diff --git a/benchmark/mmmu/README.md b/benchmark/mmmu/README.md
index 62a5a2f12..80db21921 100644
--- a/benchmark/mmmu/README.md
+++ b/benchmark/mmmu/README.md
@@ -27,6 +27,18 @@ python -m sglang.launch_server --model-path microsoft/Phi-4-multimodal-instruct
python -m benchmark/mmmu/bench_sglang.py --concurrency 8 --lora-path vision
```
+You can use `--response-answer-regex` to specify how to extract the answer from the response string. E.g.,
+```
+python3 -m sglang.launch_server --model-path zai-org/GLM-4.1V-9B-Thinking --reasoning-parser glm45
+
+python3 bench_sglang.py --response-answer-regex "<\|begin_of_box\|>(.*)<\|end_of_box\|>" --concurrency 64
+```
+
+You can use `--extra-request-body` to specify additional OpenAI request parameters. E.g.,
+```
+python3 bench_sglang.py --extra-request-body '{"max_new_tokens": 128, "temperature": 0.01}'
+```
+
### Evaluate hf
```
diff --git a/benchmark/mmmu/bench_sglang.py b/benchmark/mmmu/bench_sglang.py
index 524beb7bc..372bfeed8 100644
--- a/benchmark/mmmu/bench_sglang.py
+++ b/benchmark/mmmu/bench_sglang.py
@@ -11,6 +11,7 @@ The eval output will be logged
import argparse
import asyncio
+import re
import sys
import time
import traceback
@@ -145,7 +146,17 @@ async def eval_mmmu(args) -> None:
_, response = await process_sample(
client, sample, sampling_params, lora_path
)
- process_result(response, sample, answer_dict, out_samples)
+ answer = (
+ re.search(args.response_answer_regex, response)
+ if response is not None
+ else None
+ )
+ process_result(
+ answer.group(1) if answer else response,
+ sample,
+ answer_dict,
+ out_samples,
+ )
else:
semaphore = asyncio.Semaphore(args.concurrency)
tasks = [
@@ -157,7 +168,17 @@ async def eval_mmmu(args) -> None:
for coro in tqdm(asyncio.as_completed(tasks), total=len(tasks)):
sample, response = await coro
- process_result(response, sample, answer_dict, out_samples)
+ answer = (
+ re.search(args.response_answer_regex, response)
+ if response is not None
+ else None
+ )
+ process_result(
+ answer.group(1) if answer else response,
+ sample,
+ answer_dict,
+ out_samples,
+ )
if args.profile:
print("Stopping profiler...")
diff --git a/benchmark/mmmu/eval_utils.py b/benchmark/mmmu/eval_utils.py
index 2ec669155..83f6dd7fb 100644
--- a/benchmark/mmmu/eval_utils.py
+++ b/benchmark/mmmu/eval_utils.py
@@ -35,6 +35,7 @@ class EvalArgs:
profile: bool = False
profile_number: int = 5
concurrency: int = 1
+ response_answer_regex: str = "(.*)"
lora_path: Optional[str] = None
@staticmethod
@@ -92,6 +93,12 @@ class EvalArgs:
default=EvalArgs.concurrency,
help="Number of concurrent requests to make during evaluation. Default is 1, which means no concurrency.",
)
+ parser.add_argument(
+ "--response-answer-regex",
+ type=str,
+ default=EvalArgs.response_answer_regex,
+ help="Specific regex to capture the answer from the response, string",
+ )
parser.add_argument(
"--lora-path",
type=str,
diff --git a/docs/supported_models/multimodal_language_models.md b/docs/supported_models/multimodal_language_models.md
index 66de3d8a1..a2adf99cb 100644
--- a/docs/supported_models/multimodal_language_models.md
+++ b/docs/supported_models/multimodal_language_models.md
@@ -39,3 +39,4 @@ in the GitHub search bar.
| **Mistral-Small-3.1-24B** | `mistralai/Mistral-Small-3.1-24B-Instruct-2503` | `mistral` | Mistral 3.1 is a multimodal model that can generate text from text or images input. It also supports tool calling and structured output. |
| **Phi-4-multimodal-instruct** | `microsoft/Phi-4-multimodal-instruct` | `phi-4-mm` | Phi-4-multimodal-instruct is the multimodal variant of the Phi-4-mini model, enhanced with LoRA for improved multimodal capabilities. It supports text, vision and audio modalities in SGLang. |
| **MiMo-VL** (7B) | `XiaomiMiMo/MiMo-VL-7B-RL` | `mimo-vl` | Xiaomi's compact yet powerful vision-language model featuring a native resolution ViT encoder for fine-grained visual details, an MLP projector for cross-modal alignment, and the MiMo-7B language model optimized for complex reasoning tasks. |
+| **GLM-4.5V** (106B) / **GLM-4.1V**(9B) | `zai-org/GLM-4.5V` | `glm-4v` | GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning |
diff --git a/python/sglang/lang/chat_template.py b/python/sglang/lang/chat_template.py
index ef348d27e..80ea6d963 100644
--- a/python/sglang/lang/chat_template.py
+++ b/python/sglang/lang/chat_template.py
@@ -505,6 +505,22 @@ register_chat_template(
)
)
+# Reference: https://huggingface.co/docs/transformers/main/model_doc/glm4_v#usage-example
+register_chat_template(
+ ChatTemplate(
+ name="glm-4v",
+ default_system_prompt=None,
+ role_prefix_and_suffix={
+ "system": ("<|system|>\n", "\n"),
+ "user": ("<|user|>\n", "\n"),
+ "assistant": ("<|assistant|>\n", "\n"),
+ },
+ style=ChatTemplateStyle.PLAIN,
+ stop_str=["<|user|>", "<|endoftext|>", "<|observation|>"],
+ image_token="<|image|>",
+ )
+)
+
@register_chat_template_matching_function
def match_deepseek(model_path: str):
@@ -562,6 +578,8 @@ def match_chat_ml(model_path: str):
return "chatml"
if re.search(r"qwen.*vl", model_path, re.IGNORECASE):
return "qwen2-vl"
+ if re.search(r"glm[-_]?4(\.\d+)?v", model_path, re.IGNORECASE):
+ return "glm-4v"
if re.search(r"qwen.*(chat|instruct)", model_path, re.IGNORECASE) and not re.search(
r"llava", model_path, re.IGNORECASE
):
diff --git a/python/sglang/srt/configs/model_config.py b/python/sglang/srt/configs/model_config.py
index da70ec740..1b96ae678 100644
--- a/python/sglang/srt/configs/model_config.py
+++ b/python/sglang/srt/configs/model_config.py
@@ -659,6 +659,8 @@ multimodal_model_archs = [
"DeepseekVL2ForCausalLM",
"Gemma3ForConditionalGeneration",
"Gemma3nForConditionalGeneration",
+ "Glm4vForConditionalGeneration",
+ "Glm4vMoeForConditionalGeneration",
"Grok1VForCausalLM",
"Grok1AForCausalLM",
"LlavaLlamaForCausalLM",
diff --git a/python/sglang/srt/function_call/ebnf_composer.py b/python/sglang/srt/function_call/ebnf_composer.py
index 1db7da6d8..d41968ea7 100644
--- a/python/sglang/srt/function_call/ebnf_composer.py
+++ b/python/sglang/srt/function_call/ebnf_composer.py
@@ -316,6 +316,7 @@ class EBNFComposer:
combined_args = "".join(rule_parts)
arguments_rule = args_template.format(arg_rules=combined_args)
+ arguments_rule = arguments_rule or '""'
# Add the function call rule and its arguments rule
ebnf_lines.append(
diff --git a/python/sglang/srt/function_call/glm4_moe_detector.py b/python/sglang/srt/function_call/glm4_moe_detector.py
index 705bbcdb3..39822fb19 100644
--- a/python/sglang/srt/function_call/glm4_moe_detector.py
+++ b/python/sglang/srt/function_call/glm4_moe_detector.py
@@ -158,7 +158,7 @@ class Glm4MoeDetector(BaseFormatDetector):
individual_call_end_token=self.eot_token,
tool_call_separator="\\n",
function_format="xml",
- call_rule_fmt='"{name}" "\\n" {arguments_rule} "\\n"',
+ call_rule_fmt='"{name}" "\\n" ( {arguments_rule} "\\n" )?',
key_value_rule_fmt='"{key}" "\\n" "" {valrule} ""',
key_value_separator="\\n",
)
diff --git a/python/sglang/srt/jinja_template_utils.py b/python/sglang/srt/jinja_template_utils.py
index e23aa9226..be7d44097 100644
--- a/python/sglang/srt/jinja_template_utils.py
+++ b/python/sglang/srt/jinja_template_utils.py
@@ -102,6 +102,12 @@ def detect_jinja_template_content_format(chat_template: str) -> str:
if _is_var_or_elems_access(loop_iter, "message", "content"):
return "openai" # Found content iteration → openai format
+ # Also check for patterns like: {%- for item in msg.content -%} or {%- for item in m.content -%}
+ if _is_var_or_elems_access(
+ loop_iter, "msg", "content"
+ ) or _is_var_or_elems_access(loop_iter, "m", "content"):
+ return "openai" # Found content iteration → openai format (glm4v)
+
return "string" # No content loops found → string format
except Exception as e:
logger.debug(f"Error when parsing AST of Jinja template: {e}")
diff --git a/python/sglang/srt/layers/rotary_embedding.py b/python/sglang/srt/layers/rotary_embedding.py
index 252362201..c583a5d23 100644
--- a/python/sglang/srt/layers/rotary_embedding.py
+++ b/python/sglang/srt/layers/rotary_embedding.py
@@ -1,6 +1,7 @@
# Adapted from https://raw.githubusercontent.com/vllm-project/vllm/refs/tags/v0.6.6.post1/vllm/model_executor/layers/rotary_embedding.py
"""Rotary Positional Embeddings."""
+import itertools
import math
from typing import Any, Dict, List, Optional, Tuple, Union
@@ -946,7 +947,37 @@ class MRotaryEmbedding(RotaryEmbedding):
self.mrope_section = mrope_section
if self.mrope_section:
- assert sum(self.mrope_section) == rotary_dim // 2
+ expected_sum = rotary_dim // 2
+ actual_sum = sum(self.mrope_section)
+ if actual_sum != expected_sum:
+ print(
+ f"MRoPE section sum mismatch: expected {expected_sum}, got {actual_sum}. "
+ f"Adjusting mrope_section to match rotary_dim // 2 = {expected_sum}"
+ )
+ # Auto-correct by scaling the mrope_section proportionally
+ if actual_sum > 0:
+ scale_factor = expected_sum / actual_sum
+ self.mrope_section = [
+ max(1, int(section * scale_factor))
+ for section in self.mrope_section
+ ]
+ # Ensure the sum exactly matches by adjusting the last element
+ current_sum = sum(self.mrope_section)
+ if current_sum != expected_sum:
+ self.mrope_section[-1] += expected_sum - current_sum
+ else:
+ # If all sections are 0, create a default distribution
+ self.mrope_section = [
+ expected_sum // len(self.mrope_section)
+ ] * len(self.mrope_section)
+ # Handle remainder
+ remainder = expected_sum % len(self.mrope_section)
+ for i in range(remainder):
+ self.mrope_section[i] += 1
+
+ print(
+ f"Corrected mrope_section: {self.mrope_section} (sum={sum(self.mrope_section)})"
+ )
def forward(
self,
@@ -1153,6 +1184,204 @@ class MRotaryEmbedding(RotaryEmbedding):
mrope_position_deltas = max_position_ids + 1 - s
return position_ids, mrope_position_deltas
+ # Adapted from https://github.com/vllm-project/vllm/blob/3779eb8c81449b924a23457fc77e45a0e6171178/vllm/model_executor/layers/rotary_embedding.py#L1120
+ @staticmethod
+ def get_rope_index_glm4v(
+ input_ids: torch.Tensor,
+ hf_config: Any,
+ image_grid_thw: Union[list[list[int]], torch.Tensor],
+ video_grid_thw: Union[list[list[int]], torch.Tensor],
+ attention_mask: torch.Tensor,
+ **kwargs,
+ ) -> tuple[torch.Tensor, torch.Tensor]:
+ """Get mrope input positions and delta value for GLM4V."""
+ image_token_id = hf_config.image_token_id
+ video_start_token_id = hf_config.video_start_token_id
+ video_end_token_id = hf_config.video_end_token_id
+ spatial_merge_size = hf_config.vision_config.spatial_merge_size
+
+ mrope_position_deltas = []
+ if input_ids is not None and (
+ image_grid_thw is not None or video_grid_thw is not None
+ ):
+ total_input_ids = input_ids
+ if attention_mask is None:
+ attention_mask = torch.ones_like(total_input_ids)
+ position_ids = torch.ones(
+ 3,
+ input_ids.shape[0],
+ input_ids.shape[1],
+ dtype=input_ids.dtype,
+ device=input_ids.device,
+ )
+ image_index, video_index = 0, 0
+ video_group_index = 0
+ attention_mask = attention_mask.to(total_input_ids.device)
+ for i, input_ids in enumerate(total_input_ids):
+ input_ids = input_ids[attention_mask[i] == 1]
+ input_tokens = input_ids.tolist()
+
+ input_token_type = []
+ video_check_flg = False
+ for token in input_tokens:
+ if token == video_start_token_id:
+ video_check_flg = True
+ elif token == video_end_token_id:
+ video_check_flg = False
+
+ if token == image_token_id and not video_check_flg:
+ input_token_type.append("image")
+ elif token == image_token_id and video_check_flg:
+ input_token_type.append("video")
+ else:
+ input_token_type.append("text")
+
+ input_type_group = []
+ for key, group in itertools.groupby(
+ enumerate(input_token_type), lambda x: x[1]
+ ):
+ group = list(group)
+ start_index = group[0][0]
+ end_index = group[-1][0] + 1
+ input_type_group.append((key, start_index, end_index))
+
+ llm_pos_ids_list = []
+ video_frame_num = 1
+ for modality_type, start_idx, end_idx in input_type_group:
+ st_idx = (
+ llm_pos_ids_list[-1].max() + 1
+ if len(llm_pos_ids_list) > 0
+ else 0
+ )
+
+ if modality_type == "image":
+ t, h, w = (
+ image_grid_thw[image_index][0],
+ image_grid_thw[image_index][1],
+ image_grid_thw[image_index][2],
+ )
+ llm_grid_t, llm_grid_h, llm_grid_w = (
+ t.item(),
+ h.item() // spatial_merge_size,
+ w.item() // spatial_merge_size,
+ )
+
+ t_index = (
+ torch.arange(llm_grid_t)
+ .view(-1, 1)
+ .expand(-1, llm_grid_h * llm_grid_w)
+ .flatten()
+ )
+ h_index = (
+ torch.arange(llm_grid_h)
+ .view(1, -1, 1)
+ .expand(llm_grid_t, -1, llm_grid_w)
+ .flatten()
+ )
+ w_index = (
+ torch.arange(llm_grid_w)
+ .view(1, 1, -1)
+ .expand(llm_grid_t, llm_grid_h, -1)
+ .flatten()
+ )
+ llm_pos_ids_list.append(
+ torch.stack([t_index, h_index, w_index]) + st_idx
+ )
+
+ image_index += 1
+ video_frame_num = 1
+
+ elif modality_type == "video":
+ t, h, w = (
+ video_frame_num,
+ video_grid_thw[video_index][1],
+ video_grid_thw[video_index][2],
+ )
+
+ llm_grid_t, llm_grid_h, llm_grid_w = (
+ t,
+ h.item() // spatial_merge_size,
+ w.item() // spatial_merge_size,
+ )
+
+ for t_idx in range(llm_grid_t):
+ t_index = (
+ torch.tensor(t_idx)
+ .view(-1, 1)
+ .expand(-1, llm_grid_h * llm_grid_w)
+ .flatten()
+ )
+
+ h_index = (
+ torch.arange(llm_grid_h)
+ .view(1, -1, 1)
+ .expand(1, -1, llm_grid_w)
+ .flatten()
+ )
+ w_index = (
+ torch.arange(llm_grid_w)
+ .view(1, 1, -1)
+ .expand(1, llm_grid_h, -1)
+ .flatten()
+ )
+ llm_pos_ids_list.append(
+ torch.stack([t_index, h_index, w_index]) + st_idx
+ )
+
+ video_group_index += 1
+
+ if video_group_index >= video_grid_thw[video_index][0]:
+ video_index += 1
+ video_group_index = 0
+
+ video_frame_num += 1
+
+ else:
+ text_len = end_idx - start_idx
+ llm_pos_ids_list.append(
+ torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx
+ )
+
+ video_frame_num = 1
+
+ llm_positions = torch.cat(llm_pos_ids_list, dim=1).reshape(3, -1)
+ position_ids[..., i, attention_mask[i] == 1] = llm_positions.to(
+ position_ids.device
+ )
+ mrope_position_deltas.append(
+ llm_positions.max() + 1 - len(total_input_ids[i])
+ )
+ mrope_position_deltas = torch.tensor(
+ mrope_position_deltas, device=input_ids.device
+ ).unsqueeze(1)
+ return position_ids, mrope_position_deltas
+ else:
+ if attention_mask is not None:
+ position_ids = attention_mask.long().cumsum(-1) - 1
+ position_ids.masked_fill_(attention_mask == 0, 1)
+ position_ids = (
+ position_ids.unsqueeze(0)
+ .expand(3, -1, -1)
+ .to(attention_mask.device)
+ )
+ max_position_ids = position_ids.max(0, keepdim=False)[0].max(
+ -1, keepdim=True
+ )[0]
+ mrope_position_deltas = max_position_ids + 1 - attention_mask.shape[-1]
+ else:
+ position_ids = (
+ torch.arange(input_ids.shape[1], device=input_ids.device)
+ .view(1, 1, -1)
+ .expand(3, input_ids.shape[0], -1)
+ )
+ mrope_position_deltas = torch.zeros(
+ [input_ids.shape[0], 1],
+ device=input_ids.device,
+ dtype=input_ids.dtype,
+ )
+
+ return position_ids, mrope_position_deltas
+
@staticmethod
def get_next_input_positions(
mrope_position_delta: int,
diff --git a/python/sglang/srt/models/glm4.py b/python/sglang/srt/models/glm4.py
index 0ef11d6d0..7357e5f82 100644
--- a/python/sglang/srt/models/glm4.py
+++ b/python/sglang/srt/models/glm4.py
@@ -218,6 +218,12 @@ class Glm4Model(nn.Module):
self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+ def get_input_embeddings(self) -> nn.Embedding:
+ return self.embed_tokens
+
+ def dtype(self) -> torch.dtype:
+ return next(self.parameters()).dtype
+
@torch.no_grad()
def forward(
self,
diff --git a/python/sglang/srt/models/glm4v.py b/python/sglang/srt/models/glm4v.py
new file mode 100644
index 000000000..fbd757849
--- /dev/null
+++ b/python/sglang/srt/models/glm4v.py
@@ -0,0 +1,589 @@
+import logging
+from functools import lru_cache, partial
+from typing import Iterable, List, Optional, Tuple
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers.models.glm4v.configuration_glm4v import Glm4vConfig, Glm4vVisionConfig
+
+from sglang.srt.hf_transformers_utils import get_processor
+from sglang.srt.layers.activation import SiluAndMul
+from sglang.srt.layers.layernorm import RMSNorm
+from sglang.srt.layers.linear import (
+ ColumnParallelLinear,
+ MergedColumnParallelLinear,
+ RowParallelLinear,
+)
+from sglang.srt.layers.logits_processor import LogitsProcessor
+from sglang.srt.layers.pooler import Pooler, PoolingType
+from sglang.srt.layers.quantization.base_config import QuantizationConfig
+from sglang.srt.layers.vocab_parallel_embedding import ParallelLMHead
+from sglang.srt.managers.schedule_batch import MultimodalDataItem
+from sglang.srt.model_loader.weight_utils import default_weight_loader
+from sglang.srt.models.glm4 import Glm4Model
+from sglang.srt.models.qwen2_5_vl import (
+ Qwen2_5_VisionBlock,
+ Qwen2_5_VLForConditionalGeneration,
+)
+from sglang.srt.utils import add_prefix
+
+logger = logging.getLogger(__name__)
+
+cached_get_processor = lru_cache(get_processor)
+
+
+class Glm4vRMSNorm(RMSNorm):
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ original_shape = x.shape
+ x_2d = x.contiguous().reshape(-1, original_shape[-1])
+ x_2d = super().forward(x_2d)
+ x = x_2d.reshape(original_shape)
+ return x
+
+
+class Glm4vVisionMLP(nn.Module):
+ def __init__(
+ self,
+ in_features: int,
+ hidden_features: int,
+ bias: bool = False,
+ quant_config: Optional[QuantizationConfig] = None,
+ prefix: str = "",
+ ):
+ super().__init__()
+ self.gate_up_proj = MergedColumnParallelLinear(
+ input_size=in_features,
+ output_sizes=[hidden_features] * 2,
+ bias=bias,
+ quant_config=quant_config,
+ prefix=add_prefix("gate_up_proj", prefix),
+ )
+ self.down_proj = RowParallelLinear(
+ hidden_features,
+ in_features,
+ bias=bias,
+ quant_config=quant_config,
+ prefix=add_prefix("down_proj", prefix),
+ )
+ self.act_fn = SiluAndMul()
+
+ def forward(self, x: torch.Tensor):
+ gate_up, _ = self.gate_up_proj(x)
+ x = self.act_fn(gate_up)
+ x, _ = self.down_proj(x)
+ return x
+
+
+class Glm4vVisionBlock(Qwen2_5_VisionBlock):
+ def __init__(
+ self,
+ config: Glm4vVisionConfig,
+ norm_layer: Optional[nn.Module] = None,
+ quant_config: Optional[QuantizationConfig] = None,
+ prefix: str = "",
+ ) -> None:
+ super().__init__(
+ dim=config.hidden_size,
+ intermediate_dim=config.out_hidden_size,
+ num_heads=config.num_heads,
+ hidden_act=config.hidden_act,
+ norm_layer=norm_layer,
+ quant_config=quant_config,
+ prefix=prefix,
+ )
+ self.norm1 = Glm4vRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+ self.norm2 = Glm4vRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+ self.mlp = Glm4vVisionMLP(
+ config.hidden_size,
+ config.out_hidden_size,
+ bias=False,
+ quant_config=quant_config,
+ prefix=add_prefix("mlp", prefix),
+ )
+
+
+class Glm4vVisionPatchEmbed(nn.Module):
+ def __init__(
+ self,
+ patch_size: int = 14,
+ temporal_patch_size: int = 2,
+ in_channels: int = 3,
+ hidden_size: int = 1536,
+ ) -> None:
+ super().__init__()
+ self.patch_size = patch_size
+ self.temporal_patch_size = temporal_patch_size
+ self.hidden_size = hidden_size
+ self.in_channels = in_channels
+
+ kernel_size = (temporal_patch_size, patch_size, patch_size)
+ self.proj = nn.Conv3d(
+ in_channels,
+ hidden_size,
+ kernel_size=kernel_size,
+ stride=kernel_size,
+ bias=True,
+ )
+
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ x = x.view(
+ -1,
+ self.in_channels,
+ self.temporal_patch_size,
+ self.patch_size,
+ self.patch_size,
+ )
+ x = self.proj(x).view(-1, self.hidden_size)
+ return x
+
+
+class Glm4vPatchMerger(nn.Module):
+ def __init__(
+ self,
+ d_model: int,
+ context_dim: int,
+ quant_config: Optional[QuantizationConfig] = None,
+ bias: bool = False,
+ prefix: str = "",
+ ) -> None:
+ super().__init__()
+ self.hidden_size = d_model
+ self.proj = ColumnParallelLinear(
+ self.hidden_size,
+ self.hidden_size,
+ bias=bias,
+ quant_config=quant_config,
+ prefix=add_prefix("proj", prefix),
+ gather_output=True,
+ )
+ self.post_projection_norm = nn.LayerNorm(self.hidden_size)
+ self.gate_up_proj = MergedColumnParallelLinear(
+ input_size=self.hidden_size,
+ output_sizes=[context_dim] * 2,
+ bias=bias,
+ quant_config=quant_config,
+ prefix=add_prefix("gate_up_proj", prefix),
+ )
+ self.down_proj = RowParallelLinear(
+ context_dim,
+ self.hidden_size,
+ bias=bias,
+ quant_config=quant_config,
+ prefix=add_prefix("down_proj", prefix),
+ )
+ self.extra_activation_func = nn.GELU()
+
+ def forward(self, x: torch.Tensor):
+ x, _ = self.proj(x)
+ x = self.extra_activation_func(self.post_projection_norm(x))
+ gate_up, _ = self.gate_up_proj(x)
+ gate, up = gate_up.chunk(2, dim=-1)
+ x = F.silu(gate) * up
+ x, _ = self.down_proj(x)
+ return x
+
+
+class Glm4vVisionEmbeddings(nn.Module):
+ def __init__(self, config: Glm4vVisionConfig):
+ super().__init__()
+ self.config = config
+ self.embed_dim = config.hidden_size
+ self.image_size = config.image_size
+ self.patch_size = config.patch_size
+
+ self.num_patches = (self.image_size // self.patch_size) ** 2
+ self.num_positions = self.num_patches
+ self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim)
+ self.register_buffer(
+ "position_ids",
+ torch.arange(self.num_positions).expand((1, -1)),
+ persistent=False,
+ )
+
+ def forward(
+ self, embeddings, lengths, image_shapes, h_coords, w_coords
+ ) -> torch.Tensor:
+ pos_embed_weight = self.position_embedding.weight
+ hidden_size = pos_embed_weight.shape[1]
+ total_seq = h_coords.shape[0]
+ device = pos_embed_weight.device
+
+ # Move coordinates to correct device
+ h_coords, w_coords = h_coords.to(device), w_coords.to(device)
+
+ # Handle empty sequence case
+ if total_seq == 0:
+ adapted_pos_embed = torch.empty(
+ 0, hidden_size, device=device, dtype=pos_embed_weight.dtype
+ )
+ else:
+ # Convert inputs to tensors if needed
+ if isinstance(lengths, list):
+ lengths = torch.tensor(lengths, device=device, dtype=torch.long)
+ if not isinstance(image_shapes, torch.Tensor):
+ image_shapes = torch.tensor(
+ image_shapes, device=device, dtype=torch.long
+ )
+
+ # Prepare 2D position embedding
+ orig_size_sq = pos_embed_weight.shape[0]
+ orig_size = int(orig_size_sq**0.5)
+ pos_embed_2d = (
+ pos_embed_weight.view(orig_size, orig_size, hidden_size)
+ .permute(2, 0, 1)
+ .unsqueeze(0)
+ .to(device=device, dtype=torch.float32)
+ )
+
+ # Calculate target dimensions for each patch
+ target_h = torch.cat(
+ [image_shapes[i, 1].repeat(lengths[i]) for i in range(len(lengths))]
+ ).to(device=device, dtype=torch.float32)
+ target_w = torch.cat(
+ [image_shapes[i, 2].repeat(lengths[i]) for i in range(len(lengths))]
+ ).to(device=device, dtype=torch.float32)
+
+ # Normalize coordinates to [-1, 1] range for grid_sample
+ h_coords = h_coords.to(device=device, dtype=torch.float32)
+ w_coords = w_coords.to(device=device, dtype=torch.float32)
+ norm_w = ((w_coords + 0.5) / target_w) * 2 - 1
+ norm_h = ((h_coords + 0.5) / target_h) * 2 - 1
+
+ # Create sampling grid
+ grid = torch.stack((norm_w, norm_h), dim=-1).unsqueeze(0).unsqueeze(2)
+
+ # Perform bicubic interpolation
+ interpolated_embed_fp32 = F.grid_sample(
+ pos_embed_2d,
+ grid,
+ mode="bicubic",
+ align_corners=False,
+ padding_mode="border",
+ )
+
+ # Reshape and convert back to original dtype
+ adapted_pos_embed_fp32 = (
+ interpolated_embed_fp32.squeeze(0).squeeze(-1).permute(1, 0)
+ )
+ adapted_pos_embed = adapted_pos_embed_fp32.to(pos_embed_weight.dtype).to(
+ embeddings.device
+ )
+
+ # Add adapted position encoding to embeddings
+ embeddings = embeddings + adapted_pos_embed
+ return embeddings
+
+
+class Glm4vVisionRotaryEmbedding(nn.Module):
+ def __init__(self, dim: int, theta: float = 10000.0) -> None:
+ super().__init__()
+ self.dim = dim
+ self.theta = theta
+ inv_freq = 1.0 / (theta ** (torch.arange(0, dim, 2, dtype=torch.float) / dim))
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
+ self._seq_len_cached = 0
+ self._freqs_cached = None
+
+ def update_freqs_cache(self, seqlen: int) -> None:
+ if seqlen > self._seq_len_cached:
+ seqlen *= 2
+ self._seq_len_cached = seqlen
+ self.inv_freq = 1.0 / (
+ self.theta
+ ** (
+ torch.arange(
+ 0,
+ self.dim,
+ 2,
+ dtype=torch.float,
+ device=self.inv_freq.device,
+ )
+ / self.dim
+ )
+ )
+ seq = torch.arange(
+ seqlen, device=self.inv_freq.device, dtype=self.inv_freq.dtype
+ )
+ freqs = torch.outer(seq, self.inv_freq)
+ self._freqs_cached = freqs
+
+ def forward(self, seqlen: int) -> torch.Tensor:
+ self.update_freqs_cache(seqlen)
+ return self._freqs_cached[:seqlen]
+
+
+class Glm4vVisionModel(nn.Module):
+ def __init__(
+ self,
+ vision_config: Glm4vVisionConfig,
+ norm_eps: float = 1e-6,
+ quant_config: Optional[QuantizationConfig] = None,
+ prefix: str = "",
+ ) -> None:
+ super().__init__()
+
+ patch_size = vision_config.patch_size
+ temporal_patch_size = vision_config.temporal_patch_size
+ in_channels = vision_config.in_channels
+ depth = vision_config.depth
+ self.hidden_size = vision_config.hidden_size
+ self.num_heads = vision_config.num_heads
+
+ self.patch_size = vision_config.patch_size
+ self.spatial_merge_size = vision_config.spatial_merge_size
+ self.out_hidden_size = vision_config.out_hidden_size
+
+ self.patch_embed = Glm4vVisionPatchEmbed(
+ patch_size=patch_size,
+ temporal_patch_size=temporal_patch_size,
+ in_channels=in_channels,
+ hidden_size=self.hidden_size,
+ )
+
+ norm_layer = partial(Glm4vRMSNorm, eps=norm_eps)
+ head_dim = self.hidden_size // self.num_heads
+ self.rotary_pos_emb = Glm4vVisionRotaryEmbedding(head_dim // 2)
+
+ self.blocks = nn.ModuleList(
+ [
+ Glm4vVisionBlock(
+ config=vision_config,
+ norm_layer=norm_layer,
+ quant_config=quant_config,
+ prefix=add_prefix(f"blocks.{layer_idx}", prefix),
+ )
+ for layer_idx in range(depth)
+ ]
+ )
+
+ self.merger = Glm4vPatchMerger(
+ d_model=vision_config.out_hidden_size,
+ context_dim=vision_config.intermediate_size,
+ quant_config=quant_config,
+ bias=False,
+ prefix=add_prefix("merger", prefix),
+ )
+
+ self.embeddings = Glm4vVisionEmbeddings(vision_config)
+
+ self.post_conv_layernorm = Glm4vRMSNorm(
+ vision_config.hidden_size, eps=vision_config.rms_norm_eps
+ )
+ self.downsample = nn.Conv2d(
+ in_channels=vision_config.hidden_size,
+ out_channels=vision_config.out_hidden_size,
+ kernel_size=vision_config.spatial_merge_size,
+ stride=vision_config.spatial_merge_size,
+ )
+ self.post_layernorm = Glm4vRMSNorm(
+ vision_config.hidden_size, eps=vision_config.rms_norm_eps
+ )
+
+ @property
+ def dtype(self) -> torch.dtype:
+ return self.patch_embed.proj.weight.dtype
+
+ @property
+ def device(self) -> torch.device:
+ return self.patch_embed.proj.weight.device
+
+ def rot_pos_emb(self, grid_thw: torch.Tensor) -> torch.Tensor:
+ pos_ids = []
+ for t, h, w in grid_thw:
+ hpos_ids = torch.arange(h).unsqueeze(1).expand(-1, w)
+ wpos_ids = torch.arange(w).unsqueeze(0).expand(h, -1)
+ hpos_ids = (
+ hpos_ids.reshape(
+ h // self.spatial_merge_size,
+ self.spatial_merge_size,
+ w // self.spatial_merge_size,
+ self.spatial_merge_size,
+ )
+ .permute(0, 2, 1, 3)
+ .flatten()
+ )
+ wpos_ids = (
+ wpos_ids.reshape(
+ h // self.spatial_merge_size,
+ self.spatial_merge_size,
+ w // self.spatial_merge_size,
+ self.spatial_merge_size,
+ )
+ .permute(0, 2, 1, 3)
+ .flatten()
+ )
+ pos_ids.append(torch.stack([hpos_ids, wpos_ids], dim=-1).repeat(t, 1))
+ pos_ids = torch.cat(pos_ids, dim=0)
+ max_grid_size = grid_thw[:, 1:].max()
+ rotary_pos_emb_full = self.rotary_pos_emb(max_grid_size)
+ rotary_pos_emb = rotary_pos_emb_full[pos_ids].flatten(1)
+ return rotary_pos_emb, pos_ids
+
+ def forward(self, x: torch.Tensor, grid_thw: torch.Tensor) -> torch.Tensor:
+ # patchify
+ x = x.to(device=self.device, dtype=self.dtype)
+ x = self.patch_embed(x)
+ x = self.post_conv_layernorm(x)
+
+ # compute position embedding
+ rotary_pos_emb, image_type_ids = self.rot_pos_emb(grid_thw)
+ # compute cu_seqlens
+ cu_seqlens = torch.repeat_interleave(
+ grid_thw[:, 1] * grid_thw[:, 2], grid_thw[:, 0]
+ ).cumsum(dim=0, dtype=torch.int32)
+ cu_seqlens = F.pad(cu_seqlens, (1, 0), "constant", 0)
+
+ seqlens = (cu_seqlens[1:] - cu_seqlens[:-1]).tolist()
+ x = self.embeddings(
+ x, seqlens, grid_thw, image_type_ids[:, 0], image_type_ids[:, 1]
+ )
+
+ emb = torch.cat((rotary_pos_emb, rotary_pos_emb), dim=-1)
+ rotary_pos_emb_tuple = (emb.cos(), emb.sin())
+
+ # x.shape: (s, b, d) where b=1 for vision processing
+ # transformers
+ x = x.unsqueeze(1)
+ for blk in self.blocks:
+ x = blk(x, cu_seqlens=cu_seqlens, position_embeddings=rotary_pos_emb_tuple)
+
+ # adapter
+ x = self.post_layernorm(x)
+ x = x.view(-1, self.spatial_merge_size, self.spatial_merge_size, x.shape[-1])
+ x = x.permute(0, 3, 1, 2)
+ x = self.downsample(x).view(-1, self.out_hidden_size)
+ x = self.merger(x)
+
+ return x
+
+
+class Glm4vForConditionalGeneration(Qwen2_5_VLForConditionalGeneration):
+ def __init__(
+ self,
+ config: Glm4vConfig,
+ quant_config: Optional[QuantizationConfig] = None,
+ prefix: str = "",
+ ) -> None:
+ nn.Module.__init__(self)
+
+ self.config = config
+
+ self.model = Glm4Model(
+ config,
+ quant_config,
+ prefix=add_prefix("model", prefix),
+ )
+ self.visual = Glm4vVisionModel(
+ config.vision_config,
+ norm_eps=getattr(config, "rms_norm_eps", 1e-5),
+ quant_config=quant_config,
+ prefix=add_prefix("visual", prefix),
+ )
+
+ if config.tie_word_embeddings:
+ self.lm_head = self.model.embed_tokens
+ else:
+ self.lm_head = ParallelLMHead(
+ config.vocab_size,
+ config.hidden_size,
+ quant_config=quant_config,
+ prefix=add_prefix("lm_head", prefix),
+ )
+
+ self.logits_processor = LogitsProcessor(config)
+ self.pooler = Pooler(pooling_type=PoolingType.LAST, normalize=True)
+ self.is_mrope_enabled = "mrope_section" in self.config.rope_scaling
+
+ def get_image_feature(self, items: List[MultimodalDataItem]) -> torch.Tensor:
+ pixel_values = torch.cat(
+ [item.feature.squeeze(0) for item in items], dim=0
+ ).type(self.visual.dtype)
+ image_grid_thw = torch.concat([item.image_grid_thw for item in items], dim=0)
+ # For multi-image, pixel_values is [num_of_images, L, C] shape
+ # assert pixel_values.dim() == 2, pixel_values.dim()
+ assert image_grid_thw.dim() == 2, image_grid_thw.dim()
+ image_embeds = self.visual(pixel_values, grid_thw=image_grid_thw)
+ split_sizes = (
+ image_grid_thw.prod(-1) // self.visual.spatial_merge_size**2
+ ).tolist()
+ image_embeds = torch.split(image_embeds, split_sizes)
+ return torch.cat(image_embeds)
+
+ def get_video_feature(self, items: List[MultimodalDataItem]) -> torch.Tensor:
+ pixel_values_videos = torch.cat(
+ [item.feature.squeeze(0) for item in items], dim=0
+ ).type(self.visual.dtype)
+ video_grid_thw = torch.concat([item.video_grid_thw for item in items], dim=0)
+ # For multi-video, pixel_values_videos is [num_of_videos, L, C] shape
+ # assert pixel_values_videos.dim() == 2, pixel_values_videos.dim()
+ assert video_grid_thw.dim() == 2, video_grid_thw.dim()
+
+ # reshape video_grid_thw -> [b, 3] -> [1, h, w] * frames
+ temp_frames_hw = []
+ for t, h, w in video_grid_thw:
+ repeated_row = (
+ torch.tensor([1, h.item(), w.item()]).unsqueeze(0).repeat(t, 1)
+ )
+ temp_frames_hw.append(repeated_row)
+ flattened_video_grid_thw = torch.cat(temp_frames_hw, dim=0)
+ video_embeds = self.visual(
+ pixel_values_videos, grid_thw=flattened_video_grid_thw
+ )
+ split_sizes = (
+ video_grid_thw.prod(-1) // self.visual.spatial_merge_size**2
+ ).tolist()
+ video_embeds = torch.split(video_embeds, split_sizes)
+ return torch.cat(video_embeds)
+
+ def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
+ stacked_params_mapping = [
+ # (param_name, shard_name, shard_id)
+ (".qkv_proj", ".q_proj", "q"),
+ (".qkv_proj", ".k_proj", "k"),
+ (".qkv_proj", ".v_proj", "v"),
+ (".gate_up_proj", ".up_proj", 1),
+ (".gate_up_proj", ".gate_proj", 0),
+ ]
+ params_dict = dict(self.named_parameters(remove_duplicate=False))
+ for name, loaded_weight in weights:
+ if "language_model." in name:
+ name = name.replace("language_model.", "")
+ if "model.visual." in name:
+ name = name.replace("model.visual.", "visual.")
+
+ if "rotary_emb.inv_freq" in name:
+ continue
+
+ for param_name, weight_name, shard_id in stacked_params_mapping:
+ if weight_name not in name:
+ continue
+ name = name.replace(weight_name, param_name)
+
+ # Skip loading extra bias for GPTQ models.
+ if name.endswith(".bias") and name not in params_dict:
+ continue
+ param = params_dict[name]
+ weight_loader = param.weight_loader
+ weight_loader(param, loaded_weight, shard_id)
+ break
+ else:
+ if "visual" in name:
+ # adapt to VisionAttention
+ name = name.replace(r"attn.qkv.", r"attn.qkv_proj.")
+
+ try:
+ # Skip loading extra bias for GPTQ models.
+ if name.endswith(".bias") and name not in params_dict:
+ continue
+ param = params_dict[name]
+ except KeyError:
+ print(params_dict.keys())
+ raise
+
+ weight_loader = getattr(param, "weight_loader", default_weight_loader)
+ weight_loader(param, loaded_weight)
+
+
+EntryClass = [Glm4vForConditionalGeneration]
diff --git a/python/sglang/srt/models/glm4v_moe.py b/python/sglang/srt/models/glm4v_moe.py
new file mode 100644
index 000000000..140b6e135
--- /dev/null
+++ b/python/sglang/srt/models/glm4v_moe.py
@@ -0,0 +1,400 @@
+import logging
+from functools import lru_cache
+from typing import Iterable, Optional, Tuple
+
+import torch
+import torch.nn as nn
+from transformers.models.glm4v_moe.configuration_glm4v_moe import Glm4vMoeConfig
+
+from sglang.srt.distributed import (
+ get_moe_expert_parallel_world_size,
+ get_tensor_model_parallel_rank,
+ get_tensor_model_parallel_world_size,
+ parallel_state,
+ tensor_model_parallel_all_reduce,
+)
+from sglang.srt.hf_transformers_utils import get_processor
+from sglang.srt.layers.dp_attention import (
+ get_attention_tp_rank,
+ get_attention_tp_size,
+ get_local_attention_dp_size,
+)
+from sglang.srt.layers.logits_processor import LogitsProcessor
+from sglang.srt.layers.moe.ep_moe.layer import get_moe_impl_class
+from sglang.srt.layers.pooler import Pooler, PoolingType
+from sglang.srt.layers.quantization.base_config import QuantizationConfig
+from sglang.srt.layers.vocab_parallel_embedding import ParallelLMHead
+from sglang.srt.managers.schedule_batch import global_server_args_dict
+from sglang.srt.model_loader.weight_utils import default_weight_loader
+from sglang.srt.models.glm4_moe import Glm4MoeModel
+from sglang.srt.models.glm4v import Glm4vForConditionalGeneration, Glm4vVisionModel
+from sglang.srt.utils import add_prefix, is_cuda, log_info_on_rank0
+
+_is_cuda = is_cuda()
+
+logger = logging.getLogger(__name__)
+
+cached_get_processor = lru_cache(get_processor)
+
+
+class Glm4vMoeForConditionalGeneration(Glm4vForConditionalGeneration):
+ def __init__(
+ self,
+ config: Glm4vMoeConfig,
+ quant_config: Optional[QuantizationConfig] = None,
+ prefix: str = "",
+ ) -> None:
+ nn.Module.__init__(self)
+
+ config.moe_layer_freq = 1
+ self.config = config
+ self.tp_size = get_tensor_model_parallel_world_size()
+ self.dp_size = get_local_attention_dp_size()
+ self.quant_config = quant_config
+ self.determine_num_fused_shared_experts("Glm4MoeForCausalLM")
+ self.num_fused_shared_experts = (
+ 0
+ if global_server_args_dict["disable_shared_experts_fusion"]
+ else config.n_shared_experts
+ )
+
+ self.model = Glm4MoeModel(
+ config,
+ quant_config,
+ prefix=add_prefix("language_model", prefix),
+ )
+ self.visual = Glm4vVisionModel(
+ config.vision_config,
+ norm_eps=getattr(config, "rms_norm_eps", 1e-5),
+ quant_config=quant_config,
+ prefix=add_prefix("visual", prefix),
+ )
+
+ self.lm_head = ParallelLMHead(
+ config.vocab_size,
+ config.hidden_size,
+ quant_config=quant_config,
+ prefix=add_prefix("lm_head", prefix),
+ use_attn_tp_group=global_server_args_dict["enable_dp_lm_head"],
+ )
+ self.logits_processor = LogitsProcessor(config)
+ self.pooler = Pooler(pooling_type=PoolingType.LAST, normalize=True)
+ self.is_mrope_enabled = "mrope_section" in self.config.rope_scaling
+
+ def determine_num_fused_shared_experts(
+ self, architecture: str = "Glm4MoeForCausalLM"
+ ):
+ self.num_fused_shared_experts = 0
+ if global_server_args_dict["disable_shared_experts_fusion"]:
+ return
+
+ # Only Deepseek V3/R1 can use shared experts fusion optimization now.
+ disable_reason = None
+ if (
+ not _is_cuda
+ or torch.cuda.get_device_capability("cuda") < (8, 0)
+ or self.config.architectures[0] != architecture
+ or self.config.n_shared_experts != 1
+ ):
+ disable_reason = "Only GLM-4.5 on NV-platform with capability >= 80 can use shared experts fusion optimization."
+ elif get_moe_expert_parallel_world_size() > 1:
+ disable_reason = "Deepseek and GLM-4.5 can not use shared experts fusion optimization under expert parallelism."
+
+ if disable_reason is not None:
+ global_server_args_dict["disable_shared_experts_fusion"] = True
+ self.num_fused_shared_experts = 0
+ log_info_on_rank0(
+ logger,
+ f"{disable_reason} Shared experts fusion optimization is disabled.",
+ )
+ return
+
+ self.num_fused_shared_experts = self.config.n_shared_experts
+
+ def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]], is_nextn=False):
+
+ if is_nextn:
+ if hasattr(self.config, "num_nextn_predict_layers"):
+ num_nextn_layers = self.config.num_nextn_predict_layers
+ assert num_nextn_layers == 1, "Only 1 nextn layer is supported"
+ # compatible with old design
+ nextn_layer_id = (
+ 0
+ if self.config.num_hidden_layers == 1
+ else self.config.num_hidden_layers
+ )
+ else:
+ raise ValueError("num_nextn_predict_layers is not in the config")
+
+ stacked_params_mapping = [
+ # (param_name, shard_name, shard_id)
+ ("qkv_proj", "q_proj", "q"),
+ ("qkv_proj", "k_proj", "k"),
+ ("qkv_proj", "v_proj", "v"),
+ ("gate_up_proj", "gate_proj", 0),
+ ("gate_up_proj", "up_proj", 1),
+ ]
+ if self.num_fused_shared_experts > 0:
+ assert self.num_fused_shared_experts == 1
+ weights_list = list(weights)
+ weights_dict = dict(weights_list)
+ if self.quant_config is not None:
+ if self.quant_config.get_name() == "w8a8_int8":
+ suffix_list = [
+ "down_proj.weight",
+ "down_proj.weight_scale",
+ "gate_proj.weight",
+ "gate_proj.weight_scale",
+ "up_proj.weight",
+ "up_proj.weight_scale",
+ ]
+ elif (
+ self.quant_config.get_name() == "fp8"
+ or self.quant_config.get_name() == "blockwise_int8"
+ or self.quant_config.get_name() == "compressed_tensors"
+ ):
+ suffix_list = [
+ "down_proj.weight",
+ "down_proj.weight_scale",
+ "gate_proj.weight",
+ "gate_proj.weight_scale",
+ "up_proj.weight",
+ "up_proj.weight_scale",
+ ]
+ elif self.quant_config.get_name() == "awq":
+ suffix_list = [
+ "down_proj.qweight",
+ "down_proj.qzeros",
+ "down_proj.scales",
+ "gate_proj.qweight",
+ "gate_proj.qzeros",
+ "gate_proj.scales",
+ "up_proj.qweight",
+ "up_proj.qzeros",
+ "up_proj.scales",
+ ]
+ elif self.quant_config.get_name() == "modelopt_fp4":
+ suffix_list = [
+ "down_proj.weight",
+ "down_proj.weight_scale",
+ "down_proj.weight_scale_2",
+ "down_proj.input_scale",
+ "gate_proj.weight",
+ "gate_proj.weight_scale",
+ "gate_proj.weight_scale_2",
+ "gate_proj.input_scale",
+ "up_proj.weight",
+ "up_proj.weight_scale",
+ "up_proj.weight_scale_2",
+ "up_proj.input_scale",
+ ]
+ else:
+ raise ValueError(
+ f"Unsupported shared expert fusion for quantization: {self.quant_config.get_name()}."
+ )
+ else:
+ suffix_list = [
+ "down_proj.weight",
+ "gate_proj.weight",
+ "up_proj.weight",
+ ]
+ names_to_remove = []
+
+ moe_layers = (
+ range(
+ self.config.first_k_dense_replace,
+ self.config.num_hidden_layers,
+ self.config.moe_layer_freq,
+ )
+ if not is_nextn
+ else [nextn_layer_id]
+ )
+
+ for moe_layer in moe_layers:
+ for suffix in suffix_list:
+ shared_expert_weight_name = (
+ f"model.layers.{moe_layer}.mlp.shared_experts.{suffix}"
+ )
+ # online fp8 quantization does not load weight_scale
+ if shared_expert_weight_name not in weights_dict:
+ continue
+ weights_list.append(
+ (
+ f"model.layers.{moe_layer}."
+ f"mlp.experts."
+ f"{self.config.n_routed_experts + 0}"
+ f".{suffix}",
+ weights_dict[shared_expert_weight_name],
+ )
+ )
+ names_to_remove += [shared_expert_weight_name]
+ weights = [w for w in weights_list if w[0] not in names_to_remove]
+
+ # Params for weights, fp8 weight scales, fp8 activation scales
+ # (param_name, weight_name, expert_id, shard_id)
+ expert_params_mapping = get_moe_impl_class().make_expert_params_mapping(
+ ckpt_gate_proj_name="gate_proj",
+ ckpt_down_proj_name="down_proj",
+ ckpt_up_proj_name="up_proj",
+ num_experts=self.config.n_routed_experts + self.num_fused_shared_experts,
+ )
+
+ # Fuse q_a_proj and kv_a_proj_with_mqa along output dimension when q_lora_rank is not None
+ fuse_qkv_a_proj = hasattr(self.config, "q_lora_rank") and (
+ self.config.q_lora_rank is not None
+ )
+ cached_a_proj = {} if fuse_qkv_a_proj else None
+
+ if is_nextn:
+ nextn_layer_prefix = f"model.layers.{nextn_layer_id}"
+ nextn_spec_weight_names = [
+ "shared_head.norm",
+ "eh_proj",
+ "enorm",
+ "hnorm",
+ ]
+
+ params_dict = dict(self.named_parameters())
+ weight_names = []
+ for name, loaded_weight in weights:
+ weight_names.append(name)
+
+ if not is_nextn:
+ if hasattr(self.config, "num_nextn_predict_layers"):
+ num_nextn_layers = self.config.num_nextn_predict_layers
+ if num_nextn_layers > 0 and name.startswith("model.layers"):
+ name_list = name.split(".")
+ if (
+ len(name_list) >= 3
+ and int(name_list[2]) >= self.config.num_hidden_layers
+ ):
+ continue
+ else:
+ if not name.startswith(nextn_layer_prefix):
+ continue
+
+ # Use shared head and embed weights from target model
+ if "shared_head.head" in name or "embed_tokens" in name:
+ continue
+
+ is_decoder = True
+ # For nextn specific weights
+ for weight_name in nextn_spec_weight_names:
+ if weight_name in name:
+ name = name.replace(nextn_layer_prefix, "model")
+ is_decoder = False
+ break
+ # For decoder layer weights
+ if is_decoder:
+ name = name.replace(nextn_layer_prefix, "model.decoder")
+
+ if "language_model." in name:
+ name = name.replace("language_model.", "")
+ if "model.visual." in name:
+ name = name.replace("model.visual.", "visual.")
+ if "rotary_emb.inv_freq" in name:
+ continue
+ for param_name, weight_name, shard_id in stacked_params_mapping:
+ # Skip non-stacked layers and experts (experts handled below).
+ if weight_name not in name:
+ continue
+ # We have mlp.experts[0].gate_proj in the checkpoint.
+ # Since we handle the experts below in expert_params_mapping,
+ # we need to skip here BEFORE we update the name, otherwise
+ # name will be updated to mlp.experts[0].gate_up_proj, which
+ # will then be updated below in expert_params_mapping
+ # for mlp.experts[0].gate_gate_up_proj, which breaks load.
+ if ("mlp.experts." in name) and name not in params_dict:
+ continue
+ name = name.replace(weight_name, param_name)
+ # Skip loading extra bias for GPTQ models.
+ if name.endswith(".bias") and name not in params_dict:
+ continue
+ param = params_dict[name]
+
+ weight_loader = param.weight_loader
+ weight_loader(param, loaded_weight, shard_id)
+ break
+ else:
+ for mapping in expert_params_mapping:
+ param_name, weight_name, expert_id, shard_id = mapping
+ if weight_name not in name:
+ continue
+ name = name.replace(weight_name, param_name)
+ param = params_dict[name]
+ weight_loader = param.weight_loader
+ weight_loader(
+ param,
+ loaded_weight,
+ name,
+ shard_id=shard_id,
+ expert_id=expert_id,
+ )
+ break
+ else:
+ if "visual" in name:
+ # adapt to VisionAttention
+ name = name.replace(r"attn.qkv.", r"attn.qkv_proj.")
+
+ # Skip loading extra bias for GPTQ models.
+ if name.endswith(".bias") and name not in params_dict:
+ continue
+ if fuse_qkv_a_proj and (
+ "q_a_proj" in name or "kv_a_proj_with_mqa" in name
+ ):
+ cached_a_proj[name] = loaded_weight
+ q_a_proj_name = (
+ name
+ if "q_a_proj" in name
+ else name.replace("kv_a_proj_with_mqa", "q_a_proj")
+ )
+ kv_a_proj_name = (
+ name
+ if "kv_a_proj_with_mqa" in name
+ else name.replace("q_a_proj", "kv_a_proj_with_mqa")
+ )
+
+ # When both q_a_proj and kv_a_proj_with_mqa has been cached, load the fused weight to parameter
+ if (
+ q_a_proj_name in cached_a_proj
+ and kv_a_proj_name in cached_a_proj
+ ):
+ q_a_proj_weight = cached_a_proj[q_a_proj_name]
+ kv_a_proj_weight = cached_a_proj[kv_a_proj_name]
+ fused_weight = torch.cat(
+ [q_a_proj_weight, kv_a_proj_weight], dim=0
+ )
+ param_name = (
+ name.replace("q_a_proj", "fused_qkv_a_proj_with_mqa")
+ if "q_a_proj" in name
+ else name.replace(
+ "kv_a_proj_with_mqa", "fused_qkv_a_proj_with_mqa"
+ )
+ )
+ param = params_dict[param_name]
+
+ weight_loader = getattr(
+ param, "weight_loader", default_weight_loader
+ )
+ weight_loader(param, fused_weight)
+ cached_a_proj.pop(q_a_proj_name)
+ cached_a_proj.pop(kv_a_proj_name)
+ else:
+ if (
+ "k_scale" in name or "v_scale" in name
+ ) and name not in params_dict:
+ # modelopt attn kv scale is named differently
+ if any(scale in name for scale in ["k_scale", "v_scale"]):
+ name = name.replace("_proj", "attn_mqa")
+ else:
+ logger.warning(
+ f"Unknown scale found in checkpoint: {name}"
+ )
+ param = params_dict[name]
+ weight_loader = getattr(
+ param, "weight_loader", default_weight_loader
+ )
+ weight_loader(param, loaded_weight)
+
+
+EntryClass = [Glm4vMoeForConditionalGeneration]
diff --git a/python/sglang/srt/multimodal/processors/base_processor.py b/python/sglang/srt/multimodal/processors/base_processor.py
index 760d3c26f..db80184c3 100644
--- a/python/sglang/srt/multimodal/processors/base_processor.py
+++ b/python/sglang/srt/multimodal/processors/base_processor.py
@@ -22,13 +22,19 @@ class BaseMultiModalProcessorOutput:
input_text: str
# frames loaded from image, in given order
- images: Optional[list[Union[Image.Image, dict]]] = None
+ images: Optional[list[Union[Image.Image, dict]]] = dataclasses.field(
+ default_factory=list
+ )
# videos
- videos: Optional[list[Union[torch.Tensor, dict]]] = None
+ videos: Optional[list[Union[torch.Tensor, dict]]] = dataclasses.field(
+ default_factory=list
+ )
# audios
- audios: Optional[list[Union[np.ndarray, dict]]] = None
+ audios: Optional[list[Union[np.ndarray, dict]]] = dataclasses.field(
+ default_factory=list
+ )
def organize_results(self) -> List[Tuple[Modality, Any]]:
"""
diff --git a/python/sglang/srt/multimodal/processors/glm4v.py b/python/sglang/srt/multimodal/processors/glm4v.py
new file mode 100644
index 000000000..58c55c0f8
--- /dev/null
+++ b/python/sglang/srt/multimodal/processors/glm4v.py
@@ -0,0 +1,132 @@
+import re
+from typing import List, Union
+
+from decord import VideoReader
+from transformers.video_utils import VideoMetadata
+
+from sglang.srt.layers.rotary_embedding import MRotaryEmbedding
+from sglang.srt.models.glm4v import Glm4vForConditionalGeneration
+from sglang.srt.models.glm4v_moe import Glm4vMoeForConditionalGeneration
+from sglang.srt.multimodal.processors.base_processor import (
+ BaseMultimodalProcessor as SGLangBaseProcessor,
+)
+from sglang.srt.multimodal.processors.base_processor import (
+ BaseMultiModalProcessorOutput,
+ MultimodalSpecialTokens,
+)
+
+
+class Glm4vImageProcessor(SGLangBaseProcessor):
+ models = [Glm4vForConditionalGeneration, Glm4vMoeForConditionalGeneration]
+
+ def __init__(self, hf_config, server_args, _processor, *args, **kwargs):
+ super().__init__(hf_config, server_args, _processor, *args, **kwargs)
+
+ # GLM-4.1V and GLM-4.5V specific tokens
+ self.IMAGE_TOKEN = "<|image|>"
+ self.VIDEO_TOKEN = "<|video|>"
+ self.IMAGE_START_TOKEN = "<|begin_of_image|>"
+ self.IMAGE_END_TOKEN = "<|end_of_image|>"
+ self.VIDEO_START_TOKEN = "<|begin_of_video|>"
+ self.VIDEO_END_TOKEN = "<|end_of_video|>"
+
+ # Token IDs
+ self.IM_TOKEN_ID = hf_config.image_token_id
+ self.VIDEO_TOKEN_ID = hf_config.video_token_id
+ self.IMAGE_START_TOKEN_ID = hf_config.image_start_token_id
+ self.IMAGE_END_TOKEN_ID = hf_config.image_end_token_id
+ self.VIDEO_START_TOKEN_ID = hf_config.video_start_token_id
+ self.VIDEO_END_TOKEN_ID = hf_config.video_end_token_id
+
+ # Vision config
+ self.IMAGE_FACTOR = 28
+ self.MIN_PIXELS = 112 * 112
+ self.MAX_PIXELS = 30000 * 28 * 28 * 2
+
+ self.mm_tokens = MultimodalSpecialTokens(
+ image_token=self.IMAGE_TOKEN,
+ image_token_id=self.IM_TOKEN_ID,
+ video_token=self.VIDEO_TOKEN,
+ # Note: For GLM4v videos, it uses the video token before tokenization but uses image token after tokenization
+ video_token_id=self.IM_TOKEN_ID,
+ ).build(_processor)
+
+ # adapted from https://github.com/huggingface/transformers/blob/369c99d0cea403b77bd0aef818527106453fd9fc/src/transformers/video_utils.py#L312
+ async def preprocess_video(self, vr: VideoReader):
+ """
+ Preprocess video using VideoReader from Decord backend.
+
+ Args:
+ vr (VideoReader): VideoReader object from decord
+
+ Returns:
+ tuple: A tuple containing processed frames and metadata
+ """
+ video_fps = vr.get_avg_fps()
+ total_num_frames = len(vr)
+ duration = total_num_frames / video_fps if video_fps else 0
+
+ metadata = VideoMetadata(
+ total_num_frames=int(total_num_frames),
+ fps=float(video_fps),
+ duration=float(duration),
+ video_backend="decord",
+ )
+
+ # Extract all frames
+ indices = list(range(total_num_frames))
+ frames = vr.get_batch(indices).asnumpy()
+ metadata.frames_indices = indices
+
+ return frames, metadata
+
+ async def process_mm_data_async(
+ self,
+ image_data: List[Union[str, bytes]],
+ input_text,
+ request_obj,
+ *args,
+ **kwargs,
+ ):
+ base_output = self.load_mm_data(
+ prompt=input_text,
+ image_data=image_data,
+ video_data=request_obj.video_data,
+ multimodal_tokens=self.mm_tokens,
+ )
+
+ video_metadata = None
+
+ if base_output.videos:
+ videos_processed = [
+ await self.preprocess_video(video) for video in base_output.videos
+ ]
+ base_output.videos, video_metadata = map(list, zip(*videos_processed))
+ # transformer requires the video inputs to be under this format
+ base_output.videos = [base_output.videos]
+ video_metadata = [video_metadata]
+
+ mm_items, input_ids, ret = self.process_and_combine_mm_data(
+ base_output, self.mm_tokens, video_metadata=video_metadata
+ )
+
+ input_ids = input_ids.flatten()
+ mrope_positions, mrope_position_delta = MRotaryEmbedding.get_rope_index_glm4v(
+ input_ids=input_ids.unsqueeze(0),
+ hf_config=self.hf_config,
+ image_grid_thw=getattr(ret, "image_grid_thw", None),
+ video_grid_thw=getattr(ret, "video_grid_thw", None),
+ attention_mask=getattr(ret, "attention_mask", None),
+ )
+ mrope_positions = mrope_positions.squeeze(1)
+
+ mm_inputs = {
+ "input_ids": input_ids.tolist(),
+ "mm_items": mm_items,
+ "im_token_id": self.mm_tokens.image_token_id,
+ "video_token_id": self.mm_tokens.video_token_id,
+ "mrope_positions": mrope_positions,
+ "mrope_position_delta": mrope_position_delta,
+ }
+
+ return mm_inputs
diff --git a/python/sglang/srt/utils.py b/python/sglang/srt/utils.py
index edf441945..a234e7547 100644
--- a/python/sglang/srt/utils.py
+++ b/python/sglang/srt/utils.py
@@ -815,7 +815,7 @@ def load_video(video_file: Union[str, bytes], use_gpu: bool = True):
vr = VideoReader(tmp_file.name, ctx=ctx)
elif video_file.startswith("data:"):
_, encoded = video_file.split(",", 1)
- video_bytes = base64.b64decode(encoded)
+ video_bytes = pybase64.b64decode(encoded)
tmp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4")
tmp_file.write(video_bytes)
tmp_file.close()
@@ -823,7 +823,7 @@ def load_video(video_file: Union[str, bytes], use_gpu: bool = True):
elif os.path.isfile(video_file):
vr = VideoReader(video_file, ctx=ctx)
else:
- video_bytes = base64.b64decode(video_file)
+ video_bytes = pybase64.b64decode(video_file)
tmp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4")
tmp_file.write(video_bytes)
tmp_file.close()
diff --git a/test/srt/openai_server/function_call/test_openai_function_calling.py b/test/srt/openai_server/function_call/test_openai_function_calling.py
index 4efc04386..291ef98b7 100644
--- a/test/srt/openai_server/function_call/test_openai_function_calling.py
+++ b/test/srt/openai_server/function_call/test_openai_function_calling.py
@@ -948,5 +948,6 @@ class TestOpenAIPythonicFunctionCalling(CustomTestCase):
# def test_function_calling_multiturn(self):
# self._test_function_calling_multiturn()
+
if __name__ == "__main__":
unittest.main()
diff --git a/test/srt/test_function_call_parser.py b/test/srt/test_function_call_parser.py
index afbba82e3..0c8cabfa6 100644
--- a/test/srt/test_function_call_parser.py
+++ b/test/srt/test_function_call_parser.py
@@ -497,6 +497,17 @@ class TestEBNFGeneration(unittest.TestCase):
},
),
),
+ Tool(
+ type="function",
+ function=Function(
+ name="empty_param_func",
+ description="Function with empty parameters",
+ parameters={
+ "properties": {},
+ "required": [],
+ },
+ ),
+ ),
]
self.tokenizer = get_tokenizer(DEFAULT_SMALL_MODEL_NAME_FOR_TEST)
@@ -630,16 +641,21 @@ class TestEBNFGeneration(unittest.TestCase):
self.assertIsNotNone(ebnf)
# Check that the EBNF contains expected patterns for XML format
self.assertIn('"" function_call ""', ebnf)
- self.assertIn('"get_weather" "\\n" arguments_get_weather', ebnf)
+ self.assertIn('"get_weather" "\\n" ( arguments_get_weather "\\n" )?', ebnf)
self.assertIn(
'"location" "\\n" "" xml_text "" ( "\\n" ( "unit" "\\n" "" ("celsius" | "fahrenheit") "" ) )?',
ebnf,
)
- self.assertIn('"search" "\\n" arguments_search', ebnf)
+ self.assertIn('"search" "\\n" ( arguments_search "\\n" )?', ebnf)
self.assertIn(
'"query" "\\n" "" xml_text ""',
ebnf,
)
+ self.assertIn(
+ '"empty_param_func" "\\n" ( arguments_empty_param_func "\\n" )?', ebnf
+ )
+ self.assertIn('arguments_empty_param_func ::= ""', ebnf)
+
# Validate that the EBNF can be compiled by GrammarCompiler
try:
ctx = self.grammar_compiler.compile_grammar(ebnf)
diff --git a/test/srt/test_jinja_template_utils.py b/test/srt/test_jinja_template_utils.py
index 7764659d2..a861ac824 100644
--- a/test/srt/test_jinja_template_utils.py
+++ b/test/srt/test_jinja_template_utils.py
@@ -60,6 +60,86 @@ class TestTemplateContentFormatDetection(CustomTestCase):
result = detect_jinja_template_content_format("")
self.assertEqual(result, "string")
+ def test_detect_msg_content_pattern(self):
+ """Test detection of template with msg.content pattern (should be 'openai' format)."""
+ msg_content_pattern = """
+[gMASK]
+{%- for msg in messages %}
+ {%- if msg.role == 'system' %}
+<|system|>
+{{ msg.content }}
+ {%- elif msg.role == 'user' %}
+<|user|>{{ '\n' }}
+ {%- if msg.content is string %}
+{{ msg.content }}
+ {%- else %}
+ {%- for item in msg.content %}
+ {%- if item.type == 'video' or 'video' in item %}
+<|begin_of_video|><|video|><|end_of_video|>
+ {%- elif item.type == 'image' or 'image' in item %}
+<|begin_of_image|><|image|><|end_of_image|>
+ {%- elif item.type == 'text' %}
+{{ item.text }}
+ {%- endif %}
+ {%- endfor %}
+ {%- endif %}
+ {%- elif msg.role == 'assistant' %}
+ {%- if msg.metadata %}
+<|assistant|>{{ msg.metadata }}
+{{ msg.content }}
+ {%- else %}
+<|assistant|>
+{{ msg.content }}
+ {%- endif %}
+ {%- endif %}
+{%- endfor %}
+{% if add_generation_prompt %}<|assistant|>
+{% endif %}
+ """
+
+ result = detect_jinja_template_content_format(msg_content_pattern)
+ self.assertEqual(result, "openai")
+
+ def test_detect_m_content_pattern(self):
+ """Test detection of template with m.content pattern (should be 'openai' format)."""
+ msg_content_pattern = """
+[gMASK]
+{%- for m in messages %}
+ {%- if m.role == 'system' %}
+<|system|>
+{{ m.content }}
+ {%- elif m.role == 'user' %}
+<|user|>{{ '\n' }}
+ {%- if m.content is string %}
+{{ m.content }}
+ {%- else %}
+ {%- for item in m.content %}
+ {%- if item.type == 'video' or 'video' in item %}
+<|begin_of_video|><|video|><|end_of_video|>
+ {%- elif item.type == 'image' or 'image' in item %}
+<|begin_of_image|><|image|><|end_of_image|>
+ {%- elif item.type == 'text' %}
+{{ item.text }}
+ {%- endif %}
+ {%- endfor %}
+ {%- endif %}
+ {%- elif m.role == 'assistant' %}
+ {%- if m.metadata %}
+<|assistant|>{{ m.metadata }}
+{{ m.content }}
+ {%- else %}
+<|assistant|>
+{{ m.content }}
+ {%- endif %}
+ {%- endif %}
+{%- endfor %}
+{% if add_generation_prompt %}<|assistant|>
+{% endif %}
+ """
+
+ result = detect_jinja_template_content_format(msg_content_pattern)
+ self.assertEqual(result, "openai")
+
def test_process_content_openai_format(self):
"""Test content processing for openai format."""
msg_dict = {
diff --git a/test/srt/test_vision_openai_server_b.py b/test/srt/test_vision_openai_server_b.py
index f954aee48..95941149d 100644
--- a/test/srt/test_vision_openai_server_b.py
+++ b/test/srt/test_vision_openai_server_b.py
@@ -348,6 +348,33 @@ class TestVILAServer(TestOpenAIVisionServer):
cls.base_url += "/v1"
+# Skip for ci test
+# class TestGLM41VServer(TestOpenAIVisionServer):
+# @classmethod
+# def setUpClass(cls):
+# cls.model = "zai-org/GLM-4.1V-9B-Thinking"
+# cls.base_url = DEFAULT_URL_FOR_TEST
+# cls.api_key = "sk-123456"
+# cls.process = popen_launch_server(
+# cls.model,
+# cls.base_url,
+# timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
+# other_args=[
+# "--trust-remote-code",
+# "--mem-fraction-static",
+# "0.68",
+# "--cuda-graph-max-bs",
+# "4",
+# "--reasoning-parser",
+# "glm45",
+# ],
+# )
+# cls.base_url += "/v1"
+
+# def test_video_chat_completion(self):
+# self._test_video_chat_completion()
+
+
if __name__ == "__main__":
del TestOpenAIVisionServer
unittest.main()
diff --git a/test/srt/test_vision_openai_server_common.py b/test/srt/test_vision_openai_server_common.py
index a8c0aac38..7e30b3de2 100644
--- a/test/srt/test_vision_openai_server_common.py
+++ b/test/srt/test_vision_openai_server_common.py
@@ -96,8 +96,13 @@ class TestOpenAIVisionServer(CustomTestCase):
), f"text: {text}, should contain cab, taxi, SUV, vehicle or car"
# MiniCPMO fails to recognize `iron`, but `hanging`
assert (
- "iron" in text or "hang" in text or "cloth" in text or "holding" in text
- ), f"text: {text}, should contain iron, hang, cloth or holding"
+ "iron" in text
+ or "hang" in text
+ or "cloth" in text
+ or "coat" in text
+ or "holding" in text
+ or "outfit" in text
+ ), f"text: {text}, should contain iron, hang, cloth, coat or holding or outfit"
assert response.id
assert response.created
assert response.usage.prompt_tokens > 0
@@ -193,11 +198,15 @@ class TestOpenAIVisionServer(CustomTestCase):
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
- ), f"text: {text}, should contain man, cab, SUV or taxi"
+ "man" in text
+ or "cab" in text
+ or "SUV" in text
+ or "taxi" in text
+ or "car" in text
+ ), f"text: {text}, should contain man, cab, SUV, taxi or car"
assert (
- "logo" in text or '"S"' in text or "SG" in text
- ), f"text: {text}, should contain logo, S or SG"
+ "logo" in text or '"S"' in text or "SG" in text or "graphic" in text
+ ), f"text: {text}, should contain logo, S or SG or graphic"
assert response.id
assert response.created
assert response.usage.prompt_tokens > 0
@@ -320,11 +329,12 @@ class TestOpenAIVisionServer(CustomTestCase):
or "individual" in video_response
or "speaker" in video_response
or "Steve" in video_response
+ or "hand" in video_response
), f"""
====================== video_response =====================
{video_response}
===========================================================
- should contain 'man' or 'person' or 'individual' or 'speaker'
+ should contain 'man' or 'person' or 'individual' or 'speaker' or 'hand'
"""
assert (
"present" in video_response
@@ -375,7 +385,8 @@ class TestOpenAIVisionServer(CustomTestCase):
or "person" in video_response
or "individual" in video_response
or "speaker" in video_response
- ), f"video_response: {video_response}, should either have 'man' in video_response, or 'person' in video_response, or 'individual' in video_response or 'speaker' in video_response"
+ or "hand" in video_response
+ ), f"video_response: {video_response}, should either have 'man' in video_response, or 'person' in video_response, or 'individual' in video_response, or 'speaker' in video_response or 'hand' in video_response"
assert (
"present" in video_response
or "examine" in video_response