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GLM-4.5V with new class name at transformers (#22520)

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Signed-off-by: zRzRzRzRzRzRzR <2448370773@qq.com>
Signed-off-by: Isotr0py <mozf@mail2.sysu.edu.cn>
Co-authored-by: Isotr0py <mozf@mail2.sysu.edu.cn>
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Yuxuan Zhang 2025-08-09 15:50:21 +08:00 committed by GitHub
parent 2be07a0db1
commit a6022e6fbc
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5 changed files with 13 additions and 6 deletions
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4
docs/models/supported_models.md
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@ -352,6 +352,7 @@ th {
| `Gemma3nForConditionalGeneration` | Gemma 3n | `google/gemma-3n-E2B-it`, `google/gemma-3n-E4B-it`, etc. | | | ✅︎ |
| `GlmForCausalLM` | GLM-4 | `zai-org/glm-4-9b-chat-hf`, etc. | ✅︎ | ✅︎ | ✅︎ |
| `Glm4ForCausalLM` | GLM-4-0414 | `zai-org/GLM-4-32B-0414`, etc. | ✅︎ | ✅︎ | ✅︎ |
| `Glm4MoeForCausalLM` | GLM-4.5 | `zai-org/GLM-4.5`, etc. | ✅︎ | ✅︎ | ✅︎ |
| `GPT2LMHeadModel` | GPT-2 | `gpt2`, `gpt2-xl`, etc. | | ✅︎ | ✅︎ |
| `GPTBigCodeForCausalLM` | StarCoder, SantaCoder, WizardCoder | `bigcode/starcoder`, `bigcode/gpt_bigcode-santacoder`, `WizardLM/WizardCoder-15B-V1.0`, etc. | ✅︎ | ✅︎ | ✅︎ |
| `GPTJForCausalLM` | GPT-J | `EleutherAI/gpt-j-6b`, `nomic-ai/gpt4all-j`, etc. | | ✅︎ | ✅︎ |
@ -609,8 +610,7 @@ These models primarily accept the [`LLM.generate`](./generative_models.md#llmgen
| `Gemma3ForConditionalGeneration` | Gemma 3 | T + I<sup>+</sup> | `google/gemma-3-4b-it`, `google/gemma-3-27b-it`, etc. | ✅︎ | ✅︎ | ⚠️ |
| `GLM4VForCausalLM`<sup>^</sup> | GLM-4V | T + I | `zai-org/glm-4v-9b`, `zai-org/cogagent-9b-20241220`, etc. | ✅︎ | ✅︎ | ✅︎ |
| `Glm4vForConditionalGeneration` | GLM-4.1V-Thinking | T + I<sup>E+</sup> + V<sup>E+</sup> | `zai-org/GLM-4.1V-9B-Thinking`, etc. | ✅︎ | ✅︎ | ✅︎ |
| `Glm4MoeForCausalLM` | GLM-4.5 | T + I<sup>E+</sup> + V<sup>E+</sup> | `zai-org/GLM-4.5`, etc. | ✅︎ | ✅︎ | ✅︎ |
| `Glm4v_moeForConditionalGeneration` | GLM-4.5V | T + I<sup>E+</sup> + V<sup>E+</sup> | `zai-org/GLM-4.5V`, etc. | ✅︎ | ✅︎ | ✅︎ |
| `Glm4vMoeForConditionalGeneration` | GLM-4.5V | T + I<sup>E+</sup> + V<sup>E+</sup> | `zai-org/GLM-4.5V`, etc. | ✅︎ | ✅︎ | ✅︎ |
| `GraniteSpeechForConditionalGeneration` | Granite Speech | T + A | `ibm-granite/granite-speech-3.3-8b` | ✅︎ | ✅︎ | ✅︎ |
| `H2OVLChatModel` | H2OVL | T + I<sup>E+</sup> | `h2oai/h2ovl-mississippi-800m`, `h2oai/h2ovl-mississippi-2b`, etc. | | ✅︎ | ✅︎ |
| `Idefics3ForConditionalGeneration` | Idefics3 | T + I | `HuggingFaceM4/Idefics3-8B-Llama3`, etc. | ✅︎ | | ✅︎ |

2
tests/models/registry.py
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@ -396,7 +396,7 @@ _MULTIMODAL_EXAMPLE_MODELS = {
trust_remote_code=True,
hf_overrides={"architectures": ["GLM4VForCausalLM"]}), # noqa: E501
"Glm4vForConditionalGeneration": _HfExamplesInfo("zai-org/GLM-4.1V-9B-Thinking"), # noqa: E501
"Glm4v_moeForConditionalGeneration": _HfExamplesInfo("zai-org/GLM-4.5V",
"Glm4vMoeForConditionalGeneration": _HfExamplesInfo("zai-org/GLM-4.5V",
is_available_online=False), # noqa: E501
"H2OVLChatModel": _HfExamplesInfo("h2oai/h2ovl-mississippi-800m",
trust_remote_code=True,

8
vllm/model_executor/models/glm4_moe.py
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@ -372,7 +372,13 @@ class Glm4MoeDecoderLayer(nn.Module):
return hidden_states, residual
@support_torch_compile
@support_torch_compile(
dynamic_arg_dims={
"input_ids": 0,
"positions": -1,
"intermediate_tensors": 0,
"inputs_embeds": 0,
})
class Glm4MoeModel(nn.Module):
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):

2
vllm/model_executor/models/registry.py
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@ -207,7 +207,7 @@ _MULTIMODAL_MODELS = {
"Gemma3ForConditionalGeneration": ("gemma3_mm", "Gemma3ForConditionalGeneration"), # noqa: E501
"GLM4VForCausalLM": ("glm4v", "GLM4VForCausalLM"),
"Glm4vForConditionalGeneration": ("glm4_1v", "Glm4vForConditionalGeneration"), # noqa: E501
"Glm4v_moeForConditionalGeneration": ("glm4_1v", "Glm4vForConditionalGeneration"), # noqa: E501
"Glm4vMoeForConditionalGeneration": ("glm4_1v", "Glm4vForConditionalGeneration"), # noqa: E501
"GraniteSpeechForConditionalGeneration": ("granite_speech", "GraniteSpeechForConditionalGeneration"), # noqa: E501
"H2OVLChatModel": ("h2ovl", "H2OVLChatModel"),
"InternVLChatModel": ("internvl", "InternVLChatModel"),

3
vllm/transformers_utils/config.py
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@ -254,7 +254,8 @@ def _uses_mrope(config: PretrainedConfig) -> bool:
def uses_mrope(config: PretrainedConfig) -> bool:
"""Detect if the model with this config uses M-ROPE."""
return _uses_mrope(config) or thinker_uses_mrope(config)
return _uses_mrope(config) or _uses_mrope(
config.get_text_config()) or thinker_uses_mrope(config)
def thinker_uses_mrope(config: PretrainedConfig) -> bool: