A high-throughput and memory-efficient inference and serving engine for LLMs
Mirrored from https://github.com/tduncan-lum/Nvidia-Optimization-Stack---VLLM.git
Synced Nov 7, 15:15 UTC
Revert "Add GlmOcrConfig for GLM-OCR model type recognition" (#35512)
Harry Mellor committed
Feb 27, 2026 at 14:13 UTC
fbe3f0120a5e786a1459c982c72185311c78a276
3 files changed
-96
vllm/transformers_utils/config.py
-1
@@ -82,7 +82,6 @@ _CONFIG_REGISTRY: dict[str, type[PretrainedConfig]] = LazyConfigDict(
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deepseek_v32="DeepseekV3Config",
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flex_olmo="FlexOlmoConfig",
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funaudiochat="FunAudioChatConfig",
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- glm_ocr="GlmOcrConfig",
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hunyuan_vl="HunYuanVLConfig",
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isaac="IsaacConfig",
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kimi_linear="KimiLinearConfig",
vllm/transformers_utils/configs/__init__.py
-4
@@ -28,8 +28,6 @@ _CLASS_TO_MODULE: dict[str, str] = {
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"FlexOlmoConfig": "vllm.transformers_utils.configs.flex_olmo",
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"FunAudioChatConfig": "vllm.transformers_utils.configs.funaudiochat",
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"FunAudioChatAudioEncoderConfig": "vllm.transformers_utils.configs.funaudiochat",
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- "GlmOcrConfig": "vllm.transformers_utils.configs.glm_ocr",
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- "GlmOcrVisionConfig": "vllm.transformers_utils.configs.glm_ocr",
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"HunYuanVLConfig": "vllm.transformers_utils.configs.hunyuan_vl",
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"HunYuanVLTextConfig": "vllm.transformers_utils.configs.hunyuan_vl",
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"HunYuanVLVisionConfig": "vllm.transformers_utils.configs.hunyuan_vl",
@@ -85,8 +83,6 @@ __all__ = [
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"FlexOlmoConfig",
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"FunAudioChatConfig",
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"FunAudioChatAudioEncoderConfig",
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- "GlmOcrConfig",
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- "GlmOcrVisionConfig",
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"HunYuanVLConfig",
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"HunYuanVLTextConfig",
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"HunYuanVLVisionConfig",
vllm/transformers_utils/configs/glm_ocr.py
deleted
-91
@@ -1,91 +0,0 @@
1
-# SPDX-License-Identifier: Apache-2.0
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-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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-from __future__ import annotations
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-
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-from typing import Any
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-
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-from transformers.configuration_utils import PretrainedConfig
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-
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-
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-class GlmOcrVisionConfig(PretrainedConfig):
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- model_type = "glm_ocr_vision"
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-
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- def __init__(
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- self,
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- hidden_size: int = 1024,
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- depth: int = 24,
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- num_heads: int = 16,
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- attention_bias: bool = True,
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- intermediate_size: int = 4096,
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- hidden_act: str = "silu",
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- hidden_dropout_prob: float = 0.0,
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- initializer_range: float = 0.02,
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- image_size: int = 336,
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- in_channels: int = 3,
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- patch_size: int = 14,
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- out_hidden_size: int = 1536,
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- rms_norm_eps: float = 1e-5,
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- spatial_merge_size: int = 2,
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- temporal_patch_size: int = 2,
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- **kwargs: Any,
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- ):
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- super().__init__(**kwargs)
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- self.hidden_size = hidden_size
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- self.depth = depth
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- self.num_heads = num_heads
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- self.attention_bias = attention_bias
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- self.intermediate_size = intermediate_size
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- self.hidden_act = hidden_act
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- self.hidden_dropout_prob = hidden_dropout_prob
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- self.initializer_range = initializer_range
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- self.image_size = image_size
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- self.in_channels = in_channels
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- self.patch_size = patch_size
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- self.out_hidden_size = out_hidden_size
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- self.rms_norm_eps = rms_norm_eps
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- self.spatial_merge_size = spatial_merge_size
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- self.temporal_patch_size = temporal_patch_size
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-
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-
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-class GlmOcrConfig(PretrainedConfig):
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- model_type = "glm_ocr"
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-
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- def __init__(
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- self,
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- text_config: dict | None = None,
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- vision_config: dict | None = None,
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- image_start_token_id: int = 59256,
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- image_end_token_id: int = 59257,
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- video_start_token_id: int = 59258,
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- video_end_token_id: int = 59259,
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- image_token_id: int = 59280,
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- video_token_id: int = 59281,
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- **kwargs: Any,
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- ):
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- super().__init__(**kwargs)
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- self.image_start_token_id = image_start_token_id
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- self.image_end_token_id = image_end_token_id
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- self.video_start_token_id = video_start_token_id
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- self.video_end_token_id = video_end_token_id
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- self.image_token_id = image_token_id
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- self.video_token_id = video_token_id
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- self.vision_config = GlmOcrVisionConfig(**(vision_config or {}))
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-
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- if isinstance(text_config, dict):
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- from transformers import AutoConfig
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-
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- model_type = text_config.get("model_type", "chatglm")
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- self.text_config = AutoConfig.for_model(model_type, **text_config)
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- elif text_config is None:
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- from transformers import AutoConfig
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-
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- self.text_config = AutoConfig.for_model("chatglm")
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- else:
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- self.text_config = text_config
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-
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- def get_text_config(self) -> PretrainedConfig:
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- return self.text_config
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-
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- def save_pretrained(self, save_directory, **kwargs):
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- self._auto_class = None
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- super().save_pretrained(save_directory, **kwargs)