[MODEL ADDITION] Ovis2 Model Addition (#15826)

Signed-off-by: Marco <121761685+mlinmg@users.noreply.github.com>
Signed-off-by: Isotr0py <2037008807@qq.com>
Signed-off-by: isotr0py <2037008807@qq.com>
Co-authored-by: Isotr0py <2037008807@qq.com>
Co-authored-by: Isotr0py <mozf@mail2.sysu.edu.cn>

vllm/transformers_utils/configs/__init__.py

@@ -23,6 +23,7 @@ from vllm.transformers_utils.configs.moonvit import MoonViTConfig
 from vllm.transformers_utils.configs.mpt import MPTConfig
 from vllm.transformers_utils.configs.nemotron import NemotronConfig
 from vllm.transformers_utils.configs.nvlm_d import NVLM_D_Config
+from vllm.transformers_utils.configs.ovis2 import OvisConfig
 from vllm.transformers_utils.configs.skyworkr1v import SkyworkR1VChatConfig
 from vllm.transformers_utils.configs.solar import SolarConfig
 from vllm.transformers_utils.configs.telechat2 import Telechat2Config
@@ -49,6 +50,7 @@ __all__ = [
     "KimiVLConfig",
     "NemotronConfig",
     "NVLM_D_Config",
+    "OvisConfig",
     "SkyworkR1VChatConfig",
     "SolarConfig",
     "Telechat2Config",

vllm/transformers_utils/configs/ovis2.py

@@ -0,0 +1,170 @@
+# SPDX-License-Identifier: Apache-2.0
+
+# yapf: disable
+# ruff: noqa: E501
+# copied from https://huggingface.co/AIDC-AI/Ovis2-1B/blob/main/configuration_aimv2.py
+# and https://huggingface.co/AIDC-AI/Ovis2-1B/blob/main/configuration_ovis.py
+from typing import Any, Optional, Union
+
+from transformers import AutoConfig, PretrainedConfig
+
+
+class AIMv2Config(PretrainedConfig):
+    """This is the configuration class to store the configuration of an [`AIMv2Model`].
+
+    Instantiating a configuration with the defaults will yield a similar configuration
+    to that of the [apple/aimv2-large-patch14-224](https://huggingface.co/apple/aimv2-large-patch14-224).
+
+    Args:
+        hidden_size: Dimension of the hidden representations.
+        intermediate_size: Dimension of the SwiGLU representations.
+        num_hidden_layers: Number of hidden layers in the Transformer.
+        num_attention_heads: Number of attention heads for each attention layer
+            in the Transformer.
+        num_channels: Number of input channels.
+        image_size: Image size.
+        patch_size: Patch size.
+        rms_norm_eps: Epsilon value used for the RMS normalization layer.
+        attention_dropout: Dropout ratio for attention probabilities.
+        projection_dropout: Dropout ratio for the projection layer after the attention.
+        qkv_bias: Whether to add a bias to the queries, keys and values.
+        use_bias: Whether to add a bias in the feed-forward and projection layers.
+        kwargs: Keyword arguments for the [`PretrainedConfig`].
+    """
+
+    model_type: str = "aimv2"
+
+    def __init__(
+        self,
+        hidden_size: int = 1024,
+        intermediate_size: int = 2816,
+        num_hidden_layers: int = 24,
+        num_attention_heads: int = 8,
+        num_channels: int = 3,
+        image_size: int = 224,
+        patch_size: int = 14,
+        rms_norm_eps: float = 1e-5,
+        attention_dropout: float = 0.0,
+        projection_dropout: float = 0.0,
+        qkv_bias: bool = False,
+        use_bias: bool = False,
+        **kwargs: Any,
+    ):
+        super().__init__(**kwargs)
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.num_channels = num_channels
+        self.patch_size = patch_size
+        self.image_size = image_size
+        self.attention_dropout = attention_dropout
+        self.rms_norm_eps = rms_norm_eps
+
+        self.projection_dropout = projection_dropout
+        self.qkv_bias = qkv_bias
+        self.use_bias = use_bias
+
+
+IGNORE_ID = -100
+IMAGE_TOKEN_ID = -200
+IMAGE_TOKEN = "<image>"
+IMAGE_ATOM_ID = -300
+IMAGE_INDICATOR_IDS = [-301, -302, -303, -304, -305]
+
+AutoConfig.register("aimv2", AIMv2Config)
+
+
+# ----------------------------------------------------------------------
+#                     Visual Tokenizer Configuration
+# ----------------------------------------------------------------------
+class BaseVisualTokenizerConfig(PretrainedConfig):
+
+    def __init__(self,
+                 vocab_size=16384,
+                 tokenize_function="softmax",
+                 tau=1.0,
+                 depths=None,
+                 drop_cls_token=False,
+                 backbone_config: Optional[Union[PretrainedConfig,
+                                                 dict]] = None,
+                 hidden_stride: int = 1,
+                 **kwargs):
+        super().__init__(**kwargs)
+        self.vocab_size = vocab_size
+        self.tokenize_function = tokenize_function
+        self.tau = tau
+        if isinstance(depths, str):
+            depths = [int(x) for x in depths.split('|')]
+        self.depths = depths
+        self.backbone_kwargs = dict[str, Any]()
+        self.drop_cls_token = drop_cls_token
+        if backbone_config is not None:
+            assert isinstance(backbone_config, (PretrainedConfig, dict)), \
+                f"expect `backbone_config` to be instance of PretrainedConfig or dict, but got {type(backbone_config)} type"
+            if not isinstance(backbone_config, PretrainedConfig):
+                model_type = backbone_config['model_type']
+                backbone_config.pop('model_type')
+                backbone_config = AutoConfig.for_model(model_type,
+                                                       **backbone_config)
+        self.backbone_config = backbone_config
+        self.hidden_stride = hidden_stride
+
+
+class Aimv2VisualTokenizerConfig(BaseVisualTokenizerConfig):
+    model_type = "aimv2_visual_tokenizer"
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        if self.drop_cls_token:
+            self.drop_cls_token = False
+        if self.depths:
+            assert len(self.depths) == 1
+            self.backbone_kwargs['num_hidden_layers'] = self.depths[0]
+
+
+AutoConfig.register("aimv2_visual_tokenizer", Aimv2VisualTokenizerConfig)
+
+
+# ----------------------------------------------------------------------
+#                           Ovis Configuration
+# ----------------------------------------------------------------------
+class OvisConfig(PretrainedConfig):
+    model_type = "ovis"
+
+    def __init__(self,
+                 llm_config: Optional[Union[PretrainedConfig, dict]] = None,
+                 visual_tokenizer_config: Optional[Union[PretrainedConfig,
+                                                         dict]] = None,
+                 multimodal_max_length=8192,
+                 hidden_size=None,
+                 conversation_formatter_class=None,
+                 llm_attn_implementation=None,
+                 disable_tie_weight=False,
+                 **kwargs):
+        super().__init__(**kwargs)
+        if llm_config is not None:
+            assert isinstance(llm_config, (PretrainedConfig, dict)), \
+                f"expect `llm_config` to be instance of PretrainedConfig or dict, but got {type(llm_config)} type"
+            if not isinstance(llm_config, PretrainedConfig):
+                model_type = llm_config['model_type']
+                llm_config.pop('model_type')
+                llm_config = AutoConfig.for_model(model_type, **llm_config)
+
+        # map llm_config to text_config
+        self.text_config = llm_config
+        if visual_tokenizer_config is not None:
+            assert isinstance(visual_tokenizer_config, (PretrainedConfig, dict)), \
+                f"expect `visual_tokenizer_config` to be instance of PretrainedConfig or dict, but got {type(visual_tokenizer_config)} type"
+            if not isinstance(visual_tokenizer_config, PretrainedConfig):
+                model_type = visual_tokenizer_config['model_type']
+                visual_tokenizer_config.pop('model_type')
+                visual_tokenizer_config = AutoConfig.for_model(
+                    model_type, **visual_tokenizer_config)
+
+        self.visual_tokenizer_config = visual_tokenizer_config
+        self.multimodal_max_length = multimodal_max_length
+        self.hidden_size = hidden_size
+        self.conversation_formatter_class = conversation_formatter_class
+        self.llm_attn_implementation = llm_attn_implementation
+        self.disable_tie_weight = disable_tie_weight