# coding=utf-8 # Copyright 2025 The HustVL Team. # Copyright 2025 The Qwen Team and The HuggingFace Inc. team. All rights reserved. # # This code is based on Qwen2.5-VL, which is derived from EleutherAI's GPT-NeoX library # and the GPT-NeoX and OPT implementations. It has been modified to create InfiniteVL. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from transformers.configuration_utils import PretrainedConfig, layer_type_validation from transformers.modeling_rope_utils import rope_config_validation class InfiniteVLVisionConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`InfiniteVLVisionModel`]. Args: depth (`int`, *optional*, defaults to 32): The number of layers in the vision transformer. hidden_size (`int`, *optional*, defaults to 3584): Dimensionality of the encoder layers and the pooler layer. hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): The non-linear activation function (function or string) in the encoder and pooler. intermediate_size (`int`, *optional*, defaults to 3420): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. num_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer encoder. in_channels (`int`, *optional*, defaults to 3): Number of input channels. patch_size (`int`, *optional*, defaults to 14): The size (resolution) of each patch. spatial_merge_size (`int`, *optional*, defaults to 2): The scaling factor for spatial merging of patches. temporal_patch_size (`int`, *optional*, defaults to 2): The size of patches along the temporal dimension. tokens_per_second (`int`, *optional*, defaults to 4): Number of tokens processed per second for video inputs. window_size (`int`, *optional*, defaults to 112): The window size for windowed attention mechanisms. out_hidden_size (`int`, *optional*, defaults to 3584): Dimensionality of the output hidden states. fullatt_block_indexes (`list`, *optional*): Indices of blocks that use full attention instead of windowed attention. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. """ model_type = "infinite_vl" base_config_key = "vision_config" def __init__( self, depth=32, hidden_size=3584, hidden_act="silu", intermediate_size=3420, num_heads=16, in_channels=3, patch_size=14, spatial_merge_size=2, temporal_patch_size=2, tokens_per_second=4, window_size=112, out_hidden_size=3584, fullatt_block_indexes=None, initializer_range=0.02, **kwargs, ): super().__init__(**kwargs) if fullatt_block_indexes is None: fullatt_block_indexes = [7, 15, 23, 31] self.depth = depth self.hidden_size = hidden_size self.hidden_act = hidden_act self.intermediate_size = intermediate_size self.num_heads = num_heads self.in_channels = in_channels self.patch_size = patch_size self.spatial_merge_size = spatial_merge_size self.temporal_patch_size = temporal_patch_size self.tokens_per_second = tokens_per_second self.window_size = window_size self.fullatt_block_indexes = fullatt_block_indexes self.out_hidden_size = out_hidden_size self.initializer_range = initializer_range class InfiniteVLTextConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`InfiniteVLTextModel`]. It is used to instantiate an InfiniteVL model according to the specified arguments, defining the model architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 152064): Vocabulary size of the InfiniteVL model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`InfiniteVLModel`] hidden_size (`int`, *optional*, defaults to 8192): Dimension of the hidden representations. intermediate_size (`int`, *optional*, defaults to 29568): Dimension of the MLP representations. num_hidden_layers (`int`, *optional*, defaults to 80): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 64): Number of attention heads for each attention layer in the Transformer encoder. num_key_value_heads (`int`, *optional*, defaults to 8): This is the number of key_value heads that should be used to implement Grouped Query Attention. If `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. hidden_act (`str` or `function`, *optional*, defaults to `"silu"`): The non-linear activation function (function or string) in the decoder. max_position_embeddings (`int`, *optional*, defaults to 32768): The maximum sequence length that this model might ever be used with. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. rms_norm_eps (`float`, *optional*, defaults to 1e-05): The epsilon used by the rms normalization layers. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if `config.is_decoder=True`. tie_word_embeddings (`bool`, *optional*, defaults to `False`): Whether the model's input and output word embeddings should be tied. rope_theta (`float`, *optional*, defaults to 1000000.0): The base period of the RoPE embeddings. use_sliding_window (`bool`, *optional*, defaults to `False`): Whether to use sliding window attention. sliding_window (`int`, *optional*, defaults to 32768): Sliding window attention (SWA) window size. max_window_layers (`int`, *optional*, defaults to 80): The number of layers using full attention. The first `max_window_layers` layers will use full attention, while any additional layer afterwards will use SWA (Sliding Window Attention). layer_types (`list`, *optional*): Attention pattern for each layer. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. rope_scaling (`Dict`, *optional*): Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value accordingly. expand_v (`float`, *optional*, defaults to 2): Expansion factor for the value dimension in the linear attention/DeltaNet layer. mode (`str`, *optional*, defaults to `"chunk"`): Execution mode for the linear attention layer (e.g., "chunk" or "fused_recurrent"). use_gate (`bool`, *optional*, defaults to `True`): Whether to use the gating mechanism in the DeltaNet layer. use_short_conv (`bool`, *optional*, defaults to `True`): Whether to use short convolution in the linear attention layer. conv_size (`int`, *optional*, defaults to 4): Kernel size for the short convolution. conv_bias (`bool`, *optional*, defaults to `False`): Whether to use bias in the short convolution. num_linear_key_value_heads (`int`, *optional*, defaults to 16): Number of key/value heads used in the linear attention layers. num_linear_heads (`int`, *optional*, defaults to 16): Number of query heads used in the linear attention layers. linear_head_dim (`int`, *optional*, defaults to 128): Dimension of each head in the linear attention layers. norm_eps (`float`, *optional*, defaults to 1e-5): Epsilon value for normalization layers in the linear attention branch. ```python >>> from transformers import InfiniteVLTextModel, InfiniteVLConfig >>> # Initializing an InfiniteVL style configuration >>> configuration = InfiniteVLConfig() >>> # Initializing a model from the InfiniteVL style configuration >>> model = InfiniteVLTextModel(configuration.text_config) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "infinite_vl_text" base_config_key = "text_config" keys_to_ignore_at_inference = ["past_key_values"] # Default tensor parallel plan for base model `InfiniteVL` base_model_tp_plan = { "layers.*.self_attn.q_proj": "colwise", "layers.*.self_attn.k_proj": "colwise", "layers.*.self_attn.v_proj": "colwise", "layers.*.self_attn.o_proj": "rowwise", "layers.*.mlp.gate_proj": "colwise", "layers.*.mlp.up_proj": "colwise", "layers.*.mlp.down_proj": "rowwise", } base_model_pp_plan = { "embed_tokens": (["input_ids"], ["inputs_embeds"]), "layers": (["hidden_states", "attention_mask"], ["hidden_states"]), "norm": (["hidden_states"], ["hidden_states"]), } def __init__( self, vocab_size=152064, hidden_size=8192, intermediate_size=29568, num_hidden_layers=80, num_attention_heads=64, num_key_value_heads=8, head_dim=128, hidden_act="silu", max_position_embeddings=32768, initializer_range=0.02, rms_norm_eps=1e-05, norm_eps=1e-5, use_cache=True, tie_word_embeddings=False, rope_theta=1000000.0, use_sliding_window=False, sliding_window=32768, max_window_layers=80, layer_types=None, attention_dropout=0.0, rope_scaling=None, expand_v: float = 2, mode: str = "chunk", use_gate: bool = True, use_short_conv: bool = True, conv_size: int = 4, conv_bias: bool = False, num_linear_key_value_heads: int = 16, num_linear_heads: int = 16, linear_head_dim: int = 128, **kwargs, ): self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings 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.head_dim = head_dim self.use_sliding_window = use_sliding_window self.sliding_window = sliding_window if self.use_sliding_window else None self.max_window_layers = max_window_layers # for backward compatibility if num_key_value_heads is None: num_key_value_heads = num_attention_heads self.num_key_value_heads = num_key_value_heads self.hidden_act = hidden_act self.initializer_range = initializer_range self.rms_norm_eps = rms_norm_eps self.use_cache = use_cache self.rope_theta = rope_theta self.attention_dropout = attention_dropout self.rope_scaling = rope_scaling # DeltaNet / linear branch self.expand_v = expand_v self.mode = mode self.use_gate = use_gate self.use_short_conv = use_short_conv self.conv_size = conv_size self.conv_bias = conv_bias self.num_linear_key_value_heads = num_linear_key_value_heads self.num_linear_heads = num_linear_heads self.linear_head_dim = linear_head_dim self.norm_eps = norm_eps self.layer_types = layer_types if self.layer_types is None: # Default: one sliding_attention layer followed by three linear_attention layers (period = 4) self.layer_types = [ "linear_attention" if bool(i % 4) else "sliding_attention" for i in range(self.num_hidden_layers) ] layer_type_validation(self.layer_types, self.num_hidden_layers) # Validate the correctness of rotary position embeddings parameters # Backward Compatibility: if there is a 'type' field, move it to 'rope_type'. # Also change type from 'mrope' to 'default' because `mrope` uses default RoPE calculations in this architecture. if self.rope_scaling is not None and "type" in self.rope_scaling: if self.rope_scaling["type"] == "mrope": self.rope_scaling["type"] = "default" self.rope_scaling["rope_type"] = self.rope_scaling["type"] rope_config_validation(self, ignore_keys={"mrope_section"}) super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs) class InfiniteVLConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`InfiniteVLModel`]. It is used to instantiate an InfiniteVL model according to the specified arguments, defining the model architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: text_config (`Union[PreTrainedConfig, dict]`, *optional*, defaults to `InfiniteVLTextConfig`): The config object or dictionary of the text backbone. vision_config (`Union[PreTrainedConfig, dict]`, *optional*, defaults to `InfiniteVLVisionConfig`): The config object or dictionary of the vision backbone. image_token_id (`int`, *optional*, defaults to 151655): The image token index to encode the image prompt. video_token_id (`int`, *optional*, defaults to 151656): The video token index to encode the video prompt. vision_start_token_id (`int`, *optional*, defaults to 151652): The token index to denote start of vision input. vision_end_token_id (`int`, *optional*, defaults to 151653): The token index to denote end of vision input. ```python >>> from transformers import InfiniteVLQwen2_5_VLForConditionalGeneration, InfiniteVLConfig >>> # Initializing an InfiniteVL style configuration >>> configuration = InfiniteVLConfig() >>> # Initializing a model from the InfiniteVL style configuration >>> model = InfiniteVLQwen2_5_VLForConditionalGeneration(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "infinite_vl" sub_configs = {"vision_config": InfiniteVLVisionConfig, "text_config": InfiniteVLTextConfig} keys_to_ignore_at_inference = ["past_key_values"] def __init__( self, text_config=None, vision_config=None, image_token_id=151655, video_token_id=151656, vision_start_token_id=151652, vision_end_token_id=151653, **kwargs, ): # We need to init super() here so that it does not reset values # that are in text config to the BaseClass defaults. The Base # config has many text related defaults and not all defaults are same as for `InfiniteVLTextConfig` super().__init__(**kwargs) if isinstance(vision_config, dict): self.vision_config = self.sub_configs["vision_config"](**vision_config) elif vision_config is None: self.vision_config = self.sub_configs["vision_config"]() if isinstance(text_config, dict): self.text_config = self.sub_configs["text_config"](**text_config) elif text_config is None: # For BC use all kwargs to init `TextConfig` self.text_config = self.sub_configs["text_config"](**kwargs) self.image_token_id = image_token_id self.video_token_id = video_token_id self.vision_start_token_id = vision_start_token_id self.vision_end_token_id = vision_end_token_id # Attention implementation to use. It sets it recursively on sub-configs so we call it again in the end self._attn_implementation = kwargs.pop("attn_implementation", None) def __setattr__(self, key, value): if ( (text_config := super().__getattribute__("__dict__").get("text_config")) is not None and key not in ["dtype", "_attn_implementation_internal"] and key in text_config.__dict__ ): setattr(text_config, key, value) else: super().__setattr__(key, value) def __getattribute__(self, key): if "text_config" in super().__getattribute__("__dict__") and key not in [ "dtype", "_attn_implementation_internal", ]: text_config = super().__getattribute__("text_config") if key in text_config.__dict__: return getattr(text_config, key) return super().__getattribute__(key) __all__ = ["InfiniteVLConfig", "InfiniteVLTextConfig", "InfiniteVLVisionConfig"]