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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

added_tokens.json

@@ -0,0 +1,24 @@
+{
+  "</tool_call>": 151658,
+  "<tool_call>": 151657,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

chat_template.json

@@ -0,0 +1,3 @@
+{
+  "chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}"
+}

config.json

@@ -0,0 +1,72 @@
+{
+  "attn_implementation": "flash_attention_2",
+  "architectures": [
+    "InfiniteVLQwen2_5_VLForConditionalGeneration"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_infinitevl.InfiniteVLConfig",
+    "AutoModel": "modeling_infinitevl.InfiniteVLQwen2_5_VLForConditionalGeneration",
+    "AutoModelForCausalLM": "modeling_infinitevl.InfiniteVLQwen2_5_VLForConditionalGeneration",
+    "AutoProcessor": "Qwen2_5_VLProcessor"
+  },
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "image_token_id": 151655,
+  "initializer_range": 0.02,
+  "intermediate_size": 11008,
+  "max_position_embeddings": 128000,
+  "max_window_layers": 70,
+  "model_type": "infinite_vl",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 2,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": {
+    "mrope_section": [
+      16,
+      24,
+      24
+    ],
+    "rope_type": "default",
+    "type": "default"
+  },
+  "rope_theta": 1000000.0,
+  "sliding_window": 8192,
+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.50.0",
+  "use_cache": false,
+  "use_sliding_window": true,
+  "video_token_id": 151656,
+  "vision_config": {
+    "depth": 32,
+    "fullatt_block_indexes": [
+      7,
+      15,
+      23,
+      31
+    ],
+    "hidden_act": "silu",
+    "hidden_size": 1280,
+    "in_channels": 3,
+    "in_chans": 3,
+    "intermediate_size": 3420,
+    "model_type": "infinite_vl",
+    "num_heads": 16,
+    "out_hidden_size": 2048,
+    "patch_size": 14,
+    "spatial_merge_size": 2,
+    "spatial_patch_size": 14,
+    "temporal_patch_size": 2,
+    "tokens_per_second": 2,
+    "torch_dtype": "bfloat16",
+    "window_size": 112
+  },
+  "vision_end_token_id": 151653,
+  "vision_start_token_id": 151652,
+  "vision_token_id": 151654,
+  "vocab_size": 151936
+}

configuration_infinitevl.py

@@ -0,0 +1,395 @@
+# 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"]

generation_config.json

@@ -0,0 +1,12 @@
+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "repetition_penalty": 1.05,
+  "temperature": 1e-06,
+  "transformers_version": "4.57.0"
+}

model.safetensors.index.json

@@ -0,0 +1,994 @@
+{
+  "metadata": {
+    "total_parameters": 4408381536,
+    "total_size": 8816763072
+  },
+  "weight_map": {
+    "model.embed_tokens.weight": "model-00001-of-00002.safetensors",
+    "model.layers.0.input_layernorm.weight": "model-00001-of-00002.safetensors",
+    "model.layers.0.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.0.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.0.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.0.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
+    "model.layers.0.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
+    "model.layers.0.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
+    "model.layers.0.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
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+  }
+}

modular_infinitevl.py

@@ -0,0 +1,1089 @@
+# 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.
+"""PyTorch InfiniteVL model (built on top of Qwen2-VL/Qwen2.5-VL)."""
+
+from typing import List, Optional, Tuple, Union
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache
+from transformers.configuration_utils import PretrainedConfig
+from transformers.feature_extraction_utils import BatchFeature
+from transformers.image_utils import ImageInput
+from transformers.modeling_flash_attention_utils import is_flash_attn_available
+from transformers.modeling_layers import GradientCheckpointingLayer
+from transformers.processing_utils import MultiModalData, ProcessingKwargs, Unpack, VideosKwargs
+from transformers.tokenization_utils_base import PreTokenizedInput, TextInput
+from transformers.utils import is_torchdynamo_compiling, logging
+from transformers.video_utils import VideoInput
+
+# Import base Qwen2-VL components to extend/wrap
+from transformers.models.qwen2_vl.configuration_qwen2_vl import Qwen2VLConfig, Qwen2VLTextConfig
+from transformers.models.qwen2_vl.modeling_qwen2_vl import (
+    PatchEmbed,
+    PatchMerger,
+    Qwen2RMSNorm,
+    Qwen2VLCausalLMOutputWithPast,
+    Qwen2VLForConditionalGeneration,
+    Qwen2VLModel,
+    Qwen2VLModelOutputWithPast,
+    Qwen2VLPreTrainedModel,
+    TransformersKwargs,
+    VisionAttention,
+    VisionRotaryEmbedding,
+)
+from transformers.models.qwen2_vl.processing_qwen2_vl import Qwen2VLImagesKwargs, Qwen2VLProcessor
+
+
+if is_flash_attn_available():
+    # We keep this conditional import pattern for future flash-attn
+    # specific branches without changing the public API.
+    pass
+
+
+logger = logging.get_logger(__name__)
+
+
+# ---------------------------------------------------------------------------
+# Configs
+# ---------------------------------------------------------------------------
+
+
+class InfiniteVLVisionConfig(PretrainedConfig):
+    """
+    Vision backbone configuration for InfiniteVL.
+
+    This mirrors the Qwen2.5-VL vision encoder but is exposed under the
+    InfiniteVL naming for clarity. It is used as a sub-config inside
+    :class:`InfiniteVLConfig`.
+    """
+
+    model_type = "infinite_vl"
+    base_config_key = "vision_config"
+
+    def __init__(
+        self,
+        depth: int = 32,
+        hidden_size: int = 3584,
+        hidden_act: str = "silu",
+        intermediate_size: int = 3420,
+        num_heads: int = 16,
+        in_channels: int = 3,
+        patch_size: int = 14,
+        spatial_merge_size: int = 2,
+        temporal_patch_size: int = 2,
+        tokens_per_second: int = 4,
+        window_size: int = 112,
+        out_hidden_size: int = 3584,
+        fullatt_block_indexes: Optional[List[int]] = None,
+        initializer_range: float = 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 = list(fullatt_block_indexes)
+        self.out_hidden_size = out_hidden_size
+        self.initializer_range = initializer_range
+
+
+class InfiniteVLTextConfig(Qwen2VLTextConfig):
+    """
+    Text backbone configuration for InfiniteVL.
+
+    This class currently reuses :class:`Qwen2VLTextConfig` as a base and
+    only overrides the model_type to keep InfiniteVL text separate at
+    the configuration level, while remaining fully compatible with
+    the parent implementation.
+    """
+
+    model_type = "infinite_vl_text"
+
+
+class InfiniteVLConfig(Qwen2VLConfig):
+    """
+    Top-level InfiniteVL configuration.
+
+    This extends :class:`Qwen2VLConfig` and swaps in the InfiniteVL
+    vision/text config classes via ``sub_configs`` so that downstream
+    models can transparently use InfiniteVL while remaining compatible
+    with Qwen2-VL tooling and loading code.
+    """
+
+    model_type = "infinite_vl"
+    sub_configs = {"vision_config": InfiniteVLVisionConfig, "text_config": InfiniteVLTextConfig}
+
+
+# ---------------------------------------------------------------------------
+# Vision backbone
+# ---------------------------------------------------------------------------
+
+
+class InfiniteVLMLP(nn.Module):
+    """
+    Standard gated MLP used in the InfiniteVL vision backbone.
+    """
+
+    def __init__(self, config: InfiniteVLVisionConfig, bias: bool = False):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+
+        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=bias)
+        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=bias)
+        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=bias)
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, hidden_state: torch.Tensor) -> torch.Tensor:
+        gated = self.act_fn(self.gate_proj(hidden_state))
+        return self.down_proj(gated * self.up_proj(hidden_state))
+
+
+class InfiniteVisionPatchEmbed(PatchEmbed):
+    """
+    Wrapper around the Qwen2-VL patch embedder kept for naming
+    consistency in the InfiniteVL codebase.
+    """
+
+    pass
+
+
+class InfiniteVisionRotaryEmbedding(VisionRotaryEmbedding):
+    """
+    Rotary embedding for the InfiniteVL vision backbone. This is a direct
+    alias for the Qwen2-VL implementation, exposed under an InfiniteVL
+    name for clarity.
+    """
+
+    pass
+
+
+class InfiniteVLPatchMerger(PatchMerger):
+    """
+    Patch merger with Qwen2-style RMSNorm on the query side.
+    """
+
+    def __init__(self, dim: int, context_dim: int, spatial_merge_size: int = 2) -> None:
+        super().__init__(dim, context_dim, spatial_merge_size)
+        self.ln_q = Qwen2RMSNorm(context_dim, eps=1e-6)
+
+
+class InfiniteVLVisionAttention(VisionAttention):
+    """
+    Vision attention wrapper that exposes the hidden size via ``dim``
+    for convenience.
+    """
+
+    def __init__(self, config: InfiniteVLVisionConfig) -> None:
+        super().__init__(config)
+        self.dim = config.hidden_size
+
+
+class InfiniteVLVisionBlock(GradientCheckpointingLayer):
+    """
+    A single InfiniteVL vision transformer block consisting of:
+      - Qwen2-style RMSNorm
+      - multi-head attention
+      - gated MLP
+    """
+
+    def __init__(self, config: InfiniteVLVisionConfig, attn_implementation: str = "sdpa") -> None:
+        super().__init__()
+        self.norm1 = Qwen2RMSNorm(config.hidden_size, eps=1e-6)
+        self.norm2 = Qwen2RMSNorm(config.hidden_size, eps=1e-6)
+        self.attn = InfiniteVLVisionAttention(config=config)
+        self.mlp = InfiniteVLMLP(config, bias=True)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        cu_seqlens: torch.Tensor,
+        rotary_pos_emb: Optional[torch.Tensor] = None,
+        position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
+        **kwargs,
+    ) -> torch.Tensor:
+        hidden_states = hidden_states + self.attn(
+            self.norm1(hidden_states),
+            cu_seqlens=cu_seqlens,
+            rotary_pos_emb=rotary_pos_emb,
+            position_embeddings=position_embeddings,
+            **kwargs,
+        )
+        hidden_states = hidden_states + self.mlp(self.norm2(hidden_states))
+        return hidden_states
+
+
+# ---------------------------------------------------------------------------
+# Base model wrappers
+# ---------------------------------------------------------------------------
+
+
+class InfiniteVLPreTrainedModel(Qwen2VLPreTrainedModel):
+    """
+    Pretrained model wrapper so that InfiniteVL can plug into the same
+    utilities as Qwen2-VL.
+    """
+
+    pass
+
+
+class InfiniteVisionTransformerPretrainedModel(InfiniteVLPreTrainedModel):
+    """
+    InfiniteVL vision transformer that adapts the Qwen2.5-VL visual
+    encoder to the modular InfiniteVL stack.
+    """
+
+    config: InfiniteVLVisionConfig
+    _no_split_modules = ["InfiniteVLVisionBlock"]
+
+    def __init__(self, config: InfiniteVLVisionConfig, *inputs, **kwargs) -> None:
+        super().__init__(config, *inputs, **kwargs)
+        self.spatial_merge_size = config.spatial_merge_size
+        self.patch_size = config.patch_size
+        self.fullatt_block_indexes = config.fullatt_block_indexes
+        self.window_size = config.window_size
+        self.spatial_merge_unit = self.spatial_merge_size * self.spatial_merge_size
+
+        self.patch_embed = InfiniteVisionPatchEmbed(
+            patch_size=config.patch_size,
+            temporal_patch_size=config.temporal_patch_size,
+            in_channels=config.in_channels,
+            embed_dim=config.hidden_size,
+        )
+
+        head_dim = config.hidden_size // config.num_heads
+        self.rotary_pos_emb = InfiniteVisionRotaryEmbedding(head_dim // 2)
+
+        self.blocks = nn.ModuleList([InfiniteVLVisionBlock(config) for _ in range(config.depth)])
+        self.merger = InfiniteVLPatchMerger(
+            dim=config.out_hidden_size,
+            context_dim=config.hidden_size,
+            spatial_merge_size=config.spatial_merge_size,
+        )
+        self.gradient_checkpointing = False
+
+    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)
+            hpos_ids = hpos_ids.reshape(
+                h // self.spatial_merge_size,
+                self.spatial_merge_size,
+                w // self.spatial_merge_size,
+                self.spatial_merge_size,
+            )
+            hpos_ids = hpos_ids.permute(0, 2, 1, 3)
+            hpos_ids = hpos_ids.flatten()
+
+            wpos_ids = torch.arange(w).unsqueeze(0).expand(h, -1)
+            wpos_ids = wpos_ids.reshape(
+                h // self.spatial_merge_size,
+                self.spatial_merge_size,
+                w // self.spatial_merge_size,
+                self.spatial_merge_size,
+            )
+            wpos_ids = wpos_ids.permute(0, 2, 1, 3)
+            wpos_ids = wpos_ids.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
+
+    def get_window_index(self, grid_thw: torch.Tensor) -> Tuple[torch.Tensor, List[int]]:
+        window_index: List[torch.Tensor] = []
+        cu_window_seqlens: List[int] = [0]
+        window_index_id = 0
+        vit_merger_window_size = self.window_size // self.spatial_merge_size // self.patch_size
+
+        for grid_t, grid_h, grid_w in grid_thw:
+            llm_grid_h, llm_grid_w = (
+                grid_h // self.spatial_merge_size,
+                grid_w // self.spatial_merge_size,
+            )
+            index = torch.arange(grid_t * llm_grid_h * llm_grid_w).reshape(grid_t, llm_grid_h, llm_grid_w)
+            pad_h = vit_merger_window_size - llm_grid_h % vit_merger_window_size
+            pad_w = vit_merger_window_size - llm_grid_w % vit_merger_window_size
+            num_windows_h = (llm_grid_h + pad_h) // vit_merger_window_size
+            num_windows_w = (llm_grid_w + pad_w) // vit_merger_window_size
+            index_padded = F.pad(index, (0, pad_w, 0, pad_h), "constant", -100)
+            index_padded = index_padded.reshape(
+                grid_t,
+                num_windows_h,
+                vit_merger_window_size,
+                num_windows_w,
+                vit_merger_window_size,
+            )
+            index_padded = index_padded.permute(0, 1, 3, 2, 4).reshape(
+                grid_t,
+                num_windows_h * num_windows_w,
+                vit_merger_window_size,
+                vit_merger_window_size,
+            )
+            seqlens = (index_padded != -100).sum([2, 3]).reshape(-1)
+            index_padded = index_padded.reshape(-1)
+            index_new = index_padded[index_padded != -100]
+            window_index.append(index_new + window_index_id)
+            cu_seqlens_tmp = seqlens.cumsum(0) * self.spatial_merge_unit + cu_window_seqlens[-1]
+            cu_window_seqlens.extend(cu_seqlens_tmp.tolist())
+            window_index_id += (grid_t * llm_grid_h * llm_grid_w).item()
+        window_index_tensor = torch.cat(window_index, dim=0)
+
+        return window_index_tensor, cu_window_seqlens
+
+    def forward(self, hidden_states: torch.Tensor, grid_thw: torch.Tensor, **kwargs) -> torch.Tensor:
+        """
+        Args:
+            hidden_states (`torch.Tensor` of shape `(seq_len, hidden_size)`):
+                The final hidden states of the model.
+            grid_thw (`torch.Tensor` of shape `(num_images_or_videos, 3)`):
+                The temporal, height and width of feature shape of each image in LLM.
+
+        Returns:
+            `torch.Tensor`: hidden_states.
+        """
+        hidden_states = self.patch_embed(hidden_states)
+        rotary_pos_emb = self.rot_pos_emb(grid_thw)
+        window_index, cu_window_seqlens = self.get_window_index(grid_thw)
+        cu_window_seqlens_tensor = torch.tensor(
+            cu_window_seqlens,
+            device=hidden_states.device,
+            dtype=grid_thw.dtype if torch.jit.is_tracing() else torch.int32,
+        )
+        cu_window_seqlens_tensor = torch.unique_consecutive(cu_window_seqlens_tensor)
+
+        seq_len, _ = hidden_states.size()
+        hidden_states = hidden_states.reshape(seq_len // self.spatial_merge_unit, self.spatial_merge_unit, -1)
+        hidden_states = hidden_states[window_index, :, :]
+        hidden_states = hidden_states.reshape(seq_len, -1)
+
+        rotary_pos_emb = rotary_pos_emb.reshape(seq_len // self.spatial_merge_unit, self.spatial_merge_unit, -1)
+        rotary_pos_emb = rotary_pos_emb[window_index, :, :]
+        rotary_pos_emb = rotary_pos_emb.reshape(seq_len, -1)
+        emb = torch.cat((rotary_pos_emb, rotary_pos_emb), dim=-1)
+        position_embeddings = (emb.cos(), emb.sin())
+
+        cu_seqlens = torch.repeat_interleave(grid_thw[:, 1] * grid_thw[:, 2], grid_thw[:, 0]).cumsum(
+            dim=0,
+            # Select dtype based on the following factors:
+            #  - FA2 requires that cu_seqlens_q must have dtype int32
+            #  - torch.onnx.export requires that cu_seqlens_q must have same dtype as grid_thw
+            # See https://github.com/huggingface/transformers/pull/34852 for more information
+            dtype=grid_thw.dtype if torch.jit.is_tracing() else torch.int32,
+        )
+        cu_seqlens = F.pad(cu_seqlens, (1, 0), value=0)
+
+        for layer_num, blk in enumerate(self.blocks):
+            if layer_num in self.fullatt_block_indexes:
+                cu_seqlens_now = cu_seqlens
+            else:
+                cu_seqlens_now = cu_window_seqlens_tensor
+
+            hidden_states = blk(
+                hidden_states,
+                cu_seqlens=cu_seqlens_now,
+                position_embeddings=position_embeddings,
+                **kwargs,
+            )
+
+        hidden_states = self.merger(hidden_states)
+        reverse_indices = torch.argsort(window_index)
+        hidden_states = hidden_states[reverse_indices, :]
+
+        return hidden_states
+
+
+# ---------------------------------------------------------------------------
+# Language model wrappers
+# ---------------------------------------------------------------------------
+
+
+class InfiniteVLModelOutputWithPast(Qwen2VLModelOutputWithPast):
+    """
+    Output type for :class:`InfiniteVLModel`. This simply extends the
+    Qwen2-VL output to also track ``rope_deltas``.
+    """
+
+    pass
+
+
+class InfiniteVLModel(Qwen2VLModel):
+    """
+    InfiniteVL multimodal model that reuses the Qwen2-VL language model,
+    but swaps in the InfiniteVL vision encoder and a custom 3D RoPE
+    indexing strategy.
+    """
+
+    config: InfiniteVLConfig
+    base_model_prefix = ""
+    _no_split_modules = ["InfiniteVLDecoderLayer", "InfiniteVLVisionBlock"]
+    # Reference: fix gemma3 grad acc #37208
+    accepts_loss_kwargs = False
+
+    def __init__(self, config: InfiniteVLConfig):
+        super().__init__(config)
+        self.visual = InfiniteVisionTransformerPretrainedModel._from_config(config.vision_config)
+
+    def get_rope_index(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        image_grid_thw: Optional[torch.LongTensor] = None,
+        video_grid_thw: Optional[torch.LongTensor] = None,
+        second_per_grid_ts: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Calculate the 3D RoPE index based on image and video temporal, height
+        and width in the LLM token space.
+
+        See the original Qwen2.5-VL paper and implementation for more
+        background on the 3D M-ROPE design.
+        """
+        spatial_merge_size = self.config.vision_config.spatial_merge_size
+        image_token_id = self.config.image_token_id
+        video_token_id = self.config.video_token_id
+        vision_start_token_id = self.config.vision_start_token_id
+        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 not None:
+                attention_mask = attention_mask == 1
+            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
+            for i, input_ids_row in enumerate(total_input_ids):
+                if attention_mask is not None:
+                    input_ids_row = input_ids_row[attention_mask[i]]
+
+                image_nums, video_nums = 0, 0
+                vision_start_indices = torch.argwhere(input_ids_row == vision_start_token_id).squeeze(1)
+                vision_tokens = input_ids_row[vision_start_indices + 1]
+                image_nums = (vision_tokens == image_token_id).sum()
+                video_nums = (vision_tokens == video_token_id).sum()
+                input_tokens = input_ids_row.tolist()
+
+                llm_pos_ids_list: List[torch.Tensor] = []
+                st = 0
+                remain_images, remain_videos = image_nums, video_nums
+                for _ in range(image_nums + video_nums):
+                    if image_token_id in input_tokens and remain_images > 0:
+                        ed_image = input_tokens.index(image_token_id, st)
+                    else:
+                        ed_image = len(input_tokens) + 1
+                    if video_token_id in input_tokens and remain_videos > 0:
+                        ed_video = input_tokens.index(video_token_id, st)
+                    else:
+                        ed_video = len(input_tokens) + 1
+                    if ed_image < ed_video:
+                        t, h, w = (
+                            image_grid_thw[image_index][0],
+                            image_grid_thw[image_index][1],
+                            image_grid_thw[image_index][2],
+                        )
+                        second_per_grid_t = 0
+                        image_index += 1
+                        remain_images -= 1
+                        ed = ed_image
+                    else:
+                        t, h, w = (
+                            video_grid_thw[video_index][0],
+                            video_grid_thw[video_index][1],
+                            video_grid_thw[video_index][2],
+                        )
+                        if second_per_grid_ts is not None:
+                            second_per_grid_t = second_per_grid_ts[video_index]
+                        else:
+                            second_per_grid_t = 1.0
+                        video_index += 1
+                        remain_videos -= 1
+                        ed = ed_video
+
+                    llm_grid_t, llm_grid_h, llm_grid_w = (
+                        t.item(),
+                        h.item() // spatial_merge_size,
+                        w.item() // spatial_merge_size,
+                    )
+                    text_len = ed - st
+
+                    st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
+                    llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx)
+
+                    range_tensor = torch.arange(llm_grid_t).view(-1, 1)
+                    expanded_range = range_tensor.expand(-1, llm_grid_h * llm_grid_w)
+
+                    # normalize type, send to device
+                    second_per_grid_t = torch.as_tensor(
+                        second_per_grid_t,
+                        dtype=range_tensor.dtype,
+                        device=range_tensor.device,
+                    )
+
+                    time_tensor = expanded_range * second_per_grid_t * self.config.vision_config.tokens_per_second
+                    time_tensor_long = time_tensor.long()
+                    t_index = time_tensor_long.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]) + text_len + st_idx)
+                    st = ed + llm_grid_t * llm_grid_h * llm_grid_w
+
+                if st < len(input_tokens):
+                    st_idx = llm_pos_ids_list[-1].max() + 1 if len(llm_pos_ids_list) > 0 else 0
+                    text_len = len(input_tokens) - st
+                    llm_pos_ids_list.append(torch.arange(text_len).view(1, -1).expand(3, -1) + st_idx)
+
+                llm_positions = torch.cat(llm_pos_ids_list, dim=1).reshape(3, -1)
+                if attention_mask is not None:
+                    position_ids[..., i, attention_mask[i]] = llm_positions.to(position_ids.device)
+                else:
+                    position_ids[..., i, :] = llm_positions.to(position_ids.device)
+                mrope_position_deltas.append(llm_positions.max() + 1 - len(total_input_ids[i]))
+
+            mrope_position_deltas_tensor = torch.tensor(mrope_position_deltas).unsqueeze(1).to(
+                device=input_ids.device
+            )
+            return position_ids, mrope_position_deltas_tensor
+
+        # Pure text case – fall back to standard 1D RoPE indexing.
+        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
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Cache] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        pixel_values: Optional[torch.Tensor] = None,
+        pixel_values_videos: Optional[torch.FloatTensor] = None,
+        image_grid_thw: Optional[torch.LongTensor] = None,
+        video_grid_thw: Optional[torch.LongTensor] = None,
+        rope_deltas: Optional[torch.LongTensor] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        second_per_grid_ts: Optional[torch.Tensor] = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> Union[tuple, InfiniteVLModelOutputWithPast]:
+        r"""
+        image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*):
+            The temporal, height and width of feature shape of each image in LLM.
+        video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*):
+            The temporal, height and width of feature shape of each video in LLM.
+        rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*):
+            The RoPE index difference between sequence length and multimodal RoPE.
+        second_per_grid_ts (`torch.Tensor` of shape `(num_videos)`, *optional*):
+            The time interval (in seconds) for each grid along the temporal dimension
+            in the 3D position IDs.
+        """
+
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if inputs_embeds is None:
+            inputs_embeds = self.get_input_embeddings()(input_ids)
+
+        if pixel_values is not None:
+            image_embeds = self.get_image_features(pixel_values, image_grid_thw)
+            image_embeds = torch.cat(image_embeds, dim=0).to(inputs_embeds.device, inputs_embeds.dtype)
+            image_mask, _ = self.get_placeholder_mask(
+                input_ids, inputs_embeds=inputs_embeds, image_features=image_embeds
+            )
+            inputs_embeds = inputs_embeds.masked_scatter(image_mask, image_embeds)
+
+        if pixel_values_videos is not None:
+            video_embeds = self.get_video_features(pixel_values_videos, video_grid_thw)
+            video_embeds = torch.cat(video_embeds, dim=0).to(inputs_embeds.device, inputs_embeds.dtype)
+            _, video_mask = self.get_placeholder_mask(
+                input_ids, inputs_embeds=inputs_embeds, video_features=video_embeds
+            )
+            inputs_embeds = inputs_embeds.masked_scatter(video_mask, video_embeds)
+
+        if position_ids is None:
+            # Calculate RoPE index once per generation in the pre-fill stage only.
+            # When compiling, we can't check tensor values thus we check only input length
+            # It is safe to assume that `length!=1` means we're in pre-fill because compiled
+            # models currently cannot do assisted decoding.
+            prefill_compiled_stage = is_torchdynamo_compiling() and (
+                (input_ids is not None and input_ids.shape[1] != 1)
+                or (inputs_embeds is not None and inputs_embeds.shape[1] != 1)
+            )
+            prefill_noncompiled_stage = not is_torchdynamo_compiling() and (
+                (cache_position is not None and cache_position[0] == 0)
+                or (past_key_values is None or past_key_values.get_seq_length() == 0)
+            )
+            if (prefill_compiled_stage or prefill_noncompiled_stage) or self.rope_deltas is None:
+                position_ids, rope_deltas = self.get_rope_index(
+                    input_ids,
+                    image_grid_thw,
+                    video_grid_thw,
+                    second_per_grid_ts=second_per_grid_ts,
+                    attention_mask=attention_mask,
+                )
+                self.rope_deltas = rope_deltas
+            else:
+                batch_size, seq_length, _ = inputs_embeds.shape
+                position_ids = torch.arange(seq_length, device=inputs_embeds.device)
+                position_ids = position_ids.view(1, 1, -1).expand(3, batch_size, -1)
+                if cache_position is not None:
+                    delta = (cache_position[0] + self.rope_deltas).to(inputs_embeds.device)
+                else:
+                    delta = torch.zeros((batch_size, seq_length), device=inputs_embeds.device)
+                delta = delta.repeat_interleave(batch_size // delta.shape[0], dim=1)
+                position_ids = position_ids + delta.to(position_ids.device)
+
+        outputs = self.language_model(
+            input_ids=None,
+            position_ids=position_ids,
+            attention_mask=attention_mask,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=True,
+            cache_position=cache_position,
+            **kwargs,
+        )
+
+        output = InfiniteVLModelOutputWithPast(
+            last_hidden_state=outputs.last_hidden_state,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            rope_deltas=self.rope_deltas,
+        )
+        return output if return_dict else output.to_tuple()
+
+
+# ---------------------------------------------------------------------------
+# Causal LM wrapper
+# ---------------------------------------------------------------------------
+
+
+class InfiniteVLCausalLMOutputWithPast(Qwen2VLCausalLMOutputWithPast):
+    """
+    Output type for :class:`InfiniteVLQwen2_5_VLForConditionalGeneration`.
+    """
+
+    pass
+
+
+class InfiniteVLQwen2_5_VLForConditionalGeneration(Qwen2VLForConditionalGeneration):
+    """
+    InfiniteVL causal language model head on top of :class:`InfiniteVLModel`.
+    """
+
+    # Reference: fix gemma3 grad acc #37208
+    accepts_loss_kwargs = False
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[Cache] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        pixel_values: Optional[torch.Tensor] = None,
+        pixel_values_videos: Optional[torch.FloatTensor] = None,
+        image_grid_thw: Optional[torch.LongTensor] = None,
+        video_grid_thw: Optional[torch.LongTensor] = None,
+        rope_deltas: Optional[torch.LongTensor] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        second_per_grid_ts: Optional[torch.Tensor] = None,
+        logits_to_keep: Union[int, torch.Tensor] = 0,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> Union[tuple, InfiniteVLCausalLMOutputWithPast]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the masked language modeling loss. Indices should either be in
+            ``[0, ..., config.vocab_size]`` or ``-100`` (see ``input_ids`` docstring). Tokens with indices set to
+            ``-100`` are ignored (masked), the loss is only computed for the tokens with labels in
+            ``[0, ..., config.vocab_size]``.
+        image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*):
+            The temporal, height and width of feature shape of each image in LLM.
+        video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*):
+            The temporal, height and width of feature shape of each video in LLM.
+        rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*):
+            The RoPE index difference between sequence length and multimodal RoPE.
+        second_per_grid_ts (`torch.Tensor` of shape `(num_videos)`, *optional*):
+            The time interval (in seconds) for each grid along the temporal dimension in the 3D position IDs.
+        """
+
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+
+        outputs = self.model(
+            input_ids=input_ids,
+            pixel_values=pixel_values,
+            pixel_values_videos=pixel_values_videos,
+            image_grid_thw=image_grid_thw,
+            video_grid_thw=video_grid_thw,
+            second_per_grid_ts=second_per_grid_ts,
+            position_ids=position_ids,
+            attention_mask=attention_mask,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=True,
+            cache_position=cache_position,
+            **kwargs,
+        )
+
+        hidden_states = outputs[0]
+
+        # Only compute necessary logits, and do not upcast them to float
+        # if we are not computing the loss.
+        slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+        logits = self.lm_head(hidden_states[:, slice_indices, :])
+
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(
+                logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size, **kwargs
+            )
+
+        return InfiniteVLCausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            rope_deltas=outputs.rope_deltas,
+        )
+
+    def prepare_inputs_for_generation(
+        self,
+        input_ids,
+        past_key_values=None,
+        attention_mask=None,
+        inputs_embeds=None,
+        cache_position=None,
+        position_ids=None,
+        use_cache=True,
+        pixel_values=None,
+        pixel_values_videos=None,
+        image_grid_thw=None,
+        video_grid_thw=None,
+        second_per_grid_ts=None,
+        **kwargs,
+    ):
+        # Overwritten -- in specific circumstances we don't want to forward image inputs to the model.
+        model_inputs = super().prepare_inputs_for_generation(
+            input_ids,
+            past_key_values=past_key_values,
+            attention_mask=attention_mask,
+            inputs_embeds=inputs_embeds,
+            cache_position=cache_position,
+            position_ids=position_ids,
+            pixel_values=pixel_values,
+            pixel_values_videos=pixel_values_videos,
+            image_grid_thw=image_grid_thw,
+            video_grid_thw=video_grid_thw,
+            second_per_grid_ts=second_per_grid_ts,
+            use_cache=use_cache,
+            **kwargs,
+        )
+
+        # InfiniteVL position_ids are prepared with rope_deltas
+        if position_ids is None:
+            # Calculate RoPE index once per generation in the pre-fill stage only.
+            # When compiling, we can't check tensor values thus we check only input length
+            # It is safe to assume that `length!=1` means we're in pre-fill because compiled
+            # models currently cannot do assisted decoding.
+            if cache_position[0] == 0 or self.model.rope_deltas is None:
+                vision_positions, rope_deltas = self.model.get_rope_index(
+                    model_inputs.get("input_ids", None),
+                    image_grid_thw=image_grid_thw,
+                    video_grid_thw=video_grid_thw,
+                    second_per_grid_ts=second_per_grid_ts,
+                    attention_mask=attention_mask,
+                )
+                self.model.rope_deltas = rope_deltas
+            # then use the previous pre-calculated rope-deltas to get the correct position ids
+            elif "position_ids" in model_inputs:
+                batch_size, seq_length = model_inputs["position_ids"].shape
+                device = model_inputs["position_ids"].device
+                position_ids = torch.arange(seq_length, device=device)
+                position_ids = position_ids.view(1, 1, -1).expand(3, batch_size, -1)
+                delta = cache_position[0] + self.model.rope_deltas
+                delta = delta.repeat_interleave(batch_size // delta.shape[0], dim=0)
+                vision_positions = position_ids + delta.expand_as(position_ids)
+
+            # Concatenate "text + vision" positions into [4, bs, seq-len]
+            text_positions = model_inputs["position_ids"][None, ...]
+            model_inputs["position_ids"] = torch.cat([text_positions, vision_positions], dim=0)
+
+        if cache_position[0] != 0:
+            model_inputs["pixel_values"] = None
+            model_inputs["pixel_values_videos"] = None
+
+        return model_inputs
+
+
+# ---------------------------------------------------------------------------
+# Processor
+# ---------------------------------------------------------------------------
+
+
+class InfiniteVLVideosProcessorKwargs(VideosKwargs, total=False):
+    fps: Union[list[float], float]
+
+
+class InfiniteVLImagesKwargs(Qwen2VLImagesKwargs):
+    pass
+
+
+class InfiniteVLProcessorKwargs(ProcessingKwargs, total=False):
+    images_kwargs: InfiniteVLImagesKwargs
+    videos_kwargs: InfiniteVLVideosProcessorKwargs
+    _defaults = {
+        "text_kwargs": {
+            "padding": False,
+            "return_mm_token_type_ids": False,
+        },
+    }
+
+
+class InfiniteVLProcessor(Qwen2VLProcessor):
+    r"""
+    Constructs an InfiniteVL processor which wraps a Qwen2-VL image processor
+    and a Qwen2 tokenizer into a single processor.
+
+    :class:`InfiniteVLProcessor` offers all the functionalities of
+    :class:`Qwen2VLImageProcessor` and :class:`Qwen2TokenizerFast`. See
+    :meth:`InfiniteVLProcessor.__call__` and :meth:`InfiniteVLProcessor.decode`
+    for more information.
+
+    Args:
+        image_processor (:class:`Qwen2VLImageProcessor`, *optional*):
+            The image processor is a required input.
+        tokenizer (:class:`Qwen2TokenizerFast`, *optional*):
+            The tokenizer is a required input.
+        video_processor (:class:`InfiniteVLVideoProcessor`, *optional*):
+            The video processor is a required input.
+        chat_template (`str`, *optional*):
+            A Jinja template which will be used to convert lists of messages
+            in a chat into a tokenizable string.
+    """
+
+    image_processor_class = "AutoImageProcessor"
+
+    @property
+    def model_input_names(self):
+        tokenizer_input_names = self.tokenizer.model_input_names
+        image_processor_input_names = self.image_processor.model_input_names
+        names_from_processor = list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
+        return names_from_processor + ["second_per_grid_ts"]
+
+    def __call__(
+        self,
+        images: Optional[ImageInput] = None,
+        text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
+        videos: Optional[VideoInput] = None,
+        **kwargs: Unpack[InfiniteVLProcessorKwargs],
+    ) -> BatchFeature:
+        """
+        Main method to prepare for the model one or several sequence(s) and image(s).
+
+        This method forwards the ``text`` and ``kwargs`` arguments to
+        :class:`Qwen2TokenizerFast.__call__` if ``text`` is not ``None``
+        to encode the text. To prepare the vision inputs, this method
+        forwards the ``images`` / ``videos`` and ``kwargs`` arguments to
+        :class:`Qwen2VLImageProcessor.__call__` and the corresponding
+        video processor when they are not ``None``.
+        """
+        output_kwargs = self._merge_kwargs(
+            InfiniteVLProcessorKwargs,
+            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
+            **kwargs,
+        )
+
+        image_inputs = videos_inputs = {}
+        if images is not None:
+            image_inputs = self.image_processor(images=images, **output_kwargs["images_kwargs"])
+            image_grid_thw = image_inputs["image_grid_thw"]
+
+        if videos is not None:
+            fps = output_kwargs["videos_kwargs"].get("fps", 2.0)
+            videos_inputs = self.video_processor(videos=videos, **output_kwargs["videos_kwargs"])
+            video_grid_thw = videos_inputs["video_grid_thw"]
+
+            if isinstance(fps, (int, float)):
+                second_per_grid_ts = [self.video_processor.temporal_patch_size / fps] * len(video_grid_thw)
+            elif hasattr(fps, "__len__") and len(fps) == len(video_grid_thw):
+                second_per_grid_ts = [self.video_processor.temporal_patch_size / tmp for tmp in fps]
+            else:
+                raise ValueError(
+                    f"The length of fps ({len(fps) if hasattr(fps, '__len__') else fps}) must be equal to the "
+                    f"length of video_grid_thw ({len(video_grid_thw)}) or fps should be a single number."
+                )
+            videos_inputs.update({"second_per_grid_ts": second_per_grid_ts})
+
+        if not isinstance(text, list):
+            text = [text]
+
+        # below lines change text in-place
+        text = text.copy()
+        if images is not None:
+            merge_length = self.image_processor.merge_size**2
+            index = 0
+            for i in range(len(text)):
+                while self.image_token in text[i]:
+                    num_image_tokens = image_grid_thw[index].prod() // merge_length
+                    text[i] = text[i].replace(self.image_token, "<|placeholder|>" * num_image_tokens, 1)
+                    index += 1
+                text[i] = text[i].replace("<|placeholder|>", self.image_token)
+
+        if videos is not None:
+            merge_length = self.video_processor.merge_size**2
+            index = 0
+            for i in range(len(text)):
+                while self.video_token in text[i]:
+                    num_video_tokens = video_grid_thw[index].prod() // merge_length
+                    text[i] = text[i].replace(self.video_token, "<|placeholder|>" * num_video_tokens, 1)
+                    index += 1
+                text[i] = text[i].replace("<|placeholder|>", self.video_token)
+
+        return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
+        return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", None)
+        text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
+        self._check_special_mm_tokens(text, text_inputs, modalities=["image", "video"])
+
+        if return_mm_token_type_ids:
+            array_ids = np.array(text_inputs["input_ids"])
+            mm_token_type_ids = np.zeros_like(text_inputs["input_ids"])
+            mm_token_type_ids[array_ids == self.image_token_id] = 1
+            text_inputs["mm_token_type_ids"] = mm_token_type_ids.tolist()
+
+        return BatchFeature(data={**text_inputs, **image_inputs, **videos_inputs}, tensor_type=return_tensors)
+
+    def _get_num_multimodal_tokens(self, image_sizes=None, video_sizes=None, **kwargs) -> MultiModalData:
+        """
+        Computes the number of placeholder tokens needed for multimodal inputs with the given sizes.
+
+        Args:
+            image_sizes (`list[list[int]]`, *optional*):
+                The input sizes formatted as (height, width) per each image.
+            video_sizes (`list[list[int]]`, *optional*):
+                The input sizes formatted as (num_frames, height, width) per each video.
+
+        Returns:
+            :class:`MultiModalData`: A :class:`MultiModalData` object holding number of tokens per each of the provided
+            input modalities, along with other useful data.
+        """
+
+        vision_data = {}
+        merge_size: Optional[int] = None
+
+        if image_sizes is not None:
+            images_kwargs = InfiniteVLProcessorKwargs._defaults.get("images_kwargs", {})
+            images_kwargs.update(kwargs)
+            merge_size = images_kwargs.get("merge_size", None) or self.image_processor.merge_size
+
+            num_image_patches = [
+                self.image_processor.get_number_of_image_patches(*image_size, images_kwargs)
+                for image_size in image_sizes
+            ]
+            num_image_tokens = [(num_patches // merge_size**2) for num_patches in num_image_patches]
+            vision_data.update({"num_image_tokens": num_image_tokens, "num_image_patches": num_image_patches})
+
+        if video_sizes is not None:
+            videos_kwargs = InfiniteVLProcessorKwargs._defaults.get("videos_kwargs", {})
+            videos_kwargs.update(kwargs)
+            # For videos we should also respect a potential merge_size override.
+            video_merge_size = videos_kwargs.get("merge_size", None) or self.video_processor.merge_size
+
+            num_video_patches = [
+                self.video_processor.get_number_of_video_patches(*video_size, videos_kwargs)
+                for video_size in video_sizes
+            ]
+            num_video_tokens = [
+                (num_patches // video_merge_size**2) for num_patches in num_video_patches
+            ]
+            vision_data["num_video_tokens"] = num_video_tokens
+
+        return MultiModalData(**vision_data)
+
+
+__all__ = [
+    # Preferred InfiniteVL names
+    "InfiniteVLConfig",
+    "InfiniteVLTextConfig",
+    "InfiniteVLQwen2_5_VLForConditionalGeneration",
+    "InfiniteVLModel",
+    "InfiniteVLPreTrainedModel",
+    "InfiniteVLProcessor",
+]

preprocessor_config.json

@@ -0,0 +1,29 @@
+{
+  "do_convert_rgb": true,
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.48145466,
+    0.4578275,
+    0.40821073
+  ],
+  "image_processor_type": "Qwen2VLImageProcessor",
+  "image_std": [
+    0.26862954,
+    0.26130258,
+    0.27577711
+  ],
+  "max_pixels": 12845056,
+  "merge_size": 2,
+  "min_pixels": 3136,
+  "patch_size": 14,
+  "processor_class": "Qwen2_5_VLProcessor",
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "longest_edge": 12845056,
+    "shortest_edge": 3136
+  },
+  "temporal_patch_size": 2
+}

processing_infinitevl.py

@@ -0,0 +1,272 @@
+# coding=utf-8
+# Copyright 2025 The Qwen Team and The HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# 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 typing import Optional, Union
+
+import numpy as np
+
+from transformers.feature_extraction_utils import BatchFeature
+from transformers.image_utils import ImageInput
+from transformers.processing_utils import ImagesKwargs, MultiModalData, ProcessingKwargs, ProcessorMixin, Unpack, VideosKwargs
+from transformers.tokenization_utils_base import PreTokenizedInput, TextInput
+from transformers.video_utils import VideoInput
+
+
+class Qwen2_5_VLVideosProcessorKwargs(VideosKwargs, total=False):
+    fps: Union[list[float], float]
+
+
+class Qwen2_5_VLImagesKwargs(ImagesKwargs):
+    min_pixels: Optional[int]
+    max_pixels: Optional[int]
+    patch_size: Optional[int]
+    temporal_patch_size: Optional[int]
+    merge_size: Optional[int]
+
+
+class Qwen2_5_VLProcessorKwargs(ProcessingKwargs, total=False):
+    images_kwargs: Qwen2_5_VLImagesKwargs
+    videos_kwargs: Qwen2_5_VLVideosProcessorKwargs
+    _defaults = {
+        "text_kwargs": {
+            "padding": False,
+            "return_mm_token_type_ids": False,
+        },
+    }
+
+
+class Qwen2_5_VLProcessor(ProcessorMixin):
+    r"""
+    Constructs a Qwen2.5-VL processor which wraps a Qwen2.5-VL image processor and a Qwen2 tokenizer into a single processor.
+    [`Qwen2_5_VLProcessor`] offers all the functionalities of [`Qwen2VLImageProcessor`] and [`Qwen2TokenizerFast`]. See the
+    [`~Qwen2_5_VLProcessor.__call__`] and [`~Qwen2_5_VLProcessor.decode`] for more information.
+    Args:
+        image_processor ([`Qwen2VLImageProcessor`], *optional*):
+            The image processor is a required input.
+        tokenizer ([`Qwen2TokenizerFast`], *optional*):
+            The tokenizer is a required input.
+        video_processor ([`Qwen2_5_VLVideoProcessor`], *optional*):
+            The video processor is a required input.
+        chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages
+            in a chat into a tokenizable string.
+    """
+
+    attributes = ["image_processor", "tokenizer", "video_processor"]
+
+    image_processor_class = "AutoImageProcessor"
+    video_processor_class = "AutoVideoProcessor"
+    tokenizer_class = ("Qwen2Tokenizer", "Qwen2TokenizerFast")
+
+    def __init__(self, image_processor=None, tokenizer=None, video_processor=None, chat_template=None, **kwargs):
+        self.image_token = "<|image_pad|>" if not hasattr(tokenizer, "image_token") else tokenizer.image_token
+        self.video_token = "<|video_pad|>" if not hasattr(tokenizer, "video_token") else tokenizer.video_token
+        self.image_token_id = (
+            tokenizer.image_token_id
+            if getattr(tokenizer, "image_token_id", None)
+            else tokenizer.convert_tokens_to_ids(self.image_token)
+        )
+        self.video_token_id = (
+            tokenizer.video_token_id
+            if getattr(tokenizer, "video_token_id", None)
+            else tokenizer.convert_tokens_to_ids(self.video_token)
+        )
+        super().__init__(image_processor, tokenizer, video_processor, chat_template=chat_template)
+
+    def __call__(
+        self,
+        images: Optional[ImageInput] = None,
+        text: Union[TextInput, PreTokenizedInput, list[TextInput], list[PreTokenizedInput]] = None,
+        videos: Optional[VideoInput] = None,
+        **kwargs: Unpack[Qwen2_5_VLProcessorKwargs],
+    ) -> BatchFeature:
+        """
+        Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
+        and `kwargs` arguments to Qwen2TokenizerFast's [`~Qwen2TokenizerFast.__call__`] if `text` is not `None` to encode
+        the text. To prepare the vision inputs, this method forwards the `vision_infos` and `kwargs` arguments to
+        Qwen2VLImageProcessor's [`~Qwen2VLImageProcessor.__call__`] if `vision_infos` is not `None`.
+
+        Args:
+            images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `list[PIL.Image.Image]`, `list[np.ndarray]`, `list[torch.Tensor]`):
+                The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
+                tensor. Both channels-first and channels-last formats are supported.
+            text (`str`, `list[str]`, `list[list[str]]`):
+                The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
+                (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
+                `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
+            videos (`np.ndarray`, `torch.Tensor`, `list[np.ndarray]`, `list[torch.Tensor]`):
+                The image or batch of videos to be prepared. Each video can be a 4D NumPy array or PyTorch
+                tensor, or a nested list of 3D frames. Both channels-first and channels-last formats are supported.
+            return_tensors (`str` or [`~utils.TensorType`], *optional*):
+                If set, will return tensors of a particular framework. Acceptable values are:
+                - `'tf'`: Return TensorFlow `tf.constant` objects.
+                - `'pt'`: Return PyTorch `torch.Tensor` objects.
+                - `'np'`: Return NumPy `np.ndarray` objects.
+                - `'jax'`: Return JAX `jnp.ndarray` objects.
+
+        Returns:
+            [`BatchFeature`]: A [`BatchFeature`] with the following fields:
+
+            - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
+            - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
+              `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
+              `None`).
+            - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
+            - **pixel_values_videos** -- Pixel values of videos to be fed to a model. Returned when `videos` is not `None`.
+            - **image_grid_thw** -- List of image 3D grid in LLM. Returned when `images` is not `None`.
+            - **video_grid_thw** -- List of video 3D grid in LLM. Returned when `videos` is not `None`.
+            - **second_per_grid_ts** -- List of video seconds per time grid. Returned when `videos` is not `None`.
+        """
+        output_kwargs = self._merge_kwargs(
+            Qwen2_5_VLProcessorKwargs,
+            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
+            **kwargs,
+        )
+
+        image_inputs = videos_inputs = {}
+        if images is not None:
+            image_inputs = self.image_processor(images=images, **output_kwargs["images_kwargs"])
+            image_grid_thw = image_inputs["image_grid_thw"]
+
+        if videos is not None:
+            fps = output_kwargs["videos_kwargs"].get("fps", 2.0)
+            videos_inputs = self.video_processor(videos=videos, **output_kwargs["videos_kwargs"])
+            video_grid_thw = videos_inputs["video_grid_thw"]
+
+            if isinstance(fps, (int, float)):
+                second_per_grid_ts = [self.video_processor.temporal_patch_size / fps] * len(video_grid_thw)
+            elif hasattr(fps, "__len__") and len(fps) == len(video_grid_thw):
+                second_per_grid_ts = [self.video_processor.temporal_patch_size / tmp for tmp in fps]
+            else:
+                raise ValueError(
+                    f"The length of fps ({len(fps) if hasattr(fps, '__len__') else fps}) must be equal to the length of video_grid_thw ({len(video_grid_thw)}) or fps should be a single number."
+                )
+            videos_inputs.update({"second_per_grid_ts": second_per_grid_ts})
+
+        if not isinstance(text, list):
+            text = [text]
+
+        text = text.copy()  # below lines change text in-place
+        if images is not None:
+            merge_length = self.image_processor.merge_size**2
+            index = 0
+            for i in range(len(text)):
+                while self.image_token in text[i]:
+                    num_image_tokens = image_grid_thw[index].prod() // merge_length
+                    text[i] = text[i].replace(self.image_token, "<|placeholder|>" * num_image_tokens, 1)
+                    index += 1
+                text[i] = text[i].replace("<|placeholder|>", self.image_token)
+
+        if videos is not None:
+            merge_length = self.video_processor.merge_size**2
+            index = 0
+            for i in range(len(text)):
+                while self.video_token in text[i]:
+                    num_video_tokens = video_grid_thw[index].prod() // merge_length
+                    text[i] = text[i].replace(self.video_token, "<|placeholder|>" * num_video_tokens, 1)
+                    index += 1
+                text[i] = text[i].replace("<|placeholder|>", self.video_token)
+
+        return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
+        return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", None)
+        text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
+        self._check_special_mm_tokens(text, text_inputs, modalities=["image", "video"])
+
+        if return_mm_token_type_ids:
+            array_ids = np.array(text_inputs["input_ids"])
+            mm_token_type_ids = np.zeros_like(text_inputs["input_ids"])
+            mm_token_type_ids[array_ids == self.image_token_id] = 1
+            text_inputs["mm_token_type_ids"] = mm_token_type_ids.tolist()
+
+        return BatchFeature(data={**text_inputs, **image_inputs, **videos_inputs}, tensor_type=return_tensors)
+
+    def _get_num_multimodal_tokens(self, image_sizes=None, video_sizes=None, **kwargs):
+        """
+        Computes the number of placeholder tokens needed for multimodal inputs with the given sizes.
+        Args:
+            image_sizes (`list[list[int]]`, *optional*):
+                The input sizes formatted as (height, width) per each image.
+            video_sizes (`list[list[int]]`, *optional*):
+                The input sizes formatted as (num_frames, height, width) per each video.
+        Returns:
+            `MultiModalData`: A `MultiModalData` object holding number of tokens per each of the provided
+            input modalities, along with other useful data.
+        """
+
+        vision_data = {}
+        if image_sizes is not None:
+            images_kwargs = Qwen2_5_VLProcessorKwargs._defaults.get("images_kwargs", {})
+            images_kwargs.update(kwargs)
+            merge_size = images_kwargs.get("merge_size", None) or self.image_processor.merge_size
+
+            num_image_patches = [
+                self.image_processor.get_number_of_image_patches(*image_size, images_kwargs)
+                for image_size in image_sizes
+            ]
+            num_image_tokens = [(num_patches // merge_size**2) for num_patches in num_image_patches]
+            vision_data.update({"num_image_tokens": num_image_tokens, "num_image_patches": num_image_patches})
+
+        if video_sizes is not None:
+            videos_kwargs = Qwen2_5_VLProcessorKwargs._defaults.get("videos_kwargs", {})
+            videos_kwargs.update(kwargs)
+            num_video_patches = [
+                self.video_processor.get_number_of_video_patches(*video_size, videos_kwargs)
+                for video_size in video_sizes
+            ]
+            num_video_tokens = [(num_patches // merge_size**2) for num_patches in num_video_patches]
+            vision_data["num_video_tokens"] = num_video_tokens
+
+        return MultiModalData(**vision_data)
+
+    def post_process_image_text_to_text(
+        self, generated_outputs, skip_special_tokens=True, clean_up_tokenization_spaces=False, **kwargs
+    ):
+        """
+        Post-process the output of the model to decode the text.
+
+        Args:
+            generated_outputs (`torch.Tensor` or `np.ndarray`):
+                The output of the model `generate` function. The output is expected to be a tensor of shape `(batch_size, sequence_length)`
+                or `(sequence_length,)`.
+            skip_special_tokens (`bool`, *optional*, defaults to `True`):
+                Whether or not to remove special tokens in the output. Argument passed to the tokenizer's `batch_decode` method.
+            clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
+                Whether or not to clean up the tokenization spaces. Argument passed to the tokenizer's `batch_decode` method.
+            **kwargs:
+                Additional arguments to be passed to the tokenizer's `batch_decode method`.
+
+        Returns:
+            `list[str]`: The decoded text.
+        """
+        return self.tokenizer.batch_decode(
+            generated_outputs,
+            skip_special_tokens=skip_special_tokens,
+            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+            **kwargs,
+        )
+
+    @property
+    def model_input_names(self):
+        tokenizer_input_names = self.tokenizer.model_input_names
+        image_processor_input_names = self.image_processor.model_input_names
+        names_from_processor = list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
+        return names_from_processor + ["second_per_grid_ts"]
+
+
+__all__ = ["Qwen2_5_VLProcessor"]

special_tokens_map.json

@@ -0,0 +1,31 @@
+{
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "eos_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9c5ae00e602b8860cbd784ba82a8aa14e8feecec692e7076590d014d7b7fdafa
+size 11421896

tokenizer_config.json

@@ -0,0 +1,209 @@
+{
+  "add_bos_token": false,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "151643": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151644": {
+      "content": "<|im_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151645": {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151646": {
+      "content": "<|object_ref_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151647": {
+      "content": "<|object_ref_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151648": {
+      "content": "<|box_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151649": {
+      "content": "<|box_end|>",
+      "lstrip": false,
+      "normalized": false,
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+}