Upload Videollama3Qwen2ForCausalLM

config.json

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+{
+  "architectures": [
+    "Videollama3Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "auto_map": {
+    "AutoConfig": "configuration_videollama3.Videollama3Qwen2Config",
+    "AutoModelForCausalLM": "modeling_videollama3.Videollama3Qwen2ForCausalLM"
+  },
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 3584,
+  "image_token_index": 151665,
+  "initializer_range": 0.02,
+  "intermediate_size": 18944,
+  "max_position_embeddings": 32768,
+  "max_window_layers": 28,
+  "mm_projector_type": "mlp2x_gelu",
+  "model_type": "videollama3_qwen2",
+  "num_attention_heads": 28,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 4,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000.0,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.46.3",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "use_token_compression": true,
+  "vision_encoder": null,
+  "vision_encoder_config": {
+    "hidden_size": 1152,
+    "intermediate_size": 4304,
+    "model_type": "videollama3_vision_encoder",
+    "num_attention_heads": 16,
+    "num_hidden_layers": 27,
+    "patch_size": 14
+  },
+  "vocab_size": 152064
+}

configuration_videollama3.py

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+"""VideoLLaMA3 model configuration."""
+
+import importlib.util
+import os.path as osp
+from typing import Optional, Dict, Any
+
+from transformers import AutoConfig, AutoModel, PretrainedConfig, Qwen2Config
+
+try:
+    from .configuration_videollama3_encoder import Videollama3VisionEncoderConfig
+except ModuleNotFoundError:
+    spec = importlib.util.spec_from_file_location(
+        "configuration_videollama3_encoder",
+        osp.join(osp.dirname(__file__), "configuration_videollama3_encoder.py"),
+    )
+    configuration_videollama3_encoder = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(configuration_videollama3_encoder)
+    Videollama3VisionEncoderConfig = getattr(
+        configuration_videollama3_encoder,
+        "Videollama3VisionEncoderConfig",
+    )
+
+try:
+    from .modeling_videollama3_encoder import Videollama3VisionEncoderModel
+except ModuleNotFoundError:
+    spec = importlib.util.spec_from_file_location(
+        "modeling_videollama3_encoder",
+        osp.join(osp.dirname(__file__), "modeling_videollama3_encoder.py"),
+    )
+    modeling_videollama3_encoder = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(modeling_videollama3_encoder)
+    Videollama3VisionEncoderModel = getattr(
+        modeling_videollama3_encoder,
+        "Videollama3VisionEncoderModel",
+    )
+
+AutoConfig.register("videollama3_vision_encoder", Videollama3VisionEncoderConfig)
+AutoModel.register(Videollama3VisionEncoderConfig, Videollama3VisionEncoderModel)
+
+
+class Videollama3Qwen2Config(Qwen2Config):
+
+    model_type = "videollama3_qwen2"
+    sub_configs = {"vision_encoder_config": Videollama3VisionEncoderConfig}
+
+    def __init__(
+        self,
+        vision_encoder: Optional[str] = None,
+        vision_encoder_config: Dict[str, Any] = {},
+        mm_projector_type: str = "mlp2x_gelu",
+        use_token_compression: bool = True,
+        image_token_index: int = -1,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.model_type = "videollama3_qwen2"
+
+        self.vision_encoder = vision_encoder
+        if vision_encoder_config is not None and not isinstance(vision_encoder_config, PretrainedConfig):
+            vision_encoder_config = Videollama3VisionEncoderConfig(**vision_encoder_config)
+        self.vision_encoder_config = vision_encoder_config
+
+        self.mm_projector_type = mm_projector_type
+        self.use_token_compression = use_token_compression
+        self.image_token_index = image_token_index

configuration_videollama3_encoder.py

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+"""VideoLLaMA3 vision encoder model configuration."""
+
+from transformers import PretrainedConfig
+
+
+class Videollama3VisionEncoderConfig(PretrainedConfig):
+
+    model_type = "videollama3_vision_encoder"
+
+    def __init__(
+        self,
+        hidden_size=768,
+        intermediate_size=3072,
+        num_hidden_layers=12,
+        num_attention_heads=12,
+        num_channels=3,
+        patch_size=16,
+        hidden_act="gelu_pytorch_tanh",
+        layer_norm_eps=1e-6,
+        attention_dropout=0.0,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.num_channels = num_channels
+        self.patch_size = patch_size
+        self.attention_dropout = attention_dropout
+        self.layer_norm_eps = layer_norm_eps
+        self.hidden_act = hidden_act

generation_config.json

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+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "repetition_penalty": 1.05,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "4.46.3"
+}

model-00001-of-00004.safetensors

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model.safetensors.index.json

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+    "model.vision_encoder.post_layernorm.bias": "model-00004-of-00004.safetensors",
+    "model.vision_encoder.post_layernorm.weight": "model-00004-of-00004.safetensors"
+  }
+}

modeling_videollama3.py

@@ -0,0 +1,454 @@
+# Adopted from https://github.com/haotian-liu/LLaVA.
+# Below is the original copyright:
+# Copyright 2023 Haotian Liu
+#
+# 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 VideoLLaMA3 model."""
+
+import importlib.util
+import os.path as osp
+import re
+from abc import ABC, abstractmethod
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+import torch.utils.checkpoint
+
+from transformers import AutoModel, Qwen2ForCausalLM, Qwen2Model
+from transformers.generation.utils import GenerateOutput
+from transformers.modeling_outputs import CausalLMOutputWithPast
+
+try:
+    from .configuration_videollama3 import Videollama3Qwen2Config
+except ModuleNotFoundError:
+    spec = importlib.util.spec_from_file_location(
+        "configuration_videollama3",
+        osp.join(osp.dirname(__file__), "configuration_videollama3.py"),
+    )
+    configuration_videollama3 = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(configuration_videollama3)
+    Videollama3Qwen2Config = getattr(
+        configuration_videollama3,
+        "Videollama3Qwen2Config",
+    )
+
+
+def build_mlp(depth, hidden_size, output_hidden_size):
+    modules = [nn.Linear(hidden_size, output_hidden_size)]
+    for _ in range(1, depth):
+        modules.append(nn.GELU())
+        modules.append(nn.Linear(output_hidden_size, output_hidden_size))
+    return nn.Sequential(*modules)
+
+
+def build_vision_projector(config, delay_load=False, **kwargs):
+    # videollama3 projector only support image-wise operation now, i.e., prohibit the temporal aggregation
+    projector_type = getattr(config, 'mm_projector_type', 'linear')
+    if projector_type == "linear":
+        # NOTE: for both linear and mlp2x_gelu projector type, mean pooling is adopted to aggreate video features
+        return nn.Linear(config.mm_hidden_size, config.hidden_size)
+    elif projector_type.startswith("mlp"):
+        return MlpGeluProjector(config, projector_type)
+    else:
+        raise ValueError(f'Unknown projector type: {projector_type}')
+
+
+class MlpGeluProjector(nn.Module):
+
+    def __init__(self, config, projector_type):
+        super().__init__()
+
+        mlp_gelu_match = re.match(r"^mlp(\d+)x_gelu$", projector_type)
+        mlp_depth = int(mlp_gelu_match.group(1))
+
+        self.readout = build_mlp(mlp_depth, config.vision_encoder_config.hidden_size, config.hidden_size)
+
+    def forward(self, x):
+        x = self.readout(x)
+        return x
+
+
+class Videollama3MetaModel:
+
+    def __init__(self, config):
+        super(Videollama3MetaModel, self).__init__(config)
+        if config.vision_encoder is not None:
+            self.vision_encoder = AutoModel.from_pretrained(
+                config.vision_encoder,
+                attn_implementation=self.config._attn_implementation,
+                torch_dtype=self.dtype,
+            )
+            self.config.vision_encoder_config = self.vision_encoder.config
+            self.config.vision_encoder = None
+        elif config.vision_encoder_config is not None:
+            self.vision_encoder = AutoModel.from_config(
+                self.config.vision_encoder_config,
+                attn_implementation=self.config._attn_implementation,
+                torch_dtype=self.dtype,
+            )
+        else:
+            raise ValueError("Vision encoder is not provided in config")
+        self.mm_projector = build_vision_projector(config)
+
+    def get_vision_encoder(self):
+        return self.vision_encoder
+
+    def get_mm_projector(self):
+        return self.mm_projector
+
+
+class Videollama3Qwen2Model(Videollama3MetaModel, Qwen2Model):
+
+    config_class = Videollama3Qwen2Config
+
+    def __init__(self, config: Videollama3Qwen2Config):
+        super(Videollama3Qwen2Model, self).__init__(config)
+
+
+class Videollama3MetaForCausalLM(ABC):
+
+    @abstractmethod
+    def get_model(self):
+        pass
+
+    def get_vision_encoder(self):
+        return self.get_model().get_vision_encoder()
+
+    def get_mm_projector(self):
+        return self.get_model().get_mm_projector()
+
+    def encode_images(
+        self,
+        pixel_values: torch.FloatTensor,
+        grid_sizes: torch.LongTensor,
+        merge_sizes: torch.LongTensor,
+    ) -> torch.FloatTensor:
+        mm_features = self.get_model().get_vision_encoder()(
+            pixel_values=pixel_values,
+            grid_sizes=grid_sizes,
+            merge_sizes=merge_sizes,
+        )
+        mm_features = self.get_model().mm_projector(mm_features)
+        return mm_features
+
+    def _get_valid_visual_tokens(
+        self,
+        mm_features: torch.FloatTensor,
+        batched_num_patches: torch.LongTensor,
+        modals: List[str],
+    ):
+        valid_masks = []
+        for num_patches, modal in zip(batched_num_patches, modals):
+            valid_mask = torch.full((num_patches, ), modal != "text", dtype=torch.bool, device=mm_features.device)
+            valid_masks.append(valid_mask)
+        mm_features = mm_features[torch.cat(valid_masks)]
+        return mm_features
+
+    def _maybe_truncate_visual_tokens(
+        self,
+        mm_features: torch.FloatTensor,
+        compression_mask: torch.BoolTensor,
+        batched_num_patches: torch.LongTensor,
+        modals: List[str],
+        input_ids: torch.LongTensor,
+        position_ids: Optional[torch.LongTensor] = None,
+    ):
+        if position_ids is None or mm_features.shape[0] == input_ids.eq(self.config.image_token_index).sum():
+            return mm_features, compression_mask
+
+        truncation_mask = []
+        for num_patches, modal in zip(batched_num_patches, modals):
+            if modal == "text":
+                truncation_mask.append(torch.ones((0,), dtype=torch.bool, device=input_ids.device))
+            else:
+                truncation_mask.append(torch.ones((num_patches,), dtype=torch.bool, device=input_ids.device))
+
+        seq_end_indices = torch.nonzero(position_ids == 0)[:, 0]
+        seq_end_indices = seq_end_indices[seq_end_indices > 0].tolist()+ [len(input_ids)]
+        seq_start_indices = [0] + seq_end_indices[:-1]
+        num_visual_tokens = [
+            input_ids[start:end].eq(self.config.image_token_index).sum()
+            for start, end in zip(seq_start_indices, seq_end_indices)
+        ]
+
+        for n, mask in zip(num_visual_tokens, truncation_mask):
+            if len(mask) > 0:
+                mask[n:] = False
+        truncation_mask = torch.cat(truncation_mask)
+
+        return mm_features[truncation_mask], compression_mask[truncation_mask]
+
+    def _get_compression_mask(
+        self,
+        pixel_values: torch.FloatTensor,
+        batched_num_patches: torch.LongTensor,
+        grid_sizes: torch.LongTensor,
+        merge_sizes: torch.LongTensor,
+        modals: List[str],
+        threshold: float = 0.1,
+        min_tokens: int = 1,
+    ) -> torch.BoolTensor:
+        batched_images = pixel_values.split(grid_sizes.prod(dim=1).tolist(), dim=0)
+        compression_masks = []
+
+        for images, num_patches, grid_size, merge_size, modal in zip(
+            batched_images, batched_num_patches, grid_sizes, merge_sizes, modals
+        ):
+            t, h, w = grid_size
+            if modal == "image" or (modal == "video" and t == 1):
+                compression_masks.append(torch.ones((num_patches,), dtype=torch.bool, device=images.device))
+
+            elif modal == "video":
+                # NOTE: video token compressor
+                images = images.view(t, (h // merge_size) * (w // merge_size), -1)
+
+                pixel_diff = images[1:] - images[:-1]
+                pixel_diff = torch.abs(pixel_diff).mean(dim=-1) * 255
+                pixel_diff = torch.cat([torch.full_like(pixel_diff[0:1], threshold + 1), pixel_diff], dim=0)
+                mask = pixel_diff > threshold
+                padding_ids = torch.nonzero(mask.sum(dim=1) < min_tokens)[:, 0]
+                # mask[padding_ids, torch.randperm(min_tokens)] = 1
+                mask[padding_ids, :min_tokens] = 1
+                compression_masks.append(mask.flatten())
+
+            else:
+                # in case of psuedo image
+                compression_masks.append(torch.ones((0,), dtype=torch.bool, device=images.device))
+
+        return torch.cat(compression_masks)
+
+    def _compress_visual_tokens(
+        self,
+        compression_mask: torch.BoolTensor,
+        mm_features: torch.FloatTensor,
+        input_ids: torch.LongTensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+    ):
+        mm_features = mm_features[compression_mask]
+        image_selected = (input_ids == self.config.image_token_index)
+
+        text_masks = torch.logical_not(image_selected)
+        text_masks[image_selected] = compression_mask
+        input_ids = input_ids[text_masks]
+
+        if attention_mask is not None:
+            attention_mask = attention_mask[text_masks]
+        if labels is not None:
+            labels = labels[text_masks]
+        if position_ids is not None:
+            # FIXME: assume the first position_id is always 0
+            position_ids = position_ids[text_masks]
+            pos_start = [0] + torch.nonzero(position_ids == 0)[:, 0].tolist()
+            pos_end = pos_start[1:] + [len(input_ids)]
+            position_ids = torch.cat([torch.arange(end - start, device=input_ids.device) for start, end in zip(pos_start, pos_end)])
+
+        return mm_features, input_ids, attention_mask, position_ids, labels
+
+    def prepare_inputs_labels_for_multimodal(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        labels: Optional[torch.LongTensor] = None,
+        pixel_values: Optional[torch.FloatTensor] = None,
+        grid_sizes: Optional[torch.LongTensor] = None,
+        merge_sizes: Optional[torch.LongTensor] = None,
+        modals: Optional[List[str]] = None,
+    ):
+        vision_encoder = self.get_vision_encoder()
+        # NOTE: text-only situation
+        if vision_encoder is None or pixel_values is None or input_ids.shape[1] == 1:
+            return input_ids, attention_mask, position_ids, past_key_values, None, labels
+
+        # 1. flatten text inputs
+        B, N = input_ids.shape
+        input_ids = input_ids.view(B * N)
+        if attention_mask is not None:
+            attention_mask = attention_mask.view(B * N)
+        if position_ids is not None:
+            position_ids = position_ids.view(B * N)
+        if labels is not None:
+            labels = labels.view(B * N)
+
+        # 2. embed visual tokens
+        batched_num_patches = grid_sizes.prod(dim=1).div(merge_sizes ** 2).long()
+        mm_features = self.encode_images(pixel_values, grid_sizes, merge_sizes)
+        mm_features = self._get_valid_visual_tokens(mm_features, batched_num_patches, modals)
+
+        compression_mask = self._get_compression_mask(
+            pixel_values, batched_num_patches, grid_sizes, merge_sizes, modals
+        )
+        mm_features, compression_mask = self._maybe_truncate_visual_tokens(
+            mm_features, compression_mask, batched_num_patches, modals, input_ids, position_ids
+        )
+
+        # 3. compress visual tokens
+        if self.config.use_token_compression:
+            assert B == 1, "Token compression is only supported for batch_size=1"
+            mm_features, input_ids, attention_mask, labels, position_ids = self._compress_visual_tokens(
+                compression_mask, mm_features, input_ids, attention_mask, labels, position_ids
+            )
+
+        # 4. embed text tokens
+        inputs_embeds = self.get_model().embed_tokens(input_ids).clone()
+
+        # 5. replace multimodal tokens with features
+        image_selected = (input_ids == self.config.image_token_index)
+        inputs_embeds[image_selected] = inputs_embeds[image_selected] * 0.0 + mm_features   
+
+        # 6. reshape back to batched format
+        C = inputs_embeds.shape[-1]
+        inputs_embeds = inputs_embeds.reshape(B, -1, C)
+        if attention_mask is not None:
+            attention_mask = attention_mask.view(B, -1)
+        if labels is not None:
+            labels = labels.view(B, -1)
+        if position_ids is not None:
+            position_ids = position_ids.view(B, -1)
+
+        return None, attention_mask, position_ids, past_key_values, inputs_embeds, labels
+
+
+class Videollama3Qwen2ForCausalLM(Qwen2ForCausalLM, Videollama3MetaForCausalLM):
+
+    config_class = Videollama3Qwen2Config
+
+    def __init__(self, config, **kwargs):
+        super(Qwen2ForCausalLM, self).__init__(config)
+        self.model = Videollama3Qwen2Model(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_model(self):
+        return self.model
+
+    # NOTE: arguments are copied from transformers==4.46.3
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = 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,
+        return_dict: Optional[bool] = None,
+        cache_position: Optional[torch.LongTensor] = None,
+        num_logits_to_keep: int = 0,
+        # multimodal inputs
+        pixel_values: Optional[torch.FloatTensor] = None,
+        grid_sizes: Optional[torch.LongTensor] = None,
+        merge_sizes: Optional[torch.LongTensor] = None,
+        modals: Optional[List[str]] = None,
+        **loss_kwargs,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        if inputs_embeds is None:
+            (
+                input_ids,
+                attention_mask,
+                position_ids,
+                past_key_values,
+                inputs_embeds,
+                labels,
+            ) = self.prepare_inputs_labels_for_multimodal(
+                input_ids=input_ids,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                past_key_values=past_key_values,
+                labels=labels,
+                pixel_values=pixel_values,
+                grid_sizes=grid_sizes,
+                merge_sizes=merge_sizes,
+                modals=modals,
+            )
+
+        return super().forward(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            labels=labels,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            cache_position=cache_position,
+            num_logits_to_keep=num_logits_to_keep,
+            **loss_kwargs,
+        )
+
+    @torch.no_grad()
+    def generate(
+        self,
+        # multimodal inputs
+        pixel_values: Optional[torch.FloatTensor] = None,
+        grid_sizes: Optional[torch.LongTensor] = None,
+        merge_sizes: Optional[torch.LongTensor] = None,
+        modals: Optional[List[str]] = None,
+        **kwargs,
+    ) -> Union[GenerateOutput, torch.LongTensor]:
+        input_ids = kwargs.pop("input_ids", None)
+        attention_mask = kwargs.pop("attention_mask", None)
+        position_ids = kwargs.pop("position_ids", None)
+        past_key_values = kwargs.pop("past_key_values", None)
+
+        if "inputs_embeds" in kwargs:
+            raise NotImplementedError("`inputs_embeds` is not supported")
+
+        if pixel_values is not None:
+            (
+                input_ids,
+                attention_mask,
+                position_ids,
+                past_key_values,
+                inputs_embeds,
+                labels,
+            ) = self.prepare_inputs_labels_for_multimodal(
+                input_ids=input_ids,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                past_key_values=past_key_values,
+                labels=None,
+                pixel_values=pixel_values,
+                grid_sizes=grid_sizes,
+                merge_sizes=merge_sizes,
+                modals=modals,
+            )
+        else:
+            inputs_embeds = self.get_model().embed_tokens(input_ids)
+
+        return super().generate(
+            position_ids=position_ids,
+            attention_mask=attention_mask,
+            inputs_embeds=inputs_embeds,
+            **kwargs
+        )
+
+    def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs):
+        images = kwargs.pop("images", None)
+        _inputs = super().prepare_inputs_for_generation(
+            input_ids, past_key_values=past_key_values, inputs_embeds=inputs_embeds, **kwargs
+        )
+        if images is not None:
+            _inputs['images'] = images
+        return _inputs

modeling_videollama3_encoder.py

@@ -0,0 +1,534 @@
+# Adopted from https://github.com/huggingface/transformers/blob/main/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py.
+# Below is the original copyright:
+# Copyright 2024 The Qwen team, Alibaba Group 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.
+"""PyTorch VideoLLaMA3 vision encoder model."""
+
+import importlib.util
+import os.path as osp
+import math
+import warnings
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch.nn.init import _calculate_fan_in_and_fan_out
+
+from transformers.activations import ACT2FN
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import is_flash_attn_2_available
+
+if is_flash_attn_2_available():
+    from flash_attn import flash_attn_varlen_func
+else:
+    flash_attn_varlen_func = None
+
+try:
+    from .configuration_videollama3_encoder import Videollama3VisionEncoderConfig
+except ImportError:
+    spec = importlib.util.spec_from_file_location(
+        "configuration_videollama3_encoder",
+        osp.join(osp.dirname(__file__), "configuration_videollama3_encoder.py"),
+    )
+    configuration_videollama3_encoder = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(configuration_videollama3_encoder)
+    Videollama3VisionEncoderConfig = getattr(
+        configuration_videollama3_encoder,
+        "Videollama3VisionEncoderConfig",
+    )
+
+
+def _trunc_normal_(tensor, mean, std, a, b):
+    # Cut & paste from PyTorch official master until it's in a few official releases - RW
+    # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
+    def norm_cdf(x):
+        # Computes standard normal cumulative distribution function
+        return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0
+
+    if (mean < a - 2 * std) or (mean > b + 2 * std):
+        warnings.warn(
+            "mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
+            "The distribution of values may be incorrect.",
+            stacklevel=2,
+        )
+
+    # Values are generated by using a truncated uniform distribution and
+    # then using the inverse CDF for the normal distribution.
+    # Get upper and lower cdf values
+    l = norm_cdf((a - mean) / std)
+    u = norm_cdf((b - mean) / std)
+
+    # Uniformly fill tensor with values from [l, u], then translate to
+    # [2l-1, 2u-1].
+    tensor.uniform_(2 * l - 1, 2 * u - 1)
+
+    # Use inverse cdf transform for normal distribution to get truncated
+    # standard normal
+    tensor.erfinv_()
+
+    # Transform to proper mean, std
+    tensor.mul_(std * math.sqrt(2.0))
+    tensor.add_(mean)
+
+    # Clamp to ensure it's in the proper range
+    tensor.clamp_(min=a, max=b)
+
+
+def trunc_normal_tf_(
+    tensor: torch.Tensor, mean: float = 0.0, std: float = 1.0, a: float = -2.0, b: float = 2.0
+) -> torch.Tensor:
+    """Fills the input Tensor with values drawn from a truncated
+    normal distribution. The values are effectively drawn from the
+    normal distribution :math:`\\mathcal{N}(\text{mean}, \text{std}^2)`
+    with values outside :math:`[a, b]` redrawn until they are within
+    the bounds. The method used for generating the random values works
+    best when :math:`a \\leq \text{mean} \\leq b`.
+
+    NOTE: this 'tf' variant behaves closer to Tensorflow / JAX impl where the
+    bounds [a, b] are applied when sampling the normal distribution with mean=0, std=1.0
+    and the result is subsequently scaled and shifted by the mean and std args.
+
+    Args:
+        tensor: an n-dimensional `torch.Tensor`
+        mean: the mean of the normal distribution
+        std: the standard deviation of the normal distribution
+        a: the minimum cutoff value
+        b: the maximum cutoff value
+    """
+    with torch.no_grad():
+        _trunc_normal_(tensor, 0, 1.0, a, b)
+        tensor.mul_(std).add_(mean)
+
+
+def variance_scaling_(tensor, scale=1.0, mode="fan_in", distribution="normal"):
+    fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor)
+    if mode == "fan_in":
+        denom = fan_in
+    elif mode == "fan_out":
+        denom = fan_out
+    elif mode == "fan_avg":
+        denom = (fan_in + fan_out) / 2
+
+    variance = scale / denom
+
+    if distribution == "truncated_normal":
+        # constant is stddev of standard normal truncated to (-2, 2)
+        trunc_normal_tf_(tensor, std=math.sqrt(variance) / 0.87962566103423978)
+    elif distribution == "normal":
+        with torch.no_grad():
+            tensor.normal_(std=math.sqrt(variance))
+    elif distribution == "uniform":
+        bound = math.sqrt(3 * variance)
+        with torch.no_grad():
+            tensor.uniform_(-bound, bound)
+    else:
+        raise ValueError(f"invalid distribution {distribution}")
+
+
+def lecun_normal_(tensor):
+    variance_scaling_(tensor, mode="fan_in", distribution="truncated_normal")
+
+
+def default_flax_embed_init(tensor):
+    variance_scaling_(tensor, mode="fan_in", distribution="normal")
+
+
+# Copied from transformers.models.llama.modeling_llama.rotate_half
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb_vision(tensor: torch.Tensor, freqs: torch.Tensor) -> torch.Tensor:
+    orig_dtype = tensor.dtype
+    tensor = tensor.float()
+    cos = freqs.cos()
+    sin = freqs.sin()
+    cos = cos.unsqueeze(1).repeat(1, 1, 2).unsqueeze(0).float()
+    sin = sin.unsqueeze(1).repeat(1, 1, 2).unsqueeze(0).float()
+    output = (tensor * cos) + (rotate_half(tensor) * sin)
+    output = output.to(orig_dtype)
+    return output
+
+
+class VisionRotaryEmbedding(nn.Module):
+
+    def __init__(self, dim: int, theta: float = 10000.0) -> None:
+        super().__init__()
+        inv_freq = 1.0 / (theta ** (torch.arange(0, dim, 2, dtype=torch.float) / dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+    def forward(self, seqlen: int) -> torch.Tensor:
+        seq = torch.arange(seqlen, device=self.inv_freq.device, dtype=self.inv_freq.dtype)
+        freqs = torch.outer(seq, self.inv_freq)
+        return freqs
+    
+
+class Videollama3VisionEmbeddings(nn.Module):
+
+    def __init__(self, config: Videollama3VisionEncoderConfig):
+        super().__init__()
+        self.config = config
+        self.embed_dim = config.hidden_size
+        self.patch_size = config.patch_size
+
+        self.patch_embedding = nn.Conv2d(
+            in_channels=config.num_channels,
+            out_channels=self.embed_dim,
+            kernel_size=self.patch_size,
+            stride=self.patch_size,
+            padding="valid",
+        )
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = hidden_states.view(
+            -1, self.config.num_channels, self.patch_size, self.patch_size
+        )
+        patch_embeds = self.patch_embedding(hidden_states)  # shape = [*, width, grid, grid]
+        # embeddings = patch_embeds.flatten(2).transpose(1, 2)
+        embeddings = patch_embeds.view(-1, self.embed_dim)
+
+        return embeddings
+
+
+class VisionAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    # Copied from transformers.models.clip.modeling_clip.CLIPAttention.__init__
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.embed_dim = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.embed_dim // self.num_heads
+        if self.head_dim * self.num_heads != self.embed_dim:
+            raise ValueError(
+                f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:"
+                f" {self.num_heads})."
+            )
+        self.scale = self.head_dim**-0.5
+        self.dropout = config.attention_dropout
+
+        self.k_proj = nn.Linear(self.embed_dim, self.embed_dim)
+        self.v_proj = nn.Linear(self.embed_dim, self.embed_dim)
+        self.q_proj = nn.Linear(self.embed_dim, self.embed_dim)
+        self.out_proj = nn.Linear(self.embed_dim, self.embed_dim)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        cu_seqlens: torch.Tensor,
+        rotary_pos_emb: torch.Tensor = None,
+    ) -> torch.Tensor:
+        """Input shape: Time x Channel"""
+
+        q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        query_states = query_states.view(q_len, self.num_heads, self.head_dim)
+        key_states = key_states.view(q_len, self.num_heads, self.head_dim)
+        value_states = value_states.view(q_len, self.num_heads, self.head_dim)
+
+        query_states = apply_rotary_pos_emb_vision(query_states.unsqueeze(0), rotary_pos_emb).squeeze(0)
+        key_states = apply_rotary_pos_emb_vision(key_states.unsqueeze(0), rotary_pos_emb).squeeze(0)
+
+        attention_mask = torch.zeros([1, q_len, q_len], device=query_states.device, dtype=torch.bool)
+        for i in range(1, len(cu_seqlens)):
+            attention_mask[..., cu_seqlens[i - 1] : cu_seqlens[i], cu_seqlens[i - 1] : cu_seqlens[i]] = True
+
+        query_states = query_states.transpose(0, 1)
+        key_states = key_states.transpose(0, 1)
+        value_states = value_states.transpose(0, 1)
+
+        attn_weights = torch.matmul(query_states, key_states.transpose(1, 2)) / math.sqrt(self.head_dim)
+        attn_weights = attn_weights + attention_mask
+
+        # upcast attention to fp32
+        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+        attn_weights = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
+        attn_output = torch.matmul(attn_weights, value_states)
+
+        attn_output = attn_output.transpose(0, 1)
+        attn_output = attn_output.reshape(q_len, -1)
+        attn_output = self.out_proj(attn_output)
+
+        return attn_output
+
+
+class VisionFlashAttention2(VisionAttention):
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    # Adapted from transformers.models.llama.modeling_llama.LlamaFlashAttention2.forward
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        cu_seqlens: torch.Tensor,
+        rotary_pos_emb: torch.Tensor = None,
+    ) -> torch.Tensor:
+        q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        # Flash attention requires the input to have the shape
+        # batch_size x seq_length x head_dim x hidden_dim
+        # therefore we just need to keep the original shape
+        query_states = query_states.view(q_len, self.num_heads, self.head_dim)
+        key_states = key_states.view(q_len, self.num_heads, self.head_dim)
+        value_states = value_states.view(q_len, self.num_heads, self.head_dim)
+        query_states = apply_rotary_pos_emb_vision(query_states.unsqueeze(0), rotary_pos_emb).squeeze(0)
+        key_states = apply_rotary_pos_emb_vision(key_states.unsqueeze(0), rotary_pos_emb).squeeze(0)
+        
+        max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max().item()
+        attn_output = flash_attn_varlen_func(query_states, key_states, value_states, cu_seqlens, cu_seqlens, max_seqlen, max_seqlen).reshape(
+            q_len, -1
+        )
+        attn_output = self.out_proj(attn_output)
+        
+        return attn_output
+
+
+class VisionSdpaAttention(VisionAttention):
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        cu_seqlens: torch.Tensor,
+        rotary_pos_emb: torch.Tensor = None,
+    ) -> torch.Tensor:
+        seq_length = hidden_states.shape[0]
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        query_states = query_states.view(seq_length, self.num_heads, self.head_dim)
+        key_states = key_states.view(seq_length, self.num_heads, self.head_dim)
+        value_states = value_states.view(seq_length, self.num_heads, self.head_dim)
+
+        query_states = apply_rotary_pos_emb_vision(query_states.unsqueeze(0), rotary_pos_emb).squeeze(0)
+        key_states = apply_rotary_pos_emb_vision(key_states.unsqueeze(0), rotary_pos_emb).squeeze(0)
+
+        attention_mask = torch.zeros([1, seq_length, seq_length], device=query_states.device, dtype=torch.bool)
+        for i in range(1, len(cu_seqlens)):
+            attention_mask[..., cu_seqlens[i - 1] : cu_seqlens[i], cu_seqlens[i - 1] : cu_seqlens[i]] = True
+
+        query_states = query_states.transpose(0, 1)
+        key_states = key_states.transpose(0, 1)
+        value_states = value_states.transpose(0, 1)
+        attn_output = F.scaled_dot_product_attention(query_states, key_states, value_states, attention_mask, dropout_p=0.0)
+        attn_output = attn_output.transpose(0, 1)
+        attn_output = attn_output.reshape(seq_length, -1)
+        attn_output = self.proj(attn_output)
+        return attn_output
+
+
+VISION_ATTENTION_CLASSES = {
+    "eager": VisionAttention,
+    "flash_attention_2": VisionFlashAttention2,
+    "sdpa": VisionSdpaAttention,
+}
+
+
+# Copied from transformers.models.clip.modeling_clip.CLIPMLP with CLIP->Videollama3
+class Videollama3VisionMLP(nn.Module):
+
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.activation_fn = ACT2FN[config.hidden_act]
+        self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
+        self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.fc1(hidden_states)
+        hidden_states = self.activation_fn(hidden_states)
+        hidden_states = self.fc2(hidden_states)
+        return hidden_states
+
+
+class Videollama3VisionEncoderLayer(nn.Module):
+
+    def __init__(self, config: Videollama3VisionEncoderConfig):
+        super().__init__()
+        self.embed_dim = config.hidden_size
+        self.self_attn = VISION_ATTENTION_CLASSES[config._attn_implementation](config=config)
+        self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
+        self.mlp = Videollama3VisionMLP(config)
+        self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
+
+    # Ignore copy
+    def forward(self, hidden_states, cu_seqlens, rotary_pos_emb) -> torch.Tensor:
+        hidden_states = hidden_states + self.self_attn(
+            self.layer_norm1(hidden_states), cu_seqlens=cu_seqlens, rotary_pos_emb=rotary_pos_emb
+        )
+        hidden_states = hidden_states + self.mlp(self.layer_norm2(hidden_states))
+        return hidden_states
+
+
+class Videollama3VisionTransformerEncoder(nn.Module):
+
+    def __init__(self, config: Videollama3VisionEncoderConfig):
+        super().__init__()
+        self.config = config
+        head_dim = config.hidden_size // config.num_attention_heads
+        self.rotary_pos_emb = VisionRotaryEmbedding(head_dim // 2)
+        self.layers = nn.ModuleList([Videollama3VisionEncoderLayer(config) for _ in range(config.num_hidden_layers)])
+        self.gradient_checkpointing = False
+
+    def rot_pos_emb(self, grid_sizes, merge_sizes):
+        pos_ids = []
+        for (t, h, w), merge_size in zip(grid_sizes, merge_sizes):
+            hpos_ids = torch.arange(h).unsqueeze(1).expand(-1, w)
+            hpos_ids = hpos_ids.reshape(
+                h // merge_size,
+                merge_size,
+                w // merge_size,
+                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 // merge_size,
+                merge_size,
+                w // merge_size,
+                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_sizes[:, 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 forward(self, hidden_states, grid_sizes, merge_sizes) -> torch.Tensor:
+        rotary_pos_emb = self.rot_pos_emb(grid_sizes, merge_sizes)
+
+        cu_seqlens = torch.repeat_interleave(grid_sizes[:, 1] * grid_sizes[:, 2], grid_sizes[:, 0]).cumsum(dim=0, dtype=torch.int32)
+        cu_seqlens = F.pad(cu_seqlens, (1, 0), value=0)
+
+        for blk in self.layers:
+            if self.gradient_checkpointing and self.training:
+                hidden_states = self._gradient_checkpointing_func(
+                    blk.__call__,
+                    hidden_states,
+                    cu_seqlens,
+                    rotary_pos_emb
+                )
+            else:
+                hidden_states = blk(hidden_states, cu_seqlens=cu_seqlens, rotary_pos_emb=rotary_pos_emb)
+
+        return hidden_states
+
+
+class Videollama3VisionEncoderModel(PreTrainedModel):
+
+    config_class = Videollama3VisionEncoderConfig
+    base_model_prefix = "videollama3"
+    main_input_name = "pixel_values"
+    supports_gradient_checkpointing = True
+    _no_split_modules = [
+        "Videollama3VisionEncoderLayer",
+        "Videollama3VisionEmbeddings",
+    ]
+    _supports_flash_attn_2 = True
+    _supports_sdpa = True
+
+    def __init__(self, config: Videollama3VisionEncoderConfig):
+        super().__init__(config=config)
+        embed_dim = config.hidden_size
+
+        self.embeddings = Videollama3VisionEmbeddings(config)
+        self.encoder = Videollama3VisionTransformerEncoder(config)
+        self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
+
+        self.post_init()
+
+    def forward(self, pixel_values, grid_sizes, merge_sizes=None) -> torch.Tensor:
+        hidden_states = self.embeddings(pixel_values)
+        hidden_states = self.encoder(hidden_states, grid_sizes, merge_sizes)
+        hidden_states = self.post_layernorm(hidden_states)
+
+        hidden_states_chunks = hidden_states.split(grid_sizes.prod(dim=1).tolist(), dim=0)
+        outputs = []
+
+        for hidden_states, grid_size, merge_size in zip(hidden_states_chunks, grid_sizes, merge_sizes):
+            # NOTE: previous implementation, which supports downsampling with any factor
+            c = hidden_states.shape[-1]
+            hidden_states = hidden_states.view(
+                grid_size[0], grid_size[1] // merge_size, grid_size[2] // merge_size, merge_size, merge_size,  c
+            ).permute(0, 1, 3, 2, 4, 5)
+            hidden_states = hidden_states.reshape(
+                grid_size[0], grid_size[1], grid_size[2], c
+            ).permute(0, 3, 1, 2)
+            hidden_states = torch.nn.functional.interpolate(
+                hidden_states,
+                size=(grid_size[1] // merge_size, grid_size[2] // merge_size),
+                mode='bilinear'
+            )
+            hidden_states = hidden_states.permute(0, 2, 3, 1).view(-1, c)
+
+            # NOTE: simplified implementation, which only supports downsampling with integer factor
+            # NOTE: this implementation is mathematically equivalent to the previous one when merge_size is 1 or 2 but may cause slightly different results
+            # hidden_states = hidden_states.view(-1, merge_size * merge_size, hidden_states.size(-1))
+            # hidden_states = hidden_states.mean(dim=1)
+
+            outputs.append(hidden_states)
+
+        return torch.cat(outputs, dim=0)
+
+    def _init_weights(self, module):
+        """Initialize the weights"""
+        if isinstance(module, nn.Embedding):
+            default_flax_embed_init(module.weight)
+        elif isinstance(module, VisionAttention):
+            nn.init.xavier_uniform_(module.q_proj.weight)
+            nn.init.xavier_uniform_(module.k_proj.weight)
+            nn.init.xavier_uniform_(module.v_proj.weight)
+            nn.init.xavier_uniform_(module.out_proj.weight)
+            nn.init.zeros_(module.q_proj.bias)
+            nn.init.zeros_(module.k_proj.bias)
+            nn.init.zeros_(module.v_proj.bias)
+            nn.init.zeros_(module.out_proj.bias)
+        elif isinstance(module, Videollama3VisionMLP):
+            nn.init.xavier_uniform_(module.fc1.weight)
+            nn.init.xavier_uniform_(module.fc2.weight)
+            nn.init.normal_(module.fc1.bias, std=1e-6)
+            nn.init.normal_(module.fc2.bias, std=1e-6)
+        elif isinstance(module, (nn.Linear, nn.Conv2d)):
+            lecun_normal_(module.weight)
+            if module.bias is not None:
+                nn.init.zeros_(module.bias)
+        elif isinstance(module, nn.LayerNorm):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)