AutoAndroidController/py/venv/Lib/site-packages/transformers/models/internvl/modular_internvl.py

# coding=utf-8
# Copyright 2025 HuggingFace Inc. team. All rights reserved.
#
# 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.


import collections.abc
from dataclasses import dataclass
from typing import Callable, Optional, Union

import torch
import torch.nn as nn

from ...activations import ACT2FN
from ...cache_utils import Cache
from ...modeling_layers import GradientCheckpointingLayer
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
from ...processing_utils import Unpack
from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, logging, torch_int
from ...utils.generic import check_model_inputs
from ..clip.modeling_clip import CLIPMLP
from ..janus.modeling_janus import JanusVisionAttention
from ..llama.modeling_llama import LlamaRMSNorm
from ..llava.modeling_llava import (
    LlavaCausalLMOutputWithPast,
    LlavaForConditionalGeneration,
    LlavaModel,
    LlavaModelOutputWithPast,
    LlavaPreTrainedModel,
)
from .configuration_internvl import InternVLConfig, InternVLVisionConfig


logger = logging.get_logger(__name__)


def eager_attention_forward(
    module: nn.Module,
    query: torch.Tensor,
    key: torch.Tensor,
    value: torch.Tensor,
    attention_mask: Optional[torch.Tensor],
    scaling: float,
    dropout: float = 0.0,
    **kwargs,
):
    key_states = key
    value_states = value

    attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
    if attention_mask is not None:
        causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
        attn_weights = attn_weights + causal_mask

    # No upcasting of the attention weights to float32 in this implementation
    attn_weights = nn.functional.softmax(attn_weights, dim=-1)
    attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
    attn_output = torch.matmul(attn_weights, value_states)
    attn_output = attn_output.transpose(1, 2).contiguous()

    return attn_output, attn_weights


class InternVLVisionRMSNorm(LlamaRMSNorm):
    pass


class InternVLVisionAttention(JanusVisionAttention):
    def __init__(self, config: InternVLVisionConfig):
        super().__init__(config)
        del self.num_key_value_groups

        # Needed for flash attention
        self.is_causal = False
        qk_norm = config.use_qk_norm

        self.q_norm = InternVLVisionRMSNorm(self.embed_dim) if qk_norm else nn.Identity()
        self.k_norm = InternVLVisionRMSNorm(self.embed_dim) if qk_norm else nn.Identity()

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        **kwargs: Unpack[TransformersKwargs],
    ):
        batch_size, seq_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 = self.q_norm(query_states)
        key_states = self.k_norm(key_states)

        query_states = query_states.reshape(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2)
        key_states = key_states.reshape(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2)
        value_states = value_states.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2)

        attention_interface: Callable = eager_attention_forward
        if self.config._attn_implementation != "eager":
            attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]

        attn_output, attn_weights = attention_interface(
            self,
            query_states,
            key_states,
            value_states,
            attention_mask,
            dropout=0.0 if not self.training else self.attention_dropout,
            scaling=self.scale,
            is_causal=False,
            **kwargs,
        )
        attn_output = attn_output.reshape(batch_size, seq_len, self.embed_dim)

        output = self.projection_layer(attn_output)
        output = self.projection_dropout(output)

        return output, attn_weights


@dataclass
@auto_docstring(
    custom_intro="""
    Class for outputs of [`InternVLVisionModel`].
    """
)
class InternVLVisionModelOutputWithPooling(BaseModelOutputWithPooling):
    r"""
    pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`):
        Average of the last layer hidden states of the patch tokens (excluding the *[CLS]* token) if
        *config.use_mean_pooling* is set to True. If set to False, then the final hidden state of the *[CLS]* token
        will be returned.
    """


class InternVLVisionPatchEmbeddings(nn.Module):
    """
    This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial
    `hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a
    Transformer.
    """

    def __init__(self, config):
        super().__init__()
        image_size, patch_size = config.image_size, config.patch_size
        num_channels, hidden_size = config.num_channels, config.hidden_size

        num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
        patch_shape = (image_size[0] // patch_size[0], image_size[1] // patch_size[1])
        self.image_size = image_size
        self.patch_size = patch_size
        self.num_channels = num_channels
        self.num_patches = num_patches
        self.patch_shape = patch_shape

        self.projection = nn.Conv2d(num_channels, hidden_size, kernel_size=patch_size, stride=patch_size)

    def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
        batch_size, num_channels, height, width = pixel_values.shape
        if num_channels != self.num_channels:
            raise ValueError(
                "Make sure that the channel dimension of the pixel values match with the one set in the configuration."
            )

        embeddings = self.projection(pixel_values)
        patch_height, patch_width = embeddings.shape[2], embeddings.shape[3]
        embeddings = embeddings.flatten(2).transpose(1, 2)

        return embeddings, (patch_height, patch_width)


# Based on timm implementation, which can be found here:
# https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
class InternVLVisionEmbeddings(nn.Module):
    """
    Construct the CLS token, position and patch embeddings. Optionally, also the mask token.

    """

    def __init__(self, config: InternVLVisionConfig) -> None:
        super().__init__()

        self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
        if config.use_mask_token:
            self.mask_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
        else:
            self.mask_token = None
        self.patch_embeddings = InternVLVisionPatchEmbeddings(config)
        self.patch_size = config.patch_size
        self.image_size = (
            config.image_size
            if isinstance(config.image_size, collections.abc.Iterable)
            else (config.image_size, config.image_size)
        )
        num_patches = self.patch_embeddings.num_patches
        if config.use_absolute_position_embeddings:
            self.position_embeddings = nn.Parameter(torch.zeros(1, num_patches + 1, config.hidden_size))
        else:
            self.position_embeddings = None
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

    def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor:
        """
        This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher resolution
        images. This method is also adapted to support torch.jit tracing.

        Adapted from:
        - https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174-L194, and
        - https://github.com/facebookresearch/dinov2/blob/e1277af2ba9496fbadf7aec6eba56e8d882d1e35/dinov2/models/vision_transformer.py#L179-L211
        """

        num_patches = embeddings.shape[1] - 1
        num_positions = self.position_embeddings.shape[1] - 1

        # always interpolate when tracing to ensure the exported model works for dynamic input shapes
        if not torch.jit.is_tracing() and num_patches == num_positions and height == width:
            return self.position_embeddings

        class_pos_embed = self.position_embeddings[:, :1]
        patch_pos_embed = self.position_embeddings[:, 1:]

        dim = embeddings.shape[-1]

        new_height = height // self.patch_size[0]
        new_width = width // self.patch_size[1]

        sqrt_num_positions = torch_int(num_positions**0.5)
        patch_pos_embed = patch_pos_embed.reshape(1, sqrt_num_positions, sqrt_num_positions, dim)
        patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)

        patch_pos_embed = nn.functional.interpolate(
            patch_pos_embed,
            size=(new_height, new_width),
            mode="bicubic",
            align_corners=False,
        )

        patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)

        return torch.cat((class_pos_embed, patch_pos_embed), dim=1)

    def forward(
        self,
        pixel_values: torch.Tensor,
        bool_masked_pos: Optional[torch.BoolTensor] = None,
    ) -> torch.Tensor:
        _, _, height, width = pixel_values.shape
        embeddings, (patch_height, patch_width) = self.patch_embeddings(pixel_values)
        batch_size, seq_len, _ = embeddings.size()

        if bool_masked_pos is not None:
            mask_tokens = self.mask_token.expand(batch_size, seq_len, -1)
            # replace the masked visual tokens by mask_tokens
            w = bool_masked_pos.unsqueeze(-1).type_as(mask_tokens)
            embeddings = embeddings * (1 - w) + mask_tokens * w

        cls_tokens = self.cls_token.expand(batch_size, -1, -1)
        embeddings = torch.cat((cls_tokens, embeddings), dim=1)

        if self.position_embeddings is not None:
            embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)

        embeddings = self.dropout(embeddings)

        return embeddings, (patch_height, patch_width)


class InternVLVisionMLP(CLIPMLP):
    pass


NORM2FN = {"layer_norm": nn.LayerNorm, "rms_norm": InternVLVisionRMSNorm}


class InternVLVisionLayer(GradientCheckpointingLayer):
    """This corresponds to the Block class in the timm implementation."""

    def __init__(self, config: InternVLVisionConfig) -> None:
        super().__init__()
        self.chunk_size_feed_forward = config.chunk_size_feed_forward
        self.seq_len_dim = 1
        self.attention = InternVLVisionAttention(config)
        self.mlp = InternVLVisionMLP(config)
        # InternVL uses different layernorm implementations for different models
        self.layernorm_before = NORM2FN[config.norm_type](config.hidden_size, eps=config.layer_norm_eps)
        self.layernorm_after = NORM2FN[config.norm_type](config.hidden_size, eps=config.layer_norm_eps)

        init_values = config.layer_scale_init_value
        self.lambda_1 = nn.Parameter(init_values * torch.ones(config.hidden_size), requires_grad=True)
        self.lambda_2 = nn.Parameter(init_values * torch.ones(config.hidden_size), requires_grad=True)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)

    def forward(
        self,
        hidden_states: torch.Tensor,
    ) -> Union[tuple[torch.Tensor], tuple[torch.Tensor, torch.Tensor]]:
        attention_output, _ = self.attention(
            self.layernorm_before(hidden_states),  # in InternVLVision, layernorm is applied before self-attention
        )

        attention_output = self.lambda_1 * attention_output

        # first residual connection
        hidden_states = attention_output + hidden_states

        # in InternVLVision, layernorm is also applied after self-attention
        layer_output = self.layernorm_after(hidden_states)

        layer_output = self.mlp(layer_output)
        layer_output = self.dropout(layer_output)

        if self.lambda_2 is not None:
            layer_output = self.lambda_2 * layer_output

        # second residual connection
        layer_output = layer_output + hidden_states

        return layer_output


class InternVLVisionEncoder(nn.Module):
    def __init__(self, config: InternVLVisionConfig) -> None:
        super().__init__()
        self.config = config
        self.layer = nn.ModuleList([InternVLVisionLayer(config) for i in range(config.num_hidden_layers)])
        self.gradient_checkpointing = False

    def forward(
        self,
        hidden_states: torch.Tensor,
    ) -> Union[tuple, BaseModelOutput]:
        for layer_module in self.layer:
            hidden_states = layer_module(hidden_states)

        return BaseModelOutput(
            last_hidden_state=hidden_states,
        )


@auto_docstring
class InternVLVisionPreTrainedModel(PreTrainedModel):
    config: InternVLVisionConfig
    base_model_prefix = "internvl_vision"
    main_input_name = "pixel_values"
    supports_gradient_checkpointing = True
    _no_split_modules = ["InternVLVisionLayer"]
    _supports_sdpa = True
    _supports_flash_attn = True
    _supports_flex_attn = True
    _supports_attention_backend = True

    _can_record_outputs = {
        "hidden_states": InternVLVisionLayer,
        "attentions": InternVLVisionAttention,
    }

    def _init_weights(self, module):
        """Initialize the weights"""
        super()._init_weights(module)
        if isinstance(module, InternVLVisionEmbeddings):
            module.cls_token.data.zero_()
            if module.mask_token is not None:
                module.mask_token.data.zero_()
            if module.position_embeddings is not None:
                module.position_embeddings.data.zero_()
        elif isinstance(module, InternVLVisionLayer):
            module.lambda_1.data.fill_(self.config.layer_scale_init_value)
            module.lambda_2.data.fill_(self.config.layer_scale_init_value)


@auto_docstring
class InternVLVisionModel(InternVLVisionPreTrainedModel):
    def __init__(self, config: InternVLVisionConfig) -> None:
        super().__init__(config)
        self.config = config

        self.embeddings = InternVLVisionEmbeddings(config)
        self.encoder = InternVLVisionEncoder(config)

        self.layernorm = (
            nn.Identity() if config.use_mean_pooling else nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        )

        # Initialize weights and apply final processing
        self.post_init()

    def get_input_embeddings(self):
        return self.embeddings.patch_embeddings

    @check_model_inputs(tie_last_hidden_states=False)
    @auto_docstring
    def forward(
        self,
        pixel_values: torch.Tensor,
        bool_masked_pos: Optional[torch.BoolTensor] = None,
    ) -> Union[tuple, InternVLVisionModelOutputWithPooling]:
        r"""
        bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, num_patches)`, *optional*):
            Boolean masked positions. Indicates which patches are masked (1) and which aren't (0).
        """
        embedding_output, _ = self.embeddings(pixel_values, bool_masked_pos=bool_masked_pos)

        encoder_outputs = self.encoder(embedding_output)
        sequence_output = encoder_outputs[0]
        sequence_output = self.layernorm(sequence_output)

        return InternVLVisionModelOutputWithPooling(
            last_hidden_state=sequence_output,
            hidden_states=encoder_outputs.hidden_states,
            attentions=encoder_outputs.attentions,
        )


class InternVLPreTrainedModel(LlavaPreTrainedModel):
    pass


INTERNVL_INPUTS_DOCSTRING = None


class InternVLMultiModalProjector(nn.Module):
    def __init__(self, config: InternVLConfig):
        super().__init__()
        self.layer_norm = nn.LayerNorm(config.vision_config.hidden_size * int(1 / config.downsample_ratio) ** 2)
        self.linear_1 = nn.Linear(
            config.vision_config.hidden_size * int(1 / config.downsample_ratio) ** 2, config.text_config.hidden_size
        )
        self.act = ACT2FN[config.projector_hidden_act]
        self.linear_2 = nn.Linear(config.text_config.hidden_size, config.text_config.hidden_size)

    def forward(self, image_features):
        hidden_states = self.layer_norm(image_features)
        hidden_states = self.linear_1(hidden_states)
        hidden_states = self.act(hidden_states)
        hidden_states = self.linear_2(hidden_states)
        return hidden_states


class InternVLModelOutputWithPast(LlavaModelOutputWithPast):
    pass


class InternVLModel(LlavaModel):
    def pixel_shuffle(self, vision_features: torch.Tensor, scale_factor: float = 0.5):
        """Perform pixel shuffle downsampling on vision features.

        Args:
            vision_features (`torch.Tensor`):
                Input tensor of shape (batch_size, width, height, channels).
            scale_factor (`float`, *optional*, defaults to `0.5`):
                Factor by which to downsample. Default is 0.5, which halves the dimensions.

        Returns:
            vision_features (`torch.Tensor`):
                Downsampled tensor of shape (batch_size, height*scale_factor, width*scale_factor, channels/(scale_factor^2)).
        """
        batch_size, width, height, channels = vision_features.size()

        if height % scale_factor != 0 or width % scale_factor != 0:
            raise ValueError("Height and width must be divisible by scale_factor for proper downsampling.")

        # Reshape to allow downsampling
        vision_features = vision_features.view(
            batch_size, width, int(height * scale_factor), int(channels / scale_factor)
        )
        # Permute dimensions to align downsampled axis correctly
        vision_features = vision_features.permute(0, 2, 1, 3).contiguous()

        # Reshape to achieve final downsampled dimensions
        vision_features = vision_features.view(
            batch_size, int(height * scale_factor), int(width * scale_factor), int(channels / (scale_factor**2))
        )

        # Swap height and width back for proper orientation
        vision_features = vision_features.permute(0, 2, 1, 3).contiguous()

        return vision_features

    def get_image_features(
        self,
        pixel_values: torch.FloatTensor,
        vision_feature_layer: Optional[Union[int, list[int]]] = None,
        vision_feature_select_strategy: Optional[str] = None,
        **kwargs,
    ):
        """
        Obtains image last hidden states from the vision tower and apply multimodal projection.

        Args:
            pixel_values (`torch.FloatTensor]` of shape `(batch_size, channels, height, width)`)
               The tensors corresponding to the input images.
            vision_feature_layer (`int` or `list[int]`):
                Layer index or list of layer indices to extract features from.
        Returns:
            vision_features (`torch.Tensor`): Image feature tensor of shape `(num_images, image_length, embed_dim)`.
        """
        vision_feature_layer = (
            vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer
        )
        vision_feature_select_strategy = (
            vision_feature_select_strategy
            if vision_feature_select_strategy is not None
            else self.config.vision_feature_select_strategy
        )
        pixel_values = pixel_values.to(dtype=self.dtype)  # fp16 compatibility

        downsample_ratio = self.config.downsample_ratio
        if vision_feature_layer == -1:
            vision_features = self.vision_tower(pixel_values=pixel_values).last_hidden_state
        else:
            vision_features = self.vision_model(pixel_values=pixel_values).hidden_states[vision_feature_layer]
        if vision_feature_select_strategy == "default":
            vision_features = vision_features[:, 1:, :]

        # Calculate dimensions based on vision features
        channels = vision_features.shape[1]
        feature_size = int(channels**0.5)
        batch_size = vision_features.shape[0]

        # Reshape tensor to spatial dimensions
        vision_features = vision_features.reshape(batch_size, feature_size, feature_size, -1)

        # Apply downsampling using pixel shuffle
        vision_features = self.pixel_shuffle(vision_features, scale_factor=downsample_ratio)

        # Reshape tensor to prepare for projection
        vision_features = vision_features.reshape(batch_size, -1, vision_features.shape[-1])

        # Project features through multi-modal projector
        vision_features = self.multi_modal_projector(vision_features)
        return vision_features

    @can_return_tuple
    @auto_docstring
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        pixel_values: Optional[torch.FloatTensor] = 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,
        vision_feature_layer: Optional[Union[int, list[int]]] = None,
        vision_feature_select_strategy: Optional[str] = None,
        cache_position: Optional[torch.LongTensor] = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> Union[tuple, InternVLModelOutputWithPast]:
        vision_feature_layer = (
            vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer
        )
        vision_feature_select_strategy = (
            vision_feature_select_strategy
            if vision_feature_select_strategy is not None
            else self.config.vision_feature_select_strategy
        )

        if (input_ids is None) ^ (inputs_embeds is not None):
            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")

        if inputs_embeds is None:
            inputs_embeds = self.get_input_embeddings()(input_ids)

        if pixel_values is not None:
            image_features = self.get_image_features(
                pixel_values=pixel_values,
                vision_feature_layer=vision_feature_layer,
                vision_feature_select_strategy=vision_feature_select_strategy,
            )
            image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype)
            special_image_mask = self.get_placeholder_mask(
                input_ids, inputs_embeds=inputs_embeds, image_features=image_features
            )
            inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features)

        outputs = self.language_model(
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            cache_position=cache_position,
            **kwargs,
        )

        return InternVLModelOutputWithPast(
            last_hidden_state=outputs.last_hidden_state,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
            image_hidden_states=image_features if pixel_values is not None else None,
        )


class InternVLCausalLMOutputWithPast(LlavaCausalLMOutputWithPast):
    pass


class InternVLForConditionalGeneration(LlavaForConditionalGeneration):
    def forward(**super_kwargs):
        r"""
        Example:

        ```python
        >>> import torch
        >>> from transformers import AutoProcessor, AutoModelForImageTextToText

        >>> torch_device = "cuda"
        >>> processor = AutoProcessor.from_pretrained("OpenGVLab/InternVL3-1B-hf")
        >>> model = AutoModelForImageTextToText.from_pretrained(
        ...     "OpenGVLab/InternVL3-1B-hf", dtype=torch.bfloat16, device_map=torch_device
        ... )

        >>> messages = [
        ...     {
        ...         "role": "user",
        ...         "content": [
        ...             {
        ...                 "type": "image",
        ...                 "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg",
        ...             },
        ...             {
        ...                 "type": "image",
        ...                 "url": "https://thumbs.dreamstime.com/b/golden-gate-bridge-san-francisco-purple-flowers-california-echium-candicans-36805947.jpg",
        ...             },
        ...             {"type": "text", "text": "These images depict two different landmarks. Can you identify them?"},
        ...         ],
        ...     },
        ... ]

        >>> inputs = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt").to(torch_device)
        >>> generate_ids = model.generate(**inputs, max_new_tokens=200)
        >>> print(processor.decode(generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True))
        The images depict the Statue of Liberty and the Golden Gate Bridge.
        ```"""
        super().forward(**super_kwargs)


__all__ = [
    "InternVLVisionPreTrainedModel",
    "InternVLVisionModel",
    "InternVLPreTrainedModel",
    "InternVLModel",
    "InternVLForConditionalGeneration",
]