image-match/python/py/Lib/site-packages/transformers/models/aimv2/modular_aimv2.py

# Copyright 2025 Apple Inc. and The HuggingFace 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.

"""Pytorch implementation of AIMv2 Model"""

import math

import torch
import torch.nn.functional as F
from huggingface_hub.dataclasses import strict
from torch import nn

from ... import initialization as init
from ...configuration_utils import PreTrainedConfig
from ...masking_utils import create_causal_mask
from ...modeling_layers import GradientCheckpointingLayer
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
from ...modeling_utils import PreTrainedModel
from ...processing_utils import Unpack
from ...utils import TransformersKwargs, auto_docstring, can_return_tuple
from ...utils.generic import merge_with_config_defaults
from ...utils.output_capturing import capture_outputs
from ..clip.modeling_clip import CLIPModel, CLIPTextEmbeddings, _get_vector_norm
from ..llama.modeling_llama import LlamaMLP, LlamaRMSNorm
from ..siglip.configuration_siglip import SiglipConfig, SiglipTextConfig, SiglipVisionConfig
from ..siglip.modeling_siglip import SiglipAttention, SiglipEncoder, SiglipOutput


@auto_docstring(checkpoint="apple/aimv2-large-patch14-224-lit")
@strict
class Aimv2VisionConfig(SiglipVisionConfig):
    r"""
    use_head (`str`, *optional*, defaults to `True`):
        Whether to use Attention Pooling Head or Not.
    is_native (`str`, *optional*, defaults to `False`):
        Whether to use ckpt trained for image native resolution or not.

    Example:

    ```python
    >>> from transformers import SiglipVisionConfig, SiglipVisionModel

    >>> # Initializing a Aimv2VisionConfig with apple/aimv2-large-patch14-224 style configuration
    >>> configuration = Aimv2VisionConfig()

    >>> # Initializing a Aimv2VisionModel (with random weights) from the apple/aimv2-large-patch14-224 style configuration
    >>> model = Aimv2VisionModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""

    hidden_size: int = 1024
    intermediate_size: int = 2816
    num_hidden_layers: int = 24
    num_attention_heads: int = 8
    patch_size: int | list[int] | tuple[int, int] = 14
    rms_norm_eps: float = 1e-5
    attention_dropout: float | int = 0.0
    qkv_bias: bool = False
    mlp_bias: bool = False
    hidden_act: str = "silu"
    initializer_range: float = 0.02
    use_head: bool = True
    is_native: bool = False

    layer_norm_eps = AttributeError()


@auto_docstring(checkpoint="apple/aimv2-large-patch14-224-lit")
@strict
class Aimv2TextConfig(SiglipTextConfig):
    vocab_size: int = 49408
    hidden_size: int = 768
    intermediate_size: int = 2048
    num_hidden_layers: int = 12
    num_attention_heads: int = 6
    max_position_embeddings: int = 77
    hidden_act: str = "silu"
    rms_norm_eps: float = 1e-5
    qkv_bias: bool = False
    mlp_bias: bool = False
    initializer_range: float = 0.02
    bos_token_id = AttributeError()
    pad_token_id = AttributeError()
    layer_norm_eps = AttributeError()
    projection_size = AttributeError()

    def __post_init__(self, **kwargs):
        PreTrainedConfig.__post_init__(**kwargs)


@auto_docstring(checkpoint="apple/aimv2-large-patch14-224-lit")
@strict
class Aimv2Config(SiglipConfig):
    r"""
    max_logit_scale (`float`, *optional*, defaults to `100.0`):
        The maximum logit scale to use

    Example:

    ```python
    >>> from transformers import Aimv2Config, Aimv2Model

    >>> # Initializing a Aimv2Config with apple/aimv2-large-patch14-224-lit style configuration
    >>> configuration = Aimv2Config()

    >>> # Initializing a Aimv2Model (with random weights) from the apple/aimv2-large-patch14-224-lit style configuration
    >>> model = Aimv2Model(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config

    >>> # We can also initialize a Aimv2Config from a Aimv2TextConfig and a Aimv2VisionConfig
    >>> from transformers import Aimv2TextConfig, Aimv2VisionConfig

    >>> # Initializing a AIMv2Text and AIMv2Vision configuration
    >>> config_text = Aimv2TextConfig()
    >>> config_vision = Aimv2VisionConfig()

    >>> config = Aimv2Config(text_config=config_text, vision_config=config_vision)
    ```"""

    projection_dim: int = 512
    logit_scale_init_value: float = 2.6592
    max_logit_scale: float = 100.0


class Aimv2Output(SiglipOutput):
    pass


class Aimv2RMSNorm(LlamaRMSNorm):
    pass


class Aimv2MLP(LlamaMLP):
    pass


class Aimv2VisionEmbeddings(nn.Module):
    def __init__(self, config: Aimv2VisionConfig):
        super().__init__()
        self.config = config
        self.patch_size = config.patch_size
        self.patch_embed = nn.Conv2d(
            config.num_channels, config.hidden_size, kernel_size=config.patch_size, stride=config.patch_size
        )
        self.rms_norm = Aimv2RMSNorm(config.hidden_size, config.rms_norm_eps)

        num_patches = (config.image_size // config.patch_size) ** 2
        if not self.config.is_native:
            self.position_embedding = nn.Embedding(num_patches, config.hidden_size)
        self.register_buffer("position_ids", torch.arange(num_patches).expand((1, -1)), persistent=False)

    @staticmethod
    def build_2d_sincos_position_embedding(
        height, width, embed_dim=256, temperature=10000.0, device="cpu", dtype=torch.float32
    ) -> torch.Tensor:
        grid_w = torch.arange(int(width), dtype=dtype, device=device)
        grid_h = torch.arange(int(height), dtype=dtype, device=device)
        grid_h, grid_w = torch.meshgrid(grid_w, grid_h, indexing="xy")

        pos_dim = embed_dim // 4
        omega = torch.arange(pos_dim, dtype=dtype, device=device) / pos_dim
        omega = 1.0 / (temperature**omega)

        out_h = grid_h.flatten()[..., None] @ omega[None, :]
        out_w = grid_w.flatten()[..., None] @ omega[None, :]

        return torch.concat([out_h.sin(), out_h.cos(), out_w.sin(), out_w.cos()], dim=1)[None, :, :]

    def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
        _, _, height, width = pixel_values.size()
        hidden_states = self.patch_embed(pixel_values).flatten(2).transpose(1, 2)
        hidden_states = self.rms_norm(hidden_states)

        if self.config.is_native:
            pos_embed = self.build_2d_sincos_position_embedding(
                height // self.patch_size,
                width // self.patch_size,
                embed_dim=self.config.hidden_size,
                device=hidden_states.device,
                dtype=hidden_states.dtype,
            )
        else:
            pos_embed = self.position_embedding(self.position_ids)

        hidden_states = hidden_states + pos_embed
        return hidden_states


class Aimv2TextEmbeddings(CLIPTextEmbeddings):
    pass


class Aimv2Attention(SiglipAttention):
    def __init__(self, config):
        super().__init__(config)
        self.k_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=config.qkv_bias)
        self.v_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=config.qkv_bias)
        self.q_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=config.qkv_bias)
        self.out_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=config.qkv_bias)


class Aimv2EncoderLayer(GradientCheckpointingLayer):
    def __init__(self, config: Aimv2VisionConfig):
        super().__init__()
        self.attention = Aimv2Attention(config)
        self.ffn = Aimv2MLP(config)
        self.rms_norm1 = Aimv2RMSNorm(config.hidden_size, config.rms_norm_eps)
        self.rms_norm2 = Aimv2RMSNorm(config.hidden_size, config.rms_norm_eps)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: torch.Tensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> torch.Tensor:
        norm_hidden_states = self.rms_norm1(hidden_states)
        attn_output, _ = self.attention(hidden_states=norm_hidden_states, attention_mask=attention_mask, **kwargs)

        hidden_states = hidden_states + attn_output
        norm_hidden_states = self.rms_norm2(hidden_states)
        mlp_output = self.ffn(norm_hidden_states)

        hidden_states = hidden_states + mlp_output
        return hidden_states


class Aimv2Encoder(SiglipEncoder):
    pass


class Aimv2AttentionPoolingHead(nn.Module):
    def __init__(self, config: Aimv2VisionConfig):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.num_heads = config.num_attention_heads

        self.k_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=config.qkv_bias)
        self.v_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=config.qkv_bias)

        self.cls_token = nn.Parameter(torch.zeros(1, 1, self.hidden_size))
        self.output_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=True)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        batch_size, seq_len, hidden_dim = hidden_states.shape

        cls_token = self.cls_token.expand(batch_size, -1, -1)

        key = self.k_proj(hidden_states).reshape(batch_size, seq_len, self.num_heads, hidden_dim // self.num_heads)
        value = self.v_proj(hidden_states).reshape(batch_size, seq_len, self.num_heads, hidden_dim // self.num_heads)
        query = cls_token.reshape(batch_size, 1, self.num_heads, hidden_dim // self.num_heads)

        key = key.permute(0, 2, 1, 3)
        value = value.permute(0, 2, 1, 3)
        query = query.permute(0, 2, 1, 3)

        attn_output = F.scaled_dot_product_attention(query, key, value)

        attn_output = attn_output.transpose(1, 2).reshape(batch_size, 1, hidden_dim)
        attn_output = attn_output.mean(dim=1)

        output = self.output_proj(attn_output)
        return output


@auto_docstring
class Aimv2PreTrainedModel(PreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models. The model is only intended for inference and doesn't support finetuning.
    """

    config: Aimv2Config
    base_model_prefix = "aimv2"
    input_modalities = ("image",)
    supports_gradient_checkpointing = True
    _no_split_modules = [
        "Aimv2EncoderLayer",
        "Aimv2AttentionPoolingHead",
        "Aimv2VisionEmbeddings",
        "Aimv2TextEmbeddings",
    ]
    _supports_sdpa = True
    _supports_flash_attn = True
    _supports_flex_attn = True

    @torch.no_grad()
    def _init_weights(self, module):
        super()._init_weights(module)
        if hasattr(module, "logit_scale"):
            if isinstance(module.logit_scale, nn.Parameter):
                init.constant_(module.logit_scale, math.log(1 / 0.07))
        elif isinstance(module, Aimv2AttentionPoolingHead):
            init.normal_(module.cls_token, mean=0.0, std=self.config.initializer_range)
        elif isinstance(module, Aimv2VisionEmbeddings):
            init.copy_(module.position_ids, torch.arange(module.position_ids.shape[-1]).expand((1, -1)))
        elif isinstance(module, Aimv2TextEmbeddings):
            init.copy_(module.position_ids, torch.arange(module.position_ids.shape[-1]).expand((1, -1)))


@auto_docstring(
    custom_intro="""
    The Vision model from AIMv2 without any head or projection on top.
    """
)
class Aimv2VisionModel(Aimv2PreTrainedModel):
    config: Aimv2VisionConfig
    main_input_name = "pixel_values"
    _can_record_outputs = {
        "hidden_states": Aimv2EncoderLayer,
        "attentions": Aimv2Attention,
    }

    def __init__(self, config: Aimv2VisionConfig):
        super().__init__(config)
        self.config = config
        self.embeddings = Aimv2VisionEmbeddings(config)
        self.encoder = Aimv2Encoder(config)
        # The only change from SiglipVisionTransformer is, layernorm -> rms_norm.
        self.rms_norm = Aimv2RMSNorm(config.hidden_size, config.rms_norm_eps)

        self.use_head = config.use_head
        if self.use_head:
            self.head = Aimv2AttentionPoolingHead(config)

        self.post_init()

    def get_input_embeddings(self) -> nn.Module:
        return self.embeddings.patch_embed

    @merge_with_config_defaults
    @capture_outputs(tie_last_hidden_states=False)
    @auto_docstring
    def forward(
        self,
        pixel_values,
        **kwargs: Unpack[TransformersKwargs],
    ) -> BaseModelOutputWithPooling:
        r"""
        Examples:

        ```python
        >>> from PIL import Image
        >>> import httpx
        >>> from io import BytesIO
        >>> from transformers import AutoProcessor, Siglip2VisionModel

        >>> model = Aimv2VisionModel.from_pretrained("apple/aimv2-large-patch14-native")
        >>> processor = AutoProcessor.from_pretrained("apple/aimv2-large-patch14-native")

        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
        >>> with httpx.stream("GET", url) as response:
        ...     image = Image.open(BytesIO(response.read()))

        >>> inputs = processor(images=image, return_tensors="pt")

        >>> outputs = model(**inputs)
        >>> last_hidden_state = outputs.last_hidden_state
        >>> pooled_output = outputs.pooler_output  # pooled features
        ```"""
        hidden_states = self.embeddings(pixel_values)

        encoder_outputs: BaseModelOutput = self.encoder(
            inputs_embeds=hidden_states,
            **kwargs,
        )

        last_hidden_state = encoder_outputs.last_hidden_state
        last_hidden_state = self.rms_norm(last_hidden_state)

        pooler_output = self.head(last_hidden_state) if self.use_head else None

        return BaseModelOutputWithPooling(
            last_hidden_state=last_hidden_state,
            pooler_output=pooler_output,
        )


@auto_docstring(
    custom_intro="""
    The text model from AIMv2 without any head or projection on top.
    """
)
class Aimv2TextModel(Aimv2PreTrainedModel):
    main_input_name = "input_ids"

    _can_record_outputs = {
        "hidden_states": Aimv2EncoderLayer,
        "attentions": Aimv2Attention,
    }

    def __init__(self, config: Aimv2TextConfig):
        super().__init__(config)
        self.config = config
        self.embeddings = Aimv2TextEmbeddings(config)
        self.encoder = Aimv2Encoder(config)
        self.rms_norm = Aimv2RMSNorm(config.hidden_size, config.rms_norm_eps)

        self.eos_token_id = config.eos_token_id

        self.post_init()

    def get_input_embeddings(self) -> nn.Module:
        return self.embeddings.token_embedding

    def set_input_embeddings(self, value):
        self.embeddings.token_embedding = value

    @merge_with_config_defaults
    @capture_outputs(tie_last_hidden_states=False)
    @auto_docstring
    def forward(
        self,
        input_ids,
        attention_mask: torch.Tensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> BaseModelOutputWithPooling:
        hidden_states = self.embeddings(input_ids)
        batch_size, seq_len, _ = hidden_states.shape

        position_ids = torch.arange(seq_len, dtype=torch.long, device=hidden_states.device)
        position_ids = position_ids.unsqueeze(0).expand(batch_size, -1)
        if attention_mask is not None:
            attention_mask = create_causal_mask(
                config=self.config,
                inputs_embeds=hidden_states,
                position_ids=position_ids,
                attention_mask=attention_mask,
                past_key_values=None,
            )

        encoder_outputs = self.encoder(
            inputs_embeds=hidden_states,
            attention_mask=attention_mask,
            **kwargs,
        )

        last_hidden_state = encoder_outputs.last_hidden_state
        last_hidden_state = self.rms_norm(last_hidden_state)

        # Get pooled output
        pooled_output = last_hidden_state[
            torch.arange(last_hidden_state.shape[0], device=last_hidden_state.device),
            (input_ids.to(dtype=torch.int, device=last_hidden_state.device) == self.eos_token_id).int().argmax(dim=-1),
        ]

        return BaseModelOutputWithPooling(
            last_hidden_state=last_hidden_state,
            pooler_output=pooled_output,
        )


@auto_docstring
class Aimv2Model(CLIPModel):
    _supports_flash_attn = True

    def __init__(self, config: Aimv2Config):
        PreTrainedModel.__init__(self, config)

        self.projection_dim = config.projection_dim
        self.vision_embed_dim = config.vision_config.hidden_size
        self.text_embed_dim = config.text_config.hidden_size

        self.vision_model = Aimv2VisionModel._from_config(config.vision_config)
        self.text_model = Aimv2TextModel._from_config(config.text_config)

        self.visual_projection = nn.Linear(self.vision_embed_dim, self.projection_dim, bias=False)
        self.text_projection = nn.Linear(self.text_embed_dim, self.projection_dim, bias=False)

        self.logit_scale = nn.Parameter(torch.tensor(self.config.logit_scale_init_value))
        self.max_log_logit_scale = math.log(config.max_logit_scale)

        self.post_init()

    @auto_docstring
    @can_return_tuple
    def forward(
        self,
        input_ids: torch.LongTensor | None = None,
        pixel_values: torch.FloatTensor | None = None,
        attention_mask: torch.Tensor | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> Aimv2Output:
        r"""
        Examples:

        ```python
        >>> from PIL import Image
        >>> import httpx
        >>> from io import BytesIO
        >>> from transformers import AutoProcessor, Aimv2Model

        >>> model = Aimv2Model.from_pretrained("apple/aimv2-large-patch14-224-lit")
        >>> processor = AutoProcessor.from_pretrained("apple/aimv2-large-patch14-224-lit")

        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
        >>> with httpx.stream("GET", url) as response:
        ...     image = Image.open(BytesIO(response.read()))

        >>> inputs = processor(
        ...     text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="pt", padding=True
        ... )

        >>> outputs = model(**inputs)
        >>> logits_per_image = outputs.logits_per_image  # this is the image-text similarity score
        >>> probs = logits_per_image.softmax(dim=1)  # we can take the softmax to get the label probabilities
        ```"""
        vision_outputs: BaseModelOutputWithPooling = self.vision_model(
            pixel_values=pixel_values,
            **kwargs,
        )

        text_outputs: BaseModelOutputWithPooling = self.text_model(
            input_ids=input_ids,
            attention_mask=attention_mask,
            **kwargs,
        )

        image_embeds = vision_outputs.pooler_output
        image_embeds = self.visual_projection(image_embeds)

        text_embeds = text_outputs.pooler_output
        text_embeds = self.text_projection(text_embeds)

        # normalized features
        image_embeds = image_embeds / _get_vector_norm(image_embeds)
        text_embeds = text_embeds / _get_vector_norm(text_embeds)

        logit_scale = self.logit_scale.clamp(0.0, self.max_log_logit_scale).exp().to(text_embeds.device)
        logits_per_text = (logit_scale * text_embeds) @ image_embeds.t()
        logits_per_image = logits_per_text.t()

        return Aimv2Output(
            logits_per_image=logits_per_image,
            logits_per_text=logits_per_text,
            text_embeds=text_embeds,
            image_embeds=image_embeds,
            text_model_output=text_outputs,
            vision_model_output=vision_outputs,
        )


__all__ = [
    "Aimv2Config",
    "Aimv2VisionConfig",
    "Aimv2TextConfig",
    "Aimv2VisionModel",
    "Aimv2Model",
    "Aimv2PreTrainedModel",
    "Aimv2TextModel",
]