aphrodite-engine/aphrodite/modeling/models/na_vit.py

import logging
import math
import os
import warnings
from typing import Optional, Tuple, Union

import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
from torch.nn.init import _calculate_fan_in_and_fan_out
from transformers.activations import ACT2FN
from transformers.configuration_utils import PretrainedConfig
from transformers.modeling_attn_mask_utils import _prepare_4d_attention_mask
from transformers.modeling_outputs import (BaseModelOutput,
                                           BaseModelOutputWithPooling)
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import (ModelOutput, is_flash_attn_2_available,
                                replace_return_docstrings)

logger = logging.getLogger("aphrodite")


# For Siglip: copied from
#   HuggingFaceM4/siglip-so400m-14-980-flash-attn2-navit and add tgt_sizes
# Remove hints as there's little possibility to change these code.
class SiglipVisionConfig(PretrainedConfig):

    model_type = "siglip_vision_model"

    def __init__(
        self,
        hidden_size=768,
        intermediate_size=3072,
        num_hidden_layers=12,
        num_attention_heads=12,
        num_channels=3,
        image_size=224,
        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.image_size = image_size
        self.attention_dropout = attention_dropout
        self.layer_norm_eps = layer_norm_eps
        self.hidden_act = hidden_act

    @classmethod
    def from_pretrained(cls, pretrained_model_name_or_path: Union[str,
                                                                  os.PathLike],
                        **kwargs) -> "PretrainedConfig":
        cls._set_token_in_kwargs(kwargs)

        config_dict, kwargs = cls.get_config_dict(
            pretrained_model_name_or_path, **kwargs)

        # get the vision config dict if we are loading from SiglipConfig
        if config_dict.get("model_type") == "siglip":
            config_dict = config_dict["vision_config"]

        if "model_type" in config_dict and hasattr(
                cls,
                "model_type") and config_dict["model_type"] != cls.model_type:
            logger.warning(
                f"You are using a model of type {config_dict['model_type']} to"
                f" instantiate a model of type {cls.model_type}. "
                "This is not supported for all configurations of models and "
                "can yield errors.")

        return cls.from_dict(config_dict, **kwargs)


_CHECKPOINT_FOR_DOC = "google/siglip-base-patch16-224"

SIGLIP_PRETRAINED_MODEL_ARCHIVE_LIST = [
    "google/siglip-base-patch16-224",
    # See all SigLIP models at https://huggingface.co/models?filter=siglip
]

if is_flash_attn_2_available():
    from flash_attn import flash_attn_func, flash_attn_varlen_func
    from flash_attn.bert_padding import pad_input  # noqa
    from flash_attn.bert_padding import index_first_axis, unpad_input


# Copied from transformers.models.llama.modeling_llama._get_unpad_data
def _get_unpad_data(attention_mask):
    seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
    indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
    max_seqlen_in_batch = seqlens_in_batch.max().item()
    cu_seqlens = F.pad(
        torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
    return (
        indices,
        cu_seqlens,
        max_seqlen_in_batch,
    )


def _trunc_normal_(tensor, mean, std, a, b):

    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
    if tensor.dtype in [torch.float16, torch.bfloat16]:
        # The `erfinv_` op is not (yet?) defined in float16+cpu, bfloat16+gpu
        og_dtype = tensor.dtype
        tensor = tensor.to(torch.float32)
        tensor.erfinv_()
        tensor = tensor.to(og_dtype)
    else:
        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
    if tensor.dtype == torch.float16:
        # The `clamp_` op is not (yet?) defined in float16+cpu
        tensor = tensor.to(torch.float32)
        tensor.clamp_(min=a, max=b)
        tensor = tensor.to(torch.float16)
    else:
        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:
    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")


class SiglipVisionModelOutput(ModelOutput):
    image_embeds: Optional[torch.FloatTensor] = None
    last_hidden_state: torch.FloatTensor = None
    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
    attentions: Optional[Tuple[torch.FloatTensor]] = None


class SiglipVisionEmbeddings(nn.Module):

    def __init__(self, config: SiglipVisionConfig):
        super().__init__()
        self.config = config
        self.embed_dim = config.hidden_size
        self.image_size = config.image_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",
        )

        self.num_patches_per_side = self.image_size // self.patch_size
        self.num_patches = self.num_patches_per_side**2
        self.num_positions = self.num_patches
        self.position_embedding = nn.Embedding(self.num_positions,
                                               self.embed_dim)

    def forward(self,
                pixel_values: torch.FloatTensor,
                patch_attention_mask: torch.BoolTensor,
                tgt_sizes: Optional[torch.IntTensor] = None) -> torch.Tensor:
        batch_size = pixel_values.size(0)

        patch_embeds = self.patch_embedding(pixel_values)
        embeddings = patch_embeds.flatten(2).transpose(1, 2)

        max_im_h, max_im_w = pixel_values.size(2), pixel_values.size(3)
        max_nb_patches_h, max_nb_patches_w = (max_im_h // self.patch_size,
                                              max_im_w // self.patch_size)
        boundaries = torch.arange(1 / self.num_patches_per_side, 1.0,
                                  1 / self.num_patches_per_side)
        position_ids = torch.full(
            size=(
                batch_size,
                max_nb_patches_h * max_nb_patches_w,
            ),
            fill_value=0,
        )

        for batch_idx, p_attn_mask in enumerate(patch_attention_mask):
            if tgt_sizes is not None:
                nb_patches_h = tgt_sizes[batch_idx][0]
                nb_patches_w = tgt_sizes[batch_idx][1]
            else:
                nb_patches_h = p_attn_mask[:, 0].sum()
                nb_patches_w = p_attn_mask[0].sum()

            fractional_coords_h = torch.arange(0, 1 - 1e-6, 1 / nb_patches_h)
            fractional_coords_w = torch.arange(0, 1 - 1e-6, 1 / nb_patches_w)

            bucket_coords_h = torch.bucketize(fractional_coords_h,
                                              boundaries,
                                              right=True)
            bucket_coords_w = torch.bucketize(fractional_coords_w,
                                              boundaries,
                                              right=True)

            pos_ids = (bucket_coords_h[:, None] * self.num_patches_per_side +
                       bucket_coords_w).flatten()
            position_ids[batch_idx][p_attn_mask.view(-1).cpu()] = pos_ids

        position_ids = position_ids.to(self.position_embedding.weight.device)

        embeddings = embeddings + self.position_embedding(position_ids)
        return embeddings


class SiglipAttention(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(
                "embed_dim must be divisible by num_heads (got `embed_dim`: "
                f"{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,
        attention_mask: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = False,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor],
               Optional[Tuple[torch.Tensor]]]:
        """Input shape: Batch x Time x Channel"""

        batch_size, 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(batch_size, q_len, self.num_heads,
                                         self.head_dim).transpose(1, 2)
        key_states = key_states.view(batch_size, q_len, self.num_heads,
                                     self.head_dim).transpose(1, 2)
        value_states = value_states.view(batch_size, q_len, self.num_heads,
                                         self.head_dim).transpose(1, 2)

        k_v_seq_len = key_states.shape[-2]
        attn_weights = torch.matmul(query_states, key_states.transpose(
            2, 3)) * self.scale

        if attn_weights.size() != (batch_size, self.num_heads, q_len,
                                   k_v_seq_len):
            raise ValueError(
                "Attention weights should be of size "
                f"{(batch_size, self.num_heads, q_len, k_v_seq_len)}, but is"
                f" {attn_weights.size()}")

        if attention_mask is not None:
            if attention_mask.size() != (batch_size, 1, q_len, k_v_seq_len):
                raise ValueError(
                    "Attention mask should be of size "
                    f"{(batch_size, 1, q_len, k_v_seq_len)}",
                    f"but is {attention_mask.size()}")
            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)

        if attn_output.size() != (batch_size, self.num_heads, q_len,
                                  self.head_dim):
            raise ValueError(
                "`attn_output` should be of size "
                f"{(batch_size, self.num_heads, q_len, self.head_dim)}, "
                "but is"
                f" {attn_output.size()}")

        attn_output = attn_output.transpose(1, 2).contiguous()
        attn_output = attn_output.reshape(batch_size, q_len, self.embed_dim)

        attn_output = self.out_proj(attn_output)

        return attn_output, attn_weights


class SiglipFlashAttention2(SiglipAttention):

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.is_causal = False  # Hack to make sure we don't use a causal mask

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.LongTensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_value: Optional[Tuple[torch.Tensor]] = None,
        output_attentions: bool = False,
        use_cache: bool = False,
        **kwargs,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor],
               Optional[Tuple[torch.Tensor]]]:
        output_attentions = False

        bsz, 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(bsz, q_len, self.num_heads,
                                         self.head_dim).transpose(1, 2)
        key_states = key_states.view(bsz, q_len, self.num_heads,
                                     self.head_dim).transpose(1, 2)
        value_states = value_states.view(bsz, q_len, self.num_heads,
                                         self.head_dim).transpose(1, 2)

        kv_seq_len = key_states.shape[-2]
        if past_key_value is not None:
            kv_seq_len += past_key_value.get_usable_length(
                kv_seq_len, self.layer_idx)

        query_states = query_states.transpose(1, 2)
        key_states = key_states.transpose(1, 2)
        value_states = value_states.transpose(1, 2)

        dropout_rate = self.dropout if self.training else 0.0

        input_dtype = query_states.dtype
        if input_dtype == torch.float32:
            if torch.is_autocast_enabled():
                target_dtype = torch.get_autocast_gpu_dtype()
            # Handle the case where the model is quantized
            elif hasattr(self.config, "_pre_quantization_dtype"):
                target_dtype = self.config._pre_quantization_dtype
            else:
                target_dtype = self.q_proj.weight.dtype

            logger.warning(
                "The input hidden states seems to be "
                "silently casted in float32, "
                "this might be related to the fact "
                "you have upcasted embedding or layer norm layers in float32. "
                "We will cast back the input in"
                f" {target_dtype}.")

            query_states = query_states.to(target_dtype)
            key_states = key_states.to(target_dtype)
            value_states = value_states.to(target_dtype)

        attn_output = self._flash_attention_forward(query_states,
                                                    key_states,
                                                    value_states,
                                                    attention_mask,
                                                    q_len,
                                                    dropout=dropout_rate)

        attn_output = attn_output.reshape(bsz, q_len,
                                          self.embed_dim).contiguous()
        attn_output = self.out_proj(attn_output)

        if not output_attentions:
            attn_weights = None

        return attn_output, attn_weights

    def _flash_attention_forward(self,
                                 query_states,
                                 key_states,
                                 value_states,
                                 attention_mask,
                                 query_length,
                                 dropout=0.0,
                                 softmax_scale=None):
        causal = self.is_causal and query_length != 1

        # Contains at least one padding token in the sequence
        if attention_mask is not None:
            batch_size = query_states.shape[0]
            (query_states, key_states, value_states, indices_q, cu_seq_lens,
             max_seq_lens) = self._upad_input(query_states, key_states,
                                              value_states, attention_mask,
                                              query_length)

            cu_seqlens_q, cu_seqlens_k = cu_seq_lens
            max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens

            attn_output_unpad = flash_attn_varlen_func(
                query_states,
                key_states,
                value_states,
                cu_seqlens_q=cu_seqlens_q,
                cu_seqlens_k=cu_seqlens_k,
                max_seqlen_q=max_seqlen_in_batch_q,
                max_seqlen_k=max_seqlen_in_batch_k,
                dropout_p=dropout,
                softmax_scale=softmax_scale,
                causal=causal,
            )

            attn_output = pad_input(attn_output_unpad, indices_q, batch_size,
                                    query_length)
        else:
            attn_output = flash_attn_func(query_states,
                                          key_states,
                                          value_states,
                                          dropout,
                                          softmax_scale=softmax_scale,
                                          causal=causal)

        return attn_output

    def _upad_input(self, query_layer, key_layer, value_layer, attention_mask,
                    query_length):
        indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(
            attention_mask)
        batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape

        key_layer = index_first_axis(
            key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads,
                              head_dim), indices_k)
        value_layer = index_first_axis(
            value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads,
                                head_dim), indices_k)
        if query_length == kv_seq_len:
            query_layer = index_first_axis(
                query_layer.reshape(batch_size * kv_seq_len, self.num_heads,
                                    head_dim), indices_k)
            cu_seqlens_q = cu_seqlens_k
            max_seqlen_in_batch_q = max_seqlen_in_batch_k
            indices_q = indices_k
        elif query_length == 1:
            max_seqlen_in_batch_q = 1
            cu_seqlens_q = torch.arange(
                batch_size + 1, dtype=torch.int32, device=query_layer.device
            )  # There is a memcpy here, that is very bad.
            indices_q = cu_seqlens_q[:-1]
            query_layer = query_layer.squeeze(1)
        else:
            # The -q_len: slice assumes left padding.
            attention_mask = attention_mask[:, -query_length:]
            (query_layer, indices_q, cu_seqlens_q,
             max_seqlen_in_batch_q) = unpad_input(query_layer, attention_mask)

        return (
            query_layer,
            key_layer,
            value_layer,
            indices_q,
            (cu_seqlens_q, cu_seqlens_k),
            (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
        )


# Copied from transformers.models.clip.modeling_clip.CLIPMLP with CLIP->Siglip
class SiglipMLP(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


# Copied from transformers.models.clip.modeling_clip.CLIPEncoderLayer
# with CLIP->Siglip
class SiglipEncoderLayer(nn.Module):

    def __init__(self, config: SiglipVisionConfig):
        super().__init__()
        self.embed_dim = config.hidden_size
        self._use_flash_attention_2 = (
            config._attn_implementation == "flash_attention_2")
        self.self_attn = (SiglipAttention(config)
                          if not self._use_flash_attention_2 else
                          SiglipFlashAttention2(config))
        self.layer_norm1 = nn.LayerNorm(self.embed_dim,
                                        eps=config.layer_norm_eps)
        self.mlp = SiglipMLP(config)
        self.layer_norm2 = nn.LayerNorm(self.embed_dim,
                                        eps=config.layer_norm_eps)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: torch.Tensor,
        output_attentions: Optional[bool] = False,
    ) -> Tuple[torch.FloatTensor]:
        residual = hidden_states

        hidden_states = self.layer_norm1(hidden_states)
        hidden_states, attn_weights = self.self_attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            output_attentions=output_attentions,
        )
        hidden_states = residual + hidden_states

        residual = hidden_states
        hidden_states = self.layer_norm2(hidden_states)
        hidden_states = self.mlp(hidden_states)
        hidden_states = residual + hidden_states

        outputs = (hidden_states, )

        if output_attentions:
            outputs += (attn_weights, )

        return outputs


class SiglipPreTrainedModel(PreTrainedModel):
    config_class = SiglipVisionConfig
    base_model_prefix = "siglip"
    supports_gradient_checkpointing = True

    def _init_weights(self, module):
        """Initialize the weights"""

        if isinstance(module, SiglipVisionEmbeddings):
            width = self.config.hidden_size
            nn.init.normal_(module.position_embedding.weight,
                            std=1 / np.sqrt(width))
        elif isinstance(module, nn.Embedding):
            default_flax_embed_init(module.weight)
        elif isinstance(module, SiglipAttention):
            nn.init.normal_(module.q_proj.weight)
            nn.init.normal_(module.k_proj.weight)
            nn.init.normal_(module.v_proj.weight)
            nn.init.normal_(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, SiglipMLP):
            nn.init.normal_(module.fc1.weight)
            nn.init.normal_(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)


# Copied from transformers.models.clip.modeling_clip.CLIPEncoder
# with CLIP->Siglip
class SiglipEncoder(nn.Module):

    def __init__(self, config: SiglipVisionConfig):
        super().__init__()
        self.config = config
        self.layers = nn.ModuleList([
            SiglipEncoderLayer(config) for _ in range(config.num_hidden_layers)
        ])
        self.gradient_checkpointing = False

    # Ignore copy
    def forward(
        self,
        inputs_embeds,
        attention_mask: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, BaseModelOutput]:
        output_attentions = output_attentions if output_attentions is not None \
                                else self.config.output_attentions
        output_hidden_states = (output_hidden_states
                                if output_hidden_states is not None else
                                self.config.output_hidden_states)
        return_dict = return_dict if return_dict is not None \
                        else self.config.use_return_dict

        encoder_states = () if output_hidden_states else None
        all_attentions = () if output_attentions else None

        hidden_states = inputs_embeds
        for encoder_layer in self.layers:
            if output_hidden_states:
                encoder_states = encoder_states + (hidden_states, )
            if self.gradient_checkpointing and self.training:
                layer_outputs = self._gradient_checkpointing_func(
                    encoder_layer.__call__,
                    hidden_states,
                    attention_mask,
                    output_attentions,
                )
            else:
                layer_outputs = encoder_layer(
                    hidden_states,
                    attention_mask,
                    output_attentions=output_attentions,
                )

            hidden_states = layer_outputs[0]

            if output_attentions:
                all_attentions = all_attentions + (layer_outputs[1], )

        if output_hidden_states:
            encoder_states = encoder_states + (hidden_states, )

        if not return_dict:
            return tuple(
                v for v in [hidden_states, encoder_states, all_attentions]
                if v is not None)
        return BaseModelOutput(last_hidden_state=hidden_states,
                               hidden_states=encoder_states,
                               attentions=all_attentions)


class SiglipVisionTransformer(SiglipPreTrainedModel):
    config_class = SiglipVisionConfig
    main_input_name = "pixel_values"
    _supports_flash_attn_2 = True

    def __init__(self, config: SiglipVisionConfig):
        super().__init__(config)
        self.config = config
        embed_dim = config.hidden_size

        self.embeddings = SiglipVisionEmbeddings(config)
        self.encoder = SiglipEncoder(config)
        self.post_layernorm = nn.LayerNorm(embed_dim,
                                           eps=config.layer_norm_eps)
        self._use_flash_attention_2 = (
            config._attn_implementation == "flash_attention_2")

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

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

    @replace_return_docstrings(output_type=BaseModelOutputWithPooling,
                               config_class=SiglipVisionConfig)
    def forward(
        self,
        pixel_values,
        patch_attention_mask: Optional[torch.BoolTensor] = None,
        tgt_sizes: Optional[torch.IntTensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, BaseModelOutputWithPooling]:
        r"""
        Returns:
        """
        output_attentions = output_attentions if output_attentions is not None \
                                else self.config.output_attentions
        output_hidden_states = (output_hidden_states
                                if output_hidden_states is not None else
                                self.config.output_hidden_states)
        return_dict = return_dict if return_dict is not None \
                        else self.config.use_return_dict

        batch_size = pixel_values.size(0)
        if patch_attention_mask is None:
            patch_attention_mask = torch.ones(
                size=(
                    batch_size,
                    pixel_values.size(2) // self.config.patch_size,
                    pixel_values.size(3) // self.config.patch_size,
                ),
                dtype=torch.bool,
                device=pixel_values.device,
            )

        hidden_states = self.embeddings(
            pixel_values=pixel_values,
            patch_attention_mask=patch_attention_mask,
            tgt_sizes=tgt_sizes)

        patch_attention_mask = patch_attention_mask.view(batch_size, -1)
        # The call to `_upad_input` in `_flash_attention_forward` is expensive
        # So when the `patch_attention_mask` is full of 1s
        # (i.e. attending to the whole sequence),
        # avoiding passing the attention_mask,
        # which is equivalent to attending to the full sequence
        if not torch.any(~patch_attention_mask):
            attention_mask = None
        else:
            attention_mask = (_prepare_4d_attention_mask(
                patch_attention_mask, hidden_states.dtype)
                              if not self._use_flash_attention_2 else
                              patch_attention_mask)

        encoder_outputs = self.encoder(
            inputs_embeds=hidden_states,
            attention_mask=attention_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        last_hidden_state = encoder_outputs[0]
        last_hidden_state = self.post_layernorm(last_hidden_state)

        if not return_dict:
            return (last_hidden_state, None) + encoder_outputs[1:]

        return BaseModelOutputWithPooling(
            last_hidden_state=last_hidden_state,
            pooler_output=None,
            hidden_states=encoder_outputs.hidden_states,
            attentions=encoder_outputs.attentions,
        )