vllm.model_executor.models.siglip

class SiglipAttention(nn.Module):

    def __init__(
        self,
        config: SiglipVisionConfig,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ) -> None:
        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.qkv_proj = QKVParallelLinear(
            hidden_size=self.embed_dim,
            head_size=self.head_dim,
            total_num_heads=self.num_heads,
            quant_config=quant_config,
            prefix=f"{prefix}.qkv_proj",
        )

        self.out_proj = RowParallelLinear(
            input_size=self.embed_dim,
            output_size=self.embed_dim,
            quant_config=quant_config,
            prefix=f"{prefix}.out_proj",
        )

        self.tp_size = get_tensor_model_parallel_world_size()
        self.num_heads_per_partition = divide(self.num_heads, self.tp_size)

        self.attn = MultiHeadAttention(self.num_heads_per_partition,
                                       self.head_dim, self.scale)

    def forward(
        self,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor:
        """Input shape: Batch x Time x Channel"""
        qkv_states, _ = self.qkv_proj(hidden_states)
        query_states, key_states, value_states = qkv_states.chunk(3, dim=-1)

        out = self.attn(query_states, key_states, value_states)
        attn_output, _ = self.out_proj(out)

        return attn_output, None

class SiglipEncoder(nn.Module):

    def __init__(
        self,
        config: SiglipVisionConfig,
        quant_config: Optional[QuantizationConfig] = None,
        num_hidden_layers_override: Optional[int] = None,
        prefix: str = "",
    ) -> None:
        super().__init__()

        self.config = config

        if num_hidden_layers_override is None:
            num_hidden_layers = config.num_hidden_layers
        else:
            num_hidden_layers = num_hidden_layers_override

        self.layers = nn.ModuleList([
            SiglipEncoderLayer(config,
                               quant_config=quant_config,
                               prefix=f"{prefix}.layers.{layer_idx}")
            for layer_idx in range(num_hidden_layers)
        ])

    def forward(
        self,
        inputs_embeds: torch.Tensor,
        return_all_hidden_states: bool,
    ) -> Union[torch.Tensor, list[torch.Tensor]]:
        hidden_states_pool = [inputs_embeds]
        hidden_states = inputs_embeds

        for encoder_layer in self.layers:
            hidden_states, _ = encoder_layer(hidden_states)
            if return_all_hidden_states:
                hidden_states_pool.append(hidden_states)
        # If we have multiple feature sample layers, we return all hidden
        # states in order and grab the ones we need by index.
        if return_all_hidden_states:
            return hidden_states_pool
        return hidden_states

class SiglipEncoderInfo(VisionEncoderInfo[SiglipVisionConfig]):

    def get_num_image_tokens(
        self,
        *,
        image_width: int,
        image_height: int,
    ) -> int:
        return self.get_patch_grid_length()**2

    def get_image_size(self) -> int:
        return self.vision_config.image_size

    def get_patch_size(self) -> int:
        return self.vision_config.patch_size

    def get_patch_grid_length(self) -> int:
        image_size, patch_size = self.get_image_size(), self.get_patch_size()
        return image_size // patch_size

class SiglipEncoderLayer(nn.Module):

    def __init__(
        self,
        config: SiglipVisionConfig,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ) -> None:
        super().__init__()

        self.embed_dim = config.hidden_size

        self.self_attn = SiglipAttention(
            config,
            quant_config=quant_config,
            prefix=f"{prefix}.self_attn",
        )
        self.layer_norm1 = nn.LayerNorm(self.embed_dim,
                                        eps=config.layer_norm_eps)
        self.mlp = SiglipMLP(
            config,
            quant_config=quant_config,
            prefix=f"{prefix}.mlp",
        )
        self.layer_norm2 = nn.LayerNorm(self.embed_dim,
                                        eps=config.layer_norm_eps)

    def forward(
        self,
        hidden_states: torch.Tensor,
    ) -> tuple[torch.Tensor, None]:
        residual = hidden_states

        hidden_states = self.layer_norm1(hidden_states)
        hidden_states, _ = self.self_attn(hidden_states=hidden_states)
        hidden_states += residual

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

        return hidden_states, None

class SiglipMLP(nn.Module):

    def __init__(
        self,
        config: SiglipVisionConfig,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ) -> None:
        super().__init__()

        self.config = config
        self.activation_fn = get_act_fn(config.hidden_act)
        # Special handling for BNB and torchao quantization
        if quant_config and quant_config.get_name() in [
                "bitsandbytes", "torchao"
        ]:
            quantizable = True
        else:
            # For other quantization, we require the hidden size to be a
            # multiple of 64
            quantizable = (config.hidden_size % 64 == 0
                           and config.intermediate_size % 64 == 0)
        self.fc1 = ColumnParallelLinear(
            config.hidden_size,
            config.intermediate_size,
            quant_config=quant_config if quantizable else None,
            prefix=f"{prefix}.fc1",
        )
        self.fc2 = RowParallelLinear(
            config.intermediate_size,
            config.hidden_size,
            quant_config=quant_config if quantizable else None,
            prefix=f"{prefix}.fc2",
        )

    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 SiglipMultiheadAttentionPoolingHead(nn.Module):
    """Multihead Attention Pooling."""

    def __init__(
        self,
        config: SiglipVisionConfig,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ) -> None:
        super().__init__()

        self.probe = nn.Parameter(torch.randn(1, 1, config.hidden_size))
        # TODO(ChristopherCho): Implement vLLM version of MultiheadAttention
        self.attention = torch.nn.MultiheadAttention(
            config.hidden_size, config.num_attention_heads, batch_first=True)
        self.layernorm = nn.LayerNorm(config.hidden_size,
                                      eps=config.layer_norm_eps)
        self.mlp = SiglipMLP(config=config,
                             quant_config=quant_config,
                             prefix=f"{prefix}.mlp")

    def forward(self, hidden_state: torch.Tensor) -> torch.Tensor:
        batch_size = hidden_state.shape[0]
        probe = self.probe.repeat(batch_size, 1, 1)

        hidden_state = self.attention(probe, hidden_state, hidden_state)[0]

        residual = hidden_state
        hidden_state = self.layernorm(hidden_state)
        hidden_state = self.mlp(hidden_state)
        hidden_state += residual

        return hidden_state[:, 0]

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 = (self.image_size // self.patch_size)**2
        self.num_positions = self.num_patches
        self.position_embedding = VocabParallelEmbedding(
            self.num_positions, self.embed_dim)
        self.register_buffer(
            "position_ids",
            torch.arange(self.num_positions, dtype=torch.int64).expand(
                (1, -1)),
            persistent=False,
        )

    def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int,
                                 width: int) -> torch.Tensor:
        """
        This method is an adapted method for SigLIP (due to SigLIP not having
        class embedding unlike other ViTs) that allows the model to interpolate
        the pre-trained position encodings such that it can be usable on higher
        resolution images.

        Source:
        https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174
        """
        position_embeddings = self.position_embedding.weight.unsqueeze(0)
        num_patches = embeddings.shape[1]
        num_positions = position_embeddings.shape[1]
        if num_patches == num_positions and height == width:
            return position_embeddings

        dim = embeddings.shape[-1]
        height = height // self.patch_size
        width = width // self.patch_size
        # we add a small number to avoid floating point error
        # in the interpolation
        # see discussion at https://github.com/facebookresearch/dino/issues/8
        height, width = height + 0.1, width + 0.1

        patch_pos_embed = position_embeddings.reshape(
            1, int(math.sqrt(num_positions)), int(math.sqrt(num_positions)),
            dim)
        patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)
        patch_pos_embed = nn.functional.interpolate(
            patch_pos_embed,
            scale_factor=(
                height / math.sqrt(num_positions),
                width / math.sqrt(num_positions),
            ),
            mode="bicubic",
            align_corners=False,
        )
        if (int(height) != patch_pos_embed.shape[-2]
                or int(width) != patch_pos_embed.shape[-1]):
            raise ValueError("Width or height does not match with "
                             "the interpolated position embeddings")

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

    def forward(self,
                pixel_values: torch.Tensor,
                interpolate_pos_encoding: bool = False) -> torch.Tensor:
        _, _, height, width = pixel_values.shape
        target_dtype = self.patch_embedding.weight.dtype
        patch_embeds = self.patch_embedding(pixel_values.to(
            dtype=target_dtype))  # shape = [*, width, grid, grid]
        embeddings = patch_embeds.flatten(2).transpose(1, 2)

        if interpolate_pos_encoding:
            embeddings += self.interpolate_pos_encoding(
                embeddings, height, width)
        else:
            embeddings += self.position_embedding(self.position_ids)
        return embeddings