vllm.model_executor.models.blip

class BlipAttention(nn.Module):
    """Multi-headed attention from 'Attention Is All You Need' paper"""

    def __init__(
        self,
        config: Union[BlipVisionConfig, Blip2VisionConfig],
        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(
                "embed_dim must be divisible by num_heads "
                f"(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 = QKVParallelLinear(
            self.embed_dim,
            self.head_dim,
            self.num_heads,
            bias=config.qkv_bias,
            quant_config=quant_config,
            prefix=f"{prefix}.qkv",
        )
        self.projection = RowParallelLinear(
            self.embed_dim,
            self.embed_dim,
            quant_config=quant_config,
            prefix=f"{prefix}.projection",
        )

        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 _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
        return tensor.view(bsz, seq_len, self.num_heads,
                           self.head_dim).transpose(1, 2).contiguous()

    def forward(
        self,
        hidden_states: torch.Tensor,
    ):
        """Input shape: Batch x Time x Channel"""

        qkv_states, _ = self.qkv(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.projection(out)

        return attn_output, None

class BlipEncoder(nn.Module):
    """
    Transformer encoder consisting of `config.num_hidden_layers` self 
    attention layers. Each layer is a [`BlipEncoderLayer`].

    Args:
        config: BlipConfig
    """

    def __init__(
        self,
        config: BlipVisionConfig,
        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([
            BlipEncoderLayer(config=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):
        hidden_states = inputs_embeds
        for encoder_layer in self.layers:
            hidden_states = encoder_layer(hidden_states)

        return hidden_states

class BlipEncoderLayer(nn.Module):

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

        # fallback to sdpa attention if tp unavailable
        self.self_attn = BlipAttention(
            config,
            quant_config=quant_config,
            prefix=f"{prefix}.self_attn",
        )
        self.layer_norm1 = nn.LayerNorm(config.hidden_size,
                                        eps=config.layer_norm_eps)
        self.mlp = BlipMLP(config,
                           quant_config=quant_config,
                           prefix=f"{prefix}.mlp")
        self.layer_norm2 = nn.LayerNorm(config.hidden_size,
                                        eps=config.layer_norm_eps)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        residual = hidden_states

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

        return hidden_states

class BlipMLP(nn.Module):

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

        self.config = config

        self.activation_fn = get_act_fn(config.hidden_act)
        self.fc1 = ColumnParallelLinear(config.hidden_size,
                                        config.intermediate_size,
                                        bias=True,
                                        quant_config=quant_config,
                                        prefix=f"{prefix}.fc1")
        self.fc2 = RowParallelLinear(config.intermediate_size,
                                     config.hidden_size,
                                     bias=True,
                                     quant_config=quant_config,
                                     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 BlipVisionEmbeddings(nn.Module):

    def __init__(self, config: Union[BlipVisionConfig, Blip2VisionConfig]):
        super().__init__()

        self.config = config
        self.embed_dim = config.hidden_size
        self.image_size = config.image_size
        self.patch_size = config.patch_size

        self.class_embedding = nn.Parameter(torch.randn(1, 1, self.embed_dim))

        self.patch_embedding = nn.Conv2d(
            in_channels=3,
            out_channels=self.embed_dim,
            kernel_size=self.patch_size,
            stride=self.patch_size,
        )

        self.num_patches = get_blip_num_patches(image_size=self.image_size,
                                                patch_size=self.patch_size)
        self.num_positions = self.num_patches + 1

        self.position_embedding = nn.Parameter(
            torch.randn(1, self.num_positions, self.embed_dim))

    def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
        batch_size = pixel_values.shape[0]
        target_dtype = self.patch_embedding.weight.dtype
        patch_embeds = self.patch_embedding(pixel_values.to(
            dtype=target_dtype))  # shape = [*, width, grid, grid]
        patch_embeds = patch_embeds.flatten(2).transpose(1, 2)

        class_embeds = self.class_embedding.expand(batch_size, 1, -1)
        embeddings = torch.cat([class_embeds, patch_embeds], dim=1)

        position_embeds = self.position_embedding.to(target_dtype)
        embeddings = embeddings + position_embeds[:, :embeddings.size(1), :]

        return embeddings

class BlipVisionModel(nn.Module, SupportsQuant):
    config_class = BlipVisionConfig
    main_input_name = "pixel_values"
    packed_modules_mapping = {"qkv_proj": ["q_proj", "k_proj", "v_proj"]}

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

        self.embeddings = BlipVisionEmbeddings(config)
        self.encoder = BlipEncoder(
            config=config,
            quant_config=quant_config,
            num_hidden_layers_override=num_hidden_layers_override,
            prefix=f"{prefix}.encoder",
        )

        num_hidden_layers = config.num_hidden_layers
        if len(self.encoder.layers) > config.num_hidden_layers:
            raise ValueError(
                f"The original encoder only has {num_hidden_layers} "
                f"layers, but you requested {len(self.encoder.layers)} layers."
            )

        # If possible, skip post_layernorm to conserve memory
        if require_post_norm is None:
            require_post_norm = len(self.encoder.layers) == num_hidden_layers

        if require_post_norm:
            self.post_layernorm = nn.LayerNorm(config.hidden_size,
                                               eps=config.layer_norm_eps)
        else:
            self.post_layernorm = None

    def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
        hidden_states = self.embeddings(pixel_values)
        hidden_states = self.encoder(inputs_embeds=hidden_states)

        if self.post_layernorm is None:
            return hidden_states

        return self.post_layernorm(hidden_states)

    def load_weights(self, weights: Iterable[tuple[str,
                                                   torch.Tensor]]) -> set[str]:
        stacked_params_mapping = [
            # (param_name, shard_name, shard_id)
            ("qkv_proj", "q_proj", "q"),
            ("qkv_proj", "k_proj", "k"),
            ("qkv_proj", "v_proj", "v"),
        ]
        params_dict = dict(self.named_parameters())
        loaded_params: set[str] = set()
        layer_count = len(self.encoder.layers)

        for name, loaded_weight in weights:
            # post_layernorm is not needed in BlipVisionModel
            if (name.startswith("post_layernorm")
                    and self.post_layernorm is None):
                continue

            # omit layers when num_hidden_layers_override is set
            if name.startswith("encoder.layers"):
                layer_idx = int(name.split(".")[2])
                if layer_idx >= layer_count:
                    continue

            for (param_name, weight_name, shard_id) in stacked_params_mapping:
                if weight_name not in name:
                    continue
                name = name.replace(weight_name, param_name)
                param = params_dict[name]
                weight_loader = param.weight_loader
                weight_loader(param, loaded_weight, shard_id)
                break
            else:
                param = params_dict[name]
                weight_loader = getattr(param, "weight_loader",
                                        default_weight_loader)
                weight_loader(param, loaded_weight)
            loaded_params.add(name)
        return loaded_params