vllm.model_executor.models.intern_vit ¶

class InternMLP(nn.Module):
    def __init__(
        self,
        config: PretrainedConfig,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
        use_data_parallel: bool = False,
    ) -> 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",
            disable_tp=use_data_parallel,
        )
        self.fc2 = RowParallelLinear(
            config.intermediate_size,
            config.hidden_size,
            bias=True,
            quant_config=quant_config,
            prefix=f"{prefix}.fc2",
            disable_tp=use_data_parallel,
        )

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

    def __init__(
        self,
        config: PretrainedConfig,
        quant_config: Optional[QuantizationConfig] = None,
        *,
        num_dummy_heads: int = 0,
        prefix: str = "",
        use_data_parallel: bool = False,
    ) -> 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 "
                f"(got `embed_dim`: {self.embed_dim} and `num_heads`:"
                f" {self.num_heads})."
            )

        self.tp_size = (
            1 if use_data_parallel else get_tensor_model_parallel_world_size()
        )
        self.tp_rank = 0 if use_data_parallel else get_tensor_model_parallel_rank()

        # Additional dummy heads are used to enable TP for common GPU counts.
        self.dummy_dim = (num_dummy_heads + self.num_heads) * self.head_dim
        self.num_heads_per_partition = divide(
            num_dummy_heads + self.num_heads, self.tp_size
        )

        self.scale = self.head_dim**-0.5
        self.qkv = QKVParallelLinear(
            self.embed_dim,
            self.head_dim,
            num_dummy_heads + self.num_heads,
            bias=config.qkv_bias,
            quant_config=quant_config,
            prefix=f"{prefix}.qkv",
            disable_tp=use_data_parallel,
        )

        self.qk_normalization = config.qk_normalization

        if self.qk_normalization:
            self.q_norm = RMSNorm(
                self.dummy_dim,
                eps=config.layer_norm_eps,
                var_hidden_size=self.embed_dim,
            )
            self.k_norm = RMSNorm(
                self.dummy_dim,
                eps=config.layer_norm_eps,
                var_hidden_size=self.embed_dim,
            )

        self.proj = RowParallelLinear(
            self.dummy_dim,
            self.embed_dim,
            quant_config=quant_config,
            prefix=f"{prefix}.proj",
            disable_tp=use_data_parallel,
        )

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

    def _apply_qk_norm(self, q: torch.Tensor, k: torch.Tensor):
        if self.tp_size > 1:
            q = tensor_model_parallel_all_gather(q.contiguous())
            k = tensor_model_parallel_all_gather(k.contiguous())
        q = self.q_norm(q)
        k = self.k_norm(k)
        if self.tp_size > 1:
            splitter = partial(split_tensor_along_last_dim, num_partitions=self.tp_size)
            q = splitter(q)[self.tp_rank]
            k = splitter(k)[self.tp_rank]
        return q, k

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        B, N, _ = x.shape
        qkv, _ = self.qkv(x)
        q, k, v = qkv.chunk(3, dim=-1)

        if self.qk_normalization:
            q, k = self._apply_qk_norm(q, k)

        out = self.attn(q, k, v)
        out, _ = self.proj(out)
        return out

class InternVisionEmbeddings(nn.Module):
    def __init__(self, config: PretrainedConfig):
        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 = (self.image_size // self.patch_size) ** 2
        self.num_positions = self.num_patches + 1

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

    def _get_pos_embed(self, pos_embed: torch.Tensor, H: int, W: int):
        target_dtype = pos_embed.dtype
        pos_embed = (
            pos_embed.float()
            .reshape(
                1,
                self.image_size // self.patch_size,
                self.image_size // self.patch_size,
                -1,
            )
            .permute(0, 3, 1, 2)
        )
        pos_embed = F.interpolate(
            pos_embed, size=(H, W), mode="bicubic", align_corners=False
        )
        return pos_embed.reshape(1, -1, H * W).permute(0, 2, 1).to(target_dtype)

    def _get_position_embedding(self, H: int, W: int) -> torch.Tensor:
        position_embedding = self.position_embedding
        if self.num_patches == H * W:
            return position_embedding

        return torch.cat(
            [
                position_embedding[:, :1, :],
                self._get_pos_embed(position_embedding[:, 1:, :], H, W),
            ],
            dim=1,
        )

    def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor:
        target_dtype = self.patch_embedding.weight.dtype
        patch_embeds = self.patch_embedding(
            pixel_values.to(target_dtype)
        )  # shape = [*, channel, width, height]
        batch_size, _, height, width = patch_embeds.shape
        patch_embeds = patch_embeds.flatten(2).transpose(1, 2)
        class_embeds = self.class_embedding.expand(batch_size, 1, -1).to(target_dtype)
        embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
        position_embedding = self._get_position_embedding(height, width)
        embeddings = embeddings + position_embedding.to(target_dtype)
        return embeddings

class InternVisionEncoder(nn.Module):
    def __init__(
        self,
        config: PretrainedConfig,
        quant_config: Optional[QuantizationConfig] = None,
        *,
        num_hidden_layers_override: Optional[int] = None,
        num_dummy_heads: int = 0,
        prefix: str = "",
        use_data_parallel: bool = False,
    ):
        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(
            [
                InternVisionEncoderLayer(
                    config,
                    quant_config,
                    num_dummy_heads=num_dummy_heads,
                    prefix=f"{prefix}.layers.{layer_idx}",
                    use_data_parallel=use_data_parallel,
                )
                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 InternVisionEncoderLayer(nn.Module):
    def __init__(
        self,
        config: PretrainedConfig,
        quant_config: Optional[QuantizationConfig] = None,
        *,
        num_dummy_heads: int = 0,
        prefix: str = "",
        use_data_parallel: bool = False,
    ) -> None:
        super().__init__()

        self.embed_dim = config.hidden_size
        self.intermediate_size = config.intermediate_size
        self.norm_type = config.norm_type

        self.attn = self._init_attn(
            config,
            quant_config,
            num_dummy_heads=num_dummy_heads,
            prefix=f"{prefix}.attn",
            use_data_parallel=use_data_parallel,
        )

        self.mlp = InternMLP(
            config,
            quant_config=quant_config,
            prefix=f"{prefix}.mlp",
            use_data_parallel=use_data_parallel,
        )
        self.norm1 = NORM2FN[self.norm_type](self.embed_dim, eps=config.layer_norm_eps)
        self.norm2 = NORM2FN[self.norm_type](self.embed_dim, eps=config.layer_norm_eps)

        self.ls1 = nn.Parameter(config.initializer_factor * torch.ones(self.embed_dim))
        self.ls2 = nn.Parameter(config.initializer_factor * torch.ones(self.embed_dim))

    def _init_attn(
        self,
        config: PretrainedConfig,
        quant_config: Optional[QuantizationConfig],
        *,
        num_dummy_heads: int,
        prefix: str = "",
        use_data_parallel: bool = False,
    ):
        # fallback to sdpa attention if tp unavailable
        tp_size = 1 if use_data_parallel else get_tensor_model_parallel_world_size()
        num_heads = config.num_attention_heads

        # if the number of heads is not divisible by tp_size,
        # we also disable Attention's TP
        use_data_parallel = (
            use_data_parallel or (num_heads + num_dummy_heads) % tp_size != 0
        )
        return InternParallelAttention(
            config,
            quant_config=quant_config,
            num_dummy_heads=num_dummy_heads,
            prefix=prefix,
            use_data_parallel=use_data_parallel,
        )

    def forward(
        self,
        hidden_states: torch.Tensor,
    ):
        hidden_states = hidden_states + self.attn(self.norm1(hidden_states)) * self.ls1

        hidden_states = hidden_states + self.mlp(self.norm2(hidden_states)) * self.ls2

        return hidden_states