vllm.model_executor.models.glm4v ¶

class EVA2CLIPAttention(nn.Module):
    def __init__(
        self,
        config,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.tp_size = get_tensor_model_parallel_world_size()
        self.num_heads_per_rank = config.num_heads // self.tp_size
        self.head_dim = config.hidden_size // config.num_heads
        self.scale = self.head_dim**-0.5

        self.query_key_value = QKVParallelLinear(
            config.hidden_size,
            self.head_dim,
            config.num_heads,
            quant_config=quant_config,
            prefix=f"{prefix}.query_key_value",
        )
        self.dense = RowParallelLinear(
            config.hidden_size,
            config.hidden_size,
            quant_config=quant_config,
            prefix=f"{prefix}.dense",
        )

        self.attn = MultiHeadAttention(
            self.num_heads_per_rank, self.head_dim, self.scale
        )
        self.output_dropout = torch.nn.Dropout(config.dropout_prob)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        qkv, _ = self.query_key_value(x)  # B, L, 3 * H * D
        q, k, v = qkv.chunk(3, dim=-1)

        out = self.attn(q, k, v)
        output, _ = self.dense(out)
        output = self.output_dropout(output)
        return output

class EVA2CLIPGLU(nn.Module):
    def __init__(
        self,
        config,
        in_features,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ):
        """
        The original implementation is the same as:
        ```python
        self.dense_h_to_4h = ColumnParallelLinear(
            config.hidden_size,
            config.ffn_hidden_size,
            bias=False,
            quant_config=quant_config,
        )

        self.gate_proj = ColumnParallelLinear(
            config.hidden_size,
            config.ffn_hidden_size,
            bias=False,
            quant_config=quant_config,
        )
        ```
        ```
        gate_proj_output, _ = self.gate_proj(x)
        dense_h_to_4h_output, _ = self.dense_h_to_4h(x)
        x = torch.cat([gate_proj_output, dense_h_to_4h_output], dim=-1)
        ```

        We merge two ColumnParallelLinear into one MergedColumnParallelLinear:
        ```
        self.merged_proj = MergedColumnParallelLinear(
            config.hidden_size,
            [config.ffn_hidden_size] * 2,
            bias=False,
            quant_config=quant_config,
        )
        ```
        ```
        x, _ = self.merged_proj(x)
        ```
        """
        super().__init__()
        self.linear_proj = ReplicatedLinear(
            in_features,
            config.hidden_size,
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.linear_proj",
        )
        self.norm1 = nn.LayerNorm(config.hidden_size)
        self.act1 = nn.GELU()
        self.act2 = SiluAndMul()

        self.merged_proj = MergedColumnParallelLinear(
            config.hidden_size,
            [config.ffn_hidden_size] * 2,
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.merged_proj",
        )

        self.dense_4h_to_h = RowParallelLinear(
            config.ffn_hidden_size,
            config.hidden_size,
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.dense_4h_to_h",
        )

    def forward(self, x):
        x, _ = self.linear_proj(x)
        x = self.act1(self.norm1(x))
        x, _ = self.merged_proj(x)
        x = self.act2(x)
        x, _ = self.dense_4h_to_h(x)
        return x

class EVA2CLIPMLP(nn.Module):
    def __init__(
        self,
        config,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ):
        super().__init__()
        self.config = config
        self.activation_fn = get_act_fn(config.hidden_act)
        self.fc1 = ColumnParallelLinear(
            config.hidden_size,
            config.intermediate_size,
            quant_config=quant_config,
            prefix=f"{prefix}.fc1",
        )
        self.fc2 = RowParallelLinear(
            config.intermediate_size,
            config.hidden_size,
            quant_config=quant_config,
            prefix=f"{prefix}.fc2",
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x, _ = self.fc1(x)
        x = self.activation_fn(x)
        x, _ = self.fc2(x)
        return x

class EVA2CLIPModel(nn.Module):
    def __init__(
        self,
        config,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ):
        super().__init__()
        vision_config = Namespace(**config.vision_config)
        self.patch_embedding = EVA2CLIPPatchEmbedding(vision_config)
        self.transformer = EVA2CLIPTransformer(
            vision_config, quant_config=quant_config, prefix=f"{prefix}.transformer"
        )
        self.linear_proj = EVA2CLIPGLU(
            config,
            in_features=config.hidden_size,
            quant_config=quant_config,
            prefix=f"{prefix}.linear_proj",
        )
        self.conv = nn.Conv2d(
            in_channels=vision_config.hidden_size,
            out_channels=config.hidden_size,
            kernel_size=2,
            stride=2,
        )
        self.boi = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
        self.eoi = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
        self.scaling_factor = vision_config.scaling_factor

    def forward(self, images: torch.Tensor) -> torch.Tensor:
        """
        Parameters:
        images : torch.Tensor
            Input image tensor with shape (B, C, H, W)

        Returns:
        torch.Tensor
            Transformed tensor with shape (B, L, D)
        """
        x = self.patch_embedding(images)
        x = self.transformer(x)
        x = x[:, 1:]

        b, s, h = x.shape
        grid_size = int(s**0.5)
        x = x.view(b, grid_size, grid_size, h).permute(0, 3, 1, 2)
        x = self.conv(x)

        x = x.flatten(2).transpose(1, 2)
        x = self.linear_proj(x)
        boi = self.boi.expand(x.shape[0], -1, -1)
        eoi = self.eoi.expand(x.shape[0], -1, -1)
        x = torch.cat((boi, x, eoi), dim=1)
        x = x / self.scaling_factor
        return x

class EVA2CLIPPatchEmbedding(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.proj = nn.Conv2d(
            config.in_channels,
            config.hidden_size,
            kernel_size=config.patch_size,
            stride=config.patch_size,
        )
        self.cls_embedding = nn.Parameter(torch.zeros(1, config.hidden_size))
        self.position_embedding = nn.Embedding(config.num_positions, config.hidden_size)

    def forward(self, images: torch.Tensor) -> torch.Tensor:
        """
        Parameters:
        images : torch.Tensor
            Input image tensor with shape (B, C, H, W)

        Returns:
        torch.Tensor
            Transformed tensor with shape (B, L, D)
        """
        images = images.to(device=self.proj.weight.device, dtype=self.proj.weight.dtype)
        x = self.proj(images)
        x = x.flatten(2).transpose(1, 2)
        cls_token = self.cls_embedding.expand(x.shape[0], -1, -1)
        x = torch.cat((cls_token, x), dim=1)
        x += self.position_embedding.weight.unsqueeze(0)
        return x

class EVA2CLIPTransformer(nn.Module):
    def __init__(
        self,
        config,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ):
        super().__init__()
        self.layers = nn.ModuleList(
            [
                EVA2CLIPTransformerLayer(
                    config,
                    quant_config=quant_config,
                    prefix=f"{prefix}.layers.{layer_idx}",
                )
                for layer_idx in range(config.num_hidden_layers)
            ]
        )

    def forward(self, hidden_states):
        for layer_module in self.layers:
            hidden_states = layer_module(hidden_states)
        return hidden_states

class EVA2CLIPTransformerLayer(nn.Module):
    def __init__(
        self,
        config,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ):
        super().__init__()
        self.input_layernorm = LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.attention = EVA2CLIPAttention(
            config, quant_config=quant_config, prefix=f"{prefix}.attention"
        )
        self.mlp = EVA2CLIPMLP(
            config, quant_config=quant_config, prefix=f"{prefix}.mlp"
        )
        self.post_attention_layernorm = LayerNorm(
            config.hidden_size, eps=config.layer_norm_eps
        )

    def forward(self, hidden_states):
        attention_input = hidden_states
        attention_output = self.input_layernorm(self.attention(attention_input))
        hidden_states = attention_input + attention_output
        mlp_input = hidden_states
        mlp_output = self.post_attention_layernorm(self.mlp(mlp_input))
        output = mlp_input + mlp_output
        return output

class GLM4VDummyInputsBuilder(BaseDummyInputsBuilder[GLM4VProcessingInfo]):
    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
        num_images = mm_counts.get("image", 0)

        base_text = "<|begin_of_image|><|endoftext|><|end_of_image|>"

        return base_text * num_images

    def get_dummy_mm_data(
        self,
        seq_len: int,
        mm_counts: Mapping[str, int],
        mm_options: Optional[Mapping[str, BaseDummyOptions]] = None,
    ) -> MultiModalDataDict:
        hf_config = self.info.get_hf_config()
        vision_config = hf_config.vision_config

        target_width = target_height = vision_config["image_size"]
        num_images = mm_counts.get("image", 0)

        image_overrides = mm_options.get("image") if mm_options else None

        return {
            "image": self._get_dummy_images(
                width=target_width,
                height=target_height,
                num_images=num_images,
                overrides=image_overrides,
            )
        }