chore: import upstream snapshot with attribution

python/sglang/srt/models/glm4v.py

@@ -0,0 +1,814 @@
+# Copyright 2023-2024 SGLang Team
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+# Modeling from:
+# ./llama.py and
+# https://github.com/huggingface/transformers/blob/main/src/transformers/models/glm4v/modular_glm4v.py
+"""Inference-only GLM-4.1V model compatible with HuggingFace weights."""
+
+import logging
+from functools import lru_cache
+from typing import Iterable, List, Optional, Tuple
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange
+from transformers.models.glm4v.configuration_glm4v import Glm4vConfig, Glm4vVisionConfig
+
+from sglang.srt.distributed.parallel_state import get_pp_group
+from sglang.srt.layers.activation import SiluAndMul
+from sglang.srt.layers.attention import vision_utils
+from sglang.srt.layers.attention.vision import VisionAttention
+from sglang.srt.layers.conv import Conv3dLayer
+from sglang.srt.layers.layernorm import LayerNorm, RMSNorm
+from sglang.srt.layers.linear import (
+    MergedColumnParallelLinear,
+    ReplicatedLinear,
+    RowParallelLinear,
+)
+from sglang.srt.layers.logits_processor import LogitsProcessor
+from sglang.srt.layers.pooler import Pooler, PoolingType
+from sglang.srt.layers.quantization.base_config import QuantizationConfig
+from sglang.srt.layers.rotary_embedding import get_rope
+from sglang.srt.layers.utils import PPMissingLayer
+from sglang.srt.layers.vocab_parallel_embedding import ParallelLMHead
+from sglang.srt.managers.mm_utils import (
+    MultiModalityDataPaddingPatternMultimodalTokens,
+    general_mm_embed_routine,
+)
+from sglang.srt.managers.schedule_batch import MultimodalDataItem, MultimodalInputs
+from sglang.srt.model_executor.forward_batch_info import ForwardBatch, PPProxyTensors
+from sglang.srt.model_loader.weight_utils import default_weight_loader
+from sglang.srt.models.glm4 import Glm4Model
+from sglang.srt.multimodal.mm_utils import run_dp_sharded_mrope_vision_model
+from sglang.srt.runtime_context import get_parallel, get_server_args
+from sglang.srt.utils import add_prefix, is_npu
+from sglang.srt.utils.hf_transformers_utils import get_processor
+
+logger = logging.getLogger(__name__)
+
+cached_get_processor = lru_cache(get_processor)
+
+
+class Glm4vRMSNorm(RMSNorm):
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        original_shape = x.shape
+        x_2d = x.contiguous().reshape(-1, original_shape[-1])
+        x_2d = super().forward(x_2d)
+        x = x_2d.reshape(original_shape)
+        return x
+
+
+class Glm4vVisionMLP(nn.Module):
+    def __init__(
+        self,
+        in_features: int,
+        hidden_features: int,
+        bias: bool = False,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+        use_data_parallel: bool = False,
+    ):
+        super().__init__()
+        self.tp_size = 1 if use_data_parallel else get_parallel().tp_size
+        self.tp_rank = 0 if use_data_parallel else get_parallel().tp_rank
+        self.gate_up_proj = MergedColumnParallelLinear(
+            input_size=in_features,
+            output_sizes=[hidden_features] * 2,  # [gate_proj, up_proj]
+            bias=bias,
+            quant_config=quant_config,
+            prefix=add_prefix("gate_up_proj", prefix),
+            tp_size=self.tp_size,
+            tp_rank=self.tp_rank,
+        )
+        self.down_proj = RowParallelLinear(
+            hidden_features,
+            in_features,
+            bias=bias,
+            quant_config=quant_config,
+            prefix=add_prefix("down_proj", prefix),
+            tp_size=self.tp_size,
+            tp_rank=self.tp_rank,
+        )
+        self.act_fn = SiluAndMul()
+
+    def forward(self, x: torch.Tensor):
+        gate_up, _ = self.gate_up_proj(x)
+        x = self.act_fn(gate_up)
+        x, _ = self.down_proj(x)
+        return x
+
+
+class Glm4vVisionBlock(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        intermediate_dim: int,
+        num_heads: int,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+        attn_qkv_bias: bool = True,
+        num_dummy_heads: int = 0,
+        rms_norm_eps: float = 1e-5,
+        use_data_parallel: bool = False,
+    ) -> None:
+        super().__init__()
+        self.norm1 = RMSNorm(dim, eps=rms_norm_eps)
+        self.norm2 = RMSNorm(dim, eps=rms_norm_eps)
+
+        self.attn = VisionAttention(
+            embed_dim=dim,
+            num_heads=num_heads,
+            projection_size=dim,
+            use_qkv_parallel=True,
+            proj_bias=False,
+            qkv_bias=attn_qkv_bias,
+            flatten_batch=True,
+            quant_config=quant_config,
+            prefix=add_prefix("attn", prefix),
+            num_dummy_heads=num_dummy_heads,
+            use_data_parallel=use_data_parallel,
+        )
+        self.mlp = Glm4vVisionMLP(
+            dim,
+            intermediate_dim,
+            quant_config=quant_config,
+            prefix=add_prefix("mlp", prefix),
+            use_data_parallel=use_data_parallel,
+        )
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        cu_seqlens: torch.Tensor,
+        rotary_pos_emb_cos: torch.Tensor,
+        rotary_pos_emb_sin: torch.Tensor,
+    ) -> torch.Tensor:
+        S, B, H = x.shape
+        # norm1: flatten to 2D -> [S*B, H], then reshape back
+        x2d = x.reshape(-1, H)
+        hidden_states = self.norm1(x2d).reshape(S, B, H)
+
+        # Attention expects [B, S, H]
+        hidden_states = rearrange(hidden_states, "s b h -> b s h")
+        attn = self.attn(
+            hidden_states,
+            cu_seqlens=cu_seqlens,
+            rotary_pos_emb_cos=rotary_pos_emb_cos,
+            rotary_pos_emb_sin=rotary_pos_emb_sin,
+        )
+        attn = rearrange(attn, "b s h -> s b h")
+
+        # norm2 with fused residual-add: also 2D
+        attn2d = attn.reshape(-1, H)
+        x_norm_2d, x_after_add_2d = self.norm2(x2d, residual=attn2d)
+        x_norm = x_norm_2d.reshape(S, B, H)
+        x_after_add = x_after_add_2d.reshape(S, B, H)
+
+        # MLP and final residual
+        mlp_out = self.mlp(x_norm)
+        x = x_after_add + mlp_out
+        return x
+
+
+class Glm4vVisionPatchEmbed(nn.Module):
+    def __init__(
+        self,
+        patch_size: int = 14,
+        temporal_patch_size: int = 2,
+        in_channels: int = 3,
+        hidden_size: int = 1536,
+    ) -> None:
+        super().__init__()
+        self.patch_size = patch_size
+        self.temporal_patch_size = temporal_patch_size
+        self.hidden_size = hidden_size
+        self.in_channels = in_channels
+
+        kernel_size = (temporal_patch_size, patch_size, patch_size)
+        self.proj = Conv3dLayer(
+            in_channels,
+            hidden_size,
+            kernel_size=kernel_size,
+            stride=kernel_size,
+            bias=True,
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        # Input x is 2-D: (num_patches, C * T * P * P)
+        # Reshape to 5-D for Conv3dLayer, then flatten back.
+        x = x.view(
+            -1,
+            self.in_channels,
+            self.temporal_patch_size,
+            self.patch_size,
+            self.patch_size,
+        )
+        return self.proj(x).view(-1, self.hidden_size)
+
+
+class Glm4vPatchMerger(nn.Module):
+    def __init__(
+        self,
+        d_model: int,
+        context_dim: int,
+        quant_config: Optional[QuantizationConfig] = None,
+        bias: bool = False,
+        prefix: str = "",
+        use_data_parallel: bool = False,
+    ) -> None:
+        super().__init__()
+        self.hidden_size = d_model
+        tp_size = 1 if use_data_parallel else get_parallel().tp_size
+        tp_rank = 0 if use_data_parallel else get_parallel().tp_rank
+        self.proj = ReplicatedLinear(
+            self.hidden_size,
+            self.hidden_size,
+            bias=bias,
+            quant_config=quant_config,
+            prefix=add_prefix("proj", prefix),
+        )
+        self.post_projection_norm = LayerNorm(self.hidden_size)
+        self.gate_up_proj = MergedColumnParallelLinear(
+            input_size=self.hidden_size,
+            output_sizes=[context_dim] * 2,
+            bias=bias,
+            quant_config=quant_config,
+            prefix=add_prefix("gate_up_proj", prefix),
+            tp_size=tp_size,
+            tp_rank=tp_rank,
+        )
+        self.down_proj = RowParallelLinear(
+            context_dim,
+            self.hidden_size,
+            bias=bias,
+            quant_config=quant_config,
+            prefix=add_prefix("down_proj", prefix),
+            tp_size=tp_size,
+            tp_rank=tp_rank,
+        )
+        self.extra_activation_func = nn.GELU()
+
+    def forward(self, x: torch.Tensor):
+        x, _ = self.proj(x)
+        x = self.extra_activation_func(self.post_projection_norm(x))
+        gate_up, _ = self.gate_up_proj(x)
+        gate, up = gate_up.chunk(2, dim=-1)
+        x = F.silu(gate) * up
+        x, _ = self.down_proj(x)
+        return x
+
+
+class Glm4vVisionEmbeddings(nn.Module):
+    def __init__(self, config: Glm4vVisionConfig):
+        super().__init__()
+        self.config = config
+        self.embed_dim = config.hidden_size
+        self.image_size = config.image_size
+        self.patch_size = config.patch_size
+
+        self.num_patches = (self.image_size // self.patch_size) ** 2
+        self.num_positions = self.num_patches
+        self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim)
+        self.register_buffer(
+            "position_ids",
+            torch.arange(self.num_positions).expand((1, -1)),
+            persistent=False,
+        )
+
+    def forward(
+        self, embeddings, lengths, image_shapes, h_coords, w_coords
+    ) -> torch.Tensor:
+        pos_embed_weight = self.position_embedding.weight
+        hidden_size = pos_embed_weight.shape[1]
+        total_seq = h_coords.shape[0]
+        device = pos_embed_weight.device
+
+        # Move coordinates to correct device
+        h_coords, w_coords = h_coords.to(device), w_coords.to(device)
+
+        # Handle empty sequence case
+        if total_seq == 0:
+            adapted_pos_embed = torch.empty(
+                0, hidden_size, device=device, dtype=pos_embed_weight.dtype
+            )
+        else:
+            # Convert inputs to tensors if needed
+            if isinstance(lengths, list):
+                lengths = torch.tensor(lengths, device=device, dtype=torch.long)
+            if not isinstance(image_shapes, torch.Tensor):
+                image_shapes = torch.tensor(
+                    image_shapes, device=device, dtype=torch.long
+                )
+
+            # Prepare 2D position embedding
+            orig_size_sq = pos_embed_weight.shape[0]
+            orig_size = int(orig_size_sq**0.5)
+            pos_embed_2d = (
+                pos_embed_weight.view(orig_size, orig_size, hidden_size)
+                .permute(2, 0, 1)
+                .unsqueeze(0)
+                .to(device=device, dtype=torch.float32)
+            )
+
+            # Calculate target dimensions for each patch
+            target_h = torch.cat(
+                [image_shapes[i, 1].repeat(lengths[i]) for i in range(len(lengths))]
+            ).to(device=device, dtype=torch.float32)
+            target_w = torch.cat(
+                [image_shapes[i, 2].repeat(lengths[i]) for i in range(len(lengths))]
+            ).to(device=device, dtype=torch.float32)
+
+            # Normalize coordinates to [-1, 1] range for grid_sample
+            h_coords = h_coords.to(device=device, dtype=torch.float32)
+            w_coords = w_coords.to(device=device, dtype=torch.float32)
+            norm_w = ((w_coords + 0.5) / target_w) * 2 - 1
+            norm_h = ((h_coords + 0.5) / target_h) * 2 - 1
+
+            # Create sampling grid
+            grid = torch.stack((norm_w, norm_h), dim=-1).unsqueeze(0).unsqueeze(2)
+
+            # Perform bicubic interpolation
+            interpolated_embed_fp32 = F.grid_sample(
+                pos_embed_2d,
+                grid,
+                mode="bicubic",
+                align_corners=False,
+                padding_mode="border",
+            )
+
+            # Reshape and convert back to original dtype
+            adapted_pos_embed_fp32 = (
+                interpolated_embed_fp32.squeeze(0).squeeze(-1).permute(1, 0)
+            )
+            adapted_pos_embed = adapted_pos_embed_fp32.to(pos_embed_weight.dtype).to(
+                embeddings.device
+            )
+
+        # Add adapted position encoding to embeddings
+        embeddings = embeddings + adapted_pos_embed
+        return embeddings
+
+
+class Glm4vVisionModel(nn.Module):
+    def __init__(
+        self,
+        vision_config: Glm4vVisionConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+        use_data_parallel: bool = False,
+    ) -> None:
+        super().__init__()
+
+        patch_size = vision_config.patch_size
+        temporal_patch_size = vision_config.temporal_patch_size
+        in_channels = vision_config.in_channels
+        depth = vision_config.depth
+        self.hidden_size = vision_config.hidden_size
+        self.num_heads = vision_config.num_heads
+
+        self.patch_size = vision_config.patch_size
+        self.spatial_merge_size = vision_config.spatial_merge_size
+        self.out_hidden_size = vision_config.out_hidden_size
+        self.use_data_parallel = use_data_parallel
+
+        self.patch_embed = Glm4vVisionPatchEmbed(
+            patch_size=patch_size,
+            temporal_patch_size=temporal_patch_size,
+            in_channels=in_channels,
+            hidden_size=self.hidden_size,
+        )
+
+        head_dim = self.hidden_size // self.num_heads
+        self.rotary_pos_emb = get_rope(
+            head_size=head_dim,
+            rotary_dim=head_dim // 2,
+            max_position=8192,
+            base=10000.0,
+            is_neox_style=True,
+        )
+
+        self.blocks = nn.ModuleList(
+            [
+                Glm4vVisionBlock(
+                    dim=self.hidden_size,
+                    intermediate_dim=self.out_hidden_size,
+                    num_heads=self.num_heads,
+                    quant_config=quant_config,
+                    prefix=add_prefix(f"blocks.{layer_idx}", prefix),
+                    num_dummy_heads=vision_config.num_dummy_heads,
+                    rms_norm_eps=vision_config.rms_norm_eps,
+                    attn_qkv_bias=vision_config.attention_bias,
+                    use_data_parallel=use_data_parallel,
+                )
+                for layer_idx in range(depth)
+            ]
+        )
+
+        self.merger = Glm4vPatchMerger(
+            d_model=vision_config.out_hidden_size,
+            context_dim=vision_config.intermediate_size,
+            quant_config=quant_config,
+            bias=False,
+            prefix=add_prefix("merger", prefix),
+            use_data_parallel=use_data_parallel,
+        )
+
+        self.embeddings = Glm4vVisionEmbeddings(vision_config)
+
+        self.post_conv_layernorm = Glm4vRMSNorm(
+            vision_config.hidden_size, eps=vision_config.rms_norm_eps
+        )
+        self.downsample = nn.Conv2d(
+            in_channels=vision_config.hidden_size,
+            out_channels=vision_config.out_hidden_size,
+            kernel_size=vision_config.spatial_merge_size,
+            stride=vision_config.spatial_merge_size,
+        )
+        self.post_layernorm = Glm4vRMSNorm(
+            vision_config.hidden_size, eps=vision_config.rms_norm_eps
+        )
+
+    @property
+    def dtype(self) -> torch.dtype:
+        return self.patch_embed.proj.weight.dtype
+
+    @property
+    def device(self) -> torch.device:
+        return self.patch_embed.proj.weight.device
+
+    def rot_pos_emb(
+        self, grid_thw: torch.Tensor
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        pos_ids = []
+        for t, h, w in grid_thw:
+            hpos_ids = torch.arange(h).unsqueeze(1).expand(-1, w)
+            wpos_ids = torch.arange(w).unsqueeze(0).expand(h, -1)
+            hpos_ids = (
+                hpos_ids.reshape(
+                    h // self.spatial_merge_size,
+                    self.spatial_merge_size,
+                    w // self.spatial_merge_size,
+                    self.spatial_merge_size,
+                )
+                .permute(0, 2, 1, 3)
+                .flatten()
+            )
+            wpos_ids = (
+                wpos_ids.reshape(
+                    h // self.spatial_merge_size,
+                    self.spatial_merge_size,
+                    w // self.spatial_merge_size,
+                    self.spatial_merge_size,
+                )
+                .permute(0, 2, 1, 3)
+                .flatten()
+            )
+            pos_ids.append(torch.stack([hpos_ids, wpos_ids], dim=-1).repeat(t, 1))
+        pos_ids = torch.cat(pos_ids, dim=0).to(self.device, non_blocking=True)
+        max_grid_size = grid_thw[:, 1:].max()
+
+        # Use pre-computed cos_sin_cache from RotaryEmbedding
+        cos, sin = self.rotary_pos_emb.get_cos_sin(max_grid_size)
+
+        cos_combined = cos[pos_ids].flatten(1)
+        sin_combined = sin[pos_ids].flatten(1)
+        return cos_combined, sin_combined, pos_ids
+
+    def forward(self, x: torch.Tensor, grid_thw: torch.Tensor) -> torch.Tensor:
+        # patchify
+        x = x.to(device=self.device, dtype=self.dtype)
+        x = self.patch_embed(x)
+        x = self.post_conv_layernorm(x)
+
+        # compute position embedding
+        rotary_pos_emb_cos, rotary_pos_emb_sin, image_type_ids = self.rot_pos_emb(
+            grid_thw
+        )
+        # compute cu_seqlens
+        cu_seqlens = torch.repeat_interleave(
+            grid_thw[:, 1] * grid_thw[:, 2], grid_thw[:, 0]
+        ).cumsum(dim=0, dtype=torch.int32)
+        cu_seqlens = torch.cat([cu_seqlens.new_zeros(1), cu_seqlens])
+
+        seqlens = (cu_seqlens[1:] - cu_seqlens[:-1]).tolist()
+        x = self.embeddings(
+            x, seqlens, grid_thw, image_type_ids[:, 0], image_type_ids[:, 1]
+        )
+
+        rotary_pos_emb_cos = torch.cat([rotary_pos_emb_cos, rotary_pos_emb_cos], dim=-1)
+        rotary_pos_emb_sin = torch.cat([rotary_pos_emb_sin, rotary_pos_emb_sin], dim=-1)
+
+        # cu_seqlens must be on cpu because of npu_flash_attention_unpad operator restriction
+        if is_npu():
+            cu_seqlens = cu_seqlens.to("cpu")
+
+        # x.shape: (s, b, d) where b=1 for vision processing
+        # transformers
+        x = x.unsqueeze(1)
+        for blk in self.blocks:
+            x = blk(
+                x,
+                cu_seqlens=cu_seqlens,
+                rotary_pos_emb_cos=rotary_pos_emb_cos,
+                rotary_pos_emb_sin=rotary_pos_emb_sin,
+            )
+
+        # adapter
+        x = self.post_layernorm(x)
+        x = x.view(-1, self.spatial_merge_size, self.spatial_merge_size, x.shape[-1])
+        x = x.permute(0, 3, 1, 2)
+        x = self.downsample(x).view(-1, self.out_hidden_size)
+        x = self.merger(x)
+
+        return x
+
+
+class Glm4vForConditionalGeneration(nn.Module):
+    def __init__(
+        self,
+        config: Glm4vConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+
+        self.pp_group = get_pp_group()
+        self.config = config
+        self.use_data_parallel = get_server_args().mm_enable_dp_encoder
+        vision_utils.update_vit_attn_dummy_heads_config(self.config)
+        self.visual = Glm4vVisionModel(
+            config.vision_config,
+            quant_config=quant_config,
+            prefix=add_prefix("visual", prefix),
+            use_data_parallel=self.use_data_parallel,
+        )
+
+        self.model = Glm4Model(
+            config,
+            quant_config=quant_config,
+            prefix=add_prefix("model", prefix),
+        )
+
+        if self.pp_group.is_last_rank:
+            if self.pp_group.world_size == 1 and self.config.tie_word_embeddings:
+                self.lm_head = self.model.embed_tokens
+            else:
+                self.lm_head = ParallelLMHead(
+                    self.config.vocab_size,
+                    self.config.hidden_size,
+                    quant_config=quant_config,
+                    prefix=add_prefix("lm_head", prefix),
+                )
+        else:
+            # ranks other than the last rank will have a placeholder layer
+            self.lm_head = PPMissingLayer()
+
+        self.is_mrope_enabled = "mrope_section" in self.config.rope_scaling
+
+        self.logits_processor = LogitsProcessor(config)
+        self.pooler = Pooler(pooling_type=PoolingType.LAST, normalize=True)
+
+        # For EAGLE3 support
+        self.capture_aux_hidden_states = False
+
+    def pad_input_ids(self, input_ids: List[int], mm_inputs: MultimodalInputs):
+        pattern = MultiModalityDataPaddingPatternMultimodalTokens()
+        return pattern.pad_input_tokens(input_ids, mm_inputs)
+
+    def get_image_feature(self, items: List[MultimodalDataItem]) -> torch.Tensor:
+        # in GLM-V, last dim is the same
+        pixel_values = torch.cat([item.feature for item in items], dim=0).type(
+            self.visual.dtype
+        )
+        image_grid_thw = torch.concat([item.image_grid_thw for item in items], dim=0)
+        assert pixel_values.dim() == 2, pixel_values.dim()
+        assert image_grid_thw.dim() == 2, image_grid_thw.dim()
+        if self.use_data_parallel:
+            return run_dp_sharded_mrope_vision_model(
+                self.visual, pixel_values, image_grid_thw.tolist(), rope_type="rope_3d"
+            )
+        else:
+            image_embeds = self.visual(pixel_values, grid_thw=image_grid_thw)
+        return image_embeds
+
+    def get_video_feature(self, items: List[MultimodalDataItem]) -> torch.Tensor:
+        # in GLM-V, last dim is the same
+        pixel_values = torch.cat([item.feature for item in items], dim=0).type(
+            self.visual.dtype
+        )
+        video_grid_thw = torch.concat([item.video_grid_thw for item in items], dim=0)
+
+        # reshape video_grid_thw -> [b, 3] -> [1, h, w] * frames
+        temp_frames_hw = []
+        for t, h, w in video_grid_thw:
+            repeated_row = (
+                torch.tensor([1, h.item(), w.item()]).unsqueeze(0).repeat(t, 1)
+            )
+            temp_frames_hw.append(repeated_row)
+        flattened_video_grid_thw = torch.cat(temp_frames_hw, dim=0)
+
+        assert pixel_values.dim() == 2, pixel_values.dim()
+        assert video_grid_thw.dim() == 2, video_grid_thw.dim()
+        if self.use_data_parallel:
+            return run_dp_sharded_mrope_vision_model(
+                self.visual,
+                pixel_values,
+                flattened_video_grid_thw.tolist(),
+                rope_type="rope_3d",
+            )
+        else:
+            video_embeds = self.visual(pixel_values, grid_thw=flattened_video_grid_thw)
+        return video_embeds
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    @torch.no_grad()
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        forward_batch: ForwardBatch,
+        get_embedding: bool = False,
+        pp_proxy_tensors: Optional[PPProxyTensors] = None,
+    ):
+        """Run forward pass for GLM-4.1V.
+
+        Args:
+            input_ids: Flattened (concatenated) input_ids corresponding to a
+                batch.
+            positions: Flattened (concatenated) position ids corresponding to a
+                batch.
+                **NOTE**: If mrope is enabled (default setting for GLM-4.1V
+                opensource models), the shape will be `(3, seq_len)`,
+                otherwise it will be `(seq_len,).
+                (Use input_metadata.mrope_positions to replace it)
+        """
+        if self.is_mrope_enabled:
+            positions = forward_batch.mrope_positions
+
+        if not (
+            forward_batch.forward_mode.is_decode()
+            or not forward_batch.contains_image_inputs()
+        ):
+            if self.is_mrope_enabled:
+                assert positions.ndim == 2 and positions.size(0) == 3, (
+                    "multimodal section rotary embedding requires "
+                    f"(3, seq_len) positions, but got {positions.size()}"
+                )
+
+        hidden_states = general_mm_embed_routine(
+            input_ids=input_ids,
+            forward_batch=forward_batch,
+            language_model=self.model,
+            multimodal_model=self,
+            positions=positions,
+            pp_proxy_tensors=pp_proxy_tensors,
+        )
+
+        aux_hidden_states = None
+        if self.capture_aux_hidden_states:
+            hidden_states, aux_hidden_states = hidden_states
+
+        if self.pp_group.is_last_rank:
+            if not get_embedding:
+                return self.logits_processor(
+                    input_ids,
+                    hidden_states,
+                    self.lm_head,
+                    forward_batch,
+                )
+            else:
+                return self.pooler(hidden_states, forward_batch)
+        else:
+            return hidden_states
+
+    def _pad_vit_attn_dummy_heads(self, name: str, loaded_weight: torch.Tensor):
+        """pad attn qkv weights for dummy heads"""
+        num_dummy_heads = self.config.vision_config.num_dummy_heads
+        if num_dummy_heads == 0:
+            return loaded_weight
+        head_dim = self.config.vision_config.head_dim
+
+        if "attn.qkv_proj" in name:
+            wq, wk, wv = loaded_weight.chunk(3, dim=0)
+            if name.endswith(".weight"):
+                dummy_shape = [num_dummy_heads, head_dim, wq.shape[-1]]
+            elif name.endswith(".bias"):
+                dummy_shape = [num_dummy_heads, head_dim]
+            else:
+                raise RuntimeError(f"Unsupported weight with name={name}")
+            pad_func = lambda x: torch.cat(
+                [x.unflatten(0, (-1, head_dim)), x.new_zeros(dummy_shape)], dim=0
+            ).flatten(0, 1)
+            wq, wk, wv = pad_func(wq), pad_func(wk), pad_func(wv)
+            loaded_weight = torch.cat([wq, wk, wv], dim=0)
+        elif "attn.proj.weight" in name:
+            padded_weight = loaded_weight.new_zeros(
+                loaded_weight.shape[0], head_dim * num_dummy_heads
+            )
+            loaded_weight = torch.cat([loaded_weight, padded_weight], dim=-1)
+        elif "attn.q_norm.weight" in name or "attn.k_norm.weight" in name:
+            padded_weight = loaded_weight.new_zeros(head_dim * num_dummy_heads)
+            loaded_weight = torch.cat([loaded_weight, padded_weight], dim=0)
+        return loaded_weight
+
+    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            (".qkv_proj", ".q_proj", "q"),
+            (".qkv_proj", ".k_proj", "k"),
+            (".qkv_proj", ".v_proj", "v"),
+            (".gate_up_proj", ".up_proj", 1),
+            (".gate_up_proj", ".gate_proj", 0),
+        ]
+        params_dict = dict(self.named_parameters(remove_duplicate=False))
+
+        # For the PP case, we add special handling for lm_head.weight,
+        # - On non–last ranks: we continue, because this stage is supposed to
+        #   be just an empty PPMissingLayer shell.
+        # - On the last rank: params_dict is expected to contain lm_head.weight,
+        #   so it will never hit the branch "if name not in params_dict".
+        #
+        # For all other parameters, such like
+        # "model.visual.blocks.20.mlp.gate_proj.weight", the unified rule is:
+        # If this name does not exist in the current rank’s params_dict,
+        # it does not belong to this pipeline stage, thus we simply continue.
+        for name, loaded_weight in weights:
+            if "rotary_emb.inv_freq" in name:
+                continue
+            if "language_model" in name:
+                name = name.replace(r"model.language_model.", r"model.")
+            if "model.visual." in name:
+                name = name.replace("model.visual.", "visual.")
+
+            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)
+
+                # Skip loading extra bias for GPTQ models.
+                if name.endswith(".bias") and name not in params_dict:
+                    continue
+
+                if name not in params_dict:
+                    continue
+
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                if "visual" in name:
+                    # adapt to VisionAttention
+                    name = name.replace(r"attn.qkv.", r"attn.qkv_proj.")
+
+                try:
+                    # Skip loading extra bias for GPTQ models.
+                    if name.endswith(".bias") and name not in params_dict:
+                        continue
+
+                    if name not in params_dict:
+                        continue
+
+                    param = params_dict[name]
+                except KeyError:
+                    print(params_dict.keys())
+                    raise
+
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                if "visual" in name:
+                    loaded_weight = vision_utils.pad_vit_attn_dummy_heads(
+                        self.config, name, loaded_weight
+                    )
+                weight_loader(param, loaded_weight)
+
+    def get_embed_and_head(self):
+        return self.model.embed_tokens.weight, self.lm_head.weight
+
+    def set_embed_and_head(self, embed, head):
+        del self.model.embed_tokens.weight
+        self.model.embed_tokens.weight = embed
+        if self.config.tie_word_embeddings:
+            self.lm_head = self.model.embed_tokens
+        else:
+            del self.lm_head.weight
+            self.lm_head.weight = head
+        torch.cuda.empty_cache()
+        torch.cuda.synchronize()
+
+
+EntryClass = [Glm4vForConditionalGeneration]