import logging from functools import lru_cache, partial from typing import Iterable, List, Optional, Tuple import torch import torch.nn as nn import torch.nn.functional as F from transformers.models.glm4v.configuration_glm4v import Glm4vConfig, Glm4vVisionConfig from sglang.srt.hf_transformers_utils import get_processor from sglang.srt.layers.activation import SiluAndMul from sglang.srt.layers.attention import vision_utils from sglang.srt.layers.layernorm import RMSNorm from sglang.srt.layers.linear import ( ColumnParallelLinear, MergedColumnParallelLinear, 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.vocab_parallel_embedding import ParallelLMHead from sglang.srt.managers.schedule_batch import MultimodalDataItem from sglang.srt.model_loader.weight_utils import default_weight_loader from sglang.srt.models.glm4 import Glm4Model from sglang.srt.models.qwen2_5_vl import ( Qwen2_5_VisionBlock, Qwen2_5_VLForConditionalGeneration, ) from sglang.srt.utils import add_prefix 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 = "", ): super().__init__() self.gate_up_proj = MergedColumnParallelLinear( input_size=in_features, output_sizes=[hidden_features] * 2, bias=bias, quant_config=quant_config, prefix=add_prefix("gate_up_proj", prefix), ) self.down_proj = RowParallelLinear( hidden_features, in_features, bias=bias, quant_config=quant_config, prefix=add_prefix("down_proj", prefix), ) 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(Qwen2_5_VisionBlock): def __init__( self, config: Glm4vVisionConfig, norm_layer: Optional[nn.Module] = None, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", ) -> None: super().__init__( dim=config.hidden_size, intermediate_dim=config.out_hidden_size, num_heads=config.num_heads, hidden_act=config.hidden_act, norm_layer=norm_layer, quant_config=quant_config, prefix=prefix, num_dummy_heads=config.num_dummy_heads, rms_norm_eps=config.rms_norm_eps, ) self.mlp = Glm4vVisionMLP( config.hidden_size, config.out_hidden_size, bias=False, quant_config=quant_config, prefix=add_prefix("mlp", prefix), ) 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 = nn.Conv3d( in_channels, hidden_size, kernel_size=kernel_size, stride=kernel_size, bias=True, ) def forward(self, x: torch.Tensor) -> torch.Tensor: x = x.view( -1, self.in_channels, self.temporal_patch_size, self.patch_size, self.patch_size, ) x = self.proj(x).view(-1, self.hidden_size) return x class Glm4vPatchMerger(nn.Module): def __init__( self, d_model: int, context_dim: int, quant_config: Optional[QuantizationConfig] = None, bias: bool = False, prefix: str = "", ) -> None: super().__init__() self.hidden_size = d_model self.proj = ColumnParallelLinear( self.hidden_size, self.hidden_size, bias=bias, quant_config=quant_config, prefix=add_prefix("proj", prefix), gather_output=True, ) self.post_projection_norm = nn.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), ) self.down_proj = RowParallelLinear( context_dim, self.hidden_size, bias=bias, quant_config=quant_config, prefix=add_prefix("down_proj", prefix), ) 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 Glm4vVisionRotaryEmbedding(nn.Module): def __init__(self, dim: int, theta: float = 10000.0) -> None: super().__init__() self.dim = dim self.theta = theta inv_freq = 1.0 / (theta ** (torch.arange(0, dim, 2, dtype=torch.float) / dim)) self.register_buffer("inv_freq", inv_freq, persistent=False) self._seq_len_cached = 0 self._freqs_cached = None def update_freqs_cache(self, seqlen: int) -> None: if seqlen > self._seq_len_cached: seqlen *= 2 self._seq_len_cached = seqlen self.inv_freq = 1.0 / ( self.theta ** ( torch.arange( 0, self.dim, 2, dtype=torch.float, device=self.inv_freq.device, ) / self.dim ) ) seq = torch.arange( seqlen, device=self.inv_freq.device, dtype=self.inv_freq.dtype ) freqs = torch.outer(seq, self.inv_freq) self._freqs_cached = freqs def forward(self, seqlen: int) -> torch.Tensor: self.update_freqs_cache(seqlen) return self._freqs_cached[:seqlen] class Glm4vVisionModel(nn.Module): def __init__( self, vision_config: Glm4vVisionConfig, norm_eps: float = 1e-6, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", ) -> 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.patch_embed = Glm4vVisionPatchEmbed( patch_size=patch_size, temporal_patch_size=temporal_patch_size, in_channels=in_channels, hidden_size=self.hidden_size, ) norm_layer = partial(Glm4vRMSNorm, eps=norm_eps) head_dim = self.hidden_size // self.num_heads self.rotary_pos_emb = Glm4vVisionRotaryEmbedding(head_dim // 2) self.blocks = nn.ModuleList( [ Glm4vVisionBlock( config=vision_config, norm_layer=norm_layer, quant_config=quant_config, prefix=add_prefix(f"blocks.{layer_idx}", prefix), ) 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), ) 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) -> 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) max_grid_size = grid_thw[:, 1:].max() rotary_pos_emb_full = self.rotary_pos_emb(max_grid_size) rotary_pos_emb = rotary_pos_emb_full[pos_ids].flatten(1) return rotary_pos_emb, 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, 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 = F.pad(cu_seqlens, (1, 0), "constant", 0) seqlens = (cu_seqlens[1:] - cu_seqlens[:-1]).tolist() x = self.embeddings( x, seqlens, grid_thw, image_type_ids[:, 0], image_type_ids[:, 1] ) emb = torch.cat((rotary_pos_emb, rotary_pos_emb), dim=-1) rotary_pos_emb_tuple = (emb.cos(), emb.sin()) # 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, position_embeddings=rotary_pos_emb_tuple) # 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(Qwen2_5_VLForConditionalGeneration): def __init__( self, config: Glm4vConfig, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", ) -> None: nn.Module.__init__(self) self.config = config vision_utils.update_vit_attn_dummy_heads_config(self.config) self.model = Glm4Model( config, quant_config, prefix=add_prefix("model", prefix), ) self.visual = Glm4vVisionModel( config.vision_config, norm_eps=getattr(config, "rms_norm_eps", 1e-5), quant_config=quant_config, prefix=add_prefix("visual", prefix), ) if config.tie_word_embeddings: self.lm_head = self.model.embed_tokens else: self.lm_head = ParallelLMHead( config.vocab_size, config.hidden_size, quant_config=quant_config, prefix=add_prefix("lm_head", prefix), ) self.logits_processor = LogitsProcessor(config) self.pooler = Pooler(pooling_type=PoolingType.LAST, normalize=True) self.is_mrope_enabled = "mrope_section" in self.config.rope_scaling # For EAGLE3 support self.capture_aux_hidden_states = False def get_image_feature(self, items: List[MultimodalDataItem]) -> torch.Tensor: pixel_values = torch.cat( [item.feature.squeeze(0) 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) # For multi-image, pixel_values is [num_of_images, L, C] shape # assert pixel_values.dim() == 2, pixel_values.dim() assert image_grid_thw.dim() == 2, image_grid_thw.dim() image_embeds = self.visual(pixel_values, grid_thw=image_grid_thw) split_sizes = ( image_grid_thw.prod(-1) // self.visual.spatial_merge_size**2 ).tolist() image_embeds = torch.split(image_embeds, split_sizes) return torch.cat(image_embeds) def get_video_feature(self, items: List[MultimodalDataItem]) -> torch.Tensor: pixel_values_videos = torch.cat( [item.feature.squeeze(0) 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) # For multi-video, pixel_values_videos is [num_of_videos, L, C] shape # assert pixel_values_videos.dim() == 2, pixel_values_videos.dim() assert video_grid_thw.dim() == 2, video_grid_thw.dim() # 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) video_embeds = self.visual( pixel_values_videos, grid_thw=flattened_video_grid_thw ) split_sizes = ( video_grid_thw.prod(-1) // self.visual.spatial_merge_size**2 ).tolist() video_embeds = torch.split(video_embeds, split_sizes) return torch.cat(video_embeds) def _update_hf_config(self): """update hf config to ensure vision attention num_attention_heads is divisible by tp_size""" tp_size = get_attention_tp_size() num_heads = self.config.vision_config.num_heads head_dim = self.config.vision_config.hidden_size // num_heads num_dummy_heads = 0 if num_heads % tp_size != 0: num_dummy_heads = ( (num_heads + tp_size - 1) // tp_size ) * tp_size - num_heads setattr(self.config.vision_config, "head_dim", head_dim) setattr(self.config.vision_config, "num_dummy_heads", num_dummy_heads) 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 name, loaded_weight in weights: if "language_model." in name: name = name.replace("language_model.", "") if "model.visual." in name: name = name.replace("model.visual.", "visual.") if "rotary_emb.inv_freq" in name: 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) # Skip loading extra bias for GPTQ models. if name.endswith(".bias") and 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 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) EntryClass = [Glm4vForConditionalGeneration]