# 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 import ( get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size, ) 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.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.server_args import get_global_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_tensor_model_parallel_world_size() ) self.tp_rank = 0 if use_data_parallel else get_tensor_model_parallel_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 = 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 = "", use_data_parallel: bool = False, ) -> None: super().__init__() self.hidden_size = d_model tp_size = 1 if use_data_parallel else get_tensor_model_parallel_world_size() tp_rank = 0 if use_data_parallel else get_tensor_model_parallel_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), 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_global_server_args().mm_enable_dp_encoder self.visual = Glm4vVisionModel( config.vision_config, quant_config=quant_config, prefix=add_prefix("visual", prefix), use_data_parallel=self.use_data_parallel, ) vision_utils.update_vit_attn_dummy_heads_config(self.config) 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]