# 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/glm4/modular_glm4.py """Inference-only GLM-4-0414 model compatible with HuggingFace weights.""" import logging from typing import Any, Dict, Iterable, Optional, Tuple, Union import torch from torch import nn from sglang.srt.distributed import ( get_pp_group, get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size, ) from sglang.srt.layers.activation import SiluAndMul from sglang.srt.layers.dp_attention import is_dp_attention_enabled from sglang.srt.layers.layernorm import RMSNorm from sglang.srt.layers.linear import ( MergedColumnParallelLinear, QKVParallelLinear, 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.radix_attention import RadixAttention from sglang.srt.layers.rotary_embedding import get_rope from sglang.srt.layers.utils import PPMissingLayer, get_layer_id from sglang.srt.layers.vocab_parallel_embedding import ( ParallelLMHead, VocabParallelEmbedding, ) from sglang.srt.model_executor.forward_batch_info import ForwardBatch, PPProxyTensors from sglang.srt.model_loader.weight_utils import ( default_weight_loader, kv_cache_scales_loader, ) from sglang.srt.utils import add_prefix, make_layers Glm4Config = None logger = logging.getLogger(__name__) class Glm4MLP(nn.Module): def __init__( self, hidden_size: int, intermediate_size: int, hidden_act: str, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", reduce_results: bool = True, ) -> None: super().__init__() self.gate_up_proj = MergedColumnParallelLinear( hidden_size, [intermediate_size] * 2, bias=False, quant_config=quant_config, prefix=add_prefix("gate_up_proj", prefix), ) self.down_proj = RowParallelLinear( intermediate_size, hidden_size, bias=False, quant_config=quant_config, prefix=add_prefix("down_proj", prefix), reduce_results=reduce_results, ) if hidden_act != "silu": raise ValueError( f"Unsupported activation: {hidden_act}. Only silu is supported for now." ) self.act_fn = SiluAndMul() def forward( self, x, forward_batch=None, use_reduce_scatter: bool = False, ): gate_up, _ = self.gate_up_proj(x) x = self.act_fn(gate_up) x, _ = self.down_proj( x, skip_all_reduce=use_reduce_scatter, ) return x class Glm4Attention(nn.Module): def __init__( self, hidden_size: int, num_heads: int, num_kv_heads: int, head_dim: Optional[int] = None, layer_id: int = 0, rope_theta: float = 1000000, rope_scaling: Optional[Dict[str, Any]] = None, max_position_embeddings: int = 131072, quant_config: Optional[QuantizationConfig] = None, dual_chunk_attention_config: Optional[dict[str, Any]] = None, partial_rotary_factor: float = 0.5, bias: bool = True, prefix: str = "", ) -> None: super().__init__() self.hidden_size = hidden_size tp_size = get_tensor_model_parallel_world_size() self.total_num_heads = num_heads assert self.total_num_heads % tp_size == 0 self.num_heads = self.total_num_heads // tp_size self.total_num_kv_heads = num_kv_heads if self.total_num_kv_heads >= tp_size: # Number of KV heads is greater than TP size, so we partition # the KV heads across multiple tensor parallel GPUs. assert self.total_num_kv_heads % tp_size == 0 else: # Number of KV heads is less than TP size, so we replicate # the KV heads across multiple tensor parallel GPUs. assert tp_size % self.total_num_kv_heads == 0 self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size) if head_dim is not None: self.head_dim = head_dim else: self.head_dim = hidden_size // self.total_num_heads self.q_size = self.num_heads * self.head_dim self.kv_size = self.num_kv_heads * self.head_dim self.scaling = self.head_dim**-0.5 self.rope_theta = rope_theta self.max_position_embeddings = max_position_embeddings self.partial_rotary_factor = partial_rotary_factor self.qkv_proj = QKVParallelLinear( hidden_size, self.head_dim, self.total_num_heads, self.total_num_kv_heads, bias=bias, quant_config=quant_config, prefix=add_prefix("qkv_proj", prefix), ) self.o_proj = RowParallelLinear( self.total_num_heads * self.head_dim, hidden_size, bias=False, quant_config=quant_config, prefix=add_prefix("o_proj", prefix), ) self.rotary_emb = get_rope( self.head_dim, rotary_dim=self.head_dim, max_position=max_position_embeddings, base=rope_theta, rope_scaling=rope_scaling, dual_chunk_attention_config=dual_chunk_attention_config, partial_rotary_factor=partial_rotary_factor, is_neox_style=False, ) self.attn = RadixAttention( self.num_heads, self.head_dim, self.scaling, num_kv_heads=self.num_kv_heads, layer_id=layer_id, quant_config=quant_config, prefix=add_prefix("attn", prefix), ) def forward( self, positions: torch.Tensor, hidden_states: torch.Tensor, forward_batch: ForwardBatch, ) -> torch.Tensor: qkv, _ = self.qkv_proj(hidden_states) q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1) q, k = self.rotary_emb(positions, q, k) attn_output = self.attn(q, k, v, forward_batch) output, _ = self.o_proj(attn_output) return output class Glm4DecoderLayer(nn.Module): """A single transformer layer. Transformer layer takes input with size [s, b, h] and returns an output of the same size. """ def __init__( self, config: Glm4Config, layer_id: int = 0, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", alt_stream: Optional[torch.cuda.Stream] = None, ) -> None: super().__init__() self.hidden_size = config.hidden_size rp = getattr(config, "rope_parameters", None) if isinstance(rp, dict): rope_theta = rp.get("rope_theta", getattr(config, "rope_theta", 1000000)) partial_rotary_factor = rp.get( "partial_rotary_factor", getattr(config, "partial_rotary_factor", 0.5), ) rope_scaling = getattr(config, "rope_scaling", None) else: rope_theta = getattr(config, "rope_theta", 1000000) rope_scaling = getattr(config, "rope_scaling", None) partial_rotary_factor = getattr(config, "partial_rotary_factor", 0.5) bias = getattr(config, "attention_bias", True) max_position_embeddings = getattr(config, "max_position_embeddings", 32768) head_dim = getattr(config, "head_dim", None) dual_chunk_attention_config = getattr( config, "dual_chunk_attention_config", None ) self.self_attn = Glm4Attention( hidden_size=self.hidden_size, num_heads=config.num_attention_heads, num_kv_heads=config.num_key_value_heads, head_dim=head_dim, layer_id=layer_id, rope_theta=rope_theta, rope_scaling=rope_scaling, max_position_embeddings=max_position_embeddings, quant_config=quant_config, dual_chunk_attention_config=dual_chunk_attention_config, partial_rotary_factor=partial_rotary_factor, bias=bias, prefix=add_prefix("self_attn", prefix), ) # MLP self.mlp = Glm4MLP( config.hidden_size, intermediate_size=config.intermediate_size, hidden_act=config.hidden_act, quant_config=quant_config, prefix=add_prefix("mlp", prefix), ) self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.post_attention_layernorm = RMSNorm( config.hidden_size, eps=config.rms_norm_eps ) self.post_self_attn_layernorm = RMSNorm( config.hidden_size, eps=config.rms_norm_eps ) self.post_mlp_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) def forward( self, positions: torch.Tensor, hidden_states: torch.Tensor, forward_batch: ForwardBatch, residual: Optional[torch.Tensor], ) -> Tuple[torch.Tensor, torch.Tensor]: # Self Attention if residual is None: residual = hidden_states hidden_states = self.input_layernorm(hidden_states) else: hidden_states, residual = self.input_layernorm(hidden_states, residual) hidden_states = self.self_attn( positions=positions, hidden_states=hidden_states, forward_batch=forward_batch, ) hidden_states = self.post_self_attn_layernorm(hidden_states) # Fully Connected hidden_states, residual = self.post_attention_layernorm(hidden_states, residual) hidden_states = self.mlp(hidden_states) hidden_states = self.post_mlp_layernorm(hidden_states) return hidden_states, residual class Glm4Model(nn.Module): def __init__( self, config: Glm4Config, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", decoder_layer_type: type[nn.Module] = Glm4DecoderLayer, alt_stream: Optional[torch.cuda.Stream] = None, ) -> None: super().__init__() self.config = config self.padding_idx = config.pad_token_id self.vocab_size = config.vocab_size self.pp_group = get_pp_group() if self.pp_group.is_first_rank: self.embed_tokens = VocabParallelEmbedding( config.vocab_size, config.hidden_size, quant_config=quant_config, use_attn_tp_group=is_dp_attention_enabled(), prefix=add_prefix("embed_tokens", prefix), ) else: self.embed_tokens = PPMissingLayer() # Use the provided decoder layer type or default to Glm4DecoderLayer decoder_layer_type = decoder_layer_type or Glm4DecoderLayer self.layers, self.start_layer, self.end_layer = make_layers( config.num_hidden_layers, lambda idx, prefix: decoder_layer_type( layer_id=idx, config=config, quant_config=quant_config, prefix=prefix, alt_stream=alt_stream, ), pp_rank=self.pp_group.rank_in_group, pp_size=self.pp_group.world_size, prefix=add_prefix("layers", prefix), ) if self.pp_group.is_last_rank: self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) else: self.norm = PPMissingLayer(return_tuple=True) # For EAGLE3 support self.layers_to_capture = [] def get_input_embeddings(self) -> nn.Embedding: return self.embed_tokens def forward( self, input_ids: torch.Tensor, positions: torch.Tensor, forward_batch: ForwardBatch, input_embeds: torch.Tensor = None, pp_proxy_tensors: Optional[PPProxyTensors] = None, ) -> Union[torch.Tensor, PPProxyTensors]: if self.pp_group.is_first_rank: if input_embeds is None: hidden_states = self.embed_tokens(input_ids) else: hidden_states = input_embeds residual = None else: assert pp_proxy_tensors is not None hidden_states = pp_proxy_tensors["hidden_states"] residual = pp_proxy_tensors["residual"] aux_hidden_states = [] for i in range(self.start_layer, self.end_layer): if i in self.layers_to_capture: aux_hidden_states.append( hidden_states + residual if residual is not None else hidden_states ) layer = self.layers[i] hidden_states, residual = layer( positions, hidden_states, forward_batch, residual, ) if not self.pp_group.is_last_rank: return PPProxyTensors( { "hidden_states": hidden_states, "residual": residual, } ) else: if hidden_states.shape[0] != 0: if residual is None: hidden_states = self.norm(hidden_states) else: hidden_states, _ = self.norm(hidden_states, residual) if len(aux_hidden_states) == 0: return hidden_states return hidden_states, aux_hidden_states # If this function is called, it should always initialize KV cache scale # factors (or else raise an exception). Thus, handled exceptions should # make sure to leave KV cache scale factors in a known good (dummy) state def load_kv_cache_scales(self, quantization_param_path: str) -> None: tp_size = get_tensor_model_parallel_world_size() tp_rank = get_tensor_model_parallel_rank() for layer_idx, scaling_factor in kv_cache_scales_loader( quantization_param_path, tp_rank, tp_size, self.config.num_hidden_layers, self.config.__class__.model_type, ): if not isinstance(self.layers[layer_idx], nn.Identity): layer_self_attn = self.layers[layer_idx].self_attn if hasattr(layer_self_attn.attn, "k_scale"): layer_self_attn.attn.k_scale = scaling_factor layer_self_attn.attn.v_scale = scaling_factor else: raise RuntimeError( "Self attention has no KV cache scaling factor attribute!" ) class Glm4ForCausalLM(nn.Module): def __init__( self, config: Glm4Config, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", ) -> None: super().__init__() self.pp_group = get_pp_group() self.config = config self.quant_config = quant_config self.model = Glm4Model( config, quant_config=quant_config, prefix=add_prefix("model", prefix) ) # handle the lm head on different pp ranks if self.pp_group.is_last_rank: if self.pp_group.world_size == 1 and 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), ) else: # ranks other than the last rank will have a placeholder layer self.lm_head = PPMissingLayer() # perform weight tying for PP if self.pp_group.world_size > 1 and config.tie_word_embeddings: if self.pp_group.is_first_rank: self.pp_group.send( self.model.embed_tokens.weight, dst=self.pp_group.last_rank ) else: emb_token_weight = self.pp_group.recv( size=(config.vocab_size, config.hidden_size), dtype=next(self.model.parameters()).dtype, src=self.pp_group.first_rank, ) self.lm_head.weight.copy_(emb_token_weight) self.logits_processor = LogitsProcessor(config) self.pooler = Pooler(pooling_type=PoolingType.LAST, normalize=True) # For EAGLE3 support self.capture_aux_hidden_states = False def get_input_embedding(self, input_ids: torch.Tensor) -> torch.Tensor: return self.model.get_input_embedding(input_ids) def get_input_embeddings(self) -> nn.Embedding: return self.model.embed_tokens @torch.no_grad() def forward( self, input_ids: torch.Tensor, positions: torch.Tensor, forward_batch: ForwardBatch, input_embeds: torch.Tensor = None, get_embedding: bool = False, pp_proxy_tensors: Optional[PPProxyTensors] = None, ) -> torch.Tensor: hidden_states = self.model( input_ids, positions, forward_batch, input_embeds, 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, aux_hidden_states, ) else: return self.pooler(hidden_states, forward_batch) else: return hidden_states @torch.no_grad() def forward_split_prefill( self, input_ids: torch.Tensor, positions: torch.Tensor, forward_batch: ForwardBatch, split_interval: Tuple[int, int], # [start, end) 0-based input_embeds: torch.Tensor = None, ): start, end = split_interval # embed if start == 0: if input_embeds is None: forward_batch.hidden_states = self.model.embed_tokens(input_ids) else: forward_batch.hidden_states = input_embeds # decoder layer for i in range(start, end): layer = self.model.layers[i] forward_batch.hidden_states, forward_batch.residual = layer( positions, forward_batch.hidden_states, forward_batch, forward_batch.residual, ) if end == self.model.config.num_hidden_layers: # norm hidden_states, _ = self.model.norm( forward_batch.hidden_states, forward_batch.residual ) forward_batch.hidden_states = hidden_states # logits process result = self.logits_processor( input_ids, forward_batch.hidden_states, self.lm_head, forward_batch ) else: result = None return result @property def start_layer(self): return self.model.start_layer @property def end_layer(self): return self.model.end_layer 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()) for name, loaded_weight in weights: layer_id = get_layer_id(name) if ( layer_id is not None and hasattr(self.model, "start_layer") and ( layer_id < self.model.start_layer or layer_id >= self.model.end_layer ) ): continue if "rotary_emb.inv_freq" in name or "projector" in name: continue if self.config.tie_word_embeddings and "lm_head.weight" in name: if self.pp_group.world_size > 1 and self.pp_group.is_last_rank: # Handle pp weight tying here # find the embed_tokens.weight in the weights embed_token_weights = next( filter(lambda x: x[0] == "model.embed_tokens.weight", weights) )[1] loaded_weight = embed_token_weights else: 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 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: # Skip loading extra bias for GPTQ models. if name.endswith(".bias") and name not in params_dict: continue if name in params_dict.keys(): param = params_dict[name] weight_loader = getattr( param, "weight_loader", default_weight_loader ) weight_loader(param, loaded_weight) else: logger.warning(f"Parameter {name} not found in params_dict") 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 del self.lm_head.weight self.model.embed_tokens.weight = embed self.lm_head.weight = head torch.cuda.empty_cache() torch.cuda.synchronize() def load_kv_cache_scales(self, quantization_param_path: str) -> None: self.model.load_kv_cache_scales(quantization_param_path) EntryClass = [Glm4ForCausalLM]