from collections.abc import Iterable from typing import Any, List, Optional, Tuple, Union import torch from torch import nn from transformers import Exaone4Config from sglang.srt.distributed import get_pp_group, get_tensor_model_parallel_world_size from sglang.srt.layers.activation import SiluAndMul from sglang.srt.layers.dp_attention import ( get_attention_tp_rank, get_attention_tp_size, get_local_attention_dp_size, ) from sglang.srt.layers.layernorm import RMSNorm from sglang.srt.layers.linear import ( MergedColumnParallelLinear, QKVParallelLinear, RowParallelLinear, ) from sglang.srt.layers.logits_processor import LogitsProcessor, LogitsProcessorOutput 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, maybe_remap_kv_scale_name, ) from sglang.srt.server_args import get_global_server_args from sglang.srt.utils import add_prefix, make_layers from sglang.utils import get_exception_traceback, logger # Aligned with HF's implementation, using sliding window inclusive with the last token # SGLang assumes exclusive def get_attention_sliding_window_size(config): if getattr(config, "sliding_window", None) is not None: return config.sliding_window - 1 else: return None class Exaone4GatedMLP(nn.Module): def __init__( self, hidden_size: int, intermediate_size: int, hidden_act: str, quant_config: Optional[QuantizationConfig] = None, bias: bool = False, prefix: str = "", ) -> None: super().__init__() self.gate_up_proj = MergedColumnParallelLinear( hidden_size, [intermediate_size] * 2, bias=bias, quant_config=quant_config, prefix=add_prefix("gate_up_proj", prefix), ) self.down_proj = RowParallelLinear( intermediate_size, hidden_size, bias=bias, quant_config=quant_config, prefix=add_prefix("down_proj", prefix), ) 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): gate_up, _ = self.gate_up_proj(x) x = self.act_fn(gate_up) x, _ = self.down_proj(x) return x class Exaone4Attention(nn.Module): def __init__( self, config, hidden_size: int, num_heads: int, num_kv_heads: int, layer_id: int = 0, head_dim: Optional[int] = None, rms_norm_eps: float = 1e-06, rope_theta: float = 10000, rope_scaling: Optional[dict[str, Any]] = None, max_position_embeddings: int = 8192, quant_config: Optional[QuantizationConfig] = None, bias: bool = False, bias_o_proj: bool = False, prefix: str = "", ) -> None: super().__init__() self.hidden_size = hidden_size tp_size = get_tensor_model_parallel_world_size() attn_tp_rank = get_attention_tp_rank() attn_tp_size = get_attention_tp_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) self.head_dim = head_dim or 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.qkv_proj = QKVParallelLinear( hidden_size=hidden_size, head_size=self.head_dim, total_num_heads=self.total_num_heads, total_num_kv_heads=self.total_num_kv_heads, bias=bias, quant_config=quant_config, prefix=add_prefix("qkv_proj", prefix), tp_rank=attn_tp_rank, tp_size=attn_tp_size, ) self.o_proj = RowParallelLinear( input_size=self.total_num_heads * self.head_dim, output_size=hidden_size, bias=bias_o_proj, quant_config=quant_config, prefix=add_prefix("o_proj", prefix), tp_rank=attn_tp_rank, tp_size=attn_tp_size, ) is_neox_style = True if quant_config is not None and quant_config.get_name() == "gguf": is_neox_style = False interleaved_sliding_window = get_attention_sliding_window_size(config) self.sliding_window_pattern = getattr(config, "sliding_window_pattern", None) self.is_sliding = False if self.sliding_window_pattern: if (layer_id + 1) % len(self.sliding_window_pattern) != 0: self.is_sliding = True 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, is_neox_style=is_neox_style, ) self.attn = RadixAttention( self.num_heads, self.head_dim, self.scaling, num_kv_heads=self.num_kv_heads, layer_id=layer_id, sliding_window_size=( interleaved_sliding_window if self.is_sliding else None ), quant_config=quant_config, prefix=add_prefix("attn", prefix), ) self.q_norm = RMSNorm(self.head_dim, eps=rms_norm_eps) self.k_norm = RMSNorm(self.head_dim, eps=rms_norm_eps) 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) # Add qk-norm q_shape = q.shape q = q.reshape(-1, self.head_dim) q = self.q_norm(q) q = q.reshape(q_shape) k_shape = k.shape k = k.reshape(-1, self.head_dim) k = self.k_norm(k) k = k.reshape(k_shape) if not self.sliding_window_pattern or self.is_sliding: 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 Exaone4DecoderLayer(nn.Module): def __init__( self, config: Exaone4Config, layer_id: int = 0, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", ) -> None: super().__init__() self.layer_id = layer_id self.hidden_size = config.hidden_size rope_theta = getattr(config, "rope_theta", 1000000) rope_scaling = getattr(config, "rope_scaling", None) if rope_scaling is not None and getattr( config, "original_max_position_embeddings", None ): rope_scaling["original_max_position_embeddings"] = ( config.original_max_position_embeddings ) max_position_embeddings = getattr(config, "max_position_embeddings", 8192) self.local_dp_size = get_local_attention_dp_size() self.attn_tp_size = get_attention_tp_size() self.attn_tp_rank = get_attention_tp_rank() self.self_attn = Exaone4Attention( config=config, hidden_size=self.hidden_size, num_heads=config.num_attention_heads, num_kv_heads=getattr( config, "num_key_value_heads", config.num_key_value_heads ), layer_id=layer_id, rope_theta=rope_theta, rope_scaling=rope_scaling, max_position_embeddings=max_position_embeddings, quant_config=quant_config, prefix=add_prefix("self_attn", prefix), ) self.mlp = Exaone4GatedMLP( hidden_size=self.hidden_size, intermediate_size=config.intermediate_size, hidden_act=config.hidden_act, quant_config=quant_config, prefix=add_prefix("mlp", prefix), ) self.post_attention_layernorm = RMSNorm( self.hidden_size, eps=config.rms_norm_eps ) self.post_feedforward_layernorm = RMSNorm( self.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]: if residual is None: residual = hidden_states # Self Attention hidden_states = self.self_attn( positions=positions, hidden_states=hidden_states, forward_batch=forward_batch, ) # Use post-LN hidden_states = self.post_attention_layernorm(hidden_states) hidden_states = hidden_states + residual residual = hidden_states # Fully Connected hidden_states = self.mlp(hidden_states) # Use post-LN hidden_states = self.post_feedforward_layernorm(hidden_states) hidden_states = hidden_states + residual residual = hidden_states return hidden_states, residual class Exaone4Model(nn.Module): def __init__( self, config, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", ): super().__init__() self.config = config self.quant_config = quant_config 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, prefix=add_prefix("embed_tokens", prefix), ) else: self.embed_tokens = PPMissingLayer() self.layers, self.start_layer, self.end_layer = make_layers( config.num_hidden_layers, lambda idx, prefix: Exaone4DecoderLayer( config=config, quant_config=quant_config, layer_id=idx, prefix=prefix, ), 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) def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: return self.embed_tokens(input_ids) def forward( self, input_ids: torch.Tensor, positions: torch.Tensor, forward_batch: ForwardBatch, input_embeds: Optional[torch.Tensor] = None, pp_proxy_tensors: Optional[PPProxyTensors] = None, ) -> Union[torch.Tensor, Tuple[torch.Tensor, List[torch.Tensor]], PPProxyTensors]: if self.pp_group.is_first_rank: if input_embeds is None: hidden_states = self.get_input_embeddings(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"] for i in range(len(self.layers)): 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: hidden_states = self.norm(hidden_states) return hidden_states class Exaone4ForCausalLM(nn.Module): _tied_weights_keys = ["lm_head.weight"] _tp_plan = {"lm_head": "colwise_rep"} _pp_plan = {"lm_head": (["hidden_states"], ["logits"])} base_model_prefix = "language_model" # BitandBytes specific attributes default_bitsandbytes_target_modules = [ ".gate_proj.", ".down_proj.", ".up_proj.", ".q_proj.", ".k_proj.", ".v_proj.", ".o_proj.", ] bitsandbytes_stacked_params_mapping = { ".q_proj": (".qkv_proj", 0), ".k_proj": (".qkv_proj", 1), ".v_proj": (".qkv_proj", 2), ".gate_proj": (".gate_up_proj", 0), ".up_proj": (".gate_up_proj", 1), } packed_modules_mapping = { "qkv_proj": [ "q_proj", "k_proj", "v_proj", ], "gate_up_proj": [ "gate_proj", "up_proj", ], } def __init__( self, config, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", ): super().__init__() self.pp_group = get_pp_group() self.config = config self.quant_config = quant_config self.model = self._init_model(config, quant_config, add_prefix("model", prefix)) # Exaone-4.0 32B set tie_word_embeddins to False # Exaone-4.0 1.2B set tie_word_embeddins to True 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), use_attn_tp_group=get_global_server_args().enable_dp_lm_head, ) self.logits_processor = LogitsProcessor(config) self.pooler = Pooler(pooling_type=PoolingType.LAST, normalize=True) def _init_model( self, config, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", ): return Exaone4Model(config, quant_config=quant_config, prefix=prefix) 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: Optional[torch.Tensor] = None, get_embedding: bool = False, pp_proxy_tensors: Optional[PPProxyTensors] = None, ) -> LogitsProcessorOutput: hidden_states = self.model( input_ids, positions, forward_batch, input_embeds, pp_proxy_tensors=pp_proxy_tensors, ) 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 @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 get_attention_sliding_window_size(self): return get_attention_sliding_window_size(self.config) 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", ".gate_proj", 0), (".gate_up_proj", ".up_proj", 1), ] 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 "rotary_emb.cos_cached" in name or "rotary_emb.sin_cached" in name: # Models trained using ColossalAI may include these tensors in # the checkpoint. Skip them. continue if name.startswith("model.vision_tower") and name not in params_dict: continue if self.config.tie_word_embeddings and "lm_head.weight" in name: continue # Handle FP8 kv-scale remapping if "scale" in name: name = maybe_remap_kv_scale_name(name, params_dict) if name is None: 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 # Skip loading kv_scale from ckpts towards new design. if name.endswith(".kv_scale") 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_weights_by_name( self, name: str, truncate_size: int = 100, tp_size: int = 1 ) -> Optional[torch.Tensor]: """Get the weights of the parameter by its name. Similar to `get_parameter` in Hugging Face. Only used for unit test with an unoptimized performance. For optimized performance, please use torch.save and torch.load. """ try: if name == "lm_head.weight" and self.config.tie_word_embeddings: logger.info( "word embedding is tied for this model, return embed_tokens.weight as lm_head.weight." ) return ( self.model.embed_tokens.weight.cpu() .to(torch.float32) .numpy() .tolist()[:truncate_size] ) mapped_name = name mapped_shard_id = None for param_name, weight_name, shard_id in self.stacked_params_mapping: if weight_name in name: mapped_name = name.replace(weight_name, param_name) mapped_shard_id = shard_id break params_dict = dict(self.named_parameters()) param = params_dict[mapped_name] if mapped_shard_id is not None: if mapped_shard_id in ["q", "k", "v"]: num_heads = self.config.num_attention_heads // tp_size num_kv_heads = self.config.num_key_value_heads // tp_size head_dim = ( self.config.hidden_size // self.config.num_attention_heads ) if mapped_shard_id == "q": offset = 0 size = num_heads * head_dim elif mapped_shard_id == "k": offset = num_heads * head_dim size = num_kv_heads * head_dim elif mapped_shard_id == "v": offset = (num_heads + num_kv_heads) * head_dim size = num_kv_heads * head_dim weight = param.data.narrow(0, offset, size) elif mapped_shard_id in [0, 1]: intermediate_size = self.config.intermediate_size slice_size = intermediate_size // tp_size if mapped_shard_id == 0: # gate_proj offset = 0 size = slice_size elif mapped_shard_id == 1: # up_proj offset = slice_size size = slice_size weight = param.data.narrow(0, offset, size) else: weight = param.data else: weight = param.data if tp_size > 1 and ("o_proj" in name or "down_proj" in name): gathered_weights = [torch.zeros_like(weight) for _ in range(tp_size)] torch.distributed.all_gather(gathered_weights, weight) weight = torch.cat(gathered_weights, dim=1) return weight.cpu().to(torch.float32).numpy().tolist()[:truncate_size] except Exception: logger.error( f"Error getting weights by name {name} in Exaone4ForCausalLM: {get_exception_traceback()}" ) return None 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 get_embed(self): return self.model.embed_tokens.weight def set_embed(self, embed): # NOTE: If draft hidden size != target hidden size, the embed weight cannot be shared for EAGLE3 if ( hasattr(self.config, "target_hidden_size") and self.config.target_hidden_size != self.config.hidden_size ): return del self.model.embed_tokens.weight self.model.embed_tokens.weight = embed 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 = Exaone4ForCausalLM