# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project # ruff: noqa: E501 # 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. """Inference-only K-EXAONE-236B-A22B model compatible with HuggingFace weights.""" from collections.abc import Iterable from itertools import islice import torch from torch import nn from transformers import PretrainedConfig from vllm.compilation.decorators import support_torch_compile from vllm.config import CacheConfig, VllmConfig, get_current_vllm_config from vllm.distributed import ( get_ep_group, get_pp_group, get_tensor_model_parallel_world_size, ) from vllm.model_executor.layers.attention import Attention from vllm.model_executor.layers.fused_moe import FusedMoEFactory from vllm.model_executor.layers.layernorm import RMSNorm from vllm.model_executor.layers.linear import ( QKVParallelLinear, ReplicatedLinear, RowParallelLinear, ) from vllm.model_executor.layers.logits_processor import LogitsProcessor from vllm.model_executor.layers.quantization import QuantizationConfig from vllm.model_executor.layers.rotary_embedding import get_rope from vllm.model_executor.layers.vocab_parallel_embedding import ( ParallelLMHead, VocabParallelEmbedding, ) from vllm.sequence import IntermediateTensors from vllm.transformers_utils.config import set_default_rope_theta from .exaone4 import Exaone4GatedMLP as ExaoneMoeGatedMLP from .interfaces import SupportsLoRA, SupportsPP from .utils import ( AutoWeightsLoader, PPMissingLayer, WeightsMapper, extract_layer_index, make_empty_intermediate_tensors_factory, make_layers, maybe_prefix, ) class ExaoneMoe(nn.Module): def __init__( self, config: PretrainedConfig, swiglu_limit: float | None = None, quant_config: QuantizationConfig | None = None, prefix: str = "", enable_eplb: bool = False, ): super().__init__() self.tp_size = get_tensor_model_parallel_world_size() self.routed_scaling_factor = config.routed_scaling_factor self.ep_group = get_ep_group().device_group self.ep_size = self.ep_group.size() self.n_routed_experts = config.num_experts if self.tp_size > config.num_experts: raise ValueError( f"Tensor parallel size {self.tp_size} is greater than " f"the number of experts {config.num_experts}." ) self.gate = ReplicatedLinear( config.hidden_size, config.num_experts, bias=False, quant_config=None, prefix=f"{prefix}.gate", ) self.e_score_correction_bias = nn.Parameter( torch.empty(config.num_experts, dtype=torch.float32) ) # Load balancing settings. vllm_config = get_current_vllm_config() eplb_config = vllm_config.parallel_config.eplb_config self.enable_eplb = enable_eplb self.n_logical_experts = self.n_routed_experts eplb_config.num_redundant_experts = ( eplb_config.num_redundant_experts if eplb_config.num_redundant_experts is not None else 0 ) self.n_redundant_experts = eplb_config.num_redundant_experts self.n_physical_experts = self.n_logical_experts + self.n_redundant_experts self.n_local_physical_experts = self.n_physical_experts // self.ep_size self.shared_experts: ExaoneMoeGatedMLP | None if getattr(config, "num_shared_experts", 0) > 0: intermediate_size = config.moe_intermediate_size * config.num_shared_experts self.shared_experts = ExaoneMoeGatedMLP( hidden_size=config.hidden_size, intermediate_size=intermediate_size, hidden_act=config.hidden_act, quant_config=quant_config, reduce_results=False, swiglu_limit=swiglu_limit, prefix=f"{prefix}.shared_experts", ) else: self.shared_experts = None self.experts = FusedMoEFactory( shared_experts=self.shared_experts, gate=self.gate, num_experts=self.n_routed_experts, top_k=config.num_experts_per_tok, hidden_size=config.hidden_size, intermediate_size=config.moe_intermediate_size, renormalize=config.norm_topk_prob, quant_config=quant_config, activation="silu", swiglu_limit=swiglu_limit, use_grouped_topk=True, num_expert_group=config.n_group, topk_group=config.topk_group, prefix=f"{prefix}.experts", scoring_func="sigmoid", routed_scaling_factor=self.routed_scaling_factor, e_score_correction_bias=self.e_score_correction_bias, enable_eplb=self.enable_eplb, num_redundant_experts=self.n_redundant_experts, ) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: # NOTE: hidden_states can have either 1D or 2D shape. orig_shape = hidden_states.shape hidden_dim = hidden_states.shape[-1] hidden_states = hidden_states.view(-1, hidden_dim) final_hidden_states = self.experts( hidden_states=hidden_states, router_logits=hidden_states ) return final_hidden_states.view(orig_shape) class ExaoneMoeAttention(nn.Module): def __init__( self, config, hidden_size: int, num_heads: int, num_kv_heads: int, max_position_embeddings: int = 8192, quant_config: QuantizationConfig | None = None, bias: bool = False, cache_config: CacheConfig | None = None, prefix: str = "", is_mtp: bool = False, ) -> 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) # MistralConfig has an optional head_dim introduced by Mistral-Nemo self.head_dim = getattr(config, "head_dim", None) if self.head_dim is None: self.head_dim = self.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.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=f"{prefix}.qkv_proj", ) self.o_proj = RowParallelLinear( input_size=self.total_num_heads * self.head_dim, output_size=hidden_size, bias=bias, quant_config=quant_config, prefix=f"{prefix}.o_proj", ) self.q_norm = RMSNorm(self.head_dim, eps=config.rms_norm_eps) self.k_norm = RMSNorm(self.head_dim, eps=config.rms_norm_eps) is_neox_style = True if quant_config is not None and quant_config.get_name() == "gguf": is_neox_style = False layer_idx = extract_layer_index(prefix) if config.sliding_windows is not None: self.sliding_window_size = config.sliding_windows[layer_idx] if config.layer_types[layer_idx] == "full_attention": self.sliding_window_size = None if is_mtp: self.sliding_window = ( config.mtp_layer_types[layer_idx] == "sliding_attention" ) if config.mtp_sliding_windows is not None: self.sliding_window_size = config.mtp_sliding_windows[layer_idx] if config.mtp_layer_types[layer_idx] == "full_attention": self.sliding_window_size = None # apply rotary embeddings to every layer in full attention models self.apply_rope_all_layers = "sliding_attention" not in config.layer_types set_default_rope_theta(config, default_theta=1000000) self.rotary_emb = get_rope( self.head_dim, max_position=max_position_embeddings, rope_parameters=config.rope_parameters, is_neox_style=is_neox_style, ) self.attn = Attention( self.num_heads, self.head_dim, self.scaling, num_kv_heads=self.num_kv_heads, cache_config=cache_config, quant_config=quant_config, per_layer_sliding_window=self.sliding_window_size, prefix=f"{prefix}.attn", ) def forward( self, positions: torch.Tensor, hidden_states: torch.Tensor, ) -> 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 = q.unflatten(-1, (self.num_heads, self.head_dim)) q = self.q_norm(q) q = q.flatten(-2, -1) k = k.unflatten(-1, (self.num_kv_heads, self.head_dim)) k = self.k_norm(k) k = k.flatten(-2, -1) if self.sliding_window_size or self.apply_rope_all_layers: q, k = self.rotary_emb(positions, q, k) attn_output = self.attn(q, k, v) output, _ = self.o_proj(attn_output) return output class ExaoneMoeDecoderLayer(nn.Module): def __init__( self, config: PretrainedConfig, cache_config: CacheConfig | None = None, quant_config: QuantizationConfig | None = None, is_mtp: bool = False, prefix: str = "", ) -> None: super().__init__() layer_idx = extract_layer_index(prefix) self.hidden_size = config.hidden_size max_position_embeddings = getattr(config, "max_position_embeddings", 8192) # Support abacusai/Smaug-72B-v0.1 with attention_bias attention_bias = getattr(config, "attention_bias", False) or getattr( config, "bias", False ) self.self_attn = ExaoneMoeAttention( config=config, hidden_size=self.hidden_size, num_heads=config.num_attention_heads, num_kv_heads=getattr( config, "num_key_value_heads", config.num_attention_heads ), max_position_embeddings=max_position_embeddings, quant_config=quant_config, bias=attention_bias, cache_config=cache_config, prefix=f"{prefix}.self_attn", is_mtp=is_mtp, ) swiglu_limits = getattr(config, "swiglu_limits", None) if swiglu_limits is not None: swiglu_limit = swiglu_limits[layer_idx] swiglu_limit = swiglu_limit if swiglu_limit > 0 else None else: swiglu_limit = None if config.mlp_layer_types[layer_idx] == "sparse" and not is_mtp: self.mlp = ExaoneMoe( config=config, swiglu_limit=swiglu_limit, quant_config=quant_config, prefix=f"{prefix}.mlp", ) else: self.mlp = ExaoneMoeGatedMLP( hidden_size=self.hidden_size, intermediate_size=config.intermediate_size, hidden_act=config.hidden_act, swiglu_limit=swiglu_limit, quant_config=quant_config, bias=getattr(config, "mlp_bias", False), prefix=f"{prefix}.mlp", ) 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 ) def forward( self, positions: torch.Tensor, hidden_states: torch.Tensor, residual: torch.Tensor | None, ) -> tuple[torch.Tensor, torch.Tensor]: if residual is None: residual = hidden_states hidden_states = self.input_layernorm(hidden_states) else: hidden_states, residual = self.input_layernorm(hidden_states, residual) # Self Attention hidden_states = self.self_attn( positions=positions, hidden_states=hidden_states, ) # Fully Connected hidden_states, residual = self.post_attention_layernorm(hidden_states, residual) hidden_states = self.mlp(hidden_states) return hidden_states, residual @support_torch_compile class ExaoneMoeModel(nn.Module): def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): super().__init__() config = vllm_config.model_config.hf_config cache_config = vllm_config.cache_config quant_config = vllm_config.quant_config self.num_redundant_experts = ( vllm_config.parallel_config.eplb_config.num_redundant_experts ) self.config = config self.quant_config = quant_config self.vocab_size = config.vocab_size if get_pp_group().is_first_rank or ( config.tie_word_embeddings and get_pp_group().is_last_rank ): self.embed_tokens = VocabParallelEmbedding( self.vocab_size, config.hidden_size, quant_config=quant_config, ) else: self.embed_tokens = PPMissingLayer() self.start_layer, self.end_layer, self.layers = make_layers( config.num_hidden_layers, lambda prefix: ExaoneMoeDecoderLayer( config=config, cache_config=cache_config, quant_config=quant_config, prefix=prefix, ), prefix=f"{prefix}.layers", ) if get_pp_group().is_last_rank: self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) else: self.norm = PPMissingLayer() self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory( ["hidden_states", "residual"], config.hidden_size ) def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor: return self.embed_tokens(input_ids) def forward( self, input_ids: torch.Tensor | None, positions: torch.Tensor, intermediate_tensors: IntermediateTensors | None, inputs_embeds: torch.Tensor | None = None, ) -> torch.Tensor | IntermediateTensors: if get_pp_group().is_first_rank: if inputs_embeds is not None: hidden_states = inputs_embeds else: hidden_states = self.embed_input_ids(input_ids) residual = None else: assert intermediate_tensors is not None hidden_states = intermediate_tensors["hidden_states"] residual = intermediate_tensors["residual"] for layer in islice(self.layers, self.start_layer, self.end_layer): hidden_states, residual = layer( positions, hidden_states, residual, ) if not get_pp_group().is_last_rank: return IntermediateTensors( {"hidden_states": hidden_states, "residual": residual} ) hidden_states, _ = self.norm(hidden_states, residual) return hidden_states class ExaoneMoeForCausalLM(nn.Module, SupportsLoRA, SupportsPP): hf_to_vllm_mapper = WeightsMapper( orig_to_new_stacked={ # weight_name: (param_name, shard_id) ".q_proj": (".qkv_proj", "q"), ".k_proj": (".qkv_proj", "k"), ".v_proj": (".qkv_proj", "v"), # .experts.gate_up_proj must be handled by MoERunner.load_weights for EP ".mlp.gate_proj": (".mlp.gate_up_proj", 0), ".mlp.up_proj": (".mlp.gate_up_proj", 1), ".shared_experts.gate_proj": (".shared_experts.gate_up_proj", 0), ".shared_experts.up_proj": (".shared_experts.gate_up_proj", 1), }, orig_to_new_prefix={"mtp.": None}, ) packed_modules_mapping = { "qkv_proj": [ "q_proj", "k_proj", "v_proj", ], "gate_up_proj": [ "gate_proj", "up_proj", ], } # LoRA specific attributes embedding_modules = { "embed_tokens": "input_embeddings", "lm_head": "output_embeddings", } embedding_padding_modules = ["lm_head"] def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): super().__init__() config = vllm_config.model_config.hf_config.get_text_config() quant_config = vllm_config.quant_config self.config = config self.quant_config = quant_config self.model = ExaoneMoeModel( vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model"), ) if get_pp_group().is_last_rank: self.unpadded_vocab_size = config.vocab_size self.lm_head = ParallelLMHead( self.unpadded_vocab_size, config.hidden_size, quant_config=quant_config, ) if config.tie_word_embeddings: self.lm_head = self.lm_head.tie_weights(self.model.embed_tokens) logit_scale = getattr(config, "logit_scale", 1.0) self.logits_processor = LogitsProcessor( self.unpadded_vocab_size, config.vocab_size, logit_scale ) else: self.lm_head = PPMissingLayer() self.make_empty_intermediate_tensors = ( self.model.make_empty_intermediate_tensors ) def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor: return self.model.embed_input_ids(input_ids) def forward( self, input_ids: torch.Tensor | None, positions: torch.Tensor, intermediate_tensors: IntermediateTensors | None = None, inputs_embeds: torch.Tensor | None = None, ) -> torch.Tensor | IntermediateTensors: model_output = self.model( input_ids, positions, intermediate_tensors, inputs_embeds ) return model_output def compute_logits( self, hidden_states: torch.Tensor, ) -> torch.Tensor | None: logits = self.logits_processor(self.lm_head, hidden_states) return logits def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]: loader = AutoWeightsLoader( self, # Skip loading extra parameters for GPTQ/modelopt models. ignore_unexpected_suffixes=[ ".bias", "_bias", ".k_scale", "_k_scale", ".v_scale", "_v_scale", ".weight_scale", "_weight_scale", ".input_scale", "_input_scale", ], ) return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)