# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project # Copyright 2025 The Baidu team. # Copyright 2023 The vLLM team. # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. # # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX # and OPT implementations in this library. It has been modified from its # original forms to accommodate minor architectural differences compared # to GPT-NeoX and OPT used by the Meta AI team that trained the model. # # 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 ErineMoE model compatible with HuggingFace weights.""" import typing from collections.abc import Callable, Iterable from itertools import islice from typing import Any import torch from torch import nn from transformers import PretrainedConfig from vllm.attention.layer import Attention 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.logger import init_logger from vllm.model_executor.layers.activation import SiluAndMul from vllm.model_executor.layers.fused_moe import SharedFusedMoE from vllm.model_executor.layers.layernorm import RMSNorm from vllm.model_executor.layers.linear import ( MergedColumnParallelLinear, 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.model_executor.model_loader.weight_utils import ( default_weight_loader, maybe_remap_kv_scale_name, ) from vllm.sequence import IntermediateTensors from vllm.transformers_utils.config import set_default_rope_theta from .interfaces import MixtureOfExperts, SupportsLoRA, SupportsPP from .utils import ( AutoWeightsLoader, PPMissingLayer, extract_layer_index, is_pp_missing_parameter, make_empty_intermediate_tensors_factory, make_layers, maybe_prefix, ) logger = init_logger(__name__) class Ernie4_5_MoeMLP(nn.Module): def __init__( self, hidden_size: int, intermediate_size: int, hidden_act: str, use_bias: bool = False, quant_config: QuantizationConfig | None = None, reduce_results: bool = True, prefix: str = "", ) -> None: super().__init__() self.gate_up_proj = MergedColumnParallelLinear( hidden_size, [intermediate_size] * 2, bias=use_bias, quant_config=quant_config, prefix=f"{prefix}.gate_up_proj", ) self.down_proj = RowParallelLinear( intermediate_size, hidden_size, bias=use_bias, quant_config=quant_config, reduce_results=reduce_results, prefix=f"{prefix}.down_proj", ) 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 Ernie4_5_MoeMoE(nn.Module): def __init__( self, config: PretrainedConfig, quant_config: QuantizationConfig | None = None, prefix: str = "", enable_eplb: bool = False, ): super().__init__() layer_idx = extract_layer_index(prefix) self.layer_idx = layer_idx self.tp_size = get_tensor_model_parallel_world_size() self.moe_num_shared_experts = getattr(config, "moe_num_shared_experts", None) self.ep_group = get_ep_group().device_group self.ep_rank = get_ep_group().rank_in_group self.ep_size = self.ep_group.size() self.n_routed_experts: int = config.moe_num_experts self.n_shared_experts: int = self.moe_num_shared_experts # Load balancing settings. vllm_config = get_current_vllm_config() eplb_config = vllm_config.parallel_config.eplb_config self.enable_eplb = enable_eplb self.n_redundant_experts = eplb_config.num_redundant_experts self.n_logical_experts = self.n_routed_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.physical_expert_start = self.ep_rank * self.n_local_physical_experts self.physical_expert_end = ( self.physical_expert_start + self.n_local_physical_experts ) self.has_shared_experts = getattr(config, "moe_num_shared_experts", 0) > 0 if self.tp_size > config.moe_num_experts: raise ValueError( f"Tensor parallel size {self.tp_size} is greater than " f"the number of experts {config.moe_num_experts}." ) self.gate = ReplicatedLinear( config.hidden_size, config.moe_num_experts, bias=False, params_dtype=torch.float32, quant_config=None, prefix=f"{prefix}.gate", ) self.gate.e_score_correction_bias = nn.Parameter( torch.empty(config.moe_num_experts, dtype=torch.float32) ) if self.has_shared_experts: intermediate_size = ( config.moe_intermediate_size * config.moe_num_shared_experts ) self.shared_experts = Ernie4_5_MoeMLP( hidden_size=config.hidden_size, intermediate_size=intermediate_size, hidden_act=config.hidden_act, quant_config=quant_config, prefix=f"{prefix}.shared_experts", reduce_results=False, ) else: self.shared_experts = None self.experts = SharedFusedMoE( shared_experts=self.shared_experts, num_experts=config.moe_num_experts, top_k=config.moe_k, hidden_size=config.hidden_size, intermediate_size=config.moe_intermediate_size, reduce_results=False, renormalize=True, quant_config=quant_config, prefix=f"{prefix}.experts", e_score_correction_bias=self.gate.e_score_correction_bias, enable_eplb=self.enable_eplb, num_redundant_experts=self.n_redundant_experts, router_logits_dtype=torch.float32, ) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: orig_shape = hidden_states.shape hidden_dim = hidden_states.shape[-1] hidden_states = hidden_states.view(-1, hidden_dim) router_logits, _ = self.gate(hidden_states.to(dtype=torch.float32)) final_hidden_states = self.experts( hidden_states=hidden_states, router_logits=router_logits ) if self.has_shared_experts: final_hidden_states = final_hidden_states[0] + final_hidden_states[1] else: final_hidden_states = final_hidden_states[1] if self.tp_size > 1: final_hidden_states = self.experts.maybe_all_reduce_tensor_model_parallel( final_hidden_states ) return final_hidden_states.view(orig_shape) class Ernie4_5_MoeAttention(nn.Module): def __init__( self, hidden_size: int, num_heads: int, num_kv_heads: int, rope_parameters: dict[str, Any], head_dim: int | None = None, max_position_embeddings: int = 131072, rms_norm_eps: float = 1e-05, qkv_bias: bool = False, cache_config: CacheConfig | None = None, quant_config: QuantizationConfig | None = None, prefix: str = "", ) -> None: super().__init__() layer_idx = extract_layer_index(prefix) if len(prefix) > 0 else 0 self.layer_idx = layer_idx 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) 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.max_position_embeddings = max_position_embeddings self.qkv_proj = QKVParallelLinear( hidden_size, self.head_dim, self.total_num_heads, self.total_num_kv_heads, bias=qkv_bias, quant_config=quant_config, prefix=f"{prefix}.qkv_proj", ) self.o_proj = RowParallelLinear( self.total_num_heads * self.head_dim, hidden_size, bias=False, quant_config=quant_config, prefix=f"{prefix}.o_proj", ) self.rotary_emb = get_rope( self.head_dim, max_position=max_position_embeddings, rope_parameters=rope_parameters, is_neox_style=False, ) 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, 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, k = self.rotary_emb(positions, q, k) # Attention attn_output = self.attn(q, k, v) # Output projection output, _ = self.o_proj(attn_output) return output class Ernie4_5_MoeDecoderLayer(nn.Module): def __init__( self, config: PretrainedConfig, cache_config: CacheConfig | None = None, quant_config: QuantizationConfig | None = None, prefix: str = "", enable_eplb: bool = False, ) -> None: super().__init__() self.hidden_size = config.hidden_size set_default_rope_theta(config, default_theta=500000) max_position_embeddings = getattr(config, "max_position_embeddings", 131072) self.self_attn = Ernie4_5_MoeAttention( hidden_size=self.hidden_size, num_heads=config.num_attention_heads, num_kv_heads=config.num_key_value_heads, head_dim=getattr(config, "head_dim", None), rope_parameters=config.rope_parameters, max_position_embeddings=max_position_embeddings, rms_norm_eps=config.rms_norm_eps, qkv_bias=getattr(config, "use_bias", False), cache_config=cache_config, quant_config=quant_config, prefix=f"{prefix}.self_attn", ) layer_idx = extract_layer_index(prefix) self.layer_idx = layer_idx # MoE moe_num_experts = getattr(config, "moe_num_experts", 0) moe_layer_start_index = getattr(config, "moe_layer_start_index", 0) moe_layer_end_index = getattr( config, "moe_layer_end_index", config.num_hidden_layers - 1 ) moe_layer_interval = getattr(config, "moe_layer_interval", 1) use_moe = getattr(config, "use_moe", moe_num_experts > 0) if ( use_moe and ((layer_idx + 1) % moe_layer_interval == 0) and layer_idx >= moe_layer_start_index and layer_idx <= moe_layer_end_index ): self.mlp = Ernie4_5_MoeMoE( config=config, quant_config=quant_config, prefix=f"{prefix}.mlp", enable_eplb=enable_eplb, ) else: self.mlp = Ernie4_5_MoeMLP( hidden_size=config.hidden_size, intermediate_size=config.intermediate_size, hidden_act=config.hidden_act, use_bias=getattr(config, "use_bias", False), quant_config=quant_config, 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, ) -> 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, ) # 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 Ernie4_5_MoeModel(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.padding_idx = config.pad_token_id self.vocab_size = config.vocab_size self.config = config parallel_config = vllm_config.parallel_config eplb_config = parallel_config.eplb_config enable_eplb = parallel_config.enable_eplb self.num_redundant_experts = eplb_config.num_redundant_experts if get_pp_group().is_first_rank: self.embed_tokens = VocabParallelEmbedding( config.vocab_size, config.hidden_size, quant_config=quant_config, prefix=f"{prefix}.embed_tokens", ) else: self.embed_tokens = PPMissingLayer() self.start_layer, self.end_layer, self.layers = make_layers( config.num_hidden_layers, lambda prefix: Ernie4_5_MoeDecoderLayer( config=config, cache_config=cache_config, quant_config=quant_config, prefix=prefix, enable_eplb=enable_eplb, ), 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, positions: torch.Tensor, intermediate_tensors: IntermediateTensors | None = 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 def get_expert_mapping(self) -> list[tuple[str, str, int, str]]: # Params for weights, fp8 weight scales, fp8 activation scales # (param_name, weight_name, expert_id, shard_id) return SharedFusedMoE.make_expert_params_mapping( self, ckpt_gate_proj_name="gate_proj", ckpt_down_proj_name="down_proj", ckpt_up_proj_name="up_proj", num_experts=self.config.moe_num_experts, num_redundant_experts=self.num_redundant_experts, ) def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]: 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()) loaded_params: set[str] = set() expert_params_mapping = self.get_expert_mapping() for name, loaded_weight in weights: if self.config.tie_word_embeddings and name.endswith("lm_head.weight"): continue # MTP will be supported soon. if "mtp" in name: continue if "e_score_correction_bias" in name: name = name.replace("moe_statics", "gate") loaded_weight = loaded_weight.squeeze(0) for param_name, weight_name, shard_id in stacked_params_mapping: # Skip non-stacked layers and experts (experts handled below). if weight_name not in name: continue if ("mlp.experts." in name) and name not in params_dict: continue name = name.replace(weight_name, param_name) # Skip loading extra bias for GPTQ models. if ( name.endswith(".bias") or name.endswith("_bias") ) and name not in params_dict: continue # Skip layers on other devices. if is_pp_missing_parameter(name, self): continue param = params_dict[name] weight_loader = param.weight_loader weight_loader(param, loaded_weight, shard_id) break else: is_expert_weight = False for mapping in expert_params_mapping: param_name, weight_name, expert_id, shard_id = mapping if weight_name not in name: continue # Anyway, this is an expert weight and should not be # attempted to load as other weights later is_expert_weight = True # Do not modify `name` since the loop may continue here # Instead, create a new variable name_mapped = name.replace(weight_name, param_name) # Skip layers on other devices. if is_pp_missing_parameter(name_mapped, self): continue # Skip loading extra bias for GPTQ models. if ( name_mapped.endswith(".bias") or name_mapped.endswith("_bias") ) and name_mapped not in params_dict: continue param = params_dict[name_mapped] # We should ask the weight loader to return success or not # here since otherwise we may skip experts with other # available replicas. weight_loader = typing.cast( Callable[..., bool], param.weight_loader ) success = weight_loader( param, loaded_weight, name_mapped, shard_id=shard_id, expert_id=expert_id, return_success=True, ) if success: name = name_mapped break else: if is_expert_weight: # We've checked that this is an expert weight # However it's not mapped locally to this rank # So we simply skip it continue # Skip loading extra bias for GPTQ models. if ( name.endswith(".bias") or name.endswith("_bias") ) and name not in params_dict: continue # Skip layers on other devices. if is_pp_missing_parameter(name, self): continue # Remapping the name of FP8 kv-scale. name = maybe_remap_kv_scale_name(name, params_dict) if name is None: continue param = params_dict[name] weight_loader = getattr( param, "weight_loader", default_weight_loader ) weight_loader(param, loaded_weight) loaded_params.add(name) return loaded_params class Ernie4_5_MoeForCausalLM(nn.Module, SupportsPP, SupportsLoRA, MixtureOfExperts): packed_modules_mapping = { "qkv_proj": [ "q_proj", "k_proj", "v_proj", ], "gate_up_proj": [ "gate_proj", "up_proj", ], } fall_back_to_pt_during_load = False def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): super().__init__() config = vllm_config.model_config.hf_config quant_config = vllm_config.quant_config self.config = config self.quant_config = quant_config self.model = Ernie4_5_MoeModel( vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model") ) if get_pp_group().is_last_rank: self.lm_head = ParallelLMHead( config.vocab_size, config.hidden_size, quant_config=quant_config, prefix=maybe_prefix(prefix, "lm_head"), ) else: self.lm_head = PPMissingLayer() if self.config.tie_word_embeddings: self.lm_head.weight = self.model.embed_tokens.weight self.logits_processor = LogitsProcessor(config.vocab_size) self.make_empty_intermediate_tensors = ( self.model.make_empty_intermediate_tensors ) self.expert_weights = [] # Set MoE hyperparameters moe_layers_indices = [ i for i in range(config.num_hidden_layers) if ( i >= config.moe_layer_start_index and i <= config.moe_layer_end_index and (i + 1) % config.moe_layer_interval == 0 ) ] self.num_moe_layers = len(moe_layers_indices) self.num_expert_groups = 1 self.moe_layers: list[SharedFusedMoE] = [] example_moe = None for layer in self.model.layers: if isinstance(layer, PPMissingLayer): continue assert isinstance(layer, Ernie4_5_MoeDecoderLayer) if isinstance(layer.mlp, Ernie4_5_MoeMoE): example_moe = layer.mlp self.moe_layers.append(layer.mlp.experts) if example_moe is None: logger.warning("No Ernie4_5_MoeMoE layer found in model.layers.") self.num_logical_experts = 0 self.num_physical_experts = 0 self.num_local_physical_experts = 0 self.num_routed_experts = 0 self.num_shared_experts = 0 self.num_redundant_experts = 0 else: self.num_logical_experts = example_moe.n_logical_experts self.num_physical_experts = example_moe.n_physical_experts self.num_local_physical_experts = example_moe.n_local_physical_experts self.num_routed_experts = example_moe.n_routed_experts self.num_shared_experts = example_moe.n_shared_experts self.num_redundant_experts = example_moe.n_redundant_experts def update_physical_experts_metadata( self, num_physical_experts: int, num_local_physical_experts: int, ) -> None: assert self.num_local_physical_experts == num_local_physical_experts self.num_physical_experts = num_physical_experts self.num_local_physical_experts = num_local_physical_experts self.num_redundant_experts = num_physical_experts - self.num_logical_experts for layer in self.model.layers: if isinstance(layer.mlp, Ernie4_5_MoeMoE): moe = layer.mlp moe.n_local_physical_experts = num_local_physical_experts moe.n_physical_experts = num_physical_experts moe.n_redundant_experts = self.num_redundant_experts moe.experts.update_expert_map() 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, positions: torch.Tensor, intermediate_tensors: IntermediateTensors | None = None, inputs_embeds: torch.Tensor | None = None, ) -> torch.Tensor | IntermediateTensors: hidden_states = self.model( input_ids, positions, intermediate_tensors, inputs_embeds ) return hidden_states 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_prefixes=(["lm_head."] if self.config.tie_word_embeddings else None), ) return loader.load_weights(weights) def get_expert_mapping(self) -> list[tuple[str, str, int, str]]: return self.model.get_expert_mapping()