# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project # coding=utf-8 # Copyright 2026 The HY 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 HY 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.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.attention import Attention from vllm.model_executor.layers.fused_moe import FusedMoE, GateLinear from vllm.model_executor.layers.layernorm import RMSNorm from vllm.model_executor.layers.linear import ( MergedColumnParallelLinear, QKVParallelLinear, 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.configs.hy_v3 import HYV3Config from .interfaces import SupportsLoRA, SupportsPP from .utils import ( AutoWeightsLoader, PPMissingLayer, is_pp_missing_parameter, make_empty_intermediate_tensors_factory, make_layers, maybe_prefix, ) logger = init_logger(__name__) class HYV3FeedForward(nn.Module): def __init__( self, hidden_size: int, intermediate_size: int, hidden_act: str, quant_config: QuantizationConfig | None = None, reduce_results: bool = True, expert_gate: torch.nn.Linear | None = None, prefix: str = "", ) -> None: super().__init__() self.gate_up_proj = MergedColumnParallelLinear( hidden_size, [intermediate_size] * 2, bias=False, quant_config=quant_config, prefix=f"{prefix}.gate_up_proj", ) self.down_proj = RowParallelLinear( intermediate_size, hidden_size, bias=False, 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) out = self.act_fn(gate_up) out, _ = self.down_proj(out) return out class HYV3MoEFused(nn.Module): def __init__( self, config: HYV3Config, quant_config: QuantizationConfig | None = None, prefix: str = "", enable_eplb: bool = False, ): super().__init__() self.tp_size = get_tensor_model_parallel_world_size() 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 = 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}." ) top_k = config.num_experts_per_tok intermediate_size = config.expert_hidden_dim router_scaling_factor = getattr(config, "router_scaling_factor", 1.0) 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 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.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.gate = GateLinear( config.hidden_size, config.num_experts, bias=False, out_dtype=torch.float32, params_dtype=torch.float32, prefix=f"{prefix}.gate", ) if config.num_shared_experts > 0: self.shared_mlp = HYV3FeedForward( hidden_size=config.hidden_size, intermediate_size=config.expert_hidden_dim * config.num_shared_experts, hidden_act=config.hidden_act, quant_config=quant_config, prefix=f"{prefix}", reduce_results=False, ) else: self.shared_mlp = None self.expert_bias = nn.Parameter(torch.empty(config.num_experts)) scoring_func = "sigmoid" e_score_correction_bias = self.expert_bias self.experts = FusedMoE( num_experts=self.n_routed_experts, top_k=top_k, hidden_size=config.hidden_size, intermediate_size=intermediate_size, renormalize=config.route_norm, quant_config=quant_config, prefix=f"{prefix}.experts", enable_eplb=self.enable_eplb, num_redundant_experts=self.n_redundant_experts, scoring_func=scoring_func, use_grouped_topk=True, num_expert_group=1, topk_group=1, routed_scaling_factor=router_scaling_factor, e_score_correction_bias=e_score_correction_bias, n_shared_experts=config.num_shared_experts, shared_experts=self.shared_mlp, ) 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: (num_tokens, n_experts) router_logits, _ = self.gate(hidden_states) final_hidden_states = self.experts( hidden_states=hidden_states, router_logits=router_logits ) return final_hidden_states.view(orig_shape) class HYV3Attention(nn.Module): def __init__( self, config: PretrainedConfig, hidden_size: int, num_heads: int, num_kv_heads: int, rope_parameters: dict[str, Any], max_position_embeddings: int = 8192, head_dim: int | None = None, rms_norm_eps: float = 1e-5, cache_config: CacheConfig | None = None, quant_config: QuantizationConfig | None = None, prefix: str = "", dual_chunk_attention_config: dict[str, Any] | None = None, ) -> 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 hasattr(config, "head_dim") and config.head_dim: self.head_dim = config.head_dim else: 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.use_qk_norm = getattr(config, "qk_norm", False) 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, quant_config=quant_config, bias=None, 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=True, ) 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", ) if self.use_qk_norm: self.q_norm = RMSNorm(self.head_dim, rms_norm_eps) self.k_norm = RMSNorm(self.head_dim, rms_norm_eps) 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) output_shape = None if self.use_qk_norm: q_by_head = q.view( *q.shape[:-1], q.shape[-1] // self.head_dim, self.head_dim ) q_by_head = self.q_norm(q_by_head) q = q_by_head.view(q.shape) k_by_head = k.view( *k.shape[:-1], k.shape[-1] // self.head_dim, self.head_dim ) k_by_head = self.k_norm(k_by_head) k = k_by_head.view(k.shape) q, k = self.rotary_emb(positions, q, k) attn_output = self.attn(q, k, v, output_shape) attn_output = attn_output.view(q.shape[0], -1) output, _ = self.o_proj(attn_output) return output class HYV3DecoderLayer(nn.Module): def __init__( self, config: PretrainedConfig, cache_config: CacheConfig | None = None, quant_config: QuantizationConfig | None = None, prefix: str = "", ) -> None: super().__init__() self.hidden_size = config.hidden_size layer_idx = int(prefix.split(".")[-1]) max_position_embeddings = getattr(config, "max_position_embeddings", 8192) self.self_attn = HYV3Attention( config=config, hidden_size=self.hidden_size, num_heads=config.num_attention_heads, num_kv_heads=config.num_key_value_heads, rope_parameters=config.rope_parameters, max_position_embeddings=max_position_embeddings, head_dim=config.head_dim, rms_norm_eps=config.rms_norm_eps, cache_config=cache_config, quant_config=quant_config, prefix=f"{prefix}.self_attn", ) self.input_layernorm = RMSNorm(config.hidden_size, config.rms_norm_eps) self.post_attention_layernorm = RMSNorm(config.hidden_size, config.rms_norm_eps) if not hasattr(config, "first_k_dense_replace"): raise ValueError("first_k_dense_replace not exist,please check config") if layer_idx < config.first_k_dense_replace: self.mlp = HYV3FeedForward( hidden_size=config.hidden_size, intermediate_size=config.intermediate_size, hidden_act=config.hidden_act, quant_config=quant_config, prefix=f"{prefix}.mlp", ) self.block_type = "feedforward" else: self.mlp = HYV3MoEFused( config=config, quant_config=quant_config, prefix=f"{prefix}.mlp" ) self.block_type = "moe" def forward( self, positions: torch.Tensor, hidden_states: torch.Tensor, residual: torch.Tensor | None, idx: int = -1, ) -> 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) hidden_states = self.self_attn( positions=positions, hidden_states=hidden_states, ) hidden_states, residual = self.post_attention_layernorm(hidden_states, residual) hidden_states = self.mlp(hidden_states) return hidden_states, residual @support_torch_compile class HYV3Model(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 parallel_config = vllm_config.parallel_config eplb_config = parallel_config.eplb_config self.num_redundant_experts = eplb_config.num_redundant_experts self.vocab_size = config.vocab_size self.config = config self.quant_config = quant_config self.embed_tokens = VocabParallelEmbedding( config.vocab_size, config.hidden_size, ) self.start_layer, self.end_layer, self.layers = make_layers( config.num_hidden_layers, lambda prefix: HYV3DecoderLayer( config=config, cache_config=cache_config, quant_config=quant_config, prefix=prefix, ), prefix=f"{prefix}.layers", ) self.norm = RMSNorm(config.hidden_size, config.rms_norm_eps) self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory( ["hidden_states", "residual"], config.hidden_size ) # Set MoE hyperparameters self.expert_weights = [] self.num_expert_groups = 1 self.moe_layers = [] example_layer = None for layer in self.layers: if isinstance(layer, PPMissingLayer): continue assert isinstance(layer, HYV3DecoderLayer) if layer.block_type == "moe": example_layer = layer.mlp self.moe_layers.append(layer.mlp.experts) if example_layer is None: self.num_moe_layers = 0 raise RuntimeError("No MoE layer found in model.layers.") self.num_moe_layers = len(self.moe_layers) self.num_logical_experts = getattr(example_layer, "n_logical_experts", None) self.num_physical_experts = getattr(example_layer, "n_physical_experts", None) self.num_local_physical_experts = getattr( example_layer, "n_local_physical_experts", None ) self.num_routed_experts = getattr(example_layer, "n_routed_experts", None) self.num_redundant_experts = getattr(example_layer, "n_redundant_experts", None) def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor: return self.embed_tokens(input_ids) 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.layers: if isinstance(layer.mlp, HYV3MoEFused): 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 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 FusedMoE.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.num_experts, ) 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 idx, layer in enumerate( islice(self.layers, self.start_layer, self.end_layer) ): hidden_states, residual = layer(positions, hidden_states, residual, idx=idx) if not get_pp_group().is_last_rank: return IntermediateTensors( {"hidden_states": hidden_states, "residual": residual} ) hidden_states = hidden_states + residual residual = hidden_states hidden_states = self.norm(hidden_states) return hidden_states 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()) expert_params_mapping = self.get_expert_mapping() loaded_params: set[str] = set() for name, loaded_weight in weights: if self.config.tie_word_embeddings and "lm_head.weight" in name: continue if self.quant_config is not None and ( scale_name := self.quant_config.get_cache_scale(name) ): param = params_dict[scale_name] weight_loader = getattr(param, "weight_loader", default_weight_loader) loaded_weight = ( loaded_weight if loaded_weight.dim() == 0 else loaded_weight[0] ) weight_loader(param, loaded_weight) loaded_params.add(scale_name) continue if "scale" in name: # Remapping the name of FP8 kv-scale. name = maybe_remap_kv_scale_name(name, params_dict) if name is None: continue is_found = False for param_name, weight_name, shard_id in stacked_params_mapping: if weight_name not in name: continue if "mlp.experts" 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 # 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) loaded_params.add(name) is_found = True break if is_found: continue if name.endswith(".bias") and name not in params_dict: continue 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 is_expert_weight = True name_mapped = name.replace(weight_name, param_name) # Skip layers on other devices. if is_pp_missing_parameter(name_mapped, self): continue param = params_dict[name_mapped] 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 if name is None: continue if is_pp_missing_parameter(name, self): continue if "router.gate." in name: name = name.replace("router.", "") 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 def get_spec_layer_idx_from_weight_name( config: PretrainedConfig, weight_name: str ) -> int | None: # HYV3MTP is enabled only when num_nextn_predict_layers is greater than 1 if ( hasattr(config, "num_nextn_predict_layers") and config.num_nextn_predict_layers > 0 ): layer_idx = config.num_hidden_layers for i in range(config.num_nextn_predict_layers): if weight_name.startswith(f"model.layers.{layer_idx + i}."): return layer_idx + i return None class HYV3ForCausalLM(nn.Module, SupportsPP, SupportsLoRA): packed_modules_mapping = { "qkv_proj": ["q_proj", "k_proj", "v_proj"], "gate_up_proj": ["gate_proj", "up_proj"], } 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 parallel_config = vllm_config.parallel_config eplb_config = parallel_config.eplb_config self.num_redundant_experts = eplb_config.num_redundant_experts self.model = HYV3Model( vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model") ) self.lm_head = ParallelLMHead( config.vocab_size, config.hidden_size, quant_config=quant_config, prefix=maybe_prefix(prefix, "lm_head"), ) 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 ) 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]: def _filter_weights(weights): for name, weight in weights: spec_layer = get_spec_layer_idx_from_weight_name(self.config, name) if spec_layer is not None: continue yield name, weight loader = AutoWeightsLoader( self, skip_prefixes=(["lm_head."] if self.config.tie_word_embeddings else None), ) return loader.load_weights(_filter_weights(weights)) def get_expert_mapping(self) -> list[tuple[str, str, int, str]]: return self.model.get_expert_mapping()