# coding=utf-8 # Copyright 2023 Antgroup 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. """SGLang BailingMoE model.""" import logging from typing import Iterable, List, Optional, Tuple, Union import torch import torch.nn.functional as F from torch import nn from transformers import PretrainedConfig from sglang.srt.distributed import ( get_pp_group, get_tensor_model_parallel_world_size, parallel_state, tensor_model_parallel_all_reduce, ) from sglang.srt.eplb.expert_distribution import get_global_expert_distribution_recorder from sglang.srt.eplb.expert_location import ModelConfigForExpertLocation from sglang.srt.eplb.expert_location_dispatch import ExpertLocationDispatchInfo from sglang.srt.layers.activation import SiluAndMul from sglang.srt.layers.communicator import ( LayerCommunicator, LayerScatterModes, enable_moe_dense_fully_dp, ) from sglang.srt.layers.dp_attention import ( get_attention_dp_size, get_attention_tp_rank, get_attention_tp_size, 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.moe import ( get_deepep_mode, get_moe_a2a_backend, should_use_flashinfer_cutlass_moe_fp4_allgather, ) from sglang.srt.layers.moe.ep_moe.layer import get_moe_impl_class from sglang.srt.layers.moe.fused_moe_triton.layer import FusedMoE from sglang.srt.layers.moe.token_dispatcher import DeepEPDispatcher from sglang.srt.layers.moe.topk import TopK from sglang.srt.layers.moe.utils import filter_moe_weight_param_global_expert 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 from sglang.srt.layers.vocab_parallel_embedding import ( ParallelLMHead, VocabParallelEmbedding, ) from sglang.srt.model_executor.cuda_graph_runner import get_is_capture_mode from sglang.srt.model_executor.forward_batch_info import ForwardBatch, PPProxyTensors from sglang.srt.model_loader.weight_utils import default_weight_loader from sglang.srt.models.utils import ( apply_qk_norm, create_fused_set_kv_buffer_arg, enable_fused_set_kv_buffer, ) from sglang.srt.server_args import get_global_server_args from sglang.srt.utils import add_prefix, is_cuda, is_non_idle_and_non_empty, make_layers LoraConfig = None logger = logging.getLogger(__name__) _is_cuda = is_cuda() class BailingMoEMLP(nn.Module): def __init__( self, intermediate_size: int, config: PretrainedConfig, quant_config: Optional[QuantizationConfig] = None, reduce_results: Optional[bool] = True, prefix: str = "", tp_rank: Optional[int] = None, tp_size: Optional[int] = None, ) -> None: super().__init__() self.tp_size = tp_size self.gate_up_proj = MergedColumnParallelLinear( config.hidden_size, [intermediate_size] * 2, bias=config.use_bias, quant_config=quant_config, prefix=add_prefix("gate_up_proj", prefix), tp_rank=tp_rank, tp_size=tp_size, ) self.down_proj = RowParallelLinear( intermediate_size, config.hidden_size, bias=config.use_bias, reduce_results=reduce_results, quant_config=quant_config, prefix=add_prefix("down_proj", prefix), tp_rank=tp_rank, tp_size=tp_size, ) if config.hidden_act != "silu": raise ValueError("Unsupported activation. Only silu is supported for now.") self.act_fn = SiluAndMul() def forward( self, hidden_states: torch.Tensor, forward_batch: Optional[ForwardBatch] = None, should_allreduce_fusion: bool = False, use_reduce_scatter: bool = False, ) -> torch.Tensor: if (self.tp_size == 1) and hidden_states.shape[0] == 0: return hidden_states gate_up, _ = self.gate_up_proj(hidden_states) hidden_states = self.act_fn(gate_up) hidden_states, _ = self.down_proj( hidden_states, skip_all_reduce=should_allreduce_fusion or use_reduce_scatter ) return hidden_states class BailingMoEGate(nn.Module): def __init__( self, config, params_dtype: Optional[torch.dtype] = None, prefix: str = "", ): super().__init__() if params_dtype is None: params_dtype = torch.get_default_dtype() self.params_dtype = params_dtype self.weight = nn.Parameter( torch.empty( (config.num_experts, config.hidden_size), dtype=self.params_dtype, ), ) if getattr(config, "moe_router_enable_expert_bias", False): self.expert_bias = nn.Parameter( torch.empty((config.num_experts,), dtype=torch.float32), ) else: self.expert_bias = None def forward(self, hidden_states): logits = F.linear(hidden_states.to(self.weight.dtype), self.weight, None).to( hidden_states.dtype ) return logits class BailingMoESparseMoeBlock(nn.Module): def __init__( self, layer_id: int, config: PretrainedConfig, quant_config: Optional[QuantizationConfig] = None, alt_stream: Optional[torch.cuda.Stream] = None, prefix: str = "", ): super().__init__() self.layer_id = layer_id self.alt_stream = alt_stream self.tp_size = get_tensor_model_parallel_world_size() self.top_k = config.num_experts_per_tok self.norm_topk_prob = config.norm_topk_prob self.hidden_size = config.hidden_size self.num_shared_experts = config.num_shared_experts self.routed_scaling_factor = getattr(config, "routed_scaling_factor", 1.0) self.score_function = getattr(config, "score_function", None) if config.hidden_act != "silu": raise ValueError( f"Unsupported activation: {config.hidden_act}. " "Only silu is supported for now." ) # Gate always runs at half / full precision for now. router_dtype = getattr(config, "router_dtype", None) if router_dtype is None: self.router_dtype = None elif router_dtype == "fp32": self.router_dtype = torch.float32 else: self.router_dtype = torch.bfloat16 # TODO global_server_args.ep_num_redundant_experts is used for eplb, not supported now assert get_global_server_args().ep_num_redundant_experts == 0 # check group topk self.num_expert_group = getattr(config, "n_group", 0) self.topk_group = getattr(config, "topk_group", 0) if self.num_expert_group > 0 or self.topk_group > 0: assert ( self.num_expert_group > 0 and 0 < self.topk_group <= self.num_expert_group ) self.use_grouped_topk = True else: self.num_expert_group = self.topk_group = None self.use_grouped_topk = False self.num_experts = ( config.num_experts + get_global_server_args().ep_num_redundant_experts ) self.gate = BailingMoEGate( config=config, params_dtype=self.router_dtype, prefix=add_prefix("gate", prefix), ) self.correction_bias = ( self.gate.expert_bias.data if self.gate.expert_bias is not None else None ) if self.score_function is not None: assert ( self.score_function == "softmax" and self.correction_bias is None ) or ( self.score_function == "sigmoid" and self.correction_bias is not None ), "score_function and correction_bias should be in 2 combination (softmax, None) or (sigmoid, not None)" self.topk = TopK( top_k=self.top_k, renormalize=self.norm_topk_prob, use_grouped_topk=self.use_grouped_topk, num_expert_group=self.num_expert_group, # num_fused_shared_experts=self.num_fused_shared_experts, topk_group=self.topk_group, correction_bias=self.correction_bias, routed_scaling_factor=self.routed_scaling_factor, ) self.experts = get_moe_impl_class(quant_config)( num_experts=self.num_experts, top_k=self.top_k, layer_id=self.layer_id, hidden_size=config.hidden_size, intermediate_size=config.moe_intermediate_size, quant_config=quant_config, routed_scaling_factor=self.routed_scaling_factor, prefix=add_prefix("experts", prefix), ) # shared expert if config.num_shared_experts is not None: if hasattr(config, "moe_shared_expert_intermediate_size"): intermediate_size = config.moe_shared_expert_intermediate_size else: intermediate_size = config.moe_intermediate_size intermediate_size *= config.num_shared_experts # disable tp for shared experts when enable deepep moe self.shared_experts = BailingMoEMLP( intermediate_size=intermediate_size, config=config, quant_config=quant_config, reduce_results=False, prefix=add_prefix("shared_experts", prefix), **( dict(tp_rank=0, tp_size=1) if get_moe_a2a_backend().is_deepep() else {} ), ) # dispatcher if get_moe_a2a_backend().is_deepep(): # TODO: we will support tp < ep in the future self.ep_size = get_tensor_model_parallel_world_size() self.deepep_dispatcher = DeepEPDispatcher( group=parallel_state.get_tp_group().device_group, router_topk=self.top_k, permute_fusion=True, num_experts=self.num_experts, num_local_experts=config.num_experts // self.tp_size, hidden_size=config.hidden_size, params_dtype=config.torch_dtype, deepep_mode=get_deepep_mode(), async_finish=True, # TODO return_recv_hook=True, ) def forward( self, hidden_states: torch.Tensor, forward_batch: Optional[ForwardBatch] = None, should_allreduce_fusion: bool = False, use_reduce_scatter: bool = False, ) -> torch.Tensor: if not get_moe_a2a_backend().is_deepep(): return self.forward_normal( hidden_states, should_allreduce_fusion, use_reduce_scatter, ) else: return self.forward_deepep(hidden_states, forward_batch) def get_moe_weights(self): return [ x.data for name, x in self.experts.named_parameters() if name not in ["correction_bias"] and filter_moe_weight_param_global_expert( name, x, self.experts.num_local_experts ) ] def _forward_shared_experts(self, hidden_states: torch.Tensor): shared_output = None if self.num_shared_experts > 0: shared_output = self.shared_experts(hidden_states) return shared_output def _forward_router_experts(self, hidden_states: torch.Tensor): # router_logits: (num_tokens, n_experts) router_logits = self.gate(hidden_states) topk_output = self.topk(hidden_states, router_logits) return self.experts(hidden_states, topk_output) def forward_normal_dual_stream( self, hidden_states: torch.Tensor, ) -> torch.Tensor: current_stream = torch.cuda.current_stream() self.alt_stream.wait_stream(current_stream) shared_output = self._forward_shared_experts(hidden_states.clone()) with torch.cuda.stream(self.alt_stream): router_output = self._forward_router_experts(hidden_states) current_stream.wait_stream(self.alt_stream) return router_output, shared_output def forward_normal( self, hidden_states: torch.Tensor, should_allreduce_fusion: bool = False, use_reduce_scatter: bool = False, ) -> torch.Tensor: num_tokens, hidden_size = hidden_states.shape hidden_states = hidden_states.view(-1, hidden_size) if ( self.alt_stream is not None and hidden_states.shape[0] > 0 and get_is_capture_mode() ): final_hidden_states, shared_output = self.forward_normal_dual_stream( hidden_states ) else: shared_output = self._forward_shared_experts(hidden_states) final_hidden_states = self._forward_router_experts(hidden_states) if self.num_shared_experts > 0: final_hidden_states = final_hidden_states + shared_output if ( self.tp_size > 1 and not should_allreduce_fusion and not use_reduce_scatter and not should_use_flashinfer_cutlass_moe_fp4_allgather() ): final_hidden_states = tensor_model_parallel_all_reduce(final_hidden_states) return final_hidden_states.view(num_tokens, hidden_size) def forward_deepep( self, hidden_states: torch.Tensor, forward_batch: ForwardBatch ) -> torch.Tensor: shared_output = None forward_mode = forward_batch.forward_mode if is_non_idle_and_non_empty(forward_mode, hidden_states): router_logits = self.gate(hidden_states) if self.num_shared_experts > 0: shared_output = self.shared_experts(hidden_states) topk_output = self.topk( hidden_states, router_logits, num_token_non_padded=forward_batch.num_token_non_padded, expert_location_dispatch_info=ExpertLocationDispatchInfo.init_new( layer_id=self.layer_id, ), ) else: topk_output = self.topk.empty_topk_output(hidden_states.device) final_hidden_states = self.experts( hidden_states=hidden_states, topk_output=topk_output, ) if shared_output is not None: final_hidden_states += shared_output return final_hidden_states class BailingMoEAttention(nn.Module): def __init__( self, config: PretrainedConfig, layer_id: int = 0, quant_config: Optional[QuantizationConfig] = None, reduce_results: bool = True, prefix: str = "", alt_stream: Optional[torch.cuda.Stream] = None, ): super().__init__() self.hidden_size = config.hidden_size self.total_num_heads = config.num_attention_heads self.total_kv_heads = config.num_key_value_heads self.dp_size = get_attention_dp_size() attn_tp_rank = get_attention_tp_rank() attn_tp_size = get_attention_tp_size() assert self.total_num_heads % attn_tp_size == 0 if self.total_kv_heads >= attn_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_kv_heads % attn_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 attn_tp_size % self.total_kv_heads == 0 assert self.total_num_heads >= self.total_kv_heads self.num_heads = self.total_num_heads // attn_tp_size self.head_dim = config.head_dim or (self.hidden_size // self.total_num_heads) self.q_size = self.head_dim * self.num_heads self.num_kv_heads = max(1, self.total_kv_heads // attn_tp_size) self.kv_size = max(1, self.num_kv_heads * self.head_dim) self.scale = self.head_dim**-0.5 self.use_qk_norm = getattr(config, "use_qk_norm", False) self.query_key_value = QKVParallelLinear( self.hidden_size, self.head_dim, self.total_num_heads, self.total_kv_heads, bias=(config.use_bias or config.use_qkv_bias), quant_config=quant_config, prefix=add_prefix("query_key_value", prefix), tp_rank=attn_tp_rank, tp_size=attn_tp_size, ) if self.use_qk_norm: self.query_layernorm = RMSNorm(self.head_dim, eps=config.rms_norm_eps) self.key_layernorm = RMSNorm(self.head_dim, eps=config.rms_norm_eps) self.dense = RowParallelLinear( self.total_num_heads * self.head_dim, self.hidden_size, bias=config.use_bias, quant_config=quant_config, reduce_results=reduce_results, prefix=add_prefix("dense", prefix), tp_rank=attn_tp_rank, tp_size=attn_tp_size, ) if hasattr(config, "partial_rotary_factor"): self.rotary_dim = int(self.head_dim * config.partial_rotary_factor) elif hasattr(config, "rotary_dim"): self.rotary_dim = config.rotary_dim else: self.rotary_dim = self.head_dim self.rotary_emb = get_rope( self.head_dim, rotary_dim=self.rotary_dim, max_position=config.max_position_embeddings, base=config.rope_theta, rope_scaling=config.rope_scaling, ) self.attn = RadixAttention( self.num_heads, self.head_dim, self.scale, num_kv_heads=self.num_kv_heads, layer_id=layer_id, prefix=add_prefix("attn", prefix), ) self.alt_stream = alt_stream def forward( self, positions: torch.Tensor, hidden_states: torch.Tensor, forward_batch: ForwardBatch, ) -> torch.Tensor: if hidden_states.shape[0] == 0: return hidden_states qkv, _ = self.query_key_value(hidden_states) q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1) if self.use_qk_norm: q, k = apply_qk_norm( q=q, k=k, q_norm=self.query_layernorm, k_norm=self.key_layernorm, head_dim=self.head_dim, alt_stream=self.alt_stream, ) q, k = self.rotary_emb( positions, q, k, fused_set_kv_buffer_arg=( create_fused_set_kv_buffer_arg( value=v, layer=self.attn, forward_batch=forward_batch, ) if enable_fused_set_kv_buffer(forward_batch) else None ), ) context_layer = self.attn( q, k, v, forward_batch, save_kv_cache=not enable_fused_set_kv_buffer(forward_batch), ) attn_output, _ = self.dense(context_layer) return attn_output class BailingMoEBlock(nn.Module): def __init__( self, config: PretrainedConfig, layer_id: int = 0, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", alt_stream: Optional[torch.cuda.Stream] = None, ): super().__init__() self.config = config hidden_size = config.hidden_size self.input_layernorm = RMSNorm(hidden_size, eps=config.rms_norm_eps) self.dp_size = get_attention_dp_size() self.attention = BailingMoEAttention( config, layer_id, quant_config, reduce_results=False, prefix=add_prefix("attention", prefix), alt_stream=alt_stream, ) self.layer_id = layer_id self.attn_tp_size = get_attention_tp_size() self.attn_tp_rank = get_attention_tp_rank() self.is_layer_sparse = self._is_layer_sparse( config, layer_id=layer_id, is_nextn=False ) is_previous_layer_sparse = self._is_layer_sparse( config, layer_id=layer_id - 1, is_nextn=False ) is_next_layer_sparse = self._is_layer_sparse( config, layer_id=layer_id + 1, is_nextn=False ) self.layer_scatter_modes = LayerScatterModes.init_new( layer_id=layer_id, num_layers=config.num_hidden_layers, is_layer_sparse=self.is_layer_sparse, is_previous_layer_sparse=is_previous_layer_sparse, is_next_layer_sparse=is_next_layer_sparse, ) self.is_last_layer = self.layer_id == config.num_hidden_layers - 1 if self.is_layer_sparse: self.mlp = BailingMoESparseMoeBlock( layer_id=layer_id, config=config, quant_config=quant_config, alt_stream=alt_stream, prefix=add_prefix("mlp", prefix), ) else: if enable_moe_dense_fully_dp(): mlp_tp_rank, mlp_tp_size = 0, 1 else: mlp_tp_rank, mlp_tp_size = None, None self.mlp = BailingMoEMLP( intermediate_size=config.intermediate_size, config=config, quant_config=quant_config, prefix=add_prefix("mlp", prefix), tp_rank=mlp_tp_rank, tp_size=mlp_tp_size, ) self.post_attention_layernorm = RMSNorm(hidden_size, eps=config.rms_norm_eps) self.layer_communicator = LayerCommunicator( layer_scatter_modes=self.layer_scatter_modes, input_layernorm=self.input_layernorm, post_attention_layernorm=self.post_attention_layernorm, allow_reduce_scatter=True, is_last_layer=(self.layer_id == self.config.num_hidden_layers - 1), ) def _is_layer_sparse( self, config: PretrainedConfig, layer_id: int, is_nextn: bool ) -> bool: return is_nextn or ( config.num_experts is not None and layer_id >= config.first_k_dense_replace ) def forward( self, positions: torch.Tensor, hidden_states: torch.Tensor, forward_batch: ForwardBatch, residual: Optional[torch.Tensor], captured_last_layer_outputs: Optional[List[torch.Tensor]] = None, ) -> torch.Tensor: hidden_states, residual = ( self.layer_communicator.prepare_attn_and_capture_last_layer_outputs( hidden_states, residual, forward_batch, captured_last_layer_outputs=captured_last_layer_outputs, ) ) if hidden_states.shape[0] != 0: hidden_states = self.attention( positions=positions, hidden_states=hidden_states, forward_batch=forward_batch, ) hidden_states, residual = self.layer_communicator.prepare_mlp( hidden_states=hidden_states, residual=residual, forward_batch=forward_batch, ) should_allreduce_fusion = ( self.layer_communicator.should_fuse_mlp_allreduce_with_next_layer( forward_batch ) ) # For DP with padding, reduce scatter can be used instead of all-reduce. use_reduce_scatter = self.layer_communicator.should_use_reduce_scatter( forward_batch ) hidden_states = self.mlp( hidden_states, forward_batch, should_allreduce_fusion, use_reduce_scatter ) if should_allreduce_fusion: hidden_states._sglang_needs_allreduce_fusion = True else: hidden_states, residual = self.layer_communicator.postprocess_layer( hidden_states, residual, forward_batch ) return hidden_states, residual class BailingMoEModel(nn.Module): def __init__( self, config: PretrainedConfig, quant_config: Optional[QuantizationConfig] = None, alt_stream: Optional[torch.cuda.Stream] = None, prefix: str = "", ): super().__init__() self.pp_group = get_pp_group() self.config = config self.vocab_size = config.vocab_size self.embed_dim = config.hidden_size if self.pp_group.is_first_rank: self.word_embeddings = VocabParallelEmbedding( self.vocab_size, self.embed_dim, quant_config=quant_config, prefix=add_prefix("word_embeddings", prefix), use_attn_tp_group=is_dp_attention_enabled(), ) else: self.word_embeddings = PPMissingLayer() self.embedding_dropout = torch.nn.Dropout(config.embedding_dropout) self.layers, self.start_layer, self.end_layer = make_layers( config.num_hidden_layers, lambda idx, prefix: BailingMoEBlock( 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(self.embed_dim, eps=config.rms_norm_eps) else: self.norm = PPMissingLayer(return_tuple=True) self.layers_to_capture = [] 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.word_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"] aux_hidden_states = [] for i in range(self.start_layer, self.end_layer): with get_global_expert_distribution_recorder().with_current_layer(i): if i in self.layers_to_capture: aux_hidden_states.append( hidden_states if residual is None else hidden_states + residual ) layer = self.layers[i] hidden_states, residual = layer( positions, hidden_states, forward_batch, residual, captured_last_layer_outputs=( aux_hidden_states if getattr(layer, "_is_layer_to_capture", False) else None ), ) if not self.pp_group.is_last_rank: return PPProxyTensors( { "hidden_states": hidden_states, "residual": residual, } ) else: if not forward_batch.forward_mode.is_idle(): 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 class BailingMoEForCausalLM(nn.Module): def __init__( self, config: PretrainedConfig, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", ): super().__init__() self.pp_group = get_pp_group() self.config = config self.quant_config = quant_config alt_stream = torch.cuda.Stream() if _is_cuda else None self.model = BailingMoEModel( config, quant_config, alt_stream=alt_stream, prefix=add_prefix("model", ""), ) # tie_word_embeddings为true，复用tie_word_embeddings，反之是独立的 if config.tie_word_embeddings: self.lm_head = self.model.word_embeddings else: # TODO something wrong with ParallelLMHead with DP attention enabled 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.capture_aux_hidden_states = False @property def start_layer(self): return self.model.start_layer @property def end_layer(self): return self.model.end_layer def get_embed_and_head(self): """Used by the eagle_worker.""" return self.model.word_embeddings.weight, self.lm_head.weight def set_embed_and_head(self, embed, head): """Used by the eagle_worker.""" del self.model.word_embeddings.weight del self.lm_head.weight self.model.word_embeddings.weight = embed self.lm_head.weight = head torch.cuda.empty_cache() torch.cuda.synchronize() @torch.no_grad() def forward( self, input_ids: torch.Tensor, positions: torch.Tensor, forward_batch: ForwardBatch, input_embeds: torch.Tensor = None, 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: return self.logits_processor( input_ids, hidden_states, self.lm_head, forward_batch, aux_hidden_states ) else: return hidden_states def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]], is_nextn=False): if is_nextn: if hasattr(self.config, "num_nextn_predict_layers"): num_nextn_layers = self.config.num_nextn_predict_layers assert num_nextn_layers == 1, "Only 1 nextn layer is supported" # compatible with old design nextn_layer_id = ( 0 if self.config.num_hidden_layers == 1 else self.config.num_hidden_layers ) else: raise ValueError("num_nextn_predict_layers is not in the config") stacked_params_mapping = [ # (param_name, shard_name, shard_id) ("gate_up_proj", "gate_proj", 0), ("gate_up_proj", "up_proj", 1), ] if is_nextn: nextn_layer_prefix = f"model.layers.{nextn_layer_id}" nextn_spec_weight_names = [ "final_layernorm", "eh_proj", "enorm", "hnorm", ] # Params for weights, fp8 weight scales, fp8 activation scales # (param_name, weight_name, expert_id, shard_id) expert_params_mapping = FusedMoE.make_expert_params_mapping( ckpt_gate_proj_name="gate_proj", ckpt_down_proj_name="down_proj", ckpt_up_proj_name="up_proj", num_experts=self.config.num_experts, ) params_dict = dict(self.named_parameters()) for name, loaded_weight in weights: if ( ("v_head" in name) or ("inv_freq" in name) or (self.config.tie_word_embeddings and "lm_head" in name) ): continue if ( hasattr(self.config, "norm_head") and self.config.norm_head and "lm_head.weight" in name ): import torch.nn.functional as F loaded_weight = F.normalize(loaded_weight, dim=0, p=2, eps=1e-7) if is_nextn: if not name.startswith(nextn_layer_prefix): continue # Use shared head and embed weights from target model if "shared_head.head" in name or "embed_tokens" in name: continue is_decoder = True # For nextn specific weights for weight_name in nextn_spec_weight_names: if weight_name in name: name = name.replace(nextn_layer_prefix, "model") is_decoder = False break # For decoder layer weights if is_decoder: name = name.replace(nextn_layer_prefix, "model.decoder") for param_name, weight_name, shard_id in stacked_params_mapping: if weight_name not in name: continue # We have mlp.experts[0].gate_proj in the checkpoint. # Since we handle the experts below in expert_params_mapping, # we need to skip here BEFORE we update the name, otherwise # name will be updated to mlp.experts[0].gate_up_proj, which # will then be updated below in expert_params_mapping # for mlp.experts[0].gate_gate_up_proj, which breaks load. 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 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: for mapping in expert_params_mapping: param_name, weight_name, expert_id, shard_id = mapping if weight_name not in name: continue name = name.replace(weight_name, param_name) if name not in params_dict: continue param = params_dict[name] weight_loader = param.weight_loader weight_loader( param, loaded_weight, name, shard_id=shard_id, expert_id=expert_id, ) break else: # 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 = getattr( param, "weight_loader", default_weight_loader ) weight_loader(param, loaded_weight) if not is_nextn: self.routed_experts_weights_of_layer = { layer_id: layer.mlp.get_moe_weights() for layer_id, layer in enumerate(self.model.layers) if not isinstance(layer, PPMissingLayer) and isinstance(layer.mlp, BailingMoESparseMoeBlock) } @classmethod def get_model_config_for_expert_location(cls, config): num_groups = getattr(config, "n_group", 0) return ModelConfigForExpertLocation( num_layers=config.num_hidden_layers, num_logical_experts=config.num_experts, num_groups=None if num_groups == 0 else num_groups, ) def set_eagle3_layers_to_capture(self, layer_ids: Optional[List[int]] = None): if not self.pp_group.is_last_rank: return self.capture_aux_hidden_states = True if layer_ids is None: num_layers = self.config.num_hidden_layers self.model.layers_to_capture = [2, num_layers // 2, num_layers - 3] else: # Add +1 because in SGLang, for the i-th layer, the auxiliary hidden state # corresponds to the output of layer (i - 1). self.model.layers_to_capture = [val + 1 for val in layer_ids] class BailingMoeForCausalLM(BailingMoEForCausalLM): pass class BailingMoeV2ForCausalLM(BailingMoEForCausalLM): pass EntryClass = [BailingMoEForCausalLM, BailingMoeForCausalLM, BailingMoeV2ForCausalLM]