# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project # Copyright 2025 The ZhipuAI 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 GLM-4.7-Flash model compatible with HuggingFace weights.""" import typing from collections.abc import Callable, Iterable from itertools import islice from typing import TYPE_CHECKING import torch from torch import nn if TYPE_CHECKING: from transformers.models.glm4_moe_lite import Glm4MoeLiteConfig from vllm._aiter_ops import rocm_aiter_ops from vllm.compilation.decorators import support_torch_compile from vllm.config import VllmConfig from vllm.distributed import ( get_pp_group, ) from vllm.logger import init_logger from vllm.model_executor.layers.fused_moe import SharedFusedMoE from vllm.model_executor.layers.layernorm import RMSNorm from vllm.model_executor.layers.logits_processor import LogitsProcessor 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.model_executor.models.deepseek_v2 import ( DeepseekV2Attention, DeepseekV2MLAAttention, ) from vllm.model_executor.models.glm4_moe import ( Glm4MixtureOfExperts, Glm4MoE, Glm4MoeMLP, ) from vllm.platforms import current_platform from vllm.sequence import IntermediateTensors 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 Glm4MoeLiteMLP(Glm4MoeMLP): pass class Glm4MoeLite(Glm4MoE): pass class Glm4LiteMixtureOfExperts(Glm4MixtureOfExperts): pass class Glm4MoeLiteAttention(DeepseekV2Attention): pass class Glm4MoeLiteMLAAttention(DeepseekV2MLAAttention): pass class Glm4MoeLiteDecoderLayer(nn.Module): def __init__( self, vllm_config: VllmConfig, prefix: str, config: "Glm4MoeLiteConfig | None" = None, topk_indices_buffer: torch.Tensor | None = None, ) -> None: super().__init__() if config is None: config = vllm_config.model_config.hf_config model_config = vllm_config.model_config cache_config = vllm_config.cache_config quant_config = vllm_config.quant_config self.hidden_size = config.hidden_size max_position_embeddings = getattr(config, "max_position_embeddings", 8192) moe_layer_freq = getattr(config, "moe_layer_freq", 1) # DecoderLayers are created with `make_layers` which passes the prefix # with the layer's index. layer_idx = int(prefix.split(sep=".")[-1]) self.layer_idx = layer_idx # verify MLA attention specific fields qk_nope_head_dim = getattr(config, "qk_nope_head_dim", 0) qk_rope_head_dim = getattr(config, "qk_rope_head_dim", 0) v_head_dim = getattr(config, "v_head_dim", 0) kv_lora_rank = getattr(config, "kv_lora_rank", 0) if model_config.use_mla: attn_cls = Glm4MoeLiteMLAAttention else: attn_cls = Glm4MoeLiteAttention self.self_attn = attn_cls( vllm_config=vllm_config, config=config, hidden_size=self.hidden_size, num_heads=config.num_attention_heads, qk_nope_head_dim=qk_nope_head_dim, qk_rope_head_dim=qk_rope_head_dim, v_head_dim=v_head_dim, q_lora_rank=config.q_lora_rank if hasattr(config, "q_lora_rank") else None, kv_lora_rank=kv_lora_rank, max_position_embeddings=max_position_embeddings, cache_config=cache_config, quant_config=quant_config, prefix=f"{prefix}.self_attn", topk_indices_buffer=topk_indices_buffer, ) if ( config.n_routed_experts is not None and layer_idx >= config.first_k_dense_replace and layer_idx % moe_layer_freq == 0 ): self.mlp = Glm4MoeLite( config=config, quant_config=quant_config, prefix=f"{prefix}.mlp", ) else: self.mlp = Glm4MoeLiteMLP( hidden_size=config.hidden_size, intermediate_size=config.intermediate_size, hidden_act=config.hidden_act, 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 ) self.routed_scaling_factor = getattr(config, "routed_scaling_factor", 1.0) def forward( self, positions: torch.Tensor, hidden_states: torch.Tensor, residual: torch.Tensor | None, llama_4_scaling: torch.Tensor | None = None, ) -> torch.Tensor: # Self Attention if residual is None: residual = hidden_states.clone() hidden_states = self.input_layernorm(hidden_states) else: hidden_states, residual = self.input_layernorm(hidden_states, residual) attn_kwargs = { "positions": positions, "hidden_states": hidden_states, } attn_kwargs["llama_4_scaling"] = llama_4_scaling hidden_states = self.self_attn(**attn_kwargs) hidden_states, residual = self.post_attention_layernorm(hidden_states, residual) hidden_states = self.mlp(hidden_states) return hidden_states, residual @support_torch_compile( dynamic_arg_dims={ "input_ids": 0, "positions": -1, "intermediate_tensors": 0, "inputs_embeds": 0, } ) class Glm4MoeLiteModel(nn.Module): 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.device = current_platform.device_type self.vocab_size = config.vocab_size self.is_v32 = hasattr(config, "index_topk") if self.is_v32: topk_tokens = config.index_topk topk_indices_buffer = torch.empty( vllm_config.scheduler_config.max_num_batched_tokens, topk_tokens, dtype=torch.int32, device=self.device, ) else: topk_indices_buffer = None 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: Glm4MoeLiteDecoderLayer( vllm_config=vllm_config, config=config, prefix=prefix, topk_indices_buffer=topk_indices_buffer, ), 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 make_empty_intermediate_tensors( self, batch_size: int, dtype: torch.dtype, device: torch.device ) -> IntermediateTensors: return IntermediateTensors( { "hidden_states": torch.zeros( (batch_size, self.config.hidden_size), dtype=dtype, device=device ), "residual": torch.zeros( (batch_size, self.config.hidden_size), dtype=dtype, device=device ), } ) 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.n_routed_experts, ) def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]: rocm_aiter_moe_shared_expert_enabled = ( rocm_aiter_ops.is_fusion_moe_shared_experts_enabled() ) stacked_params_mapping = [ # (param_name, shard_name, shard_id) ("gate_up_proj", "gate_proj", 0), ("gate_up_proj", "up_proj", 1), ] mla_params_mapping = [ ("fused_qkv_a_proj", "q_a_proj", 0), ("fused_qkv_a_proj", "kv_a_proj_with_mqa", 1), ] stacked_params_mapping.extend(mla_params_mapping) # Params for weights, fp8 weight scales, fp8 activation scales # (param_name, weight_name, expert_id, shard_id) expert_params_mapping = 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.n_routed_experts + ( self.config.n_shared_experts if rocm_aiter_moe_shared_expert_enabled else 0 ), ) params_dict = dict(self.named_parameters()) loaded_params: set[str] = set() for name, loaded_weight in weights: if "rotary_emb.inv_freq" in name: continue spec_layer = get_spec_layer_idx_from_weight_name(self.config, name) if spec_layer is not None: continue # skip spec decode layers for main model is_fusion_moe_shared_experts_layer = ( rocm_aiter_moe_shared_expert_enabled and ("mlp.shared_experts" in name) ) 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 # 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) and name not in params_dict: continue if is_fusion_moe_shared_experts_layer: continue name_mapped = name.replace(weight_name, param_name) # QKV fusion is optional, fall back to normal # weight loading if it's not enabled # if go with fusion option, then update name if ( param_name == "fused_qkv_a_proj" ) and name_mapped not in params_dict: continue else: name = name_mapped # Skip loading extra bias for GPTQ models. if name.endswith(".bias") and name not in params_dict: continue 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 # Special handling: when AITER fusion_shared_experts is enabled, # checkpoints may provide a single widened shared_experts tensor # without explicit expert indices # (e.g. ...mlp.shared_experts.gate_proj.weight). # For models with multiple shared experts, split that tensor # evenly into per-shared-expert slices and load them into # appended expert slots mlp.experts.{n_routed_experts + j}.* # accordingly. num_chunks = 1 if is_fusion_moe_shared_experts_layer: num_chunks = getattr(self.config, "n_shared_experts", 1) or 1 # Determine split axis based on op type # gate/up: ColumnParallel → split along dim 0 # down: RowParallel → split along dim 1 split_dim = 1 if "down_proj.weight" in name else 0 total = loaded_weight.shape[split_dim] assert total % num_chunks == 0, ( f"Shared expert weight dim {total} " f"not divisible by num_chunks {num_chunks}" ) chunk_size = total // num_chunks for j in range(num_chunks): chunk_name = name weight_to_load = loaded_weight if is_fusion_moe_shared_experts_layer: if split_dim == 0: weight_to_load = loaded_weight[ j * chunk_size : (j + 1) * chunk_size, : ] else: weight_to_load = loaded_weight[ :, j * chunk_size : (j + 1) * chunk_size ] # Synthesize an expert-style name so expert mapping # can route it chunk_name = name.replace( "mlp.shared_experts", f"mlp.experts.{self.config.n_routed_experts + j}", ) # Use expert_params_mapping to locate the destination # param and delegate to its expert-aware weight_loader # with expert_id. for mapping in expert_params_mapping: param_name, weight_name, expert_id, shard_id = mapping if weight_name not in chunk_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 = chunk_name.replace(weight_name, param_name) if is_pp_missing_parameter(name_mapped, self): 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, weight_to_load, name_mapped, shard_id=shard_id, expert_id=expert_id, return_success=True, ) if success: if not is_fusion_moe_shared_experts_layer: name = name_mapped else: loaded_params.add(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") and name not in params_dict: continue # Remapping the name of FP8 kv-scale. name = maybe_remap_kv_scale_name(name, params_dict) if name is None: continue if is_pp_missing_parameter(name, self): continue param = params_dict[name] weight_loader = getattr( param, "weight_loader", default_weight_loader ) weight_loader(param, loaded_weight) if not is_fusion_moe_shared_experts_layer: loaded_params.add(name) return loaded_params class Glm4MoeLiteForCausalLM( nn.Module, SupportsPP, SupportsLoRA, Glm4LiteMixtureOfExperts ): packed_modules_mapping = { "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 qk_nope_head_dim = getattr(config, "qk_nope_head_dim", 0) qk_rope_head_dim = getattr(config, "qk_rope_head_dim", 0) self.use_mha = config.model_type == "deepseek" or all( dim == 0 for dim in (qk_nope_head_dim, qk_rope_head_dim) ) if self.use_mha: self.packed_modules_mapping["qkv_proj"] = ["q_proj", "k_proj", "v_proj"] # `packed_modules_mapping` needs to be modified before # initializing DeepseekV2Model, as it is passed inplace to # quantization config init and may be used to select the # quant_method for relevant layers during initialization. self.fuse_qkv_a_proj = ( hasattr(config, "q_lora_rank") and config.q_lora_rank is not None ) if self.fuse_qkv_a_proj: self.packed_modules_mapping["fused_qkv_a_proj"] = [ "q_a_proj", "kv_a_proj_with_mqa", ] self.model = Glm4MoeLiteModel( 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() self.logits_processor = LogitsProcessor(config.vocab_size) self.make_empty_intermediate_tensors = ( self.model.make_empty_intermediate_tensors ) # Set MoE hyperparameters self.num_moe_layers = ( self.config.num_hidden_layers - self.config.first_k_dense_replace ) self.set_moe_parameters() def set_moe_parameters(self): self.expert_weights = [] self.num_expert_groups = getattr(self.config, "n_group", 1) self.moe_layers = [] self.moe_mlp_layers = [] example_moe = None for layer in self.model.layers: if isinstance(layer, PPMissingLayer): continue assert isinstance(layer, Glm4MoeLiteDecoderLayer) if isinstance(layer.mlp, Glm4MoeLite): # Pick last one layer since the first ones may be dense layers. example_moe = layer.mlp self.moe_mlp_layers.append(layer.mlp) self.moe_layers.append(layer.mlp.experts) self.extract_moe_parameters(example_moe) 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 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( ckpt_gate_proj_name="gate_proj", ckpt_down_proj_name="down_proj", ckpt_up_proj_name="up_proj", num_experts=self.config.n_routed_experts, num_redundant_experts=0, ) def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]: loader = AutoWeightsLoader(self) return loader.load_weights(weights) def get_spec_layer_idx_from_weight_name( config: "Glm4MoeLiteConfig", weight_name: str ) -> int | None: 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 f"layers.{layer_idx + i}." in weight_name: return layer_idx + i return None