# Copyright 2024 Bytedance Ltd. and/or its affiliates # # 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. from msgspec import field from packaging import version as vs from vllm.lora.models import LoRAModel from vllm.lora.request import LoRARequest from vllm.lora.utils import get_adapter_absolute_path from vllm.lora.worker_manager import LRUCacheWorkerLoRAManager from verl.third_party.vllm import get_version # To support different vLLM versions, we add the model into SUPPORTED_MOE_MODELS separately to avoid triggering # unsupported issues. SUPPORTED_MOE_MODELS = [] try: from vllm.model_executor.models.deepseek_v2 import DeepseekV2ForCausalLM, DeepseekV3ForCausalLM SUPPORTED_MOE_MODELS.append(DeepseekV2ForCausalLM) SUPPORTED_MOE_MODELS.append(DeepseekV3ForCausalLM) except ImportError: pass try: from vllm.model_executor.models.mixtral import MixtralForCausalLM SUPPORTED_MOE_MODELS.append(MixtralForCausalLM) except ImportError: pass try: from vllm.model_executor.models.qwen2_moe import Qwen2MoeForCausalLM SUPPORTED_MOE_MODELS.append(Qwen2MoeForCausalLM) except ImportError: pass try: from vllm.model_executor.models.qwen3_moe import Qwen3MoeForCausalLM SUPPORTED_MOE_MODELS.append(Qwen3MoeForCausalLM) except ImportError: pass try: from vllm.model_executor.models.kimi_vl import KimiVLForConditionalGeneration SUPPORTED_MOE_MODELS.append(KimiVLForConditionalGeneration) except ImportError: pass def patch_vllm_moe_model_weight_loader(model): # this is a work around to load the weight of vllm fused moe model # it is from a bug from vllm 0.8.2 # all the weights are supposed to have a weight_loader, but the moe weights # do not have a weight_loader, so we need to patch it # (True, 'model.embed_tokens.weight') # (True, 'model.layers.0.self_attn.qkv_proj.weight') # (True, 'model.layers.0.self_attn.qkv_proj.bias') # (True, 'model.layers.0.self_attn.o_proj.weight') # (True, 'model.layers.0.mlp.gate.weight') # (True, 'model.layers.0.mlp.shared_expert.gate_up_proj.weight') # (True, 'model.layers.0.mlp.shared_expert.down_proj.weight') # (False, 'model.layers.0.mlp.shared_expert_gate.weight') use default # (False, 'model.layers.0.input_layernorm.weight') use default # (False, 'model.layers.0.post_attention_layernorm.weight') use default # (False, 'model.layers.0.mlp.experts.w13_weight') use mlp.experts.weight_loader # (False, 'model.layers.0.mlp.experts.w2_weight') use mlp.experts.weight_loader # Define MLP attribute mapping for different model types MLP_ATTR_MAPPING = { MixtralForCausalLM: "block_sparse_moe", } DEFAULT_MLP_ATTR = "mlp" if not isinstance(model, tuple(SUPPORTED_MOE_MODELS)): return model = getattr(model, "model", None) or getattr(model, "language_model", None) if model is None: raise ValueError("The provided model does not have a valid 'model' or 'language_model' attribute.") for layer in model.layers: mlp_attr = MLP_ATTR_MAPPING.get(type(model), DEFAULT_MLP_ATTR) mlp = getattr(layer, mlp_attr) param_dict = dict(mlp.named_parameters()) for name, param in param_dict.items(): if "w13_weight" in name or "w2_weight" in name: param.weight_loader = mlp.experts.weight_loader class TensorLoRARequest(LoRARequest): peft_config: dict = field(default=None) lora_tensors: dict = field(default=None) class VLLMHijack: @staticmethod def hijack(): def hijack__load_adapter(self, lora_request: TensorLoRARequest) -> LoRAModel: """ based on vllm.lora.worker_manager.WorkerLoRAManager._load_adapter, support load adapter with lora tensors Reason: VLLM does not support adding LoRA from tensors directly. It only supports adding LoRA via file paths. To synchronize the LoRA tensors of the actor model, we need to find a workaround to enable VLLM to load memory-based LoRA tensors. """ try: supported_lora_modules = self._adapter_manager.supported_lora_modules packed_modules_mapping = self._adapter_manager.packed_modules_mapping expected_lora_modules: list[str] = [] for module in supported_lora_modules: if module in packed_modules_mapping: expected_lora_modules.extend(packed_modules_mapping[module]) else: expected_lora_modules.append(module) expected_lora_modules = list(set(expected_lora_modules)) lora_tensors = None from vllm.lora.peft_helper import PEFTHelper if isinstance(lora_request, TensorLoRARequest): peft_config = lora_request.peft_config lora_tensors = lora_request.lora_tensors peft_helper = PEFTHelper.from_dict(peft_config) else: lora_path = get_adapter_absolute_path(lora_request.lora_path) peft_helper = PEFTHelper.from_local_dir(lora_path, self.max_position_embeddings) # Validates the LoRA configuration against requirements before # loading weights, throwing an exception if validation fails. peft_helper.validate_legal(self.lora_config) # For some models like Qwen2VL, we need to use hf_to_vllm_mapper # to ensure correct loading of lora weights. model = self._adapter_manager.model hf_to_vllm_mapper = None if hasattr(model, "hf_to_vllm_mapper") and model.hf_to_vllm_mapper is not None: hf_to_vllm_mapper = model.hf_to_vllm_mapper if isinstance(lora_request, TensorLoRARequest): lora = self._lora_model_cls.from_lora_tensors( lora_model_id=lora_request.lora_int_id, tensors=lora_tensors, peft_helper=peft_helper, device="cpu", dtype=self.lora_config.lora_dtype, embeddings=None, target_embedding_padding=self.vocab_size + self.lora_config.lora_extra_vocab_size, embedding_modules=self.embedding_modules, embedding_padding_modules=self.embedding_padding_modules, weights_mapper=hf_to_vllm_mapper, ) else: lora = self._lora_model_cls.from_local_checkpoint( lora_path, expected_lora_modules, peft_helper=peft_helper, lora_model_id=lora_request.lora_int_id, device="cpu", dtype=self.lora_config.lora_dtype, target_embedding_padding=self.vocab_size + self.lora_config.lora_extra_vocab_size, embedding_modules=self.embedding_modules, embedding_padding_modules=self.embedding_padding_modules, weights_mapper=hf_to_vllm_mapper, ) except Exception as e: raise e if lora.extra_vocab_size > self.lora_config.lora_extra_vocab_size: raise ValueError( f"LoRA added vocab size {lora.extra_vocab_size} is greater than lora_extra_vocab_size " f"{self.lora_config.lora_extra_vocab_size}." ) return lora def do_hijack(target_cls, target_method_name, hooking_method): setattr(target_cls, target_method_name, hooking_method) do_hijack(LRUCacheWorkerLoRAManager, "_load_adapter", hijack__load_adapter) def is_version_ge(pkg: str = "vllm", minver: str = "0.7.3"): """check if the package version is greater than or equal to the minimum version""" return vs.parse(get_version(pkg)) >= vs.parse(minver)