# # Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved. # # 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. # This file is a part of the vllm-ascend project. # # Todo: Once https://github.com/vllm-project/vllm/issues/22246 is merged in vllm. Remove this adaptor. import json from typing import Any import torch import torch.distributed as dist from vllm.logger import logger from vllm_ascend.eplb.adaptor.abstract_adaptor import EplbAdaptor class VllmEplbAdaptor(EplbAdaptor): def __init__(self, model, **args): super().__init__(**args) self.model = model self.rank_id = dist.get_rank() self.world_size = dist.get_world_size() self.param_dict = dict(self.model.named_parameters()) if self.model.config.model_type == "qwen3_moe": self.num_dense_layers = 0 self.global_expert_num = self.model.config.num_experts else: self.num_dense_layers = self.model.config.first_k_dense_replace self.global_expert_num = self.model.config.n_routed_experts self.num_moe_layers = self.model.config.num_hidden_layers - self.num_dense_layers for i in range(self.num_dense_layers, self.model.config.num_hidden_layers): self.param_dict["model.layers." + str(i) + ".mlp.experts." + "w13_weight_list"] = \ self.model.model.layers[i].mlp.experts.w13_weight_list self.param_dict["model.layers." + str(i) + ".mlp.experts." + "w2_weight_list"] = \ self.model.model.layers[i].mlp.experts.w2_weight_list self.param_dict["model.layers." + str(i) + ".mlp.experts." + "w13_weight_scale_fp32_list"] = \ self.model.model.layers[i].mlp.experts.w13_weight_scale_fp32_list self.param_dict["model.layers." + str(i) + ".mlp.experts." + "w2_weight_scale_list"] = \ self.model.model.layers[i].mlp.experts.w2_weight_scale_list self.param_dict["model.layers." + str(i) + ".mlp.experts." + "w2_weight_scale_fp32_list"] = \ self.model.model.layers[i].mlp.experts.w2_weight_scale_fp32_list # TODO: init self.expert_weight_names depending on different model types, only deepseek v3 w8a8 and qwen3-moe is supported here if self.model.quant_config is not None: self.expert_weight_names = [ "w13_weight_list", "w2_weight_list", "w13_weight_scale_fp32_list", "w13_weight_offset", "w2_weight_scale_list", "w2_weight_offset", "w2_weight_scale_fp32_list" ] else: self.expert_weight_names = ["w13_weight", "w2_weight"] self.expert_map_per_layer = dict( ) # reference to expert map on device for expert map update self.expert_map_per_layer_cpu = dict( ) # copy of expert map on CPU to avoid device synchronize frequently for layer_idx in range(self.num_moe_layers): self.expert_map_per_layer[self.num_dense_layers + layer_idx] = \ self.model.get_expert_map(self.num_dense_layers + layer_idx) # TODO: here we set number of buffer tensor equal to number of expert in each laryer, which can be improved num_buffer_tensor = torch.where( self.expert_map_per_layer[self.num_dense_layers] != -1)[0].numel() self.buffer_tensor_list: list[list[Any]] = [ [] for _ in range(num_buffer_tensor) ] self.init_buffer_tensor(num_buffer_tensor) self.expert_param_per_layer = dict() self.init_expert_param_per_layer() self.log2phy_map_per_layer = dict() for layer_idx in range(self.num_moe_layers): self.log2phy_map_per_layer[self.num_dense_layers + layer_idx] = \ self.model.get_log2phy_map(self.num_dense_layers + layer_idx) self.all_topk_ids = [] def init_buffer_tensor(self, num_buffer_tensor): for buffer_id in range(num_buffer_tensor): for name in self.expert_weight_names: complete_name = "model.layers." + str( self.num_dense_layers) + ".mlp.experts." + name if name in [ "w13_weight_list", "w2_weight_list", "w13_weight_scale_fp32_list", "w2_weight_scale_list", "w2_weight_scale_fp32_list" ]: expert_tensor = self.param_dict[complete_name][0] expert_tensor = expert_tensor.clone() else: expert_tensor = self.param_dict[complete_name][0].data[0] buffer_tensor = torch.empty_like(expert_tensor) self.buffer_tensor_list[buffer_id].append(buffer_tensor) def init_expert_param_per_layer(self): key = f"model.layers.{self.num_dense_layers}.mlp.experts.{self.expert_weight_names[0]}" num_local_expert = len(self.param_dict[key]) for moe_layer_id in range(self.num_moe_layers): layer_idx = self.num_dense_layers + moe_layer_id self.expert_param_per_layer[layer_idx] = list() for local_expert_id in range(num_local_expert): per_expert_param = list() for name in self.expert_weight_names: if name in [ "w13_weight_list", "w2_weight_list", "w13_weight_scale_fp32_list", "w2_weight_scale_list", "w2_weight_scale_fp32_list" ]: per_expert_param.append( self.param_dict["model.layers." + str(layer_idx) + ".mlp.experts." + name][local_expert_id]) else: per_expert_param.append( self.param_dict["model.layers." + str(layer_idx) + ".mlp.experts." + name][0].data[local_expert_id]) self.expert_param_per_layer[layer_idx].append(per_expert_param) def get_rank_expert_workload(self) -> torch.Tensor: self.moe_load = self.model.get_all_moe_loads() return self.moe_load def _export_tensor_to_file(self, expert_maps, expert_map_record_path: str): if self.rank_id == 0: num_local_experts = expert_maps.max() + 1 expert_maps_list = expert_maps.tolist() record: dict[str, Any] = { "moe_layer_count": len(expert_maps_list), "layer_list": [] } for layer_idx, layer_data in enumerate(expert_maps_list): layer_record: dict[str, Any] = { "layer_id": layer_idx, "device_count": len(layer_data), "device_list": [] } for device_idx, experts in enumerate(layer_data): placement = [ experts.index(i) for i in range(num_local_experts) ] device_record = { "device_id": device_idx, "device_expert": placement } layer_record["device_list"].append(device_record) record["layer_list"].append(layer_record) with open(expert_map_record_path, "w") as f: json.dump(record, f, indent=4) def do_update_expert_map(self, layer_id, updated_expert_map): self.expert_map_per_layer[layer_id].copy_(updated_expert_map) self.expert_map_per_layer_cpu[layer_id].copy_(updated_expert_map) def do_update_expert_weight(self, layer_id, local_expert_to_replace, buffer_tensor_id): for expert_tensor, buffer_tensor in zip( self.expert_param_per_layer[layer_id][local_expert_to_replace], self.buffer_tensor_list[buffer_tensor_id]): expert_tensor.copy_(buffer_tensor) logger.debug(f"Expert tensor shape is :{expert_tensor.shape}") def do_update_log2phy_map(self, layer_id, updated_log2phy_map): if self.log2phy_map_per_layer[layer_id] is not None: self.log2phy_map_per_layer[layer_id].copy_(updated_log2phy_map) def get_global_expert_map(self): all_layer_global_expert_map = [] for layer_id in range(self.num_moe_layers): map_cpu = self.model.model.layers[ layer_id].mlp.experts.global_expert_map.cpu() all_layer_global_expert_map.append(map_cpu) self.expert_map_per_layer_cpu[self.num_dense_layers + layer_id] = map_cpu[self.rank_id] return torch.stack(all_layer_global_expert_map)