ecf2fa482e
### What this PR does / why we need it?
The initialization method of expert_map used by the eplb module is
different from that used by the fused_moe module. This PR deletes the
expert_map initialization method used by the eplb module to make the
initialization methods consistent.
#### before bugfix
self._expert_map=tensor([64, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
-1, -1, -1, -1, -1, -1, 1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 1, 2,
3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22,
23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,
41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58,
59, 60, 61,62, 63], device='npu:1', dtype=torch.int32)
self.shared_dict["expert_maps"][0]=tensor([-1, -1, -1, -1, -1, -1, -1,
-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,
-1, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,
18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,
36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64]], dtype=torch.int32)
### How was this patch tested?
#### qwen3-235B-w8a8 aime
| dataset | version | metric | mode | vllm-api-general-chat |
|----- | ----- | ----- | ----- | -----|
| aime2024 | 604a78 | accuracy | gen | 86.67 |
- vLLM version: v0.13.0
- vLLM main:
2f4e6548ef
Signed-off-by: shenchuxiaofugui <1311027364@qq.com>
197 lines
9.2 KiB
Python
197 lines
9.2 KiB
Python
#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# This file is a part of the vllm-ascend project.
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#
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# Todo: Once https://github.com/vllm-project/vllm/issues/22246 is merged in vllm. Remove this adaptor.
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import json
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from typing import Any
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import torch
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import torch.distributed as dist
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from vllm.logger import logger
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from vllm_ascend.eplb.adaptor.abstract_adaptor import EplbAdaptor
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class VllmEplbAdaptor(EplbAdaptor):
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def __init__(self, model, **args):
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super().__init__(**args)
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self.model = model
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self.rank_id = dist.get_rank()
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self.world_size = dist.get_world_size()
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self.param_dict = dict(self.model.named_parameters())
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if self.model.config.model_type == "qwen3_moe":
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self.num_dense_layers = 0
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self.global_expert_num = self.model.config.num_experts
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else:
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self.num_dense_layers = self.model.config.first_k_dense_replace
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self.global_expert_num = self.model.config.n_routed_experts
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self.num_moe_layers = self.model.config.num_hidden_layers - self.num_dense_layers
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for i in range(self.num_dense_layers,
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self.model.config.num_hidden_layers):
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self.param_dict["model.layers." + str(i) + ".mlp.experts." + "w13_weight_list"] = \
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self.model.model.layers[i].mlp.experts.w13_weight_list
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self.param_dict["model.layers." + str(i) + ".mlp.experts." + "w2_weight_list"] = \
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self.model.model.layers[i].mlp.experts.w2_weight_list
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self.param_dict["model.layers." + str(i) + ".mlp.experts." + "w13_weight_scale_fp32_list"] = \
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self.model.model.layers[i].mlp.experts.w13_weight_scale_fp32_list
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self.param_dict["model.layers." + str(i) + ".mlp.experts." + "w2_weight_scale_list"] = \
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self.model.model.layers[i].mlp.experts.w2_weight_scale_list
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self.param_dict["model.layers." + str(i) + ".mlp.experts." + "w2_weight_scale_fp32_list"] = \
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self.model.model.layers[i].mlp.experts.w2_weight_scale_fp32_list
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# TODO: init self.expert_weight_names depending on different model types, only deepseek v3 w8a8 and qwen3-moe is supported here
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if self.model.quant_config is not None:
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self.expert_weight_names = [
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"w13_weight_list", "w2_weight_list",
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"w13_weight_scale_fp32_list", "w13_weight_offset",
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"w2_weight_scale_list", "w2_weight_offset",
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"w2_weight_scale_fp32_list"
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]
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else:
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self.expert_weight_names = ["w13_weight", "w2_weight"]
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self.expert_map_per_layer = dict(
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) # reference to expert map on device for expert map update
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self.expert_map_per_layer_cpu = dict(
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) # copy of expert map on CPU to avoid device synchronize frequently
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for layer_idx in range(self.num_moe_layers):
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self.expert_map_per_layer[self.num_dense_layers + layer_idx] = \
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self.model.get_expert_map(self.num_dense_layers + layer_idx)
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# TODO: here we set number of buffer tensor equal to number of expert in each laryer, which can be improved
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num_buffer_tensor = torch.where(
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self.expert_map_per_layer[self.num_dense_layers] != -1)[0].numel()
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self.buffer_tensor_list: list[list[Any]] = [
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[] for _ in range(num_buffer_tensor)
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]
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self.init_buffer_tensor(num_buffer_tensor)
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self.expert_param_per_layer = dict()
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self.init_expert_param_per_layer()
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self.log2phy_map_per_layer = dict()
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for layer_idx in range(self.num_moe_layers):
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self.log2phy_map_per_layer[self.num_dense_layers + layer_idx] = \
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self.model.get_log2phy_map(self.num_dense_layers + layer_idx)
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self.all_topk_ids = []
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def init_buffer_tensor(self, num_buffer_tensor):
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for buffer_id in range(num_buffer_tensor):
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for name in self.expert_weight_names:
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complete_name = "model.layers." + str(
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self.num_dense_layers) + ".mlp.experts." + name
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if name in [
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"w13_weight_list", "w2_weight_list",
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"w13_weight_scale_fp32_list", "w2_weight_scale_list",
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"w2_weight_scale_fp32_list"
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]:
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expert_tensor = self.param_dict[complete_name][0]
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expert_tensor = expert_tensor.clone()
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else:
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expert_tensor = self.param_dict[complete_name][0].data[0]
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buffer_tensor = torch.empty_like(expert_tensor)
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self.buffer_tensor_list[buffer_id].append(buffer_tensor)
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def init_expert_param_per_layer(self):
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key = f"model.layers.{self.num_dense_layers}.mlp.experts.{self.expert_weight_names[0]}"
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num_local_expert = len(self.param_dict[key])
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for moe_layer_id in range(self.num_moe_layers):
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layer_idx = self.num_dense_layers + moe_layer_id
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self.expert_param_per_layer[layer_idx] = list()
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for local_expert_id in range(num_local_expert):
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per_expert_param = list()
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for name in self.expert_weight_names:
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if name in [
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"w13_weight_list", "w2_weight_list",
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"w13_weight_scale_fp32_list",
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"w2_weight_scale_list", "w2_weight_scale_fp32_list"
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]:
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per_expert_param.append(
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self.param_dict["model.layers." + str(layer_idx) +
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".mlp.experts." +
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name][local_expert_id])
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else:
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per_expert_param.append(
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self.param_dict["model.layers." + str(layer_idx) +
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".mlp.experts." +
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name][0].data[local_expert_id])
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self.expert_param_per_layer[layer_idx].append(per_expert_param)
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def get_rank_expert_workload(self) -> torch.Tensor:
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self.moe_load = self.model.get_all_moe_loads()
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return self.moe_load
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def _export_tensor_to_file(self, expert_maps, expert_map_record_path: str):
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if self.rank_id == 0:
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num_local_experts = expert_maps.max() + 1
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expert_maps_list = expert_maps.tolist()
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record: dict[str, Any] = {
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"moe_layer_count": len(expert_maps_list),
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"layer_list": []
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}
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for layer_idx, layer_data in enumerate(expert_maps_list):
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layer_record: dict[str, Any] = {
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"layer_id": layer_idx,
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"device_count": len(layer_data),
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"device_list": []
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}
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for device_idx, experts in enumerate(layer_data):
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placement = [
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experts.index(i) for i in range(num_local_experts)
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]
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device_record = {
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"device_id": device_idx,
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"device_expert": placement
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}
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layer_record["device_list"].append(device_record)
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record["layer_list"].append(layer_record)
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with open(expert_map_record_path, "w") as f:
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json.dump(record, f, indent=4)
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def do_update_expert_map(self, layer_id, updated_expert_map):
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self.expert_map_per_layer[layer_id].copy_(updated_expert_map)
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self.expert_map_per_layer_cpu[layer_id].copy_(updated_expert_map)
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def do_update_expert_weight(self, layer_id, local_expert_to_replace,
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buffer_tensor_id):
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for expert_tensor, buffer_tensor in zip(
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self.expert_param_per_layer[layer_id][local_expert_to_replace],
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self.buffer_tensor_list[buffer_tensor_id]):
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expert_tensor.copy_(buffer_tensor)
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logger.debug(f"Expert tensor shape is :{expert_tensor.shape}")
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def do_update_log2phy_map(self, layer_id, updated_log2phy_map):
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if self.log2phy_map_per_layer[layer_id] is not None:
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self.log2phy_map_per_layer[layer_id].copy_(updated_log2phy_map)
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def get_global_expert_map(self):
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all_layer_global_expert_map = []
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for layer_id in range(self.num_moe_layers):
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map_cpu = self.model.model.layers[
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layer_id].mlp.experts.global_expert_map.cpu()
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all_layer_global_expert_map.append(map_cpu)
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self.expert_map_per_layer_cpu[self.num_dense_layers +
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layer_id] = map_cpu[self.rank_id]
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return torch.stack(all_layer_global_expert_map)
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