FastDeploy/fastdeploy/worker/experts_manager.py

"""
# Copyright (c) 2024 PaddlePaddle Authors. 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.
"""
"""
redundant expert manger
"""

import numpy as np
import paddle
from paddleformers.utils.log import logger

from .eplb import rebalance_experts


class RedundantExpertManger:
    """
    RedundantExpertManger
    """

    def __init__(self, n_routed_experts, num_hidden_layers,
                 redundant_experts_num, ep_size):

        self.num_expert = n_routed_experts
        self.redundant_experts_num = redundant_experts_num
        self.num_hidden_layers = num_hidden_layers

        self.num_replicas = self.num_expert + self.redundant_experts_num
        self.num_nodes = max(ep_size // 8, 1)
        self.num_gpus = ep_size
        self.num_groups = 1

        self.export_per_rank = self.num_replicas // ep_size
        assert self.num_replicas % ep_size == 0, \
            f"num_replicas must be divisible by ep_size, \
                but got num_replicas = {self.num_replicas}, ep_size = {ep_size}"

        self.model_ep_rank_to_expert_id_list = paddle.full(shape=[
            self.num_hidden_layers,
            self.num_expert + self.redundant_experts_num
        ],
                                                           fill_value=-1,
                                                           dtype="int32")
        self.model_expert_id_to_ep_rank_array = paddle.full(shape=[
            self.num_hidden_layers, self.num_expert,
            self.redundant_experts_num + 1
        ],
                                                            fill_value=-1,
                                                            dtype="int32")
        self.model_expert_in_rank_num_list = paddle.full(
            shape=[self.num_hidden_layers, self.num_expert],
            fill_value=0,
            dtype="int32")
        # self.model_ep_rank_to_expert_id_list = paddle.arange(
        #     self.num_expert + self.redundant_experts_num,
        #     dtype="int32").tile([self.num_hidden_layers, 1])
        # self.model_expert_id_to_ep_rank_array = paddle.arange(
        #     self.num_expert,
        #     dtype="int32").reshape([self.num_expert, 1]).tile([self.num_hidden_layers, 1, 1])
        # self.model_expert_in_rank_num_list = paddle.full(
        #     shape=[self.num_hidden_layers, self.num_expert],
        #     fill_value=1,
        #     dtype="int32")

        self.model_tokens_per_expert_stats_list = paddle.ones(
            shape=[self.num_hidden_layers, self.num_expert], dtype="int32")

        rank_expert_list, \
            logical_to_physical_map, \
            expert_count = rebalance_experts(
                                self.model_tokens_per_expert_stats_list.cpu().numpy(),
                                self.num_replicas,
                                self.num_groups,
                                self.num_nodes,
                                self.num_gpus)

        self.update_expert_rank_table(rank_expert_list,
                                      logical_to_physical_map, expert_count,
                                      False)

        logger.info(
            f"moe experts table manager init successfully, ep_size {ep_size} \
            num_replicas {self.num_replicas} export_per_rank {self.export_per_rank}"
        )

    def get_ep_rank_to_expert_id_list_by_layer(self, layer_id):
        """
        get_ep_rank_to_expert_id_list_by_layer
        """
        return self.model_ep_rank_to_expert_id_list[layer_id],  \
               self.model_expert_id_to_ep_rank_array[layer_id], \
               self.model_expert_in_rank_num_list[layer_id], \
               self.model_tokens_per_expert_stats_list[layer_id]

    def get_ep_rank_to_expert_id_list(self, layer_id):
        """
        get_ep_rank_to_expert_id_list
        """
        return self.model_ep_rank_to_expert_id_list[layer_id],  \
               self.model_expert_id_to_ep_rank_array[layer_id], \
               self.model_expert_in_rank_num_list[layer_id], \
               self.model_tokens_per_expert_stats_list[layer_id]

    def get_expert_tokens_stats(self,
                                verbose: bool = False,
                                clear_stat: bool = False):
        """
        get_per_expert_tokens_stats
        """
        try:
            if verbose:
                return self.model_tokens_per_expert_stats_list.cpu().numpy(), \
                    self.model_expert_id_to_ep_rank_array.cpu().numpy(), \
                    self.model_ep_rank_to_expert_id_list.cpu().numpy(), \
                    self.model_expert_in_rank_num_list.cpu().numpy()
            return self.model_tokens_per_expert_stats_list.cpu().numpy(
            ), None, None, None
        finally:
            if clear_stat:
                self.model_tokens_per_expert_stats_list.zero_()

    def get_expert_id_to_ep_rank_array(self):
        """
        get_expert_id_to_ep_rank_array
        """
        return self.model_expert_id_to_ep_rank_array.cpu().numpy()

    def update_expert_rank_table(self,
                                 rank_expert_list: np.ndarray,
                                 logical_to_physical_map: np.ndarray,
                                 expert_count: np.ndarray,
                                 clear_stat: bool = True):
        """
        update_expert_rank_table
        """
        #update model info
        self.model_ep_rank_to_expert_id_list.copy_(
            paddle.to_tensor(rank_expert_list), True)
        self.model_expert_id_to_ep_rank_array.fill_(-1)
        self.model_expert_id_to_ep_rank_array[:, :, :logical_to_physical_map.shape[-1]] = \
            paddle.to_tensor(logical_to_physical_map)
        self.model_expert_in_rank_num_list.copy_(
            paddle.to_tensor(expert_count), True)

        # reset
        if clear_stat:
            self.model_tokens_per_expert_stats_list.zero_()


if __name__ == '__main__':
    print(RedundantExpertManger(64, 2, 8, 8).model_expert_id_to_ep_rank_array)