# -*- coding=utf-8 -*- # Copyright (c) 2020 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. """ Training use fluid with DistributeTranspiler. """ from __future__ import print_function import time import logging import numpy as np import paddle.fluid as fluid from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler import fleet from paddlerec.core.utils import envs from paddlerec.core.trainers.framework.startup import StartupBase from paddlerec.core.trainer import EngineMode logging.basicConfig(format="%(asctime)s - %(levelname)s - %(message)s") logger = logging.getLogger("fluid") logger.setLevel(logging.INFO) special_param = ["TDM_Tree_Travel", "TDM_Tree_Layer", "TDM_Tree_Info"] class Startup(StartupBase): def startup(self, context): logger.info("Run TDM Trainer Startup Pass") if context["engine"] == EngineMode.SINGLE: self._single_startup(context) else: self._cluster_startup(context) context['status'] = 'train_pass' def _single_startup(self, context): load_tree_from_numpy = envs.get_global_env( "hyper_parameters.tree.load_tree_from_numpy", False) model_dict = context("env")["phase"][0] with fluid.scope_guard(context["model"][model_dict["name"]]["scope"]): context["exe"].run(context["model"][model_dict["name"]][ "startup_program"]) if load_tree_from_numpy: logger.info("load tree from numpy") self.tree_layer_path = envs.get_global_env( "hyper_parameters.tree.tree_layer_path", "") self.tree_travel_path = envs.get_global_env( "hyper_parameters.tree.tree_travel_path", "") self.tree_info_path = envs.get_global_env( "hyper_parameters.tree.tree_info_path", "") self.tree_emb_path = envs.get_global_env( "hyper_parameters.tree.tree_emb_path", "", ) for param_name in special_param: param_t = fluid.global_scope().find_var( param_name).get_tensor() param_array = self._tdm_prepare(param_name) if param_name == 'TDM_Tree_Emb': param_t.set( param_array.astype('float32'), context["place"]) else: param_t.set( param_array.astype('int32'), context["place"]) logger.info("Begin Save Init model.") fluid.io.save_persistables( executor=context["exe"], main_program=context["model"][model_dict["name"]][ "main_program"], dirname="./init_model") logger.info("End Save Init model.") load_paddle_model = envs.get_global_env( "hyper_parameters.tree.load_paddle_model", False) assert load_tree_from_numpy != load_paddle_model, "Please Don't use load_tree_from_numpy & load_paddle_model at the same time" warmup_model_path = envs.get_global_env( "runner." + context["runner_name"] + ".init_model_path", None) if load_paddle_model: # 从paddle二进制模型加载参数 assert warmup_model_path != None, "set runner.init_model_path for loading model" fluid.io.load_persistables( executor=context["exe"], dirname=warmup_model_path, main_program=context["model"][model_dict["name"]][ "main_program"]) logger.info("Load persistables from \"{}\"".format( warmup_model_path)) def _cluster_startup(self, context): warmup_model_path = envs.get_global_env( "runner." + context["runner_name"] + ".init_model_path", None) assert warmup_model_path != None, "set runner.init_model_path for loading model" model_dict = context("env")["phase"][0] with fluid.scope_guard(context["model"][model_dict["name"]]["scope"]): context["exe"].run(context["model"][model_dict["name"]][ "startup_program"]) def is_tdm_tree_var(var): res = var.name in special_param return res fluid.io.load_vars( context["exe"], dirname=warmup_model_path, main_program=context["model"][model_dict["name"]][ "main_program"], predicate=is_tdm_tree_var) """ -------- tree file load detail --------- """ def _tdm_prepare(self, param_name): if param_name == "TDM_Tree_Travel": travel_array = self._tdm_travel_prepare() return travel_array elif param_name == "TDM_Tree_Layer": layer_array, _ = self._tdm_layer_prepare() return layer_array elif param_name == "TDM_Tree_Info": info_array = self._tdm_info_prepare() return info_array else: raise " {} is not a special tdm param name".format(param_name) def _tdm_travel_prepare(self): """load tdm tree param from npy/list file""" travel_array = np.load(self.tree_travel_path) logger.info("TDM Tree leaf node nums: {}".format(travel_array.shape[ 0])) return travel_array def _tdm_layer_prepare(self): """load tdm tree param from npy/list file""" layer_list = [] layer_list_flat = [] with open(self.tree_layer_path, 'r') as fin: for line in fin.readlines(): l = [] layer = (line.split('\n'))[0].split(',') for node in layer: if node: layer_list_flat.append(node) l.append(node) layer_list.append(l) layer_array = np.array(layer_list_flat) layer_array = layer_array.reshape([-1, 1]) logger.info("TDM Tree max layer: {}".format(len(layer_list))) logger.info("TDM Tree layer_node_num_list: {}".format( [len(i) for i in layer_list])) return layer_array, layer_list def _tdm_info_prepare(self): """load tdm tree param from list file""" info_array = np.load(self.tree_info_path) return info_array