From 6ea133716fb6aa02607e56e788175eff2084396e Mon Sep 17 00:00:00 2001 From: HengZhang Date: Thu, 21 Nov 2024 12:37:27 +0800 Subject: [PATCH] modifications on original FS modifications on original FS --- .../core/auxiliaries/data_builder.py | 8 ++- .../core/auxiliaries/model_builder.py | 12 +++- .../core/auxiliaries/splitter_builder.py | 4 ++ .../core/auxiliaries/trainer_builder.py | 8 +++ federatedscope/core/configs/cfg_data.py | 19 +++++++ federatedscope/core/configs/cfg_model.py | 15 +++++ .../core/configs/cfg_trafficflow.py | 57 +++++++++++++++++++ federatedscope/core/configs/cfg_training.py | 21 +++++++ federatedscope/core/data/utils.py | 54 ++++++++++-------- .../core/monitors/metric_calculator.py | 3 + federatedscope/core/trainers/torch_trainer.py | 11 +++- federatedscope/core/trainers/trainer_FedEM.py | 7 ++- .../core/trainers/trainer_multi_model.py | 15 ++++- federatedscope/core/workers/client.py | 2 +- federatedscope/mf/dataset/movielens.py | 7 ++- federatedscope/mf/trainer/trainer_sgdmf.py | 34 ++++++++--- setup.py | 8 ++- 17 files changed, 241 insertions(+), 44 deletions(-) create mode 100644 federatedscope/core/configs/cfg_trafficflow.py diff --git a/federatedscope/core/auxiliaries/data_builder.py b/federatedscope/core/auxiliaries/data_builder.py index cddabef..694dc2c 100644 --- a/federatedscope/core/auxiliaries/data_builder.py +++ b/federatedscope/core/auxiliaries/data_builder.py @@ -9,6 +9,10 @@ import federatedscope.register as register logger = logging.getLogger(__name__) +# Modifications: +# 1. Add FedDGCN support. Line 203 +# (2024-10-8, czzhangheng) + try: from federatedscope.contrib.data import * except ImportError as error: @@ -23,13 +27,14 @@ TRANS_DATA_MAP = { '.*?@.*?', 'hiv', 'proteins', 'imdb-binary', 'bbbp', 'tox21', 'bace', 'sider', 'clintox', 'esol', 'freesolv', 'lipo', 'cifar4cl', 'cifar4lp' ], + # Add trafficflow to 'DummyDataTranslator' 'DummyDataTranslator': [ 'toy', 'quadratic', 'femnist', 'celeba', 'shakespeare', 'twitter', 'subreddit', 'synthetic', 'ciao', 'epinions', '.*?vertical_fl_data.*?', '.*?movielens.*?', '.*?netflix.*?', '.*?cikmcup.*?', 'graph_multi_domain.*?', 'cora', 'citeseer', 'pubmed', 'dblp_conf', 'dblp_org', 'csbm.*?', 'fb15k-237', 'wn18', 'adult', 'abalone', - 'credit', 'blog' + 'credit', 'blog', 'trafficflow' ], # Dummy for FL dataset 'RawDataTranslator': ['hetero_nlp_tasks'], } @@ -111,6 +116,7 @@ def get_data(config, client_cfgs=None): HFLMovieLens10M Recommendation VFLNetflix Recommendation HFLNetflix Recommendation + trafficflow Traffic Flow Prediction ================================== =========================== """ # Fix the seed for data generation diff --git a/federatedscope/core/auxiliaries/model_builder.py b/federatedscope/core/auxiliaries/model_builder.py index d3ba682..25cc31d 100644 --- a/federatedscope/core/auxiliaries/model_builder.py +++ b/federatedscope/core/auxiliaries/model_builder.py @@ -4,6 +4,10 @@ import federatedscope.register as register logger = logging.getLogger(__name__) +# Modifications: +# 1. Do a my_gcn demo Line 75 +# (2024-9-1, czzhangheng) + try: from federatedscope.contrib.model import * except ImportError as error: @@ -72,7 +76,10 @@ def get_shape_from_data(data, model_config, backend='torch'): elif backend == 'torch': import torch if issubclass(type(data_representative), torch.utils.data.DataLoader): - x, _ = next(iter(data_representative)) + if model_config.type == 'my_gcn': + x = next(iter(data_representative)) + return x.x.shape + x = next(iter(data_representative)) if isinstance(x, list): return x[0].shape return x.shape @@ -197,6 +204,9 @@ def get_model(model_config, local_data=None, backend='torch'): elif model_config.type.lower() in ['atc_model']: from federatedscope.nlp.hetero_tasks.model import ATCModel model = ATCModel(model_config) + elif model_config.type.lower() in ['feddgcn']: + from federatedscope.trafficflow.model.FedDGCN import FedDGCN + model = FedDGCN(model_config) else: raise ValueError('Model {} is not provided'.format(model_config.type)) diff --git a/federatedscope/core/auxiliaries/splitter_builder.py b/federatedscope/core/auxiliaries/splitter_builder.py index 6f91684..1cc81ef 100644 --- a/federatedscope/core/auxiliaries/splitter_builder.py +++ b/federatedscope/core/auxiliaries/splitter_builder.py @@ -75,6 +75,10 @@ def get_splitter(config): elif config.data.splitter == 'iid': from federatedscope.core.splitters.generic import IIDSplitter splitter = IIDSplitter(client_num) + # Add traffic flow splitter + elif config.data.splitter == 'trafficflowprediction': + from federatedscope.trafficflow.splitters.trafficSplitter import TrafficSplitter + splitter = TrafficSplitter(client_num) else: logger.warning(f'Splitter {config.data.splitter} not found or not ' f'used.') diff --git a/federatedscope/core/auxiliaries/trainer_builder.py b/federatedscope/core/auxiliaries/trainer_builder.py index b32baf7..f7fd18f 100644 --- a/federatedscope/core/auxiliaries/trainer_builder.py +++ b/federatedscope/core/auxiliaries/trainer_builder.py @@ -176,6 +176,14 @@ def get_trainer(model=None, data=data, device=device, monitor=monitor) + # Add traffic flow trainer + elif config.trainer.type.lower() in ['trafficflowtrainer']: + from federatedscope.trafficflow.trainer.trafficflow_trainer import call_trafficflow_trainer + trainer = call_trafficflow_trainer(config=config, + model=model, + data=data, + device=device, + monitor=monitor) else: # try to find user registered trainer trainer = None diff --git a/federatedscope/core/configs/cfg_data.py b/federatedscope/core/configs/cfg_data.py index d0e4212..585684f 100644 --- a/federatedscope/core/configs/cfg_data.py +++ b/federatedscope/core/configs/cfg_data.py @@ -82,6 +82,25 @@ def extend_data_cfg(cfg): cfg.data.num_contrast = 0 cfg.data.is_debug = False + # Traffic Flow data parameters, These are only default values. + # Please modify the specific parameters directly in the YAML files. + cfg.data.root = 'data/trafficflow/PeMS04' + cfg.data.type = 'trafficflow' + cfg.data.num_nodes = 307 + cfg.data.lag = 12 + cfg.data.horizon = 12 + cfg.data.val_ratio = 0.2 + cfg.data.test_ratio = 0.2 + cfg.data.tod = False + cfg.data.normalizer = 'std' + cfg.data.column_wise = False + cfg.data.default_graph = True + cfg.data.add_time_in_day = True + cfg.data.add_day_in_week = True + cfg.data.steps_per_day = 288 + cfg.data.days_per_week = 7 + cfg.data.scaler = [0,0] + # feature engineering cfg.feat_engr = CN() cfg.feat_engr.type = '' diff --git a/federatedscope/core/configs/cfg_model.py b/federatedscope/core/configs/cfg_model.py index 8f9cd27..9737f4f 100644 --- a/federatedscope/core/configs/cfg_model.py +++ b/federatedscope/core/configs/cfg_model.py @@ -49,6 +49,21 @@ def extend_model_cfg(cfg): cfg.model.contrast_topk = 100 cfg.model.contrast_temp = 1.0 + # Traffic Flow model parameters, These are only default values. + # Please modify the specific parameters directly in the baselines/YAML files. + cfg.model.num_nodes = 0 + cfg.model.rnn_units = 64 + cfg.model.dropout = 0.1 + cfg.model.horizon = 12 + cfg.model.input_dim = 1 # If 0, model will be built by data.shape + cfg.model.output_dim = 1 + cfg.model.embed_dim = 10 + cfg.model.num_layers = 1 # In GPR-GNN, K = layer + cfg.model.cheb_order = 1 # A tuple, e.g., (in_channel, h, w) + cfg.model.use_day = True + cfg.model.use_week = True + + # ---------------------------------------------------------------------- # # Criterion related options # ---------------------------------------------------------------------- # diff --git a/federatedscope/core/configs/cfg_trafficflow.py b/federatedscope/core/configs/cfg_trafficflow.py new file mode 100644 index 0000000..73ade59 --- /dev/null +++ b/federatedscope/core/configs/cfg_trafficflow.py @@ -0,0 +1,57 @@ +from federatedscope.core.configs.config import CN +from federatedscope.register import register_config + +""" +The parameter settings for traffic flow prediction are located in the YAML files under +the baseline folder within the trafficflow package. These are only default values. +Please modify the specific parameters directly in the YAML files. +""" + + +def extend_trafficflow_cfg(cfg): + # ---------------------------------------------------------------------- # + # Model related options + # ---------------------------------------------------------------------- # + cfg.model = CN() + + cfg.model.model_num_per_trainer = 1 # some methods may leverage more + # than one model in each trainer + cfg.model.type = 'trafficflow' + cfg.model.use_bias = True + cfg.model.task = 'trafficflowprediction' + cfg.model.num_nodes = 0 + cfg.model.rnn_units = 64 + cfg.model.dropout = 0.1 + cfg.model.horizon = 12 + cfg.model.input_dim = 1 # If 0, model will be built by data.shape + cfg.model.output_dim = 1 + cfg.model.embed_dim = 10 + cfg.model.num_layers = 1 # In GPR-GNN, K = layer + cfg.model.cheb_order = 1 # A tuple, e.g., (in_channel, h, w) + cfg.model.use_day = True + cfg.model.use_week = True + + # ---------------------------------------------------------------------- # + # Criterion related options + # ---------------------------------------------------------------------- # + cfg.criterion = CN() + + cfg.criterion.type = 'L1Loss' + + # ---------------------------------------------------------------------- # + # regularizer related options + # ---------------------------------------------------------------------- # + cfg.regularizer = CN() + + cfg.regularizer.type = '' + cfg.regularizer.mu = 0. + + # --------------- register corresponding check function ---------- + cfg.register_cfg_check_fun(assert_model_cfg) + + +def assert_model_cfg(cfg): + pass + + +register_config("trafficflow", extend_trafficflow_cfg) diff --git a/federatedscope/core/configs/cfg_training.py b/federatedscope/core/configs/cfg_training.py index baa8f91..55a7e76 100644 --- a/federatedscope/core/configs/cfg_training.py +++ b/federatedscope/core/configs/cfg_training.py @@ -24,6 +24,7 @@ def extend_training_cfg(cfg): # atc (TODO: merge later) cfg.trainer.disp_freq = 50 cfg.trainer.val_freq = 100000000 # eval freq across batches + cfg.trainer.log_dir = '' # ---------------------------------------------------------------------- # # Training related options @@ -38,6 +39,23 @@ def extend_training_cfg(cfg): cfg.train.optimizer.type = 'SGD' cfg.train.optimizer.lr = 0.1 + # trafficflow + cfg.train.loss_func = 'mae' + cfg.train.seed = 10 + cfg.train.batch_size = 64 + cfg.train.epochs = 300 + cfg.train.lr_init = 0.003 + cfg.train.weight_decay = 0 + cfg.train.lr_decay = False + cfg.train.lr_decay_rate = 0.3 + cfg.train.lr_decay_step = [5, 20, 40, 70] + cfg.train.early_stop = True + cfg.train.early_stop_patience = 15 + cfg.train.grad_norm = False + cfg.train.max_grad_norm = 5 + cfg.train.real_value = True + + # you can add new arguments 'aa' by `cfg.train.scheduler.aa = 'bb'` cfg.train.scheduler = CN(new_allowed=True) cfg.train.scheduler.type = '' @@ -91,6 +109,9 @@ def extend_training_cfg(cfg): # Early stop when no improve to last `patience` round, in ['mean', 'best'] cfg.early_stop.improve_indicator_mode = 'best' + # TODO:trafficflow + + # --------------- register corresponding check function ---------- cfg.register_cfg_check_fun(assert_training_cfg) diff --git a/federatedscope/core/data/utils.py b/federatedscope/core/data/utils.py index be785cb..b1efa69 100644 --- a/federatedscope/core/data/utils.py +++ b/federatedscope/core/data/utils.py @@ -1,19 +1,20 @@ -import copy import inspect import logging import os +import os.path as osp import re import ssl import urllib.request +from collections import defaultdict +from random import shuffle import numpy as np -import os.path as osp - -from random import shuffle -from collections import defaultdict logger = logging.getLogger(__name__) +""" +Modification Record: This file has been reformatted. +""" class RegexInverseMap: def __init__(self, n_dic, val): @@ -57,16 +58,16 @@ def load_dataset(config, client_cfgs=None): from federatedscope.cl.dataloader import load_cifar_dataset dataset, modified_config = load_cifar_dataset(config) elif config.data.type.lower() in [ - 'shakespeare', 'twitter', 'subreddit', 'synthetic' + 'shakespeare', 'twitter', 'subreddit', 'synthetic' ]: from federatedscope.nlp.dataloader import load_nlp_dataset dataset, modified_config = load_nlp_dataset(config) elif config.data.type.lower() in [ - 'cora', - 'citeseer', - 'pubmed', - 'dblp_conf', - 'dblp_org', + 'cora', + 'citeseer', + 'pubmed', + 'dblp_conf', + 'dblp_org', ] or config.data.type.lower().startswith('csbm'): from federatedscope.gfl.dataloader import load_nodelevel_dataset dataset, modified_config = load_nodelevel_dataset(config) @@ -74,13 +75,13 @@ def load_dataset(config, client_cfgs=None): from federatedscope.gfl.dataloader import load_linklevel_dataset dataset, modified_config = load_linklevel_dataset(config) elif config.data.type.lower() in [ - 'hiv', 'proteins', 'imdb-binary', 'bbbp', 'tox21', 'bace', 'sider', - 'clintox', 'esol', 'freesolv', 'lipo', 'cikmcup' + 'hiv', 'proteins', 'imdb-binary', 'bbbp', 'tox21', 'bace', 'sider', + 'clintox', 'esol', 'freesolv', 'lipo', 'cikmcup' ] or config.data.type.startswith('graph_multi_domain'): from federatedscope.gfl.dataloader import load_graphlevel_dataset dataset, modified_config = load_graphlevel_dataset(config) elif config.data.type.lower() in [ - 'synthetic_vfl_data', 'adult', 'abalone', 'credit', 'blog' + 'synthetic_vfl_data', 'adult', 'abalone', 'credit', 'blog' ]: from federatedscope.vertical_fl.dataloader import load_vertical_data generate = config.data.type.lower() == 'synthetic_vfl_data' @@ -97,10 +98,17 @@ def load_dataset(config, client_cfgs=None): elif '@' in config.data.type.lower(): from federatedscope.core.data.utils import load_external_data dataset, modified_config = load_external_data(config) + elif 'cora' in config.data.type.lower(): + from federatedscope.contrib.data.my_cora import call_my_data + dataset, modified_config = call_my_data(config, client_cfgs) elif config.data.type is None or config.data.type == "": # The participant (only for server in this version) does not own data dataset = None modified_config = config + elif config.data.type.lower() in [ + 'trafficflow']: + from federatedscope.trafficflow.dataloader.traffic_dataloader import load_traffic_data + dataset, modified_config = load_traffic_data(config, client_cfgs) else: raise ValueError('Dataset {} not found.'.format(config.data.type)) return dataset, modified_config @@ -305,8 +313,8 @@ def load_external_data(config=None): config.data.transform.append({}) vocab = getattr(import_module('torchtext.vocab'), config.data.transform[0])( - dim=config.model.in_channels, - **config.data.transform[1]) + dim=config.model.in_channels, + **config.data.transform[1]) if 'classification' in config.model.task.lower(): data = [ @@ -360,7 +368,7 @@ def load_external_data(config=None): lengths = [train_size, val_size] data_split_dict['train'], data_split_dict[ 'val'] = torch.utils.data.dataset.random_split( - data_split_dict['train'], lengths) + data_split_dict['train'], lengths) else: # Use config.data.splits data_split_dict = {} @@ -370,7 +378,7 @@ def load_external_data(config=None): lengths = [train_size, val_size, test_size] data_split_dict['train'], data_split_dict['val'], data_split_dict[ 'test'] = torch.utils.data.dataset.random_split( - data_list[0], lengths) + data_list[0], lengths) return data_split_dict @@ -458,7 +466,7 @@ def load_external_data(config=None): original_train_size = len(data_split_dict["train"]) if "half_val_dummy_test" in raw_args and raw_args[ - "half_val_dummy_test"]: + "half_val_dummy_test"]: # since the "test" set from GLUE dataset may be masked, we need to # submit to get the ground-truth, for fast FL experiments, # we split the validation set into two parts with the same size as @@ -467,22 +475,22 @@ def load_external_data(config=None): dataset['validation'][1])] data_split_dict["val"], data_split_dict[ "test"] = original_val[:len(original_val) // - 2], original_val[len(original_val) // - 2:] + 2], original_val[len(original_val) // + 2:] if "val_as_dummy_test" in raw_args and raw_args["val_as_dummy_test"]: # use the validation set as tmp test set, # and partial training set as validation set data_split_dict["test"] = data_split_dict["val"] data_split_dict["val"] = [] if "part_train_dummy_val" in raw_args and 1 > raw_args[ - "part_train_dummy_val"] > 0: + "part_train_dummy_val"] > 0: new_val_part = int(original_train_size * raw_args["part_train_dummy_val"]) data_split_dict["val"].extend( data_split_dict["train"][:new_val_part]) data_split_dict["train"] = data_split_dict["train"][new_val_part:] if "part_train_dummy_test" in raw_args and 1 > raw_args[ - "part_train_dummy_test"] > 0: + "part_train_dummy_test"] > 0: new_test_part = int(original_train_size * raw_args["part_train_dummy_test"]) data_split_dict["test"] = data_split_dict["val"] diff --git a/federatedscope/core/monitors/metric_calculator.py b/federatedscope/core/monitors/metric_calculator.py index 1a65bb5..7e2f4f8 100644 --- a/federatedscope/core/monitors/metric_calculator.py +++ b/federatedscope/core/monitors/metric_calculator.py @@ -140,8 +140,11 @@ class MetricCalculator(object): if torch is not None and isinstance(y_prob, torch.Tensor): y_prob = y_prob.detach().cpu().numpy() + # Add traffic flow metrics if 'regression' in ctx.cfg.model.task.lower(): y_pred = None + elif 'trafficflowprediction' in ctx.cfg.model.task.lower(): + y_pred = None else: # classification task if y_true.ndim == 1: diff --git a/federatedscope/core/trainers/torch_trainer.py b/federatedscope/core/trainers/torch_trainer.py index 6ac7b98..14552e8 100644 --- a/federatedscope/core/trainers/torch_trainer.py +++ b/federatedscope/core/trainers/torch_trainer.py @@ -25,6 +25,9 @@ from federatedscope.core.monitors.monitor import Monitor logger = logging.getLogger(__name__) +# Modifications: +# 1. Fixed some errors in the flop_counter. +# (2024-10-06, Heng-Zhang) class GeneralTorchTrainer(Trainer): def get_model_para(self): @@ -161,8 +164,7 @@ class GeneralTorchTrainer(Trainer): ================================== =========================== Attribute Operation ================================== =========================== - ``ctx.model`` Wrap ``nn.Module` to \ - `nn.DataParallel` + ``ctx.model`` Wrap ``nn.Module` to `nn.DataParallel` ================================== =========================== """ if isinstance(ctx.model, torch.nn.DataParallel): @@ -325,7 +327,10 @@ class GeneralTorchTrainer(Trainer): try: x, y = [_.to(ctx.device) for _ in ctx.data_batch] from fvcore.nn import FlopCountAnalysis - flops_one_batch = FlopCountAnalysis(ctx.model, x).total() + # Something wrong!! + flop_counter = FlopCountAnalysis(ctx.model, x) + flop_counter.unsupported_ops_warnings(False) + flops_one_batch = flop_counter.total() if self.model_nums > 1 and ctx.mirrored_models: flops_one_batch *= self.model_nums logger.warning( diff --git a/federatedscope/core/trainers/trainer_FedEM.py b/federatedscope/core/trainers/trainer_FedEM.py index 2a391d3..0553053 100644 --- a/federatedscope/core/trainers/trainer_FedEM.py +++ b/federatedscope/core/trainers/trainer_FedEM.py @@ -10,6 +10,10 @@ from federatedscope.core.trainers.torch_trainer import GeneralTorchTrainer from federatedscope.core.trainers.trainer_multi_model import \ GeneralMultiModelTrainer +# Modifications: +# 1. Fix issue where the trainer cannot access the monitor. Line 31 +# (2024-10-6, czzhangheng) + class FedEMTrainer(GeneralMultiModelTrainer): """ @@ -25,10 +29,11 @@ class FedEMTrainer(GeneralMultiModelTrainer): data=None, device=None, config=None, + monitor=None, base_trainer: Type[GeneralTorchTrainer] = None): super(FedEMTrainer, self).__init__(model_nums, models_interact_mode, model, data, - device, config, base_trainer) + device, config, monitor=monitor) device = self.ctx.device # --------------- attribute-level modifications ---------------------- diff --git a/federatedscope/core/trainers/trainer_multi_model.py b/federatedscope/core/trainers/trainer_multi_model.py index d230070..f86aeb4 100644 --- a/federatedscope/core/trainers/trainer_multi_model.py +++ b/federatedscope/core/trainers/trainer_multi_model.py @@ -7,6 +7,10 @@ from federatedscope.core.trainers.torch_trainer import GeneralTorchTrainer import numpy as np +# Modifications: +# 1. Fix issue where the trainer cannot access the monitor. Line 68 +# 2. Fix issue where deepcopy cannot copy items Line 77 +# (2024-10-6, czzhangheng) class GeneralMultiModelTrainer(GeneralTorchTrainer): def __init__(self, @@ -16,6 +20,7 @@ class GeneralMultiModelTrainer(GeneralTorchTrainer): data=None, device=None, config=None, + monitor=None, base_trainer: Type[GeneralTorchTrainer] = None): """ `GeneralMultiModelTrainer` supports train/eval via multiple @@ -65,7 +70,7 @@ class GeneralMultiModelTrainer(GeneralTorchTrainer): "data, device, config) should not be " \ "None" super(GeneralMultiModelTrainer, - self).__init__(model, data, device, config) + self).__init__(model, data, device, config, monitor=monitor) else: assert isinstance(base_trainer, GeneralMultiModelTrainer) or \ issubclass(type(base_trainer), GeneralMultiModelTrainer) \ @@ -74,7 +79,13 @@ class GeneralMultiModelTrainer(GeneralTorchTrainer): "can only copy instances of `GeneralMultiModelTrainer` " \ "and its subclasses, or " \ "`GeneralTorchTrainer` and its subclasses" - self.__dict__ = copy.deepcopy(base_trainer.__dict__) + # self.__dict__ = copy.deepcopy(base_trainer.__dict__) + # 逐个复制 base_trainer 的属性，跳过不可拷贝的对象 + for key, value in base_trainer.__dict__.items(): + try: + self.__dict__[key] = copy.deepcopy(value) + except TypeError: + self.__dict__[key] = value # 如果不能 deepcopy，使用浅拷贝 assert models_interact_mode in ["sequential", "parallel"], \ f"Invalid models_interact_mode, should be `sequential` or " \ diff --git a/federatedscope/core/workers/client.py b/federatedscope/core/workers/client.py index d176382..db54960 100644 --- a/federatedscope/core/workers/client.py +++ b/federatedscope/core/workers/client.py @@ -530,7 +530,7 @@ class Client(BaseClient): # TODO: The time cost of evaluation is not considered here eval_metrics = self.trainer.evaluate( target_data_split_name=split) - + if self._cfg.federate.mode == 'distributed': logger.info( self._monitor.format_eval_res(eval_metrics, diff --git a/federatedscope/mf/dataset/movielens.py b/federatedscope/mf/dataset/movielens.py index 4bd0c77..70bac2e 100644 --- a/federatedscope/mf/dataset/movielens.py +++ b/federatedscope/mf/dataset/movielens.py @@ -14,6 +14,9 @@ import numpy as np logger = logging.getLogger(__name__) +# Modifications: +# 1. Fix issue where iteritems() is deprecated, items() instead. Line 160 +# (2024-10-8, czzhangheng) class VMFDataset: """Dataset of matrix factorization task in vertical federated learning. @@ -157,8 +160,8 @@ class MovieLensData(object): }, {mid: idx for idx, mid in enumerate(unique_id_user)} - row = [mapping_user[mid] for _, mid in data["userId"].iteritems()] - col = [mapping_item[mid] for _, mid in data["movieId"].iteritems()] + row = [mapping_user[mid] for _, mid in data["userId"].items()] + col = [mapping_item[mid] for _, mid in data["movieId"].items()] ratings = coo_matrix((data["rating"], (row, col)), shape=(n_user, n_item)) diff --git a/federatedscope/mf/trainer/trainer_sgdmf.py b/federatedscope/mf/trainer/trainer_sgdmf.py index c139885..a0f966c 100644 --- a/federatedscope/mf/trainer/trainer_sgdmf.py +++ b/federatedscope/mf/trainer/trainer_sgdmf.py @@ -9,6 +9,11 @@ import torch logger = logging.getLogger(__name__) +# Modifications: +# +# 1. Fix issue where embed_user.shape is deprecated, embed_user.weight.shape instead. Line 91 +# It may be casued by torch.Embedding update. +# (2024-9-8, czzhangheng) def wrap_MFTrainer(base_trainer: Type[MFTrainer]) -> Type[MFTrainer]: """Build `SGDMFTrainer` with a plug-in manner, by registering new @@ -74,26 +79,39 @@ def hook_on_batch_backward(ctx): ctx.optimizer.zero_grad() ctx.loss_task.backward() + if ctx.model.embed_user.weight.grad.is_sparse: + dense_user_grad = ctx.model.embed_user.weight.grad.to_dense() + else: + dense_user_grad = ctx.model.embed_user.weight.grad + + if ctx.model.embed_item.weight.grad.is_sparse: + dense_item_grad = ctx.model.embed_item.weight.grad.to_dense() + else: + dense_item_grad = ctx.model.embed_item.weight.grad + # Inject noise - ctx.model.embed_user.grad.data += get_random( + dense_user_grad.data += get_random( "Normal", - sample_shape=ctx.model.embed_user.shape, + sample_shape=ctx.model.embed_user.weight.shape, params={ "loc": 0, "scale": ctx.scale }, - device=ctx.model.embed_user.device) - ctx.model.embed_item.grad.data += get_random( + device=ctx.model.embed_user.weight.device) + dense_item_grad.data += get_random( "Normal", - sample_shape=ctx.model.embed_item.shape, + sample_shape=ctx.model.embed_item.weight.shape, params={ "loc": 0, "scale": ctx.scale }, - device=ctx.model.embed_item.device) + device=ctx.model.embed_item.weight.device) + + ctx.model.embed_user.weight.grad = dense_user_grad.to_sparse() + ctx.model.embed_item.weight.grad = dense_item_grad.to_sparse() ctx.optimizer.step() # Embedding clipping with torch.no_grad(): - embedding_clip(ctx.model.embed_user, ctx.sgdmf_R) - embedding_clip(ctx.model.embed_item, ctx.sgdmf_R) + embedding_clip(ctx.model.embed_user.weight, ctx.sgdmf_R) + embedding_clip(ctx.model.embed_item.weight, ctx.sgdmf_R) diff --git a/setup.py b/setup.py index 3f579c7..9fb4638 100644 --- a/setup.py +++ b/setup.py @@ -2,13 +2,17 @@ from __future__ import absolute_import, division, print_function import setuptools +# Modifications: +# 1. Change the package requirement for suit latest package +# (2024-10-10, czzhangheng) + __name__ = 'federatedscope' __version__ = '0.3.0' URL = 'https://github.com/alibaba/FederatedScope' minimal_requires = [ - 'numpy<1.23.0', 'scikit-learn==1.0.2', 'scipy==1.7.3', 'pandas', - 'grpcio>=1.45.0', 'grpcio-tools', 'pyyaml>=5.1', 'fvcore', 'iopath', + 'numpy', 'scikit-learn', 'scipy', 'pandas', + 'grpcio', 'grpcio-tools', 'pyyaml>=5.1', 'fvcore', 'iopath', 'wandb', 'tensorboard', 'tensorboardX', 'pympler', 'protobuf==3.19.4', 'matplotlib' ]