import numpy
from wandb.wandb_torch import torch
from federatedscope.core.trainers.enums import LIFECYCLE
from federatedscope.core.monitors import Monitor
from federatedscope.core.trainers.context import CtxVar
from federatedscope.mf.dataloader.dataloader import MFDataLoader
from federatedscope.core.trainers import GeneralTorchTrainer
from federatedscope.register import register_trainer
import logging
logger = logging.getLogger(__name__)
[docs]class MFTrainer(GeneralTorchTrainer):
"""Trainer for MF task
Arguments:
model (torch.nn.module): MF model.
data (dict): input data
device (str): device.
"""
[docs] def parse_data(self, data):
"""Populate "{}_data", "{}_loader" and "num_{}_data" for different
modes
"""
init_dict = dict()
if isinstance(data, dict):
for mode in ["train", "val", "test"]:
init_dict["{}_data".format(mode)] = None
init_dict["{}_loader".format(mode)] = None
init_dict["num_{}_data".format(mode)] = 0
if data.get(mode, None) is not None:
if isinstance(data.get(mode), MFDataLoader):
init_dict["{}_loader".format(mode)] = data.get(mode)
init_dict["num_{}_data".format(mode)] = data.get(
mode).n_rating
else:
raise TypeError(
"Type {} is not supported for MFTrainer.".format(
type(data.get(mode))))
else:
raise TypeError("Type of data should be dict.")
return init_dict
[docs] def _hook_on_fit_end(self, ctx):
results = {
f"{ctx.cur_mode}_avg_loss": ctx.loss_batch_total / ctx.num_samples,
f"{ctx.cur_mode}_total": ctx.num_samples
}
setattr(ctx, 'eval_metrics', results)
[docs] def _hook_on_batch_forward(self, ctx):
indices, ratings = ctx.data_batch
pred, label = ctx.model(indices, ratings)
ctx.loss_batch = CtxVar(ctx.criterion(pred, label), LIFECYCLE.BATCH)
ctx.batch_size = len(ratings)
[docs] def _hook_on_batch_end(self, ctx):
# update statistics
ctx.num_samples += ctx.batch_size
ctx.loss_batch_total += ctx.loss_batch.item() * ctx.batch_size
ctx.loss_regular_total += float(ctx.get("loss_regular", 0.))
[docs] def _hook_on_batch_forward_flop_count(self, ctx):
if not isinstance(self.ctx.monitor, Monitor):
logger.warning(
f"The trainer {type(self)} does contain a valid monitor, "
f"this may be caused by "
f"initializing trainer subclasses without passing a valid "
f"monitor instance."
f"Plz check whether this is you want.")
return
if self.cfg.eval.count_flops and self.ctx.monitor.flops_per_sample \
== 0:
# calculate the flops_per_sample
try:
indices, ratings = ctx.data_batch
if isinstance(indices, numpy.ndarray):
indices = torch.from_numpy(indices)
if isinstance(ratings, numpy.ndarray):
ratings = torch.from_numpy(ratings)
from fvcore.nn import FlopCountAnalysis
flops_one_batch = FlopCountAnalysis(
ctx.model, (indices, ratings)).total()
if self.model_nums > 1 and ctx.mirrored_models:
flops_one_batch *= self.model_nums
logger.warning(
"the flops_per_batch is multiplied by internal model "
"nums as self.mirrored_models=True."
"if this is not the case you want, "
"please customize the count hook")
self.ctx.monitor.track_avg_flops(flops_one_batch,
ctx.batch_size)
except:
logger.warning(
"current flop count implementation is for general "
"NodeFullBatchTrainer case: "
"1) the ctx.model takes tuple (indices, ratings) as input."
"Please check the forward format or implement your own "
"flop_count function")
self.ctx.monitor.flops_per_sample = -1 # warning at the
# first failure
# by default, we assume the data has the same input shape,
# thus simply multiply the flops to avoid redundant forward
self.ctx.monitor.total_flops += self.ctx.monitor.flops_per_sample * \
ctx.batch_size
def call_mf_trainer(trainer_type):
if trainer_type == "mftrainer":
trainer_builder = MFTrainer
return trainer_builder
register_trainer("mftrainer", call_mf_trainer)