mirror of
https://github.com/twitter/the-algorithm.git
synced 2024-11-18 17:39:26 +01:00
ef4c5eb65e
Please note we have force-pushed a new initial commit in order to remove some publicly-available Twitter user information. Note that this process may be required in the future.
402 lines
13 KiB
Python
402 lines
13 KiB
Python
from datetime import datetime
|
|
from importlib import import_module
|
|
import os
|
|
|
|
from toxicity_ml_pipeline.data.data_preprocessing import (
|
|
DefaultENNoPreprocessor,
|
|
DefaultENPreprocessor,
|
|
)
|
|
from toxicity_ml_pipeline.data.dataframe_loader import ENLoader, ENLoaderWithSampling
|
|
from toxicity_ml_pipeline.data.mb_generator import BalancedMiniBatchLoader
|
|
from toxicity_ml_pipeline.load_model import load, get_last_layer
|
|
from toxicity_ml_pipeline.optim.callbacks import (
|
|
AdditionalResultLogger,
|
|
ControlledStoppingCheckpointCallback,
|
|
GradientLoggingTensorBoard,
|
|
SyncingTensorBoard,
|
|
)
|
|
from toxicity_ml_pipeline.optim.schedulers import WarmUp
|
|
from toxicity_ml_pipeline.settings.default_settings_abs import GCS_ADDRESS as ABS_GCS
|
|
from toxicity_ml_pipeline.settings.default_settings_tox import (
|
|
GCS_ADDRESS as TOX_GCS,
|
|
MODEL_DIR,
|
|
RANDOM_SEED,
|
|
REMOTE_LOGDIR,
|
|
WARM_UP_PERC,
|
|
)
|
|
from toxicity_ml_pipeline.utils.helpers import check_gpu, set_seeds, upload_model
|
|
|
|
import numpy as np
|
|
import tensorflow as tf
|
|
|
|
|
|
try:
|
|
from tensorflow_addons.optimizers import AdamW
|
|
except ModuleNotFoundError:
|
|
print("No TFA")
|
|
|
|
|
|
class Trainer(object):
|
|
OPTIMIZERS = ["Adam", "AdamW"]
|
|
|
|
def __init__(
|
|
self,
|
|
optimizer_name,
|
|
weight_decay,
|
|
learning_rate,
|
|
mb_size,
|
|
train_epochs,
|
|
content_loss_weight=1,
|
|
language="en",
|
|
scope='TOX',
|
|
project=...,
|
|
experiment_id="default",
|
|
gradient_clipping=None,
|
|
fold="time",
|
|
seed=RANDOM_SEED,
|
|
log_gradients=False,
|
|
kw="",
|
|
stopping_epoch=None,
|
|
test=False,
|
|
):
|
|
self.seed = seed
|
|
self.weight_decay = weight_decay
|
|
self.learning_rate = learning_rate
|
|
self.mb_size = mb_size
|
|
self.train_epochs = train_epochs
|
|
self.gradient_clipping = gradient_clipping
|
|
|
|
if optimizer_name not in self.OPTIMIZERS:
|
|
raise ValueError(
|
|
f"Optimizer {optimizer_name} not implemented. Accepted values {self.OPTIMIZERS}."
|
|
)
|
|
self.optimizer_name = optimizer_name
|
|
self.log_gradients = log_gradients
|
|
self.test = test
|
|
self.fold = fold
|
|
self.stopping_epoch = stopping_epoch
|
|
self.language = language
|
|
if scope == 'TOX':
|
|
GCS_ADDRESS = TOX_GCS.format(project=project)
|
|
elif scope == 'ABS':
|
|
GCS_ADDRESS = ABS_GCS
|
|
else:
|
|
raise ValueError
|
|
GCS_ADDRESS = GCS_ADDRESS.format(project=project)
|
|
try:
|
|
self.setting_file = import_module(f"toxicity_ml_pipeline.settings.{scope.lower()}{project}_settings")
|
|
except ModuleNotFoundError:
|
|
raise ValueError(f"You need to define a setting file for your project {project}.")
|
|
experiment_settings = self.setting_file.experiment_settings
|
|
|
|
self.project = project
|
|
self.remote_logdir = REMOTE_LOGDIR.format(GCS_ADDRESS=GCS_ADDRESS, project=project)
|
|
self.model_dir = MODEL_DIR.format(GCS_ADDRESS=GCS_ADDRESS, project=project)
|
|
|
|
if experiment_id not in experiment_settings:
|
|
raise ValueError("This is not an experiment id as defined in the settings file.")
|
|
|
|
for var, default_value in experiment_settings["default"].items():
|
|
override_val = experiment_settings[experiment_id].get(var, default_value)
|
|
print("Setting ", var, override_val)
|
|
self.__setattr__(var, override_val)
|
|
|
|
self.content_loss_weight = content_loss_weight if self.dual_head else None
|
|
|
|
self.mb_loader = BalancedMiniBatchLoader(
|
|
fold=self.fold,
|
|
seed=self.seed,
|
|
perc_training_tox=self.perc_training_tox,
|
|
mb_size=self.mb_size,
|
|
n_outer_splits="time",
|
|
scope=scope,
|
|
project=project,
|
|
dual_head=self.dual_head,
|
|
sample_weights=self.sample_weights,
|
|
huggingface=("bertweet" in self.model_type),
|
|
)
|
|
self._init_dirnames(kw=kw, experiment_id=experiment_id)
|
|
print("------- Checking there is a GPU")
|
|
check_gpu()
|
|
|
|
def _init_dirnames(self, kw, experiment_id):
|
|
kw = "test" if self.test else kw
|
|
hyper_param_kw = ""
|
|
if self.optimizer_name == "AdamW":
|
|
hyper_param_kw += f"{self.weight_decay}_"
|
|
if self.gradient_clipping:
|
|
hyper_param_kw += f"{self.gradient_clipping}_"
|
|
if self.content_loss_weight:
|
|
hyper_param_kw += f"{self.content_loss_weight}_"
|
|
experiment_name = (
|
|
f"{self.language}{str(datetime.now()).replace(' ', '')[:-7]}{kw}_{experiment_id}{self.fold}_"
|
|
f"{self.optimizer_name}_"
|
|
f"{self.learning_rate}_"
|
|
f"{hyper_param_kw}"
|
|
f"{self.mb_size}_"
|
|
f"{self.perc_training_tox}_"
|
|
f"{self.train_epochs}_seed{self.seed}"
|
|
)
|
|
print("------- Experiment name: ", experiment_name)
|
|
self.logdir = (
|
|
f"..."
|
|
if self.test
|
|
else f"..."
|
|
)
|
|
self.checkpoint_path = f"{self.model_dir}/{experiment_name}"
|
|
|
|
@staticmethod
|
|
def _additional_writers(logdir, metric_name):
|
|
return tf.summary.create_file_writer(os.path.join(logdir, metric_name))
|
|
|
|
def get_callbacks(self, fold, val_data, test_data):
|
|
fold_logdir = self.logdir + f"_fold{fold}"
|
|
fold_checkpoint_path = self.checkpoint_path + f"_fold{fold}/{{epoch:02d}}"
|
|
|
|
tb_args = {
|
|
"log_dir": fold_logdir,
|
|
"histogram_freq": 0,
|
|
"update_freq": 500,
|
|
"embeddings_freq": 0,
|
|
"remote_logdir": f"{self.remote_logdir}_{self.language}"
|
|
if not self.test
|
|
else f"{self.remote_logdir}_test",
|
|
}
|
|
tensorboard_callback = (
|
|
GradientLoggingTensorBoard(loader=self.mb_loader, val_data=val_data, freq=10, **tb_args)
|
|
if self.log_gradients
|
|
else SyncingTensorBoard(**tb_args)
|
|
)
|
|
|
|
callbacks = [tensorboard_callback]
|
|
if "bertweet" in self.model_type:
|
|
from_logits = True
|
|
dataset_transform_func = self.mb_loader.make_huggingface_tensorflow_ds
|
|
else:
|
|
from_logits = False
|
|
dataset_transform_func = None
|
|
|
|
fixed_recall = 0.85 if not self.dual_head else 0.5
|
|
val_callback = AdditionalResultLogger(
|
|
data=val_data,
|
|
set_="validation",
|
|
from_logits=from_logits,
|
|
dataset_transform_func=dataset_transform_func,
|
|
dual_head=self.dual_head,
|
|
fixed_recall=fixed_recall
|
|
)
|
|
if val_callback is not None:
|
|
callbacks.append(val_callback)
|
|
|
|
test_callback = AdditionalResultLogger(
|
|
data=test_data,
|
|
set_="test",
|
|
from_logits=from_logits,
|
|
dataset_transform_func=dataset_transform_func,
|
|
dual_head=self.dual_head,
|
|
fixed_recall=fixed_recall
|
|
)
|
|
callbacks.append(test_callback)
|
|
|
|
checkpoint_args = {
|
|
"filepath": fold_checkpoint_path,
|
|
"verbose": 0,
|
|
"monitor": "val_pr_auc",
|
|
"save_weights_only": True,
|
|
"mode": "max",
|
|
"save_freq": "epoch",
|
|
}
|
|
if self.stopping_epoch:
|
|
checkpoint_callback = ControlledStoppingCheckpointCallback(
|
|
**checkpoint_args,
|
|
stopping_epoch=self.stopping_epoch,
|
|
save_best_only=False,
|
|
)
|
|
callbacks.append(checkpoint_callback)
|
|
|
|
return callbacks
|
|
|
|
def get_lr_schedule(self, steps_per_epoch):
|
|
total_num_steps = steps_per_epoch * self.train_epochs
|
|
|
|
warm_up_perc = WARM_UP_PERC if self.learning_rate >= 1e-3 else 0
|
|
warm_up_steps = int(total_num_steps * warm_up_perc)
|
|
if self.linear_lr_decay:
|
|
learning_rate_fn = tf.keras.optimizers.schedules.PolynomialDecay(
|
|
self.learning_rate,
|
|
total_num_steps - warm_up_steps,
|
|
end_learning_rate=0.0,
|
|
power=1.0,
|
|
cycle=False,
|
|
)
|
|
else:
|
|
print('Constant learning rate')
|
|
learning_rate_fn = self.learning_rate
|
|
|
|
if warm_up_perc > 0:
|
|
print(f".... using warm-up for {warm_up_steps} steps")
|
|
warm_up_schedule = WarmUp(
|
|
initial_learning_rate=self.learning_rate,
|
|
decay_schedule_fn=learning_rate_fn,
|
|
warmup_steps=warm_up_steps,
|
|
)
|
|
return warm_up_schedule
|
|
return learning_rate_fn
|
|
|
|
def get_optimizer(self, schedule):
|
|
optim_args = {
|
|
"learning_rate": schedule,
|
|
"beta_1": 0.9,
|
|
"beta_2": 0.999,
|
|
"epsilon": 1e-6,
|
|
"amsgrad": False,
|
|
}
|
|
if self.gradient_clipping:
|
|
optim_args["global_clipnorm"] = self.gradient_clipping
|
|
|
|
print(f".... {self.optimizer_name} w global clipnorm {self.gradient_clipping}")
|
|
if self.optimizer_name == "Adam":
|
|
return tf.keras.optimizers.Adam(**optim_args)
|
|
|
|
if self.optimizer_name == "AdamW":
|
|
optim_args["weight_decay"] = self.weight_decay
|
|
return AdamW(**optim_args)
|
|
raise NotImplementedError
|
|
|
|
def get_training_actors(self, steps_per_epoch, val_data, test_data, fold):
|
|
callbacks = self.get_callbacks(fold=fold, val_data=val_data, test_data=test_data)
|
|
schedule = self.get_lr_schedule(steps_per_epoch=steps_per_epoch)
|
|
|
|
optimizer = self.get_optimizer(schedule)
|
|
|
|
return optimizer, callbacks
|
|
|
|
def load_data(self):
|
|
if self.project == 435 or self.project == 211:
|
|
if self.dataset_type is None:
|
|
data_loader = ENLoader(project=self.project, setting_file=self.setting_file)
|
|
dataset_type_args = {}
|
|
else:
|
|
data_loader = ENLoaderWithSampling(project=self.project, setting_file=self.setting_file)
|
|
dataset_type_args = self.dataset_type
|
|
|
|
df = data_loader.load_data(
|
|
language=self.language, test=self.test, reload=self.dataset_reload, **dataset_type_args
|
|
)
|
|
|
|
return df
|
|
|
|
def preprocess(self, df):
|
|
if self.project == 435 or self.project == 211:
|
|
if self.preprocessing is None:
|
|
data_prepro = DefaultENNoPreprocessor()
|
|
elif self.preprocessing == "default":
|
|
data_prepro = DefaultENPreprocessor()
|
|
else:
|
|
raise NotImplementedError
|
|
|
|
return data_prepro(
|
|
df=df,
|
|
label_column=self.label_column,
|
|
class_weight=self.perc_training_tox if self.sample_weights == 'class_weight' else None,
|
|
filter_low_agreements=self.filter_low_agreements,
|
|
num_classes=self.num_classes,
|
|
)
|
|
|
|
def load_model(self, optimizer):
|
|
smart_bias_value = (
|
|
np.log(self.perc_training_tox / (1 - self.perc_training_tox)) if self.smart_bias_init else 0
|
|
)
|
|
model = load(
|
|
optimizer,
|
|
seed=self.seed,
|
|
trainable=self.trainable,
|
|
model_type=self.model_type,
|
|
loss_name=self.loss_name,
|
|
num_classes=self.num_classes,
|
|
additional_layer=self.additional_layer,
|
|
smart_bias_value=smart_bias_value,
|
|
content_num_classes=self.content_num_classes,
|
|
content_loss_name=self.content_loss_name,
|
|
content_loss_weight=self.content_loss_weight
|
|
)
|
|
|
|
if self.model_reload is not False:
|
|
model_folder = upload_model(full_gcs_model_path=os.path.join(self.model_dir, self.model_reload))
|
|
model.load_weights(model_folder)
|
|
if self.scratch_last_layer:
|
|
print('Putting the last layer back to scratch')
|
|
model.layers[-1] = get_last_layer(seed=self.seed,
|
|
num_classes=self.num_classes,
|
|
smart_bias_value=smart_bias_value)
|
|
|
|
return model
|
|
|
|
def _train_single_fold(self, mb_generator, test_data, steps_per_epoch, fold, val_data=None):
|
|
steps_per_epoch = 100 if self.test else steps_per_epoch
|
|
|
|
optimizer, callbacks = self.get_training_actors(
|
|
steps_per_epoch=steps_per_epoch, val_data=val_data, test_data=test_data, fold=fold
|
|
)
|
|
print("Loading model")
|
|
model = self.load_model(optimizer)
|
|
print(f"Nb of steps per epoch: {steps_per_epoch} ---- launching training")
|
|
training_args = {
|
|
"epochs": self.train_epochs,
|
|
"steps_per_epoch": steps_per_epoch,
|
|
"batch_size": self.mb_size,
|
|
"callbacks": callbacks,
|
|
"verbose": 2,
|
|
}
|
|
|
|
model.fit(mb_generator, **training_args)
|
|
return
|
|
|
|
def train_full_model(self):
|
|
print("Setting up random seed.")
|
|
set_seeds(self.seed)
|
|
|
|
print(f"Loading {self.language} data")
|
|
df = self.load_data()
|
|
df = self.preprocess(df=df)
|
|
|
|
print("Going to train on everything but the test dataset")
|
|
mini_batches, test_data, steps_per_epoch = self.mb_loader.simple_cv_load(df)
|
|
|
|
self._train_single_fold(
|
|
mb_generator=mini_batches, test_data=test_data, steps_per_epoch=steps_per_epoch, fold="full"
|
|
)
|
|
|
|
def train(self):
|
|
print("Setting up random seed.")
|
|
set_seeds(self.seed)
|
|
|
|
print(f"Loading {self.language} data")
|
|
df = self.load_data()
|
|
df = self.preprocess(df=df)
|
|
|
|
print("Loading MB generator")
|
|
i = 0
|
|
if self.project == 435 or self.project == 211:
|
|
mb_generator, steps_per_epoch, val_data, test_data = self.mb_loader.no_cv_load(full_df=df)
|
|
self._train_single_fold(
|
|
mb_generator=mb_generator,
|
|
val_data=val_data,
|
|
test_data=test_data,
|
|
steps_per_epoch=steps_per_epoch,
|
|
fold=i,
|
|
)
|
|
else:
|
|
raise ValueError("Sure you want to do multiple fold training")
|
|
for mb_generator, steps_per_epoch, val_data, test_data in self.mb_loader(full_df=df):
|
|
self._train_single_fold(
|
|
mb_generator=mb_generator,
|
|
val_data=val_data,
|
|
test_data=test_data,
|
|
steps_per_epoch=steps_per_epoch,
|
|
fold=i,
|
|
)
|
|
i += 1
|
|
if i == 3:
|
|
break
|