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