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run_fewshot_distillation.py
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run_fewshot_distillation.py
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import argparse
import json
import os
import pathlib
import sys
from shutil import copyfile
from typing import List
from warnings import simplefilter
from xmlrpc.client import Boolean
import pandas as pd
from datasets import Dataset, DatasetDict, concatenate_datasets, load_dataset
from distillation_baseline import BaselineDistillation
from sentence_transformers import losses
from setfit import DistillationSetFitTrainer, SetFitModel, SetFitTrainer
from setfit.modeling import SetFitBaseModel
from setfit.utils import DEV_DATASET_TO_METRIC, TEST_DATASET_TO_METRIC
TEACHER_SEED = [0]
STUDENT_SEEDS = [1, 2, 3, 4, 5]
# ignore all future warnings
simplefilter(action="ignore", category=FutureWarning)
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--teacher_model", default="paraphrase-mpnet-base-v2")
parser.add_argument("--student_model", default="paraphrase-MiniLM-L3-v2")
parser.add_argument("--baseline_student_model", default="nreimers/MiniLM-L3-H384-uncased")
parser.add_argument(
"--datasets",
nargs="+",
default=["sst2"],
)
parser.add_argument("--teacher_sample_sizes", type=int, nargs="+", default=[16])
parser.add_argument(
"--student_sample_sizes",
type=int,
nargs="+",
default=[8, 16, 32, 64, 100, 200, 1000],
)
parser.add_argument("--num_iterations_teacher", type=int, default=20)
parser.add_argument("--num_iterations_student", type=int, default=20)
parser.add_argument("--num_epochs", type=int, default=1)
parser.add_argument("--batch_size_teacher", type=int, default=16)
parser.add_argument("--batch_size_student", type=int, default=16)
parser.add_argument("--max_seq_length", type=int, default=256)
parser.add_argument("--baseline_model_epochs", type=int, default=10)
parser.add_argument("--baseline_model_batch_size", type=int, default=16)
parser.add_argument(
"--classifier",
default="logistic_regression",
choices=[
"logistic_regression",
"svc-rbf",
"svc-rbf-norm",
"knn",
"pytorch",
"pytorch_complex",
],
)
parser.add_argument("--loss", default="CosineSimilarityLoss")
parser.add_argument("--exp_name", default="")
parser.add_argument("--add_normalization_layer", default=False, action="store_true")
parser.add_argument("--optimizer_name", default="AdamW")
parser.add_argument("--lr", type=float, default=0.001)
parser.add_argument("--is_dev_set", type=bool, default=False)
parser.add_argument("--is_test_set", type=bool, default=False)
args = parser.parse_args()
return args
class RunFewShotDistill:
def __init__(self, args, mode, trained_teacher_model, teacher_train_dataset, student_train_dataset) -> None:
# Prepare directory for results
self.args = args
# these attributes refer to the different modes to run the training
self.TEACHER = 0
self.SETFIT_STUDENT = 1
self.BASELINE_STUDENT = 2
if mode == self.TEACHER:
model = args.teacher_model
path_prefix = f"setfit_teacher_{args.teacher_model.replace('/', '-')}"
self.mode = self.TEACHER
if mode == self.SETFIT_STUDENT:
model = args.student_model
path_prefix = f"setfit_student_{args.student_model.replace('/', '-')}"
self.trained_teacher_model = trained_teacher_model
self.teacher_train_dataset = teacher_train_dataset
self.mode = self.SETFIT_STUDENT
if mode == self.BASELINE_STUDENT:
model = args.baseline_student_model
path_prefix = f"baseline_student_{args.student_model.replace('/', '-')}"
self.trained_teacher_model = trained_teacher_model
self.teacher_train_dataset = teacher_train_dataset
self.student_train_dataset = student_train_dataset
self.mode = self.BASELINE_STUDENT
self.bl_stdnt_distill = BaselineDistillation(
args.baseline_student_model,
args.baseline_model_epochs,
args.baseline_model_batch_size,
)
parent_directory = pathlib.Path(__file__).parent.absolute()
self.output_path = (
parent_directory
/ "results"
/ f"{path_prefix}-{args.loss}-{args.classifier}-student_iters_{args.num_iterations_student}-batch_{args.batch_size_student}-{args.exp_name}".rstrip(
"-"
)
)
os.makedirs(self.output_path, exist_ok=True)
# Save a copy of this training script and the run command in results directory
train_script_path = os.path.join(self.output_path, "train_script.py")
copyfile(__file__, train_script_path)
with open(train_script_path, "a") as f_out:
f_out.write("\n\n# Script was called via:\n#python " + " ".join(sys.argv))
# Configure dataset <> metric mapping. Defaults to accuracy
if args.is_dev_set:
self.dataset_to_metric = DEV_DATASET_TO_METRIC
elif args.is_test_set:
self.dataset_to_metric = TEST_DATASET_TO_METRIC
else:
self.dataset_to_metric = {dataset: "accuracy" for dataset in args.datasets}
# Configure loss function
self.loss_class = losses.CosineSimilarityLoss
self.model_name = model
# Load SetFit Model
self.model_wrapper = SetFitBaseModel(
# self.args.model, max_seq_length=args.max_seq_length, add_normalization_layer=args.add_normalization_layer
model,
max_seq_length=args.max_seq_length,
add_normalization_layer=args.add_normalization_layer,
)
self.model = self.model_wrapper.model
def create_samples(self, df: pd.DataFrame, sample_size: int, seed: int, mode) -> pd.DataFrame:
"""Samples a DataFrame to create an equal number of samples per class (when possible)."""
examples = []
if mode == self.TEACHER:
for label in df["label"].unique():
subset = df.query(f"label == {label}")
if len(subset) > sample_size:
examples.append(subset.sample(sample_size, random_state=seed, replace=False))
else:
examples.append(subset)
examples = pd.concat(examples)
if mode == self.SETFIT_STUDENT:
examples = df.sample(sample_size, random_state=seed, replace=False)
return examples
def create_fewshot_splits(self, dataset: Dataset, sample_sizes: List[int], seeds, mode: Boolean) -> DatasetDict:
"""Creates training splits from the dataset with an equal number of samples per class (when possible)."""
splits_ds = DatasetDict()
df = dataset.to_pandas()
for sample_size in sample_sizes:
for idx, seed in enumerate(seeds):
split_df = self.create_samples(df, sample_size, seed, mode)
splits_ds[f"train-{sample_size}-{idx}"] = Dataset.from_pandas(split_df, preserve_index=False)
return splits_ds
def train(self):
for dataset, metric in self.dataset_to_metric.items():
if self.mode == self.TEACHER:
print("\n\n\n=========== Training Teacher =========")
if self.mode == self.SETFIT_STUDENT:
print("\n\n\n======== Training SetFit Student ======")
if self.mode == self.BASELINE_STUDENT:
print("\n\n\n======== Training Baseline Student ======")
print(f"\n\n\n============== {dataset} ============")
# Load one of the SetFit training sets from the Hugging Face Hub
train_ds = load_dataset(f"SetFit/{dataset}", split="train")
eval_dataset = load_dataset(f"SetFit/{dataset}", split="test")
print(f"Test set: {len(eval_dataset)}")
# if teacher training use only 1 split (send only 1 seed. seed= 0)
if self.mode == self.TEACHER:
fewshot_ds = self.create_fewshot_splits(
train_ds,
self.args.teacher_sample_sizes,
seeds=TEACHER_SEED,
mode=self.TEACHER,
)
if self.mode == self.SETFIT_STUDENT:
fewshot_ds = self.create_fewshot_splits(
train_ds,
self.args.student_sample_sizes,
seeds=STUDENT_SEEDS,
mode=self.SETFIT_STUDENT,
)
self.student_train_dataset = fewshot_ds
# for training baseline student use the same data that was used for training setfit student
if self.mode == self.BASELINE_STUDENT:
fewshot_ds = self.student_train_dataset
num_classes = len(train_ds.unique("label"))
self.bl_stdnt_distill.update_metric(metric)
for name in fewshot_ds:
results_path = os.path.join(self.output_path, dataset, name, "results.json")
print(f"\n\n======== {os.path.dirname(results_path)} =======")
os.makedirs(os.path.dirname(results_path), exist_ok=True)
if self.mode == self.TEACHER:
teacher_model = SetFitModel.from_pretrained(self.model_name)
teacher_trainer = SetFitTrainer(
model=teacher_model,
train_dataset=fewshot_ds[name],
eval_dataset=eval_dataset,
loss_class=losses.CosineSimilarityLoss,
metric=metric,
batch_size=self.args.batch_size_teacher,
num_iterations=self.args.num_iterations_teacher, # The number of text pairs to generate for contrastive learning
num_epochs=1, # The number of epochs to use for contrastive learning
)
teacher_trainer.train()
# Evaluate the model on the test data
metrics = teacher_trainer.evaluate()
print("Teacher metrics: ", metrics)
self.teacher_train_dataset = fewshot_ds[name] # save teacher training data
self.trained_teacher_model = teacher_trainer.model
if self.mode == self.SETFIT_STUDENT:
# student train data = teacher train data + unlabeled data
student_train_dataset = concatenate_datasets([self.teacher_train_dataset, fewshot_ds[name]])
student_model = SetFitModel.from_pretrained(self.model_name)
student_trainer = DistillationSetFitTrainer(
teacher_model=self.trained_teacher_model,
train_dataset=student_train_dataset,
student_model=student_model,
eval_dataset=eval_dataset,
loss_class=losses.CosineSimilarityLoss,
metric="accuracy",
batch_size=self.args.batch_size_student,
num_iterations=self.args.num_iterations_student, # The number of text pairs to generate for contrastive learning
# column_mapping={"sentence": "text", "label": "label"} # Map dataset columns to text/label expected by trainer
)
# Student Train and evaluate
student_trainer.train()
metrics = student_trainer.evaluate()
print("Student metrics: ", metrics)
if self.mode == self.BASELINE_STUDENT:
student_train_dataset = concatenate_datasets([self.teacher_train_dataset, fewshot_ds[name]])
metrics = self.train_baseline_student(student_train_dataset, eval_dataset, num_classes)
print("Baseline model score: ", round(metrics[metric] * 100, 3))
with open(results_path, "w") as f_out:
json.dump(
{"score": round(metrics[metric] * 100, 3), "measure": metric},
f_out,
sort_keys=True,
)
def train_baseline_student(self, train_data, test_data, num_classes):
x_train = train_data["text"]
x_test = test_data["text"]
y_test = test_data["label"]
x_train_embd_student = self.trained_teacher_model.model_body.encode(x_train)
# baseline student uses teacher probabilities (converted to logits) for training
y_train_teacher_pred_prob = self.trained_teacher_model.model_head.predict_proba(x_train_embd_student)
train_raw_student_prob = Dataset.from_dict({"text": x_train, "score": list(y_train_teacher_pred_prob)})
metric = self.bl_stdnt_distill.standard_model_distillation(train_raw_student_prob, x_test, y_test, num_classes)
return metric
def main():
args = parse_args()
TEACHER = 0
SETFIT_STUDENT = 1
BASELINE_STUDENT = 2
# 1. Train few-shot teacher
fewshot_teacher = RunFewShotDistill(
args,
mode=TEACHER,
trained_teacher_model=None,
teacher_train_dataset=None,
student_train_dataset=None,
)
fewshot_teacher.train()
# 2. Train few-shot setfit student
setfit_student = RunFewShotDistill(
args,
mode=SETFIT_STUDENT,
trained_teacher_model=fewshot_teacher.trained_teacher_model,
teacher_train_dataset=fewshot_teacher.teacher_train_dataset,
student_train_dataset=None,
)
setfit_student.train()
# 3. Train few-shot baseline student
baseline_student = RunFewShotDistill(
args,
mode=BASELINE_STUDENT,
trained_teacher_model=fewshot_teacher.trained_teacher_model,
teacher_train_dataset=fewshot_teacher.teacher_train_dataset,
student_train_dataset=setfit_student.student_train_dataset,
)
baseline_student.train()
if __name__ == "__main__":
main()