Spaces:
Sleeping
Sleeping
File size: 7,579 Bytes
72e2b6e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 | import os
import mlflow
import numpy as np
import torch
from torch.utils.data import DataLoader
from transformers import EarlyStoppingCallback, Trainer, TrainingArguments
from src.data.loader import load_clinc150, load_splits, save_splits
from src.data.preprocessor import preprocess
from src.evaluation.metrics import (
compute_classification_metrics,
compute_latency,
get_classification_report,
)
from src.models.transformer import IntentDatasetHF, TransformerModel
from src.storage.s3 import upload_artifact
from src.utils.config import load_config
from src.utils.mlflow_utils import (
get_or_create_experiment,
log_confusion_matrix,
log_metrics,
)
from src.utils.settings import settings
DATA_DIR = "data/raw"
MODEL_DIR = "artifacts/models/distilbert"
def load_or_download_data(config: dict) -> tuple:
train_path = os.path.join(DATA_DIR, "train.csv")
if os.path.exists(train_path):
print("loading data from disk...")
splits = load_splits(DATA_DIR)
else:
print("downloading CLINC150...")
splits = load_clinc150(config["data"]["subset"])
save_splits(splits, DATA_DIR)
processed, label_map = preprocess(splits)
return processed, label_map
def compute_metrics_hf(eval_pred) -> dict:
logits, labels = eval_pred
preds = np.argmax(logits, axis=1)
metrics = compute_classification_metrics(labels, preds)
return {
"accuracy": metrics["accuracy"],
"macro_f1": metrics["macro_f1"],
}
def predict_all(
transformer: TransformerModel,
dataset: IntentDatasetHF,
batch_size: int,
device: torch.device,
) -> tuple[np.ndarray, np.ndarray]:
transformer.model.eval()
transformer.model.to(device)
loader = DataLoader(dataset, batch_size=batch_size, shuffle=False)
all_preds = []
all_labels = []
with torch.no_grad():
for batch in loader:
input_ids = batch["input_ids"].to(device)
attention_mask = batch["attention_mask"].to(device)
labels = batch["labels"]
outputs = transformer.model(input_ids=input_ids, attention_mask=attention_mask)
preds = outputs.logits.argmax(dim=1).cpu().numpy()
all_preds.extend(preds)
all_labels.extend(labels.numpy())
return np.array(all_preds), np.array(all_labels)
def main():
config = load_config("transformer")
torch.manual_seed(config["training"]["random_state"])
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"device: {device}")
processed, label_map = load_or_download_data(config)
model_cfg = config["model"]
training_cfg = config["training"]
max_length = config["data"]["max_length"]
print(f"loading {model_cfg['name']}...")
transformer = TransformerModel(
model_name=model_cfg["name"],
num_labels=model_cfg["num_labels"],
dropout=model_cfg["dropout"],
)
print("tokenizing datasets...")
train_dataset = IntentDatasetHF(
processed["train"]["text"].tolist(),
processed["train"]["label_id"].tolist(),
transformer.tokenizer,
max_length,
)
val_dataset = IntentDatasetHF(
processed["validation"]["text"].tolist(),
processed["validation"]["label_id"].tolist(),
transformer.tokenizer,
max_length,
)
test_dataset = IntentDatasetHF(
processed["test"]["text"].tolist(),
processed["test"]["label_id"].tolist(),
transformer.tokenizer,
max_length,
)
mlflow.set_tracking_uri(settings.mlflow_tracking_uri)
experiment_id = get_or_create_experiment(config["mlflow"]["experiment_name"])
with mlflow.start_run(
experiment_id=experiment_id,
run_name=config["mlflow"]["run_name"],
) as run:
mlflow.log_params(
{
"model.name": model_cfg["name"],
"model.num_labels": model_cfg["num_labels"],
"model.dropout": model_cfg["dropout"],
"training.epochs": training_cfg["epochs"],
"training.batch_size": training_cfg["batch_size"],
"training.learning_rate": training_cfg["learning_rate"],
"training.warmup_steps": training_cfg["warmup_steps"],
"training.weight_decay": training_cfg["weight_decay"],
"data.max_length": max_length,
}
)
training_args = TrainingArguments(
output_dir=MODEL_DIR,
num_train_epochs=training_cfg["epochs"],
per_device_train_batch_size=training_cfg["batch_size"],
per_device_eval_batch_size=training_cfg["batch_size"],
learning_rate=training_cfg["learning_rate"],
warmup_steps=training_cfg["warmup_steps"],
weight_decay=training_cfg["weight_decay"],
eval_strategy="epoch",
save_strategy="epoch",
load_best_model_at_end=True,
metric_for_best_model="macro_f1",
greater_is_better=True,
logging_steps=50,
report_to="none",
seed=training_cfg["random_state"],
)
trainer = Trainer(
model=transformer.model,
args=training_args,
train_dataset=train_dataset,
eval_dataset=val_dataset,
compute_metrics=compute_metrics_hf,
callbacks=[EarlyStoppingCallback(early_stopping_patience=2)],
)
print("\nfine-tuning distilbert...")
trainer.train()
print("\nloading best checkpoint...")
transformer.save(MODEL_DIR)
id_to_label = {v: k for k, v in label_map.items()}
label_names = [id_to_label[i] for i in range(len(label_map))]
print("evaluating on test set...")
test_preds, test_labels = predict_all(transformer, test_dataset, training_cfg["batch_size"], device)
test_metrics = compute_classification_metrics(test_labels, test_preds)
test_metrics_logged = {f"test_{k}": v for k, v in test_metrics.items()}
log_metrics(test_metrics_logged)
def predict_fn(dataset):
predict_all(transformer, dataset, training_cfg["batch_size"], device)
latency = compute_latency(predict_fn, test_dataset, n_runs=20)
log_metrics(latency)
report = get_classification_report(test_labels, test_preds, label_names)
report_path = "artifacts/distilbert_report.txt"
with open(report_path, "w") as f:
f.write(report)
mlflow.log_artifact(report_path)
log_confusion_matrix(
test_labels,
test_preds,
label_names,
save_path="artifacts/distilbert_confusion_matrix.png",
)
mlflow.log_artifact(MODEL_DIR)
print("uploading to S3...")
for fname in os.listdir(MODEL_DIR):
local_path = os.path.join(MODEL_DIR, fname)
if os.path.isfile(local_path):
upload_artifact(local_path, f"{config['s3']['prefix']}/distilbert/{fname}")
print(f"\ntest accuracy : {test_metrics['accuracy']}")
print(f"test macro_f1 : {test_metrics['macro_f1']}")
print(f"latency p50 : {latency['latency_p50_ms']}ms")
print(f"run id : {run.info.run_id}")
if __name__ == "__main__":
main()
|