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import os.path
import time
from pathlib import Path
from typing import Callable, Optional, Tuple
import pandas as pd
from datasets import Dataset
from optimum.onnxruntime import (
ORTModelForSequenceClassification,
ORTOptimizer,
ORTQuantizer,
)
from optimum.onnxruntime.configuration import (
AutoCalibrationConfig,
AutoOptimizationConfig,
AutoQuantizationConfig,
)
from optimum.pipelines import pipeline as opt_pipeline
from pynvml import nvmlDeviceGetHandleByIndex, nvmlDeviceGetMemoryInfo, nvmlInit
from sklearn.metrics import roc_auc_score
from transformers import AutoTokenizer, PreTrainedModel, PreTrainedTokenizer, pipeline
from transformers.pipelines.base import KeyDataset
from detoxify.detoxify import load_checkpoint
def get_gpu_utilization() -> int:
nvmlInit()
handle = nvmlDeviceGetHandleByIndex(0)
info = nvmlDeviceGetMemoryInfo(handle)
return info.used // 1024**2 # memory in MB
def load_data(base_path: Path, nrows: Optional[int] = None) -> pd.DataFrame:
labels_path = base_path / "test_labels.csv"
test_path = base_path / "test.csv"
labels_df = pd.read_csv(labels_path, index_col=0, nrows=nrows)
test_df = pd.read_csv(test_path, index_col=0, nrows=nrows)
test_df["label"] = labels_df
return test_df
def get_toxicity(result):
return list(filter(lambda r: r["label"] == "toxicity", result))[0]["score"]
def evaluate_devices(data_path: Path, evaluate_model_fn: Callable, **kwargs):
small_df = load_data(data_path, nrows=1000)
cpu_eval = evaluate_model_fn("cpu", small_df, **kwargs)
big_df = load_data(data_path)
gpu_eval = evaluate_model_fn("cuda:0", big_df, **kwargs)
return {
"scores": gpu_eval["scores"],
"samples_per_second_cpu": len(small_df) / cpu_eval["time_seconds"],
"samples_per_second_gpu": len(big_df) / gpu_eval["time_seconds"],
"gpu_memory_mb": gpu_eval["gpu_memory_mb"],
}
def evaluate_pipeline(pipe, df):
results = pipe(
KeyDataset(Dataset.from_pandas(df), "content"),
top_k=None,
batch_size=4,
padding="longest",
truncation=True,
)
t1 = time.time()
toxicity_pred = pd.Series(map(get_toxicity, results), index=df.index)
t2 = time.time()
scores = {
"all": roc_auc_score(df.label, toxicity_pred),
}
languages = ["it", "fr", "ru", "pt", "es", "tr"]
for lang in languages:
idx = df.lang == lang
scores[lang] = roc_auc_score(df[idx].label, toxicity_pred[idx])
return {
"scores": scores,
"time_seconds": t2 - t1,
"gpu_memory_mb": get_gpu_utilization(),
}
def load_original_model(device: str) -> Tuple[PreTrainedModel, PreTrainedTokenizer]:
model, tokenizer, class_names = load_checkpoint(
model_type="multilingual", device=device
)
identity_classes = [
"male",
"female",
"homosexual_gay_or_lesbian",
"christian",
"jewish",
"muslim",
"black",
"white",
"psychiatric_or_mental_illness",
]
model.config.id2label = {n: c for n, c in enumerate(class_names + identity_classes)}
model.config.label2id = {c: n for n, c in enumerate(class_names + identity_classes)}
return model, tokenizer
def evaluate_original_model(device: str, test_df: pd.DataFrame):
model, tokenizer = load_original_model(device)
pipe = pipeline(
model=model,
task="text-classification",
tokenizer=tokenizer,
function_to_apply="sigmoid",
device=device,
)
return evaluate_pipeline(pipe, test_df)
def save_original_model(base_path: Path = Path(".")):
model, tokenizer = load_original_model("cpu")
pipe = pipeline(
model=model,
task="text-classification",
tokenizer=tokenizer,
function_to_apply="sigmoid",
)
pipe.save_pretrained(base_path)
def evaluate_ort_model(device: str, test_df: pd.DataFrame, base_path: Path = Path(".")):
model = ORTModelForSequenceClassification.from_pretrained(base_path, export=True)
tokenizer = AutoTokenizer.from_pretrained(base_path, device=device)
pipe = opt_pipeline(
model=model,
task="text-classification",
tokenizer=tokenizer,
function_to_apply="sigmoid",
device=device,
accelerator="ort",
)
return evaluate_pipeline(pipe, test_df)
def evaluate_ort_optimize_model(
device: str, test_df: pd.DataFrame, base_path: Path = Path(".")
):
tokenizer = AutoTokenizer.from_pretrained(base_path, device=device)
if not os.path.exists(base_path / "model_optimized.onnx"):
model = ORTModelForSequenceClassification.from_pretrained(
base_path, export=True
)
# oconfig = AutoOptimizationConfig.O1(fp16=True)
oconfig = AutoOptimizationConfig.O4()
optimizer = ORTOptimizer.from_pretrained(model)
optimizer.optimize(
save_dir=base_path,
optimization_config=oconfig,
)
model = ORTModelForSequenceClassification.from_pretrained(
base_path, file_name="model_optimized.onnx"
)
pipe = opt_pipeline(
model=model,
task="text-classification",
function_to_apply="sigmoid",
device=device,
accelerator="ort",
tokenizer=tokenizer,
)
return evaluate_pipeline(pipe, test_df)
def evaluate_ort_quantize_model(
device: str,
test_df: pd.DataFrame,
base_path: Path = Path("."),
overwrite: bool = False,
):
tokenizer = AutoTokenizer.from_pretrained(base_path, device=device)
if overwrite or not os.path.exists(base_path / "model_quantized.onnx"):
model = ORTModelForSequenceClassification.from_pretrained(
base_path, export=True
)
qconfig = AutoQuantizationConfig.avx2(is_static=True, per_channel=False)
quantizer = ORTQuantizer.from_pretrained(model)
def preprocess_fn(ex):
return tokenizer(ex["content"])
# Calibrate based on the dataset
calibration_dataset = (
Dataset.from_pandas(test_df)
.map(preprocess_fn)
.select_columns(["input_ids", "attention_mask"])
)
calibration_config = AutoCalibrationConfig.minmax(calibration_dataset)
ranges = quantizer.fit(
dataset=calibration_dataset,
calibration_config=calibration_config,
operators_to_quantize=qconfig.operators_to_quantize,
)
quantizer.quantize(
save_dir=base_path,
quantization_config=qconfig,
calibration_tensors_range=ranges,
)
model = ORTModelForSequenceClassification.from_pretrained(
base_path,
file_name="model_quantized.onnx",
foo="bar",
)
pipe = opt_pipeline(
model=model,
task="text-classification",
function_to_apply="sigmoid",
device=device,
accelerator="ort",
tokenizer=tokenizer,
)
return evaluate_pipeline(pipe, test_df)
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser()
parser.add_argument(
"data_path",
type=str,
help="Path to jigsaw multilingual toxic comment data. "
'For example: "jigsaw_data/jigsaw-multilingual-toxic-comment-classification"',
)
parser.add_argument(
"--models_path",
type=str,
default=".",
help="Path to model weights directory (root of the repo)",
)
parser.add_argument(
"model", type=str, help="Model to evaluate (original, ort, optimized, quantized)."
)
args = parser.parse_args()
data = Path(args.data_path)
models_p = Path(args.models_path)
if args.model == "original":
print(evaluate_devices(data, evaluate_original_model))
elif args.model == "ort":
print(evaluate_devices(data, evaluate_ort_model, base_path=models_p))
elif args.model == "optimized":
print(evaluate_devices(data, evaluate_ort_optimize_model, base_path=models_p))
elif args.model == "quantized":
print(evaluate_devices(data, evaluate_ort_quantize_model, base_path=models_p))
else:
raise ValueError(f"Invalid model received: {args.model!r}")
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