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#Run inference benchmarks
import argparse
import logging
import os
import pathlib
import time
import numpy as np
import torch
from transformers import AutoTokenizer
from transformers import AutoModelForSequenceClassification
from transformers import BertConfig, BertForSequenceClassification
from utils.process_data import read_and_preprocess_data, REVERSE_MAPPING
def inference(predict_fn, batch, n_runs) -> float:
"""Run inference using the provided `predict_fn`
Args:
predict_fn: prediction function to use
batch: data batch from a data loader
n_runs: number of benchmark runs to time
Returns:
float : Average prediction time
"""
times = []
predictions = []
with torch.no_grad():
for _ in range(2 + n_runs):
start = time.time()
res = predict_fn(batch)
end = time.time()
predictions.append(res)
times.append(end - start)
avg_time = np.mean(times[2:])
return avg_time
def main(flags) -> None:
"""Setup model for inference and perform benchmarking
Args:
FLAGS: benchmarking flags
"""
if flags.logfile == "":
logging.basicConfig(level=logging.DEBUG)
else:
path = pathlib.Path(flags.logfile)
path.parent.mkdir(parents=True, exist_ok=True)
logging.basicConfig(filename=flags.logfile, level=logging.DEBUG)
logger = logging.getLogger()
if not os.path.exists(flags.saved_model_dir):
logger.error("Saved model %s not found!", flags.saved_model_dir)
return
# Load dataset into memory
tokenizer = AutoTokenizer.from_pretrained(flags.saved_model_dir)
try:
test_dataset = read_and_preprocess_data(
flags.input_file,
tokenizer,
max_length=flags.seq_length,
include_label=False
)
test_loader = torch.utils.data.DataLoader(
test_dataset, batch_size=flags.batch_size, shuffle=False
)
except FileNotFoundError as exc:
logger.error("Please follow instructions to download data.")
logger.error(exc, exc_info=True)
return
# Load model into memory, if INC, need special loading
model = AutoModelForSequenceClassification.from_pretrained(flags.saved_model_dir)
# JIT model for faster execution
batch = next(iter(test_loader))
token_ids = batch['input_ids']
mask = batch['attention_mask']
jit_inputs = (token_ids, mask)
logger.info("Using stock model")
model.eval()
model = torch.jit.trace(model,jit_inputs,check_trace=False,strict=False)
model = torch.jit.freeze(model)
def predict(
batch
) -> torch.Tensor:
"""Predicts the output for the given batch
using the given PyTorch model.
Args:
batch (torch.Tensor): data batch from data loader
transformers tokenizer
Returns:
torch.Tensor: predicted quantities
"""
return model(
input_ids=batch['input_ids'],
attention_mask=batch['attention_mask'],
)
if flags.benchmark_mode:
logger.info("Running experiment n = %d, b = %d, l = %d",
flags.n_runs, flags.batch_size, flags.seq_length)
average_time = inference(predict, batch, FLAGS.n_runs)
logger.info('Avg time per batch : %.3f s', average_time)
else:
predictions = []
index = 0
for _, batch in enumerate(test_loader):
pred_probs = torch.softmax(
predict(batch)['logits'], axis=1
).detach().numpy()
for i in range(len(pred_probs)):
probs = {
REVERSE_MAPPING[x]: pred_probs[i, x]
for x in np.argsort(pred_probs[i, :])[::-1][:5]
}
predictions.append(
{'id': index, 'prognosis': probs}
)
index += 1
print({"predictions": predictions})
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument(
'--saved_model_dir',
required=True,
help="saved pretrained model to benchmark",
type=str
)
parser.add_argument(
'--input_file',
required=True,
help="input to make predictions on",
type=str
)
parser.add_argument(
'--batch_size',
default=-1,
type=int,
help="batch size to use. if -1, uses all entries in input."
)
parser.add_argument(
'--benchmark_mode',
default=False,
help="Benchmark instead of get predictions.",
action="store_true"
)
parser.add_argument(
'--seq_length',
default=512,
help="sequence length to use. defaults to 512.",
type=int
)
parser.add_argument(
'--logfile',
help="logfile to use.",
default="",
type=str
)
parser.add_argument(
'--n_runs',
default=100,
help="number of trials to test. defaults to 100.",
type=int
)
FLAGS = parser.parse_args()
main(FLAGS)
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