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"""
A quantized model executes some or all of the operations with integers rather than floating point values. This allows for a more compact models and the use of high performance vectorized operations on many hardware platforms.
As a result, you get about 40% smaller and faster models. The speed-up depends on your CPU and how PyTorch was build and can be anywhere between 10% speed-up and 300% speed-up.
Note: Quantized models are only available for CPUs. Use a GPU, if available, for optimal performance.
For more details:
https://pytorch.org/docs/stable/quantization.html
"""
import logging
import os
import torch
from sentence_transformers import LoggingHandler, SentenceTransformer, util, InputExample
from sentence_transformers.evaluation import EmbeddingSimilarityEvaluator
from torch.nn import Embedding, Linear
from torch.quantization import quantize_dynamic
import gzip
import csv
import time
#### Just some code to print debug information to stdout
logging.basicConfig(format='%(asctime)s - %(message)s',
datefmt='%Y-%m-%d %H:%M:%S',
level=logging.INFO,
handlers=[LoggingHandler()])
#### /print debug information to stdout
#Check if dataset exsist. If not, download and extract it
sts_dataset_path = 'datasets/stsbenchmark.tsv.gz'
if not os.path.exists(sts_dataset_path):
util.http_get('https://sbert.net/datasets/stsbenchmark.tsv.gz', sts_dataset_path)
#Limit torch to 4 threads
torch.set_num_threads(4)
#### Just some code to print debug information to stdout
logging.basicConfig(format='%(asctime)s - %(message)s',
datefmt='%Y-%m-%d %H:%M:%S',
level=logging.INFO)
### /print debug information to stdout
model_name = 'all-distilroberta-v1'
# Load a named sentence model (based on BERT). This will download the model from our server.
# Alternatively, you can also pass a filepath to SentenceTransformer()
model = SentenceTransformer(model_name, device='cpu')
q_model = quantize_dynamic(model, {Linear, Embedding})
# Convert the dataset to a DataLoader ready for training
logging.info("Read STSbenchmark dataset")
test_samples = []
sentences = []
with gzip.open(sts_dataset_path, 'rt', encoding='utf8') as fIn:
reader = csv.DictReader(fIn, delimiter='\t', quoting=csv.QUOTE_NONE)
for row in reader:
score = float(row['score']) / 5.0 # Normalize score to range 0 ... 1
inp_example = InputExample(texts=[row['sentence1'], row['sentence2']], label=score)
sentences.append(row['sentence1'])
sentences.append(row['sentence2'])
if row['split'] == 'test':
test_samples.append(inp_example)
sentences = sentences[0:10000]
logging.info("Evaluating speed of unquantized model")
start_time = time.time()
emb = model.encode(sentences, show_progress_bar=True)
diff_normal = time.time() - start_time
logging.info("Done after {:.2f} sec. {:.2f} sentences / sec".format(diff_normal, len(sentences) / diff_normal))
logging.info("Evaluating speed of quantized model")
start_time = time.time()
emb = q_model.encode(sentences, show_progress_bar=True)
diff_quantized = time.time() - start_time
logging.info("Done after {:.2f} sec. {:.2f} sentences / sec".format(diff_quantized, len(sentences) / diff_quantized))
logging.info("Speed-up: {:.2f}".format(diff_normal / diff_quantized))
#########
evaluator = EmbeddingSimilarityEvaluator.from_input_examples(test_samples, name='sts-test')
logging.info("Evaluate regular model")
model.evaluate(evaluator)
print("\n\n")
logging.info("Evaluate quantized model")
q_model.evaluate(evaluator)
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