Hi,
I have tried BERT uncased model with Tensorflow and Pytorch but Pyt is slower . In pytorch if I increase the batch size to 512 the system gets hang.
transformers 4.35.2
tensorflow 2.15.0
torch 2.1.1
Corresponding codes are in BERT model is slow in Pytorch 路 Issue #27756 路 huggingface/transformers 路 GitHub
This might silly question but often neglected so I will ask.
Do you aware that the batch_size in tensorflow and pytorch is not same?
Also It鈥檚 hard to see your code. Would you put it in your post?
I am not expert but I am interested in your problem. thx!
The TF code :
import os
import sys
from time import time
import tensorflow as tf
from transformers import BertTokenizer, TFBertModel
import numpy as np
#intra_threads = int(sys.argv[1])
#inter_threads = int(sys.argv[2])
#batch_size = int(sys.argv[3])
batch_size = 4
#print(intra_threads)
#print(inter_threads)
#print(batch_size)
intra_threads = int(4)
#tf.config.threading.set_inter_op_parallelism_threads(inter_threads)
tf.config.threading.set_intra_op_parallelism_threads(intra_threads)
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = TFBertModel.from_pretrained("bert-base-uncased")
text = batch_size*["""jsdzlhxkgburnfyijmmocanfmurasggxxfzsjszirhwgvlamms
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"""]
encoded_input = tokenizer.encode(text, padding=True, truncation=True, return_tensors='tf')
# warmup
# we run model for 5 iterations
for i in range(5):
output=model.predict(encoded_input, verbose=False)[0]
times=[]
for i in range(25):
start_time = time()
output = model.predict(encoded_input, verbose=False)[0]
times.append(time() - start_time)
print("average time (seconds) for bert Inference: %.2fms " %(np.average(times[1:])*1e3))
PyT code :
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from transformers import BertTokenizerFast, BertModel
from time import time
import numpy as np
import os
os.environ['OMP_NUM_THREADS'] = '4'
batch_size = 4
# Create an instance of the BertModel class
model = BertModel.from_pretrained("bert-base-uncased")
#model.load_state_dict(torch.load("path/to/your/pretrained/model/weights"))
#print(model.config)
# Tokenize the text and create a batch of padded and truncated tokens
tokenizer = BertTokenizerFast.from_pretrained("bert-base-uncased")
text = batch_size * ["""jsdzlhxkgburnfyijmmocanfmurasggxxfzsjszirhwgvlamms
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"""]
neuron_inputs = tokenizer(text, return_tensors='pt', padding='max_length', truncation=True)
inputs = tuple(neuron_inputs.values())
# Inference
with torch.no_grad():
# Warm Up
for i in range(5):
#model(**inputs)
predictions = model(*inputs)[0]
prof = torch.profiler.profile(
record_shapes=True, with_stack=True)
# profile after warmupSS
times=[]
for i in range(10):
start_time = time()
predictions = model(*inputs)[0]
#model(**inputs)
times.append(time() - start_time)
prof.start()
predictions = model(*inputs)[0]
#model(**inputs)
prof.stop()
# logits = model(**inputs).logits
# print(prof.key_averages().table(row_limit=10000))
print("average time (seconds) for bert Inference: %.2fms " %(np.average(times[1:])*1e3))
# print (et-st)
#del os.environ['OMP_NUM_THREADS']
del os.environ['OMP_NUM_THREADS']
@Hakase-Noonna ,
Thank you. pytorch is slower than tf if the same batch size and input is used and if the batch size increased to 512 the pyt code gets hang.
Hey, Would you check encoded_input in tensorflow script one more time?
I got the shape with (1,6). It should be same as pytorch shape (4, 512).
thx!