Please, I need help to run my model and I am stuck!
I try here to train a Siamese BERT model, using a particular dataset (that I transformed in dataloader…)

But I got this error (the GPU is : ‘Tesla P100-PCIE-16GB’)
RuntimeError                              Traceback (most recent call last)
/tmp/ipykernel_34/2427691835.py in 
11         optim.zero_grad()
12         # prepare batches and more all to the active device
—> 13         inputs_ids_a = batch[‘code_file1_input_ids’].to(device)
14         inputs_ids_b = batch[‘code_file2_input_ids’].to(device)
15         attention_a = batch[‘code_file1_attention_mask’].to(device)
RuntimeError: CUDA error: device-side assert triggered
CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1.
from tqdm.auto import tqdm

for epoch in range(4):
    model.train()  # make sure model is in training mode
    # initialize the dataloader loop with tqdm (tqdm == progress bar)
    loop = tqdm(loader, leave=True)
    for batch in loop:
        # zero all gradients on each new step
        optim.zero_grad()
        # prepare batches and more all to the active device
        inputs_ids_a = batch['code_file1_input_ids'].to(device)
        inputs_ids_b = batch['code_file2_input_ids'].to(device)
        attention_a = batch['code_file1_attention_mask'].to(device)
        attention_b = batch['code_file2_attention_mask'].to(device)
        label = batch['similar_or_different'].to(device)
        # extract token embeddings from BERT
        u = model(inputs_ids_a, attention_mask=attention_a)[0]  # all token embeddings A
        v = model(inputs_ids_b, attention_mask=attention_b)[0]  # all token embeddings B
        .........
       ..............
        # process concatenated tensor through FFNN
        x = ffnn(x)
        # calculate the 'softmax-loss' between predicted and true label
        loss = loss_func(x, label)
        # using loss, calculate gradients and then optimize
        loss.backward()
        optim.step()


Knowing that: I reduced the batch_size to 1
And when I tried, just before this code, this one: It passes!!!
from tqdm.auto import tqdm
loop = tqdm(loader, leave=True)
for batch in loop:
    inputs_ids_a = batch['code_file1_input_ids'].to(device)

Here the output (I couldn’t add the other screenshot becaue i was told new users can’t integrate two images… )