Understanding gpu usage huggingface classification - Total optimization steps

Beginners
1

I am training huggingface longformer for a classification problem and got below output.

  1. I am confused about Total optimization steps. As I have 7000 training data points and 5 epochs and Total train batch size (w. parallel, distributed & accumulation) = 64, shouldn’t I get
    7000*5/64 steps? that comes to 546.875? why is it showing Total optimization steps = 545

  2. Why in the below output, there are 16 steps of Input ids are automatically padded from 1500 to 1536 to be a multiple of config.attention_window: 512 then [ 23/545 14:24 < 5:58:16, 0.02 it/s, Epoch 0.20/5]? what are these steps?

==========================================================

***** Running training *****
  Num examples = 7000
  Num Epochs = 5
  Instantaneous batch size per device = 4
  Total train batch size (w. parallel, distributed & accumulation) = 64
  Gradient Accumulation steps = 16
  Total optimization steps = 545
Initializing global attention on CLS token...
Input ids are automatically padded from 1500 to 1536 to be a multiple of `config.attention_window`: 512
Initializing global attention on CLS token...
Input ids are automatically padded from 1500 to 1536 to be a multiple of `config.attention_window`: 512
Initializing global attention on CLS token...
Input ids are automatically padded from 1500 to 1536 to be a multiple of `config.attention_window`: 512
Initializing global attention on CLS token...
Input ids are automatically padded from 1500 to 1536 to be a multiple of `config.attention_window`: 512
Initializing global attention on CLS token...
Input ids are automatically padded from 1500 to 1536 to be a multiple of `config.attention_window`: 512
Initializing global attention on CLS token...
Input ids are automatically padded from 1500 to 1536 to be a multiple of `config.attention_window`: 512
Initializing global attention on CLS token...
Input ids are automatically padded from 1500 to 1536 to be a multiple of `config.attention_window`: 512
Initializing global attention on CLS token...
Input ids are automatically padded from 1500 to 1536 to be a multiple of `config.attention_window`: 512
Initializing global attention on CLS token...
Input ids are automatically padded from 1500 to 1536 to be a multiple of `config.attention_window`: 512
Initializing global attention on CLS token...
Input ids are automatically padded from 1500 to 1536 to be a multiple of `config.attention_window`: 512
Initializing global attention on CLS token...
Input ids are automatically padded from 1500 to 1536 to be a multiple of `config.attention_window`: 512
Initializing global attention on CLS token...
Input ids are automatically padded from 1500 to 1536 to be a multiple of `config.attention_window`: 512
Initializing global attention on CLS token...
Input ids are automatically padded from 1500 to 1536 to be a multiple of `config.attention_window`: 512
Initializing global attention on CLS token...
Input ids are automatically padded from 1500 to 1536 to be a multiple of `config.attention_window`: 512
Initializing global attention on CLS token...
Input ids are automatically padded from 1500 to 1536 to be a multiple of `config.attention_window`: 512
Initializing global attention on CLS token...
Input ids are automatically padded from 1500 to 1536 to be a multiple of `config.attention_window`: 512
 [ 23/545 14:24 < 5:58:16, 0.02 it/s, Epoch 0.20/5]
Epoch	Training Loss	Validation Loss