This PR implements support for `batch size > 1 `and tracks the progress
of the warmup of multiple different
`prompt-length/max-decode/batch-size` shapes.

### Contributions:

- Introduce env var and interpret `BATCH_SIZE` as list of values
(similar to `MIN_PAD_LENGTH` and `MAX_NEW_TOKENS`)
- Adapt warmup loop over the zipped list containing **pad length**,
**max new tokens** and **batch size**
- Support batch dimension for input arguments (tokens, positions, masks)
in warmup algorithm
- Add batch dimension support in update function for the attention mask
(`update_mask()` in sendnn.py)
- Alter test scripts to work with `batch size > 1 `

#### The code has been tested in the following settings: 

- On **CPU**: `batch size = 4` and `batch size = 8` with
`torch.compile(backend=inductor)`
- On **AIU**: `batch size = 1` 

in both **offline** and **online** mode.


### Open questions (including unaddressed questions from
[PR23](https://github.ibm.com/ai-foundation/vllm/pull/23)):
- [x] verify code functionality for `batch size = 4` and `batch size =
8` on **AIU**
- [ ] ideally, the `SENDNNWorker` checks how many compiled shapes fit in
AIU memory before starting to warmup all of them. Unclear how to decide,
implementation missing.
- [ ] How to handle requests that are too long? Right now there are just
cut to maximum padding length (we should probably fail the request and inform the client)
- [ ] verify output of example prompts