fix: providing a tensor to `cache_position` in `model.generate` kwargs always crashes because of boolean test
Author: guicho271828
Pull request: yes

Currently, giving `cache_position` to `model.generate` is broken because providing any tensor value to it results in performing a boolean test, which causes a runtime error. This PR fixes it.

This is basically an oversight in the change commited in #37986 --- the code path is untested.

<!--
Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost.
-->

```
Traceback (most recent call last):
  File "/home/masataro/test-kv.py", line 77, in <module>
    outputs = model.generate(**inputs_trimmed, do_sample=False, max_new_tokens=256, past_key_values=past_key_values, cache_position=cache_position)
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/masataro/miniforge3/envs/test/lib/python3.12/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
    return func(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^
  File "/home/masataro/miniforge3/envs/test/lib/python3.12/site-packages/transformers/generation/utils.py", line 2623, in generate
    result = self._sample(
             ^^^^^^^^^^^^^
  File "/home/masataro/miniforge3/envs/test/lib/python3.12/site-packages/transformers/generation/utils.py", line 3568, in _sample
    model_kwargs = self._get_initial_cache_position(cur_len, input_ids.device, model_kwargs)
                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/masataro/miniforge3/envs/test/lib/python3.12/site-packages/transformers/generation/utils.py", line 1799, in _get_initial_cache_position
    if "cache_position" in model_kwargs and model_kwargs["cache_position"]:
                                            ~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^
RuntimeError: Boolean value of Tensor with more than one value is ambiguous
```

sample code (test-kv.py):
```python
import torch
from transformers import AutoTokenizer,AutoModelForCausalLM
from transformers.cache_utils import (
    DynamicCache,
)

model_id = "ibm-granite/granite-3.2-8b-instruct"
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained(model_id)

user_prompts = ["Hello, what's your name?",
                "Btw, yesterday I was on a rock concert.",
                "The song was 'we are the world'",
                ]

# with kv cache for all past context
past_key_values = DynamicCache()
last_output_length=0
messages = []
for prompt in user_prompts:
    #                      outputs[0].shape[1]
    #                  /~~~~~~~~~~~~~~~~~~~~~~~~~~\ output contains this string
    # IIIIIIIIOOOOOOOOOOIIIIIIIIIOOOOOOOOOOOOOOOOOO
    #                  |        +-- input length  |
    #                  +--last output length      + next output length
    #                           \-----------------/
    #                               completion
    print(prompt)
    messages.append({"role": "user", "content": prompt})
    inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt", return_dict=True).to(model.device)
    input_length = inputs["input_ids"].shape[1]
    inputs_trimmed={
        'input_ids':inputs["input_ids"][:, last_output_length:],
        'attention_mask':inputs["attention_mask"],
    }
    cache_position = torch.arange(last_output_length, input_length, dtype=torch.int64, device=model.device)
    assert inputs_trimmed["input_ids"].shape[1] == cache_position.shape[0]
    outputs = model.generate(**inputs_trimmed,
                             do_sample=False,
                             max_new_tokens=256,
                             use_cache=True,
                             past_key_values=past_key_values,
                             cache_position=cache_position)
    completion = tokenizer.decode(outputs[0, input_length - last_output_length: ], skip_special_tokens=True)
    print(completion)
    messages.append({"role": "assistant", "content": completion})
    last_output_length += outputs.shape[1]

```

## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case). --- *does not apply*
- [X] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
      Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case. --- *could not find any similar issue*
- [ ] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation). --- *does not apply*
- [ ] Did you write any new necessary tests? --- *help needed.* 


## Who can review?

@FremyCompany @ArthurZucker @zucchini-nlp @gante

Comments:
Done. I wonder if this test is correct.

@guicho271828 ideally, the test would be an integration test (runs once per CI run) rather than a mixin test (runs once per model per CI run 😬 ) i.e. keep it in the same file, but in `GenerationIntegrationTests` instead of in `GenerationTesterMixin`, using a dummy model checkpoint (e.g. `hf-internal-testing/tiny-random-MistralForCausalLM`)

if it's too much hassle for you, lmk and I'll push the changes :)

> if it's too much hassle for you, lmk and I'll push the changes :)

Yeah I don't know much about the testing mechanism here; Thanks a lot.

The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/transformers/pr_39261). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

@guicho271828 sorry, I did something wrong with your branch and it closed this PR 👀 I've opened a copy here #39300

You'll still be attributed as an author, since it has your commits 🤗