CUDA: Add backend sampler for penalties sampler (#25262)

CUDA: Add backend sampler for penalties sampler (#25262)

* sampling: enhance penalty handling in common_sampler_init

- Set default value for penalty_last_n based on model context if not specified.
- Ensure penalty_last_n and n_prev are non-negative.
- Update llama_sampler_penalties structure to inherit from llama_sampler_backend and add backend input handling for penalties.
- Implement backend initialization and application logic for penalties, including frequency and presence adjustments.

* tests: add backend penalties sampling tests and utility functions

- Introduced `accept_prompt` and `unique_prompt_tokens` functions to handle prompt acceptance and token uniqueness.
- Implemented `compare_penalties_logits` to compare logits from backend and CPU samplers with penalties.
- Added `test_backend_penalties_sampling` to validate backend penalties with various configurations.
- Enhanced the test suite for better coverage of penalty handling in sampling.

* sampling: add support for top-k penalties in backend sampling

* sampling: add fix to ensure  stable numerical results. Preserve masked logits as -Inf and no longer generate NaN.

* sampling: enhance penalty comparison tests with masking penalties logic

* add comments on padding

* sampling: add comments on modifications

* add the unit test to cover masked-out token as -INF

* validate repeat penalty to ensure it is finite and greater than 0; add tests for invalid values

* refactor: test functions to share logic and be less verbose

* add test to cover case where previously penalized token is not part of candidates

* remove comments

* remove redundant penalty_last_n initialization and validation in common_sampler_init

* add support for penalties in sampler chain with configurable positions

* add validation for penalty parameters and enhance tests for non-finite values

* add context parameter to common_sampler_init and set default for penalty_last_n

* add llama_n_ctx parameter to common_sampler_init for improved sampler initialization

* replace penalty_last_n x n_candidates comparison matrix with a vocabulary-sized count tensor

* add tests for backend penalties sampling without filler entries , token_count.size() == n_active == n_max == 64

* add test for backend penalties sampling  after top-p with large history window

* remove as unused

* add is_disabled method, tensor logits reshape, add rest review suggestions

* clarify comment