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

include/llama.h

@@ -1256,6 +1256,7 @@ extern "C" {
         struct ggml_tensor * probs;
         struct ggml_tensor * sampled;
         struct ggml_tensor * candidates;
+        int64_t              n_vocab;
     };
 
     // user code can implement the interface below in order to create custom llama_sampler
@@ -1425,9 +1426,9 @@ extern "C" {
     /// NOTE: Avoid using on the full vocabulary as searching for repeated tokens can become slow. For example, apply top-k or top-p sampling first.
     LLAMA_API struct llama_sampler * llama_sampler_init_penalties(
                              int32_t   penalty_last_n,   // last n tokens to penalize (0 = disable penalty, -1 = context size)
-                               float   penalty_repeat,   // 1.0 = disabled
-                               float   penalty_freq,     // 0.0 = disabled
-                               float   penalty_present); // 0.0 = disabled
+                               float   penalty_repeat,   // must be > 0.0, 1.0 = disabled
+                               float   penalty_freq,     // must be finite, 0.0 = disabled
+                               float   penalty_present); // must be finite, 0.0 = disabled
 
     ///  @details DRY sampler, designed by p-e-w, as described in: https://github.com/oobabooga/text-generation-webui/pull/5677, porting Koboldcpp implementation authored by pi6am: https://github.com/LostRuins/koboldcpp/pull/982
     LLAMA_API struct llama_sampler * llama_sampler_init_dry(