CANN: Update several operators to support FP16 data format (#16251)
Many Ascend operators internally use FP16 precision for computation.
If input data is in FP32, it must first be cast to FP16 before
computation, and then cast back to FP32 after computation, which
introduces unnecessary cast operations. Moreover, FP16 computation
requires significantly less workload compared to FP32, leading to
noticeable efficiency improvements.
In this change, `get_rows`, `rms_norm`, and `flash_attn_ext` are extended
to support multiple data types. Validation on the Qwen2 0.5b model shows
correct accuracy and about 10% performance gain in concurrent scenarios.
Co-authored-by: noemotiovon <757486878@qq.com>

metal : add opt_step_adamw and op_sum (#16529)
* scaffold to support opt step adamw on metal (not written so far)
* add opt-step-adamw kernel for metal
* pass op->src[4] as a separate buffer to the pipeline
* add bounds check to opt-step-adamw kernel
* complete scaffold for GGML_OP_SUM
* naive GGML_OP_SUM kernel
* remove unwanted comment
* change OP_SUM capability gate
* Add has_simdgroup_reduction to both ops to pass CI

[SYCL] fix UT fault cases: count-equal, argsort, pad OPs (#16521)
* fix/refactor OP argsort, pad
* fix count-equal op
* update SYCL OP list
* fix format issue
---------
Co-authored-by: Zhang Jianyu <zhang.jianyu@outlook.com>

common : handle unicode during partial json parsing (#16526)
* common : handle unicode during partial json parsing
* common : set missing `ensure_ascii = true` during json dump

ggml : Fix FP16 ELU positive branch (#16519)
Co-authored-by: Aaron <shelhamer.aaron@gmail.com>

hparams : add check for layer index in is_recurrent (#16511)
* hparams : add check for layer index in is_recurrent
This commit adds a check in the is_recurrent method to ensure that the
provided layer index is within the valid range.
The motivation for this change is to prevent potential out-of-bounds
and also be consistent with other methods in the class that perform
similar checks, like is_swa.

ggml: Correct SVE implementation in ggml_vec_dot_f16_unroll (#16518)
The previous SVE implementation for `ggml_vec_dot_f16_unroll` contained a bug due to a copy-paste error. The wrong variable was used in an FMA instruction, leading to incorrect results. This commit corrects the variable usage and improves the clarity of the code by renaming variables to avoid confusion.
Co-authored-by: Aaron <shelhamer.aaron@gmail.com>

CUDA: faster tile FA, add oob checks, more HSs (#16492)

metal : fix mul-mm condition + fix mul-mv permuted kernels (#16494)

server / ranking : add sorting and management of top_n (#16403)
* server / ranking : add sorting and management of top_n
* Make the retro compatible if no top_n will return
all results
here is a script to make some test
```script
URL=${1:-https://127.0.0.1:8181}
curl "$URL/v1/rerank" -H "Content-Type: application/json" \
 -d '{ "model": "M", "query": "What is the recipe to make bread ?",
 "return_text" : true,
 "texts" : true,
 "top_n": 6,
 "documents": [
 "voici la recette pour faire du pain, il faut de la farine de l eau et du levain et du sel",
 "it is a bear",
 "bread recipe : floor, water, yest, salt",
 "The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.",
 "here is the ingedients to bake bread : 500g floor, 350g water, 120g fresh refresh yest, 15g salt",
 "recipe to make cookies : floor, eggs, water, chocolat",
 "here is the recipe to make bread : 500g floor, 350g water, 120g fresh refresh yest, 15g salt",
 "il fait tres beau aujourd hui",
 "je n ai pas faim, je ne veux pas manger",
 "je suis a paris"
 ] }' | jq
```
* use resize() instead for(...)
* simplify top_n init since no need to return error
result to test :
./tests.sh unit/test_rerank.py -v -x
==================================================== test session starts =====================================================
platform linux -- Python 3.12.3, pytest-8.3.5, pluggy-1.6.0 -- /home/yann/dev/yann/llama.cpp/tools/server/tests/test/bin/python3
cachedir: .pytest_cache
rootdir: /home/yann/dev/yann/llama.cpp/tools/server/tests
configfile: pytest.ini
plugins: anyio-4.11.0
collected 8 items
unit/test_rerank.py::test_rerank PASSED                                                                                [ 12%]
unit/test_rerank.py::test_rerank_tei_format PASSED                                                                     [ 25%]
unit/test_rerank.py::test_invalid_rerank_req[documents0] PASSED                                                        [ 37%]
unit/test_rerank.py::test_invalid_rerank_req[None] PASSED                                                              [ 50%]
unit/test_rerank.py::test_invalid_rerank_req[123] PASSED                                                               [ 62%]
unit/test_rerank.py::test_invalid_rerank_req[documents3] PASSED                                                        [ 75%]
unit/test_rerank.py::test_rerank_usage[Machine learning is-A machine-Learning is-19] PASSED                            [ 87%]
unit/test_rerank.py::test_rerank_usage[Which city?-Machine learning is -Paris, capitale de la-26] PASSED               [100%]
===================================================== 8 passed in 4.31s ======================================================
* add rerank top_n unit test
here is the result :
./tests.sh unit/test_rerank.py -v -x
=================================================================== test session starts ===================================================================
platform linux -- Python 3.12.3, pytest-8.3.5, pluggy-1.6.0 -- /home/yann/dev/yann/llama.cpp/tools/server/tests/test/bin/python3
cachedir: .pytest_cache
rootdir: /home/yann/dev/yann/llama.cpp/tools/server/tests
configfile: pytest.ini
plugins: anyio-4.11.0
collected 16 items
unit/test_rerank.py::test_rerank PASSED                                                                                                             [  6%]
unit/test_rerank.py::test_rerank_tei_format PASSED                                                                                                  [ 12%]
unit/test_rerank.py::test_invalid_rerank_req[documents0] PASSED                                                                                     [ 18%]
unit/test_rerank.py::test_invalid_rerank_req[None] PASSED                                                                                           [ 25%]
unit/test_rerank.py::test_invalid_rerank_req[123] PASSED                                                                                            [ 31%]
unit/test_rerank.py::test_invalid_rerank_req[documents3] PASSED                                                                                     [ 37%]
unit/test_rerank.py::test_rerank_usage[Machine learning is-A machine-Learning is-19] PASSED                                                         [ 43%]
unit/test_rerank.py::test_rerank_usage[Which city?-Machine learning is -Paris, capitale de la-26] PASSED                                            [ 50%]
unit/test_rerank.py::test_rerank_top_n[None-4] PASSED                                                                                               [ 56%]
unit/test_rerank.py::test_rerank_top_n[2-2] PASSED                                                                                                  [ 62%]
unit/test_rerank.py::test_rerank_top_n[4-4] PASSED                                                                                                  [ 68%]
unit/test_rerank.py::test_rerank_top_n[99-4] PASSED                                                                                                 [ 75%]
unit/test_rerank.py::test_rerank_tei_top_n[None-4] PASSED                                                                                           [ 81%]
unit/test_rerank.py::test_rerank_tei_top_n[2-2] PASSED                                                                                              [ 87%]
unit/test_rerank.py::test_rerank_tei_top_n[4-4] PASSED                                                                                              [ 93%]
unit/test_rerank.py::test_rerank_tei_top_n[99-4] PASSED                                                                                             [100%]
=================================================================== 16 passed in 8.84s ===================================================================
* editor config check fix