* Metal memory: Small memory leak on init, dangling pointer, and unused autorelease pool in graph compute

* SPM header potential fix

* Reverting symlinks

* winogrande: simple implementation

It doesn't look like it is working - why?
For Mistral-7B it is barely better than
random chance (score ~60% for 1267 tasks), while I see
Mistral-7B scoring 78.4% on the HF leader board.
1-sigma statistical uncertainty for 1267 tasks is ~1.4,
so no way the difference is due to statistics.

* winogrande: somewhat better

Score for Mistrali7-B is now 68.9 on the validation set of
winogrande_debiased. Still far from the reported 78.4, but
better than what I had before.

* winogrande: improving

Mistral-7B score is now 73.56.
Still not quite 78.4 but getting there.
We are also getting a lower score on HellaSwag
compared to HF leader board, so I'm not expecting
we will get up to 78.4 anyway.

It looks like it is better to skip the choice word(s)
when evaluating the average log-likelihood. This kind of
makes sense because a more common word (in Winogrande this is
often a name) will have a higher probability without knowing
about the follow up context, and this will skew the log-likelihood
towards the more common word. We can only do this if the
choice words are not last in the sentence.

It also looks like it is better to skip the punctuation at the
end of the sentence, provided the choice words are not last.

* winogrande: add dataset instructions

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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>

* perplexity : faster HellaSwag

ggml-ci

* perplexity : clean-up

ggml-ci

* perplexity : no need for decode_helper

ggml-ci

* perplexity : add comments

* perplexity : option to specify max batched tasks via `n_parallel`

* perplexity : remove HellaSwag restruction for n_batch

For Mistral-7B and fp16, time on my system goes down from 536 seconds
to 423 seconds for the full evaluation dataset (10042 tasks).

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>

PR #4818 (merged last week) reintroduced a config check for vocab_size that was addressed in PR #4258 (merged 2023-11-30).

Without the fix, llama2 models can't be converted. The error is:

`ValueError: The model's vocab size is set to -1 in params.json. Please update it manually. Maybe 32000?`

* server: defer task when no slot is available

* remove unnecessary log

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Co-authored-by: Xuan Son Nguyen <xuanson.nguyen@snowpack.eu>

* falcon arch fix for tied output embeddings

* Update llama.cpp

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* Update llama.cpp

* Update llama.cpp

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* Update llama.cpp

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* perplexity : faster Winogrande via batching

ggml-ci

* perplexity : remove unused function

* perplexity : only tokenize selected tasks for Winogrande

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>

* llama: add codeshell support

* llama.cpp: fix codeshell with NeoX rope

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

This is a relatively minor performance tweak resulting in
~10% speedup on my system.

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>

* Fix issue with alloc causing max_compute_size to be calculated

* remove ggml_allocr_free as suggested in issue #4791