FlashMLA-2: reduce compute buffer size (CUDA and CPU) (#260)

* FlashMLA-2: eliminate intermediate f32 tensors

This works on the CPU. PP performance is ~13% better for 16k tokens
and compute buffer is quite a bit smaller.

* FlashMLA-2: enable fast path only on the CPU for now

I did implement the necessary ops on CUDA, but something is
still wrong there, so for now we only use it when running
CPU-only.

* FlashMLA-2: slightly smaller computer buffer size

* Prepare wk_b when loading DeepSeek models (if wk_b is missing)

* Add some comments

* Fix case where wkv_b is quantized with k- or i-quants.

* Fix CUDA

There is an issue with quantized GEMV on CUDA when the left operand
(the matrix) is not contiguous. So, for now, we also create wv_b
during model loading and use that instead of the 3D view of wkv_b.

* FlashMLA-2: avoid conversions to f32 also on CUDA

* Be able to compute for more than 65535 tokens

On CUDA just a quick hack that allows us to cancatenate tensors
with more than 65535 rows along zroth dimension as needed by
FlashMLA-2. Also needed some care in the perplexity tool to
avoid int overflows when evaluating the computed logits.

* Reduce memory usage for FlashMLA-2

Oh, also fix int overflow in the CUDA concat implementation.

It is funny how the llama.cpp 64-bit police has gone (almost) everywhere
and replaced 32-bit ints with 64-bit ints, needed or not,
but hasn't done it where it is actually needed.

---------

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

0 files changed, 0 insertions, 0 deletions