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The GGML flash attention kernel has specific requirements for
padding and permutation. This adds support to the KV cache
for conforming to these requirements so that flash attention
can be enabled.

Flash attention can be used in the same situations as the llama
engine and is enabled by the user in the same way.

defer the cancel to guarantee it runs

As are adding support for weighted sampling we have seen some performance
regressions, bypassing the sampler logic for now and defaulting to greedy
until we can benchmark the new sampler logic.

Currently the following parameters are in the runner but not used:
 - numGPULayers
 - mainGPU
 - threads
 - tensorSplit

This passes them through to the backend, which is where they would
actually get used. However, the GGML backend does not yet do anything
with them.

This provides integration with the new Ollama engine
(58245413 next ollama runner (#7913)) and the rest of the Ollama
infrastructure such as the runner and Ollama server.

In addition, it also builds out the KV cache infrastructure to
support requirements of how Ollama runs models such as:
 - Parallel processing
 - Memory management for defragmentation and shifting
 - Multi-modal modals

Both old and new engines continue to be supported. By default, only
the old engine is used. To enable the new engine:

Start the server with the OLLAMA_NEW_ENGINE environment variable set:
OLLAMA_NEW_ENGINE=1 ./ollama serve

Start a model that is supported by the Ollama engine. This one is Llama 3.1 8b Q4_K_M:
./ollama run jessegross/llama3.1