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some tensors should be created on specific backends to reduce number of
copies and improve performance

use a similar strategy as llama.cpp for deciding where tensors should be
allocated. this will be improved later to be aware of usable memory
before assigning the tensor

* Include unified vision layers in memory prediction

For newer vision models with a single gguf, include
the projection estimates.

* Adjust CLI to handle both styles of vision model metadata

* Wire up new tokenizers for new engine

If we're loading the new engine, utilize the new model
text processor instead of calling into cgo wrappers for
llama.cpp.  This also cleans up some tech debt from the
older tokenization flow for the C++ server which was
no longer used.

This also adjusts the grammar handling logic to pass
through to the new engine instead of utilizing the cgo
schema to grammar call.

* Lay foundation for auto selection of new engine

Prior to performing attention, we need to permute query, key
and value. Currently we call Contiguous after each of these
permutations, which is correct but expensive. Avoiding the
3 calls to Contiguous increases performance by over 20%.

The permutations of query and key do not violate the continuity
rules for mulmat and the Contiguous call can be simply removed.

Value requires a different permutation and does require Contiguous.
However, we can use the copy into the cache as a way to perform this
without further overhead.

To support this and avoid unexpected tensor shapes that are seen by
models, we need tighter integration between attention, cache
and backend. Future optimization will also likely need this structure
 - for example, flash attention has special padding requirements in
the cache and other backends may have their own needs.

This further contains the operations that go into attention so that
these and other optimizations can be handled transparently. Models
that have special requirements for attention can still implement
their own version of it.

There are two benefits to doing this:
 - Provide a library function that models can use, reducing code for
   each model implementation
 - Enables a single place to drop in optimized implementations of
   attention based on the backend or other factors. One is provided for
   GGML.

On CUDA this improves token generation rate by about 3%. It does not
have a significant effect on Metal.
Co-authored-by: Daniel Hiltgen <daniel@ollama.com>

Currently Rows is called as the last step in a model computation
to get the values for the output tokens. However, if we move it
earlier in the process then we can trim out computations that
never get used. This is similar to how models are defined in
llama.cpp.

Changing the model definition in this way improves token generation
performance by approximately 8%.

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

This allows there to be a file that is a list of models that is
not mixed into the runner code.