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Similar to the llama engine, quantizing the KV cache requires
flash attention to be enabled through the Ollama server.

Debug logging of every token has previously caused test timeouts
on slower machines.

This change bring in various interface cleanups along with greatly improving the performance of the sampler.

Tested with llama3.2 on local machine.
Improves performance from ~ 70 tokens/s -> 135 tokens/s with topK(40) enabled.
Without topK performance is ~ 110 tokens/s

Various vision models have different requirements for how they
receive their inputs. For example:
 - Mllama wants images together with text and the image embeddings
   don't themselves have positions or get stored in the main KV cache
 - Llava-style models feed in embeddings similar to tokens and
   images correspond to a varying number of tokens in the cache.

In addition, the strategy for providing inputs must support batching
and multiple sequences, which are managed by the runner. At the same
time, we want to keep data handling fully in the model so that new
architectures are not bottlenecked by runner code which does not
understand their particular requirements.

This provides a method for models to edit the input stream so that
it meets their needs while still being in a format that the runner
understands. This allows the runner to avoid special processing
for different models.

In addition, this fixes a regression where non-vision models may
try to incorrectly interpret images.

We sometimes tokenize partial strings. For example, with
multimodal inputs, we split the input string around the images
and then tokenize each piece. In these cases, we should only add
the special tokens on the first piece.

- output backend system info when initializing the backend. this ensures
  this information is always present without needing to be called
  explicitly
- convert to structured logging
- enumerate devices rather than backends since devices are ordered
- track device indices grouped by device name

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.

We currently print system info before the GGML backends are loaded.
This results in only getting information about the default lowest
common denominator runner. If we move up the GGML init then we can
see what we are actually running.

Before:
time=2025-02-14T11:15:07.606-08:00 level=INFO source=runner.go:935 msg=system info="CPU : LLAMAFILE = 1 | CPU : LLAMAFILE = 1 | cgo(gcc)" threads=24

After:
time=2025-02-14T11:16:02.936-08:00 level=INFO source=runner.go:935 msg=system info="CPU : LLAMAFILE = 1 | CPU : LLAMAFILE = 1 | CUDA : ARCHS = 890 | USE_GRAPHS = 1 | PEER_MAX_BATCH_SIZE = 128 | CPU : SSE3 = 1 | SSSE3 = 1 | AVX = 1 | AVX2 = 1 | F16C = 1 | FMA = 1 | AVX512 = 1 | AVX512_VBMI = 1 | AVX512_VNNI = 1 | LLAMAFILE = 1 | cgo(gcc)" threads=24

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