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- 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

expand backend loading error handling to catch more problems and log
them instead of panicing

sapphire rapids has amx support but it ends up having a negative
performance impact.

emerald rapids also has amx support with a positive performance impact
however there's no reasonable way in ggml to differentiate between the
two. the impact is small (~6%) so disable amx entirely for simplicity

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

* wrap ggml_backend_load_best in try/catch
* ignore non-ollama paths

Shield the code processing the embedding result
from subsequent calls that may overwrite the same
buffer to process a second input when retrieving
model embeddings.

* chore: update gitattributes
* chore: add build info source

This reverts commit bea1f1fa.

* add build to .dockerignore

* test: only build one arch

* add build to .gitignore

* fix ccache path

* filter amdgpu targets

* only filter if autodetecting

* Don't clobber gpu list for default runner

This ensures the GPU specific environment variables are set properly

* explicitly set CXX compiler for HIP

* Update build_windows.ps1

This isn't complete, but is close.  Dependencies are missing, and it only builds the "default" preset.

* build: add ollama subdir

* add .git to .dockerignore

* docs: update development.md

* update build_darwin.sh

* remove unused scripts

* llm: add cwd and build/lib/ollama to library paths

* default DYLD_LIBRARY_PATH to LD_LIBRARY_PATH in runner on macOS

* add additional cmake output vars for msvc

* interim edits to make server detection logic work with dll directories like lib/ollama/cuda_v12

* remove unncessary filepath.Dir, cleanup

* add hardware-specific directory to path

* use absolute server path

* build: linux arm

* cmake install targets

* remove unused files

* ml: visit each library path once

* build: skip cpu variants on arm

* build: install cpu targets

* build: fix workflow

* shorter names

* fix rocblas install

* docs: clean up development.md

* consistent build dir removal in development.md

* silence -Wimplicit-function-declaration build warnings in ggml-cpu

* update readme

* update development readme

* llm: update library lookup logic now that there is one runner (#8587)

* tweak development.md

* update docs

* add windows cuda/rocm tests

---------
Co-authored-by: jmorganca <jmorganca@gmail.com>
Co-authored-by: Daniel Hiltgen <daniel@ollama.com>

This change adds a test to catch a regression in schema_to_grammar where
the order of keys in the JSON schema is not preserved in the generated
grammar, which is critical for step-by-step reasoning.

Sometimes the KV cache requires defragmentation even without
triggering the threshold heuristic. In this case, decoding
will not being able to find a KV cache slot. This is particularly
difficult for the caller to handle if it happens in between
ubatches. To avoid this, we should immediately trigger a defrag.

In addition, a heavily fragmented cache can require more than
max_moves to defragment. Currently, we stop when we hit the limit
but this can leave a cache that still does not have adequate space
even after defragmentation is triggered. Instead, we should do
multiple batches of processing until everything is complete.

Fixes #7949

This puts the low-level runner logging back on stderr for consistency with prior releases

Previously we decoded and re-encoded JSON schemas during validation,
which served no purpose since json.RawMessage already validates JSON
syntax. Worse, the re-encoding lost field ordering from the original
schema, which affects inference quality during step-by-step reasoning.

While fixing this ordering issue by using json.RawMessage directly,
testing revealed that schema_to_grammar (from llama.cpp) also fails to
preserve field order during grammar generation. This appears to be the
root cause of inference degradation.

This change prevents us from mangling the user's original schema order,
but we still need to address the ordering issue in schema_to_grammar.
That will be a separate change.

Updates #7978

The final implementation of #7499 removed dynamic vector requirements
in favor of a simpler filename based model, and this was left over logic that
is no longer needed.

* llama: wire up builtin runner

This adds a new entrypoint into the ollama CLI to run the cgo built runner.
On Mac arm64, this will have GPU support, but on all other platforms it will
be the lowest common denominator CPU build.  After we fully transition
to the new Go runners more tech-debt can be removed and we can stop building
the "default" runner via make and rely on the builtin always.

* build: Make target improvements

Add a few new targets and help for building locally.
This also adjusts the runner lookup to favor local builds, then
runners relative to the executable, and finally payloads.

* Support customized CPU flags for runners

This implements a simplified custom CPU flags pattern for the runners.
When built without overrides, the runner name contains the vector flag
we check for (AVX) to ensure we don't try to run on unsupported systems
and crash.  If the user builds a customized set, we omit the naming
scheme and don't check for compatibility.  This avoids checking
requirements at runtime, so that logic has been removed as well.  This
can be used to build GPU runners with no vector flags, or CPU/GPU
runners with additional flags (e.g. AVX512) enabled.

* Use relative paths

If the user checks out the repo in a path that contains spaces, make gets
really confused so use relative paths for everything in-repo to avoid breakage.

* Remove payloads from main binary

* install: clean up prior libraries

This removes support for v0.3.6 and older versions (before the tar bundle)
and ensures we clean up prior libraries before extracting the bundle(s).
Without this change, runners and dependent libraries could leak when we
update and lead to subtle runtime errors.

Adds structured outputs to chat endpoint
---------
Co-authored-by: Michael Yang <mxyng@pm.me>
Co-authored-by: Hieu Nguyen <hieunguyen1053@outlook.com>