* Optimize container images for startup
This change adjusts how to handle runner payloads to support
container builds where we keep them extracted in the filesystem.
This makes it easier to optimize the cpu/cuda vs cpu/rocm images for
size, and should result in faster startup times for container images.
* Refactor payload logic and add buildx support for faster builds
* Move payloads around
* Review comments
* Converge to buildx based helper scripts
* Use docker buildx action for release
This adjusts linux to follow a similar model to windows with a discrete archive
(zip/tgz) to cary the primary executable, and dependent libraries. Runners are
still carried as payloads inside the main binary
Darwin retain the payload model where the go binary is fully self contained.
For simplicity, perform parallelization of embedding requests in the API handler instead of offloading this to the subprocess runner. This keeps the scheduling story simpler as it builds on existing parallel requests, similar to existing text completion functionality.
Don't allow loading models that would lead to memory exhaustion (across vram, system memory and disk paging). This check was already applied on Linux but should also be applied on Windows as well.
If the system has multiple numa nodes, enable numa support in llama.cpp
If we detect numactl in the path, use that, else use the basic "distribute" mode.
Make sure if something goes wrong spawning the process, the user gets
enough info to be able to try to self correct, or at least file a bug
with details so we can fix it. Once the process starts, we immediately
change back to the recommended setting to prevent the blocking dialog.
This ensures if the model fails to load (OOM, unsupported model type,
etc.) the process will exit quickly and we can scan the stdout/stderr
of the subprocess for the reason to report via API.
On windows, the exit status winds up being the search term many
users search for and end up piling in on issues that are unrelated.
This refines the reporting so that if we have a more detailed message
we'll suppress the exit status portion of the message.
The v5 hip library returns unsupported GPUs which wont enumerate at
inference time in the runner so this makes sure we align discovery. The
gfx906 cards are no longer supported so we shouldn't compile with that
GPU type as it wont enumerate at runtime.
* Initial Batch Embedding
* Revert "Initial Batch Embedding"
This reverts commit c22d54895a280b54c727279d85a5fc94defb5a29.
* Initial Draft
* mock up notes
* api/embed draft
* add server function
* check normalization
* clean up
* normalization
* playing around with truncate stuff
* Truncation
* Truncation
* move normalization to go
* Integration Test Template
* Truncation Integration Tests
* Clean up
* use float32
* move normalize
* move normalize test
* refactoring
* integration float32
* input handling and handler testing
* Refactoring of legacy and new
* clear comments
* merge conflicts
* touches
* embedding type 64
* merge conflicts
* fix hanging on single string
* refactoring
* test values
* set context length
* clean up
* testing clean up
* testing clean up
* remove function closure
* Revert "remove function closure"
This reverts commit 55d48c6ed17abe42e7a122e69d603ef0c1506787.
* remove function closure
* remove redundant error check
* clean up
* more clean up
* clean up
* llm: avoid loading model if system memory is too small
* update log
* Instrument swap free space
On linux and windows, expose how much swap space is available
so we can take that into consideration when scheduling models
* use `systemSwapFreeMemory` in check
---------
Co-authored-by: Daniel Hiltgen <daniel@ollama.com>
When ollama is running a long time, tmp cleaners can remove the
runners. This tightens up a few corner cases on arm macs where
we failed with "server cpu not listed in available servers map[]"
Previously, some costly things were causing the loading of GGUF files
and their metadata and tensor information to be VERY slow:
* Too many allocations when decoding strings
* Hitting disk for each read of each key and value, resulting in a
not-okay amount of syscalls/disk I/O.
The show API is now down to 33ms from 800ms+ for llama3 on a macbook pro
m3.
This commit also prevents collecting large arrays of values when
decoding GGUFs (if desired). When such keys are encountered, their
values are null, and are encoded as such in JSON.
Also, this fixes a broken test that was not encoding valid GGUF.
This adjusts our default settings to enable multiple models and parallel
requests to a single model. Users can still override these by the same
env var settings as before. Parallel has a direct impact on
num_ctx, which in turn can have a significant impact on small VRAM GPUs
so this change also refines the algorithm so that when parallel is not
explicitly set by the user, we try to find a reasonable default that fits
the model on their GPU(s). As before, multiple models will only load
concurrently if they fully fit in VRAM.
Prior to this change, we logged the memory prediction multiple times
as the scheduler iterates to find a suitable configuration, which can be
confusing since only the last log before the server starts is actually valid.
This now logs once just before starting the server on the final configuration.
It also reports what library instead of always saying "offloading to gpu" when
using CPU.
On Windows, recent llama.cpp changes make mmap slower in most
cases, so default to off. This also implements a tri-state for
use_mmap so we can detect the difference between a user provided
value of true/false, or unspecified.
We update the PATH on windows to get the CLI mapped, but this has
an unintended side effect of causing other apps that may use our bundled
DLLs to get terminated when we upgrade.
Still not complete, needs some refinement to our prediction to understand the
discrete GPUs available space so we can see how many layers fit in each one
since we can't split one layer across multiple GPUs we can't treat free space
as one logical block
