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.
In mult-brand GPU setups, if we couldn't fully load the model we
would fall through the scheduler and mistakenly try to load across
a mix of brands. This makes sure we find the set of GPU(s) that
best fit for the partial load.
* 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
This change fixes the handling of keep_alive so that if client
request omits the setting, we only set this on initial load. Once
the model is loaded, if new requests leave this unset, we'll keep
whatever keep_alive was there.
Users may not realize the siny new model they're trying to load
fits on their disk, but can't load into system+GPU memory. Today
we crash, but with this fix, we'll give them a better error message
before even trying to load it.
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.
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
This moves all the env var reading into one central module
and logs the loaded config once at startup which should
help in troubleshooting user server logs
Prior refactoring passes accidentally removed the logic to bypass VRAM
checks for CPU loads. This adds that back, along with test coverage.
This also fixes loaded map access in the unit test to be behind the mutex which was
likely the cause of various flakes in the tests.
This change adds support for multiple concurrent requests, as well as
loading multiple models by spawning multiple runners. The default
settings are currently set at 1 concurrent request per model and only 1
loaded model at a time, but these can be adjusted by setting
OLLAMA_NUM_PARALLEL and OLLAMA_MAX_LOADED_MODELS.
