This PR detects embedding models and sets batch_size = context_size so the full input fits in a single batch.
Previously, if batch size was smaller than the input, tokens could be split across batches and cause a SIGTRAP crash.
This change ensures all tokens stay in one batch and prevents crashes.
Fixes: #12938#13054
Co-authored-by: Jesse Gross <jesse@ollama.com>
Adds logprobs support to Ollama's API including support for Ollama's
OpenAI-compatible API. By specifying the new 'logprobs' boolean parameter
in the API, Ollama will return the log probabilities for each token generated.
'top_logprobs', an integer value can also be specified up to the value 20.
When specified, the API will also provide the number of most likely tokens to
return at each token position
Co-authored-by: Baptiste Jamin <baptiste@crisp.chat>
Currently, checking the length of prompts for embeddings to ensure
they fit in the context window (and possible truncation) occurs in
two places - the Ollama server and runner. This can lead to
inconsistencies in both the checks and reported number of tokens
processed. Since we have to do this processing in the runner, this
consolidates all of the logic there.
* test: harden scheduler tests
This removes reschedDelay which was stale code, and adds
a new configurable timeout for the waitForVRAMRecovery so
tests can now set the timeout to be very short to avoid the
scheduler getting stuck and hitting a test timeout.
* test: tune tests for partial loads
Give stress tests more time when the model is split between CPU/GPU
This revamps how we discover GPUs in the system by leveraging the Ollama
runner. This should eliminate inconsistency between our GPU discovery and the
runners capabilities at runtime, particularly for cases where we try to filter
out unsupported GPUs. Now the runner does that implicitly based on the actual
device list. In some cases free VRAM reporting can be unreliable which can
leaad to scheduling mistakes, so this also includes a patch to leverage more
reliable VRAM reporting libraries if available.
Automatic workarounds have been removed as only one GPU leveraged this, which
is now documented. This GPU will soon fall off the support matrix with the next
ROCm bump.
Additional cleanup of the scheduler and discovery packages can be done in the
future once we have switched on the new memory management code, and removed
support for the llama runner.
* tests: add single threaded history test
Also tidies up some existing tests to handle more model output variation
* test: add support for testing specific architectures
* tests: reduce stress on CPU to 2 models
This should avoid flakes due to systems getting overloaded with 3 (or more) models running concurrently
* tests: allow slow systems to pass on timeout
If a slow system is still streaming a response, and the response
will pass validation, don't fail just because the system is slow.
* test: unload embedding models more quickly
The context must always be able to store the current batch, so
if the user requests a small context then we should also shrink
the batch to match. This also fixes the TestLongInputContext
test on the new engine. (The old engine already has this behavior.)
* perf: build graph for next batch in parallel to keep GPU busy
This refactors the main run loop of the ollama runner to perform the main GPU
intensive tasks (Compute+Floats) in a go routine so we can prepare the next
batch in parallel to reduce the amount of time the GPU stalls waiting for the
next batch of work.
* tests: tune integration tests for ollama engine
This tunes the integration tests to focus more on models supported
by the new engine.
* test: improve scheduler/concurrency stress tests
The scheduler test used to use approximate memory figures and would often
over or under shoot a systems capcity leading to flaky test results.
This should improve the reliability of this scenario by leveraging
ps output to determinie exactly how many models it takes to
trigger thrashing.
The concurrency test is also refined to target num_parallel + 1 and handle
timeouts better.
With these refinements, TestMultiModelConcurrency was redundant
* test: add parallel generate with history
TestGenerateWithHistory will help verify caching and context
are properly handled while making requests
* test: focus embed tests on embedding models
remove non-embedding models from the embedding tests
* Move quantization logic to GGML via new backend
This moves the model aware logic to Go code and calls GGMLs quantization code for model creation.
* Remove "add model quantizations"
This is no longer needed now that quantization is implemented in Go+GGML code directly.
Models may require that a set of inputs all be processed as part
of the same batch. For example, if an image has multiple patches
with fully connected attention between them, we should not split
the batch in the middle of an image.
Fixes#9697
Fragmentation of the KV cache can occur due to cache shifting or
different sequences getting processed. Decode uses a heuristic to
decide if it should defrag. However, this heuristic isn't 100%
accurate, so decoding can sometimes fail by surprise.
For these cases, if decode indicates that there is no KV cache space,
we should defrag and then try again.
* Give unicode test more time to run
Some slower GPUs (or partial CPU/GPU loads) can take more than the default 30s to complete this test
* Give more time for concurrency test
CPU inference can be very slow under stress
