The encoder cache needs to know the position of images in the input
stream so that it knows when to delete them. Previously images didn't
have a position, so we implied one by breaking batches before an
image and then assuming the image was in the first position. However,
multimodal objects are now given explicit positions in the input
stream, so we can use that instead.
Breaking batches was also a way to simulate a cross attention mask
for mllama. However, given that it only supports a single sequence
and a single image, this mask doesn't serve any real purpose.
Removing the batch break does not appear to affect the quality of
the output.
Most of this is simply moving the input data structures to a new
package to avoid import cycles.
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.
* Include unified vision layers in memory prediction
For newer vision models with a single gguf, include
the projection estimates.
* Adjust CLI to handle both styles of vision model metadata
* Wire up new tokenizers for new engine
If we're loading the new engine, utilize the new model
text processor instead of calling into cgo wrappers for
llama.cpp. This also cleans up some tech debt from the
older tokenization flow for the C++ server which was
no longer used.
This also adjusts the grammar handling logic to pass
through to the new engine instead of utilizing the cgo
schema to grammar call.
* Lay foundation for auto selection of new engine
update Context.Forward to accept multiple tensors to match
Context.Compute signature
update Context.Forward to return Context such that it can be chained
with Context.Compute
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.
This provides integration with the new Ollama engine
(5824541 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
Currently, if a model uses an interface for its data structures (as mllama
does) then the tensor data in the structs implementing that interface will
not get loaded.
There are two cases where we may not have an output after computing:
- Prompt processing where the length of the input exceeds the batch
size
- Internal memory management operations such as cache defrag and shift
feat: add new Ollama engine using ggml through cgo
This change introduces a new way to run pretrained models. It introduces 3 high level interfaces and a bunch of smaller helper interfaces to facilitate this.
- `model.Model` defines the interface for a model architecture. Models such as `llama` and `mllama`, which are provided as examples, can implement the model's forward propagation in the `Forward` method. This method will be called to generate completions. This interface can be found in `model/model.go`
- `ml.Backend` defines the interface for a backend tensor library, in this case `ggml`. Among other things, a Backend is responsible for loading a pretrained model into hardware (GPU, CPU, etc) and providing an interface for Models to access loaded tensors. This interface can be found in `ml/backend.go`
- `ml.Tensor` defines the interface for a tensor and tensor operations
This is the first implementation of the new engine. Follow up PRs will implement more features:
- non-greedy sampling (#8410)
- integration with Ollama and KV caching (#8301)
- more model support (#9080) with more coming soon
Co-authored-by: Bruce MacDonald <brucewmacdonald@gmail.com>
