Prior to performing attention, we need to permute query, key
and value. Currently we call Contiguous after each of these
permutations, which is correct but expensive. Avoiding the
3 calls to Contiguous increases performance by over 20%.
The permutations of query and key do not violate the continuity
rules for mulmat and the Contiguous call can be simply removed.
Value requires a different permutation and does require Contiguous.
However, we can use the copy into the cache as a way to perform this
without further overhead.
To support this and avoid unexpected tensor shapes that are seen by
models, we need tighter integration between attention, cache
and backend. Future optimization will also likely need this structure
- for example, flash attention has special padding requirements in
the cache and other backends may have their own needs.
This further contains the operations that go into attention so that
these and other optimizations can be handled transparently. Models
that have special requirements for attention can still implement
their own version of it.
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
There are two benefits to doing this:
- Provide a library function that models can use, reducing code for
each model implementation
- Enables a single place to drop in optimized implementations of
attention based on the backend or other factors. One is provided for
GGML.
On CUDA this improves token generation rate by about 3%. It does not
have a significant effect on Metal.
Co-authored-by: Daniel Hiltgen <daniel@ollama.com>
We don't need to create and destroy the GGML scheduler for every
context. This introduces extra CPU overhead for every forward
pass and extra memory for contexts that don't actually get scheduled
(for example, KV caches). We can instead just have one scheduler
for the backend and reset it each time we call Compute.
This improves token generation performance by 1-2% and removes
scheduler create/destroy from profile traces.
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
We need to sync before retrieving data after async computation.
It is also important to ensure that the Go buffer is not moved by
the GC across function calls so we do a synchronous copy.
Passing in a Go buffer is not safe because the garbage collector could
free or move the memory while the context is still open. However, if
we pass in the size and a nil pointer then GGML will allocate it from
the C side.
Most tensor backends try to optimize performance by using a lower
precision for matmuls. However, some operations (such as kq) on
some models are sensitive to this and require full precision.
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
Currently there is a mixture of int and int64 used when dealing with
tensor dimensions and shapes, which causes unnecessary conversions -
they all should be the same type.
In general, most interfaces (such as Pytorch) use int64 for
generality but most implementations (such as CUDA) use int32 for
performance. There isn't much benefit to us to being more flexible
than the implementations we are likely to run on.
In addition, as a practical matter, a model with a tensor with a single
dimension larger than 32 bits is unlikely to run on a 32-bit machine.
It is not common to return errors with close/free operations - most
people won't check it and even if they did there's probably not much
that can do. It's better to not give implementations false expectations.
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>
Jesse Gross
Michael Yang
Daniel Hiltgen