7 Commits

Author SHA1 Message Date
Jesse Gross
21aa666a1e ml: Enable support for flash attention
The GGML flash attention kernel has specific requirements for
padding and permutation. This adds support to the KV cache
for conforming to these requirements so that flash attention
can be enabled.

Flash attention can be used in the same situations as the llama
engine and is enabled by the user in the same way.
2025-03-01 20:53:23 -08:00
Jesse Gross
ee141cc821 ml: Empty tensor constructor for tensors
In cases where we allocate a tensor and then fully overwrite it with
copied data, it is wasteful to first zero out the memory.
2025-03-01 20:53:23 -08:00
Jesse Gross
854a9195f3 attention: Remove unnecessary contiguous operations
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.
2025-03-01 20:53:23 -08:00
Michael Yang
8b194b7520 kvcache: update tests 2025-02-27 22:27:16 +00:00
Michael Yang
3e8b8a1933 ml: update Context.Forward interface
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
2025-02-27 22:27:16 +00:00
Daniel Hiltgen
df2680b4b9
Wire up system info log for new engine (#9123) 2025-02-14 15:55:33 -08:00
Jesse Gross
ed443a0393 Runner for Ollama engine
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
2025-02-13 17:09:26 -08:00