repeat is a convenience operation for repeating a tensor n times along a
dimention. this can replace instances where the same tensors are stacked
together
Mistral is a popular research lab making open source models. This updates
the forward pass of llama architecture models to support both llama models
and mistral models by accounting for additional metadata present in mistral
models, and finding the correct dimensions for the output projection.
Close chunked writers as soon as downloads complete, rather than
deferring closure until Pull exits. This prevents exhausting file
descriptors when pulling many layers.
Instead of unbounded defers, use a WaitGroup and background goroutine
to close each chunked writer as soon as its downloads finish.
Also rename 'total' to 'received' for clarity.
Currently sliding window attention allocates and uses the full
context size and just masks out any tokens that are outside of the
window. However, we really only need (roughly) the sliding window
size.
At large context sizes this improves two things:
- Memory allocated - since the fully context size is allocated up front,
memory requirements drop substantially. On Gemma3:4b with a 32k
context window, total memory usage (including weights and non-sliding
layers) drops from ~20GB to ~8GB.
- Computation - ranges that are completely outside of the sliding
window are now removed from the tensors that are returned from the
cache rather than simply being masked out. This results in more
efficient processing, scaling with the size of the context that
has actually been used.
Notable, this does not update the scheduler for any model to be aware of
the smaller memory requirements. This is difficult for Gemma3 because
the layers are heterogeneous between sliding and non-sliding attention.
As a result, while actual memory consumption will be reduced, the
scheduler will over-estimate the requirements of the model. This means
that splitting between GPUs or GPUs and CPUs will still be suboptimal.
Bug #9730
Currently the runner computes the kv size needed and creates a
cache of that size. This is the context size times number of
parallel sequences.
Cache implementations can make better decisions about their memory
usage, so instead pass in the required capacity, number of sequences
and maximum batch size. For now, the causal cache just uses this to
compute the size in the same way as before.
This enables the runner to report progress back to the Ollama server,
both for showing status to the user and also to prevent the server
from killing the runner if it thinks things have stalled.
Most of the infrastructure was already there, this extends it to
be available to the backends.
Defragging the KV cache can generate a lot of operations, so we
need to be careful that we don't overflow the number that the graph
can support. We currently account for all of the nodes that we add
to the graph for each move but we also need to include the original
cache tensors as well.
Fixes#9904
Rather than directly giving the input data to models, we can
pass a tensor instead. In the short term, this saves some duplicated
code.
Longer term, we will want to overlap setting up the next batch with
processing of the current one. In this case, we will only have the
shape of tensor but it will not be loaded with data at the time of
graph generation. By passing only a tensor to models now, we set up
this possibility and prevent them from relying on data that they won't
have in the future.
Although the same could be done for Positions and Outputs, in some
cases we either need the raw input data or don't use them at all.
Therefore, for now we leave them as they are and allow models to
convert them to tensors as needed.
