For every forward pass through the model, we need to allocate input
tensors: tokens, images, positions, outputs and masks. These get
allocated in system memory.
However, when we close the context that the tensors were allocated
through, the metadata gets freed but the actual backend memory does
not. This results in a significant memory leak.
This makes it so that all the memory allocated through a context
gets freed when it is closed.
Fixes#10040
Allocating (and in particular, freeing) memory from CUDA host buffers
is expensive and can cause a significant performance hit if we do
it for every token. Using normal system memory avoids this issue
and also gives the OS more flexibility to manage it.
There is no performance impact from this patch directly (either
positive or negative) but it makes a difference once we start
freeing memory correctly.
Context is currently mixed between pointer and value receivers. Change
this to be all pointer receivers so don't have to reason about whether
the things we are updating in the struct will be retained.
Sometimes loading the GGUF file fails with:
panic: context canceled
This is probably a filesystem error but it doesn't provide any
information about what happened.
Currently, the KV cache and graph are lazily allocated as needed.
The cache is fully allocated on first use of the corresponding
layer whereas the graph grows with the size of the context.
This can be an issue if another application allocates more VRAM
after we do our calculations - Ollama will crash in the middle of
inference. If we instead allocate the maximum needed memory at
startup of the runner, we will either succeed or fail at that point
rather than at some surprising time in the future.
Currently, this only generates a worst case batch for text, which
means that vision models may get a partial allocation and continue
to lazily allocate the rest.
If there is a CUDA OOM, we currently don't check the return value
and will evetually segfault. This checks for the problem and generates
a Go error. At the moment, this will still result in a panic but having
the error is the first step to being able to handle it more gracefully.
improves model loading times on network-based filesystems
such as GCS fuse by creating a dedicated file descriptor for each
section of the file being read, reducing seeking
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.
Model implementations should use Input for all of their tensors
supplied to the model. This includes tensors that relate to the
outputs, which is confusing since there is also an Output funciton.
Since Output is only used internally in GGML and not used by any
model implementations, we can remove it from the interface to
reduce confusion.
When converting a ggml model if there is a failure to read tensor data a nil error value was being returned. It should be assigned to the actual error from reading.
some tensors are expected to be used in repeating layers but are not
themselves repeated. this change copies these tensors into the same
backends as their repeating counterparts to minimize copying tensors
between backends
use a similar strategy as llama.cpp for deciding where tensors should be
allocated. this will be improved later to be aware of usable memory
before assigning the tensor
- output backend system info when initializing the backend. this ensures
this information is always present without needing to be called
explicitly
- convert to structured logging
- enumerate devices rather than backends since devices are ordered
- track device indices grouped by device name
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.
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.
