This wires up some new logic to start using sysfs to discover AMD GPU
information and detects old cards we can't yet support so we can fallback to CPU mode.
At least with the ROCm libraries, its possible to have the library
present with zero GPUs. This fix avoids a divide by zero bug in llm.go
when we try to calculate GPU memory with zero GPUs.
We build the GPU libraries with AVX enabled to ensure that if not all
layers fit on the GPU we get better performance in a mixed mode.
If the user is using a virtualization/emulation system that lacks AVX
this used to result in an illegal instruction error and crash before this
fix. Now we will report a warning in the server log, and just use
CPU mode to ensure we don't crash.
Fix an ordering glitch of dlerr/dlclose and add more logging to help
root cause some crashes users are hitting. This also refines the
function pointer names to use the underlying function names instead
of simplified names for readability.
This adds additional calls to both CUDA and ROCm management libraries to
discover additional attributes about the GPU(s) detected in the system, and
wires up runtime verbosity selection. When users hit problems with GPUs we can
ask them to run with `OLLAMA_DEBUG=1 ollama serve` and share the results.
Update gpu.go initGPUHandles() to declare gpuHandles variable before
reading it. This resolves an "invalid memory address or nil pointer
dereference" error.
Update dyn_ext_server.c to avoid setting the RTLD_DEEPBIND flag under
__TERMUX__ (Android).
This reduces the built-in linux version to not use any vector extensions
which enables the resulting builds to run under Rosetta on MacOS in
Docker. Then at runtime it checks for the actual CPU vector
extensions and loads the best CPU library available
In some cases we may want multiple variants for a given GPU type or CPU.
This adds logic to have an optional Variant which we can use to select
an optimal library, but also allows us to try multiple variants in case
some fail to load.
This can be useful for scenarios such as ROCm v5 vs v6 incompatibility
or potentially CPU features.
* increase minimum cuda overhead and fix minimum overhead for multi-gpu
* fix multi gpu overhead
* limit overhead to 10% of all gpus
* better wording
* allocate fixed amount before layers
* fixed only includes graph alloc
When there are multiple management libraries installed on a system
not every one will be compatible with the current driver. This change
improves our management library algorithm to build up a set of discovered
libraries based on glob patterns, and then try all of them until we're able to
load one without error.
If you attempt to run the current CUDA build on compute capability 5.2
cards, you'll hit the following failure:
cuBLAS error 15 at ggml-cuda.cu:7956: the requested functionality is not supported
* select layers based on estimated model memory usage
* always account for scratch vram
* dont load +1 layers
* better estmation for graph alloc
* Update gpu/gpu_darwin.go
Co-authored-by: Bruce MacDonald <brucewmacdonald@gmail.com>
* Update llm/llm.go
Co-authored-by: Bruce MacDonald <brucewmacdonald@gmail.com>
* Update llm/llm.go
* add overhead for cuda memory
* Update llm/llm.go
Co-authored-by: Bruce MacDonald <brucewmacdonald@gmail.com>
* fix build error on linux
* address comments
---------
Co-authored-by: Bruce MacDonald <brucewmacdonald@gmail.com>
If we try to load the CUDA library on an old GPU, it panics and crashes
the server. This checks the compute capability before we load the
library so we can gracefully fall back to CPU mode.
Refactor where we store build outputs, and support a fully dynamic loading
model on windows so the base executable has no special dependencies thus
doesn't require a special PATH.
This switches the default llama.cpp to be CPU based, and builds the GPU variants
as dynamically loaded libraries which we can select at runtime.
This also bumps the ROCm library to version 6 given 5.7 builds don't work
on the latest ROCm library that just shipped.
