The CUDA APIs for reporting free VRAM are useless on NVIDIA iGPU
systems as they only return the kernels actual free memory and ignore
buff/cache allocations which on a typical system will quickly fill up
most of the free system memory. As a result, we incorrectly think
there's very little available for GPU allocations which is wrong.
This will likely yield builds that have problems with unicode characters
but at least we can start testing the release while we try to find an
alternate clang compiler for windows, or mingw ships a fixed version.
As we automatically enable flash attention for more models, there
are likely some cases where we get it wrong. This allows setting
OLLAMA_FLASH_ATTENTION=0 to disable it, even for models that usually
have flash attention.
This revamps how we discover GPUs in the system by leveraging the Ollama
runner. This should eliminate inconsistency between our GPU discovery and the
runners capabilities at runtime, particularly for cases where we try to filter
out unsupported GPUs. Now the runner does that implicitly based on the actual
device list. In some cases free VRAM reporting can be unreliable which can
leaad to scheduling mistakes, so this also includes a patch to leverage more
reliable VRAM reporting libraries if available.
Automatic workarounds have been removed as only one GPU leveraged this, which
is now documented. This GPU will soon fall off the support matrix with the next
ROCm bump.
Additional cleanup of the scheduler and discovery packages can be done in the
future once we have switched on the new memory management code, and removed
support for the llama runner.
The GGML CUDA backend allocates additional memory for intermediate
results during calculation. This memory isn't currently allocated
during worst case graph reservation and therefore not included in
scheduling. This means that as these buffers potentially grow
with context length, we could crash.
This extends the memory allocation system down layer from the GGML
graph to the CUDA layer, preallocating the worst case memory there
as well.
Fixes#11753
The GGML scale kernel uses signed 32-bit ints to represent
the number of elements in the tensor. For large images,
mistral-small3.2 overflows this, triggering CUDA errors due
to negative arguments.
Currently, this can happen when the user passes a large image
to mistral-small3.2. However, with upcoming changes to reserve
CUDA memory, it happens every time mistral-small is loaded as
we reserve using a worst case batch.
This patch is part of an upstream GGML commit and should be removed
after GGML is updated past 0a1b398 "ggml: add ops for WAN video model
(cuda && cpu) (#15669)".
Fixes#10388
For each memory allocation we report the size of the (attempted)
allocation and whether it succeeded or failed. The latter status
reporting proved to be not that useful in practice as systems
such as Windows can automatically overflow from VRAM into RAM,
resultings in successful allocations even when there isn't
enough memory where we wanted.
As a result, this information is only used for debug logging,
which isn't worthwhile enough for the amount of code. It
also isn't fully accurate, as multiple allocations may result
in partial failures.
There are two bugs when using `/load <model>` for a model that doesn't exist, namely:
1. it will not restore the current model settings if the current model is a thinking model; and
2. it will crash is the current model is a non-thinking model
This bug fix saves the current runOptions and then restores them if the model load
doesn't happen. It also fixes the crash happening for non-thinking models.
When trimming whitespace at the end of every chunk, we were iterating
backwards over the string byte-by-byte instead of rune-by-rune.
As an example of how this can cause corruption, suppose we have the
multi-byte character ✅ (`"\u2705"`), which is represented in utf-8 as
the three bytes `0xE2 0x9C 0x85`. It happens that `0x85` is NEL, which
passes `unicode.IsSpace()`. Because we were iterating byte-by-byte, this
caused us to mistakenly slice in the middle of the rune, removing `0x85`
and leaving `0xE2 0x9C`, which beyond being the incorrect place to
slice, is not even a valid utf-8 character.
`trailingWhitespaceLen()` was modified to count from the end in a
rune-aware way. Tests with various multibyte unicode characters were
also added.
Fixes: #12414
* init deepseek model file
* temp removal of flash attention implementation
* shapes and proper, can make a pass
* query, key, value have good cosine similarity, but the max diff is a bit high
* Attention block is working! ** with eager for now, have not added the mask line
* Attention block is working! ** with eager for now, have not added the mask line
* working MoE at around 0.95 cosine sim
* added cosine similarity function
* Starting end to end structure
* Trying (and failing) to get rope to work, going to test full thing on tater
* running on tater36... just not the right outputs
* we have the right values for rope... but its still not working?
* chnage Extrapolation Factor to 1
* removed adding residuals twice, removed normalization from shared expert, refactored Norms (Attention, MLP) to be outside the (Attention, MLP) blocks and in the Transformer block instead, add cache setLayer
* Temporary modelfiles for cpu
* change kpass intermediate step to kv, two layer outputs [0,1] look fine
* this calls for 16 chicken nuggets
* whoops
* cleaning up code
* delete stuff we dont need
* getting rid of debug statements for llama cpp
* working with long contexts
* fix long context view error
* reverting some changes I made for files that are not apart of pr
* Added proper tokenizer for deeepseek3
* clean up model and go test
* remove Modelfile
* not passing the tests
* whoops
* how to pass the ci tests
* resolving some of the comments
* rename
* linted and renamed deepseek3 -> deepseek2
* remove name go
* addressed changes - main change was adopting qwen3 naming scheme
* I cannot with linters
* clean up logs
* clean up logs
---------
Co-authored-by: Grace Guo <graceguo@Graces-MBP.localdomain>
Co-authored-by: Grace Guo <graceguo@Graces-MacBook-Pro.local>
Co-authored-by: graceguo <graceguo@tater36.localdomain>
Inforithmics
Jesse Gross
Grace
Daniel Hiltgen
Patrick Devine
Devon Rifkin
Michael Yang
羊撅撅
Gabe Goodhart
Daniel Hiltgen