* CUDA: Simplify and improve CUDA graphs through use of indirect copy pointers
Previously there was complexity in the CUDA graphs implementation due
frequently changing parameters to copy kernels associated with K and V
cache pointers. This patch simplifies by using indirection to avoid
such parameters frequently changing, avoiding the need for frequent
graph updates.
Fixes#12152
* Addressed comments
* fix HIP builds
* properly sync to stream
* removed ggml_cuda_cpy_fn_ptrs
* move stream sync before free
* guard to only use indirection with graphs
* style fixes
* check for errors
---------
Co-authored-by: slaren <slarengh@gmail.com>
When using group query attention, we have one workgroup per KV batch and this
can be very few workgroups (e.g. just 8 in some models). Enable split_k to
spread the work across SMs. This helps a lot when the KV cache is large.
When adjacent batches of Q share the same batches of K/V, batch them into
the same workgroup. For example, when:
dst(128,32,1,1) = FA(q(128,1,32,1), k(128,16640,8,1), v(128,16640,8,1))
previously we would run 32 workgroups computing 1 result each, now we will
run 8 workgroups computing 4 results each.
This doesn't directly translate to better performance (at least when you have
>=32 SMs), but in a subsequent change I'll enable split_k which will scale much
better with 4x fewer workgroups.
* model : print tensor size during load
* cont : fix units MB -> MiB
Co-authored-by: Diego Devesa <slarengh@gmail.com>
---------
Co-authored-by: Diego Devesa <slarengh@gmail.com>
* (wip) refactor downloading system [no ci]
* fix all examples
* fix mmproj with -hf
* gemma3: update readme
* only handle mmproj in llava example
* fix multi-shard download
* windows: fix problem with std::min and std::max
* fix 2
* Rename oneMKL Interface to oneMath
* Use oneMath for Intel vendor
* Rename occurences to mkl
* clang-format
* Silence verbose warnings
* Set oneMath HIP_TARGETS
* Fix silence warnings
* Remove step to build oneMath from build instructions
* Use fixed oneMath version
* Remove INTEL_CPU
* Fold CMake oneDNN conditions
* Use Intel oneMKL for Intel devices
* Improve CMake message
* Link against MKL::MKL_SYCL::BLAS only
* Move oneMath documentation to Nvidia and AMD sections
* vocab : add special infill tokens for CodeLlama
The commit adds the following special tokens for CodeLlama infill:
- `▁<PRE>`
- `▁<SUF>`
- `▁<MID>`
The motivation for this is that currently the infill example uses
CodeLlama as a suggested model. But when using this model the following
error is generated:
```console
/llama.cpp-debug/examples/infill/infill.cpp:165: GGML_ASSERT(llama_vocab_fim_pre(vocab) >= 0) failed
Could not attach to process. If your uid matches the uid of the target
process, check the setting of /proc/sys/kernel/yama/ptrace_scope, or try
again as the root user. For more details, see /etc/sysctl.d/10-ptrace.conf
ptrace: Operation not permitted.
No stack.
The program is not being run.
305251 Aborted (core dumped)
./build/bin/llama-infill -t 10 -ngl 0 -m models/codellama-13b.Q5_K_S.gguf \
-c 4096 --temp 0.7 --repeat_penalty 1.1 -n 20 \
--in-prefix "def helloworld():\n print(\"hell" \
--in-suffix "\n print(\"goodbye world\")\n "
```
* squash! vocab : add special infill tokens for CodeLlama
Add _<EOT> as well.
* tts.cpp : llama tokens console output is done using LOG_INF instead of printf(). Therefore the options '--log-disable' and '--log-file' have now uniform impact on all output.
This commit adds debug level logging for the native build options and
variables to ggml/CMakeLists.txt.
The motivation for this is that it can be useful to see the effective
result of `GGML_NATIVE`, `GGML_NATIVE_DEFAULT`, and `INS_ENB` for a
cmake build. I've found myself adding similar logging a few times now,
so I thought it might be a good idea to add this.
Example output, specifying `-DCMAKE_MESSAGE_LOG_LEVEL=DEBUG` when
running cmake produces the following output:
```console
-- GGML_NATIVE : OFF
-- GGML_NATIVE_DEFAULT : OFF
-- INS_ENB : OFF
```
Nauful
a3sh
Chenguang Li
Alan Gray
hipudding
lhez
Jeff Bolz
bandoti
0cc4m
Georgi Gerganov
Diego Devesa
Sigbjørn Skjæret
Xuan-Son Nguyen
Junil Kim
jklincn
R0CKSTAR
Wagner Bruna
Romain Biessy
Akarshan Biswas
Daniel Bevenius
Sandro Hanea
marcoStocchi
Juyoung Suk
Sergei Vorobyov
cmdr2