* Implement host pool for matrix_info
Creating a new memory pool on the host to store memory location for
matrix_info needed to launch gemm_batch from oneMKL/oneMath.
Removing complex support in gemm_batch since it is not used in llama.cpp
* Remove unnecessary headers and cast
* Reorder member variable to avoid warning on initialization
* Formatting
* Remove unused variable
* Address PR review feedback - remove warning
---------
Signed-off-by: nscipione <nicolo.scipione@codeplay.com>
This is a fork of linenoise that is C++17 compatible. I intend on
adding it to llama-run so we can do things like traverse prompt
history via the up and down arrows:
https://github.com/ericcurtin/linenoise.cpp
Signed-off-by: Eric Curtin <ecurtin@redhat.com>
* Added the ability to use guide tokens for OuteTTS, greatly improving TTS recitation accuracy over long input sequences.
* applied linting suggestions, updated to latest llama_vocab changes, added a safety check, added newline to guide token start
Add code similar to mul_mm_cm2 to force alignment of strides, to avoid
a performance regression.
Add noncontiguous FA tests in test-backend-ops.
Fixes#11268.
* vulkan: support copy from f32 to q4_0/q4_1/q5_0/q5_1/q8_0/iq4_nl
Shaders are based on cpy.cu.
* vulkan: support copy from q4_0/q4_1/q5_0/q5_1/q8_0/iq4_nl to f32
* ggml: copy q->f32 assumes some contiguity in the destination
* Add SVE support for q4_K_q8_K
* Update ggml/src/ggml-cpu/ggml-cpu-quants.c
change to use K_SCALE_SIZE
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* q6_k scale caching
* 16 bit unpack
* q4_k test (slow)
* revert it
* q3_k
* q2_k
* little stuff
* try precalculating products of a and q2_k scales
* Revert "try precalculating products of a and q2_k scales"
This reverts commit 65110b81f23f66331a50c6e889a7c1ab9470a86b.
* unpack should be u16, add vim swap to gitignore (about time)
* better q4_k scales
* q5_k
* better q6_k with separate paths for all threads and partial threads in use, plus some more optimizations
* q2_k better dequant
* q3_k optimizations
* q3_k use hmask simd from cpu avx version
* make the caches happy
* q3_k separate out calculation
* q2_k separate out
* little stuff
* use calc_superblock everywhere
* q2_k optimize scale calculation
* more barriers
* fix: ggml: fix vulkan-shaders-gen build
The vulkan-shaders-gen target was not being built correctly
in case of cross-compilation.
Other outputs need to be built for the cross compile target,
but vulkan-shaders-gen needs to be built for the host.
* refactor: ggml: Improve vulkan-shaders-gen toolchain setup
- Add GGML_SHADERS_GEN_TOOLCHAIN CMake option.
- Auto-detect host toolchain if not set.
* refactor: ggml: Improve vulkan-shaders-gen toolchain setup
Use configure_file to generate host_toolchain.cmake from template
* fix: ggml: Fix compile error
Fix compile error not finding vulkan-shaders-gen
* fix: vulkan-shaders-gen build and path handling
Fix build issues with vulkan-shaders-gen:
- Add target dependency for correct build order
- Use CMAKE_HOST_SYSTEM_NAME for executable suffix
- Fix MSVC output directory in host toolchain
- Normalize path handling for cross-compilation
* fix: improve host compiler detection in vulkan shader build
Improve host compiler detection for vulkan shader generation:
- Add NO_CMAKE_FIND_ROOT_PATH to all compiler searches
- Consolidate compiler detection logic
- Fix Windows-specific MSVC detection
- Ensure correct compiler search in cross-compilation
* refactor: Simplify CMake function for detecting host compiler
Simplified the CMake function to improve the process of detecting the host compiler.
* fix: Remove unnecessary Vulkan library linkage in CMakeLists.txt
Since `vulkan-shader-gen.cpp` only requires the `glslc` executable
and not the Vulkan headers or libraries, CMakeLists.txt needs to
be corrected.
(See: ecc93d0558)
* refactor: Rename host_toolchain.cmake.in
- Rename host_toolchain.cmake.in to cmake/host-toolchain.cmake.in
* refactor: GGML_VULKAN_SHADERS_GEN_TOOLCHAIN
Rename the macro GGML_SHADERS_GEN_TOOLCHAIN to GGML_VULKAN_SHADERS_GEN_TOOLCHAIN
This commit contains a suggestion for adding the missing embd_to_audio
function from tts.cpp to tts-outetts.py. This introduces a depencency
numpy which I was not sure if that is acceptable or not (only PyTorch
was mentioned in referened PR).
Also the README has been updated with instructions to run the example
with llama-server and the python script.
Refs: https://github.com/ggerganov/llama.cpp/pull/10784#issuecomment-2548377734
