* The "main" example now allows a response-file as the sole parameter.
A response-file is a text file with command-line parameters, one per line.
Prefix the name of the response-file with "@" to identify it as such.
It's used under MS Windows to work around command-line length limits.
It may be useful under other platforms to simplify character-escaping.
* minor : style
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Allow a regular expression to describe tokens to suppress.
Example: --suppress-tokens-re "[,\.]|[ ]?[0-9]+" will suppress commas, periods, and numeric tokens.
Technique inspired by https://github.com/openai/whisper/discussions/1041
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Blind change to fix Java test.
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* ggml : update mul_mat_id to use the same tensor for all the experts
* update cuda
* minor
* update metal
* update test-backend-ops
* fix cuda
* Update ggml-metal.m
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* update convert.py
* update convert-hf-to-gguf.py
* update convert.py for mixtral hf models
* Update convert-hf-to-gguf.py
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* cuda : support non-pow-2 number of experts
* allow quantize to work for split and merged experts models in the same way
* cleanup + disable mmap automatically with split tensors models
* update imatrix
* test-backend-ops : test qwen argsort
* update grok model loading
* llama : add merged experts tensors to the grok tensor map
* minor
* gguf : bump version
* fix quantizing of merged experts
* convert-hf-to-gguf.py : update grok (untested)
* make linter happy
* cuda/argsort : use shared memory instead of pool memory
* convert : fix grok tensor names
* metal : add support for non-pow-2 argsort
* llama : more loader cleanup, better error checking
* cuda : fix warning
* llama : still use mmap for loading old models, but copy the data to a host buffer
* add review note
* llama : remove ffn tensor counting + add sanity check
ggml-ci
* convert : fix handling of n_experts == None
ggml-ci
* imatrix : fix ncall counters
* llama : produce error if imatrix size does not match
* quantize : terminate on errors + trace logs
ggml-ci
* metal : pad shared memory to 16 bytes
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Fix Vulkan no kv offload incoherence
* Add k-quant mul mat mat shaders
* Rework working buffer allocation, reduces vram use noticeably
Clean up cpu assist code, replaced with ggml-backend offload function
* Default to all dedicated GPUs
* Add fallback for integrated GPUs if no dedicated GPUs are found
* Add debug info which device is allocating memory
* Fix Intel dequant issue
Fix validation issue
* Fix Vulkan GGML_OP_GET_ROWS implementation
* Clean up merge artifacts
* Remove Vulkan warning
* make : use pkg-config for finding CFLAGS & LDFLAGS needed by OpenBLAS
That way building on *nix like environments (including MSYS2 on Windows)
with WHISPER_OPENBLAS=1 works out of the box.
Fix handling of WHISPER_OPENBLAS, so that empty value or 0 won't be
misinterpreted by make as enabled. Mind that it's not intended to
detect CMake false constants (OFF NO FALSE N). make is not CMake.
By default OpenBLAS with 64-bit interface is used, but that can be
changed with `WHISPER_OPENBLAS_INTERFACE64=0` if 32-bit one is desired.
If OpenBLAS headers and library are respectively in include/ and lib/
subdirectories of given path, then you can specify it, e.g.
`OPENBLAS_PATH=/usr/local/openblas`, and this will take precedence over
any pkg-config file.
If there is no pkg-config file (.pc) for OpenBLAS and OPENBLAS_PATH is
empty, then headers are assumed to be in /usr/include/openblas and
library as assumed to be called 'openblas64' (or 'openblas' if
`WHISPER_OPENBLAS_INTERFACE64=0`). If different headers location should
be used, then it can be done, e.g.
`WHISPER_BLAS_CFLAGS=-I/usr/local/include/openblas`.
If different library should be used, it can be specified, e.g.
`WHISPER_BLAS_LIB=openblasp64` (pthreads version as seen on Fedora), or
you can provide LDFLAGS needed to link with OpenBLAS directly:
`WHISPER_BLAS_LDFLAGS="-L/usr/local/lib/openblas -lopenblas64"`.
Current solution is flexible enough to handle most cases out there
without needlessly hardcoding possible OpenBLAS installation details.
* cmake : fix how pkg-config is used for finding include dirs and libraries needed by OpenBLAS
That way building on *nix like environments (including MSYS2 on Windows)
with -DWHISPER_OPENBLAS=ON should work out of the box as long as you
have CMake 3.25 or newer.
Make OPENBLAS_PATH environment variable supported not only on Windows.
It sets OpenBLAS include dir to ${OPENBLAS_PATH}/include and library to
${WHISPER_BLAS_LIB} (name without prefixes and suffixes) in
${OPENBLAS_PATH}/lib and avoids further package finding.
By default OpenBLAS with 64-bit interface is used (equivalent to setting
`-DWHISPER_BLAS_LIB=openblas64`), but that can be changed with
`-DWHISPER_OPENBLAS_INTERFACE64=OFF` (equivalent to setting
`-DWHISPER_BLAS_LIB=openblas`) if 32-bit one is desired.
Turn on BLA_STATIC for FindBLAS only when WHISPER_STATIC is enabled.
BLA_STATIC may not work as expected for pkg-config based operation.
Get rid of supporting BLAS_HOME environment variable. If OPENBLAS_PATH
is insufficient in your case, there is no pkg-config file to rely on,
then you can manually specify include dir, e.g.
`-DBLAS_INCLUDE_DIRS=/usr/local/include/openblas`, and library, e.g.
`-DBLAS_LIBRARIES=/usr/local/lib/libopenblas.so`.
* make / cmake : use OpenBLAS with 32-bit interface by default.
OpenBLAS w/o INTERFACE64=1 vel USE_64BITINT=1 seems to be more common.
* cmake : hardcode "lib" prefix for OpenBLAS lib filename (even on Windows)
* cmake : hardcode OpenBLAS library name when building in MSVC (Windows)
Most *nix like environments (including MSYS2 on Windows) have OpenBLAS
packages that allow coexistence of OpenBLAS builds with 32-bit and
64-bit interface (w/o and w/ OPENBLAS_USE64BITINT defined) and they
differ by not having or having "64" suffix in their library filenames.
That's not the case for OpenBLAS prebuilt libraries for Windows.
* Implemented command-style grammar in the main example.
Mostly just copied the relevant parts from the command example.
* main : code style
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* whisper.cpp: impl dtw algo
* WIP: producing and placing DTW timestamps on tokens
* Fix compile and assertion errors. Attempt to DTW timestamp with single_segment=false.
* Fix mistake causing incorrect alignment of dtw timestamps
* implement N_TOP_MOST and CUSTOM alignment heads setting
* whisper: fix typo on alignment heads enum
* Fix issues related to changes in whisper.cpp
* Fixed excessive memory use when using DTW timestamps. Other minor fixes to DTW timestamping function
* decoder: save cross QKs only if requested
* Calling median filter with ggml_map_custom1
* Reimpl aheads n_top_most and custom. Sanity checks on chosen aheads
* Copying cross QKs from decoder backend correctly
* dtw: cleanup
* Fix incorrect n_frames passed to dtw when near end of audio
* Fix aheads_masks_init for backend != CPU
* whisper : minor style
* main : add dtw (wip)
* whisper: fix invalid memory access in aheads_masks_init
* main : add dtw (cont)
* whisper : minor
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* llama : add pipeline parallelism support for batch processing with multiple CUDA GPUs
ggml-ci
* server : add -ub, --ubatch-size parameter
* fix server embedding test
* llama : fix Mamba inference for pipeline parallelism
Tested to work correctly with both `main` and `parallel` examples.
* llama : limit max batch size to n_batch
* add LLAMA_SCHED_MAX_COPIES to configure the number of input copies for pipeline parallelism
default increase to 4 (from 2)
changing this value may improve performance for some systems, but increases memory usage
* fix hip build
* fix sycl build (disable cpy_tensor_async)
* fix hip build
* llama : limit n_batch and n_ubatch to n_ctx during context creation
* llama : fix norm backend
* batched-bench : sync after decode
* swiftui : sync after decode
* ggml : allow ggml_get_rows to use multiple threads if they are available
* check n_ubatch >= n_tokens with non-casual attention
* llama : do not limit n_batch to n_ctx with non-casual attn
* server : construct batch with size of llama_n_batch
* ggml_backend_cpu_graph_compute : fix return value when alloc fails
* llama : better n_batch and n_ubatch comment
* fix merge
* small fix
* reduce default n_batch to 2048
---------
Co-authored-by: Francis Couture-Harpin <git@compilade.net>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* iq1_s: we can do even better
Spent one of the 4 scale bits on a signs of a 0.125 shift.
I.e., quants are now -1 + delta, delta, 1 + delta, where delta
is +/- 0.125.
CUDA works, same performance as before.
PPL(LLaMA-v2-7B) is now 11.85!
* iq1_s: make scalar and AVX2 work with the new version
* iq1_s: make Neon work with new version.
~10% drop in performance, so will need some more work.
* iq1_s: make Metal work with new version
* iq1_s: very slightly faster dequantize on Metal
* iq1_s: fix dequantize on the CPU
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* windows arm ci
* fix `error C2078: too many initializers` with ggml_vld1q_u32 macro for MSVC ARM64
* fix `warning C4146: unary minus operator applied to unsigned type, result still unsigned`
* fix `error C2065: '__fp16': undeclared identifier`
* Trying blocvks of 16 for IQ1_S - seems slightly better
* iq1s_blocks16: Adjust scale fudge factor to 1.125
* iq1s_blocks16: going to blocks of 32
with 2048 lattice points, so same bpw.
This is even better than blocks of 16.
Should I try blocks of 64? But to keep the same
bpw, when I go to 4096 lattice points, I need to
remove blocks alltogether and just have superblocks of
256 weights.
* iq1s_blocks16: Use 2*<x^2> as sigma2 in weight adjustment
* iq1s_blocks16: scalar and AVX2 dot products
* iq1s_blocks16: CUDA dot product
* iq1s_blocks16: Metal works, Neon does not
Metal works but TG is dog slow (35 t/s). PP is OKish (493 t/s).
Not seeing the bug in the Neon implementation for now.
* iq1s_blocks16: fixed Neon
* iq1s_blocks16: very slightly faster TG on Metal
Still pathetic at 37 t/s
* iq1s_blocks16: speedup Metal by packing codebook into uint32_t's
* Formatting
* iq1s_blocks16: uint32_t codebook is also better in CUDA
TG-128 is now 204 t/s up from 194 t/s.
PP-512 is 5890 t/s, so significantly better than other quants
* iq1s_blocks16: slightly faster Neon dot product
* iq1s_blocks16: faster AVX2 dot product
* iq1s_blocks16: adjust to ggml-common.h
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
