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401 Commits

Author SHA1 Message Date
ceb77363cd ggml : disable CUDA graphs for non-llama.cpp projects 2024-06-26 20:14:22 +03:00
dc8cc2dd6f whisper : disable CUDA mel + fix FFMPEG 2024-06-26 20:11:38 +03:00
3efedb9511 sync : ggml 2024-06-26 19:40:23 +03:00
e30c679928 whisper : reorganize source code + improve CMake (#2256)
* scripts : update sync [no ci]

* files : reorganize [no ci]

* sync : llama.cpp

* cmake : link math library

* cmake : build normal ggml library

* files : move headers to include

* objc : fix path to ggml-metal.h

* ci : fix WHISPER_CUDA -> GGML_CUDA

* scripts : sync LICENSE [no ci]
2024-06-26 19:34:09 +03:00
bf4cb4abad whisper : optimize fft() function (#2242)
Co-authored-by: Mike Fan <60965742+mike-fzy@users.noreply.github.com>
2024-06-18 18:10:33 +03:00
e293f17d34 talk-llama : sync llama.cpp 2024-06-18 09:45:37 +03:00
5d950c4b8d whisper : use ggml_backend_sched (#2239)
* whisper : use ggml_backend_sched (wip)

* use sched in whisper_allocr

* whisper : single backend in whisper_context

* whisper : remove whisper_state->backends_used

* whisper : remove whisper_context->backend

* whisper : reset scheduler after init

* whisper : fix external encoder (e.g. CoreML)

* whisper : cleanup

* whisper : handle null GPU buffer types + fix sycl

---------

Co-authored-by: slaren <slarengh@gmail.com>
2024-06-18 09:39:40 +03:00
820446e230 fix : remove extra files 2024-06-18 09:39:40 +03:00
54d5823ebe scripts : sync ggml-blas 2024-06-18 09:39:40 +03:00
5181494e9f build : update make / cmake 2024-06-18 09:39:40 +03:00
4a6e6e8b30 sync : ggml 2024-06-18 09:39:40 +03:00
de29b193f6 move BLAS to a separate backend (cont) (llama/6210)
ggml-ci
2024-06-18 09:39:40 +03:00
922971041b Vulkan Shader Refactor, Memory Debugging Option (llama/7947)
* Refactor shaders, extract GLSL code from ggml_vk_generate_shaders.py into vulkan-shaders directory

* Improve debug log code

* Add memory debug output option

* Fix flake8

* Fix unnecessary high llama-3 VRAM use
2024-06-18 09:39:40 +03:00
63a767a134 scripts : stop sync whisper example from ggml 2024-06-18 09:39:40 +03:00
30841fa786 cmake : fix sycl build (#0) 2024-06-16 18:19:48 +03:00
3b1ac03828 ggml : remove OpenCL (#0) 2024-06-16 18:19:48 +03:00
990de617b5 sycl : sync (#0) 2024-06-16 18:19:48 +03:00
6975600b4b cuda : enable CUDA graphs (#0) 2024-06-16 18:19:48 +03:00
061eeb9f61 talk-llama : sync llama.cpp 2024-06-16 18:19:48 +03:00
4942b1b428 cmake : fix CUDA build (#0) 2024-06-16 18:19:48 +03:00
3c7cc5c437 sync : ggml
ggml-ci
2024-06-16 18:19:48 +03:00
5cd42ee2cc ggml : fix and optimize ppc64le (ggml/849)
* fix compile issues introduced by loongarch_asx

* restore quant changes to merge

* fix compile issues introduced by loongarch_asx

* further optimize by using vec_msum & vec_sum4s on ppc64le
2024-06-16 18:19:48 +03:00
ee718f3da6 ggml : remove duplicate include of ggml-common.h (ggml/853)
Signed-off-by: Daniel Bevenius <daniel.bevenius@gmail.com>
2024-06-16 18:19:48 +03:00
63eac1f608 remove global variables (llama/7710)
* separate DPCT helpers outside

* replace global variables with context

* remove useless extra

* update mul_mat condition

* remove duplicate buft initialization

* remove duplicate extra and global work group size

* remove useless backend check

* remove duplicated extras

* use macro for group_size and remove cuda-related
2024-06-16 18:19:48 +03:00
b17ba2815b CUDA: faster q2_K, q3_K MMQ + int8 tensor cores (llama/7921)
* CUDA: faster q2_K, q3_K MMQ + int8 tensor cores

* try CI fix

* try CI fix

* try CI fix

* fix data race

* rever q2_K precision related changes
2024-06-16 18:19:48 +03:00
7a489af2f3 metal : utilize max shared memory for mul_mat_id (llama/7935) 2024-06-16 18:19:48 +03:00
4a4ea13d6d rpc : fix ggml_backend_rpc_supports_buft() (llama/7918) 2024-06-16 18:19:48 +03:00
174a461fc6 move BLAS to a separate backend (llama/6210)
* move BLAS to a separate backend

* rename GGML_USE_OPENBLAS to GGML_USE_BLAS

* alloc : reuse same buffer when the same buffer type if used multiple times

* set number of threads automatically for openblas and blis

* sched : print assignments when GGML_SCHED_DEBUG env variable is set

* sched : allow ops with weights on an incompatible buffer type

This will cause the weight to be copied to a backend that supports the
op, which is very costly. The weight should have been stored in a buffer
of a backend that can run the op, but llama.cpp cannot do this
automatically at the moment.

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-06-16 18:19:48 +03:00
d8b7a24bc9 CUDA: fix broken oob check for FA vec f32 kernel (llama/7904) 2024-06-16 18:19:48 +03:00
acf3832c9c tests : add non-cont unary tests (llama/7857)
* tests : add non-cont unary tests

* ggml : update unary asserts and "supports_op"

ggml-ci
2024-06-16 18:19:48 +03:00
d29ac44303 ggml : improve ggml_is_contiguous logic (llama/7856)
* ggml : improve ggml_is_contiguous logic

ggml-ci

* ggml : support more contiguous cases

ggml-ci
2024-06-16 18:19:48 +03:00
12638dfef0 vulkan: select only one device for single gpu with multiple drivers (llama/7582) 2024-06-16 18:19:48 +03:00
f100b3b523 Update Vulkan RoPE implementation (llama/7818)
* Update Vulkan RoPE implementation

* Return nullptr on alloc_buffer when allocation fails, instead of throwing an exception

Minor fixes

* Fix segfault when running out of VRAM

Co-authored-by: slaren <slarengh@gmail.com>

---------

Co-authored-by: slaren <slarengh@gmail.com>
2024-06-16 18:19:48 +03:00
a99e213a82 CUDA: int8 tensor cores for MMQ (q4_K, q5_K, q6_K) (llama/7860) 2024-06-16 18:19:48 +03:00
7483d2b61c CUDA: use tensor cores for MMQ (llama/7676)
* CUDA: int8 tensor cores for MMQ (legacy quants)

* fix out-of-bounds writes

* __builtin_assume -> GGML_CUDA_ASSUME

* fix writeback returning too early
2024-06-16 18:19:48 +03:00
1fe5948227 use the correct SYCL context for host USM allocations (llama/7777)
Signed-off-by: Ben Ashbaugh <ben.ashbaugh@intel.com>
2024-06-16 18:19:48 +03:00
760497e1ab CUDA: revise q8_1 data layout for mul_mat_q (llama/7824) 2024-06-16 18:19:48 +03:00
b172e7714c vulkan : reuse parent extra for views (llama/7806)
* vulkan : reuse parent extra for views

* Fix validation error when multiple compute contexts are used in a graph

---------

Co-authored-by: 0cc4m <picard12@live.de>
2024-06-16 18:19:48 +03:00
dc01aadb18 fix softmax r2r result wrong issue (llama/7811) 2024-06-16 18:19:48 +03:00
e08c62149b CUDA: refactor mmq, dmmv, mmvq (llama/7716)
* CUDA: refactor mmq, dmmv, mmvq

* fix out-of-bounds write

* struct for qk, qr, qi

* fix cmake build

* mmq_type_traits
2024-06-16 18:19:48 +03:00
abab4500fa ggml : refactor rope norm/neox (llama/7634)
* ggml : unify rope norm/neox (CPU)

* ggml : fix compile warning

* ggml : remove GLM rope mode

ggml-ci

* metal : better rope implementation

ggml-ci

* cuda : better rope implementation

ggml-ci

* naming : n_orig_ctx -> n_ctx_orig

ggml-ci

* dev : add reminders to update backends

ggml-ci

* vulkan : fix ggml_rope_ext() usage

* cuda : fix array size + indents

ggml-ci
2024-06-16 18:19:48 +03:00
e666315fa8 Allow number of nodes in CUDA graph to change (llama/7738)
Previously the code would have failed to cope in the case that the
number of nodes changes in an existing CUDA graph. This fixes the
issue by removing an unnecessary conditional.
2024-06-16 18:19:48 +03:00
3f869af14c ggml : remove OpenCL (llama/7735)
ggml-ci
2024-06-16 18:19:48 +03:00
cbacb7634c ggml : prevent builds with -ffinite-math-only (llama/7726)
This enforces a check that -fno-finite-math-only was set and that the operating
compiling mode is not in finite maths mode. This is because during rewriting of
silu and softmax for cpu #7154 there emerged an issue where the result that was
observed when >1 slot was nondeterministic as found by @JohannesGaessler.

@LostRuins narrowed the problem down to -ffinite-math-only which was theorised
to be due to SiLU, instead of flushing small values to 0, returns NaN or some
other garbage. @jart proposed a fix that @ggerganov then implemented in this fix

ref https://github.com/ggerganov/llama.cpp/pull/7154#issuecomment-2145661825
2024-06-16 18:19:48 +03:00
6cc3b022ee llama : offload to RPC in addition to other backends (llama/7640)
* llama : offload to RPC in addition to other backends

* - fix copy_tensor being called on the src buffer instead of the dst buffer

- always initialize views in the view_src buffer

- add RPC backend to Makefile build

- add endpoint to all RPC object names

* add rpc-server to Makefile

* Update llama.cpp

Co-authored-by: slaren <slarengh@gmail.com>

---------

Co-authored-by: slaren <slarengh@gmail.com>
2024-06-16 18:19:48 +03:00
e5e38d4920 ggml : use OpenMP as a thread pool (llama/7606)
* ggml: Added OpenMP for multi-threads processing

* ggml : Limit the number of threads used to avoid deadlock

* update shared state n_threads in parallel region

* clear numa affinity for main thread even with openmp

* enable openmp by default

* fix msvc build

* disable openmp on macos

* ci : disable openmp with thread sanitizer

* Update ggml.c

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

---------

Co-authored-by: slaren <slarengh@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-06-16 18:19:48 +03:00
2a6bab5655 Vulkan Mixture of Experts (MoE) support (llama/7628)
* Finish Vulkan mul_mat_id implementation

* Add Vulkan sum_rows and div ops

* Fix MUL_MAT_ID matrix matrix shader

* Fix MUL_MAT_ID matrix vector shader dispatch size

* Fix MUL_MAT_ID matrix vector shader and dispatch code

* Update Vulkan CPU offload for MUL_MAT_ID

* Fix crash when using split mode none and setting a main GPU
2024-06-16 18:19:48 +03:00
8c01c9b85c kompute : implement op_getrows_f32 (llama/6403)
op_getrows_f32 is required since https://github.com/ggerganov/llama.cpp/pull/6122
for the Vulkan w/ Kompute backend to be functional.

As such, implement this op to make this backend functional again.
2024-06-16 18:19:48 +03:00
d1123d795e fix bug introduced in using calloc (llama/7701)
compilade pointed this out on the previous MR
2024-06-16 18:19:48 +03:00
9b3d784020 Fix FlashAttention debug test, FP32 assert (llama/7684) 2024-06-16 18:19:48 +03:00
a16137d13d CUDA: fix Pascal FA, deq. KV to FP16 for batch > 8 (llama/7681) 2024-06-16 18:19:48 +03:00
5582039d0a CUDA: quantized KV support for FA vec (llama/7527)
* CUDA: quantized KV support for FA vec

* try CI fix

* fix commented-out kernel variants

* add q8_0 q4_0 tests

* fix nwarps > batch size

* split fattn compile via extern templates

* fix flake8

* fix metal tests

* fix cmake

* make generate_cu_files.py executable

* add autogenerated .cu files

* fix AMD

* error if type_v != FP16 and not flash_attn

* remove obsolete code
2024-06-16 18:19:48 +03:00
9a16c643e2 ggml : fix loongson compile warnings (llama/7537)
* ggml : fix loongson compile warnings

ggml-ci

* Fix loongarch quantize test fail.

Fix unexpected error introduced during rebase code.

* tests : disable json test due to lack of python on the CI node

ggml-ci

---------

Co-authored-by: junchao-loongson <zhaojunchao@loongson.cn>
2024-06-16 18:19:48 +03:00
10a8a23100 faster avx512 exp implementation (llama/7551)
* faster avx512 exp implementation

* x->r

* improve accuracy, handle special cases

* remove `e`
2024-06-16 18:19:48 +03:00
29cfeef77f ggml : fix loongarch build (O2 issue) (llama/7636) 2024-06-16 18:19:48 +03:00
e66e9ea25b metal : remove invalid asserts (llama/7617) 2024-06-16 18:19:48 +03:00
276779a849 metal : add missing asserts (llama/7617) 2024-06-16 18:19:48 +03:00
1f35ce61c1 ggml : fix YARN + add tests + add asserts (llama/7617)
* tests : add rope tests

ggml-ci

* ggml : fixes (hopefully)

ggml-ci

* tests : add non-cont tests

ggml-ci

* cuda : add asserts for rope/norm + fix DS2

ggml-ci

* ggml : assert contiguousness

* tests : reduce RoPE tests

ggml-ci
2024-06-16 18:19:48 +03:00
4b19cc3ed4 cuda : non-cont concat support (llama/7610)
* tests : add non-cont concat tests

* cuda : non-cont concat support

ggml-ci
2024-06-16 18:19:48 +03:00
a535d348dd llama-bench : add support for the RPC backend (llama/7435) 2024-06-16 18:19:48 +03:00
8f5dc729d9 ggml : use atomic_flag for critical section (llama/7598)
* ggml : use atomic_flag for critical section

* add windows shims
2024-06-16 18:19:48 +03:00
02fc147a0b examples : adapt to new ggml_concat (ggml/0) 2024-06-16 18:19:48 +03:00
109148ac84 ggml : fix typo in ggml.c (llama/7603) 2024-06-16 18:19:48 +03:00
3563473d2c Align GEMM dispatch (llama/7566)
* align GEMM dispatch
2024-06-16 18:19:48 +03:00
046834198d sycl : fix assert (llama/7563) 2024-06-16 18:19:48 +03:00
0a2ad9de06 vulkan: properly initialize vulkan devices for LLAMA_SPLIT_MODE_NONE (llama/7552) 2024-06-16 18:19:48 +03:00
39b0640b09 rpc : resource management rework (llama/7562)
* rpc : resource management rework

* address review comments
2024-06-16 18:19:48 +03:00
8dca71de64 fix ggml_sycl_mul_mat_id() to match the change of api (llama/7436)
* fix mul_mat_id to match the change of api

* rm comment

* rm unused or duplicated code, rename as review comment
2024-06-16 18:19:48 +03:00
812787cbc5 ggml : generalize GGML_OP_CONCAT (llama/7563)
* ggml : generalize GGML_OP_CONCAT (WIP)

ggml-ci

* tests : add dim != 2 tests

* metal : generalize concat kernel

* tests : naming

* cuda : generalize concat kernel

ggml-ci

* sycl : add warning and assert

* ggml : fix op params handling

* metal : bugfix kernel

ggml-ci

* ggml : reimplement CPU and Metal

* cuda : add asserts

ggml-ci

* ggml : fix ptrs

ggml-ci
2024-06-16 18:19:48 +03:00
68ef10805e update HIP_UMA #7399 (llama/7414)
* update HIP_UMA #7399

add use of hipMemAdviseSetCoarseGrain when LLAMA_HIP_UMA is enable.
- get x2 on prompte eval and x1.5 on token gen with rocm6.0 on ryzen 7940HX iGPU (780M/gfx1103)

* simplify code, more consistent style

---------

Co-authored-by: slaren <slarengh@gmail.com>
2024-06-16 18:19:48 +03:00
96fdb90f5f Allow multiple copy function pointers for CUDA graph kernel param updates (llama/7565)
CUDA graphs require parameter updates to kernels associated with
GGML_OP_CPY nodes. Previously the implementation only checked for a
single CUDA kernel in such nodes, but this caused a bug in cases where
2 such kernels exist. This fixes the issue by using a vector to allow
multiple function pointers to be stored and checked against.

Fixes #7942
2024-06-16 18:19:48 +03:00
e98f9ac554 Fix q_xxs using mul_mat_q (llama/7459) 2024-06-16 18:19:48 +03:00
02d481595b Add freq factors (llama/7495) 2024-06-16 18:19:48 +03:00
7091c7ab5a metal : add GGML_OP_REPEAT kernels (llama/7557)
ggml-ci
2024-06-16 18:19:48 +03:00
d70ccb75f5 metal : disable FA kernel for HS=256 (llama/7556)
ggml-ci
2024-06-16 18:19:48 +03:00
5ee048eb67 ggml : restore ggml_rope_xpos_inplace (ggml/0)
ggml-ci
2024-06-16 18:19:48 +03:00
37ed71c964 ggml: aarch64: SVE kernels for q8_0_q8_0, q4_0_q8_0 vector dot (llama/7433)
* Add SVE support for q4_0_q8_0 q8_0_q8_0

* remove ifdef
2024-06-16 18:19:48 +03:00
8cd7a3df37 ggml : silence UB sanitizer error during iq2_xxs quantization (llama/0) 2024-06-16 18:19:48 +03:00
04a3279320 ggml : remove ggml_flash_attn and ggml_flash_ff (llama/7463)
ggml-ci
2024-06-16 18:19:48 +03:00
45ddda8e0c ggml : drop support for QK_K=64 (llama/7473)
* ggml : drop support for QK_K=64

ggml-ci

* opencl : restore QK_K=256 define
2024-06-16 18:19:48 +03:00
c41317fd66 Update vulkan rope implementation to support frequency factors (llama/7475) 2024-06-16 18:19:48 +03:00
96b8419b27 CUDA: fix FA out-of-bounds reads (llama/7479) 2024-06-16 18:19:48 +03:00
3c63f4cf35 CUDA: fix FA out-of-bounds writes (llama/7465) 2024-06-16 18:19:48 +03:00
5848dfd9c8 cuda : fix compile warning (llama/7454) 2024-06-16 18:19:48 +03:00
29ab5d0326 CUDA: remove incorrect precision check (llama/7454) 2024-06-16 18:19:48 +03:00
c4d6958b3e cuda : fix rope + add tests (llama/7452)
* cuda : fix rope pos data

ggml-ci

* ggml : drop mode & 1 == 1 support for ggml_rope

ggml-ci

* ggml : support freq_factors for f16 rope (CPU)

ggml-ci

* tests : add rope tests using frequency factors

ggml-ci
2024-06-16 18:19:48 +03:00
c9dcb75118 llama : add phi3 128K model support (llama/7225)
* add phi3 128k support in convert-hf-to-gguf

* add phi3 128k support in cuda

* address build warnings on llama.cpp

* adjust index value in cuda long rope freq factors

* add long rope support in ggml cpu backend

* make freq factors only depend on ctx size

* remove unused rope scaling type 'su' frin gguf converter

* fix flint warnings on convert-hf-to-gguf.py

* set to the short freq factor when context size is small than trained context size

* add one line of comments

* metal : support rope freq_factors

* ggml : update ggml_rope_ext API to support freq. factors

* backends : add dev messages to support rope freq. factors

* minor : style

* tests : update to use new rope API

* backends : fix pragma semicolons

* minor : cleanup

* llama : move rope factors from KV header to tensors

* llama : remove tmp assert

* cuda : fix compile warning

* convert : read/write n_head_kv

* llama : fix uninitialized tensors

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-06-16 18:19:48 +03:00
bbdbc3fc62 metal : handle F16 inf values, fix FA partial offload (llama/7434)
ggml-ci
2024-06-16 18:19:48 +03:00
28c207a541 CUDA: fix unused warning in mmq.cu (llama/7442) 2024-06-16 18:19:48 +03:00
c23f830983 CUDA: deduplicate mmq code (llama/7397) 2024-06-16 18:19:48 +03:00
caeeb32b41 rpc : track allocated buffers (llama/7411)
* rpc : track allocated buffers

ref: #7407

* rpc : pack rpc_tensor tightly
2024-06-16 18:19:48 +03:00
584cc1177a Update SYCL upscale operation (llama/7321)
* Update SYCL upscale operation

* Formatting

* Remove messages
2024-06-16 18:19:48 +03:00
cc1ae10989 ggml-opencl, llama: using reserve() if count already known (llama/7272) 2024-06-16 18:19:48 +03:00
eb26f55b40 ggml : add loongarch lsx and lasx support (llama/6454)
* add loongarch lsx and lasx optimize code

* Add loongarch compilation support to makefile

* revert stb_image.h

* opt bytes_from_nibbles_32 and sum_i16_pairs_float

* fix undeclared

* format code

* update

* update 2

---------

Co-authored-by: Jinyang He <hejinyang@loongson.cn>
2024-06-16 18:19:48 +03:00
eb2b086584 Add provisions for windows support for BF16 code including CMake provision for enabling AVX512_BF16 (llama/7258) 2024-06-16 18:19:48 +03:00
67919cfe11 Vulkan Embedding Fix (llama/7360)
* Fix empty Vulkan host buffers

Add fp32 fp16 matmul shader

Fix matmul shader alignment

* Remove deprecated tensor->backend uses

* Fix Vulkan validation errors on embedding models with no offloaded layers

* Fix Vulkan llava segfault when not offloading layers
2024-06-16 18:19:48 +03:00
bf5fc81a8a ggml : fix another case of quants nans (llama/7387) 2024-06-16 18:19:48 +03:00
2b07dc3186 ggml: implement quantized KV cache for FA (llama/7372) 2024-06-16 18:19:48 +03:00
951c463d39 cuda : clear error after buffer allocation failure (llama/7376) 2024-06-16 18:19:48 +03:00
7f257b210f Capture CUDA logging output (llama/7298)
* logging: output capture in cuda module

* fix compile error

* fix: vsnprintf terminates with 0, string use not correct

* post review

* Update llama.cpp

Co-authored-by: slaren <slarengh@gmail.com>

* Update llama.cpp

Co-authored-by: slaren <slarengh@gmail.com>

---------

Co-authored-by: slaren <slarengh@gmail.com>
2024-06-16 18:19:48 +03:00
705fe30a02 android : use "ci-android" branch for CI (llama/7341)
* android : use "ci-android" branch for CI

* ggml : disable SIMD exp and silu for 32-bit ARM

ggml-ci

* android : do not fetch, use add_subdirectory instead

* cmake : provide binary dir
2024-06-16 18:19:48 +03:00
45b5b95e29 CUDA: deduplicate FlashAttention code (llama/7352) 2024-06-16 18:19:48 +03:00
f2c47d1e6a cuda : add half2 __shfl_xor() for ROCm 5.5 (llama/7263) 2024-06-16 18:19:48 +03:00
b4bb9b9036 Update and fix Vulkan soft_max and argsort implementations (llama/7237)
* Update and fix Vulkan softmax implementation

* Update and fix Vulkan argsort implementation
2024-06-16 18:19:48 +03:00
2bc6483299 ggml : fix quants nans when all the group weights are very close to zero (llama/7313) 2024-06-16 18:19:48 +03:00
ec52f900e4 CUDA: faster large batch FA without tensor cores (llama/7314) 2024-06-16 18:19:48 +03:00
77d708fabb rpc : set SO_REUSEADDR for the server socket (llama/7320)
ref: #7293
2024-06-16 18:19:48 +03:00
c00149c861 ggml-quants, llama : removed excess checks (llama/7274) 2024-06-16 18:19:48 +03:00
574661f2e6 ggml : rewrite silu and softmax for cpu (llama/7154)
This change upstreams llamafile's vectorized expf() functions. This lets
us compute softmax and silu more accurately than the short[65536] lookup
table that GGML previously used to make this operation go faster. We can
support aarch64 and sse2+ with the worst case rounding error of 2ulp. It
makes make -j8 tests && ./tests/test-backend-ops -o SOFT_MAX -b CPU perf
go 1.5x faster for SSE2+FMA, 1.9x faster for AVX2+FMA and 2.1x on AVX512
2024-06-16 18:19:48 +03:00
7bd69349bf rpc : add command line arg for specifying backend memory
ref: #7293
2024-06-16 18:19:48 +03:00
488ad99c13 Add support for properly optimized Windows ARM64 builds with LLVM and MSVC (llama/7191)
* logging: add proper checks for clang to avoid errors and warnings with VA_ARGS

* build: add CMake Presets and toolchian files for Windows ARM64

* matmul-int8: enable matmul-int8 with MSVC and fix Clang warnings

* ci: add support for optimized Windows ARM64 builds with MSVC and LLVM

* matmul-int8: fixed typos in q8_0_q8_0 matmuls

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* matmul-int8: remove unnecessary casts in q8_0_q8_0

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-06-16 18:19:48 +03:00
7178cceeaa ggml : use dynamic thread scheduling for matrix multiplication (llama/6915)
* Just reordering some structs.

* Adding in the calls to mm_pause

* Passing around the state

* Renaming and moving a bunch of variables around.

* Extracting the logic to it's own function.

* Moving some variable definitions into the chunk function.

* Moving some variables around

* moving src1_cont inside

* Moving row_size

* adding the current_chunk

* Reorg the code.

* Formatting to match the orig patch

* starting to setup the chunking variables

* Starting the buildup of the loop

* The yield shouldn't be necessary.

* adding the looping structure based on the chunk configuration.

* Add in the re-chunking code.

* Making it much more likely to rechunk.

* disable resizing if numa is enabled.

* Updating comments with what we've learned.

* Fix formatting

* Couple more formatting fixes.

* More style fixes.

* Fix Warnings

* Going with unused because there's conditional logic that needs it.

* Update ggml.c

* Update ggml.c

---------
2024-06-16 18:19:48 +03:00
8d55ccdb8c Avoid unnecessarily disabling CUDA graphs (llama/7302)
As discussed in PR #6766, CUDA graphs were being disabled in the presence of long prompts.
This fixes the issue by avoiding the consective update counter from incrementing unnecessarily
for tokens in which cuda graphs are disabled due to batch size > 1.
2024-06-16 18:19:48 +03:00
37a72cb170 ggml : tag ggml_tensor::backend as deprecated (llama/7290) 2024-06-16 18:19:48 +03:00
bf9b69284f Add missing " (llama/7303) 2024-06-16 18:19:48 +03:00
c4de1e19df ggml : add ggml_upscale_ext (ggml/814)
* initial commit with CPU implementation of upscale to shape and test, cuda implementation next

* experimental commit to see if dst shape is correct

* test version

* test

* removed unnecessary params

* refactor

* fixed tests

* ggml : metal impl + cleanup + sycl dev warnings

* patched ggml_upscale cuda op to handle non-contiguous tensors, added test for non-contiguous behavior

* metal : fix upsacle op to support nb00 + style

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-06-16 18:19:48 +03:00
5b7073cae1 scripts : update sync 2024-06-16 12:41:42 +03:00
b29b3b2924 whisper : use ggml-cuda in mel calc, set appropriate device (#2236)
* whisper : use ggml-cuda in mel calc, set appropriate device

* whisper : forbid cuda mel calc on devices with compute < 600, workaround for #2230
2024-06-13 13:16:07 +03:00
420b6abc54 cuda : fix HIPBLAS build (#2234) 2024-06-11 19:14:38 +03:00
99804b0f3e cuda : fix bounds check for src0 rows in MMVQ kernel (#2231)
* cuda : fix bounds check for src0 rows in MMVQ kernel

* Update ggml-cuda/mmvq.cu

Co-authored-by: Johannes Gäßler <johannesg@5d6.de>

---------

Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
2024-06-11 17:39:01 +03:00
c55964c956 ci : fix CUDA builds (#2232) 2024-06-11 17:21:30 +03:00
20c542c713 whisper : auto-grow working areas for mel_calc_cuda (#2227)
* whisper : auto-grow working areas for mel_calc_cuda, fixes #2226

* whisper : only calculate mel spectrogram on GPU if audio is <= 5 min
2024-06-10 21:51:32 +03:00
c2bdb960cd whisper : free whisper_mel instances (#2220) 2024-06-10 11:00:15 +03:00
87acd6d629 whisper : whisper_state/backend fixes (#2217)
* whisper : fixes

* ci : WHISPER_CUBLAS -> WHISPER_CUDA
2024-06-06 18:51:36 +03:00
f842d31171 whisper : calculate mel spectrogram directly into a ggml_tensor (#2208)
* whisper : calculate mel spectrogram directly into a ggml_tensor

* whisper : remove unused temp buffer from state

* whisper : fix not initializing wstate.embd_enc
2024-06-06 16:20:46 +03:00
ffef323c4c whisper : add CUDA-specific computation mel spectrograms (#2206)
* whisper : use polymorphic class to calculate mel spectrogram

* whisper : add cuda-specific mel spectrogram calculation

* whisper : conditionally compile cufftGetErrorString to avoid warnings

* build : add new files to makefile

* ruby : add new files to conf script

* build : fix typo in makefile

* whisper : suppress cub warning for deprecated C++ std in whisper-mel-cuda
2024-06-04 09:32:23 +03:00
af5833e298 whisper : remove speed_up and phase_vocoder* functions (#2198)
* whisper : fix cast warning

* whisper : remove phase_vocoder functions, ref #2195

* whisper : remove speed_up from whisper_full_params, closes #2195
2024-05-31 11:37:29 +03:00
b87494bb8f readme : add conan badge (#2196)
* Add conan badge

* Fix markdown formating
2024-05-30 15:43:28 +03:00
ad130431aa readme : add install instructions for Conan (#2189) 2024-05-30 15:06:15 +03:00
e130b66642 whisper: use global cache for sin/cos vals and Hann window (#2194)
- also rename Hanning to Hann as it's named after Julius von Hann
 as per Wikipedia
2024-05-29 19:09:21 +03:00
c7b6988678 release : v1.6.2 2024-05-27 10:35:09 +03:00
05042a782d Revert "whisper : remove extra backend instance (huh?)" (#2182)
This reverts commit 4caa64b73e.
2024-05-27 10:20:25 +03:00
a7dc2aab16 server : fix typo (#2181)
A simple comment typo, PR can be dismissed
2024-05-25 10:46:22 +03:00
22d46b7ba4 ruby : update bindings (#2154)
* update library files

* update whispercpp

* not needed for gem
2024-05-22 23:02:52 +03:00
c10db6ea28 release : v1.6.1 2024-05-21 18:44:37 +03:00
1b51fdf170 examples : add support for decoding input with ffmpeg (Linux) (#2133)
- search for ffmpeg libs/headers at cmake time
- added ffmpeg-transcode.cpp into libcommon if ffmpeg on
- hooked ffmpeg trancoding in common read_wav(...)
- passed test:
./main -m ggml-base.en.bin -f samples/jfk.mp3
2024-05-21 18:31:41 +03:00
adee3f9c1f node : add flash_attn param (#2170) 2024-05-20 09:08:48 +03:00
4798be1f9a ci: Update build.yml to suppress warnings about node.js versions (#2166)
* Update actions to suppress warnings about old node.js

https://github.blog/changelog/2023-09-22-github-actions-transitioning-from-node-16-to-node-20/

* Update actions/upload-artifact, specify android cmdline-tools-version

* Use java 20

gradle 8.1 complains against 21
https://docs.gradle.org/current/userguide/compatibility.html
2024-05-19 11:49:26 +03:00
08981d1bac release : v1.6.0 2024-05-15 09:59:48 +03:00
7094ea5e75 whisper : use flash attention (#2152)
* whisper : use flash attention in the encoder

* whisper : add kv_pad

* whisper : remove extra backend instance (huh?)

* whisper : use FA for cross-attention

* whisper : use FA for self-attention

* whisper : simplify encoder FA

* whisper : add flash_attn runtime parameter

* scripts : add bench log

* scripts : add M1 Pro bench log
2024-05-15 09:38:19 +03:00
9d5771ae43 talk-llama : reject runs without required arguments (#2153)
* Extended talk-llama example to reject runs without required arguments.

Print warning and exit if models are not specified on the command line.

* Update examples/talk-llama/talk-llama.cpp

* Update examples/talk-llama/talk-llama.cpp

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-05-14 21:32:41 +03:00
f56b8305c4 sync : ggml 2024-05-14 19:16:32 +03:00
1056ad762c metal : support FA without mask + add asserts (llama/7278)
* ggml : fa without mask + add asserts

ggml-ci

* metal : support non-contiguous KV

ggml-ci
2024-05-14 19:16:29 +03:00
c451080c8b ggml : add RPC backend (llama/6829)
* ggml : add RPC backend

The RPC backend proxies all operations to a remote server which runs a
regular backend (CPU, CUDA, Metal, etc).

* set TCP_NODELAY

* add CI workflows

* Address review comments

* fix warning

* implement llama_max_devices() for RPC

* Address review comments

* Address review comments

* wrap sockfd into a struct

* implement get_alignment and get_max_size

* add get_device_memory

* fix warning

* win32 support

* add README

* readme : trim trailing whitespace

* Address review comments

* win32 fix

* Address review comments

* fix compile warnings on macos
2024-05-14 19:16:29 +03:00
8e7c22fbdb rm wait() (llama/7233) 2024-05-14 19:16:29 +03:00
e57e95eb0d CUDA: add FP32 FlashAttention vector kernel (llama/7188)
* CUDA: add FP32 FlashAttention vector kernel

* fixup! CUDA: add FP32 FlashAttention vector kernel

* fixup! fixup! CUDA: add FP32 FlashAttention vector kernel

* fixup! fixup! fixup! CUDA: add FP32 FlashAttention vector kernel
2024-05-14 19:16:29 +03:00
130f43e4b8 scripts : sync ggml-rpc 2024-05-14 19:15:35 +03:00
d8356a1cc2 whisper : fix model path encoding in windows (#2086)
* fix: model path encoding in windows

* fix: convert model path to wide string only for MSVC compiler
2024-05-14 09:43:41 +03:00
4ef8d9f44e server : return utf-8 (#2138) 2024-05-13 15:33:46 +03:00
3928dbd206 node : add audio_ctx and audio buffer params (#2123)
* node : add audio_ctx param

* node : support passing audio buffer directly

* node : parse audio_ctx in index.js

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-05-13 15:22:23 +03:00
2ced6f0742 cmake : fix HIP/ROCm build (#2102) 2024-05-13 15:18:43 +03:00
30f73109b8 node : add additional params (#2000)
* Add additional params to addon.node

* Add comma_in_time as parameter

* Fix tests
2024-05-13 15:15:43 +03:00
17fa62d3d3 js : remove un-needed request header from fetchRemote (#2119) 2024-05-13 15:13:19 +03:00
1da5edcde0 cmake : fix metal embed sources path (#2110) 2024-05-13 15:09:59 +03:00
0bb05b113d main : dont print timings with --no-prints (#2108)
Signed-off-by: Daniel Ziegenberg <daniel@ziegenberg.at>
2024-05-13 15:00:19 +03:00
f141b2b938 main : add options for temperature control (#2088)
Add two options:

```
-tp,       --temperature N     [0.00   ] The sampling temperature, between 0 and 1
-tpi,      --temperature-inc N [0.20   ] The increment of temperature, between 0 and 1
```

The sampling temperature, between 0 and 1. Higher values like 0.8 will
make the output more random, while lower values like 0.2 will make it
more focused and deterministic. If set to 0, the model will use log
probability to automatically increase the temperature until certain
thresholds are hit.

Signed-off-by: Daniel Ziegenberg <daniel@ziegenberg.at>
2024-05-13 14:59:44 +03:00
2b434c449e whisper : switch back to F32 mask (#0) 2024-05-13 14:43:43 +03:00
e93081f83f whisper.android : update example, add field to print timestamp (#2072) 2024-05-13 14:30:03 +03:00
b6bbce4ae9 cmake : fix json INTERFACE library (#2069) 2024-05-13 14:29:39 +03:00
7705dc52da main : fix double quote escaping in csv output (#2090) 2024-05-13 11:55:32 +03:00
e6acaf9d91 metal : tune soft_max number of threads (#0) 2024-05-13 11:02:26 +03:00
2c81e6fd51 whisper : remove old flash attn code (#0) 2024-05-13 11:02:26 +03:00
9506267ce5 ggml : try fix ppc64 (#0) 2024-05-13 11:02:26 +03:00
fbeb80b5f0 ggml : remove oboslete alibi code (skipme) (#0) 2024-05-13 11:02:26 +03:00
3fa7d29876 talk-llama : sync llama.cpp 2024-05-13 11:02:26 +03:00
fe179ae0cc sync : ggml 2024-05-13 11:02:26 +03:00
40aeeeecc4 ggml : optimize for ppc64le using VSX intrinsics (ggml/784)
* optimize for ppc64le using VSX intrinsics

* 1. code clean up by removing comments about overflow concern.

2. fix typo in suffix of scaling.

* Continue to fix typo in suffix of scaling for QK_K <> 256

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-05-13 11:02:26 +03:00
5a863fbe18 metal : fix indent (ggml/0) 2024-05-13 11:02:26 +03:00
91c646c61d ggml : restore sigmoid decl order (ggml/0) 2024-05-13 11:02:26 +03:00
accada542a ggml : resolve merge (ggml/0)
ggml-ci
2024-05-13 11:02:26 +03:00
e54329da7b ggml : full ALiBi support (llama/7192)
* ggml : full ALiBi support

* ggml : update ggml_soft_max_ext() CUDA, SYCL

* ggml : ggml_flash_attn_ext() support ALiBi (CPU)

* ggml : ggml_flash_attn_ext() support ALiBi (Metal)

* ggml : fix warning

* ggml : ggml_flash_attn_ext() support ALiBi (CUDA)

ggml-ci

* ggml : fix assert message

* vulkan : add dev notes

* ggml : require mask when using ALiBi

ggml-ci

* convert : fix convert for refact models
2024-05-13 11:02:26 +03:00
284fac39fb metal : fix flash attention kernel requirements (llama/7169)
* metal : fix flash attention kernel requirements

ggml-ci

* metal : fix ggml_metal_supports_op

ggml-ci
2024-05-13 11:02:26 +03:00
fe454b8d9e Minor arithmetic improvement to mmvq wrapper kernel (llama/7172) 2024-05-13 11:02:26 +03:00
c114b75aee Vulkan Bugfixes and Improvements (llama/7084)
* Modify mat mat mul shader for mul_mat_id, modify mat vec mul shaders for single call batch operation

* Further work towards MoE, disabled for now

* Disable MoE code (not ready yet), fix a number of bugs in shaders and Vulkan code

* Add softmax with f16 mask and pos buffer support

* Disable mul_mat_id shaders for now

* Fix flake8

* Fix validation errors caused by empty buffers on larger batch sizes
2024-05-13 11:02:26 +03:00
4be936b88b CUDA: generalize FP16 fattn vec kernel (llama/7061)
* CUDA: generalize FP16 fattn vec kernel

* disable unsupported head sizes for AMD in test

* try AMD fix

* fix batch size 2-8

* partially revert changes
2024-05-13 11:02:26 +03:00
26c550f772 opencl : alignment size converted from bits to bytes (llama/7090)
* opencl alignment size should be converted from bits to bytes

Reference: https://registry.khronos.org/OpenCL/specs/3.0-unified/html/OpenCL_API.html#CL_DEVICE_MEM_BASE_ADDR_ALIGN

> Alignment requirement (in bits) for sub-buffer offsets.

* Update ggml-opencl.cpp for readability using division instead of shift

Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com>

---------

Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com>
2024-05-13 11:02:26 +03:00
24f0aa460b Introduction of CUDA Graphs to LLama.cpp (llama/6766)
* DRAFT: Introduction of CUDA Graphs to LLama.cpp

* FIx issues raised in comments

* Tidied to now only use CUDA runtime (not mixed with driver calls)

* disable for multi-gpu and batch size > 1

* Disable CUDA graphs for old GPU arch and with env var

* added missing CUDA_CHECKs

* Addressed comments

* further addressed comments

* limit to GGML_ALLOW_CUDA_GRAPHS defined in llama.cpp cmake

* Added more comprehensive graph node checking

* With mechanism to fall back if graph capture fails

* Revert "With mechanism to fall back if graph capture fails"

This reverts commit eb9f15fb6fcb81384f732c4601a5b25c016a5143.

* Fall back if graph capture fails and address other comments

* - renamed GGML_ALLOW_CUDA_GRAPHS to GGML_CUDA_USE_GRAPHS

- rename env variable to disable CUDA graphs to GGML_CUDA_DISABLE_GRAPHS

- updated Makefile build to enable CUDA graphs

- removed graph capture failure checking in ggml_cuda_error
  using a global variable to track this is not thread safe, but I am also not safistied with checking an error by string
  if this is necessary to workaround some issues with graph capture with eg. cuBLAS, we can pass the ggml_backend_cuda_context to the error checking macro and store the result in the context

- fixed several resource leaks

- fixed issue with zero node graphs

- changed fixed size arrays to vectors

- removed the count of number of evaluations before start capturing, and instead changed the capture mode to relaxed

- removed the check for multiple devices so that it is still possible to use a single device, instead checks for split buffers to disable cuda graphs with -sm row

- changed the op for checking batch size to GGML_OP_ADD, should be more reliable than GGML_OP_SOFT_MAX

- code style fixes

- things to look into
  - VRAM usage of the cudaGraphExec_t, if it is significant we may need to make it optional
  - possibility of using cudaStreamBeginCaptureToGraph to keep track of which ggml graph nodes correspond to which cuda graph nodes

* fix build without cuda graphs

* remove outdated comment

* replace minimum cc value with a constant

---------

Co-authored-by: slaren <slarengh@gmail.com>
2024-05-13 11:02:26 +03:00
69efc39d5c metal : use vm_allocate instead of posix_memalign on macOS (llama/7078)
* fix: use `malloc` instead of `posix_memalign` in `ggml-metal.m` to make it not crash Electron proccesses

* fix: typo

* fix: use `vm_allocate` instead of `posix_memalign`

* fix: don't call `newBufferWithBytesNoCopy` with `NULL` when `ggml_metal_host_malloc` returns `NULL`

* fix: use `vm_allocate` only on macOS
2024-05-13 11:02:26 +03:00
a2ad810118 ggml : introduce bfloat16 support (llama/6412)
* Introduce bfloat16 support

Many models on Hugging Face (e.g. Mistral, TinyLLaMA) use bfloat16 as
their canonical floating point format.

      ┌sign
      │
      │   ┌exponent
      │   │
      │   │      ┌mantissa
      │   │      │
      │┌──┴───┐┌─┴───┐
    0b0000000000000000 brain16

This encoding has the same number of exponent bits as float32. That
makes conversion relatively straightforward, even in the absence of
hardware support. For example, converting brain16 to binary32 means
simply shifting 16 bits to the left.

      ┌sign
      │
      │   ┌exponent
      │   │
      │   │      ┌mantissa
      │   │      │
      │┌──┴───┐┌─┴───────────────────┐
    0b00000000000000000000000000000000 IEEE binary32

The issue is that converting bf16 to fp16 can result in information
loss. Only 13% of bf16 numbers can be precisely represented in fp16
which in practice ends up being 99.71% of Mistral 7b v0.2's weights
however there is currently no way other than fp32 to get the others

      ┌sign
      │
      │  ┌exponent
      │  │
      │  │    ┌mantissa
      │  │    │
      │┌─┴─┐┌─┴──────┐
    0b0000000000000000 IEEE binary16

This change fixes that, by adding a bf16 data type to GGML. Support
for CPU inference has been implemented along with optimizations for
the AVX2, AVX512, and AVX512BF16 ISAs. Perplexity on Mistral 7b 0.2
improves somewhere around -0.0024 to -0.0046 compared to using fp16

* Remove GGML code that's not needed

* Minimize the GGML API surface area for BF16

* Remove bf16 luts

* Make the GGML header look nicer

* Fix documentation

* Apply ggerganov's fixes for test-backend-ops

* Add BF16 code for new ggml_validate_row_data() function
2024-05-13 11:02:26 +03:00
1ae1a9cd56 metal : fix unused warning 2024-05-13 11:02:26 +03:00
b5521fea19 Add an option to build without CUDA VMM (llama/7067)
Add an option to build ggml cuda without CUDA VMM
resolves
https://github.com/ggerganov/llama.cpp/issues/6889
https://forums.developer.nvidia.com/t/potential-nvshmem-allocated-memory-performance-issue/275416/4
2024-05-13 11:02:26 +03:00
9b84195225 gguf-split: add --no-tensor-first-split (llama/7072) 2024-05-13 11:02:26 +03:00
11c1df0436 CUDA: CUDART < 11.7 workaround for __hmax, __hmax2 (llama/7019) 2024-05-13 11:02:26 +03:00
c754494fdd switch to using localizedDescription (llama/7010) 2024-05-13 11:02:26 +03:00
1bce67999d metal : remove deprecated error code (llama/7008) 2024-05-13 11:02:26 +03:00
6c39ea46b6 metal : log more info on error (llama/6987) 2024-05-13 11:02:26 +03:00
156a33a990 ggml : add Flash Attention (llama/5021)
* ggml : add ggml_flash_attn_ext API

* ggml : fix GQA support in ggml_flash_attn_ext

* ggml : online attention (CPU)

* metal : initial implementation

* metal : f16 precision

* metal : reduce branches

* metal : specialize for head size

* wip : 8 rows per simd group

* wip : 4 rows per simd group

* wip : template for rows per warp

* metal : parallelize across KV size

* metal : parallel reduce across heads

* metal : efficient flash_attn_f16 implementation

* metal : avoid redundant loads of the attention

* metal : scale and mask in matrix form

* metal : fix comment

* llama : avoid ggml_cast, use F32 query

* metal : add parallel reduce version (disabled)

* metal : move output into local memory + optimize

- the result from each simdgroup now stays in the registers
- significantly reduced SRAM usage
- more efficient skipping of -INF blocks
- avoid simdgroup barrier in hot loop
- add comments

* metal : add tests, fix scaling, support C > 32

* metal : improve precision

* ggml : fix f16 mad

* metal : minor

* metal : support Q > 8

* tests : add ATTN tests

* metal : disable buffer allocation logs

* tests : more

* metal : faster inner loop for C == 32

* metal : fix array initialization

* tests : ifdef

* ggml : switch to padded F16 mask for ggml_soft_max, ggml_flash_attn_ext

* ggml : fix ggml_soft_max mask requirement

* cuda : fix soft_max to use correct mask size

* cuda : add flash_attn kernel (wip)

* metal : optimize softmax for C > 32

* metal : optimize softmax

* tests : minor fix

* cuda : avoid zeroing fragments

* tests : update dims

* cuda : fix __hisinf() result check

* cuda : avoid warp_reduce for smax

* cuda : use int instead of int64_t

Noticeably improves performance (thanks to Johannes)

* cuda : make loops use the same loop values

Thanks Johannes again for the tip

* cuda : unroll some of the loops

* cuda : avoid __hisinf branches

* cuda : use half2 in softmax

* cuda : switch to 1 warp for bs > 16

* cuda : speed-up reduce part of the kernel

* cuda : unroll Q*K^T loop

* cuda : fix -INF block check

* cuda : simplify softmax

* cuda : fix matrix names

* cuda : minor

* llama : adapt to F16 KQ_pos

* llama : adapt new models to F16 KQ_mask

* ggml : fix F16 store (ARM NEON)

* llama : fix type of KQ_mask and KQ_pos

* ggml : fix CPU soft_max

* tests : add hs=256

* cuda : fix build

* metal : improve perf via smaller int registers

* cuda : adapt soft_max to F16 mask and pos

* CUDA: faster FlashAttention, kernel for bs == 1

* 16 cols for Phi-2

* no vec for hs, no hs==256 ncols==32 for Volta

* adjust kernel selection logic

* 4 warps, 256 stride for all D

* no ncols == 64

* Multiple parallel blocks for batch size 1

* fix compile warnings

* fix excessive KQ_b loads

* fix cmake build

* fix KV cache padding, NaN from INFINITY (llama/6438)

* llama : flash_attn cparam + fix defrag

* server: support flash_attn param

* server: bench: enable flash_attn param

* CUDA: refactor host code, dyn. par. blocks

* fix flash_attn_vec_f16 race condition

* flush softmax exp below threshold to 0

* store temp KQ in registers

* Calculate KQ as FP32 if KQV has GGML_PREC_F32

* Add __hgt2_mask implementation for CUDA 11

* fix KQ FP32 precision fpr parallel_blocks > 1

* llama-bench : add -fa,--flash-attn arg

* metal : add BS=1 kernel for flash attention (llama/6508)

* metal : add BS=1 kernel for flash attention (wip)

* metal : support more than 1 warps

* metal : opts

* metal : opt

* metal : switch to parallel reduce

* metal : reduce registers

* metal : simplify

* metal : initial FA vec kernel

* metal : use F32 attention accumulators

* batched-bench : add fattn arg

* llama : simplify llama_build_kv_store

ggml-ci

* llama : adapt build_olmo to changes

* ggml : fix arm fp16 store on windows

* metal : clean-up

* metal : clean-up kernel code

* metal : minor

* tests : remove benchmarks

ggml-ci

* ggml : fix avx512 const correctness

ggml-ci

* ggml : fix soft_max with bias on CPU

ggml-ci

* common : print --flash-attn in help

* ggml : fix num dimensions in ggml_flash_attn_ext

* llama : force disable flash attention for incompatible models

* ggml : ggml_soft_max support F16/F32 mask/pos

ggml-ci

* cuda : uint -> uint32_t

* cuda : "constexpr dim3" -> "const dim3"

ggml-ci

* cuda : try to fix __hgt2_mask

ggml-ci

* ggml : add TODO's for F16/F32 mask/pos support in other backends

* llama : replace bool need_kq_pos with use_alibi

* llama : prep ALiBi support for BERT models

ggml-ci

* llama : fix n_batch requirements

ggml-ci

* cont

* server : add help for --flash-attn arg

* llama : disable FA for AMD

* tests : remove TMP_ATTN_BENCH

ggml-ci

* llama : support save/load state with FA enabled

ggml-ci

* ci : add CUDA save-load-state tests

ggml-ci

* llama : llama_kv_cache_clear zeroes data + fix save-load seq

ggml-ci

* llama : fix copy-paste errors, add TODO

* llama : disallow incompatible states

* llama : update llama_state_get_size after v_trans field

* metal : remove tmp log

* llama : add static reminder for llama_state_get_size

* metal : fix max nsg

ggml-ci

* ci : fix arg order

ggml-ci

---------

Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
Co-authored-by: Pierrick HYMBERT <pierrick.hymbert@gmail.com>
2024-05-13 11:02:26 +03:00
5167ebdfca ggml : fix __MSC_VER -> _MSC_VER (llama/6977)
ggml-ci
2024-05-13 11:02:26 +03:00
b574646d75 Fix more int overflow during quant (PPL/CUDA). (llama/6563)
* Fix more int overflow during quant.

* Fix some more int overflow in softmax.

* Revert back to int64_t.
2024-05-13 11:02:26 +03:00
388c3462a6 gguf : enforce that tensor names are unique (llama/6905)
* not allow adding duplicated tensor name

* no duplicated tensor while reading gguf

* typo

* throw exception inside llama_model_loader

Co-authored-by: slaren <slarengh@gmail.com>

---------

Co-authored-by: slaren <slarengh@gmail.com>
2024-05-13 11:02:26 +03:00
9ad202bee9 add device version in device list (llama/6959)
Co-authored-by: arthw <>
2024-05-13 11:02:26 +03:00
f0d3fb4a7e Reset schedule earlier to allow overlap with ggml graph computation on device (llama/6933)
* Reset schedule earlier to allow overlap with graph computation on device
2024-05-13 11:02:26 +03:00
9d4c8b8aa5 add basic tensor data validation function (llama/6884)
* add basic tensor data validation function

* add --check-tensors command line argument

tensor validation is disabled by default and can be enabled by adding
`--check-tensors` to the command line arguments.

quantize always validates tensors.
2024-05-13 11:02:26 +03:00
ecfac1e240 gguf : fix mismatch between alloc and free functions (llama/6929) 2024-05-13 11:02:26 +03:00
6f7140f568 Merge pull request from GHSA-p5mv-gjc5-mwqv
* always use calloc

clamp n_kv on failure to read a kv

* ggml : alternative ctx->header.n_kv update

---------

Co-authored-by: slaren <slarengh@gmail.com>
2024-05-13 11:02:26 +03:00
05b17112cf ggml : fix redefinition of vaddvq_f32 for 32-bit ARM (llama/6906) 2024-05-13 11:02:26 +03:00
a15fb5cd79 ggml : fix MIN / MAX macros (llama/6904)
ggml-ci
2024-05-13 11:02:26 +03:00
63fd148d8f ggml : move 32-bit arm compat in ggml-impl.h (llama/6865)
ggml-ci
2024-05-13 11:02:26 +03:00
6c3971b29b llamafile : improve sgemm.cpp (llama/6796)
* llamafile : improve sgemm.cpp

- Re-enable by default
- Fix issue described in #6716
- Make code more abstract, elegant, and maintainable
- Faster handling of weirdly shaped `m` an `n` edge cases

* Address review comments

* Help clang produce fma instructions

* Address review comments
2024-05-13 11:02:26 +03:00
a6d264f331 ggml : fix calloc argument ordering. (llama/6820)
Latest gcc complains here:
/home/airlied/devel/llama.cpp/ggml-alloc.c: In function ‘ggml_gallocr_new_n’:
/home/airlied/devel/llama.cpp/ggml-alloc.c:374:59: warning: ‘calloc’ sizes specified with ‘sizeof’ in the earlier argument and not in the later argument [-Wcalloc-transposed-args]
  374 |     ggml_gallocr_t galloc = (ggml_gallocr_t)calloc(sizeof(struct ggml_gallocr), 1);
      |                                                           ^~~~~~
/home/airlied/devel/llama.cpp/ggml-alloc.c:374:59: note: earlier argument should specify number of elements, later size of each element

and a bunch more.

calloc is specified to take nmemb first then size, so realign the code.

In a couple of places there was a * x, 1 so I fixed those to use calloc properly.
2024-05-13 11:02:26 +03:00
2959686019 ggml : fix ggml_backend_cpu_supports_op() for CPY (llama/0) 2024-05-13 11:02:26 +03:00
c96b0a938e ggml : group all experts in a single ggml_mul_mat_id (llama/6505)
* ggml : group all experts in a single ggml_mul_mat_id
cuda : improve mmid row copy

* cuda : fix bin bcast with non-cont src0

* test-backend-ops : only run all mul mat tests for base types

* llama : disable moe offloading with SYCL

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-05-13 11:02:26 +03:00
c97796aa0f ggml : fix llamafile sgemm wdata offsets (llama/6710)
ggml-ci
2024-05-13 11:02:26 +03:00
7a4f7d825e ggml : add llamafile sgemm (llama/6414)
This change upstreams llamafile's cpu matrix multiplication kernels
which improve image and prompt evaluation speed. For starters, Q4_0
and Q8_0 weights should go ~40% faster on CPU. The biggest benefits
are with data types like f16 / f32, which process prompts 2x faster
thus making them faster than quantized data types for prompt evals.

This change also introduces bona fide AVX512 support since tinyBLAS
is able to exploit the larger register file. For example, on my CPU
llama.cpp llava-cli processes an image prompt at 305 tokens/second,
using the Q4_K and Q4_0 types, which has always been faster than if
we used f16 LLaVA weights, which at HEAD go 188 tokens/second. With
this change, f16 LLaVA performance leap frogs to 464 tokens/second.

On Intel Core i9-14900K this change improves F16 prompt perf by 5x.
For example, using llama.cpp at HEAD with Mistral 7b f16 to process
a 215 token prompt will go 13 tok/sec. This change has fixes making
it go 52 tok/sec. It's mostly thanks to my vectorized outer product
kernels but also because I added support for correctly counting the
number of cores on Alderlake, so the default thread count discounts
Intel's new efficiency cores. Only Linux right now can count cores.

This work was sponsored by Mozilla who's given permission to change
the license of this code from Apache 2.0 to MIT. To read more about
what's improved, and how it works, see: https://justine.lol/matmul/
2024-05-13 11:02:26 +03:00
fdb2c87350 llama : add qwen2moe (llama/6074)
* support qwen2moe

* fix-review

* metal : support unary ops for nelements % 4 != 0

* metal : require contiguousness for float4 unary kernels

* metal : require contiguousness for float4 unary kernels (cont)

* fix-review

* names : for brevity "SHARED_EXP" -> "SHEXP"

* llama : reuse build_moe_ffn()

* llama : add model type name

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-05-13 11:02:26 +03:00
98c0b77e0c fix mul_mat_id() for new input, make the ut pass (llama/6682) 2024-05-13 11:02:26 +03:00
9d6d50d933 Added support for GGML_OP_CLAMP in Metal (llama/6662)
* Added support for GGML_OP_CLAMP in Metal

* Corrected size

---------

Co-authored-by: dave-fl <dave@Davids-MacBook-Pro.local>
2024-05-13 11:02:26 +03:00
c1320c1f0c fix memcpy() crash, add missed cmd in guide, fix softmax (llama/6622)
* disable mmap to fix memcpy crash, add missed cmd in guide, fix softmax

* refactor to disable mmap for SYCL backend

* fix compile error in other os

* refactor the solution, use host buf to fix it, instead of disable mmap

* keep to support mmap()

* use host buff to reduce malloc times

* revert to malloc/free solution, for threaad safe
2024-05-13 11:02:26 +03:00
66aaf03a7a CUDA: fix matrix multiplication logic for tests (llama/6667) 2024-05-13 11:02:26 +03:00
00a0947c65 metal : unify mul_mv_id kernels (llama/6556) 2024-05-13 11:02:26 +03:00
60f3713026 llama : add gguf_remove_key + remove split meta during quantize (llama/6591)
* Remove split metadata when quantize model shards

* Find metadata key by enum

* Correct loop range for gguf_remove_key and code format

* Free kv memory

---------

Co-authored-by: z5269887 <z5269887@unsw.edu.au>
2024-05-13 11:02:26 +03:00
37e6757453 feat: implemented sigmoid function (ggml/806)
* added sigmoid function

* implemented metal kernel for sigmoid

* implemented cuda kernel for sigmoid

* added sigmoid unary op and incremented count
2024-05-13 11:02:26 +03:00
8dcefdf4a9 build: fix and ignore msvc warnings (ggml/805) 2024-05-13 11:02:26 +03:00
73d13ad19a ggml : expose SSE3 and SSSE3 for MSVC when AVX is available (#2128) 2024-05-08 18:33:43 +03:00
b6680fab50 build : improve disabling AVX-512 (#2129)
* cmake : make WHISPER_NO_AVX512=ON disable all subsets of AVX-512

Previously it happened only for MSVC, but it makes sense to have the
same behavior for other compilers too.

* make : reorder x86 ISA extensions in chronological order

And update compiler flags at the end to ease modifying conditions.

* make : support WHISPER_NO_AVX512=1 for disabling all AVX-512 subsets.

That way you do not have to override each AVX-512 subset setting
individually if it has been turned on during autodetection.
2024-05-08 18:32:43 +03:00
f760756078 minor: add CMakeSettings.json to gitignore (#2094) 2024-05-08 11:03:21 +03:00
58210d6a76 examples : fix node compilation (#2115)
* node : fix compilation and update examples

* node : fix readme

* Update addon.node test
2024-05-02 22:52:55 +01:00
8fac6455ff make : change GNU make default CXX from g++ to c++ (#2100) 2024-04-28 22:54:21 +01:00
22b6598cc9 Remove unnecessary memory reallocation in fft (#2080)
fft_out needs to be twice the frame_size, not the frame_step.  It is resized in fft() anyway, but this change prevents an unnecessary reallocation.

n_fft must match the mel filter size, so it is best not to calculate it from the framesize.

We only need to get the magnitudes for half the spectrum since the other half is a mirror and not used in the mel filter loop later.
2024-04-28 18:36:12 +01:00
858452d58d models : disable old script (#2079) 2024-04-24 14:56:30 +03:00
7f85e1d7fd whisper : more prominent log message for sub-1s audio (#2065) 2024-04-24 14:46:06 +03:00
b0c3cbf2e8 main : pass nullptr when regex is empty (#2070) 2024-04-17 12:23:47 +03:00
a750868428 readme : add up-to-date repository for Python bindings (#2063)
README
2024-04-16 14:15:52 +03:00
7395c70a74 release : v1.5.5 2024-04-16 14:08:31 +03:00
9fab28135c server : add dtw (#2044)
* server.cpp: add dtw

* Update examples/server/server.cpp

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-15 22:16:58 +03:00
08d3eef97d build : fix embedded Metal library generation (#2045) 2024-04-15 20:23:05 +03:00
1b5439a6c2 node : support no timestamps (#2048)
* fix: node: do not compute timestamps if you do not need them

* feat: add no_timestamps parameter to node addon
2024-04-15 20:03:34 +03:00
c7f95b7ca2 build : detect AVX512 in Makefile, add AVX512 option in CMake (#2043)
* make : add AVX512 detection to Makefile and CMakeLists.txt

* make : autodetect more AVX512 instruction subsets

* cmake : do not default to AVX512, must be enabled explicitly

* cmake : enable a set of AVX512 subsets, when AVX512 is turned on

* make : consolidate AVX512 subsets, add AVX512 VBMI

* cmake : revert to NO AVX512 setting, add settings for AVX512 VNNI and VBMI

* make : re-introduce AVX512VNNI back

* cmake : remove superfluous comment line
2024-04-15 20:02:09 +03:00
5c554c04ff whisper.nvim : fix missing reference to "model" variable (#2049) 2024-04-15 19:41:28 +03:00
c383f091a1 whisper : update grammar-parser.cpp (#2058)
preceeding -> preceding
2024-04-15 19:40:27 +03:00
8f253ef3af sync : ggml 2024-04-09 20:27:55 +03:00
c7dc37f97c license : update copyright notice + add AUTHORS 2024-04-09 20:27:44 +03:00
526332873b llama : add Command R Plus support (llama/6491)
* Add Command R Plus GGUF

* Add Command R Plus GGUF

* Loading works up to LayerNorm2D

* Export new tensors in 1D so they are not quantized.

* Fix embedding layer based on Noeda's example

* Whitespace

* Add line

* Fix unexpected tokens on MPS. Re-add F16 fix. ((Noeda)

* dranger003: Fix block index overflow in CUDA dequantizing.

* Reverted blocked multiplication code as it still has issues and could affect other Llama arches

* export norms as f32

* fix overflow issues during quant and other cleanup

* Type convention

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* dranger003: Fix more int overflow during quant.

---------

Co-authored-by: S <seast@Ss-Mac-Studio.local>
Co-authored-by: S <s@example.com>
Co-authored-by: slaren <slarengh@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-09 20:26:18 +03:00
1d2721ca72 remove row=1 cond (llama/6532) 2024-04-09 20:26:18 +03:00
219e601dab support/fix OPs GGML_TYPE_IQ4_NL, GGML_TYPE_IQ4_XS, GGML_TYPE_IQ3_XXS, GGML_TYPE_IQ3_S, GGML_TYPE_IQ2_XXS, GGML_TYPE_IQ2_XS, GGML_TYPE_IQ2_S, GGML_TYPE_IQ1_S, GGML_TYPE_IQ1_M (llama/6521) 2024-04-09 20:26:18 +03:00
3b8aade3c2 scripts : update sync 2024-04-09 20:25:50 +03:00
52ccd4a3a8 files : rename ./extra to ./scripts 2024-04-09 20:13:41 +03:00
5275074d37 whisper : fix DTW memory access (#2012)
* Fix DTW memory access

* Memory fix - Apply changes from denersc
2024-04-09 18:38:19 +03:00
c15b4cda7d common : fix file-handle leak in read_wav() (#2026)
Now it cleans up in case of error.
2024-04-09 18:34:34 +03:00
d3cfb6ca2b main : set stdin to binary mode on Windows (#2025) 2024-04-09 18:33:32 +03:00
956ef860bc cmake : support for CPU BLAS build via Intel MKL (#2024) 2024-04-09 18:32:46 +03:00
671b4bde6c main : allow a response-file as the sole parameter (#2019)
* 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>
2024-04-09 18:31:16 +03:00
c8eeb93a6a whisper : suppress tokens with a regex (#1997)
* 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>
2024-04-09 18:27:28 +03:00
319fe5146e cmake : create solution folders (#2004)
* Create solution folders in the CMake build.

* Fixed non-SDL2 build.

* Fixed emscripten build.
2024-04-09 18:23:33 +03:00
13c22321d1 sync : ggml 2024-04-07 17:04:56 +03:00
ccbe9d5676 extra : sync grammar-parser 2024-04-07 17:04:22 +03:00
81a3c41aa0 talk-llama : sync llama.cpp 2024-04-07 16:21:08 +03:00
a50207c65d sync : ggml 2024-04-07 16:18:11 +03:00
97878e53fd sync : llama.cpp (skip)
ggml-ci
2024-04-07 16:15:57 +03:00
61b05815e0 Fixed minor bug when enabling FP16 for non intel targets (llama/6464)
* moved INTEL_MKL guard from gemm_impl to gemm (wrapper)

* Update ggml-sycl.cpp

Co-authored-by: AidanBeltonS <87009434+AidanBeltonS@users.noreply.github.com>

---------

Co-authored-by: AidanBeltonS <87009434+AidanBeltonS@users.noreply.github.com>
2024-04-07 16:15:57 +03:00
1dce94cf26 ggml : mul_mat_id use the same tensor for all the experts (llama/6387)
* 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>
2024-04-07 16:15:57 +03:00
f12e982c0b Disable iqx on windows as WA (llama/6435)
* disable iqx on windows as WA

* array instead of global_memory
2024-04-07 16:15:57 +03:00
fa966b9b40 Vulkan k-quant mmq and ggml-backend offload functionality (llama/6155)
* 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
2024-04-07 16:15:57 +03:00
b83a9fc9d3 fix set main gpu crash (llama/6339) 2024-04-07 16:15:56 +03:00
3adbf2fb03 ggml : fix bounds checking of zero size views (llama/6347) 2024-04-07 16:15:56 +03:00
700d146127 backend : fix typo in scheduler documentation (ggml/781)
Signed-off-by: Daniel Bevenius <daniel.bevenius@gmail.com>
2024-04-07 16:15:56 +03:00
a74fde9b4c extra : sync ggml-cuda folder 2024-04-07 16:10:44 +03:00
1d7657f409 ggml: bypass code incompatible with CUDA < 11.1 (#2020)
`cudaHostRegisterReadOnly` parameter was only introduced in CUDA 11.1

See this issue for more details:
https://github.com/ggerganov/whisper.cpp/issues/2007
2024-04-04 14:49:24 +02:00
ac283dbce7 ci : add building in MSYS2 environments (Windows) (#1994) 2024-03-30 09:20:20 +02:00
1e8f28c42a build : use pkg-config for OpenBLAS (#1778)
* 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.
2024-03-29 15:53:26 +02:00
fc366b807a main : add command-style grammar (#1998)
* 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>
2024-03-28 12:02:10 +02:00
9fb308d90f make : add grammar parser to common objects 2024-03-28 11:59:48 +02:00
2948c740a2 sync : ggml (#2001)
* sync : update scripts

* sync : ggml

* talk-llama : sync llama.cpp

* make : WHISPER_CUBLAS -> WHISPER_CUDA

* ci : try to fix sycl build

* talk-llama : fix make build
2024-03-27 18:55:10 +02:00
1558ec5a16 whisper : improve handling of prompts (#1981)
* whisper : improve handling of prompts

* whisper : add whisper_token_count helper
2024-03-25 14:48:19 +02:00
fff24a0148 whisper : improve support for distil-large-v3 (#1982) 2024-03-21 18:53:30 +02:00
48a145207e ruby : fix build (#1980) 2024-03-21 07:40:09 +02:00
79d5765e7e docker : libcuda.so.1 in PATH (#1966) 2024-03-20 18:45:15 +02:00
04e48094e4 readme : add Fedora dependencies (#1970)
* README.md

fix documentaion and added fedora liunx dependencies for stream build

* fix documentaion and added fedora liunx dependencies for command build

* fix documentaion and added fedora liunx dependencies for talk build

* fix documentaion and added fedora liunx dependencies for talk-llama build

* reverted back mistakenly removed MacOS documentaion
2024-03-20 18:42:11 +02:00
741abb162c whisper : token-level timestamps with DTW (#1485)
* 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>
2024-03-20 18:25:26 +02:00
e7794a868f examples : rename --audio-context to --audio-ctx per help text (#1953) 2024-03-18 17:53:33 +02:00
725350d4ea whisper : set outputs from conv graph (#1959) 2024-03-16 17:30:55 +02:00
906c73b219 alloc : fix allocation data of pre-allocated leafs 2024-03-16 17:15:45 +02:00
00d80ff965 cmake : copy ggml-common.h to bin 2024-03-16 17:15:44 +02:00
1b553b9817 gitignore : .vimspector.json 2024-03-16 16:26:35 +02:00
de4d067f1e talk-llama : sync llama.cpp 2024-03-15 14:21:59 +02:00
e715f6a601 sync : ggml 2024-03-15 14:12:19 +02:00
f60ccfd83b update examples and tests 2024-03-15 14:01:14 +02:00
3753a2b2a8 ggml : add ggml-common.h 2024-03-15 14:01:14 +02:00
592dd25615 ggml : designate enum vals for integer types (llama/6050) 2024-03-15 14:01:14 +02:00
c8709d4604 metal : build metallib + fix embed path (llama/6015)
* metal : build metallib + fix embed path

ggml-ci

* metal : fix embed build + update library load logic

ggml-ci

* metal : fix embeded library build

ggml-ci

* ci : fix iOS builds to use embedded library
2024-03-15 14:01:14 +02:00
8932c2d6ce llama : add pipeline parallelism support (llama/6017)
* 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>
2024-03-15 14:01:13 +02:00
2bddfdd7c8 Update get version (llama/6025) 2024-03-15 14:01:13 +02:00
46e3c3f112 ggml : reuse quantum structs across backends (llama/5943)
* ggml : reuse quant blocks across backends

ggml-ci

* ggml : define helper constants only for CUDA and SYCL

ggml-ci

* ggml : define helper quantum constants for SYCL

ggml-ci
2024-03-15 14:01:13 +02:00
ef24ae0c7d ggml : fix UB in IQ2_S and IQ3_S (llama/6012) 2024-03-15 14:01:13 +02:00
a753926f02 sycl : update IQ1_S kernels (WIP - not working!) (llama/5995)
* sycl : try to fix after IQ1_S changes

* sycl : iq1s_grid -> iq1s_grid_gpu

* sycl : fix grid type
2024-03-15 14:01:13 +02:00
9dc60fc02d 1.5 bit: we can do even better (llama/5999)
* 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>
2024-03-15 14:01:13 +02:00
d73a63629e ggml, ci : Windows ARM runner and build fixes (llama/5979)
* 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`
2024-03-15 14:01:13 +02:00
f79d0d4f74 Better 1.5 bit quantization (llama/5971)
* 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>
2024-03-15 14:01:12 +02:00
4f88940ff6 Add q3_s and q1_s (llama/5886)
* Add q3_s and q1_s

* fix compilation

* fix build

* fix build

* fix build

* enable ops

* rm macro

* increase grid space
2024-03-15 14:01:12 +02:00
7bdb1de9ec metal : move mm_id indices to shared mem (llama/5982) 2024-03-15 14:01:12 +02:00
653d2e8ff9 ggml : fix unnecessary f32 -> f16 -> f32 casts (mmla) (llama/5951) 2024-03-15 14:01:12 +02:00
2fef660d0a ggml : remove old quantization functions (llama/5942)
* ggml : remove old quantization functions

ggml-ci

* ggml : simplify ggml_quantize_chunk

ggml-ci

* ggml : restrict correctness

ggml-ci

* ggml : remove hist data from the quantization API

ggml-ci

* tests : remove hist usage in test-backend-ops

ggml-ci

* vulkan : remove hist and fix typo
2024-03-15 14:01:12 +02:00
24eba5a2ff ggml : add ggml-common.h to deduplicate shared code (llama/5940)
* ggml : add ggml-common.h to shared code

ggml-ci

* scripts : update sync scripts

* sycl : reuse quantum tables

ggml-ci

* ggml : minor

* ggml : minor

* sycl : try to fix build
2024-03-15 14:01:12 +02:00
6e9d3aa32d llama : support Mamba Selective State Space Models (llama/5328)
* mamba : begin working on support for Mamba SSM

* mamba : begin figuring out how to (ab)use the kv cache for Mamba

* mamba : recurrent inference almost works, but incoherent

* mamba : recurrent inference WORKS!!!

* convert : optionally use d_conv and d_state from config.json for Mamba

* mamba : refactor recurrent conv, resulting in 20% perf increase

It's still slower than I'd like, but I did not really optimize `ggml_exp` yet.

I also refactored `ggml_exp` to work with tensors with more than 2 dimensions.

* ggml : parallelize ggml_exp

This results in 8% faster token generation for Mamba-130M.

* mamba : simplify the conv step with a self-overlapping view

Turns out the conv_state can be made smaller by one column.
Note that this breaks existing GGUFs of Mamba,
because the key_value_length field is tied to the conv_state size.

Convolution with a self-overlapping view is cool!
And it's much simpler than what I initially thought would be necessary
to make the convolution step work with more than 1 token at a time.

Next step is to make the SSM step work on batches of tokens too,
and thus I need to figure out a way to make a parallel selective scan
which will keep the ssm_state small and won't make it bigger
by a factor of (n_layer * batch_size).

* llama : fix Mamba KV self size wrongly displaying as f16 instead of f32

Relatedly, I also tried to see if other types than f32 worked for the states,
but they don't, because of the operators used.
It's probably better anyway to keep lots of precision there,
since the states are small anyway.

* mamba : fix self-overlapping view depth stride

* mamba : handle batches of more than 1 token

This means running Mamba no longer crashes when using the default settings!
And probably also slightly faster prompt processing.
Both batched and non-batched processing yield the same output.

Previously, the state was not cleared when starting a sequence.
Next step is to make the KV cache API work as expected for Mamba models.

* ggml: add ggml_ssm_scan to help with parallel selective scan

If the selective scan was implemented without a custom operator,
there would be waaay too many nodes in the graph. For example,
for Mamba-130M, with a batch size of 512 (the default),
a naive selective scan could add at least 24*512=12288 nodes,
which is more than LLAMA_MAX_NODES (8192),
and that's only for the smallest Mamba model.
So it's much cleaner with a custom operator.
Not sure about the name, though.

* ggml : in ggml_ssm_scan, merge multiple rows in the same vec operation

This will help with performance on CPU if ggml_vec_mul_f32
and ggml_vec_add_f32 are ever optimized with SIMD.

* mamba : very basic quantization support

Mostly works, but there is currently no difference
between the variants of a k-quant (e.g. Q4_K_S and Q4_K_M are the same).
Most of the SSM-specific weights can be kept in f32 without affecting
the size that much, since they are relatively small.
(the linear projection weights are responsible for most of Mamba's size)

Too much quantization seems to make the state degrade quite fast, and
the model begins to output gibberish.
It seems to affect bigger models to a lesser extent than small models,
but I'm not sure by how much.

Experimentation will be needed to figure out which weights are more important
for the _M (and _L?) variants of k-quants for Mamba.

* convert : fix wrong name for layer norm weight of offical Mamba models

I was using Q-bert/Mamba-* models before, which have a slighlty different
naming scheme for the weights.
(they start with "model.layers" instead of "backbone.layers")

* mamba : fuse more steps of the SSM scan in the ggml_ssm_scan operator

This increases performance on CPU by around 30% for prompt processing,
and by around 20% for text generation.

However, it also makes the ggml_exp and ggml_soft_plus operators unused.
Whether or not they should be kept will be decided later.

* convert : for Mamba, also consider the "MambaLMHeadModel" arch name

It's the name of the class of the official implementation,
though they don't use it (yet) in the "architectures" field of config.json

* mamba : fix vocab size problems with official models

The perplexity was waaaay to high for models with a non-round vocab size.
Not sure why, but it needed to be fixed in the metadata.

Note that this breaks existing GGUF-converted Mamba models,
but **only if** the vocab size was not already rounded.

* ggml : remove ggml_exp and ggml_soft_plus

They did not exist anyway outside of this branch,
and since ggml_ssm_scan fused operations together, they are unused.
It's always possible to bring them back if needed.

* mamba : remove some useless comments

No code change.

* convert : fix flake8 linter errors

* mamba : apply suggestions from code review

* mamba : remove unecessary branch for row-wise ssm_state and C multiplication

It was previously done to avoid permuting when only one token is processed
at a time (like when generating text), but permuting is cheap,
and dynamically changing the compute graph is not future-proof.

* ggml : in ggml_ssm_scan, use more appropriate asserts

* ggml : rename the destination pointer in ggml_compute_forward_ssm_scan_f32

* mamba : multiple sequences, but one at a time

This is a step towards making this Mamba implementation usable
with the server example (the way the system prompt is kept when clearing
the client slots will need to be changed before this can work, though).

The KV cache size for this kind of model is tied to the maximum number
of sequences kept at any single time.
For now, this number is obtained from n_parallel (plus one,
to have an extra sequence to dedicate to the system prompt),
but there might be a better way to do this which won't also
make the main example use 2 cells even if only 1 is really used.
(for this specific case, --parallel 0 helps)

Simultaneous sequence processing will probably require changes to
ggml_ssm_scan, and possibly a new operator for the conv step.

* mamba : support llama_kv_cache_seq_cp

This (mis)uses the logic around K shifts, because tokens in a state
can't be shifted anyway, and because inp_K_shift has the right shape and type.
Using ggml_get_rows is a nice way to do copies, but copy chains can't work.
Fortunately, copy chains don't really seem to be used in the examples.

Each KV cell is dedicated to the sequence ID corresponding to its own index.

* mamba : use a state mask

It's cleaner than the previous heuristic of
checking for the pos of the first token in the batch.

inp_KQ_mask could not be re-used for this, because it has the wrong shape
and because it seems more suited to the next step of
simultaneous sequence processing (helping with the problem of
remembering which token belongs to which sequence(s)/state(s)).

* llama : replace the usage of n_ctx with kv_self.size in many places

* mamba : use n_tokens directly instead of n_tok

* mamba : in comments, properly refer to KV cells instead of slots

* mamba : reduce memory usage of ggml_ssm_scan

From 290.37 MiB to 140.68 MiB of CPU compute buffer size
with Mamba 3B with a batch size of 512.

The result tensor of ggml_ssm_scan was previously a big part
of the CPU compute buffer size. To make it smaller,
it does not contain the intermediate ssm states anymore.
Both y and the last ssm state are combined in the result tensor,
because it seems only a single tensor can be returned by an operator
with the way the graph is built.

* mamba : simultaneous sequence processing

A batch can now contain tokens from multiple sequences.

This is necessary for at least the parallel example, the server example,
and the HellaSwag test in the perplexity example.

However, for this to be useful, uses of llama_kv_cache_seq_rm/cp
will need to be changed to work on whole sequences.

* ggml : add ggml_ssm_conv as a new operator for the conv step of Mamba

This operator makes it possible to use and update the correct states
for each token of the batch in the same way as ggml_ssm_scan.
Other solutions which use existing operators would need loops which would
add too many nodes to the graph (at least the ones I thought of).

Using this operator further reduces the size of the CPU compute buffer
from 140.68 MiB to 103.20 MiB with Mamba 3B with a batch size of 512.
And (at least on CPU), it's a bit faster than before.

Note that "ggml_ssm_conv" is probably not the most appropriate name,
and it could be changed if a better one is found.

* llama : add inp_s_seq as a new input tensor

The most convenient implementation to select the correct state (for Mamba)
for each token is to directly get the correct index from a tensor.
This is why inp_s_seq is storing int32_t and not floats.

The other, less convenient way to select the correct state would be
to have inp_KQ_mask contain 1.0f for each state used by a token
and 0.0f otherwise. This complicates quickly fetching the first used
state of a token, and is also less efficient because a whole row
of the mask would always need to be read for each token.

Using indexes makes it easy to stop searching when there are
no more sequences for a token, and the first sequence assigned
is always very quickly available (it's the first element of each row).

* mamba : support llama_kv_cache_seq_cp copy chains

* mamba : support shifting and dividing the kv cache pos

* mamba : make the server and parallel examples work with whole sequences

A seq_id is dedicated to the system prompt in both cases.

* llama : make llama_kv_cache_seq_rm return whether it succeeded or not

* mamba : dedicate an input tensor for state copy indices

This is cleaner and makes it easier to adapt when/if token positions
(and by extension, inp_K_shift) are no longer integers.

* mamba : adapt perplexity, batched, and batched-bench examples

* perplexity : limit the max number of sequences

This adapts to what the loaded model can provide.

* llama : add llama_n_max_seq to get the upper limit for seq_ids

Used by the perplexity example.

* batched : pass n_parallel to the model's context params

This should have been there already, but it wasn't.

* batched-bench : reserve sequences to support Mamba

* batched-bench : fix tokens being put in wrong sequences

Generation quality isn't what's measured in there anyway,
but at least using the correct sequences avoids using non-consecutive
token positions.

* mamba : stop abusing attention metadata

This breaks existing converted-to-GGUF Mamba models,
but will allow supporting mixed architectures like MambaFormer
without needing to break Mamba models.

This will also allow changing the size of Mamba's states
without having to reconvert models in the future.
(e.g. using something else than d_conv - 1 columns for the conv_states
 will not require breaking existing converted Mamba models again)

* gguf-py : add new KV metadata key-value pairs for Mamba

* llama : add new metadata key-value pairs for Mamba

* llama : guard against divisions by zero when n_head is 0

* mamba : rename "unlimited" KV cache property to "recurrent"

* mamba : more correctly update the "used" field of the KV cache

* ggml : in ggml_ssm_scan, use a threshold for soft_plus

This is how the official Mamba implementation does it,
and it's also what torch.nn.Softplus does.

* convert : for Mamba, fallback to internal NeoX tokenizer

The resulting models are exactly the same
as if the tokenizer.json and tokenizer_config.json of GPT-NeoX were there.

* mamba : support state saving and restoring

* ggml : implicitly pass src tensors through dst for Mamba-related ops

* mamba : clarify some comments

* server : fix cache_tokens not getting correctly resized

Otherwise, when the "we have to evaluate at least 1 token" special case
was triggered, an extra token was kept in cache_tokens even if it was
removed from the KV cache.

For Mamba, this caused useless prompt reprocessing when the previous
request triggered the above case.

* convert-hf : support new metadata keys for Mamba

For the models available at
https://huggingface.co/collections/state-spaces/transformers-compatible-mamba-65e7b40ab87e5297e45ae406

* mamba : rename metadata to be more similar to transformers library

This breaks existing converted-to-GGUF models,
but the metadata names are more "standard".

* mamba : support mamba-*-hf models

These models share their token_embd.weight with their output.weight

* mamba : add missing spaces

This is purely a formatting change.

* convert-hf : omit output.weight when identical with token_embd.weight

Only for Mamba for now, but it might be relevant for other models eventually.
Most Mamba models actually share these two tensors, albeit implicitly.

* readme : add Mamba to supported models, and add recent API changes

* mamba : move state_seq and state_mask views outside layer loop

A few tensors were also missing `struct` in front of `ggml_tensor`.
2024-03-15 14:01:12 +02:00
9ae0d18856 extra : update sync scripts after ggml-common.h 2024-03-15 14:00:53 +02:00
a56f435fd4 whisper : document whisper_batch.n_seq_id (#1942)
To prevent other people from attempting to remove it, as I did.
2024-03-10 16:55:22 +02:00
ec166499d8 whisper : improve beam search candidate diversity (#1947)
As of #1486, whisper.cpp uses a unified KV cache with KQ masking.
As a result, depending on their location in the batch,
identical sequences in a batch can have slightly different outputs
due to floating point rounding errors during reduction.
See the discussion in #1941 for more details.

The beam search code used "has identical sum of log probabilities"
as a shorthand for "is an identical token sequence". However, per above,
identical tokens do not necessarily result in identical probabilities.

Instead, explicitly compare on sequences.
This is linear in cost when they are identical,
but the lengths are always small and the comparisons are cheap.

This increases diversity during beam search.

This improves output quality for some short samples I've been working
with, at no detectable performance cost.
I haven't checked against larger corpuses.

Fixes #1941
2024-03-10 16:54:43 +02:00
ccf022f970 bindings/go : add linker flags to make metal work (#1944)
The first two are required to build.
The last one is to make it actually detect the GPU.

Fixes #1899, at least for me
2024-03-09 18:50:44 +02:00
2852e1af55 whisper : make beam candidate sort more stable (#1943)
All else being otherwise equal, this encourages the beam candidate
selection to re-use the same decoder, which slightly
reduces the cache size.

I wouldn't expect it to make much of a performance difference,
but it helps when debug printing the cache and beam.

Added as part of understanding #1941.
2024-03-09 18:50:03 +02:00
ce945b50c3 ggml : try fix 32-bit arm compat (#1938)
* ggml : try fix 32-bit arm compat

* ggml : fix cont
2024-03-08 23:45:07 +02:00
2f5a5a66dd talk-llama : use llama_decode instead of llama_eval 2024-03-08 12:04:43 +02:00
8e409d1113 talk-llama : sync llama.cpp 2024-03-08 11:55:50 +02:00
05d1b61af4 talk-llama : sync llama.cpp 2024-03-08 11:52:47 +02:00
647cae178a sync : ggml 2024-03-08 11:39:34 +02:00
bae7c23fbf Revert "[SYCL] fix error when set main gpu to non-zero (llama/5901)" (llama/5918)
This reverts commit ceca1aef0738b57951cd12c603c3477e75312dec.
2024-03-08 11:38:33 +02:00
18ea187d42 fix error when set main gpu to non-zero (llama/5901)
* fix error when set main gpu to non-zero

* fix delete condition
2024-03-08 11:38:33 +02:00
1daeffca54 ggml : use SYS_get_cpu if SYS_getcpu is not defined (llama/5906)
Fixes #5694
Fixes ggerganov/whisper.cpp#1894
2024-03-08 11:38:33 +02:00
2f6f1d4465 ggml : use uint8x16_t return type for ggml_vqtbl1q_u8 (llama/5894)
* use uint8x16_t

* Update ggml-quants.c
2024-03-08 11:38:33 +02:00
7ff1894c34 add wait() to make code stable (llama/5895) 2024-03-08 11:38:33 +02:00
8edfc54c2b quants : use MM256_SET_M128I consistently to fix gcc 7 build (llama/5889) 2024-03-08 11:38:33 +02:00
9c399689ec Vulkan Improvements (llama/5835)
* Improve dequant shaders, add fast q4_0 dequant

* Optimize dmmv non-kquants for GCN

Remove unnecessary SPIR-V shader duplication

* Fix q4_0 dequant dispatch sizes

Fix backend free bug

* Optimize dequant shaders for q4_1, q5_0, q5_1 and q8_0

* Add unary and binary op shader templates

* Fix Vulkan check results

* Enable non-contiguous support for simple ops

* Add argsort

Basic q4_0 mmq shader and unit test

* Speed up q4_0 dequant code, enable mmq for q4_0

* Rework matmul pipeline selection

* Add soft_max alibi support

* Add q4_1, q5_0, q5_1 and q8_0 dequant mat mat mul shaders

* Add environment variable GGML_VK_FORCE_MAX_ALLOCATION_SIZE to limit max buffer size

Rename GGML_VULKAN_DISABLE_F16 to GGML_VK_DISABLE_F16 for consistency
2024-03-08 11:38:33 +02:00
9d9a405cfd fix mul_mat fault in CI/unit-test (llama/5862)
* fix mul_mat fault in cpy_f32_f16

* rm unused function

* add wait() for memcpy

* restore ci/run.sh, rename struct defination, fix bug in ggml_sycl_op_mul_mat_sycl

* fix format issue

* llama : fix segfault from unknown model arch name (llama/5820)

* llama : fix segfault from unknown model arch name

* llama : make all LLM maps const

This also requires using `std::map::at` instead of its `operator[]`
which does not exist for const maps.

* llama : name LLM_ARCH_UNKNOWN to "(unknown)"

This avoids errors from `std::map::at` when
getting the general name of the model architecture.
Using "(unknown)" instead of an empty string as per suggestion
https://github.com/ggerganov/llama.cpp/pull/5820#issuecomment-1973735284

* llama : remove redundant inner const for LLM_TENSOR_NAMES

The extra const won't do anything here as const maps
return const references to values.

Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com>

* llama : remove redundant nullptr check in llm_arch_from_string

Since LLM_ARCH_NAMES is a const map, no spurious elements
with a NULL name are inserted anymore, so this check is dead code.

---------

Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com>

* llama : refactor internal quantization functions (llama/5830)

* scripts : add pod-llama.sh

* ggml : IQ3_S improvements (llama/5829)

* iq3_s: somewhat faster AVX2 dot product

On Ryzen a 7950X TG-128 increases to 16 t/s from 15.5 t/s using
16 threads. For 8 threads it is 13.85 t/s vs 11.75 t/s.
PP-512 increases to 28.5 t/s from 23.8 t/s.

* iq3_s: somewhat faster ARM_NEON dot product

Still dog slow - 10.7 t/s up from 9.9 t/s.

* iq3_s: another small ARM_NEON improvement

10.7 -> 11.0 t/s. Using vmulq_s8 is faster than the xor - sub trick
that works best on AVX2.

* iq3_s: minor improvement on Metal

49.4 t/s -> 50.3 t/s

* iq3_s: PPL improvement

E.g., for a context of 4096 LLaMA-v2-7B goes to 5.1340 from 5.1653.

* iq3_s: use new grid everywhere

* Fix ARM_NEON

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>

* convert-hf : make model class definitions self-contained (llama/5825)

* convert : automatically fall back to HfVocab if tokenizer.model doesn't exist (llama/5821)

* ggml : fix IQ3_S AVX implementation (llama/5834)

ggml-ci

* llama : add abort_callback to interrupt computation (llama/5409)

* using abort_callback from ggml to stop llama computation

* format fix

* a brief explaining comment

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* server: tests: passkey challenge /  self-extend with context shift demo (llama/5832)

* server: tests: add models endpoint scenario

* server: /v1/models add some metadata

* server: tests: add debug field in context before scenario

* server: tests: download model from HF, add batch size

* server: tests: add passkey test

* server: tests: add group attention params

* server: do not truncate prompt tokens if self-extend through group attention is enabled

* server: logs: do not truncate log values

* server: tests - passkey - first good working value of nga

* server: tests: fix server timeout

* server: tests: fix passkey, add doc, fix regex content matching, fix timeout

* server: tests: fix regex content matching

* server: tests: schedule slow tests on master

* server: metrics: fix when no prompt processed

* server: tests: self-extend add llama-2-7B and Mixtral-8x7B-v0.1

* server: tests: increase timeout for completion

* server: tests: keep only the PHI-2 test

* server: tests: passkey add a negative test

* flake.lock: Update (llama/5842)

Flake lock file updates:

• Updated input 'flake-parts':
    'github:hercules-ci/flake-parts/b253292d9c0a5ead9bc98c4e9a26c6312e27d69f' (2024-02-01)
  → 'github:hercules-ci/flake-parts/f7b3c975cf067e56e7cda6cb098ebe3fb4d74ca2' (2024-03-01)
• Updated input 'flake-parts/nixpkgs-lib':
    'github:NixOS/nixpkgs/97b17f32362e475016f942bbdfda4a4a72a8a652?dir=lib' (2024-01-29)
  → 'github:NixOS/nixpkgs/1536926ef5621b09bba54035ae2bb6d806d72ac8?dir=lib' (2024-02-29)
• Updated input 'nixpkgs':
    'github:NixOS/nixpkgs/cbc4211f0afffe6dfd2478a62615dd5175a13f9a' (2024-02-23)
  → 'github:NixOS/nixpkgs/1536926ef5621b09bba54035ae2bb6d806d72ac8' (2024-02-29)

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>

* server : init http requests thread pool with --parallel if set (llama/5836)

* ci : schedule slow server tests only on Release or on demand (llama/5839)

* llama : fix llama_copy_state_data with fragmented KV cache (llama/5840)

The row size of the saved states was based on kv_self.head while
it should be based on llama_kv_cache_cell_max.

Existing session files should still work.

* llama : fix llama_kv_cache_cell_max inability to return 1

I've also changed its return type to uint32_t,
because this function is always used to set the value of uint32_t variables,
and because the index already has this type.

* llama : fix state size calculation

Some bytes in the state were unaccounted for in llama_get_state_size.
Since the logits reserve so much space, it did not cause problems.

* gguf-dump : support i-quants (llama/5841)

Co-authored-by: Black_Fox <radekliska@gmail.com>

* llama : allow for user specified embedding pooling type (llama/5849)

* allow for user specified pooling type

* llama : use enum types over int

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* readme : add API changes section

* cuda : fix data race in soft max (llama/5853)

* main : support special tokens as reverse/anti prompt (llama/5847)

* Support special tokens as reverse/anti prompt.

* Tokenize antiprompts only once.

* main : minor

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* common : use LLAMA_DEFAULT_SEED (llama/5855)

* add some new ops, fix some operators and add batch operations to certain operators. (ggml/747)

* cuda: fix group_norm

* cuda: add batch inference support for ggml_pad/ggml_upscale

* add ggml_arrange

* add ggml_timestep_embedding

* update ggml_arange/ggml_timestep_embedding tests

* cuda: fix im2col

* add ggml_arange/ggml_timestep_embbeding support for metal backend

* fix some bugs

* fix some bugs

* Update ggml.h

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* Update ggml-cuda.cu

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* Update ggml-metal.m

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* Update ggml-metal.m

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* Update ggml-metal.metal

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* modify according to the review comments

* ggml : fix compile warnings + code style

* ggml : normalize compute_forward calls + fix seg fault in debug

* minor

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: slaren <slarengh@gmail.com>

* sync : ggml

* add alias for chat template (llama/5858)

* speculative : implement stochastic speculative sampling (llama/5625)

* (WIP) Implement stochastic speculative decoding

* sample from residual distribution on draft accept failure

* fix #5657: force greedy sampling with probs when temp is 0

* remove p_accept parameter

* fix style

* remove unused variables

* add srand() in speculative.cpp

* replace use of rand() with mt19937 sampling

* fixes based on review (@JohannesGaessler)

* fix r random generation

* randomly select next sequence to verify + fix bug in memory freeing

* fix bug in active_seqs sync

* fix uniform int distribution initialization

* remove warnings from comparison between int and size_t

* check grammar in `llama_sample_probability_distribution_impl`

* remove malloc code by utilizing vectors

* add PR link to README

* cmake : handle cases where git index is not found in .git (llama/5844)

* Update CMakeLists.txt

* Update CMakeLists.txt

* ggml : introduce ggml_status (ggml/750)

* using enum as an exit code instead of macros

* update return type from enum to unsigned int

* indentation fix

* compound update
ggml_compute_exit_code -> ggml_status
changed ggml_status from a bit-field type to simple codes
ggml_status to string cast

* ggml_status to string cast

* GGML_CALL was removed

Co-authored-by: slaren <slarengh@gmail.com>

---------

Co-authored-by: slaren <slarengh@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* sync : ggml

ggml-ci

* ggml : fix unknown status (llama/0)

* flake : fix

* llama : fix embeddings (llama/5796)

* llama : fix embeddings

ggml-ci

* llama : do not use KV cache for non-causal models

ggml-ci

* embeddings : fix llama_batch_init arg

* llama : add pooling switch

* llama : distinguish token vs sequence embeddings

ggml-ci

* llama : assert pooling tensor

* llama : simplify causal mask condition

ggml-ci

* llama : assert input batch with pooling enabled

* readme : update API changes list

* nix: static build (llama/5814)

* fix speculative decoding build on windows (llama/5874)

* rebase and rm tailing space

---------

Co-authored-by: LiangtaoJin <liang-tao.jin@intel.com>
Co-authored-by: compilade <113953597+compilade@users.noreply.github.com>
Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com>
Co-authored-by: Xuan Son Nguyen <thichthat@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: Kawrakow <48489457+ikawrakow@users.noreply.github.com>
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
Co-authored-by: Jared Van Bortel <jared@nomic.ai>
Co-authored-by: Michael Podvitskiy <podvitskiymichael@gmail.com>
Co-authored-by: Pierrick Hymbert <pierrick.hymbert@gmail.com>
Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
Co-authored-by: Nindaleth <Nindaleth@users.noreply.github.com>
Co-authored-by: Black_Fox <radekliska@gmail.com>
Co-authored-by: Douglas Hanley <thesecretaryofwar@gmail.com>
Co-authored-by: slaren <slarengh@gmail.com>
Co-authored-by: DAN™ <dranger003@gmail.com>
Co-authored-by: leejet <leejet714@gmail.com>
Co-authored-by: Minsoo Cheong <54794500+mscheong01@users.noreply.github.com>
Co-authored-by: Dane Madsen <dane_madsen@hotmail.com>
Co-authored-by: hutli <6594598+hutli@users.noreply.github.com>
Co-authored-by: Jeffrey Quesnelle <emozilla@nousresearch.com>
2024-03-08 11:38:32 +02:00
edd8b38a75 ggml : fix unknown status (llama/0) 2024-03-08 11:38:32 +02:00
ed76818700 whisper : fix compute helper return (ggml/750) 2024-03-08 11:38:32 +02:00
9a0b59d990 ggml : introduce ggml_status (ggml/750)
* using enum as an exit code instead of macros

* update return type from enum to unsigned int

* indentation fix

* compound update
ggml_compute_exit_code -> ggml_status
changed ggml_status from a bit-field type to simple codes
ggml_status to string cast

* ggml_status to string cast

* GGML_CALL was removed

Co-authored-by: slaren <slarengh@gmail.com>

---------

Co-authored-by: slaren <slarengh@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-03-08 11:38:32 +02:00
93a84a143b cuda : fix data race in soft max (llama/5853) 2024-03-08 11:38:32 +02:00
bd26876267 ggml : fix IQ3_S AVX implementation (llama/5834)
ggml-ci
2024-03-08 11:38:32 +02:00
21d295180d ggml : IQ3_S improvements (llama/5829)
* iq3_s: somewhat faster AVX2 dot product

On Ryzen a 7950X TG-128 increases to 16 t/s from 15.5 t/s using
16 threads. For 8 threads it is 13.85 t/s vs 11.75 t/s.
PP-512 increases to 28.5 t/s from 23.8 t/s.

* iq3_s: somewhat faster ARM_NEON dot product

Still dog slow - 10.7 t/s up from 9.9 t/s.

* iq3_s: another small ARM_NEON improvement

10.7 -> 11.0 t/s. Using vmulq_s8 is faster than the xor - sub trick
that works best on AVX2.

* iq3_s: minor improvement on Metal

49.4 t/s -> 50.3 t/s

* iq3_s: PPL improvement

E.g., for a context of 4096 LLaMA-v2-7B goes to 5.1340 from 5.1653.

* iq3_s: use new grid everywhere

* Fix ARM_NEON

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2024-03-08 11:38:32 +02:00
c3bfc9bfda Support multiple GPUs (split mode) on SYCL backend (llama/5806)
* suport multiple cards: split-mode - layer|row

* rm warning

* rebase with master, support tow new OPs, close feature for -sm=row, fix for unit test

* update news

* fix merge error

* update according to review comments
2024-03-08 11:38:32 +02:00
422a6b16fc ggml-vulkan: fix VULKAN_CHECK_RESULTS flag, which was previously broken (llama/5813) 2024-03-08 11:38:32 +02:00
11dd0d4482 Use batched mul_mat pathway (llama/5591)
* Use batched mul_mat pathway

* rm extra line

* Explicitly state scaled data type

---------

Co-authored-by: Abhilash Majumder <30946547+abhilash1910@users.noreply.github.com>
2024-03-08 11:38:31 +02:00
Eve
26dd2f06ac make portability_enumeration_ext apple only (llama/5757) 2024-03-08 11:38:31 +02:00
8cee7c08b6 add some new ops, fix some operators and add batch operations to certain operators. (ggml/747)
* cuda: fix group_norm

* cuda: add batch inference support for ggml_pad/ggml_upscale

* add ggml_arrange

* add ggml_timestep_embedding

* update ggml_arange/ggml_timestep_embedding tests

* cuda: fix im2col

* add ggml_arange/ggml_timestep_embbeding support for metal backend

* fix some bugs

* fix some bugs

* Update ggml.h

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* Update ggml-cuda.cu

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* Update ggml-metal.m

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* Update ggml-metal.m

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* Update ggml-metal.metal

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* modify according to the review comments

* ggml : fix compile warnings + code style

* ggml : normalize compute_forward calls + fix seg fault in debug

* minor

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: slaren <slarengh@gmail.com>
2024-03-08 11:38:31 +02:00
2e2626b167 examples : Auto lowercase language parameter in main.cpp (#1928)
* Auto lowercase language parameter

* Update examples/main/main.cpp

Co-authored-by: bobqianic <129547291+bobqianic@users.noreply.github.com>

---------

Co-authored-by: bobqianic <129547291+bobqianic@users.noreply.github.com>
2024-03-06 22:25:10 +00:00
c0c0ae2dea examples : fix typo in bench.cpp (#1933) 2024-03-06 22:21:44 +00:00
897412b5b6 whisper : fix typo (#1925) 2024-03-05 17:06:31 +02:00
f22d27a385 whisper.android.java : fix returns in JNI (#1929) 2024-03-05 15:59:26 +02:00
ccd7c1d2da cmake : add library versioning (#1352)
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-03-04 21:17:48 +02:00
c713eb5e2a readme : recommend MacOS Sonoma for Core ML (#1917) 2024-03-04 21:16:13 +02:00
25d313b38b talk-llama : sync llama.cpp 2024-02-28 13:04:05 +02:00
3168dbf23b sync : ggml 2024-02-28 13:01:33 +02:00
1711bb3881 sync : llama.cpp (ggml/0) 2024-02-28 13:00:30 +02:00
2533305596 ggml : make i-quants work with super-blocks of 64 (CPU,Metal) (llama/5760)
* WIP: make i-quants work for QK_K = 64

* iq2_xs: attempt to fix AVX dot product for QK_K = 64

Tests pass, but I get gibberish.

* QK_K = 64 tests pass on ARM_NEON and Metal

Sadly, that does not mean it actually works.

* Make CUDA compile with QK_K = 64

Tests don't pass, plus we get misaligned access

* Q2_K: fixed bug in imatrix quantization for QK_K = 64

* iq1_s: turn off SIMD implementation for QK_K = 64 (it does not work)

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2024-02-28 13:00:30 +02:00
0eca512ac8 Attempt to fix android build (llama/5752)
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2024-02-28 13:00:30 +02:00
013e394a4b IQ4_XS: a 4.25 bpw quantization (llama/5747)
* Try IQ4_NL with blocks of 64 - does not look good

* iq4_xs: go to super-blocks of 256 and 6-bit scales for blocks of 32

* iq4_xs: CUDA works - 133.2 t/s

* iq4_xs: AVX2 dot product

* iq4_xs: ARM_NEON dot product

* iq4_nl: Metal implementation

As usual, Metal / Apple Silicon don't like my quants.

* iq3_xs: minor fix

* iq4_xs: shrink by using IQ3_S for attn_k and attn_q

* iq4_xs: revert using IQ3_S for attn_k and attn_v

PPL vs size is good, but CPU performance suffers: on M2 Max
TG-128 drops to 21.7 t/s from 28.8, and on a Ryzen-7950X
to 14.5 t/s from 15.8 t/s. On CUDA we have 135 t/s when
using IQ3_S vs 133 t/s with pure IQ4_XS.

* Fix CI

* iq4_xs: Added forgotten check for 256 divisibility

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2024-02-28 13:00:29 +02:00
d83f371b5f cuda : replace remaining shfl_xor with calls to warp_reduce functions (llama/5744) 2024-02-28 13:00:29 +02:00
1c71816eab ggml-quants : fix avx2 iq1_s vec_dot when compiled with gcc (llama/5742) 2024-02-28 13:00:29 +02:00
7b1d8ea7e0 Adding IQ2_S and IQ2_M to complete coverage of the 2-3 bit quantization range (llama/5721)
* Adding IQ2_S and IQ2_M as a single cumulative commit

* Update examples/quantize/quantize.cpp

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-02-28 13:00:29 +02:00
b1f7223a0a CUDA: fix DEBUG_CUDA_MALLOC (llama/5729) 2024-02-28 13:00:29 +02:00
8408a4be8e Add support for soft_max ALiBi (llama/5639)
* Add support for bias

* Update pre-processor

* rm commented code

* fix format

* fix CI

---------

Co-authored-by: Abhilash Majumder <30946547+abhilash1910@users.noreply.github.com>
2024-02-28 13:00:29 +02:00
72849c24ba ggml-quants : provide ggml_vqtbl1q_u8 for 64bit compatibility (llama/5711)
* [ggml-quants] Provide ggml_vqtbl1q_u8 for 64bit compatibility

vqtbl1q_u8 is not part of arm v7 neon library

* [android-example] Remove abi filter after arm v7a fix

* [github-workflows] Do not skip Android armeabi-v7a build
2024-02-28 13:00:28 +02:00
c19c28be71 add google magika inference example (ggml/748)
* add magika inference example

* ggml : fix unaligned accesses in custom ops

* ggml : fix FP32 GELU for values that exceed the FP16 range

* use ggml_pool_1d

* add README

* Update README.md

* pad inputs if the files are too small

* cleanup

ggml-ci
2024-02-28 13:00:28 +02:00
0d8fd8483a stream.wasm : fix invalid memory access when no segments (#1902)
No segments may be returned when a smaller sample buffer (EG 2048 samples) is sent to the worker.
2024-02-26 10:12:35 +02:00
3170841ed9 talk-llama : sync llama.cpp 2024-02-25 20:00:10 +02:00
7a6e385c1b sync : ggml 2024-02-25 19:59:34 +02:00
578e47e70c sync : llama.cpp (ggml/0) 2024-02-25 19:58:46 +02:00
fac5b43830 code : normalize enum names (llama/5697)
* coda : normalize enum names

ggml-ci

* code : cont

* code : cont
2024-02-25 19:58:46 +02:00
9e7c5212a1 IQ3_S: a much better alternative to Q3_K (llama/5676)
* iq4_nl: squash commits for easier rebase

* Basics (quantize, dequantize)
* CUDA dequantize and dot product
* Slightly faster CUDA dot product (120 t/s)
* Switch to 6-bit scales
* Scalar dot product
* AVX2 dot product
* ARM_NEON dot product
* Works on metal, but still slow
* Slightly better Metal dot product
* Another small Metal improvement
* Metal dot product is getting there
* Faster CUDA dot product
* Add 1/8 ffn_down layers as Q5_K when no imatrix has been provided
* Report the actual bpw
* Add _xs mix that is 4.05 bpw for non-MoE models
* Remove IQ4_XS for now, slightly adjust kvalues_iq4nl
* AVX2 dot product uses Q8_0 instead of Q8_K
* Add to test-backend-ops
* Minor fix
* Also use use Q5_K for attn_output in MoE models
* Fixes after merging latest master
* Switching to blocks of 32
* AVX2 for blocks of 32
* Scaler dot product for blocks of 32
* ARM_NEON dot product for blocks of 32
* Metal kernels for blocks of 32
* Slightly faster Metal kernels

* Resurrecting iq3_xs

After all the experimentation, nothing was better than this.

* Minor PPL improvement via a block scale fudge factor

* Minor improvement via 3 neighbours

* iq3_xs: working scalar and AVX2 dot products

* iq3_xs: ARM_NEON dot product - works but extremely slow (10 t/s)

* iq3_xs: working Metal implementation

* Adding IQ3_M - IQ3_XS mix with mostly Q4_K

* iiq3_xs: a 3.4375 bpw variant

* iq3_xs: make CUDA work for new version

* iq3_xs: make scalar and AVX2 work for new version

* iq3_s: make ARM_NEON work with new version

* iq3_xs: make new version work on metal

Performance is very similar to Q3_K_S

* iq3_xs: tiny Metal speed improvement

* iq3_xs: tiny Metal speed improvement

* Fix stupid warning

* Q3_K_XS now uses a mix of IQ3_XS and IQ3_XXS

* iq3_xs: rename to iq3_s

* iq3_s: make tests pass

* Move Q3_K_XS mix to 3.25 bpw

* Attempt to fix failing tests

* Another attempt to fix the Windows builds

* Attempt to fix ROCm

* ROCm again

* iq3_s: partial fix for QK_K = 64

* iq3_s: make it work on metal for QK_K = 64

Pleasent surprise: the coding was super-block size independent,
so all it took was to delete some QK_K == 256 guards.

* Will this fix ROCm?

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2024-02-25 19:58:46 +02:00
1cb64f7368 Introduce backend GUIDs (ggml/743)
* Introduce backend GUIDs

Initial proposed implementation of backend GUIDs
(Discussed in https://github.com/ggerganov/ggml/pull/741)

Hardcoded CPU backend GUID (for now)
Change ggml_backend_is_cpu logic to use GUID

* Remove redundant functions

Remove redundant functions `ggml_backend_i::get_name` and `ggml_backend_guid` which are not desired for future expansion

* Add spaces to match style

Co-authored-by: slaren <slarengh@gmail.com>

* Fix brace style to match

Co-authored-by: slaren <slarengh@gmail.com>

* Add void to () in function signature

Co-authored-by: slaren <slarengh@gmail.com>

* Add back ggml_backend_guid and make CPU_GUID a local static in ggml_backend_cpu_guid

* add guids to all backends

ggml-ci

---------

Co-authored-by: slaren <slarengh@gmail.com>
2024-02-25 19:58:45 +02:00
f18738f247 talk, talk-llama : pass text_to_speak as a file (#1865)
* talk-llama: pass file instead of arg

it is too hard to quote text in a portable way

* talk-llama: pass heard_ok as a file

* talk-llama: let eleven-labs.py accept options

Options: -v voice, -s savefile, -p (--play)

* talk-llama: check installed commands in "speak"

Pass "-q" to eleven-labs.py to skip checking whether elevenlabs is installed

* talk-llama: pass voice_id again

in order to sync talk with talk-llama

* talk: sync with talk-llama

Passing text_to_speak as a file is safer and more portable
cf. https://stackoverflow.com/a/59036879/45375

* talk and talk-llama: get all installed voices in speak.ps1

* talk and talk-llama: get voices from api

* talk and talk-llama: add more options to eleven-labs.py

and remove DEFAULT_VOICE because it is deprecated (https://www.reddit.com/r/ElevenLabs/comments/1830abt/what_happened_to_bella/)

```
usage: eleven-labs.py [-q] [-l] [-h] [-n NAME | -v NUMBER] [-f KEY=VAL] [-s FILE | -p] [TEXTFILE]

options:
  -q, --quick           skip checking the required library

action:
  TEXTFILE              read the text file (default: stdin)
  -l, --list            show the list of voices and exit
  -h, --help            show this help and exit

voice selection:
  -n NAME, --name NAME  get a voice object by name (default: Arnold)
  -v NUMBER, --voice NUMBER
                        get a voice object by number (see --list)
  -f KEY=VAL, --filter KEY=VAL
                        filter voices by labels (default: "use case=narration")
                        this option can be used multiple times
                        filtering will be disabled if the first -f has no "=" (e.g. -f "any")

output:
  -s FILE, --save FILE  save the TTS to a file (default: audio.mp3)
  -p, --play            play the TTS with ffplay
```

* examples: add speak_with_file()

as suggested in the review

* talk and talk-llama: ignore to_speak.txt
2024-02-24 09:24:47 +02:00
a0ddd8392c whisper : add SYCL support (#1863)
* add changes from llama upstream

* add sycl abstraction

* add sycl build

* update cmake

* add sycl build config

* fix bug

* fix bug

* refactor build

* fix bug

* update build

* call build

* use sycl header

* add examples

* add target

* fix typecast in quant.c

* readd fp16 and readme

* fix quant typecast

* add sample

* add readme

* remove cxx file check
2024-02-23 09:22:24 +02:00
a2506909b1 talk-llama : sync llama.cpp 2024-02-22 23:30:53 +02:00
7b1ff212d9 sync : ggml 2024-02-22 23:25:38 +02:00
e5d06cfc0f ggml : always define ggml_fp16_t as uint16_t (llama/5666)
* ggml : always define ggml_fp16_t as uint16_t

ggml-ci

* ggml : cont

ggml-ci

* ggml : cont

* ggml : cont

ggml-ci

* ggml : cont

ggml-ci

* cuda : no longer ggml headers last

ggml-ci

* ggml : fix q6_K FP16 -> FP32 conversion

ggml-ci

* ggml : more FP16 -> FP32 conversion fixes

ggml-ci
2024-02-22 23:25:33 +02:00
31891db2e3 ci : fix whitespace 2024-02-22 20:20:34 +02:00
5fdb27ff80 ggml : 32-bit arm compat (#1891)
* ggml : 32-bit arm compat

* ggml : add ggml_vqtbl1q_s8 impl

* ggml : cont
2024-02-22 18:31:40 +02:00
6b16927d18 sync : ggml 2024-02-22 15:15:38 +02:00
ce411498f6 sync : llama.cpp (ggml/0)
ggml-ci
2024-02-22 15:12:36 +02:00
208de95ac7 conext add name (llama/5624)
* [SYCL] conext add name

* name should start with SYCL*
2024-02-22 15:12:36 +02:00
c2ce39c795 Update ggml_sycl_op_mul_mat_vec_q (llama/5502)
* Update ggml_sycl_op_mul_mat_vec_q

* Apply suggestions from code review

Co-authored-by: Abhilash Majumder <30946547+abhilash1910@users.noreply.github.com>

* revert suggestion on macro

* fix bug

* Add quant type GGML_TYPE_IQ1_S to unsupported

* fix format

---------

Co-authored-by: Abhilash Majumder <30946547+abhilash1910@users.noreply.github.com>
2024-02-22 15:12:36 +02:00
8daa534818 Refactor validation and enumeration platform checks into functions to clean up ggml_vk_instance_init() 2024-02-22 15:12:36 +02:00
9fca69b410 Add check for VK_KHR_portability_enumeration for MoltenVK support 2024-02-22 15:12:36 +02:00
b26c645420 Add preprocessor checks for Apple devices.
Based on work by @rbourgeat in https://github.com/ggerganov/llama.cpp/pull/5322/files
2024-02-22 15:12:36 +02:00
1879ec556e Resolve ErrorIncompatibleDriver with Vulkan on MacOS.
Refs:
- https://chat.openai.com/share/7020ce72-65fc-45ec-b7be-9d9d798a5f3f
- https://github.com/SaschaWillems/Vulkan/issues/954
- https://github.com/haasn/libplacebo/issues/128
- https://github.com/KhronosGroup/Vulkan-Samples/issues/476
2024-02-22 15:12:35 +02:00
c6e53cfc46 Allow for Vulkan build with Accelerate.
Closes #5304
2024-02-22 15:12:35 +02:00
b19f2fb815 cuda : ignore peer access already enabled errors (llama/5597)
* cuda : ignore peer access already enabled errors

* fix hip
2024-02-22 15:12:35 +02:00
a6b0950916 ggml : compute forward no longer pass src tensors (ggml/729)
* refactored compute forward to not pass in the src tensors each time

* fix merge issues with flags

* missed one place in the last commit to fix the is_param / flags issue

* minor spacing fix

* fixed some variable assignments so all tests locally are passing

* new change after merge fix

---------

Co-authored-by: siddharthvader <siddharth@coinlist.co>
2024-02-22 15:12:35 +02:00
d352dbd163 ggml : fix conv_2d batch mode (ggml/737)
Co-authored-by: bssrdf <bssrdf@gmail.com>
2024-02-22 15:12:32 +02:00
eb23f4ef16 openvino : fix convert-whisper-to-openvino.py (#1890)
Fix issue: Conversion from Whisper to OpenVino failed #1870

convert-whisper-to-openvino.py stopped working with OpenVINO version 2023.0.0-10926-b4452d56304-releases/2023/0 .

Error was: TypeError: load(): incompatible function arguments. The following argument types are supported:
    1. (self: openvino._pyopenvino.FrontEnd, path: object) -> ov::frontend::InputModel

Tested successfully with a large-v3 conversion.

Co-authored-by: Stefan Grundmann <grundmanns@sandiego.gov>
2024-02-22 15:11:35 +02:00
c56344b509 main : fix file existence check in main.cpp (#1889)
In commit dda4b0e of PR #1872, I've introduced a check for the
existence of files before loading the model. However, I haven't
considered the case where whisper.cpp might read from stdin as well,
and in such cases, the checks should ignore the "-" argument as it
does not represent a regular file.

Additionally, this commit removes the usage of 'stat()' in favor of
the recently introduced function 'is_file_exist()' in common.cpp from
PR #1871.

Apologies for the bug introduced in the previous PR and any
inconvenience it may have caused.
2024-02-22 15:01:08 +02:00
59119f4f20 talk-llama : sync llama.cpp 2024-02-20 12:09:57 +02:00
276615d708 make : fix CUBLAS link with WSL (#1878) 2024-02-20 12:05:38 +02:00
b602819b6e sync : ggml 2024-02-19 15:54:25 +02:00
c2c606f05b ggml : resolve merge conflicts (ggml/0)
ggml-ci
2024-02-19 15:53:25 +02:00
83afebe872 common : add IQ1_S (ggml/0)
ggml-ci
2024-02-19 15:53:25 +02:00
a4d8f9d559 ci : enable -Werror for CUDA builds (llama/5579)
* cmake : pass -Werror through -Xcompiler

ggml-ci

* make, cmake : enable CUDA errors on warnings

ggml-ci
2024-02-19 15:53:24 +02:00
5ec1e0edfa cuda, metal : fix nans in soft_max (llama/5574)
* cuda : fix nans in soft_max

* metal : fix nans in soft_max

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-02-19 15:53:24 +02:00
30a11b1ab8 ggml : android and old glibc NUMA incompatibility bugfixes (llama/5557)
* #ifdef out some code NUMA blocks for Android due to lack of support

* added in some __ANDROID__ if def gates around numa code and forced GLIBC prior to 2.29 to use a syscall for getcpu instead of the wrapper

* Changed gates on numa platform specific stuff to __gnu_linux__ to skip any platforms without glibc

* harmonizing #if defined blocks for numa code to __gnu_linux__ since that's the only model that's being followed anyways

---------

Co-authored-by: root <root@nenya.lothlorien.ca>
2024-02-19 15:53:24 +02:00
f04e6b87d7 ggml : restore vec dot stride arg names (llama/5453) 2024-02-19 15:53:24 +02:00
0c33928b55 ci : fix wikitext url + compile warnings (llama/5569)
ggml-ci
2024-02-19 15:53:24 +02:00
0775374750 metal : fix unused warnings (llama/0) 2024-02-19 15:53:24 +02:00
7d90bb035b ggml, common, examples, tests : fixed type arguments in printf (llama/5528) 2024-02-19 15:53:24 +02:00
2c1ad21ba8 1.5 bit quantization (llama/5453)
* iq1_s: WIP basics

* iq1_s: CUDA is working

* iq1_s: scalar CPU dot product

* iq1_s: WIP AVX2 dot product - something is not right

* Fix tests

* Fix shadow warnings

* Fix after merge with latest master

* iq1_s: AVX2 finally works

* iq1_s: ARM_NEON dot product. Works, but not very fast

* iq1_s: better grid

* iq1_s: use IQ2_XXS for attn_output

At a cost of 0.04 extra bpw this gives a big improvement in PPL.

* iq1_s: Metal basics

Dequantize works, but not dot product

* iq1_s: Metal works, but quite slow

As usual, Apple Silicon does not like the code I write.

* iq1_s: Tests

* iq1_s: slightly faster dot product

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2024-02-19 15:53:23 +02:00
eca5ff9868 ggml : add ALiBi support for ggml_soft_max_ext (llama/5488) 2024-02-19 15:53:23 +02:00
1b25d2fa0a ci : add an option to fail on compile warning (llama/3952)
* feat(ci): add an option to fail on compile warning

* Update CMakeLists.txt

* minor : fix compile warnings

ggml-ci

* ggml : fix unreachable code warnings

ggml-ci

* ci : disable fatal warnings for windows, ios and tvos

* ggml : fix strncpy warning

* ci : disable fatal warnings for MPI build

* ci : add fatal warnings to ggml-ci

ggml-ci

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-02-19 15:53:23 +02:00
74a6acc999 cmake : fix VULKAN and ROCm builds (llama/5525)
* cmake : fix VULKAN and ROCm builds

* cmake : fix (cont)

* vulkan : fix compile warnings

ggml-ci

* cmake : fix

ggml-ci

* cmake : minor

ggml-ci
2024-02-19 15:53:23 +02:00
a4ed8a0821 ggml : add numa options (llama/5377)
* Added numa options to allow finer grained control as well as plumbing for a new mirror mode that will require numa.h

* Reverted Makefile

* Fixed include

* Removed sched.h from ggml.h, moved ggml_get_numa_affinity into ggml.c, removed trailing whitespace and fixed up a few inconsistent variables

* removed trailing whitespace

* Added numa options to allow finer grained control as well as plumbing for a new mirror mode that will require numa.h

* Reverting Makefile

* Fixed a number of issues with the move from BOOL to ggml_numa_strategies. Added a note about mirror mode note being implemented yet

* Removing MIRROR_MODE code for this PR

* Removing last bit of MIRROR_MODE code for this PR

* Removing unneeded branch in server.cpp example and moving get_numa_affinity and making it static

* Fixed lingering init_llama_backend() bool calls in tests and examples

* Remote enum llama_numa_strategies

* Revert bad merge with dynatemp flags

* add missing enum ggml_numa_strategies declaration and revert sync problem with master

* add missing enum ggml_numa_strategies declaration

* fixed ggml_init_numa variable

* Update ggml.h

Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com>

* Update READMEs with info about numa flags, change INTERLEAVE strategy name to DISTRIBUTE everywhere, implement the improved distribution strategy from @rankaiyx, fix a spelling mistake and un-merge some bad merges

* split numa init out from llama_backend_init and created llama_numa_init. Updated all code paths and samples

* Fix up some boolean vs enum comparisons

* Added #ifdefs for non-Linux OS that don't have cpu_set_t datatype

* Update ggml.h

Align enum values

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* Update ggml.c

Remove whitespace

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* Update ggml.c

align paremeters

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* Update examples/server/server.cpp

remove whitespace and align brace

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* Update common/common.cpp

Remove whitespace and align brace

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* unified ggml_numa_strategy enum and fixed text alignment in server.cpp example

* Update ggml.c

simplified return for platforms without NUMA support

Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com>

* removed redundant else from cli argument processing of --numa

* whitespace

---------

Co-authored-by: root <root@nenya.lothlorien.ca>
Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: Jared Van Bortel <jared@nomic.ai>
2024-02-19 15:53:23 +02:00
9f675e021c cuda : print message when initialization fails (llama/5512)
* cuda : print message when initialization fails

* use CUDA_NAME both times
2024-02-19 15:53:23 +02:00
a38efcb9fd vulkan: Find optimal memory type but with fallback (llama/5381)
* @0cc4m feedback

* More feedback @0cc4m
2024-02-19 15:53:22 +02:00
AT
31591649a0 Early return for zero size calls to get_tensor. (llama/5482)
* Early return for zero size calls to get_tensor.

Signed-off-by: Adam Treat <treat.adam@gmail.com>

* Update ggml-kompute.cpp

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* Update ggml-kompute.cpp

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* Add an early return to the get/set tensor when the size is null.

Signed-off-by: Adam Treat <treat.adam@gmail.com>

* Early return after the assertions.

Signed-off-by: Adam Treat <treat.adam@gmail.com>

* Since we do the early return in the generic backend now no reason to do so here as well.

Signed-off-by: Adam Treat <treat.adam@gmail.com>

---------

Signed-off-by: Adam Treat <treat.adam@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-02-19 15:53:22 +02:00
4f5c46a84f ggml-quants : fix compiler warnings (shadow variable) (llama/5472)
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2024-02-19 15:53:22 +02:00
462ffc58db ggml-sycl: Replace 3d ops with macro (llama/5458)
* use macro

* use macro

* fix format
2024-02-19 15:53:21 +02:00
65faae0b6a build : update CBLAS flags + fix unused var warning (#0) 2024-02-19 14:44:46 +02:00
dda4b0ed06 main : check if input files exist before proceeding (#1872)
Until the most recent commit (3d42463), the main.cpp sample file does
not check whether the input files exist or not. Consequently, the
model is loaded first before reporting whether there was a failure or
not when processing a file. In environments with HDD, this can take
about 50 seconds or more, depending on the loaded model.

This commit addresses this issue by checking in advance whether the
input files exist or not.
2024-02-19 10:51:26 +02:00
07d04280be examples : clean up common code (#1871)
move some utility functions into common.h
2024-02-19 10:50:15 +02:00
917c56ded4 models : fix openvino setup info (#1874) 2024-02-19 02:19:47 +00:00
3d42463845 models : add update py requirements 2024-02-13 11:51:32 +02:00
3ffc83d90a swift : package no longer use ggml dependency (#1861)
* Revert "swift : update Package.swift to use ggml as package dependency (#1701)"

This reverts commit 993acb5d41.

* spm : add ggml.h
2024-02-12 19:54:11 +02:00
e3c5e2cba8 whisper : fix external encoder (#1860) 2024-02-12 19:53:51 +02:00
b742f13e70 sync : ggml 2024-02-12 19:07:56 +02:00
52c529eeb1 ggml-alloc : allocate all leafs as if they were inputs (ggml/731)
* ggml-alloc : allocate all leafs as if they were inputs

* ensure static leafs are allocated

* gpt-2-backend : remove unnecesary ggml_new_tensor

* update other gpt-2 examples to remove ggml_new_tensor calls in the graph
2024-02-12 19:07:38 +02:00
468 changed files with 259016 additions and 84415 deletions

View File

@ -21,7 +21,7 @@ COPY . .
# Set nvcc architecture # Set nvcc architecture
ENV CUDA_DOCKER_ARCH=${CUDA_DOCKER_ARCH} ENV CUDA_DOCKER_ARCH=${CUDA_DOCKER_ARCH}
# Enable cuBLAS # Enable cuBLAS
ENV WHISPER_CUBLAS=1 ENV GGML_CUDA=1
RUN make RUN make

View File

@ -14,7 +14,7 @@ ARG CUDA_DOCKER_ARCH=all
# Set nvcc architecture # Set nvcc architecture
ENV CUDA_DOCKER_ARCH=${CUDA_DOCKER_ARCH} ENV CUDA_DOCKER_ARCH=${CUDA_DOCKER_ARCH}
# Enable cuBLAS # Enable cuBLAS
ENV WHISPER_CUBLAS=1 ENV GGML_CUDA=1
RUN apt-get update && \ RUN apt-get update && \
apt-get install -y build-essential \ apt-get install -y build-essential \
@ -28,6 +28,8 @@ COPY .. .
RUN make RUN make
FROM ${BASE_CUDA_RUN_CONTAINER} AS runtime FROM ${BASE_CUDA_RUN_CONTAINER} AS runtime
ENV CUDA_MAIN_VERSION=12.3
ENV LD_LIBRARY_PATH /usr/local/cuda-${CUDA_MAIN_VERSION}/compat:$LD_LIBRARY_PATH
WORKDIR /app WORKDIR /app
RUN apt-get update && \ RUN apt-get update && \

View File

@ -15,10 +15,10 @@ jobs:
steps: steps:
- name: Clone - name: Clone
uses: actions/checkout@v3 uses: actions/checkout@v4
- name: Set up QEMU - name: Set up QEMU
uses: docker/setup-qemu-action@v2 uses: docker/setup-qemu-action@v3
- name: Build ${{ matrix.arch }} - name: Build ${{ matrix.arch }}
run: | run: |
@ -36,7 +36,7 @@ jobs:
steps: steps:
- name: Clone - name: Clone
uses: actions/checkout@v3 uses: actions/checkout@v4
- name: Dependencies - name: Dependencies
run: | run: |
@ -53,10 +53,10 @@ jobs:
steps: steps:
- name: Clone - name: Clone
uses: actions/checkout@v3 uses: actions/checkout@v4
- name: Build - name: Build
uses: cross-platform-actions/action@v0.15.0 uses: cross-platform-actions/action@v0.24.0
with: with:
operating_system: freebsd operating_system: freebsd
version: '13.2' version: '13.2'
@ -77,10 +77,10 @@ jobs:
steps: steps:
- name: Clone - name: Clone
uses: actions/checkout@v3 uses: actions/checkout@v4
- name: Set up QEMU - name: Set up QEMU
uses: docker/setup-qemu-action@v2 uses: docker/setup-qemu-action@v3
- name: Build ${{ matrix.arch }} - name: Build ${{ matrix.arch }}
run: | run: |
@ -101,14 +101,17 @@ jobs:
fail-fast: false fail-fast: false
matrix: matrix:
build: [Debug, Release] build: [Debug, Release]
arch: [linux/amd64, linux/arm64, linux/arm/v7, linux/ppc64le] #arch: [linux/amd64, linux/arm64, linux/arm/v7, linux/ppc64le]
# TODO: arm/v7 disabled due to clang bug
# https://github.com/ggerganov/whisper.cpp/actions/runs/9657764109/job/26637633042?pr=2256#step:4:1990
arch: [linux/amd64, linux/arm64, linux/ppc64le]
steps: steps:
- name: Clone - name: Clone
uses: actions/checkout@v3 uses: actions/checkout@v4
- name: Set up QEMU - name: Set up QEMU
uses: docker/setup-qemu-action@v2 uses: docker/setup-qemu-action@v3
- name: Build ${{ matrix.arch }} - name: Build ${{ matrix.arch }}
run: | run: |
@ -133,10 +136,10 @@ jobs:
steps: steps:
- name: Clone - name: Clone
uses: actions/checkout@v3 uses: actions/checkout@v4
- name: Set up QEMU - name: Set up QEMU
uses: docker/setup-qemu-action@v2 uses: docker/setup-qemu-action@v3
- name: Build ${{ matrix.arch }} - name: Build ${{ matrix.arch }}
run: | run: |
@ -150,6 +153,164 @@ jobs:
make make
ctest -L gh --output-on-failure' ctest -L gh --output-on-failure'
ubuntu-22-cmake-sycl:
runs-on: ubuntu-22.04
strategy:
fail-fast: false
matrix:
dwhisper_sycl: [ON]
dcmake_c_compiler: [icx]
dcmake_cxx_compiler: [icpx]
arch: [linux/amd64, linux/arm64, linux/arm/v7, linux/ppc64le]
continue-on-error: true
steps:
- name: Clone
uses: actions/checkout@v4
- name: add oneAPI to apt
shell: bash
run: |
cd /tmp
wget https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB
sudo apt-key add GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB
rm GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB
sudo add-apt-repository "deb https://apt.repos.intel.com/oneapi all main"
- name: install oneAPI dpcpp compiler
shell: bash
run: |
sudo apt update
sudo apt install intel-oneapi-compiler-dpcpp-cpp
- name: install oneAPI MKL library
shell: bash
run: |
sudo apt install intel-oneapi-mkl-devel
- name: Clone
id: checkout
uses: actions/checkout@v4
- name: Build
id: cmake_build
run: |
source /opt/intel/oneapi/setvars.sh
mkdir build
cd build
cmake -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx ..
cmake --build . --config Release -j $(nproc)
ubuntu-22-cmake-sycl-fp16:
runs-on: ubuntu-22.04
strategy:
fail-fast: false
matrix:
dwhisper_sycl: [ON]
dcmake_c_compiler: [icx]
dcmake_cxx_compiler: [icpx]
arch: [linux/amd64, linux/arm64, linux/arm/v7, linux/ppc64le]
continue-on-error: true
steps:
- name: Clone
uses: actions/checkout@v4
- name: add oneAPI to apt
shell: bash
run: |
cd /tmp
wget https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB
sudo apt-key add GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB
rm GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB
sudo add-apt-repository "deb https://apt.repos.intel.com/oneapi all main"
- name: install oneAPI dpcpp compiler
shell: bash
run: |
sudo apt update
sudo apt install intel-oneapi-compiler-dpcpp-cpp
- name: install oneAPI MKL library
shell: bash
run: |
sudo apt install intel-oneapi-mkl-devel
- name: Clone
id: checkout
uses: actions/checkout@v4
- name: Build
id: cmake_build
run: |
source /opt/intel/oneapi/setvars.sh
mkdir build
cd build
cmake -DGGML_SYCL_F16=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx ..
cmake --build . --config Release -j $(nproc)
windows-msys2:
runs-on: windows-latest
strategy:
fail-fast: false
matrix:
include:
- { sys: UCRT64, env: ucrt-x86_64, build: Release }
- { sys: CLANG64, env: clang-x86_64, build: Release }
steps:
- name: Clone
uses: actions/checkout@v4
- name: Setup ${{ matrix.sys }}
uses: msys2/setup-msys2@v2
with:
update: true
msystem: ${{matrix.sys}}
install: >-
base-devel
mingw-w64-${{matrix.env}}-toolchain
mingw-w64-${{matrix.env}}-cmake
mingw-w64-${{matrix.env}}-SDL2
mingw-w64-${{matrix.env}}-openblas
- name: Build using make
shell: msys2 {0}
run: |
make -j $(nproc)
- name: Clean after building using make
shell: msys2 {0}
run: |
make clean
- name: Build using make w/ OpenBLAS
shell: msys2 {0}
run: |
make GGML_OPENBLAS=1 -j $(nproc)
- name: Build using CMake
shell: msys2 {0}
run: |
cmake -B build
cmake --build build --config ${{ matrix.build }} -j $(nproc)
- name: Clean after building using CMake
shell: msys2 {0}
run: |
rm -rf build
- name: Build using CMake w/ OpenBLAS
shell: msys2 {0}
run: |
cmake -B build -DGGML_OPENBLAS=ON
cmake --build build --config ${{ matrix.build }} -j $(nproc)
windows: windows:
runs-on: windows-latest runs-on: windows-latest
@ -170,10 +331,10 @@ jobs:
steps: steps:
- name: Clone - name: Clone
uses: actions/checkout@v3 uses: actions/checkout@v4
- name: Add msbuild to PATH - name: Add msbuild to PATH
uses: microsoft/setup-msbuild@v1 uses: microsoft/setup-msbuild@v2
- name: Fetch SDL2 and set SDL2_DIR - name: Fetch SDL2 and set SDL2_DIR
if: matrix.sdl2 == 'ON' if: matrix.sdl2 == 'ON'
@ -198,14 +359,14 @@ jobs:
run: copy "$env:SDL2_DIR/../lib/${{ matrix.s2arc }}/SDL2.dll" build/bin/${{ matrix.build }} run: copy "$env:SDL2_DIR/../lib/${{ matrix.s2arc }}/SDL2.dll" build/bin/${{ matrix.build }}
- name: Upload dll - name: Upload dll
uses: actions/upload-artifact@v3 uses: actions/upload-artifact@v4
with: with:
name: ${{ matrix.jnaPath }}_whisper.dll name: ${{ matrix.jnaPath }}_whisper.dll
path: build/bin/${{ matrix.build }}/whisper.dll path: build/bin/${{ matrix.build }}/whisper.dll
- name: Upload binaries - name: Upload binaries
if: matrix.sdl2 == 'ON' if: matrix.sdl2 == 'ON'
uses: actions/upload-artifact@v1 uses: actions/upload-artifact@v4
with: with:
name: whisper-bin-${{ matrix.arch }} name: whisper-bin-${{ matrix.arch }}
path: build/bin/${{ matrix.build }} path: build/bin/${{ matrix.build }}
@ -223,21 +384,18 @@ jobs:
- arch: Win32 - arch: Win32
obzip: https://github.com/OpenMathLib/OpenBLAS/releases/download/v0.3.25/OpenBLAS-0.3.25-x86.zip obzip: https://github.com/OpenMathLib/OpenBLAS/releases/download/v0.3.25/OpenBLAS-0.3.25-x86.zip
s2arc: x86 s2arc: x86
clblast: OFF
- arch: x64 - arch: x64
obzip: https://github.com/OpenMathLib/OpenBLAS/releases/download/v0.3.25/OpenBLAS-0.3.25-x64.zip obzip: https://github.com/OpenMathLib/OpenBLAS/releases/download/v0.3.25/OpenBLAS-0.3.25-x64.zip
s2arc: x64 s2arc: x64
clblast: ON
clver: 1.6.1
- sdl2: ON - sdl2: ON
s2ver: 2.28.5 s2ver: 2.28.5
steps: steps:
- name: Clone - name: Clone
uses: actions/checkout@v3 uses: actions/checkout@v4
- name: Add msbuild to PATH - name: Add msbuild to PATH
uses: microsoft/setup-msbuild@v1 uses: microsoft/setup-msbuild@v2
- name: Fetch OpenBLAS - name: Fetch OpenBLAS
if: matrix.blas == 'ON' if: matrix.blas == 'ON'
@ -255,26 +413,13 @@ jobs:
7z x sdl2.zip 7z x sdl2.zip
echo "SDL2_DIR=$env:GITHUB_WORKSPACE/SDL2-${{ matrix.s2ver }}/cmake" >> $env:GITHUB_ENV echo "SDL2_DIR=$env:GITHUB_WORKSPACE/SDL2-${{ matrix.s2ver }}/cmake" >> $env:GITHUB_ENV
- name: Install OpenCL
if: matrix.clblast == 'ON'
run: vcpkg.exe --triplet=${{ matrix.arch }}-windows install opencl
- name: Fetch CLBlast and set CLBlast_DIR
if: matrix.clblast == 'ON'
run: |
C:/msys64/usr/bin/wget.exe -qO clblast.zip https://github.com/CNugteren/CLBlast/releases/download/${{ matrix.clver }}/CLBlast-${{ matrix.clver }}-windows-x64.zip
7z x clblast.zip
7z x CLBlast-${{ matrix.clver }}-windows-x64.7z
echo "CLBlast_DIR=$env:GITHUB_WORKSPACE/CLBlast-${{ matrix.clver }}-windows-x64/lib/cmake/CLBlast" >> $env:GITHUB_ENV
- name: Configure - name: Configure
run: > run: >
cmake -S . -B ./build -A ${{ matrix.arch }} cmake -S . -B ./build -A ${{ matrix.arch }}
-DCMAKE_BUILD_TYPE=${{ matrix.build }} -DCMAKE_BUILD_TYPE=${{ matrix.build }}
-DWHISPER_OPENBLAS=${{ matrix.blas }} -DGGML_OPENBLAS=${{ matrix.blas }}
-DCMAKE_LIBRARY_PATH="$env:OPENBLAS_PATH/lib" -DCMAKE_LIBRARY_PATH="$env:OPENBLAS_PATH/lib"
-DWHISPER_SDL2=${{ matrix.sdl2 }} -DWHISPER_SDL2=${{ matrix.sdl2 }}
-DWHISPER_CLBLAST=${{ matrix.clblast }}
- name: Build - name: Build
run: | run: |
@ -289,19 +434,15 @@ jobs:
if: matrix.sdl2 == 'ON' if: matrix.sdl2 == 'ON'
run: copy "$env:SDL2_DIR/../lib/${{ matrix.s2arc }}/SDL2.dll" build/bin/${{ matrix.build }} run: copy "$env:SDL2_DIR/../lib/${{ matrix.s2arc }}/SDL2.dll" build/bin/${{ matrix.build }}
- name: Copy clblast.dll
if: matrix.clblast == 'ON'
run: copy "$env:CLBlast_DIR/../../clblast.dll" build/bin/${{ matrix.build }}
- name: Upload binaries - name: Upload binaries
if: matrix.blas == 'ON' && matrix.sdl2 == 'ON' if: matrix.blas == 'ON' && matrix.sdl2 == 'ON'
uses: actions/upload-artifact@v1 uses: actions/upload-artifact@v4
with: with:
name: whisper-blas${{ matrix.clblast == 'ON' && '-clblast' || ''}}-bin-${{ matrix.arch }} name: whisper-blas-bin-${{ matrix.arch }}
path: build/bin/${{ matrix.build }} path: build/bin/${{ matrix.build }}
windows-cublas: windows-cublas:
runs-on: windows-latest runs-on: windows-2019
strategy: strategy:
matrix: matrix:
@ -318,14 +459,14 @@ jobs:
steps: steps:
- name: Clone - name: Clone
uses: actions/checkout@v3 uses: actions/checkout@v4
- name: Add msbuild to PATH - name: Add msbuild to PATH
uses: microsoft/setup-msbuild@v1 uses: microsoft/setup-msbuild@v2
- name: Install CUDA Toolkit - name: Install CUDA Toolkit
id: cuda-toolkit id: cuda-toolkit
uses: Jimver/cuda-toolkit@v0.2.11 uses: Jimver/cuda-toolkit@v0.2.15
with: with:
cuda: '${{ matrix.cuda-toolkit }}' cuda: '${{ matrix.cuda-toolkit }}'
@ -340,7 +481,7 @@ jobs:
run: > run: >
cmake -S . -B ./build -A ${{ matrix.arch }} cmake -S . -B ./build -A ${{ matrix.arch }}
-DCMAKE_BUILD_TYPE=${{ matrix.build }} -DCMAKE_BUILD_TYPE=${{ matrix.build }}
-DWHISPER_CUBLAS=${{ matrix.cublas }} -DGGML_CUDA=${{ matrix.cublas }}
-DWHISPER_SDL2=${{ matrix.sdl2 }} -DWHISPER_SDL2=${{ matrix.sdl2 }}
- name: Build ${{ matrix.cuda-toolkit }} - name: Build ${{ matrix.cuda-toolkit }}
@ -361,7 +502,7 @@ jobs:
- name: Upload binaries - name: Upload binaries
if: matrix.sdl2 == 'ON' if: matrix.sdl2 == 'ON'
uses: actions/upload-artifact@v1 uses: actions/upload-artifact@v4
with: with:
name: whisper-cublas-${{ matrix.cuda-toolkit }}-bin-${{ matrix.arch }} name: whisper-cublas-${{ matrix.cuda-toolkit }}-bin-${{ matrix.arch }}
path: build/bin/${{ matrix.build }} path: build/bin/${{ matrix.build }}
@ -375,10 +516,10 @@ jobs:
steps: steps:
- name: Clone - name: Clone
uses: actions/checkout@v3 uses: actions/checkout@v4
- name: Setup emsdk - name: Setup emsdk
uses: mymindstorm/setup-emsdk@v12 uses: mymindstorm/setup-emsdk@v14
- name: Verify - name: Verify
run: emcc -v run: emcc -v
@ -397,7 +538,7 @@ jobs:
steps: steps:
- name: Clone - name: Clone
uses: actions/checkout@v3 uses: actions/checkout@v4
- name: Configure - name: Configure
run: | run: |
@ -415,24 +556,24 @@ jobs:
steps: steps:
- name: Clone - name: Clone
uses: actions/checkout@v3 uses: actions/checkout@v4
with: with:
path: whisper path: whisper
- name: Clone - name: Clone
uses: actions/checkout@v3 uses: actions/checkout@v4
with: with:
repository: ggerganov/ggml repository: ggerganov/ggml
path: ggml path: ggml
- name: Install Java - name: Install Java
uses: actions/setup-java@v3 uses: actions/setup-java@v4
with: with:
distribution: zulu distribution: zulu
java-version: 17 java-version: 21
- name: Setup Android SDK - name: Setup Android SDK
uses: android-actions/setup-android@v2 uses: android-actions/setup-android@v3
- name: Build - name: Build
run: | run: |
@ -450,40 +591,40 @@ jobs:
steps: steps:
- name: Clone - name: Clone
uses: actions/checkout@v3 uses: actions/checkout@v4
- name: set up JDK 11 - name: set up JDK 11
uses: actions/setup-java@v3 uses: actions/setup-java@v4
with: with:
java-version: '11' java-version: '11'
distribution: 'temurin' distribution: 'temurin'
cache: gradle cache: gradle
- name: Setup Android SDK - name: Setup Android SDK
uses: android-actions/setup-android@v2 uses: android-actions/setup-android@v3
with: with:
api-level: 30 cmdline-tools-version: 9.0
build-tools-version: 30.0.3
- name: Build - name: Build
run: | run: |
cd examples/whisper.android.java cd examples/whisper.android.java
chmod +x ./gradlew chmod +x ./gradlew
./gradlew assembleRelease ./gradlew assembleRelease
java: java:
needs: [ 'windows' ] needs: [ 'windows' ]
runs-on: windows-latest runs-on: windows-latest
steps: steps:
- uses: actions/checkout@v3 - uses: actions/checkout@v4
- name: Install Java - name: Install Java
uses: actions/setup-java@v1 uses: actions/setup-java@v4
with: with:
java-version: 17 distribution: zulu
java-version: 20
- name: Download Windows lib - name: Download Windows lib
uses: actions/download-artifact@v3 uses: actions/download-artifact@v4
with: with:
name: win32-x86-64_whisper.dll name: win32-x86-64_whisper.dll
path: bindings/java/build/generated/resources/main/win32-x86-64 path: bindings/java/build/generated/resources/main/win32-x86-64
@ -496,7 +637,7 @@ jobs:
./gradlew build ./gradlew build
- name: Upload jar - name: Upload jar
uses: actions/upload-artifact@v3 uses: actions/upload-artifact@v4
with: with:
name: whispercpp.jar name: whispercpp.jar
path: bindings/java/build/libs/whispercpp-*.jar path: bindings/java/build/libs/whispercpp-*.jar
@ -518,7 +659,7 @@ jobs:
steps: steps:
- name: Clone - name: Clone
uses: actions/checkout@v3 uses: actions/checkout@v4
- name: Test quantize - name: Test quantize
run: | run: |

View File

@ -37,7 +37,7 @@ jobs:
run: npm install run: npm install
- name: Compile addon.node - name: Compile addon.node
run: npx cmake-js compile -T whisper-addon -B Release run: npx cmake-js compile -T addon.node -B Release
- name: Download test model - name: Download test model
run: | run: |

15
.gitignore vendored
View File

@ -6,18 +6,11 @@
.vs/ .vs/
.vscode/ .vscode/
.DS_Store .DS_Store
.vimspector.json
/CMakeSettings.json
build/ build/
build-coreml/ build-*/
build-em/
build-debug/
build-release/
build-rwdi/
build-static/
build-cublas/
build-no-accel/
build-sanitize-addr/
build-sanitize-thread/
# SPM # SPM
.build/ .build/
@ -58,4 +51,4 @@ benchmark_results.csv
cmake-build-debug/ cmake-build-debug/
.cxx/ .cxx/
.gradle/ .gradle/
local.properties local.properties

3
.gitmodules vendored
View File

@ -1,3 +0,0 @@
[submodule "bindings/ios"]
path = bindings/ios
url = https://github.com/ggerganov/whisper.spm

301
AUTHORS Normal file
View File

@ -0,0 +1,301 @@
# date: Tue Apr 9 20:27:03 EEST 2024
# this file is auto-generated by scripts/gen-authors.sh
0/0 <zero@imaskeleton.me>
0cc4m <picard12@live.de>
0xsourcecode <134374803+0xsourcecode@users.noreply.github.com>
AT <manyoso@users.noreply.github.com>
Aarni Koskela <akx@iki.fi>
Aaron Pham <29749331+aarnphm@users.noreply.github.com>
Aaron Taylor <aaron@exphat.com>
Abhilash Majumder <30946547+abhilash1910@users.noreply.github.com>
Abitofevrything <54505189+abitofevrything@users.noreply.github.com>
AfryMask <AfryMask@163.com>
Ahmad Bilal <ahmad.bilal@empglabs.com>
AidanBeltonS <87009434+AidanBeltonS@users.noreply.github.com>
Akash Mahajan <akash7190@gmail.com>
Akash Mahajan <akashmjn@stanford.edu>
Al Hoang <3811822-hoanga@users.noreply.gitlab.com>
Alan <unknown>
Aleksander Andrzejewski <18704749+aleksanderandrzejewski@users.noreply.github.com>
Alex Azarov <alex@azarov.by>
Alex Bacart <13940752+alex-bacart@users.noreply.github.com>
Alex Evgrashin <aevgrashin@yandex.ru>
Alexandr Graschenkov <alexandr.graschenkov91@gmail.com>
Alexandru Mariuti <alex@mariuti.com>
Alexey Kharlamov <alexey@kharlamov.biz>
Alfredo Montesinos <alfredo.montesinos@g.austincc.edu>
Ali Alameh <ali.alameh@isae.edu.lb>
Ananta Bastola <anantarajbastola@gmail.com>
Andreu Huguet <andreuhuguet@gmail.com>
Andrew Huynh <a5thuynh@gmail.com>
Andrew S <andrews54757@gmail.com>
Andy Maloney <asmaloney@gmail.com>
Anton Kostin <masguit42@users.noreply.github.com>
Artyom Mezin <psycho.fading@gmail.com>
Asad Memon <asad.lionpk@gmail.com>
Ashraful Islam <ashraful.meche@gmail.com>
AsukaMinato <asukaminato@nyan.eu.org>
AustinMroz <austinmroz@utexas.edu>
Avik Sengupta <avik@sengupta.net>
Bader-eddine Ouaich <49657842+baderouaich@users.noreply.github.com>
Baffin Lee <baffinlee@gmail.com>
Ben Nortier <bjnortier@gmail.com>
Benjamin Heiniger <benjamin.heiniger@bluewin.ch>
Bo-Yi Wu <appleboy.tw@gmail.com>
Boris Bliznioukov <blib@mail.com>
Borislav Stanimirov <b.stanimirov@abv.bg>
Brad Murray <59848399+bradmurray-dt@users.noreply.github.com>
Brian Murray <brian@bmurray.ca>
CRD716 <crd716@gmail.com>
Canis Lupus <Canis-UK@users.noreply.github.com>
Carolinabanana <140120812+Carolinabanana@users.noreply.github.com>
ChangSeok Oh <shivamidow@users.noreply.github.com>
Chaoqun <27287694+OpenWaygate@users.noreply.github.com>
Chia-Hsiang Cheng <88014292+garychia@users.noreply.github.com>
Chidi Williams <williamschidi1@gmail.com>
Christian <12550267+iceychris@users.noreply.github.com>
Clifford Heath <clifford.heath@gmail.com>
Colin <github@whoisc.cc>
DGdev91 <DGdev91@users.noreply.github.com>
Damian Czaja <trojan295@protonmail.com>
Daniel Bevenius <daniel.bevenius@gmail.com>
David <dnhkng@gmail.com>
David Thorpe <djt@mutablelogic.com>
Davidson Francis <davidsondfgl@gmail.com>
Dener Stassun <denerstassun@gmail.com>
Didzis Gosko <didzis@users.noreply.github.com>
Digipom <admin@digipom.com>
Dimo <dimo@ieee.org>
Dody Suria Wijaya <dodysw@gmail.com>
Dr. Tom Murphy VII Ph.D <499244+tom7@users.noreply.github.com>
Duncan McConnell <ddmcconnell4@gmail.com>
Egor Egorov <me@egorfine.com>
Elkana Bardugo <ttv200@gmail.com>
Emmanuel Schmidbauer <eschmidbauer@gmail.com>
Engininja2 <139037756+Engininja2@users.noreply.github.com>
Eric Swanson <eswanson@alloscomp.com>
Eric Tendian <erictendian@gmail.com>
Erik Scholz <Green-Sky@users.noreply.github.com>
Evan Jones <evan.q.jones@gmail.com>
Evan Martin <evan.martin@gmail.com>
Eve <139727413+netrunnereve@users.noreply.github.com>
Evgeny Kuznetsov <evgeny@kuznetsov.md>
F1L1P <78918286+F1L1Pv2@users.noreply.github.com>
Fangjun Kuang <csukuangfj@gmail.com>
Felix <stenbackfelix@gmail.com>
Finn Voorhees <finnvoorhees@gmail.com>
FlippFuzz <41221030+FlippFuzz@users.noreply.github.com>
Gang Chen <goncha@gmail.com>
Gavin Cai <gavin1818@hotmail.com>
George Hindle <george@georgehindle.com>
Georgi Gerganov <ggerganov@gmail.com>
GitAritron <103900385+GitAritron@users.noreply.github.com>
GiviMAD <GiviMAD@users.noreply.github.com>
Gleicon Moraes <gleicon@gmail.com>
Gregor Jasny <gjasny@googlemail.com>
Guillaume Wenzek <gwenzek@users.noreply.github.com>
HY. Kelvin Lee <34256578+hykelvinlee42@users.noreply.github.com>
Halalaluyafail3 <55773281+Halalaluyafail3@users.noreply.github.com>
Hang <bebound@gmail.com>
Herman Semenov <GermanAizek@yandex.ru>
Hrishikesh Barman <geekodour@users.noreply.github.com>
Ian Bicking <ian@ianbicking.org>
Ian Bull <irbull@eclipsesource.com>
Ikko Ashimine <eltociear@gmail.com>
InconsolableCellist <23345188+InconsolableCellist@users.noreply.github.com>
Ismatulla Mansurov <47342870+sapoepsilon@users.noreply.github.com>
Ivan Gorin <ivangorin21@gmail.com>
JJ <103335846+computerscienceiscool@users.noreply.github.com>
Jack Mousseau <jmousseau@users.noreply.github.com>
JacobLinCool <jacoblincool@gmail.com>
Jakub Ráček <blizzcz@gmail.com>
Jared Van Bortel <jared@nomic.ai>
Jay Binks <jaybinks@gmail.com>
Jhen-Jie Hong <developer@jhen.me>
Jhen-Jie Hong <iainst0409@gmail.com>
JidongZhang-THU <1119708529@qq.com>
Jo Liss <joliss42@gmail.com>
Johan <jr.raffin@gmail.com>
Johannes Gäßler <johannesg@5d6.de>
John Balis <phobossystems@gmail.com>
Jonathan Soo <jcsoo@agora.com>
Jonno <1160532+razodactyl@users.noreply.github.com>
Joonas Pihlajamaa <joonas.pihlajamaa@iki.fi>
Jose <34888496+Jerry-Master@users.noreply.github.com>
Josh Bleecher Snyder <josharian@gmail.com>
Judd <foldl@users.noreply.github.com>
Jumper775 <78500318+jumpers775@users.noreply.github.com>
Justine Tunney <jtunney@gmail.com>
KP Kaiser <kirk@zothcorp.com>
Kamilake <exjang0@gmail.com>
Kartik Saranathan <278928+Kartiku@users.noreply.github.com>
Kasumi <90275229+kasumi-1@users.noreply.github.com>
Kawrakow <48489457+ikawrakow@users.noreply.github.com>
Kevin Brothaler <admin@digipom.com>
Konstantin Zhuravlyov <konstantin.zhuravlyov@amd.com>
Kreijstal <rainb@tfwno.gf>
Kylin <56434533+KyL0N@users.noreply.github.com>
LBlue <153975653+lbluep@users.noreply.github.com>
Larry Battle <larry.battle.tech@gmail.com>
Laytan Laats <laytanlaats@hotmail.com>
Leo Moll <leo.moll@yeasoft.com>
Lexevolution <31176843+Lexevolution@users.noreply.github.com>
LittleLoli <26589867+WhichWho@users.noreply.github.com>
Lucas Zanek <57494138+LucasZNK@users.noreply.github.com>
Luis Herrera <herrera-luis@users.noreply.github.com>
Lukas Rist <glaslos@gmail.com>
M. A. Ali <73258591+MightyStud@users.noreply.github.com>
M. Eren Akbiyik <erenakbiyik@gmail.com>
Maciek <maciek.mab122@gmail.com>
Marcin Mielniczuk <marmistrz.dev@zoho.eu>
Martin Warnaar <martinwarnaar@gmail.com>
Matheus de Sousa <23645013+keyehzy@users.noreply.github.com>
Mathijs de Bruin <mathijs@mathijsfietst.nl>
Matija Pevec <mightymatth@users.noreply.github.com>
Maximiliano Levi <8160966+maxilevi@users.noreply.github.com>
Meng, Hengyu <hengyu.meng@intel.com>
Michael Podvitskiy <podvitskiymichael@gmail.com>
Michael Rienstra <mrienstra@gmail.com>
Mikhail Grigorev <sleuthhound@gmail.com>
Mohammadreza Hendiani <hendiani.mohammadreza@gmail.com>
Mohit Agarwal <mohit@sdf.org>
Murilo Santana <mvrilo@gmail.com>
Neil Chudleigh <nchudleigh@users.noreply.github.com>
Neo Zhang Jianyu <jianyu.zhang@intel.com>
Neuman Vong <neuman.vong@gmail.com>
Nicholas Albion <nalbion@yahoo.com>
Niels Mayer <Niels.Mayer@gmail.com>
Okabintaro <103938900+Okabintaro@users.noreply.github.com>
Oleg Sidorov <me@whitebox.io>
Oleg Sidorov <oleg@sidorov.nl>
Ondrej Kokes <ondrej.kokes@gmail.com>
Ouadie EL FAROUKI <ouadie.elfarouki@codeplay.com>
Paul Tsochantaris <ptsochantaris@icloud.com>
Philipp Zabel <philipp.zabel@gmail.com>
Philippe Normand <phil@base-art.net>
Przemysław Pawełczyk <przemoc@gmail.com>
Qianhe Chen <54462604+chenqianhe@users.noreply.github.com>
Radosław Gryta <radek.gryta@gmail.com>
Reinforce-II <fate@eastal.com>
Reinis Muiznieks <muiznieks.reinis@gmail.com>
RelatedTitle <r3latedtitle@gmail.com>
RhinoDevel <RhinoDevel@users.noreply.github.com>
Rich Jones <miserlou@gmail.com>
Robin <robin.xw@hotmail.com>
Roddur Dasgupta <roddurd@gmail.com>
Roland Rabien <figbug@gmail.com>
Rotem Dan <rotemdan@gmail.com>
Ryan Hitchman <hitchmanr@gmail.com>
Ryan Metcalfe <107415876+RyanMetcalfeInt8@users.noreply.github.com>
RyanChang <ftes90015@gmail.com>
Sam <49637763+Onlyartist9@users.noreply.github.com>
Sam Pullara <spullara@gmail.com>
Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
Sergio López <slp@sinrega.org>
Siddharth Ramakrishnan <srr2141@columbia.edu>
Simon Moisselin <simon.moisstoll@gmail.com>
Sindre Sorhus <sindresorhus@gmail.com>
Slava Primenko <primenko.s@gmail.com>
Syahmi Azhar <prsyahmi@gmail.com>
Syed Jafri <syedjafri97@gmail.com>
Sơn Phan Trung <phantrungson17@gmail.com>
Taisei Mima <bhbstar.me@gmail.com>
Takeshi Inoue <inoue.takeshi@gmail.com>
Tamotsu Takahashi <ttakah+github@gmail.com>
Taras Glek <taras@thegp.com>
Tauseef Mohiuddin <35351464+tauseefmohammed2@users.noreply.github.com>
Thijs Raymakers <thijs@raymakers.nl>
Thomas Fitzsimmons <fitzsim@fitzsim.org>
Tiago Fassoni <tiagofassoni@users.noreply.github.com>
Tienshiao Ma <tienshiao@tienshiao.org>
Timothy Cronin <40186632+4imothy@users.noreply.github.com>
Tobrun <tobrun.van.nuland@gmail.com>
Todd <taf2@users.noreply.github.com>
Tong Li <31761981+litongjava@users.noreply.github.com>
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Xiang (Kevin) Li <kevinli020508@gmail.com>
Xiao-Yong Jin <jinxiaoyong@gmail.com>
XiaotaoChen <chenxiaotao1234@gmail.com>
Yajing Tang <phillis@google.com>
Yang Shen <aplshenyang@gmail.com>
Yunès <jean.baptiste.yunes@free.fr>
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Zigfrid Zvezdin <ziggerZZ@gmail.com>
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ai-at-home <149282006+ai-at-home@users.noreply.github.com>
alonfaraj <alonfaraj@gmail.com>
andypayne <apayne@gmail.com>
ardfork <134447697+ardfork@users.noreply.github.com>
automaticcat <daogiatuank54@gmail.com>
be-next <jerome.ramette@gmail.com>
bert hubert <bert@hubertnet.nl>
bmwl <brian.marshall@tolko.com>
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clach04 <Chris.Clark@actian.com>
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fitzsim <fitzsim@fitzsim.org>
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genevera (she/her) <genevera@users.noreply.github.com>
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greeshmay <greeshmay@gmail.com>
hydai <z54981220@gmail.com>
iamthad <thadeus.j.fleming@gmail.com>
james wolf <contractorwolf@hotmail.com>
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jorismertz <35079666+jorismertz@users.noreply.github.com>
junkfood <69683722+JunkFood02@users.noreply.github.com>
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kamranjon <kamranjon@gmail.com>
katsu560 <katsu560oo-@docomo.ne.jp>
kennethge <57784063+kenneth-ge@users.noreply.github.com>
keyehzy <msamuel@aluno.puc-rio.br>
leejet <leejet714@gmail.com>
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rlapray <lapray.romain@gmail.com>
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shibukazu <61775791+shibukazu@users.noreply.github.com>
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texmex76 <40733439+texmex76@users.noreply.github.com>
thefinaldegree <thefinaldegree@gmail.com>
trixirt <trix@redhat.com>
ulatekh <ulatekh@yahoo.com>
undef <undefdev@gmail.com>
venkr <venkateshrameshkumar+1@gmail.com>
vicalloy <zbirder@gmail.com>
xdrudis <xavierdrudis@yahoo.es>
zhouwg <6889919+zhouwg@users.noreply.github.com>
布客飞龙 <562826179@qq.com>
Артём Земляк <azemlyak@smart-consulting.ru>

View File

@ -1,21 +1,31 @@
cmake_minimum_required (VERSION 3.5) cmake_minimum_required(VERSION 3.5) # for add_link_options and implicit target directories.
project("whisper.cpp" C CXX)
project("whisper.cpp" VERSION 1.6.2)
include(CheckIncludeFileCXX)
project(whisper.cpp VERSION 1.5.4) set(SOVERSION 1)
#set(CMAKE_WARN_DEPRECATED YES)
set(CMAKE_WARN_UNUSED_CLI YES)
set(CMAKE_EXPORT_COMPILE_COMMANDS ON)
if (NOT XCODE AND NOT MSVC AND NOT CMAKE_BUILD_TYPE)
set(CMAKE_BUILD_TYPE Release CACHE STRING "Build type" FORCE)
set_property(CACHE CMAKE_BUILD_TYPE PROPERTY STRINGS "Debug" "Release" "MinSizeRel" "RelWithDebInfo")
endif()
# Add path to modules # Add path to modules
list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/cmake/") list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/cmake/")
set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/bin) set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/bin)
if(CMAKE_SOURCE_DIR STREQUAL CMAKE_CURRENT_SOURCE_DIR) if (CMAKE_SOURCE_DIR STREQUAL CMAKE_CURRENT_SOURCE_DIR)
set(WHISPER_STANDALONE ON) set(WHISPER_STANDALONE ON)
include(GitVars)
include(BuildTypes) include(git-vars)
# configure project version # configure project version
if (EXISTS "${CMAKE_SOURCE_DIR}/bindings/ios/Makefile-tmpl")
configure_file(${CMAKE_SOURCE_DIR}/bindings/ios/Makefile-tmpl ${CMAKE_SOURCE_DIR}/bindings/ios/Makefile @ONLY)
endif()
configure_file(${CMAKE_SOURCE_DIR}/bindings/javascript/package-tmpl.json ${CMAKE_SOURCE_DIR}/bindings/javascript/package.json @ONLY) configure_file(${CMAKE_SOURCE_DIR}/bindings/javascript/package-tmpl.json ${CMAKE_SOURCE_DIR}/bindings/javascript/package.json @ONLY)
else() else()
set(WHISPER_STANDALONE OFF) set(WHISPER_STANDALONE OFF)
@ -25,6 +35,11 @@ if (EMSCRIPTEN)
set(BUILD_SHARED_LIBS_DEFAULT OFF) set(BUILD_SHARED_LIBS_DEFAULT OFF)
option(WHISPER_WASM_SINGLE_FILE "whisper: embed WASM inside the generated whisper.js" ON) option(WHISPER_WASM_SINGLE_FILE "whisper: embed WASM inside the generated whisper.js" ON)
# TODO: without these, we get the following error:
# wasm-ld: error: --shared-memory is disallowed by whisper.cpp.o because it was not compiled with 'atomics' or 'bulk-memory' features.
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -pthread -s TOTAL_STACK=5242880")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -pthread -s TOTAL_STACK=5242880")
else() else()
if (MINGW) if (MINGW)
set(BUILD_SHARED_LIBS_DEFAULT OFF) set(BUILD_SHARED_LIBS_DEFAULT OFF)
@ -33,570 +48,145 @@ else()
endif() endif()
endif() endif()
# options option(BUILD_SHARED_LIBS "build shared libraries" ${BUILD_SHARED_LIBS_DEFAULT})
if (APPLE) #
set(WHISPER_METAL_DEFAULT ON) # option list
else() #
set(WHISPER_METAL_DEFAULT OFF)
endif()
option(BUILD_SHARED_LIBS "whisper: build shared libs" ${BUILD_SHARED_LIBS_DEFAULT}) # general
option(WHISPER_CCACHE "whisper: use ccache if available" ON)
# debug
option(WHISPER_ALL_WARNINGS "whisper: enable all compiler warnings" ON) option(WHISPER_ALL_WARNINGS "whisper: enable all compiler warnings" ON)
option(WHISPER_ALL_WARNINGS_3RD_PARTY "whisper: enable all compiler warnings in 3rd party libs" OFF) option(WHISPER_ALL_WARNINGS_3RD_PARTY "whisper: enable all compiler warnings in 3rd party libs" OFF)
option(WHISPER_SANITIZE_THREAD "whisper: enable thread sanitizer" OFF) # build
option(WHISPER_SANITIZE_ADDRESS "whisper: enable address sanitizer" OFF) option(WHISPER_FATAL_WARNINGS "whisper: enable -Werror flag" OFF)
option(WHISPER_SANITIZE_UNDEFINED "whisper: enable undefined sanitizer" OFF)
option(WHISPER_BUILD_TESTS "whisper: build tests" ${WHISPER_STANDALONE})
option(WHISPER_BUILD_EXAMPLES "whisper: build examples" ${WHISPER_STANDALONE})
option(WHISPER_SDL2 "whisper: support for libSDL2" OFF)
option(WHISPER_NO_AVX "whisper: disable AVX" OFF)
option(WHISPER_NO_AVX2 "whisper: disable AVX2" OFF)
option(WHISPER_NO_FMA "whisper: disable FMA" OFF)
option(WHISPER_NO_F16C "whisper: disable F16c" OFF)
option(WHISPER_OPENVINO "whisper: support for OpenVINO" OFF)
if (APPLE)
option(WHISPER_NO_ACCELERATE "whisper: disable Accelerate framework" OFF)
option(WHISPER_METAL "whisper: use Metal" ${WHISPER_METAL_DEFAULT})
option(WHISPER_METAL_NDEBUG "whisper: disable Metal debugging" OFF)
option(WHISPER_COREML "whisper: enable Core ML framework" OFF)
option(WHISPER_COREML_ALLOW_FALLBACK "whisper: allow non-CoreML fallback" OFF)
option(WHISPER_METAL_EMBED_LIBRARY "whisper: embed Metal library" OFF)
else()
option(WHISPER_BLAS "whisper: use BLAS libraries" OFF)
option(WHISPER_BLAS_VENDOR "whisper: BLAS library vendor" Generic)
option(WHISPER_OPENBLAS "whisper: prefer OpenBLAS" OFF)
option(WHISPER_CUBLAS "whisper: support for cuBLAS" OFF)
option(WHISPER_HIPBLAS "whisper: support for hipBLAS" OFF)
option(WHISPER_CLBLAST "whisper: use CLBlast" OFF)
endif()
option(WHISPER_PERF "whisper: enable perf timings" OFF)
# sanitizers # sanitizers
option(WHISPER_SANITIZE_THREAD "whisper: enable thread sanitizer" OFF)
option(WHISPER_SANITIZE_ADDRESS "whisper: enable address sanitizer" OFF)
option(WHISPER_SANITIZE_UNDEFINED "whisper: enable undefined sanitizer" OFF)
if (NOT MSVC) # extra artifacts
if (WHISPER_SANITIZE_THREAD) option(WHISPER_BUILD_TESTS "whisper: build tests" ${WHISPER_STANDALONE})
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -fsanitize=thread") option(WHISPER_BUILD_EXAMPLES "whisper: build examples" ${WHISPER_STANDALONE})
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsanitize=thread") option(WHISPER_BUILD_SERVER "whisper: build server example" ${WHISPER_STANDALONE})
endif()
if (WHISPER_SANITIZE_ADDRESS) # 3rd party libs
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -fsanitize=address -fno-omit-frame-pointer") option(WHISPER_CURL "whisper: use libcurl to download model from an URL" OFF)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsanitize=address -fno-omit-frame-pointer") option(WHISPER_SDL2 "whisper: support for libSDL2" OFF)
endif()
if (WHISPER_SANITIZE_UNDEFINED)
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -fsanitize=undefined")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsanitize=undefined")
endif()
endif()
#set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -ffast-math")
#set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -march=native")
# dependencies
find_package(Threads REQUIRED)
# on APPLE
if (APPLE)
# include Accelerate framework
if (NOT WHISPER_NO_ACCELERATE)
find_library(ACCELERATE_FRAMEWORK Accelerate)
if (ACCELERATE_FRAMEWORK)
message(STATUS "Accelerate framework found")
set(WHISPER_EXTRA_LIBS ${WHISPER_EXTRA_LIBS} ${ACCELERATE_FRAMEWORK})
set(WHISPER_EXTRA_FLAGS ${WHISPER_EXTRA_FLAGS} -DGGML_USE_ACCELERATE)
else()
message(FATAL_ERROR "Accelerate framework not found")
endif()
endif()
if (WHISPER_METAL)
find_library(FOUNDATION_LIBRARY Foundation REQUIRED)
find_library(METAL_FRAMEWORK Metal REQUIRED)
find_library(METALKIT_FRAMEWORK MetalKit REQUIRED)
if (METAL_FRAMEWORK)
message(STATUS "Metal framework found")
set(WHISPER_EXTRA_LIBS ${WHISPER_EXTRA_LIBS}
${FOUNDATION_LIBRARY}
${METAL_FRAMEWORK}
${METALKIT_FRAMEWORK}
)
set(WHISPER_EXTRA_FLAGS ${WHISPER_EXTRA_FLAGS} -DGGML_USE_METAL)
if (WHISPER_METAL_NDEBUG)
set(WHISPER_EXTRA_FLAGS ${WHISPER_EXTRA_FLAGS} -DGGML_METAL_NDEBUG)
endif()
else()
message(FATAL_ERROR "Metal framework not found")
endif()
set(GGML_SOURCES_METAL ggml-metal.m ggml-metal.h)
# copy ggml-metal.metal to bin directory
configure_file(ggml-metal.metal bin/ggml-metal.metal COPYONLY)
if (WHISPER_METAL_EMBED_LIBRARY)
enable_language(ASM)
set(WHISPER_EXTRA_FLAGS ${WHISPER_EXTRA_FLAGS} -DGGML_METAL_EMBED_LIBRARY)
set(METALLIB_SOURCE "${CMAKE_SOURCE_DIR}/ggml-metal.metal")
file(MAKE_DIRECTORY "${CMAKE_BINARY_DIR}/autogenerated")
set(EMBED_METALLIB_ASSEMBLY "${CMAKE_BINARY_DIR}/autogenerated/ggml-embed-metallib.s")
add_custom_command(
OUTPUT ${EMBED_METALLIB_ASSEMBLY}
COMMAND echo ".section __DATA,__ggml_metallib" > ${EMBED_METALLIB_ASSEMBLY}
COMMAND echo ".globl _ggml_metallib_start" >> ${EMBED_METALLIB_ASSEMBLY}
COMMAND echo "_ggml_metallib_start:" >> ${EMBED_METALLIB_ASSEMBLY}
COMMAND echo ".incbin \\\"${METALLIB_SOURCE}\\\"" >> ${EMBED_METALLIB_ASSEMBLY}
COMMAND echo ".globl _ggml_metallib_end" >> ${EMBED_METALLIB_ASSEMBLY}
COMMAND echo "_ggml_metallib_end:" >> ${EMBED_METALLIB_ASSEMBLY}
DEPENDS ${METALLIB_SOURCE}
COMMENT "Generate assembly for embedded Metal library"
)
set(GGML_SOURCES_METAL ${GGML_SOURCES_METAL} ${EMBED_METALLIB_ASSEMBLY})
endif()
endif()
if (WHISPER_COREML)
find_library(FOUNDATION_FRAMEWORK Foundation)
find_library(COREML_FRAMEWORK CoreML)
if (COREML_FRAMEWORK)
message(STATUS "CoreML framework found")
set(WHISPER_EXTRA_FLAGS ${WHISPER_EXTRA_FLAGS} -DWHISPER_USE_COREML)
else()
message(FATAL_ERROR "CoreML framework not found")
endif()
if (WHISPER_COREML_ALLOW_FALLBACK)
set(WHISPER_EXTRA_FLAGS ${WHISPER_EXTRA_FLAGS} -DWHISPER_COREML_ALLOW_FALLBACK)
endif()
endif()
endif()
if (WHISPER_OPENBLAS)
set(WHISPER_BLAS_VENDOR "OpenBLAS")
set(WHISPER_BLAS ON)
endif()
if (WHISPER_BLAS)
if (WIN32)
if(DEFINED ENV{OPENBLAS_PATH})
set(BLAS_LIBRARIES $ENV{OPENBLAS_PATH}/lib/libopenblas.dll.a)
message(STATUS "Libraries ${BLAS_LIBRARIES}")
set(WHISPER_EXTRA_FLAGS ${WHISPER_EXTRA_FLAGS} -DGGML_USE_OPENBLAS)
include_directories($ENV{OPENBLAS_PATH}/include)
set(WHISPER_EXTRA_LIBS ${WHISPER_EXTRA_LIBS} ${BLAS_LIBRARIES})
else ()
message(FATAL_ERROR "BLAS library was not found. Environment variable OPENBLAS_PATH not defined.")
endif ()
else ()
set(BLA_STATIC 1)
set(BLA_VENDOR ${WHISPER_BLAS_VENDOR})
set(BLA_SIZEOF_INTEGER 8)
set(BLA_PREFER_PKGCONFIG 1)
find_package(BLAS)
if(BLAS_FOUND)
message(STATUS "BLAS compatible library found")
message(STATUS "Libraries ${BLAS_LIBRARIES}")
find_path(BLAS_INCLUDE_DIRS cblas.h /usr/include/openblas /usr/local/include/openblas $ENV{BLAS_HOME}/include)
set(WHISPER_EXTRA_FLAGS ${WHISPER_EXTRA_FLAGS} -DGGML_USE_OPENBLAS)
include_directories(${BLAS_INCLUDE_DIRS})
set(WHISPER_EXTRA_LIBS ${WHISPER_EXTRA_LIBS} ${BLAS_LIBRARIES})
else()
message(FATAL_ERROR "BLAS library was not found")
endif()
endif ()
endif ()
if (WHISPER_CUBLAS)
cmake_minimum_required(VERSION 3.17)
find_package(CUDAToolkit)
if (CUDAToolkit_FOUND)
message(STATUS "cuBLAS found")
enable_language(CUDA)
set(GGML_SOURCES_CUDA ggml-cuda.cu ggml-cuda.h)
add_compile_definitions(GGML_USE_CUBLAS)
if (WHISPER_STATIC)
if (WIN32)
# As of 12.3.1 CUDA Tookit for Windows does not offer a static cublas library
set(WHISPER_EXTRA_LIBS ${WHISPER_EXTRA_LIBS} CUDA::cudart_static CUDA::cublas CUDA::cublasLt)
else ()
set(WHISPER_EXTRA_LIBS ${WHISPER_EXTRA_LIBS} CUDA::cudart_static CUDA::cublas_static CUDA::cublasLt_static)
endif()
else()
set(WHISPER_EXTRA_LIBS ${WHISPER_EXTRA_LIBS} CUDA::cudart CUDA::cublas CUDA::cublasLt)
endif()
set(WHISPER_EXTRA_LIBS ${WHISPER_EXTRA_LIBS} CUDA::cuda_driver)
else()
message(FATAL_ERROR "cuBLAS not found")
endif()
endif()
if (WHISPER_HIPBLAS)
list(APPEND CMAKE_PREFIX_PATH /opt/rocm)
if (NOT ${CMAKE_C_COMPILER_ID} MATCHES "Clang")
message(WARNING "Only LLVM is supported for HIP, hint: CC=/opt/rocm/llvm/bin/clang")
endif()
if (NOT ${CMAKE_CXX_COMPILER_ID} MATCHES "Clang")
message(WARNING "Only LLVM is supported for HIP, hint: CXX=/opt/rocm/llvm/bin/clang++")
endif()
find_package(hip)
find_package(hipblas)
find_package(rocblas)
if (${hipblas_FOUND} AND ${hip_FOUND})
message(STATUS "HIP and hipBLAS found")
add_compile_definitions(GGML_USE_HIPBLAS GGML_USE_CUBLAS)
add_library(ggml-rocm OBJECT ggml-cuda.cu ggml-cuda.h)
set_property(TARGET ggml-rocm PROPERTY POSITION_INDEPENDENT_CODE ON)
set_source_files_properties(ggml-cuda.cu PROPERTIES LANGUAGE CXX)
target_link_libraries(ggml-rocm PRIVATE hip::device PUBLIC hip::host roc::rocblas roc::hipblas)
if (WHISPER_STATIC)
message(FATAL_ERROR "Static linking not supported for HIP/ROCm")
endif()
set(WHISPER_EXTRA_LIBS ${WHISPER_EXTRA_LIBS} ggml-rocm)
else()
message(FATAL_ERROR "hipBLAS or HIP not found. Try setting CMAKE_PREFIX_PATH=/opt/rocm")
endif()
endif()
if (WHISPER_CLBLAST)
find_package(CLBlast)
if (CLBlast_FOUND)
message(STATUS "CLBlast found")
set(GGML_SOURCES_OPENCL ggml-opencl.cpp ggml-opencl.h)
add_compile_definitions(GGML_USE_CLBLAST)
set(WHISPER_EXTRA_LIBS ${WHISPER_EXTRA_LIBS} clblast)
else()
message(FATAL_ERROR "CLBlast not found")
endif()
endif()
if( WHISPER_OPENVINO )
find_package(OpenVINO REQUIRED COMPONENTS Runtime)
endif()
# compiler flags
if (NOT CMAKE_BUILD_TYPE AND NOT CMAKE_CONFIGURATION_TYPES)
set(CMAKE_BUILD_TYPE Release CACHE STRING "Build type" FORCE)
set_property(CACHE CMAKE_BUILD_TYPE PROPERTY STRINGS "Debug" "Release" "RelWithDebInfo")
endif ()
if (WHISPER_ALL_WARNINGS)
if (NOT MSVC)
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} \
-Wall \
-Wextra \
-Wpedantic \
-Wshadow \
-Wcast-qual \
-Wstrict-prototypes \
-Wpointer-arith \
-Wno-unused-function \
")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} \
-Wall \
-Wextra \
-Wpedantic \
-Wcast-qual \
")
else()
# todo : msvc
endif()
endif()
if (NOT MSVC)
# TODO: temporary disabled until we figure out ggml-metal.m
#set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -Werror=vla")
#set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -fno-math-errno -ffinite-math-only -funsafe-math-optimizations")
endif()
message(STATUS "CMAKE_SYSTEM_PROCESSOR: ${CMAKE_SYSTEM_PROCESSOR}")
if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "arm" OR ${CMAKE_SYSTEM_PROCESSOR} MATCHES "aarch64")
message(STATUS "ARM detected")
elseif(${CMAKE_SYSTEM_PROCESSOR} MATCHES "ppc64le")
message(STATUS "PowerPC detected")
else()
message(STATUS "x86 detected")
if (MSVC)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /utf-8")
set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} /utf-8")
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} /utf-8")
if(NOT WHISPER_NO_AVX2)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /arch:AVX2")
set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} /arch:AVX2")
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} /arch:AVX2")
else()
if(NOT WHISPER_NO_AVX)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /arch:AVX")
set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} /arch:AVX")
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} /arch:AVX")
endif()
endif()
else()
if (EMSCRIPTEN)
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -pthread -s TOTAL_STACK=5242880")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -pthread -s TOTAL_STACK=5242880")
else()
if(NOT WHISPER_NO_AVX)
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -mavx")
endif()
if(NOT WHISPER_NO_AVX2)
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -mavx2")
endif()
if(NOT WHISPER_NO_FMA)
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -mfma")
endif()
if(NOT WHISPER_NO_F16C)
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -mf16c")
endif()
endif()
endif()
endif()
#
# POSIX conformance
#
# clock_gettime came in POSIX.1b (1993)
# CLOCK_MONOTONIC came in POSIX.1-2001 / SUSv3 as optional
# posix_memalign came in POSIX.1-2001 / SUSv3
# M_PI is an XSI extension since POSIX.1-2001 / SUSv3, came in XPG1 (1985)
add_compile_definitions(_XOPEN_SOURCE=600)
# Somehow in OpenBSD whenever POSIX conformance is specified
# some string functions rely on locale_t availability,
# which was introduced in POSIX.1-2008, forcing us to go higher
if (CMAKE_SYSTEM_NAME MATCHES "OpenBSD")
remove_definitions(-D_XOPEN_SOURCE=600)
add_compile_definitions(_XOPEN_SOURCE=700)
endif()
# Data types, macros and functions related to controlling CPU affinity
# are available on Linux through GNU extensions in libc
if (CMAKE_SYSTEM_NAME MATCHES "Linux") if (CMAKE_SYSTEM_NAME MATCHES "Linux")
add_compile_definitions(_GNU_SOURCE) option(WHISPER_FFMPEG "whisper: support building and linking with ffmpeg libs (avcodec, swresample, ...)" OFF)
endif() endif()
# RLIMIT_MEMLOCK came in BSD, is not specified in POSIX.1, option(WHISPER_COREML "whisper: enable Core ML framework" OFF)
# and on macOS its availability depends on enabling Darwin extensions option(WHISPER_COREML_ALLOW_FALLBACK "whisper: allow non-CoreML fallback" OFF)
# similarly on DragonFly, enabling BSD extensions is necessary option(WHISPER_OPENVINO "whisper: support for OpenVINO" OFF)
if (CMAKE_SYSTEM_NAME MATCHES "Darwin")
add_compile_definitions(_DARWIN_C_SOURCE)
endif()
if (CMAKE_SYSTEM_NAME MATCHES "DragonFly")
add_compile_definitions(_DARWIN_C_SOURCE)
endif()
# alloca is a non-standard interface that is not visible on BSDs when # Required for relocatable CMake package
# POSIX conformance is specified, but not all of them provide a clean way include(${CMAKE_CURRENT_SOURCE_DIR}/cmake/build-info.cmake)
# to enable it in such cases
if (CMAKE_SYSTEM_NAME MATCHES "FreeBSD")
add_compile_definitions(__BSD_VISIBLE)
endif()
if (CMAKE_SYSTEM_NAME MATCHES "NetBSD")
add_compile_definitions(_NETBSD_SOURCE)
endif()
if (CMAKE_SYSTEM_NAME MATCHES "OpenBSD")
add_compile_definitions(_BSD_SOURCE)
endif()
if (WHISPER_PERF) # override ggml options
set(WHISPER_EXTRA_FLAGS ${WHISPER_EXTRA_FLAGS} -DGGML_PERF) set(GGML_CCACHE ${WHISPER_CCACHE})
endif() set(GGML_SANITIZE_THREAD ${WHISPER_SANITIZE_THREAD})
set(GGML_SANITIZE_ADDRESS ${WHISPER_SANITIZE_ADDRESS})
set(GGML_SANITIZE_UNDEFINED ${WHISPER_SANITIZE_UNDEFINED})
set(GGML_ALL_WARNINGS ${WHISPER_ALL_WARNINGS})
set(GGML_FATAL_WARNINGS ${WHISPER_FATAL_WARNINGS})
# # transition helpers
# whisper.coreml - Core ML support function (whisper_option_depr TYPE OLD NEW)
# if (${OLD})
message(${TYPE} "${OLD} is deprecated and will be removed in the future.\nUse ${NEW} instead\n")
if (WHISPER_COREML) set(${NEW} ON)
set(TARGET whisper.coreml)
add_library(${TARGET}
coreml/whisper-encoder.h
coreml/whisper-encoder.mm
coreml/whisper-encoder-impl.h
coreml/whisper-encoder-impl.m
)
include(DefaultTargetOptions)
target_include_directories(${TARGET} PUBLIC
.
)
target_link_libraries(${TARGET} PRIVATE ${FOUNDATION_FRAMEWORK} ${COREML_FRAMEWORK})
set_target_properties(${TARGET} PROPERTIES
COMPILE_FLAGS "-fobjc-arc"
)
endif()
if (WHISPER_OPENVINO)
set(TARGET whisper.openvino)
add_library(${TARGET} OBJECT
openvino/whisper-openvino-encoder.h
openvino/whisper-openvino-encoder.cpp
)
target_include_directories(${TARGET} PUBLIC
.
)
set_property(TARGET ${TARGET} PROPERTY POSITION_INDEPENDENT_CODE ON)
set(WHISPER_EXTRA_FLAGS ${WHISPER_EXTRA_FLAGS} -DWHISPER_USE_OPENVINO)
target_link_libraries(${TARGET} PRIVATE openvino::runtime)
endif()
#
# whisper - this is the main library of the project
#
set(TARGET whisper)
add_library(${TARGET}
ggml.h
ggml.c
ggml-alloc.h
ggml-alloc.c
ggml-backend.h
ggml-backend.c
ggml-quants.h
ggml-quants.c
${GGML_SOURCES_METAL}
${GGML_SOURCES_CUDA}
${GGML_SOURCES_OPENCL}
whisper.h
whisper.cpp
)
include(DefaultTargetOptions)
target_include_directories(${TARGET} PUBLIC
.
)
if (WHISPER_COREML)
target_link_libraries(${TARGET} PRIVATE whisper.coreml)
endif()
if (WHISPER_OPENVINO)
target_link_libraries(${TARGET} PRIVATE whisper.openvino)
endif()
if (MSVC)
target_link_libraries(${TARGET} PRIVATE ${WHISPER_EXTRA_LIBS} ${CMAKE_THREAD_LIBS_INIT})
set(WHISPER_EXTRA_FLAGS ${WHISPER_EXTRA_FLAGS} -D_CRT_SECURE_NO_WARNINGS)
else()
target_link_libraries(${TARGET} PRIVATE m ${WHISPER_EXTRA_LIBS} ${CMAKE_THREAD_LIBS_INIT})
endif()
if (BUILD_SHARED_LIBS)
set_target_properties(${TARGET} PROPERTIES POSITION_INDEPENDENT_CODE ON)
target_link_libraries(${TARGET} PUBLIC
${CMAKE_DL_LIBS}
)
target_compile_definitions(${TARGET} PUBLIC
WHISPER_SHARED
GGML_SHARED
)
target_compile_definitions(${TARGET} PRIVATE
WHISPER_BUILD
GGML_BUILD
)
if (WHISPER_METAL)
# TODO: I think this should make ggml-metal.m "see" the ggml-metal.metal file from the "bin" directory
# but for some reason it does not work here like it does in llama.cpp
set_target_properties(${TARGET} PROPERTIES RESOURCE "${CMAKE_CURRENT_SOURCE_DIR}/ggml-metal.metal")
endif() endif()
endif() endfunction()
if (GGML_SOURCES_CUDA) whisper_option_depr(FATAL_ERROR WHISPER_CUBLAS GGML_CUDA)
message(STATUS "GGML CUDA sources found, configuring CUDA architecture") whisper_option_depr(WARNING WHISPER_CUDA GGML_CUDA)
# Only configure gmml CUDA architectures is not globally set whisper_option_depr(WARNING WHISPER_KOMPUTE GGML_KOMPUTE)
if (NOT DEFINED GGML_CUDA_ARCHITECTURES) whisper_option_depr(WARNING WHISPER_METAL GGML_METAL)
# Not overriden by user, so set defaults whisper_option_depr(WARNING WHISPER_METAL_EMBED_LIBRARY GGML_METAL_EMBED_LIBRARY)
set(GGML_CUDA_ARCHITECTURES 52 61 70) whisper_option_depr(WARNING WHISPER_NATIVE GGML_NATIVE)
endif() whisper_option_depr(WARNING WHISPER_OPENMP GGML_OPENMP)
message(STATUS "GGML Configuring CUDA architectures ${GGML_CUDA_ARCHITECTURES}") whisper_option_depr(WARNING WHISPER_RPC GGML_RPC)
set_property(TARGET whisper PROPERTY CUDA_ARCHITECTURES ${GGML_CUDA_ARCHITECTURES}) whisper_option_depr(WARNING WHISPER_SYCL GGML_SYCL)
set_property(TARGET whisper PROPERTY CUDA_SELECT_NVCC_ARCH_FLAGS "Auto") whisper_option_depr(WARNING WHISPER_SYCL_F16 GGML_SYCL_F16)
endif()
if (EMSCRIPTEN) #
set_target_properties(${TARGET} PROPERTIES COMPILE_FLAGS "-msimd128") # build the library
endif() #
target_compile_definitions(${TARGET} PUBLIC add_subdirectory(ggml)
${WHISPER_EXTRA_FLAGS} add_subdirectory(src)
)
set_target_properties(${TARGET} PROPERTIES PUBLIC_HEADER "ggml.h;whisper.h") #
# install
#
include(GNUInstallDirs) include(GNUInstallDirs)
include(CMakePackageConfigHelpers)
install(TARGETS ${TARGET} set(WHISPER_BUILD_NUMBER ${BUILD_NUMBER})
LIBRARY DESTINATION lib set(WHISPER_BUILD_COMMIT ${BUILD_COMMIT})
ARCHIVE DESTINATION lib/static set(WHISPER_INSTALL_VERSION ${CMAKE_PROJECT_VERSION})
RUNTIME DESTINATION bin
RESOURCE DESTINATION bin
PUBLIC_HEADER DESTINATION include
)
# set(WHISPER_INCLUDE_INSTALL_DIR ${CMAKE_INSTALL_INCLUDEDIR} CACHE PATH "Location of header files")
# bindings set(WHISPER_LIB_INSTALL_DIR ${CMAKE_INSTALL_LIBDIR} CACHE PATH "Location of library files")
# set(WHISPER_BIN_INSTALL_DIR ${CMAKE_INSTALL_BINDIR} CACHE PATH "Location of binary files")
add_subdirectory(bindings) get_directory_property(WHISPER_TRANSIENT_DEFINES COMPILE_DEFINITIONS)
set_target_properties(whisper PROPERTIES PUBLIC_HEADER ${CMAKE_CURRENT_SOURCE_DIR}/include/whisper.h)
install(TARGETS whisper LIBRARY PUBLIC_HEADER)
configure_package_config_file(
${CMAKE_CURRENT_SOURCE_DIR}/cmake/whisper-config.cmake.in
${CMAKE_CURRENT_BINARY_DIR}/whisper-config.cmake
INSTALL_DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/whisper
PATH_VARS
WHISPER_INCLUDE_INSTALL_DIR
WHISPER_LIB_INSTALL_DIR
WHISPER_BIN_INSTALL_DIR )
write_basic_package_version_file(
${CMAKE_CURRENT_BINARY_DIR}/whisper-version.cmake
VERSION ${WHISPER_INSTALL_VERSION}
COMPATIBILITY SameMajorVersion)
install(FILES ${CMAKE_CURRENT_BINARY_DIR}/whisper-config.cmake
${CMAKE_CURRENT_BINARY_DIR}/whisper-version.cmake
DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/whisper)
install(
FILES convert-hf-to-gguf.py
PERMISSIONS
OWNER_READ
OWNER_WRITE
OWNER_EXECUTE
GROUP_READ
GROUP_EXECUTE
WORLD_READ
WORLD_EXECUTE
DESTINATION ${CMAKE_INSTALL_BINDIR})
configure_file(cmake/whisper.pc.in
"${CMAKE_CURRENT_BINARY_DIR}/whisper.pc"
@ONLY)
install(FILES "${CMAKE_CURRENT_BINARY_DIR}/whisper.pc"
DESTINATION lib/pkgconfig)
# #
# programs, examples and tests # programs, examples and tests
# #
if (WHISPER_BUILD_TESTS AND NOT CMAKE_JS_VERSION) if (WHISPER_BUILD_TESTS AND NOT CMAKE_JS_VERSION)
enable_testing() #include(CTest)
add_subdirectory(tests) #add_subdirectory(tests)
endif () endif ()
if (WHISPER_BUILD_EXAMPLES) if (WHISPER_BUILD_EXAMPLES)

View File

@ -1,6 +1,6 @@
MIT License MIT License
Copyright (c) 2023 Georgi Gerganov Copyright (c) 2023-2024 The ggml authors
Permission is hereby granted, free of charge, to any person obtaining a copy Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal of this software and associated documentation files (the "Software"), to deal

1198
Makefile

File diff suppressed because it is too large Load Diff

View File

@ -13,13 +13,9 @@ let package = Package(
products: [ products: [
.library(name: "whisper", targets: ["whisper"]), .library(name: "whisper", targets: ["whisper"]),
], ],
dependencies: [
.package(url: "https://github.com/ggerganov/ggml.git", .branch("release"))
],
targets: [ targets: [
.target( .target(
name: "whisper", name: "whisper",
dependencies: ["ggml"],
path: ".", path: ".",
exclude: [ exclude: [
"bindings", "bindings",
@ -31,13 +27,17 @@ let package = Package(
"samples", "samples",
"tests", "tests",
"CMakeLists.txt", "CMakeLists.txt",
"ggml-cuda.cu",
"ggml-cuda.h",
"Makefile" "Makefile"
], ],
sources: [ sources: [
"whisper.cpp", "ggml/src/ggml.c",
"src/whisper.cpp",
"ggml/src/ggml-alloc.c",
"ggml/src/ggml-backend.c",
"ggml/src/ggml-quants.c",
"ggml/src/ggml-metal.m"
], ],
resources: [.process("ggml-metal.metal")],
publicHeadersPath: "spm-headers", publicHeadersPath: "spm-headers",
cSettings: [ cSettings: [
.unsafeFlags(["-Wno-shorten-64-to-32", "-O3", "-DNDEBUG"]), .unsafeFlags(["-Wno-shorten-64-to-32", "-O3", "-DNDEBUG"]),

View File

@ -4,9 +4,10 @@
[![Actions Status](https://github.com/ggerganov/whisper.cpp/workflows/CI/badge.svg)](https://github.com/ggerganov/whisper.cpp/actions) [![Actions Status](https://github.com/ggerganov/whisper.cpp/workflows/CI/badge.svg)](https://github.com/ggerganov/whisper.cpp/actions)
[![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT) [![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT)
[![Conan Center](https://shields.io/conan/v/whisper-cpp)](https://conan.io/center/whisper-cpp)
[![npm](https://img.shields.io/npm/v/whisper.cpp.svg)](https://www.npmjs.com/package/whisper.cpp/) [![npm](https://img.shields.io/npm/v/whisper.cpp.svg)](https://www.npmjs.com/package/whisper.cpp/)
Stable: [v1.5.4](https://github.com/ggerganov/whisper.cpp/releases/tag/v1.5.4) / [Roadmap | F.A.Q.](https://github.com/ggerganov/whisper.cpp/discussions/126) Stable: [v1.6.2](https://github.com/ggerganov/whisper.cpp/releases/tag/v1.6.0) / [Roadmap | F.A.Q.](https://github.com/ggerganov/whisper.cpp/discussions/126)
High-performance inference of [OpenAI's Whisper](https://github.com/openai/whisper) automatic speech recognition (ASR) model: High-performance inference of [OpenAI's Whisper](https://github.com/openai/whisper) automatic speech recognition (ASR) model:
@ -19,7 +20,6 @@ High-performance inference of [OpenAI's Whisper](https://github.com/openai/whisp
- Zero memory allocations at runtime - Zero memory allocations at runtime
- Support for CPU-only inference - Support for CPU-only inference
- [Efficient GPU support for NVIDIA](https://github.com/ggerganov/whisper.cpp#nvidia-gpu-support-via-cublas) - [Efficient GPU support for NVIDIA](https://github.com/ggerganov/whisper.cpp#nvidia-gpu-support-via-cublas)
- [Partial OpenCL GPU support via CLBlast](https://github.com/ggerganov/whisper.cpp#opencl-gpu-support-via-clblast)
- [OpenVINO Support](https://github.com/ggerganov/whisper.cpp#openvino-support) - [OpenVINO Support](https://github.com/ggerganov/whisper.cpp#openvino-support)
- [C-style API](https://github.com/ggerganov/whisper.cpp/blob/master/whisper.h) - [C-style API](https://github.com/ggerganov/whisper.cpp/blob/master/whisper.h)
@ -278,6 +278,7 @@ speed-up - more than x3 faster compared with CPU-only execution. Here are the in
- To ensure `coremltools` operates correctly, please confirm that [Xcode](https://developer.apple.com/xcode/) is installed and execute `xcode-select --install` to install the command-line tools. - To ensure `coremltools` operates correctly, please confirm that [Xcode](https://developer.apple.com/xcode/) is installed and execute `xcode-select --install` to install the command-line tools.
- Python 3.10 is recommended. - Python 3.10 is recommended.
- MacOS Sonoma (version 14) or newer is recommended, as older versions of MacOS might experience issues with transcription hallucination.
- [OPTIONAL] It is recommended to utilize a Python version management system, such as [Miniconda](https://docs.conda.io/en/latest/miniconda.html) for this step: - [OPTIONAL] It is recommended to utilize a Python version management system, such as [Miniconda](https://docs.conda.io/en/latest/miniconda.html) for this step:
- To create an environment, use: `conda create -n py310-whisper python=3.10 -y` - To create an environment, use: `conda create -n py310-whisper python=3.10 -y`
- To activate the environment, use: `conda activate py310-whisper` - To activate the environment, use: `conda activate py310-whisper`
@ -339,7 +340,7 @@ This can result in significant speedup in encoder performance. Here are the inst
python -m venv openvino_conv_env python -m venv openvino_conv_env
openvino_conv_env\Scripts\activate openvino_conv_env\Scripts\activate
python -m pip install --upgrade pip python -m pip install --upgrade pip
pip install -r openvino-conversion-requirements.txt pip install -r requirements-openvino.txt
``` ```
Linux and macOS: Linux and macOS:
@ -349,7 +350,7 @@ This can result in significant speedup in encoder performance. Here are the inst
python3 -m venv openvino_conv_env python3 -m venv openvino_conv_env
source openvino_conv_env/bin/activate source openvino_conv_env/bin/activate
python -m pip install --upgrade pip python -m pip install --upgrade pip
pip install -r openvino-conversion-requirements.txt pip install -r requirements-openvino.txt
``` ```
- Generate an OpenVINO encoder model. For example, to generate a `base.en` model, use: - Generate an OpenVINO encoder model. For example, to generate a `base.en` model, use:
@ -413,35 +414,13 @@ For more information about the Core ML implementation please refer to PR [#1037]
With NVIDIA cards the processing of the models is done efficiently on the GPU via cuBLAS and custom CUDA kernels. With NVIDIA cards the processing of the models is done efficiently on the GPU via cuBLAS and custom CUDA kernels.
First, make sure you have installed `cuda`: https://developer.nvidia.com/cuda-downloads First, make sure you have installed `cuda`: https://developer.nvidia.com/cuda-downloads
Now build `whisper.cpp` with cuBLAS support: Now build `whisper.cpp` with CUDA support:
``` ```
make clean make clean
WHISPER_CUBLAS=1 make -j GGML_CUDA=1 make -j
``` ```
## OpenCL GPU support via CLBlast
For cards and integrated GPUs that support OpenCL, the Encoder processing can be largely offloaded to the GPU through CLBlast. This is especially useful for users with AMD APUs or low end devices for up to ~2x speedup.
First, make sure you have installed `CLBlast` for your OS or Distribution: https://github.com/CNugteren/CLBlast
Now build `whisper.cpp` with CLBlast support:
```
Makefile:
cd whisper.cpp
make clean
WHISPER_CLBLAST=1 make -j
CMake:
cd whisper.cpp
cmake -B build -DWHISPER_CLBLAST=ON
cmake --build build -j --config Release
```
Run all the examples as usual.
## BLAS CPU support via OpenBLAS ## BLAS CPU support via OpenBLAS
Encoder processing can be accelerated on the CPU via OpenBLAS. Encoder processing can be accelerated on the CPU via OpenBLAS.
@ -451,7 +430,22 @@ Now build `whisper.cpp` with OpenBLAS support:
``` ```
make clean make clean
WHISPER_OPENBLAS=1 make -j GGML_OPENBLAS=1 make -j
```
## BLAS CPU support via Intel MKL
Encoder processing can be accelerated on the CPU via the BLAS compatible interface of Intel's Math Kernel Library.
First, make sure you have installed Intel's MKL runtime and development packages: https://www.intel.com/content/www/us/en/developer/tools/oneapi/onemkl-download.html
Now build `whisper.cpp` with Intel MKL BLAS support:
```
source /opt/intel/oneapi/setvars.sh
mkdir build
cd build
cmake -DWHISPER_MKL=ON ..
WHISPER_MKL=1 make -j
``` ```
## Docker ## Docker
@ -486,6 +480,16 @@ docker run -it --rm \
whisper.cpp:main "./main -m /models/ggml-base.bin -f ./samples/jfk.wav" whisper.cpp:main "./main -m /models/ggml-base.bin -f ./samples/jfk.wav"
``` ```
## Installing with Conan
You can install pre-built binaries for whisper.cpp or build it from source using [Conan](https://conan.io/). Use the following command:
```
conan install --requires="whisper-cpp/[*]" --build=missing
```
For detailed instructions on how to use Conan, please refer to the [Conan documentation](https://docs.conan.io/2/).
## Limitations ## Limitations
- Inference only - Inference only
@ -694,7 +698,7 @@ The [main](examples/main) example provides support for output of karaoke-style m
currently pronounced word is highlighted. Use the `-wts` argument and run the generated bash script. currently pronounced word is highlighted. Use the `-wts` argument and run the generated bash script.
This requires to have `ffmpeg` installed. This requires to have `ffmpeg` installed.
Here are a few *"typical"* examples: Here are a few _"typical"_ examples:
```bash ```bash
./main -m ./models/ggml-base.en.bin -f ./samples/jfk.wav -owts ./main -m ./models/ggml-base.en.bin -f ./samples/jfk.wav -owts
@ -728,10 +732,10 @@ https://user-images.githubusercontent.com/1991296/199337538-b7b0c7a3-2753-4a88-a
## Video comparison of different models ## Video comparison of different models
Use the [extra/bench-wts.sh](https://github.com/ggerganov/whisper.cpp/blob/master/extra/bench-wts.sh) script to generate a video in the following format: Use the [scripts/bench-wts.sh](https://github.com/ggerganov/whisper.cpp/blob/master/scripts/bench-wts.sh) script to generate a video in the following format:
```bash ```bash
./extra/bench-wts.sh samples/jfk.wav ./scripts/bench-wts.sh samples/jfk.wav
ffplay ./samples/jfk.wav.all.mp4 ffplay ./samples/jfk.wav.all.mp4
``` ```
@ -752,7 +756,7 @@ Additionally a script to run whisper.cpp with different models and audio files i
You can run it with the following command, by default it will run against any standard model in the models folder. You can run it with the following command, by default it will run against any standard model in the models folder.
```bash ```bash
python3 extra/bench.py -f samples/jfk.wav -t 2,4,8 -p 1,2 python3 scripts/bench.py -f samples/jfk.wav -t 2,4,8 -p 1,2
``` ```
It is written in python with the intention of being easy to modify and extend for your benchmarking use case. It is written in python with the intention of being easy to modify and extend for your benchmarking use case.
@ -792,6 +796,7 @@ For more details, see the conversion script [models/convert-pt-to-ggml.py](model
- [NickDarvey/whisper](https://github.com/NickDarvey/whisper) - [NickDarvey/whisper](https://github.com/NickDarvey/whisper)
- [x] Python: | [#9](https://github.com/ggerganov/whisper.cpp/issues/9) - [x] Python: | [#9](https://github.com/ggerganov/whisper.cpp/issues/9)
- [stlukey/whispercpp.py](https://github.com/stlukey/whispercpp.py) (Cython) - [stlukey/whispercpp.py](https://github.com/stlukey/whispercpp.py) (Cython)
- [AIWintermuteAI/whispercpp](https://github.com/AIWintermuteAI/whispercpp) (Updated fork of aarnphm/whispercpp)
- [aarnphm/whispercpp](https://github.com/aarnphm/whispercpp) (Pybind11) - [aarnphm/whispercpp](https://github.com/aarnphm/whispercpp) (Pybind11)
- [x] R: [bnosac/audio.whisper](https://github.com/bnosac/audio.whisper) - [x] R: [bnosac/audio.whisper](https://github.com/bnosac/audio.whisper)
- [x] Unity: [macoron/whisper.unity](https://github.com/Macoron/whisper.unity) - [x] Unity: [macoron/whisper.unity](https://github.com/Macoron/whisper.unity)

249
README_sycl.md Normal file
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@ -0,0 +1,249 @@
# whisper.cpp for SYCL
[Background](#background)
[OS](#os)
[Intel GPU](#intel-gpu)
[Linux](#linux)
[Environment Variable](#environment-variable)
[Known Issue](#known-issue)
[Todo](#todo)
## Background
SYCL is a higher-level programming model to improve programming productivity on various hardware accelerators<72>such as CPUs, GPUs, and FPGAs. It is a single-source embedded domain-specific language based on pure C++17.
oneAPI is a specification that is open and standards-based, supporting multiple architecture types including but not limited to GPU, CPU, and FPGA. The spec has both direct programming and API-based programming paradigms.
Intel uses the SYCL as direct programming language to support CPU, GPUs and FPGAs.
To avoid re-inventing the wheel, this code refers other code paths in llama.cpp (like OpenBLAS, cuBLAS, CLBlast). We use a open-source tool [SYCLomatic](https://github.com/oneapi-src/SYCLomatic) (Commercial release [Intel<EFBFBD> DPC++ Compatibility Tool](https://www.intel.com/content/www/us/en/developer/tools/oneapi/dpc-compatibility-tool.html)) migrate to SYCL.
The whisper.cpp for SYCL is used to support Intel GPUs.
For Intel CPU, recommend to use whisper.cpp for X86 (Intel MKL build).
## OS
|OS|Status|Verified|
|-|-|-|
|Linux|Support|Ubuntu 22.04|
|Windows|Ongoing| |
## Intel GPU
|Intel GPU| Status | Verified Model|
|-|-|-|
|Intel Data Center Max Series| Support| Max 1550|
|Intel Data Center Flex Series| Support| Flex 170|
|Intel Arc Series| Support| Arc 770|
|Intel built-in Arc GPU| Support| built-in Arc GPU in Meteor Lake|
|Intel iGPU| Support| iGPU in i5-1250P, i7-1165G7|
## Linux
### Setup Environment
1. Install Intel GPU driver.
a. Please install Intel GPU driver by official guide: [Install GPU Drivers](https://dgpu-docs.intel.com/driver/installation.html).
Note: for iGPU, please install the client GPU driver.
b. Add user to group: video, render.
```
sudo usermod -aG render username
sudo usermod -aG video username
```
Note: re-login to enable it.
c. Check
```
sudo apt install clinfo
sudo clinfo -l
```
Output (example):
```
Platform #0: Intel(R) OpenCL Graphics
`-- Device #0: Intel(R) Arc(TM) A770 Graphics
Platform #0: Intel(R) OpenCL HD Graphics
`-- Device #0: Intel(R) Iris(R) Xe Graphics [0x9a49]
```
2. Install Intel<65> oneAPI Base toolkit.
a. Please follow the procedure in [Get the Intel<65> oneAPI Base Toolkit ](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html).
Recommend to install to default folder: **/opt/intel/oneapi**.
Following guide use the default folder as example. If you use other folder, please modify the following guide info with your folder.
b. Check
```
source /opt/intel/oneapi/setvars.sh
sycl-ls
```
There should be one or more level-zero devices. Like **[ext_oneapi_level_zero:gpu:0]**.
Output (example):
```
[opencl:acc:0] Intel(R) FPGA Emulation Platform for OpenCL(TM), Intel(R) FPGA Emulation Device OpenCL 1.2 [2023.16.10.0.17_160000]
[opencl:cpu:1] Intel(R) OpenCL, 13th Gen Intel(R) Core(TM) i7-13700K OpenCL 3.0 (Build 0) [2023.16.10.0.17_160000]
[opencl:gpu:2] Intel(R) OpenCL Graphics, Intel(R) Arc(TM) A770 Graphics OpenCL 3.0 NEO [23.30.26918.50]
[ext_oneapi_level_zero:gpu:0] Intel(R) Level-Zero, Intel(R) Arc(TM) A770 Graphics 1.3 [1.3.26918]
```
2. Build locally:
```
mkdir -p build
cd build
source /opt/intel/oneapi/setvars.sh
#for FP16
#cmake .. -DWHISPER_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DWHISPER_SYCL_F16=ON
#for FP32
cmake .. -DWHISPER_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
#build example/main only
#cmake --build . --config Release --target main
#build all binary
cmake --build . --config Release -v
```
or
```
./examples/sycl/build.sh
```
Note:
- By default, it will build for all binary files. It will take more time. To reduce the time, we recommend to build for **example/main** only.
### Run
1. Put model file to folder **models**
2. Enable oneAPI running environment
```
source /opt/intel/oneapi/setvars.sh
```
3. List device ID
Run without parameter:
```
./build/bin/ls-sycl-device
or
./build/bin/main
```
Check the ID in startup log, like:
```
found 4 SYCL devices:
Device 0: Intel(R) Arc(TM) A770 Graphics, compute capability 1.3,
max compute_units 512, max work group size 1024, max sub group size 32, global mem size 16225243136
Device 1: Intel(R) FPGA Emulation Device, compute capability 1.2,
max compute_units 24, max work group size 67108864, max sub group size 64, global mem size 67065057280
Device 2: 13th Gen Intel(R) Core(TM) i7-13700K, compute capability 3.0,
max compute_units 24, max work group size 8192, max sub group size 64, global mem size 67065057280
Device 3: Intel(R) Arc(TM) A770 Graphics, compute capability 3.0,
max compute_units 512, max work group size 1024, max sub group size 32, global mem size 16225243136
```
|Attribute|Note|
|-|-|
|compute capability 1.3|Level-zero running time, recommended |
|compute capability 3.0|OpenCL running time, slower than level-zero in most cases|
4. Set device ID and execute whisper.cpp
Set device ID = 0 by **GGML_SYCL_DEVICE=0**
```
GGML_SYCL_DEVICE=0 ./build/bin/main -m models/ggml-base.en.bin -f samples/jfk.wav
```
or run by script:
```
./examples/sycl/run_whisper.sh
```
5. Check the device ID in output
Like:
```
Using device **0** (Intel(R) Arc(TM) A770 Graphics) as main device
```
## Environment Variable
#### Build
|Name|Value|Function|
|-|-|-|
|WHISPER_SYCL|ON (mandatory)|Enable build with SYCL code path. <br>For FP32/FP16, WHISPER_SYCL=ON is mandatory.|
|WHISPER_SYCL_F16|ON (optional)|Enable FP16 build with SYCL code path.For FP32, do not set it.|
|CMAKE_C_COMPILER|icx|Use icx compiler for SYCL code path|
|CMAKE_CXX_COMPILER|icpx|use icpx for SYCL code path|
#### Running
|Name|Value|Function|
|-|-|-|
|GGML_SYCL_DEVICE|0 (default) or 1|Set the device id used. Check the device ids by default running output|
|GGML_SYCL_DEBUG|0 (default) or 1|Enable log function by macro: GGML_SYCL_DEBUG|
## Known Issue
- Error: `error while loading shared libraries: libsycl.so.7: cannot open shared object file: No such file or directory`.
Miss to enable oneAPI running environment.
Install oneAPI base toolkit and enable it by: `source /opt/intel/oneapi/setvars.sh`.
- Hang during startup
llama.cpp use mmap as default way to read model file and copy to GPU. In some system, memcpy will be abnormal and block.
Solution: add **--no-mmap**.
## Todo
- Support to build in Windows.
- Support multiple cards.

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@ -68,10 +68,6 @@ func (flags *Flags) GetOut() string {
return strings.ToLower(flags.Lookup("out").Value.String()) return strings.ToLower(flags.Lookup("out").Value.String())
} }
func (flags *Flags) IsSpeedup() bool {
return flags.Lookup("speedup").Value.String() == "true"
}
func (flags *Flags) IsTokens() bool { func (flags *Flags) IsTokens() bool {
return flags.Lookup("tokens").Value.String() == "true" return flags.Lookup("tokens").Value.String() == "true"
} }
@ -111,10 +107,6 @@ func (flags *Flags) SetParams(context whisper.Context) error {
fmt.Fprintf(flags.Output(), "Setting duration to %v\n", duration) fmt.Fprintf(flags.Output(), "Setting duration to %v\n", duration)
context.SetDuration(duration) context.SetDuration(duration)
} }
if flags.IsSpeedup() {
fmt.Fprintf(flags.Output(), "Setting speedup to true\n")
context.SetSpeedup(true)
}
if threads := flags.GetThreads(); threads != 0 { if threads := flags.GetThreads(); threads != 0 {
fmt.Fprintf(flags.Output(), "Setting threads to %d\n", threads) fmt.Fprintf(flags.Output(), "Setting threads to %d\n", threads)
context.SetThreads(threads) context.SetThreads(threads)
@ -146,7 +138,6 @@ func registerFlags(flag *Flags) {
flag.Duration("offset", 0, "Time offset") flag.Duration("offset", 0, "Time offset")
flag.Duration("duration", 0, "Duration of audio to process") flag.Duration("duration", 0, "Duration of audio to process")
flag.Uint("threads", 0, "Number of threads to use") flag.Uint("threads", 0, "Number of threads to use")
flag.Bool("speedup", false, "Enable speedup")
flag.Uint("max-len", 0, "Maximum segment length in characters") flag.Uint("max-len", 0, "Maximum segment length in characters")
flag.Uint("max-tokens", 0, "Maximum tokens per segment") flag.Uint("max-tokens", 0, "Maximum tokens per segment")
flag.Float64("word-thold", 0, "Maximum segment score") flag.Float64("word-thold", 0, "Maximum segment score")

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@ -47,10 +47,6 @@ func (p *Params) SetPrintTimestamps(v bool) {
p.print_timestamps = toBool(v) p.print_timestamps = toBool(v)
} }
func (p *Params) SetSpeedup(v bool) {
p.speed_up = toBool(v)
}
// Set language id // Set language id
func (p *Params) SetLanguage(lang int) error { func (p *Params) SetLanguage(lang int) error {
if lang == -1 { if lang == -1 {
@ -177,9 +173,6 @@ func (p *Params) String() string {
if p.token_timestamps { if p.token_timestamps {
str += " token_timestamps" str += " token_timestamps"
} }
if p.speed_up {
str += " speed_up"
}
return str + ">" return str + ">"
} }

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@ -76,11 +76,6 @@ func (context *context) SetTranslate(v bool) {
context.params.SetTranslate(v) context.params.SetTranslate(v)
} }
// Set speedup flag
func (context *context) SetSpeedup(v bool) {
context.params.SetSpeedup(v)
}
func (context *context) SetSplitOnWord(v bool) { func (context *context) SetSplitOnWord(v bool) {
context.params.SetSplitOnWord(v) context.params.SetSplitOnWord(v)
} }

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@ -41,7 +41,6 @@ type Context interface {
SetOffset(time.Duration) // Set offset SetOffset(time.Duration) // Set offset
SetDuration(time.Duration) // Set duration SetDuration(time.Duration) // Set duration
SetThreads(uint) // Set number of threads to use SetThreads(uint) // Set number of threads to use
SetSpeedup(bool) // Set speedup flag
SetSplitOnWord(bool) // Set split on word flag SetSplitOnWord(bool) // Set split on word flag
SetTokenThreshold(float32) // Set timestamp token probability threshold SetTokenThreshold(float32) // Set timestamp token probability threshold
SetTokenSumThreshold(float32) // Set timestamp token sum probability threshold SetTokenSumThreshold(float32) // Set timestamp token sum probability threshold

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@ -10,7 +10,7 @@ import (
/* /*
#cgo LDFLAGS: -lwhisper -lm -lstdc++ #cgo LDFLAGS: -lwhisper -lm -lstdc++
#cgo darwin LDFLAGS: -framework Accelerate #cgo darwin LDFLAGS: -framework Accelerate -framework Metal -framework Foundation -framework CoreGraphics
#include <whisper.h> #include <whisper.h>
#include <stdlib.h> #include <stdlib.h>

Submodule bindings/ios deleted from b21b6ff325

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@ -20,7 +20,7 @@ public interface WhisperCppJnaLibrary extends Library {
* @return Whisper context on success, null on failure * @return Whisper context on success, null on failure
*/ */
Pointer whisper_init_from_file(String path_model); Pointer whisper_init_from_file(String path_model);
/** /**
* Provides default params which can be used with `whisper_init_from_file_with_params()` etc. * Provides default params which can be used with `whisper_init_from_file_with_params()` etc.
* Because this function allocates memory for the params, the caller must call either: * Because this function allocates memory for the params, the caller must call either:
@ -304,14 +304,6 @@ public interface WhisperCppJnaLibrary extends Library {
/** Language id associated with the provided state */ /** Language id associated with the provided state */
int whisper_full_lang_id_from_state(Pointer state); int whisper_full_lang_id_from_state(Pointer state);
/**
* Convert RAW PCM audio to log mel spectrogram but applies a Phase Vocoder to speed up the audio x2.
* The resulting spectrogram is stored inside the default state of the provided whisper context.
* @return 0 on success
*/
int whisper_pcm_to_mel_phase_vocoder(Pointer ctx, final float[] samples, int n_samples, int n_threads);
int whisper_pcm_to_mel_phase_vocoder_with_state(Pointer ctx, Pointer state, final float[] samples, int n_samples, int n_threads);
/** Get the start time of the specified segment. */ /** Get the start time of the specified segment. */
long whisper_full_get_segment_t0(Pointer ctx, int i_segment); long whisper_full_get_segment_t0(Pointer ctx, int i_segment);

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@ -129,14 +129,6 @@ public class WhisperFullParams extends Structure {
/** Maximum tokens per segment (0, default = no limit) */ /** Maximum tokens per segment (0, default = no limit) */
public int max_tokens; public int max_tokens;
/** Flag to speed up the audio by 2x using Phase Vocoder. (default = false) */
public CBool speed_up;
/** Flag to speed up the audio by 2x using Phase Vocoder. (default = false) */
public void speedUp(boolean enable) {
speed_up = enable ? CBool.TRUE : CBool.FALSE;
}
/** Overwrite the audio context size (0 = use default). */ /** Overwrite the audio context size (0 = use default). */
public int audio_ctx; public int audio_ctx;
@ -148,6 +140,9 @@ public class WhisperFullParams extends Structure {
tdrz_enable = enable ? CBool.TRUE : CBool.FALSE; tdrz_enable = enable ? CBool.TRUE : CBool.FALSE;
} }
/** Regular expression matching tokens to suppress. */
public String suppress_regex;
/** Tokens to provide to the whisper decoder as an initial prompt. /** Tokens to provide to the whisper decoder as an initial prompt.
* These are prepended to any existing text context from a previous call. */ * These are prepended to any existing text context from a previous call. */
public String initial_prompt; public String initial_prompt;
@ -318,8 +313,8 @@ public class WhisperFullParams extends Structure {
return Arrays.asList("strategy", "n_threads", "n_max_text_ctx", "offset_ms", "duration_ms", "translate", return Arrays.asList("strategy", "n_threads", "n_max_text_ctx", "offset_ms", "duration_ms", "translate",
"no_context", "single_segment", "no_timestamps", "no_context", "single_segment", "no_timestamps",
"print_special", "print_progress", "print_realtime", "print_timestamps", "token_timestamps", "print_special", "print_progress", "print_realtime", "print_timestamps", "token_timestamps",
"thold_pt", "thold_ptsum", "max_len", "split_on_word", "max_tokens", "speed_up", "audio_ctx", "thold_pt", "thold_ptsum", "max_len", "split_on_word", "max_tokens", "audio_ctx",
"tdrz_enable", "initial_prompt", "prompt_tokens", "prompt_n_tokens", "language", "detect_language", "tdrz_enable", "suppress_regex", "initial_prompt", "prompt_tokens", "prompt_n_tokens", "language", "detect_language",
"suppress_blank", "suppress_non_speech_tokens", "temperature", "max_initial_ts", "length_penalty", "suppress_blank", "suppress_non_speech_tokens", "temperature", "max_initial_ts", "length_penalty",
"temperature_inc", "entropy_thold", "logprob_thold", "no_speech_thold", "greedy", "beam_search", "temperature_inc", "entropy_thold", "logprob_thold", "no_speech_thold", "greedy", "beam_search",
"new_segment_callback", "new_segment_callback_user_data", "new_segment_callback", "new_segment_callback_user_data",

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@ -1,6 +1,6 @@
{ {
"name": "whisper.cpp", "name": "whisper.cpp",
"version": "1.5.4", "version": "1.6.2",
"description": "Whisper speech recognition", "description": "Whisper speech recognition",
"main": "whisper.js", "main": "whisper.js",
"scripts": { "scripts": {

12
bindings/ruby/Rakefile Normal file
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@ -0,0 +1,12 @@
require 'rake/clean'
require 'rubygems/package'
desc 'Build gem'
task :package do
spec_source = File.read File.join(File.dirname(__FILE__),'whispercpp.gemspec')
spec = nil
# see: http://gist.github.com/16215
Thread.new { spec = eval("#{spec_source}") }.join
spec.validate
Gem::Package.build(spec)
end

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@ -1,6 +1,7 @@
require 'mkmf' require 'mkmf'
system("cp #{File.join(File.dirname(__FILE__),'..','..','..','whisper.cpp')} .") system("cp #{File.join(File.dirname(__FILE__),'..','..','..','whisper.cpp')} .")
system("cp #{File.join(File.dirname(__FILE__),'..','..','..','whisper.h')} .") system("cp #{File.join(File.dirname(__FILE__),'..','..','..','whisper.h')} .")
system("cp #{File.join(File.dirname(__FILE__),'..','..','..','whisper-mel.hpp')} .")
system("cp #{File.join(File.dirname(__FILE__),'..','..','..','ggml.h')} .") system("cp #{File.join(File.dirname(__FILE__),'..','..','..','ggml.h')} .")
system("cp #{File.join(File.dirname(__FILE__),'..','..','..','ggml.c')} .") system("cp #{File.join(File.dirname(__FILE__),'..','..','..','ggml.c')} .")
system("cp #{File.join(File.dirname(__FILE__),'..','..','..','ggml-impl.h')} .") system("cp #{File.join(File.dirname(__FILE__),'..','..','..','ggml-impl.h')} .")
@ -9,6 +10,7 @@ system("cp #{File.join(File.dirname(__FILE__),'..','..','..','ggml-alloc.c')} ."
system("cp #{File.join(File.dirname(__FILE__),'..','..','..','ggml-backend-impl.h')} .") system("cp #{File.join(File.dirname(__FILE__),'..','..','..','ggml-backend-impl.h')} .")
system("cp #{File.join(File.dirname(__FILE__),'..','..','..','ggml-backend.h')} .") system("cp #{File.join(File.dirname(__FILE__),'..','..','..','ggml-backend.h')} .")
system("cp #{File.join(File.dirname(__FILE__),'..','..','..','ggml-backend.c')} .") system("cp #{File.join(File.dirname(__FILE__),'..','..','..','ggml-backend.c')} .")
system("cp #{File.join(File.dirname(__FILE__),'..','..','..','ggml-common.h')} .")
system("cp #{File.join(File.dirname(__FILE__),'..','..','..','ggml-quants.h')} .") system("cp #{File.join(File.dirname(__FILE__),'..','..','..','ggml-quants.h')} .")
system("cp #{File.join(File.dirname(__FILE__),'..','..','..','ggml-quants.c')} .") system("cp #{File.join(File.dirname(__FILE__),'..','..','..','ggml-quants.c')} .")
system("cp #{File.join(File.dirname(__FILE__),'..','..','..','examples','dr_wav.h')} .") system("cp #{File.join(File.dirname(__FILE__),'..','..','..','examples','dr_wav.h')} .")

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@ -12,31 +12,63 @@ extern "C" {
// Backend buffer // Backend buffer
// //
// buffer type
typedef void * ggml_backend_buffer_type_context_t;
struct ggml_backend_buffer_type_i {
const char * (*GGML_CALL get_name) (ggml_backend_buffer_type_t buft);
ggml_backend_buffer_t (*GGML_CALL alloc_buffer) (ggml_backend_buffer_type_t buft, size_t size);
size_t (*GGML_CALL get_alignment) (ggml_backend_buffer_type_t buft); // tensor alignment
size_t (*GGML_CALL get_max_size) (ggml_backend_buffer_type_t buft); // allocation max size
size_t (*GGML_CALL get_alloc_size) (ggml_backend_buffer_type_t buft, const struct ggml_tensor * tensor); // data size needed to allocate the tensor, including padding
bool (*GGML_CALL supports_backend)(ggml_backend_buffer_type_t buft, ggml_backend_t backend); // check if the buffer type is usable by the backend
// check if tensor data is in host memory
// should be equivalent to supports_backend(buft, ggml_backend_cpu_init())
bool (*GGML_CALL is_host) (ggml_backend_buffer_type_t buft);
};
struct ggml_backend_buffer_type {
struct ggml_backend_buffer_type_i iface;
ggml_backend_buffer_type_context_t context;
};
// buffer
typedef void * ggml_backend_buffer_context_t; typedef void * ggml_backend_buffer_context_t;
struct ggml_backend_buffer_i { struct ggml_backend_buffer_i {
void (*free_buffer) (ggml_backend_buffer_t buffer); const char * (*GGML_CALL get_name) (ggml_backend_buffer_t buffer);
void * (*get_base) (ggml_backend_buffer_t buffer); // get base pointer void (*GGML_CALL free_buffer)(ggml_backend_buffer_t buffer);
size_t (*get_alloc_size)(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); // pre-allocation callback void * (*GGML_CALL get_base) (ggml_backend_buffer_t buffer);
void (*init_tensor) (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); // post-allocation callback void (*GGML_CALL init_tensor)(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
void (*free_tensor) (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); // pre-free callback void (*GGML_CALL set_tensor) (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
void (*GGML_CALL get_tensor) (ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);
bool (*GGML_CALL cpy_tensor) (ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst); // dst is in the buffer, src may be in any buffer
void (*GGML_CALL clear) (ggml_backend_buffer_t buffer, uint8_t value);
void (*GGML_CALL reset) (ggml_backend_buffer_t buffer); // reset any internal state due to tensor initialization, such as tensor extras
}; };
struct ggml_backend_buffer { struct ggml_backend_buffer {
struct ggml_backend_buffer_i iface; struct ggml_backend_buffer_i iface;
ggml_backend_buffer_type_t buft;
ggml_backend_t backend;
ggml_backend_buffer_context_t context; ggml_backend_buffer_context_t context;
size_t size; size_t size;
enum ggml_backend_buffer_usage usage;
}; };
GGML_API ggml_backend_buffer_t ggml_backend_buffer_init( GGML_CALL ggml_backend_buffer_t ggml_backend_buffer_init(
struct ggml_backend * backend, ggml_backend_buffer_type_t buft,
struct ggml_backend_buffer_i iface, struct ggml_backend_buffer_i iface,
ggml_backend_buffer_context_t context, ggml_backend_buffer_context_t context,
size_t size); size_t size);
// do not use directly, use ggml_backend_tensor_copy instead
bool ggml_backend_buffer_copy_tensor(const struct ggml_tensor * src, struct ggml_tensor * dst);
// buffer that contains a collection of buffers
GGML_CALL ggml_backend_buffer_t ggml_backend_multi_buffer_alloc_buffer(ggml_backend_buffer_t * buffers, size_t n_buffers);
GGML_CALL bool ggml_backend_buffer_is_multi_buffer(ggml_backend_buffer_t buffer);
GGML_CALL void ggml_backend_multi_buffer_set_usage(ggml_backend_buffer_t buffer, enum ggml_backend_buffer_usage usage);
// //
// Backend // Backend
// //
@ -44,44 +76,66 @@ extern "C" {
typedef void * ggml_backend_context_t; typedef void * ggml_backend_context_t;
struct ggml_backend_i { struct ggml_backend_i {
const char * (*get_name)(ggml_backend_t backend); const char * (*GGML_CALL get_name)(ggml_backend_t backend);
void (*free)(ggml_backend_t backend); void (*GGML_CALL free)(ggml_backend_t backend);
// buffer allocation // buffer allocation
ggml_backend_buffer_t (*alloc_buffer)(ggml_backend_t backend, size_t size); ggml_backend_buffer_type_t (*GGML_CALL get_default_buffer_type)(ggml_backend_t backend);
// get buffer alignment // (optional) asynchronous tensor data access
size_t (*get_alignment)(ggml_backend_t backend); void (*GGML_CALL set_tensor_async)(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
void (*GGML_CALL get_tensor_async)(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);
bool (*GGML_CALL cpy_tensor_async)(ggml_backend_t backend_src, ggml_backend_t backend_dst, const struct ggml_tensor * src, struct ggml_tensor * dst);
// tensor data access // (optional) complete all pending operations
// these functions can be asynchronous, helper functions are provided for synchronous access that automatically call synchronize void (*GGML_CALL synchronize)(ggml_backend_t backend);
void (*set_tensor_async)(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
void (*get_tensor_async)(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);
void (*synchronize) (ggml_backend_t backend);
// (optional) copy tensor between different backends, allow for single-copy tranfers // compute graph with a plan (not used currently)
void (*cpy_tensor_from)(ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst); ggml_backend_graph_plan_t (*GGML_CALL graph_plan_create) (ggml_backend_t backend, const struct ggml_cgraph * cgraph);
void (*cpy_tensor_to) (ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst); void (*GGML_CALL graph_plan_free) (ggml_backend_t backend, ggml_backend_graph_plan_t plan);
// compute graph with a plan // compute graph with a plan
ggml_backend_graph_plan_t (*graph_plan_create) (ggml_backend_t backend, struct ggml_cgraph * cgraph); enum ggml_status (*GGML_CALL graph_plan_compute)(ggml_backend_t backend, ggml_backend_graph_plan_t plan);
void (*graph_plan_free) (ggml_backend_t backend, ggml_backend_graph_plan_t plan); // compute graph without a plan (async)
void (*graph_plan_compute)(ggml_backend_t backend, ggml_backend_graph_plan_t plan); enum ggml_status (*GGML_CALL graph_compute) (ggml_backend_t backend, struct ggml_cgraph * cgraph);
// compute graph without a plan
bool (*graph_compute)(ggml_backend_t backend, struct ggml_cgraph * cgraph);
// check if the backend supports an operation // check if the backend supports an operation
bool (*supports_op)(ggml_backend_t backend, const struct ggml_tensor * op); bool (*GGML_CALL supports_op)(ggml_backend_t backend, const struct ggml_tensor * op);
// check if the backend wants to run an operation, even if the weights are allocated in a CPU buffer
// these should be expensive operations with large batch sizes that may benefit from running on this backend
// even if the weight has to be copied from the CPU temporarily
bool (*GGML_CALL offload_op)(ggml_backend_t backend, const struct ggml_tensor * op);
// (optional) event synchronization
ggml_backend_event_t (*GGML_CALL event_new) (ggml_backend_t backend);
void (*GGML_CALL event_free) (ggml_backend_event_t event);
void (*GGML_CALL event_record) (ggml_backend_event_t event);
void (*GGML_CALL event_wait) (ggml_backend_t backend, ggml_backend_event_t event);
void (*GGML_CALL event_synchronize) (ggml_backend_event_t event);
}; };
struct ggml_backend { struct ggml_backend {
struct ggml_backend_i iface; ggml_guid_t guid;
struct ggml_backend_i iface;
ggml_backend_context_t context; ggml_backend_context_t context;
}; };
struct ggml_backend_event {
ggml_backend_t backend;
void * context;
};
//
// Backend registry
//
typedef ggml_backend_t (*GGML_CALL ggml_backend_init_fn)(const char * params, void * user_data);
GGML_CALL void ggml_backend_register(const char * name, ggml_backend_init_fn init_fn, ggml_backend_buffer_type_t default_buffer_type, void * user_data);
#ifdef __cplusplus #ifdef __cplusplus
} }
#endif #endif

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@ -7,69 +7,123 @@
extern "C" { extern "C" {
#endif #endif
typedef struct ggml_backend_buffer_type * ggml_backend_buffer_type_t;
typedef struct ggml_backend_buffer * ggml_backend_buffer_t;
typedef struct ggml_backend_event * ggml_backend_event_t;
typedef struct ggml_backend * ggml_backend_t;
typedef void * ggml_backend_graph_plan_t;
// //
// Backend buffer // Backend buffer
// //
struct ggml_backend_buffer; // buffer type
typedef struct ggml_backend_buffer * ggml_backend_buffer_t; GGML_API const char * ggml_backend_buft_name (ggml_backend_buffer_type_t buft);
GGML_API GGML_CALL ggml_backend_buffer_t ggml_backend_buft_alloc_buffer (ggml_backend_buffer_type_t buft, size_t size);
GGML_API size_t ggml_backend_buft_get_alignment (ggml_backend_buffer_type_t buft);
GGML_API size_t ggml_backend_buft_get_max_size (ggml_backend_buffer_type_t buft);
GGML_API GGML_CALL size_t ggml_backend_buft_get_alloc_size (ggml_backend_buffer_type_t buft, struct ggml_tensor * tensor);
GGML_API bool ggml_backend_buft_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend);
GGML_API bool ggml_backend_buft_is_host (ggml_backend_buffer_type_t buft);
// backend buffer functions // buffer
GGML_API void ggml_backend_buffer_free (ggml_backend_buffer_t buffer); enum ggml_backend_buffer_usage {
GGML_API size_t ggml_backend_buffer_get_alignment (ggml_backend_buffer_t buffer); GGML_BACKEND_BUFFER_USAGE_ANY = 0,
GGML_API void * ggml_backend_buffer_get_base (ggml_backend_buffer_t buffer); GGML_BACKEND_BUFFER_USAGE_WEIGHTS = 1,
GGML_API size_t ggml_backend_buffer_get_size (ggml_backend_buffer_t buffer); };
GGML_API size_t ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
GGML_API void ggml_backend_buffer_init_tensor (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); GGML_API const char * ggml_backend_buffer_name (ggml_backend_buffer_t buffer);
GGML_API void ggml_backend_buffer_free_tensor (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); GGML_API void ggml_backend_buffer_free (ggml_backend_buffer_t buffer);
GGML_API void * ggml_backend_buffer_get_base (ggml_backend_buffer_t buffer);
GGML_API size_t ggml_backend_buffer_get_size (ggml_backend_buffer_t buffer);
GGML_API GGML_CALL void ggml_backend_buffer_init_tensor (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
GGML_API size_t ggml_backend_buffer_get_alignment (ggml_backend_buffer_t buffer);
GGML_API size_t ggml_backend_buffer_get_max_size (ggml_backend_buffer_t buffer);
GGML_API size_t ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
GGML_API void ggml_backend_buffer_clear (ggml_backend_buffer_t buffer, uint8_t value);
GGML_API bool ggml_backend_buffer_is_host (ggml_backend_buffer_t buffer);
GGML_API void ggml_backend_buffer_set_usage (ggml_backend_buffer_t buffer, enum ggml_backend_buffer_usage usage);
GGML_API ggml_backend_buffer_type_t ggml_backend_buffer_get_type (ggml_backend_buffer_t buffer);
GGML_API void ggml_backend_buffer_reset (ggml_backend_buffer_t buffer);
// //
// Backend // Backend
// //
struct ggml_backend; GGML_API ggml_guid_t ggml_backend_guid(ggml_backend_t backend);
typedef struct ggml_backend * ggml_backend_t;
typedef void * ggml_backend_graph_plan_t;
GGML_API ggml_backend_t ggml_get_backend(const struct ggml_tensor * tensor);
GGML_API const char * ggml_backend_name(ggml_backend_t backend); GGML_API const char * ggml_backend_name(ggml_backend_t backend);
GGML_API void ggml_backend_free(ggml_backend_t backend); GGML_API void ggml_backend_free(ggml_backend_t backend);
GGML_API ggml_backend_buffer_t ggml_backend_alloc_buffer(ggml_backend_t backend, size_t size); GGML_API ggml_backend_buffer_type_t ggml_backend_get_default_buffer_type(ggml_backend_t backend);
GGML_API ggml_backend_buffer_t ggml_backend_alloc_buffer(ggml_backend_t backend, size_t size);
GGML_API size_t ggml_backend_get_alignment(ggml_backend_t backend);
GGML_API size_t ggml_backend_get_max_size(ggml_backend_t backend);
GGML_API size_t ggml_backend_get_alignment(ggml_backend_t backend); GGML_API void ggml_backend_tensor_set_async(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
GGML_API void ggml_backend_tensor_get_async(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);
GGML_API void ggml_backend_tensor_set_async( struct ggml_tensor * tensor, const void * data, size_t offset, size_t size); GGML_API GGML_CALL void ggml_backend_tensor_set( struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
GGML_API void ggml_backend_tensor_get_async(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size); GGML_API GGML_CALL void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);
GGML_API void ggml_backend_tensor_set( struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
GGML_API void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);
GGML_API void ggml_backend_synchronize(ggml_backend_t backend); GGML_API void ggml_backend_synchronize(ggml_backend_t backend);
GGML_API ggml_backend_graph_plan_t ggml_backend_graph_plan_create (ggml_backend_t backend, struct ggml_cgraph * cgraph); GGML_API ggml_backend_graph_plan_t ggml_backend_graph_plan_create(ggml_backend_t backend, struct ggml_cgraph * cgraph);
GGML_API void ggml_backend_graph_plan_free (ggml_backend_t backend, ggml_backend_graph_plan_t plan);
GGML_API void ggml_backend_graph_plan_free (ggml_backend_t backend, ggml_backend_graph_plan_t plan); GGML_API enum ggml_status ggml_backend_graph_plan_compute (ggml_backend_t backend, ggml_backend_graph_plan_t plan);
GGML_API void ggml_backend_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan); GGML_API enum ggml_status ggml_backend_graph_compute (ggml_backend_t backend, struct ggml_cgraph * cgraph);
GGML_API bool ggml_backend_graph_compute (ggml_backend_t backend, struct ggml_cgraph * cgraph); GGML_API enum ggml_status ggml_backend_graph_compute_async(ggml_backend_t backend, struct ggml_cgraph * cgraph);
GGML_API bool ggml_backend_supports_op (ggml_backend_t backend, const struct ggml_tensor * op); GGML_API bool ggml_backend_supports_op(ggml_backend_t backend, const struct ggml_tensor * op);
GGML_API bool ggml_backend_offload_op(ggml_backend_t backend, const struct ggml_tensor * op);
// tensor copy between different backends // tensor copy between different backends
GGML_API void ggml_backend_tensor_copy(struct ggml_tensor * src, struct ggml_tensor * dst); GGML_API void ggml_backend_tensor_copy(struct ggml_tensor * src, struct ggml_tensor * dst);
// asynchronous copy
// the copy is performed after all the currently queued operations in backend_src
// backend_dst will wait for the copy to complete before performing other operations
// automatic fallback to sync copy if async is not supported
GGML_API void ggml_backend_tensor_copy_async(ggml_backend_t backend_src, ggml_backend_t backend_dst, struct ggml_tensor * src, struct ggml_tensor * dst);
// events
GGML_API ggml_backend_event_t ggml_backend_event_new (ggml_backend_t backend);
GGML_API void ggml_backend_event_free (ggml_backend_event_t event);
GGML_API void ggml_backend_event_record (ggml_backend_event_t event);
GGML_API void ggml_backend_event_synchronize(ggml_backend_event_t event);
GGML_API void ggml_backend_event_wait (ggml_backend_t backend, ggml_backend_event_t event); // wait async on event
// //
// CPU backend // CPU backend
// //
GGML_API ggml_backend_t ggml_backend_cpu_init(void); GGML_API ggml_backend_t ggml_backend_cpu_init(void);
GGML_API bool ggml_backend_is_cpu(ggml_backend_t backend); GGML_API GGML_CALL bool ggml_backend_is_cpu (ggml_backend_t backend);
GGML_API void ggml_backend_cpu_set_n_threads(ggml_backend_t backend_cpu, int n_threads); GGML_API void ggml_backend_cpu_set_n_threads (ggml_backend_t backend_cpu, int n_threads);
GGML_API void ggml_backend_cpu_set_abort_callback(ggml_backend_t backend_cpu, ggml_abort_callback abort_callback, void * abort_callback_data);
// Create a backend buffer from an existing pointer // Create a backend buffer from an existing pointer
GGML_API ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(ggml_backend_t backend_cpu, void * ptr, size_t size); GGML_API GGML_CALL ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(void * ptr, size_t size);
GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void);
#ifdef GGML_USE_CPU_HBM
GGML_API ggml_backend_buffer_type_t ggml_backend_cpu_hbm_buffer_type(void);
#endif
//
// Backend registry
//
// The backend registry is a registry of all the available backends, and allows initializing backends in a generic way
GGML_API size_t ggml_backend_reg_get_count(void);
GGML_API size_t ggml_backend_reg_find_by_name(const char * name);
GGML_API ggml_backend_t ggml_backend_reg_init_backend_from_str(const char * backend_str); // str is name[:params]
GGML_API const char * ggml_backend_reg_get_name(size_t i);
GGML_API ggml_backend_t ggml_backend_reg_init_backend(size_t i, const char * params); // params is backend-specific
GGML_API ggml_backend_buffer_type_t ggml_backend_reg_get_default_buffer_type(size_t i);
GGML_API ggml_backend_buffer_t ggml_backend_reg_alloc_buffer(size_t i, size_t size);
// //
// Backend scheduler // Backend scheduler
@ -83,53 +137,96 @@ extern "C" {
/* /*
Example usage: Example usage:
sched = ggml_backend_sched_new({backend_gpu, backend_gpu2, backend_cpu}, num_backends); // operations that use tensors allocated in a buffer with USAGE_WEIGHTS will be assigned
// sched is initialized with measure allocators and cannot be used until allocated with a measure graph // preferrably to run on the same backend as the buffer
ggml_backend_buffer_set_usage(buf_weights, GGML_BACKEND_BUFFER_USAGE_WEIGHTS);
// initialize buffers from a measure graph sched = ggml_backend_sched_new({backend_gpu, backend_gpu2, backend_cpu}, NULL, num_backends, GGML_DEFAULT_GRAPH_SIZE, false);
measure_graph = build_graph(sched); // use the allocr to allocate inputs as needed
// in build_graph: // initialize buffers from a max size graph (optional)
build_graph(...) { reserve_graph = build_graph(sched, max_batch_size);
// allocating tensors in a specific backend (optional, recommended: pre-allocate inputs in a different buffer)
alloc_cpu = ggml_backend_sched_get_allocr(sched, backend_cpu);
ggml_allocr_alloc(alloc_cpu, tensor);
// manually assigning nodes to a backend (optional, shouldn't be needed in most cases) // manually assign nodes to a backend (optional, should not be needed in most cases)
struct ggml_tensor * node = ggml_mul_mat(ctx, ...); struct ggml_tensor * node = ggml_mul_mat(ctx, ...);
ggml_backend_sched_set_node_backend(sched, node, backend_gpu); ggml_backend_sched_set_tensor_backend(sched, node, backend_gpu);
}
// allocate backend buffers from measure graph ggml_backend_sched_reserve(sched, reserve_graph);
ggml_backend_sched_init_measure(sched, measure_graph);
// the scheduler is now ready to compute graphs
// compute // compute
graph = build_graph(sched); graph = build_graph(sched);
ggml_backend_sched_graph_compute(sched, graph); ggml_backend_sched_graph_compute(sched, graph);
// if there are graph inputs:
ggml_backend_sched_reset(sched);
ggml_backend_sched_alloc_graph(sched, graph);
ggml_backend_tensor_set(input_tensor, ...);
ggml_backend_sched_graph_compute(sched, graph);
}
*/ */
struct ggml_backend_sched; struct ggml_backend_sched;
typedef struct ggml_backend_sched * ggml_backend_sched_t; typedef struct ggml_backend_sched * ggml_backend_sched_t;
// Initialize a backend scheduler // when ask == true, the scheduler wants to know if the user wants to observe this node
GGML_API ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, int n_backends); // this allows the scheduler to batch nodes together in order to evaluate them in a single call
//
// when ask == false, the scheduler is passing the node tensor to the user for observation
// if the user returns false, the scheduler will cancel the graph compute
//
typedef bool (*ggml_backend_sched_eval_callback)(struct ggml_tensor * t, bool ask, void * user_data);
GGML_API void ggml_backend_sched_free(ggml_backend_sched_t sched); // Initialize a backend scheduler
GGML_API ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, ggml_backend_buffer_type_t * bufts, int n_backends, size_t graph_size, bool parallel);
GGML_API void ggml_backend_sched_free(ggml_backend_sched_t sched);
// Initialize backend buffers from a measure graph // Initialize backend buffers from a measure graph
GGML_API void ggml_backend_sched_init_measure(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph); GGML_API bool ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph);
GGML_API ggml_tallocr_t ggml_backend_sched_get_tallocr(ggml_backend_sched_t sched, ggml_backend_t backend); // Get the number of splits of the last graph
GGML_API ggml_backend_buffer_t ggml_backend_sched_get_buffer (ggml_backend_sched_t sched, ggml_backend_t backend); GGML_API int ggml_backend_sched_get_n_splits(ggml_backend_sched_t sched);
GGML_API int ggml_backend_sched_get_n_copies(ggml_backend_sched_t sched);
GGML_API void ggml_backend_sched_set_node_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend); GGML_API size_t ggml_backend_sched_get_buffer_size(ggml_backend_sched_t sched, ggml_backend_t backend);
GGML_API void ggml_backend_sched_set_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend);
GGML_API ggml_backend_t ggml_backend_sched_get_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node);
// Allocate and compute graph on the backend scheduler
GGML_API bool ggml_backend_sched_alloc_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph);
GGML_API enum ggml_status ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, struct ggml_cgraph * graph);
GGML_API enum ggml_status ggml_backend_sched_graph_compute_async(ggml_backend_sched_t sched, struct ggml_cgraph * graph);
GGML_API void ggml_backend_sched_synchronize(ggml_backend_sched_t sched);
// Reset all assignments and allocators - must be called before changing the node backends
GGML_API void ggml_backend_sched_reset(ggml_backend_sched_t sched);
// Set a callback to be called for each resulting node during graph compute
GGML_API void ggml_backend_sched_set_eval_callback(ggml_backend_sched_t sched, ggml_backend_sched_eval_callback callback, void * user_data);
//
// Utils
//
struct ggml_backend_graph_copy {
ggml_backend_buffer_t buffer;
struct ggml_context * ctx_allocated;
struct ggml_context * ctx_unallocated;
struct ggml_cgraph * graph;
};
// Copy a graph to a different backend
GGML_API struct ggml_backend_graph_copy ggml_backend_graph_copy(ggml_backend_t backend, struct ggml_cgraph * graph);
GGML_API void ggml_backend_graph_copy_free(struct ggml_backend_graph_copy copy);
typedef bool (*GGML_CALL ggml_backend_eval_callback)(int node_index, struct ggml_tensor * t1, struct ggml_tensor * t2, void * user_data);
// Compare the output of two backends
GGML_API bool ggml_backend_compare_graph_backend(ggml_backend_t backend1, ggml_backend_t backend2, struct ggml_cgraph * graph, ggml_backend_eval_callback callback, void * user_data);
// Tensor initialization
GGML_API void ggml_backend_tensor_alloc(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, void * addr);
GGML_API void ggml_backend_view_init(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
// Allocate a graph on the backend scheduler
GGML_API void ggml_backend_sched_graph_compute(
ggml_backend_sched_t sched,
struct ggml_cgraph * graph);
#ifdef __cplusplus #ifdef __cplusplus
} }

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@ -17,29 +17,17 @@ extern "C" {
#define GGML_CUDA_MAX_DEVICES 16 #define GGML_CUDA_MAX_DEVICES 16
// Always success. To check if CUDA is actually loaded, use `ggml_cublas_loaded`.
GGML_API GGML_CALL void ggml_init_cublas(void);
// Returns `true` if there are available CUDA devices and cublas loads successfully; otherwise, it returns `false`.
GGML_API GGML_CALL bool ggml_cublas_loaded(void);
GGML_API GGML_CALL void * ggml_cuda_host_malloc(size_t size);
GGML_API GGML_CALL void ggml_cuda_host_free(void * ptr);
GGML_API GGML_CALL bool ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst);
GGML_API GGML_CALL bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor);
GGML_API GGML_CALL int ggml_cuda_get_device_count(void);
GGML_API GGML_CALL void ggml_cuda_get_device_description(int device, char * description, size_t description_size);
// backend API // backend API
GGML_API GGML_CALL ggml_backend_t ggml_backend_cuda_init(int device); GGML_API GGML_CALL ggml_backend_t ggml_backend_cuda_init(int device);
GGML_API GGML_CALL bool ggml_backend_is_cuda(ggml_backend_t backend); GGML_API GGML_CALL bool ggml_backend_is_cuda(ggml_backend_t backend);
// device buffer
GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device); GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device);
// split tensor buffer that splits matrices by rows across multiple devices // split tensor buffer that splits matrices by rows across multiple devices
GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(const float * tensor_split); GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_split_buffer_type(const float * tensor_split);
// pinned host buffer for use with the CPU backend for faster copies between CPU and GPU // pinned host buffer for use with the CPU backend for faster copies between CPU and GPU
GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type(void); GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type(void);
@ -47,6 +35,9 @@ GGML_API GGML_CALL int ggml_backend_cuda_get_device_count(void);
GGML_API GGML_CALL void ggml_backend_cuda_get_device_description(int device, char * description, size_t description_size); GGML_API GGML_CALL void ggml_backend_cuda_get_device_description(int device, char * description, size_t description_size);
GGML_API GGML_CALL void ggml_backend_cuda_get_device_memory(int device, size_t * free, size_t * total); GGML_API GGML_CALL void ggml_backend_cuda_get_device_memory(int device, size_t * free, size_t * total);
GGML_API GGML_CALL bool ggml_backend_cuda_register_host_buffer(void * buffer, size_t size);
GGML_API GGML_CALL void ggml_backend_cuda_unregister_host_buffer(void * buffer);
#ifdef __cplusplus #ifdef __cplusplus
} }
#endif #endif

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@ -5,6 +5,7 @@
// GGML internal header // GGML internal header
#include <assert.h> #include <assert.h>
#include <stdlib.h> // load `stdlib.h` before other headers to work around MinGW bug: https://sourceforge.net/p/mingw-w64/bugs/192/
#include <stddef.h> #include <stddef.h>
#include <stdbool.h> #include <stdbool.h>
#include <string.h> // memcpy #include <string.h> // memcpy
@ -18,6 +19,7 @@ extern "C" {
// fall back to the _Static_assert C11 keyword. // fall back to the _Static_assert C11 keyword.
// if C99 - static_assert is noop // if C99 - static_assert is noop
// ref: https://stackoverflow.com/a/53923785/4039976 // ref: https://stackoverflow.com/a/53923785/4039976
#ifndef __cplusplus
#ifndef static_assert #ifndef static_assert
#if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 201100L) #if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 201100L)
#define static_assert(cond, msg) _Static_assert(cond, msg) #define static_assert(cond, msg) _Static_assert(cond, msg)
@ -25,6 +27,7 @@ extern "C" {
#define static_assert(cond, msg) struct global_scope_noop_trick #define static_assert(cond, msg) struct global_scope_noop_trick
#endif #endif
#endif #endif
#endif
// __FMA__ and __F16C__ are not defined in MSVC, however they are implied with AVX2/AVX512 // __FMA__ and __F16C__ are not defined in MSVC, however they are implied with AVX2/AVX512
#if defined(_MSC_VER) && (defined(__AVX2__) || defined(__AVX512F__)) #if defined(_MSC_VER) && (defined(__AVX2__) || defined(__AVX512F__))
@ -34,16 +37,17 @@ extern "C" {
#ifndef __F16C__ #ifndef __F16C__
#define __F16C__ #define __F16C__
#endif #endif
#endif
// __SSE3__ and __SSSE3__ are not defined in MSVC, but SSE3/SSSE3 are present when AVX/AVX2/AVX512 are available
#if defined(_MSC_VER) && (defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__))
#ifndef __SSE3__ #ifndef __SSE3__
#define __SSE3__ #define __SSE3__
#endif #endif
#ifndef __SSSE3__
#define __SSSE3__
#endif
#endif #endif
#undef MIN
#undef MAX
#define MIN(a, b) ((a) < (b) ? (a) : (b))
#define MAX(a, b) ((a) > (b) ? (a) : (b))
// 16-bit float // 16-bit float
// on Arm, we use __fp16 // on Arm, we use __fp16
@ -56,14 +60,30 @@ extern "C" {
// //
#include <arm_neon.h> #include <arm_neon.h>
#define GGML_COMPUTE_FP16_TO_FP32(x) ((float) (x)) typedef __fp16 ggml_fp16_internal_t;
#define GGML_COMPUTE_FP32_TO_FP16(x) (x)
#define GGML_FP16_TO_FP32(x) ((float) (x)) #define GGML_COMPUTE_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x)
#define GGML_FP32_TO_FP16(x) (x) #define GGML_COMPUTE_FP32_TO_FP16(x) ggml_compute_fp32_to_fp16(x)
#define GGML_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x)
static inline float ggml_compute_fp16_to_fp32(ggml_fp16_t h) {
ggml_fp16_internal_t tmp;
memcpy(&tmp, &h, sizeof(ggml_fp16_t));
return (float)tmp;
}
static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) {
ggml_fp16_t res;
ggml_fp16_internal_t tmp = f;
memcpy(&res, &tmp, sizeof(ggml_fp16_t));
return res;
}
#else #else
typedef uint16_t ggml_fp16_internal_t;
#ifdef __wasm_simd128__ #ifdef __wasm_simd128__
#include <wasm_simd128.h> #include <wasm_simd128.h>
#else #else
@ -217,8 +237,7 @@ extern float ggml_table_f32_f16[1 << 16];
// On ARM NEON, it's quicker to directly convert x -> x instead of calling into ggml_lookup_fp16_to_fp32, // On ARM NEON, it's quicker to directly convert x -> x instead of calling into ggml_lookup_fp16_to_fp32,
// so we define GGML_FP16_TO_FP32 and GGML_FP32_TO_FP16 elsewhere for NEON. // so we define GGML_FP16_TO_FP32 and GGML_FP32_TO_FP16 elsewhere for NEON.
// This is also true for POWER9. // This is also true for POWER9.
#if !defined(GGML_FP16_TO_FP32) || !defined(GGML_FP32_TO_FP16) #if !defined(GGML_FP16_TO_FP32)
inline static float ggml_lookup_fp16_to_fp32(ggml_fp16_t f) { inline static float ggml_lookup_fp16_to_fp32(ggml_fp16_t f) {
uint16_t s; uint16_t s;
memcpy(&s, &f, sizeof(uint16_t)); memcpy(&s, &f, sizeof(uint16_t));
@ -226,19 +245,23 @@ inline static float ggml_lookup_fp16_to_fp32(ggml_fp16_t f) {
} }
#define GGML_FP16_TO_FP32(x) ggml_lookup_fp16_to_fp32(x) #define GGML_FP16_TO_FP32(x) ggml_lookup_fp16_to_fp32(x)
#define GGML_FP32_TO_FP16(x) GGML_COMPUTE_FP32_TO_FP16(x) #endif
#if !defined(GGML_FP32_TO_FP16)
#define GGML_FP32_TO_FP16(x) GGML_COMPUTE_FP32_TO_FP16(x)
#endif #endif
#define GGML_HASHTABLE_FULL ((size_t)-1) #define GGML_HASHTABLE_FULL ((size_t)-1)
#define GGML_HASHTABLE_ALREADY_EXISTS ((size_t)-2) #define GGML_HASHTABLE_ALREADY_EXISTS ((size_t)-2)
struct ggml_hash_set ggml_hash_set_new(size_t size);
bool ggml_hash_contains (const struct ggml_hash_set hash_set, struct ggml_tensor * key); bool ggml_hash_contains (const struct ggml_hash_set hash_set, struct ggml_tensor * key);
// returns GGML_HASHTABLE_FULL if table is full, otherwise the current index of the key or where it should be inserted // returns GGML_HASHTABLE_FULL if table is full, otherwise the current index of the key or where it should be inserted
size_t ggml_hash_find (const struct ggml_hash_set hash_set, struct ggml_tensor * key); size_t ggml_hash_find (const struct ggml_hash_set hash_set, struct ggml_tensor * key);
// returns GGML_HAHSHTABLE_ALREADY_EXISTS if key already exists, index otherwise, asserts if table is full // returns GGML_HASHTABLE_ALREADY_EXISTS if key already exists, index otherwise, asserts if table is full
size_t ggml_hash_insert ( struct ggml_hash_set hash_set, struct ggml_tensor * key); size_t ggml_hash_insert ( struct ggml_hash_set hash_set, struct ggml_tensor * key);
// return index, asserts if table is full // return index, asserts if table is full

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@ -1,224 +1,133 @@
#pragma once #pragma once
#include "ggml-impl.h" #define GGML_COMMON_DECL_C
#include "ggml-common.h"
#include "ggml.h"
// GGML internal header // GGML internal header
#include <stdint.h> #ifdef __cplusplus
#include <stddef.h> extern "C" {
#define QK4_0 32
typedef struct {
ggml_fp16_t d; // delta
uint8_t qs[QK4_0 / 2]; // nibbles / quants
} block_q4_0;
static_assert(sizeof(block_q4_0) == sizeof(ggml_fp16_t) + QK4_0 / 2, "wrong q4_0 block size/padding");
#define QK4_1 32
typedef struct {
ggml_fp16_t d; // delta
ggml_fp16_t m; // min
uint8_t qs[QK4_1 / 2]; // nibbles / quants
} block_q4_1;
static_assert(sizeof(block_q4_1) == 2 * sizeof(ggml_fp16_t) + QK4_1 / 2, "wrong q4_1 block size/padding");
#define QK5_0 32
typedef struct {
ggml_fp16_t d; // delta
uint8_t qh[4]; // 5-th bit of quants
uint8_t qs[QK5_0 / 2]; // nibbles / quants
} block_q5_0;
static_assert(sizeof(block_q5_0) == sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_0 / 2, "wrong q5_0 block size/padding");
#define QK5_1 32
typedef struct {
ggml_fp16_t d; // delta
ggml_fp16_t m; // min
uint8_t qh[4]; // 5-th bit of quants
uint8_t qs[QK5_1 / 2]; // nibbles / quants
} block_q5_1;
static_assert(sizeof(block_q5_1) == 2 * sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_1 / 2, "wrong q5_1 block size/padding");
#define QK8_0 32
typedef struct {
ggml_fp16_t d; // delta
int8_t qs[QK8_0]; // quants
} block_q8_0;
static_assert(sizeof(block_q8_0) == sizeof(ggml_fp16_t) + QK8_0, "wrong q8_0 block size/padding");
#define QK8_1 32
typedef struct {
float d; // delta
float s; // d * sum(qs[i])
int8_t qs[QK8_1]; // quants
} block_q8_1;
static_assert(sizeof(block_q8_1) == 2*sizeof(float) + QK8_1, "wrong q8_1 block size/padding");
//
// Super-block quantization structures
//
// Super-block size
#ifdef GGML_QKK_64
#define QK_K 64
#define K_SCALE_SIZE 4
#else
#define QK_K 256
#define K_SCALE_SIZE 12
#endif #endif
// 2-bit quantization
// weight is represented as x = a * q + b
// 16 blocks of 16 elements each
// Effectively 2.5625 bits per weight
typedef struct {
uint8_t scales[QK_K/16]; // scales and mins, quantized with 4 bits
uint8_t qs[QK_K/4]; // quants
ggml_fp16_t d; // super-block scale for quantized scales
ggml_fp16_t dmin; // super-block scale for quantized mins
} block_q2_K;
static_assert(sizeof(block_q2_K) == 2*sizeof(ggml_fp16_t) + QK_K/16 + QK_K/4, "wrong q2_K block size/padding");
// 3-bit quantization
// weight is represented as x = a * q
// 16 blocks of 16 elements each
// Effectively 3.4375 bits per weight
#ifdef GGML_QKK_64
typedef struct {
uint8_t hmask[QK_K/8]; // quants - high bit
uint8_t qs[QK_K/4]; // quants - low 2 bits
uint8_t scales[2];
ggml_fp16_t d; // super-block scale
} block_q3_K;
static_assert(sizeof(block_q3_K) == sizeof(ggml_fp16_t) + QK_K / 4 + QK_K / 8 + 2, "wrong q3_K block size/padding");
#else
typedef struct {
uint8_t hmask[QK_K/8]; // quants - high bit
uint8_t qs[QK_K/4]; // quants - low 2 bits
uint8_t scales[12]; // scales, quantized with 6 bits
ggml_fp16_t d; // super-block scale
} block_q3_K;
static_assert(sizeof(block_q3_K) == sizeof(ggml_fp16_t) + QK_K / 4 + QK_K / 8 + 12, "wrong q3_K block size/padding");
#endif
// 4-bit quantization
// 8 blocks of 32 elements each
// weight is represented as x = a * q + b
// Effectively 4.5 bits per weight
#ifdef GGML_QKK_64
typedef struct {
ggml_fp16_t d[2]; // super-block scales/mins
uint8_t scales[2]; // 4-bit block scales/mins
uint8_t qs[QK_K/2]; // 4--bit quants
} block_q4_K;
static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_fp16_t) + QK_K/2 + 2, "wrong q4_K block size/padding");
#else
typedef struct {
ggml_fp16_t d; // super-block scale for quantized scales
ggml_fp16_t dmin; // super-block scale for quantized mins
uint8_t scales[K_SCALE_SIZE]; // scales and mins, quantized with 6 bits
uint8_t qs[QK_K/2]; // 4--bit quants
} block_q4_K;
static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_fp16_t) + K_SCALE_SIZE + QK_K/2, "wrong q4_K block size/padding");
#endif
// 5-bit quantization
// 8 blocks of 32 elements each
// weight is represented as x = a * q + b
// Effectively 5.5 bits per weight
#ifdef GGML_QKK_64
typedef struct {
ggml_fp16_t d; // super-block scale
int8_t scales[QK_K/16]; // 8-bit block scales
uint8_t qh[QK_K/8]; // quants, high bit
uint8_t qs[QK_K/2]; // quants, low 4 bits
} block_q5_K;
static_assert(sizeof(block_q5_K) == sizeof(ggml_fp16_t) + QK_K/2 + QK_K/8 + QK_K/16, "wrong q5_K block size/padding");
#else
typedef struct {
ggml_fp16_t d; // super-block scale for quantized scales
ggml_fp16_t dmin; // super-block scale for quantized mins
uint8_t scales[K_SCALE_SIZE]; // scales and mins, quantized with 6 bits
uint8_t qh[QK_K/8]; // quants, high bit
uint8_t qs[QK_K/2]; // quants, low 4 bits
} block_q5_K;
static_assert(sizeof(block_q5_K) == 2*sizeof(ggml_fp16_t) + K_SCALE_SIZE + QK_K/2 + QK_K/8, "wrong q5_K block size/padding");
#endif
// 6-bit quantization
// weight is represented as x = a * q
// 16 blocks of 16 elements each
// Effectively 6.5625 bits per weight
typedef struct {
uint8_t ql[QK_K/2]; // quants, lower 4 bits
uint8_t qh[QK_K/4]; // quants, upper 2 bits
int8_t scales[QK_K/16]; // scales, quantized with 8 bits
ggml_fp16_t d; // super-block scale
} block_q6_K;
static_assert(sizeof(block_q6_K) == sizeof(ggml_fp16_t) + QK_K / 16 + 3*QK_K/4, "wrong q6_K block size/padding");
// This is only used for intermediate quantization and dot products
typedef struct {
float d; // delta
int8_t qs[QK_K]; // quants
int16_t bsums[QK_K/16]; // sum of quants in groups of 16
} block_q8_K;
static_assert(sizeof(block_q8_K) == sizeof(float) + QK_K + QK_K/16*sizeof(int16_t), "wrong q8_K block size/padding");
// Quantization // Quantization
void quantize_row_q4_0_reference(const float * restrict x, block_q4_0 * restrict y, int k); void quantize_row_q4_0_reference(const float * GGML_RESTRICT x, block_q4_0 * GGML_RESTRICT y, int64_t k);
void quantize_row_q4_1_reference(const float * restrict x, block_q4_1 * restrict y, int k); void quantize_row_q4_1_reference(const float * GGML_RESTRICT x, block_q4_1 * GGML_RESTRICT y, int64_t k);
void quantize_row_q5_0_reference(const float * restrict x, block_q5_0 * restrict y, int k); void quantize_row_q5_0_reference(const float * GGML_RESTRICT x, block_q5_0 * GGML_RESTRICT y, int64_t k);
void quantize_row_q5_1_reference(const float * restrict x, block_q5_1 * restrict y, int k); void quantize_row_q5_1_reference(const float * GGML_RESTRICT x, block_q5_1 * GGML_RESTRICT y, int64_t k);
void quantize_row_q8_0_reference(const float * restrict x, block_q8_0 * restrict y, int k); void quantize_row_q8_0_reference(const float * GGML_RESTRICT x, block_q8_0 * GGML_RESTRICT y, int64_t k);
void quantize_row_q8_1_reference(const float * restrict x, block_q8_1 * restrict y, int k); void quantize_row_q8_1_reference(const float * GGML_RESTRICT x, block_q8_1 * GGML_RESTRICT y, int64_t k);
void quantize_row_q2_K_reference(const float * restrict x, block_q2_K * restrict y, int k); void quantize_row_q2_K_reference(const float * GGML_RESTRICT x, block_q2_K * GGML_RESTRICT y, int64_t k);
void quantize_row_q3_K_reference(const float * restrict x, block_q3_K * restrict y, int k); void quantize_row_q3_K_reference(const float * GGML_RESTRICT x, block_q3_K * GGML_RESTRICT y, int64_t k);
void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict y, int k); void quantize_row_q4_K_reference(const float * GGML_RESTRICT x, block_q4_K * GGML_RESTRICT y, int64_t k);
void quantize_row_q5_K_reference(const float * restrict x, block_q5_K * restrict y, int k); void quantize_row_q5_K_reference(const float * GGML_RESTRICT x, block_q5_K * GGML_RESTRICT y, int64_t k);
void quantize_row_q6_K_reference(const float * restrict x, block_q6_K * restrict y, int k); void quantize_row_q6_K_reference(const float * GGML_RESTRICT x, block_q6_K * GGML_RESTRICT y, int64_t k);
void quantize_row_q8_K_reference(const float * restrict x, block_q8_K * restrict y, int k); void quantize_row_q8_K_reference(const float * GGML_RESTRICT x, block_q8_K * GGML_RESTRICT y, int64_t k);
void quantize_row_q4_0(const float * restrict x, void * restrict y, int k); void quantize_row_iq3_xxs_reference(const float * GGML_RESTRICT x, block_iq3_xxs * GGML_RESTRICT y, int64_t k);
void quantize_row_q4_1(const float * restrict x, void * restrict y, int k); void quantize_row_iq4_nl_reference (const float * GGML_RESTRICT x, block_iq4_nl * GGML_RESTRICT y, int64_t k);
void quantize_row_q5_0(const float * restrict x, void * restrict y, int k); void quantize_row_iq4_xs_reference (const float * GGML_RESTRICT x, block_iq4_xs * GGML_RESTRICT y, int64_t k);
void quantize_row_q5_1(const float * restrict x, void * restrict y, int k); void quantize_row_iq3_s_reference (const float * GGML_RESTRICT x, block_iq3_s * GGML_RESTRICT y, int64_t k);
void quantize_row_q8_0(const float * restrict x, void * restrict y, int k); void quantize_row_iq2_s_reference (const float * GGML_RESTRICT x, block_iq2_s * GGML_RESTRICT y, int64_t k);
void quantize_row_q8_1(const float * restrict x, void * restrict y, int k);
void quantize_row_q2_K(const float * restrict x, void * restrict y, int k); void quantize_row_q4_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
void quantize_row_q3_K(const float * restrict x, void * restrict y, int k); void quantize_row_q4_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
void quantize_row_q4_K(const float * restrict x, void * restrict y, int k); void quantize_row_q5_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
void quantize_row_q5_K(const float * restrict x, void * restrict y, int k); void quantize_row_q5_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
void quantize_row_q6_K(const float * restrict x, void * restrict y, int k); void quantize_row_q8_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
void quantize_row_q8_K(const float * restrict x, void * restrict y, int k); void quantize_row_q8_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
void quantize_row_q2_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
void quantize_row_q3_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
void quantize_row_q4_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
void quantize_row_q5_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
void quantize_row_q6_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
void quantize_row_q8_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
void quantize_row_iq3_xxs(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
void quantize_row_iq4_nl (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
void quantize_row_iq4_xs (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
void quantize_row_iq3_s (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
void quantize_row_iq2_s (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k);
// Dequantization // Dequantization
void dequantize_row_q4_0(const block_q4_0 * restrict x, float * restrict y, int k); void dequantize_row_q4_0(const block_q4_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
void dequantize_row_q4_1(const block_q4_1 * restrict x, float * restrict y, int k); void dequantize_row_q4_1(const block_q4_1 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
void dequantize_row_q5_0(const block_q5_0 * restrict x, float * restrict y, int k); void dequantize_row_q5_0(const block_q5_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
void dequantize_row_q5_1(const block_q5_1 * restrict x, float * restrict y, int k); void dequantize_row_q5_1(const block_q5_1 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
void dequantize_row_q8_0(const block_q8_0 * restrict x, float * restrict y, int k); void dequantize_row_q8_0(const block_q8_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
//void dequantize_row_q8_1(const block_q8_1 * restrict x, float * restrict y, int k); //void dequantize_row_q8_1(const block_q8_1 * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
void dequantize_row_q2_K(const block_q2_K * restrict x, float * restrict y, int k); void dequantize_row_q2_K(const block_q2_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
void dequantize_row_q3_K(const block_q3_K * restrict x, float * restrict y, int k); void dequantize_row_q3_K(const block_q3_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
void dequantize_row_q4_K(const block_q4_K * restrict x, float * restrict y, int k); void dequantize_row_q4_K(const block_q4_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
void dequantize_row_q5_K(const block_q5_K * restrict x, float * restrict y, int k); void dequantize_row_q5_K(const block_q5_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
void dequantize_row_q6_K(const block_q6_K * restrict x, float * restrict y, int k); void dequantize_row_q6_K(const block_q6_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
void dequantize_row_q8_K(const block_q8_K * restrict x, float * restrict y, int k); void dequantize_row_q8_K(const block_q8_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
void dequantize_row_iq2_xxs(const block_iq2_xxs * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
void dequantize_row_iq2_xs (const block_iq2_xs * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
void dequantize_row_iq2_s (const block_iq2_s * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
void dequantize_row_iq3_xxs(const block_iq3_xxs * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
void dequantize_row_iq1_s (const block_iq1_s * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
void dequantize_row_iq1_m (const block_iq1_m * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
void dequantize_row_iq4_nl (const block_iq4_nl * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
void dequantize_row_iq4_xs (const block_iq4_xs * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
void dequantize_row_iq3_s (const block_iq3_s * GGML_RESTRICT x, float * GGML_RESTRICT y, int64_t k);
// Dot product // Dot product
void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, const void * restrict vx, const void * restrict vy); void ggml_vec_dot_q4_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, const void * restrict vx, const void * restrict vy); void ggml_vec_dot_q4_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
void ggml_vec_dot_q5_0_q8_0(int n, float * restrict s, const void * restrict vx, const void * restrict vy); void ggml_vec_dot_q5_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, const void * restrict vx, const void * restrict vy); void ggml_vec_dot_q5_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, const void * restrict vx, const void * restrict vy); void ggml_vec_dot_q8_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
void ggml_vec_dot_q2_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
void ggml_vec_dot_q3_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
void ggml_vec_dot_q4_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
void ggml_vec_dot_q5_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
void ggml_vec_dot_q6_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
void ggml_vec_dot_iq2_xxs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
void ggml_vec_dot_iq2_xs_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
void ggml_vec_dot_iq2_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
void ggml_vec_dot_iq3_xxs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
void ggml_vec_dot_iq1_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
void ggml_vec_dot_iq1_m_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
void ggml_vec_dot_iq4_nl_q8_0 (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
void ggml_vec_dot_iq4_xs_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
void ggml_vec_dot_iq3_s_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
// Quantization utilizing an importance matrix (a.k.a. "Activation aWare Quantization")
size_t quantize_iq2_xxs(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
size_t quantize_iq2_xs (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
size_t quantize_iq2_s (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
size_t quantize_iq3_xxs(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
size_t quantize_iq1_s (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
size_t quantize_iq1_m (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
size_t quantize_iq4_nl (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
size_t quantize_iq4_xs (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
size_t quantize_iq3_s (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
size_t quantize_q2_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
size_t quantize_q3_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
size_t quantize_q4_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
size_t quantize_q5_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
size_t quantize_q6_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
size_t quantize_q4_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
size_t quantize_q4_1(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
size_t quantize_q5_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
size_t quantize_q5_1(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
size_t quantize_q8_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int64_t nrows, int64_t n_per_row, const float * imatrix);
void iq2xs_init_impl(enum ggml_type type);
void iq2xs_free_impl(enum ggml_type type);
void iq3xs_init_impl(int grid_size);
void iq3xs_free_impl(int grid_size);
#ifdef __cplusplus
}
#endif
void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);
void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy);

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@ -0,0 +1,49 @@
//
// MIT license
// Copyright (C) 2024 Intel Corporation
// SPDX-License-Identifier: MIT
//
#pragma once
#include "ggml.h"
#include "ggml-backend.h"
#ifdef __cplusplus
extern "C" {
#endif
#define GGML_SYCL_MAX_DEVICES 48
#define GGML_SYCL_NAME "SYCL"
// backend API
GGML_API ggml_backend_t ggml_backend_sycl_init(int device);
// devide buffer
GGML_API ggml_backend_buffer_type_t ggml_backend_sycl_buffer_type(int device);
// split tensor buffer that splits matrices by rows across multiple devices
GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_sycl_split_buffer_type(const float * tensor_split);
// pinned host buffer for use with the CPU backend for faster copies between CPU and GPU
GGML_API ggml_backend_buffer_type_t ggml_backend_sycl_host_buffer_type(void);
GGML_API void ggml_backend_sycl_print_sycl_devices(void);
GGML_API GGML_CALL void ggml_sycl_get_gpu_list(int *id_list, int max_len);
GGML_API GGML_CALL void ggml_sycl_get_device_description(int device, char *description, size_t description_size);
GGML_API GGML_CALL int ggml_backend_sycl_get_device_count();
GGML_API GGML_CALL void ggml_backend_sycl_get_device_memory(int device, size_t *free, size_t *total);
GGML_API GGML_CALL int ggml_backend_sycl_get_device_index(int device_id);
// TODO: these are temporary
// ref: https://github.com/ggerganov/llama.cpp/pull/6022#issuecomment-1992615670
GGML_API GGML_CALL int ggml_backend_sycl_get_device_id(int device_index);
GGML_API GGML_CALL void ggml_backend_sycl_set_single_device_mode(int main_gpu_id);
GGML_API GGML_CALL void ggml_backend_sycl_set_mul_device_mode();
// SYCL doesn't support registering host memory, keep here for reference
// GGML_API GGML_CALL bool ggml_backend_sycl_register_host_buffer(void * buffer, size_t size);
// GGML_API GGML_CALL void ggml_backend_sycl_unregister_host_buffer(void * buffer);
#ifdef __cplusplus
}
#endif

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@ -10,17 +10,7 @@ extern "C" {
#define GGML_VK_NAME "Vulkan" #define GGML_VK_NAME "Vulkan"
#define GGML_VK_MAX_DEVICES 16 #define GGML_VK_MAX_DEVICES 16
GGML_API void ggml_vk_init_cpu_assist(void); GGML_API void ggml_vk_instance_init(void);
GGML_API void ggml_vk_preallocate_buffers_graph_cpu_assist(struct ggml_tensor * node);
GGML_API void ggml_vk_preallocate_buffers_cpu_assist(void);
GGML_API void ggml_vk_build_graph_cpu_assist(struct ggml_tensor * node, bool last_node);
GGML_API bool ggml_vk_compute_forward_cpu_assist(struct ggml_compute_params * params, struct ggml_tensor * tensor);
#ifdef GGML_VULKAN_CHECK_RESULTS
void ggml_vk_check_results_1_cpu_assist(struct ggml_compute_params * params, struct ggml_tensor * tensor);
#endif
GGML_API void ggml_vk_graph_cleanup_cpu_assist(void);
GGML_API void ggml_vk_free_cpu_assist(void);
// backend API // backend API
GGML_API GGML_CALL ggml_backend_t ggml_backend_vk_init(size_t dev_num); GGML_API GGML_CALL ggml_backend_t ggml_backend_vk_init(size_t dev_num);

View File

@ -311,12 +311,6 @@ static VALUE ruby_whisper_params_get_split_on_word(VALUE self) {
static VALUE ruby_whisper_params_set_split_on_word(VALUE self, VALUE value) { static VALUE ruby_whisper_params_set_split_on_word(VALUE self, VALUE value) {
BOOL_PARAMS_SETTER(self, split_on_word, value) BOOL_PARAMS_SETTER(self, split_on_word, value)
} }
static VALUE ruby_whisper_params_get_speed_up(VALUE self) {
BOOL_PARAMS_GETTER(self, speed_up)
}
static VALUE ruby_whisper_params_set_speed_up(VALUE self, VALUE value) {
BOOL_PARAMS_SETTER(self, speed_up, value)
}
static VALUE ruby_whisper_params_get_diarize(VALUE self) { static VALUE ruby_whisper_params_get_diarize(VALUE self) {
ruby_whisper_params *rwp; ruby_whisper_params *rwp;
Data_Get_Struct(self, ruby_whisper_params, rwp); Data_Get_Struct(self, ruby_whisper_params, rwp);
@ -408,8 +402,6 @@ void Init_whisper() {
rb_define_method(cParams, "token_timestamps=", ruby_whisper_params_set_token_timestamps, 1); rb_define_method(cParams, "token_timestamps=", ruby_whisper_params_set_token_timestamps, 1);
rb_define_method(cParams, "split_on_word", ruby_whisper_params_get_split_on_word, 0); rb_define_method(cParams, "split_on_word", ruby_whisper_params_get_split_on_word, 0);
rb_define_method(cParams, "split_on_word=", ruby_whisper_params_set_split_on_word, 1); rb_define_method(cParams, "split_on_word=", ruby_whisper_params_set_split_on_word, 1);
rb_define_method(cParams, "speed_up", ruby_whisper_params_get_speed_up, 0);
rb_define_method(cParams, "speed_up=", ruby_whisper_params_set_speed_up, 1);
rb_define_method(cParams, "diarize", ruby_whisper_params_get_diarize, 0); rb_define_method(cParams, "diarize", ruby_whisper_params_get_diarize, 0);
rb_define_method(cParams, "diarize=", ruby_whisper_params_set_diarize, 1); rb_define_method(cParams, "diarize=", ruby_whisper_params_set_diarize, 1);

View File

@ -117,13 +117,6 @@ class TestWhisper < Test::Unit::TestCase
assert !@params.split_on_word assert !@params.split_on_word
end end
def test_speed_up
@params.speed_up = true
assert @params.speed_up
@params.speed_up = false
assert !@params.speed_up
end
def test_whisper def test_whisper
@whisper = Whisper::Context.new(File.join(TOPDIR, '..', '..', 'models', 'ggml-base.en.bin')) @whisper = Whisper::Context.new(File.join(TOPDIR, '..', '..', 'models', 'ggml-base.en.bin'))
params = Whisper::Params.new params = Whisper::Params.new

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@ -0,0 +1,28 @@
Gem::Specification.new do |s|
s.name = "whispercpp"
s.authors = ["Georgi Gerganov", "Todd A. Fisher"]
s.version = '1.3.0'
s.date = '2024-05-14'
s.description = %q{High-performance inference of OpenAI's Whisper automatic speech recognition (ASR) model via Ruby}
s.email = 'todd.fisher@gmail.com'
s.extra_rdoc_files = ['LICENSE', 'README.md']
s.files = ["LICENSE", "README.md", "Rakefile", "ext/extconf.rb", "ext/ggml.c", "ext/ruby_whisper.cpp", "ext/whisper.cpp", "ext/dr_wav.h", "ext/ggml.h", "ext/ruby_whisper.h", "ext/whisper.h"]
#### Load-time details
s.require_paths = ['lib','ext']
s.summary = %q{Ruby whisper.cpp bindings}
s.test_files = ["tests/test_whisper.rb"]
s.extensions << 'ext/extconf.rb'
#### Documentation and testing.
s.homepage = 'https://github.com/ggerganov/whisper.cpp'
s.rdoc_options = ['--main', '../../README.md']
s.platform = Gem::Platform::RUBY
s.licenses = ['MIT']
end

View File

@ -1,54 +0,0 @@
# Add new build types
# ReleaseGG - Release with enabled asserts
SET(CMAKE_CXX_FLAGS_RELEASEGG
"-O3"
CACHE STRING "Flags used by the c++ compiler during release builds with enabled asserts."
FORCE )
SET(CMAKE_C_FLAGS_RELEASEGG
"-O3"
CACHE STRING "Flags used by the compiler during release builds with enabled asserts."
FORCE )
SET(CMAKE_EXE_LINKER_FLAGS_RELEASEGG
""
CACHE STRING "Flags used for linking binaries during release builds with enabled asserts."
FORCE )
SET(CMAKE_SHARED_LINKER_FLAGS_RELEASEGG
""
CACHE STRING "Flags used by the shared libraries linker during release builds with enabled asserts."
FORCE )
MARK_AS_ADVANCED(
CMAKE_CXX_FLAGS_RELEASEGG
CMAKE_C_FLAGS_RELEASEGG
CMAKE_EXE_LINKER_FLAGS_RELEASEGG
CMAKE_SHARED_LINKER_FLAGS_RELEASEGG )
# RelWithDebInfoGG - RelWithDebInfo with enabled asserts
SET(CMAKE_CXX_FLAGS_RELWITHDEBINFOGG
"-O2 -g"
CACHE STRING "Flags used by the c++ compiler during release builds with debug symbols and enabled asserts."
FORCE )
SET(CMAKE_C_FLAGS_RELWITHDEBINFOGG
"-O2 -g"
CACHE STRING "Flags used by the compiler during release builds with debug symbols and enabled asserts."
FORCE )
SET(CMAKE_EXE_LINKER_FLAGS_RELWITHDEBINFOGG
""
CACHE STRING "Flags used for linking binaries during release builds with debug symbols and enabled asserts."
FORCE )
SET(CMAKE_SHARED_LINKER_FLAGS_RELWITHDEBINFOGG
""
CACHE STRING "Flags used by the shared libraries linker during release builds with debug symbols and enabled asserts."
FORCE )
MARK_AS_ADVANCED(
CMAKE_CXX_FLAGS_RELWITHDEBINFOGG
CMAKE_C_FLAGS_RELWITHDEBINFOGG
CMAKE_EXE_LINKER_FLAGS_RELWITHDEBINFOGG
CMAKE_SHARED_LINKER_FLAGS_RELWITHDEBINFOGG )
if (NOT XCODE AND NOT MSVC AND NOT CMAKE_BUILD_TYPE)
set(CMAKE_BUILD_TYPE Release CACHE STRING "Build type" FORCE)
set_property(CACHE CMAKE_BUILD_TYPE PROPERTY STRINGS "Debug" "Release" "MinSizeRel" "RelWithDebInfo" "ReleaseGG" "RelWithDebInfoGG")
endif()

163
cmake/FindFFmpeg.cmake Normal file
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@ -0,0 +1,163 @@
# From
# https://github.com/snikulov/cmake-modules/blob/master/FindFFmpeg.cmake
#
# vim: ts=2 sw=2
# - Try to find the required ffmpeg components(default: AVFORMAT, AVUTIL, AVCODEC)
#
# Once done this will define
# FFMPEG_FOUND - System has the all required components.
# FFMPEG_INCLUDE_DIRS - Include directory necessary for using the required components headers.
# FFMPEG_LIBRARIES - Link these to use the required ffmpeg components.
# FFMPEG_DEFINITIONS - Compiler switches required for using the required ffmpeg components.
#
# For each of the components it will additionally set.
# - AVCODEC
# - AVDEVICE
# - AVFORMAT
# - AVFILTER
# - AVUTIL
# - POSTPROC
# - SWSCALE
# the following variables will be defined
# <component>_FOUND - System has <component>
# <component>_INCLUDE_DIRS - Include directory necessary for using the <component> headers
# <component>_LIBRARIES - Link these to use <component>
# <component>_DEFINITIONS - Compiler switches required for using <component>
# <component>_VERSION - The components version
#
# Copyright (c) 2006, Matthias Kretz, <kretz@kde.org>
# Copyright (c) 2008, Alexander Neundorf, <neundorf@kde.org>
# Copyright (c) 2011, Michael Jansen, <kde@michael-jansen.biz>
#
# Redistribution and use is allowed according to the terms of the BSD license.
# For details see the accompanying COPYING-CMAKE-SCRIPTS file.
include(FindPackageHandleStandardArgs)
# The default components were taken from a survey over other FindFFMPEG.cmake files
if (NOT FFmpeg_FIND_COMPONENTS)
set(FFmpeg_FIND_COMPONENTS AVFORMAT AVCODEC AVUTIL SWRESAMPLE)
endif()
#
### Macro: set_component_found
#
# Marks the given component as found if both *_LIBRARIES AND *_INCLUDE_DIRS is present.
#
macro(set_component_found _component )
if (${_component}_LIBRARIES AND ${_component}_INCLUDE_DIRS)
message(DEBUG " - ${_component} found.")
set(${_component}_FOUND TRUE)
else ()
message(DEBUG " - ${_component} not found.")
endif ()
endmacro()
#
### Macro: find_component
#
# Checks for the given component by invoking pkgconfig and then looking up the libraries and
# include directories.
#
macro(find_component _component _pkgconfig _library _header)
if (NOT WIN32)
# use pkg-config to get the directories and then use these values
# in the FIND_PATH() and FIND_LIBRARY() calls
find_package(PkgConfig)
if (PKG_CONFIG_FOUND)
pkg_check_modules(PC_${_component} ${_pkgconfig})
message(STATUS "Pkgconfig found: ${PC_${_component}_INCLUDEDIR}")
message(STATUS "Pkgconfig found: ${PC_${_component}_INCLUDE_DIRS}")
message(STATUS "${PC_${_component}_CFLAGS}")
endif ()
endif (NOT WIN32)
find_path(${_component}_INCLUDE_DIRS ${_header}
HINTS
${PC_${_component}_INCLUDEDIR}
${PC_${_component}_INCLUDE_DIRS}
PATH_SUFFIXES
ffmpeg
)
# CMake's default is to search first for shared libraries and then for static libraries.
# Todo later: add option to prefer static libs over dynamic:
find_library(${_component}_LIBRARIES NAMES ${_library} lib${_library}.a
HINTS
${PC_${_component}_LIBDIR}
${PC_${_component}_LIBRARY_DIRS}
)
set(${_component}_DEFINITIONS ${PC_${_component}_CFLAGS_OTHER} CACHE STRING "The ${_component} CFLAGS.")
set(${_component}_VERSION ${PC_${_component}_VERSION} CACHE STRING "The ${_component} version number.")
set_component_found(${_component})
mark_as_advanced(
${_component}_INCLUDE_DIRS
${_component}_LIBRARIES
${_component}_DEFINITIONS
${_component}_VERSION)
endmacro()
# Check for cached results. If there are skip the costly part.
if (NOT FFMPEG_LIBRARIES)
# Check for all possible component.
find_component(AVCODEC libavcodec avcodec libavcodec/avcodec.h)
find_component(AVFORMAT libavformat avformat libavformat/avformat.h)
find_component(AVDEVICE libavdevice avdevice libavdevice/avdevice.h)
#find_component(AVRESAMPLE libavresample avresample libavresample/avresample.h) # old name for swresample
find_component(AVUTIL libavutil avutil libavutil/avutil.h)
find_component(AVFILTER libavfilter avfilter libavfilter/avfilter.h)
find_component(SWSCALE libswscale swscale libswscale/swscale.h)
find_component(POSTPROC libpostproc postproc libpostproc/postprocess.h)
find_component(SWRESAMPLE libswresample swresample libswresample/swresample.h)
# Check if the required components were found and add their stuff to the FFMPEG_* vars.
foreach (_component ${FFmpeg_FIND_COMPONENTS})
if (${_component}_FOUND)
# message(STATUS "Required component ${_component} present.")
set(FFMPEG_LIBRARIES ${FFMPEG_LIBRARIES} ${${_component}_LIBRARIES})
set(FFMPEG_DEFINITIONS ${FFMPEG_DEFINITIONS} ${${_component}_DEFINITIONS})
list(APPEND FFMPEG_INCLUDE_DIRS ${${_component}_INCLUDE_DIRS})
else ()
# message(STATUS "Required component ${_component} missing.")
endif ()
endforeach ()
# Build the include path with duplicates removed.
if (FFMPEG_INCLUDE_DIRS)
list(REMOVE_DUPLICATES FFMPEG_INCLUDE_DIRS)
endif ()
# cache the vars.
set(FFMPEG_INCLUDE_DIRS ${FFMPEG_INCLUDE_DIRS} CACHE STRING "The FFmpeg include directories." FORCE)
set(FFMPEG_LIBRARIES ${FFMPEG_LIBRARIES} CACHE STRING "The FFmpeg libraries." FORCE)
set(FFMPEG_DEFINITIONS ${FFMPEG_DEFINITIONS} CACHE STRING "The FFmpeg cflags." FORCE)
mark_as_advanced(FFMPEG_INCLUDE_DIRS
FFMPEG_LIBRARIES
FFMPEG_DEFINITIONS)
endif ()
# Now set the noncached _FOUND vars for the components.
# whisper.cpp does not need SWSCALE
foreach (_component AVCODEC AVDEVICE AVFORMAT AVRESAMPLE AVUTIL POSTPROCESS)
set_component_found(${_component})
endforeach ()
# Compile the list of required vars
set(_FFmpeg_REQUIRED_VARS FFMPEG_LIBRARIES FFMPEG_INCLUDE_DIRS)
foreach (_component ${FFmpeg_FIND_COMPONENTS})
list(APPEND _FFmpeg_REQUIRED_VARS ${_component}_LIBRARIES ${_component}_INCLUDE_DIRS)
endforeach ()
# Give a nice error message if some of the required vars are missing.
find_package_handle_standard_args(FFmpeg DEFAULT_MSG ${_FFmpeg_REQUIRED_VARS})

58
cmake/build-info.cmake Normal file
View File

@ -0,0 +1,58 @@
set(BUILD_NUMBER 0)
set(BUILD_COMMIT "unknown")
set(BUILD_COMPILER "unknown")
set(BUILD_TARGET "unknown")
# Look for git
find_package(Git)
if(NOT Git_FOUND)
find_program(GIT_EXECUTABLE NAMES git git.exe)
if(GIT_EXECUTABLE)
set(Git_FOUND TRUE)
message(STATUS "Found Git: ${GIT_EXECUTABLE}")
else()
message(WARNING "Git not found. Build info will not be accurate.")
endif()
endif()
# Get the commit count and hash
if(Git_FOUND)
execute_process(
COMMAND ${GIT_EXECUTABLE} rev-parse --short HEAD
WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}
OUTPUT_VARIABLE HEAD
OUTPUT_STRIP_TRAILING_WHITESPACE
RESULT_VARIABLE RES
)
if (RES EQUAL 0)
set(BUILD_COMMIT ${HEAD})
endif()
execute_process(
COMMAND ${GIT_EXECUTABLE} rev-list --count HEAD
WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}
OUTPUT_VARIABLE COUNT
OUTPUT_STRIP_TRAILING_WHITESPACE
RESULT_VARIABLE RES
)
if (RES EQUAL 0)
set(BUILD_NUMBER ${COUNT})
endif()
endif()
if(MSVC)
set(BUILD_COMPILER "${CMAKE_C_COMPILER_ID} ${CMAKE_C_COMPILER_VERSION}")
set(BUILD_TARGET ${CMAKE_VS_PLATFORM_NAME})
else()
execute_process(
COMMAND sh -c "$@ --version | head -1" _ ${CMAKE_C_COMPILER}
OUTPUT_VARIABLE OUT
OUTPUT_STRIP_TRAILING_WHITESPACE
)
set(BUILD_COMPILER ${OUT})
execute_process(
COMMAND ${CMAKE_C_COMPILER} -dumpmachine
OUTPUT_VARIABLE OUT
OUTPUT_STRIP_TRAILING_WHITESPACE
)
set(BUILD_TARGET ${OUT})
endif()

View File

@ -0,0 +1,65 @@
set(LLAMA_VERSION @LLAMA_INSTALL_VERSION@)
set(LLAMA_BUILD_COMMIT @LLAMA_BUILD_COMMIT@)
set(LLAMA_BUILD_NUMBER @LLAMA_BUILD_NUMBER@)
set(LLAMA_SHARED_LIB @BUILD_SHARED_LIBS@)
set(GGML_BLAS @GGML_BLAS@)
set(GGML_CUDA @GGML_CUDA@)
set(GGML_METAL @GGML_METAL@)
set(GGML_HIPBLAS @GGML_HIPBLAS@)
set(GGML_ACCELERATE @GGML_ACCELERATE@)
@PACKAGE_INIT@
set_and_check(LLAMA_INCLUDE_DIR "@PACKAGE_LLAMA_INCLUDE_INSTALL_DIR@")
set_and_check(LLAMA_LIB_DIR "@PACKAGE_LLAMA_LIB_INSTALL_DIR@")
set_and_check(LLAMA_BIN_DIR "@PACKAGE_LLAMA_BIN_INSTALL_DIR@")
# Ensure transient dependencies satisfied
find_package(Threads REQUIRED)
if (APPLE AND GGML_ACCELERATE)
find_library(ACCELERATE_FRAMEWORK Accelerate REQUIRED)
endif()
if (GGML_BLAS)
find_package(BLAS REQUIRED)
endif()
if (GGML_CUDA)
find_package(CUDAToolkit REQUIRED)
endif()
if (GGML_METAL)
find_library(FOUNDATION_LIBRARY Foundation REQUIRED)
find_library(METAL_FRAMEWORK Metal REQUIRED)
find_library(METALKIT_FRAMEWORK MetalKit REQUIRED)
endif()
if (GGML_HIPBLAS)
find_package(hip REQUIRED)
find_package(hipblas REQUIRED)
find_package(rocblas REQUIRED)
endif()
find_library(llama_LIBRARY llama
REQUIRED
HINTS ${LLAMA_LIB_DIR})
set(_llama_link_deps "Threads::Threads" "@LLAMA_EXTRA_LIBS@")
set(_llama_transient_defines "@LLAMA_TRANSIENT_DEFINES@")
add_library(llama UNKNOWN IMPORTED)
set_target_properties(llama
PROPERTIES
INTERFACE_INCLUDE_DIRECTORIES "${LLAMA_INCLUDE_DIR}"
INTERFACE_LINK_LIBRARIES "${_llama_link_deps}"
INTERFACE_COMPILE_DEFINITIONS "${_llama_transient_defines}"
IMPORTED_LINK_INTERFACE_LANGUAGES "CXX"
IMPORTED_LOCATION "${llama_LIBRARY}"
INTERFACE_COMPILE_FEATURES cxx_std_11
POSITION_INDEPENDENT_CODE ON )
check_required_components(Llama)

10
cmake/whisper.pc.in Normal file
View File

@ -0,0 +1,10 @@
prefix=@CMAKE_INSTALL_PREFIX@
exec_prefix=${prefix}
libdir=${exec_prefix}/lib
includedir=${prefix}/include
Name: whisper
Description: Port of OpenAI's Whisper model in C/C++
Version: @PROJECT_VERSION@
Libs: -L${libdir} -lwhisper
Cflags: -I${includedir}

View File

@ -11,7 +11,7 @@ if (WHISPER_SDL2)
string(STRIP "${SDL2_LIBRARIES}" SDL2_LIBRARIES) string(STRIP "${SDL2_LIBRARIES}" SDL2_LIBRARIES)
message(STATUS "SDL2_INCLUDE_DIRS = ${SDL2_INCLUDE_DIRS}") message(STATUS "SDL2_INCLUDE_DIRS = ${SDL2_INCLUDE_DIRS}")
message(STATUS "SDL2_LIBRARIES = ${SDL2_LIBRARIES}") message(STATUS "SDL2_LIBRARIES = ${SDL2_LIBRARIES}")
endif() endif()
if (WHISPER_CLBLAST) if (WHISPER_CLBLAST)
@ -22,19 +22,51 @@ endif()
set(TARGET common) set(TARGET common)
unset(COMMON_EXTRA_LIBS)
if (WHISPER_FFMPEG)
# As of cmake 3.27, there is no official cmake support for FindFFmpeg.
# Consequnelty we added a FindFFmpeg.cmake script the cmake subfolder:
# whisper.cpp does not need the full ffmpeg libs, just AVFORMAT AVCODEC AVUTIL SWRESAMPLE
# libswresample performs highly optimized audio resampling, rematrixing and sample format conversion operations
# libavcodec provides a generic encoding/decoding framework and contains multiple decoders and encoders for audio, video and subtitle streams, and several bitstream filters.
# libavformat provides a generic framework for multiplexing and demultiplexing (muxing and demuxing) audio, video and subtitle streams.
find_package(FFmpeg REQUIRED)
if (NOT ${FFMPEG_FOUND})
message(FATAL_ERROR "Cannot find ffmpeg libs/headers")
endif()
message(STATUS "Found ffmpeg libs: ${FFMPEG_LIBRARIES}")
message(STATUS "Found ffmpeg headers in: ${FFMPEG_INCLUDE_DIRS}")
message(STATUS "ffmpeg definitions: ${FFMPEG_DEFINITIONS}")
message(STATUS "Found avformat ${AVFORMAT_VERSION}")
include_directories(${FFMPEG_INCLUDE_DIRS})
add_compile_definitions(WHISPER_FFMPEG)
list(APPEND COMMON_EXTRA_LIBS ${FFMPEG_LIBRARIES})
set(COMMON_SOURCES_FFMPEG ffmpeg-transcode.cpp)
endif()
add_library(${TARGET} STATIC add_library(${TARGET} STATIC
common.h common.h
common.cpp common.cpp
common-ggml.h common-ggml.h
common-ggml.cpp common-ggml.cpp
grammar-parser.h
grammar-parser.cpp grammar-parser.cpp
${COMMON_SOURCES_FFMPEG}
) )
include(DefaultTargetOptions) include(DefaultTargetOptions)
target_link_libraries(${TARGET} PRIVATE whisper) target_link_libraries(${TARGET} PRIVATE whisper ${COMMON_EXTRA_LIBS})
set_target_properties(${TARGET} PROPERTIES POSITION_INDEPENDENT_CODE ON) set_target_properties(${TARGET} PROPERTIES POSITION_INDEPENDENT_CODE ON)
set_target_properties(${TARGET} PROPERTIES FOLDER "libs")
if (WHISPER_SDL2) if (WHISPER_SDL2)
# common-sdl # common-sdl
@ -48,34 +80,67 @@ if (WHISPER_SDL2)
include(DefaultTargetOptions) include(DefaultTargetOptions)
target_include_directories(${TARGET} PUBLIC ${SDL2_INCLUDE_DIRS}) target_include_directories(${TARGET} PUBLIC ${SDL2_INCLUDE_DIRS})
target_link_libraries(${TARGET} PRIVATE ${SDL2_LIBRARIES}) target_link_libraries (${TARGET} PRIVATE ${SDL2_LIBRARIES})
set_target_properties(${TARGET} PROPERTIES POSITION_INDEPENDENT_CODE ON) set_target_properties(${TARGET} PROPERTIES POSITION_INDEPENDENT_CODE ON)
set_target_properties(${TARGET} PROPERTIES FOLDER "libs")
endif() endif()
# add json lib
add_library(json_cpp INTERFACE)
target_include_directories(json_cpp INTERFACE ${CMAKE_CURRENT_SOURCE_DIR})
# examples # examples
include_directories(${CMAKE_CURRENT_SOURCE_DIR}) include_directories(${CMAKE_CURRENT_SOURCE_DIR})
if (EMSCRIPTEN) if (EMSCRIPTEN)
add_subdirectory(whisper.wasm) add_subdirectory(whisper.wasm)
set_target_properties(libmain PROPERTIES FOLDER "libs")
add_subdirectory(stream.wasm) add_subdirectory(stream.wasm)
set_target_properties(libstream PROPERTIES FOLDER "libs")
add_subdirectory(command.wasm) add_subdirectory(command.wasm)
set_target_properties(libcommand PROPERTIES FOLDER "libs")
add_subdirectory(talk.wasm) add_subdirectory(talk.wasm)
set_target_properties(libtalk PROPERTIES FOLDER "libs")
add_subdirectory(bench.wasm) add_subdirectory(bench.wasm)
set_target_properties(libbench PROPERTIES FOLDER "libs")
elseif(CMAKE_JS_VERSION) elseif(CMAKE_JS_VERSION)
add_subdirectory(addon.node) add_subdirectory(addon.node)
set_target_properties(addon.node PROPERTIES FOLDER "examples")
else() else()
add_subdirectory(main) add_subdirectory(main)
set_target_properties(main PROPERTIES FOLDER "examples")
if (WHISPER_SDL2)
add_subdirectory(stream) add_subdirectory(stream)
set_target_properties(stream PROPERTIES FOLDER "examples")
endif (WHISPER_SDL2)
add_subdirectory(server) add_subdirectory(server)
set_target_properties(server PROPERTIES FOLDER "examples")
if (WHISPER_SDL2)
add_subdirectory(command) add_subdirectory(command)
set_target_properties(command PROPERTIES FOLDER "examples")
endif (WHISPER_SDL2)
add_subdirectory(bench) add_subdirectory(bench)
set_target_properties(bench PROPERTIES FOLDER "examples")
add_subdirectory(quantize) add_subdirectory(quantize)
set_target_properties(quantize PROPERTIES FOLDER "examples")
if (WHISPER_SDL2)
add_subdirectory(talk) add_subdirectory(talk)
set_target_properties(talk PROPERTIES FOLDER "examples")
add_subdirectory(talk-llama) add_subdirectory(talk-llama)
set_target_properties(talk-llama PROPERTIES FOLDER "examples")
add_subdirectory(lsp) add_subdirectory(lsp)
set_target_properties(lsp PROPERTIES FOLDER "examples")
if (GGML_SYCL)
add_subdirectory(sycl)
set_target_properties(sycl PROPERTIES FOLDER "examples")
endif()
endif (WHISPER_SDL2)
endif() endif()
add_subdirectory(wchess) if (WHISPER_SDL2)
add_subdirectory(wchess)
set_target_properties(wchess PROPERTIES FOLDER "examples")
endif (WHISPER_SDL2)

View File

@ -1,4 +1,4 @@
set(TARGET whisper-addon) set(TARGET addon.node)
# Base settings # Base settings
#================================================================== #==================================================================

View File

@ -14,14 +14,14 @@ npm install
Make sure it is in the project root directory and compiled with make-js. Make sure it is in the project root directory and compiled with make-js.
```shell ```shell
npx cmake-js compile -T whisper-addon -B Release npx cmake-js compile -T addon.node -B Release
``` ```
For Electron addon and cmake-js options, you can see [cmake-js](https://github.com/cmake-js/cmake-js) and make very few configuration changes. For Electron addon and cmake-js options, you can see [cmake-js](https://github.com/cmake-js/cmake-js) and make very few configuration changes.
> Such as appointing special cmake path: > Such as appointing special cmake path:
> ```shell > ```shell
> npx cmake-js compile -c 'xxx/cmake' -T whisper-addon -B Release > npx cmake-js compile -c 'xxx/cmake' -T addon.node -B Release
> ``` > ```
## Run ## Run

View File

@ -1,7 +1,7 @@
const path = require("path"); const path = require("path");
const { whisper } = require(path.join( const { whisper } = require(path.join(
__dirname, __dirname,
"../../../build/Release/whisper-addon" "../../../build/Release/addon.node"
)); ));
const { promisify } = require("util"); const { promisify } = require("util");
@ -12,6 +12,12 @@ const whisperParamsMock = {
model: path.join(__dirname, "../../../models/ggml-base.en.bin"), model: path.join(__dirname, "../../../models/ggml-base.en.bin"),
fname_inp: path.join(__dirname, "../../../samples/jfk.wav"), fname_inp: path.join(__dirname, "../../../samples/jfk.wav"),
use_gpu: true, use_gpu: true,
flash_attn: false,
no_prints: true,
comma_in_time: false,
translate: true,
no_timestamps: false,
audio_ctx: 0,
}; };
describe("Run whisper.node", () => { describe("Run whisper.node", () => {

View File

@ -19,12 +19,12 @@ struct whisper_params {
int32_t max_len = 0; int32_t max_len = 0;
int32_t best_of = 5; int32_t best_of = 5;
int32_t beam_size = -1; int32_t beam_size = -1;
int32_t audio_ctx = 0;
float word_thold = 0.01f; float word_thold = 0.01f;
float entropy_thold = 2.4f; float entropy_thold = 2.4f;
float logprob_thold = -1.0f; float logprob_thold = -1.0f;
bool speed_up = false;
bool translate = false; bool translate = false;
bool diarize = false; bool diarize = false;
bool output_txt = false; bool output_txt = false;
@ -36,7 +36,10 @@ struct whisper_params {
bool print_colors = false; bool print_colors = false;
bool print_progress = false; bool print_progress = false;
bool no_timestamps = false; bool no_timestamps = false;
bool no_prints = false;
bool use_gpu = true; bool use_gpu = true;
bool flash_attn = false;
bool comma_in_time = true;
std::string language = "en"; std::string language = "en";
std::string prompt; std::string prompt;
@ -44,6 +47,8 @@ struct whisper_params {
std::vector<std::string> fname_inp = {}; std::vector<std::string> fname_inp = {};
std::vector<std::string> fname_out = {}; std::vector<std::string> fname_out = {};
std::vector<float> pcmf32 = {}; // mono-channel F32 PCM
}; };
struct whisper_print_user_data { struct whisper_print_user_data {
@ -52,27 +57,6 @@ struct whisper_print_user_data {
const std::vector<std::vector<float>> * pcmf32s; const std::vector<std::vector<float>> * pcmf32s;
}; };
// 500 -> 00:05.000
// 6000 -> 01:00.000
std::string to_timestamp(int64_t t, bool comma = false) {
int64_t msec = t * 10;
int64_t hr = msec / (1000 * 60 * 60);
msec = msec - hr * (1000 * 60 * 60);
int64_t min = msec / (1000 * 60);
msec = msec - min * (1000 * 60);
int64_t sec = msec / 1000;
msec = msec - sec * 1000;
char buf[32];
snprintf(buf, sizeof(buf), "%02d:%02d:%02d%s%03d", (int) hr, (int) min, (int) sec, comma ? "," : ".", (int) msec);
return std::string(buf);
}
int timestamp_to_sample(int64_t t, int n_samples) {
return std::max(0, std::min((int) n_samples - 1, (int) ((t*WHISPER_SAMPLE_RATE)/100)));
}
void whisper_print_segment_callback(struct whisper_context * ctx, struct whisper_state * state, int n_new, void * user_data) { void whisper_print_segment_callback(struct whisper_context * ctx, struct whisper_state * state, int n_new, void * user_data) {
const auto & params = *((whisper_print_user_data *) user_data)->params; const auto & params = *((whisper_print_user_data *) user_data)->params;
const auto & pcmf32s = *((whisper_print_user_data *) user_data)->pcmf32s; const auto & pcmf32s = *((whisper_print_user_data *) user_data)->pcmf32s;
@ -104,8 +88,8 @@ void whisper_print_segment_callback(struct whisper_context * ctx, struct whisper
if (params.diarize && pcmf32s.size() == 2) { if (params.diarize && pcmf32s.size() == 2) {
const int64_t n_samples = pcmf32s[0].size(); const int64_t n_samples = pcmf32s[0].size();
const int64_t is0 = timestamp_to_sample(t0, n_samples); const int64_t is0 = timestamp_to_sample(t0, n_samples, WHISPER_SAMPLE_RATE);
const int64_t is1 = timestamp_to_sample(t1, n_samples); const int64_t is1 = timestamp_to_sample(t1, n_samples, WHISPER_SAMPLE_RATE);
double energy0 = 0.0f; double energy0 = 0.0f;
double energy1 = 0.0f; double energy1 = 0.0f;
@ -141,9 +125,15 @@ void whisper_print_segment_callback(struct whisper_context * ctx, struct whisper
} }
} }
void cb_log_disable(enum ggml_log_level, const char *, void *) {}
int run(whisper_params &params, std::vector<std::vector<std::string>> &result) { int run(whisper_params &params, std::vector<std::vector<std::string>> &result) {
if (params.fname_inp.empty()) { if (params.no_prints) {
fprintf(stderr, "error: no input files specified\n"); whisper_log_set(cb_log_disable, NULL);
}
if (params.fname_inp.empty() && params.pcmf32.empty()) {
fprintf(stderr, "error: no input files or audio buffer specified\n");
return 2; return 2;
} }
@ -156,6 +146,7 @@ int run(whisper_params &params, std::vector<std::vector<std::string>> &result) {
struct whisper_context_params cparams = whisper_context_default_params(); struct whisper_context_params cparams = whisper_context_default_params();
cparams.use_gpu = params.use_gpu; cparams.use_gpu = params.use_gpu;
cparams.flash_attn = params.flash_attn;
struct whisper_context * ctx = whisper_init_from_file_with_params(params.model.c_str(), cparams); struct whisper_context * ctx = whisper_init_from_file_with_params(params.model.c_str(), cparams);
if (ctx == nullptr) { if (ctx == nullptr) {
@ -163,6 +154,14 @@ int run(whisper_params &params, std::vector<std::vector<std::string>> &result) {
return 3; return 3;
} }
// if params.pcmf32 is provided, set params.fname_inp to "buffer"
// this is simpler than further modifications in the code
if (!params.pcmf32.empty()) {
fprintf(stderr, "info: using audio buffer as input\n");
params.fname_inp.clear();
params.fname_inp.emplace_back("buffer");
}
for (int f = 0; f < (int) params.fname_inp.size(); ++f) { for (int f = 0; f < (int) params.fname_inp.size(); ++f) {
const auto fname_inp = params.fname_inp[f]; const auto fname_inp = params.fname_inp[f];
const auto fname_out = f < (int)params.fname_out.size() && !params.fname_out[f].empty() ? params.fname_out[f] : params.fname_inp[f]; const auto fname_out = f < (int)params.fname_out.size() && !params.fname_out[f].empty() ? params.fname_out[f] : params.fname_inp[f];
@ -170,20 +169,25 @@ int run(whisper_params &params, std::vector<std::vector<std::string>> &result) {
std::vector<float> pcmf32; // mono-channel F32 PCM std::vector<float> pcmf32; // mono-channel F32 PCM
std::vector<std::vector<float>> pcmf32s; // stereo-channel F32 PCM std::vector<std::vector<float>> pcmf32s; // stereo-channel F32 PCM
if (!::read_wav(fname_inp, pcmf32, pcmf32s, params.diarize)) { // read the input audio file if params.pcmf32 is not provided
fprintf(stderr, "error: failed to read WAV file '%s'\n", fname_inp.c_str()); if (params.pcmf32.empty()) {
continue; if (!::read_wav(fname_inp, pcmf32, pcmf32s, params.diarize)) {
fprintf(stderr, "error: failed to read WAV file '%s'\n", fname_inp.c_str());
continue;
}
} else {
pcmf32 = params.pcmf32;
} }
// print system information // print system information
{ if (!params.no_prints) {
fprintf(stderr, "\n"); fprintf(stderr, "\n");
fprintf(stderr, "system_info: n_threads = %d / %d | %s\n", fprintf(stderr, "system_info: n_threads = %d / %d | %s\n",
params.n_threads*params.n_processors, std::thread::hardware_concurrency(), whisper_print_system_info()); params.n_threads*params.n_processors, std::thread::hardware_concurrency(), whisper_print_system_info());
} }
// print some info about the processing // print some info about the processing
{ if (!params.no_prints) {
fprintf(stderr, "\n"); fprintf(stderr, "\n");
if (!whisper_is_multilingual(ctx)) { if (!whisper_is_multilingual(ctx)) {
if (params.language != "en" || params.translate) { if (params.language != "en" || params.translate) {
@ -192,12 +196,13 @@ int run(whisper_params &params, std::vector<std::vector<std::string>> &result) {
fprintf(stderr, "%s: WARNING: model is not multilingual, ignoring language and translation options\n", __func__); fprintf(stderr, "%s: WARNING: model is not multilingual, ignoring language and translation options\n", __func__);
} }
} }
fprintf(stderr, "%s: processing '%s' (%d samples, %.1f sec), %d threads, %d processors, lang = %s, task = %s, timestamps = %d ...\n", fprintf(stderr, "%s: processing '%s' (%d samples, %.1f sec), %d threads, %d processors, lang = %s, task = %s, timestamps = %d, audio_ctx = %d ...\n",
__func__, fname_inp.c_str(), int(pcmf32.size()), float(pcmf32.size())/WHISPER_SAMPLE_RATE, __func__, fname_inp.c_str(), int(pcmf32.size()), float(pcmf32.size())/WHISPER_SAMPLE_RATE,
params.n_threads, params.n_processors, params.n_threads, params.n_processors,
params.language.c_str(), params.language.c_str(),
params.translate ? "translate" : "transcribe", params.translate ? "translate" : "transcribe",
params.no_timestamps ? 0 : 1); params.no_timestamps ? 0 : 1,
params.audio_ctx);
fprintf(stderr, "\n"); fprintf(stderr, "\n");
} }
@ -224,14 +229,15 @@ int run(whisper_params &params, std::vector<std::vector<std::string>> &result) {
wparams.entropy_thold = params.entropy_thold; wparams.entropy_thold = params.entropy_thold;
wparams.logprob_thold = params.logprob_thold; wparams.logprob_thold = params.logprob_thold;
wparams.max_len = params.output_wts && params.max_len == 0 ? 60 : params.max_len; wparams.max_len = params.output_wts && params.max_len == 0 ? 60 : params.max_len;
wparams.audio_ctx = params.audio_ctx;
wparams.speed_up = params.speed_up;
wparams.greedy.best_of = params.best_of; wparams.greedy.best_of = params.best_of;
wparams.beam_search.beam_size = params.beam_size; wparams.beam_search.beam_size = params.beam_size;
wparams.initial_prompt = params.prompt.c_str(); wparams.initial_prompt = params.prompt.c_str();
wparams.no_timestamps = params.no_timestamps;
whisper_print_user_data user_data = { &params, &pcmf32s }; whisper_print_user_data user_data = { &params, &pcmf32s };
// this callback is called on each new segment // this callback is called on each new segment
@ -267,8 +273,8 @@ int run(whisper_params &params, std::vector<std::vector<std::string>> &result) {
const int64_t t0 = whisper_full_get_segment_t0(ctx, i); const int64_t t0 = whisper_full_get_segment_t0(ctx, i);
const int64_t t1 = whisper_full_get_segment_t1(ctx, i); const int64_t t1 = whisper_full_get_segment_t1(ctx, i);
result[i].emplace_back(to_timestamp(t0, true)); result[i].emplace_back(to_timestamp(t0, params.comma_in_time));
result[i].emplace_back(to_timestamp(t1, true)); result[i].emplace_back(to_timestamp(t1, params.comma_in_time));
result[i].emplace_back(text); result[i].emplace_back(text);
} }
@ -319,11 +325,33 @@ Napi::Value whisper(const Napi::CallbackInfo& info) {
std::string model = whisper_params.Get("model").As<Napi::String>(); std::string model = whisper_params.Get("model").As<Napi::String>();
std::string input = whisper_params.Get("fname_inp").As<Napi::String>(); std::string input = whisper_params.Get("fname_inp").As<Napi::String>();
bool use_gpu = whisper_params.Get("use_gpu").As<Napi::Boolean>(); bool use_gpu = whisper_params.Get("use_gpu").As<Napi::Boolean>();
bool flash_attn = whisper_params.Get("flash_attn").As<Napi::Boolean>();
bool no_prints = whisper_params.Get("no_prints").As<Napi::Boolean>();
bool no_timestamps = whisper_params.Get("no_timestamps").As<Napi::Boolean>();
int32_t audio_ctx = whisper_params.Get("audio_ctx").As<Napi::Number>();
bool comma_in_time = whisper_params.Get("comma_in_time").As<Napi::Boolean>();
Napi::Value pcmf32Value = whisper_params.Get("pcmf32");
std::vector<float> pcmf32_vec;
if (pcmf32Value.IsTypedArray()) {
Napi::Float32Array pcmf32 = pcmf32Value.As<Napi::Float32Array>();
size_t length = pcmf32.ElementLength();
pcmf32_vec.reserve(length);
for (size_t i = 0; i < length; i++) {
pcmf32_vec.push_back(pcmf32[i]);
}
}
params.language = language; params.language = language;
params.model = model; params.model = model;
params.fname_inp.emplace_back(input); params.fname_inp.emplace_back(input);
params.use_gpu = use_gpu; params.use_gpu = use_gpu;
params.flash_attn = flash_attn;
params.no_prints = no_prints;
params.no_timestamps = no_timestamps;
params.audio_ctx = audio_ctx;
params.pcmf32 = pcmf32_vec;
params.comma_in_time = comma_in_time;
Napi::Function callback = info[1].As<Napi::Function>(); Napi::Function callback = info[1].As<Napi::Function>();
Worker* worker = new Worker(callback, params); Worker* worker = new Worker(callback, params);

View File

@ -1,7 +1,7 @@
const path = require("path"); const path = require("path");
const { whisper } = require(path.join( const { whisper } = require(path.join(
__dirname, __dirname,
"../../build/Release/whisper-addon" "../../build/Release/addon.node"
)); ));
const { promisify } = require("util"); const { promisify } = require("util");
@ -10,15 +10,27 @@ const whisperAsync = promisify(whisper);
const whisperParams = { const whisperParams = {
language: "en", language: "en",
model: path.join(__dirname, "../../models/ggml-base.en.bin"), model: path.join(__dirname, "../../models/ggml-base.en.bin"),
fname_inp: "../../samples/jfk.wav", fname_inp: path.join(__dirname, "../../samples/jfk.wav"),
use_gpu: true, use_gpu: true,
flash_attn: false,
no_prints: true,
comma_in_time: false,
translate: true,
no_timestamps: false,
audio_ctx: 0,
}; };
const arguments = process.argv.slice(2); const arguments = process.argv.slice(2);
const params = Object.fromEntries( const params = Object.fromEntries(
arguments.reduce((pre, item) => { arguments.reduce((pre, item) => {
if (item.startsWith("--")) { if (item.startsWith("--")) {
return [...pre, item.slice(2).split("=")]; const [key, value] = item.slice(2).split("=");
if (key === "audio_ctx") {
whisperParams[key] = parseInt(value);
} else {
whisperParams[key] = value;
}
return pre;
} }
return pre; return pre;
}, []) }, [])
@ -33,5 +45,6 @@ for (const key in params) {
console.log("whisperParams =", whisperParams); console.log("whisperParams =", whisperParams);
whisperAsync(whisperParams).then((result) => { whisperAsync(whisperParams).then((result) => {
console.log(`Result from whisper: ${result}`); console.log();
console.log(result);
}); });

View File

@ -1,5 +1,5 @@
{ {
"name": "whisper-addon", "name": "addon.node",
"version": "0.0.0", "version": "0.0.0",
"description": "", "description": "",
"main": "index.js", "main": "index.js",

View File

@ -8,16 +8,17 @@
// command-line parameters // command-line parameters
struct whisper_params { struct whisper_params {
int32_t n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency()); int32_t n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
int32_t what = 0; // what to benchmark: 0 - whisper ecoder, 1 - memcpy, 2 - ggml_mul_mat int32_t what = 0; // what to benchmark: 0 - whisper encoder, 1 - memcpy, 2 - ggml_mul_mat
std::string model = "models/ggml-base.en.bin"; std::string model = "models/ggml-base.en.bin";
bool use_gpu = true; bool use_gpu = true;
bool flash_attn = false;
}; };
void whisper_print_usage(int argc, char ** argv, const whisper_params & params); void whisper_print_usage(int argc, char ** argv, const whisper_params & params);
bool whisper_params_parse(int argc, char ** argv, whisper_params & params) { static bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
for (int i = 1; i < argc; i++) { for (int i = 1; i < argc; i++) {
std::string arg = argv[i]; std::string arg = argv[i];
@ -25,10 +26,11 @@ bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
whisper_print_usage(argc, argv, params); whisper_print_usage(argc, argv, params);
exit(0); exit(0);
} }
else if (arg == "-t" || arg == "--threads") { params.n_threads = std::stoi(argv[++i]); } else if (arg == "-t" || arg == "--threads") { params.n_threads = std::stoi(argv[++i]); }
else if (arg == "-m" || arg == "--model") { params.model = argv[++i]; } else if (arg == "-m" || arg == "--model") { params.model = argv[++i]; }
else if (arg == "-w" || arg == "--what") { params.what = atoi(argv[++i]); } else if (arg == "-w" || arg == "--what") { params.what = atoi(argv[++i]); }
else if (arg == "-ng" || arg == "--no-gpu") { params.use_gpu = false; } else if (arg == "-ng" || arg == "--no-gpu") { params.use_gpu = false; }
else if (arg == "-fa" || arg == "--flash-attn") { params.flash_attn = true; }
else { else {
fprintf(stderr, "error: unknown argument: %s\n", arg.c_str()); fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
whisper_print_usage(argc, argv, params); whisper_print_usage(argc, argv, params);
@ -49,17 +51,20 @@ void whisper_print_usage(int /*argc*/, char ** argv, const whisper_params & para
fprintf(stderr, " -m FNAME, --model FNAME [%-7s] model path\n", params.model.c_str()); fprintf(stderr, " -m FNAME, --model FNAME [%-7s] model path\n", params.model.c_str());
fprintf(stderr, " -w N, --what N [%-7d] what to benchmark:\n", params.what); fprintf(stderr, " -w N, --what N [%-7d] what to benchmark:\n", params.what);
fprintf(stderr, " -ng, --no-gpu [%-7s] disable GPU\n", params.use_gpu ? "false" : "true"); fprintf(stderr, " -ng, --no-gpu [%-7s] disable GPU\n", params.use_gpu ? "false" : "true");
fprintf(stderr, " -fa, --flash-attn [%-7s] enable flash attention\n", params.flash_attn ? "true" : "false");
fprintf(stderr, " %-7s 0 - whisper\n", ""); fprintf(stderr, " %-7s 0 - whisper\n", "");
fprintf(stderr, " %-7s 1 - memcpy\n", ""); fprintf(stderr, " %-7s 1 - memcpy\n", "");
fprintf(stderr, " %-7s 2 - ggml_mul_mat\n", ""); fprintf(stderr, " %-7s 2 - ggml_mul_mat\n", "");
fprintf(stderr, "\n"); fprintf(stderr, "\n");
} }
int whisper_bench_full(const whisper_params & params) { static int whisper_bench_full(const whisper_params & params) {
// whisper init // whisper init
struct whisper_context_params cparams = whisper_context_default_params(); struct whisper_context_params cparams = whisper_context_default_params();
cparams.use_gpu = params.use_gpu;
cparams.use_gpu = params.use_gpu;
cparams.flash_attn = params.flash_attn;
struct whisper_context * ctx = whisper_init_from_file_with_params(params.model.c_str(), cparams); struct whisper_context * ctx = whisper_init_from_file_with_params(params.model.c_str(), cparams);

View File

@ -37,9 +37,13 @@ https://user-images.githubusercontent.com/1991296/207435352-8fc4ed3f-bde5-4555-9
The `command` tool depends on SDL2 library to capture audio from the microphone. You can build it like this: The `command` tool depends on SDL2 library to capture audio from the microphone. You can build it like this:
```bash ```bash
# Install SDL2 on Linux # Install SDL2
# On Debian based linux distributions:
sudo apt-get install libsdl2-dev sudo apt-get install libsdl2-dev
# On Fedora Linux:
sudo dnf install SDL2 SDL2-devel
# Install SDL2 on Mac OS # Install SDL2 on Mac OS
brew install sdl2 brew install sdl2

View File

@ -22,11 +22,6 @@
#include <vector> #include <vector>
#include <map> #include <map>
bool file_exists(const std::string & fname) {
std::ifstream f(fname.c_str());
return f.good();
}
// command-line parameters // command-line parameters
struct whisper_params { struct whisper_params {
int32_t n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency()); int32_t n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
@ -43,12 +38,12 @@ struct whisper_params {
grammar_parser::parse_state grammar_parsed; grammar_parser::parse_state grammar_parsed;
bool speed_up = false;
bool translate = false; bool translate = false;
bool print_special = false; bool print_special = false;
bool print_energy = false; bool print_energy = false;
bool no_timestamps = true; bool no_timestamps = true;
bool use_gpu = true; bool use_gpu = true;
bool flash_attn = false;
std::string language = "en"; std::string language = "en";
std::string model = "models/ggml-base.en.bin"; std::string model = "models/ggml-base.en.bin";
@ -57,11 +52,14 @@ struct whisper_params {
std::string prompt; std::string prompt;
std::string context; std::string context;
std::string grammar; std::string grammar;
// A regular expression that matches tokens to suppress
std::string suppress_regex;
}; };
void whisper_print_usage(int argc, char ** argv, const whisper_params & params); void whisper_print_usage(int argc, char ** argv, const whisper_params & params);
bool whisper_params_parse(int argc, char ** argv, whisper_params & params) { static bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
for (int i = 1; i < argc; i++) { for (int i = 1; i < argc; i++) {
std::string arg = argv[i]; std::string arg = argv[i];
@ -77,11 +75,11 @@ bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
else if (arg == "-ac" || arg == "--audio-ctx") { params.audio_ctx = std::stoi(argv[++i]); } else if (arg == "-ac" || arg == "--audio-ctx") { params.audio_ctx = std::stoi(argv[++i]); }
else if (arg == "-vth" || arg == "--vad-thold") { params.vad_thold = std::stof(argv[++i]); } else if (arg == "-vth" || arg == "--vad-thold") { params.vad_thold = std::stof(argv[++i]); }
else if (arg == "-fth" || arg == "--freq-thold") { params.freq_thold = std::stof(argv[++i]); } else if (arg == "-fth" || arg == "--freq-thold") { params.freq_thold = std::stof(argv[++i]); }
else if (arg == "-su" || arg == "--speed-up") { params.speed_up = true; }
else if (arg == "-tr" || arg == "--translate") { params.translate = true; } else if (arg == "-tr" || arg == "--translate") { params.translate = true; }
else if (arg == "-ps" || arg == "--print-special") { params.print_special = true; } else if (arg == "-ps" || arg == "--print-special") { params.print_special = true; }
else if (arg == "-pe" || arg == "--print-energy") { params.print_energy = true; } else if (arg == "-pe" || arg == "--print-energy") { params.print_energy = true; }
else if (arg == "-ng" || arg == "--no-gpu") { params.use_gpu = false; } else if (arg == "-ng" || arg == "--no-gpu") { params.use_gpu = false; }
else if (arg == "-fa" || arg == "--flash-attn") { params.flash_attn = true; }
else if (arg == "-l" || arg == "--language") { params.language = argv[++i]; } else if (arg == "-l" || arg == "--language") { params.language = argv[++i]; }
else if (arg == "-m" || arg == "--model") { params.model = argv[++i]; } else if (arg == "-m" || arg == "--model") { params.model = argv[++i]; }
else if (arg == "-f" || arg == "--file") { params.fname_out = argv[++i]; } else if (arg == "-f" || arg == "--file") { params.fname_out = argv[++i]; }
@ -90,6 +88,7 @@ bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
else if (arg == "-ctx" || arg == "--context") { params.context = argv[++i]; } else if (arg == "-ctx" || arg == "--context") { params.context = argv[++i]; }
else if ( arg == "--grammar") { params.grammar = argv[++i]; } else if ( arg == "--grammar") { params.grammar = argv[++i]; }
else if ( arg == "--grammar-penalty") { params.grammar_penalty = std::stof(argv[++i]); } else if ( arg == "--grammar-penalty") { params.grammar_penalty = std::stof(argv[++i]); }
else if ( arg == "--suppress-regex") { params.suppress_regex = argv[++i]; }
else { else {
fprintf(stderr, "error: unknown argument: %s\n", arg.c_str()); fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
whisper_print_usage(argc, argv, params); whisper_print_usage(argc, argv, params);
@ -114,11 +113,11 @@ void whisper_print_usage(int /*argc*/, char ** argv, const whisper_params & para
fprintf(stderr, " -ac N, --audio-ctx N [%-7d] audio context size (0 - all)\n", params.audio_ctx); fprintf(stderr, " -ac N, --audio-ctx N [%-7d] audio context size (0 - all)\n", params.audio_ctx);
fprintf(stderr, " -vth N, --vad-thold N [%-7.2f] voice activity detection threshold\n", params.vad_thold); fprintf(stderr, " -vth N, --vad-thold N [%-7.2f] voice activity detection threshold\n", params.vad_thold);
fprintf(stderr, " -fth N, --freq-thold N [%-7.2f] high-pass frequency cutoff\n", params.freq_thold); fprintf(stderr, " -fth N, --freq-thold N [%-7.2f] high-pass frequency cutoff\n", params.freq_thold);
fprintf(stderr, " -su, --speed-up [%-7s] speed up audio by x2 (reduced accuracy)\n", params.speed_up ? "true" : "false");
fprintf(stderr, " -tr, --translate [%-7s] translate from source language to english\n", params.translate ? "true" : "false"); fprintf(stderr, " -tr, --translate [%-7s] translate from source language to english\n", params.translate ? "true" : "false");
fprintf(stderr, " -ps, --print-special [%-7s] print special tokens\n", params.print_special ? "true" : "false"); fprintf(stderr, " -ps, --print-special [%-7s] print special tokens\n", params.print_special ? "true" : "false");
fprintf(stderr, " -pe, --print-energy [%-7s] print sound energy (for debugging)\n", params.print_energy ? "true" : "false"); fprintf(stderr, " -pe, --print-energy [%-7s] print sound energy (for debugging)\n", params.print_energy ? "true" : "false");
fprintf(stderr, " -ng, --no-gpu [%-7s] disable GPU\n", params.use_gpu ? "false" : "true"); fprintf(stderr, " -ng, --no-gpu [%-7s] disable GPU\n", params.use_gpu ? "false" : "true");
fprintf(stderr, " -fa, --flash-attn [%-7s] flash attention\n", params.flash_attn ? "true" : "false");
fprintf(stderr, " -l LANG, --language LANG [%-7s] spoken language\n", params.language.c_str()); fprintf(stderr, " -l LANG, --language LANG [%-7s] spoken language\n", params.language.c_str());
fprintf(stderr, " -m FNAME, --model FNAME [%-7s] model path\n", params.model.c_str()); fprintf(stderr, " -m FNAME, --model FNAME [%-7s] model path\n", params.model.c_str());
fprintf(stderr, " -f FNAME, --file FNAME [%-7s] text output file name\n", params.fname_out.c_str()); fprintf(stderr, " -f FNAME, --file FNAME [%-7s] text output file name\n", params.fname_out.c_str());
@ -127,10 +126,11 @@ void whisper_print_usage(int /*argc*/, char ** argv, const whisper_params & para
fprintf(stderr, " -ctx, --context [%-7s] sample text to help the transcription\n", params.context.c_str()); fprintf(stderr, " -ctx, --context [%-7s] sample text to help the transcription\n", params.context.c_str());
fprintf(stderr, " --grammar GRAMMAR [%-7s] GBNF grammar to guide decoding\n", params.grammar.c_str()); fprintf(stderr, " --grammar GRAMMAR [%-7s] GBNF grammar to guide decoding\n", params.grammar.c_str());
fprintf(stderr, " --grammar-penalty N [%-7.1f] scales down logits of nongrammar tokens\n", params.grammar_penalty); fprintf(stderr, " --grammar-penalty N [%-7.1f] scales down logits of nongrammar tokens\n", params.grammar_penalty);
fprintf(stderr, " --suppress-regex REGEX [%-7s] regular expression matching tokens to suppress\n", params.suppress_regex.c_str());
fprintf(stderr, "\n"); fprintf(stderr, "\n");
} }
std::string transcribe( static std::string transcribe(
whisper_context * ctx, whisper_context * ctx,
const whisper_params & params, const whisper_params & params,
const std::vector<float> & pcmf32, const std::vector<float> & pcmf32,
@ -162,7 +162,6 @@ std::string transcribe(
wparams.n_threads = params.n_threads; wparams.n_threads = params.n_threads;
wparams.audio_ctx = params.audio_ctx; wparams.audio_ctx = params.audio_ctx;
wparams.speed_up = params.speed_up;
wparams.temperature = 0.4f; wparams.temperature = 0.4f;
wparams.temperature_inc = 1.0f; wparams.temperature_inc = 1.0f;
@ -172,6 +171,8 @@ std::string transcribe(
wparams.initial_prompt = params.context.data(); wparams.initial_prompt = params.context.data();
wparams.suppress_regex = params.suppress_regex.c_str();
const auto & grammar_parsed = params.grammar_parsed; const auto & grammar_parsed = params.grammar_parsed;
auto grammar_rules = grammar_parsed.c_rules(); auto grammar_rules = grammar_parsed.c_rules();
@ -215,7 +216,7 @@ std::string transcribe(
return result; return result;
} }
std::vector<std::string> read_allowed_commands(const std::string & fname) { static std::vector<std::string> read_allowed_commands(const std::string & fname) {
std::vector<std::string> allowed_commands; std::vector<std::string> allowed_commands;
std::ifstream ifs(fname); std::ifstream ifs(fname);
@ -237,7 +238,7 @@ std::vector<std::string> read_allowed_commands(const std::string & fname) {
return allowed_commands; return allowed_commands;
} }
std::vector<std::string> get_words(const std::string &txt) { static std::vector<std::string> get_words(const std::string &txt) {
std::vector<std::string> words; std::vector<std::string> words;
std::istringstream iss(txt); std::istringstream iss(txt);
@ -251,7 +252,7 @@ std::vector<std::string> get_words(const std::string &txt) {
// command-list mode // command-list mode
// guide the transcription to match the most likely command from a provided list // guide the transcription to match the most likely command from a provided list
int process_command_list(struct whisper_context * ctx, audio_async &audio, const whisper_params &params) { static int process_command_list(struct whisper_context * ctx, audio_async &audio, const whisper_params &params) {
fprintf(stderr, "\n"); fprintf(stderr, "\n");
fprintf(stderr, "%s: guided mode\n", __func__); fprintf(stderr, "%s: guided mode\n", __func__);
@ -366,7 +367,6 @@ int process_command_list(struct whisper_context * ctx, audio_async &audio, const
wparams.n_threads = params.n_threads; wparams.n_threads = params.n_threads;
wparams.audio_ctx = params.audio_ctx; wparams.audio_ctx = params.audio_ctx;
wparams.speed_up = params.speed_up;
wparams.prompt_tokens = k_tokens.data(); wparams.prompt_tokens = k_tokens.data();
wparams.prompt_n_tokens = k_tokens.size(); wparams.prompt_n_tokens = k_tokens.size();
@ -463,7 +463,7 @@ int process_command_list(struct whisper_context * ctx, audio_async &audio, const
// always-prompt mode // always-prompt mode
// transcribe the voice into text after valid prompt // transcribe the voice into text after valid prompt
int always_prompt_transcription(struct whisper_context * ctx, audio_async & audio, const whisper_params & params) { static int always_prompt_transcription(struct whisper_context * ctx, audio_async & audio, const whisper_params & params) {
bool is_running = true; bool is_running = true;
bool ask_prompt = true; bool ask_prompt = true;
@ -543,7 +543,7 @@ int always_prompt_transcription(struct whisper_context * ctx, audio_async & audi
// general-purpose mode // general-purpose mode
// freely transcribe the voice into text // freely transcribe the voice into text
int process_general_transcription(struct whisper_context * ctx, audio_async & audio, const whisper_params & params) { static int process_general_transcription(struct whisper_context * ctx, audio_async & audio, const whisper_params & params) {
bool is_running = true; bool is_running = true;
bool have_prompt = false; bool have_prompt = false;
bool ask_prompt = true; bool ask_prompt = true;
@ -694,7 +694,9 @@ int main(int argc, char ** argv) {
// whisper init // whisper init
struct whisper_context_params cparams = whisper_context_default_params(); struct whisper_context_params cparams = whisper_context_default_params();
cparams.use_gpu = params.use_gpu;
cparams.use_gpu = params.use_gpu;
cparams.flash_attn = params.flash_attn;
struct whisper_context * ctx = whisper_init_from_file_with_params(params.model.c_str(), cparams); struct whisper_context * ctx = whisper_init_from_file_with_params(params.model.c_str(), cparams);
@ -736,7 +738,7 @@ int main(int argc, char ** argv) {
if (!params.grammar.empty()) { if (!params.grammar.empty()) {
auto & grammar = params.grammar_parsed; auto & grammar = params.grammar_parsed;
if (file_exists(params.grammar.c_str())) { if (is_file_exist(params.grammar.c_str())) {
// read grammar from file // read grammar from file
std::ifstream ifs(params.grammar.c_str()); std::ifstream ifs(params.grammar.c_str());
const std::string txt = std::string((std::istreambuf_iterator<char>(ifs)), std::istreambuf_iterator<char>()); const std::string txt = std::string((std::istreambuf_iterator<char>(ifs)), std::istreambuf_iterator<char>());

View File

@ -64,7 +64,14 @@ bool ggml_common_quantize_0(
case GGML_FTYPE_MOSTLY_Q4_1_SOME_F16: case GGML_FTYPE_MOSTLY_Q4_1_SOME_F16:
case GGML_FTYPE_MOSTLY_IQ2_XXS: case GGML_FTYPE_MOSTLY_IQ2_XXS:
case GGML_FTYPE_MOSTLY_IQ2_XS: case GGML_FTYPE_MOSTLY_IQ2_XS:
case GGML_FTYPE_MOSTLY_IQ2_S:
case GGML_FTYPE_MOSTLY_IQ3_XXS: case GGML_FTYPE_MOSTLY_IQ3_XXS:
case GGML_FTYPE_MOSTLY_IQ3_S:
case GGML_FTYPE_MOSTLY_IQ1_S:
case GGML_FTYPE_MOSTLY_IQ4_NL:
case GGML_FTYPE_MOSTLY_IQ4_XS:
case GGML_FTYPE_MOSTLY_IQ1_M:
case GGML_FTYPE_MOSTLY_BF16:
{ {
fprintf(stderr, "%s: invalid model type %d\n", __func__, ftype); fprintf(stderr, "%s: invalid model type %d\n", __func__, ftype);
return false; return false;
@ -85,8 +92,6 @@ bool ggml_common_quantize_0(
std::vector<ggml_fp16_t> data_f16; std::vector<ggml_fp16_t> data_f16;
std::vector<float> data_f32; std::vector<float> data_f32;
std::vector<int64_t> hist_all(1 << 4, 0);
while (true) { while (true) {
int32_t n_dims; int32_t n_dims;
int32_t length; int32_t length;
@ -171,8 +176,6 @@ bool ggml_common_quantize_0(
work.resize(nelements); // for quantization work.resize(nelements); // for quantization
size_t cur_size = 0; size_t cur_size = 0;
std::vector<int64_t> hist_cur(1 << 4, 0);
switch ((ggml_type) ttype) { switch ((ggml_type) ttype) {
case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_0:
case GGML_TYPE_Q4_1: case GGML_TYPE_Q4_1:
@ -185,18 +188,27 @@ bool ggml_common_quantize_0(
case GGML_TYPE_Q5_K: case GGML_TYPE_Q5_K:
case GGML_TYPE_Q6_K: case GGML_TYPE_Q6_K:
{ {
cur_size = ggml_quantize_chunk((ggml_type) ttype, data_f32.data(), work.data(), 0, nelements/ne[0], ne[0], hist_cur.data(), nullptr); cur_size = ggml_quantize_chunk((ggml_type) ttype, data_f32.data(), work.data(), 0, nelements/ne[0], ne[0], nullptr);
} break; } break;
case GGML_TYPE_F32: case GGML_TYPE_F32:
case GGML_TYPE_F16: case GGML_TYPE_F16:
case GGML_TYPE_I8: case GGML_TYPE_I8:
case GGML_TYPE_I16: case GGML_TYPE_I16:
case GGML_TYPE_I32: case GGML_TYPE_I32:
case GGML_TYPE_I64:
case GGML_TYPE_F64:
case GGML_TYPE_Q8_1: case GGML_TYPE_Q8_1:
case GGML_TYPE_Q8_K: case GGML_TYPE_Q8_K:
case GGML_TYPE_IQ2_XXS: case GGML_TYPE_IQ2_XXS:
case GGML_TYPE_IQ2_XS: case GGML_TYPE_IQ2_XS:
case GGML_TYPE_IQ2_S:
case GGML_TYPE_IQ3_XXS: case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ3_S:
case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS:
case GGML_TYPE_IQ1_M:
case GGML_TYPE_BF16:
case GGML_TYPE_COUNT: case GGML_TYPE_COUNT:
{ {
fprintf(stderr, "%s: unsupported quantization type %d (%s)\n", __func__, ttype, ggml_type_name((ggml_type) ttype)); fprintf(stderr, "%s: unsupported quantization type %d (%s)\n", __func__, ttype, ggml_type_name((ggml_type) ttype));
@ -207,15 +219,7 @@ bool ggml_common_quantize_0(
fout.write(reinterpret_cast<char *>(work.data()), cur_size); fout.write(reinterpret_cast<char *>(work.data()), cur_size);
total_size_new += cur_size; total_size_new += cur_size;
printf("size = %8.2f MB -> %8.2f MB | hist: ", nelements * sizeof(float)/1024.0/1024.0, cur_size/1024.0/1024.0); printf("size = %8.2f MB -> %8.2f MB\n", nelements * sizeof(float)/1024.0/1024.0, cur_size/1024.0/1024.0);
for (int i = 0; i < (int) hist_cur.size(); ++i) {
hist_all[i] += hist_cur[i];
}
for (int i = 0; i < (int) hist_cur.size(); ++i) {
printf("%5.3f ", hist_cur[i] / (float)nelements);
}
printf("\n");
} else { } else {
printf("size = %8.3f MB\n", data_u8.size()/1024.0/1024.0); printf("size = %8.3f MB\n", data_u8.size()/1024.0/1024.0);
fout.write(reinterpret_cast<char *>(data_u8.data()), data_u8.size()); fout.write(reinterpret_cast<char *>(data_u8.data()), data_u8.size());
@ -228,18 +232,5 @@ bool ggml_common_quantize_0(
printf("%s: model size = %8.2f MB\n", __func__, total_size_org/1024.0/1024.0); printf("%s: model size = %8.2f MB\n", __func__, total_size_org/1024.0/1024.0);
printf("%s: quant size = %8.2f MB | ftype = %d (%s)\n", __func__, total_size_new/1024.0/1024.0, ftype, ggml_type_name(qtype)); printf("%s: quant size = %8.2f MB | ftype = %d (%s)\n", __func__, total_size_new/1024.0/1024.0, ftype, ggml_type_name(qtype));
{
int64_t sum_all = 0;
for (int i = 0; i < (int) hist_all.size(); ++i) {
sum_all += hist_all[i];
}
printf("%s: hist: ", __func__);
for (int i = 0; i < (int) hist_all.size(); ++i) {
printf("%5.3f ", hist_all[i] / (float)sum_all);
}
printf("\n");
}
return true; return true;
} }

View File

@ -219,7 +219,7 @@ bool sdl_poll_events() {
case SDL_QUIT: case SDL_QUIT:
{ {
return false; return false;
} break; }
default: default:
break; break;
} }

View File

@ -19,8 +19,18 @@
#pragma warning(disable: 4244 4267) // possible loss of data #pragma warning(disable: 4244 4267) // possible loss of data
#endif #endif
#ifdef _WIN32
#include <fcntl.h>
#include <io.h>
#endif
#ifdef WHISPER_FFMPEG
// as implemented in ffmpeg_trancode.cpp only embedded in common lib if whisper built with ffmpeg support
extern bool ffmpeg_decode_audio(const std::string & ifname, std::vector<uint8_t> & wav_data);
#endif
// Function to check if the next argument exists // Function to check if the next argument exists
std::string get_next_arg(int& i, int argc, char** argv, const std::string& flag, gpt_params& params) { static std::string get_next_arg(int& i, int argc, char** argv, const std::string& flag, gpt_params& params) {
if (i + 1 < argc && argv[i + 1][0] != '-') { if (i + 1 < argc && argv[i + 1][0] != '-') {
return argv[++i]; return argv[++i];
} else { } else {
@ -336,7 +346,7 @@ std::vector<gpt_vocab::id> gpt_tokenize(const gpt_vocab & vocab, const std::stri
return tokens; return tokens;
} }
std::vector<gpt_vocab::id> parse_tokens_from_string(const std::string& input, char delimiter) { static std::vector<gpt_vocab::id> parse_tokens_from_string(const std::string& input, char delimiter) {
std::vector<gpt_vocab::id> output; std::vector<gpt_vocab::id> output;
std::stringstream ss(input); std::stringstream ss(input);
std::string token; std::string token;
@ -348,7 +358,7 @@ std::vector<gpt_vocab::id> parse_tokens_from_string(const std::string& input, ch
return output; return output;
} }
std::map<std::string, std::vector<gpt_vocab::id>> extract_tests_from_file(const std::string & fpath_test){ static std::map<std::string, std::vector<gpt_vocab::id>> extract_tests_from_file(const std::string & fpath_test){
if (fpath_test.empty()){ if (fpath_test.empty()){
fprintf(stderr, "%s : No test file found.\n", __func__); fprintf(stderr, "%s : No test file found.\n", __func__);
return std::map<std::string, std::vector<gpt_vocab::id>>(); return std::map<std::string, std::vector<gpt_vocab::id>>();
@ -632,10 +642,14 @@ bool is_wav_buffer(const std::string buf) {
bool read_wav(const std::string & fname, std::vector<float>& pcmf32, std::vector<std::vector<float>>& pcmf32s, bool stereo) { bool read_wav(const std::string & fname, std::vector<float>& pcmf32, std::vector<std::vector<float>>& pcmf32s, bool stereo) {
drwav wav; drwav wav;
std::vector<uint8_t> wav_data; // used for pipe input from stdin std::vector<uint8_t> wav_data; // used for pipe input from stdin or ffmpeg decoding output
if (fname == "-") { if (fname == "-") {
{ {
#ifdef _WIN32
_setmode(_fileno(stdin), _O_BINARY);
#endif
uint8_t buf[1024]; uint8_t buf[1024];
while (true) while (true)
{ {
@ -661,27 +675,42 @@ bool read_wav(const std::string & fname, std::vector<float>& pcmf32, std::vector
} }
} }
else if (drwav_init_file(&wav, fname.c_str(), nullptr) == false) { else if (drwav_init_file(&wav, fname.c_str(), nullptr) == false) {
#if defined(WHISPER_FFMPEG)
if (ffmpeg_decode_audio(fname, wav_data) != 0) {
fprintf(stderr, "error: failed to ffmpeg decode '%s' \n", fname.c_str());
return false;
}
if (drwav_init_memory(&wav, wav_data.data(), wav_data.size(), nullptr) == false) {
fprintf(stderr, "error: failed to read wav data as wav \n");
return false;
}
#else
fprintf(stderr, "error: failed to open '%s' as WAV file\n", fname.c_str()); fprintf(stderr, "error: failed to open '%s' as WAV file\n", fname.c_str());
return false; return false;
#endif
} }
if (wav.channels != 1 && wav.channels != 2) { if (wav.channels != 1 && wav.channels != 2) {
fprintf(stderr, "%s: WAV file '%s' must be mono or stereo\n", __func__, fname.c_str()); fprintf(stderr, "%s: WAV file '%s' must be mono or stereo\n", __func__, fname.c_str());
drwav_uninit(&wav);
return false; return false;
} }
if (stereo && wav.channels != 2) { if (stereo && wav.channels != 2) {
fprintf(stderr, "%s: WAV file '%s' must be stereo for diarization\n", __func__, fname.c_str()); fprintf(stderr, "%s: WAV file '%s' must be stereo for diarization\n", __func__, fname.c_str());
drwav_uninit(&wav);
return false; return false;
} }
if (wav.sampleRate != COMMON_SAMPLE_RATE) { if (wav.sampleRate != COMMON_SAMPLE_RATE) {
fprintf(stderr, "%s: WAV file '%s' must be %i kHz\n", __func__, fname.c_str(), COMMON_SAMPLE_RATE/1000); fprintf(stderr, "%s: WAV file '%s' must be %i kHz\n", __func__, fname.c_str(), COMMON_SAMPLE_RATE/1000);
drwav_uninit(&wav);
return false; return false;
} }
if (wav.bitsPerSample != 16) { if (wav.bitsPerSample != 16) {
fprintf(stderr, "%s: WAV file '%s' must be 16-bit\n", __func__, fname.c_str()); fprintf(stderr, "%s: WAV file '%s' must be 16-bit\n", __func__, fname.c_str());
drwav_uninit(&wav);
return false; return false;
} }
@ -836,3 +865,48 @@ void sam_print_usage(int /*argc*/, char ** argv, const sam_params & params) {
fprintf(stderr, " output file (default: %s)\n", params.fname_out.c_str()); fprintf(stderr, " output file (default: %s)\n", params.fname_out.c_str());
fprintf(stderr, "\n"); fprintf(stderr, "\n");
} }
// 500 -> 00:05.000
// 6000 -> 01:00.000
std::string to_timestamp(int64_t t, bool comma) {
int64_t msec = t * 10;
int64_t hr = msec / (1000 * 60 * 60);
msec = msec - hr * (1000 * 60 * 60);
int64_t min = msec / (1000 * 60);
msec = msec - min * (1000 * 60);
int64_t sec = msec / 1000;
msec = msec - sec * 1000;
char buf[32];
snprintf(buf, sizeof(buf), "%02d:%02d:%02d%s%03d", (int) hr, (int) min, (int) sec, comma ? "," : ".", (int) msec);
return std::string(buf);
}
int timestamp_to_sample(int64_t t, int n_samples, int whisper_sample_rate) {
return std::max(0, std::min((int) n_samples - 1, (int) ((t*whisper_sample_rate)/100)));
}
bool is_file_exist(const char *fileName)
{
std::ifstream infile(fileName);
return infile.good();
}
bool speak_with_file(const std::string & command, const std::string & text, const std::string & path, int voice_id)
{
std::ofstream speak_file(path.c_str());
if (speak_file.fail()) {
fprintf(stderr, "%s: failed to open speak_file\n", __func__);
return false;
} else {
speak_file.write(text.c_str(), text.size());
speak_file.close();
int ret = system((command + " " + std::to_string(voice_id) + " " + path).c_str());
if (ret != 0) {
fprintf(stderr, "%s: failed to speak\n", __func__);
return false;
}
}
return true;
}

View File

@ -21,7 +21,7 @@ struct gpt_params {
int32_t n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency()); int32_t n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
int32_t n_predict = 200; // new tokens to predict int32_t n_predict = 200; // new tokens to predict
int32_t n_parallel = 1; // number of parallel streams int32_t n_parallel = 1; // number of parallel streams
int32_t n_batch = 8; // batch size for prompt processing int32_t n_batch = 32; // batch size for prompt processing
int32_t n_ctx = 2048; // context size (this is the KV cache max size) int32_t n_ctx = 2048; // context size (this is the KV cache max size)
int32_t n_gpu_layers = 0; // number of layers to offlload to the GPU int32_t n_gpu_layers = 0; // number of layers to offlload to the GPU
@ -185,7 +185,7 @@ private:
// It is assumed that PCM data is normalized to a range from -1 to 1 // It is assumed that PCM data is normalized to a range from -1 to 1
bool write_audio(const float * data, size_t length) { bool write_audio(const float * data, size_t length) {
for (size_t i = 0; i < length; ++i) { for (size_t i = 0; i < length; ++i) {
const int16_t intSample = data[i] * 32767; const int16_t intSample = int16_t(data[i] * 32767);
file.write(reinterpret_cast<const char *>(&intSample), sizeof(int16_t)); file.write(reinterpret_cast<const char *>(&intSample), sizeof(int16_t));
dataSize += sizeof(int16_t); dataSize += sizeof(int16_t);
} }
@ -281,3 +281,31 @@ struct sam_params {
bool sam_params_parse(int argc, char ** argv, sam_params & params); bool sam_params_parse(int argc, char ** argv, sam_params & params);
void sam_print_usage(int argc, char ** argv, const sam_params & params); void sam_print_usage(int argc, char ** argv, const sam_params & params);
//
// Terminal utils
//
// Terminal color map. 10 colors grouped in ranges [0.0, 0.1, ..., 0.9]
// Lowest is red, middle is yellow, highest is green.
const std::vector<std::string> k_colors = {
"\033[38;5;196m", "\033[38;5;202m", "\033[38;5;208m", "\033[38;5;214m", "\033[38;5;220m",
"\033[38;5;226m", "\033[38;5;190m", "\033[38;5;154m", "\033[38;5;118m", "\033[38;5;82m",
};
//
// Other utils
//
// convert timestamp to string, 6000 -> 01:00.000
std::string to_timestamp(int64_t t, bool comma = false);
// given a timestamp get the sample
int timestamp_to_sample(int64_t t, int n_samples, int whisper_sample_rate);
// check if file exists using ifstream
bool is_file_exist(const char *fileName);
// write text to file, and call system("command voice_id file")
bool speak_with_file(const std::string & command, const std::string & text, const std::string & path, int voice_id);

View File

@ -0,0 +1,350 @@
/* SPDX-License-Identifier: GPL-2.0 */
/*
* transcode.c - convert audio file to WAVE
*
* Copyright (C) 2019 Andrew Clayton <andrew@digital-domain.net>
* Copyright (C) 2024 William Tambellini <william.tambellini@gmail.com>
*/
// Just for conveninent C++ API
#include <vector>
#include <string>
// C
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <stdbool.h>
#include <stdint.h>
#include <sys/types.h>
#include <sys/stat.h>
#include <fcntl.h>
#include <unistd.h>
#include <sys/mman.h>
extern "C" {
#include <libavutil/opt.h>
#include <libavcodec/avcodec.h>
#include <libavformat/avformat.h>
#include <libswresample/swresample.h>
}
typedef uint64_t u64;
typedef int64_t s64;
typedef uint32_t u32;
typedef int32_t s32;
typedef uint16_t u16;
typedef int16_t s16;
typedef uint8_t u8;
typedef int8_t s8;
#define WAVE_SAMPLE_RATE 16000
#define AVIO_CTX_BUF_SZ 4096
static const char* ffmpegLog = getenv("FFMPEG_LOG");
// Todo: add __FILE__ __LINE__
#define LOG(...) \
do { if (ffmpegLog) fprintf(stderr, __VA_ARGS__); } while(0) // C99
/*
* WAVE file header based on definition from
* https://gist.github.com/Jon-Schneider/8b7c53d27a7a13346a643dac9c19d34f
*
* We must ensure this structure doesn't have any holes or
* padding so we can just map it straight to the WAVE data.
*/
struct wave_hdr {
/* RIFF Header: "RIFF" */
char riff_header[4];
/* size of audio data + sizeof(struct wave_hdr) - 8 */
int wav_size;
/* "WAVE" */
char wav_header[4];
/* Format Header */
/* "fmt " (includes trailing space) */
char fmt_header[4];
/* Should be 16 for PCM */
int fmt_chunk_size;
/* Should be 1 for PCM. 3 for IEEE Float */
s16 audio_format;
s16 num_channels;
int sample_rate;
/*
* Number of bytes per second
* sample_rate * num_channels * bit_depth/8
*/
int byte_rate;
/* num_channels * bytes per sample */
s16 sample_alignment;
/* bits per sample */
s16 bit_depth;
/* Data Header */
/* "data" */
char data_header[4];
/*
* size of audio
* number of samples * num_channels * bit_depth/8
*/
int data_bytes;
} __attribute__((__packed__));
struct audio_buffer {
u8 *ptr;
int size; /* size left in the buffer */
};
static void set_wave_hdr(wave_hdr& wh, size_t size) {
memcpy(&wh.riff_header, "RIFF", 4);
wh.wav_size = size + sizeof(struct wave_hdr) - 8;
memcpy(&wh.wav_header, "WAVE", 4);
memcpy(&wh.fmt_header, "fmt ", 4);
wh.fmt_chunk_size = 16;
wh.audio_format = 1;
wh.num_channels = 1;
wh.sample_rate = WAVE_SAMPLE_RATE;
wh.sample_alignment = 2;
wh.bit_depth = 16;
wh.byte_rate = wh.sample_rate * wh.sample_alignment;
memcpy(&wh.data_header, "data", 4);
wh.data_bytes = size;
}
static void write_wave_hdr(int fd, size_t size) {
struct wave_hdr wh;
set_wave_hdr(wh, size);
write(fd, &wh, sizeof(struct wave_hdr));
}
static int map_file(int fd, u8 **ptr, size_t *size)
{
struct stat sb;
fstat(fd, &sb);
*size = sb.st_size;
*ptr = (u8*)mmap(NULL, *size, PROT_READ|PROT_WRITE, MAP_PRIVATE, fd, 0);
if (*ptr == MAP_FAILED) {
perror("mmap");
return -1;
}
return 0;
}
static int read_packet(void *opaque, u8 *buf, int buf_size)
{
struct audio_buffer *audio_buf = (audio_buffer*)opaque;
buf_size = FFMIN(buf_size, audio_buf->size);
/* copy internal buffer data to buf */
memcpy(buf, audio_buf->ptr, buf_size);
audio_buf->ptr += buf_size;
audio_buf->size -= buf_size;
return buf_size;
}
static void convert_frame(struct SwrContext *swr, AVCodecContext *codec,
AVFrame *frame, s16 **data, int *size, bool flush)
{
int nr_samples;
s64 delay;
u8 *buffer;
delay = swr_get_delay(swr, codec->sample_rate);
nr_samples = av_rescale_rnd(delay + frame->nb_samples,
WAVE_SAMPLE_RATE, codec->sample_rate,
AV_ROUND_UP);
av_samples_alloc(&buffer, NULL, 1, nr_samples, AV_SAMPLE_FMT_S16, 0);
/*
* !flush is used to check if we are flushing any remaining
* conversion buffers...
*/
nr_samples = swr_convert(swr, &buffer, nr_samples,
!flush ? (const u8 **)frame->data : NULL,
!flush ? frame->nb_samples : 0);
*data = (s16*)realloc(*data, (*size + nr_samples) * sizeof(s16));
memcpy(*data + *size, buffer, nr_samples * sizeof(s16));
*size += nr_samples;
av_freep(&buffer);
}
static bool is_audio_stream(const AVStream *stream)
{
if (stream->codecpar->codec_type == AVMEDIA_TYPE_AUDIO)
return true;
return false;
}
// Return non zero on error, 0 on success
// audio_buffer: input memory
// data: decoded output audio data (wav file)
// size: size of output data
static int decode_audio(struct audio_buffer *audio_buf, s16 **data, int *size)
{
LOG("decode_audio: input size: %d\n", audio_buf->size);
AVFormatContext *fmt_ctx;
AVIOContext *avio_ctx;
AVStream *stream;
AVCodecContext *codec;
AVPacket packet;
AVFrame *frame;
struct SwrContext *swr;
u8 *avio_ctx_buffer;
unsigned int i;
int stream_index = -1;
int err;
const size_t errbuffsize = 1024;
char errbuff[errbuffsize];
av_register_all(); // from avformat. Still a must-have call for ffmpeg v3! (can be skipped for later versions)
fmt_ctx = avformat_alloc_context();
avio_ctx_buffer = (u8*)av_malloc(AVIO_CTX_BUF_SZ);
LOG("Creating an avio context: AVIO_CTX_BUF_SZ=%d\n", AVIO_CTX_BUF_SZ);
avio_ctx = avio_alloc_context(avio_ctx_buffer, AVIO_CTX_BUF_SZ, 0, audio_buf, &read_packet, NULL, NULL);
fmt_ctx->pb = avio_ctx;
// open the input stream and read header
err = avformat_open_input(&fmt_ctx, NULL, NULL, NULL);
if (err) {
LOG("Could not read audio buffer: %d: %s\n", err, av_make_error_string(errbuff, errbuffsize, err));
return err;
}
err = avformat_find_stream_info(fmt_ctx, NULL);
if (err < 0) {
LOG("Could not retrieve stream info from audio buffer: %d\n", err);
return err;
}
for (i = 0; i < fmt_ctx->nb_streams; i++) {
if (is_audio_stream(fmt_ctx->streams[i])) {
stream_index = i;
break;
}
}
if (stream_index == -1) {
LOG("Could not retrieve audio stream from buffer\n");
return -1;
}
stream = fmt_ctx->streams[stream_index];
codec = avcodec_alloc_context3(
avcodec_find_decoder(stream->codecpar->codec_id));
avcodec_parameters_to_context(codec, stream->codecpar);
err = avcodec_open2(codec, avcodec_find_decoder(codec->codec_id),
NULL);
if (err) {
LOG("Failed to open decoder for stream #%d in audio buffer\n", stream_index);
return err;
}
/* prepare resampler */
swr = swr_alloc();
av_opt_set_int(swr, "in_channel_count", codec->channels, 0);
av_opt_set_int(swr, "out_channel_count", 1, 0);
av_opt_set_int(swr, "in_channel_layout", codec->channel_layout, 0);
av_opt_set_int(swr, "out_channel_layout", AV_CH_LAYOUT_MONO, 0);
av_opt_set_int(swr, "in_sample_rate", codec->sample_rate, 0);
av_opt_set_int(swr, "out_sample_rate", WAVE_SAMPLE_RATE, 0);
av_opt_set_sample_fmt(swr, "in_sample_fmt", codec->sample_fmt, 0);
av_opt_set_sample_fmt(swr, "out_sample_fmt", AV_SAMPLE_FMT_S16, 0);
swr_init(swr);
if (!swr_is_initialized(swr)) {
LOG("Resampler has not been properly initialized\n");
return -1;
}
av_init_packet(&packet);
frame = av_frame_alloc();
if (!frame) {
LOG("Error allocating the frame\n");
return -1;
}
/* iterate through frames */
*data = NULL;
*size = 0;
while (av_read_frame(fmt_ctx, &packet) >= 0) {
avcodec_send_packet(codec, &packet);
err = avcodec_receive_frame(codec, frame);
if (err == AVERROR(EAGAIN))
continue;
convert_frame(swr, codec, frame, data, size, false);
}
/* Flush any remaining conversion buffers... */
convert_frame(swr, codec, frame, data, size, true);
av_frame_free(&frame);
swr_free(&swr);
//avio_context_free(); // todo?
avcodec_close(codec);
avformat_close_input(&fmt_ctx);
avformat_free_context(fmt_ctx);
if (avio_ctx) {
av_freep(&avio_ctx->buffer);
av_freep(&avio_ctx);
}
return 0;
}
// in mem decoding/conversion/resampling:
// ifname: input file path
// owav_data: in mem wav file. Can be forwarded as it to whisper/drwav
// return 0 on success
int ffmpeg_decode_audio(const std::string &ifname, std::vector<uint8_t>& owav_data) {
LOG("ffmpeg_decode_audio: %s\n", ifname.c_str());
int ifd = open(ifname.c_str(), O_RDONLY);
if (ifd == -1) {
fprintf(stderr, "Couldn't open input file %s\n", ifname.c_str());
return -1;
}
u8 *ibuf = NULL;
size_t ibuf_size;
int err = map_file(ifd, &ibuf, &ibuf_size);
if (err) {
LOG("Couldn't map input file %s\n", ifname.c_str());
return err;
}
LOG("Mapped input file: %x size: %d\n", ibuf, ibuf_size);
struct audio_buffer inaudio_buf;
inaudio_buf.ptr = ibuf;
inaudio_buf.size = ibuf_size;
s16 *odata=NULL;
int osize=0;
err = decode_audio(&inaudio_buf, &odata, &osize);
LOG("decode_audio returned %d \n", err);
if (err != 0) {
LOG("decode_audio failed\n");
return err;
}
LOG("decode_audio output size: %d\n", osize);
wave_hdr wh;
const size_t outdatasize = osize * sizeof(s16);
set_wave_hdr(wh, outdatasize);
owav_data.resize(sizeof(wave_hdr) + outdatasize);
// header:
memcpy(owav_data.data(), &wh, sizeof(wave_hdr));
// the data:
memcpy(owav_data.data() + sizeof(wave_hdr), odata, osize* sizeof(s16));
return 0;
}

View File

@ -9,7 +9,7 @@
namespace grammar_parser { namespace grammar_parser {
// NOTE: assumes valid utf8 (but checks for overrun) // NOTE: assumes valid utf8 (but checks for overrun)
// copied from whisper.cpp // copied from whisper.cpp
std::pair<uint32_t, const char *> decode_utf8(const char * src) { static std::pair<uint32_t, const char *> decode_utf8(const char * src) {
static const int lookup[] = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 4 }; static const int lookup[] = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 4 };
uint8_t first_byte = static_cast<uint8_t>(*src); uint8_t first_byte = static_cast<uint8_t>(*src);
uint8_t highbits = first_byte >> 4; uint8_t highbits = first_byte >> 4;
@ -24,19 +24,19 @@ namespace grammar_parser {
return std::make_pair(value, pos); return std::make_pair(value, pos);
} }
uint32_t get_symbol_id(parse_state & state, const char * src, size_t len) { static uint32_t get_symbol_id(parse_state & state, const char * src, size_t len) {
uint32_t next_id = static_cast<uint32_t>(state.symbol_ids.size()); uint32_t next_id = static_cast<uint32_t>(state.symbol_ids.size());
auto result = state.symbol_ids.insert(std::make_pair(std::string(src, len), next_id)); auto result = state.symbol_ids.insert(std::make_pair(std::string(src, len), next_id));
return result.first->second; return result.first->second;
} }
uint32_t generate_symbol_id(parse_state & state, const std::string & base_name) { static uint32_t generate_symbol_id(parse_state & state, const std::string & base_name) {
uint32_t next_id = static_cast<uint32_t>(state.symbol_ids.size()); uint32_t next_id = static_cast<uint32_t>(state.symbol_ids.size());
state.symbol_ids[base_name + '_' + std::to_string(next_id)] = next_id; state.symbol_ids[base_name + '_' + std::to_string(next_id)] = next_id;
return next_id; return next_id;
} }
void add_rule( static void add_rule(
parse_state & state, parse_state & state,
uint32_t rule_id, uint32_t rule_id,
const std::vector<whisper_grammar_element> & rule) { const std::vector<whisper_grammar_element> & rule) {
@ -46,11 +46,11 @@ namespace grammar_parser {
state.rules[rule_id] = rule; state.rules[rule_id] = rule;
} }
bool is_word_char(char c) { static bool is_word_char(char c) {
return ('a' <= c && c <= 'z') || ('A' <= c && c <= 'Z') || c == '-' || ('0' <= c && c <= '9'); return ('a' <= c && c <= 'z') || ('A' <= c && c <= 'Z') || c == '-' || ('0' <= c && c <= '9');
} }
std::pair<uint32_t, const char *> parse_hex(const char * src, int size) { static std::pair<uint32_t, const char *> parse_hex(const char * src, int size) {
const char * pos = src; const char * pos = src;
const char * end = src + size; const char * end = src + size;
uint32_t value = 0; uint32_t value = 0;
@ -73,7 +73,7 @@ namespace grammar_parser {
return std::make_pair(value, pos); return std::make_pair(value, pos);
} }
const char * parse_space(const char * src, bool newline_ok) { static const char * parse_space(const char * src, bool newline_ok) {
const char * pos = src; const char * pos = src;
while (*pos == ' ' || *pos == '\t' || *pos == '#' || while (*pos == ' ' || *pos == '\t' || *pos == '#' ||
(newline_ok && (*pos == '\r' || *pos == '\n'))) { (newline_ok && (*pos == '\r' || *pos == '\n'))) {
@ -88,7 +88,7 @@ namespace grammar_parser {
return pos; return pos;
} }
const char * parse_name(const char * src) { static const char * parse_name(const char * src) {
const char * pos = src; const char * pos = src;
while (is_word_char(*pos)) { while (is_word_char(*pos)) {
pos++; pos++;
@ -99,7 +99,7 @@ namespace grammar_parser {
return pos; return pos;
} }
std::pair<uint32_t, const char *> parse_char(const char * src) { static std::pair<uint32_t, const char *> parse_char(const char * src) {
if (*src == '\\') { if (*src == '\\') {
switch (src[1]) { switch (src[1]) {
case 'x': return parse_hex(src + 2, 2); case 'x': return parse_hex(src + 2, 2);
@ -122,14 +122,14 @@ namespace grammar_parser {
throw std::runtime_error("unexpected end of input"); throw std::runtime_error("unexpected end of input");
} }
const char * parse_alternates( static const char * parse_alternates(
parse_state & state, parse_state & state,
const char * src, const char * src,
const std::string & rule_name, const std::string & rule_name,
uint32_t rule_id, uint32_t rule_id,
bool is_nested); bool is_nested);
const char * parse_sequence( static const char * parse_sequence(
parse_state & state, parse_state & state,
const char * src, const char * src,
const std::string & rule_name, const std::string & rule_name,
@ -190,7 +190,7 @@ namespace grammar_parser {
pos = parse_space(pos + 1, is_nested); pos = parse_space(pos + 1, is_nested);
} else if (*pos == '*' || *pos == '+' || *pos == '?') { // repetition operator } else if (*pos == '*' || *pos == '+' || *pos == '?') { // repetition operator
if (last_sym_start == out_elements.size()) { if (last_sym_start == out_elements.size()) {
throw std::runtime_error(std::string("expecting preceeding item to */+/? at ") + pos); throw std::runtime_error(std::string("expecting preceding item to */+/? at ") + pos);
} }
// apply transformation to previous symbol (last_sym_start to end) according to // apply transformation to previous symbol (last_sym_start to end) according to
@ -229,7 +229,7 @@ namespace grammar_parser {
return pos; return pos;
} }
const char * parse_alternates( static const char * parse_alternates(
parse_state & state, parse_state & state,
const char * src, const char * src,
const std::string & rule_name, const std::string & rule_name,
@ -247,7 +247,7 @@ namespace grammar_parser {
return pos; return pos;
} }
const char * parse_rule(parse_state & state, const char * src) { static const char * parse_rule(parse_state & state, const char * src) {
const char * name_end = parse_name(src); const char * name_end = parse_name(src);
const char * pos = parse_space(name_end, false); const char * pos = parse_space(name_end, false);
size_t name_len = name_end - src; size_t name_len = name_end - src;
@ -285,7 +285,7 @@ namespace grammar_parser {
} }
} }
void print_grammar_char(FILE * file, uint32_t c) { static void print_grammar_char(FILE * file, uint32_t c) {
if (0x20 <= c && c <= 0x7f) { if (0x20 <= c && c <= 0x7f) {
fprintf(file, "%c", static_cast<char>(c)); fprintf(file, "%c", static_cast<char>(c));
} else { } else {
@ -294,7 +294,7 @@ namespace grammar_parser {
} }
} }
bool is_char_element(whisper_grammar_element elem) { static bool is_char_element(whisper_grammar_element elem) {
switch (elem.type) { switch (elem.type) {
case WHISPER_GRETYPE_CHAR: return true; case WHISPER_GRETYPE_CHAR: return true;
case WHISPER_GRETYPE_CHAR_NOT: return true; case WHISPER_GRETYPE_CHAR_NOT: return true;
@ -304,7 +304,7 @@ namespace grammar_parser {
} }
} }
void print_rule_binary(FILE * file, const std::vector<whisper_grammar_element> & rule) { static void print_rule_binary(FILE * file, const std::vector<whisper_grammar_element> & rule) {
for (auto elem : rule) { for (auto elem : rule) {
switch (elem.type) { switch (elem.type) {
case WHISPER_GRETYPE_END: fprintf(file, "END"); break; case WHISPER_GRETYPE_END: fprintf(file, "END"); break;
@ -334,7 +334,7 @@ namespace grammar_parser {
fprintf(file, "\n"); fprintf(file, "\n");
} }
void print_rule( static void print_rule(
FILE * file, FILE * file,
uint32_t rule_id, uint32_t rule_id,
const std::vector<whisper_grammar_element> & rule, const std::vector<whisper_grammar_element> & rule,
@ -413,7 +413,7 @@ namespace grammar_parser {
} }
} }
std::vector<const whisper_grammar_element *> parse_state::c_rules() const{ std::vector<const whisper_grammar_element *> parse_state::c_rules() const {
std::vector<const whisper_grammar_element *> ret; std::vector<const whisper_grammar_element *> ret;
for (const auto & rule : rules) { for (const auto & rule : rules) {
ret.push_back(rule.data()); ret.push_back(rule.data());

View File

@ -34,9 +34,6 @@ async function fetchRemote(url, cbProgress, cbPrint) {
url, url,
{ {
method: 'GET', method: 'GET',
headers: {
'Content-Type': 'application/octet-stream',
},
} }
); );

View File

@ -5,5 +5,5 @@ if (WHISPER_SDL2)
include(DefaultTargetOptions) include(DefaultTargetOptions)
target_link_libraries(${TARGET} PRIVATE common common-sdl whisper ${CMAKE_THREAD_LIBS_INIT}) target_link_libraries(${TARGET} PRIVATE common json_cpp common-sdl whisper ${CMAKE_THREAD_LIBS_INIT})
endif () endif ()

View File

@ -26,11 +26,11 @@ struct whisper_params {
float vad_thold = 0.6f; float vad_thold = 0.6f;
float freq_thold = 100.0f; float freq_thold = 100.0f;
bool speed_up = false;
bool translate = false; bool translate = false;
bool print_special = false; bool print_special = false;
bool print_energy = false; bool print_energy = false;
bool use_gpu = true; bool use_gpu = true;
bool flash_attn = false;
std::string language = "en"; std::string language = "en";
std::string model = "models/ggml-base.en.bin"; std::string model = "models/ggml-base.en.bin";
@ -53,7 +53,7 @@ struct commandset {
void whisper_print_usage(int argc, char ** argv, const whisper_params & params); void whisper_print_usage(int argc, char ** argv, const whisper_params & params);
bool whisper_params_parse(int argc, char ** argv, whisper_params & params) { static bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
for (int i = 1; i < argc; i++) { for (int i = 1; i < argc; i++) {
std::string arg = argv[i]; std::string arg = argv[i];
@ -69,11 +69,11 @@ bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
else if (arg == "-ac" || arg == "--audio-ctx") { params.audio_ctx = std::stoi(argv[++i]); } else if (arg == "-ac" || arg == "--audio-ctx") { params.audio_ctx = std::stoi(argv[++i]); }
else if (arg == "-vth" || arg == "--vad-thold") { params.vad_thold = std::stof(argv[++i]); } else if (arg == "-vth" || arg == "--vad-thold") { params.vad_thold = std::stof(argv[++i]); }
else if (arg == "-fth" || arg == "--freq-thold") { params.freq_thold = std::stof(argv[++i]); } else if (arg == "-fth" || arg == "--freq-thold") { params.freq_thold = std::stof(argv[++i]); }
else if (arg == "-su" || arg == "--speed-up") { params.speed_up = true; }
else if (arg == "-tr" || arg == "--translate") { params.translate = true; } else if (arg == "-tr" || arg == "--translate") { params.translate = true; }
else if (arg == "-ps" || arg == "--print-special") { params.print_special = true; } else if (arg == "-ps" || arg == "--print-special") { params.print_special = true; }
else if (arg == "-pe" || arg == "--print-energy") { params.print_energy = true; } else if (arg == "-pe" || arg == "--print-energy") { params.print_energy = true; }
else if (arg == "-ng" || arg == "--no-gpu") { params.use_gpu = false; } else if (arg == "-ng" || arg == "--no-gpu") { params.use_gpu = false; }
else if (arg == "-fa" || arg == "--flash-attn") { params.flash_attn = true; }
else if (arg == "-l" || arg == "--language") { params.language = argv[++i]; } else if (arg == "-l" || arg == "--language") { params.language = argv[++i]; }
else if (arg == "-m" || arg == "--model") { params.model = argv[++i]; } else if (arg == "-m" || arg == "--model") { params.model = argv[++i]; }
else { else {
@ -100,16 +100,16 @@ void whisper_print_usage(int /*argc*/, char ** argv, const whisper_params & para
fprintf(stderr, " -ac N, --audio-ctx N [%-7d] audio context size (0 - all)\n", params.audio_ctx); fprintf(stderr, " -ac N, --audio-ctx N [%-7d] audio context size (0 - all)\n", params.audio_ctx);
fprintf(stderr, " -vth N, --vad-thold N [%-7.2f] voice activity detection threshold\n", params.vad_thold); fprintf(stderr, " -vth N, --vad-thold N [%-7.2f] voice activity detection threshold\n", params.vad_thold);
fprintf(stderr, " -fth N, --freq-thold N [%-7.2f] high-pass frequency cutoff\n", params.freq_thold); fprintf(stderr, " -fth N, --freq-thold N [%-7.2f] high-pass frequency cutoff\n", params.freq_thold);
fprintf(stderr, " -su, --speed-up [%-7s] speed up audio by x2 (reduced accuracy)\n", params.speed_up ? "true" : "false");
fprintf(stderr, " -tr, --translate [%-7s] translate from source language to english\n", params.translate ? "true" : "false"); fprintf(stderr, " -tr, --translate [%-7s] translate from source language to english\n", params.translate ? "true" : "false");
fprintf(stderr, " -ps, --print-special [%-7s] print special tokens\n", params.print_special ? "true" : "false"); fprintf(stderr, " -ps, --print-special [%-7s] print special tokens\n", params.print_special ? "true" : "false");
fprintf(stderr, " -pe, --print-energy [%-7s] print sound energy (for debugging)\n", params.print_energy ? "true" : "false"); fprintf(stderr, " -pe, --print-energy [%-7s] print sound energy (for debugging)\n", params.print_energy ? "true" : "false");
fprintf(stderr, " -ng, --no-gpu [%-7s] disable GPU\n", params.use_gpu ? "false" : "true"); fprintf(stderr, " -ng, --no-gpu [%-7s] disable GPU\n", params.use_gpu ? "false" : "true");
fprintf(stderr, " -fa, --flash-attn [%-7s] flash attention\n", params.flash_attn ? "true" : "false");
fprintf(stderr, " -l LANG, --language LANG [%-7s] spoken language\n", params.language.c_str()); fprintf(stderr, " -l LANG, --language LANG [%-7s] spoken language\n", params.language.c_str());
fprintf(stderr, " -m FNAME, --model FNAME [%-7s] model path\n", params.model.c_str()); fprintf(stderr, " -m FNAME, --model FNAME [%-7s] model path\n", params.model.c_str());
fprintf(stderr, "\n"); fprintf(stderr, "\n");
} }
uint64_t wait_for_vad(audio_async & audio, json jparams, const whisper_params & params, uint64_t maxlength_ms, std::vector<float> & pcmf32) { static uint64_t wait_for_vad(audio_async & audio, json jparams, const whisper_params & params, uint64_t maxlength_ms, std::vector<float> & pcmf32) {
using namespace std::chrono; using namespace std::chrono;
uint64_t time_now = time_point_cast<milliseconds>(system_clock::now()).time_since_epoch().count(); uint64_t time_now = time_point_cast<milliseconds>(system_clock::now()).time_since_epoch().count();
uint64_t start_time = time_now; uint64_t start_time = time_now;
@ -153,7 +153,7 @@ uint64_t wait_for_vad(audio_async & audio, json jparams, const whisper_params &
return time_now; return time_now;
} }
json unguided_transcription(struct whisper_context * ctx, audio_async &audio, json jparams, const whisper_params &params) { static json unguided_transcription(struct whisper_context * ctx, audio_async &audio, json jparams, const whisper_params &params) {
std::vector<whisper_token> prompt_tokens; std::vector<whisper_token> prompt_tokens;
std::vector<float> pcmf32; std::vector<float> pcmf32;
uint64_t unprocessed_audio_timestamp = wait_for_vad(audio, jparams, params, 10000U, pcmf32); uint64_t unprocessed_audio_timestamp = wait_for_vad(audio, jparams, params, 10000U, pcmf32);
@ -181,7 +181,6 @@ json unguided_transcription(struct whisper_context * ctx, audio_async &audio, js
wparams.n_threads = params.n_threads; wparams.n_threads = params.n_threads;
wparams.audio_ctx = params.audio_ctx; wparams.audio_ctx = params.audio_ctx;
wparams.speed_up = params.speed_up;
wparams.suppress_non_speech_tokens = true; wparams.suppress_non_speech_tokens = true;
// run the transformer and a single decoding pass // run the transformer and a single decoding pass
if (whisper_full(ctx, wparams, pcmf32.data(), pcmf32.size()) != 0) { if (whisper_full(ctx, wparams, pcmf32.data(), pcmf32.size()) != 0) {
@ -200,7 +199,7 @@ json unguided_transcription(struct whisper_context * ctx, audio_async &audio, js
// command-list mode // command-list mode
// guide the transcription to match the most likely command from a provided list // guide the transcription to match the most likely command from a provided list
json guided_transcription(struct whisper_context * ctx, audio_async &audio, const whisper_params &params, json jparams, std::vector<struct commandset> commandset_list) { static json guided_transcription(struct whisper_context * ctx, audio_async &audio, const whisper_params &params, json jparams, std::vector<struct commandset> commandset_list) {
struct commandset cs = commandset_list[jparams.value("commandset_index", commandset_list.size()-1)]; struct commandset cs = commandset_list[jparams.value("commandset_index", commandset_list.size()-1)];
std::vector<float> pcmf32; std::vector<float> pcmf32;
uint64_t unprocessed_audio_timestamp = wait_for_vad(audio, jparams, params, 2000U, pcmf32); uint64_t unprocessed_audio_timestamp = wait_for_vad(audio, jparams, params, 2000U, pcmf32);
@ -220,7 +219,6 @@ json guided_transcription(struct whisper_context * ctx, audio_async &audio, cons
wparams.n_threads = params.n_threads; wparams.n_threads = params.n_threads;
wparams.audio_ctx = params.audio_ctx; wparams.audio_ctx = params.audio_ctx;
wparams.speed_up = params.speed_up;
// TODO: Do some time testing. Does an overly long prompt slow down processing? // TODO: Do some time testing. Does an overly long prompt slow down processing?
// Set up command sets/precompute prompts // Set up command sets/precompute prompts
@ -287,7 +285,7 @@ json guided_transcription(struct whisper_context * ctx, audio_async &audio, cons
} }
} }
json register_commandset(struct whisper_context * ctx, json jparams, std::vector<struct commandset> &commandset_list) { static json register_commandset(struct whisper_context * ctx, json jparams, std::vector<struct commandset> &commandset_list) {
// TODO: check for token collision // TODO: check for token collision
struct commandset cs; struct commandset cs;
@ -327,7 +325,8 @@ json register_commandset(struct whisper_context * ctx, json jparams, std::vector
commandset_list.push_back(cs); commandset_list.push_back(cs);
return json{{"index",index}}; return json{{"index",index}};
} }
json seek(struct whisper_context * /*ctx*/, audio_async & /*audio*/, json /*params*/) {
static json seek(struct whisper_context * /*ctx*/, audio_async & /*audio*/, json /*params*/) {
// whisper_state has the pertinent offsets, but there also seem to be a large // whisper_state has the pertinent offsets, but there also seem to be a large
// number of scratch buffers that would prevent rewinding context in a manner similar to llama // number of scratch buffers that would prevent rewinding context in a manner similar to llama
// I'll give this a another pass once everything else is implemented, // I'll give this a another pass once everything else is implemented,
@ -337,7 +336,8 @@ json seek(struct whisper_context * /*ctx*/, audio_async & /*audio*/, json /*para
{"message", "Seeking is not yet supported."} {"message", "Seeking is not yet supported."}
}; };
} }
json parse_job(const json &body, struct whisper_context * ctx, audio_async &audio, const whisper_params &params, std::vector<struct commandset> &commandset_list) {
static json parse_job(const json &body, struct whisper_context * ctx, audio_async &audio, const whisper_params &params, std::vector<struct commandset> &commandset_list) {
// See: https://www.jsonrpc.org/specification // See: https://www.jsonrpc.org/specification
json id = body.at("id"); json id = body.at("id");
try { try {
@ -377,7 +377,7 @@ json parse_job(const json &body, struct whisper_context * ctx, audio_async &audi
} }
} }
void process_loop(struct whisper_context * ctx, audio_async &audio, const whisper_params &params) { static void process_loop(struct whisper_context * ctx, audio_async &audio, const whisper_params &params) {
std::deque<json> jobqueue; std::deque<json> jobqueue;
std::vector<struct commandset> commandset_list; std::vector<struct commandset> commandset_list;
while (true) { while (true) {
@ -436,7 +436,10 @@ int main(int argc, char ** argv) {
// whisper init // whisper init
struct whisper_context_params cparams = whisper_context_default_params(); struct whisper_context_params cparams = whisper_context_default_params();
cparams.use_gpu = params.use_gpu;
cparams.use_gpu = params.use_gpu;
cparams.flash_attn = params.flash_attn;
struct whisper_context * ctx = whisper_init_from_file_with_params(params.model.c_str(), cparams); struct whisper_context * ctx = whisper_init_from_file_with_params(params.model.c_str(), cparams);
// init audio // init audio

View File

@ -3,4 +3,4 @@ add_executable(${TARGET} main.cpp)
include(DefaultTargetOptions) include(DefaultTargetOptions)
target_link_libraries(${TARGET} PRIVATE common whisper ${CMAKE_THREAD_LIBS_INIT}) target_link_libraries(${TARGET} PRIVATE common whisper ${FFMPEG_LIBRARIES} ${CMAKE_THREAD_LIBS_INIT})

View File

@ -1,10 +1,12 @@
#include "common.h" #include "common.h"
#include "whisper.h" #include "whisper.h"
#include "grammar-parser.h"
#include <cmath> #include <cmath>
#include <fstream> #include <fstream>
#include <cstdio> #include <cstdio>
#include <regex>
#include <string> #include <string>
#include <thread> #include <thread>
#include <vector> #include <vector>
@ -14,36 +16,8 @@
#pragma warning(disable: 4244 4267) // possible loss of data #pragma warning(disable: 4244 4267) // possible loss of data
#endif #endif
// Terminal color map. 10 colors grouped in ranges [0.0, 0.1, ..., 0.9]
// Lowest is red, middle is yellow, highest is green.
const std::vector<std::string> k_colors = {
"\033[38;5;196m", "\033[38;5;202m", "\033[38;5;208m", "\033[38;5;214m", "\033[38;5;220m",
"\033[38;5;226m", "\033[38;5;190m", "\033[38;5;154m", "\033[38;5;118m", "\033[38;5;82m",
};
// 500 -> 00:05.000
// 6000 -> 01:00.000
std::string to_timestamp(int64_t t, bool comma = false) {
int64_t msec = t * 10;
int64_t hr = msec / (1000 * 60 * 60);
msec = msec - hr * (1000 * 60 * 60);
int64_t min = msec / (1000 * 60);
msec = msec - min * (1000 * 60);
int64_t sec = msec / 1000;
msec = msec - sec * 1000;
char buf[32];
snprintf(buf, sizeof(buf), "%02d:%02d:%02d%s%03d", (int) hr, (int) min, (int) sec, comma ? "," : ".", (int) msec);
return std::string(buf);
}
int timestamp_to_sample(int64_t t, int n_samples) {
return std::max(0, std::min((int) n_samples - 1, (int) ((t*WHISPER_SAMPLE_RATE)/100)));
}
// helper function to replace substrings // helper function to replace substrings
void replace_all(std::string & s, const std::string & search, const std::string & replace) { static void replace_all(std::string & s, const std::string & search, const std::string & replace) {
for (size_t pos = 0; ; pos += replace.length()) { for (size_t pos = 0; ; pos += replace.length()) {
pos = s.find(search, pos); pos = s.find(search, pos);
if (pos == std::string::npos) break; if (pos == std::string::npos) break;
@ -54,23 +28,25 @@ void replace_all(std::string & s, const std::string & search, const std::string
// command-line parameters // command-line parameters
struct whisper_params { struct whisper_params {
int32_t n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency()); int32_t n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
int32_t n_processors = 1; int32_t n_processors = 1;
int32_t offset_t_ms = 0; int32_t offset_t_ms = 0;
int32_t offset_n = 0; int32_t offset_n = 0;
int32_t duration_ms = 0; int32_t duration_ms = 0;
int32_t progress_step = 5; int32_t progress_step = 5;
int32_t max_context = -1; int32_t max_context = -1;
int32_t max_len = 0; int32_t max_len = 0;
int32_t best_of = whisper_full_default_params(WHISPER_SAMPLING_GREEDY).greedy.best_of; int32_t best_of = whisper_full_default_params(WHISPER_SAMPLING_GREEDY).greedy.best_of;
int32_t beam_size = whisper_full_default_params(WHISPER_SAMPLING_BEAM_SEARCH).beam_search.beam_size; int32_t beam_size = whisper_full_default_params(WHISPER_SAMPLING_BEAM_SEARCH).beam_search.beam_size;
int32_t audio_ctx = 0; int32_t audio_ctx = 0;
float word_thold = 0.01f; float word_thold = 0.01f;
float entropy_thold = 2.40f; float entropy_thold = 2.40f;
float logprob_thold = -1.00f; float logprob_thold = -1.00f;
float grammar_penalty = 100.0f;
float temperature = 0.0f;
float temperature_inc = 0.2f;
bool speed_up = false;
bool debug_mode = false; bool debug_mode = false;
bool translate = false; bool translate = false;
bool detect_language = false; bool detect_language = false;
@ -93,24 +69,42 @@ struct whisper_params {
bool no_timestamps = false; bool no_timestamps = false;
bool log_score = false; bool log_score = false;
bool use_gpu = true; bool use_gpu = true;
bool flash_attn = false;
std::string language = "en"; std::string language = "en";
std::string prompt; std::string prompt;
std::string font_path = "/System/Library/Fonts/Supplemental/Courier New Bold.ttf"; std::string font_path = "/System/Library/Fonts/Supplemental/Courier New Bold.ttf";
std::string model = "models/ggml-base.en.bin"; std::string model = "models/ggml-base.en.bin";
std::string grammar;
std::string grammar_rule;
// [TDRZ] speaker turn string // [TDRZ] speaker turn string
std::string tdrz_speaker_turn = " [SPEAKER_TURN]"; // TODO: set from command line std::string tdrz_speaker_turn = " [SPEAKER_TURN]"; // TODO: set from command line
// A regular expression that matches tokens to suppress
std::string suppress_regex;
std::string openvino_encode_device = "CPU"; std::string openvino_encode_device = "CPU";
std::string dtw = "";
std::vector<std::string> fname_inp = {}; std::vector<std::string> fname_inp = {};
std::vector<std::string> fname_out = {}; std::vector<std::string> fname_out = {};
grammar_parser::parse_state grammar_parsed;
}; };
void whisper_print_usage(int argc, char ** argv, const whisper_params & params); static void whisper_print_usage(int argc, char ** argv, const whisper_params & params);
bool whisper_params_parse(int argc, char ** argv, whisper_params & params) { static char * whisper_param_turn_lowercase(char * in){
int string_len = strlen(in);
for (int i = 0; i < string_len; i++){
*(in+i) = tolower((unsigned char)*(in+i));
}
return in;
}
static bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
for (int i = 1; i < argc; i++) { for (int i = 1; i < argc; i++) {
std::string arg = argv[i]; std::string arg = argv[i];
@ -137,11 +131,12 @@ bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
else if (arg == "-ml" || arg == "--max-len") { params.max_len = std::stoi(argv[++i]); } else if (arg == "-ml" || arg == "--max-len") { params.max_len = std::stoi(argv[++i]); }
else if (arg == "-bo" || arg == "--best-of") { params.best_of = std::stoi(argv[++i]); } else if (arg == "-bo" || arg == "--best-of") { params.best_of = std::stoi(argv[++i]); }
else if (arg == "-bs" || arg == "--beam-size") { params.beam_size = std::stoi(argv[++i]); } else if (arg == "-bs" || arg == "--beam-size") { params.beam_size = std::stoi(argv[++i]); }
else if (arg == "-ac" || arg == "--audio-context") { params.audio_ctx = std::stoi(argv[++i]); } else if (arg == "-ac" || arg == "--audio-ctx") { params.audio_ctx = std::stoi(argv[++i]); }
else if (arg == "-wt" || arg == "--word-thold") { params.word_thold = std::stof(argv[++i]); } else if (arg == "-wt" || arg == "--word-thold") { params.word_thold = std::stof(argv[++i]); }
else if (arg == "-et" || arg == "--entropy-thold") { params.entropy_thold = std::stof(argv[++i]); } else if (arg == "-et" || arg == "--entropy-thold") { params.entropy_thold = std::stof(argv[++i]); }
else if (arg == "-lpt" || arg == "--logprob-thold") { params.logprob_thold = std::stof(argv[++i]); } else if (arg == "-lpt" || arg == "--logprob-thold") { params.logprob_thold = std::stof(argv[++i]); }
// else if (arg == "-su" || arg == "--speed-up") { params.speed_up = true; } else if (arg == "-tp" || arg == "--temperature") { params.temperature = std::stof(argv[++i]); }
else if (arg == "-tpi" || arg == "--temperature-inc") { params.temperature_inc = std::stof(argv[++i]); }
else if (arg == "-debug"|| arg == "--debug-mode") { params.debug_mode = true; } else if (arg == "-debug"|| arg == "--debug-mode") { params.debug_mode = true; }
else if (arg == "-tr" || arg == "--translate") { params.translate = true; } else if (arg == "-tr" || arg == "--translate") { params.translate = true; }
else if (arg == "-di" || arg == "--diarize") { params.diarize = true; } else if (arg == "-di" || arg == "--diarize") { params.diarize = true; }
@ -163,14 +158,20 @@ bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
else if (arg == "-pc" || arg == "--print-colors") { params.print_colors = true; } else if (arg == "-pc" || arg == "--print-colors") { params.print_colors = true; }
else if (arg == "-pp" || arg == "--print-progress") { params.print_progress = true; } else if (arg == "-pp" || arg == "--print-progress") { params.print_progress = true; }
else if (arg == "-nt" || arg == "--no-timestamps") { params.no_timestamps = true; } else if (arg == "-nt" || arg == "--no-timestamps") { params.no_timestamps = true; }
else if (arg == "-l" || arg == "--language") { params.language = argv[++i]; } else if (arg == "-l" || arg == "--language") { params.language = whisper_param_turn_lowercase(argv[++i]); }
else if (arg == "-dl" || arg == "--detect-language") { params.detect_language = true; } else if (arg == "-dl" || arg == "--detect-language") { params.detect_language = true; }
else if ( arg == "--prompt") { params.prompt = argv[++i]; } else if ( arg == "--prompt") { params.prompt = argv[++i]; }
else if (arg == "-m" || arg == "--model") { params.model = argv[++i]; } else if (arg == "-m" || arg == "--model") { params.model = argv[++i]; }
else if (arg == "-f" || arg == "--file") { params.fname_inp.emplace_back(argv[++i]); } else if (arg == "-f" || arg == "--file") { params.fname_inp.emplace_back(argv[++i]); }
else if (arg == "-oved" || arg == "--ov-e-device") { params.openvino_encode_device = argv[++i]; } else if (arg == "-oved" || arg == "--ov-e-device") { params.openvino_encode_device = argv[++i]; }
else if (arg == "-dtw" || arg == "--dtw") { params.dtw = argv[++i]; }
else if (arg == "-ls" || arg == "--log-score") { params.log_score = true; } else if (arg == "-ls" || arg == "--log-score") { params.log_score = true; }
else if (arg == "-ng" || arg == "--no-gpu") { params.use_gpu = false; } else if (arg == "-ng" || arg == "--no-gpu") { params.use_gpu = false; }
else if (arg == "-fa" || arg == "--flash-attn") { params.flash_attn = true; }
else if ( arg == "--suppress-regex") { params.suppress_regex = argv[++i]; }
else if ( arg == "--grammar") { params.grammar = argv[++i]; }
else if ( arg == "--grammar-rule") { params.grammar_rule = argv[++i]; }
else if ( arg == "--grammar-penalty") { params.grammar_penalty = std::stof(argv[++i]); }
else { else {
fprintf(stderr, "error: unknown argument: %s\n", arg.c_str()); fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
whisper_print_usage(argc, argv, params); whisper_print_usage(argc, argv, params);
@ -181,7 +182,7 @@ bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
return true; return true;
} }
void whisper_print_usage(int /*argc*/, char ** argv, const whisper_params & params) { static void whisper_print_usage(int /*argc*/, char ** argv, const whisper_params & params) {
fprintf(stderr, "\n"); fprintf(stderr, "\n");
fprintf(stderr, "usage: %s [options] file0.wav file1.wav ...\n", argv[0]); fprintf(stderr, "usage: %s [options] file0.wav file1.wav ...\n", argv[0]);
fprintf(stderr, "\n"); fprintf(stderr, "\n");
@ -201,7 +202,8 @@ void whisper_print_usage(int /*argc*/, char ** argv, const whisper_params & para
fprintf(stderr, " -wt N, --word-thold N [%-7.2f] word timestamp probability threshold\n", params.word_thold); fprintf(stderr, " -wt N, --word-thold N [%-7.2f] word timestamp probability threshold\n", params.word_thold);
fprintf(stderr, " -et N, --entropy-thold N [%-7.2f] entropy threshold for decoder fail\n", params.entropy_thold); fprintf(stderr, " -et N, --entropy-thold N [%-7.2f] entropy threshold for decoder fail\n", params.entropy_thold);
fprintf(stderr, " -lpt N, --logprob-thold N [%-7.2f] log probability threshold for decoder fail\n", params.logprob_thold); fprintf(stderr, " -lpt N, --logprob-thold N [%-7.2f] log probability threshold for decoder fail\n", params.logprob_thold);
// fprintf(stderr, " -su, --speed-up [%-7s] speed up audio by x2 (reduced accuracy)\n", params.speed_up ? "true" : "false"); fprintf(stderr, " -tp, --temperature N [%-7.2f] The sampling temperature, between 0 and 1\n", params.temperature);
fprintf(stderr, " -tpi, --temperature-inc N [%-7.2f] The increment of temperature, between 0 and 1\n",params.temperature_inc);
fprintf(stderr, " -debug, --debug-mode [%-7s] enable debug mode (eg. dump log_mel)\n", params.debug_mode ? "true" : "false"); fprintf(stderr, " -debug, --debug-mode [%-7s] enable debug mode (eg. dump log_mel)\n", params.debug_mode ? "true" : "false");
fprintf(stderr, " -tr, --translate [%-7s] translate from source language to english\n", params.translate ? "true" : "false"); fprintf(stderr, " -tr, --translate [%-7s] translate from source language to english\n", params.translate ? "true" : "false");
fprintf(stderr, " -di, --diarize [%-7s] stereo audio diarization\n", params.diarize ? "true" : "false"); fprintf(stderr, " -di, --diarize [%-7s] stereo audio diarization\n", params.diarize ? "true" : "false");
@ -224,12 +226,18 @@ void whisper_print_usage(int /*argc*/, char ** argv, const whisper_params & para
fprintf(stderr, " -nt, --no-timestamps [%-7s] do not print timestamps\n", params.no_timestamps ? "true" : "false"); fprintf(stderr, " -nt, --no-timestamps [%-7s] do not print timestamps\n", params.no_timestamps ? "true" : "false");
fprintf(stderr, " -l LANG, --language LANG [%-7s] spoken language ('auto' for auto-detect)\n", params.language.c_str()); fprintf(stderr, " -l LANG, --language LANG [%-7s] spoken language ('auto' for auto-detect)\n", params.language.c_str());
fprintf(stderr, " -dl, --detect-language [%-7s] exit after automatically detecting language\n", params.detect_language ? "true" : "false"); fprintf(stderr, " -dl, --detect-language [%-7s] exit after automatically detecting language\n", params.detect_language ? "true" : "false");
fprintf(stderr, " --prompt PROMPT [%-7s] initial prompt\n", params.prompt.c_str()); fprintf(stderr, " --prompt PROMPT [%-7s] initial prompt (max n_text_ctx/2 tokens)\n", params.prompt.c_str());
fprintf(stderr, " -m FNAME, --model FNAME [%-7s] model path\n", params.model.c_str()); fprintf(stderr, " -m FNAME, --model FNAME [%-7s] model path\n", params.model.c_str());
fprintf(stderr, " -f FNAME, --file FNAME [%-7s] input WAV file path\n", ""); fprintf(stderr, " -f FNAME, --file FNAME [%-7s] input WAV file path\n", "");
fprintf(stderr, " -oved D, --ov-e-device DNAME [%-7s] the OpenVINO device used for encode inference\n", params.openvino_encode_device.c_str()); fprintf(stderr, " -oved D, --ov-e-device DNAME [%-7s] the OpenVINO device used for encode inference\n", params.openvino_encode_device.c_str());
fprintf(stderr, " -dtw MODEL --dtw MODEL [%-7s] compute token-level timestamps\n", params.dtw.c_str());
fprintf(stderr, " -ls, --log-score [%-7s] log best decoder scores of tokens\n", params.log_score?"true":"false"); fprintf(stderr, " -ls, --log-score [%-7s] log best decoder scores of tokens\n", params.log_score?"true":"false");
fprintf(stderr, " -ng, --no-gpu [%-7s] disable GPU\n", params.use_gpu ? "false" : "true"); fprintf(stderr, " -ng, --no-gpu [%-7s] disable GPU\n", params.use_gpu ? "false" : "true");
fprintf(stderr, " -fa, --flash-attn [%-7s] flash attention\n", params.flash_attn ? "true" : "false");
fprintf(stderr, " --suppress-regex REGEX [%-7s] regular expression matching tokens to suppress\n", params.suppress_regex.c_str());
fprintf(stderr, " --grammar GRAMMAR [%-7s] GBNF grammar to guide decoding\n", params.grammar.c_str());
fprintf(stderr, " --grammar-rule RULE [%-7s] top-level GBNF grammar rule name\n", params.grammar_rule.c_str());
fprintf(stderr, " --grammar-penalty N [%-7.1f] scales down logits of nongrammar tokens\n", params.grammar_penalty);
fprintf(stderr, "\n"); fprintf(stderr, "\n");
} }
@ -240,12 +248,12 @@ struct whisper_print_user_data {
int progress_prev; int progress_prev;
}; };
std::string estimate_diarization_speaker(std::vector<std::vector<float>> pcmf32s, int64_t t0, int64_t t1, bool id_only = false) { static std::string estimate_diarization_speaker(std::vector<std::vector<float>> pcmf32s, int64_t t0, int64_t t1, bool id_only = false) {
std::string speaker = ""; std::string speaker = "";
const int64_t n_samples = pcmf32s[0].size(); const int64_t n_samples = pcmf32s[0].size();
const int64_t is0 = timestamp_to_sample(t0, n_samples); const int64_t is0 = timestamp_to_sample(t0, n_samples, WHISPER_SAMPLE_RATE);
const int64_t is1 = timestamp_to_sample(t1, n_samples); const int64_t is1 = timestamp_to_sample(t1, n_samples, WHISPER_SAMPLE_RATE);
double energy0 = 0.0f; double energy0 = 0.0f;
double energy1 = 0.0f; double energy1 = 0.0f;
@ -272,7 +280,8 @@ std::string estimate_diarization_speaker(std::vector<std::vector<float>> pcmf32s
return speaker; return speaker;
} }
void whisper_print_progress_callback(struct whisper_context * /*ctx*/, struct whisper_state * /*state*/, int progress, void * user_data) {
static void whisper_print_progress_callback(struct whisper_context * /*ctx*/, struct whisper_state * /*state*/, int progress, void * user_data) {
int progress_step = ((whisper_print_user_data *) user_data)->params->progress_step; int progress_step = ((whisper_print_user_data *) user_data)->params->progress_step;
int * progress_prev = &(((whisper_print_user_data *) user_data)->progress_prev); int * progress_prev = &(((whisper_print_user_data *) user_data)->progress_prev);
if (progress >= *progress_prev + progress_step) { if (progress >= *progress_prev + progress_step) {
@ -281,7 +290,7 @@ void whisper_print_progress_callback(struct whisper_context * /*ctx*/, struct wh
} }
} }
void whisper_print_segment_callback(struct whisper_context * ctx, struct whisper_state * /*state*/, int n_new, void * user_data) { static void whisper_print_segment_callback(struct whisper_context * ctx, struct whisper_state * /*state*/, int n_new, void * user_data) {
const auto & params = *((whisper_print_user_data *) user_data)->params; const auto & params = *((whisper_print_user_data *) user_data)->params;
const auto & pcmf32s = *((whisper_print_user_data *) user_data)->pcmf32s; const auto & pcmf32s = *((whisper_print_user_data *) user_data)->pcmf32s;
@ -350,7 +359,7 @@ void whisper_print_segment_callback(struct whisper_context * ctx, struct whisper
} }
} }
bool output_txt(struct whisper_context * ctx, const char * fname, const whisper_params & params, std::vector<std::vector<float>> pcmf32s) { static bool output_txt(struct whisper_context * ctx, const char * fname, const whisper_params & params, std::vector<std::vector<float>> pcmf32s) {
std::ofstream fout(fname); std::ofstream fout(fname);
if (!fout.is_open()) { if (!fout.is_open()) {
fprintf(stderr, "%s: failed to open '%s' for writing\n", __func__, fname); fprintf(stderr, "%s: failed to open '%s' for writing\n", __func__, fname);
@ -377,7 +386,7 @@ bool output_txt(struct whisper_context * ctx, const char * fname, const whisper_
return true; return true;
} }
bool output_vtt(struct whisper_context * ctx, const char * fname, const whisper_params & params, std::vector<std::vector<float>> pcmf32s) { static bool output_vtt(struct whisper_context * ctx, const char * fname, const whisper_params & params, std::vector<std::vector<float>> pcmf32s) {
std::ofstream fout(fname); std::ofstream fout(fname);
if (!fout.is_open()) { if (!fout.is_open()) {
fprintf(stderr, "%s: failed to open '%s' for writing\n", __func__, fname); fprintf(stderr, "%s: failed to open '%s' for writing\n", __func__, fname);
@ -409,7 +418,7 @@ bool output_vtt(struct whisper_context * ctx, const char * fname, const whisper_
return true; return true;
} }
bool output_srt(struct whisper_context * ctx, const char * fname, const whisper_params & params, std::vector<std::vector<float>> pcmf32s) { static bool output_srt(struct whisper_context * ctx, const char * fname, const whisper_params & params, std::vector<std::vector<float>> pcmf32s) {
std::ofstream fout(fname); std::ofstream fout(fname);
if (!fout.is_open()) { if (!fout.is_open()) {
fprintf(stderr, "%s: failed to open '%s' for writing\n", __func__, fname); fprintf(stderr, "%s: failed to open '%s' for writing\n", __func__, fname);
@ -438,7 +447,7 @@ bool output_srt(struct whisper_context * ctx, const char * fname, const whisper_
return true; return true;
} }
char *escape_double_quotes_and_backslashes(const char *str) { static char * escape_double_quotes_and_backslashes(const char * str) {
if (str == NULL) { if (str == NULL) {
return NULL; return NULL;
} }
@ -451,7 +460,7 @@ char *escape_double_quotes_and_backslashes(const char *str) {
} }
} }
char *escaped = (char *)calloc(escaped_length, 1); // pre-zeroed char * escaped = (char *)calloc(escaped_length, 1); // pre-zeroed
if (escaped == NULL) { if (escaped == NULL) {
return NULL; return NULL;
} }
@ -469,7 +478,39 @@ char *escape_double_quotes_and_backslashes(const char *str) {
return escaped; return escaped;
} }
bool output_csv(struct whisper_context * ctx, const char * fname, const whisper_params & params, std::vector<std::vector<float>> pcmf32s) { // double quote should be escaped by another double quote. (rfc4180)
static char * escape_double_quotes_in_csv(const char * str) {
if (str == NULL) {
return NULL;
}
size_t escaped_length = strlen(str) + 1;
for (size_t i = 0; str[i] != '\0'; i++) {
if (str[i] == '"') {
escaped_length++;
}
}
char *escaped = (char *)calloc(escaped_length, 1); // pre-zeroed
if (escaped == NULL) {
return NULL;
}
size_t pos = 0;
for (size_t i = 0; str[i] != '\0'; i++) {
if (str[i] == '"') {
escaped[pos++] = '"';
}
escaped[pos++] = str[i];
}
// no need to set zero due to calloc() being used prior
return escaped;
}
static bool output_csv(struct whisper_context * ctx, const char * fname, const whisper_params & params, std::vector<std::vector<float>> pcmf32s) {
std::ofstream fout(fname); std::ofstream fout(fname);
if (!fout.is_open()) { if (!fout.is_open()) {
fprintf(stderr, "%s: failed to open '%s' for writing\n", __func__, fname); fprintf(stderr, "%s: failed to open '%s' for writing\n", __func__, fname);
@ -490,7 +531,7 @@ bool output_csv(struct whisper_context * ctx, const char * fname, const whisper_
const char * text = whisper_full_get_segment_text(ctx, i); const char * text = whisper_full_get_segment_text(ctx, i);
const int64_t t0 = whisper_full_get_segment_t0(ctx, i); const int64_t t0 = whisper_full_get_segment_t0(ctx, i);
const int64_t t1 = whisper_full_get_segment_t1(ctx, i); const int64_t t1 = whisper_full_get_segment_t1(ctx, i);
char * text_escaped = escape_double_quotes_and_backslashes(text); char * text_escaped = escape_double_quotes_in_csv(text);
//need to multiply times returned from whisper_full_get_segment_t{0,1}() by 10 to get milliseconds. //need to multiply times returned from whisper_full_get_segment_t{0,1}() by 10 to get milliseconds.
fout << 10 * t0 << "," << 10 * t1 << ","; fout << 10 * t0 << "," << 10 * t1 << ",";
@ -504,7 +545,7 @@ bool output_csv(struct whisper_context * ctx, const char * fname, const whisper_
return true; return true;
} }
bool output_score(struct whisper_context * ctx, const char * fname, const whisper_params & /*params*/, std::vector<std::vector<float>> /*pcmf32s*/) { static bool output_score(struct whisper_context * ctx, const char * fname, const whisper_params & /*params*/, std::vector<std::vector<float>> /*pcmf32s*/) {
std::ofstream fout(fname); std::ofstream fout(fname);
fprintf(stderr, "%s: saving output to '%s'\n", __func__, fname); fprintf(stderr, "%s: saving output to '%s'\n", __func__, fname);
@ -523,7 +564,7 @@ bool output_score(struct whisper_context * ctx, const char * fname, const whispe
return true; return true;
} }
bool output_json( static bool output_json(
struct whisper_context * ctx, struct whisper_context * ctx,
const char * fname, const char * fname,
const whisper_params & params, const whisper_params & params,
@ -669,7 +710,8 @@ bool output_json(
times_o(token.t0, token.t1, false); times_o(token.t0, token.t1, false);
} }
value_i("id", token.id, false); value_i("id", token.id, false);
value_f("p", token.p, true); value_f("p", token.p, false);
value_f("t_dtw", token.t_dtw, true);
end_obj(j == (n - 1)); end_obj(j == (n - 1));
} }
end_arr(!params.diarize && !params.tinydiarize); end_arr(!params.diarize && !params.tinydiarize);
@ -693,7 +735,7 @@ bool output_json(
// karaoke video generation // karaoke video generation
// outputs a bash script that uses ffmpeg to generate a video with the subtitles // outputs a bash script that uses ffmpeg to generate a video with the subtitles
// TODO: font parameter adjustments // TODO: font parameter adjustments
bool output_wts(struct whisper_context * ctx, const char * fname, const char * fname_inp, const whisper_params & params, float t_sec, std::vector<std::vector<float>> pcmf32s) { static bool output_wts(struct whisper_context * ctx, const char * fname, const char * fname_inp, const whisper_params & params, float t_sec, std::vector<std::vector<float>> pcmf32s) {
std::ofstream fout(fname); std::ofstream fout(fname);
fprintf(stderr, "%s: saving output to '%s'\n", __func__, fname); fprintf(stderr, "%s: saving output to '%s'\n", __func__, fname);
@ -818,7 +860,7 @@ bool output_wts(struct whisper_context * ctx, const char * fname, const char * f
return true; return true;
} }
bool output_lrc(struct whisper_context * ctx, const char * fname, const whisper_params & params, std::vector<std::vector<float>> pcmf32s) { static bool output_lrc(struct whisper_context * ctx, const char * fname, const whisper_params & params, std::vector<std::vector<float>> pcmf32s) {
std::ofstream fout(fname); std::ofstream fout(fname);
if (!fout.is_open()) { if (!fout.is_open()) {
fprintf(stderr, "%s: failed to open '%s' for writing\n", __func__, fname); fprintf(stderr, "%s: failed to open '%s' for writing\n", __func__, fname);
@ -859,16 +901,58 @@ bool output_lrc(struct whisper_context * ctx, const char * fname, const whisper_
} }
void cb_log_disable(enum ggml_log_level , const char * , void * ) { } static void cb_log_disable(enum ggml_log_level , const char * , void * ) { }
int main(int argc, char ** argv) { int main(int argc, char ** argv) {
whisper_params params; whisper_params params;
// If the only argument starts with "@", read arguments line-by-line
// from the given file.
std::vector<std::string> vec_args;
if (argc == 2 && argv != nullptr && argv[1] != nullptr && argv[1][0] == '@') {
// Save the name of the executable.
vec_args.push_back(argv[0]);
// Open the response file.
char const * rspfile = argv[1] + sizeof(char);
std::ifstream fin(rspfile);
if (fin.is_open() == false) {
fprintf(stderr, "error: response file '%s' not found\n", rspfile);
return 1;
}
// Read the entire response file.
std::string line;
while (std::getline(fin, line)) {
vec_args.push_back(line);
}
// Use the contents of the response file as the command-line arguments.
argc = static_cast<int>(vec_args.size());
argv = static_cast<char **>(alloca(argc * sizeof (char *)));
for (int i = 0; i < argc; ++i) {
argv[i] = const_cast<char *>(vec_args[i].c_str());
}
}
if (whisper_params_parse(argc, argv, params) == false) { if (whisper_params_parse(argc, argv, params) == false) {
whisper_print_usage(argc, argv, params); whisper_print_usage(argc, argv, params);
return 1; return 1;
} }
// remove non-existent files
for (auto it = params.fname_inp.begin(); it != params.fname_inp.end();) {
const auto fname_inp = it->c_str();
if (*it != "-" && !is_file_exist(fname_inp)) {
fprintf(stderr, "error: input file not found '%s'\n", fname_inp);
it = params.fname_inp.erase(it);
continue;
}
it++;
}
if (params.fname_inp.empty()) { if (params.fname_inp.empty()) {
fprintf(stderr, "error: no input files specified\n"); fprintf(stderr, "error: no input files specified\n");
whisper_print_usage(argc, argv, params); whisper_print_usage(argc, argv, params);
@ -894,7 +978,31 @@ int main(int argc, char ** argv) {
// whisper init // whisper init
struct whisper_context_params cparams = whisper_context_default_params(); struct whisper_context_params cparams = whisper_context_default_params();
cparams.use_gpu = params.use_gpu;
cparams.use_gpu = params.use_gpu;
cparams.flash_attn = params.flash_attn;
if (!params.dtw.empty()) {
cparams.dtw_token_timestamps = true;
cparams.dtw_aheads_preset = WHISPER_AHEADS_NONE;
if (params.dtw == "tiny") cparams.dtw_aheads_preset = WHISPER_AHEADS_TINY;
if (params.dtw == "tiny.en") cparams.dtw_aheads_preset = WHISPER_AHEADS_TINY_EN;
if (params.dtw == "base") cparams.dtw_aheads_preset = WHISPER_AHEADS_BASE;
if (params.dtw == "base.en") cparams.dtw_aheads_preset = WHISPER_AHEADS_BASE_EN;
if (params.dtw == "small") cparams.dtw_aheads_preset = WHISPER_AHEADS_SMALL;
if (params.dtw == "small.en") cparams.dtw_aheads_preset = WHISPER_AHEADS_SMALL_EN;
if (params.dtw == "medium") cparams.dtw_aheads_preset = WHISPER_AHEADS_MEDIUM;
if (params.dtw == "medium.en") cparams.dtw_aheads_preset = WHISPER_AHEADS_MEDIUM_EN;
if (params.dtw == "large.v1") cparams.dtw_aheads_preset = WHISPER_AHEADS_LARGE_V1;
if (params.dtw == "large.v2") cparams.dtw_aheads_preset = WHISPER_AHEADS_LARGE_V2;
if (params.dtw == "large.v3") cparams.dtw_aheads_preset = WHISPER_AHEADS_LARGE_V3;
if (cparams.dtw_aheads_preset == WHISPER_AHEADS_NONE) {
fprintf(stderr, "error: unknown DTW preset '%s'\n", params.dtw.c_str());
return 3;
}
}
struct whisper_context * ctx = whisper_init_from_file_with_params(params.model.c_str(), cparams); struct whisper_context * ctx = whisper_init_from_file_with_params(params.model.c_str(), cparams);
@ -906,6 +1014,29 @@ int main(int argc, char ** argv) {
// initialize openvino encoder. this has no effect on whisper.cpp builds that don't have OpenVINO configured // initialize openvino encoder. this has no effect on whisper.cpp builds that don't have OpenVINO configured
whisper_ctx_init_openvino_encoder(ctx, nullptr, params.openvino_encode_device.c_str(), nullptr); whisper_ctx_init_openvino_encoder(ctx, nullptr, params.openvino_encode_device.c_str(), nullptr);
if (!params.grammar.empty()) {
auto & grammar = params.grammar_parsed;
if (is_file_exist(params.grammar.c_str())) {
// read grammar from file
std::ifstream ifs(params.grammar.c_str());
const std::string txt = std::string((std::istreambuf_iterator<char>(ifs)), std::istreambuf_iterator<char>());
grammar = grammar_parser::parse(txt.c_str());
} else {
// read grammar from string
grammar = grammar_parser::parse(params.grammar.c_str());
}
// will be empty (default) if there are parse errors
if (grammar.rules.empty()) {
fprintf(stderr, "error: failed to parse grammar \"%s\"\n", params.grammar.c_str());
return 4;
} else {
fprintf(stderr, "%s: grammar:\n", __func__);
grammar_parser::print_grammar(stderr, grammar);
fprintf(stderr, "\n");
}
}
for (int f = 0; f < (int) params.fname_inp.size(); ++f) { for (int f = 0; f < (int) params.fname_inp.size(); ++f) {
const auto fname_inp = params.fname_inp[f]; const auto fname_inp = params.fname_inp[f];
const auto fname_out = f < (int) params.fname_out.size() && !params.fname_out[f].empty() ? params.fname_out[f] : params.fname_inp[f]; const auto fname_out = f < (int) params.fname_out.size() && !params.fname_out[f].empty() ? params.fname_out[f] : params.fname_inp[f];
@ -952,7 +1083,8 @@ int main(int argc, char ** argv) {
{ {
whisper_full_params wparams = whisper_full_default_params(WHISPER_SAMPLING_GREEDY); whisper_full_params wparams = whisper_full_default_params(WHISPER_SAMPLING_GREEDY);
wparams.strategy = params.beam_size > 1 ? WHISPER_SAMPLING_BEAM_SEARCH : WHISPER_SAMPLING_GREEDY; const bool use_grammar = (!params.grammar_parsed.rules.empty() && !params.grammar_rule.empty());
wparams.strategy = (params.beam_size > 1 || use_grammar) ? WHISPER_SAMPLING_BEAM_SEARCH : WHISPER_SAMPLING_GREEDY;
wparams.print_realtime = false; wparams.print_realtime = false;
wparams.print_progress = params.print_progress; wparams.print_progress = params.print_progress;
@ -972,17 +1104,20 @@ int main(int argc, char ** argv) {
wparams.split_on_word = params.split_on_word; wparams.split_on_word = params.split_on_word;
wparams.audio_ctx = params.audio_ctx; wparams.audio_ctx = params.audio_ctx;
wparams.speed_up = params.speed_up;
wparams.debug_mode = params.debug_mode; wparams.debug_mode = params.debug_mode;
wparams.tdrz_enable = params.tinydiarize; // [TDRZ] wparams.tdrz_enable = params.tinydiarize; // [TDRZ]
wparams.suppress_regex = params.suppress_regex.empty() ? nullptr : params.suppress_regex.c_str();
wparams.initial_prompt = params.prompt.c_str(); wparams.initial_prompt = params.prompt.c_str();
wparams.greedy.best_of = params.best_of; wparams.greedy.best_of = params.best_of;
wparams.beam_search.beam_size = params.beam_size; wparams.beam_search.beam_size = params.beam_size;
wparams.temperature_inc = params.no_fallback ? 0.0f : wparams.temperature_inc; wparams.temperature_inc = params.no_fallback ? 0.0f : params.temperature_inc;
wparams.temperature = params.temperature;
wparams.entropy_thold = params.entropy_thold; wparams.entropy_thold = params.entropy_thold;
wparams.logprob_thold = params.logprob_thold; wparams.logprob_thold = params.logprob_thold;
@ -990,6 +1125,20 @@ int main(int argc, char ** argv) {
whisper_print_user_data user_data = { &params, &pcmf32s, 0 }; whisper_print_user_data user_data = { &params, &pcmf32s, 0 };
const auto & grammar_parsed = params.grammar_parsed;
auto grammar_rules = grammar_parsed.c_rules();
if (use_grammar) {
if (grammar_parsed.symbol_ids.find(params.grammar_rule) == grammar_parsed.symbol_ids.end()) {
fprintf(stderr, "%s: warning: grammar rule '%s' not found - skipping grammar sampling\n", __func__, params.grammar_rule.c_str());
} else {
wparams.grammar_rules = grammar_rules.data();
wparams.n_grammar_rules = grammar_rules.size();
wparams.i_start_rule = grammar_parsed.symbol_ids.at(params.grammar_rule);
wparams.grammar_penalty = params.grammar_penalty;
}
}
// this callback is called on each new segment // this callback is called on each new segment
if (!wparams.print_realtime) { if (!wparams.print_realtime) {
wparams.new_segment_callback = whisper_print_segment_callback; wparams.new_segment_callback = whisper_print_segment_callback;
@ -1086,7 +1235,9 @@ int main(int argc, char ** argv) {
} }
} }
whisper_print_timings(ctx); if (!params.no_prints) {
whisper_print_timings(ctx);
}
whisper_free(ctx); whisper_free(ctx);
return 0; return 0;

View File

@ -36,7 +36,7 @@ struct whisper_filters {
}; };
// quantize a model // quantize a model
bool whisper_model_quantize(const std::string & fname_inp, const std::string & fname_out, ggml_ftype ftype) { static bool whisper_model_quantize(const std::string & fname_inp, const std::string & fname_out, ggml_ftype ftype) {
gpt_vocab vocab; gpt_vocab vocab;
printf("%s: loading model from '%s'\n", __func__, fname_inp.c_str()); printf("%s: loading model from '%s'\n", __func__, fname_inp.c_str());

View File

@ -1,9 +1,9 @@
set(TARGET server) set(TARGET server)
add_executable(${TARGET} server.cpp httplib.h json.hpp) add_executable(${TARGET} server.cpp httplib.h)
include(DefaultTargetOptions) include(DefaultTargetOptions)
target_link_libraries(${TARGET} PRIVATE common whisper ${CMAKE_THREAD_LIBS_INIT}) target_link_libraries(${TARGET} PRIVATE common json_cpp whisper ${CMAKE_THREAD_LIBS_INIT})
if (WIN32) if (WIN32)
target_link_libraries(${TARGET} PRIVATE ws2_32) target_link_libraries(${TARGET} PRIVATE ws2_32)

File diff suppressed because it is too large Load Diff

View File

@ -22,13 +22,6 @@ using json = nlohmann::ordered_json;
namespace { namespace {
// Terminal color map. 10 colors grouped in ranges [0.0, 0.1, ..., 0.9]
// Lowest is red, middle is yellow, highest is green.
const std::vector<std::string> k_colors = {
"\033[38;5;196m", "\033[38;5;202m", "\033[38;5;208m", "\033[38;5;214m", "\033[38;5;220m",
"\033[38;5;226m", "\033[38;5;190m", "\033[38;5;154m", "\033[38;5;118m", "\033[38;5;82m",
};
// output formats // output formats
const std::string json_format = "json"; const std::string json_format = "json";
const std::string text_format = "text"; const std::string text_format = "text";
@ -68,7 +61,6 @@ struct whisper_params {
float temperature = 0.00f; float temperature = 0.00f;
float temperature_inc = 0.20f; float temperature_inc = 0.20f;
bool speed_up = false;
bool debug_mode = false; bool debug_mode = false;
bool translate = false; bool translate = false;
bool detect_language = false; bool detect_language = false;
@ -82,6 +74,7 @@ struct whisper_params {
bool print_progress = false; bool print_progress = false;
bool no_timestamps = false; bool no_timestamps = false;
bool use_gpu = true; bool use_gpu = true;
bool flash_attn = false;
std::string language = "en"; std::string language = "en";
std::string prompt = ""; std::string prompt = "";
@ -94,35 +87,10 @@ struct whisper_params {
std::string tdrz_speaker_turn = " [SPEAKER_TURN]"; // TODO: set from command line std::string tdrz_speaker_turn = " [SPEAKER_TURN]"; // TODO: set from command line
std::string openvino_encode_device = "CPU"; std::string openvino_encode_device = "CPU";
std::string dtw = "";
}; };
// 500 -> 00:05.000
// 6000 -> 01:00.000
std::string to_timestamp(int64_t t, bool comma = false) {
int64_t msec = t * 10;
int64_t hr = msec / (1000 * 60 * 60);
msec = msec - hr * (1000 * 60 * 60);
int64_t min = msec / (1000 * 60);
msec = msec - min * (1000 * 60);
int64_t sec = msec / 1000;
msec = msec - sec * 1000;
char buf[32];
snprintf(buf, sizeof(buf), "%02d:%02d:%02d%s%03d", (int) hr, (int) min, (int) sec, comma ? "," : ".", (int) msec);
return std::string(buf);
}
int timestamp_to_sample(int64_t t, int n_samples) {
return std::max(0, std::min((int) n_samples - 1, (int) ((t*WHISPER_SAMPLE_RATE)/100)));
}
bool is_file_exist(const char *fileName)
{
std::ifstream infile(fileName);
return infile.good();
}
void whisper_print_usage(int /*argc*/, char ** argv, const whisper_params & params, const server_params& sparams) { void whisper_print_usage(int /*argc*/, char ** argv, const whisper_params & params, const server_params& sparams) {
fprintf(stderr, "\n"); fprintf(stderr, "\n");
fprintf(stderr, "usage: %s [options] \n", argv[0]); fprintf(stderr, "usage: %s [options] \n", argv[0]);
@ -143,7 +111,6 @@ void whisper_print_usage(int /*argc*/, char ** argv, const whisper_params & para
fprintf(stderr, " -wt N, --word-thold N [%-7.2f] word timestamp probability threshold\n", params.word_thold); fprintf(stderr, " -wt N, --word-thold N [%-7.2f] word timestamp probability threshold\n", params.word_thold);
fprintf(stderr, " -et N, --entropy-thold N [%-7.2f] entropy threshold for decoder fail\n", params.entropy_thold); fprintf(stderr, " -et N, --entropy-thold N [%-7.2f] entropy threshold for decoder fail\n", params.entropy_thold);
fprintf(stderr, " -lpt N, --logprob-thold N [%-7.2f] log probability threshold for decoder fail\n", params.logprob_thold); fprintf(stderr, " -lpt N, --logprob-thold N [%-7.2f] log probability threshold for decoder fail\n", params.logprob_thold);
// fprintf(stderr, " -su, --speed-up [%-7s] speed up audio by x2 (reduced accuracy)\n", params.speed_up ? "true" : "false");
fprintf(stderr, " -debug, --debug-mode [%-7s] enable debug mode (eg. dump log_mel)\n", params.debug_mode ? "true" : "false"); fprintf(stderr, " -debug, --debug-mode [%-7s] enable debug mode (eg. dump log_mel)\n", params.debug_mode ? "true" : "false");
fprintf(stderr, " -tr, --translate [%-7s] translate from source language to english\n", params.translate ? "true" : "false"); fprintf(stderr, " -tr, --translate [%-7s] translate from source language to english\n", params.translate ? "true" : "false");
fprintf(stderr, " -di, --diarize [%-7s] stereo audio diarization\n", params.diarize ? "true" : "false"); fprintf(stderr, " -di, --diarize [%-7s] stereo audio diarization\n", params.diarize ? "true" : "false");
@ -160,6 +127,7 @@ void whisper_print_usage(int /*argc*/, char ** argv, const whisper_params & para
fprintf(stderr, " -m FNAME, --model FNAME [%-7s] model path\n", params.model.c_str()); fprintf(stderr, " -m FNAME, --model FNAME [%-7s] model path\n", params.model.c_str());
fprintf(stderr, " -oved D, --ov-e-device DNAME [%-7s] the OpenVINO device used for encode inference\n", params.openvino_encode_device.c_str()); fprintf(stderr, " -oved D, --ov-e-device DNAME [%-7s] the OpenVINO device used for encode inference\n", params.openvino_encode_device.c_str());
// server params // server params
fprintf(stderr, " -dtw MODEL --dtw MODEL [%-7s] compute token-level timestamps\n", params.dtw.c_str());
fprintf(stderr, " --host HOST, [%-7s] Hostname/ip-adress for the server\n", sparams.hostname.c_str()); fprintf(stderr, " --host HOST, [%-7s] Hostname/ip-adress for the server\n", sparams.hostname.c_str());
fprintf(stderr, " --port PORT, [%-7d] Port number for the server\n", sparams.port); fprintf(stderr, " --port PORT, [%-7d] Port number for the server\n", sparams.port);
fprintf(stderr, " --public PATH, [%-7s] Path to the public folder\n", sparams.public_path.c_str()); fprintf(stderr, " --public PATH, [%-7s] Path to the public folder\n", sparams.public_path.c_str());
@ -185,11 +153,10 @@ bool whisper_params_parse(int argc, char ** argv, whisper_params & params, serve
else if (arg == "-ml" || arg == "--max-len") { params.max_len = std::stoi(argv[++i]); } else if (arg == "-ml" || arg == "--max-len") { params.max_len = std::stoi(argv[++i]); }
else if (arg == "-bo" || arg == "--best-of") { params.best_of = std::stoi(argv[++i]); } else if (arg == "-bo" || arg == "--best-of") { params.best_of = std::stoi(argv[++i]); }
else if (arg == "-bs" || arg == "--beam-size") { params.beam_size = std::stoi(argv[++i]); } else if (arg == "-bs" || arg == "--beam-size") { params.beam_size = std::stoi(argv[++i]); }
else if (arg == "-ac" || arg == "--audio-context") { params.audio_ctx = std::stoi(argv[++i]); } else if (arg == "-ac" || arg == "--audio-ctx") { params.audio_ctx = std::stoi(argv[++i]); }
else if (arg == "-wt" || arg == "--word-thold") { params.word_thold = std::stof(argv[++i]); } else if (arg == "-wt" || arg == "--word-thold") { params.word_thold = std::stof(argv[++i]); }
else if (arg == "-et" || arg == "--entropy-thold") { params.entropy_thold = std::stof(argv[++i]); } else if (arg == "-et" || arg == "--entropy-thold") { params.entropy_thold = std::stof(argv[++i]); }
else if (arg == "-lpt" || arg == "--logprob-thold") { params.logprob_thold = std::stof(argv[++i]); } else if (arg == "-lpt" || arg == "--logprob-thold") { params.logprob_thold = std::stof(argv[++i]); }
// else if (arg == "-su" || arg == "--speed-up") { params.speed_up = true; }
else if (arg == "-debug"|| arg == "--debug-mode") { params.debug_mode = true; } else if (arg == "-debug"|| arg == "--debug-mode") { params.debug_mode = true; }
else if (arg == "-tr" || arg == "--translate") { params.translate = true; } else if (arg == "-tr" || arg == "--translate") { params.translate = true; }
else if (arg == "-di" || arg == "--diarize") { params.diarize = true; } else if (arg == "-di" || arg == "--diarize") { params.diarize = true; }
@ -207,7 +174,9 @@ bool whisper_params_parse(int argc, char ** argv, whisper_params & params, serve
else if ( arg == "--prompt") { params.prompt = argv[++i]; } else if ( arg == "--prompt") { params.prompt = argv[++i]; }
else if (arg == "-m" || arg == "--model") { params.model = argv[++i]; } else if (arg == "-m" || arg == "--model") { params.model = argv[++i]; }
else if (arg == "-oved" || arg == "--ov-e-device") { params.openvino_encode_device = argv[++i]; } else if (arg == "-oved" || arg == "--ov-e-device") { params.openvino_encode_device = argv[++i]; }
else if (arg == "-dtw" || arg == "--dtw") { params.dtw = argv[++i]; }
else if (arg == "-ng" || arg == "--no-gpu") { params.use_gpu = false; } else if (arg == "-ng" || arg == "--no-gpu") { params.use_gpu = false; }
else if (arg == "-fa" || arg == "--flash-attn") { params.flash_attn = true; }
// server params // server params
else if ( arg == "--port") { sparams.port = std::stoi(argv[++i]); } else if ( arg == "--port") { sparams.port = std::stoi(argv[++i]); }
else if ( arg == "--host") { sparams.hostname = argv[++i]; } else if ( arg == "--host") { sparams.hostname = argv[++i]; }
@ -274,8 +243,8 @@ std::string estimate_diarization_speaker(std::vector<std::vector<float>> pcmf32s
std::string speaker = ""; std::string speaker = "";
const int64_t n_samples = pcmf32s[0].size(); const int64_t n_samples = pcmf32s[0].size();
const int64_t is0 = timestamp_to_sample(t0, n_samples); const int64_t is0 = timestamp_to_sample(t0, n_samples, WHISPER_SAMPLE_RATE);
const int64_t is1 = timestamp_to_sample(t1, n_samples); const int64_t is1 = timestamp_to_sample(t1, n_samples, WHISPER_SAMPLE_RATE);
double energy0 = 0.0f; double energy0 = 0.0f;
double energy1 = 0.0f; double energy1 = 0.0f;
@ -532,7 +501,53 @@ int main(int argc, char ** argv) {
} }
// whisper init // whisper init
struct whisper_context_params cparams = whisper_context_default_params(); struct whisper_context_params cparams = whisper_context_default_params();
cparams.use_gpu = params.use_gpu;
cparams.use_gpu = params.use_gpu;
cparams.flash_attn = params.flash_attn;
if (!params.dtw.empty()) {
cparams.dtw_token_timestamps = true;
cparams.dtw_aheads_preset = WHISPER_AHEADS_NONE;
if (params.dtw == "tiny") {
cparams.dtw_aheads_preset = WHISPER_AHEADS_TINY;
}
if (params.dtw == "tiny.en") {
cparams.dtw_aheads_preset = WHISPER_AHEADS_TINY_EN;
}
if (params.dtw == "base") {
cparams.dtw_aheads_preset = WHISPER_AHEADS_BASE;
}
if (params.dtw == "base.en") {
cparams.dtw_aheads_preset = WHISPER_AHEADS_BASE_EN;
}
if (params.dtw == "small") {
cparams.dtw_aheads_preset = WHISPER_AHEADS_SMALL;
}
if (params.dtw == "small.en") {
cparams.dtw_aheads_preset = WHISPER_AHEADS_SMALL_EN;
}
if (params.dtw == "medium") {
cparams.dtw_aheads_preset = WHISPER_AHEADS_MEDIUM;
}
if (params.dtw == "medium.en") {
cparams.dtw_aheads_preset = WHISPER_AHEADS_MEDIUM_EN;
}
if (params.dtw == "large.v1") {
cparams.dtw_aheads_preset = WHISPER_AHEADS_LARGE_V1;
}
if (params.dtw == "large.v2") {
cparams.dtw_aheads_preset = WHISPER_AHEADS_LARGE_V2;
}
if (params.dtw == "large.v3") {
cparams.dtw_aheads_preset = WHISPER_AHEADS_LARGE_V3;
}
if (cparams.dtw_aheads_preset == WHISPER_AHEADS_NONE) {
fprintf(stderr, "error: unknown DTW preset '%s'\n", params.dtw.c_str());
return 3;
}
}
struct whisper_context * ctx = whisper_init_from_file_with_params(params.model.c_str(), cparams); struct whisper_context * ctx = whisper_init_from_file_with_params(params.model.c_str(), cparams);
@ -629,7 +644,7 @@ int main(int argc, char ** argv) {
return false; return false;
}); });
svr.Options(sparams.request_path + "/inference", [&](const Request &req, Response &res){ svr.Options(sparams.request_path + "/inference", [&](const Request &, Response &){
}); });
svr.Post(sparams.request_path + "/inference", [&](const Request &req, Response &res){ svr.Post(sparams.request_path + "/inference", [&](const Request &req, Response &res){
@ -750,7 +765,6 @@ int main(int argc, char ** argv) {
wparams.split_on_word = params.split_on_word; wparams.split_on_word = params.split_on_word;
wparams.audio_ctx = params.audio_ctx; wparams.audio_ctx = params.audio_ctx;
wparams.speed_up = params.speed_up;
wparams.debug_mode = params.debug_mode; wparams.debug_mode = params.debug_mode;
wparams.tdrz_enable = params.tinydiarize; // [TDRZ] wparams.tdrz_enable = params.tinydiarize; // [TDRZ]
@ -818,7 +832,7 @@ int main(int argc, char ** argv) {
if (params.response_format == text_format) if (params.response_format == text_format)
{ {
std::string results = output_str(ctx, params, pcmf32s); std::string results = output_str(ctx, params, pcmf32s);
res.set_content(results.c_str(), "text/html"); res.set_content(results.c_str(), "text/html; charset=utf-8");
} }
else if (params.response_format == srt_format) else if (params.response_format == srt_format)
{ {
@ -899,6 +913,7 @@ int main(int argc, char ** argv) {
if (!params.no_timestamps) { if (!params.no_timestamps) {
word["start"] = token.t0 * 0.01; word["start"] = token.t0 * 0.01;
word["end"] = token.t1 * 0.01; word["end"] = token.t1 * 0.01;
word["t_dtw"] = token.t_dtw;
} }
word["probability"] = token.p; word["probability"] = token.p;
total_logprob += token.plog; total_logprob += token.plog;
@ -928,7 +943,7 @@ int main(int argc, char ** argv) {
"application/json"); "application/json");
} }
// reset params to thier defaults // reset params to their defaults
params = default_params; params = default_params;
}); });
svr.Post(sparams.request_path + "/load", [&](const Request &req, Response &res){ svr.Post(sparams.request_path + "/load", [&](const Request &req, Response &res){

View File

@ -103,11 +103,11 @@ void stream_main(size_t index) {
{ {
const int n_segments = whisper_full_n_segments(ctx); const int n_segments = whisper_full_n_segments(ctx);
for (int i = n_segments - 1; i < n_segments; ++i) { if (n_segments > 0) {
const char * text = whisper_full_get_segment_text(ctx, i); const char * text = whisper_full_get_segment_text(ctx, n_segments - 1);
const int64_t t0 = whisper_full_get_segment_t0(ctx, i); const int64_t t0 = whisper_full_get_segment_t0(ctx, n_segments - 1);
const int64_t t1 = whisper_full_get_segment_t1(ctx, i); const int64_t t1 = whisper_full_get_segment_t1(ctx, n_segments - 1);
printf("transcribed: %s\n", text); printf("transcribed: %s\n", text);

View File

@ -30,9 +30,13 @@ a transcription block that is suitable for parsing.
The `stream` tool depends on SDL2 library to capture audio from the microphone. You can build it like this: The `stream` tool depends on SDL2 library to capture audio from the microphone. You can build it like this:
```bash ```bash
# Install SDL2 on Linux # Install SDL2
# On Debian based linux distributions:
sudo apt-get install libsdl2-dev sudo apt-get install libsdl2-dev
# On Fedora Linux:
sudo dnf install SDL2 SDL2-devel
# Install SDL2 on Mac OS # Install SDL2 on Mac OS
brew install sdl2 brew install sdl2

View File

@ -14,20 +14,6 @@
#include <fstream> #include <fstream>
// 500 -> 00:05.000
// 6000 -> 01:00.000
std::string to_timestamp(int64_t t) {
int64_t sec = t/100;
int64_t msec = t - sec*100;
int64_t min = sec/60;
sec = sec - min*60;
char buf[32];
snprintf(buf, sizeof(buf), "%02d:%02d.%03d", (int) min, (int) sec, (int) msec);
return std::string(buf);
}
// command-line parameters // command-line parameters
struct whisper_params { struct whisper_params {
int32_t n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency()); int32_t n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
@ -41,7 +27,6 @@ struct whisper_params {
float vad_thold = 0.6f; float vad_thold = 0.6f;
float freq_thold = 100.0f; float freq_thold = 100.0f;
bool speed_up = false;
bool translate = false; bool translate = false;
bool no_fallback = false; bool no_fallback = false;
bool print_special = false; bool print_special = false;
@ -50,6 +35,7 @@ struct whisper_params {
bool tinydiarize = false; bool tinydiarize = false;
bool save_audio = false; // save audio to wav file bool save_audio = false; // save audio to wav file
bool use_gpu = true; bool use_gpu = true;
bool flash_attn = false;
std::string language = "en"; std::string language = "en";
std::string model = "models/ggml-base.en.bin"; std::string model = "models/ggml-base.en.bin";
@ -58,7 +44,7 @@ struct whisper_params {
void whisper_print_usage(int argc, char ** argv, const whisper_params & params); void whisper_print_usage(int argc, char ** argv, const whisper_params & params);
bool whisper_params_parse(int argc, char ** argv, whisper_params & params) { static bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
for (int i = 1; i < argc; i++) { for (int i = 1; i < argc; i++) {
std::string arg = argv[i]; std::string arg = argv[i];
@ -75,7 +61,6 @@ bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
else if (arg == "-ac" || arg == "--audio-ctx") { params.audio_ctx = std::stoi(argv[++i]); } else if (arg == "-ac" || arg == "--audio-ctx") { params.audio_ctx = std::stoi(argv[++i]); }
else if (arg == "-vth" || arg == "--vad-thold") { params.vad_thold = std::stof(argv[++i]); } else if (arg == "-vth" || arg == "--vad-thold") { params.vad_thold = std::stof(argv[++i]); }
else if (arg == "-fth" || arg == "--freq-thold") { params.freq_thold = std::stof(argv[++i]); } else if (arg == "-fth" || arg == "--freq-thold") { params.freq_thold = std::stof(argv[++i]); }
else if (arg == "-su" || arg == "--speed-up") { params.speed_up = true; }
else if (arg == "-tr" || arg == "--translate") { params.translate = true; } else if (arg == "-tr" || arg == "--translate") { params.translate = true; }
else if (arg == "-nf" || arg == "--no-fallback") { params.no_fallback = true; } else if (arg == "-nf" || arg == "--no-fallback") { params.no_fallback = true; }
else if (arg == "-ps" || arg == "--print-special") { params.print_special = true; } else if (arg == "-ps" || arg == "--print-special") { params.print_special = true; }
@ -86,6 +71,7 @@ bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
else if (arg == "-tdrz" || arg == "--tinydiarize") { params.tinydiarize = true; } else if (arg == "-tdrz" || arg == "--tinydiarize") { params.tinydiarize = true; }
else if (arg == "-sa" || arg == "--save-audio") { params.save_audio = true; } else if (arg == "-sa" || arg == "--save-audio") { params.save_audio = true; }
else if (arg == "-ng" || arg == "--no-gpu") { params.use_gpu = false; } else if (arg == "-ng" || arg == "--no-gpu") { params.use_gpu = false; }
else if (arg == "-fa" || arg == "--flash-attn") { params.flash_attn = true; }
else { else {
fprintf(stderr, "error: unknown argument: %s\n", arg.c_str()); fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
@ -112,7 +98,6 @@ void whisper_print_usage(int /*argc*/, char ** argv, const whisper_params & para
fprintf(stderr, " -ac N, --audio-ctx N [%-7d] audio context size (0 - all)\n", params.audio_ctx); fprintf(stderr, " -ac N, --audio-ctx N [%-7d] audio context size (0 - all)\n", params.audio_ctx);
fprintf(stderr, " -vth N, --vad-thold N [%-7.2f] voice activity detection threshold\n", params.vad_thold); fprintf(stderr, " -vth N, --vad-thold N [%-7.2f] voice activity detection threshold\n", params.vad_thold);
fprintf(stderr, " -fth N, --freq-thold N [%-7.2f] high-pass frequency cutoff\n", params.freq_thold); fprintf(stderr, " -fth N, --freq-thold N [%-7.2f] high-pass frequency cutoff\n", params.freq_thold);
fprintf(stderr, " -su, --speed-up [%-7s] speed up audio by x2 (reduced accuracy)\n", params.speed_up ? "true" : "false");
fprintf(stderr, " -tr, --translate [%-7s] translate from source language to english\n", params.translate ? "true" : "false"); fprintf(stderr, " -tr, --translate [%-7s] translate from source language to english\n", params.translate ? "true" : "false");
fprintf(stderr, " -nf, --no-fallback [%-7s] do not use temperature fallback while decoding\n", params.no_fallback ? "true" : "false"); fprintf(stderr, " -nf, --no-fallback [%-7s] do not use temperature fallback while decoding\n", params.no_fallback ? "true" : "false");
fprintf(stderr, " -ps, --print-special [%-7s] print special tokens\n", params.print_special ? "true" : "false"); fprintf(stderr, " -ps, --print-special [%-7s] print special tokens\n", params.print_special ? "true" : "false");
@ -123,6 +108,7 @@ void whisper_print_usage(int /*argc*/, char ** argv, const whisper_params & para
fprintf(stderr, " -tdrz, --tinydiarize [%-7s] enable tinydiarize (requires a tdrz model)\n", params.tinydiarize ? "true" : "false"); fprintf(stderr, " -tdrz, --tinydiarize [%-7s] enable tinydiarize (requires a tdrz model)\n", params.tinydiarize ? "true" : "false");
fprintf(stderr, " -sa, --save-audio [%-7s] save the recorded audio to a file\n", params.save_audio ? "true" : "false"); fprintf(stderr, " -sa, --save-audio [%-7s] save the recorded audio to a file\n", params.save_audio ? "true" : "false");
fprintf(stderr, " -ng, --no-gpu [%-7s] disable GPU inference\n", params.use_gpu ? "false" : "true"); fprintf(stderr, " -ng, --no-gpu [%-7s] disable GPU inference\n", params.use_gpu ? "false" : "true");
fprintf(stderr, " -fa, --flash-attn [%-7s] flash attention during inference\n", params.flash_attn ? "true" : "false");
fprintf(stderr, "\n"); fprintf(stderr, "\n");
} }
@ -167,7 +153,9 @@ int main(int argc, char ** argv) {
} }
struct whisper_context_params cparams = whisper_context_default_params(); struct whisper_context_params cparams = whisper_context_default_params();
cparams.use_gpu = params.use_gpu;
cparams.use_gpu = params.use_gpu;
cparams.flash_attn = params.flash_attn;
struct whisper_context * ctx = whisper_init_from_file_with_params(params.model.c_str(), cparams); struct whisper_context * ctx = whisper_init_from_file_with_params(params.model.c_str(), cparams);
@ -323,7 +311,6 @@ int main(int argc, char ** argv) {
wparams.n_threads = params.n_threads; wparams.n_threads = params.n_threads;
wparams.audio_ctx = params.audio_ctx; wparams.audio_ctx = params.audio_ctx;
wparams.speed_up = params.speed_up;
wparams.tdrz_enable = params.tinydiarize; // [TDRZ] wparams.tdrz_enable = params.tinydiarize; // [TDRZ]
@ -372,7 +359,7 @@ int main(int argc, char ** argv) {
const int64_t t0 = whisper_full_get_segment_t0(ctx, i); const int64_t t0 = whisper_full_get_segment_t0(ctx, i);
const int64_t t1 = whisper_full_get_segment_t1(ctx, i); const int64_t t1 = whisper_full_get_segment_t1(ctx, i);
std::string output = "[" + to_timestamp(t0) + " --> " + to_timestamp(t1) + "] " + text; std::string output = "[" + to_timestamp(t0, false) + " --> " + to_timestamp(t1, false) + "] " + text;
if (whisper_full_get_segment_speaker_turn_next(ctx, i)) { if (whisper_full_get_segment_speaker_turn_next(ctx, i)) {
output += " [SPEAKER_TURN]"; output += " [SPEAKER_TURN]";

View File

@ -0,0 +1,9 @@
# MIT license
# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: MIT
set(TARGET ls-sycl-device)
add_executable(${TARGET} ls-sycl-device.cpp)
install(TARGETS ${TARGET} RUNTIME)
target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_17)

47
examples/sycl/README.md Normal file
View File

@ -0,0 +1,47 @@
# llama.cpp/example/sycl
This example program provide the tools for llama.cpp for SYCL on Intel GPU.
## Tool
|Tool Name| Function|Status|
|-|-|-|
|ls-sycl-device| List all SYCL devices with ID, compute capability, max work group size, ect.|Support|
### ls-sycl-device
List all SYCL devices with ID, compute capability, max work group size, ect.
1. Build the llama.cpp for SYCL for all targets.
2. Enable oneAPI running environment
```
source /opt/intel/oneapi/setvars.sh
```
3. Execute
```
./build/bin/ls-sycl-device
```
Check the ID in startup log, like:
```
found 4 SYCL devices:
Device 0: Intel(R) Arc(TM) A770 Graphics, compute capability 1.3,
max compute_units 512, max work group size 1024, max sub group size 32, global mem size 16225243136
Device 1: Intel(R) FPGA Emulation Device, compute capability 1.2,
max compute_units 24, max work group size 67108864, max sub group size 64, global mem size 67065057280
Device 2: 13th Gen Intel(R) Core(TM) i7-13700K, compute capability 3.0,
max compute_units 24, max work group size 8192, max sub group size 64, global mem size 67065057280
Device 3: Intel(R) Arc(TM) A770 Graphics, compute capability 3.0,
max compute_units 512, max work group size 1024, max sub group size 32, global mem size 16225243136
```
|Attribute|Note|
|-|-|
|compute capability 1.3|Level-zero running time, recommended |
|compute capability 3.0|OpenCL running time, slower than level-zero in most cases|

19
examples/sycl/build.sh Normal file
View File

@ -0,0 +1,19 @@
# MIT license
# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: MIT
mkdir -p build
cd build
source /opt/intel/oneapi/setvars.sh
#for FP16
#cmake .. -DWHISPER_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DWHISPER_SYCL_F16=ON # faster for long-prompt inference
#for FP32
cmake .. -DWHISPER_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
#build example/main only
#cmake --build . --config Release --target main
#build all binary
cmake --build . --config Release -v

View File

@ -0,0 +1,11 @@
/*MIT license
Copyright (C) 2024 Intel Corporation
SPDX-License-Identifier: MIT
*/
#include "ggml-sycl.h"
int main(int argc, char ** argv) {
ggml_backend_sycl_print_sycl_devices();
return 0;
}

View File

@ -0,0 +1,17 @@
#!/bin/bash
# MIT license
# Copyright (C) 2024 Intel Corporation
# SPDX-License-Identifier: MIT
INPUT2="Building a website can be done in 10 simple steps:\nStep 1:"
source /opt/intel/oneapi/setvars.sh
if [ $# -gt 0 ]; then
export GGML_SYCL_DEVICE=$1
else
export GGML_SYCL_DEVICE=0
fi
echo GGML_SYCL_DEVICE=$GGML_SYCL_DEVICE
#export GGML_SYCL_DEBUG=1
./build/bin/main -m models/ggml-base.en.bin -f samples/jfk.wav

View File

@ -1 +1,2 @@
audio.mp3 audio.mp3
to_speak.txt

View File

@ -1,7 +1,7 @@
if (WHISPER_SDL2) if (WHISPER_SDL2)
# talk-llama # talk-llama
set(TARGET talk-llama) set(TARGET talk-llama)
add_executable(${TARGET} talk-llama.cpp llama.cpp) add_executable(${TARGET} talk-llama.cpp llama.cpp unicode.cpp unicode-data.cpp)
target_include_directories(${TARGET} PRIVATE ${SDL2_INCLUDE_DIRS}) target_include_directories(${TARGET} PRIVATE ${SDL2_INCLUDE_DIRS})
if (WHISPER_CLBLAST) if (WHISPER_CLBLAST)

View File

@ -15,9 +15,13 @@ https://github.com/ggerganov/whisper.cpp/assets/1991296/d97a3788-bf2a-4756-9a43-
The `talk-llama` tool depends on SDL2 library to capture audio from the microphone. You can build it like this: The `talk-llama` tool depends on SDL2 library to capture audio from the microphone. You can build it like this:
```bash ```bash
# Install SDL2 on Linux # Install SDL2
# On Debian based linux distributions:
sudo apt-get install libsdl2-dev sudo apt-get install libsdl2-dev
# On Fedora Linux:
sudo dnf install SDL2 SDL2-devel
# Install SDL2 on Mac OS # Install SDL2 on Mac OS
brew install sdl2 brew install sdl2

View File

@ -1,20 +1,80 @@
import sys import sys
import importlib.util import argparse
import textwrap
if importlib.util.find_spec("elevenlabs") is None: parser = argparse.ArgumentParser(add_help=False,
print("elevenlabs library is not installed, you can install it to your enviroment using 'pip install elevenlabs'") formatter_class=argparse.RawTextHelpFormatter)
parser.add_argument("-q", "--quick", action="store_true",
help="skip checking the required library")
modes = parser.add_argument_group("action")
modes.add_argument("inputfile", metavar="TEXTFILE",
nargs='?', type=argparse.FileType(), default=sys.stdin,
help="read the text file (default: stdin)")
modes.add_argument("-l", "--list", action="store_true",
help="show the list of voices and exit")
modes.add_argument("-h", "--help", action="help",
help="show this help and exit")
selopts = parser.add_argument_group("voice selection")
selmodes = selopts.add_mutually_exclusive_group()
selmodes.add_argument("-n", "--name",
default="Arnold",
help="get a voice object by name (default: Arnold)")
selmodes.add_argument("-v", "--voice", type=int, metavar="NUMBER",
help="get a voice object by number (see --list)")
selopts.add_argument("-f", "--filter", action="append", metavar="KEY=VAL",
default=["use case=narration"],
help=textwrap.dedent('''\
filter voices by labels (default: "use case=narration")
this option can be used multiple times
filtering will be disabled if the first -f has no "=" (e.g. -f "any")
'''))
outmodes = parser.add_argument_group("output")
outgroup = outmodes.add_mutually_exclusive_group()
outgroup.add_argument("-s", "--save", metavar="FILE",
default="audio.mp3",
help="save the TTS to a file (default: audio.mp3)")
outgroup.add_argument("-p", "--play", action="store_true",
help="play the TTS with ffplay")
args = parser.parse_args()
if not args.quick:
import importlib.util
if importlib.util.find_spec("elevenlabs") is None:
print("elevenlabs library is not installed, you can install it to your enviroment using 'pip install elevenlabs'")
sys.exit()
from elevenlabs import voices, generate, play, save
if args.filter and "=" in args.filter[0]:
voicelist = voices()
for f in args.filter:
label, value = f.split("=")
voicelist = filter(lambda x: x.labels.get(label) == value, voicelist)
voicelist = list(voicelist)
else:
voicelist = list(voices())
if args.list:
for i, v in enumerate(voicelist):
print(str(i) + ": " + v.name + " " + str(v.labels))
sys.exit() sys.exit()
from elevenlabs import generate, play, save if args.voice:
voice = voicelist[args.voice % len(voicelist)]
else:
voice = args.name
# if -n should consult -f, use the following
#voice = next(x for x in voicelist if x.name == args.name)
# Get a Voice object, by name or UUID
voice = "Arnold" #Possible Voices: Adam Antoni Arnold Bella Domi Elli Josh
# Generate the TTS
audio = generate( audio = generate(
text=str(sys.argv[2:]), text=str(args.inputfile.read()),
voice=voice voice=voice
) )
if args.play:
# Save the TTS to a file play(audio)
save(audio, "audio.mp3") else:
save(audio, args.save)

File diff suppressed because it is too large Load Diff

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@ -37,9 +37,13 @@
#define LLAMA_FILE_MAGIC_GGLA 0x67676c61u // 'ggla' #define LLAMA_FILE_MAGIC_GGLA 0x67676c61u // 'ggla'
#define LLAMA_FILE_MAGIC_GGSN 0x6767736eu // 'ggsn' #define LLAMA_FILE_MAGIC_GGSN 0x6767736eu // 'ggsn'
#define LLAMA_FILE_MAGIC_GGSQ 0x67677371u // 'ggsq'
#define LLAMA_SESSION_MAGIC LLAMA_FILE_MAGIC_GGSN #define LLAMA_SESSION_MAGIC LLAMA_FILE_MAGIC_GGSN
#define LLAMA_SESSION_VERSION 4 #define LLAMA_SESSION_VERSION 6
#define LLAMA_STATE_SEQ_MAGIC LLAMA_FILE_MAGIC_GGSQ
#define LLAMA_STATE_SEQ_VERSION 1
#ifdef __cplusplus #ifdef __cplusplus
extern "C" { extern "C" {
@ -59,12 +63,42 @@ extern "C" {
typedef int32_t llama_seq_id; typedef int32_t llama_seq_id;
enum llama_vocab_type { enum llama_vocab_type {
LLAMA_VOCAB_TYPE_SPM = 0, // SentencePiece LLAMA_VOCAB_TYPE_NONE = 0, // For models without vocab
LLAMA_VOCAB_TYPE_BPE = 1, // Byte Pair Encoding LLAMA_VOCAB_TYPE_SPM = 1, // LLaMA tokenizer based on byte-level BPE with byte fallback
LLAMA_VOCAB_TYPE_WPM = 2, // WordPiece LLAMA_VOCAB_TYPE_BPE = 2, // GPT-2 tokenizer based on byte-level BPE
LLAMA_VOCAB_TYPE_WPM = 3, // BERT tokenizer based on WordPiece
}; };
enum llama_token_type { // pre-tokenization types
enum llama_vocab_pre_type {
LLAMA_VOCAB_PRE_TYPE_DEFAULT = 0,
LLAMA_VOCAB_PRE_TYPE_LLAMA3 = 1,
LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_LLM = 2,
LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_CODER = 3,
LLAMA_VOCAB_PRE_TYPE_FALCON = 4,
LLAMA_VOCAB_PRE_TYPE_MPT = 5,
LLAMA_VOCAB_PRE_TYPE_STARCODER = 6,
LLAMA_VOCAB_PRE_TYPE_GPT2 = 7,
LLAMA_VOCAB_PRE_TYPE_REFACT = 8,
LLAMA_VOCAB_PRE_TYPE_COMMAND_R = 9,
LLAMA_VOCAB_PRE_TYPE_STABLELM2 = 10,
LLAMA_VOCAB_PRE_TYPE_QWEN2 = 11,
LLAMA_VOCAB_PRE_TYPE_OLMO = 12,
LLAMA_VOCAB_PRE_TYPE_DBRX = 13,
LLAMA_VOCAB_PRE_TYPE_SMAUG = 14,
LLAMA_VOCAB_PRE_TYPE_PORO = 15,
};
// note: these values should be synchronized with ggml_rope
// TODO: maybe move this enum to ggml.h (ggml_rope_type)
enum llama_rope_type {
LLAMA_ROPE_TYPE_NONE = -1,
LLAMA_ROPE_TYPE_NORM = 0,
LLAMA_ROPE_TYPE_NEOX = 2,
LLAMA_ROPE_TYPE_GLM = 4,
};
enum llama_token_type { //TODO: remove, required until per token attributes are available from GGUF file
LLAMA_TOKEN_TYPE_UNDEFINED = 0, LLAMA_TOKEN_TYPE_UNDEFINED = 0,
LLAMA_TOKEN_TYPE_NORMAL = 1, LLAMA_TOKEN_TYPE_NORMAL = 1,
LLAMA_TOKEN_TYPE_UNKNOWN = 2, LLAMA_TOKEN_TYPE_UNKNOWN = 2,
@ -74,6 +108,20 @@ extern "C" {
LLAMA_TOKEN_TYPE_BYTE = 6, LLAMA_TOKEN_TYPE_BYTE = 6,
}; };
enum llama_token_attr {
LLAMA_TOKEN_ATTR_UNDEFINED = 0,
LLAMA_TOKEN_ATTR_UNKNOWN = 1 << 0,
LLAMA_TOKEN_ATTR_UNUSED = 1 << 1,
LLAMA_TOKEN_ATTR_NORMAL = 1 << 2,
LLAMA_TOKEN_ATTR_CONTROL = 1 << 3, // SPECIAL?
LLAMA_TOKEN_ATTR_USER_DEFINED = 1 << 4,
LLAMA_TOKEN_ATTR_BYTE = 1 << 5,
LLAMA_TOKEN_ATTR_NORMALIZED = 1 << 6,
LLAMA_TOKEN_ATTR_LSTRIP = 1 << 7,
LLAMA_TOKEN_ATTR_RSTRIP = 1 << 8,
LLAMA_TOKEN_ATTR_SINGLE_WORD = 1 << 9,
};
// model file types // model file types
enum llama_ftype { enum llama_ftype {
LLAMA_FTYPE_ALL_F32 = 0, LLAMA_FTYPE_ALL_F32 = 0,
@ -98,24 +146,41 @@ extern "C" {
LLAMA_FTYPE_MOSTLY_IQ2_XXS = 19, // except 1d tensors LLAMA_FTYPE_MOSTLY_IQ2_XXS = 19, // except 1d tensors
LLAMA_FTYPE_MOSTLY_IQ2_XS = 20, // except 1d tensors LLAMA_FTYPE_MOSTLY_IQ2_XS = 20, // except 1d tensors
LLAMA_FTYPE_MOSTLY_Q2_K_S = 21, // except 1d tensors LLAMA_FTYPE_MOSTLY_Q2_K_S = 21, // except 1d tensors
LLAMA_FTYPE_MOSTLY_Q3_K_XS = 22, // except 1d tensors LLAMA_FTYPE_MOSTLY_IQ3_XS = 22, // except 1d tensors
LLAMA_FTYPE_MOSTLY_IQ3_XXS = 23, // except 1d tensors LLAMA_FTYPE_MOSTLY_IQ3_XXS = 23, // except 1d tensors
LLAMA_FTYPE_MOSTLY_IQ1_S = 24, // except 1d tensors
LLAMA_FTYPE_MOSTLY_IQ4_NL = 25, // except 1d tensors
LLAMA_FTYPE_MOSTLY_IQ3_S = 26, // except 1d tensors
LLAMA_FTYPE_MOSTLY_IQ3_M = 27, // except 1d tensors
LLAMA_FTYPE_MOSTLY_IQ2_S = 28, // except 1d tensors
LLAMA_FTYPE_MOSTLY_IQ2_M = 29, // except 1d tensors
LLAMA_FTYPE_MOSTLY_IQ4_XS = 30, // except 1d tensors
LLAMA_FTYPE_MOSTLY_IQ1_M = 31, // except 1d tensors
LLAMA_FTYPE_MOSTLY_BF16 = 32, // except 1d tensors
LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file
}; };
enum llama_rope_scaling_type { enum llama_rope_scaling_type {
LLAMA_ROPE_SCALING_UNSPECIFIED = -1, LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED = -1,
LLAMA_ROPE_SCALING_NONE = 0, LLAMA_ROPE_SCALING_TYPE_NONE = 0,
LLAMA_ROPE_SCALING_LINEAR = 1, LLAMA_ROPE_SCALING_TYPE_LINEAR = 1,
LLAMA_ROPE_SCALING_YARN = 2, LLAMA_ROPE_SCALING_TYPE_YARN = 2,
LLAMA_ROPE_SCALING_MAX_VALUE = LLAMA_ROPE_SCALING_YARN, LLAMA_ROPE_SCALING_TYPE_MAX_VALUE = LLAMA_ROPE_SCALING_TYPE_YARN,
};
enum llama_pooling_type {
LLAMA_POOLING_TYPE_UNSPECIFIED = -1,
LLAMA_POOLING_TYPE_NONE = 0,
LLAMA_POOLING_TYPE_MEAN = 1,
LLAMA_POOLING_TYPE_CLS = 2,
LLAMA_POOLING_TYPE_LAST = 3,
}; };
enum llama_split_mode { enum llama_split_mode {
LLAMA_SPLIT_NONE = 0, // single GPU LLAMA_SPLIT_MODE_NONE = 0, // single GPU
LLAMA_SPLIT_LAYER = 1, // split layers and KV across GPUs LLAMA_SPLIT_MODE_LAYER = 1, // split layers and KV across GPUs
LLAMA_SPLIT_ROW = 2, // split rows across GPUs LLAMA_SPLIT_MODE_ROW = 2, // split rows across GPUs
}; };
typedef struct llama_token_data { typedef struct llama_token_data {
@ -130,7 +195,7 @@ extern "C" {
bool sorted; bool sorted;
} llama_token_data_array; } llama_token_data_array;
typedef bool (*llama_progress_callback)(float progress, void *ctx); typedef bool (*llama_progress_callback)(float progress, void * user_data);
// Input data for llama_decode // Input data for llama_decode
// A llama_batch object can contain input about one or many sequences // A llama_batch object can contain input about one or many sequences
@ -140,7 +205,7 @@ extern "C" {
// - embd : token embeddings (i.e. float vector of size n_embd) (used when token is NULL) // - embd : token embeddings (i.e. float vector of size n_embd) (used when token is NULL)
// - pos : the positions of the respective token in the sequence // - pos : the positions of the respective token in the sequence
// - seq_id : the sequence to which the respective token belongs // - seq_id : the sequence to which the respective token belongs
// - logits : if zero, the logits for the respective token will not be output // - logits : if zero, the logits (and/or the embeddings) for the respective token will not be output
// //
typedef struct llama_batch { typedef struct llama_batch {
int32_t n_tokens; int32_t n_tokens;
@ -150,7 +215,7 @@ extern "C" {
llama_pos * pos; llama_pos * pos;
int32_t * n_seq_id; int32_t * n_seq_id;
llama_seq_id ** seq_id; llama_seq_id ** seq_id;
int8_t * logits; int8_t * logits; // TODO: rename this to "output"
// NOTE: helpers for smooth API transition - can be deprecated in the future // NOTE: helpers for smooth API transition - can be deprecated in the future
// for future-proof code, use the above fields instead and ignore everything below // for future-proof code, use the above fields instead and ignore everything below
@ -163,18 +228,22 @@ extern "C" {
} llama_batch; } llama_batch;
enum llama_model_kv_override_type { enum llama_model_kv_override_type {
LLAMA_KV_OVERRIDE_INT, LLAMA_KV_OVERRIDE_TYPE_INT,
LLAMA_KV_OVERRIDE_FLOAT, LLAMA_KV_OVERRIDE_TYPE_FLOAT,
LLAMA_KV_OVERRIDE_BOOL, LLAMA_KV_OVERRIDE_TYPE_BOOL,
LLAMA_KV_OVERRIDE_TYPE_STR,
}; };
struct llama_model_kv_override { struct llama_model_kv_override {
char key[128];
enum llama_model_kv_override_type tag; enum llama_model_kv_override_type tag;
char key[128];
union { union {
int64_t int_value; int64_t val_i64;
double float_value; double val_f64;
bool bool_value; bool val_bool;
char val_str[128];
}; };
}; };
@ -191,6 +260,9 @@ extern "C" {
// proportion of the model (layers or rows) to offload to each GPU, size: llama_max_devices() // proportion of the model (layers or rows) to offload to each GPU, size: llama_max_devices()
const float * tensor_split; const float * tensor_split;
// comma separated list of RPC servers to use for offloading
const char * rpc_servers;
// Called with a progress value between 0.0 and 1.0. Pass NULL to disable. // Called with a progress value between 0.0 and 1.0. Pass NULL to disable.
// If the provided progress_callback returns true, model loading continues. // If the provided progress_callback returns true, model loading continues.
// If it returns false, model loading is immediately aborted. // If it returns false, model loading is immediately aborted.
@ -203,18 +275,25 @@ extern "C" {
const struct llama_model_kv_override * kv_overrides; const struct llama_model_kv_override * kv_overrides;
// Keep the booleans together to avoid misalignment during copy-by-value. // Keep the booleans together to avoid misalignment during copy-by-value.
bool vocab_only; // only load the vocabulary, no weights bool vocab_only; // only load the vocabulary, no weights
bool use_mmap; // use mmap if possible bool use_mmap; // use mmap if possible
bool use_mlock; // force system to keep model in RAM bool use_mlock; // force system to keep model in RAM
bool check_tensors; // validate model tensor data
}; };
// NOTE: changing the default values of parameters marked as [EXPERIMENTAL] may cause crashes or incorrect results in certain configurations
// https://github.com/ggerganov/llama.cpp/pull/7544
struct llama_context_params { struct llama_context_params {
uint32_t seed; // RNG seed, -1 for random uint32_t seed; // RNG seed, -1 for random
uint32_t n_ctx; // text context, 0 = from model uint32_t n_ctx; // text context, 0 = from model
uint32_t n_batch; // prompt processing maximum batch size uint32_t n_batch; // logical maximum batch size that can be submitted to llama_decode
uint32_t n_ubatch; // physical maximum batch size
uint32_t n_seq_max; // max number of sequences (i.e. distinct states for recurrent models)
uint32_t n_threads; // number of threads to use for generation uint32_t n_threads; // number of threads to use for generation
uint32_t n_threads_batch; // number of threads to use for batch processing uint32_t n_threads_batch; // number of threads to use for batch processing
int32_t rope_scaling_type; // RoPE scaling type, from `enum llama_rope_scaling_type`
enum llama_rope_scaling_type rope_scaling_type; // RoPE scaling type, from `enum llama_rope_scaling_type`
enum llama_pooling_type pooling_type; // whether to pool (sum) embedding results by sequence id
// ref: https://github.com/ggerganov/llama.cpp/pull/2054 // ref: https://github.com/ggerganov/llama.cpp/pull/2054
float rope_freq_base; // RoPE base frequency, 0 = from model float rope_freq_base; // RoPE base frequency, 0 = from model
@ -224,29 +303,40 @@ extern "C" {
float yarn_beta_fast; // YaRN low correction dim float yarn_beta_fast; // YaRN low correction dim
float yarn_beta_slow; // YaRN high correction dim float yarn_beta_slow; // YaRN high correction dim
uint32_t yarn_orig_ctx; // YaRN original context size uint32_t yarn_orig_ctx; // YaRN original context size
float defrag_thold; // defragment the KV cache if holes/size > thold, < 0 disabled (default)
ggml_backend_sched_eval_callback cb_eval; ggml_backend_sched_eval_callback cb_eval;
void * cb_eval_user_data; void * cb_eval_user_data;
enum ggml_type type_k; // data type for K cache enum ggml_type type_k; // data type for K cache [EXPERIMENTAL]
enum ggml_type type_v; // data type for V cache enum ggml_type type_v; // data type for V cache [EXPERIMENTAL]
// Keep the booleans together to avoid misalignment during copy-by-value. // Keep the booleans together to avoid misalignment during copy-by-value.
bool mul_mat_q; // if true, use experimental mul_mat_q kernels (DEPRECATED - always true) bool logits_all; // the llama_decode() call computes all logits, not just the last one (DEPRECATED - set llama_batch.logits instead)
bool logits_all; // the llama_eval() call computes all logits, not just the last one (DEPRECATED - set llama_batch.logits instead) bool embeddings; // if true, extract embeddings (together with logits)
bool embedding; // embedding mode only
bool offload_kqv; // whether to offload the KQV ops (including the KV cache) to GPU bool offload_kqv; // whether to offload the KQV ops (including the KV cache) to GPU
bool flash_attn; // whether to use flash attention [EXPERIMENTAL]
// Abort callback
// if it returns true, execution of llama_decode() will be aborted
// currently works only with CPU execution
ggml_abort_callback abort_callback;
void * abort_callback_data;
}; };
// model quantization parameters // model quantization parameters
typedef struct llama_model_quantize_params { typedef struct llama_model_quantize_params {
int32_t nthread; // number of threads to use for quantizing, if <=0 will use std::thread::hardware_concurrency() int32_t nthread; // number of threads to use for quantizing, if <=0 will use std::thread::hardware_concurrency()
enum llama_ftype ftype; // quantize to this llama_ftype enum llama_ftype ftype; // quantize to this llama_ftype
bool allow_requantize; // allow quantizing non-f32/f16 tensors enum ggml_type output_tensor_type; // output tensor type
bool quantize_output_tensor; // quantize output.weight enum ggml_type token_embedding_type; // itoken embeddings tensor type
bool only_copy; // only copy tensors - ftype, allow_requantize and quantize_output_tensor are ignored bool allow_requantize; // allow quantizing non-f32/f16 tensors
bool pure; // disable k-quant mixtures and quantize all tensors to the same type bool quantize_output_tensor; // quantize output.weight
void * imatrix; // pointer to importance matrix data bool only_copy; // only copy tensors - ftype, allow_requantize and quantize_output_tensor are ignored
bool pure; // quantize all tensors to the default type
bool keep_split; // quantize to the same number of shards
void * imatrix; // pointer to importance matrix data
void * kv_overrides; // pointer to vector containing overrides
} llama_model_quantize_params; } llama_model_quantize_params;
// grammar types // grammar types
@ -276,6 +366,9 @@ extern "C" {
// modifies a preceding LLAMA_GRETYPE_CHAR or // modifies a preceding LLAMA_GRETYPE_CHAR or
// LLAMA_GRETYPE_CHAR_RNG_UPPER to add an alternate char to match ([ab], [a-zA]) // LLAMA_GRETYPE_CHAR_RNG_UPPER to add an alternate char to match ([ab], [a-zA])
LLAMA_GRETYPE_CHAR_ALT = 6, LLAMA_GRETYPE_CHAR_ALT = 6,
// any character (.)
LLAMA_GRETYPE_CHAR_ANY = 7,
}; };
typedef struct llama_grammar_element { typedef struct llama_grammar_element {
@ -297,6 +390,12 @@ extern "C" {
int32_t n_eval; int32_t n_eval;
}; };
// used in chat template
typedef struct llama_chat_message {
const char * role;
const char * content;
} llama_chat_message;
// Helpers for getting default parameters // Helpers for getting default parameters
LLAMA_API struct llama_model_params llama_model_default_params(void); LLAMA_API struct llama_model_params llama_model_default_params(void);
LLAMA_API struct llama_context_params llama_context_default_params(void); LLAMA_API struct llama_context_params llama_context_default_params(void);
@ -305,7 +404,10 @@ extern "C" {
// Initialize the llama + ggml backend // Initialize the llama + ggml backend
// If numa is true, use NUMA optimizations // If numa is true, use NUMA optimizations
// Call once at the start of the program // Call once at the start of the program
LLAMA_API void llama_backend_init(bool numa); LLAMA_API void llama_backend_init(void);
//optional:
LLAMA_API void llama_numa_init(enum ggml_numa_strategy numa);
// Call once at the end of the program - currently only used for MPI // Call once at the end of the program - currently only used for MPI
LLAMA_API void llama_backend_free(void); LLAMA_API void llama_backend_free(void);
@ -331,19 +433,22 @@ extern "C" {
LLAMA_API bool llama_supports_mlock (void); LLAMA_API bool llama_supports_mlock (void);
LLAMA_API bool llama_supports_gpu_offload(void); LLAMA_API bool llama_supports_gpu_offload(void);
LLAMA_API DEPRECATED(bool llama_mmap_supported (void), "use llama_supports_mmap() instead");
LLAMA_API DEPRECATED(bool llama_mlock_supported(void), "use llama_supports_mlock() instead");
LLAMA_API const struct llama_model * llama_get_model(const struct llama_context * ctx); LLAMA_API const struct llama_model * llama_get_model(const struct llama_context * ctx);
LLAMA_API uint32_t llama_n_ctx (const struct llama_context * ctx); LLAMA_API uint32_t llama_n_ctx (const struct llama_context * ctx);
LLAMA_API uint32_t llama_n_batch (const struct llama_context * ctx); LLAMA_API uint32_t llama_n_batch (const struct llama_context * ctx);
LLAMA_API uint32_t llama_n_ubatch (const struct llama_context * ctx);
LLAMA_API uint32_t llama_n_seq_max (const struct llama_context * ctx);
LLAMA_API enum llama_vocab_type llama_vocab_type(const struct llama_model * model); LLAMA_API enum llama_pooling_type llama_pooling_type(const struct llama_context * ctx);
LLAMA_API enum llama_vocab_type llama_vocab_type (const struct llama_model * model);
LLAMA_API enum llama_rope_type llama_rope_type (const struct llama_model * model);
LLAMA_API int32_t llama_n_vocab (const struct llama_model * model); LLAMA_API int32_t llama_n_vocab (const struct llama_model * model);
LLAMA_API int32_t llama_n_ctx_train(const struct llama_model * model); LLAMA_API int32_t llama_n_ctx_train(const struct llama_model * model);
LLAMA_API int32_t llama_n_embd (const struct llama_model * model); LLAMA_API int32_t llama_n_embd (const struct llama_model * model);
LLAMA_API int32_t llama_n_layer (const struct llama_model * model);
// Get the model's RoPE frequency scaling factor // Get the model's RoPE frequency scaling factor
LLAMA_API float llama_rope_freq_scale_train(const struct llama_model * model); LLAMA_API float llama_rope_freq_scale_train(const struct llama_model * model);
@ -389,20 +494,26 @@ extern "C" {
// The model needs to be reloaded before applying a new adapter, otherwise the adapter // The model needs to be reloaded before applying a new adapter, otherwise the adapter
// will be applied on top of the previous one // will be applied on top of the previous one
// Returns 0 on success // Returns 0 on success
LLAMA_API DEPRECATED(int32_t llama_apply_lora_from_file(
struct llama_context * ctx,
const char * path_lora,
float scale,
const char * path_base_model,
int32_t n_threads),
"use llama_model_apply_lora_from_file instead");
LLAMA_API int32_t llama_model_apply_lora_from_file( LLAMA_API int32_t llama_model_apply_lora_from_file(
const struct llama_model * model, const struct llama_model * model,
const char * path_lora, const char * path_lora,
float scale, float scale,
const char * path_base_model, const char * path_base_model,
int32_t n_threads); int32_t n_threads);
// Apply a loaded control vector to a llama_context, or if data is NULL, clear
// the currently loaded vector.
// n_embd should be the size of a single layer's control, and data should point
// to an n_embd x n_layers buffer starting from layer 1.
// il_start and il_end are the layer range the vector should apply to (both inclusive)
// See llama_control_vector_load in common to load a control vector.
LLAMA_API int32_t llama_control_vector_apply(
struct llama_context * lctx,
const float * data,
size_t len,
int32_t n_embd,
int32_t il_start,
int32_t il_end);
// //
// KV cache // KV cache
@ -423,7 +534,7 @@ extern "C" {
// Maximum number of sequences that can exist in a cell. It's not an error // Maximum number of sequences that can exist in a cell. It's not an error
// if there are more sequences in a cell than this value, however they will // if there are more sequences in a cell than this value, however they will
// not be visible in the view cells_sequences. // not be visible in the view cells_sequences.
int32_t n_max_seq; int32_t n_seq_max;
// Number of tokens in the cache. For example, if there are two populated // Number of tokens in the cache. For example, if there are two populated
// cells, the first with 1 sequence id in it and the second with 2 sequence // cells, the first with 1 sequence id in it and the second with 2 sequence
@ -443,12 +554,12 @@ extern "C" {
// Information for an individual cell. // Information for an individual cell.
struct llama_kv_cache_view_cell * cells; struct llama_kv_cache_view_cell * cells;
// The sequences for each cell. There will be n_max_seq items per cell. // The sequences for each cell. There will be n_seq_max items per cell.
llama_seq_id * cells_sequences; llama_seq_id * cells_sequences;
}; };
// Create an empty KV cache view. (use only for debugging purposes) // Create an empty KV cache view. (use only for debugging purposes)
LLAMA_API struct llama_kv_cache_view llama_kv_cache_view_init(const struct llama_context * ctx, int32_t n_max_seq); LLAMA_API struct llama_kv_cache_view llama_kv_cache_view_init(const struct llama_context * ctx, int32_t n_seq_max);
// Free a KV cache view. (use only for debugging purposes) // Free a KV cache view. (use only for debugging purposes)
LLAMA_API void llama_kv_cache_view_free(struct llama_kv_cache_view * view); LLAMA_API void llama_kv_cache_view_free(struct llama_kv_cache_view * view);
@ -463,15 +574,16 @@ extern "C" {
// Returns the number of used KV cells (i.e. have at least one sequence assigned to them) // Returns the number of used KV cells (i.e. have at least one sequence assigned to them)
LLAMA_API int32_t llama_get_kv_cache_used_cells(const struct llama_context * ctx); LLAMA_API int32_t llama_get_kv_cache_used_cells(const struct llama_context * ctx);
// Clear the KV cache // Clear the KV cache - both cell info is erased and KV data is zeroed
LLAMA_API void llama_kv_cache_clear( LLAMA_API void llama_kv_cache_clear(
struct llama_context * ctx); struct llama_context * ctx);
// Removes all tokens that belong to the specified sequence and have positions in [p0, p1) // Removes all tokens that belong to the specified sequence and have positions in [p0, p1)
// Returns false if a partial sequence cannot be removed. Removing a whole sequence never fails
// seq_id < 0 : match any sequence // seq_id < 0 : match any sequence
// p0 < 0 : [0, p1] // p0 < 0 : [0, p1]
// p1 < 0 : [p0, inf) // p1 < 0 : [p0, inf)
LLAMA_API void llama_kv_cache_seq_rm( LLAMA_API bool llama_kv_cache_seq_rm(
struct llama_context * ctx, struct llama_context * ctx,
llama_seq_id seq_id, llama_seq_id seq_id,
llama_pos p0, llama_pos p0,
@ -494,10 +606,12 @@ extern "C" {
llama_seq_id seq_id); llama_seq_id seq_id);
// Adds relative position "delta" to all tokens that belong to the specified sequence and have positions in [p0, p1) // Adds relative position "delta" to all tokens that belong to the specified sequence and have positions in [p0, p1)
// If the KV cache is RoPEd, the KV data is updated accordingly // If the KV cache is RoPEd, the KV data is updated accordingly:
// - lazily on next llama_decode()
// - explicitly with llama_kv_cache_update()
// p0 < 0 : [0, p1] // p0 < 0 : [0, p1]
// p1 < 0 : [p0, inf) // p1 < 0 : [p0, inf)
LLAMA_API void llama_kv_cache_seq_shift( LLAMA_API void llama_kv_cache_seq_add(
struct llama_context * ctx, struct llama_context * ctx,
llama_seq_id seq_id, llama_seq_id seq_id,
llama_pos p0, llama_pos p0,
@ -505,7 +619,9 @@ extern "C" {
llama_pos delta); llama_pos delta);
// Integer division of the positions by factor of `d > 1` // Integer division of the positions by factor of `d > 1`
// If the KV cache is RoPEd, the KV data is updated accordingly // If the KV cache is RoPEd, the KV data is updated accordingly:
// - lazily on next llama_decode()
// - explicitly with llama_kv_cache_update()
// p0 < 0 : [0, p1] // p0 < 0 : [0, p1]
// p1 < 0 : [p0, inf) // p1 < 0 : [p0, inf)
LLAMA_API void llama_kv_cache_seq_div( LLAMA_API void llama_kv_cache_seq_div(
@ -515,66 +631,117 @@ extern "C" {
llama_pos p1, llama_pos p1,
int d); int d);
// Returns the largest position present in the KV cache for the specified sequence
LLAMA_API llama_pos llama_kv_cache_seq_pos_max(
struct llama_context * ctx,
llama_seq_id seq_id);
// Defragment the KV cache
// This will be applied:
// - lazily on next llama_decode()
// - explicitly with llama_kv_cache_update()
LLAMA_API void llama_kv_cache_defrag(struct llama_context * ctx);
// Apply the KV cache updates (such as K-shifts, defragmentation, etc.)
LLAMA_API void llama_kv_cache_update(struct llama_context * ctx);
// //
// State / sessions // State / sessions
// //
// Returns the maximum size in bytes of the state (rng, logits, embedding // Returns the maximum size in bytes of the state (rng, logits, embedding
// and kv_cache) - will often be smaller after compacting tokens // and kv_cache) - will often be smaller after compacting tokens
LLAMA_API size_t llama_get_state_size(const struct llama_context * ctx); LLAMA_API size_t llama_state_get_size(const struct llama_context * ctx);
LLAMA_API DEPRECATED(size_t llama_get_state_size(const struct llama_context * ctx),
"use llama_state_get_size instead");
// Copies the state to the specified destination address. // Copies the state to the specified destination address.
// Destination needs to have allocated enough memory. // Destination needs to have allocated enough memory.
// Returns the number of bytes copied // Returns the number of bytes copied
LLAMA_API size_t llama_copy_state_data( LLAMA_API size_t llama_state_get_data(
struct llama_context * ctx, struct llama_context * ctx,
uint8_t * dst); uint8_t * dst);
LLAMA_API DEPRECATED(size_t llama_copy_state_data(
struct llama_context * ctx,
uint8_t * dst),
"use llama_state_get_data instead");
// Set the state reading from the specified address // Set the state reading from the specified address
// Returns the number of bytes read // Returns the number of bytes read
LLAMA_API size_t llama_set_state_data( LLAMA_API size_t llama_state_set_data(
struct llama_context * ctx, struct llama_context * ctx,
uint8_t * src); const uint8_t * src);
LLAMA_API DEPRECATED(size_t llama_set_state_data(
struct llama_context * ctx,
const uint8_t * src),
"use llama_state_set_data instead");
// Save/load session file // Save/load session file
LLAMA_API bool llama_load_session_file( LLAMA_API bool llama_state_load_file(
struct llama_context * ctx, struct llama_context * ctx,
const char * path_session, const char * path_session,
llama_token * tokens_out, llama_token * tokens_out,
size_t n_token_capacity, size_t n_token_capacity,
size_t * n_token_count_out); size_t * n_token_count_out);
LLAMA_API DEPRECATED(bool llama_load_session_file(
struct llama_context * ctx,
const char * path_session,
llama_token * tokens_out,
size_t n_token_capacity,
size_t * n_token_count_out),
"use llama_state_load_file instead");
LLAMA_API bool llama_save_session_file( LLAMA_API bool llama_state_save_file(
struct llama_context * ctx, struct llama_context * ctx,
const char * path_session, const char * path_session,
const llama_token * tokens, const llama_token * tokens,
size_t n_token_count); size_t n_token_count);
LLAMA_API DEPRECATED(bool llama_save_session_file(
struct llama_context * ctx,
const char * path_session,
const llama_token * tokens,
size_t n_token_count),
"use llama_state_save_file instead");
// Get the exact size needed to copy the KV cache of a single sequence
LLAMA_API size_t llama_state_seq_get_size(
struct llama_context * ctx,
llama_seq_id seq_id);
// Copy the KV cache of a single sequence into the specified buffer
LLAMA_API size_t llama_state_seq_get_data(
struct llama_context * ctx,
uint8_t * dst,
llama_seq_id seq_id);
// Copy the sequence data (originally copied with `llama_state_seq_get_data`) into the specified sequence
// Returns:
// - Positive: Ok
// - Zero: Failed to load
LLAMA_API size_t llama_state_seq_set_data(
struct llama_context * ctx,
const uint8_t * src,
llama_seq_id dest_seq_id);
LLAMA_API size_t llama_state_seq_save_file(
struct llama_context * ctx,
const char * filepath,
llama_seq_id seq_id,
const llama_token * tokens,
size_t n_token_count);
LLAMA_API size_t llama_state_seq_load_file(
struct llama_context * ctx,
const char * filepath,
llama_seq_id dest_seq_id,
llama_token * tokens_out,
size_t n_token_capacity,
size_t * n_token_count_out);
// //
// Decoding // Decoding
// //
// Run the llama inference to obtain the logits and probabilities for the next token(s).
// tokens + n_tokens is the provided batch of new tokens to process
// n_past is the number of tokens to use from previous eval calls
// Returns 0 on success
// DEPRECATED: use llama_decode() instead
LLAMA_API DEPRECATED(int llama_eval(
struct llama_context * ctx,
llama_token * tokens,
int32_t n_tokens,
int32_t n_past),
"use llama_decode() instead");
// Same as llama_eval, but use float matrix input directly.
// DEPRECATED: use llama_decode() instead
LLAMA_API DEPRECATED(int llama_eval_embd(
struct llama_context * ctx,
float * embd,
int32_t n_tokens,
int32_t n_past),
"use llama_decode() instead");
// Return batch for single sequence of tokens starting at pos_0 // Return batch for single sequence of tokens starting at pos_0
// //
// NOTE: this is a helper function to facilitate transition to the new batch API - avoid using it // NOTE: this is a helper function to facilitate transition to the new batch API - avoid using it
@ -613,21 +780,61 @@ extern "C" {
// n_threads_batch is the number of threads used for prompt and batch processing (multiple tokens) // n_threads_batch is the number of threads used for prompt and batch processing (multiple tokens)
LLAMA_API void llama_set_n_threads(struct llama_context * ctx, uint32_t n_threads, uint32_t n_threads_batch); LLAMA_API void llama_set_n_threads(struct llama_context * ctx, uint32_t n_threads, uint32_t n_threads_batch);
// Token logits obtained from the last call to llama_eval() // Get the number of threads used for generation of a single token.
// The logits for the last token are stored in the last row LLAMA_API uint32_t llama_n_threads(struct llama_context * ctx);
// Logits for which llama_batch.logits[i] == 0 are undefined
// Rows: n_tokens provided with llama_batch // Get the number of threads used for prompt and batch processing (multiple token).
LLAMA_API uint32_t llama_n_threads_batch(struct llama_context * ctx);
// Set whether the model is in embeddings mode or not
// If true, embeddings will be returned but logits will not
LLAMA_API void llama_set_embeddings(struct llama_context * ctx, bool embeddings);
// Set whether to use causal attention or not
// If set to true, the model will only attend to the past tokens
LLAMA_API void llama_set_causal_attn(struct llama_context * ctx, bool causal_attn);
// Set abort callback
LLAMA_API void llama_set_abort_callback(struct llama_context * ctx, ggml_abort_callback abort_callback, void * abort_callback_data);
// Wait until all computations are finished
// This is automatically done when using one of the functions below to obtain the computation results
// and is not necessary to call it explicitly in most cases
LLAMA_API void llama_synchronize(struct llama_context * ctx);
// Token logits obtained from the last call to llama_decode()
// The logits for which llama_batch.logits[i] != 0 are stored contiguously
// in the order they have appeared in the batch.
// Rows: number of tokens for which llama_batch.logits[i] != 0
// Cols: n_vocab // Cols: n_vocab
LLAMA_API float * llama_get_logits(struct llama_context * ctx); LLAMA_API float * llama_get_logits(struct llama_context * ctx);
// Logits for the ith token. Equivalent to: // Logits for the ith token. For positive indices, Equivalent to:
// llama_get_logits(ctx) + i*n_vocab // llama_get_logits(ctx) + ctx->output_ids[i]*n_vocab
// Negative indicies can be used to access logits in reverse order, -1 is the last logit.
// returns NULL for invalid ids.
LLAMA_API float * llama_get_logits_ith(struct llama_context * ctx, int32_t i); LLAMA_API float * llama_get_logits_ith(struct llama_context * ctx, int32_t i);
// Get the embeddings for the input // Get all output token embeddings.
// shape: [n_embd] (1-dimensional) // when pooling_type == LLAMA_POOLING_TYPE_NONE or when using a generative model,
// the embeddings for which llama_batch.logits[i] != 0 are stored contiguously
// in the order they have appeared in the batch.
// shape: [n_outputs*n_embd]
// Otherwise, returns NULL.
LLAMA_API float * llama_get_embeddings(struct llama_context * ctx); LLAMA_API float * llama_get_embeddings(struct llama_context * ctx);
// Get the embeddings for the ith token. For positive indices, Equivalent to:
// llama_get_embeddings(ctx) + ctx->output_ids[i]*n_embd
// Negative indicies can be used to access embeddings in reverse order, -1 is the last embedding.
// shape: [n_embd] (1-dimensional)
// returns NULL for invalid ids.
LLAMA_API float * llama_get_embeddings_ith(struct llama_context * ctx, int32_t i);
// Get the embeddings for a sequence id
// Returns NULL if pooling_type is LLAMA_POOLING_TYPE_NONE
// shape: [n_embd] (1-dimensional)
LLAMA_API float * llama_get_embeddings_seq(struct llama_context * ctx, llama_seq_id seq_id);
// //
// Vocab // Vocab
// //
@ -636,11 +843,19 @@ extern "C" {
LLAMA_API float llama_token_get_score(const struct llama_model * model, llama_token token); LLAMA_API float llama_token_get_score(const struct llama_model * model, llama_token token);
LLAMA_API enum llama_token_type llama_token_get_type(const struct llama_model * model, llama_token token); LLAMA_API enum llama_token_attr llama_token_get_attr(const struct llama_model * model, llama_token token);
// Check if the token is supposed to end generation (end-of-generation, eg. EOS, EOT, etc.)
LLAMA_API bool llama_token_is_eog(const struct llama_model * model, llama_token token);
// Identify if Token Id is a control token or a render-able token
LLAMA_API bool llama_token_is_control(const struct llama_model * model, llama_token token);
// Special tokens // Special tokens
LLAMA_API llama_token llama_token_bos(const struct llama_model * model); // beginning-of-sentence LLAMA_API llama_token llama_token_bos(const struct llama_model * model); // beginning-of-sentence
LLAMA_API llama_token llama_token_eos(const struct llama_model * model); // end-of-sentence LLAMA_API llama_token llama_token_eos(const struct llama_model * model); // end-of-sentence
LLAMA_API llama_token llama_token_cls(const struct llama_model * model); // classification
LLAMA_API llama_token llama_token_sep(const struct llama_model * model); // sentence separator
LLAMA_API llama_token llama_token_nl (const struct llama_model * model); // next-line LLAMA_API llama_token llama_token_nl (const struct llama_model * model); // next-line
// Returns -1 if unknown, 1 for true or 0 for false. // Returns -1 if unknown, 1 for true or 0 for false.
@ -649,7 +864,7 @@ extern "C" {
// Returns -1 if unknown, 1 for true or 0 for false. // Returns -1 if unknown, 1 for true or 0 for false.
LLAMA_API int32_t llama_add_eos_token(const struct llama_model * model); LLAMA_API int32_t llama_add_eos_token(const struct llama_model * model);
// codellama infill tokens // Codellama infill tokens
LLAMA_API llama_token llama_token_prefix(const struct llama_model * model); // Beginning of infill prefix LLAMA_API llama_token llama_token_prefix(const struct llama_model * model); // Beginning of infill prefix
LLAMA_API llama_token llama_token_middle(const struct llama_model * model); // Beginning of infill middle LLAMA_API llama_token llama_token_middle(const struct llama_model * model); // Beginning of infill middle
LLAMA_API llama_token llama_token_suffix(const struct llama_model * model); // Beginning of infill suffix LLAMA_API llama_token llama_token_suffix(const struct llama_model * model); // Beginning of infill suffix
@ -661,27 +876,48 @@ extern "C" {
/// @details Convert the provided text into tokens. /// @details Convert the provided text into tokens.
/// @param tokens The tokens pointer must be large enough to hold the resulting tokens. /// @param tokens The tokens pointer must be large enough to hold the resulting tokens.
/// @return Returns the number of tokens on success, no more than n_max_tokens /// @return Returns the number of tokens on success, no more than n_tokens_max
/// @return Returns a negative number on failure - the number of tokens that would have been returned /// @return Returns a negative number on failure - the number of tokens that would have been returned
/// @param special Allow tokenizing special and/or control tokens which otherwise are not exposed and treated as plaintext. /// @param parse_special Allow tokenizing special and/or control tokens which otherwise are not exposed and treated
/// Does not insert a leading space. /// as plaintext. Does not insert a leading space.
LLAMA_API int32_t llama_tokenize( LLAMA_API int32_t llama_tokenize(
const struct llama_model * model, const struct llama_model * model,
const char * text, const char * text,
int32_t text_len, int32_t text_len,
llama_token * tokens, llama_token * tokens,
int32_t n_max_tokens, int32_t n_tokens_max,
bool add_bos, bool add_special,
bool special); bool parse_special);
// Token Id -> Piece. // Token Id -> Piece.
// Uses the vocabulary in the provided context. // Uses the vocabulary in the provided context.
// Does not write null terminator to the buffer. // Does not write null terminator to the buffer.
// User code is responsible to remove the leading whitespace of the first non-BOS token when decoding multiple tokens. // User code is responsible to remove the leading whitespace of the first non-BOS token when decoding multiple tokens.
// @param special If true, special tokens are rendered in the output.
LLAMA_API int32_t llama_token_to_piece( LLAMA_API int32_t llama_token_to_piece(
const struct llama_model * model, const struct llama_model * model,
llama_token token, llama_token token,
char * buf, char * buf,
int32_t length,
bool special);
/// Apply chat template. Inspired by hf apply_chat_template() on python.
/// Both "model" and "custom_template" are optional, but at least one is required. "custom_template" has higher precedence than "model"
/// NOTE: This function does not use a jinja parser. It only support a pre-defined list of template. See more: https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template
/// @param tmpl A Jinja template to use for this chat. If this is nullptr, the models default chat template will be used instead.
/// @param chat Pointer to a list of multiple llama_chat_message
/// @param n_msg Number of llama_chat_message in this chat
/// @param add_ass Whether to end the prompt with the token(s) that indicate the start of an assistant message.
/// @param buf A buffer to hold the output formatted prompt. The recommended alloc size is 2 * (total number of characters of all messages)
/// @param length The size of the allocated buffer
/// @return The total number of bytes of the formatted prompt. If is it larger than the size of buffer, you may need to re-alloc it and then re-apply the template.
LLAMA_API int32_t llama_chat_apply_template(
const struct llama_model * model,
const char * tmpl,
const struct llama_chat_message * chat,
size_t n_msg,
bool add_ass,
char * buf,
int32_t length); int32_t length);
// //
@ -725,13 +961,6 @@ extern "C" {
float * logits_guidance, float * logits_guidance,
float scale); float scale);
LLAMA_API DEPRECATED(void llama_sample_classifier_free_guidance(
struct llama_context * ctx,
llama_token_data_array * candidates,
struct llama_context * guidance_ctx,
float scale),
"use llama_sample_apply_guidance() instead");
/// @details Sorts candidate tokens by their logits in descending order and calculate probabilities based on logits. /// @details Sorts candidate tokens by their logits in descending order and calculate probabilities based on logits.
LLAMA_API void llama_sample_softmax( LLAMA_API void llama_sample_softmax(
struct llama_context * ctx, struct llama_context * ctx,
@ -785,12 +1014,6 @@ extern "C" {
llama_token_data_array * candidates, llama_token_data_array * candidates,
float temp); float temp);
LLAMA_API DEPRECATED(void llama_sample_temperature(
struct llama_context * ctx,
llama_token_data_array * candidates,
float temp),
"use llama_sample_temp instead");
/// @details Apply constraints from grammar /// @details Apply constraints from grammar
LLAMA_API void llama_sample_grammar( LLAMA_API void llama_sample_grammar(
struct llama_context * ctx, struct llama_context * ctx,
@ -829,7 +1052,7 @@ extern "C" {
struct llama_context * ctx, struct llama_context * ctx,
llama_token_data_array * candidates); llama_token_data_array * candidates);
/// @details Randomly selects a token from the candidates based on their probabilities. /// @details Randomly selects a token from the candidates based on their probabilities using the RNG of ctx.
LLAMA_API llama_token llama_sample_token( LLAMA_API llama_token llama_sample_token(
struct llama_context * ctx, struct llama_context * ctx,
llama_token_data_array * candidates); llama_token_data_array * candidates);
@ -841,48 +1064,18 @@ extern "C" {
llama_token token); llama_token token);
// //
// Beam search // Model split
// //
struct llama_beam_view { /// @details Build a split GGUF final path for this chunk.
const llama_token * tokens; /// llama_split_path(split_path, sizeof(split_path), "/models/ggml-model-q4_0", 2, 4) => split_path = "/models/ggml-model-q4_0-00002-of-00004.gguf"
// Returns the split_path length.
LLAMA_API int llama_split_path(char * split_path, size_t maxlen, const char * path_prefix, int split_no, int split_count);
size_t n_tokens; /// @details Extract the path prefix from the split_path if and only if the split_no and split_count match.
float p; // Cumulative beam probability (renormalized relative to all beams) /// llama_split_prefix(split_prefix, 64, "/models/ggml-model-q4_0-00002-of-00004.gguf", 2, 4) => split_prefix = "/models/ggml-model-q4_0"
bool eob; // Callback should set this to true when a beam is at end-of-beam. // Returns the split_prefix length.
}; LLAMA_API int llama_split_prefix(char * split_prefix, size_t maxlen, const char * split_path, int split_no, int split_count);
// Passed to beam_search_callback function.
// Whenever 0 < common_prefix_length, this number of tokens should be copied from any of the beams
// (e.g. beams[0]) as they will be removed (shifted) from all beams in all subsequent callbacks.
// These pointers are valid only during the synchronous callback, so should not be saved.
struct llama_beams_state {
struct llama_beam_view * beam_views;
size_t n_beams; // Number of elements in beam_views[].
size_t common_prefix_length; // Current max length of prefix tokens shared by all beams.
bool last_call; // True iff this is the last callback invocation.
};
// Type of pointer to the beam_search_callback function.
// void* callback_data is any custom data passed to llama_beam_search, that is subsequently
// passed back to beam_search_callback. This avoids having to use global variables in the callback.
typedef void (*llama_beam_search_callback_fn_t)(void * callback_data, struct llama_beams_state);
/// @details Deterministically returns entire sentence constructed by a beam search.
/// @param ctx Pointer to the llama_context.
/// @param callback Invoked for each iteration of the beam_search loop, passing in beams_state.
/// @param callback_data A pointer that is simply passed back to callback.
/// @param n_beams Number of beams to use.
/// @param n_past Number of tokens already evaluated.
/// @param n_predict Maximum number of tokens to predict. EOS may occur earlier.
LLAMA_API void llama_beam_search(
struct llama_context * ctx,
llama_beam_search_callback_fn_t callback,
void * callback_data,
size_t n_beams,
int32_t n_past,
int32_t n_predict);
// Performance information // Performance information
LLAMA_API struct llama_timings llama_get_timings(struct llama_context * ctx); LLAMA_API struct llama_timings llama_get_timings(struct llama_context * ctx);
@ -906,15 +1099,49 @@ extern "C" {
// Internal API to be implemented by llama.cpp and used by tests/benchmarks only // Internal API to be implemented by llama.cpp and used by tests/benchmarks only
#ifdef LLAMA_API_INTERNAL #ifdef LLAMA_API_INTERNAL
#include <vector> #include <random>
#include <string> #include <string>
#include <vector>
struct ggml_tensor; struct ggml_tensor;
struct llama_partial_utf8 {
uint32_t value; // bit value so far (unshifted)
int n_remain; // num bytes remaining; -1 indicates invalid sequence
};
struct llama_grammar {
const std::vector<std::vector<llama_grammar_element>> rules;
std::vector<std::vector<const llama_grammar_element *>> stacks;
// buffer for partially generated UTF-8 sequence from accepted tokens
llama_partial_utf8 partial_utf8;
};
struct llama_grammar_candidate {
size_t index;
const uint32_t * code_points;
llama_partial_utf8 partial_utf8;
};
const std::vector<std::pair<std::string, struct ggml_tensor *>> & llama_internal_get_tensor_map( const std::vector<std::pair<std::string, struct ggml_tensor *>> & llama_internal_get_tensor_map(
struct llama_context * ctx struct llama_context * ctx
); );
void llama_grammar_accept(
const std::vector<std::vector<llama_grammar_element>> & rules,
const std::vector<std::vector<const llama_grammar_element *>> & stacks,
const uint32_t chr,
std::vector<std::vector<const llama_grammar_element *>> & new_stacks);
std::pair<std::vector<uint32_t>, llama_partial_utf8> decode_utf8(
const std::string & src,
llama_partial_utf8 partial_start);
// Randomly selects a token from the candidates based on their probabilities using given std::mt19937.
// This is a temporary workaround in order to fix race conditions when sampling with multiple sequences.
llama_token llama_sample_token_with_rng(struct llama_context * ctx, llama_token_data_array * candidates, std::mt19937 & rng);
#endif // LLAMA_API_INTERNAL #endif // LLAMA_API_INTERNAL
#endif // LLAMA_H #endif // LLAMA_H

View File

@ -1,32 +1,40 @@
#!/bin/bash #!/bin/bash
# Usage: # Usage:
# speak.sh <voice_id> <text-to-speak> # speak <voice_id> <textfile>
# espeak function installed() { command -v $1 >/dev/null 2>&1; }
# Mac OS: brew install espeak
# Linux: apt-get install espeak
#
#espeak -v en-us+m$1 -s 225 -p 50 -a 200 -g 5 -k 5 "$2"
# piper if installed espeak; then
# espeak -v en-us+m$1 -s 225 -p 50 -a 200 -g 5 -k 5 -f $2
# https://github.com/rhasspy/piper
# elif installed piper && installed aplay; then
# Tested with Linux: cat $2 | piper --model ~/en_US-lessac-medium.onnx --output-raw | aplay -q -r 22050 -f S16_LE -t raw -
#
#echo "$2" | piper --model ~/en_US-lessac-medium.onnx --output-raw | aplay -q -r 22050 -f S16_LE -t raw -
# for Mac # for Mac
say "$2" elif installed say; then
say -f $2
# Eleven Labs # Eleven Labs
# To use it, install the elevenlabs module from pip (pip install elevenlabs) elif installed python3 && \
# It's possible to use the API for free with limited number of characters. To increase this limit register to https://beta.elevenlabs.io to get an api key and paste it after 'ELEVEN_API_KEY=' python3 -c 'import importlib.util; exit(not importlib.util.find_spec("elevenlabs"))' && \
#Keep the line commented to use the free version whitout api key installed ffplay; then
# # It's possible to use the API for free with limited number of characters.
#export ELEVEN_API_KEY=your_api_key # To increase this limit register to https://beta.elevenlabs.io to get an api key
#wd=$(dirname $0) # and paste it after 'ELEVEN_API_KEY='
#script=$wd/eleven-labs.py # Keep the line commented to use the free version without api key
#python3 $script $1 "$2" >/dev/null 2>&1 #export ELEVEN_API_KEY=your_api_key
#ffplay -autoexit -nodisp -loglevel quiet -hide_banner -i ./audio.mp3 >/dev/null 2>&1 wd=$(dirname $0)
script=$wd/eleven-labs.py
python3 $script -q -p -v $1 $2 >/dev/null 2>&1
# Uncomment to keep the audio file
#python3 $script -q -s ./audio.mp3 -v $1 $2 >/dev/null 2>&1
#ffplay -autoexit -nodisp -loglevel quiet -hide_banner -i ./audio.mp3 >/dev/null 2>&1
else
echo 'Install espeak ("brew install espeak" or "apt-get install espeak"),'
echo 'piper ("pip install piper-tts" or https://github.com/rhasspy/piper) with aplay,'
echo 'or elevenlabs ("pip install elevenlabs") with ffplay.'
echo '(export ELEVEN_API_KEY if you have an api key from https://beta.elevenlabs.io)'
fi

View File

@ -1 +1 @@
@powershell -ExecutionPolicy Bypass -F examples\talk\speak.ps1 %1 %2 @powershell -ExecutionPolicy Bypass -F examples\talk-llama\speak.ps1 %1 %2

View File

@ -1,12 +1,14 @@
# Set-ExecutionPolicy -ExecutionPolicy Bypass -Scope CurrentUser # Set-ExecutionPolicy -ExecutionPolicy Bypass -Scope CurrentUser
param( param(
# voice options are David or Zira [Parameter(Mandatory=$true)][int]$voicenum,
[Parameter(Mandatory=$true)][string]$voice, [Parameter(Mandatory=$true)][string]$textfile
[Parameter(Mandatory=$true)][string]$text
) )
Add-Type -AssemblyName System.Speech; Add-Type -AssemblyName System.Speech;
$speak = New-Object System.Speech.Synthesis.SpeechSynthesizer; $speak = New-Object System.Speech.Synthesis.SpeechSynthesizer;
$speak.SelectVoice("Microsoft $voice Desktop"); $voiceoptions = $speak.GetInstalledVoices("en-US");
$voice = $voiceoptions[$voicenum % $voiceoptions.count];
$speak.SelectVoice($voice.VoiceInfo.Name);
$speak.Rate="0"; $speak.Rate="0";
$text = Get-Content -Path $textfile;
$speak.Speak($text); $speak.Speak($text);

View File

@ -16,7 +16,7 @@
#include <regex> #include <regex>
#include <sstream> #include <sstream>
std::vector<llama_token> llama_tokenize(struct llama_context * ctx, const std::string & text, bool add_bos) { static std::vector<llama_token> llama_tokenize(struct llama_context * ctx, const std::string & text, bool add_bos) {
auto * model = llama_get_model(ctx); auto * model = llama_get_model(ctx);
// upper limit for the number of tokens // upper limit for the number of tokens
@ -33,12 +33,12 @@ std::vector<llama_token> llama_tokenize(struct llama_context * ctx, const std::s
return result; return result;
} }
std::string llama_token_to_piece(const struct llama_context * ctx, llama_token token) { static std::string llama_token_to_piece(const struct llama_context * ctx, llama_token token) {
std::vector<char> result(8, 0); std::vector<char> result(8, 0);
const int n_tokens = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size()); const int n_tokens = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size(), false);
if (n_tokens < 0) { if (n_tokens < 0) {
result.resize(-n_tokens); result.resize(-n_tokens);
int check = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size()); int check = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size(), false);
GGML_ASSERT(check == -n_tokens); GGML_ASSERT(check == -n_tokens);
} else { } else {
result.resize(n_tokens); result.resize(n_tokens);
@ -59,13 +59,13 @@ struct whisper_params {
float vad_thold = 0.6f; float vad_thold = 0.6f;
float freq_thold = 100.0f; float freq_thold = 100.0f;
bool speed_up = false;
bool translate = false; bool translate = false;
bool print_special = false; bool print_special = false;
bool print_energy = false; bool print_energy = false;
bool no_timestamps = true; bool no_timestamps = true;
bool verbose_prompt = false; bool verbose_prompt = false;
bool use_gpu = true; bool use_gpu = true;
bool flash_attn = false;
std::string person = "Georgi"; std::string person = "Georgi";
std::string bot_name = "LLaMA"; std::string bot_name = "LLaMA";
@ -75,6 +75,7 @@ struct whisper_params {
std::string model_wsp = "models/ggml-base.en.bin"; std::string model_wsp = "models/ggml-base.en.bin";
std::string model_llama = "models/ggml-llama-7B.bin"; std::string model_llama = "models/ggml-llama-7B.bin";
std::string speak = "./examples/talk-llama/speak"; std::string speak = "./examples/talk-llama/speak";
std::string speak_file = "./examples/talk-llama/to_speak.txt";
std::string prompt = ""; std::string prompt = "";
std::string fname_out; std::string fname_out;
std::string path_session = ""; // path to file for saving/loading model eval state std::string path_session = ""; // path to file for saving/loading model eval state
@ -82,7 +83,7 @@ struct whisper_params {
void whisper_print_usage(int argc, char ** argv, const whisper_params & params); void whisper_print_usage(int argc, char ** argv, const whisper_params & params);
bool whisper_params_parse(int argc, char ** argv, whisper_params & params) { static bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
for (int i = 1; i < argc; i++) { for (int i = 1; i < argc; i++) {
std::string arg = argv[i]; std::string arg = argv[i];
@ -98,12 +99,12 @@ bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
else if (arg == "-ngl" || arg == "--n-gpu-layers") { params.n_gpu_layers = std::stoi(argv[++i]); } else if (arg == "-ngl" || arg == "--n-gpu-layers") { params.n_gpu_layers = std::stoi(argv[++i]); }
else if (arg == "-vth" || arg == "--vad-thold") { params.vad_thold = std::stof(argv[++i]); } else if (arg == "-vth" || arg == "--vad-thold") { params.vad_thold = std::stof(argv[++i]); }
else if (arg == "-fth" || arg == "--freq-thold") { params.freq_thold = std::stof(argv[++i]); } else if (arg == "-fth" || arg == "--freq-thold") { params.freq_thold = std::stof(argv[++i]); }
else if (arg == "-su" || arg == "--speed-up") { params.speed_up = true; }
else if (arg == "-tr" || arg == "--translate") { params.translate = true; } else if (arg == "-tr" || arg == "--translate") { params.translate = true; }
else if (arg == "-ps" || arg == "--print-special") { params.print_special = true; } else if (arg == "-ps" || arg == "--print-special") { params.print_special = true; }
else if (arg == "-pe" || arg == "--print-energy") { params.print_energy = true; } else if (arg == "-pe" || arg == "--print-energy") { params.print_energy = true; }
else if (arg == "-vp" || arg == "--verbose-prompt") { params.verbose_prompt = true; } else if (arg == "-vp" || arg == "--verbose-prompt") { params.verbose_prompt = true; }
else if (arg == "-ng" || arg == "--no-gpu") { params.use_gpu = false; } else if (arg == "-ng" || arg == "--no-gpu") { params.use_gpu = false; }
else if (arg == "-fa" || arg == "--flash-attn") { params.flash_attn = true; }
else if (arg == "-p" || arg == "--person") { params.person = argv[++i]; } else if (arg == "-p" || arg == "--person") { params.person = argv[++i]; }
else if (arg == "-bn" || arg == "--bot-name") { params.bot_name = argv[++i]; } else if (arg == "-bn" || arg == "--bot-name") { params.bot_name = argv[++i]; }
else if (arg == "--session") { params.path_session = argv[++i]; } else if (arg == "--session") { params.path_session = argv[++i]; }
@ -113,6 +114,7 @@ bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
else if (arg == "-mw" || arg == "--model-whisper") { params.model_wsp = argv[++i]; } else if (arg == "-mw" || arg == "--model-whisper") { params.model_wsp = argv[++i]; }
else if (arg == "-ml" || arg == "--model-llama") { params.model_llama = argv[++i]; } else if (arg == "-ml" || arg == "--model-llama") { params.model_llama = argv[++i]; }
else if (arg == "-s" || arg == "--speak") { params.speak = argv[++i]; } else if (arg == "-s" || arg == "--speak") { params.speak = argv[++i]; }
else if (arg == "-sf" || arg == "--speak-file") { params.speak_file = argv[++i]; }
else if (arg == "--prompt-file") { else if (arg == "--prompt-file") {
std::ifstream file(argv[++i]); std::ifstream file(argv[++i]);
std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(params.prompt)); std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(params.prompt));
@ -121,7 +123,6 @@ bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
} }
} }
else if (arg == "-f" || arg == "--file") { params.fname_out = argv[++i]; } else if (arg == "-f" || arg == "--file") { params.fname_out = argv[++i]; }
else if (arg == "-ng" || arg == "--no-gpu") { params.use_gpu = false; }
else { else {
fprintf(stderr, "error: unknown argument: %s\n", arg.c_str()); fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
whisper_print_usage(argc, argv, params); whisper_print_usage(argc, argv, params);
@ -146,12 +147,12 @@ void whisper_print_usage(int /*argc*/, char ** argv, const whisper_params & para
fprintf(stderr, " -ngl N, --n-gpu-layers N [%-7d] number of layers to store in VRAM\n", params.n_gpu_layers); fprintf(stderr, " -ngl N, --n-gpu-layers N [%-7d] number of layers to store in VRAM\n", params.n_gpu_layers);
fprintf(stderr, " -vth N, --vad-thold N [%-7.2f] voice activity detection threshold\n", params.vad_thold); fprintf(stderr, " -vth N, --vad-thold N [%-7.2f] voice activity detection threshold\n", params.vad_thold);
fprintf(stderr, " -fth N, --freq-thold N [%-7.2f] high-pass frequency cutoff\n", params.freq_thold); fprintf(stderr, " -fth N, --freq-thold N [%-7.2f] high-pass frequency cutoff\n", params.freq_thold);
fprintf(stderr, " -su, --speed-up [%-7s] speed up audio by x2 (reduced accuracy)\n", params.speed_up ? "true" : "false");
fprintf(stderr, " -tr, --translate [%-7s] translate from source language to english\n", params.translate ? "true" : "false"); fprintf(stderr, " -tr, --translate [%-7s] translate from source language to english\n", params.translate ? "true" : "false");
fprintf(stderr, " -ps, --print-special [%-7s] print special tokens\n", params.print_special ? "true" : "false"); fprintf(stderr, " -ps, --print-special [%-7s] print special tokens\n", params.print_special ? "true" : "false");
fprintf(stderr, " -pe, --print-energy [%-7s] print sound energy (for debugging)\n", params.print_energy ? "true" : "false"); fprintf(stderr, " -pe, --print-energy [%-7s] print sound energy (for debugging)\n", params.print_energy ? "true" : "false");
fprintf(stderr, " -vp, --verbose-prompt [%-7s] print prompt at start\n", params.verbose_prompt ? "true" : "false"); fprintf(stderr, " -vp, --verbose-prompt [%-7s] print prompt at start\n", params.verbose_prompt ? "true" : "false");
fprintf(stderr, " -ng, --no-gpu [%-7s] disable GPU\n", params.use_gpu ? "false" : "true"); fprintf(stderr, " -ng, --no-gpu [%-7s] disable GPU\n", params.use_gpu ? "false" : "true");
fprintf(stderr, " -fa, --flash-attn [%-7s] flash attention\n", params.flash_attn ? "true" : "false");
fprintf(stderr, " -p NAME, --person NAME [%-7s] person name (for prompt selection)\n", params.person.c_str()); fprintf(stderr, " -p NAME, --person NAME [%-7s] person name (for prompt selection)\n", params.person.c_str());
fprintf(stderr, " -bn NAME, --bot-name NAME [%-7s] bot name (to display)\n", params.bot_name.c_str()); fprintf(stderr, " -bn NAME, --bot-name NAME [%-7s] bot name (to display)\n", params.bot_name.c_str());
fprintf(stderr, " -w TEXT, --wake-command T [%-7s] wake-up command to listen for\n", params.wake_cmd.c_str()); fprintf(stderr, " -w TEXT, --wake-command T [%-7s] wake-up command to listen for\n", params.wake_cmd.c_str());
@ -160,13 +161,14 @@ void whisper_print_usage(int /*argc*/, char ** argv, const whisper_params & para
fprintf(stderr, " -mw FILE, --model-whisper [%-7s] whisper model file\n", params.model_wsp.c_str()); fprintf(stderr, " -mw FILE, --model-whisper [%-7s] whisper model file\n", params.model_wsp.c_str());
fprintf(stderr, " -ml FILE, --model-llama [%-7s] llama model file\n", params.model_llama.c_str()); fprintf(stderr, " -ml FILE, --model-llama [%-7s] llama model file\n", params.model_llama.c_str());
fprintf(stderr, " -s FILE, --speak TEXT [%-7s] command for TTS\n", params.speak.c_str()); fprintf(stderr, " -s FILE, --speak TEXT [%-7s] command for TTS\n", params.speak.c_str());
fprintf(stderr, " -sf FILE, --speak-file [%-7s] file to pass to TTS\n", params.speak_file.c_str());
fprintf(stderr, " --prompt-file FNAME [%-7s] file with custom prompt to start dialog\n", ""); fprintf(stderr, " --prompt-file FNAME [%-7s] file with custom prompt to start dialog\n", "");
fprintf(stderr, " --session FNAME file to cache model state in (may be large!) (default: none)\n"); fprintf(stderr, " --session FNAME file to cache model state in (may be large!) (default: none)\n");
fprintf(stderr, " -f FNAME, --file FNAME [%-7s] text output file name\n", params.fname_out.c_str()); fprintf(stderr, " -f FNAME, --file FNAME [%-7s] text output file name\n", params.fname_out.c_str());
fprintf(stderr, "\n"); fprintf(stderr, "\n");
} }
std::string transcribe( static std::string transcribe(
whisper_context * ctx, whisper_context * ctx,
const whisper_params & params, const whisper_params & params,
const std::vector<float> & pcmf32, const std::vector<float> & pcmf32,
@ -200,7 +202,6 @@ std::string transcribe(
wparams.prompt_n_tokens = prompt_tokens.empty() ? 0 : prompt_tokens.size(); wparams.prompt_n_tokens = prompt_tokens.empty() ? 0 : prompt_tokens.size();
wparams.audio_ctx = params.audio_ctx; wparams.audio_ctx = params.audio_ctx;
wparams.speed_up = params.speed_up;
if (whisper_full(ctx, wparams, pcmf32.data(), pcmf32.size()) != 0) { if (whisper_full(ctx, wparams, pcmf32.data(), pcmf32.size()) != 0) {
return ""; return "";
@ -234,7 +235,7 @@ std::string transcribe(
return result; return result;
} }
std::vector<std::string> get_words(const std::string &txt) { static std::vector<std::string> get_words(const std::string &txt) {
std::vector<std::string> words; std::vector<std::string> words;
std::istringstream iss(txt); std::istringstream iss(txt);
@ -282,13 +283,19 @@ int main(int argc, char ** argv) {
// whisper init // whisper init
struct whisper_context_params cparams = whisper_context_default_params(); struct whisper_context_params cparams = whisper_context_default_params();
cparams.use_gpu = params.use_gpu;
cparams.use_gpu = params.use_gpu;
cparams.flash_attn = params.flash_attn;
struct whisper_context * ctx_wsp = whisper_init_from_file_with_params(params.model_wsp.c_str(), cparams); struct whisper_context * ctx_wsp = whisper_init_from_file_with_params(params.model_wsp.c_str(), cparams);
if (!ctx_wsp) {
fprintf(stderr, "No whisper.cpp model specified. Please provide using -mw <modelfile>\n");
return 1;
}
// llama init // llama init
llama_backend_init(true); llama_backend_init();
auto lmparams = llama_model_default_params(); auto lmparams = llama_model_default_params();
if (!params.use_gpu) { if (!params.use_gpu) {
@ -298,6 +305,10 @@ int main(int argc, char ** argv) {
} }
struct llama_model * model_llama = llama_load_model_from_file(params.model_llama.c_str(), lmparams); struct llama_model * model_llama = llama_load_model_from_file(params.model_llama.c_str(), lmparams);
if (!model_llama) {
fprintf(stderr, "No llama.cpp model specified. Please provide using -ml <modelfile>\n");
return 1;
}
llama_context_params lcparams = llama_context_default_params(); llama_context_params lcparams = llama_context_default_params();
@ -305,6 +316,7 @@ int main(int argc, char ** argv) {
lcparams.n_ctx = 2048; lcparams.n_ctx = 2048;
lcparams.seed = 1; lcparams.seed = 1;
lcparams.n_threads = params.n_threads; lcparams.n_threads = params.n_threads;
lcparams.flash_attn = params.flash_attn;
struct llama_context * ctx_llama = llama_new_context_with_model(model_llama, lcparams); struct llama_context * ctx_llama = llama_new_context_with_model(model_llama, lcparams);
@ -388,6 +400,8 @@ int main(int argc, char ** argv) {
prompt_llama = ::replace(prompt_llama, "{4}", chat_symb); prompt_llama = ::replace(prompt_llama, "{4}", chat_symb);
llama_batch batch = llama_batch_init(llama_n_ctx(ctx_llama), 0, 1);
// init session // init session
std::string path_session = params.path_session; std::string path_session = params.path_session;
std::vector<llama_token> session_tokens; std::vector<llama_token> session_tokens;
@ -423,8 +437,21 @@ int main(int argc, char ** argv) {
printf("\n"); printf("\n");
printf("%s : initializing - please wait ...\n", __func__); printf("%s : initializing - please wait ...\n", __func__);
if (llama_eval(ctx_llama, embd_inp.data(), embd_inp.size(), 0)) { // prepare batch
fprintf(stderr, "%s : failed to eval\n", __func__); {
batch.n_tokens = embd_inp.size();
for (int i = 0; i < batch.n_tokens; i++) {
batch.token[i] = embd_inp[i];
batch.pos[i] = i;
batch.n_seq_id[i] = 1;
batch.seq_id[i][0] = 0;
batch.logits[i] = i == batch.n_tokens - 1;
}
}
if (llama_decode(ctx_llama, batch)) {
fprintf(stderr, "%s : failed to decode\n", __func__);
return 1; return 1;
} }
@ -546,10 +573,7 @@ int main(int argc, char ** argv) {
// optionally give audio feedback that the current text is being processed // optionally give audio feedback that the current text is being processed
if (!params.heard_ok.empty()) { if (!params.heard_ok.empty()) {
int ret = system((params.speak + " " + std::to_string(voice_id) + " '" + params.heard_ok + "'").c_str()); speak_with_file(params.speak, params.heard_ok, params.speak_file, voice_id);
if (ret != 0) {
fprintf(stderr, "%s: failed to speak\n", __func__);
}
} }
// remove text between brackets using regex // remove text between brackets using regex
@ -647,8 +671,21 @@ int main(int argc, char ** argv) {
n_session_consumed = session_tokens.size(); n_session_consumed = session_tokens.size();
} }
if (llama_eval(ctx_llama, embd.data(), embd.size(), n_past)) { // prepare batch
fprintf(stderr, "%s : failed to eval\n", __func__); {
batch.n_tokens = embd.size();
for (int i = 0; i < batch.n_tokens; i++) {
batch.token[i] = embd[i];
batch.pos[i] = n_past + i;
batch.n_seq_id[i] = 1;
batch.seq_id[i][0] = 0;
batch.logits[i] = i == batch.n_tokens - 1;
}
}
if (llama_decode(ctx_llama, batch)) {
fprintf(stderr, "%s : failed to decode\n", __func__);
return 1; return 1;
} }
} }
@ -748,11 +785,7 @@ int main(int argc, char ** argv) {
} }
} }
text_to_speak = ::replace(text_to_speak, "'", "'\"'\"'"); speak_with_file(params.speak, text_to_speak, params.speak_file, voice_id);
int ret = system((params.speak + " " + std::to_string(voice_id) + " '" + text_to_speak + "'").c_str());
if (ret != 0) {
fprintf(stderr, "%s: failed to speak\n", __func__);
}
audio.clear(); audio.clear();
} }

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@ -0,0 +1,20 @@
#pragma once
#include <cstdint>
#include <vector>
#include <unordered_map>
#include <unordered_set>
struct range_nfd {
uint32_t first;
uint32_t last;
uint32_t nfd;
};
static const uint32_t MAX_CODEPOINTS = 0x110000;
extern const std::vector<std::pair<uint32_t, uint16_t>> unicode_ranges_flags;
extern const std::unordered_set<uint32_t> unicode_set_whitespace;
extern const std::unordered_map<uint32_t, uint32_t> unicode_map_lowercase;
extern const std::unordered_map<uint32_t, uint32_t> unicode_map_uppercase;
extern const std::vector<range_nfd> unicode_ranges_nfd;

View File

@ -0,0 +1,810 @@
#include "unicode.h"
#include "unicode-data.h"
#include <cassert>
#include <cstddef>
#include <cstdint>
#include <map>
#include <regex>
#include <stdexcept>
#include <string>
#include <unordered_map>
#include <unordered_set>
#include <utility>
#include <vector>
#include <locale>
#include <codecvt>
static std::string unicode_cpts_to_utf8(const std::vector<uint32_t> & cps) {
std::string result;
for (size_t i = 0; i < cps.size(); ++i) {
result.append(unicode_cpt_to_utf8(cps[i]));
}
return result;
}
static uint32_t unicode_cpt_from_utf8(const std::string & utf8, size_t & offset) {
assert(offset < utf8.size());
if (!(utf8[offset + 0] & 0x80)) {
auto result = utf8[offset + 0];
offset += 1;
return result;
}
if (!(utf8[offset + 0] & 0x40)) {
throw std::invalid_argument("invalid character");
}
if (!(utf8[offset + 0] & 0x20)) {
if (offset + 1 >= utf8.size() || ! ((utf8[offset + 1] & 0xc0) == 0x80)) {
throw std::invalid_argument("invalid character");
}
auto result = ((utf8[offset + 0] & 0x1f) << 6) | (utf8[offset + 1] & 0x3f);
offset += 2;
return result;
}
if (!(utf8[offset + 0] & 0x10)) {
if (offset + 2 >= utf8.size() || ! ((utf8[offset + 1] & 0xc0) == 0x80) || ! ((utf8[offset + 2] & 0xc0) == 0x80)) {
throw std::invalid_argument("invalid character");
}
auto result = ((utf8[offset + 0] & 0x0f) << 12) | ((utf8[offset + 1] & 0x3f) << 6) | (utf8[offset + 2] & 0x3f);
offset += 3;
return result;
}
if (!(utf8[offset + 0] & 0x08)) {
if (offset + 3 >= utf8.size() || ! ((utf8[offset + 1] & 0xc0) == 0x80) || ! ((utf8[offset + 2] & 0xc0) == 0x80) || !((utf8[offset + 3] & 0xc0) == 0x80)) {
throw std::invalid_argument("invalid character");
}
auto result = ((utf8[offset + 0] & 0x07) << 18) | ((utf8[offset + 1] & 0x3f) << 12) | ((utf8[offset + 2] & 0x3f) << 6) | (utf8[offset + 3] & 0x3f);
offset += 4;
return result;
}
throw std::invalid_argument("failed to convert utf8 to codepoint");
}
//static std::vector<uint16_t> unicode_cpt_to_utf16(uint32_t cp) {
// std::vector<uint16_t> result;
// if (/* 0x0000 <= cp && */ cp <= 0xffff) {
// result.emplace_back(cp);
// return result;
// }
// if (0x10000 <= cp && cp <= 0x10ffff) {
// result.emplace_back(0xd800 | ((cp - 0x10000) >> 10));
// result.emplace_back(0xdc00 | ((cp - 0x10000) & 0x03ff));
// return result;
// }
// throw std::invalid_argument("failed to convert codepoint to utf16");
//}
//static std::vector<uint16_t> unicode_cpts_to_utf16(const std::vector<uint32_t> & cps) {
// std::vector<uint16_t> result;
// for (size_t i = 0; i < cps.size(); ++i) {
// auto temp = unicode_cpt_to_utf16(cps[i]);
// result.insert(result.end(), temp.begin(), temp.end());
// }
// return result;
//}
//static uint32_t unicode_cpt_from_utf16(const std::vector<uint16_t> & utf16, size_t & offset) {
// assert(offset < utf16.size());
// if (((utf16[0] >> 10) << 10) != 0xd800) {
// auto result = utf16[offset + 0];
// offset += 1;
// return result;
// }
//
// if (offset + 1 >= utf16.size() || !((utf16[1] & 0xdc00) == 0xdc00)) {
// throw std::invalid_argument("invalid character");
// }
//
// auto result = 0x10000 + (((utf16[0] & 0x03ff) << 10) | (utf16[1] & 0x03ff));
// offset += 2;
// return result;
//}
//static std::vector<uint32_t> unicode_cpts_from_utf16(const std::vector<uint16_t> & utf16) {
// std::vector<uint32_t> result;
// size_t offset = 0;
// while (offset < utf16.size()) {
// result.push_back(unicode_cpt_from_utf16(utf16, offset));
// }
// return result;
//}
static std::vector<codepoint_flags> unicode_cpt_flags_array() {
std::vector<codepoint_flags> cpt_flags(MAX_CODEPOINTS, codepoint_flags::UNDEFINED);
assert (unicode_ranges_flags.front().first == 0);
assert (unicode_ranges_flags.back().first == MAX_CODEPOINTS);
for (size_t i = 1; i < unicode_ranges_flags.size(); ++i) {
const auto range_ini = unicode_ranges_flags[i-1]; // codepoint_ini, flags
const auto range_end = unicode_ranges_flags[i]; // codepoint_end, flags
for (uint32_t cpt = range_ini.first; cpt < range_end.first; ++cpt) {
cpt_flags[cpt] = range_ini.second;
}
}
for (auto cpt : unicode_set_whitespace) {
cpt_flags[cpt].is_whitespace = true;
}
for (auto p : unicode_map_lowercase) {
cpt_flags[p.second].is_lowercase = true;
}
for (auto p : unicode_map_uppercase) {
cpt_flags[p.second].is_uppercase = true;
}
for (auto &range : unicode_ranges_nfd) { // start, last, nfd
cpt_flags[range.nfd].is_nfd = true;
}
return cpt_flags;
}
static std::unordered_map<uint8_t, std::string> unicode_byte_to_utf8_map() {
std::unordered_map<uint8_t, std::string> map;
for (int ch = 0x21; ch <= 0x7E; ++ch) { // u'!' to u'~'
assert(0 <= ch && ch < 256);
map[ch] = unicode_cpt_to_utf8(ch);
}
for (int ch = 0xA1; ch <= 0xAC; ++ch) { // u'¡' to u'¬'
assert(0 <= ch && ch < 256);
map[ch] = unicode_cpt_to_utf8(ch);
}
for (int ch = 0xAE; ch <= 0xFF; ++ch) { // u'®' to u'ÿ'
assert(0 <= ch && ch < 256);
map[ch] = unicode_cpt_to_utf8(ch);
}
auto n = 0;
for (int ch = 0; ch < 256; ++ch) {
if (map.find(ch) == map.end()) {
map[ch] = unicode_cpt_to_utf8(256 + n);
++n;
}
}
return map;
}
static std::unordered_map<std::string, uint8_t> unicode_utf8_to_byte_map() {
std::unordered_map<std::string, uint8_t> map;
for (int ch = 0x21; ch <= 0x7E; ++ch) { // u'!' to u'~'
assert(0 <= ch && ch < 256);
map[unicode_cpt_to_utf8(ch)] = ch;
}
for (int ch = 0xA1; ch <= 0xAC; ++ch) { // u'¡' to u'¬'
assert(0 <= ch && ch < 256);
map[unicode_cpt_to_utf8(ch)] = ch;
}
for (int ch = 0xAE; ch <= 0xFF; ++ch) { // u'®' to u'ÿ'
assert(0 <= ch && ch < 256);
map[unicode_cpt_to_utf8(ch)] = ch;
}
auto n = 0;
for (int ch = 0; ch < 256; ++ch) {
if (map.find(unicode_cpt_to_utf8(ch)) == map.end()) {
map[unicode_cpt_to_utf8(256 + n)] = ch;
++n;
}
}
return map;
}
static inline std::wstring unicode_wstring_from_utf8(const std::string & s) {
std::wstring_convert<std::codecvt_utf8<wchar_t>> conv;
return conv.from_bytes(s);
}
static std::vector<std::string> unicode_byte_encoding_process(const std::vector<std::string> & bpe_words) {
std::vector<std::string> bpe_encoded_words;
for (const auto & word : bpe_words) {
std::string text_utf;
auto utf_word = unicode_cpts_from_utf8(word);
for (size_t i = 0; i < utf_word.size(); ++i) {
text_utf += unicode_cpt_to_utf8(utf_word[i]);
}
std::string encoded_token;
for (char & c : text_utf) {
encoded_token += unicode_byte_to_utf8(c);
}
bpe_encoded_words.emplace_back(encoded_token);
}
return bpe_encoded_words;
}
// GPT2 system regex: 's|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+
static std::vector<size_t> unicode_regex_split_custom_gpt2(const std::string & text, const std::vector<size_t> & offsets) {
std::vector<size_t> bpe_offsets; // store the offset of each word
bpe_offsets.reserve(offsets.size()); // Reserve memory for the approximate size
const auto cpts = unicode_cpts_from_utf8(text);
size_t start = 0;
for (auto offset : offsets) {
const size_t offset_ini = start;
const size_t offset_end = start + offset;
assert(offset_end <= cpts.size());
start = offset_end;
static const uint32_t OUT_OF_RANGE = 0xFFFFFFFF;
auto _get_cpt = [&] (const size_t pos) -> uint32_t {
return (offset_ini <= pos && pos < offset_end) ? cpts[pos] : OUT_OF_RANGE;
};
auto _get_flags = [&] (const size_t pos) -> codepoint_flags {
static const codepoint_flags undef(codepoint_flags::UNDEFINED);
return (offset_ini <= pos && pos < offset_end) ? unicode_cpt_flags(cpts[pos]) : undef;
};
size_t _prev_end = offset_ini;
auto _add_token = [&] (const size_t end) -> size_t {
assert(_prev_end <= end && end <= offset_end);
size_t len = end - _prev_end;
if (len > 0) {
bpe_offsets.push_back(len);
}
_prev_end = end;
//if (len > 0) {
// std::string s = "";
// for(size_t p = end-len; p < end; p++)
// s += unicode_cpt_to_utf8(cpts[p]);
// printf(">>> '%s'\n", s.c_str());
//}
return len;
};
for (size_t pos = offset_ini; pos < offset_end; /*pos++*/ ) {
const uint32_t cpt = _get_cpt(pos);
const auto flags = _get_flags(pos);
// regex: 's|'t|'re|'ve|'m|'ll|'d
if (cpt == '\'' && pos+1 < offset_end) {
uint32_t cpt_next = _get_cpt(pos+1);
if (cpt_next == 's' || cpt_next == 't' || cpt_next == 'm' || cpt_next == 'd') {
pos += _add_token(pos+2);
continue;
}
if (pos+2 < offset_end) {
uint32_t cpt_next_next = _get_cpt(pos+2);
if ((cpt_next == 'r' && cpt_next_next == 'e') ||
(cpt_next == 'v' && cpt_next_next == 'e') ||
(cpt_next == 'l' && cpt_next_next == 'l')) {
pos += _add_token(pos+3);
continue;
}
}
}
auto flags2 = (cpt == ' ' ? _get_flags(pos+1) : flags);
// regex: <space>?\p{L}+
if (flags2.is_letter) {
pos += (cpt == ' ');
while (flags2.is_letter) {
flags2 = _get_flags(++pos);
}
_add_token(pos);
continue;
}
// regex: <space>?\p{N}+
if (flags2.is_number) {
pos += (cpt == ' ');
while (flags2.is_number) {
flags2 = _get_flags(++pos);
}
_add_token(pos);
continue;
}
// regex: <space>?[^\s\p{L}\p{N}]+
if (!(flags2.is_whitespace || flags2.is_letter || flags2.is_number || flags2.is_undefined)) {
pos += (cpt == ' ');
while (!(flags2.is_whitespace || flags2.is_letter || flags2.is_number || flags2.is_undefined)) {
flags2 = _get_flags(++pos);
}
_add_token(pos);
continue;
}
size_t num_whitespaces = 0;
while (_get_flags(pos+num_whitespaces).is_whitespace) {
num_whitespaces++;
}
// regex: \s+(?!\S)
if (num_whitespaces > 1 && _get_cpt(pos+num_whitespaces) != OUT_OF_RANGE) {
pos += num_whitespaces - 1;
_add_token(pos);
continue;
}
// regex: \s+
if (num_whitespaces > 0) {
pos += num_whitespaces;
_add_token(pos);
continue;
}
// no matches
_add_token(++pos);
}
}
return bpe_offsets;
}
// LLAMA3 system regex: "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}{1,3}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+"
static std::vector<size_t> unicode_regex_split_custom_llama3(const std::string & text, const std::vector<size_t> & offsets) {
std::vector<size_t> bpe_offsets; // store the offset of each word
bpe_offsets.reserve(offsets.size()); // Reserve memory for the approximate size
const auto cpts = unicode_cpts_from_utf8(text);
size_t start = 0;
for (auto offset : offsets) {
const size_t offset_ini = start;
const size_t offset_end = start + offset;
assert(offset_end <= cpts.size());
start = offset_end;
static const uint32_t OUT_OF_RANGE = 0xFFFFFFFF;
auto _get_cpt = [&] (const size_t pos) -> uint32_t {
return (offset_ini <= pos && pos < offset_end) ? cpts[pos] : OUT_OF_RANGE;
};
auto _get_flags = [&] (const size_t pos) -> codepoint_flags {
static const codepoint_flags undef(codepoint_flags::UNDEFINED);
return (offset_ini <= pos && pos < offset_end) ? unicode_cpt_flags(cpts[pos]) : undef;
};
size_t _prev_end = offset_ini;
auto _add_token = [&] (const size_t end) -> size_t {
assert(_prev_end <= end && end <= offset_end);
size_t len = end - _prev_end;
if (len > 0) {
bpe_offsets.push_back(len);
}
_prev_end = end;
//if (len > 0) {
// std::string s = "";
// for(size_t p = end-len; p < end; p++)
// s += unicode_cpt_to_utf8(cpts[p]);
// printf(">>> '%s'\n", s.c_str());
//}
return len;
};
for (size_t pos = offset_ini; pos < offset_end; /*pos++*/ ) {
const uint32_t cpt = _get_cpt(pos);
const auto flags = _get_flags(pos);
// regex: (?i:'s|'t|'re|'ve|'m|'ll|'d) // case insensitive
if (cpt == '\'' && pos+1 < offset_end) {
uint32_t cpt_next = unicode_tolower(_get_cpt(pos+1));
if (cpt_next == 's' || cpt_next == 't' || cpt_next == 'm' || cpt_next == 'd') {
pos += _add_token(pos+2);
continue;
}
if (pos+2 < offset_end) {
uint32_t cpt_next_next = unicode_tolower(_get_cpt(pos+2));
if ((cpt_next == 'r' && cpt_next_next == 'e') ||
(cpt_next == 'v' && cpt_next_next == 'e') ||
(cpt_next == 'l' && cpt_next_next == 'l')) {
pos += _add_token(pos+3);
continue;
}
}
}
// regex: [^\r\n\p{L}\p{N}]?\p{L}+ //####FIXME: the first \p{L} is correct?
if (!(cpt == '\r' || cpt == '\n' || /*flags.is_letter |*/ flags.is_number)) {
if (flags.is_letter || _get_flags(pos+1).is_letter) { // one or more letters
pos++;
while (_get_flags(pos).is_letter) {
pos++;
}
_add_token(pos);
continue;
}
}
// regex: \p{N}{1,3}
if (flags.is_number) {
size_t ini = pos;
while (_get_flags(pos).is_number) {
if (++pos - ini >= 3 ) {
_add_token(pos);
ini = pos;
}
}
_add_token(pos);
continue;
}
// regex: <space>?[^\s\p{L}\p{N}]+[\r\n]*
auto flags2 = (cpt == ' ' ? _get_flags(pos+1) : flags);
if (!(flags2.is_whitespace || flags2.is_letter || flags2.is_number || flags2.is_undefined)) {
pos += (cpt == ' ');
while (!(flags2.is_whitespace || flags2.is_letter || flags2.is_number || flags2.is_undefined)) {
flags2 = _get_flags(++pos);
}
uint32_t cpt2 = _get_cpt(pos);
while (cpt2 == '\r' || cpt2 == '\n') {
cpt2 = _get_cpt(++pos);
}
_add_token(pos);
continue;
}
size_t num_whitespaces = 0;
size_t last_end_r_or_n = 0;
while (_get_flags(pos+num_whitespaces).is_whitespace) {
uint32_t cpt2 = _get_cpt(pos+num_whitespaces);
if (cpt2 == '\r' || cpt2 == '\n') {
last_end_r_or_n = pos + num_whitespaces + 1;
}
num_whitespaces++;
}
// regex: \s*[\r\n]+
if (last_end_r_or_n > 0) {
pos = last_end_r_or_n;
_add_token(pos);
continue;
}
// regex: \s+(?!\S)
if (num_whitespaces > 1 && _get_cpt(pos+num_whitespaces) != OUT_OF_RANGE) {
pos += num_whitespaces - 1;
_add_token(pos);
continue;
}
// regex: \s+
if (num_whitespaces > 0) {
pos += num_whitespaces;
_add_token(pos);
continue;
}
// no matches
_add_token(++pos);
}
}
return bpe_offsets;
}
// use std::wregex to split the text
static std::vector<size_t> unicode_regex_split_stl(const std::wstring & wtext, const std::wstring & regex_expr, const std::vector<size_t> & offsets) {
std::wregex expr(regex_expr);
std::vector<size_t> bpe_offsets; // store the offset of each word
bpe_offsets.reserve(offsets.size()); // Reserve memory for the approximate size
size_t start = 0;
for (auto offset : offsets) {
std::wcregex_iterator it(wtext.data() + start, wtext.data() + start + offset, expr);
std::wcregex_iterator end;
int64_t start_idx = 0;
while (it != end) {
std::wcmatch match = *it;
if (match.position() > start_idx) {
bpe_offsets.emplace_back(match.position() - start_idx);
}
bpe_offsets.emplace_back(match.length());
start_idx = match.position() + match.length();
++it;
}
if (start_idx < (int64_t) offset) {
bpe_offsets.emplace_back(offset - start_idx);
}
start += offset;
}
return bpe_offsets;
}
// use std::regex to split the text
static std::vector<size_t> unicode_regex_split_stl(const std::string & text, const std::string & regex_expr, const std::vector<size_t> & offsets) {
std::regex expr(regex_expr);
std::vector<size_t> bpe_offsets; // store the offset of each word
bpe_offsets.reserve(offsets.size()); // Reserve memory for the approximate size
size_t start = 0;
for (auto offset : offsets) {
std::cregex_iterator it(text.data() + start, text.data() + start + offset, expr);
std::cregex_iterator end;
int64_t start_idx = 0;
while (it != end) {
std::cmatch match = *it;
if (match.position() > start_idx) {
bpe_offsets.emplace_back(match.position() - start_idx);
}
bpe_offsets.emplace_back(match.length());
start_idx = match.position() + match.length();
++it;
}
if (start_idx < (int64_t) offset) {
bpe_offsets.emplace_back(offset - start_idx);
}
start += offset;
}
return bpe_offsets;
}
static std::vector<size_t> unicode_regex_split_custom(const std::string & text, const std::string & regex_expr, const std::vector<size_t> & offsets) {
std::vector<size_t> bpe_offsets;
if (regex_expr == "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)") {
bpe_offsets = unicode_regex_split_custom_gpt2(text, offsets);
} else if (
regex_expr == "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+" ||
regex_expr == "(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+") {
bpe_offsets = unicode_regex_split_custom_llama3(text, offsets);
}
return bpe_offsets;
}
//
// interface
//
std::string unicode_cpt_to_utf8(uint32_t cp) {
std::string result;
if (/* 0x00 <= cp && */ cp <= 0x7f) {
result.push_back(cp);
return result;
}
if (0x80 <= cp && cp <= 0x7ff) {
result.push_back(0xc0 | ((cp >> 6) & 0x1f));
result.push_back(0x80 | (cp & 0x3f));
return result;
}
if (0x800 <= cp && cp <= 0xffff) {
result.push_back(0xe0 | ((cp >> 12) & 0x0f));
result.push_back(0x80 | ((cp >> 6) & 0x3f));
result.push_back(0x80 | (cp & 0x3f));
return result;
}
if (0x10000 <= cp && cp <= 0x10ffff) {
result.push_back(0xf0 | ((cp >> 18) & 0x07));
result.push_back(0x80 | ((cp >> 12) & 0x3f));
result.push_back(0x80 | ((cp >> 6) & 0x3f));
result.push_back(0x80 | (cp & 0x3f));
return result;
}
throw std::invalid_argument("invalid codepoint");
}
std::vector<uint32_t> unicode_cpts_normalize_nfd(const std::vector<uint32_t> & cpts) {
auto comp = [] (const uint32_t cpt, const range_nfd & range) {
return cpt < range.first;
};
std::vector<uint32_t> result(cpts.size());
for (size_t i = 0; i < cpts.size(); ++i) {
const uint32_t cpt = cpts[i];
auto it = std::upper_bound(unicode_ranges_nfd.cbegin(), unicode_ranges_nfd.cend(), cpt, comp) - 1;
result[i] = (it->first <= cpt && cpt <= it->last) ? it->nfd : cpt;
}
return result;
}
std::vector<uint32_t> unicode_cpts_from_utf8(const std::string & utf8) {
std::vector<uint32_t> result;
result.reserve(utf8.size());
size_t offset = 0;
while (offset < utf8.size()) {
result.push_back(unicode_cpt_from_utf8(utf8, offset));
}
return result;
}
codepoint_flags unicode_cpt_flags(const uint32_t cp) {
static const codepoint_flags undef(codepoint_flags::UNDEFINED);
static const auto cpt_flags = unicode_cpt_flags_array();
return cp < cpt_flags.size() ? cpt_flags[cp] : undef;
}
codepoint_flags unicode_cpt_flags(const std::string & utf8) {
static const codepoint_flags undef(codepoint_flags::UNDEFINED);
if (utf8.empty()) {
return undef; // undefined
}
size_t offset = 0;
return unicode_cpt_flags(unicode_cpt_from_utf8(utf8, offset));
}
std::string unicode_byte_to_utf8(uint8_t byte) {
static std::unordered_map<uint8_t, std::string> map = unicode_byte_to_utf8_map();
return map.at(byte);
}
uint8_t unicode_utf8_to_byte(const std::string & utf8) {
static std::unordered_map<std::string, uint8_t> map = unicode_utf8_to_byte_map();
return map.at(utf8);
}
uint32_t unicode_tolower(uint32_t cp) {
auto it = unicode_map_lowercase.find(cp);
return it == unicode_map_lowercase.end() ? cp : it->second;
}
std::vector<std::string> unicode_regex_split(const std::string & text, const std::vector<std::string> & regex_exprs) {
// unicode categories
static const std::map<std::string, int> k_ucat_enum = {
{ "\\p{N}", codepoint_flags::NUMBER },
{ "\\p{L}", codepoint_flags::LETTER },
{ "\\p{P}", codepoint_flags::PUNCTUATION },
};
static const std::map<int, int> k_ucat_cpt = {
{ codepoint_flags::NUMBER, 0xD1 },
{ codepoint_flags::LETTER, 0xD2 },
{ codepoint_flags::PUNCTUATION, 0xD3 },
};
static const std::map<int, std::string> k_ucat_map = {
{ codepoint_flags::NUMBER, "\x30-\x39" }, // 0-9
{ codepoint_flags::LETTER, "\x41-\x5A\x61-\x7A" }, // A-Za-z
{ codepoint_flags::PUNCTUATION, "\x21-\x23\x25-\x2A\x2C-\x2F\x3A-\x3B\x3F-\x40\\\x5B-\\\x5D\x5F\\\x7B\\\x7D" }, // !-#%-*,-/:-;?-@\[-\]_\{\}
};
// compute collapsed codepoints only if needed by at least one regex
bool need_collapse = false;
for (auto & regex_expr : regex_exprs) {
// search for unicode categories
for (const auto & ucat : k_ucat_enum) {
if (std::string::npos != regex_expr.find(ucat.first)) {
need_collapse = true;
break;
}
}
}
const auto cpts = unicode_cpts_from_utf8(text);
// generate a "collapsed" representation of the text, where all codepoints are replaced by a single byte
// ref: https://github.com/ggerganov/llama.cpp/pull/6920#issuecomment-2081479935
std::string text_collapsed;
if (need_collapse) {
// collapse all unicode categories
text_collapsed.resize(cpts.size());
for (size_t i = 0; i < cpts.size(); ++i) {
// keep single-byte codepoints as is
if (cpts[i] < 128) {
text_collapsed[i] = cpts[i];
continue;
}
const auto flags = unicode_cpt_flags(cpts[i]);
if (flags.is_whitespace) {
//NOTE: C++ std::regex \s does not mach 0x85, Rust and Python regex does.
//text_collapsed[i] = (char) 0x85; // <Next Line> as whitespace fallback
text_collapsed[i] = (char) 0x0B; // <vertical tab> as whitespace fallback
} else if (k_ucat_cpt.find(flags.category_flag()) != k_ucat_cpt.end()) {
text_collapsed[i] = k_ucat_cpt.at(flags.category_flag());
} else {
text_collapsed[i] = (char) 0xD0; // fallback
}
}
}
std::vector<size_t> bpe_offsets = { cpts.size() };
for (auto & regex_expr : regex_exprs) {
// first, see if we have an efficient custom regex implementation
auto tmp = unicode_regex_split_custom(text, regex_expr, bpe_offsets);
if (!tmp.empty()) {
bpe_offsets = std::move(tmp);
continue;
}
// fallback to general-purpose std::regex / std::wregex
try {
// if a unicode category is used in the regex, we use the collapsed text and replace the unicode category
// with the corresponding collapsed representation
bool use_collapsed = false;
for (auto & ucat : k_ucat_enum) {
if (std::string::npos != regex_expr.find(ucat.first)) {
use_collapsed = true;
break;
}
}
if (use_collapsed) {
// sanity-check that the original regex does not contain any non-ASCII characters
const auto cpts_regex = unicode_cpts_from_utf8(regex_expr);
for (size_t i = 0; i < cpts_regex.size(); ++i) {
if (cpts_regex[i] >= 128) {
throw std::runtime_error("Regex includes both unicode categories and non-ASCII characters - not supported");
}
}
// generate a collapsed representation of the regex
std::string regex_expr_collapsed;
// track if we are inside [], because nested [] are not allowed
bool inside = false;
for (size_t i = 0; i < regex_expr.size(); ++i) {
if (regex_expr[i] == '[' && (i == 0 || regex_expr[i - 1] != '\\')) {
regex_expr_collapsed += '[';
inside = true;
continue;
}
if (inside && regex_expr[i] == ']' && regex_expr[i - 1] != '\\') {
regex_expr_collapsed += ']';
inside = false;
continue;
}
if (regex_expr[i + 0] == '\\' && i + 4 < regex_expr.size() &&
regex_expr[i + 1] == 'p' &&
regex_expr[i + 2] == '{' &&
regex_expr[i + 4] == '}') {
const std::string pat = regex_expr.substr(i, 5);
if (k_ucat_enum.find(pat) != k_ucat_enum.end()) {
if (!inside) {
regex_expr_collapsed += '[';
}
regex_expr_collapsed += k_ucat_cpt.at(k_ucat_enum.at(pat));
regex_expr_collapsed += k_ucat_map.at(k_ucat_enum.at(pat));
if (!inside) {
regex_expr_collapsed += ']';
}
i += 4;
continue;
}
}
regex_expr_collapsed += regex_expr[i];
}
//printf("text_collapsed: %s\n", text_collapsed.c_str());
//printf("regex_expr_collapsed: %s\n", regex_expr_collapsed.c_str());
bpe_offsets = unicode_regex_split_stl(text_collapsed, regex_expr_collapsed, bpe_offsets);
} else {
// no unicode category used, we can use std::wregex directly
const std::wstring wregex_expr = unicode_wstring_from_utf8(regex_expr);
// std::wregex \s does not mach non-ASCII whitespaces, using 0x0B as fallback
std::wstring wtext(cpts.begin(), cpts.end());
for (size_t i = 0; i < wtext.size(); ++i) {
if (wtext[i] > 0x7F && unicode_cpt_flags(wtext[i]).is_whitespace) {
wtext[i] = 0x0B;
}
}
//printf("text: %s\n", text.c_str());
//printf("regex_expr: %s\n", regex_expr.c_str());
bpe_offsets = unicode_regex_split_stl(wtext, wregex_expr, bpe_offsets);
}
} catch (std::regex_error & e) {
fprintf(stderr, "Failed to process regex: '%s'\n", regex_expr.c_str());
fprintf(stderr, "Regex error: %s\n", e.what());
throw std::runtime_error("Failed to process regex");
}
}
std::vector<std::string> bpe_words;
bpe_words.reserve(bpe_offsets.size()); // reserve memory for the approximate size
size_t start = 0;
for (size_t & offset : bpe_offsets) {
bpe_words.emplace_back();
for (size_t i = start; i < start + offset; ++i) {
bpe_words.back() += unicode_cpt_to_utf8(cpts[i]);
}
start += offset;
}
return unicode_byte_encoding_process(bpe_words);
}

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@ -1,463 +1,63 @@
#pragma once #pragma once
#include <cassert> #include <cstdint>
#include <stdexcept>
#include <string> #include <string>
#include <unordered_map>
#include <vector> #include <vector>
static const std::vector<std::pair<uint32_t, uint32_t>> digit_ranges = { struct codepoint_flags {
{0x30, 0x39}, {0xB2, 0xB3}, {0xB9, 0xB9}, {0x660, 0x669}, {0x6F0, 0x6F9}, {0x7C0, 0x7C9}, {0x966, 0x96F}, {0x9E6, 0x9EF}, {0xA66, 0xA6F}, {0xAE6, 0xAEF}, {0xB66, 0xB6F}, {0xBE6, 0xBEF}, {0xC66, 0xC6F}, enum {
{0xCE6, 0xCEF}, {0xD66, 0xD6F}, {0xDE6, 0xDEF}, {0xE50, 0xE59}, {0xED0, 0xED9}, {0xF20, 0xF29}, {0x1040, 0x1049}, {0x1090, 0x1099}, {0x1369, 0x1371}, {0x17E0, 0x17E9}, {0x1810, 0x1819}, {0x1946, 0x194F}, UNDEFINED = 0x0001,
{0x19D0, 0x19DA}, {0x1A80, 0x1A89}, {0x1A90, 0x1A99}, {0x1B50, 0x1B59}, {0x1BB0, 0x1BB9}, {0x1C40, 0x1C49}, {0x1C50, 0x1C59}, {0x2070, 0x2070}, {0x2074, 0x2079}, {0x2080, 0x2089}, {0x2460, 0x2468}, NUMBER = 0x0002, // regex: \p{N}
{0x2474, 0x247C}, {0x2488, 0x2490}, {0x24EA, 0x24EA}, {0x24F5, 0x24FD}, {0x24FF, 0x24FF}, {0x2776, 0x277E}, {0x2780, 0x2788}, {0x278A, 0x2792}, {0xA620, 0xA629}, {0xA8D0, 0xA8D9}, {0xA900, 0xA909}, LETTER = 0x0004, // regex: \p{L}
{0xA9D0, 0xA9D9}, {0xA9F0, 0xA9F9}, {0xAA50, 0xAA59}, {0xABF0, 0xABF9}, {0xFF10, 0xFF19}, {0x104A0, 0x104A9}, {0x10A40, 0x10A43}, {0x10D30, 0x10D39}, {0x10E60, 0x10E68}, {0x11052, 0x1105A}, SEPARATOR = 0x0008, // regex: \p{Z}
{0x11066, 0x1106F}, {0x110F0, 0x110F9}, {0x11136, 0x1113F}, {0x111D0, 0x111D9}, {0x112F0, 0x112F9}, {0x11450, 0x11459}, {0x114D0, 0x114D9}, {0x11650, 0x11659}, {0x116C0, 0x116C9}, {0x11730, 0x11739}, ACCENT_MARK = 0x0010, // regex: \p{M}
{0x118E0, 0x118E9}, {0x11950, 0x11959}, {0x11C50, 0x11C59}, {0x11D50, 0x11D59}, {0x11DA0, 0x11DA9}, {0x16A60, 0x16A69}, {0x16B50, 0x16B59}, {0x1D7CE, 0x1D7FF}, {0x1E140, 0x1E149}, {0x1E2F0, 0x1E2F9}, PUNCTUATION = 0x0020, // regex: \p{P}
{0x1E950, 0x1E959}, {0x1F100, 0x1F10A}, {0x1FBF0, 0x1FBF9}, SYMBOL = 0x0040, // regex: \p{S}
CONTROL = 0x0080, // regex: \p{C}
MASK_CATEGORIES = 0x00FF,
};
// codepoint type
uint16_t is_undefined : 1;
uint16_t is_number : 1; // regex: \p{N}
uint16_t is_letter : 1; // regex: \p{L}
uint16_t is_separator : 1; // regex: \p{Z}
uint16_t is_accent_mark : 1; // regex: \p{M}
uint16_t is_punctuation : 1; // regex: \p{P}
uint16_t is_symbol : 1; // regex: \p{S}
uint16_t is_control : 1; // regex: \p{C}
// helper flags
uint16_t is_whitespace : 1; // regex: \s
uint16_t is_lowercase : 1;
uint16_t is_uppercase : 1;
uint16_t is_nfd : 1;
// decode from uint16
inline codepoint_flags(const uint16_t flags=0) {
*reinterpret_cast<uint16_t*>(this) = flags;
}
inline uint16_t as_uint() const {
return *reinterpret_cast<const uint16_t*>(this);
}
inline uint16_t category_flag() const {
return this->as_uint() & MASK_CATEGORIES;
}
}; };
static const std::vector<std::pair<uint32_t, uint32_t>> letter_ranges = {
{0x41, 0x5A}, {0x61, 0x7A}, {0xAA, 0xAA}, {0xB5, 0xB5}, {0xBA, 0xBA}, {0xC0, 0xD6}, {0xD8, 0xF6}, {0xF8, 0x2C1}, {0x2C6, 0x2D1}, {0x2E0, 0x2E4}, {0x2EC, 0x2EC}, {0x2EE, 0x2EE}, {0x370, 0x374},
{0x376, 0x377}, {0x37A, 0x37D}, {0x37F, 0x37F}, {0x386, 0x386}, {0x388, 0x38A}, {0x38C, 0x38C}, {0x38E, 0x3A1}, {0x3A3, 0x3F5}, {0x3F7, 0x481}, {0x48A, 0x52F}, {0x531, 0x556}, {0x559, 0x559},
{0x560, 0x588}, {0x5D0, 0x5EA}, {0x5EF, 0x5F2}, {0x620, 0x64A}, {0x66E, 0x66F}, {0x671, 0x6D3}, {0x6D5, 0x6D5}, {0x6E5, 0x6E6}, {0x6EE, 0x6EF}, {0x6FA, 0x6FC}, {0x6FF, 0x6FF}, {0x710, 0x710},
{0x712, 0x72F}, {0x74D, 0x7A5}, {0x7B1, 0x7B1}, {0x7CA, 0x7EA}, {0x7F4, 0x7F5}, {0x7FA, 0x7FA}, {0x800, 0x815}, {0x81A, 0x81A}, {0x824, 0x824}, {0x828, 0x828}, {0x840, 0x858}, {0x860, 0x86A},
{0x8A0, 0x8B4}, {0x8B6, 0x8C7}, {0x904, 0x939}, {0x93D, 0x93D}, {0x950, 0x950}, {0x958, 0x961}, {0x971, 0x980}, {0x985, 0x98C}, {0x98F, 0x990}, {0x993, 0x9A8}, {0x9AA, 0x9B0}, {0x9B2, 0x9B2},
{0x9B6, 0x9B9}, {0x9BD, 0x9BD}, {0x9CE, 0x9CE}, {0x9DC, 0x9DD}, {0x9DF, 0x9E1}, {0x9F0, 0x9F1}, {0x9FC, 0x9FC}, {0xA05, 0xA0A}, {0xA0F, 0xA10}, {0xA13, 0xA28}, {0xA2A, 0xA30}, {0xA32, 0xA33},
{0xA35, 0xA36}, {0xA38, 0xA39}, {0xA59, 0xA5C}, {0xA5E, 0xA5E}, {0xA72, 0xA74}, {0xA85, 0xA8D}, {0xA8F, 0xA91}, {0xA93, 0xAA8}, {0xAAA, 0xAB0}, {0xAB2, 0xAB3}, {0xAB5, 0xAB9}, {0xABD, 0xABD},
{0xAD0, 0xAD0}, {0xAE0, 0xAE1}, {0xAF9, 0xAF9}, {0xB05, 0xB0C}, {0xB0F, 0xB10}, {0xB13, 0xB28}, {0xB2A, 0xB30}, {0xB32, 0xB33}, {0xB35, 0xB39}, {0xB3D, 0xB3D}, {0xB5C, 0xB5D}, {0xB5F, 0xB61},
{0xB71, 0xB71}, {0xB83, 0xB83}, {0xB85, 0xB8A}, {0xB8E, 0xB90}, {0xB92, 0xB95}, {0xB99, 0xB9A}, {0xB9C, 0xB9C}, {0xB9E, 0xB9F}, {0xBA3, 0xBA4}, {0xBA8, 0xBAA}, {0xBAE, 0xBB9}, {0xBD0, 0xBD0},
{0xC05, 0xC0C}, {0xC0E, 0xC10}, {0xC12, 0xC28}, {0xC2A, 0xC39}, {0xC3D, 0xC3D}, {0xC58, 0xC5A}, {0xC60, 0xC61}, {0xC80, 0xC80}, {0xC85, 0xC8C}, {0xC8E, 0xC90}, {0xC92, 0xCA8}, {0xCAA, 0xCB3},
{0xCB5, 0xCB9}, {0xCBD, 0xCBD}, {0xCDE, 0xCDE}, {0xCE0, 0xCE1}, {0xCF1, 0xCF2}, {0xD04, 0xD0C}, {0xD0E, 0xD10}, {0xD12, 0xD3A}, {0xD3D, 0xD3D}, {0xD4E, 0xD4E}, {0xD54, 0xD56}, {0xD5F, 0xD61},
{0xD7A, 0xD7F}, {0xD85, 0xD96}, {0xD9A, 0xDB1}, {0xDB3, 0xDBB}, {0xDBD, 0xDBD}, {0xDC0, 0xDC6}, {0xE01, 0xE30}, {0xE32, 0xE33}, {0xE40, 0xE46}, {0xE81, 0xE82}, {0xE84, 0xE84}, {0xE86, 0xE8A},
{0xE8C, 0xEA3}, {0xEA5, 0xEA5}, {0xEA7, 0xEB0}, {0xEB2, 0xEB3}, {0xEBD, 0xEBD}, {0xEC0, 0xEC4}, {0xEC6, 0xEC6}, {0xEDC, 0xEDF}, {0xF00, 0xF00}, {0xF40, 0xF47}, {0xF49, 0xF6C}, {0xF88, 0xF8C},
{0x1000, 0x102A}, {0x103F, 0x103F}, {0x1050, 0x1055}, {0x105A, 0x105D}, {0x1061, 0x1061}, {0x1065, 0x1066}, {0x106E, 0x1070}, {0x1075, 0x1081}, {0x108E, 0x108E}, {0x10A0, 0x10C5}, {0x10C7, 0x10C7},
{0x10CD, 0x10CD}, {0x10D0, 0x10FA}, {0x10FC, 0x1248}, {0x124A, 0x124D}, {0x1250, 0x1256}, {0x1258, 0x1258}, {0x125A, 0x125D}, {0x1260, 0x1288}, {0x128A, 0x128D}, {0x1290, 0x12B0}, {0x12B2, 0x12B5},
{0x12B8, 0x12BE}, {0x12C0, 0x12C0}, {0x12C2, 0x12C5}, {0x12C8, 0x12D6}, {0x12D8, 0x1310}, {0x1312, 0x1315}, {0x1318, 0x135A}, {0x1380, 0x138F}, {0x13A0, 0x13F5}, {0x13F8, 0x13FD}, {0x1401, 0x166C},
{0x166F, 0x167F}, {0x1681, 0x169A}, {0x16A0, 0x16EA}, {0x16F1, 0x16F8}, {0x1700, 0x170C}, {0x170E, 0x1711}, {0x1720, 0x1731}, {0x1740, 0x1751}, {0x1760, 0x176C}, {0x176E, 0x1770}, {0x1780, 0x17B3},
{0x17D7, 0x17D7}, {0x17DC, 0x17DC}, {0x1820, 0x1878}, {0x1880, 0x1884}, {0x1887, 0x18A8}, {0x18AA, 0x18AA}, {0x18B0, 0x18F5}, {0x1900, 0x191E}, {0x1950, 0x196D}, {0x1970, 0x1974}, {0x1980, 0x19AB},
{0x19B0, 0x19C9}, {0x1A00, 0x1A16}, {0x1A20, 0x1A54}, {0x1AA7, 0x1AA7}, {0x1B05, 0x1B33}, {0x1B45, 0x1B4B}, {0x1B83, 0x1BA0}, {0x1BAE, 0x1BAF}, {0x1BBA, 0x1BE5}, {0x1C00, 0x1C23}, {0x1C4D, 0x1C4F},
{0x1C5A, 0x1C7D}, {0x1C80, 0x1C88}, {0x1C90, 0x1CBA}, {0x1CBD, 0x1CBF}, {0x1CE9, 0x1CEC}, {0x1CEE, 0x1CF3}, {0x1CF5, 0x1CF6}, {0x1CFA, 0x1CFA}, {0x1D00, 0x1DBF}, {0x1E00, 0x1F15}, {0x1F18, 0x1F1D},
{0x1F20, 0x1F45}, {0x1F48, 0x1F4D}, {0x1F50, 0x1F57}, {0x1F59, 0x1F59}, {0x1F5B, 0x1F5B}, {0x1F5D, 0x1F5D}, {0x1F5F, 0x1F7D}, {0x1F80, 0x1FB4}, {0x1FB6, 0x1FBC}, {0x1FBE, 0x1FBE}, {0x1FC2, 0x1FC4},
{0x1FC6, 0x1FCC}, {0x1FD0, 0x1FD3}, {0x1FD6, 0x1FDB}, {0x1FE0, 0x1FEC}, {0x1FF2, 0x1FF4}, {0x1FF6, 0x1FFC}, {0x2071, 0x2071}, {0x207F, 0x207F}, {0x2090, 0x209C}, {0x2102, 0x2102}, {0x2107, 0x2107},
{0x210A, 0x2113}, {0x2115, 0x2115}, {0x2119, 0x211D}, {0x2124, 0x2124}, {0x2126, 0x2126}, {0x2128, 0x2128}, {0x212A, 0x212D}, {0x212F, 0x2139}, {0x213C, 0x213F}, {0x2145, 0x2149}, {0x214E, 0x214E},
{0x2183, 0x2184}, {0x2C00, 0x2C2E}, {0x2C30, 0x2C5E}, {0x2C60, 0x2CE4}, {0x2CEB, 0x2CEE}, {0x2CF2, 0x2CF3}, {0x2D00, 0x2D25}, {0x2D27, 0x2D27}, {0x2D2D, 0x2D2D}, {0x2D30, 0x2D67}, {0x2D6F, 0x2D6F},
{0x2D80, 0x2D96}, {0x2DA0, 0x2DA6}, {0x2DA8, 0x2DAE}, {0x2DB0, 0x2DB6}, {0x2DB8, 0x2DBE}, {0x2DC0, 0x2DC6}, {0x2DC8, 0x2DCE}, {0x2DD0, 0x2DD6}, {0x2DD8, 0x2DDE}, {0x2E2F, 0x2E2F}, {0x3005, 0x3006},
{0x3031, 0x3035}, {0x303B, 0x303C}, {0x3041, 0x3096}, {0x309D, 0x309F}, {0x30A1, 0x30FA}, {0x30FC, 0x30FF}, {0x3105, 0x312F}, {0x3131, 0x318E}, {0x31A0, 0x31BF}, {0x31F0, 0x31FF}, {0x3400, 0x4DBF},
{0x4E00, 0x9FFC}, {0xA000, 0xA48C}, {0xA4D0, 0xA4FD}, {0xA500, 0xA60C}, {0xA610, 0xA61F}, {0xA62A, 0xA62B}, {0xA640, 0xA66E}, {0xA67F, 0xA69D}, {0xA6A0, 0xA6E5}, {0xA717, 0xA71F}, {0xA722, 0xA788},
{0xA78B, 0xA7BF}, {0xA7C2, 0xA7CA}, {0xA7F5, 0xA801}, {0xA803, 0xA805}, {0xA807, 0xA80A}, {0xA80C, 0xA822}, {0xA840, 0xA873}, {0xA882, 0xA8B3}, {0xA8F2, 0xA8F7}, {0xA8FB, 0xA8FB}, {0xA8FD, 0xA8FE},
{0xA90A, 0xA925}, {0xA930, 0xA946}, {0xA960, 0xA97C}, {0xA984, 0xA9B2}, {0xA9CF, 0xA9CF}, {0xA9E0, 0xA9E4}, {0xA9E6, 0xA9EF}, {0xA9FA, 0xA9FE}, {0xAA00, 0xAA28}, {0xAA40, 0xAA42}, {0xAA44, 0xAA4B},
{0xAA60, 0xAA76}, {0xAA7A, 0xAA7A}, {0xAA7E, 0xAAAF}, {0xAAB1, 0xAAB1}, {0xAAB5, 0xAAB6}, {0xAAB9, 0xAABD}, {0xAAC0, 0xAAC0}, {0xAAC2, 0xAAC2}, {0xAADB, 0xAADD}, {0xAAE0, 0xAAEA}, {0xAAF2, 0xAAF4},
{0xAB01, 0xAB06}, {0xAB09, 0xAB0E}, {0xAB11, 0xAB16}, {0xAB20, 0xAB26}, {0xAB28, 0xAB2E}, {0xAB30, 0xAB5A}, {0xAB5C, 0xAB69}, {0xAB70, 0xABE2}, {0xAC00, 0xD7A3}, {0xD7B0, 0xD7C6}, {0xD7CB, 0xD7FB},
{0xF900, 0xFA6D}, {0xFA70, 0xFAD9}, {0xFB00, 0xFB06}, {0xFB13, 0xFB17}, {0xFB1D, 0xFB1D}, {0xFB1F, 0xFB28}, {0xFB2A, 0xFB36}, {0xFB38, 0xFB3C}, {0xFB3E, 0xFB3E}, {0xFB40, 0xFB41}, {0xFB43, 0xFB44},
{0xFB46, 0xFBB1}, {0xFBD3, 0xFD3D}, {0xFD50, 0xFD8F}, {0xFD92, 0xFDC7}, {0xFDF0, 0xFDFB}, {0xFE70, 0xFE74}, {0xFE76, 0xFEFC}, {0xFF21, 0xFF3A}, {0xFF41, 0xFF5A}, {0xFF66, 0xFFBE}, {0xFFC2, 0xFFC7},
{0xFFCA, 0xFFCF}, {0xFFD2, 0xFFD7}, {0xFFDA, 0xFFDC}, {0x10000, 0x1000B}, {0x1000D, 0x10026}, {0x10028, 0x1003A}, {0x1003C, 0x1003D}, {0x1003F, 0x1004D}, {0x10050, 0x1005D}, {0x10080, 0x100FA},
{0x10280, 0x1029C}, {0x102A0, 0x102D0}, {0x10300, 0x1031F}, {0x1032D, 0x10340}, {0x10342, 0x10349}, {0x10350, 0x10375}, {0x10380, 0x1039D}, {0x103A0, 0x103C3}, {0x103C8, 0x103CF}, {0x10400, 0x1049D},
{0x104B0, 0x104D3}, {0x104D8, 0x104FB}, {0x10500, 0x10527}, {0x10530, 0x10563}, {0x10600, 0x10736}, {0x10740, 0x10755}, {0x10760, 0x10767}, {0x10800, 0x10805}, {0x10808, 0x10808}, {0x1080A, 0x10835},
{0x10837, 0x10838}, {0x1083C, 0x1083C}, {0x1083F, 0x10855}, {0x10860, 0x10876}, {0x10880, 0x1089E}, {0x108E0, 0x108F2}, {0x108F4, 0x108F5}, {0x10900, 0x10915}, {0x10920, 0x10939}, {0x10980, 0x109B7},
{0x109BE, 0x109BF}, {0x10A00, 0x10A00}, {0x10A10, 0x10A13}, {0x10A15, 0x10A17}, {0x10A19, 0x10A35}, {0x10A60, 0x10A7C}, {0x10A80, 0x10A9C}, {0x10AC0, 0x10AC7}, {0x10AC9, 0x10AE4}, {0x10B00, 0x10B35},
{0x10B40, 0x10B55}, {0x10B60, 0x10B72}, {0x10B80, 0x10B91}, {0x10C00, 0x10C48}, {0x10C80, 0x10CB2}, {0x10CC0, 0x10CF2}, {0x10D00, 0x10D23}, {0x10E80, 0x10EA9}, {0x10EB0, 0x10EB1}, {0x10F00, 0x10F1C},
{0x10F27, 0x10F27}, {0x10F30, 0x10F45}, {0x10FB0, 0x10FC4}, {0x10FE0, 0x10FF6}, {0x11003, 0x11037}, {0x11083, 0x110AF}, {0x110D0, 0x110E8}, {0x11103, 0x11126}, {0x11144, 0x11144}, {0x11147, 0x11147},
{0x11150, 0x11172}, {0x11176, 0x11176}, {0x11183, 0x111B2}, {0x111C1, 0x111C4}, {0x111DA, 0x111DA}, {0x111DC, 0x111DC}, {0x11200, 0x11211}, {0x11213, 0x1122B}, {0x11280, 0x11286}, {0x11288, 0x11288},
{0x1128A, 0x1128D}, {0x1128F, 0x1129D}, {0x1129F, 0x112A8}, {0x112B0, 0x112DE}, {0x11305, 0x1130C}, {0x1130F, 0x11310}, {0x11313, 0x11328}, {0x1132A, 0x11330}, {0x11332, 0x11333}, {0x11335, 0x11339},
{0x1133D, 0x1133D}, {0x11350, 0x11350}, {0x1135D, 0x11361}, {0x11400, 0x11434}, {0x11447, 0x1144A}, {0x1145F, 0x11461}, {0x11480, 0x114AF}, {0x114C4, 0x114C5}, {0x114C7, 0x114C7}, {0x11580, 0x115AE},
{0x115D8, 0x115DB}, {0x11600, 0x1162F}, {0x11644, 0x11644}, {0x11680, 0x116AA}, {0x116B8, 0x116B8}, {0x11700, 0x1171A}, {0x11800, 0x1182B}, {0x118A0, 0x118DF}, {0x118FF, 0x11906}, {0x11909, 0x11909},
{0x1190C, 0x11913}, {0x11915, 0x11916}, {0x11918, 0x1192F}, {0x1193F, 0x1193F}, {0x11941, 0x11941}, {0x119A0, 0x119A7}, {0x119AA, 0x119D0}, {0x119E1, 0x119E1}, {0x119E3, 0x119E3}, {0x11A00, 0x11A00},
{0x11A0B, 0x11A32}, {0x11A3A, 0x11A3A}, {0x11A50, 0x11A50}, {0x11A5C, 0x11A89}, {0x11A9D, 0x11A9D}, {0x11AC0, 0x11AF8}, {0x11C00, 0x11C08}, {0x11C0A, 0x11C2E}, {0x11C40, 0x11C40}, {0x11C72, 0x11C8F},
{0x11D00, 0x11D06}, {0x11D08, 0x11D09}, {0x11D0B, 0x11D30}, {0x11D46, 0x11D46}, {0x11D60, 0x11D65}, {0x11D67, 0x11D68}, {0x11D6A, 0x11D89}, {0x11D98, 0x11D98}, {0x11EE0, 0x11EF2}, {0x11FB0, 0x11FB0},
{0x12000, 0x12399}, {0x12480, 0x12543}, {0x13000, 0x1342E}, {0x14400, 0x14646}, {0x16800, 0x16A38}, {0x16A40, 0x16A5E}, {0x16AD0, 0x16AED}, {0x16B00, 0x16B2F}, {0x16B40, 0x16B43}, {0x16B63, 0x16B77},
{0x16B7D, 0x16B8F}, {0x16E40, 0x16E7F}, {0x16F00, 0x16F4A}, {0x16F50, 0x16F50}, {0x16F93, 0x16F9F}, {0x16FE0, 0x16FE1}, {0x16FE3, 0x16FE3}, {0x17000, 0x187F7}, {0x18800, 0x18CD5}, {0x18D00, 0x18D08},
{0x1B000, 0x1B11E}, {0x1B150, 0x1B152}, {0x1B164, 0x1B167}, {0x1B170, 0x1B2FB}, {0x1BC00, 0x1BC6A}, {0x1BC70, 0x1BC7C}, {0x1BC80, 0x1BC88}, {0x1BC90, 0x1BC99}, {0x1D400, 0x1D454}, {0x1D456, 0x1D49C},
{0x1D49E, 0x1D49F}, {0x1D4A2, 0x1D4A2}, {0x1D4A5, 0x1D4A6}, {0x1D4A9, 0x1D4AC}, {0x1D4AE, 0x1D4B9}, {0x1D4BB, 0x1D4BB}, {0x1D4BD, 0x1D4C3}, {0x1D4C5, 0x1D505}, {0x1D507, 0x1D50A}, {0x1D50D, 0x1D514},
{0x1D516, 0x1D51C}, {0x1D51E, 0x1D539}, {0x1D53B, 0x1D53E}, {0x1D540, 0x1D544}, {0x1D546, 0x1D546}, {0x1D54A, 0x1D550}, {0x1D552, 0x1D6A5}, {0x1D6A8, 0x1D6C0}, {0x1D6C2, 0x1D6DA}, {0x1D6DC, 0x1D6FA},
{0x1D6FC, 0x1D714}, {0x1D716, 0x1D734}, {0x1D736, 0x1D74E}, {0x1D750, 0x1D76E}, {0x1D770, 0x1D788}, {0x1D78A, 0x1D7A8}, {0x1D7AA, 0x1D7C2}, {0x1D7C4, 0x1D7CB}, {0x1E100, 0x1E12C}, {0x1E137, 0x1E13D},
{0x1E14E, 0x1E14E}, {0x1E2C0, 0x1E2EB}, {0x1E800, 0x1E8C4}, {0x1E900, 0x1E943}, {0x1E94B, 0x1E94B}, {0x1EE00, 0x1EE03}, {0x1EE05, 0x1EE1F}, {0x1EE21, 0x1EE22}, {0x1EE24, 0x1EE24}, {0x1EE27, 0x1EE27},
{0x1EE29, 0x1EE32}, {0x1EE34, 0x1EE37}, {0x1EE39, 0x1EE39}, {0x1EE3B, 0x1EE3B}, {0x1EE42, 0x1EE42}, {0x1EE47, 0x1EE47}, {0x1EE49, 0x1EE49}, {0x1EE4B, 0x1EE4B}, {0x1EE4D, 0x1EE4F}, {0x1EE51, 0x1EE52},
{0x1EE54, 0x1EE54}, {0x1EE57, 0x1EE57}, {0x1EE59, 0x1EE59}, {0x1EE5B, 0x1EE5B}, {0x1EE5D, 0x1EE5D}, {0x1EE5F, 0x1EE5F}, {0x1EE61, 0x1EE62}, {0x1EE64, 0x1EE64}, {0x1EE67, 0x1EE6A}, {0x1EE6C, 0x1EE72},
{0x1EE74, 0x1EE77}, {0x1EE79, 0x1EE7C}, {0x1EE7E, 0x1EE7E}, {0x1EE80, 0x1EE89}, {0x1EE8B, 0x1EE9B}, {0x1EEA1, 0x1EEA3}, {0x1EEA5, 0x1EEA9}, {0x1EEAB, 0x1EEBB}, {0x20000, 0x2A6DD}, {0x2A700, 0x2B734},
{0x2B740, 0x2B81D}, {0x2B820, 0x2CEA1}, {0x2CEB0, 0x2EBE0}, {0x2F800, 0x2FA1D}, {0x30000, 0x3134A},
};
static const std::vector<std::pair<uint32_t, uint32_t>> whitespace_ranges = { std::string unicode_cpt_to_utf8(uint32_t cp);
{0x9, 0xD}, {0x1C, 0x20}, {0x85, 0x85}, {0xA0, 0xA0}, {0x1680, 0x1680}, {0x2000, 0x200A}, {0x2028, 0x2029}, {0x202F, 0x202F}, {0x205F, 0x205F}, {0x3000, 0x3000}, std::vector<uint32_t> unicode_cpts_from_utf8(const std::string & utf8);
};
static const std::vector<std::pair<uint32_t, uint32_t>> accent_mark_ranges = { std::vector<uint32_t> unicode_cpts_normalize_nfd(const std::vector<uint32_t> & cpts);
{0x300, 0x36F}, {0x483, 0x489}, {0x591, 0x5BD}, {0x5BF, 0x5BF}, {0x5C1, 0x5C2}, {0x5C4, 0x5C5}, {0x5C7, 0x5C7}, {0x610, 0x61A}, {0x64B, 0x65F}, {0x670, 0x670}, {0x6D6, 0x6DC}, {0x6DF, 0x6E4},
{0x6E7, 0x6E8}, {0x6EA, 0x6ED}, {0x711, 0x711}, {0x730, 0x74A}, {0x7A6, 0x7B0}, {0x7EB, 0x7F3}, {0x7FD, 0x7FD}, {0x816, 0x819}, {0x81B, 0x823}, {0x825, 0x827}, {0x829, 0x82D}, {0x859, 0x85B},
{0x8D3, 0x8E1}, {0x8E3, 0x903}, {0x93A, 0x93C}, {0x93E, 0x94F}, {0x951, 0x957}, {0x962, 0x963}, {0x981, 0x983}, {0x9BC, 0x9BC}, {0x9BE, 0x9C4}, {0x9C7, 0x9C8}, {0x9CB, 0x9CD}, {0x9D7, 0x9D7},
{0x9E2, 0x9E3}, {0x9FE, 0x9FE}, {0xA01, 0xA03}, {0xA3C, 0xA3C}, {0xA3E, 0xA42}, {0xA47, 0xA48}, {0xA4B, 0xA4D}, {0xA51, 0xA51}, {0xA70, 0xA71}, {0xA75, 0xA75}, {0xA81, 0xA83}, {0xABC, 0xABC},
{0xABE, 0xAC5}, {0xAC7, 0xAC9}, {0xACB, 0xACD}, {0xAE2, 0xAE3}, {0xAFA, 0xAFF}, {0xB01, 0xB03}, {0xB3C, 0xB3C}, {0xB3E, 0xB44}, {0xB47, 0xB48}, {0xB4B, 0xB4D}, {0xB55, 0xB57}, {0xB62, 0xB63},
{0xB82, 0xB82}, {0xBBE, 0xBC2}, {0xBC6, 0xBC8}, {0xBCA, 0xBCD}, {0xBD7, 0xBD7}, {0xC00, 0xC04}, {0xC3E, 0xC44}, {0xC46, 0xC48}, {0xC4A, 0xC4D}, {0xC55, 0xC56}, {0xC62, 0xC63}, {0xC81, 0xC83},
{0xCBC, 0xCBC}, {0xCBE, 0xCC4}, {0xCC6, 0xCC8}, {0xCCA, 0xCCD}, {0xCD5, 0xCD6}, {0xCE2, 0xCE3}, {0xD00, 0xD03}, {0xD3B, 0xD3C}, {0xD3E, 0xD44}, {0xD46, 0xD48}, {0xD4A, 0xD4D}, {0xD57, 0xD57},
{0xD62, 0xD63}, {0xD81, 0xD83}, {0xDCA, 0xDCA}, {0xDCF, 0xDD4}, {0xDD6, 0xDD6}, {0xDD8, 0xDDF}, {0xDF2, 0xDF3}, {0xE31, 0xE31}, {0xE34, 0xE3A}, {0xE47, 0xE4E}, {0xEB1, 0xEB1}, {0xEB4, 0xEBC},
{0xEC8, 0xECD}, {0xF18, 0xF19}, {0xF35, 0xF35}, {0xF37, 0xF37}, {0xF39, 0xF39}, {0xF3E, 0xF3F}, {0xF71, 0xF84}, {0xF86, 0xF87}, {0xF8D, 0xF97}, {0xF99, 0xFBC}, {0xFC6, 0xFC6}, {0x102B, 0x103E},
{0x1056, 0x1059}, {0x105E, 0x1060}, {0x1062, 0x1064}, {0x1067, 0x106D}, {0x1071, 0x1074}, {0x1082, 0x108D}, {0x108F, 0x108F}, {0x109A, 0x109D}, {0x135D, 0x135F}, {0x1712, 0x1714}, {0x1732, 0x1734},
{0x1752, 0x1753}, {0x1772, 0x1773}, {0x17B4, 0x17D3}, {0x17DD, 0x17DD}, {0x180B, 0x180D}, {0x1885, 0x1886}, {0x18A9, 0x18A9}, {0x1920, 0x192B}, {0x1930, 0x193B}, {0x1A17, 0x1A1B}, {0x1A55, 0x1A5E},
{0x1A60, 0x1A7C}, {0x1A7F, 0x1A7F}, {0x1AB0, 0x1AC0}, {0x1B00, 0x1B04}, {0x1B34, 0x1B44}, {0x1B6B, 0x1B73}, {0x1B80, 0x1B82}, {0x1BA1, 0x1BAD}, {0x1BE6, 0x1BF3}, {0x1C24, 0x1C37}, {0x1CD0, 0x1CD2},
{0x1CD4, 0x1CE8}, {0x1CED, 0x1CED}, {0x1CF4, 0x1CF4}, {0x1CF7, 0x1CF9}, {0x1DC0, 0x1DF9}, {0x1DFB, 0x1DFF}, {0x20D0, 0x20F0}, {0x2CEF, 0x2CF1}, {0x2D7F, 0x2D7F}, {0x2DE0, 0x2DFF}, {0x302A, 0x302F},
{0x3099, 0x309A}, {0xA66F, 0xA672}, {0xA674, 0xA67D}, {0xA69E, 0xA69F}, {0xA6F0, 0xA6F1}, {0xA802, 0xA802}, {0xA806, 0xA806}, {0xA80B, 0xA80B}, {0xA823, 0xA827}, {0xA82C, 0xA82C}, {0xA880, 0xA881},
{0xA8B4, 0xA8C5}, {0xA8E0, 0xA8F1}, {0xA8FF, 0xA8FF}, {0xA926, 0xA92D}, {0xA947, 0xA953}, {0xA980, 0xA983}, {0xA9B3, 0xA9C0}, {0xA9E5, 0xA9E5}, {0xAA29, 0xAA36}, {0xAA43, 0xAA43}, {0xAA4C, 0xAA4D},
{0xAA7B, 0xAA7D}, {0xAAB0, 0xAAB0}, {0xAAB2, 0xAAB4}, {0xAAB7, 0xAAB8}, {0xAABE, 0xAABF}, {0xAAC1, 0xAAC1}, {0xAAEB, 0xAAEF}, {0xAAF5, 0xAAF6}, {0xABE3, 0xABEA}, {0xABEC, 0xABED}, {0xFB1E, 0xFB1E},
{0xFE00, 0xFE0F}, {0xFE20, 0xFE2F}, {0x101FD, 0x101FD}, {0x102E0, 0x102E0}, {0x10376, 0x1037A}, {0x10A01, 0x10A03}, {0x10A05, 0x10A06}, {0x10A0C, 0x10A0F}, {0x10A38, 0x10A3A}, {0x10A3F, 0x10A3F},
{0x10AE5, 0x10AE6}, {0x10D24, 0x10D27}, {0x10EAB, 0x10EAC}, {0x10F46, 0x10F50}, {0x11000, 0x11002}, {0x11038, 0x11046}, {0x1107F, 0x11082}, {0x110B0, 0x110BA}, {0x11100, 0x11102}, {0x11127, 0x11134},
{0x11145, 0x11146}, {0x11173, 0x11173}, {0x11180, 0x11182}, {0x111B3, 0x111C0}, {0x111C9, 0x111CC}, {0x111CE, 0x111CF}, {0x1122C, 0x11237}, {0x1123E, 0x1123E}, {0x112DF, 0x112EA}, {0x11300, 0x11303},
{0x1133B, 0x1133C}, {0x1133E, 0x11344}, {0x11347, 0x11348}, {0x1134B, 0x1134D}, {0x11357, 0x11357}, {0x11362, 0x11363}, {0x11366, 0x1136C}, {0x11370, 0x11374}, {0x11435, 0x11446}, {0x1145E, 0x1145E},
{0x114B0, 0x114C3}, {0x115AF, 0x115B5}, {0x115B8, 0x115C0}, {0x115DC, 0x115DD}, {0x11630, 0x11640}, {0x116AB, 0x116B7}, {0x1171D, 0x1172B}, {0x1182C, 0x1183A}, {0x11930, 0x11935}, {0x11937, 0x11938},
{0x1193B, 0x1193E}, {0x11940, 0x11940}, {0x11942, 0x11943}, {0x119D1, 0x119D7}, {0x119DA, 0x119E0}, {0x119E4, 0x119E4}, {0x11A01, 0x11A0A}, {0x11A33, 0x11A39}, {0x11A3B, 0x11A3E}, {0x11A47, 0x11A47},
{0x11A51, 0x11A5B}, {0x11A8A, 0x11A99}, {0x11C2F, 0x11C36}, {0x11C38, 0x11C3F}, {0x11C92, 0x11CA7}, {0x11CA9, 0x11CB6}, {0x11D31, 0x11D36}, {0x11D3A, 0x11D3A}, {0x11D3C, 0x11D3D}, {0x11D3F, 0x11D45},
{0x11D47, 0x11D47}, {0x11D8A, 0x11D8E}, {0x11D90, 0x11D91}, {0x11D93, 0x11D97}, {0x11EF3, 0x11EF6}, {0x16AF0, 0x16AF4}, {0x16B30, 0x16B36}, {0x16F4F, 0x16F4F}, {0x16F51, 0x16F87}, {0x16F8F, 0x16F92},
{0x16FE4, 0x16FE4}, {0x16FF0, 0x16FF1}, {0x1BC9D, 0x1BC9E}, {0x1D165, 0x1D169}, {0x1D16D, 0x1D172}, {0x1D17B, 0x1D182}, {0x1D185, 0x1D18B}, {0x1D1AA, 0x1D1AD}, {0x1D242, 0x1D244}, {0x1DA00, 0x1DA36},
{0x1DA3B, 0x1DA6C}, {0x1DA75, 0x1DA75}, {0x1DA84, 0x1DA84}, {0x1DA9B, 0x1DA9F}, {0x1DAA1, 0x1DAAF}, {0x1E000, 0x1E006}, {0x1E008, 0x1E018}, {0x1E01B, 0x1E021}, {0x1E023, 0x1E024}, {0x1E026, 0x1E02A},
{0x1E130, 0x1E136}, {0x1E2EC, 0x1E2EF}, {0x1E8D0, 0x1E8D6}, {0x1E944, 0x1E94A}, {0xE0100, 0xE01EF},
};
static const std::vector<std::pair<uint32_t, uint32_t>> punctuation_ranges = { codepoint_flags unicode_cpt_flags(const uint32_t cp);
{0x21, 0x23}, {0x25, 0x2A}, {0x2C, 0x2F}, {0x3A, 0x3B}, {0x3F, 0x40}, {0x5B, 0x5D}, {0x5F, 0x5F}, {0x7B, 0x7B}, {0x7D, 0x7D}, {0xA1, 0xA1}, {0xA7, 0xA7}, {0xAB, 0xAB}, {0xB6, 0xB7}, {0xBB, 0xBB}, codepoint_flags unicode_cpt_flags(const std::string & utf8);
{0xBF, 0xBF}, {0x37E, 0x37E}, {0x387, 0x387}, {0x55A, 0x55F}, {0x589, 0x58A}, {0x5BE, 0x5BE}, {0x5C0, 0x5C0}, {0x5C3, 0x5C3}, {0x5C6, 0x5C6}, {0x5F3, 0x5F4}, {0x609, 0x60A}, {0x60C, 0x60D},
{0x61B, 0x61B}, {0x61E, 0x61F}, {0x66A, 0x66D}, {0x6D4, 0x6D4}, {0x700, 0x70D}, {0x7F7, 0x7F9}, {0x830, 0x83E}, {0x85E, 0x85E}, {0x964, 0x965}, {0x970, 0x970}, {0x9FD, 0x9FD}, {0xA76, 0xA76},
{0xAF0, 0xAF0}, {0xC77, 0xC77}, {0xC84, 0xC84}, {0xDF4, 0xDF4}, {0xE4F, 0xE4F}, {0xE5A, 0xE5B}, {0xF04, 0xF12}, {0xF14, 0xF14}, {0xF3A, 0xF3D}, {0xF85, 0xF85}, {0xFD0, 0xFD4}, {0xFD9, 0xFDA},
{0x104A, 0x104F}, {0x10FB, 0x10FB}, {0x1360, 0x1368}, {0x1400, 0x1400}, {0x166E, 0x166E}, {0x169B, 0x169C}, {0x16EB, 0x16ED}, {0x1735, 0x1736}, {0x17D4, 0x17D6}, {0x17D8, 0x17DA}, {0x1800, 0x180A},
{0x1944, 0x1945}, {0x1A1E, 0x1A1F}, {0x1AA0, 0x1AA6}, {0x1AA8, 0x1AAD}, {0x1B5A, 0x1B60}, {0x1BFC, 0x1BFF}, {0x1C3B, 0x1C3F}, {0x1C7E, 0x1C7F}, {0x1CC0, 0x1CC7}, {0x1CD3, 0x1CD3}, {0x2010, 0x2027},
{0x2030, 0x2043}, {0x2045, 0x2051}, {0x2053, 0x205E}, {0x207D, 0x207E}, {0x208D, 0x208E}, {0x2308, 0x230B}, {0x2329, 0x232A}, {0x2768, 0x2775}, {0x27C5, 0x27C6}, {0x27E6, 0x27EF}, {0x2983, 0x2998},
{0x29D8, 0x29DB}, {0x29FC, 0x29FD}, {0x2CF9, 0x2CFC}, {0x2CFE, 0x2CFF}, {0x2D70, 0x2D70}, {0x2E00, 0x2E2E}, {0x2E30, 0x2E4F}, {0x2E52, 0x2E52}, {0x3001, 0x3003}, {0x3008, 0x3011}, {0x3014, 0x301F},
{0x3030, 0x3030}, {0x303D, 0x303D}, {0x30A0, 0x30A0}, {0x30FB, 0x30FB}, {0xA4FE, 0xA4FF}, {0xA60D, 0xA60F}, {0xA673, 0xA673}, {0xA67E, 0xA67E}, {0xA6F2, 0xA6F7}, {0xA874, 0xA877}, {0xA8CE, 0xA8CF},
{0xA8F8, 0xA8FA}, {0xA8FC, 0xA8FC}, {0xA92E, 0xA92F}, {0xA95F, 0xA95F}, {0xA9C1, 0xA9CD}, {0xA9DE, 0xA9DF}, {0xAA5C, 0xAA5F}, {0xAADE, 0xAADF}, {0xAAF0, 0xAAF1}, {0xABEB, 0xABEB}, {0xFD3E, 0xFD3F},
{0xFE10, 0xFE19}, {0xFE30, 0xFE52}, {0xFE54, 0xFE61}, {0xFE63, 0xFE63}, {0xFE68, 0xFE68}, {0xFE6A, 0xFE6B}, {0xFF01, 0xFF03}, {0xFF05, 0xFF0A}, {0xFF0C, 0xFF0F}, {0xFF1A, 0xFF1B}, {0xFF1F, 0xFF20},
{0xFF3B, 0xFF3D}, {0xFF3F, 0xFF3F}, {0xFF5B, 0xFF5B}, {0xFF5D, 0xFF5D}, {0xFF5F, 0xFF65}, {0x10100, 0x10102}, {0x1039F, 0x1039F}, {0x103D0, 0x103D0}, {0x1056F, 0x1056F}, {0x10857, 0x10857},
{0x1091F, 0x1091F}, {0x1093F, 0x1093F}, {0x10A50, 0x10A58}, {0x10A7F, 0x10A7F}, {0x10AF0, 0x10AF6}, {0x10B39, 0x10B3F}, {0x10B99, 0x10B9C}, {0x10EAD, 0x10EAD}, {0x10F55, 0x10F59}, {0x11047, 0x1104D},
{0x110BB, 0x110BC}, {0x110BE, 0x110C1}, {0x11140, 0x11143}, {0x11174, 0x11175}, {0x111C5, 0x111C8}, {0x111CD, 0x111CD}, {0x111DB, 0x111DB}, {0x111DD, 0x111DF}, {0x11238, 0x1123D}, {0x112A9, 0x112A9},
{0x1144B, 0x1144F}, {0x1145A, 0x1145B}, {0x1145D, 0x1145D}, {0x114C6, 0x114C6}, {0x115C1, 0x115D7}, {0x11641, 0x11643}, {0x11660, 0x1166C}, {0x1173C, 0x1173E}, {0x1183B, 0x1183B}, {0x11944, 0x11946},
{0x119E2, 0x119E2}, {0x11A3F, 0x11A46}, {0x11A9A, 0x11A9C}, {0x11A9E, 0x11AA2}, {0x11C41, 0x11C45}, {0x11C70, 0x11C71}, {0x11EF7, 0x11EF8}, {0x11FFF, 0x11FFF}, {0x12470, 0x12474}, {0x16A6E, 0x16A6F},
{0x16AF5, 0x16AF5}, {0x16B37, 0x16B3B}, {0x16B44, 0x16B44}, {0x16E97, 0x16E9A}, {0x16FE2, 0x16FE2}, {0x1BC9F, 0x1BC9F}, {0x1DA87, 0x1DA8B}, {0x1E95E, 0x1E95F},
};
static const std::vector<std::pair<uint32_t, uint32_t>> symbol_ranges = { std::string unicode_byte_to_utf8(uint8_t byte);
{0x24, 0x24}, {0x2B, 0x2B}, {0x3C, 0x3E}, {0x5E, 0x5E}, {0x60, 0x60}, {0x7C, 0x7C}, {0x7E, 0x7E}, {0xA2, 0xA6}, {0xA8, 0xA9}, {0xAC, 0xAC}, {0xAE, 0xB1}, {0xB4, 0xB4}, {0xB8, 0xB8}, {0xD7, 0xD7}, uint8_t unicode_utf8_to_byte(const std::string & utf8);
{0xF7, 0xF7}, {0x2C2, 0x2C5}, {0x2D2, 0x2DF}, {0x2E5, 0x2EB}, {0x2ED, 0x2ED}, {0x2EF, 0x2FF}, {0x375, 0x375}, {0x384, 0x385}, {0x3F6, 0x3F6}, {0x482, 0x482}, {0x58D, 0x58F}, {0x606, 0x608},
{0x60B, 0x60B}, {0x60E, 0x60F}, {0x6DE, 0x6DE}, {0x6E9, 0x6E9}, {0x6FD, 0x6FE}, {0x7F6, 0x7F6}, {0x7FE, 0x7FF}, {0x9F2, 0x9F3}, {0x9FA, 0x9FB}, {0xAF1, 0xAF1}, {0xB70, 0xB70}, {0xBF3, 0xBFA},
{0xC7F, 0xC7F}, {0xD4F, 0xD4F}, {0xD79, 0xD79}, {0xE3F, 0xE3F}, {0xF01, 0xF03}, {0xF13, 0xF13}, {0xF15, 0xF17}, {0xF1A, 0xF1F}, {0xF34, 0xF34}, {0xF36, 0xF36}, {0xF38, 0xF38}, {0xFBE, 0xFC5},
{0xFC7, 0xFCC}, {0xFCE, 0xFCF}, {0xFD5, 0xFD8}, {0x109E, 0x109F}, {0x1390, 0x1399}, {0x166D, 0x166D}, {0x17DB, 0x17DB}, {0x1940, 0x1940}, {0x19DE, 0x19FF}, {0x1B61, 0x1B6A}, {0x1B74, 0x1B7C},
{0x1FBD, 0x1FBD}, {0x1FBF, 0x1FC1}, {0x1FCD, 0x1FCF}, {0x1FDD, 0x1FDF}, {0x1FED, 0x1FEF}, {0x1FFD, 0x1FFE}, {0x2044, 0x2044}, {0x2052, 0x2052}, {0x207A, 0x207C}, {0x208A, 0x208C}, {0x20A0, 0x20BF},
{0x2100, 0x2101}, {0x2103, 0x2106}, {0x2108, 0x2109}, {0x2114, 0x2114}, {0x2116, 0x2118}, {0x211E, 0x2123}, {0x2125, 0x2125}, {0x2127, 0x2127}, {0x2129, 0x2129}, {0x212E, 0x212E}, {0x213A, 0x213B},
{0x2140, 0x2144}, {0x214A, 0x214D}, {0x214F, 0x214F}, {0x218A, 0x218B}, {0x2190, 0x2307}, {0x230C, 0x2328}, {0x232B, 0x2426}, {0x2440, 0x244A}, {0x249C, 0x24E9}, {0x2500, 0x2767}, {0x2794, 0x27C4},
{0x27C7, 0x27E5}, {0x27F0, 0x2982}, {0x2999, 0x29D7}, {0x29DC, 0x29FB}, {0x29FE, 0x2B73}, {0x2B76, 0x2B95}, {0x2B97, 0x2BFF}, {0x2CE5, 0x2CEA}, {0x2E50, 0x2E51}, {0x2E80, 0x2E99}, {0x2E9B, 0x2EF3},
{0x2F00, 0x2FD5}, {0x2FF0, 0x2FFB}, {0x3004, 0x3004}, {0x3012, 0x3013}, {0x3020, 0x3020}, {0x3036, 0x3037}, {0x303E, 0x303F}, {0x309B, 0x309C}, {0x3190, 0x3191}, {0x3196, 0x319F}, {0x31C0, 0x31E3},
{0x3200, 0x321E}, {0x322A, 0x3247}, {0x3250, 0x3250}, {0x3260, 0x327F}, {0x328A, 0x32B0}, {0x32C0, 0x33FF}, {0x4DC0, 0x4DFF}, {0xA490, 0xA4C6}, {0xA700, 0xA716}, {0xA720, 0xA721}, {0xA789, 0xA78A},
{0xA828, 0xA82B}, {0xA836, 0xA839}, {0xAA77, 0xAA79}, {0xAB5B, 0xAB5B}, {0xAB6A, 0xAB6B}, {0xFB29, 0xFB29}, {0xFBB2, 0xFBC1}, {0xFDFC, 0xFDFD}, {0xFE62, 0xFE62}, {0xFE64, 0xFE66}, {0xFE69, 0xFE69},
{0xFF04, 0xFF04}, {0xFF0B, 0xFF0B}, {0xFF1C, 0xFF1E}, {0xFF3E, 0xFF3E}, {0xFF40, 0xFF40}, {0xFF5C, 0xFF5C}, {0xFF5E, 0xFF5E}, {0xFFE0, 0xFFE6}, {0xFFE8, 0xFFEE}, {0xFFFC, 0xFFFD}, {0x10137, 0x1013F},
{0x10179, 0x10189}, {0x1018C, 0x1018E}, {0x10190, 0x1019C}, {0x101A0, 0x101A0}, {0x101D0, 0x101FC}, {0x10877, 0x10878}, {0x10AC8, 0x10AC8}, {0x1173F, 0x1173F}, {0x11FD5, 0x11FF1}, {0x16B3C, 0x16B3F},
{0x16B45, 0x16B45}, {0x1BC9C, 0x1BC9C}, {0x1D000, 0x1D0F5}, {0x1D100, 0x1D126}, {0x1D129, 0x1D164}, {0x1D16A, 0x1D16C}, {0x1D183, 0x1D184}, {0x1D18C, 0x1D1A9}, {0x1D1AE, 0x1D1E8}, {0x1D200, 0x1D241},
{0x1D245, 0x1D245}, {0x1D300, 0x1D356}, {0x1D6C1, 0x1D6C1}, {0x1D6DB, 0x1D6DB}, {0x1D6FB, 0x1D6FB}, {0x1D715, 0x1D715}, {0x1D735, 0x1D735}, {0x1D74F, 0x1D74F}, {0x1D76F, 0x1D76F}, {0x1D789, 0x1D789},
{0x1D7A9, 0x1D7A9}, {0x1D7C3, 0x1D7C3}, {0x1D800, 0x1D9FF}, {0x1DA37, 0x1DA3A}, {0x1DA6D, 0x1DA74}, {0x1DA76, 0x1DA83}, {0x1DA85, 0x1DA86}, {0x1E14F, 0x1E14F}, {0x1E2FF, 0x1E2FF}, {0x1ECAC, 0x1ECAC},
{0x1ECB0, 0x1ECB0}, {0x1ED2E, 0x1ED2E}, {0x1EEF0, 0x1EEF1}, {0x1F000, 0x1F02B}, {0x1F030, 0x1F093}, {0x1F0A0, 0x1F0AE}, {0x1F0B1, 0x1F0BF}, {0x1F0C1, 0x1F0CF}, {0x1F0D1, 0x1F0F5}, {0x1F10D, 0x1F1AD},
{0x1F1E6, 0x1F202}, {0x1F210, 0x1F23B}, {0x1F240, 0x1F248}, {0x1F250, 0x1F251}, {0x1F260, 0x1F265}, {0x1F300, 0x1F6D7}, {0x1F6E0, 0x1F6EC}, {0x1F6F0, 0x1F6FC}, {0x1F700, 0x1F773}, {0x1F780, 0x1F7D8},
{0x1F7E0, 0x1F7EB}, {0x1F800, 0x1F80B}, {0x1F810, 0x1F847}, {0x1F850, 0x1F859}, {0x1F860, 0x1F887}, {0x1F890, 0x1F8AD}, {0x1F8B0, 0x1F8B1}, {0x1F900, 0x1F978}, {0x1F97A, 0x1F9CB}, {0x1F9CD, 0x1FA53},
{0x1FA60, 0x1FA6D}, {0x1FA70, 0x1FA74}, {0x1FA78, 0x1FA7A}, {0x1FA80, 0x1FA86}, {0x1FA90, 0x1FAA8}, {0x1FAB0, 0x1FAB6}, {0x1FAC0, 0x1FAC2}, {0x1FAD0, 0x1FAD6}, {0x1FB00, 0x1FB92}, {0x1FB94, 0x1FBCA},
};
static const std::vector<std::pair<uint32_t, uint32_t>> control_ranges = { uint32_t unicode_tolower(uint32_t cp);
{0x0, 0x8}, {0xE, 0x1B}, {0x7F, 0x84}, {0x86, 0x9F}, {0xAD, 0xAD}, {0x378, 0x379}, {0x380, 0x383}, {0x38B, 0x38B}, {0x38D, 0x38D}, {0x3A2, 0x3A2}, {0x530, 0x530}, {0x557, 0x558}, {0x58B, 0x58C},
{0x590, 0x590}, {0x5C8, 0x5CF}, {0x5EB, 0x5EE}, {0x5F5, 0x605}, {0x61C, 0x61D}, {0x6DD, 0x6DD}, {0x70E, 0x70F}, {0x74B, 0x74C}, {0x7B2, 0x7BF}, {0x7FB, 0x7FC}, {0x82E, 0x82F}, {0x83F, 0x83F},
{0x85C, 0x85D}, {0x85F, 0x85F}, {0x86B, 0x89F}, {0x8B5, 0x8B5}, {0x8C8, 0x8D2}, {0x8E2, 0x8E2}, {0x984, 0x984}, {0x98D, 0x98E}, {0x991, 0x992}, {0x9A9, 0x9A9}, {0x9B1, 0x9B1}, {0x9B3, 0x9B5},
{0x9BA, 0x9BB}, {0x9C5, 0x9C6}, {0x9C9, 0x9CA}, {0x9CF, 0x9D6}, {0x9D8, 0x9DB}, {0x9DE, 0x9DE}, {0x9E4, 0x9E5}, {0x9FF, 0xA00}, {0xA04, 0xA04}, {0xA0B, 0xA0E}, {0xA11, 0xA12}, {0xA29, 0xA29},
{0xA31, 0xA31}, {0xA34, 0xA34}, {0xA37, 0xA37}, {0xA3A, 0xA3B}, {0xA3D, 0xA3D}, {0xA43, 0xA46}, {0xA49, 0xA4A}, {0xA4E, 0xA50}, {0xA52, 0xA58}, {0xA5D, 0xA5D}, {0xA5F, 0xA65}, {0xA77, 0xA80},
{0xA84, 0xA84}, {0xA8E, 0xA8E}, {0xA92, 0xA92}, {0xAA9, 0xAA9}, {0xAB1, 0xAB1}, {0xAB4, 0xAB4}, {0xABA, 0xABB}, {0xAC6, 0xAC6}, {0xACA, 0xACA}, {0xACE, 0xACF}, {0xAD1, 0xADF}, {0xAE4, 0xAE5},
{0xAF2, 0xAF8}, {0xB00, 0xB00}, {0xB04, 0xB04}, {0xB0D, 0xB0E}, {0xB11, 0xB12}, {0xB29, 0xB29}, {0xB31, 0xB31}, {0xB34, 0xB34}, {0xB3A, 0xB3B}, {0xB45, 0xB46}, {0xB49, 0xB4A}, {0xB4E, 0xB54},
{0xB58, 0xB5B}, {0xB5E, 0xB5E}, {0xB64, 0xB65}, {0xB78, 0xB81}, {0xB84, 0xB84}, {0xB8B, 0xB8D}, {0xB91, 0xB91}, {0xB96, 0xB98}, {0xB9B, 0xB9B}, {0xB9D, 0xB9D}, {0xBA0, 0xBA2}, {0xBA5, 0xBA7},
{0xBAB, 0xBAD}, {0xBBA, 0xBBD}, {0xBC3, 0xBC5}, {0xBC9, 0xBC9}, {0xBCE, 0xBCF}, {0xBD1, 0xBD6}, {0xBD8, 0xBE5}, {0xBFB, 0xBFF}, {0xC0D, 0xC0D}, {0xC11, 0xC11}, {0xC29, 0xC29}, {0xC3A, 0xC3C},
{0xC45, 0xC45}, {0xC49, 0xC49}, {0xC4E, 0xC54}, {0xC57, 0xC57}, {0xC5B, 0xC5F}, {0xC64, 0xC65}, {0xC70, 0xC76}, {0xC8D, 0xC8D}, {0xC91, 0xC91}, {0xCA9, 0xCA9}, {0xCB4, 0xCB4}, {0xCBA, 0xCBB},
{0xCC5, 0xCC5}, {0xCC9, 0xCC9}, {0xCCE, 0xCD4}, {0xCD7, 0xCDD}, {0xCDF, 0xCDF}, {0xCE4, 0xCE5}, {0xCF0, 0xCF0}, {0xCF3, 0xCFF}, {0xD0D, 0xD0D}, {0xD11, 0xD11}, {0xD45, 0xD45}, {0xD49, 0xD49},
{0xD50, 0xD53}, {0xD64, 0xD65}, {0xD80, 0xD80}, {0xD84, 0xD84}, {0xD97, 0xD99}, {0xDB2, 0xDB2}, {0xDBC, 0xDBC}, {0xDBE, 0xDBF}, {0xDC7, 0xDC9}, {0xDCB, 0xDCE}, {0xDD5, 0xDD5}, {0xDD7, 0xDD7},
{0xDE0, 0xDE5}, {0xDF0, 0xDF1}, {0xDF5, 0xE00}, {0xE3B, 0xE3E}, {0xE5C, 0xE80}, {0xE83, 0xE83}, {0xE85, 0xE85}, {0xE8B, 0xE8B}, {0xEA4, 0xEA4}, {0xEA6, 0xEA6}, {0xEBE, 0xEBF}, {0xEC5, 0xEC5},
{0xEC7, 0xEC7}, {0xECE, 0xECF}, {0xEDA, 0xEDB}, {0xEE0, 0xEFF}, {0xF48, 0xF48}, {0xF6D, 0xF70}, {0xF98, 0xF98}, {0xFBD, 0xFBD}, {0xFCD, 0xFCD}, {0xFDB, 0xFFF}, {0x10C6, 0x10C6}, {0x10C8, 0x10CC},
{0x10CE, 0x10CF}, {0x1249, 0x1249}, {0x124E, 0x124F}, {0x1257, 0x1257}, {0x1259, 0x1259}, {0x125E, 0x125F}, {0x1289, 0x1289}, {0x128E, 0x128F}, {0x12B1, 0x12B1}, {0x12B6, 0x12B7}, {0x12BF, 0x12BF},
{0x12C1, 0x12C1}, {0x12C6, 0x12C7}, {0x12D7, 0x12D7}, {0x1311, 0x1311}, {0x1316, 0x1317}, {0x135B, 0x135C}, {0x137D, 0x137F}, {0x139A, 0x139F}, {0x13F6, 0x13F7}, {0x13FE, 0x13FF}, {0x169D, 0x169F},
{0x16F9, 0x16FF}, {0x170D, 0x170D}, {0x1715, 0x171F}, {0x1737, 0x173F}, {0x1754, 0x175F}, {0x176D, 0x176D}, {0x1771, 0x1771}, {0x1774, 0x177F}, {0x17DE, 0x17DF}, {0x17EA, 0x17EF}, {0x17FA, 0x17FF},
{0x180E, 0x180F}, {0x181A, 0x181F}, {0x1879, 0x187F}, {0x18AB, 0x18AF}, {0x18F6, 0x18FF}, {0x191F, 0x191F}, {0x192C, 0x192F}, {0x193C, 0x193F}, {0x1941, 0x1943}, {0x196E, 0x196F}, {0x1975, 0x197F},
{0x19AC, 0x19AF}, {0x19CA, 0x19CF}, {0x19DB, 0x19DD}, {0x1A1C, 0x1A1D}, {0x1A5F, 0x1A5F}, {0x1A7D, 0x1A7E}, {0x1A8A, 0x1A8F}, {0x1A9A, 0x1A9F}, {0x1AAE, 0x1AAF}, {0x1AC1, 0x1AFF}, {0x1B4C, 0x1B4F},
{0x1B7D, 0x1B7F}, {0x1BF4, 0x1BFB}, {0x1C38, 0x1C3A}, {0x1C4A, 0x1C4C}, {0x1C89, 0x1C8F}, {0x1CBB, 0x1CBC}, {0x1CC8, 0x1CCF}, {0x1CFB, 0x1CFF}, {0x1DFA, 0x1DFA}, {0x1F16, 0x1F17}, {0x1F1E, 0x1F1F},
{0x1F46, 0x1F47}, {0x1F4E, 0x1F4F}, {0x1F58, 0x1F58}, {0x1F5A, 0x1F5A}, {0x1F5C, 0x1F5C}, {0x1F5E, 0x1F5E}, {0x1F7E, 0x1F7F}, {0x1FB5, 0x1FB5}, {0x1FC5, 0x1FC5}, {0x1FD4, 0x1FD5}, {0x1FDC, 0x1FDC},
{0x1FF0, 0x1FF1}, {0x1FF5, 0x1FF5}, {0x1FFF, 0x1FFF}, {0x200B, 0x200F}, {0x202A, 0x202E}, {0x2060, 0x206F}, {0x2072, 0x2073}, {0x208F, 0x208F}, {0x209D, 0x209F}, {0x20C0, 0x20CF}, {0x20F1, 0x20FF},
{0x218C, 0x218F}, {0x2427, 0x243F}, {0x244B, 0x245F}, {0x2B74, 0x2B75}, {0x2B96, 0x2B96}, {0x2C2F, 0x2C2F}, {0x2C5F, 0x2C5F}, {0x2CF4, 0x2CF8}, {0x2D26, 0x2D26}, {0x2D28, 0x2D2C}, {0x2D2E, 0x2D2F},
{0x2D68, 0x2D6E}, {0x2D71, 0x2D7E}, {0x2D97, 0x2D9F}, {0x2DA7, 0x2DA7}, {0x2DAF, 0x2DAF}, {0x2DB7, 0x2DB7}, {0x2DBF, 0x2DBF}, {0x2DC7, 0x2DC7}, {0x2DCF, 0x2DCF}, {0x2DD7, 0x2DD7}, {0x2DDF, 0x2DDF},
{0x2E53, 0x2E7F}, {0x2E9A, 0x2E9A}, {0x2EF4, 0x2EFF}, {0x2FD6, 0x2FEF}, {0x2FFC, 0x2FFF}, {0x3040, 0x3040}, {0x3097, 0x3098}, {0x3100, 0x3104}, {0x3130, 0x3130}, {0x318F, 0x318F}, {0x31E4, 0x31EF},
{0x321F, 0x321F}, {0x9FFD, 0x9FFF}, {0xA48D, 0xA48F}, {0xA4C7, 0xA4CF}, {0xA62C, 0xA63F}, {0xA6F8, 0xA6FF}, {0xA7C0, 0xA7C1}, {0xA7CB, 0xA7F4}, {0xA82D, 0xA82F}, {0xA83A, 0xA83F}, {0xA878, 0xA87F},
{0xA8C6, 0xA8CD}, {0xA8DA, 0xA8DF}, {0xA954, 0xA95E}, {0xA97D, 0xA97F}, {0xA9CE, 0xA9CE}, {0xA9DA, 0xA9DD}, {0xA9FF, 0xA9FF}, {0xAA37, 0xAA3F}, {0xAA4E, 0xAA4F}, {0xAA5A, 0xAA5B}, {0xAAC3, 0xAADA},
{0xAAF7, 0xAB00}, {0xAB07, 0xAB08}, {0xAB0F, 0xAB10}, {0xAB17, 0xAB1F}, {0xAB27, 0xAB27}, {0xAB2F, 0xAB2F}, {0xAB6C, 0xAB6F}, {0xABEE, 0xABEF}, {0xABFA, 0xABFF}, {0xD7A4, 0xD7AF}, {0xD7C7, 0xD7CA},
{0xD7FC, 0xF8FF}, {0xFA6E, 0xFA6F}, {0xFADA, 0xFAFF}, {0xFB07, 0xFB12}, {0xFB18, 0xFB1C}, {0xFB37, 0xFB37}, {0xFB3D, 0xFB3D}, {0xFB3F, 0xFB3F}, {0xFB42, 0xFB42}, {0xFB45, 0xFB45}, {0xFBC2, 0xFBD2},
{0xFD40, 0xFD4F}, {0xFD90, 0xFD91}, {0xFDC8, 0xFDEF}, {0xFDFE, 0xFDFF}, {0xFE1A, 0xFE1F}, {0xFE53, 0xFE53}, {0xFE67, 0xFE67}, {0xFE6C, 0xFE6F}, {0xFE75, 0xFE75}, {0xFEFD, 0xFF00}, {0xFFBF, 0xFFC1},
{0xFFC8, 0xFFC9}, {0xFFD0, 0xFFD1}, {0xFFD8, 0xFFD9}, {0xFFDD, 0xFFDF}, {0xFFE7, 0xFFE7}, {0xFFEF, 0xFFFB}, {0xFFFE, 0xFFFF}, {0x1000C, 0x1000C}, {0x10027, 0x10027}, {0x1003B, 0x1003B},
{0x1003E, 0x1003E}, {0x1004E, 0x1004F}, {0x1005E, 0x1007F}, {0x100FB, 0x100FF}, {0x10103, 0x10106}, {0x10134, 0x10136}, {0x1018F, 0x1018F}, {0x1019D, 0x1019F}, {0x101A1, 0x101CF}, {0x101FE, 0x1027F},
{0x1029D, 0x1029F}, {0x102D1, 0x102DF}, {0x102FC, 0x102FF}, {0x10324, 0x1032C}, {0x1034B, 0x1034F}, {0x1037B, 0x1037F}, {0x1039E, 0x1039E}, {0x103C4, 0x103C7}, {0x103D6, 0x103FF}, {0x1049E, 0x1049F},
{0x104AA, 0x104AF}, {0x104D4, 0x104D7}, {0x104FC, 0x104FF}, {0x10528, 0x1052F}, {0x10564, 0x1056E}, {0x10570, 0x105FF}, {0x10737, 0x1073F}, {0x10756, 0x1075F}, {0x10768, 0x107FF}, {0x10806, 0x10807},
{0x10809, 0x10809}, {0x10836, 0x10836}, {0x10839, 0x1083B}, {0x1083D, 0x1083E}, {0x10856, 0x10856}, {0x1089F, 0x108A6}, {0x108B0, 0x108DF}, {0x108F3, 0x108F3}, {0x108F6, 0x108FA}, {0x1091C, 0x1091E},
{0x1093A, 0x1093E}, {0x10940, 0x1097F}, {0x109B8, 0x109BB}, {0x109D0, 0x109D1}, {0x10A04, 0x10A04}, {0x10A07, 0x10A0B}, {0x10A14, 0x10A14}, {0x10A18, 0x10A18}, {0x10A36, 0x10A37}, {0x10A3B, 0x10A3E},
{0x10A49, 0x10A4F}, {0x10A59, 0x10A5F}, {0x10AA0, 0x10ABF}, {0x10AE7, 0x10AEA}, {0x10AF7, 0x10AFF}, {0x10B36, 0x10B38}, {0x10B56, 0x10B57}, {0x10B73, 0x10B77}, {0x10B92, 0x10B98}, {0x10B9D, 0x10BA8},
{0x10BB0, 0x10BFF}, {0x10C49, 0x10C7F}, {0x10CB3, 0x10CBF}, {0x10CF3, 0x10CF9}, {0x10D28, 0x10D2F}, {0x10D3A, 0x10E5F}, {0x10E7F, 0x10E7F}, {0x10EAA, 0x10EAA}, {0x10EAE, 0x10EAF}, {0x10EB2, 0x10EFF},
{0x10F28, 0x10F2F}, {0x10F5A, 0x10FAF}, {0x10FCC, 0x10FDF}, {0x10FF7, 0x10FFF}, {0x1104E, 0x11051}, {0x11070, 0x1107E}, {0x110BD, 0x110BD}, {0x110C2, 0x110CF}, {0x110E9, 0x110EF}, {0x110FA, 0x110FF},
{0x11135, 0x11135}, {0x11148, 0x1114F}, {0x11177, 0x1117F}, {0x111E0, 0x111E0}, {0x111F5, 0x111FF}, {0x11212, 0x11212}, {0x1123F, 0x1127F}, {0x11287, 0x11287}, {0x11289, 0x11289}, {0x1128E, 0x1128E},
{0x1129E, 0x1129E}, {0x112AA, 0x112AF}, {0x112EB, 0x112EF}, {0x112FA, 0x112FF}, {0x11304, 0x11304}, {0x1130D, 0x1130E}, {0x11311, 0x11312}, {0x11329, 0x11329}, {0x11331, 0x11331}, {0x11334, 0x11334},
{0x1133A, 0x1133A}, {0x11345, 0x11346}, {0x11349, 0x1134A}, {0x1134E, 0x1134F}, {0x11351, 0x11356}, {0x11358, 0x1135C}, {0x11364, 0x11365}, {0x1136D, 0x1136F}, {0x11375, 0x113FF}, {0x1145C, 0x1145C},
{0x11462, 0x1147F}, {0x114C8, 0x114CF}, {0x114DA, 0x1157F}, {0x115B6, 0x115B7}, {0x115DE, 0x115FF}, {0x11645, 0x1164F}, {0x1165A, 0x1165F}, {0x1166D, 0x1167F}, {0x116B9, 0x116BF}, {0x116CA, 0x116FF},
{0x1171B, 0x1171C}, {0x1172C, 0x1172F}, {0x11740, 0x117FF}, {0x1183C, 0x1189F}, {0x118F3, 0x118FE}, {0x11907, 0x11908}, {0x1190A, 0x1190B}, {0x11914, 0x11914}, {0x11917, 0x11917}, {0x11936, 0x11936},
{0x11939, 0x1193A}, {0x11947, 0x1194F}, {0x1195A, 0x1199F}, {0x119A8, 0x119A9}, {0x119D8, 0x119D9}, {0x119E5, 0x119FF}, {0x11A48, 0x11A4F}, {0x11AA3, 0x11ABF}, {0x11AF9, 0x11BFF}, {0x11C09, 0x11C09},
{0x11C37, 0x11C37}, {0x11C46, 0x11C4F}, {0x11C6D, 0x11C6F}, {0x11C90, 0x11C91}, {0x11CA8, 0x11CA8}, {0x11CB7, 0x11CFF}, {0x11D07, 0x11D07}, {0x11D0A, 0x11D0A}, {0x11D37, 0x11D39}, {0x11D3B, 0x11D3B},
{0x11D3E, 0x11D3E}, {0x11D48, 0x11D4F}, {0x11D5A, 0x11D5F}, {0x11D66, 0x11D66}, {0x11D69, 0x11D69}, {0x11D8F, 0x11D8F}, {0x11D92, 0x11D92}, {0x11D99, 0x11D9F}, {0x11DAA, 0x11EDF}, {0x11EF9, 0x11FAF},
{0x11FB1, 0x11FBF}, {0x11FF2, 0x11FFE}, {0x1239A, 0x123FF}, {0x1246F, 0x1246F}, {0x12475, 0x1247F}, {0x12544, 0x12FFF}, {0x1342F, 0x143FF}, {0x14647, 0x167FF}, {0x16A39, 0x16A3F}, {0x16A5F, 0x16A5F},
{0x16A6A, 0x16A6D}, {0x16A70, 0x16ACF}, {0x16AEE, 0x16AEF}, {0x16AF6, 0x16AFF}, {0x16B46, 0x16B4F}, {0x16B5A, 0x16B5A}, {0x16B62, 0x16B62}, {0x16B78, 0x16B7C}, {0x16B90, 0x16E3F}, {0x16E9B, 0x16EFF},
{0x16F4B, 0x16F4E}, {0x16F88, 0x16F8E}, {0x16FA0, 0x16FDF}, {0x16FE5, 0x16FEF}, {0x16FF2, 0x16FFF}, {0x187F8, 0x187FF}, {0x18CD6, 0x18CFF}, {0x18D09, 0x1AFFF}, {0x1B11F, 0x1B14F}, {0x1B153, 0x1B163},
{0x1B168, 0x1B16F}, {0x1B2FC, 0x1BBFF}, {0x1BC6B, 0x1BC6F}, {0x1BC7D, 0x1BC7F}, {0x1BC89, 0x1BC8F}, {0x1BC9A, 0x1BC9B}, {0x1BCA0, 0x1CFFF}, {0x1D0F6, 0x1D0FF}, {0x1D127, 0x1D128}, {0x1D173, 0x1D17A},
{0x1D1E9, 0x1D1FF}, {0x1D246, 0x1D2DF}, {0x1D2F4, 0x1D2FF}, {0x1D357, 0x1D35F}, {0x1D379, 0x1D3FF}, {0x1D455, 0x1D455}, {0x1D49D, 0x1D49D}, {0x1D4A0, 0x1D4A1}, {0x1D4A3, 0x1D4A4}, {0x1D4A7, 0x1D4A8},
{0x1D4AD, 0x1D4AD}, {0x1D4BA, 0x1D4BA}, {0x1D4BC, 0x1D4BC}, {0x1D4C4, 0x1D4C4}, {0x1D506, 0x1D506}, {0x1D50B, 0x1D50C}, {0x1D515, 0x1D515}, {0x1D51D, 0x1D51D}, {0x1D53A, 0x1D53A}, {0x1D53F, 0x1D53F},
{0x1D545, 0x1D545}, {0x1D547, 0x1D549}, {0x1D551, 0x1D551}, {0x1D6A6, 0x1D6A7}, {0x1D7CC, 0x1D7CD}, {0x1DA8C, 0x1DA9A}, {0x1DAA0, 0x1DAA0}, {0x1DAB0, 0x1DFFF}, {0x1E007, 0x1E007}, {0x1E019, 0x1E01A},
{0x1E022, 0x1E022}, {0x1E025, 0x1E025}, {0x1E02B, 0x1E0FF}, {0x1E12D, 0x1E12F}, {0x1E13E, 0x1E13F}, {0x1E14A, 0x1E14D}, {0x1E150, 0x1E2BF}, {0x1E2FA, 0x1E2FE}, {0x1E300, 0x1E7FF}, {0x1E8C5, 0x1E8C6},
{0x1E8D7, 0x1E8FF}, {0x1E94C, 0x1E94F}, {0x1E95A, 0x1E95D}, {0x1E960, 0x1EC70}, {0x1ECB5, 0x1ED00}, {0x1ED3E, 0x1EDFF}, {0x1EE04, 0x1EE04}, {0x1EE20, 0x1EE20}, {0x1EE23, 0x1EE23}, {0x1EE25, 0x1EE26},
{0x1EE28, 0x1EE28}, {0x1EE33, 0x1EE33}, {0x1EE38, 0x1EE38}, {0x1EE3A, 0x1EE3A}, {0x1EE3C, 0x1EE41}, {0x1EE43, 0x1EE46}, {0x1EE48, 0x1EE48}, {0x1EE4A, 0x1EE4A}, {0x1EE4C, 0x1EE4C}, {0x1EE50, 0x1EE50},
{0x1EE53, 0x1EE53}, {0x1EE55, 0x1EE56}, {0x1EE58, 0x1EE58}, {0x1EE5A, 0x1EE5A}, {0x1EE5C, 0x1EE5C}, {0x1EE5E, 0x1EE5E}, {0x1EE60, 0x1EE60}, {0x1EE63, 0x1EE63}, {0x1EE65, 0x1EE66}, {0x1EE6B, 0x1EE6B},
{0x1EE73, 0x1EE73}, {0x1EE78, 0x1EE78}, {0x1EE7D, 0x1EE7D}, {0x1EE7F, 0x1EE7F}, {0x1EE8A, 0x1EE8A}, {0x1EE9C, 0x1EEA0}, {0x1EEA4, 0x1EEA4}, {0x1EEAA, 0x1EEAA}, {0x1EEBC, 0x1EEEF}, {0x1EEF2, 0x1EFFF},
{0x1F02C, 0x1F02F}, {0x1F094, 0x1F09F}, {0x1F0AF, 0x1F0B0}, {0x1F0C0, 0x1F0C0}, {0x1F0D0, 0x1F0D0}, {0x1F0F6, 0x1F0FF}, {0x1F1AE, 0x1F1E5}, {0x1F203, 0x1F20F}, {0x1F23C, 0x1F23F}, {0x1F249, 0x1F24F},
{0x1F252, 0x1F25F}, {0x1F266, 0x1F2FF}, {0x1F6D8, 0x1F6DF}, {0x1F6ED, 0x1F6EF}, {0x1F6FD, 0x1F6FF}, {0x1F774, 0x1F77F}, {0x1F7D9, 0x1F7DF}, {0x1F7EC, 0x1F7FF}, {0x1F80C, 0x1F80F}, {0x1F848, 0x1F84F},
{0x1F85A, 0x1F85F}, {0x1F888, 0x1F88F}, {0x1F8AE, 0x1F8AF}, {0x1F8B2, 0x1F8FF}, {0x1F979, 0x1F979}, {0x1F9CC, 0x1F9CC}, {0x1FA54, 0x1FA5F}, {0x1FA6E, 0x1FA6F}, {0x1FA75, 0x1FA77}, {0x1FA7B, 0x1FA7F},
{0x1FA87, 0x1FA8F}, {0x1FAA9, 0x1FAAF}, {0x1FAB7, 0x1FABF}, {0x1FAC3, 0x1FACF}, {0x1FAD7, 0x1FAFF}, {0x1FB93, 0x1FB93}, {0x1FBCB, 0x1FBEF}, {0x1FBFA, 0x1FFFF}, {0x2A6DE, 0x2A6FF}, {0x2B735, 0x2B73F},
{0x2B81E, 0x2B81F}, {0x2CEA2, 0x2CEAF}, {0x2EBE1, 0x2F7FF}, {0x2FA1E, 0x2FFFF}, {0x3134B, 0xE00FF}, {0xE01F0, 0x10FFFF},
};
static std::string codepoint_to_utf8(uint32_t cp) {
std::string result;
if (/* 0x00 <= cp && */ cp <= 0x7f) {
result.push_back(cp);
}
else if (0x80 <= cp && cp <= 0x7ff) {
result.push_back(0xc0 | ((cp >> 6) & 0x1f));
result.push_back(0x80 | (cp & 0x3f));
}
else if (0x800 <= cp && cp <= 0xffff) {
result.push_back(0xe0 | ((cp >> 12) & 0x0f));
result.push_back(0x80 | ((cp >> 6) & 0x3f));
result.push_back(0x80 | (cp & 0x3f));
}
else if (0x10000 <= cp && cp <= 0x10ffff) {
result.push_back(0xf0 | ((cp >> 18) & 0x07));
result.push_back(0x80 | ((cp >> 12) & 0x3f));
result.push_back(0x80 | ((cp >> 6) & 0x3f));
result.push_back(0x80 | (cp & 0x3f));
}
else {
throw std::invalid_argument("invalid codepoint");
}
return result;
}
static std::string codepoints_to_utf8(const std::vector<uint32_t> & cps) {
std::string result;
for (size_t i = 0; i < cps.size(); ++i) {
result.append(codepoint_to_utf8(cps[i]));
}
return result;
}
static uint32_t codepoint_from_utf8(const std::string & utf8, size_t & offset) {
assert(offset < utf8.size());
if (!(utf8[offset + 0] & 0x80)) {
auto result = utf8[offset + 0];
offset += 1;
return result;
}
else if (!(utf8[offset + 0] & 0x40)) {
throw std::invalid_argument("invalid character");
}
else if (!(utf8[offset + 0] & 0x20)) {
if (offset + 1 >= utf8.size() || ! ((utf8[offset + 1] & 0xc0) == 0x80))
throw std::invalid_argument("invalid character");
auto result = ((utf8[offset + 0] & 0x1f) << 6) | (utf8[offset + 1] & 0x3f);
offset += 2;
return result;
}
else if (!(utf8[offset + 0] & 0x10)) {
if (offset + 2 >= utf8.size() || ! ((utf8[offset + 1] & 0xc0) == 0x80) || ! ((utf8[offset + 2] & 0xc0) == 0x80))
throw std::invalid_argument("invalid character");
auto result = ((utf8[offset + 0] & 0x0f) << 12) | ((utf8[offset + 1] & 0x3f) << 6) | (utf8[offset + 2] & 0x3f);
offset += 3;
return result;
}
else if (!(utf8[offset + 0] & 0x08)) {
if (offset + 3 >= utf8.size() || ! ((utf8[offset + 1] & 0xc0) == 0x80) || ! ((utf8[offset + 2] & 0xc0) == 0x80) || !((utf8[offset + 3] & 0xc0) == 0x80))
throw std::invalid_argument("invalid character");
auto result = ((utf8[offset + 0] & 0x07) << 18) | ((utf8[offset + 1] & 0x3f) << 12) | ((utf8[offset + 2] & 0x3f) << 6) | (utf8[offset + 3] & 0x3f);
offset += 4;
return result;
}
throw std::invalid_argument("invalid string");
}
static std::vector<uint32_t> codepoints_from_utf8(const std::string & utf8) {
std::vector<uint32_t> result;
size_t offset = 0;
while (offset < utf8.size()) {
result.push_back(codepoint_from_utf8(utf8, offset));
}
return result;
}
static std::vector<uint16_t> codepoint_to_utf16(uint32_t cp) {
std::vector<uint16_t> result;
if (/* 0x0000 <= cp && */ cp <= 0xffff) {
result.emplace_back(cp);
}
else if (0x10000 <= cp && cp <= 0x10ffff) {
result.emplace_back(0xd800 | ((cp - 0x10000) >> 10));
result.emplace_back(0xdc00 | ((cp - 0x10000) & 0x03ff));
}
else {
throw std::invalid_argument("invalid codepoint");
}
return result;
}
static std::vector<uint16_t> codepoints_to_utf16(const std::vector<uint32_t> & cps) {
std::vector<uint16_t> result;
for (size_t i = 0; i < cps.size(); ++i) {
auto temp = codepoint_to_utf16(cps[i]);
result.insert(result.end(), temp.begin(), temp.end());
}
return result;
}
static uint32_t codepoint_from_utf16(const std::vector<uint16_t> & utf16, size_t & offset) {
assert(offset < utf16.size());
if (((utf16[0] >> 10) << 10) != 0xd800) {
auto result = utf16[offset + 0];
offset += 1;
return result;
}
else {
if (offset + 1 >= utf16.size() || !((utf16[1] & 0xdc00) == 0xdc00))
throw std::invalid_argument("invalid character");
auto result = 0x10000 + (((utf16[0] & 0x03ff) << 10) | (utf16[1] & 0x03ff));
offset += 2;
return result;
}
throw std::invalid_argument("invalid string");
}
static std::vector<uint32_t> codepoints_from_utf16(const std::vector<uint16_t> & utf16) {
std::vector<uint32_t> result;
size_t offset = 0;
while (offset < utf16.size())
result.push_back(codepoint_from_utf16(utf16, offset));
return result;
}
#define CODEPOINT_TYPE_UNIDENTIFIED 0
#define CODEPOINT_TYPE_DIGIT 1
#define CODEPOINT_TYPE_LETTER 2
#define CODEPOINT_TYPE_WHITESPACE 3
#define CODEPOINT_TYPE_ACCENT_MARK 4
#define CODEPOINT_TYPE_PUNCTUATION 5
#define CODEPOINT_TYPE_SYMBOL 6
#define CODEPOINT_TYPE_CONTROL 7
static std::unordered_map<uint32_t, int> codepoint_type_map() {
std::unordered_map<uint32_t, int> codepoint_types;
for (auto p : digit_ranges) {
for(auto i = p.first; i <= p.second; ++ i)
codepoint_types[i] = CODEPOINT_TYPE_DIGIT;
}
for(auto p : letter_ranges) {
for(auto i = p.first; i <= p.second; ++ i)
codepoint_types[i] = CODEPOINT_TYPE_LETTER;
}
for(auto p : whitespace_ranges) {
for(auto i = p.first; i <= p.second; ++ i)
codepoint_types[i] = CODEPOINT_TYPE_WHITESPACE;
}
for(auto p : accent_mark_ranges) {
for(auto i = p.first; i <= p.second; ++ i)
codepoint_types[i] = CODEPOINT_TYPE_ACCENT_MARK;
}
for(auto p : punctuation_ranges) {
for(auto i = p.first; i <= p.second; ++ i)
codepoint_types[i] = CODEPOINT_TYPE_PUNCTUATION;
}
for (auto p : symbol_ranges) {
for (auto i = p.first; i <= p.second; ++i)
codepoint_types[i] = CODEPOINT_TYPE_SYMBOL;
}
for(auto p : control_ranges) {
for(auto i = p.first; i <= p.second; ++ i)
codepoint_types[i] = CODEPOINT_TYPE_CONTROL;
}
return codepoint_types;
}
static int codepoint_type(uint32_t cp) {
static std::unordered_map<uint32_t, int> codepoint_types = codepoint_type_map();
return codepoint_types[cp];
}
static int codepoint_type(const std::string & utf8) {
if (utf8.length() == 0)
return CODEPOINT_TYPE_UNIDENTIFIED;
size_t offset = 0;
return codepoint_type(codepoint_from_utf8(utf8, offset));
}
static std::unordered_map<uint8_t, std::string> bytes_to_unicode_map_bpe() {
std::unordered_map<uint8_t, std::string> map;
for (int ch = u'!'; ch <= u'~'; ++ch) {
assert(0 <= ch && ch < 256);
map[ch] = codepoint_to_utf8(ch);
}
for (int ch = u'¡'; ch <= u'¬'; ++ch) {
assert(0 <= ch && ch < 256);
map[ch] = codepoint_to_utf8(ch);
}
for (int ch = u'®'; ch <= u'ÿ'; ++ch) {
assert(0 <= ch && ch < 256);
map[ch] = codepoint_to_utf8(ch);
}
auto n = 0;
for (int ch = 0; ch < 256; ++ch) {
if (map.find(ch) == map.end()) {
map[ch] = codepoint_to_utf8(256 + n);
++n;
}
}
return map;
}
static std::string bytes_to_unicode_bpe(uint8_t byte) {
static std::unordered_map<uint8_t, std::string> map = bytes_to_unicode_map_bpe();
return map.at(byte);
}
static std::unordered_map<std::string, uint8_t> unicode_to_bytes_map_bpe() {
std::unordered_map<std::string, uint8_t> map;
for (int ch = u'!'; ch <= u'~'; ++ch) {
assert(0 <= ch && ch < 256);
map[codepoint_to_utf8(ch)] = ch;
}
for (int ch = u'¡'; ch <= u'¬'; ++ch) {
assert(0 <= ch && ch < 256);
map[codepoint_to_utf8(ch)] = ch;
}
for (int ch = u'®'; ch <= u'ÿ'; ++ch) {
assert(0 <= ch && ch < 256);
map[codepoint_to_utf8(ch)] = ch;
}
auto n = 0;
for (int ch = 0; ch < 256; ++ch) {
if (map.find(codepoint_to_utf8(ch)) == map.end()) {
map[codepoint_to_utf8(256 + n)] = ch;
++n;
}
}
return map;
}
static uint8_t unicode_to_bytes_bpe(const std::string & utf8) {
static std::unordered_map<std::string, uint8_t> map = unicode_to_bytes_map_bpe();
return map.at(utf8);
}
std::vector<std::string> unicode_regex_split(const std::string & text, const std::vector<std::string> & regex_exprs);

View File

@ -29,18 +29,6 @@ std::string g_status_forced = "";
std::vector<float> g_pcmf32; std::vector<float> g_pcmf32;
std::string to_timestamp(int64_t t) {
int64_t sec = t/100;
int64_t msec = t - sec*100;
int64_t min = sec/60;
sec = sec - min*60;
char buf[32];
snprintf(buf, sizeof(buf), "%02d:%02d.%03d", (int) min, (int) sec, (int) msec);
return std::string(buf);
}
void talk_set_status(const std::string & status) { void talk_set_status(const std::string & status) {
std::lock_guard<std::mutex> lock(g_mutex); std::lock_guard<std::mutex> lock(g_mutex);
g_status = status; g_status = status;

View File

@ -1 +1,2 @@
audio.mp3 audio.mp3
to_speak.txt

View File

@ -11,9 +11,13 @@ Web version: [examples/talk.wasm](/examples/talk.wasm)
The `talk` tool depends on SDL2 library to capture audio from the microphone. You can build it like this: The `talk` tool depends on SDL2 library to capture audio from the microphone. You can build it like this:
```bash ```bash
# Install SDL2 on Linux # Install SDL2
# On Debian based linux distributions:
sudo apt-get install libsdl2-dev sudo apt-get install libsdl2-dev
# On Fedora Linux:
sudo dnf install SDL2 SDL2-devel
# Install SDL2 on Mac OS # Install SDL2 on Mac OS
brew install sdl2 brew install sdl2

View File

@ -1,20 +1,80 @@
import sys import sys
import importlib.util import argparse
import textwrap
if importlib.util.find_spec("elevenlabs") is None: parser = argparse.ArgumentParser(add_help=False,
print("elevenlabs library is not installed, you can install it to your enviroment using 'pip install elevenlabs'") formatter_class=argparse.RawTextHelpFormatter)
parser.add_argument("-q", "--quick", action="store_true",
help="skip checking the required library")
modes = parser.add_argument_group("action")
modes.add_argument("inputfile", metavar="TEXTFILE",
nargs='?', type=argparse.FileType(), default=sys.stdin,
help="read the text file (default: stdin)")
modes.add_argument("-l", "--list", action="store_true",
help="show the list of voices and exit")
modes.add_argument("-h", "--help", action="help",
help="show this help and exit")
selopts = parser.add_argument_group("voice selection")
selmodes = selopts.add_mutually_exclusive_group()
selmodes.add_argument("-n", "--name",
default="Arnold",
help="get a voice object by name (default: Arnold)")
selmodes.add_argument("-v", "--voice", type=int, metavar="NUMBER",
help="get a voice object by number (see --list)")
selopts.add_argument("-f", "--filter", action="append", metavar="KEY=VAL",
default=["use case=narration"],
help=textwrap.dedent('''\
filter voices by labels (default: "use case=narration")
this option can be used multiple times
filtering will be disabled if the first -f has no "=" (e.g. -f "any")
'''))
outmodes = parser.add_argument_group("output")
outgroup = outmodes.add_mutually_exclusive_group()
outgroup.add_argument("-s", "--save", metavar="FILE",
default="audio.mp3",
help="save the TTS to a file (default: audio.mp3)")
outgroup.add_argument("-p", "--play", action="store_true",
help="play the TTS with ffplay")
args = parser.parse_args()
if not args.quick:
import importlib.util
if importlib.util.find_spec("elevenlabs") is None:
print("elevenlabs library is not installed, you can install it to your enviroment using 'pip install elevenlabs'")
sys.exit()
from elevenlabs import voices, generate, play, save
if args.filter and "=" in args.filter[0]:
voicelist = voices()
for f in args.filter:
label, value = f.split("=")
voicelist = filter(lambda x: x.labels.get(label) == value, voicelist)
voicelist = list(voicelist)
else:
voicelist = list(voices())
if args.list:
for i, v in enumerate(voicelist):
print(str(i) + ": " + v.name + " " + str(v.labels))
sys.exit() sys.exit()
from elevenlabs import generate, play, save if args.voice:
voice = voicelist[args.voice % len(voicelist)]
else:
voice = args.name
# if -n should consult -f, use the following
#voice = next(x for x in voicelist if x.name == args.name)
# Get a Voice object, by name or UUID
voice = "Arnold" #Possible Voices: Adam Antoni Arnold Bella Domi Elli Josh
# Generate the TTS
audio = generate( audio = generate(
text=str(sys.argv[2:]), text=str(args.inputfile.read()),
voice=voice voice=voice
) )
if args.play:
# Save the TTS to a file play(audio)
save(audio, "audio.mp3") else:
save(audio, args.save)

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@ -72,7 +72,7 @@ struct gpt2_model {
}; };
// load the model's weights from a file // load the model's weights from a file
bool gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab & vocab) { static bool gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab & vocab) {
printf("%s: loading model from '%s'\n", __func__, fname.c_str()); printf("%s: loading model from '%s'\n", __func__, fname.c_str());
auto fin = std::ifstream(fname, std::ios::binary); auto fin = std::ifstream(fname, std::ios::binary);
@ -380,7 +380,7 @@ bool gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab &
// - embd_w: the predicted logits for the next token // - embd_w: the predicted logits for the next token
// //
// TODO: sync latest version from ggml repo // TODO: sync latest version from ggml repo
bool gpt2_eval( static bool gpt2_eval(
const gpt2_model & model, const gpt2_model & model,
const int n_threads, const int n_threads,
const int n_past, const int n_past,

View File

@ -1,24 +1,40 @@
#!/bin/bash #!/bin/bash
# Usage: # Usage:
# speak.sh <voice_id> <text-to-speak> # speak <voice_id> <textfile>
# espeak function installed() { command -v $1 >/dev/null 2>&1; }
# Mac OS: brew install espeak
# Linux: apt-get install espeak
#
#espeak -v en-us+m$1 -s 175 -p 50 -a 200 -g 5 -k 5 "$2"
# Mac OS "say" command if installed espeak; then
say "$2" espeak -v en-us+m$1 -s 225 -p 50 -a 200 -g 5 -k 5 -f $2
elif installed piper && installed aplay; then
cat $2 | piper --model ~/en_US-lessac-medium.onnx --output-raw | aplay -q -r 22050 -f S16_LE -t raw -
# for Mac
elif installed say; then
say -f $2
# Eleven Labs # Eleven Labs
# To use it, install the elevenlabs module from pip (pip install elevenlabs) elif installed python3 && \
# It's possible to use the API for free with limited number of characters. To increase this limit register to https://beta.elevenlabs.io to get an api key and paste it after 'ELEVEN_API_KEY=' python3 -c 'import importlib.util; exit(not importlib.util.find_spec("elevenlabs"))' && \
#Keep the line commented to use the free version without api key installed ffplay; then
# # It's possible to use the API for free with limited number of characters.
#export ELEVEN_API_KEY=your_api_key # To increase this limit register to https://beta.elevenlabs.io to get an api key
#wd=$(dirname $0) # and paste it after 'ELEVEN_API_KEY='
#script=$wd/eleven-labs.py # Keep the line commented to use the free version without api key
#python3 $script $1 "$2" #export ELEVEN_API_KEY=your_api_key
#ffplay -autoexit -nodisp -loglevel quiet -hide_banner -i ./audio.mp3 wd=$(dirname $0)
script=$wd/eleven-labs.py
python3 $script -q -p -v $1 $2 >/dev/null 2>&1
# Uncomment to keep the audio file
#python3 $script -q -s ./audio.mp3 -v $1 $2 >/dev/null 2>&1
#ffplay -autoexit -nodisp -loglevel quiet -hide_banner -i ./audio.mp3 >/dev/null 2>&1
else
echo 'Install espeak ("brew install espeak" or "apt-get install espeak"),'
echo 'piper ("pip install piper-tts" or https://github.com/rhasspy/piper) with aplay,'
echo 'or elevenlabs ("pip install elevenlabs") with ffplay.'
echo '(export ELEVEN_API_KEY if you have an api key from https://beta.elevenlabs.io)'
fi

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