This commit introduces a mechanism to embed all licenses directly
into the compiled binaries.
This eliminates the need to distribute separate LICENSE files alongside
the executable, making the binaries self-contained and simplifying
deployment.
this shouldn't do anything (+2 squashed commit)
Squashed commit:
[1cdcb302a] another attempt to tip the scales, part 2
[8f647b709] another attempt to tip the scales (volta)
Previously, cmake was forcing `_WIN32_WINNT=0x0A00` for MinGW builds,
This caused "macro redefined" warnings with toolchains that define the version.
This also removes the `GGML_WIN_VER` variable as it is no longer needed.
Signed-off-by: Adrien Gallouët <angt@huggingface.co>
* Faster tensors (#8)
Add fast matrix and matrix/vector multiplication.
* Use map for shader replacements instead of pair of strings
* Wasm (#9)
* webgpu : fix build on emscripten
* more debugging stuff
* test-backend-ops: force single thread on wasm
* fix single-thread case for init_tensor_uniform
* use jspi
* add pthread
* test: remember to set n_thread for cpu backend
* Add buffer label and enable dawn-specific toggles to turn off some checks
* Intermediate state
* Fast working f16/f32 vec4
* Working float fast mul mat
* Clean up naming of mul_mat to match logical model, start work on q mul_mat
* Setup for subgroup matrix mat mul
* Basic working subgroup matrix
* Working subgroup matrix tiling
* Handle weirder sg matrix sizes (but still % sg matrix size)
* Working start to gemv
* working f16 accumulation with shared memory staging
* Print out available subgroup matrix configurations
* Vectorize dst stores for sg matrix shader
* Gemv working scalar
* Minor set_rows optimization (#4)
* updated optimization, fixed errors
* non vectorized version now dispatches one thread per element
* Simplify
* Change logic for set_rows pipelines
---------
Co-authored-by: Neha Abbas <nehaabbas@macbookpro.lan>
Co-authored-by: Neha Abbas <nehaabbas@ReeseLevines-MacBook-Pro.local>
Co-authored-by: Reese Levine <reeselevine1@gmail.com>
* Comment on dawn toggles
* Working subgroup matrix code for (semi)generic sizes
* Remove some comments
* Cleanup code
* Update dawn version and move to portable subgroup size
* Try to fix new dawn release
* Update subgroup size comment
* Only check for subgroup matrix configs if they are supported
* Add toggles for subgroup matrix/f16 support on nvidia+vulkan
* Make row/col naming consistent
* Refactor shared memory loading
* Move sg matrix stores to correct file
* Working q4_0
* Formatting
* Work with emscripten builds
* Fix test-backend-ops emscripten for f16/quantized types
* Use emscripten memory64 to support get_memory
* Add build flags and try ci
---------
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
* Remove extra whitespace
* Move wasm single-thread logic out of test-backend-ops for cpu backend
* Disable multiple threads for emscripten single-thread builds in ggml_graph_plan
* Fix .gitignore
* Add memory64 option and remove unneeded macros for setting threads to 1
---------
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
Introduce a new `LLAMA_OPENSSL` option, enabled by default.
This preserves the previous default (libcurl first, OpenSSL as fallback),
while allowing OpenSSL to be disabled if desired.
Signed-off-by: Adrien Gallouët <angt@huggingface.co>
* examples/finetune -opt SGD (stochastic gradient descent) memory opt
add unit tested GGML_OPT_OPTIMIZER_SGD to ggml - avoids allocating
m, v tensors.
support finetune.cpp arg -opt SGD (or sgd). (default adamw as before)
llama 3.2-1b-F32 result: observed 11gb gpu ram (41 sec/epoch)
when using SGD instead of 19gb (55 sec/epoch) using adamw.
(wikipedia 100 lines finetune)
(
using the same GPU memory, adamw can only do before OOM 512
batch/context, reaching:
train: [███████▉] data=0000140/0000140 loss=0.02575±0.00099 acc=99.52±0.03% t=00:00:47 ETA=00:00:00
val: [███████▉] data=0000008/0000008 loss=4.76565±0.28810 acc=41.46±0.77% t=00:00:00 ETA=00:00:00
SGD is superior, though it converges slower, with max before OOM 1728
batch/context (esp see the better validation perf):
train: [███████▉] data=0000039/0000039 loss=0.00371±0.00010 acc=99.96±0.01% t=00:00:41 ETA=00:00:00
val: [███████▉] data=0000003/0000003 loss=5.11406±0.76034 acc=48.01±0.69% t=00:00:01 ETA=00:00:00
)
note: when finetuning long enough (or w/ enough -lr),
validation accuracy *eventually* drops ('catastrophic forgetting')
-lr-half (halflife) option useful for SGD to avoid oscillation or
super slow underdamped learning (makes setting -lr more forgiving).
terminal -lr for now is set by lr-halvings i.e. if you want at most
1/8 the inital -lr you set -lr-halvings 3.
note: objective loss not directly comparable between adamw, sgd? -
check perplexity or accuracy or consider relative improvements
for convergence
new finetune args -wd 1e-9 to enable weight decay in sgd or adamw,
and max -epochs N (default 2 as before)
cache (1 - wd*alpha) in 'adamw' opt struct -
no noticeable perf benefit, disabled (still done
for new SGD though)
since opt. memory is pre-allocated, the ggml_opt_get_optimizer_params
would probably be able to change between SGD and AdamW with each epoch
but would need to use adamw for the first (unconfirmed - no cmdline arg
to set such a policy yet)
test-opt checks adamw as before and now sgd (except for a few disabled
tests for sgd only; probably just needs logging values and adding
alternate reference values); tolerance on the 'regression'
test is broader for sgd (so we don't need many more epochs)
* Vulkan: Implement GGML_OP_OPT_STEP_SGD
* tests: Fix OPT_STEP_SGD test-backend-ops
* SGD op param store weight-decay and not 1-alpha*wd
* minor + cosmetic changes
* fix vulkan sgd
* try CI fix
---------
Co-authored-by: 0cc4m <picard12@live.de>
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
there is so much duplicate code in each cpu arch, i expect upstream will prune it eventually
arch detection has no fallback if all the arches are not found, by right we should set GGML_CPU_GENERIC
i should be relaxing its the weekend
this should finally work (+21 squashed commit)
Squashed commit:
[5edac5b59] Revert "quick dbg"
This reverts commit fd62a997cc6684bb89242d5e7b0ae2aed83fd27f.
[fd62a997c] quick dbg
[bcccae7e6] sanity check 2
[568e2eb08] sanity check
[2f30d573a] please work 2
[cf8765221] please work
[c535e60d9] try a small trick
[d4ba79b80] 2022 test
[3f146b000] t2
[4a3b9a9b4] revert and test
[4bdc9a149] reverted test2
[5081cb4a3] reverted test
[ea9a826f3] broken test
[3c11ae389] compare 2019
[8ecec4fec] not for cu12
[0be964f3a] added vs2019 for the other runners
[5d24641cb] debugging 4
[1dee79207] debugging 3
[ab172f133] more debugging 2
[b1a895e84] more debugging
[5d21d8bd0] vs2019 setup
Squashed commit:
[e050f83db] Revert "test cu11 build on win2022"
This reverts commit 1bf989f2b3789c99aa9883cfe70550de6c26db23.
[1bf989f2b] test cu11 build on win2022
[5dc94eae8] updated lite
Squashed commits:
[3c5112398] 117 (+10 squashed commit)
Squashed commit:
[4f01bb2d4] 117 graphs 80v
[7549034ea] 117 graphs
[dabf9cb99] checking if cuda 11.5.2 works
[ba7ccdb7a] another try cu11.7 only
[752cf2ae5] increase aria2c download log rate
[dc4f198fd] test send turing to wmma flash attention
[496a22e83] temp build test cu11.7.0
[ca759c424] temp build test cu11.7
[c46ada17c] test build: enable virtual80 for oldcpu
[3ccfd939a] test build: with cuda graphs for all
Concedo
Hugo
Georgi Gerganov
Adrien Gallouët
Jeff Bolz
Reese Levine
Xuan-Son Nguyen
Yuri Khrustalev
Jonathan Graehl
Mathieu Baudier
Christian Kastner
Diego Devesa