Commit Graph

16 Commits

Author SHA1 Message Date
Gilad S
527ac800cf ggml: load all backends from a user-provided search path (llama/10699)
* feat: load all backends from a user-provided search path

* fix: Windows search path

* refactor: rename `ggml_backend_load_all_in_search_path` to `ggml_backend_load_all_from_path`

* refactor: rename `search_path` to `dir_path`

* fix: change `NULL` to `nullptr`

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

* fix: change `NULL` to `nullptr`

---------

Co-authored-by: Diego Devesa <slarengh@gmail.com>
2024-12-18 12:52:16 +02:00
Diego Devesa
77e3e4a090 ggml : add support for dynamic loading of backends (llama/10469)
* ggml : add support for dynamic loading of backends

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-12-08 20:14:35 +02:00
Johannes Gäßler
c9541741e6 ggml: new optimization interface (ggml/988)
* ggml: new optimization interface

remove test2.c, test3.c

store adamw params in tensor

move grads from tensor to graph

* avoid segfault upon API misuse

* add ggml-opt.h to public headers

* remove dependence of ggml-opt.cpp on ggml-cpu.h
2024-11-20 21:00:08 +02:00
Diego Devesa
746bf2596f ggml : build backends as libraries (llama/10256)
* ggml : build backends as libraries

---------

Signed-off-by: Xiaodong Ye <xiaodong.ye@mthreads.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: R0CKSTAR <xiaodong.ye@mthreads.com>
2024-11-20 21:00:08 +02:00
Diego Devesa
9c817edb48 ggml : move CPU backend to a separate file (llama/10144) 2024-11-15 15:21:04 +02:00
Diego Devesa
1d48457aa6 llama : refactor model loader with backend registry (llama/10026) 2024-11-15 15:21:04 +02:00
Diego Devesa
44bc2767fd ggml : add backend registry / device interfaces to BLAS backend (llama/9752)
* ggml : add backend registry / device interfaces to BLAS backend

* fix mmap usage when using host buffers
2024-11-01 10:19:05 +02:00
Georgi Gerganov
315364d7de ggml : add metal backend registry / device (llama/9713)
* ggml : add metal backend registry / device

ggml-ci

* metal : fix names [no ci]

* metal : global registry and device instances

ggml-ci

* cont : alternative initialization of global objects

ggml-ci

* llama : adapt to backend changes

ggml-ci

* fixes

* metal : fix indent

* metal : fix build when MTLGPUFamilyApple3 is not available

ggml-ci

* fix merge

* metal : avoid unnecessary singleton accesses

ggml-ci

* metal : minor fix [no ci]

* metal : g_state -> g_ggml_ctx_dev_main [no ci]

* metal : avoid reference of device context in the backend context

ggml-ci

* metal : minor [no ci]

* metal : fix maxTransferRate check

* metal : remove transfer rate stuff

---------

Co-authored-by: slaren <slarengh@gmail.com>
2024-11-01 10:19:05 +02:00
Diego Devesa
cf977670e6 ggml-backend : add device and backend reg interfaces (llama/9707)
Also:

- metal : fix compute pass descriptor autorelease crash
- ggml-backend : add device description to CPU backend
- ggml: unify backend logging mechanism
2024-10-05 15:23:51 +03:00
Diego Devesa
1acfadb721 ggml-backend : add device and backend reg interfaces (llama/9707)
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
2024-10-05 15:23:51 +03:00
Johannes Gäßler
bc92c2f8f0 ggml: refactor cross entropy loss CPU impl. (ggml/976) 2024-10-05 15:23:51 +03:00
Johannes Gäßler
c7515b0995 ggml/examples: add backend support for numerical optimization (ggml/949)
* CUDA eval works

* stochastic gradient descent op

* Adam except decay

* CUDA CROSS_ENTROPY_LOSS_BACK

* CUDA mnist-fc training works

* backend CLI arg

* refactor gguf load

* remove sched from opt_step_adam

* implement l1 regularization (weight decay)

* extra call to add optimizer

* initialize gradients with ggml_graph_reset

* gradient accumulation

* increment iter per eval instead of epoch

* adjust backend interfaces

* fix ggml_graph_reset without backend

* fix ggml graph export/import

* fixup

* rename

* revert ggml_opt changes

* more general CUDA repeat_back

* update documentation, fix CNN

* validation split

* add clarifying comment

* optimize PyTorch training

* adjust buffer size, thread count

* fix 0.0f validation split

* Update examples/mnist/mnist-common.cpp

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

* fix gradient accumulation

* tensor flag for accumulators -> tensor hash set

* Update include/ggml.h

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

* Update tests/test-backend-ops.cpp

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

* Update tests/test-backend-ops.cpp

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

* fix test prints

* Update src/ggml-backend.c

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

* better CUDA support for noncontiguous out_prod

* add comment

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: slaren <slarengh@gmail.com>
2024-09-24 19:45:08 +03:00
Faisal Zaghloul
38d40b9972 Threadpool: take 2 (llama/8672)
* Introduce ggml_compute_threadpool

- OpenMP functional: check
- Vanilla ggml functional: Check
- ggml w/threadpool functional: Check
- OpenMP no regression: No glaring problems
- Vanilla ggml no regression: No glaring problems
- ggml w/threadpool no regression: No glaring problems

* Minor fixes

* fixed use after release bug

* fixed a harmless race condition

* Fix Android bulid issue

* fix more race conditions

* fix deadlock for cases where cgraph.n_nodes == 1

and fix --poll case

* threadpool: use cpu_get_num_math to set the default number of threadpool threads

This way we avoid using E-Cores and Hyperthreaded siblings.

* bench: create fresh threadpool for each test

For benchmarking it's better to start a fresh pool for each test with the exact number of threads
needed for that test. Having larger pools is suboptimal (causes more load, etc).

* atomics: always use stdatomics with clang and use relaxed memory order when polling in ggml_barrier

This also removes sched_yield() calls from ggml_barrier() to match OpenMP behavior.

* threadpool: make polling the default to match openmp behavior

All command line args now allow for setting poll to 0 (false).

* threadpool: do not wakeup threads in already paused threadpool

* fix potential race condition in check_for_work

* threadpool: do not create two threadpools if their params are identical

* threadpool: reduce pause/resume/wakeup overhead in common cases

We now start threadpool in paused state only if we have two.
The resume is now implicit (ie new work) which allows for reduced locking and context-switch overhead.

* threadpool: add support for hybrid polling

poll params (--poll, ...) now specify "polling level", i.e. how aggresively we poll before waiting on cond.var.
poll=0 means no polling, 1 means poll for 128K rounds then wait, 2 for 256K rounds, ...

The default value of 50 (ie 50x128K rounds) seems like a decent default across modern platforms.
We can tune this further as things evolve.

* threadpool: reduce the number of barrier required

New work is now indicated with an atomic counter that is incremented for
each new graph that needs to be computed.
This removes the need for extra barrier for clearing the "new_work" and
removes the special case for trivial graphs.

* threadpool: remove special-casing for disposable threadpools

With the efficient hybrid polling there is no need to make disposable pools any different.
This simplifies the overall logic and reduces branching.

Include n_threads in debug print for disposable threadpool.

Declare pause and stop flags as atomic_bool
This doesn't actually generate any memory barriers and simply informs
the thread sanitizer that these flags can be written & read by different
threads without locking.

* threadpool: do not clear barrier counters between graphs computes (fixes race with small graphs)

This fixes the race condition with very small graphs where the main thread happens to
start a new graph while the workers are just about to exit from barriers.

* threadpool: use relaxed order for chunk sync

Full memory barrier is an overkill for this since each thread works on different chunk

* threadpool: remove abort_callback from threadpool state

* threadpool: better naming for thread/cpumask releated functions

* threadpool: consistent use of int type for n_threads params

* threadpool: add support for ggml_threadpool_params_default/init

Also removes the need for explicit mask_specified param.
all-zero cpumask means use default (usually inherited) cpu affinity mask.

* threadpool: move typedef into ggml.h

* threadpool: fix apply_priority() function name

* threadpool: fix swift wrapper errors due to n_threads int type cleanup

* threadpool: enable --cpu-mask and other threadpool related options only if threadpool is enabled

* threadpool: replace checks for compute_thread ret code with proper status check

* threadpool: simplify threadpool init logic and fix main thread affinity application

Most of the init code is now exactly the same between threadpool and openmp.

* threadpool: update threadpool resume/pause function names

* threadpool: enable openmp by default for now

* threadpool: don't forget to free workers state when omp is enabled

* threadpool: avoid updating process priority on the platforms that do not require it

On Windows we need to change overall process priority class in order to set thread priorities,
but on Linux, Mac, etc we do not need to touch the overall process settings.

* threadpool: update calling thread prio and affinity only at start/resume

This avoids extra syscalls for each graph_compute()

* llama-bench: turn threadpool params into vectors, add output headers, etc

* llama-bench: add support for cool off between tests --delay

This helps for long running tests on platforms that are thermally limited (phones, laptops, etc).
--delay (disabled by default) introduces the sleep for N seconds before starting each test.

* threadpool: move process priority setting into the apps (bench and cli)

This avoids changing the overall process priority on Windows for the apps
that use ggml/llama.cpp directy.

* threadpool: move all pause/resume logic into ggml

* threadpool: futher api cleanup and prep for future refactoring

All threadpool related functions and structs use ggml_threadpool prefix.

* threadpool: minor indent fixes

* threadpool: improve setprioty error message

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

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

* threadpool: fix indent in set_threadpool call

* use int32_t for n_thread type in public llama.cpp API

* threadpool: use _new and _free instead of _create and _release

* fix two more public APIs to use int32_t for n_threads

* build: set _GNU_SOURCE for Adroid

---------

Co-authored-by: Max Krasnyansky <quic_maxk@quicinc.com>
Co-authored-by: fmz <quic_fzaghlou@quic.com>
Co-authored-by: Max Krasnyansky <max.krasnyansky@gmail.com>
Co-authored-by: slaren <slarengh@gmail.com>
2024-09-24 19:45:08 +03:00
Johannes Gäßler
8954769aa2 feat: ref. cross entropy, add CUDA, fix grad test (ggml/929) 2024-08-28 13:22:20 +03:00
Johannes Gäßler
a8ab3abe09 CUDA: fix partial offloading for ne0 % 256 != 0 (llama/8572) 2024-08-08 22:48:46 +03:00
Georgi Gerganov
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