Commit Graph

8 Commits

Author SHA1 Message Date
Faisal Zaghloul
42c76d1358
Threadpool: take 2 (#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>
2024-08-30 01:20:53 +02:00
Zhenwei Jin
4af8420afb
common : remove duplicate function llama_should_add_bos_token (#8778) 2024-08-15 10:23:23 +03:00
Liu Jia
0a4ce78681
common : Changed tuple to struct (TODO fix) (#8823)
* common : Changed tuple to struct (TODO fix)

Use struct `llama_init_result` to replace the previous
std::tuple<struct llama_model *, struct llama_context *>

* delete llama_init_default_params()

* delete the extra whitespace
2024-08-05 18:14:10 +02:00
Xuan Son Nguyen
49c03c79cd
cvector: better prompt handling, add "mean vector" method (#8069)
* remove completions file

* fix inverted vector

* add mean method

* code style

* remove inverted pca hotfix
2024-06-25 13:59:54 +02:00
Xuan Son Nguyen
3e58b0ee35
cvector: fix CI + correct help message (#8064)
* cvector: fix CI + correct help message

* also correct --pca-iter
2024-06-22 18:11:30 +02:00
HatsuneMikuUwU33
adf480c3ab
cvector-generator: Moe Moe Fixie-Fixie for Lots of Formats~! ♡(ᐢ ᴥ ᐢ)♡ (#8052)
* Update negative.txt

* Update positive.txt

* Update cvector-generator.cpp

* Update cvector-generator.cpp
2024-06-22 17:19:37 +02:00
Calvin Laurenson
43b35e38ba
Add support for sqrt on CUDA (#7953)
* cuda sqrt support

* enable cuda in pca

* fix comments in pca

* add test

* add sqrt to ggml_backend_cuda_supports_op

* fix test

* new line

* Use F32 sqrtf instead of F64 sqrt

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

---------

Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
2024-06-17 00:23:04 +02:00
Xuan Son Nguyen
0c7b3595b9
Add cvector-generator example (#7514)
* add control-vector-generator

* calc diff

* add comments

* proof-of-concept stdlib implementation

Implements PCA and file writing using mostly standard libraries. The output is recognized as a functional control vector, but outputs gibberish.

* param parsing, refactor, comments

Added basic command-line parameters for outfile and one each positive/negative prompt.

Refactored some messy code in PCA computation and GGUF exporting.

Left a bunch of comments regarding further work needed.

* example template completions

Implements an example template set built from the positive/negative prompts like the control vector Python implementation.

* add multi prompts, multi-thread for PCA

* fix mem error

* add debugs

* fix matrix transpose multiplication

you have got to be kidding me

* preliminary template/multiprompt support

model is running out of context and that ought to be fixed (segfaulting) but other than that it looks goodish

* fix zero output & param parsing, functional templating

fixed a bug where the output file had no tensor data/was all zero

fixed a bug where single hyphen flags were not being correctly parsed

implements creation of templated prompts from input (still need to adapt based on model)

* fix square_diff matmul index range and CRLF->LF line endings

fixed a logic error where square_diff would not multiply all rows

fixed a formatting error where the provided completions.txt had CRLF line endings

* add command-line args for num threads, num completions file lines, always reload model

refactored a few things and did what the commit message says on the tin

* code aestheticization

* fix compiler warnings

* in-series multithreading for prompt embedding?

added commented-out code to attempt to start implementing mutlithreading for embedding in main

* remove unnecessary multithreading

* interim fix memory leak

* translated everything but PCA (I think)

* tentatively translate the rest

* fix ggml errors and make new ones

at least it compiles and runs

* fix cb_eval

* temporary commit while I move dev environments

it finally outputs a functioning control vector - "functioning" in the sense that it can be loaded and it clearly has the right idea, but makes the model incoherent

* update debug statements

* pre-tokenize so we can allocate correct memory to ctx_diffs_wrapped

* update comments

* (wip) refactor

* clean up PCA ggml implementation

* fix shape of v_diff_original

* add n_batch for pca

* working version

* remember to copy back the last_eigenvector

* fix n_completions

* bring back n_completions

* default n_pca_batch to 20

* fix macos build

* add to makefile all targets

* use ggml_format_name

* add readme

* fix .editorconfig

* use ggml_backend_tensor_copy

* attemp to fix compile problem on mac

* fix compile warn

* reuse allocr

* move param parser to common

* better error handling

* clean up a bit

* add print_usage

* shorten help msg

* beautify help msg

* escape prompt by default

* change compile target to llama-cvector-generator

* typo

* disable GPU for PCA

* code style

---------

Co-authored-by: Christian Zhou-Zheng <christianzhouzheng@gmail.com>
2024-06-15 18:53:40 +02:00