Commit Graph

86 Commits

Author SHA1 Message Date
ditsuke
b0a46993df build(python): Package scripts with pip-0517 compliance 2024-07-04 15:39:13 +00:00
Georgi Gerganov
f3f65429c4
llama : reorganize source code + improve CMake (#8006)
* scripts : update sync [no ci]

* files : relocate [no ci]

* ci : disable kompute build [no ci]

* cmake : fixes [no ci]

* server : fix mingw build

ggml-ci

* cmake : minor [no ci]

* cmake : link math library [no ci]

* cmake : build normal ggml library (not object library) [no ci]

* cmake : fix kompute build

ggml-ci

* make,cmake : fix LLAMA_CUDA + replace GGML_CDEF_PRIVATE

ggml-ci

* move public backend headers to the public include directory (#8122)

* move public backend headers to the public include directory

* nix test

* spm : fix metal header

---------

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

* scripts : fix sync paths [no ci]

* scripts : sync ggml-blas.h [no ci]

---------

Co-authored-by: slaren <slarengh@gmail.com>
2024-06-26 18:33:02 +03:00
Michael de Gans
a7854743c5
un-ignore build-info.cmake and build-info.sh (#7996)
* un-ignore `build-info.cmake` and `build-info.sh`

I am assuming that ignoring them was unintentional. If they are ignored, some tools, like cargo, will consider the files inexistent, even if they're comitted, for the purpose of publishing. This leads to the build failing in such cases.

* un-ignore `build-info.cpp.in`

For the same reason as the previous two files.

* Reorganize `.gitignore`

* Add exceptions for files mentioned by @slaren

I did leave .clang-tidy since it was explicitly ignored before.

* Add comments for organization
* Sort some lines for pretty
* Test with `make` and `cmake` builds to ensure no build artifacts might be comitted

* Remove `.clang-tidy` from `.gitignore`

Per comment by @ggerganov

* Remove `IDEWorkspaceChecks.plist` from root-level `.gitignore`
2024-06-19 22:10:42 +02:00
Olivier Chafik
1c641e6aac
build: rename main → llama-cli, server → llama-server, llava-cli → llama-llava-cli, etc... (#7809)
* `main`/`server`: rename to `llama` / `llama-server` for consistency w/ homebrew

* server: update refs -> llama-server

gitignore llama-server

* server: simplify nix package

* main: update refs -> llama

fix examples/main ref

* main/server: fix targets

* update more names

* Update build.yml

* rm accidentally checked in bins

* update straggling refs

* Update .gitignore

* Update server-llm.sh

* main: target name -> llama-cli

* Prefix all example bins w/ llama-

* fix main refs

* rename {main->llama}-cmake-pkg binary

* prefix more cmake targets w/ llama-

* add/fix gbnf-validator subfolder to cmake

* sort cmake example subdirs

* rm bin files

* fix llama-lookup-* Makefile rules

* gitignore /llama-*

* rename Dockerfiles

* rename llama|main -> llama-cli; consistent RPM bin prefixes

* fix some missing -cli suffixes

* rename dockerfile w/ llama-cli

* rename(make): llama-baby-llama

* update dockerfile refs

* more llama-cli(.exe)

* fix test-eval-callback

* rename: llama-cli-cmake-pkg(.exe)

* address gbnf-validator unused fread warning (switched to C++ / ifstream)

* add two missing llama- prefixes

* Updating docs for eval-callback binary to use new `llama-` prefix.

* Updating a few lingering doc references for rename of main to llama-cli

* Updating `run-with-preset.py` to use new binary names.
Updating docs around `perplexity` binary rename.

* Updating documentation references for lookup-merge and export-lora

* Updating two small `main` references missed earlier in the finetune docs.

* Update apps.nix

* update grammar/README.md w/ new llama-* names

* update llama-rpc-server bin name + doc

* Revert "update llama-rpc-server bin name + doc"

This reverts commit e474ef1df4.

* add hot topic notice to README.md

* Update README.md

* Update README.md

* rename gguf-split & quantize bins refs in **/tests.sh

---------

Co-authored-by: HanClinto <hanclinto@gmail.com>
2024-06-13 00:41:52 +01:00
zhouwg
b226c1227b
refine .gitignore (#7688)
This adds tags and android ndk into the git ignore list
2024-06-04 21:21:26 +10:00
Austin
7c4e5b7eae
chore : add ignore rule for generated server themes (#7689) 2024-06-02 20:39:08 +03:00
Olivier Chafik
8843a98c2b
Improve usability of --model-url & related flags (#6930)
* args: default --model to models/ + filename from --model-url or --hf-file (or else legacy models/7B/ggml-model-f16.gguf)

* args: main & server now call gpt_params_handle_model_default

* args: define DEFAULT_MODEL_PATH + update cli docs

* curl: check url of previous download (.json metadata w/ url, etag & lastModified)

* args: fix update to quantize-stats.cpp

* curl: support legacy .etag / .lastModified companion files

* curl: rm legacy .etag file support

* curl: reuse regex across headers callback calls

* curl: unique_ptr to manage lifecycle of curl & outfile

* curl: nit: no need for multiline regex flag

* curl: update failed test (model file collision) + gitignore *.gguf.json
2024-04-30 00:52:50 +01:00
Georgi Gerganov
f4ab2a4147
llama : fix BPE pre-tokenization (#6920)
* merged the changes from deepseeker models to main branch

* Moved regex patterns to unicode.cpp and updated unicode.h

* Moved header files

* Resolved issues

* added and refactored unicode_regex_split and related functions

* Updated/merged the deepseek coder pr

* Refactored code

* Adding unicode regex mappings

* Adding unicode regex function

* Added needed functionality, testing remains

* Fixed issues

* Fixed issue with gpt2 regex custom preprocessor

* unicode : fix? unicode_wstring_to_utf8

* lint : fix whitespaces

* tests : add tokenizer tests for numbers

* unicode : remove redundant headers

* tests : remove and rename tokenizer test scripts

* tests : add sample usage

* gguf-py : reader prints warnings on duplicate keys

* llama : towards llama3 tokenization support (wip)

* unicode : shot in the dark to fix tests on Windows

* unicode : first try custom implementations

* convert : add "tokenizer.ggml.pre" GGUF KV (wip)

* llama : use new pre-tokenizer type

* convert : fix pre-tokenizer type writing

* lint : fix

* make : add test-tokenizer-0-llama-v3

* wip

* models : add llama v3 vocab file

* llama : adapt punctuation regex + add llama 3 regex

* minor

* unicode : set bomb

* unicode : set bomb

* unicode : always use std::wregex

* unicode : support \p{N}, \p{L} and \p{P} natively

* unicode : try fix windows

* unicode : category support via std::regex

* unicode : clean-up

* unicode : simplify

* convert : add convert-hf-to-gguf-update.py

ggml-ci

* lint : update

* convert : add falcon

ggml-ci

* unicode : normalize signatures

* lint : fix

* lint : fix

* convert : remove unused functions

* convert : add comments

* convert : exercise contractions

ggml-ci

* lint : fix

* cmake : refactor test targets

* tests : refactor vocab tests

ggml-ci

* tests : add more vocabs and tests

ggml-ci

* unicode : cleanup

* scripts : ignore new update script in check-requirements.sh

* models : add phi-3, mpt, gpt-2, starcoder

* tests : disable obsolete

ggml-ci

* tests : use faster bpe test

ggml-ci

* llama : more prominent warning for old BPE models

* tests : disable test-tokenizer-1-bpe due to slowness

ggml-ci

---------

Co-authored-by: Jaggzh <jaggz.h@gmail.com>
Co-authored-by: Kazim Abrar Mahi <kazimabrarmahi135@gmail.com>
2024-04-29 16:58:41 +03:00
Olivier Chafik
5cf5e7d490
build: generate hex dump of server assets during build (#6661)
* `build`: generate hex dumps of server assets on the fly

* build: workaround lack of -n on gnu xxd

* build: don't use xxd in cmake

* build: don't call xxd from build.zig

* build: more idiomatic hexing

* build: don't use xxd in Makefile (od hackery instead)

* build: avoid exceeding max cmd line limit in makefile hex dump

* build: hex dump assets at cmake build time (not config time)
2024-04-21 18:48:53 +01:00
Pierrick Hymbert
b804b1ef77
eval-callback: Example how to use eval callback for debugging (#6576)
* gguf-debug: Example how to use ggml callback for debugging

* gguf-debug: no mutex, verify type, fix stride.

* llama: cv eval: move cb eval field in common gpt_params

* ggml_debug: use common gpt_params to pass cb eval.
Fix get tensor SIGV random.

* ggml_debug: ci: add tests

* ggml_debug: EOL in CMakeLists.txt

* ggml_debug: Remove unused param n_batch, no batching here

* ggml_debug: fix trailing spaces

* ggml_debug: fix trailing spaces

* common: fix cb_eval and user data not initialized

* ci: build revert label

* ggml_debug: add main test label

* doc: add a model: add a link to ggml-debug

* ggml-debug: add to make toolchain

* ggml-debug: tests add the main label

* ggml-debug: ci add test curl label

* common: allow the warmup to be disabled in llama_init_from_gpt_params

* ci: add curl test

* ggml-debug: better tensor type support

* gitignore : ggml-debug

* ggml-debug: printing also the sum of each tensor

* ggml-debug: remove block size

* eval-callback: renamed from ggml-debug

* eval-callback: fix make toolchain

---------

Co-authored-by: slaren <slarengh@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-11 14:51:07 +02:00
Minsoo Cheong
64e7b47c69
examples : add "retrieval" (#6193)
* add `retrieval` example

* add README

* minor fixes

* cast filepos on print

* remove use of variable sized array

* store similarities in separate vector

* print error on insufficient batch size

* fix error message printing

* assign n_batch value to n_ubatch

* fix param definitions

* define retrieval-only parameters in retrieval.cpp

* fix `--context-file` option to be provided multiple times for multiple files

* use vector for `query_emb`

* add usage description in README

* fix merge conflict

* fix usage printing

* remove seed setting

* fix lint

* increase file read buffer size

* retrieval : minor

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-03-25 09:38:22 +02:00
Georgi Gerganov
95562175f8
gitignore : gguf-split 2024-03-23 21:35:23 +02:00
Johannes Gäßler
50ccaf5eac
lookup: complement data from context with general text statistics (#5479)
* lookup: evaluation tools, use corpus/previous gens

* fixup! lookup: evaluation tools, use corpus/previous gens

* fixup! lookup: evaluation tools, use corpus/previous gens

* fixup! lookup: evaluation tools, use corpus/previous gens

* fixup! lookup: evaluation tools, use corpus/previous gens
2024-03-23 01:24:36 +01:00
Olivier Chafik
5b7b0ac8df
json-schema-to-grammar improvements (+ added to server) (#5978)
* json: fix arrays (disallow `[,1]`)

* json: support tuple types (`[number, string]`)

* json: support additionalProperties (`{[k: string]: [string,number][]}`)

* json: support required / optional properties

* json: add support for pattern

* json: resolve $ref (and support https schema urls)

* json: fix $ref resolution

* join: support union types (mostly for nullable types I think)

* json: support allOf + nested anyOf

* json: support any (`{}` or `{type: object}`)

* json: fix merge

* json: temp fix for escapes

* json: spaces in output and unrestricted output spaces

* json: add typings

* json:fix typo

* Create ts-type-to-grammar.sh

* json: fix _format_literal (json.dumps already escapes quotes)

* json: merge lit sequences and handle negatives

{"type": "string", "pattern": "^({\"question\": \"[^\"]+\", \"response\": \"[^\"]+\"}\\n)+$"}

* json: handle pattern repetitions

* Update json-schema-to-grammar.mjs

* Create regex-to-grammar.py

* json: extract repeated regexp patterns to subrule

* Update json-schema-to-grammar.py

* Update json-schema-to-grammar.py

* Update json-schema-to-grammar.py

* json: handle schema from pydantic Optional fields

* Update json-schema-to-grammar.py

* Update json-schema-to-grammar.py

* Update ts-type-to-grammar.sh

* Update ts-type-to-grammar.sh

* json: simplify nullable fields handling

* json: accept duplicate identical rules

* json: revert space to 1 at most

* json: reuse regexp pattern subrules

* json: handle uuid string format

* json: fix literal escapes

* json: add --allow-fetch

* json: simplify range escapes

* json: support negative ranges in patterns

* Delete commit.txt

* json: custom regex parser, adds dot support & JS-portable

* json: rm trailing spaces

* Update json-schema-to-grammar.mjs

* json: updated server & chat `( cd examples/server && ./deps.sh )`

* json: port fixes from mjs to python

* Update ts-type-to-grammar.sh

* json: support prefixItems alongside array items

* json: add date format + fix uuid

* json: add date, time, date-time formats

* json: preserve order of props from TS defs

* json: port schema converter to C++, wire in ./server

* json: nits

* Update json-schema-to-grammar.cpp

* Update json-schema-to-grammar.cpp

* Update json-schema-to-grammar.cpp

* json: fix mjs implementation + align outputs

* Update json-schema-to-grammar.mjs.hpp

* json: test C++, JS & Python versions

* json: nits + regen deps

* json: cleanup test

* json: revert from c++17 to 11

* json: nit fixes

* json: dirty include for test

* json: fix zig build

* json: pass static command to std::system in tests (fixed temp files)

* json: fix top-level $refs

* json: don't use c++20 designated initializers

* nit

* json: basic support for reserved names `{number:{number:{root:number}}}`

* Revamp test cmake to allow args (WORKING_DIRECTORY needed for JSON test)

* json: re-ran server deps.sh

* json: simplify test

* json: support mix of additional props & required/optional

* json: add tests for some expected failures

* json: fix type=const in c++, add failure expectations for non-str const&enum

* json: test (& simplify output of) empty schema

* json: check parsing in test + fix value & string refs

* json: add server tests for OAI JSON response_format

* json: test/fix top-level anyOf

* json: improve grammar parsing failures

* json: test/fix additional props corner cases

* json: fix string patterns (was missing quotes)

* json: ws nit

* json: fix json handling in server when there's no response_format

* json: catch schema conversion errors in server

* json: don't complain about unknown format type in server if unset

* json: cleaner build of test

* json: create examples/json-schema-pydantic-example.py

* json: fix date pattern

* json: move json.hpp & json-schema-to-grammar.{cpp,h} to common

* json: indent 4 spaces

* json: fix naming of top-level c++ function (+ drop unused one)

* json: avoid using namespace std

* json: fix zig build

* Update server.feature

* json: iostream -> fprintf

* json: space before & refs for consistency

* json: nits
2024-03-21 11:50:43 +00:00
Georgi Gerganov
6b7e76d28c
gitignore : ignore curl-related files 2024-03-20 14:17:34 +02:00
Georgi Gerganov
381da2d9f0
metal : build metallib + fix embed path (#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
2024-03-14 11:55:23 +02:00
DAN™
bcebd7dbf6
llama : add support for GritLM (#5959)
* add gritlm example

* gritlm results match

* tabs to spaces

* comment out debug printing

* rebase to new embed

* gritlm embeddings are back babeee

* add to gitignore

* allow to toggle embedding mode

* Clean-up GritLM sample code.

* Fix types.

* Flush stdout and output ending newline if streaming.

* mostly style fixes; correct KQ_mask comment

* add causal_attn flag to llama_cparams

* gritml : minor

* llama : minor

---------

Co-authored-by: Douglas Hanley <thesecretaryofwar@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-03-10 17:56:30 +02:00
clibdev
d250c9d61d
gitignore : update for CLion IDE (#5544) 2024-02-17 18:28:37 +02:00
Neo Zhang Jianyu
01684139c3
support SYCL backend windows build (#5208)
* support SYCL backend windows build

* add windows build in CI

* add for win build CI

* correct install oneMKL

* fix install issue

* fix ci

* fix install cmd

* fix install cmd

* fix install cmd

* fix install cmd

* fix install cmd

* fix win build

* fix win build

* fix win build

* restore other CI part

* restore as base

* rm no new line

* fix no new line issue, add -j

* fix grammer issue

* allow to trigger manually, fix format issue

* fix format

* add newline

* fix format

* fix format

* fix format issuse

---------

Co-authored-by: Abhilash Majumder <30946547+abhilash1910@users.noreply.github.com>
2024-01-31 08:08:07 +05:30
crasm
413e7b0559
ci : add model tests + script wrapper (#4586)
* scripts : add lib.sh and lib_test.sh

* scripts : stub out new ci-run.sh script

* scripts : switch to PascalCase for functions

This looks a little odd at first, but I find it very useful as a
convention to know if a command is part of our code vs a builtin.

* scripts : add some fancy conversion from snake_case to PascalCase

* Add venv to ci/run.sh

* Revert scripts work

* scripts : add wrapper script for local use of ci/run.sh

* Simplify .gitignore for tests, clang-tidy fixes

* Label all ctest tests

* ci : ctest uses -L main

* Attempt at writing ctest_with_model

* Update test-model-load-cancel

* ci : add ctest_with_model for debug and release

ggml-ci

* Fix gg_get_model function

ggml-ci

* got stuck on CMake

* Add get_model.cpp to tests/CMakeLists.txt

ggml-ci

* Fix README.md output for ctest_with_model

ggml-ci

* workflows : use `-L main` for all ctest

ggml-ci

* Fixes

* GG_RUN_CTEST_MODELFILE => LLAMACPP_TESTMODELFILE
* Always show warning rather than failing if model file variable is not
  set

* scripts : update usage text for ci-run.sh
2024-01-26 14:18:00 +02:00
Georgi Gerganov
c918fe8dca
metal : create autorelease pool during library build (#4970)
* metal : create autorelease pool during library build

ggml-ci

* test : simplify

ggml-ci
2024-01-17 18:38:39 +02:00
Georgi Gerganov
5537d9d36b
gitignore : imatrix 2024-01-12 14:33:21 +02:00
Georgi Gerganov
b0034d93ce
examples : add passkey test (#3856)
* examples : add passkey test

* passkey : better prints

* passkey : select pass key pos from CLI

* passkey : simplify n_past logic

* make : add passkey target

* passkey : add "self-extend"-like context extension (#4810)

* llama : "self-extend"-like context extension

* passkey : add comment

* passkey : add readme
2024-01-08 11:14:04 +02:00
LeonEricsson
7082d24cec
lookup : add prompt lookup decoding example (#4484)
* initial commit, going through initializations

* main loop finished, starting to debug

* BUG: generates gibberish/repeating tokens after a while

* kv_cache management

* Added colors to distinguish drafted tokens (--color). Updated README

* lookup : fix token positions in the draft batch

* lookup : use n_draft from CLI params

* lookup : final touches

---------

Co-authored-by: Leon Ericsson <leon.ericsson@icloud.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-12-22 18:05:56 +02:00
Georgi Gerganov
fe680e3d10
sync : ggml (new ops, tests, backend, etc.) (#4359)
* sync : ggml (part 1)

* sync : ggml (part 2, CUDA)

* sync : ggml (part 3, Metal)

* ggml : build fixes

ggml-ci

* cuda : restore lost changes

* cuda : restore lost changes (StableLM rope)

* cmake : enable separable compilation for CUDA

ggml-ci

* ggml-cuda : remove device side dequantize

* Revert "cmake : enable separable compilation for CUDA"

This reverts commit 09e35d04b1.

* cuda : remove assert for rope

* tests : add test-backend-ops

* ggml : fix bug in ggml_concat

* ggml : restore `ggml_get_n_tasks()` logic in `ggml_graph_plan()`

* ci : try to fix macOS

* ggml-backend : remove backend self-registration

* ci : disable Metal for macOS cmake build

ggml-ci

* metal : fix "supports family" call

* metal : fix assert

* metal : print resource path

ggml-ci

---------

Co-authored-by: slaren <slarengh@gmail.com>
2023-12-07 22:26:54 +02:00
Jared Van Bortel
15f5d96037
build : fix build info generation and cleanup Makefile (#3920)
* cmake : fix joining of REAL_GIT_DIR

* fix includes with help from include-what-you-use

* make : remove unneeded deps and add test-rope target

* fix C includes in C++ source files

* Revert "fix includes with help from include-what-you-use"

This reverts commit 635e9fadfd.
2023-12-01 00:23:08 +02:00
Georgi Gerganov
922754a8d6
lookahead : add example for lookahead decoding (#4207)
* lookahead : init

* lookahead : generate and store n-grams

* lookahead : use loop instead recursion to generate n-grams

* lookahead : initial working implementation

* lookahead : filter repeating n-grams

* lookahead : use deterministic init

* lookahead : add to Makefile

* lookahead : fix a bug in the seq_id of the lookahead tokens

* lookahead : add comments

---------

Co-authored-by: slaren <slarengh@gmail.com>
2023-11-26 20:33:07 +02:00
Georgi Gerganov
35985acffa
gitignore : tokenize 2023-11-19 18:50:49 +02:00
Damian Stewart
381efbf480
llava : expose as a shared library for downstream projects (#3613)
* wip llava python bindings compatibility

* add external llava API

* add base64 in-prompt image support

* wip refactor image loading

* refactor image load out of llava init

* cleanup

* further cleanup; move llava-cli into its own file and rename

* move base64.hpp into common/

* collapse clip and llava libraries

* move llava into its own subdir

* wip

* fix bug where base64 string was not removed from the prompt

* get libllava to output in the right place

* expose llava methods in libllama.dylib

* cleanup memory usage around clip_image_*

* cleanup and refactor *again*

* update headerdoc

* build with cmake, not tested (WIP)

* Editorconfig

* Editorconfig

* Build with make

* Build with make

* Fix cyclical depts on Windows

* attempt to fix build on Windows

* attempt to fix build on Windows

* Upd TODOs

* attempt to fix build on Windows+CUDA

* Revert changes in cmake

* Fix according to review comments

* Support building as a shared library

* address review comments

---------

Co-authored-by: M. Yusuf Sarıgöz <yusufsarigoz@gmail.com>
Co-authored-by: Jared Van Bortel <jared@nomic.ai>
2023-11-07 00:36:23 +03:00
cebtenzzre
b12fa0d1c1
build : link against build info instead of compiling against it (#3879)
* cmake : fix build when .git does not exist

* cmake : simplify BUILD_INFO target

* cmake : add missing dependencies on BUILD_INFO

* build : link against build info instead of compiling against it

* zig : make build info a .cpp source instead of a header

Co-authored-by: Matheus C. França <matheus-catarino@hotmail.com>

* cmake : revert change to CMP0115

---------

Co-authored-by: Matheus C. França <matheus-catarino@hotmail.com>
2023-11-02 08:50:16 +02:00
Adrian Hesketh
ca190bca8e
server : re-enable completion and embedded at the same time (#3876) 2023-11-01 11:28:28 +02:00
Georgi Gerganov
438c2ca830
server : parallel decoding and multimodal (#3677)
* implementing parallel decoding in server example

* crash fixed

* save dev progress

* refactored sampling function

* completion endpoint working

* multiple client support

* grammar + no stream completion

* cached prompt support

* chat.mjs support cached prompt + some fixes

* server ui now support multiple clients

* unused change reverted

* fixed timings per slot

* add context swap

* add changes to README.md

* llava multimodal integration

* fixed tokens probs

* add multimodal input - alfa

* refactor code + remove unused comments + improved README.md

* fix compilation errors with llvm

* notify the user from server ui that multimodality is unavialable

* some ci fixes

* fix ci make build undefined ref errors

* fix long prompt than ctx proposed in #3639

* fixed premature end due stop word

* context shift fixed

* fix llava implementation

* sync README.md changes

* readme change

* update api like OpenAI

* multimodal support enabled by default

* fix make bui;d errors

* fix multiple clients

* fix zig build

* new sampling API

* latest changes of sampling API

* server : coding-style normalization

* server : coding-style normalization (part 2)

* server : remove beam-search functionality

* server : bug fix in ingest_images

n_tokens is incremented internally by llama_batch_add

* server : use refs + use llama_batch_clear()

* server : snake case

* server : minor sync

* added thread safe pipeline

* server : bach has to be allocated for n_parallel sequences

* server : no need for atomic int - already using mutex

* server : logs + minor code style

* server : fix multibyte handle in partial response (#3706)

* fix image load + view image in chat

* make : silence stb warnings

* clip : link to ggml, not to llama

* server : fix switch fallthrough

* server : fix crash in Debug on macOS (I have no idea why this fixes it!?)

* server : refactor ctx_sampling init + n_ctx + names

* server : bug fix for prompt caching

* Do not save/load image_data to localStorage

* editorconfig : new line in index.html

* server : completion requests remember slot_id

* Update readme to document multimodal in server

* server : minor style

* Update readme to document multimodal in server

* server : hide ctx_sampling->prev behind API (#3696)

* server : apply fix from #3722

* server : fix slot reuse

* server : add comment about changing slot_state to bool

---------

Co-authored-by: FSSRepo <go778sgt@gmail.com>
Co-authored-by: Damian Stewart <d@damianstewart.com>
Co-authored-by: Steward Garcia <57494570+FSSRepo@users.noreply.github.com>
Co-authored-by: Jhen-Jie Hong <iainst0409@gmail.com>
Co-authored-by: M. Yusuf Sarıgöz <yusufsarigoz@gmail.com>
2023-10-22 22:53:08 +03:00
M. Yusuf Sarıgöz
370359e5ba
examples: support LLaVA v1.5 (multimodal model) (#3436)
* WIP: start implementing LLaVA

* rm scratch buf for now, will revert after cleanup

* LLaVA image encoder is working. will combine with llama

* Add llava inference code, but it's buggy. debugging

* LLaVA is working e2e, needs to optimize memory allocation + cleanup

* Use ggml_allocr + rm unnecessary code

* fix: crlf -> lf

* fix: new line at EoF

* fix: trailing whitespace

* Add readme

* Update readme

* Some cleanup

* Are you happy editorconfig?

* rm unused batch image preprocessing

* rm unused import

* fix: rm designated initializers

* introduce pad-to-square mode for non-square images

* are you happy editorconfig?

* gitignore /llava

* Handle cases where image file does not exist

* add llava target to Makefile

* add support for 13b model variant

* Maybe seed is unlucky?

* Check if apples are compared to apples

* are you happy editorconfig?

* Use temperature = 0.1 by default

* command line: use gpt_params_parse()

* minor

* handle default n_predict

* fix typo

* llava : code formatting, rename files, fix compile warnings

* do not use Wno-cast-qual for MSVC

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-10-12 18:23:18 +03:00
Georgi Gerganov
8c70a5ff25
batched : add bench tool (#3545)
* batched : add bench tool

* batched : minor fix table

* batched-bench : add readme + n_kv_max is now configurable

* batched-bench : init warm-up batch

* batched-bench : pass custom set of PP, TG and PL

* batched-bench : add mmq CLI arg
2023-10-11 21:25:33 +03:00
Jhen-Jie Hong
c26765a0a1
metal : support default.metallib load & reuse code for swift package (#3522)
* metal : support load default.metallib & reuse code for swift package

* metal : use SWIFT_PACKAGE def instead of define GGML_SWIFT
2023-10-07 11:40:27 +03:00
goerch
ff5a3f0c09
Work on the BPE tokenizer (#3252)
* Work on the BPE tokenizer

Tokenizer tests work for Falcon-7B

* Try to fix build problem

* Fix debug assertion failure

* Fix MSVC Unicode BOM problem

* Cleanup and an improvement

* Fix compiler warning

* Cleanup

* Test doesn't work over the full range of Unicodes

* Update .gitignore and Makefile

* Another Makefile rule

* Testing Aquila

* Moving byte decoding back to `token_to_piece` ...

... because everyone is using it.

* Guarding some unusable code pathes

* Streamlining code and adding some more assertions

Important change: I'm classifying added tokens as control tokens now for BPE.

* Adding a comment

* Adding another assertion

* Fixed vocabulary guarding assertions

* Fix PR for recent change

* Fix PR for recent change

* Fix for compiler warning

* Fix PR for recent change

* Fix PR for recent change

* Fix PR for recent change

* Fix for compiler warning

* Fixes for more compiler warnings

* Remove unused code

* Fix initialization of static maps

* Add scores and token types back, adapt gptneox

* Update llama.cpp

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

* Update unicode.h

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

* Update unicode.h

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

* Ported Starcoder and added some assertions

* Fix coding style

* Apply @jploski 's fix for missing tokens

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-10-03 09:16:26 +02:00
vvhg1
c97f01c362
infill : add new example + extend server API (#3296)
* vvhg-code-infill (#1)

* infill in separate example (#2)

* reverted changes to main and added infill example

* cleanup

* naming improvement

* make : add missing blank line

* fix missing semicolon

* brought infill up to current main code

* cleanup

---------

Co-authored-by: Cebtenzzre <cebtenzzre@gmail.com>
2023-10-02 10:42:02 +03:00
Cebtenzzre
bc39553c90
build : enable more non-default compiler warnings (#3200) 2023-09-28 17:41:44 -04:00
xaedes
0e76a8992c
train : finetune LORA (#2632)
* fix track_max_mem in forward_batch_wo_cache_flash_attn_train

* remove unnecessary Adam(W) optimizer tensors.

reduces optimizer memory overhead from 7*modelsize to 2*modelsize.

additionally allows to optimize models with more than 2^31 parameters by replacing int with int64_t.

bumps training checkpoint file version, but old checkpoints can still be read.
new version with less tensors is saved.

* add gradient clipping to AdamW

* Fix reset of unused g->nodes and g->grads to NULL

* implement gradient checkpointing for training

reduces memory overhead from O(n_layer) to O(sqrt(n_layer))

as explained in readme of https://github.com/cybertronai/gradient-checkpointing

* remove unused compute buffer 3

* add and use function ggml_build_backward_expand to avoid stack overflows with large maximum number of nodes

GGML_API void ggml_build_backward_expand(struct ggml_context * ctx, struct ggml_cgraph * gf, struct ggml_cgraph * gb, bool keep);

* change AdamW decay parameter to work like the torch AdamW decay parameter

It is now relative to Adam learning rate `alpha*sched`.
Before that it was relative to `sched` only.

`alpha` being the maximum learning rate and `sched` being a scaling parameter in [0..1]

* change default AdamW weight decay parameter used in training to 0.1 as used in nanoGPT

* change default AdamW weight decay parameter defined in ggml to 0.0, making Adam default instead of AdamW

btw: the default weight decay parameter for torch.optim.AdamW is 0.01

* bug fixes for cross entropy loss

ggml_cross_entropy_loss: sums where not correctly added in workload of each thread
ggml_cross_entropy_loss_back: simplify backward process, reducing numerical issues

guard usage of exp f16 lookup in cross entropy by #define GGML_CROSS_ENTROPY_EXP_FP16

cross entropy loss is only used once during training, but it is quite sensitive to numerical errors introduced by exp-f16-lookup.
so exp-f16-lookup for cross entropy loss is disabled by default, trading better gradients for very slightly worse runtime performance.

* fix test-grad0 for cross_entropy_loss

the second argument to cross_entropy_loss must sum up to 1 for each row

* fix test-grad0 for soft_max

dont use only sum as aggregation, because sum of softmax is always 1 -> finite differences should not work
instead use sum(log(soft_max()*(1-eps)+eps)); use eps to avoid log(0)

* improve finite differences of test-grad0 by using double instead of float

* change cross_entropy_loss to output average over all rows

this helps keeping the loss and gradients in a sane range

* improve gradient checkpointing

sqrt(n_layers) is only the best checkpoint step when mem size of checkpoints and mem size of layers are equal.
since layers require more memory than the single-tensor-checkpoint we use, the optimal values are compute different:

```
  given: n, u, v
  objective: minimize(a*u+b*v) where a*b=n, a>0, b>0
  b=n/a
  minimize(a*u+v*n/a)
  diff(a*u+v*n/a, a) = u - (v*n/a)/a
  diff(a*u+v*n/a, a) == 0
  u - (v*n/a)/a == 0
  u == v*n/(a*a)
  u*a*a = v*n
  a*a = v*n/u
  a = sqrt(n*v/u)
```

this change results in more checkpoints, requiring less layers to store between checkpoints, overall improving memory usage.

* disable gradient checkpointing debug output

* llama : fix rope usage in train-text-from-scratch after ChatGLM change

* add more training parameters:

--enable-restart N         Only for Adam optimizer. Enable restarts of cos-decay
--disable-restart N        Only for Adam optimizer. Disable restarts of cos-decay
--opt-past N               Number of optimization iterations to track for delta convergence test. Disabled when zero.
--opt-delta N              Maximum delta for delta convergence test. Disabled when <= zero.
--opt-max-no-improvement N Maximum number of optimization iterations with no improvement. Disabled when <= zero.
--adam-epsf N              AdamW epsilon for convergence test. Disabled when <= zero.
--adam-min-alpha N         Adam minimum learning rate alpha, usually 0.1 * alpha

* replace memcpy with reshape operation so that the graph is not cut at the input

this makes it possible to store other values into the input tensor and then simply recompute the graph without rebuilding it

* remove unused function argument from get_example_targets_batch

* measure and print total training time

* add optimization callback to ggml_opt_resume_g

this callback is called before each iteration with custom data and pointer to learning schedule parameter (only used in Adam(W)).

can be used for dynamic learning schedule and setting input data for batches before each iteration

* use optimization callback in training

allows dynamic learning schedule and different batch data for each iteration without relying on low n_iter and high n_examples parameters

reduces runtime by avoiding restart of optimization function and improves training convergence by providing a different batch for each iteration

* add minimum number of tensor dimensions to apply weight decay (default 2)

this allows to not apply weight decay to bias parameters

* rename training parameter cos-decay-alpha to cos-decay-min and clarify that adam-min-alpha also applies to warmup

* fix increase of model.train_samples and model.train_tokens

now that each optimizer iteration gets its own batch we need to multiply by number of opt iterations

* change sampling parameters for prediction after training to defaults of common.h

and clarify what is context for prediction and what are generated tokens

* tighten abs error bounds for cross_entropy_loss in test-grad0

* add conditional compilation of using F16 exp in flash attention

uncomment `// #define GGML_FLASH_ATTN_EXP_FP16` to enable usage of f16 exp in flash attention

* tighten abs error bounds for flash_attn in test-grad0

* tighten abs error bounds for sqrt in test-grad0

* remove out-commented vectorized code of opt_adam

the vectorized code might be bit faster for low number of parameters, but it had a big memory usage overhead

* ggml : update ggml_rms_norm_back with configurable eps

* llama training : fix ggml_rms_norm_back calls to pass configurable eps

* remove trailing whitespace

* add train function using automatic gradient checkpointing backward pass and allocator

* in train function replace add_inplace by regular add

because using add_inplace seems to result in different gradients

* don't use allocate hash_map on context

because the context has no_alloc=True when using memory allocator resulting in NULL data pointers

* correctly clone reshape and permute operations by also cloning tensor->nb values

* fix variable name and add missing type cast

* terminate recursive tensor cloning when reaching tensor without src tensors

* correctly clone view tensors by setting data pointers

without this the checkpointing would only work when being used together with memory allocator

* fix variable names

* swap arguments to commutative ops to be the same as in `forward_batch_wo_cache_flash_attn`

* add input tensors as checkpoints

so that recursive tensor cloning of gradient checkpointing terminates on input tensors

* fix variable name and add missing boolean negation

* make sure some tensors are not reallocated by inserting new temporary nodes depending on them:

output and parameter gradient tensors need to be available at the end of the graph execution

parameter gradient tensors also need to be available before the graph execution because they are set to zero before each optimizer iteration

checkpoint tensors are allocated all together to reduce memory allocator fragmentation

afterwards, in addition to the temporary nodes, we also need to reset the temporary leafs

* fix ASSERT to work with zero layers

* add training options whether to use allocator and/or unified training function

* integrate unified training function which may use memory allocator

the unified training function also supports arguments whether to use flash attention and/or gradient checkpointing

* format name of cloned tensors with " (clone)" suffix

* set names for tensors in unified train function for easier debugging

* allocate graph on context using ggml_new_graph

* remove handwritten training functions

* remove unused training parameters "use_scratch" and "use_unified"

* remove trailing whitespace

* remove unused train params: mem_compute1_gb & mem_compute2_gb

mem_compute_gb is used for compute when automatic memory allocator is not enabled, otherwise it can be very small to only hold the tensor definitions
mem_compute0_gb is used for automatic memory allocator (as long as measurement of max required size is not implemented)

* remove unused forward_batch function

* add debug asserts in ggml_allocr_alloc to some common pitfalls when using this function directly

* only use ggml_allocr_alloc when tensor has NULL data and is no view

* fix test when to create temporary backward graph

temporary backward graph is only necessary when using checkpointing

* fix memory "leak" in optimizers

each iteration a new cplan with new memory for work data was allocated.
now cplan creation only happens at the start of optimization, with each iteration reusing the cplan and its work data.

* reverse order of for loop in ggml_build_backward_expand to save memory when using gradient checkpointing and allocator

with this loop order gradient checkpointing with allocator on 16 layer model saves 13% memory; 2 layer memory it saves 2% memory.

the computation results are the same

* add API functions to access llama model tensors

* add stub example for finetuning, based on train-text-from-scratch

* move and remove code

* add API functions to access remaining model parameters:

mult, head and rot

* first draft for LORA finetune training

* remove const model and layer arguments in API functions for accessing model tensors

* bug fixes to make finetune compile

automatic allocator does not work yet

* add debug prints for training memory improvements

* fix names of lora tensors

* avoid stack overflow resulting from big ggml_cgraph

replace stack allocation and ggml_build_forward by ggml_new_graph in combination with ggml_build_forward_expand

* replace llama API functions to get model tensors by one function to get model tensor by name

LLAMA_API struct ggml_tensor * llama_get_model_tensor(struct llama_model * model, const char * name);

* remove unused call to not existing llama_get_layer_from_model

* implement ggml_compute_forward_out_prod_q_f32

* remove trailing whitespace

* add lora finetune support on quantized base model tensors

* add ggml_add_cast API function

this function works like ggml_add, but accepts a data type for the resulting tensor.
only supported for quantized src0 input.

* use ggml_add_cast in finetuning

lora-applied weights will now have data type F32, which improves gradients when finetuning quantized base models

* bug fix: actually use result type passed to ggml_add_cast

* make sure base model tensors data cannot be used in viewable operations

memory allocator would try to make lora application inplace on base model tensors.
since those are memory mapped this will result in memory access violations

* fix bug in ggml_out_prod which resulted in wrong n_dims of result tensors

* avoid keeping in memory ALL of the gradients

The problem here stems from ggml_graph_reset. This function is called in the optimization function, before each graph computation, to reset the gradients to zero. This required a unique memory slot for each gradient: allocating memory from a previosly freed memory location might lead to non-zero input gradients.

During ggml_compute_backward the gradients are build stepwise by adding or substracting new values, starting from a OP_NONE tensor which needs to contain zero-values. This requires the graph reset.

To avoid this I now remember in ggml_build_backward_expand the original OP_NONE gradient tensors in a hash table, which is passed to ggml_compute_backward. There instead of using add (or sub or similar) I test whether the existing gradient to be changed is a zero-valued-tensor by looking up its existence in the hash table. When it is such a zero-tensor it will not be modified, but replaced by the value to be added, otherwise the regular add (not inplace, allocator will take care of this) will be used. This way none of those zero-tensor values will be necessary in the final backward graph and more importantly they won't need a unique memory slot, just to make them zero.

* remove trailing whitespace

* remove debug prints and function to compute tensor data hash

* improve optimization iteration prints

* adjust maximal values to support finetuning 3B models

* change default finetune params lora_r and lora_alpha to match the n_rank parameters of 4

* bug fix: make sure finetune input gradient is allocated at begin and kept until end

* remove unnecessary src tensor from ggml_get_rows_back

we don't need data of src[2] for computation, only to setup the correct output shape.
remove dependency on src[2], so that allocator can work more freely.

the computational graph is still completely determined, because the output shape is naturally included.
this is similar to how ggml_reshape does it.

* remove unnecessary src tensor from ggml_repeat & ggml_repeat_back

we don't need data of src[1] for computation, only to setup the correct output shape.
remove dependency on src[1], so that allocator can work more freely.

the computational graph is still completely determined, because the output shape is naturally included

* resolve todo

allocator will only make it inplace when they are of the same type

* mixing multiple LORA adapters is now possible

pass more than one '--lora FNAME' argument to apply more than one LORA.
use '--lora-scaled FNAME S' when you want to specify a user-defined scale for an adapter.

* add option to save finetune output every N iterations

* also save latest finetune output with ITERATION="LATEST" and print where files are saved

saving with LATEST makes it easier to resume training from the latest checkpoint
the string "LATEST" can be configured with command line option "--fn-latest STR"

* update checkpoint train stats before saving via "--save-every"

* add command line option `--rank-wo N` for rank of wo tensor

* update finetune README

* fix dump_non_result_info_yaml to output multiple lora adapters

* bug fix: replace GGML_TYPE_SIZE[t] by ggml_type_size(t)

* replace llama_n_mult by llama_n_ff

* finetune bug fixes to compile with merged in code from master

* remove prediction related code to reduce duplicated code with main

use main instead

* reduce large memory overhead in train-text-from-scratch

all gradients had to be pinned so that graph_reset works correctly.
this is no longer necessary with the changes to ggml_compute_backward introduced in this PR.

* add comment explaining why finetune checkpoints are allocated in one block

* make default value of float member a float literal

* handle rms_norm and rope parameters the same as in train-text-from-scratch

* remove unused code

* remove vocab related code as it is unnecessary

* add LLM_KV_TRAINING_TYPE to train-text-from-scratch checkpoints

so that they can be differentiated from lora finetune checkpoints

* add gguf constants and load/save functions from train-text-from-scratch

* add load & save lora finetune checkpoints via gguf

* add python script to convert old finetune checkpoint files to gguf

* remove old checkpoint save & load code

* remove code to print data checksums which was used to verify correctness of new gguf code

* omit tokenization when training is disabled, only save llama lora adapter

training can be disabled by passing '-n 0' to finetune

* remove trailing whitespace

* update README.md

* implement ggml_compute_forward_repeat_f16

* avoid stack overflow of large cgraphs in test-grad0

* add ggml API functions ggml_unravel_index, ggml_get_i32_nd and its analogs for set and for f32

ggml_get_i32_1d, ggml_set_i32_1d, ggml_get_f32_1d, ggml_set_f32_1d now support non-contiguous tensors.
in case of non-contiguous tensor, the 1d index is unraveled into a multi index using ggml_unravel_index to be passed to '_nd' function equivalent.

this fixes a bug in test-grad0 which happens due to ggml_build_backward not building purely contiguous tensors anymore

* increase test-grad0 context mem size to accommodate for bigger cgraph

* add sanity check to ggml_compute_backward, asserting the correct shape of gradients

* fix ggml_acc_or_set to return tensor of correct shape

* remove unused 'inplace' argument from ggml_compute_backward function

inplace operations to add gradients are no longer created by ggml_compute_backward
use allocator to automatically make inplace operations

* add missing argument 'int i0' to ggml_get_i32_nd & ggml_set_i32_nd header declarations

* fix error message in ggml_allocr_alloc to display actual max_avail

* fix check_gradient

ggml_build_backward_expand was previously replaced by ggml_build_backward, but the assignment of forward graph to backward graph missing

* use tensor->view_src instead of ggml_is_view and get_view_source

* move gradient checkpointing code into ggml, new API function:

// build gradient checkpointing backward graph gb for gf using provided checkpoints
// gb_tmp will contain original backward graph with rewritten backward process nodes,
// but without the second forward pass nodes.
GGML_API void ggml_build_backward_gradient_checkpointing(
        struct ggml_context   * ctx,
        struct ggml_cgraph    * gf,
        struct ggml_cgraph    * gb,
        struct ggml_cgraph    * gb_tmp,
        struct ggml_tensor  * * checkpoints,
        int                     n_checkpoints);

* replace custom data getters and setters by ggml functions

* train-text-from-scratch can train (full finetune) gguf models

just pass the gguf model via `--checkpoint-in FN`.
after this, to continue training, pass the generated checkpoint instead of the original gguf model.

tested with smaller models, bigger models may exceed available memory.
use (LORA) finetune for those.

* remove trailing whitespace

* add option to save train-text-from-scratch output every N iterations

* update README.md

* fix warnings

* fix warnings

* remove finetune option to disable allocator

the allocator should always be used.
by making sure that it is always used it gets easier to implement automatic memory requirements computation

* add tensor checkpoints only when gradient checkpointing is enabled

* initialize opt ggml context if none was provided

* add ggml-alloc API function 'ggml_allocr_max_size' to get max size of alloc

GGML_API size_t ggml_allocr_max_size(struct ggml_allocr * alloc);

* finetune: automatically allocate all memory and changes to command line options

remove '--n_examples N' parameter, as it no longer makes sense to call optimization process multiple times in a loop.
add '--only_write_lora' command line option: will skip tokenization and training, to only write a llama.cpp comptabile LORA adapter.
remove memory buffer related command line options.
improve iteration console output.

* add finetune to Makefile

* update README.md

* print time per iteration and estimate remaining time

* increase measured alloc size by tensor_alignment

ggml_allocr_reset will reduce the given size by up to tensor_alignment-1

* fix README.md

* add some more allocator debug prints

* bug fix, probably solves the 'ggml_allocr_alloc: not enough space in the buffer' issue

* revert last commit

"bug fix, probably solves the 'ggml_allocr_alloc: not enough space in the buffer' issue"

"alloc was freeing an externally allocated tensor, because it calculated the end of allocator memory as alloc->data + alloc->max_size instead of alloc->data + alloc->size."

This is intentional to reduce the risk of freeing external tensors when measuring. Unless max_size is not properly calculated, I don't see why this is an issue.

* remove unnecessary "0x" before "%p" output

* move measurement memory segment to upper region of the address space

* update README.md

* fix printf format warnings

* add missing gguf_free in load_checkpoint_lora_file

* load default rms_norm and rope parameters from base model

* add gradient accumulation

specify number accumulation steps with '--grad-acc N'.
this will simulate a bigger batch size of grad_acc*batch.

* fix tracking of train_samples and train_tokens

* build : fix compile warnings

* ggml : fix L-BFGS linesearch loop

* improve finetune time measurement

fix printf warnings on system where int64_t is (long int).
change time datatypes to double because values get big with long training times.
exclude file saving from time measurement.
converge faster to actual time per iteration by removing very small first duration before first iteration was performed.
fix bug in output of total training time, the reported value was 1000 times to small.

* specify default lora rank with '--lora-r N'

'--lora-r N' will specify default rank for all tensors
'--rank-wq N', etc. will override this default rank for specific tensor types.

* fix gradient accumulation bug where the same batch was used for each microstep

* fix gradient accumulation bug where the same batch was used for each microstep

* support grouped-query-attention in ggml_flash_attn and ggml_flash_attn_back

k and v can now be repeated in q along ne[2]

in forward pass just use modulo to compute k and v indices, like ik2 = iq2 % nek2.

in backard pass this won't work as easy, because multiple threads will compete to accumulate to the same k->grad[:,ik1,ik2,ik3] and v->grad[:,iv1,iv2,iv3].
so we change the parallelization over q rows to be over k rows. this ensures non-overlapping (ik2,ik3) across threads.
in each thread we then iterate over the number of repetitions of k/v in q to compute iq2 as iq2 = ik2 + irep*nek2.

since ne2 is not the same for q,k and v we also change how the gradients are concatenated into the result tensor.
additionally the offsets of gradq, gradk and gradv in the result tensor are now memory aligned.

we also simplify the compute_backward part of flash_attn to use ggml_reshape instead of switching over the number of dimensions.
this needs a small change to ggml_reshape, removing the assertion of second argument to be contiguous.
since only the shape (ne) of the second reshape argument is of relevance, its memory layout (nb) is irrelevant -> it can very well be non-contiguous.

change test-grad0 to also test for repeated k/v in q.

this changes the rng and now results in small gradient differences in softmax. these solely come from using f16 exp table lookup in forward softmax: when temporarily changing softmax to use actual exp function, the reported gradient differences go away. gradient differences coming solely from f16 table lookup are acceptable.
added a note to explain this.

* add llama API functions to get grouped-query-attention n_head parameter 'n_head_kv'.

* fix finetune to support grouped-query-attention (using flash-attention)

note: ggml changes to ggml_out_prod are necessary to support grouped-query-attention without flash-attention.

* support broadcastable a in out_prod(a, b) and backward pass of broadcasting mul_mat(a, b)

* test broadcasting mul_mat backward pass

* decouple random number generator of each operation test

when changing one test the rng of others tests is not influenced anymore

* add comment briefly describing what ggml_repeat_back does

* simplify broadcasting mul_mat backward using ggml_repeat_back

* add cgraph evaluation order member and corresponding enum type

this controls in which order ggml_build_forward visits source nodes.
by default the nodes are visited left to right, i.e. src[0] first.
in some cases it is beneficial for ggml-alloc to visit in a different order.
two possible orders are supported: left-to-right (src[0] first) and right-to-left (src[0] last).

* measure max compute size for each cgraph eval order and use best order

this can bring huge memory savings:
e.g. codellama-34b with n_ctx=64, n_batch=1 goes from 92927.8mb down to 4627.6 MB

* remove unused command line options

* add sample start patterns and options to force new or by default resume last shuffling

* update shuffle rng state on reshuffle

* exclude known zero values from computations in flash_attn_f32 & flash_attn_back_f32

* remove probably unnecessary exception type flags from stringstream

* pass correct max number of tokens to llama_tokenize

* account for possible leading whitespace that will be added by tokenizer
e.g. '\t' will be tokenized by llama spm tokenizer to [29871, 12]

* use unrolled vec_mad in out_prod

y is vec_mad result vec.
x is vec_mad input vec.
v is vec_mad input scalar.

ggml_vec_mad_f32_unroll will internally loop over x and v with same y.

GGML_VEC_MAD_UNROLL is by default defined to 32.

This value is empirical optimized using performance test runs of out-prod in openllama-3b finetune with 256 context length and batch size 1. It gives 23% performance boost for out_prod.

Full measurements of out-prod runtime in ms:
	unroll_xv	unroll_yv
1	67014.643	87826.469
2	77117.552	89077.656
4	72091.311	109121.657
8	61077.543	88678.334
16	56914.67	79514.947
24	59024.595	84350.254
28	55952.446	83368.73
32	51476.658	85177.745
36	55973.792	84659.92
40	55139.616	93844.738
48	60736.392	93330.267
64	99856.878	116994.99

Second column is when unrollying yv instead of xv

* set lora_alpha to value of lora_r if it is not set via command line

otherwise only changing lora_r will change scaling of lora adapter used in prediction

* reshuffle original sample order instead of the previous shuffled order

otherwise resumed reshuffle will not result in same sample order

* block tiling for out-prod inspired by mul-mat

block sizes are empirically optimized

roughly doubles the flops of out-prod

* exclude some more known zero values from computations in flash_attn_f32 & flash_attn_back_f32

* add static keywords

* remove outcommented old code

* update train-text-from-scratch with tokenization, sample selection and shuffling from finetune

* remove lbfgs related train parameters

* move common train functions into common/train.[h|cpp]

* move train state into struct train_state

* move train data saving code into callback to unify code of opt_callback

train_params are still different in finetune and train-text-from-scratch, so it can't yet be moved to train.h|cpp

* move common train params into common/train

* move common opt_callback into common/train

* fix consume_common_train_arg

* save and load head_count_kv in lora checkpoints

* increase train_samples by used_samples instead of number of batches

on batch can contain more than one sample when option "fill_with_next_samples" is used

* fix usage of llama_tokenize

* remove static from process_escape since we need it exposed in header

* fix code formating of long function declarations

* fix condition in load_train_state_gguf

* use die("msg") instead of replace GGML_ASSERT(!"msg") or throw std::runtime_error("msg")

* fix saving and loading of training type

* remove terminating '\0' from tokenization

(llama_tokenize is now passed the string length instead of relying on terminating '\0')

* fix compile warnings

* fix compile warnings

* use new/delete for train_state instead of malloc/free

using malloc may result in seg faults when trying to assign string fields

* assert that sample_count > 0, avoiding division by zero

* fix frand to return value in interval [0,1)

* add train option "--sample-random-offsets"

Use samples beginning at random offsets.
The offset is only applied to the first sample in each batch context window.
Together with "--fill-with-next-samples" this may help for training endless text generation.

For example given a dataset containing samples "abcd", "ABCD", "0123".
With context size of 8 and options "--fill-with-next-samples", "--no-separate-with-eos", "--no-separate-with-bos",
the context windows of batches could only be filled with "abcdABCD", "ABCDabcd", "0123abcd", etc.

With "--sample-random-offsets" it can also be filled with "23abcdAB", "bcd0123A", etc.

* deduplicate code into function

* remove n_rot hparam, as it must always be hparam.n_embd_head()

* align code

* assert correct base model tensor shapes

* move some params from lora hparams into model hparams and load model params from gguf

this equalizes the model definition in finetune and text-from-scratch and removes the need for additional llama api functions to get model parameters

* remove now unnecessary llama API functions to get model params that where added by this PR

* train-text-from-scratch: automatically allocate model tensors, remove option '--mem-model N'

* train-text-from-scratch: automatically allocate opt context

* train-text-from-scratch: automatically allocate input tensors

* train-text-from-scratch: automatically allocate compute memory

* remove unused options and equalize train-text-from-scratch with finetune

* initialize opt->loss_after with zero

* add export-lora program

* remove trailing whitespace

* add export-lora build in Makefile

* remove unused struct tensor_info from export-lora

* add export-lora build dependency to llama

because it depends on common, which depends on llama

* update finetune README.md

* cancel optimization when specified number of epochs is completed

* improve handling of export-lora arguments

print errors and warnings when files could not be read or created

* Fix export-lora.cpp "not enough space in the context's memory pool" (#1)

* Fix export-lora.cpp "not enough space in the context's memory pool"

Without this patch, export-lora would sometimes error with "not enough space in the context's memory pool (needed 656784, available 656800)".

* increase required context size by 5*GGML_MEM_ALIGN instead of plain 16

---------

Co-authored-by: xaedes <xaedes@gmail.com>

* improve handling of not yet supported tensor types

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: meatbag-18a <145869052+meatbag-18a@users.noreply.github.com>
2023-09-28 21:40:11 +03:00
Georgi Gerganov
ec893798b7
llama : custom attention mask + parallel decoding + no context swaps (#3228)
* tests : verify that RoPE is "additive"

* llama : replace ggml_diag_mask_inf with ggml_add (custom -inf mask)

* ggml : ggml_rope now takes a vector with positions instead of n_past

* metal : add rope_f16 kernel + optimize cpy kernels

* llama : unified KV cache + batch inference API

* llama : add new llama_decode() API that works with llama_batch

* llama : add cell_max heuristic for more efficient kv_cache

* llama : extend llama_kv_cache API

* llama : more robust cell_max heuristic + wip shift

* metal : disable concurrency optimization

* llama : add llama_kv_cache_shift_seq + no more context swaps

* llama : apply K-cache roping for Falcon and Baichuan

* speculative : fix KV cache management

* parallel : example for serving multiple users in parallel

* parallel : disable hot-plug to avoid cache fragmentation

* fixes : speculative KV cache + llama worst-case graph

* llama : extend batch API to select which logits to output

* llama : fix worst case graph build

* ggml-cuda : update rope implementation for parallel decoding (#3254)

* ggml-cuda : update rope implementation for parallel decoding

* better solution for p0 computation

* fix rope

* simpler rope implementation

---------

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

* make : add parallel to build + fix static functions in llama.cpp

* simple : fix token counting

* parallel : various improvements

* llama : fix cell_max logic + rename functions

* parallel : try smaller batches when the KV cache is fragmented

* parallel : fix sequence termination criteria

* llama : silence errors KV cache errors

* parallel : remove new line from prompt

* parallel : process system prompt once + configurable paramters + llama API

* parallel : remove question with short answers

* parallel : count cache misses

* parallel : print misses on each request

* parallel : minor

* llama : fix n_kv to never become 0

* parallel : rename hot-plug to continuous-batching

* llama : improve llama_batch API + simplify parallel example

* simple : add parallel decoding support

* simple : improve comments + free batch

* ggml-cuda : add rope f16, restore performance with parallel decoding (#3272)

* ggml-cuda : add rope f16, restore performance

* offload KQ_mask with all models

* fix rope shift

---------

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

* llama : disable MPI for now

ggml-ci

* train : make KQ_pos memory buffer permanent via dummy scale op

* ggml : revert change to ggml_cpy, add ggml_cont_Nd instead (#3275)

ggml-ci

* parallel : fix bug (extra BOS) + smaller token_prev array

* parallel : fix cases where the input prompts can overflow the batch

* parallel : add disabled experimental batch chunking in powers of two

* llama : llama.h formatting + comments

* simple : add README.md

* llama : fix kv cache heuristic when context is less than 32

* parallel : fix crash when `-n -1`

* llama : simplify returns if/else branches

* metal : use mm kernels for batch size > 2

* examples : utilize new llama_get_logits_ith()

* examples : add example for batched decoding

* examples : do not eval prompt 2 times (close #3348)

* server : clear the KV cache beyond n_past before llama_decode

* server : avoid context swaps by shifting the KV cache

---------

Co-authored-by: slaren <slarengh@gmail.com>
2023-09-28 19:04:36 +03:00
Georgi Gerganov
e36ecdccc8
build : on Mac OS enable Metal by default (#2901)
* build : on Mac OS enable Metal by default

* make : try to fix build on Linux

* make : move targets back to the top

* make : fix target clean

* llama : enable GPU inference by default with Metal

* llama : fix vocab_only logic when GPU is enabled

* common : better `n_gpu_layers` assignment

* readme : update Metal instructions

* make : fix merge conflict remnants

* gitignore : metal
2023-09-04 22:26:24 +03:00
Leng Yue
5b8530d88c
make : add speculative example (#3003) 2023-09-04 13:39:57 +03:00
Alon
afc43d5f82
cov : add Code Coverage and codecov.io integration (#2928)
* update .gitignore

* makefile: add coverage support (lcov, gcovr)

* add code-coverage workflow

* update code coverage workflow

* wun on ubuntu 20.04

* use gcc-8

* check why the job hang

* add env vars

* add LLAMA_CODE_COVERAGE=1 again

* - add CODECOV_TOKEN
- add missing make lcov-report

* install lcov

* update make file -pb flag

* remove unused  GGML_NITER from workflows

* wrap coverage output files in COV_TARGETS
2023-09-03 11:48:49 +03:00
Georgi Gerganov
c90d135eb4
examples : fix underscore in beam-search + .gitignore (close #2900) 2023-08-30 12:53:24 +03:00
staviq
8341a25957
main : log file (#2748)
* initial, base LOG macro

* add *.log to .gitignore

* added basic log file handler

* reverted log auto endline to better mimic printf

* remove atomics and add dynamic log target

* log_enable/disable, LOG_TEE, basic usage doc

* update .gitignore

* mv include to common, params, help msg

* log tostring helpers, token vectors pretty prints

* main: replaced fprintf/LOG_TEE, some trace logging

* LOG_DISABLE_LOGS compile flag, wrapped f in macros

* fix LOG_TEELN and configchecker

* stub LOG_DUMP_CMDLINE for WIN32 for now

* fix msvc

* cleanup main.cpp:273

* fix stray whitespace after master sync

* log : fix compile warnings

- do not use C++20 stuff
- use PRIu64 to print uint64_t
- avoid string copies by using const ref
- fix ", ##__VA_ARGS__" warnings
- compare strings with == and !=

* log : do not append to existing log + disable file line func by default

* log : try to fix Windows build

* main : wip logs

* main : add trace log

* review: macro f lowercase, str append to sstream

* review: simplify ifs and str comparisons

* fix MSVC, formatting, FMT/VAL placeholders

* review: if/else cleanup

* review: if/else cleanup (2)

* replace _ prefix with _impl suffix

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-08-30 09:29:32 +03:00
alonfaraj
75fafcbccc
make : fix tests build (#2855)
* makefile:
- fix test name
- add missing tests build

* editorconfig : fixes

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-08-28 18:38:35 +03:00
Henri Vasserman
6bbc598a63
ROCm Port (#1087)
* use hipblas based on cublas
* Update Makefile for the Cuda kernels
* Expand arch list and make it overrideable
* Fix multi GPU on multiple amd architectures with rocblas_initialize() (#5)
* add hipBLAS to README
* new build arg LLAMA_CUDA_MMQ_Y
* fix half2 decomposition
* Add intrinsics polyfills for AMD
* AMD assembly optimized __dp4a
* Allow overriding CC_TURING
* use "ROCm" instead of "CUDA"
* ignore all build dirs
* Add Dockerfiles
* fix llama-bench
* fix -nommq help for non CUDA/HIP

---------

Co-authored-by: YellowRoseCx <80486540+YellowRoseCx@users.noreply.github.com>
Co-authored-by: ardfork <134447697+ardfork@users.noreply.github.com>
Co-authored-by: funnbot <22226942+funnbot@users.noreply.github.com>
Co-authored-by: Engininja2 <139037756+Engininja2@users.noreply.github.com>
Co-authored-by: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com>
Co-authored-by: jammm <2500920+jammm@users.noreply.github.com>
Co-authored-by: jdecourval <7315817+jdecourval@users.noreply.github.com>
2023-08-25 12:09:42 +03:00
M. Yusuf Sarıgöz
87e3733f24
gguf : make gguf pip-installable
* gitignore : add dist and rm pyproject.toml

* gguf: prepare as Pip package

* gguf: prepare as Pip package

* gguf : fix line endings

* requirements : add gguf

* gguf : update readme with build notes

* gguf : update readme with build notes

* gguf : add notes for tests
2023-08-25 09:26:05 +03:00
akawrykow
cc34dbda96
gitignore : fix for windows (#2729) 2023-08-23 17:31:34 +03:00
Georgi Gerganov
6381d4e110
gguf : new file format with flexible meta data (beta) (#2398)
* gguf : first API pass

* gguf : read header + meta data

* gguf : read tensor info

* gguf : initial model loading - not tested

* gguf : add gguf_get_tensor_name()

* gguf : do not support passing existing ggml_context to gguf_init

* gguf : simplify gguf_get_val

* gguf : gguf.c is now part of ggml.c

* gguf : read / write sample models

* gguf : add comments

* refactor : reduce code duplication and better API (#2415)

* gguf : expose the gguf_type enum through the API for now

* gguf : add array support

* gguf.py : some code style changes

* convert.py : start a new simplified implementation by removing old stuff

* convert.py : remove GGML vocab + other obsolete stuff

* GGUF : write tensor (#2426)

* WIP: Write tensor

* GGUF : Support writing tensors in Python

* refactor : rm unused import and upd todos

* fix : fix errors upd writing example

* rm example.gguf

* gitignore *.gguf

* undo formatting

* gguf : add gguf_find_key (#2438)

* gguf.cpp : find key example

* ggml.h : add gguf_find_key

* ggml.c : add gguf_find_key

* gguf : fix writing tensors

* gguf : do not hardcode tensor names to read

* gguf : write sample tensors to read

* gguf : add tokenization constants

* quick and dirty conversion example

* gguf : fix writing gguf arrays

* gguf : write tensors one by one and code reuse

* gguf : fix writing gguf arrays

* gguf : write tensors one by one

* gguf : write tensors one by one

* gguf : write tokenizer data

* gguf : upd gguf conversion script

* Update convert-llama-h5-to-gguf.py

* gguf : handle already encoded string

* ggml.h : get array str and f32

* ggml.c : get arr str and f32

* gguf.py : support any type

* Update convert-llama-h5-to-gguf.py

* gguf : fix set is not subscriptable

* gguf : update convert-llama-h5-to-gguf.py

* constants.py : add layer norm eps

* gguf.py : add layer norm eps and merges

* ggml.h : increase GGML_MAX_NAME to 64

* ggml.c : add gguf_get_arr_n

* Update convert-llama-h5-to-gguf.py

* add gptneox gguf example

* Makefile : add gptneox gguf example

* Update convert-llama-h5-to-gguf.py

* add gptneox gguf example

* Update convert-llama-h5-to-gguf.py

* Update convert-gptneox-h5-to-gguf.py

* Update convert-gptneox-h5-to-gguf.py

* Update convert-llama-h5-to-gguf.py

* gguf : support custom alignment value

* gguf : fix typo in function call

* gguf : mmap tensor data example

* fix : update convert-llama-h5-to-gguf.py

* Update convert-llama-h5-to-gguf.py

* convert-gptneox-h5-to-gguf.py : Special tokens

* gptneox-main.cpp : special tokens

* Update gptneox-main.cpp

* constants.py : special tokens

* gguf.py : accumulate kv and tensor info data + special tokens

* convert-gptneox-h5-to-gguf.py : accumulate kv and ti + special tokens

* gguf : gguf counterpart of llama-util.h

* gguf-util.h : update note

* convert-llama-h5-to-gguf.py : accumulate kv / ti + special tokens

* convert-llama-h5-to-gguf.py : special tokens

* Delete gptneox-common.cpp

* Delete gptneox-common.h

* convert-gptneox-h5-to-gguf.py : gpt2bpe tokenizer

* gptneox-main.cpp : gpt2 bpe tokenizer

* gpt2 bpe tokenizer (handles merges and unicode)

* Makefile : remove gptneox-common

* gguf.py : bytesarray for gpt2bpe tokenizer

* cmpnct_gpt2bpe.hpp : comments

* gguf.py : use custom alignment if present

* gguf : minor stuff

* Update gptneox-main.cpp

* map tensor names

* convert-gptneox-h5-to-gguf.py : map tensor names

* convert-llama-h5-to-gguf.py : map tensor names

* gptneox-main.cpp : map tensor names

* gguf : start implementing libllama in GGUF (WIP)

* gguf : start implementing libllama in GGUF (WIP)

* rm binary commited by mistake

* upd .gitignore

* gguf : calculate n_mult

* gguf :  inference with 7B model working (WIP)

* gguf : rm deprecated function

* gguf : start implementing gguf_file_saver (WIP)

* gguf : start implementing gguf_file_saver (WIP)

* gguf : start implementing gguf_file_saver (WIP)

* gguf : add gguf_get_kv_type

* gguf : add gguf_get_kv_type

* gguf : write metadata in gguf_file_saver (WIP)

* gguf : write metadata in gguf_file_saver (WIP)

* gguf : write metadata in gguf_file_saver

* gguf : rm references to old file formats

* gguf : shorter name for member variable

* gguf : rm redundant method

* gguf : get rid of n_mult, read n_ff from file

* Update gguf_tensor_map.py

* Update gptneox-main.cpp

* gguf : rm references to old file magics

* gguf : start implementing quantization (WIP)

* gguf : start implementing quantization (WIP)

* gguf : start implementing quantization (WIP)

* gguf : start implementing quantization (WIP)

* gguf : start implementing quantization (WIP)

* gguf : start implementing quantization (WIP)

* gguf : quantization is working

* gguf : roper closing of file

* gguf.py : no need to convert tensors twice

* convert-gptneox-h5-to-gguf.py : no need to convert tensors twice

* convert-llama-h5-to-gguf.py : no need to convert tensors twice

* convert-gptneox-h5-to-gguf.py : simplify nbytes

* convert-llama-h5-to-gguf.py : simplify nbytes

* gptneox-main.cpp : n_layer --> n_block

* constants.py : n_layer --> n_block

* gguf.py : n_layer --> n_block

* convert-gptneox-h5-to-gguf.py : n_layer --> n_block

* convert-llama-h5-to-gguf.py : n_layer --> n_block

* gptneox-main.cpp : n_layer --> n_block

* Update gguf_tensor_map.py

* convert-gptneox-h5-to-gguf.py : load model in parts to save memory

* convert-llama-h5-to-gguf.py : load model in parts to save memory

* convert : write more metadata for LLaMA

* convert : rm quantization version

* convert-gptneox-h5-to-gguf.py : add file_type key

* gptneox-main.cpp : add file_type key

* fix conflicts

* gguf : add todos and comments

* convert-gptneox-h5-to-gguf.py : tensor name map changes

* Create gguf_namemap.py : tensor name map changes

* Delete gguf_tensor_map.py

* gptneox-main.cpp : tensor name map changes

* convert-llama-h5-to-gguf.py : fixes

* gguf.py : dont add empty strings

* simple : minor style changes

* gguf : use UNIX line ending

* Create convert-llama-7b-pth-to-gguf.py

* llama : sync gguf-llama.cpp with latest llama.cpp (#2608)

* llama : sync gguf-llama.cpp with latest llama.cpp

* minor : indentation + assert

* llama : refactor gguf_buffer and gguf_ctx_buffer

* llama : minor

* gitignore : add gptneox-main

* llama : tokenizer fixes (#2549)

* Merge tokenizer fixes into the gguf branch.

* Add test vocabularies

* convert : update convert-new.py with tokenizer fixes (#2614)

* Merge tokenizer fixes into the gguf branch.

* Add test vocabularies

* Adapt convert-new.py (and fix a clang-cl compiler error on windows)

* llama : sync gguf-llama with llama (#2613)

* llama : sync gguf-llama with llama

* tests : fix build + warnings (test-tokenizer-1 still fails)

* tests : fix wstring_convert

* convert : fix layer names

* llama : sync gguf-llama.cpp

* convert : update HF converter to new tokenizer voodoo magics

* llama : update tokenizer style

* convert-llama-h5-to-gguf.py : add token types

* constants.py : add token types

* gguf.py : add token types

* convert-llama-7b-pth-to-gguf.py : add token types

* gguf-llama.cpp :  fix n_head_kv

* convert-llama-h5-to-gguf.py : add 70b gqa support

* gguf.py : add tensor data layout

* convert-llama-h5-to-gguf.py : add tensor data layout

* convert-llama-7b-pth-to-gguf.py : add tensor data layout

* gptneox-main.cpp : add tensor data layout

* convert-llama-h5-to-gguf.py : clarify the reverse permute

* llama : refactor model loading code (#2620)

* llama : style formatting + remove helper methods

* llama : fix quantization using gguf tool

* llama : simplify gguf_file_saver

* llama : fix method names

* llama : simplify write_header()

* llama : no need to pass full file loader to the file saver

just gguf_ctx

* llama : gguf_file_saver write I32

* llama : refactor tensor names (#2622)

* gguf: update tensor names searched in quantization

* gguf : define tensor names as constants

* gguf : initial write API (not tested yet)

* gguf : write to file API (not tested)

* gguf : initial write API ready + example

* gguf : fix header write

* gguf : fixes + simplify example + add ggml_nbytes_pad()

* gguf : minor

* llama : replace gguf_file_saver with new gguf write API

* gguf : streaming support when writing files

* gguf : remove oboslete write methods

* gguf : remove obosolete gguf_get_arr_xxx API

* llama : simplify gguf_file_loader

* llama : move hparams and vocab from gguf_file_loader to llama_model_loader

* llama : merge gguf-util.h in llama.cpp

* llama : reorder definitions in .cpp to match .h

* llama : minor simplifications

* llama : refactor llama_model_loader (WIP)

wip : remove ggml_ctx from llama_model_loader

wip : merge gguf_file_loader in llama_model_loader

* llama : fix shape prints

* llama : fix Windows build + fix norm_rms_eps key

* llama : throw error on missing KV paris in model meta data

* llama : improve printing + log meta data

* llama : switch print order of meta data

---------

Co-authored-by: M. Yusuf Sarıgöz <yusufsarigoz@gmail.com>

* gguf : deduplicate (#2629)

* gguf : better type names

* dedup : CPU + Metal is working

* ggml : fix warnings about unused results

* llama.cpp : fix line feed and compiler warning

* llama : fix strncpy warning + note token_to_str does not write null

* llama : restore the original load/save session implementation

Will migrate this to GGUF in the future

* convert-llama-h5-to-gguf.py : support alt ctx param name

* ggml : assert when using ggml_mul with non-F32 src1

* examples : dedup simple

---------

Co-authored-by: klosax <131523366+klosax@users.noreply.github.com>

* gguf.py : merge all files in gguf.py

* convert-new.py : pick #2427 for HF 70B support

* examples/gguf : no need to keep q option for quantization any more

* llama.cpp : print actual model size

* llama.cpp : use ggml_elements()

* convert-new.py : output gguf (#2635)

* convert-new.py : output gguf (WIP)

* convert-new.py : add gguf key-value pairs

* llama : add hparams.ctx_train + no longer print ftype

* convert-new.py : minor fixes

* convert-new.py : vocab-only option should work now

* llama : fix tokenizer to use llama_char_to_byte

* tests : add new ggml-vocab-llama.gguf

* convert-new.py : tensor name mapping

* convert-new.py : add map for skipping tensor serialization

* convert-new.py : convert script now works

* gguf.py : pick some of the refactoring from #2644

* convert-new.py : minor fixes

* convert.py : update to support GGUF output

* Revert "ci : disable CI temporary to not waste energy"

This reverts commit 7e82d25f40.

* convert.py : n_head_kv optional and .gguf file extension

* convert.py : better always have n_head_kv and default it to n_head

* llama : sync with recent PRs on master

* editorconfig : ignore models folder

ggml-ci

* ci : update ".bin" to ".gguf" extension

ggml-ci

* llama : fix llama_model_loader memory leak

* gptneox : move as a WIP example

* llama : fix lambda capture

ggml-ci

* ggml : fix bug in gguf_set_kv

ggml-ci

* common.h : .bin --> .gguf

* quantize-stats.cpp : .bin --> .gguf

* convert.py : fix HF tensor permuting / unpacking

ggml-ci

* llama.cpp : typo

* llama : throw error if gguf fails to init from file

ggml-ci

* llama : fix tensor name grepping during quantization

ggml-ci

* gguf.py : write tensors in a single pass (#2644)

* gguf : single pass for writing tensors + refactoring writer

* gguf : single pass for writing tensors + refactoring writer

* gguf : single pass for writing tensors + refactoring writer

* gguf : style fixes in simple conversion script

* gguf : refactor gptneox conversion script

* gguf : rename h5 to hf (for HuggingFace)

* gguf : refactor pth to gguf conversion script

* gguf : rm file_type key and method

* gguf.py : fix vertical alignment

* gguf.py : indentation

---------

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

* convert-gptneox-hf-to-gguf.py : fixes

* gguf.py : gptneox mapping

* convert-llama-hf-to-gguf.py : fixes

* convert-llama-7b-pth-to-gguf.py : fixes

* ggml.h : reverse GGUF_MAGIC

* gguf.py : reverse GGUF_MAGIC

* test-tokenizer-0.cpp : fix warning

* llama.cpp : print kv general.name

* llama.cpp : get special token kv and linefeed token id

* llama : print number of tensors per type + print arch + style

* tests : update vocab file with new magic

* editorconfig : fix whitespaces

* llama : re-order functions

* llama : remove C++ API + reorganize common source in /common dir

* llama : minor API updates

* llama : avoid hardcoded special tokens

* llama : fix MPI build

ggml-ci

* llama : introduce enum llama_vocab_type + remove hardcoded string constants

* convert-falcon-hf-to-gguf.py : falcon HF --> gguf conversion, not tested

* falcon-main.cpp : falcon inference example

* convert-falcon-hf-to-gguf.py : remove extra kv

* convert-gptneox-hf-to-gguf.py : remove extra kv

* convert-llama-7b-pth-to-gguf.py : remove extra kv

* convert-llama-hf-to-gguf.py : remove extra kv

* gguf.py : fix for falcon 40b

* falcon-main.cpp : fix for falcon 40b

* convert-falcon-hf-to-gguf.py : update ref

* convert-falcon-hf-to-gguf.py : add tensor data layout

* cmpnct_gpt2bpe.hpp : fixes

* falcon-main.cpp : fixes

* gptneox-main.cpp : fixes

* cmpnct_gpt2bpe.hpp : remove non-general stuff

* Update examples/server/README.md

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

* cmpnct_gpt2bpe.hpp : cleanup

* convert-llama-hf-to-gguf.py : special tokens

* convert-llama-7b-pth-to-gguf.py : special tokens

* convert-permute-debug.py : permute debug print

* convert-permute-debug-master.py : permute debug for master

* convert-permute-debug.py : change permute type of attn_q

* convert.py : 70b model working (change attn_q permute)

* Delete convert-permute-debug-master.py

* Delete convert-permute-debug.py

* convert-llama-hf-to-gguf.py : fix attn_q permute

* gguf.py : fix rope scale kv

* convert-llama-hf-to-gguf.py : rope scale and added tokens

* convert-llama-7b-pth-to-gguf.py : rope scale and added tokens

* llama.cpp : use rope scale kv

* convert-llama-7b-pth-to-gguf.py : rope scale fix

* convert-llama-hf-to-gguf.py : rope scale fix

* py : fix whitespace

* gguf : add Python script to convert GGMLv3 LLaMA models to GGUF (#2682)

* First pass at converting GGMLv3 LLaMA models to GGUF

* Cleanups, better output during conversion

* Fix vocab space conversion logic

* More vocab conversion fixes

* Add description to converted GGUF files

* Improve help text, expand warning

* Allow specifying name and description for output GGUF

* Allow overriding vocab and hyperparams from original model metadata

* Use correct params override var name

* Fix wrong type size for Q8_K

Better handling of original style metadata

* Set default value for gguf add_tensor raw_shape KW arg

* llama : improve token type support (#2668)

* Merge tokenizer fixes into the gguf branch.

* Add test vocabularies

* Adapt convert-new.py (and fix a clang-cl compiler error on windows)

* Improved tokenizer test

But does it work on MacOS?

* Improve token type support

- Added @klosax code to convert.py
- Improved token type support in vocabulary

* Exclude platform dependent tests

* More sentencepiece compatibility by eliminating magic numbers

* Restored accidentally removed comment

* llama : add API for token type

ggml-ci

* tests : use new tokenizer type API (#2692)

* Merge tokenizer fixes into the gguf branch.

* Add test vocabularies

* Adapt convert-new.py (and fix a clang-cl compiler error on windows)

* Improved tokenizer test

But does it work on MacOS?

* Improve token type support

- Added @klosax code to convert.py
- Improved token type support in vocabulary

* Exclude platform dependent tests

* More sentencepiece compatibility by eliminating magic numbers

* Restored accidentally removed comment

* Improve commentary

* Use token type API in test-tokenizer-1.cpp

* py : cosmetics

* readme : add notice about new file format

ggml-ci

---------

Co-authored-by: M. Yusuf Sarıgöz <yusufsarigoz@gmail.com>
Co-authored-by: klosax <131523366+klosax@users.noreply.github.com>
Co-authored-by: goerch <jhr.walter@t-online.de>
Co-authored-by: slaren <slarengh@gmail.com>
Co-authored-by: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com>
2023-08-21 23:07:43 +03:00