Commit Graph

170 Commits

Author SHA1 Message Date
Brian
e236528e76
gguf_hash.py: Add sha256 (#8470)
* gguf_hash.py: Add sha256

* gguf_hash.py: rename string UUIDv5 --> uuid

* Apply suggestions from code review

Co-authored-by: compilade <git@compilade.net>

---------

Co-authored-by: compilade <git@compilade.net>
2024-07-14 16:47:14 +10:00
M. Yusuf Sarıgöz
83321c6958
gguf-py rel pipeline (#8410)
* Upd gguf-py/readme

* Bump patch version for release
2024-07-10 15:12:35 +03:00
compilade
7d0e23d72e
gguf-py : do not use internal numpy types (#7472) 2024-07-09 01:04:49 -04:00
compilade
3fd62a6b1c
py : type-check all Python scripts with Pyright (#8341)
* py : type-check all Python scripts with Pyright

* server-tests : use trailing slash in openai base_url

* server-tests : add more type annotations

* server-tests : strip "chat" from base_url in oai_chat_completions

* server-tests : model metadata is a dict

* ci : disable pip cache in type-check workflow

The cache is not shared between branches, and it's 250MB in size,
so it would become quite a big part of the 10GB cache limit of the repo.

* py : fix new type errors from master branch

* tests : fix test-tokenizer-random.py

Apparently, gcc applies optimisations even when pre-processing,
which confuses pycparser.

* ci : only show warnings and errors in python type-check

The "information" level otherwise has entries
from 'examples/pydantic_models_to_grammar.py',
which could be confusing for someone trying to figure out what failed,
considering that these messages can safely be ignored
even though they look like errors.
2024-07-07 15:04:39 -04:00
Brian
f7cab35ef9
gguf-hash: model wide and per tensor hashing using xxhash and sha1 (#8048)
CLI to hash GGUF files to detect difference on a per model and per tensor level

The hash type we support is:

- `--xxh64`: use xhash 64bit hash mode (default)
- `--sha1`: use sha1
- `--uuid`: use uuid
- `--sha256`: use sha256

While most POSIX systems already have hash checking programs like sha256sum, it
is designed to check entire files. This is not ideal for our purpose if we want
to check for consistency of the tensor data even if the metadata content of the
gguf KV store has been updated.

This program is designed to hash a gguf tensor payload on a 'per tensor layer'
in addition to a 'entire tensor model' hash. The intent is that the entire
tensor layer can be checked first but if there is any detected inconsistencies,
then the per tensor hash can be used to narrow down the specific tensor layer
that has inconsistencies.

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-07-07 22:58:43 +10:00
toyer
905942abdb
llama : support glm3 and glm4 (#8031)
* add chatglm3-6b model support huggingface model:
 https://hf-mirror.com/THUDM/chatglm3-6b

Signed-off-by: XingXing Qiao <qiaoxx@dingdao.com>

* remove .rotary_pos_emb.inv_freq and unuse code for chatglm3 model

Signed-off-by: XingXing Qiao <qiaoxx@dingdao.com>

* fix lint error

Signed-off-by: XingXing Qiao <qiaoxx@dingdao.com>

* optimize convert-hf-to-gguf.py for chatglm model

Signed-off-by: XingXing Qiao <qiaoxx@dingdao.com>

* support glm-4-9b-chat

Signed-off-by: XingXing Qiao <qiaoxx@dingdao.com>

* fix eos tokens to glm4

* remove unused log

* add preprocess to chatglm3 and chatglm4

* add eos_id_list to llama.cpp

* fix code style

* fix code style

* fix conflicts

* fix conflicts

* Revert "add eos_id_list to llama.cpp"

This reverts commit 3a4d5790bf.

* set <|endoftext|> as eos and <|user|> as eot

* fix chat template bug

* add comment to glm prefix and suffix

* fix conflicts and add rope_ratio & ChatGLMForConditionalGeneration

* fix chat template bug

* fix codestyle

* fix conflicts

* modified the general name of glm model

* fix conflicts

* remove prefix and suffix

* use normal glm4 chattempalte & use LLM_FFN_SWIGLU in phi3

* fix: resolve Flake8 errors in `convert-hf-to-gguf.py`

- Fix E302 by adding two blank lines before top-level function definitions
- Replace print statements to fix NP100
- Fix E303 by ensuring only one blank line between lines of code

* fix rope ratio to solve incorrect answers

* fix by comments

---------

Signed-off-by: XingXing Qiao <qiaoxx@dingdao.com>
Co-authored-by: XingXing Qiao <qiaoxx@dingdao.com>
Co-authored-by: Umpire2018 <138990495+Umpire2018@users.noreply.github.com>
2024-07-07 15:52:10 +03:00
Georgi Gerganov
e235b267a2
py : switch to snake_case (#8305)
* py : switch to snake_case

ggml-ci

* cont

ggml-ci

* cont

ggml-ci

* cont : fix link

* gguf-py : use snake_case in scripts entrypoint export

* py : rename requirements for convert_legacy_llama.py

Needed for scripts/check-requirements.sh

---------

Co-authored-by: Francis Couture-Harpin <git@compilade.net>
2024-07-05 07:53:33 +03:00
Icecream95
d7fd29fff1
llama : add OpenELM support (#7359)
* Initial OpenELM support (270M only so far)

* Fill out missing entries in llama_model_type_name

* fixup! Initial OpenELM support (270M only so far)

Fix formatting

* llama : support all OpenELM models

* llama : add variable GQA and variable FFN sizes

Some metadata keys can now also be arrays to support setting
their value per-layer for models like OpenELM.

* llama : minor spacing changes

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

* llama : use std::array for per-layer hparams

* llama : fix save/load state

* llama : do not print hparams for vocab-only models

* llama : handle n_head == 0

* llama : use const ref for print_f and fix division by zero

* llama : fix t5 uses of n_head and n_ff

* llama : minor comment

---------

Co-authored-by: Francis Couture-Harpin <git@compilade.net>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-07-04 20:14:21 +03:00
Faisal Zaghloul
968967376d
Add JAIS model(s) (#8118)
* Add `JAIS` model(s)

* cleanup

* address review comments

* remove hack

* un-hardcode max-alibi-bias

* minor tweaks

---------

Co-authored-by: fmz <quic_fzaghlou@quic.com>
2024-07-02 16:36:00 +02:00
Xuan Son Nguyen
49122a873f
gemma2: add sliding window mask (#8227)
* gemma2: add sliding window mask

* fix data_swa uninitialized

* better naming

* add co-author

Co-authored-by: Arlo Phoenix <arlo-phoenix@users.noreply.github.com>

* replace list with single tensor

* update

* llama : minor styling

* convert : add sanity check for query_pre_attn_scalar

* fix small typo in README

---------

Co-authored-by: Arlo Phoenix <arlo-phoenix@users.noreply.github.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-07-01 18:48:34 +02:00
Andrei
1c5eba6f8e
llama: Add attention and final logit soft-capping, update scaling factor to Gemma2 (#8197)
* Add attention and final logit softcapping.

* fix

* Add custom add_ functions

* Disable flash attention for Gemma2

* Update src/llama.cpp

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

* Add default value for attention and final logit softcap value

* Add custom kq scaling from Gemma2Attention

* Remove custom pre attention scaling and use computed value instead.

---------

Co-authored-by: slaren <slarengh@gmail.com>
2024-06-29 23:44:08 -04:00
pculliton
e57dc62057
llama: Add support for Gemma2ForCausalLM (#8156)
* Inference support for Gemma 2 model family

* Update convert-hf-to-gguf.py, constants, and tensor mappings

* cleanup

* format fix

* Fix special token vocab bug

* Don't add space prefix

* fix deleted lines

* Update src/llama.cpp

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

* Add model type names

* Add control vector

* Fix model type identification

---------

Co-authored-by: Andrei Betlen <abetlen@gmail.com>
Co-authored-by: slaren <slarengh@gmail.com>
2024-06-27 21:00:43 -07:00
Brian
c8ad35955a
Gguf dump start data offset via --data-offset and some extra refactor (#8054)
* gguf-dump: add --data-offset

* gguf-dump: add tensor data offset table

* gguf-dump: refactor GGUFReader for clarity

* gguf-dump: add --data-alignment

* gguf-dump.py: Rename variables and adjust comments

start_data_offset --> data_offset

_build_tensors_info_fields --> _build_tensor_info
2024-06-25 22:03:25 +10:00
fairydreaming
d62e4aaa02
gguf-py : fix tensor groups for encoder-decoder models in gguf-dump.py (#8090)
Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
Co-authored-by: Brian <mofosyne@gmail.com>
2024-06-24 14:13:39 +02:00
Christian Zhou-Zheng
52fc8705a0
Option to split during conversion (#6942)
* support splits in convert.py

* Support split by size and dry run to write estimated shards/filesizes

* Move split functionality to new GGUFManager class

* fix improper function signature

* tentative push of convert-hf-to-gguf support

* resolve merge + SplitArguments for easier parsing

* Fix eager tensor memory leak and remove convert.py changes

Removed a memory leak caused by unexpected reference retention to eager tensors.

Also removed GGUFManager functionality in convert.py in favor of specializing for convert-hf-to-gguf.py.

* refactor SplitStrategy to be a deque

Instead of having SplitStrategy have a `data` field that is a deque, just have SplitStrategy be a subclass of deque itself.

* fix Q8 quantization

* remove unnecessary imports in gguf_manager

* fix final? merge issue

* fix gguf_writer placement and remove comments

* oops, actually fix gguf_writer placement

* reduce duplicated code from gguf_writer

* further simplify GGUFManager

* simplify even further and standardize with GGUFWriter

* reduce diffs with master

* form shards while adding tensors, SHA256 sums agree with master

* re-add type hint

Co-authored-by: compilade <git@compilade.net>

* GGUFWriter compatibility fix

Co-authored-by: compilade <git@compilade.net>

* Shard dataclass and un-negative dont_add_architecture

* type consistency in format_n_bytes_to_str

* move kv keys to constants.py

* make pathlib explicit

* base-1024 bytes to base-1000

* rename GGUFManager to GGUFWriterSplit

* Update gguf-py/gguf/constants.py

Co-authored-by: compilade <git@compilade.net>

* fix convert-hf-to-gguf.py permissions

* fix line endings

* Update gguf-py/gguf/gguf_writer_split.py

Co-authored-by: compilade <git@compilade.net>

* convert-hf : restore executable file permission

* examples/convert-legacy-llama.py: restore executable file permission

* reinstate original gguf package import and fix type annotation

* attempt to appease the linter

* attempt 2 to appease the linter

* attempt 3 to appease the linter

* comma consistency

* Update convert-hf-to-gguf.py

Co-authored-by: compilade <git@compilade.net>

* edit cmd line args

* use simplification from #7827

* kv/ti data are still wrong

* try to refactor kv data (still fails)

* fix ti data messiness

* tidy up

* fix linting

* actually make the linter happy

* cleanup round 1

* remove SplitStrategy, SplitArguments

* appease linter

* fix typing and clean up

* fix linting

* Update gguf-py/gguf/gguf_writer.py

Co-authored-by: compilade <git@compilade.net>

* progress bar, fix split logic

* Update gguf-py/gguf/gguf_writer.py

Co-authored-by: compilade <git@compilade.net>

* catch oversights

* Update gguf-py/gguf/gguf_writer.py

Co-authored-by: compilade <git@compilade.net>

* Update gguf-py/gguf/gguf_writer.py

Co-authored-by: compilade <git@compilade.net>

* Update gguf-py/gguf/gguf_writer.py

Co-authored-by: compilade <git@compilade.net>

* Update gguf-py/gguf/gguf_writer.py

Co-authored-by: compilade <git@compilade.net>

* Update gguf-py/gguf/gguf_writer.py

Co-authored-by: compilade <git@compilade.net>

* swap bar orders

* Update gguf-py/gguf/gguf_writer.py

Co-authored-by: compilade <git@compilade.net>

* Update gguf-py/gguf/gguf_writer.py

Co-authored-by: compilade <git@compilade.net>

* compatibility fix

* Update gguf-py/gguf/gguf_writer.py

Co-authored-by: compilade <git@compilade.net>

* Update convert-hf-to-gguf.py

Co-authored-by: compilade <git@compilade.net>

---------

Co-authored-by: Brian <mofosyne@gmail.com>
Co-authored-by: compilade <git@compilade.net>
2024-06-24 19:42:03 +10:00
fairydreaming
de0d6a68ac
gguf-py, convert-hf : model conversion support for T5 and FLAN-T5 model variants (#5763)
* gguf-py : add T5 model architecture

* gguf-py : add separate tensors for encoder and decoder

* gguf-py : add new model header parameters: decoder_start_token_id, attention.relative_buckets_count, tokenizer.ggml.remove_extra_whitespaces, tokenizer.ggml.precompiled_charsmap

* convert-hf : add model conversion support for T5ForConditionalGeneration and T5WithLMHeadModel

---------

Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
2024-06-24 07:06:05 +02:00
Eddie-Wang
e112b610a1
llama : add support for BitnetForCausalLM (#7931)
* hf bitnet v1

* hf bitnet e2e v2

* finish bitnet e2e

* finish f16 hf bitnet e2e

* remove unsed

* finish bitnet i2 e2e

* move i2s to quantize v1

* move i2 to quantize

* clean code

* clean code 2

* fix codestyle

* fix code

* fix

* fix code

* fix merge

* remove unused

* change table name

* fix whitespace

* delete redundant

* i2_s to absmax

* finish i2_s/i8_s vec_dot x86 simd

* i2s->q22

* fix code

* remove block scale

* add dequantize

* fix seq

* update avx2

* remove q2_2

* remove q22_grid

* fix whitespace

* reuse llm_build_kv

* fix bo

---------

Co-authored-by: root <root@wangjinheng>
2024-06-23 21:27:57 +03:00
Ștefan-Gabriel Muscalu
a94e6ff877
update: support Qwen2-57B-A14B (#7835)
* update: convert-hf-to-gguf.py to support Qwen2-57B-A14B

* fix: QWEN2MOE support for expert_feed_forward_length

previously, expert ff was taken from n_ff (intermediate size) but it is now properly taken from LLM_KV_EXPERT_FEED_FORWARD_LENGTH

n_ff_exp and n_ff_shared_exp are now properly calculated

* update: convert-hf-to-gguf.py cleanup for Qwen2MoeForCausalLM

* fix: QWEN2MOE support for expert_feed_forward_length

previously, expert ff was taken from n_ff (intermediate size) but it is now properly taken from LLM_KV_EXPERT_FEED_FORWARD_LENGTH

n_ff_exp and n_ff_shexp are now properly calculated
2024-06-17 21:08:46 +02:00
Brian
006167aaf6
gguf-dump.py: add --markdown dump output (#7853)
* gguf-dump.py: add --markdown dump output

* gguf-dump.py: Add toc

* gguf-dump.py: use standard tensor name lookup. Also add tensor ID field

* gguf-dump.py: Add tensor overview count

* gguf-dump.py: fix array preview

* gguf-dump.py: markdownTableWithAlignmentSupport() added

* Add type hints and spacing

Co-authored-by: compilade <git@compilade.net>

* gguf-dump.py: prettyfy dimention

* gguf-dump: right align element count

* gguf-dump.py: element count autosizing

* Apply suggestions from code review

Co-authored-by: compilade <git@compilade.net>

---------

Co-authored-by: compilade <git@compilade.net>
2024-06-17 15:25:20 +10:00
compilade
ed9f252118
gguf-py : decouple adding metadata from writing in GGUFWriter (#7827)
Main changes of this PR is to consolidate GGUFWriter.add_key and GGUFWriter.add_val into GGUFWriter.add_key_value. 

In addition use_temp_file is now opt-in instead of opt-out defaulting to False.

Also GGUFWriter now does not require output file name until when actually writing to it.

And GGUFWriter doesn't really need to eagerly prepare the data layout of the metadata
2024-06-09 12:34:29 +10:00
Joan Fontanals
f5d7b268ec
llama : add jina v2 base code (#7596)
* feat: add changes to handle jina v2 base code

* fix: do not complicate things

* fix: fix the usage of the code model

* fix: fix comments

* fix: fix linting issues

* fix: remove ollama patches

* style : minor

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-06-06 10:22:41 +03:00
zhangkaihuo
6f28a333c1
llama : MiniCPM support tied embeddings (#7664)
* support lm_head

* remove the code block

---------

Co-authored-by: zhangkaihuo <zhangkaihuo@modelbest.cn>
2024-06-03 10:49:30 +03:00
Galunid
9c4c9cc83f
Move convert.py to examples/convert-legacy-llama.py (#7430)
* Move convert.py to examples/convert-no-torch.py

* Fix CI, scripts, readme files

* convert-no-torch -> convert-legacy-llama

* Move vocab thing to vocab.py

* Fix convert-no-torch -> convert-legacy-llama

* Fix lost convert.py in ci/run.sh

* Fix imports

* Fix gguf not imported correctly

* Fix flake8 complaints

* Fix check-requirements.sh

* Get rid of ADDED_TOKENS_FILE, FAST_TOKENIZER_FILE

* Review fixes
2024-05-30 21:40:00 +10:00
Galunid
eb57fee51f
gguf-py : Add tokenizer.ggml.pre to gguf-new-metadata.py (#7627) 2024-05-30 02:10:40 +02:00
fairydreaming
ee3dff6b8e
Add support for DeepseekV2ForCausalLM (#7519)
* common : increase max number of experts to 160

* common : add tensors ATTN_Q_A, ATTN_Q_A_NORM, ATTN_Q_B, ATTN_KV_A_MQA, ATTN_KV_A_NORM, ATTN_KV_B needed by DeepSeek-V2 MLA (multi-head latent attention) architecture

* common : add model header parameters: leading_dense_block_count, expert_feed_forward_length, expert_shared_count, expert_weights_scale, attention.q_lora_rank, attention.kv_lora_rank, rope.scaling.yarn_log_multiplier

* convert-hf : add model conversion support for DeepseekV2ForCausalLM

* llama : add model types for DeepSeek-V2 and DeepSeek-V2-Lite models

* llama : add two new llm_build_moe_ffn() arguments: scale_w (whether to scale weights of selected MoE experts) and w_scale (numerical value of the scaling factor)

* llama : add inference support for LLM_ARCH_DEEPSEEK2

---------

Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
2024-05-28 17:07:05 +02:00
compilade
b83bab15a5
gguf-py : fix and simplify quantized shape round-trip (#7483)
* gguf-py : fix and simplify quantized shape round-trip

* gguf-py : remove unused import
2024-05-25 11:11:48 +10:00
fairydreaming
fbca2f27fc
Add support for ArcticForCausalLM (#7020)
* common : increase max number of experts to 128

* common : add tensor LLM_TENSOR_FFN_NORM_EXPS for normalization before MoE that runs in parallel to attention + ffn

* gguf-py : add architecture-specific block mappings that override selected general block mappings

* convert-hf : add model conversion support for ArcticForCausalLM

* convert-hf : use added_tokens_decoder from tokenizer_config.json to redefine tokens from SentencePiece model (only for ArcticForCausalLM)

* llama : add inference support for LLM_ARCH_ARCTIC

---------

Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
2024-05-24 14:31:13 +02:00
Georgi Gerganov
e84b71c2c6
ggml : drop support for QK_K=64 (#7473)
* ggml : drop support for QK_K=64

ggml-ci

* opencl : restore QK_K=256 define
2024-05-23 10:00:21 +03:00
liuwei-git
201cc11afa
llama : add phi3 128K model support (#7225)
* add phi3 128k support in convert-hf-to-gguf

* add phi3 128k support in cuda

* address build warnings on llama.cpp

* adjust index value in cuda long rope freq factors

* add long rope support in ggml cpu backend

* make freq factors only depend on ctx size

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

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

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

* add one line of comments

* metal : support rope freq_factors

* ggml : update ggml_rope_ext API to support freq. factors

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

* minor : style

* tests : update to use new rope API

* backends : fix pragma semicolons

* minor : cleanup

* llama : move rope factors from KV header to tensors

* llama : remove tmp assert

* cuda : fix compile warning

* convert : read/write n_head_kv

* llama : fix uninitialized tensors

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-05-21 23:28:32 +03:00
Georgi Gerganov
fabf30b4c4
llama : remove Persimmon (#7408)
* llama : remove Persimmon

* requirements : remove
2024-05-21 02:35:28 +10:00
compilade
ee52225067
convert-hf : support direct Q8_0 conversion (#7234)
* convert-hf : support q8_0 conversion

* convert-hf : add missing ftype

This was messing with the checksums otherwise.

* convert-hf : add missing ftype to Baichuan and Xverse

I didn't notice these on my first pass.
2024-05-13 14:10:51 -04:00
compilade
5a419926b0
convert-hf : support bfloat16 conversion (#7158)
* convert-hf : support bfloat16 conversion

* gguf-py : flake8 fixes

* convert-hf : add missing space after comma

* convert-hf : get bit-exact same output as ./quantize

The quantization version was missing.

* convert-hf : don't round bf16 NANs

* convert-hf : save some memory with np.int16 intermediate bf16 weights

* convert-hf : more closely match llama.cpp with which weights to keep in f32

* convert-hf : add --outtype auto-f16

A reason for this to exist is for model quantizers who want an initial
GGUF with the most fidelity to the original model while still using
a 16-bit float type instead of 32-bit floats.

* convert-hf : remove a semicolon because flake8 doesn't like it

It's a reflex from when programming in C/C++, I guess.

* convert-hf : support outtype templating in outfile name

* convert-hf : rename --outtype auto-f16 to --outtype auto
2024-05-11 11:06:26 -04:00
Joan Fontanals
b83cc3f5b3
llama : add Jina Embeddings architecture (#6826)
* feat: first things to do

* feat: create tensors for Jina architecture

* fix: use other tensors

* feat: embedding gets results

* fix: fix usage of ALIBI

* fix: clean prints

* fix: do some cleanup unused vars

* fix: revert changes to Makefile and CMakeLists

* fix: revert some changes

* fix: fix small detail

* fix: fix convert formatting

* fix: fix linting and editor

* feat: set proper vocab settings

* fix: JinaBertForMaskedLM registration

* feat: support q_normalization and k_normalization in Jina arch

* feat: handle gpt2 tokenizer with Jina architecture

* feat: example comments in embedding

* feat: rename Jina Bert to Jina Bert V2

* fix: add some changes as per review

* feat: proper KQ_pos for Jina embeddings

* feat: add capacity to load models ES and DE for Spanish

* llama : fix pre-tokenizers

* ggml : full ALiBi support

* ggml : update ggml_soft_max_ext() CUDA, SYCL

* ggml : ggml_flash_attn_ext() support ALiBi (CPU)

* ggml : ggml_flash_attn_ext() support ALiBi (Metal)

* ggml : fix warning

* ggml : ggml_flash_attn_ext() support ALiBi (CUDA)

ggml-ci

* minor : clean-up

* embedding : add warning about missing SEP

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-05-11 10:46:09 +03:00
Georgi Gerganov
9cb317f77e
ggml : full ALiBi support (#7192)
* ggml : full ALiBi support

* ggml : update ggml_soft_max_ext() CUDA, SYCL

* ggml : ggml_flash_attn_ext() support ALiBi (CPU)

* ggml : ggml_flash_attn_ext() support ALiBi (Metal)

* ggml : fix warning

* ggml : ggml_flash_attn_ext() support ALiBi (CUDA)

ggml-ci

* ggml : fix assert message

* vulkan : add dev notes

* ggml : require mask when using ALiBi

ggml-ci

* convert : fix convert for refact models
2024-05-11 10:32:41 +03:00
Sigbjørn Skjæret
22842164bc
gguf-py : add special token modification capability (#7166)
* Add special token modification capability

To be able to fix/amend special tokens in a GGUF let's add two new arguments:
* `--special-token <name> <value>` where `<name>` can be bos, eos, prefix, middle, etc. while `<value>` is the token value, f.ex. `"<|fim▁begin|>"`
* `--special-token-by-id <name> <id>` where `<id>` is the ID of the token, f.ex. 32006

So, in order to f.ex. add fill-in-middle tokens to a GGUF you would do the following:
```bash
python3 gguf-new-metadata.py input.gguf output.gguf --special-token prefix "<|fim▁begin|>" --special-token middle "<|fim▁hole|>" --special-token suffix "<|fim▁end|>"
```

* improve help text

* flake--

* fix multiple tokens warning

* make script executable

* switch to namedtuple, no need to dataclass

* typing++

* add progress bar

* Add special token modification capability

To be able to fix/amend special tokens in a GGUF let's add two new arguments:
* `--special-token <name> <value>` where `<name>` can be bos, eos, prefix, middle, etc. while `<value>` is the token value, f.ex. `"<|fim▁begin|>"`
* `--special-token-by-id <name> <id>` where `<id>` is the ID of the token, f.ex. 32006

So, in order to f.ex. add fill-in-middle tokens to a GGUF you would do the following:
```bash
gguf-new-metadata.py input.gguf output.gguf --special-token prefix "<|fim▁begin|>" --special-token middle "<|fim▁end|>" --special-token suffix "<|fim▁hole|>"
```
(yes, fim_end is the `middle` token, because completion is a `prefix`/`suffix`/`middle` sequence (where `middle` is unfilled))
or
```bash
gguf-new-metadata.py input.gguf output.gguf --special-token prefix "<fim_prefix>" --special-token middle "<fim_middle>" --special-token suffix "<fim_suffix>"
```
etc...

NB: The tokens have to exist already, trying to add non-existent token name/IDs will be ignored (with a warning), while non-existent values will fail (with an error).

* improve help text

* flake--

* fix multiple tokens warning

* make script executable

* switch to namedtuple, no need to dataclass

* typing++

* add progress bar

* fail on invalid token id
2024-05-09 13:56:00 +03:00
compilade
f98eb31c51
convert-hf : save memory with lazy evaluation (#7075)
* convert-hf : begin refactoring write_tensor

* convert : upgrade to sentencepiece v0.2.0

* convert-hf : remove unused n_dims in extra_*_tensors

* convert-hf : simplify MoE weights stacking

* convert-hf : flake8 linter doesn't like semicolons

* convert-hf : allow unusual model part names

For example, loading `model-00001-of-00001.safetensors` now works.

* convert-hf : fix stacking MoE expert tensors

`torch.stack` and `torch.cat` don't do the same thing.

* convert-hf : fix Mamba conversion

Tested to work even with a SentencePiece-based tokenizer.

* convert : use a string for the SentencePiece tokenizer path

* convert-hf : display tensor shape

* convert-hf : convert norms to f32 by default

* convert-hf : sort model part names

`os.listdir` is said to list files in arbitrary order.
Sorting the file names should let "model-00009-of-00042.safetensors"
be loaded before "model-00010-of-00042.safetensors".

* convert-hf : use an ABC for Model again

It seems Protocol can't be used as a statically type-checked ABC,
because its subclasses also can't be instantiated. (why did it seem to work?)

At least there's still a way to throw an error when forgetting to define
the `model_arch` property of any registered Model subclasses.

* convert-hf : use a plain class for Model, and forbid direct instantiation

There are no abstract methods used anyway,
so using ABC isn't really necessary.

* convert-hf : more consistent formatting of cmdline args

* convert-hf : align the message logged for converted tensors

* convert-hf : fix Refact conversion

* convert-hf : save memory with lazy evaluation

* convert-hf : flake8 doesn't like lowercase L as a variable name

* convert-hf : remove einops requirement for InternLM2

* convert-hf : faster model parts loading

Instead of pre-loading them all into a dict, iterate on the tensors
in the model parts progressively as needed in Model.write_tensors

Conversion for some architectures relies on checking for the presence
of specific tensor names, so for multi-part models, the weight map is read
from the relevant json file to quickly get these names up-front.

* convert-hf : minor changes for consistency

* gguf-py : add tqdm as a dependency

It's small, and used for a progress bar
in GGUFWriter.write_tensors_to_file
2024-05-08 18:16:38 -04:00
Justine Tunney
3855416027
ggml : introduce bfloat16 support (#6412)
* Introduce bfloat16 support

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

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

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

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

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

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

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

* Remove GGML code that's not needed

* Minimize the GGML API surface area for BF16

* Remove bf16 luts

* Make the GGML header look nicer

* Fix documentation

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

* Add BF16 code for new ggml_validate_row_data() function
2024-05-08 09:30:09 +03:00
Brian
a2ac89d6ef
convert.py : add python logging instead of print() (#6511)
* convert.py: add python logging instead of print()

* convert.py: verbose flag takes priority over dump flag log suppression

* convert.py: named instance logging

* convert.py: use explicit logger id string

* convert.py: convert extra print() to named logger

* convert.py: sys.stderr.write --> logger.error

* *.py: Convert all python scripts to use logging module

* requirements.txt: remove extra line

* flake8: update flake8 ignore and exclude to match ci settings

* gh-actions: add flake8-no-print to flake8 lint step

* pre-commit: add flake8-no-print to flake8 and also update pre-commit version

* convert-hf-to-gguf.py: print() to logger conversion

* *.py: logging basiconfig refactor to use conditional expression

* *.py: removed commented out logging

* fixup! *.py: logging basiconfig refactor to use conditional expression

* constant.py: logger.error then exit should be a raise exception instead

* *.py: Convert logger error and sys.exit() into a raise exception (for atypical error)

* gguf-convert-endian.py: refactor convert_byteorder() to use tqdm progressbar

* verify-checksum-model.py: This is the result of the program, it should be printed to stdout.

* compare-llama-bench.py: add blank line for readability during missing repo response

* reader.py: read_gguf_file() use print() over logging

* convert.py: warning goes to stderr and won't hurt the dump output

* gguf-dump.py: dump_metadata() should print to stdout

* convert-hf-to-gguf.py: print --> logger.debug or ValueError()

* verify-checksum-models.py: use print() for printing table

* *.py: refactor logging.basicConfig()

* gguf-py/gguf/*.py: use __name__ as logger name

Since they will be imported and not run directly.

* python-lint.yml: use .flake8 file instead

* constants.py: logger no longer required

* convert-hf-to-gguf.py: add additional logging

* convert-hf-to-gguf.py: print() --> logger

* *.py: fix flake8 warnings

* revert changes to convert-hf-to-gguf.py for get_name()

* convert-hf-to-gguf-update.py: use triple quoted f-string instead

* *.py: accidentally corrected the wrong line

* *.py: add compilade warning suggestions and style fixes
2024-05-03 22:36:41 +03:00
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
Xuan Son Nguyen
7bb36ccf91
gguf : enforce that tensor names are unique (#6905)
* not allow adding duplicated tensor name

* no duplicated tensor while reading gguf

* typo

* throw exception inside llama_model_loader

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

---------

Co-authored-by: slaren <slarengh@gmail.com>
2024-04-28 17:36:18 +02:00
liuwei-git
c8297c6af5
llama : add phi3 support (#6852)
* add explicit phi3 support

* add explicit phi3 support

* remove unused code

* convert : add BOS token

* llama : match EOT token <|end|>

* llama : minor / style

* llama : tabs -> spaces

* convert : fix lint checks

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-24 10:00:37 +03:00
pmysl
c1386c936e
gguf-py : add IQ1_M to GGML_QUANT_SIZES (#6761) 2024-04-21 15:49:30 +03:00
nopperl
9958c81b79
Implement the OLMo architecture (#6741)
* implement olmo architecture

* remove unused variable

* remove unused moe branch

* remove check for weight

* remove superfluous moe, bias and rope tensors

* clarified comment

* fix clamp_kqv setting

* remove obsolete parameter name filter
2024-04-19 11:35:54 +02:00
Sigbjørn Skjæret
03c0946d73
convert : support models with multiple chat templates (#6588)
* Support converting models with multiple chat templates

Adds the following metadata:
* tokenizer.chat_templates
* tokenizer.chat_template.<name1>
* tokenizer.chat_template.<name2>
* tokenizer.chat_template.<...>

Where `tokenizer.chat_templates` is an array of the template names (except `default`), `default` is added to the regular `tokenizer.chat_template`.

* replace filtered characters with underscore

* New script to add/modify/remove metadata

This scripts creates a copy of a GGUF file and allows you to add/modify/remove metadata in the process.

Most importantly this allows you to update chat templates, either as a string or directly from an updated tokenizer_config.json file.

* Add files via upload

add new script to project/readme

* flake--
2024-04-18 14:49:01 +03:00
Ashish
dbceec87c0
llama : add StableLM2 12B (#6635)
* StableLM2 12B support for huggingface -> GGUF

* StableLM12 tensormapping and constants

* StableLM-2-12b model support

* fix

* Added 12B support

* Removed autoformatting; resolved bug where model_arch was not selecting StableLM2

* Formatting

* Do QK norm stacking in model conversion step

* Converge StableLM and StableLM2 code to simplify graph construction

* Fix accidental removal

* Removed warnings

* Revert formatter

* Move QK norm stack to private function so it's easier to read

* refactor stablelm graph builder to support 1.6, 3b and 12b more efficiently

* Proper check for None type for new_name to avoid crash; formatting; revert change to base class `write_tensors()`

* Format

* Formatting

* format

Co-authored-by: compilade <git@compilade.net>

* Fix incorrect check for K norm

* space after commas; Keep indentation multiple of 4 spaces

* Flake8 format

* Removed unnecessary conditional branches

* Removed unused comment

* Fixed incorrect tensor passing

* Format

---------

Co-authored-by: compilade <git@compilade.net>
2024-04-16 18:48:35 +03:00
Shijie
f4dea7da18
llama : add qwen2moe (#6074)
* support qwen2moe

* fix-review

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

* metal : require contiguousness for float4 unary kernels

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

* fix-review

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

* llama : reuse build_moe_ffn()

* llama : add model type name

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-16 18:40:48 +03:00
Daniel Bevenius
4fbd8098e6
gguf : add special tokens metadata for FIM/Infill (#6689)
This commit adds special token metadata for Fill-In-the-Middle
(FIM)/Infill to the GGUF model.

The motivation for this is that currently there is support for CodeLlama
but other models exist now like CodeGemma, but the different models use
different token ids for the special tokens and this commit allows for
supporting multiple models.

Signed-off-by: Daniel Bevenius <daniel.bevenius@gmail.com>
2024-04-16 09:13:13 +03:00
Pierrick Hymbert
4bd0f93e4a
model: support arch DbrxForCausalLM (#6515)
* model: dbrx convert to gguf
#6344

* llama: support dbrx
#6344

* doc: dbrx: add the model as supported

* scripts: get-wikitext-2 add unzip

* llama: increase maximum experts allowed

* llama: factorize moe graph implementation between grok, mixtral and dbrx


---------

Co-authored-by: Megha Agarwal <16129366+megha95@users.noreply.github.com>
2024-04-13 11:33:52 +02:00
Carolinabanana
5dc9dd7152
llama : add Command R Plus support (#6491)
* Add Command R Plus GGUF

* Add Command R Plus GGUF

* Loading works up to LayerNorm2D

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

* Fix embedding layer based on Noeda's example

* Whitespace

* Add line

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

* dranger003: Fix block index overflow in CUDA dequantizing.

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

* export norms as f32

* fix overflow issues during quant and other cleanup

* Type convention

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

* dranger003: Fix more int overflow during quant.

---------

Co-authored-by: S <seast@Ss-Mac-Studio.local>
Co-authored-by: S <s@example.com>
Co-authored-by: slaren <slarengh@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-09 11:16:13 +03:00
Brian
a8bd14d557
gguf.py : add licence and version to gguf writer (#6504) 2024-04-05 21:41:38 +03:00
bryanSwk
bb43cf7e9d
llama : add SEA-LION support (#6448)
* initial commit for sealion support

* add sealion support

* minor fix

* q/k ln and pos_embd only if required

* Apply suggestions from code review

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

* minor : clear whitespaces

---------

Co-authored-by: bryan <bryansiow@aisingapore.org>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-03 21:05:10 +03:00
slaren
08a0c02060
ggml : mul_mat_id use the same tensor for all the experts (#6387)
* ggml : update mul_mat_id to use the same tensor for all the experts

* update cuda

* minor

* update metal

* update test-backend-ops

* fix cuda

* Update ggml-metal.m

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

* update convert.py

* update convert-hf-to-gguf.py

* update convert.py for mixtral hf models

* Update convert-hf-to-gguf.py

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

* cuda : support non-pow-2 number of experts

* allow quantize to work for split and merged experts models in the same way

* cleanup + disable mmap automatically with split tensors models

* update imatrix

* test-backend-ops : test qwen argsort

* update grok model loading

* llama : add merged experts tensors to the grok tensor map

* minor

* gguf : bump version

* fix quantizing of merged experts

* convert-hf-to-gguf.py : update grok (untested)

* make linter happy

* cuda/argsort : use shared memory instead of pool memory

* convert : fix grok tensor names

* metal : add support for non-pow-2 argsort

* llama : more loader cleanup, better error checking

* cuda : fix warning

* llama : still use mmap for loading old models, but copy the data to a host buffer

* add review note

* llama : remove ffn tensor counting + add sanity check

ggml-ci

* convert : fix handling of n_experts == None

ggml-ci

* imatrix : fix ncall counters

* llama : produce error if imatrix size does not match

* quantize : terminate on errors + trace logs

ggml-ci

* metal : pad shared memory to 16 bytes

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-04-03 16:07:05 +03:00
hxer7963
069574775c
[Model] Add support for xverse (#6301)
* Support xverse model convert to gguf format.

* 1. Convert xverse models to gguf;
2. Add LLM_ARCH_XVERSE inference in llama.cpp;
3. Add xverse item in Supported models in README.md;

* * gguf-py: remove redundant logs
* llama: remove the init_mapping_prefetch custom parameter

* llama.cpp: Include the changes from #6122 to exclude the unused outputs of the last layers.

* - Fix format issues
- Remove duplicate set kqv_out to llm_build_kv

* Update llama.cpp

---------

Co-authored-by: willhe <willhe@xverse.cn>
Co-authored-by: willhe <hexin@xverse.cn>
2024-03-29 14:37:03 +01:00
Kawrakow
55c1b2a3bb
IQ1_M: 1.75 bpw quantization (#6302)
* iq1_m: basics

* iq1_m: basics-2

* iq1_m: CUDA dequantize works

Very 1st shot I get PPL = 9.76 for LLaMA-v2-7B.

* iq1_m: separate shifts for each group of 8 in a block

We get
PPL(LLaMA-v2-7B ) = 9.2810
PPL(LLaMA-v2-13B) = 6.8105

Not bad, but slightly higher than
  sqrt(PPL(IQ1_S) * PPL(IQ2_XXS))
which is the expected outcome given that IQ1_M is
halfway between IQ1_S and IQ2_XXS in terms of bpw.
From this, we would expect
 PPL = 9.14 for LLaMA-v2-7B
 PPL = 6.63 for LLaMA-v2-13B

* iq1_m: go to 3-bit scales

There is slight increase in PPL, but the 0.0625 bpw reduction
in size is totally worth it.

We now have
PPL(LLaMA-v2-7B ) = 9.4469 at 1.96 bpw
PPL(LLaMA-v2-13B) = 6.8717 at 1.93 bpw
PPL(LLaMA-v2-70B) = 4.8568 at 1.85 bpw

* iq1_m: scalar dot product

* iq1_m: AVX2 dot product

* iq1_m: very slightly faster AVX2 dot product

* iq1_m: ARM_NEON dot product

Works, but very slow (10.5 t/s)

* iq1_m: Metal - dequantize works, dot product does not

* iq1_m: Metal now works

About the same performance as iq1_s.

* iq1_m: minor

* iq1_m: checking pure iq1_m quantization

It is pretty bad: PPL(LLaMA-v2-7B) = 34 if we quantize output.weight
with Q4_K.

* iiq1_m: slightly faster ARM_NEON dot product

10.5 t/s -> 11.65 t/s

* iq1_m: faster ARM_NEON dot product

11.65 t/s -> 14.9 t/s

* iq1_m: another minor ARM_NEON dot product improvement

14.9 -> 15.0 t/s

* iq1_m: small PPL improvement via super-block scale adjustment

After quantizing block scales redo the super-block scale fit.

PPL(LLaMA-v2-7B ) = 9.3346
PPL(LLaMA-v2-13B) = 6.8419
PPL(LLaMA-v2-70B) = 4.8294
PPL(Mistral-7B  ) = 8.1624

* iq1_m: adapt to CUDA refactoring

* iq1_m: remove unused variable

We have progressed to warnings being errors.

* iq1_m: add to backend-ops tests

* iq1_m: fix Windows ARM

* iq1_m: use common definition of iq1m_scale_t

* cuda: assert -> NO_DEVICE_CODE

* iq1_M: PR comments

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2024-03-26 15:21:27 +01:00
Julius Arkenberg
476b0251b2
llama : add grok-1 support (#6204)
* Add support for Grok model architecture

* Revert convert-hf-to-gguf to default options

* Fixed f_norm_rms_eps bug

* Fix whitespaces

* llama : fix grok rope type

* llama : minor

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-03-23 18:41:53 +02:00
Andrew Canis
12247f4c69
llama : add Command-R support (#6033)
Information about the Command-R 35B model (128k context) can be found at:
	https://huggingface.co/CohereForAI/c4ai-command-r-v01

Based on the llama2 model with a few changes:

1) New hyper parameter to scale output logits (logit_scale)
2) Uses LayerNorm instead of RMSNorm
3) Transfomer layers have a single shared LayerNorm that feeds into both the
   self-attention and FFN layers in parallel. There is no post-attention LayerNorm.
4) No support for Rotary Position Embeddings (RoPE) scaling
5) No biases used

Find GGUF files here:
	https://huggingface.co/andrewcanis/c4ai-command-r-v01-GGUF

To convert model to GGUF format yourself:

1) Download Command-R Hugging Face safetensors:
	git lfs install
	git clone https://huggingface.co/CohereForAI/c4ai-command-r-v01

2) Run:
	python3 convert-hf-to-gguf.py --outtype f16 ./c4ai-command-r-v01
2024-03-15 22:41:22 +02:00
Ondřej Čertík
7ce2c77f88
gguf : add support for I64 and F64 arrays (#6062)
* gguf : add support for I64 and F64 arrays

GGML currently does not support I64 or F64 arrays and they are not often
used in machine learning, however if in the future the need arises, it
would be nice to add them now, so that the types are next to the other
types I8, I16, I32 in the enums, and it also reserves their type number.

Furthermore, with this addition the GGUF format becomes very usable for
most computational applications of NumPy (being compatible with the most
common NumPy dtypes: i8, i16, i32, i64, f32, f64), providing a faster,
and more versatile alternative to the `npz` format, and a simpler
alternative to the `hdf5` format.

The change in this PR seems small, not significantly increasing the
maintenance burden. I tested this from Python using GGUFWriter/Reader
and `gguf-dump`, as well as from C, everything seems to work.

* Fix compiler warnings
2024-03-15 10:46:51 +02:00
Ondřej Čertík
727107707a
gguf-py : bump version to 0.8.0 (#6060) 2024-03-14 19:57:31 +02:00
Michael Podvitskiy
69ff61397d
llama : support models without vocabulary (#5798)
* additional methods to read model and ctx parameters

* vocab size as a part of a model metadata

* models without vocabulary, convert.py part

* models without vocabulary, llama.cpp part

* PR clean up

* converter scrypt fixes

* llama_vocab_type update (renamed the new key)

* pr review fixes

* revert function renaming

* one more NoVocab assert
2024-03-14 18:21:56 +02:00
Georgi Gerganov
77178eedc8
gguf-py : fix dtype check (#6045) 2024-03-14 13:32:14 +02:00
Ondřej Čertík
3ca23481dd
gguf-py : add support for I8, I16 and I32 (#6045)
* Refactor dtype handling to be extensible

This code is equivalent as before, but now it is prepared to easily add
more NumPy dtypes.

* Add support for I8, I16 and I32

These types are allowed in the GGUF specification.

* Add support for I8, I16 and I32 to gguf_writer

* Add support for I8, I16, I32 to gguf_reader
2024-03-14 12:40:14 +02:00
compilade
c2101a2e90
llama : support Mamba Selective State Space Models (#5328)
* mamba : begin working on support for Mamba SSM

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

* mamba : recurrent inference almost works, but incoherent

* mamba : recurrent inference WORKS!!!

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

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

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

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

* ggml : parallelize ggml_exp

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

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

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

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

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

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

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

* mamba : fix self-overlapping view depth stride

* mamba : handle batches of more than 1 token

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

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

* ggml: add ggml_ssm_scan to help with parallel selective scan

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

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

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

* mamba : very basic quantization support

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

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

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

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

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

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

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

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

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

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

* mamba : fix vocab size problems with official models

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

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

* ggml : remove ggml_exp and ggml_soft_plus

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

* mamba : remove some useless comments

No code change.

* convert : fix flake8 linter errors

* mamba : apply suggestions from code review

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

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

* ggml : in ggml_ssm_scan, use more appropriate asserts

* ggml : rename the destination pointer in ggml_compute_forward_ssm_scan_f32

* mamba : multiple sequences, but one at a time

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

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

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

* mamba : support llama_kv_cache_seq_cp

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

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

* mamba : use a state mask

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

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

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

* mamba : use n_tokens directly instead of n_tok

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

* mamba : reduce memory usage of ggml_ssm_scan

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

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

* mamba : simultaneous sequence processing

A batch can now contain tokens from multiple sequences.

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

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

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

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

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

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

* llama : add inp_s_seq as a new input tensor

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

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

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

* mamba : support llama_kv_cache_seq_cp copy chains

* mamba : support shifting and dividing the kv cache pos

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

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

* llama : make llama_kv_cache_seq_rm return whether it succeeded or not

* mamba : dedicate an input tensor for state copy indices

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

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

* perplexity : limit the max number of sequences

This adapts to what the loaded model can provide.

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

Used by the perplexity example.

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

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

* batched-bench : reserve sequences to support Mamba

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

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

* mamba : stop abusing attention metadata

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

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

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

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

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

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

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

* ggml : in ggml_ssm_scan, use a threshold for soft_plus

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

* convert : for Mamba, fallback to internal NeoX tokenizer

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

* mamba : support state saving and restoring

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

* mamba : clarify some comments

* server : fix cache_tokens not getting correctly resized

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

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

* convert-hf : support new metadata keys for Mamba

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

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

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

* mamba : support mamba-*-hf models

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

* mamba : add missing spaces

This is purely a formatting change.

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

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

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

* mamba : move state_seq and state_mask views outside layer loop

A few tensors were also missing `struct` in front of `ggml_tensor`.
2024-03-08 17:31:00 -05:00
Nindaleth
87c2e8b279
gguf-dump : support i-quants (#5841)
Co-authored-by: Black_Fox <radekliska@gmail.com>
2024-03-03 10:43:42 +02:00
Jared Van Bortel
c7a0ad8ec9
convert-hf : make model class definitions self-contained (#5825) 2024-03-02 12:21:47 -05:00
Sourab Mangrulkar
c29af7e225
llama : add StarCoder2 support (#5795)
* Add support for starcoder2

* handle rope type

* skip rope freq and rotary embeddings from being serialized

* resolve comments

* Update llama.cpp

* remove redundant changes

* handle `rope-theta`

* llama : change starcoder2 rope type

* address comment

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-03-01 21:30:46 +02:00
postmasters
580111d42b
llama : add gemma model (#5631)
There are couple things in this architecture:

1. Shared input and output embedding parameters.
2. Key length and value length are not derived from `n_embd`.

More information about the models can be found at
https://ai.google.dev/gemma. GGUFs can be downloaded from
https://huggingface.co/google.
2024-02-21 15:08:22 +02:00
Douglas Hanley
4524290e87
Use correct type of pooling for embedding models (#5500)
Use correct type of pooling for embedding models
2024-02-15 12:21:49 -05:00
Michaël de Vries
73122473ff
fix(gguf-py): special tokens are no longer skipped when add_<token>_token is set to false (#5487)
* fix(gguf-py): special tokens are no longer skipped when add_<token>_token is set to false

* fix(gguf-py): added missing cls and mask token ids to the gguf metadata
2024-02-15 14:14:37 +01:00
John
6c00a06692
gguf : add python reader example (#5216)
* Update CMakeLists.txt

* Create reader.py

* Update reader.py

* Update reader.py

another whitespace :|

* Update reader.py

* lintlintlint
2024-02-13 19:56:38 +02:00
Jared Van Bortel
ea9c8e1143
llama : add support for Nomic Embed (#5468) 2024-02-13 12:03:53 -05:00
Douglas Hanley
03bf161eb6
llama : support batched embeddings (#5466)
* batched embedding: pool outputs by sequence id. updated embedding example

* bring back non-causal attention

* embd : minor improvements

* llama : minor

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-02-13 14:06:58 +02:00
Douglas Hanley
2891c8aa9a
Add support for BERT embedding models (#5423)
* BERT model graph construction (build_bert)
* WordPiece tokenizer (llm_tokenize_wpm)
* Add flag for non-causal attention models
* Allow for models that only output embeddings
* Support conversion of BERT models to GGUF
* Based on prior work by @xyzhang626 and @skeskinen

---------

Co-authored-by: Jared Van Bortel <jared@nomic.ai>
Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-02-11 11:21:38 -05:00
runfuture
316c7faf77
llama : add MiniCPM support (#5346)
* support minicpm arch.

* fix tab/space typo.

* convert minicpm model via convert-hf-gguf.py

* try to make tokenizer work

* fix bug for quantize minicpm

* fix for flake8 lint

* remove convert-minicpm.py

* fix for editorconfig

* correct minicpm model type (size)

* constants expanded for minicpm

* Minor change of the constant names for minicpm
2024-02-07 08:15:56 +02:00
Guoteng
ce32060198
llama : support InternLM2 (#5184)
* support InternLM2 inference
  * add add_space_prefix KV pair
2024-02-01 11:19:51 +02:00
sharpHL
f2e69d28c0
llama : add support for Orion-14B (#5118)
* add support for Orion-14B(https://huggingface.co/OrionStarAI/Orion-14B-Chat)

* flake8 support

* Update llama.cpp

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

* Update llama.cpp

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

* Update llama.cpp

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

* Update llama.cpp

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

* Update llama.cpp

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

* Update llama.cpp

* Update llama.cpp

---------

Co-authored-by: lixiaopu <lixiaopu@cmcm.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: slaren <slarengh@gmail.com>
2024-01-28 10:00:30 +02:00
Riceball LEE
38b431de23
gguf : fix "general.alignment" type in gguf_reader.py (#5136) 2024-01-26 11:10:28 +02:00
Shijie
9b75cb2b3c
llama : support upcoming Qwen2 (#5037) 2024-01-19 13:53:13 +02:00
chiranko
2b3b999cac
llama : add CodeShell support (#5016)
* llama: add codeshell support

* llama.cpp: fix codeshell with NeoX rope

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

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-19 11:07:27 +02:00
Georgi Gerganov
15ebe59210
convert : update phi-2 to latest HF repo (#4903)
* convert : update phi-2 to latest HF repo

ggml-ci

* py : try to fix flake stuff
2024-01-13 13:44:37 +02:00
Georgi Gerganov
f445c0e68c
llama : fix llm_build_k_shift to use correct n_rot (#4889)
* llama : fix llm_build_k_shift to use correct n_rot

ggml-ci

* llama : always use hparams.n_rot for ggml_rope_custom

ggml-ci

* convert : fix persimmon conversion to write correct n_rot
2024-01-12 13:01:56 +02:00
postmasters
83e633c27e
llama : differentiate the KV dims in the attention (#4657)
* Add n_key_dim and n_value_dim

Some models use values that are not derived from `n_embd`.
Also remove `n_embd_head` and `n_embd_gqa` because it is not clear
which "head" is referred to (key or value).

Fix issue #4648.

* Fix `llm_build_kqv` to use `n_value_gqa`

* Rebase

* Rename variables

* Fix llm_build_kqv to be more generic wrt n_embd_head_k

* Update default values for n_embd_head_k and n_embd_head_v

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

* Fix llm_load_tensors: the asserts were not backcompat

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-02 13:51:28 +02:00
manikbhandari
ea5497df5d
gpt2 : Add gpt2 architecture integration (#4555) 2023-12-28 15:03:57 +01:00
Nam D. Tran
f6793491b5
llama : add AWQ for llama, llama2, mpt, and mistral models (#4593)
* update: awq support llama-7b model

* update: change order

* update: benchmark results for llama2-7b

* update: mistral 7b v1 benchmark

* update: support 4 models

* fix: Readme

* update: ready for PR

* update: readme

* fix: readme

* update: change order import

* black

* format code

* update: work for bot mpt and awqmpt

* update: readme

* Rename to llm_build_ffn_mpt_awq

* Formatted other files

* Fixed params count

* fix: remove code

* update: more detail for mpt

* fix: readme

* fix: readme

* update: change folder architecture

* fix: common.cpp

* fix: readme

* fix: remove ggml_repeat

* update: cicd

* update: cicd

* uppdate: remove use_awq arg

* update: readme

* llama : adapt plamo to new ffn

ggml-ci

---------

Co-authored-by: Trần Đức Nam <v.namtd12@vinai.io>
Co-authored-by: Le Hoang Anh <v.anhlh33@vinai.io>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-12-27 17:39:45 +02:00
Shintarou Okada
753be377b6
llama : add PLaMo model (#3557)
* add plamo mock

* add tensor loading

* plamo convert

* update norm

* able to compile

* fix norm_rms_eps hparam

* runnable

* use inp_pos

* seems ok

* update kqv code

* remove develop code

* update README

* shuffle attn_q.weight and attn_output.weight for broadcasting

* remove plamo_llm_build_kqv and use llm_build_kqv

* fix style

* update

* llama : remove obsolete KQ_scale

* plamo : fix tensor names for correct GPU offload

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-12-24 15:35:49 +02:00
Georgi Gerganov
769a7bc85e
gguf-py : fix broken link 2023-12-21 23:20:36 +02:00
howlger
880e352277
py : open merges file as 'utf-8' (#4566)
Otherwise, on Windows converting bling-phi-2-v0 (<https://huggingface.co/llmware/bling-phi-2-v0>) via convert-hf-to-gguf.py will fail with the following error:

```
Traceback (most recent call last):
  File "C:\Users\User\git\gguf\convert-hf-to-gguf.py", line 1061, in <module>
    model_instance.set_vocab()
  File "C:\Users\User\git\gguf\convert-hf-to-gguf.py", line 52, in set_vocab
    self._set_vocab_gpt2()
  File "C:\Users\User\git\gguf\convert-hf-to-gguf.py", line 264, in _set_vocab_gpt2
    special_vocab = gguf.SpecialVocab(dir_model, load_merges=True)
  File "C:\Users\User\git\gguf\gguf\vocab.py", line 33, in __init__
    self._load(Path(path))
  File "C:\Users\User\git\gguf\gguf\vocab.py", line 81, in _load
    self._try_load_merges_txt(path)
  File "C:\Users\User\git\gguf\gguf\vocab.py", line 95, in _try_load_merges_txt
    for line in fp:
  File "C:\Users\User\miniconda3\envs\gguf\lib\encodings\cp1252.py", line 23, in decode
    return codecs.charmap_decode(input,self.errors,decoding_table)[0]
UnicodeDecodeError: 'charmap' codec can't decode byte 0x81 in position 1415: character maps to <undefined>
```
2023-12-21 19:07:34 +02:00
Ebey Abraham
b9e74f9bca
llama : add phi-2 + fix NeoX rope + ggml_mul_mat_set_prec (#4490)
* phi2 implementation

* fix breaking change

* phi-2 : various fixes

* phi-2 : use layer norm eps

* py : whitespaces

* llama : fix meta KV override bug

* convert : phi don't add BOS token

* convert : revert "added_tokens_decoder" change

* phi-2 : scale Q instead of KQ for better precision

* ggml : fix NeoX rope to rotate just first n_dims

* cuda : less diff in the rope_neox kernel

* ggml : add ggml_mul_mat_set_prec

ggml-ci

* Update ggml-cuda.cu

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

* Update ggml-cuda.cu

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

* cuda : ggml_cuda_op_mul_mat_cublas support F32 precision

* cuda : remove oboslete comment

---------

Co-authored-by: Ebey Abraham <ebeyabraham@microsoft.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: slaren <slarengh@gmail.com>
2023-12-18 19:27:47 +02:00
Jared Van Bortel
f7f468a97d
gguf-py : fail fast on nonsensical special token IDs (#4489) 2023-12-17 10:45:46 -05:00
slaren
799a1cb13b
llama : add Mixtral support (#4406)
* convert : support Mixtral as LLAMA arch

* convert : fix n_ff typo

* llama : model loading

* ggml : sync latest ggml_mul_mat_id

* llama : update graph to support MoE

* llama : fix cur -> cur_expert

* llama : first working version

* llama : fix expert weighting in the FFN

* ggml : ggml_get_rows support 2D indexing [n_tokens, n_experts] (cpu only)

* ggml : add n_as argument to ggml_mul_mat_id

* ggml : fix ggml_get_rows to take into account ne02 / ne11

* metal : add more general support for ggml_get_rows + tests

* llama : add basic support for offloading moe with CUDA

* metal : add/mul/div use general kernel when src1 not cont

* metal : reduce the kernel launches for ggml_mul_mat_id

* ggml : get_rows : support non-contiguos tensors with gaps, generalize up to 3D

* ggml : update get_rows f16 and q

* cuda : support non-contiguous src1 in get_rows

* llama : offload missing ffn_moe_silu

* metal : fix ggml_get_rows to work with non-cont src1

* metal : add indirect mat-vec kernels for all quantization types

* llama : do not quantize expert gating tensors

* llama : add n_expert and n_expert_used to hparams + change quants

* test-backend-ops : add moe test

* cuda : fix get_rows when ncols is odd

* convert : determine n_ctx correctly

* metal : fix ggml_mul_mat_id for F32

* test-backend-ops : make experts more evenly probable (test_moe)

* test-backend-ops : cleanup, add moe test for batches

* test-backend-ops : add cpy from f32 -> all types test

* test-backend-ops : fix dequantize block offset

* llama : fix hard-coded number of experts

* test-backend-ops : simplify and disable slow tests to avoid CI timeout

* test-backend-ops : disable MOE test with thread sanitizer

* cuda : fix mul_mat_id with multi gpu

* convert : use 1e6 rope_freq_base for mixtral

* convert : fix style

* convert : support safetensors format

* gguf-py : bump version

* metal : add cpy f16 -> f32 kernel

* metal : fix binary ops for ne10 % 4 != 0

* test-backend-ops : add one more sum_rows test

* ggml : do not use BLAS with ggml_mul_mat_id

* convert-hf : support for mixtral-instruct (#4428)

* convert : typo fix, add additional hyperparameters, use LLaMA arch for Mixtral-instruct

* convert : use sentencepiece tokenizer for Mixtral-instruct

* convert : make flake8 happy

* metal : fix soft_max kernels

ref: 1914017863

* metal : limit kernels to not use more than the allowed threads

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: Radek Pilar <github@mrkva.eu>
2023-12-13 14:04:25 +02:00
Richard Kiss
9494d7c477
english : use typos to fix comments and logs (#4354) 2023-12-12 11:53:36 +02:00
Shijie
37c746d687
llama : add Qwen support (#4281)
* enable qwen to llama.cpp

* llama : do not GPU split bias tensors

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-12-01 20:16:31 +02:00
Galunid
f23c0359a3
ci : add flake8 to github actions (python linting) (#4129)
Disabled rules:

* E203 Whitespace before ':' - disabled because we often use 'C' Style where values are aligned

* E211 Whitespace before '(' (E211) - disabled because we often use 'C' Style where values are aligned

* E221 Multiple spaces before operator - disabled because we often use 'C' Style where values are aligned

* E225 Missing whitespace around operator - disabled because it's broken so often it seems like a standard

* E231 Missing whitespace after ',', ';', or ':' - disabled because we often use 'C' Style where values are aligned

* E241 Multiple spaces after ',' - disabled because we often use 'C' Style where values are aligned

* E251 Unexpected spaces around keyword / parameter equals - disabled because it's broken so often it seems like a standard

* E261 At least two spaces before inline comment - disabled because it's broken so often it seems like a standard

* E266 Too many leading '#' for block comment - sometimes used as "section" separator

* E501 Line too long - disabled because it's broken so often it seems like a standard

* E701 Multiple statements on one line (colon) - broken only in convert.py when defining abstract methods (we can use# noqa instead)

* E704 Multiple statements on one line - broken only in convert.py when defining abstract methods (we can use# noqa instead)
2023-11-20 11:35:47 +01:00
slaren
e937066420
gguf-py : export chat templates (#4125)
* gguf-py : export chat templates

* llama.cpp : escape new lines in gguf kv info prints

* gguf-py : bump version

* gguf-py : check chat_template type

* gguf-py : initialize chat_template
2023-11-19 11:10:52 +01:00
Kerfuffle
91f6499393
Respect tokenizer.ggml.add_bos_token value when tokenizing (#4040)
* gguf-py: gguf-dump: Respect --no-tensor flag in JSON mode.

* Respect add_bos_token GGUF metadata value

* gguf-py: Try to fix SpecialVocab giving up too easily for the Nth time
2023-11-16 19:14:37 -07:00
Galunid
36eed0c42c
stablelm : StableLM support (#3586)
* Add support for stablelm-3b-4e1t
* Supports GPU offloading of (n-1) layers
2023-11-14 11:17:12 +01:00
Kerfuffle
21fd874c8d
gguf-py: gguf_writer: Use bytearray to build metadata (#4051)
* gguf-py: gguf_writer: Use BytesIO to build metadata

* Use bytearray instead

Bump gguf-py package version
2023-11-12 16:39:37 -07:00
M. Yusuf Sarıgöz
e86fc56f75
Fix gguf-convert-endian script (#4037)
* Fix gguf-convert-endian script

* Bump version and update description
2023-11-11 08:35:31 -07:00
Kerfuffle
34b0a08207
gguf-py: Refactor and allow reading/modifying existing GGUF files (#3981)
* gguf-py: Refactor and add file reading support

* Replay changes from #3871

Credit to @cebtenzzre for that pull

* Various type annotation fixes.

* sort imports with isort (again)

* Fix missing return statement in add_tensor

* style cleanup with flake8

* fix NamedTuple and Enum usage

* Fix an issue with state init in GGUFReader

Move examples to an examples/ directory

Clean up examples

Add an example of modifying keys in a GGUF file

Update documentation with info on examples

Try to support people importing gguf/gguf.py directly

* Damagage is not a word.

* Clean up gguf-py/examples/modify_gguf.py whitespace

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

* Update gguf-py/examples/modify_gguf.py formatting

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

* Update gguf-py/gguf/gguf_reader.py type hint

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

* Make examples executable, formatting changes

* Add more information to GGUFReader and examples comments

* Include a gguf Python package version bump

* Add convert-gguf-endian.py script

* cleanup

* gguf-py : bump minor version

* Reorganize scripts

* Make GGUFReader endian detection less arbitrary

* Add JSON dumping support to gguf-dump.py

Which I kind of regret now

* A few for gguf-dump.py cleanups

* Murder accidental tuple in gguf-py/scripts/gguf-dump.py

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

* cleanup

* constants : remove unneeded type annotations

* fix python 3.8 compat

* Set up gguf- scripts in pyproject.toml

* And include scripts/__init__.py, derp

* convert.py: We can't currently support Q8_0 on big endian.

* gguf-py: SpecialVocab: Always try available sources for special token ids

gguf-py: SpecialVocab: Try to load merges from merges.txt if not in tokenizer.json

gguf-py: SpecialVocab: Add 'add_bos_token' type bools to GGUF metadata
u

* cleanup

* Promote add_X_token to GGUF metadata for BOS and EOS

---------

Co-authored-by: Jared Van Bortel <jared@nomic.ai>
Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com>
2023-11-11 08:04:50 +03:00
Jared Van Bortel
0a7c980b6f
gguf : track writer state, free unneeded tensors, cleanup (#3871) 2023-11-07 12:43:04 -05:00
Kerfuffle
f28af0d81a
gguf-py: Support 01.AI Yi models (#3943) 2023-11-04 16:20:34 -06:00