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205 commits

Author SHA1 Message Date
Georgi Gerganov
6a9e6375b5
gguf.py : indentation 2023-08-17 21:53:15 +03:00
Georgi Gerganov
307e09cd85
Merge branch 'gguf' into gguf-write-single-pass 2023-08-17 21:51:15 +03:00
Georgi Gerganov
e426b3cfc8
gguf.py : fix vertical alignment 2023-08-17 21:50:01 +03:00
Georgi Gerganov
5484737d58
llama : fix tensor name grepping during quantization
ggml-ci
2023-08-17 21:40:51 +03:00
Georgi Gerganov
57eaadb853
llama : throw error if gguf fails to init from file
ggml-ci
2023-08-17 21:32:14 +03:00
klosax
b3cc182990
llama.cpp : typo 2023-08-17 20:27:50 +02:00
Georgi Gerganov
acaa98234a
convert.py : fix HF tensor permuting / unpacking
ggml-ci
2023-08-17 21:06:45 +03:00
klosax
78e1e57862
quantize-stats.cpp : .bin --> .gguf 2023-08-17 19:18:24 +02:00
klosax
fb11dd3f92
common.h : .bin --> .gguf 2023-08-17 19:16:35 +02:00
Georgi Gerganov
e72c8c2124
ggml : fix bug in gguf_set_kv
ggml-ci
2023-08-17 20:13:48 +03:00
M. Yusuf Sarıgöz
4dbce7d009 gguf : rm file_type key and method 2023-08-17 20:02:38 +03:00
M. Yusuf Sarıgöz
1d93d04ce2 gguf : refactor pth to gguf conversion script 2023-08-17 19:58:27 +03:00
Georgi Gerganov
899f9a5350
llama : fix lambda capture
ggml-ci
2023-08-17 19:49:45 +03:00
Georgi Gerganov
93f285bdf1
gptneox : move as a WIP example 2023-08-17 19:49:45 +03:00
M. Yusuf Sarıgöz
f71704177f gguf : rename h5 to hf (for HuggingFace) 2023-08-17 19:49:15 +03:00
Georgi Gerganov
81a2c2a6f4
llama : fix llama_model_loader memory leak 2023-08-17 19:49:02 +03:00
M. Yusuf Sarıgöz
9f02694c91 gguf : refactor gptneox conversion script 2023-08-17 19:45:06 +03:00
Georgi Gerganov
dd9e2fc988
ci : update ".bin" to ".gguf" extension
ggml-ci
2023-08-17 19:32:14 +03:00
Georgi Gerganov
c3b739374e
editorconfig : ignore models folder
ggml-ci
2023-08-17 19:17:25 +03:00
M. Yusuf Sarıgöz
22c61c5b45 gguf : style fixes in simple conversion script 2023-08-17 19:05:43 +03:00
Georgi Gerganov
6d66ef96eb
Merge branch 'master' into gguf 2023-08-17 19:04:59 +03:00
Georgi Gerganov
11bf4366c2
llama : sync with recent PRs on master 2023-08-17 19:03:15 +03:00
M. Yusuf Sarıgöz
2f8fc92d86 gguf : fix conflicts 2023-08-17 18:51:14 +03:00
Georgi Gerganov
8ace03ad3d
convert.py : better always have n_head_kv and default it to n_head 2023-08-17 18:47:06 +03:00
klosax
d646c4efce
convert.py : n_head_kv optional and .gguf file extension 2023-08-17 17:20:36 +02:00
Georgi Gerganov
dd016cc246
Revert "ci : disable CI temporary to not waste energy"
This reverts commit 7e82d25f40.
2023-08-17 17:23:16 +03:00
Georgi Gerganov
2ddd9681d6
convert.py : update to support GGUF output 2023-08-17 17:22:43 +03:00
Georgi Gerganov
e0429d38e4
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
2023-08-17 17:19:52 +03:00
M. Yusuf Sarıgöz
5f97a48fc1 gguf : single pass for writing tensors + refactoring writer 2023-08-17 16:57:50 +03:00
M. Yusuf Sarıgöz
dce07c3121 gguf : single pass for writing tensors + refactoring writer 2023-08-17 16:48:49 +03:00
klosax
d6fd53afd6
llama.cpp : use ggml_elements() 2023-08-17 15:24:35 +02:00
klosax
5a0a2c5685
llama.cpp : print actual model size 2023-08-17 15:18:16 +02:00
M. Yusuf Sarıgöz
f31e9230ad gguf : single pass for writing tensors + refactoring writer 2023-08-17 15:19:30 +03:00
M. Yusuf Sarıgöz
42f8fe1927 examples/gguf : no need to keep q option for quantization any more 2023-08-17 08:56:42 +03:00
Georgi Gerganov
5ec18934ad
convert-new.py : pick #2427 for HF 70B support 2023-08-16 20:16:15 +03:00
Georgi Gerganov
c8ee87f141
gguf.py : merge all files in gguf.py 2023-08-16 19:55:49 +03:00
Georgi Gerganov
88b5769487
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>
2023-08-16 19:25:29 +03:00
Georgi Gerganov
758ff1bbb5
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>
2023-08-16 14:34:03 +03:00
klosax
ea5615a03a
convert-llama-h5-to-gguf.py : clarify the reverse permute 2023-08-16 11:23:15 +02:00
klosax
4a1741aa2d
gptneox-main.cpp : add tensor data layout 2023-08-15 19:56:19 +02:00
klosax
2ae0e985b3
convert-llama-7b-pth-to-gguf.py : add tensor data layout 2023-08-15 19:55:13 +02:00
klosax
66756c82af
convert-llama-h5-to-gguf.py : add tensor data layout 2023-08-15 19:54:33 +02:00
klosax
b6056c3db8
gguf.py : add tensor data layout 2023-08-15 19:53:44 +02:00
klosax
2dd5d2c92c
convert-llama-h5-to-gguf.py : add 70b gqa support 2023-08-15 00:43:10 +02:00
klosax
ca4758290c
gguf-llama.cpp : fix n_head_kv 2023-08-14 23:18:41 +02:00
klosax
ab2cbd03ca
convert-llama-7b-pth-to-gguf.py : add token types 2023-08-14 22:10:50 +02:00
klosax
cedb4870c6
gguf.py : add token types 2023-08-14 22:08:40 +02:00
klosax
5d518d421f
constants.py : add token types 2023-08-14 22:07:53 +02:00
klosax
7ec125b1dc
convert-llama-h5-to-gguf.py : add token types 2023-08-14 22:06:33 +02:00
Georgi Gerganov
6c63550f63
llama : update tokenizer style 2023-08-14 22:11:57 +03:00
Georgi Gerganov
7494c78428
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
2023-08-14 21:33:33 +03:00
goerch
afc4ca2889
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)
2023-08-14 20:20:04 +03:00
goerch
ec1b100720
llama : tokenizer fixes (#2549)
* Merge tokenizer fixes into the gguf branch.

* Add test vocabularies
2023-08-14 19:30:28 +03:00
Georgi Gerganov
8af3a99ff1
Merge branch 'master' into gguf 2023-08-14 16:39:18 +03:00
Georgi Gerganov
6f14854880
gitignore : add gptneox-main 2023-08-14 16:39:02 +03:00
Georgi Gerganov
f00780b2ee
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
2023-08-14 16:28:44 +03:00
klosax
6f64b6c0f8
Create convert-llama-7b-pth-to-gguf.py 2023-08-14 13:51:09 +02:00
Georgi Gerganov
62490f1380
gguf : use UNIX line ending 2023-08-14 13:04:35 +03:00
Georgi Gerganov
0c19ae70d5
simple : minor style changes 2023-08-14 12:58:12 +03:00
klosax
5c5a95ba2d
gguf.py : dont add empty strings 2023-08-14 11:22:06 +02:00
klosax
a7d226f871
convert-llama-h5-to-gguf.py : fixes 2023-08-14 11:14:24 +02:00
klosax
d753dfbcc8
gptneox-main.cpp : tensor name map changes 2023-08-14 10:59:18 +02:00
klosax
806a15749d
Delete gguf_tensor_map.py 2023-08-14 10:57:19 +02:00
klosax
51939d7d1b
Create gguf_namemap.py : tensor name map changes 2023-08-14 10:56:59 +02:00
klosax
5d22a9db13
convert-gptneox-h5-to-gguf.py : tensor name map changes 2023-08-14 10:55:44 +02:00
Georgi Gerganov
56a1f32072
Merge branch 'master' into gguf 2023-08-14 10:14:05 +03:00
M. Yusuf Sarıgöz
196b50fee7 gguf : add todos and comments 2023-08-14 08:50:47 +03:00
M. Yusuf Sarıgöz
24f48833ab fix conflicts 2023-08-13 16:55:42 +03:00
klosax
6beebf3fd9
gptneox-main.cpp : add file_type key 2023-08-13 14:11:01 +02:00
klosax
2827b840e4
convert-gptneox-h5-to-gguf.py : add file_type key 2023-08-13 13:54:10 +02:00
M. Yusuf Sarıgöz
bf2dad3100 convert : rm quantization version 2023-08-13 14:38:53 +03:00
M. Yusuf Sarıgöz
1d60468eee fix conflicts 2023-08-13 13:35:40 +03:00
M. Yusuf Sarıgöz
91d4bfd536 convert : write more metadata for LLaMA 2023-08-13 13:29:46 +03:00
klosax
17800cd80f
convert-llama-h5-to-gguf.py : load model in parts to save memory 2023-08-13 12:20:02 +02:00
klosax
e3d1f07eb1
convert-gptneox-h5-to-gguf.py : load model in parts to save memory 2023-08-13 12:18:34 +02:00
klosax
9bf5a7efcb
Update gguf_tensor_map.py 2023-08-13 01:27:38 +02:00
klosax
c7bd8c147c
gptneox-main.cpp : n_layer --> n_block 2023-08-13 00:03:32 +02:00
klosax
e91a2224e4
convert-llama-h5-to-gguf.py : n_layer --> n_block 2023-08-13 00:02:44 +02:00
klosax
489616e126
convert-gptneox-h5-to-gguf.py : n_layer --> n_block 2023-08-13 00:02:04 +02:00
klosax
d2ce9cfe8d
gguf.py : n_layer --> n_block 2023-08-13 00:01:20 +02:00
klosax
8b5f0c5067
constants.py : n_layer --> n_block 2023-08-13 00:00:32 +02:00
klosax
5e58ffa1ed
gptneox-main.cpp : n_layer --> n_block 2023-08-12 23:50:58 +02:00
klosax
e606ffeaee
convert-llama-h5-to-gguf.py : simplify nbytes 2023-08-12 22:30:35 +02:00
klosax
f8218477b3
convert-gptneox-h5-to-gguf.py : simplify nbytes 2023-08-12 22:29:35 +02:00
klosax
4cef57c81a
convert-llama-h5-to-gguf.py : no need to convert tensors twice 2023-08-12 21:50:24 +02:00
klosax
8f09157ec9
convert-gptneox-h5-to-gguf.py : no need to convert tensors twice 2023-08-12 21:48:58 +02:00
klosax
5d81a715d4
gguf.py : no need to convert tensors twice 2023-08-12 21:45:45 +02:00
M. Yusuf Sarıgöz
60d540831b gguf : roper closing of file 2023-08-12 21:42:31 +03:00
M. Yusuf Sarıgöz
202eab04d3 gguf : quantization is working 2023-08-12 16:39:05 +03:00
M. Yusuf Sarıgöz
1fc3d30b71 gguf : start implementing quantization (WIP) 2023-08-12 16:09:47 +03:00
M. Yusuf Sarıgöz
fa7c39540c gguf : start implementing quantization (WIP) 2023-08-12 15:55:58 +03:00
M. Yusuf Sarıgöz
b2571af255 gguf : start implementing quantization (WIP) 2023-08-12 14:28:17 +03:00
M. Yusuf Sarıgöz
c4f02b4f74 gguf : start implementing quantization (WIP) 2023-08-12 12:01:17 +03:00
M. Yusuf Sarıgöz
0e1a3c7e7d gguf : start implementing quantization (WIP) 2023-08-12 11:32:34 +03:00
M. Yusuf Sarıgöz
4fa017a1f9 gguf : start implementing quantization (WIP) 2023-08-12 10:40:56 +03:00
M. Yusuf Sarıgöz
186c496fdf Merge branch 'gguf' of https://github.com//ggerganov/llama.cpp into gguf 2023-08-12 07:25:10 +03:00
M. Yusuf Sarıgöz
2f52008b20 gguf : rm references to old file magics 2023-08-12 07:24:46 +03:00
klosax
e76c59d524
Update gptneox-main.cpp 2023-08-11 23:09:49 +02:00
klosax
2a5ac7af44
Update gguf_tensor_map.py 2023-08-11 23:08:48 +02:00
M. Yusuf Sarıgöz
e732423280 gguf : get rid of n_mult, read n_ff from file 2023-08-11 23:50:38 +03:00
M. Yusuf Sarıgöz
f44bbd3d88 gguf : rm redundant method 2023-08-11 21:00:51 +03:00
M. Yusuf Sarıgöz
7009cf581c gguf : shorter name for member variable 2023-08-11 20:43:02 +03:00
M. Yusuf Sarıgöz
61919c1a8f gguf : rm references to old file formats 2023-08-11 20:36:11 +03:00
M. Yusuf Sarıgöz
d09fd10713 gguf : write metadata in gguf_file_saver 2023-08-11 20:07:43 +03:00
M. Yusuf Sarıgöz
781b9ec3f5 gguf : write metadata in gguf_file_saver (WIP) 2023-08-11 18:01:26 +03:00
M. Yusuf Sarıgöz
28abfc90fa gguf : write metadata in gguf_file_saver (WIP) 2023-08-11 13:27:58 +03:00
M. Yusuf Sarıgöz
e3a4960953 gguf : add gguf_get_kv_type 2023-08-11 13:03:23 +03:00
M. Yusuf Sarıgöz
eb8ca6996f gguf : add gguf_get_kv_type 2023-08-11 12:24:08 +03:00
M. Yusuf Sarıgöz
b2440f1943 gguf : start implementing gguf_file_saver (WIP) 2023-08-11 11:29:50 +03:00
M. Yusuf Sarıgöz
a356b0e228 gguf : start implementing gguf_file_saver (WIP) 2023-08-11 10:50:02 +03:00
M. Yusuf Sarıgöz
e7d346c37c gguf : start implementing gguf_file_saver (WIP) 2023-08-11 09:52:01 +03:00
M. Yusuf Sarıgöz
f316b94c7c gguf : rm deprecated function 2023-08-10 20:20:22 +03:00
M. Yusuf Sarıgöz
cfb8e35b73 gguf : inference with 7B model working (WIP) 2023-08-10 19:56:56 +03:00
M. Yusuf Sarıgöz
42cc04d11d gguf : calculate n_mult 2023-08-10 18:49:08 +03:00
M. Yusuf Sarıgöz
22de6c5c4c upd .gitignore 2023-08-10 18:09:49 +03:00
M. Yusuf Sarıgöz
4c0f64e302 rm binary commited by mistake 2023-08-10 18:07:41 +03:00
M. Yusuf Sarıgöz
4f865181aa gguf : start implementing libllama in GGUF (WIP) 2023-08-10 17:49:31 +03:00
M. Yusuf Sarıgöz
1c4d8bf981 gguf : start implementing libllama in GGUF (WIP) 2023-08-10 16:52:08 +03:00
klosax
0246d0dd6f
gptneox-main.cpp : map tensor names 2023-08-09 00:54:21 +02:00
klosax
7d5f4522dd
convert-llama-h5-to-gguf.py : map tensor names 2023-08-09 00:52:16 +02:00
klosax
f4d137d98c
convert-gptneox-h5-to-gguf.py : map tensor names 2023-08-09 00:50:11 +02:00
klosax
ece4fc185e
map tensor names 2023-08-09 00:48:33 +02:00
klosax
65559a23c8
Update gptneox-main.cpp 2023-08-07 22:28:43 +02:00
Georgi Gerganov
8083ae347a gguf : minor stuff 2023-08-07 19:02:18 +03:00
Georgi Gerganov
1da82c551f Merge branch 'master' into gguf 2023-08-07 18:53:03 +03:00
klosax
4357e692ac
gguf.py : use custom alignment if present 2023-08-07 13:51:26 +02:00
klosax
db5618ad99
cmpnct_gpt2bpe.hpp : comments 2023-08-04 04:57:51 +02:00
klosax
278ada9572
gguf.py : bytesarray for gpt2bpe tokenizer 2023-08-04 04:07:57 +02:00
klosax
fb0b243705
Makefile : remove gptneox-common 2023-08-04 04:02:10 +02:00
klosax
5d98989cf6
gpt2 bpe tokenizer (handles merges and unicode) 2023-08-04 03:58:44 +02:00
klosax
e6f19ba240
gptneox-main.cpp : gpt2 bpe tokenizer 2023-08-04 03:56:37 +02:00
klosax
2922280a1a
convert-gptneox-h5-to-gguf.py : gpt2bpe tokenizer 2023-08-04 03:55:23 +02:00
klosax
6691aa8797
Delete gptneox-common.h 2023-08-04 03:52:01 +02:00
klosax
23abbe8e00
Delete gptneox-common.cpp 2023-08-04 03:51:43 +02:00
klosax
c5ba5efda2
convert-llama-h5-to-gguf.py : special tokens 2023-08-02 11:26:07 +02:00
klosax
e1e9b28547
convert-llama-h5-to-gguf.py : accumulate kv / ti + special tokens 2023-08-02 11:15:33 +02:00
M. Yusuf Sarıgöz
c3a65c4bbe gguf-util.h : update note 2023-08-02 11:16:23 +03:00
M. Yusuf Sarıgöz
cf365fbc20 gguf : gguf counterpart of llama-util.h 2023-08-02 11:13:56 +03:00
klosax
1b4f9c8eb9
convert-gptneox-h5-to-gguf.py : accumulate kv and ti + special tokens 2023-08-01 23:40:50 +02:00
klosax
49380a23a3
gguf.py : accumulate kv and tensor info data + special tokens 2023-08-01 23:37:48 +02:00
klosax
ff1cb02397
constants.py : special tokens 2023-08-01 23:17:21 +02:00
klosax
36a36c32a3
Update gptneox-main.cpp 2023-08-01 14:44:28 +02:00
klosax
c77fabb1f9
gptneox-main.cpp : special tokens 2023-08-01 14:32:53 +02:00
klosax
e7a741695c
convert-gptneox-h5-to-gguf.py : Special tokens 2023-08-01 14:30:00 +02:00
klosax
da4900e835
Update convert-llama-h5-to-gguf.py 2023-07-31 23:04:03 +02:00
M. Yusuf Sarıgöz
f3de876a12 fix : update convert-llama-h5-to-gguf.py 2023-07-31 23:58:29 +03:00
M. Yusuf Sarıgöz
bb42aefaeb gguf : mmap tensor data example 2023-07-31 17:46:12 +03:00
M. Yusuf Sarıgöz
b26f5b2e43 gguf : fix typo in function call 2023-07-31 16:23:54 +03:00
M. Yusuf Sarıgöz
7aa0a0e7f7 gguf : support custom alignment value 2023-07-31 09:59:36 +03:00
klosax
6b3a7b9f4f
Update convert-llama-h5-to-gguf.py 2023-07-31 03:02:00 +02:00
klosax
4f5b6224be
Update convert-gptneox-h5-to-gguf.py 2023-07-31 03:00:20 +02:00
klosax
2a0914673c
Update convert-gptneox-h5-to-gguf.py 2023-07-30 17:31:11 +02:00
klosax
068a8e0fbe
Update convert-llama-h5-to-gguf.py 2023-07-30 17:29:56 +02:00
klosax
30c4ea47e6
add gptneox gguf example 2023-07-30 16:59:26 +02:00
klosax
2fabc176ce
Update convert-llama-h5-to-gguf.py 2023-07-30 16:28:08 +02:00
klosax
f175b05872
Makefile : add gptneox gguf example 2023-07-30 15:08:37 +02:00
klosax
e9192b0135
add gptneox gguf example 2023-07-30 15:05:37 +02:00
klosax
4ed98bf1ab
Update convert-llama-h5-to-gguf.py 2023-07-30 15:01:47 +02:00
klosax
b19c11750b
ggml.c : add gguf_get_arr_n 2023-07-30 14:58:50 +02:00
klosax
b4676ee447
ggml.h : increase GGML_MAX_NAME to 64 2023-07-30 14:51:37 +02:00
klosax
ccd81a751b
gguf.py : add layer norm eps and merges 2023-07-30 14:48:14 +02:00
klosax
0790c121aa
constants.py : add layer norm eps 2023-07-30 14:46:36 +02:00
M. Yusuf Sarıgöz
87c34e4dd4 gguf : update convert-llama-h5-to-gguf.py 2023-07-30 01:09:22 +03:00
M. Yusuf Sarıgöz
32e037ffbe gguf : fix set is not subscriptable 2023-07-30 01:01:13 +03:00
klosax
06c3e4a1a7
Update convert-llama-h5-to-gguf.py 2023-07-29 21:38:01 +02:00
klosax
9577821487
gguf.py : support any type 2023-07-29 21:29:07 +02:00
klosax
2c22e3bcdb
ggml.c : get arr str and f32 2023-07-29 20:37:47 +02:00
klosax
34469b9ea7
ggml.h : get array str and f32 2023-07-29 20:36:06 +02:00
M. Yusuf Sarıgöz
0f5e57f01d gguf : handle already encoded string 2023-07-29 19:56:06 +03:00
klosax
8ad7cd49fb
Update convert-llama-h5-to-gguf.py 2023-07-29 16:47:00 +02:00
M. Yusuf Sarıgöz
0317c41d98 gguf : upd gguf conversion script 2023-07-29 13:31:07 +03:00
M. Yusuf Sarıgöz
cc3dd7f042 gguf : write tokenizer data 2023-07-29 13:30:22 +03:00
M. Yusuf Sarıgöz
8a76dd8a85 gguf : write tensors one by one 2023-07-29 13:17:28 +03:00
M. Yusuf Sarıgöz
c861e234f4 gguf : write tensors one by one 2023-07-29 12:49:01 +03:00
M. Yusuf Sarıgöz
0c219fb5b5 gguf : fix writing gguf arrays 2023-07-29 12:42:54 +03:00
M. Yusuf Sarıgöz
93f7f7aef7 gguf : write tensors one by one and code reuse 2023-07-29 12:34:35 +03:00
M. Yusuf Sarıgöz
aa99562d70 Merge branch 'gguf' of https://github.com//ggerganov/llama.cpp into gguf 2023-07-29 12:26:11 +03:00
M. Yusuf Sarıgöz
ea5f9ad2ca gguf : fix writing gguf arrays 2023-07-29 12:25:43 +03:00
klosax
999431c4b6
quick and dirty conversion example 2023-07-29 11:20:05 +02:00
M. Yusuf Sarıgöz
d54f53ca51 gguf : add tokenization constants 2023-07-29 12:04:45 +03:00
M. Yusuf Sarıgöz
06f423a8e1 gguf : write sample tensors to read 2023-07-29 10:26:26 +03:00
M. Yusuf Sarıgöz
08dc8fd884 gguf : do not hardcode tensor names to read 2023-07-29 10:24:46 +03:00
M. Yusuf Sarıgöz
9475cdb7a3 Merge branch 'gguf-write-tokenization' into gguf 2023-07-29 00:36:35 +03:00
M. Yusuf Sarıgöz
1495735aac gguf : fix writing tensors 2023-07-29 00:26:22 +03:00
klosax
3492f848d7
gguf : add gguf_find_key (#2438)
* gguf.cpp : find key example

* ggml.h : add gguf_find_key

* ggml.c : add gguf_find_key
2023-07-28 23:45:24 +03:00
M. Yusuf Sarıgöz
11ef380c2a
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
2023-07-28 11:34:16 +03:00
Georgi Gerganov
d2bb3ac10b
convert.py : remove GGML vocab + other obsolete stuff 2023-07-27 16:36:35 +03:00
Georgi Gerganov
68f53485e4
convert.py : start a new simplified implementation by removing old stuff 2023-07-27 15:56:53 +03:00
Georgi Gerganov
158be8f7f4
gguf.py : some code style changes 2023-07-27 15:37:06 +03:00
Georgi Gerganov
d2b6ca13ad
gguf : add array support 2023-07-27 14:53:07 +03:00
Georgi Gerganov
d89533dff6
gguf : expose the gguf_type enum through the API for now 2023-07-27 11:10:34 +03:00
M. Yusuf Sarıgöz
c85d3178b3
refactor : reduce code duplication and better API (#2415) 2023-07-27 10:29:29 +03:00
Georgi Gerganov
d8491fc7e3
gguf : add comments 2023-07-26 23:00:24 +03:00
Georgi Gerganov
5628ec7163
gguf : read / write sample models 2023-07-26 22:40:45 +03:00
Georgi Gerganov
e46870f5af
gguf : gguf.c is now part of ggml.c 2023-07-26 18:55:32 +03:00
Georgi Gerganov
d313c0fa33
gguf : simplify gguf_get_val 2023-07-26 18:53:57 +03:00
Georgi Gerganov
cb871fa022
gguf : do not support passing existing ggml_context to gguf_init 2023-07-26 18:48:52 +03:00
Georgi Gerganov
860c9c63ce
gguf : add gguf_get_tensor_name() 2023-07-26 18:21:14 +03:00
Georgi Gerganov
78b226a959
gguf : initial model loading - not tested 2023-07-26 18:21:14 +03:00
Georgi Gerganov
d91b985d2d
gguf : read tensor info 2023-07-26 18:21:13 +03:00
Georgi Gerganov
8d6acfec12
gguf : read header + meta data 2023-07-26 18:21:13 +03:00
Georgi Gerganov
6873148771
gguf : first API pass 2023-07-26 18:21:13 +03:00
Georgi Gerganov
7e82d25f40
ci : disable CI temporary to not waste energy 2023-07-26 18:21:13 +03:00
M. Yusuf Sarıgöz
bae6b125f6
wip : implement GGUF (#2397)
* Add LLAMA_DEFAULT_RMS_EPS so we can change the default (#2384)

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>

* WIP: python class to write GGUF, incomplete C apı for reading

---------

Co-authored-by: Kawrakow <48489457+ikawrakow@users.noreply.github.com>
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2023-07-26 18:21:13 +03:00
Georgi Gerganov
4d698495ea
gguf : init 2023-07-26 18:21:12 +03:00
40 changed files with 7626 additions and 2672 deletions

4
.gitignore vendored
View file

@ -1,6 +1,7 @@
*.o
*.a
*.so
*.gguf
*.bin
.DS_Store
.build/
@ -47,6 +48,8 @@ models-mnt
/server
/Pipfile
/embd-input-test
/gguf
/gguf-llama-simple
/libllama.so
build-info.h
arm_neon.h
@ -64,7 +67,6 @@ perf-*.txt
examples/jeopardy/results.txt
pyproject.toml
poetry.lock
poetry.toml

View file

@ -527,7 +527,6 @@ endif()
add_library(llama
llama.cpp
llama.h
llama-util.h
)
target_include_directories(llama PUBLIC .)

View file

@ -1,5 +1,5 @@
# Define the default target now so that it is always the first target
BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot train-text-from-scratch convert-llama2c-to-ggml simple server embd-input-test
BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot train-text-from-scratch convert-llama2c-to-ggml simple server embd-input-test gguf
# Binaries only useful for tests
TEST_TARGETS = tests/test-llama-grammar tests/test-grammar-parser tests/test-double-float tests/test-grad0 tests/test-opt tests/test-quantize-fns tests/test-quantize-perf tests/test-sampling tests/test-tokenizer-0
@ -329,7 +329,7 @@ ggml-alloc.o: ggml-alloc.c ggml.h ggml-alloc.h
OBJS += ggml-alloc.o
llama.o: llama.cpp ggml.h ggml-alloc.h ggml-cuda.h ggml-metal.h llama.h llama-util.h
llama.o: llama.cpp ggml.h ggml-alloc.h ggml-cuda.h ggml-metal.h llama.h
$(CXX) $(CXXFLAGS) -c $< -o $@
common.o: examples/common.cpp examples/common.h
@ -345,7 +345,7 @@ libllama.so: llama.o ggml.o $(OBJS)
$(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS)
clean:
rm -vf *.o *.so *.dll main quantize quantize-stats perplexity embedding benchmark-matmult save-load-state server simple vdot train-text-from-scratch convert-llama2c-to-ggml embd-input-test build-info.h $(TEST_TARGETS)
rm -vf *.o *.so *.dll main quantize quantize-stats perplexity embedding benchmark-matmult save-load-state server simple vdot train-text-from-scratch convert-llama2c-to-ggml embd-input-test gguf build-info.h $(TEST_TARGETS)
#
# Examples
@ -385,6 +385,9 @@ $(LIB_PRE)embdinput$(DSO_EXT): examples/embd-input/embd-input.h examples/embd-in
embd-input-test: $(LIB_PRE)embdinput$(DSO_EXT) examples/embd-input/embd-input-test.cpp build-info.h ggml.o llama.o common.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %$(DSO_EXT),$(filter-out %.h,$(filter-out %.hpp,$^))) -o $@ $(LDFLAGS) -L. -lembdinput
gguf: examples/gguf/gguf.cpp build-info.h ggml.o llama.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
train-text-from-scratch: examples/train-text-from-scratch/train-text-from-scratch.cpp build-info.h ggml.o llama.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)

View file

@ -284,7 +284,7 @@ When built with Metal support, you can enable GPU inference with the `--gpu-laye
Any value larger than 0 will offload the computation to the GPU. For example:
```bash
./main -m ./models/7B/ggml-model-q4_0.bin -n 128 -ngl 1
./main -m ./models/7B/ggml-model-q4_0.gguf -n 128 -ngl 1
```
### MPI Build
@ -323,7 +323,7 @@ The above will distribute the computation across 2 processes on the first host a
Finally, you're ready to run a computation using `mpirun`:
```bash
mpirun -hostfile hostfile -n 3 ./main -m ./models/7B/ggml-model-q4_0.bin -n 128
mpirun -hostfile hostfile -n 3 ./main -m ./models/7B/ggml-model-q4_0.gguf -n 128
```
### BLAS Build
@ -506,10 +506,10 @@ python3 convert.py models/7B/
python convert.py models/7B/ --vocabtype bpe
# quantize the model to 4-bits (using q4_0 method)
./quantize ./models/7B/ggml-model-f16.bin ./models/7B/ggml-model-q4_0.bin q4_0
./quantize ./models/7B/ggml-model-f16.gguf ./models/7B/ggml-model-q4_0.gguf q4_0
# run the inference
./main -m ./models/7B/ggml-model-q4_0.bin -n 128
./main -m ./models/7B/ggml-model-q4_0.gguf -n 128
```
When running the larger models, make sure you have enough disk space to store all the intermediate files.
@ -565,7 +565,7 @@ Here is an example of a few-shot interaction, invoked with the command
./examples/chat-13B.sh
# custom arguments using a 13B model
./main -m ./models/13B/ggml-model-q4_0.bin -n 256 --repeat_penalty 1.0 --color -i -r "User:" -f prompts/chat-with-bob.txt
./main -m ./models/13B/ggml-model-q4_0.gguf -n 256 --repeat_penalty 1.0 --color -i -r "User:" -f prompts/chat-with-bob.txt
```
Note the use of `--color` to distinguish between user input and generated text. Other parameters are explained in more detail in the [README](examples/main/README.md) for the `main` example program.
@ -628,6 +628,8 @@ OpenLLaMA is an openly licensed reproduction of Meta's original LLaMA model. It
### Using [GPT4All](https://github.com/nomic-ai/gpt4all)
*Note: these instructions are likely obsoleted by the GGUF update*
- Obtain the `tokenizer.model` file from LLaMA model and put it to `models`
- Obtain the `added_tokens.json` file from Alpaca model and put it to `models`
- Obtain the `gpt4all-lora-quantized.bin` file from GPT4All model and put it to `models/gpt4all-7B`
@ -703,7 +705,7 @@ If your issue is with model generation quality, then please at least scan the fo
#### How to run
1. Download/extract: https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip?ref=salesforce-research
2. Run `./perplexity -m models/7B/ggml-model-q4_0.bin -f wiki.test.raw`
2. Run `./perplexity -m models/7B/ggml-model-q4_0.gguf -f wiki.test.raw`
3. Output:
```
perplexity : calculating perplexity over 655 chunks
@ -802,13 +804,13 @@ docker run -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:full --all-in-
On completion, you are ready to play!
```bash
docker run -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:full --run -m /models/7B/ggml-model-q4_0.bin -p "Building a website can be done in 10 simple steps:" -n 512
docker run -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:full --run -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512
```
or with a light image:
```bash
docker run -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:light -m /models/7B/ggml-model-q4_0.bin -p "Building a website can be done in 10 simple steps:" -n 512
docker run -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:light -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512
```
### Docker With CUDA
@ -839,8 +841,8 @@ The resulting images, are essentially the same as the non-CUDA images:
After building locally, Usage is similar to the non-CUDA examples, but you'll need to add the `--gpus` flag. You will also want to use the `--n-gpu-layers` flag.
```bash
docker run --gpus all -v /path/to/models:/models local/llama.cpp:full-cuda --run -m /models/7B/ggml-model-q4_0.bin -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1
docker run --gpus all -v /path/to/models:/models local/llama.cpp:light-cuda -m /models/7B/ggml-model-q4_0.bin -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1
docker run --gpus all -v /path/to/models:/models local/llama.cpp:full-cuda --run -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1
docker run --gpus all -v /path/to/models:/models local/llama.cpp:light-cuda -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1
```
### Contributing

View file

@ -159,17 +159,17 @@ function gg_run_open_llama_3b_v2 {
python3 ../convert.py ${path_models}
model_f16="${path_models}/ggml-model-f16.bin"
model_q8_0="${path_models}/ggml-model-q8_0.bin"
model_q4_0="${path_models}/ggml-model-q4_0.bin"
model_q4_1="${path_models}/ggml-model-q4_1.bin"
model_q5_0="${path_models}/ggml-model-q5_0.bin"
model_q5_1="${path_models}/ggml-model-q5_1.bin"
model_q2_k="${path_models}/ggml-model-q2_k.bin"
model_q3_k="${path_models}/ggml-model-q3_k.bin"
model_q4_k="${path_models}/ggml-model-q4_k.bin"
model_q5_k="${path_models}/ggml-model-q5_k.bin"
model_q6_k="${path_models}/ggml-model-q6_k.bin"
model_f16="${path_models}/ggml-model-f16.gguf"
model_q8_0="${path_models}/ggml-model-q8_0.gguf"
model_q4_0="${path_models}/ggml-model-q4_0.gguf"
model_q4_1="${path_models}/ggml-model-q4_1.gguf"
model_q5_0="${path_models}/ggml-model-q5_0.gguf"
model_q5_1="${path_models}/ggml-model-q5_1.gguf"
model_q2_k="${path_models}/ggml-model-q2_k.gguf"
model_q3_k="${path_models}/ggml-model-q3_k.gguf"
model_q4_k="${path_models}/ggml-model-q4_k.gguf"
model_q5_k="${path_models}/ggml-model-q5_k.gguf"
model_q6_k="${path_models}/ggml-model-q6_k.gguf"
wiki_test_60="${path_wiki}/wiki.test-60.raw"
@ -285,17 +285,17 @@ function gg_run_open_llama_7b_v2 {
python3 ../convert.py ${path_models}
model_f16="${path_models}/ggml-model-f16.bin"
model_q8_0="${path_models}/ggml-model-q8_0.bin"
model_q4_0="${path_models}/ggml-model-q4_0.bin"
model_q4_1="${path_models}/ggml-model-q4_1.bin"
model_q5_0="${path_models}/ggml-model-q5_0.bin"
model_q5_1="${path_models}/ggml-model-q5_1.bin"
model_q2_k="${path_models}/ggml-model-q2_k.bin"
model_q3_k="${path_models}/ggml-model-q3_k.bin"
model_q4_k="${path_models}/ggml-model-q4_k.bin"
model_q5_k="${path_models}/ggml-model-q5_k.bin"
model_q6_k="${path_models}/ggml-model-q6_k.bin"
model_f16="${path_models}/ggml-model-f16.gguf"
model_q8_0="${path_models}/ggml-model-q8_0.gguf"
model_q4_0="${path_models}/ggml-model-q4_0.gguf"
model_q4_1="${path_models}/ggml-model-q4_1.gguf"
model_q5_0="${path_models}/ggml-model-q5_0.gguf"
model_q5_1="${path_models}/ggml-model-q5_1.gguf"
model_q2_k="${path_models}/ggml-model-q2_k.gguf"
model_q3_k="${path_models}/ggml-model-q3_k.gguf"
model_q4_k="${path_models}/ggml-model-q4_k.gguf"
model_q5_k="${path_models}/ggml-model-q5_k.gguf"
model_q6_k="${path_models}/ggml-model-q6_k.gguf"
wiki_test="${path_wiki}/wiki.test.raw"

View file

@ -0,0 +1,268 @@
# HF gptneox--> gguf conversion
import gguf
import os
import sys
import struct
import json
import numpy as np
import torch
from typing import Any, List
from pathlib import Path
from transformers import AutoTokenizer
# ref: https://github.com/openai/gpt-2/blob/master/src/encoder.py
def bytes_to_unicode():
"""
Returns list of utf-8 byte and a corresponding list of unicode strings.
The reversible bpe codes work on unicode strings.
This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
This is a significant percentage of your normal, say, 32K bpe vocab.
To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
And avoids mapping to whitespace/control characters the bpe code barfs on.
"""
bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1))
cs = bs[:]
n = 0
for b in range(2**8):
if b not in bs:
bs.append(b)
cs.append(2**8+n)
n += 1
cs = [chr(n) for n in cs]
return dict(zip(bs, cs))
def count_model_parts(dir_model: str) -> int:
num_parts = 0
for filename in os.listdir(dir_model):
if filename.startswith("pytorch_model-"):
num_parts += 1
if num_parts > 0:
print("gguf: found " + str(num_parts) + " model parts")
return num_parts
if len(sys.argv) < 3:
print("Usage: convert-h5-to-ggml.py dir-model ftype\n")
print(" ftype == 0 -> float32")
print(" ftype == 1 -> float16")
sys.exit(1)
# output in the same directory as the model
dir_model = sys.argv[1]
last_dir = os.path.basename(os.path.normpath(dir_model))
# possible tensor data types
# ftype == 0 -> float32
# ftype == 1 -> float16
# map from ftype to string
ftype_str = ["f32", "f16"]
ftype = 1
if len(sys.argv) > 2:
ftype = int(sys.argv[2])
if ftype < 0 or ftype > 1:
print("Invalid ftype: " + str(ftype))
sys.exit(1)
fname_out = sys.argv[1] + "/ggml-model-" + ftype_str[ftype] + ".gguf"
print("gguf: loading model "+last_dir)
with open(dir_model + "/config.json", "r", encoding="utf-8") as f:
hparams = json.load(f)
if hparams["architectures"][0] != "GPTNeoXForCausalLM":
print("Model architecture not supported: " + hparams["architectures"][0])
sys.exit()
# get number of model parts
num_parts = count_model_parts(dir_model)
llm_arch = "gptneox"
gguf_writer = gguf.GGUFWriter(fname_out, arch=llm_arch)
print("gguf: get model metadata")
block_count = hparams["num_hidden_layers"]
gguf_writer.add_architecture()
gguf_writer.add_name(last_dir)
gguf_writer.add_context_length(hparams["max_position_embeddings"])
gguf_writer.add_embedding_length(hparams["hidden_size"])
gguf_writer.add_block_count(block_count)
gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
gguf_writer.add_rope_dimension_count(int(hparams["rotary_pct"]*(hparams["hidden_size"]//hparams["num_attention_heads"])))
gguf_writer.add_head_count(hparams["num_attention_heads"])
gguf_writer.add_parallel_residual(hparams["use_parallel_residual"] if "use_parallel_residual" in hparams else True)
gguf_writer.add_layer_norm_eps(hparams["layer_norm_eps"])
# TOKENIZATION
print("gguf: get tokenizer metadata")
tokens: List[str] = []
merges: List[str] = []
if Path(dir_model + "/tokenizer.json").is_file():
# gpt2 tokenizer
gguf_writer.add_tokenizer_model("gpt2")
print("gguf: get gpt2 tokenizer merges")
with open(dir_model + "/tokenizer.json", "r", encoding="utf-8") as f:
tokenizer_json = json.load(f)
merges = tokenizer_json["model"]["merges"]
gguf_writer.add_token_merges(merges)
print("gguf: get gpt2 tokenizer vocab")
vocab_size = len(tokenizer_json["model"]["vocab"])
# ref: https://github.com/cmp-nct/ggllm.cpp/blob/master/falcon_convert.py
tokenizer = AutoTokenizer.from_pretrained(dir_model)
reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()}
byte_encoder = bytes_to_unicode()
byte_decoder = {v: k for k, v in byte_encoder.items()}
for i in range(vocab_size):
if i in reverse_vocab:
try:
text = bytearray([byte_decoder[c] for c in reverse_vocab[i]])
except KeyError:
text = bytearray()
for c in reverse_vocab[i]:
if ord(c) < 256: # single byte character
text.append(byte_decoder[ord(c)])
else: # multibyte special token character
text.extend(c.encode('utf-8'))
else:
print(f"Key {i} not in tokenizer vocabulary. Padding with an arbitrary token.")
pad_token = f"[PAD{i}]".encode("utf8")
text = bytearray(pad_token)
tokens.append(text)
gguf_writer.add_token_list(tokens)
if "added_tokens" in tokenizer_json and Path(dir_model + "/tokenizer_config.json").is_file():
print("gguf: get special token ids")
with open(dir_model + "/tokenizer_config.json", "r", encoding="utf-8") as f:
tokenizer_config = json.load(f)
# find special token ids
if "bos_token" in tokenizer_config:
for key in tokenizer_json["added_tokens"]:
if key["content"] == tokenizer_config["bos_token"]:
gguf_writer.add_bos_token_id(key["id"])
if "eos_token" in tokenizer_config:
for key in tokenizer_json["added_tokens"]:
if key["content"] == tokenizer_config["eos_token"]:
gguf_writer.add_eos_token_id(key["id"])
if "unk_token" in tokenizer_config:
for key in tokenizer_json["added_tokens"]:
if key["content"] == tokenizer_config["unk_token"]:
gguf_writer.add_unk_token_id(key["id"])
if "sep_token" in tokenizer_config:
for key in tokenizer_json["added_tokens"]:
if key["content"] == tokenizer_config["sep_token"]:
gguf_writer.add_sep_token_id(key["id"])
if "pad_token" in tokenizer_config:
for key in tokenizer_json["added_tokens"]:
if key["content"] == tokenizer_config["pad_token"]:
gguf_writer.add_pad_token_id(key["id"])
# TENSORS
tensor_map = gguf.get_tensor_name_map(block_count)
# tensor info
print("gguf: get tensor metadata")
if num_parts == 0:
part_names = ("pytorch_model.bin",)
else:
part_names = (
f"pytorch_model-{n:05}-of-{num_parts:05}.bin" for n in range(1, num_parts + 1)
)
for part_name in part_names:
print("gguf: loading model part '" + part_name + "'")
model_part = torch.load(f"{dir_model}/{part_name}", map_location="cpu")
for name in model_part.keys():
data = model_part[name]
# we don't need these
if name.endswith(".attention.masked_bias") or name.endswith(".attention.bias") or name.endswith(".attention.rotary_emb.inv_freq"):
continue
# convert any unsupported data types to float32
if data.dtype != torch.float16 and data.dtype != torch.float32:
data = data.to(torch.float32)
data = data.squeeze().numpy()
# map tensor names
if name.endswith(".weight") and name[:-7] in tensor_map:
name = tensor_map[name[:-7]] + ".weight"
elif name.endswith(".bias") and name[:-5] in tensor_map:
name = tensor_map[name[:-5]] + ".bias"
else:
print("Can not map tensor '" + name + "'")
sys.exit()
n_dims = len(data.shape)
data_dtype = data.dtype
old_dtype = data_dtype
# if f32 desired, convert any float16 to float32
if ftype == 0 and data.dtype == np.float16:
data_dtype = np.float32
# TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
if ftype == 1 and data.dtype == np.float16 and n_dims == 1:
data_dtype = np.float32
# if f16 desired, convert any float32 2-dim weight tensors to float16
if ftype == 1 and data.dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
data_dtype = np.float16
print(name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data_dtype))
data = data.astype(data_dtype)
gguf_writer.add_tensor(name, data)
print("gguf: write header")
gguf_writer.write_header_to_file()
print("gguf: write metadata")
gguf_writer.write_kv_data_to_file()
print("gguf: write tensors")
gguf_writer.write_tensors_to_file()
gguf_writer.close()
print("gguf: model successfully exported to '" + fname_out + "'")
print("")

View file

@ -0,0 +1,265 @@
# 7b pth llama --> gguf conversion, GQA/70b not supported
# Only models with a single datafile are supported, like 7B
# HF files required in the model dir: config.json tokenizer_config.json tokenizer.json tokenizer.model
import gguf
import os
import sys
import struct
import json
import numpy as np
import torch
from typing import Any, List
from pathlib import Path
from sentencepiece import SentencePieceProcessor
#NDArray = np.ndarray[Any, Any]
# compatible with python < 3.9
NDArray: 'TypeAlias' = 'np.ndarray[Any, Any]'
def count_model_parts(dir_model: str) -> int:
num_parts = 0
for filename in os.listdir(dir_model):
if filename.startswith("consolidated."):
num_parts += 1
if num_parts > 0:
print("gguf: found " + str(num_parts) + " model parts")
return num_parts
if len(sys.argv) < 3:
print("Usage: convert-h5-to-ggml.py dir-model ftype\n")
print(" ftype == 0 -> float32")
print(" ftype == 1 -> float16")
sys.exit(1)
# output in the same directory as the model
dir_model = sys.argv[1]
last_dir = os.path.basename(os.path.normpath(dir_model))
# possible tensor data types
# ftype == 0 -> float32
# ftype == 1 -> float16
# map from ftype to string
ftype_str = ["f32", "f16"]
ftype = 1
if len(sys.argv) > 2:
ftype = int(sys.argv[2])
if ftype < 0 or ftype > 1:
print("Invalid ftype: " + str(ftype))
sys.exit(1)
fname_out = sys.argv[1] + "/ggml-model-" + ftype_str[ftype] + ".gguf"
print("gguf: loading model "+last_dir)
with open(dir_model + "/config.json", "r", encoding="utf-8") as f:
hparams = json.load(f)
if hparams["architectures"][0] != "LlamaForCausalLM":
print("Model architecture not supported: " + hparams["architectures"][0])
sys.exit()
# get number of model parts
num_parts = count_model_parts(dir_model)
if num_parts > 1:
print("gguf: Only models with a single datafile are supported.")
sys.exit()
llm_arch = "llama"
gguf_writer = gguf.GGUFWriter(fname_out, arch=llm_arch)
print("gguf: get model metadata")
block_count = hparams["num_hidden_layers"]
head_count = hparams["num_attention_heads"]
if "num_key_value_heads" in hparams:
head_count_kv = hparams["num_key_value_heads"]
else:
head_count_kv = head_count
if "_name_or_path" in hparams:
hf_repo = hparams["_name_or_path"]
else:
hf_repo = ""
gguf_writer.add_architecture()
gguf_writer.add_name(last_dir)
gguf_writer.add_source_hf_repo(hf_repo)
gguf_writer.add_tensor_data_layout("Meta AI original pth")
gguf_writer.add_context_length(hparams["max_position_embeddings"])
gguf_writer.add_embedding_length(hparams["hidden_size"])
gguf_writer.add_block_count(block_count)
gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
gguf_writer.add_rope_dimension_count(hparams["hidden_size"] // hparams["num_attention_heads"])
gguf_writer.add_head_count(head_count)
gguf_writer.add_head_count_kv(head_count_kv)
gguf_writer.add_layer_norm_rms_eps(hparams["rms_norm_eps"])
# TOKENIZATION
print("gguf: get tokenizer metadata")
tokens: List[bytes] = []
scores: List[float] = []
toktypes: List[int] = []
if Path(dir_model + "/tokenizer.model").is_file():
# vocab type sentencepiece
print("gguf: get sentencepiece tokenizer vocab and scores")
tokenizer = SentencePieceProcessor(dir_model + "/tokenizer.model")
for i in range(tokenizer.vocab_size()):
text: bytes
score: float
piece = tokenizer.id_to_piece(i)
text = piece.encode("utf-8")
score = tokenizer.get_score(i)
toktype = 1 # defualt to normal token type
if tokenizer.is_unknown(i):
toktype = 2
if tokenizer.is_control(i):
toktype = 3
# TODO: How to determinate if a token is user defined?
# ref: https://github.com/google/sentencepiece/blob/master/src/sentencepiece_model.proto
# if tokenizer.is_user_defined(i): toktype = 4
if tokenizer.is_unused(i):
toktype = 5
if tokenizer.is_byte(i):
toktype = 6
tokens.append(text)
scores.append(score)
toktypes.append(toktype)
gguf_writer.add_tokenizer_model("llama")
gguf_writer.add_token_list(tokens)
gguf_writer.add_token_scores(scores)
gguf_writer.add_token_types(toktypes)
if Path(dir_model + "/tokenizer.json").is_file():
with open(dir_model + "/tokenizer.json", "r", encoding="utf-8") as f:
tokenizer = json.load(f)
if "added_tokens" in tokenizer and Path(dir_model + "/tokenizer_config.json").is_file():
print("gguf: get special token ids")
with open(dir_model + "/tokenizer_config.json", "r", encoding="utf-8") as f:
tokenizer_config = json.load(f)
# find special token ids
if "bos_token" in tokenizer_config and tokenizer_config["bos_token"] != None:
for key in tokenizer["added_tokens"]:
if key["content"] == tokenizer_config["bos_token"]["content"]:
gguf_writer.add_bos_token_id(key["id"])
if "eos_token" in tokenizer_config and tokenizer_config["eos_token"] != None:
for key in tokenizer["added_tokens"]:
if key["content"] == tokenizer_config["eos_token"]["content"]:
gguf_writer.add_eos_token_id(key["id"])
if "unk_token" in tokenizer_config and tokenizer_config["unk_token"] != None:
for key in tokenizer["added_tokens"]:
if key["content"] == tokenizer_config["unk_token"]["content"]:
gguf_writer.add_unk_token_id(key["id"])
if "sep_token" in tokenizer_config and tokenizer_config["sep_token"] != None:
for key in tokenizer["added_tokens"]:
if key["content"] == tokenizer_config["sep_token"]["content"]:
gguf_writer.add_sep_token_id(key["id"])
if "pad_token" in tokenizer_config and tokenizer_config["pad_token"] != None:
for key in tokenizer["added_tokens"]:
if key["content"] == tokenizer_config["pad_token"]["content"]:
gguf_writer.add_pad_token_id(key["id"])
# TENSORS
tensor_map = gguf.get_tensor_name_map(block_count)
# tensor info
print("gguf: get tensor metadata")
part_names = (f"consolidated.{n:02}.pth" for n in range(0, num_parts))
for part_name in part_names:
print("gguf: loading model part '" + part_name + "'")
model_part = torch.load(f"{dir_model}/{part_name}", map_location="cpu")
for name in model_part.keys():
data = model_part[name]
# we don't need these
if name == "rope.freqs":
continue
# convert any unsupported data types to float32
if data.dtype != torch.float16 and data.dtype != torch.float32:
data = data.to(torch.float32)
data = data.squeeze().numpy()
# map tensor names
if name.endswith(".weight") and name[:-7] in tensor_map:
name = tensor_map[name[:-7]] + ".weight"
elif name.endswith(".bias") and name[:-5] in tensor_map:
name = tensor_map[name[:-5]] + ".bias"
else:
print("Can not map tensor '" + name + "'")
sys.exit()
n_dims = len(data.shape)
data_dtype = data.dtype
old_dtype = data_dtype
# if f32 desired, convert any float16 to float32
if ftype == 0 and data.dtype == np.float16:
data_dtype = np.float32
# TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
if ftype == 1 and data_dtype == np.float16 and n_dims == 1:
data_dtype = np.float32
# if f16 desired, convert any float32 2-dim weight tensors to float16
if ftype == 1 and data.dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
data_dtype = np.float16
print(name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data_dtype))
data = data.astype(data_dtype)
gguf_writer.add_tensor(name, data)
print("gguf: write header")
gguf_writer.write_header_to_file()
print("gguf: write metadata")
gguf_writer.write_kv_data_to_file()
print("gguf: write tensors")
gguf_writer.write_tensors_to_file()
gguf_writer.close()
print("gguf: model successfully exported to '" + fname_out + "'")
print("")

294
convert-llama-hf-to-gguf.py Normal file
View file

@ -0,0 +1,294 @@
# HF llama --> gguf conversion
import gguf
import os
import sys
import struct
import json
import numpy as np
import torch
from typing import Any, List, Optional
from pathlib import Path
from sentencepiece import SentencePieceProcessor
#NDArray = np.ndarray[Any, Any]
# compatible with python < 3.9
NDArray: 'TypeAlias' = 'np.ndarray[Any, Any]'
# reverse HF permute back to original pth layout
# https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/convert_llama_weights_to_hf.py
def reverse_hf_permute(weights: NDArray, n_head: int, n_kv_head: Optional[int] = None) -> NDArray:
if n_kv_head is not None and n_head != n_kv_head:
n_head //= n_kv_head
return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
.swapaxes(1, 2)
.reshape(weights.shape))
def count_model_parts(dir_model: str) -> int:
num_parts = 0
for filename in os.listdir(dir_model):
if filename.startswith("pytorch_model-"):
num_parts += 1
if num_parts > 0:
print("gguf: found " + str(num_parts) + " model parts")
return num_parts
if len(sys.argv) < 3:
print("Usage: convert-h5-to-ggml.py dir-model ftype\n")
print(" ftype == 0 -> float32")
print(" ftype == 1 -> float16")
sys.exit(1)
# output in the same directory as the model
dir_model = sys.argv[1]
last_dir = os.path.basename(os.path.normpath(dir_model))
# possible tensor data types
# ftype == 0 -> float32
# ftype == 1 -> float16
# map from ftype to string
ftype_str = ["f32", "f16"]
ftype = 1
if len(sys.argv) > 2:
ftype = int(sys.argv[2])
if ftype < 0 or ftype > 1:
print("Invalid ftype: " + str(ftype))
sys.exit(1)
fname_out = sys.argv[1] + "/ggml-model-" + ftype_str[ftype] + ".gguf"
print("gguf: loading model "+last_dir)
with open(dir_model + "/config.json", "r", encoding="utf-8") as f:
hparams = json.load(f)
if hparams["architectures"][0] != "LlamaForCausalLM":
print("Model architecture not supported: " + hparams["architectures"][0])
sys.exit()
# get number of model parts
num_parts = count_model_parts(dir_model)
gguf_writer = gguf.GGUFWriter(fname_out, arch="llama")
print("gguf: get model metadata")
block_count = hparams["num_hidden_layers"]
head_count = hparams["num_attention_heads"]
if "num_key_value_heads" in hparams:
head_count_kv = hparams["num_key_value_heads"]
else:
head_count_kv = head_count
if "_name_or_path" in hparams:
hf_repo = hparams["_name_or_path"]
else:
hf_repo = ""
if "max_sequence_length" in hparams:
ctx_length = hparams["max_sequence_length"]
elif "max_position_embeddings" in hparams:
ctx_length = hparams["max_position_embeddings"]
else:
print("gguf: can not find ctx length parameter.")
sys.exit()
gguf_writer.add_architecture()
gguf_writer.add_name(last_dir)
gguf_writer.add_source_hf_repo(hf_repo)
gguf_writer.add_tensor_data_layout("Meta AI original pth")
gguf_writer.add_context_length(ctx_length)
gguf_writer.add_embedding_length(hparams["hidden_size"])
gguf_writer.add_block_count(block_count)
gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
gguf_writer.add_rope_dimension_count(hparams["hidden_size"] // hparams["num_attention_heads"])
gguf_writer.add_head_count(head_count)
gguf_writer.add_head_count_kv(head_count_kv)
gguf_writer.add_layer_norm_rms_eps(hparams["rms_norm_eps"])
# TOKENIZATION
print("gguf: get tokenizer metadata")
tokens: List[bytes] = []
scores: List[float] = []
toktypes: List[int] = []
if Path(dir_model + "/tokenizer.model").is_file():
# vocab type sentencepiece
print("gguf: get sentencepiece tokenizer vocab, scores and token types")
tokenizer = SentencePieceProcessor(dir_model + "/tokenizer.model")
for i in range(tokenizer.vocab_size()):
text: bytes
score: float
piece = tokenizer.id_to_piece(i)
text = piece.encode("utf-8")
score = tokenizer.get_score(i)
toktype = 1 # defualt to normal token type
if tokenizer.is_unknown(i):
toktype = 2
if tokenizer.is_control(i):
toktype = 3
# TODO: How to determinate if a token is user defined?
# ref: https://github.com/google/sentencepiece/blob/master/src/sentencepiece_model.proto
# if tokenizer.is_user_defined(i): toktype = 4
if tokenizer.is_unused(i):
toktype = 5
if tokenizer.is_byte(i):
toktype = 6
tokens.append(text)
scores.append(score)
toktypes.append(toktype)
gguf_writer.add_tokenizer_model("llama")
gguf_writer.add_token_list(tokens)
gguf_writer.add_token_scores(scores)
gguf_writer.add_token_types(toktypes)
if Path(dir_model + "/tokenizer.json").is_file():
with open(dir_model + "/tokenizer.json", "r", encoding="utf-8") as f:
tokenizer = json.load(f)
if "added_tokens" in tokenizer and Path(dir_model + "/tokenizer_config.json").is_file():
print("gguf: get special token ids")
with open(dir_model + "/tokenizer_config.json", "r", encoding="utf-8") as f:
tokenizer_config = json.load(f)
# find special token ids
if "bos_token" in tokenizer_config and tokenizer_config["bos_token"] != None:
for key in tokenizer["added_tokens"]:
if key["content"] == tokenizer_config["bos_token"]["content"]:
gguf_writer.add_bos_token_id(key["id"])
if "eos_token" in tokenizer_config and tokenizer_config["eos_token"] != None:
for key in tokenizer["added_tokens"]:
if key["content"] == tokenizer_config["eos_token"]["content"]:
gguf_writer.add_eos_token_id(key["id"])
if "unk_token" in tokenizer_config and tokenizer_config["unk_token"] != None:
for key in tokenizer["added_tokens"]:
if key["content"] == tokenizer_config["unk_token"]["content"]:
gguf_writer.add_unk_token_id(key["id"])
if "sep_token" in tokenizer_config and tokenizer_config["sep_token"] != None:
for key in tokenizer["added_tokens"]:
if key["content"] == tokenizer_config["sep_token"]["content"]:
gguf_writer.add_sep_token_id(key["id"])
if "pad_token" in tokenizer_config and tokenizer_config["pad_token"] != None:
for key in tokenizer["added_tokens"]:
if key["content"] == tokenizer_config["pad_token"]["content"]:
gguf_writer.add_pad_token_id(key["id"])
# TENSORS
tensor_map = gguf.get_tensor_name_map(block_count)
# tensor info
print("gguf: get tensor metadata")
if num_parts == 0:
part_names = ("pytorch_model.bin",)
else:
part_names = (
f"pytorch_model-{n:05}-of-{num_parts:05}.bin" for n in range(1, num_parts + 1)
)
for part_name in part_names:
print("gguf: loading model part '" + part_name + "'")
model_part = torch.load(f"{dir_model}/{part_name}", map_location="cpu")
for name in model_part.keys():
data = model_part[name]
# we don't need these
if name.endswith(".rotary_emb.inv_freq"):
continue
# convert any unsupported data types to float32
if data.dtype != torch.float16 and data.dtype != torch.float32:
data = data.to(torch.float32)
data = data.squeeze().numpy()
# reverse permute these
if name.endswith(".q_proj.weight") or name.endswith(".k_proj.weight"):
data = reverse_hf_permute(data, head_count, head_count_kv)
# map tensor names
if name.endswith(".weight") and name[:-7] in tensor_map:
name = tensor_map[name[:-7]] + ".weight"
elif name.endswith(".bias") and name[:-5] in tensor_map:
name = tensor_map[name[:-5]] + ".bias"
else:
print("Can not map tensor '" + name + "'")
sys.exit()
n_dims = len(data.shape)
data_dtype = data.dtype
old_dtype = data_dtype
# if f32 desired, convert any float16 to float32
if ftype == 0 and data.dtype == np.float16:
data_dtype = np.float32
# TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
if ftype == 1 and data_dtype == np.float16 and n_dims == 1:
data_dtype = np.float32
# if f16 desired, convert any float32 2-dim weight tensors to float16
if ftype == 1 and data.dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
data_dtype = np.float16
data = data.astype(data_dtype)
print(name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype))
gguf_writer.add_tensor(name, data)
print("gguf: write header")
gguf_writer.write_header_to_file()
print("gguf: write metadata")
gguf_writer.write_kv_data_to_file()
print("gguf: write tensors")
gguf_writer.write_tensors_to_file()
gguf_writer.close()
print("gguf: model successfully exported to '" + fname_out + "'")
print("")

989
convert.py Normal file → Executable file

File diff suppressed because it is too large Load diff

View file

@ -3,7 +3,7 @@
## Verifying that the model is running on the GPU with cuBLAS
Make sure you compiled llama with the correct env variables according to [this guide](../README.md#cublas), so that llama accepts the `-ngl N` (or `--n-gpu-layers N`) flag. When running llama, you may configure `N` to be very large, and llama will offload the maximum possible number of layers to the GPU, even if it's less than the number you configured. For example:
```shell
./main -m "path/to/model.bin" -ngl 200000 -p "Please sir, may I have some "
./main -m "path/to/model.gguf" -ngl 200000 -p "Please sir, may I have some "
```
When running llama, before it starts the inference work, it will output diagnostic information that shows whether cuBLAS is offloading work to the GPU. Look for these lines:
@ -25,9 +25,9 @@ GPU: A6000 (48GB VRAM)
CPU: 7 physical cores
RAM: 32GB
Model: `TheBloke_Wizard-Vicuna-30B-Uncensored-GGML/Wizard-Vicuna-30B-Uncensored.ggmlv3.q4_0.bin` (30B parameters, 4bit quantization, GGML)
Model: `TheBloke_Wizard-Vicuna-30B-Uncensored-GGML/Wizard-Vicuna-30B-Uncensored.q4_0.gguf` (30B parameters, 4bit quantization, GGML)
Run command: `./main -m "path/to/model.bin" -p "-p "An extremely detailed description of the 10 best ethnic dishes will follow, with recipes: " -n 1000 [additional benchmark flags]`
Run command: `./main -m "path/to/model.gguf" -p "An extremely detailed description of the 10 best ethnic dishes will follow, with recipes: " -n 1000 [additional benchmark flags]`
Result:

View file

@ -170,18 +170,6 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
break;
}
params.n_ctx = std::stoi(argv[i]);
} else if (arg == "-gqa" || arg == "--gqa") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.n_gqa = std::stoi(argv[i]);
} else if (arg == "-eps" || arg == "--rms-norm-eps") {
if (++i >= argc) {
invalid_param = true;
break;
}
params.rms_norm_eps = std::stof(argv[i]);
} else if (arg == "--rope-freq-base") {
if (++i >= argc) {
invalid_param = true;
@ -561,8 +549,6 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
fprintf(stdout, " -n N, --n-predict N number of tokens to predict (default: %d, -1 = infinity, -2 = until context filled)\n", params.n_predict);
fprintf(stdout, " -c N, --ctx-size N size of the prompt context (default: %d)\n", params.n_ctx);
fprintf(stdout, " -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch);
fprintf(stdout, " -gqa N, --gqa N grouped-query attention factor (TEMP!!! use 8 for LLaMAv2 70B) (default: %d)\n", params.n_gqa);
fprintf(stdout, " -eps N, --rms-norm-eps N rms norm eps (TEMP!!! use 1e-5 for LLaMAv2) (default: %.1e)\n", params.rms_norm_eps);
fprintf(stdout, " --top-k N top-k sampling (default: %d, 0 = disabled)\n", params.top_k);
fprintf(stdout, " --top-p N top-p sampling (default: %.1f, 1.0 = disabled)\n", (double)params.top_p);
fprintf(stdout, " --tfs N tail free sampling, parameter z (default: %.1f, 1.0 = disabled)\n", (double)params.tfs_z);
@ -650,24 +636,11 @@ std::string gpt_random_prompt(std::mt19937 & rng) {
return "The";
}
// TODO: not great allocating this every time
std::vector<llama_token> llama_tokenize(struct llama_context * ctx, const std::string & text, bool add_bos) {
// initialize to prompt numer of chars, since n_tokens <= n_prompt_chars
std::vector<llama_token> res(text.size() + (int) add_bos);
const int n = llama_tokenize(ctx, text.c_str(), res.data(), res.size(), add_bos);
assert(n >= 0);
res.resize(n);
return res;
}
struct llama_context_params llama_context_params_from_gpt_params(const gpt_params & params) {
auto lparams = llama_context_default_params();
lparams.n_ctx = params.n_ctx;
lparams.n_batch = params.n_batch;
lparams.n_gqa = params.n_gqa;
lparams.rms_norm_eps = params.rms_norm_eps;
lparams.n_gpu_layers = params.n_gpu_layers;
lparams.main_gpu = params.main_gpu;
lparams.tensor_split = params.tensor_split;

View file

@ -2,6 +2,7 @@
#pragma once
#define LLAMA_API_CPP // TODO: eliminate me
#include "llama.h"
#include <string>
@ -22,14 +23,12 @@ struct gpt_params {
int32_t n_predict = -1; // new tokens to predict
int32_t n_ctx = 512; // context size
int32_t n_batch = 512; // batch size for prompt processing (must be >=32 to use BLAS)
int32_t n_gqa = 1; // grouped-query attention factor (TODO: move to hparams)
int32_t n_keep = 0; // number of tokens to keep from initial prompt
int32_t n_chunks = -1; // max number of chunks to process (-1 = unlimited)
int32_t n_gpu_layers = 0; // number of layers to store in VRAM
int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors
float tensor_split[LLAMA_MAX_DEVICES] = {0}; // how split tensors should be distributed across GPUs
int32_t n_probs = 0; // if greater than 0, output the probabilities of top n_probs tokens.
float rms_norm_eps = LLAMA_DEFAULT_RMS_EPS; // rms norm epsilon
float rope_freq_base = 10000.0f; // RoPE base frequency
float rope_freq_scale = 1.0f; // RoPE frequency scaling factor
@ -53,7 +52,7 @@ struct gpt_params {
std::string cfg_negative_prompt; // string to help guidance
float cfg_scale = 1.f; // How strong is guidance
std::string model = "models/7B/ggml-model.bin"; // model path
std::string model = "models/7B/ggml-model-f16.gguf"; // model path
std::string model_alias = "unknown"; // model alias
std::string prompt = "";
std::string path_prompt_cache = ""; // path to file for saving/loading prompt eval state
@ -100,12 +99,6 @@ void gpt_print_usage(int argc, char ** argv, const gpt_params & params);
std::string gpt_random_prompt(std::mt19937 & rng);
//
// Vocab utils
//
std::vector<llama_token> llama_tokenize(struct llama_context * ctx, const std::string & text, bool add_bos);
//
// Model utils
//

View file

@ -1,5 +1,6 @@
#include "ggml.h"
#include "llama.h"
#include <unordered_map>
#include <vector>
#include <cassert>
@ -502,7 +503,7 @@ bool is_ggml_file(const char *filename) {
return false;
}
uint32_t magic = file.read_u32();
return magic == LLAMA_FILE_MAGIC;
return magic == GGUF_MAGIC;
}
void load_vocab(const char *filename, Config *config, struct llama_vocab *vocab) {
@ -590,75 +591,80 @@ void save_as_llama_model(struct llama_vocab * vocab, struct my_llama_model * mod
if (file.fp == NULL) {
return;
}
// write_magic
file.write_u32(LLAMA_FILE_MAGIC); // magic
file.write_u32(LLAMA_FILE_VERSION); // version
// write_hparams
file.write_u32(model->hparams.n_vocab);
file.write_u32(model->hparams.n_embd);
file.write_u32(model->hparams.n_mult);
file.write_u32(model->hparams.n_head);
file.write_u32(model->hparams.n_layer);
file.write_u32(model->hparams.n_rot);
file.write_u32(LLAMA_FTYPE_ALL_F32);
// write_vocab - for now we are just writing the existing BPE voc. assuming karpathy's vocabulary is the same. idk.
uint32_t n_vocab = model->hparams.n_vocab;
for (uint32_t i = 0; i < n_vocab; i++) {
const auto & token_score = vocab->id_to_token.at(i);
file.write_u32((uint32_t) token_score.tok.size());
file.write_raw(token_score.tok.data(), token_score.tok.size());
file.write_raw(&token_score.score, sizeof(token_score.score));
}
// stuff AK weights into GG weights one by one.
// w->token_embedding_table -> model->tok_embeddings
// float* -> struct ggml_tensor
stuff_karpathy_weights_into_gg(model->tok_embeddings, w->token_embedding_table);
stuff_karpathy_weights_into_gg(model->output, w->token_embedding_table);
stuff_karpathy_weights_into_gg(model->norm, w->rms_final_weight);
//print_row(model->norm, 0);
// for rms-att-weight
int row_length = model->hparams.n_embd;
const auto & hparams = model->hparams;
//int n_ff = model->hparams.n_embd;
int n_ff = get_n_ff(&hparams);
for (uint32_t i = 0; i < model->hparams.n_layer; ++i){
auto & layer = model->layers[i];
// 1d
stuff_karpathy_weights_into_gg(layer.attention_norm, &w->rms_att_weight[i*row_length]);
stuff_karpathy_weights_into_gg(layer.ffn_norm , &w->rms_ffn_weight[i*row_length]);
// from 3d matrix layer x dim x dim to 2d matrix dim x dim
stuff_karpathy_weights_into_gg(layer.wq , &w->wq[i*row_length*row_length]);
stuff_karpathy_weights_into_gg(layer.wk , &w->wk[i*row_length*row_length]);
stuff_karpathy_weights_into_gg(layer.wv , &w->wv[i*row_length*row_length]);
stuff_karpathy_weights_into_gg(layer.wo , &w->wo[i*row_length*row_length]);
stuff_karpathy_weights_into_gg(layer.w1 , &w->w1[i*row_length*n_ff]);
stuff_karpathy_weights_into_gg(layer.w2 , &w->w2[i*n_ff*row_length]);
stuff_karpathy_weights_into_gg(layer.w3 , &w->w3[i*row_length*n_ff]);
}
// write tensors
write_tensor(&file, model->tok_embeddings);
write_tensor(&file, model->norm);
write_tensor(&file, model->output); // ?
for (uint32_t i = 0; i < model->hparams.n_layer; ++i) {
auto & layer = model->layers[i];
write_tensor(&file, layer.attention_norm);
write_tensor(&file, layer.wq);
write_tensor(&file, layer.wk);
write_tensor(&file, layer.wv);
write_tensor(&file, layer.wo);
write_tensor(&file, layer.ffn_norm);
write_tensor(&file, layer.w1);
write_tensor(&file, layer.w2);
write_tensor(&file, layer.w3);
}
#pragma message("TODO: implement file saving using gguf")
(void) vocab;
(void) model;
(void) w;
// // write_magic
// file.write_u32(LLAMA_FILE_MAGIC); // magic
// file.write_u32(LLAMA_FILE_VERSION); // version
// // write_hparams
// file.write_u32(model->hparams.n_vocab);
// file.write_u32(model->hparams.n_embd);
// file.write_u32(model->hparams.n_mult);
// file.write_u32(model->hparams.n_head);
// file.write_u32(model->hparams.n_layer);
// file.write_u32(model->hparams.n_rot);
// file.write_u32(LLAMA_FTYPE_ALL_F32);
//
// // write_vocab - for now we are just writing the existing BPE voc. assuming karpathy's vocabulary is the same. idk.
// uint32_t n_vocab = model->hparams.n_vocab;
// for (uint32_t i = 0; i < n_vocab; i++) {
// const auto & token_score = vocab->id_to_token.at(i);
// file.write_u32((uint32_t) token_score.tok.size());
// file.write_raw(token_score.tok.data(), token_score.tok.size());
// file.write_raw(&token_score.score, sizeof(token_score.score));
// }
//
// // stuff AK weights into GG weights one by one.
// // w->token_embedding_table -> model->tok_embeddings
// // float* -> struct ggml_tensor
// stuff_karpathy_weights_into_gg(model->tok_embeddings, w->token_embedding_table);
// stuff_karpathy_weights_into_gg(model->output, w->token_embedding_table);
//
// stuff_karpathy_weights_into_gg(model->norm, w->rms_final_weight);
// //print_row(model->norm, 0);
//
// // for rms-att-weight
// int row_length = model->hparams.n_embd;
// const auto & hparams = model->hparams;
// //int n_ff = model->hparams.n_embd;
// int n_ff = get_n_ff(&hparams);
//
// for (uint32_t i = 0; i < model->hparams.n_layer; ++i){
// auto & layer = model->layers[i];
// // 1d
// stuff_karpathy_weights_into_gg(layer.attention_norm, &w->rms_att_weight[i*row_length]);
// stuff_karpathy_weights_into_gg(layer.ffn_norm , &w->rms_ffn_weight[i*row_length]);
//
// // from 3d matrix layer x dim x dim to 2d matrix dim x dim
// stuff_karpathy_weights_into_gg(layer.wq , &w->wq[i*row_length*row_length]);
// stuff_karpathy_weights_into_gg(layer.wk , &w->wk[i*row_length*row_length]);
// stuff_karpathy_weights_into_gg(layer.wv , &w->wv[i*row_length*row_length]);
// stuff_karpathy_weights_into_gg(layer.wo , &w->wo[i*row_length*row_length]);
//
// stuff_karpathy_weights_into_gg(layer.w1 , &w->w1[i*row_length*n_ff]);
// stuff_karpathy_weights_into_gg(layer.w2 , &w->w2[i*n_ff*row_length]);
// stuff_karpathy_weights_into_gg(layer.w3 , &w->w3[i*row_length*n_ff]);
// }
// // write tensors
// write_tensor(&file, model->tok_embeddings);
// write_tensor(&file, model->norm);
// write_tensor(&file, model->output); // ?
// for (uint32_t i = 0; i < model->hparams.n_layer; ++i) {
// auto & layer = model->layers[i];
//
// write_tensor(&file, layer.attention_norm);
// write_tensor(&file, layer.wq);
// write_tensor(&file, layer.wk);
// write_tensor(&file, layer.wv);
// write_tensor(&file, layer.wo);
// write_tensor(&file, layer.ffn_norm);
// write_tensor(&file, layer.w1);
// write_tensor(&file, layer.w2);
// write_tensor(&file, layer.w3);
// }
}
struct train_params get_default_train_params() {

View file

@ -67,7 +67,7 @@ int main(int argc, char ** argv) {
fprintf(stderr, "%s: prompt: '%s'\n", __func__, params.prompt.c_str());
fprintf(stderr, "%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
for (int i = 0; i < (int) embd_inp.size(); i++) {
fprintf(stderr, "%6d -> '%s'\n", embd_inp[i], llama_token_to_str(ctx, embd_inp[i]));
fprintf(stderr, "%6d -> '%s'\n", embd_inp[i], llama_token_to_str(ctx, embd_inp[i]).c_str());
}
fprintf(stderr, "\n");
}

246
examples/gguf/gguf.cpp Normal file
View file

@ -0,0 +1,246 @@
#include "ggml.h"
#include "llama.h"
#include <cstdio>
#include <cinttypes>
#include <string>
#include <sstream>
#include <fstream>
#include <vector>
#undef MIN
#undef MAX
#define MIN(a, b) ((a) < (b) ? (a) : (b))
#define MAX(a, b) ((a) > (b) ? (a) : (b))
template<typename T>
static std::string to_string(const T & val) {
std::stringstream ss;
ss << val;
return ss.str();
}
bool gguf_ex_write(const std::string & fname) {
struct gguf_context * ctx = gguf_init_empty();
gguf_set_val_u8 (ctx, "some.parameter.uint8", 0x12);
gguf_set_val_i8 (ctx, "some.parameter.int8", -0x13);
gguf_set_val_u16 (ctx, "some.parameter.uint16", 0x1234);
gguf_set_val_i16 (ctx, "some.parameter.int16", -0x1235);
gguf_set_val_u32 (ctx, "some.parameter.uint32", 0x12345678);
gguf_set_val_i32 (ctx, "some.parameter.int32", -0x12345679);
gguf_set_val_f32 (ctx, "some.parameter.float32", 0.123456789f);
gguf_set_val_bool(ctx, "some.parameter.bool", true);
gguf_set_val_str (ctx, "some.parameter.string", "hello world");
gguf_set_arr_data(ctx, "some.parameter.arr.i16", GGUF_TYPE_INT16, std::vector<int16_t>{ 1, 2, 3, 4, }.data(), 4);
gguf_set_arr_data(ctx, "some.parameter.arr.f32", GGUF_TYPE_FLOAT32, std::vector<float>{ 3.145f, 2.718f, 1.414f, }.data(), 3);
gguf_set_arr_str (ctx, "some.parameter.arr.str", std::vector<const char *>{ "hello", "world", "!" }.data(), 3);
struct ggml_init_params params = {
/*.mem_size =*/ 128ull*1024ull*1024ull,
/*.mem_buffer =*/ NULL,
/*.no_alloc =*/ false,
};
struct ggml_context * ctx_data = ggml_init(params);
const int n_tensors = 10;
// tensor infos
for (int i = 0; i < n_tensors; ++i) {
const std::string name = "tensor_" + to_string(i);
int64_t ne[GGML_MAX_DIMS] = { 1 };
int32_t n_dims = rand() % GGML_MAX_DIMS + 1;
for (int j = 0; j < n_dims; ++j) {
ne[j] = rand() % 10 + 1;
}
struct ggml_tensor * cur = ggml_new_tensor(ctx_data, GGML_TYPE_F32, n_dims, ne);
ggml_set_name(cur, name.c_str());
{
float * data = (float *) cur->data;
for (int j = 0; j < ggml_nelements(cur); ++j) {
data[j] = 100 + i;
}
}
gguf_add_tensor(ctx, cur);
}
gguf_write_to_file(ctx, fname.c_str(), false);
fprintf(stdout, "%s: wrote file '%s;\n", __func__, fname.c_str());
ggml_free(ctx_data);
gguf_free(ctx);
return true;
}
// just read tensor info
bool gguf_ex_read_0(const std::string & fname) {
struct gguf_init_params params = {
/*.no_alloc = */ false,
/*.ctx = */ NULL,
};
struct gguf_context * ctx = gguf_init_from_file(fname.c_str(), params);
fprintf(stdout, "%s: version: %d\n", __func__, gguf_get_version(ctx));
fprintf(stdout, "%s: alignment: %zu\n", __func__, gguf_get_alignment(ctx));
fprintf(stdout, "%s: data offset: %zu\n", __func__, gguf_get_data_offset(ctx));
// kv
{
const int n_kv = gguf_get_n_kv(ctx);
fprintf(stdout, "%s: n_kv: %d\n", __func__, n_kv);
for (int i = 0; i < n_kv; ++i) {
const char * key = gguf_get_key(ctx, i);
fprintf(stdout, "%s: kv[%d]: key = %s\n", __func__, i, key);
}
}
// find kv string
{
const char * findkey = "some.parameter.string";
const int keyidx = gguf_find_key(ctx, findkey);
if (keyidx == -1) {
fprintf(stdout, "%s: find key: %s not found.\n", __func__, findkey);
} else {
const char * key_value = gguf_get_val_str(ctx, keyidx);
fprintf(stdout, "%s: find key: %s found, kv[%d] value = %s\n", __func__, findkey, keyidx, key_value);
}
}
// tensor info
{
const int n_tensors = gguf_get_n_tensors(ctx);
fprintf(stdout, "%s: n_tensors: %d\n", __func__, n_tensors);
for (int i = 0; i < n_tensors; ++i) {
const char * name = gguf_get_tensor_name (ctx, i);
const size_t offset = gguf_get_tensor_offset(ctx, i);
fprintf(stdout, "%s: tensor[%d]: name = %s, offset = %zu\n", __func__, i, name, offset);
}
}
gguf_free(ctx);
return true;
}
// read and create ggml_context containing the tensors and their data
bool gguf_ex_read_1(const std::string & fname) {
struct ggml_context * ctx_data = NULL;
struct gguf_init_params params = {
/*.no_alloc = */ false,
/*.ctx = */ &ctx_data,
};
struct gguf_context * ctx = gguf_init_from_file(fname.c_str(), params);
fprintf(stdout, "%s: version: %d\n", __func__, gguf_get_version(ctx));
fprintf(stdout, "%s: alignment: %zu\n", __func__, gguf_get_alignment(ctx));
fprintf(stdout, "%s: data offset: %zu\n", __func__, gguf_get_data_offset(ctx));
// kv
{
const int n_kv = gguf_get_n_kv(ctx);
fprintf(stdout, "%s: n_kv: %d\n", __func__, n_kv);
for (int i = 0; i < n_kv; ++i) {
const char * key = gguf_get_key(ctx, i);
fprintf(stdout, "%s: kv[%d]: key = %s\n", __func__, i, key);
}
}
// tensor info
{
const int n_tensors = gguf_get_n_tensors(ctx);
fprintf(stdout, "%s: n_tensors: %d\n", __func__, n_tensors);
for (int i = 0; i < n_tensors; ++i) {
const char * name = gguf_get_tensor_name (ctx, i);
const size_t offset = gguf_get_tensor_offset(ctx, i);
fprintf(stdout, "%s: tensor[%d]: name = %s, offset = %zu\n", __func__, i, name, offset);
}
}
// data
{
const int n_tensors = gguf_get_n_tensors(ctx);
for (int i = 0; i < n_tensors; ++i) {
fprintf(stdout, "%s: reading tensor %d data\n", __func__, i);
const char * name = gguf_get_tensor_name(ctx, i);
struct ggml_tensor * cur = ggml_get_tensor(ctx_data, name);
fprintf(stdout, "%s: tensor[%d]: n_dims = %d, name = %s, data = %p\n", __func__, i, cur->n_dims, cur->name, cur->data);
// print first 10 elements
const float * data = (const float *) cur->data;
printf("%s data[:10] : ", name);
for (int j = 0; j < MIN(10, ggml_nelements(cur)); ++j) {
printf("%f ", data[j]);
}
printf("\n\n");
// check data
{
const float * data = (const float *) cur->data;
for (int j = 0; j < ggml_nelements(cur); ++j) {
if (data[j] != 100 + i) {
fprintf(stderr, "%s: tensor[%d]: data[%d] = %f\n", __func__, i, j, data[j]);
return false;
}
}
}
}
}
fprintf(stdout, "%s: ctx_data size: %zu\n", __func__, ggml_get_mem_size(ctx_data));
ggml_free(ctx_data);
gguf_free(ctx);
return true;
}
int main(int argc, char ** argv) {
if (argc < 3) {
fprintf(stdout, "usage: %s data.gguf r|w\n", argv[0]);
return -1;
}
const std::string fname(argv[1]);
const std::string mode (argv[2]);
GGML_ASSERT((mode == "r" || mode == "w") && "mode must be r or w");
if (mode == "w") {
GGML_ASSERT(gguf_ex_write(fname) && "failed to write gguf file");
} else if (mode == "r") {
GGML_ASSERT(gguf_ex_read_0(fname) && "failed to read gguf file");
GGML_ASSERT(gguf_ex_read_1(fname) && "failed to read gguf file");
}
return 0;
}

File diff suppressed because one or more lines are too long

File diff suppressed because it is too large Load diff

View file

@ -191,10 +191,6 @@ int main(int argc, char ** argv) {
// tokenize the prompt
std::vector<llama_token> embd_inp;
// Add a space in front of the first character to match OG llama tokenizer behavior
params.prompt.insert(0, 1, ' ');
if (params.interactive_first || params.instruct || !params.prompt.empty() || session_tokens.empty()) {
embd_inp = ::llama_tokenize(ctx, params.prompt, true);
} else {
@ -270,15 +266,12 @@ int main(int argc, char ** argv) {
params.interactive = true;
}
// determine newline token
auto llama_token_newline = ::llama_tokenize(ctx, "\n", false);
if (params.verbose_prompt) {
fprintf(stderr, "\n");
fprintf(stderr, "%s: prompt: '%s'\n", __func__, params.prompt.c_str());
fprintf(stderr, "%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
for (int i = 0; i < (int) embd_inp.size(); i++) {
fprintf(stderr, "%6d -> '%s'\n", embd_inp[i], llama_token_to_str(ctx, embd_inp[i]));
fprintf(stderr, "%6d -> '%s'\n", embd_inp[i], llama_token_to_str(ctx, embd_inp[i]).c_str());
}
if (ctx_guidance) {
@ -286,14 +279,14 @@ int main(int argc, char ** argv) {
fprintf(stderr, "%s: negative prompt: '%s'\n", __func__, params.cfg_negative_prompt.c_str());
fprintf(stderr, "%s: number of tokens in negative prompt = %zu\n", __func__, guidance_inp.size());
for (int i = 0; i < (int) guidance_inp.size(); i++) {
fprintf(stderr, "%6d -> '%s'\n", guidance_inp[i], llama_token_to_str(ctx, guidance_inp[i]));
fprintf(stderr, "%6d -> '%s'\n", guidance_inp[i], llama_token_to_str(ctx, guidance_inp[i]).c_str());
}
}
if (params.n_keep > 0) {
fprintf(stderr, "%s: static prompt based on n_keep: '", __func__);
for (int i = 0; i < params.n_keep; i++) {
fprintf(stderr, "%s", llama_token_to_str(ctx, embd_inp[i]));
fprintf(stderr, "%s", llama_token_to_str(ctx, embd_inp[i]).c_str());
}
fprintf(stderr, "'\n");
}
@ -311,7 +304,7 @@ int main(int argc, char ** argv) {
auto console_ctrl_handler = +[](DWORD ctrl_type) -> BOOL {
return (ctrl_type == CTRL_C_EVENT) ? (sigint_handler(SIGINT), true) : false;
};
SetConsoleCtrlHandler(static_cast<PHANDLER_ROUTINE>(console_ctrl_handler), true);
SetConsoleCtrlHandler(reinterpret_cast<PHANDLER_ROUTINE>(console_ctrl_handler), true);
#endif
fprintf(stderr, "%s: interactive mode on.\n", __func__);
@ -662,7 +655,7 @@ int main(int argc, char ** argv) {
// display text
if (input_echo) {
for (auto id : embd) {
printf("%s", llama_token_to_str(ctx, id));
printf("%s", llama_token_to_str(ctx, id).c_str());
}
fflush(stdout);
}
@ -782,8 +775,7 @@ int main(int argc, char ** argv) {
if (grammar != NULL) {
llama_grammar_free(grammar);
std::vector<const llama_grammar_element *> grammar_rules(
parsed_grammar.c_rules());
std::vector<const llama_grammar_element *> grammar_rules( parsed_grammar.c_rules());
grammar = llama_grammar_init(
grammar_rules.data(), grammar_rules.size(),
parsed_grammar.symbol_ids.at("root"));

View file

@ -2,7 +2,7 @@
//
// - First, export a LLaMA graph:
//
// $ ./bin/main -m ../models/7B/ggml-model-q4_0.bin --export
// $ ./bin/main -m ../models/7B/ggml-model-q4_0.gguf --export
//
// - Run this tool to evaluate the exported graph:
//

View file

@ -1,6 +1,7 @@
#include "ggml.h"
#include "build-info.h"
#define LLAMA_API_CPP // TODO: eliminate me
#define LLAMA_API_INTERNAL
#include "llama.h"
@ -24,7 +25,7 @@
#endif
struct quantize_stats_params {
std::string model = "models/7B/ggml-model-f16.bin";
std::string model = "models/7B/ggml-model-f16.gguf";
bool verbose = false;
bool per_layer_stats = false;
bool print_histogram = false;

View file

@ -68,10 +68,10 @@ bool try_parse_ftype(const std::string & ftype_str_in, llama_ftype & ftype, std:
}
// usage:
// ./quantize [--allow-requantize] [--leave-output-tensor] models/llama/ggml-model.bin [models/llama/ggml-model-quant.bin] type [nthreads]
// ./quantize [--allow-requantize] [--leave-output-tensor] models/llama/ggml-model.gguf [models/llama/ggml-model-quant.gguf] type [nthreads]
//
void usage(const char * executable) {
fprintf(stderr, "usage: %s [--help] [--allow-requantize] [--leave-output-tensor] model-f32.bin [model-quant.bin] type [nthreads]\n\n", executable);
fprintf(stderr, "usage: %s [--help] [--allow-requantize] [--leave-output-tensor] model-f32.gguf [model-quant.gguf] type [nthreads]\n\n", executable);
fprintf(stderr, " --allow-requantize: Allows requantizing tensors that have already been quantized. Warning: This can severely reduce quality compared to quantizing from 16bit or 32bit\n");
fprintf(stderr, " --leave-output-tensor: Will leave output.weight un(re)quantized. Increases model size but may also increase quality, especially when requantizing\n");
fprintf(stderr, "\nAllowed quantization types:\n");
@ -118,8 +118,8 @@ int main(int argc, char ** argv) {
if (pos != std::string::npos) {
fpath = fname_inp.substr(0, pos + 1);
}
// export as [inp path]/ggml-model-[ftype].bin
fname_out = fpath + "ggml-model-" + ftype_str + ".bin";
// export as [inp path]/ggml-model-[ftype].gguf
fname_out = fpath + "ggml-model-" + ftype_str + ".gguf";
arg_idx++;
}
else {

View file

@ -26,7 +26,6 @@ int main(int argc, char ** argv) {
auto lparams = llama_context_default_params();
lparams.n_ctx = params.n_ctx;
lparams.n_gqa = params.n_gqa;
lparams.seed = params.seed;
lparams.f16_kv = params.memory_f16;
lparams.use_mmap = params.use_mmap;
@ -45,9 +44,8 @@ int main(int argc, char ** argv) {
llama_free_model(model);
return 1;
}
auto tokens = std::vector<llama_token>(params.n_ctx);
auto n_prompt_tokens = llama_tokenize(ctx, params.prompt.c_str(), tokens.data(), int(tokens.size()), true);
auto tokens = llama_tokenize(ctx, params.prompt.c_str(), true);
auto n_prompt_tokens = tokens.size();
if (n_prompt_tokens < 1) {
fprintf(stderr, "%s : failed to tokenize prompt\n", __func__);
llama_free(ctx);
@ -92,7 +90,7 @@ int main(int argc, char ** argv) {
auto next_token_str = llama_token_to_str(ctx, next_token);
last_n_tokens_data.push_back(next_token);
printf("%s", next_token_str);
printf("%s", next_token_str.c_str());
if (llama_eval(ctx, &next_token, 1, n_past, params.n_threads)) {
fprintf(stderr, "\n%s : failed to evaluate\n", __func__);
llama_free(ctx);
@ -152,7 +150,7 @@ int main(int argc, char ** argv) {
auto next_token_str = llama_token_to_str(ctx2, next_token);
last_n_tokens_data.push_back(next_token);
printf("%s", next_token_str);
printf("%s", next_token_str.c_str());
if (llama_eval(ctx2, &next_token, 1, n_past, params.n_threads)) {
fprintf(stderr, "\n%s : failed to evaluate\n", __func__);
llama_free(ctx2);

View file

@ -5,7 +5,7 @@ This example demonstrates a simple HTTP API server and a simple web front end to
Command line options:
- `--threads N`, `-t N`: Set the number of threads to use during computation.
- `-m FNAME`, `--model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.bin`).
- `-m FNAME`, `--model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.gguf`).
- `-m ALIAS`, `--alias ALIAS`: Set an alias for the model. The alias will be returned in API responses.
- `-c N`, `--ctx-size N`: Set the size of the prompt context. The default is 512, but LLaMA models were built with a context of 2048, which will provide better results for longer input/inference. The size may differ in other models, for example, baichuan models were build with a context of 4096.
- `-ngl N`, `--n-gpu-layers N`: When compiled with appropriate support (currently CLBlast or cuBLAS), this option allows offloading some layers to the GPU for computation. Generally results in increased performance.
@ -48,14 +48,12 @@ To get started right away, run the following command, making sure to use the cor
### Unix-based systems (Linux, macOS, etc.):
```bash
./server -m models/7B/ggml-model.bin -c 2048
./server -m models/7B/ggml-model.gguf -c 2048
```
### Windows:
```powershell
server.exe -m models\7B\ggml-model.bin -c 2048
```
The above command will start a server that by default listens on `127.0.0.1:8080`.
You can consume the endpoints with Postman or NodeJS with axios library. You can visit the web front end at the same url.

View file

@ -652,8 +652,6 @@ static void server_print_usage(const char *argv0, const gpt_params &params,
fprintf(stdout, " -v, --verbose verbose output (default: %s)\n", server_verbose ? "enabled" : "disabled");
fprintf(stdout, " -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads);
fprintf(stdout, " -c N, --ctx-size N size of the prompt context (default: %d)\n", params.n_ctx);
fprintf(stdout, " -gqa N, --gqa N grouped-query attention factor (TEMP!!! use 8 for LLaMAv2 70B) (default: %d)\n", params.n_gqa);
fprintf(stdout, " -eps N, --rms-norm-eps N rms norm eps (TEMP!!! use 1e-5 for LLaMAv2) (default: %.1e)\n", params.rms_norm_eps);
fprintf(stdout, " --rope-freq-base N RoPE base frequency (default: %.1f)\n", params.rope_freq_base);
fprintf(stdout, " --rope-freq-scale N RoPE frequency scaling factor (default: %g)\n", params.rope_freq_scale);
fprintf(stdout, " -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch);
@ -774,23 +772,6 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
}
params.n_ctx = std::stoi(argv[i]);
}
else if (arg == "-gqa" || arg == "--gqa")
{
if (++i >= argc)
{
invalid_param = true;
break;
}
params.n_gqa = std::stoi(argv[i]);
}
else if (arg == "-eps" || arg == "--rms-norm-eps") {
if (++i >= argc)
{
invalid_param = true;
break;
}
params.rms_norm_eps = std::stof(argv[i]);
}
else if (arg == "--rope-freq-base")
{
if (++i >= argc)

View file

@ -2,180 +2,129 @@
#define _GNU_SOURCE
#endif
#include "common.h"
#include "llama.h"
#include "build-info.h"
#include <cassert>
#include <cinttypes>
#include "common.h"
#include "llama.h"
#include <cmath>
#include <cstdio>
#include <cstring>
#include <ctime>
#include <fstream>
#include <iostream>
#include <string>
#include <vector>
#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
#include <signal.h>
#include <unistd.h>
#elif defined (_WIN32)
#define WIN32_LEAN_AND_MEAN
#define NOMINMAX
#include <windows.h>
#include <signal.h>
#endif
int main(int argc, char ** argv)
{
int main(int argc, char ** argv) {
gpt_params params;
//---------------------------------
// Print help :
//---------------------------------
if ( argc == 1 || argv[1][0] == '-' )
{
printf( "usage: %s MODEL_PATH [PROMPT]\n" , argv[0] );
if (argc == 1 || argv[1][0] == '-') {
printf("usage: %s MODEL_PATH [PROMPT]\n" , argv[0]);
return 1 ;
}
//---------------------------------
// Load parameters :
//---------------------------------
if ( argc >= 2 )
{
if (argc >= 2) {
params.model = argv[1];
}
if ( argc >= 3 )
{
if (argc >= 3) {
params.prompt = argv[2];
}
if ( params.prompt.empty() )
{
if (params.prompt.empty()) {
params.prompt = "Hello my name is";
}
//---------------------------------
// Init LLM :
//---------------------------------
// init LLM
llama_backend_init(params.numa);
llama_model * model;
llama_context * ctx;
llama_context_params ctx_params = llama_context_default_params();
std::tie(model, ctx) = llama_init_from_gpt_params( params );
llama_model * model = llama_load_model_from_file(params.model.c_str(), ctx_params);
if ( model == NULL )
{
fprintf( stderr , "%s: error: unable to load model\n" , __func__ );
if (model == NULL) {
fprintf(stderr , "%s: error: unable to load model\n" , __func__);
return 1;
}
//---------------------------------
// Tokenize the prompt :
//---------------------------------
llama_context * ctx = llama_new_context_with_model(model, ctx_params);
// tokenize the prompt
std::vector<llama_token> tokens_list;
tokens_list = ::llama_tokenize( ctx , params.prompt , true );
tokens_list = ::llama_tokenize(ctx, params.prompt, true);
const int max_context_size = llama_n_ctx( ctx );
const int max_tokens_list_size = max_context_size - 4 ;
const int max_context_size = llama_n_ctx(ctx);
const int max_tokens_list_size = max_context_size - 4;
if ( (int)tokens_list.size() > max_tokens_list_size )
{
fprintf( stderr , "%s: error: prompt too long (%d tokens, max %d)\n" ,
__func__ , (int)tokens_list.size() , max_tokens_list_size );
if ((int) tokens_list.size() > max_tokens_list_size) {
fprintf(stderr, "%s: error: prompt too long (%d tokens, max %d)\n", __func__, (int) tokens_list.size(), max_tokens_list_size);
return 1;
}
fprintf( stderr, "\n\n" );
fprintf(stderr, "\n\n");
// Print the tokens from the prompt :
for( auto id : tokens_list )
{
printf( "%s" , llama_token_to_str( ctx , id ) );
for (auto id : tokens_list) {
fprintf(stderr, "%s", llama_token_to_str(ctx, id).c_str());
}
fflush(stdout);
fflush(stderr);
//---------------------------------
// Main prediction loop :
//---------------------------------
// main loop
// The LLM keeps a contextual cache memory of previous token evaluation.
// Usually, once this cache is full, it is required to recompute a compressed context based on previous
// tokens (see "infinite text generation via context swapping" in the main example), but in this minimalist
// example, we will just stop the loop once this cache is full or once an end of stream is detected.
while ( llama_get_kv_cache_token_count( ctx ) < max_context_size )
{
//---------------------------------
// Evaluate the tokens :
//---------------------------------
const int n_gen = std::min(32, max_context_size);
if ( llama_eval( ctx , tokens_list.data() , int(tokens_list.size()) , llama_get_kv_cache_token_count( ctx ) , params.n_threads ) )
{
fprintf( stderr, "%s : failed to eval\n" , __func__ );
while (llama_get_kv_cache_token_count(ctx) < n_gen) {
// evaluate the transformer
if (llama_eval(ctx, tokens_list.data(), int(tokens_list.size()), llama_get_kv_cache_token_count(ctx), params.n_threads)) {
fprintf(stderr, "%s : failed to eval\n", __func__);
return 1;
}
tokens_list.clear();
//---------------------------------
// Select the best prediction :
//---------------------------------
// sample the next token
llama_token new_token_id = 0;
auto logits = llama_get_logits( ctx );
auto n_vocab = llama_n_vocab( ctx ); // the size of the LLM vocabulary (in tokens)
auto logits = llama_get_logits(ctx);
auto n_vocab = llama_n_vocab(ctx);
std::vector<llama_token_data> candidates;
candidates.reserve( n_vocab );
candidates.reserve(n_vocab);
for( llama_token token_id = 0 ; token_id < n_vocab ; token_id++ )
{
candidates.emplace_back( llama_token_data{ token_id , logits[ token_id ] , 0.0f } );
for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
candidates.emplace_back(llama_token_data{ token_id, logits[token_id], 0.0f });
}
llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
// Select it using the "Greedy sampling" method :
new_token_id = llama_sample_token_greedy( ctx , &candidates_p );
new_token_id = llama_sample_token_greedy(ctx , &candidates_p);
// is it an end of stream ?
if ( new_token_id == llama_token_eos() )
{
if (new_token_id == llama_token_eos()) {
fprintf(stderr, " [end of text]\n");
break;
}
// Print the new token :
printf( "%s" , llama_token_to_str( ctx , new_token_id ) );
fflush( stdout );
// print the new token :
printf("%s", llama_token_to_str(ctx, new_token_id).c_str());
fflush(stdout);
// Push this new token for next evaluation :
tokens_list.push_back( new_token_id );
// push this new token for next evaluation
tokens_list.push_back(new_token_id);
}
} // wend of main loop
llama_free( ctx );
llama_free_model( model );
llama_free(ctx);
llama_free_model(model);
llama_backend_free();
fprintf(stderr, "\n\n");
return 0;
}
// EOF

View file

@ -1,4 +1,5 @@
#include "ggml.h"
#include "common.h"
#include "llama.h"
#include <unordered_map>
#include <vector>
@ -16,7 +17,7 @@
#pragma warning(disable: 4244 4267) // possible loss of data
#endif
static const float rms_norm_eps = LLAMA_DEFAULT_RMS_EPS;
static const float rms_norm_eps = 1e-5f;
struct random_normal_distribution {
std::mt19937 gen;
@ -1961,7 +1962,7 @@ void print_matrix(struct ggml_tensor * probs) {
void print_token(struct llama_context * ctx, llama_token token) {
printf("%s", llama_token_to_str(ctx, token));
printf("%s", llama_token_to_str(ctx, token).c_str());
}
void print_tokens(struct llama_context* ctx, struct ggml_tensor * tokens) {
@ -2188,11 +2189,10 @@ int tokenize_file(struct llama_context * lctx, const char * filename, std::vecto
f.read_raw(buf.data(), f.size);
buf[f.size] = '\0';
out.resize(buf.size());
int n_tokens = llama_tokenize(lctx, buf.data(), out.data(), buf.size(), false);
if (n_tokens >= 0) {
out.resize(n_tokens);
int n_tokens = llama_tokenize(lctx, buf.data(), out.data(), out.size(), false);
if (n_tokens < 0) {
out.resize(-n_tokens);
llama_tokenize(lctx, buf.data(), out.data(), out.size(), false);
}
bool verify = false;
@ -2200,17 +2200,17 @@ int tokenize_file(struct llama_context * lctx, const char * filename, std::vecto
const char * in = buf.data();
const char * end = buf.data() + buf.size();
for (int i = 0; i < (int) out.size(); ++i) {
const char * s = llama_token_to_str(lctx, out[i]);
int len = strlen(s);
std::string s = llama_token_to_str(lctx, out[i]);
int len = s.length();
if (in >= end) {
printf("%s: unexpected end of original text.\n", __func__);
break;
}
const bool matches = (strncmp(in, s, len) == 0);
const bool matches = (strncmp(in, s.c_str(), len) == 0);
if (matches) {
in += len;
} else {
printf("%s: mismatch: expected '%s', but got '%s'\n", __func__, std::string(in, len).c_str(), s);
printf("%s: mismatch: expected '%s', but got '%s'\n", __func__, std::string(in, len).c_str(), s.c_str());
}
}
}
@ -2612,42 +2612,45 @@ void save_as_llama_model(struct llama_vocab * vocab, struct my_llama_model * mod
return;
}
// write_magic
file.write_u32(LLAMA_FILE_MAGIC); // magic
file.write_u32(LLAMA_FILE_VERSION); // version
// write_hparams
file.write_u32(model->hparams.n_vocab);
file.write_u32(model->hparams.n_embd);
file.write_u32(model->hparams.n_mult);
file.write_u32(model->hparams.n_head);
file.write_u32(model->hparams.n_layer);
file.write_u32(model->hparams.n_rot);
file.write_u32(LLAMA_FTYPE_ALL_F32);
// write_vocab
uint32_t n_vocab = model->hparams.n_vocab;
for (uint32_t i = 0; i < n_vocab; i++) {
const auto & token_score = vocab->id_to_token.at(i);
file.write_u32((uint32_t) token_score.tok.size());
file.write_raw(token_score.tok.data(), token_score.tok.size());
file.write_raw(&token_score.score, sizeof(token_score.score));
}
// write tensors
write_tensor(&file, model->tok_embeddings);
write_tensor(&file, model->norm);
write_tensor(&file, model->output);
for (uint32_t i = 0; i < model->hparams.n_layer; ++i) {
auto & layer = model->layers[i];
write_tensor(&file, layer.attention_norm);
write_tensor(&file, layer.wq);
write_tensor(&file, layer.wk);
write_tensor(&file, layer.wv);
write_tensor(&file, layer.wo);
write_tensor(&file, layer.ffn_norm);
write_tensor(&file, layer.w1);
write_tensor(&file, layer.w2);
write_tensor(&file, layer.w3);
}
#pragma message("TODO: implement file saving using gguf")
(void) vocab;
(void) model;
// // write_magic
// file.write_u32(LLAMA_FILE_MAGIC); // magic
// file.write_u32(LLAMA_FILE_VERSION); // version
// // write_hparams
// file.write_u32(model->hparams.n_vocab);
// file.write_u32(model->hparams.n_embd);
// file.write_u32(model->hparams.n_mult);
// file.write_u32(model->hparams.n_head);
// file.write_u32(model->hparams.n_layer);
// file.write_u32(model->hparams.n_rot);
// file.write_u32(LLAMA_FTYPE_ALL_F32);
// // write_vocab
// uint32_t n_vocab = model->hparams.n_vocab;
// for (uint32_t i = 0; i < n_vocab; i++) {
// const auto & token_score = vocab->id_to_token.at(i);
// file.write_u32((uint32_t) token_score.tok.size());
// file.write_raw(token_score.tok.data(), token_score.tok.size());
// file.write_raw(&token_score.score, sizeof(token_score.score));
// }
// // write tensors
// write_tensor(&file, model->tok_embeddings);
// write_tensor(&file, model->norm);
// write_tensor(&file, model->output);
// for (uint32_t i = 0; i < model->hparams.n_layer; ++i) {
// auto & layer = model->layers[i];
//
// write_tensor(&file, layer.attention_norm);
// write_tensor(&file, layer.wq);
// write_tensor(&file, layer.wk);
// write_tensor(&file, layer.wv);
// write_tensor(&file, layer.wo);
// write_tensor(&file, layer.ffn_norm);
// write_tensor(&file, layer.w1);
// write_tensor(&file, layer.w2);
// write_tensor(&file, layer.w3);
// }
}
float cosine_decay(const int decay_steps, const float alpha, int step) {

View file

@ -38,6 +38,9 @@ struct ggml_metal_context;
struct ggml_metal_context * ggml_metal_init(int n_cb);
void ggml_metal_free(struct ggml_metal_context * ctx);
void * ggml_metal_host_malloc(size_t n);
void ggml_metal_host_free (void * data);
// set the number of command buffers to use
void ggml_metal_set_n_cb(struct ggml_metal_context * ctx, int n_cb);

View file

@ -237,6 +237,21 @@ void ggml_metal_free(struct ggml_metal_context * ctx) {
free(ctx);
}
void * ggml_metal_host_malloc(size_t n) {
void * data = NULL;
const int result = posix_memalign((void **) &data, getpagesize(), n);
if (result != 0) {
fprintf(stderr, "%s: error: posix_memalign failed\n", __func__);
return NULL;
}
return data;
}
void ggml_metal_host_free(void * data) {
free(data);
}
void ggml_metal_set_n_cb(struct ggml_metal_context * ctx, int n_cb) {
ctx->n_cb = n_cb;
}

1014
ggml.c

File diff suppressed because it is too large Load diff

122
ggml.h
View file

@ -207,14 +207,18 @@
#define GGML_MAX_PARAMS 256
#define GGML_MAX_CONTEXTS 64
#define GGML_MAX_SRC 6
#define GGML_MAX_NAME 48
#define GGML_MAX_NAME 64
#define GGML_MAX_OP_PARAMS 32
#define GGML_DEFAULT_N_THREADS 4
#define GGML_EXIT_SUCCESS 0
#define GGML_EXIT_ABORTED 1
#define GGUF_MAGIC 0x47475546 // "GGUF"
#define GGUF_VERSION 1
#define GGUF_DEFAULT_ALIGNMENT 32
#define GGML_UNUSED(x) (void)(x)
#define GGML_PAD(x, n) (((x) + (n) - 1) & ~((n) - 1))
@ -562,6 +566,7 @@ extern "C" {
GGML_API int64_t ggml_nelements (const struct ggml_tensor * tensor);
GGML_API int64_t ggml_nrows (const struct ggml_tensor * tensor);
GGML_API size_t ggml_nbytes (const struct ggml_tensor * tensor);
GGML_API size_t ggml_nbytes_pad (const struct ggml_tensor * tensor); // same as ggml_nbytes() but padded to GGML_MEM_ALIGN
GGML_API size_t ggml_nbytes_split(const struct ggml_tensor * tensor, int nrows_split);
GGML_API int ggml_blck_size (enum ggml_type type);
@ -1494,7 +1499,6 @@ extern "C" {
struct ggml_context * ctx,
struct ggml_tensor * tensor);
GGML_API void ggml_build_forward_expand(struct ggml_cgraph * cgraph, struct ggml_tensor * tensor);
GGML_API struct ggml_cgraph ggml_build_forward (struct ggml_tensor * tensor);
@ -1703,6 +1707,118 @@ extern "C" {
GGML_API size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, int start, int n, int64_t * hist);
//
// gguf
//
enum gguf_type {
GGUF_TYPE_UINT8 = 0,
GGUF_TYPE_INT8 = 1,
GGUF_TYPE_UINT16 = 2,
GGUF_TYPE_INT16 = 3,
GGUF_TYPE_UINT32 = 4,
GGUF_TYPE_INT32 = 5,
GGUF_TYPE_FLOAT32 = 6,
GGUF_TYPE_BOOL = 7,
GGUF_TYPE_STRING = 8,
GGUF_TYPE_ARRAY = 9,
GGUF_TYPE_COUNT, // marks the end of the enum
};
struct gguf_context;
struct gguf_init_params {
bool no_alloc;
// if not NULL, create a ggml_context and allocate the tensor data in it
struct ggml_context ** ctx;
};
GGML_API struct gguf_context * gguf_init_empty(void);
GGML_API struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_params params);
//GGML_API struct gguf_context * gguf_init_from_buffer(..);
GGML_API void gguf_free(struct gguf_context * ctx);
GGML_API const char * gguf_type_name(enum gguf_type type);
GGML_API int gguf_get_version (struct gguf_context * ctx);
GGML_API size_t gguf_get_alignment (struct gguf_context * ctx);
GGML_API size_t gguf_get_data_offset(struct gguf_context * ctx);
GGML_API void * gguf_get_data (struct gguf_context * ctx);
GGML_API int gguf_get_n_kv(struct gguf_context * ctx);
GGML_API int gguf_find_key(struct gguf_context * ctx, const char * key);
GGML_API const char * gguf_get_key (struct gguf_context * ctx, int i);
GGML_API enum gguf_type gguf_get_kv_type (struct gguf_context * ctx, int i);
GGML_API enum gguf_type gguf_get_arr_type(struct gguf_context * ctx, int i);
// results are undefined if the wrong type is used for the key
GGML_API uint8_t gguf_get_val_u8 (struct gguf_context * ctx, int i);
GGML_API int8_t gguf_get_val_i8 (struct gguf_context * ctx, int i);
GGML_API uint16_t gguf_get_val_u16 (struct gguf_context * ctx, int i);
GGML_API int16_t gguf_get_val_i16 (struct gguf_context * ctx, int i);
GGML_API uint32_t gguf_get_val_u32 (struct gguf_context * ctx, int i);
GGML_API int32_t gguf_get_val_i32 (struct gguf_context * ctx, int i);
GGML_API float gguf_get_val_f32 (struct gguf_context * ctx, int i);
GGML_API bool gguf_get_val_bool(struct gguf_context * ctx, int i);
GGML_API const char * gguf_get_val_str (struct gguf_context * ctx, int i);
GGML_API int gguf_get_arr_n (struct gguf_context * ctx, int i);
GGML_API const void * gguf_get_arr_data(struct gguf_context * ctx, int i);
GGML_API const char * gguf_get_arr_str (struct gguf_context * ctx, int key_id, int i);
GGML_API int gguf_get_n_tensors (struct gguf_context * ctx);
GGML_API int gguf_find_tensor (struct gguf_context * ctx, const char * name);
GGML_API size_t gguf_get_tensor_offset(struct gguf_context * ctx, int i);
GGML_API char * gguf_get_tensor_name (struct gguf_context * ctx, int i);
// overrides existing values or adds a new one
GGML_API void gguf_set_val_u8 (struct gguf_context * ctx, const char * key, uint8_t val);
GGML_API void gguf_set_val_i8 (struct gguf_context * ctx, const char * key, int8_t val);
GGML_API void gguf_set_val_u16 (struct gguf_context * ctx, const char * key, uint16_t val);
GGML_API void gguf_set_val_i16 (struct gguf_context * ctx, const char * key, int16_t val);
GGML_API void gguf_set_val_u32 (struct gguf_context * ctx, const char * key, uint32_t val);
GGML_API void gguf_set_val_i32 (struct gguf_context * ctx, const char * key, int32_t val);
GGML_API void gguf_set_val_f32 (struct gguf_context * ctx, const char * key, float val);
GGML_API void gguf_set_val_bool(struct gguf_context * ctx, const char * key, bool val);
GGML_API void gguf_set_val_str (struct gguf_context * ctx, const char * key, const char * val);
GGML_API void gguf_set_arr_data(struct gguf_context * ctx, const char * key, enum gguf_type type, const void * data, int n);
GGML_API void gguf_set_arr_str (struct gguf_context * ctx, const char * key, const char ** data, int n);
// set or add KV pairs from another context
GGML_API void gguf_set_kv(struct gguf_context * ctx, struct gguf_context * src);
// manage tensor info
GGML_API void gguf_add_tensor(struct gguf_context * ctx, const struct ggml_tensor * tensor);
GGML_API void gguf_set_tensor_type(struct gguf_context * ctx, const char * name, enum ggml_type type);
GGML_API void gguf_set_tensor_data(struct gguf_context * ctx, const char * name, const void * data, size_t size);
// writing gguf files can be done in 2 ways:
//
// - write the entire gguf_context to a binary file in a single pass:
//
// gguf_write_to_file(ctx, fname);
//
// - first prepare a file with a placeholder for the meta data, write the tensor data, then write the meta data:
//
// FILE * f = fopen(fname, "wb");
// fseek(f, gguf_get_meta_size(ctx), SEEK_SET);
// fwrite(f, ...);
// void * data = gguf_meta_get_meta_data(ctx);
// fseek(f, 0, SEEK_SET);
// fwrite(f, data, gguf_get_meta_size(ctx));
// free(data);
// fclose(f);
//
// write the entire context to a binary file
GGML_API void gguf_write_to_file(struct gguf_context * ctx, const char * fname, bool only_meta);
// get the size in bytes of the meta data (header, kv pairs, tensor info) including padding
GGML_API size_t gguf_get_meta_size(struct gguf_context * ctx);
GGML_API void gguf_get_meta_data(struct gguf_context * ctx, void * data);
//
// system info
//

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import shutil
import sys
import struct
import tempfile
import numpy as np
from enum import IntEnum, auto
from typing import Any, IO, List
#
# constants
#
GGUF_MAGIC = 0x47475546
GGUF_VERSION = 1
GGUF_DEFAULT_ALIGNMENT = 32
# general
KEY_GENERAL_ARCHITECTURE = "general.architecture"
KEY_GENERAL_QUANTIZATION_VERSION = "general.quantization_version"
KEY_GENERAL_ALIGNMENT = "general.alignment"
KEY_GENERAL_NAME = "general.name"
KEY_GENERAL_AUTHOR = "general.author"
KEY_GENERAL_URL = "general.url"
KEY_GENERAL_DESCRIPTION = "general.description"
KEY_GENERAL_LICENSE = "general.license"
KEY_GENERAL_SOURCE_URL = "general.source.url"
KEY_GENERAL_SOURCE_HF_REPO = "general.source.hugginface.repository"
# LLM
KEY_LLM_CONTEXT_LENGTH = "{arch}.context_length"
KEY_LLM_EMBEDDING_LENGTH = "{arch}.embedding_length"
KEY_LLM_BLOCK_COUNT = "{arch}.block_count"
KEY_LLM_FEED_FORWARD_LENGTH = "{arch}.feed_forward_length"
KEY_LLM_USE_PARALLEL_RESIDUAL = "{arch}.use_parallel_residual"
KEY_LLM_TENSOR_DATA_LAYOUT = "{arch}.tensor_data_layout"
# attention
KEY_ATTENTION_HEAD_COUNT = "{arch}.attention.head_count"
KEY_ATTENTION_HEAD_COUNT_KV = "{arch}.attention.head_count_kv"
KEY_ATTENTION_MAX_ALIBI_BIAS = "{arch}.attention.max_alibi_bias"
KEY_ATTENTION_CLAMP_KQV = "{arch}.attention.clamp_kqv"
KEY_ATTENTION_LAYERNORM_EPS = "{arch}.attention.layer_norm_epsilon"
KEY_ATTENTION_LAYERNORM_RMS_EPS = "{arch}.attention.layer_norm_rms_epsilon"
# RoPE
KEY_ROPE_DIMENSION_COUNT = "{arch}.rope.dimension_count"
KEY_ROPE_SCALE = "{arch}.rope.scale"
# tokenization
KEY_TOKENIZER_MODEL = "tokenizer.ggml.model"
KEY_TOKENIZER_LIST = "tokenizer.ggml.tokens"
KEY_TOKENIZER_TOKEN_TYPE = "tokenizer.ggml.token_type"
KEY_TOKENIZER_SCORES = "tokenizer.ggml.scores"
KEY_TOKENIZER_MERGES = "tokenizer.ggml.merges"
KEY_TOKENIZER_BOS_ID = "tokenizer.ggml.bos_token_id"
KEY_TOKENIZER_EOS_ID = "tokenizer.ggml.eos_token_id"
KEY_TOKENIZER_UNK_ID = "tokenizer.ggml.unknown_token_id"
KEY_TOKENIZER_SEP_ID = "tokenizer.ggml.seperator_token_id"
KEY_TOKENIZER_PAD_ID = "tokenizer.ggml.padding_token_id"
KEY_TOKENIZER_HF_JSON = "tokenizer.huggingface.json"
KEY_TOKENIZER_RWKV = "tokenizer.rwkv.world"
#
# recommended mapping of model tensor names for storage in gguf
#
class MODEL_ARCH(IntEnum):
LLAMA = auto()
FALCON = auto()
GPT2 = auto()
GPTJ = auto()
GPTNEOX = auto()
MPT = auto()
class MODEL_TENSOR(IntEnum):
TOKEN_EMBD = auto()
POS_EMBD = auto()
OUTPUT = auto()
OUTPUT_NORM = auto()
ROPE_FREQS = auto()
ATTN_Q = auto()
ATTN_K = auto()
ATTN_V = auto()
ATTN_QKV = auto()
ATTN_OUT = auto()
ATTN_NORM = auto()
ATTN_NORM_2 = auto()
ATTN_ROT_EMBD = auto()
FFN_GATE = auto()
FFN_DOWN = auto()
FFN_UP = auto()
FFN_NORM = auto()
MODEL_ARCH_NAMES = {
MODEL_ARCH.LLAMA: "llama",
MODEL_ARCH.FALCON: "falcon",
MODEL_ARCH.GPT2: "gpt2",
MODEL_ARCH.GPTJ: "gptj",
MODEL_ARCH.GPTNEOX: "gptneox",
MODEL_ARCH.MPT: "mpt",
}
MODEL_TENSOR_NAMES = {
MODEL_ARCH.LLAMA: {
MODEL_TENSOR.TOKEN_EMBD: "token_embd",
MODEL_TENSOR.OUTPUT_NORM: "output_norm",
MODEL_TENSOR.OUTPUT: "output",
MODEL_TENSOR.ROPE_FREQS: "rope_freqs",
MODEL_TENSOR.ATTN_NORM: "blk.{bid}.attn_norm",
MODEL_TENSOR.ATTN_Q: "blk.{bid}.attn_q",
MODEL_TENSOR.ATTN_K: "blk.{bid}.attn_k",
MODEL_TENSOR.ATTN_V: "blk.{bid}.attn_v",
MODEL_TENSOR.ATTN_OUT: "blk.{bid}.attn_output",
MODEL_TENSOR.ATTN_ROT_EMBD: "blk.{bid}.attn_rot_embd",
MODEL_TENSOR.FFN_NORM: "blk.{bid}.ffn_norm",
MODEL_TENSOR.FFN_GATE: "blk.{bid}.ffn_gate",
MODEL_TENSOR.FFN_DOWN: "blk.{bid}.ffn_down",
MODEL_TENSOR.FFN_UP: "blk.{bid}.ffn_up",
},
MODEL_ARCH.FALCON: {
MODEL_TENSOR.TOKEN_EMBD: "token_embd",
MODEL_TENSOR.OUTPUT_NORM: "output_norm",
MODEL_TENSOR.OUTPUT: "output",
MODEL_TENSOR.ATTN_NORM: "blk.{bid}.attn_norm",
MODEL_TENSOR.ATTN_NORM_2: "blk.{bid}.attn_norm_2",
MODEL_TENSOR.ATTN_QKV: "blk.{bid}.attn_qkv",
MODEL_TENSOR.ATTN_OUT: "blk.{bid}.attn_output",
MODEL_TENSOR.FFN_DOWN: "blk.{bid}.ffn_down",
MODEL_TENSOR.FFN_UP: "blk.{bid}.ffn_up",
},
MODEL_ARCH.GPT2: {
# TODO
},
# TODO
}
# tensors that will not be serialized
MODEL_TENSOR_SKIP = {
MODEL_ARCH.LLAMA: [
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_ROT_EMBD,
],
}
# TODO: the following helper functions should be removed
# instead, get_tensor_name_map should return tuples of (name, MODEL_TENSOR)
# however, my Python is very bad, and I couldn't figure out how to do this, hence these functions
# REMOVE
def should_skip_tensor_TMP(arch: MODEL_ARCH, n_blocks: int, name: str) -> bool:
for skip in MODEL_TENSOR_SKIP.get(arch, []):
for i in range(n_blocks):
if name == MODEL_TENSOR_NAMES[arch][skip].format(bid=i):
return True
return False
def get_tensor_name_map(arch: MODEL_ARCH, n_blocks: int) -> dict:
tensor_map = {}
# Token embeddings
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.TOKEN_EMBD, None)
tensor_map["gpt_neox.embed_in"] = mapped_to # gptneox
tensor_map["transformer.wte"] = mapped_to # gpt2 mpt
tensor_map["transformer.word_embeddings"] = mapped_to # falcon
tensor_map["model.embed_tokens"] = mapped_to # llama-hf
tensor_map["tok_embeddings"] = mapped_to # llama-pth
# Position embeddings
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.POS_EMBD, None)
tensor_map["transformer.wpe"] = mapped_to # gpt2
# Output
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.OUTPUT, None)
tensor_map["embed_out"] = mapped_to # gptneox
tensor_map["lm_head"] = mapped_to # gpt2 mpt falcon llama-hf
tensor_map["output"] = mapped_to # llama-pth
# Output norm
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.OUTPUT_NORM, None)
tensor_map["gpt_neox.final_layer_norm"] = mapped_to # gptneox
tensor_map["transformer.ln_f"] = mapped_to # gpt2 falcon
tensor_map["transformer.norm_f"] = mapped_to # mpt
tensor_map["model.norm"] = mapped_to # llama-hf
tensor_map["norm"] = mapped_to # llama-pth
# Rope frequencies
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ROPE_FREQS, None)
tensor_map["rope.freqs"] = mapped_to # llama-pth
# Attention and feed-forward blocks
for i in range(0, n_blocks):
# Attention norm
# TODO: is there are simpler way to write these 2 lines in Python?
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_NORM, None)
mapped_to = mapped_to.format(bid=i) if mapped_to else None
tensor_map["gpt_neox.layers."+str(i)+".input_layernorm"] = mapped_to # gptneox
tensor_map["transformer.h."+str(i)+".ln_1"] = mapped_to # gpt2
tensor_map["transformer.blocks."+str(i)+".norm_1"] = mapped_to # mpt
tensor_map["transformer.h."+str(i)+".input_layernorm"] = mapped_to # falcon7b
tensor_map["transformer.h."+str(i)+".ln_attn"] = mapped_to # falcon40b
tensor_map["model.layers."+str(i)+".input_layernorm"] = mapped_to # llama-hf
tensor_map["layers."+str(i)+".attention_norm"] = mapped_to # llama-pth
# Attention norm 2
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_NORM_2, None)
mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
tensor_map["transformer.h."+str(i)+".ln_mlp"] = mapped_to # falcon40b
# Attention query-key-value
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_QKV, None)
mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
tensor_map["gpt_neox.layers."+str(i)+".attention.query_key_value"] = mapped_to # gptneox
tensor_map["transformer.h."+str(i)+".attn.c_attn"] = mapped_to # gpt2
tensor_map["transformer.blocks."+str(i)+".attn.Wqkv"] = mapped_to # mpt
tensor_map["transformer.h."+str(i)+".self_attention.query_key_value"] = mapped_to # falcon
# Attention query
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_Q, None)
mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
tensor_map["model.layers."+str(i)+".self_attn.q_proj"] = mapped_to # llama-hf
tensor_map["layers."+str(i)+".attention.wq"] = mapped_to # llama-pth
# Attention key
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_K, None)
mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
tensor_map["model.layers."+str(i)+".self_attn.k_proj"] = mapped_to # llama-hf
tensor_map["layers."+str(i)+".attention.wk"] = mapped_to # llama-pth
# Attention value
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_V, None)
mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
tensor_map["model.layers."+str(i)+".self_attn.v_proj"] = mapped_to # llama-hf
tensor_map["layers."+str(i)+".attention.wv"] = mapped_to # llama-pth
# Attention output
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_OUT, None)
mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
tensor_map["gpt_neox.layers."+str(i)+".attention.dense"] = mapped_to # gptneox
tensor_map["transformer.h."+str(i)+".attn.c_proj"] = mapped_to # gpt2
tensor_map["transformer.blocks."+str(i)+".attn.out_proj"] = mapped_to # mpt
tensor_map["transformer.h."+str(i)+".self_attention.dense"] = mapped_to # falcon
tensor_map["model.layers."+str(i)+".self_attn.o_proj"] = mapped_to # llama-hf
tensor_map["layers."+str(i)+".attention.wo"] = mapped_to # llama-pth
# Rotary embeddings
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_ROT_EMBD, None)
mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
tensor_map["model.layers."+str(i)+".self_attn.rotary_emb.inv_freq"] = mapped_to # llama-hf
tensor_map["layers."+str(i)+".attention.inner_attention.rope.freqs"] = mapped_to # llama-pth
# Feed-forward norm
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.FFN_NORM, None)
mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
tensor_map["gpt_neox.layers."+str(i)+".post_attention_layernorm"] = mapped_to # gptneox
tensor_map["transformer.h."+str(i)+".ln_2"] = mapped_to # gpt2
tensor_map["transformer.blocks."+str(i)+".norm_2"] = mapped_to # mpt
tensor_map["model.layers."+str(i)+".post_attention_layernorm"] = mapped_to # llama-hf
tensor_map["layers."+str(i)+".ffn_norm"] = mapped_to # llama-pth
# Feed-forward up
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.FFN_UP, None)
mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
tensor_map["gpt_neox.layers."+str(i)+".mlp.dense_h_to_4h"] = mapped_to # gptneox
tensor_map["transformer.h."+str(i)+".mlp.c_fc"] = mapped_to # gpt2
tensor_map["transformer.blocks."+str(i)+".ffn.up_proj"] = mapped_to # mpt
tensor_map["transformer.h."+str(i)+".mlp.dense_h_to_4h"] = mapped_to # falcon
tensor_map["model.layers."+str(i)+".mlp.up_proj"] = mapped_to # llama-hf
tensor_map["layers."+str(i)+".feed_forward.w3"] = mapped_to # llama-pth
# Feed-forward gate
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.FFN_GATE, None)
mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
tensor_map["model.layers."+str(i)+".mlp.gate_proj"] = mapped_to # llama-hf
tensor_map["layers."+str(i)+".feed_forward.w1"] = mapped_to # llama-pth
# Feed-forward down
mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.FFN_DOWN, None)
mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
tensor_map["gpt_neox.layers."+str(i)+".mlp.dense_4h_to_h"] = mapped_to # gptneox
tensor_map["transformer.h."+str(i)+".mlp.c_proj"] = mapped_to # gpt2
tensor_map["transformer.blocks."+str(i)+".ffn.down_proj"] = mapped_to # mpt
tensor_map["transformer.h."+str(i)+".mlp.dense_4h_to_h"] = mapped_to # falcon
tensor_map["model.layers."+str(i)+".mlp.down_proj"] = mapped_to # llama-hf
tensor_map["layers."+str(i)+".feed_forward.w2"] = mapped_to # llama-pth
return tensor_map
#
# implementation
#
class GGMLQuantizationType(IntEnum):
F32 = 0
F16 = 1
class GGUFValueType(IntEnum):
UINT8 = 0
INT8 = 1
UINT16 = 2
INT16 = 3
UINT32 = 4
INT32 = 5
FLOAT32 = 6
BOOL = 7
STRING = 8
ARRAY = 9
@staticmethod
def get_type(val):
if isinstance(val, str) or isinstance(val, bytes) or isinstance(val, bytearray):
return GGUFValueType.STRING
elif isinstance(val, list):
return GGUFValueType.ARRAY
elif isinstance(val, float):
return GGUFValueType.FLOAT32
elif isinstance(val, bool):
return GGUFValueType.BOOL
elif isinstance(val, int):
return GGUFValueType.INT32
else:
print("Unknown type: "+str(type(val)))
sys.exit()
class GGUFWriter:
def __init__(self, path: str, arch: str):
self.fout = open(path, "wb")
self.arch = arch
self.offset_tensor = 0
self.data_alignment = GGUF_DEFAULT_ALIGNMENT
self.kv_data = b""
self.kv_data_count = 0
self.ti_data = b""
self.ti_data_count = 0
self.add_architecture()
def write_header_to_file(self):
self.fout.write(struct.pack("<I", GGUF_MAGIC))
self.fout.write(struct.pack("<I", GGUF_VERSION))
self.fout.write(struct.pack("<I", self.ti_data_count))
self.fout.write(struct.pack("<I", self.kv_data_count))
self.flush()
# print("tensors " + str(self.ti_data_count) + " kv " + str(self.kv_data_count))
def write_kv_data_to_file(self):
self.fout.write(self.kv_data)
self.flush()
def write_ti_data_to_file(self):
self.fout.write(self.ti_data)
self.flush()
def add_key(self, key: str):
self.add_val(key, GGUFValueType.STRING, add_vtype=False)
def add_uint8(self, key: str, val: int):
self.add_key(key)
self.add_val(val, GGUFValueType.UINT8)
def add_int8(self, key: str, val: int):
self.add_key(key)
self.add_val(val, GGUFValueType.INT8)
def add_uint16(self, key: str, val: int):
self.add_key(key)
self.add_val(val, GGUFValueType.UINT16)
def add_int16(self, key: str, val: int):
self.add_key(key)
self.add_val(val, GGUFValueType.INT16)
def add_uint32(self, key: str, val: int):
self.add_key(key)
self.add_val(val, GGUFValueType.UINT32)
def add_int32(self, key: str, val: int):
self.add_key(key)
self.add_val(val, GGUFValueType.INT32)
def add_float32(self, key: str, val: float):
self.add_key(key)
self.add_val(val, GGUFValueType.FLOAT32)
def add_bool(self, key: str, val: bool):
self.add_key(key)
self.add_val(val, GGUFValueType.BOOL)
def add_string(self, key: str, val: str):
if len(val) == 0:
return
self.add_key(key)
self.add_val(val, GGUFValueType.STRING)
def add_array(self, key: str, val: list):
if not isinstance(val, list):
raise ValueError("Value must be a list for array type")
self.add_key(key)
self.add_val(val, GGUFValueType.ARRAY)
def add_val(self: str, val: Any, vtype: GGUFValueType = None, add_vtype: bool = True):
if vtype is None:
vtype = GGUFValueType.get_type(val)
if add_vtype:
self.kv_data += struct.pack("<I", vtype)
self.kv_data_count += 1
if vtype == GGUFValueType.UINT8:
self.kv_data += struct.pack("<B", val)
elif vtype == GGUFValueType.INT8:
self.kv_data += struct.pack("<b", val)
elif vtype == GGUFValueType.UINT16:
self.kv_data += struct.pack("<H", val)
elif vtype == GGUFValueType.INT16:
self.kv_data += struct.pack("<h", val)
elif vtype == GGUFValueType.UINT32:
self.kv_data += struct.pack("<I", val)
elif vtype == GGUFValueType.INT32:
self.kv_data += struct.pack("<i", val)
elif vtype == GGUFValueType.FLOAT32:
self.kv_data += struct.pack("<f", val)
elif vtype == GGUFValueType.BOOL:
self.kv_data += struct.pack("?", val)
elif vtype == GGUFValueType.STRING:
encoded_val = val.encode("utf8") if isinstance(val, str) else val
self.kv_data += struct.pack("<I", len(encoded_val))
self.kv_data += encoded_val
elif vtype == GGUFValueType.ARRAY:
ltype = set([GGUFValueType.get_type(item) for item in val])
assert len(ltype) == 1, "All items in a GGUF array should be of the same type"
self.kv_data += struct.pack("<I", list(ltype)[0])
self.kv_data += struct.pack("<I", len(val))
for item in val:
self.add_val(item, add_vtype=False)
else:
raise ValueError("Invalid GGUF metadata value type")
@staticmethod
def ggml_pad(x: int, n: int) -> int:
return ((x + n - 1) // n) * n
def add_tensor_info(self, name: str, tensor_shape: np.ndarray, tensor_dtype: np.dtype, tensor_nbytes: int):
assert tensor_dtype in (np.float32, np.float16), "Only F32 and F16 tensors are supported for now"
encoded_name = name.encode("utf8")
self.ti_data += struct.pack("<I", len(encoded_name))
self.ti_data += encoded_name
n_dims = len(tensor_shape)
self.ti_data += struct.pack("<I", n_dims)
for i in range(n_dims):
self.ti_data += struct.pack("<I", tensor_shape[n_dims - 1 - i])
dtype = GGMLQuantizationType.F32 if tensor_dtype == np.float32 else GGMLQuantizationType.F16
self.ti_data += struct.pack("<I", dtype)
self.ti_data += struct.pack("<Q", self.offset_tensor)
self.offset_tensor += GGUFWriter.ggml_pad(tensor_nbytes, self.data_alignment)
self.ti_data_count += 1
def add_tensor(self, name: str, tensor: np.ndarray):
if not hasattr(self, "temp_file"):
self.temp_file = tempfile.SpooledTemporaryFile(mode="w+b", max_size=256*1024*1024)
self.temp_file.seek(0)
self.add_tensor_info(name, tensor.shape, tensor.dtype, tensor.nbytes)
tensor.tofile(self.temp_file)
pad = GGUFWriter.ggml_pad(tensor.nbytes, self.data_alignment) - tensor.nbytes
if pad != 0:
self.temp_file.write(bytes([0] * pad))
def write_tensor_data(self, tensor: np.ndarray):
pad = GGUFWriter.ggml_pad(self.fout.tell(), self.data_alignment) - self.fout.tell()
if pad != 0:
self.fout.write(bytes([0] * pad))
tensor.tofile(self.fout)
pad = GGUFWriter.ggml_pad(tensor.nbytes, self.data_alignment) - tensor.nbytes
if pad != 0:
self.fout.write(bytes([0] * pad))
def write_tensors_to_file(self):
self.write_ti_data_to_file()
pad = GGUFWriter.ggml_pad(self.fout.tell(), self.data_alignment) - self.fout.tell()
if pad != 0:
self.fout.write(bytes([0] * pad))
self.temp_file.seek(0)
shutil.copyfileobj(self.temp_file, self.fout)
self.flush()
self.temp_file.close()
def flush(self):
self.fout.flush()
def close(self):
self.fout.close()
def add_architecture(self):
self.add_string(KEY_GENERAL_ARCHITECTURE, self.arch)
def add_author(self, author: str):
self.add_string(KEY_GENERAL_AUTHOR, author)
def add_tensor_data_layout(self, layout: str):
self.add_string(KEY_LLM_TENSOR_DATA_LAYOUT.format(arch=self.arch), layout)
def add_url(self, url: str):
self.add_string(KEY_GENERAL_URL, url)
def add_description(self, description: str):
self.add_string(KEY_GENERAL_DESCRIPTION, description)
def add_source_url(self, url: str):
self.add_string(KEY_GENERAL_SOURCE_URL, url)
def add_source_hf_repo(self, repo: str):
self.add_string(KEY_GENERAL_SOURCE_HF_REPO, repo)
def add_name(self, name: str):
self.add_string(KEY_GENERAL_NAME, name)
def add_quantization_version(self, quantization_version: GGMLQuantizationType):
self.add_uint32(
KEY_GENERAL_QUANTIZATION_VERSION, quantization_version)
def add_custom_alignment(self, alignment: int):
self.data_alignment = alignment
self.add_uint32(KEY_GENERAL_ALIGNMENT, alignment)
def add_context_length(self, length: int):
self.add_uint32(
KEY_LLM_CONTEXT_LENGTH.format(arch=self.arch), length)
def add_embedding_length(self, length: int):
self.add_uint32(
KEY_LLM_EMBEDDING_LENGTH.format(arch=self.arch), length)
def add_block_count(self, length: int):
self.add_uint32(
KEY_LLM_BLOCK_COUNT.format(arch=self.arch), length)
def add_feed_forward_length(self, length: int):
self.add_uint32(
KEY_LLM_FEED_FORWARD_LENGTH.format(arch=self.arch), length)
def add_parallel_residual(self, use: bool):
self.add_bool(
KEY_LLM_USE_PARALLEL_RESIDUAL.format(arch=self.arch), use)
def add_tensor_data_layout(self, layout: str):
self.add_string(
KEY_LLM_TENSOR_DATA_LAYOUT.format(arch=self.arch), layout)
def add_head_count(self, count: int):
self.add_uint32(
KEY_ATTENTION_HEAD_COUNT.format(arch=self.arch), count)
def add_head_count_kv(self, count: int):
self.add_uint32(
KEY_ATTENTION_HEAD_COUNT_KV.format(arch=self.arch), count)
def add_max_alibi_bias(self, bias: float):
self.add_float32(
KEY_ATTENTION_MAX_ALIBI_BIAS.format(arch=self.arch), bias)
def add_clamp_kqv(self, value: float):
self.add_float32(
KEY_ATTENTION_CLAMP_KQV.format(arch=self.arch), value)
def add_layer_norm_eps(self, value: float):
self.add_float32(
KEY_ATTENTION_LAYERNORM_EPS.format(arch=self.arch), value)
def add_layer_norm_rms_eps(self, value: float):
self.add_float32(
KEY_ATTENTION_LAYERNORM_RMS_EPS.format(arch=self.arch), value)
def add_rope_dimension_count(self, count: int):
self.add_uint32(
KEY_ROPE_DIMENSION_COUNT.format(arch=self.arch), count)
def add_rope_scale(self, value: float):
self.add_float32(KEY_ROPE_SCALE.format(arch=self.arch), value)
def add_tokenizer_model(self, model: str):
self.add_string(KEY_TOKENIZER_MODEL, model)
def add_token_list(self, tokens: List):
self.add_array(KEY_TOKENIZER_LIST, tokens)
def add_token_merges(self, merges: List):
self.add_array(KEY_TOKENIZER_MERGES, merges)
def add_token_types(self, types: List[int]):
self.add_array(KEY_TOKENIZER_TOKEN_TYPE, types)
def add_token_scores(self, scores: List[float]):
self.add_array(KEY_TOKENIZER_SCORES, scores)
def add_bos_token_id(self, id: int):
self.add_uint32(KEY_TOKENIZER_BOS_ID, id)
def add_eos_token_id(self, id: int):
self.add_uint32(KEY_TOKENIZER_EOS_ID, id)
def add_unk_token_id(self, id: int):
self.add_uint32(KEY_TOKENIZER_UNK_ID, id)
def add_sep_token_id(self, id: int):
self.add_uint32(KEY_TOKENIZER_SEP_ID, id)
def add_pad_token_id(self, id: int):
self.add_uint32(KEY_TOKENIZER_PAD_ID, id)
# Example usage:
if __name__ == "__main__":
# Example usage with a file
gguf_writer = GGUFWriter("example.gguf", "llama")
gguf_writer.add_architecture()
gguf_writer.add_block_count(12)
gguf_writer.add_uint32("answer", 42) # Write a 32-bit integer
gguf_writer.add_float32("answer_in_float", 42.0) # Write a 32-bit float
gguf_writer.add_custom_alignment(64)
tensor1 = np.ones((32,), dtype=np.float32) * 100.0
tensor2 = np.ones((64,), dtype=np.float32) * 101.0
tensor3 = np.ones((96,), dtype=np.float32) * 102.0
gguf_writer.add_tensor("tensor1", tensor1)
gguf_writer.add_tensor("tensor2", tensor2)
gguf_writer.add_tensor("tensor3", tensor3)
gguf_writer.write_header_to_file()
gguf_writer.write_kv_data_to_file()
gguf_writer.write_tensors_to_file()
gguf_writer.close()

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@ -1,553 +0,0 @@
// Internal header to be included only by llama.cpp.
// Contains wrappers around OS interfaces.
#ifndef LLAMA_UTIL_H
#define LLAMA_UTIL_H
#include <cstdio>
#include <cstdint>
#include <cerrno>
#include <cstring>
#include <cstdarg>
#include <cstdlib>
#include <climits>
#include <string>
#include <vector>
#include <stdexcept>
#ifdef __has_include
#if __has_include(<unistd.h>)
#include <unistd.h>
#if defined(_POSIX_MAPPED_FILES)
#include <sys/mman.h>
#endif
#if defined(_POSIX_MEMLOCK_RANGE)
#include <sys/resource.h>
#endif
#endif
#endif
#if defined(_WIN32)
#define WIN32_LEAN_AND_MEAN
#ifndef NOMINMAX
#define NOMINMAX
#endif
#include <windows.h>
#include <io.h>
#include <stdio.h> // for _fseeki64
#endif
#define LLAMA_ASSERT(x) \
do { \
if (!(x)) { \
fprintf(stderr, "LLAMA_ASSERT: %s:%d: %s\n", __FILE__, __LINE__, #x); \
abort(); \
} \
} while (0)
#ifdef __GNUC__
#ifdef __MINGW32__
__attribute__((format(gnu_printf, 1, 2)))
#else
__attribute__((format(printf, 1, 2)))
#endif
#endif
static std::string format(const char * fmt, ...) {
va_list ap, ap2;
va_start(ap, fmt);
va_copy(ap2, ap);
int size = vsnprintf(NULL, 0, fmt, ap);
LLAMA_ASSERT(size >= 0 && size < INT_MAX);
std::vector<char> buf(size + 1);
int size2 = vsnprintf(buf.data(), size + 1, fmt, ap2);
LLAMA_ASSERT(size2 == size);
va_end(ap2);
va_end(ap);
return std::string(buf.data(), size);
}
struct llama_file {
// use FILE * so we don't have to re-open the file to mmap
FILE * fp;
size_t size;
llama_file(const char * fname, const char * mode) {
fp = std::fopen(fname, mode);
if (fp == NULL) {
throw std::runtime_error(format("failed to open %s: %s", fname, strerror(errno)));
}
seek(0, SEEK_END);
size = tell();
seek(0, SEEK_SET);
}
size_t tell() const {
#ifdef _WIN32
__int64 ret = _ftelli64(fp);
#else
long ret = std::ftell(fp);
#endif
LLAMA_ASSERT(ret != -1); // this really shouldn't fail
return (size_t) ret;
}
void seek(size_t offset, int whence) {
#ifdef _WIN32
int ret = _fseeki64(fp, (__int64) offset, whence);
#else
int ret = std::fseek(fp, (long) offset, whence);
#endif
LLAMA_ASSERT(ret == 0); // same
}
void read_raw(void * ptr, size_t len) const {
if (len == 0) {
return;
}
errno = 0;
std::size_t ret = std::fread(ptr, len, 1, fp);
if (ferror(fp)) {
throw std::runtime_error(format("read error: %s", strerror(errno)));
}
if (ret != 1) {
throw std::runtime_error(std::string("unexpectedly reached end of file"));
}
}
std::uint32_t read_u32() {
std::uint32_t ret;
read_raw(&ret, sizeof(ret));
return ret;
}
std::string read_string(std::uint32_t len) {
std::vector<char> chars(len);
read_raw(chars.data(), len);
return std::string(chars.data(), len);
}
void write_raw(const void * ptr, size_t len) const {
if (len == 0) {
return;
}
errno = 0;
size_t ret = std::fwrite(ptr, len, 1, fp);
if (ret != 1) {
throw std::runtime_error(format("write error: %s", strerror(errno)));
}
}
void write_u32(std::uint32_t val) {
write_raw(&val, sizeof(val));
}
~llama_file() {
if (fp) {
std::fclose(fp);
}
}
};
// llama_context_data
struct llama_data_context {
virtual void write(const void * src, size_t size) = 0;
virtual size_t get_size_written() = 0;
virtual ~llama_data_context() = default;
};
struct llama_data_buffer_context : llama_data_context {
uint8_t* ptr;
size_t size_written = 0;
llama_data_buffer_context(uint8_t * p) : ptr(p) {}
void write(const void * src, size_t size) override {
memcpy(ptr, src, size);
ptr += size;
size_written += size;
}
size_t get_size_written() override {
return size_written;
}
};
struct llama_data_file_context : llama_data_context {
llama_file* file;
size_t size_written = 0;
llama_data_file_context(llama_file * f) : file(f) {}
void write(const void * src, size_t size) override {
file->write_raw(src, size);
size_written += size;
}
size_t get_size_written() override {
return size_written;
}
};
#if defined(_WIN32)
static std::string llama_format_win_err(DWORD err) {
LPSTR buf;
size_t size = FormatMessageA(FORMAT_MESSAGE_ALLOCATE_BUFFER | FORMAT_MESSAGE_FROM_SYSTEM | FORMAT_MESSAGE_IGNORE_INSERTS,
NULL, err, MAKELANGID(LANG_NEUTRAL, SUBLANG_DEFAULT), (LPSTR)&buf, 0, NULL);
if (!size) {
return "FormatMessageA failed";
}
std::string ret(buf, size);
LocalFree(buf);
return ret;
}
#endif
struct llama_mmap {
void * addr;
size_t size;
llama_mmap(const llama_mmap &) = delete;
#ifdef _POSIX_MAPPED_FILES
static constexpr bool SUPPORTED = true;
llama_mmap(struct llama_file * file, size_t prefetch = (size_t) -1 /* -1 = max value */, bool numa = false) {
size = file->size;
int fd = fileno(file->fp);
int flags = MAP_SHARED;
// prefetch/readahead impairs performance on NUMA systems
if (numa) { prefetch = 0; }
#ifdef __linux__
if (prefetch >= file->size) { flags |= MAP_POPULATE; }
#endif
addr = mmap(NULL, file->size, PROT_READ, flags, fd, 0);
if (addr == MAP_FAILED) {
throw std::runtime_error(format("mmap failed: %s", strerror(errno)));
}
if (prefetch > 0) {
// Advise the kernel to preload the mapped memory
if (madvise(addr, std::min(file->size, prefetch), MADV_WILLNEED)) {
fprintf(stderr, "warning: madvise(.., MADV_WILLNEED) failed: %s\n",
strerror(errno));
}
}
if (numa) {
// advise the kernel not to use readahead
// (because the next page might not belong on the same node)
if (madvise(addr, file->size, MADV_RANDOM)) {
fprintf(stderr, "warning: madvise(.., MADV_RANDOM) failed: %s\n",
strerror(errno));
}
}
}
~llama_mmap() {
munmap(addr, size);
}
#elif defined(_WIN32)
static constexpr bool SUPPORTED = true;
llama_mmap(struct llama_file * file, bool prefetch = true, bool numa = false) {
(void) numa;
size = file->size;
HANDLE hFile = (HANDLE) _get_osfhandle(_fileno(file->fp));
HANDLE hMapping = CreateFileMappingA(hFile, NULL, PAGE_READONLY, 0, 0, NULL);
DWORD error = GetLastError();
if (hMapping == NULL) {
throw std::runtime_error(format("CreateFileMappingA failed: %s", llama_format_win_err(error).c_str()));
}
addr = MapViewOfFile(hMapping, FILE_MAP_READ, 0, 0, 0);
error = GetLastError();
CloseHandle(hMapping);
if (addr == NULL) {
throw std::runtime_error(format("MapViewOfFile failed: %s", llama_format_win_err(error).c_str()));
}
if (prefetch) {
// The PrefetchVirtualMemory API is only present on Windows 8 and above, so we
// will dynamically load it using GetProcAddress.
BOOL (WINAPI *pPrefetchVirtualMemory) (HANDLE, ULONG_PTR, PWIN32_MEMORY_RANGE_ENTRY, ULONG);
HMODULE hKernel32;
// This call is guaranteed to succeed.
hKernel32 = GetModuleHandleW(L"kernel32.dll");
// This call may fail if on a pre-Win8 system.
pPrefetchVirtualMemory = reinterpret_cast<decltype(pPrefetchVirtualMemory)> (GetProcAddress(hKernel32, "PrefetchVirtualMemory"));
if (pPrefetchVirtualMemory) {
// Advise the kernel to preload the mapped memory.
WIN32_MEMORY_RANGE_ENTRY range;
range.VirtualAddress = addr;
range.NumberOfBytes = (SIZE_T)size;
if (!pPrefetchVirtualMemory(GetCurrentProcess(), 1, &range, 0)) {
fprintf(stderr, "warning: PrefetchVirtualMemory failed: %s\n",
llama_format_win_err(GetLastError()).c_str());
}
}
}
}
~llama_mmap() {
if (!UnmapViewOfFile(addr)) {
fprintf(stderr, "warning: UnmapViewOfFile failed: %s\n",
llama_format_win_err(GetLastError()).c_str());
}
}
#else
static constexpr bool SUPPORTED = false;
llama_mmap(struct llama_file *, bool prefetch = true, bool numa = false) {
(void) prefetch;
(void) numa;
throw std::runtime_error(std::string("mmap not supported"));
}
#endif
};
// Represents some region of memory being locked using mlock or VirtualLock;
// will automatically unlock on destruction.
struct llama_mlock {
void * addr = NULL;
size_t size = 0;
bool failed_already = false;
llama_mlock() {}
llama_mlock(const llama_mlock &) = delete;
~llama_mlock() {
if (size) {
raw_unlock(addr, size);
}
}
void init(void * ptr) {
LLAMA_ASSERT(addr == NULL && size == 0);
addr = ptr;
}
void grow_to(size_t target_size) {
LLAMA_ASSERT(addr);
if (failed_already) {
return;
}
size_t granularity = lock_granularity();
target_size = (target_size + granularity - 1) & ~(granularity - 1);
if (target_size > size) {
if (raw_lock((uint8_t *) addr + size, target_size - size)) {
size = target_size;
} else {
failed_already = true;
}
}
}
#ifdef _POSIX_MEMLOCK_RANGE
static constexpr bool SUPPORTED = true;
size_t lock_granularity() {
return (size_t) sysconf(_SC_PAGESIZE);
}
#ifdef __APPLE__
#define MLOCK_SUGGESTION \
"Try increasing the sysctl values 'vm.user_wire_limit' and 'vm.global_user_wire_limit' and/or " \
"decreasing 'vm.global_no_user_wire_amount'. Also try increasing RLIMIT_MLOCK (ulimit -l).\n"
#else
#define MLOCK_SUGGESTION \
"Try increasing RLIMIT_MLOCK ('ulimit -l' as root).\n"
#endif
bool raw_lock(const void * addr, size_t size) {
if (!mlock(addr, size)) {
return true;
} else {
char* errmsg = std::strerror(errno);
bool suggest = (errno == ENOMEM);
// Check if the resource limit is fine after all
struct rlimit lock_limit;
if (suggest && getrlimit(RLIMIT_MEMLOCK, &lock_limit))
suggest = false;
if (suggest && (lock_limit.rlim_max > lock_limit.rlim_cur + size))
suggest = false;
fprintf(stderr, "warning: failed to mlock %zu-byte buffer (after previously locking %zu bytes): %s\n%s",
size, this->size, errmsg, suggest ? MLOCK_SUGGESTION : "");
return false;
}
}
#undef MLOCK_SUGGESTION
void raw_unlock(void * addr, size_t size) {
if (munlock(addr, size)) {
fprintf(stderr, "warning: failed to munlock buffer: %s\n", std::strerror(errno));
}
}
#elif defined(_WIN32)
static constexpr bool SUPPORTED = true;
size_t lock_granularity() {
SYSTEM_INFO si;
GetSystemInfo(&si);
return (size_t) si.dwPageSize;
}
bool raw_lock(void * ptr, size_t len) {
for (int tries = 1; ; tries++) {
if (VirtualLock(ptr, len)) {
return true;
}
if (tries == 2) {
fprintf(stderr, "warning: failed to VirtualLock %zu-byte buffer (after previously locking %zu bytes): %s\n",
len, size, llama_format_win_err(GetLastError()).c_str());
return false;
}
// It failed but this was only the first try; increase the working
// set size and try again.
SIZE_T min_ws_size, max_ws_size;
if (!GetProcessWorkingSetSize(GetCurrentProcess(), &min_ws_size, &max_ws_size)) {
fprintf(stderr, "warning: GetProcessWorkingSetSize failed: %s\n",
llama_format_win_err(GetLastError()).c_str());
return false;
}
// Per MSDN: "The maximum number of pages that a process can lock
// is equal to the number of pages in its minimum working set minus
// a small overhead."
// Hopefully a megabyte is enough overhead:
size_t increment = len + 1048576;
// The minimum must be <= the maximum, so we need to increase both:
min_ws_size += increment;
max_ws_size += increment;
if (!SetProcessWorkingSetSize(GetCurrentProcess(), min_ws_size, max_ws_size)) {
fprintf(stderr, "warning: SetProcessWorkingSetSize failed: %s\n",
llama_format_win_err(GetLastError()).c_str());
return false;
}
}
}
void raw_unlock(void * ptr, size_t len) {
if (!VirtualUnlock(ptr, len)) {
fprintf(stderr, "warning: failed to VirtualUnlock buffer: %s\n",
llama_format_win_err(GetLastError()).c_str());
}
}
#else
static constexpr bool SUPPORTED = false;
size_t lock_granularity() {
return (size_t) 65536;
}
bool raw_lock(const void * addr, size_t len) {
fprintf(stderr, "warning: mlock not supported on this system\n");
return false;
}
void raw_unlock(const void * addr, size_t len) {}
#endif
};
// Replacement for std::vector<uint8_t> that doesn't require zero-initialization.
struct llama_buffer {
uint8_t * addr = NULL;
size_t size = 0;
llama_buffer() = default;
void resize(size_t len) {
#ifdef GGML_USE_METAL
free(addr);
int result = posix_memalign((void **) &addr, getpagesize(), len);
if (result == 0) {
memset(addr, 0, len);
}
else {
addr = NULL;
}
#else
delete[] addr;
addr = new uint8_t[len];
#endif
size = len;
}
~llama_buffer() {
#ifdef GGML_USE_METAL
free(addr);
#else
delete[] addr;
#endif
addr = NULL;
}
// disable copy and move
llama_buffer(const llama_buffer&) = delete;
llama_buffer(llama_buffer&&) = delete;
llama_buffer& operator=(const llama_buffer&) = delete;
llama_buffer& operator=(llama_buffer&&) = delete;
};
#ifdef GGML_USE_CUBLAS
#include "ggml-cuda.h"
struct llama_ctx_buffer {
uint8_t * addr = NULL;
bool is_cuda;
size_t size = 0;
llama_ctx_buffer() = default;
void resize(size_t size) {
free();
addr = (uint8_t *) ggml_cuda_host_malloc(size);
if (addr) {
is_cuda = true;
}
else {
// fall back to pageable memory
addr = new uint8_t[size];
is_cuda = false;
}
this->size = size;
}
void free() {
if (addr) {
if (is_cuda) {
ggml_cuda_host_free(addr);
}
else {
delete[] addr;
}
}
addr = NULL;
}
~llama_ctx_buffer() {
free();
}
// disable copy and move
llama_ctx_buffer(const llama_ctx_buffer&) = delete;
llama_ctx_buffer(llama_ctx_buffer&&) = delete;
llama_ctx_buffer& operator=(const llama_ctx_buffer&) = delete;
llama_ctx_buffer& operator=(llama_ctx_buffer&&) = delete;
};
#else
typedef llama_buffer llama_ctx_buffer;
#endif
#endif

2835
llama.cpp

File diff suppressed because it is too large Load diff

121
llama.h
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@ -34,29 +34,18 @@
# define DEPRECATED(func, hint) func
#endif
#define LLAMA_FILE_MAGIC_GGJT 0x67676a74u // 'ggjt'
#define LLAMA_FILE_MAGIC_GGLA 0x67676c61u // 'ggla'
#define LLAMA_FILE_MAGIC_GGMF 0x67676d66u // 'ggmf'
#define LLAMA_FILE_MAGIC_GGML 0x67676d6cu // 'ggml'
#define LLAMA_FILE_MAGIC_GGSN 0x6767736eu // 'ggsn'
#define LLAMA_DEFAULT_SEED 0xFFFFFFFF
#define LLAMA_FILE_VERSION 3
#define LLAMA_FILE_MAGIC LLAMA_FILE_MAGIC_GGJT
#define LLAMA_FILE_MAGIC_UNVERSIONED LLAMA_FILE_MAGIC_GGML
#define LLAMA_SESSION_MAGIC LLAMA_FILE_MAGIC_GGSN
#define LLAMA_SESSION_VERSION 1
#define LLAMA_FILE_MAGIC_GGSN 0x6767736eu // 'ggsn'
#define LLAMA_DEFAULT_SEED 0xFFFFFFFF
#define LLAMA_SESSION_MAGIC LLAMA_FILE_MAGIC_GGSN
#define LLAMA_SESSION_VERSION 1
#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST) || defined(GGML_USE_METAL)
// Defined when llama.cpp is compiled with support for offloading model layers to GPU.
#define LLAMA_SUPPORTS_GPU_OFFLOAD
#endif
#ifndef LLAMA_DEFAULT_RMS_EPS
#define LLAMA_DEFAULT_RMS_EPS 5e-6f
#endif
#ifdef __cplusplus
extern "C" {
#endif
@ -103,8 +92,6 @@ extern "C" {
uint32_t seed; // RNG seed, -1 for random
int32_t n_ctx; // text context
int32_t n_batch; // prompt processing batch size
int32_t n_gqa; // grouped-query attention (TEMP - will be moved to model hparams)
float rms_norm_eps; // rms norm epsilon (TEMP - will be moved to model hparams)
int32_t n_gpu_layers; // number of layers to store in VRAM
int32_t main_gpu; // the GPU that is used for scratch and small tensors
@ -129,6 +116,7 @@ extern "C" {
bool use_mlock; // force system to keep model in RAM
bool embedding; // embedding mode only
};
// model file types
enum llama_ftype {
LLAMA_FTYPE_ALL_F32 = 0,
@ -155,7 +143,7 @@ extern "C" {
// model quantization parameters
typedef struct llama_model_quantize_params {
int nthread; // number of threads to use for quantizing, if <=0 will use std::thread::hardware_concurrency()
enum llama_ftype ftype; // quantize to this llama_ftype
enum llama_ftype ftype; // quantize to this llama_ftype
bool allow_requantize; // allow quantizing non-f32/f16 tensors
bool quantize_output_tensor; // quantize output.weight
} llama_model_quantize_params;
@ -208,17 +196,12 @@ extern "C" {
int32_t n_eval;
};
// Set callback for all future logging events.
// If this is not called, or NULL is supplied, everything is output on stderr.
LLAMA_API void llama_log_set(llama_log_callback log_callback, void * user_data);
LLAMA_API struct llama_context_params llama_context_default_params(void);
LLAMA_API struct llama_model_quantize_params llama_model_quantize_default_params(void);
LLAMA_API int llama_max_devices();
LLAMA_API struct llama_context_params llama_context_default_params();
LLAMA_API struct llama_model_quantize_params llama_model_quantize_default_params();
LLAMA_API bool llama_mmap_supported();
LLAMA_API bool llama_mlock_supported();
LLAMA_API int llama_max_devices(void);
LLAMA_API bool llama_mmap_supported(void);
LLAMA_API bool llama_mlock_supported(void);
// TODO: not great API - very likely to change
// Initialize the llama + ggml backend
@ -226,9 +209,9 @@ extern "C" {
// Call once at the start of the program
LLAMA_API void llama_backend_init(bool numa);
// Call once at the end of the program - currently only used for MPI
LLAMA_API void llama_backend_free();
LLAMA_API void llama_backend_free(void);
LLAMA_API int64_t llama_time_us();
LLAMA_API int64_t llama_time_us(void);
LLAMA_API struct llama_model * llama_load_model_from_file(
const char * path_model,
@ -240,13 +223,6 @@ extern "C" {
struct llama_model * model,
struct llama_context_params params);
// Various functions for loading a ggml llama model.
// Allocate (almost) all memory needed for the model.
// Return NULL on failure
LLAMA_API DEPRECATED(struct llama_context * llama_init_from_file(
const char * path_model,
struct llama_context_params params),
"please use llama_load_model_from_file combined with llama_new_context_with_model instead");
// Frees all allocated memory
LLAMA_API void llama_free(struct llama_context * ctx);
@ -336,6 +312,13 @@ extern "C" {
int n_max_tokens,
bool add_bos);
LLAMA_API int llama_tokenize_bpe(
struct llama_context * ctx,
const char * text,
llama_token * tokens,
int n_max_tokens,
bool add_bos);
LLAMA_API int llama_tokenize_with_model(
const struct llama_model * model,
const char * text,
@ -377,18 +360,28 @@ extern "C" {
LLAMA_API float * llama_get_embeddings(struct llama_context * ctx);
// Token Id -> String. Uses the vocabulary in the provided context
LLAMA_API const char * llama_token_to_str(
// Does not write null terminator to the buffer
LLAMA_API int llama_token_to_str(
const struct llama_context * ctx,
llama_token token);
llama_token token,
char * buf,
int length);
LLAMA_API const char * llama_token_to_str_with_model(
LLAMA_API int llama_token_to_str_bpe(
const struct llama_context * ctx,
llama_token token,
char * buf,
int length);
LLAMA_API int llama_token_to_str_with_model(
const struct llama_model * model,
llama_token token);
llama_token token,
char * buf,
int length);
// Special tokens
LLAMA_API llama_token llama_token_bos(); // beginning-of-sentence
LLAMA_API llama_token llama_token_eos(); // end-of-sentence
LLAMA_API llama_token llama_token_nl(); // next-line
LLAMA_API llama_token llama_token_bos(void); // beginning-of-sentence
LLAMA_API llama_token llama_token_eos(void); // end-of-sentence
LLAMA_API llama_token llama_token_nl(void); // next-line
// Grammar
//
@ -468,19 +461,51 @@ extern "C" {
// Print system information
LLAMA_API const char * llama_print_system_info(void);
// Set callback for all future logging events.
// If this is not called, or NULL is supplied, everything is output on stderr.
LLAMA_API void llama_log_set(llama_log_callback log_callback, void * user_data);
#ifdef __cplusplus
}
#endif
// Internal API to be implemented by llama.cpp and used by tests/benchmarks only
#ifdef LLAMA_API_INTERNAL
// C++ API, will be moving to common.h soon (TM)
#ifdef LLAMA_API_CPP
#include <vector>
#include <string>
//
// Vocab utils
//
std::vector<llama_token> llama_tokenize(
struct llama_context * ctx,
const std::string & text,
bool add_bos);
std::vector<llama_token> llama_tokenize_bpe(
struct llama_context * ctx,
const std::string & text,
bool add_bos);
std::string llama_token_to_str(
const struct llama_context * ctx,
llama_token token);
std::string llama_token_to_str_bpe(
const struct llama_context * ctx,
llama_token token);
// Internal API to be implemented by llama.cpp and used by tests/benchmarks only
#ifdef LLAMA_API_INTERNAL
struct ggml_tensor;
const std::vector<std::pair<std::string, struct ggml_tensor *>>& llama_internal_get_tensor_map(struct llama_context * ctx);
#endif
#endif // LLAMA_API_CPP
#endif // LLAMA_API_INTERNAL
#endif // LLAMA_H

1
models/.editorconfig Normal file
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@ -0,0 +1 @@
root = true

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@ -1,4 +1,19 @@
function(llama_add_test source)
function(llama_build_executable source)
get_filename_component(TEST_TARGET ${source} NAME_WE)
add_executable(${TEST_TARGET} ${source})
install(TARGETS ${TEST_TARGET} RUNTIME)
target_link_libraries(${TEST_TARGET} PRIVATE llama)
endfunction()
function(llama_test_executable name source)
get_filename_component(TEST_TARGET ${source} NAME_WE)
# add_executable(${TEST_TARGET} ${source})
# install(TARGETS ${TEST_TARGET} RUNTIME)
# target_link_libraries(${TEST_TARGET} PRIVATE llama)
add_test(NAME ${name} COMMAND $<TARGET_FILE:${TEST_TARGET}> ${ARGN})
endfunction()
function(llama_build_and_test_executable source)
get_filename_component(TEST_TARGET ${source} NAME_WE)
add_executable(${TEST_TARGET} ${source})
install(TARGETS ${TEST_TARGET} RUNTIME)
@ -6,12 +21,16 @@ function(llama_add_test source)
add_test(NAME ${TEST_TARGET} COMMAND $<TARGET_FILE:${TEST_TARGET}> ${ARGN})
endfunction()
# llama_add_test(test-double-float.cpp) # SLOW
llama_add_test(test-quantize-fns.cpp)
llama_add_test(test-quantize-perf.cpp)
llama_add_test(test-sampling.cpp)
llama_add_test(test-tokenizer-0.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab.bin)
llama_add_test(test-grammar-parser.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../examples/grammar-parser.cpp)
llama_add_test(test-llama-grammar.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../examples/grammar-parser.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../llama.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../examples/common.cpp)
llama_add_test(test-grad0.cpp) # SLOW
# llama_add_test(test-opt.cpp) # SLOW
# llama_build_and_test_executable(test-double-float.cpp) # SLOW
llama_build_and_test_executable(test-quantize-fns.cpp)
llama_build_and_test_executable(test-quantize-perf.cpp)
llama_build_and_test_executable(test-sampling.cpp)
llama_build_executable(test-tokenizer-0.cpp)
llama_test_executable(test-tokenizer-0.llama test-tokenizer-0.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama.gguf)
llama_build_executable(test-tokenizer-1.cpp)
llama_test_executable(test-tokenizer-1.llama test-tokenizer-1.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama.gguf)
#llama_test_executable(test-tokenizer-1.aquila test-tokenizer-1.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-aquila.gguf)
llama_build_and_test_executable(test-grammar-parser.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../examples/grammar-parser.cpp)
llama_build_and_test_executable(test-llama-grammar.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../examples/grammar-parser.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../llama.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../examples/common.cpp)
llama_build_and_test_executable(test-grad0.cpp) # SLOW
# llama_build_and_test_executable(test-opt.cpp) # SLOW

View file

@ -1,3 +1,4 @@
#define LLAMA_API_CPP // TODO: eliminate me
#include "llama.h"
#include <cstdio>
@ -5,16 +6,40 @@
#include <map>
#include <vector>
static const std::map<std::string, std::vector<llama_token>> & k_tests()
{
static std::string unescape_whitespace(llama_context* ctx, const std::vector<llama_token>& tokens) {
std::string result;
for (size_t i = 0; i < tokens.size(); ++i) {
result += llama_token_to_str(ctx, tokens[i]);
}
return result;
}
static const std::map<std::string, std::vector<llama_token>> & k_tests() {
static std::map<std::string, std::vector<llama_token>> _k_tests = {
{ "Hello World", { 1, 10994, 2787, }, },
{ " Hello World", { 1, 15043, 2787, }, },
{ " Hello World!", { 1, 15043, 2787, 29991, }, },
{ " this is 🦙.cpp", { 1, 445, 338, 29871, 243, 162, 169, 156, 29889, 8223, }, },
{ "w048 7tuijk dsdfhu", { 1, 29893, 29900, 29946, 29947, 29871, 29955, 9161, 13535, 18031, 2176, 6905, }, },
{ "нещо на Български", { 1, 821, 4851, 665, 1386, 29713, 1305, }, },
{ " ", {1, 259, }, },
{ "\t", { 1, 29871, 12, }, },
{ "\n", { 1, 29871, 13, }, },
{ "\t\n", { 1, 29871, 12, 13, }, },
{ "Hello world", { 1, 15043, 3186, }, },
{ " Hello world", { 1, 29871, 15043, 3186, }, },
{ "Hello World", { 1, 15043, 2787, }, },
{ " Hello World", { 1, 29871, 15043, 2787, }, },
{ " Hello World!", { 1, 29871, 15043, 2787, 29991, }, },
{ " this is 🦙.cpp", { 1, 29871, 445, 338, 29871, 243, 162, 169, 156, 29889, 8223, }, },
{ "w048 7tuijk dsdfhu", { 1, 281, 29900, 29946, 29947, 29871, 29955, 9161, 13535, 18031, 2176, 6905, }, },
{ "нещо на Български", { 1, 1538, 4851, 665, 1386, 29713, 1305, }, },
{ "កាន់តែពិសេសអាចខលចេញ", { 1, 29871, 31849, 31324, 31934, 228, 162, 142, 228, 161,
146, 228, 162, 133, 228, 161, 153, 228, 161, 186,
31708, 228, 162, 132, 31708, 228, 161, 165, 31324, 228,
161, 136, 228, 161, 132, 228, 161, 158, 228, 161,
136, 228, 162, 132, 228, 161, 140, }, },
{ "🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)",
{ 1, 29871, 243, 162, 157, 131, 313, 8945, 29897, 29871,
243, 162, 155, 185, 30722, 243, 162, 143, 174, 30598,
313, 20787, 953, 3848, 275, 16125, 630, 29897, 29871, 31681,
313, 6194, 953, 29877, 2397, 393, 756, 967, 1914, 5993, 29897, }, },
};
return _k_tests;
};
@ -64,10 +89,12 @@ int main(int argc, char **argv) {
return 2;
}
bool success = true;
for (const auto & test_kv : k_tests()) {
std::vector<llama_token> res(test_kv.first.size());
const int n = llama_tokenize(ctx, test_kv.first.c_str(), res.data(), int(res.size()), true);
res.resize(n);
std::vector<llama_token> res = llama_tokenize(ctx, test_kv.first, true);
fprintf(stderr, "%s : '%s' tokenized to '%s'\n",
__func__, test_kv.first.c_str(), unescape_whitespace(ctx, res).c_str());
bool correct = res.size() == test_kv.second.size();
@ -78,7 +105,8 @@ int main(int argc, char **argv) {
}
if (!correct) {
fprintf(stderr, "%s : failed test: '%s'\n", __func__, test_kv.first.c_str());
fprintf(stderr, "%s : failed test: '%s'\n", __func__, test_kv.first.c_str());
fprintf(stderr, "%s : detokenized to: '%s'\n", __func__, unescape_whitespace(ctx, test_kv.second).c_str());
fprintf(stderr, "%s : expected tokens: ", __func__);
for (const auto & t : test_kv.second) {
fprintf(stderr, "%6d, ", t);
@ -90,9 +118,7 @@ int main(int argc, char **argv) {
}
fprintf(stderr, "\n");
llama_free_model(model);
llama_free(ctx);
return 3;
success = false;
}
}
@ -101,5 +127,5 @@ int main(int argc, char **argv) {
llama_backend_free();
return 0;
return success ? 0 : 3;
}

128
tests/test-tokenizer-1.cpp Normal file
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@ -0,0 +1,128 @@
#define LLAMA_API_CPP // TODO: eliminate me
#include "llama.h"
#include <cassert>
#include <cstdio>
#include <cstring>
#include <string>
#include <codecvt>
#include <map>
#include <vector>
#include <locale>
static std::string vocab_type(llama_context * ctx) {
return llama_n_vocab(ctx) == 32000 ? "spm": "bpe";
}
static std::string escape_whitespace(const std::string& text) {
std::string result;
bool escaping = false;
result += "\xe2\x96\x81";
for (size_t offs = 0; offs < text.length(); ++offs) {
if (text[offs] == ' ') {
if (!escaping) {
result += "\xe2\x96\x81";
escaping = true;
}
}
else {
escaping = false;
result += text[offs];
}
}
return result;
}
static std::string unescape_whitespace(llama_context * ctx, const std::vector<llama_token> & tokens) {
std::string result;
for (size_t i = 0; i < tokens.size(); ++i) {
result += llama_token_to_str(ctx, tokens[i]);
}
return result;
}
int main(int argc, char **argv) {
if (argc < 2) {
fprintf(stderr, "Usage: %s <vocab-file>\n", argv[0]);
return 1;
}
const std::string fname = argv[1];
fprintf(stderr, "%s : reading vocab from: '%s'\n", __func__, fname.c_str());
llama_model * model;
llama_context * ctx;
llama_backend_init(false);
// load the vocab
{
auto lparams = llama_context_default_params();
lparams.vocab_only = true;
model = llama_load_model_from_file(fname.c_str(), lparams);
if (model == NULL) {
fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str());
return 1;
}
ctx = llama_new_context_with_model(model, lparams);
if (ctx == NULL) {
fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str());
llama_free_model(model);
return 1;
}
}
const int n_vocab = llama_n_vocab(ctx);
for (int i = 0; i < n_vocab; ++i) {
std::string forward = llama_token_to_str_bpe(ctx, i);
std::vector<llama_token> tokens = llama_tokenize_bpe(ctx, forward, false);
if (tokens.size() == 1) {
if (i != tokens[0]) {
std::string backward = llama_token_to_str(ctx, tokens[0]);
fprintf(stderr, "%s : error: token %d is string %s but bpe returns token %d %s\n",
__func__, i, llama_token_to_str(ctx, i).c_str(), tokens[0], backward.c_str());
return 2;
}
} else {
if ((vocab_type(ctx) == "spm" && i <= 258) ||
(vocab_type(ctx) == "bpe" && (i == 0 || i >= 100000))) {
fprintf(stderr, "%s : info: token %d is string %s and bpe returns tokens %s\n",
__func__, i, llama_token_to_str(ctx, i).c_str(), unescape_whitespace(ctx, tokens).c_str());
} else {
fprintf(stderr, "%s : error: token %d is string %s but bpe returns tokens %s\n",
__func__, i, llama_token_to_str(ctx, i).c_str(), unescape_whitespace(ctx, tokens).c_str());
return 2;
}
}
}
std::wstring_convert<typename std::codecvt_utf8<wchar_t>, wchar_t> converter;
for (wchar_t ch = 0x0000; ch < 0xffff; ++ch) {
std::wstring wstr(1, ch);
std::string str;
try {
str = converter.to_bytes(wstr);
} catch (std::exception & e) {
continue;
}
std::vector<llama_token> tokens = llama_tokenize(ctx, escape_whitespace(str), false);
if (tokens.size() == 1) {
fprintf(stderr, "%s : info: %s tokenized to %d \n",
__func__, str.c_str(), tokens[0]);
}
}
llama_free_model(model);
llama_free(ctx);
llama_backend_free();
return 0;
}