Commit Graph

61 Commits

Author SHA1 Message Date
Georgi Gerganov
289313716f metal : add kernel_get_rows_i32
ggml-ci
2024-01-03 14:38:38 +02:00
Georgi Gerganov
f3f62f0d83
metal : optimize ggml_mul_mat_id (faster Mixtral PP) (#4725)
* ggml : disable fast-math for Metal (cmake build only)

ggml-ci

* metal : fix Metal API debug warnings

* cmake : add -fno-inline for Metal build (#4545)

* metal : fix API debug warnings

* metal : fix compile warnings

* metal : use uint64_t for strides

* cmake : rename option to LLAMA_METAL_SHADER_DEBUG

* metal : fix mat-vec Q8_0 kernel for BS > 1

* metal : normalize mat-vec kernel signatures

* cmake : respect LLAMA_QKK_64 option

* metal : fix mat-vec Q4_K kernel for QK_K == 64

* metal : optimizing ggml_mul_mat_id (wip)

* metal : minor fix

* metal : opt mul_mm_id
2024-01-02 21:07:47 +02:00
Georgi Gerganov
58ba655af0
metal : enable shader debugging (cmake option) (#4705)
* ggml : disable fast-math for Metal (cmake build only)

ggml-ci

* metal : fix Metal API debug warnings

* cmake : add -fno-inline for Metal build (#4545)

* metal : fix API debug warnings

* metal : fix compile warnings

* metal : use uint64_t for strides

* cmake : rename option to LLAMA_METAL_SHADER_DEBUG

* metal : fix mat-vec Q8_0 kernel for BS > 1

* metal : normalize mat-vec kernel signatures

* cmake : respect LLAMA_QKK_64 option

* metal : fix mat-vec Q4_K kernel for QK_K == 64

ggml-ci
2024-01-02 10:57:44 +02:00
Ebey Abraham
b9e74f9bca
llama : add phi-2 + fix NeoX rope + ggml_mul_mat_set_prec (#4490)
* phi2 implementation

* fix breaking change

* phi-2 : various fixes

* phi-2 : use layer norm eps

* py : whitespaces

* llama : fix meta KV override bug

* convert : phi don't add BOS token

* convert : revert "added_tokens_decoder" change

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

* ggml : fix NeoX rope to rotate just first n_dims

* cuda : less diff in the rope_neox kernel

* ggml : add ggml_mul_mat_set_prec

ggml-ci

* Update ggml-cuda.cu

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

* Update ggml-cuda.cu

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

* cuda : ggml_cuda_op_mul_mat_cublas support F32 precision

* cuda : remove oboslete comment

---------

Co-authored-by: Ebey Abraham <ebeyabraham@microsoft.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: slaren <slarengh@gmail.com>
2023-12-18 19:27:47 +02:00
Georgi Gerganov
4d98d9a656
sync : ggml (SD ops, tests, kernels) (#4444)
* sync : ggml (SD ops, tests, kernels)

ggml-ci

* cuda : restore im2col

ggml-ci

* metal : fix accuracy of dequantization kernels

ggml-ci

* cuda : restore correct im2col

ggml-ci

* metal : try to fix moe test by reducing expert size

ggml-ci

* cuda : fix bin bcast when src1 and dst have different types

ggml-ci

---------

Co-authored-by: slaren <slarengh@gmail.com>
2023-12-13 21:54:54 +02:00
slaren
799a1cb13b
llama : add Mixtral support (#4406)
* convert : support Mixtral as LLAMA arch

* convert : fix n_ff typo

* llama : model loading

* ggml : sync latest ggml_mul_mat_id

* llama : update graph to support MoE

* llama : fix cur -> cur_expert

* llama : first working version

* llama : fix expert weighting in the FFN

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

* ggml : add n_as argument to ggml_mul_mat_id

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

* metal : add more general support for ggml_get_rows + tests

* llama : add basic support for offloading moe with CUDA

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

* metal : reduce the kernel launches for ggml_mul_mat_id

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

* ggml : update get_rows f16 and q

* cuda : support non-contiguous src1 in get_rows

* llama : offload missing ffn_moe_silu

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

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

* llama : do not quantize expert gating tensors

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

* test-backend-ops : add moe test

* cuda : fix get_rows when ncols is odd

* convert : determine n_ctx correctly

* metal : fix ggml_mul_mat_id for F32

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

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

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

* test-backend-ops : fix dequantize block offset

* llama : fix hard-coded number of experts

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

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

* cuda : fix mul_mat_id with multi gpu

* convert : use 1e6 rope_freq_base for mixtral

* convert : fix style

* convert : support safetensors format

* gguf-py : bump version

* metal : add cpy f16 -> f32 kernel

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

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

* ggml : do not use BLAS with ggml_mul_mat_id

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

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

* convert : use sentencepiece tokenizer for Mixtral-instruct

* convert : make flake8 happy

* metal : fix soft_max kernels

ref: 1914017863

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

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: Radek Pilar <github@mrkva.eu>
2023-12-13 14:04:25 +02:00
Georgi Gerganov
fe680e3d10
sync : ggml (new ops, tests, backend, etc.) (#4359)
* sync : ggml (part 1)

* sync : ggml (part 2, CUDA)

* sync : ggml (part 3, Metal)

* ggml : build fixes

ggml-ci

* cuda : restore lost changes

* cuda : restore lost changes (StableLM rope)

* cmake : enable separable compilation for CUDA

ggml-ci

* ggml-cuda : remove device side dequantize

* Revert "cmake : enable separable compilation for CUDA"

This reverts commit 09e35d04b1.

* cuda : remove assert for rope

* tests : add test-backend-ops

* ggml : fix bug in ggml_concat

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

* ci : try to fix macOS

* ggml-backend : remove backend self-registration

* ci : disable Metal for macOS cmake build

ggml-ci

* metal : fix "supports family" call

* metal : fix assert

* metal : print resource path

ggml-ci

---------

Co-authored-by: slaren <slarengh@gmail.com>
2023-12-07 22:26:54 +02:00
Georgi Gerganov
bcc0eb4591
llama : per-layer KV cache + quantum K cache (#4309)
* per-layer KV

* remove unnecessary copies

* less code duplication, offload k and v separately

* llama : offload KV cache per-layer

* llama : offload K shift tensors

* llama : offload for rest of the model arches

* llama : enable offload debug temporarily

* llama : keep the KV related layers on the device

* llama : remove mirrors, perform Device -> Host when partial offload

* common : add command-line arg to disable KV cache offloading

* llama : update session save/load

* llama : support quantum K cache (#4312)

* llama : support quantum K cache (wip)

* metal : add F32 -> Q8_0 copy kernel

* cuda : add F32 -> Q8_0 copy kernel

ggml-ci

* cuda : use mmv kernel for quantum cache ops

* llama : pass KV cache type through API

* llama : fix build

ggml-ci

* metal : add F32 -> Q4_0 copy kernel

* metal : add F32 -> Q4_1 copy kernel

* cuda : wip

* cuda : add F32 -> Q4_0 and F32 -> Q4_1 copy kernels

* llama-bench : support type_k/type_v

* metal : use mm kernel only for quantum KV cache

* cuda : add comment

* llama : remove memory_f16 and kv_f16 flags

---------

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

* readme : add API change notice

---------

Co-authored-by: slaren <slarengh@gmail.com>
2023-12-07 13:03:17 +02:00
Georgi Gerganov
ef47ec18da
ggml : add ggml_soft_max_ext (#4256)
* metal : implement soft_max_ext

* cuda : implement soft_max_ext

* ggml : implement soft_max_ext (CPU)

* batched-bench : print threads

ggml-ci

* metal : simplify soft_max encoding

ggml-ci

* cuda : use 512 threads for soft_max instead of 32

* ggml : update soft max cpu

* cuda : do warp-based block reduce

* cuda : increase max block size to 1024

* cuda : fix warp reduction initialization of shared mem

* metal : warp-based reduction for soft max kernel

* metal : warp-based reduce for rms_norm

* metal : simplify soft max kernel

ggml-ci

* alloc : fix build with debug
2023-12-01 10:51:24 +02:00
Georgi Gerganov
3d68f364f1
ggml : sync (im2col, GPU conv, 32-bit arm compat) (#4060)
ggml-ci
2023-11-13 16:55:52 +02:00
Georgi Gerganov
183b3fac6c
metal : fix build errors and kernel sig after #2268 (#3898) 2023-11-02 08:33:37 +02:00
cebtenzzre
898aeca90a
llama : implement YaRN RoPE scaling (#2268)
Co-authored-by: cebtenzzre <cebtenzzre@gmail.com>
Co-authored-by: Jeffrey Quesnelle <jquesnelle@gmail.com>
2023-11-01 18:04:33 -04:00
Georgi Gerganov
e16b9fa4ba
metal : multi-simd softmax (#3710)
ggml-ci
2023-11-01 21:25:00 +02:00
Georgi Gerganov
469c9addef
metal : handle ggml_scale for n%4 != 0 (close #3754)
ggml-ci
2023-10-24 09:47:22 +03:00
Jhen-Jie Hong
c67fe68e41
metal : implement q5_0 and q5_1 kernels (#3648)
* metal : implement dequantize_q5_0

* metal : block_q_n_dot_y for block_q5_0 (broken)

* metal : revert unnecessary change

* metal : implement dequantize_q5_1

* metal : block_q_n_dot_y for q5_1 (broken)

* metal : fix block_q_n_dot_y

* minor : spaces / formatting

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-10-18 15:21:48 +03:00
Georgi Gerganov
fcca0a7004
refact : fix convert script + zero out KV cache to avoid nans (#3523)
* refact : fix convert script + zero out KV cache to avoid nans

* ggml : silu(-inf) should never happen

* metal : assert various kernel requirements
2023-10-09 14:32:17 +03:00
Georgi Gerganov
b0ec5218c3
metal : support MTLGPUFamily < Apple7, formatting, style (#3524)
* metal : improve decoding speed for batches of 2-16

* metal : rename kernels mul_mat_ to mul_mv_

* metal : indentations

* minor

* metal : print more GPU info + disable mul_mm for MTLGPUFamiliy < Apple7
2023-10-08 10:01:53 +03:00
Phillip Kravtsov
0e797c2fc5
llm : support Adept Persimmon 8B (#3410)
* Produces garbage output

* wip: correct tensors up to RoPE

* correct tensors thru RoPE

* Correct outputs through masked & softmax'd KQ

* fp32 works

* Rename adept->persimmon

* Produces correct outputs

* clean up convert scripts

* remove printing logic from ggml.c

* remove prints from llama.cpp & fix merge

* trivial cleanups

* Add offload funcs

* update conversion script to directly take adept artifacts rather than .saftensors file

* Fix norm eps bug

* Support sqr and concat on metal, persimmon-8b-q4 runs correctly

* Small changes from review

* Formatting changes

* Minor changes to conversion script

* Remove old script

* Fix editorconfig formatting

* Fix build

* add overlooked offload code ggml-ci
2023-10-07 10:12:43 +03:00
Jiahao Li
f56e1baec3
metal : alibi for arbitrary number of heads (#3426) 2023-10-03 19:55:21 +03:00
Georgi Gerganov
ec893798b7
llama : custom attention mask + parallel decoding + no context swaps (#3228)
* tests : verify that RoPE is "additive"

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

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

* metal : add rope_f16 kernel + optimize cpy kernels

* llama : unified KV cache + batch inference API

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

* llama : add cell_max heuristic for more efficient kv_cache

* llama : extend llama_kv_cache API

* llama : more robust cell_max heuristic + wip shift

* metal : disable concurrency optimization

* llama : add llama_kv_cache_shift_seq + no more context swaps

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

* speculative : fix KV cache management

* parallel : example for serving multiple users in parallel

* parallel : disable hot-plug to avoid cache fragmentation

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

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

* llama : fix worst case graph build

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

* ggml-cuda : update rope implementation for parallel decoding

* better solution for p0 computation

* fix rope

* simpler rope implementation

---------

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

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

* simple : fix token counting

* parallel : various improvements

* llama : fix cell_max logic + rename functions

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

* parallel : fix sequence termination criteria

* llama : silence errors KV cache errors

* parallel : remove new line from prompt

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

* parallel : remove question with short answers

* parallel : count cache misses

* parallel : print misses on each request

* parallel : minor

* llama : fix n_kv to never become 0

* parallel : rename hot-plug to continuous-batching

* llama : improve llama_batch API + simplify parallel example

* simple : add parallel decoding support

* simple : improve comments + free batch

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

* ggml-cuda : add rope f16, restore performance

* offload KQ_mask with all models

* fix rope shift

---------

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

* llama : disable MPI for now

ggml-ci

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

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

ggml-ci

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

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

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

* llama : llama.h formatting + comments

* simple : add README.md

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

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

* llama : simplify returns if/else branches

* metal : use mm kernels for batch size > 2

* examples : utilize new llama_get_logits_ith()

* examples : add example for batched decoding

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

* server : clear the KV cache beyond n_past before llama_decode

* server : avoid context swaps by shifting the KV cache

---------

Co-authored-by: slaren <slarengh@gmail.com>
2023-09-28 19:04:36 +03:00
Georgi Gerganov
c6f1491da0
metal : fix bug in soft_max kernels (out-of-bounds access) (#3194) 2023-09-15 20:17:24 +03:00
Georgi Gerganov
8c00b7a6ff
sync : ggml (Metal F32 support + reduce ggml-alloc size) (#3192)
* sync : ggml (Metal F32 support + reduce ggml-alloc size)

ggml-ci

* llama-bench : fix ggml_cpu_has_metal() duplicate function

ggml-ci
2023-09-15 19:06:03 +03:00
Georgi Gerganov
a51b687657
metal : relax conditions on fast matrix multiplication kernel (#3168)
* metal : relax conditions on fast matrix multiplication kernel

* metal : revert the concurrnecy change because it was wrong

* llama : remove experimental stuff
2023-09-15 11:09:24 +03:00
Kawrakow
f31b6f4e2d
metal : PP speedup (#3084)
* Minor speed gains for all quantization types

* metal: faster kernel_scale via float4

* Various other speedups for "small" kernels

* metal: faster soft_max vial float4

* metal: faster diagonal infinity

Although, to me it looks like one should simply
fuse scale + diagnonal infinity + soft_max on the
KQtensor.

* Another faster f16 x f32 matrix multiply kernel

* Reverting the diag infinity change

It does work for PP, but somehow it fails for TG.
Need to look more into it.

* metal: add back faster diagonal infinity

This time more carefully

* metal : minor (readibility)

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-09-11 10:30:11 +03:00
Kawrakow
ba7ffbb251
metal : Q3_K speedup (#2995)
* Slightly faster Q3_K and Q5_K on metal

* Another Q3_K speedup on metal

Combined with previous commit, we are now +9.6% for TG.
PP is not affected as this happens via the matrix multiplication
templates.

* Slowly progressing on Q3_K on metal

We are now 13% faster than master

* nother small improvement for Q3_K on metal

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2023-09-08 19:01:04 +03:00
Kawrakow
be8c9c245b
metal : parallel RoPE on Metal (#3024)
* Parallel RoPE on metal

* PR suggestion

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2023-09-07 16:45:01 +03:00
Kawrakow
be6beeb8d7
metal : correct fix of kernel_norm (#3060)
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-09-07 16:42:42 +03:00
Georgi Gerganov
c4f496648c
metal : fix kernel_norm (fixes Falcon on Metal) (#3057)
* metal : fix kernel_norm

ggml-ci

* metal : put warning in kernel_norm to not combine the loops

* metal : restore original F16 mat-vec multiplication

It works after the norm fixes

* common : don't do warm-up with more than n_batch tokens (close #3058)

ggml-ci

* metal : minor
2023-09-07 15:49:09 +03:00
Georgi Gerganov
b7f2aa9e51
metal : restore 363f0bf and fix reduce in F16_F32 kernels (#2986) 2023-09-03 13:23:33 +03:00
Georgi Gerganov
d9151e6f57
metal : revert 6af0bab until we fix it
This restores the generated text to be the same as before #2959
2023-09-03 12:40:56 +03:00
Kawrakow
ca82cf7bac
metal : more optimizations (#2959)
* Very minor speedup via simd-group synchronization in f16 x f32

* Another very minor speedup on metal

* Quite significant PP speedup on metal

* Another attempt

* Minor

* Massive improvement for TG for fp16

* ~4-5% improvement for Q8_0 TG on metal

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-09-03 11:06:22 +03:00
Georgi Gerganov
13268c5331
metal : slight speed-up for add and mul kernels (#2917) 2023-09-01 13:42:41 +03:00
Kawrakow
e8d9158925
metal: somewhat faster f16 x f32 matrix multiply kernel (#2951)
* Somewhat faster f16 x f32 matrix multiply kernel

* Better use 32 thread groups for f16 x f32

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2023-09-01 11:15:57 +03:00
Georgi Gerganov
d67777c202
metal : add Q8_0 support (#2763)
* metal : add dequantize_q8_0 kernel

* metal : add mul_mat_q8_0_f32 kernel

* metal : add Q8_0 mul_mm kernel
2023-08-24 16:19:57 +03:00
Georgi Gerganov
cf658adc83
llm : add Falcon support (#2717)
* llama : refactor GGUF constants into static maps

* llama : check if model architecture is known

* llama : refactor llama_model_load_internal()

* gguf : add KV constant maps

* llm : read arch-specific KVs

* convert : add dummy scores + types

* falcon : load tensor data (CPU only)

* llama : fix loading progress bar

* llama : add arch member to llama_model

* falcon : CPU inference working

* falcon : support non-40B models

* falcon : minor

* llama : minor updates

ggml-ci

* convert-falcon-hf-to-gguf.py : fix special token mapping

* llama.cpp : llama default UNK token = id 0

* llama.cpp : fix bpe tokenizer

* llama.cpp : fix the fix of bpe tokenizer

* ggml : pass eps to ggml_norm

* metal : implement RoPE (mode = 2) + avoid ggml_repeat

* ggml : ggml_repeat always creates new tensor

* falcon : copy-paste self-attention from LLaMA

* metal : print extra compute pipeline info

* falcon : minor changes (still chasing the Metal problem)

* llama.cpp : fix linefeed token

* metal : fix GELU kernel numerical stability by using precise::tanh

* metal : temporary workaround for the concurrency optimization bug

* falcon : add CUDA offloading (#2739)

* llama : better model naming and size reporting

* llama : prep new tokenizer support

* llama : advanced BPE tokenizer based on ggllm.cpp imlpementation

* llama : remove oboslete comment

ggml-ci

* common : remove obsolete BPE API + disable test-tokenizer-1

* llama : revert BPE special-case in llama_byte_to_token()

* cuda : add TODOs for RoPE NeoX implementation

* llama : default special tokens based on vocab type

* perplexity : add log for start of tokenization

---------

Co-authored-by: klosax <131523366+klosax@users.noreply.github.com>
Co-authored-by: slaren <slarengh@gmail.com>
2023-08-23 23:08:04 +03:00
Shouzheng Liu
14b1d7e6f7
metal : add missing barriers for mul-mat (#2699) 2023-08-22 09:18:40 +03:00
Shouzheng Liu
dadbed99e6
metal : fix synchronization in new matrix multiplication kernel (#2686) 2023-08-21 13:59:29 +03:00
Shouzheng Liu
bf83bff674
metal : matrix-matrix multiplication kernel (#2615)
* metal: matrix-matrix multiplication kernel

This commit removes MPS and uses custom matrix-matrix multiplication
kernels for all quantization types. This commit also adds grouped-query
attention to support llama2 70B.

* metal: fix performance degradation from gqa

Integers are slow on the GPU, and 64-bit divides are extremely slow.
In the context of GQA, we introduce a 64-bit divide that cannot be
optimized out by the compiler, which results in a decrease of ~8% in
inference performance. This commit fixes that issue by calculating a
part of the offset with a 32-bit divide. Naturally, this limits the
size of a single matrix to ~4GB. However, this limitation should
suffice for the near future.

* metal: fix bugs for GQA and perplexity test.

I mixed up ne02 and nb02 in previous commit.
2023-08-16 23:07:04 +03:00
Matteo Boschini
1873ff586b
metal : add gqa8 kernel to allow llama-2-70B on metal (#2459)
* Added gqa8 kernel to allow llama-2-70B on metal

* Update ggml-metal.m

Co-authored-by: Cebtenzzre <cebtenzzre@gmail.com>

* Extend kernel_mul_mat_f16_f32 to handle gqa broadcast

* Added ne03==ne13 assertion

---------

Co-authored-by: Cebtenzzre <cebtenzzre@gmail.com>
2023-08-01 10:43:12 +03:00
Kawrakow
9a08eaf3c4
Another speed gain for Q4_0 and Q4_1 on Metal (#2375)
* Another speed gain for Q4_0 and Q4_1 on Metal

* Have N_DST, etc., be template parameters

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2023-07-25 13:48:29 +03:00
Jiahao Li
83a00ce69b
metal : support bcast add & dup & cont op (#2323) 2023-07-23 14:00:37 +03:00
Kawrakow
4d76a5f49b
Faster Q3_K implementation on Metal (#2307)
* Faster Q3_K on Metal

* Additional Q3_K speedup on Metal

* Q3_K for QK_K = 64

* Better Q3_K for QK_K = 64

21.6 ms/t -> 21.1 ms/t

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2023-07-21 17:05:30 +03:00
Kawrakow
e68c96f7fe
Faster Q2_K on Metal (#2297)
* Faster Q2_K on Metal

* Deleting unnoticed and dangereous trailing white space

* Fixed bug in new metal Q2_K implementation

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2023-07-21 10:44:40 +03:00
Kawrakow
e782c9e735
Faster Q5_K and Q6_K on Metal (#2294)
* Faster Q6_K on Metal

* Faster Q5_K on Metal

* Another Q5_K speedup

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2023-07-20 18:19:45 +03:00
Kawrakow
785829dfe8
Faster Q4_K on Metal (#2290)
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2023-07-20 15:18:43 +03:00
Shouzheng Liu
417a85a001
metal: minor q4 optimization and reduce code size (#2248)
* metal: use uint16_t instead of uint8_t.

Apple GPU doesn't like uint8_t. For every operation on uint8_t
the gpu need to copy the uint8_t to an empty 16 bit register, then
it can issue other instructions.

For the matrix-vector multiplication kernel only, we observed a
340~350 GB/s memory read speed on M1 Max after this commit, which is
very close to the reported hardware limit.

* metal: update rms_norm kernel

This commit double the speed of rms_norm operations by using 512 threads
per threadgroup, combining with SIMD primitives to minimize the need for
thread group barriers.

* metal: use template to reduce size

Revert modifications on block_q4_0 and block_q4_1.
2023-07-20 13:32:22 +03:00
Xiao-Yong Jin
6e7cca4047
llama : add custom RoPE (#2054)
* Implement customizable RoPE

The original RoPE has pre-defined parameters

theta_i = 10000^(−2(i−1)/d), for i in [1, 2, ..., d/2]

Our customizable RoPE, ggml_rope_custom_inplace, uses

theta_i = scale * base^(−2(i−1)/d), for i in [1, 2, ..., d/2]

with the default matches the original

scale = 1.0
base = 10000

The new command line arguments
--rope-freq-base
--rope-freq-scale
set the two new RoPE parameter.

Recent researches show changing these two parameters extends the context limit with minimal loss.

1. Extending Context to 8K
   kaiokendev
   https://kaiokendev.github.io/til#extending-context-to-8k

2. Extending Context Window of Large Language Models via Positional Interpolation
   Shouyuan Chen, Sherman Wong, Liangjian Chen, Yuandong Tian
   https://arxiv.org/abs/2306.15595

3. NTK-Aware Scaled RoPE allows LLaMA models to have extended (8k+) context size without any fine-tuning and minimal perplexity degradation.
   https://www.reddit.com/user/bloc97
   https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/

For the bold, try adding the following command line parameters to your favorite model:
-c 16384 --rope-freq-base 80000 --rope-freq-scale 0.5

* ggml-metal: fix custom rope

* common: fix argument names in help

* llama: increase MEM_REQ_EVAL for MODEL_3B

It avoids crashing for quantized weights on CPU.
Better ways to calculate the required buffer size would be better.

* llama: make MEM_REQ_EVAL depend on n_ctx

* server: use proper Content-Type in curl examples

Without the header Content-Type: application/json, curl will POST with
Content-Type: application/x-www-form-urlencoded

Though our simple server doesn't care, the httplib.h used has a limit
with CPPHTTPLIB_FORM_URL_ENCODED_PAYLOAD_MAX_LENGTH 8192

With Content-Type: application/json, we can send large json data.

* style : minor fixes, mostly indentations

* ggml : fix asserts

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-07-15 13:34:16 +03:00
Kawrakow
27ad57a69b
Metal: faster Q4_0 and Q4_1 matrix x vector kernels (#2212)
* 3-5% faster Q4_0 on Metal

* 7-25% faster Q4_1 on Metal

* Oops, forgot to delete the original Q4_1 kernel

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2023-07-14 11:46:21 +02:00
Shouzheng Liu
1cbf561466
metal : new q4_0 matrix-vector kernel (#2188)
Prefetch data to improve GPU utilization. ~48% faster for 33B model.
2023-07-12 23:10:55 +03:00
Kawrakow
6769e944c7
k-quants : support for super-block size of 64 (#2001)
* k_quants: WIP super-blocks with 64 weights

* k_quants: WIP super-blocks with 64 weights

Q6_K scalar and AVX2 works

* k_quants: WIP super-blocks with 64 weights

Q4_K scalar and AVX2 works

* k_quants: WIP super-blocks with 64 weights

Q2_K scalar and AVX2 works. Q2_K is way too slow (it is actually slower
than the scalar implementation)

* k_quants: WIP super-blocks with 64 weights

Q3_K scalar and AVX2 works.

* k_quants: WIP super-blocks with 64 weights

Q5_K scalar and AVX2 works, and with that all
k_quants are done on AVX2 and scalar

* k_quants: WIP super-blocks with 64 weights

Q6_K working on CUDA. Cannot make it run quite as gast as
with super-blocks with 256 weigths: 8% slower on 4080,
20% slower on the 1660 (but there we fit 1 less layer on the
GPU because pf the larger model size), so some fraction of
these 20% is due to that,

* k_quants: WIP super-blocks with 64 weights

Q4_K working on CUDA. ~10% slower on GTX-1660,
16% slower on 4080.

* k_quants: WIP super-blocks with 64 weights

Q2_K working on CUDA. ~3% slower on GTX-1660,
10% slower on 4080.

* k_quants: WIP super-blocks with 64 weights

Q3_K working on CUDA.

* k_quants: WIP super-blocks with 64 weights

Q5_K working on CUDA, and with this CUDA is done.

* k_quants: WIP super-blocks with 64 weights

Q6_K working on ARM_NEON

* k_quants: WIP super-blocks with 64 weights

Q4_K working on ARM_NEON, but quite a bit slower than 256 weights

* k_quants: WIP super-blocks with 64 weights

Q2_K working on ARM_NEON, but quite a bit slower than 256 weights

* k_quants: WIP super-blocks with 64 weights

Q3_K working on ARM_NEON, but quite a bit slower than 256 weights.

* k_quants: WIP super-blocks with 64 weights

Q5_K working on ARM_NEON, but quite a bit slower than 256 weights.

With that, we have full support for ARM_NEON, although
performance is not quite there.

* k_quants: WIP super-blocks with 64 weights

Slightly more efficient Q3_K and Q5_K

* k_quants: WIP super-blocks with 64 weights

Another small improvement for Q3_K and Q5_K on ARM_NEON

* k_quants: WIP super-blocks with 64 weights

Yet another speedup for Q5_K on ARM_NEON.
We are now within 10% of the QK_K = 256 version.

* k_quants: WIP super-blocks with 64 weights

* We are able to pass preprocessor macros to the Metal
  compiler
* Q6_K works and is actually slightly more efficient than
  the QK_K = 256 version (25.2 ms vs 25.8 ms)

* k_quants: WIP super-blocks with 64 weights

Q4_K works on Metal and is actually slightly faster
than QK_K = 256 (21.95 ms vs 24.0 ms).

* k_quants: WIP super-blocks with 64 weights

Q2_K works on Metal and is very slightly faster
than QK_K = 256 (23.8 ms vs 24.2 ms).

* k_quants: WIP super-blocks with 64 weights

Q3_K works on Metal and is slightly faster
than QK_K = 256 (26.6 ms vs 28.3 ms).

* k_quants: WIP super-blocks with 64 weights

Q5_K works on Metal and is slightly faster
than QK_K = 256 (23.7 ms vs 26.3 ms).

* k_quants: call them _K, not _k, also on Metal

* k_quants: correctly define QK_K in llama.cpp

* Fixed bug in q4_K quantization added with the 64-block addition

* Simplify via lambda

* k_quants: swicth Q3_K to 4-bit scales when QK_K = 64

Otherwise there isn't much benefit from this
quantization type. There is some very slight loss
in accuracy, but we reduce size by ~7%.
E.g., for OpenLLaMA-3B, Q3_K_S perplexity is
8.6131 with 8-bit scales and 8.6352 with 4-bit,
while file size decreases from 1.53G to 1.44G.

* k_quants: switch Q4_K to 4-bit scales when QK_K = 64

 Here the loss in accuracy is greater than for Q3_K,
 but the Q4_K points still move further to the left on
 the perplexity vs size curve.

* k_quants: forgot to add the Metal changes in last commit

* k_quants: change Q5_K to be type 0 when QK_K = 64

Still needs AVX2 implementation

* k_quants: AVX2 implementation for new 64-weight Q5_K

* k_quants: 10% faster ARM_NEON Q5_K dot product

* k_quants: fixed issue caused by merging with master

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2023-06-26 19:43:07 +03:00