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104 Commits

Author SHA1 Message Date
slaren
f5ef5cfb18 ggml-cuda : perform cublas mat mul of quantized types as f16 (#3412) 
* ggml-cuda : perform cublas matrix multiplication of quantized types as fp16

* rename CC_TURING to CC_VOLTA

* disable fp16 mat mul completely with multi GPU
 2023-09-30 18:12:57 +02:00
slaren
16bc66d947 llama.cpp : split llama_context_params into model and context params (#3301) 
* llama.cpp : split llama_context_params into model and context params

ggml-ci

* fix metal build

* fix freq_base/scale default to model value

* llama-bench : keep the same model between tests when possible

* move n_threads to llama_context_params, add n_threads_batch

* fix mpi build

* remove kv_size(), cuda scratch fixes

* remove low-vram option

* add n_threads_batch to system info, refactor to get_system_info()

* add documentation about --threads-batch to the READMEs

* llama-bench fix

* main : fix rope freq/scale warning

* llama.cpp : add llama_get_model
common : add llama_tokenize from model

* remove duplicated ctx/model functions

ggml-ci

* cuda : print total VRAM used
 2023-09-28 22:42:38 +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
slaren
da0400344b ggml-cuda : perform cublas fp16 matrix multiplication as fp16 (#3370) 
* ggml-cuda : perform cublas fp16 matrix multiplication as fp16

* try to fix rocm build

* restrict fp16 mat mul to volta and up
 2023-09-28 13:08:28 +03:00
Johannes Gäßler
ee66942d7e CUDA: fix peer access logic (#3231) 2023-09-17 23:35:20 +02:00
Johannes Gäßler
111163e246 CUDA: enable peer access between devices (#2470) 2023-09-17 16:37:53 +02:00
Johannes Gäßler
578d8c8f5c CUDA: fix scratch malloced on non-main device (#3220) 2023-09-17 14:16:22 +02:00
Vlad
5dbc2b3213 Enable build with CUDA 11.0 (make) (#3132) 
* CUDA 11.0 fixes

* Cleaner CUDA/host flags separation

Also renamed GGML_ASSUME into GGML_CUDA_ASSUME
 2023-09-16 16:55:43 +02:00
Johannes Gäßler
0a5eebb45d CUDA: mul_mat_q RDNA2 tunings (#2910) 
* CUDA: mul_mat_q RDNA2 tunings

* Update ggml-cuda.cu

Co-authored-by: Henri Vasserman <henv@hot.ee>

---------

Co-authored-by: Henri Vasserman <henv@hot.ee>
 2023-09-13 11:20:24 +02:00
Johannes Gäßler
4f7cd6ba9c CUDA: fix LoRAs (#3130) 2023-09-13 00:15:33 +02:00
Johannes Gäßler
89e89599fd CUDA: fix mul_mat_q not used for output tensor (#3127) 2023-09-11 22:58:41 +02:00
Johannes Gäßler
d54a4027a6 CUDA: lower GPU latency + fix Windows performance (#3110) 2023-09-11 19:55:51 +02:00
Johannes Gäßler
8a4ca9af56 CUDA: add device number to error messages (#3112) 2023-09-11 13:00:24 +02:00
Georgi Gerganov
b3e9852e47 sync : ggml (CUDA GLM RoPE + POSIX) (#3082) 
ggml-ci
 2023-09-08 17:58:07 +03:00
Jiahao Li
35195689cd 2x faster (rms) norm cuda kernels (3.7% e2e improvement) (#2985) 
* 2x faster (rms) norm cuda kernels

* Fix code style
 2023-09-04 08:53:30 +02:00
Engininja2
f04d002844 cuda : vsubss4 for older versions of ROCm/clang (#2942) 2023-09-01 23:33:19 +02:00
Johannes Gäßler
92b1bbd2ec CUDA: fix RoPE asserts, block sizes (#2833) 2023-08-28 14:23:55 +03:00
Georgi Gerganov
eaa13a48ff falcon : fix CUDA inference by making K and Q contiguous (#2830) 
* falcon : fix CUDA inference by making K and Q contiguous

ggml-ci

* cuda : add assert to guard from non-cont ropes
 2023-08-27 16:40:48 +03:00
Kawrakow
a6d1189fdd k_quants tuning for Falcon-7b (#2816) 
* Make ggml-cuda.cu build with QK_K = 64

Using LLAMA_CUDA_FORCE_DMMV = ON and -nommq it runs and produces
a meaningful result.

* k_quants tuning for Falcon-7b

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
 2023-08-27 15:19:59 +03:00
Henri Vasserman
6bbc598a63 ROCm Port (#1087) 
* use hipblas based on cublas
* Update Makefile for the Cuda kernels
* Expand arch list and make it overrideable
* Fix multi GPU on multiple amd architectures with rocblas_initialize() (#5)
* add hipBLAS to README
* new build arg LLAMA_CUDA_MMQ_Y
* fix half2 decomposition
* Add intrinsics polyfills for AMD
* AMD assembly optimized __dp4a
* Allow overriding CC_TURING
* use "ROCm" instead of "CUDA"
* ignore all build dirs
* Add Dockerfiles
* fix llama-bench
* fix -nommq help for non CUDA/HIP

---------

Co-authored-by: YellowRoseCx <80486540+YellowRoseCx@users.noreply.github.com>
Co-authored-by: ardfork <134447697+ardfork@users.noreply.github.com>
Co-authored-by: funnbot <22226942+funnbot@users.noreply.github.com>
Co-authored-by: Engininja2 <139037756+Engininja2@users.noreply.github.com>
Co-authored-by: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com>
Co-authored-by: jammm <2500920+jammm@users.noreply.github.com>
Co-authored-by: jdecourval <7315817+jdecourval@users.noreply.github.com>
 2023-08-25 12:09:42 +03:00
Georgi Gerganov
3f460a2b72 cuda : add RoPE kernel for mode == 2 (NeoX) (#2760) 
* cuda : add RoPE kernel for mode == 2 (NeoX)

* falcon : do not offload the embeddings layer
 2023-08-25 11:55:59 +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
Johannes Gäßler
c63bb1d16a CUDA: use mul_mat_q kernels by default (#2683) 2023-08-22 22:47:05 +02:00
Jiahao Li
800c9635b4 Fix CUDA softmax by subtracting max value before exp (#2665) 2023-08-22 20:27:06 +02:00
slaren
1123f7fbdf ggml-cuda : use graph allocator (#2684) 
use a different function for no_alloc to avoid breaking backwards compat, fixes lora

remove 512 n_batch limit

fixed 2048 batch size

cleanup

Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
 2023-08-22 15:25:19 +02:00
Georgi Gerganov
ef3f333d37 ggml : sync latest (SAM + SD operators, CUDA alibi) (#2709) 
* ggml : sync latest (SAM + SD operators, CUDA alibi)

ggml-ci

* ggml : fix tabs
 2023-08-22 14:22:08 +03:00
slaren
097e121e2f llama : add benchmark example (#2626) 
* llama : add benchmark example

* add to examples CMakeLists.txt

* fix msvc build

* add missing include

* add Bessel's correction to stdev calculation

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

* improve markdown formatting

* add missing include

* print warning is NDEBUG is not defined

* remove n_prompt and n_gen from the matrix, use each value separately instead

* better checks for non-optimized builds

* llama.cpp : fix MEM_REQ_SCRATCH0 reusing the value of n_ctx of the first call

* fix json formatting

* add sql output

* add basic cpu and gpu info (linx/cuda only)

* markdown: also show values that differ from the default

* markdown: add build id

* cleanup

* improve formatting

* formatting

---------

Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
 2023-08-18 12:44:58 +02:00
Johannes Gäßler
1cd06fa25e CUDA: launch_bounds, small q4_K, q5_K mmq refactor (#2596) 2023-08-14 10:41:22 +02:00
Johannes Gäßler
f64d44a9b9 CUDA: Fixed OpenLLaMA 3b mmq, reduced compile time (#2590) 2023-08-13 00:24:45 +02:00
Johannes Gäßler
25d43e0eb5 CUDA: tuned mul_mat_q kernels (#2546) 2023-08-09 09:42:34 +02:00
Johannes Gäßler
f514d1b306 CUDA: faster k-quant mul_mat_q kernels (#2525) 2023-08-05 18:20:44 +02:00
Cebtenzzre
4329d1acb0 CUDA: use min compute capability of GPUs actually used (#2506) 2023-08-04 17:35:22 +02:00
Cebtenzzre
02f9d96a86 CUDA: check if event is NULL before cudaStreamWaitEvent (#2505) 
Fixes #2503
 2023-08-04 17:34:32 +02:00
Johannes Gäßler
468ea24fb4 CUDA: faster non k-quant mul_mat_q kernels (#2483) 2023-08-02 18:04:04 +02:00
Johannes Gäßler
4f6b60c776 CUDA: Fix models with output size != 32000 (#2480) 2023-08-02 16:48:10 +02:00
Johannes Gäßler
0728c5a8b9 CUDA: mmq CLI option, fixed mmq build issues (#2453) 2023-07-31 15:44:35 +02:00
Johannes Gäßler
1215ed7d5c CUDA: Implemented row flattening for non-glm RoPE (#2468) 2023-07-31 14:32:30 +02:00
Johannes Gäßler
2dbf518911 CUDA: fewer memory bank conflicts for mul_mat_q (#2458) 2023-07-31 13:18:51 +02:00
Johannes Gäßler
11f3ca06b8 CUDA: Quantized matrix matrix multiplication (#2160) 
* mmq implementation for non k-quants

* q6_K

* q2_K

* q3_k

* q4_K

* vdr

* q5_K

* faster q8_1 loading

* loop unrolling

* add __restrict__

* q2_K sc_high

* GGML_CUDA_MMQ_Y

* Updated Makefile

* Update Makefile

* DMMV_F16 -> F16

* Updated README, CMakeLists

* Fix CMakeLists.txt

* Fix CMakeLists.txt

* Fix multi GPU out-of-bounds
 2023-07-29 23:04:44 +02:00
Johannes Gäßler
9baf9ef304 CUDA: faster multi GPU synchronization (#2448) 2023-07-29 23:04:10 +02:00
Kawrakow
129d844c87 Fix Q4_K and Q5_K for QK_K = 64 on CUDA (#2359) 
* Fix Q4_K and Q5_K for QK_K = 64

* Very slightly better Q5_K bit fiddling

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
 2023-07-25 13:48:04 +03:00
slaren
41c674161f make rms_norm_eps a parameter (#2374) 
* make rms_norm_eps a parameter

* add rms_norm_eps to command line

* fix baby llama, test-grad0

* use scientific notation for eps param in the help

ggml-ci
 2023-07-24 17:57:12 +02:00
Georgi Gerganov
5b2b2dc6ae ggml : sync (unary ops refactor, static-correctness) (#2370) 
* ggml : sync (unary ops, tests)

ggml-ci

* tests : remove unnecessary funcs
 2023-07-24 14:46:21 +03:00
Kawrakow
2f9cf974a0 Some more Q4_K and Q5_K speedup on CUDA (#2346) 
* Faster Q5_K on CUDA

* Small Q5_K improvement on older GPUs

* Spped up Q4_K on CUDA

GTX1660: 29.5 ms/t -> 25.6 ms/t
RTX4080: 8.40 ms/t -> 8.25 ms/t

* Spped up Q4_K on CUDA

GTX1660: 36.7 ms/t -> 35.6 ms/t
RTX4080:  9.8 ms/t ->  9.5 ms/t

* Address PR comments

* Add some comments to satisfy PR reviewer

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
 2023-07-24 00:19:47 +03:00
slaren
95a6c595e7 ggml: move op parameters from tensors to ggml_tensor::op_params (#2333) 
* ggml: move op parameters from tensors to ggml_tensor::op_params

* alibi: use memcpy for float params

* remove `src[1] = NULL` in ops
 2023-07-23 14:36:02 +02:00
Georgi Gerganov
e76d630df1 llama : grouped-query attention + LLaMAv2 70B support (#2276) 
* CUDA: GQA implementation

* llama : support for GQA and LLaMAv2 70B

ggml-ci

* py : fix hparams parsing (if-else blocks)

ggml-ci

* py : oh boy ..

ggml-ci

* help : fix gqa value for 70B

ggml-ci

---------

Co-authored-by: JohannesGaessler <johannesg@5d6.de>
 2023-07-23 15:09:47 +03:00
Kawrakow
d2a43664f9 Speed up Q4_K (#2322) 
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
 2023-07-23 08:49:20 +03:00
Johannes Gäßler
b9b7d94fc1 CUDA: Fixed 7b q3_K_S with mul_mat_vec_q (#2313) 2023-07-22 21:27:34 +02:00
Kawrakow
d924522a46 Custom RoPE + bettter memory management for CUDA (#2295) 
* Custom RoPE + bettter memory management for CUDA

* Adjusted look ahead in ggml_cuda_pool_malloc to 5%

This is sufficient it seems.
We end up using about 200 MB less VRAM that way when running
the 13B model with context 8192.

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
 2023-07-21 17:27:51 +03:00
Georgi Gerganov
ae178ab46b llama : make tensor_split ptr instead of array (#2272) 2023-07-21 13:10:51 +03:00