Commit Graph

143 Commits

Author SHA1 Message Date
JohannesGaessler
4b12881329 WAKE ME UP 2023-05-11 22:47:38 +02:00
JohannesGaessler
3ed4588e22 Store layers in VRAM 2023-05-09 11:05:58 +02:00
swittk
1b0fd45465
ggml : Allow usage of CLBlast alongside Accelerate.framework (#1336)
Minor edit in ggml.c which originally would prevent OpenCL from loading completely if GGML_USE_ACCELERATE was defined.
Minor speedup in prompt eval time.
2023-05-06 23:03:23 -04:00
Ron Jailall
20fbf2a2a0
ggml : change immintrin.h to intrin.h for compatibility (#1307)
* change immintrin.h to intrin.h for compatibility

Building on windows11 arm throws an error on this line. Seems like using intrin.h covers x86 and and arm

* conditional def of intrin.h

* fix typo in ggml.c
2023-05-04 18:05:59 +03:00
Georgi Gerganov
799fdc1b5d
ggml : vectorize Q8_0 quantization
https://github.com/ggerganov/ggml/pull/127#issuecomment-1533648531
2023-05-03 23:24:20 +03:00
Georgi Gerganov
5d5817ca60
ggml : fix 32-bit ARM 2023-05-02 22:14:50 +03:00
Marvin Gießing
cc0bb7235c
ggml : fix ppc64le build error and make cmake detect Power processors (#1284)
* Fix ppc64le build issue

* Added support to detect ppc64* processors
2023-05-02 19:42:16 +03:00
slaren
2d099e5193
ggml: add names to tensors (#1268)
* ggml: add names to tensors

* minor improvements to dot file formatting
2023-05-02 16:03:00 +02:00
slaren
58b367c2d7
cuBLAS: refactor and optimize f16 mat mul performance (#1259)
* cuBLAS: refactor, convert fp16 to fp32 on device

* cuBLAS: use multiple streams, choose smartly between mul_mat_q and mul_mat_f16

* fix build

* cuBLAS: update block_q5_1
2023-05-01 18:11:07 +02:00
Kerfuffle
2bdc09646d
ggml : fix ggml_used_mem() (#1264) 2023-05-01 14:56:07 +03:00
Georgi Gerganov
7ff0dcd320
ggml : fix UB (int << 31) 2023-04-30 22:28:51 +03:00
Georgi Gerganov
6bc4400e67
ggml : add Q5 WASM SIMD + GGML_FTYPE 2023-04-30 19:07:43 +03:00
Georgi Gerganov
3e5aa8a1c4
ggml : fix labels for GGML_OP_ALIBI 2023-04-30 10:25:46 +03:00
Georgi Gerganov
c3ca7a5f05
ggml : fix 32-bit ARM NEON 2023-04-29 21:34:23 +03:00
Georgi Gerganov
e8c051611a
ggml : use vzip instead of vuzp for consistency 2023-04-29 21:12:56 +03:00
Georgi Gerganov
0b5a935099
ggml : fix visibility and unused warnings 2023-04-29 19:28:36 +03:00
Georgi Gerganov
ec728e44d7
ggml : fix #if for f32_f32 mul_mat (CLBlast) (#1229) 2023-04-29 18:43:42 +03:00
Georgi Gerganov
214b6a3570
ggml : adjust mul_mat_f16 work memory (#1226)
* llama : minor - remove explicity int64_t cast

* ggml : reduce memory buffer for F16 mul_mat when not using cuBLAS

* ggml : add asserts to guard for incorrect wsize
2023-04-29 18:43:28 +03:00
slaren
7fc50c051a
cuBLAS: use host pinned memory and dequantize while copying (#1207)
* cuBLAS: dequantize simultaneously while copying memory

* cuBLAS: use host pinned memory

* cuBLAS: improve ggml_compute_forward_mul_mat_f16_f32 with pinned memory

* cuBLAS: also pin kv cache

* fix rebase
2023-04-29 02:04:18 +02:00
Henri Vasserman
b1ee8f59b4
cuBLAS: non-contiguous tensor support (#1215)
* Cuda: non-contiguous tensor support

* remove extra stuff

* rename

* fix error

* more fixes, now OpenBLAS and CLBlast build too

* now then?
2023-04-29 01:31:56 +02:00
Stephan Walter
36d19a603b
Remove Q4_3 which is no better than Q5 (#1218) 2023-04-28 23:10:43 +00:00
Georgi Gerganov
55390bcaf2
ggml : sync ggml (ggml_alibi) 2023-04-28 20:51:05 +03:00
Georgi Gerganov
11d902364b
ggml : add helper debug printf in soft_max 2023-04-28 17:59:08 +03:00
0cc4m
7296c961d9
ggml : add CLBlast support (#1164)
* Allow use of OpenCL GPU-based BLAS using ClBlast instead of OpenBLAS for context processing

* Improve ClBlast implementation, avoid recreating buffers, remove redundant transfers

* Finish merge of ClBlast support

* Move CLBlast implementation to separate file

Add buffer reuse code (adapted from slaren's cuda implementation)

* Add q4_2 and q4_3 CLBlast support, improve code

* Double CLBlast speed by disabling OpenBLAS thread workaround

Co-authored-by: Concedo <39025047+LostRuins@users.noreply.github.com>
Co-authored-by: slaren <2141330+slaren@users.noreply.github.com>

* Fix device selection env variable names

* Fix cast in opencl kernels

* Add CLBlast to CMakeLists.txt

* Replace buffer pool with static buffers a, b, qb, c

Fix compile warnings

* Fix typos, use GGML_TYPE defines, improve code

* Improve btype dequant kernel selection code, add error if type is unsupported

* Improve code quality

* Move internal stuff out of header
* Use internal enums instead of CLBlast enums
* Remove leftover C++ includes and defines
* Make event use easier to read

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

* Use c compiler for opencl files

* Simplify code, fix include

* First check error, then release event

* Make globals static, fix indentation

* Rename dequant kernels file to conform with other file names

* Fix import cl file name

---------

Co-authored-by: Concedo <39025047+LostRuins@users.noreply.github.com>
Co-authored-by: slaren <2141330+slaren@users.noreply.github.com>
Co-authored-by: Henri Vasserman <henv@hot.ee>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-04-28 17:57:16 +03:00
Yann Follet
04aaae1d79
add avx2 for dot_q8_0_q8_0, 2x faster than scalar (#1211) 2023-04-28 11:59:48 +00:00
Stephan Walter
0b2da20538
ggml : slightly faster AVX2 implementation for Q5 (#1197) 2023-04-26 23:26:42 +03:00
Georgi Gerganov
574406dc7e
ggml : add Q5_0 and Q5_1 quantization (#1187)
* ggml : add Q5_0 quantization (cuBLAS only)

* ggml : fix Q5_0 qh -> uint32_t

* ggml : fix q5_0 histogram stats

* ggml : q5_0 scalar dot product

* ggml : q5_0 ARM NEON dot

* ggml : q5_0 more efficient ARM NEON using uint64_t masks

* ggml : rename Q5_0 -> Q5_1

* ggml : adding Q5_0 mode

* quantize : add Q5_0 and Q5_1 to map

* ggml : AVX2 optimizations for Q5_0, Q5_1 (#1195)

---------

Co-authored-by: Stephan Walter <stephan@walter.name>
2023-04-26 23:14:13 +03:00
Georgi Gerganov
7a32fcb3b2
ggml : add Q8_0 quantization format (rename the old one to Q8_1) (ARM NEON) (#1179)
* ggml : add Q8_0 quantization format (rename the old one to Q8_1)

* tests : fix test-quantize-fns

* ggml : finalize Q8_0 implementation

* ggml : use q4_0_q8_0 and q4_2_q8_0

* ggml : fix Q8_0 dot product bug (ARM)

* ggml : Q8_0 unroll x2

* ggml : fix bug - using wrong block type

* ggml : extend quantize_fns_t with "vec_dot_type"

* ggml : fix Q8_0 to use 255 values out of 256

* ggml : fix assert using wrong QK4_2 instead of QK4_3
2023-04-25 23:40:51 +03:00
unbounded
dd0eabc049
ggml : use full range for Q4_0 and Q4_2 quantization (#729)
* Use full range for q4_0 quantization

By keeping the sign of the highest magnitude, we can make sure the
highest value maps to -8, which is currently unused.
This is a bit of a freebie since it is fully backwards compatible with
the current format.

* Update quantize_row_q4_0 for AVX/AVX2

* Update quantize_row_q4_0 for WASM

Untested

* Update quantize_row_q4_0 for Arm NEON

* Update quantize_row_q4_0 for PowerPC

Untested

* Use full range for q4_2 quantization
2023-04-25 20:20:46 +03:00
xaedes
54bb60e268
ggml : fix bug in ggml_compute_forward_sum_f32 (#1162)
The sum over all rows is now computed instead of just the last row
2023-04-24 23:02:02 +02:00
Stephan Walter
2ec83428de
Fix build for gcc 8 and test in CI (#1154) 2023-04-24 15:38:26 +00:00
Georgi Gerganov
ec9cdb6752
ggml : do not print perf ops that have not been used at all 2023-04-23 18:32:52 +03:00
Georgi Gerganov
e4422e299c
ggml : better PERF prints + support "LLAMA_PERF=1 make" 2023-04-23 18:15:39 +03:00
Stephan Walter
53c8434398
Improve AVX2 for vec_dot_q4_3_q8_0 (#1138) 2023-04-23 11:01:03 +00:00
Yishuo Wang
c9e2c26f41
A better packNibbles and mul_sum_i8_pairs_float implementation using AVX512 (#1119) 2023-04-23 07:57:05 +00:00
Georgi Gerganov
0e018fe008
ggml : fix Q4_3 cuBLAS 2023-04-22 16:32:07 +03:00
Stephan Walter
c50b628810
Fix CI: ARM NEON, quantization unit tests, editorconfig (#1122) 2023-04-22 10:54:13 +00:00
Georgi Gerganov
872c365a91 ggml : fix AVX build + update to new Q8_0 format 2023-04-22 11:08:12 +03:00
Georgi Gerganov
955ef9a5d5
ggml : alternative Q4_3 implementation using modified Q8_0 (#1109)
* ggml : prefer vzip to vuzp

This way we always use the same type of instruction across all quantizations

* ggml : alternative Q4_3 implementation using modified Q8_0

* ggml : fix Q4_3 scalar imlpementation

* ggml : slight improvement of Q4_3 - no need for loop unrolling

* ggml : fix AVX paths for Q8_0 quantization
2023-04-22 10:55:35 +03:00
Stephan Walter
c5aa5e5777
ggml : AVX2 optimization for vec_dot_q4_3_q8_0 and refactoring (#1099)
* AVX2 optimization for vec_dot_q4_3_q8_0 and refactoring

* finish AVX vectorization of quantize_row_q8_0

* Rename hsum_int_8 to hsum_i32_8
2023-04-22 10:37:05 +03:00
slaren
50cb666b8a
Improve cuBLAS performance by using a memory pool (#1094)
* Improve cuBLAS performance by using a memory pool

* Move cuda specific definitions to ggml-cuda.h/cu

* Add CXX flags to nvcc

* Change memory pool synchronization mechanism to a spin lock
General code cleanup
2023-04-21 21:59:17 +02:00
Kawrakow
1bfc153e2f
ggml : a faster version for Q4_1 x Q8_0 dot products (#1083)
* A faster version for Q4_1 x Q8_0 dot products

The idea nehind being that Q8_0 quantized
values get used many times in the matrix multiplications
where they are involved. In the current implementations,
when we are evaluating the dot products, we need to compute
the sum of the quants in the Q8_0 vector, so the same
operation is repeated many times. Here we pre-compute
the sum during Q8_0 quantization, store it in the
now modified block_q8_0 struct, and then reuse this
result in the subsequent dot products.

In a synthetic benchmark (just compute a bunch of dot
products), this change speeds up the Q4_1 * Q8_0 dot
product by 80%, making the performance identical to
Q4_0 * Q8_0.

In practical application, I see a ~15% gain in speed for
token prediction on M2, and ~5% gain on Ryzen 7950X.
The speed gain in the prompt evaluation is much bigger
(around 50%).

I have only done the change for the scalar version,
ARM_NEON, and AVX2, so we still need an AVX implementation.

* Cleaning up

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
2023-04-21 18:18:26 +03:00
Georgi Gerganov
12b5900dbc
ggml : sync ggml (add GPT-NeoX RoPE implementation) 2023-04-20 23:32:59 +03:00
Georgi Gerganov
9ff334f3c9
ggml : fix bug in ggml_compute_forward_dup_f32() 2023-04-20 21:58:38 +03:00
Georgi Gerganov
8a1756abdf
ggml : do not break cuBLAS build (Q4_3 is not yet implemented) 2023-04-20 21:43:50 +03:00
Georgi Gerganov
66aab46079
ggml : fix Q4_3 quantization
Broke it during conflict resolution in last PR
2023-04-20 20:44:05 +03:00
Kawrakow
38de86a711
llama : multi-threaded quantization (#1075)
* Multi-threading quantization.

Not much gain for simple quantizations, bit it will be important
for quantizations that require more CPU cycles.

* Multi-threading for quantize-stats

It now does the job in ~14 seconds on my Mac for
Q4_0, Q4_1 and Q4_2. Single-threaded it was taking
more than 2 minutes after adding the more elaborate
version of Q4_2.

* Reviewer comments

* Avoiding compiler confusion

After changing chunk_size to const int as suggested by
@ggerganov, clang and GCC starting to warn me that I don't
need to capture it in the lambda. So, I removed it from the
capture list. But that makes the MSVC build fail. So,
making it a constexpr to make every compiler happy.

* Still fighting with lambda captures in MSVC

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-04-20 20:42:27 +03:00
Georgi Gerganov
e0305ead3a
ggml : add Q4_3 quantization (#1082) 2023-04-20 20:35:53 +03:00
Stephan Walter
c8c2c52482
AVX2 optimization for vec_dot_q4_2_q8_0 (#1068) 2023-04-20 08:45:41 +02:00
slaren
02d6988121
Improve cuBLAS performance by dequantizing on the GPU (#1065) 2023-04-20 03:14:14 +02:00