Commit Graph

134 Commits

Author SHA1 Message Date
Concedo 1b9b9068b1 merged q4_2 and q4_3 dequants and FIXED CLBLAST SLOWNESS! 2023-04-24 21:33:01 +08:00
Concedo 8e615c8245 Merge branch 'master' into concedo_experimental
# Conflicts:
#	README.md
2023-04-24 12:20:08 +08:00
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
Concedo 7c60441d71 Merge branch 'master' into concedo
# Conflicts:
#	.github/workflows/build.yml
#	CMakeLists.txt
2023-04-22 23:46:14 +08:00
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
Concedo 1b7aa2b815 Merge branch 'master' into concedo
# Conflicts:
#	.github/workflows/build.yml
#	CMakeLists.txt
#	Makefile
2023-04-22 16:22:08 +08:00
Georgi Gerganov 872c365a91 ggml : fix AVX build + update to new Q8_0 format 2023-04-22 11:08:12 +03:00
Concedo 1ea0e15292 Merge branch 'master' into concedo
# Conflicts:
#	llama.cpp
2023-04-22 16:07:27 +08:00
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
Concedo 7b3d04e5d4 Merge branch 'master' into concedo_experimental
# Conflicts:
#	CMakeLists.txt
2023-04-22 10:58:16 +08:00
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
Concedo cee018960e Merge branch 'master' into concedo_experimental 2023-04-22 00:19:50 +08:00
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
Concedo 82d74ca1a6 Merge branch 'master' into concedo
# Conflicts:
#	.github/workflows/build.yml
2023-04-21 16:24:30 +08:00
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
Concedo 4605074245 Merge branch 'master' into concedo_experimental
# Conflicts:
#	CMakeLists.txt
#	Makefile
#	README.md
#	ggml.c
2023-04-20 17:30:54 +08:00
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
Kawrakow f7d05095b4 Q4_2 quantization with rmse-optimized scale and quants (#1062)
* Q4_2 quantization with rmse-optimized scale and quants

For quantize-stats we get
q4_2: rmse 0.00159301, maxerr 0.17480469, 95pct<0.0030, median<0.0012

For 7B perplexity with BLAS enabled we get 6.2038 after 655 chunks.

Quantization is slow (~90 seconds on my Mac for 7B) as not
multi-threaded as in PR #896.

* ggml : satisfy the sanitizer builds

Not sure why this makes them fail

* Better follow ggml conventions for function names

* Fixed type as per reviewer comment

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-04-19 20:20:14 +02:00
Georgi Gerganov 884e7d7a2b ggml : use 8-bit precision for Q4_1 intermediate results (#1047)
* ggml : use 8-bit precision for Q4_1 intermediate results (ARM)

* ggml : optimize ggml_vec_dot_q4_1_q8_0() via vmalq_n_f32

56 ms/token with Q4_1 !

* ggml : AVX2 implementation of ggml_vec_dot_q4_1_q8_0 (#1051)

* gitignore : ignore ppl-*.txt files

---------

Co-authored-by: slaren <2141330+slaren@users.noreply.github.com>
2023-04-19 20:10:08 +03:00
Stephan Walter f3d4edf504 ggml : Q4 cleanup - remove 4-bit dot product code (#1061)
* Q4 cleanup

* Remove unused AVX512 Q4_0 code
2023-04-19 19:06:37 +03:00
Concedo be1222c36e Merged the upstream cublas feature, 2023-04-19 20:45:37 +08:00
slaren 8944a13296 Add NVIDIA cuBLAS support (#1044) 2023-04-19 11:22:45 +02:00
Concedo f662a9a230 Merge branch 'master' into concedo
# Conflicts:
#	.github/workflows/build.yml
#	.github/workflows/docker.yml
#	CMakeLists.txt
#	Makefile
#	README.md
2023-04-19 16:34:51 +08:00
slaren 6667401238 Multi-threaded ggml_cpy (#1035)
* Multi-threaded ggml_cpy

* Update ggml.c

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

* Also fix wdata offset in ggml_compute_forward_add_q_f32

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-04-19 00:53:24 +02:00
Georgi Gerganov 77a73403ca ggml : add new Q4_2 quantization (ARM only) (#1046)
* ggml : Q4_2 ARM

* ggml : add ggml_is_quantized()

* llama : update llama_type_name() with Q4_2 entry

* ggml : speed-up q4_2

- 4 threads: ~100ms -> ~90ms
- 8 threads:  ~55ms -> ~50ms

* ggml : optimize q4_2 using vmlaq_n_f32 + vmulq_n_f32
2023-04-18 23:54:57 +03:00
Georgi Gerganov 50a8a2af97 ggml : scratch that - vmlaq_n_f32 is always better
Had a background process that was messing with the timings
2023-04-18 23:11:23 +03:00
Georgi Gerganov dcdd65e296 ggml : optimize ggml_vec_dot_q4_0_q8_0() using vectorized accumulators 2023-04-18 22:59:17 +03:00
Concedo ac61e34d5f Merge branch 'master' into concedo_experimental
# Conflicts:
#	CMakeLists.txt
#	README.md
2023-04-18 17:38:10 +08:00
slaren 315a95a4d3 Add LoRA support (#820) 2023-04-17 17:28:55 +02:00
Georgi Gerganov 69b740289f ggml : avoid using ggml_fp16_to_fp32() and ggml_fp32_to_fp16() in ggml.c 2023-04-17 16:16:23 +03:00
Ivan Komarov f266259ad9 Speedup the AVX-512 implementation of ggml_vec_dot_q4_0() (#933) 2023-04-17 15:10:57 +02:00
Concedo 5a4d1b5d15 Merge branch 'master' into concedo
# Conflicts:
#	CMakeLists.txt
#	Makefile
2023-04-16 14:08:23 +08:00
Stephan Walter 2f7c8e014e Fix potential int8 overflow in non-SIMD vec_dot (#986) 2023-04-15 18:28:56 +00:00
Concedo 3e992eabb4 Merge remote-tracking branch 'occam/clblast-gpu-dequant' into concedo 2023-04-16 00:26:54 +08:00
Stephan Walter 0ad964631f Refactor ggml.c for future tensor types (#1001) 2023-04-15 16:25:38 +00:00
0cc4m 57d046eeb6 Enable dequantization on GPU for ClBlast 2023-04-15 18:04:24 +02:00
Georgi Gerganov e95b6554b4 ggml : add Q8_0 quantization for intermediate results (#951)
* ggml : add Q8_0 quantization for intermediate results

* quantize-stats : fix test + add it to Makefile default

* Q8: use int8_t, AVX/AVX2 optimizations

* ggml : fix quantize_row_q8_0() ARM_NEON rounding

* minor : updates after rebase to latest master

* quantize-stats : delete obsolete strings

* ggml : fix q4_1 dot func

---------

Co-authored-by: Stephan Walter <stephan@walter.name>
2023-04-15 17:53:22 +03:00
Georgi Gerganov aa485cee33 ggml : use posix_memalign on non-Windows env 2023-04-15 14:25:45 +03:00
Concedo d00b865eb1 Merge branch 'master' into concedo
# Conflicts:
#	.devops/full.Dockerfile
#	Makefile
#	flake.nix
2023-04-15 11:33:43 +08:00