JohannesGaessler
e7b9d97bae
More int mult, less float mult, worse performance
2023-05-12 09:11:47 +02:00
JohannesGaessler
d882d1c2fe
Performance no longer terrible
2023-05-11 23:27:06 +02:00
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
JohannesGaessler
d052a0ed4c
Faster than CPU without 80% runtime memcpy
2023-05-09 09:47:55 +02:00
JohannesGaessler
229aa1f504
Works but slower than CPU
2023-05-09 09:47:55 +02:00
Johannes Gäßler
1f48b0abcf
Documented CUDA reproducibility, added warning ( #1346 )
2023-05-08 02:42:01 +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
slaren
b925f1f1b0
cuBLAS: fall back to pageable memory if pinned alloc fails ( #1233 )
...
* cuBLAS: fall back to pageable memory if pinned alloc fails
* cuBLAS: do not use pinned memory if env variable GGML_CUDA_NO_PINNED is set
2023-05-01 13:32:22 +02:00
slaren
7fc50c051a
cuBLAS: use host pinned memory and dequantize while copying ( #1207 )
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* 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 )
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* 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
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
slaren
50cb666b8a
Improve cuBLAS performance by using a memory pool ( #1094 )
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* 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
slaren
2005469ea1
Add Q4_3 support to cuBLAS ( #1086 )
2023-04-20 20:49:53 +02:00
slaren
02d6988121
Improve cuBLAS performance by dequantizing on the GPU ( #1065 )
2023-04-20 03:14:14 +02:00
