.. |
template-instances
|
CUDA: refactor mmq, dmmv, mmvq (llama/7716)
|
2024-06-16 18:19:48 +03:00 |
acc.cu
|
sync : ggml (#2001)
|
2024-03-27 18:55:10 +02:00 |
acc.cuh
|
sync : ggml (#2001)
|
2024-03-27 18:55:10 +02:00 |
arange.cu
|
sync : ggml (#2001)
|
2024-03-27 18:55:10 +02:00 |
arange.cuh
|
sync : ggml (#2001)
|
2024-03-27 18:55:10 +02:00 |
argsort.cu
|
CUDA: faster q2_K, q3_K MMQ + int8 tensor cores (llama/7921)
|
2024-06-16 18:19:48 +03:00 |
argsort.cuh
|
sync : ggml (#2001)
|
2024-03-27 18:55:10 +02:00 |
binbcast.cu
|
ggml : group all experts in a single ggml_mul_mat_id (llama/6505)
|
2024-05-13 11:02:26 +03:00 |
binbcast.cuh
|
sync : ggml (#2001)
|
2024-03-27 18:55:10 +02:00 |
clamp.cu
|
Introduction of CUDA Graphs to LLama.cpp (llama/6766)
|
2024-05-13 11:02:26 +03:00 |
clamp.cuh
|
sync : ggml (#2001)
|
2024-03-27 18:55:10 +02:00 |
common.cuh
|
CUDA: faster q2_K, q3_K MMQ + int8 tensor cores (llama/7921)
|
2024-06-16 18:19:48 +03:00 |
concat.cu
|
cuda : non-cont concat support (llama/7610)
|
2024-06-16 18:19:48 +03:00 |
concat.cuh
|
sync : ggml (#2001)
|
2024-03-27 18:55:10 +02:00 |
convert.cu
|
ggml : drop support for QK_K=64 (llama/7473)
|
2024-06-16 18:19:48 +03:00 |
convert.cuh
|
llama : add Command R Plus support (llama/6491)
|
2024-04-09 20:26:18 +03:00 |
cpy.cu
|
Introduction of CUDA Graphs to LLama.cpp (llama/6766)
|
2024-05-13 11:02:26 +03:00 |
cpy.cuh
|
Introduction of CUDA Graphs to LLama.cpp (llama/6766)
|
2024-05-13 11:02:26 +03:00 |
dequantize.cuh
|
llama : add Command R Plus support (llama/6491)
|
2024-04-09 20:26:18 +03:00 |
diagmask.cu
|
sync : ggml (#2001)
|
2024-03-27 18:55:10 +02:00 |
diagmask.cuh
|
sync : ggml (#2001)
|
2024-03-27 18:55:10 +02:00 |
dmmv.cu
|
CUDA: refactor mmq, dmmv, mmvq (llama/7716)
|
2024-06-16 18:19:48 +03:00 |
dmmv.cuh
|
sync : llama.cpp (skip)
|
2024-04-07 16:15:57 +03:00 |
fattn-common.cuh
|
CUDA: use tensor cores for MMQ (llama/7676)
|
2024-06-16 18:19:48 +03:00 |
fattn-tile-f16.cu
|
CUDA: use tensor cores for MMQ (llama/7676)
|
2024-06-16 18:19:48 +03:00 |
fattn-tile-f16.cuh
|
CUDA: faster large batch FA without tensor cores (llama/7314)
|
2024-06-16 18:19:48 +03:00 |
fattn-tile-f32.cu
|
CUDA: fix Pascal FA, deq. KV to FP16 for batch > 8 (llama/7681)
|
2024-06-16 18:19:48 +03:00 |
fattn-tile-f32.cuh
|
CUDA: faster large batch FA without tensor cores (llama/7314)
|
2024-06-16 18:19:48 +03:00 |
fattn-vec-f16.cuh
|
CUDA: use tensor cores for MMQ (llama/7676)
|
2024-06-16 18:19:48 +03:00 |
fattn-vec-f32.cuh
|
CUDA: fix broken oob check for FA vec f32 kernel (llama/7904)
|
2024-06-16 18:19:48 +03:00 |
fattn-wmma-f16.cuh
|
CUDA: use tensor cores for MMQ (llama/7676)
|
2024-06-16 18:19:48 +03:00 |
fattn.cu
|
CUDA: fix Pascal FA, deq. KV to FP16 for batch > 8 (llama/7681)
|
2024-06-16 18:19:48 +03:00 |
fattn.cuh
|
ggml : add Flash Attention (llama/5021)
|
2024-05-13 11:02:26 +03:00 |
getrows.cu
|
sync : ggml (#2001)
|
2024-03-27 18:55:10 +02:00 |
getrows.cuh
|
sync : ggml (#2001)
|
2024-03-27 18:55:10 +02:00 |
im2col.cu
|
sync : ggml (#2001)
|
2024-03-27 18:55:10 +02:00 |
im2col.cuh
|
sync : ggml (#2001)
|
2024-03-27 18:55:10 +02:00 |
mma.cuh
|
CUDA: int8 tensor cores for MMQ (q4_K, q5_K, q6_K) (llama/7860)
|
2024-06-16 18:19:48 +03:00 |
mmq.cu
|
CUDA: revise q8_1 data layout for mul_mat_q (llama/7824)
|
2024-06-16 18:19:48 +03:00 |
mmq.cuh
|
CUDA: faster q2_K, q3_K MMQ + int8 tensor cores (llama/7921)
|
2024-06-16 18:19:48 +03:00 |
mmvq.cu
|
CUDA: refactor mmq, dmmv, mmvq (llama/7716)
|
2024-06-16 18:19:48 +03:00 |
mmvq.cuh
|
sync : ggml (#2001)
|
2024-03-27 18:55:10 +02:00 |
norm.cu
|
ggml : fix YARN + add tests + add asserts (llama/7617)
|
2024-06-16 18:19:48 +03:00 |
norm.cuh
|
sync : ggml (#2001)
|
2024-03-27 18:55:10 +02:00 |
pad.cu
|
sync : ggml (#2001)
|
2024-03-27 18:55:10 +02:00 |
pad.cuh
|
sync : ggml (#2001)
|
2024-03-27 18:55:10 +02:00 |
pool2d.cu
|
sync : ggml (#2001)
|
2024-03-27 18:55:10 +02:00 |
pool2d.cuh
|
sync : ggml (#2001)
|
2024-03-27 18:55:10 +02:00 |
quantize.cu
|
CUDA: revise q8_1 data layout for mul_mat_q (llama/7824)
|
2024-06-16 18:19:48 +03:00 |
quantize.cuh
|
CUDA: revise q8_1 data layout for mul_mat_q (llama/7824)
|
2024-06-16 18:19:48 +03:00 |
rope.cu
|
ggml : refactor rope norm/neox (llama/7634)
|
2024-06-16 18:19:48 +03:00 |
rope.cuh
|
sync : ggml (#2001)
|
2024-03-27 18:55:10 +02:00 |
scale.cu
|
Introduction of CUDA Graphs to LLama.cpp (llama/6766)
|
2024-05-13 11:02:26 +03:00 |
scale.cuh
|
sync : ggml (#2001)
|
2024-03-27 18:55:10 +02:00 |
softmax.cu
|
CUDA: faster q2_K, q3_K MMQ + int8 tensor cores (llama/7921)
|
2024-06-16 18:19:48 +03:00 |
softmax.cuh
|
sync : ggml (#2001)
|
2024-03-27 18:55:10 +02:00 |
sumrows.cu
|
sync : ggml (#2001)
|
2024-03-27 18:55:10 +02:00 |
sumrows.cuh
|
sync : ggml (#2001)
|
2024-03-27 18:55:10 +02:00 |
tsembd.cu
|
sync : ggml (#2001)
|
2024-03-27 18:55:10 +02:00 |
tsembd.cuh
|
sync : ggml (#2001)
|
2024-03-27 18:55:10 +02:00 |
unary.cu
|
tests : add non-cont unary tests (llama/7857)
|
2024-06-16 18:19:48 +03:00 |
unary.cuh
|
feat: implemented sigmoid function (ggml/806)
|
2024-05-13 11:02:26 +03:00 |
upscale.cu
|
ggml : add ggml_upscale_ext (ggml/814)
|
2024-06-16 18:19:48 +03:00 |
upscale.cuh
|
sync : ggml (#2001)
|
2024-03-27 18:55:10 +02:00 |
vecdotq.cuh
|
CUDA: faster q2_K, q3_K MMQ + int8 tensor cores (llama/7921)
|
2024-06-16 18:19:48 +03:00 |