This commit updates the include directive in cumsum.cu to use
cub/cub.cuh instead of cub/block/block_scan.cuh.
The motivation of this change is that without it compilation fails
with the following error:
```console
/llama.cpp/ggml/src/ggml-cuda/cumsum.cu(196): error: name followed by "::" must be a class or namespace name
cub::DeviceScan::InclusiveSum(nullptr,
^
/llama.cpp/ggml/src/ggml-cuda/cumsum.cu(207): error: name followed by "::" must be a class or namespace name
cub::DeviceScan::InclusiveSum((void *) tmp_alloc.get(), tmp_size, src, dst, ne, stream);
^
2 errors detected in the compilation of "/llama.cpp/ggml/src/ggml-cuda/cumsum.cu".
gmake[2]: *** [ggml/src/ggml-cuda/CMakeFiles/ggml-cuda.dir/build.make:317: ggml/src/ggml-cuda/CMakeFiles/ggml-cuda.dir/cumsum.cu.o] Error 2
```
Commit 83b3b1c271 ("cuda: optimize
cumsum cub path (#18362)") updated the include directive replacing
device_scan.cuh which is causing this issue.
This commit uses cub/cub.cuh umbrella header which is consistent with
other files in the ggml-cuda directory like mean.cu, sum.cu, etc.
* vulkan: Use BK=32 for coopmat2 mul_mat_id
* vulkan: optimize decodeFuncB in coopmat2 mul_mat_id shader
Disable robustness, remove the OOB check in decodeFuncB, and initialize the
row_ids to zero to avoid OOB access.
Don't slice/offset the B matrix to ic * BN, only to adjust the coord back down
to the range [0, BN) in decodeFuncB. Instead just slice with a row offset of
zero and remove the '& (BN - 1)'. This allows the compiler to common some of
the shared memory loads.
* ggml-cuda: fix blackwell native builds
Replace 12x in native architectures by 12xa
* replace for GGML_NATIVE=OFF too
* only replace for native
* remove 120f-virtual for default compilation
---------
Co-authored-by: Aman Gupta <aman>
* CONV_TRANSPOSE_1D kernel_size>255
* remove condition check
* fix the bug of type conversion
* removing trailing whitespaces
* fix: return true in the switch case
* refactor: replace ggml_hexagon_mul_mat with template-based binary operation for improved flexibility
* refactor: replace ggml_hexagon_mul_mat_id with template-based binary operation for improved flexibility
* refactor: initialize buffer types and streamline dspqueue_buffers_init calls for clarity
* add comment
* refactor: remove redundant buffer checks in hexagon supported operations
* wip
* add missing include to fix weak symbol warning
* add ggml_hexagon_op_generic
* refactor: simplify tensor operation initialization and buffer management in hexagon implementation
* refactor: streamline hexagon operation initialization and buffer management
* refactor: update function signatures and streamline request handling in hexagon operations
* wip
* ggml-hexagon: clean up code formatting and improve unary operation handling
* wip
* rename
* fix: add support for permuted F16 tensors and enhance quantization checks in matrix operations
* refactor: replace ggml_hexagon_mul_mat with template-based binary operation for improved flexibility
refactor: replace ggml_hexagon_mul_mat_id with template-based binary operation for improved flexibility
refactor: initialize buffer types and streamline dspqueue_buffers_init calls for clarity
refactor: remove redundant buffer checks in hexagon supported operations
add missing include to fix weak symbol warning
add ggml_hexagon_op_generic
refactor: simplify tensor operation initialization and buffer management in hexagon implementation
refactor: streamline hexagon operation initialization and buffer management
refactor: update function signatures and streamline request handling in hexagon operations
ggml-hexagon: clean up code formatting and improve unary operation handling
fix: add support for permuted F16 tensors and enhance quantization checks in matrix operations
# Conflicts:
# ggml/src/ggml-hexagon/ggml-hexagon.cpp
* hexagon: fix merge conflicts
* hexagon: minor cleanup for buffer support checks
* hexagon: factor out op_desc and the overal op logging
* hexagon: further simplify and cleanup op dispatch logic
* snapdragon: update adb scripts to use llama-cli and llama-completion
* fix pipeline failure
---------
Co-authored-by: Max Krasnyansky <maxk@qti.qualcomm.com>
* feat: working gelu with src0 put on vtcm
* feat: gelu ping-pong for both in and out
* fix: fixu compile error
* break: distinguish dma ddr->vtcm and vtcm->ddr operation
* fix: fix dma queue size
* break: update dma api to either pop src or dst ptr
* fix: fix activation vtcm allocation issue for src1 when swapperd
* refactor: ping-pong gelu logic to avoid unnecessary if else
* dma: improved queue interface and prefetch handling
* gelu: fix N+2 block prefetch
---------
Co-authored-by: Max Krasnyansky <maxk@qti.qualcomm.com>
The goal is to enable the async loading code paths in
llama_model_loader::load_all_data, originally from #7896. This works and the
loads themselves are faster, but with host visible vidmem I think the cost of
allocating/mapping vidmem moves and becomes more expensive, and I don't see a
benefit by default. But with GGML_VK_DISABLE_HOST_VISIBLE_VIDMEM=1 I do see a
significant improvement in model loading time.
I updated test_topk_moe to more closely match llm_graph_context::build_moe_ffn
and added coverage for exp_probs_b and some other missing combinations. This
exposed a bug in both CUDA and Vulkan backends where they were assuming the
input to argsort and the input to get_rows are the same. I'd like to optimize
this graph in another change, but for now just get it functional.
CUDA also had a bug where it got n_experts from the wrong place, leading to
GGML_ASSERT failures in some of the new tests.
* Some improvement on mul_mat_iq2_xs
Refactor calculations for db values and grid data to optimize performance and reduce redundancy.
* Fix trailing whitespace
Georgi Gerganov
Charles Xu
Aman Gupta
Daniel Bevenius
Johannes Gäßler
uvos
o7si
Boian Berberov
QDelta
lhez
Jeff Bolz
Eve
0Marble
Penglin Cai
Aadeshveer Singh
Wang Weixuan
Ruben Ortlam
TianHao324
Chris Rohlf
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Shouyu
Taimur Ahmad
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