* Biased mmvq: minor optimization
* Fusing Q and K rms_norm for TG on CUDA
* Remove commented out code
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* Don't use vector kernels if K or V are quantized
* Correctly determine if FA is supported
* Also wmma
* Minor
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* Fuse Q, K, V gemv+add
* More gemv+add fusing
* Faster copy when tensors are contiguous
Relevant for storing data into the KV cache. I see ~1% speedup
for fast models (Ling-mini-2.0, gpt-oss-20b, etc.)
* Cleanup
* Make sure the bias really is 1 row to use fusion
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* Args for MMVQ functions
* WIP
* Fused ffn_up*unary_op(ffn_gate) for MMVQ (no bias)
We see nearly 2% TG speedup for Ling-mini-2.0 and
about 1% for DeepSeek-Lite.
* Fused ffn_up*unary_op(ffn_gate) for MMVQ (with bias)
* Fusing also for iqk/trellis/repacked quants
* Fusing mmvq also in non-MoE up+gate
* Fuse mul_mat_id and add_id into a single kernel for mmvq
* Also iqk quants
* Split mmvq.cu and iqk_mmvq.cu into separate template instances
* Put iqk mmvq implementations into template instances
* Somehow I forgot to change the ggml_type in the legacy template calls
* Add disagnostics
* Disable assert
* Fix TG fused up*nary(gate) when down cannot be fused
The wrong memory buffer got used in that case
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* Adding fused mul+multi_add + CPU implementation
* fused mul+multi_add: command line argument to disable it
* Faster tensor name formatting
We gain ~1% for Ling-mini-2.0 when running on CUDA.
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* Adding fused mul+multi_add + CPU implementation
* fused mul+multi_add: CUDA
* fused mul+multi_add: command line argument to disable it
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* Fuse add+add+fused_rms
* Try this
* Macro to easily enable/disable fusion
* Various:
* Check that all tensors involved are on the same device before applying fusion
* Fuse sigmoid+scale+sum_rows+div
* Fix the fused bailingmoe2 experts selection
The issue there was that the bias was not per row, but per
expert group, so only the first n_per_group biases were used
for al experts.
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* Combine all calls to llm_build_norm to a single line
so more easily check what kind of arguments are being passed
by simply using grep.
* Combine add + fused_rms_norm
For many models this happens at each layer: the result of the
layer is added to the ayer input, which then becomes the input
to the next layer, which then is typically normalized via
fused_rms_norm.
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* Fuse sigmoid+add+grouped_topk+get_rows (CPU)
* Fix CPU + CUDA
but CUDA is somehow not 100% correct as I get a slightly different
PPL (lower!)
* Minor
* Fuse sigmoid+add+topk+get_rows (CUDA)
* Fuse sigmoid+add+topk+get_rows (CPU)
* Fuse topk+view+get_rows+reshape+softmax (CPU)
* Fuse topk+view+get_rows+reshape+softmax (CUDA)
* cpu: turn off the openai topk fusing for now
Something is not right and I don't see the bug.
On the CPU one doesn't gain much if anything, so not a big loss.
* Also fuse sum_rows and div
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* Better argsort (CPU)
* Attemt at grouped topk
* This seems to do the trick for grouped experts routing
* Cleanup
* Trying to merge, something is not right
* Working merged grouped top_k (CPU)
* Add command line option to enable grouped expert routing
* Add grouped expert routing option to llama-bench
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* Parallelize mask
We see non-negligible PP gains for long contexts.
More importantly, the strange drop in performance
observed for GPT-OSS for context >= 32k tokens is gone.
* Whith FA on, create mask as f16 directly
* WIP
* Reduce KQ mask padding to 16
Why was it 64 in the first place?
I don't observe any issues, while TG performance
for long contexts improves by 2-4%.
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* Add mtmd: the beginning
* Add mtmd: mtmd.cpp compiles
* Add mtmd: clip initialization compiles
* Add mtmd: clip.cpp compiles
* Add mtmd: builds successfully
* Add CPU implementation for GGML_OP_GLU
* Add CUDA implementation for GGML_OP_GLU
* Add CPU implementation for GGML_OP_CONV_2D and GGML_OP_CONV_2D_DW
* Add CUDA implementation for GGML_OP_CONV_2D and GGML_OP_CONV_2D_DW
* Add mtmd: refresh CPU rope
* Add mtmd: refresh CUDA rope
* Add mtmd: add Qwen2-VL
* Add mtmd: Qwen2.5-VL text seems to work with this change
* Add mtmd: fix swiglu
* Add mtmd: use LOG_TEE so generated tokens show up in terminal
* Add mtmd: do not attempt to load a GPU backend if none are available
* GLU, not GPU
* Fix typo
* Fix new/free mismatch
* LOG stuff
* Add mtmd: this fixes gibberish on second image
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* Avoid computing FA chunks where the mask is -infinity
* Avoid computing FA chunks where the mask is -infinity also for f16/bf16
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* Quick attempt to fuse the Q, K, V GEMMs
Doesn't do much on the CPU
* Doesn't do much on the GPU either
* Use llm_build_mul_mat_qkv
* This is not needed
* Revert timing on committed by mistake
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* Offload only activated experts
* This seems to do the trick for -fmoe
* Do not recalculate activated expers for fused up/gate
* Log out of bounds access details
* Add a command line argument
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* Bounds for flash attention
* Add n_swa to FA parameters
* Fix it
* This seems very slightly better
* Using vec kernel when we have SWA
* Need also this
* f32 vec kernel
* This is slightly better
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* Fused up+gate+unary for regular (not MoE) FFN - CPU
* WIP CUDA
* Seems to be working on CUDA
For a dense model we get 2-3% speedup for PP and ~0.6% for TG.
* Add command line option
This time the option is ON by default, and one needs to turn it
off via -no-fug or --no-fused-up-gate
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* Skip the row id computation for the ffn_down op
Sadly, almost negligible performance gain.
* Also this doesn't do much
* Also this barely moves the needle
* This is slightly better
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* Check for NaNs while loading the model.
* Also tell which experts have NaNs.
* Add command line option to validate quants
* Add checks for more quantization types
* Add checks for more quantizagtion types
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* This fixes confusion around Q8_0 on AVX2
* This does it for iq4_nl, including FA
* This does it for iq4_nl on Zen4, but FA does not work
* Slightly more clear
* Adding forgotten q8_0_r8 to num_rows()
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* Use bperm trick for iq2_ks gemm -> 7% gain
* Use bperm trick for iq2_k gemm -> ~5% gain
* Use bperm trick for iq2_k_r4 gemm -> ~3% gain
* Use bperm trick for iq2_ks gemv -> ~7% gain
* Use bperm trick for iq2_k gemv -> ~3% gain
* Use bperm trick for iq2_k_r4 gemv -> ~7% gain
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
