* Merge Q and K into a single tensor
* Make V mul mat follow QK mul mat
so they can be fused, which gives a slightly bbetter TG performance.
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* server: add support for vision model
webui: add support for vision model
* server : remove hack for extra parallel slot#10187
* llama : fix KV shift for qwen2vl #13870
* add no-context-shift parameter
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Co-authored-by: firecoperana <firecoperana>
* Introducing rope cache
When computing RoPE, the rotation angles in each layer
are exactly the same, and only depend on the token positions
(and other constant, model dependent parameters).
So, I wonder, why don't we compute the angles just once
and then reuse for the Q and K RoPE in each layer?
This commit does it as a POC on the CPU, and uses it in
the Qwen3-MoE compute graph.
* cuda: neox works
* WIP
* rope_cache: norm works
* Fused rope+rope
* Fused rope+rope (norm)
* Fused rms+rms+rope+rope (neox) - not working
* WIP
* Also qwen3
* Add command line arg to disable rope cache
* Disable RoPE cache if rope type is not neox or norm
* Add missing break after merge with main
* Fused fused_rms+fused_rms+rope+rope (with -mqkv)
* Fused fused_rms+fused_rms+rope+rope (without -mqkv)
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* Biased mmvq: minor optimization
* Fusing Q and K rms_norm for TG on CUDA
* Remove commented out code
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* POC: merge Q, K, V into a single, contiguous tensor
Done just for Qwen3-MoE, where I see a 4% uplift in TG.
PP performance gain is sub-percent, if any.
Still, it seems it makes sense to do it in general given
the TG performance gain.
* WIP
* merge_qkv: it works for gpt-oss
...but we see a smaller TG gain (~1.5%)
* WIP
* Don't ignore the return value of create_tensors()
else, when q, k, v get merged and we are running on the CPU,
we get a crash because the backend is trying to use mmap,
but that no longer works.
* merge_qkv: bias can be required, optional, or mandatory
* merge_qkv: glm4.5moe
* merge_qkv: add command loine argument to enable
* merge_qkv: fix tensor dimensions
* merge_qkv: llama-4
* merge_qkv: qwen3 (dense)
* merge_qkv: simplify build_qwen3moe
* cohere2 - simplify graph building
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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>
* 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.
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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
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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.
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* Do not allocate KV cache for unused layers
* Do not apply experts weight scale if it is 1
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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%.
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* llama_model and llama_hparams
* llama_build_context
Surprisingly small reduction in llama.cpp compile time given
the reduction in LOCs (22k -> 14k)
* LLM_TN
llama.cpp compilation: 50 s -> 33 s
* llama_quantize
* arch names
* All graph building is now in llm-build-context.cpp
* hparams loading
llama.cpp is now just 9300 LOC, but still takes 32 seconds to compile.
* We are now at 6 seconds to build the src folder
* load -> create
We are not actually loading the tensors, but just creating them.
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
