PP is already better than split mode layer, but TG for zero context
is kind of low - 60 vs 92 t/s. TG becomes better than split mode layer
at around 20k tokens. PP at 26k tokens is 1.55X of sm layer.
Granularity for Wq, Wo is not just head size, but
head size * gqa_ratio.
Else the Wk, Wv tensors end up not being a multiple of the
head size when we divide the split determined by Wo with
the gqa_ratio.
the graph is still not being computed in parallel.
Why? Because the scheduler creates graph splits where the
result of the computation on one GPU becomes an input for the
other split. Hence, to trigger the computation on the second GPU
one needs to wait for the computation on the first GPU to finish,
even thiough the two can be done in parallel up to the sunchronization
point. So, all that is left to do is to trick the scheduler to create
to splits that can be done in parallel, and then have a graph split
where the results get combined.
But it also looks like the backend scheduler is not going to help:
* It copies mask and input positions to GPU 0
* => RoPE ops must run on GPU 0
* => To proceed attn evaluation, GPU 1 must wait for GPU 0 to finish its
entire attn calculation
* Same with FFN. The rms_norm gets scheduled on GPU 0. Hence, GPU 1 must
wait for GPU 0 to finish its entore FFN calculation before it can
start (as it needs to copy the result of rms_norm from GPU 0)
* => Seems useless without writing a bespoke TP scheduling
* Fixing Gigachat support
* Gigachat: CUDA FA (needs 192 x 192 for MLA = 3)
* Gigachat: CPU FA (needs 192 x 192 for MLA = 3)
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* 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.
---------
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
---------
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
---------
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>
