* WIP: absorb adding input into std_attn and std_ffn
* WIP: NCCL infra
* WIP: add reduce and fake_cpy ops
* WIP
* WIP: graph appears to work, layer is broken
* WIP: Qwen3-MoE works with graph, layer still broken
* WIP: GLM-4.5 graph works
* WIP: fix sm layer (dense)
* WIP: fix sm layer (MoE)
* WIP: fast PP with bespoke 4-GPU NCCL
I guess, I'm not using NCCL the right way as PP is very
low with a single communicator group for 3 or more GPUs.
But if I create 4 communicator groups for pairs of GPUs
(0,1, 2,3, 0,2, 1,3) and use that, PP is fast: I'm hitting
1500 t/s for L3-70B on the 4x3090 system, which is
~20% better than the previous sm graph without NCCL.
But that cannot be the solution (I cannot be creating pairwise
communicators and associated logic for every possible number of GPUs).
* WIP: Cohere2
* Explicitely set device
* Bespoke 3-GPU case
* WIP
* Do not repeat get_rows multiple times
* Fix 3 GPUs
* OK, let's leave it in
* Implement the reduce op without NCCL available
* Be able to build without NCCL
cmake -DGGML_NCCL=OFF disables it
* Make --max-gpu work again
* Slightly better for 4 GPUs without NCCL
* Cleanup
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* This should do the trick for PP
* Command line option to set max. extra VRAM that the scheduler can use
* Fix bug and cleanup
* Looks like with this change it is working with tensor overrides
* Nah, it is not working
* OK, this seems to be working
* Disable split scheduling with tensor overrides
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* This works and TG is descent, but PP is low
* Better
* Apply f_logit_scale before mul mat with output tensor
* This is better for PP: 600 t/s -> 700 t/s
* To not lose this again
* WIP
* Equal split
* WIP
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* Rearrange graph nodes
So that we can do graph portions that are the same on 2 or more
GPUs at the same time.
* Separate graph compute implementation for split mode graph
* This is better
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
Required adding the "temperature scaling" to the standard attention
implementation.
But in this way split mode "graph" is automatically supported.
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* Remove most of split mode row
* WIP
* WIP: also allocate the KV cache using tensor split
* WIP: it runs with wrong result
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
* WIP
* This works, but it is slow
* This is slightly better
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.
* Playing games with the scheduler
This change tricks it into doing the right thing^TM.
Still quite a bit slower than split mode layer for the 8B LlaMA model.
But for the 70B LlaMA it now beats split mode layer for TG:
28 t/s vs 24.4 t/s. PP is 627 t/s vs 744 t/s.
In comparison, split mode "row" in mainline gets
484 t/s PP and 19.3 t/s TG.
* Fix attn split
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.
* Show memory used per device
* Make it work with partial offload
but no tensor overrides yet, just ngl < num_layers.
* Allow for f16 source in fused_rms_norm
* This results in faster PP.
Now PP is faster than split mode layer for L3-70B.
* Rename split mode "row" to split mode "graph"
* Leave FFN partial results as f16
* WIP GLM4.5 - runs with wrong results
* WIP GLM4.5 - this works
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.
* Work around compiler bug
It issues a warning that there is an extra semicolon outside of a function,
but there isn't. If I remove the anonymous namespace and turn the
functions inside into static, the warning disapears, so clearly
a compiler bug.
* Make graph reuse work with split mode graph
* Remove more split mode row remnants
* WIP tensor overrides
Runs with wrong results, don't see where the issue could be.
* This works but is slow
Still does not work for row-interleaved quants
* Slightly better
* Slightly better
* Row-interleaved quants work
* Better
* Minor
* Guarad against using split mode "graph" for unsupported models
* Guards against using merge_qkv with split mode "graph"
* WIP split mode attn
Works for LlaMA models, but not for GLM-4.5.
Doesn't seem to improve performance, so I guess no point in trying to
fix it.
* Split mode graph for qwen3moe
* Try to better distribute the splits
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* 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>
* Add mainline compatible FA command line option
* Graph reuse: add command line argument to turn it on
* WIP
* This seems to work
* This is perhaps cleaner
* Change the command line option to -gr
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* Fix q5_0_r4
The issue waqs in the tail part. As almost all models have tensor
rows that are multiple of 128, that part was never triggered in testing.
But ithe gpt-oss models have an embedding size of 2880, so we end
up there and trigger the bug.
* Fix q6_0_r4
Same fix as q5_0_r4
* Fix q4_0_r8
* Fix q5_0_r4 and q6_0_r4 also on Zen4
* Fix q4_0_r8 also on Zen4
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
so more recent users that haven't followed the history of FlashMLA
evolution and hence don't know about the MLA options get the best setting
without having to add -mla 3 on the command line.
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* Use new-new-mma also for MLA=3, and use mask bounds
This gives us ~25% better PP at 32k tokens compared to main
* This seems better
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* Fuse concat and copy into K cache
* Avoid ggml_cont() when n_token = 1
Combined effect: about +2% in TG performance with full GPU offload
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* Use mmq_id in mul_mat_id
* Better
* Also use it in the fused up+gate op
* Better -no-fmoe TG on CUDA
Still much slower than -fmoe, but abot 20-25% faster than what
we had before.
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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>
* 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
---------
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>
* Don't use vector kernels if K or V are quantized
* Correctly determine if FA is supported
* Also wmma
* Minor
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
