* Add GLM-4-0414 model support
Based on zRzRzRzRzRzRzR's PR on mainline llama.cpp.
Still some issues where it doesn't work:
* offloading >=60 layers to GPU
* no flash attention
* Remove seemingly unused llm_tensor enums
Both of these seem unused and LLM_TENSOR_ATTN_POST_NORM already
existed which seems pretty similar? Don't think they were used in the
python code either...
So removed these as possibly just cruft:
* LLM_TENSOR_POST_ATTN_NORM
* LLM_TENSOR_POST_MLP_NORM
* Set flash attention precision to f32 on GLM4 arch
* Set non flash attention precision to f32 on GLM4
* Remove reshape_3d() for Vcur in build_glm4()
This fixes the non-flash-attention inferencing on both CPU and CUDA.
* Slightly better CPU TG performance for GQA
* Better CPU FA implementation for TG when GQA
* Minor
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* imatrix: collect layer influence statistics
* imatrix: collect layer influence statiscs also for the last layer
For the last layer we need to use the input for the output.weight
tensor. Last layer(s) tend(s) to be important, so it is useful to also
have its influence metric.
* imatrix: separate metric for attention and ffn importance
* Use stripped tensor name, not src0->name
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* Metal: WIP to update Metal FA implementation
Dk=192, Dv=128 works, but not Dk = 576, Dv = 512
* Metal FA: go to float
* WIP
* Metal FA: MLA options now all work
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* MoE improvements on Metal
This version beats mainline, there are things I don't understand:
* Mianline has effectively gone to GEMV for MUL_MAT_ID. We can do the
same, but we are 30% slower. Why?
* Using actual GEMM, we beat mainline with ubtach size of 128. But then
performance degrades. Why?
* Some cleanup
* Much better
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* iq3_k: slightly better quantization
Not much of a difference for most models, but this change
avoids what it looks like a catastrophic failure for DeepSeek-Lite
(PPL is now 7.041 vs 7.314 on main).
* Small improvement for type-1 quants
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* Make sure no interleaved quants are being used for token embeddings
also with `--pure` and/or `--custom-q`.
* Simplify
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* Better make_qx_quants
Tested with q4_0 and q3_K (pure, imatrix), and the improvement is
quite significant.
* Sae for iq4_nl, iq4_xs
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* WIP - not working
* q8_0 without bells and wistles works
* It works for q8_0
* Use bf16 instead of f16,int16
* q4_0_r8
* q5_0_r4
* q6_0_r4
* Also q4_1 and q5_1
* Add check if selected type is possible with --pure
I often want to quantize with --pure to see quantization performance
without quantization mixes. But for models where there qre tensors
with row sizes that are not multiple of 256, this results in a crash
for k- and i-quants. Hence, lets add a check if the quant selected
via --pure is applicable, and change it if not.
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* WIP - not working
* q8_0 without bells and wistles works
* It works for q8_0
* Use bf16 instead of f16,int16
* q4_0_r8
* q5_0_r4
* q6_0_r4
* Also q4_1 and q5_1
* q8_0_r8 on avx2
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* llama-bench: enable having different number of threads for tg and pp
* Add -tgb to usage
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* Make fused MoE reproducible
As a bonus, peak performance at pp2048 with u_batch = 2048 is
~8% better.
* Slightly better
* Also do it for non-fused mul_mat_id
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* Improve DeepSeek batched processing speed
* Revert the commented out section in iqk_mul_mat.cpp
It does have some benefit at long contexts.
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* Adding ability to use THP on Linux
* Use the actual page size4 used for mmap also in munmap
* Add -thp to llama-bench
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* WIP Gemma3: not working
* gemma3: build_gemma3 seems to be working now
* Revert changes to convert_hf_to_gguf.py
It wasn't working, so I guess, it is better to leave the
conversion up tp upstream.
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
