Age | Commit message (Collapse) | Author |
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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* Enable MLA-3 in crippled GGUFs: WIP
* Enable MLA-3 in crippled GGUFs: seems to work
* Add newly created tensors to model.tensors_by_name
Else they don't get run-time repacked.
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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* New DeepSeek FlashMLA
Does not work because the RoPE portion is stored at the end
in our case, while in mainline it is stored at the beginning,
and the FA kernel assumes that.
* Rearrange MLA K cache so it first new CUDA FA implementation
* constexpr and minor changes
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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* Adding GPU offload policy
* Minor
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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This reverts commit 36e6e888b75ae93fb5aac212bb0e147d8379ae23.
I should have tested. We get NaNs.
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Reference: https://github.com/ggml-org/llama.cpp/pull/13438
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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* cuda: Remove unnecessary device to host copy of row ids
We get 3-4% TG speed improvement for DeepSeek-Lite just from that.
* CPU: fix get_rows when SER is used
With smart experts reduction (SER), one potentially uses fewer
experts than specified by the model. This is accomplished by setting
the ID of the not seected tensors to -1. Most of the necessary
stuff was implemented when I added the SER option, but I forgot
to update get_rows() for not quantized tensors. As a result, we
get random garbage for the weights of the not-selected epxerts,
which leads to garbage output. This commit fixes it on the CPU.
I'm not quite sure yet why the GPU is not working.
* CUDA: fix TG with SER
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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* lora : fix llama conversion script with ROPE_FREQS
* convert : refactor rope_freqs generation
This should also fix vocab-only conversion for Phi-3.
* convert : adapt MiniCPM3 to separate rope_freqs insertion
MiniCPM3's tokenizer is treated as a SentencePiece tokenizer to avoid
having to run its custom Python code which mixes tokenization
in the same file as tool calls.
gguf-py : add long and short RoPE factors to tensor mappings
Empty, but the key names are used to populate the mappings.
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Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
Co-authored-by: Francis Couture-Harpin <git@compilade.net>
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@saood06 Thanks!
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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* conflict resolution
* Changes to make work and add longrope support
* Changes to n_attention_wv rule
* Untested support of 253B
* DeciLMCausalModel now reads rope_theta from config.json properly
* Remove errant Granite mentions
* Better n_attention_vw rule
* Update vocab.py
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Co-authored-by: Yee Man Chan <ymchan@gmail.com>
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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* CUDA WIP: support for FlashMLA-3
* Much better
The issue was that I did not change the number of warps
used for 3D matrix multiplications (wk_b * kv_cache, MoE),
so we ended up using 4 warps for TG. By going to 1 warp
in these cases, we get a significant boost in TG performance
(tested with DeepSeek-Lite)
* Sadly, the previous commit was wrong
* Finalizing
* Also add these
* Minor
* Minor tweak
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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* cmake: force MSVC compiler charset to utf-8
* build: apply MSVC /bigobj option to c/cpp files only
* Update CMakeLists.txt
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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* WIP
* WIP: still getting illegal memory access
* CUDA: MMQ for iq4_ks now works
~25% faster than dequantize+cuBLAS, ~10% slower than Q4_0 MMQ.
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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* cuda: WIP MMA FA
* Use MMA for TG also when quantized
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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* Another attempt to fix #367
* Yet another
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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* Fix FA bug on AVX2
* Also this was wrong
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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Co-authored-by: junhuihe <junhui-he@outlook.com>
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* Update README.md
* Edits
* Updates
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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* Fix IQK_FA_ALL_QUANTS on AVX2
* Make it also work, not just compile
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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Add @ubergarm
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* FA: provide work buffer for K repacking
* Add header to avoid comp0iler warnings
* WIP
* WIP
* WIP
* WIP
* Slightly better
* WIP (Zen4)
* WIP
* Try to improve for unusual number of heads/number of threads
* Use mul_mat_qX_0_q8_2_Tx for q6_0 in FA
* Use mul_mat_qX_0_q8_2_Tx for q4_0 in FA
* Use Sum4q4 for q4_0
* WIP
* WIP
* Much better FA TG with q8_0 KV cache
Just repack it even for TG. But do the repacking for k_step rows,
not the whole K tensor.
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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* 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.
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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* Add support for Cohere2
* Fixe IQ4_NL on AVX2
* Command-A needs fp32 precision for K*Q
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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* Update GGMLQuantizationType
* Update LlamaFileType
* Update GGML_QUANT_SIZES
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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* add support for bitnet2b_2501 model
* Fixes
* Support both model names
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Co-authored-by: potassiummmm <zhou.hansong@outlook.com>
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* Attempt fix
* Attempt fix 2
* Attempt fix 3
* Attempt fix 4
* Attempt fix 5
* Attempt fix 6
* Attempt fix 7
* Attempt fix 8
* Attempt fix 9
* Attempt fix 10
* Attempt fix 11
* Attempt fix 12
* Attempt fix 13
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* 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>
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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* Allow q8_0 KV cache for head size 256
* We need also these
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Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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