* Samplers sequence: simplified and input field.
* Removed unused function
* Modify and use `settings-modal-short-input`
* rename "name" --> "label"
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Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
Compute two result elements per workgroup (for Q{4,5}_{0,1}). This reuses
the B loads across the rows and also reuses some addressing calculations.
This required manually partially unrolling the loop, since the compiler
is less willing to unroll outer loops.
Add bounds-checking on the last iteration of the loop. I think this was at
least partly broken before.
Optimize the Q4_K shader to vectorize most loads and reduce the number of
bit twiddling instructions.
* use 128 bit loads (i've tried 256->128 to death and its slower)
* double accumulator
* avx bf16 vec dot
* +3% q4_0 inference
* +7% tg +5% pp compared to master
* slower f16c version, kep for reference
* 256b version, also slow. i tried :)
* revert f16
* faster with madd
* split to functions
* Q8_0 and IQ4_NL, 5-7% faster
* fix potential overflow (performance reduced)
* 16 bit add for q4_0 only
* merge
* server : (web ui) add copy btn for code blocks
* fix problem with api key
* use settings-modal-short-input component
* always show copy btn for code snippet
* sycl: Use syclcompat::dp4a
* Using the syclcompat version allow the compiler to optimize the
operation with native function
* Update news section
* Update CI Windows oneAPI version to 2025.0
* Reword doc
* Call syclcompat::dp4a inside dpct::dp4a
This reverts commit 90cb61d692.
Reuse the index calculations across all of src0/src1/dst. Add a shader
variant for when src0/src1 are the same dimensions and additional modulus
for src1 aren't needed. Div/mod are slow, so add "fast" div/mod that
have a fast path when the calculation isn't needed or can be done more
cheaply.
* llama: propagating the results of `graph_compute` to the user interface
* llama: reverting kv_cache in case of failed compute
* llama: `llama_kv_cache_state` was removed, only the result of `llama_graph_compute` is returned
* llama: restore a kv_cache in case of failed computation
* llama: correct reverting of the entire batch.
also updates `llama_kv_cache_find_slot`, will correctly count the number of `used` cells for recurrent models
* llama: updated comments
* llama : add comments about KV cache state after error
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Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
