Sync master with upstream release b6264 #217
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* [CANN] Optimize RMS_NORM using cache Signed-off-by: noemotiovon <[email protected]> * fix typo Signed-off-by: noemotiovon <[email protected]> * fix review comment Signed-off-by: noemotiovon <[email protected]> * codestyle adjustment Signed-off-by: noemotiovon <[email protected]> --------- Signed-off-by: noemotiovon <[email protected]>
* Support untied embeddings * Increase number of image tokens to 1024 * Add LFM2-VL to readme * Actually use untied embeddings
… format (ggml-org#15108) - Use server_tokens in more places in server and util.cpp - Convert most functions that used llama_tokens to server_tokens - Modify input tokenizer to handle JSON objects as subprompts - Break out MTMD prompt parsing into utility function - Support JSON objects with multimodal_data arrays for MTMD prompts along with other existing types - Add capability to model endpoint to indicate if client can send multimodal data - Add tests.
* ggml-cpu: initial q5_0 impl for s390x Signed-off-by: Aaron Teo <[email protected]> * ggml-cpu: updated q5_0 code for better performance Signed-off-by: Aaron Teo <[email protected]> * ggml-cpu: use optimised hsum for better performance Signed-off-by: Aaron Teo <[email protected]> * ggml-cpu: introduce q5_1 simd + refactor q5_0 Signed-off-by: Aaron Teo <[email protected]> * ggml-cpu: fix incorrect return type vec_hsum Signed-off-by: Aaron Teo <[email protected]> * ggml-cpu: q5_0 incomplete refactor + table_b2b_0 activation Signed-off-by: Aaron Teo <[email protected]> * ggml-cpu: refactor q5_1 Signed-off-by: Aaron Teo <[email protected]> * ggml-cpu: q5_1 update loop unroll to 4 Signed-off-by: Aaron Teo <[email protected]> * ggml-cpu: update q5_0 unroll to 4 Signed-off-by: Aaron Teo <[email protected]> * ggml-cpu: update build-s390x docs Signed-off-by: Aaron Teo <[email protected]> * ggml-cpu: update unused variables q5_0 Signed-off-by: Aaron Teo <[email protected]> * docs: update the last update date Signed-off-by: Aaron Teo <[email protected]> --------- Signed-off-by: Aaron Teo <[email protected]>
* Add Pad Reflect 1D CUDA support * Update ggml/src/ggml-cuda/pad_reflect_1d.cu Co-authored-by: Johannes Gäßler <[email protected]> --------- Co-authored-by: Johannes Gäßler <[email protected]>
* Begin work on set_rows * Work on set rows * Add error buffers for reporting unsupported SET_ROWS indices * Remove extra comments * Work on templating for different types in shaders * Work on shader type generation * Working q4_0 mul_mat and some templating for different types * Add q4_0_f16 matmul and fix device init * Add matmul support for basic quantization types * Add q2_k and q3_k quantization * Add rest of k-quants * Get firt i-quant working * Closer to supporting all i-quants * Support rest of i-quants * Cleanup code * Fix python formatting * debug * Bugfix for memset * Add padding to end of buffers on creation * Simplify bit-shifting * Update usage of StringView
…org#15427) - Spread the work across the whole workgroup. Using more threads seems to far outweigh the synchronization overhead. - Specialize the code for when the division is by a power of two.
* vulkan : support ggml_mean * vulkan : support sum, sum_rows and mean with non-contiguous tensors * vulkan : fix subbuffer size not accounting for misalign offset * tests : add backend-op tests for non-contiguous sum_rows * cuda : require contiguous src for SUM_ROWS, MEAN support * sycl : require contiguous src for SUM, SUM_ROWS, ARGSORT support * require ggml_contiguous_rows in supports_op and expect nb00=1 in the shader
Signed-off-by: Xiaodong Ye <[email protected]>
…gml-org#15489) Track a list of nodes that need synchronization, and only sync if the new node depends on them (or overwrites them). This allows some overlap which can improve performance, and centralizes a big chunk of the synchronization logic. The remaining synchronization logic involves writes to memory other than the nodes, e.g. for dequantization or split_k. Each of these allocations has a bool indicating whether they were in use and need to be synced. This should be checked before they are written to, and set to true after they are done being consumed.
* First draft * Fix linter errors * Added missing sinks nullptr * Don't forget the llama-arch! * We're through to the generation stage. * Fix post-attention norm * Apply suggestions from code review Co-authored-by: Sigbjørn Skjæret <[email protected]> * Fix RoPE type * Fix tensor name and reorder llm_types * Update gguf-py/gguf/constants.py Remove nonexistent FFN_POST_NORM tensor Co-authored-by: Sigbjørn Skjæret <[email protected]> * Update src/llama-model.h Co-authored-by: Sigbjørn Skjæret <[email protected]> * Add basic chat template * Add chat template tests * Remake chat template test * Apply suggestions from code review Co-authored-by: Sigbjørn Skjæret <[email protected]> * Update src/llama-chat.cpp Co-authored-by: Sigbjørn Skjæret <[email protected]> * Reorder llm type descriptions * Update src/llama-model.cpp Co-authored-by: Sigbjørn Skjæret <[email protected]> --------- Co-authored-by: Sigbjørn Skjæret <[email protected]>
…le SMs (ggml-org#15281) * vulkan: optimize rms_norm, and allow the work to spread across multiple SMs There are really two parts to this change: (1) Some optimizations similar to what we have in soft_max, to unroll with different numbers of iterations. (2) A fusion optimization where we detect add followed by rms_norm, and make the add shader atomically accumulate the values^2 into memory. Then the rms_norm shader can just load that sum. This allows the rms_norm to be parallelized across multiple workgroups, it just becomes a simple per-element multiply. The fusion optimization is currently only applied when the rms_norm is on a single vector. This previously always ran on a single SM. It could apply more broadly, but when there are other dimensions the work can already spread across SMs, and there would be some complexity to tracking multiple atomic sums. * Change add+rms_norm optimization to write out an array of partial sums rather than using atomic add, to make it deterministic. The rms_norm shader fetches a subgroup's worth in parallel and uses subgroupAdd to add them up. * complete rebase against fused adds - multi_add shader can also compute partial sums * fix validation errors * disable add_rms_fusion for Intel due to possible driver bug * resolve against ggml-org#15489, sync after clearing partial sums
) * vulkan: workaround MoltenVK compile failure in multi_add * Update ggml/src/ggml-vulkan/vulkan-shaders/multi_add.comp Co-authored-by: 0cc4m <[email protected]>
The scalar FA shader already handled multiples of 8. The coopmat1 FA shader assumed 16x16x16 and the shared memory allocations need the HSK dimensions padded to a multiple of 16. NVIDIA's coopmat2 implementation requires multiples of 16 for N and K, and needs the matrix dimensions padded and loads clamped. Store the FA pipelines in a map, indexed by the pipeline state.
* kv-cache : support layer reuse ggml-ci * cont : update comments [no ci]
…ggml-org#15524) * vulkan: use subgroup function for mul_mat_id shader even without coopmat * vulkan: fix compile warnings * vulkan: properly check for subgroup size control and require full subgroups for subgroup mul_mat_id * vulkan: disable subgroup mul_mat_id on devices with subgroups < 16
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Minh141120
approved these changes
Aug 25, 2025
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Updates dev branch with latest release (b6264) from ggml-org/llama.cpp