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CUDA: fix quantized KV cache + multiple sequences #14822
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JohannesGaessler
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Jul 23, 2025
Merged
CUDA: fix quantized KV cache + multiple sequences #14822
JohannesGaessler
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ggml-org:gg/tests-fa-non-cont
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JohannesGaessler:cuda-fa-nc-quant
Jul 23, 2025
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ggerganov
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Tested and works correctly after removing the extra alloc.
23 tasks
Co-authored-by: Georgi Gerganov <[email protected]>
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ggerganov
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* CUDA: fix quantized KV cache + multiple sequences * Update ggml/src/ggml-cuda/fattn-common.cuh Co-authored-by: Georgi Gerganov <[email protected]> --------- Co-authored-by: Georgi Gerganov <[email protected]>
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* origin/master: (49 commits) ci : correct label refactor->refactoring (ggml-org#14832) CUDA: fix quantized KV cache + multiple sequences (ggml-org#14822) tests : add non-cont K,V FA tests memory : handle saving/loading null layers in recurrent memory (ggml-org#14675) ggml: fix loongarch quantize_row_q8_1 error (ggml-org#14827) CANN: weight format to NZ for Ascend310P3 (ggml-org#14407) CUDA: add fused rms norm (ggml-org#14800) ggml : model card yaml tab->2xspace (ggml-org#14819) vulkan: fix rms_norm_mul to handle broadcasting dim0 (ggml-org#14817) llama : add model type detection for rwkv7 7B&14B (ggml-org#14816) imatrix: add option to display importance score statistics for a given imatrix file (ggml-org#12718) Mtmd: add a way to select device for vision encoder (ggml-org#14236) cuda : implement bf16 cpy ops and enable bf16 cont (ggml-org#14763) opencl: remove unreachable `return` (ggml-org#14806) server : allow setting `--reverse-prompt` arg (ggml-org#14799) cuda: remove linking to cublasLt (ggml-org#14790) opencl: fix `im2col` when `KW!=KH` (ggml-org#14803) opencl: add conv2d kernel (ggml-org#14403) sycl: Fix im2col (ggml-org#14797) kleidiai: add support for get_rows (ggml-org#14676) ...
Getting compile errors on this, see #14834 |
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Jul 25, 2025
* CUDA: fix quantized KV cache + multiple sequences * Update ggml/src/ggml-cuda/fattn-common.cuh Co-authored-by: Georgi Gerganov <[email protected]> --------- Co-authored-by: Georgi Gerganov <[email protected]>
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ggml
changes relating to the ggml tensor library for machine learning
Nvidia GPU
Issues specific to Nvidia GPUs
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This PR extends the generic dequantization kernel with support for non-contiguous inputs. As it is the dequantization can be expected to be faster if a contiguous allocation of the data is assumed. Long-term the dequantization code should be refactored more generally.