Sync master with upstream release b10375 - #619
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* Switch ROCm from 7.2.1 to 7.14 ROCm 7.14 is the first production release using TheRock build system. It can be installed using multi-arch deliverables from wheels, debs, rpms, tarballs or runfiles. Adjust ROCm targets for Linux and Windows to use this instead. * ci: switch all other Windows ROCm jobs to ROCm 7.14 wheels Move the shared windows-setup-rocm composite action from the HIP SDK PRO Edition installer to the multi-arch ROCm wheels (rocm[libraries,devel]). The wheel-install logic that previously lived inline in release.yml is now in the shared action, and both build-cache.yml and release.yml call it. Also migrate the build-cuda-windows.yml hip job to the same wheel-based layout (cache path/key, rocm-sdk environment setup, llvm/bin compiler paths) so it keeps working after the action's contract changed; drop its now-unused ROCm 7.2.1 rocWMMA download and stale include path.
…ml-org#26566) * test new flash_attn test * rebase and fix to disable subgrou matrices when max_kv_tile == 0 * delete log output * Add i32 support to cpy and enables the all ops test * restore the non target ci tests * comment out of TODO of build-cpu.yml * fix format
* model : fix SWA not being enabled for EXAONE 4.5 load_arch_hparams tests `hparams.n_layer() == 64` before LLM_KV_NEXTN_PREDICT_LAYERS has been read. n_layer() returns n_layer_all - n_layer_nextn and n_layer_nextn defaults to 0, so a GGUF carrying the MTP head (block_count=65, nextn=1) evaluates to 65 and the whole SWA block is skipped. The model type switch further down in the same function reads 64, because by then the key has been loaded. n_swa is still filled in by the unconditional get_key below the block, so llama_model_n_swa() reports 4096 and the logs look correct while only swa_type stays LLAMA_SWA_TYPE_NONE. This affects the official LGAI-EXAONE GGUF release as well. EXAONE 4.0 has no MTP head, so block_count is 64 there and the check matches. * model-loader : skip TENSOR_SKIP tensors in the metadata-only path create_tensor asserts on a null buffer type when building from metadata alone, but buft_for_tensor returns null by design for tensors marked TENSOR_SKIP, which is how architectures with nextn/MTP layers mark theirs. Those models cannot be constructed by llama_model_init_from_user at all. The file-backed path below already returns nullptr for the same tensors, so callers see the same thing either way. * tests : cover exaone4 hparams ordering Builds a synthetic exaone4 model with the layout the shipped EXAONE 4.5 GGUFs use (block_count 65 + nextn 1). The swa_type check is the one that catches the ordering bug; the n_layer_nextn and n_layer() checks only tell a broken fixture apart from a real regression. Fails before the ordering fix with "swa_type is not STANDARD", passes after. * Revert "tests : cover exaone4 hparams ordering" This reverts commit d2f3baf. * Revert "model-loader : skip TENSOR_SKIP tensors in the metadata-only path" This reverts commit aecb9bc.
* test-backend-sampler: skip multi_output_sampling_chain on HIP The new multi_output_sampling_chain test uses top_k, whose backend probs path needs CUB (unavailable on HIP), so sampled_probs is null and the test aborts. Add it to the existing HIP skip list alongside the other TOP_K tests. * ci: keep gpu-rocm logs in a per-run dir keyed by GitHub run id The self-hosted gpu-rocm runner can't upload logs to Azure blob (egress firewalled), so a run's logs were wiped by the next run. Write each run's logs to $OUT/run-<run_id>-<attempt>/ so an Actions run URL maps to its logs. * test-backend-sampler: also skip multi_output_cpu on HIP Like the other TOP_K-based subtests, multi_output_cpu's backend sampler never initializes on HIP (no CUB TOP_K), so it aborts. Add it to the skip list. --------- Co-authored-by: Jim Wu <ywu@xilinx.com>
* tests : remove fetch_server_test_models.py * ci : use tests.sh wrapper of pytest
…rg#26081) * llama: add new default load-mode auto which picks mmap unless a non-Metal iGPU is used * Update ggml/src/ggml-hexagon/ggml-hexagon.cpp Co-authored-by: Max Krasnyansky <maxk@qti.qualcomm.com> * set mmap_support to false on OpenCL backend * fix order of load modes * use -1 for auto * resolve load mode auto earlier to correctly pick gpu host or cpu memory * add load mode auto to llama-bench * bump virtgpu api version, regenerate docs --------- Co-authored-by: Piotr Wilkin (ilintar) <piotr.wilkin@syndatis.com> Co-authored-by: Max Krasnyansky <maxk@qti.qualcomm.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
ggml-org#26890) This commit updates the python script that runs the original model to generate embeddings for the causal model, to use save_output_data which stores the token ids and the prompt in addition to logits. The motivation for this is that the embedding logits verification will fail as it expects these files (-prompt.txt and -tokens.bin) to exist. With the changes in this commit the causal-verify-embeddings target works again.
Most of the old ones have been resolved (yay) but the recent refactor of mmq paramters has caused some symbol names to change, leaving a couple of non-ignored failures
* webui: hide loaded model in context gauge at single-model mode * webui: keep context gauge details open state across reopens
* adapt the api * text model ok * working impl, need verify and clean up * mtmd: build the pocket-tts transposed convolutions as GEMM + col2im ggml_conv_transpose_1d has no grouped mode, so the depthwise upsample was built as one convolution and one concat per channel, which floods the graph with small nodes and makes kernel launches dominate the decoder. Fold both cases into the column form the seanet decoder already needs: the general case reshapes the kernel to [IC, K * OC] and matmuls it with the input, the depthwise case batches a matmul over the channels so a step scales its own kernel. A single col2im_1d then scatter-adds the columns back to the signal, with the same shape as before, so the overlap-add tail, the streaming state and the bias are untouched. Generation time per frame drops by 80% on CUDA and by 50% on CPU. The output matches the previous implementation sample for sample, with a correlation of 0.999994 and identical frame counts. * flow_temp + frames_after_eos * chunking * mtmd: carry the remaining pocket-tts per-pack settings The language packs also tune the end-of-speech padding and the padding of short prompts, next to the temperature already carried in the mmproj: french_24l asks for 8 tail frames instead of the guessed 3, english_2026-01 asks for short prompts to be padded with spaces. Write both in the mmproj as clip.gen.audio.frames_after_eos and clip.gen.audio.pad_short_text, keyed on the pack in the conversion script like the temperature. The loader keeps them optional, so a mmproj without them behaves as before. Map semicolons to commas for every pack instead, the reference only asks for it on three of them and it costs nothing elsewhere. Existing mmproj files must be converted again to carry the two keys. On a long french text the port now lands within 2% of the reference: 22.96s against 23.44s, with the same peak level and the same amount of silence. * clip.gen.audio.model_variant * clean up code comments * nit: drop the dead flow_temp hparam, the pack table holds the default * update docs * address security problems * less invasive base.py * lint * add mtmd_gen_inp_default * add docs * rm gen_flow_temp --------- Co-authored-by: Pascal <admin@serveurperso.com>
* conversion: skip untrained DFlash embeddings * Add Nemotron DFlash support * Add DFlash NVFP4 support * Address review comments * add missing output_s for nvfp4 * Include change for keeping residual for last layer also if requested in future dflash models * Update conversion/qwen.py Defensive check, not needed Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co> * Fixing bug introduced by merge conflict --------- Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
…g#26903) Signed-off-by: ynankani <ynankani@nvidia.com>
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Updates dev branch with latest release (b10375) from ggml-org/llama.cpp