Jimwu.sync upstream 20260807 - #81
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* hexagon: add IM2COL op Add Hexagon IM2COL support targeting only patch-embedding convolutions. * hexagon: im2col refactor and cleanup * hex-im2col: instrument and update im2col. * hex-im2col: add local htp_vtcm_layout computation.
* move server_pipe to common * init impl * vendor: update subprocess.h * add server_mcp_stdio * stderr drain * server_mcp_transport * server_mcp_stdio is now framing-only, no json * internal/mcp-stdio: integration + tests + fixes (ggml-org#26075) * server-mcp: harden transport and wire up the tool integration Builds on the transport/manager architecture (server_mcp_transport + server_pipe) with the hardening and integration the draft did not yet have. Hardening: * Reader and stderr pumps are polled (running-aware) instead of blocking on a read that only ends at EOF. subprocess_terminate() SIGKILLs only the direct child, so a grandchild the MCP server spawned that inherited the pipe would otherwise keep the write end open and hang teardown (both warmup shutdown at startup and process shutdown). The writer is likewise non-blocking + polled. * Windows: resolve the command through PATHEXT so "npx" (npm ships npx.cmd, never npx.exe) spawns, matching POSIX's PATH search; and enumerate the parent environment as UTF-8 (GetEnvironmentStringsW) instead of the active code page. * server_pipe gains an opt-in max_size (default unbounded, so the router's streaming use is unchanged); the MCP reply queue uses it so a server that streams unsolicited notifications between requests cannot grow it without bound. Integration: * --mcp-servers-config / --mcp-servers-json flags; enabling MCP restricts default CORS to localhost, same as --tools. * MCP tools are exposed through /tools (and chat-completions) as <server>_<tool>, skipping names that collide with a built-in or another MCP tool. * Manager lifecycle wired into llama_server(): warmup at start, shutdown() from the signal handler before the HTTP server drains, blocking teardown in clean_up(). * SIGPIPE ignored so a child dying mid-write yields EPIPE rather than killing us. Assisted-By: Claude Opus 4.8 <noreply@anthropic.com> * server-mcp: add MCP test suite with grandchild deadlock regression test 21 tests over the /tools endpoint: tool discovery/invocation, timeouts, crash recovery and respawn cooldown, warmup partial failure, malformed and batched notification+response output, tool-definition shape, and prompt shutdown during a slow call. The last test spawns an MCP server that leaves a grandchild inheriting its stdout/stderr and asserts the server both starts and stops promptly. Verified it fails (5s SIGKILL fallback on a deadlocked reader-join) when the pump is made to ignore the running flag, and passes with the polled reader. Assisted-By: Claude Opus 4.8 <noreply@anthropic.com> * clean up * clean up 2 * even stricter life cycle * nits * nits 2 --------- Co-authored-by: Xuan Son Nguyen <son@huggingface.co> * fix some edge cases * fix last_error data race * fix response schema + docs * server: fix MCP zombie leak and timeout-induced transport teardown join_pumps() never reaped the child, leaking one zombie per spawn: call subprocess_join() before subprocess_destroy(). A per-call timeout permanently closed from_server and got a healthy transport evicted: add close_on_stop to server_pipe::read() and pass false from send_rpc(), where should_stop is a per-request deadline and a late reply is already skipped on id mismatch. Also drop the unreachable disconnect cancellation in server_mcp_tool::invoke(): support_stream is false, st is always null. (cherry picked from commit e6de1ec) Assisted-by: Claude Opus 4.8 * server: make MCP test fixtures JSON-RPC 2.0 compliant Add the missing notification guard to mcp_malformed_server.py and mcp_burst_server.py (the latter treated id 0 as a notification and replied to unknown ones; its notification table is now unused). Return -32602 instead of -32601 for unknown tools: tools/call is a valid method, the tool name is the invalid parameter. Also fix the test module docstring: tools are named <server>_<tool>. (cherry picked from commit 74a08e8) Assisted-by: Claude Opus 4.8 --------- Co-authored-by: Piotr Wilkin (ilintar) <piotr.wilkin@syndatis.com> Co-authored-by: Pascal <admin@serveurperso.com>
* Fix emscripten compilation with openmp * Separate wasm job to its own workflow * Add flags necessary for newer emsdk * Just disable openmp * Update triggers
…nd (ggml-org#26099) The context gauge card starts monitoring like the dial does, because its own processing state instance only follows the live stream while its monitoring flag is set. It also gets back the text-sm and ring classes the removed hover card wrapper used to inject, which restores its layout. Routing to a conversation now lands at the bottom instantly and keeps the pin one frame at a time until the page height settles, since content-visibility size realizations and syntax highlight passes grow the page without DOM mutations.
…rg#24908) * Add preliminary MiniMax-M3 support Text-only port that re-uses existing components: MiniMax-M2 style GQA with per-head QK-norm and partial rotary, DeepSeek-V3 style leading-dense and routed/shared experts, and swigluoai activation. Sparse attention is not yet supported (dense fallback); vision tower and MTP heads are dropped. * MiniMax-M3 vision tower (mmproj + clip graph) * Delete m3_vision_ref.py * Update clip.cpp * MSA * Update constants.py * Update minimax.py * Cache creation. Working withotu flash attention * Added flash attention for sparse layers * Decomposed slow cpu OP into GPU + CPU ops. Massive speedup over long ctx * Rewrote indexer op to be cuda native. Modified flash attention to match per group block picking * Implement sparse attention calc out of stock ops. * Fix a cache allocation and cont issue * Fixed -fa auto crash, flagged debug spots * Delete vocab.json * Delete model.safetensors.index.json * Delete generation_config.json * Delete Minimax directory * Handled multi stream case to fall back on Dense Attention * Development scaffolding cleanup. No functional change to the decode or 4-way paths. Full debug harness remains at <8136a9c68ed7a5eb009aa67bba3fda8062f4648f> for reproducing the selection-parity validation. * Remove redundant comment from minimax-m3.cpp * Changed 3 Gelu Ops for vision into Gelu_erf ops * Assert that n_kv is multiple of 128 * Rename MSA index tensors to indexer convention Note: All GGUFs generated before this change will need to be regenerated. * Fix incorrect Assert * Review driven changes (#3) * Remove comment from conversion minimax.py Co-authored-by: Sigbjørn Skjæret <1629204+CISC@users.noreply.github.com> * Remove whitespaces from constants.py Co-authored-by: Sigbjørn Skjæret <1629204+CISC@users.noreply.github.com> * Tighten comment in minimax.py Co-authored-by: Sigbjørn Skjæret <1629204+CISC@users.noreply.github.com> * inherit MiniMax-M3 from MiniMax-M2 * drop dead text_config fallbacks * Add indexer writer methods * Reuse LLM_FFN_SWIGLU_OAI_MOE * Remove duplicate indexer setters, add only block_size/local_blocks, follow value naming convention * Fix conversion error /gguf_writer.py Co-authored-by: Sigbjørn Skjæret <1629204+CISC@users.noreply.github.com> * Update gguf-py/gguf/gguf_writer.py Co-authored-by: Sigbjørn Skjæret <1629204+CISC@users.noreply.github.com> * Update gguf-py/gguf/tensor_mapping.py Co-authored-by: Sigbjørn Skjæret <1629204+CISC@users.noreply.github.com> * Update conversion/minimax.py Co-authored-by: Sigbjørn Skjæret <1629204+CISC@users.noreply.github.com> * Update conversion/minimax.py Co-authored-by: Sigbjørn Skjæret <1629204+CISC@users.noreply.github.com> * Remove whitespace in src/llama-kv-cache.cpp Co-authored-by: Sigbjørn Skjæret <1629204+CISC@users.noreply.github.com> * Remove Whitespace in Update src/llama-model.h Co-authored-by: Sigbjørn Skjæret <1629204+CISC@users.noreply.github.com> * Remove whitespace in src/llama-hparams.h Co-authored-by: Sigbjørn Skjæret <1629204+CISC@users.noreply.github.com> * remove multimodal code upon maintainer request. Will be made as a separate PR * Whitespace clean in tensor_mapping.py * Log cache size on launch, block ctx shift, support prompt caching Log indexer cache size on launch Disallow ctx shift Support prompt caching * Update minimax-m3.cpp * Optimize implementation, add multi stream support. Fully rewrote minimax-m3.cpp for speed and buffer size gains: Unified the 4-way + decode, 1 FA call per layer instead of 4, with the groups mapped onto ne[3] Custom CPU op now emits block-level mask, expanded on GPU, which causes CPU to GPU transfer to shrinks at prefill Decode: ~25 nodes/layer vs ~50, no per-group concats/conts Unified selection semantics, so both regimes rank bs + local bias (position-anchored local force), which means prefill/decode can no longer disagree on selection can_reuse on the MSA bias input. Graph reuse at decode restored (was rebuilding the full graph every token) In-place mask adds, shrinking compute buffer ~6.8 to ~4.2 GiB at ub2048/62k Multi-stream: MSA now runs with -np N when kv_unified=false. Decode stays batched across streams (still 1 FA call), prefill loops per stream. dense fallback only for --kv-unified + multi-seq Measured effect on expert offload bound setup: decode 6.2(4WAY)–7.15(MSA_decode) -> 7.7~7.8 t/s, flat from 5k to 60k+. prefill around 10% faster. buffer about 20% smaller, multi-user support. * set default cache type to F32 * Fix potential DSA double indexer cache allocation bug, only allocate in-cache k_idx for archs that opt in * remove F16 downcasts in MSA attention, force F32 indexer score accum * Add Minimax eos to llama vocab * Guard edge case where idx cache can become stale after a tail trim * Update llama-kv-cache.h * Update llama-kv-cache.cpp * Update llama-kv-cache.cpp * Update llama-kv-cache.h * Update llama-kv-cache.cpp * Review driven changes * style fix * indexer hparams are required * fix tests * fix lint --------- Co-authored-by: Daniel Han <danielhanchen@gmail.com> Co-authored-by: Sigbjørn Skjæret <1629204+CISC@users.noreply.github.com> Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
Co-authored-by: Eric Hartford <eric@quixi.ai>
…g#26121) * ui: detect the conversation import format from file contents iOS resolves every accept entry to a UTI and has none for ".jsonl", so the picker greyed out exported conversations. Drop the accept filter and pick the parser from the file contents: ZIP magic bytes, then a first "session" record for JSONL, otherwise the legacy JSON format. Also remove the unused importConversations() picker and an orphan doc comment, and cover each format with unit tests. * ui: report what a conversation import actually wrote The import summary echoed the selection back, so re-importing conversations already in the database claimed success while nothing was written and only a console warning said otherwise. Return the imported and skipped conversations from the database layer, list the written ones in the summary, and count the rest in a toast. * ui: name the literals of the JSONL conversation format Introduce SessionRecordType and SESSION_HARNESS, and reuse the existing NEWLINE constant, so the record format lives in one place. This also covers the writer side, which predates the import path under review and carried the same literals: an enum stated by the reader alone lets the two sides drift. Values are unchanged, so an export stays byte identical.
…g#26144) * Keep Minimax's indexer tensors at F32 for speed and accuracy * name -> new_name
…g#26006) The in-conversation system-message edit textarea had a fixed min-height and no auto-resize, so long messages were crammed to 2 lines. Reused existing autoResizeTextarea helper on input and when the editor opens, and added max-height to cap growth.
* Add preliminary MiniMax-M3 support Text-only port that re-uses existing components: MiniMax-M2 style GQA with per-head QK-norm and partial rotary, DeepSeek-V3 style leading-dense and routed/shared experts, and swigluoai activation. Sparse attention is not yet supported (dense fallback); vision tower and MTP heads are dropped. * MiniMax-M3 vision tower (mmproj + clip graph) * Delete m3_vision_ref.py * Update clip.cpp * MSA * Update constants.py * Update minimax.py * Cache creation. Working withotu flash attention * Added flash attention for sparse layers * Decomposed slow cpu OP into GPU + CPU ops. Massive speedup over long ctx * Rewrote indexer op to be cuda native. Modified flash attention to match per group block picking * Implement sparse attention calc out of stock ops. * Fix a cache allocation and cont issue * Fixed -fa auto crash, flagged debug spots * Delete vocab.json * Delete model.safetensors.index.json * Delete generation_config.json * Delete Minimax directory * Handled multi stream case to fall back on Dense Attention * Development scaffolding cleanup. No functional change to the decode or 4-way paths. Full debug harness remains at <8136a9c68ed7a5eb009aa67bba3fda8062f4648f> for reproducing the selection-parity validation. * Remove redundant comment from minimax-m3.cpp * Changed 3 Gelu Ops for vision into Gelu_erf ops * Assert that n_kv is multiple of 128 * Rename MSA index tensors to indexer convention Note: All GGUFs generated before this change will need to be regenerated. * Fix incorrect Assert * Review driven changes (#3) * Remove comment from conversion minimax.py Co-authored-by: Sigbjørn Skjæret <1629204+CISC@users.noreply.github.com> * Remove whitespaces from constants.py Co-authored-by: Sigbjørn Skjæret <1629204+CISC@users.noreply.github.com> * Tighten comment in minimax.py Co-authored-by: Sigbjørn Skjæret <1629204+CISC@users.noreply.github.com> * inherit MiniMax-M3 from MiniMax-M2 * drop dead text_config fallbacks * Add indexer writer methods * Reuse LLM_FFN_SWIGLU_OAI_MOE * Remove duplicate indexer setters, add only block_size/local_blocks, follow value naming convention * Fix conversion error /gguf_writer.py Co-authored-by: Sigbjørn Skjæret <1629204+CISC@users.noreply.github.com> * Update gguf-py/gguf/gguf_writer.py Co-authored-by: Sigbjørn Skjæret <1629204+CISC@users.noreply.github.com> * Update gguf-py/gguf/tensor_mapping.py Co-authored-by: Sigbjørn Skjæret <1629204+CISC@users.noreply.github.com> * Update conversion/minimax.py Co-authored-by: Sigbjørn Skjæret <1629204+CISC@users.noreply.github.com> * Update conversion/minimax.py Co-authored-by: Sigbjørn Skjæret <1629204+CISC@users.noreply.github.com> * Remove whitespace in src/llama-kv-cache.cpp Co-authored-by: Sigbjørn Skjæret <1629204+CISC@users.noreply.github.com> * Remove Whitespace in Update src/llama-model.h Co-authored-by: Sigbjørn Skjæret <1629204+CISC@users.noreply.github.com> * Remove whitespace in src/llama-hparams.h Co-authored-by: Sigbjørn Skjæret <1629204+CISC@users.noreply.github.com> * Update minimax_m3.cpp Rewrite code comment based on feedback and to better reflect the actual architecture, and reuse existing build_vit * Rename minimax_m3.cpp to minimax-m3.cpp * Update CMakeLists.txt * Remove debug code from clip.cpp * Update clip.cpp * Update comments in tools/mtmd/models/minimax-m3.cpp * Permute Q/K at conversion, drop precomputed sin/cos * Log cache size on launch, block ctx shift, support prompt caching Log indexer cache size on launch Disallow ctx shift Support prompt caching * Update minimax-m3.cpp * Optimize implementation, add multi stream support. Fully rewrote minimax-m3.cpp for speed and buffer size gains: Unified the 4-way + decode, 1 FA call per layer instead of 4, with the groups mapped onto ne[3] Custom CPU op now emits block-level mask, expanded on GPU, which causes CPU to GPU transfer to shrinks at prefill Decode: ~25 nodes/layer vs ~50, no per-group concats/conts Unified selection semantics, so both regimes rank bs + local bias (position-anchored local force), which means prefill/decode can no longer disagree on selection can_reuse on the MSA bias input. Graph reuse at decode restored (was rebuilding the full graph every token) In-place mask adds, shrinking compute buffer ~6.8 to ~4.2 GiB at ub2048/62k Multi-stream: MSA now runs with -np N when kv_unified=false. Decode stays batched across streams (still 1 FA call), prefill loops per stream. dense fallback only for --kv-unified + multi-seq Measured effect on expert offload bound setup: decode 6.2(4WAY)–7.15(MSA_decode) -> 7.7~7.8 t/s, flat from 5k to 60k+. prefill around 10% faster. buffer about 20% smaller, multi-user support. * set default cache type to F32 * Fix potential DSA double indexer cache allocation bug, only allocate in-cache k_idx for archs that opt in * remove F16 downcasts in MSA attention, force F32 indexer score accum * Add Minimax eos to llama vocab * Guard edge case where idx cache can become stale after a tail trim * Update llama-kv-cache.h * Update llama-kv-cache.cpp * Update llama-kv-cache.cpp * Update llama-kv-cache.h * Change resize Pad to none, resize alg to Bicubic Pillow * Review driven changes * Update llama-kv-cache.cpp * rm unrotated pos_t * fused rope w + pad * rename merge --> merger for consistency * add review skill for mtmd * graph should use hparams n_merge * fix lint --------- Co-authored-by: Daniel Han <danielhanchen@gmail.com> Co-authored-by: Sigbjørn Skjæret <1629204+CISC@users.noreply.github.com> Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
* Update sandbox.ts * Update sandbox.ts * Update sandbox.ts * Update sandbox.ts * Revise nerdamer description in sandbox constants Updated NERDAMER_DESCRIPTION to clarify usage and warnings.
…ml-org#26137) * server + ui: refactor resumable stream routes to query string conv_id The conversation id can embed a model name containing slashes (ggml-org/...) in router mode, which the decoded path splits before the :conv_id param is captured, so stop and resume never matched the session. Move the id to the conv_id query string on the public routes and on the internal router -> child hop, where slashes survive encoding. Handlers are unchanged since query and path params land in the same map. Add a regression test with a slashed model name. * server: move stream route docs to server-stream.h Address review: ngxson wants the main server.cpp registration code kept clean and simple, with route-level explanations living in the header. Move the query string rationale and the lookup ownership note next to the handler declarations in server-stream.h, and shorten the wiring comment to a pointer. * server: cancel a pending request when its stream is stopped during model load The conversation was registered in the conv map only after the blocking autoload wait, so a stop issued while the model loaded found nothing to cancel and the request went on to generate an orphan once the load ended. Register the conversation before the wait and give the entry a ticket: a stop erases the entry, and the parked request checks its ticket after the wait and aborts with 400 instead of starting. A newer request on the same conversation replaces the entry, so only the stopped request is cancelled. Add a regression test that stops during the load window. * server + ui: resume a stream after a page reload during model load A pending request died with the client socket when the page was reloaded while its model was loading, so no session ever existed and the conversation had nothing to recover. A session request that waited for a load now detaches from the client socket and reaches the child regardless, the session buffer receives the generation, and the resume route answers 503 while the owner is loading so the client retries instead of dropping its state. The WebUI persists the pending stream at send time, quietly polls on 503, and attaches once the session exists. Add a regression test that drops the client during the load window. * ui: show the model load progress again after a page refresh The resume wait was invisible, so a conversation refreshed while its model was loading showed nothing until the first byte. On a 503 from the resume probe, mark the conversation as loading again so the assistant row persisted at send time renders the processing info, and target the model frozen in the persisted stream state for the progress, since the row has no model yet and the dropdown may not be restored. * fix CI * fix CI bis
* arg: add `-lm mlock` where it mlocks but doesnt mmap Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> * docs: rm unwanted docs changes Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> * docs: revert auto-formatting Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> * bench: fix automated review point 3 Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> * arg: revert the meaning of --mlock to non-mmap'ed mlock Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> * docs: update docs Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> * docs: remove extra changes from `llama-gen-docs` Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> --------- Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>
* docs: add exception about weight folding * add example
…ml-org#26165) * common: fix explicit -md precedence over draft sidecar resolution Follow-up of ggml-org#25955, an explicit --model-draft file given with -hfd was silently overridden by the sidecar resolution of the draft repo, and its path was never resolved to a local file. An explicit draft file selection now disables the sidecar resolution, so the manual CLI configuration wins over the automatic one. * common: apply the -hfd tag to the sidecar resolution The sidecar selection was anchored on the primary of the draft plan, so a tag without a matching full model aborted the whole plan, and the sidecar quant silently followed the default model pick. The tag now anchors the sidecar directly: exact tag match first, then closest quant to the tag, and a requested sidecar resolves even when no full model matches the tag. A wired draft sidecar also counts as an explicit draft, so the main plan no longer downloads a second one. * common: promote speculative load logs from trace to info Show the loaded draft model and the MTP draft context at the default verbosity, for consistency with the mmproj and primary logs. Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
…25832) * ggml : adjust logic for offloading ops to weight's backend * llama : dsv4 graph fixes
* support nanbeige4.2 model * fix * fix flake8 Lint check * fix loop bound check and drop redundant head_dim --------- Co-authored-by: root <lizongqiang@kanzhun.com>
Signed-off-by: Adrien Gallouët <angt@huggingface.co>
* gguf converter for mimo audio * fix conv * cpp impl * nits * nits 2
* contrib : add guideline about the "merge ready" label * cont : add ref [no ci]
…causes of DeviceLost drivers errors (ggml-org#26371) * vulkan: add debug tooling to get more information about a DeviceLost error * fix submission threshold applied too late * use logging macros, throw instead of aborting * clean up circular dependency
…6665) This commit adds the --model-name flag to the causual and embedding model conversion scripts. The motivation for this is that this is the name used for the metadata field general.name and it can be useful to specify this explicitely if the default (the basename of the model path) is not what we want.
* ci: prepare for amd rocm ci Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> * ci: fix editorconfig-checker Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> * ci: fix device not recognised Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> * ci: rename gpu-amd to gpu-hip Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> * ci: gpu-hip to gpu-rocm haha Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> * CUDA: allow integrated-GPU host output buffer in debug assert On integrated GPUs (APUs), the scheduler can legitimately place a graph node's output on the host-visible buffer, which ggml_cuda_compute_forward already handles. The debug assert in ggml_cuda_graph_evaluate_and_capture required every node output to be on the device buffer, so a debug build aborts on such a node (e.g. attn_residual ADD -> ROCm_Host on RDNA3.5). The source-tensor assert directly below already permits this via the integrated + cuda_host exception; apply the same exception to the node's own output buffer. Debug-only; no effect on release/compute. Fixes test-recurrent-state-rollback on gfx1151 (Strix Halo). * ci: enable unified memory for ROCm gfx1151 job Work around a coherence issue on integrated RDNA3.5 (gfx1151) where GPU kernels reading mmap-loaded weights can return incorrect output, which makes test-llama-archs (and real inference) intermittently wrong. GGML_CUDA_ENABLE_UNIFIED_MEMORY=1 uses managed memory, which restores coherence. Remove once the underlying ROCm/HIP issue is fixed. * test-llama-archs: skip jamba on HIP backend jamba produces incorrect output (~0.55 NMSE vs CPU) on the HIP backend on RDNA3.5 (gfx1151); the SSM kernels need separate investigation. Skip it for now, matching the existing per-backend carve-outs (WebGPU), so the ROCm CI can run the test for the remaining architectures. * ci: use HIP_LAUNCH_BLOCKING for ROCm gfx1151 job The gfx1151 ROCm CI job produced incorrect inference output (qwen3 perplexity ~88 vs ~9.4) due to an async-execution correctness issue in the HIP path. Serializing kernel launches with HIP_LAUNCH_BLOCKING=1 restores correctness. This replaces the earlier GGML_CUDA_ENABLE_UNIFIED_MEMORY workaround, which did not fix batched inference. * test-backend-sampler: skip top-k subtests on HIP backend The ROCm backend does not support the TOP_K/ARGSORT op at vocab scale (no CUB; bitonic argsort is capped at ncols <= 1024), so top-k/top-p backend samplers cannot be offloaded. The penalties, set_sampler, mixed, and top_p subtests assert that offload happened, so they fail on HIP. Skip them until TOP_K is supported on the ROCm backend. * Update tests/test-backend-sampler.cpp Co-authored-by: Aaron Teo <taronaeo@gmail.com> * Update tests/test-backend-sampler.cpp Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> --------- Signed-off-by: Aaron Teo <aaron.teo1@ibm.com> Co-authored-by: Aaron Teo <aaron.teo1@ibm.com> Co-authored-by: Jim Wu <ywu@xilinx.com> Co-authored-by: Aaron Teo <taronaeo@gmail.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* mtmd: add chunk save/load function * nits * add tests * rn _MAX --> _COUNT
…tning_indexer (ggml-org#26646) - In MSL, declaring an array of matrix types like `threadgroup half4x4` causes a 'no matching constructor' compilation error because MSL matrix types do not have zero-argument default constructors and threadgroup variables cannot have initializers. - Fix this by declaring a POD `threadgroup half` array instead and casting to `threadgroup half4x4 *` for matrix indexing. Signed-off-by: JamePeng <jame_peng@sina.com>
1. Abort CI if build requirements are missing. 2. Add check to make sure Git LFS has been configured. 3. Add trailing newlines to log messages.
…#26515) * support fp16 to fp16/fp32 * support all missed data types in set_rows * refactor the code to support all data types
…DSV4_HC_PRE (ggml-org#26568) * support DSv4 OPs: LIGHTNING_INDEXER,DSV4_HC_COMB,DSV4_HC_POST,DSV4_HC_PREwq * update ops.md * fix format issue
* tests: add SWIGLU perf cases perf mode had no GLU coverage. Adds SWIGLU at 17408 columns, 512 and 2048 tokens, f16 and f32, with the operands both fused and split. * sycl: consolidate fused-GLU kernels They differed only in which op_* they called, so take the op as an argument and share a common launcher. Their block sizes were all 256, so launch geometry is unchanged; SYCL_GELU_BLOCK_SIZE and SYCL_SILU_BLOCK_SIZE lose their last users so are dropped. * sycl: contiguous fast path for the fused GLU ops o0 == n and o1 == n collapse the de-interleave index math to the identity, so dispatch a flat kernel in that case. It fires for ggml_glu_split with packed operands; a fused [gate|up] tensor keeps the strided path. test-backend-ops perf -o SWIGLU on an Arc Pro B70: split +14% f16 and +4% f32, fused unchanged.
The NVFP4 quantization format stores a scaling factor for every group of 16 weights, packed into a single UE4M3 byte. The SYCL GPU code was converting these scale values using the E4M3 path, but that's *signed*, and these are unsigned values.
* ui: read model modalities from the router model list The router advertises input modalities for every model, loaded or not. Reading them at list build time lets the UI accept image and audio uploads for a model selected through ?model=, which has no /props yet. * enum
…ml-org#25554) * ggml : add fallback definitions for missing aarch64 HWCAP bits * ggml : require HWCAP_ASIMDHP for the aarch64 fp16 cpu variants Also rename has_fp16_va to has_fp16, the field gates the whole FEAT_FP16 extension, scalar and vector half-precision arithmetic together.
…gml-org#26706) The reference implementation has two mutually exclusive prompt layouts. In non streaming mode the prefill carries the whole utterance text plus tts_eos summed with codec_pad, and the trailing text hidden collapses to a single tts_pad row. In streaming mode the prefill carries only the first text token and the trailing rows stream the rest of the text followed by tts_eos. The pipeline built the non streaming prefill but the streaming overlay, so the talker saw the utterance a second time during generation and read it twice before emitting codec_eos. The overlay is now the single tts_pad row that matches the prefill.
* add lru_sched * handle coalescing (req leaves waiting queue) * add tests * fix stream case * address review comments
…ggml-org#26711) * ui: set npm `min-release-age` to protect against supply-chain attacks * ui: bump to 7 days
…26707) * webui: load the model selected via ?model= Opening the WebUI with ?model= selects the model but doesn't load it. The load only starts when you send your first message, so you wait for it then. This loads it as soon as the page opens, while you're still typing your prompt. It's what the model dropdown already does, and it isn't awaited, so the UI still works while the model loads. This is the path the Llama macOS app uses to open the WebUI, so it's a common way in. * webui: gate the load behind ?load=true Loading on landing is opt-in, so a plain ?model= link behaves as before and doesn't allocate memory on its own. * webui: name the chat URL params Collects the query params the chat routes read into a URL_PARAMS constant, instead of repeating the literals across three files. NEW_CHAT_PARAM folds into it.
…am-20260807 Sync AMD-Ecosystem master with upstream ggml-org/master (up to 9ba73fd). Keep the fork's README.md (retirement/deprecation notice).
…H (default OFF) -funsafe-math-optimizations enables -fassociative-math, which reassociates FP reductions. On RDNA3.5 (gfx1151) this perturbs the greedy argmax enough to make MTP speculative-decode output diverge from the non-speculative baseline that was previously bit-identical. Gate the flag behind a new option, default OFF, so deterministic/MTP runs are IEEE-conformant by default; set -DGGML_HIP_UNSAFE_MATH=ON to restore the fast-math speedup.
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Sync to latest upstream