fix: Gemma 4 + TurboQuant KV no longer crashes on second prompt when --cache-reuse enabled - #10
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Ooooze merged 1 commit intoMay 12, 2026
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…--cache-reuse enabled TurboQuant (turbo2/3/4) uses kernel-level WHT rotation which is position-invariant -- WHT preserves inner products so no RoPE correction is needed after a KV position shift. build_graph_shift() assumed standard quantized tensors with upstream rotation, but TurboQuant sets attn_rot_k=0 and handles rotation at kernel level. Building the shift graph with turbo-padded tensors causes a null buffer assert and segfault on the second prompt. Fix: skip build_graph_shift() layers and get_has_shift() entirely for turbo KV types. Position tracking via seq_add() still works correctly -- only the broken RoPE re-rotation kernel is skipped.
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merged commit May 12, 2026
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…gml-org#16038) Initalizing RESERVED_NAME in is_reserved_name() is not thread safe and leads to corrupted memory when used from multiple threads as can be seen in the asan trace below. This fixes the initialization to make it thread-safe. #0 0x000100abd018 in std::__1::pair<std::__1::__hash_iterator<std::__1::__hash_node<std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char>>, void*>*>, bool> std::__1::__hash_table<std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char>>, std::__1::hash<std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char>>>, std::__1::equal_to<std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char>>>, std::__1::allocator<std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char>>>>::__emplace_unique_key_args<std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char>>, std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char>> const&>(std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char>> const&, std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char>> const&) __hash_table:1565 AtomicBot-ai#1 0x000100ab0320 in SchemaConverter::visit(nlohmann::json_abi_v3_12_0::basic_json<nlohmann::json_abi_v3_12_0::ordered_map, std::__1::vector, std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char>>, bool, long long, unsigned long long, double, std::__1::allocator, nlohmann::json_abi_v3_12_0::adl_serializer, std::__1::vector<unsigned char, std::__1::allocator<unsigned char>>, void> const&, std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char>> const&) json-schema-to-grammar.cpp:802 AtomicBot-ai#2 0x000100aafc48 in std::__1::__function::__func<build_grammar(std::__1::function<void (common_grammar_builder const&)> const&, common_grammar_options const&)::$_2, std::__1::allocator<build_grammar(std::__1::function<void (common_grammar_builder const&)> const&, common_grammar_options const&)::$_2>, std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char>> (std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char>> const&, nlohmann::json_abi_v3_12_0::basic_json<nlohmann::json_abi_v3_12_0::ordered_map, std::__1::vector, std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char>>, bool, long long, unsigned long long, double, std::__1::allocator, nlohmann::json_abi_v3_12_0::adl_serializer, std::__1::vector<unsigned char, std::__1::allocator<unsigned char>>, void> const&)>::operator()(std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char>> const&, nlohmann::json_abi_v3_12_0::basic_json<nlohmann::json_abi_v3_12_0::ordered_map, std::__1::vector, std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char>>, bool, long long, unsigned long long, double, std::__1::allocator, nlohmann::json_abi_v3_12_0::adl_serializer, std::__1::vector<unsigned char, std::__1::allocator<unsigned char>>, void> const&) function.h:319 AtomicBot-ai#3 0x000100a2c938 in std::__1::__function::__func<common_chat_params_init_llama_3_x(minja::chat_template const&, templates_params const&, bool)::$_0::operator()(common_grammar_builder const&) const::'lambda'(nlohmann::json_abi_v3_12_0::basic_json<nlohmann::json_abi_v3_12_0::ordered_map, std::__1::vector, std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char>>, bool, long long, unsigned long long, double, std::__1::allocator, nlohmann::json_abi_v3_12_0::adl_serializer, std::__1::vector<unsigned char, std::__1::allocator<unsigned char>>, void> const&), std::__1::allocator<common_chat_params_init_llama_3_x(minja::chat_template const&, templates_params const&, bool)::$_0::operator()(common_grammar_builder const&) const::'lambda'(nlohmann::json_abi_v3_12_0::basic_json<nlohmann::json_abi_v3_12_0::ordered_map, std::__1::vector, std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char>>, bool, long long, unsigned long long, double, std::__1::allocator, nlohmann::json_abi_v3_12_0::adl_serializer, std::__1::vector<unsigned char, std::__1::allocator<unsigned char>>, void> const&)>, void (nlohmann::json_abi_v3_12_0::basic_json<nlohmann::json_abi_v3_12_0::ordered_map, std::__1::vector, std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char>>, bool, long long, unsigned long long, double, std::__1::allocator, nlohmann::json_abi_v3_12_0::adl_serializer, std::__1::vector<unsigned char, std::__1::allocator<unsigned char>>, void> const&)>::operator()(nlohmann::json_abi_v3_12_0::basic_json<nlohmann::json_abi_v3_12_0::ordered_map, std::__1::vector, std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char>>, bool, long long, unsigned long long, double, std::__1::allocator, nlohmann::json_abi_v3_12_0::adl_serializer, std::__1::vector<unsigned char, std::__1::allocator<unsigned char>>, void> const&) function.h:319 AtomicBot-ai#4 0x000100a139f8 in foreach_function(nlohmann::json_abi_v3_12_0::basic_json<nlohmann::json_abi_v3_12_0::ordered_map, std::__1::vector, std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char>>, bool, long long, unsigned long long, double, std::__1::allocator, nlohmann::json_abi_v3_12_0::adl_serializer, std::__1::vector<unsigned char, std::__1::allocator<unsigned char>>, void> const&, std::__1::function<void (nlohmann::json_abi_v3_12_0::basic_json<nlohmann::json_abi_v3_12_0::ordered_map, std::__1::vector, std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char>>, bool, long long, unsigned long long, double, std::__1::allocator, nlohmann::json_abi_v3_12_0::adl_serializer, std::__1::vector<unsigned char, std::__1::allocator<unsigned char>>, void> const&)> const&) chat.cpp:762 AtomicBot-ai#5 0x000100a2a7f4 in std::__1::__function::__func<common_chat_params_init_llama_3_x(minja::chat_template const&, templates_params const&, bool)::$_0, std::__1::allocator<common_chat_params_init_llama_3_x(minja::chat_template const&, templates_params const&, bool)::$_0>, void (common_grammar_builder const&)>::operator()(common_grammar_builder const&) function.h:319 AtomicBot-ai#6 0x000100aa98f4 in build_grammar(std::__1::function<void (common_grammar_builder const&)> const&, common_grammar_options const&) json-schema-to-grammar.cpp:982 AtomicBot-ai#7 0x0001009c9314 in common_chat_params_init_llama_3_x(minja::chat_template const&, templates_params const&, bool) chat.cpp:1110 AtomicBot-ai#8 0x0001009b8afc in common_chat_templates_apply_jinja(common_chat_templates const*, common_chat_templates_inputs const&) chat.cpp:1992 AtomicBot-ai#9 0x0001009b533c in common_chat_templates_apply(common_chat_templates const*, common_chat_templates_inputs const&) chat.cpp:2074 AtomicBot-ai#10 0x000100810120 in llamacpp_apply_chat_template+0x724 (predict_oai-98384e17fb94e863:arm64+0x100090120) ... ==45482==Register values: x[0] = 0x00006020004147f8 x[1] = 0x00006080000013c8 x[2] = 0x0000000000000000 x[3] = 0x0000604006289738 x[4] = 0x0000000000000002 x[5] = 0x0000000000000001 x[6] = 0x04034000004b4000 x[7] = 0x0000000000000001 x[8] = 0xbebebebebebebebe x[9] = 0x17d7d7d7d7d7d7d7 x[10] = 0x00000c04000828ff x[11] = 0x0000000000000001 x[12] = 0x000000002018d383 x[13] = 0x0000000000000000 x[14] = 0xfa0000000000fafa x[15] = 0x000010700001ffff x[16] = 0x000000019dc012c0 x[17] = 0x00000001021284f8 x[18] = 0x0000000000000000 x[19] = 0x00000001700acdc0 x[20] = 0x0000000000000002 x[21] = 0x000000002018d384 x[22] = 0x16dd16fd2e731151 x[23] = 0x0000007000020000 x[24] = 0x0000000100c69c08 x[25] = 0x0000000100c69c20 x[26] = 0x00006080000013c7 x[27] = 0x0000000100c69c00 x[28] = 0x00000001700acd60 fp = 0x00000001700aceb0 lr = 0x0000000100abce30 sp = 0x00000001700acd60 AddressSanitizer can not provide additional info. SUMMARY: AddressSanitizer: SEGV __hash_table:1565 in std::__1::pair<std::__1::__hash_iterator<std::__1::__hash_node<std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char>>, void*>*>, bool> std::__1::__hash_table<std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char>>, std::__1::hash<std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char>>>, std::__1::equal_to<std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char>>>, std::__1::allocator<std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char>>>>::__emplace_unique_key_args<std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char>>, std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char>> const&>(std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char>> const&, std::__1::basic_string<char, std::__1::char_traits<char>, std::__1::allocator<char>> const&) Thread T5 created by T0 here: #0 0x0001020b99d4 in pthread_create+0x5c (libclang_rt.asan_osx_dynamic.dylib:arm64e+0x359d4) AtomicBot-ai#1 0x000100873910 in std::sys::pal::unix::thread::Thread::new::h77254fdd87a28e05+0x118 (predict_oai-98384e17fb94e863:arm64+0x1000f3910) AtomicBot-ai#2 0x0001007c7a1c in test::run_test::haeb3c2bcd5ed6cf6+0x76c (predict_oai-98384e17fb94e863:arm64+0x100047a1c) AtomicBot-ai#3 0x0001007aedb0 in test::console::run_tests_console::he9d142d704f3a986+0x149c (predict_oai-98384e17fb94e863:arm64+0x10002edb0) AtomicBot-ai#4 0x0001007c5758 in test::test_main::hf86a5e20735245b9+0x118 (predict_oai-98384e17fb94e863:arm64+0x100045758) AtomicBot-ai#5 0x0001007c5da0 in test::test_main_static::h61ee9c8fd30abca0+0x54 (predict_oai-98384e17fb94e863:arm64+0x100045da0) ... ==45482==ABORTING
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…rg#17764) * Squashed commit of the following: commit b3c6bf4b0450d8d452b934df27a0fb7cb53cd755 Author: Abhijit Ramesh <abhijitramesh2k@gmail.com> Date: Mon Dec 1 18:29:00 2025 -0800 ggml webgpu: fix xielu parameter passing (AtomicBot-ai#11) The XIELU operation was incorrectly using static_cast to convert float parameters to uint32_t, which converted numeric values instead of preserving IEEE 754 bit patterns. This caused incorrect values to be interpreted by the GPU shader. * Use reinterpret_cast to preserve float bit patterns when passing through uint32_t params buffer * Update WGSL shader parameter types from u32 to f32 * Re-enable XIELU support (was disabled due to numerical issues) Fixes NMSE test failures for XIELU operation on WebGPU backend. commit 5ca9b5e Author: neha-ha <137219201+neha-ha@users.noreply.github.com> Date: Tue Nov 18 12:17:00 2025 -0800 Refactored pipelines and workgroup calculations (AtomicBot-ai#10) * refactored pipelines * refactored workgroup calculation * removed commented out block of prior maps * Clean up ceiling division pattern --------- Co-authored-by: Neha Abbas <nehaabbas@eduroam-169-233-141-223.ucsc.edu> Co-authored-by: Reese Levine <reeselevine1@gmail.com> Author: James Contini <jamescontini@gmail.com> Date: Wed Oct 29 23:13:06 2025 -0700 formatted embed wgsl and ggml-webgpu.cpp commit e1f6bae Author: James Contini <jamescontini@gmail.com> Date: Wed Oct 29 23:08:37 2025 -0700 implemented REPL_Template support and removed bug in unary operators kernel commit 8c70b8f Author: James Contini <jamescontini@gmail.com> Date: Wed Oct 15 16:14:20 2025 -0700 responded and dealt with PR comments commit f9282c6 Author: James Contini <jamescontini@gmail.com> Date: Sun Oct 12 13:41:41 2025 -0700 removed unnecesarry checking if node->src[1] exists for unary operators commit 4cf28d7 Author: James Contini <jamescontini@gmail.com> Date: Sun Oct 12 13:32:45 2025 -0700 All operators (inlcluding xielu) working commit 74c6add Author: James Contini <jamescontini@gmail.com> Date: Fri Oct 10 13:16:48 2025 -0700 fixed autoconfig commit 3627499 Author: James Contini <jamescontini@gmail.com> Date: Fri Oct 10 13:10:46 2025 -0700 removed vestigial files commit cb08583 Author: James Contini <jamescontini@gmail.com> Date: Fri Oct 10 12:59:32 2025 -0700 abides by editor-config commit 5360e28 Author: James Contini <jamescontini@gmail.com> Date: Fri Oct 10 12:45:57 2025 -0700 rms_norm double declaration bug atoned commit 7b09baa Merge: 8a6ec84 1579702 Author: James Contini <jamescontini@gmail.com> Date: Fri Oct 10 11:50:03 2025 -0700 resolving merge conflicts commit 8a6ec84 Author: James Contini <jamescontini@gmail.com> Date: Wed Oct 8 18:06:47 2025 -0700 unary operators pass ggml tests commit c3ae382 Author: James Contini <jamescontini@gmail.com> Date: Wed Oct 1 16:22:40 2025 -0700 neg passes backend test commit aa1c9b2 Author: James Contini <jamescontini@gmail.com> Date: Tue Sep 30 23:55:27 2025 -0700 neg f16xf32xip builds and runs, havent actually ran a model that uses neg kernel yet though Co-authored-by: James Contini <jamescontini@gmail.com> Co-authored-by: Neha Abbas <neabbas@ucsc.edu> Co-authored-by: Abhijit Ramesh <abhijitramesh2k@gmail.com> * Remove extra code and format * Add ops documentation (finally) * Update ggml/src/ggml-webgpu/wgsl-shaders/embed_wgsl.py Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com> --------- Co-authored-by: James Contini <jamescontini@gmail.com> Co-authored-by: Neha Abbas <neabbas@ucsc.edu> Co-authored-by: Abhijit Ramesh <abhijitramesh2k@gmail.com> Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
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* FlashAttention (AtomicBot-ai#13) * Add inplace softmax * Move rms_norm to split row approach * Update debug for supports_op * clean up debug statements * neg f16xf32xip builds and runs, havent actually ran a model that uses neg kernel yet though * neg passes backend test * unary operators pass ggml tests * rms_norm double declaration bug atoned * abides by editor-config * removed vestigial files * fixed autoconfig * All operators (inlcluding xielu) working * removed unnecesarry checking if node->src[1] exists for unary operators * responded and dealt with PR comments * implemented REPL_Template support and removed bug in unary operators kernel * formatted embed wgsl and ggml-webgpu.cpp * Faster tensors (AtomicBot-ai#8) Add fast matrix and matrix/vector multiplication. * Use map for shader replacements instead of pair of strings * Wasm (AtomicBot-ai#9) * webgpu : fix build on emscripten * more debugging stuff * test-backend-ops: force single thread on wasm * fix single-thread case for init_tensor_uniform * use jspi * add pthread * test: remember to set n_thread for cpu backend * Add buffer label and enable dawn-specific toggles to turn off some checks * Intermediate state * Fast working f16/f32 vec4 * Working float fast mul mat * Clean up naming of mul_mat to match logical model, start work on q mul_mat * Setup for subgroup matrix mat mul * Basic working subgroup matrix * Working subgroup matrix tiling * Handle weirder sg matrix sizes (but still % sg matrix size) * Working start to gemv * working f16 accumulation with shared memory staging * Print out available subgroup matrix configurations * Vectorize dst stores for sg matrix shader * Gemv working scalar * Minor set_rows optimization (AtomicBot-ai#4) * updated optimization, fixed errors * non vectorized version now dispatches one thread per element * Simplify * Change logic for set_rows pipelines --------- Co-authored-by: Neha Abbas <nehaabbas@macbookpro.lan> Co-authored-by: Neha Abbas <nehaabbas@ReeseLevines-MacBook-Pro.local> Co-authored-by: Reese Levine <reeselevine1@gmail.com> * Comment on dawn toggles * Working subgroup matrix code for (semi)generic sizes * Remove some comments * Cleanup code * Update dawn version and move to portable subgroup size * Try to fix new dawn release * Update subgroup size comment * Only check for subgroup matrix configs if they are supported * Add toggles for subgroup matrix/f16 support on nvidia+vulkan * Make row/col naming consistent * Refactor shared memory loading * Move sg matrix stores to correct file * Working q4_0 * Formatting * Work with emscripten builds * Fix test-backend-ops emscripten for f16/quantized types * Use emscripten memory64 to support get_memory * Add build flags and try ci --------- Co-authored-by: Xuan Son Nguyen <son@huggingface.co> * Remove extra whitespace * Move wasm single-thread logic out of test-backend-ops for cpu backend * Disable multiple threads for emscripten single-thread builds in ggml_graph_plan * Refactored pipelines and workgroup calculations (AtomicBot-ai#10) * refactored pipelines * refactored workgroup calculation * removed commented out block of prior maps * Clean up ceiling division pattern --------- Co-authored-by: Neha Abbas <nehaabbas@eduroam-169-233-141-223.ucsc.edu> Co-authored-by: Reese Levine <reeselevine1@gmail.com> * Start work on flash attention * Shader structure set up (many bugs still) * debugging * Working first test * Working with head grouping, head sizes to 128, logit softcap, mask/sinks enabled, f32 * Generalize softmax to work with multiple subgroups, f16 accumulation, mask shared memory tiling * Start work on integrating pre-wgsl * Separate structs/initial shader compilation library into separate files * Work on compilation choices for flashattention * Work on subgroup matrix/tile size portability * subgroup size agnostic online softmax * Cleanups, quantization types * more cleanup * fix wasm build * Refactor flashattention to increase parallelism, use direct loads for KV in somce cases * Checkpoint * formatting * Update to account for default kv cache padding * formatting shader * Add workflow for ggml-ci webgpu * Try passing absolute path to dawn in ggml-ci * Avoid error on device destruction, add todos for proper cleanup * Fix unused warning * Forgot one parameter unused * Move some flashattn computation to f32 for correctness
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…d per-thread state (ggml-org#18976) * Squashed commit of the following: commit b3c6bf4b0450d8d452b934df27a0fb7cb53cd755 Author: Abhijit Ramesh <abhijitramesh2k@gmail.com> Date: Mon Dec 1 18:29:00 2025 -0800 ggml webgpu: fix xielu parameter passing (AtomicBot-ai#11) The XIELU operation was incorrectly using static_cast to convert float parameters to uint32_t, which converted numeric values instead of preserving IEEE 754 bit patterns. This caused incorrect values to be interpreted by the GPU shader. * Use reinterpret_cast to preserve float bit patterns when passing through uint32_t params buffer * Update WGSL shader parameter types from u32 to f32 * Re-enable XIELU support (was disabled due to numerical issues) Fixes NMSE test failures for XIELU operation on WebGPU backend. commit 5ca9b5e Author: neha-ha <137219201+neha-ha@users.noreply.github.com> Date: Tue Nov 18 12:17:00 2025 -0800 Refactored pipelines and workgroup calculations (AtomicBot-ai#10) * refactored pipelines * refactored workgroup calculation * removed commented out block of prior maps * Clean up ceiling division pattern --------- Co-authored-by: Neha Abbas <nehaabbas@eduroam-169-233-141-223.ucsc.edu> Co-authored-by: Reese Levine <reeselevine1@gmail.com> Author: James Contini <jamescontini@gmail.com> Date: Wed Oct 29 23:13:06 2025 -0700 formatted embed wgsl and ggml-webgpu.cpp commit e1f6bae Author: James Contini <jamescontini@gmail.com> Date: Wed Oct 29 23:08:37 2025 -0700 implemented REPL_Template support and removed bug in unary operators kernel commit 8c70b8f Author: James Contini <jamescontini@gmail.com> Date: Wed Oct 15 16:14:20 2025 -0700 responded and dealt with PR comments commit f9282c6 Author: James Contini <jamescontini@gmail.com> Date: Sun Oct 12 13:41:41 2025 -0700 removed unnecesarry checking if node->src[1] exists for unary operators commit 4cf28d7 Author: James Contini <jamescontini@gmail.com> Date: Sun Oct 12 13:32:45 2025 -0700 All operators (inlcluding xielu) working commit 74c6add Author: James Contini <jamescontini@gmail.com> Date: Fri Oct 10 13:16:48 2025 -0700 fixed autoconfig commit 3627499 Author: James Contini <jamescontini@gmail.com> Date: Fri Oct 10 13:10:46 2025 -0700 removed vestigial files commit cb08583 Author: James Contini <jamescontini@gmail.com> Date: Fri Oct 10 12:59:32 2025 -0700 abides by editor-config commit 5360e28 Author: James Contini <jamescontini@gmail.com> Date: Fri Oct 10 12:45:57 2025 -0700 rms_norm double declaration bug atoned commit 7b09baa Merge: 8a6ec84 1579702 Author: James Contini <jamescontini@gmail.com> Date: Fri Oct 10 11:50:03 2025 -0700 resolving merge conflicts commit 8a6ec84 Author: James Contini <jamescontini@gmail.com> Date: Wed Oct 8 18:06:47 2025 -0700 unary operators pass ggml tests commit c3ae382 Author: James Contini <jamescontini@gmail.com> Date: Wed Oct 1 16:22:40 2025 -0700 neg passes backend test commit aa1c9b2 Author: James Contini <jamescontini@gmail.com> Date: Tue Sep 30 23:55:27 2025 -0700 neg f16xf32xip builds and runs, havent actually ran a model that uses neg kernel yet though Co-authored-by: James Contini <jamescontini@gmail.com> Co-authored-by: Neha Abbas <neabbas@ucsc.edu> Co-authored-by: Abhijit Ramesh <abhijitramesh2k@gmail.com> * Remove extra code and format * Add ops documentation (finally) * ggml webgpu: add SOFTPLUS unary operator Implements SOFTPLUS (log(1 + exp(x))) with f16/f32 support. Uses f32 precision for intermediate calculations to prevent f16 overflow. * Add shader implementation and 4 variants (f32/f16, inplace/non-inplace) * Register pipelines and device support * Follow Vulkan backend numerical stability pattern * ggml webgpu: add EXPM1 unary operator Implements EXPM1 (exp(x) - 1) with f16/f32 support. * Add shader implementation and 4 variants (f32/f16, inplace/non-inplace) * Register pipelines and device support * ggml webgpu: add FLOOR unary operator Implements FLOOR (rounds down to nearest integer) with f16/f32 support. * Add shader implementation and 4 variants (f32/f16, inplace/non-inplace) * Register pipelines and device support * ggml webgpu: add CEIL unary operator Implements CEIL (rounds up to nearest integer) with f16/f32 support. * Add shader implementation and 4 variants (f32/f16, inplace/non-inplace) * Register pipelines and device support * ggml webgpu: add ROUND unary operator Implements ROUND (rounds to nearest integer) with f16/f32 support. * Add shader implementation and 4 variants (f32/f16, inplace/non-inplace) * Register pipelines and device support * ggml webgpu: add TRUNC unary operator Implements TRUNC (truncates towards zero) with f16/f32 support. * Add shader implementation and 4 variants (f32/f16, inplace/non-inplace) * Register pipelines and device support * docs : update WebGPU support for unary operators (FLOOR, CEIL, ROUND, TRUNC, EXPM1, SOFTPLUS) * Updates to webgpu get_memory * Move shared state (webgpu_context) and device creation out of registration context, device context, and buffer context, and move into backend context * Small cleanup * Move Instance, Device, Adapter, Device creation, and capabilities to global state while moving Queue, pipelines, and buffers to per-thread state. * Cleanups * More cleanup * Move staging_buf mutex to global context * Resolve merge * Resolve merge * Resolve merge * Clean up merge errors, delete forward declaration, and run clang-format * Rename device_init to backend_init * Move webgpu_context to backend_context * Move buffer context members into global context and refactor function calls * Run clang-format * Remove commends * Move parameter buffers to per-thread, add single memset_tensor param buf * Fix CI compilation issue * Fix builds for emscripten not supporting subgroups * cleanup * cleanup --------- Co-authored-by: Reese Levine <reeselevine1@gmail.com>
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Complete experiment log: AtomicBot-ai#1 4-mag LUT: 15.1 at 8K (BEST, +38%) AtomicBot-ai#2 Batched extract: 13.7 (+25%) AtomicBot-ai#3 Inline FA block: 13.5 (I-cache pressure) AtomicBot-ai#4 Deferred norm: 12.9 (loses ILP) AtomicBot-ai#5 2-pair half2: 12.0 (ternary overhead) AtomicBot-ai#6 Select chain: 11.9 (branches kill) AtomicBot-ai#7 Bit-arithmetic: 11.6 (ALU too heavy) AtomicBot-ai#8 FMA branchless: 11.4 (ALU still too heavy) AtomicBot-ai#9 Named-reg ternary: 10.3 (branches worst) AtomicBot-ai#10 Main (8-LUT): 10.95 (baseline) AtomicBot-ai#11 Non-vec FA: 10.2 (wrong kernel) Ceiling: 24.5 (no dequant) Apple8 hardware truth: 1 divergent constant read < 7 ALU ops (even with fma) Branches cost MORE than divergent constant reads Array indexing ALWAYS spills on Metal 4 constant addresses is the sweet spot The 4-mag LUT is the dequant-level ceiling on Apple Silicon. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> Co-Authored-By: tturney@psyguard.ai
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…hift-gemma4 fix: Gemma 4 + TurboQuant KV no longer crashes on second prompt when --cache-reuse enabled
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Brings in Gemma 4 + TurboQuant KV cache fixes: - fix/turbo-rope-shift-gemma4 (PR AtomicBot-ai#10) - fix/iswa-get-can-shift-gemma4 (PR AtomicBot-ai#9) - fix/mtp-assistant-tensor-prefix (PR AtomicBot-ai#7)
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Status-Update nach Abschluss aller verfuegbaren Cherry-Picks: BLOCKIERT (erfordern Refactor/Infrastruktur): - B3: KV-Cache Fixes (erfordert KV-Cache-Refactor, 30-40h) - C2: gzip compression (andere WebUI-Architektur) - C3: DeepSeek V3.2 (erfordert KV-Cache-Refactor) - AtomicBot-ai#9: CUDA PDL MTP (PDL-Infrastruktur fehlt im Fork) BEREITS VORHANDEN / NICHT RELEVANT: - AtomicBot-ai#10: SVE FWHT Runtime Width (bereits in ops.cpp) - #17b: Gemma 4 Audio Embedding (Granite Speech, nicht unser Fork) - C1: Reasoning Interruption (WebUI nicht genutzt) IN MASTER GEMERGET: - AtomicBot-ai#13: Vulkan iq1 shared memory - AtomicBot-ai#14: Vulkan host memory lock contention - #17a: Gemma 4 Unified Conversion Fix ZURUECKGESTELLT: - C4: Self-Conditioning DiffusionGemma (nicht stabil genug) - D3: Video-Input (nice-to-have, nicht dringend)
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…scal) Nach git filter-repo path-rename (styx→pascal) waren interne Referenzen inkonsistent: scripts/AGENTS.md, systemd-Service und Skript-Kommentare referenzierten Pascal-Host-server.sh statt pascal-server.sh. DOKU_BEOBACHTUNGEN.md: Sicherheitsverletzungen AtomicBot-ai#8,AtomicBot-ai#10,AtomicBot-ai#12,AtomicBot-ai#13,AtomicBot-ai#16 als ERLEDIGT markiert.
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…i#10 UMA nicht umsetzbar AtomicBot-ai#6 MUL_MAT_ID Subgroup (PR ggml-org#15524): bereits vollständig im Fork integriert - MatMulIdType::SUBGROUP, mul_mm_id_funcs.glsl, matmul_id_subgroup_* Shader - Pipelines aktiv (subgroup_ballot && subgroup_require_full_support) - Vorheriger Revert war fehlerhafter Merge, aktueller Stand sauber AtomicBot-ai#10 UMA Zero-Copy (PR ggml-org#22462): nicht implementieren - Verwandte UMA-PRs (ggml-org#22455, ggml-org#22930, ggml-org#23770) bereits revertiert - RCA-Masterplan: System-RAM langsamer als GTT für GPU-Compute auf Mars - PR ggml-org#22462 ist open/unstable
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…udit abgeschlossen AtomicBot-ai#7 Vulkan FA Refactor (PR ggml-org#19625): bereits im Fork (Commit 66e999e) - get_fa_tuning_params_scalar(), row_split, shmem_staging - Q caching in registers, vendor-specific Br selection - 28 Referenzen im Code ROADMAP-Audit Ergebnis: - M1: ✅ abgeschlossen - M2: ✅ evaluiert (AtomicBot-ai#6✅, AtomicBot-ai#7✅ bereits integriert, AtomicBot-ai#9❌, AtomicBot-ai#10❌) - M3: ⏳ teilweise (AtomicBot-ai#3✅, AtomicBot-ai#13❌, AtomicBot-ai#14⏭️, AtomicBot-ai#15 offen) - M4-M6: Noch nicht begonnen (Tier 2-4, 1+ Wochen Aufwand)
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Systematische Prüfung aller ROADMAP-Items gegen Fork-Code: - AtomicBot-ai#11 EAGLE-3 (PR ggml-org#18039): bereits integriert (Commit 57774253c) - AtomicBot-ai#12 Coopmat2 (PR ggml-org#19075): bereits integriert (flash_attn_cm2.comp, SPV generiert) - AtomicBot-ai#20 Tensor Parallelism (PR ggml-org#19378): bereits integriert (Commit d850df3) - AtomicBot-ai#28 Adaptive MTP (PR ggml-org#22931): Fork hat eigene Implementierung (LLAMA_MTP_SKIP_STREAK_THRESHOLD), PR closed/inkompatibel Meilenstein-Status: - M1: ✅ abgeschlossen - M2: ✅ evaluiert (AtomicBot-ai#6✅, AtomicBot-ai#7✅, AtomicBot-ai#12✅ bereits integriert, AtomicBot-ai#9❌, AtomicBot-ai#10❌) - M3: ⏳ teilweise (AtomicBot-ai#3✅, AtomicBot-ai#13❌, AtomicBot-ai#14⏭️, AtomicBot-ai#15 offen) - M4: ✅ abgeschlossen (AtomicBot-ai#11✅, AtomicBot-ai#28✅ eigene Implementierung) - M5: ⏳ teilweise (AtomicBot-ai#12✅, AtomicBot-ai#20✅ bereits integriert, AtomicBot-ai#21 offen) - M6: ☐ offen (Tier 4 Forschung)
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Overview
The previous cache bug #9 prevented the discovery of a knock on problem in the RoPE implementation. This fix is necessary to allow TurboQuant to function properly with cache reuse with gemma 4.
TurboQuant (turbo2/3/4) uses kernel-level WHT rotation, which is position-invariant -- WHT preserves inner products so no RoPE correction is needed after a KV position shift.
build_graph_shift() assumed standard quantized tensors with upstream rotation, but TurboQuant sets attn_rot_k=0 and handles rotation at kernel level. Building the shift graph with turbo-padded tensors causes a null buffer assert and segfault on the second prompt.
Fix: skip build_graph_shift() layers and get_has_shift() entirely for turbo KV types. Position tracking via seq_add() still works correctly -- only the broken RoPE re-rotation kernel is skipped.
Additional information
Combined with the previous PR that recognises caching in Gemma 4 this leads to near instataneous chat conversations on llama-sever web gui, when previously there was a reprocessing lag of 7 seconds plus, and a crash with any prompt causing a sliding window shift.
I have tested to around 6k of available 250k context and working flawlessly now.
Requirements