Refactor: Move matrix packing outside GEMM kernels #10
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In class tinyBLAS_PPC, previously, packing of input matrices A and B was performed on-the-fly within each GEMM microkernel. This patch refactors the code to decouple packing from kernel by introducing a preprocessing step that packs matrices once before any kernel is invoked. Benefits: - Enables better memory locality and data reuse - Simplifies the kernel logic by focusing purely on computation - Improves overall GEMM performance, especially for large matrix sizes Signed-off-by: Shalini Salomi Bodapati <[email protected]>
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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 #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 #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 #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 #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 #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 #6 0x000100aa98f4 in build_grammar(std::__1::function<void (common_grammar_builder const&)> const&, common_grammar_options const&) json-schema-to-grammar.cpp:982 #7 0x0001009c9314 in common_chat_params_init_llama_3_x(minja::chat_template const&, templates_params const&, bool) chat.cpp:1110 #8 0x0001009b8afc in common_chat_templates_apply_jinja(common_chat_templates const*, common_chat_templates_inputs const&) chat.cpp:1992 #9 0x0001009b533c in common_chat_templates_apply(common_chat_templates const*, common_chat_templates_inputs const&) chat.cpp:2074 #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) #1 0x000100873910 in std::sys::pal::unix::thread::Thread::new::h77254fdd87a28e05+0x118 (predict_oai-98384e17fb94e863:arm64+0x1000f3910) #2 0x0001007c7a1c in test::run_test::haeb3c2bcd5ed6cf6+0x76c (predict_oai-98384e17fb94e863:arm64+0x100047a1c) #3 0x0001007aedb0 in test::console::run_tests_console::he9d142d704f3a986+0x149c (predict_oai-98384e17fb94e863:arm64+0x10002edb0) #4 0x0001007c5758 in test::test_main::hf86a5e20735245b9+0x118 (predict_oai-98384e17fb94e863:arm64+0x100045758) #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 b3c6bf4 Author: Abhijit Ramesh <[email protected]> Date: Mon Dec 1 18:29:00 2025 -0800 ggml webgpu: fix xielu parameter passing (#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 <[email protected]> Date: Tue Nov 18 12:17:00 2025 -0800 Refactored pipelines and workgroup calculations (#10) * refactored pipelines * refactored workgroup calculation * removed commented out block of prior maps * Clean up ceiling division pattern --------- Co-authored-by: Neha Abbas <[email protected]> Co-authored-by: Reese Levine <[email protected]> Author: James Contini <[email protected]> Date: Wed Oct 29 23:13:06 2025 -0700 formatted embed wgsl and ggml-webgpu.cpp commit e1f6bae Author: James Contini <[email protected]> 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 <[email protected]> Date: Wed Oct 15 16:14:20 2025 -0700 responded and dealt with PR comments commit f9282c6 Author: James Contini <[email protected]> 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 <[email protected]> Date: Sun Oct 12 13:32:45 2025 -0700 All operators (inlcluding xielu) working commit 74c6add Author: James Contini <[email protected]> Date: Fri Oct 10 13:16:48 2025 -0700 fixed autoconfig commit 3627499 Author: James Contini <[email protected]> Date: Fri Oct 10 13:10:46 2025 -0700 removed vestigial files commit cb08583 Author: James Contini <[email protected]> Date: Fri Oct 10 12:59:32 2025 -0700 abides by editor-config commit 5360e28 Author: James Contini <[email protected]> Date: Fri Oct 10 12:45:57 2025 -0700 rms_norm double declaration bug atoned commit 7b09baa Merge: 8a6ec84 74b8fc1 Author: James Contini <[email protected]> Date: Fri Oct 10 11:50:03 2025 -0700 resolving merge conflicts commit 8a6ec84 Author: James Contini <[email protected]> Date: Wed Oct 8 18:06:47 2025 -0700 unary operators pass ggml tests commit c3ae382 Author: James Contini <[email protected]> Date: Wed Oct 1 16:22:40 2025 -0700 neg passes backend test commit aa1c9b2 Author: James Contini <[email protected]> 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 <[email protected]> Co-authored-by: Neha Abbas <[email protected]> Co-authored-by: Abhijit Ramesh <[email protected]> * Remove extra code and format * Add ops documentation (finally) * Update ggml/src/ggml-webgpu/wgsl-shaders/embed_wgsl.py Co-authored-by: Sigbjørn Skjæret <[email protected]> --------- Co-authored-by: James Contini <[email protected]> Co-authored-by: Neha Abbas <[email protected]> Co-authored-by: Abhijit Ramesh <[email protected]> Co-authored-by: Sigbjørn Skjæret <[email protected]>
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* FlashAttention (#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 (#8) Add fast matrix and matrix/vector multiplication. * Use map for shader replacements instead of pair of strings * Wasm (#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 (#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 <[email protected]> Co-authored-by: Neha Abbas <[email protected]> Co-authored-by: Reese Levine <[email protected]> * 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 <[email protected]> * 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 (#10) * refactored pipelines * refactored workgroup calculation * removed commented out block of prior maps * Clean up ceiling division pattern --------- Co-authored-by: Neha Abbas <[email protected]> Co-authored-by: Reese Levine <[email protected]> * 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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In class tinyBLAS_PPC, previously, packing of input matrices A and B was performed on-the-fly within each GEMM microkernel. This patch refactors the code to decouple packing from kernel by introducing a preprocessing step that packs matrices once before any kernel is invoked.
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