From 6b93b378cdfdcbe99f55087888b6578c5d184d9f Mon Sep 17 00:00:00 2001 From: Reese Levine Date: Wed, 3 Dec 2025 14:59:15 -0800 Subject: [PATCH 01/21] Squashed commit of the following: commit b3c6bf4b0450d8d452b934df27a0fb7cb53cd755 Author: Abhijit Ramesh 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 5ca9b5e49ea7cddc9ab7c8b43a11a9c76a4dff4a Author: neha-ha <137219201+neha-ha@users.noreply.github.com> 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 Co-authored-by: Reese Levine Author: James Contini Date: Wed Oct 29 23:13:06 2025 -0700 formatted embed wgsl and ggml-webgpu.cpp commit e1f6baea31645e5d96ad53664acae856f74b96f4 Author: James Contini Date: Wed Oct 29 23:08:37 2025 -0700 implemented REPL_Template support and removed bug in unary operators kernel commit 8c70b8fece445cdc9a8c660dbddbf201e52da2bb Author: James Contini Date: Wed Oct 15 16:14:20 2025 -0700 responded and dealt with PR comments commit f9282c660c10dec4487d434549bdb707a9cd9f37 Author: James Contini Date: Sun Oct 12 13:41:41 2025 -0700 removed unnecesarry checking if node->src[1] exists for unary operators commit 4cf28d7dec41c29186d66152735b244c5699f9dc Author: James Contini Date: Sun Oct 12 13:32:45 2025 -0700 All operators (inlcluding xielu) working commit 74c6add1761a59d2c2ff60b60e8ad3c8300f6d3e Author: James Contini Date: Fri Oct 10 13:16:48 2025 -0700 fixed autoconfig commit 362749910be4f0120c8ffb21ceddeb7d2c088e51 Author: James Contini Date: Fri Oct 10 13:10:46 2025 -0700 removed vestigial files commit cb0858333785757804c5104e59c4981843207c16 Author: James Contini Date: Fri Oct 10 12:59:32 2025 -0700 abides by editor-config commit 5360e2852a4b51197d7d67d0a5d42e908b02d7ed Author: James Contini Date: Fri Oct 10 12:45:57 2025 -0700 rms_norm double declaration bug atoned commit 7b09baa4aa53711be5a126043670cc182c78bfcd Merge: 8a6ec843 74b8fc17 Author: James Contini Date: Fri Oct 10 11:50:03 2025 -0700 resolving merge conflicts commit 8a6ec843a50ab82f8cef59b4558eb63f318ba02d Author: James Contini Date: Wed Oct 8 18:06:47 2025 -0700 unary operators pass ggml tests commit c3ae38278a2db236adc5912c9140e4f0d63f2c19 Author: James Contini Date: Wed Oct 1 16:22:40 2025 -0700 neg passes backend test commit aa1c9b2f8877a405470ca56709c42a1fd43713de Author: James Contini 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 Co-authored-by: Neha Abbas Co-authored-by: Abhijit Ramesh --- ggml/include/ggml.h | 6 + ggml/src/ggml-cpu/ggml-cpu.cpp | 7 + ggml/src/ggml-webgpu/ggml-webgpu.cpp | 969 +++++++++++------- .../ggml-webgpu/wgsl-shaders/embed_wgsl.py | 15 +- .../ggml-webgpu/wgsl-shaders/unary_op.wgsl | 461 +++++++++ tests/test-backend-ops.cpp | 6 + 6 files changed, 1099 insertions(+), 365 deletions(-) create mode 100644 ggml/src/ggml-webgpu/wgsl-shaders/unary_op.wgsl diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h index e665614670..5af0692692 100644 --- a/ggml/include/ggml.h +++ b/ggml/include/ggml.h @@ -225,7 +225,13 @@ # define GGML_MAX_NAME 64 #endif +// For single-thread WASM builds, only use 1 thread +#if !defined(__EMSCRIPTEN__) || defined(__EMSCRIPTEN_PTHREADS__) #define GGML_DEFAULT_N_THREADS 4 +#else +#define GGML_DEFAULT_N_THREADS 1 +#endif + #define GGML_DEFAULT_GRAPH_SIZE 2048 #if UINTPTR_MAX == 0xFFFFFFFF diff --git a/ggml/src/ggml-cpu/ggml-cpu.cpp b/ggml/src/ggml-cpu/ggml-cpu.cpp index 3191faaa4c..3c5cc14102 100644 --- a/ggml/src/ggml-cpu/ggml-cpu.cpp +++ b/ggml/src/ggml-cpu/ggml-cpu.cpp @@ -246,8 +246,11 @@ bool ggml_backend_is_cpu(ggml_backend_t backend) { void ggml_backend_cpu_set_n_threads(ggml_backend_t backend_cpu, int n_threads) { GGML_ASSERT(ggml_backend_is_cpu(backend_cpu)); +// For single-thread WASM builds, do not allow changing the number of threads +#if !defined(_EMSCRIPTEN_) || defined(__EMSCRIPTEN_PTHREADS__) struct ggml_backend_cpu_context * ctx = (struct ggml_backend_cpu_context *)backend_cpu->context; ctx->n_threads = n_threads; +#endif } void ggml_backend_cpu_set_threadpool(ggml_backend_t backend_cpu, ggml_threadpool_t threadpool) { @@ -622,10 +625,14 @@ static ggml_backend_feature * ggml_backend_cpu_get_features(ggml_backend_reg_t r } static void * ggml_backend_cpu_get_proc_address(ggml_backend_reg_t reg, const char * name) { + +// For single-thread WASM builds, do not expose a set_n_threads function +#if !defined(__EMSCRIPTEN__) || defined(__EMSCRIPTEN_PTHREADS__) if (strcmp(name, "ggml_backend_set_n_threads") == 0) { ggml_backend_set_n_threads_t fct = ggml_backend_cpu_set_n_threads; return (void *)fct; } +#endif if (strcmp(name, "ggml_backend_dev_get_extra_bufts") == 0) { ggml_backend_dev_get_extra_bufts_t fct = ggml_backend_cpu_device_get_extra_buffers_type; return (void *)fct; diff --git a/ggml/src/ggml-webgpu/ggml-webgpu.cpp b/ggml/src/ggml-webgpu/ggml-webgpu.cpp index a7476b109d..56dc24681f 100644 --- a/ggml/src/ggml-webgpu/ggml-webgpu.cpp +++ b/ggml/src/ggml-webgpu/ggml-webgpu.cpp @@ -25,6 +25,9 @@ #include #include +#define ROUNDUP_POW2(x, pow2) (((x) + ((pow2) - 1)) & ~((pow2) - 1)) +#define CEIL_DIV(M, N) (((M) + (N) - 1) / (N)) + #ifdef GGML_WEBGPU_DEBUG # define WEBGPU_LOG_DEBUG(msg) std::cout << msg << std::endl # define WEBGPU_DEBUG_BUF_ELEMS 32 @@ -64,6 +67,9 @@ /* Constants */ +// Track https://github.com/gpuweb/gpuweb/issues/5315 for fixes to implementations so this can be removed. +#define WEBGPU_MAX_WG_SIZE 288 + #define WEBGPU_MUL_MAT_WG_SIZE 256 #define WEBGPU_NUM_PARAM_BUFS 32u #define WEBGPU_COMMAND_SUBMIT_BATCH_SIZE 8u @@ -272,36 +278,33 @@ struct webgpu_context_struct { wgpu::SubgroupMatrixConfig subgroup_matrix_config; #endif - // Separate this out from limits since on some Metal systems, the limit returned by - // querying the limits is higher than the actual allowed maximum. - uint32_t max_wg_size_x; - std::recursive_mutex mutex; std::atomic_uint inflight_threads = 0; webgpu_buf_pool param_buf_pool; webgpu_buf_pool set_rows_error_buf_pool; - webgpu_pipeline memset_pipeline; + std::map memset_pipelines; // variant or type index std::map>> mul_mat_pipelines; // src0_type, src1_type, vectorized std::map>> mul_mat_vec_pipelines; // src0_type, src1_type, vectorized - webgpu_pipeline mul_mat_pipeline[30][2]; - webgpu_pipeline set_rows_pipeline[1][2]; // dst->type, vectorized - webgpu_pipeline get_rows_pipeline[30]; - webgpu_pipeline get_rows_f32_no_vec_pipeline; - webgpu_pipeline cpy_pipeline[2][2]; // src type, dst type - webgpu_pipeline add_pipeline[2][2]; // type, inplace - webgpu_pipeline sub_pipeline[2][2]; // type, inplace - webgpu_pipeline mul_pipeline[2][2]; // type, inplace - webgpu_pipeline div_pipeline[2][2]; // type, inplace - webgpu_pipeline rms_norm_pipeline[2]; // inplace - webgpu_pipeline rope_pipeline[2][2][2]; // type, ff, inplace - webgpu_pipeline glu_pipeline[7][2][2]; // glu-op, type, split - webgpu_pipeline scale_pipeline[2]; // inplace - webgpu_pipeline soft_max_pipeline[3][2][2]; // (no_mask, f32_mask, f16_mask), has_sink, inplace + std::map> set_rows_pipelines; // dst_type, vectorized + std::map> get_rows_pipelines; // src_type, vectorized + + std::map> cpy_pipelines; // src_type, dst_type + std::map> add_pipelines; // type, inplace + std::map> sub_pipelines; // type, inplace + std::map> mul_pipelines; // type, inplace + std::map> div_pipelines; // type, inplace + + std::map rms_norm_pipelines; // inplace + std::map>> rope_pipelines; // type, ff, inplace + std::map>> glu_pipelines; // glu_op, type, split + std::map scale_pipelines; // inplace + std::map>> soft_max_pipelines; // mask_type, has_sink, inplace + std::map>> unary_pipelines; // unary_op, type, inplace size_t memset_bytes_per_thread; @@ -381,35 +384,10 @@ static std::string ggml_webgpu_process_shader_repls(const char * return s; } -static void ggml_webgpu_create_pipeline(wgpu::Device & device, - webgpu_pipeline & pipeline, - const char * shader_code, - const char * label, - const std::vector & constants = {}) { - wgpu::ShaderSourceWGSL shader_source; - shader_source.code = shader_code; - - wgpu::ShaderModuleDescriptor shader_desc; - shader_desc.nextInChain = &shader_source; - - wgpu::ShaderModule shader_module = device.CreateShaderModule(&shader_desc); - - wgpu::ComputePipelineDescriptor pipeline_desc; - pipeline_desc.label = label; - pipeline_desc.compute.module = shader_module; - pipeline_desc.compute.entryPoint = "main"; // Entry point in the WGSL code - pipeline_desc.layout = nullptr; // nullptr means auto layout - if (constants.size() > 0) { - pipeline_desc.compute.constants = constants.data(); - pipeline_desc.compute.constantCount = constants.size(); - } - pipeline = { device.CreateComputePipeline(&pipeline_desc), label }; -} - -static webgpu_pipeline ggml_webgpu_create_pipeline2(wgpu::Device & device, - const char * shader_code, - const char * label, - const std::vector & constants = {}) { +static webgpu_pipeline ggml_webgpu_create_pipeline(wgpu::Device & device, + const char * shader_code, + const char * label, + const std::vector & constants = {}) { wgpu::ShaderSourceWGSL shader_source; shader_source.code = shader_code; @@ -678,10 +656,10 @@ static void ggml_backend_webgpu_buffer_memset(webgpu_context & ctx, std::vector entries = { { .binding = 0, .buffer = buf, .offset = 0, .size = buf.GetSize() } }; - size_t bytes_per_wg = ctx->max_wg_size_x * ctx->memset_bytes_per_thread; - uint32_t wg_x = ((size + 3) + bytes_per_wg - 1) / bytes_per_wg; + size_t bytes_per_wg = WEBGPU_MAX_WG_SIZE * ctx->memset_bytes_per_thread; + uint32_t wg_x = CEIL_DIV(size + 3, bytes_per_wg); - webgpu_command command = ggml_backend_webgpu_build(ctx, ctx->memset_pipeline, params, entries, wg_x); + webgpu_command command = ggml_backend_webgpu_build(ctx, ctx->memset_pipelines[0], params, entries, wg_x); std::vector futures = { ggml_backend_webgpu_submit(ctx, { command }) }; ggml_backend_webgpu_wait(ctx, futures); } @@ -763,8 +741,7 @@ static size_t ggml_webgpu_tensor_align_offset(webgpu_context & ctx, ggml_tensor } static size_t ggml_webgpu_tensor_binding_size(webgpu_context & ctx, ggml_tensor * t) { - return (ggml_nbytes(t) + ggml_webgpu_tensor_misalignment(ctx, t) + WEBGPU_STORAGE_BUF_BINDING_MULT - 1) & - ~(WEBGPU_STORAGE_BUF_BINDING_MULT - 1); + return ROUNDUP_POW2(ggml_nbytes(t) + ggml_webgpu_tensor_misalignment(ctx, t), WEBGPU_STORAGE_BUF_BINDING_MULT); } // Used to determine if two tensors are the same for in-place operations @@ -800,9 +777,8 @@ static webgpu_command ggml_webgpu_cpy(webgpu_context & ctx, ggml_tensor * src, g .size = ggml_webgpu_tensor_binding_size(ctx, dst) } }; - size_t max_wg_size = ctx->max_wg_size_x; - uint32_t wg_x = (ne + max_wg_size - 1) / max_wg_size; - return ggml_backend_webgpu_build(ctx, ctx->cpy_pipeline[src->type][dst->type], params, entries, wg_x); + uint32_t wg_x = CEIL_DIV(ne, WEBGPU_MAX_WG_SIZE); + return ggml_backend_webgpu_build(ctx, ctx->cpy_pipelines[src->type][dst->type], params, entries, wg_x); } static std::optional ggml_webgpu_set_rows(webgpu_context & ctx, @@ -851,10 +827,8 @@ static std::optional ggml_webgpu_set_rows(webgpu_context & ctx, { .binding = 3, .buffer = error_bufs.dev_buf, .offset = 0, .size = error_bufs.dev_buf.GetSize() } }; - size_t max_wg_size = ctx->max_wg_size_x; - int vectorized = src->ne[0] % 4 == 0; - webgpu_pipeline pipeline = ctx->set_rows_pipeline[0][vectorized]; + webgpu_pipeline pipeline = ctx->set_rows_pipelines[0][vectorized]; uint32_t threads; if (vectorized) { threads = (src->ne[1] * src->ne[2] * src->ne[3]) * (src->ne[0] / 4); @@ -862,7 +836,7 @@ static std::optional ggml_webgpu_set_rows(webgpu_context & ctx, threads = src->ne[0] * src->ne[1] * src->ne[2] * src->ne[3]; } - uint32_t wg_x = (threads + max_wg_size - 1) / max_wg_size; + uint32_t wg_x = CEIL_DIV(threads, WEBGPU_MAX_WG_SIZE); return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x, 1, error_bufs); } @@ -902,13 +876,10 @@ static webgpu_command ggml_webgpu_get_rows(webgpu_context & ctx, .size = ggml_webgpu_tensor_binding_size(ctx, dst) } }; - size_t max_wg_size = ctx->max_wg_size_x; - uint32_t wg_x = (dst->ne[1] * dst->ne[2] * dst->ne[3] + max_wg_size - 1) / max_wg_size; + uint32_t wg_x = CEIL_DIV(dst->ne[1] * dst->ne[2] * dst->ne[3], WEBGPU_MAX_WG_SIZE); - webgpu_pipeline pipeline = ctx->get_rows_pipeline[src->type]; - if (src->type == GGML_TYPE_F32 && dst->ne[0] % 4 != 0) { - pipeline = ctx->get_rows_f32_no_vec_pipeline; - } + uint32_t vectorized = src->type == GGML_TYPE_F32 && dst->ne[0] % 4 == 0; + webgpu_pipeline pipeline = ctx->get_rows_pipelines[src->type][vectorized]; return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x); } @@ -950,10 +921,9 @@ static webgpu_command ggml_webgpu_mul_mat(webgpu_context & ctx, .size = ggml_webgpu_tensor_binding_size(ctx, dst) }, }; - webgpu_pipeline pipeline = ctx->mul_mat_pipeline[src0->type][src1->type]; + webgpu_pipeline pipeline = ctx->mul_mat_pipelines[src0->type][src1->type][0]; - uint32_t wg_x = - (dst->ne[0] * dst->ne[1] * dst->ne[2] * dst->ne[3] + WEBGPU_MUL_MAT_WG_SIZE - 1) / WEBGPU_MUL_MAT_WG_SIZE; + uint32_t wg_x = CEIL_DIV(dst->ne[0] * dst->ne[1] * dst->ne[2] * dst->ne[3], WEBGPU_MUL_MAT_WG_SIZE); uint32_t wg_y = 1; bool use_fast = false; @@ -980,15 +950,13 @@ static webgpu_command ggml_webgpu_mul_mat(webgpu_context & ctx, int vectorized = src0->ne[0] % 4 == 0 && dst->ne[0] % 4 == 0 && dst->ne[1] % 4 == 0; if (dst->ne[1] == 1) { // We don't support vectorized mul_mat_vec for quantized types - vectorized = vectorized && (src0->type < 2); - pipeline = ctx->mul_mat_vec_pipelines[src0->type][src1->type][vectorized]; - uint32_t batches = dst->ne[2] * dst->ne[3]; - uint32_t output_groups = - (dst->ne[0] + WEBGPU_MUL_MAT_VEC_OUTPUTS_PER_WG - 1) / WEBGPU_MUL_MAT_VEC_OUTPUTS_PER_WG; - uint32_t total_wg = output_groups * batches; - wg_x = total_wg % ctx->limits.maxComputeWorkgroupsPerDimension; - wg_y = (total_wg + ctx->limits.maxComputeWorkgroupsPerDimension - 1) / - ctx->limits.maxComputeWorkgroupsPerDimension; + vectorized = vectorized && (src0->type < 2); + pipeline = ctx->mul_mat_vec_pipelines[src0->type][src1->type][vectorized]; + uint32_t batches = dst->ne[2] * dst->ne[3]; + uint32_t output_groups = CEIL_DIV(dst->ne[0], WEBGPU_MUL_MAT_VEC_OUTPUTS_PER_WG); + uint32_t total_wg = output_groups * batches; + wg_x = total_wg % ctx->limits.maxComputeWorkgroupsPerDimension; + wg_y = CEIL_DIV(total_wg, ctx->limits.maxComputeWorkgroupsPerDimension); } else { pipeline = ctx->mul_mat_pipelines[src0->type][src1->type][vectorized]; uint32_t wg_m; @@ -998,16 +966,16 @@ static webgpu_command ggml_webgpu_mul_mat(webgpu_context & ctx, // The total number of subgroups/workgroups needed per matrix. uint32_t wg_m_sg_tile = WEBGPU_MUL_MAT_SUBGROUP_M * WEBGPU_MUL_MAT_SUBGROUP_MATRIX_M * ctx->subgroup_matrix_config.M; - wg_m = (dst->ne[0] + wg_m_sg_tile - 1) / wg_m_sg_tile; + wg_m = CEIL_DIV(dst->ne[0], wg_m_sg_tile); uint32_t wg_n_sg_tile = WEBGPU_MUL_MAT_SUBGROUP_N * WEBGPU_MUL_MAT_SUBGROUP_MATRIX_N * ctx->subgroup_matrix_config.N; - wg_n = (dst->ne[1] + wg_n_sg_tile - 1) / wg_n_sg_tile; + wg_n = CEIL_DIV(dst->ne[1], wg_n_sg_tile); } else { #endif uint32_t tile_m_s = WEBGPU_MUL_MAT_TILE_M * WEBGPU_MUL_MAT_WG_SIZE_M; uint32_t tile_n_s = WEBGPU_MUL_MAT_TILE_N * WEBGPU_MUL_MAT_WG_SIZE_N; - wg_m = (dst->ne[0] + tile_m_s - 1) / tile_m_s; - wg_n = (dst->ne[1] + tile_n_s - 1) / tile_n_s; + wg_m = CEIL_DIV(dst->ne[0], tile_m_s); + wg_n = CEIL_DIV(dst->ne[1], tile_n_s); #ifndef __EMSCRIPTEN__ } #endif @@ -1018,6 +986,59 @@ static webgpu_command ggml_webgpu_mul_mat(webgpu_context & ctx, return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x, wg_y); } +static webgpu_command ggml_webgpu_unary_op(webgpu_context & ctx, ggml_tensor * src, ggml_tensor * dst) { + uint32_t ne = (uint32_t) ggml_nelements(dst); + ggml_unary_op unary_op = ggml_get_unary_op(dst); + uint32_t inplace = ggml_webgpu_tensor_equal(src, dst); + + std::vector params = { + ne, (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src) / ggml_type_size(src->type)), + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)), + // Convert byte-strides to element-strides + (uint32_t) (src->nb[0] / ggml_type_size(src->type)), (uint32_t) (src->nb[1] / ggml_type_size(src->type)), + (uint32_t) (src->nb[2] / ggml_type_size(src->type)), (uint32_t) (src->nb[3] / ggml_type_size(src->type)), + (uint32_t) (dst->nb[0] / ggml_type_size(dst->type)), (uint32_t) (dst->nb[1] / ggml_type_size(dst->type)), + (uint32_t) (dst->nb[2] / ggml_type_size(dst->type)), (uint32_t) (dst->nb[3] / ggml_type_size(dst->type)), + // Logical shapes + (uint32_t) src->ne[0], (uint32_t) src->ne[1], (uint32_t) src->ne[2], (uint32_t) dst->ne[0], + (uint32_t) dst->ne[1], (uint32_t) dst->ne[2] + }; + + switch (unary_op) { + case GGML_UNARY_OP_XIELU: { + // Get float parameters and reinterpret their bit patterns as uint32_t + // for passing through the params buffer + float alpha_n = ggml_get_op_params_f32(dst, 1); + float alpha_p = ggml_get_op_params_f32(dst, 2); + float beta = ggml_get_op_params_f32(dst, 3); + float eps = ggml_get_op_params_f32(dst, 4); + params.push_back(*reinterpret_cast(&alpha_n)); + params.push_back(*reinterpret_cast(&alpha_p)); + params.push_back(*reinterpret_cast(&beta)); + params.push_back(*reinterpret_cast(&eps)); + break; + } + default: + break; + } + + std::vector entries = { + { .binding = 0, + .buffer = ggml_webgpu_tensor_buf(src), + .offset = ggml_webgpu_tensor_align_offset(ctx, src), + .size = ggml_webgpu_tensor_binding_size(ctx, src) }, + }; + if (!inplace) { + entries.push_back({ .binding = 1, + .buffer = ggml_webgpu_tensor_buf(dst), + .offset = ggml_webgpu_tensor_align_offset(ctx, dst), + .size = ggml_webgpu_tensor_binding_size(ctx, dst) }); + } + + uint32_t wg_x = CEIL_DIV(ne, WEBGPU_MAX_WG_SIZE); + return ggml_backend_webgpu_build(ctx, ctx->unary_pipelines[unary_op][dst->type][inplace], params, entries, wg_x); +} + static webgpu_command ggml_webgpu_binary_op(webgpu_context & ctx, ggml_tensor * src0, ggml_tensor * src1, @@ -1059,8 +1080,7 @@ static webgpu_command ggml_webgpu_binary_op(webgpu_context & ctx, .size = ggml_webgpu_tensor_binding_size(ctx, dst) }); } - size_t max_wg_size = ctx->max_wg_size_x; - uint32_t wg_x = (ggml_nelements(dst) + max_wg_size - 1) / max_wg_size; + uint32_t wg_x = CEIL_DIV(ggml_nelements(dst), WEBGPU_MAX_WG_SIZE); return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x); } @@ -1096,7 +1116,7 @@ static webgpu_command ggml_webgpu_rms_norm(webgpu_context & ctx, ggml_tensor * s .size = ggml_webgpu_tensor_binding_size(ctx, dst) }); } - return ggml_backend_webgpu_build(ctx, ctx->rms_norm_pipeline[inplace], params, entries, ggml_nrows(src)); + return ggml_backend_webgpu_build(ctx, ctx->rms_norm_pipelines[inplace], params, entries, ggml_nrows(src)); } static webgpu_command ggml_webgpu_rope(webgpu_context & ctx, @@ -1181,9 +1201,8 @@ static webgpu_command ggml_webgpu_rope(webgpu_context & ctx, .size = ggml_webgpu_tensor_binding_size(ctx, dst) }); } - webgpu_pipeline pipeline = ctx->rope_pipeline[dst->type][has_freq_factor][inplace]; - size_t max_wg_size = ctx->max_wg_size_x; - uint32_t wg_x = (ggml_nelements(src0) / 2 + max_wg_size - 1) / max_wg_size; + webgpu_pipeline pipeline = ctx->rope_pipelines[dst->type][has_freq_factor][inplace]; + uint32_t wg_x = CEIL_DIV(ggml_nelements(dst), WEBGPU_MAX_WG_SIZE); return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x); } @@ -1234,9 +1253,8 @@ static webgpu_command ggml_webgpu_glu(webgpu_context & ctx, ggml_tensor * src0, .offset = ggml_webgpu_tensor_align_offset(ctx, dst), .size = ggml_webgpu_tensor_binding_size(ctx, dst) }); - webgpu_pipeline pipeline = ctx->glu_pipeline[ggml_get_glu_op(dst)][dst->type][split]; - size_t max_wg_size = ctx->max_wg_size_x; - uint32_t wg_x = (ggml_nelements(dst) + max_wg_size - 1) / max_wg_size; + webgpu_pipeline pipeline = ctx->glu_pipelines[ggml_get_glu_op(dst)][dst->type][split]; + uint32_t wg_x = CEIL_DIV(ggml_nelements(dst), WEBGPU_MAX_WG_SIZE); return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x); } @@ -1273,9 +1291,8 @@ static webgpu_command ggml_webgpu_scale(webgpu_context & ctx, ggml_tensor * src, .size = ggml_webgpu_tensor_binding_size(ctx, dst) }); } - size_t max_wg_size = ctx->max_wg_size_x; - uint32_t wg_x = (ggml_nelements(dst) + max_wg_size - 1) / max_wg_size; - return ggml_backend_webgpu_build(ctx, ctx->scale_pipeline[inplace], params, entries, wg_x); + uint32_t wg_x = CEIL_DIV(ggml_nelements(dst), WEBGPU_MAX_WG_SIZE); + return ggml_backend_webgpu_build(ctx, ctx->scale_pipelines[inplace], params, entries, wg_x); } static webgpu_command ggml_webgpu_soft_max(webgpu_context & ctx, @@ -1347,7 +1364,7 @@ static webgpu_command ggml_webgpu_soft_max(webgpu_context & ctx, .size = ggml_webgpu_tensor_binding_size(ctx, dst) }); } - return ggml_backend_webgpu_build(ctx, ctx->soft_max_pipeline[mask_type][has_sink][inplace], params, entries, + return ggml_backend_webgpu_build(ctx, ctx->soft_max_pipelines[mask_type][has_sink][inplace], params, entries, ggml_nrows(dst)); } @@ -1382,22 +1399,22 @@ static std::optional ggml_webgpu_encode_node(webgpu_context ctx, case GGML_OP_ADD: { int inplace = ggml_webgpu_tensor_equal(src0, node); - return ggml_webgpu_binary_op(ctx, src0, src1, node, ctx->add_pipeline[node->type][inplace], inplace); + return ggml_webgpu_binary_op(ctx, src0, src1, node, ctx->add_pipelines[node->type][inplace], inplace); } case GGML_OP_SUB: { int inplace = ggml_webgpu_tensor_equal(src0, node); - return ggml_webgpu_binary_op(ctx, src0, src1, node, ctx->sub_pipeline[node->type][inplace], inplace); + return ggml_webgpu_binary_op(ctx, src0, src1, node, ctx->sub_pipelines[node->type][inplace], inplace); } case GGML_OP_MUL: { int inplace = ggml_webgpu_tensor_equal(src0, node); - return ggml_webgpu_binary_op(ctx, src0, src1, node, ctx->mul_pipeline[node->type][inplace], inplace); + return ggml_webgpu_binary_op(ctx, src0, src1, node, ctx->mul_pipelines[node->type][inplace], inplace); } case GGML_OP_DIV: { int inplace = ggml_webgpu_tensor_equal(src0, node); - return ggml_webgpu_binary_op(ctx, src0, src1, node, ctx->div_pipeline[node->type][inplace], inplace); + return ggml_webgpu_binary_op(ctx, src0, src1, node, ctx->div_pipelines[node->type][inplace], inplace); } case GGML_OP_RMS_NORM: return ggml_webgpu_rms_norm(ctx, src0, node); @@ -1409,6 +1426,8 @@ static std::optional ggml_webgpu_encode_node(webgpu_context ctx, return ggml_webgpu_scale(ctx, src0, node); case GGML_OP_SOFT_MAX: return ggml_webgpu_soft_max(ctx, src0, src1, src2, node); + case GGML_OP_UNARY: + return ggml_webgpu_unary_op(ctx, src0, node); default: return std::nullopt; } @@ -1641,8 +1660,7 @@ static ggml_backend_buffer_t ggml_backend_webgpu_buffer_type_alloc_buffer(ggml_b ggml_backend_webgpu_device_context * ctx = static_cast(buft->device->context); wgpu::Buffer buf; - ggml_webgpu_create_buffer(ctx->webgpu_ctx->device, buf, - (size + WEBGPU_STORAGE_BUF_BINDING_MULT - 1) & ~(WEBGPU_STORAGE_BUF_BINDING_MULT - 1), + ggml_webgpu_create_buffer(ctx->webgpu_ctx->device, buf, ROUNDUP_POW2(size, WEBGPU_STORAGE_BUF_BINDING_MULT), wgpu::BufferUsage::Storage | wgpu::BufferUsage::CopySrc | wgpu::BufferUsage::CopyDst, buf_name.c_str()); @@ -1717,58 +1735,64 @@ static std::vector ggml_webgpu_wg_size_entry(uint32_t wg_si static void ggml_webgpu_init_memset_pipeline(webgpu_context & webgpu_ctx) { // we use the maximum workgroup size for the memset pipeline - size_t max_wg_size = webgpu_ctx->max_wg_size_x; - size_t max_threads = max_wg_size * webgpu_ctx->limits.maxComputeWorkgroupsPerDimension; + size_t max_threads = WEBGPU_MAX_WG_SIZE * webgpu_ctx->limits.maxComputeWorkgroupsPerDimension; // Size the bytes_per_thread so that the largest buffer size can be handled - webgpu_ctx->memset_bytes_per_thread = - (webgpu_ctx->limits.maxStorageBufferBindingSize + max_threads - 1) / max_threads; + webgpu_ctx->memset_bytes_per_thread = CEIL_DIV(webgpu_ctx->limits.maxStorageBufferBindingSize, max_threads); std::vector constants(2); - constants[0].key = "wg_size"; - constants[0].value = max_wg_size; - constants[1].key = "bytes_per_thread"; - constants[1].value = webgpu_ctx->memset_bytes_per_thread; - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->memset_pipeline, wgsl_memset, "memset", constants); + constants[0].key = "wg_size"; + constants[0].value = WEBGPU_MAX_WG_SIZE; + constants[1].key = "bytes_per_thread"; + constants[1].value = webgpu_ctx->memset_bytes_per_thread; + webgpu_ctx->memset_pipelines[0] = ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_memset, "memset", constants); } static void ggml_webgpu_init_mul_mat_pipeline(webgpu_context & webgpu_ctx) { - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->mul_mat_pipeline[GGML_TYPE_Q4_0][GGML_TYPE_F32], - wgsl_mul_mat_q4_0_f32, "mul_mat_q4_0_f32"); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->mul_mat_pipeline[GGML_TYPE_Q4_1][GGML_TYPE_F32], - wgsl_mul_mat_q4_1_f32, "mul_mat_q4_1_f32"); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->mul_mat_pipeline[GGML_TYPE_Q5_0][GGML_TYPE_F32], - wgsl_mul_mat_q5_0_f32, "mul_mat_q5_0_f32"); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->mul_mat_pipeline[GGML_TYPE_Q5_1][GGML_TYPE_F32], - wgsl_mul_mat_q5_1_f32, "mul_mat_q5_1_f32"); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->mul_mat_pipeline[GGML_TYPE_Q8_0][GGML_TYPE_F32], - wgsl_mul_mat_q8_0_f32, "mul_mat_q8_0_f32"); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->mul_mat_pipeline[GGML_TYPE_Q2_K][GGML_TYPE_F32], - wgsl_mul_mat_q2_k_f32, "mul_mat_q2_k_f32"); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->mul_mat_pipeline[GGML_TYPE_Q3_K][GGML_TYPE_F32], - wgsl_mul_mat_q3_k_f32, "mul_mat_q3_k_f32"); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->mul_mat_pipeline[GGML_TYPE_Q4_K][GGML_TYPE_F32], - wgsl_mul_mat_q4_k_f32, "mul_mat_q4_k_f32"); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->mul_mat_pipeline[GGML_TYPE_Q5_K][GGML_TYPE_F32], - wgsl_mul_mat_q5_k_f32, "mul_mat_q5_k_f32"); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->mul_mat_pipeline[GGML_TYPE_Q6_K][GGML_TYPE_F32], - wgsl_mul_mat_q6_k_f32, "mul_mat_q6_k_f32"); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->mul_mat_pipeline[GGML_TYPE_IQ2_XXS][GGML_TYPE_F32], - wgsl_mul_mat_iq2_xxs_f32, "mul_mat_iq2_xxs_f32"); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->mul_mat_pipeline[GGML_TYPE_IQ2_XS][GGML_TYPE_F32], - wgsl_mul_mat_iq2_xs_f32, "mul_mat_iq2_xs_f32"); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->mul_mat_pipeline[GGML_TYPE_IQ2_S][GGML_TYPE_F32], - wgsl_mul_mat_iq2_s_f32, "mul_mat_iq2_s_f32"); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->mul_mat_pipeline[GGML_TYPE_IQ3_XXS][GGML_TYPE_F32], - wgsl_mul_mat_iq3_xxs_f32, "mul_mat_iq3_xxs_f32"); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->mul_mat_pipeline[GGML_TYPE_IQ3_S][GGML_TYPE_F32], - wgsl_mul_mat_iq3_s_f32, "mul_mat_iq3_s_f32"); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->mul_mat_pipeline[GGML_TYPE_IQ1_S][GGML_TYPE_F32], - wgsl_mul_mat_iq1_s_f32, "mul_mat_iq1_s_f32"); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->mul_mat_pipeline[GGML_TYPE_IQ1_M][GGML_TYPE_F32], - wgsl_mul_mat_iq1_m_f32, "mul_mat_iq1_m_f32"); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->mul_mat_pipeline[GGML_TYPE_IQ4_NL][GGML_TYPE_F32], - wgsl_mul_mat_iq4_nl_f32, "mul_mat_iq4_nl_f32"); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->mul_mat_pipeline[GGML_TYPE_IQ4_XS][GGML_TYPE_F32], - wgsl_mul_mat_iq4_xs_f32, "mul_mat_iq4_xs_f32"); + // Q4/Q5/Q8 classic quantizations + webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q4_0][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_q4_0_f32, "mul_mat_q4_0_f32"); + webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q4_1][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_q4_1_f32, "mul_mat_q4_1_f32"); + webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q5_0][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_q5_0_f32, "mul_mat_q5_0_f32"); + webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q5_1][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_q5_1_f32, "mul_mat_q5_1_f32"); + webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q8_0][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_q8_0_f32, "mul_mat_q8_0_f32"); + + // K-quantizations + webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q2_K][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_q2_k_f32, "mul_mat_q2_k_f32"); + webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q3_K][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_q3_k_f32, "mul_mat_q3_k_f32"); + webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q4_K][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_q4_k_f32, "mul_mat_q4_k_f32"); + webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q5_K][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_q5_k_f32, "mul_mat_q5_k_f32"); + webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q6_K][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_q6_k_f32, "mul_mat_q6_k_f32"); + + // IQ quantizations (2-, 3-, 4-bit variants) + webgpu_ctx->mul_mat_pipelines[GGML_TYPE_IQ2_XXS][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_iq2_xxs_f32, "mul_mat_iq2_xxs_f32"); + webgpu_ctx->mul_mat_pipelines[GGML_TYPE_IQ2_XS][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_iq2_xs_f32, "mul_mat_iq2_xs_f32"); + webgpu_ctx->mul_mat_pipelines[GGML_TYPE_IQ2_S][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_iq2_s_f32, "mul_mat_iq2_s_f32"); + + webgpu_ctx->mul_mat_pipelines[GGML_TYPE_IQ3_XXS][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_iq3_xxs_f32, "mul_mat_iq3_xxs_f32"); + webgpu_ctx->mul_mat_pipelines[GGML_TYPE_IQ3_S][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_iq3_s_f32, "mul_mat_iq3_s_f32"); + + // 1-bit and 4-bit IQ variants + webgpu_ctx->mul_mat_pipelines[GGML_TYPE_IQ1_S][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_iq1_s_f32, "mul_mat_iq1_s_f32"); + webgpu_ctx->mul_mat_pipelines[GGML_TYPE_IQ1_M][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_iq1_m_f32, "mul_mat_iq1_m_f32"); + webgpu_ctx->mul_mat_pipelines[GGML_TYPE_IQ4_NL][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_iq4_nl_f32, "mul_mat_iq4_nl_f32"); + webgpu_ctx->mul_mat_pipelines[GGML_TYPE_IQ4_XS][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_iq4_xs_f32, "mul_mat_iq4_xs_f32"); std::string proc_mul_mat_f32_f32; std::string proc_mul_mat_f32_f32_vec; @@ -1828,21 +1852,21 @@ static void ggml_webgpu_init_mul_mat_pipeline(webgpu_context & webgpu_ctx) { } #endif - webgpu_ctx->mul_mat_pipelines[GGML_TYPE_F32][GGML_TYPE_F32][0] = ggml_webgpu_create_pipeline2( + webgpu_ctx->mul_mat_pipelines[GGML_TYPE_F32][GGML_TYPE_F32][0] = ggml_webgpu_create_pipeline( webgpu_ctx->device, proc_mul_mat_f32_f32.c_str(), "mul_mat_f32_f32", mul_mat_constants); - webgpu_ctx->mul_mat_pipelines[GGML_TYPE_F32][GGML_TYPE_F32][1] = ggml_webgpu_create_pipeline2( + webgpu_ctx->mul_mat_pipelines[GGML_TYPE_F32][GGML_TYPE_F32][1] = ggml_webgpu_create_pipeline( webgpu_ctx->device, proc_mul_mat_f32_f32_vec.c_str(), "mul_mat_f32_f32_vec", mul_mat_constants); - webgpu_ctx->mul_mat_pipelines[GGML_TYPE_F16][GGML_TYPE_F32][0] = ggml_webgpu_create_pipeline2( + webgpu_ctx->mul_mat_pipelines[GGML_TYPE_F16][GGML_TYPE_F32][0] = ggml_webgpu_create_pipeline( webgpu_ctx->device, proc_mul_mat_f16_f32.c_str(), "mul_mat_f16_f32", mul_mat_constants); - webgpu_ctx->mul_mat_pipelines[GGML_TYPE_F16][GGML_TYPE_F32][1] = ggml_webgpu_create_pipeline2( + webgpu_ctx->mul_mat_pipelines[GGML_TYPE_F16][GGML_TYPE_F32][1] = ggml_webgpu_create_pipeline( webgpu_ctx->device, proc_mul_mat_f16_f32_vec.c_str(), "mul_mat_f16_f32_vec", mul_mat_constants); - webgpu_ctx->mul_mat_pipelines[GGML_TYPE_F16][GGML_TYPE_F16][0] = ggml_webgpu_create_pipeline2( + webgpu_ctx->mul_mat_pipelines[GGML_TYPE_F16][GGML_TYPE_F16][0] = ggml_webgpu_create_pipeline( webgpu_ctx->device, proc_mul_mat_f16_f16.c_str(), "mul_mat_f16_f16", mul_mat_constants); - webgpu_ctx->mul_mat_pipelines[GGML_TYPE_F16][GGML_TYPE_F16][1] = ggml_webgpu_create_pipeline2( + webgpu_ctx->mul_mat_pipelines[GGML_TYPE_F16][GGML_TYPE_F16][1] = ggml_webgpu_create_pipeline( webgpu_ctx->device, proc_mul_mat_f16_f16_vec.c_str(), "mul_mat_f16_f16_vec", mul_mat_constants); - webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q4_0][GGML_TYPE_F32][0] = ggml_webgpu_create_pipeline2( + webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q4_0][GGML_TYPE_F32][0] = ggml_webgpu_create_pipeline( webgpu_ctx->device, proc_mul_mat_q4_0_f32.c_str(), "mul_mat_q4_0_f32", mul_mat_constants); - webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q4_0][GGML_TYPE_F32][1] = ggml_webgpu_create_pipeline2( + webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q4_0][GGML_TYPE_F32][1] = ggml_webgpu_create_pipeline( webgpu_ctx->device, proc_mul_mat_q4_0_f32_vec.c_str(), "mul_mat_q4_0_f32_vec", mul_mat_constants); std::vector mul_mat_vec_constants(3); @@ -1853,257 +1877,444 @@ static void ggml_webgpu_init_mul_mat_pipeline(webgpu_context & webgpu_ctx) { mul_mat_vec_constants[2].key = "OUTPUTS_PER_WG"; mul_mat_vec_constants[2].value = WEBGPU_MUL_MAT_VEC_OUTPUTS_PER_WG; - webgpu_ctx->mul_mat_vec_pipelines[GGML_TYPE_F32][GGML_TYPE_F32][0] = ggml_webgpu_create_pipeline2( + webgpu_ctx->mul_mat_vec_pipelines[GGML_TYPE_F32][GGML_TYPE_F32][0] = ggml_webgpu_create_pipeline( webgpu_ctx->device, wgsl_mul_mat_vec_f32_f32, "mul_mat_vec_f32_f32", mul_mat_vec_constants); - webgpu_ctx->mul_mat_vec_pipelines[GGML_TYPE_F32][GGML_TYPE_F32][1] = ggml_webgpu_create_pipeline2( + webgpu_ctx->mul_mat_vec_pipelines[GGML_TYPE_F32][GGML_TYPE_F32][1] = ggml_webgpu_create_pipeline( webgpu_ctx->device, wgsl_mul_mat_vec_f32_f32_vec, "mul_mat_vec_f32_f32_vec", mul_mat_vec_constants); - webgpu_ctx->mul_mat_vec_pipelines[GGML_TYPE_F16][GGML_TYPE_F32][0] = ggml_webgpu_create_pipeline2( + webgpu_ctx->mul_mat_vec_pipelines[GGML_TYPE_F16][GGML_TYPE_F32][0] = ggml_webgpu_create_pipeline( webgpu_ctx->device, wgsl_mul_mat_vec_f16_f32, "mul_mat_vec_f16_f32", mul_mat_vec_constants); - webgpu_ctx->mul_mat_vec_pipelines[GGML_TYPE_F16][GGML_TYPE_F32][1] = ggml_webgpu_create_pipeline2( + webgpu_ctx->mul_mat_vec_pipelines[GGML_TYPE_F16][GGML_TYPE_F32][1] = ggml_webgpu_create_pipeline( webgpu_ctx->device, wgsl_mul_mat_vec_f16_f32_vec, "mul_mat_vec_f16_f32_vec", mul_mat_vec_constants); - webgpu_ctx->mul_mat_vec_pipelines[GGML_TYPE_F16][GGML_TYPE_F16][0] = ggml_webgpu_create_pipeline2( + webgpu_ctx->mul_mat_vec_pipelines[GGML_TYPE_F16][GGML_TYPE_F16][0] = ggml_webgpu_create_pipeline( webgpu_ctx->device, wgsl_mul_mat_vec_f16_f16, "mul_mat_vec_f16_f16", mul_mat_vec_constants); - webgpu_ctx->mul_mat_vec_pipelines[GGML_TYPE_F16][GGML_TYPE_F16][1] = ggml_webgpu_create_pipeline2( + webgpu_ctx->mul_mat_vec_pipelines[GGML_TYPE_F16][GGML_TYPE_F16][1] = ggml_webgpu_create_pipeline( webgpu_ctx->device, wgsl_mul_mat_vec_f16_f16_vec, "mul_mat_vec_f16_f16_vec", mul_mat_vec_constants); - webgpu_ctx->mul_mat_vec_pipelines[GGML_TYPE_Q4_0][GGML_TYPE_F32][0] = ggml_webgpu_create_pipeline2( + webgpu_ctx->mul_mat_vec_pipelines[GGML_TYPE_Q4_0][GGML_TYPE_F32][0] = ggml_webgpu_create_pipeline( webgpu_ctx->device, wgsl_mul_mat_vec_q4_0_f32, "mul_mat_vec_q4_0_f32", mul_mat_vec_constants); } static void ggml_webgpu_init_set_rows_pipeline(webgpu_context & webgpu_ctx) { - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->set_rows_pipeline[0][0], wgsl_set_rows_f16, - "set_rows_f16", ggml_webgpu_wg_size_entry(webgpu_ctx->max_wg_size_x)); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->set_rows_pipeline[0][1], wgsl_set_rows_f16_vec, - "set_rows_f16_vec", ggml_webgpu_wg_size_entry(webgpu_ctx->max_wg_size_x)); + webgpu_ctx->set_rows_pipelines[0][0] = ggml_webgpu_create_pipeline( + webgpu_ctx->device, wgsl_set_rows_f16, "set_rows_f16", ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE)); + webgpu_ctx->set_rows_pipelines[0][1] = ggml_webgpu_create_pipeline( + webgpu_ctx->device, wgsl_set_rows_f16_vec, "set_rows_f16_vec", ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE)); } static void ggml_webgpu_init_get_rows_pipeline(webgpu_context & webgpu_ctx) { - std::vector constants = ggml_webgpu_wg_size_entry(webgpu_ctx->max_wg_size_x); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->get_rows_pipeline[GGML_TYPE_F32], wgsl_get_rows_f32_vec, - "get_rows_f32_vec", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->get_rows_f32_no_vec_pipeline, wgsl_get_rows_f32, - "get_rows_f32", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->get_rows_pipeline[GGML_TYPE_F16], wgsl_get_rows_f16, - "get_rows_f16", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->get_rows_pipeline[GGML_TYPE_I32], wgsl_get_rows_i32, - "get_rows_i32", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->get_rows_pipeline[GGML_TYPE_Q4_0], wgsl_get_rows_q4_0, - "get_rows_q4_0", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->get_rows_pipeline[GGML_TYPE_Q4_1], wgsl_get_rows_q4_1, - "get_rows_q4_1", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->get_rows_pipeline[GGML_TYPE_Q5_0], wgsl_get_rows_q5_0, - "get_rows_q5_0", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->get_rows_pipeline[GGML_TYPE_Q5_1], wgsl_get_rows_q5_1, - "get_rows_q5_1", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->get_rows_pipeline[GGML_TYPE_Q8_0], wgsl_get_rows_q8_0, - "get_rows_q8_0", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->get_rows_pipeline[GGML_TYPE_Q2_K], wgsl_get_rows_q2_k, - "get_rows_q2_k", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->get_rows_pipeline[GGML_TYPE_Q3_K], wgsl_get_rows_q3_k, - "get_rows_q3_k", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->get_rows_pipeline[GGML_TYPE_Q4_K], wgsl_get_rows_q4_k, - "get_rows_q4_k", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->get_rows_pipeline[GGML_TYPE_Q5_K], wgsl_get_rows_q5_k, - "get_rows_q5_k", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->get_rows_pipeline[GGML_TYPE_Q6_K], wgsl_get_rows_q6_k, - "get_rows_q6_k", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->get_rows_pipeline[GGML_TYPE_IQ2_XXS], - wgsl_get_rows_iq2_xxs, "get_rows_iq2_xxs", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->get_rows_pipeline[GGML_TYPE_IQ2_XS], - wgsl_get_rows_iq2_xs, "get_rows_iq2_xs", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->get_rows_pipeline[GGML_TYPE_IQ2_S], wgsl_get_rows_iq2_s, - "get_rows_iq2_s", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->get_rows_pipeline[GGML_TYPE_IQ3_XXS], - wgsl_get_rows_iq3_xxs, "get_rows_iq3_xxs", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->get_rows_pipeline[GGML_TYPE_IQ3_S], wgsl_get_rows_iq3_s, - "get_rows_iq3_s", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->get_rows_pipeline[GGML_TYPE_IQ1_S], wgsl_get_rows_iq1_s, - "get_rows_iq1_s", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->get_rows_pipeline[GGML_TYPE_IQ1_M], wgsl_get_rows_iq1_m, - "get_rows_iq1_m", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->get_rows_pipeline[GGML_TYPE_IQ4_NL], - wgsl_get_rows_iq4_nl, "get_rows_iq4_nl", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->get_rows_pipeline[GGML_TYPE_IQ4_XS], - wgsl_get_rows_iq4_xs, "get_rows_iq4_xs", constants); + std::vector constants = ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE); + + webgpu_ctx->get_rows_pipelines[GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_f32, "get_rows_f32", constants); + webgpu_ctx->get_rows_pipelines[GGML_TYPE_F32][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_f32_vec, "get_rows_f32_vec", constants); + + webgpu_ctx->get_rows_pipelines[GGML_TYPE_F16][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_f16, "get_rows_f16", constants); + webgpu_ctx->get_rows_pipelines[GGML_TYPE_I32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_i32, "get_rows_i32", constants); + webgpu_ctx->get_rows_pipelines[GGML_TYPE_Q4_0][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_q4_0, "get_rows_q4_0", constants); + webgpu_ctx->get_rows_pipelines[GGML_TYPE_Q4_1][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_q4_1, "get_rows_q4_1", constants); + webgpu_ctx->get_rows_pipelines[GGML_TYPE_Q5_0][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_q5_0, "get_rows_q5_0", constants); + webgpu_ctx->get_rows_pipelines[GGML_TYPE_Q5_1][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_q5_1, "get_rows_q5_1", constants); + webgpu_ctx->get_rows_pipelines[GGML_TYPE_Q8_0][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_q8_0, "get_rows_q8_0", constants); + + webgpu_ctx->get_rows_pipelines[GGML_TYPE_Q2_K][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_q2_k, "get_rows_q2_k", constants); + webgpu_ctx->get_rows_pipelines[GGML_TYPE_Q3_K][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_q3_k, "get_rows_q3_k", constants); + webgpu_ctx->get_rows_pipelines[GGML_TYPE_Q4_K][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_q4_k, "get_rows_q4_k", constants); + webgpu_ctx->get_rows_pipelines[GGML_TYPE_Q5_K][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_q5_k, "get_rows_q5_k", constants); + webgpu_ctx->get_rows_pipelines[GGML_TYPE_Q6_K][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_q6_k, "get_rows_q6_k", constants); + + webgpu_ctx->get_rows_pipelines[GGML_TYPE_IQ2_XXS][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_iq2_xxs, "get_rows_iq2_xxs", constants); + webgpu_ctx->get_rows_pipelines[GGML_TYPE_IQ2_XS][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_iq2_xs, "get_rows_iq2_xs", constants); + webgpu_ctx->get_rows_pipelines[GGML_TYPE_IQ2_S][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_iq2_s, "get_rows_iq2_s", constants); + webgpu_ctx->get_rows_pipelines[GGML_TYPE_IQ3_XXS][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_iq3_xxs, "get_rows_iq3_xxs", constants); + webgpu_ctx->get_rows_pipelines[GGML_TYPE_IQ3_S][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_iq3_s, "get_rows_iq3_s", constants); + webgpu_ctx->get_rows_pipelines[GGML_TYPE_IQ1_S][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_iq1_s, "get_rows_iq1_s", constants); + webgpu_ctx->get_rows_pipelines[GGML_TYPE_IQ1_M][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_iq1_m, "get_rows_iq1_m", constants); + webgpu_ctx->get_rows_pipelines[GGML_TYPE_IQ4_NL][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_iq4_nl, "get_rows_iq4_nl", constants); + webgpu_ctx->get_rows_pipelines[GGML_TYPE_IQ4_XS][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_iq4_xs, "get_rows_iq4_xs", constants); } static void ggml_webgpu_init_cpy_pipeline(webgpu_context & webgpu_ctx) { - std::vector constants = ggml_webgpu_wg_size_entry(webgpu_ctx->max_wg_size_x); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->cpy_pipeline[GGML_TYPE_F32][GGML_TYPE_F32], - wgsl_cpy_f32_f32, "cpy_f32_f32", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->cpy_pipeline[GGML_TYPE_F32][GGML_TYPE_F16], - wgsl_cpy_f32_f16, "cpy_f32_f16", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->cpy_pipeline[GGML_TYPE_F16][GGML_TYPE_F32], - wgsl_cpy_f16_f32, "cpy_f16_f32", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->cpy_pipeline[GGML_TYPE_F16][GGML_TYPE_F16], - wgsl_cpy_f16_f16, "cpy_f16_f16", constants); + std::vector constants = ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE); + + webgpu_ctx->cpy_pipelines[GGML_TYPE_F32][GGML_TYPE_F32] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_cpy_f32_f32, "cpy_f32_f32", constants); + webgpu_ctx->cpy_pipelines[GGML_TYPE_F32][GGML_TYPE_F16] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_cpy_f32_f16, "cpy_f32_f16", constants); + webgpu_ctx->cpy_pipelines[GGML_TYPE_F16][GGML_TYPE_F32] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_cpy_f16_f32, "cpy_f16_f32", constants); + webgpu_ctx->cpy_pipelines[GGML_TYPE_F16][GGML_TYPE_F16] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_cpy_f16_f16, "cpy_f16_f16", constants); } static void ggml_webgpu_init_add_pipeline(webgpu_context & webgpu_ctx) { - std::vector constants = ggml_webgpu_wg_size_entry(webgpu_ctx->max_wg_size_x); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->add_pipeline[GGML_TYPE_F32][0], wgsl_add_f32, "add_f32", - constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->add_pipeline[GGML_TYPE_F16][0], wgsl_add_f16, "add_f16", - constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->add_pipeline[GGML_TYPE_F32][1], wgsl_add_f32_inplace, - "add_f32_inplace", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->add_pipeline[GGML_TYPE_F16][1], wgsl_add_f16_inplace, - "add_f16_inplace", constants); + std::vector constants = ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE); + + webgpu_ctx->add_pipelines[GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_add_f32, "add_f32", constants); + webgpu_ctx->add_pipelines[GGML_TYPE_F16][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_add_f16, "add_f16", constants); + webgpu_ctx->add_pipelines[GGML_TYPE_F32][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_add_f32_inplace, "add_f32_inplace", constants); + webgpu_ctx->add_pipelines[GGML_TYPE_F16][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_add_f16_inplace, "add_f16_inplace", constants); } static void ggml_webgpu_init_sub_pipeline(webgpu_context & webgpu_ctx) { - std::vector constants = ggml_webgpu_wg_size_entry(webgpu_ctx->max_wg_size_x); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->sub_pipeline[GGML_TYPE_F32][0], wgsl_sub_f32, "sub_f32", - constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->sub_pipeline[GGML_TYPE_F16][0], wgsl_sub_f16, "sub_f16", - constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->sub_pipeline[GGML_TYPE_F32][1], wgsl_sub_f32_inplace, - "sub_f32_inplace", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->sub_pipeline[GGML_TYPE_F16][1], wgsl_sub_f16_inplace, - "sub_f16_inplace", constants); + std::vector constants = ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE); + + webgpu_ctx->sub_pipelines[GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_sub_f32, "sub_f32", constants); + webgpu_ctx->sub_pipelines[GGML_TYPE_F16][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_sub_f16, "sub_f16", constants); + webgpu_ctx->sub_pipelines[GGML_TYPE_F32][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_sub_f32_inplace, "sub_f32_inplace", constants); + webgpu_ctx->sub_pipelines[GGML_TYPE_F16][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_sub_f16_inplace, "sub_f16_inplace", constants); } static void ggml_webgpu_init_mul_pipeline(webgpu_context & webgpu_ctx) { - std::vector constants = ggml_webgpu_wg_size_entry(webgpu_ctx->max_wg_size_x); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->mul_pipeline[GGML_TYPE_F32][0], wgsl_mul_f32, "mul_f32", - constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->mul_pipeline[GGML_TYPE_F16][0], wgsl_mul_f16, "mul_f16", - constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->mul_pipeline[GGML_TYPE_F32][1], wgsl_mul_f32_inplace, - "mul_f32_inplace", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->mul_pipeline[GGML_TYPE_F16][1], wgsl_mul_f16_inplace, - "mul_f16_inplace", constants); + std::vector constants = ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE); + + webgpu_ctx->mul_pipelines[GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_f32, "mul_f32", constants); + webgpu_ctx->mul_pipelines[GGML_TYPE_F16][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_f16, "mul_f16", constants); + webgpu_ctx->mul_pipelines[GGML_TYPE_F32][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_f32_inplace, "mul_f32_inplace", constants); + webgpu_ctx->mul_pipelines[GGML_TYPE_F16][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_f16_inplace, "mul_f16_inplace", constants); } static void ggml_webgpu_init_div_pipeline(webgpu_context & webgpu_ctx) { - std::vector constants = ggml_webgpu_wg_size_entry(webgpu_ctx->max_wg_size_x); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->div_pipeline[GGML_TYPE_F32][0], wgsl_div_f32, "div_f32", - constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->div_pipeline[GGML_TYPE_F16][0], wgsl_div_f16, "div_f16", - constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->div_pipeline[GGML_TYPE_F32][1], wgsl_div_f32_inplace, - "div_f32_inplace", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->div_pipeline[GGML_TYPE_F16][1], wgsl_div_f16_inplace, - "div_f16_inplace", constants); + std::vector constants = ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE); + + webgpu_ctx->div_pipelines[GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_div_f32, "div_f32", constants); + webgpu_ctx->div_pipelines[GGML_TYPE_F16][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_div_f16, "div_f16", constants); + webgpu_ctx->div_pipelines[GGML_TYPE_F32][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_div_f32_inplace, "div_f32_inplace", constants); + webgpu_ctx->div_pipelines[GGML_TYPE_F16][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_div_f16_inplace, "div_f16_inplace", constants); } static void ggml_webgpu_init_rms_norm_pipeline(webgpu_context & webgpu_ctx) { std::vector constants = ggml_webgpu_wg_size_entry(WEBGPU_ROW_SPLIT_WG_SIZE); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->rms_norm_pipeline[0], wgsl_rms_norm, "rms_norm", - constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->rms_norm_pipeline[1], wgsl_rms_norm_inplace, - "rms_norm_inplace", constants); + + webgpu_ctx->rms_norm_pipelines[0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_rms_norm, "rms_norm", constants); + webgpu_ctx->rms_norm_pipelines[1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_rms_norm_inplace, "rms_norm_inplace", constants); } static void ggml_webgpu_init_rope_pipeline(webgpu_context & webgpu_ctx) { - std::vector constants = ggml_webgpu_wg_size_entry(webgpu_ctx->max_wg_size_x); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->rope_pipeline[GGML_TYPE_F32][0][0], wgsl_rope_f32, - "rope_f32", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->rope_pipeline[GGML_TYPE_F32][0][1], - wgsl_rope_f32_inplace, "rope_f32_inplace", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->rope_pipeline[GGML_TYPE_F32][1][0], wgsl_rope_f32_ff, - "rope_f32_ff", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->rope_pipeline[GGML_TYPE_F32][1][1], - wgsl_rope_f32_ff_inplace, "rope_f32_ff_inplace", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->rope_pipeline[GGML_TYPE_F16][0][0], wgsl_rope_f16, - "rope_f16", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->rope_pipeline[GGML_TYPE_F16][0][1], - wgsl_rope_f16_inplace, "rope_f16_inplace", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->rope_pipeline[GGML_TYPE_F16][1][0], wgsl_rope_f16_ff, - "rope_f16_ff", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->rope_pipeline[GGML_TYPE_F16][1][1], - wgsl_rope_f16_ff_inplace, "rope_f16_ff_inplace", constants); + std::vector constants = ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE); + + webgpu_ctx->rope_pipelines[GGML_TYPE_F32][0][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_rope_f32, "rope_f32", constants); + webgpu_ctx->rope_pipelines[GGML_TYPE_F32][0][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_rope_f32_inplace, "rope_f32_inplace", constants); + webgpu_ctx->rope_pipelines[GGML_TYPE_F32][1][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_rope_f32_ff, "rope_f32_ff", constants); + webgpu_ctx->rope_pipelines[GGML_TYPE_F32][1][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_rope_f32_ff_inplace, "rope_f32_ff_inplace", constants); + + webgpu_ctx->rope_pipelines[GGML_TYPE_F16][0][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_rope_f16, "rope_f16", constants); + webgpu_ctx->rope_pipelines[GGML_TYPE_F16][0][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_rope_f16_inplace, "rope_f16_inplace", constants); + webgpu_ctx->rope_pipelines[GGML_TYPE_F16][1][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_rope_f16_ff, "rope_f16_ff", constants); + webgpu_ctx->rope_pipelines[GGML_TYPE_F16][1][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_rope_f16_ff_inplace, "rope_f16_ff_inplace", constants); } static void ggml_webgpu_init_glu_pipeline(webgpu_context & webgpu_ctx) { - std::vector constants = ggml_webgpu_wg_size_entry(webgpu_ctx->max_wg_size_x); - // reglu - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->glu_pipeline[GGML_GLU_OP_REGLU][GGML_TYPE_F32][0], - wgsl_reglu_f32, "reglu_f32", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->glu_pipeline[GGML_GLU_OP_REGLU][GGML_TYPE_F16][0], - wgsl_reglu_f16, "reglu_f16", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->glu_pipeline[GGML_GLU_OP_REGLU][GGML_TYPE_F32][1], - wgsl_reglu_f32_split, "reglu_f32_split", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->glu_pipeline[GGML_GLU_OP_REGLU][GGML_TYPE_F16][1], - wgsl_reglu_f16_split, "reglu_f16_split", constants); - // geglu - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->glu_pipeline[GGML_GLU_OP_GEGLU][GGML_TYPE_F32][0], - wgsl_geglu_f32, "geglu_f32", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->glu_pipeline[GGML_GLU_OP_GEGLU][GGML_TYPE_F16][0], - wgsl_geglu_f16, "geglu_f16", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->glu_pipeline[GGML_GLU_OP_GEGLU][GGML_TYPE_F32][1], - wgsl_geglu_f32_split, "geglu_f32_split", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->glu_pipeline[GGML_GLU_OP_GEGLU][GGML_TYPE_F16][1], - wgsl_geglu_f16_split, "geglu_f16_split", constants); - // swiglu - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->glu_pipeline[GGML_GLU_OP_SWIGLU][GGML_TYPE_F32][0], - wgsl_swiglu_f32, "swiglu_f32", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->glu_pipeline[GGML_GLU_OP_SWIGLU][GGML_TYPE_F16][0], - wgsl_swiglu_f16, "swiglu_f16", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->glu_pipeline[GGML_GLU_OP_SWIGLU][GGML_TYPE_F32][1], - wgsl_swiglu_f32_split, "swiglu_f32_split", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->glu_pipeline[GGML_GLU_OP_SWIGLU][GGML_TYPE_F16][1], - wgsl_swiglu_f16_split, "swiglu_f16_split", constants); - // swiglu_oai - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->glu_pipeline[GGML_GLU_OP_SWIGLU_OAI][GGML_TYPE_F32][0], - wgsl_swiglu_oai_f32, "swiglu_oai_f32", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->glu_pipeline[GGML_GLU_OP_SWIGLU_OAI][GGML_TYPE_F32][1], - wgsl_swiglu_oai_f32_split, "swiglu_oai_f32_split", constants); - // geglu_erf - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->glu_pipeline[GGML_GLU_OP_GEGLU_ERF][GGML_TYPE_F32][0], - wgsl_geglu_erf_f32, "geglu_erf_f32", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->glu_pipeline[GGML_GLU_OP_GEGLU_ERF][GGML_TYPE_F16][0], - wgsl_geglu_erf_f16, "geglu_erf_f16", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->glu_pipeline[GGML_GLU_OP_GEGLU_ERF][GGML_TYPE_F32][1], - wgsl_geglu_erf_f32_split, "geglu_erf_f32_split", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->glu_pipeline[GGML_GLU_OP_GEGLU_ERF][GGML_TYPE_F16][1], - wgsl_geglu_erf_f16_split, "geglu_erf_f16_split", constants); - // geglu_quick - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->glu_pipeline[GGML_GLU_OP_GEGLU_QUICK][GGML_TYPE_F32][0], - wgsl_geglu_quick_f32, "geglu_quick_f32", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->glu_pipeline[GGML_GLU_OP_GEGLU_QUICK][GGML_TYPE_F16][0], - wgsl_geglu_quick_f16, "geglu_quick_f16", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->glu_pipeline[GGML_GLU_OP_GEGLU_QUICK][GGML_TYPE_F32][1], - wgsl_geglu_quick_f32_split, "geglu_quick_f32_split", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->glu_pipeline[GGML_GLU_OP_GEGLU_QUICK][GGML_TYPE_F16][1], - wgsl_geglu_quick_f16_split, "geglu_quick_f16_split", constants); + std::vector constants = ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE); + + // REGLU + webgpu_ctx->glu_pipelines[GGML_GLU_OP_REGLU][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_reglu_f32, "reglu_f32", constants); + webgpu_ctx->glu_pipelines[GGML_GLU_OP_REGLU][GGML_TYPE_F16][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_reglu_f16, "reglu_f16", constants); + webgpu_ctx->glu_pipelines[GGML_GLU_OP_REGLU][GGML_TYPE_F32][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_reglu_f32_split, "reglu_f32_split", constants); + webgpu_ctx->glu_pipelines[GGML_GLU_OP_REGLU][GGML_TYPE_F16][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_reglu_f16_split, "reglu_f16_split", constants); + + // GEGLU + webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_geglu_f32, "geglu_f32", constants); + webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU][GGML_TYPE_F16][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_geglu_f16, "geglu_f16", constants); + webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU][GGML_TYPE_F32][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_geglu_f32_split, "geglu_f32_split", constants); + webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU][GGML_TYPE_F16][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_geglu_f16_split, "geglu_f16_split", constants); + + // SWIGLU + webgpu_ctx->glu_pipelines[GGML_GLU_OP_SWIGLU][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_swiglu_f32, "swiglu_f32", constants); + webgpu_ctx->glu_pipelines[GGML_GLU_OP_SWIGLU][GGML_TYPE_F16][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_swiglu_f16, "swiglu_f16", constants); + webgpu_ctx->glu_pipelines[GGML_GLU_OP_SWIGLU][GGML_TYPE_F32][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_swiglu_f32_split, "swiglu_f32_split", constants); + webgpu_ctx->glu_pipelines[GGML_GLU_OP_SWIGLU][GGML_TYPE_F16][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_swiglu_f16_split, "swiglu_f16_split", constants); + + // SWIGLU_OAI + webgpu_ctx->glu_pipelines[GGML_GLU_OP_SWIGLU_OAI][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_swiglu_oai_f32, "swiglu_oai_f32", constants); + webgpu_ctx->glu_pipelines[GGML_GLU_OP_SWIGLU_OAI][GGML_TYPE_F32][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_swiglu_oai_f32_split, "swiglu_oai_f32_split", constants); + + // GEGLU_ERF + webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU_ERF][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_geglu_erf_f32, "geglu_erf_f32", constants); + webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU_ERF][GGML_TYPE_F16][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_geglu_erf_f16, "geglu_erf_f16", constants); + webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU_ERF][GGML_TYPE_F32][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_geglu_erf_f32_split, "geglu_erf_f32_split", constants); + webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU_ERF][GGML_TYPE_F16][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_geglu_erf_f16_split, "geglu_erf_f16_split", constants); + + // GEGLU_QUICK + webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU_QUICK][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_geglu_quick_f32, "geglu_quick_f32", constants); + webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU_QUICK][GGML_TYPE_F16][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_geglu_quick_f16, "geglu_quick_f16", constants); + webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU_QUICK][GGML_TYPE_F32][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_geglu_quick_f32_split, "geglu_quick_f32_split", constants); + webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU_QUICK][GGML_TYPE_F16][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_geglu_quick_f16_split, "geglu_quick_f16_split", constants); +} + +static void ggml_webgpu_init_unary_pipeline(webgpu_context & webgpu_ctx) { + std::vector constants = ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE); + + // ABS + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_ABS][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_abs_f32, "abs_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_ABS][GGML_TYPE_F16][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_abs_f16, "abs_f16", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_ABS][GGML_TYPE_F32][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_abs_inplace_f32, "abs_inplace_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_ABS][GGML_TYPE_F16][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_abs_inplace_f16, "abs_inplace_f16", constants); + + // SGN + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_SGN][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_sgn_f32, "sgn_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_SGN][GGML_TYPE_F16][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_sgn_f16, "sgn_f16", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_SGN][GGML_TYPE_F32][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_sgn_inplace_f32, "sgn_inplace_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_SGN][GGML_TYPE_F16][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_sgn_inplace_f16, "sgn_inplace_f16", constants); + + // NEG + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_NEG][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_neg_f32, "neg_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_NEG][GGML_TYPE_F16][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_neg_f16, "neg_f16", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_NEG][GGML_TYPE_F32][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_neg_inplace_f32, "neg_inplace_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_NEG][GGML_TYPE_F16][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_neg_inplace_f16, "neg_inplace_f16", constants); + + // STEP + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_STEP][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_step_f32, "step_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_STEP][GGML_TYPE_F16][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_step_f16, "step_f16", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_STEP][GGML_TYPE_F32][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_step_inplace_f32, "step_inplace_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_STEP][GGML_TYPE_F16][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_step_inplace_f16, "step_inplace_f16", constants); + + // TANH + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_TANH][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_tanh_f32, "tanh_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_TANH][GGML_TYPE_F16][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_tanh_f16, "tanh_f16", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_TANH][GGML_TYPE_F32][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_tanh_inplace_f32, "tanh_inplace_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_TANH][GGML_TYPE_F16][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_tanh_inplace_f16, "tanh_inplace_f16", constants); + + // ELU + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_ELU][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_elu_f32, "elu_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_ELU][GGML_TYPE_F16][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_elu_f16, "elu_f16", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_ELU][GGML_TYPE_F32][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_elu_inplace_f32, "elu_inplace_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_ELU][GGML_TYPE_F16][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_elu_inplace_f16, "elu_inplace_f16", constants); + + // RELU + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_RELU][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_relu_f32, "relu_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_RELU][GGML_TYPE_F16][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_relu_f16, "relu_f16", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_RELU][GGML_TYPE_F32][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_relu_inplace_f32, "relu_inplace_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_RELU][GGML_TYPE_F16][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_relu_inplace_f16, "relu_inplace_f16", constants); + + // SIGMOID + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_SIGMOID][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_sigmoid_f32, "sigmoid_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_SIGMOID][GGML_TYPE_F16][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_sigmoid_f16, "sigmoid_f16", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_SIGMOID][GGML_TYPE_F32][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_sigmoid_inplace_f32, "sigmoid_inplace_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_SIGMOID][GGML_TYPE_F16][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_sigmoid_inplace_f16, "sigmoid_inplace_f16", constants); + + // GELU + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_GELU][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_gelu_f32, "gelu_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_GELU][GGML_TYPE_F16][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_gelu_f16, "gelu_f16", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_GELU][GGML_TYPE_F32][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_gelu_inplace_f32, "gelu_inplace_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_GELU][GGML_TYPE_F16][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_gelu_inplace_f16, "gelu_inplace_f16", constants); + + // GELU_QUICK + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_GELU_QUICK][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_gelu_quick_f32, "gelu_quick_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_GELU_QUICK][GGML_TYPE_F16][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_gelu_quick_f16, "gelu_quick_f16", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_GELU_QUICK][GGML_TYPE_F32][1] = ggml_webgpu_create_pipeline( + webgpu_ctx->device, wgsl_gelu_quick_inplace_f32, "gelu_quick_inplace_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_GELU_QUICK][GGML_TYPE_F16][1] = ggml_webgpu_create_pipeline( + webgpu_ctx->device, wgsl_gelu_quick_inplace_f16, "gelu_quick_inplace_f16", constants); + + // SILU + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_SILU][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_silu_f32, "silu_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_SILU][GGML_TYPE_F16][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_silu_f16, "silu_f16", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_SILU][GGML_TYPE_F32][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_silu_inplace_f32, "silu_inplace_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_SILU][GGML_TYPE_F16][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_silu_inplace_f16, "silu_inplace_f16", constants); + + // HARDSWISH + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_HARDSWISH][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_hardswish_f32, "hardswish_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_HARDSWISH][GGML_TYPE_F16][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_hardswish_f16, "hardswish_f16", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_HARDSWISH][GGML_TYPE_F32][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_hardswish_inplace_f32, "hardswish_inplace_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_HARDSWISH][GGML_TYPE_F16][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_hardswish_inplace_f16, "hardswish_inplace_f16", constants); + + // HARDSIGMOID + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_HARDSIGMOID][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_hardsigmoid_f32, "hardsigmoid_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_HARDSIGMOID][GGML_TYPE_F16][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_hardsigmoid_f16, "hardsigmoid_f16", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_HARDSIGMOID][GGML_TYPE_F32][1] = ggml_webgpu_create_pipeline( + webgpu_ctx->device, wgsl_hardsigmoid_inplace_f32, "hardsigmoid_inplace_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_HARDSIGMOID][GGML_TYPE_F16][1] = ggml_webgpu_create_pipeline( + webgpu_ctx->device, wgsl_hardsigmoid_inplace_f16, "hardsigmoid_inplace_f16", constants); + + // EXP + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_EXP][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_exp_f32, "exp_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_EXP][GGML_TYPE_F16][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_exp_f16, "exp_f16", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_EXP][GGML_TYPE_F32][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_exp_inplace_f32, "exp_inplace_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_EXP][GGML_TYPE_F16][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_exp_inplace_f16, "exp_inplace_f16", constants); + + // GELU_ERF + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_GELU_ERF][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_gelu_erf_f32, "gelu_erf_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_GELU_ERF][GGML_TYPE_F16][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_gelu_erf_f16, "gelu_erf_f16", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_GELU_ERF][GGML_TYPE_F32][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_gelu_erf_inplace_f32, "gelu_erf_inplace_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_GELU_ERF][GGML_TYPE_F16][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_gelu_erf_inplace_f16, "gelu_erf_inplace_f16", constants); + + // XIELU + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_XIELU][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_xielu_f32, "xielu_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_XIELU][GGML_TYPE_F16][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_xielu_f16, "xielu_f16", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_XIELU][GGML_TYPE_F32][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_xielu_inplace_f32, "xielu_inplace_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_XIELU][GGML_TYPE_F16][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_xielu_inplace_f16, "xielu_inplace_f16", constants); } static void ggml_webgpu_init_scale_pipeline(webgpu_context & webgpu_ctx) { - std::vector constants = ggml_webgpu_wg_size_entry(webgpu_ctx->max_wg_size_x); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->scale_pipeline[0], wgsl_scale_f32, "scale_f32", - constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->scale_pipeline[1], wgsl_scale_f32_inplace, - "scale_f32_inplace", constants); + std::vector constants = ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE); + + webgpu_ctx->scale_pipelines[0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_scale_f32, "scale_f32", constants); + webgpu_ctx->scale_pipelines[1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_scale_f32_inplace, "scale_f32_inplace", constants); } static void ggml_webgpu_init_soft_max_pipeline(webgpu_context & webgpu_ctx) { std::vector constants = ggml_webgpu_wg_size_entry(WEBGPU_ROW_SPLIT_WG_SIZE); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->soft_max_pipeline[2][0][0], wgsl_soft_max_f32, - "soft_max_f32", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->soft_max_pipeline[2][0][1], wgsl_soft_max_f32_inplace, - "soft_max_f32_inplace", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->soft_max_pipeline[2][1][0], wgsl_soft_max_f32_sink, - "soft_max_f32_sink", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->soft_max_pipeline[2][1][1], - wgsl_soft_max_f32_sink_inplace, "soft_max_f32_sink_inplace", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->soft_max_pipeline[0][0][0], wgsl_soft_max_f32_mask_f32, - "soft_max_f32_mask_f32", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->soft_max_pipeline[0][0][1], - wgsl_soft_max_f32_mask_f32_inplace, "soft_max_f32_mask_f32_inplace", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->soft_max_pipeline[1][0][0], wgsl_soft_max_f32_mask_f16, - "soft_max_f32_mask_f16", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->soft_max_pipeline[1][0][1], - wgsl_soft_max_f32_mask_f16_inplace, "soft_max_f32_mask_f16_inplace", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->soft_max_pipeline[0][1][0], - wgsl_soft_max_f32_mask_f32_sink, "soft_max_f32_mask_f32_sink", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->soft_max_pipeline[0][1][1], - wgsl_soft_max_f32_mask_f32_sink_inplace, "soft_max_f32_mask_f32_sink_inplace", - constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->soft_max_pipeline[1][1][0], - wgsl_soft_max_f32_mask_f16_sink, "soft_max_f32_mask_f16_sink", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->soft_max_pipeline[1][1][1], - wgsl_soft_max_f32_mask_f16_sink_inplace, "soft_max_f32_mask_f16_sink_inplace", - constants); + + // f32 (no mask) + webgpu_ctx->soft_max_pipelines[2][0][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_soft_max_f32, "soft_max_f32", constants); + webgpu_ctx->soft_max_pipelines[2][0][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_soft_max_f32_inplace, "soft_max_f32_inplace", constants); + webgpu_ctx->soft_max_pipelines[2][1][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_soft_max_f32_sink, "soft_max_f32_sink", constants); + webgpu_ctx->soft_max_pipelines[2][1][1] = ggml_webgpu_create_pipeline( + webgpu_ctx->device, wgsl_soft_max_f32_sink_inplace, "soft_max_f32_sink_inplace", constants); + + // f32 mask (mask_type = 0) + webgpu_ctx->soft_max_pipelines[0][0][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_soft_max_f32_mask_f32, "soft_max_f32_mask_f32", constants); + webgpu_ctx->soft_max_pipelines[0][0][1] = ggml_webgpu_create_pipeline( + webgpu_ctx->device, wgsl_soft_max_f32_mask_f32_inplace, "soft_max_f32_mask_f32_inplace", constants); + webgpu_ctx->soft_max_pipelines[0][1][0] = ggml_webgpu_create_pipeline( + webgpu_ctx->device, wgsl_soft_max_f32_mask_f32_sink, "soft_max_f32_mask_f32_sink", constants); + webgpu_ctx->soft_max_pipelines[0][1][1] = ggml_webgpu_create_pipeline( + webgpu_ctx->device, wgsl_soft_max_f32_mask_f32_sink_inplace, "soft_max_f32_mask_f32_sink_inplace", constants); + + // f16 mask (mask_type = 1) + webgpu_ctx->soft_max_pipelines[1][0][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_soft_max_f32_mask_f16, "soft_max_f32_mask_f16", constants); + webgpu_ctx->soft_max_pipelines[1][0][1] = ggml_webgpu_create_pipeline( + webgpu_ctx->device, wgsl_soft_max_f32_mask_f16_inplace, "soft_max_f32_mask_f16_inplace", constants); + webgpu_ctx->soft_max_pipelines[1][1][0] = ggml_webgpu_create_pipeline( + webgpu_ctx->device, wgsl_soft_max_f32_mask_f16_sink, "soft_max_f32_mask_f16_sink", constants); + webgpu_ctx->soft_max_pipelines[1][1][1] = ggml_webgpu_create_pipeline( + webgpu_ctx->device, wgsl_soft_max_f32_mask_f16_sink_inplace, "soft_max_f32_mask_f16_sink_inplace", constants); } static ggml_backend_t ggml_backend_webgpu_device_init(ggml_backend_dev_t dev, const char * params) { @@ -2125,12 +2336,12 @@ static ggml_backend_t ggml_backend_webgpu_device_init(ggml_backend_dev_t dev, co /* .device = */ dev, /* .context = */ &backend_ctx, }; - return &backend; } static ggml_backend_buffer_type_t ggml_backend_webgpu_device_get_buffer_type(ggml_backend_dev_t dev) { // See GGML Backend Buffer Type Interface section + static struct ggml_backend_buffer_type ggml_backend_webgpu_buffer_type = { /* .iface = */ { /* .get_name = */ ggml_backend_webgpu_buffer_type_get_name, @@ -2295,6 +2506,36 @@ static bool ggml_backend_webgpu_device_supports_op(ggml_backend_dev_t dev, const case GGML_OP_SOFT_MAX: supports_op = op->type == GGML_TYPE_F32; break; + case GGML_OP_UNARY: + { + const ggml_unary_op UNARY_OP = ggml_get_unary_op(op); + + switch (UNARY_OP) { + case GGML_UNARY_OP_ABS: + case GGML_UNARY_OP_SGN: + case GGML_UNARY_OP_NEG: + case GGML_UNARY_OP_STEP: + case GGML_UNARY_OP_TANH: + case GGML_UNARY_OP_ELU: + case GGML_UNARY_OP_RELU: + case GGML_UNARY_OP_SIGMOID: + case GGML_UNARY_OP_GELU: + case GGML_UNARY_OP_GELU_QUICK: + case GGML_UNARY_OP_SILU: + case GGML_UNARY_OP_HARDSWISH: + case GGML_UNARY_OP_HARDSIGMOID: + case GGML_UNARY_OP_EXP: + case GGML_UNARY_OP_GELU_ERF: + case GGML_UNARY_OP_XIELU: + supports_op = supports_op = + (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16) && (src0->type == op->type); + break; + default: + break; + } + } + break; + default: break; } @@ -2388,7 +2629,6 @@ static ggml_backend_dev_t ggml_backend_webgpu_reg_get_device(ggml_backend_reg_t GGML_ASSERT(ctx->adapter != nullptr); ctx->adapter.GetLimits(&ctx->limits); - ctx->max_wg_size_x = 288; // default value wgpu::AdapterInfo info{}; #ifndef __EMSCRIPTEN__ @@ -2522,6 +2762,7 @@ static ggml_backend_dev_t ggml_backend_webgpu_reg_get_device(ggml_backend_reg_t ggml_webgpu_init_glu_pipeline(ctx); ggml_webgpu_init_scale_pipeline(ctx); ggml_webgpu_init_soft_max_pipeline(ctx); + ggml_webgpu_init_unary_pipeline(ctx); #ifdef GGML_WEBGPU_DEBUG // Initialize debug buffers diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/embed_wgsl.py b/ggml/src/ggml-webgpu/wgsl-shaders/embed_wgsl.py index ed8068d416..22ec09cca3 100755 --- a/ggml/src/ggml-webgpu/wgsl-shaders/embed_wgsl.py +++ b/ggml/src/ggml-webgpu/wgsl-shaders/embed_wgsl.py @@ -18,6 +18,13 @@ def parse_decls(decls_text): decls[name.strip()] = code.strip() return decls +def replace_repl_placeholders(variant, template_map): + for repl, code in variant["REPLS"].items(): + for key, val in template_map.items(): + # Match "key" and avoid matching subsequences using by using \b + code = re.sub(rf'\b{re.escape(str(key))}\b', str(val), code) + variant["REPLS"][repl] = code + return variant def replace_placeholders(shader_text, replacements): for key, val in replacements.items(): @@ -71,6 +78,10 @@ def generate_variants(fname, input_dir, output_dir, outfile): decls_map = parse_decls(extract_block(text, "DECLS")) except ValueError: decls_map = {} + try: + templates_map = ast.literal_eval(extract_block(text, "REPL_TEMPLATES")) + except ValueError: + templates_map = {} for fname in sorted(os.listdir(input_dir)): if fname.endswith(".tmpl"): @@ -90,9 +101,11 @@ def generate_variants(fname, input_dir, output_dir, outfile): if key not in decls_map: raise ValueError(f"DECLS key '{key}' not found.") decls_code += decls_map[key] + "\n\n" - final_shader = re.sub(r'\bDECLS\b', decls_code, shader_template) if "REPLS" in variant: + variant = replace_repl_placeholders(variant, templates_map) + final_shader = replace_placeholders(final_shader, variant["REPLS"]) + # second run to expand placeholders in repl_template final_shader = replace_placeholders(final_shader, variant["REPLS"]) final_shader = expand_includes(final_shader, input_dir) diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/unary_op.wgsl b/ggml/src/ggml-webgpu/wgsl-shaders/unary_op.wgsl new file mode 100644 index 0000000000..d474ab107b --- /dev/null +++ b/ggml/src/ggml-webgpu/wgsl-shaders/unary_op.wgsl @@ -0,0 +1,461 @@ +#define(REPL_TEMPLATES) + +{ + "XIELU_FUNC": "{{MUTATE}}[dst_i] = select(((exp(min(src[src_i], {{TYPE}}(params.eps))) - 1.0) - src[src_i]) * {{TYPE}}(params.alpha_n) + {{TYPE}}(params.beta) * src[src_i], {{TYPE}}(params.alpha_p) * src[src_i] * src[src_i] + {{TYPE}}(params.beta) * src[src_i], src[src_i] > 0.0);", + "ABS_FUNC": "{{MUTATE}}[dst_i] = abs(src[src_i]);", + "SGN_FUNC": "{{MUTATE}}[dst_i] = select({{TYPE}}(select(0.0, -1.0, src[src_i] < 0.0)), {{TYPE}}(1.0), src[src_i] > 0.0);", + "NEG_FUNC": "{{MUTATE}}[dst_i] = -src[src_i];", + "STEP_FUNC": "{{MUTATE}}[dst_i] = {{TYPE}}(select(0.0, 1.0, src[src_i] > 0.0));", + "TANH_FUNC": "{{MUTATE}}[dst_i] = tanh(clamp(src[src_i], -9.010913, 9.010913)); // Regarding tanh() domain restrictions in wgsl https://github.com/gpuweb/gpuweb/issues/4458", + "RELU_FUNC": "{{MUTATE}}[dst_i] = select(0.0, src[src_i], src[src_i] > 0.0);", + "ELU_FUNC": "{{MUTATE}}[dst_i] = select(exp(src[src_i]) - 1.0, src[src_i], src[src_i] > 0.0);", + "HARDSIGMOID_FUNC": "{{MUTATE}}[dst_i] = min(1.0, max(0.0, (src[src_i] + 3.0) / 6.0));", + "SIGMOID_FUNC": "{{MUTATE}}[dst_i] = 1.0 / (1.0 + exp(-src[src_i]));", + "SILU_FUNC": "{{MUTATE}}[dst_i] = src[src_i] / (1.0 + exp(-src[src_i]));", + "EXP_FUNC": "{{MUTATE}}[dst_i] = exp(src[src_i]);", + "HARDSWISH_FUNC": "{{MUTATE}}[dst_i] = src[src_i] * min(1.0, max(0.0, (src[src_i] + 3.0) / 6.0));", + "GELU_FUNC": "{{MUTATE}}[dst_i] = 0.5 * src[src_i] * (1.0 + tanh(clamp(sqrt(2.0 / 3.14159265) * (src[src_i] + 0.044715 * pow(src[src_i], 3.0)), -9.010913, 9.010913))); // Regarding tanh() domain restrictions in wgsl https://github.com/gpuweb/gpuweb/issues/4458", + "GELU_QUICK_FUNC": "{{MUTATE}}[dst_i] = src[src_i] * 0.5 * (1.0 + tanh(clamp(0.79788456 * (src[src_i] + 0.044715 * src[src_i] * src[src_i] * src[src_i]), -9.010913, 9.010913))); // Regarding tanh() domain restrictions in wgsl https://github.com/gpuweb/gpuweb/issues/4458", + "GELU_ERF_FUNC": "{{MUTATE}}[dst_i] = 0.5 * src[src_i] * (1.0 + tanh(clamp(0.79788456 * (src[src_i] + 0.044715 * src[src_i] * src[src_i] * src[src_i]), -9.010913, 9.010913))); // Regarding tanh() domain restrictions in wgsl https://github.com/gpuweb/gpuweb/issues/4458" +} + +#end(REPL_TEMPLATES) + +#define(VARIANTS) + +[ + { + "SHADER_NAME": "abs_f32", + "REPLS": { "TYPE": "f32", "FUNC": "ABS_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "abs_f16", + "REPLS": { "TYPE": "f16", "FUNC": "ABS_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "abs_inplace_f32", + "REPLS": { "TYPE": "f32", "FUNC": "ABS_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + { + "SHADER_NAME": "abs_inplace_f16", + "REPLS": { "TYPE": "f16", "FUNC": "ABS_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + + { + "SHADER_NAME": "sgn_f32", + "REPLS": { "TYPE": "f32", "FUNC": "SGN_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "sgn_f16", + "REPLS": { "TYPE": "f16", "FUNC": "SGN_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "sgn_inplace_f32", + "REPLS": { "TYPE": "f32", "FUNC": "SGN_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + { + "SHADER_NAME": "sgn_inplace_f16", + "REPLS": { "TYPE": "f16", "FUNC": "SGN_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + + { + "SHADER_NAME": "neg_f32", + "REPLS": { "TYPE": "f32", "FUNC": "NEG_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "neg_f16", + "REPLS": { "TYPE": "f16", "FUNC": "NEG_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "neg_inplace_f32", + "REPLS": { "TYPE": "f32", "FUNC": "NEG_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + { + "SHADER_NAME": "neg_inplace_f16", + "REPLS": { "TYPE": "f16", "FUNC": "NEG_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + + { + "SHADER_NAME": "step_f32", + "REPLS": { "TYPE": "f32", "FUNC": "STEP_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "step_f16", + "REPLS": { "TYPE": "f16", "FUNC": "STEP_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "step_inplace_f32", + "REPLS": { "TYPE": "f32", "FUNC": "STEP_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + { + "SHADER_NAME": "step_inplace_f16", + "REPLS": { "TYPE": "f16", "FUNC": "STEP_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + + { + "SHADER_NAME": "tanh_f32", + "REPLS": { "TYPE": "f32", "FUNC": "TANH_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "tanh_f16", + "REPLS": { "TYPE": "f16", "FUNC": "TANH_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "tanh_inplace_f32", + "REPLS": { "TYPE": "f32", "FUNC": "TANH_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + { + "SHADER_NAME": "tanh_inplace_f16", + "REPLS": { "TYPE": "f16", "FUNC": "TANH_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + + { + "SHADER_NAME": "elu_f32", + "REPLS": { "TYPE": "f32", "FUNC": "ELU_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "elu_f16", + "REPLS": { "TYPE": "f16", "FUNC": "ELU_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "elu_inplace_f32", + "REPLS": { "TYPE": "f32", "FUNC": "ELU_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + { + "SHADER_NAME": "elu_inplace_f16", + "REPLS": { "TYPE": "f16", "FUNC": "ELU_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + + { + "SHADER_NAME": "relu_f32", + "REPLS": { "TYPE": "f32", "FUNC": "RELU_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "relu_f16", + "REPLS": { "TYPE": "f16", "FUNC": "RELU_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "relu_inplace_f32", + "REPLS": { "TYPE": "f32", "FUNC": "RELU_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + { + "SHADER_NAME": "relu_inplace_f16", + "REPLS": { "TYPE": "f16", "FUNC": "RELU_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + + { + "SHADER_NAME": "sigmoid_f32", + "REPLS": { "TYPE": "f32", "FUNC": "SIGMOID_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "sigmoid_f16", + "REPLS": { "TYPE": "f16", "FUNC": "SIGMOID_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "sigmoid_inplace_f32", + "REPLS": { "TYPE": "f32", "FUNC": "SIGMOID_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + { + "SHADER_NAME": "sigmoid_inplace_f16", + "REPLS": { "TYPE": "f16", "FUNC": "SIGMOID_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + + { + "SHADER_NAME": "silu_f32", + "REPLS": { "TYPE": "f32", "FUNC": "SILU_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "silu_f16", + "REPLS": { "TYPE": "f16", "FUNC": "SILU_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "silu_inplace_f32", + "REPLS": { "TYPE": "f32", "FUNC": "SILU_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + { + "SHADER_NAME": "silu_inplace_f16", + "REPLS": { "TYPE": "f16", "FUNC": "SILU_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + + { + "SHADER_NAME": "exp_f32", + "REPLS": { "TYPE": "f32", "FUNC": "EXP_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "exp_f16", + "REPLS": { "TYPE": "f16", "FUNC": "EXP_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "exp_inplace_f32", + "REPLS": { "TYPE": "f32", "FUNC": "EXP_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + { + "SHADER_NAME": "exp_inplace_f16", + "REPLS": { "TYPE": "f16", "FUNC": "EXP_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + + { + "SHADER_NAME": "hardsigmoid_f32", + "REPLS": { "TYPE": "f32", "FUNC": "HARDSIGMOID_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "hardsigmoid_f16", + "REPLS": { "TYPE": "f16", "FUNC": "HARDSIGMOID_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "hardsigmoid_inplace_f32", + "REPLS": { "TYPE": "f32", "FUNC": "HARDSIGMOID_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + { + "SHADER_NAME": "hardsigmoid_inplace_f16", + "REPLS": { "TYPE": "f16", "FUNC": "HARDSIGMOID_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + + { + "SHADER_NAME": "hardswish_f32", + "REPLS": { "TYPE": "f32", "FUNC": "HARDSWISH_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "hardswish_f16", + "REPLS": { "TYPE": "f16", "FUNC": "HARDSWISH_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "hardswish_inplace_f32", + "REPLS": { "TYPE": "f32", "FUNC": "HARDSWISH_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + { + "SHADER_NAME": "hardswish_inplace_f16", + "REPLS": { "TYPE": "f16", "FUNC": "HARDSWISH_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + + { + "SHADER_NAME": "gelu_f32", + "REPLS": { "TYPE": "f32", "FUNC": "GELU_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "gelu_f16", + "REPLS": { "TYPE": "f16", "FUNC": "GELU_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "gelu_inplace_f32", + "REPLS": { "TYPE": "f32", "FUNC": "GELU_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + { + "SHADER_NAME": "gelu_inplace_f16", + "REPLS": { "TYPE": "f16", "FUNC": "GELU_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + + { + "SHADER_NAME": "gelu_quick_f32", + "REPLS": { "TYPE": "f32", "FUNC": "GELU_QUICK_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "gelu_quick_f16", + "REPLS": { "TYPE": "f16", "FUNC": "GELU_QUICK_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "gelu_quick_inplace_f32", + "REPLS": { "TYPE": "f32", "FUNC": "GELU_QUICK_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + { + "SHADER_NAME": "gelu_quick_inplace_f16", + "REPLS": { "TYPE": "f16", "FUNC": "GELU_QUICK_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + + { + "SHADER_NAME": "xielu_f32", + "REPLS": { "TYPE": "f32", "FUNC": "XIELU_FUNC", "EXT_PARAMS": "alpha_n: f32, alpha_p: f32, beta: f32, eps: f32", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "xielu_f16", + "REPLS": { "TYPE": "f16", "FUNC": "XIELU_FUNC", "EXT_PARAMS": "alpha_n: f32, alpha_p: f32, beta: f32, eps: f32", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "xielu_inplace_f32", + "REPLS": { "TYPE": "f32", "FUNC": "XIELU_FUNC", "EXT_PARAMS": "alpha_n: f32, alpha_p: f32, beta: f32, eps: f32", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + { + "SHADER_NAME": "xielu_inplace_f16", + "REPLS": { "TYPE": "f16", "FUNC": "XIELU_FUNC", "EXT_PARAMS": "alpha_n: f32, alpha_p: f32, beta: f32, eps: f32", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + { + "SHADER_NAME": "gelu_erf_f32", + "REPLS": { "TYPE": "f32", "FUNC": "GELU_ERF_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "gelu_erf_f16", + "REPLS": { "TYPE": "f16", "FUNC": "GELU_ERF_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "gelu_erf_inplace_f32", + "REPLS": { "TYPE": "f32", "FUNC": "GELU_ERF_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + { + "SHADER_NAME": "gelu_erf_inplace_f16", + "REPLS": { "TYPE": "f16", "FUNC": "GELU_ERF_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + } +] + +#end(VARIANTS) + +#define(DECLS) + +#decl(INPLACE) + +@group(0) @binding(1) +var params: Params; + +#enddecl(INPLACE) + +#decl(NOT_INPLACE) + +@group(0) @binding(1) +var dst: array<{{TYPE}}>; + +@group(0) @binding(2) +var params: Params; + +#enddecl(NOT_INPLACE) + +#end(DECLS) + +#define(SHADER) + +enable f16; + +fn update(dst_i: u32, src_i: u32) { + {{FUNC}} +} + +@group(0) @binding(0) +var src: array<{{TYPE}}>; + +DECLS + +struct Params { + ne: u32, // total number of elements + offset_src: u32, // in elements + offset_dst: u32, // in elements + + // Strides (in elements) — may be permuted + stride_src0: u32, + stride_src1: u32, + stride_src2: u32, + stride_src3: u32, + + stride_dst0: u32, + stride_dst1: u32, + stride_dst2: u32, + stride_dst3: u32, + + // Logical shapes + src_ne0: u32, + src_ne1: u32, + src_ne2: u32, + + dst_ne0: u32, + dst_ne1: u32, + dst_ne2: u32, + + {{EXT_PARAMS}} +}; + +override wg_size: u32; +@compute @workgroup_size(wg_size) +fn main(@builtin(global_invocation_id) gid: vec3) { + if (gid.x >= params.ne) { + return; + } + + var i = gid.x; + let i3 = i / (params.src_ne2 * params.src_ne1 * params.src_ne0); + i = i % (params.src_ne2 * params.src_ne1 * params.src_ne0); + let i2 = i / (params.src_ne1 * params.src_ne0); + i = i % (params.src_ne1 * params.src_ne0); + let i1 = i / params.src_ne0; + let i0 = i % params.src_ne0; + + var j = gid.x; + let j3 = j / (params.dst_ne2 * params.dst_ne1 * params.dst_ne0); + j = j % (params.dst_ne2 * params.dst_ne1 * params.dst_ne0); + let j2 = j / (params.dst_ne1 * params.dst_ne0); + j = j % (params.dst_ne1 * params.dst_ne0); + let j1 = j / params.dst_ne0; + let j0 = j % params.dst_ne0; + + let src_idx = i0 * params.stride_src0 + i1 * params.stride_src1 + + i2 * params.stride_src2 + i3 * params.stride_src3; + + let dst_idx = j0 * params.stride_dst0 + j1 * params.stride_dst1 + + j2 * params.stride_dst2 + j3 * params.stride_dst3; + + + update(params.offset_dst + dst_idx, params.offset_src + src_idx); +} + +#end(SHADER) + diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index 7ef7f2ad81..495fffd5d0 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -47,6 +47,12 @@ # define N_THREADS std::thread::hardware_concurrency() #endif +#ifdef __EMSCRIPTEN__ +# define N_THREADS 1 +#else +# define N_THREADS std::thread::hardware_concurrency() +#endif + static void init_tensor_uniform(ggml_tensor * tensor, float min = -1.0f, float max = 1.0f) { size_t nels = ggml_nelements(tensor); std::vector data(nels); From 893e6afea58078afb0f610d7f901bd9fcd5022ea Mon Sep 17 00:00:00 2001 From: Reese Levine Date: Thu, 4 Dec 2025 10:17:38 -0800 Subject: [PATCH 02/21] Remove extra code and format --- ggml/include/ggml.h | 6 ------ ggml/src/ggml-cpu/ggml-cpu.cpp | 7 ------- ggml/src/ggml-webgpu/ggml-webgpu.cpp | 27 ++++++++++++++------------- tests/test-backend-ops.cpp | 6 ------ 4 files changed, 14 insertions(+), 32 deletions(-) diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h index 5af0692692..e665614670 100644 --- a/ggml/include/ggml.h +++ b/ggml/include/ggml.h @@ -225,13 +225,7 @@ # define GGML_MAX_NAME 64 #endif -// For single-thread WASM builds, only use 1 thread -#if !defined(__EMSCRIPTEN__) || defined(__EMSCRIPTEN_PTHREADS__) #define GGML_DEFAULT_N_THREADS 4 -#else -#define GGML_DEFAULT_N_THREADS 1 -#endif - #define GGML_DEFAULT_GRAPH_SIZE 2048 #if UINTPTR_MAX == 0xFFFFFFFF diff --git a/ggml/src/ggml-cpu/ggml-cpu.cpp b/ggml/src/ggml-cpu/ggml-cpu.cpp index 3c5cc14102..3191faaa4c 100644 --- a/ggml/src/ggml-cpu/ggml-cpu.cpp +++ b/ggml/src/ggml-cpu/ggml-cpu.cpp @@ -246,11 +246,8 @@ bool ggml_backend_is_cpu(ggml_backend_t backend) { void ggml_backend_cpu_set_n_threads(ggml_backend_t backend_cpu, int n_threads) { GGML_ASSERT(ggml_backend_is_cpu(backend_cpu)); -// For single-thread WASM builds, do not allow changing the number of threads -#if !defined(_EMSCRIPTEN_) || defined(__EMSCRIPTEN_PTHREADS__) struct ggml_backend_cpu_context * ctx = (struct ggml_backend_cpu_context *)backend_cpu->context; ctx->n_threads = n_threads; -#endif } void ggml_backend_cpu_set_threadpool(ggml_backend_t backend_cpu, ggml_threadpool_t threadpool) { @@ -625,14 +622,10 @@ static ggml_backend_feature * ggml_backend_cpu_get_features(ggml_backend_reg_t r } static void * ggml_backend_cpu_get_proc_address(ggml_backend_reg_t reg, const char * name) { - -// For single-thread WASM builds, do not expose a set_n_threads function -#if !defined(__EMSCRIPTEN__) || defined(__EMSCRIPTEN_PTHREADS__) if (strcmp(name, "ggml_backend_set_n_threads") == 0) { ggml_backend_set_n_threads_t fct = ggml_backend_cpu_set_n_threads; return (void *)fct; } -#endif if (strcmp(name, "ggml_backend_dev_get_extra_bufts") == 0) { ggml_backend_dev_get_extra_bufts_t fct = ggml_backend_cpu_device_get_extra_buffers_type; return (void *)fct; diff --git a/ggml/src/ggml-webgpu/ggml-webgpu.cpp b/ggml/src/ggml-webgpu/ggml-webgpu.cpp index 56dc24681f..d0e99b6fe2 100644 --- a/ggml/src/ggml-webgpu/ggml-webgpu.cpp +++ b/ggml/src/ggml-webgpu/ggml-webgpu.cpp @@ -1005,19 +1005,20 @@ static webgpu_command ggml_webgpu_unary_op(webgpu_context & ctx, ggml_tensor * s }; switch (unary_op) { - case GGML_UNARY_OP_XIELU: { - // Get float parameters and reinterpret their bit patterns as uint32_t - // for passing through the params buffer - float alpha_n = ggml_get_op_params_f32(dst, 1); - float alpha_p = ggml_get_op_params_f32(dst, 2); - float beta = ggml_get_op_params_f32(dst, 3); - float eps = ggml_get_op_params_f32(dst, 4); - params.push_back(*reinterpret_cast(&alpha_n)); - params.push_back(*reinterpret_cast(&alpha_p)); - params.push_back(*reinterpret_cast(&beta)); - params.push_back(*reinterpret_cast(&eps)); - break; - } + case GGML_UNARY_OP_XIELU: + { + // Get float parameters and reinterpret their bit patterns as uint32_t + // for passing through the params buffer + float alpha_n = ggml_get_op_params_f32(dst, 1); + float alpha_p = ggml_get_op_params_f32(dst, 2); + float beta = ggml_get_op_params_f32(dst, 3); + float eps = ggml_get_op_params_f32(dst, 4); + params.push_back(*reinterpret_cast(&alpha_n)); + params.push_back(*reinterpret_cast(&alpha_p)); + params.push_back(*reinterpret_cast(&beta)); + params.push_back(*reinterpret_cast(&eps)); + break; + } default: break; } diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index 495fffd5d0..7ef7f2ad81 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -47,12 +47,6 @@ # define N_THREADS std::thread::hardware_concurrency() #endif -#ifdef __EMSCRIPTEN__ -# define N_THREADS 1 -#else -# define N_THREADS std::thread::hardware_concurrency() -#endif - static void init_tensor_uniform(ggml_tensor * tensor, float min = -1.0f, float max = 1.0f) { size_t nels = ggml_nelements(tensor); std::vector data(nels); From 417fa792dd2912d270b72c76cf8a4be15cb14a04 Mon Sep 17 00:00:00 2001 From: Reese Levine Date: Thu, 4 Dec 2025 10:25:59 -0800 Subject: [PATCH 03/21] Add ops documentation (finally) --- docs/ops.md | 216 +- docs/ops/WebGPU.csv | 18741 ++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 18849 insertions(+), 108 deletions(-) create mode 100644 docs/ops/WebGPU.csv diff --git a/docs/ops.md b/docs/ops.md index fe5e6c1819..29bfe0cd55 100644 --- a/docs/ops.md +++ b/docs/ops.md @@ -12,111 +12,111 @@ Legend: - 🟡 Partially supported by this backend - ❌ Not supported by this backend -| Operation | BLAS | CANN | CPU | CUDA | Metal | OpenCL | SYCL | Vulkan | zDNN | -|-----------|------|------|------|------|------|------|------|------|------| -| ABS | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ❌ | -| ACC | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | -| ADD | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ❌ | -| ADD1 | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | -| ADD_ID | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | -| ARANGE | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | -| ARGMAX | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | -| ARGSORT | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | -| CEIL | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ❌ | -| CLAMP | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | -| CONCAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ✅ | ❌ | -| CONT | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | -| CONV_2D | ❌ | ❌ | ✅ | ✅ | ❌ | ✅ | ❌ | ✅ | ❌ | -| CONV_2D_DW | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | -| CONV_3D | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | -| CONV_TRANSPOSE_1D | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | -| CONV_TRANSPOSE_2D | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | -| COS | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | 🟡 | ❌ | -| COUNT_EQUAL | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | -| CPY | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | -| CROSS_ENTROPY_LOSS | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | -| CROSS_ENTROPY_LOSS_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | -| CUMSUM | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | -| DIAG_MASK_INF | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ❌ | -| DIV | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ❌ | -| DUP | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ✅ | ❌ | -| ELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | ❌ | ❌ | -| EXP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ❌ | -| EXPM1 | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | ❌ | ❌ | ❌ | -| FILL | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | -| FLASH_ATTN_EXT | ❌ | 🟡 | ✅ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ | -| FLOOR | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ❌ | -| GATED_LINEAR_ATTN | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | -| GEGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ | -| GEGLU_ERF | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ | -| GEGLU_QUICK | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ | -| GELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ❌ | -| GELU_ERF | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ❌ | -| GELU_QUICK | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ❌ | -| GET_ROWS | ❌ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | ❌ | -| GET_ROWS_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ❌ | ❌ | -| GROUP_NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | -| GROUP_NORM_MUL_ADD | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | -| HARDSIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ❌ | -| HARDSWISH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ❌ | -| IM2COL | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | -| IM2COL_3D | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | -| L2_NORM | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | -| LEAKY_RELU | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | 🟡 | ❌ | -| LOG | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | 🟡 | ✅ | ❌ | -| MEAN | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | -| MUL | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ❌ | -| MUL_MAT | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | -| MUL_MAT_ID | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | -| NEG | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ❌ | -| NORM | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ | -| NORM_MUL_ADD | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | -| OPT_STEP_ADAMW | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | -| OPT_STEP_SGD | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | -| OUT_PROD | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ | ❌ | -| PAD | ❌ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | -| PAD_REFLECT_1D | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | -| POOL_2D | ❌ | 🟡 | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | -| REGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ | -| RELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ❌ | -| REPEAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | ❌ | -| REPEAT_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | -| RMS_NORM | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | -| RMS_NORM_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | -| RMS_NORM_MUL_ADD | ❌ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | -| ROLL | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | -| ROPE | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | -| ROPE_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | -| ROUND | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ❌ | -| RWKV_WKV6 | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | -| RWKV_WKV7 | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | -| SCALE | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | -| SET | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | 🟡 | ❌ | ❌ | -| SET_ROWS | ❌ | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | -| SGN | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | ❌ | ❌ | -| SIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ❌ | -| SILU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ❌ | -| SILU_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | -| SIN | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | 🟡 | ❌ | -| SOFTCAP | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | -| SOFTPLUS | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | ❌ | 🟡 | ❌ | -| SOFT_MAX | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | -| SOFT_MAX_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ✅ | ❌ | -| SOLVE_TRI | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | 🟡 | ❌ | -| SQR | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | 🟡 | ❌ | -| SQRT | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | 🟡 | ❌ | -| SSM_CONV | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | -| SSM_SCAN | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | 🟡 | ❌ | -| STEP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ❌ | -| SUB | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ❌ | -| SUM | ❌ | ✅ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ❌ | -| SUM_ROWS | ❌ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | -| SWIGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ | -| SWIGLU_OAI | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | 🟡 | ❌ | -| TANH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | 🟡 | ❌ | -| TIMESTEP_EMBEDDING | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | -| TOP_K | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | 🟡 | ❌ | -| TRI | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | -| TRUNC | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ❌ | -| UPSCALE | ❌ | 🟡 | ✅ | ✅ | 🟡 | ✅ | 🟡 | 🟡 | ❌ | -| XIELU | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | +| Operation | BLAS | CANN | CPU | CUDA | Metal | OpenCL | SYCL | Vulkan | WebGPU | zDNN | +|-----------|------|------|------|------|------|------|------|------|------|------| +| ABS | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | +| ACC | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | +| ADD | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ❌ | +| ADD1 | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | +| ADD_ID | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | +| ARANGE | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | +| ARGMAX | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | +| ARGSORT | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | +| CEIL | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | +| CLAMP | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ | +| CONCAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ✅ | ❌ | ❌ | +| CONT | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ❌ | +| CONV_2D | ❌ | ❌ | ✅ | ✅ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | +| CONV_2D_DW | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | +| CONV_3D | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | +| CONV_TRANSPOSE_1D | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | +| CONV_TRANSPOSE_2D | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | +| COS | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | +| COUNT_EQUAL | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | +| CPY | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | +| CROSS_ENTROPY_LOSS | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | +| CROSS_ENTROPY_LOSS_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | +| CUMSUM | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | +| DIAG_MASK_INF | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ❌ | ❌ | +| DIV | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ❌ | +| DUP | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ✅ | ❌ | ❌ | +| ELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | ❌ | ✅ | ❌ | +| EXP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | +| EXPM1 | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | +| FILL | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | +| FLASH_ATTN_EXT | ❌ | 🟡 | ✅ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ | ❌ | +| FLOOR | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | +| GATED_LINEAR_ATTN | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | +| GEGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | +| GEGLU_ERF | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | +| GEGLU_QUICK | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | +| GELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | +| GELU_ERF | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | +| GELU_QUICK | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | +| GET_ROWS | ❌ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | +| GET_ROWS_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | +| GROUP_NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | +| GROUP_NORM_MUL_ADD | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | +| HARDSIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | +| HARDSWISH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | +| IM2COL | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ | +| IM2COL_3D | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | +| L2_NORM | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | +| LEAKY_RELU | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | 🟡 | ❌ | ❌ | +| LOG | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | 🟡 | ✅ | ❌ | ❌ | +| MEAN | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | +| MUL | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ❌ | +| MUL_MAT | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | +| MUL_MAT_ID | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ❌ | +| NEG | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | +| NORM | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ | ❌ | +| NORM_MUL_ADD | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | +| OPT_STEP_ADAMW | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | +| OPT_STEP_SGD | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | +| OUT_PROD | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | +| PAD | ❌ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ | +| PAD_REFLECT_1D | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | +| POOL_2D | ❌ | 🟡 | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | +| REGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | +| RELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | +| REPEAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | ❌ | ❌ | +| REPEAT_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | +| RMS_NORM | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | +| RMS_NORM_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | +| RMS_NORM_MUL_ADD | ❌ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | +| ROLL | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | +| ROPE | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | +| ROPE_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | +| ROUND | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | +| RWKV_WKV6 | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | +| RWKV_WKV7 | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | +| SCALE | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | +| SET | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | 🟡 | ❌ | ❌ | ❌ | +| SET_ROWS | ❌ | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | +| SGN | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | ❌ | ✅ | ❌ | +| SIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | +| SILU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | +| SILU_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | +| SIN | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | +| SOFTCAP | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | +| SOFTPLUS | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | ❌ | 🟡 | ❌ | ❌ | +| SOFT_MAX | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | +| SOFT_MAX_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ✅ | ❌ | ❌ | +| SOLVE_TRI | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | 🟡 | ❌ | ❌ | +| SQR | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | +| SQRT | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | +| SSM_CONV | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | +| SSM_SCAN | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | 🟡 | ❌ | ❌ | +| STEP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | +| SUB | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ❌ | +| SUM | ❌ | ✅ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | +| SUM_ROWS | ❌ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ | +| SWIGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | +| SWIGLU_OAI | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | 🟡 | ✅ | ❌ | +| TANH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | +| TIMESTEP_EMBEDDING | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | +| TOP_K | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | 🟡 | ❌ | ❌ | +| TRI | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | +| TRUNC | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | +| UPSCALE | ❌ | 🟡 | ✅ | ✅ | 🟡 | ✅ | 🟡 | 🟡 | ❌ | ❌ | +| XIELU | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | diff --git a/docs/ops/WebGPU.csv b/docs/ops/WebGPU.csv new file mode 100644 index 0000000000..bfff75e66f --- /dev/null +++ b/docs/ops/WebGPU.csv @@ -0,0 +1,18741 @@ +"backend_name","op_name","op_params","test_mode","supported","error_message","backend_reg_name" +"WebGPU: WebGPU","ABS","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","ABS","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","SGN","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","SGN","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","NEG","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","NEG","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","STEP","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","STEP","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","TANH","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","TANH","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","ELU","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","ELU","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","RELU","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","RELU","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","SIGMOID","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","SIGMOID","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","GELU","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","GELU","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","GELU_QUICK","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","GELU_QUICK","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","SILU","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","SILU","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","HARDSWISH","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","HARDSWISH","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","HARDSIGMOID","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","HARDSIGMOID","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","EXP","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","EXP","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","EXPM1","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","WebGPU" +"WebGPU: WebGPU","EXPM1","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","WebGPU" +"WebGPU: WebGPU","SOFTPLUS","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","WebGPU" +"WebGPU: WebGPU","SOFTPLUS","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","WebGPU" +"WebGPU: WebGPU","GELU_ERF","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","GELU_ERF","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLOOR","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","WebGPU" +"WebGPU: 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WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","WebGPU" +"WebGPU: WebGPU","CROSS_ENTROPY_LOSS","type=f32,ne=[10,5,4,3]","support","0","no","WebGPU" +"WebGPU: WebGPU","CROSS_ENTROPY_LOSS","type=f32,ne=[30000,1,1,1]","support","0","no","WebGPU" +"WebGPU: WebGPU","CROSS_ENTROPY_LOSS_BACK","type=f32,ne=[10,5,4,3]","support","0","no","WebGPU" +"WebGPU: WebGPU","CROSS_ENTROPY_LOSS_BACK","type=f32,ne=[30000,1,1,1]","support","0","no","WebGPU" +"WebGPU: WebGPU","OPT_STEP_ADAMW","type=f32,ne=[10,5,4,3]","support","0","no","WebGPU" +"WebGPU: WebGPU","OPT_STEP_SGD","type=f32,ne=[10,5,4,3]","support","0","no","WebGPU" From 13ad711c1ac9b1edd9beba1409b07480b7964e50 Mon Sep 17 00:00:00 2001 From: Abhijit Ramesh Date: Thu, 18 Dec 2025 15:24:15 +0400 Subject: [PATCH 04/21] 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/src/ggml-webgpu/ggml-webgpu.cpp | 11 +++++++++ .../ggml-webgpu/wgsl-shaders/unary_op.wgsl | 23 ++++++++++++++++++- 2 files changed, 33 insertions(+), 1 deletion(-) diff --git a/ggml/src/ggml-webgpu/ggml-webgpu.cpp b/ggml/src/ggml-webgpu/ggml-webgpu.cpp index d0e99b6fe2..5807ab0fad 100644 --- a/ggml/src/ggml-webgpu/ggml-webgpu.cpp +++ b/ggml/src/ggml-webgpu/ggml-webgpu.cpp @@ -2273,6 +2273,16 @@ static void ggml_webgpu_init_unary_pipeline(webgpu_context & webgpu_ctx) { ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_xielu_inplace_f32, "xielu_inplace_f32", constants); webgpu_ctx->unary_pipelines[GGML_UNARY_OP_XIELU][GGML_TYPE_F16][1] = ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_xielu_inplace_f16, "xielu_inplace_f16", constants); + + // SOFTPLUS + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_SOFTPLUS][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_softplus_f32, "softplus_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_SOFTPLUS][GGML_TYPE_F16][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_softplus_f16, "softplus_f16", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_SOFTPLUS][GGML_TYPE_F32][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_softplus_inplace_f32, "softplus_inplace_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_SOFTPLUS][GGML_TYPE_F16][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_softplus_inplace_f16, "softplus_inplace_f16", constants); } static void ggml_webgpu_init_scale_pipeline(webgpu_context & webgpu_ctx) { @@ -2527,6 +2537,7 @@ static bool ggml_backend_webgpu_device_supports_op(ggml_backend_dev_t dev, const case GGML_UNARY_OP_HARDSIGMOID: case GGML_UNARY_OP_EXP: case GGML_UNARY_OP_GELU_ERF: + case GGML_UNARY_OP_SOFTPLUS: case GGML_UNARY_OP_XIELU: supports_op = supports_op = (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16) && (src0->type == op->type); diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/unary_op.wgsl b/ggml/src/ggml-webgpu/wgsl-shaders/unary_op.wgsl index d474ab107b..582e34f161 100644 --- a/ggml/src/ggml-webgpu/wgsl-shaders/unary_op.wgsl +++ b/ggml/src/ggml-webgpu/wgsl-shaders/unary_op.wgsl @@ -16,7 +16,8 @@ "HARDSWISH_FUNC": "{{MUTATE}}[dst_i] = src[src_i] * min(1.0, max(0.0, (src[src_i] + 3.0) / 6.0));", "GELU_FUNC": "{{MUTATE}}[dst_i] = 0.5 * src[src_i] * (1.0 + tanh(clamp(sqrt(2.0 / 3.14159265) * (src[src_i] + 0.044715 * pow(src[src_i], 3.0)), -9.010913, 9.010913))); // Regarding tanh() domain restrictions in wgsl https://github.com/gpuweb/gpuweb/issues/4458", "GELU_QUICK_FUNC": "{{MUTATE}}[dst_i] = src[src_i] * 0.5 * (1.0 + tanh(clamp(0.79788456 * (src[src_i] + 0.044715 * src[src_i] * src[src_i] * src[src_i]), -9.010913, 9.010913))); // Regarding tanh() domain restrictions in wgsl https://github.com/gpuweb/gpuweb/issues/4458", - "GELU_ERF_FUNC": "{{MUTATE}}[dst_i] = 0.5 * src[src_i] * (1.0 + tanh(clamp(0.79788456 * (src[src_i] + 0.044715 * src[src_i] * src[src_i] * src[src_i]), -9.010913, 9.010913))); // Regarding tanh() domain restrictions in wgsl https://github.com/gpuweb/gpuweb/issues/4458" + "GELU_ERF_FUNC": "{{MUTATE}}[dst_i] = 0.5 * src[src_i] * (1.0 + tanh(clamp(0.79788456 * (src[src_i] + 0.044715 * src[src_i] * src[src_i] * src[src_i]), -9.010913, 9.010913))); // Regarding tanh() domain restrictions in wgsl https://github.com/gpuweb/gpuweb/issues/4458", + "SOFTPLUS_FUNC": "{ let src_f32 = f32(src[src_i]); {{MUTATE}}[dst_i] = {{TYPE}}(select(log(1.0 + exp(src_f32)), src_f32, src_f32 > 20.0)); } // Cast to f32 to prevent exp() overflow with f16 (exp(x) overflows f16 for x > ~11)" } #end(REPL_TEMPLATES) @@ -357,6 +358,26 @@ "SHADER_NAME": "gelu_erf_inplace_f16", "REPLS": { "TYPE": "f16", "FUNC": "GELU_ERF_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, "DECLS": ["INPLACE"] + }, + { + "SHADER_NAME": "softplus_f32", + "REPLS": { "TYPE": "f32", "FUNC": "SOFTPLUS_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "softplus_f16", + "REPLS": { "TYPE": "f16", "FUNC": "SOFTPLUS_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "softplus_inplace_f32", + "REPLS": { "TYPE": "f32", "FUNC": "SOFTPLUS_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + { + "SHADER_NAME": "softplus_inplace_f16", + "REPLS": { "TYPE": "f16", "FUNC": "SOFTPLUS_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] } ] From ac51c622bf2eb3af2a900d4e369812c936a8c4ce Mon Sep 17 00:00:00 2001 From: Abhijit Ramesh Date: Thu, 18 Dec 2025 15:48:25 +0400 Subject: [PATCH 05/21] 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/src/ggml-webgpu/ggml-webgpu.cpp | 11 ++++++++ .../ggml-webgpu/wgsl-shaders/unary_op.wgsl | 25 +++++++++++++++++-- 2 files changed, 34 insertions(+), 2 deletions(-) diff --git a/ggml/src/ggml-webgpu/ggml-webgpu.cpp b/ggml/src/ggml-webgpu/ggml-webgpu.cpp index 5807ab0fad..ec55486887 100644 --- a/ggml/src/ggml-webgpu/ggml-webgpu.cpp +++ b/ggml/src/ggml-webgpu/ggml-webgpu.cpp @@ -2283,6 +2283,16 @@ static void ggml_webgpu_init_unary_pipeline(webgpu_context & webgpu_ctx) { ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_softplus_inplace_f32, "softplus_inplace_f32", constants); webgpu_ctx->unary_pipelines[GGML_UNARY_OP_SOFTPLUS][GGML_TYPE_F16][1] = ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_softplus_inplace_f16, "softplus_inplace_f16", constants); + + // EXPM1 + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_EXPM1][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_expm1_f32, "expm1_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_EXPM1][GGML_TYPE_F16][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_expm1_f16, "expm1_f16", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_EXPM1][GGML_TYPE_F32][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_expm1_inplace_f32, "expm1_inplace_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_EXPM1][GGML_TYPE_F16][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_expm1_inplace_f16, "expm1_inplace_f16", constants); } static void ggml_webgpu_init_scale_pipeline(webgpu_context & webgpu_ctx) { @@ -2538,6 +2548,7 @@ static bool ggml_backend_webgpu_device_supports_op(ggml_backend_dev_t dev, const case GGML_UNARY_OP_EXP: case GGML_UNARY_OP_GELU_ERF: case GGML_UNARY_OP_SOFTPLUS: + case GGML_UNARY_OP_EXPM1: case GGML_UNARY_OP_XIELU: supports_op = supports_op = (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16) && (src0->type == op->type); diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/unary_op.wgsl b/ggml/src/ggml-webgpu/wgsl-shaders/unary_op.wgsl index 582e34f161..7a1f267d29 100644 --- a/ggml/src/ggml-webgpu/wgsl-shaders/unary_op.wgsl +++ b/ggml/src/ggml-webgpu/wgsl-shaders/unary_op.wgsl @@ -17,7 +17,8 @@ "GELU_FUNC": "{{MUTATE}}[dst_i] = 0.5 * src[src_i] * (1.0 + tanh(clamp(sqrt(2.0 / 3.14159265) * (src[src_i] + 0.044715 * pow(src[src_i], 3.0)), -9.010913, 9.010913))); // Regarding tanh() domain restrictions in wgsl https://github.com/gpuweb/gpuweb/issues/4458", "GELU_QUICK_FUNC": "{{MUTATE}}[dst_i] = src[src_i] * 0.5 * (1.0 + tanh(clamp(0.79788456 * (src[src_i] + 0.044715 * src[src_i] * src[src_i] * src[src_i]), -9.010913, 9.010913))); // Regarding tanh() domain restrictions in wgsl https://github.com/gpuweb/gpuweb/issues/4458", "GELU_ERF_FUNC": "{{MUTATE}}[dst_i] = 0.5 * src[src_i] * (1.0 + tanh(clamp(0.79788456 * (src[src_i] + 0.044715 * src[src_i] * src[src_i] * src[src_i]), -9.010913, 9.010913))); // Regarding tanh() domain restrictions in wgsl https://github.com/gpuweb/gpuweb/issues/4458", - "SOFTPLUS_FUNC": "{ let src_f32 = f32(src[src_i]); {{MUTATE}}[dst_i] = {{TYPE}}(select(log(1.0 + exp(src_f32)), src_f32, src_f32 > 20.0)); } // Cast to f32 to prevent exp() overflow with f16 (exp(x) overflows f16 for x > ~11)" + "SOFTPLUS_FUNC": "{ let src_f32 = f32(src[src_i]); {{MUTATE}}[dst_i] = {{TYPE}}(select(log(1.0 + exp(src_f32)), src_f32, src_f32 > 20.0)); } // Cast to f32 to prevent exp() overflow with f16 (exp(x) overflows f16 for x > ~11)", + "EXPM1_FUNC": "{{MUTATE}}[dst_i] = exp(src[src_i]) - 1.0;" } #end(REPL_TEMPLATES) @@ -378,7 +379,27 @@ "SHADER_NAME": "softplus_inplace_f16", "REPLS": { "TYPE": "f16", "FUNC": "SOFTPLUS_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, "DECLS": ["INPLACE"] - } + }, + { + "SHADER_NAME": "expm1_f32", + "REPLS": { "TYPE": "f32", "FUNC": "EXPM1_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "expm1_f16", + "REPLS": { "TYPE": "f16", "FUNC": "EXPM1_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "expm1_inplace_f32", + "REPLS": { "TYPE": "f32", "FUNC": "EXPM1_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + { + "SHADER_NAME": "expm1_inplace_f16", + "REPLS": { "TYPE": "f16", "FUNC": "EXPM1_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, ] #end(VARIANTS) From e2a00cfb0a4eafec0785063433e64fa002e9b6da Mon Sep 17 00:00:00 2001 From: Abhijit Ramesh Date: Thu, 18 Dec 2025 16:06:27 +0400 Subject: [PATCH 06/21] 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/src/ggml-webgpu/ggml-webgpu.cpp | 11 +++++++++ .../ggml-webgpu/wgsl-shaders/unary_op.wgsl | 23 ++++++++++++++++++- 2 files changed, 33 insertions(+), 1 deletion(-) diff --git a/ggml/src/ggml-webgpu/ggml-webgpu.cpp b/ggml/src/ggml-webgpu/ggml-webgpu.cpp index ec55486887..4e09d4e9c8 100644 --- a/ggml/src/ggml-webgpu/ggml-webgpu.cpp +++ b/ggml/src/ggml-webgpu/ggml-webgpu.cpp @@ -2293,6 +2293,16 @@ static void ggml_webgpu_init_unary_pipeline(webgpu_context & webgpu_ctx) { ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_expm1_inplace_f32, "expm1_inplace_f32", constants); webgpu_ctx->unary_pipelines[GGML_UNARY_OP_EXPM1][GGML_TYPE_F16][1] = ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_expm1_inplace_f16, "expm1_inplace_f16", constants); + + // FLOOR + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_FLOOR][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_floor_f32, "floor_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_FLOOR][GGML_TYPE_F16][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_floor_f16, "floor_f16", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_FLOOR][GGML_TYPE_F32][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_floor_inplace_f32, "floor_inplace_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_FLOOR][GGML_TYPE_F16][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_floor_inplace_f16, "floor_inplace_f16", constants); } static void ggml_webgpu_init_scale_pipeline(webgpu_context & webgpu_ctx) { @@ -2549,6 +2559,7 @@ static bool ggml_backend_webgpu_device_supports_op(ggml_backend_dev_t dev, const case GGML_UNARY_OP_GELU_ERF: case GGML_UNARY_OP_SOFTPLUS: case GGML_UNARY_OP_EXPM1: + case GGML_UNARY_OP_FLOOR: case GGML_UNARY_OP_XIELU: supports_op = supports_op = (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16) && (src0->type == op->type); diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/unary_op.wgsl b/ggml/src/ggml-webgpu/wgsl-shaders/unary_op.wgsl index 7a1f267d29..e369f47006 100644 --- a/ggml/src/ggml-webgpu/wgsl-shaders/unary_op.wgsl +++ b/ggml/src/ggml-webgpu/wgsl-shaders/unary_op.wgsl @@ -18,7 +18,8 @@ "GELU_QUICK_FUNC": "{{MUTATE}}[dst_i] = src[src_i] * 0.5 * (1.0 + tanh(clamp(0.79788456 * (src[src_i] + 0.044715 * src[src_i] * src[src_i] * src[src_i]), -9.010913, 9.010913))); // Regarding tanh() domain restrictions in wgsl https://github.com/gpuweb/gpuweb/issues/4458", "GELU_ERF_FUNC": "{{MUTATE}}[dst_i] = 0.5 * src[src_i] * (1.0 + tanh(clamp(0.79788456 * (src[src_i] + 0.044715 * src[src_i] * src[src_i] * src[src_i]), -9.010913, 9.010913))); // Regarding tanh() domain restrictions in wgsl https://github.com/gpuweb/gpuweb/issues/4458", "SOFTPLUS_FUNC": "{ let src_f32 = f32(src[src_i]); {{MUTATE}}[dst_i] = {{TYPE}}(select(log(1.0 + exp(src_f32)), src_f32, src_f32 > 20.0)); } // Cast to f32 to prevent exp() overflow with f16 (exp(x) overflows f16 for x > ~11)", - "EXPM1_FUNC": "{{MUTATE}}[dst_i] = exp(src[src_i]) - 1.0;" + "EXPM1_FUNC": "{{MUTATE}}[dst_i] = exp(src[src_i]) - 1.0;", + "FLOOR_FUNC": "{{MUTATE}}[dst_i] = floor(src[src_i]);" } #end(REPL_TEMPLATES) @@ -400,6 +401,26 @@ "REPLS": { "TYPE": "f16", "FUNC": "EXPM1_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, "DECLS": ["INPLACE"] }, + { + "SHADER_NAME": "floor_f32", + "REPLS": { "TYPE": "f32", "FUNC": "FLOOR_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "floor_f16", + "REPLS": { "TYPE": "f16", "FUNC": "FLOOR_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "floor_inplace_f32", + "REPLS": { "TYPE": "f32", "FUNC": "FLOOR_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + { + "SHADER_NAME": "floor_inplace_f16", + "REPLS": { "TYPE": "f16", "FUNC": "FLOOR_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, ] #end(VARIANTS) From 267d3b4285c4292121bf7dac403055eb532df310 Mon Sep 17 00:00:00 2001 From: Abhijit Ramesh Date: Thu, 18 Dec 2025 16:38:27 +0400 Subject: [PATCH 07/21] 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/src/ggml-webgpu/ggml-webgpu.cpp | 11 +++++++++ .../ggml-webgpu/wgsl-shaders/unary_op.wgsl | 23 ++++++++++++++++++- 2 files changed, 33 insertions(+), 1 deletion(-) diff --git a/ggml/src/ggml-webgpu/ggml-webgpu.cpp b/ggml/src/ggml-webgpu/ggml-webgpu.cpp index 4e09d4e9c8..5d94cbc338 100644 --- a/ggml/src/ggml-webgpu/ggml-webgpu.cpp +++ b/ggml/src/ggml-webgpu/ggml-webgpu.cpp @@ -2303,6 +2303,16 @@ static void ggml_webgpu_init_unary_pipeline(webgpu_context & webgpu_ctx) { ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_floor_inplace_f32, "floor_inplace_f32", constants); webgpu_ctx->unary_pipelines[GGML_UNARY_OP_FLOOR][GGML_TYPE_F16][1] = ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_floor_inplace_f16, "floor_inplace_f16", constants); + + // CEIL + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_CEIL][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_ceil_f32, "ceil_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_CEIL][GGML_TYPE_F16][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_ceil_f16, "ceil_f16", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_CEIL][GGML_TYPE_F32][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_ceil_inplace_f32, "ceil_inplace_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_CEIL][GGML_TYPE_F16][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_ceil_inplace_f16, "ceil_inplace_f16", constants); } static void ggml_webgpu_init_scale_pipeline(webgpu_context & webgpu_ctx) { @@ -2560,6 +2570,7 @@ static bool ggml_backend_webgpu_device_supports_op(ggml_backend_dev_t dev, const case GGML_UNARY_OP_SOFTPLUS: case GGML_UNARY_OP_EXPM1: case GGML_UNARY_OP_FLOOR: + case GGML_UNARY_OP_CEIL: case GGML_UNARY_OP_XIELU: supports_op = supports_op = (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16) && (src0->type == op->type); diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/unary_op.wgsl b/ggml/src/ggml-webgpu/wgsl-shaders/unary_op.wgsl index e369f47006..1d7c751fc9 100644 --- a/ggml/src/ggml-webgpu/wgsl-shaders/unary_op.wgsl +++ b/ggml/src/ggml-webgpu/wgsl-shaders/unary_op.wgsl @@ -19,7 +19,8 @@ "GELU_ERF_FUNC": "{{MUTATE}}[dst_i] = 0.5 * src[src_i] * (1.0 + tanh(clamp(0.79788456 * (src[src_i] + 0.044715 * src[src_i] * src[src_i] * src[src_i]), -9.010913, 9.010913))); // Regarding tanh() domain restrictions in wgsl https://github.com/gpuweb/gpuweb/issues/4458", "SOFTPLUS_FUNC": "{ let src_f32 = f32(src[src_i]); {{MUTATE}}[dst_i] = {{TYPE}}(select(log(1.0 + exp(src_f32)), src_f32, src_f32 > 20.0)); } // Cast to f32 to prevent exp() overflow with f16 (exp(x) overflows f16 for x > ~11)", "EXPM1_FUNC": "{{MUTATE}}[dst_i] = exp(src[src_i]) - 1.0;", - "FLOOR_FUNC": "{{MUTATE}}[dst_i] = floor(src[src_i]);" + "FLOOR_FUNC": "{{MUTATE}}[dst_i] = floor(src[src_i]);", + "CEIL_FUNC": "{{MUTATE}}[dst_i] = ceil(src[src_i]);" } #end(REPL_TEMPLATES) @@ -421,6 +422,26 @@ "REPLS": { "TYPE": "f16", "FUNC": "FLOOR_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, "DECLS": ["INPLACE"] }, + { + "SHADER_NAME": "ceil_f32", + "REPLS": { "TYPE": "f32", "FUNC": "CEIL_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "ceil_f16", + "REPLS": { "TYPE": "f16", "FUNC": "CEIL_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "ceil_inplace_f32", + "REPLS": { "TYPE": "f32", "FUNC": "CEIL_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + { + "SHADER_NAME": "ceil_inplace_f16", + "REPLS": { "TYPE": "f16", "FUNC": "CEIL_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, ] #end(VARIANTS) From 0e5948740cd9bd000c5303abca0dd9f68e063e2a Mon Sep 17 00:00:00 2001 From: Abhijit Ramesh Date: Thu, 25 Dec 2025 13:26:14 +0530 Subject: [PATCH 08/21] 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/src/ggml-webgpu/ggml-webgpu.cpp | 11 +++++++++ .../ggml-webgpu/wgsl-shaders/unary_op.wgsl | 23 ++++++++++++++++++- 2 files changed, 33 insertions(+), 1 deletion(-) diff --git a/ggml/src/ggml-webgpu/ggml-webgpu.cpp b/ggml/src/ggml-webgpu/ggml-webgpu.cpp index 5d94cbc338..b521176244 100644 --- a/ggml/src/ggml-webgpu/ggml-webgpu.cpp +++ b/ggml/src/ggml-webgpu/ggml-webgpu.cpp @@ -2313,6 +2313,16 @@ static void ggml_webgpu_init_unary_pipeline(webgpu_context & webgpu_ctx) { ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_ceil_inplace_f32, "ceil_inplace_f32", constants); webgpu_ctx->unary_pipelines[GGML_UNARY_OP_CEIL][GGML_TYPE_F16][1] = ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_ceil_inplace_f16, "ceil_inplace_f16", constants); + + // ROUND + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_ROUND][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_round_f32, "round_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_ROUND][GGML_TYPE_F16][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_round_f16, "round_f16", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_ROUND][GGML_TYPE_F32][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_round_inplace_f32, "round_inplace_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_ROUND][GGML_TYPE_F16][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_round_inplace_f16, "round_inplace_f16", constants); } static void ggml_webgpu_init_scale_pipeline(webgpu_context & webgpu_ctx) { @@ -2571,6 +2581,7 @@ static bool ggml_backend_webgpu_device_supports_op(ggml_backend_dev_t dev, const case GGML_UNARY_OP_EXPM1: case GGML_UNARY_OP_FLOOR: case GGML_UNARY_OP_CEIL: + case GGML_UNARY_OP_ROUND: case GGML_UNARY_OP_XIELU: supports_op = supports_op = (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16) && (src0->type == op->type); diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/unary_op.wgsl b/ggml/src/ggml-webgpu/wgsl-shaders/unary_op.wgsl index 1d7c751fc9..ab43bfc4a2 100644 --- a/ggml/src/ggml-webgpu/wgsl-shaders/unary_op.wgsl +++ b/ggml/src/ggml-webgpu/wgsl-shaders/unary_op.wgsl @@ -20,7 +20,8 @@ "SOFTPLUS_FUNC": "{ let src_f32 = f32(src[src_i]); {{MUTATE}}[dst_i] = {{TYPE}}(select(log(1.0 + exp(src_f32)), src_f32, src_f32 > 20.0)); } // Cast to f32 to prevent exp() overflow with f16 (exp(x) overflows f16 for x > ~11)", "EXPM1_FUNC": "{{MUTATE}}[dst_i] = exp(src[src_i]) - 1.0;", "FLOOR_FUNC": "{{MUTATE}}[dst_i] = floor(src[src_i]);", - "CEIL_FUNC": "{{MUTATE}}[dst_i] = ceil(src[src_i]);" + "CEIL_FUNC": "{{MUTATE}}[dst_i] = ceil(src[src_i]);", + "ROUND_FUNC": "{ let src_f32 = f32(src[src_i]); let result = select(ceil(src_f32 - 0.5), floor(src_f32 + 0.5), src_f32 >= 0.0); {{MUTATE}}[dst_i] = {{TYPE}}(result); } // Round half away from zero to match C roundf() behavior, not WGSL round() which uses round-to-even" } #end(REPL_TEMPLATES) @@ -442,6 +443,26 @@ "REPLS": { "TYPE": "f16", "FUNC": "CEIL_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, "DECLS": ["INPLACE"] }, + { + "SHADER_NAME": "round_f32", + "REPLS": { "TYPE": "f32", "FUNC": "ROUND_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "round_f16", + "REPLS": { "TYPE": "f16", "FUNC": "ROUND_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "round_inplace_f32", + "REPLS": { "TYPE": "f32", "FUNC": "ROUND_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + { + "SHADER_NAME": "round_inplace_f16", + "REPLS": { "TYPE": "f16", "FUNC": "ROUND_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, ] #end(VARIANTS) From 4f358f761ccc2c917183c9c5d7ff99f9ebd55ced Mon Sep 17 00:00:00 2001 From: Abhijit Ramesh Date: Thu, 25 Dec 2025 13:47:02 +0530 Subject: [PATCH 09/21] 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 --- ggml/src/ggml-webgpu/ggml-webgpu.cpp | 11 +++++++++ .../ggml-webgpu/wgsl-shaders/unary_op.wgsl | 23 ++++++++++++++++++- 2 files changed, 33 insertions(+), 1 deletion(-) diff --git a/ggml/src/ggml-webgpu/ggml-webgpu.cpp b/ggml/src/ggml-webgpu/ggml-webgpu.cpp index b521176244..d9552fbf41 100644 --- a/ggml/src/ggml-webgpu/ggml-webgpu.cpp +++ b/ggml/src/ggml-webgpu/ggml-webgpu.cpp @@ -2323,6 +2323,16 @@ static void ggml_webgpu_init_unary_pipeline(webgpu_context & webgpu_ctx) { ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_round_inplace_f32, "round_inplace_f32", constants); webgpu_ctx->unary_pipelines[GGML_UNARY_OP_ROUND][GGML_TYPE_F16][1] = ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_round_inplace_f16, "round_inplace_f16", constants); + + // TRUNC + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_TRUNC][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_trunc_f32, "trunc_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_TRUNC][GGML_TYPE_F16][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_trunc_f16, "trunc_f16", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_TRUNC][GGML_TYPE_F32][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_trunc_inplace_f32, "trunc_inplace_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_TRUNC][GGML_TYPE_F16][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_trunc_inplace_f16, "trunc_inplace_f16", constants); } static void ggml_webgpu_init_scale_pipeline(webgpu_context & webgpu_ctx) { @@ -2582,6 +2592,7 @@ static bool ggml_backend_webgpu_device_supports_op(ggml_backend_dev_t dev, const case GGML_UNARY_OP_FLOOR: case GGML_UNARY_OP_CEIL: case GGML_UNARY_OP_ROUND: + case GGML_UNARY_OP_TRUNC: case GGML_UNARY_OP_XIELU: supports_op = supports_op = (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16) && (src0->type == op->type); diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/unary_op.wgsl b/ggml/src/ggml-webgpu/wgsl-shaders/unary_op.wgsl index ab43bfc4a2..90efb78bfa 100644 --- a/ggml/src/ggml-webgpu/wgsl-shaders/unary_op.wgsl +++ b/ggml/src/ggml-webgpu/wgsl-shaders/unary_op.wgsl @@ -21,7 +21,8 @@ "EXPM1_FUNC": "{{MUTATE}}[dst_i] = exp(src[src_i]) - 1.0;", "FLOOR_FUNC": "{{MUTATE}}[dst_i] = floor(src[src_i]);", "CEIL_FUNC": "{{MUTATE}}[dst_i] = ceil(src[src_i]);", - "ROUND_FUNC": "{ let src_f32 = f32(src[src_i]); let result = select(ceil(src_f32 - 0.5), floor(src_f32 + 0.5), src_f32 >= 0.0); {{MUTATE}}[dst_i] = {{TYPE}}(result); } // Round half away from zero to match C roundf() behavior, not WGSL round() which uses round-to-even" + "ROUND_FUNC": "{ let src_f32 = f32(src[src_i]); let result = select(ceil(src_f32 - 0.5), floor(src_f32 + 0.5), src_f32 >= 0.0); {{MUTATE}}[dst_i] = {{TYPE}}(result); } // Round half away from zero to match C roundf() behavior, not WGSL round() which uses round-to-even", + "TRUNC_FUNC": "{{MUTATE}}[dst_i] = trunc(src[src_i]);" } #end(REPL_TEMPLATES) @@ -463,6 +464,26 @@ "REPLS": { "TYPE": "f16", "FUNC": "ROUND_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, "DECLS": ["INPLACE"] }, + { + "SHADER_NAME": "trunc_f32", + "REPLS": { "TYPE": "f32", "FUNC": "TRUNC_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "trunc_f16", + "REPLS": { "TYPE": "f16", "FUNC": "TRUNC_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "trunc_inplace_f32", + "REPLS": { "TYPE": "f32", "FUNC": "TRUNC_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + { + "SHADER_NAME": "trunc_inplace_f16", + "REPLS": { "TYPE": "f16", "FUNC": "TRUNC_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, ] #end(VARIANTS) From c4c4f7796f18c1e4ecd5521acc2aea165ac2a800 Mon Sep 17 00:00:00 2001 From: Abhijit Ramesh Date: Thu, 25 Dec 2025 14:38:02 +0530 Subject: [PATCH 10/21] docs : update WebGPU support for unary operators (FLOOR, CEIL, ROUND, TRUNC, EXPM1, SOFTPLUS) --- docs/ops.md | 13 +- docs/ops/WebGPU.csv | 778 ++++++++++++++++++++++---------------------- 2 files changed, 402 insertions(+), 389 deletions(-) diff --git a/docs/ops.md b/docs/ops.md index ef1febccae..8d466cd1f3 100644 --- a/docs/ops.md +++ b/docs/ops.md @@ -22,7 +22,7 @@ Legend: | ARANGE | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | | ARGMAX | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | | ARGSORT | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | -| CEIL | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | +| CEIL | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ | | CLAMP | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ | ❌ | | CONCAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ | | CONT | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ❌ | ❌ | @@ -37,15 +37,16 @@ Legend: | CROSS_ENTROPY_LOSS | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | | CROSS_ENTROPY_LOSS_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | | CUMSUM | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | +| DIAG | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | | DIAG_MASK_INF | ❌ | ✅ | ✅ | ✅ | ❌ | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ | | DIV | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ | | DUP | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ | | ELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | | EXP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ | -| EXPM1 | ❌ | ❌ | ✅ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | +| EXPM1 | ❌ | ❌ | ✅ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | | FILL | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | | FLASH_ATTN_EXT | ❌ | 🟡 | ✅ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | -| FLOOR | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | +| FLOOR | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ | | GATED_LINEAR_ATTN | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | | GEGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ | | GEGLU_ERF | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ | @@ -87,7 +88,7 @@ Legend: | ROLL | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | | ROPE | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | | ROPE_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | -| ROUND | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | +| ROUND | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ | | RWKV_WKV6 | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | | RWKV_WKV7 | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | | SCALE | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | @@ -99,7 +100,7 @@ Legend: | SILU_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | | SIN | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | | SOFTCAP | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | -| SOFTPLUS | ❌ | ❌ | ✅ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | +| SOFTPLUS | ❌ | ❌ | ✅ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ✅ | ❌ | ❌ | | SOFT_MAX | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | | SOFT_MAX_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ✅ | ❌ | ❌ | ❌ | | SOLVE_TRI | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | @@ -117,6 +118,6 @@ Legend: | TIMESTEP_EMBEDDING | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | | TOP_K | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | | TRI | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | -| TRUNC | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | +| TRUNC | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ | | UPSCALE | ❌ | 🟡 | ✅ | ✅ | 🟡 | ✅ | 🟡 | 🟡 | ❌ | ❌ | ❌ | | XIELU | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | diff --git a/docs/ops/WebGPU.csv b/docs/ops/WebGPU.csv index bfff75e66f..0c71ac7897 100644 --- a/docs/ops/WebGPU.csv +++ b/docs/ops/WebGPU.csv @@ -27,20 +27,20 @@ "WebGPU: WebGPU","HARDSIGMOID","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","WebGPU" "WebGPU: WebGPU","EXP","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","WebGPU" "WebGPU: WebGPU","EXP","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","EXPM1","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","WebGPU" -"WebGPU: WebGPU","EXPM1","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","WebGPU" -"WebGPU: WebGPU","SOFTPLUS","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","WebGPU" -"WebGPU: WebGPU","SOFTPLUS","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","WebGPU" +"WebGPU: WebGPU","EXPM1","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","EXPM1","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","SOFTPLUS","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","SOFTPLUS","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","WebGPU" "WebGPU: WebGPU","GELU_ERF","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","WebGPU" "WebGPU: WebGPU","GELU_ERF","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","FLOOR","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","WebGPU" -"WebGPU: WebGPU","FLOOR","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","WebGPU" -"WebGPU: WebGPU","CEIL","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","WebGPU" -"WebGPU: WebGPU","CEIL","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","WebGPU" -"WebGPU: WebGPU","ROUND","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","WebGPU" -"WebGPU: WebGPU","ROUND","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","WebGPU" -"WebGPU: WebGPU","TRUNC","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","WebGPU" -"WebGPU: WebGPU","TRUNC","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","WebGPU" +"WebGPU: WebGPU","FLOOR","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLOOR","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","CEIL","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","CEIL","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","ROUND","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","ROUND","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","TRUNC","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","TRUNC","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","WebGPU" "WebGPU: WebGPU","ABS","type=f16,ne_a=[128,2,2,2],v=1","support","1","yes","WebGPU" "WebGPU: WebGPU","ABS","type=f16,ne_a=[5,7,11,13],v=1","support","1","yes","WebGPU" "WebGPU: WebGPU","SGN","type=f16,ne_a=[128,2,2,2],v=1","support","1","yes","WebGPU" @@ -69,20 +69,20 @@ "WebGPU: WebGPU","HARDSIGMOID","type=f16,ne_a=[5,7,11,13],v=1","support","1","yes","WebGPU" "WebGPU: WebGPU","EXP","type=f16,ne_a=[128,2,2,2],v=1","support","1","yes","WebGPU" "WebGPU: WebGPU","EXP","type=f16,ne_a=[5,7,11,13],v=1","support","1","yes","WebGPU" -"WebGPU: WebGPU","EXPM1","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","WebGPU" -"WebGPU: WebGPU","EXPM1","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","WebGPU" -"WebGPU: WebGPU","SOFTPLUS","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","WebGPU" -"WebGPU: WebGPU","SOFTPLUS","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","WebGPU" +"WebGPU: WebGPU","EXPM1","type=f16,ne_a=[128,2,2,2],v=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","EXPM1","type=f16,ne_a=[5,7,11,13],v=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","SOFTPLUS","type=f16,ne_a=[128,2,2,2],v=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","SOFTPLUS","type=f16,ne_a=[5,7,11,13],v=1","support","1","yes","WebGPU" "WebGPU: WebGPU","GELU_ERF","type=f16,ne_a=[128,2,2,2],v=1","support","1","yes","WebGPU" "WebGPU: WebGPU","GELU_ERF","type=f16,ne_a=[5,7,11,13],v=1","support","1","yes","WebGPU" -"WebGPU: WebGPU","FLOOR","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","WebGPU" -"WebGPU: WebGPU","FLOOR","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","WebGPU" -"WebGPU: WebGPU","CEIL","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","WebGPU" -"WebGPU: WebGPU","CEIL","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","WebGPU" -"WebGPU: WebGPU","ROUND","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","WebGPU" -"WebGPU: WebGPU","ROUND","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","WebGPU" -"WebGPU: WebGPU","TRUNC","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","WebGPU" -"WebGPU: WebGPU","TRUNC","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","WebGPU" +"WebGPU: WebGPU","FLOOR","type=f16,ne_a=[128,2,2,2],v=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLOOR","type=f16,ne_a=[5,7,11,13],v=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","CEIL","type=f16,ne_a=[128,2,2,2],v=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","CEIL","type=f16,ne_a=[5,7,11,13],v=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","ROUND","type=f16,ne_a=[128,2,2,2],v=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","ROUND","type=f16,ne_a=[5,7,11,13],v=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","TRUNC","type=f16,ne_a=[128,2,2,2],v=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","TRUNC","type=f16,ne_a=[5,7,11,13],v=1","support","1","yes","WebGPU" "WebGPU: WebGPU","ABS","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","WebGPU" "WebGPU: WebGPU","ABS","type=f32,ne_a=[5,7,11,13],v=0","support","1","yes","WebGPU" "WebGPU: WebGPU","SGN","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","WebGPU" @@ -111,20 +111,20 @@ "WebGPU: WebGPU","HARDSIGMOID","type=f32,ne_a=[5,7,11,13],v=0","support","1","yes","WebGPU" "WebGPU: WebGPU","EXP","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","WebGPU" "WebGPU: WebGPU","EXP","type=f32,ne_a=[5,7,11,13],v=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","EXPM1","type=f32,ne_a=[128,2,2,2],v=0","support","0","no","WebGPU" -"WebGPU: WebGPU","EXPM1","type=f32,ne_a=[5,7,11,13],v=0","support","0","no","WebGPU" -"WebGPU: WebGPU","SOFTPLUS","type=f32,ne_a=[128,2,2,2],v=0","support","0","no","WebGPU" -"WebGPU: WebGPU","SOFTPLUS","type=f32,ne_a=[5,7,11,13],v=0","support","0","no","WebGPU" +"WebGPU: WebGPU","EXPM1","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","EXPM1","type=f32,ne_a=[5,7,11,13],v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","SOFTPLUS","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","SOFTPLUS","type=f32,ne_a=[5,7,11,13],v=0","support","1","yes","WebGPU" "WebGPU: WebGPU","GELU_ERF","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","WebGPU" "WebGPU: WebGPU","GELU_ERF","type=f32,ne_a=[5,7,11,13],v=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","FLOOR","type=f32,ne_a=[128,2,2,2],v=0","support","0","no","WebGPU" -"WebGPU: WebGPU","FLOOR","type=f32,ne_a=[5,7,11,13],v=0","support","0","no","WebGPU" -"WebGPU: WebGPU","CEIL","type=f32,ne_a=[128,2,2,2],v=0","support","0","no","WebGPU" -"WebGPU: WebGPU","CEIL","type=f32,ne_a=[5,7,11,13],v=0","support","0","no","WebGPU" -"WebGPU: WebGPU","ROUND","type=f32,ne_a=[128,2,2,2],v=0","support","0","no","WebGPU" -"WebGPU: WebGPU","ROUND","type=f32,ne_a=[5,7,11,13],v=0","support","0","no","WebGPU" -"WebGPU: WebGPU","TRUNC","type=f32,ne_a=[128,2,2,2],v=0","support","0","no","WebGPU" -"WebGPU: WebGPU","TRUNC","type=f32,ne_a=[5,7,11,13],v=0","support","0","no","WebGPU" +"WebGPU: WebGPU","FLOOR","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLOOR","type=f32,ne_a=[5,7,11,13],v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","CEIL","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","CEIL","type=f32,ne_a=[5,7,11,13],v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","ROUND","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","ROUND","type=f32,ne_a=[5,7,11,13],v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","TRUNC","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","TRUNC","type=f32,ne_a=[5,7,11,13],v=0","support","1","yes","WebGPU" "WebGPU: WebGPU","ABS","type=f32,ne_a=[128,2,2,2],v=1","support","1","yes","WebGPU" "WebGPU: WebGPU","ABS","type=f32,ne_a=[5,7,11,13],v=1","support","1","yes","WebGPU" "WebGPU: WebGPU","SGN","type=f32,ne_a=[128,2,2,2],v=1","support","1","yes","WebGPU" @@ -153,20 +153,20 @@ "WebGPU: WebGPU","HARDSIGMOID","type=f32,ne_a=[5,7,11,13],v=1","support","1","yes","WebGPU" "WebGPU: WebGPU","EXP","type=f32,ne_a=[128,2,2,2],v=1","support","1","yes","WebGPU" "WebGPU: WebGPU","EXP","type=f32,ne_a=[5,7,11,13],v=1","support","1","yes","WebGPU" -"WebGPU: WebGPU","EXPM1","type=f32,ne_a=[128,2,2,2],v=1","support","0","no","WebGPU" -"WebGPU: WebGPU","EXPM1","type=f32,ne_a=[5,7,11,13],v=1","support","0","no","WebGPU" -"WebGPU: WebGPU","SOFTPLUS","type=f32,ne_a=[128,2,2,2],v=1","support","0","no","WebGPU" -"WebGPU: WebGPU","SOFTPLUS","type=f32,ne_a=[5,7,11,13],v=1","support","0","no","WebGPU" +"WebGPU: WebGPU","EXPM1","type=f32,ne_a=[128,2,2,2],v=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","EXPM1","type=f32,ne_a=[5,7,11,13],v=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","SOFTPLUS","type=f32,ne_a=[128,2,2,2],v=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","SOFTPLUS","type=f32,ne_a=[5,7,11,13],v=1","support","1","yes","WebGPU" "WebGPU: WebGPU","GELU_ERF","type=f32,ne_a=[128,2,2,2],v=1","support","1","yes","WebGPU" "WebGPU: WebGPU","GELU_ERF","type=f32,ne_a=[5,7,11,13],v=1","support","1","yes","WebGPU" -"WebGPU: WebGPU","FLOOR","type=f32,ne_a=[128,2,2,2],v=1","support","0","no","WebGPU" -"WebGPU: WebGPU","FLOOR","type=f32,ne_a=[5,7,11,13],v=1","support","0","no","WebGPU" -"WebGPU: WebGPU","CEIL","type=f32,ne_a=[128,2,2,2],v=1","support","0","no","WebGPU" -"WebGPU: WebGPU","CEIL","type=f32,ne_a=[5,7,11,13],v=1","support","0","no","WebGPU" -"WebGPU: WebGPU","ROUND","type=f32,ne_a=[128,2,2,2],v=1","support","0","no","WebGPU" -"WebGPU: WebGPU","ROUND","type=f32,ne_a=[5,7,11,13],v=1","support","0","no","WebGPU" -"WebGPU: WebGPU","TRUNC","type=f32,ne_a=[128,2,2,2],v=1","support","0","no","WebGPU" -"WebGPU: WebGPU","TRUNC","type=f32,ne_a=[5,7,11,13],v=1","support","0","no","WebGPU" +"WebGPU: WebGPU","FLOOR","type=f32,ne_a=[128,2,2,2],v=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLOOR","type=f32,ne_a=[5,7,11,13],v=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","CEIL","type=f32,ne_a=[128,2,2,2],v=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","CEIL","type=f32,ne_a=[5,7,11,13],v=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","ROUND","type=f32,ne_a=[128,2,2,2],v=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","ROUND","type=f32,ne_a=[5,7,11,13],v=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","TRUNC","type=f32,ne_a=[128,2,2,2],v=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","TRUNC","type=f32,ne_a=[5,7,11,13],v=1","support","1","yes","WebGPU" "WebGPU: WebGPU","REGLU","type=f16,ne_a=[128,2,2,2],v=0,swapped=0","support","1","yes","WebGPU" "WebGPU: WebGPU","REGLU","type=f16,ne_a=[5,7,11,13],v=0,swapped=0","support","1","yes","WebGPU" "WebGPU: WebGPU","REGLU","type=f16,ne_a=[128,2,2,2],v=0,swapped=1","support","1","yes","WebGPU" @@ -4964,6 +4964,7 @@ "WebGPU: WebGPU","CONV_TRANSPOSE_1D","ne_input=[2,1,1,1],ne_kernel=[3,1,1,1],s0=1,p0=0,d0=1","support","0","no","WebGPU" "WebGPU: WebGPU","CONV_TRANSPOSE_2D","ne_input=[3,2,3,1],ne_kernel=[2,2,1,3],stride=1","support","0","no","WebGPU" "WebGPU: WebGPU","CONV_TRANSPOSE_2D","ne_input=[10,10,9,1],ne_kernel=[3,3,1,9],stride=2","support","0","no","WebGPU" +"WebGPU: WebGPU","CONV_TRANSPOSE_2D","ne_input=[129,63,35,1],ne_kernel=[3,3,48,35],stride=1","support","0","no","WebGPU" "WebGPU: WebGPU","COUNT_EQUAL","type=f32,ne=[4,500,1,1]","support","0","no","WebGPU" "WebGPU: WebGPU","COUNT_EQUAL","type=f32,ne=[4,5000,1,1]","support","0","no","WebGPU" "WebGPU: WebGPU","ARGMAX","type=f32,ne=[32,1,1,1]","support","0","no","WebGPU" @@ -8661,10 +8662,10 @@ "WebGPU: WebGPU","COS","type=f16,ne=[10,2,2,2]","support","0","no","WebGPU" "WebGPU: WebGPU","CLAMP","type=f16,ne=[10,5,4,3],min=-0.500000,max=0.500000","support","0","no","WebGPU" "WebGPU: WebGPU","LEAKY_RELU","type=f16,ne_a=[10,5,4,3],negative_slope=0.100000","support","0","no","WebGPU" -"WebGPU: WebGPU","FLOOR","type=f16,ne=[10,2,2,2]","support","0","no","WebGPU" -"WebGPU: WebGPU","CEIL","type=f16,ne=[10,2,2,2]","support","0","no","WebGPU" -"WebGPU: WebGPU","ROUND","type=f16,ne=[10,2,2,2]","support","0","no","WebGPU" -"WebGPU: WebGPU","TRUNC","type=f16,ne=[10,2,2,2]","support","0","no","WebGPU" +"WebGPU: WebGPU","FLOOR","type=f16,ne=[10,2,2,2]","support","1","yes","WebGPU" +"WebGPU: WebGPU","CEIL","type=f16,ne=[10,2,2,2]","support","1","yes","WebGPU" +"WebGPU: WebGPU","ROUND","type=f16,ne=[10,2,2,2]","support","1","yes","WebGPU" +"WebGPU: WebGPU","TRUNC","type=f16,ne=[10,2,2,2]","support","1","yes","WebGPU" "WebGPU: WebGPU","SQR","type=f16,ne=[7,1,5,3]","support","0","no","WebGPU" "WebGPU: WebGPU","SQRT","type=f16,ne=[7,1,5,3]","support","0","no","WebGPU" "WebGPU: WebGPU","LOG","type=f16,ne=[7,1,5,3]","support","0","no","WebGPU" @@ -8672,14 +8673,14 @@ "WebGPU: WebGPU","COS","type=f16,ne=[7,1,5,3]","support","0","no","WebGPU" "WebGPU: WebGPU","CLAMP","type=f16,ne=[7,1,5,3],min=-0.500000,max=0.500000","support","0","no","WebGPU" "WebGPU: WebGPU","LEAKY_RELU","type=f16,ne_a=[7,1,5,3],negative_slope=0.100000","support","0","no","WebGPU" -"WebGPU: WebGPU","FLOOR","type=f16,ne=[7,1,5,3]","support","0","no","WebGPU" -"WebGPU: WebGPU","FLOOR","type=f16,ne=[1024,1024,1,1]","support","0","no","WebGPU" -"WebGPU: WebGPU","CEIL","type=f16,ne=[7,1,5,3]","support","0","no","WebGPU" -"WebGPU: WebGPU","CEIL","type=f16,ne=[1024,1024,1,1]","support","0","no","WebGPU" -"WebGPU: WebGPU","ROUND","type=f16,ne=[7,1,5,3]","support","0","no","WebGPU" -"WebGPU: WebGPU","ROUND","type=f16,ne=[1024,1024,1,1]","support","0","no","WebGPU" -"WebGPU: WebGPU","TRUNC","type=f16,ne=[7,1,5,3]","support","0","no","WebGPU" -"WebGPU: WebGPU","TRUNC","type=f16,ne=[1024,1024,1,1]","support","0","no","WebGPU" +"WebGPU: WebGPU","FLOOR","type=f16,ne=[7,1,5,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","FLOOR","type=f16,ne=[1024,1024,1,1]","support","1","yes","WebGPU" +"WebGPU: WebGPU","CEIL","type=f16,ne=[7,1,5,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","CEIL","type=f16,ne=[1024,1024,1,1]","support","1","yes","WebGPU" +"WebGPU: WebGPU","ROUND","type=f16,ne=[7,1,5,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","ROUND","type=f16,ne=[1024,1024,1,1]","support","1","yes","WebGPU" +"WebGPU: WebGPU","TRUNC","type=f16,ne=[7,1,5,3]","support","1","yes","WebGPU" +"WebGPU: WebGPU","TRUNC","type=f16,ne=[1024,1024,1,1]","support","1","yes","WebGPU" "WebGPU: WebGPU","SQR","type=f32,ne=[10,5,4,3]","support","0","no","WebGPU" "WebGPU: WebGPU","SQRT","type=f32,ne=[10,3,3,2]","support","0","no","WebGPU" "WebGPU: WebGPU","LOG","type=f32,ne=[10,5,4,3]","support","0","no","WebGPU" @@ -8687,10 +8688,10 @@ "WebGPU: WebGPU","COS","type=f32,ne=[10,2,2,2]","support","0","no","WebGPU" "WebGPU: WebGPU","CLAMP","type=f32,ne=[10,5,4,3],min=-0.500000,max=0.500000","support","0","no","WebGPU" "WebGPU: WebGPU","LEAKY_RELU","type=f32,ne_a=[10,5,4,3],negative_slope=0.100000","support","0","no","WebGPU" -"WebGPU: WebGPU","FLOOR","type=f32,ne=[10,2,2,2]","support","0","no","WebGPU" -"WebGPU: WebGPU","CEIL","type=f32,ne=[10,2,2,2]","support","0","no","WebGPU" -"WebGPU: WebGPU","ROUND","type=f32,ne=[10,2,2,2]","support","0","no","WebGPU" -"WebGPU: WebGPU","TRUNC","type=f32,ne=[10,2,2,2]","support","0","no","WebGPU" +"WebGPU: WebGPU","FLOOR","type=f32,ne=[10,2,2,2]","support","1","yes","WebGPU" +"WebGPU: WebGPU","CEIL","type=f32,ne=[10,2,2,2]","support","1","yes","WebGPU" +"WebGPU: WebGPU","ROUND","type=f32,ne=[10,2,2,2]","support","1","yes","WebGPU" +"WebGPU: WebGPU","TRUNC","type=f32,ne=[10,2,2,2]","support","1","yes","WebGPU" "WebGPU: WebGPU","SQR","type=f32,ne=[7,1,5,3]","support","0","no","WebGPU" "WebGPU: WebGPU","SQRT","type=f32,ne=[7,1,5,3]","support","0","no","WebGPU" "WebGPU: WebGPU","LOG","type=f32,ne=[7,1,5,3]","support","0","no","WebGPU" @@ -8698,14 +8699,14 @@ "WebGPU: WebGPU","COS","type=f32,ne=[7,1,5,3]","support","0","no","WebGPU" "WebGPU: WebGPU","CLAMP","type=f32,ne=[7,1,5,3],min=-0.500000,max=0.500000","support","0","no","WebGPU" "WebGPU: 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WebGPU","CUMSUM","type=f32,ne=[128,5,4,3]","support","0","no","WebGPU" +"WebGPU: WebGPU","CUMSUM","type=f32,ne=[128,128,4,4]","support","0","no","WebGPU" "WebGPU: WebGPU","CUMSUM","type=f32,ne=[255,5,4,3]","support","0","no","WebGPU" "WebGPU: WebGPU","CUMSUM","type=f32,ne=[256,5,4,3]","support","0","no","WebGPU" "WebGPU: WebGPU","CUMSUM","type=f32,ne=[511,5,4,3]","support","0","no","WebGPU" @@ -9922,6 +9925,9 @@ "WebGPU: WebGPU","FILL","type=f32,ne=[303,207,11,3],c=2.000000","support","0","no","WebGPU" "WebGPU: WebGPU","FILL","type=f32,ne=[800,600,4,4],c=-152.000000","support","0","no","WebGPU" "WebGPU: WebGPU","FILL","type=f32,ne=[2048,512,2,2],c=3.500000","support","0","no","WebGPU" +"WebGPU: WebGPU","DIAG","type=f32,ne=[10,1,4,3]","support","0","no","WebGPU" +"WebGPU: WebGPU","DIAG","type=f32,ne=[79,1,19,13]","support","0","no","WebGPU" +"WebGPU: WebGPU","DIAG","type=f32,ne=[256,1,8,16]","support","0","no","WebGPU" "WebGPU: WebGPU","SOLVE_TRI","type=f32,ne_lhs=[10,10,4,3],ne_rhs=[3,10,4,3]","support","0","no","WebGPU" "WebGPU: WebGPU","SOLVE_TRI","type=f32,ne_lhs=[11,11,1,1],ne_rhs=[5,11,1,1]","support","0","no","WebGPU" "WebGPU: WebGPU","SOLVE_TRI","type=f32,ne_lhs=[17,17,2,4],ne_rhs=[9,17,2,4]","support","0","no","WebGPU" @@ -9929,10 +9935,16 @@ "WebGPU: WebGPU","SOLVE_TRI","type=f32,ne_lhs=[42,42,5,2],ne_rhs=[10,42,5,2]","support","0","no","WebGPU" "WebGPU: WebGPU","SOLVE_TRI","type=f32,ne_lhs=[64,64,2,2],ne_rhs=[10,64,2,2]","support","0","no","WebGPU" "WebGPU: WebGPU","SOLVE_TRI","type=f32,ne_lhs=[100,100,4,4],ne_rhs=[41,100,4,4]","support","0","no","WebGPU" -"WebGPU: WebGPU","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=0","support","0","no","WebGPU" -"WebGPU: WebGPU","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=0","support","0","no","WebGPU" -"WebGPU: WebGPU","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=1","support","0","no","WebGPU" -"WebGPU: WebGPU","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=1","support","0","no","WebGPU" +"WebGPU: WebGPU","SOLVE_TRI","type=f32,ne_lhs=[128,128,4,4],ne_rhs=[31,128,4,4]","support","0","no","WebGPU" +"WebGPU: WebGPU","SOLVE_TRI","type=f32,ne_lhs=[64,64,4,4],ne_rhs=[300,64,4,4]","support","0","no","WebGPU" +"WebGPU: WebGPU","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=0,circular=0","support","0","no","WebGPU" +"WebGPU: WebGPU","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=0,circular=0","support","0","no","WebGPU" +"WebGPU: WebGPU","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=0,circular=1","support","0","no","WebGPU" +"WebGPU: WebGPU","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=0,circular=1","support","0","no","WebGPU" +"WebGPU: WebGPU","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=1,circular=0","support","0","no","WebGPU" +"WebGPU: WebGPU","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=1,circular=0","support","0","no","WebGPU" +"WebGPU: WebGPU","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=1,circular=1","support","0","no","WebGPU" +"WebGPU: WebGPU","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=1,circular=1","support","0","no","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","0","no","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","WebGPU" "WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","WebGPU" From 0ba2cc15f3175ffcc89c316671904ba8e475633b Mon Sep 17 00:00:00 2001 From: Reese Levine Date: Thu, 8 Jan 2026 10:48:06 -0800 Subject: [PATCH 11/21] Updates to webgpu get_memory --- ggml/src/ggml-webgpu/ggml-webgpu.cpp | 16 +++++++++++++--- 1 file changed, 13 insertions(+), 3 deletions(-) diff --git a/ggml/src/ggml-webgpu/ggml-webgpu.cpp b/ggml/src/ggml-webgpu/ggml-webgpu.cpp index f64f94b96f..5b8f7f72d5 100644 --- a/ggml/src/ggml-webgpu/ggml-webgpu.cpp +++ b/ggml/src/ggml-webgpu/ggml-webgpu.cpp @@ -19,6 +19,7 @@ #include #include +#include #include #include #include @@ -1880,9 +1881,18 @@ static const char * ggml_backend_webgpu_device_get_description(ggml_backend_dev_ static void ggml_backend_webgpu_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) { ggml_backend_webgpu_device_context * ctx = static_cast(dev->context); - // TODO: what do we actually want to return here? maxBufferSize might not be the full available memory. - *free = ctx->webgpu_ctx->limits.maxBufferSize; - *total = ctx->webgpu_ctx->limits.maxBufferSize; + // TODO: for now, return maxBufferSize as both free and total memory + // Track https://github.com/gpuweb/gpuweb/issues/5505 for updates. + uint64_t max_buffer_size = ctx->webgpu_ctx->limits.maxBufferSize; + // If we're on a 32-bit system, clamp to UINTPTR_MAX +#if UINTPTR_MAX < UINT64_MAX + uint64_t max_ptr_size = static_cast(UINTPTR_MAX); + if (max_buffer_size > max_ptr_size) { + max_buffer_size = max_ptr_size; + } +#endif + *free = static_cast(max_buffer_size); + *total = static_cast(max_buffer_size); } static enum ggml_backend_dev_type ggml_backend_webgpu_device_get_type(ggml_backend_dev_t dev) { From 08f7937c34fc16e8a257b2abd777155b7e641795 Mon Sep 17 00:00:00 2001 From: Nikhil Jain Date: Mon, 12 Jan 2026 17:03:40 -0800 Subject: [PATCH 12/21] Move shared state (webgpu_context) and device creation out of registration context, device context, and buffer context, and move into backend context --- ggml/src/ggml-webgpu/ggml-webgpu.cpp | 514 +++++++++++++++------------ 1 file changed, 283 insertions(+), 231 deletions(-) diff --git a/ggml/src/ggml-webgpu/ggml-webgpu.cpp b/ggml/src/ggml-webgpu/ggml-webgpu.cpp index 5b8f7f72d5..c577954e8c 100644 --- a/ggml/src/ggml-webgpu/ggml-webgpu.cpp +++ b/ggml/src/ggml-webgpu/ggml-webgpu.cpp @@ -309,7 +309,8 @@ struct flash_attn_pipeline_key_hash { // All the base objects needed to run operations on a WebGPU device struct webgpu_context_struct { - wgpu::Instance instance; + // Points to global instance owned by ggml_backend_webgpu_reg_context + wgpu::Instance * instance; wgpu::Adapter adapter; wgpu::Device device; wgpu::Queue queue; @@ -381,34 +382,46 @@ struct webgpu_context_struct { typedef std::shared_ptr webgpu_context; +// Metadata required for the ggml backend registration/discovery interface struct ggml_backend_webgpu_reg_context { - webgpu_context webgpu_ctx; + // Since the Instance is a global entrypoint into the WebGPU api, it lives here + std::unique_ptr instance; size_t device_count; const char * name; }; +// Per-device struct struct ggml_backend_webgpu_device_context { - webgpu_context webgpu_ctx; + ggml_backend_webgpu_reg_context * reg_context; std::string device_name; std::string device_desc; }; +// Per-thread data required to actually run webgpu operations in a backend instance struct ggml_backend_webgpu_context { + ggml_backend_webgpu_device_context * device_context; + std::once_flag init_once; webgpu_context webgpu_ctx; std::string name; }; +// Per-thread data related to buffers struct ggml_backend_webgpu_buffer_context { - webgpu_context webgpu_ctx; - wgpu::Buffer buffer; - std::string label; - - ggml_backend_webgpu_buffer_context(webgpu_context ctx, wgpu::Buffer buf, std::string lbl) : - webgpu_ctx(std::move(ctx)), - buffer(std::move(buf)), - label(std::move(lbl)) {} + ggml_backend_webgpu_context * backend_ctx; + wgpu::Buffer buffer; + std::string label; + + ggml_backend_webgpu_buffer_context(ggml_backend_webgpu_context * bctx, + wgpu::Buffer buf, + std::string lbl) + : backend_ctx(bctx), + buffer(std::move(buf)), + label(std::move(lbl)) {} }; +// Forward declaration +static ggml_backend_webgpu_context * ggml_backend_webgpu_get_thread_backend_ctx(ggml_backend_dev_t dev); + /* WebGPU object initializations */ // Process a WGSL shader string, replacing tokens of the form {{KEY}} with @@ -474,7 +487,7 @@ static void ggml_webgpu_create_buffer(wgpu::Device & device, /** WebGPU Actions */ // Wait for the queue to finish processing all submitted work -static void ggml_backend_webgpu_wait(webgpu_context & ctx, +static void ggml_backend_webgpu_wait(webgpu_context & ctx, std::vector & futures, bool block = true) { // If we have too many in-flight submissions, wait on the oldest one first. If there are many threads, @@ -483,12 +496,12 @@ static void ggml_backend_webgpu_wait(webgpu_context & ct uint32_t inflight_threads = ctx->inflight_threads; uint32_t inflight_max = WEBGPU_MAX_INFLIGHT_SUBS_PER_THREAD / std::max(inflight_threads, 1u); while (futures.size() >= inflight_max && futures.size() > 0) { - ctx->instance.WaitAny(futures[0].futures.size(), futures[0].futures.data(), UINT64_MAX); + ctx->instance->WaitAny(futures[0].futures.size(), futures[0].futures.data(), UINT64_MAX); futures.erase(futures.begin()); } size_t i = 0; while (i < futures.size()) { - auto waitStatus = ctx->instance.WaitAny(futures[i].futures.size(), futures[i].futures.data(), timeout_ms); + auto waitStatus = ctx->instance->WaitAny(futures[i].futures.size(), futures[i].futures.data(), timeout_ms); switch (waitStatus) { case wgpu::WaitStatus::Success: futures.erase(futures.begin() + i); @@ -511,7 +524,7 @@ static void ggml_backend_webgpu_map_buffer(webgpu_context & ctx, wgpu::MapMode mode, size_t offset, size_t size) { - ctx->instance.WaitAny(buffer.MapAsync(mode, offset, size, wgpu::CallbackMode::AllowSpontaneous, + ctx->instance->WaitAny(buffer.MapAsync(mode, offset, size, wgpu::CallbackMode::AllowSpontaneous, [](wgpu::MapAsyncStatus status, wgpu::StringView message) { if (status != wgpu::MapAsyncStatus::Success) { GGML_LOG_ERROR("ggml_webgpu: Failed to map buffer: %s\n", @@ -1639,7 +1652,7 @@ static ggml_status ggml_backend_webgpu_graph_compute(ggml_backend_t backend, str if (commands.size() >= batch_size) { futures.push_back(ggml_backend_webgpu_submit(ctx, commands)); // Process events and check for completed submissions - ctx->instance.ProcessEvents(); + ctx->instance->ProcessEvents(); ggml_backend_webgpu_wait(ctx, futures, false); commands.clear(); } @@ -1708,8 +1721,8 @@ static void ggml_backend_webgpu_buffer_memset_tensor(ggml_backend_buffer_t buffe // This is a trick to set all bytes of a u32 to the same 1 byte value. uint32_t val32 = (uint32_t) value * 0x01010101; - ggml_backend_webgpu_buffer_memset(buf_ctx->webgpu_ctx, buf_ctx->buffer, val32, total_offset, size); - WEBGPU_CPU_PROFILE_TOTAL_END(memset_tensor, buf_ctx->webgpu_ctx); + ggml_backend_webgpu_buffer_memset(buf_ctx->backend_ctx->webgpu_ctx, buf_ctx->buffer, val32, total_offset, size); + WEBGPU_CPU_PROFILE_TOTAL_END(memset_tensor, buf_ctx->backend_ctx->webgpu_ctx); } static void ggml_backend_webgpu_buffer_set_tensor(ggml_backend_buffer_t buffer, @@ -1719,7 +1732,7 @@ static void ggml_backend_webgpu_buffer_set_tensor(ggml_backend_buffer_t buffer, size_t size) { WEBGPU_CPU_PROFILE_TOTAL_START(set_tensor); ggml_backend_webgpu_buffer_context * buf_ctx = (ggml_backend_webgpu_buffer_context *) buffer->context; - webgpu_context webgpu_ctx = buf_ctx->webgpu_ctx; + webgpu_context webgpu_ctx = buf_ctx->backend_ctx->webgpu_ctx; WEBGPU_LOG_DEBUG("ggml_backend_webgpu_buffer_set_tensor(" << buf_ctx->label << ", " << tensor << ", " << data << ", " << offset << ", " << size << ")"); @@ -1743,7 +1756,7 @@ static void ggml_backend_webgpu_buffer_set_tensor(ggml_backend_buffer_t buffer, remaining_size); } else { // wait for WriteBuffer to complete - webgpu_ctx->instance.WaitAny( + webgpu_ctx->instance->WaitAny( webgpu_ctx->queue.OnSubmittedWorkDone(wgpu::CallbackMode::AllowSpontaneous, [](wgpu::QueueWorkDoneStatus status, wgpu::StringView message) { if (status != wgpu::QueueWorkDoneStatus::Success) { @@ -1765,7 +1778,7 @@ static void ggml_backend_webgpu_buffer_get_tensor(ggml_backend_buffer_t buffer, ggml_backend_webgpu_buffer_context * buf_ctx = (ggml_backend_webgpu_buffer_context *) buffer->context; WEBGPU_LOG_DEBUG("ggml_backend_webgpu_buffer_get_tensor(" << buf_ctx->label << ", " << tensor << ", " << data << ", " << offset << ", " << size << ")"); - webgpu_context webgpu_ctx = buf_ctx->webgpu_ctx; + webgpu_context webgpu_ctx = buf_ctx->backend_ctx->webgpu_ctx; wgpu::Device device = webgpu_ctx->device; size_t total_offset = webgpu_tensor_offset(tensor) + tensor->view_offs + offset; @@ -1810,8 +1823,8 @@ static void ggml_backend_webgpu_buffer_clear(ggml_backend_buffer_t buffer, uint8 WEBGPU_LOG_DEBUG("ggml_backend_webgpu_buffer_clear(" << buffer << ", " << (uint32_t) value << ")"); WEBGPU_CPU_PROFILE_TOTAL_START(clear); ggml_backend_webgpu_buffer_context * buf_ctx = (ggml_backend_webgpu_buffer_context *) buffer->context; - ggml_backend_webgpu_buffer_memset(buf_ctx->webgpu_ctx, buf_ctx->buffer, value, 0, buffer->size); - WEBGPU_CPU_PROFILE_TOTAL_END(clear, buf_ctx->webgpu_ctx); + ggml_backend_webgpu_buffer_memset(buf_ctx->backend_ctx->webgpu_ctx, buf_ctx->buffer, value, 0, buffer->size); + WEBGPU_CPU_PROFILE_TOTAL_END(clear, buf_ctx->backend_ctx->webgpu_ctx); } static ggml_backend_buffer_i ggml_backend_webgpu_buffer_interface = { @@ -1841,7 +1854,8 @@ static ggml_backend_buffer_t ggml_backend_webgpu_buffer_type_alloc_buffer(ggml_b int buffer_id = buffer_count++; std::string buf_name = "tensor_buf" + std::to_string(buffer_id); WEBGPU_LOG_DEBUG("ggml_backend_webgpu_buffer_type_alloc_buffer_" << buffer_id << ": " << size << " bytes"); - ggml_backend_webgpu_device_context * ctx = static_cast(buft->device->context); + + ggml_backend_webgpu_context * ctx = static_cast(buft->context); wgpu::Buffer buf; ggml_webgpu_create_buffer(ctx->webgpu_ctx->device, buf, ROUNDUP_POW2(size, WEBGPU_STORAGE_BUF_BINDING_MULT), @@ -1849,19 +1863,19 @@ static ggml_backend_buffer_t ggml_backend_webgpu_buffer_type_alloc_buffer(ggml_b buf_name.c_str()); ggml_backend_webgpu_buffer_context * buf_ctx = - new ggml_backend_webgpu_buffer_context(ctx->webgpu_ctx, buf, buf_name); + new ggml_backend_webgpu_buffer_context(ctx, buf, buf_name); return ggml_backend_buffer_init(buft, ggml_backend_webgpu_buffer_interface, buf_ctx, size); } static size_t ggml_backend_webgpu_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { - ggml_backend_webgpu_device_context * ctx = static_cast(buft->device->context); + ggml_backend_webgpu_context * ctx = static_cast(buft->context); return ctx->webgpu_ctx->limits.minStorageBufferOffsetAlignment; } // maxBufferSize might be larger, but you can't bind more than maxStorageBufferBindingSize to a single binding. static size_t ggml_backend_webgpu_buffer_type_get_max_size(ggml_backend_buffer_type_t buft) { - ggml_backend_webgpu_device_context * ctx = static_cast(buft->device->context); + ggml_backend_webgpu_context * ctx = static_cast(buft->context); return ctx->webgpu_ctx->limits.maxStorageBufferBindingSize; } @@ -1880,10 +1894,11 @@ static const char * ggml_backend_webgpu_device_get_description(ggml_backend_dev_ } static void ggml_backend_webgpu_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) { - ggml_backend_webgpu_device_context * ctx = static_cast(dev->context); + ggml_backend_webgpu_context * bctx = ggml_backend_webgpu_get_thread_backend_ctx(dev); + // TODO: for now, return maxBufferSize as both free and total memory // Track https://github.com/gpuweb/gpuweb/issues/5505 for updates. - uint64_t max_buffer_size = ctx->webgpu_ctx->limits.maxBufferSize; + uint64_t max_buffer_size = bctx->webgpu_ctx->limits.maxBufferSize; // If we're on a 32-bit system, clamp to UINTPTR_MAX #if UINTPTR_MAX < UINT64_MAX uint64_t max_ptr_size = static_cast(UINTPTR_MAX); @@ -2520,33 +2535,249 @@ static void ggml_webgpu_init_soft_max_pipeline(webgpu_context & webgpu_ctx) { webgpu_ctx->device, wgsl_soft_max_f32_mask_f16_sink_inplace, "soft_max_f32_mask_f16_sink_inplace", constants); } -// TODO: move most initialization logic here + +static bool create_webgpu_device(webgpu_context ctx) { + + wgpu::RequestAdapterOptions options = {}; + +#ifndef __EMSCRIPTEN__ + // TODO: track need for these toggles: https://issues.chromium.org/issues/42251215 + const char * const adapterEnabledToggles[] = { "vulkan_enable_f16_on_nvidia", "use_vulkan_memory_model" }; + wgpu::DawnTogglesDescriptor adapterTogglesDesc; + adapterTogglesDesc.enabledToggles = adapterEnabledToggles; + adapterTogglesDesc.enabledToggleCount = 2; + options.nextInChain = &adapterTogglesDesc; +#endif + + ctx->instance->WaitAny(ctx->instance->RequestAdapter( + &options, wgpu::CallbackMode::AllowSpontaneous, + [&ctx](wgpu::RequestAdapterStatus status, wgpu::Adapter adapter, const char * message) { + if (status != wgpu::RequestAdapterStatus::Success) { + GGML_LOG_ERROR("ggml_webgpu: Failed to get an adapter: %s\n", message); + return; + } + ctx->adapter = std::move(adapter); + }), + UINT64_MAX); + GGML_ASSERT(ctx->adapter != nullptr); + + ctx->adapter.GetLimits(&ctx->limits); + + wgpu::AdapterInfo info{}; +#ifndef __EMSCRIPTEN__ + wgpu::AdapterPropertiesSubgroupMatrixConfigs subgroup_matrix_configs{}; + if (ctx->adapter.HasFeature(wgpu::FeatureName::ChromiumExperimentalSubgroupMatrix)) { + info.nextInChain = &subgroup_matrix_configs; + } +#endif + ctx->adapter.GetInfo(&info); + + wgpu::SupportedFeatures features; + ctx->adapter.GetFeatures(&features); + // we require f16 support + GGML_ASSERT(ctx->adapter.HasFeature(wgpu::FeatureName::ShaderF16)); + +#ifndef __EMSCRIPTEN__ + // Only support square f16 matrices of size 8 or 16 for now + bool valid_subgroup_matrix_config = false; + if (ctx->adapter.HasFeature(wgpu::FeatureName::ChromiumExperimentalSubgroupMatrix)) { + for (size_t i = 0; i < subgroup_matrix_configs.configCount; i++) { + const wgpu::SubgroupMatrixConfig config = subgroup_matrix_configs.configs[i]; + if (config.M == config.N && config.N == config.K && (config.K == 8 || config.K == 16) && + config.componentType == wgpu::SubgroupMatrixComponentType::F16 && + config.resultComponentType == wgpu::SubgroupMatrixComponentType::F16) { + ctx->sg_mat_m = config.M; + ctx->sg_mat_n = config.N; + ctx->sg_mat_k = config.K; + valid_subgroup_matrix_config = true; + break; + } + } + } + + ctx->supports_subgroup_matrix = valid_subgroup_matrix_config; +#endif + // For subgroup matrix code to be the most efficient, we would like the subgroup size to be consistent and accurate. + // Unfortunately, that is not possible, so we use the maximum subgroup size reported by the adapter. + ctx->max_subgroup_size = info.subgroupMaxSize; + + // Initialize device + std::vector required_features = { wgpu::FeatureName::ShaderF16 }; + +#ifndef __EMSCRIPTEN__ + required_features.push_back(wgpu::FeatureName::ImplicitDeviceSynchronization); + if (ctx->supports_subgroup_matrix) { + required_features.push_back(wgpu::FeatureName::Subgroups); + required_features.push_back(wgpu::FeatureName::ChromiumExperimentalSubgroupMatrix); + } +#endif + +#ifdef GGML_WEBGPU_GPU_PROFILE + required_features.push_back(wgpu::FeatureName::TimestampQuery); +#endif + + wgpu::DeviceDescriptor dev_desc; + dev_desc.requiredLimits = &ctx->limits; + dev_desc.requiredFeatures = required_features.data(); + dev_desc.requiredFeatureCount = required_features.size(); + dev_desc.SetDeviceLostCallback( + wgpu::CallbackMode::AllowSpontaneous, + [](const wgpu::Device & device, wgpu::DeviceLostReason reason, wgpu::StringView message) { + GGML_UNUSED(device); + GGML_UNUSED(reason); + GGML_UNUSED(message); + //TODO: uncomment once proper free logic is in place + //GGML_LOG_ERROR("ggml_webgpu: Device lost! Reason: %d, Message: %s\n", static_cast(reason), + //std::string(message).c_str()); + }); + dev_desc.SetUncapturedErrorCallback( + [](const wgpu::Device & device, wgpu::ErrorType reason, wgpu::StringView message) { + GGML_UNUSED(device); + GGML_ABORT("ggml_webgpu: Device error! Reason: %d, Message: %s\n", static_cast(reason), + std::string(message).c_str()); + }); + +#ifndef __EMSCRIPTEN__ + // Enable Dawn-specific toggles to increase native performance + // TODO: Maybe WebGPU needs a "fast" mode where you can request compilers skip adding checks like these, + // only for native performance? + const char * const deviceEnabledToggles[] = { "skip_validation", "disable_robustness", "disable_workgroup_init", + "disable_polyfills_on_integer_div_and_mod" }; + const char * const deviceDisabledToggles[] = { "timestamp_quantization" }; + wgpu::DawnTogglesDescriptor deviceTogglesDesc; + deviceTogglesDesc.enabledToggles = deviceEnabledToggles; + deviceTogglesDesc.enabledToggleCount = 4; + deviceTogglesDesc.disabledToggles = deviceDisabledToggles; + deviceTogglesDesc.disabledToggleCount = 1; + + dev_desc.nextInChain = &deviceTogglesDesc; +#endif + + ctx->instance->WaitAny(ctx->adapter.RequestDevice( + &dev_desc, wgpu::CallbackMode::AllowSpontaneous, + [ctx](wgpu::RequestDeviceStatus status, wgpu::Device device, wgpu::StringView message) { + if (status != wgpu::RequestDeviceStatus::Success) { + GGML_LOG_ERROR("ggml_webgpu: Failed to get a device: %s\n", + std::string(message).c_str()); + return; + } + ctx->device = std::move(device); + }), + UINT64_MAX); + GGML_ASSERT(ctx->device != nullptr); + + // Initialize (compute) queue + ctx->queue = ctx->device.GetQueue(); + + // Create buffer pool for shader parameters + ctx->param_buf_pool.init(ctx->device, WEBGPU_NUM_PARAM_BUFS, WEBGPU_PARAMS_BUF_SIZE_BYTES, + wgpu::BufferUsage::CopyDst | wgpu::BufferUsage::Uniform, + wgpu::BufferUsage::CopySrc | wgpu::BufferUsage::MapWrite); + +#ifdef GGML_WEBGPU_GPU_PROFILE + // Initialize buffer pool for timestamp queries (profiling) + ctx->timestamp_query_buf_pool.init(ctx->device, WEBGPU_NUM_TIMESTAMP_QUERY_BUFS, + WEBGPU_TIMESTAMP_QUERY_BUF_SIZE_BYTES, + wgpu::BufferUsage::QueryResolve | wgpu::BufferUsage::CopySrc, + wgpu::BufferUsage::MapRead | wgpu::BufferUsage::CopyDst); +#endif + + ctx->set_rows_error_buf_pool.init(ctx->device, WEBGPU_NUM_SET_ROWS_ERROR_BUFS, WEBGPU_SET_ROWS_ERROR_BUF_SIZE_BYTES, + wgpu::BufferUsage::CopySrc | wgpu::BufferUsage::Storage, + wgpu::BufferUsage::CopyDst | wgpu::BufferUsage::MapRead); + + ggml_webgpu_init_memset_pipeline(ctx); + ggml_webgpu_init_mul_mat_pipeline(ctx); + ggml_webgpu_init_set_rows_pipeline(ctx); + ggml_webgpu_init_get_rows_pipeline(ctx); + ggml_webgpu_init_cpy_pipeline(ctx); + ggml_webgpu_init_add_pipeline(ctx); + ggml_webgpu_init_sub_pipeline(ctx); + ggml_webgpu_init_mul_pipeline(ctx); + ggml_webgpu_init_div_pipeline(ctx); + ggml_webgpu_init_rms_norm_pipeline(ctx); + ggml_webgpu_init_rope_pipeline(ctx); + ggml_webgpu_init_glu_pipeline(ctx); + ggml_webgpu_init_scale_pipeline(ctx); + ggml_webgpu_init_soft_max_pipeline(ctx); + ggml_webgpu_init_unary_pipeline(ctx); + +#ifdef GGML_WEBGPU_DEBUG + // Initialize debug buffers + ggml_webgpu_create_buffer(ctx->device, ctx->debug_host_buf, WEBGPU_DEBUG_BUF_ELEMS * sizeof(uint32_t), + wgpu::BufferUsage::CopyDst | wgpu::BufferUsage::MapRead, "debug_host_buf"); + ggml_webgpu_create_buffer(ctx->device, ctx->debug_dev_buf, WEBGPU_DEBUG_BUF_ELEMS * sizeof(uint32_t), + wgpu::BufferUsage::Storage | wgpu::BufferUsage::CopySrc, "debug_dev_buf"); +#endif + GGML_LOG_INFO( + "ggml_webgpu: adapter_info: vendor_id: %u | vendor: %s | architecture: %s | device_id: %u | name: %s | " + "device_desc: %s\n", + info.vendorID, std::string(info.vendor).c_str(), std::string(info.architecture).c_str(), info.deviceID, + std::string(info.device).c_str(), std::string(info.description).c_str()); + return true; +} + +// Create the webgpu device at most once per ggml_backend_webgpu_context +static void ensure_webgpu_device_initialized(ggml_backend_webgpu_context * backend_ctx) { + GGML_ASSERT(backend_ctx); + GGML_ASSERT(backend_ctx->device_context); + GGML_ASSERT(backend_ctx->device_context->reg_context); + GGML_ASSERT(backend_ctx->webgpu_ctx); + + std::call_once(backend_ctx->init_once, [&] { + backend_ctx->webgpu_ctx->instance = backend_ctx->device_context->reg_context->instance.get(); + create_webgpu_device(backend_ctx->webgpu_ctx); + }); +} + +// Fetch the backend context (and associated webgpu_context) for a device running on this thread +static ggml_backend_webgpu_context * ggml_backend_webgpu_get_thread_backend_ctx(ggml_backend_dev_t dev) { + thread_local std::unordered_map map; + + ggml_backend_webgpu_device_context * dev_ctx = (ggml_backend_webgpu_device_context *) dev->context; + auto it = map.find(dev_ctx); + if (it != map.end()) { + return it->second; + } + + auto * bctx = new ggml_backend_webgpu_context(); + bctx->device_context = dev_ctx; + bctx->webgpu_ctx = std::make_shared(); + bctx->name = std::string(GGML_WEBGPU_NAME); + ensure_webgpu_device_initialized(bctx); + + map[dev_ctx] = bctx; + return bctx; +} + static ggml_backend_t ggml_backend_webgpu_device_init(ggml_backend_dev_t dev, const char * params) { GGML_UNUSED(params); WEBGPU_LOG_DEBUG("ggml_backend_webgpu_device_init()"); - ggml_backend_webgpu_device_context * dev_ctx = static_cast(dev->context); - webgpu_context webgpu_ctx = dev_ctx->webgpu_ctx; + ggml_backend_webgpu_device_context * dev_ctx = static_cast(dev->context); - static ggml_backend_webgpu_context backend_ctx; - backend_ctx.name = GGML_WEBGPU_NAME + std::string(": ") + dev_ctx->device_name; - backend_ctx.webgpu_ctx = webgpu_ctx; + auto * backend_ctx = new ggml_backend_webgpu_context(); + backend_ctx->name = GGML_WEBGPU_NAME + std::string(": ") + dev_ctx->device_name; + backend_ctx->device_context = dev_ctx; + backend_ctx->webgpu_ctx = std::make_shared(); + ensure_webgpu_device_initialized(backend_ctx); - // See GGML Backend Interface section - static ggml_backend backend = { + auto * backend = new ggml_backend(); + *backend = { /* .guid = */ ggml_backend_webgpu_guid(), /* .interface = */ ggml_backend_webgpu_i, /* .device = */ dev, - /* .context = */ &backend_ctx, + /* .context = */ backend_ctx, }; - return &backend; + return backend; } static ggml_backend_buffer_type_t ggml_backend_webgpu_device_get_buffer_type(ggml_backend_dev_t dev) { // See GGML Backend Buffer Type Interface section - - static struct ggml_backend_buffer_type ggml_backend_webgpu_buffer_type = { + + // Keep this thread_local, since we need to get the webgpu_context from the ggml_backend_webgpu_context for this thread. + thread_local struct ggml_backend_buffer_type ggml_backend_webgpu_buffer_type = { /* .iface = */ { /* .get_name = */ ggml_backend_webgpu_buffer_type_get_name, /* .alloc_buffer = */ ggml_backend_webgpu_buffer_type_alloc_buffer, @@ -2557,7 +2788,8 @@ static ggml_backend_buffer_type_t ggml_backend_webgpu_device_get_buffer_type(ggm }, /* .device = */ dev, - /* .context = */ NULL, + /* .context = */ + ggml_backend_webgpu_get_thread_backend_ctx(dev), }; return &ggml_backend_webgpu_buffer_type; @@ -2596,9 +2828,9 @@ static bool ggml_webgpu_supported_qtype(ggml_type type) { } static bool ggml_backend_webgpu_device_supports_op(ggml_backend_dev_t dev, const ggml_tensor * op) { - ggml_backend_webgpu_device_context * ctx = static_cast(dev->context); + auto * bctx = ggml_backend_webgpu_get_thread_backend_ctx(dev); - webgpu_context webgpu_ctx = ctx->webgpu_ctx; + webgpu_context webgpu_ctx = bctx->webgpu_ctx; ggml_tensor * src0 = op->src[0]; ggml_tensor * src1 = op->src[1]; @@ -2824,6 +3056,7 @@ static size_t ggml_backend_webgpu_reg_get_device_count(ggml_backend_reg_t reg) { // TODO: Does this need to be thread safe? Is it only called once? // TODO: move most logic to device_init function so backend can be freed/initialized properly // Only one device is supported for now + static ggml_backend_dev_t ggml_backend_webgpu_reg_get_device(ggml_backend_reg_t reg, size_t index) { GGML_ASSERT(index == 0); WEBGPU_LOG_DEBUG("ggml_backend_reg_get_device()"); @@ -2832,190 +3065,11 @@ static ggml_backend_dev_t ggml_backend_webgpu_reg_get_device(ggml_backend_reg_t ggml_backend_webgpu_reg_context * reg_ctx = static_cast(reg->context); - webgpu_context ctx = reg_ctx->webgpu_ctx; - - wgpu::RequestAdapterOptions options = {}; - -#ifndef __EMSCRIPTEN__ - // TODO: track need for these toggles: https://issues.chromium.org/issues/42251215 - const char * const adapterEnabledToggles[] = { "vulkan_enable_f16_on_nvidia", "use_vulkan_memory_model" }; - wgpu::DawnTogglesDescriptor adapterTogglesDesc; - adapterTogglesDesc.enabledToggles = adapterEnabledToggles; - adapterTogglesDesc.enabledToggleCount = 2; - options.nextInChain = &adapterTogglesDesc; -#endif - - ctx->instance.WaitAny(ctx->instance.RequestAdapter( - &options, wgpu::CallbackMode::AllowSpontaneous, - [&ctx](wgpu::RequestAdapterStatus status, wgpu::Adapter adapter, const char * message) { - if (status != wgpu::RequestAdapterStatus::Success) { - GGML_LOG_ERROR("ggml_webgpu: Failed to get an adapter: %s\n", message); - return; - } - ctx->adapter = std::move(adapter); - }), - UINT64_MAX); - GGML_ASSERT(ctx->adapter != nullptr); - - ctx->adapter.GetLimits(&ctx->limits); - - wgpu::AdapterInfo info{}; -#ifndef __EMSCRIPTEN__ - wgpu::AdapterPropertiesSubgroupMatrixConfigs subgroup_matrix_configs{}; - if (ctx->adapter.HasFeature(wgpu::FeatureName::ChromiumExperimentalSubgroupMatrix)) { - info.nextInChain = &subgroup_matrix_configs; - } -#endif - ctx->adapter.GetInfo(&info); - - wgpu::SupportedFeatures features; - ctx->adapter.GetFeatures(&features); - // we require f16 support - GGML_ASSERT(ctx->adapter.HasFeature(wgpu::FeatureName::ShaderF16)); - -#ifndef __EMSCRIPTEN__ - // Only support square f16 matrices of size 8 or 16 for now - bool valid_subgroup_matrix_config = false; - if (ctx->adapter.HasFeature(wgpu::FeatureName::ChromiumExperimentalSubgroupMatrix)) { - for (size_t i = 0; i < subgroup_matrix_configs.configCount; i++) { - const wgpu::SubgroupMatrixConfig config = subgroup_matrix_configs.configs[i]; - if (config.M == config.N && config.N == config.K && (config.K == 8 || config.K == 16) && - config.componentType == wgpu::SubgroupMatrixComponentType::F16 && - config.resultComponentType == wgpu::SubgroupMatrixComponentType::F16) { - ctx->sg_mat_m = config.M; - ctx->sg_mat_n = config.N; - ctx->sg_mat_k = config.K; - valid_subgroup_matrix_config = true; - break; - } - } - } - - ctx->supports_subgroup_matrix = valid_subgroup_matrix_config; -#endif - // For subgroup matrix code to be the most efficient, we would like the subgroup size to be consistent and accurate. - // Unfortunately, that is not possible, so we use the maximum subgroup size reported by the adapter. - ctx->max_subgroup_size = info.subgroupMaxSize; - - // Initialize device - std::vector required_features = { wgpu::FeatureName::ShaderF16 }; - -#ifndef __EMSCRIPTEN__ - required_features.push_back(wgpu::FeatureName::ImplicitDeviceSynchronization); - if (ctx->supports_subgroup_matrix) { - required_features.push_back(wgpu::FeatureName::Subgroups); - required_features.push_back(wgpu::FeatureName::ChromiumExperimentalSubgroupMatrix); - } -#endif - -#ifdef GGML_WEBGPU_GPU_PROFILE - required_features.push_back(wgpu::FeatureName::TimestampQuery); -#endif - - wgpu::DeviceDescriptor dev_desc; - dev_desc.requiredLimits = &ctx->limits; - dev_desc.requiredFeatures = required_features.data(); - dev_desc.requiredFeatureCount = required_features.size(); - dev_desc.SetDeviceLostCallback( - wgpu::CallbackMode::AllowSpontaneous, - [](const wgpu::Device & device, wgpu::DeviceLostReason reason, wgpu::StringView message) { - GGML_UNUSED(device); - GGML_UNUSED(reason); - GGML_UNUSED(message); - //TODO: uncomment once proper free logic is in place - //GGML_LOG_ERROR("ggml_webgpu: Device lost! Reason: %d, Message: %s\n", static_cast(reason), - //std::string(message).c_str()); - }); - dev_desc.SetUncapturedErrorCallback( - [](const wgpu::Device & device, wgpu::ErrorType reason, wgpu::StringView message) { - GGML_UNUSED(device); - GGML_ABORT("ggml_webgpu: Device error! Reason: %d, Message: %s\n", static_cast(reason), - std::string(message).c_str()); - }); - -#ifndef __EMSCRIPTEN__ - // Enable Dawn-specific toggles to increase native performance - // TODO: Maybe WebGPU needs a "fast" mode where you can request compilers skip adding checks like these, - // only for native performance? - const char * const deviceEnabledToggles[] = { "skip_validation", "disable_robustness", "disable_workgroup_init", - "disable_polyfills_on_integer_div_and_mod" }; - const char * const deviceDisabledToggles[] = { "timestamp_quantization" }; - wgpu::DawnTogglesDescriptor deviceTogglesDesc; - deviceTogglesDesc.enabledToggles = deviceEnabledToggles; - deviceTogglesDesc.enabledToggleCount = 4; - deviceTogglesDesc.disabledToggles = deviceDisabledToggles; - deviceTogglesDesc.disabledToggleCount = 1; - - dev_desc.nextInChain = &deviceTogglesDesc; -#endif - - ctx->instance.WaitAny(ctx->adapter.RequestDevice( - &dev_desc, wgpu::CallbackMode::AllowSpontaneous, - [ctx](wgpu::RequestDeviceStatus status, wgpu::Device device, wgpu::StringView message) { - if (status != wgpu::RequestDeviceStatus::Success) { - GGML_LOG_ERROR("ggml_webgpu: Failed to get a device: %s\n", - std::string(message).c_str()); - return; - } - ctx->device = std::move(device); - }), - UINT64_MAX); - GGML_ASSERT(ctx->device != nullptr); - - // Initialize (compute) queue - ctx->queue = ctx->device.GetQueue(); - - // Create buffer pool for shader parameters - ctx->param_buf_pool.init(ctx->device, WEBGPU_NUM_PARAM_BUFS, WEBGPU_PARAMS_BUF_SIZE_BYTES, - wgpu::BufferUsage::CopyDst | wgpu::BufferUsage::Uniform, - wgpu::BufferUsage::CopySrc | wgpu::BufferUsage::MapWrite); - -#ifdef GGML_WEBGPU_GPU_PROFILE - // Initialize buffer pool for timestamp queries (profiling) - ctx->timestamp_query_buf_pool.init(ctx->device, WEBGPU_NUM_TIMESTAMP_QUERY_BUFS, - WEBGPU_TIMESTAMP_QUERY_BUF_SIZE_BYTES, - wgpu::BufferUsage::QueryResolve | wgpu::BufferUsage::CopySrc, - wgpu::BufferUsage::MapRead | wgpu::BufferUsage::CopyDst); -#endif - - ctx->set_rows_error_buf_pool.init(ctx->device, WEBGPU_NUM_SET_ROWS_ERROR_BUFS, WEBGPU_SET_ROWS_ERROR_BUF_SIZE_BYTES, - wgpu::BufferUsage::CopySrc | wgpu::BufferUsage::Storage, - wgpu::BufferUsage::CopyDst | wgpu::BufferUsage::MapRead); - - ggml_webgpu_init_memset_pipeline(ctx); - ggml_webgpu_init_mul_mat_pipeline(ctx); - ggml_webgpu_init_set_rows_pipeline(ctx); - ggml_webgpu_init_get_rows_pipeline(ctx); - ggml_webgpu_init_cpy_pipeline(ctx); - ggml_webgpu_init_add_pipeline(ctx); - ggml_webgpu_init_sub_pipeline(ctx); - ggml_webgpu_init_mul_pipeline(ctx); - ggml_webgpu_init_div_pipeline(ctx); - ggml_webgpu_init_rms_norm_pipeline(ctx); - ggml_webgpu_init_rope_pipeline(ctx); - ggml_webgpu_init_glu_pipeline(ctx); - ggml_webgpu_init_scale_pipeline(ctx); - ggml_webgpu_init_soft_max_pipeline(ctx); - ggml_webgpu_init_unary_pipeline(ctx); - -#ifdef GGML_WEBGPU_DEBUG - // Initialize debug buffers - ggml_webgpu_create_buffer(ctx->device, ctx->debug_host_buf, WEBGPU_DEBUG_BUF_ELEMS * sizeof(uint32_t), - wgpu::BufferUsage::CopyDst | wgpu::BufferUsage::MapRead, "debug_host_buf"); - ggml_webgpu_create_buffer(ctx->device, ctx->debug_dev_buf, WEBGPU_DEBUG_BUF_ELEMS * sizeof(uint32_t), - wgpu::BufferUsage::Storage | wgpu::BufferUsage::CopySrc, "debug_dev_buf"); -#endif - static ggml_backend_webgpu_device_context device_ctx; - device_ctx.webgpu_ctx = ctx; device_ctx.device_name = GGML_WEBGPU_NAME; - device_ctx.device_desc = info.description; - - GGML_LOG_INFO( - "ggml_webgpu: adapter_info: vendor_id: %u | vendor: %s | architecture: %s | device_id: %u | name: %s | " - "device_desc: %s\n", - info.vendorID, std::string(info.vendor).c_str(), std::string(info.architecture).c_str(), info.deviceID, - std::string(info.device).c_str(), std::string(info.description).c_str()); + // TODO: we get the device description when actually initializing the webgpu device, so move this to backend context? + device_ctx.device_desc = GGML_WEBGPU_NAME; + device_ctx.reg_context = reg_ctx; // See GGML Backend Device Interface section static ggml_backend_device device = { @@ -3040,10 +3094,7 @@ static const struct ggml_backend_reg_i ggml_backend_webgpu_reg_i = { ggml_backend_reg_t ggml_backend_webgpu_reg() { WEBGPU_LOG_DEBUG("ggml_backend_webgpu_reg()"); - webgpu_context webgpu_ctx = std::make_shared(); - static ggml_backend_webgpu_reg_context ctx; - ctx.webgpu_ctx = webgpu_ctx; ctx.name = GGML_WEBGPU_NAME; ctx.device_count = 1; @@ -3060,7 +3111,8 @@ ggml_backend_reg_t ggml_backend_webgpu_reg() { instance_descriptor.nextInChain = &instanceTogglesDesc; #endif - webgpu_ctx->instance = wgpu::CreateInstance(&instance_descriptor); + wgpu::Instance inst = wgpu::CreateInstance(&instance_descriptor); + ctx.instance = std::make_unique(std::move(inst)); #ifdef __EMSCRIPTEN__ if (webgpu_ctx->instance == nullptr) { @@ -3068,7 +3120,7 @@ ggml_backend_reg_t ggml_backend_webgpu_reg() { return nullptr; } #endif - GGML_ASSERT(webgpu_ctx->instance != nullptr); + GGML_ASSERT(ctx.instance != nullptr); static ggml_backend_reg reg = { /* .api_version = */ GGML_BACKEND_API_VERSION, From c0373f5f90fa39b87b73a726e0d5692715ec4658 Mon Sep 17 00:00:00 2001 From: Nikhil Jain Date: Mon, 12 Jan 2026 17:34:43 -0800 Subject: [PATCH 13/21] Small cleanup --- ggml/src/ggml-webgpu/ggml-webgpu.cpp | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/ggml/src/ggml-webgpu/ggml-webgpu.cpp b/ggml/src/ggml-webgpu/ggml-webgpu.cpp index c577954e8c..2612acebf6 100644 --- a/ggml/src/ggml-webgpu/ggml-webgpu.cpp +++ b/ggml/src/ggml-webgpu/ggml-webgpu.cpp @@ -2763,6 +2763,7 @@ static ggml_backend_t ggml_backend_webgpu_device_init(ggml_backend_dev_t dev, co backend_ctx->webgpu_ctx = std::make_shared(); ensure_webgpu_device_initialized(backend_ctx); + // See GGML Backend Interface section auto * backend = new ggml_backend(); *backend = { /* .guid = */ ggml_backend_webgpu_guid(), @@ -2770,7 +2771,8 @@ static ggml_backend_t ggml_backend_webgpu_device_init(ggml_backend_dev_t dev, co /* .device = */ dev, /* .context = */ backend_ctx, }; - return backend; + return backend; + } static ggml_backend_buffer_type_t ggml_backend_webgpu_device_get_buffer_type(ggml_backend_dev_t dev) { From 51b392c146a101d3dd865d0049a4d8edb33c5c9f Mon Sep 17 00:00:00 2001 From: Nikhil Jain Date: Wed, 14 Jan 2026 23:01:40 -0800 Subject: [PATCH 14/21] Move Instance, Device, Adapter, Device creation, and capabilities to global state while moving Queue, pipelines, and buffers to per-thread state. --- ggml/src/ggml-webgpu/ggml-webgpu.cpp | 772 ++++++++++++++------------- 1 file changed, 409 insertions(+), 363 deletions(-) diff --git a/ggml/src/ggml-webgpu/ggml-webgpu.cpp b/ggml/src/ggml-webgpu/ggml-webgpu.cpp index 2612acebf6..2d03e100e7 100644 --- a/ggml/src/ggml-webgpu/ggml-webgpu.cpp +++ b/ggml/src/ggml-webgpu/ggml-webgpu.cpp @@ -307,16 +307,23 @@ struct flash_attn_pipeline_key_hash { } }; +// Forward declaration +struct webgpu_global_context; // All the base objects needed to run operations on a WebGPU device struct webgpu_context_struct { - // Points to global instance owned by ggml_backend_webgpu_reg_context - wgpu::Instance * instance; - wgpu::Adapter adapter; - wgpu::Device device; + // Points to global instances owned by ggml_backend_webgpu_reg_context + webgpu_global_context * global_ctx; + // wgpu::Instance * instance; + // wgpu::Adapter * adapter; + // wgpu::Device * device; + // wgpu::Buffer * get_tensor_staging_buf; + // uint32_t max_subgroup_size; + wgpu::Queue queue; - wgpu::Limits limits; + webgpu_buf_pool param_buf_pool; + // TODO: I got rid of this...update uses to read from global context + // wgpu::Limits limits; - uint32_t max_subgroup_size; bool supports_subgroup_matrix = false; uint32_t sg_mat_m; @@ -326,7 +333,6 @@ struct webgpu_context_struct { std::recursive_mutex mutex; std::atomic_uint inflight_threads = 0; - webgpu_buf_pool param_buf_pool; webgpu_buf_pool set_rows_error_buf_pool; pre_wgsl::Preprocessor p; @@ -357,9 +363,6 @@ struct webgpu_context_struct { size_t memset_bytes_per_thread; - // Staging buffer for reading data from the GPU - wgpu::Buffer get_tensor_staging_buf; - #ifdef GGML_WEBGPU_DEBUG wgpu::Buffer debug_host_buf; wgpu::Buffer debug_dev_buf; @@ -382,15 +385,33 @@ struct webgpu_context_struct { typedef std::shared_ptr webgpu_context; +struct webgpu_capabilities { + wgpu::Limits limits; + bool supports_subgroup_matrix = false; + wgpu::SubgroupMatrixConfig subgroup_matrix_config{}; + uint32_t subgroup_size = 0; + uint32_t max_subgroup_size = 0; +}; + +// Stores global webgpu members +struct webgpu_global_context { + wgpu::Instance instance; + wgpu::Adapter adapter; + wgpu::Device device; + // Shared buffer to move data from device to host + wgpu::Buffer get_tensor_staging_buf; + webgpu_capabilities capabilities; +} ; + // Metadata required for the ggml backend registration/discovery interface struct ggml_backend_webgpu_reg_context { // Since the Instance is a global entrypoint into the WebGPU api, it lives here - std::unique_ptr instance; + webgpu_global_context wgpu_global_ctx; size_t device_count; const char * name; }; -// Per-device struct +// Per-device struct for the global logical device interface struct ggml_backend_webgpu_device_context { ggml_backend_webgpu_reg_context * reg_context; std::string device_name; @@ -399,28 +420,26 @@ struct ggml_backend_webgpu_device_context { // Per-thread data required to actually run webgpu operations in a backend instance struct ggml_backend_webgpu_context { - ggml_backend_webgpu_device_context * device_context; + ggml_backend_webgpu_device_context * device_context; // TODO(nikhil.jain): delete? std::once_flag init_once; - webgpu_context webgpu_ctx; std::string name; }; // Per-thread data related to buffers struct ggml_backend_webgpu_buffer_context { - ggml_backend_webgpu_context * backend_ctx; wgpu::Buffer buffer; std::string label; - ggml_backend_webgpu_buffer_context(ggml_backend_webgpu_context * bctx, + ggml_backend_webgpu_buffer_context( wgpu::Buffer buf, std::string lbl) - : backend_ctx(bctx), + : buffer(std::move(buf)), label(std::move(lbl)) {} }; // Forward declaration -static ggml_backend_webgpu_context * ggml_backend_webgpu_get_thread_backend_ctx(ggml_backend_dev_t dev); +static webgpu_context get_per_thread_webgpu_context(ggml_backend_dev_t dev); /* WebGPU object initializations */ @@ -467,7 +486,7 @@ static webgpu_pipeline ggml_webgpu_create_pipeline(wgpu::Device & return { device.CreateComputePipeline(&pipeline_desc), label }; } -static void ggml_webgpu_create_buffer(wgpu::Device & device, +static void ggml_webgpu_create_buffer(wgpu::Device & device, wgpu::Buffer & buffer, size_t size, wgpu::BufferUsage usage, @@ -496,12 +515,12 @@ static void ggml_backend_webgpu_wait(webgpu_context & ctx, uint32_t inflight_threads = ctx->inflight_threads; uint32_t inflight_max = WEBGPU_MAX_INFLIGHT_SUBS_PER_THREAD / std::max(inflight_threads, 1u); while (futures.size() >= inflight_max && futures.size() > 0) { - ctx->instance->WaitAny(futures[0].futures.size(), futures[0].futures.data(), UINT64_MAX); + ctx->global_ctx->instance.WaitAny(futures[0].futures.size(), futures[0].futures.data(), UINT64_MAX); futures.erase(futures.begin()); } size_t i = 0; while (i < futures.size()) { - auto waitStatus = ctx->instance->WaitAny(futures[i].futures.size(), futures[i].futures.data(), timeout_ms); + auto waitStatus = ctx->global_ctx->instance.WaitAny(futures[i].futures.size(), futures[i].futures.data(), timeout_ms); switch (waitStatus) { case wgpu::WaitStatus::Success: futures.erase(futures.begin() + i); @@ -524,7 +543,7 @@ static void ggml_backend_webgpu_map_buffer(webgpu_context & ctx, wgpu::MapMode mode, size_t offset, size_t size) { - ctx->instance->WaitAny(buffer.MapAsync(mode, offset, size, wgpu::CallbackMode::AllowSpontaneous, + ctx->global_ctx->instance.WaitAny(buffer.MapAsync(mode, offset, size, wgpu::CallbackMode::AllowSpontaneous, [](wgpu::MapAsyncStatus status, wgpu::StringView message) { if (status != wgpu::MapAsyncStatus::Success) { GGML_LOG_ERROR("ggml_webgpu: Failed to map buffer: %s\n", @@ -539,7 +558,7 @@ static void ggml_backend_webgpu_map_buffer(webgpu_context & ctx, // To use, add a bind group entry to the setup for the shader you are debugging, add the buffer and // debug statements in the shader, and then call this function after encoding the commands and submitting them. static void ggml_backend_webgpu_debug(webgpu_context & ctx) { - wgpu::CommandEncoder encoder = ctx->device.CreateCommandEncoder(); + wgpu::CommandEncoder encoder = ctx->global_ctx->device.CreateCommandEncoder(); encoder.CopyBufferToBuffer(ctx->debug_dev_buf, 0, ctx->debug_host_buf, 0, ctx->debug_host_buf.GetSize()); wgpu::CommandBuffer commands = encoder.Finish(); ctx->queue.Submit(1, &commands); @@ -651,9 +670,9 @@ static webgpu_command ggml_backend_webgpu_build(webgpu_context & bind_group_desc.entryCount = bind_group_entries.size(); bind_group_desc.entries = bind_group_entries.data(); bind_group_desc.label = pipeline.name.c_str(); - wgpu::BindGroup bind_group = ctx->device.CreateBindGroup(&bind_group_desc); + wgpu::BindGroup bind_group = ctx->global_ctx->device.CreateBindGroup(&bind_group_desc); - wgpu::CommandEncoder encoder = ctx->device.CreateCommandEncoder(); + wgpu::CommandEncoder encoder = ctx->global_ctx->device.CreateCommandEncoder(); encoder.CopyBufferToBuffer(params_bufs.host_buf, 0, params_bufs.dev_buf, 0, params_bufs.dev_buf.GetSize()); #ifdef GGML_WEBGPU_GPU_PROFILE @@ -787,12 +806,12 @@ static wgpu::Buffer ggml_webgpu_tensor_buf(const ggml_tensor * tensor) { static size_t ggml_webgpu_tensor_misalignment(webgpu_context & ctx, const ggml_tensor * t) { size_t offset = ggml_webgpu_tensor_offset(t); - return offset & (ctx->limits.minStorageBufferOffsetAlignment - 1); + return offset & (ctx->global_ctx->capabilities.limits.minStorageBufferOffsetAlignment - 1); } static size_t ggml_webgpu_tensor_align_offset(webgpu_context & ctx, const ggml_tensor * t) { size_t offset = ggml_webgpu_tensor_offset(t); - return offset & ~(ctx->limits.minStorageBufferOffsetAlignment - 1); + return offset & ~(ctx->global_ctx->capabilities.limits.minStorageBufferOffsetAlignment - 1); } static size_t ggml_webgpu_tensor_binding_size(webgpu_context & ctx, ggml_tensor * t) { @@ -1010,8 +1029,8 @@ static webgpu_command ggml_webgpu_mul_mat(webgpu_context & ctx, uint32_t batches = dst->ne[2] * dst->ne[3]; uint32_t output_groups = CEIL_DIV(dst->ne[0], WEBGPU_MUL_MAT_VEC_OUTPUTS_PER_WG); uint32_t total_wg = output_groups * batches; - wg_x = total_wg % ctx->limits.maxComputeWorkgroupsPerDimension; - wg_y = CEIL_DIV(total_wg, ctx->limits.maxComputeWorkgroupsPerDimension); + wg_x = total_wg % ctx->global_ctx->capabilities.limits.maxComputeWorkgroupsPerDimension; + wg_y = CEIL_DIV(total_wg, ctx->global_ctx->capabilities.limits.maxComputeWorkgroupsPerDimension); } else { pipeline = ctx->mul_mat_pipelines[src0->type][src1->type][vectorized]; uint32_t wg_m; @@ -1162,12 +1181,12 @@ static webgpu_command ggml_webgpu_flash_attn(webgpu_context & ctx, .sg_mat_n = ctx->sg_mat_n, .sg_mat_k = ctx->sg_mat_k, .wg_mem_limit_bytes = - ctx->limits.maxComputeWorkgroupStorageSize, - .max_subgroup_size = ctx->max_subgroup_size }; + ctx->global_ctx->capabilities.limits.maxComputeWorkgroupStorageSize, + .max_subgroup_size = ctx->global_ctx->capabilities.max_subgroup_size }; ggml_webgpu_processed_shader processed = ggml_webgpu_preprocess_flash_attn_shader(ctx->p, wgsl_flash_attn, shader_lib_ctx); - pipeline = ggml_webgpu_create_pipeline(ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); + pipeline = ggml_webgpu_create_pipeline(ctx->global_ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); pipeline.context = new ggml_webgpu_flash_attn_shader_decisions(processed.decisions); ctx->flash_attn_pipelines.emplace(key, pipeline); decisions = processed.decisions; @@ -1632,8 +1651,7 @@ static std::optional ggml_webgpu_encode_node(webgpu_context ctx, static ggml_status ggml_backend_webgpu_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { WEBGPU_LOG_DEBUG("ggml_backend_webgpu_graph_compute(" << cgraph->n_nodes << " nodes)"); - ggml_backend_webgpu_context * backend_ctx = static_cast(backend->context); - webgpu_context ctx = backend_ctx->webgpu_ctx; + webgpu_context ctx = get_per_thread_webgpu_context(backend->device); WEBGPU_CPU_PROFILE_TOTAL_START(graph_compute); @@ -1652,7 +1670,7 @@ static ggml_status ggml_backend_webgpu_graph_compute(ggml_backend_t backend, str if (commands.size() >= batch_size) { futures.push_back(ggml_backend_webgpu_submit(ctx, commands)); // Process events and check for completed submissions - ctx->instance->ProcessEvents(); + ctx->global_ctx->instance.ProcessEvents(); ggml_backend_webgpu_wait(ctx, futures, false); commands.clear(); } @@ -1712,6 +1730,7 @@ static void ggml_backend_webgpu_buffer_memset_tensor(ggml_backend_buffer_t buffe WEBGPU_CPU_PROFILE_TOTAL_START(memset_tensor); + webgpu_context webgpu_ctx = get_per_thread_webgpu_context(buffer->buft->device); ggml_backend_webgpu_buffer_context * buf_ctx = (ggml_backend_webgpu_buffer_context *) buffer->context; WEBGPU_LOG_DEBUG("ggml_backend_webgpu_buffer_memset_tensor(" << buf_ctx->label << ", " << tensor << ", " << value @@ -1721,8 +1740,8 @@ static void ggml_backend_webgpu_buffer_memset_tensor(ggml_backend_buffer_t buffe // This is a trick to set all bytes of a u32 to the same 1 byte value. uint32_t val32 = (uint32_t) value * 0x01010101; - ggml_backend_webgpu_buffer_memset(buf_ctx->backend_ctx->webgpu_ctx, buf_ctx->buffer, val32, total_offset, size); - WEBGPU_CPU_PROFILE_TOTAL_END(memset_tensor, buf_ctx->backend_ctx->webgpu_ctx); + ggml_backend_webgpu_buffer_memset(webgpu_ctx, buf_ctx->buffer, val32, total_offset, size); + WEBGPU_CPU_PROFILE_TOTAL_END(memset_tensor, webgpu_ctx); } static void ggml_backend_webgpu_buffer_set_tensor(ggml_backend_buffer_t buffer, @@ -1732,7 +1751,7 @@ static void ggml_backend_webgpu_buffer_set_tensor(ggml_backend_buffer_t buffer, size_t size) { WEBGPU_CPU_PROFILE_TOTAL_START(set_tensor); ggml_backend_webgpu_buffer_context * buf_ctx = (ggml_backend_webgpu_buffer_context *) buffer->context; - webgpu_context webgpu_ctx = buf_ctx->backend_ctx->webgpu_ctx; + webgpu_context webgpu_ctx = get_per_thread_webgpu_context(buffer->buft->device); WEBGPU_LOG_DEBUG("ggml_backend_webgpu_buffer_set_tensor(" << buf_ctx->label << ", " << tensor << ", " << data << ", " << offset << ", " << size << ")"); @@ -1756,7 +1775,7 @@ static void ggml_backend_webgpu_buffer_set_tensor(ggml_backend_buffer_t buffer, remaining_size); } else { // wait for WriteBuffer to complete - webgpu_ctx->instance->WaitAny( + webgpu_ctx->global_ctx->instance.WaitAny( webgpu_ctx->queue.OnSubmittedWorkDone(wgpu::CallbackMode::AllowSpontaneous, [](wgpu::QueueWorkDoneStatus status, wgpu::StringView message) { if (status != wgpu::QueueWorkDoneStatus::Success) { @@ -1778,8 +1797,8 @@ static void ggml_backend_webgpu_buffer_get_tensor(ggml_backend_buffer_t buffer, ggml_backend_webgpu_buffer_context * buf_ctx = (ggml_backend_webgpu_buffer_context *) buffer->context; WEBGPU_LOG_DEBUG("ggml_backend_webgpu_buffer_get_tensor(" << buf_ctx->label << ", " << tensor << ", " << data << ", " << offset << ", " << size << ")"); - webgpu_context webgpu_ctx = buf_ctx->backend_ctx->webgpu_ctx; - wgpu::Device device = webgpu_ctx->device; + webgpu_context webgpu_ctx = get_per_thread_webgpu_context(buffer->buft->device); + wgpu::Device device = webgpu_ctx->global_ctx->device; size_t total_offset = webgpu_tensor_offset(tensor) + tensor->view_offs + offset; @@ -1791,31 +1810,31 @@ static void ggml_backend_webgpu_buffer_get_tensor(ggml_backend_buffer_t buffer, std::lock_guard lock(webgpu_ctx->mutex); - if (webgpu_ctx->get_tensor_staging_buf == nullptr || webgpu_ctx->get_tensor_staging_buf.GetSize() < final_size) { + if (webgpu_ctx->global_ctx->get_tensor_staging_buf == nullptr || webgpu_ctx->global_ctx->get_tensor_staging_buf.GetSize() < final_size) { // Create a new staging buffer if it doesn't exist or is too small - if (webgpu_ctx->get_tensor_staging_buf) { - webgpu_ctx->get_tensor_staging_buf.Destroy(); + if (webgpu_ctx->global_ctx->get_tensor_staging_buf) { + webgpu_ctx->global_ctx->get_tensor_staging_buf.Destroy(); } - ggml_webgpu_create_buffer(device, webgpu_ctx->get_tensor_staging_buf, final_size, + ggml_webgpu_create_buffer(device, webgpu_ctx->global_ctx->get_tensor_staging_buf, final_size, wgpu::BufferUsage::CopyDst | wgpu::BufferUsage::MapRead, "get_tensor_staging_buf"); } // Copy the data from the buffer to the staging buffer wgpu::CommandEncoder encoder = device.CreateCommandEncoder(); - encoder.CopyBufferToBuffer(buf_ctx->buffer, total_offset, webgpu_ctx->get_tensor_staging_buf, 0, final_size); + encoder.CopyBufferToBuffer(buf_ctx->buffer, total_offset, webgpu_ctx->global_ctx->get_tensor_staging_buf, 0, final_size); wgpu::CommandBuffer commands = encoder.Finish(); // Submit the command buffer to the queue webgpu_ctx->queue.Submit(1, &commands); // Map the staging buffer to read the data - ggml_backend_webgpu_map_buffer(webgpu_ctx, webgpu_ctx->get_tensor_staging_buf, wgpu::MapMode::Read, 0, final_size); + ggml_backend_webgpu_map_buffer(webgpu_ctx, webgpu_ctx->global_ctx->get_tensor_staging_buf, wgpu::MapMode::Read, 0, final_size); // Must specify size here since the staging buffer might be larger than the tensor size - const void * mapped_range = webgpu_ctx->get_tensor_staging_buf.GetConstMappedRange(0, final_size); + const void * mapped_range = webgpu_ctx->global_ctx->get_tensor_staging_buf.GetConstMappedRange(0, final_size); // Copy the data from the mapped range to the output buffer std::memcpy(data, mapped_range, size); - webgpu_ctx->get_tensor_staging_buf.Unmap(); + webgpu_ctx->global_ctx->get_tensor_staging_buf.Unmap(); WEBGPU_CPU_PROFILE_TOTAL_END(get_tensor, webgpu_ctx); } @@ -1823,8 +1842,9 @@ static void ggml_backend_webgpu_buffer_clear(ggml_backend_buffer_t buffer, uint8 WEBGPU_LOG_DEBUG("ggml_backend_webgpu_buffer_clear(" << buffer << ", " << (uint32_t) value << ")"); WEBGPU_CPU_PROFILE_TOTAL_START(clear); ggml_backend_webgpu_buffer_context * buf_ctx = (ggml_backend_webgpu_buffer_context *) buffer->context; - ggml_backend_webgpu_buffer_memset(buf_ctx->backend_ctx->webgpu_ctx, buf_ctx->buffer, value, 0, buffer->size); - WEBGPU_CPU_PROFILE_TOTAL_END(clear, buf_ctx->backend_ctx->webgpu_ctx); + webgpu_context webgpu_ctx = get_per_thread_webgpu_context(buffer->buft->device); + ggml_backend_webgpu_buffer_memset(webgpu_ctx, buf_ctx->buffer, value, 0, buffer->size); + WEBGPU_CPU_PROFILE_TOTAL_END(clear, webgpu_ctx); } static ggml_backend_buffer_i ggml_backend_webgpu_buffer_interface = { @@ -1855,28 +1875,29 @@ static ggml_backend_buffer_t ggml_backend_webgpu_buffer_type_alloc_buffer(ggml_b std::string buf_name = "tensor_buf" + std::to_string(buffer_id); WEBGPU_LOG_DEBUG("ggml_backend_webgpu_buffer_type_alloc_buffer_" << buffer_id << ": " << size << " bytes"); - ggml_backend_webgpu_context * ctx = static_cast(buft->context); + webgpu_context ctx = get_per_thread_webgpu_context(buft->device); + wgpu::Buffer buf; - ggml_webgpu_create_buffer(ctx->webgpu_ctx->device, buf, ROUNDUP_POW2(size, WEBGPU_STORAGE_BUF_BINDING_MULT), + ggml_webgpu_create_buffer(ctx->global_ctx->device, buf, ROUNDUP_POW2(size, WEBGPU_STORAGE_BUF_BINDING_MULT), wgpu::BufferUsage::Storage | wgpu::BufferUsage::CopySrc | wgpu::BufferUsage::CopyDst, buf_name.c_str()); ggml_backend_webgpu_buffer_context * buf_ctx = - new ggml_backend_webgpu_buffer_context(ctx, buf, buf_name); + new ggml_backend_webgpu_buffer_context(buf, buf_name); return ggml_backend_buffer_init(buft, ggml_backend_webgpu_buffer_interface, buf_ctx, size); } static size_t ggml_backend_webgpu_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { - ggml_backend_webgpu_context * ctx = static_cast(buft->context); - return ctx->webgpu_ctx->limits.minStorageBufferOffsetAlignment; + webgpu_context ctx = get_per_thread_webgpu_context(buft->device); + return ctx->global_ctx->capabilities.limits.minStorageBufferOffsetAlignment; } // maxBufferSize might be larger, but you can't bind more than maxStorageBufferBindingSize to a single binding. static size_t ggml_backend_webgpu_buffer_type_get_max_size(ggml_backend_buffer_type_t buft) { - ggml_backend_webgpu_context * ctx = static_cast(buft->context); - return ctx->webgpu_ctx->limits.maxStorageBufferBindingSize; + webgpu_context ctx = get_per_thread_webgpu_context(buft->device); + return ctx->global_ctx->capabilities.limits.maxStorageBufferBindingSize; } /* End GGML Backend Buffer Type Interface */ @@ -1894,11 +1915,11 @@ static const char * ggml_backend_webgpu_device_get_description(ggml_backend_dev_ } static void ggml_backend_webgpu_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) { - ggml_backend_webgpu_context * bctx = ggml_backend_webgpu_get_thread_backend_ctx(dev); + webgpu_context webgpu_ctx = get_per_thread_webgpu_context(dev); // TODO: for now, return maxBufferSize as both free and total memory // Track https://github.com/gpuweb/gpuweb/issues/5505 for updates. - uint64_t max_buffer_size = bctx->webgpu_ctx->limits.maxBufferSize; + uint64_t max_buffer_size = webgpu_ctx->global_ctx->capabilities.limits.maxBufferSize; // If we're on a 32-bit system, clamp to UINTPTR_MAX #if UINTPTR_MAX < UINT64_MAX uint64_t max_ptr_size = static_cast(UINTPTR_MAX); @@ -1943,64 +1964,64 @@ static std::vector ggml_webgpu_wg_size_entry(uint32_t wg_si static void ggml_webgpu_init_memset_pipeline(webgpu_context & webgpu_ctx) { // we use the maximum workgroup size for the memset pipeline - size_t max_threads = WEBGPU_MAX_WG_SIZE * webgpu_ctx->limits.maxComputeWorkgroupsPerDimension; + size_t max_threads = WEBGPU_MAX_WG_SIZE * webgpu_ctx->global_ctx->capabilities.limits.maxComputeWorkgroupsPerDimension; // Size the bytes_per_thread so that the largest buffer size can be handled - webgpu_ctx->memset_bytes_per_thread = CEIL_DIV(webgpu_ctx->limits.maxStorageBufferBindingSize, max_threads); + webgpu_ctx->memset_bytes_per_thread = CEIL_DIV(webgpu_ctx->global_ctx->capabilities.limits.maxStorageBufferBindingSize, max_threads); std::vector constants(2); constants[0].key = "wg_size"; constants[0].value = WEBGPU_MAX_WG_SIZE; constants[1].key = "bytes_per_thread"; constants[1].value = webgpu_ctx->memset_bytes_per_thread; - webgpu_ctx->memset_pipelines[0] = ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_memset, "memset", constants); + webgpu_ctx->memset_pipelines[0] = ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_memset, "memset", constants); } static void ggml_webgpu_init_mul_mat_pipeline(webgpu_context & webgpu_ctx) { // Q4/Q5/Q8 classic quantizations webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q4_0][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_q4_0_f32, "mul_mat_q4_0_f32"); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_mat_q4_0_f32, "mul_mat_q4_0_f32"); webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q4_1][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_q4_1_f32, "mul_mat_q4_1_f32"); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_mat_q4_1_f32, "mul_mat_q4_1_f32"); webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q5_0][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_q5_0_f32, "mul_mat_q5_0_f32"); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_mat_q5_0_f32, "mul_mat_q5_0_f32"); webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q5_1][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_q5_1_f32, "mul_mat_q5_1_f32"); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_mat_q5_1_f32, "mul_mat_q5_1_f32"); webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q8_0][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_q8_0_f32, "mul_mat_q8_0_f32"); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_mat_q8_0_f32, "mul_mat_q8_0_f32"); // K-quantizations webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q2_K][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_q2_k_f32, "mul_mat_q2_k_f32"); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_mat_q2_k_f32, "mul_mat_q2_k_f32"); webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q3_K][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_q3_k_f32, "mul_mat_q3_k_f32"); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_mat_q3_k_f32, "mul_mat_q3_k_f32"); webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q4_K][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_q4_k_f32, "mul_mat_q4_k_f32"); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_mat_q4_k_f32, "mul_mat_q4_k_f32"); webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q5_K][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_q5_k_f32, "mul_mat_q5_k_f32"); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_mat_q5_k_f32, "mul_mat_q5_k_f32"); webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q6_K][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_q6_k_f32, "mul_mat_q6_k_f32"); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_mat_q6_k_f32, "mul_mat_q6_k_f32"); // IQ quantizations (2-, 3-, 4-bit variants) webgpu_ctx->mul_mat_pipelines[GGML_TYPE_IQ2_XXS][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_iq2_xxs_f32, "mul_mat_iq2_xxs_f32"); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_mat_iq2_xxs_f32, "mul_mat_iq2_xxs_f32"); webgpu_ctx->mul_mat_pipelines[GGML_TYPE_IQ2_XS][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_iq2_xs_f32, "mul_mat_iq2_xs_f32"); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_mat_iq2_xs_f32, "mul_mat_iq2_xs_f32"); webgpu_ctx->mul_mat_pipelines[GGML_TYPE_IQ2_S][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_iq2_s_f32, "mul_mat_iq2_s_f32"); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_mat_iq2_s_f32, "mul_mat_iq2_s_f32"); webgpu_ctx->mul_mat_pipelines[GGML_TYPE_IQ3_XXS][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_iq3_xxs_f32, "mul_mat_iq3_xxs_f32"); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_mat_iq3_xxs_f32, "mul_mat_iq3_xxs_f32"); webgpu_ctx->mul_mat_pipelines[GGML_TYPE_IQ3_S][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_iq3_s_f32, "mul_mat_iq3_s_f32"); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_mat_iq3_s_f32, "mul_mat_iq3_s_f32"); // 1-bit and 4-bit IQ variants webgpu_ctx->mul_mat_pipelines[GGML_TYPE_IQ1_S][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_iq1_s_f32, "mul_mat_iq1_s_f32"); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_mat_iq1_s_f32, "mul_mat_iq1_s_f32"); webgpu_ctx->mul_mat_pipelines[GGML_TYPE_IQ1_M][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_iq1_m_f32, "mul_mat_iq1_m_f32"); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_mat_iq1_m_f32, "mul_mat_iq1_m_f32"); webgpu_ctx->mul_mat_pipelines[GGML_TYPE_IQ4_NL][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_iq4_nl_f32, "mul_mat_iq4_nl_f32"); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_mat_iq4_nl_f32, "mul_mat_iq4_nl_f32"); webgpu_ctx->mul_mat_pipelines[GGML_TYPE_IQ4_XS][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_iq4_xs_f32, "mul_mat_iq4_xs_f32"); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_mat_iq4_xs_f32, "mul_mat_iq4_xs_f32"); std::string proc_mul_mat_f32_f32; std::string proc_mul_mat_f32_f32_vec; @@ -2015,15 +2036,15 @@ static void ggml_webgpu_init_mul_mat_pipeline(webgpu_context & webgpu_ctx) { #ifndef __EMSCRIPTEN__ if (webgpu_ctx->supports_subgroup_matrix) { std::map sg_matrix_repls; - sg_matrix_repls["WEBGPU_MAX_SUBGROUP_SIZE"] = std::to_string(webgpu_ctx->max_subgroup_size); + sg_matrix_repls["WEBGPU_MAX_SUBGROUP_SIZE"] = std::to_string(webgpu_ctx->global_ctx->capabilities.max_subgroup_size); sg_matrix_repls["WEBGPU_TILE_K"] = std::to_string(WEBGPU_MUL_MAT_TILE_K); sg_matrix_repls["WEBGPU_SUBGROUP_M"] = std::to_string(WEBGPU_MUL_MAT_SUBGROUP_M); sg_matrix_repls["WEBGPU_SUBGROUP_N"] = std::to_string(WEBGPU_MUL_MAT_SUBGROUP_N); sg_matrix_repls["WEBGPU_SUBGROUP_MATRIX_M"] = std::to_string(WEBGPU_MUL_MAT_SUBGROUP_MATRIX_M); sg_matrix_repls["WEBGPU_SUBGROUP_MATRIX_N"] = std::to_string(WEBGPU_MUL_MAT_SUBGROUP_MATRIX_N); - sg_matrix_repls["WEBGPU_SG_MAT_M_SIZE"] = std::to_string(webgpu_ctx->sg_mat_m); - sg_matrix_repls["WEBGPU_SG_MAT_N_SIZE"] = std::to_string(webgpu_ctx->sg_mat_n); - sg_matrix_repls["WEBGPU_SG_MAT_K_SIZE"] = std::to_string(webgpu_ctx->sg_mat_k); + sg_matrix_repls["WEBGPU_SG_MAT_M_SIZE"] = std::to_string(webgpu_ctx->global_ctx->capabilities.subgroup_matrix_config.M); + sg_matrix_repls["WEBGPU_SG_MAT_N_SIZE"] = std::to_string(webgpu_ctx->global_ctx->capabilities.subgroup_matrix_config.N); + sg_matrix_repls["WEBGPU_SG_MAT_K_SIZE"] = std::to_string(webgpu_ctx->global_ctx->capabilities.subgroup_matrix_config.K); proc_mul_mat_f32_f32 = ggml_webgpu_process_shader_repls(wgsl_mul_mat_subgroup_matrix_f32_f32, sg_matrix_repls); proc_mul_mat_f32_f32_vec = @@ -2061,21 +2082,21 @@ static void ggml_webgpu_init_mul_mat_pipeline(webgpu_context & webgpu_ctx) { #endif webgpu_ctx->mul_mat_pipelines[GGML_TYPE_F32][GGML_TYPE_F32][0] = ggml_webgpu_create_pipeline( - webgpu_ctx->device, proc_mul_mat_f32_f32.c_str(), "mul_mat_f32_f32", mul_mat_constants); + webgpu_ctx->global_ctx->device, proc_mul_mat_f32_f32.c_str(), "mul_mat_f32_f32", mul_mat_constants); webgpu_ctx->mul_mat_pipelines[GGML_TYPE_F32][GGML_TYPE_F32][1] = ggml_webgpu_create_pipeline( - webgpu_ctx->device, proc_mul_mat_f32_f32_vec.c_str(), "mul_mat_f32_f32_vec", mul_mat_constants); + webgpu_ctx->global_ctx->device, proc_mul_mat_f32_f32_vec.c_str(), "mul_mat_f32_f32_vec", mul_mat_constants); webgpu_ctx->mul_mat_pipelines[GGML_TYPE_F16][GGML_TYPE_F32][0] = ggml_webgpu_create_pipeline( - webgpu_ctx->device, proc_mul_mat_f16_f32.c_str(), "mul_mat_f16_f32", mul_mat_constants); + webgpu_ctx->global_ctx->device, proc_mul_mat_f16_f32.c_str(), "mul_mat_f16_f32", mul_mat_constants); webgpu_ctx->mul_mat_pipelines[GGML_TYPE_F16][GGML_TYPE_F32][1] = ggml_webgpu_create_pipeline( - webgpu_ctx->device, proc_mul_mat_f16_f32_vec.c_str(), "mul_mat_f16_f32_vec", mul_mat_constants); + webgpu_ctx->global_ctx->device, proc_mul_mat_f16_f32_vec.c_str(), "mul_mat_f16_f32_vec", mul_mat_constants); webgpu_ctx->mul_mat_pipelines[GGML_TYPE_F16][GGML_TYPE_F16][0] = ggml_webgpu_create_pipeline( - webgpu_ctx->device, proc_mul_mat_f16_f16.c_str(), "mul_mat_f16_f16", mul_mat_constants); + webgpu_ctx->global_ctx->device, proc_mul_mat_f16_f16.c_str(), "mul_mat_f16_f16", mul_mat_constants); webgpu_ctx->mul_mat_pipelines[GGML_TYPE_F16][GGML_TYPE_F16][1] = ggml_webgpu_create_pipeline( - webgpu_ctx->device, proc_mul_mat_f16_f16_vec.c_str(), "mul_mat_f16_f16_vec", mul_mat_constants); + webgpu_ctx->global_ctx->device, proc_mul_mat_f16_f16_vec.c_str(), "mul_mat_f16_f16_vec", mul_mat_constants); webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q4_0][GGML_TYPE_F32][0] = ggml_webgpu_create_pipeline( - webgpu_ctx->device, proc_mul_mat_q4_0_f32.c_str(), "mul_mat_q4_0_f32", mul_mat_constants); + webgpu_ctx->global_ctx->device, proc_mul_mat_q4_0_f32.c_str(), "mul_mat_q4_0_f32", mul_mat_constants); webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q4_0][GGML_TYPE_F32][1] = ggml_webgpu_create_pipeline( - webgpu_ctx->device, proc_mul_mat_q4_0_f32_vec.c_str(), "mul_mat_q4_0_f32_vec", mul_mat_constants); + webgpu_ctx->global_ctx->device, proc_mul_mat_q4_0_f32_vec.c_str(), "mul_mat_q4_0_f32_vec", mul_mat_constants); std::vector mul_mat_vec_constants(3); mul_mat_vec_constants[0].key = "WORKGROUP_SIZE"; @@ -2086,176 +2107,176 @@ static void ggml_webgpu_init_mul_mat_pipeline(webgpu_context & webgpu_ctx) { mul_mat_vec_constants[2].value = WEBGPU_MUL_MAT_VEC_OUTPUTS_PER_WG; webgpu_ctx->mul_mat_vec_pipelines[GGML_TYPE_F32][GGML_TYPE_F32][0] = ggml_webgpu_create_pipeline( - webgpu_ctx->device, wgsl_mul_mat_vec_f32_f32, "mul_mat_vec_f32_f32", mul_mat_vec_constants); + webgpu_ctx->global_ctx->device, wgsl_mul_mat_vec_f32_f32, "mul_mat_vec_f32_f32", mul_mat_vec_constants); webgpu_ctx->mul_mat_vec_pipelines[GGML_TYPE_F32][GGML_TYPE_F32][1] = ggml_webgpu_create_pipeline( - webgpu_ctx->device, wgsl_mul_mat_vec_f32_f32_vec, "mul_mat_vec_f32_f32_vec", mul_mat_vec_constants); + webgpu_ctx->global_ctx->device, wgsl_mul_mat_vec_f32_f32_vec, "mul_mat_vec_f32_f32_vec", mul_mat_vec_constants); webgpu_ctx->mul_mat_vec_pipelines[GGML_TYPE_F16][GGML_TYPE_F32][0] = ggml_webgpu_create_pipeline( - webgpu_ctx->device, wgsl_mul_mat_vec_f16_f32, "mul_mat_vec_f16_f32", mul_mat_vec_constants); + webgpu_ctx->global_ctx->device, wgsl_mul_mat_vec_f16_f32, "mul_mat_vec_f16_f32", mul_mat_vec_constants); webgpu_ctx->mul_mat_vec_pipelines[GGML_TYPE_F16][GGML_TYPE_F32][1] = ggml_webgpu_create_pipeline( - webgpu_ctx->device, wgsl_mul_mat_vec_f16_f32_vec, "mul_mat_vec_f16_f32_vec", mul_mat_vec_constants); + webgpu_ctx->global_ctx->device, wgsl_mul_mat_vec_f16_f32_vec, "mul_mat_vec_f16_f32_vec", mul_mat_vec_constants); webgpu_ctx->mul_mat_vec_pipelines[GGML_TYPE_F16][GGML_TYPE_F16][0] = ggml_webgpu_create_pipeline( - webgpu_ctx->device, wgsl_mul_mat_vec_f16_f16, "mul_mat_vec_f16_f16", mul_mat_vec_constants); + webgpu_ctx->global_ctx->device, wgsl_mul_mat_vec_f16_f16, "mul_mat_vec_f16_f16", mul_mat_vec_constants); webgpu_ctx->mul_mat_vec_pipelines[GGML_TYPE_F16][GGML_TYPE_F16][1] = ggml_webgpu_create_pipeline( - webgpu_ctx->device, wgsl_mul_mat_vec_f16_f16_vec, "mul_mat_vec_f16_f16_vec", mul_mat_vec_constants); + webgpu_ctx->global_ctx->device, wgsl_mul_mat_vec_f16_f16_vec, "mul_mat_vec_f16_f16_vec", mul_mat_vec_constants); webgpu_ctx->mul_mat_vec_pipelines[GGML_TYPE_Q4_0][GGML_TYPE_F32][0] = ggml_webgpu_create_pipeline( - webgpu_ctx->device, wgsl_mul_mat_vec_q4_0_f32, "mul_mat_vec_q4_0_f32", mul_mat_vec_constants); + webgpu_ctx->global_ctx->device, wgsl_mul_mat_vec_q4_0_f32, "mul_mat_vec_q4_0_f32", mul_mat_vec_constants); } static void ggml_webgpu_init_set_rows_pipeline(webgpu_context & webgpu_ctx) { webgpu_ctx->set_rows_pipelines[0][0] = ggml_webgpu_create_pipeline( - webgpu_ctx->device, wgsl_set_rows_f16, "set_rows_f16", ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE)); + webgpu_ctx->global_ctx->device, wgsl_set_rows_f16, "set_rows_f16", ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE)); webgpu_ctx->set_rows_pipelines[0][1] = ggml_webgpu_create_pipeline( - webgpu_ctx->device, wgsl_set_rows_f16_vec, "set_rows_f16_vec", ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE)); + webgpu_ctx->global_ctx->device, wgsl_set_rows_f16_vec, "set_rows_f16_vec", ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE)); } static void ggml_webgpu_init_get_rows_pipeline(webgpu_context & webgpu_ctx) { std::vector constants = ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE); webgpu_ctx->get_rows_pipelines[GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_f32, "get_rows_f32", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_f32, "get_rows_f32", constants); webgpu_ctx->get_rows_pipelines[GGML_TYPE_F32][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_f32_vec, "get_rows_f32_vec", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_f32_vec, "get_rows_f32_vec", constants); webgpu_ctx->get_rows_pipelines[GGML_TYPE_F16][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_f16, "get_rows_f16", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_f16, "get_rows_f16", constants); webgpu_ctx->get_rows_pipelines[GGML_TYPE_I32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_i32, "get_rows_i32", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_i32, "get_rows_i32", constants); webgpu_ctx->get_rows_pipelines[GGML_TYPE_Q4_0][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_q4_0, "get_rows_q4_0", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_q4_0, "get_rows_q4_0", constants); webgpu_ctx->get_rows_pipelines[GGML_TYPE_Q4_1][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_q4_1, "get_rows_q4_1", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_q4_1, "get_rows_q4_1", constants); webgpu_ctx->get_rows_pipelines[GGML_TYPE_Q5_0][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_q5_0, "get_rows_q5_0", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_q5_0, "get_rows_q5_0", constants); webgpu_ctx->get_rows_pipelines[GGML_TYPE_Q5_1][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_q5_1, "get_rows_q5_1", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_q5_1, "get_rows_q5_1", constants); webgpu_ctx->get_rows_pipelines[GGML_TYPE_Q8_0][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_q8_0, "get_rows_q8_0", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_q8_0, "get_rows_q8_0", constants); webgpu_ctx->get_rows_pipelines[GGML_TYPE_Q2_K][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_q2_k, "get_rows_q2_k", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_q2_k, "get_rows_q2_k", constants); webgpu_ctx->get_rows_pipelines[GGML_TYPE_Q3_K][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_q3_k, "get_rows_q3_k", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_q3_k, "get_rows_q3_k", constants); webgpu_ctx->get_rows_pipelines[GGML_TYPE_Q4_K][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_q4_k, "get_rows_q4_k", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_q4_k, "get_rows_q4_k", constants); webgpu_ctx->get_rows_pipelines[GGML_TYPE_Q5_K][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_q5_k, "get_rows_q5_k", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_q5_k, "get_rows_q5_k", constants); webgpu_ctx->get_rows_pipelines[GGML_TYPE_Q6_K][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_q6_k, "get_rows_q6_k", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_q6_k, "get_rows_q6_k", constants); webgpu_ctx->get_rows_pipelines[GGML_TYPE_IQ2_XXS][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_iq2_xxs, "get_rows_iq2_xxs", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_iq2_xxs, "get_rows_iq2_xxs", constants); webgpu_ctx->get_rows_pipelines[GGML_TYPE_IQ2_XS][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_iq2_xs, "get_rows_iq2_xs", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_iq2_xs, "get_rows_iq2_xs", constants); webgpu_ctx->get_rows_pipelines[GGML_TYPE_IQ2_S][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_iq2_s, "get_rows_iq2_s", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_iq2_s, "get_rows_iq2_s", constants); webgpu_ctx->get_rows_pipelines[GGML_TYPE_IQ3_XXS][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_iq3_xxs, "get_rows_iq3_xxs", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_iq3_xxs, "get_rows_iq3_xxs", constants); webgpu_ctx->get_rows_pipelines[GGML_TYPE_IQ3_S][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_iq3_s, "get_rows_iq3_s", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_iq3_s, "get_rows_iq3_s", constants); webgpu_ctx->get_rows_pipelines[GGML_TYPE_IQ1_S][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_iq1_s, "get_rows_iq1_s", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_iq1_s, "get_rows_iq1_s", constants); webgpu_ctx->get_rows_pipelines[GGML_TYPE_IQ1_M][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_iq1_m, "get_rows_iq1_m", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_iq1_m, "get_rows_iq1_m", constants); webgpu_ctx->get_rows_pipelines[GGML_TYPE_IQ4_NL][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_iq4_nl, "get_rows_iq4_nl", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_iq4_nl, "get_rows_iq4_nl", constants); webgpu_ctx->get_rows_pipelines[GGML_TYPE_IQ4_XS][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_iq4_xs, "get_rows_iq4_xs", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_iq4_xs, "get_rows_iq4_xs", constants); } static void ggml_webgpu_init_cpy_pipeline(webgpu_context & webgpu_ctx) { std::vector constants = ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE); webgpu_ctx->cpy_pipelines[GGML_TYPE_F32][GGML_TYPE_F32] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_cpy_f32_f32, "cpy_f32_f32", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_cpy_f32_f32, "cpy_f32_f32", constants); webgpu_ctx->cpy_pipelines[GGML_TYPE_F32][GGML_TYPE_F16] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_cpy_f32_f16, "cpy_f32_f16", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_cpy_f32_f16, "cpy_f32_f16", constants); webgpu_ctx->cpy_pipelines[GGML_TYPE_F16][GGML_TYPE_F32] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_cpy_f16_f32, "cpy_f16_f32", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_cpy_f16_f32, "cpy_f16_f32", constants); webgpu_ctx->cpy_pipelines[GGML_TYPE_F16][GGML_TYPE_F16] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_cpy_f16_f16, "cpy_f16_f16", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_cpy_f16_f16, "cpy_f16_f16", constants); } static void ggml_webgpu_init_add_pipeline(webgpu_context & webgpu_ctx) { std::vector constants = ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE); webgpu_ctx->add_pipelines[GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_add_f32, "add_f32", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_add_f32, "add_f32", constants); webgpu_ctx->add_pipelines[GGML_TYPE_F16][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_add_f16, "add_f16", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_add_f16, "add_f16", constants); webgpu_ctx->add_pipelines[GGML_TYPE_F32][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_add_f32_inplace, "add_f32_inplace", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_add_f32_inplace, "add_f32_inplace", constants); webgpu_ctx->add_pipelines[GGML_TYPE_F16][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_add_f16_inplace, "add_f16_inplace", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_add_f16_inplace, "add_f16_inplace", constants); } static void ggml_webgpu_init_sub_pipeline(webgpu_context & webgpu_ctx) { std::vector constants = ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE); webgpu_ctx->sub_pipelines[GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_sub_f32, "sub_f32", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_sub_f32, "sub_f32", constants); webgpu_ctx->sub_pipelines[GGML_TYPE_F16][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_sub_f16, "sub_f16", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_sub_f16, "sub_f16", constants); webgpu_ctx->sub_pipelines[GGML_TYPE_F32][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_sub_f32_inplace, "sub_f32_inplace", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_sub_f32_inplace, "sub_f32_inplace", constants); webgpu_ctx->sub_pipelines[GGML_TYPE_F16][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_sub_f16_inplace, "sub_f16_inplace", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_sub_f16_inplace, "sub_f16_inplace", constants); } static void ggml_webgpu_init_mul_pipeline(webgpu_context & webgpu_ctx) { std::vector constants = ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE); webgpu_ctx->mul_pipelines[GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_f32, "mul_f32", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_f32, "mul_f32", constants); webgpu_ctx->mul_pipelines[GGML_TYPE_F16][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_f16, "mul_f16", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_f16, "mul_f16", constants); webgpu_ctx->mul_pipelines[GGML_TYPE_F32][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_f32_inplace, "mul_f32_inplace", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_f32_inplace, "mul_f32_inplace", constants); webgpu_ctx->mul_pipelines[GGML_TYPE_F16][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_f16_inplace, "mul_f16_inplace", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_mul_f16_inplace, "mul_f16_inplace", constants); } static void ggml_webgpu_init_div_pipeline(webgpu_context & webgpu_ctx) { std::vector constants = ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE); webgpu_ctx->div_pipelines[GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_div_f32, "div_f32", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_div_f32, "div_f32", constants); webgpu_ctx->div_pipelines[GGML_TYPE_F16][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_div_f16, "div_f16", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_div_f16, "div_f16", constants); webgpu_ctx->div_pipelines[GGML_TYPE_F32][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_div_f32_inplace, "div_f32_inplace", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_div_f32_inplace, "div_f32_inplace", constants); webgpu_ctx->div_pipelines[GGML_TYPE_F16][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_div_f16_inplace, "div_f16_inplace", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_div_f16_inplace, "div_f16_inplace", constants); } static void ggml_webgpu_init_rms_norm_pipeline(webgpu_context & webgpu_ctx) { std::vector constants = ggml_webgpu_wg_size_entry(WEBGPU_ROW_SPLIT_WG_SIZE); webgpu_ctx->rms_norm_pipelines[0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_rms_norm, "rms_norm", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_rms_norm, "rms_norm", constants); webgpu_ctx->rms_norm_pipelines[1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_rms_norm_inplace, "rms_norm_inplace", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_rms_norm_inplace, "rms_norm_inplace", constants); } static void ggml_webgpu_init_rope_pipeline(webgpu_context & webgpu_ctx) { std::vector constants = ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE); webgpu_ctx->rope_pipelines[GGML_TYPE_F32][0][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_rope_f32, "rope_f32", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_rope_f32, "rope_f32", constants); webgpu_ctx->rope_pipelines[GGML_TYPE_F32][0][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_rope_f32_inplace, "rope_f32_inplace", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_rope_f32_inplace, "rope_f32_inplace", constants); webgpu_ctx->rope_pipelines[GGML_TYPE_F32][1][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_rope_f32_ff, "rope_f32_ff", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_rope_f32_ff, "rope_f32_ff", constants); webgpu_ctx->rope_pipelines[GGML_TYPE_F32][1][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_rope_f32_ff_inplace, "rope_f32_ff_inplace", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_rope_f32_ff_inplace, "rope_f32_ff_inplace", constants); webgpu_ctx->rope_pipelines[GGML_TYPE_F16][0][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_rope_f16, "rope_f16", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_rope_f16, "rope_f16", constants); webgpu_ctx->rope_pipelines[GGML_TYPE_F16][0][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_rope_f16_inplace, "rope_f16_inplace", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_rope_f16_inplace, "rope_f16_inplace", constants); webgpu_ctx->rope_pipelines[GGML_TYPE_F16][1][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_rope_f16_ff, "rope_f16_ff", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_rope_f16_ff, "rope_f16_ff", constants); webgpu_ctx->rope_pipelines[GGML_TYPE_F16][1][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_rope_f16_ff_inplace, "rope_f16_ff_inplace", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_rope_f16_ff_inplace, "rope_f16_ff_inplace", constants); } static void ggml_webgpu_init_glu_pipeline(webgpu_context & webgpu_ctx) { @@ -2263,59 +2284,59 @@ static void ggml_webgpu_init_glu_pipeline(webgpu_context & webgpu_ctx) { // REGLU webgpu_ctx->glu_pipelines[GGML_GLU_OP_REGLU][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_reglu_f32, "reglu_f32", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_reglu_f32, "reglu_f32", constants); webgpu_ctx->glu_pipelines[GGML_GLU_OP_REGLU][GGML_TYPE_F16][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_reglu_f16, "reglu_f16", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_reglu_f16, "reglu_f16", constants); webgpu_ctx->glu_pipelines[GGML_GLU_OP_REGLU][GGML_TYPE_F32][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_reglu_f32_split, "reglu_f32_split", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_reglu_f32_split, "reglu_f32_split", constants); webgpu_ctx->glu_pipelines[GGML_GLU_OP_REGLU][GGML_TYPE_F16][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_reglu_f16_split, "reglu_f16_split", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_reglu_f16_split, "reglu_f16_split", constants); // GEGLU webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_geglu_f32, "geglu_f32", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_geglu_f32, "geglu_f32", constants); webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU][GGML_TYPE_F16][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_geglu_f16, "geglu_f16", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_geglu_f16, "geglu_f16", constants); webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU][GGML_TYPE_F32][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_geglu_f32_split, "geglu_f32_split", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_geglu_f32_split, "geglu_f32_split", constants); webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU][GGML_TYPE_F16][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_geglu_f16_split, "geglu_f16_split", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_geglu_f16_split, "geglu_f16_split", constants); // SWIGLU webgpu_ctx->glu_pipelines[GGML_GLU_OP_SWIGLU][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_swiglu_f32, "swiglu_f32", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_swiglu_f32, "swiglu_f32", constants); webgpu_ctx->glu_pipelines[GGML_GLU_OP_SWIGLU][GGML_TYPE_F16][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_swiglu_f16, "swiglu_f16", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_swiglu_f16, "swiglu_f16", constants); webgpu_ctx->glu_pipelines[GGML_GLU_OP_SWIGLU][GGML_TYPE_F32][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_swiglu_f32_split, "swiglu_f32_split", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_swiglu_f32_split, "swiglu_f32_split", constants); webgpu_ctx->glu_pipelines[GGML_GLU_OP_SWIGLU][GGML_TYPE_F16][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_swiglu_f16_split, "swiglu_f16_split", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_swiglu_f16_split, "swiglu_f16_split", constants); // SWIGLU_OAI webgpu_ctx->glu_pipelines[GGML_GLU_OP_SWIGLU_OAI][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_swiglu_oai_f32, "swiglu_oai_f32", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_swiglu_oai_f32, "swiglu_oai_f32", constants); webgpu_ctx->glu_pipelines[GGML_GLU_OP_SWIGLU_OAI][GGML_TYPE_F32][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_swiglu_oai_f32_split, "swiglu_oai_f32_split", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_swiglu_oai_f32_split, "swiglu_oai_f32_split", constants); // GEGLU_ERF webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU_ERF][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_geglu_erf_f32, "geglu_erf_f32", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_geglu_erf_f32, "geglu_erf_f32", constants); webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU_ERF][GGML_TYPE_F16][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_geglu_erf_f16, "geglu_erf_f16", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_geglu_erf_f16, "geglu_erf_f16", constants); webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU_ERF][GGML_TYPE_F32][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_geglu_erf_f32_split, "geglu_erf_f32_split", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_geglu_erf_f32_split, "geglu_erf_f32_split", constants); webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU_ERF][GGML_TYPE_F16][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_geglu_erf_f16_split, "geglu_erf_f16_split", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_geglu_erf_f16_split, "geglu_erf_f16_split", constants); // GEGLU_QUICK webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU_QUICK][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_geglu_quick_f32, "geglu_quick_f32", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_geglu_quick_f32, "geglu_quick_f32", constants); webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU_QUICK][GGML_TYPE_F16][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_geglu_quick_f16, "geglu_quick_f16", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_geglu_quick_f16, "geglu_quick_f16", constants); webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU_QUICK][GGML_TYPE_F32][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_geglu_quick_f32_split, "geglu_quick_f32_split", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_geglu_quick_f32_split, "geglu_quick_f32_split", constants); webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU_QUICK][GGML_TYPE_F16][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_geglu_quick_f16_split, "geglu_quick_f16_split", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_geglu_quick_f16_split, "geglu_quick_f16_split", constants); } static void ggml_webgpu_init_unary_pipeline(webgpu_context & webgpu_ctx) { @@ -2323,182 +2344,182 @@ static void ggml_webgpu_init_unary_pipeline(webgpu_context & webgpu_ctx) { // ABS webgpu_ctx->unary_pipelines[GGML_UNARY_OP_ABS][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_abs_f32, "abs_f32", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_abs_f32, "abs_f32", constants); webgpu_ctx->unary_pipelines[GGML_UNARY_OP_ABS][GGML_TYPE_F16][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_abs_f16, "abs_f16", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_abs_f16, "abs_f16", constants); webgpu_ctx->unary_pipelines[GGML_UNARY_OP_ABS][GGML_TYPE_F32][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_abs_inplace_f32, "abs_inplace_f32", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_abs_inplace_f32, "abs_inplace_f32", constants); webgpu_ctx->unary_pipelines[GGML_UNARY_OP_ABS][GGML_TYPE_F16][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_abs_inplace_f16, "abs_inplace_f16", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_abs_inplace_f16, "abs_inplace_f16", constants); // SGN webgpu_ctx->unary_pipelines[GGML_UNARY_OP_SGN][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_sgn_f32, "sgn_f32", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_sgn_f32, "sgn_f32", constants); webgpu_ctx->unary_pipelines[GGML_UNARY_OP_SGN][GGML_TYPE_F16][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_sgn_f16, "sgn_f16", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_sgn_f16, "sgn_f16", constants); webgpu_ctx->unary_pipelines[GGML_UNARY_OP_SGN][GGML_TYPE_F32][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_sgn_inplace_f32, "sgn_inplace_f32", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_sgn_inplace_f32, "sgn_inplace_f32", constants); webgpu_ctx->unary_pipelines[GGML_UNARY_OP_SGN][GGML_TYPE_F16][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_sgn_inplace_f16, "sgn_inplace_f16", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_sgn_inplace_f16, "sgn_inplace_f16", constants); // NEG webgpu_ctx->unary_pipelines[GGML_UNARY_OP_NEG][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_neg_f32, "neg_f32", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_neg_f32, "neg_f32", constants); webgpu_ctx->unary_pipelines[GGML_UNARY_OP_NEG][GGML_TYPE_F16][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_neg_f16, "neg_f16", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_neg_f16, "neg_f16", constants); webgpu_ctx->unary_pipelines[GGML_UNARY_OP_NEG][GGML_TYPE_F32][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_neg_inplace_f32, "neg_inplace_f32", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_neg_inplace_f32, "neg_inplace_f32", constants); webgpu_ctx->unary_pipelines[GGML_UNARY_OP_NEG][GGML_TYPE_F16][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_neg_inplace_f16, "neg_inplace_f16", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_neg_inplace_f16, "neg_inplace_f16", constants); // STEP webgpu_ctx->unary_pipelines[GGML_UNARY_OP_STEP][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_step_f32, "step_f32", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_step_f32, "step_f32", constants); webgpu_ctx->unary_pipelines[GGML_UNARY_OP_STEP][GGML_TYPE_F16][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_step_f16, "step_f16", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_step_f16, "step_f16", constants); webgpu_ctx->unary_pipelines[GGML_UNARY_OP_STEP][GGML_TYPE_F32][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_step_inplace_f32, "step_inplace_f32", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_step_inplace_f32, "step_inplace_f32", constants); webgpu_ctx->unary_pipelines[GGML_UNARY_OP_STEP][GGML_TYPE_F16][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_step_inplace_f16, "step_inplace_f16", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_step_inplace_f16, "step_inplace_f16", constants); // TANH webgpu_ctx->unary_pipelines[GGML_UNARY_OP_TANH][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_tanh_f32, "tanh_f32", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_tanh_f32, "tanh_f32", constants); webgpu_ctx->unary_pipelines[GGML_UNARY_OP_TANH][GGML_TYPE_F16][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_tanh_f16, "tanh_f16", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_tanh_f16, "tanh_f16", constants); webgpu_ctx->unary_pipelines[GGML_UNARY_OP_TANH][GGML_TYPE_F32][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_tanh_inplace_f32, "tanh_inplace_f32", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_tanh_inplace_f32, "tanh_inplace_f32", constants); webgpu_ctx->unary_pipelines[GGML_UNARY_OP_TANH][GGML_TYPE_F16][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_tanh_inplace_f16, "tanh_inplace_f16", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_tanh_inplace_f16, "tanh_inplace_f16", constants); // ELU webgpu_ctx->unary_pipelines[GGML_UNARY_OP_ELU][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_elu_f32, "elu_f32", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_elu_f32, "elu_f32", constants); webgpu_ctx->unary_pipelines[GGML_UNARY_OP_ELU][GGML_TYPE_F16][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_elu_f16, "elu_f16", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_elu_f16, "elu_f16", constants); webgpu_ctx->unary_pipelines[GGML_UNARY_OP_ELU][GGML_TYPE_F32][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_elu_inplace_f32, "elu_inplace_f32", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_elu_inplace_f32, "elu_inplace_f32", constants); webgpu_ctx->unary_pipelines[GGML_UNARY_OP_ELU][GGML_TYPE_F16][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_elu_inplace_f16, "elu_inplace_f16", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_elu_inplace_f16, "elu_inplace_f16", constants); // RELU webgpu_ctx->unary_pipelines[GGML_UNARY_OP_RELU][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_relu_f32, "relu_f32", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_relu_f32, "relu_f32", constants); webgpu_ctx->unary_pipelines[GGML_UNARY_OP_RELU][GGML_TYPE_F16][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_relu_f16, "relu_f16", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_relu_f16, "relu_f16", constants); webgpu_ctx->unary_pipelines[GGML_UNARY_OP_RELU][GGML_TYPE_F32][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_relu_inplace_f32, "relu_inplace_f32", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_relu_inplace_f32, "relu_inplace_f32", constants); webgpu_ctx->unary_pipelines[GGML_UNARY_OP_RELU][GGML_TYPE_F16][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_relu_inplace_f16, "relu_inplace_f16", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_relu_inplace_f16, "relu_inplace_f16", constants); // SIGMOID webgpu_ctx->unary_pipelines[GGML_UNARY_OP_SIGMOID][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_sigmoid_f32, "sigmoid_f32", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_sigmoid_f32, "sigmoid_f32", constants); webgpu_ctx->unary_pipelines[GGML_UNARY_OP_SIGMOID][GGML_TYPE_F16][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_sigmoid_f16, "sigmoid_f16", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_sigmoid_f16, "sigmoid_f16", constants); webgpu_ctx->unary_pipelines[GGML_UNARY_OP_SIGMOID][GGML_TYPE_F32][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_sigmoid_inplace_f32, "sigmoid_inplace_f32", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_sigmoid_inplace_f32, "sigmoid_inplace_f32", constants); webgpu_ctx->unary_pipelines[GGML_UNARY_OP_SIGMOID][GGML_TYPE_F16][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_sigmoid_inplace_f16, "sigmoid_inplace_f16", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_sigmoid_inplace_f16, "sigmoid_inplace_f16", constants); // GELU webgpu_ctx->unary_pipelines[GGML_UNARY_OP_GELU][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_gelu_f32, "gelu_f32", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_gelu_f32, "gelu_f32", constants); webgpu_ctx->unary_pipelines[GGML_UNARY_OP_GELU][GGML_TYPE_F16][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_gelu_f16, "gelu_f16", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_gelu_f16, "gelu_f16", constants); webgpu_ctx->unary_pipelines[GGML_UNARY_OP_GELU][GGML_TYPE_F32][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_gelu_inplace_f32, "gelu_inplace_f32", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_gelu_inplace_f32, "gelu_inplace_f32", constants); webgpu_ctx->unary_pipelines[GGML_UNARY_OP_GELU][GGML_TYPE_F16][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_gelu_inplace_f16, "gelu_inplace_f16", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_gelu_inplace_f16, "gelu_inplace_f16", constants); // GELU_QUICK webgpu_ctx->unary_pipelines[GGML_UNARY_OP_GELU_QUICK][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_gelu_quick_f32, "gelu_quick_f32", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_gelu_quick_f32, "gelu_quick_f32", constants); webgpu_ctx->unary_pipelines[GGML_UNARY_OP_GELU_QUICK][GGML_TYPE_F16][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_gelu_quick_f16, "gelu_quick_f16", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_gelu_quick_f16, "gelu_quick_f16", constants); webgpu_ctx->unary_pipelines[GGML_UNARY_OP_GELU_QUICK][GGML_TYPE_F32][1] = ggml_webgpu_create_pipeline( - webgpu_ctx->device, wgsl_gelu_quick_inplace_f32, "gelu_quick_inplace_f32", constants); + webgpu_ctx->global_ctx->device, wgsl_gelu_quick_inplace_f32, "gelu_quick_inplace_f32", constants); webgpu_ctx->unary_pipelines[GGML_UNARY_OP_GELU_QUICK][GGML_TYPE_F16][1] = ggml_webgpu_create_pipeline( - webgpu_ctx->device, wgsl_gelu_quick_inplace_f16, "gelu_quick_inplace_f16", constants); + webgpu_ctx->global_ctx->device, wgsl_gelu_quick_inplace_f16, "gelu_quick_inplace_f16", constants); // SILU webgpu_ctx->unary_pipelines[GGML_UNARY_OP_SILU][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_silu_f32, "silu_f32", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_silu_f32, "silu_f32", constants); webgpu_ctx->unary_pipelines[GGML_UNARY_OP_SILU][GGML_TYPE_F16][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_silu_f16, "silu_f16", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_silu_f16, "silu_f16", constants); webgpu_ctx->unary_pipelines[GGML_UNARY_OP_SILU][GGML_TYPE_F32][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_silu_inplace_f32, "silu_inplace_f32", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_silu_inplace_f32, "silu_inplace_f32", constants); webgpu_ctx->unary_pipelines[GGML_UNARY_OP_SILU][GGML_TYPE_F16][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_silu_inplace_f16, "silu_inplace_f16", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_silu_inplace_f16, "silu_inplace_f16", constants); // HARDSWISH webgpu_ctx->unary_pipelines[GGML_UNARY_OP_HARDSWISH][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_hardswish_f32, "hardswish_f32", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_hardswish_f32, "hardswish_f32", constants); webgpu_ctx->unary_pipelines[GGML_UNARY_OP_HARDSWISH][GGML_TYPE_F16][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_hardswish_f16, "hardswish_f16", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_hardswish_f16, "hardswish_f16", constants); webgpu_ctx->unary_pipelines[GGML_UNARY_OP_HARDSWISH][GGML_TYPE_F32][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_hardswish_inplace_f32, "hardswish_inplace_f32", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_hardswish_inplace_f32, "hardswish_inplace_f32", constants); webgpu_ctx->unary_pipelines[GGML_UNARY_OP_HARDSWISH][GGML_TYPE_F16][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_hardswish_inplace_f16, "hardswish_inplace_f16", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_hardswish_inplace_f16, "hardswish_inplace_f16", constants); // HARDSIGMOID webgpu_ctx->unary_pipelines[GGML_UNARY_OP_HARDSIGMOID][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_hardsigmoid_f32, "hardsigmoid_f32", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_hardsigmoid_f32, "hardsigmoid_f32", constants); webgpu_ctx->unary_pipelines[GGML_UNARY_OP_HARDSIGMOID][GGML_TYPE_F16][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_hardsigmoid_f16, "hardsigmoid_f16", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_hardsigmoid_f16, "hardsigmoid_f16", constants); webgpu_ctx->unary_pipelines[GGML_UNARY_OP_HARDSIGMOID][GGML_TYPE_F32][1] = ggml_webgpu_create_pipeline( - webgpu_ctx->device, wgsl_hardsigmoid_inplace_f32, "hardsigmoid_inplace_f32", constants); + webgpu_ctx->global_ctx->device, wgsl_hardsigmoid_inplace_f32, "hardsigmoid_inplace_f32", constants); webgpu_ctx->unary_pipelines[GGML_UNARY_OP_HARDSIGMOID][GGML_TYPE_F16][1] = ggml_webgpu_create_pipeline( - webgpu_ctx->device, wgsl_hardsigmoid_inplace_f16, "hardsigmoid_inplace_f16", constants); + webgpu_ctx->global_ctx->device, wgsl_hardsigmoid_inplace_f16, "hardsigmoid_inplace_f16", constants); // EXP webgpu_ctx->unary_pipelines[GGML_UNARY_OP_EXP][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_exp_f32, "exp_f32", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_exp_f32, "exp_f32", constants); webgpu_ctx->unary_pipelines[GGML_UNARY_OP_EXP][GGML_TYPE_F16][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_exp_f16, "exp_f16", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_exp_f16, "exp_f16", constants); webgpu_ctx->unary_pipelines[GGML_UNARY_OP_EXP][GGML_TYPE_F32][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_exp_inplace_f32, "exp_inplace_f32", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_exp_inplace_f32, "exp_inplace_f32", constants); webgpu_ctx->unary_pipelines[GGML_UNARY_OP_EXP][GGML_TYPE_F16][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_exp_inplace_f16, "exp_inplace_f16", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_exp_inplace_f16, "exp_inplace_f16", constants); // GELU_ERF webgpu_ctx->unary_pipelines[GGML_UNARY_OP_GELU_ERF][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_gelu_erf_f32, "gelu_erf_f32", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_gelu_erf_f32, "gelu_erf_f32", constants); webgpu_ctx->unary_pipelines[GGML_UNARY_OP_GELU_ERF][GGML_TYPE_F16][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_gelu_erf_f16, "gelu_erf_f16", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_gelu_erf_f16, "gelu_erf_f16", constants); webgpu_ctx->unary_pipelines[GGML_UNARY_OP_GELU_ERF][GGML_TYPE_F32][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_gelu_erf_inplace_f32, "gelu_erf_inplace_f32", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_gelu_erf_inplace_f32, "gelu_erf_inplace_f32", constants); webgpu_ctx->unary_pipelines[GGML_UNARY_OP_GELU_ERF][GGML_TYPE_F16][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_gelu_erf_inplace_f16, "gelu_erf_inplace_f16", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_gelu_erf_inplace_f16, "gelu_erf_inplace_f16", constants); // XIELU webgpu_ctx->unary_pipelines[GGML_UNARY_OP_XIELU][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_xielu_f32, "xielu_f32", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_xielu_f32, "xielu_f32", constants); webgpu_ctx->unary_pipelines[GGML_UNARY_OP_XIELU][GGML_TYPE_F16][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_xielu_f16, "xielu_f16", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_xielu_f16, "xielu_f16", constants); webgpu_ctx->unary_pipelines[GGML_UNARY_OP_XIELU][GGML_TYPE_F32][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_xielu_inplace_f32, "xielu_inplace_f32", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_xielu_inplace_f32, "xielu_inplace_f32", constants); webgpu_ctx->unary_pipelines[GGML_UNARY_OP_XIELU][GGML_TYPE_F16][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_xielu_inplace_f16, "xielu_inplace_f16", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_xielu_inplace_f16, "xielu_inplace_f16", constants); // CEIL webgpu_ctx->unary_pipelines[GGML_UNARY_OP_CEIL][GGML_TYPE_F32][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_ceil_f32, "ceil_f32", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_ceil_f32, "ceil_f32", constants); webgpu_ctx->unary_pipelines[GGML_UNARY_OP_CEIL][GGML_TYPE_F16][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_ceil_f16, "ceil_f16", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_ceil_f16, "ceil_f16", constants); webgpu_ctx->unary_pipelines[GGML_UNARY_OP_CEIL][GGML_TYPE_F32][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_ceil_inplace_f32, "ceil_inplace_f32", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_ceil_inplace_f32, "ceil_inplace_f32", constants); webgpu_ctx->unary_pipelines[GGML_UNARY_OP_CEIL][GGML_TYPE_F16][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_ceil_inplace_f16, "ceil_inplace_f16", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_ceil_inplace_f16, "ceil_inplace_f16", constants); } static void ggml_webgpu_init_scale_pipeline(webgpu_context & webgpu_ctx) { std::vector constants = ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE); webgpu_ctx->scale_pipelines[0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_scale_f32, "scale_f32", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_scale_f32, "scale_f32", constants); webgpu_ctx->scale_pipelines[1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_scale_f32_inplace, "scale_f32_inplace", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_scale_f32_inplace, "scale_f32_inplace", constants); } static void ggml_webgpu_init_soft_max_pipeline(webgpu_context & webgpu_ctx) { @@ -2506,37 +2527,37 @@ static void ggml_webgpu_init_soft_max_pipeline(webgpu_context & webgpu_ctx) { // f32 (no mask) webgpu_ctx->soft_max_pipelines[2][0][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_soft_max_f32, "soft_max_f32", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_soft_max_f32, "soft_max_f32", constants); webgpu_ctx->soft_max_pipelines[2][0][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_soft_max_f32_inplace, "soft_max_f32_inplace", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_soft_max_f32_inplace, "soft_max_f32_inplace", constants); webgpu_ctx->soft_max_pipelines[2][1][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_soft_max_f32_sink, "soft_max_f32_sink", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_soft_max_f32_sink, "soft_max_f32_sink", constants); webgpu_ctx->soft_max_pipelines[2][1][1] = ggml_webgpu_create_pipeline( - webgpu_ctx->device, wgsl_soft_max_f32_sink_inplace, "soft_max_f32_sink_inplace", constants); + webgpu_ctx->global_ctx->device, wgsl_soft_max_f32_sink_inplace, "soft_max_f32_sink_inplace", constants); // f32 mask (mask_type = 0) webgpu_ctx->soft_max_pipelines[0][0][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_soft_max_f32_mask_f32, "soft_max_f32_mask_f32", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_soft_max_f32_mask_f32, "soft_max_f32_mask_f32", constants); webgpu_ctx->soft_max_pipelines[0][0][1] = ggml_webgpu_create_pipeline( - webgpu_ctx->device, wgsl_soft_max_f32_mask_f32_inplace, "soft_max_f32_mask_f32_inplace", constants); + webgpu_ctx->global_ctx->device, wgsl_soft_max_f32_mask_f32_inplace, "soft_max_f32_mask_f32_inplace", constants); webgpu_ctx->soft_max_pipelines[0][1][0] = ggml_webgpu_create_pipeline( - webgpu_ctx->device, wgsl_soft_max_f32_mask_f32_sink, "soft_max_f32_mask_f32_sink", constants); + webgpu_ctx->global_ctx->device, wgsl_soft_max_f32_mask_f32_sink, "soft_max_f32_mask_f32_sink", constants); webgpu_ctx->soft_max_pipelines[0][1][1] = ggml_webgpu_create_pipeline( - webgpu_ctx->device, wgsl_soft_max_f32_mask_f32_sink_inplace, "soft_max_f32_mask_f32_sink_inplace", constants); + webgpu_ctx->global_ctx->device, wgsl_soft_max_f32_mask_f32_sink_inplace, "soft_max_f32_mask_f32_sink_inplace", constants); // f16 mask (mask_type = 1) webgpu_ctx->soft_max_pipelines[1][0][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_soft_max_f32_mask_f16, "soft_max_f32_mask_f16", constants); + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_soft_max_f32_mask_f16, "soft_max_f32_mask_f16", constants); webgpu_ctx->soft_max_pipelines[1][0][1] = ggml_webgpu_create_pipeline( - webgpu_ctx->device, wgsl_soft_max_f32_mask_f16_inplace, "soft_max_f32_mask_f16_inplace", constants); + webgpu_ctx->global_ctx->device, wgsl_soft_max_f32_mask_f16_inplace, "soft_max_f32_mask_f16_inplace", constants); webgpu_ctx->soft_max_pipelines[1][1][0] = ggml_webgpu_create_pipeline( - webgpu_ctx->device, wgsl_soft_max_f32_mask_f16_sink, "soft_max_f32_mask_f16_sink", constants); + webgpu_ctx->global_ctx->device, wgsl_soft_max_f32_mask_f16_sink, "soft_max_f32_mask_f16_sink", constants); webgpu_ctx->soft_max_pipelines[1][1][1] = ggml_webgpu_create_pipeline( - webgpu_ctx->device, wgsl_soft_max_f32_mask_f16_sink_inplace, "soft_max_f32_mask_f16_sink_inplace", constants); + webgpu_ctx->global_ctx->device, wgsl_soft_max_f32_mask_f16_sink_inplace, "soft_max_f32_mask_f16_sink_inplace", constants); } -static bool create_webgpu_device(webgpu_context ctx) { +static bool create_webgpu_device(ggml_backend_webgpu_reg_context * ctx) { wgpu::RequestAdapterOptions options = {}; @@ -2549,64 +2570,65 @@ static bool create_webgpu_device(webgpu_context ctx) { options.nextInChain = &adapterTogglesDesc; #endif - ctx->instance->WaitAny(ctx->instance->RequestAdapter( + ctx->wgpu_global_ctx.instance.WaitAny(ctx->wgpu_global_ctx.instance.RequestAdapter( &options, wgpu::CallbackMode::AllowSpontaneous, [&ctx](wgpu::RequestAdapterStatus status, wgpu::Adapter adapter, const char * message) { if (status != wgpu::RequestAdapterStatus::Success) { GGML_LOG_ERROR("ggml_webgpu: Failed to get an adapter: %s\n", message); return; } - ctx->adapter = std::move(adapter); + ctx->wgpu_global_ctx.adapter = std::move(adapter); }), UINT64_MAX); - GGML_ASSERT(ctx->adapter != nullptr); + GGML_ASSERT(ctx->wgpu_global_ctx.adapter != nullptr); - ctx->adapter.GetLimits(&ctx->limits); + // TODO: get webgpu_ctx.limits + ctx->wgpu_global_ctx.adapter.GetLimits(&ctx->wgpu_global_ctx.capabilities.limits); wgpu::AdapterInfo info{}; #ifndef __EMSCRIPTEN__ wgpu::AdapterPropertiesSubgroupMatrixConfigs subgroup_matrix_configs{}; - if (ctx->adapter.HasFeature(wgpu::FeatureName::ChromiumExperimentalSubgroupMatrix)) { + if (ctx->wgpu_global_ctx.adapter.HasFeature(wgpu::FeatureName::ChromiumExperimentalSubgroupMatrix)) { info.nextInChain = &subgroup_matrix_configs; } #endif - ctx->adapter.GetInfo(&info); + ctx->wgpu_global_ctx.adapter.GetInfo(&info); wgpu::SupportedFeatures features; - ctx->adapter.GetFeatures(&features); + ctx->wgpu_global_ctx.adapter.GetFeatures(&features); // we require f16 support - GGML_ASSERT(ctx->adapter.HasFeature(wgpu::FeatureName::ShaderF16)); + GGML_ASSERT(ctx->wgpu_global_ctx.adapter.HasFeature(wgpu::FeatureName::ShaderF16)); #ifndef __EMSCRIPTEN__ // Only support square f16 matrices of size 8 or 16 for now bool valid_subgroup_matrix_config = false; - if (ctx->adapter.HasFeature(wgpu::FeatureName::ChromiumExperimentalSubgroupMatrix)) { + if (ctx->wgpu_global_ctx.adapter.HasFeature(wgpu::FeatureName::ChromiumExperimentalSubgroupMatrix)) { for (size_t i = 0; i < subgroup_matrix_configs.configCount; i++) { const wgpu::SubgroupMatrixConfig config = subgroup_matrix_configs.configs[i]; if (config.M == config.N && config.N == config.K && (config.K == 8 || config.K == 16) && config.componentType == wgpu::SubgroupMatrixComponentType::F16 && config.resultComponentType == wgpu::SubgroupMatrixComponentType::F16) { - ctx->sg_mat_m = config.M; - ctx->sg_mat_n = config.N; - ctx->sg_mat_k = config.K; + // todo: what about sg_mat_m,n,k? is this global state as well? + ctx->wgpu_global_ctx.capabilities.subgroup_matrix_config = config; valid_subgroup_matrix_config = true; break; } } } - - ctx->supports_subgroup_matrix = valid_subgroup_matrix_config; + ctx->wgpu_global_ctx.capabilities.supports_subgroup_matrix = valid_subgroup_matrix_config; #endif + // For subgroup matrix code to be the most efficient, we would like the subgroup size to be consistent and accurate. // Unfortunately, that is not possible, so we use the maximum subgroup size reported by the adapter. - ctx->max_subgroup_size = info.subgroupMaxSize; + ctx->wgpu_global_ctx.capabilities.max_subgroup_size = info.subgroupMaxSize; // Initialize device std::vector required_features = { wgpu::FeatureName::ShaderF16 }; #ifndef __EMSCRIPTEN__ required_features.push_back(wgpu::FeatureName::ImplicitDeviceSynchronization); - if (ctx->supports_subgroup_matrix) { + // this too? + if (ctx->wgpu_global_ctx.capabilities.supports_subgroup_matrix) { required_features.push_back(wgpu::FeatureName::Subgroups); required_features.push_back(wgpu::FeatureName::ChromiumExperimentalSubgroupMatrix); } @@ -2617,7 +2639,7 @@ static bool create_webgpu_device(webgpu_context ctx) { #endif wgpu::DeviceDescriptor dev_desc; - dev_desc.requiredLimits = &ctx->limits; + dev_desc.requiredLimits = &ctx->wgpu_global_ctx.capabilities.limits; dev_desc.requiredFeatures = required_features.data(); dev_desc.requiredFeatureCount = required_features.size(); dev_desc.SetDeviceLostCallback( @@ -2653,7 +2675,7 @@ static bool create_webgpu_device(webgpu_context ctx) { dev_desc.nextInChain = &deviceTogglesDesc; #endif - ctx->instance->WaitAny(ctx->adapter.RequestDevice( + ctx->wgpu_global_ctx.instance.WaitAny(ctx->wgpu_global_ctx.adapter.RequestDevice( &dev_desc, wgpu::CallbackMode::AllowSpontaneous, [ctx](wgpu::RequestDeviceStatus status, wgpu::Device device, wgpu::StringView message) { if (status != wgpu::RequestDeviceStatus::Success) { @@ -2661,54 +2683,58 @@ static bool create_webgpu_device(webgpu_context ctx) { std::string(message).c_str()); return; } - ctx->device = std::move(device); + ctx->wgpu_global_ctx.device = std::move(device); }), UINT64_MAX); - GGML_ASSERT(ctx->device != nullptr); + GGML_ASSERT(ctx->wgpu_global_ctx.device != nullptr); // Initialize (compute) queue - ctx->queue = ctx->device.GetQueue(); + // MOVE THIS TO THREAD + // ctx->queue = ctx->device.GetQueue(); // Create buffer pool for shader parameters - ctx->param_buf_pool.init(ctx->device, WEBGPU_NUM_PARAM_BUFS, WEBGPU_PARAMS_BUF_SIZE_BYTES, - wgpu::BufferUsage::CopyDst | wgpu::BufferUsage::Uniform, - wgpu::BufferUsage::CopySrc | wgpu::BufferUsage::MapWrite); - -#ifdef GGML_WEBGPU_GPU_PROFILE - // Initialize buffer pool for timestamp queries (profiling) - ctx->timestamp_query_buf_pool.init(ctx->device, WEBGPU_NUM_TIMESTAMP_QUERY_BUFS, - WEBGPU_TIMESTAMP_QUERY_BUF_SIZE_BYTES, - wgpu::BufferUsage::QueryResolve | wgpu::BufferUsage::CopySrc, - wgpu::BufferUsage::MapRead | wgpu::BufferUsage::CopyDst); -#endif - - ctx->set_rows_error_buf_pool.init(ctx->device, WEBGPU_NUM_SET_ROWS_ERROR_BUFS, WEBGPU_SET_ROWS_ERROR_BUF_SIZE_BYTES, - wgpu::BufferUsage::CopySrc | wgpu::BufferUsage::Storage, - wgpu::BufferUsage::CopyDst | wgpu::BufferUsage::MapRead); - - ggml_webgpu_init_memset_pipeline(ctx); - ggml_webgpu_init_mul_mat_pipeline(ctx); - ggml_webgpu_init_set_rows_pipeline(ctx); - ggml_webgpu_init_get_rows_pipeline(ctx); - ggml_webgpu_init_cpy_pipeline(ctx); - ggml_webgpu_init_add_pipeline(ctx); - ggml_webgpu_init_sub_pipeline(ctx); - ggml_webgpu_init_mul_pipeline(ctx); - ggml_webgpu_init_div_pipeline(ctx); - ggml_webgpu_init_rms_norm_pipeline(ctx); - ggml_webgpu_init_rope_pipeline(ctx); - ggml_webgpu_init_glu_pipeline(ctx); - ggml_webgpu_init_scale_pipeline(ctx); - ggml_webgpu_init_soft_max_pipeline(ctx); - ggml_webgpu_init_unary_pipeline(ctx); - -#ifdef GGML_WEBGPU_DEBUG - // Initialize debug buffers - ggml_webgpu_create_buffer(ctx->device, ctx->debug_host_buf, WEBGPU_DEBUG_BUF_ELEMS * sizeof(uint32_t), - wgpu::BufferUsage::CopyDst | wgpu::BufferUsage::MapRead, "debug_host_buf"); - ggml_webgpu_create_buffer(ctx->device, ctx->debug_dev_buf, WEBGPU_DEBUG_BUF_ELEMS * sizeof(uint32_t), - wgpu::BufferUsage::Storage | wgpu::BufferUsage::CopySrc, "debug_dev_buf"); -#endif + // MOVE THIS TO THREAD + // ctx->param_buf_pool.init(ctx->device, WEBGPU_NUM_PARAM_BUFS, WEBGPU_PARAMS_BUF_SIZE_BYTES, + // wgpu::BufferUsage::CopyDst | wgpu::BufferUsage::Uniform, + // wgpu::BufferUsage::CopySrc | wgpu::BufferUsage::MapWrite); + + // Moved to Thread Local +// #ifdef GGML_WEBGPU_GPU_PROFILE +// // Initialize buffer pool for timestamp queries (profiling) +// ctx->timestamp_query_buf_pool.init(ctx->wgpu_global_ctx->device, WEBGPU_NUM_TIMESTAMP_QUERY_BUFS, +// WEBGPU_TIMESTAMP_QUERY_BUF_SIZE_BYTES, +// wgpu::BufferUsage::QueryResolve | wgpu::BufferUsage::CopySrc, +// wgpu::BufferUsage::MapRead | wgpu::BufferUsage::CopyDst); +// #endif + + // ctx->set_rows_error_buf_pool.init(ctx->wgpu_global_ctx->device, WEBGPU_NUM_SET_ROWS_ERROR_BUFS, WEBGPU_SET_ROWS_ERROR_BUF_SIZE_BYTES, + // wgpu::BufferUsage::CopySrc | wgpu::BufferUsage::Storage, + // wgpu::BufferUsage::CopyDst | wgpu::BufferUsage::MapRead); + + // todo: this is thread_local as well? + // ggml_webgpu_init_memset_pipeline(ctx); + // ggml_webgpu_init_mul_mat_pipeline(ctx); + // ggml_webgpu_init_set_rows_pipeline(ctx); + // ggml_webgpu_init_get_rows_pipeline(ctx); + // ggml_webgpu_init_cpy_pipeline(ctx); + // ggml_webgpu_init_add_pipeline(ctx); + // ggml_webgpu_init_sub_pipeline(ctx); + // ggml_webgpu_init_mul_pipeline(ctx); + // ggml_webgpu_init_div_pipeline(ctx); + // ggml_webgpu_init_rms_norm_pipeline(ctx); + // ggml_webgpu_init_rope_pipeline(ctx); + // ggml_webgpu_init_glu_pipeline(ctx); + // ggml_webgpu_init_scale_pipeline(ctx); + // ggml_webgpu_init_soft_max_pipeline(ctx); + // ggml_webgpu_init_unary_pipeline(ctx); + +// #ifdef GGML_WEBGPU_DEBUG +// // Initialize debug buffers +// ggml_webgpu_create_buffer(ctx->wgpu_global_ctx->device, ctx->debug_host_buf, WEBGPU_DEBUG_BUF_ELEMS * sizeof(uint32_t), +// wgpu::BufferUsage::CopyDst | wgpu::BufferUsage::MapRead, "debug_host_buf"); +// ggml_webgpu_create_buffer(ctx->wgpu_global_ctx->device, ctx->debug_dev_buf, WEBGPU_DEBUG_BUF_ELEMS * sizeof(uint32_t), +// wgpu::BufferUsage::Storage | wgpu::BufferUsage::CopySrc, "debug_dev_buf"); +// #endif GGML_LOG_INFO( "ggml_webgpu: adapter_info: vendor_id: %u | vendor: %s | architecture: %s | device_id: %u | name: %s | " "device_desc: %s\n", @@ -2717,37 +2743,60 @@ static bool create_webgpu_device(webgpu_context ctx) { return true; } -// Create the webgpu device at most once per ggml_backend_webgpu_context -static void ensure_webgpu_device_initialized(ggml_backend_webgpu_context * backend_ctx) { - GGML_ASSERT(backend_ctx); - GGML_ASSERT(backend_ctx->device_context); - GGML_ASSERT(backend_ctx->device_context->reg_context); - GGML_ASSERT(backend_ctx->webgpu_ctx); - - std::call_once(backend_ctx->init_once, [&] { - backend_ctx->webgpu_ctx->instance = backend_ctx->device_context->reg_context->instance.get(); - create_webgpu_device(backend_ctx->webgpu_ctx); - }); -} -// Fetch the backend context (and associated webgpu_context) for a device running on this thread -static ggml_backend_webgpu_context * ggml_backend_webgpu_get_thread_backend_ctx(ggml_backend_dev_t dev) { - thread_local std::unordered_map map; +// Fetch the webgpu_context for this thread (indexed by device for multiple devices running per thread) +// If it hasn't been created yet, then initiailize one. +static webgpu_context get_per_thread_webgpu_context(ggml_backend_dev_t dev) { + thread_local std::unordered_map map; ggml_backend_webgpu_device_context * dev_ctx = (ggml_backend_webgpu_device_context *) dev->context; - auto it = map.find(dev_ctx); - if (it != map.end()) { + if (auto it = map.find(dev_ctx); it != map.end()) { return it->second; } - auto * bctx = new ggml_backend_webgpu_context(); - bctx->device_context = dev_ctx; - bctx->webgpu_ctx = std::make_shared(); - bctx->name = std::string(GGML_WEBGPU_NAME); - ensure_webgpu_device_initialized(bctx); + webgpu_context webgpu_ctx = std::make_shared(); + webgpu_ctx->global_ctx = &(dev_ctx->reg_context->wgpu_global_ctx); + webgpu_ctx->queue = webgpu_ctx->global_ctx->device.GetQueue(); + webgpu_ctx->param_buf_pool.init(webgpu_ctx->global_ctx->device, + WEBGPU_NUM_PARAM_BUFS, WEBGPU_PARAMS_BUF_SIZE_BYTES, + wgpu::BufferUsage::CopyDst | wgpu::BufferUsage::Uniform, + wgpu::BufferUsage::CopySrc | wgpu::BufferUsage::MapWrite); +#ifdef GGML_WEBGPU_GPU_PROFILE + // Initialize buffer pool for timestamp queries, used for profiling + webgpu_ctx->timestamp_query_buf_pool.init(webgpu_ctx->wgpu_global_ctx->device, WEBGPU_NUM_TIMESTAMP_QUERY_BUFS, + WEBGPU_TIMESTAMP_QUERY_BUF_SIZE_BYTES, + wgpu::BufferUsage::QueryResolve | wgpu::BufferUsage::CopySrc, + wgpu::BufferUsage::MapRead | wgpu::BufferUsage::CopyDst); +#endif + webgpu_ctx->set_rows_error_buf_pool.init(webgpu_ctx->global_ctx->device, WEBGPU_NUM_SET_ROWS_ERROR_BUFS, WEBGPU_SET_ROWS_ERROR_BUF_SIZE_BYTES, + wgpu::BufferUsage::CopySrc | wgpu::BufferUsage::Storage, + wgpu::BufferUsage::CopyDst | wgpu::BufferUsage::MapRead); + + ggml_webgpu_init_memset_pipeline(webgpu_ctx); + ggml_webgpu_init_mul_mat_pipeline(webgpu_ctx); + ggml_webgpu_init_set_rows_pipeline(webgpu_ctx); + ggml_webgpu_init_get_rows_pipeline(webgpu_ctx); + ggml_webgpu_init_cpy_pipeline(webgpu_ctx); + ggml_webgpu_init_add_pipeline(webgpu_ctx); + ggml_webgpu_init_sub_pipeline(webgpu_ctx); + ggml_webgpu_init_mul_pipeline(webgpu_ctx); + ggml_webgpu_init_div_pipeline(webgpu_ctx); + ggml_webgpu_init_rms_norm_pipeline(webgpu_ctx); + ggml_webgpu_init_rope_pipeline(webgpu_ctx); + ggml_webgpu_init_glu_pipeline(webgpu_ctx); + ggml_webgpu_init_scale_pipeline(webgpu_ctx); + ggml_webgpu_init_soft_max_pipeline(webgpu_ctx); + ggml_webgpu_init_unary_pipeline(webgpu_ctx); +#ifdef GGML_WEBGPU_DEBUG + // Initialize debug buffers + ggml_webgpu_create_buffer(webgpu_ctx->global_ctx->device, webgpu_ctx->debug_host_buf, WEBGPU_DEBUG_BUF_ELEMS * sizeof(uint32_t), + wgpu::BufferUsage::CopyDst | wgpu::BufferUsage::MapRead, "debug_host_buf"); + ggml_webgpu_create_buffer(webgpu_ctx->global_ctx->device, webgpu_ctx->debug_dev_buf, WEBGPU_DEBUG_BUF_ELEMS * sizeof(uint32_t), + wgpu::BufferUsage::Storage | wgpu::BufferUsage::CopySrc, "debug_dev_buf"); +#endif - map[dev_ctx] = bctx; - return bctx; + map.try_emplace(dev_ctx, webgpu_ctx); + return webgpu_ctx; } static ggml_backend_t ggml_backend_webgpu_device_init(ggml_backend_dev_t dev, const char * params) { @@ -2760,8 +2809,6 @@ static ggml_backend_t ggml_backend_webgpu_device_init(ggml_backend_dev_t dev, co auto * backend_ctx = new ggml_backend_webgpu_context(); backend_ctx->name = GGML_WEBGPU_NAME + std::string(": ") + dev_ctx->device_name; backend_ctx->device_context = dev_ctx; - backend_ctx->webgpu_ctx = std::make_shared(); - ensure_webgpu_device_initialized(backend_ctx); // See GGML Backend Interface section auto * backend = new ggml_backend(); @@ -2778,8 +2825,7 @@ static ggml_backend_t ggml_backend_webgpu_device_init(ggml_backend_dev_t dev, co static ggml_backend_buffer_type_t ggml_backend_webgpu_device_get_buffer_type(ggml_backend_dev_t dev) { // See GGML Backend Buffer Type Interface section - // Keep this thread_local, since we need to get the webgpu_context from the ggml_backend_webgpu_context for this thread. - thread_local struct ggml_backend_buffer_type ggml_backend_webgpu_buffer_type = { + static struct ggml_backend_buffer_type ggml_backend_webgpu_buffer_type = { /* .iface = */ { /* .get_name = */ ggml_backend_webgpu_buffer_type_get_name, /* .alloc_buffer = */ ggml_backend_webgpu_buffer_type_alloc_buffer, @@ -2791,7 +2837,7 @@ static ggml_backend_buffer_type_t ggml_backend_webgpu_device_get_buffer_type(ggm /* .device = */ dev, /* .context = */ - ggml_backend_webgpu_get_thread_backend_ctx(dev), + NULL }; return &ggml_backend_webgpu_buffer_type; @@ -2830,18 +2876,16 @@ static bool ggml_webgpu_supported_qtype(ggml_type type) { } static bool ggml_backend_webgpu_device_supports_op(ggml_backend_dev_t dev, const ggml_tensor * op) { - auto * bctx = ggml_backend_webgpu_get_thread_backend_ctx(dev); - - webgpu_context webgpu_ctx = bctx->webgpu_ctx; + webgpu_context webgpu_ctx = get_per_thread_webgpu_context(dev); ggml_tensor * src0 = op->src[0]; ggml_tensor * src1 = op->src[1]; ggml_tensor * src2 = op->src[2]; // on smaller devices (or CI), tensors may be larger than the max storage buffer size - if (ggml_nbytes(op) > webgpu_ctx->limits.maxStorageBufferBindingSize || - (src0 != nullptr && ggml_nbytes(src0) > webgpu_ctx->limits.maxStorageBufferBindingSize) || - (src1 != nullptr && ggml_nbytes(src1) > webgpu_ctx->limits.maxStorageBufferBindingSize)) { + if (ggml_nbytes(op) > webgpu_ctx->global_ctx->capabilities.limits.maxStorageBufferBindingSize || + (src0 != nullptr && ggml_nbytes(src0) > webgpu_ctx->global_ctx->capabilities.limits.maxStorageBufferBindingSize) || + (src1 != nullptr && ggml_nbytes(src1) > webgpu_ctx->global_ctx->capabilities.limits.maxStorageBufferBindingSize)) { return false; } @@ -2922,7 +2966,7 @@ static bool ggml_backend_webgpu_device_supports_op(ggml_backend_dev_t dev, const break; } // Head dimensions must fit in workgroup memory with minimum tile sizes - size_t limit_bytes = webgpu_ctx->limits.maxComputeWorkgroupStorageSize; + size_t limit_bytes = webgpu_ctx->global_ctx->capabilities.limits.maxComputeWorkgroupStorageSize; const bool has_mask = op->src[3] != nullptr; const bool kv_direct = src1->type == GGML_TYPE_F16 && (src0->ne[0] % webgpu_ctx->sg_mat_k) == 0 && (src1->ne[1] % GGML_WEBGPU_KV_SEQ_PAD) == 0; @@ -3001,10 +3045,10 @@ static bool ggml_backend_webgpu_device_supports_op(ggml_backend_dev_t dev, const default: break; } - if (ggml_nbytes(op) > webgpu_ctx->limits.maxStorageBufferBindingSize || - (src0 != nullptr && ggml_nbytes(src0) > webgpu_ctx->limits.maxStorageBufferBindingSize) || - (src1 != nullptr && ggml_nbytes(src1) > webgpu_ctx->limits.maxStorageBufferBindingSize) || - (src2 != nullptr && ggml_nbytes(src2) > webgpu_ctx->limits.maxStorageBufferBindingSize)) { + if (ggml_nbytes(op) > webgpu_ctx->global_ctx->capabilities.limits.maxStorageBufferBindingSize || + (src0 != nullptr && ggml_nbytes(src0) > webgpu_ctx->global_ctx->capabilities.limits.maxStorageBufferBindingSize) || + (src1 != nullptr && ggml_nbytes(src1) > webgpu_ctx->global_ctx->capabilities.limits.maxStorageBufferBindingSize) || + (src2 != nullptr && ggml_nbytes(src2) > webgpu_ctx->global_ctx->capabilities.limits.maxStorageBufferBindingSize)) { supports_op = false; WEBGPU_LOG_DEBUG("ggml_webgpu op not supported due to size: "); } @@ -3067,6 +3111,8 @@ static ggml_backend_dev_t ggml_backend_webgpu_reg_get_device(ggml_backend_reg_t ggml_backend_webgpu_reg_context * reg_ctx = static_cast(reg->context); + create_webgpu_device(reg_ctx); + static ggml_backend_webgpu_device_context device_ctx; device_ctx.device_name = GGML_WEBGPU_NAME; // TODO: we get the device description when actually initializing the webgpu device, so move this to backend context? @@ -3114,7 +3160,7 @@ ggml_backend_reg_t ggml_backend_webgpu_reg() { #endif wgpu::Instance inst = wgpu::CreateInstance(&instance_descriptor); - ctx.instance = std::make_unique(std::move(inst)); + ctx.wgpu_global_ctx.instance = std::move(inst); #ifdef __EMSCRIPTEN__ if (webgpu_ctx->instance == nullptr) { @@ -3122,7 +3168,7 @@ ggml_backend_reg_t ggml_backend_webgpu_reg() { return nullptr; } #endif - GGML_ASSERT(ctx.instance != nullptr); + GGML_ASSERT(ctx.wgpu_global_ctx.instance != nullptr); static ggml_backend_reg reg = { /* .api_version = */ GGML_BACKEND_API_VERSION, From 5f6457542c3cad8d5c7ba022eb7c912110558903 Mon Sep 17 00:00:00 2001 From: Nikhil Jain Date: Wed, 14 Jan 2026 23:05:08 -0800 Subject: [PATCH 15/21] Cleanups --- ggml/src/ggml-webgpu/ggml-webgpu.cpp | 52 ---------------------------- 1 file changed, 52 deletions(-) diff --git a/ggml/src/ggml-webgpu/ggml-webgpu.cpp b/ggml/src/ggml-webgpu/ggml-webgpu.cpp index 2d03e100e7..73c37cfbb3 100644 --- a/ggml/src/ggml-webgpu/ggml-webgpu.cpp +++ b/ggml/src/ggml-webgpu/ggml-webgpu.cpp @@ -420,7 +420,6 @@ struct ggml_backend_webgpu_device_context { // Per-thread data required to actually run webgpu operations in a backend instance struct ggml_backend_webgpu_context { - ggml_backend_webgpu_device_context * device_context; // TODO(nikhil.jain): delete? std::once_flag init_once; std::string name; }; @@ -2582,7 +2581,6 @@ static bool create_webgpu_device(ggml_backend_webgpu_reg_context * ctx) { UINT64_MAX); GGML_ASSERT(ctx->wgpu_global_ctx.adapter != nullptr); - // TODO: get webgpu_ctx.limits ctx->wgpu_global_ctx.adapter.GetLimits(&ctx->wgpu_global_ctx.capabilities.limits); wgpu::AdapterInfo info{}; @@ -2608,7 +2606,6 @@ static bool create_webgpu_device(ggml_backend_webgpu_reg_context * ctx) { if (config.M == config.N && config.N == config.K && (config.K == 8 || config.K == 16) && config.componentType == wgpu::SubgroupMatrixComponentType::F16 && config.resultComponentType == wgpu::SubgroupMatrixComponentType::F16) { - // todo: what about sg_mat_m,n,k? is this global state as well? ctx->wgpu_global_ctx.capabilities.subgroup_matrix_config = config; valid_subgroup_matrix_config = true; break; @@ -2627,7 +2624,6 @@ static bool create_webgpu_device(ggml_backend_webgpu_reg_context * ctx) { #ifndef __EMSCRIPTEN__ required_features.push_back(wgpu::FeatureName::ImplicitDeviceSynchronization); - // this too? if (ctx->wgpu_global_ctx.capabilities.supports_subgroup_matrix) { required_features.push_back(wgpu::FeatureName::Subgroups); required_features.push_back(wgpu::FeatureName::ChromiumExperimentalSubgroupMatrix); @@ -2688,53 +2684,6 @@ static bool create_webgpu_device(ggml_backend_webgpu_reg_context * ctx) { UINT64_MAX); GGML_ASSERT(ctx->wgpu_global_ctx.device != nullptr); - // Initialize (compute) queue - // MOVE THIS TO THREAD - // ctx->queue = ctx->device.GetQueue(); - - // Create buffer pool for shader parameters - // MOVE THIS TO THREAD - // ctx->param_buf_pool.init(ctx->device, WEBGPU_NUM_PARAM_BUFS, WEBGPU_PARAMS_BUF_SIZE_BYTES, - // wgpu::BufferUsage::CopyDst | wgpu::BufferUsage::Uniform, - // wgpu::BufferUsage::CopySrc | wgpu::BufferUsage::MapWrite); - - // Moved to Thread Local -// #ifdef GGML_WEBGPU_GPU_PROFILE -// // Initialize buffer pool for timestamp queries (profiling) -// ctx->timestamp_query_buf_pool.init(ctx->wgpu_global_ctx->device, WEBGPU_NUM_TIMESTAMP_QUERY_BUFS, -// WEBGPU_TIMESTAMP_QUERY_BUF_SIZE_BYTES, -// wgpu::BufferUsage::QueryResolve | wgpu::BufferUsage::CopySrc, -// wgpu::BufferUsage::MapRead | wgpu::BufferUsage::CopyDst); -// #endif - - // ctx->set_rows_error_buf_pool.init(ctx->wgpu_global_ctx->device, WEBGPU_NUM_SET_ROWS_ERROR_BUFS, WEBGPU_SET_ROWS_ERROR_BUF_SIZE_BYTES, - // wgpu::BufferUsage::CopySrc | wgpu::BufferUsage::Storage, - // wgpu::BufferUsage::CopyDst | wgpu::BufferUsage::MapRead); - - // todo: this is thread_local as well? - // ggml_webgpu_init_memset_pipeline(ctx); - // ggml_webgpu_init_mul_mat_pipeline(ctx); - // ggml_webgpu_init_set_rows_pipeline(ctx); - // ggml_webgpu_init_get_rows_pipeline(ctx); - // ggml_webgpu_init_cpy_pipeline(ctx); - // ggml_webgpu_init_add_pipeline(ctx); - // ggml_webgpu_init_sub_pipeline(ctx); - // ggml_webgpu_init_mul_pipeline(ctx); - // ggml_webgpu_init_div_pipeline(ctx); - // ggml_webgpu_init_rms_norm_pipeline(ctx); - // ggml_webgpu_init_rope_pipeline(ctx); - // ggml_webgpu_init_glu_pipeline(ctx); - // ggml_webgpu_init_scale_pipeline(ctx); - // ggml_webgpu_init_soft_max_pipeline(ctx); - // ggml_webgpu_init_unary_pipeline(ctx); - -// #ifdef GGML_WEBGPU_DEBUG -// // Initialize debug buffers -// ggml_webgpu_create_buffer(ctx->wgpu_global_ctx->device, ctx->debug_host_buf, WEBGPU_DEBUG_BUF_ELEMS * sizeof(uint32_t), -// wgpu::BufferUsage::CopyDst | wgpu::BufferUsage::MapRead, "debug_host_buf"); -// ggml_webgpu_create_buffer(ctx->wgpu_global_ctx->device, ctx->debug_dev_buf, WEBGPU_DEBUG_BUF_ELEMS * sizeof(uint32_t), -// wgpu::BufferUsage::Storage | wgpu::BufferUsage::CopySrc, "debug_dev_buf"); -// #endif GGML_LOG_INFO( "ggml_webgpu: adapter_info: vendor_id: %u | vendor: %s | architecture: %s | device_id: %u | name: %s | " "device_desc: %s\n", @@ -2808,7 +2757,6 @@ static ggml_backend_t ggml_backend_webgpu_device_init(ggml_backend_dev_t dev, co auto * backend_ctx = new ggml_backend_webgpu_context(); backend_ctx->name = GGML_WEBGPU_NAME + std::string(": ") + dev_ctx->device_name; - backend_ctx->device_context = dev_ctx; // See GGML Backend Interface section auto * backend = new ggml_backend(); From 2e4c9f84c63252783efd5094ee7ebd26e4e0e2d1 Mon Sep 17 00:00:00 2001 From: Nikhil Jain Date: Wed, 14 Jan 2026 23:31:19 -0800 Subject: [PATCH 16/21] More cleanup --- ggml/src/ggml-webgpu/ggml-webgpu.cpp | 8 -------- 1 file changed, 8 deletions(-) diff --git a/ggml/src/ggml-webgpu/ggml-webgpu.cpp b/ggml/src/ggml-webgpu/ggml-webgpu.cpp index 73c37cfbb3..57ee643ad3 100644 --- a/ggml/src/ggml-webgpu/ggml-webgpu.cpp +++ b/ggml/src/ggml-webgpu/ggml-webgpu.cpp @@ -313,17 +313,9 @@ struct webgpu_global_context; struct webgpu_context_struct { // Points to global instances owned by ggml_backend_webgpu_reg_context webgpu_global_context * global_ctx; - // wgpu::Instance * instance; - // wgpu::Adapter * adapter; - // wgpu::Device * device; - // wgpu::Buffer * get_tensor_staging_buf; - // uint32_t max_subgroup_size; wgpu::Queue queue; webgpu_buf_pool param_buf_pool; - // TODO: I got rid of this...update uses to read from global context - // wgpu::Limits limits; - bool supports_subgroup_matrix = false; uint32_t sg_mat_m; From 1dd5676a9d9694590fc4b9e5e4a07e233d598dab Mon Sep 17 00:00:00 2001 From: Nikhil Jain Date: Tue, 20 Jan 2026 11:37:34 -0800 Subject: [PATCH 17/21] Move staging_buf mutex to global context --- ggml/src/ggml-webgpu/ggml-webgpu.cpp | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/ggml/src/ggml-webgpu/ggml-webgpu.cpp b/ggml/src/ggml-webgpu/ggml-webgpu.cpp index 57ee643ad3..67eed4fd17 100644 --- a/ggml/src/ggml-webgpu/ggml-webgpu.cpp +++ b/ggml/src/ggml-webgpu/ggml-webgpu.cpp @@ -322,7 +322,7 @@ struct webgpu_context_struct { uint32_t sg_mat_n; uint32_t sg_mat_k; - std::recursive_mutex mutex; + // std::recursive_mutex mutex; std::atomic_uint inflight_threads = 0; webgpu_buf_pool set_rows_error_buf_pool; @@ -392,6 +392,8 @@ struct webgpu_global_context { wgpu::Device device; // Shared buffer to move data from device to host wgpu::Buffer get_tensor_staging_buf; + // Mutex to lock get_tensor_staging_buf + std::recursive_mutex mutex; webgpu_capabilities capabilities; } ; @@ -1155,7 +1157,7 @@ static webgpu_command ggml_webgpu_flash_attn(webgpu_context & ctx, pipeline = it->second; decisions = *static_cast(pipeline.context); } else { - std::lock_guard lock(ctx->mutex); + std::lock_guard lock(ctx->global_ctx->mutex); it = ctx->flash_attn_pipelines.find(key); if (it != ctx->flash_attn_pipelines.end()) { pipeline = it->second; @@ -1799,7 +1801,7 @@ static void ggml_backend_webgpu_buffer_get_tensor(ggml_backend_buffer_t buffer, final_size = size + (4 - (size % 4)); } - std::lock_guard lock(webgpu_ctx->mutex); + std::lock_guard lock(webgpu_ctx->global_ctx->mutex); if (webgpu_ctx->global_ctx->get_tensor_staging_buf == nullptr || webgpu_ctx->global_ctx->get_tensor_staging_buf.GetSize() < final_size) { // Create a new staging buffer if it doesn't exist or is too small From 07838a10745e3b4e604b30d705fddf023d50824b Mon Sep 17 00:00:00 2001 From: Nikhil Jain Date: Tue, 20 Jan 2026 14:06:16 -0800 Subject: [PATCH 18/21] Resolve merge --- docs/ops.md | 44 +++++++++++------------ docs/ops/WebGPU.csv | 88 --------------------------------------------- 2 files changed, 22 insertions(+), 110 deletions(-) diff --git a/docs/ops.md b/docs/ops.md index 59eff00f9e..c066ab5a85 100644 --- a/docs/ops.md +++ b/docs/ops.md @@ -20,10 +20,10 @@ Legend: | ADD1 | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | | ADD_ID | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | | ARANGE | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | -| ARGMAX | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | -| ARGSORT | ❌ | ✅ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ❌ | ❌ | +| ARGMAX | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | +| ARGSORT | ❌ | ✅ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ❌ | ❌ | | CEIL | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ | -| CLAMP | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ❌ | ❌ | ❌ | +| CLAMP | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ | | CONCAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ | | CONT | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ❌ | ❌ | | CONV_2D | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | @@ -34,9 +34,9 @@ Legend: | COS | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 | ❌ | ❌ | ❌ | | COUNT_EQUAL | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | | CPY | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ | -| CROSS_ENTROPY_LOSS | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | +| CROSS_ENTROPY_LOSS | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | | CROSS_ENTROPY_LOSS_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | -| CUMSUM | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | +| CUMSUM | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | | DIAG | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | | DIAG_MASK_INF | ❌ | ✅ | ✅ | ✅ | ❌ | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ | | DIV | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | @@ -44,10 +44,10 @@ Legend: | ELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | | EXP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ | | EXPM1 | ❌ | ❌ | ✅ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | -| FILL | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | -| FLASH_ATTN_EXT | ❌ | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | ❌ | 🟡 | ❌ | ❌ | ❌ | -| FLOOR | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | 🟡 | ❌ | ❌ | -| GATED_LINEAR_ATTN | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | +| FILL | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | +| FLASH_ATTN_EXT | ❌ | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | +| FLOOR | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ | +| GATED_LINEAR_ATTN | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | | GEGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ | | GEGLU_ERF | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ | | GEGLU_QUICK | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ | @@ -61,9 +61,9 @@ Legend: | HARDSWISH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ | | IM2COL | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | | IM2COL_3D | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | -| L2_NORM | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | +| L2_NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | | LEAKY_RELU | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 | ❌ | ❌ | ❌ | -| LOG | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | +| LOG | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | | MEAN | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | | MUL | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | | MUL_MAT | 🟡 | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | @@ -72,9 +72,10 @@ Legend: | NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | 🟡 | ❌ | ❌ | ❌ | | OPT_STEP_ADAMW | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | | OPT_STEP_SGD | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | -| OUT_PROD | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | 🟡 | -| PAD | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | ✅ | ❌ | ❌ | ❌ | +| OUT_PROD | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | 🟡 | +| PAD | ❌ | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | ✅ | ✅ | ❌ | ❌ | | PAD_REFLECT_1D | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | +| POOL_1D | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | | POOL_2D | ❌ | 🟡 | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | | REGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ | | RELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ | @@ -82,16 +83,15 @@ Legend: | REPEAT_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | | RMS_NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | | RMS_NORM_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | -| RMS_NORM_MUL_ADD | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | | ROLL | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | -| ROPE | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | +| ROPE | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | | ROPE_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | -| ROUND | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | 🟡 | ❌ | ❌ | +| ROUND | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ | | RWKV_WKV6 | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | | RWKV_WKV7 | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | | SCALE | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | | SET | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | ❌ | -| SET_ROWS | ❌ | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ | +| SET_ROWS | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ | | SGN | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | | SIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ | | SILU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ | @@ -103,18 +103,18 @@ Legend: | SOLVE_TRI | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | | SQR | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ | ❌ | ❌ | | SQRT | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ | ❌ | ❌ | -| SSM_CONV | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | +| SSM_CONV | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | | SSM_SCAN | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | | STEP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ | | SUB | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | -| SUM | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | -| SUM_ROWS | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | 🟡 | ✅ | ❌ | ❌ | ❌ | +| SUM | ❌ | 🟡 | ✅ | 🟡 | 🟡 | ❌ | 🟡 | 🟡 | 🟡 | ❌ | ❌ | +| SUM_ROWS | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | 🟡 | ✅ | ✅ | ❌ | ❌ | | SWIGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ | | SWIGLU_OAI | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ | | TANH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ | | TIMESTEP_EMBEDDING | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | -| TOP_K | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | +| TOP_K | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | 🟡 | ✅ | ❌ | ❌ | | TRI | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | -| TRUNC | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | 🟡 | ❌ | ❌ | +| TRUNC | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ✅ | ❌ | ❌ | | UPSCALE | ❌ | 🟡 | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ | ❌ | | XIELU | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | diff --git a/docs/ops/WebGPU.csv b/docs/ops/WebGPU.csv index 98f301da15..835dbc08ba 100644 --- a/docs/ops/WebGPU.csv +++ b/docs/ops/WebGPU.csv @@ -33,16 +33,6 @@ "WebGPU: WebGPU","SOFTPLUS","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","WebGPU" "WebGPU: WebGPU","GELU_ERF","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","WebGPU" "WebGPU: WebGPU","GELU_ERF","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","WebGPU" -<<<<<<< HEAD -"WebGPU: WebGPU","FLOOR","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","WebGPU" -"WebGPU: WebGPU","FLOOR","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","WebGPU" -"WebGPU: WebGPU","CEIL","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","CEIL","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","ROUND","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","WebGPU" -"WebGPU: WebGPU","ROUND","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","WebGPU" -"WebGPU: WebGPU","TRUNC","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","WebGPU" -"WebGPU: WebGPU","TRUNC","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","WebGPU" -======= "WebGPU: WebGPU","FLOOR","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","WebGPU" "WebGPU: WebGPU","FLOOR","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","WebGPU" "WebGPU: WebGPU","CEIL","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","WebGPU" @@ -51,7 +41,6 @@ "WebGPU: WebGPU","ROUND","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","WebGPU" "WebGPU: WebGPU","TRUNC","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","WebGPU" "WebGPU: WebGPU","TRUNC","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","WebGPU" ->>>>>>> abhijit/abhijit/unary "WebGPU: WebGPU","ABS","type=f16,ne_a=[128,2,2,2],v=1","support","1","yes","WebGPU" "WebGPU: WebGPU","ABS","type=f16,ne_a=[5,7,11,13],v=1","support","1","yes","WebGPU" "WebGPU: WebGPU","SGN","type=f16,ne_a=[128,2,2,2],v=1","support","1","yes","WebGPU" @@ -86,16 +75,6 @@ "WebGPU: WebGPU","SOFTPLUS","type=f16,ne_a=[5,7,11,13],v=1","support","1","yes","WebGPU" "WebGPU: WebGPU","GELU_ERF","type=f16,ne_a=[128,2,2,2],v=1","support","1","yes","WebGPU" "WebGPU: WebGPU","GELU_ERF","type=f16,ne_a=[5,7,11,13],v=1","support","1","yes","WebGPU" -<<<<<<< HEAD -"WebGPU: WebGPU","FLOOR","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","WebGPU" -"WebGPU: WebGPU","FLOOR","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","WebGPU" -"WebGPU: WebGPU","CEIL","type=f16,ne_a=[128,2,2,2],v=1","support","1","yes","WebGPU" -"WebGPU: WebGPU","CEIL","type=f16,ne_a=[5,7,11,13],v=1","support","1","yes","WebGPU" -"WebGPU: WebGPU","ROUND","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","WebGPU" -"WebGPU: WebGPU","ROUND","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","WebGPU" -"WebGPU: WebGPU","TRUNC","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","WebGPU" -"WebGPU: WebGPU","TRUNC","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","WebGPU" -======= "WebGPU: WebGPU","FLOOR","type=f16,ne_a=[128,2,2,2],v=1","support","1","yes","WebGPU" "WebGPU: WebGPU","FLOOR","type=f16,ne_a=[5,7,11,13],v=1","support","1","yes","WebGPU" "WebGPU: WebGPU","CEIL","type=f16,ne_a=[128,2,2,2],v=1","support","1","yes","WebGPU" @@ -104,7 +83,6 @@ "WebGPU: WebGPU","ROUND","type=f16,ne_a=[5,7,11,13],v=1","support","1","yes","WebGPU" "WebGPU: WebGPU","TRUNC","type=f16,ne_a=[128,2,2,2],v=1","support","1","yes","WebGPU" "WebGPU: WebGPU","TRUNC","type=f16,ne_a=[5,7,11,13],v=1","support","1","yes","WebGPU" ->>>>>>> abhijit/abhijit/unary "WebGPU: WebGPU","ABS","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","WebGPU" "WebGPU: WebGPU","ABS","type=f32,ne_a=[5,7,11,13],v=0","support","1","yes","WebGPU" "WebGPU: WebGPU","SGN","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","WebGPU" @@ -139,16 +117,6 @@ "WebGPU: WebGPU","SOFTPLUS","type=f32,ne_a=[5,7,11,13],v=0","support","1","yes","WebGPU" "WebGPU: WebGPU","GELU_ERF","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","WebGPU" "WebGPU: WebGPU","GELU_ERF","type=f32,ne_a=[5,7,11,13],v=0","support","1","yes","WebGPU" -<<<<<<< HEAD -"WebGPU: WebGPU","FLOOR","type=f32,ne_a=[128,2,2,2],v=0","support","0","no","WebGPU" -"WebGPU: WebGPU","FLOOR","type=f32,ne_a=[5,7,11,13],v=0","support","0","no","WebGPU" -"WebGPU: WebGPU","CEIL","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","CEIL","type=f32,ne_a=[5,7,11,13],v=0","support","1","yes","WebGPU" -"WebGPU: WebGPU","ROUND","type=f32,ne_a=[128,2,2,2],v=0","support","0","no","WebGPU" -"WebGPU: WebGPU","ROUND","type=f32,ne_a=[5,7,11,13],v=0","support","0","no","WebGPU" -"WebGPU: WebGPU","TRUNC","type=f32,ne_a=[128,2,2,2],v=0","support","0","no","WebGPU" -"WebGPU: WebGPU","TRUNC","type=f32,ne_a=[5,7,11,13],v=0","support","0","no","WebGPU" -======= "WebGPU: WebGPU","FLOOR","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","WebGPU" "WebGPU: WebGPU","FLOOR","type=f32,ne_a=[5,7,11,13],v=0","support","1","yes","WebGPU" "WebGPU: WebGPU","CEIL","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","WebGPU" @@ -157,7 +125,6 @@ "WebGPU: WebGPU","ROUND","type=f32,ne_a=[5,7,11,13],v=0","support","1","yes","WebGPU" "WebGPU: WebGPU","TRUNC","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","WebGPU" "WebGPU: WebGPU","TRUNC","type=f32,ne_a=[5,7,11,13],v=0","support","1","yes","WebGPU" ->>>>>>> abhijit/abhijit/unary "WebGPU: WebGPU","ABS","type=f32,ne_a=[128,2,2,2],v=1","support","1","yes","WebGPU" "WebGPU: WebGPU","ABS","type=f32,ne_a=[5,7,11,13],v=1","support","1","yes","WebGPU" "WebGPU: WebGPU","SGN","type=f32,ne_a=[128,2,2,2],v=1","support","1","yes","WebGPU" @@ -192,16 +159,6 @@ "WebGPU: WebGPU","SOFTPLUS","type=f32,ne_a=[5,7,11,13],v=1","support","1","yes","WebGPU" "WebGPU: WebGPU","GELU_ERF","type=f32,ne_a=[128,2,2,2],v=1","support","1","yes","WebGPU" "WebGPU: WebGPU","GELU_ERF","type=f32,ne_a=[5,7,11,13],v=1","support","1","yes","WebGPU" -<<<<<<< HEAD -"WebGPU: WebGPU","FLOOR","type=f32,ne_a=[128,2,2,2],v=1","support","0","no","WebGPU" -"WebGPU: WebGPU","FLOOR","type=f32,ne_a=[5,7,11,13],v=1","support","0","no","WebGPU" -"WebGPU: WebGPU","CEIL","type=f32,ne_a=[128,2,2,2],v=1","support","1","yes","WebGPU" -"WebGPU: WebGPU","CEIL","type=f32,ne_a=[5,7,11,13],v=1","support","1","yes","WebGPU" -"WebGPU: WebGPU","ROUND","type=f32,ne_a=[128,2,2,2],v=1","support","0","no","WebGPU" -"WebGPU: WebGPU","ROUND","type=f32,ne_a=[5,7,11,13],v=1","support","0","no","WebGPU" -"WebGPU: WebGPU","TRUNC","type=f32,ne_a=[128,2,2,2],v=1","support","0","no","WebGPU" -"WebGPU: WebGPU","TRUNC","type=f32,ne_a=[5,7,11,13],v=1","support","0","no","WebGPU" -======= "WebGPU: WebGPU","FLOOR","type=f32,ne_a=[128,2,2,2],v=1","support","1","yes","WebGPU" "WebGPU: WebGPU","FLOOR","type=f32,ne_a=[5,7,11,13],v=1","support","1","yes","WebGPU" "WebGPU: WebGPU","CEIL","type=f32,ne_a=[128,2,2,2],v=1","support","1","yes","WebGPU" @@ -210,7 +167,6 @@ "WebGPU: WebGPU","ROUND","type=f32,ne_a=[5,7,11,13],v=1","support","1","yes","WebGPU" "WebGPU: WebGPU","TRUNC","type=f32,ne_a=[128,2,2,2],v=1","support","1","yes","WebGPU" "WebGPU: WebGPU","TRUNC","type=f32,ne_a=[5,7,11,13],v=1","support","1","yes","WebGPU" ->>>>>>> abhijit/abhijit/unary "WebGPU: WebGPU","REGLU","type=f16,ne_a=[128,2,2,2],v=0,swapped=0","support","1","yes","WebGPU" "WebGPU: WebGPU","REGLU","type=f16,ne_a=[5,7,11,13],v=0,swapped=0","support","1","yes","WebGPU" "WebGPU: WebGPU","REGLU","type=f16,ne_a=[128,2,2,2],v=0,swapped=1","support","1","yes","WebGPU" @@ -8716,17 +8672,10 @@ "WebGPU: WebGPU","COS","type=f16,ne=[10,2,2,2]","support","0","no","WebGPU" "WebGPU: WebGPU","CLAMP","type=f16,ne=[10,5,4,3],min=-0.500000,max=0.500000","support","0","no","WebGPU" "WebGPU: WebGPU","LEAKY_RELU","type=f16,ne_a=[10,5,4,3],negative_slope=0.100000","support","0","no","WebGPU" -<<<<<<< HEAD -"WebGPU: WebGPU","FLOOR","type=f16,ne=[10,2,2,2]","support","0","no","WebGPU" -"WebGPU: WebGPU","CEIL","type=f16,ne=[10,2,2,2]","support","1","yes","WebGPU" -"WebGPU: WebGPU","ROUND","type=f16,ne=[10,2,2,2]","support","0","no","WebGPU" -"WebGPU: WebGPU","TRUNC","type=f16,ne=[10,2,2,2]","support","0","no","WebGPU" -======= "WebGPU: WebGPU","FLOOR","type=f16,ne=[10,2,2,2]","support","1","yes","WebGPU" "WebGPU: WebGPU","CEIL","type=f16,ne=[10,2,2,2]","support","1","yes","WebGPU" "WebGPU: WebGPU","ROUND","type=f16,ne=[10,2,2,2]","support","1","yes","WebGPU" "WebGPU: WebGPU","TRUNC","type=f16,ne=[10,2,2,2]","support","1","yes","WebGPU" ->>>>>>> abhijit/abhijit/unary "WebGPU: WebGPU","SQR","type=f16,ne=[7,1,5,3]","support","0","no","WebGPU" "WebGPU: WebGPU","SQRT","type=f16,ne=[7,1,5,3]","support","0","no","WebGPU" "WebGPU: WebGPU","LOG","type=f16,ne=[7,1,5,3]","support","0","no","WebGPU" @@ -8734,16 +8683,6 @@ "WebGPU: WebGPU","COS","type=f16,ne=[7,1,5,3]","support","0","no","WebGPU" "WebGPU: WebGPU","CLAMP","type=f16,ne=[7,1,5,3],min=-0.500000,max=0.500000","support","0","no","WebGPU" "WebGPU: WebGPU","LEAKY_RELU","type=f16,ne_a=[7,1,5,3],negative_slope=0.100000","support","0","no","WebGPU" -<<<<<<< HEAD -"WebGPU: WebGPU","FLOOR","type=f16,ne=[7,1,5,3]","support","0","no","WebGPU" -"WebGPU: WebGPU","FLOOR","type=f16,ne=[1024,1024,1,1]","support","0","no","WebGPU" -"WebGPU: WebGPU","CEIL","type=f16,ne=[7,1,5,3]","support","1","yes","WebGPU" -"WebGPU: WebGPU","CEIL","type=f16,ne=[1024,1024,1,1]","support","1","yes","WebGPU" -"WebGPU: WebGPU","ROUND","type=f16,ne=[7,1,5,3]","support","0","no","WebGPU" -"WebGPU: WebGPU","ROUND","type=f16,ne=[1024,1024,1,1]","support","0","no","WebGPU" -"WebGPU: WebGPU","TRUNC","type=f16,ne=[7,1,5,3]","support","0","no","WebGPU" -"WebGPU: WebGPU","TRUNC","type=f16,ne=[1024,1024,1,1]","support","0","no","WebGPU" -======= "WebGPU: WebGPU","FLOOR","type=f16,ne=[7,1,5,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLOOR","type=f16,ne=[1024,1024,1,1]","support","1","yes","WebGPU" "WebGPU: WebGPU","CEIL","type=f16,ne=[7,1,5,3]","support","1","yes","WebGPU" @@ -8752,7 +8691,6 @@ "WebGPU: WebGPU","ROUND","type=f16,ne=[1024,1024,1,1]","support","1","yes","WebGPU" "WebGPU: WebGPU","TRUNC","type=f16,ne=[7,1,5,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","TRUNC","type=f16,ne=[1024,1024,1,1]","support","1","yes","WebGPU" ->>>>>>> abhijit/abhijit/unary "WebGPU: WebGPU","SQR","type=f32,ne=[10,5,4,3]","support","0","no","WebGPU" "WebGPU: WebGPU","SQRT","type=f32,ne=[10,3,3,2]","support","0","no","WebGPU" "WebGPU: WebGPU","LOG","type=f32,ne=[10,5,4,3]","support","0","no","WebGPU" @@ -8760,17 +8698,10 @@ "WebGPU: WebGPU","COS","type=f32,ne=[10,2,2,2]","support","0","no","WebGPU" "WebGPU: WebGPU","CLAMP","type=f32,ne=[10,5,4,3],min=-0.500000,max=0.500000","support","0","no","WebGPU" "WebGPU: WebGPU","LEAKY_RELU","type=f32,ne_a=[10,5,4,3],negative_slope=0.100000","support","0","no","WebGPU" -<<<<<<< HEAD -"WebGPU: WebGPU","FLOOR","type=f32,ne=[10,2,2,2]","support","0","no","WebGPU" -"WebGPU: WebGPU","CEIL","type=f32,ne=[10,2,2,2]","support","1","yes","WebGPU" -"WebGPU: WebGPU","ROUND","type=f32,ne=[10,2,2,2]","support","0","no","WebGPU" -"WebGPU: WebGPU","TRUNC","type=f32,ne=[10,2,2,2]","support","0","no","WebGPU" -======= "WebGPU: WebGPU","FLOOR","type=f32,ne=[10,2,2,2]","support","1","yes","WebGPU" "WebGPU: WebGPU","CEIL","type=f32,ne=[10,2,2,2]","support","1","yes","WebGPU" "WebGPU: WebGPU","ROUND","type=f32,ne=[10,2,2,2]","support","1","yes","WebGPU" "WebGPU: WebGPU","TRUNC","type=f32,ne=[10,2,2,2]","support","1","yes","WebGPU" ->>>>>>> abhijit/abhijit/unary "WebGPU: WebGPU","SQR","type=f32,ne=[7,1,5,3]","support","0","no","WebGPU" "WebGPU: WebGPU","SQRT","type=f32,ne=[7,1,5,3]","support","0","no","WebGPU" "WebGPU: WebGPU","LOG","type=f32,ne=[7,1,5,3]","support","0","no","WebGPU" @@ -8778,16 +8709,6 @@ "WebGPU: WebGPU","COS","type=f32,ne=[7,1,5,3]","support","0","no","WebGPU" "WebGPU: WebGPU","CLAMP","type=f32,ne=[7,1,5,3],min=-0.500000,max=0.500000","support","0","no","WebGPU" "WebGPU: WebGPU","LEAKY_RELU","type=f32,ne_a=[7,1,5,3],negative_slope=0.100000","support","0","no","WebGPU" -<<<<<<< HEAD -"WebGPU: WebGPU","FLOOR","type=f32,ne=[7,1,5,3]","support","0","no","WebGPU" -"WebGPU: WebGPU","FLOOR","type=f32,ne=[1024,1024,1,1]","support","0","no","WebGPU" -"WebGPU: WebGPU","CEIL","type=f32,ne=[7,1,5,3]","support","1","yes","WebGPU" -"WebGPU: WebGPU","CEIL","type=f32,ne=[1024,1024,1,1]","support","1","yes","WebGPU" -"WebGPU: WebGPU","ROUND","type=f32,ne=[7,1,5,3]","support","0","no","WebGPU" -"WebGPU: WebGPU","ROUND","type=f32,ne=[1024,1024,1,1]","support","0","no","WebGPU" -"WebGPU: WebGPU","TRUNC","type=f32,ne=[7,1,5,3]","support","0","no","WebGPU" -"WebGPU: WebGPU","TRUNC","type=f32,ne=[1024,1024,1,1]","support","0","no","WebGPU" -======= "WebGPU: WebGPU","FLOOR","type=f32,ne=[7,1,5,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","FLOOR","type=f32,ne=[1024,1024,1,1]","support","1","yes","WebGPU" "WebGPU: WebGPU","CEIL","type=f32,ne=[7,1,5,3]","support","1","yes","WebGPU" @@ -8796,7 +8717,6 @@ "WebGPU: WebGPU","ROUND","type=f32,ne=[1024,1024,1,1]","support","1","yes","WebGPU" "WebGPU: WebGPU","TRUNC","type=f32,ne=[7,1,5,3]","support","1","yes","WebGPU" "WebGPU: WebGPU","TRUNC","type=f32,ne=[1024,1024,1,1]","support","1","yes","WebGPU" ->>>>>>> abhijit/abhijit/unary "WebGPU: WebGPU","DIAG_MASK_INF","type=f32,ne=[10,10,1,1],n_past=5","support","0","no","WebGPU" "WebGPU: WebGPU","DIAG_MASK_INF","type=f32,ne=[10,10,3,1],n_past=5","support","0","no","WebGPU" "WebGPU: WebGPU","DIAG_MASK_INF","type=f32,ne=[10,10,3,2],n_past=5","support","0","no","WebGPU" @@ -10063,15 +9983,7 @@ "WebGPU: WebGPU","SOLVE_TRI","type=f32,ne_lhs=[95,95,8,8],ne_rhs=[40,95,8,8]","support","0","no","WebGPU" "WebGPU: WebGPU","SOLVE_TRI","type=f32,ne_lhs=[100,100,4,4],ne_rhs=[41,100,4,4]","support","0","no","WebGPU" "WebGPU: WebGPU","SOLVE_TRI","type=f32,ne_lhs=[128,128,4,4],ne_rhs=[31,128,4,4]","support","0","no","WebGPU" -<<<<<<< HEAD -"WebGPU: WebGPU","SOLVE_TRI","type=f32,ne_lhs=[128,128,4,4],ne_rhs=[32,128,4,4]","support","0","no","WebGPU" -"WebGPU: WebGPU","SOLVE_TRI","type=f32,ne_lhs=[128,128,3,4],ne_rhs=[32,128,3,4]","support","0","no","WebGPU" -"WebGPU: WebGPU","SOLVE_TRI","type=f32,ne_lhs=[128,128,4,1],ne_rhs=[32,128,4,1]","support","0","no","WebGPU" -"WebGPU: WebGPU","SOLVE_TRI","type=f32,ne_lhs=[64,64,4,4],ne_rhs=[200,64,4,4]","support","0","no","WebGPU" -"WebGPU: WebGPU","SOLVE_TRI","type=f32,ne_lhs=[64,64,4,4],ne_rhs=[384,64,4,4]","support","0","no","WebGPU" -======= "WebGPU: WebGPU","SOLVE_TRI","type=f32,ne_lhs=[64,64,4,4],ne_rhs=[300,64,4,4]","support","0","no","WebGPU" ->>>>>>> abhijit/abhijit/unary "WebGPU: WebGPU","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=0,circular=0","support","0","no","WebGPU" "WebGPU: WebGPU","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=0,circular=0","support","0","no","WebGPU" "WebGPU: WebGPU","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=0,circular=1","support","0","no","WebGPU" From c036856ea6a2b8fd061b16e399acb5d824c8aabb Mon Sep 17 00:00:00 2001 From: Nikhil Jain Date: Tue, 20 Jan 2026 14:19:50 -0800 Subject: [PATCH 19/21] Resolve merge --- ggml/src/ggml-webgpu/ggml-webgpu.cpp | 61 ++++------------------------ 1 file changed, 8 insertions(+), 53 deletions(-) diff --git a/ggml/src/ggml-webgpu/ggml-webgpu.cpp b/ggml/src/ggml-webgpu/ggml-webgpu.cpp index c3d38eaae6..49da77d3a2 100644 --- a/ggml/src/ggml-webgpu/ggml-webgpu.cpp +++ b/ggml/src/ggml-webgpu/ggml-webgpu.cpp @@ -267,51 +267,6 @@ struct webgpu_command { #endif }; -<<<<<<< HEAD -struct flash_attn_pipeline_key { - int q_type; - int kv_type; - int dst_type; - uint32_t head_dim_qk; - uint32_t head_dim_v; - bool kv_direct; - bool has_mask; - bool has_sinks; - bool uses_logit_softcap; - - bool operator==(const flash_attn_pipeline_key & other) const { - return q_type == other.q_type && kv_type == other.kv_type && dst_type == other.dst_type && - head_dim_qk == other.head_dim_qk && head_dim_v == other.head_dim_v && kv_direct == other.kv_direct && - has_mask == other.has_mask && has_sinks == other.has_sinks && - uses_logit_softcap == other.uses_logit_softcap; - } -}; - -// Same hash combine function as in boost -template inline void ggml_webgpu_hash_combine(size_t & seed, const T & value) { - seed ^= std::hash{}(value) + 0x9e3779b9 + (seed << 6) + (seed >> 2); -} - -struct flash_attn_pipeline_key_hash { - size_t operator()(const flash_attn_pipeline_key & key) const { - size_t seed = 0; - ggml_webgpu_hash_combine(seed, key.q_type); - ggml_webgpu_hash_combine(seed, key.kv_type); - ggml_webgpu_hash_combine(seed, key.dst_type); - ggml_webgpu_hash_combine(seed, key.head_dim_qk); - ggml_webgpu_hash_combine(seed, key.head_dim_v); - ggml_webgpu_hash_combine(seed, key.kv_direct); - ggml_webgpu_hash_combine(seed, key.has_mask); - ggml_webgpu_hash_combine(seed, key.has_sinks); - ggml_webgpu_hash_combine(seed, key.uses_logit_softcap); - return seed; - } -}; - -// Forward declaration -struct webgpu_global_context; -======= ->>>>>>> upstream/master // All the base objects needed to run operations on a WebGPU device struct webgpu_context_struct { // Points to global instances owned by ggml_backend_webgpu_reg_context @@ -900,7 +855,7 @@ static webgpu_command ggml_webgpu_pad(webgpu_context & ctx, ggml_tensor * src, g webgpu_pipeline pipeline; { // TODO: remove guard once pipeline caches are per-thread - std::lock_guard lock(ctx->mutex); + std::lock_guard lock(ctx->global_ctx->mutex); auto it = ctx->pad_pipelines.find(pipeline_key); if (it != ctx->pad_pipelines.end()) { pipeline = it->second; @@ -982,7 +937,7 @@ static std::optional ggml_webgpu_set_rows(webgpu_context & ctx, webgpu_pipeline pipeline; // TODO: remove guard once pipeline caches are per-thread { - std::lock_guard lock(ctx->mutex); + std::lock_guard lock(ctx->global_ctx->mutex); auto it = ctx->set_rows_pipelines.find(key); if (it != ctx->set_rows_pipelines.end()) { pipeline = it->second; @@ -1294,7 +1249,7 @@ static webgpu_command ggml_webgpu_flash_attn(webgpu_context & ctx, webgpu_pipeline pipeline; // TODO: remove guard once pipeline caches are per-thread { - std::lock_guard lock(ctx->mutex); + std::lock_guard lock(ctx->global_ctx->mutex); auto it = ctx->flash_attn_pipelines.find(key); if (it != ctx->flash_attn_pipelines.end()) { pipeline = it->second; @@ -1339,7 +1294,7 @@ static webgpu_command ggml_webgpu_unary_op(webgpu_context & ctx, ggml_tensor * s webgpu_pipeline pipeline; { // TODO: remove guard once pipeline caches are per-thread - std::lock_guard lock(ctx->mutex); + std::lock_guard lock(ctx->global_ctx->mutex); auto it = ctx->unary_pipelines.find(pipeline_key); if (it != ctx->unary_pipelines.end()) { pipeline = it->second; @@ -1770,7 +1725,7 @@ static webgpu_command ggml_webgpu_argmax(webgpu_context & ctx, ggml_tensor * src webgpu_pipeline pipeline; { // TODO: remove guard once pipeline caches are per-thread - std::lock_guard lock(ctx->mutex); + std::lock_guard lock(ctx->global_ctx->mutex); auto it = ctx->argmax_pipelines.find(shader_lib_ctx.vec4); if (it != ctx->argmax_pipelines.end()) { pipeline = it->second; @@ -1796,7 +1751,7 @@ static webgpu_command ggml_webgpu_argsort(webgpu_context & ctx, ggml_tensor * sr ctx->limits.maxComputeWorkgroupStorageSize, .order = order }; - std::lock_guard lock(ctx->mutex); + std::lock_guard lock(ctx->global_ctx->mutex); webgpu_pipeline argsort_pipeline; auto it = ctx->argsort_pipelines.find(order); if (it != ctx->argsort_pipelines.end()) { @@ -1985,7 +1940,7 @@ static webgpu_command ggml_webgpu_cumsum(webgpu_context & ctx, ggml_tensor * src webgpu_pipeline pipeline; // TODO: remove guard once pipeline caches are per-thread { - std::lock_guard lock(ctx->mutex); + std::lock_guard lock(ctx->global_ctx->mutex); auto it = ctx->cumsum_pipelines.find(1); if (it != ctx->cumsum_pipelines.end()) { pipeline = it->second; @@ -2030,7 +1985,7 @@ static webgpu_command ggml_webgpu_sum_rows(webgpu_context & ctx, ggml_tensor * s webgpu_pipeline pipeline; { // TODO: remove guard once pipeline caches are per-thread - std::lock_guard lock(ctx->mutex); + std::lock_guard lock(ctx->global_ctx->mutex); auto it = ctx->sum_rows_pipelines.find(1); if (it != ctx->sum_rows_pipelines.end()) { pipeline = it->second; From 94101f1abef3893e19a24b0278cab22e5d69e40c Mon Sep 17 00:00:00 2001 From: Nikhil Jain Date: Tue, 20 Jan 2026 14:28:04 -0800 Subject: [PATCH 20/21] Resolve merge --- ggml/src/ggml-webgpu/ggml-webgpu.cpp | 47 ++++++++++++++-------------- 1 file changed, 24 insertions(+), 23 deletions(-) diff --git a/ggml/src/ggml-webgpu/ggml-webgpu.cpp b/ggml/src/ggml-webgpu/ggml-webgpu.cpp index 49da77d3a2..a5a5fe4920 100644 --- a/ggml/src/ggml-webgpu/ggml-webgpu.cpp +++ b/ggml/src/ggml-webgpu/ggml-webgpu.cpp @@ -267,6 +267,9 @@ struct webgpu_command { #endif }; +// Forward declaration +struct webgpu_global_context; + // All the base objects needed to run operations on a WebGPU device struct webgpu_context_struct { // Points to global instances owned by ggml_backend_webgpu_reg_context @@ -640,7 +643,7 @@ static webgpu_command ggml_backend_webgpu_build_multi( bind_group_desc.entryCount = entries.size(); bind_group_desc.entries = entries.data(); bind_group_desc.label = pipelines[i].name.c_str(); - bind_groups.push_back(ctx->device.CreateBindGroup(&bind_group_desc)); + bind_groups.push_back(ctx->global_ctx->device.CreateBindGroup(&bind_group_desc)); params_bufs_list.push_back(params_bufs); } @@ -850,7 +853,7 @@ static webgpu_command ggml_webgpu_pad(webgpu_context & ctx, ggml_tensor * src, g ggml_webgpu_pad_pipeline_key pipeline_key = { .circular = circular }; ggml_webgpu_pad_shader_lib_context shader_lib_ctx = { .key = pipeline_key, .max_wg_size = - ctx->limits.maxComputeInvocationsPerWorkgroup }; + ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup }; webgpu_pipeline pipeline; { @@ -862,7 +865,7 @@ static webgpu_command ggml_webgpu_pad(webgpu_context & ctx, ggml_tensor * src, g } else { ggml_webgpu_processed_shader processed = ggml_webgpu_preprocess_pad_shader(ctx->p, wgsl_pad, shader_lib_ctx); - pipeline = ggml_webgpu_create_pipeline(ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); + pipeline = ggml_webgpu_create_pipeline(ctx->global_ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); pipeline.context = processed.decisions; ctx->pad_pipelines.emplace(pipeline_key, pipeline); } @@ -932,7 +935,7 @@ static std::optional ggml_webgpu_set_rows(webgpu_context & ctx, ggml_webgpu_set_rows_shader_lib_context shader_lib_ctx = { .key = key, .max_wg_size = - ctx->limits.maxComputeInvocationsPerWorkgroup }; + ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup }; webgpu_pipeline pipeline; // TODO: remove guard once pipeline caches are per-thread @@ -944,7 +947,7 @@ static std::optional ggml_webgpu_set_rows(webgpu_context & ctx, } else { ggml_webgpu_processed_shader processed = ggml_webgpu_preprocess_set_rows_shader(ctx->p, wgsl_set_rows, shader_lib_ctx); - pipeline = ggml_webgpu_create_pipeline(ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); + pipeline = ggml_webgpu_create_pipeline(ctx->global_ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); pipeline.context = processed.decisions; ctx->set_rows_pipelines.emplace(key, pipeline); } @@ -1289,7 +1292,7 @@ static webgpu_command ggml_webgpu_unary_op(webgpu_context & ctx, ggml_tensor * s }; ggml_webgpu_unary_shader_lib_context shader_lib_ctx = { .key = pipeline_key, .max_wg_size = - ctx->limits.maxComputeInvocationsPerWorkgroup }; + ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup }; webgpu_pipeline pipeline; { @@ -1301,7 +1304,7 @@ static webgpu_command ggml_webgpu_unary_op(webgpu_context & ctx, ggml_tensor * s } else { ggml_webgpu_processed_shader processed = ggml_webgpu_preprocess_unary_shader(ctx->p, wgsl_unary, shader_lib_ctx); - pipeline = ggml_webgpu_create_pipeline(ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); + pipeline = ggml_webgpu_create_pipeline(ctx->global_ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); pipeline.context = processed.decisions; ctx->unary_pipelines.emplace(pipeline_key, pipeline); } @@ -1719,7 +1722,7 @@ static webgpu_command ggml_webgpu_argmax(webgpu_context & ctx, ggml_tensor * src ggml_webgpu_generic_shader_lib_context shader_lib_ctx = { .vec4 = src->ne[0] % 4 == 0, - .max_wg_size = ctx->limits.maxComputeInvocationsPerWorkgroup, + .max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup, }; webgpu_pipeline pipeline; @@ -1732,7 +1735,7 @@ static webgpu_command ggml_webgpu_argmax(webgpu_context & ctx, ggml_tensor * src } else { ggml_webgpu_processed_shader processed = ggml_webgpu_preprocess_generic_shader(ctx->p, wgsl_argmax, shader_lib_ctx, "argmax"); - pipeline = ggml_webgpu_create_pipeline(ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); + pipeline = ggml_webgpu_create_pipeline(ctx->global_ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); ctx->argmax_pipelines.emplace(shader_lib_ctx.vec4, pipeline); } } @@ -1746,9 +1749,9 @@ static webgpu_command ggml_webgpu_argsort(webgpu_context & ctx, ggml_tensor * sr const int32_t order = is_top_k ? (int32_t) GGML_SORT_ORDER_DESC : (int32_t) ggml_get_op_params_i32(dst, 0); ggml_webgpu_argsort_shader_lib_context shader_lib_ctx = { .max_wg_size = - ctx->limits.maxComputeInvocationsPerWorkgroup, + ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup, .wg_mem_limit_bytes = - ctx->limits.maxComputeWorkgroupStorageSize, + ctx->global_ctx->capabilities.limits.maxComputeWorkgroupStorageSize, .order = order }; std::lock_guard lock(ctx->global_ctx->mutex); @@ -1759,7 +1762,7 @@ static webgpu_command ggml_webgpu_argsort(webgpu_context & ctx, ggml_tensor * sr } else { ggml_webgpu_processed_shader processed = ggml_webgpu_preprocess_argsort_shader(ctx->p, wgsl_argsort, shader_lib_ctx); - argsort_pipeline = ggml_webgpu_create_pipeline(ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); + argsort_pipeline = ggml_webgpu_create_pipeline(ctx->global_ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); argsort_pipeline.context = processed.decisions; ctx->argsort_pipelines.emplace(order, argsort_pipeline); } @@ -1774,7 +1777,7 @@ static webgpu_command ggml_webgpu_argsort(webgpu_context & ctx, ggml_tensor * sr ggml_webgpu_processed_shader processed = ggml_webgpu_preprocess_argsort_merge_shader(ctx->p, wgsl_argsort_merge, shader_lib_ctx); argsort_merge_pipeline = - ggml_webgpu_create_pipeline(ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); + ggml_webgpu_create_pipeline(ctx->global_ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); argsort_merge_pipeline.context = processed.decisions; ctx->argsort_merge_pipelines.emplace(order, argsort_merge_pipeline); } @@ -1805,7 +1808,7 @@ static webgpu_command ggml_webgpu_argsort(webgpu_context & ctx, ggml_tensor * sr const size_t dst_offset = ggml_webgpu_tensor_offset(dst); const size_t idx_nbytes = out_ne0 * ggml_nrows(dst) * sizeof(int32_t); - const size_t tmp_offset = ROUNDUP_POW2(dst_offset + idx_nbytes, ctx->limits.minStorageBufferOffsetAlignment); + const size_t tmp_offset = ROUNDUP_POW2(dst_offset + idx_nbytes, ctx->global_ctx->capabilities.limits.minStorageBufferOffsetAlignment); const size_t tmp_binding_size = ROUNDUP_POW2(idx_nbytes, WEBGPU_STORAGE_BUF_BINDING_MULT); const size_t dst_binding_size = ROUNDUP_POW2(idx_nbytes + ggml_webgpu_tensor_misalignment(ctx, dst), WEBGPU_STORAGE_BUF_BINDING_MULT); @@ -1836,7 +1839,7 @@ static webgpu_command ggml_webgpu_argsort(webgpu_context & ctx, ggml_tensor * sr }; const uint32_t total_wg_init = npr * nrows; - const uint32_t max_wg = ctx->limits.maxComputeWorkgroupsPerDimension; + const uint32_t max_wg = ctx->global_ctx->capabilities.limits.maxComputeWorkgroupsPerDimension; const uint32_t wg_x_init = std::min(total_wg_init, max_wg); const uint32_t wg_y_init = CEIL_DIV(total_wg_init, wg_x_init); std::vector init_entries = { @@ -1935,7 +1938,7 @@ static webgpu_command ggml_webgpu_cumsum(webgpu_context & ctx, ggml_tensor * src ggml_webgpu_generic_shader_lib_context shader_lib_ctx = { .vec4 = false, - .max_wg_size = ctx->limits.maxComputeInvocationsPerWorkgroup, + .max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup, }; webgpu_pipeline pipeline; // TODO: remove guard once pipeline caches are per-thread @@ -1947,7 +1950,7 @@ static webgpu_command ggml_webgpu_cumsum(webgpu_context & ctx, ggml_tensor * src } else { ggml_webgpu_processed_shader processed = ggml_webgpu_preprocess_generic_shader(ctx->p, wgsl_cumsum, shader_lib_ctx, "cumsum"); - pipeline = ggml_webgpu_create_pipeline(ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); + pipeline = ggml_webgpu_create_pipeline(ctx->global_ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); ctx->cumsum_pipelines.emplace(1, pipeline); } } @@ -1979,7 +1982,7 @@ static webgpu_command ggml_webgpu_sum_rows(webgpu_context & ctx, ggml_tensor * s ggml_webgpu_generic_shader_lib_context shader_lib_ctx = { .vec4 = false, - .max_wg_size = ctx->limits.maxComputeInvocationsPerWorkgroup, + .max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup, }; webgpu_pipeline pipeline; @@ -1992,7 +1995,7 @@ static webgpu_command ggml_webgpu_sum_rows(webgpu_context & ctx, ggml_tensor * s } else { ggml_webgpu_processed_shader processed = ggml_webgpu_preprocess_generic_shader(ctx->p, wgsl_sum_rows, shader_lib_ctx, "sum_rows"); - pipeline = ggml_webgpu_create_pipeline(ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); + pipeline = ggml_webgpu_create_pipeline(ctx->global_ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); ctx->sum_rows_pipelines.emplace(1, pipeline); } } @@ -2348,7 +2351,7 @@ static size_t ggml_backend_webgpu_buffer_type_get_alloc_size(ggml_backend_buffer size_t res = ggml_nbytes(tensor); switch (tensor->op) { case GGML_OP_ARGSORT: - res = ROUNDUP_POW2(res * 2 + ctx->webgpu_ctx->limits.minStorageBufferOffsetAlignment, + res = ROUNDUP_POW2(res * 2 + ctx->reg_context->wgpu_global_ctx.capabilities.limits.minStorageBufferOffsetAlignment, WEBGPU_STORAGE_BUF_BINDING_MULT); break; case GGML_OP_TOP_K: @@ -2356,7 +2359,7 @@ static size_t ggml_backend_webgpu_buffer_type_get_alloc_size(ggml_backend_buffer const ggml_tensor * src0 = tensor->src[0]; if (src0) { const size_t full = sizeof(int32_t) * ggml_nelements(src0); - res = ROUNDUP_POW2(full * 2 + ctx->webgpu_ctx->limits.minStorageBufferOffsetAlignment, + res = ROUNDUP_POW2(full * 2 + ctx->reg_context->wgpu_global_ctx.capabilities.limits.minStorageBufferOffsetAlignment, WEBGPU_STORAGE_BUF_BINDING_MULT); } } @@ -3012,7 +3015,6 @@ static webgpu_context get_per_thread_webgpu_context(ggml_backend_dev_t dev) { ggml_webgpu_init_memset_pipeline(webgpu_ctx); ggml_webgpu_init_mul_mat_pipeline(webgpu_ctx); - ggml_webgpu_init_set_rows_pipeline(webgpu_ctx); ggml_webgpu_init_get_rows_pipeline(webgpu_ctx); ggml_webgpu_init_cpy_pipeline(webgpu_ctx); ggml_webgpu_init_add_pipeline(webgpu_ctx); @@ -3024,7 +3026,6 @@ static webgpu_context get_per_thread_webgpu_context(ggml_backend_dev_t dev) { ggml_webgpu_init_glu_pipeline(webgpu_ctx); ggml_webgpu_init_scale_pipeline(webgpu_ctx); ggml_webgpu_init_soft_max_pipeline(webgpu_ctx); - ggml_webgpu_init_unary_pipeline(webgpu_ctx); #ifdef GGML_WEBGPU_DEBUG // Initialize debug buffers ggml_webgpu_create_buffer(webgpu_ctx->global_ctx->device, webgpu_ctx->debug_host_buf, WEBGPU_DEBUG_BUF_ELEMS * sizeof(uint32_t), From 8d04416b207b6f6ac1c971c9b5e12b1a0a7e5623 Mon Sep 17 00:00:00 2001 From: Nikhil Jain Date: Tue, 20 Jan 2026 16:19:32 -0800 Subject: [PATCH 21/21] Clean up merge errors, delete forward declaration, and run clang-format --- ggml/src/ggml-webgpu/ggml-webgpu.cpp | 468 +++++++------- .../ggml-webgpu/wgsl-shaders/embed_wgsl.py | 7 - .../ggml-webgpu/wgsl-shaders/unary_op.wgsl | 587 ------------------ 3 files changed, 244 insertions(+), 818 deletions(-) delete mode 100644 ggml/src/ggml-webgpu/wgsl-shaders/unary_op.wgsl diff --git a/ggml/src/ggml-webgpu/ggml-webgpu.cpp b/ggml/src/ggml-webgpu/ggml-webgpu.cpp index a5a5fe4920..f7f3dbd445 100644 --- a/ggml/src/ggml-webgpu/ggml-webgpu.cpp +++ b/ggml/src/ggml-webgpu/ggml-webgpu.cpp @@ -267,15 +267,32 @@ struct webgpu_command { #endif }; -// Forward declaration -struct webgpu_global_context; +struct webgpu_capabilities { + wgpu::Limits limits; + bool supports_subgroup_matrix = false; + wgpu::SubgroupMatrixConfig subgroup_matrix_config{}; + uint32_t subgroup_size = 0; + uint32_t max_subgroup_size = 0; +}; + +// Stores global webgpu members +struct webgpu_global_context { + wgpu::Instance instance; + wgpu::Adapter adapter; + wgpu::Device device; + // Shared buffer to move data from device to host + wgpu::Buffer get_tensor_staging_buf; + // Mutex to lock get_tensor_staging_buf + std::recursive_mutex mutex; + webgpu_capabilities capabilities; +}; // All the base objects needed to run operations on a WebGPU device struct webgpu_context_struct { // Points to global instances owned by ggml_backend_webgpu_reg_context webgpu_global_context * global_ctx; - - wgpu::Queue queue; + + wgpu::Queue queue; webgpu_buf_pool param_buf_pool; bool supports_subgroup_matrix = false; @@ -283,8 +300,7 @@ struct webgpu_context_struct { uint32_t sg_mat_n; uint32_t sg_mat_k; - // std::recursive_mutex mutex; - std::atomic_uint inflight_threads = 0; + std::atomic_uint inflight_threads = 0; webgpu_buf_pool set_rows_error_buf_pool; @@ -348,42 +364,22 @@ struct webgpu_context_struct { typedef std::shared_ptr webgpu_context; -struct webgpu_capabilities { - wgpu::Limits limits; - bool supports_subgroup_matrix = false; - wgpu::SubgroupMatrixConfig subgroup_matrix_config{}; - uint32_t subgroup_size = 0; - uint32_t max_subgroup_size = 0; -}; - -// Stores global webgpu members -struct webgpu_global_context { - wgpu::Instance instance; - wgpu::Adapter adapter; - wgpu::Device device; - // Shared buffer to move data from device to host - wgpu::Buffer get_tensor_staging_buf; - // Mutex to lock get_tensor_staging_buf - std::recursive_mutex mutex; - webgpu_capabilities capabilities; -} ; - -// Metadata required for the ggml backend registration/discovery interface +// Metadata required for the ggml backend registration/discovery interface struct ggml_backend_webgpu_reg_context { - // Since the Instance is a global entrypoint into the WebGPU api, it lives here + // Since the Instance is a global entrypoint into the WebGPU api, it lives here webgpu_global_context wgpu_global_ctx; - size_t device_count; - const char * name; + size_t device_count; + const char * name; }; -// Per-device struct for the global logical device interface +// Per-device struct for the global logical device interface struct ggml_backend_webgpu_device_context { ggml_backend_webgpu_reg_context * reg_context; - std::string device_name; - std::string device_desc; + std::string device_name; + std::string device_desc; }; -// Per-thread data required to actually run webgpu operations in a backend instance +// Per-thread data required to actually run webgpu operations in a backend instance struct ggml_backend_webgpu_context { std::once_flag init_once; std::string name; @@ -391,15 +387,12 @@ struct ggml_backend_webgpu_context { // Per-thread data related to buffers struct ggml_backend_webgpu_buffer_context { - wgpu::Buffer buffer; - std::string label; - - ggml_backend_webgpu_buffer_context( - wgpu::Buffer buf, - std::string lbl) - : - buffer(std::move(buf)), - label(std::move(lbl)) {} + wgpu::Buffer buffer; + std::string label; + + ggml_backend_webgpu_buffer_context(wgpu::Buffer buf, std::string lbl) : + buffer(std::move(buf)), + label(std::move(lbl)) {} }; // Forward declaration @@ -450,7 +443,7 @@ static webgpu_pipeline ggml_webgpu_create_pipeline(wgpu::Device & return { device.CreateComputePipeline(&pipeline_desc), label }; } -static void ggml_webgpu_create_buffer(wgpu::Device & device, +static void ggml_webgpu_create_buffer(wgpu::Device & device, wgpu::Buffer & buffer, size_t size, wgpu::BufferUsage usage, @@ -470,7 +463,7 @@ static void ggml_webgpu_create_buffer(wgpu::Device & device, /** WebGPU Actions */ // Wait for the queue to finish processing all submitted work -static void ggml_backend_webgpu_wait(webgpu_context & ctx, +static void ggml_backend_webgpu_wait(webgpu_context & ctx, std::vector & futures, bool block = true) { // If we have too many in-flight submissions, wait on the oldest one first. If there are many threads, @@ -484,7 +477,8 @@ static void ggml_backend_webgpu_wait(webgpu_context & ctx, } size_t i = 0; while (i < futures.size()) { - auto waitStatus = ctx->global_ctx->instance.WaitAny(futures[i].futures.size(), futures[i].futures.data(), timeout_ms); + auto waitStatus = + ctx->global_ctx->instance.WaitAny(futures[i].futures.size(), futures[i].futures.data(), timeout_ms); switch (waitStatus) { case wgpu::WaitStatus::Success: futures.erase(futures.begin() + i); @@ -508,13 +502,13 @@ static void ggml_backend_webgpu_map_buffer(webgpu_context & ctx, size_t offset, size_t size) { ctx->global_ctx->instance.WaitAny(buffer.MapAsync(mode, offset, size, wgpu::CallbackMode::AllowSpontaneous, - [](wgpu::MapAsyncStatus status, wgpu::StringView message) { - if (status != wgpu::MapAsyncStatus::Success) { - GGML_LOG_ERROR("ggml_webgpu: Failed to map buffer: %s\n", - message.data); - } - }), - UINT64_MAX); + [](wgpu::MapAsyncStatus status, wgpu::StringView message) { + if (status != wgpu::MapAsyncStatus::Success) { + GGML_LOG_ERROR("ggml_webgpu: Failed to map buffer: %s\n", + message.data); + } + }), + UINT64_MAX); } #ifdef GGML_WEBGPU_DEBUG @@ -851,9 +845,9 @@ static webgpu_command ggml_webgpu_pad(webgpu_context & ctx, ggml_tensor * src, g const bool circular = ggml_get_op_params_i32(dst, 8) != 0; ggml_webgpu_pad_pipeline_key pipeline_key = { .circular = circular }; - ggml_webgpu_pad_shader_lib_context shader_lib_ctx = { .key = pipeline_key, - .max_wg_size = - ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup }; + ggml_webgpu_pad_shader_lib_context shader_lib_ctx = { + .key = pipeline_key, .max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup + }; webgpu_pipeline pipeline; { @@ -865,7 +859,8 @@ static webgpu_command ggml_webgpu_pad(webgpu_context & ctx, ggml_tensor * src, g } else { ggml_webgpu_processed_shader processed = ggml_webgpu_preprocess_pad_shader(ctx->p, wgsl_pad, shader_lib_ctx); - pipeline = ggml_webgpu_create_pipeline(ctx->global_ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); + pipeline = + ggml_webgpu_create_pipeline(ctx->global_ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); pipeline.context = processed.decisions; ctx->pad_pipelines.emplace(pipeline_key, pipeline); } @@ -933,9 +928,9 @@ static std::optional ggml_webgpu_set_rows(webgpu_context & ctx, .vec4 = src->ne[0] % 4 == 0, .i64_idx = idx->type == GGML_TYPE_I64 }; - ggml_webgpu_set_rows_shader_lib_context shader_lib_ctx = { .key = key, - .max_wg_size = - ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup }; + ggml_webgpu_set_rows_shader_lib_context shader_lib_ctx = { + .key = key, .max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup + }; webgpu_pipeline pipeline; // TODO: remove guard once pipeline caches are per-thread @@ -947,7 +942,8 @@ static std::optional ggml_webgpu_set_rows(webgpu_context & ctx, } else { ggml_webgpu_processed_shader processed = ggml_webgpu_preprocess_set_rows_shader(ctx->p, wgsl_set_rows, shader_lib_ctx); - pipeline = ggml_webgpu_create_pipeline(ctx->global_ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); + pipeline = + ggml_webgpu_create_pipeline(ctx->global_ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); pipeline.context = processed.decisions; ctx->set_rows_pipelines.emplace(key, pipeline); } @@ -1125,7 +1121,7 @@ static webgpu_command ggml_webgpu_mul_mat(webgpu_context & ctx, uint32_t output_groups = CEIL_DIV(dst->ne[0], WEBGPU_MUL_MAT_VEC_OUTPUTS_PER_WG); uint32_t total_wg = output_groups * batches; wg_x = total_wg % ctx->global_ctx->capabilities.limits.maxComputeWorkgroupsPerDimension; - wg_y = CEIL_DIV(total_wg, ctx->global_ctx->capabilities.limits.maxComputeWorkgroupsPerDimension); + wg_y = CEIL_DIV(total_wg, ctx->global_ctx->capabilities.limits.maxComputeWorkgroupsPerDimension); } else { pipeline = ctx->mul_mat_pipelines[src0->type][src1->type][vectorized]; uint32_t wg_m; @@ -1249,25 +1245,27 @@ static webgpu_command ggml_webgpu_flash_attn(webgpu_context & ctx, .uses_logit_softcap = logit_softcap != 0.0f, }; - webgpu_pipeline pipeline; + webgpu_pipeline pipeline; // TODO: remove guard once pipeline caches are per-thread { std::lock_guard lock(ctx->global_ctx->mutex); auto it = ctx->flash_attn_pipelines.find(key); if (it != ctx->flash_attn_pipelines.end()) { - pipeline = it->second; + pipeline = it->second; } else { - ggml_webgpu_flash_attn_shader_lib_context shader_lib_ctx = { .key = key, - .sg_mat_m = ctx->sg_mat_m, - .sg_mat_n = ctx->sg_mat_n, - .sg_mat_k = ctx->sg_mat_k, - .wg_mem_limit_bytes = - ctx->global_ctx->capabilities.limits.maxComputeWorkgroupStorageSize, - .max_subgroup_size = ctx->global_ctx->capabilities.max_subgroup_size }; + ggml_webgpu_flash_attn_shader_lib_context shader_lib_ctx = { + .key = key, + .sg_mat_m = ctx->sg_mat_m, + .sg_mat_n = ctx->sg_mat_n, + .sg_mat_k = ctx->sg_mat_k, + .wg_mem_limit_bytes = ctx->global_ctx->capabilities.limits.maxComputeWorkgroupStorageSize, + .max_subgroup_size = ctx->global_ctx->capabilities.max_subgroup_size + }; ggml_webgpu_processed_shader processed = ggml_webgpu_preprocess_flash_attn_shader(ctx->p, wgsl_flash_attn, shader_lib_ctx); - pipeline = ggml_webgpu_create_pipeline(ctx->global_ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); + pipeline = + ggml_webgpu_create_pipeline(ctx->global_ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); pipeline.context = processed.decisions; ctx->flash_attn_pipelines.emplace(key, pipeline); } @@ -1276,7 +1274,6 @@ static webgpu_command ggml_webgpu_flash_attn(webgpu_context & ctx, ggml_webgpu_flash_attn_shader_decisions decisions = *static_cast(pipeline.context); - uint32_t wg_per_head = CEIL_DIV(Q->ne[1], decisions.q_tile); uint32_t wg_x = wg_per_head * Q->ne[2] * Q->ne[3]; // wg per head * number of heads * number of batches return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x); @@ -1290,9 +1287,9 @@ static webgpu_command ggml_webgpu_unary_op(webgpu_context & ctx, ggml_tensor * s ggml_webgpu_unary_pipeline_key pipeline_key = { .type = dst->type, .op = op, .is_unary = is_unary, .inplace = inplace }; - ggml_webgpu_unary_shader_lib_context shader_lib_ctx = { .key = pipeline_key, - .max_wg_size = - ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup }; + ggml_webgpu_unary_shader_lib_context shader_lib_ctx = { + .key = pipeline_key, .max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup + }; webgpu_pipeline pipeline; { @@ -1304,7 +1301,8 @@ static webgpu_command ggml_webgpu_unary_op(webgpu_context & ctx, ggml_tensor * s } else { ggml_webgpu_processed_shader processed = ggml_webgpu_preprocess_unary_shader(ctx->p, wgsl_unary, shader_lib_ctx); - pipeline = ggml_webgpu_create_pipeline(ctx->global_ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); + pipeline = + ggml_webgpu_create_pipeline(ctx->global_ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); pipeline.context = processed.decisions; ctx->unary_pipelines.emplace(pipeline_key, pipeline); } @@ -1735,7 +1733,8 @@ static webgpu_command ggml_webgpu_argmax(webgpu_context & ctx, ggml_tensor * src } else { ggml_webgpu_processed_shader processed = ggml_webgpu_preprocess_generic_shader(ctx->p, wgsl_argmax, shader_lib_ctx, "argmax"); - pipeline = ggml_webgpu_create_pipeline(ctx->global_ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); + pipeline = + ggml_webgpu_create_pipeline(ctx->global_ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); ctx->argmax_pipelines.emplace(shader_lib_ctx.vec4, pipeline); } } @@ -1748,11 +1747,11 @@ static webgpu_command ggml_webgpu_argsort(webgpu_context & ctx, ggml_tensor * sr // ascending order is 0, descending order is 1 const int32_t order = is_top_k ? (int32_t) GGML_SORT_ORDER_DESC : (int32_t) ggml_get_op_params_i32(dst, 0); - ggml_webgpu_argsort_shader_lib_context shader_lib_ctx = { .max_wg_size = - ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup, - .wg_mem_limit_bytes = - ctx->global_ctx->capabilities.limits.maxComputeWorkgroupStorageSize, - .order = order }; + ggml_webgpu_argsort_shader_lib_context shader_lib_ctx = { + .max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup, + .wg_mem_limit_bytes = ctx->global_ctx->capabilities.limits.maxComputeWorkgroupStorageSize, + .order = order + }; std::lock_guard lock(ctx->global_ctx->mutex); webgpu_pipeline argsort_pipeline; @@ -1762,7 +1761,8 @@ static webgpu_command ggml_webgpu_argsort(webgpu_context & ctx, ggml_tensor * sr } else { ggml_webgpu_processed_shader processed = ggml_webgpu_preprocess_argsort_shader(ctx->p, wgsl_argsort, shader_lib_ctx); - argsort_pipeline = ggml_webgpu_create_pipeline(ctx->global_ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); + argsort_pipeline = + ggml_webgpu_create_pipeline(ctx->global_ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); argsort_pipeline.context = processed.decisions; ctx->argsort_pipelines.emplace(order, argsort_pipeline); } @@ -1806,9 +1806,10 @@ static webgpu_command ggml_webgpu_argsort(webgpu_context & ctx, ggml_tensor * sr const bool start_in_tmp = (merge_passes % 2) == 1; - const size_t dst_offset = ggml_webgpu_tensor_offset(dst); - const size_t idx_nbytes = out_ne0 * ggml_nrows(dst) * sizeof(int32_t); - const size_t tmp_offset = ROUNDUP_POW2(dst_offset + idx_nbytes, ctx->global_ctx->capabilities.limits.minStorageBufferOffsetAlignment); + const size_t dst_offset = ggml_webgpu_tensor_offset(dst); + const size_t idx_nbytes = out_ne0 * ggml_nrows(dst) * sizeof(int32_t); + const size_t tmp_offset = + ROUNDUP_POW2(dst_offset + idx_nbytes, ctx->global_ctx->capabilities.limits.minStorageBufferOffsetAlignment); const size_t tmp_binding_size = ROUNDUP_POW2(idx_nbytes, WEBGPU_STORAGE_BUF_BINDING_MULT); const size_t dst_binding_size = ROUNDUP_POW2(idx_nbytes + ggml_webgpu_tensor_misalignment(ctx, dst), WEBGPU_STORAGE_BUF_BINDING_MULT); @@ -1839,10 +1840,10 @@ static webgpu_command ggml_webgpu_argsort(webgpu_context & ctx, ggml_tensor * sr }; const uint32_t total_wg_init = npr * nrows; - const uint32_t max_wg = ctx->global_ctx->capabilities.limits.maxComputeWorkgroupsPerDimension; - const uint32_t wg_x_init = std::min(total_wg_init, max_wg); - const uint32_t wg_y_init = CEIL_DIV(total_wg_init, wg_x_init); - std::vector init_entries = { + const uint32_t max_wg = ctx->global_ctx->capabilities.limits.maxComputeWorkgroupsPerDimension; + const uint32_t wg_x_init = std::min(total_wg_init, max_wg); + const uint32_t wg_y_init = CEIL_DIV(total_wg_init, wg_x_init); + std::vector init_entries = { { .binding = 0, .buffer = ggml_webgpu_tensor_buf(src), .offset = ggml_webgpu_tensor_align_offset(ctx, src), @@ -1950,7 +1951,8 @@ static webgpu_command ggml_webgpu_cumsum(webgpu_context & ctx, ggml_tensor * src } else { ggml_webgpu_processed_shader processed = ggml_webgpu_preprocess_generic_shader(ctx->p, wgsl_cumsum, shader_lib_ctx, "cumsum"); - pipeline = ggml_webgpu_create_pipeline(ctx->global_ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); + pipeline = + ggml_webgpu_create_pipeline(ctx->global_ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); ctx->cumsum_pipelines.emplace(1, pipeline); } } @@ -1995,7 +1997,8 @@ static webgpu_command ggml_webgpu_sum_rows(webgpu_context & ctx, ggml_tensor * s } else { ggml_webgpu_processed_shader processed = ggml_webgpu_preprocess_generic_shader(ctx->p, wgsl_sum_rows, shader_lib_ctx, "sum_rows"); - pipeline = ggml_webgpu_create_pipeline(ctx->global_ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); + pipeline = + ggml_webgpu_create_pipeline(ctx->global_ctx->device, processed.wgsl.c_str(), processed.variant.c_str()); ctx->sum_rows_pipelines.emplace(1, pipeline); } } @@ -2096,7 +2099,7 @@ static std::optional ggml_webgpu_encode_node(webgpu_context ctx, static ggml_status ggml_backend_webgpu_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { WEBGPU_LOG_DEBUG("ggml_backend_webgpu_graph_compute(" << cgraph->n_nodes << " nodes)"); - webgpu_context ctx = get_per_thread_webgpu_context(backend->device); + webgpu_context ctx = get_per_thread_webgpu_context(backend->device); WEBGPU_CPU_PROFILE_TOTAL_START(graph_compute); @@ -2175,8 +2178,8 @@ static void ggml_backend_webgpu_buffer_memset_tensor(ggml_backend_buffer_t buffe WEBGPU_CPU_PROFILE_TOTAL_START(memset_tensor); - webgpu_context webgpu_ctx = get_per_thread_webgpu_context(buffer->buft->device); - ggml_backend_webgpu_buffer_context * buf_ctx = (ggml_backend_webgpu_buffer_context *) buffer->context; + webgpu_context webgpu_ctx = get_per_thread_webgpu_context(buffer->buft->device); + ggml_backend_webgpu_buffer_context * buf_ctx = (ggml_backend_webgpu_buffer_context *) buffer->context; WEBGPU_LOG_DEBUG("ggml_backend_webgpu_buffer_memset_tensor(" << buf_ctx->label << ", " << tensor << ", " << value << ", " << offset << ", " << size << ")"); @@ -2196,7 +2199,7 @@ static void ggml_backend_webgpu_buffer_set_tensor(ggml_backend_buffer_t buffer, size_t size) { WEBGPU_CPU_PROFILE_TOTAL_START(set_tensor); ggml_backend_webgpu_buffer_context * buf_ctx = (ggml_backend_webgpu_buffer_context *) buffer->context; - webgpu_context webgpu_ctx = get_per_thread_webgpu_context(buffer->buft->device); + webgpu_context webgpu_ctx = get_per_thread_webgpu_context(buffer->buft->device); WEBGPU_LOG_DEBUG("ggml_backend_webgpu_buffer_set_tensor(" << buf_ctx->label << ", " << tensor << ", " << data << ", " << offset << ", " << size << ")"); @@ -2243,7 +2246,7 @@ static void ggml_backend_webgpu_buffer_get_tensor(ggml_backend_buffer_t buffer, WEBGPU_LOG_DEBUG("ggml_backend_webgpu_buffer_get_tensor(" << buf_ctx->label << ", " << tensor << ", " << data << ", " << offset << ", " << size << ")"); webgpu_context webgpu_ctx = get_per_thread_webgpu_context(buffer->buft->device); - wgpu::Device device = webgpu_ctx->global_ctx->device; + wgpu::Device device = webgpu_ctx->global_ctx->device; size_t total_offset = webgpu_tensor_offset(tensor) + tensor->view_offs + offset; @@ -2255,7 +2258,8 @@ static void ggml_backend_webgpu_buffer_get_tensor(ggml_backend_buffer_t buffer, std::lock_guard lock(webgpu_ctx->global_ctx->mutex); - if (webgpu_ctx->global_ctx->get_tensor_staging_buf == nullptr || webgpu_ctx->global_ctx->get_tensor_staging_buf.GetSize() < final_size) { + if (webgpu_ctx->global_ctx->get_tensor_staging_buf == nullptr || + webgpu_ctx->global_ctx->get_tensor_staging_buf.GetSize() < final_size) { // Create a new staging buffer if it doesn't exist or is too small if (webgpu_ctx->global_ctx->get_tensor_staging_buf) { webgpu_ctx->global_ctx->get_tensor_staging_buf.Destroy(); @@ -2266,14 +2270,16 @@ static void ggml_backend_webgpu_buffer_get_tensor(ggml_backend_buffer_t buffer, // Copy the data from the buffer to the staging buffer wgpu::CommandEncoder encoder = device.CreateCommandEncoder(); - encoder.CopyBufferToBuffer(buf_ctx->buffer, total_offset, webgpu_ctx->global_ctx->get_tensor_staging_buf, 0, final_size); + encoder.CopyBufferToBuffer(buf_ctx->buffer, total_offset, webgpu_ctx->global_ctx->get_tensor_staging_buf, 0, + final_size); wgpu::CommandBuffer commands = encoder.Finish(); // Submit the command buffer to the queue webgpu_ctx->queue.Submit(1, &commands); // Map the staging buffer to read the data - ggml_backend_webgpu_map_buffer(webgpu_ctx, webgpu_ctx->global_ctx->get_tensor_staging_buf, wgpu::MapMode::Read, 0, final_size); + ggml_backend_webgpu_map_buffer(webgpu_ctx, webgpu_ctx->global_ctx->get_tensor_staging_buf, wgpu::MapMode::Read, 0, + final_size); // Must specify size here since the staging buffer might be larger than the tensor size const void * mapped_range = webgpu_ctx->global_ctx->get_tensor_staging_buf.GetConstMappedRange(0, final_size); @@ -2286,8 +2292,8 @@ static void ggml_backend_webgpu_buffer_get_tensor(ggml_backend_buffer_t buffer, static void ggml_backend_webgpu_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { WEBGPU_LOG_DEBUG("ggml_backend_webgpu_buffer_clear(" << buffer << ", " << (uint32_t) value << ")"); WEBGPU_CPU_PROFILE_TOTAL_START(clear); - ggml_backend_webgpu_buffer_context * buf_ctx = (ggml_backend_webgpu_buffer_context *) buffer->context; - webgpu_context webgpu_ctx = get_per_thread_webgpu_context(buffer->buft->device); + ggml_backend_webgpu_buffer_context * buf_ctx = (ggml_backend_webgpu_buffer_context *) buffer->context; + webgpu_context webgpu_ctx = get_per_thread_webgpu_context(buffer->buft->device); ggml_backend_webgpu_buffer_memset(webgpu_ctx, buf_ctx->buffer, value, 0, buffer->size); WEBGPU_CPU_PROFILE_TOTAL_END(clear, webgpu_ctx); } @@ -2320,28 +2326,26 @@ static ggml_backend_buffer_t ggml_backend_webgpu_buffer_type_alloc_buffer(ggml_b std::string buf_name = "tensor_buf" + std::to_string(buffer_id); WEBGPU_LOG_DEBUG("ggml_backend_webgpu_buffer_type_alloc_buffer_" << buffer_id << ": " << size << " bytes"); - webgpu_context ctx = get_per_thread_webgpu_context(buft->device); - + webgpu_context ctx = get_per_thread_webgpu_context(buft->device); wgpu::Buffer buf; ggml_webgpu_create_buffer(ctx->global_ctx->device, buf, ROUNDUP_POW2(size, WEBGPU_STORAGE_BUF_BINDING_MULT), wgpu::BufferUsage::Storage | wgpu::BufferUsage::CopySrc | wgpu::BufferUsage::CopyDst, buf_name.c_str()); - ggml_backend_webgpu_buffer_context * buf_ctx = - new ggml_backend_webgpu_buffer_context(buf, buf_name); + ggml_backend_webgpu_buffer_context * buf_ctx = new ggml_backend_webgpu_buffer_context(buf, buf_name); return ggml_backend_buffer_init(buft, ggml_backend_webgpu_buffer_interface, buf_ctx, size); } static size_t ggml_backend_webgpu_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { - webgpu_context ctx = get_per_thread_webgpu_context(buft->device); + webgpu_context ctx = get_per_thread_webgpu_context(buft->device); return ctx->global_ctx->capabilities.limits.minStorageBufferOffsetAlignment; } // maxBufferSize might be larger, but you can't bind more than maxStorageBufferBindingSize to a single binding. static size_t ggml_backend_webgpu_buffer_type_get_max_size(ggml_backend_buffer_type_t buft) { - webgpu_context ctx = get_per_thread_webgpu_context(buft->device); + webgpu_context ctx = get_per_thread_webgpu_context(buft->device); return ctx->global_ctx->capabilities.limits.maxStorageBufferBindingSize; } @@ -2351,16 +2355,19 @@ static size_t ggml_backend_webgpu_buffer_type_get_alloc_size(ggml_backend_buffer size_t res = ggml_nbytes(tensor); switch (tensor->op) { case GGML_OP_ARGSORT: - res = ROUNDUP_POW2(res * 2 + ctx->reg_context->wgpu_global_ctx.capabilities.limits.minStorageBufferOffsetAlignment, - WEBGPU_STORAGE_BUF_BINDING_MULT); + res = ROUNDUP_POW2( + res * 2 + ctx->reg_context->wgpu_global_ctx.capabilities.limits.minStorageBufferOffsetAlignment, + WEBGPU_STORAGE_BUF_BINDING_MULT); break; case GGML_OP_TOP_K: { const ggml_tensor * src0 = tensor->src[0]; if (src0) { const size_t full = sizeof(int32_t) * ggml_nelements(src0); - res = ROUNDUP_POW2(full * 2 + ctx->reg_context->wgpu_global_ctx.capabilities.limits.minStorageBufferOffsetAlignment, - WEBGPU_STORAGE_BUF_BINDING_MULT); + res = ROUNDUP_POW2( + full * 2 + + ctx->reg_context->wgpu_global_ctx.capabilities.limits.minStorageBufferOffsetAlignment, + WEBGPU_STORAGE_BUF_BINDING_MULT); } } break; @@ -2434,15 +2441,18 @@ static std::vector ggml_webgpu_wg_size_entry(uint32_t wg_si static void ggml_webgpu_init_memset_pipeline(webgpu_context & webgpu_ctx) { // we use the maximum workgroup size for the memset pipeline - size_t max_threads = WEBGPU_MAX_WG_SIZE * webgpu_ctx->global_ctx->capabilities.limits.maxComputeWorkgroupsPerDimension; + size_t max_threads = + WEBGPU_MAX_WG_SIZE * webgpu_ctx->global_ctx->capabilities.limits.maxComputeWorkgroupsPerDimension; // Size the bytes_per_thread so that the largest buffer size can be handled - webgpu_ctx->memset_bytes_per_thread = CEIL_DIV(webgpu_ctx->global_ctx->capabilities.limits.maxStorageBufferBindingSize, max_threads); + webgpu_ctx->memset_bytes_per_thread = + CEIL_DIV(webgpu_ctx->global_ctx->capabilities.limits.maxStorageBufferBindingSize, max_threads); std::vector constants(2); - constants[0].key = "wg_size"; - constants[0].value = WEBGPU_MAX_WG_SIZE; - constants[1].key = "bytes_per_thread"; - constants[1].value = webgpu_ctx->memset_bytes_per_thread; - webgpu_ctx->memset_pipelines[0] = ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_memset, "memset", constants); + constants[0].key = "wg_size"; + constants[0].value = WEBGPU_MAX_WG_SIZE; + constants[1].key = "bytes_per_thread"; + constants[1].value = webgpu_ctx->memset_bytes_per_thread; + webgpu_ctx->memset_pipelines[0] = + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_memset, "memset", constants); } static void ggml_webgpu_init_mul_mat_pipeline(webgpu_context & webgpu_ctx) { @@ -2506,15 +2516,19 @@ static void ggml_webgpu_init_mul_mat_pipeline(webgpu_context & webgpu_ctx) { #ifndef __EMSCRIPTEN__ if (webgpu_ctx->supports_subgroup_matrix) { std::map sg_matrix_repls; - sg_matrix_repls["WEBGPU_MAX_SUBGROUP_SIZE"] = std::to_string(webgpu_ctx->global_ctx->capabilities.max_subgroup_size); + sg_matrix_repls["WEBGPU_MAX_SUBGROUP_SIZE"] = + std::to_string(webgpu_ctx->global_ctx->capabilities.max_subgroup_size); sg_matrix_repls["WEBGPU_TILE_K"] = std::to_string(WEBGPU_MUL_MAT_TILE_K); sg_matrix_repls["WEBGPU_SUBGROUP_M"] = std::to_string(WEBGPU_MUL_MAT_SUBGROUP_M); sg_matrix_repls["WEBGPU_SUBGROUP_N"] = std::to_string(WEBGPU_MUL_MAT_SUBGROUP_N); sg_matrix_repls["WEBGPU_SUBGROUP_MATRIX_M"] = std::to_string(WEBGPU_MUL_MAT_SUBGROUP_MATRIX_M); sg_matrix_repls["WEBGPU_SUBGROUP_MATRIX_N"] = std::to_string(WEBGPU_MUL_MAT_SUBGROUP_MATRIX_N); - sg_matrix_repls["WEBGPU_SG_MAT_M_SIZE"] = std::to_string(webgpu_ctx->global_ctx->capabilities.subgroup_matrix_config.M); - sg_matrix_repls["WEBGPU_SG_MAT_N_SIZE"] = std::to_string(webgpu_ctx->global_ctx->capabilities.subgroup_matrix_config.N); - sg_matrix_repls["WEBGPU_SG_MAT_K_SIZE"] = std::to_string(webgpu_ctx->global_ctx->capabilities.subgroup_matrix_config.K); + sg_matrix_repls["WEBGPU_SG_MAT_M_SIZE"] = + std::to_string(webgpu_ctx->global_ctx->capabilities.subgroup_matrix_config.M); + sg_matrix_repls["WEBGPU_SG_MAT_N_SIZE"] = + std::to_string(webgpu_ctx->global_ctx->capabilities.subgroup_matrix_config.N); + sg_matrix_repls["WEBGPU_SG_MAT_K_SIZE"] = + std::to_string(webgpu_ctx->global_ctx->capabilities.subgroup_matrix_config.K); proc_mul_mat_f32_f32 = ggml_webgpu_process_shader_repls(wgsl_mul_mat_subgroup_matrix_f32_f32, sg_matrix_repls); proc_mul_mat_f32_f32_vec = @@ -2597,8 +2611,8 @@ static void ggml_webgpu_init_get_rows_pipeline(webgpu_context & webgpu_ctx) { webgpu_ctx->get_rows_pipelines[GGML_TYPE_F32][0] = ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_f32, "get_rows_f32", constants); - webgpu_ctx->get_rows_pipelines[GGML_TYPE_F32][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_f32_vec, "get_rows_f32_vec", constants); + webgpu_ctx->get_rows_pipelines[GGML_TYPE_F32][1] = ggml_webgpu_create_pipeline( + webgpu_ctx->global_ctx->device, wgsl_get_rows_f32_vec, "get_rows_f32_vec", constants); webgpu_ctx->get_rows_pipelines[GGML_TYPE_F16][0] = ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_f16, "get_rows_f16", constants); @@ -2626,14 +2640,14 @@ static void ggml_webgpu_init_get_rows_pipeline(webgpu_context & webgpu_ctx) { webgpu_ctx->get_rows_pipelines[GGML_TYPE_Q6_K][0] = ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_q6_k, "get_rows_q6_k", constants); - webgpu_ctx->get_rows_pipelines[GGML_TYPE_IQ2_XXS][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_iq2_xxs, "get_rows_iq2_xxs", constants); + webgpu_ctx->get_rows_pipelines[GGML_TYPE_IQ2_XXS][0] = ggml_webgpu_create_pipeline( + webgpu_ctx->global_ctx->device, wgsl_get_rows_iq2_xxs, "get_rows_iq2_xxs", constants); webgpu_ctx->get_rows_pipelines[GGML_TYPE_IQ2_XS][0] = ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_iq2_xs, "get_rows_iq2_xs", constants); webgpu_ctx->get_rows_pipelines[GGML_TYPE_IQ2_S][0] = ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_iq2_s, "get_rows_iq2_s", constants); - webgpu_ctx->get_rows_pipelines[GGML_TYPE_IQ3_XXS][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_iq3_xxs, "get_rows_iq3_xxs", constants); + webgpu_ctx->get_rows_pipelines[GGML_TYPE_IQ3_XXS][0] = ggml_webgpu_create_pipeline( + webgpu_ctx->global_ctx->device, wgsl_get_rows_iq3_xxs, "get_rows_iq3_xxs", constants); webgpu_ctx->get_rows_pipelines[GGML_TYPE_IQ3_S][0] = ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_get_rows_iq3_s, "get_rows_iq3_s", constants); webgpu_ctx->get_rows_pipelines[GGML_TYPE_IQ1_S][0] = @@ -2718,8 +2732,8 @@ static void ggml_webgpu_init_rms_norm_pipeline(webgpu_context & webgpu_ctx) { webgpu_ctx->rms_norm_pipelines[0] = ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_rms_norm, "rms_norm", constants); - webgpu_ctx->rms_norm_pipelines[1] = - ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_rms_norm_inplace, "rms_norm_inplace", constants); + webgpu_ctx->rms_norm_pipelines[1] = ggml_webgpu_create_pipeline( + webgpu_ctx->global_ctx->device, wgsl_rms_norm_inplace, "rms_norm_inplace", constants); } static void ggml_webgpu_init_rope_pipeline(webgpu_context & webgpu_ctx) { @@ -2727,21 +2741,21 @@ static void ggml_webgpu_init_rope_pipeline(webgpu_context & webgpu_ctx) { webgpu_ctx->rope_pipelines[GGML_TYPE_F32][0][0] = ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_rope_f32, "rope_f32", constants); - webgpu_ctx->rope_pipelines[GGML_TYPE_F32][0][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_rope_f32_inplace, "rope_f32_inplace", constants); + webgpu_ctx->rope_pipelines[GGML_TYPE_F32][0][1] = ggml_webgpu_create_pipeline( + webgpu_ctx->global_ctx->device, wgsl_rope_f32_inplace, "rope_f32_inplace", constants); webgpu_ctx->rope_pipelines[GGML_TYPE_F32][1][0] = ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_rope_f32_ff, "rope_f32_ff", constants); - webgpu_ctx->rope_pipelines[GGML_TYPE_F32][1][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_rope_f32_ff_inplace, "rope_f32_ff_inplace", constants); + webgpu_ctx->rope_pipelines[GGML_TYPE_F32][1][1] = ggml_webgpu_create_pipeline( + webgpu_ctx->global_ctx->device, wgsl_rope_f32_ff_inplace, "rope_f32_ff_inplace", constants); webgpu_ctx->rope_pipelines[GGML_TYPE_F16][0][0] = ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_rope_f16, "rope_f16", constants); - webgpu_ctx->rope_pipelines[GGML_TYPE_F16][0][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_rope_f16_inplace, "rope_f16_inplace", constants); + webgpu_ctx->rope_pipelines[GGML_TYPE_F16][0][1] = ggml_webgpu_create_pipeline( + webgpu_ctx->global_ctx->device, wgsl_rope_f16_inplace, "rope_f16_inplace", constants); webgpu_ctx->rope_pipelines[GGML_TYPE_F16][1][0] = ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_rope_f16_ff, "rope_f16_ff", constants); - webgpu_ctx->rope_pipelines[GGML_TYPE_F16][1][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_rope_f16_ff_inplace, "rope_f16_ff_inplace", constants); + webgpu_ctx->rope_pipelines[GGML_TYPE_F16][1][1] = ggml_webgpu_create_pipeline( + webgpu_ctx->global_ctx->device, wgsl_rope_f16_ff_inplace, "rope_f16_ff_inplace", constants); } static void ggml_webgpu_init_glu_pipeline(webgpu_context & webgpu_ctx) { @@ -2772,36 +2786,36 @@ static void ggml_webgpu_init_glu_pipeline(webgpu_context & webgpu_ctx) { ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_swiglu_f32, "swiglu_f32", constants); webgpu_ctx->glu_pipelines[GGML_GLU_OP_SWIGLU][GGML_TYPE_F16][0] = ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_swiglu_f16, "swiglu_f16", constants); - webgpu_ctx->glu_pipelines[GGML_GLU_OP_SWIGLU][GGML_TYPE_F32][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_swiglu_f32_split, "swiglu_f32_split", constants); - webgpu_ctx->glu_pipelines[GGML_GLU_OP_SWIGLU][GGML_TYPE_F16][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_swiglu_f16_split, "swiglu_f16_split", constants); + webgpu_ctx->glu_pipelines[GGML_GLU_OP_SWIGLU][GGML_TYPE_F32][1] = ggml_webgpu_create_pipeline( + webgpu_ctx->global_ctx->device, wgsl_swiglu_f32_split, "swiglu_f32_split", constants); + webgpu_ctx->glu_pipelines[GGML_GLU_OP_SWIGLU][GGML_TYPE_F16][1] = ggml_webgpu_create_pipeline( + webgpu_ctx->global_ctx->device, wgsl_swiglu_f16_split, "swiglu_f16_split", constants); // SWIGLU_OAI webgpu_ctx->glu_pipelines[GGML_GLU_OP_SWIGLU_OAI][GGML_TYPE_F32][0] = ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_swiglu_oai_f32, "swiglu_oai_f32", constants); - webgpu_ctx->glu_pipelines[GGML_GLU_OP_SWIGLU_OAI][GGML_TYPE_F32][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_swiglu_oai_f32_split, "swiglu_oai_f32_split", constants); + webgpu_ctx->glu_pipelines[GGML_GLU_OP_SWIGLU_OAI][GGML_TYPE_F32][1] = ggml_webgpu_create_pipeline( + webgpu_ctx->global_ctx->device, wgsl_swiglu_oai_f32_split, "swiglu_oai_f32_split", constants); // GEGLU_ERF webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU_ERF][GGML_TYPE_F32][0] = ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_geglu_erf_f32, "geglu_erf_f32", constants); webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU_ERF][GGML_TYPE_F16][0] = ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_geglu_erf_f16, "geglu_erf_f16", constants); - webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU_ERF][GGML_TYPE_F32][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_geglu_erf_f32_split, "geglu_erf_f32_split", constants); - webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU_ERF][GGML_TYPE_F16][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_geglu_erf_f16_split, "geglu_erf_f16_split", constants); + webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU_ERF][GGML_TYPE_F32][1] = ggml_webgpu_create_pipeline( + webgpu_ctx->global_ctx->device, wgsl_geglu_erf_f32_split, "geglu_erf_f32_split", constants); + webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU_ERF][GGML_TYPE_F16][1] = ggml_webgpu_create_pipeline( + webgpu_ctx->global_ctx->device, wgsl_geglu_erf_f16_split, "geglu_erf_f16_split", constants); // GEGLU_QUICK webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU_QUICK][GGML_TYPE_F32][0] = ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_geglu_quick_f32, "geglu_quick_f32", constants); webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU_QUICK][GGML_TYPE_F16][0] = ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_geglu_quick_f16, "geglu_quick_f16", constants); - webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU_QUICK][GGML_TYPE_F32][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_geglu_quick_f32_split, "geglu_quick_f32_split", constants); - webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU_QUICK][GGML_TYPE_F16][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_geglu_quick_f16_split, "geglu_quick_f16_split", constants); + webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU_QUICK][GGML_TYPE_F32][1] = ggml_webgpu_create_pipeline( + webgpu_ctx->global_ctx->device, wgsl_geglu_quick_f32_split, "geglu_quick_f32_split", constants); + webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU_QUICK][GGML_TYPE_F16][1] = ggml_webgpu_create_pipeline( + webgpu_ctx->global_ctx->device, wgsl_geglu_quick_f16_split, "geglu_quick_f16_split", constants); } static void ggml_webgpu_init_scale_pipeline(webgpu_context & webgpu_ctx) { @@ -2809,8 +2823,8 @@ static void ggml_webgpu_init_scale_pipeline(webgpu_context & webgpu_ctx) { webgpu_ctx->scale_pipelines[0] = ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_scale_f32, "scale_f32", constants); - webgpu_ctx->scale_pipelines[1] = - ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_scale_f32_inplace, "scale_f32_inplace", constants); + webgpu_ctx->scale_pipelines[1] = ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_scale_f32_inplace, + "scale_f32_inplace", constants); } static void ggml_webgpu_init_soft_max_pipeline(webgpu_context & webgpu_ctx) { @@ -2819,37 +2833,37 @@ static void ggml_webgpu_init_soft_max_pipeline(webgpu_context & webgpu_ctx) { // f32 (no mask) webgpu_ctx->soft_max_pipelines[2][0][0] = ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_soft_max_f32, "soft_max_f32", constants); - webgpu_ctx->soft_max_pipelines[2][0][1] = - ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_soft_max_f32_inplace, "soft_max_f32_inplace", constants); - webgpu_ctx->soft_max_pipelines[2][1][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_soft_max_f32_sink, "soft_max_f32_sink", constants); + webgpu_ctx->soft_max_pipelines[2][0][1] = ggml_webgpu_create_pipeline( + webgpu_ctx->global_ctx->device, wgsl_soft_max_f32_inplace, "soft_max_f32_inplace", constants); + webgpu_ctx->soft_max_pipelines[2][1][0] = ggml_webgpu_create_pipeline( + webgpu_ctx->global_ctx->device, wgsl_soft_max_f32_sink, "soft_max_f32_sink", constants); webgpu_ctx->soft_max_pipelines[2][1][1] = ggml_webgpu_create_pipeline( webgpu_ctx->global_ctx->device, wgsl_soft_max_f32_sink_inplace, "soft_max_f32_sink_inplace", constants); // f32 mask (mask_type = 0) - webgpu_ctx->soft_max_pipelines[0][0][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_soft_max_f32_mask_f32, "soft_max_f32_mask_f32", constants); + webgpu_ctx->soft_max_pipelines[0][0][0] = ggml_webgpu_create_pipeline( + webgpu_ctx->global_ctx->device, wgsl_soft_max_f32_mask_f32, "soft_max_f32_mask_f32", constants); webgpu_ctx->soft_max_pipelines[0][0][1] = ggml_webgpu_create_pipeline( webgpu_ctx->global_ctx->device, wgsl_soft_max_f32_mask_f32_inplace, "soft_max_f32_mask_f32_inplace", constants); webgpu_ctx->soft_max_pipelines[0][1][0] = ggml_webgpu_create_pipeline( webgpu_ctx->global_ctx->device, wgsl_soft_max_f32_mask_f32_sink, "soft_max_f32_mask_f32_sink", constants); - webgpu_ctx->soft_max_pipelines[0][1][1] = ggml_webgpu_create_pipeline( - webgpu_ctx->global_ctx->device, wgsl_soft_max_f32_mask_f32_sink_inplace, "soft_max_f32_mask_f32_sink_inplace", constants); + webgpu_ctx->soft_max_pipelines[0][1][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_soft_max_f32_mask_f32_sink_inplace, + "soft_max_f32_mask_f32_sink_inplace", constants); // f16 mask (mask_type = 1) - webgpu_ctx->soft_max_pipelines[1][0][0] = - ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_soft_max_f32_mask_f16, "soft_max_f32_mask_f16", constants); + webgpu_ctx->soft_max_pipelines[1][0][0] = ggml_webgpu_create_pipeline( + webgpu_ctx->global_ctx->device, wgsl_soft_max_f32_mask_f16, "soft_max_f32_mask_f16", constants); webgpu_ctx->soft_max_pipelines[1][0][1] = ggml_webgpu_create_pipeline( webgpu_ctx->global_ctx->device, wgsl_soft_max_f32_mask_f16_inplace, "soft_max_f32_mask_f16_inplace", constants); webgpu_ctx->soft_max_pipelines[1][1][0] = ggml_webgpu_create_pipeline( webgpu_ctx->global_ctx->device, wgsl_soft_max_f32_mask_f16_sink, "soft_max_f32_mask_f16_sink", constants); - webgpu_ctx->soft_max_pipelines[1][1][1] = ggml_webgpu_create_pipeline( - webgpu_ctx->global_ctx->device, wgsl_soft_max_f32_mask_f16_sink_inplace, "soft_max_f32_mask_f16_sink_inplace", constants); + webgpu_ctx->soft_max_pipelines[1][1][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->global_ctx->device, wgsl_soft_max_f32_mask_f16_sink_inplace, + "soft_max_f32_mask_f16_sink_inplace", constants); } - static bool create_webgpu_device(ggml_backend_webgpu_reg_context * ctx) { - wgpu::RequestAdapterOptions options = {}; #ifndef __EMSCRIPTEN__ @@ -2861,16 +2875,17 @@ static bool create_webgpu_device(ggml_backend_webgpu_reg_context * ctx) { options.nextInChain = &adapterTogglesDesc; #endif - ctx->wgpu_global_ctx.instance.WaitAny(ctx->wgpu_global_ctx.instance.RequestAdapter( - &options, wgpu::CallbackMode::AllowSpontaneous, - [&ctx](wgpu::RequestAdapterStatus status, wgpu::Adapter adapter, const char * message) { - if (status != wgpu::RequestAdapterStatus::Success) { - GGML_LOG_ERROR("ggml_webgpu: Failed to get an adapter: %s\n", message); - return; - } - ctx->wgpu_global_ctx.adapter = std::move(adapter); - }), - UINT64_MAX); + ctx->wgpu_global_ctx.instance.WaitAny( + ctx->wgpu_global_ctx.instance.RequestAdapter( + &options, wgpu::CallbackMode::AllowSpontaneous, + [&ctx](wgpu::RequestAdapterStatus status, wgpu::Adapter adapter, const char * message) { + if (status != wgpu::RequestAdapterStatus::Success) { + GGML_LOG_ERROR("ggml_webgpu: Failed to get an adapter: %s\n", message); + return; + } + ctx->wgpu_global_ctx.adapter = std::move(adapter); + }), + UINT64_MAX); GGML_ASSERT(ctx->wgpu_global_ctx.adapter != nullptr); ctx->wgpu_global_ctx.adapter.GetLimits(&ctx->wgpu_global_ctx.capabilities.limits); @@ -2899,7 +2914,7 @@ static bool create_webgpu_device(ggml_backend_webgpu_reg_context * ctx) { config.componentType == wgpu::SubgroupMatrixComponentType::F16 && config.resultComponentType == wgpu::SubgroupMatrixComponentType::F16) { ctx->wgpu_global_ctx.capabilities.subgroup_matrix_config = config; - valid_subgroup_matrix_config = true; + valid_subgroup_matrix_config = true; break; } } @@ -2963,55 +2978,54 @@ static bool create_webgpu_device(ggml_backend_webgpu_reg_context * ctx) { dev_desc.nextInChain = &deviceTogglesDesc; #endif - ctx->wgpu_global_ctx.instance.WaitAny(ctx->wgpu_global_ctx.adapter.RequestDevice( - &dev_desc, wgpu::CallbackMode::AllowSpontaneous, - [ctx](wgpu::RequestDeviceStatus status, wgpu::Device device, wgpu::StringView message) { - if (status != wgpu::RequestDeviceStatus::Success) { - GGML_LOG_ERROR("ggml_webgpu: Failed to get a device: %s\n", - std::string(message).c_str()); - return; - } - ctx->wgpu_global_ctx.device = std::move(device); - }), - UINT64_MAX); + ctx->wgpu_global_ctx.instance.WaitAny( + ctx->wgpu_global_ctx.adapter.RequestDevice( + &dev_desc, wgpu::CallbackMode::AllowSpontaneous, + [ctx](wgpu::RequestDeviceStatus status, wgpu::Device device, wgpu::StringView message) { + if (status != wgpu::RequestDeviceStatus::Success) { + GGML_LOG_ERROR("ggml_webgpu: Failed to get a device: %s\n", std::string(message).c_str()); + return; + } + ctx->wgpu_global_ctx.device = std::move(device); + }), + UINT64_MAX); GGML_ASSERT(ctx->wgpu_global_ctx.device != nullptr); GGML_LOG_INFO( "ggml_webgpu: adapter_info: vendor_id: %u | vendor: %s | architecture: %s | device_id: %u | name: %s | " "device_desc: %s\n", - info.vendorID, std::string(info.vendor).c_str(), std::string(info.architecture).c_str(), info.deviceID, + info.vendorID, std::string(info.vendor).c_str(), std::string(info.architecture).c_str(), info.deviceID, std::string(info.device).c_str(), std::string(info.description).c_str()); return true; } - // Fetch the webgpu_context for this thread (indexed by device for multiple devices running per thread) // If it hasn't been created yet, then initiailize one. static webgpu_context get_per_thread_webgpu_context(ggml_backend_dev_t dev) { - thread_local std::unordered_map map; - + thread_local std::unordered_map map; + ggml_backend_webgpu_device_context * dev_ctx = (ggml_backend_webgpu_device_context *) dev->context; if (auto it = map.find(dev_ctx); it != map.end()) { return it->second; } - - webgpu_context webgpu_ctx = std::make_shared(); - webgpu_ctx->global_ctx = &(dev_ctx->reg_context->wgpu_global_ctx); - webgpu_ctx->queue = webgpu_ctx->global_ctx->device.GetQueue(); - webgpu_ctx->param_buf_pool.init(webgpu_ctx->global_ctx->device, - WEBGPU_NUM_PARAM_BUFS, WEBGPU_PARAMS_BUF_SIZE_BYTES, + + webgpu_context webgpu_ctx = std::make_shared(); + webgpu_ctx->global_ctx = &(dev_ctx->reg_context->wgpu_global_ctx); + webgpu_ctx->queue = webgpu_ctx->global_ctx->device.GetQueue(); + webgpu_ctx->param_buf_pool.init(webgpu_ctx->global_ctx->device, WEBGPU_NUM_PARAM_BUFS, WEBGPU_PARAMS_BUF_SIZE_BYTES, wgpu::BufferUsage::CopyDst | wgpu::BufferUsage::Uniform, - wgpu::BufferUsage::CopySrc | wgpu::BufferUsage::MapWrite); + wgpu::BufferUsage::CopySrc | wgpu::BufferUsage::MapWrite); #ifdef GGML_WEBGPU_GPU_PROFILE // Initialize buffer pool for timestamp queries, used for profiling webgpu_ctx->timestamp_query_buf_pool.init(webgpu_ctx->wgpu_global_ctx->device, WEBGPU_NUM_TIMESTAMP_QUERY_BUFS, - WEBGPU_TIMESTAMP_QUERY_BUF_SIZE_BYTES, - wgpu::BufferUsage::QueryResolve | wgpu::BufferUsage::CopySrc, - wgpu::BufferUsage::MapRead | wgpu::BufferUsage::CopyDst); + WEBGPU_TIMESTAMP_QUERY_BUF_SIZE_BYTES, + wgpu::BufferUsage::QueryResolve | wgpu::BufferUsage::CopySrc, + wgpu::BufferUsage::MapRead | wgpu::BufferUsage::CopyDst); #endif - webgpu_ctx->set_rows_error_buf_pool.init(webgpu_ctx->global_ctx->device, WEBGPU_NUM_SET_ROWS_ERROR_BUFS, WEBGPU_SET_ROWS_ERROR_BUF_SIZE_BYTES, - wgpu::BufferUsage::CopySrc | wgpu::BufferUsage::Storage, - wgpu::BufferUsage::CopyDst | wgpu::BufferUsage::MapRead); + webgpu_ctx->set_rows_error_buf_pool.init(webgpu_ctx->global_ctx->device, WEBGPU_NUM_SET_ROWS_ERROR_BUFS, + WEBGPU_SET_ROWS_ERROR_BUF_SIZE_BYTES, + wgpu::BufferUsage::CopySrc | wgpu::BufferUsage::Storage, + wgpu::BufferUsage::CopyDst | wgpu::BufferUsage::MapRead); ggml_webgpu_init_memset_pipeline(webgpu_ctx); ggml_webgpu_init_mul_mat_pipeline(webgpu_ctx); @@ -3028,9 +3042,11 @@ static webgpu_context get_per_thread_webgpu_context(ggml_backend_dev_t dev) { ggml_webgpu_init_soft_max_pipeline(webgpu_ctx); #ifdef GGML_WEBGPU_DEBUG // Initialize debug buffers - ggml_webgpu_create_buffer(webgpu_ctx->global_ctx->device, webgpu_ctx->debug_host_buf, WEBGPU_DEBUG_BUF_ELEMS * sizeof(uint32_t), + ggml_webgpu_create_buffer(webgpu_ctx->global_ctx->device, webgpu_ctx->debug_host_buf, + WEBGPU_DEBUG_BUF_ELEMS * sizeof(uint32_t), wgpu::BufferUsage::CopyDst | wgpu::BufferUsage::MapRead, "debug_host_buf"); - ggml_webgpu_create_buffer(webgpu_ctx->global_ctx->device, webgpu_ctx->debug_dev_buf, WEBGPU_DEBUG_BUF_ELEMS * sizeof(uint32_t), + ggml_webgpu_create_buffer(webgpu_ctx->global_ctx->device, webgpu_ctx->debug_dev_buf, + WEBGPU_DEBUG_BUF_ELEMS * sizeof(uint32_t), wgpu::BufferUsage::Storage | wgpu::BufferUsage::CopySrc, "debug_dev_buf"); #endif @@ -3046,23 +3062,22 @@ static ggml_backend_t ggml_backend_webgpu_device_init(ggml_backend_dev_t dev, co ggml_backend_webgpu_device_context * dev_ctx = static_cast(dev->context); auto * backend_ctx = new ggml_backend_webgpu_context(); - backend_ctx->name = GGML_WEBGPU_NAME + std::string(": ") + dev_ctx->device_name; + backend_ctx->name = GGML_WEBGPU_NAME + std::string(": ") + dev_ctx->device_name; // See GGML Backend Interface section auto * backend = new ggml_backend(); - *backend = { + *backend = { /* .guid = */ ggml_backend_webgpu_guid(), /* .interface = */ ggml_backend_webgpu_i, /* .device = */ dev, /* .context = */ backend_ctx, }; - return backend; - + return backend; } static ggml_backend_buffer_type_t ggml_backend_webgpu_device_get_buffer_type(ggml_backend_dev_t dev) { // See GGML Backend Buffer Type Interface section - + static struct ggml_backend_buffer_type ggml_backend_webgpu_buffer_type = { /* .iface = */ { /* .get_name = */ ggml_backend_webgpu_buffer_type_get_name, @@ -3122,8 +3137,10 @@ static bool ggml_backend_webgpu_device_supports_op(ggml_backend_dev_t dev, const // on smaller devices (or CI), tensors may be larger than the max storage buffer size if (ggml_nbytes(op) > webgpu_ctx->global_ctx->capabilities.limits.maxStorageBufferBindingSize || - (src0 != nullptr && ggml_nbytes(src0) > webgpu_ctx->global_ctx->capabilities.limits.maxStorageBufferBindingSize) || - (src1 != nullptr && ggml_nbytes(src1) > webgpu_ctx->global_ctx->capabilities.limits.maxStorageBufferBindingSize)) { + (src0 != nullptr && + ggml_nbytes(src0) > webgpu_ctx->global_ctx->capabilities.limits.maxStorageBufferBindingSize) || + (src1 != nullptr && + ggml_nbytes(src1) > webgpu_ctx->global_ctx->capabilities.limits.maxStorageBufferBindingSize)) { return false; } @@ -3319,9 +3336,12 @@ static bool ggml_backend_webgpu_device_supports_op(ggml_backend_dev_t dev, const break; } if (ggml_nbytes(op) > webgpu_ctx->global_ctx->capabilities.limits.maxStorageBufferBindingSize || - (src0 != nullptr && ggml_nbytes(src0) > webgpu_ctx->global_ctx->capabilities.limits.maxStorageBufferBindingSize) || - (src1 != nullptr && ggml_nbytes(src1) > webgpu_ctx->global_ctx->capabilities.limits.maxStorageBufferBindingSize) || - (src2 != nullptr && ggml_nbytes(src2) > webgpu_ctx->global_ctx->capabilities.limits.maxStorageBufferBindingSize)) { + (src0 != nullptr && + ggml_nbytes(src0) > webgpu_ctx->global_ctx->capabilities.limits.maxStorageBufferBindingSize) || + (src1 != nullptr && + ggml_nbytes(src1) > webgpu_ctx->global_ctx->capabilities.limits.maxStorageBufferBindingSize) || + (src2 != nullptr && + ggml_nbytes(src2) > webgpu_ctx->global_ctx->capabilities.limits.maxStorageBufferBindingSize)) { supports_op = false; WEBGPU_LOG_DEBUG("ggml_webgpu op not supported due to size: "); } @@ -3385,11 +3405,11 @@ static ggml_backend_dev_t ggml_backend_webgpu_reg_get_device(ggml_backend_reg_t ggml_backend_webgpu_reg_context * reg_ctx = static_cast(reg->context); create_webgpu_device(reg_ctx); - + static ggml_backend_webgpu_device_context device_ctx; device_ctx.device_name = GGML_WEBGPU_NAME; // TODO: we get the device description when actually initializing the webgpu device, so move this to backend context? - device_ctx.device_desc = GGML_WEBGPU_NAME; + device_ctx.device_desc = GGML_WEBGPU_NAME; device_ctx.reg_context = reg_ctx; // See GGML Backend Device Interface section @@ -3432,7 +3452,7 @@ ggml_backend_reg_t ggml_backend_webgpu_reg() { instance_descriptor.nextInChain = &instanceTogglesDesc; #endif - wgpu::Instance inst = wgpu::CreateInstance(&instance_descriptor); + wgpu::Instance inst = wgpu::CreateInstance(&instance_descriptor); ctx.wgpu_global_ctx.instance = std::move(inst); #ifdef __EMSCRIPTEN__ diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/embed_wgsl.py b/ggml/src/ggml-webgpu/wgsl-shaders/embed_wgsl.py index 4df49759f7..d61df5bb9e 100755 --- a/ggml/src/ggml-webgpu/wgsl-shaders/embed_wgsl.py +++ b/ggml/src/ggml-webgpu/wgsl-shaders/embed_wgsl.py @@ -18,13 +18,6 @@ def parse_decls(decls_text): decls[name.strip()] = code.strip() return decls -def replace_repl_placeholders(variant, template_map): - for repl, code in variant["REPLS"].items(): - for key, val in template_map.items(): - # Match "key" and avoid matching subsequences using by using \b - code = re.sub(rf'\b{re.escape(str(key))}\b', str(val), code) - variant["REPLS"][repl] = code - return variant def replace_repl_placeholders(variant, template_map): for repl, code in variant["REPLS"].items(): diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/unary_op.wgsl b/ggml/src/ggml-webgpu/wgsl-shaders/unary_op.wgsl deleted file mode 100644 index 90efb78bfa..0000000000 --- a/ggml/src/ggml-webgpu/wgsl-shaders/unary_op.wgsl +++ /dev/null @@ -1,587 +0,0 @@ -#define(REPL_TEMPLATES) - -{ - "XIELU_FUNC": "{{MUTATE}}[dst_i] = select(((exp(min(src[src_i], {{TYPE}}(params.eps))) - 1.0) - src[src_i]) * {{TYPE}}(params.alpha_n) + {{TYPE}}(params.beta) * src[src_i], {{TYPE}}(params.alpha_p) * src[src_i] * src[src_i] + {{TYPE}}(params.beta) * src[src_i], src[src_i] > 0.0);", - "ABS_FUNC": "{{MUTATE}}[dst_i] = abs(src[src_i]);", - "SGN_FUNC": "{{MUTATE}}[dst_i] = select({{TYPE}}(select(0.0, -1.0, src[src_i] < 0.0)), {{TYPE}}(1.0), src[src_i] > 0.0);", - "NEG_FUNC": "{{MUTATE}}[dst_i] = -src[src_i];", - "STEP_FUNC": "{{MUTATE}}[dst_i] = {{TYPE}}(select(0.0, 1.0, src[src_i] > 0.0));", - "TANH_FUNC": "{{MUTATE}}[dst_i] = tanh(clamp(src[src_i], -9.010913, 9.010913)); // Regarding tanh() domain restrictions in wgsl https://github.com/gpuweb/gpuweb/issues/4458", - "RELU_FUNC": "{{MUTATE}}[dst_i] = select(0.0, src[src_i], src[src_i] > 0.0);", - "ELU_FUNC": "{{MUTATE}}[dst_i] = select(exp(src[src_i]) - 1.0, src[src_i], src[src_i] > 0.0);", - "HARDSIGMOID_FUNC": "{{MUTATE}}[dst_i] = min(1.0, max(0.0, (src[src_i] + 3.0) / 6.0));", - "SIGMOID_FUNC": "{{MUTATE}}[dst_i] = 1.0 / (1.0 + exp(-src[src_i]));", - "SILU_FUNC": "{{MUTATE}}[dst_i] = src[src_i] / (1.0 + exp(-src[src_i]));", - "EXP_FUNC": "{{MUTATE}}[dst_i] = exp(src[src_i]);", - "HARDSWISH_FUNC": "{{MUTATE}}[dst_i] = src[src_i] * min(1.0, max(0.0, (src[src_i] + 3.0) / 6.0));", - "GELU_FUNC": "{{MUTATE}}[dst_i] = 0.5 * src[src_i] * (1.0 + tanh(clamp(sqrt(2.0 / 3.14159265) * (src[src_i] + 0.044715 * pow(src[src_i], 3.0)), -9.010913, 9.010913))); // Regarding tanh() domain restrictions in wgsl https://github.com/gpuweb/gpuweb/issues/4458", - "GELU_QUICK_FUNC": "{{MUTATE}}[dst_i] = src[src_i] * 0.5 * (1.0 + tanh(clamp(0.79788456 * (src[src_i] + 0.044715 * src[src_i] * src[src_i] * src[src_i]), -9.010913, 9.010913))); // Regarding tanh() domain restrictions in wgsl https://github.com/gpuweb/gpuweb/issues/4458", - "GELU_ERF_FUNC": "{{MUTATE}}[dst_i] = 0.5 * src[src_i] * (1.0 + tanh(clamp(0.79788456 * (src[src_i] + 0.044715 * src[src_i] * src[src_i] * src[src_i]), -9.010913, 9.010913))); // Regarding tanh() domain restrictions in wgsl https://github.com/gpuweb/gpuweb/issues/4458", - "SOFTPLUS_FUNC": "{ let src_f32 = f32(src[src_i]); {{MUTATE}}[dst_i] = {{TYPE}}(select(log(1.0 + exp(src_f32)), src_f32, src_f32 > 20.0)); } // Cast to f32 to prevent exp() overflow with f16 (exp(x) overflows f16 for x > ~11)", - "EXPM1_FUNC": "{{MUTATE}}[dst_i] = exp(src[src_i]) - 1.0;", - "FLOOR_FUNC": "{{MUTATE}}[dst_i] = floor(src[src_i]);", - "CEIL_FUNC": "{{MUTATE}}[dst_i] = ceil(src[src_i]);", - "ROUND_FUNC": "{ let src_f32 = f32(src[src_i]); let result = select(ceil(src_f32 - 0.5), floor(src_f32 + 0.5), src_f32 >= 0.0); {{MUTATE}}[dst_i] = {{TYPE}}(result); } // Round half away from zero to match C roundf() behavior, not WGSL round() which uses round-to-even", - "TRUNC_FUNC": "{{MUTATE}}[dst_i] = trunc(src[src_i]);" -} - -#end(REPL_TEMPLATES) - -#define(VARIANTS) - -[ - { - "SHADER_NAME": "abs_f32", - "REPLS": { "TYPE": "f32", "FUNC": "ABS_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, - "DECLS": ["NOT_INPLACE"] - }, - { - "SHADER_NAME": "abs_f16", - "REPLS": { "TYPE": "f16", "FUNC": "ABS_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, - "DECLS": ["NOT_INPLACE"] - }, - { - "SHADER_NAME": "abs_inplace_f32", - "REPLS": { "TYPE": "f32", "FUNC": "ABS_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, - "DECLS": ["INPLACE"] - }, - { - "SHADER_NAME": "abs_inplace_f16", - "REPLS": { "TYPE": "f16", "FUNC": "ABS_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, - "DECLS": ["INPLACE"] - }, - - { - "SHADER_NAME": "sgn_f32", - "REPLS": { "TYPE": "f32", "FUNC": "SGN_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, - "DECLS": ["NOT_INPLACE"] - }, - { - "SHADER_NAME": "sgn_f16", - "REPLS": { "TYPE": "f16", "FUNC": "SGN_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, - "DECLS": ["NOT_INPLACE"] - }, - { - "SHADER_NAME": "sgn_inplace_f32", - "REPLS": { "TYPE": "f32", "FUNC": "SGN_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, - "DECLS": ["INPLACE"] - }, - { - "SHADER_NAME": "sgn_inplace_f16", - "REPLS": { "TYPE": "f16", "FUNC": "SGN_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, - "DECLS": ["INPLACE"] - }, - - { - "SHADER_NAME": "neg_f32", - "REPLS": { "TYPE": "f32", "FUNC": "NEG_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, - "DECLS": ["NOT_INPLACE"] - }, - { - "SHADER_NAME": "neg_f16", - "REPLS": { "TYPE": "f16", "FUNC": "NEG_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, - "DECLS": ["NOT_INPLACE"] - }, - { - "SHADER_NAME": "neg_inplace_f32", - "REPLS": { "TYPE": "f32", "FUNC": "NEG_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, - "DECLS": ["INPLACE"] - }, - { - "SHADER_NAME": "neg_inplace_f16", - "REPLS": { "TYPE": "f16", "FUNC": "NEG_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, - "DECLS": ["INPLACE"] - }, - - { - "SHADER_NAME": "step_f32", - "REPLS": { "TYPE": "f32", "FUNC": "STEP_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, - "DECLS": ["NOT_INPLACE"] - }, - { - "SHADER_NAME": "step_f16", - "REPLS": { "TYPE": "f16", "FUNC": "STEP_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, - "DECLS": ["NOT_INPLACE"] - }, - { - "SHADER_NAME": "step_inplace_f32", - "REPLS": { "TYPE": "f32", "FUNC": "STEP_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, - "DECLS": ["INPLACE"] - }, - { - "SHADER_NAME": "step_inplace_f16", - "REPLS": { "TYPE": "f16", "FUNC": "STEP_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, - "DECLS": ["INPLACE"] - }, - - { - "SHADER_NAME": "tanh_f32", - "REPLS": { "TYPE": "f32", "FUNC": "TANH_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, - "DECLS": ["NOT_INPLACE"] - }, - { - "SHADER_NAME": "tanh_f16", - "REPLS": { "TYPE": "f16", "FUNC": "TANH_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, - "DECLS": ["NOT_INPLACE"] - }, - { - "SHADER_NAME": "tanh_inplace_f32", - "REPLS": { "TYPE": "f32", "FUNC": "TANH_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, - "DECLS": ["INPLACE"] - }, - { - "SHADER_NAME": "tanh_inplace_f16", - "REPLS": { "TYPE": "f16", "FUNC": "TANH_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, - "DECLS": ["INPLACE"] - }, - - { - "SHADER_NAME": "elu_f32", - "REPLS": { "TYPE": "f32", "FUNC": "ELU_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, - "DECLS": ["NOT_INPLACE"] - }, - { - "SHADER_NAME": "elu_f16", - "REPLS": { "TYPE": "f16", "FUNC": "ELU_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, - "DECLS": ["NOT_INPLACE"] - }, - { - "SHADER_NAME": "elu_inplace_f32", - "REPLS": { "TYPE": "f32", "FUNC": "ELU_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, - "DECLS": ["INPLACE"] - }, - { - "SHADER_NAME": "elu_inplace_f16", - "REPLS": { "TYPE": "f16", "FUNC": "ELU_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, - "DECLS": ["INPLACE"] - }, - - { - "SHADER_NAME": "relu_f32", - "REPLS": { "TYPE": "f32", "FUNC": "RELU_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, - "DECLS": ["NOT_INPLACE"] - }, - { - "SHADER_NAME": "relu_f16", - "REPLS": { "TYPE": "f16", "FUNC": "RELU_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, - "DECLS": ["NOT_INPLACE"] - }, - { - "SHADER_NAME": "relu_inplace_f32", - "REPLS": { "TYPE": "f32", "FUNC": "RELU_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, - "DECLS": ["INPLACE"] - }, - { - "SHADER_NAME": "relu_inplace_f16", - "REPLS": { "TYPE": "f16", "FUNC": "RELU_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, - "DECLS": ["INPLACE"] - }, - - { - "SHADER_NAME": "sigmoid_f32", - "REPLS": { "TYPE": "f32", "FUNC": "SIGMOID_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, - "DECLS": ["NOT_INPLACE"] - }, - { - "SHADER_NAME": "sigmoid_f16", - "REPLS": { "TYPE": "f16", "FUNC": "SIGMOID_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, - "DECLS": ["NOT_INPLACE"] - }, - { - "SHADER_NAME": "sigmoid_inplace_f32", - "REPLS": { "TYPE": "f32", "FUNC": "SIGMOID_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, - "DECLS": ["INPLACE"] - }, - { - "SHADER_NAME": "sigmoid_inplace_f16", - "REPLS": { "TYPE": "f16", "FUNC": "SIGMOID_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, - "DECLS": ["INPLACE"] - }, - - { - "SHADER_NAME": "silu_f32", - "REPLS": { "TYPE": "f32", "FUNC": "SILU_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, - "DECLS": ["NOT_INPLACE"] - }, - { - "SHADER_NAME": "silu_f16", - "REPLS": { "TYPE": "f16", "FUNC": "SILU_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, - "DECLS": ["NOT_INPLACE"] - }, - { - "SHADER_NAME": "silu_inplace_f32", - "REPLS": { "TYPE": "f32", "FUNC": "SILU_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, - "DECLS": ["INPLACE"] - }, - { - "SHADER_NAME": "silu_inplace_f16", - "REPLS": { "TYPE": "f16", "FUNC": "SILU_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, - "DECLS": ["INPLACE"] - }, - - { - "SHADER_NAME": "exp_f32", - "REPLS": { "TYPE": "f32", "FUNC": "EXP_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, - "DECLS": ["NOT_INPLACE"] - }, - { - "SHADER_NAME": "exp_f16", - "REPLS": { "TYPE": "f16", "FUNC": "EXP_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, - "DECLS": ["NOT_INPLACE"] - }, - { - "SHADER_NAME": "exp_inplace_f32", - "REPLS": { "TYPE": "f32", "FUNC": "EXP_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, - "DECLS": ["INPLACE"] - }, - { - "SHADER_NAME": "exp_inplace_f16", - "REPLS": { "TYPE": "f16", "FUNC": "EXP_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, - "DECLS": ["INPLACE"] - }, - - { - "SHADER_NAME": "hardsigmoid_f32", - "REPLS": { "TYPE": "f32", "FUNC": "HARDSIGMOID_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, - "DECLS": ["NOT_INPLACE"] - }, - { - "SHADER_NAME": "hardsigmoid_f16", - "REPLS": { "TYPE": "f16", "FUNC": "HARDSIGMOID_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, - "DECLS": ["NOT_INPLACE"] - }, - { - "SHADER_NAME": "hardsigmoid_inplace_f32", - "REPLS": { "TYPE": "f32", "FUNC": "HARDSIGMOID_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, - "DECLS": ["INPLACE"] - }, - { - "SHADER_NAME": "hardsigmoid_inplace_f16", - "REPLS": { "TYPE": "f16", "FUNC": "HARDSIGMOID_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, - "DECLS": ["INPLACE"] - }, - - { - "SHADER_NAME": "hardswish_f32", - "REPLS": { "TYPE": "f32", "FUNC": "HARDSWISH_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, - "DECLS": ["NOT_INPLACE"] - }, - { - "SHADER_NAME": "hardswish_f16", - "REPLS": { "TYPE": "f16", "FUNC": "HARDSWISH_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, - "DECLS": ["NOT_INPLACE"] - }, - { - "SHADER_NAME": "hardswish_inplace_f32", - "REPLS": { "TYPE": "f32", "FUNC": "HARDSWISH_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, - "DECLS": ["INPLACE"] - }, - { - "SHADER_NAME": "hardswish_inplace_f16", - "REPLS": { "TYPE": "f16", "FUNC": "HARDSWISH_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, - "DECLS": ["INPLACE"] - }, - - { - "SHADER_NAME": "gelu_f32", - "REPLS": { "TYPE": "f32", "FUNC": "GELU_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, - "DECLS": ["NOT_INPLACE"] - }, - { - "SHADER_NAME": "gelu_f16", - "REPLS": { "TYPE": "f16", "FUNC": "GELU_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, - "DECLS": ["NOT_INPLACE"] - }, - { - "SHADER_NAME": "gelu_inplace_f32", - "REPLS": { "TYPE": "f32", "FUNC": "GELU_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, - "DECLS": ["INPLACE"] - }, - { - "SHADER_NAME": "gelu_inplace_f16", - "REPLS": { "TYPE": "f16", "FUNC": "GELU_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, - "DECLS": ["INPLACE"] - }, - - { - "SHADER_NAME": "gelu_quick_f32", - "REPLS": { "TYPE": "f32", "FUNC": "GELU_QUICK_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, - "DECLS": ["NOT_INPLACE"] - }, - { - "SHADER_NAME": "gelu_quick_f16", - "REPLS": { "TYPE": "f16", "FUNC": "GELU_QUICK_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, - "DECLS": ["NOT_INPLACE"] - }, - { - "SHADER_NAME": "gelu_quick_inplace_f32", - "REPLS": { "TYPE": "f32", "FUNC": "GELU_QUICK_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, - "DECLS": ["INPLACE"] - }, - { - "SHADER_NAME": "gelu_quick_inplace_f16", - "REPLS": { "TYPE": "f16", "FUNC": "GELU_QUICK_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, - "DECLS": ["INPLACE"] - }, - - { - "SHADER_NAME": "xielu_f32", - "REPLS": { "TYPE": "f32", "FUNC": "XIELU_FUNC", "EXT_PARAMS": "alpha_n: f32, alpha_p: f32, beta: f32, eps: f32", "MUTATE": "dst" }, - "DECLS": ["NOT_INPLACE"] - }, - { - "SHADER_NAME": "xielu_f16", - "REPLS": { "TYPE": "f16", "FUNC": "XIELU_FUNC", "EXT_PARAMS": "alpha_n: f32, alpha_p: f32, beta: f32, eps: f32", "MUTATE": "dst" }, - "DECLS": ["NOT_INPLACE"] - }, - { - "SHADER_NAME": "xielu_inplace_f32", - "REPLS": { "TYPE": "f32", "FUNC": "XIELU_FUNC", "EXT_PARAMS": "alpha_n: f32, alpha_p: f32, beta: f32, eps: f32", "MUTATE": "src" }, - "DECLS": ["INPLACE"] - }, - { - "SHADER_NAME": "xielu_inplace_f16", - "REPLS": { "TYPE": "f16", "FUNC": "XIELU_FUNC", "EXT_PARAMS": "alpha_n: f32, alpha_p: f32, beta: f32, eps: f32", "MUTATE": "src" }, - "DECLS": ["INPLACE"] - }, - { - "SHADER_NAME": "gelu_erf_f32", - "REPLS": { "TYPE": "f32", "FUNC": "GELU_ERF_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, - "DECLS": ["NOT_INPLACE"] - }, - { - "SHADER_NAME": "gelu_erf_f16", - "REPLS": { "TYPE": "f16", "FUNC": "GELU_ERF_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, - "DECLS": ["NOT_INPLACE"] - }, - { - "SHADER_NAME": "gelu_erf_inplace_f32", - "REPLS": { "TYPE": "f32", "FUNC": "GELU_ERF_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, - "DECLS": ["INPLACE"] - }, - { - "SHADER_NAME": "gelu_erf_inplace_f16", - "REPLS": { "TYPE": "f16", "FUNC": "GELU_ERF_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, - "DECLS": ["INPLACE"] - }, - { - "SHADER_NAME": "softplus_f32", - "REPLS": { "TYPE": "f32", "FUNC": "SOFTPLUS_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, - "DECLS": ["NOT_INPLACE"] - }, - { - "SHADER_NAME": "softplus_f16", - "REPLS": { "TYPE": "f16", "FUNC": "SOFTPLUS_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, - "DECLS": ["NOT_INPLACE"] - }, - { - "SHADER_NAME": "softplus_inplace_f32", - "REPLS": { "TYPE": "f32", "FUNC": "SOFTPLUS_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, - "DECLS": ["INPLACE"] - }, - { - "SHADER_NAME": "softplus_inplace_f16", - "REPLS": { "TYPE": "f16", "FUNC": "SOFTPLUS_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, - "DECLS": ["INPLACE"] - }, - { - "SHADER_NAME": "expm1_f32", - "REPLS": { "TYPE": "f32", "FUNC": "EXPM1_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, - "DECLS": ["NOT_INPLACE"] - }, - { - "SHADER_NAME": "expm1_f16", - "REPLS": { "TYPE": "f16", "FUNC": "EXPM1_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, - "DECLS": ["NOT_INPLACE"] - }, - { - "SHADER_NAME": "expm1_inplace_f32", - "REPLS": { "TYPE": "f32", "FUNC": "EXPM1_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, - "DECLS": ["INPLACE"] - }, - { - "SHADER_NAME": "expm1_inplace_f16", - "REPLS": { "TYPE": "f16", "FUNC": "EXPM1_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, - "DECLS": ["INPLACE"] - }, - { - "SHADER_NAME": "floor_f32", - "REPLS": { "TYPE": "f32", "FUNC": "FLOOR_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, - "DECLS": ["NOT_INPLACE"] - }, - { - "SHADER_NAME": "floor_f16", - "REPLS": { "TYPE": "f16", "FUNC": "FLOOR_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, - "DECLS": ["NOT_INPLACE"] - }, - { - "SHADER_NAME": "floor_inplace_f32", - "REPLS": { "TYPE": "f32", "FUNC": "FLOOR_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, - "DECLS": ["INPLACE"] - }, - { - "SHADER_NAME": "floor_inplace_f16", - "REPLS": { "TYPE": "f16", "FUNC": "FLOOR_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, - "DECLS": ["INPLACE"] - }, - { - "SHADER_NAME": "ceil_f32", - "REPLS": { "TYPE": "f32", "FUNC": "CEIL_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, - "DECLS": ["NOT_INPLACE"] - }, - { - "SHADER_NAME": "ceil_f16", - "REPLS": { "TYPE": "f16", "FUNC": "CEIL_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, - "DECLS": ["NOT_INPLACE"] - }, - { - "SHADER_NAME": "ceil_inplace_f32", - "REPLS": { "TYPE": "f32", "FUNC": "CEIL_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, - "DECLS": ["INPLACE"] - }, - { - "SHADER_NAME": "ceil_inplace_f16", - "REPLS": { "TYPE": "f16", "FUNC": "CEIL_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, - "DECLS": ["INPLACE"] - }, - { - "SHADER_NAME": "round_f32", - "REPLS": { "TYPE": "f32", "FUNC": "ROUND_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, - "DECLS": ["NOT_INPLACE"] - }, - { - "SHADER_NAME": "round_f16", - "REPLS": { "TYPE": "f16", "FUNC": "ROUND_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, - "DECLS": ["NOT_INPLACE"] - }, - { - "SHADER_NAME": "round_inplace_f32", - "REPLS": { "TYPE": "f32", "FUNC": "ROUND_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, - "DECLS": ["INPLACE"] - }, - { - "SHADER_NAME": "round_inplace_f16", - "REPLS": { "TYPE": "f16", "FUNC": "ROUND_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, - "DECLS": ["INPLACE"] - }, - { - "SHADER_NAME": "trunc_f32", - "REPLS": { "TYPE": "f32", "FUNC": "TRUNC_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, - "DECLS": ["NOT_INPLACE"] - }, - { - "SHADER_NAME": "trunc_f16", - "REPLS": { "TYPE": "f16", "FUNC": "TRUNC_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, - "DECLS": ["NOT_INPLACE"] - }, - { - "SHADER_NAME": "trunc_inplace_f32", - "REPLS": { "TYPE": "f32", "FUNC": "TRUNC_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, - "DECLS": ["INPLACE"] - }, - { - "SHADER_NAME": "trunc_inplace_f16", - "REPLS": { "TYPE": "f16", "FUNC": "TRUNC_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, - "DECLS": ["INPLACE"] - }, -] - -#end(VARIANTS) - -#define(DECLS) - -#decl(INPLACE) - -@group(0) @binding(1) -var params: Params; - -#enddecl(INPLACE) - -#decl(NOT_INPLACE) - -@group(0) @binding(1) -var dst: array<{{TYPE}}>; - -@group(0) @binding(2) -var params: Params; - -#enddecl(NOT_INPLACE) - -#end(DECLS) - -#define(SHADER) - -enable f16; - -fn update(dst_i: u32, src_i: u32) { - {{FUNC}} -} - -@group(0) @binding(0) -var src: array<{{TYPE}}>; - -DECLS - -struct Params { - ne: u32, // total number of elements - offset_src: u32, // in elements - offset_dst: u32, // in elements - - // Strides (in elements) — may be permuted - stride_src0: u32, - stride_src1: u32, - stride_src2: u32, - stride_src3: u32, - - stride_dst0: u32, - stride_dst1: u32, - stride_dst2: u32, - stride_dst3: u32, - - // Logical shapes - src_ne0: u32, - src_ne1: u32, - src_ne2: u32, - - dst_ne0: u32, - dst_ne1: u32, - dst_ne2: u32, - - {{EXT_PARAMS}} -}; - -override wg_size: u32; -@compute @workgroup_size(wg_size) -fn main(@builtin(global_invocation_id) gid: vec3) { - if (gid.x >= params.ne) { - return; - } - - var i = gid.x; - let i3 = i / (params.src_ne2 * params.src_ne1 * params.src_ne0); - i = i % (params.src_ne2 * params.src_ne1 * params.src_ne0); - let i2 = i / (params.src_ne1 * params.src_ne0); - i = i % (params.src_ne1 * params.src_ne0); - let i1 = i / params.src_ne0; - let i0 = i % params.src_ne0; - - var j = gid.x; - let j3 = j / (params.dst_ne2 * params.dst_ne1 * params.dst_ne0); - j = j % (params.dst_ne2 * params.dst_ne1 * params.dst_ne0); - let j2 = j / (params.dst_ne1 * params.dst_ne0); - j = j % (params.dst_ne1 * params.dst_ne0); - let j1 = j / params.dst_ne0; - let j0 = j % params.dst_ne0; - - let src_idx = i0 * params.stride_src0 + i1 * params.stride_src1 + - i2 * params.stride_src2 + i3 * params.stride_src3; - - let dst_idx = j0 * params.stride_dst0 + j1 * params.stride_dst1 + - j2 * params.stride_dst2 + j3 * params.stride_dst3; - - - update(params.offset_dst + dst_idx, params.offset_src + src_idx); -} - -#end(SHADER) -