From 6016d0bd414d0512a78aebccded6af50bc6d71aa Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Fri, 5 Dec 2025 13:47:52 +0100 Subject: [PATCH 01/23] HIP : fix RDNA4 build (#17792) --- ggml/src/ggml-cuda/mma.cuh | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/ggml/src/ggml-cuda/mma.cuh b/ggml/src/ggml-cuda/mma.cuh index 625a367a5b..0b13293da9 100644 --- a/ggml/src/ggml-cuda/mma.cuh +++ b/ggml/src/ggml-cuda/mma.cuh @@ -560,7 +560,7 @@ namespace ggml_cuda_mma { xi[0] = xs[0]; xi[1] = xs[1]; #endif // defined(RDNA4) - }else if constexpr (I == 16 && J == 8) { + } else if constexpr (I == 16 && J == 8) { int64_t * xi = (int64_t *) t.x; #if defined(RDNA4) const int64_t * xs = (int64_t *) ((const int *) xs0 + (threadIdx.x % t.I) * stride + 4 * (threadIdx.x / t.I)); @@ -577,14 +577,13 @@ namespace ggml_cuda_mma { const int64_t * xs1 = xs + 2; xi[2] = xs1[0]; xi[3] = xs1[1]; - - }else{ +#endif // defined(RDNA4) + } else { NO_DEVICE_CODE; } } else { NO_DEVICE_CODE; } -#endif // defined(RDNA4) #else #pragma unroll for (int l = 0; l < t.ne; ++l) { From c41bde6fbda169b504a37e590798be6cd058f60f Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 5 Dec 2025 19:38:54 +0200 Subject: [PATCH 02/23] metal : add residency sets keep-alive heartbeat (#17766) * examples : add idle * metal : attach residency sets to queue * idle : add link * idle : adjust intervals * metal : add residency sets keep-alive heartbeat * cont : adjust default keep-alive time --- examples/CMakeLists.txt | 1 + examples/idle/CMakeLists.txt | 5 + examples/idle/README.md | 3 + examples/idle/idle.cpp | 110 +++++++++++++ ggml/src/ggml-metal/ggml-metal-context.m | 42 +++-- ggml/src/ggml-metal/ggml-metal-device.h | 15 ++ ggml/src/ggml-metal/ggml-metal-device.m | 187 +++++++++++++++++++---- 7 files changed, 320 insertions(+), 43 deletions(-) create mode 100644 examples/idle/CMakeLists.txt create mode 100644 examples/idle/README.md create mode 100644 examples/idle/idle.cpp diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt index dab795fb90..91797cf78a 100644 --- a/examples/CMakeLists.txt +++ b/examples/CMakeLists.txt @@ -20,6 +20,7 @@ else() add_subdirectory(gguf-hash) add_subdirectory(gguf) + add_subdirectory(idle) add_subdirectory(lookahead) add_subdirectory(lookup) add_subdirectory(parallel) diff --git a/examples/idle/CMakeLists.txt b/examples/idle/CMakeLists.txt new file mode 100644 index 0000000000..d5018fec4b --- /dev/null +++ b/examples/idle/CMakeLists.txt @@ -0,0 +1,5 @@ +set(TARGET llama-idle) +add_executable(${TARGET} idle.cpp) +install(TARGETS ${TARGET} RUNTIME) +target_link_libraries(${TARGET} PRIVATE llama common ${CMAKE_THREAD_LIBS_INIT}) +target_compile_features(${TARGET} PRIVATE cxx_std_11) diff --git a/examples/idle/README.md b/examples/idle/README.md new file mode 100644 index 0000000000..d38e607994 --- /dev/null +++ b/examples/idle/README.md @@ -0,0 +1,3 @@ +# llama.cpp/example/idle + +https://github.com/ggml-org/llama.cpp/pull/17766 diff --git a/examples/idle/idle.cpp b/examples/idle/idle.cpp new file mode 100644 index 0000000000..000427143d --- /dev/null +++ b/examples/idle/idle.cpp @@ -0,0 +1,110 @@ +#include "arg.h" +#include "common.h" +#include "log.h" +#include "llama.h" + +#include +#include +#include +#include +#include +#include + +static void print_usage(int /*argc*/, char ** argv) { + printf("\nexample usage:\n"); + printf("\n %s -m model.gguf [-ngl n_gpu_layers]\n", argv[0]); + printf("\n"); +} + +int main(int argc, char ** argv) { + common_params params; + + if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON, print_usage)) { + return 1; + } + + common_init(); + + // init LLM + + llama_backend_init(); + llama_numa_init(params.numa); + + // initialize the model + + llama_model_params model_params = common_model_params_to_llama(params); + + llama_model * model = llama_model_load_from_file(params.model.path.c_str(), model_params); + + if (model == NULL) { + LOG_ERR("%s: error: unable to load model\n" , __func__); + return 1; + } + + const llama_vocab * vocab = llama_model_get_vocab(model); + + // we need just a dummy token to evaluate + std::vector prompt_tokens(1, llama_vocab_bos(vocab)); + + llama_context_params ctx_params = llama_context_default_params(); + ctx_params.n_ctx = 512; + ctx_params.n_batch = 512; + ctx_params.no_perf = false; + + llama_context * ctx = llama_init_from_model(model, ctx_params); + if (ctx == NULL) { + fprintf(stderr , "%s: error: failed to create the llama_context\n" , __func__); + return 1; + } + + llama_batch batch = llama_batch_get_one(prompt_tokens.data(), prompt_tokens.size()); + + const int n_iters = 3; + + // warm-up + llama_decode(ctx, batch); + llama_memory_clear(llama_get_memory(ctx), true); + llama_synchronize(ctx); + + for (int64_t t_pause_ms = 0; t_pause_ms <= 4000; t_pause_ms += 800) { + double t_sum_us = 0.0; + double t_sum2_us = 0.0; + + for (int i = 0; i < n_iters; i++) { + // this pause is important - it simulates "idle GPU" + std::this_thread::sleep_for(std::chrono::milliseconds(t_pause_ms)); + + const int64_t t_start_us = llama_time_us(); + + // this should take constant time + llama_decode(ctx, batch); + llama_synchronize(ctx); + + const int64_t t_end_us = llama_time_us(); + + const double t_cur_us = t_end_us - t_start_us; + +#if 1 + // print individual decode times + printf(" - decode time: %8.2f ms\n", t_cur_us / 1000); +#endif + + t_sum_us += t_cur_us; + t_sum2_us += t_cur_us * t_cur_us; + + llama_memory_clear(llama_get_memory(ctx), true); + llama_synchronize(ctx); // just in case + } + + const double t_avg_us = t_sum_us / n_iters; + const double t_dev_us = sqrt((t_sum2_us / (n_iters - 1)) - (t_avg_us * t_avg_us * n_iters) / (n_iters - 1)); + + printf("iters: %4d, pause: %5d ms, avg decode time: %8.2f +/- %4.2f ms\n", n_iters, (int) t_pause_ms, t_avg_us / 1000, t_dev_us / 1000); + fflush(stdout); + } + + llama_free(ctx); + llama_model_free(model); + + return 0; +} diff --git a/ggml/src/ggml-metal/ggml-metal-context.m b/ggml/src/ggml-metal/ggml-metal-context.m index e66646284d..42a35736ee 100644 --- a/ggml/src/ggml-metal/ggml-metal-context.m +++ b/ggml/src/ggml-metal/ggml-metal-context.m @@ -24,9 +24,6 @@ struct ggml_metal_command_buffer { }; struct ggml_metal { - id device; - id queue; // currently a pointer to the device queue, but might become separate queue [TAG_QUEUE_PER_BACKEND] - ggml_metal_device_t dev; ggml_metal_library_t lib; @@ -91,15 +88,15 @@ ggml_metal_t ggml_metal_init(ggml_metal_device_t dev) { // init context ggml_metal_t res = calloc(1, sizeof(struct ggml_metal)); - res->device = ggml_metal_device_get_obj(dev); + id device = ggml_metal_device_get_obj(dev); - GGML_LOG_INFO("%s: picking default device: %s\n", __func__, [[res->device name] UTF8String]); + GGML_LOG_INFO("%s: picking default device: %s\n", __func__, [[device name] UTF8String]); // TODO: would it be better to have one queue for the backend and one queue for the device? // the graph encoders and async ops would use the backend queue while the sync ops would use the device queue? //res->queue = [device newCommandQueue]; [TAG_QUEUE_PER_BACKEND] - res->queue = ggml_metal_device_get_queue(dev); - if (res->queue == nil) { + id queue = ggml_metal_device_get_queue(dev); + if (queue == nil) { GGML_LOG_ERROR("%s: error: failed to create command queue\n", __func__); return NULL; } @@ -274,7 +271,8 @@ static struct ggml_metal_buffer_id ggml_metal_get_buffer_id(const struct ggml_te void ggml_metal_set_tensor_async(ggml_metal_t ctx, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) { @autoreleasepool { // wrap the source data into a Metal buffer - id buf_src = [ctx->device newBufferWithBytes:data + id device = ggml_metal_device_get_obj(ctx->dev); + id buf_src = [device newBufferWithBytes:data length:size options:MTLResourceStorageModeShared]; @@ -289,7 +287,8 @@ void ggml_metal_set_tensor_async(ggml_metal_t ctx, struct ggml_tensor * tensor, // queue the copy operation into the queue of the Metal context // this will be queued at the end, after any currently ongoing GPU operations - id cmd_buf = [ctx->queue commandBuffer]; + id queue = ggml_metal_device_get_queue(ctx->dev); + id cmd_buf = [queue commandBuffer]; id encoder = [cmd_buf blitCommandEncoder]; [encoder copyFromBuffer:buf_src @@ -315,7 +314,8 @@ void ggml_metal_set_tensor_async(ggml_metal_t ctx, struct ggml_tensor * tensor, void ggml_metal_get_tensor_async(ggml_metal_t ctx, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) { @autoreleasepool { - id buf_dst = [ctx->device newBufferWithBytesNoCopy:data + id device = ggml_metal_device_get_obj(ctx->dev); + id buf_dst = [device newBufferWithBytesNoCopy:data length:size options:MTLResourceStorageModeShared deallocator:nil]; @@ -331,7 +331,8 @@ void ggml_metal_get_tensor_async(ggml_metal_t ctx, const struct ggml_tensor * te // queue the copy operation into the queue of the Metal context // this will be queued at the end, after any currently ongoing GPU operations - id cmd_buf = [ctx->queue commandBuffer]; + id queue = ggml_metal_device_get_queue(ctx->dev); + id cmd_buf = [queue commandBuffer]; id encoder = [cmd_buf blitCommandEncoder]; [encoder copyFromBuffer:bid_src.metal @@ -362,6 +363,9 @@ enum ggml_status ggml_metal_graph_compute(ggml_metal_t ctx, struct ggml_cgraph * // number of threads in addition to the main thread const int n_cb = ctx->n_cb; + // keep the memory wired + ggml_metal_device_rsets_keep_alive(ctx->dev); + // submit the ggml compute graph to the GPU by creating command buffers and encoding the ops in them // the first n_nodes_0 are encoded and submitted for processing directly by the calling thread // while these nodes are processing, we start n_cb threads to enqueue the rest of the nodes @@ -389,7 +393,8 @@ enum ggml_status ggml_metal_graph_compute(ggml_metal_t ctx, struct ggml_cgraph * if (!ctx->capture_started) { // create capture scope - ctx->capture_scope = [[MTLCaptureManager sharedCaptureManager] newCaptureScopeWithDevice:ctx->device]; + id device = ggml_metal_device_get_obj(ctx->dev); + ctx->capture_scope = [[MTLCaptureManager sharedCaptureManager] newCaptureScopeWithDevice:device]; MTLCaptureDescriptor * descriptor = [MTLCaptureDescriptor new]; descriptor.captureObject = ctx->capture_scope; @@ -406,10 +411,13 @@ enum ggml_status ggml_metal_graph_compute(ggml_metal_t ctx, struct ggml_cgraph * } } + // short-hand + id queue = ggml_metal_device_get_queue(ctx->dev); + // the main thread commits the first few commands immediately // cmd_buf[n_cb] { - id cmd_buf = [ctx->queue commandBufferWithUnretainedReferences]; + id cmd_buf = [queue commandBufferWithUnretainedReferences]; [cmd_buf retain]; if (ctx->cmd_bufs[n_cb].obj) { @@ -428,7 +436,7 @@ enum ggml_status ggml_metal_graph_compute(ggml_metal_t ctx, struct ggml_cgraph * // prepare the rest of the command buffers asynchronously (optional) // cmd_buf[0.. n_cb) for (int cb_idx = 0; cb_idx < n_cb; ++cb_idx) { - id cmd_buf = [ctx->queue commandBufferWithUnretainedReferences]; + id cmd_buf = [queue commandBufferWithUnretainedReferences]; [cmd_buf retain]; if (ctx->cmd_bufs[cb_idx].obj) { @@ -589,9 +597,11 @@ void ggml_metal_set_abort_callback(ggml_metal_t ctx, ggml_abort_callback abort_c } bool ggml_metal_supports_family(ggml_metal_t ctx, int family) { - GGML_ASSERT(ctx->device != nil); + GGML_ASSERT(ctx->dev != nil); - return [ctx->device supportsFamily:(MTLGPUFamilyApple1 + family - 1)]; + id device = ggml_metal_device_get_obj(ctx->dev); + + return [device supportsFamily:(MTLGPUFamilyApple1 + family - 1)]; } void ggml_metal_capture_next_compute(ggml_metal_t ctx) { diff --git a/ggml/src/ggml-metal/ggml-metal-device.h b/ggml/src/ggml-metal/ggml-metal-device.h index 4be0432ea7..77f2e98cfe 100644 --- a/ggml/src/ggml-metal/ggml-metal-device.h +++ b/ggml/src/ggml-metal/ggml-metal-device.h @@ -186,6 +186,16 @@ struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_flash_att int32_t dv, int32_t nwg); +// MTLResidencySet wrapper + +typedef void * ggml_metal_rset_t; + +// a collection of residency sets (non-owning) +typedef struct ggml_metal_rsets * ggml_metal_rsets_t; + +ggml_metal_rsets_t ggml_metal_rsets_init(void); +void ggml_metal_rsets_free(ggml_metal_rsets_t rsets); + // // device // @@ -219,6 +229,11 @@ void * ggml_metal_device_get_queue(ggml_metal_device_t dev); // id @@ -519,11 +518,101 @@ struct ggml_metal_device { // ref: https://github.com/ggml-org/llama.cpp/pull/15906 id mtl_queue; + ggml_metal_rsets_t rsets; + ggml_metal_library_t library; struct ggml_metal_device_props props; }; +// +// MTLResidenceSet wrapper +// + +struct ggml_metal_rsets { + NSLock * lock; + + NSMutableArray * data; + + // number of seconds since the last graph computation + // keep the residency sets wired for that amount of time to avoid being collected by the OS + int keep_alive_s; + + // background heartbeat thread to keep the residency sets alive + atomic_bool d_stop; + atomic_int d_loop; + + dispatch_group_t d_group; +}; + +ggml_metal_rsets_t ggml_metal_rsets_init(void) { + ggml_metal_rsets_t res = calloc(1, sizeof(struct ggml_metal_rsets)); + + res->lock = [[NSLock alloc] init]; + res->data = [[NSMutableArray alloc] init]; + + // by default keep the memory wired for 3 minutes + res->keep_alive_s = 3*60; + + const char * GGML_METAL_RESIDENCY_KEEP_ALIVE_S = getenv("GGML_METAL_RESIDENCY_KEEP_ALIVE_S"); + if (GGML_METAL_RESIDENCY_KEEP_ALIVE_S) { + res->keep_alive_s = atoi(GGML_METAL_RESIDENCY_KEEP_ALIVE_S); + } + + if (res->keep_alive_s <= 0) { + res->keep_alive_s = 3*60; + } + + GGML_LOG_INFO("%s: creating a residency set collection (keep_alive = %d s)\n", __func__, res->keep_alive_s); + + atomic_store_explicit(&res->d_stop, false, memory_order_relaxed); + atomic_store_explicit(&res->d_loop, 2*res->keep_alive_s, memory_order_relaxed); + + res->d_group = dispatch_group_create(); + + // start a background thread that periodically requests residency for all the currently active sets in the collection + // the requests stop after a certain amount of time (keep_alive_s) of inactivity + dispatch_queue_t d_queue = dispatch_get_global_queue(QOS_CLASS_DEFAULT, 0); + dispatch_group_async(res->d_group, d_queue, ^{ + while (!atomic_load_explicit(&res->d_stop, memory_order_relaxed)) { + if (atomic_load_explicit(&res->d_loop, memory_order_relaxed) > 0) { + [res->lock lock]; + + for (int i = 0; i < (int) res->data.count; ++i) { + [res->data[i] requestResidency]; + } + + atomic_fetch_sub_explicit(&res->d_loop, 1, memory_order_relaxed); + + [res->lock unlock]; + } + + // half a second + usleep(500 * 1000); + } + }); + + return res; +} + +void ggml_metal_rsets_free(ggml_metal_rsets_t rsets) { + if (rsets == NULL) { + return; + } + + GGML_ASSERT([rsets->data count] == 0); + + atomic_store_explicit(&rsets->d_stop, true, memory_order_relaxed); + + dispatch_group_wait(rsets->d_group, DISPATCH_TIME_FOREVER); + dispatch_release(rsets->d_group); + + [rsets->data release]; + [rsets->lock release]; + + free(rsets); +} + ggml_metal_device_t ggml_metal_device_init(void) { ggml_metal_device_t dev = calloc(1, sizeof(struct ggml_metal_device)); @@ -692,6 +781,13 @@ ggml_metal_device_t ggml_metal_device_init(void) { GGML_LOG_ERROR("%s: error: failed to create library\n", __func__); } + if (dev->props.use_residency_sets) { + dev->rsets = ggml_metal_rsets_init(); + } else { + dev->rsets = nil; + } + + // -------------------------------------------------- // print MTL GPU family: @@ -745,6 +841,8 @@ ggml_metal_device_t ggml_metal_device_init(void) { void ggml_metal_device_free(ggml_metal_device_t dev) { assert(dev != NULL); + ggml_metal_rsets_free(dev->rsets); + ggml_metal_library_free(dev->library); dev->library = NULL; @@ -773,6 +871,42 @@ ggml_metal_library_t ggml_metal_device_get_library(ggml_metal_device_t dev) { return dev->library; } +void ggml_metal_device_rsets_add(ggml_metal_device_t dev, ggml_metal_rset_t rset) { + if (rset == nil) { + return; + } + + GGML_ASSERT(dev->rsets); + + [dev->rsets->lock lock]; + + [dev->rsets->data addObject:rset]; + + [dev->rsets->lock unlock]; +} + +void ggml_metal_device_rsets_rm(ggml_metal_device_t dev, ggml_metal_rset_t rset) { + if (rset == nil) { + return; + } + + GGML_ASSERT(dev->rsets); + + [dev->rsets->lock lock]; + + [dev->rsets->data removeObject:rset]; + + [dev->rsets->lock unlock]; +} + +void ggml_metal_device_rsets_keep_alive(ggml_metal_device_t dev) { + if (dev->rsets == NULL) { + return; + } + + atomic_store_explicit(&dev->rsets->d_loop, 2*dev->rsets->keep_alive_s, memory_order_relaxed); +} + void ggml_metal_device_get_memory(ggml_metal_device_t dev, size_t * free, size_t * total) { if (@available(macOS 10.12, iOS 16.0, *)) { *total = dev->mtl_device.recommendedMaxWorkingSetSize; @@ -1066,9 +1200,8 @@ struct ggml_metal_buffer { // note: cannot use explicity "id" here because it is not available on certain OSes id rset; - // pointers to global device objects - id device; - id queue; + // pointers to global device + ggml_metal_device_t dev; }; static void ggml_metal_log_allocated_size(id device, size_t size_aligned) { @@ -1111,7 +1244,7 @@ static bool ggml_metal_buffer_rset_init(ggml_metal_buffer_t buf) { desc.initialCapacity = buf->n_buffers; NSError * error; - buf->rset = [buf->device newResidencySetWithDescriptor:desc error:&error]; + buf->rset = [buf->dev->mtl_device newResidencySetWithDescriptor:desc error:&error]; if (error) { GGML_LOG_ERROR("%s: error: %s\n", __func__, [[error description] UTF8String]); [desc release]; @@ -1172,6 +1305,8 @@ static void * ggml_metal_host_malloc(size_t n) { ggml_metal_buffer_t ggml_metal_buffer_init(ggml_metal_device_t dev, size_t size, bool shared) { ggml_metal_buffer_t res = calloc(1, sizeof(struct ggml_metal_buffer)); + res->dev = dev; + const size_t size_page = sysconf(_SC_PAGESIZE); size_t size_aligned = size; @@ -1196,9 +1331,6 @@ ggml_metal_buffer_t ggml_metal_buffer_init(ggml_metal_device_t dev, size_t size, res->owned = true; - res->device = ggml_metal_device_get_obj(dev); - res->queue = ggml_metal_device_get_queue(dev); - res->n_buffers = 1; if (res->all_data != NULL) { @@ -1207,12 +1339,12 @@ ggml_metal_buffer_t ggml_metal_buffer_init(ggml_metal_device_t dev, size_t size, if (size_aligned > 0) { if (props_dev->use_shared_buffers && shared) { - res->buffers[0].metal = [res->device newBufferWithBytesNoCopy:res->all_data + res->buffers[0].metal = [res->dev->mtl_device newBufferWithBytesNoCopy:res->all_data length:size_aligned options:MTLResourceStorageModeShared deallocator:nil]; } else { - res->buffers[0].metal = [res->device newBufferWithLength:size_aligned options:MTLResourceStorageModePrivate]; + res->buffers[0].metal = [res->dev->mtl_device newBufferWithLength:size_aligned options:MTLResourceStorageModePrivate]; } } @@ -1233,6 +1365,8 @@ ggml_metal_buffer_t ggml_metal_buffer_init(ggml_metal_device_t dev, size_t size, return NULL; } + ggml_metal_device_rsets_add(dev, res->rset); + //ggml_metal_log_allocated_size(device, size_aligned); return res; @@ -1241,6 +1375,8 @@ ggml_metal_buffer_t ggml_metal_buffer_init(ggml_metal_device_t dev, size_t size, ggml_metal_buffer_t ggml_metal_buffer_map(ggml_metal_device_t dev, void * ptr, size_t size, size_t max_tensor_size) { ggml_metal_buffer_t res = calloc(1, sizeof(struct ggml_metal_buffer)); + res->dev = dev; + res->all_data = ptr; res->all_size = size; @@ -1263,9 +1399,6 @@ ggml_metal_buffer_t ggml_metal_buffer_map(ggml_metal_device_t dev, void * ptr, s size_aligned += (size_page - (size_aligned % size_page)); } - res->device = ggml_metal_device_get_obj(dev); - res->queue = ggml_metal_device_get_queue(dev); - const struct ggml_metal_device_props * props_dev = ggml_metal_device_get_props(dev); // the buffer fits into the max buffer size allowed by the device @@ -1275,7 +1408,7 @@ ggml_metal_buffer_t ggml_metal_buffer_map(ggml_metal_device_t dev, void * ptr, s res->buffers[res->n_buffers].metal = nil; if (size_aligned > 0) { - res->buffers[res->n_buffers].metal = [res->device newBufferWithBytesNoCopy:ptr length:size_aligned options:MTLResourceStorageModeShared deallocator:nil]; + res->buffers[res->n_buffers].metal = [res->dev->mtl_device newBufferWithBytesNoCopy:ptr length:size_aligned options:MTLResourceStorageModeShared deallocator:nil]; if (res->buffers[res->n_buffers].metal == nil) { GGML_LOG_ERROR("%s: error: failed to allocate buffer, size = %8.2f MiB\n", __func__, size_aligned / 1024.0 / 1024.0); @@ -1284,7 +1417,7 @@ ggml_metal_buffer_t ggml_metal_buffer_map(ggml_metal_device_t dev, void * ptr, s } } - ggml_metal_log_allocated_size(res->device, size_aligned); + ggml_metal_log_allocated_size(res->dev->mtl_device, size_aligned); ++res->n_buffers; } else { @@ -1302,7 +1435,7 @@ ggml_metal_buffer_t ggml_metal_buffer_map(ggml_metal_device_t dev, void * ptr, s res->buffers[res->n_buffers].metal = nil; if (size_step_aligned > 0) { - res->buffers[res->n_buffers].metal = [res->device newBufferWithBytesNoCopy:(void *) ((uint8_t *) ptr + i) length:size_step_aligned options:MTLResourceStorageModeShared deallocator:nil]; + res->buffers[res->n_buffers].metal = [res->dev->mtl_device newBufferWithBytesNoCopy:(void *) ((uint8_t *) ptr + i) length:size_step_aligned options:MTLResourceStorageModeShared deallocator:nil]; if (res->buffers[res->n_buffers].metal == nil) { GGML_LOG_ERROR("%s: error: failed to allocate buffer, size = %8.2f MiB\n", __func__, size_step_aligned / 1024.0 / 1024.0); @@ -1311,7 +1444,7 @@ ggml_metal_buffer_t ggml_metal_buffer_map(ggml_metal_device_t dev, void * ptr, s } } - ggml_metal_log_allocated_size(res->device, size_step_aligned); + ggml_metal_log_allocated_size(res->dev->mtl_device, size_step_aligned); if (i + size_step < size) { GGML_LOG_INFO("\n"); @@ -1329,10 +1462,14 @@ ggml_metal_buffer_t ggml_metal_buffer_map(ggml_metal_device_t dev, void * ptr, s return NULL; } + ggml_metal_device_rsets_add(dev, res->rset); + return res; } void ggml_metal_buffer_free(ggml_metal_buffer_t buf) { + ggml_metal_device_rsets_rm(buf->dev, buf->rset); + for (int i = 0; i < buf->n_buffers; i++) { [buf->buffers[i].metal release]; } @@ -1369,8 +1506,7 @@ void ggml_metal_buffer_memset_tensor(ggml_metal_buffer_t buf, struct ggml_tensor struct ggml_metal_buffer_id bid_dst = ggml_metal_buffer_get_id(buf, tensor); bid_dst.offs += offset; - id queue = buf->queue; - id cmd_buf = [queue commandBufferWithUnretainedReferences]; + id cmd_buf = [buf->dev->mtl_queue commandBufferWithUnretainedReferences]; { id encoder = [cmd_buf blitCommandEncoder]; @@ -1396,7 +1532,7 @@ void ggml_metal_buffer_set_tensor(ggml_metal_buffer_t buf, struct ggml_tensor * @autoreleasepool { // src void * data_ptr = (void *)(uintptr_t) data; // "const cast" the src data - id buf_src = [buf->device newBufferWithBytesNoCopy:data_ptr + id buf_src = [buf->dev->mtl_device newBufferWithBytesNoCopy:data_ptr length:size options:MTLResourceStorageModeShared deallocator:nil]; @@ -1411,8 +1547,7 @@ void ggml_metal_buffer_set_tensor(ggml_metal_buffer_t buf, struct ggml_tensor * // this is alternative to waitUntilCompleted, which should be faster, but don't seem to make much difference dispatch_semaphore_t completion_semaphore = dispatch_semaphore_create(0); - id queue = buf->queue; - id cmd_buf = [queue commandBufferWithUnretainedReferences]; + id cmd_buf = [buf->dev->mtl_queue commandBufferWithUnretainedReferences]; { id encoder = [cmd_buf blitCommandEncoder]; @@ -1454,15 +1589,14 @@ void ggml_metal_buffer_get_tensor(ggml_metal_buffer_t buf, const struct ggml_ten bid_src.offs += offset; // dst - id buf_dst = [buf->device newBufferWithBytesNoCopy:data + id buf_dst = [buf->dev->mtl_device newBufferWithBytesNoCopy:data length:size options:MTLResourceStorageModeShared deallocator:nil]; GGML_ASSERT(buf_dst); - id queue = buf->queue; - id cmd_buf = [queue commandBufferWithUnretainedReferences]; + id cmd_buf = [buf->dev->mtl_queue commandBufferWithUnretainedReferences]; { id encoder = [cmd_buf blitCommandEncoder]; @@ -1488,8 +1622,7 @@ void ggml_metal_buffer_clear(ggml_metal_buffer_t buf, uint8_t value) { } @autoreleasepool { - id queue = buf->queue; - id cmd_buf = [queue commandBufferWithUnretainedReferences]; + id cmd_buf = [buf->dev->mtl_queue commandBufferWithUnretainedReferences]; { id encoder = [cmd_buf blitCommandEncoder]; From 8160b38a5fa8a25490ca33ffdd200cda51405688 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 5 Dec 2025 19:39:04 +0200 Subject: [PATCH 03/23] rpc : fix alloc size logic (#17116) * rpc : fix alloc size logic * rpc : bump version --- ggml/include/ggml-rpc.h | 3 +-- ggml/src/ggml-rpc/ggml-rpc.cpp | 46 ++++++++++++++++++++++++++++------ 2 files changed, 39 insertions(+), 10 deletions(-) diff --git a/ggml/include/ggml-rpc.h b/ggml/include/ggml-rpc.h index 832c26c61d..df1ad2a516 100644 --- a/ggml/include/ggml-rpc.h +++ b/ggml/include/ggml-rpc.h @@ -1,6 +1,5 @@ #pragma once -#include "ggml.h" #include "ggml-backend.h" #ifdef __cplusplus @@ -8,7 +7,7 @@ extern "C" { #endif #define RPC_PROTO_MAJOR_VERSION 3 -#define RPC_PROTO_MINOR_VERSION 5 +#define RPC_PROTO_MINOR_VERSION 6 #define RPC_PROTO_PATCH_VERSION 0 #define GGML_RPC_MAX_SERVERS 16 diff --git a/ggml/src/ggml-rpc/ggml-rpc.cpp b/ggml/src/ggml-rpc/ggml-rpc.cpp index 48fd99a762..cf656b6a08 100644 --- a/ggml/src/ggml-rpc/ggml-rpc.cpp +++ b/ggml/src/ggml-rpc/ggml-rpc.cpp @@ -128,6 +128,7 @@ struct rpc_msg_device_count_rsp { struct rpc_msg_get_alloc_size_req { uint32_t device; rpc_tensor tensor; + rpc_tensor srcs[GGML_MAX_SRC]; }; struct rpc_msg_get_alloc_size_rsp { @@ -572,6 +573,11 @@ static void * ggml_backend_rpc_buffer_get_base(ggml_backend_buffer_t buffer) { static rpc_tensor serialize_tensor(const ggml_tensor * tensor) { rpc_tensor result; + if (!tensor) { + memset(&result, 0, sizeof(result)); + return result; + } + result.id = reinterpret_cast(tensor); result.type = tensor->type; if (tensor->buffer) { @@ -753,23 +759,41 @@ static size_t ggml_backend_rpc_get_max_size(ggml_backend_buffer_type_t buft) { } static size_t ggml_backend_rpc_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) { + // should we query the remote server for the actual size + bool rpc_get = false; + // See comments in init_tensor. - if (ggml_is_quantized(tensor->type) && (tensor->ne[0] % 512 != 0) && (tensor->view_src == nullptr)) { + rpc_get |= ggml_is_quantized(tensor->type) && (tensor->ne[0] % 512 != 0) && (tensor->view_src == nullptr); + + // ops that require additional memory for fleeting data on certain backends + // ref: https://github.com/ggml-org/llama.cpp/pull/15966 + rpc_get |= tensor->op == GGML_OP_FLASH_ATTN_EXT; + rpc_get |= tensor->op == GGML_OP_MUL_MAT_ID; + + if (rpc_get) { ggml_backend_rpc_buffer_type_context * buft_ctx = (ggml_backend_rpc_buffer_type_context *)buft->context; auto sock = get_socket(buft_ctx->endpoint); - rpc_msg_get_alloc_size_req request; - request.device = buft_ctx->device; - request.tensor = serialize_tensor(tensor); + rpc_msg_get_alloc_size_req request = { + /*.device =*/ buft_ctx->device, + /*.tensor =*/ serialize_tensor(tensor), + /*.srcs =*/ {}, + }; + // .get_alloc_size could be a function of the tensor's srcs, so we must serialize them as well + for (int i = 0; i < GGML_MAX_SRC; i++) { + request.srcs[i] = serialize_tensor(tensor->src[i]); + } + + // TODO: cache the alloc responses to avoid extra RPC calls? rpc_msg_get_alloc_size_rsp response; bool status = send_rpc_cmd(sock, RPC_CMD_GET_ALLOC_SIZE, &request, sizeof(request), &response, sizeof(response)); RPC_STATUS_ASSERT(status); return response.alloc_size; - } else { - return ggml_nbytes(tensor); } + + return ggml_nbytes(tensor); } static ggml_backend_buffer_type_i ggml_backend_rpc_buffer_type_interface = { @@ -1017,7 +1041,7 @@ bool rpc_server::get_alloc_size(const rpc_msg_get_alloc_size_req & request, rpc_ } ggml_backend_buffer_type_t buft; struct ggml_init_params params { - /*.mem_size =*/ ggml_tensor_overhead(), + /*.mem_size =*/ ggml_tensor_overhead()*(1 + GGML_MAX_SRC), /*.mem_buffer =*/ NULL, /*.no_alloc =*/ true, }; @@ -1025,12 +1049,18 @@ bool rpc_server::get_alloc_size(const rpc_msg_get_alloc_size_req & request, rpc_ ggml_context_ptr ctx_ptr { ggml_init(params) }; GGML_ASSERT(ctx_ptr != nullptr); ggml_context * ctx = ctx_ptr.get(); - ggml_tensor * tensor = deserialize_tensor(ctx, &request.tensor); + ggml_tensor * tensor = deserialize_tensor(ctx, &request.tensor); if (tensor == nullptr) { GGML_LOG_ERROR("Null tensor pointer passed to server get_alloc_size function.\n"); return false; } + for (int i = 0; i < GGML_MAX_SRC; i++) { + if (request.srcs[i].id != 0) { + tensor->src[i] = deserialize_tensor(ctx, &request.srcs[i]); + } + } + LOG_DBG("[%s] device: %d, buffer: %p, data: %p\n", __func__, dev_id, (void*)tensor->buffer, tensor->data); if (tensor->buffer == nullptr) { //No buffer allocated. From 93bb92664e37086cb4adab24f4911f7681724fce Mon Sep 17 00:00:00 2001 From: Jeff Bolz Date: Fri, 5 Dec 2025 14:21:04 -0600 Subject: [PATCH 04/23] vulkan: set all memory allocations to high priority (#17624) * vulkan: set all memory allocations to high priority * gate by env var --- ggml/src/ggml-vulkan/ggml-vulkan.cpp | 22 +++++++++++++++++++++- 1 file changed, 21 insertions(+), 1 deletion(-) diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp index f917a745d5..97b0fd742e 100644 --- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp +++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp @@ -519,6 +519,7 @@ struct vk_device_struct { bool fp16; bool bf16; bool pipeline_robustness; + bool memory_priority; vk::Device device; uint32_t vendor_id; vk::DriverId driver_id; @@ -2369,7 +2370,13 @@ static vk_buffer ggml_vk_create_buffer(vk_device& device, size_t size, const std vk::PhysicalDeviceMemoryProperties mem_props = device->physical_device.getMemoryProperties(); - const vk::MemoryAllocateFlagsInfo mem_flags_info { mem_flags }; + const vk::MemoryPriorityAllocateInfoEXT mem_priority_info { 1.0f }; + + vk::MemoryAllocateFlagsInfo mem_flags_info { mem_flags }; + + if (device->memory_priority) { + mem_flags_info.setPNext(&mem_priority_info); + } for (auto it = req_flags_list.begin(); it != req_flags_list.end(); it++) { const auto & req_flags = *it; @@ -4340,6 +4347,9 @@ static vk_device ggml_vk_get_device(size_t idx) { #endif } else if (strcmp("VK_KHR_pipeline_executable_properties", properties.extensionName) == 0) { pipeline_executable_properties_support = true; + } else if (strcmp("VK_EXT_memory_priority", properties.extensionName) == 0 && + getenv("GGML_VK_ENABLE_MEMORY_PRIORITY")) { + device->memory_priority = true; } } @@ -4531,6 +4541,16 @@ static vk_device ggml_vk_get_device(size_t idx) { device_extensions.push_back("VK_EXT_pipeline_robustness"); } + VkPhysicalDeviceMemoryPriorityFeaturesEXT memory_priority_features; + memory_priority_features.pNext = nullptr; + memory_priority_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_MEMORY_PRIORITY_FEATURES_EXT; + memory_priority_features.memoryPriority = VK_FALSE; + if (device->memory_priority) { + last_struct->pNext = (VkBaseOutStructure *)&memory_priority_features; + last_struct = (VkBaseOutStructure *)&memory_priority_features; + device_extensions.push_back("VK_EXT_memory_priority"); + } + VkPhysicalDeviceSubgroupSizeControlFeaturesEXT subgroup_size_control_features; subgroup_size_control_features.pNext = nullptr; subgroup_size_control_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_SUBGROUP_SIZE_CONTROL_FEATURES_EXT; From 6ab0d6496074f51644def999f94686d1d939785e Mon Sep 17 00:00:00 2001 From: Jeff Bolz Date: Fri, 5 Dec 2025 14:21:57 -0600 Subject: [PATCH 05/23] vulkan: enable mmvq for q2_k on NVIDIA (#17675) --- ggml/src/ggml-vulkan/ggml-vulkan.cpp | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp index 97b0fd742e..c8f0449a94 100644 --- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp +++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp @@ -6948,6 +6948,10 @@ static bool ggml_vk_should_use_mmvq(const vk_device& device, uint32_t m, uint32_ // Quantization overhead is not worth it for small k switch (device->vendor_id) { case VK_VENDOR_ID_NVIDIA: + if (src0_type == GGML_TYPE_Q2_K) { + return true; + } + if (k <= 4096) { return false; } From fd57b24c0f2b28f54c1375481f470b8e589909eb Mon Sep 17 00:00:00 2001 From: Reese Levine Date: Fri, 5 Dec 2025 12:25:51 -0800 Subject: [PATCH 06/23] ggml webgpu: unary op suppport, code refactoring, ops support (#17764) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * 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 * Remove extra code and format * Add ops documentation (finally) * Update ggml/src/ggml-webgpu/wgsl-shaders/embed_wgsl.py Co-authored-by: SigbjΓΈrn SkjΓ¦ret --------- Co-authored-by: James Contini Co-authored-by: Neha Abbas Co-authored-by: Abhijit Ramesh Co-authored-by: SigbjΓΈrn SkjΓ¦ret --- docs/ops.md | 216 +- docs/ops/WebGPU.csv | 18741 ++++++++++++++++ ggml/src/ggml-webgpu/ggml-webgpu.cpp | 970 +- .../ggml-webgpu/wgsl-shaders/embed_wgsl.py | 17 +- .../ggml-webgpu/wgsl-shaders/unary_op.wgsl | 461 + 5 files changed, 19932 insertions(+), 473 deletions(-) create mode 100644 docs/ops/WebGPU.csv create mode 100644 ggml/src/ggml-webgpu/wgsl-shaders/unary_op.wgsl diff --git a/docs/ops.md b/docs/ops.md index 4136e8ab3b..65e33b5120 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 | ❌ | βœ… | βœ… | 🟑 | 🟑 | ❌ | βœ… | 🟑 | ❌ | -| ACC | ❌ | βœ… | βœ… | βœ… | βœ… | ❌ | βœ… | βœ… | ❌ | -| ADD | ❌ | βœ… | βœ… | βœ… | 🟑 | 🟑 | βœ… | βœ… | ❌ | -| ADD1 | ❌ | βœ… | βœ… | βœ… | ❌ | ❌ | βœ… | βœ… | ❌ | -| ADD_ID | ❌ | ❌ | βœ… | βœ… | βœ… | ❌ | ❌ | βœ… | ❌ | -| ARANGE | ❌ | βœ… | βœ… | βœ… | βœ… | ❌ | βœ… | βœ… | ❌ | -| ARGMAX | ❌ | βœ… | βœ… | βœ… | βœ… | ❌ | βœ… | βœ… | ❌ | -| ARGSORT | ❌ | βœ… | βœ… | βœ… | βœ… | βœ… | βœ… | βœ… | ❌ | -| CEIL | ❌ | ❌ | βœ… | 🟑 | ❌ | ❌ | 🟑 | 🟑 | ❌ | -| CLAMP | ❌ | βœ… | βœ… | βœ… | 🟑 | 🟑 | 🟑 | 🟑 | ❌ | -| CONCAT | ❌ | βœ… | βœ… | 🟑 | βœ… | 🟑 | βœ… | βœ… | ❌ | -| CONT | ❌ | 🟑 | βœ… | βœ… | βœ… | 🟑 | 🟑 | βœ… | ❌ | -| CONV_2D | ❌ | ❌ | βœ… | βœ… | βœ… | βœ… | ❌ | βœ… | ❌ | -| CONV_2D_DW | ❌ | ❌ | βœ… | βœ… | ❌ | ❌ | ❌ | βœ… | ❌ | -| CONV_3D | ❌ | ❌ | βœ… | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | -| CONV_TRANSPOSE_1D | ❌ | βœ… | βœ… | βœ… | βœ… | ❌ | βœ… | βœ… | ❌ | -| CONV_TRANSPOSE_2D | ❌ | ❌ | βœ… | βœ… | βœ… | ❌ | ❌ | βœ… | ❌ | -| COS | ❌ | βœ… | βœ… | βœ… | 🟑 | ❌ | 🟑 | 🟑 | ❌ | -| COUNT_EQUAL | ❌ | βœ… | βœ… | βœ… | ❌ | ❌ | βœ… | βœ… | ❌ | -| CPY | ❌ | 🟑 | 🟑 | 🟑 | 🟑 | 🟑 | 🟑 | 🟑 | ❌ | -| CROSS_ENTROPY_LOSS | ❌ | ❌ | βœ… | βœ… | ❌ | ❌ | ❌ | ❌ | ❌ | -| CROSS_ENTROPY_LOSS_BACK | ❌ | ❌ | βœ… | βœ… | ❌ | ❌ | ❌ | ❌ | ❌ | -| CUMSUM | ❌ | ❌ | βœ… | ❌ | βœ… | ❌ | ❌ | βœ… | ❌ | -| DIAG_MASK_INF | ❌ | βœ… | βœ… | βœ… | ❌ | 🟑 | βœ… | βœ… | ❌ | -| DIV | ❌ | βœ… | βœ… | βœ… | 🟑 | 🟑 | βœ… | βœ… | ❌ | -| DUP | ❌ | βœ… | βœ… | 🟑 | 🟑 | 🟑 | βœ… | βœ… | ❌ | -| ELU | ❌ | βœ… | βœ… | 🟑 | 🟑 | ❌ | βœ… | ❌ | ❌ | -| EXP | ❌ | βœ… | βœ… | 🟑 | 🟑 | ❌ | βœ… | 🟑 | ❌ | -| EXPM1 | ❌ | ❌ | βœ… | 🟑 | 🟑 | ❌ | ❌ | ❌ | ❌ | -| FILL | ❌ | ❌ | βœ… | ❌ | βœ… | ❌ | ❌ | βœ… | ❌ | -| FLASH_ATTN_EXT | ❌ | 🟑 | βœ… | 🟑 | 🟑 | ❌ | ❌ | 🟑 | ❌ | -| FLOOR | ❌ | ❌ | βœ… | 🟑 | ❌ | ❌ | 🟑 | 🟑 | ❌ | -| GATED_LINEAR_ATTN | ❌ | ❌ | βœ… | βœ… | ❌ | ❌ | βœ… | ❌ | ❌ | -| GEGLU | ❌ | βœ… | βœ… | βœ… | 🟑 | βœ… | βœ… | 🟑 | ❌ | -| GEGLU_ERF | ❌ | βœ… | βœ… | βœ… | 🟑 | βœ… | βœ… | 🟑 | ❌ | -| GEGLU_QUICK | ❌ | βœ… | βœ… | βœ… | 🟑 | βœ… | βœ… | 🟑 | ❌ | -| GELU | ❌ | βœ… | βœ… | 🟑 | 🟑 | 🟑 | βœ… | 🟑 | ❌ | -| GELU_ERF | ❌ | βœ… | βœ… | 🟑 | 🟑 | 🟑 | βœ… | 🟑 | ❌ | -| GELU_QUICK | ❌ | βœ… | βœ… | 🟑 | 🟑 | 🟑 | βœ… | 🟑 | ❌ | -| GET_ROWS | ❌ | 🟑 | βœ… | 🟑 | βœ… | 🟑 | 🟑 | 🟑 | ❌ | -| GET_ROWS_BACK | ❌ | ❌ | 🟑 | 🟑 | ❌ | ❌ | ❌ | ❌ | ❌ | -| GROUP_NORM | ❌ | βœ… | βœ… | βœ… | βœ… | βœ… | βœ… | βœ… | ❌ | -| GROUP_NORM_MUL_ADD | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | -| HARDSIGMOID | ❌ | βœ… | βœ… | 🟑 | 🟑 | ❌ | βœ… | 🟑 | ❌ | -| HARDSWISH | ❌ | βœ… | βœ… | 🟑 | 🟑 | ❌ | βœ… | 🟑 | ❌ | -| IM2COL | ❌ | βœ… | βœ… | βœ… | βœ… | βœ… | βœ… | βœ… | ❌ | -| IM2COL_3D | ❌ | ❌ | βœ… | βœ… | ❌ | ❌ | ❌ | βœ… | ❌ | -| L2_NORM | ❌ | ❌ | βœ… | βœ… | βœ… | ❌ | βœ… | βœ… | ❌ | -| LEAKY_RELU | ❌ | βœ… | βœ… | βœ… | 🟑 | ❌ | βœ… | 🟑 | ❌ | -| LOG | ❌ | βœ… | βœ… | βœ… | 🟑 | ❌ | 🟑 | βœ… | ❌ | -| MEAN | ❌ | βœ… | βœ… | βœ… | βœ… | ❌ | βœ… | βœ… | ❌ | -| MUL | ❌ | βœ… | βœ… | βœ… | 🟑 | 🟑 | βœ… | βœ… | ❌ | -| MUL_MAT | 🟑 | 🟑 | 🟑 | 🟑 | βœ… | 🟑 | 🟑 | 🟑 | 🟑 | -| MUL_MAT_ID | ❌ | 🟑 | βœ… | βœ… | βœ… | 🟑 | 🟑 | βœ… | ❌ | -| NEG | ❌ | βœ… | βœ… | 🟑 | 🟑 | ❌ | βœ… | 🟑 | ❌ | -| NORM | ❌ | βœ… | βœ… | βœ… | βœ… | βœ… | βœ… | 🟑 | ❌ | -| NORM_MUL_ADD | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | -| OPT_STEP_ADAMW | ❌ | ❌ | βœ… | βœ… | βœ… | ❌ | ❌ | βœ… | ❌ | -| OPT_STEP_SGD | ❌ | ❌ | βœ… | βœ… | βœ… | ❌ | ❌ | βœ… | ❌ | -| OUT_PROD | 🟑 | ❌ | 🟑 | 🟑 | ❌ | ❌ | 🟑 | ❌ | ❌ | -| PAD | ❌ | βœ… | βœ… | 🟑 | 🟑 | βœ… | 🟑 | βœ… | ❌ | -| PAD_REFLECT_1D | ❌ | βœ… | βœ… | βœ… | βœ… | ❌ | βœ… | ❌ | ❌ | -| POOL_2D | ❌ | 🟑 | βœ… | βœ… | βœ… | ❌ | βœ… | βœ… | ❌ | -| REGLU | ❌ | βœ… | βœ… | βœ… | 🟑 | βœ… | βœ… | 🟑 | ❌ | -| RELU | ❌ | βœ… | βœ… | 🟑 | 🟑 | 🟑 | βœ… | 🟑 | ❌ | -| REPEAT | ❌ | βœ… | βœ… | 🟑 | βœ… | 🟑 | βœ… | 🟑 | ❌ | -| REPEAT_BACK | ❌ | ❌ | βœ… | βœ… | ❌ | ❌ | βœ… | βœ… | ❌ | -| RMS_NORM | ❌ | βœ… | βœ… | βœ… | βœ… | βœ… | βœ… | βœ… | ❌ | -| RMS_NORM_BACK | ❌ | ❌ | βœ… | βœ… | ❌ | ❌ | βœ… | βœ… | ❌ | -| RMS_NORM_MUL_ADD | ❌ | βœ… | ❌ | ❌ | ❌ | βœ… | ❌ | ❌ | ❌ | -| ROLL | ❌ | ❌ | βœ… | βœ… | ❌ | ❌ | βœ… | βœ… | ❌ | -| ROPE | ❌ | 🟑 | βœ… | βœ… | βœ… | βœ… | βœ… | βœ… | ❌ | -| ROPE_BACK | ❌ | ❌ | βœ… | βœ… | ❌ | ❌ | ❌ | βœ… | ❌ | -| ROUND | ❌ | ❌ | βœ… | 🟑 | ❌ | ❌ | 🟑 | 🟑 | ❌ | -| RWKV_WKV6 | ❌ | ❌ | βœ… | βœ… | βœ… | ❌ | βœ… | βœ… | ❌ | -| RWKV_WKV7 | ❌ | ❌ | βœ… | βœ… | βœ… | ❌ | βœ… | βœ… | ❌ | -| SCALE | ❌ | 🟑 | βœ… | βœ… | βœ… | βœ… | βœ… | βœ… | ❌ | -| SET | ❌ | ❌ | βœ… | βœ… | ❌ | ❌ | 🟑 | ❌ | ❌ | -| SET_ROWS | ❌ | ❌ | 🟑 | 🟑 | 🟑 | 🟑 | 🟑 | 🟑 | ❌ | -| SGN | ❌ | βœ… | βœ… | 🟑 | 🟑 | ❌ | βœ… | ❌ | ❌ | -| SIGMOID | ❌ | βœ… | βœ… | 🟑 | 🟑 | 🟑 | βœ… | 🟑 | ❌ | -| SILU | ❌ | βœ… | βœ… | 🟑 | 🟑 | 🟑 | βœ… | 🟑 | ❌ | -| SILU_BACK | ❌ | ❌ | βœ… | βœ… | ❌ | ❌ | ❌ | βœ… | ❌ | -| SIN | ❌ | βœ… | βœ… | βœ… | 🟑 | ❌ | 🟑 | 🟑 | ❌ | -| SOFTCAP | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | -| SOFTPLUS | ❌ | ❌ | βœ… | 🟑 | 🟑 | ❌ | ❌ | 🟑 | ❌ | -| SOFT_MAX | ❌ | 🟑 | βœ… | βœ… | βœ… | βœ… | βœ… | βœ… | ❌ | -| SOFT_MAX_BACK | ❌ | ❌ | 🟑 | 🟑 | ❌ | ❌ | 🟑 | βœ… | ❌ | -| SOLVE_TRI | ❌ | ❌ | βœ… | ❌ | ❌ | ❌ | ❌ | 🟑 | ❌ | -| SQR | ❌ | βœ… | βœ… | βœ… | 🟑 | ❌ | 🟑 | 🟑 | ❌ | -| SQRT | ❌ | βœ… | βœ… | βœ… | 🟑 | ❌ | 🟑 | 🟑 | ❌ | -| SSM_CONV | ❌ | ❌ | βœ… | βœ… | βœ… | ❌ | βœ… | βœ… | ❌ | -| SSM_SCAN | ❌ | ❌ | βœ… | βœ… | βœ… | ❌ | ❌ | 🟑 | ❌ | -| STEP | ❌ | βœ… | βœ… | 🟑 | 🟑 | ❌ | βœ… | 🟑 | ❌ | -| SUB | ❌ | βœ… | βœ… | βœ… | 🟑 | 🟑 | βœ… | βœ… | ❌ | -| SUM | ❌ | βœ… | βœ… | 🟑 | 🟑 | ❌ | 🟑 | 🟑 | ❌ | -| SUM_ROWS | ❌ | βœ… | βœ… | 🟑 | βœ… | βœ… | 🟑 | βœ… | ❌ | -| SWIGLU | ❌ | βœ… | βœ… | βœ… | 🟑 | βœ… | βœ… | 🟑 | ❌ | -| SWIGLU_OAI | ❌ | ❌ | βœ… | βœ… | βœ… | ❌ | ❌ | 🟑 | ❌ | -| TANH | ❌ | βœ… | βœ… | 🟑 | 🟑 | βœ… | βœ… | 🟑 | ❌ | -| TIMESTEP_EMBEDDING | ❌ | βœ… | βœ… | βœ… | βœ… | βœ… | βœ… | βœ… | ❌ | -| TOP_K | ❌ | ❌ | ❌ | ❌ | βœ… | ❌ | ❌ | 🟑 | ❌ | -| TRI | ❌ | ❌ | βœ… | ❌ | βœ… | ❌ | ❌ | βœ… | ❌ | -| TRUNC | ❌ | ❌ | βœ… | 🟑 | ❌ | ❌ | 🟑 | 🟑 | ❌ | -| UPSCALE | ❌ | 🟑 | βœ… | βœ… | 🟑 | βœ… | 🟑 | 🟑 | ❌ | -| XIELU | ❌ | ❌ | βœ… | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | +| Operation | BLAS | CANN | CPU | CUDA | Metal | OpenCL | SYCL | Vulkan | WebGPU | zDNN | +|-----------|------|------|------|------|------|------|------|------|------|------| +| ABS | ❌ | βœ… | βœ… | 🟑 | 🟑 | ❌ | βœ… | 🟑 | βœ… | ❌ | +| ACC | ❌ | βœ… | βœ… | βœ… | βœ… | ❌ | βœ… | βœ… | ❌ | ❌ | +| ADD | ❌ | βœ… | βœ… | βœ… | 🟑 | 🟑 | βœ… | βœ… | βœ… | ❌ | +| ADD1 | ❌ | βœ… | βœ… | βœ… | ❌ | ❌ | βœ… | βœ… | ❌ | ❌ | +| ADD_ID | ❌ | ❌ | βœ… | βœ… | βœ… | ❌ | ❌ | βœ… | ❌ | ❌ | +| ARANGE | ❌ | βœ… | βœ… | βœ… | βœ… | ❌ | βœ… | βœ… | ❌ | ❌ | +| ARGMAX | ❌ | βœ… | βœ… | βœ… | βœ… | ❌ | βœ… | βœ… | ❌ | ❌ | +| ARGSORT | ❌ | βœ… | βœ… | βœ… | βœ… | βœ… | βœ… | βœ… | ❌ | ❌ | +| CEIL | ❌ | ❌ | βœ… | 🟑 | ❌ | ❌ | 🟑 | 🟑 | ❌ | ❌ | +| CLAMP | ❌ | βœ… | βœ… | βœ… | 🟑 | 🟑 | 🟑 | 🟑 | ❌ | ❌ | +| CONCAT | ❌ | βœ… | βœ… | 🟑 | βœ… | 🟑 | βœ… | βœ… | ❌ | ❌ | +| CONT | ❌ | 🟑 | βœ… | βœ… | βœ… | 🟑 | 🟑 | βœ… | 🟑 | ❌ | +| CONV_2D | ❌ | ❌ | βœ… | βœ… | βœ… | βœ… | ❌ | βœ… | ❌ | ❌ | +| CONV_2D_DW | ❌ | ❌ | βœ… | βœ… | ❌ | ❌ | ❌ | βœ… | ❌ | ❌ | +| CONV_3D | ❌ | ❌ | βœ… | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | +| CONV_TRANSPOSE_1D | ❌ | βœ… | βœ… | βœ… | βœ… | ❌ | βœ… | βœ… | ❌ | ❌ | +| CONV_TRANSPOSE_2D | ❌ | ❌ | βœ… | βœ… | βœ… | ❌ | ❌ | βœ… | ❌ | ❌ | +| COS | ❌ | βœ… | βœ… | βœ… | 🟑 | ❌ | 🟑 | 🟑 | ❌ | ❌ | +| COUNT_EQUAL | ❌ | βœ… | βœ… | βœ… | ❌ | ❌ | βœ… | βœ… | ❌ | ❌ | +| CPY | ❌ | 🟑 | 🟑 | 🟑 | 🟑 | 🟑 | 🟑 | 🟑 | 🟑 | ❌ | +| CROSS_ENTROPY_LOSS | ❌ | ❌ | βœ… | βœ… | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | +| CROSS_ENTROPY_LOSS_BACK | ❌ | ❌ | βœ… | βœ… | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | +| CUMSUM | ❌ | ❌ | βœ… | ❌ | βœ… | ❌ | ❌ | βœ… | ❌ | ❌ | +| DIAG_MASK_INF | ❌ | βœ… | βœ… | βœ… | ❌ | 🟑 | βœ… | βœ… | ❌ | ❌ | +| DIV | ❌ | βœ… | βœ… | βœ… | 🟑 | 🟑 | βœ… | βœ… | βœ… | ❌ | +| DUP | ❌ | βœ… | βœ… | 🟑 | 🟑 | 🟑 | βœ… | βœ… | ❌ | ❌ | +| ELU | ❌ | βœ… | βœ… | 🟑 | 🟑 | ❌ | βœ… | ❌ | βœ… | ❌ | +| EXP | ❌ | βœ… | βœ… | 🟑 | 🟑 | ❌ | βœ… | 🟑 | βœ… | ❌ | +| EXPM1 | ❌ | ❌ | βœ… | 🟑 | 🟑 | ❌ | ❌ | ❌ | ❌ | ❌ | +| FILL | ❌ | ❌ | βœ… | ❌ | βœ… | ❌ | ❌ | βœ… | ❌ | ❌ | +| FLASH_ATTN_EXT | ❌ | 🟑 | βœ… | 🟑 | 🟑 | ❌ | ❌ | 🟑 | ❌ | ❌ | +| FLOOR | ❌ | ❌ | βœ… | 🟑 | ❌ | ❌ | 🟑 | 🟑 | ❌ | ❌ | +| GATED_LINEAR_ATTN | ❌ | ❌ | βœ… | βœ… | ❌ | ❌ | βœ… | ❌ | ❌ | ❌ | +| GEGLU | ❌ | βœ… | βœ… | βœ… | 🟑 | βœ… | βœ… | 🟑 | βœ… | ❌ | +| GEGLU_ERF | ❌ | βœ… | βœ… | βœ… | 🟑 | βœ… | βœ… | 🟑 | βœ… | ❌ | +| GEGLU_QUICK | ❌ | βœ… | βœ… | βœ… | 🟑 | βœ… | βœ… | 🟑 | βœ… | ❌ | +| GELU | ❌ | βœ… | βœ… | 🟑 | 🟑 | 🟑 | βœ… | 🟑 | βœ… | ❌ | +| GELU_ERF | ❌ | βœ… | βœ… | 🟑 | 🟑 | 🟑 | βœ… | 🟑 | βœ… | ❌ | +| GELU_QUICK | ❌ | βœ… | βœ… | 🟑 | 🟑 | 🟑 | βœ… | 🟑 | βœ… | ❌ | +| GET_ROWS | ❌ | 🟑 | βœ… | 🟑 | βœ… | 🟑 | 🟑 | 🟑 | 🟑 | ❌ | +| GET_ROWS_BACK | ❌ | ❌ | 🟑 | 🟑 | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | +| GROUP_NORM | ❌ | βœ… | βœ… | βœ… | βœ… | βœ… | βœ… | βœ… | ❌ | ❌ | +| GROUP_NORM_MUL_ADD | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | +| HARDSIGMOID | ❌ | βœ… | βœ… | 🟑 | 🟑 | ❌ | βœ… | 🟑 | βœ… | ❌ | +| HARDSWISH | ❌ | βœ… | βœ… | 🟑 | 🟑 | ❌ | βœ… | 🟑 | βœ… | ❌ | +| IM2COL | ❌ | βœ… | βœ… | βœ… | βœ… | βœ… | βœ… | βœ… | ❌ | ❌ | +| IM2COL_3D | ❌ | ❌ | βœ… | βœ… | ❌ | ❌ | ❌ | βœ… | ❌ | ❌ | +| L2_NORM | ❌ | ❌ | βœ… | βœ… | βœ… | ❌ | βœ… | βœ… | ❌ | ❌ | +| LEAKY_RELU | ❌ | βœ… | βœ… | βœ… | 🟑 | ❌ | βœ… | 🟑 | ❌ | ❌ | +| LOG | ❌ | βœ… | βœ… | βœ… | 🟑 | ❌ | 🟑 | βœ… | ❌ | ❌ | +| MEAN | ❌ | βœ… | βœ… | βœ… | βœ… | ❌ | βœ… | βœ… | ❌ | ❌ | +| MUL | ❌ | βœ… | βœ… | βœ… | 🟑 | 🟑 | βœ… | βœ… | βœ… | ❌ | +| MUL_MAT | 🟑 | 🟑 | 🟑 | 🟑 | βœ… | 🟑 | 🟑 | 🟑 | 🟑 | 🟑 | +| MUL_MAT_ID | ❌ | 🟑 | βœ… | βœ… | βœ… | 🟑 | 🟑 | βœ… | ❌ | ❌ | +| NEG | ❌ | βœ… | βœ… | 🟑 | 🟑 | ❌ | βœ… | 🟑 | βœ… | ❌ | +| NORM | ❌ | βœ… | βœ… | βœ… | βœ… | βœ… | βœ… | 🟑 | ❌ | ❌ | +| NORM_MUL_ADD | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | +| OPT_STEP_ADAMW | ❌ | ❌ | βœ… | βœ… | βœ… | ❌ | ❌ | βœ… | ❌ | ❌ | +| OPT_STEP_SGD | ❌ | ❌ | βœ… | βœ… | βœ… | ❌ | ❌ | βœ… | ❌ | ❌ | +| OUT_PROD | 🟑 | ❌ | 🟑 | 🟑 | ❌ | ❌ | 🟑 | ❌ | ❌ | ❌ | +| PAD | ❌ | βœ… | βœ… | 🟑 | 🟑 | βœ… | 🟑 | βœ… | ❌ | ❌ | +| PAD_REFLECT_1D | ❌ | βœ… | βœ… | βœ… | βœ… | ❌ | βœ… | ❌ | ❌ | ❌ | +| POOL_2D | ❌ | 🟑 | βœ… | βœ… | βœ… | ❌ | βœ… | βœ… | ❌ | ❌ | +| REGLU | ❌ | βœ… | βœ… | βœ… | 🟑 | βœ… | βœ… | 🟑 | βœ… | ❌ | +| RELU | ❌ | βœ… | βœ… | 🟑 | 🟑 | 🟑 | βœ… | 🟑 | βœ… | ❌ | +| REPEAT | ❌ | βœ… | βœ… | 🟑 | βœ… | 🟑 | βœ… | 🟑 | ❌ | ❌ | +| REPEAT_BACK | ❌ | ❌ | βœ… | βœ… | ❌ | ❌ | βœ… | βœ… | ❌ | ❌ | +| RMS_NORM | ❌ | βœ… | βœ… | βœ… | βœ… | βœ… | βœ… | βœ… | βœ… | ❌ | +| RMS_NORM_BACK | ❌ | ❌ | βœ… | βœ… | ❌ | ❌ | βœ… | βœ… | ❌ | ❌ | +| RMS_NORM_MUL_ADD | ❌ | βœ… | ❌ | ❌ | ❌ | βœ… | ❌ | ❌ | ❌ | ❌ | +| ROLL | ❌ | ❌ | βœ… | βœ… | ❌ | ❌ | βœ… | βœ… | ❌ | ❌ | +| ROPE | ❌ | 🟑 | βœ… | βœ… | βœ… | βœ… | βœ… | βœ… | βœ… | ❌ | +| ROPE_BACK | ❌ | ❌ | βœ… | βœ… | ❌ | ❌ | ❌ | βœ… | ❌ | ❌ | +| ROUND | ❌ | ❌ | βœ… | 🟑 | ❌ | ❌ | 🟑 | 🟑 | ❌ | ❌ | +| RWKV_WKV6 | ❌ | ❌ | βœ… | βœ… | βœ… | ❌ | βœ… | βœ… | ❌ | ❌ | +| RWKV_WKV7 | ❌ | ❌ | βœ… | βœ… | βœ… | ❌ | βœ… | βœ… | ❌ | ❌ | +| SCALE | ❌ | 🟑 | βœ… | βœ… | βœ… | βœ… | βœ… | βœ… | βœ… | ❌ | +| SET | ❌ | ❌ | βœ… | βœ… | ❌ | ❌ | 🟑 | ❌ | ❌ | ❌ | +| SET_ROWS | ❌ | ❌ | 🟑 | 🟑 | 🟑 | 🟑 | 🟑 | 🟑 | 🟑 | ❌ | +| SGN | ❌ | βœ… | βœ… | 🟑 | 🟑 | ❌ | βœ… | ❌ | βœ… | ❌ | +| SIGMOID | ❌ | βœ… | βœ… | 🟑 | 🟑 | 🟑 | βœ… | 🟑 | βœ… | ❌ | +| SILU | ❌ | βœ… | βœ… | 🟑 | 🟑 | 🟑 | βœ… | 🟑 | βœ… | ❌ | +| SILU_BACK | ❌ | ❌ | βœ… | βœ… | ❌ | ❌ | ❌ | βœ… | ❌ | ❌ | +| SIN | ❌ | βœ… | βœ… | βœ… | 🟑 | ❌ | 🟑 | 🟑 | ❌ | ❌ | +| SOFTCAP | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | +| SOFTPLUS | ❌ | ❌ | βœ… | 🟑 | 🟑 | ❌ | ❌ | 🟑 | ❌ | ❌ | +| SOFT_MAX | ❌ | 🟑 | βœ… | βœ… | βœ… | βœ… | βœ… | βœ… | βœ… | ❌ | +| SOFT_MAX_BACK | ❌ | ❌ | 🟑 | 🟑 | ❌ | ❌ | 🟑 | βœ… | ❌ | ❌ | +| SOLVE_TRI | ❌ | ❌ | βœ… | ❌ | ❌ | ❌ | ❌ | 🟑 | ❌ | ❌ | +| SQR | ❌ | βœ… | βœ… | βœ… | 🟑 | ❌ | 🟑 | 🟑 | ❌ | ❌ | +| SQRT | ❌ | βœ… | βœ… | βœ… | 🟑 | ❌ | 🟑 | 🟑 | ❌ | ❌ | +| SSM_CONV | ❌ | ❌ | βœ… | βœ… | βœ… | ❌ | βœ… | βœ… | ❌ | ❌ | +| SSM_SCAN | ❌ | ❌ | βœ… | βœ… | βœ… | ❌ | ❌ | 🟑 | ❌ | ❌ | +| STEP | ❌ | βœ… | βœ… | 🟑 | 🟑 | ❌ | βœ… | 🟑 | βœ… | ❌ | +| SUB | ❌ | βœ… | βœ… | βœ… | 🟑 | 🟑 | βœ… | βœ… | βœ… | ❌ | +| SUM | ❌ | βœ… | βœ… | 🟑 | 🟑 | ❌ | 🟑 | 🟑 | ❌ | ❌ | +| SUM_ROWS | ❌ | βœ… | βœ… | 🟑 | βœ… | βœ… | 🟑 | βœ… | ❌ | ❌ | +| SWIGLU | ❌ | βœ… | βœ… | βœ… | 🟑 | βœ… | βœ… | 🟑 | βœ… | ❌ | +| SWIGLU_OAI | ❌ | ❌ | βœ… | βœ… | βœ… | ❌ | ❌ | 🟑 | βœ… | ❌ | +| TANH | ❌ | βœ… | βœ… | 🟑 | 🟑 | βœ… | βœ… | 🟑 | βœ… | ❌ | +| TIMESTEP_EMBEDDING | ❌ | βœ… | βœ… | βœ… | βœ… | βœ… | βœ… | βœ… | ❌ | ❌ | +| TOP_K | ❌ | ❌ | ❌ | ❌ | βœ… | ❌ | ❌ | 🟑 | ❌ | ❌ | +| TRI | ❌ | ❌ | βœ… | ❌ | βœ… | ❌ | ❌ | βœ… | ❌ | ❌ | +| TRUNC | ❌ | ❌ | βœ… | 🟑 | ❌ | ❌ | 🟑 | 🟑 | ❌ | ❌ | +| UPSCALE | ❌ | 🟑 | βœ… | βœ… | 🟑 | βœ… | 🟑 | 🟑 | ❌ | ❌ | +| XIELU | ❌ | ❌ | βœ… | ❌ | ❌ | ❌ | ❌ | ❌ | βœ… | ❌ | 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: 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","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" +"WebGPU: WebGPU","SGN","type=f16,ne_a=[5,7,11,13],v=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","NEG","type=f16,ne_a=[128,2,2,2],v=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","NEG","type=f16,ne_a=[5,7,11,13],v=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","STEP","type=f16,ne_a=[128,2,2,2],v=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","STEP","type=f16,ne_a=[5,7,11,13],v=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","TANH","type=f16,ne_a=[128,2,2,2],v=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","TANH","type=f16,ne_a=[5,7,11,13],v=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","ELU","type=f16,ne_a=[128,2,2,2],v=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","ELU","type=f16,ne_a=[5,7,11,13],v=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","RELU","type=f16,ne_a=[128,2,2,2],v=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","RELU","type=f16,ne_a=[5,7,11,13],v=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","SIGMOID","type=f16,ne_a=[128,2,2,2],v=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","SIGMOID","type=f16,ne_a=[5,7,11,13],v=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","GELU","type=f16,ne_a=[128,2,2,2],v=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","GELU","type=f16,ne_a=[5,7,11,13],v=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","GELU_QUICK","type=f16,ne_a=[128,2,2,2],v=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","GELU_QUICK","type=f16,ne_a=[5,7,11,13],v=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","SILU","type=f16,ne_a=[128,2,2,2],v=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","SILU","type=f16,ne_a=[5,7,11,13],v=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","HARDSWISH","type=f16,ne_a=[128,2,2,2],v=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","HARDSWISH","type=f16,ne_a=[5,7,11,13],v=1","support","1","yes","WebGPU" +"WebGPU: WebGPU","HARDSIGMOID","type=f16,ne_a=[128,2,2,2],v=1","support","1","yes","WebGPU" +"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","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: 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WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,sinks=0,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=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,sinks=0,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=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,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","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,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=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,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=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","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=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=3,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","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,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=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,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=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,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","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=f32,permute=[0,1,2,3]","support","0","no","WebGPU" +"WebGPU: 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=f16,permute=[0,1,2,3]","support","0","no","WebGPU" +"WebGPU: 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=bf16,permute=[0,1,2,3]","support","0","no","WebGPU" +"WebGPU: 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=q8_0,permute=[0,1,2,3]","support","0","no","WebGPU" +"WebGPU: 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" diff --git a/ggml/src/ggml-webgpu/ggml-webgpu.cpp b/ggml/src/ggml-webgpu/ggml-webgpu.cpp index a7476b109d..d0e99b6fe2 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 +986,60 @@ 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 +1081,7 @@ 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 +1117,7 @@ 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 +1202,8 @@ 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 +1254,8 @@ 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 +1292,8 @@ 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 +1365,7 @@ 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 +1400,22 @@ 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 +1427,8 @@ 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 +1661,7 @@ 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 +1736,64 @@ 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 +1853,21 @@ 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 +1878,444 @@ 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 +2337,12 @@ 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 +2507,36 @@ 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 +2630,6 @@ 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 +2763,7 @@ 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..d61df5bb9e 100755 --- a/ggml/src/ggml-webgpu/wgsl-shaders/embed_wgsl.py +++ b/ggml/src/ggml-webgpu/wgsl-shaders/embed_wgsl.py @@ -19,6 +19,15 @@ def parse_decls(decls_text): 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(): # Match {{KEY}} literally, where KEY is escaped @@ -71,6 +80,10 @@ 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 +103,11 @@ 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) + From e15cd06a94fce1fafe68f44db01ca69963623df4 Mon Sep 17 00:00:00 2001 From: Acly Date: Fri, 5 Dec 2025 21:46:39 +0100 Subject: [PATCH 07/23] vulkan : support conv-2d with large output size (#17685) --- ggml/src/ggml-vulkan/ggml-vulkan.cpp | 329 ++++-------------- .../ggml-vulkan/vulkan-shaders/conv2d_mm.comp | 21 +- tests/test-backend-ops.cpp | 3 + 3 files changed, 84 insertions(+), 269 deletions(-) diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp index c8f0449a94..f57199b1ad 100644 --- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp +++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp @@ -353,10 +353,17 @@ enum vk_conv_shapes { CONV_SHAPE_COUNT, }; -uint32_t conv_shapes_wg_denoms[][3] = { - { 128, 128, 1 }, - { 64, 32, 1 }, - { 32, 256, 1 }, +struct vk_conv_block_size { + uint32_t K; + uint32_t NPQ; + uint32_t CRS; +}; + +vk_conv_block_size vk_conv_block_sizes[CONV_SHAPE_COUNT] = { + // K NPQ CRS + { 128, 128, 16 }, // CONV_SHAPE_128x128 + { 64, 32, 32 }, // CONV_SHAPE_64x32 + { 32, 256, 16 }, // CONV_SHAPE_32x256 }; enum dmmv_wg_sizes { @@ -1344,20 +1351,11 @@ struct vk_op_conv2d_push_constants { uint32_t Cin; uint32_t N; - uint32_t KW; - uint32_t KH; uint32_t W; uint32_t H; uint32_t OW; uint32_t OH; - uint32_t s0; - uint32_t s1; - uint32_t p0; - uint32_t p1; - uint32_t d0; - uint32_t d1; - uint32_t nb01; uint32_t nb02; uint32_t nb03; @@ -1381,48 +1379,6 @@ template <> void init_pushconst_fastdiv(vk_op_conv2d_push_constants &p) { init_fastdiv_values(p.OW*p.OH, p.OWOHmp, p.OWOHL); } -struct vk_op_conv_transpose_2d_push_constants { - uint32_t Cout; - uint32_t Cin; - uint32_t N; - - uint32_t KW; - uint32_t KH; - uint32_t W; - uint32_t H; - uint32_t OW; - uint32_t OH; - - uint32_t s0; - uint32_t s1; - uint32_t p0; - uint32_t p1; - uint32_t d0; - uint32_t d1; - - uint32_t nb01; - uint32_t nb02; - uint32_t nb03; - - uint32_t nb11; - uint32_t nb12; - uint32_t nb13; - - uint32_t nb1; - uint32_t nb2; - uint32_t nb3; - - // init_fastdiv_values constants for dividing by OW, OW*OH - uint32_t OWmp; uint32_t OWL; - uint32_t OWOHmp; uint32_t OWOHL; -}; - -template <> void init_pushconst_fastdiv(vk_op_conv_transpose_2d_push_constants &p) { - // Compute magic values to divide by OW, OW*OH - init_fastdiv_values(p.OW, p.OWmp, p.OWL); - init_fastdiv_values(p.OW*p.OH, p.OWOHmp, p.OWOHL); -} - struct vk_op_conv2d_dw_push_constants { uint32_t ne; uint32_t batches; @@ -4126,12 +4082,10 @@ static void ggml_vk_load_shaders(vk_device& device) { // conv2d, conv_transpose_2d for (uint32_t s = 0; s < CONV_SHAPE_COUNT; ++s) { uint32_t conv2d_WG_SIZE = 256; - uint32_t conv2d_BS_K = 128; - uint32_t conv2d_BS_CRS = 16; uint32_t use_collectives = 0; // Enables subgroup ops for preventing the re-calculation of indices. - uint32_t conv2d_BS_NPQ = 128; - uint32_t conv2d_TS_K = 8; + uint32_t conv2d_TS_K = (s == CONV_SHAPE_64x32) ? 4 : 8; uint32_t conv2d_SHMEM_PAD = 4; + vk_conv_block_size conv2d_BS = vk_conv_block_sizes[s]; bool conv2d_UNROLL = true; #if defined(GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT) @@ -4145,29 +4099,9 @@ static void ggml_vk_load_shaders(vk_device& device) { conv2d_UNROLL = false; } else if (device->vendor_id == VK_VENDOR_ID_AMD) { conv2d_SHMEM_PAD = device->architecture == vk_device_architecture::AMD_GCN ? 1 : 4; - } - - switch (s) { - default: - case CONV_SHAPE_128x128: - conv2d_BS_K = conv_shapes_wg_denoms[CONV_SHAPE_128x128][0]; - conv2d_BS_NPQ = conv_shapes_wg_denoms[CONV_SHAPE_128x128][1]; - conv2d_BS_CRS = 16; - if (device->vendor_id == VK_VENDOR_ID_AMD && device->architecture != vk_device_architecture::AMD_GCN) { + if (s == CONV_SHAPE_128x128 && device->architecture != vk_device_architecture::AMD_GCN) { conv2d_UNROLL = false; } - break; - case CONV_SHAPE_64x32: - conv2d_BS_K = conv_shapes_wg_denoms[CONV_SHAPE_64x32][0]; - conv2d_BS_NPQ = conv_shapes_wg_denoms[CONV_SHAPE_64x32][1]; - conv2d_BS_CRS = 32; - conv2d_TS_K = 4; - break; - case CONV_SHAPE_32x256: - conv2d_BS_K = conv_shapes_wg_denoms[CONV_SHAPE_32x256][0]; - conv2d_BS_NPQ = conv_shapes_wg_denoms[CONV_SHAPE_32x256][1]; - conv2d_BS_CRS = 16; - break; } // Use collectives on pre-Turing NVIDIA GPUs and GCN AMD cards, which had slower integer math. @@ -4181,22 +4115,22 @@ static void ggml_vk_load_shaders(vk_device& device) { allow_collectives_nv && allow_collectives_amd) { use_collectives = 1; - conv2d_BS_CRS = std::min( + conv2d_BS.CRS = std::min( device->subgroup_size, - conv2d_BS_CRS); // CRS block size should be capped at subgroup size for correctness when shuffle is used. + conv2d_BS.CRS); // CRS block size should be capped at subgroup size for correctness when shuffle is used. } uint32_t conv2d_shmem_req = - (conv2d_BS_K * (conv2d_BS_CRS + conv2d_SHMEM_PAD) + conv2d_BS_CRS * (conv2d_BS_NPQ + conv2d_SHMEM_PAD)) * sizeof(float); + (conv2d_BS.K * (conv2d_BS.CRS + conv2d_SHMEM_PAD) + conv2d_BS.CRS * (conv2d_BS.NPQ + conv2d_SHMEM_PAD)) * sizeof(float); if (device->properties.limits.maxComputeSharedMemorySize < conv2d_shmem_req) { - conv2d_BS_CRS = 8; + conv2d_BS.CRS = 8; if (use_collectives) { - conv2d_BS_CRS = std::min(device->subgroup_size, conv2d_BS_CRS); + conv2d_BS.CRS = std::min(device->subgroup_size, conv2d_BS.CRS); } } - std::array wg_denoms = { conv2d_BS_K, conv2d_BS_NPQ, 1 }; - std::vector spec_constants = { conv2d_WG_SIZE, conv2d_BS_K, conv2d_BS_CRS, conv2d_BS_NPQ, conv2d_TS_K, use_collectives, conv2d_SHMEM_PAD }; + std::array wg_denoms = { conv2d_BS.K, 1, 1 }; + std::vector spec_constants = { conv2d_WG_SIZE, conv2d_BS.K, conv2d_BS.CRS, conv2d_BS.NPQ, conv2d_TS_K, use_collectives, conv2d_SHMEM_PAD }; #define CREATE_CONV(name, type_suffix, spv_suffix) \ for (auto &c : device->pipeline_##name##type_suffix[s]) { \ @@ -4213,15 +4147,13 @@ static void ggml_vk_load_shaders(vk_device& device) { ggml_vk_create_pipeline( \ device, c.second, #name #type_suffix, \ name##type_suffix##spv_suffix##_len, name##type_suffix##spv_suffix##_data, "main", 3, \ - sizeof(vk_op_##name##_push_constants), wg_denoms, spec_constants_cpy, 1, true, use_collectives); \ + sizeof(vk_op_conv2d_push_constants), wg_denoms, spec_constants_cpy, 1, true, use_collectives); \ } #define CREATE_CONVS(spv_suffix) \ CREATE_CONV(conv2d, _f32, spv_suffix) \ CREATE_CONV(conv2d, _f16_f32, spv_suffix) \ - if (device->properties.limits.maxPushConstantsSize >= sizeof(vk_op_conv_transpose_2d_push_constants)) { \ - CREATE_CONV(conv_transpose_2d, _f32, spv_suffix) \ - CREATE_CONV(conv_transpose_2d, _f16_f32, spv_suffix) \ - } + CREATE_CONV(conv_transpose_2d, _f32, spv_suffix) \ + CREATE_CONV(conv_transpose_2d, _f16_f32, spv_suffix) #if defined(GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT) if (device->coopmat2) { CREATE_CONVS(_cm2) @@ -8284,59 +8216,23 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx } } -static std::array ggml_vk_get_conv_elements(const ggml_tensor *dst) { - const ggml_tensor *src0 = dst->src[0]; - const ggml_tensor *src1 = dst->src[1]; - - // src0 - kernel: [KW, KH, Cin, Cout] - // src1 - input: [W, H, Cin, N] - // dst - result: [OW, OH, Cout, N] - - // Copied from ggml.c: int64_t ggml_calc_conv_output_size(int64_t ins, int64_t ks, int s, int p, int d) - auto calc_conv_output_size = [](int64_t ins, int64_t ks, int s, int p, int d) -> int64_t { - return (ins + 2 * p - d * (ks - 1) - 1) / s + 1; +static vk_conv_shapes ggml_vk_conv_select_shape(ggml_backend_vk_context * ctx, uint32_t K, uint32_t NPQ) { + auto n_tiles = [&](vk_conv_shapes s) { + return CEIL_DIV(K, vk_conv_block_sizes[s].K) + * CEIL_DIV(NPQ, vk_conv_block_sizes[s].NPQ); }; - // parallelize in {OW/BS_K, OH/BS_NPQ, 1} - int64_t W = src1->ne[0]; - int64_t H = src1->ne[1]; - int64_t KW = src0->ne[0]; - int64_t KH = src0->ne[1]; - int64_t Cout = src0->ne[3]; - int64_t N = src1->ne[3]; - int64_t OH = calc_conv_output_size(H, KH, dst->op_params[1], dst->op_params[3], dst->op_params[5]); - int64_t OW = calc_conv_output_size(W, KW, dst->op_params[0], dst->op_params[2], dst->op_params[4]); - int64_t NPQ = N * OW * OH; - // Tile output matrix to (K/NB_K, NPQ/NB_NPQ, 1) workgroups - std::array elements = { static_cast(Cout), static_cast(NPQ), 1 }; - return elements; -} + // We can't query number of shader cores on Intel, use 32 as a placeholder + // so small convolutions will still choose a smaller tile. + const uint32_t shader_core_count = ctx->device->shader_core_count > 0 ? ctx->device->shader_core_count : 32; -static std::array ggml_vk_get_conv_transpose_2d_elements(const ggml_tensor *dst) { - const ggml_tensor *src0 = dst->src[0]; - const ggml_tensor *src1 = dst->src[1]; - - // src0 - kernel: [KW, KH, Cout, Cin] - // src1 - input: [W, H, Cin, N] - // dst - result: [OW, OH, Cout, N] - - auto calc_conv_output_size = [](int64_t ins, int64_t ks, int s, int p, int d) -> int64_t { - return (ins - 1) * s - 2 * p + (ks - 1) * d + 1; - }; - // parallelize in {OW/BS_K, OH/BS_NPQ, 1} - int64_t W = src1->ne[0]; - int64_t H = src1->ne[1]; - int64_t KW = src0->ne[0]; - int64_t KH = src0->ne[1]; - int64_t Cout = src0->ne[2]; - int64_t N = src1->ne[3]; - int64_t OH = calc_conv_output_size(H, KH, dst->op_params[0], 0, 1); - int64_t OW = calc_conv_output_size(W, KW, dst->op_params[0], 0, 1); - int64_t NPQ = N * OW * OH; - - // Tile output matrix to (K/NB_K, NPQ/NB_NPQ, 1) workgroups - std::array elements = { static_cast(Cout), static_cast(NPQ), 1 }; - return elements; + if (K > 64 && n_tiles(CONV_SHAPE_128x128) >= shader_core_count * 2) { + return CONV_SHAPE_128x128; + } else if (K <= 32 && n_tiles(CONV_SHAPE_32x256) >= shader_core_count * 2) { + return CONV_SHAPE_32x256; + } else { + return CONV_SHAPE_64x32; + } } static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, const ggml_tensor * dst, ggml_op op) { @@ -8799,39 +8695,20 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const return nullptr; case GGML_OP_CONV_2D: case GGML_OP_CONV_TRANSPOSE_2D: - if (src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32 && - ggml_is_contiguous(src0) && ggml_is_contiguous(src1) && ggml_is_contiguous(dst)) { - std::array elements{}; - if (op == GGML_OP_CONV_2D) elements = ggml_vk_get_conv_elements(dst); - else if (op == GGML_OP_CONV_TRANSPOSE_2D) elements = ggml_vk_get_conv_transpose_2d_elements(dst); - vk_conv_shapes shape; - - uint32_t tiles[CONV_SHAPE_COUNT]; - for (uint32_t i = 0; i < CONV_SHAPE_COUNT; ++i) { - tiles[i] = CEIL_DIV(elements[0], conv_shapes_wg_denoms[i][0]) * CEIL_DIV(elements[1], conv_shapes_wg_denoms[i][1]); - } - - // We can't query number of shader cores on Intel, use 32 as a placeholder - // so small convolutions will still choose a smaller tile. - const uint32_t shader_core_count = ctx->device->shader_core_count > 0 ? ctx->device->shader_core_count : 32; - - if (elements[0] > 64 && tiles[CONV_SHAPE_128x128] >= shader_core_count * 2) { - shape = CONV_SHAPE_128x128; - } else if (elements[0] <= 32 && tiles[CONV_SHAPE_32x256] >= shader_core_count * 2) { - shape = CONV_SHAPE_32x256; - } else { - shape = CONV_SHAPE_64x32; - } - - uint32_t KW = static_cast(src0->ne[0]); - uint32_t KH = static_cast(src0->ne[1]); - uint32_t s0 = static_cast(dst->op_params[0]); - uint32_t s1 = op == GGML_OP_CONV_2D ? static_cast(dst->op_params[1]) : static_cast(dst->op_params[0]); - uint32_t p0 = op == GGML_OP_CONV_2D ? static_cast(dst->op_params[2]) : 0; - uint32_t p1 = op == GGML_OP_CONV_2D ? static_cast(dst->op_params[3]) : 0; - uint32_t d0 = op == GGML_OP_CONV_2D ? static_cast(dst->op_params[4]) : 1; - uint32_t d1 = op == GGML_OP_CONV_2D ? static_cast(dst->op_params[5]) : 1; + if (src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + uint32_t K = dst->ne[2]; // Cout + uint32_t NPQ = dst->ne[3] * dst->ne[1] * dst->ne[0]; // N * OH * OW + vk_conv_shapes shape = ggml_vk_conv_select_shape(ctx, K, NPQ); + bool transpose = dst->op == GGML_OP_CONV_TRANSPOSE_2D; + uint32_t KW = (uint32_t)src0->ne[0]; + uint32_t KH = (uint32_t)src0->ne[1]; + uint32_t s0 = (uint32_t)(ggml_get_op_params_i32(dst, 0)); + uint32_t s1 = !transpose ? (uint32_t)ggml_get_op_params_i32(dst, 1) : s0; + uint32_t p0 = !transpose ? (uint32_t)ggml_get_op_params_i32(dst, 2) : 0; + uint32_t p1 = !transpose ? (uint32_t)ggml_get_op_params_i32(dst, 3) : 0; + uint32_t d0 = !transpose ? (uint32_t)ggml_get_op_params_i32(dst, 4) : 1; + uint32_t d1 = !transpose ? (uint32_t)ggml_get_op_params_i32(dst, 5) : 1; vk_conv2d_pipeline_state conv2d_pipeline_state(s0, s1, p0, p1, d0, d1, KW, KH); std::map *pipelines = nullptr; @@ -9150,13 +9027,21 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co elements = { N * OC * OH * OW, 1, 1}; } break; case GGML_OP_CONV_2D: - { - elements = ggml_vk_get_conv_elements(dst); - } break; case GGML_OP_CONV_TRANSPOSE_2D: - { - elements = ggml_vk_get_conv_transpose_2d_elements(dst); - } break; + if constexpr (std::is_same_v) { + const uint32_t NPQ = pc.N * pc.OH * pc.OW; + const vk_conv_shapes shape = ggml_vk_conv_select_shape(ctx, pc.Cout, NPQ); + const uint32_t NPQ_blocks = CEIL_DIV(NPQ, vk_conv_block_sizes[shape].NPQ); + + elements = { pc.Cout, NPQ_blocks, 1 }; + if (elements[1] > 512) { + elements[2] = CEIL_DIV(elements[1], 512); + elements[1] = 512; + } + } else { + GGML_ABORT("invalid push constant type for CONV_2D"); + } + break; case GGML_OP_ADD: case GGML_OP_SUB: case GGML_OP_DIV: @@ -10707,30 +10592,24 @@ static void ggml_vk_conv_2d(ggml_backend_vk_context * ctx, vk_context & subctx, GGML_ASSERT(dst->type == GGML_TYPE_F32); GGML_TENSOR_BINARY_OP_LOCALS - GGML_ASSERT(nb00 == sizeof(float) || nb00 == sizeof(ggml_fp16_t)); GGML_ASSERT(nb10 == sizeof(float)); GGML_ASSERT(nb0 == sizeof(float)); + bool transpose = dst->op == GGML_OP_CONV_TRANSPOSE_2D; + vk_op_conv2d_push_constants p{}; - p.Cout = static_cast(ne03); - p.Cin = static_cast(ne02); + p.Cout = static_cast(!transpose ? ne03 : ne02); + p.Cin = static_cast(!transpose ? ne02 : ne03); p.N = static_cast(ne13); + GGML_ASSERT(p.Cout == ne2); + GGML_ASSERT(p.Cin == ne12); - p.KW = static_cast(ne00); - p.KH = static_cast(ne01); p.W = static_cast(ne10); p.H = static_cast(ne11); p.OW = static_cast(ne0); p.OH = static_cast(ne1); - p.s0 = static_cast(dst->op_params[0]); - p.s1 = static_cast(dst->op_params[1]); - p.p0 = static_cast(dst->op_params[2]); - p.p1 = static_cast(dst->op_params[3]); - p.d0 = static_cast(dst->op_params[4]); - p.d1 = static_cast(dst->op_params[5]); - p.nb01 = static_cast(nb01 / nb00); p.nb02 = static_cast(nb02 / nb00); p.nb03 = static_cast(nb03 / nb00); @@ -10743,59 +10622,7 @@ static void ggml_vk_conv_2d(ggml_backend_vk_context * ctx, vk_context & subctx, p.nb2 = static_cast(nb2 / nb0); p.nb3 = static_cast(nb3 / nb0); - GGML_ASSERT(ne03 == ne2); - GGML_ASSERT(ne02 == ne12); - - ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, nullptr, dst, GGML_OP_CONV_2D, std::move(p)); -} - -static void ggml_vk_conv_transpose_2d(ggml_backend_vk_context * ctx, vk_context & subctx, const ggml_tensor * src0, - const ggml_tensor * src1, ggml_tensor * dst) { - GGML_ASSERT(src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT(dst->type == GGML_TYPE_F32); - - GGML_TENSOR_BINARY_OP_LOCALS - - GGML_ASSERT(nb00 == sizeof(float) || nb00 == sizeof(ggml_fp16_t)); - GGML_ASSERT(nb10 == sizeof(float)); - GGML_ASSERT(nb0 == sizeof(float)); - - vk_op_conv_transpose_2d_push_constants p{}; - p.Cout = static_cast(ne02); - p.Cin = static_cast(ne03); - p.N = static_cast(ne13); - - p.KW = static_cast(ne00); - p.KH = static_cast(ne01); - p.W = static_cast(ne10); - p.H = static_cast(ne11); - p.OW = static_cast(ne0); - p.OH = static_cast(ne1); - - p.s0 = static_cast(dst->op_params[0]); - p.s1 = static_cast(dst->op_params[0]); - p.p0 = 0; - p.p1 = 0; - p.d0 = 1; - p.d1 = 1; - - p.nb01 = static_cast(nb01 / nb00); - p.nb02 = static_cast(nb02 / nb00); - p.nb03 = static_cast(nb03 / nb00); - - p.nb11 = static_cast(nb11 / nb10); - p.nb12 = static_cast(nb12 / nb10); - p.nb13 = static_cast(nb13 / nb10); - - p.nb1 = static_cast(nb1 / nb0); - p.nb2 = static_cast(nb2 / nb0); - p.nb3 = static_cast(nb3 / nb0); - - GGML_ASSERT(ne02 == ne2); - GGML_ASSERT(ne03 == ne12); - - ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, nullptr, dst, GGML_OP_CONV_TRANSPOSE_2D, std::move(p)); + ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, nullptr, dst, dst->op, std::move(p)); } static void ggml_vk_conv_2d_dw(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { @@ -12166,11 +11993,8 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr break; case GGML_OP_CONV_2D: - ggml_vk_conv_2d(ctx, compute_ctx, src0, src1, node); - - break; case GGML_OP_CONV_TRANSPOSE_2D: - ggml_vk_conv_transpose_2d(ctx, compute_ctx, src0, src1, node); + ggml_vk_conv_2d(ctx, compute_ctx, src0, src1, node); break; case GGML_OP_CONV_2D_DW: @@ -14279,13 +14103,6 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm case GGML_OP_CONV_2D: case GGML_OP_CONV_TRANSPOSE_2D: { - // Op is disabled for Apple because it segfaults at pipeline create time on MoltenVK - ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)dev->context; - const vk_device& device = ggml_vk_get_device(ctx->device); - if (op->op == GGML_OP_CONV_TRANSPOSE_2D && - device->properties.limits.maxPushConstantsSize < sizeof(vk_op_conv_transpose_2d_push_constants)) { - return false; - } // Channel-contiguous format is not supported yet. return ((op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16) && op->src[1]->type == GGML_TYPE_F32 && diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/conv2d_mm.comp b/ggml/src/ggml-vulkan/vulkan-shaders/conv2d_mm.comp index e9bdbf7db5..875c012cd3 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/conv2d_mm.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/conv2d_mm.comp @@ -32,22 +32,12 @@ layout(push_constant) uniform parameter { uint32_t Cin; uint32_t N; - // Tensor spatial sizes: kernel, input, output - uint32_t KW; - uint32_t KH; + // Tensor spatial sizes: input, output uint32_t W; uint32_t H; uint32_t OW; uint32_t OH; - // Parameters: stride, padding, dilation - 0=y, 1=x - uint32_t s0; - uint32_t s1; - uint32_t p0; - uint32_t p1; - uint32_t d0; - uint32_t d1; - // Strides in elements uint32_t nb01; uint32_t nb02; @@ -77,13 +67,14 @@ layout(constant_id = 3) const uint BS_NPQ = 128; layout(constant_id = 4) const uint TS_K = 8; layout(constant_id = 5) const uint use_collectives = 1; layout(constant_id = 6) const uint SHMEM_PAD = 4; - +// Stride, padding, dilation layout(constant_id = 7) const uint s0 = 1; layout(constant_id = 8) const uint s1 = 1; layout(constant_id = 9) const uint p0 = 0; layout(constant_id = 10) const uint p1 = 0; layout(constant_id = 11) const uint d0 = 1; layout(constant_id = 12) const uint d1 = 1; +// Kernel spatial sizes layout(constant_id = 13) const uint KW = 1; layout(constant_id = 14) const uint KH = 1; @@ -138,7 +129,7 @@ P,Q=OH,OW */ uint32_t B_idx_K = gl_WorkGroupID.x; -uint32_t B_idx_NPQ = gl_WorkGroupID.y; +uint32_t B_idx_NPQ = gl_WorkGroupID.y + gl_WorkGroupID.z * 512; uint32_t T_y = tid / NT_NPQ; uint32_t T_x = tid % NT_NPQ; @@ -178,6 +169,10 @@ ACC_TYPE perElemOpStore(const in uint32_t r, const in uint32_t c, const in ACC_T #endif void main() { + if (B_idx_NPQ * BS_NPQ >= NPQ) { + return; + } + #ifdef COOPMAT2 coopmat matC; matC = coopmat(0.0); diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index 844db455d2..ca30537749 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -6982,6 +6982,7 @@ static std::vector> make_test_cases_eval() { test_cases.emplace_back(new test_conv_transpose_2d({3, 2, 3, 1}, {2, 2, 1, 3}, 1)); test_cases.emplace_back(new test_conv_transpose_2d({10, 10, 9, 1}, {3, 3, 1, 9}, 2)); + test_cases.emplace_back(new test_conv_transpose_2d({129, 63, 35, 1}, {3, 3, 48, 35}, 1)); test_cases.emplace_back(new test_count_equal(GGML_TYPE_F32, {4, 500, 1, 1})); test_cases.emplace_back(new test_count_equal(GGML_TYPE_F32, {4, 5000, 1, 1})); @@ -7897,6 +7898,8 @@ static std::vector> make_test_cases_perf() { { 58, 3, 64, 32, 8 }, // A deep layer of a ConvNet, several images in the batch { 16, 3, 512, 128, 8 }, + // High resolution output (large NPQ) + {1536, 3, 64, 32, 1 }, }; for (auto kernel_type : {GGML_TYPE_F32, GGML_TYPE_F16}) { From a0f3897d53e0e956982ca23abb0d381fe71722f8 Mon Sep 17 00:00:00 2001 From: Jeff Bolz Date: Fri, 5 Dec 2025 15:03:19 -0600 Subject: [PATCH 08/23] vulkan: fix top_k bug when there are ties in the input (#17659) * vulkan: Reduce temporary memory usage for TOP_K - Compute row size for the temp buffer based on the output of the first pass. - Update shader addressing math to use the output row size - Pass the output row size as "ncols_output", what used to be "ncols_output" is now "k" For the common case of K=40 and src0=(200000,1,1,1), this reduces the temporary buffer from about 3.2MB to 500KB. * vulkan: fix top_k bug when there are ties in the input I noticed by inspection a bug in the vulkan top_k shader where if the least value in the top_k appears multiple times we could end up writing those extra copies out rather than some larger values (if the larger values are on higher numbered threads). I rewrote the test verification to handle this case, where the final index set is not necessarily the same. * Update tests/test-backend-ops.cpp Co-authored-by: Georgi Gerganov --------- Co-authored-by: Georgi Gerganov --- ggml/src/ggml-vulkan/ggml-vulkan.cpp | 2 +- .../vulkan-shaders/topk_nary_search.comp | 76 ++++++++++---- tests/test-backend-ops.cpp | 99 +++++++++++++++---- 3 files changed, 139 insertions(+), 38 deletions(-) diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp index f57199b1ad..4a6ec1be77 100644 --- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp +++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp @@ -4013,7 +4013,7 @@ static void ggml_vk_load_shaders(vk_device& device) { uint32_t nary_shmem = 2 * sizeof(int) * BLOCK_SIZE + sizeof(int) * device->subgroup_size + 2 * sizeof(int) + - (BLOCK_SIZE / device->subgroup_size) * sizeof(int); + 2 * (BLOCK_SIZE / device->subgroup_size) * sizeof(int); if (device->subgroup_arithmetic && device->subgroup_require_full_support && device->subgroup_shuffle && device->subgroup_ballot && nary_shmem <= device->properties.limits.maxComputeSharedMemorySize) { ggml_vk_create_pipeline2(device, device->pipeline_topk_f32[i], "topk_f32_"+std::to_string(i), topk_nary_search_f32_len, topk_nary_search_f32_data, "main", 2, sizeof(vk_op_topk_push_constants), {BLOCK_SIZE, 1, 1}, {BLOCK_SIZE, device->subgroup_size, device->subgroup_size_log2}, 1, true, true, device->subgroup_size); diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/topk_nary_search.comp b/ggml/src/ggml-vulkan/vulkan-shaders/topk_nary_search.comp index f794285ee1..0b757f38e1 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/topk_nary_search.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/topk_nary_search.comp @@ -38,6 +38,7 @@ shared int counts[SUBGROUP_SIZE]; shared int sh_min_idx; shared uint sh_total; shared uint offset_partials[BLOCK_SIZE / SUBGROUP_SIZE]; +shared uint eq_min_partials[BLOCK_SIZE / SUBGROUP_SIZE]; // Map float values to uint such that comparisons still work. // Positive values set the high bit, negative values are inverted. @@ -156,25 +157,66 @@ void topk(const uint row) { // We need to compact these values to the start of the dst_row array. // Have each subgroup count how many items it'll store, so other // subgroups can compute their base offset. - bool top = f2ui(intBitsToFloat(v.y)) >= range_min; - uvec4 b = subgroupBallot(top); - uint bit_count = subgroupBallotBitCount(b); - if ((tid % SUBGROUP_SIZE) == 0) { - offset_partials[tid / SUBGROUP_SIZE] = bit_count; - } - barrier(); - - uint out_idx = 0; - [[unroll]] for (int i = 0; i < BLOCK_SIZE / SUBGROUP_SIZE; ++i) { - if (i < tid / SUBGROUP_SIZE) { - out_idx += offset_partials[i]; + // Values strictly greater than range_min must be stored. For values equal + // to range_min, there can be ties and it's possible we'll need to store + // an arbitrary subset of them. + // If total == p.k, have a fast path where we don't need to handle ties. + if (total == p.k) { + bool top = f2ui(intBitsToFloat(v.y)) >= range_min; + uvec4 b = subgroupBallot(top); + uint bit_count = subgroupBallotBitCount(b); + if ((tid % SUBGROUP_SIZE) == 0) { + offset_partials[tid / SUBGROUP_SIZE] = bit_count; } - } + barrier(); - uint bit_count_ex = subgroupBallotExclusiveBitCount(b); - if (top) { - // TODO: Copy directly to the output? - dst_row[out_idx + bit_count_ex] = v; + uint out_idx = 0; + [[unroll]] for (int i = 0; i < BLOCK_SIZE / SUBGROUP_SIZE; ++i) { + if (i < tid / SUBGROUP_SIZE) { + out_idx += offset_partials[i]; + } + } + + uint bit_count_ex = subgroupBallotExclusiveBitCount(b); + if (top) { + // TODO: Copy directly to the output? + dst_row[out_idx + bit_count_ex] = v; + } + } else { + bool top = f2ui(intBitsToFloat(v.y)) > range_min; + bool eq_min = f2ui(intBitsToFloat(v.y)) == range_min; + uvec4 b_top = subgroupBallot(top); + uvec4 b_eq_min = subgroupBallot(eq_min); + uint bit_count_top = subgroupBallotBitCount(b_top); + uint bit_count_eq_min = subgroupBallotBitCount(b_eq_min); + if ((tid % SUBGROUP_SIZE) == 0) { + offset_partials[tid / SUBGROUP_SIZE] = bit_count_top; + eq_min_partials[tid / SUBGROUP_SIZE] = bit_count_eq_min; + } + barrier(); + + uint out_idx = 0; + uint eq_min_base = 0; + uint eq_min_idx = 0; + [[unroll]] for (int i = 0; i < BLOCK_SIZE / SUBGROUP_SIZE; ++i) { + if (i < tid / SUBGROUP_SIZE) { + out_idx += offset_partials[i]; + eq_min_idx += eq_min_partials[i]; + } + eq_min_base += offset_partials[i]; + } + // range_min values are stored at the end + eq_min_idx += eq_min_base; + + uint bit_count_ex_top = subgroupBallotExclusiveBitCount(b_top); + uint bit_count_ex_eq_min = subgroupBallotExclusiveBitCount(b_eq_min); + if (top) { + // TODO: Copy directly to the output? + dst_row[out_idx + bit_count_ex_top] = v; + } + if (eq_min && eq_min_idx + bit_count_ex_eq_min < p.k) { + dst_row[eq_min_idx + bit_count_ex_eq_min] = v; + } } barrier(); diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index ca30537749..9d7b0152af 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -286,10 +286,11 @@ static double nmse(const float * a, const float * b, size_t n) { return mse_a_b / mse_a_0; } -// difference between 2 integer sets (Jaccard distance, 0 - no difference, 1 - no overlap) -static double jdst(const int32_t * a, const int32_t * b, size_t n) { - std::unordered_map set_a; - std::unordered_map set_b; +// difference between 2 sets (Jaccard distance, 0 - no difference, 1 - no overlap) +template +static double jdst(const T * a, const T * b, size_t n) { + std::unordered_map set_a; + std::unordered_map set_b; for (size_t i = 0; i < n; ++i) { set_a[a[i]]++; @@ -5001,42 +5002,94 @@ struct test_top_k : public test_case { const ggml_type type; const std::array ne; const int k; + const bool ties; + ggml_tensor * input {}; std::string vars() override { - return VARS_TO_STR3(type, ne, k); + return VARS_TO_STR4(type, ne, k, ties); } test_top_k(ggml_type type = GGML_TYPE_F32, std::array ne = {16, 10, 10, 10}, - int k = 4) - : type(type), ne(ne), k(k) {} + int k = 4, bool ties = false) + : type(type), ne(ne), k(k), ties(ties) {} double max_err() override { return 0.0; } + // When there are ties, only validate the final result. + // The logic in err can't handle the sentinel tensors. + bool run_whole_graph() override { return ties; } + double err(const float * a, const float * b, size_t n) override { - std::vector ia(n); - std::vector ib(n); + // When there are no ties, we expect the exact same set of indices, + // but possibly in a different order. When there are ties, the indices + // can be different but the input values they correspond to should be + // the same. The logic for ties could work for non-ties, but only for + // the output tensor, not for the sentinel tensors. + if (ties) { + std::vector src(ggml_nelements(input)); - double diff = 0.0f; + ggml_backend_tensor_get(input, src.data(), 0, ggml_nelements(input) * ggml_type_size(type)); - for (size_t i = 0; i < n; i++) { - ia[i] = (int32_t) a[i]; - ib[i] = (int32_t) b[i]; + double diff = 0.0f; - // penalize the result if the data is not integer valued - diff += std::fabs(a[i] - ia[i]); - diff += std::fabs(b[i] - ib[i]); + GGML_ASSERT(n == (size_t)(ggml_nrows(input) * k)); + int64_t cols = input->ne[0]; + std::vector ia(k); + std::vector ib(k); + std::vector asrc(k); + std::vector bsrc(k); + for (int64_t r = 0; r < ggml_nrows(input); r++) { + // Convert indices for the row back to integer + for (int64_t c = 0; c < k; c++) { + ia[c] = (int32_t)a[r * k + c]; + ib[c] = (int32_t)b[r * k + c]; + } + // The src values for each row should match. + for (int64_t c = 0; c < k; c++) { + asrc[c] = src[r * cols + ia[c]]; + bsrc[c] = src[r * cols + ib[c]]; + } + diff += jdst(asrc.data(), bsrc.data(), k); + // There should be no duplicate indices + std::sort(ia.begin(), ia.end()); + std::sort(ib.begin(), ib.end()); + if (std::adjacent_find(ia.begin(), ia.end()) != ia.end()) { + diff += 1; + } + if (std::adjacent_find(ib.begin(), ib.end()) != ib.end()) { + diff += 1; + } + } + return diff; + } else { + std::vector ia(n); + std::vector ib(n); + + double diff = 0.0f; + + for (size_t i = 0; i < n; i++) { + ia[i] = (int32_t) a[i]; + ib[i] = (int32_t) b[i]; + + // penalize the result if the data is not integer valued + diff += std::fabs(a[i] - ia[i]); + diff += std::fabs(b[i] - ib[i]); + } + + return diff + jdst(ia.data(), ib.data(), n); } - - return diff + jdst(ia.data(), ib.data(), n); } ggml_tensor * build_graph(ggml_context * ctx) override { ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data()); ggml_set_name(a, "a"); + // Save 'a' for err() + input = a; + ggml_tensor * out = ggml_top_k(ctx, a, k); ggml_set_name(out, "out"); @@ -5047,11 +5100,16 @@ struct test_top_k : public test_case { std::random_device rd; std::default_random_engine rng(rd()); for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) { - // initialize with unique values to avoid ties + int tie_denom = std::max(1, std::min(10, k / 2)); for (int64_t r = 0; r < ggml_nrows(t); r++) { std::vector data(t->ne[0]); for (int i = 0; i < t->ne[0]; i++) { - data[i] = i; + if (ties) { + // integer division to introduce duplicates + data[i] = i / tie_denom; + } else { + data[i] = i; + } } std::shuffle(data.begin(), data.end(), rng); ggml_backend_tensor_set(t, data.data(), r * t->nb[1], t->ne[0] * sizeof(float)); @@ -7657,6 +7715,7 @@ static std::vector> make_test_cases_eval() { if (k <= 1< Date: Fri, 5 Dec 2025 15:08:56 -0600 Subject: [PATCH 09/23] vulkan: add more num_blocks instantiations in rms_norm (#17701) --- ggml/src/ggml-vulkan/vulkan-shaders/rms_norm.comp | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/rms_norm.comp b/ggml/src/ggml-vulkan/vulkan-shaders/rms_norm.comp index 3a47949d5a..9d6d366542 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/rms_norm.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/rms_norm.comp @@ -131,8 +131,12 @@ void main() { rms_norm(num_blocks); } else if (num_blocks > 16) { rms_norm(32); - } else if (num_blocks > 8) { + } else if (num_blocks > 12) { rms_norm(16); + } else if (num_blocks > 10) { + rms_norm(12); + } else if (num_blocks > 8) { + rms_norm(10); } else if (num_blocks > 4) { rms_norm(8); } else if (num_blocks == 4) { From d8c0a7b085302017f6583dbb309e4da83084087a Mon Sep 17 00:00:00 2001 From: Masato Nakasaka Date: Sat, 6 Dec 2025 14:23:30 +0900 Subject: [PATCH 10/23] vulkan: Fix mismatch in TOPK_MOE unit test (#17541) * Fix shader to support 2D workgroup mapping to a single subgroup * Set required_subgroup_size topk_moe shader requires static WARP_SIZE and actual subgroup size to match --- ggml/src/ggml-vulkan/ggml-vulkan.cpp | 6 +++--- .../ggml-vulkan/vulkan-shaders/topk_moe.comp | 19 ++++++++++--------- 2 files changed, 13 insertions(+), 12 deletions(-) diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp index 4a6ec1be77..f468458153 100644 --- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp +++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp @@ -4174,9 +4174,9 @@ static void ggml_vk_load_shaders(vk_device& device) { ggml_vk_create_pipeline(device, device->pipeline_conv2d_dw_cwhn_f16_f32, "conv2d_dw_cwhn_f16_f32", conv2d_dw_cwhn_f16_f32_len, conv2d_dw_cwhn_f16_f32_data, "main", 3, sizeof(vk_op_conv2d_dw_push_constants), {512, 1, 1}, {}, 1); for (uint32_t i = 0; i < num_topk_moe_pipelines; ++i) { - ggml_vk_create_pipeline2(device, device->pipeline_topk_moe[i][TOPK_MOE_EARLY_SOFTMAX], "topk_moe_f32_early_softmax_"+std::to_string(i), topk_moe_f32_len, topk_moe_f32_data, "main", 3, sizeof(vk_op_topk_moe_push_constants), {1, 1, 1}, {device->subgroup_size, 1u<pipeline_topk_moe[i][TOPK_MOE_EARLY_SOFTMAX_NORM], "topk_moe_f32_early_softmax_norm"+std::to_string(i), topk_moe_f32_len, topk_moe_f32_data, "main", 3, sizeof(vk_op_topk_moe_push_constants), {1, 1, 1}, {device->subgroup_size, 1u<pipeline_topk_moe[i][TOPK_MOE_LATE_SOFTMAX], "topk_moe_f32_late_softmax"+std::to_string(i), topk_moe_f32_len, topk_moe_f32_data, "main", 3, sizeof(vk_op_topk_moe_push_constants), {1, 1, 1}, {device->subgroup_size, 1u<pipeline_topk_moe[i][TOPK_MOE_EARLY_SOFTMAX], "topk_moe_f32_early_softmax_"+std::to_string(i), topk_moe_f32_len, topk_moe_f32_data, "main", 3, sizeof(vk_op_topk_moe_push_constants), {1, 1, 1}, {device->subgroup_size, 1u<subgroup_size); + ggml_vk_create_pipeline2(device, device->pipeline_topk_moe[i][TOPK_MOE_EARLY_SOFTMAX_NORM], "topk_moe_f32_early_softmax_norm"+std::to_string(i), topk_moe_f32_len, topk_moe_f32_data, "main", 3, sizeof(vk_op_topk_moe_push_constants), {1, 1, 1}, {device->subgroup_size, 1u<subgroup_size); + ggml_vk_create_pipeline2(device, device->pipeline_topk_moe[i][TOPK_MOE_LATE_SOFTMAX], "topk_moe_f32_late_softmax"+std::to_string(i), topk_moe_f32_len, topk_moe_f32_data, "main", 3, sizeof(vk_op_topk_moe_push_constants), {1, 1, 1}, {device->subgroup_size, 1u<subgroup_size); } for (auto &c : compiles) { diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/topk_moe.comp b/ggml/src/ggml-vulkan/vulkan-shaders/topk_moe.comp index bc1c278bf4..5cd0785d20 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/topk_moe.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/topk_moe.comp @@ -75,7 +75,7 @@ void softmax_warp_inplace(inout float vals[experts_per_thread], const uint limit } void main() { - const uint row = gl_WorkGroupID.x * gl_WorkGroupSize.y + gl_LocalInvocationID.y; + const uint row = gl_WorkGroupID.x * gl_WorkGroupSize.y + gl_SubgroupID; if (row >= n_rows) { return; } @@ -83,17 +83,18 @@ void main() { const uint logits_offset = n_experts * row; const uint weights_offset = n_expert_used * row; const uint ids_offset = n_experts * row; + const uint lane = gl_SubgroupInvocationID; float wt[experts_per_thread]; [[unroll]] for (uint i = 0; i < n_experts; i += WARP_SIZE) { - const uint expert = i + gl_LocalInvocationID.x; + const uint expert = i + lane; wt[i / WARP_SIZE] = (n_experts % WARP_SIZE == 0 || expert < n_experts) ? logits[logits_offset + expert] : -INFINITY; } if (!late_softmax) { - softmax_warp_inplace(wt, n_experts, gl_LocalInvocationID.x, false); + softmax_warp_inplace(wt, n_experts, lane, false); } // at this point, each thread holds a portion of softmax, @@ -111,11 +112,11 @@ void main() { for (int k = 0; k < n_expert_used; k++) { float max_val = wt[0]; - uint max_expert = gl_LocalInvocationID.x; + uint max_expert = lane; [[unroll]] for (int i = 1; i < experts_per_thread; i++) { - const uint expert = gl_LocalInvocationID.x + i * WARP_SIZE; + const uint expert = lane + i * WARP_SIZE; if ((n_experts % WARP_SIZE == 0 || expert < n_experts) && wt[i] > max_val) { max_val = wt[i]; max_expert = expert; @@ -132,11 +133,11 @@ void main() { } } - if ((k & (WARP_SIZE - 1)) == gl_LocalInvocationID.x) { + if ((k & (WARP_SIZE - 1)) == lane) { output_weights[k / WARP_SIZE] = max_val; } - if ((max_expert & (WARP_SIZE - 1)) == gl_LocalInvocationID.x) { + if ((max_expert & (WARP_SIZE - 1)) == lane) { wt[max_expert / WARP_SIZE] = -INFINITY; ids[ids_offset + k] = max_expert; @@ -158,12 +159,12 @@ void main() { } if (late_softmax) { - softmax_warp_inplace(output_weights, n_expert_used, gl_LocalInvocationID.x, true); + softmax_warp_inplace(output_weights, n_expert_used, lane, true); } [[unroll]] for (uint i = 0; i < experts_per_thread; ++i) { - uint idx = i * WARP_SIZE + gl_LocalInvocationID.x; + uint idx = i * WARP_SIZE + lane; if (idx < n_expert_used) { weights[weights_offset + idx] = output_weights[i]; } From 67788f68468e1d8c0da40d7015e2e8b704c42f8a Mon Sep 17 00:00:00 2001 From: Masato Nakasaka Date: Sat, 6 Dec 2025 14:39:42 +0900 Subject: [PATCH 11/23] vulkan: Replace deprecated VK_EXT_validation_features (#17637) * replaced deprecated VK_EXT_validation_features * forgot to remove old code --- ggml/src/ggml-vulkan/ggml-vulkan.cpp | 42 +++++++++++++--------------- 1 file changed, 20 insertions(+), 22 deletions(-) diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp index f468458153..b40b356ce2 100644 --- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp +++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp @@ -5062,7 +5062,7 @@ static void ggml_vk_print_gpu_info(size_t idx) { } } -static bool ggml_vk_instance_validation_ext_available(); +static bool ggml_vk_instance_layer_settings_available(); static bool ggml_vk_instance_portability_enumeration_ext_available(const std::vector& instance_extensions); static bool ggml_vk_instance_debug_utils_ext_available(const std::vector & instance_extensions); static bool ggml_vk_device_is_supported(const vk::PhysicalDevice & vkdev); @@ -5091,19 +5091,19 @@ static void ggml_vk_instance_init() { vk::ApplicationInfo app_info{ "ggml-vulkan", 1, nullptr, 0, api_version }; const std::vector instance_extensions = vk::enumerateInstanceExtensionProperties(); - const bool validation_ext = ggml_vk_instance_validation_ext_available(); + const bool layer_settings = ggml_vk_instance_layer_settings_available(); #ifdef __APPLE__ const bool portability_enumeration_ext = ggml_vk_instance_portability_enumeration_ext_available(instance_extensions); #endif const bool debug_utils_ext = ggml_vk_instance_debug_utils_ext_available(instance_extensions) && getenv("GGML_VK_DEBUG_MARKERS") != nullptr; std::vector layers; - if (validation_ext) { + if (layer_settings) { layers.push_back("VK_LAYER_KHRONOS_validation"); } std::vector extensions; - if (validation_ext) { - extensions.push_back("VK_EXT_validation_features"); + if (layer_settings) { + extensions.push_back("VK_EXT_layer_settings"); } #ifdef __APPLE__ if (portability_enumeration_ext) { @@ -5113,26 +5113,24 @@ static void ggml_vk_instance_init() { if (debug_utils_ext) { extensions.push_back("VK_EXT_debug_utils"); } - vk::InstanceCreateInfo instance_create_info(vk::InstanceCreateFlags{}, &app_info, layers, extensions); + VkBool32 enable_best_practice = layer_settings; + std::vector settings = { + { + "VK_LAYER_KHRONOS_validation", + "validate_best_practices", + vk::LayerSettingTypeEXT::eBool32, + 1, + &enable_best_practice + }, + }; + vk::LayerSettingsCreateInfoEXT layer_setting_info(settings); + vk::InstanceCreateInfo instance_create_info(vk::InstanceCreateFlags{}, &app_info, layers, extensions, &layer_setting_info); #ifdef __APPLE__ if (portability_enumeration_ext) { instance_create_info.flags |= vk::InstanceCreateFlagBits::eEnumeratePortabilityKHR; } #endif - std::vector features_enable; - vk::ValidationFeaturesEXT validation_features; - - if (validation_ext) { - features_enable = { vk::ValidationFeatureEnableEXT::eBestPractices }; - validation_features = { - features_enable, - {}, - }; - validation_features.setPNext(nullptr); - instance_create_info.setPNext(&validation_features); - GGML_LOG_DEBUG("ggml_vulkan: Validation layers enabled\n"); - } vk_instance.instance = vk::createInstance(instance_create_info); vk_instance_initialized = true; @@ -14227,21 +14225,21 @@ ggml_backend_reg_t ggml_backend_vk_reg() { } // Extension availability -static bool ggml_vk_instance_validation_ext_available() { +static bool ggml_vk_instance_layer_settings_available() { #ifdef GGML_VULKAN_VALIDATE // Check if validation layer provides the extension const std::string layer_name = "VK_LAYER_KHRONOS_validation"; for (const auto& layer : vk::enumerateInstanceLayerProperties()) { if (layer_name == layer.layerName.data()) { for (const auto& ext : vk::enumerateInstanceExtensionProperties(layer_name)) { - if (strcmp("VK_EXT_validation_features", ext.extensionName.data()) == 0) { + if (strcmp("VK_EXT_layer_settings", ext.extensionName.data()) == 0) { return true; } } } } - std::cerr << "ggml_vulkan: WARNING: Validation layer or layer extension VK_EXT_validation_features not found." << std::endl; + std::cerr << "ggml_vulkan: WARNING: Validation layer or layer extension VK_EXT_layer_settings not found." << std::endl; #endif return false; } From 8ce774a102cd1b24f06cb74c056d05415cddb08e Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 6 Dec 2025 09:33:59 +0200 Subject: [PATCH 12/23] metal : fix build(#17799) * metal : fix build * tests : fix context destruction --- examples/save-load-state/save-load-state.cpp | 6 ++++ ggml/src/ggml-metal/ggml-metal-device.m | 32 +++++++++++--------- 2 files changed, 23 insertions(+), 15 deletions(-) diff --git a/examples/save-load-state/save-load-state.cpp b/examples/save-load-state/save-load-state.cpp index 1065ec6bb0..4cd3071f76 100644 --- a/examples/save-load-state/save-load-state.cpp +++ b/examples/save-load-state/save-load-state.cpp @@ -241,6 +241,12 @@ int main(int argc, char ** argv) { llama_batch_free(batch); + // this one is managed by common_init_result + //llama_free(ctx); + + llama_free(ctx2); + llama_free(ctx3); + if (result0 != result2) { fprintf(stderr, "\n%s : error : the seq restore generation is different\n", __func__); return 1; diff --git a/ggml/src/ggml-metal/ggml-metal-device.m b/ggml/src/ggml-metal/ggml-metal-device.m index c53ec506cd..64fd9bdbf6 100644 --- a/ggml/src/ggml-metal/ggml-metal-device.m +++ b/ggml/src/ggml-metal/ggml-metal-device.m @@ -574,22 +574,26 @@ ggml_metal_rsets_t ggml_metal_rsets_init(void) { // the requests stop after a certain amount of time (keep_alive_s) of inactivity dispatch_queue_t d_queue = dispatch_get_global_queue(QOS_CLASS_DEFAULT, 0); dispatch_group_async(res->d_group, d_queue, ^{ - while (!atomic_load_explicit(&res->d_stop, memory_order_relaxed)) { - if (atomic_load_explicit(&res->d_loop, memory_order_relaxed) > 0) { - [res->lock lock]; +#if defined(GGML_METAL_HAS_RESIDENCY_SETS) + if (@available(macOS 15.0, iOS 18.0, tvOS 18.0, visionOS 2.0, *)) { + while (!atomic_load_explicit(&res->d_stop, memory_order_relaxed)) { + if (atomic_load_explicit(&res->d_loop, memory_order_relaxed) > 0) { + [res->lock lock]; - for (int i = 0; i < (int) res->data.count; ++i) { - [res->data[i] requestResidency]; + for (int i = 0; i < (int) res->data.count; ++i) { + [res->data[i] requestResidency]; + } + + atomic_fetch_sub_explicit(&res->d_loop, 1, memory_order_relaxed); + + [res->lock unlock]; } - atomic_fetch_sub_explicit(&res->d_loop, 1, memory_order_relaxed); - - [res->lock unlock]; + // half a second + usleep(500 * 1000); } - - // half a second - usleep(500 * 1000); - } + } +#endif }); return res; @@ -600,6 +604,7 @@ void ggml_metal_rsets_free(ggml_metal_rsets_t rsets) { return; } + // note: if you hit this assert, most likely you haven't deallocated all Metal resources before exiting GGML_ASSERT([rsets->data count] == 0); atomic_store_explicit(&rsets->d_stop, true, memory_order_relaxed); @@ -787,9 +792,6 @@ ggml_metal_device_t ggml_metal_device_init(void) { dev->rsets = nil; } - - // -------------------------------------------------- - // print MTL GPU family: GGML_LOG_INFO("%s: GPU name: %s\n", __func__, dev->props.name); From 8e5f4987b1c0f41ab80d4e95355e79eb6d169b8b Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 6 Dec 2025 09:34:18 +0200 Subject: [PATCH 13/23] contrib : stale PRs (#17803) --- CONTRIBUTING.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 875eb766f3..e4f05258db 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -16,7 +16,7 @@ The project differentiates between 3 levels of contributors: - If you modified a `ggml` operator or added a new one, add the corresponding test cases to `test-backend-ops` - Create separate PRs for each feature or fix. Avoid combining unrelated changes in a single PR - Consider allowing write access to your branch for faster reviews, as reviewers can push commits directly -- If your PR becomes stale, don't hesitate to ping the maintainers in the comments +- If your PR becomes stale, rebase it on top of latest `master` to get maintainers attention - Maintainers will rely on your insights and approval when making a final decision to approve and merge a PR - Consider adding yourself to [CODEOWNERS](CODEOWNERS) to indicate your availability for reviewing related PRs - Using AI to generate PRs is permitted. However, you must (1) explicitly disclose how AI was used and (2) conduct a thorough manual review before publishing the PR. Note that trivial tab autocompletions do not require disclosure. From c6c5e859798163c2e41d848d1157438467a2a34a Mon Sep 17 00:00:00 2001 From: Jeff Bolz Date: Sat, 6 Dec 2025 01:56:45 -0600 Subject: [PATCH 14/23] vulkan: support solve_tri with larger N/K values (#17781) Split N into chunks to fit into shared memory. If K > 128, use a larger workgroup with enough invocations. Add perf tests matching qwen3next. --- ggml/src/ggml-vulkan/ggml-vulkan.cpp | 14 +++- .../ggml-vulkan/vulkan-shaders/solve_tri.comp | 67 +++++++++++-------- tests/test-backend-ops.cpp | 15 +++++ 3 files changed, 64 insertions(+), 32 deletions(-) diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp index b40b356ce2..6a52f1342e 100644 --- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp +++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp @@ -4033,10 +4033,16 @@ static void ggml_vk_load_shaders(vk_device& device) { for (auto &s : device->pipeline_solve_tri_f32) { const vk_solve_tri_pipeline_state &state = s.first; + + // Max number of rows to load at a time, limited by shared memory + const uint32_t batch_N = device->properties.limits.maxComputeSharedMemorySize / ((state.N + state.K) * sizeof(float)); + // Need at least K invocations, and prefer a minimum of 128 to spread out loading shared memory + const uint32_t block_size = std::max(128u, 1u << (uint32_t)ceilf(log2f(float(state.K)))); + ggml_vk_create_pipeline( device, s.second, "solve_tri_f32", solve_tri_f32_len, solve_tri_f32_data, "main", 3, - sizeof(vk_op_binary_push_constants), {1, 1, 1}, { 0, state.N, state.K }, 1, true); + sizeof(vk_op_binary_push_constants), {1, 1, 1}, { 0, state.N, state.K, batch_N, block_size }, 1, true); } #define IM2COL(bda) \ @@ -14025,10 +14031,12 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm const uint32_t N = op->src[0]->ne[0]; const uint32_t K = op->src[1]->ne[0]; // K dimension limited to workgroup size - if (K > 128) { + if (K > 1u << device->max_workgroup_size_log2) { return false; } - if (N * N * sizeof(float) + N * K * sizeof(float) > device->properties.limits.maxComputeSharedMemorySize) { + const uint32_t batch_N = device->properties.limits.maxComputeSharedMemorySize / ((N + K) * sizeof(float)); + + if (batch_N == 0) { return false; } return true; diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/solve_tri.comp b/ggml/src/ggml-vulkan/vulkan-shaders/solve_tri.comp index 253a9e7efe..3b65145032 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/solve_tri.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/solve_tri.comp @@ -5,8 +5,9 @@ layout (constant_id = 1) const uint N = 64; layout (constant_id = 2) const uint K = 32; +layout (constant_id = 3) const uint BATCH_N = 32; -layout(local_size_x = 128, local_size_y = 1, local_size_z = 1) in; +layout(local_size_x_id = 4, local_size_y = 1, local_size_z = 1) in; uint a_base, b_base, x_base; @@ -22,8 +23,8 @@ void store_x(uint r, uint c, FLOAT_TYPE v) { data_d[x_base + r * p.nb21 + c * p.nb20] = D_TYPE(v); } -shared FLOAT_TYPE shA[N * N]; -shared FLOAT_TYPE shB[N * K]; +shared FLOAT_TYPE shA[BATCH_N * N]; +shared FLOAT_TYPE shB[BATCH_N * K]; void main() { const uint batch = gl_WorkGroupID.z * 262144 + gl_WorkGroupID.y * 512 + gl_WorkGroupID.x; @@ -39,34 +40,42 @@ void main() { b_base = get_boffset() + i2 * p.nb12 + i3 * p.nb13; x_base = get_doffset() + i2 * p.nb22 + i3 * p.nb23; - // Load the A matrix into shA - [[unroll]] for (uint i = 0; i < N * N; i += gl_WorkGroupSize.x) { - uint idx = i + tid; - if (((N * N) % gl_WorkGroupSize.x == 0) || idx < N * N) { - shA[idx] = get_a(idx / N, idx % N); - } - } - // Load the B matrix into shB - [[unroll]] for (uint i = 0; i < N * K; i += gl_WorkGroupSize.x) { - uint idx = i + tid; - if (((N * K) % gl_WorkGroupSize.x == 0) || idx < N * K) { - shB[idx] = get_b(idx / K, idx % K); - } - } - barrier(); - FLOAT_TYPE X[N]; - // Each thread solves one column - if (tid < K) { - [[unroll]] for (int r = 0; r < N; ++r) { - FLOAT_TYPE b = shB[r * K + tid]; - // Compute x[r,c] = (b[r,c] - sum(a[r,c]*x[c])) / a[r,r] - [[unroll]] for (int c = 0; c < r; ++c) { - b -= shA[r * N + c] * X[c]; + + // Loop over batches of rows + [[unroll]] for (uint row_base = 0; row_base < N; row_base += BATCH_N) { + const uint cur_N = min(BATCH_N, N - row_base); + + // Load the A matrix batch into shA + [[unroll]] for (uint i = 0; i < cur_N * N; i += gl_WorkGroupSize.x) { + uint idx = i + tid; + if (((cur_N * N) % gl_WorkGroupSize.x == 0) || idx < cur_N * N) { + shA[idx] = get_a(row_base + idx / N, idx % N); } - FLOAT_TYPE x = b / shA[r * N + r]; - X[r] = x; - store_x(r, tid, x); } + // Load the B matrix batch into shB + [[unroll]] for (uint i = 0; i < cur_N * K; i += gl_WorkGroupSize.x) { + uint idx = i + tid; + if (((cur_N * K) % gl_WorkGroupSize.x == 0) || idx < cur_N * K) { + shB[idx] = get_b(row_base + idx / K, idx % K); + } + } + barrier(); + + // Each thread solves one column + if (tid < K) { + [[unroll]] for (uint row_offset = 0; row_offset < cur_N; ++row_offset) { + uint r = row_base + row_offset; + FLOAT_TYPE b = shB[row_offset * K + tid]; + // Compute x[r,c] = (b[r,c] - sum(a[r,c]*x[c])) / a[r,r] + [[unroll]] for (int c = 0; c < r; ++c) { + b -= shA[row_offset * N + c] * X[c]; + } + FLOAT_TYPE x = b / shA[row_offset * N + r]; + X[r] = x; + store_x(r, tid, x); + } + } + barrier(); } } diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index 9d7b0152af..316b589769 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -6204,6 +6204,15 @@ struct test_solve_tri : public test_case { std::string vars() override { return VARS_TO_STR3(type, ne_lhs, ne_rhs); } + uint64_t op_flops(ggml_tensor * t) override { + GGML_UNUSED(t); + int64_t n = ne_lhs[0]; + int64_t k = ne_rhs[0]; + int64_t batch = ne_lhs[2] * ne_lhs[3]; + // n * (n + 1) / 2 non-zero elements of lhs, 2 flops each, for each col of rhs + return n * (n + 1) * k * batch; + } + test_solve_tri(ggml_type type = GGML_TYPE_F32, std::array ne_lhs = { 10, 10, 4, 3 }, std::array ne_rhs = { 3, 10, 4, 3 } @@ -7816,6 +7825,8 @@ static std::vector> make_test_cases_eval() { test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 42, 42, 5, 2 }, { 10, 42, 5, 2 })); test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 64, 64, 2, 2 }, { 10, 64, 2, 2 })); test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 100, 100, 4, 4 }, { 41, 100, 4, 4 })); + test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 128, 128, 4, 4 }, { 31, 128, 4, 4 })); + test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 64, 64, 4, 4 }, { 300, 64, 4, 4 })); for (bool v : {false, true}) { test_cases.emplace_back(new test_pad_ext(GGML_TYPE_F32, {512, 512, 1, 1}, 0, 1, 0, 1, 0, 0, 0, 0, v)); @@ -8016,6 +8027,10 @@ static std::vector> make_test_cases_perf() { test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 64, 64, 4, 2 }, { 6, 64, 4, 2 })); test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 128, 128, 4, 1 }, { 8, 128, 4, 1 })); + // qwen3next with CHUNK_SIZE 64 + test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 64, 64, 8, 32 }, { 64, 64, 8, 32 })); + // qwen3next with CHUNK_SIZE 128 + test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 128, 128, 4, 32 }, { 128, 128, 4, 32 })); test_cases.emplace_back(new test_tri(GGML_TRI_TYPE_LOWER, GGML_TYPE_F32, { 256, 256, 4, 4 })); test_cases.emplace_back(new test_tri(GGML_TRI_TYPE_UPPER_DIAG, GGML_TYPE_F32, { 1024, 1024, 8, 4 })); From dbc15a79672e72e0b9c1832adddf3334f5c9229c Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Sat, 6 Dec 2025 10:49:33 +0100 Subject: [PATCH 15/23] convert: support Mistral 3 Large MoE (#17730) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * convert: support Mistral 3 Large MoE * filter out vision tensors, add missing keys * handle vocab * add temperature_length * fix mscale_all_dim * clean up * Apply suggestions from code review Co-authored-by: Sigbjørn Skjæret * fix * Update gguf-py/gguf/tensor_mapping.py Co-authored-by: Sigbjørn Skjæret --------- Co-authored-by: Sigbjørn Skjæret --- convert_hf_to_gguf.py | 260 ++++++++++++++++++++++----------- gguf-py/gguf/tensor_mapping.py | 11 ++ 2 files changed, 188 insertions(+), 83 deletions(-) diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index 4590b23921..c641989c19 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -1524,6 +1524,79 @@ class TextModel(ModelBase): special_vocab._set_special_token("bos", 151643) special_vocab.add_to_gguf(self.gguf_writer) + def _set_vocab_mistral(self): + if not _mistral_common_installed: + raise ImportError(_mistral_import_error_msg) + + vocab = MistralVocab(self.dir_model) + logger.info( + f"Converting tokenizer {vocab.tokenizer_type} of size {vocab.vocab_size}." + ) + + self.gguf_writer.add_tokenizer_model(vocab.gguf_tokenizer_model) + + tokens = [] + scores = [] + toktypes = [] + + for text, score, toktype in vocab.all_tokens(): + tokens.append(text) + scores.append(score) + toktypes.append(toktype) + + assert len(tokens) == vocab.vocab_size, ( + f"token count ({len(tokens)}) != vocab size ({vocab.vocab_size})" + ) + + if vocab.tokenizer_type == MistralTokenizerType.tekken: + self.gguf_writer.add_tokenizer_pre("tekken") + self.gguf_writer.add_token_merges( + vocab.extract_vocab_merges_from_model() + ) + + logger.info( + f"Setting bos, eos, unk and pad token IDs to {vocab.bos_id}, {vocab.eos_id}, {vocab.unk_id}, {vocab.pad_id}." + ) + + self.gguf_writer.add_bos_token_id(vocab.bos_id) + self.gguf_writer.add_eos_token_id(vocab.eos_id) + self.gguf_writer.add_unk_token_id(vocab.unk_id) + self.gguf_writer.add_pad_token_id(vocab.pad_id) + + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_scores(scores) + self.gguf_writer.add_token_types(toktypes) + self.gguf_writer.add_vocab_size(vocab.vocab_size) + + self.gguf_writer.add_add_bos_token(True) + self.gguf_writer.add_add_eos_token(False) + + local_template_file_path = self.dir_model / "chat_template.jinja" + + if self.is_mistral_format and local_template_file_path.is_file(): + # Ministral-3 and other new Mistral models come with chat templates. + # ref: https://huggingface.co/mistralai/Ministral-3-14B-Instruct-2512/tree/main + logger.info("Using an existing Mistral local chat template.") + + with open(local_template_file_path, "r", encoding="utf-8") as f: + template = f.read() + elif not self.is_mistral_format or not self.disable_mistral_community_chat_template: + template_dir = Path(__file__).parent / "models/templates/" + + # Log only for Mistral format that the official tokenization and detokenization is via `mistral-common`. + if self.is_mistral_format: + logger.info( + "Using a Mistral community chat template. These templates can be subject to errors in early days or weeks after a release. " + "Mistral recommends to use `mistral-common` to perform tokenization and detokenization." + ) + template = MistralModel.get_community_chat_template(vocab, template_dir, self.is_mistral_format) + else: + logger.info("Not using a Mistral local or community chat template. Ensure to perform the tokenization and detokenization via `mistral-common`.") + template = None + + if template is not None: + self.gguf_writer.add_chat_template(template) + class MmprojModel(ModelBase): model_type = ModelType.MMPROJ @@ -2294,79 +2367,6 @@ class LlamaModel(TextModel): if self.hf_arch == "VLlama3ForCausalLM": self.hparams["num_attention_heads"] = self.hparams.get("num_attention_heads", 32) - def _set_vocab_mistral(self): - if not _mistral_common_installed: - raise ImportError(_mistral_import_error_msg) - - vocab = MistralVocab(self.dir_model) - logger.info( - f"Converting tokenizer {vocab.tokenizer_type} of size {vocab.vocab_size}." - ) - - self.gguf_writer.add_tokenizer_model(vocab.gguf_tokenizer_model) - - tokens = [] - scores = [] - toktypes = [] - - for text, score, toktype in vocab.all_tokens(): - tokens.append(text) - scores.append(score) - toktypes.append(toktype) - - assert len(tokens) == vocab.vocab_size, ( - f"token count ({len(tokens)}) != vocab size ({vocab.vocab_size})" - ) - - if vocab.tokenizer_type == MistralTokenizerType.tekken: - self.gguf_writer.add_tokenizer_pre("tekken") - self.gguf_writer.add_token_merges( - vocab.extract_vocab_merges_from_model() - ) - - logger.info( - f"Setting bos, eos, unk and pad token IDs to {vocab.bos_id}, {vocab.eos_id}, {vocab.unk_id}, {vocab.pad_id}." - ) - - self.gguf_writer.add_bos_token_id(vocab.bos_id) - self.gguf_writer.add_eos_token_id(vocab.eos_id) - self.gguf_writer.add_unk_token_id(vocab.unk_id) - self.gguf_writer.add_pad_token_id(vocab.pad_id) - - self.gguf_writer.add_token_list(tokens) - self.gguf_writer.add_token_scores(scores) - self.gguf_writer.add_token_types(toktypes) - self.gguf_writer.add_vocab_size(vocab.vocab_size) - - self.gguf_writer.add_add_bos_token(True) - self.gguf_writer.add_add_eos_token(False) - - local_template_file_path = self.dir_model / "chat_template.jinja" - - if self.is_mistral_format and local_template_file_path.is_file(): - # Ministral-3 and other new Mistral models come with chat templates. - # ref: https://huggingface.co/mistralai/Ministral-3-14B-Instruct-2512/tree/main - logger.info("Using an existing Mistral local chat template.") - - with open(local_template_file_path, "r", encoding="utf-8") as f: - template = f.read() - elif not self.is_mistral_format or not self.disable_mistral_community_chat_template: - template_dir = Path(__file__).parent / "models/templates/" - - # Log only for Mistral format that the official tokenization and detokenization is via `mistral-common`. - if self.is_mistral_format: - logger.info( - "Using a Mistral community chat template. These templates can be subject to errors in early days or weeks after a release. " - "Mistral recommends to use `mistral-common` to perform tokenization and detokenization." - ) - template = MistralModel.get_community_chat_template(vocab, template_dir, self.is_mistral_format) - else: - logger.info("Not using a Mistral local or community chat template. Ensure to perform the tokenization and detokenization via `mistral-common`.") - template = None - - if template is not None: - self.gguf_writer.add_chat_template(template) - def set_vocab(self): if self.is_mistral_format: return self._set_vocab_mistral() @@ -9924,17 +9924,109 @@ class MistralModel(LlamaModel): def set_gguf_parameters(self): super().set_gguf_parameters() - if "yarn" in self.hparams: - yarn_params = self.hparams["yarn"] - self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN) - self.gguf_writer.add_rope_scaling_factor(yarn_params["factor"]) - self.gguf_writer.add_rope_scaling_yarn_beta_fast(yarn_params["beta"]) - self.gguf_writer.add_rope_scaling_yarn_beta_slow(yarn_params["alpha"]) - self.gguf_writer.add_rope_scaling_yarn_log_mul(1.0) # mscale_all_dim - self.gguf_writer.add_rope_scaling_orig_ctx_len(yarn_params["original_max_position_embeddings"]) + MistralModel.set_mistral_config(self.gguf_writer, self.hparams) - if "llama_4_scaling" in self.hparams: - self.gguf_writer.add_attn_temperature_scale(self.hparams["llama_4_scaling"]["beta"]) + @staticmethod + def set_mistral_config(gguf_writer: gguf.GGUFWriter, hparams: dict): + if "yarn" in hparams: + yarn_params = hparams["yarn"] + gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN) + gguf_writer.add_rope_scaling_factor(yarn_params["factor"]) + gguf_writer.add_rope_scaling_yarn_beta_fast(yarn_params["beta"]) + gguf_writer.add_rope_scaling_yarn_beta_slow(yarn_params["alpha"]) + gguf_writer.add_rope_scaling_yarn_log_mul(1.0) # mscale_all_dim + gguf_writer.add_rope_scaling_orig_ctx_len(yarn_params["original_max_position_embeddings"]) + + if "llama_4_scaling" in hparams: + gguf_writer.add_attn_temperature_scale(hparams["llama_4_scaling"]["beta"]) + + +class MistralMoeModel(DeepseekV2Model): + model_arch = gguf.MODEL_ARCH.DEEPSEEK2 + model_name = "Mistral" + hf_arch = "" + is_mistral_format = True + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + logger.info("Using MistralMoeModel") + # remap hparams from Mistral MoE format to DeepseekV2 format + # we do this way to be able to reuse DeepseekV2Model set_gguf_parameters logic + # ref: https://github.com/vllm-project/vllm/blob/b294e28db2c5dee61bc25157664edcada8b90b31/vllm/transformers_utils/configs/mistral.py + config = self.hparams + # Mistral key -> HF key + config_mapping = { + "dim": "hidden_size", + "norm_eps": "rms_norm_eps", + "n_kv_heads": "num_key_value_heads", + "n_layers": "num_hidden_layers", + "n_heads": "num_attention_heads", + "hidden_dim": "intermediate_size", + } + # HF key -> (Mistral key, default value) + top_level_mapping_with_default = { + "model_type": ("model_type", "transformer"), + "hidden_act": ("activation", "silu"), + "tie_word_embeddings": ("tied_embeddings", False), + "max_seq_len": ("max_seq_len", config.get("max_position_embeddings", 128_000)), + "max_position_embeddings": ("max_position_embeddings", 128_000), + } + # mapping top-level keys + for key, new_key in config_mapping.items(): + if key in config: + config[new_key] = config[key] + for new_key, (key, default_value) in top_level_mapping_with_default.items(): + config[new_key] = config.get(key, default_value) + # mapping MoE-specific keys + moe_config_map = { + "route_every_n": "moe_layer_freq", + "first_k_dense_replace": "first_k_dense_replace", + "num_experts_per_tok": "num_experts_per_tok", + "num_experts": "n_routed_experts", + "expert_hidden_dim": "moe_intermediate_size", + "routed_scale": "routed_scaling_factor", + "num_shared_experts": "n_shared_experts", + "num_expert_groups": "n_group", + "num_expert_groups_per_tok": "topk_group", + } + moe = config["moe"] + for key, new_key in moe_config_map.items(): + if key in moe: + config[new_key] = moe[key] + # provide missing values + config["topk_method"] = None + config["norm_topk_prob"] = True + config["scoring_func"] = "softmax" + + def set_vocab(self): + self._set_vocab_mistral() + + def set_gguf_parameters(self): + super().set_gguf_parameters() + MistralModel.set_mistral_config(self.gguf_writer, self.hparams) + yarn_params = self.hparams["yarn"] + self.gguf_writer.add_attn_temperature_length(yarn_params["original_max_position_embeddings"]) + self.gguf_writer.add_rope_scaling_yarn_log_mul(0.1) # mscale_all_dim * 0.1 + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None): + if name.startswith("vision_") or name.startswith("patch_merger.") or "mm_projector" in name: + return [] + + # rename certain tensors so that we can reuse DeepseekV2Model modify_tensors logic + if name.endswith(".qscale_act"): + name = name.replace(".qscale_act", ".input_scale") + if name.endswith(".qscale_weight"): + name = name.replace(".qscale_weight", ".weight_scale") + if ".wkv_b." in name: + name = name.replace(".wkv_b.", ".kv_b_proj.") + if ".experts." in name: + name = name.replace(".experts.", ".mlp.experts.") + name = name.replace(".w1.", ".gate_proj.") + name = name.replace(".w2.", ".down_proj.") + name = name.replace(".w3.", ".up_proj.") + name = "model." + name + + return super().modify_tensors(data_torch, name, bid) class PixtralModel(LlavaVisionModel): @@ -10490,6 +10582,8 @@ def main() -> None: elif args.mmproj: assert hparams.get("vision_encoder") is not None, "This model does not support multimodal" model_class = PixtralModel + elif "moe" in hparams: + model_class = MistralMoeModel else: model_class = MistralModel diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index a7b0973979..d9c87da194 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -376,6 +376,7 @@ class TensorNameMap: "model.layers.{bid}.block_sparse_moe.primary_router", # smallthinker "model.layers.{bid}.feed_forward.gate", # lfm2moe "model.layers.{bid}.mlp.router.gate", # afmoe + "layers.{bid}.gate", # mistral-large ), MODEL_TENSOR.FFN_GATE_INP_SHEXP: ( @@ -450,6 +451,7 @@ class TensorNameMap: "model.layers.{bid}.feed_forward.shared_expert.up_proj", # llama4 "model.layers.{bid}.feed_forward.down_proj", "model.layers.{bid}.mlp.shared_mlp.up_proj", # hunyuan + "layers.{bid}.shared_experts.w3", # mistral-large ), MODEL_TENSOR.FFN_UP_CHEXP: ( @@ -496,6 +498,7 @@ class TensorNameMap: "model.layers.{bid}.mlp.shared_experts.gate_proj", # deepseek deepseek2 "model.layers.{bid}.feed_forward.shared_expert.gate_proj", # llama4 "model.layers.{bid}.mlp.shared_mlp.gate_proj", # hunyuan + "layers.{bid}.shared_experts.w1", # mistral-large ), MODEL_TENSOR.FFN_GATE_CHEXP: ( @@ -557,6 +560,7 @@ class TensorNameMap: "model.layers.{bid}.feed_forward.shared_expert.down_proj", # llama4 "model.layers.{bid}.shared_mlp.output_linear", # granitemoe "model.layers.{bid}.mlp.shared_mlp.down_proj", # hunyuan + "layers.{bid}.shared_experts.w2", # mistral-large ), MODEL_TENSOR.FFN_DOWN_CHEXP: ( @@ -924,14 +928,17 @@ class TensorNameMap: MODEL_TENSOR.ATTN_Q_A: ( "model.layers.{bid}.self_attn.q_a_proj", # deepseek2 + "layers.{bid}.attention.wq_a", # mistral-large ), MODEL_TENSOR.ATTN_Q_B: ( "model.layers.{bid}.self_attn.q_b_proj", # deepseek2 + "layers.{bid}.attention.wq_b", # mistral-large ), MODEL_TENSOR.ATTN_KV_A_MQA: ( "model.layers.{bid}.self_attn.kv_a_proj_with_mqa", # deepseek2 + "layers.{bid}.attention.wkv_a_with_mqa", # mistral-large ), MODEL_TENSOR.ATTN_KV_B: ( @@ -940,18 +947,22 @@ class TensorNameMap: MODEL_TENSOR.ATTN_K_B: ( "model.layers.{bid}.self_attn.k_b_proj", # deepseek2 + "layers.{bid}.attention.k_b_proj", # mistral-large ), MODEL_TENSOR.ATTN_V_B: ( "model.layers.{bid}.self_attn.v_b_proj", # deepseek2 + "layers.{bid}.attention.v_b_proj", # mistral-large ), MODEL_TENSOR.ATTN_Q_A_NORM: ( "model.layers.{bid}.self_attn.q_a_layernorm", # deepseek2 + "layers.{bid}.attention.q_a_norm", # mistral-large ), MODEL_TENSOR.ATTN_KV_A_NORM: ( "model.layers.{bid}.self_attn.kv_a_layernorm", # deepseek2 + "layers.{bid}.attention.kv_a_norm", # mistral-large ), MODEL_TENSOR.ATTN_SUB_NORM: ( From 2960eb2975da26f0d0b1e30cb6e09f25fe3dac0e Mon Sep 17 00:00:00 2001 From: Jeff Bolz Date: Sat, 6 Dec 2025 04:12:26 -0600 Subject: [PATCH 16/23] vulkan: Use one row per workgroup for f32 mmv (#17711) The MoE models have a mul_mat_vec with very small m (32, 64, 128) right before the topk_moe selection. Running multiple rows per wg doesn't utilize the SMs well. I think even for larger m, f32 is so bandwidth-limited that running multiple rows doesn't help. --- ggml/src/ggml-vulkan/ggml-vulkan.cpp | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp index 6a52f1342e..d7c35ed101 100644 --- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp +++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp @@ -3537,7 +3537,7 @@ static void ggml_vk_load_shaders(vk_device& device) { SHADER_REDUCTION_MODE_SHMEM; for (uint32_t i = 0; i < mul_mat_vec_max_cols; ++i) { - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_F32 ][i], "mul_mat_vec_f32_f32_f32", arr_dmmv_f32_f32_f32_len[reduc], arr_dmmv_f32_f32_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1, false, use_subgroups, force_subgroup_size); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_F32 ][i], "mul_mat_vec_f32_f32_f32", arr_dmmv_f32_f32_f32_len[reduc], arr_dmmv_f32_f32_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {wg_size_subgroup, 1, i+1}, 1, false, use_subgroups, force_subgroup_size); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_F16 ][i], "mul_mat_vec_f16_f32_f32", arr_dmmv_f16_f32_f32_len[reduc], arr_dmmv_f16_f32_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1, false, use_subgroups, force_subgroup_size); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_BF16][i], "mul_mat_vec_bf16_f32_f32", arr_dmmv_bf16_f32_f32_len[reduc], arr_dmmv_bf16_f32_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1, false, use_subgroups, force_subgroup_size); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q4_0][i], "mul_mat_vec_q4_0_f32_f32", arr_dmmv_q4_0_f32_f32_len[reduc], arr_dmmv_q4_0_f32_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size); @@ -3561,7 +3561,7 @@ static void ggml_vk_load_shaders(vk_device& device) { ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ4_NL][i], "mul_mat_vec_iq4_nl_f32_f32", arr_dmmv_iq4_nl_f32_f32_len[reduc16], arr_dmmv_iq4_nl_f32_f32_data[reduc16], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_MXFP4][i], "mul_mat_vec_mxfp4_f32_f32", arr_dmmv_mxfp4_f32_f32_len[reduc16], arr_dmmv_mxfp4_f32_f32_data[reduc16], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_F32 ][i], "mul_mat_vec_f32_f16_f32", arr_dmmv_f32_f16_f32_len[reduc], arr_dmmv_f32_f16_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1, false, use_subgroups, force_subgroup_size); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_F32 ][i], "mul_mat_vec_f32_f16_f32", arr_dmmv_f32_f16_f32_len[reduc], arr_dmmv_f32_f16_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {wg_size_subgroup, 1, i+1}, 1, false, use_subgroups, force_subgroup_size); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_F16 ][i], "mul_mat_vec_f16_f16_f32", arr_dmmv_f16_f16_f32_len[reduc], arr_dmmv_f16_f16_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1, false, use_subgroups, force_subgroup_size); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_BF16][i], "mul_mat_vec_bf16_f16_f32", arr_dmmv_bf16_f16_f32_len[reduc], arr_dmmv_bf16_f16_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1, false, use_subgroups, force_subgroup_size); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q4_0][i], "mul_mat_vec_q4_0_f16_f32", arr_dmmv_q4_0_f16_f32_len[reduc], arr_dmmv_q4_0_f16_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size); @@ -3607,7 +3607,7 @@ static void ggml_vk_load_shaders(vk_device& device) { #endif // GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT } - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_F32 ], "mul_mat_vec_id_f32_f32", arr_dmmv_id_f32_f32_f32_len[reduc], arr_dmmv_id_f32_f32_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {wg_size_subgroup, 2}, 1, false, use_subgroups, force_subgroup_size); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_F32 ], "mul_mat_vec_id_f32_f32", arr_dmmv_id_f32_f32_f32_len[reduc], arr_dmmv_id_f32_f32_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, {wg_size_subgroup, 1}, 1, false, use_subgroups, force_subgroup_size); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_F16 ], "mul_mat_vec_id_f16_f32", arr_dmmv_id_f16_f32_f32_len[reduc], arr_dmmv_id_f16_f32_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {wg_size_subgroup, 2}, 1, false, use_subgroups, force_subgroup_size); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_BF16], "mul_mat_vec_id_bf16_f32", arr_dmmv_id_bf16_f32_f32_len[reduc], arr_dmmv_id_bf16_f32_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {wg_size_subgroup, 2}, 1, false, use_subgroups, force_subgroup_size); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_Q4_0], "mul_mat_vec_id_q4_0_f32", arr_dmmv_id_q4_0_f32_f32_len[reduc], arr_dmmv_id_q4_0_f32_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq}, 1, true, use_subgroups, force_subgroup_size); From 444f00b0ec814a071ce1b9dc0de5ea4b4850bd1b Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Sat, 6 Dec 2025 12:26:20 +0100 Subject: [PATCH 17/23] llama : remove quantization sanity check (#17788) * llama : remove quantization sanity check This commit removes the quantization sanity check for attention layers. The motivation for this is that there are model that are hybrid models that have recurrent layers, experts layers, and attention layers. For these models the current check fails as the experts layers are not taking into account. After consideration, it was decided that this check is not strictly necessary, and can be removed to allow for more flexible model architectures. * llama : remove unused pruned_attention_w and is_clip_model vars --- src/llama-quant.cpp | 27 --------------------------- 1 file changed, 27 deletions(-) diff --git a/src/llama-quant.cpp b/src/llama-quant.cpp index 764833749e..351dcb7baa 100644 --- a/src/llama-quant.cpp +++ b/src/llama-quant.cpp @@ -666,7 +666,6 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std:: std::map mapped; int blk_id = 0; - int pruned_attention_w = 0; // make a list of weights std::vector tensors; @@ -674,11 +673,6 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std:: for (const auto & it : ml.weights_map) { const std::string remapped_name(remap_layer(it.first, prune_list, mapped, blk_id)); if (remapped_name.empty()) { - if (it.first.find("attn_v.weight") != std::string::npos || - it.first.find("attn_qkv.weight") != std::string::npos || - it.first.find("attn_kv_b.weight") != std::string::npos) { - pruned_attention_w++; - } LLAMA_LOG_DEBUG("%s: pruning tensor %s\n", __func__, it.first.c_str()); continue; } @@ -703,7 +697,6 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std:: }); } - bool is_clip_model = false; for (const auto * it : tensors) { const struct ggml_tensor * tensor = it->tensor; @@ -717,30 +710,10 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std:: } else if (name == LLM_TN(model.arch)(LLM_TENSOR_OUTPUT, "weight")) { qs.has_output = true; } - - is_clip_model |= name.rfind("mm.", 0) == 0; // check the "mm." prefix } qs.n_ffn_down = qs.n_ffn_gate = qs.n_ffn_up = (int)model.hparams.n_layer; - // sanity checks for models that have attention layers - if (qs.n_attention_wv != 0 && !is_clip_model) - { - int32_t n_layer_all = model.hparams.n_layer; - if (llama_model_has_encoder(&model)) { - // now n_layer_all is the number of attention layers in the encoder - // for each decoder block, there are 2 attention layers - n_layer_all += 2 * model.hparams.dec_n_layer; - } - - // note: for linear-attention models (such as Qwen3 Next) this is the number of linear layers - const int32_t n_layer_recr = std::count(model.hparams.recurrent_layer_arr.begin(), model.hparams.recurrent_layer_arr.end(), true); - - LLAMA_LOG_INFO("%s: n_layer_all = %d, n_layer_recr = %d, pruned_attention_w = %d\n", __func__, n_layer_all, n_layer_recr, pruned_attention_w); - - GGML_ASSERT((qs.n_attention_wv == n_layer_all - pruned_attention_w - n_layer_recr) && "n_attention_wv is unexpected"); - } - size_t total_size_org = 0; size_t total_size_new = 0; From 7b43f5575399b18c7847efb665a3c1f16d5fcc2c Mon Sep 17 00:00:00 2001 From: Sky Date: Sat, 6 Dec 2025 19:28:16 +0800 Subject: [PATCH 18/23] ggml : improve error handling for search path existence checks (#17653) * Improve error handling for search path existence checks Refactor existence checks for search paths using std::error_code to handle potential errors. * Improve cache file existence check with error code Update fs::exists to use std::error_code for error handling. * Simplify existence check for search paths Simplify existence check for search paths * Fix logging path in error message for posix_stat * Update ggml/src/ggml-backend-reg.cpp Co-authored-by: Aman Gupta * Adapt to the coding standard --------- Co-authored-by: Aman Gupta --- ggml/src/ggml-backend-reg.cpp | 14 +++++++++++--- ggml/src/ggml-rpc/ggml-rpc.cpp | 3 ++- 2 files changed, 13 insertions(+), 4 deletions(-) diff --git a/ggml/src/ggml-backend-reg.cpp b/ggml/src/ggml-backend-reg.cpp index e96b5c403d..88e6dc45c7 100644 --- a/ggml/src/ggml-backend-reg.cpp +++ b/ggml/src/ggml-backend-reg.cpp @@ -534,8 +534,12 @@ static ggml_backend_reg_t ggml_backend_load_best(const char * name, bool silent, fs::path best_path; for (const auto & search_path : search_paths) { - if (!fs::exists(search_path)) { - GGML_LOG_DEBUG("%s: search path %s does not exist\n", __func__, path_str(search_path).c_str()); + if (std::error_code ec; !fs::exists(search_path, ec)) { + if (ec) { + GGML_LOG_DEBUG("%s: posix_stat(%s) failure, error-message: %s\n", __func__, path_str(search_path).c_str(), ec.message().c_str()); + } else { + GGML_LOG_DEBUG("%s: search path %s does not exist\n", __func__, path_str(search_path).c_str()); + } continue; } fs::directory_iterator dir_it(search_path, fs::directory_options::skip_permission_denied); @@ -575,8 +579,12 @@ static ggml_backend_reg_t ggml_backend_load_best(const char * name, bool silent, for (const auto & search_path : search_paths) { fs::path filename = backend_filename_prefix().native() + name_path.native() + backend_filename_extension().native(); fs::path path = search_path / filename; - if (fs::exists(path)) { + if (std::error_code ec; fs::exists(path, ec)) { return get_reg().load_backend(path, silent); + } else { + if (ec) { + GGML_LOG_DEBUG("%s: posix_stat(%s) failure, error-message: %s\n", __func__, path_str(path).c_str(), ec.message().c_str()); + } } } return nullptr; diff --git a/ggml/src/ggml-rpc/ggml-rpc.cpp b/ggml/src/ggml-rpc/ggml-rpc.cpp index cf656b6a08..18a45d2d96 100644 --- a/ggml/src/ggml-rpc/ggml-rpc.cpp +++ b/ggml/src/ggml-rpc/ggml-rpc.cpp @@ -1257,7 +1257,8 @@ bool rpc_server::get_cached_file(uint64_t hash, std::vector & data) { char hash_str[17]; snprintf(hash_str, sizeof(hash_str), "%016" PRIx64, hash); fs::path cache_file = fs::path(cache_dir) / hash_str; - if (!fs::exists(cache_file)) { + std::error_code ec; + if (!fs::exists(cache_file, ec)) { return false; } std::ifstream ifs(cache_file, std::ios::binary); From 21f24f27a9bd58e35ba2ba9a3366bfc660b1f095 Mon Sep 17 00:00:00 2001 From: Aleksander Grygier Date: Sat, 6 Dec 2025 13:19:05 +0100 Subject: [PATCH 19/23] webui: Per-conversation system message with UI displaying, edition & branching (#17275) * feat: Per-conversation system message with optional display in UI, edition and branching (WIP) * chore: update webui build output --- tools/server/public/index.html.gz | Bin 1442226 -> 1442523 bytes .../app/chat/ChatMessages/ChatMessage.svelte | 27 ++- .../ChatMessages/ChatMessageSystem.svelte | 216 ++++++++++++++++++ .../chat/ChatMessages/ChatMessageUser.svelte | 2 +- .../app/chat/ChatMessages/ChatMessages.svelte | 9 +- .../app/chat/ChatSettings/ChatSettings.svelte | 12 +- .../ChatSettings/ChatSettingsFields.svelte | 19 +- .../webui/src/lib/components/app/index.ts | 2 + .../app/misc/MarkdownContent.svelte | 8 +- .../src/lib/constants/settings-config.ts | 4 +- tools/server/webui/src/lib/services/chat.ts | 46 +--- .../server/webui/src/lib/services/database.ts | 43 ++++ .../webui/src/lib/stores/chat.svelte.ts | 31 ++- tools/server/webui/src/lib/types/chat.d.ts | 2 +- 14 files changed, 357 insertions(+), 64 deletions(-) create mode 100644 tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageSystem.svelte diff --git a/tools/server/public/index.html.gz b/tools/server/public/index.html.gz index 4527cb335677f2921182e4a3d5085312b79988e5..38927736683ab580fd9dceb450e6022427d773a7 100644 GIT binary patch delta 1428061 zcmV)BK*PVXgIL>zSdbil&8HspIA+)fV)Y1Ly_g95S z1KmJ(lae#>Y$m%F8z>YCg+ieK6bgU;YpL{?Lg8c(&I|azPGKI7`@^7c5#pcoWjO2= zCKrXp04cM{coB?yWq=?3+Zhg5g?=y&W}QXQE1a)?xPT}Gos1t91|a0wqh4px zX@#RsKX@|#)c@vYG<2vb(N? zogaR#bPp$=eysQEy;ZZ`UNt{;N8L~D%jeha7YFO!s2d)Cy&4QV?|YM8RDSvazq_^L z)ra@LEW5SAr{0U|@WbU*<-_QoVf#h(>U8vd_~S|c`osG-i`R$GuaCn@-MW(kbfE;{qSM>;a|NM=k@-<(dF}g`^EFs@yTi5rg7Rhe>Gfo(r7)oYQBDZAmCO< z{STwz{2cUuosSNdAKo7Id!t`g-P-We`6cLo+qgNpZ0v(4vJHbl_tm?_hxez=<5wTT z)63_J53l|?I0d~#C$*t{c#1Y$Roa(V&GzAUL*$P@-zp$}yg2H2$5%vu_|xUh_~_N} z>ZiB=Tz6i5fBoTYKAyBs-qnv!ez|PdUP>It^V5HS9j}3B)Omk1@1jke_iql4Mjxi< zuWr^qzW-(P`sC-)>FDVC$M-*nr|*w|4|Kf%&4dPP`0AJGhuWY5I!F#c<8dW; 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b/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessage.svelte @@ -3,6 +3,7 @@ import { copyToClipboard, isIMEComposing } from '$lib/utils'; import ChatMessageAssistant from './ChatMessageAssistant.svelte'; import ChatMessageUser from './ChatMessageUser.svelte'; + import ChatMessageSystem from './ChatMessageSystem.svelte'; interface Props { class?: string; @@ -140,8 +141,7 @@ } function handleSaveEdit() { - if (message.role === 'user') { - // For user messages, trim to avoid accidental whitespace + if (message.role === 'user' || message.role === 'system') { onEditWithBranching?.(message, editedContent.trim()); } else { // For assistant messages, preserve exact content including trailing whitespace @@ -167,7 +167,28 @@ } -{#if message.role === 'user'} +{#if message.role === 'system'} + +{:else if message.role === 'user'} + import { Check, X } from '@lucide/svelte'; + import { Card } from '$lib/components/ui/card'; + import { Button } from '$lib/components/ui/button'; + import { MarkdownContent } from '$lib/components/app'; + import { INPUT_CLASSES } from '$lib/constants/input-classes'; + import { config } from '$lib/stores/settings.svelte'; + import ChatMessageActions from './ChatMessageActions.svelte'; + + interface Props { + class?: string; + message: DatabaseMessage; + isEditing: boolean; + editedContent: string; + siblingInfo?: ChatMessageSiblingInfo | null; + showDeleteDialog: boolean; + deletionInfo: { + totalCount: number; + userMessages: number; + assistantMessages: number; + messageTypes: string[]; + } | null; + onCancelEdit: () => void; + onSaveEdit: () => void; + onEditKeydown: (event: KeyboardEvent) => void; + onEditedContentChange: (content: string) => void; + onCopy: () => void; + onEdit: () => void; + onDelete: () => void; + onConfirmDelete: () => void; + onNavigateToSibling?: (siblingId: string) => void; + onShowDeleteDialogChange: (show: boolean) => void; + textareaElement?: HTMLTextAreaElement; + } + + let { + class: className = '', + message, + isEditing, + editedContent, + siblingInfo = null, + showDeleteDialog, + deletionInfo, + onCancelEdit, + onSaveEdit, + onEditKeydown, + onEditedContentChange, + onCopy, + onEdit, + onDelete, + onConfirmDelete, + onNavigateToSibling, + onShowDeleteDialogChange, + textareaElement = $bindable() + }: Props = $props(); + + let isMultiline = $state(false); + let messageElement: HTMLElement | undefined = $state(); + let isExpanded = $state(false); + let contentHeight = $state(0); + const MAX_HEIGHT = 200; // pixels + const currentConfig = config(); + + let showExpandButton = $derived(contentHeight > MAX_HEIGHT); + + $effect(() => { + if (!messageElement || !message.content.trim()) return; + + if (message.content.includes('\n')) { + isMultiline = true; + } + + const resizeObserver = new ResizeObserver((entries) => { + for (const entry of entries) { + const element = entry.target as HTMLElement; + const estimatedSingleLineHeight = 24; + + isMultiline = element.offsetHeight > estimatedSingleLineHeight * 1.5; + contentHeight = element.scrollHeight; + } + }); + + resizeObserver.observe(messageElement); + + return () => { + resizeObserver.disconnect(); + }; + }); + + function toggleExpand() { + isExpanded = !isExpanded; + } + + +

+ {#if isEditing} +
+ + +
+ + + +
+
+ {:else} + {#if message.content.trim()} +
+ +
+ {/if} +
+ + {#if isExpanded && showExpandButton} +
+ +
+ {/if} + + + + {/if} + + {#if message.timestamp} +
+ +
+ {/if} + {/if} + diff --git a/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageUser.svelte b/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageUser.svelte index 8556cbef5b..3d2b8dd35b 100644 --- a/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageUser.svelte +++ b/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageUser.svelte @@ -145,7 +145,7 @@ {#if message.content.trim()} {#if currentConfig.renderUserContentAsMarkdown} diff --git a/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessages.svelte b/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessages.svelte index f307f829bc..2e5f57cb61 100644 --- a/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessages.svelte +++ b/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessages.svelte @@ -2,6 +2,7 @@ import { ChatMessage } from '$lib/components/app'; import { chatStore } from '$lib/stores/chat.svelte'; import { conversationsStore, activeConversation } from '$lib/stores/conversations.svelte'; + import { config } from '$lib/stores/settings.svelte'; import { getMessageSiblings } from '$lib/utils'; interface Props { @@ -13,6 +14,7 @@ let { class: className, messages = [], onUserAction }: Props = $props(); let allConversationMessages = $state([]); + const currentConfig = config(); function refreshAllMessages() { const conversation = activeConversation(); @@ -40,7 +42,12 @@ return []; } - return messages.map((message) => { + // Filter out system messages if showSystemMessage is false + const filteredMessages = currentConfig.showSystemMessage + ? messages + : messages.filter((msg) => msg.type !== 'system'); + + return filteredMessages.map((message) => { const siblingInfo = getMessageSiblings(allConversationMessages, message.id); return { diff --git a/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettings.svelte b/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettings.svelte index 67df20439c..45640e42a0 100644 --- a/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettings.svelte +++ b/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettings.svelte @@ -36,12 +36,6 @@ title: 'General', icon: Settings, fields: [ - { key: 'apiKey', label: 'API Key', type: 'input' }, - { - key: 'systemMessage', - label: 'System Message (will be disabled if left empty)', - type: 'textarea' - }, { key: 'theme', label: 'Theme', @@ -52,6 +46,12 @@ { value: 'dark', label: 'Dark', icon: Moon } ] }, + { key: 'apiKey', label: 'API Key', type: 'input' }, + { + key: 'systemMessage', + label: 'System Message', + type: 'textarea' + }, { key: 'pasteLongTextToFileLen', label: 'Paste long text to file length', diff --git a/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettingsFields.svelte b/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettingsFields.svelte index ef46e2d176..a6f51f47d6 100644 --- a/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettingsFields.svelte +++ b/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettingsFields.svelte @@ -95,7 +95,7 @@ {#if field.help || SETTING_CONFIG_INFO[field.key]}

- {field.help || SETTING_CONFIG_INFO[field.key]} + {@html field.help || SETTING_CONFIG_INFO[field.key]}

{/if} {:else if field.type === 'textarea'} @@ -112,13 +112,28 @@ value={String(localConfig[field.key] ?? '')} onchange={(e) => onConfigChange(field.key, e.currentTarget.value)} placeholder={`Default: ${SETTING_CONFIG_DEFAULT[field.key] ?? 'none'}`} - class="min-h-[100px] w-full md:max-w-2xl" + class="min-h-[10rem] w-full md:max-w-2xl" /> + {#if field.help || SETTING_CONFIG_INFO[field.key]}

{field.help || SETTING_CONFIG_INFO[field.key]}

{/if} + + {#if field.key === 'systemMessage'} +
+ onConfigChange('showSystemMessage', Boolean(checked))} + /> + + +
+ {/if} {:else if field.type === 'select'} {@const selectedOption = field.options?.find( (opt: { value: string; label: string; icon?: Component }) => diff --git a/tools/server/webui/src/lib/components/app/index.ts b/tools/server/webui/src/lib/components/app/index.ts index cf4d7495e2..87b24598b7 100644 --- a/tools/server/webui/src/lib/components/app/index.ts +++ b/tools/server/webui/src/lib/components/app/index.ts @@ -19,8 +19,10 @@ export { default as ChatMessage } from './chat/ChatMessages/ChatMessage.svelte'; export { default as ChatMessageActions } from './chat/ChatMessages/ChatMessageActions.svelte'; export { default as ChatMessageBranchingControls } from './chat/ChatMessages/ChatMessageBranchingControls.svelte'; export { default as ChatMessageStatistics } from './chat/ChatMessages/ChatMessageStatistics.svelte'; +export { default as ChatMessageSystem } from './chat/ChatMessages/ChatMessageSystem.svelte'; export { default as ChatMessageThinkingBlock } from './chat/ChatMessages/ChatMessageThinkingBlock.svelte'; export { default as ChatMessages } from './chat/ChatMessages/ChatMessages.svelte'; +export { default as MessageBranchingControls } from './chat/ChatMessages/ChatMessageBranchingControls.svelte'; export { default as ChatScreen } from './chat/ChatScreen/ChatScreen.svelte'; export { default as ChatScreenHeader } from './chat/ChatScreen/ChatScreenHeader.svelte'; diff --git a/tools/server/webui/src/lib/components/app/misc/MarkdownContent.svelte b/tools/server/webui/src/lib/components/app/misc/MarkdownContent.svelte index 99d6e21e13..9c37bde0d4 100644 --- a/tools/server/webui/src/lib/components/app/misc/MarkdownContent.svelte +++ b/tools/server/webui/src/lib/components/app/misc/MarkdownContent.svelte @@ -337,19 +337,23 @@ line-height: 1.75; } + div :global(:is(h1, h2, h3, h4, h5, h6):first-child) { + margin-top: 0; + } + /* Headers with consistent spacing */ div :global(h1) { font-size: 1.875rem; font-weight: 700; - margin: 1.5rem 0 0.75rem 0; line-height: 1.2; + margin: 1.5rem 0 0.75rem 0; } div :global(h2) { font-size: 1.5rem; font-weight: 600; - margin: 1.25rem 0 0.5rem 0; line-height: 1.3; + margin: 1.25rem 0 0.5rem 0; } div :global(h3) { diff --git a/tools/server/webui/src/lib/constants/settings-config.ts b/tools/server/webui/src/lib/constants/settings-config.ts index 1fc35b48c4..3764a2856b 100644 --- a/tools/server/webui/src/lib/constants/settings-config.ts +++ b/tools/server/webui/src/lib/constants/settings-config.ts @@ -3,6 +3,7 @@ export const SETTING_CONFIG_DEFAULT: Record = // Do not use nested objects, keep it single level. Prefix the key if you need to group them. apiKey: '', systemMessage: '', + showSystemMessage: true, theme: 'system', showThoughtInProgress: false, showToolCalls: false, @@ -42,8 +43,9 @@ export const SETTING_CONFIG_DEFAULT: Record = }; export const SETTING_CONFIG_INFO: Record = { - apiKey: 'Set the API Key if you are using --api-key option for the server.', + apiKey: 'Set the API Key if you are using --api-key option for the server.', systemMessage: 'The starting message that defines how model should behave.', + showSystemMessage: 'Display the system message at the top of each conversation.', theme: 'Choose the color theme for the interface. You can choose between System (follows your device settings), Light, or Dark.', pasteLongTextToFileLen: diff --git a/tools/server/webui/src/lib/services/chat.ts b/tools/server/webui/src/lib/services/chat.ts index a6a6812403..c03b764419 100644 --- a/tools/server/webui/src/lib/services/chat.ts +++ b/tools/server/webui/src/lib/services/chat.ts @@ -89,7 +89,6 @@ export class ChatService { custom, timings_per_token, // Config options - systemMessage, disableReasoningFormat } = options; @@ -103,6 +102,7 @@ export class ChatService { } }) .filter((msg) => { + // Filter out empty system messages if (msg.role === 'system') { const content = typeof msg.content === 'string' ? msg.content : ''; @@ -112,10 +112,8 @@ export class ChatService { return true; }); - const processedMessages = ChatService.injectSystemMessage(normalizedMessages, systemMessage); - const requestBody: ApiChatCompletionRequest = { - messages: processedMessages.map((msg: ApiChatMessageData) => ({ + messages: normalizedMessages.map((msg: ApiChatMessageData) => ({ role: msg.role, content: msg.content })), @@ -677,46 +675,6 @@ export class ChatService { // Utilities // ───────────────────────────────────────────────────────────────────────────── - /** - * Injects a system message at the beginning of the conversation if provided. - * Checks for existing system messages to avoid duplication. - * - * @param messages - Array of chat messages to process - * @param systemMessage - Optional system message to inject - * @returns Array of messages with system message injected at the beginning if provided - * @private - */ - private static injectSystemMessage( - messages: ApiChatMessageData[], - systemMessage?: string - ): ApiChatMessageData[] { - const trimmedSystemMessage = systemMessage?.trim(); - - if (!trimmedSystemMessage) { - return messages; - } - - if (messages.length > 0 && messages[0].role === 'system') { - if (messages[0].content !== trimmedSystemMessage) { - const updatedMessages = [...messages]; - updatedMessages[0] = { - role: 'system', - content: trimmedSystemMessage - }; - return updatedMessages; - } - - return messages; - } - - const systemMsg: ApiChatMessageData = { - role: 'system', - content: trimmedSystemMessage - }; - - return [systemMsg, ...messages]; - } - /** * Parses error response and creates appropriate error with context information * @param response - HTTP response object diff --git a/tools/server/webui/src/lib/services/database.ts b/tools/server/webui/src/lib/services/database.ts index 185a598c3b..3b24628cff 100644 --- a/tools/server/webui/src/lib/services/database.ts +++ b/tools/server/webui/src/lib/services/database.ts @@ -166,6 +166,49 @@ export class DatabaseService { return rootMessage.id; } + /** + * Creates a system prompt message for a conversation. + * + * @param convId - Conversation ID + * @param systemPrompt - The system prompt content (must be non-empty) + * @param parentId - Parent message ID (typically the root message) + * @returns The created system message + * @throws Error if systemPrompt is empty + */ + static async createSystemMessage( + convId: string, + systemPrompt: string, + parentId: string + ): Promise { + const trimmedPrompt = systemPrompt.trim(); + if (!trimmedPrompt) { + throw new Error('Cannot create system message with empty content'); + } + + const systemMessage: DatabaseMessage = { + id: uuid(), + convId, + type: 'system', + timestamp: Date.now(), + role: 'system', + content: trimmedPrompt, + parent: parentId, + thinking: '', + children: [] + }; + + await db.messages.add(systemMessage); + + const parentMessage = await db.messages.get(parentId); + if (parentMessage) { + await db.messages.update(parentId, { + children: [...parentMessage.children, systemMessage.id] + }); + } + + return systemMessage; + } + /** * Deletes a conversation and all its messages. * diff --git a/tools/server/webui/src/lib/stores/chat.svelte.ts b/tools/server/webui/src/lib/stores/chat.svelte.ts index f21e291163..dd6b77e71a 100644 --- a/tools/server/webui/src/lib/stores/chat.svelte.ts +++ b/tools/server/webui/src/lib/stores/chat.svelte.ts @@ -624,6 +624,22 @@ class ChatStore { this.clearChatStreaming(currentConv.id); try { + if (isNewConversation) { + const rootId = await DatabaseService.createRootMessage(currentConv.id); + const currentConfig = config(); + const systemPrompt = currentConfig.systemMessage?.toString().trim(); + + if (systemPrompt) { + const systemMessage = await DatabaseService.createSystemMessage( + currentConv.id, + systemPrompt, + rootId + ); + + conversationsStore.addMessageToActive(systemMessage); + } + } + const userMessage = await this.addMessage('user', content, 'text', '-1', extras); if (!userMessage) throw new Error('Failed to add user message'); if (isNewConversation && content) @@ -999,14 +1015,20 @@ class ChatStore { const activeConv = conversationsStore.activeConversation; if (!activeConv || this.isLoading) return; - const result = this.getMessageByIdWithRole(messageId, 'user'); + let result = this.getMessageByIdWithRole(messageId, 'user'); + + if (!result) { + result = this.getMessageByIdWithRole(messageId, 'system'); + } + if (!result) return; const { message: msg } = result; try { const allMessages = await conversationsStore.getConversationMessages(activeConv.id); const rootMessage = allMessages.find((m) => m.type === 'root' && m.parent === null); - const isFirstUserMessage = rootMessage && msg.parent === rootMessage.id; + const isFirstUserMessage = + msg.role === 'user' && rootMessage && msg.parent === rootMessage.id; const parentId = msg.parent || rootMessage?.id; if (!parentId) return; @@ -1037,7 +1059,10 @@ class ChatStore { ); } await conversationsStore.refreshActiveMessages(); - await this.generateResponseForMessage(newMessage.id); + + if (msg.role === 'user') { + await this.generateResponseForMessage(newMessage.id); + } } catch (error) { console.error('Failed to edit message with branching:', error); } diff --git a/tools/server/webui/src/lib/types/chat.d.ts b/tools/server/webui/src/lib/types/chat.d.ts index 0eafb80cbf..0e706b72b6 100644 --- a/tools/server/webui/src/lib/types/chat.d.ts +++ b/tools/server/webui/src/lib/types/chat.d.ts @@ -1,4 +1,4 @@ -export type ChatMessageType = 'root' | 'text' | 'think'; +export type ChatMessageType = 'root' | 'text' | 'think' | 'system'; export type ChatRole = 'user' | 'assistant' | 'system'; export interface ChatUploadedFile { From e31b5c55c3dbbf5c8e47c8dcb17ba92250f82303 Mon Sep 17 00:00:00 2001 From: Aleksander Grygier Date: Sat, 6 Dec 2025 13:23:29 +0100 Subject: [PATCH 20/23] webui: Fix context available value in Multi-model Router mode (#17804) * fix: Use context size from `/props?model=...` in ROUTER mode * chore: update webui build output --- tools/server/public/index.html.gz | Bin 1442523 -> 1441700 bytes .../webui/src/lib/stores/chat.svelte.ts | 21 +++++++++++++++++- .../webui/src/lib/stores/models.svelte.ts | 17 ++++++++++++++ 3 files 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zN`drA_Zb*8?bgCL68?XI^9te_04f9zRsU=dRKY6CXVu9R3g*#pKzanobNq#wudD59 zycu7_FjN5m;Fs^<%kSL3M8s9`E#0-n2>wpLKaZchIk`NUS5$T5v8%*6Z3#LxnNjUj(2Y6SV78D4S7Y6j>D z<9k<8P|yL^&d*|CMnIiAxV|-m=_VxW@7Uep8r%gF{eOe&RL7jFDyYRq;=5oVj^ck;h&U2XvD$M4!N!uoA&I@r z?!IL)`Nv$Rap%0riSlwAerXs>%gg_`tlpfR98AZ-?6KcFs>}-7f%bceu=nI^pGek{ zoxkl@_)ECd&albmDn}k3BV$!Y` diff --git a/tools/server/webui/src/lib/stores/chat.svelte.ts b/tools/server/webui/src/lib/stores/chat.svelte.ts index dd6b77e71a..167f24288f 100644 --- a/tools/server/webui/src/lib/stores/chat.svelte.ts +++ b/tools/server/webui/src/lib/stores/chat.svelte.ts @@ -2,7 +2,11 @@ import { DatabaseService, ChatService } from '$lib/services'; import { conversationsStore } from '$lib/stores/conversations.svelte'; import { config } from '$lib/stores/settings.svelte'; import { contextSize, isRouterMode } from '$lib/stores/server.svelte'; -import { selectedModelName, modelsStore } from '$lib/stores/models.svelte'; +import { + selectedModelName, + modelsStore, + selectedModelContextSize +} from '$lib/stores/models.svelte'; import { normalizeModelName, filterByLeafNodeId, @@ -261,6 +265,13 @@ class ChatStore { return activeState.contextTotal; } + if (isRouterMode()) { + const modelContextSize = selectedModelContextSize(); + if (modelContextSize && modelContextSize > 0) { + return modelContextSize; + } + } + const propsContextSize = contextSize(); if (propsContextSize && propsContextSize > 0) { return propsContextSize; @@ -458,6 +469,14 @@ class ChatStore { onError?: (error: Error) => void, modelOverride?: string | null ): Promise { + // Ensure model props are cached before streaming (for correct n_ctx in processing info) + if (isRouterMode()) { + const modelName = modelOverride || selectedModelName(); + if (modelName && !modelsStore.getModelProps(modelName)) { + await modelsStore.fetchModelProps(modelName); + } + } + let streamedContent = ''; let streamedReasoningContent = ''; let streamedToolCallContent = ''; diff --git a/tools/server/webui/src/lib/stores/models.svelte.ts b/tools/server/webui/src/lib/stores/models.svelte.ts index 2e834af5a0..29416c2fe5 100644 --- a/tools/server/webui/src/lib/stores/models.svelte.ts +++ b/tools/server/webui/src/lib/stores/models.svelte.ts @@ -158,6 +158,22 @@ class ModelsStore { return this.modelPropsCache.get(modelId) ?? null; } + /** + * Get context size (n_ctx) for a specific model from cached props + */ + getModelContextSize(modelId: string): number | null { + const props = this.modelPropsCache.get(modelId); + return props?.default_generation_settings?.n_ctx ?? null; + } + + /** + * Get context size for the currently selected model or null if no model is selected + */ + get selectedModelContextSize(): number | null { + if (!this.selectedModelName) return null; + return this.getModelContextSize(this.selectedModelName); + } + /** * Check if props are being fetched for a model */ @@ -579,3 +595,4 @@ export const loadedModelIds = () => modelsStore.loadedModelIds; export const loadingModelIds = () => modelsStore.loadingModelIds; export const propsCacheVersion = () => modelsStore.propsCacheVersion; export const singleModelName = () => modelsStore.singleModelName; +export const selectedModelContextSize = () => modelsStore.selectedModelContextSize; From a28e3c7567ae2f064d663db7019ca2d5da73df55 Mon Sep 17 00:00:00 2001 From: Aleksander Grygier Date: Sat, 6 Dec 2025 13:29:15 +0100 Subject: [PATCH 21/23] webui: Stop generation from chat sidebar (#17806) * feat: Add stop generation button for Conversation Item * chore: update webui build output --- tools/server/public/index.html.gz | Bin 1441700 -> 1442044 bytes .../app/chat/ChatSidebar/ChatSidebar.svelte | 6 ++ .../ChatSidebarConversationItem.svelte | 53 +++++++++++++++++- .../webui/src/lib/stores/chat.svelte.ts | 14 +++-- 4 files changed, 66 insertions(+), 7 deletions(-) diff --git a/tools/server/public/index.html.gz b/tools/server/public/index.html.gz index 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