diff --git a/.gitattributes b/.gitattributes index 05c56f5108936d77d3515239dbc8df305493510f..cdf77ff9475eb04b02cf9ea7d6d908f46fc85088 100644 --- a/.gitattributes +++ b/.gitattributes @@ -36,3 +36,28 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text backend/llama.cpp/conversion/__pycache__/base.cpython-313.pyc filter=lfs diff=lfs merge=lfs -text backend/llama.cpp/docs/android/imported-into-android-studio.jpg filter=lfs diff=lfs merge=lfs -text backend/llama.cpp/docs/development/llama-star/idea-arch.key filter=lfs diff=lfs merge=lfs -text +backend/llama.cpp/gguf-py/gguf/__pycache__/constants.cpython-313.pyc filter=lfs diff=lfs merge=lfs -text +backend/llama.cpp/gguf-py/gguf/__pycache__/gguf_writer.cpython-313.pyc filter=lfs diff=lfs merge=lfs -text +backend/llama.cpp/gguf-py/gguf/__pycache__/quants.cpython-313.pyc filter=lfs diff=lfs merge=lfs -text +backend/llama.cpp/media/llama0-banner.png filter=lfs diff=lfs merge=lfs -text +backend/llama.cpp/media/llama0-logo.png filter=lfs diff=lfs merge=lfs -text +backend/llama.cpp/media/matmul.png filter=lfs diff=lfs merge=lfs -text +backend/llama.cpp/models/ggml-vocab-aquila.gguf filter=lfs diff=lfs merge=lfs -text +backend/llama.cpp/models/ggml-vocab-baichuan.gguf filter=lfs diff=lfs merge=lfs -text +backend/llama.cpp/models/ggml-vocab-bert-bge.gguf filter=lfs diff=lfs merge=lfs -text +backend/llama.cpp/models/ggml-vocab-command-r.gguf filter=lfs diff=lfs merge=lfs -text +backend/llama.cpp/models/ggml-vocab-deepseek-coder.gguf filter=lfs diff=lfs merge=lfs -text +backend/llama.cpp/models/ggml-vocab-deepseek-llm.gguf filter=lfs diff=lfs merge=lfs -text +backend/llama.cpp/models/ggml-vocab-falcon.gguf filter=lfs diff=lfs merge=lfs -text +backend/llama.cpp/models/ggml-vocab-gemma-4.gguf filter=lfs diff=lfs merge=lfs -text +backend/llama.cpp/models/ggml-vocab-gpt-2.gguf filter=lfs diff=lfs merge=lfs -text +backend/llama.cpp/models/ggml-vocab-gpt-neox.gguf filter=lfs diff=lfs merge=lfs -text +backend/llama.cpp/models/ggml-vocab-llama-bpe.gguf filter=lfs diff=lfs merge=lfs -text +backend/llama.cpp/models/ggml-vocab-llama-spm.gguf filter=lfs diff=lfs merge=lfs -text +backend/llama.cpp/models/ggml-vocab-mpt.gguf filter=lfs diff=lfs merge=lfs -text +backend/llama.cpp/models/ggml-vocab-nomic-bert-moe.gguf filter=lfs diff=lfs merge=lfs -text +backend/llama.cpp/models/ggml-vocab-phi-3.gguf filter=lfs diff=lfs merge=lfs -text +backend/llama.cpp/models/ggml-vocab-qwen2.gguf filter=lfs diff=lfs merge=lfs -text +backend/llama.cpp/models/ggml-vocab-qwen35.gguf filter=lfs diff=lfs merge=lfs -text +backend/llama.cpp/models/ggml-vocab-refact.gguf filter=lfs diff=lfs merge=lfs -text +backend/llama.cpp/models/ggml-vocab-starcoder.gguf filter=lfs diff=lfs merge=lfs -text diff --git a/backend/llama.cpp/LICENSE b/backend/llama.cpp/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..e7dca554bcb802f98408383a864404e3aa4eacca --- /dev/null +++ b/backend/llama.cpp/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023-2026 The ggml authors + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/backend/llama.cpp/Makefile b/backend/llama.cpp/Makefile new file mode 100644 index 0000000000000000000000000000000000000000..bcbc7702059566c8e23bcaf73e12caff9b7dac8e --- /dev/null +++ b/backend/llama.cpp/Makefile @@ -0,0 +1,9 @@ +define newline + + +endef + +$(error Build system changed:$(newline)\ +The Makefile build has been replaced by CMake.$(newline)$(newline)\ +For build instructions see:$(newline)\ +https://github.com/ggml-org/llama.cpp/blob/master/docs/build.md$(newline)${newline}) diff --git a/backend/llama.cpp/ggml/src/ggml-virtgpu/virtgpu-apir.h b/backend/llama.cpp/ggml/src/ggml-virtgpu/virtgpu-apir.h new file mode 100644 index 0000000000000000000000000000000000000000..238f960acd2390551e2790cb461b98d4e3b1eeac --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-virtgpu/virtgpu-apir.h @@ -0,0 +1,15 @@ +#include "backend/shared/apir_backend.h" +#include "ggml-alloc.h" +#include "ggml-impl.h" +#include "ggml.h" +#include "virtgpu-shm.h" +#include "virtgpu-utils.h" + +struct apir_buffer_context_t { + apir_buffer_host_handle_t host_handle; + + struct virtgpu_shmem shmem; + apir_buffer_type_host_handle_t buft_host_handle; +}; + +#include "virtgpu-forward.gen.h" diff --git a/backend/llama.cpp/ggml/src/ggml-virtgpu/virtgpu-forward-backend.cpp b/backend/llama.cpp/ggml/src/ggml-virtgpu/virtgpu-forward-backend.cpp new file mode 100644 index 0000000000000000000000000000000000000000..4593690c6385592f681dfb2cd731828d5edd6ea7 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-virtgpu/virtgpu-forward-backend.cpp @@ -0,0 +1,58 @@ +#include "virtgpu-forward-impl.h" + +static long long current_time_ms() { + timespec ts; + clock_gettime(CLOCK_REALTIME, &ts); // Use CLOCK_MONOTONIC for elapsed time + return (long long) ts.tv_sec * 1000000000LL + ts.tv_nsec; +} + +ggml_status apir_backend_graph_compute(virtgpu * gpu, ggml_cgraph * cgraph) { + apir_encoder * encoder; + apir_decoder * decoder; + ApirForwardReturnCode ret; + + REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_BACKEND_GRAPH_COMPUTE); + + std::vector cgraph_data; + size_t cgraph_size = apir_serialize_ggml_cgraph(cgraph, cgraph_data); + + virtgpu_shmem temp_shmem; // Local storage for large buffers + virtgpu_shmem * shmem = &temp_shmem; + bool using_shared_shmem = false; + + if (cgraph_size <= gpu->data_shmem.mmap_size) { + // Lock mutex before using shared data_shmem buffer + if (mtx_lock(&gpu->data_shmem_mutex) != thrd_success) { + GGML_ABORT(GGML_VIRTGPU "%s: Failed to lock data_shmem mutex", __func__); + } + using_shared_shmem = true; + shmem = &gpu->data_shmem; + } else if (virtgpu_shmem_create(gpu, cgraph_size, shmem)) { + GGML_ABORT(GGML_VIRTGPU "%s: Couldn't allocate the guest-host shared buffer", __func__); + } + + apir_encode_virtgpu_shmem_res_id(encoder, shmem->res_id); + + apir_encode_size_t(encoder, &cgraph_size); + + char * shmem_data = (char *) shmem->mmap_ptr; + apir_encoder secondary_enc = apir_new_encoder(shmem_data, cgraph_size); + + apir_encode_cgraph_data(&secondary_enc, cgraph_data); + + REMOTE_CALL(gpu, encoder, decoder, ret); + + ggml_status status = GGML_STATUS_ABORTED; + apir_decode_ggml_status(decoder, &status); + + remote_call_finish(gpu, encoder, decoder); + + // Unlock mutex before cleanup + if (using_shared_shmem) { + mtx_unlock(&gpu->data_shmem_mutex); + } else { + virtgpu_shmem_destroy(gpu, shmem); + } + + return status; +} diff --git a/backend/llama.cpp/ggml/src/ggml-virtgpu/virtgpu-forward-buffer-type.cpp b/backend/llama.cpp/ggml/src/ggml-virtgpu/virtgpu-forward-buffer-type.cpp new file mode 100644 index 0000000000000000000000000000000000000000..38f8ec945e0638053d58d80a5b21427c91feddb0 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-virtgpu/virtgpu-forward-buffer-type.cpp @@ -0,0 +1,110 @@ +#include "virtgpu-forward-impl.h" + +char * apir_buffer_type_get_name(virtgpu * gpu, apir_buffer_type_host_handle_t host_handle) { + apir_encoder * encoder; + apir_decoder * decoder; + ApirForwardReturnCode ret; + + REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_BUFFER_TYPE_GET_NAME); + + apir_encode_apir_buffer_type_host_handle(encoder, host_handle); + + REMOTE_CALL(gpu, encoder, decoder, ret); + + const size_t string_size = apir_decode_array_size_unchecked(decoder); + char * string = (char *) apir_decoder_alloc_array(sizeof(char), string_size); + if (!string) { + GGML_LOG_ERROR(GGML_VIRTGPU "%s: Could not allocate the device name buffer\n", __func__); + apir_decoder_set_fatal(decoder); + } + apir_decode_char_array(decoder, string, string_size); + + remote_call_finish(gpu, encoder, decoder); + + return string; +} + +size_t apir_buffer_type_get_alignment(virtgpu * gpu, apir_buffer_type_host_handle_t host_handle) { + apir_encoder * encoder; + apir_decoder * decoder; + ApirForwardReturnCode ret; + + REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_BUFFER_TYPE_GET_ALIGNMENT); + + apir_encode_apir_buffer_type_host_handle(encoder, host_handle); + + REMOTE_CALL(gpu, encoder, decoder, ret); + + size_t alignment; + apir_decode_size_t(decoder, &alignment); + + remote_call_finish(gpu, encoder, decoder); + + return alignment; +} + +size_t apir_buffer_type_get_max_size(virtgpu * gpu, apir_buffer_type_host_handle_t host_handle) { + apir_encoder * encoder; + apir_decoder * decoder; + ApirForwardReturnCode ret; + + REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_BUFFER_TYPE_GET_MAX_SIZE); + + apir_encode_apir_buffer_type_host_handle(encoder, host_handle); + + REMOTE_CALL(gpu, encoder, decoder, ret); + + size_t max_size; + apir_decode_size_t(decoder, &max_size); + + remote_call_finish(gpu, encoder, decoder); + + return max_size; +} + +apir_buffer_context_t apir_buffer_type_alloc_buffer(virtgpu * gpu, + apir_buffer_type_host_handle_t host_handle, + size_t size) { + apir_encoder * encoder; + apir_decoder * decoder; + ApirForwardReturnCode ret; + + apir_buffer_context_t buffer_context; + + REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_BUFFER_TYPE_ALLOC_BUFFER); + + apir_encode_apir_buffer_type_host_handle(encoder, host_handle); + + apir_encode_size_t(encoder, &size); + + REMOTE_CALL(gpu, encoder, decoder, ret); + + apir_decode_apir_buffer_host_handle_t(decoder, &buffer_context.host_handle); + + remote_call_finish(gpu, encoder, decoder); + + return buffer_context; +} + +size_t apir_buffer_type_get_alloc_size(virtgpu * gpu, + apir_buffer_type_host_handle_t host_handle, + const ggml_tensor * op) { + apir_encoder * encoder; + apir_decoder * decoder; + ApirForwardReturnCode ret; + + REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_BUFFER_TYPE_GET_ALLOC_SIZE); + + apir_encode_apir_buffer_type_host_handle(encoder, host_handle); + + apir_encode_ggml_tensor_inline(encoder, op); + + REMOTE_CALL(gpu, encoder, decoder, ret); + + size_t alloc_size; + apir_decode_size_t(decoder, &alloc_size); + + remote_call_finish(gpu, encoder, decoder); + + return alloc_size; +} diff --git a/backend/llama.cpp/ggml/src/ggml-virtgpu/virtgpu-forward-buffer.cpp b/backend/llama.cpp/ggml/src/ggml-virtgpu/virtgpu-forward-buffer.cpp new file mode 100644 index 0000000000000000000000000000000000000000..228284f4a42a43d7c01633b37fdb14d6059ce325 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-virtgpu/virtgpu-forward-buffer.cpp @@ -0,0 +1,173 @@ +#include "virtgpu-forward-impl.h" + +void * apir_buffer_get_base(virtgpu * gpu, apir_buffer_context_t * buffer_context) { + apir_encoder * encoder; + apir_decoder * decoder; + ApirForwardReturnCode ret; + + REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_BUFFER_GET_BASE); + + apir_encode_apir_buffer_host_handle_t(encoder, &buffer_context->host_handle); + + REMOTE_CALL(gpu, encoder, decoder, ret); + + uintptr_t base; + apir_decode_uintptr_t(decoder, &base); + + remote_call_finish(gpu, encoder, decoder); + + return (void *) base; +} + +void apir_buffer_set_tensor(virtgpu * gpu, + apir_buffer_context_t * buffer_context, + ggml_tensor * tensor, + const void * data, + size_t offset, + size_t size) { + apir_encoder * encoder; + apir_decoder * decoder; + ApirForwardReturnCode ret; + + REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_BUFFER_SET_TENSOR); + + apir_encode_apir_buffer_host_handle_t(encoder, &buffer_context->host_handle); + apir_encode_ggml_tensor(encoder, tensor); + + virtgpu_shmem temp_shmem; // Local storage for large buffers + virtgpu_shmem * shmem = &temp_shmem; + bool using_shared_shmem = false; + + if (size <= gpu->data_shmem.mmap_size) { + // Lock mutex before using shared data_shmem buffer + if (mtx_lock(&gpu->data_shmem_mutex) != thrd_success) { + GGML_ABORT(GGML_VIRTGPU "%s: Failed to lock data_shmem mutex", __func__); + } + using_shared_shmem = true; + shmem = &gpu->data_shmem; + + } else if (virtgpu_shmem_create(gpu, size, shmem)) { + GGML_ABORT(GGML_VIRTGPU "%s: Couldn't allocate the guest-host shared buffer", __func__); + } + + memcpy(shmem->mmap_ptr, data, size); + apir_encode_virtgpu_shmem_res_id(encoder, shmem->res_id); + + apir_encode_size_t(encoder, &offset); + apir_encode_size_t(encoder, &size); + + REMOTE_CALL(gpu, encoder, decoder, ret); + + remote_call_finish(gpu, encoder, decoder); + + // Unlock mutex before cleanup + if (using_shared_shmem) { + mtx_unlock(&gpu->data_shmem_mutex); + } else { + virtgpu_shmem_destroy(gpu, shmem); + } + + return; +} + +void apir_buffer_get_tensor(virtgpu * gpu, + apir_buffer_context_t * buffer_context, + const ggml_tensor * tensor, + void * data, + size_t offset, + size_t size) { + apir_encoder * encoder; + apir_decoder * decoder; + ApirForwardReturnCode ret; + + REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_BUFFER_GET_TENSOR); + + apir_encode_apir_buffer_host_handle_t(encoder, &buffer_context->host_handle); + apir_encode_ggml_tensor(encoder, tensor); + + virtgpu_shmem temp_shmem; // Local storage for large buffers + virtgpu_shmem * shmem = &temp_shmem; + bool using_shared_shmem = false; + + if (size <= gpu->data_shmem.mmap_size) { + // Lock mutex before using shared data_shmem buffer + if (mtx_lock(&gpu->data_shmem_mutex) != thrd_success) { + GGML_ABORT(GGML_VIRTGPU "%s: Failed to lock data_shmem mutex", __func__); + } + using_shared_shmem = true; + shmem = &gpu->data_shmem; + + } else if (virtgpu_shmem_create(gpu, size, shmem)) { + GGML_ABORT(GGML_VIRTGPU "%s: Couldn't allocate the guest-host shared buffer", __func__); + } + + apir_encode_virtgpu_shmem_res_id(encoder, shmem->res_id); + apir_encode_size_t(encoder, &offset); + apir_encode_size_t(encoder, &size); + + REMOTE_CALL(gpu, encoder, decoder, ret); + + memcpy(data, shmem->mmap_ptr, size); + + remote_call_finish(gpu, encoder, decoder); + + // Unlock mutex before cleanup + if (using_shared_shmem) { + mtx_unlock(&gpu->data_shmem_mutex); + } else { + virtgpu_shmem_destroy(gpu, shmem); + } +} + +bool apir_buffer_cpy_tensor(virtgpu * gpu, + apir_buffer_context_t * buffer_context, + const ggml_tensor * src, + const ggml_tensor * dst) { + apir_encoder * encoder; + apir_decoder * decoder; + ApirForwardReturnCode ret; + + REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_BUFFER_CPY_TENSOR); + + apir_encode_apir_buffer_host_handle_t(encoder, &buffer_context->host_handle); + apir_encode_ggml_tensor(encoder, src); + apir_encode_ggml_tensor(encoder, dst); + + REMOTE_CALL(gpu, encoder, decoder, ret); + + bool ret_val; + apir_decode_bool_t(decoder, &ret_val); + + remote_call_finish(gpu, encoder, decoder); + + return ret_val; +} + +void apir_buffer_clear(virtgpu * gpu, apir_buffer_context_t * buffer_context, uint8_t value) { + apir_encoder * encoder; + apir_decoder * decoder; + ApirForwardReturnCode ret; + + REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_BUFFER_CLEAR); + + apir_encode_apir_buffer_host_handle_t(encoder, &buffer_context->host_handle); + apir_encode_uint8_t(encoder, &value); + + REMOTE_CALL(gpu, encoder, decoder, ret); + + remote_call_finish(gpu, encoder, decoder); +} + +void apir_buffer_free_buffer(virtgpu * gpu, apir_buffer_context_t * buffer_context) { + apir_encoder * encoder; + apir_decoder * decoder; + ApirForwardReturnCode ret; + + REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_BUFFER_FREE_BUFFER); + + apir_encode_apir_buffer_host_handle_t(encoder, &buffer_context->host_handle); + + REMOTE_CALL(gpu, encoder, decoder, ret); + + remote_call_finish(gpu, encoder, decoder); +} diff --git a/backend/llama.cpp/ggml/src/ggml-virtgpu/virtgpu-forward-device.cpp b/backend/llama.cpp/ggml/src/ggml-virtgpu/virtgpu-forward-device.cpp new file mode 100644 index 0000000000000000000000000000000000000000..9f513c138dd24d331ab7c2500eb06ca010c10c15 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-virtgpu/virtgpu-forward-device.cpp @@ -0,0 +1,192 @@ +#include "virtgpu-forward-impl.h" +#include "virtgpu-shm.h" + +int apir_device_get_count(virtgpu * gpu) { + apir_encoder * encoder; + apir_decoder * decoder; + ApirForwardReturnCode ret; + + REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_DEVICE_GET_COUNT); + REMOTE_CALL(gpu, encoder, decoder, ret); + + int32_t dev_count = -1; + apir_decode_int32_t(decoder, &dev_count); + + remote_call_finish(gpu, encoder, decoder); + + return dev_count; +} + +char * apir_device_get_name(virtgpu * gpu) { + apir_encoder * encoder; + apir_decoder * decoder; + ApirForwardReturnCode ret; + + REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_DEVICE_GET_NAME); + REMOTE_CALL(gpu, encoder, decoder, ret); + + const size_t string_size = apir_decode_array_size_unchecked(decoder); + char * string = (char *) apir_decoder_alloc_array(sizeof(char), string_size); + if (!string) { + GGML_LOG_ERROR(GGML_VIRTGPU "%s: Could not allocate the device name buffer\n", __func__); + return NULL; + } + apir_decode_char_array(decoder, string, string_size); + + remote_call_finish(gpu, encoder, decoder); + + return string; +} + +char * apir_device_get_description(virtgpu * gpu) { + apir_encoder * encoder; + apir_decoder * decoder; + ApirForwardReturnCode ret; + + REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_DEVICE_GET_DESCRIPTION); + + REMOTE_CALL(gpu, encoder, decoder, ret); + + const size_t string_size = apir_decode_array_size_unchecked(decoder); + char * string = (char *) apir_decoder_alloc_array(sizeof(char), string_size); + if (!string) { + GGML_LOG_ERROR(GGML_VIRTGPU "%s: Could not allocate the device description buffer\n", __func__); + + return NULL; + } + apir_decode_char_array(decoder, string, string_size); + + remote_call_finish(gpu, encoder, decoder); + + return string; +} + +uint32_t apir_device_get_type(virtgpu * gpu) { + static uint32_t dev_type = 255; + if (dev_type != 255) { + return dev_type; + } + + apir_encoder * encoder; + apir_decoder * decoder; + ApirForwardReturnCode ret; + + REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_DEVICE_GET_TYPE); + + REMOTE_CALL(gpu, encoder, decoder, ret); + + apir_decode_uint32_t(decoder, &dev_type); + + remote_call_finish(gpu, encoder, decoder); + + return dev_type; +} + +void apir_device_get_memory(virtgpu * gpu, size_t * free, size_t * total) { + static size_t dev_free = 0; + static size_t dev_total = 0; + apir_encoder * encoder; + apir_decoder * decoder; + ApirForwardReturnCode ret; + + REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_DEVICE_GET_MEMORY); + + REMOTE_CALL(gpu, encoder, decoder, ret); + + apir_decode_size_t(decoder, &dev_free); + apir_decode_size_t(decoder, &dev_total); + + *free = dev_free; + *total = dev_total; + + remote_call_finish(gpu, encoder, decoder); + + return; +} + +bool apir_device_supports_op(virtgpu * gpu, const ggml_tensor * op) { + apir_encoder * encoder; + apir_decoder * decoder; + ApirForwardReturnCode ret; + + REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_DEVICE_SUPPORTS_OP); + + apir_encode_ggml_tensor_inline(encoder, op); + + REMOTE_CALL(gpu, encoder, decoder, ret); + + bool supports_op; + apir_decode_bool_t(decoder, &supports_op); + + remote_call_finish(gpu, encoder, decoder); + + return supports_op; +} + +apir_buffer_type_host_handle_t apir_device_get_buffer_type(virtgpu * gpu) { + apir_encoder * encoder; + apir_decoder * decoder; + ApirForwardReturnCode ret; + + REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_DEVICE_GET_BUFFER_TYPE); + + REMOTE_CALL(gpu, encoder, decoder, ret); + + apir_buffer_type_host_handle_t buft_handle; + apir_decode_apir_buffer_type_host_handle_t(decoder, &buft_handle); + + remote_call_finish(gpu, encoder, decoder); + + return buft_handle; +} + +void apir_device_get_props(virtgpu * gpu, + bool * async, + bool * host_buffer, + bool * buffer_from_host_ptr, + bool * events) { + apir_encoder * encoder; + apir_decoder * decoder; + ApirForwardReturnCode ret; + + REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_DEVICE_GET_PROPS); + + REMOTE_CALL(gpu, encoder, decoder, ret); + + apir_decode_bool_t(decoder, async); + apir_decode_bool_t(decoder, host_buffer); + apir_decode_bool_t(decoder, buffer_from_host_ptr); + apir_decode_bool_t(decoder, events); + + remote_call_finish(gpu, encoder, decoder); + + return; +} + +apir_buffer_context_t apir_device_buffer_from_ptr(virtgpu * gpu, size_t size, size_t max_tensor_size) { + apir_encoder * encoder; + apir_decoder * decoder; + ApirForwardReturnCode ret; + + apir_buffer_context_t buffer_context; + + REMOTE_CALL_PREPARE(gpu, encoder, APIR_COMMAND_TYPE_DEVICE_BUFFER_FROM_PTR); + + if (virtgpu_shmem_create(gpu, size, &buffer_context.shmem)) { + GGML_ABORT(GGML_VIRTGPU "%s: Couldn't allocate %ldb of guest-host shared buffer", __func__, size); + } + + apir_encode_virtgpu_shmem_res_id(encoder, buffer_context.shmem.res_id); + + apir_encode_size_t(encoder, &size); + apir_encode_size_t(encoder, &max_tensor_size); + + REMOTE_CALL(gpu, encoder, decoder, ret); + + apir_decode_apir_buffer_host_handle_t(decoder, &buffer_context.host_handle); + buffer_context.buft_host_handle = apir_decode_apir_buffer_type_host_handle(decoder); + + remote_call_finish(gpu, encoder, decoder); + + return buffer_context; +} diff --git a/backend/llama.cpp/ggml/src/ggml-virtgpu/virtgpu-forward-impl.h b/backend/llama.cpp/ggml/src/ggml-virtgpu/virtgpu-forward-impl.h new file mode 100644 index 0000000000000000000000000000000000000000..4d0b6e05c7402d968507c7427a4fe95e05066136 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-virtgpu/virtgpu-forward-impl.h @@ -0,0 +1,36 @@ +#pragma once + +// clang-format off +#include "virtgpu.h" +#include "ggml-remoting.h" +#include "backend/shared/apir_backend.h" +#include "backend/shared/apir_cs_ggml.h" +#include "ggml-backend-impl.h" +// clang-format on + +#define REMOTE_CALL_PREPARE(gpu_dev_name, encoder_name, apir_command_type__) \ + int32_t REMOTE_CALL_PREPARE_forward_flag = (int32_t) apir_command_type__; \ + const char * REMOTE_CALL_PREPARE_command_name = apir_dispatch_command_name(apir_command_type__); \ + do { \ + encoder_name = remote_call_prepare(gpu_dev_name, APIR_COMMAND_TYPE_FORWARD, REMOTE_CALL_PREPARE_forward_flag); \ + if (!encoder_name) { \ + GGML_ABORT(GGML_VIRTGPU "%s: failed to prepare the remote call encoder", __func__); \ + } \ + } while (0) + +#define REMOTE_CALL(gpu_dev_name, encoder_name, decoder_name, ret_name) \ + do { \ + ret_name = (ApirForwardReturnCode) remote_call(gpu_dev_name, encoder_name, &decoder_name, 0, NULL); \ + if (!decoder_name) { \ + GGML_ABORT(GGML_VIRTGPU "%s: failed to kick the remote call", __func__); \ + } \ + if (ret_name < APIR_FORWARD_BASE_INDEX) { \ + GGML_ABORT(GGML_VIRTGPU "%s: failed to forward the API call: %s: code %d", __func__, \ + apir_forward_error(ret_name), ret_name); \ + } \ + ret_name = (ApirForwardReturnCode) (ret_name - APIR_FORWARD_BASE_INDEX); \ + if (ret_name != 0) { \ + GGML_ABORT(GGML_VIRTGPU "backend function '%s' failed (return code: %d)", \ + REMOTE_CALL_PREPARE_command_name, ret_name); \ + } \ + } while (0) diff --git a/backend/llama.cpp/ggml/src/ggml-virtgpu/virtgpu-forward.gen.h b/backend/llama.cpp/ggml/src/ggml-virtgpu/virtgpu-forward.gen.h new file mode 100644 index 0000000000000000000000000000000000000000..44b0ad1ffa1d1b483836bf1a0166d8800f1528d4 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-virtgpu/virtgpu-forward.gen.h @@ -0,0 +1,53 @@ +#pragma once + +/* device */ +void apir_device_get_device_count(struct virtgpu * gpu); +int apir_device_get_count(struct virtgpu * gpu); +char * apir_device_get_name(struct virtgpu * gpu); +char * apir_device_get_description(struct virtgpu * gpu); +uint32_t apir_device_get_type(struct virtgpu * gpu); +void apir_device_get_memory(struct virtgpu * gpu, size_t * free, size_t * total); +bool apir_device_supports_op(struct virtgpu * gpu, const ggml_tensor * op); +apir_buffer_type_host_handle_t apir_device_get_buffer_type(struct virtgpu * gpu); +void apir_device_get_props(struct virtgpu * gpu, + bool * async, + bool * host_buffer, + bool * buffer_from_host_ptr, + bool * events); +apir_buffer_context_t apir_device_buffer_from_ptr(struct virtgpu * gpu, size_t size, size_t max_tensor_size); + +/* buffer-type */ +char * apir_buffer_type_get_name(struct virtgpu * gpu, apir_buffer_type_host_handle_t host_handle); +size_t apir_buffer_type_get_alignment(struct virtgpu * gpu, apir_buffer_type_host_handle_t host_handle); +size_t apir_buffer_type_get_max_size(struct virtgpu * gpu, apir_buffer_type_host_handle_t host_handle); +/* apir_buffer_type_is_host is deprecated. */ +apir_buffer_context_t apir_buffer_type_alloc_buffer(struct virtgpu * gpu, + apir_buffer_type_host_handle_t host_handle, + size_t size); +size_t apir_buffer_type_get_alloc_size(struct virtgpu * gpu, + apir_buffer_type_host_handle_t host_handle, + const ggml_tensor * op); + +/* buffer */ +void * apir_buffer_get_base(struct virtgpu * gpu, apir_buffer_context_t * buffer_context); +void apir_buffer_set_tensor(struct virtgpu * gpu, + apir_buffer_context_t * buffer_context, + ggml_tensor * tensor, + const void * data, + size_t offset, + size_t size); +void apir_buffer_get_tensor(struct virtgpu * gpu, + apir_buffer_context_t * buffer_context, + const ggml_tensor * tensor, + void * data, + size_t offset, + size_t size); +bool apir_buffer_cpy_tensor(struct virtgpu * gpu, + apir_buffer_context_t * buffer_context, + const ggml_tensor * src, + const ggml_tensor * dst); +void apir_buffer_clear(struct virtgpu * gpu, apir_buffer_context_t * buffer_context, uint8_t value); +void apir_buffer_free_buffer(struct virtgpu * gpu, apir_buffer_context_t * buffer_context); + +/* backend */ +ggml_status apir_backend_graph_compute(struct virtgpu * gpu, ggml_cgraph * cgraph); diff --git a/backend/llama.cpp/ggml/src/ggml-virtgpu/virtgpu-shm.cpp b/backend/llama.cpp/ggml/src/ggml-virtgpu/virtgpu-shm.cpp new file mode 100644 index 0000000000000000000000000000000000000000..7f2c2322d919f39dd52f1eca1e298fa725fd5fae --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-virtgpu/virtgpu-shm.cpp @@ -0,0 +1,99 @@ +#include "virtgpu-shm.h" + +#include "virtgpu.h" +#include "ggml-remoting.h" + +#include + +static uint32_t virtgpu_ioctl_resource_create_blob(virtgpu * gpu, + uint32_t blob_mem, + uint32_t blob_flags, + size_t blob_size, + uint64_t blob_id, + uint32_t * res_id) { +#ifdef SIMULATE_BO_SIZE_FIX + blob_size = align64(blob_size, 4096); +#endif + + drm_virtgpu_resource_create_blob args = { + .blob_mem = blob_mem, + .blob_flags = blob_flags, + .bo_handle = 0, + .res_handle = 0, + .size = blob_size, + .pad = 0, + .cmd_size = 0, + .cmd = 0, + .blob_id = blob_id, + }; + + if (virtgpu_ioctl(gpu, DRM_IOCTL_VIRTGPU_RESOURCE_CREATE_BLOB, &args)) { + return 0; + } + + *res_id = args.res_handle; + return args.bo_handle; +} + +static void virtgpu_ioctl_gem_close(virtgpu * gpu, uint32_t gem_handle) { + drm_gem_close args = { + .handle = gem_handle, + .pad = 0, + }; + + const int ret = virtgpu_ioctl(gpu, DRM_IOCTL_GEM_CLOSE, &args); + assert(!ret); +#ifdef NDEBUG + UNUSED(ret); +#endif +} + +static void * virtgpu_ioctl_map(virtgpu * gpu, uint32_t gem_handle, size_t size) { + drm_virtgpu_map args = { + .offset = 0, + .handle = gem_handle, + .pad = 0, + }; + + if (virtgpu_ioctl(gpu, DRM_IOCTL_VIRTGPU_MAP, &args)) { + return NULL; + } + + void * ptr = mmap(NULL, size, PROT_READ | PROT_WRITE, MAP_SHARED, gpu->fd, args.offset); + if (ptr == MAP_FAILED) { + return NULL; + } + + return ptr; +} + +void virtgpu_shmem_destroy(virtgpu * gpu, virtgpu_shmem * shmem) { + munmap(shmem->mmap_ptr, shmem->mmap_size); + virtgpu_ioctl_gem_close(gpu, shmem->gem_handle); +} + +int virtgpu_shmem_create(virtgpu * gpu, size_t size, virtgpu_shmem * shmem) { + size = align64(size, 16384); + + uint32_t res_id; + uint32_t gem_handle = virtgpu_ioctl_resource_create_blob(gpu, VIRTGPU_BLOB_MEM_HOST3D, + VIRTGPU_BLOB_FLAG_USE_MAPPABLE, size, 0, &res_id); + + if (!gem_handle) { + return 1; + } + + void * ptr = virtgpu_ioctl_map(gpu, gem_handle, size); + if (!ptr) { + virtgpu_ioctl_gem_close(gpu, gem_handle); + GGML_LOG_ERROR(GGML_VIRTGPU "%s: virtgpu_ioctl_map failed\n", __func__); + return 1; + } + + shmem->res_id = res_id; + shmem->mmap_size = size; + shmem->mmap_ptr = ptr; + shmem->gem_handle = gem_handle; + + return 0; +} diff --git a/backend/llama.cpp/ggml/src/ggml-virtgpu/virtgpu-shm.h b/backend/llama.cpp/ggml/src/ggml-virtgpu/virtgpu-shm.h new file mode 100644 index 0000000000000000000000000000000000000000..606860a0946a1a15053ba7bae393e8d1d281078e --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-virtgpu/virtgpu-shm.h @@ -0,0 +1,23 @@ +#pragma once + +#include "virtgpu-utils.h" + +#include + +#include +#include +#include +#include + +struct virtgpu; + +struct virtgpu_shmem { + uint32_t res_id; + size_t mmap_size; + void * mmap_ptr; + + uint32_t gem_handle; +}; + +int virtgpu_shmem_create(virtgpu * gpu, size_t size, virtgpu_shmem * shmem); +void virtgpu_shmem_destroy(virtgpu * gpu, virtgpu_shmem * shmem); diff --git a/backend/llama.cpp/ggml/src/ggml-virtgpu/virtgpu-utils.cpp b/backend/llama.cpp/ggml/src/ggml-virtgpu/virtgpu-utils.cpp new file mode 100644 index 0000000000000000000000000000000000000000..8a2805e990233cd96d1eec697d3c47a763bfa6f5 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-virtgpu/virtgpu-utils.cpp @@ -0,0 +1,179 @@ +#include "virtgpu-utils.h" + +#include +#include + +#include + +#define NODE_ALLOC_ALIGN 64 +#define NODE_PTR_MASK (~((uintptr_t) NODE_ALLOC_ALIGN - 1)) +#define NODE_LEVEL_MASK ((uintptr_t) NODE_ALLOC_ALIGN - 1) +#define NULL_NODE 0 + +#define os_malloc_aligned(_size, _align) _aligned_malloc(_size, _align) +#define os_free_aligned(_ptr) free(_ptr) +#define p_atomic_cmpxchg(v, old, _new) __sync_val_compare_and_swap((v), (old), (_new)) + +static inline uint64_t util_logbase2_64(uint64_t n) { +#if defined(HAVE___BUILTIN_CLZLL) + return ((sizeof(uint64_t) * 8 - 1) - __builtin_clzll(n | 1)); +#else + uint64_t pos = 0ull; + if (n >= 1ull << 32) { + n >>= 32; + pos += 32; + } + if (n >= 1ull << 16) { + n >>= 16; + pos += 16; + } + if (n >= 1ull << 8) { + n >>= 8; + pos += 8; + } + if (n >= 1ull << 4) { + n >>= 4; + pos += 4; + } + if (n >= 1ull << 2) { + n >>= 2; + pos += 2; + } + if (n >= 1ull << 1) { + pos += 1; + } + return pos; +#endif +} + +void util_sparse_array_init(util_sparse_array * arr, size_t elem_size, size_t node_size) { + memset(arr, 0, sizeof(*arr)); + arr->elem_size = elem_size; + arr->node_size_log2 = util_logbase2_64(node_size); + assert(node_size >= 2 && node_size == (1ull << arr->node_size_log2)); +} + +static inline void * os_malloc_aligned(size_t size, size_t alignment) { + void * ptr; + alignment = (alignment + sizeof(void *) - 1) & ~(sizeof(void *) - 1); + if (posix_memalign(&ptr, alignment, size) != 0) { + return NULL; + } + return ptr; +} + +static inline void * _util_sparse_array_node_data(uintptr_t handle) { + return (void *) (handle & NODE_PTR_MASK); +} + +static inline unsigned _util_sparse_array_node_level(uintptr_t handle) { + return handle & NODE_LEVEL_MASK; +} + +static inline void _util_sparse_array_node_finish(util_sparse_array * arr, uintptr_t node) { + if (_util_sparse_array_node_level(node) > 0) { + uintptr_t * children = (uintptr_t *) _util_sparse_array_node_data(node); + size_t node_size = 1ull << arr->node_size_log2; + for (size_t i = 0; i < node_size; i++) { + if (children[i]) { + _util_sparse_array_node_finish(arr, children[i]); + } + } + } + + os_free_aligned(_util_sparse_array_node_data(node)); +} + +static inline uintptr_t _util_sparse_array_node(void * data, unsigned level) { + assert(data != NULL); + assert(((uintptr_t) data & NODE_LEVEL_MASK) == 0); + assert((level & NODE_PTR_MASK) == 0); + return (uintptr_t) data | level; +} + +inline uintptr_t _util_sparse_array_node_alloc(util_sparse_array * arr, unsigned level) { + size_t size; + if (level == 0) { + size = arr->elem_size << arr->node_size_log2; + } else { + size = sizeof(uintptr_t) << arr->node_size_log2; + } + + void * data = os_malloc_aligned(size, NODE_ALLOC_ALIGN); + memset(data, 0, size); + + return _util_sparse_array_node(data, level); +} + +static inline uintptr_t _util_sparse_array_set_or_free_node(uintptr_t * node_ptr, uintptr_t cmp_node, uintptr_t node) { + uintptr_t prev_node = p_atomic_cmpxchg(node_ptr, cmp_node, node); + + if (prev_node != cmp_node) { + /* We lost the race. Free this one and return the one that was already + * allocated. + */ + os_free_aligned(_util_sparse_array_node_data(node)); + return prev_node; + } else { + return node; + } +} + +void * util_sparse_array_get(util_sparse_array * arr, uint64_t idx) { + const unsigned node_size_log2 = arr->node_size_log2; + uintptr_t root = p_atomic_read(&arr->root); + if (unlikely(!root)) { + unsigned root_level = 0; + uint64_t idx_iter = idx >> node_size_log2; + while (idx_iter) { + idx_iter >>= node_size_log2; + root_level++; + } + uintptr_t new_root = _util_sparse_array_node_alloc(arr, root_level); + root = _util_sparse_array_set_or_free_node(&arr->root, NULL_NODE, new_root); + } + + while (1) { + unsigned root_level = _util_sparse_array_node_level(root); + uint64_t root_idx = idx >> (root_level * node_size_log2); + if (likely(root_idx < (1ull << node_size_log2))) { + break; + } + + /* In this case, we have a root but its level is low enough that the + * requested index is out-of-bounds. + */ + uintptr_t new_root = _util_sparse_array_node_alloc(arr, root_level + 1); + + uintptr_t * new_root_children = (uintptr_t *) _util_sparse_array_node_data(new_root); + new_root_children[0] = root; + + /* We only add one at a time instead of the whole tree because it's + * easier to ensure correctness of both the tree building and the + * clean-up path. Because we're only adding one node we never have to + * worry about trying to free multiple things without freeing the old + * things. + */ + root = _util_sparse_array_set_or_free_node(&arr->root, root, new_root); + } + + void * node_data = _util_sparse_array_node_data(root); + unsigned node_level = _util_sparse_array_node_level(root); + while (node_level > 0) { + uint64_t child_idx = (idx >> (node_level * node_size_log2)) & ((1ull << node_size_log2) - 1); + + uintptr_t * children = (uintptr_t *) node_data; + uintptr_t child = p_atomic_read(&children[child_idx]); + + if (unlikely(!child)) { + child = _util_sparse_array_node_alloc(arr, node_level - 1); + child = _util_sparse_array_set_or_free_node(&children[child_idx], NULL_NODE, child); + } + + node_data = _util_sparse_array_node_data(child); + node_level = _util_sparse_array_node_level(child); + } + + uint64_t elem_idx = idx & ((1ull << node_size_log2) - 1); + return (void *) ((char *) node_data + (elem_idx * arr->elem_size)); +} diff --git a/backend/llama.cpp/ggml/src/ggml-virtgpu/virtgpu-utils.h b/backend/llama.cpp/ggml/src/ggml-virtgpu/virtgpu-utils.h new file mode 100644 index 0000000000000000000000000000000000000000..a0036b4e2bc8efcb42c82ced8baafb2070c894d2 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-virtgpu/virtgpu-utils.h @@ -0,0 +1,86 @@ +#pragma once + +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#define unlikely(x) __builtin_expect(!!(x), 0) +#define likely(x) __builtin_expect(!!(x), 1) + +#ifndef UNUSED +# define UNUSED(x) (void) (x) +#endif + +/** Checks is a value is a power of two. Does not handle zero. */ +#define IS_POT(v) (((v) & ((v) - 1)) == 0) + +/** Checks is a value is a power of two. Zero handled. */ +#define IS_POT_NONZERO(v) ((v) != 0 && IS_POT(v)) + +/** Align a value to a power of two */ +#define ALIGN_POT(x, pot_align) (((x) + (pot_align) - 1) & ~((pot_align) - 1)) + +#define p_atomic_read(_v) __atomic_load_n((_v), __ATOMIC_ACQUIRE) + +static inline bool util_is_power_of_two_nonzero64(uint64_t v) { + return IS_POT_NONZERO(v); +} + +static inline uint64_t align64(uint64_t value, uint64_t alignment) { + assert(util_is_power_of_two_nonzero64(alignment)); + return ALIGN_POT(value, alignment); +} + +struct list_head { + list_head * prev; + list_head * next; +}; + +struct util_sparse_array { + size_t elem_size; + unsigned node_size_log2; + + uintptr_t root; +}; + +void * util_sparse_array_get(util_sparse_array * arr, uint64_t idx); +void util_sparse_array_init(util_sparse_array * arr, size_t elem_size, size_t node_size); + +inline void os_time_sleep(int64_t usecs) { + timespec time; + time.tv_sec = usecs / 1000000; + time.tv_nsec = (usecs % 1000000) * 1000; + while (clock_nanosleep(CLOCK_MONOTONIC, 0, &time, &time) == EINTR) + ; +} + +struct timer_data { + long long start; + long long total; + long long count; +}; + +static inline void start_timer(timer_data * timer) { + timespec ts; + clock_gettime(CLOCK_MONOTONIC, &ts); + timer->start = (long long) ts.tv_sec * 1000000000LL + ts.tv_nsec; +} + +// returns the duration in ns +static inline long long stop_timer(timer_data * timer) { + timespec ts; + clock_gettime(CLOCK_MONOTONIC, &ts); + long long timer_end = (long long) ts.tv_sec * 1000000000LL + ts.tv_nsec; + + long long duration = (timer_end - timer->start); + timer->total += duration; + timer->count += 1; + + return duration; +} diff --git a/backend/llama.cpp/ggml/src/ggml-virtgpu/virtgpu.cpp b/backend/llama.cpp/ggml/src/ggml-virtgpu/virtgpu.cpp new file mode 100644 index 0000000000000000000000000000000000000000..e3ae1cc75e001bd043903c911c333b828f2a1c15 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-virtgpu/virtgpu.cpp @@ -0,0 +1,545 @@ +#include "virtgpu.h" +#include "ggml-remoting.h" + +#include +#include + +#include +#include +#include + +static virt_gpu_result_t virtgpu_open_device(virtgpu * gpu, const drmDevicePtr dev); +static virt_gpu_result_t virtgpu_open(virtgpu * gpu); + +static virt_gpu_result_t virtgpu_init_capset(virtgpu * gpu); +static virt_gpu_result_t virtgpu_init_context(virtgpu * gpu); + +static int virtgpu_ioctl_context_init(virtgpu * gpu, virgl_renderer_capset capset_id); +static int virtgpu_ioctl_get_caps(virtgpu * gpu, + virgl_renderer_capset id, + uint32_t version, + void * capset, + size_t capset_size); +static uint64_t virtgpu_ioctl_getparam(virtgpu * gpu, uint64_t param); +static void virtgpu_init_renderer_info(virtgpu * gpu); + +static void log_call_duration(long long call_duration_ns, const char * name); + +const uint64_t APIR_HANDSHAKE_MAX_WAIT_MS = 2 * 1000; // 2s +const uint64_t APIR_LOADLIBRARY_MAX_WAIT_MS = 60 * 1000; // 60s + +static int virtgpu_handshake(virtgpu * gpu) { + apir_encoder * encoder; + apir_decoder * decoder; + + encoder = remote_call_prepare(gpu, APIR_COMMAND_TYPE_HANDSHAKE, 0); + if (!encoder) { + GGML_ABORT(GGML_VIRTGPU "%s: failed to prepare the remote call encoder", __func__); + return 1; + } + + /* write handshake props */ + + uint32_t guest_major = APIR_PROTOCOL_MAJOR; + uint32_t guest_minor = APIR_PROTOCOL_MINOR; + apir_encode_uint32_t(encoder, &guest_major); + apir_encode_uint32_t(encoder, &guest_minor); + + /* *** */ + + uint32_t ret_magic; + long long call_duration_ns; + ret_magic = remote_call(gpu, encoder, &decoder, APIR_HANDSHAKE_MAX_WAIT_MS, &call_duration_ns); + log_call_duration(call_duration_ns, "API Remoting handshake"); + + if (!decoder) { + GGML_ABORT(GGML_VIRTGPU + "%s: failed to initiate the communication with the virglrenderer library. " + "Most likely, the wrong virglrenderer library was loaded in the hypervisor.", + __func__); + return 1; + } + + /* read handshake return values */ + + uint32_t host_major; + uint32_t host_minor; + + if (ret_magic != APIR_HANDSHAKE_MAGIC) { + GGML_ABORT(GGML_VIRTGPU "%s: handshake with the virglrenderer failed (code=%d | %s)", __func__, ret_magic, + apir_backend_initialize_error(ret_magic)); + } else { + apir_decode_uint32_t(decoder, &host_major); + apir_decode_uint32_t(decoder, &host_minor); + } + + remote_call_finish(gpu, encoder, decoder); + + if (ret_magic != APIR_HANDSHAKE_MAGIC) { + return 1; + } + + GGML_LOG_INFO(GGML_VIRTGPU "%s: Guest is running with %u.%u\n", __func__, guest_major, guest_minor); + GGML_LOG_INFO(GGML_VIRTGPU "%s: Host is running with %u.%u\n", __func__, host_major, host_minor); + + if (guest_major != host_major) { + GGML_LOG_ERROR(GGML_VIRTGPU "Host major (%d) and guest major (%d) version differ\n", host_major, guest_major); + } else if (guest_minor != host_minor) { + GGML_LOG_WARN(GGML_VIRTGPU "Host minor (%d) and guest minor (%d) version differ\n", host_minor, guest_minor); + } + + return 0; +} + +static ApirLoadLibraryReturnCode virtgpu_load_library(virtgpu * gpu) { + apir_encoder * encoder; + apir_decoder * decoder; + ApirLoadLibraryReturnCode ret; + + encoder = remote_call_prepare(gpu, APIR_COMMAND_TYPE_LOADLIBRARY, 0); + if (!encoder) { + GGML_ABORT(GGML_VIRTGPU "%s: hypercall error: failed to prepare the API Remoting command encoder", __func__); + return APIR_LOAD_LIBRARY_HYPERCALL_INITIALIZATION_ERROR; + } + + long long call_duration_ns; + + ret = (ApirLoadLibraryReturnCode) remote_call(gpu, encoder, &decoder, APIR_LOADLIBRARY_MAX_WAIT_MS, + &call_duration_ns); + log_call_duration(call_duration_ns, "API Remoting LoadLibrary"); + + if (!decoder) { + GGML_ABORT(GGML_VIRTGPU "%s: hypercall error: failed to trigger the API Remoting hypercall.\n", __func__); + return APIR_LOAD_LIBRARY_HYPERCALL_INITIALIZATION_ERROR; + } + + remote_call_finish(gpu, encoder, decoder); + + if (ret == APIR_LOAD_LIBRARY_SUCCESS) { + GGML_LOG_INFO(GGML_VIRTGPU "The API Remoting backend was successfully loaded and initialized\n"); + + return ret; + } + + // something wrong happened, find out what. + if (ret < APIR_LOAD_LIBRARY_INIT_BASE_INDEX) { + if (ret == APIR_LOAD_LIBRARY_ENV_VAR_MISSING) { + GGML_ABORT(GGML_VIRTGPU + "%s: virglrenderer could not open the API Remoting backend library, " + "some environment variables are missing. " + "Make sure virglrenderer is correctly configured by the hypervisor. (%s)", + __func__, apir_load_library_error(ret)); + } else if (ret == APIR_LOAD_LIBRARY_CANNOT_OPEN) { + GGML_ABORT(GGML_VIRTGPU + "%s: virglrenderer could not open the API Remoting backend library. " + "Make sure virglrenderer is correctly configured by the hypervisor. (%s)", + __func__, apir_load_library_error(ret)); + } else if (ret == APIR_LOAD_LIBRARY_ENV_VAR_MISSING) { + GGML_ABORT(GGML_VIRTGPU + "%s: could not load the backend library, some symbols are missing. " + "Make sure virglrenderer is correctly configured by the hypervisor. (%s) ", + __func__, apir_load_library_error(ret)); + } else { + GGML_ABORT(GGML_VIRTGPU "%s: virglrenderer could not load the API Remoting backend library. (%s - code %d)", + __func__, apir_load_library_error(ret), ret); + } + return ret; + } + + GGML_LOG_INFO(GGML_VIRTGPU "%s: virglrenderer successfully loaded the API Remoting backend library.\n", __func__); + + ApirLoadLibraryReturnCode apir_ret = (ApirLoadLibraryReturnCode) (ret - APIR_LOAD_LIBRARY_INIT_BASE_INDEX); + + if (apir_ret == APIR_LOAD_LIBRARY_CANNOT_OPEN) { + GGML_ABORT(GGML_VIRTGPU + "%s: the API Remoting backend library couldn't load the GGML backend library. " + "Make sure virglrenderer is correctly configured by the hypervisor. (%s)", + __func__, apir_load_library_error(apir_ret)); + } else if (apir_ret == APIR_LOAD_LIBRARY_SYMBOL_MISSING) { + GGML_ABORT( + GGML_VIRTGPU + "%s: the API Remoting backend library couldn't load the GGML backend library, some symbols are missing. " + "Make sure virglrenderer is correctly configured by the hypervisor. (%s)", + __func__, apir_load_library_error(apir_ret)); + } else if (apir_ret < APIR_LOAD_LIBRARY_INIT_BASE_INDEX) { + GGML_ABORT(GGML_VIRTGPU + "%s: the API Remoting backend library couldn't load the GGML backend library: apir code=%d | %s)", + __func__, apir_ret, apir_load_library_error(apir_ret)); + } else { + uint32_t lib_ret = apir_ret - APIR_LOAD_LIBRARY_INIT_BASE_INDEX; + GGML_ABORT(GGML_VIRTGPU + "%s: the API Remoting backend library failed to initialize its backend library: apir code=%d)", + __func__, lib_ret); + } + return ret; +} + +virtgpu * create_virtgpu() { + virtgpu * gpu = new virtgpu(); + + gpu->use_apir_capset = getenv("GGML_REMOTING_USE_APIR_CAPSET") != nullptr; + util_sparse_array_init(&gpu->shmem_array, sizeof(virtgpu_shmem), 1024); + + // Initialize mutex to protect shared data_shmem buffer + if (mtx_init(&gpu->data_shmem_mutex, mtx_plain) != thrd_success) { + delete gpu; + GGML_ABORT(GGML_VIRTGPU "%s: failed to initialize data_shmem mutex", __func__); + return NULL; + } + + if (virtgpu_open(gpu) != APIR_SUCCESS) { + GGML_LOG_ERROR(GGML_VIRTGPU "%s: failed to open the virtgpu device\n", __func__); + return NULL; + } + + if (virtgpu_init_capset(gpu) != APIR_SUCCESS) { + if (gpu->use_apir_capset) { + GGML_ABORT(GGML_VIRTGPU + "%s: failed to initialize the virtgpu APIR capset. Make sure that the virglrenderer library " + "supports it.", + __func__); + } else { + GGML_ABORT(GGML_VIRTGPU "%s: failed to initialize the virtgpu Venus capset", __func__); + } + return NULL; + } + + if (virtgpu_init_context(gpu) != APIR_SUCCESS) { + GGML_ABORT(GGML_VIRTGPU "%s: failed to initialize the GPU context", __func__); + return NULL; + } + + if (virtgpu_shmem_create(gpu, SHMEM_REPLY_SIZE, &gpu->reply_shmem)) { + GGML_ABORT(GGML_VIRTGPU "%s: failed to create the shared reply memory pages", __func__); + return NULL; + } + + if (virtgpu_shmem_create(gpu, SHMEM_DATA_SIZE, &gpu->data_shmem)) { + GGML_ABORT(GGML_VIRTGPU "%s: failed to create the shared data memory pages", __func__); + return NULL; + } + + if (virtgpu_handshake(gpu)) { + GGML_ABORT(GGML_VIRTGPU "%s: failed to handshake with the virglrenderer library", __func__); + return NULL; + } + + if (virtgpu_load_library(gpu) != APIR_LOAD_LIBRARY_SUCCESS) { + GGML_ABORT(GGML_VIRTGPU "%s: failed to load the backend library", __func__); + return NULL; + } + + return gpu; +} + +static virt_gpu_result_t virtgpu_open(virtgpu * gpu) { + drmDevicePtr devs[8]; + int count = drmGetDevices2(0, devs, ARRAY_SIZE(devs)); + if (count < 0) { + GGML_LOG_ERROR(GGML_VIRTGPU "%s: failed to enumerate DRM devices\n", __func__); + return APIR_ERROR_INITIALIZATION_FAILED; + } + + virt_gpu_result_t result = APIR_ERROR_INITIALIZATION_FAILED; + for (int i = 0; i < count; i++) { + result = virtgpu_open_device(gpu, devs[i]); + if (result == APIR_SUCCESS) { + break; + } + } + + drmFreeDevices(devs, count); + + return result; +} + +static virt_gpu_result_t virtgpu_open_device(virtgpu * gpu, const drmDevicePtr dev) { + const char * node_path = dev->nodes[DRM_NODE_RENDER]; + + int fd = open(node_path, O_RDWR | O_CLOEXEC); + if (fd < 0) { + GGML_ABORT(GGML_VIRTGPU "%s: failed to open %s", __func__, node_path); + return APIR_ERROR_INITIALIZATION_FAILED; + } + + drmVersionPtr version = drmGetVersion(fd); + if (!version || strcmp(version->name, "virtio_gpu") || version->version_major != 0) { + if (version) { + GGML_LOG_ERROR(GGML_VIRTGPU "%s: unknown DRM driver %s version %d\n", __func__, version->name, + version->version_major); + } else { + GGML_LOG_ERROR(GGML_VIRTGPU "%s: failed to get DRM driver version\n", __func__); + } + + if (version) { + drmFreeVersion(version); + } + close(fd); + return APIR_ERROR_INITIALIZATION_FAILED; + } + + gpu->fd = fd; + + drmFreeVersion(version); + + GGML_LOG_INFO(GGML_VIRTGPU "using DRM device %s\n", node_path); + + return APIR_SUCCESS; +} + +static virt_gpu_result_t virtgpu_init_context(virtgpu * gpu) { + assert(!gpu->capset.version); + const int ret = virtgpu_ioctl_context_init(gpu, gpu->capset.id); + if (ret) { + GGML_LOG_ERROR(GGML_VIRTGPU "%s: failed to initialize context: %s\n", __func__, strerror(errno)); + return APIR_ERROR_INITIALIZATION_FAILED; + } + + return APIR_SUCCESS; +} + +static virt_gpu_result_t virtgpu_init_capset(virtgpu * gpu) { + if (gpu->use_apir_capset) { + GGML_LOG_INFO(GGML_VIRTGPU "Using the APIR capset\n"); + gpu->capset.id = VIRTGPU_DRM_CAPSET_APIR; + } else { + GGML_LOG_INFO(GGML_VIRTGPU "Using the Venus capset\n"); + gpu->capset.id = VIRTGPU_DRM_CAPSET_VENUS; + } + gpu->capset.version = 0; + + int ret = + virtgpu_ioctl_get_caps(gpu, gpu->capset.id, gpu->capset.version, &gpu->capset.data, sizeof(gpu->capset.data)); + + if (ret) { + GGML_LOG_ERROR(GGML_VIRTGPU "%s: failed to get APIR v%d capset: %s\n", __func__, gpu->capset.version, + strerror(errno)); + return APIR_ERROR_INITIALIZATION_FAILED; + } + + assert(gpu->capset.data.supports_blob_resources); + + return APIR_SUCCESS; +} + +static int virtgpu_ioctl_context_init(virtgpu * gpu, virgl_renderer_capset capset_id) { + drm_virtgpu_context_set_param ctx_set_params[3] = { + { + .param = VIRTGPU_CONTEXT_PARAM_CAPSET_ID, + .value = capset_id, + }, + { + .param = VIRTGPU_CONTEXT_PARAM_NUM_RINGS, + .value = 1, + }, + { + .param = VIRTGPU_CONTEXT_PARAM_POLL_RINGS_MASK, + .value = 0, /* don't generate drm_events on fence signaling */ + }, + }; + + drm_virtgpu_context_init args = { + .num_params = ARRAY_SIZE(ctx_set_params), + .pad = 0, + .ctx_set_params = (uintptr_t) &ctx_set_params, + }; + + return virtgpu_ioctl(gpu, DRM_IOCTL_VIRTGPU_CONTEXT_INIT, &args); +} + +static int virtgpu_ioctl_get_caps(virtgpu * gpu, + virgl_renderer_capset id, + uint32_t version, + void * capset, + size_t capset_size) { + drm_virtgpu_get_caps args = { + .cap_set_id = id, + .cap_set_ver = version, + .addr = (uintptr_t) capset, + .size = (__u32) capset_size, + .pad = 0, + }; + + return virtgpu_ioctl(gpu, DRM_IOCTL_VIRTGPU_GET_CAPS, &args); +} + +static uint64_t virtgpu_ioctl_getparam(virtgpu * gpu, uint64_t param) { + /* val must be zeroed because kernel only writes the lower 32 bits */ + uint64_t val = 0; + drm_virtgpu_getparam args = { + .param = param, + .value = (uintptr_t) &val, + }; + + const int ret = virtgpu_ioctl(gpu, DRM_IOCTL_VIRTGPU_GETPARAM, &args); + return ret ? 0 : val; +} + +apir_encoder * remote_call_prepare(virtgpu * gpu, ApirCommandType apir_cmd_type, int32_t cmd_flags) { + /* + * Prepare the command encoder and its buffer + */ + + thread_local char encoder_buffer[4096]; + + thread_local apir_encoder enc; + enc = { + .cur = encoder_buffer, + .start = encoder_buffer, + .end = encoder_buffer + sizeof(encoder_buffer), + .fatal = false, + }; + + /* + * Fill the command encoder with the common args: + * - cmd_type (int32_t) + * - cmd_flags (int32_t) + * - reply res id (uint32_t) + */ + + int32_t cmd_type = apir_cmd_type; + + // for testing during the hypervisor transition + if (!gpu->use_apir_capset) { + cmd_type += VENUS_COMMAND_TYPE_LENGTH; + } + apir_encode_int32_t(&enc, &cmd_type); + apir_encode_int32_t(&enc, &cmd_flags); + + uint32_t reply_res_id = gpu->reply_shmem.res_id; + apir_encode_uint32_t(&enc, &reply_res_id); + + return &enc; +} + +void remote_call_finish(virtgpu * gpu, apir_encoder * enc, apir_decoder * dec) { + UNUSED(gpu); + + if (!enc) { + GGML_ABORT(GGML_VIRTGPU "%s: Invalid (null) encoder", __func__); + } + + if (!dec) { + GGML_ABORT(GGML_VIRTGPU "%s: Invalid (null) decoder", __func__); + } + + if (apir_encoder_get_fatal(enc)) { + GGML_LOG_ERROR(GGML_VIRTGPU "%s: Failed to encode the output parameters.", __func__); + } + + if (apir_decoder_get_fatal(dec)) { + GGML_LOG_ERROR(GGML_VIRTGPU "%s: Failed to decode the input parameters.", __func__); + } +} + +uint32_t remote_call(virtgpu * gpu, + apir_encoder * encoder, + apir_decoder ** decoder, + float max_wait_ms, + long long * call_duration_ns) { + /* + * Prepare the reply notification pointer + */ + + volatile std::atomic_uint * atomic_reply_notif = (volatile std::atomic_uint *) gpu->reply_shmem.mmap_ptr; + *atomic_reply_notif = 0; + + /* + * Trigger the execbuf ioctl + */ + + drm_virtgpu_execbuffer args = { + .flags = VIRTGPU_EXECBUF_RING_IDX, + .size = (uint32_t) (encoder->cur - encoder->start), + .command = (uintptr_t) encoder->start, + + .bo_handles = 0, + .num_bo_handles = 0, + + .fence_fd = 0, + .ring_idx = 0, + .syncobj_stride = 0, + .num_in_syncobjs = 0, + .num_out_syncobjs = 0, + .in_syncobjs = 0, + .out_syncobjs = 0, + }; + + *decoder = NULL; + + int ret = drmIoctl(gpu->fd, DRM_IOCTL_VIRTGPU_EXECBUFFER, &args); + + if (ret != 0) { + GGML_ABORT(GGML_VIRTGPU "%s: the virtgpu EXECBUFFER ioctl failed (%d)", __func__, ret); + } + + /* + * Wait for the response notification + */ + timer_data wait_host_reply_timer = { 0, 0, 0 }; + + start_timer(&wait_host_reply_timer); + + timespec ts_start, ts_end; + clock_gettime(CLOCK_MONOTONIC, &ts_start); + long long start_time = (long long) ts_start.tv_sec * 1000000000LL + ts_start.tv_nsec; + + bool timedout = false; + uint32_t notif_value = 0; + while (true) { + notif_value = std::atomic_load_explicit(atomic_reply_notif, std::memory_order_acquire); + + if (notif_value != 0) { + break; + } + + int64_t base_sleep_us = 15; + + os_time_sleep(base_sleep_us); + + if (max_wait_ms) { + clock_gettime(CLOCK_MONOTONIC, &ts_end); + long long end_time = (long long) ts_end.tv_sec * 1000000000LL + ts_end.tv_nsec; + float duration_ms = (end_time - start_time) / 1000000; + + if (duration_ms > max_wait_ms) { + timedout = true; + break; + } + } + } + + if (call_duration_ns) { + *call_duration_ns = stop_timer(&wait_host_reply_timer); + } + + if (max_wait_ms && timedout) { + GGML_LOG_ERROR(GGML_VIRTGPU "%s: timed out waiting for the host answer...\n", __func__); + return APIR_FORWARD_TIMEOUT; + } + + /* + * Prepare the decoder + */ + static apir_decoder response_dec; + response_dec.cur = (char *) gpu->reply_shmem.mmap_ptr + sizeof(*atomic_reply_notif); + response_dec.end = (char *) gpu->reply_shmem.mmap_ptr + gpu->reply_shmem.mmap_size; + *decoder = &response_dec; + + // extract the actual return value from the notif flag + uint32_t returned_value = notif_value - 1; + return returned_value; +} + +static void log_call_duration(long long call_duration_ns, const char * name) { + double call_duration_ms = (double) call_duration_ns / 1e6; // 1 millisecond = 1e6 nanoseconds + double call_duration_s = (double) call_duration_ns / 1e9; // 1 second = 1e9 nanoseconds + + if (call_duration_s > 1) { + GGML_LOG_INFO(GGML_VIRTGPU "waited %.2fs for the %s host reply...\n", call_duration_s, name); + } else if (call_duration_ms > 1) { + GGML_LOG_INFO(GGML_VIRTGPU "waited %.2fms for the %s host reply...\n", call_duration_ms, name); + } else { + GGML_LOG_INFO(GGML_VIRTGPU "waited %lldns for the %s host reply...\n", call_duration_ns, name); + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-virtgpu/virtgpu.h b/backend/llama.cpp/ggml/src/ggml-virtgpu/virtgpu.h new file mode 100644 index 0000000000000000000000000000000000000000..6b8de583893e4a76cb7475dd84478f864340559b --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-virtgpu/virtgpu.h @@ -0,0 +1,115 @@ +#pragma once + +// clang-format off +#include "virtgpu-utils.h" +#include "virtgpu-shm.h" +#include "virtgpu-apir.h" + +#include "backend/shared/api_remoting.h" +#include "backend/shared/apir_cs.h" + +#include +#include +#include +#include +#include +#include +#include + +#include + +#define VIRGL_RENDERER_UNSTABLE_APIS 1 +#include "apir_hw.h" +#include +#include "venus_hw.h" +// clang-format on + +#ifndef VIRTGPU_DRM_CAPSET_APIR +// Will be defined include/drm/virtgpu_drm.h when +// https://gitlab.freedesktop.org/virgl/virglrenderer/-/merge_requests/1590/diffs +// is merged +# define VIRTGPU_DRM_CAPSET_APIR 10 +#endif + +// Mesa/Virlgrenderer Venus internal. Only necessary during the +// Venus->APIR transition in Virglrenderer +#define VENUS_COMMAND_TYPE_LENGTH 331 + +#ifndef VIRTGPU_DRM_CAPSET_VENUS // only available with Linux >= v6.16 +# define VIRTGPU_DRM_CAPSET_VENUS 4 +#endif + +typedef uint32_t virgl_renderer_capset; + +/* from src/virtio/vulkan/vn_renderer_virtgpu.c */ +#define VIRTGPU_PCI_VENDOR_ID 0x1af4 +#define VIRTGPU_PCI_DEVICE_ID 0x1050 +#define VIRTGPU_BLOB_MEM_GUEST_VRAM 0x0004 +#define VIRTGPU_PARAM_GUEST_VRAM 9 + +#define SHMEM_DATA_SIZE 0x1830000 // 24MiB +#define SHMEM_REPLY_SIZE 0x4000 + +#define ARRAY_SIZE(x) (sizeof(x) / sizeof((x)[0])) + +enum virt_gpu_result_t { + APIR_SUCCESS = 0, + APIR_ERROR_INITIALIZATION_FAILED = -1, +}; + +#define PRINTFLIKE(f, a) __attribute__((format(__printf__, f, a))) + +struct virtgpu { + bool use_apir_capset; + + int fd; + + struct { + virgl_renderer_capset id; + uint32_t version; + virgl_renderer_capset_apir data; + } capset; + + util_sparse_array shmem_array; + + /* APIR communication pages */ + virtgpu_shmem reply_shmem; + virtgpu_shmem data_shmem; + + /* Mutex to protect shared data_shmem buffer from concurrent access */ + mtx_t data_shmem_mutex; + + /* Cached device information to prevent memory leaks and race conditions */ + struct { + char * description; + char * name; + int32_t device_count; + uint32_t type; + size_t memory_free; + size_t memory_total; + } cached_device_info; + + /* Cached buffer type information to prevent memory leaks and race conditions */ + struct { + apir_buffer_type_host_handle_t host_handle; + char * name; + size_t alignment; + size_t max_size; + } cached_buffer_type; +}; + +static inline int virtgpu_ioctl(virtgpu * gpu, unsigned long request, void * args) { + return drmIoctl(gpu->fd, request, args); +} + +virtgpu * create_virtgpu(); + +apir_encoder * remote_call_prepare(virtgpu * gpu, ApirCommandType apir_cmd_type, int32_t cmd_flags); + +uint32_t remote_call(virtgpu * gpu, + apir_encoder * enc, + apir_decoder ** dec, + float max_wait_ms, + long long * call_duration_ns); + +void remote_call_finish(virtgpu * gpu, apir_encoder * enc, apir_decoder * dec); diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/CMakeLists.txt b/backend/llama.cpp/ggml/src/ggml-vulkan/CMakeLists.txt new file mode 100644 index 0000000000000000000000000000000000000000..5aeb6e97b159b8a0a0497a995096bdcd19be62cb --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/CMakeLists.txt @@ -0,0 +1,236 @@ +cmake_minimum_required(VERSION 3.19) +cmake_policy(SET CMP0114 NEW) +cmake_policy(SET CMP0116 NEW) +if (POLICY CMP0147) + # Parallel build custom build steps + cmake_policy(SET CMP0147 NEW) +endif() + +find_package(Vulkan COMPONENTS glslc REQUIRED) + +if (DEFINED ENV{VULKAN_SDK}) + list(APPEND CMAKE_PREFIX_PATH "$ENV{VULKAN_SDK}") +endif() +find_package(SPIRV-Headers CONFIG REQUIRED) + +if (CMAKE_CXX_COMPILER_ID STREQUAL "MSVC") + # Parallel build object files + add_definitions(/MP) +endif() + +function(detect_host_compiler) + if (CMAKE_HOST_SYSTEM_NAME STREQUAL "Windows") + find_program(HOST_C_COMPILER NAMES cl gcc clang NO_CMAKE_FIND_ROOT_PATH) + find_program(HOST_CXX_COMPILER NAMES cl g++ clang++ NO_CMAKE_FIND_ROOT_PATH) + else() + find_program(HOST_C_COMPILER NAMES gcc clang NO_CMAKE_FIND_ROOT_PATH) + find_program(HOST_CXX_COMPILER NAMES g++ clang++ NO_CMAKE_FIND_ROOT_PATH) + endif() + set(HOST_C_COMPILER "${HOST_C_COMPILER}" PARENT_SCOPE) + set(HOST_CXX_COMPILER "${HOST_CXX_COMPILER}" PARENT_SCOPE) +endfunction() + +# Function to test shader extension support +# Parameters: +# EXTENSION_NAME - Name of the extension to test (e.g., "GL_EXT_integer_dot_product") +# TEST_SHADER_FILE - Path to the test shader file +# RESULT_VARIABLE - Name of the variable to set (ON/OFF) based on test result +function(test_shader_extension_support EXTENSION_NAME TEST_SHADER_FILE RESULT_VARIABLE) + execute_process( + COMMAND ${Vulkan_GLSLC_EXECUTABLE} -o - -fshader-stage=compute --target-env=vulkan1.3 "${TEST_SHADER_FILE}" + OUTPUT_VARIABLE glslc_output + ERROR_VARIABLE glslc_error + ) + + if (${glslc_error} MATCHES ".*extension not supported: ${EXTENSION_NAME}.*") + message(STATUS "${EXTENSION_NAME} not supported by glslc") + set(${RESULT_VARIABLE} OFF PARENT_SCOPE) + else() + message(STATUS "${EXTENSION_NAME} supported by glslc") + set(${RESULT_VARIABLE} ON PARENT_SCOPE) + add_compile_definitions(${RESULT_VARIABLE}) + + # Ensure the extension support is forwarded to vulkan-shaders-gen + list(APPEND VULKAN_SHADER_GEN_CMAKE_ARGS -D${RESULT_VARIABLE}=ON) + set(VULKAN_SHADER_GEN_CMAKE_ARGS "${VULKAN_SHADER_GEN_CMAKE_ARGS}" PARENT_SCOPE) + endif() +endfunction() + +if (Vulkan_FOUND) + message(STATUS "Vulkan found") + + ggml_add_backend_library(ggml-vulkan + ggml-vulkan.cpp + ../../include/ggml-vulkan.h + ) + + set(VULKAN_SHADER_GEN_CMAKE_ARGS "") + + # Test all shader extensions + test_shader_extension_support( + "GL_KHR_cooperative_matrix" + "${CMAKE_CURRENT_SOURCE_DIR}/vulkan-shaders/feature-tests/coopmat.comp" + "GGML_VULKAN_COOPMAT_GLSLC_SUPPORT" + ) + + test_shader_extension_support( + "GL_NV_cooperative_matrix2" + "${CMAKE_CURRENT_SOURCE_DIR}/vulkan-shaders/feature-tests/coopmat2.comp" + "GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT" + ) + + test_shader_extension_support( + "GL_NV_cooperative_matrix_decode_vector" + "${CMAKE_CURRENT_SOURCE_DIR}/vulkan-shaders/feature-tests/coopmat2_decode_vector.comp" + "GGML_VULKAN_COOPMAT2_DECODE_VECTOR_GLSLC_SUPPORT" + ) + + test_shader_extension_support( + "GL_EXT_integer_dot_product" + "${CMAKE_CURRENT_SOURCE_DIR}/vulkan-shaders/feature-tests/integer_dot.comp" + "GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT" + ) + + test_shader_extension_support( + "GL_EXT_bfloat16" + "${CMAKE_CURRENT_SOURCE_DIR}/vulkan-shaders/feature-tests/bfloat16.comp" + "GGML_VULKAN_BFLOAT16_GLSLC_SUPPORT" + ) + + target_link_libraries(ggml-vulkan PRIVATE Vulkan::Vulkan) + target_include_directories(ggml-vulkan PRIVATE ${CMAKE_CURRENT_BINARY_DIR}) + + # Workaround to the "can't dereference invalidated vector iterator" bug in clang-cl debug build + # Possibly relevant: https://stackoverflow.com/questions/74748276/visual-studio-no-displays-the-correct-length-of-stdvector + if (MSVC AND CMAKE_CXX_COMPILER_ID STREQUAL "Clang") + add_compile_definitions(_ITERATOR_DEBUG_LEVEL=0) + endif() + + if (GGML_VULKAN_CHECK_RESULTS) + add_compile_definitions(GGML_VULKAN_CHECK_RESULTS) + # the result-checking path computes a CPU reference graph via + # ggml_graph_compute_with_ctx(), which is defined in ggml-cpu + target_link_libraries(ggml-vulkan PRIVATE ggml-cpu) + endif() + + if (GGML_VULKAN_DEBUG) + add_compile_definitions(GGML_VULKAN_DEBUG) + endif() + + if (GGML_VULKAN_MEMORY_DEBUG) + add_compile_definitions(GGML_VULKAN_MEMORY_DEBUG) + endif() + + if (GGML_VULKAN_SHADER_DEBUG_INFO) + add_compile_definitions(GGML_VULKAN_SHADER_DEBUG_INFO) + list(APPEND VULKAN_SHADER_GEN_CMAKE_ARGS -DGGML_VULKAN_SHADER_DEBUG_INFO=ON) + endif() + + if (GGML_VULKAN_VALIDATE) + add_compile_definitions(GGML_VULKAN_VALIDATE) + endif() + + if (GGML_VULKAN_RUN_TESTS) + add_compile_definitions(GGML_VULKAN_RUN_TESTS) + # the test path also calls ggml_graph_compute_with_ctx() (ggml-cpu) + target_link_libraries(ggml-vulkan PRIVATE ggml-cpu) + endif() + + # Set up toolchain for host compilation whether cross-compiling or not + if (CMAKE_CROSSCOMPILING) + if (GGML_VULKAN_SHADERS_GEN_TOOLCHAIN) + set(HOST_CMAKE_TOOLCHAIN_FILE ${GGML_VULKAN_SHADERS_GEN_TOOLCHAIN}) + else() + detect_host_compiler() + if (NOT HOST_C_COMPILER OR NOT HOST_CXX_COMPILER) + message(FATAL_ERROR "Host compiler not found") + else() + message(STATUS "Host compiler: ${HOST_C_COMPILER} ${HOST_CXX_COMPILER}") + endif() + configure_file(${CMAKE_CURRENT_SOURCE_DIR}/cmake/host-toolchain.cmake.in ${CMAKE_BINARY_DIR}/host-toolchain.cmake @ONLY) + set(HOST_CMAKE_TOOLCHAIN_FILE ${CMAKE_BINARY_DIR}/host-toolchain.cmake) + endif() + else() + # For non-cross-compiling, use empty toolchain (use host compiler) + set(HOST_CMAKE_TOOLCHAIN_FILE "") + endif() + + include(ExternalProject) + + if (CMAKE_CROSSCOMPILING) + list(APPEND VULKAN_SHADER_GEN_CMAKE_ARGS -DCMAKE_TOOLCHAIN_FILE=${HOST_CMAKE_TOOLCHAIN_FILE}) + message(STATUS "vulkan-shaders-gen toolchain file: ${HOST_CMAKE_TOOLCHAIN_FILE}") + endif() + + ExternalProject_Add( + vulkan-shaders-gen + SOURCE_DIR ${CMAKE_CURRENT_SOURCE_DIR}/vulkan-shaders + CMAKE_ARGS -DCMAKE_INSTALL_PREFIX=${CMAKE_BINARY_DIR}/$ + -DCMAKE_INSTALL_BINDIR=. + -DCMAKE_BUILD_TYPE=$ + ${VULKAN_SHADER_GEN_CMAKE_ARGS} + + BUILD_COMMAND ${CMAKE_COMMAND} --build . --config $ + BUILD_ALWAYS TRUE + + # NOTE: When DESTDIR is set using Makefile generators and + # "make install" triggers the build step, vulkan-shaders-gen + # would be installed into the DESTDIR prefix, so it is unset + # to ensure that does not happen. + + INSTALL_COMMAND ${CMAKE_COMMAND} -E env --unset=DESTDIR + ${CMAKE_COMMAND} --install . --config $ + ) + + set (_ggml_vk_host_suffix $,.exe,>) + set (_ggml_vk_genshaders_dir "${CMAKE_BINARY_DIR}/$") + set (_ggml_vk_genshaders_cmd "${_ggml_vk_genshaders_dir}/vulkan-shaders-gen${_ggml_vk_host_suffix}") + set (_ggml_vk_header "${CMAKE_CURRENT_BINARY_DIR}/ggml-vulkan-shaders.hpp") + set (_ggml_vk_input_dir "${CMAKE_CURRENT_SOURCE_DIR}/vulkan-shaders") + set (_ggml_vk_output_dir "${CMAKE_CURRENT_BINARY_DIR}/vulkan-shaders.spv") + + file(GLOB _ggml_vk_shader_files CONFIGURE_DEPENDS "${_ggml_vk_input_dir}/*.comp") + + # Because external projects do not provide source-level tracking, + # the vulkan-shaders-gen sources need to be explicitly added to + # ensure that changes will cascade into shader re-generation. + + file(GLOB _ggml_vk_shaders_gen_sources + CONFIGURE_DEPENDS "${_ggml_vk_input_dir}/*.cpp" + "${_ggml_vk_input_dir}/*.h") + + add_custom_command( + OUTPUT ${_ggml_vk_header} + COMMAND ${_ggml_vk_genshaders_cmd} + --output-dir ${_ggml_vk_output_dir} + --target-hpp ${_ggml_vk_header} + DEPENDS ${_ggml_vk_shaders_gen_sources} + vulkan-shaders-gen + COMMENT "Generate vulkan shaders header" + ) + target_sources(ggml-vulkan PRIVATE ${_ggml_vk_header}) + + foreach (file_full ${_ggml_vk_shader_files}) + get_filename_component(file ${file_full} NAME) + set (_ggml_vk_target_cpp "${CMAKE_CURRENT_BINARY_DIR}/${file}.cpp") + + add_custom_command( + OUTPUT ${_ggml_vk_target_cpp} + DEPFILE ${_ggml_vk_target_cpp}.d + COMMAND ${_ggml_vk_genshaders_cmd} + --glslc ${Vulkan_GLSLC_EXECUTABLE} + --source ${file_full} + --output-dir ${_ggml_vk_output_dir} + --target-hpp ${_ggml_vk_header} + --target-cpp ${_ggml_vk_target_cpp} + DEPENDS ${file_full} + ${_ggml_vk_shaders_gen_sources} + vulkan-shaders-gen + COMMENT "Generate vulkan shaders for ${file}" + ) + target_sources(ggml-vulkan PRIVATE ${_ggml_vk_target_cpp}) + endforeach() + +else() + message(WARNING "Vulkan not found") +endif() diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/cmake/host-toolchain.cmake.in b/backend/llama.cpp/ggml/src/ggml-vulkan/cmake/host-toolchain.cmake.in new file mode 100644 index 0000000000000000000000000000000000000000..2d8a85696d374e68822f854d6e59247c90a934a8 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/cmake/host-toolchain.cmake.in @@ -0,0 +1,15 @@ +set(CMAKE_BUILD_TYPE Release) +set(CMAKE_C_FLAGS -O2) +set(CMAKE_CXX_FLAGS -O2) +set(CMAKE_FIND_ROOT_PATH_MODE_PROGRAM NEVER) +set(CMAKE_FIND_ROOT_PATH_MODE_LIBRARY NEVER) +set(CMAKE_FIND_ROOT_PATH_MODE_INCLUDE NEVER) +set(CMAKE_C_COMPILER "@HOST_C_COMPILER@") +set(CMAKE_CXX_COMPILER "@HOST_CXX_COMPILER@") +set(CMAKE_RUNTIME_OUTPUT_DIRECTORY @CMAKE_RUNTIME_OUTPUT_DIRECTORY@) + +if("@CMAKE_C_COMPILER_ID@" STREQUAL "MSVC") + foreach(CONFIG IN ITEMS DEBUG RELEASE MINSIZEREL RELWITHDEBINFO) + set(CMAKE_RUNTIME_OUTPUT_DIRECTORY_${CONFIG} ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}) + endforeach() +endif() diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/backend/llama.cpp/ggml/src/ggml-vulkan/ggml-vulkan.cpp new file mode 100644 index 0000000000000000000000000000000000000000..1134a3bbd90c10121796641e87abb54d3392d53e --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/ggml-vulkan.cpp @@ -0,0 +1,18843 @@ +#include "ggml-vulkan.h" +#include +#if defined(GGML_VULKAN_RUN_TESTS) || defined(GGML_VULKAN_CHECK_RESULTS) +#include +#include "ggml-cpu.h" +#endif + +// See https://github.com/KhronosGroup/Vulkan-Hpp?tab=readme-ov-file#extensions--per-device-function-pointers- +#define VULKAN_HPP_DISPATCH_LOADER_DYNAMIC 1 +// We use VULKAN_HPP_DEFAULT_DISPATCHER, but not VULKAN_HPP_DEFAULT_DISPATCH_LOADER_DYNAMIC_STORAGE +// to avoid conflicts with applications or other libraries who might use it. +#if VK_HEADER_VERSION >= 301 +namespace vk::detail { class DispatchLoaderDynamic; } +using vk::detail::DispatchLoaderDynamic; +#else +namespace vk { class DispatchLoaderDynamic; } +using vk::DispatchLoaderDynamic; +#endif +DispatchLoaderDynamic & ggml_vk_default_dispatcher(); +#define VULKAN_HPP_DEFAULT_DISPATCHER ggml_vk_default_dispatcher() + +#include + +// Fallback definitions for VK_NV_cooperative_matrix_decode_vector in case the +// installed Vulkan headers predate the extension. +#ifndef VK_NV_cooperative_matrix_decode_vector +#define VK_NV_cooperative_matrix_decode_vector 1 +#define VK_NV_COOPERATIVE_MATRIX_DECODE_VECTOR_EXTENSION_NAME "VK_NV_cooperative_matrix_decode_vector" +#define VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_COOPERATIVE_MATRIX_DECODE_VECTOR_FEATURES_NV ((VkStructureType)1000689000) +typedef struct VkPhysicalDeviceCooperativeMatrixDecodeVectorFeaturesNV { + VkStructureType sType; + void* pNext; + VkBool32 cooperativeMatrixDecodeVector; +} VkPhysicalDeviceCooperativeMatrixDecodeVectorFeaturesNV; +#endif + +// SPIR-V Headers: different SDK installations expose different include paths. +// LunarG Vulkan SDK on Windows typically provides . +// Linux packages, MSYS2 and MinGW often use the Khronos layout . +#if __has_include() +# include +#elif __has_include() +# include +#elif __has_include() +# include +#else + // Fallback to let the compiler throw a standard "file not found" error +# include +#endif + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#if defined(_MSC_VER) +# define NOMINMAX 1 +# include +# define YIELD() YieldProcessor() +#elif defined(__clang__) || defined(__GNUC__) +# if defined(__x86_64__) ||defined(__i386__) +# include +# define YIELD() _mm_pause() +# elif defined(__arm__) || defined(__aarch64__) +# if defined(__clang__) +# include +# define YIELD() __yield() +# else +# define YIELD() asm volatile("yield") +# endif +# endif +#endif + +#if !defined(YIELD) +#define YIELD() +#endif + +#include "ggml-impl.h" +#include "ggml-backend-impl.h" + +#include "ggml-vulkan-shaders.hpp" + +// remove this once it's more widely available in the SDK +#if !defined(VK_KHR_shader_bfloat16) + +#define VK_KHR_shader_bfloat16 1 +#define VK_KHR_SHADER_BFLOAT16_SPEC_VERSION 1 +#define VK_KHR_SHADER_BFLOAT16_EXTENSION_NAME "VK_KHR_shader_bfloat16" +#define VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_SHADER_BFLOAT16_FEATURES_KHR ((VkStructureType)1000141000) +#define VK_COMPONENT_TYPE_BFLOAT16_KHR ((VkComponentTypeKHR)1000141000) + +typedef struct VkPhysicalDeviceShaderBfloat16FeaturesKHR { + VkStructureType sType; + void* pNext; + VkBool32 shaderBFloat16Type; + VkBool32 shaderBFloat16DotProduct; + VkBool32 shaderBFloat16CooperativeMatrix; +} VkPhysicalDeviceShaderBfloat16FeaturesKHR; +#endif + +#if !defined(VK_VALVE_shader_mixed_float_dot_product) +#define VK_VALVE_shader_mixed_float_dot_product 1 +#define VK_VALVE_SHADER_MIXED_FLOAT_DOT_PRODUCT_SPEC_VERSION 1 +#define VK_VALVE_SHADER_MIXED_FLOAT_DOT_PRODUCT_EXTENSION_NAME "VK_VALVE_shader_mixed_float_dot_product" +#define VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_SHADER_MIXED_FLOAT_DOT_PRODUCT_FEATURES_VALVE ((VkStructureType)1000673000) +typedef struct VkPhysicalDeviceShaderMixedFloatDotProductFeaturesVALVE { + VkStructureType sType; + void* pNext; + VkBool32 shaderMixedFloatDotProductFloat16AccFloat32; + VkBool32 shaderMixedFloatDotProductFloat16AccFloat16; + VkBool32 shaderMixedFloatDotProductBFloat16Acc; + VkBool32 shaderMixedFloatDotProductFloat8AccFloat32; +} VkPhysicalDeviceShaderMixedFloatDotProductFeaturesVALVE; +#endif + +#define ROUNDUP_POW2(M, N) (((M) + (N) - 1) & ~((N) - 1)) +#define CEIL_DIV(M, N) (((M) / (N)) + (((M) % (N)) != 0)) +static bool is_pow2(uint32_t x) { return x > 1 && (x & (x-1)) == 0; } + +#define VK_VENDOR_ID_AMD 0x1002 +#define VK_VENDOR_ID_APPLE 0x106b +#define VK_VENDOR_ID_INTEL 0x8086 +#define VK_VENDOR_ID_NVIDIA 0x10de +#define VK_VENDOR_ID_QUALCOMM 0x5143 + +#define VK_DEVICE_DESCRIPTOR_POOL_SIZE 256 + +#define VK_CHECK(err, msg) \ + do { \ + vk::Result err_ = (err); \ + if (err_ != vk::Result::eSuccess) { \ + fprintf(stderr, "ggml_vulkan: %s error %s at %s:%d\n", \ + #err, to_string(err_).c_str(), __FILE__, __LINE__); \ + exit(1); \ + } \ + } while (0) + +#ifdef GGML_VULKAN_DEBUG +#define VK_LOG_DEBUG(msg) std::cerr << msg << std::endl +#else +#define VK_LOG_DEBUG(msg) ((void) 0) +#endif // GGML_VULKAN_DEBUG + +struct ggml_backend_vk_context; + +#define MAX_PARAMETER_COUNT 12 +// Max number of adds that can be fused without exceeding MAX_PARAMETER_COUNT. +#define MAX_FUSED_ADDS (MAX_PARAMETER_COUNT - 3) + +typedef std::shared_ptr vk_pipeline; + +struct vk_pipeline_struct { + std::string name; + vk::ShaderModule shader_module; + vk::PipelineLayout layout; + vk::Pipeline pipeline; + uint32_t push_constant_size; + uint32_t parameter_count; + std::array wg_denoms; + uint32_t align; + // true if fields have been set by ggml_vk_create_pipeline + bool initialized {}; + // true while a compile is in flight, used to dedupe concurrent claims. + // Protected by device->compile_mutex. + bool compile_pending {}; + // set to true when the shader has been compiled + std::atomic compiled {}; + // number of registers used, extracted from pipeline executable properties + uint32_t register_count {}; + +#if defined(VK_EXT_shader_64bit_indexing) + bool is_64b_indexing {}; +#endif + // linked list of pipelines for multiple compilation variants. + // currently only used to compile a 64-bit indexing variant. + vk_pipeline next; +}; + +typedef std::weak_ptr vk_pipeline_ref; + +static void ggml_vk_destroy_pipeline(vk::Device& device, vk_pipeline& pipeline); + +struct vk_matmul_pipeline_struct { + vk_pipeline l, m, s; + vk_pipeline a_l, a_m, a_s; + // Returns true when all unaligned pipelines are null. + // We only check for unaligned variants since one of the unaligned pipelines must exist + // while aligned pipelines are optional + bool is_empty() const { + return l == nullptr && m == nullptr && s == nullptr; + } +}; +typedef std::shared_ptr vk_matmul_pipeline; + +struct vk_matmul_pipeline2 { + vk_matmul_pipeline2() { + f16acc = std::make_shared(); + f32acc = std::make_shared(); + } + vk_matmul_pipeline f32acc; + vk_matmul_pipeline f16acc; +}; + +struct vk_device_struct; +typedef std::shared_ptr vk_device; +typedef std::weak_ptr vk_device_ref; + +struct vk_buffer_struct; +typedef std::shared_ptr vk_buffer; +typedef std::weak_ptr vk_buffer_ref; + +struct ggml_backend_vk_buffer_type_context { + std::string name; + vk_device device; +}; + +struct vk_queue; + +struct vk_command_buffer { + vk::CommandBuffer buf; + uint64_t use_counter = 0; + bool in_use = false; +}; + +// Stores command pool/buffers. There's an instance of this +// for each (context,queue) pair and for each (device,queue) pair. +struct vk_command_pool { + void init(vk_device& device, vk_queue *q_); + void destroy(vk::Device& device); + + vk::CommandPool pool; + // Using deque so the pointers to command buffers + // remain valid even if we add more + std::deque cmd_buffers; + + vk_queue *q; + + size_t buffers_in_use() const { + return std::count_if(cmd_buffers.begin(), cmd_buffers.end(), + [](const auto& cb) { return cb.in_use; }); + } +}; + +// Prevent simultaneous submissions to the same queue. +// This could be per vk_queue if we stopped having two vk_queue structures +// sharing the same vk::Queue. +static std::mutex queue_mutex; + +struct vk_queue { + uint32_t queue_family_index; + vk::Queue queue; + + vk_command_pool cmd_pool; + + vk::PipelineStageFlags stage_flags; + + bool transfer_only; + + // copy everything except the cmd_pool + void copyFrom(vk_queue &other) { + queue_family_index = other.queue_family_index; + queue = other.queue; + stage_flags = other.stage_flags; + transfer_only = other.transfer_only; + } +}; + +static const char * ggml_backend_vk_buffer_type_name(ggml_backend_buffer_type_t buft); +static ggml_backend_buffer_t ggml_backend_vk_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size); +static size_t ggml_backend_vk_buffer_type_get_alignment(ggml_backend_buffer_type_t buft); +static size_t ggml_backend_vk_buffer_type_get_max_size(ggml_backend_buffer_type_t buft); +static size_t ggml_backend_vk_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor); +static ggml_backend_buffer_type_i ggml_backend_vk_buffer_type_interface = { + /* .get_name = */ ggml_backend_vk_buffer_type_name, + /* .alloc_buffer = */ ggml_backend_vk_buffer_type_alloc_buffer, + /* .get_alignment = */ ggml_backend_vk_buffer_type_get_alignment, + /* .get_max_size = */ ggml_backend_vk_buffer_type_get_max_size, + /* .get_alloc_size = */ ggml_backend_vk_buffer_type_get_alloc_size, + /* .is_host = */ NULL, +}; + +class vk_memory_logger; +class vk_perf_logger; +static void ggml_vk_destroy_buffer(vk_buffer& buf); +static void ggml_vk_synchronize(ggml_backend_vk_context * ctx); + +static constexpr uint32_t mul_mat_vec_max_cols = 8; +static constexpr uint32_t p021_max_gqa_ratio = 8; + +enum vk_device_architecture { + OTHER, + AMD_GCN, + AMD_RDNA1, + AMD_RDNA2, + AMD_RDNA3, + INTEL_XE1, + INTEL_XE2, + NVIDIA_PRE_TURING, + NVIDIA_TURING, +}; + +static vk_device_architecture get_device_architecture(const vk::PhysicalDevice& device) { + vk::PhysicalDeviceProperties props = device.getProperties(); + + if (props.vendorID == VK_VENDOR_ID_AMD) { + const std::vector ext_props = device.enumerateDeviceExtensionProperties(); + + bool amd_shader_core_properties = false; + bool integer_dot_product = false; + bool subgroup_size_control = false; + + for (const auto& properties : ext_props) { + if (strcmp("VK_AMD_shader_core_properties", properties.extensionName) == 0) { + amd_shader_core_properties = true; + } else if (strcmp("VK_KHR_shader_integer_dot_product", properties.extensionName) == 0) { + integer_dot_product = true; + } else if (strcmp("VK_EXT_subgroup_size_control", properties.extensionName) == 0) { + subgroup_size_control = true; + } + } + + if (!amd_shader_core_properties || !integer_dot_product || !subgroup_size_control) { + return vk_device_architecture::OTHER; + } + + vk::PhysicalDeviceProperties2 props2; + vk::PhysicalDeviceShaderCorePropertiesAMD shader_core_props_amd; + vk::PhysicalDeviceShaderIntegerDotProductPropertiesKHR integer_dot_props; + vk::PhysicalDeviceSubgroupSizeControlPropertiesEXT subgroup_size_control_props; + + props2.pNext = &shader_core_props_amd; + shader_core_props_amd.pNext = &integer_dot_props; + integer_dot_props.pNext = &subgroup_size_control_props; + + device.getProperties2(&props2); + + if (subgroup_size_control_props.maxSubgroupSize == 64 && subgroup_size_control_props.minSubgroupSize == 64) { + return vk_device_architecture::AMD_GCN; + } + if (subgroup_size_control_props.maxSubgroupSize == 64 && subgroup_size_control_props.minSubgroupSize == 32) { + // RDNA + if (shader_core_props_amd.wavefrontsPerSimd == 20) { + return vk_device_architecture::AMD_RDNA1; + } + if (integer_dot_props.integerDotProduct4x8BitPackedMixedSignednessAccelerated) { + return vk_device_architecture::AMD_RDNA3; + } + return vk_device_architecture::AMD_RDNA2; + } + } else if (props.vendorID == VK_VENDOR_ID_INTEL) { + const std::vector ext_props = device.enumerateDeviceExtensionProperties(); + + bool subgroup_size_control = false; + bool integer_dot_product = false; + + for (const auto& properties : ext_props) { + if (strcmp("VK_EXT_subgroup_size_control", properties.extensionName) == 0) { + subgroup_size_control = true; + } else if (strcmp("VK_KHR_shader_integer_dot_product", properties.extensionName) == 0) { + integer_dot_product = true; + } + } + + if (!subgroup_size_control || !integer_dot_product) { + return vk_device_architecture::OTHER; + } + + vk::PhysicalDeviceProperties2 props2; + vk::PhysicalDeviceSubgroupSizeControlPropertiesEXT subgroup_size_control_props; + vk::PhysicalDeviceShaderIntegerDotProductPropertiesKHR integer_dot_props; + + props2.pNext = &subgroup_size_control_props; + subgroup_size_control_props.pNext = &integer_dot_props; + device.getProperties2(&props2); + + if (subgroup_size_control_props.minSubgroupSize == 16) { + // Xe2 architecture uses SIMD16 while previous Xe and Gen architecture uses SIMD8. + // Minimum subgroup size matches the SIMD width so we distinguish architecture by checking this value. + // https://www.intel.com/content/www/us/en/content-details/824434/2024-intel-tech-tour-xe2-and-lunar-lake-s-gpu.html + // https://www.intel.com/content/www/us/en/docs/oneapi/optimization-guide-gpu/2025-0/intel-xe-gpu-architecture.html + return vk_device_architecture::INTEL_XE2; + } else if (subgroup_size_control_props.minSubgroupSize == 8 && + integer_dot_product && integer_dot_props.integerDotProduct4x8BitPackedSignedAccelerated) { + return vk_device_architecture::INTEL_XE1; + } + } else if (props.vendorID == VK_VENDOR_ID_NVIDIA) { + const std::vector ext_props = device.enumerateDeviceExtensionProperties(); + + bool cooperative_matrix = false; + bool sm_builtins = false; + + // Detect "pre-turing" based on lack of coopmat support. + for (const auto& properties : ext_props) { + if (strcmp("VK_KHR_cooperative_matrix", properties.extensionName) == 0) { + cooperative_matrix = true; + } else if (strcmp("VK_NV_shader_sm_builtins", properties.extensionName) == 0) { + sm_builtins = true; + } + } + + if (!cooperative_matrix) { + return vk_device_architecture::NVIDIA_PRE_TURING; + } + + if (sm_builtins) { + vk::PhysicalDeviceProperties2 props2; + vk::PhysicalDeviceShaderSMBuiltinsPropertiesNV sm_props; + + props2.pNext = &sm_props; + + device.getProperties2(&props2); + + // Turing has 32, following architectures have 48 + if (sm_props.shaderWarpsPerSM == 32) { + return vk_device_architecture::NVIDIA_TURING; + } + } + } + return vk_device_architecture::OTHER; +} + +enum vk_conv_shapes { + CONV_SHAPE_128x128, + CONV_SHAPE_64x32, + CONV_SHAPE_32x256, + CONV_SHAPE_64x128, + CONV_SHAPE_COUNT, +}; + +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 + { 64, 128, 16 }, // CONV_SHAPE_64x128 +}; + +enum dmmv_wg_sizes { + DMMV_WG_SIZE_SUBGROUP, + DMMV_WG_SIZE_LARGE, + DMMV_WG_SIZE_COUNT, +}; + +enum FaCodePath { + FA_SCALAR, + FA_COOPMAT1, + FA_COOPMAT2, +}; + +struct vk_fa_pipeline_state { + uint32_t HSK, HSV; + uint32_t Br, Bc; + uint32_t D_split, row_split; + bool shmem_staging; + FaCodePath path; + uint32_t workgroup_size, subgroup_size; + bool aligned; + bool f32acc; + uint32_t flags; + uint32_t limit_occupancy_shmem; + ggml_type k_type; + ggml_type v_type; + + bool operator<(const vk_fa_pipeline_state &b) const { + return std::tie(HSK, HSV, Br, Bc, D_split, row_split, shmem_staging, path, workgroup_size, subgroup_size, aligned, f32acc, flags, limit_occupancy_shmem, k_type, v_type) < + std::tie(b.HSK, b.HSV, b.Br, b.Bc, b.D_split, b.row_split, b.shmem_staging, b.path, b.workgroup_size, b.subgroup_size, b.aligned, b.f32acc, b.flags, b.limit_occupancy_shmem, b.k_type, b.v_type); + } +}; + +struct vk_conv2d_pipeline_state { + vk_conv2d_pipeline_state(uint32_t s0, uint32_t s1, uint32_t p0, uint32_t p1, uint32_t d0, uint32_t d1, uint32_t KW, uint32_t KH, uint32_t aligned) + : s0(s0), s1(s1), p0(p0), p1(p1), d0(d0), d1(d1), KW(KW), KH(KH), aligned(aligned) {} + + uint32_t s0, s1, p0, p1, d0, d1, KW, KH; + // when set, shader can skip K/CRS/NPQ bounds checks and address clamps + uint32_t aligned; + + bool operator<(const vk_conv2d_pipeline_state &b) const { + return std::tie(s0, s1, p0, p1, d0, d1, KW, KH, aligned) < + std::tie(b.s0, b.s1, b.p0, b.p1, b.d0, b.d1, b.KW, b.KH, b.aligned); + } +}; + +struct vk_conv3d_pipeline_state { + vk_conv3d_pipeline_state(uint32_t s0, uint32_t s1, uint32_t s2, uint32_t p0, uint32_t p1, uint32_t p2, + uint32_t d0, uint32_t d1, uint32_t d2, uint32_t KW, uint32_t KH, uint32_t KD, uint32_t aligned) + : s0(s0), s1(s1), s2(s2), p0(p0), p1(p1), p2(p2), d0(d0), d1(d1), d2(d2), KW(KW), KH(KH), KD(KD), aligned(aligned) {} + + uint32_t s0, s1, s2, p0, p1, p2, d0, d1, d2, KW, KH, KD; + uint32_t aligned; + + bool operator<(const vk_conv3d_pipeline_state &b) const { + return std::tie(s0, s1, s2, p0, p1, p2, d0, d1, d2, KW, KH, KD, aligned) < + std::tie(b.s0, b.s1, b.s2, b.p0, b.p1, b.p2, b.d0, b.d1, b.d2, b.KW, b.KH, b.KD, b.aligned); + } +}; + +struct vk_solve_tri_pipeline_state { + vk_solve_tri_pipeline_state(uint32_t N, uint32_t K) + : N(N), K(K) {} + + uint32_t N, K; + + bool operator<(const vk_solve_tri_pipeline_state &b) const { + return std::tie(N, K) < + std::tie(b.N, b.K); + } +}; + +enum shader_reduction_mode { + SHADER_REDUCTION_MODE_SHMEM, + SHADER_REDUCTION_MODE_HYBRID, + SHADER_REDUCTION_MODE_SUBGROUP, + SHADER_REDUCTION_MODE_COUNT, +}; + +// argsort pipelines for up to 1<<10 invocations per workgroup +static constexpr uint32_t num_argsort_pipelines = 11; +static constexpr uint32_t num_topk_moe_pipelines = 10; +static constexpr uint32_t num_topk_pipelines = 11; + +static constexpr std::initializer_list topk_moe_early_softmax_norm{ GGML_OP_SOFT_MAX, GGML_OP_RESHAPE, GGML_OP_ARGSORT, + GGML_OP_VIEW, GGML_OP_GET_ROWS, GGML_OP_RESHAPE, + GGML_OP_SUM_ROWS, GGML_OP_CLAMP, GGML_OP_DIV, + GGML_OP_RESHAPE }; + +static constexpr std::initializer_list topk_moe_sigmoid_norm_bias{ GGML_OP_UNARY, GGML_OP_RESHAPE, GGML_OP_ADD, + GGML_OP_ARGSORT, GGML_OP_VIEW, GGML_OP_GET_ROWS, + GGML_OP_RESHAPE, GGML_OP_SUM_ROWS, GGML_OP_CLAMP, + GGML_OP_DIV, GGML_OP_RESHAPE }; + +static constexpr std::initializer_list topk_moe_early_softmax { GGML_OP_SOFT_MAX, GGML_OP_RESHAPE, GGML_OP_ARGSORT, + GGML_OP_VIEW, GGML_OP_GET_ROWS }; + +static constexpr std::initializer_list topk_moe_late_softmax { GGML_OP_ARGSORT, GGML_OP_VIEW, + GGML_OP_GET_ROWS, GGML_OP_RESHAPE, + GGML_OP_SOFT_MAX, GGML_OP_RESHAPE }; + +// Snake activation: y = x + sin(a*x)^2 * inv_b. Used by the optimize_graph reorder +// pass so it keeps the chain contiguous and by the dispatcher to detect the fusion. +static constexpr std::initializer_list snake_pattern { GGML_OP_MUL, GGML_OP_SIN, + GGML_OP_SQR, GGML_OP_MUL, + GGML_OP_ADD }; + +//node #978 ( SOFT_MAX): ffn_moe_probs-15 ( 0K) [Vulka ] use=2: ffn_moe_logits-15 ( 0K) [Vulka ] +//node #979 ( RESHAPE): ffn_moe_probs-15 (re ( 0K) [Vulka ] use=1: ffn_moe_probs-15 ( 0K) [Vulka ] +//node #980 ( ARGSORT): ffn_moe_argsort-15 ( 0K) [Vulka ] use=1: ffn_moe_probs-15 ( 0K) [Vulka ] +//node #981 ( VIEW): ffn_moe_topk-15 ( 0K) [Vulka ] use=4: ffn_moe_argsort-15 ( 0K) [Vulka ] +//node #982 ( GET_ROWS): ffn_moe_weights-15 ( 0K) [Vulka ] use=1: ffn_moe_probs-15 (re ( 0K) [Vulka ] ffn_moe_topk-15 ( 0K) [Vulka ] +//node #983 ( RESHAPE): ffn_moe_weights-15 ( ( 0K) [Vulka ] use=2: ffn_moe_weights-15 ( 0K) [Vulka ] +//node #984 ( SUM_ROWS): ffn_moe_weights_sum- ( 0K) [Vulka ] use=1: ffn_moe_weights-15 ( ( 0K) [Vulka ] +//node #985 ( CLAMP): ffn_moe_weights_sum_ ( 0K) [Vulka ] use=1: ffn_moe_weights_sum- ( 0K) [Vulka ] +//node #986 ( DIV): ffn_moe_weights_norm ( 0K) [Vulka ] use=1: ffn_moe_weights-15 ( ( 0K) [Vulka ] ffn_moe_weights_sum_ ( 0K) [Vulka ] +//node #987 ( RESHAPE): ffn_moe_weights_norm ( 0K) [Vulka ] use=1: ffn_moe_weights_norm ( 0K) [Vulka ] +static constexpr std::initializer_list> topk_moe_early_softmax_norm_edges { + { 1, 0, 0 }, // reshape->src[0] == softmax + { 2, 0, 0 }, // argsort->src[0] == softmax + { 3, 0, 2 }, // view->src[0] == argsort + { 4, 0, 1 }, // get_rows->src[0] == reshape + { 4, 1, 3 }, // get_rows->src[1] == view + { 5, 0, 4 }, // reshape->src[0] == get_rows + { 6, 0, 5 }, // sum_rows->src[0] == reshape + { 7, 0, 6 }, // clamp->src[0] == sum_rows + { 8, 0, 5 }, // div->src[0] == reshape + { 8, 1, 7 }, // div->src[1] == clamp + { 9, 0, 8 }, // reshape->src[0] == div +}; + +//node #436 ( UNARY): ffn_moe_probs-10 ( 256K) [Vulka ] use=2: ffn_moe_logits-10 ( 256K) [Vulka ] +//node #437 ( RESHAPE): ffn_moe_probs-10 (re ( 256K) [Vulka ] use=1: ffn_moe_probs-10 ( 256K) [Vulka ] +//node #438 ( ADD): ffn_moe_probs_biased ( 256K) [Vulka ] use=1: ffn_moe_probs-10 ( 256K) [Vulka ] blk.10.exp_probs_b.b ( 0K) [Vulka ] +//node #439 ( ARGSORT): ffn_moe_argsort-10 ( 256K) [Vulka ] use=1: ffn_moe_probs_biased ( 256K) [Vulka ] +//node #440 ( VIEW): ffn_moe_topk-10 ( 255K) [Vulka ] use=3: ffn_moe_argsort-10 ( 256K) [Vulka ] +//node #441 ( GET_ROWS): ffn_moe_weights-10 ( 12K) [Vulka ] use=1: ffn_moe_probs-10 (re ( 256K) [Vulka ] ffn_moe_topk-10 ( 255K) [Vulka ] +//node #442 ( RESHAPE): ffn_moe_weights-10 ( ( 12K) [Vulka ] use=2: ffn_moe_weights-10 ( 12K) [Vulka ] +//node #443 ( SUM_ROWS): ffn_moe_weights_sum- ( 2K) [Vulka ] use=1: ffn_moe_weights-10 ( ( 12K) [Vulka ] +//node #444 ( CLAMP): ffn_moe_weights_sum_ ( 2K) [Vulka ] use=1: ffn_moe_weights_sum- ( 2K) [Vulka ] +//node #445 ( DIV): ffn_moe_weights_norm ( 12K) [Vulka ] use=1: ffn_moe_weights-10 ( ( 12K) [Vulka ] ffn_moe_weights_sum_ ( 2K) [Vulka ] +//node #446 ( RESHAPE): ffn_moe_weights_norm ( 12K) [Vulka ] use=1: ffn_moe_weights_norm ( 12K) [Vulka ] +static constexpr std::initializer_list> topk_moe_sigmoid_norm_bias_edges { + { 1, 0, 0 }, // reshape->src[0] == sigmoid + { 2, 0, 0 }, // add->src[0] == sigmoid + { 3, 0, 2 }, // argsort->src[0] == add + { 4, 0, 3 }, // view->src[0] == argsort + { 5, 0, 1 }, // get_rows->src[0] == reshape + { 5, 1, 4 }, // get_rows->src[1] == view + { 6, 0, 5 }, // reshape->src[0] == get_rows + { 7, 0, 6 }, // sum_rows->src[0] == reshape + { 8, 0, 7 }, // clamp->src[0] == sum_rows + { 9, 0, 6 }, // div->src[0] == reshape + { 9, 1, 8 }, // div->src[1] == clamp + {10, 0, 9 }, // reshape->src[0] == div +}; + +// same as early_softmax_norm but ending after the get_rows +static constexpr std::initializer_list> topk_moe_early_softmax_edges { + { 1, 0, 0 }, // reshape->src[0] == softmax + { 2, 0, 0 }, // argsort->src[0] == softmax + { 3, 0, 2 }, // view->src[0] == argsort + { 4, 0, 1 }, // get_rows->src[0] == reshape + { 4, 1, 3 }, // get_rows->src[1] == view +}; + +//node #652 ( ARGSORT): ffn_moe_argsort-11 ( 0K) [Vulka ] use=1: ffn_moe_probs-11 ( 0K) [Vulka ] +//node #653 ( VIEW): ffn_moe_topk-11 ( 0K) [Vulka ] use=7: ffn_moe_argsort-11 ( 0K) [Vulka ] +//node #654 ( GET_ROWS): ffn_moe_weights-11 ( 0K) [Vulka ] use=1: ffn_moe_probs-11 (re ( 0K) [Vulka ] ffn_moe_topk-11 ( 0K) [Vulka ] +//node #655 ( RESHAPE): ffn_moe_weights-11 ( ( 0K) [Vulka ] use=1: ffn_moe_weights-11 ( 0K) [Vulka ] +//node #656 ( SOFT_MAX): node_656 ( 0K) [Vulka ] use=1: ffn_moe_weights-11 ( ( 0K) [Vulka ] +//node #657 ( RESHAPE): ffn_moe_weights_soft ( 0K) [Vulka ] use=1: node_656 ( 0K) [Vulka ] +static constexpr std::initializer_list> topk_moe_late_softmax_edges { + { 1, 0, 0 }, // view->src[0] == argsort + { 2, 1, 1 }, // get_rows->src[1] == view + { 3, 0, 2 }, // reshape->src[0] == get_rows + { 4, 0, 3 }, // soft_max->src[0] == reshape + { 5, 0, 4 }, // reshape->src[0] == soft_max +}; + +enum topk_moe_mode { + TOPK_MOE_EARLY_SOFTMAX, + TOPK_MOE_EARLY_SOFTMAX_NORM, + TOPK_MOE_LATE_SOFTMAX, + TOPK_MOE_SIGMOID_NORM_BIAS, + TOPK_MOE_COUNT, +}; + +static constexpr std::initializer_list> rope_view_set_rows_edges { + { 1, 0, 0 }, // view->src[0] == rope + { 2, 0, 1 }, // set_rows->src[0] == view +}; + +static constexpr std::initializer_list> rms_norm_mul_rope_view_set_rows_edges { + { 1, 0, 0 }, // mul->src[0] == rms + { 2, 0, 1 }, // rope->src[0] == mul + { 3, 0, 2 }, // view->src[0] == rope + { 4, 0, 3 }, // set_rows->src[0] == view +}; + + +struct vk_device_struct { + std::recursive_mutex mutex; + mutable std::shared_mutex pinned_memory_mutex; + + // Guards compile_pending, all_pipelines, and the dynamic pipeline maps + // (flash_attn, fa_mask_opt, solve_tri, conv2d, etc). The actual compile + // runs with no lock held, so different pipelines can compile in parallel. + // Lock order is device->mutex -> compile_mutex, never the reverse. + std::mutex compile_mutex; + std::condition_variable compile_cv; + + vk::PhysicalDevice physical_device; + vk::PhysicalDeviceProperties properties; + std::string name; + uint64_t max_memory_allocation_size; + uint64_t max_buffer_size; + uint64_t suballocation_block_size; + uint64_t min_imported_host_pointer_alignment; + bool external_memory_host {}; + bool fp16; + bool bf16; + bool pipeline_robustness; + bool memory_priority; + vk::Device device; + uint32_t vendor_id; + vk::DriverId driver_id; + vk_device_architecture architecture; + vk_queue compute_queue; + vk_queue transfer_queue; + bool single_queue; + bool support_async; + bool async_use_transfer_queue; + uint32_t subgroup_size; + uint32_t subgroup_size_log2; + uint32_t shader_core_count; + bool uma; + bool prefer_host_memory; + bool float_controls_rte_fp16; + bool subgroup_basic; + bool subgroup_arithmetic; + bool subgroup_shuffle; + bool subgroup_ballot; + bool subgroup_clustered; + bool subgroup_vote; + bool multi_add; + bool shader_int64; + bool buffer_device_address; + bool vulkan_memory_model; + + bool add_rms_fusion; + uint32_t partials_binding_alignment; + uint32_t max_nodes_per_submit; + + bool shader_64b_indexing; + + bool integer_dot_product; + // 0: default, 1: force mmvq, -1: disable mmvq + int32_t mmvq_mode; + + bool subgroup_size_control; + uint32_t subgroup_min_size; + uint32_t subgroup_max_size; + bool subgroup_require_full_support; + + // floor(log2(maxComputeWorkGroupInvocations)) + uint32_t max_workgroup_size_log2 {}; + + bool coopmat_support; + bool coopmat_acc_f32_support {}; + bool coopmat_acc_f16_support {}; + bool coopmat_bf16_support {}; + bool coopmat_support_16x16x16_f16acc {}; + bool coopmat_support_16x16x16_f32acc {}; + bool coopmat1_fa_support {}; + uint32_t coopmat_m; + uint32_t coopmat_n; + uint32_t coopmat_k; + + bool coopmat_int_support; + uint32_t coopmat_int_m; + uint32_t coopmat_int_n; + uint32_t coopmat_int_k; + + bool coopmat2; + bool coopmat2_bf16_support {}; + bool coopmat2_decode_vector; + + bool dot2_f16 {}; + + bool pipeline_executable_properties_support {}; + + size_t idx; + + bool mul_mat_l[GGML_TYPE_COUNT]; + bool mul_mat_m[GGML_TYPE_COUNT]; + bool mul_mat_s[GGML_TYPE_COUNT]; + bool mul_mat_id_l[GGML_TYPE_COUNT]; + bool mul_mat_id_m[GGML_TYPE_COUNT]; + bool mul_mat_id_s[GGML_TYPE_COUNT]; + + // Separate flags for the q8_1 (integer dot) mmq path, whose shader uses + // a different shared-memory layout than the float matmul shaders. + bool mul_mat_l_int[GGML_TYPE_COUNT]; + bool mul_mat_m_int[GGML_TYPE_COUNT]; + bool mul_mat_s_int[GGML_TYPE_COUNT]; + bool mul_mat_id_l_int[GGML_TYPE_COUNT]; + bool mul_mat_id_m_int[GGML_TYPE_COUNT]; + bool mul_mat_id_s_int[GGML_TYPE_COUNT]; + + vk::DescriptorSetLayout dsl; + + vk_matmul_pipeline pipeline_matmul_f32 {}; + vk_matmul_pipeline pipeline_matmul_f32_f16 {}; + vk_matmul_pipeline pipeline_matmul_bf16 {}; + vk_matmul_pipeline2 pipeline_matmul_f16; + vk_matmul_pipeline2 pipeline_matmul_f16_f32; + + vk_matmul_pipeline2 pipeline_dequant_mul_mat_mat[GGML_TYPE_COUNT]; + vk_matmul_pipeline2 pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_COUNT]; + vk_matmul_pipeline2 pipeline_dequant_mul_mat_mat_q8_1[GGML_TYPE_COUNT]; + + vk_matmul_pipeline pipeline_matmul_id_f32 {}; + vk_matmul_pipeline pipeline_matmul_id_bf16 {}; + vk_matmul_pipeline2 pipeline_matmul_id_f16; + vk_matmul_pipeline2 pipeline_matmul_id_f16_f32; + + vk_matmul_pipeline2 pipeline_dequant_mul_mat_mat_id[GGML_TYPE_COUNT]; + vk_matmul_pipeline2 pipeline_dequant_mul_mat_mat_id_q8_1[GGML_TYPE_COUNT]; + + vk_pipeline pipeline_matmul_split_k_reduce; + vk_pipeline pipeline_quantize_q8_1_x4; + + vk_pipeline pipeline_dequant[GGML_TYPE_COUNT]; + vk_pipeline pipeline_dequant_mul_mat_vec_f32_f32[DMMV_WG_SIZE_COUNT][GGML_TYPE_COUNT][mul_mat_vec_max_cols]; + vk_pipeline pipeline_dequant_mul_mat_vec_f16_f32[DMMV_WG_SIZE_COUNT][GGML_TYPE_COUNT][mul_mat_vec_max_cols]; + vk_pipeline pipeline_dequant_mul_mat_vec_id_f32[DMMV_WG_SIZE_COUNT][GGML_TYPE_COUNT]; + + vk_pipeline pipeline_dequant_mul_mat_vec_q8_1_f32[DMMV_WG_SIZE_COUNT][GGML_TYPE_COUNT][mul_mat_vec_max_cols]; + vk_pipeline pipeline_dequant_mul_mat_vec_id_q8_1_f32[DMMV_WG_SIZE_COUNT][GGML_TYPE_COUNT]; + + vk_pipeline pipeline_mul_mat_vec_p021_f16_f32[p021_max_gqa_ratio]; + vk_pipeline pipeline_mul_mat_vec_nc_f16_f32; + vk_pipeline pipeline_get_rows[GGML_TYPE_COUNT]; + vk_pipeline pipeline_get_rows_f32[GGML_TYPE_COUNT]; + vk_pipeline pipeline_get_rows_back_f32; + vk_pipeline pipeline_acc_f32; + vk_pipeline pipeline_set_f32; + + // [src0 0=fp32,1=fp16][src1 0=fp32,1=fp16][dst 0=fp32,1=fp16] + vk_pipeline pipeline_add[2][2][2]; + vk_pipeline pipeline_add_norepeat[2][2][2]; + vk_pipeline pipeline_sub[2][2][2]; + vk_pipeline pipeline_sub_norepeat[2][2][2]; + vk_pipeline pipeline_mul[2][2][2]; + vk_pipeline pipeline_mul_norepeat[2][2][2]; + vk_pipeline pipeline_div[2][2][2]; + vk_pipeline pipeline_div_norepeat[2][2][2]; + vk_pipeline pipeline_add_rms[2][2][2]; + vk_pipeline pipeline_add_rms_norepeat[2][2][2]; + + // indexed by num_additional_fused_ops == num_adds - 1 + vk_pipeline pipeline_multi_add[MAX_FUSED_ADDS]; + vk_pipeline pipeline_multi_add_rms[MAX_FUSED_ADDS]; + + vk_pipeline pipeline_add_id_f32; + + vk_pipeline pipeline_concat_i8, pipeline_concat_i16, pipeline_concat_i32, pipeline_concat_i64; + vk_pipeline pipeline_upscale_nearest_f32, pipeline_upscale_bilinear_f32, pipeline_upscale_bicubic_f32, pipeline_upscale_bilinear_antialias_f32; + vk_pipeline pipeline_scale_f32; + vk_pipeline pipeline_log[2]; + vk_pipeline pipeline_tri[2]; + vk_pipeline pipeline_diag[2]; + vk_pipeline pipeline_clamp[2]; + vk_pipeline pipeline_pad_f32; + vk_pipeline pipeline_roll_f32; + vk_pipeline pipeline_repeat_i32, pipeline_repeat_back_f32; + vk_pipeline pipeline_repeat_i16; + vk_pipeline pipeline_cpy_f32_f32, pipeline_cpy_f32_f16, pipeline_cpy_f16_f16, pipeline_cpy_f16_f32, pipeline_cpy_f32_bf16, pipeline_cpy_bf16_f32, pipeline_cpy_f32_i32, pipeline_cpy_i32_f32; + vk_pipeline pipeline_contig_cpy_f32_f32, pipeline_contig_cpy_f32_f16, pipeline_contig_cpy_f16_f16, pipeline_contig_cpy_f16_f32, pipeline_contig_cpy_f32_bf16, pipeline_contig_cpy_bf16_f32, pipeline_contig_cpy_f32_i32, pipeline_contig_cpy_i32_f32; + vk_pipeline pipeline_cpy_f32_quant[GGML_TYPE_COUNT]; + vk_pipeline pipeline_cpy_quant_f32[GGML_TYPE_COUNT]; + vk_pipeline pipeline_cpy_transpose_16, pipeline_cpy_transpose_32; + vk_pipeline pipeline_set_rows_i32[GGML_TYPE_COUNT]; + vk_pipeline pipeline_set_rows_i64[GGML_TYPE_COUNT]; + vk_pipeline pipeline_norm_f32; + vk_pipeline pipeline_group_norm_f32; + vk_pipeline pipeline_rms_norm_f32; + vk_pipeline pipeline_rms_norm_mul_f32; + vk_pipeline pipeline_rms_norm_partials_f32; + vk_pipeline pipeline_rms_norm_mul_partials_f32; + vk_pipeline pipeline_rms_norm_mul_rope_f32_f32; + vk_pipeline pipeline_rms_norm_mul_rope_f32_f16; + vk_pipeline pipeline_rms_norm_back_f32; + vk_pipeline pipeline_l2_norm_f32; + + // [src/dst 0=fp32,1=fp16] + vk_pipeline pipeline_exp[2]; + vk_pipeline pipeline_expm1[2]; + vk_pipeline pipeline_elu[2]; + vk_pipeline pipeline_gelu[2]; + vk_pipeline pipeline_gelu_erf[2]; + vk_pipeline pipeline_gelu_quick[2]; + vk_pipeline pipeline_silu[2]; + vk_pipeline pipeline_relu[2]; + vk_pipeline pipeline_sqr[2]; + vk_pipeline pipeline_sqrt[2]; + vk_pipeline pipeline_sin[2]; + vk_pipeline pipeline_cos[2]; + vk_pipeline pipeline_xielu[2]; + vk_pipeline pipeline_neg[2]; + vk_pipeline pipeline_tanh[2]; + vk_pipeline pipeline_sigmoid[2]; + vk_pipeline pipeline_hardsigmoid[2]; + vk_pipeline pipeline_hardswish[2]; + vk_pipeline pipeline_abs[2]; + vk_pipeline pipeline_softplus[2]; + vk_pipeline pipeline_step[2]; + vk_pipeline pipeline_round[2]; + vk_pipeline pipeline_ceil[2]; + vk_pipeline pipeline_floor[2]; + vk_pipeline pipeline_trunc[2]; + vk_pipeline pipeline_sgn[2]; + + vk_pipeline pipeline_add1_f16_f16; + vk_pipeline pipeline_add1_f16_f32; + vk_pipeline pipeline_add1_f32_f32; + + vk_pipeline pipeline_arange_f32; + + vk_pipeline pipeline_fill_f32; + vk_pipeline pipeline_fill_f16; + + vk_pipeline pipeline_geglu[2]; + vk_pipeline pipeline_reglu[2]; + vk_pipeline pipeline_swiglu[2]; + vk_pipeline pipeline_swiglu_oai[2]; + vk_pipeline pipeline_geglu_erf[2]; + vk_pipeline pipeline_geglu_quick[2]; + + vk_pipeline pipeline_leaky_relu[2]; + vk_pipeline pipeline_silu_back_f32; + vk_pipeline pipeline_diag_mask_inf_f32; + vk_pipeline pipeline_soft_max_f32, pipeline_soft_max_f32_f16; + vk_pipeline pipeline_soft_max_f32_wg512, pipeline_soft_max_f32_f16_wg512; + vk_pipeline pipeline_soft_max_back_f32; + + vk_pipeline pipeline_soft_max_large1_f32, pipeline_soft_max_large1_f32_f16; + vk_pipeline pipeline_soft_max_large2_f32, pipeline_soft_max_large2_f32_f16; + vk_pipeline pipeline_soft_max_large3_f32, pipeline_soft_max_large3_f32_f16; + + vk_pipeline pipeline_rope_norm_f32, pipeline_rope_norm_f16, pipeline_rope_norm_f32_f16; + vk_pipeline pipeline_rope_neox_f32, pipeline_rope_neox_f16, pipeline_rope_neox_f32_f16; + vk_pipeline pipeline_rope_multi_f32, pipeline_rope_multi_f16, pipeline_rope_multi_f32_f16; + vk_pipeline pipeline_rope_vision_f32, pipeline_rope_vision_f16; + vk_pipeline pipeline_argsort_f32[num_argsort_pipelines]; + vk_pipeline pipeline_argsort_large_f32[num_argsort_pipelines]; + vk_pipeline pipeline_topk_f32[num_topk_pipelines]; + vk_pipeline pipeline_sum_rows_f32; + vk_pipeline pipeline_fwht_f32[4]; + vk_pipeline pipeline_cumsum_f32; + vk_pipeline pipeline_cumsum_small_f32; + vk_pipeline pipeline_cumsum_multipass1_f32; + vk_pipeline pipeline_cumsum_multipass2_f32; + vk_pipeline pipeline_argmax_f32; + vk_pipeline pipeline_count_equal_i32; + std::map pipeline_solve_tri_f32; + vk_pipeline pipeline_im2col_f32, pipeline_im2col_f32_f16; + vk_pipeline pipeline_im2col_3d_f32, pipeline_im2col_3d_f32_f16; + vk_pipeline pipeline_timestep_embedding_f32; + vk_pipeline pipeline_conv_transpose_1d_f32; + vk_pipeline pipeline_col2im_1d_f32; + vk_pipeline pipeline_col2im_1d_f16; + vk_pipeline pipeline_col2im_1d_bf16; + vk_pipeline pipeline_snake_f32; + vk_pipeline pipeline_snake_f16; + vk_pipeline pipeline_snake_bf16; + vk_pipeline pipeline_pool2d_f32; + vk_pipeline pipeline_rwkv_wkv6_f32; + vk_pipeline pipeline_rwkv_wkv7_f32; + // [size_idx][kda] where size_idx: 0=d16, 1=d32, 2=d64, 3=d128 + vk_pipeline pipeline_gated_delta_net[4][2]; + vk_pipeline pipeline_ssm_scan_f32_d128; + vk_pipeline pipeline_ssm_scan_f32_d256; + vk_pipeline pipeline_ssm_conv_f32; + vk_pipeline pipeline_ssm_conv_silu_f32; + vk_pipeline pipeline_ssm_conv_bias_silu_f32; + vk_pipeline pipeline_opt_step_adamw_f32; + vk_pipeline pipeline_opt_step_sgd_f32; + std::map pipeline_conv2d_f32[CONV_SHAPE_COUNT]; + std::map pipeline_conv2d_f16_f32[CONV_SHAPE_COUNT]; + std::map pipeline_conv_transpose_2d_f32[CONV_SHAPE_COUNT]; + std::map pipeline_conv_transpose_2d_f16_f32[CONV_SHAPE_COUNT]; + std::map pipeline_conv3d_f32[CONV_SHAPE_COUNT]; + std::map pipeline_conv3d_f16_f32[CONV_SHAPE_COUNT]; + vk_pipeline pipeline_conv2d_dw_whcn_f32, pipeline_conv2d_dw_whcn_f16_f32; + vk_pipeline pipeline_conv2d_dw_cwhn_f32, pipeline_conv2d_dw_cwhn_f16_f32; + + std::map pipeline_flash_attn_f32_f16; + + std::map, vk_pipeline> pipeline_fa_mask_opt; + + vk_pipeline pipeline_flash_attn_split_k_reduce; + vk_pipeline pipeline_count_experts; + + // [2] is for whether to take n_experts from spec constant (0) or push constant (1) + vk_pipeline pipeline_topk_moe[num_topk_moe_pipelines][2]; + + std::vector all_pipelines; + + std::vector> pinned_memory; + + vk::Fence fence; + vk_buffer sync_staging; + + ggml_backend_buffer_type buffer_type; + + bool disable_fusion; + bool disable_host_visible_vidmem; + bool allow_sysmem_fallback; + bool disable_graph_optimize; + + std::unique_ptr memory_logger; + + ~vk_device_struct() { + VK_LOG_DEBUG("destroy device " << name); + + device.destroyFence(fence); + + ggml_vk_destroy_buffer(sync_staging); + + compute_queue.cmd_pool.destroy(device); + transfer_queue.cmd_pool.destroy(device); + + for (auto& pipeline : all_pipelines) { + if (pipeline.expired()) { + continue; + } + + vk_pipeline pl = pipeline.lock(); + ggml_vk_destroy_pipeline(device, pl); + } + all_pipelines.clear(); + + device.destroyDescriptorSetLayout(dsl); + + device.destroy(); + } +}; + +void vk_command_pool::init(vk_device& device, vk_queue *q_) { + cmd_buffers.clear(); + q = q_; + + vk::CommandPoolCreateInfo command_pool_create_info( + vk::CommandPoolCreateFlags(VK_COMMAND_POOL_CREATE_TRANSIENT_BIT | VK_COMMAND_POOL_CREATE_RESET_COMMAND_BUFFER_BIT), + q->queue_family_index); + pool = device->device.createCommandPool(command_pool_create_info); +} + +void vk_command_pool::destroy(vk::Device& device) { + device.destroyCommandPool(pool); + pool = nullptr; + cmd_buffers.clear(); +} + +struct vk_buffer_struct { + vk::Buffer buffer = VK_NULL_HANDLE; + vk::DeviceMemory device_memory = VK_NULL_HANDLE; + vk::MemoryPropertyFlags memory_property_flags; + void * ptr; + size_t size = 0; + vk::DeviceAddress bda_addr {}; + + vk_device device; + + ~vk_buffer_struct() { + if (size == 0) { + return; + } + VK_LOG_DEBUG("~vk_buffer_struct(" << buffer << ", " << size << ")"); + + device->device.freeMemory(device_memory); + device->device.destroyBuffer(buffer); + } +}; + +struct vk_subbuffer { + vk_buffer buffer; + uint64_t offset; + uint64_t size; + + operator vk::DescriptorBufferInfo() const { + return { buffer->buffer, offset, size }; + } +}; + +struct vk_semaphore { + vk::Semaphore s; + uint64_t value; +}; + +// vk_event is used for the event-related backend interfaces. It uses vk::Events for +// event_wait and a timeline semaphore for event_synchronize. Polling on an event for +// event_synchronize wouldn't be sufficient to wait for command buffers to complete, +// and would lead to validation errors. +struct vk_event { + std::vector events_free; // Events available for reuse + std::vector events_submitted; // Events that are fully submitted and can be reused on next synchronize + vk::Event event; + bool has_event; + + vk_semaphore tl_semaphore; + vk_command_buffer* cmd_buffer = nullptr; + uint64_t cmd_buffer_use_counter = 0; +}; + +struct vk_submission { + vk_command_buffer* buffer = nullptr; + std::vector wait_semaphores; + std::vector signal_semaphores; +}; + +typedef std::vector vk_sequence; + +struct vk_mat_mat_push_constants { + uint32_t M; uint32_t N; uint32_t K; + uint32_t stride_a; uint32_t stride_b; uint32_t stride_d; + uint32_t batch_stride_a; uint32_t batch_stride_b; uint32_t batch_stride_d; + uint32_t base_work_group_z; uint32_t num_batches; + uint32_t k_split; + uint32_t ne02; uint32_t ne12; uint32_t broadcast2; uint32_t broadcast3; + uint32_t padded_N; +}; + +#define MAT_VEC_FUSION_FLAGS_BIAS0 0x1 +#define MAT_VEC_FUSION_FLAGS_BIAS1 0x2 +#define MAT_VEC_FUSION_FLAGS_SCALE0 0x4 +#define MAT_VEC_FUSION_FLAGS_SCALE1 0x8 + +struct vk_mat_vec_push_constants { + uint32_t ncols; + uint32_t stride_a; + uint32_t stride_b; + uint32_t stride_d; + uint32_t batch_stride_a; + uint32_t batch_stride_b; + uint32_t batch_stride_d; + uint32_t fusion_flags; + uint32_t base_work_group_y; + uint32_t ne02; + uint32_t ne12; + uint32_t broadcast2; + uint32_t broadcast3; +}; + +struct vk_mat_vec_p021_push_constants { + uint32_t ncols_x; + uint32_t nrows_x; + uint32_t nchannels_x; + uint32_t nchannels_y; + uint32_t b_offset; + uint32_t d_offset; + uint32_t fusion_flags; +}; + +struct vk_mat_vec_nc_push_constants { + uint32_t ncols_x; + uint32_t nrows_x; + uint32_t row_stride_x; + uint32_t channel_stride_x; + uint32_t channel_stride_y; + uint32_t channel_x_divisor; + uint32_t ne12; + uint32_t b_offset; + uint32_t d_offset; + uint32_t nb03; + uint32_t nb13; + uint32_t nb23; + uint32_t fusion_flags; +}; + +struct vk_mat_mat_id_push_constants { + uint32_t M; uint32_t N; uint32_t K; + uint32_t stride_a; uint32_t stride_b; uint32_t stride_d; + uint32_t batch_stride_a; uint32_t batch_stride_b; uint32_t batch_stride_d; + uint32_t nei0; uint32_t nei1; uint32_t nbi1; uint32_t ne11; + uint32_t padded_N; +}; +struct vk_mat_vec_id_push_constants { + uint32_t ncols; + uint32_t stride_a; + uint32_t stride_b; + uint32_t stride_d; + uint32_t batch_stride_a; + uint32_t batch_stride_b; + uint32_t batch_stride_d; + uint32_t fusion_flags; + uint32_t nei0; + uint32_t ne11; + uint32_t expert_i1; + uint32_t nbi1; +}; + +struct vk_flash_attn_push_constants { + uint32_t N; + uint32_t KV; + + uint32_t ne1; + uint32_t ne2; + uint32_t ne3; + + uint32_t neq2; + uint32_t neq3; + uint32_t nek2; + uint32_t nek3; + uint32_t nev2; + uint32_t nev3; + uint32_t nem1; + uint32_t nem2; + uint32_t nem3; + + uint32_t nb01; + uint32_t nb02; + uint32_t nb03; + uint32_t nb11; + uint32_t nb12; + uint32_t nb13; + uint32_t nb21; + uint32_t nb22; + uint32_t nb23; + + float scale; + float max_bias; + float logit_softcap; + + uint32_t mask_n_head_log2; + float m0; + float m1; + + uint32_t gqa_ratio; + uint32_t split_kv; + uint32_t k_num; +}; +static_assert(sizeof(vk_flash_attn_push_constants) <= 128, "sizeof(vk_flash_attn_push_constants) must be <= 128"); + +struct vk_op_push_constants { + uint32_t KX; + uint32_t KY; + float param1; + float param2; + float param3; + float param4; +}; + +struct vk_op_fwht_push_constants { + uint32_t n_rows; + uint32_t src_offset; + uint32_t dst_offset; + float scale; +}; + +struct vk_op_count_experts_push_constants { + uint32_t ne00; + uint32_t ne01; + uint32_t nb00; + uint32_t nb01; + uint32_t a_offset; +}; + +struct vk_op_glu_push_constants { + uint32_t N; + uint32_t ne00; + uint32_t ne20; + uint32_t mode; // 0: default, 1: swapped, 2: split + float alpha; // for swiglu_oai + float limit; + uint32_t nb00; + uint32_t nb01; + uint32_t nb02; + uint32_t nb03; + uint32_t nb10; + uint32_t nb11; + uint32_t nb12; + uint32_t nb13; + uint32_t nb20; + uint32_t nb21; + uint32_t nb22; + uint32_t nb23; + uint32_t ne21; + uint32_t ne22; + uint32_t misalign_offsets; + uint32_t ne2_012mp; uint32_t ne2_012L; + uint32_t ne2_01mp; uint32_t ne2_01L; + uint32_t ne2_0mp; uint32_t ne2_0L; +}; +static_assert(sizeof(vk_op_glu_push_constants) <= 128, "sizeof(vk_op_glu_push_constants) must be <= 128"); + +struct vk_op_unary_push_constants { + uint32_t ne; + uint32_t ne00; uint32_t ne01; uint32_t ne02; uint32_t ne03; uint32_t nb00; uint32_t nb01; uint32_t nb02; uint32_t nb03; + uint32_t ne10; uint32_t ne11; uint32_t ne12; uint32_t ne13; uint32_t nb10; uint32_t nb11; uint32_t nb12; uint32_t nb13; + uint32_t misalign_offsets; + float param1; float param2; float param3; float param4; + uint32_t ne0_012mp; uint32_t ne0_01mp; uint32_t ne0_0mp; uint32_t ne0_Ls; + uint32_t ne1_012mp; uint32_t ne1_01mp; uint32_t ne1_0mp; uint32_t ne1_Ls; +}; +static_assert(sizeof(vk_op_unary_push_constants) <= 128, "sizeof(vk_op_unary_push_constants) must be <= 128"); + +static vk_op_unary_push_constants vk_op_unary_push_constants_init(const ggml_tensor * src0, const ggml_tensor * dst, int64_t ne = 0) { + GGML_ASSERT(ne != 0 || (ggml_nelements(src0) == ggml_nelements(dst))); + ne = ne != 0 ? ne : ggml_nelements(dst); + GGML_ASSERT(ne <= (int64_t)std::numeric_limits::max()); + + vk_op_unary_push_constants p{}; + p.ne = (uint32_t)ne; + + size_t src0_tsize = ggml_type_size(src0->type); + p.ne00 = (uint32_t)src0->ne[0]; + p.ne01 = (uint32_t)src0->ne[1]; + p.ne02 = (uint32_t)src0->ne[2]; + p.ne03 = (uint32_t)src0->ne[3]; + p.nb00 = (uint32_t)(src0->nb[0] / src0_tsize); + p.nb01 = (uint32_t)(src0->nb[1] / src0_tsize); + p.nb02 = (uint32_t)(src0->nb[2] / src0_tsize); + p.nb03 = (uint32_t)(src0->nb[3] / src0_tsize); + + size_t dst_tsize = ggml_type_size(dst->type); + p.ne10 = (uint32_t)dst->ne[0]; + p.ne11 = (uint32_t)dst->ne[1]; + p.ne12 = (uint32_t)dst->ne[2]; + p.ne13 = (uint32_t)dst->ne[3]; + p.nb10 = (uint32_t)(dst->nb[0] / dst_tsize); + p.nb11 = (uint32_t)(dst->nb[1] / dst_tsize); + p.nb12 = (uint32_t)(dst->nb[2] / dst_tsize); + p.nb13 = (uint32_t)(dst->nb[3] / dst_tsize); + + return p; // offsets are initialized later in ggml_vk_op +} + +struct vk_op_pad_push_constants { + uint32_t ne; + uint32_t ne00; uint32_t ne01; uint32_t ne02; uint32_t ne03; uint32_t nb00; uint32_t nb01; uint32_t nb02; uint32_t nb03; + uint32_t ne10; uint32_t ne11; uint32_t ne12; uint32_t ne13; uint32_t nb10; uint32_t nb11; uint32_t nb12; uint32_t nb13; + uint32_t misalign_offsets; + uint32_t circular; + + uint32_t lp0; uint32_t rp0; + uint32_t lp1; uint32_t rp1; + uint32_t lp2; uint32_t rp2; + uint32_t lp3; uint32_t rp3; +}; + +static vk_op_pad_push_constants vk_op_pad_push_constants_init(const ggml_tensor * src0, const ggml_tensor * dst) { + int64_t ne = ggml_nelements(dst); + GGML_ASSERT(ne <= (int64_t)std::numeric_limits::max()); + + vk_op_pad_push_constants p{}; + p.ne = (uint32_t)ne; + + size_t src0_tsize = ggml_type_size(src0->type); + p.ne00 = (uint32_t)src0->ne[0]; + p.ne01 = (uint32_t)src0->ne[1]; + p.ne02 = (uint32_t)src0->ne[2]; + p.ne03 = (uint32_t)src0->ne[3]; + p.nb00 = (uint32_t)(src0->nb[0] / src0_tsize); + p.nb01 = (uint32_t)(src0->nb[1] / src0_tsize); + p.nb02 = (uint32_t)(src0->nb[2] / src0_tsize); + p.nb03 = (uint32_t)(src0->nb[3] / src0_tsize); + + size_t dst_tsize = ggml_type_size(dst->type); + p.ne10 = (uint32_t)dst->ne[0]; + p.ne11 = (uint32_t)dst->ne[1]; + p.ne12 = (uint32_t)dst->ne[2]; + p.ne13 = (uint32_t)dst->ne[3]; + p.nb10 = (uint32_t)(dst->nb[0] / dst_tsize); + p.nb11 = (uint32_t)(dst->nb[1] / dst_tsize); + p.nb12 = (uint32_t)(dst->nb[2] / dst_tsize); + p.nb13 = (uint32_t)(dst->nb[3] / dst_tsize); + + p.lp0 = dst->op_params[0]; + p.rp0 = dst->op_params[1]; + p.lp1 = dst->op_params[2]; + p.rp1 = dst->op_params[3]; + p.lp2 = dst->op_params[4]; + p.rp2 = dst->op_params[5]; + p.lp3 = dst->op_params[6]; + p.rp3 = dst->op_params[7]; + p.circular = dst->op_params[8]; + + return p; // fastdiv values and offsets are initialized later in ggml_vk_op +} + +// See https://gmplib.org/~tege/divcnst-pldi94.pdf figure 4.1. +// Precompute mp (m' in the paper) and L such that division +// can be computed using a multiply (high 32b of 64b result) +// and a shift: +// +// n/d = (mulhi(n, mp) + n) >> L; +static void init_fastdiv_values(uint32_t d, uint32_t &mp, uint32_t &L) +{ + // compute L = ceil(log2(d)); + L = 0; + while (L < 32 && (uint32_t{1} << L) < d) { + L++; + } + + mp = (uint32_t)((uint64_t{1} << 32) * ((uint64_t{1} << L) - d) / d + 1); +} + +static uint32_t pack_fastdiv_L(uint32_t L0, uint32_t L1, uint32_t L2) { + return L0 | (L1 << 8) | (L2 << 16); +} + +template void init_pushconst_fastdiv(T &p) { + GGML_UNUSED(p); + static_assert(!std::is_const::value, "unexpected type"); +} + +template <> void init_pushconst_fastdiv(vk_op_unary_push_constants &p) { + // Compute magic values to divide by these six numbers. + uint32_t ne0_012L; + uint32_t ne0_01L; + uint32_t ne0_0L; + uint32_t ne1_012L; + uint32_t ne1_01L; + uint32_t ne1_0L; + + init_fastdiv_values(p.ne02*p.ne01*p.ne00, p.ne0_012mp, ne0_012L); + init_fastdiv_values(p.ne01*p.ne00, p.ne0_01mp, ne0_01L); + init_fastdiv_values(p.ne00, p.ne0_0mp, ne0_0L); + init_fastdiv_values(p.ne12*p.ne11*p.ne10, p.ne1_012mp, ne1_012L); + init_fastdiv_values(p.ne11*p.ne10, p.ne1_01mp, ne1_01L); + init_fastdiv_values(p.ne10, p.ne1_0mp, ne1_0L); + + p.ne0_Ls = pack_fastdiv_L(ne0_012L, ne0_01L, ne0_0L); + p.ne1_Ls = pack_fastdiv_L(ne1_012L, ne1_01L, ne1_0L); +} + +template <> void init_pushconst_fastdiv(vk_op_glu_push_constants &p) { + // GLU linearizes over dst, then uses dst coordinates for src0/src1. + init_fastdiv_values(p.ne22*p.ne21*p.ne20, p.ne2_012mp, p.ne2_012L); + init_fastdiv_values(p.ne21*p.ne20, p.ne2_01mp, p.ne2_01L); + init_fastdiv_values(p.ne20, p.ne2_0mp, p.ne2_0L); +} + +struct vk_op_binary_push_constants { + uint32_t ne; + uint32_t ne00; uint32_t ne01; uint32_t ne02; uint32_t ne03; uint32_t nb00; uint32_t nb01; uint32_t nb02; uint32_t nb03; + uint32_t ne10; uint32_t ne11; uint32_t ne12; uint32_t ne13; uint32_t nb10; uint32_t nb11; uint32_t nb12; uint32_t nb13; + uint32_t ne20; uint32_t ne21; uint32_t ne22; uint32_t ne23; uint32_t nb20; uint32_t nb21; uint32_t nb22; uint32_t nb23; + uint32_t misalign_offsets; + float param1; float param2; int32_t param3; +}; + +struct vk_op_multi_add_push_constants { + // shape for dst + uint32_t ne20; uint32_t ne21; uint32_t ne22; uint32_t ne23; + + // strides for srcs+dst + uint32_t nb[MAX_PARAMETER_COUNT][4]; + + uint32_t rms_partials; +}; +// update multi_add.comp if this changes +static_assert(MAX_PARAMETER_COUNT == 12); +static_assert(sizeof(vk_op_multi_add_push_constants) <= 256); + +struct vk_op_topk_moe_push_constants { + uint32_t n_rows; + uint32_t n_experts_push; + uint32_t n_expert_used; + float clamp_min; + float clamp_max; + uint32_t gating_func; + uint32_t has_bias; + uint32_t with_norm; + float output_scale; + float output_bias; +}; + +struct vk_op_add_id_push_constants { + uint32_t ne0; + uint32_t ne1; + uint32_t s01; + uint32_t s02; + uint32_t s11; + uint32_t s21; +}; + +struct vk_op_diag_mask_push_constants { + uint32_t ncols; + uint32_t rows_per_channel; + int32_t n_past; +}; + +struct vk_op_rope_push_constants { + uint32_t rope_mode; + uint32_t nrows; + uint32_t n_dims; + float freq_scale; + float freq_base; + float ext_factor; + float attn_factor; + float corr_dims[2]; + float theta_scale; + uint32_t has_ff; + int32_t sections[4]; + uint32_t is_imrope; + uint32_t is_back; + uint32_t set_rows_stride; + uint32_t ne00; + uint32_t ne01; + uint32_t ne02; + uint32_t nb01; + uint32_t nb02; + uint32_t nb03; + uint32_t nb11; + uint32_t nb12; + uint32_t nb13; + uint32_t a_offset; + uint32_t d_offset; +}; +static_assert(sizeof(vk_op_rope_push_constants) <= 128, "sizeof(vk_op_rope_push_constants) must be <= 128"); + +// For fused rms_norm+mul+rope(+view+set_rows) +struct vk_op_rms_norm_mul_rope_push_constants { + vk_op_binary_push_constants bin; + vk_op_rope_push_constants rope; +}; + +struct vk_op_soft_max_push_constants { + uint32_t KX; + uint32_t KY; + uint32_t ne00; + uint32_t ne01; + uint32_t ne02; + uint32_t ne12; + uint32_t ne13; + uint32_t nb11; + uint32_t nb12; + uint32_t nb13; + float scale; + float max_bias; + float m0; + float m1; + uint32_t n_head_log2; + uint32_t nrows_x; + uint32_t has_sinks; +}; + +struct vk_op_argsort_push_constants { + uint32_t ncols; + uint32_t ncols_padded; + uint32_t ncols_padded_log2; + uint32_t nrows; + uint32_t order; + uint32_t outer_start; + uint32_t outer_end; + uint32_t inner_start; + uint32_t inner_end; +}; + +struct vk_op_topk_push_constants { + uint32_t orig_ncols; + uint32_t ncols_input; + uint32_t ncols_output; + uint32_t k; + uint32_t nrows; + uint32_t first_pass; + uint32_t last_pass; +}; + +struct vk_op_im2col_push_constants { + uint64_t dst_addr; + uint32_t batch_offset; uint32_t offset_delta; + uint32_t IC; + uint32_t IW; uint32_t IH; + uint32_t OW; uint32_t OH; + uint32_t KW; uint32_t KH; + uint32_t OH_batch; + uint32_t CHW; + int32_t s0; int32_t s1; + int32_t p0; int32_t p1; + int32_t d0; int32_t d1; + uint32_t batch_IC; +}; + +struct vk_op_im2col_3d_push_constants { + uint64_t dst_addr; + uint32_t nb10; + uint32_t nb11; + uint32_t nb12; + uint32_t nb13; + uint32_t s0; + uint32_t s1; + uint32_t s2; + uint32_t p0; + uint32_t p1; + uint32_t p2; + uint32_t d0; + uint32_t d1; + uint32_t d2; + uint32_t IW; + uint32_t IH; + uint32_t ID; + uint32_t IC; + uint32_t KW; + uint32_t OH; + uint32_t KD_KH_KW; + uint32_t KH_KW; + uint32_t IC_KD_KH_KW; + uint32_t N_OD_OH; + uint32_t OD_OH; + uint32_t OD_OH_OW_IC_KD_KH_KW; + uint32_t OH_OW_IC_KD_KH_KW; + uint32_t OW_IC_KD_KH_KW; + uint32_t misalign_offsets; +}; + +struct vk_op_timestep_embedding_push_constants { + uint32_t nb1; + uint32_t dim; + uint32_t max_period; +}; + +struct vk_op_col2im_1d_push_constants { + uint32_t T_out; + uint32_t OC; + uint32_t K_OC; + uint32_t T_in; + uint32_t K; + int32_t stride; + int32_t p0; +}; + +struct vk_op_conv_transpose_1d_push_constants { + uint32_t Cout; + uint32_t Cin; + uint32_t K; + uint32_t L; + uint32_t KL; + + uint32_t nb01; + uint32_t nb02; + uint32_t nb11; + uint32_t nb1; + + int32_t s0; +}; + +struct vk_op_snake_push_constants { + uint32_t ne0; + uint32_t ne1; +}; + +struct vk_op_pool2d_push_constants { + uint32_t IW; uint32_t IH; + uint32_t OW; uint32_t OH; + uint32_t OC; + uint32_t pelements; + uint32_t op; + int32_t k0; int32_t k1; + int32_t s0; int32_t s1; + int32_t p0; int32_t p1; +}; + +struct vk_op_rwkv_wkv6_push_constants { + uint32_t B; + uint32_t T; + uint32_t C; + uint32_t H; +}; + +struct vk_op_rwkv_wkv7_push_constants { + uint32_t B; + uint32_t T; + uint32_t C; + uint32_t H; +}; +struct vk_op_gated_delta_net_push_constants { + uint32_t H; + uint32_t n_tokens; + uint32_t n_seqs; + uint32_t s_off; + uint32_t sq1, sq2, sq3; + uint32_t sv1, sv2, sv3; + uint32_t sb1, sb2, sb3; + uint32_t neq1, rq3; + float scale; + uint32_t K; +}; + +struct vk_op_ssm_scan_push_constants { + uint32_t nb02, nb03, nb12, nb13; + uint32_t nb21, nb22, nb31; + uint32_t nb42, nb43, nb52, nb53; + uint32_t s_off; + uint32_t n_head, d_head, n_group, n_tok; +}; +struct vk_op_ssm_conv_push_constants { + uint32_t nb01, nb02; + uint32_t nb11; + uint32_t dst_nb0, dst_nb1, dst_nb2; + uint32_t nc, ncs, nr, n_t, n_s; +}; + +struct vk_op_conv2d_push_constants { + uint32_t Cout; + uint32_t Cin; + uint32_t N; + + uint32_t W; + uint32_t H; + uint32_t OW; + uint32_t OH; + + 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_conv2d_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_conv3d_push_constants { + uint32_t OC; + uint32_t IC; + uint32_t N; + + uint32_t IW; + uint32_t IH; + uint32_t ID; + uint32_t OW; + uint32_t OH; + uint32_t OD; + + 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; + + uint32_t OWmp; uint32_t OWL; + uint32_t OWOHmp; uint32_t OWOHL; + uint32_t OWOHODmp; uint32_t OWOHODL; +}; + +template <> void init_pushconst_fastdiv(vk_op_conv3d_push_constants &p) { + init_fastdiv_values(p.OW, p.OWmp, p.OWL); + init_fastdiv_values(p.OW*p.OH, p.OWOHmp, p.OWOHL); + init_fastdiv_values(p.OW*p.OH*p.OD, p.OWOHODmp, p.OWOHODL); +} + +struct vk_op_conv2d_dw_push_constants { + uint32_t ne; + uint32_t batches; + uint32_t channels; + uint32_t dst_w; + uint32_t dst_h; + uint32_t src_w; + uint32_t src_h; + uint32_t knl_w; + uint32_t knl_h; + int32_t stride_x; + int32_t stride_y; + int32_t pad_x; + int32_t pad_y; + int32_t dilation_x; + int32_t dilation_y; +}; + +struct vk_op_upscale_push_constants { + uint32_t ne; uint32_t a_offset; uint32_t d_offset; + uint32_t ne00; uint32_t ne01; + uint32_t nb00; uint32_t nb01; uint32_t nb02; uint32_t nb03; + uint32_t ne10; uint32_t ne11; uint32_t ne12; uint32_t ne13; + float sf0; float sf1; float sf2; float sf3; + float pixel_offset; +}; + +struct vk_op_sum_rows_push_constants +{ + uint32_t n_cols; + uint32_t ne01, ne02; + uint32_t nb01, nb02, nb03; + uint32_t nb11, nb12, nb13; + float weight; + uint32_t misalign_offsets; + uint32_t ne0_12mp, ne0_12L; + uint32_t ne0_1mp, ne0_1L; +}; + +static vk_op_sum_rows_push_constants vk_op_sum_rows_push_constants_init(const ggml_tensor * src, const ggml_tensor * dst, int64_t n_cols) { + uint32_t type_size = (uint32_t)ggml_type_size(src->type); + vk_op_sum_rows_push_constants p = {}; + p.n_cols = (uint32_t)n_cols; + p.ne01 = (uint32_t)src->ne[1]; + p.ne02 = (uint32_t)src->ne[2]; + p.nb01 = (uint32_t)src->nb[1] / type_size; + p.nb02 = (uint32_t)src->nb[2] / type_size; + p.nb03 = (uint32_t)src->nb[3] / type_size; + p.nb11 = (uint32_t)dst->nb[1] / type_size; + p.nb12 = (uint32_t)dst->nb[2] / type_size; + p.nb13 = (uint32_t)dst->nb[3] / type_size; + p.weight = 1.0f; + return p; +} + +template <> void init_pushconst_fastdiv(vk_op_sum_rows_push_constants &p) { + init_fastdiv_values(p.ne01*p.ne02, p.ne0_12mp, p.ne0_12L); + init_fastdiv_values(p.ne01, p.ne0_1mp, p.ne0_1L); +} + +struct vk_quantize_q8_1_push_constants { + uint32_t ne; + uint32_t num_blocks; +}; + +struct vk_op_flash_attn_split_k_reduce_push_constants { + uint32_t D; + uint32_t ne1; + uint32_t ne2; + uint32_t ne3; + uint32_t k_num; + uint32_t sinks; +}; + +struct vk_op_flash_attn_mask_opt_push_constants { + uint32_t nem0; + uint32_t nem1; + uint32_t nem2; + uint32_t nbm1; + uint32_t nbm2; + uint32_t nbm3; + uint32_t nbd1; + uint32_t nbd2; + uint32_t nbd3; +}; + +// Allow pre-recording command buffers +struct vk_staging_memcpy { + vk_staging_memcpy(void * _dst, const void * _src, size_t _n) : dst(_dst), src(_src), n(_n) {} + + void * dst; + const void * src; + size_t n; +}; + +struct vk_staging_memset { + vk_staging_memset(void * _dst, uint32_t _val, size_t _n) : dst(_dst), val(_val), n(_n) {} + + void * dst; + uint32_t val; + size_t n; +}; + +struct vk_context_struct { + vk_submission * s; + std::vector seqs; + + int exit_tensor_idx; + + std::vector in_memcpys; + std::vector out_memcpys; + std::vector memsets; + + vk_command_pool * p {}; +}; +typedef std::shared_ptr vk_context; +typedef std::weak_ptr vk_context_ref; + +struct ggml_vk_garbage_collector { + std::vector tl_semaphores; + std::vector semaphores; + std::vector events; + std::vector contexts; +}; + +static void ggml_vk_preallocate_buffers(ggml_backend_vk_context * ctx, vk_context subctx); +static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested = nullptr); +static void ggml_pipeline_allocate_descriptor_sets(ggml_backend_vk_context * ctx); + +static bool vk_memory_logger_enabled = false; + +#define VK_LOG_MEMORY(msg) if (vk_memory_logger_enabled) { std::cerr << "ggml_vulkan memory: " << msg << std::endl; } + +static std::string format_size(size_t size) { + const size_t kib = 1024; + const size_t mib = kib * 1024; + const size_t gib = mib * 1024; + + std::ostringstream oss; + oss << std::fixed << std::setprecision(2); + + if (size >= gib) { + oss << static_cast(size) / gib << " GiB"; + } else if (size >= mib) { + oss << static_cast(size) / mib << " MiB"; + } else if (size >= kib) { + oss << static_cast(size) / kib << " KiB"; + } else { + oss << size << " B"; + } + + return oss.str(); +} + +class vk_memory_logger { +public: + vk_memory_logger(): total_device(0), total_host(0) {} + void log_allocation(vk_buffer_ref buf_ref, size_t size); + void log_deallocation(vk_buffer_ref buf_ref); + +private: + std::map allocations; // Track allocations + size_t total_device; + size_t total_host; + static std::mutex log_mutex; +}; + +std::mutex vk_memory_logger::log_mutex; + +static bool vk_perf_logger_enabled = false; +static bool vk_perf_logger_concurrent = false; +static bool vk_enable_sync_logger = false; +// number of calls between perf logger prints +static uint32_t vk_perf_logger_frequency = 1; +static std::string vk_pipeline_stats_filter; + +static uint64_t ggml_vk_get_node_flops(const ggml_tensor * node) { + if (node->op == GGML_OP_MUL_MAT || node->op == GGML_OP_MUL_MAT_ID) { + const uint64_t m = node->ne[0]; + const uint64_t n = node->ne[1]; + const uint64_t k = node->src[1]->ne[0]; + const uint64_t batch = node->ne[2] * node->ne[3]; + return m * n * (k + (k - 1)) * batch; + } + if (node->op == GGML_OP_CONV_2D || node->op == GGML_OP_CONV_TRANSPOSE_2D) { + const ggml_tensor * knl = node->src[0]; + const uint64_t Cout = node->ne[2]; + const uint64_t size_K = node->src[1]->ne[2] * knl->ne[0] * knl->ne[1]; + const uint64_t size_N = node->ne[3] * node->ne[0] * node->ne[1]; + return Cout * size_N * (size_K + (size_K - 1)); + } + if (node->op == GGML_OP_CONV_3D) { + const ggml_tensor * knl = node->src[0]; + const uint64_t OC = ggml_get_op_params_i32(node, 11); + const uint64_t IC = ggml_get_op_params_i32(node, 9); + const uint64_t size_K = IC * knl->ne[0] * knl->ne[1] * knl->ne[2]; + const uint64_t size_N = node->ne[3] / OC * node->ne[0] * node->ne[1] * node->ne[2]; + return OC * size_N * (size_K + (size_K - 1)); + } + if (node->op == GGML_OP_FLASH_ATTN_EXT) { + const ggml_tensor * q = node->src[0]; + const ggml_tensor * k = node->src[1]; + const ggml_tensor * v = node->src[2]; + return 2ull * q->ne[1] * q->ne[2] * (k->ne[0] + v->ne[0]) * k->ne[1] * q->ne[3]; + } + return 0; +} + +class vk_perf_logger { + public: + void print_timings(bool force = false) { + if (timings.empty()) { + return; + } + print_count++; + if ((print_count % vk_perf_logger_frequency) != 0 && !force) { + return; + } + print_count = 0; + uint64_t total_all_op_times = 0; + std::cerr << "----------------\nVulkan Timings:" << std::endl; + for (const auto & t : timings) { + uint64_t total_op_times = 0; + for (const auto & time : t.second) { + total_op_times += time; + } + std::cerr << t.first << ": " << t.second.size() << " x " << (total_op_times / t.second.size() / 1000.0) + << " us = " << (total_op_times / 1000.0) << " us"; + + // If we have as many flops entries as timing entries for the op, then compute and log the flops/S. + auto it = flops.find(t.first); + if (it != flops.end() && (it->second).size() == t.second.size()) { + uint64_t total_op_flops = 0; + for (const auto & elem : it->second) { + total_op_flops += elem; + } + std::cerr << " (" + << (double(total_op_flops) / (1000.0 * 1000.0 * 1000.0)) / + (double(total_op_times) / (1000.0 * 1000.0 * 1000.0)) + << " GFLOPS/s)"; + } + + total_all_op_times += total_op_times; + + std::cerr << std::endl; + } + + if (timings.size() > 0) { + std::cerr << "Total time: " << total_all_op_times / 1000.0 << " us." << std::endl; + } + + timings.clear(); + flops.clear(); + } + + std::string get_node_fusion_name(const ggml_tensor * node, const char *fusion_name, uint64_t *n_flops) { + *n_flops = ggml_vk_get_node_flops(node); + std::string fusion_str; + if (fusion_name) { + fusion_str = fusion_name + std::string(" "); + } + if (node->op == GGML_OP_UNARY) { + return fusion_str + ggml_unary_op_name(ggml_get_unary_op(node)); + } + if (node->op == GGML_OP_MUL_MAT || node->op == GGML_OP_MUL_MAT_ID) { + const uint64_t m = node->ne[0]; + const uint64_t n = node->ne[1]; + const uint64_t k = node->src[1]->ne[0]; + const uint64_t batch = node->ne[2] * node->ne[3]; + std::string name = ggml_op_name(node->op); + if ((node->op == GGML_OP_MUL_MAT && n <= mul_mat_vec_max_cols) || + (node->op == GGML_OP_MUL_MAT_ID && node->src[2]->ne[1] == 1)) { + name += "_VEC"; + } + name += " "; + name += ggml_type_name(node->src[0]->type); + name += " m=" + std::to_string(m) + " n=" + std::to_string(n) + " k=" + std::to_string(k); + if (node->op == GGML_OP_MUL_MAT_ID) { + name += " n_expert=" + std::to_string(node->src[0]->ne[2]); + } + if (batch > 1) { + name += " batch=" + std::to_string(batch); + } + return fusion_str + name; + } + if (node->op == GGML_OP_CONV_2D || node->op == GGML_OP_CONV_TRANSPOSE_2D) { + std::string name = ggml_op_name(node->op); + const ggml_tensor * knl = node->src[0]; + uint64_t Cout = node->ne[2]; + uint64_t size_K = node->src[1]->ne[2] * knl->ne[0] * knl->ne[1]; + uint64_t size_N = node->ne[3] * node->ne[0] * node->ne[1]; + name += " M=Cout=" + std::to_string(Cout) + ", K=Cin*KW*KH=" + std::to_string(size_K) + + ", N=N*OW*OH=" + std::to_string(size_N); + return fusion_str + name; + } + if (node->op == GGML_OP_RMS_NORM) { + std::string name = ggml_op_name(node->op); + name += "(" + std::to_string(node->ne[0]) + "," + std::to_string(node->ne[1]) + "," + std::to_string(node->ne[2]) + "," + std::to_string(node->ne[3]) + ")"; + return fusion_str + name; + } + if (node->op == GGML_OP_FLASH_ATTN_EXT) { + const ggml_tensor * dst = node; + const ggml_tensor * q = node->src[0]; + const ggml_tensor * k = node->src[1]; + const ggml_tensor * v = node->src[2]; + const ggml_tensor * m = node->src[3]; + std::stringstream name; + name << fusion_str; + name << ggml_op_name(node->op) << + " dst(" << dst->ne[0] << "," << dst->ne[1] << "," << dst->ne[2] << "," << dst->ne[3] << "), " << + " q(" << q->ne[0] << "," << q->ne[1] << "," << q->ne[2] << "," << q->ne[3] << "), " << + " k(" << k->ne[0] << "," << k->ne[1] << "," << k->ne[2] << "," << k->ne[3] << "), " << + " v(" << v->ne[0] << "," << v->ne[1] << "," << v->ne[2] << "," << v->ne[3] << "), " << + " m(" << (m?m->ne[0]:0) << "," << (m?m->ne[1]:0) << "," << (m?m->ne[2]:0) << "," << (m?m->ne[3]:0) << ")"; + return name.str(); + } + if (node->op == GGML_OP_TOP_K) { + std::stringstream name; + name << fusion_str; + name << ggml_op_name(node->op) << + " K=" << node->ne[0] << + " (" << node->src[0]->ne[0] << "," << node->src[0]->ne[1] << "," << node->src[0]->ne[2] << "," << node->src[0]->ne[3] << ")"; + return name.str(); + } + return fusion_str + ggml_op_name(node->op); + } + + void log_timing(const ggml_tensor * node, const char *fusion_name, uint64_t time) { + uint64_t n_flops; + std::string name = get_node_fusion_name(node, fusion_name, &n_flops); + if (n_flops) { + flops[name].push_back(n_flops); + } + timings[name].push_back(time); + } + + void log_timing(const std::vector &nodes, const std::vector &names, uint64_t time) { + uint64_t total_flops = 0; + std::string name; + for (size_t n = 0; n < nodes.size(); ++n) { + uint64_t n_flops = 0; + name += get_node_fusion_name(nodes[n], names[n], &n_flops); + total_flops += n_flops; + + if (n != nodes.size() - 1) { + name += ", "; + } + } + if (total_flops) { + flops[name].push_back(total_flops); + } + timings[name].push_back(time); + } + + private: + std::map> timings; + std::map> flops; + uint32_t print_count {}; +}; + +struct ggml_backend_vk_context { + std::string name; + + vk_device device; + + size_t semaphore_idx, event_idx; + ggml_vk_garbage_collector gc; + size_t prealloc_size_x, prealloc_size_y, prealloc_size_split_k, prealloc_size_add_rms_partials, prealloc_size_add_rms_partials_offset; + vk_buffer prealloc_x, prealloc_y, prealloc_split_k, prealloc_add_rms_partials, sync_staging; + vk::Fence fence, almost_ready_fence; + bool submit_pending {}; + bool almost_ready_fence_pending {}; + // Set before op_add and unset after op_rms_norm to indicate that the add should + // write partial sums to accumulate the square of the vector components + bool do_add_rms_partials_offset_calculation; + bool do_add_rms_partials; + + uint64_t last_total_flops {UINT64_MAX}; + + // Cache most recent tensor that was converted into prealloc_y, and what pipeline it used to convert. + vk_pipeline_struct * prealloc_y_last_pipeline_used {}; + const ggml_tensor * prealloc_y_last_tensor_used {}; + // True when prealloc_y holds the padded fp16 layout used by the coopmat2 B decode-vector callback. + // If false, then it's contiguous. + bool prealloc_y_last_decode_vector_staging {}; + + // Track which nodes have been used since the last sync, and whether they were written to + std::vector unsynced_nodes_written; + std::vector unsynced_nodes_read; + // Track which prealloc buffers have pending reads that need to be synchronized. + // These are checked before writing to the buffer (and call ggml_vk_sync_buffers if set), + // and set to true after the buffer contents are consumed. + bool prealloc_x_need_sync, prealloc_y_need_sync, prealloc_split_k_need_sync; + + vk_context_ref compute_ctx; + + vk_context_ref transfer_ctx; + vk_semaphore transfer_semaphore; + uint64_t transfer_semaphore_last_submitted {}; + + std::vector tensor_ctxs; + + std::vector descriptor_pools; + std::vector descriptor_sets; + uint32_t descriptor_set_idx {}; + uint32_t pipeline_descriptor_set_requirements {}; + + vk_command_pool compute_cmd_pool; + vk_command_pool transfer_cmd_pool; + + // number of additional consecutive nodes that are being fused with the + // node currently being processed + int num_additional_fused_ops {}; + // Bitmask of which fused ops need to write an intermediate value to memory. + // Bit 'i' means nodes[start_of_fusion + i] writes to memory. + // If there's no fusion, bit 0 is still set. + int fused_ops_write_mask {}; + topk_moe_mode fused_topk_moe_mode {}; + bool fused_topk_moe_scale {}; + + // for GGML_VK_PERF_LOGGER + std::unique_ptr perf_logger; + vk::QueryPool query_pool; + std::vector query_fusion_names; + std::vector query_fusion_node_count; + std::vector query_nodes; + std::vector query_node_idx; + int32_t num_queries {}; + int32_t query_idx {}; +}; + +static void * const vk_ptr_base = (void *)(uintptr_t) 0x1000; // NOLINT + +static uint64_t vk_tensor_offset(const ggml_tensor * tensor) { + if (tensor->view_src) { + return (uint8_t *) tensor->view_src->data - (uint8_t *) vk_ptr_base; + } + return (uint8_t *) tensor->data - (uint8_t *) vk_ptr_base; +} + +static uint32_t get_misalign_bytes(const ggml_backend_vk_context * ctx, const ggml_tensor * t) +{ + return ((vk_tensor_offset(t) + t->view_offs) & (ctx->device->properties.limits.minStorageBufferOffsetAlignment - 1));; +} + +template void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, T &p, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, const ggml_tensor * src3, ggml_tensor * dst) { + GGML_UNUSED(p); + GGML_UNUSED(src0); + GGML_UNUSED(src1); + GGML_UNUSED(src2); + GGML_UNUSED(src3); + GGML_UNUSED(dst); + static_assert(!std::is_const::value, "unexpected type"); + GGML_ASSERT(!src0 || get_misalign_bytes(ctx, src0) == 0); + GGML_ASSERT(!src1 || get_misalign_bytes(ctx, src1) == 0); + GGML_ASSERT(!src2 || get_misalign_bytes(ctx, src2) == 0); + GGML_ASSERT(!src3 || get_misalign_bytes(ctx, src3) == 0); + GGML_ASSERT(!dst || get_misalign_bytes(ctx, dst) == 0); +} + +template <> void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, vk_mat_vec_p021_push_constants &p, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, const ggml_tensor * src3, ggml_tensor * dst) { + const uint32_t b_offset = get_misalign_bytes(ctx, src1) / ggml_type_size(src1->type); + const uint32_t d_offset = get_misalign_bytes(ctx, dst) / ggml_type_size(dst->type); + + p.b_offset = b_offset; + p.d_offset = d_offset; + + GGML_UNUSED(src0); + GGML_UNUSED(src2); + GGML_UNUSED(src3); +} + +template <> void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, vk_mat_vec_nc_push_constants &p, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, const ggml_tensor * src3, ggml_tensor * dst) { + const uint32_t b_offset = get_misalign_bytes(ctx, src1) / ggml_type_size(src1->type); + const uint32_t d_offset = get_misalign_bytes(ctx, dst) / ggml_type_size(dst->type); + + p.b_offset = b_offset; + p.d_offset = d_offset; + + GGML_UNUSED(src0); + GGML_UNUSED(src2); + GGML_UNUSED(src3); +} + +template <> void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, vk_op_fwht_push_constants &p, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, const ggml_tensor * src3, ggml_tensor * dst) { + p.src_offset = get_misalign_bytes(ctx, src0) / ggml_type_size(src0->type); + p.dst_offset = get_misalign_bytes(ctx, dst) / ggml_type_size(dst->type); + + GGML_UNUSED(src1); + GGML_UNUSED(src2); + GGML_UNUSED(src3); +} + +struct ggml_backend_vk_buffer_context { + vk_device_ref device; + vk_buffer dev_buffer; + std::string name; + + ggml_backend_vk_buffer_context(vk_device_ref device, vk_buffer&& dev_buffer, std::string& name) : + device(device), + dev_buffer(dev_buffer), + name(name) { + } + + ~ggml_backend_vk_buffer_context() { + ggml_vk_destroy_buffer(dev_buffer); + } +}; + +void vk_memory_logger::log_allocation(vk_buffer_ref buf_ref, size_t size) { + if (!vk_memory_logger_enabled) { + return; + } + std::lock_guard guard(log_mutex); + vk_buffer buf = buf_ref.lock(); + const bool device = bool(buf->memory_property_flags & vk::MemoryPropertyFlagBits::eDeviceLocal); + const std::string type = device ? "device" : "host"; + allocations[buf->buffer] = size; + total_device += device ? size : 0; + total_host += device ? 0 : size; + VK_LOG_MEMORY(buf->device->name << ": +" << format_size(size) << " " << type << " at " << buf->buffer << ". Total device: " << format_size(total_device) << ", total host: " << format_size(total_host)); +} + +void vk_memory_logger::log_deallocation(vk_buffer_ref buf_ref) { + if (buf_ref.expired() || buf_ref.lock()->size == 0 || !vk_memory_logger_enabled) { + return; + } + + std::lock_guard guard(log_mutex); + vk_buffer buf = buf_ref.lock(); + const bool device = bool(buf->memory_property_flags & vk::MemoryPropertyFlagBits::eDeviceLocal); + std::string type = device ? "device" : "host"; + auto it = allocations.find(buf->buffer); + if (it != allocations.end()) { + total_device -= device ? it->second : 0; + total_host -= device ? 0 : it->second; + VK_LOG_MEMORY(buf->device->name << ": -" << format_size(it->second) << " " << type << " at " << buf->buffer << ". Total device: " << format_size(total_device) << ", total host: " << format_size(total_host)); + allocations.erase(it); + } else { + VK_LOG_MEMORY("ERROR " << buf->device->name << ": Attempted to deallocate unknown " << type << " memory at " << buf->buffer); + } +} + +struct vk_instance_t { + vk::Instance instance; + + bool debug_utils_support = false; // VK_EXT_debug_utils enabled + PFN_vkSetDebugUtilsObjectNameEXT pfn_vkSetDebugUtilsObjectNameEXT = {}; + PFN_vkQueueBeginDebugUtilsLabelEXT pfn_vkQueueBeginDebugUtilsLabelEXT = {}; + PFN_vkQueueEndDebugUtilsLabelEXT pfn_vkQueueEndDebugUtilsLabelEXT = {}; + PFN_vkCmdBeginDebugUtilsLabelEXT pfn_vkCmdBeginDebugUtilsLabelEXT = {}; + PFN_vkCmdEndDebugUtilsLabelEXT pfn_vkCmdEndDebugUtilsLabelEXT = {}; + PFN_vkCmdInsertDebugUtilsLabelEXT pfn_vkCmdInsertDebugUtilsLabelEXT = {}; + + std::vector device_indices; + std::vector device_supports_membudget; + vk_device devices[GGML_VK_MAX_DEVICES]; +}; + +static bool vk_instance_initialized = false; +static vk_instance_t vk_instance; + +#ifdef GGML_VULKAN_CHECK_RESULTS +static size_t vk_skip_checks; +static size_t vk_output_tensor; + +static void ggml_vk_print_tensor(const ggml_tensor * tensor, const char * name); +static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_cgraph * cgraph, int tensor_idx); +static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_cgraph * cgraph, int tensor_idx); +#endif + +typedef void (*ggml_vk_func_t)(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); + +static void ggml_backend_vk_free(ggml_backend_t backend); + +static VkDeviceSize ggml_vk_get_max_buffer_range(const ggml_backend_vk_context * ctx, const vk_buffer &buf, const VkDeviceSize offset) { + const VkDeviceSize range = std::min(VkDeviceSize{buf->size - offset}, + VkDeviceSize{ctx->device->properties.limits.maxStorageBufferRange}); + return range; +} + +// Wait for ctx->fence to be signaled. +static void ggml_vk_wait_for_fence(ggml_backend_vk_context * ctx) { + // Use waitForFences while most of the graph executes. Hopefully the CPU can sleep + // during this wait. + if (ctx->almost_ready_fence_pending) { + VK_CHECK(ctx->device->device.waitForFences({ ctx->almost_ready_fence }, true, UINT64_MAX), "almost_ready_fence"); + ctx->device->device.resetFences({ ctx->almost_ready_fence }); + ctx->almost_ready_fence_pending = false; + } + + // Spin (w/pause) waiting for the graph to finish executing. + vk::Result result; + while ((result = ctx->device->device.getFenceStatus(ctx->fence)) != vk::Result::eSuccess) { + if (result != vk::Result::eNotReady) { + fprintf(stderr, "ggml_vulkan: error %s at %s:%d\n", to_string(result).c_str(), __FILE__, __LINE__); + exit(1); + } + for (uint32_t i = 0; i < 100; ++i) { + YIELD(); + YIELD(); + YIELD(); + YIELD(); + YIELD(); + YIELD(); + YIELD(); + YIELD(); + YIELD(); + YIELD(); + } + } + ctx->device->device.resetFences({ ctx->fence }); +} + +static constexpr uint32_t kSpvOpCooperativeMatrixLoadTensorNV = 5367; +static constexpr uint32_t kSpvCapabilityCooperativeMatrixDecodeVectorNV = 5447; +static constexpr uint32_t kSpvTensorAddressingDecodeVectorFuncBit = 0x4; + +// Remove SPV_NV_cooperative_matrix_decode_vector usage from a SPIR-V module so it +// can be loaded on drivers that only support SPV_NV_cooperative_matrix2. Drops the +// OpExtension declaration, the CooperativeMatrixDecodeVectorNV OpCapability, and the +// DecodeVectorFunc operand from any OpCooperativeMatrixLoadTensorNV instruction. +// Returns true when the input used the extension (and `out` was populated with a +// stripped copy); returns false otherwise without touching `out`. +static bool ggml_vk_strip_decode_vector(const uint32_t * code, size_t word_count, std::vector & out) { + static const char kDecodeVectorExt[] = "SPV_NV_cooperative_matrix_decode_vector"; + + if (word_count < 5) { + return false; + } + + bool uses_decode_vector = false; + for (size_t pos = 5; pos < word_count; ) { + uint32_t word = code[pos]; + uint32_t wc = word >> spv::WordCountShift; + uint32_t op = word & spv::OpCodeMask; + GGML_ASSERT(wc > 0 && pos + wc <= word_count); + if (op == spv::OpExtension && wc >= 2) { + const char * s = reinterpret_cast(&code[pos + 1]); + if (strcmp(s, kDecodeVectorExt) == 0) { + uses_decode_vector = true; + break; + } + } + pos += wc; + } + + if (!uses_decode_vector) { + return false; + } + + VK_LOG_DEBUG("ggml_vk_strip_decode_vector: stripping SPV_NV_cooperative_matrix_decode_vector"); + + // Bulk-copy unchanged runs and only break the run when an instruction needs to + // be dropped or patched. Use reserve + insert/push_back so the destination buffer + // is touched exactly once (no zero-initialization pass from resize()). + out.clear(); + out.reserve(word_count); + + size_t run_start = 0; + auto flush_run = [&](size_t up_to) { + if (up_to > run_start) { + out.insert(out.end(), code + run_start, code + up_to); + } + }; + + for (size_t pos = 5; pos < word_count; ) { + uint32_t word = code[pos]; + uint32_t wc = word >> spv::WordCountShift; + uint32_t op = word & spv::OpCodeMask; + GGML_ASSERT(wc > 0 && pos + wc <= word_count); + + if (op == spv::OpExtension && wc >= 2) { + const char * s = reinterpret_cast(&code[pos + 1]); + if (strcmp(s, kDecodeVectorExt) == 0) { + flush_run(pos); + pos += wc; + run_start = pos; + continue; + } + } + + if (op == spv::OpCapability && wc == 2 && code[pos + 1] == kSpvCapabilityCooperativeMatrixDecodeVectorNV) { + flush_run(pos); + pos += wc; + run_start = pos; + continue; + } + + if (op == kSpvOpCooperativeMatrixLoadTensorNV) { + // [opcode/wc][ResultType][Result][Pointer][Object][TensorLayout][MemOperand mask][mem extras...][TA mask][ta extras...] + GGML_ASSERT(wc >= 8); + + uint32_t mem_mask = code[pos + 6]; + size_t cur = pos + 7; + // Each of these MemoryAccess bits (when set) carries one trailing operand. + cur += (mem_mask & 0x2) ? 1 : 0; // Aligned + cur += (mem_mask & 0x8) ? 1 : 0; // MakePointerAvailable + cur += (mem_mask & 0x10) ? 1 : 0; // MakePointerVisible + cur += (mem_mask & 0x10000) ? 1 : 0; // AliasScopeINTELMask + cur += (mem_mask & 0x20000) ? 1 : 0; // NoAliasINTELMask + GGML_ASSERT(cur < pos + wc); + + uint32_t ta_mask = code[cur]; + if ((ta_mask & kSpvTensorAddressingDecodeVectorFuncBit) == 0) { + pos += wc; + continue; // leave instruction inside the current unchanged run + } + + flush_run(pos); + + // Append unchanged prefix of the instruction (header through the mem-extras). + size_t inst_start = out.size(); + size_t pre_n = cur - pos; + out.insert(out.end(), code + pos, code + pos + pre_n); + + // Emit TA mask with the DecodeVectorFunc bit cleared. + out.push_back(ta_mask & ~kSpvTensorAddressingDecodeVectorFuncBit); + + // TA extras: TensorView (0x1) and DecodeFunc (0x2) are kept verbatim; + // DecodeVectorFunc (0x4) is dropped along with its trailing id operand. + size_t keep_ta_extras = ((ta_mask & 0x1) ? 1 : 0) + ((ta_mask & 0x2) ? 1 : 0); + if (keep_ta_extras) { + out.insert(out.end(), code + cur + 1, code + cur + 1 + keep_ta_extras); + } + + GGML_ASSERT(wc == pre_n + 1 + keep_ta_extras + 1); + + // Patch the instruction header with the new (one-shorter) word count. + uint32_t new_wc = wc - 1; + out[inst_start] = (new_wc << spv::WordCountShift) | op; + + pos += wc; + run_start = pos; + continue; + } + + pos += wc; + } + + flush_run(word_count); + return true; +} + +// Remove the loop unrolling hint of the matmul shader's BK loop +// and replace it with the dont_unroll hint for better performance on +// hardware like Apple M1/M2. +// Assumes 1. code comes from mul_mm.comp 2. the K-tile loop has no loop +// control hint and 3. the BK loop is the last loop nested directly inside +// the K-tile loop. +// Returns true when the input was modified; returns false otherwise +// without touching `out`. +static bool ggml_vk_roll_bk_loop(const uint32_t * code, size_t word_count, std::vector & out) { + if (word_count < 5) { + return false; + } + + struct vk_spv_loop { + size_t header; + size_t end; + uint32_t control; + }; + + std::vector loops; + + // Collect a list of all loops in the module. + for (size_t pos = 5; pos < word_count; ) { + const uint32_t wc = code[pos] >> spv::WordCountShift; + const uint32_t op = code[pos] & spv::OpCodeMask; + if (wc == 0 || pos + wc > word_count) { + return false; + } + + if (op == spv::OpLoopMerge && wc >= 4) { loops.push_back({ pos, 0, code[pos + 3] }); } + + if (op == spv::OpLabel && wc >= 2) { + for (auto & l : loops) { + if (l.end == 0 && code[l.header + 1] == code[pos + 1]) { l.end = pos; } + } + } + + pos += wc; + } + + auto encloses = [](const vk_spv_loop & a, const vk_spv_loop & b) { + return a.header < b.header && b.header < a.end; + }; + + // Find the BK loop. + const vk_spv_loop * bk = nullptr; + for (const auto & h : loops) { + if (h.control != spv::LoopControlUnrollMask) { + continue; + } + const vk_spv_loop * parent = nullptr; + bool has_child = false; + for (const auto & g : loops) { + if (encloses(g, h) && (!parent || g.header > parent->header)) { + parent = &g; + } + if (encloses(h, g)) { + has_child = true; + } + } + // BK loop should be the last loop nested inside the loop with no hint + // and have at least one child loop. + if (parent && + parent->control == spv::LoopControlMaskNone && + has_child && + (!bk || h.header > bk->header)) { + bk = &h; + } + } + if (!bk) { + return false; + } + + // set DontUnroll instead of Unroll + out.assign(code, code + word_count); + out[bk->header + 3] = spv::LoopControlDontUnrollMask; + return true; +} + +static void ggml_vk_create_pipeline_func(vk_device& device, vk_pipeline& pipeline, size_t spv_size, const void* spv_data, const std::string entrypoint, + uint32_t parameter_count, std::array wg_denoms, std::vector specialization_constants, + bool disable_robustness, bool require_full_subgroups, uint32_t required_subgroup_size) { + VK_LOG_DEBUG("ggml_vk_create_pipeline(" << device->name << ", " << pipeline->name << ", " << entrypoint << ", " << parameter_count << + ", (" << wg_denoms[0] << "," << wg_denoms[1] << "," << wg_denoms[2] << "), specialization_constants, " << + disable_robustness << ", " << require_full_subgroups << ", " << required_subgroup_size << ")"); + GGML_ASSERT(parameter_count > 0); + GGML_ASSERT(parameter_count <= MAX_PARAMETER_COUNT); + GGML_ASSERT(wg_denoms[0] > 0 && wg_denoms[1] > 0 && wg_denoms[2] > 0); // NOLINT + + vk::ShaderModuleCreateInfo shader_module_create_info({}, spv_size, reinterpret_cast(spv_data)); + + // Patch SPIR-V to enable RTE rounding for FP16, avoiding the need for + // separate shader variants compiled with -DRTE16. + std::vector spirv; + if (device->float_controls_rte_fp16) { + const uint32_t* spv_words = reinterpret_cast(spv_data); + size_t word_count = spv_size / sizeof(uint32_t); + spirv.assign(spv_words, spv_words + word_count); + + // Find insertion points respecting SPIR-V layout order: + // Header(5) -> OpCapability -> OpExtension -> ... -> OpEntryPoint -> OpExecutionMode -> ... + size_t pos = 5; // skip header + size_t cap_insert_pos = pos; + size_t ext_insert_pos = pos; + size_t exec_insert_pos = pos; + uint32_t entry_point_id = 0; + + while (pos < spirv.size()) { + uint32_t opcode = spirv[pos] & spv::OpCodeMask; + uint32_t len = spirv[pos] >> spv::WordCountShift; + if (len == 0) break; + + if (opcode == spv::OpCapability) { + cap_insert_pos = pos + len; + ext_insert_pos = pos + len; + } else if (opcode == spv::OpExtension) { + ext_insert_pos = pos + len; + } else if (opcode == spv::OpEntryPoint) { + entry_point_id = spirv[pos + 2]; + exec_insert_pos = pos + len; + } else if (opcode == spv::OpExecutionMode || opcode == spv::OpExecutionModeId) { + exec_insert_pos = pos + len; + } else if (entry_point_id != 0) { + break; + } + + pos += len; + } + + // Insert from latest position first so earlier indices stay valid. + + // OpExecutionMode %entrypoint RoundingModeRTE 16 + uint32_t exec_mode[] = { (4u << spv::WordCountShift) | spv::OpExecutionMode, entry_point_id, spv::ExecutionModeRoundingModeRTE, 16 }; + spirv.insert(spirv.begin() + exec_insert_pos, std::begin(exec_mode), std::end(exec_mode)); + + // OpExtension "SPV_KHR_float_controls" + const char ext_str[] = "SPV_KHR_float_controls"; + size_t ext_str_words = CEIL_DIV(sizeof(ext_str), sizeof(uint32_t)); + std::vector extension(1 + ext_str_words, 0); + extension[0] = (uint32_t)((1 + ext_str_words) << spv::WordCountShift) | spv::OpExtension; + memcpy(&extension[1], ext_str, sizeof(ext_str)); + spirv.insert(spirv.begin() + ext_insert_pos, extension.begin(), extension.end()); + + // OpCapability RoundingModeRTE + uint32_t capability[] = { (2u << spv::WordCountShift) | spv::OpCapability, spv::CapabilityRoundingModeRTE }; + spirv.insert(spirv.begin() + cap_insert_pos, std::begin(capability), std::end(capability)); + + shader_module_create_info = vk::ShaderModuleCreateInfo({}, spirv.size() * sizeof(uint32_t), spirv.data()); + } + +#if defined(GGML_VULKAN_COOPMAT2_DECODE_VECTOR_GLSLC_SUPPORT) + if (device->coopmat2 && !device->coopmat2_decode_vector) { + const uint32_t * src = spirv.empty() ? reinterpret_cast(spv_data) : spirv.data(); + size_t src_n = spirv.empty() ? spv_size / sizeof(uint32_t) : spirv.size(); + std::vector stripped; + if (ggml_vk_strip_decode_vector(src, src_n, stripped)) { + spirv = std::move(stripped); + shader_module_create_info = vk::ShaderModuleCreateInfo({}, spirv.size() * sizeof(uint32_t), spirv.data()); + } + } +#endif + +#if VK_HEADER_VERSION >= 287 + // Roll the mul_mm BK loop on Asahi Linux. Skip bf16 and the mul_mmq pipelines. + if (device->driver_id == vk::DriverId::eMesaHoneykrisp && + pipeline->name.rfind("matmul", 0) == 0 && + pipeline->name.find("bf16") == std::string::npos && + pipeline->name.find("q8_1") == std::string::npos) { + const uint32_t * src = spirv.empty() ? reinterpret_cast(spv_data) : spirv.data(); + size_t src_n = spirv.empty() ? spv_size / sizeof(uint32_t) : spirv.size(); + std::vector rolled; + if (ggml_vk_roll_bk_loop(src, src_n, rolled)) { + spirv = std::move(rolled); + shader_module_create_info = vk::ShaderModuleCreateInfo({}, spirv.size() * sizeof(uint32_t), spirv.data()); + } + } +#endif + + pipeline->shader_module = device->device.createShaderModule(shader_module_create_info); + + vk::PushConstantRange pcr( + vk::ShaderStageFlagBits::eCompute, + 0, + pipeline->push_constant_size + ); + + vk::PipelineLayoutCreateInfo pipeline_layout_create_info(vk::PipelineLayoutCreateFlags(), device->dsl, pcr); + pipeline->layout = device->device.createPipelineLayout(pipeline_layout_create_info); + + std::vector specialization_entries(specialization_constants.size()); + + for (size_t i = 0; i < specialization_constants.size(); i++) { + specialization_entries[i].constantID = i; + specialization_entries[i].offset = i * sizeof(uint32_t); + specialization_entries[i].size = sizeof(uint32_t); + } + + vk::SpecializationInfo specialization_info( + specialization_entries.size(), + specialization_entries.data(), + specialization_constants.size() * sizeof(uint32_t), + specialization_constants.data() + ); + + vk::PipelineShaderStageCreateFlags pipeline_shader_stage_create_flags{}; + + if (device->subgroup_require_full_support && require_full_subgroups) { + pipeline_shader_stage_create_flags |= vk::PipelineShaderStageCreateFlagBits::eRequireFullSubgroupsEXT; + } + + vk::PipelineShaderStageCreateInfo pipeline_shader_create_info( + pipeline_shader_stage_create_flags, + vk::ShaderStageFlagBits::eCompute, + pipeline->shader_module, + entrypoint.c_str(), + &specialization_info); + + vk::PipelineShaderStageRequiredSubgroupSizeCreateInfoEXT pipeline_shader_stage_required_subgroup_size_create_info; + pipeline_shader_stage_required_subgroup_size_create_info.requiredSubgroupSize = required_subgroup_size; + if (device->subgroup_size_control && required_subgroup_size > 0) { + GGML_ASSERT(device->subgroup_min_size <= required_subgroup_size && required_subgroup_size <= device->subgroup_max_size); + pipeline_shader_create_info.setPNext(&pipeline_shader_stage_required_subgroup_size_create_info); + } + + vk::ComputePipelineCreateInfo compute_pipeline_create_info( + device->pipeline_executable_properties_support ? + vk::PipelineCreateFlagBits::eCaptureStatisticsKHR : + vk::PipelineCreateFlags{}, + pipeline_shader_create_info, + pipeline->layout); + + vk::PipelineRobustnessCreateInfoEXT rci; + + if (device->pipeline_robustness && disable_robustness) { + rci.storageBuffers = vk::PipelineRobustnessBufferBehaviorEXT::eDisabled; + rci.uniformBuffers = vk::PipelineRobustnessBufferBehaviorEXT::eDisabled; + compute_pipeline_create_info.setPNext(&rci); + } + +#if defined(VK_EXT_shader_64bit_indexing) + vk::PipelineCreateFlags2CreateInfo pipelineFlags2CreateInfo; + if (pipeline->is_64b_indexing) + { + pipelineFlags2CreateInfo.flags = vk::PipelineCreateFlagBits2::e64BitIndexingEXT; + if (device->pipeline_executable_properties_support) { + pipelineFlags2CreateInfo.flags |= vk::PipelineCreateFlagBits2::eCaptureStatisticsKHR; + } + pipelineFlags2CreateInfo.setPNext(compute_pipeline_create_info.pNext); + compute_pipeline_create_info.setPNext(&pipelineFlags2CreateInfo); + } +#endif + + try { + pipeline->pipeline = device->device.createComputePipeline(VK_NULL_HANDLE, compute_pipeline_create_info).value; + } catch (const vk::SystemError& e) { + std::cerr << "ggml_vulkan: Compute pipeline creation failed for " << pipeline->name << std::endl; + std::cerr << "ggml_vulkan: " << e.what() << std::endl; + throw e; + } + + if (vk_instance.debug_utils_support) { + vk::DebugUtilsObjectNameInfoEXT duoni; + duoni.objectType = vk::ObjectType::ePipeline; + duoni.pObjectName = pipeline->name.c_str(); + duoni.objectHandle = /*reinterpret_cast*/(uint64_t)(static_cast(pipeline->pipeline)); + vk_instance.pfn_vkSetDebugUtilsObjectNameEXT(device->device, &static_cast(duoni)); + } + + if (device->pipeline_executable_properties_support) { + vk::PipelineExecutableInfoKHR executableInfo; + executableInfo.pipeline = pipeline->pipeline; + + auto statistics = device->device.getPipelineExecutableStatisticsKHR(executableInfo); + + bool print_stats = !vk_pipeline_stats_filter.empty() && + pipeline->name.find(vk_pipeline_stats_filter) != std::string::npos; + if (print_stats) { + std::cerr << "ggml_vulkan: pipeline stats for " << pipeline->name << ":" << std::endl; + } + + for (auto & s : statistics) { + if (print_stats) { + std::cerr << "ggml_vulkan: " << s.name.data() << ": "; + switch (s.format) { + case vk::PipelineExecutableStatisticFormatKHR::eBool32: + std::cerr << (s.value.b32 ? "true" : "false"); + break; + case vk::PipelineExecutableStatisticFormatKHR::eInt64: + std::cerr << s.value.i64; + break; + case vk::PipelineExecutableStatisticFormatKHR::eUint64: + std::cerr << s.value.u64; + break; + case vk::PipelineExecutableStatisticFormatKHR::eFloat64: + std::cerr << s.value.f64; + break; + } + std::cerr << std::endl; + } + // "Register Count" is reported by NVIDIA drivers. + if (strcmp(s.name, "Register Count") == 0) { + VK_LOG_DEBUG(pipeline->name << " " << s.name << ": " << s.value.u64 << " registers"); + pipeline->register_count = (uint32_t)s.value.u64; + } + } + } + + { + std::lock_guard guard(device->compile_mutex); + device->all_pipelines.push_back(pipeline); + pipeline->compiled = true; + pipeline->compile_pending = false; + } + device->compile_cv.notify_all(); +} + +static void ggml_vk_destroy_pipeline(vk::Device& device, vk_pipeline& pipeline) { + VK_LOG_DEBUG("ggml_pipeline_destroy_pipeline(" << pipeline->name << ")"); + device.destroyPipelineLayout(pipeline->layout); + + device.destroyShaderModule(pipeline->shader_module); + + device.destroyPipeline(pipeline->pipeline); +} + +static void ggml_pipeline_request_descriptor_sets(ggml_backend_vk_context *ctx, vk_pipeline& pipeline, uint32_t n) { + VK_LOG_DEBUG("ggml_pipeline_request_descriptor_sets(" << pipeline->name << ", " << n << ")"); + ctx->pipeline_descriptor_set_requirements += n; + if (!pipeline->compiled) { + ggml_vk_load_shaders(ctx->device, pipeline); + } + ggml_pipeline_allocate_descriptor_sets(ctx); +} + +static void ggml_pipeline_allocate_descriptor_sets(ggml_backend_vk_context * ctx) { + + if (ctx->descriptor_sets.size() >= ctx->pipeline_descriptor_set_requirements) { + // Enough descriptors are available + return; + } + + vk_device& device = ctx->device; + + // Grow by 50% to avoid frequent allocations + uint32_t needed = std::max(3 * ctx->descriptor_sets.size() / 2, size_t{ctx->pipeline_descriptor_set_requirements}); + uint32_t to_alloc = needed - ctx->descriptor_sets.size(); + uint32_t pool_remaining = VK_DEVICE_DESCRIPTOR_POOL_SIZE - ctx->descriptor_sets.size() % VK_DEVICE_DESCRIPTOR_POOL_SIZE; + uint32_t pool_idx = ctx->descriptor_sets.size() / VK_DEVICE_DESCRIPTOR_POOL_SIZE; + + while (to_alloc > 0) { + const uint32_t alloc_count = std::min(pool_remaining, to_alloc); + to_alloc -= alloc_count; + pool_remaining = VK_DEVICE_DESCRIPTOR_POOL_SIZE; + + if (pool_idx >= ctx->descriptor_pools.size()) { + vk::DescriptorPoolSize descriptor_pool_size(vk::DescriptorType::eStorageBuffer, MAX_PARAMETER_COUNT * VK_DEVICE_DESCRIPTOR_POOL_SIZE); + vk::DescriptorPoolCreateInfo descriptor_pool_create_info({}, VK_DEVICE_DESCRIPTOR_POOL_SIZE, descriptor_pool_size); + ctx->descriptor_pools.push_back(device->device.createDescriptorPool(descriptor_pool_create_info)); + } + + std::vector layouts(alloc_count); + for (uint32_t i = 0; i < alloc_count; i++) { + layouts[i] = device->dsl; + } + vk::DescriptorSetAllocateInfo descriptor_set_alloc_info(ctx->descriptor_pools[pool_idx], alloc_count, layouts.data()); + std::vector sets = device->device.allocateDescriptorSets(descriptor_set_alloc_info); + ctx->descriptor_sets.insert(ctx->descriptor_sets.end(), sets.begin(), sets.end()); + + pool_idx++; + } +} + +static vk_command_buffer* ggml_vk_create_cmd_buffer(vk_device& device, vk_command_pool& p) { + VK_LOG_DEBUG("ggml_vk_create_cmd_buffer()"); + vk::CommandBufferAllocateInfo command_buffer_alloc_info( + p.pool, + vk::CommandBufferLevel::ePrimary, + 1); + const std::vector cmd_buffers = device->device.allocateCommandBuffers(command_buffer_alloc_info); + p.cmd_buffers.push_back({ cmd_buffers.front(), 0, true }); + return &p.cmd_buffers[p.cmd_buffers.size()-1]; +} + +static void ggml_vk_submit(vk_context& ctx, vk::Fence fence) { + if (ctx->seqs.empty()) { + if (fence) { + std::lock_guard guard(queue_mutex); + ctx->p->q->queue.submit({}, fence); + } + return; + } + VK_LOG_DEBUG("ggml_vk_submit(" << ctx << ", " << fence << ")"); + + std::vector> tl_wait_vals; + std::vector> tl_signal_vals; + std::vector> tl_wait_semaphores; + std::vector> tl_signal_semaphores; + std::vector tl_submit_infos; + std::vector submit_infos; + int idx = -1; + std::vector> stage_flags; + + size_t reserve = 0; + + for (const auto& sequence : ctx->seqs) { + reserve += sequence.size(); + } + + // Pre-reserve vectors to prevent reallocation, which invalidates pointers + tl_wait_semaphores.reserve(reserve); + tl_wait_vals.reserve(reserve); + tl_signal_semaphores.reserve(reserve); + tl_signal_vals.reserve(reserve); + tl_submit_infos.reserve(reserve); + submit_infos.reserve(reserve); + stage_flags.reserve(reserve); + + for (const auto& sequence : ctx->seqs) { + for (const auto& submission : sequence) { + stage_flags.push_back({}); + idx++; + tl_wait_vals.push_back({}); + tl_wait_semaphores.push_back({}); + tl_signal_vals.push_back({}); + tl_signal_semaphores.push_back({}); + for (size_t i = 0; i < submission.wait_semaphores.size(); i++) { + stage_flags[idx].push_back(ctx->p->q->stage_flags); + tl_wait_vals[idx].push_back(submission.wait_semaphores[i].value); + tl_wait_semaphores[idx].push_back(submission.wait_semaphores[i].s); + } + for (size_t i = 0; i < submission.signal_semaphores.size(); i++) { + tl_signal_vals[idx].push_back(submission.signal_semaphores[i].value); + tl_signal_semaphores[idx].push_back(submission.signal_semaphores[i].s); + } + tl_submit_infos.push_back({ + (uint32_t) submission.wait_semaphores.size(), + tl_wait_vals[idx].data(), + (uint32_t) submission.signal_semaphores.size(), + tl_signal_vals[idx].data(), + }); + tl_submit_infos[idx].sType = vk::StructureType::eTimelineSemaphoreSubmitInfo; + tl_submit_infos[idx].pNext = nullptr; + vk::SubmitInfo si{ + (uint32_t) submission.wait_semaphores.size(), + tl_wait_semaphores[idx].data(), + stage_flags[idx].data(), + 1, + &submission.buffer->buf, + (uint32_t) submission.signal_semaphores.size(), + tl_signal_semaphores[idx].data(), + }; + si.setPNext(&tl_submit_infos[idx]); + submit_infos.push_back(si); + } + } + + std::lock_guard guard(queue_mutex); + ctx->p->q->queue.submit(submit_infos, fence); + + ctx->seqs.clear(); +} + +static uint32_t ggml_vk_find_queue_family_index(std::vector& queue_family_props, const vk::QueueFlags& required, const vk::QueueFlags& avoid, int32_t compute_index, uint32_t min_num_queues) { + VK_LOG_DEBUG("ggml_vk_find_queue_family_index()"); + const uint32_t qfsize = queue_family_props.size(); + + // Try with avoid preferences first + for (uint32_t i = 0; i < qfsize; i++) { + if (queue_family_props[i].queueCount >= min_num_queues && (compute_index < 0 || i != (uint32_t) compute_index) && queue_family_props[i].queueFlags & required && !(queue_family_props[i].queueFlags & avoid)) { + return i; + } + } + + // Fall back to only required + for (size_t i = 0; i < qfsize; i++) { + if (queue_family_props[i].queueCount >= min_num_queues && (compute_index < 0 || i != (uint32_t) compute_index) && queue_family_props[i].queueFlags & required) { + return i; + } + } + + // Fall back to reusing compute queue + for (size_t i = 0; i < qfsize; i++) { + if (queue_family_props[i].queueCount >= min_num_queues && queue_family_props[i].queueFlags & required) { + return i; + } + } + + // Fall back to ignoring min_num_queries + for (size_t i = 0; i < qfsize; i++) { + if (queue_family_props[i].queueFlags & required) { + return i; + } + } + + // All commands that are allowed on a queue that supports transfer operations are also allowed on a queue that supports either graphics or compute operations. + // Thus, if the capabilities of a queue family include VK_QUEUE_GRAPHICS_BIT or VK_QUEUE_COMPUTE_BIT, then reporting the VK_QUEUE_TRANSFER_BIT capability separately for that queue family is optional. + if (compute_index >= 0) { + return compute_index; + } + + std::cerr << "ggml_vulkan: No suitable queue family index found." << std::endl; + + for(auto &q_family : queue_family_props) { + std::cerr << "Queue number: " + std::to_string(q_family.queueCount) << " flags: " + to_string(q_family.queueFlags) << std::endl; + } + abort(); +} + +static void ggml_vk_create_queue(vk_device& device, vk_queue& q, uint32_t queue_family_index, uint32_t queue_index, vk::PipelineStageFlags&& stage_flags, bool transfer_only) { + VK_LOG_DEBUG("ggml_vk_create_queue()"); + std::lock_guard guard(device->mutex); + + q.queue_family_index = queue_family_index; + q.transfer_only = transfer_only; + + q.cmd_pool.init(device, &q); + + q.queue = device->device.getQueue(queue_family_index, queue_index); + + q.stage_flags = stage_flags; +} + +static vk_context ggml_vk_create_context(ggml_backend_vk_context * ctx, vk_command_pool& p) { + vk_context result = std::make_shared(); + VK_LOG_DEBUG("ggml_vk_create_context(" << result << ")"); + ctx->gc.contexts.emplace_back(result); + result->p = &p; + return result; +} + +static vk_context ggml_vk_create_temporary_context(vk_command_pool& p) { + vk_context result = std::make_shared(); + VK_LOG_DEBUG("ggml_vk_create_temporary_context(" << result << ")"); + result->p = &p; + return result; +} + +static vk_semaphore * ggml_vk_create_binary_semaphore(ggml_backend_vk_context * ctx) { + VK_LOG_DEBUG("ggml_vk_create_timeline_semaphore()"); + vk::SemaphoreTypeCreateInfo tci{ vk::SemaphoreType::eBinary, 0 }; + vk::SemaphoreCreateInfo ci{}; + ci.setPNext(&tci); + vk::Semaphore semaphore = ctx->device->device.createSemaphore(ci); + ctx->gc.semaphores.push_back({ semaphore, 0 }); + return &ctx->gc.semaphores[ctx->gc.semaphores.size() - 1]; +} + +static vk_semaphore * ggml_vk_create_timeline_semaphore(ggml_backend_vk_context * ctx) { + VK_LOG_DEBUG("ggml_vk_create_timeline_semaphore()"); + if (ctx->semaphore_idx >= ctx->gc.tl_semaphores.size()) { + vk::SemaphoreTypeCreateInfo tci{ vk::SemaphoreType::eTimeline, 0 }; + vk::SemaphoreCreateInfo ci{}; + ci.setPNext(&tci); + vk::Semaphore semaphore = ctx->device->device.createSemaphore(ci); + ctx->gc.tl_semaphores.push_back({ semaphore, 0 }); + } + return &ctx->gc.tl_semaphores[ctx->semaphore_idx++]; +} + +static vk::Event ggml_vk_create_event(ggml_backend_vk_context * ctx) { + if (ctx->event_idx >= ctx->gc.events.size()) { + ctx->gc.events.push_back(ctx->device->device.createEvent({})); + } + return ctx->gc.events[ctx->event_idx++]; +} + +static void ggml_vk_command_pool_cleanup(vk_device& device, vk_command_pool& p) { + VK_LOG_DEBUG("ggml_vk_command_pool_cleanup()"); + + // Requires command buffers to be done + device->device.resetCommandPool(p.pool); + // Don't clear the command buffers and mark them as not in use. + // This allows us to reuse them + for (auto& cmd_buffer : p.cmd_buffers) { + cmd_buffer.in_use = false; + } +} + +static void ggml_vk_queue_command_pools_cleanup(vk_device& device) { + VK_LOG_DEBUG("ggml_vk_queue_command_pools_cleanup()"); + + // Arbitrary frequency to cleanup/reuse command buffers + static constexpr uint32_t cleanup_frequency = 10; + + if (device->compute_queue.cmd_pool.buffers_in_use() >= cleanup_frequency) { + ggml_vk_command_pool_cleanup(device, device->compute_queue.cmd_pool); + } + if (device->transfer_queue.cmd_pool.buffers_in_use() >= cleanup_frequency) { + ggml_vk_command_pool_cleanup(device, device->transfer_queue.cmd_pool); + } +} + +static std::vector ggml_vk_find_memory_properties(const vk::PhysicalDeviceMemoryProperties* mem_props, vk::MemoryRequirements* mem_req, vk::MemoryPropertyFlags flags) { + std::vector indices; + + for (uint32_t i = 0; i < mem_props->memoryTypeCount; ++i) { + vk::MemoryType memory_type = mem_props->memoryTypes[i]; + if ((mem_req->memoryTypeBits & ((uint64_t)1 << i)) && + (flags & memory_type.propertyFlags) == flags && + mem_props->memoryHeaps[memory_type.heapIndex].size >= mem_req->size) { + indices.push_back(i); + } + } + return indices; +} + +static vk_buffer ggml_vk_create_buffer(vk_device& device, size_t size, const std::initializer_list & req_flags_list, + void *import_ptr = nullptr) { + VK_LOG_DEBUG("ggml_vk_create_buffer(" << device->name << ", " << size << ", " << to_string(req_flags_list.begin()[0]) << ", " << to_string(req_flags_list.begin()[req_flags_list.size()-1]) << ")"); + if (size > device->max_buffer_size) { + throw vk::OutOfDeviceMemoryError("Requested buffer size exceeds device buffer size limit"); + } + + vk_buffer buf = std::make_shared(); + + if (size == 0) { + buf->size = 0; + return buf; + } + + vk::BufferUsageFlags usage_flags = vk::BufferUsageFlagBits::eStorageBuffer | vk::BufferUsageFlagBits::eTransferSrc | vk::BufferUsageFlagBits::eTransferDst; + vk::MemoryAllocateFlags mem_flags {}; + if (device->buffer_device_address) { + usage_flags |= vk::BufferUsageFlagBits::eShaderDeviceAddress; + mem_flags |= vk::MemoryAllocateFlagBits::eDeviceAddress; + } + + vk::BufferCreateInfo buffer_create_info{ + vk::BufferCreateFlags(), + size, + usage_flags, + vk::SharingMode::eExclusive, + 0, + nullptr, + }; + + vk::ExternalMemoryBufferCreateInfo external_memory_bci; + if (import_ptr) { + external_memory_bci.handleTypes = vk::ExternalMemoryHandleTypeFlagBits::eHostAllocationEXT; + buffer_create_info.setPNext(&external_memory_bci); + } + + buf->buffer = device->device.createBuffer(buffer_create_info); + + vk::MemoryRequirements mem_req = device->device.getBufferMemoryRequirements(buf->buffer); + + vk::PhysicalDeviceMemoryProperties mem_props = device->physical_device.getMemoryProperties(); + + 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); + } + + if (import_ptr) { + vk::MemoryHostPointerPropertiesEXT host_pointer_props; + try { + host_pointer_props = device->device.getMemoryHostPointerPropertiesEXT(vk::ExternalMemoryHandleTypeFlagBits::eHostAllocationEXT, import_ptr); + } catch (vk::SystemError& e) { + GGML_LOG_WARN("ggml_vulkan: Failed getMemoryHostPointerPropertiesEXT (%s)\n", e.what()); + device->device.destroyBuffer(buf->buffer); + return {}; + } + vk::PhysicalDeviceMemoryProperties mem_props = device->physical_device.getMemoryProperties(); + + uint32_t memory_type_idx; + vk::MemoryPropertyFlags property_flags = *req_flags_list.begin(); + for (memory_type_idx = 0; memory_type_idx < 32; ++memory_type_idx) { + if (!(host_pointer_props.memoryTypeBits & (1u << memory_type_idx))) { + continue; + } + if (!(mem_req.memoryTypeBits & (1u << memory_type_idx))) { + continue; + } + + vk::MemoryType memory_type = mem_props.memoryTypes[memory_type_idx]; + // check for visible+coherent+cached. Other flags (e.g. devicelocal) are allowed + if ((memory_type.propertyFlags & property_flags) == property_flags) { + property_flags = memory_type.propertyFlags; + break; + } + } + if (memory_type_idx == 32) { + GGML_LOG_WARN("ggml_vulkan: Memory type for host allocation not found\n"); + device->device.destroyBuffer(buf->buffer); + return {}; + } + + buf->memory_property_flags = mem_props.memoryTypes[memory_type_idx].propertyFlags; + try { + vk::ImportMemoryHostPointerInfoEXT import_info; + import_info.handleType = vk::ExternalMemoryHandleTypeFlagBits::eHostAllocationEXT; + import_info.pHostPointer = import_ptr; + import_info.setPNext(&mem_flags_info); + buf->device_memory = device->device.allocateMemory({ size, memory_type_idx, &import_info }); + } catch (const vk::SystemError& e) { + } + } else { + for (auto it = req_flags_list.begin(); it != req_flags_list.end(); it++) { + const auto & req_flags = *it; + + const std::vector memory_type_indices = ggml_vk_find_memory_properties(&mem_props, &mem_req, req_flags); + + if (memory_type_indices.empty()) { + continue; + } + + bool done = false; + + for (auto mtype_it = memory_type_indices.begin(); mtype_it != memory_type_indices.end(); mtype_it++) { + try { + buf->device_memory = device->device.allocateMemory({ mem_req.size, *mtype_it, &mem_flags_info }); + buf->memory_property_flags = mem_props.memoryTypes[*mtype_it].propertyFlags; + done = true; + break; + } catch (const vk::SystemError& e) { + // loop and retry + // during last attempt throw the exception + if (it + 1 == req_flags_list.end() && mtype_it + 1 == memory_type_indices.end()) { + device->device.destroyBuffer(buf->buffer); + throw e; + } + } + } + + if (done) { + break; + } + } + } + + if (!buf->device_memory) { + device->device.destroyBuffer(buf->buffer); + throw vk::OutOfDeviceMemoryError("No suitable memory type found"); + } + + buf->ptr = nullptr; + + if (import_ptr) { + buf->ptr = import_ptr; + } else { + if (buf->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible) { + buf->ptr = device->device.mapMemory(buf->device_memory, 0, VK_WHOLE_SIZE); + } + } + + device->device.bindBufferMemory(buf->buffer, buf->device_memory, 0); + + buf->device = device; + buf->size = size; + + if (device->buffer_device_address) { + const vk::BufferDeviceAddressInfo addressInfo(buf->buffer); + buf->bda_addr = device->device.getBufferAddress(addressInfo); + } + + device->memory_logger->log_allocation(buf, size); + + return buf; +} + +static vk_buffer ggml_vk_create_buffer_check(vk_device& device, size_t size, vk::MemoryPropertyFlags req_flags, vk::MemoryPropertyFlags fallback_flags = vk::MemoryPropertyFlags(0)) { + try { + return ggml_vk_create_buffer(device, size, {req_flags, fallback_flags}); + } catch (const vk::SystemError& e) { + std::cerr << "ggml_vulkan: Memory allocation of size " << size << " failed." << std::endl; + std::cerr << "ggml_vulkan: " << e.what() << std::endl; + throw e; + } +} + +static vk_buffer ggml_vk_create_buffer_device(vk_device& device, size_t size) { + vk_buffer buf; + try { + if (device->prefer_host_memory) { + buf = ggml_vk_create_buffer(device, size, {vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent, + vk::MemoryPropertyFlagBits::eDeviceLocal}); + } else if (device->uma) { + // On UMA, prefer host-visible memory so direct tensor borrowing works. + // If unavailable, fall back to device-local memory. + buf = ggml_vk_create_buffer(device, size, {vk::MemoryPropertyFlagBits::eDeviceLocal | vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent, + vk::MemoryPropertyFlagBits::eDeviceLocal, + vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent}); + } else if (device->disable_host_visible_vidmem) { + if (device->allow_sysmem_fallback) { + buf = ggml_vk_create_buffer(device, size, {vk::MemoryPropertyFlagBits::eDeviceLocal, + vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent}); + } else { + buf = ggml_vk_create_buffer(device, size, {vk::MemoryPropertyFlagBits::eDeviceLocal}); + } + } else { + // use rebar if available, otherwise fallback to device only visible memory + if (device->allow_sysmem_fallback) { + buf = ggml_vk_create_buffer(device, size, {vk::MemoryPropertyFlagBits::eDeviceLocal | vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent, + vk::MemoryPropertyFlagBits::eDeviceLocal, + vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent}); + } else { + buf = ggml_vk_create_buffer(device, size, {vk::MemoryPropertyFlagBits::eDeviceLocal | vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent, + vk::MemoryPropertyFlagBits::eDeviceLocal}); + } + } + } catch (const vk::SystemError& e) { + std::cerr << "ggml_vulkan: Device memory allocation of size " << size << " failed." << std::endl; + std::cerr << "ggml_vulkan: " << e.what() << std::endl; + throw e; + } + + return buf; +} + +static void ggml_vk_destroy_buffer(vk_buffer& buf) { + if (buf == nullptr) { + return; + } + + if (buf->device != nullptr) { + buf->device->memory_logger->log_deallocation(buf); + } + + buf.reset(); +} + +static vk_subbuffer ggml_vk_subbuffer(const ggml_backend_vk_context* ctx, const vk_buffer& buf, size_t offset = 0) { + return { buf, offset, ggml_vk_get_max_buffer_range(ctx, buf, offset) }; +} + +static void ggml_vk_sync_buffers(ggml_backend_vk_context* ctx, vk_context& subctx) { + VK_LOG_DEBUG("ggml_vk_sync_buffers()"); + + const bool transfer_queue = subctx->p->q->transfer_only; + + if (ctx) { + ctx->prealloc_x_need_sync = ctx->prealloc_y_need_sync = ctx->prealloc_split_k_need_sync = false; + } + + subctx->s->buffer->buf.pipelineBarrier( + subctx->p->q->stage_flags, + subctx->p->q->stage_flags, + {}, + { { + { !transfer_queue ? (vk::AccessFlagBits::eShaderRead | vk::AccessFlagBits::eShaderWrite | vk::AccessFlagBits::eTransferRead | vk::AccessFlagBits::eTransferWrite) : (vk::AccessFlagBits::eTransferRead | vk::AccessFlagBits::eTransferWrite) }, + { !transfer_queue ? (vk::AccessFlagBits::eShaderRead | vk::AccessFlagBits::eShaderWrite | vk::AccessFlagBits::eTransferRead | vk::AccessFlagBits::eTransferWrite) : (vk::AccessFlagBits::eTransferRead | vk::AccessFlagBits::eTransferWrite) } + } }, + {}, + {} + ); +} + +static void ggml_vk_reset_event(vk_context& ctx, vk::Event& event) { + VK_LOG_DEBUG("ggml_vk_set_event()"); + + ctx->s->buffer->buf.resetEvent( + event, + ctx->p->q->stage_flags + ); +} + +static void ggml_vk_set_event(vk_context& ctx, vk::Event& event) { + VK_LOG_DEBUG("ggml_vk_set_event()"); + + ctx->s->buffer->buf.setEvent( + event, + ctx->p->q->stage_flags + ); +} + +static void ggml_vk_wait_events(vk_context& ctx, std::vector&& events) { + VK_LOG_DEBUG("ggml_vk_wait_events()"); + if (events.empty()) { + return; + } + + ctx->s->buffer->buf.waitEvents( + events, + ctx->p->q->stage_flags, + ctx->p->q->stage_flags, + {}, + {}, + {} + ); +} + +struct vk_fa_tuning_params { + FaCodePath path; + uint32_t workgroup_size; + uint32_t subgroup_size; + uint32_t block_rows; + uint32_t block_cols; + uint32_t d_split; + uint32_t row_split; + bool shmem_staging; + bool disable_subgroups; + uint32_t limit_occupancy_shmem; + + void print() const { + std::cerr << "path=" << path << " workgroup_size=" << workgroup_size << " subgroup_size=" << subgroup_size << + " block_rows=" << block_rows << " block_cols=" << block_cols << " d_split=" << d_split << + " row_split=" << row_split << " shmem_staging=" << shmem_staging << " disable_subgroups=" << disable_subgroups << + " limit_occupancy_shmem=" << limit_occupancy_shmem << std::endl; + } +}; + +static bool ggml_vk_flash_attn_scalar_shmem_support(const vk_device& device, const vk_fa_tuning_params& params, uint32_t hsk, uint32_t hsv, bool f32acc, ggml_type k_type, ggml_type v_type); +static bool ggml_vk_flash_attn_coopmat_shmem_support(const vk_device& device, const vk_fa_tuning_params& params, uint32_t hsk, uint32_t hsv, bool f32acc, ggml_type k_type = GGML_TYPE_F16); + +static vk_fa_tuning_params get_fa_tuning_params_scalar(const vk_device& device, uint32_t hsk, uint32_t hsv, uint32_t n_rows, uint32_t n_kv, ggml_type k_type, ggml_type v_type, bool f32acc) { + + vk_fa_tuning_params result{}; + result.path = FA_SCALAR; + + if (device->vendor_id == VK_VENDOR_ID_INTEL) { + // Disable subgroup use due to performance issues when enforcing subgroup sizes + result.subgroup_size = 32; + result.disable_subgroups = true; + } else if (device->vendor_id == VK_VENDOR_ID_AMD && device->architecture != AMD_GCN) { + result.subgroup_size = n_rows < 4 ? 32 : device->subgroup_size; + } else { + result.subgroup_size = device->subgroup_size; + } + + // Row split splits the workgroup so that synchronization only has to happen within subgroups, which avoids barriers + uint32_t row_split_max_hsk = 64; + if (device->vendor_id == VK_VENDOR_ID_AMD && device->architecture != AMD_GCN && !device->uma) { + row_split_max_hsk = n_rows <= 8 ? 64 : 128; + } + result.row_split = (n_rows < 4 || hsk <= row_split_max_hsk) ? 1 : 4; + + if (result.subgroup_size > 32 && (n_rows < 4 || hsk < (result.row_split == 1 ? 128 : 64))) { + result.workgroup_size = result.subgroup_size * 2; + } else { + result.workgroup_size = result.subgroup_size * 4; + } + + const uint32_t D = hsk | hsv; + + const bool reduce_block_rows = D & 8 || n_kv < 1024 || device->vendor_id == VK_VENDOR_ID_INTEL; + + if (n_rows == 1) { + result.block_rows = 1; + result.block_cols = 64; + } else { + // row_split 1 means higher register use per row, so block size has to be adjusted + if (result.row_split == 1) { + result.block_rows = n_rows == 2 ? 2 : ((n_rows <= 4 || reduce_block_rows) ? 4 : 8); + } else { + result.block_rows = n_rows <= 4 ? 4 : ((n_rows <= 8 || reduce_block_rows) ? 8 : 16); + } + + result.block_cols = (D & 8) ? 64 : 32; + } + + const uint32_t D_lsb = D ^ (D & (D-1)); // extract lowest set bit + + result.d_split = std::min(std::min(result.subgroup_size, 8u), D_lsb / 4); + + result.shmem_staging = (device->vendor_id == VK_VENDOR_ID_NVIDIA && hsk < 256 && hsv < 256) ? 1 : 0; + + if (!reduce_block_rows && !ggml_vk_flash_attn_scalar_shmem_support(device, result, hsk, hsv, f32acc, k_type, v_type)) { + result.block_rows /= 2; + } + + // On AMD RDNA, for small head sizes and big batch size the shader uses few registers, so too many subgroups get scheduled + // at once and end up thrashing the cache. Fix this by setting a large (unused) shmem buffer that reduces occupancy. + // This targets an occupancy of 4 subgroups per SIMD. + if (device->vendor_id == VK_VENDOR_ID_AMD && device->properties.limits.maxComputeSharedMemorySize == 65536) { + if (device->architecture != AMD_GCN && n_rows >= 64 && hsk <= 128) { + // 30kb target for hsk > 64, 26kb for <= 64 due to smaller workgroup size + // Values are guessed, tested on RDNA2 + result.limit_occupancy_shmem = (hsk <= 64 ? 26 : 30) * 1024 / 4 / 4; + } else if (device->architecture == AMD_GCN && n_rows <= 8 && hsk >= 256) { + // Same thing for GCN, with an occupancy target of 2 subgroups per SIMD. + // Here low-batch FA with large head size is affected. + // n_rows < 4 switch because workgroup size switches from 128 to 256 there. + result.limit_occupancy_shmem = (n_rows < 4 ? 14 : 26) * 1024 / 4 / 4; + } + } + + return result; +} + +static vk_fa_tuning_params get_fa_tuning_params_coopmat1(const vk_device& device, uint32_t hsk, uint32_t hsv, uint32_t n_rows, uint32_t n_kv, ggml_type k_type, ggml_type v_type, bool f32acc) { + GGML_UNUSED(n_rows); + GGML_UNUSED(n_kv); + GGML_UNUSED(k_type); + GGML_UNUSED(v_type); + GGML_UNUSED(f32acc); + + vk_fa_tuning_params result{}; + result.path = FA_COOPMAT1; + + const uint32_t D = hsk | hsv; + + const uint32_t coopmat_block_rows = 16; + const uint32_t coopmat_block_cols = 16; + + const uint32_t num_subgroups = 4; + + result.block_rows = coopmat_block_rows; + result.block_cols = coopmat_block_cols * num_subgroups; + result.row_split = num_subgroups; + result.subgroup_size = device->subgroup_size; + result.workgroup_size = num_subgroups * result.subgroup_size; + + const uint32_t D_lsb = D ^ (D & (D-1)); // extract lowest set bit + result.d_split = std::min(std::min(result.subgroup_size, 8u), D_lsb / 4); + + result.shmem_staging = (device->vendor_id == VK_VENDOR_ID_NVIDIA && hsk < 256 && hsv < 256) ? 1 : 0; + + return result; +} + +static vk_fa_tuning_params get_fa_tuning_params_coopmat2(const vk_device& device, uint32_t hsk, uint32_t hsv, uint32_t n_rows, uint32_t n_kv, ggml_type k_type, ggml_type v_type, bool f32acc) { + GGML_UNUSED(n_kv); + GGML_UNUSED(f32acc); + + vk_fa_tuning_params result{}; + result.path = FA_COOPMAT2; + + const uint32_t D = hsk | hsv; + + const bool small_rows = n_rows < 32; + + if (small_rows) { + result.block_rows = 32; + result.block_cols = 32; + } else if (ggml_is_quantized(k_type) || ggml_is_quantized(v_type) || hsk >= 256 || hsv >= 256) { + result.block_rows = (hsk >= 512 || hsv >= 512) ? 32 : 64; + result.block_cols = 32; + } else { + result.block_rows = 64; + result.block_cols = 64; + } + + result.subgroup_size = device->subgroup_size; + result.workgroup_size = (small_rows && (D % 32) == 0) ? 256 : 128; + + return result; +} + +static vk_fa_tuning_params get_fa_tuning_params(const vk_device& device, uint32_t hsk, uint32_t hsv, uint32_t n_rows, uint32_t n_kv, ggml_type k_type, ggml_type v_type, bool f32acc) { + FaCodePath path = device->coopmat2 ? FA_COOPMAT2 : + device->coopmat1_fa_support ? FA_COOPMAT1 : FA_SCALAR; + + if (path == FA_COOPMAT2 && k_type == GGML_TYPE_BF16 && !device->coopmat2_bf16_support) { + path = FA_COOPMAT1; + } + if (path == FA_COOPMAT1 && k_type == GGML_TYPE_BF16 && !device->coopmat_bf16_support) { + path = FA_SCALAR; + } + + if (path == FA_COOPMAT1 && device->architecture == vk_device_architecture::NVIDIA_TURING) { + // Nvidia compiler bug, see https://github.com/ggml-org/llama.cpp/pull/19075#issuecomment-3820716090 + path = FA_SCALAR; + } + + if (path == FA_COOPMAT1) { + bool shape_ok = (f32acc && device->coopmat_support_16x16x16_f32acc) || + (!f32acc && device->coopmat_support_16x16x16_f16acc); + const vk_fa_tuning_params params = get_fa_tuning_params_coopmat1(device, hsk, hsv, n_rows, n_kv, k_type, v_type, f32acc); + bool shmem_ok = ggml_vk_flash_attn_coopmat_shmem_support(device, params, hsk, hsv, f32acc, k_type); + + if (!shape_ok || !shmem_ok) { + path = FA_SCALAR; + } + } + + // scalar is faster than coopmat when N==1 + if (n_rows == 1 && (path == FA_COOPMAT1 || path == FA_COOPMAT2)) { + path = FA_SCALAR; + } + + // Q1_0 K/V is only implemented on coopmat2 (flash_attn_cm2); there is no scalar FA shader for it. + if ((k_type == GGML_TYPE_Q1_0 || v_type == GGML_TYPE_Q1_0) && device->coopmat2) { + path = FA_COOPMAT2; + } + + switch (path) { + case FA_SCALAR: + return get_fa_tuning_params_scalar(device, hsk, hsv, n_rows, n_kv, k_type, v_type, f32acc); + case FA_COOPMAT1: + return get_fa_tuning_params_coopmat1(device, hsk, hsv, n_rows, n_kv, k_type, v_type, f32acc); + case FA_COOPMAT2: + return get_fa_tuning_params_coopmat2(device, hsk, hsv, n_rows, n_kv, k_type, v_type, f32acc); + default: + throw std::runtime_error("unsupported FaCodePath"); + } +} + +static vk_fa_pipeline_state get_fa_pipeline_state(const vk_device& device, const vk_fa_tuning_params& params, uint32_t hsk, uint32_t hsv, bool aligned, bool f32acc, + bool use_mask, bool use_mask_opt, bool use_logit_softcap, ggml_type k_type, ggml_type v_type) { + const bool old_amd_windows = device->vendor_id == VK_VENDOR_ID_AMD && device->driver_id == vk::DriverId::eAmdProprietary && + (device->architecture == AMD_GCN || device->architecture == AMD_RDNA1 || device->architecture == AMD_RDNA2); + + uint32_t flags = (use_mask_opt ? 1 : 0) | + (use_mask ? 2 : 0) | + (use_logit_softcap ? 4 : 0) | + (old_amd_windows ? 8 : 0); + + const uint32_t subgroup_size = params.disable_subgroups ? 0 : params.subgroup_size; + + return vk_fa_pipeline_state{hsk, hsv, params.block_rows, params.block_cols, params.d_split, params.row_split, params.shmem_staging, params.path, params.workgroup_size, subgroup_size, aligned, f32acc, flags, params.limit_occupancy_shmem, k_type, v_type}; +} + +static std::vector get_fa_spec_constants(const vk_fa_pipeline_state& state) { + const auto fa_block_bytes = [](ggml_type t) -> uint32_t { + if (t == GGML_TYPE_F32) return 16u; + return (uint32_t) ggml_type_size(t); + }; + return { + /* 0 WorkGroupSize */ state.workgroup_size, + /* 1 Br */ state.Br, + /* 2 Bc */ state.Bc, + /* 3 HSK */ state.HSK, + /* 4 HSV */ state.HSV, + /* 5 Clamp */ static_cast(!state.aligned), + /* 6 D_split */ state.D_split, + /* 7 row_split */ state.row_split, + /* 8 SubGroupSize */ state.subgroup_size, + /* 9 SHMEM_STAGING */ state.shmem_staging ? 1u : 0u, + /*10 Flags */ state.flags, + /*11 LIMIT_OCCUPANCY_SHMEM */ state.limit_occupancy_shmem, + /*12 FaTypeK */ static_cast(state.k_type), + /*13 FaTypeV */ static_cast(state.v_type), + /*14 FaBlockBytesK */ fa_block_bytes(state.k_type), + /*15 FaBlockBytesV */ fa_block_bytes(state.v_type), + }; +} + +static bool ggml_vk_matmul_shmem_support(const vk_device& device, const std::vector& warptile, bool mul_mat_id, ggml_type src0_type) { + + uint32_t lut_size = 0; + switch (src0_type) { + case GGML_TYPE_IQ1_S: + case GGML_TYPE_IQ1_M: + // Regular matmul uses the compact uint16_t IQ1 grid; the expanded + // uint32_t grid is only enabled for the q8_1/int-dot vector path. + lut_size = 2*2048; + break; + case GGML_TYPE_IQ2_XXS: + lut_size = 8*256; + break; + case GGML_TYPE_IQ2_XS: + lut_size = 8*512; + break; + case GGML_TYPE_IQ2_S: + lut_size = 8*1024; + break; + case GGML_TYPE_IQ3_XXS: + lut_size = 4*256; + break; + case GGML_TYPE_IQ3_S: + lut_size = 4*512; + break; + case GGML_TYPE_IQ4_NL: + case GGML_TYPE_IQ4_XS: + case GGML_TYPE_MXFP4: + lut_size = 4*16; + break; + case GGML_TYPE_NVFP4: + // Same kvalues budget as MXFP4 plus ue4m3_fp32_lut[128] (types.glsl, DATA_A_NVFP4). + lut_size = 4*16 + 128u * (uint32_t)sizeof(float); + break; + default: + break; + } + + // Needs to be kept up to date on shader changes + const uint32_t bank_conflict_offset = device->coopmat_support ? 8 : 1; + const uint32_t type_size = device->fp16 ? sizeof(ggml_fp16_t) : sizeof(float); + const uint32_t warps = warptile[0] / warptile[10]; + + const uint32_t load_bufs = (warptile[1] + warptile[2]) * (warptile[3] + bank_conflict_offset) * type_size; + const uint32_t mmid_row_ids = mul_mat_id ? (warptile[2] * 2 * sizeof(uint16_t)) : 0; + const uint32_t coopmat_stage = device->coopmat_support ? warptile[7] * warptile[8] / warps * sizeof(float) : 0; + const uint32_t ballots_sh = mul_mat_id ? (warps * 4 * sizeof(uint32_t)) : 0; + + const uint32_t total_size = load_bufs + mmid_row_ids + coopmat_stage + lut_size + ballots_sh; + const bool supported = total_size <= device->properties.limits.maxComputeSharedMemorySize; + + VK_LOG_DEBUG("ggml_vk_matmul_shmem_support(warptile=(" << warptile[0] << "," << warptile[1] << "," << warptile[2] << "), " + "mul_mat_id=" << mul_mat_id << ", src0_type=" << ggml_type_name(src0_type) << ", supported=" << supported); + + return supported; +} + +// Shmem usage for the q8_1 mmq shader (mul_mmq.comp), which uses +// block_a_cache / block_b_cache layouts (see mul_mmq_shmem_types.glsl) rather +// than the float load buffers checked by ggml_vk_matmul_shmem_support. +// Sizes follow std430 rules. Returns false for types without a q8_1 pipeline. +static bool ggml_vk_matmul_int_shmem_support(const vk_device& device, const std::vector& warptile, bool mul_mat_id, ggml_type src0_type) { + + // FLOAT_TYPE in the shader is float16_t with fp16 support, otherwise float. + const uint32_t fp_size = device->fp16 ? 2u : 4u; + const uint32_t fp_align = fp_size; + const uint32_t fp2_size = 2u * fp_size; + const uint32_t fp2_align = device->fp16 ? 4u : 8u; + + struct member { uint32_t size, align; }; + auto std430_size = [](std::initializer_list members) { + uint32_t off = 0, struct_align = 1; + for (const auto &m : members) { + off = (off + m.align - 1) & ~(m.align - 1); + off += m.size; + struct_align = std::max(struct_align, m.align); + } + return (off + struct_align - 1) & ~(struct_align - 1); + }; + + uint32_t block_a_size = 0; + switch (src0_type) { + case GGML_TYPE_Q4_0: block_a_size = std430_size({{16, 4}, {fp_size, fp_align}}); break; // qs[16/4] + dm + case GGML_TYPE_Q4_1: block_a_size = std430_size({{16, 4}, {fp2_size, fp2_align}}); break; // qs[16/4] + dm(vec2) + case GGML_TYPE_Q5_0: block_a_size = std430_size({{16, 4}, {4, 4}, {fp_size, fp_align}}); break; // qs[16/4] + qh + dm + case GGML_TYPE_Q5_1: block_a_size = std430_size({{16, 4}, {4, 4}, {fp2_size, fp2_align}}); break; // qs[16/4] + qh + dm(vec2) + case GGML_TYPE_Q8_0: block_a_size = std430_size({{32, 4}, {fp_size, fp_align}}); break; // qs[8] + dm + case GGML_TYPE_MXFP4: block_a_size = std430_size({{32, 4}, {fp_size, fp_align}}); break; // qs[8] + d + case GGML_TYPE_Q2_K: block_a_size = std430_size({{ 8, 4}, {2, 2}, {fp2_size, fp2_align}}); break; // qs[2] + scales(u8vec2) + dm(vec2) + case GGML_TYPE_Q3_K: block_a_size = std430_size({{16, 4}, {fp2_size, fp2_align}}); break; // qs[4] + d_scales(vec2) + case GGML_TYPE_Q4_K: block_a_size = std430_size({{16, 4}, {fp2_size, fp2_align}}); break; // qs[4] + dm(vec2) + case GGML_TYPE_Q5_K: block_a_size = std430_size({{32, 4}, {fp2_size, fp2_align}}); break; // qs[8] + dm(vec2) + case GGML_TYPE_Q6_K: block_a_size = std430_size({{32, 4}, {fp2_size, fp2_align}}); break; // qs[8] + d_scales(vec2) + default: + return false; + } + + // block_b_cache: { int32_t qs[8]; FLOAT_TYPEV2 ds; } + const uint32_t block_b_size = std430_size({{32, 4}, {fp2_size, fp2_align}}); + + const uint32_t BM = warptile[1]; + const uint32_t BN = warptile[2]; + // mul_mmq.comp: BK_STEP=1 for MUL_MAT_ID, 4 otherwise. + const uint32_t BK_STEP = mul_mat_id ? 1u : 4u; + + const uint32_t buf_a_size = BM * BK_STEP * block_a_size; + const uint32_t buf_b_size = BN * BK_STEP * block_b_size; + const uint32_t mmid_row_ids = mul_mat_id ? (BN * 2u * (uint32_t)sizeof(uint16_t)) : 0u; + + const uint32_t warps = warptile[0] / warptile[10]; + const uint32_t ballots_sh = mul_mat_id ? (warps * 4u * (uint32_t)sizeof(uint32_t)) : 0u; + + const uint32_t total_size = buf_a_size + buf_b_size + mmid_row_ids + ballots_sh; + const bool supported = total_size <= device->properties.limits.maxComputeSharedMemorySize; + + VK_LOG_DEBUG("ggml_vk_matmul_int_shmem_support(warptile=(" << warptile[0] << "," << warptile[1] << "," << warptile[2] << "), " + "mul_mat_id=" << mul_mat_id << ", src0_type=" << ggml_type_name(src0_type) << ", total=" << total_size << ", supported=" << supported); + + return supported; +} + +struct GpuPipelineConfig { + // GPU architecture identifier. + // Example: vk_device_architecture::AMD_GCN + vk_device_architecture arch; + + // Mapping of pipeline names to their specific subgroup sizes. + // Example: {"soft_max_f32", 64} + std::unordered_map pipelines; + + // Default subgroup size for this GPU. + // Defaults to 0 if not explicitly provided. + uint32_t default_subgroup_size = 0; +}; + +// Pipeline configuration for RDNA1 GPUs. +static const std::unordered_map rdna1_pipelines = { + {"soft_max", 64}, {"im2col", 64}, + {"argmax", 64}, {"mul_mat_vec", 64}, + {"mul_mat_vec_f16", 32}, {"mul_mat_vec_f32_f16", 32} +}; + +// Pipeline configuration for RDNA2 GPUs. +static const std::unordered_map rdna2_pipelines = { + {"soft_max", 64}, {"im2col", 64}, +}; + +static constexpr uint32_t RDNA_DEFAULT_SUBGROUP_SIZE = 32; + +// Define configurations for different GPUs. +static std::vector gpu_pipeline_configs = { + { + vk_device_architecture::AMD_RDNA1, + { + rdna1_pipelines, + }, + RDNA_DEFAULT_SUBGROUP_SIZE + }, + { + vk_device_architecture::AMD_RDNA2, + { + rdna2_pipelines, + }, + RDNA_DEFAULT_SUBGROUP_SIZE + }, +}; + +static uint32_t get_subgroup_size(const std::string &pipeline_name, const vk_device_architecture &arch) { + for (const auto &config : gpu_pipeline_configs) { + if (config.arch == arch) { + auto pipIt = config.pipelines.find(pipeline_name); + if (pipIt != config.pipelines.end()) { + return pipIt->second; + } + std::vector> sorted_pipelines(config.pipelines.begin(), config.pipelines.end()); + std::sort(sorted_pipelines.begin(), sorted_pipelines.end(), + [](const auto &a, const auto &b) { return a.first.size() > b.first.size(); }); + for (const auto &entry : sorted_pipelines) { + if (pipeline_name.find(entry.first) != std::string::npos) { + return entry.second; + } + } + return config.default_subgroup_size; + } + } + return 0; // If no matching configuration is found +} + +// Whether scalar flash attention will use the MMQ path for the given k_type. +static bool ggml_vk_fa_scalar_uses_mmq(const vk_device& device, ggml_type k_type) { +#if defined(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT) + return device->integer_dot_product && device->subgroup_clustered && + (k_type == GGML_TYPE_Q4_0 || k_type == GGML_TYPE_Q4_1 || + k_type == GGML_TYPE_Q5_0 || k_type == GGML_TYPE_Q5_1 || + k_type == GGML_TYPE_Q8_0); +#else + GGML_UNUSED(device); + GGML_UNUSED(k_type); + return false; +#endif +} + +// load_shaders walks the pipeline list under compile_mutex and either claims +// the requested pipeline for compilation or, if another thread is already +// compiling it, drops the lock and waits on compile_cv. Compiles themselves +// run unlocked. +struct CompileTask { + vk_pipeline pipeline; + size_t spv_size; + const void * spv_data; + std::string entrypoint; + uint32_t parameter_count; + std::array wg_denoms; + std::vector specialization_constants; + bool disable_robustness; + bool require_full_subgroups; + uint32_t required_subgroup_size; +}; + +static void ggml_vk_load_shaders(vk_device& device, vk_pipeline requested) { + VK_LOG_DEBUG("ggml_vk_load_shaders(" << device->name << ")"); + + // some shaders have a minimum subgroup size + const uint32_t subgroup_size_8 = std::max(device->subgroup_size, 8u); + const uint32_t subgroup_size_16 = std::max(device->subgroup_size, 16u); + const uint32_t subgroup_size_32 = std::max(device->subgroup_size, 32u); + + const uint32_t mul_mat_subgroup_size = (device->vendor_id == VK_VENDOR_ID_INTEL && device->subgroup_size_control) ? device->subgroup_min_size : device->subgroup_size; + const uint32_t mul_mat_subgroup_size_8 = std::max(mul_mat_subgroup_size, 8u); + const uint32_t mul_mat_subgroup_size_16 = std::max(mul_mat_subgroup_size, 16u); + const uint32_t mul_mat_subgroup_size_32 = std::max(mul_mat_subgroup_size, 32u); + + const bool subgroup_min_size_16 = (!device->subgroup_size_control && device->subgroup_size >= 16) || + (device->subgroup_size_control && device->subgroup_max_size >= 16); + + // mulmat + std::vector l_warptile, m_warptile, s_warptile, + l_warptile_id, m_warptile_id, s_warptile_id, + l_warptile_mmq, m_warptile_mmq, s_warptile_mmq, + l_warptile_mmq_int, m_warptile_mmq_int, s_warptile_mmq_int, + l_warptile_mmq_int_k, m_warptile_mmq_int_k, s_warptile_mmq_int_k, + l_warptile_mmq_k, m_warptile_mmq_k, s_warptile_mmq_k, + l_warptile_mmqid, m_warptile_mmqid, s_warptile_mmqid, + l_warptile_mmqid_int, m_warptile_mmqid_int, s_warptile_mmqid_int, + l_warptile_mmqid_int_k, m_warptile_mmqid_int_k, s_warptile_mmqid_int_k; + std::array l_wg_denoms, m_wg_denoms, s_wg_denoms, + l_mmq_wg_denoms, m_mmq_wg_denoms, s_mmq_wg_denoms, + l_mmq_wg_denoms_k, m_mmq_wg_denoms_k, s_mmq_wg_denoms_k, + l_mmqid_wg_denoms, m_mmqid_wg_denoms, s_mmqid_wg_denoms; + + uint32_t l_align, m_align, s_align; + + vk_pipeline wait_pipeline; + CompileTask claimed_task {}; + bool has_claimed_task = false; + + // The rest of the walk reads and writes shared device state, so hold the + // lock until we're done deciding what to compile. + std::unique_lock compile_lock(device->compile_mutex); + + if (device->coopmat2) { + // spec constants and tile sizes for non-quant matmul/matmul_id + l_warptile = { 256, 128, 256, 64, 1 }; + m_warptile = { 256, 128, 128, 64, 0 }; + s_warptile = { 128, 64, 64, 64, 0 }; + l_wg_denoms = {128, 256, 1 }; + m_wg_denoms = {128, 128, 1 }; + s_wg_denoms = { 64, 64, 1 }; + + // spec constants and tile sizes for quant matmul (non-Qi_K) + l_warptile_mmq = { 256, 128, 256, 64, 1 }; + m_warptile_mmq = { 256, 128, 128, 64, 1 }; + s_warptile_mmq = { 256, 32, 64, 128, 0 }; + l_mmq_wg_denoms = { 128, 256, 1 }; + m_mmq_wg_denoms = { 128, 128, 1 }; + s_mmq_wg_denoms = { 32, 64, 1 }; + + // spec constants and tile sizes for quant matmul (Qi_K) + l_warptile_mmq_k = { 256, 128, 256, 64, 1 }; + m_warptile_mmq_k = { 256, 128, 128, 64, 1 }; + s_warptile_mmq_k = { 256, 32, 64, 128, 0 }; + l_mmq_wg_denoms_k = { 128, 256, 1 }; + m_mmq_wg_denoms_k = { 128, 128, 1 }; + s_mmq_wg_denoms_k = { 32, 64, 1 }; + + // spec constants and tile sizes for quant matmul_id + const uint32_t mmqid_bk = device->coopmat2_decode_vector ? 64u : 32u; + l_warptile_mmqid = { 256, 128, 128, mmqid_bk, 1, device->subgroup_size }; + m_warptile_mmqid = { 256, 128, 64, mmqid_bk, 0, device->subgroup_size }; + s_warptile_mmqid = { 256, 128, 64, mmqid_bk, 0, device->subgroup_size }; + l_mmqid_wg_denoms = { 128, 128, 1 }; + m_mmqid_wg_denoms = { 128, 64, 1 }; + s_mmqid_wg_denoms = { 128, 64, 1 }; + + l_align = 128; + m_align = 64; + s_align = 32; + } else { + // Matrix cores require different warp group sizes + const uint32_t tm_l = device->coopmat_support ? device->coopmat_m : 4; + const uint32_t tm_m = device->coopmat_support ? device->coopmat_m : 4; + const uint32_t tm_s = device->coopmat_support ? device->coopmat_m : 2; + const uint32_t tn_l = device->coopmat_support ? device->coopmat_n : 4; + const uint32_t tn_m = device->coopmat_support ? device->coopmat_n : 2; + const uint32_t tn_s = device->coopmat_support ? device->coopmat_n : 2; + const uint32_t tk_l = device->coopmat_support ? device->coopmat_k : 1; + const uint32_t tk_m = device->coopmat_support ? device->coopmat_k : 1; + const uint32_t tk_s = device->coopmat_support ? device->coopmat_k : 1; + + const uint32_t s_warptile_wm = device->subgroup_size == 8 ? 8 : 32; + + l_warptile = { 128, 128, 128, 16, subgroup_size_8 * 2, 64, 2, tm_l, tn_l, tk_l, subgroup_size_8 }; + m_warptile = { 128, 64, 64, 16, subgroup_size_8, 32, 2, tm_m, tn_m, tk_m, subgroup_size_8 }; + s_warptile = { subgroup_size_32, 32, 32, 16, s_warptile_wm, 32, 2, tm_s, tn_s, tk_s, subgroup_size_8 }; + + l_warptile_mmq = { 128, 128, 128, 32, subgroup_size_8 * 2, 64, 2, tm_l, tn_l, tk_l, subgroup_size_8 }; + m_warptile_mmq = { 128, 64, 64, 32, subgroup_size_8, 32, 2, tm_m, tn_m, tk_m, subgroup_size_8 }; + s_warptile_mmq = { subgroup_size_32, 32, 32, 32, s_warptile_wm, 32, 2, tm_s, tn_s, tk_s, subgroup_size_8 }; + + // Integer MMQ has a smaller shared memory profile, but heavier register use + l_warptile_mmq_int = { 128, 128, 128, 32, subgroup_size_8 * 2, 64, 2, 4, 4, 1, subgroup_size_8 }; + m_warptile_mmq_int = { 128, 64, 64, 32, subgroup_size_8, 32, 2, 2, 2, 1, subgroup_size_8 }; + s_warptile_mmq_int = { subgroup_size_32, 32, 32, 32, s_warptile_wm, 32, 2, 2, 1, 1, subgroup_size_8 }; + + // K-quants use even more registers, mitigate by setting WMITER to 1 + l_warptile_mmq_int_k = { 128, 128, 128, 32, subgroup_size_8 * 2, 64, 1, 4, 4, 1, subgroup_size_8 }; + m_warptile_mmq_int_k = { 128, 64, 64, 32, subgroup_size_8, 32, 1, 2, 2, 1, subgroup_size_8 }; + s_warptile_mmq_int_k = { subgroup_size_32, 32, 32, 32, s_warptile_wm, 32, 1, 2, 1, 1, subgroup_size_8 }; + + l_warptile_id = { 128, 128, 128, 16, mul_mat_subgroup_size_16 * 2, 64, 2, tm_l, tn_l, tk_l, mul_mat_subgroup_size_16 }; + m_warptile_id = { 128, 64, 64, 16, mul_mat_subgroup_size_16, 32, 2, tm_m, tn_m, tk_m, mul_mat_subgroup_size_16 }; + s_warptile_id = { mul_mat_subgroup_size_16, 32, 32, 16, s_warptile_wm, 32, 2, tm_s, tn_s, tk_s, mul_mat_subgroup_size_16 }; + + l_warptile_mmqid = { 128, 128, 128, 32, mul_mat_subgroup_size_8 * 2, 64, 2, tm_l, tn_l, tk_l, mul_mat_subgroup_size_8 }; + m_warptile_mmqid = { 128, 64, 64, 32, mul_mat_subgroup_size_8, 32, 2, tm_m, tn_m, tk_m, mul_mat_subgroup_size_8 }; + s_warptile_mmqid = { mul_mat_subgroup_size_32, 32, 32, 32, s_warptile_wm, 32, 2, tm_s, tn_s, tk_s, mul_mat_subgroup_size_8 }; + + l_warptile_mmqid_int = { 128, 128, 128, 32, mul_mat_subgroup_size_8 * 2, 64, 2, 4, 4, 1, mul_mat_subgroup_size_8 }; + m_warptile_mmqid_int = { 128, 64, 64, 32, mul_mat_subgroup_size_8, 32, 2, 2, 2, 1, mul_mat_subgroup_size_8 }; + s_warptile_mmqid_int = { mul_mat_subgroup_size_32, 32, 32, 32, s_warptile_wm, 32, 2, 2, 1, 1, mul_mat_subgroup_size_8 }; + + l_warptile_mmqid_int_k = { 128, 128, 128, 32, mul_mat_subgroup_size_16 * 2, 64, 1, 4, 4, 1, mul_mat_subgroup_size_16 }; + m_warptile_mmqid_int_k = { 128, 64, 64, 32, mul_mat_subgroup_size_16, 32, 1, 2, 2, 1, mul_mat_subgroup_size_16 }; + s_warptile_mmqid_int_k = { mul_mat_subgroup_size_32, 32, 32, 32, s_warptile_wm, 32, 1, 2, 1, 1, mul_mat_subgroup_size_16 }; + + // chip specific tuning + if ((device->architecture == AMD_GCN) && (device->driver_id != vk::DriverId::eAmdProprietary)) { + m_warptile_mmq = m_warptile_mmq_int = { 256, 64, 64, 32, 16, 16, 2, 2, 2, 1, 16 }; + m_warptile_mmqid = m_warptile_mmqid_int = { 256, 64, 64, 32, 16, 16, 2, 2, 2, 1, 16 }; + } else if (device->vendor_id == VK_VENDOR_ID_AMD && device->coopmat_support && device->driver_id != vk::DriverId::eAmdProprietary) { + // This is intentionally using tx_m values, slight performance increase + l_warptile = { 256, 128, 128, 16, subgroup_size_8, 64, 2, tm_m, tn_m, tk_m, subgroup_size_8 }; + l_warptile_mmq = l_warptile_mmq_int = { 256, 128, 128, 32, subgroup_size_8, 64, 2, tm_m, tn_m, tk_m, subgroup_size_8 }; + l_warptile_mmq_int_k = { 256, 128, 128, 32, subgroup_size_16, 64, 1, 4, 2, 1, subgroup_size_16 }; + } else if (device->vendor_id == VK_VENDOR_ID_INTEL && device->coopmat_support) { + // Xe2/Xe3 with coopmat enabled - warptile performance tuning + l_warptile = { 512, 128, 128, 16, subgroup_size_8, 32, 2, tm_m, tn_m, tk_m, subgroup_size_8 }; + l_warptile_mmq = { 512, 128, 128, 32, subgroup_size_8, 32, 2, tm_m, tn_m, tk_m, subgroup_size_8 }; + } + + l_mmq_wg_denoms = l_wg_denoms = {128, 128, 1 }; + m_mmq_wg_denoms = m_wg_denoms = { 64, 64, 1 }; + s_mmq_wg_denoms = s_wg_denoms = { 32, 32, 1 }; + l_align = 128; + m_align = 64; + s_align = 32; + + for (uint32_t i = 0; i < GGML_TYPE_COUNT; ++i) { + ggml_type t = (ggml_type)i; + // Disable medium and large matrix multiplication if not enough shared memory is available + // Check mmq warptiles as the largest configuration + // Throw an error if not enough for any matrix multiplication is available + if (!ggml_vk_matmul_shmem_support(device, s_warptile_mmq, false, t)) { + std::cerr << "ggml_vulkan: Error: Shared memory size too small for matrix multiplication." << std::endl; + throw std::runtime_error("Shared memory size too small for matrix multiplication."); + } else if (!ggml_vk_matmul_shmem_support(device, m_warptile_mmq, false, t)) { + device->mul_mat_m[i] = false; + device->mul_mat_l[i] = false; + } else if (!ggml_vk_matmul_shmem_support(device, l_warptile_mmq, false, t)) { + device->mul_mat_l[i] = false; + } + + // Disable mul_mat_id if not enough shared memory is available + if (!ggml_vk_matmul_shmem_support(device, s_warptile_mmqid, true, t)) { + device->mul_mat_id_s[i] = false; + device->mul_mat_id_m[i] = false; + device->mul_mat_id_l[i] = false; + } else if (!ggml_vk_matmul_shmem_support(device, m_warptile_mmqid, true, t)) { + device->mul_mat_id_m[i] = false; + device->mul_mat_id_l[i] = false; + } else if (!ggml_vk_matmul_shmem_support(device, l_warptile_mmqid, true, t)) { + device->mul_mat_id_l[i] = false; + } + + // The q8_1 mmq path has its own (larger) shmem layout, check it separately. + // K-quants use the _int_k warptiles, others use _int. + const bool is_k_quant = (t == GGML_TYPE_Q2_K || t == GGML_TYPE_Q3_K || + t == GGML_TYPE_Q4_K || t == GGML_TYPE_Q5_K || + t == GGML_TYPE_Q6_K); + const auto & s_int = is_k_quant ? s_warptile_mmq_int_k : s_warptile_mmq_int; + const auto & m_int = is_k_quant ? m_warptile_mmq_int_k : m_warptile_mmq_int; + const auto & l_int = is_k_quant ? l_warptile_mmq_int_k : l_warptile_mmq_int; + const auto & s_intid = is_k_quant ? s_warptile_mmqid_int_k : s_warptile_mmqid_int; + const auto & m_intid = is_k_quant ? m_warptile_mmqid_int_k : m_warptile_mmqid_int; + const auto & l_intid = is_k_quant ? l_warptile_mmqid_int_k : l_warptile_mmqid_int; + + if (!ggml_vk_matmul_int_shmem_support(device, s_int, false, t)) { + device->mul_mat_s_int[i] = false; + device->mul_mat_m_int[i] = false; + device->mul_mat_l_int[i] = false; + } else if (!ggml_vk_matmul_int_shmem_support(device, m_int, false, t)) { + device->mul_mat_m_int[i] = false; + device->mul_mat_l_int[i] = false; + } else if (!ggml_vk_matmul_int_shmem_support(device, l_int, false, t)) { + device->mul_mat_l_int[i] = false; + } + + if (!ggml_vk_matmul_int_shmem_support(device, s_intid, true, t)) { + device->mul_mat_id_s_int[i] = false; + device->mul_mat_id_m_int[i] = false; + device->mul_mat_id_l_int[i] = false; + } else if (!ggml_vk_matmul_int_shmem_support(device, m_intid, true, t)) { + device->mul_mat_id_m_int[i] = false; + device->mul_mat_id_l_int[i] = false; + } else if (!ggml_vk_matmul_int_shmem_support(device, l_intid, true, t)) { + device->mul_mat_id_l_int[i] = false; + } + } + } + + if (!device->pipeline_matmul_f32) { + device->pipeline_matmul_f32 = std::make_shared(); + } + if (!device->pipeline_matmul_f32_f16) { + device->pipeline_matmul_f32_f16 = std::make_shared(); + } + if (!device->pipeline_matmul_id_f32) { + device->pipeline_matmul_id_f32 = std::make_shared(); + } + if (!device->pipeline_matmul_bf16) { + device->pipeline_matmul_bf16 = std::make_shared(); + } + if (!device->pipeline_matmul_id_bf16) { + device->pipeline_matmul_id_bf16 = std::make_shared(); + } + + auto const &ggml_vk_create_pipeline = [&](vk_device& device, vk_pipeline& base_pipeline, const char *name, size_t spv_size, const void* spv_data, const char *entrypoint, + uint32_t parameter_count, uint32_t push_constant_size, std::array wg_denoms, const std::vector& specialization_constants, + uint32_t align, bool disable_robustness = false, bool require_full_subgroups = false, uint32_t required_subgroup_size = 0) { + + if (!require_full_subgroups && required_subgroup_size == 0) { + required_subgroup_size = get_subgroup_size(name, device->architecture); + } + + vk_pipeline *ptr = &base_pipeline; + + int num_pipelines = 1; +#if defined(VK_EXT_shader_64bit_indexing) + if (device->shader_64b_indexing) { + num_pipelines = 2; + } +#endif + for (int i = 0; i < num_pipelines; ++i, ptr = &(*ptr)->next) { + vk_pipeline &pipeline = *ptr; + if (!pipeline) { + pipeline = std::make_shared(); + } + if (!pipeline->initialized) { + pipeline->name = name; + pipeline->parameter_count = parameter_count; + pipeline->push_constant_size = push_constant_size; + pipeline->wg_denoms = wg_denoms; + pipeline->align = align; + pipeline->initialized = true; +#if defined(VK_EXT_shader_64bit_indexing) + pipeline->is_64b_indexing = (i == 1); +#endif + } + + // We only care about the pipeline this call asked for; the rest + // (including the 64-bit indexing variant) are handled by their + // own request_descriptor_sets / load_shaders calls. + if (pipeline.get() != requested.get()) { + continue; + } + + if (pipeline->compiled) { + continue; + } + + wait_pipeline = pipeline; + + if (!pipeline->compile_pending) { + pipeline->compile_pending = true; + claimed_task.pipeline = pipeline; + claimed_task.spv_size = spv_size; + claimed_task.spv_data = spv_data; + claimed_task.entrypoint = entrypoint; + claimed_task.parameter_count = parameter_count; + claimed_task.wg_denoms = wg_denoms; + claimed_task.specialization_constants = specialization_constants; + claimed_task.disable_robustness = disable_robustness; + claimed_task.require_full_subgroups = require_full_subgroups; + claimed_task.required_subgroup_size = required_subgroup_size; + has_claimed_task = true; + } + } + }; + + auto const &ggml_vk_create_pipeline2 = [&](vk_device& device, vk_pipeline& pipeline, const std::string &name, size_t spv_size, const void* spv_data, const char *entrypoint, + uint32_t parameter_count, uint32_t push_constant_size, std::array wg_denoms, const std::vector& specialization_constants, + uint32_t align, bool disable_robustness = false, bool require_full_subgroups = false, uint32_t required_subgroup_size = 0) { + return ggml_vk_create_pipeline(device, pipeline, name.c_str(), spv_size, spv_data, entrypoint, + parameter_count, push_constant_size, wg_denoms, specialization_constants, + align, disable_robustness, require_full_subgroups, required_subgroup_size); + }; + + // FA scalar has two SPIR-V modules (MMQ vs non-MMQ); FA cm1 has one. K/V + // quant type is selected at runtime via the FaTypeK / FaTypeV spec constants. + + for (auto &fa : device->pipeline_flash_attn_f32_f16) { + if (fa.first.path != FA_SCALAR) continue; + const uint32_t Br = fa.first.Br; + const uint32_t Bc = fa.first.Bc; + const bool aligned = fa.first.aligned; + const bool f32acc = fa.first.f32acc; + const uint32_t fa_sgs = fa.first.subgroup_size; + const bool fa_ds = fa.first.subgroup_size == 0; + + const bool bf16_kv = fa.first.k_type == GGML_TYPE_BF16; + const bool use_mmq = ggml_vk_fa_scalar_uses_mmq(device, fa.first.k_type); + const void * spv_data = nullptr; + size_t spv_size = 0; + const char *name = nullptr; + if (bf16_kv) { + spv_data = flash_attn_f32_f16_fp32_data; + spv_size = flash_attn_f32_f16_fp32_len; + name = aligned ? "flash_attn_f32_bf16_aligned" : "flash_attn_f32_bf16"; + } else if (use_mmq) { +#if defined(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT) + if (device->fp16) { + if (f32acc) { spv_data = flash_attn_f32_f16_int8_data; spv_size = flash_attn_f32_f16_int8_len; } + else { spv_data = flash_attn_f32_f16_f16acc_int8_data; spv_size = flash_attn_f32_f16_f16acc_int8_len; } + } else { + spv_data = flash_attn_f32_f16_fp32_int8_data; + spv_size = flash_attn_f32_f16_fp32_int8_len; + } +#endif + name = aligned ? "flash_attn_f32_f16_aligned" : "flash_attn_f32_f16"; + } else { + if (device->fp16) { + if (device->dot2_f16) { + if (f32acc) { spv_data = flash_attn_f32_f16_dot2_data; spv_size = flash_attn_f32_f16_dot2_len; } + else { spv_data = flash_attn_f32_f16_dot2_f16acc_data; spv_size = flash_attn_f32_f16_dot2_f16acc_len; } + } else { + if (f32acc) { spv_data = flash_attn_f32_f16_data; spv_size = flash_attn_f32_f16_len; } + else { spv_data = flash_attn_f32_f16_f16acc_data; spv_size = flash_attn_f32_f16_f16acc_len; } + } + } else { + spv_data = flash_attn_f32_f16_fp32_data; + spv_size = flash_attn_f32_f16_fp32_len; + } + name = aligned ? "flash_attn_f32_f16_aligned" : "flash_attn_f32_f16"; + } + ggml_vk_create_pipeline(device, fa.second, name, spv_size, spv_data, "main", 7, + sizeof(vk_flash_attn_push_constants), {Br, 1, 1}, + get_fa_spec_constants(fa.first), aligned ? Bc : 1, true, + !fa_ds, !fa_ds ? fa_sgs : 0); + } + +#if defined(VK_KHR_cooperative_matrix) && defined(GGML_VULKAN_COOPMAT_GLSLC_SUPPORT) + if (device->coopmat1_fa_support) { + for (auto &fa : device->pipeline_flash_attn_f32_f16) { + if (fa.first.path != FA_COOPMAT1) continue; + const uint32_t Br = fa.first.Br; + const uint32_t Bc = fa.first.Bc; + const bool aligned = fa.first.aligned; + const bool f32acc = fa.first.f32acc; + const uint32_t fa_sgs = fa.first.subgroup_size; + const bool fa_ds = fa.first.subgroup_size == 0; + + const bool bf16_kv = fa.first.k_type == GGML_TYPE_BF16; + + const void * spv_data; + size_t spv_size; + const char *name; + if (bf16_kv) { +#if defined(VK_KHR_shader_bfloat16) && defined(GGML_VULKAN_BFLOAT16_GLSLC_SUPPORT) + if (!device->coopmat_bf16_support) continue; + spv_data = flash_attn_f32_f16_bf16_cm1_data; + spv_size = flash_attn_f32_f16_bf16_cm1_len; + name = aligned ? "flash_attn_f32_bf16_aligned_cm1" : "flash_attn_f32_bf16_cm1"; +#else + continue; +#endif + } else { + if (f32acc) { spv_data = flash_attn_f32_f16_cm1_data; spv_size = flash_attn_f32_f16_cm1_len; } + else { spv_data = flash_attn_f32_f16_f16acc_cm1_data; spv_size = flash_attn_f32_f16_f16acc_cm1_len; } + name = aligned ? "flash_attn_f32_f16_aligned_cm1" : "flash_attn_f32_f16_cm1"; + } + ggml_vk_create_pipeline(device, fa.second, name, spv_size, spv_data, "main", 7, + sizeof(vk_flash_attn_push_constants), {Br, 1, 1}, + get_fa_spec_constants(fa.first), aligned ? Bc : 1, true, + !fa_ds, !fa_ds ? fa_sgs : 0); + } + } +#endif + +#if defined(VK_NV_cooperative_matrix2) && defined(GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT) + if (device->coopmat2) { + for (auto &fa : device->pipeline_flash_attn_f32_f16) { + if (fa.first.path != FA_COOPMAT2) continue; + const uint32_t Br = fa.first.Br; + const uint32_t Bc = fa.first.Bc; + const bool aligned = fa.first.aligned; + const bool f32acc = fa.first.f32acc; + + const bool bf16_kv = fa.first.k_type == GGML_TYPE_BF16; + const void * spv_data; + size_t spv_size; + const char * name; + if (bf16_kv) { +#if defined(VK_KHR_shader_bfloat16) && defined(GGML_VULKAN_BFLOAT16_GLSLC_SUPPORT) + if (!device->coopmat2_bf16_support) continue; + spv_data = flash_attn_f32_f16_bf16_cm2_data; + spv_size = flash_attn_f32_f16_bf16_cm2_len; + name = aligned ? "flash_attn_f32_bf16_aligned_cm2" : "flash_attn_f32_bf16_cm2"; +#else + continue; +#endif + } else if (aligned) { + if (f32acc) { spv_data = flash_attn_f32_f16_cm2_data; spv_size = flash_attn_f32_f16_cm2_len; name = "flash_attn_f32_f16_aligned_f32acc_cm2"; } + else { spv_data = flash_attn_f32_f16_f16acc_cm2_data; spv_size = flash_attn_f32_f16_f16acc_cm2_len; name = "flash_attn_f32_f16_aligned_f16acc_cm2"; } + } else { + if (f32acc) { spv_data = flash_attn_f32_f16_cm2_data; spv_size = flash_attn_f32_f16_cm2_len; name = "flash_attn_f32_f16_f32acc_cm2"; } + else { spv_data = flash_attn_f32_f16_f16acc_cm2_data; spv_size = flash_attn_f32_f16_f16acc_cm2_len; name = "flash_attn_f32_f16_f16acc_cm2"; } + } + ggml_vk_create_pipeline(device, fa.second, name, spv_size, spv_data, "main", 7, + sizeof(vk_flash_attn_push_constants), {Br, 1, 1}, + get_fa_spec_constants(fa.first), aligned ? Bc : 1, true, false, 0); + } + } +#endif + + auto const &ggml_vk_mul_mm_spec = [](std::vector spec, bool aligned) { + spec.push_back(aligned ? 1u : 0u); + return spec; + }; + + const int mul_mat_id_param_count = 5; + +#if defined(VK_NV_cooperative_matrix2) && defined(GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT) + if (device->coopmat2) { + auto const &ggml_vk_mul_mm_cm2_spec = [](std::vector spec, bool aligned, bool mul_mat_id) { + if (mul_mat_id && spec.size() > 5) { + spec.insert(spec.begin() + 5, aligned ? 1u : 0u); + } else { + spec.push_back(aligned ? 1u : 0u); + } + if (mul_mat_id && spec.size() == 6) { + spec.push_back(32); + } + return spec; + }; + + // Create 6 variants, {s,m,l}x{unaligned,aligned} +#define CREATE_MM(PIPELINE_NAME, NAMELC, F16ACC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT) \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->l, #NAMELC #F16ACC "_l", NAMELC ## F16ACC ## _cm2_len, NAMELC ## F16ACC ## _cm2_data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, ggml_vk_mul_mm_cm2_spec(l_ ## WARPTILE, false, PARAMCOUNT == mul_mat_id_param_count), 1, true); \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->m, #NAMELC #F16ACC "_m", NAMELC ## F16ACC ## _cm2_len, NAMELC ## F16ACC ## _cm2_data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, ggml_vk_mul_mm_cm2_spec(m_ ## WARPTILE, false, PARAMCOUNT == mul_mat_id_param_count), 1, true); \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->s, #NAMELC #F16ACC "_s", NAMELC ## F16ACC ## _cm2_len, NAMELC ## F16ACC ## _cm2_data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, ggml_vk_mul_mm_cm2_spec(s_ ## WARPTILE, false, PARAMCOUNT == mul_mat_id_param_count), 1, true); \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_l, #NAMELC #F16ACC "_aligned_l", NAMELC ## F16ACC ## _cm2_len, NAMELC ## F16ACC ## _cm2_data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, ggml_vk_mul_mm_cm2_spec(l_ ## WARPTILE, true, PARAMCOUNT == mul_mat_id_param_count), l_align, true); \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_m, #NAMELC #F16ACC "_aligned_m", NAMELC ## F16ACC ## _cm2_len, NAMELC ## F16ACC ## _cm2_data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, ggml_vk_mul_mm_cm2_spec(m_ ## WARPTILE, true, PARAMCOUNT == mul_mat_id_param_count), m_align, true); \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_s, #NAMELC #F16ACC "_aligned_s", NAMELC ## F16ACC ## _cm2_len, NAMELC ## F16ACC ## _cm2_data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, ggml_vk_mul_mm_cm2_spec(s_ ## WARPTILE, true, PARAMCOUNT == mul_mat_id_param_count), s_align, true); \ + + // Create 2 variants, {f16,f32} accumulator +#define CREATE_MM2(PIPELINE_NAME, NAMELC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT) \ + CREATE_MM(PIPELINE_NAME . f16acc, NAMELC, _f16acc, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT) \ + CREATE_MM(PIPELINE_NAME . f32acc, NAMELC, , WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT) \ + + CREATE_MM2(pipeline_matmul_f16, matmul_f16, wg_denoms, warptile, vk_mat_mat_push_constants, 3) +#if defined(GGML_VULKAN_BFLOAT16_GLSLC_SUPPORT) + if (device->coopmat_bf16_support) { + CREATE_MM(pipeline_matmul_bf16, matmul_bf16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3) + } +#endif + CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q1_0], matmul_q1_0_f16, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3) + CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q4_0], matmul_q4_0_f16, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3) + CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q4_1], matmul_q4_1_f16, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3) + CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q5_0], matmul_q5_0_f16, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3) + CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q5_1], matmul_q5_1_f16, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3) + CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q8_0], matmul_q8_0_f16, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3) + CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q2_K], matmul_q2_k_f16, mmq_wg_denoms_k, warptile_mmq_k, vk_mat_mat_push_constants, 3) + CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q3_K], matmul_q3_k_f16, mmq_wg_denoms_k, warptile_mmq_k, vk_mat_mat_push_constants, 3) + CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q4_K], matmul_q4_k_f16, mmq_wg_denoms_k, warptile_mmq_k, vk_mat_mat_push_constants, 3) + CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q5_K], matmul_q5_k_f16, mmq_wg_denoms_k, warptile_mmq_k, vk_mat_mat_push_constants, 3) + CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_Q6_K], matmul_q6_k_f16, mmq_wg_denoms_k, warptile_mmq_k, vk_mat_mat_push_constants, 3) + CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_IQ1_S], matmul_iq1_s_f16, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3) + CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_IQ1_M], matmul_iq1_m_f16, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3) + CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_IQ2_XXS], matmul_iq2_xxs_f16, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3) + CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_IQ2_XS], matmul_iq2_xs_f16, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3) + CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_IQ2_S], matmul_iq2_s_f16, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3) + CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_IQ3_XXS], matmul_iq3_xxs_f16, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3) + CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_IQ3_S], matmul_iq3_s_f16, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3) + CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_IQ4_XS], matmul_iq4_xs_f16, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3) + CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_IQ4_NL], matmul_iq4_nl_f16, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3) + CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_MXFP4], matmul_mxfp4_f16, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3) + CREATE_MM2(pipeline_dequant_mul_mat_mat_f16[GGML_TYPE_NVFP4], matmul_nvfp4_f16, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3) + + GGML_ASSERT(device->subgroup_ballot); + + CREATE_MM2(pipeline_matmul_id_f16, matmul_id_subgroup_f16, wg_denoms, warptile, vk_mat_mat_id_push_constants, 5) +#if defined(GGML_VULKAN_BFLOAT16_GLSLC_SUPPORT) + if (device->coopmat_bf16_support) { + CREATE_MM(pipeline_matmul_id_bf16, matmul_id_subgroup_bf16, , wg_denoms, warptile, vk_mat_mat_id_push_constants, 5) + } +#endif + CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q1_0], matmul_id_subgroup_q1_0_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 5) + CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0], matmul_id_subgroup_q4_0_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 5) + CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1], matmul_id_subgroup_q4_1_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 5) + CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0], matmul_id_subgroup_q5_0_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 5) + CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1], matmul_id_subgroup_q5_1_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 5) + CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0], matmul_id_subgroup_q8_0_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 5) + CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K], matmul_id_subgroup_q2_k_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 5) + CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K], matmul_id_subgroup_q3_k_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 5) + CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K], matmul_id_subgroup_q4_k_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 5) + CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K], matmul_id_subgroup_q5_k_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 5) + CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K], matmul_id_subgroup_q6_k_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 5) + CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ1_S], matmul_id_subgroup_iq1_s_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 5) + CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ1_M], matmul_id_subgroup_iq1_m_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 5) + CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XXS], matmul_id_subgroup_iq2_xxs_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 5) + CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XS], matmul_id_subgroup_iq2_xs_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 5) + CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_S], matmul_id_subgroup_iq2_s_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 5) + CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_XXS], matmul_id_subgroup_iq3_xxs_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 5) + CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_S], matmul_id_subgroup_iq3_s_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 5) + CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_XS], matmul_id_subgroup_iq4_xs_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 5) + CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL], matmul_id_subgroup_iq4_nl_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 5) + CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_MXFP4], matmul_id_subgroup_mxfp4_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 5) + CREATE_MM2(pipeline_dequant_mul_mat_mat_id[GGML_TYPE_NVFP4], matmul_id_subgroup_nvfp4_f16, mmqid_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, 5) +#undef CREATE_MM +#undef CREATE_MM2 + } else +#endif // defined(VK_NV_cooperative_matrix2) && defined(GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT) +#if defined(VK_KHR_cooperative_matrix) && defined(GGML_VULKAN_COOPMAT_GLSLC_SUPPORT) + if (device->coopmat_support) { + // Create 6 variants, {s,m,l}x{unaligned,aligned} +#define CREATE_MM(TYPE, PIPELINE_NAME, NAMELC, F16ACC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID) \ + if (device->mul_mat ## ID ## _l[TYPE]) \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->l, #NAMELC #F16ACC "_l", NAMELC ## F16ACC ## _cm1_len, NAMELC ## F16ACC ## _cm1_data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, ggml_vk_mul_mm_spec(l_ ## WARPTILE, false), 1, false, true); \ + if (device->mul_mat ## ID ## _m[TYPE]) \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->m, #NAMELC #F16ACC "_m", NAMELC ## F16ACC ## _cm1_len, NAMELC ## F16ACC ## _cm1_data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, ggml_vk_mul_mm_spec(m_ ## WARPTILE, false), 1, false, true); \ + if (device->mul_mat ## ID ## _s[TYPE]) \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->s, #NAMELC #F16ACC "_s", NAMELC ## F16ACC ## _cm1_len, NAMELC ## F16ACC ## _cm1_data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, ggml_vk_mul_mm_spec(s_ ## WARPTILE, false), 1, false, true); \ + if (device->mul_mat ## ID ## _l[TYPE]) \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_l, #NAMELC #F16ACC "_aligned_l", NAMELC ## F16ACC ## _cm1_len, NAMELC ## F16ACC ## _cm1_data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, ggml_vk_mul_mm_spec(l_ ## WARPTILE, true), l_align, false, true); \ + if (device->mul_mat ## ID ## _m[TYPE]) \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_m, #NAMELC #F16ACC "_aligned_m", NAMELC ## F16ACC ## _cm1_len, NAMELC ## F16ACC ## _cm1_data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, ggml_vk_mul_mm_spec(m_ ## WARPTILE, true), m_align, false, true); \ + if (device->mul_mat ## ID ## _s[TYPE]) \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_s, #NAMELC #F16ACC "_aligned_s", NAMELC ## F16ACC ## _cm1_len, NAMELC ## F16ACC ## _cm1_data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, ggml_vk_mul_mm_spec(s_ ## WARPTILE, true), s_align, false, true); \ + + // Create 2 variants, {f16,f32} accumulator +#define CREATE_MM2(TYPE, PIPELINE_NAME, NAMELC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID) \ + if (device->coopmat_acc_f16_support) { \ + CREATE_MM(TYPE, PIPELINE_NAME . f16acc, NAMELC, _f16acc, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID) \ + } \ + if (device->coopmat_acc_f32_support) { \ + CREATE_MM(TYPE, PIPELINE_NAME . f32acc, NAMELC, , WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID) \ + } \ + + CREATE_MM(GGML_TYPE_F32, pipeline_matmul_f32, matmul_f32_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, ); + CREATE_MM(GGML_TYPE_F32, pipeline_matmul_f32_f16, matmul_f32_f16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, ); + CREATE_MM2(GGML_TYPE_F16, pipeline_matmul_f16, matmul_f16, wg_denoms, warptile, vk_mat_mat_push_constants, 3, ); + CREATE_MM2(GGML_TYPE_F16, pipeline_matmul_f16_f32, matmul_f16_f32, wg_denoms, warptile, vk_mat_mat_push_constants, 3, ); +#if defined(GGML_VULKAN_BFLOAT16_GLSLC_SUPPORT) + if (device->coopmat_bf16_support) { + CREATE_MM(GGML_TYPE_BF16, pipeline_matmul_bf16, matmul_bf16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, ) + } +#endif + + if (device->coopmat_acc_f16_support) { + CREATE_MM2(GGML_TYPE_Q1_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q1_0], matmul_q1_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, ); + CREATE_MM2(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0], matmul_q4_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, ); + CREATE_MM2(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1], matmul_q4_1_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, ); + CREATE_MM2(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0], matmul_q5_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, ); + CREATE_MM2(GGML_TYPE_Q5_1, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1], matmul_q5_1_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, ); + CREATE_MM2(GGML_TYPE_Q8_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0], matmul_q8_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, ); + + CREATE_MM2(GGML_TYPE_Q2_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K], matmul_q2_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, ); + CREATE_MM2(GGML_TYPE_Q3_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K], matmul_q3_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, ); + CREATE_MM2(GGML_TYPE_Q4_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K], matmul_q4_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, ); + CREATE_MM2(GGML_TYPE_Q5_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K], matmul_q5_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, ); + CREATE_MM2(GGML_TYPE_Q6_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K], matmul_q6_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, ); + CREATE_MM2(GGML_TYPE_IQ1_S, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ1_S], matmul_iq1_s_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, ); + CREATE_MM2(GGML_TYPE_IQ1_M, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ1_M], matmul_iq1_m_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, ); + CREATE_MM2(GGML_TYPE_IQ2_XXS, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_XXS], matmul_iq2_xxs_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, ); + CREATE_MM2(GGML_TYPE_IQ2_XS, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_XS], matmul_iq2_xs_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, ); + CREATE_MM2(GGML_TYPE_IQ2_S, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_S], matmul_iq2_s_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, ); + CREATE_MM2(GGML_TYPE_IQ3_XXS, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_XXS], matmul_iq3_xxs_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, ); + CREATE_MM2(GGML_TYPE_IQ3_S, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_S], matmul_iq3_s_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, ); + CREATE_MM2(GGML_TYPE_IQ4_XS, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_XS], matmul_iq4_xs_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, ); + CREATE_MM2(GGML_TYPE_IQ4_NL, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL], matmul_iq4_nl_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, ); + CREATE_MM2(GGML_TYPE_MXFP4, pipeline_dequant_mul_mat_mat[GGML_TYPE_MXFP4], matmul_mxfp4_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, ); + CREATE_MM2(GGML_TYPE_NVFP4, pipeline_dequant_mul_mat_mat[GGML_TYPE_NVFP4], matmul_nvfp4_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, ); + } else { + CREATE_MM(GGML_TYPE_Q1_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q1_0].f32acc, matmul_q1_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, ); + CREATE_MM(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0].f32acc, matmul_q4_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, ); + CREATE_MM(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1].f32acc, matmul_q4_1_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, ); + CREATE_MM(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0].f32acc, matmul_q5_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, ); + CREATE_MM(GGML_TYPE_Q5_1, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1].f32acc, matmul_q5_1_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, ); + CREATE_MM(GGML_TYPE_Q8_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0].f32acc, matmul_q8_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, ); + + CREATE_MM(GGML_TYPE_Q2_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K].f32acc, matmul_q2_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, ); + CREATE_MM(GGML_TYPE_Q3_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K].f32acc, matmul_q3_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, ); + CREATE_MM(GGML_TYPE_Q4_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K].f32acc, matmul_q4_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, ); + CREATE_MM(GGML_TYPE_Q5_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K].f32acc, matmul_q5_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, ); + CREATE_MM(GGML_TYPE_Q6_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K].f32acc, matmul_q6_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, ); + CREATE_MM(GGML_TYPE_IQ1_S, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ1_S].f32acc, matmul_iq1_s_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, ); + CREATE_MM(GGML_TYPE_IQ1_M, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ1_M].f32acc, matmul_iq1_m_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, ); + CREATE_MM(GGML_TYPE_IQ2_XXS, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_XXS].f32acc, matmul_iq2_xxs_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, ); + CREATE_MM(GGML_TYPE_IQ2_XS, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_XS].f32acc, matmul_iq2_xs_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, ); + CREATE_MM(GGML_TYPE_IQ2_S, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_S].f32acc, matmul_iq2_s_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, ); + CREATE_MM(GGML_TYPE_IQ3_XXS, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_XXS].f32acc, matmul_iq3_xxs_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, ); + CREATE_MM(GGML_TYPE_IQ3_S, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_S].f32acc, matmul_iq3_s_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, ); + CREATE_MM(GGML_TYPE_IQ4_XS, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_XS].f32acc, matmul_iq4_xs_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, ); + CREATE_MM(GGML_TYPE_IQ4_NL, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL].f32acc, matmul_iq4_nl_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, ); + CREATE_MM(GGML_TYPE_MXFP4, pipeline_dequant_mul_mat_mat[GGML_TYPE_MXFP4].f32acc, matmul_mxfp4_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, ); + CREATE_MM(GGML_TYPE_NVFP4, pipeline_dequant_mul_mat_mat[GGML_TYPE_NVFP4].f32acc, matmul_nvfp4_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, ); + } + + GGML_ASSERT(device->subgroup_ballot); + + CREATE_MM(GGML_TYPE_F32, pipeline_matmul_id_f32, matmul_id_subgroup_f32_f32, , wg_denoms, warptile, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id); + CREATE_MM2(GGML_TYPE_F16, pipeline_matmul_id_f16, matmul_id_subgroup_f16, wg_denoms, warptile, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id); + CREATE_MM2(GGML_TYPE_F16, pipeline_matmul_id_f16_f32, matmul_id_subgroup_f16_f32, wg_denoms, warptile, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id); +#if defined(GGML_VULKAN_BFLOAT16_GLSLC_SUPPORT) + if (device->coopmat_bf16_support) { + CREATE_MM(GGML_TYPE_BF16, pipeline_matmul_id_bf16, matmul_id_subgroup_bf16, , wg_denoms, warptile, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id); + } +#endif + + CREATE_MM2(GGML_TYPE_Q1_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q1_0], matmul_id_subgroup_q1_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id); + CREATE_MM2(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0], matmul_id_subgroup_q4_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id); + CREATE_MM2(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1], matmul_id_subgroup_q4_1_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id); + CREATE_MM2(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0], matmul_id_subgroup_q5_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id); + CREATE_MM2(GGML_TYPE_Q5_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1], matmul_id_subgroup_q5_1_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id); + CREATE_MM2(GGML_TYPE_Q8_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0], matmul_id_subgroup_q8_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id); + CREATE_MM2(GGML_TYPE_Q2_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K], matmul_id_subgroup_q2_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id); + CREATE_MM2(GGML_TYPE_Q3_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K], matmul_id_subgroup_q3_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id); + CREATE_MM2(GGML_TYPE_Q4_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K], matmul_id_subgroup_q4_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id); + CREATE_MM2(GGML_TYPE_Q5_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K], matmul_id_subgroup_q5_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id); + CREATE_MM2(GGML_TYPE_Q6_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K], matmul_id_subgroup_q6_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id); + CREATE_MM2(GGML_TYPE_IQ1_S, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ1_S], matmul_id_subgroup_iq1_s_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id); + CREATE_MM2(GGML_TYPE_IQ1_M, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ1_M], matmul_id_subgroup_iq1_m_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id); + CREATE_MM2(GGML_TYPE_IQ2_XXS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XXS], matmul_id_subgroup_iq2_xxs_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id); + CREATE_MM2(GGML_TYPE_IQ2_XS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XS], matmul_id_subgroup_iq2_xs_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id); + CREATE_MM2(GGML_TYPE_IQ2_S, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_S], matmul_id_subgroup_iq2_s_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id); + CREATE_MM2(GGML_TYPE_IQ3_XXS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_XXS], matmul_id_subgroup_iq3_xxs_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id); + CREATE_MM2(GGML_TYPE_IQ3_S, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_S], matmul_id_subgroup_iq3_s_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id); + CREATE_MM2(GGML_TYPE_IQ4_XS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_XS], matmul_id_subgroup_iq4_xs_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id); + CREATE_MM2(GGML_TYPE_IQ4_NL, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL], matmul_id_subgroup_iq4_nl_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id); + CREATE_MM2(GGML_TYPE_MXFP4, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_MXFP4], matmul_id_subgroup_mxfp4_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id); + CREATE_MM2(GGML_TYPE_NVFP4, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_NVFP4], matmul_id_subgroup_nvfp4_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id); +#undef CREATE_MM2 +#undef CREATE_MM + } else +#endif // defined(VK_KHR_cooperative_matrix) && defined(GGML_VULKAN_COOPMAT_GLSLC_SUPPORT) + if (device->fp16) { + // Create 6 variants, {s,m,l}x{unaligned,aligned} + // Selects dot2 SPIR-V variant at runtime when device->dot2_f16 is true +#define CREATE_MM(TYPE, PIPELINE_NAME, NAMELC, F16ACC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID, REQSUBGROUPSIZE) \ + if (device->mul_mat ## ID ## _l[TYPE]) \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->l, #NAMELC #F16ACC "_l", (device->dot2_f16 ? NAMELC ## _dot2 ## F16ACC ## _len : NAMELC ## F16ACC ## _len), (device->dot2_f16 ? NAMELC ## _dot2 ## F16ACC ## _data : NAMELC ## F16ACC ## _data), "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, ggml_vk_mul_mm_spec(l_ ## WARPTILE, false), 1, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE); \ + if (device->mul_mat ## ID ## _m[TYPE]) \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->m, #NAMELC #F16ACC "_m", (device->dot2_f16 ? NAMELC ## _dot2 ## F16ACC ## _len : NAMELC ## F16ACC ## _len), (device->dot2_f16 ? NAMELC ## _dot2 ## F16ACC ## _data : NAMELC ## F16ACC ## _data), "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, ggml_vk_mul_mm_spec(m_ ## WARPTILE, false), 1, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE); \ + if (device->mul_mat ## ID ## _s[TYPE]) \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->s, #NAMELC #F16ACC "_s", (device->dot2_f16 ? NAMELC ## _dot2 ## F16ACC ## _len : NAMELC ## F16ACC ## _len), (device->dot2_f16 ? NAMELC ## _dot2 ## F16ACC ## _data : NAMELC ## F16ACC ## _data), "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, ggml_vk_mul_mm_spec(s_ ## WARPTILE, false), 1, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE); \ + if (device->mul_mat ## ID ## _l[TYPE]) \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_l, #NAMELC #F16ACC "_aligned_l", (device->dot2_f16 ? NAMELC ## _dot2 ## F16ACC ## _len : NAMELC ## F16ACC ## _len), (device->dot2_f16 ? NAMELC ## _dot2 ## F16ACC ## _data : NAMELC ## F16ACC ## _data), "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, ggml_vk_mul_mm_spec(l_ ## WARPTILE, true), l_align, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE); \ + if (device->mul_mat ## ID ## _m[TYPE]) \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_m, #NAMELC #F16ACC "_aligned_m", (device->dot2_f16 ? NAMELC ## _dot2 ## F16ACC ## _len : NAMELC ## F16ACC ## _len), (device->dot2_f16 ? NAMELC ## _dot2 ## F16ACC ## _data : NAMELC ## F16ACC ## _data), "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, ggml_vk_mul_mm_spec(m_ ## WARPTILE, true), m_align, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE); \ + if (device->mul_mat ## ID ## _s[TYPE]) \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_s, #NAMELC #F16ACC "_aligned_s", (device->dot2_f16 ? NAMELC ## _dot2 ## F16ACC ## _len : NAMELC ## F16ACC ## _len), (device->dot2_f16 ? NAMELC ## _dot2 ## F16ACC ## _data : NAMELC ## F16ACC ## _data), "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, ggml_vk_mul_mm_spec(s_ ## WARPTILE, true), s_align, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE); \ + + // bf16 scalar path promotes to f32, no dot2 variant +#define CREATE_MM_NODOT2(TYPE, PIPELINE_NAME, NAMELC, F16ACC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID, REQSUBGROUPSIZE) \ + if (device->mul_mat ## ID ## _l[TYPE]) \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->l, #NAMELC #F16ACC "_l", NAMELC ## F16ACC ## _len, NAMELC ## F16ACC ## _data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, ggml_vk_mul_mm_spec(l_ ## WARPTILE, false), 1, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE); \ + if (device->mul_mat ## ID ## _m[TYPE]) \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->m, #NAMELC #F16ACC "_m", NAMELC ## F16ACC ## _len, NAMELC ## F16ACC ## _data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, ggml_vk_mul_mm_spec(m_ ## WARPTILE, false), 1, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE); \ + if (device->mul_mat ## ID ## _s[TYPE]) \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->s, #NAMELC #F16ACC "_s", NAMELC ## F16ACC ## _len, NAMELC ## F16ACC ## _data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, ggml_vk_mul_mm_spec(s_ ## WARPTILE, false), 1, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE); \ + if (device->mul_mat ## ID ## _l[TYPE]) \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_l, #NAMELC #F16ACC "_aligned_l", NAMELC ## F16ACC ## _len, NAMELC ## F16ACC ## _data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, ggml_vk_mul_mm_spec(l_ ## WARPTILE, true), l_align, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE); \ + if (device->mul_mat ## ID ## _m[TYPE]) \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_m, #NAMELC #F16ACC "_aligned_m", NAMELC ## F16ACC ## _len, NAMELC ## F16ACC ## _data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, ggml_vk_mul_mm_spec(m_ ## WARPTILE, true), m_align, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE); \ + if (device->mul_mat ## ID ## _s[TYPE]) \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_s, #NAMELC #F16ACC "_aligned_s", NAMELC ## F16ACC ## _len, NAMELC ## F16ACC ## _data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, ggml_vk_mul_mm_spec(s_ ## WARPTILE, true), s_align, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE); \ + +#define CREATE_MMQ(TYPE, PIPELINE_NAME, NAMELC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID, REQSUBGROUPSIZE) \ + if (device->mul_mat ## ID ## _l_int[TYPE]) { \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME .f32acc->l, #NAMELC "_l", NAMELC ## _len, NAMELC ## _data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, 1, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE); \ + } \ + if (device->mul_mat ## ID ## _m_int[TYPE]) { \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME .f32acc->m, #NAMELC "_m", NAMELC ## _len, NAMELC ## _data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, m_ ## WARPTILE, 1, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE); \ + } \ + if (device->mul_mat ## ID ## _s_int[TYPE]) { \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME .f32acc->s, #NAMELC "_s", NAMELC ## _len, NAMELC ## _data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, 1, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE); \ + } \ + + // Create 2 variants, {f16,f32} accumulator +#define CREATE_MM2(TYPE, PIPELINE_NAME, NAMELC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID, REQSUBGROUPSIZE) \ + CREATE_MM(TYPE, PIPELINE_NAME . f16acc, NAMELC, _f16acc, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID, REQSUBGROUPSIZE) \ + CREATE_MM(TYPE, PIPELINE_NAME . f32acc, NAMELC, , WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID, REQSUBGROUPSIZE) \ + + CREATE_MM(GGML_TYPE_F32, pipeline_matmul_f32, matmul_f32_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, , 0); + CREATE_MM(GGML_TYPE_F32, pipeline_matmul_f32_f16, matmul_f32_f16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, , 0); + CREATE_MM2(GGML_TYPE_F16, pipeline_matmul_f16, matmul_f16, wg_denoms, warptile, vk_mat_mat_push_constants, 3, , 0); + CREATE_MM2(GGML_TYPE_F16, pipeline_matmul_f16_f32, matmul_f16_f32, wg_denoms, warptile, vk_mat_mat_push_constants, 3, , 0); + + CREATE_MM_NODOT2(GGML_TYPE_BF16, pipeline_matmul_bf16, matmul_bf16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, , 0); + + CREATE_MM2(GGML_TYPE_Q1_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q1_0], matmul_q1_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); + CREATE_MM2(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0], matmul_q4_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); + CREATE_MM2(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1], matmul_q4_1_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); + CREATE_MM2(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0], matmul_q5_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); + CREATE_MM2(GGML_TYPE_Q5_1, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1], matmul_q5_1_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); + CREATE_MM2(GGML_TYPE_Q8_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0], matmul_q8_0_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); + CREATE_MM2(GGML_TYPE_Q2_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K], matmul_q2_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); + CREATE_MM2(GGML_TYPE_Q3_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K], matmul_q3_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); + CREATE_MM2(GGML_TYPE_Q4_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K], matmul_q4_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); + CREATE_MM2(GGML_TYPE_Q5_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K], matmul_q5_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); + CREATE_MM2(GGML_TYPE_Q6_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K], matmul_q6_k_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); + CREATE_MM2(GGML_TYPE_IQ1_S, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ1_S], matmul_iq1_s_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); + CREATE_MM2(GGML_TYPE_IQ1_M, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ1_M], matmul_iq1_m_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); + CREATE_MM2(GGML_TYPE_IQ2_XXS, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_XXS], matmul_iq2_xxs_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); + CREATE_MM2(GGML_TYPE_IQ2_XS, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_XS], matmul_iq2_xs_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); + CREATE_MM2(GGML_TYPE_IQ2_S, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_S], matmul_iq2_s_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); + CREATE_MM2(GGML_TYPE_IQ3_XXS, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_XXS], matmul_iq3_xxs_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); + CREATE_MM2(GGML_TYPE_IQ3_S, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_S], matmul_iq3_s_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); + CREATE_MM2(GGML_TYPE_IQ4_XS, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_XS], matmul_iq4_xs_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); + CREATE_MM2(GGML_TYPE_IQ4_NL, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL], matmul_iq4_nl_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); + CREATE_MM2(GGML_TYPE_MXFP4, pipeline_dequant_mul_mat_mat[GGML_TYPE_MXFP4], matmul_mxfp4_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); + CREATE_MM2(GGML_TYPE_NVFP4, pipeline_dequant_mul_mat_mat[GGML_TYPE_NVFP4], matmul_nvfp4_f32, mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); + +#if defined(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT) + if (device->integer_dot_product) { + CREATE_MMQ(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat_q8_1[GGML_TYPE_Q4_0], matmul_q4_0_q8_1, mmq_wg_denoms, warptile_mmq_int, vk_mat_mat_push_constants, 3, , 0); + CREATE_MMQ(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat_q8_1[GGML_TYPE_Q4_1], matmul_q4_1_q8_1, mmq_wg_denoms, warptile_mmq_int, vk_mat_mat_push_constants, 3, , 0); + CREATE_MMQ(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat_q8_1[GGML_TYPE_Q5_0], matmul_q5_0_q8_1, mmq_wg_denoms, warptile_mmq_int, vk_mat_mat_push_constants, 3, , 0); + CREATE_MMQ(GGML_TYPE_Q5_1, pipeline_dequant_mul_mat_mat_q8_1[GGML_TYPE_Q5_1], matmul_q5_1_q8_1, mmq_wg_denoms, warptile_mmq_int, vk_mat_mat_push_constants, 3, , 0); + CREATE_MMQ(GGML_TYPE_Q8_0, pipeline_dequant_mul_mat_mat_q8_1[GGML_TYPE_Q8_0], matmul_q8_0_q8_1, mmq_wg_denoms, warptile_mmq_int, vk_mat_mat_push_constants, 3, , 0); + + CREATE_MMQ(GGML_TYPE_MXFP4, pipeline_dequant_mul_mat_mat_q8_1[GGML_TYPE_MXFP4], matmul_mxfp4_q8_1, mmq_wg_denoms, warptile_mmq_int, vk_mat_mat_push_constants, 3, , 0); + + CREATE_MMQ(GGML_TYPE_Q2_K, pipeline_dequant_mul_mat_mat_q8_1[GGML_TYPE_Q2_K], matmul_q2_k_q8_1, mmq_wg_denoms, warptile_mmq_int_k, vk_mat_mat_push_constants, 3, , 0); + CREATE_MMQ(GGML_TYPE_Q3_K, pipeline_dequant_mul_mat_mat_q8_1[GGML_TYPE_Q3_K], matmul_q3_k_q8_1, mmq_wg_denoms, warptile_mmq_int_k, vk_mat_mat_push_constants, 3, , 0); + CREATE_MMQ(GGML_TYPE_Q4_K, pipeline_dequant_mul_mat_mat_q8_1[GGML_TYPE_Q4_K], matmul_q4_k_q8_1, mmq_wg_denoms, warptile_mmq_int_k, vk_mat_mat_push_constants, 3, , 0); + CREATE_MMQ(GGML_TYPE_Q5_K, pipeline_dequant_mul_mat_mat_q8_1[GGML_TYPE_Q5_K], matmul_q5_k_q8_1, mmq_wg_denoms, warptile_mmq_int_k, vk_mat_mat_push_constants, 3, , 0); + CREATE_MMQ(GGML_TYPE_Q6_K, pipeline_dequant_mul_mat_mat_q8_1[GGML_TYPE_Q6_K], matmul_q6_k_q8_1, mmq_wg_denoms, warptile_mmq_int_k, vk_mat_mat_push_constants, 3, , 0); + } +#endif + + if (device->subgroup_ballot && device->subgroup_require_full_support && subgroup_min_size_16) { + CREATE_MM(GGML_TYPE_F32, pipeline_matmul_id_f32, matmul_id_subgroup_f32_f32, , wg_denoms, warptile_id, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size_16); + CREATE_MM2(GGML_TYPE_F16, pipeline_matmul_id_f16, matmul_id_subgroup_f16, wg_denoms, warptile_id, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size_16); + CREATE_MM2(GGML_TYPE_F16, pipeline_matmul_id_f16_f32, matmul_id_subgroup_f16_f32, wg_denoms, warptile_id, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size_16); + CREATE_MM_NODOT2(GGML_TYPE_BF16, pipeline_matmul_id_bf16, matmul_id_subgroup_bf16, , wg_denoms, warptile_id, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size_16); + CREATE_MM2(GGML_TYPE_Q1_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q1_0], matmul_id_subgroup_q1_0_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); + CREATE_MM2(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0], matmul_id_subgroup_q4_0_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); + CREATE_MM2(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1], matmul_id_subgroup_q4_1_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); + CREATE_MM2(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0], matmul_id_subgroup_q5_0_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); + CREATE_MM2(GGML_TYPE_Q5_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1], matmul_id_subgroup_q5_1_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); + CREATE_MM2(GGML_TYPE_Q8_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0], matmul_id_subgroup_q8_0_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); + CREATE_MM2(GGML_TYPE_Q2_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K], matmul_id_subgroup_q2_k_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); + CREATE_MM2(GGML_TYPE_Q3_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K], matmul_id_subgroup_q3_k_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); + CREATE_MM2(GGML_TYPE_Q4_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K], matmul_id_subgroup_q4_k_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); + CREATE_MM2(GGML_TYPE_Q5_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K], matmul_id_subgroup_q5_k_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); + CREATE_MM2(GGML_TYPE_Q6_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K], matmul_id_subgroup_q6_k_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); + CREATE_MM2(GGML_TYPE_IQ1_S, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ1_S], matmul_id_subgroup_iq1_s_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); + CREATE_MM2(GGML_TYPE_IQ1_M, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ1_M], matmul_id_subgroup_iq1_m_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); + CREATE_MM2(GGML_TYPE_IQ2_XXS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XXS], matmul_id_subgroup_iq2_xxs_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); + CREATE_MM2(GGML_TYPE_IQ2_XS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XS], matmul_id_subgroup_iq2_xs_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); + CREATE_MM2(GGML_TYPE_IQ2_S, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_S], matmul_id_subgroup_iq2_s_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); + CREATE_MM2(GGML_TYPE_IQ3_XXS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_XXS], matmul_id_subgroup_iq3_xxs_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); + CREATE_MM2(GGML_TYPE_IQ3_S, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_S], matmul_id_subgroup_iq3_s_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); + CREATE_MM2(GGML_TYPE_IQ4_XS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_XS], matmul_id_subgroup_iq4_xs_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); + CREATE_MM2(GGML_TYPE_IQ4_NL, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL], matmul_id_subgroup_iq4_nl_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); + CREATE_MM2(GGML_TYPE_MXFP4, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_MXFP4], matmul_id_subgroup_mxfp4_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); + CREATE_MM2(GGML_TYPE_NVFP4, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_NVFP4], matmul_id_subgroup_nvfp4_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); + +#if defined(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT) + if (device->integer_dot_product) { + CREATE_MMQ(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat_id_q8_1[GGML_TYPE_Q4_0], matmul_id_subgroup_q4_0_q8_1, mmq_wg_denoms, warptile_mmqid_int, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); + CREATE_MMQ(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat_id_q8_1[GGML_TYPE_Q4_1], matmul_id_subgroup_q4_1_q8_1, mmq_wg_denoms, warptile_mmqid_int, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); + CREATE_MMQ(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat_id_q8_1[GGML_TYPE_Q5_0], matmul_id_subgroup_q5_0_q8_1, mmq_wg_denoms, warptile_mmqid_int, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); + CREATE_MMQ(GGML_TYPE_Q5_1, pipeline_dequant_mul_mat_mat_id_q8_1[GGML_TYPE_Q5_1], matmul_id_subgroup_q5_1_q8_1, mmq_wg_denoms, warptile_mmqid_int, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); + CREATE_MMQ(GGML_TYPE_Q8_0, pipeline_dequant_mul_mat_mat_id_q8_1[GGML_TYPE_Q8_0], matmul_id_subgroup_q8_0_q8_1, mmq_wg_denoms, warptile_mmqid_int, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); + + CREATE_MMQ(GGML_TYPE_MXFP4, pipeline_dequant_mul_mat_mat_id_q8_1[GGML_TYPE_MXFP4], matmul_id_subgroup_mxfp4_q8_1, mmq_wg_denoms, warptile_mmqid_int, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); + + CREATE_MMQ(GGML_TYPE_Q2_K, pipeline_dequant_mul_mat_mat_id_q8_1[GGML_TYPE_Q2_K], matmul_id_subgroup_q2_k_q8_1, mmq_wg_denoms, warptile_mmqid_int_k, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size_16); + CREATE_MMQ(GGML_TYPE_Q3_K, pipeline_dequant_mul_mat_mat_id_q8_1[GGML_TYPE_Q3_K], matmul_id_subgroup_q3_k_q8_1, mmq_wg_denoms, warptile_mmqid_int_k, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size_16); + CREATE_MMQ(GGML_TYPE_Q4_K, pipeline_dequant_mul_mat_mat_id_q8_1[GGML_TYPE_Q4_K], matmul_id_subgroup_q4_k_q8_1, mmq_wg_denoms, warptile_mmqid_int_k, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size_16); + CREATE_MMQ(GGML_TYPE_Q5_K, pipeline_dequant_mul_mat_mat_id_q8_1[GGML_TYPE_Q5_K], matmul_id_subgroup_q5_k_q8_1, mmq_wg_denoms, warptile_mmqid_int_k, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size_16); + CREATE_MMQ(GGML_TYPE_Q6_K, pipeline_dequant_mul_mat_mat_id_q8_1[GGML_TYPE_Q6_K], matmul_id_subgroup_q6_k_q8_1, mmq_wg_denoms, warptile_mmqid_int_k, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size_16); + } +#endif + } else { + CREATE_MM(GGML_TYPE_F32, pipeline_matmul_id_f32, matmul_id_f32_f32, , wg_denoms, warptile, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + CREATE_MM2(GGML_TYPE_F16, pipeline_matmul_id_f16, matmul_id_f16, wg_denoms, warptile, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + CREATE_MM2(GGML_TYPE_F16, pipeline_matmul_id_f16_f32, matmul_id_f16_f32, wg_denoms, warptile, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + CREATE_MM_NODOT2(GGML_TYPE_BF16, pipeline_matmul_id_bf16, matmul_id_bf16, , wg_denoms, warptile, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + CREATE_MM2(GGML_TYPE_Q1_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q1_0], matmul_id_q1_0_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + CREATE_MM2(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0], matmul_id_q4_0_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + CREATE_MM2(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1], matmul_id_q4_1_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + CREATE_MM2(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0], matmul_id_q5_0_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + CREATE_MM2(GGML_TYPE_Q5_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1], matmul_id_q5_1_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + CREATE_MM2(GGML_TYPE_Q8_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0], matmul_id_q8_0_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + CREATE_MM2(GGML_TYPE_Q2_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K], matmul_id_q2_k_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + CREATE_MM2(GGML_TYPE_Q3_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K], matmul_id_q3_k_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + CREATE_MM2(GGML_TYPE_Q4_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K], matmul_id_q4_k_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + CREATE_MM2(GGML_TYPE_Q5_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K], matmul_id_q5_k_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + CREATE_MM2(GGML_TYPE_Q6_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K], matmul_id_q6_k_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + CREATE_MM2(GGML_TYPE_IQ1_S, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ1_S], matmul_id_iq1_s_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + CREATE_MM2(GGML_TYPE_IQ1_M, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ1_M], matmul_id_iq1_m_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + CREATE_MM2(GGML_TYPE_IQ2_XXS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XXS], matmul_id_iq2_xxs_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + CREATE_MM2(GGML_TYPE_IQ2_XS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XS], matmul_id_iq2_xs_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + CREATE_MM2(GGML_TYPE_IQ2_S, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_S], matmul_id_iq2_s_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + CREATE_MM2(GGML_TYPE_IQ3_XXS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_XXS], matmul_id_iq3_xxs_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + CREATE_MM2(GGML_TYPE_IQ3_S, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_S], matmul_id_iq3_s_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + CREATE_MM2(GGML_TYPE_IQ4_XS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_XS], matmul_id_iq4_xs_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + CREATE_MM2(GGML_TYPE_IQ4_NL, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL], matmul_id_iq4_nl_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + CREATE_MM2(GGML_TYPE_MXFP4, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_MXFP4], matmul_id_mxfp4_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + CREATE_MM2(GGML_TYPE_NVFP4, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_NVFP4], matmul_id_nvfp4_f32, mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + +#if defined(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT) + if (device->integer_dot_product) { + CREATE_MMQ(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat_id_q8_1[GGML_TYPE_Q4_0], matmul_id_q4_0_q8_1, mmq_wg_denoms, warptile_mmqid_int, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + CREATE_MMQ(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat_id_q8_1[GGML_TYPE_Q4_1], matmul_id_q4_1_q8_1, mmq_wg_denoms, warptile_mmqid_int, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + CREATE_MMQ(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat_id_q8_1[GGML_TYPE_Q5_0], matmul_id_q5_0_q8_1, mmq_wg_denoms, warptile_mmqid_int, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + CREATE_MMQ(GGML_TYPE_Q5_1, pipeline_dequant_mul_mat_mat_id_q8_1[GGML_TYPE_Q5_1], matmul_id_q5_1_q8_1, mmq_wg_denoms, warptile_mmqid_int, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + CREATE_MMQ(GGML_TYPE_Q8_0, pipeline_dequant_mul_mat_mat_id_q8_1[GGML_TYPE_Q8_0], matmul_id_q8_0_q8_1, mmq_wg_denoms, warptile_mmqid_int, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + + CREATE_MMQ(GGML_TYPE_MXFP4, pipeline_dequant_mul_mat_mat_id_q8_1[GGML_TYPE_MXFP4], matmul_id_mxfp4_q8_1, mmq_wg_denoms, warptile_mmqid_int, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + + CREATE_MMQ(GGML_TYPE_Q2_K, pipeline_dequant_mul_mat_mat_id_q8_1[GGML_TYPE_Q2_K], matmul_id_q2_k_q8_1, mmq_wg_denoms, warptile_mmqid_int_k, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + CREATE_MMQ(GGML_TYPE_Q3_K, pipeline_dequant_mul_mat_mat_id_q8_1[GGML_TYPE_Q3_K], matmul_id_q3_k_q8_1, mmq_wg_denoms, warptile_mmqid_int_k, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + CREATE_MMQ(GGML_TYPE_Q4_K, pipeline_dequant_mul_mat_mat_id_q8_1[GGML_TYPE_Q4_K], matmul_id_q4_k_q8_1, mmq_wg_denoms, warptile_mmqid_int_k, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + CREATE_MMQ(GGML_TYPE_Q5_K, pipeline_dequant_mul_mat_mat_id_q8_1[GGML_TYPE_Q5_K], matmul_id_q5_k_q8_1, mmq_wg_denoms, warptile_mmqid_int_k, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + CREATE_MMQ(GGML_TYPE_Q6_K, pipeline_dequant_mul_mat_mat_id_q8_1[GGML_TYPE_Q6_K], matmul_id_q6_k_q8_1, mmq_wg_denoms, warptile_mmqid_int_k, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + } +#endif + } +#undef CREATE_MM2 +#undef CREATE_MMQ +#undef CREATE_MM +#undef CREATE_MM_NODOT2 + } else { + // Create 6 variants, {s,m,l}x{unaligned,aligned} +#define CREATE_MM(TYPE, PIPELINE_NAME, NAMELC, F16ACC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID, REQSUBGROUPSIZE) \ + if (device->mul_mat ## ID ## _l[TYPE]) \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->l, #NAMELC #F16ACC "_l", NAMELC ## F16ACC ## _fp32_len, NAMELC ## F16ACC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, ggml_vk_mul_mm_spec(l_ ## WARPTILE, false), 1, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE); \ + if (device->mul_mat ## ID ## _m[TYPE]) \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->m, #NAMELC #F16ACC "_m", NAMELC ## F16ACC ## _fp32_len, NAMELC ## F16ACC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, ggml_vk_mul_mm_spec(m_ ## WARPTILE, false), 1, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE); \ + if (device->mul_mat ## ID ## _s[TYPE]) \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->s, #NAMELC #F16ACC "_s", NAMELC ## F16ACC ## _fp32_len, NAMELC ## F16ACC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, ggml_vk_mul_mm_spec(s_ ## WARPTILE, false), 1, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE); \ + if (device->mul_mat ## ID ## _l[TYPE]) \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_l, #NAMELC #F16ACC "_aligned_l", NAMELC ## F16ACC ## _fp32_len, NAMELC ## F16ACC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, ggml_vk_mul_mm_spec(l_ ## WARPTILE, true), l_align, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE); \ + if (device->mul_mat ## ID ## _m[TYPE]) \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_m, #NAMELC #F16ACC "_aligned_m", NAMELC ## F16ACC ## _fp32_len, NAMELC ## F16ACC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, ggml_vk_mul_mm_spec(m_ ## WARPTILE, true), m_align, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE); \ + if (device->mul_mat ## ID ## _s[TYPE]) \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->a_s, #NAMELC #F16ACC "_aligned_s", NAMELC ## F16ACC ## _fp32_len, NAMELC ## F16ACC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, ggml_vk_mul_mm_spec(s_ ## WARPTILE, true), s_align, false, REQSUBGROUPSIZE > 0, REQSUBGROUPSIZE); \ + +#define CREATE_MMQ(TYPE, PIPELINE_NAME, NAMELC, WG_DENOMS, WARPTILE, PUSHCONST, PARAMCOUNT, ID) \ + if (device->mul_mat ## ID ## _l_int[TYPE]) \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->l, #NAMELC "_l", NAMELC ## _fp32_len, NAMELC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), l_ ## WG_DENOMS, l_ ## WARPTILE, 1); \ + if (device->mul_mat ## ID ## _m_int[TYPE]) \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->m, #NAMELC "_m", NAMELC ## _fp32_len, NAMELC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), m_ ## WG_DENOMS, m_ ## WARPTILE, 1); \ + if (device->mul_mat ## ID ## _s_int[TYPE]) \ + ggml_vk_create_pipeline(device, device-> PIPELINE_NAME ->s, #NAMELC "_s", NAMELC ## _fp32_len, NAMELC ## _fp32_data, "main", PARAMCOUNT, sizeof(PUSHCONST), s_ ## WG_DENOMS, s_ ## WARPTILE, 1); \ + + CREATE_MM(GGML_TYPE_F32, pipeline_matmul_f32, matmul_f32_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, , 0); + CREATE_MM(GGML_TYPE_F32, pipeline_matmul_f32_f16, matmul_f32_f16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, , 0); + CREATE_MM(GGML_TYPE_F16, pipeline_matmul_f16.f32acc, matmul_f16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, , 0); + CREATE_MM(GGML_TYPE_F16, pipeline_matmul_f16_f32.f32acc, matmul_f16_f32, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, , 0); + + CREATE_MM(GGML_TYPE_BF16, pipeline_matmul_bf16, matmul_bf16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, , 0); + + CREATE_MM(GGML_TYPE_Q1_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q1_0].f32acc, matmul_q1_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); + CREATE_MM(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0].f32acc, matmul_q4_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); + CREATE_MM(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1].f32acc, matmul_q4_1_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); + CREATE_MM(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0].f32acc, matmul_q5_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); + CREATE_MM(GGML_TYPE_Q5_1, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1].f32acc, matmul_q5_1_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); + CREATE_MM(GGML_TYPE_Q8_0, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0].f32acc, matmul_q8_0_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); + + CREATE_MM(GGML_TYPE_Q2_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K].f32acc, matmul_q2_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); + CREATE_MM(GGML_TYPE_Q3_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K].f32acc, matmul_q3_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); + CREATE_MM(GGML_TYPE_Q4_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K].f32acc, matmul_q4_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); + CREATE_MM(GGML_TYPE_Q5_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K].f32acc, matmul_q5_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); + CREATE_MM(GGML_TYPE_Q6_K, pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K].f32acc, matmul_q6_k_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); + CREATE_MM(GGML_TYPE_IQ1_S, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ1_S].f32acc, matmul_iq1_s_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); + CREATE_MM(GGML_TYPE_IQ1_M, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ1_M].f32acc, matmul_iq1_m_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); + CREATE_MM(GGML_TYPE_IQ2_XXS, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_XXS].f32acc, matmul_iq2_xxs_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); + CREATE_MM(GGML_TYPE_IQ2_XS, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_XS].f32acc, matmul_iq2_xs_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); + CREATE_MM(GGML_TYPE_IQ2_S, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ2_S].f32acc, matmul_iq2_s_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); + CREATE_MM(GGML_TYPE_IQ3_XXS, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_XXS].f32acc, matmul_iq3_xxs_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); + CREATE_MM(GGML_TYPE_IQ3_S, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ3_S].f32acc, matmul_iq3_s_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); + CREATE_MM(GGML_TYPE_IQ4_XS, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_XS].f32acc, matmul_iq4_xs_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); + CREATE_MM(GGML_TYPE_IQ4_NL, pipeline_dequant_mul_mat_mat[GGML_TYPE_IQ4_NL].f32acc, matmul_iq4_nl_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); + CREATE_MM(GGML_TYPE_MXFP4, pipeline_dequant_mul_mat_mat[GGML_TYPE_MXFP4].f32acc, matmul_mxfp4_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); + CREATE_MM(GGML_TYPE_NVFP4, pipeline_dequant_mul_mat_mat[GGML_TYPE_NVFP4].f32acc, matmul_nvfp4_f32, , mmq_wg_denoms, warptile_mmq, vk_mat_mat_push_constants, 3, , 0); + +#if defined(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT) + if (device->integer_dot_product) { + CREATE_MMQ(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat_q8_1[GGML_TYPE_Q4_0].f32acc, matmul_q4_0_q8_1, mmq_wg_denoms, warptile_mmq_int, vk_mat_mat_push_constants, 3, ); + CREATE_MMQ(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat_q8_1[GGML_TYPE_Q4_1].f32acc, matmul_q4_1_q8_1, mmq_wg_denoms, warptile_mmq_int, vk_mat_mat_push_constants, 3, ); + CREATE_MMQ(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat_q8_1[GGML_TYPE_Q5_0].f32acc, matmul_q5_0_q8_1, mmq_wg_denoms, warptile_mmq_int, vk_mat_mat_push_constants, 3, ); + CREATE_MMQ(GGML_TYPE_Q5_1, pipeline_dequant_mul_mat_mat_q8_1[GGML_TYPE_Q5_1].f32acc, matmul_q5_1_q8_1, mmq_wg_denoms, warptile_mmq_int, vk_mat_mat_push_constants, 3, ); + CREATE_MMQ(GGML_TYPE_Q8_0, pipeline_dequant_mul_mat_mat_q8_1[GGML_TYPE_Q8_0].f32acc, matmul_q8_0_q8_1, mmq_wg_denoms, warptile_mmq_int, vk_mat_mat_push_constants, 3, ); + + CREATE_MMQ(GGML_TYPE_Q2_K, pipeline_dequant_mul_mat_mat_q8_1[GGML_TYPE_Q2_K].f32acc, matmul_q2_k_q8_1, mmq_wg_denoms, warptile_mmq_int_k, vk_mat_mat_push_constants, 3, ); + CREATE_MMQ(GGML_TYPE_Q3_K, pipeline_dequant_mul_mat_mat_q8_1[GGML_TYPE_Q3_K].f32acc, matmul_q3_k_q8_1, mmq_wg_denoms, warptile_mmq_int_k, vk_mat_mat_push_constants, 3, ); + CREATE_MMQ(GGML_TYPE_Q4_K, pipeline_dequant_mul_mat_mat_q8_1[GGML_TYPE_Q4_K].f32acc, matmul_q4_k_q8_1, mmq_wg_denoms, warptile_mmq_int_k, vk_mat_mat_push_constants, 3, ); + CREATE_MMQ(GGML_TYPE_Q5_K, pipeline_dequant_mul_mat_mat_q8_1[GGML_TYPE_Q5_K].f32acc, matmul_q5_k_q8_1, mmq_wg_denoms, warptile_mmq_int_k, vk_mat_mat_push_constants, 3, ); + CREATE_MMQ(GGML_TYPE_Q6_K, pipeline_dequant_mul_mat_mat_q8_1[GGML_TYPE_Q6_K].f32acc, matmul_q6_k_q8_1, mmq_wg_denoms, warptile_mmq_int_k, vk_mat_mat_push_constants, 3, ); + } +#endif + + if (device->subgroup_ballot && device->subgroup_require_full_support && subgroup_min_size_16) { + CREATE_MM(GGML_TYPE_F32, pipeline_matmul_id_f32, matmul_id_subgroup_f32_f32, , wg_denoms, warptile_id, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size_16); + CREATE_MM(GGML_TYPE_F16, pipeline_matmul_id_f16.f32acc, matmul_id_subgroup_f16, , wg_denoms, warptile_id, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size_16); + CREATE_MM(GGML_TYPE_F16, pipeline_matmul_id_f16_f32.f32acc, matmul_id_subgroup_f16_f32, , wg_denoms, warptile_id, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size_16); + CREATE_MM(GGML_TYPE_BF16, pipeline_matmul_id_bf16, matmul_id_subgroup_bf16, , wg_denoms, warptile_id, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size_16); + + CREATE_MM(GGML_TYPE_Q1_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q1_0].f32acc, matmul_id_subgroup_q1_0_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); + CREATE_MM(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0].f32acc, matmul_id_subgroup_q4_0_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); + CREATE_MM(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1].f32acc, matmul_id_subgroup_q4_1_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); + CREATE_MM(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0].f32acc, matmul_id_subgroup_q5_0_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); + CREATE_MM(GGML_TYPE_Q5_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1].f32acc, matmul_id_subgroup_q5_1_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); + CREATE_MM(GGML_TYPE_Q8_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0].f32acc, matmul_id_subgroup_q8_0_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); + CREATE_MM(GGML_TYPE_Q2_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K].f32acc, matmul_id_subgroup_q2_k_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); + CREATE_MM(GGML_TYPE_Q3_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K].f32acc, matmul_id_subgroup_q3_k_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); + CREATE_MM(GGML_TYPE_Q4_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K].f32acc, matmul_id_subgroup_q4_k_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); + CREATE_MM(GGML_TYPE_Q5_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K].f32acc, matmul_id_subgroup_q5_k_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); + CREATE_MM(GGML_TYPE_Q6_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K].f32acc, matmul_id_subgroup_q6_k_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); + CREATE_MM(GGML_TYPE_IQ1_S, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ1_S].f32acc, matmul_id_subgroup_iq1_s_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); + CREATE_MM(GGML_TYPE_IQ1_M, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ1_M].f32acc, matmul_id_subgroup_iq1_m_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); + CREATE_MM(GGML_TYPE_IQ2_XXS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XXS].f32acc, matmul_id_subgroup_iq2_xxs_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); + CREATE_MM(GGML_TYPE_IQ2_XS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XS].f32acc, matmul_id_subgroup_iq2_xs_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); + CREATE_MM(GGML_TYPE_IQ2_S, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_S].f32acc, matmul_id_subgroup_iq2_s_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); + CREATE_MM(GGML_TYPE_IQ3_XXS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_XXS].f32acc, matmul_id_subgroup_iq3_xxs_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); + CREATE_MM(GGML_TYPE_IQ3_S, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_S].f32acc, matmul_id_subgroup_iq3_s_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); + CREATE_MM(GGML_TYPE_IQ4_XS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_XS].f32acc, matmul_id_subgroup_iq4_xs_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); + CREATE_MM(GGML_TYPE_IQ4_NL, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL].f32acc, matmul_id_subgroup_iq4_nl_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); + CREATE_MM(GGML_TYPE_MXFP4, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_MXFP4].f32acc, matmul_id_subgroup_mxfp4_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); + CREATE_MM(GGML_TYPE_NVFP4, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_NVFP4].f32acc, matmul_id_subgroup_nvfp4_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, mul_mat_subgroup_size); + } else { + CREATE_MM(GGML_TYPE_F32, pipeline_matmul_id_f32, matmul_id_f32_f32, , wg_denoms, warptile, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + CREATE_MM(GGML_TYPE_F16, pipeline_matmul_id_f16.f32acc, matmul_id_f16, , wg_denoms, warptile, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + CREATE_MM(GGML_TYPE_F16, pipeline_matmul_id_f16_f32.f32acc, matmul_id_f16_f32, , wg_denoms, warptile, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + CREATE_MM(GGML_TYPE_BF16, pipeline_matmul_id_bf16, matmul_id_bf16, , wg_denoms, warptile, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + + CREATE_MM(GGML_TYPE_Q1_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q1_0].f32acc, matmul_id_q1_0_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + CREATE_MM(GGML_TYPE_Q4_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0].f32acc, matmul_id_q4_0_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + CREATE_MM(GGML_TYPE_Q4_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1].f32acc, matmul_id_q4_1_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + CREATE_MM(GGML_TYPE_Q5_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0].f32acc, matmul_id_q5_0_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + CREATE_MM(GGML_TYPE_Q5_1, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1].f32acc, matmul_id_q5_1_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + CREATE_MM(GGML_TYPE_Q8_0, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0].f32acc, matmul_id_q8_0_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + CREATE_MM(GGML_TYPE_Q2_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K].f32acc, matmul_id_q2_k_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + CREATE_MM(GGML_TYPE_Q3_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K].f32acc, matmul_id_q3_k_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + CREATE_MM(GGML_TYPE_Q4_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K].f32acc, matmul_id_q4_k_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + CREATE_MM(GGML_TYPE_Q5_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K].f32acc, matmul_id_q5_k_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + CREATE_MM(GGML_TYPE_Q6_K, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K].f32acc, matmul_id_q6_k_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + CREATE_MM(GGML_TYPE_IQ1_S, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ1_S].f32acc, matmul_id_iq1_s_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + CREATE_MM(GGML_TYPE_IQ1_M, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ1_M].f32acc, matmul_id_iq1_m_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + CREATE_MM(GGML_TYPE_IQ2_XXS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XXS].f32acc, matmul_id_iq2_xxs_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + CREATE_MM(GGML_TYPE_IQ2_XS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_XS].f32acc, matmul_id_iq2_xs_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + CREATE_MM(GGML_TYPE_IQ2_S, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ2_S].f32acc, matmul_id_iq2_s_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + CREATE_MM(GGML_TYPE_IQ3_XXS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_XXS].f32acc, matmul_id_iq3_xxs_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + CREATE_MM(GGML_TYPE_IQ3_S, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ3_S].f32acc, matmul_id_iq3_s_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + CREATE_MM(GGML_TYPE_IQ4_XS, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_XS].f32acc, matmul_id_iq4_xs_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + CREATE_MM(GGML_TYPE_IQ4_NL, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_IQ4_NL].f32acc, matmul_id_iq4_nl_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + CREATE_MM(GGML_TYPE_MXFP4, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_MXFP4].f32acc, matmul_id_mxfp4_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + CREATE_MM(GGML_TYPE_NVFP4, pipeline_dequant_mul_mat_mat_id[GGML_TYPE_NVFP4].f32acc, matmul_id_nvfp4_f32, , mmq_wg_denoms, warptile_mmqid, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + } + } + // reusing CREATE_MM from the fp32 path + if ((device->coopmat2 || device->coopmat_support) +#if defined(GGML_VULKAN_BFLOAT16_GLSLC_SUPPORT) + && !device->coopmat_bf16_support +#endif + ) { + const uint32_t s_warptile_wm = device->subgroup_size == 8 ? 8 : 32; + + // use scalar tile sizes + l_warptile = { 128, 128, 128, 16, subgroup_size_8 * 2, 64, 2, 4, 4, 1, subgroup_size_8 }; + m_warptile = { 128, 64, 64, 16, subgroup_size_8, 32, 2, 4, 2, 1, subgroup_size_8 }; + s_warptile = { subgroup_size_32, 32, 32, 16, s_warptile_wm, 32, 2, 2, 2, 1, subgroup_size_8 }; + + l_wg_denoms = {128, 128, 1 }; + m_wg_denoms = { 64, 64, 1 }; + s_wg_denoms = { 32, 32, 1 }; + + CREATE_MM(GGML_TYPE_BF16, pipeline_matmul_bf16, matmul_bf16, , wg_denoms, warptile, vk_mat_mat_push_constants, 3, , 0); + CREATE_MM(GGML_TYPE_BF16, pipeline_matmul_id_bf16, matmul_id_bf16, , wg_denoms, warptile, vk_mat_mat_id_push_constants, mul_mat_id_param_count, _id, 0); + } +#undef CREATE_MM + + // mul mat vec + + // the number of rows computed per shader depends on GPU model and quant + uint32_t rm_stdq = 1; + uint32_t rm_kq = 2; + uint32_t rm_stdq_int = 1; + uint32_t rm_kq_int = 1; + auto const &rm_iq_int = [](uint32_t i) { return i == 0 ? 8u : 4u; }; + if (device->vendor_id == VK_VENDOR_ID_AMD) { + if (device->architecture == AMD_GCN) { + rm_stdq = 2; + rm_kq = 4; + rm_stdq_int = 4; + } + } else if (device->vendor_id == VK_VENDOR_ID_INTEL) { + rm_stdq = 2; + rm_stdq_int = 2; + } + uint32_t rm_iq = 2 * rm_kq; + + const bool use_subgroups = device->subgroup_arithmetic; + // Ensure a subgroup size >= 16 is available + const bool use_subgroups16 = use_subgroups && subgroup_min_size_16; + + const uint32_t subgroup_size = (device->vendor_id == VK_VENDOR_ID_INTEL && device->subgroup_size_control && device->subgroup_min_size <= 16 && device->subgroup_max_size >= 16) ? 16 : device->subgroup_size; + const uint32_t subgroup_size16 = std::max(subgroup_size, 16u); + + const uint32_t force_subgroup_size = use_subgroups ? subgroup_size : 0; + const uint32_t force_subgroup_size16 = use_subgroups16 ? subgroup_size16 : 0; + static constexpr uint32_t mul_mat_vec_num_bindings = 5; + static constexpr uint32_t mul_mat_vec_id_num_bindings = 6; + + for (uint32_t w = 0; w < DMMV_WG_SIZE_COUNT; ++w) { + const uint32_t wg_size_subgroup = (w == DMMV_WG_SIZE_SUBGROUP) ? subgroup_size : (subgroup_size * 4); + const uint32_t wg_size_subgroup16 = (w == DMMV_WG_SIZE_SUBGROUP) ? subgroup_size16 : (subgroup_size16 * 4); + + const shader_reduction_mode reduc = (use_subgroups && w == DMMV_WG_SIZE_SUBGROUP) ? SHADER_REDUCTION_MODE_SUBGROUP : + (use_subgroups && w == DMMV_WG_SIZE_LARGE) ? SHADER_REDUCTION_MODE_HYBRID : + SHADER_REDUCTION_MODE_SHMEM; + + const shader_reduction_mode reduc16 = (use_subgroups16 && w == DMMV_WG_SIZE_SUBGROUP) ? SHADER_REDUCTION_MODE_SUBGROUP : + (use_subgroups16 && w == DMMV_WG_SIZE_LARGE) ? SHADER_REDUCTION_MODE_HYBRID : + 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), {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_Q1_0][i], "mul_mat_vec_q1_0_f32_f32", arr_dmmv_q1_0_f32_f32_len[reduc], arr_dmmv_q1_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); + 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); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q4_1][i], "mul_mat_vec_q4_1_f32_f32", arr_dmmv_q4_1_f32_f32_len[reduc], arr_dmmv_q4_1_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); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q5_0][i], "mul_mat_vec_q5_0_f32_f32", arr_dmmv_q5_0_f32_f32_len[reduc], arr_dmmv_q5_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); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q5_1][i], "mul_mat_vec_q5_1_f32_f32", arr_dmmv_q5_1_f32_f32_len[reduc], arr_dmmv_q5_1_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); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q8_0][i], "mul_mat_vec_q8_0_f32_f32", arr_dmmv_q8_0_f32_f32_len[reduc], arr_dmmv_q8_0_f32_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {1*rm_stdq, 1, 1}, {wg_size_subgroup, 1*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q2_K][i], "mul_mat_vec_q2_k_f32_f32", arr_dmmv_q2_k_f32_f32_len[reduc16], arr_dmmv_q2_k_f32_f32_data[reduc16], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, 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_Q3_K][i], "mul_mat_vec_q3_k_f32_f32", arr_dmmv_q3_k_f32_f32_len[reduc16], arr_dmmv_q3_k_f32_f32_data[reduc16], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, 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_Q4_K][i], "mul_mat_vec_q4_k_f32_f32", arr_dmmv_q4_k_f32_f32_len[reduc16], arr_dmmv_q4_k_f32_f32_data[reduc16], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, 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_Q5_K][i], "mul_mat_vec_q5_k_f32_f32", arr_dmmv_q5_k_f32_f32_len[reduc16], arr_dmmv_q5_k_f32_f32_data[reduc16], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, 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_Q6_K][i], "mul_mat_vec_q6_k_f32_f32", arr_dmmv_q6_k_f32_f32_len[reduc16], arr_dmmv_q6_k_f32_f32_data[reduc16], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, 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_IQ1_S][i], "mul_mat_vec_iq1_s_f32_f32", arr_dmmv_iq1_s_f32_f32_len[reduc16], arr_dmmv_iq1_s_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_IQ1_M][i], "mul_mat_vec_iq1_m_f32_f32", arr_dmmv_iq1_m_f32_f32_len[reduc16], arr_dmmv_iq1_m_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_IQ2_XXS][i], "mul_mat_vec_iq2_xxs_f32_f32", arr_dmmv_iq2_xxs_f32_f32_len[reduc16], arr_dmmv_iq2_xxs_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_IQ2_XS][i], "mul_mat_vec_iq2_xs_f32_f32", arr_dmmv_iq2_xs_f32_f32_len[reduc16], arr_dmmv_iq2_xs_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_IQ2_S][i], "mul_mat_vec_iq2_s_f32_f32", arr_dmmv_iq2_s_f32_f32_len[reduc16], arr_dmmv_iq2_s_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_IQ3_XXS][i], "mul_mat_vec_iq3_xxs_f32_f32", arr_dmmv_iq3_xxs_f32_f32_len[reduc16], arr_dmmv_iq3_xxs_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_IQ3_S][i], "mul_mat_vec_iq3_s_f32_f32", arr_dmmv_iq3_s_f32_f32_len[reduc16], arr_dmmv_iq3_s_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_IQ4_XS][i], "mul_mat_vec_iq4_xs_f32_f32", arr_dmmv_iq4_xs_f32_f32_len[reduc16], arr_dmmv_iq4_xs_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_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_f32_f32[w][GGML_TYPE_NVFP4][i], "mul_mat_vec_nvfp4_f32_f32", arr_dmmv_nvfp4_f32_f32_len[reduc16], arr_dmmv_nvfp4_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), {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_Q1_0][i], "mul_mat_vec_q1_0_f16_f32", arr_dmmv_q1_0_f16_f32_len[reduc], arr_dmmv_q1_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); + 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); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q4_1][i], "mul_mat_vec_q4_1_f16_f32", arr_dmmv_q4_1_f16_f32_len[reduc], arr_dmmv_q4_1_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); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q5_0][i], "mul_mat_vec_q5_0_f16_f32", arr_dmmv_q5_0_f16_f32_len[reduc], arr_dmmv_q5_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); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q5_1][i], "mul_mat_vec_q5_1_f16_f32", arr_dmmv_q5_1_f16_f32_len[reduc], arr_dmmv_q5_1_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); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q8_0][i], "mul_mat_vec_q8_0_f16_f32", arr_dmmv_q8_0_f16_f32_len[reduc], arr_dmmv_q8_0_f16_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {1*rm_stdq, 1, 1}, {wg_size_subgroup, 1*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q2_K][i], "mul_mat_vec_q2_k_f16_f32", arr_dmmv_q2_k_f16_f32_len[reduc16], arr_dmmv_q2_k_f16_f32_data[reduc16], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, 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_Q3_K][i], "mul_mat_vec_q3_k_f16_f32", arr_dmmv_q3_k_f16_f32_len[reduc16], arr_dmmv_q3_k_f16_f32_data[reduc16], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, 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_Q4_K][i], "mul_mat_vec_q4_k_f16_f32", arr_dmmv_q4_k_f16_f32_len[reduc16], arr_dmmv_q4_k_f16_f32_data[reduc16], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, 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_Q5_K][i], "mul_mat_vec_q5_k_f16_f32", arr_dmmv_q5_k_f16_f32_len[reduc16], arr_dmmv_q5_k_f16_f32_data[reduc16], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, 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_Q6_K][i], "mul_mat_vec_q6_k_f16_f32", arr_dmmv_q6_k_f16_f32_len[reduc16], arr_dmmv_q6_k_f16_f32_data[reduc16], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, 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_IQ1_S][i], "mul_mat_vec_iq1_s_f16_f32", arr_dmmv_iq1_s_f16_f32_len[reduc16], arr_dmmv_iq1_s_f16_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_IQ1_M][i], "mul_mat_vec_iq1_m_f16_f32", arr_dmmv_iq1_m_f16_f32_len[reduc16], arr_dmmv_iq1_m_f16_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_IQ2_XXS][i], "mul_mat_vec_iq2_xxs_f16_f32", arr_dmmv_iq2_xxs_f16_f32_len[reduc16], arr_dmmv_iq2_xxs_f16_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_IQ2_XS][i], "mul_mat_vec_iq2_xs_f16_f32", arr_dmmv_iq2_xs_f16_f32_len[reduc16], arr_dmmv_iq2_xs_f16_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_IQ2_S][i], "mul_mat_vec_iq2_s_f16_f32", arr_dmmv_iq2_s_f16_f32_len[reduc16], arr_dmmv_iq2_s_f16_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_IQ3_XXS][i], "mul_mat_vec_iq3_xxs_f16_f32", arr_dmmv_iq3_xxs_f16_f32_len[reduc16], arr_dmmv_iq3_xxs_f16_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_IQ3_S][i], "mul_mat_vec_iq3_s_f16_f32", arr_dmmv_iq3_s_f16_f32_len[reduc16], arr_dmmv_iq3_s_f16_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_IQ4_XS][i], "mul_mat_vec_iq4_xs_f16_f32", arr_dmmv_iq4_xs_f16_f32_len[reduc16], arr_dmmv_iq4_xs_f16_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_IQ4_NL][i], "mul_mat_vec_iq4_nl_f16_f32", arr_dmmv_iq4_nl_f16_f32_len[reduc16], arr_dmmv_iq4_nl_f16_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_MXFP4][i], "mul_mat_vec_mxfp4_f16_f32", arr_dmmv_mxfp4_f16_f32_len[reduc16], arr_dmmv_mxfp4_f16_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_NVFP4][i], "mul_mat_vec_nvfp4_f16_f32", arr_dmmv_nvfp4_f16_f32_len[reduc16], arr_dmmv_nvfp4_f16_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); + +#if defined(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT) + if (device->integer_dot_product) { + const uint32_t subgroup_size_int = (device->vendor_id == VK_VENDOR_ID_INTEL && device->subgroup_size_control) ? device->subgroup_min_size : device->subgroup_size; + const uint32_t wg_size_subgroup_int = (w == DMMV_WG_SIZE_SUBGROUP) ? subgroup_size_int : (subgroup_size_int * 4); + + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_Q4_0][i], "mul_mat_vec_q4_0_q8_1_f32", arr_dmmv_q4_0_q8_1_f32_len[reduc], arr_dmmv_q4_0_q8_1_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {1*rm_stdq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_stdq_int, i+1}, 1, true, use_subgroups, subgroup_size_int); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_Q4_1][i], "mul_mat_vec_q4_1_q8_1_f32", arr_dmmv_q4_1_q8_1_f32_len[reduc], arr_dmmv_q4_1_q8_1_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {1*rm_stdq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_stdq_int, i+1}, 1, true, use_subgroups, subgroup_size_int); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_Q5_0][i], "mul_mat_vec_q5_0_q8_1_f32", arr_dmmv_q5_0_q8_1_f32_len[reduc], arr_dmmv_q5_0_q8_1_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {1*rm_stdq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_stdq_int, i+1}, 1, true, use_subgroups, subgroup_size_int); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_Q5_1][i], "mul_mat_vec_q5_1_q8_1_f32", arr_dmmv_q5_1_q8_1_f32_len[reduc], arr_dmmv_q5_1_q8_1_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {1*rm_stdq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_stdq_int, i+1}, 1, true, use_subgroups, subgroup_size_int); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_Q8_0][i], "mul_mat_vec_q8_0_q8_1_f32", arr_dmmv_q8_0_q8_1_f32_len[reduc], arr_dmmv_q8_0_q8_1_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {1*rm_stdq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_stdq_int, i+1}, 1, true, use_subgroups, subgroup_size_int); + + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_MXFP4][i], "mul_mat_vec_mxfp4_q8_1_f32", arr_dmmv_mxfp4_q8_1_f32_len[reduc], arr_dmmv_mxfp4_q8_1_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2*rm_stdq_int, 1, 1}, {wg_size_subgroup_int, 2*rm_stdq_int, i+1}, 1, true, use_subgroups, subgroup_size_int); + + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_Q2_K][i], "mul_mat_vec_q2_k_q8_1_f32", arr_dmmv_q2_k_q8_1_f32_len[reduc], arr_dmmv_q2_k_q8_1_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2*rm_kq_int, 1, 1}, {wg_size_subgroup_int, 2*rm_kq_int, i+1}, 1, true, use_subgroups, subgroup_size_int); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_Q3_K][i], "mul_mat_vec_q3_k_q8_1_f32", arr_dmmv_q3_k_q8_1_f32_len[reduc], arr_dmmv_q3_k_q8_1_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {1*rm_kq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_kq_int, i+1}, 1, true, use_subgroups, subgroup_size_int); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_Q4_K][i], "mul_mat_vec_q4_k_q8_1_f32", arr_dmmv_q4_k_q8_1_f32_len[reduc], arr_dmmv_q4_k_q8_1_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {1*rm_kq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_kq_int, i+1}, 1, true, use_subgroups, subgroup_size_int); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_Q5_K][i], "mul_mat_vec_q5_k_q8_1_f32", arr_dmmv_q5_k_q8_1_f32_len[reduc], arr_dmmv_q5_k_q8_1_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {1*rm_kq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_kq_int, i+1}, 1, true, use_subgroups, subgroup_size_int); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_Q6_K][i], "mul_mat_vec_q6_k_q8_1_f32", arr_dmmv_q6_k_q8_1_f32_len[reduc], arr_dmmv_q6_k_q8_1_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {1*rm_kq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_kq_int, i+1}, 1, true, use_subgroups, subgroup_size_int); + + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_IQ1_S][i], "mul_mat_vec_iq1_s_q8_1_f32", arr_dmmv_iq1_s_q8_1_f32_len[reduc], arr_dmmv_iq1_s_q8_1_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {1*rm_iq_int(i), 1, 1}, {wg_size_subgroup_int, 1*rm_iq_int(i), i+1}, 1, true, use_subgroups, subgroup_size_int); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_IQ1_M][i], "mul_mat_vec_iq1_m_q8_1_f32", arr_dmmv_iq1_m_q8_1_f32_len[reduc], arr_dmmv_iq1_m_q8_1_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {1*rm_iq_int(i), 1, 1}, {wg_size_subgroup_int, 1*rm_iq_int(i), i+1}, 1, true, use_subgroups, subgroup_size_int); + + } +#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), {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_Q1_0], "mul_mat_vec_id_q1_0_f32", arr_dmmv_id_q1_0_f32_f32_len[reduc], arr_dmmv_id_q1_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); + 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); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_Q4_1], "mul_mat_vec_id_q4_1_f32", arr_dmmv_id_q4_1_f32_f32_len[reduc], arr_dmmv_id_q4_1_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); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_Q5_0], "mul_mat_vec_id_q5_0_f32", arr_dmmv_id_q5_0_f32_f32_len[reduc], arr_dmmv_id_q5_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); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_Q5_1], "mul_mat_vec_id_q5_1_f32", arr_dmmv_id_q5_1_f32_f32_len[reduc], arr_dmmv_id_q5_1_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); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_Q8_0], "mul_mat_vec_id_q8_0_f32", arr_dmmv_id_q8_0_f32_f32_len[reduc], arr_dmmv_id_q8_0_f32_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {1*rm_stdq, 1, 1}, {wg_size_subgroup, 1*rm_stdq}, 1, true, use_subgroups, force_subgroup_size); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_Q2_K], "mul_mat_vec_id_q2_k_f32", arr_dmmv_id_q2_k_f32_f32_len[reduc16], arr_dmmv_id_q2_k_f32_f32_data[reduc16], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq}, 1, true, use_subgroups16, force_subgroup_size16); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_Q3_K], "mul_mat_vec_id_q3_k_f32", arr_dmmv_id_q3_k_f32_f32_len[reduc16], arr_dmmv_id_q3_k_f32_f32_data[reduc16], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq}, 1, true, use_subgroups16, force_subgroup_size16); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_Q4_K], "mul_mat_vec_id_q4_k_f32", arr_dmmv_id_q4_k_f32_f32_len[reduc16], arr_dmmv_id_q4_k_f32_f32_data[reduc16], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq}, 1, true, use_subgroups16, force_subgroup_size16); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_Q5_K], "mul_mat_vec_id_q5_k_f32", arr_dmmv_id_q5_k_f32_f32_len[reduc16], arr_dmmv_id_q5_k_f32_f32_data[reduc16], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq}, 1, true, use_subgroups16, force_subgroup_size16); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_Q6_K], "mul_mat_vec_id_q6_k_f32", arr_dmmv_id_q6_k_f32_f32_len[reduc16], arr_dmmv_id_q6_k_f32_f32_data[reduc16], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq}, 1, true, use_subgroups16, force_subgroup_size16); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_IQ1_S], "mul_mat_vec_id_iq1_s_f32", arr_dmmv_id_iq1_s_f32_f32_len[reduc16], arr_dmmv_id_iq1_s_f32_f32_data[reduc16], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq}, 1, true, use_subgroups16, force_subgroup_size16); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_IQ1_M], "mul_mat_vec_id_iq1_m_f32", arr_dmmv_id_iq1_m_f32_f32_len[reduc16], arr_dmmv_id_iq1_m_f32_f32_data[reduc16], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq}, 1, true, use_subgroups16, force_subgroup_size16); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_IQ2_XXS], "mul_mat_vec_id_iq2_xxs_f32", arr_dmmv_id_iq2_xxs_f32_f32_len[reduc16], arr_dmmv_id_iq2_xxs_f32_f32_data[reduc16], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq}, 1, true, use_subgroups16, force_subgroup_size16); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_IQ2_XS], "mul_mat_vec_id_iq2_xs_f32", arr_dmmv_id_iq2_xs_f32_f32_len[reduc16], arr_dmmv_id_iq2_xs_f32_f32_data[reduc16], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq}, 1, true, use_subgroups16, force_subgroup_size16); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_IQ2_S], "mul_mat_vec_id_iq2_s_f32", arr_dmmv_id_iq2_s_f32_f32_len[reduc16], arr_dmmv_id_iq2_s_f32_f32_data[reduc16], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq}, 1, true, use_subgroups16, force_subgroup_size16); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_IQ3_XXS], "mul_mat_vec_id_iq3_xxs_f32", arr_dmmv_id_iq3_xxs_f32_f32_len[reduc16], arr_dmmv_id_iq3_xxs_f32_f32_data[reduc16], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq}, 1, true, use_subgroups16, force_subgroup_size16); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_IQ3_S], "mul_mat_vec_id_iq3_s_f32", arr_dmmv_id_iq3_s_f32_f32_len[reduc16], arr_dmmv_id_iq3_s_f32_f32_data[reduc16], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq}, 1, true, use_subgroups16, force_subgroup_size16); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_IQ4_XS], "mul_mat_vec_id_iq4_xs_f32", arr_dmmv_id_iq4_xs_f32_f32_len[reduc16], arr_dmmv_id_iq4_xs_f32_f32_data[reduc16], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq}, 1, true, use_subgroups16, force_subgroup_size16); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_IQ4_NL], "mul_mat_vec_id_iq4_nl_f32", arr_dmmv_id_iq4_nl_f32_f32_len[reduc16], arr_dmmv_id_iq4_nl_f32_f32_data[reduc16], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq}, 1, true, use_subgroups16, force_subgroup_size16); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_MXFP4], "mul_mat_vec_id_mxfp4_f32", arr_dmmv_id_mxfp4_f32_f32_len[reduc16], arr_dmmv_id_mxfp4_f32_f32_data[reduc16], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq}, 1, true, use_subgroups16, force_subgroup_size16); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_NVFP4], "mul_mat_vec_id_nvfp4_f32", arr_dmmv_id_nvfp4_f32_f32_len[reduc16], arr_dmmv_id_nvfp4_f32_f32_data[reduc16], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq}, 1, true, use_subgroups16, force_subgroup_size16); + +#if defined(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT) + if (device->integer_dot_product) { + const uint32_t subgroup_size_int = (device->vendor_id == VK_VENDOR_ID_INTEL && device->subgroup_size_control) ? device->subgroup_min_size : device->subgroup_size; + const uint32_t wg_size_subgroup_int = (w == DMMV_WG_SIZE_SUBGROUP) ? subgroup_size_int : (subgroup_size_int * 4); + + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_q8_1_f32[w][GGML_TYPE_Q4_0], "mul_mat_vec_id_q4_0_q8_1_f32", arr_dmmv_id_q4_0_q8_1_f32_len[reduc], arr_dmmv_id_q4_0_q8_1_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {1*rm_stdq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_stdq_int}, 1, true, use_subgroups, subgroup_size_int); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_q8_1_f32[w][GGML_TYPE_Q4_1], "mul_mat_vec_id_q4_1_q8_1_f32", arr_dmmv_id_q4_1_q8_1_f32_len[reduc], arr_dmmv_id_q4_1_q8_1_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {1*rm_stdq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_stdq_int}, 1, true, use_subgroups, subgroup_size_int); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_q8_1_f32[w][GGML_TYPE_Q5_0], "mul_mat_vec_id_q5_0_q8_1_f32", arr_dmmv_id_q5_0_q8_1_f32_len[reduc], arr_dmmv_id_q5_0_q8_1_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {1*rm_stdq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_stdq_int}, 1, true, use_subgroups, subgroup_size_int); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_q8_1_f32[w][GGML_TYPE_Q5_1], "mul_mat_vec_id_q5_1_q8_1_f32", arr_dmmv_id_q5_1_q8_1_f32_len[reduc], arr_dmmv_id_q5_1_q8_1_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {1*rm_stdq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_stdq_int}, 1, true, use_subgroups, subgroup_size_int); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_q8_1_f32[w][GGML_TYPE_Q8_0], "mul_mat_vec_id_q8_0_q8_1_f32", arr_dmmv_id_q8_0_q8_1_f32_len[reduc], arr_dmmv_id_q8_0_q8_1_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {1*rm_stdq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_stdq_int}, 1, true, use_subgroups, subgroup_size_int); + + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_q8_1_f32[w][GGML_TYPE_MXFP4], "mul_mat_vec_id_mxfp4_q8_1_f32", arr_dmmv_id_mxfp4_q8_1_f32_len[reduc], arr_dmmv_id_mxfp4_q8_1_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {2*rm_stdq_int, 1, 1}, {wg_size_subgroup_int, 2*rm_stdq_int}, 1, true, use_subgroups, subgroup_size_int); + + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_q8_1_f32[w][GGML_TYPE_Q2_K], "mul_mat_vec_id_q2_k_q8_1_f32", arr_dmmv_id_q2_k_q8_1_f32_len[reduc], arr_dmmv_id_q2_k_q8_1_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {2*rm_kq_int, 1, 1}, {wg_size_subgroup_int, 2*rm_kq_int}, 1, true, use_subgroups, subgroup_size_int); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_q8_1_f32[w][GGML_TYPE_Q3_K], "mul_mat_vec_id_q3_k_q8_1_f32", arr_dmmv_id_q3_k_q8_1_f32_len[reduc], arr_dmmv_id_q3_k_q8_1_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {1*rm_kq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_kq_int}, 1, true, use_subgroups, subgroup_size_int); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_q8_1_f32[w][GGML_TYPE_Q4_K], "mul_mat_vec_id_q4_k_q8_1_f32", arr_dmmv_id_q4_k_q8_1_f32_len[reduc], arr_dmmv_id_q4_k_q8_1_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {1*rm_kq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_kq_int}, 1, true, use_subgroups, subgroup_size_int); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_q8_1_f32[w][GGML_TYPE_Q5_K], "mul_mat_vec_id_q5_k_q8_1_f32", arr_dmmv_id_q5_k_q8_1_f32_len[reduc], arr_dmmv_id_q5_k_q8_1_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {1*rm_kq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_kq_int}, 1, true, use_subgroups, subgroup_size_int); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_q8_1_f32[w][GGML_TYPE_Q6_K], "mul_mat_vec_id_q6_k_q8_1_f32", arr_dmmv_id_q6_k_q8_1_f32_len[reduc], arr_dmmv_id_q6_k_q8_1_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {1*rm_kq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_kq_int}, 1, true, use_subgroups, subgroup_size_int); + + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_q8_1_f32[w][GGML_TYPE_IQ1_S], "mul_mat_vec_id_iq1_s_q8_1_f32", arr_dmmv_id_iq1_s_q8_1_f32_len[reduc], arr_dmmv_id_iq1_s_q8_1_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {1*rm_iq_int(0), 1, 1}, {wg_size_subgroup_int, 1*rm_iq_int(0)}, 1, true, use_subgroups, subgroup_size_int); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_q8_1_f32[w][GGML_TYPE_IQ1_M], "mul_mat_vec_id_iq1_m_q8_1_f32", arr_dmmv_id_iq1_m_q8_1_f32_len[reduc], arr_dmmv_id_iq1_m_q8_1_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {1*rm_iq_int(0), 1, 1}, {wg_size_subgroup_int, 1*rm_iq_int(0)}, 1, true, use_subgroups, subgroup_size_int); + } +#endif // GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT + } + +#if !defined(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT) + GGML_UNUSED(rm_stdq_int); + GGML_UNUSED(rm_kq_int); + GGML_UNUSED(rm_iq_int); +#endif + + // dequant shaders + ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_F32 ], "f32_to_f16", dequant_f32_len, dequant_f32_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q1_0], "dequant_q1_0", dequant_q1_0_len, dequant_q1_0_data, "main", 2, 5 * sizeof(uint32_t), {256 * 8, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q4_0], "dequant_q4_0", dequant_q4_0_len, dequant_q4_0_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q4_1], "dequant_q4_1", dequant_q4_1_len, dequant_q4_1_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q5_0], "dequant_q5_0", dequant_q5_0_len, dequant_q5_0_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q5_1], "dequant_q5_1", dequant_q5_1_len, dequant_q5_1_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q8_0], "dequant_q8_0", dequant_q8_0_len, dequant_q8_0_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q2_K], "dequant_q2_k", dequant_q2_k_len, dequant_q2_k_data, "main", 2, 5 * sizeof(uint32_t), {256 * 64, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q3_K], "dequant_q3_k", dequant_q3_k_len, dequant_q3_k_data, "main", 2, 5 * sizeof(uint32_t), {256 * 64, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q4_K], "dequant_q4_k", dequant_q4_k_len, dequant_q4_k_data, "main", 2, 5 * sizeof(uint32_t), {256 * 32, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q5_K], "dequant_q5_k", dequant_q5_k_len, dequant_q5_k_data, "main", 2, 5 * sizeof(uint32_t), {256 * 64, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_Q6_K], "dequant_q6_k", dequant_q6_k_len, dequant_q6_k_data, "main", 2, 5 * sizeof(uint32_t), {256 * 64, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_IQ1_S], "dequant_iq1_s", dequant_iq1_s_len, dequant_iq1_s_data, "main", 2, 5 * sizeof(uint32_t), {256 * 32, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_IQ1_M], "dequant_iq1_m", dequant_iq1_m_len, dequant_iq1_m_data, "main", 2, 5 * sizeof(uint32_t), {256 * 32, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_IQ2_XXS], "dequant_iq2_xxs", dequant_iq2_xxs_len, dequant_iq2_xxs_data, "main", 2, 5 * sizeof(uint32_t), {256 * 32, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_IQ2_XS], "dequant_iq2_xs", dequant_iq2_xs_len, dequant_iq2_xs_data, "main", 2, 5 * sizeof(uint32_t), {256 * 32, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_IQ2_S], "dequant_iq2_s", dequant_iq2_s_len, dequant_iq2_s_data, "main", 2, 5 * sizeof(uint32_t), {256 * 32, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_IQ3_XXS], "dequant_iq3_xxs", dequant_iq3_xxs_len, dequant_iq3_xxs_data, "main", 2, 5 * sizeof(uint32_t), {256 * 32, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_IQ3_S], "dequant_iq3_s", dequant_iq3_s_len, dequant_iq3_s_data, "main", 2, 5 * sizeof(uint32_t), {256 * 32, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_IQ4_XS], "dequant_iq4_xs", dequant_iq4_xs_len, dequant_iq4_xs_data, "main", 2, 5 * sizeof(uint32_t), {256 * 32, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_IQ4_NL], "dequant_iq4_nl", dequant_iq4_nl_len, dequant_iq4_nl_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_MXFP4], "dequant_mxfp4", dequant_mxfp4_len, dequant_mxfp4_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_NVFP4], "dequant_nvfp4", dequant_nvfp4_len, dequant_nvfp4_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1); + + // get_rows + ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_F32 ], "get_rows_f32", get_rows_f32_len, get_rows_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_F16 ], "get_rows_f16", get_rows_f16_len, get_rows_f16_data, "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_BF16], "get_rows_bf16", get_rows_bf16_len, get_rows_bf16_data, "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_Q1_0], "get_rows_q1_0", get_rows_q1_0_len, get_rows_q1_0_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_Q4_0], "get_rows_q4_0", get_rows_q4_0_len, get_rows_q4_0_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_Q4_1], "get_rows_q4_1", get_rows_q4_1_len, get_rows_q4_1_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_Q5_0], "get_rows_q5_0", get_rows_q5_0_len, get_rows_q5_0_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_Q5_1], "get_rows_q5_1", get_rows_q5_1_len, get_rows_q5_1_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_Q8_0], "get_rows_q8_0", get_rows_q8_0_len, get_rows_q8_0_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_Q2_K], "get_rows_q2_k", get_rows_q2_k_len, get_rows_q2_k_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_Q3_K], "get_rows_q3_k", get_rows_q3_k_len, get_rows_q3_k_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_Q4_K], "get_rows_q4_k", get_rows_q4_k_len, get_rows_q4_k_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_Q5_K], "get_rows_q5_k", get_rows_q5_k_len, get_rows_q5_k_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_Q6_K], "get_rows_q6_k", get_rows_q6_k_len, get_rows_q6_k_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_IQ1_S], "get_rows_iq1_s", get_rows_iq1_s_len, get_rows_iq1_s_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_IQ1_M], "get_rows_iq1_m", get_rows_iq1_m_len, get_rows_iq1_m_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_IQ2_XXS], "get_rows_iq2_xxs", get_rows_iq2_xxs_len, get_rows_iq2_xxs_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_IQ2_XS], "get_rows_iq2_xs", get_rows_iq2_xs_len, get_rows_iq2_xs_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_IQ2_S], "get_rows_iq2_s", get_rows_iq2_s_len, get_rows_iq2_s_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_IQ3_XXS], "get_rows_iq3_xxs", get_rows_iq3_xxs_len, get_rows_iq3_xxs_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_IQ3_S], "get_rows_iq3_s", get_rows_iq3_s_len, get_rows_iq3_s_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_IQ4_XS], "get_rows_iq4_xs", get_rows_iq4_xs_len, get_rows_iq4_xs_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_IQ4_NL], "get_rows_iq4_nl", get_rows_iq4_nl_len, get_rows_iq4_nl_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_MXFP4], "get_rows_mxfp4", get_rows_mxfp4_len, get_rows_mxfp4_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_NVFP4], "get_rows_nvfp4", get_rows_nvfp4_len, get_rows_nvfp4_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_I32], "get_rows_i32", get_rows_i32_len, get_rows_i32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); + + ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_F32 ], "get_rows_f32_f32", get_rows_f32_f32_len, get_rows_f32_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_F16 ], "get_rows_f16_f32", get_rows_f16_f32_len, get_rows_f16_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_BF16], "get_rows_bf16_f32", get_rows_bf16_f32_len, get_rows_bf16_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_Q1_0], "get_rows_q1_0_f32", get_rows_q1_0_f32_len, get_rows_q1_0_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_Q4_0], "get_rows_q4_0_f32", get_rows_q4_0_f32_len, get_rows_q4_0_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_Q4_1], "get_rows_q4_1_f32", get_rows_q4_1_f32_len, get_rows_q4_1_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_Q5_0], "get_rows_q5_0_f32", get_rows_q5_0_f32_len, get_rows_q5_0_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_Q5_1], "get_rows_q5_1_f32", get_rows_q5_1_f32_len, get_rows_q5_1_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_Q8_0], "get_rows_q8_0_f32", get_rows_q8_0_f32_len, get_rows_q8_0_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_Q2_K], "get_rows_q2_k_f32", get_rows_q2_k_f32_len, get_rows_q2_k_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_Q3_K], "get_rows_q3_k_f32", get_rows_q3_k_f32_len, get_rows_q3_k_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_Q4_K], "get_rows_q4_k_f32", get_rows_q4_k_f32_len, get_rows_q4_k_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_Q5_K], "get_rows_q5_k_f32", get_rows_q5_k_f32_len, get_rows_q5_k_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_Q6_K], "get_rows_q6_k_f32", get_rows_q6_k_f32_len, get_rows_q6_k_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_IQ1_S], "get_rows_iq1_s_f32", get_rows_iq1_s_f32_len, get_rows_iq1_s_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_IQ1_M], "get_rows_iq1_m_f32", get_rows_iq1_m_f32_len, get_rows_iq1_m_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_IQ2_XXS], "get_rows_iq2_xxs_f32", get_rows_iq2_xxs_f32_len, get_rows_iq2_xxs_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_IQ2_XS], "get_rows_iq2_xs_f32", get_rows_iq2_xs_f32_len, get_rows_iq2_xs_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_IQ2_S], "get_rows_iq2_s_f32", get_rows_iq2_s_f32_len, get_rows_iq2_s_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_IQ3_XXS], "get_rows_iq3_xxs_f32", get_rows_iq3_xxs_f32_len, get_rows_iq3_xxs_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_IQ3_S], "get_rows_iq3_s_f32", get_rows_iq3_s_f32_len, get_rows_iq3_s_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_IQ4_XS], "get_rows_iq4_xs_f32", get_rows_iq4_xs_f32_len, get_rows_iq4_xs_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_IQ4_NL], "get_rows_iq4_nl_f32", get_rows_iq4_nl_f32_len, get_rows_iq4_nl_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_MXFP4], "get_rows_mxfp4_f32", get_rows_mxfp4_f32_len, get_rows_mxfp4_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_NVFP4], "get_rows_nvfp4_f32", get_rows_nvfp4_f32_len, get_rows_nvfp4_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_get_rows_back_f32, "get_rows_back_f32", get_rows_back_f32_len, get_rows_back_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {256, 1, 1}, {}, 1, true); + + ggml_vk_create_pipeline(device, device->pipeline_matmul_split_k_reduce, "split_k_reduce", split_k_reduce_len, split_k_reduce_data, "main", 2, 2 * sizeof(uint32_t), {256 * 4, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_flash_attn_split_k_reduce, "fa_split_k_reduce", fa_split_k_reduce_len, fa_split_k_reduce_data, "main", 3, sizeof(vk_op_flash_attn_split_k_reduce_push_constants), {1, device->subgroup_size, 1}, {device->subgroup_size}, 1, true); + + for (auto &it : device->pipeline_fa_mask_opt) { + auto BrBc = it.first; + ggml_vk_create_pipeline(device, it.second, "fa_mask_opt", fa_mask_opt_len, fa_mask_opt_data, "main", 2, sizeof(vk_op_flash_attn_mask_opt_push_constants), {1, 1, 1}, {128, 128 / device->subgroup_size, BrBc.first, BrBc.second}, 1, true, true, device->subgroup_size); + } + + if (device->subgroup_clustered && device->subgroup_require_full_support) { + ggml_vk_create_pipeline(device, device->pipeline_quantize_q8_1_x4, "quantize_q8_1_x4", quantize_q8_1_x4_subgroup_len, quantize_q8_1_x4_subgroup_data, "main", 2, sizeof(vk_quantize_q8_1_push_constants), {32 * device->subgroup_size / 8, 1, 1}, { device->subgroup_size }, 1, true, true); + } else { + ggml_vk_create_pipeline(device, device->pipeline_quantize_q8_1_x4, "quantize_q8_1_x4", quantize_q8_1_x4_len, quantize_q8_1_x4_data, "main", 2, sizeof(vk_quantize_q8_1_push_constants), {32 * device->subgroup_size / 8, 1, 1}, { device->subgroup_size }, 1); + } + + for (uint32_t i = 0; i < p021_max_gqa_ratio; ++i) { + if (device->subgroup_arithmetic && device->subgroup_require_full_support) { + ggml_vk_create_pipeline2(device, device->pipeline_mul_mat_vec_p021_f16_f32[i], "mul_mat_vec_p021_f16_f32"+std::to_string(i+1), mul_mat_vec_p021_f16_f32_subgroup_add_len, mul_mat_vec_p021_f16_f32_subgroup_add_data, "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_p021_push_constants), {1, 1, 1}, {device->subgroup_size, i + 1}, 1, true, true); + } else { + ggml_vk_create_pipeline2(device, device->pipeline_mul_mat_vec_p021_f16_f32[i], "mul_mat_vec_p021_f16_f32"+std::to_string(i+1), mul_mat_vec_p021_f16_f32_len, mul_mat_vec_p021_f16_f32_data, "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_p021_push_constants), {1, 1, 1}, {device->subgroup_size, i + 1}, 1, true); + } + } + ggml_vk_create_pipeline(device, device->pipeline_mul_mat_vec_nc_f16_f32, "mul_mat_vec_nc_f16_f32", mul_mat_vec_nc_f16_f32_len, mul_mat_vec_nc_f16_f32_data, "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_nc_push_constants), {1, 1, 1}, {}, 1); + + ggml_vk_create_pipeline(device, device->pipeline_norm_f32, "norm_f32", norm_f32_len, norm_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_group_norm_f32, "group_norm_f32", group_norm_f32_len, group_norm_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1); + + ggml_vk_create_pipeline(device, device->pipeline_rms_norm_f32, "rms_norm_f32", rms_norm_f32_len, rms_norm_f32_data, "main", 4, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {0, 0}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_rms_norm_mul_f32, "rms_norm_mul_f32", rms_norm_f32_len, rms_norm_f32_data, "main", 4, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {0, 1}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_rms_norm_partials_f32, "rms_norm_partials_f32", rms_norm_partials_f32_len, rms_norm_partials_f32_data, "main", 4, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {0, 0}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_rms_norm_mul_partials_f32, "rms_norm_mul_partials_f32", rms_norm_partials_f32_len, rms_norm_partials_f32_data, "main", 4, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {0, 1}, 1, true); + + if (sizeof(vk_op_rms_norm_mul_rope_push_constants) <= device->properties.limits.maxPushConstantsSize) { + ggml_vk_create_pipeline(device, device->pipeline_rms_norm_mul_rope_f32_f32, "rms_norm_mul_rope_f32_f32", rms_norm_mul_rope_f32_f32_len, rms_norm_mul_rope_f32_f32_data, "main", 7, sizeof(vk_op_rms_norm_mul_rope_push_constants), {1, 1, 1}, {0, 1}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_rms_norm_mul_rope_f32_f16, "rms_norm_mul_rope_f32_f16", rms_norm_mul_rope_f32_f16_len, rms_norm_mul_rope_f32_f16_data, "main", 7, sizeof(vk_op_rms_norm_mul_rope_push_constants), {1, 1, 1}, {0, 1}, 1, true); + } + + ggml_vk_create_pipeline(device, device->pipeline_rms_norm_back_f32, "rms_norm_back_f32", rms_norm_back_f32_len, rms_norm_back_f32_data, "main", 3, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_l2_norm_f32, "l2_norm_f32", l2_norm_f32_len, l2_norm_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {1, 1, 1}, {}, 1); + + ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_f32, "cpy_f32_f32", cpy_f32_f32_len, cpy_f32_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_f16, "cpy_f32_f16", cpy_f32_f16_len, cpy_f32_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_cpy_f16_f16, "cpy_f16_f16", cpy_f16_f16_len, cpy_f16_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_cpy_f16_f32, "cpy_f16_f32", cpy_f16_f32_len, cpy_f16_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_bf16,"cpy_f32_bf16",cpy_f32_bf16_len,cpy_f32_bf16_data,"main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_cpy_bf16_f32,"cpy_bf16_f32",cpy_bf16_f32_len,cpy_bf16_f32_data,"main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_cpy_i32_f32, "cpy_i32_f32", cpy_i32_f32_len, cpy_i32_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_i32, "cpy_f32_i32", cpy_f32_i32_len, cpy_f32_i32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); + + ggml_vk_create_pipeline(device, device->pipeline_contig_cpy_f32_f32, "contig_cpy_f32_f32", contig_cpy_f32_f32_len, contig_cpy_f32_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_contig_cpy_f32_f16, "contig_cpy_f32_f16", contig_cpy_f32_f16_len, contig_cpy_f32_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_contig_cpy_f16_f16, "contig_cpy_f16_f16", contig_cpy_f16_f16_len, contig_cpy_f16_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_contig_cpy_f16_f32, "contig_cpy_f16_f32", contig_cpy_f16_f32_len, contig_cpy_f16_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_contig_cpy_f32_bf16,"contig_cpy_f32_bf16",contig_cpy_f32_bf16_len,contig_cpy_f32_bf16_data,"main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_contig_cpy_bf16_f32,"contig_cpy_bf16_f32",contig_cpy_bf16_f32_len,contig_cpy_bf16_f32_data,"main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_contig_cpy_i32_f32, "contig_cpy_i32_f32", contig_cpy_i32_f32_len, contig_cpy_i32_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_contig_cpy_f32_i32, "contig_cpy_f32_i32", contig_cpy_f32_i32_len, contig_cpy_f32_i32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); + + ggml_vk_create_pipeline(device, device->pipeline_cpy_transpose_32, "cpy_transpose_32", cpy_transpose_32_len, cpy_transpose_32_data, "main", 2, sizeof(vk_op_unary_push_constants), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_cpy_transpose_16, "cpy_transpose_16", cpy_transpose_16_len, cpy_transpose_16_data, "main", 2, sizeof(vk_op_unary_push_constants), {1, 1, 1}, {}, 1); + + ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_quant[GGML_TYPE_Q1_0], "cpy_f32_q1_0", cpy_f32_q1_0_len, cpy_f32_q1_0_data, "main", 2, sizeof(vk_op_unary_push_constants), {32, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_quant[GGML_TYPE_Q4_0], "cpy_f32_q4_0", cpy_f32_q4_0_len, cpy_f32_q4_0_data, "main", 2, sizeof(vk_op_unary_push_constants), {32, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_quant[GGML_TYPE_Q4_1], "cpy_f32_q4_1", cpy_f32_q4_1_len, cpy_f32_q4_1_data, "main", 2, sizeof(vk_op_unary_push_constants), {32, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_quant[GGML_TYPE_Q5_0], "cpy_f32_q5_0", cpy_f32_q5_0_len, cpy_f32_q5_0_data, "main", 2, sizeof(vk_op_unary_push_constants), {32, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_quant[GGML_TYPE_Q5_1], "cpy_f32_q5_1", cpy_f32_q5_1_len, cpy_f32_q5_1_data, "main", 2, sizeof(vk_op_unary_push_constants), {32, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_quant[GGML_TYPE_Q8_0], "cpy_f32_q8_0", cpy_f32_q8_0_len, cpy_f32_q8_0_data, "main", 2, sizeof(vk_op_unary_push_constants), {32, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_quant[GGML_TYPE_IQ4_NL], "cpy_f32_iq4_nl", cpy_f32_iq4_nl_len, cpy_f32_iq4_nl_data, "main", 2, sizeof(vk_op_unary_push_constants), {32, 1, 1}, {}, 1); + +#define SET_ROWS(itype) \ + ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [GGML_TYPE_F32], "set_rows_f32" #itype, set_rows_f32 ## itype ## _len, set_rows_f32 ## itype ## _data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true); \ + ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [GGML_TYPE_F16], "set_rows_f16" #itype, set_rows_f16 ## itype ## _len, set_rows_f16 ## itype ## _data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true); \ + ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [GGML_TYPE_BF16], "set_rows_bf16" #itype, set_rows_bf16 ## itype ## _len, set_rows_bf16 ## itype ## _data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true); \ + ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [GGML_TYPE_Q1_0], "set_rows_q1_0" #itype, set_rows_q1_0 ## itype ## _len, set_rows_q1_0 ## itype ## _data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true); \ + ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [GGML_TYPE_Q4_0], "set_rows_q4_0" #itype, set_rows_q4_0 ## itype ## _len, set_rows_q4_0 ## itype ## _data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true); \ + ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [GGML_TYPE_Q4_1], "set_rows_q4_1" #itype, set_rows_q4_1 ## itype ## _len, set_rows_q4_1 ## itype ## _data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true); \ + ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [GGML_TYPE_Q5_0], "set_rows_q5_0" #itype, set_rows_q5_0 ## itype ## _len, set_rows_q5_0 ## itype ## _data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true); \ + ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [GGML_TYPE_Q5_1], "set_rows_q5_1" #itype, set_rows_q5_1 ## itype ## _len, set_rows_q5_1 ## itype ## _data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true); \ + ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [GGML_TYPE_Q8_0], "set_rows_q8_0" #itype, set_rows_q8_0 ## itype ## _len, set_rows_q8_0 ## itype ## _data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true); \ + ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [GGML_TYPE_IQ4_NL], "set_rows_iq4_nl" #itype, set_rows_iq4_nl ## itype ## _len, set_rows_iq4_nl ## itype ## _data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true); + + SET_ROWS(_i32) + SET_ROWS(_i64) +#undef SET_ROWS + + + ggml_vk_create_pipeline(device, device->pipeline_cpy_quant_f32[GGML_TYPE_Q1_0], "cpy_q1_0_f32", cpy_q1_0_f32_len, cpy_q1_0_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {(uint32_t)ggml_blck_size(GGML_TYPE_Q1_0), 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_cpy_quant_f32[GGML_TYPE_Q4_0], "cpy_q4_0_f32", cpy_q4_0_f32_len, cpy_q4_0_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {(uint32_t)ggml_blck_size(GGML_TYPE_Q4_0), 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_cpy_quant_f32[GGML_TYPE_Q4_1], "cpy_q4_1_f32", cpy_q4_1_f32_len, cpy_q4_1_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {(uint32_t)ggml_blck_size(GGML_TYPE_Q4_1), 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_cpy_quant_f32[GGML_TYPE_Q5_0], "cpy_q5_0_f32", cpy_q5_0_f32_len, cpy_q5_0_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {(uint32_t)ggml_blck_size(GGML_TYPE_Q5_0), 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_cpy_quant_f32[GGML_TYPE_Q5_1], "cpy_q5_1_f32", cpy_q5_1_f32_len, cpy_q5_1_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {(uint32_t)ggml_blck_size(GGML_TYPE_Q5_1), 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_cpy_quant_f32[GGML_TYPE_Q8_0], "cpy_q8_0_f32", cpy_q8_0_f32_len, cpy_q8_0_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {(uint32_t)ggml_blck_size(GGML_TYPE_Q8_0), 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_cpy_quant_f32[GGML_TYPE_IQ4_NL], "cpy_iq4_nl_f32", cpy_iq4_nl_f32_len, cpy_iq4_nl_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {(uint32_t)ggml_blck_size(GGML_TYPE_IQ4_NL), 1, 1}, {}, 1); + + auto get_suffix = [](bool src0_f16, bool src1_f16, bool dst_f16) { + std::string s; + s += std::string(src0_f16 ? "_f16" : "_f32"); + s += std::string(src1_f16 ? "_f16" : "_f32"); + s += std::string(dst_f16 ? "_f16" : "_f32"); + return s; + }; + +#define CREATE_BINARY(name, namemod, spec, bindings) \ + for (int s0 : {0,1}) for (int s1 : {0,1}) for (int d : {0,1}) \ + ggml_vk_create_pipeline2(device, device->pipeline_ ## name ## namemod[s0][s1][d], \ + #name + get_suffix(s0, s1, d) + #namemod, name ## _len[s0][s1][d], name ## _data[s0][s1][d], \ + "main", (bindings), sizeof(vk_op_binary_push_constants), {512, 1, 1}, spec, 1); + + CREATE_BINARY(add, , {0}, 4) + CREATE_BINARY(add, _norepeat, {1}, 4) + CREATE_BINARY(sub, , {0}, 3) + CREATE_BINARY(sub, _norepeat, {1}, 3) + CREATE_BINARY(mul, , {0}, 3) + CREATE_BINARY(mul, _norepeat, {1}, 3) + CREATE_BINARY(div, , {0}, 3) + CREATE_BINARY(div, _norepeat, {1}, 3) + CREATE_BINARY(add_rms, , {0}, 4) + CREATE_BINARY(add_rms, _norepeat, {1}, 4) +#undef CREATE_BINARY + + if (device->multi_add) { + for (uint32_t i = 0; i < MAX_FUSED_ADDS; ++i) { + ggml_vk_create_pipeline2(device, device->pipeline_multi_add[i], "multi_add_f32_" + std::to_string(i+1), multi_add_f32_len, multi_add_f32_data, "main", MAX_PARAMETER_COUNT, sizeof(vk_op_multi_add_push_constants), {512, 1, 1}, {i+2}, 1); + ggml_vk_create_pipeline2(device, device->pipeline_multi_add_rms[i], "multi_add_rms_f32_" + std::to_string(i+1), multi_add_rms_f32_len, multi_add_rms_f32_data, "main", MAX_PARAMETER_COUNT, sizeof(vk_op_multi_add_push_constants), {512, 1, 1}, {i+2}, 1); + } + } + + ggml_vk_create_pipeline(device, device->pipeline_add_id_f32, "add_id_f32", add_id_f32_len, add_id_f32_data, "main", 4, sizeof(vk_op_add_id_push_constants), {1, 1, 1}, {}, 1); + + ggml_vk_create_pipeline(device, device->pipeline_acc_f32, "acc_f32", acc_f32_len, acc_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {0, 1}, 1); + ggml_vk_create_pipeline(device, device->pipeline_set_f32, "set_f32", acc_f32_len, acc_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {0, 0}, 1); + + ggml_vk_create_pipeline(device, device->pipeline_concat_i8, "concat_i8", concat_i8_len, concat_i8_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_concat_i16, "concat_i16", concat_i16_len, concat_i16_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_concat_i32, "concat_i32", concat_i32_len, concat_i32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_concat_i64, "concat_i64", concat_i64_len, concat_i64_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1); + + ggml_vk_create_pipeline(device, device->pipeline_upscale_nearest_f32, "upscale_f32", upscale_f32_len, upscale_f32_data, "main", 2, sizeof(vk_op_upscale_push_constants), {512, 1, 1}, {GGML_SCALE_MODE_NEAREST}, 1); + ggml_vk_create_pipeline(device, device->pipeline_upscale_bilinear_f32, "upscale_f32", upscale_f32_len, upscale_f32_data, "main", 2, sizeof(vk_op_upscale_push_constants), {512, 1, 1}, {GGML_SCALE_MODE_BILINEAR}, 1); + ggml_vk_create_pipeline(device, device->pipeline_upscale_bicubic_f32, "upscale_f32", upscale_f32_len, upscale_f32_data, "main", 2, sizeof(vk_op_upscale_push_constants), {512, 1, 1}, {GGML_SCALE_MODE_BICUBIC}, 1); + ggml_vk_create_pipeline(device, device->pipeline_upscale_bilinear_antialias_f32, "upscale_f32", upscale_f32_len, upscale_f32_data, "main", 2, sizeof(vk_op_upscale_push_constants), {512, 1, 1}, {GGML_SCALE_MODE_BILINEAR | GGML_SCALE_FLAG_ANTIALIAS}, 1); + + ggml_vk_create_pipeline(device, device->pipeline_scale_f32, "scale_f32", scale_f32_len, scale_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); + + ggml_vk_create_pipeline(device, device->pipeline_log[0], "log_f32", log_f32_len, log_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_log[1], "log_f16", log_f16_len, log_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); + + ggml_vk_create_pipeline(device, device->pipeline_tri[0], "tri_f32", tri_f32_len, tri_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_tri[1], "tri_f16", tri_f16_len, tri_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); + + ggml_vk_create_pipeline(device, device->pipeline_diag[0], "diag_f32", diag_f32_len, diag_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_diag[1], "diag_f16", diag_f16_len, diag_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); + + ggml_vk_create_pipeline(device, device->pipeline_pad_f32, "pad_f32", pad_f32_len, pad_f32_data, "main", 2, sizeof(vk_op_pad_push_constants), {512, 1, 1}, {}, 1); + + ggml_vk_create_pipeline(device, device->pipeline_roll_f32, "roll_f32", roll_f32_len, roll_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); + + ggml_vk_create_pipeline(device, device->pipeline_repeat_i32, "repeat_i32", repeat_i32_len, repeat_i32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_repeat_back_f32, "repeat_back_f32", repeat_back_f32_len, repeat_back_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); + + ggml_vk_create_pipeline(device, device->pipeline_repeat_i16, "repeat_i16", repeat_i16_len, repeat_i16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); + +#define CREATE_UNARY(name) \ + ggml_vk_create_pipeline(device, device->pipeline_ ## name [0], #name "_f32", name ## _f32_len, name ## _f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); \ + ggml_vk_create_pipeline(device, device->pipeline_ ## name [1], #name "_f16", name ## _f16_len, name ## _f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); + + CREATE_UNARY(elu) + CREATE_UNARY(gelu) + CREATE_UNARY(gelu_erf) + CREATE_UNARY(gelu_quick) + CREATE_UNARY(silu) + CREATE_UNARY(relu) + CREATE_UNARY(sqr) + CREATE_UNARY(sqrt) + CREATE_UNARY(sin) + CREATE_UNARY(cos) + CREATE_UNARY(clamp) + CREATE_UNARY(leaky_relu) + CREATE_UNARY(xielu) + CREATE_UNARY(neg) + CREATE_UNARY(tanh) + CREATE_UNARY(sigmoid) + CREATE_UNARY(hardsigmoid) + CREATE_UNARY(hardswish) + CREATE_UNARY(abs) + CREATE_UNARY(softplus) + CREATE_UNARY(step) + CREATE_UNARY(round) + CREATE_UNARY(ceil) + CREATE_UNARY(floor) + CREATE_UNARY(trunc) + CREATE_UNARY(sgn) + CREATE_UNARY(exp) + CREATE_UNARY(expm1) +#undef CREATE_UNARY + + ggml_vk_create_pipeline(device, device->pipeline_add1_f16_f16, "add1_f16_f16", add1_f16_f16_len, add1_f16_f16_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_add1_f16_f32, "add1_f16_f32", add1_f16_f32_len, add1_f16_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_add1_f32_f32, "add1_f32_f32", add1_f32_f32_len, add1_f32_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1); + + ggml_vk_create_pipeline(device, device->pipeline_arange_f32, "arange_f32", arange_f32_len, arange_f32_data, "main", 1, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + + ggml_vk_create_pipeline(device, device->pipeline_fill_f32, "fill_f32", fill_f32_len, fill_f32_data, "main", 1, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_fill_f16, "fill_f16", fill_f16_len, fill_f16_data, "main", 1, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + +#define CREATE_GLU(name) \ + ggml_vk_create_pipeline(device, device->pipeline_ ## name [0], #name "_f32", name ## _f32_len, name ## _f32_data, "main", 3, sizeof(vk_op_glu_push_constants), {512, 1, 1}, {}, 1, true); \ + ggml_vk_create_pipeline(device, device->pipeline_ ## name [1], #name "_f16", name ## _f16_len, name ## _f16_data, "main", 3, sizeof(vk_op_glu_push_constants), {512, 1, 1}, {}, 1, true); + + CREATE_GLU(geglu) + CREATE_GLU(reglu) + CREATE_GLU(swiglu) + CREATE_GLU(swiglu_oai) + CREATE_GLU(geglu_erf) + CREATE_GLU(geglu_quick) +#undef CREATE_GLU + + ggml_vk_create_pipeline(device, device->pipeline_silu_back_f32, "silu_back_f32", silu_back_f32_len, silu_back_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + + ggml_vk_create_pipeline(device, device->pipeline_diag_mask_inf_f32, "diag_mask_inf_f32", diag_mask_inf_f32_len, diag_mask_inf_f32_data, "main", 2, sizeof(vk_op_diag_mask_push_constants), {1, 512, 1}, {}, 1, true); + + ggml_vk_create_pipeline(device, device->pipeline_soft_max_f32, "soft_max_f32", soft_max_f32_len, soft_max_f32_data, "main", 4, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(device, device->pipeline_soft_max_f32_wg512, "soft_max_f32_wg512", soft_max_f32_len, soft_max_f32_data, "main", 4, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { 512 }, 1); + ggml_vk_create_pipeline(device, device->pipeline_soft_max_f32_f16, "soft_max_f32_f16", soft_max_f32_f16_len, soft_max_f32_f16_data, "main", 4, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(device, device->pipeline_soft_max_f32_f16_wg512, "soft_max_f32_f16_wg512", soft_max_f32_f16_len, soft_max_f32_f16_data, "main", 4, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { 512 }, 1); + ggml_vk_create_pipeline(device, device->pipeline_soft_max_back_f32, "soft_max_back_f32", soft_max_back_f32_len, soft_max_back_f32_data, "main", 3, sizeof(vk_op_push_constants), {1, 1, 1}, { device->subgroup_size }, 1, true); + + ggml_vk_create_pipeline(device, device->pipeline_soft_max_large1_f32, "soft_max_large1_f32", soft_max_large1_f32_len, soft_max_large1_f32_data, "main", 6, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { 128, 4 }, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_soft_max_large2_f32, "soft_max_large2_f32", soft_max_large2_f32_len, soft_max_large2_f32_data, "main", 6, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { 128, 4 }, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_soft_max_large3_f32, "soft_max_large3_f32", soft_max_large3_f32_len, soft_max_large3_f32_data, "main", 6, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { 128, 4 }, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_soft_max_large1_f32_f16, "soft_max_large1_f32_f16", soft_max_large1_f32_f16_len, soft_max_large1_f32_f16_data, "main", 6, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { 128, 4 }, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_soft_max_large2_f32_f16, "soft_max_large2_f32_f16", soft_max_large2_f32_f16_len, soft_max_large2_f32_f16_data, "main", 6, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { 128, 4 }, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_soft_max_large3_f32_f16, "soft_max_large3_f32_f16", soft_max_large3_f32_f16_len, soft_max_large3_f32_f16_data, "main", 6, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { 128, 4 }, 1, true); + + ggml_vk_create_pipeline(device, device->pipeline_rope_norm_f32, "rope_norm_f32", rope_norm_f32_len, rope_norm_f32_data, "main", 5, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_rope_neox_f32, "rope_neox_f32", rope_neox_f32_len, rope_neox_f32_data, "main", 5, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_rope_multi_f32, "rope_multi_f32", rope_multi_f32_len, rope_multi_f32_data, "main", 5, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_rope_vision_f32, "rope_vision_f32", rope_vision_f32_len, rope_vision_f32_data, "main", 5, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); + + ggml_vk_create_pipeline(device, device->pipeline_rope_norm_f16, "rope_norm_f16", rope_norm_f16_len, rope_norm_f16_data, "main", 5, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_rope_neox_f16, "rope_neox_f16", rope_neox_f16_len, rope_neox_f16_data, "main", 5, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_rope_multi_f16, "rope_multi_f16", rope_multi_f16_len, rope_multi_f16_data, "main", 5, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_rope_vision_f16, "rope_vision_f16", rope_vision_f16_len, rope_vision_f16_data, "main", 5, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); + + ggml_vk_create_pipeline(device, device->pipeline_rope_norm_f32_f16, "rope_norm_f32_f16", rope_norm_f32_f16_len, rope_norm_f32_f16_data, "main", 5, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_rope_neox_f32_f16, "rope_neox_f32_f16", rope_neox_f32_f16_len, rope_neox_f32_f16_data, "main", 5, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_rope_multi_f32_f16, "rope_multi_f32_f16", rope_multi_f32_f16_len, rope_multi_f32_f16_data, "main", 5, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); + + for (uint32_t i = 0; i < num_argsort_pipelines; ++i) { + uint32_t BLOCK_SIZE = 1u << std::min(i, device->max_workgroup_size_log2); + if (i <= device->max_workgroup_size_log2 && + 2 * sizeof(int) * BLOCK_SIZE <= device->properties.limits.maxComputeSharedMemorySize) { + const uint32_t NCOLS_PADDED_LOG2 = i; + ggml_vk_create_pipeline2(device, device->pipeline_argsort_f32[i], "argsort_f32_"+std::to_string(i), argsort_f32_len, argsort_f32_data, "main", 3, sizeof(vk_op_argsort_push_constants), {BLOCK_SIZE, 1, 1}, {BLOCK_SIZE, NCOLS_PADDED_LOG2}, 1, true); + } + const uint32_t WG_UNROLL_FACTOR = BLOCK_SIZE > 1 ? 2 : 1; + BLOCK_SIZE /= WG_UNROLL_FACTOR; + ggml_vk_create_pipeline2(device, device->pipeline_argsort_large_f32[i], "argsort_large_f32_"+std::to_string(i), argsort_large_f32_len, argsort_large_f32_data, "main", 3, sizeof(vk_op_argsort_push_constants), {BLOCK_SIZE * WG_UNROLL_FACTOR, 1, 1}, {BLOCK_SIZE, WG_UNROLL_FACTOR}, 1, true); + } + + for (uint32_t i = 0; i < num_topk_pipelines; ++i) { + const uint32_t BLOCK_SIZE = 1u << i; + const uint32_t NCOLS_PADDED_LOG2 = i; + if (i <= device->max_workgroup_size_log2) { + uint32_t nary_shmem = 2 * sizeof(int) * BLOCK_SIZE + + sizeof(int) * device->subgroup_size + + 2 * 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); + } else if (2 * sizeof(int) * BLOCK_SIZE <= device->properties.limits.maxComputeSharedMemorySize) { + ggml_vk_create_pipeline2(device, device->pipeline_topk_f32[i], "topk_f32_"+std::to_string(i), topk_argsort_f32_len, topk_argsort_f32_data, "main", 2, sizeof(vk_op_topk_push_constants), {BLOCK_SIZE, 1, 1}, {BLOCK_SIZE, NCOLS_PADDED_LOG2}, 1, true); + } + } + } + + ggml_vk_create_pipeline(device, device->pipeline_argmax_f32, "argmax_f32", argmax_f32_len, argmax_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + + ggml_vk_create_pipeline(device, device->pipeline_sum_rows_f32, "sum_rows_f32", sum_rows_f32_len, sum_rows_f32_data, "main", 2, sizeof(vk_op_sum_rows_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + // Intel Arc B390 was observed segfaulting with this shader. + if (device->subgroup_basic && device->subgroup_shuffle && device->vendor_id != VK_VENDOR_ID_INTEL) { + int idx = 0; + for (uint32_t n : {64, 128, 256, 512}) { + if (device->subgroup_size <= n) { + ggml_vk_create_pipeline(device, device->pipeline_fwht_f32[idx], "fwht_f32", fwht_f32_len, fwht_f32_data, "main", 2, sizeof(vk_op_fwht_push_constants), {1, 1, 1}, { device->subgroup_size, n }, 1, true, true, device->subgroup_size); + } + ++idx; + } + } else if (device->driver_id != vk::DriverId::eIntelProprietaryWindows) { + // Disabled on Intel Windows due to a driver bug: https://github.com/ggml-org/llama.cpp/pull/23964#issuecomment-4598226147 + int idx = 0; + for (uint32_t n : {64, 128, 256, 512}) { + const uint32_t block_size = std::min(device->subgroup_size, n); + ggml_vk_create_pipeline(device, device->pipeline_fwht_f32[idx], "fwht_shmem_f32", fwht_shmem_f32_len, fwht_shmem_f32_data, "main", 2, sizeof(vk_op_fwht_push_constants), {1, 1, 1}, { block_size, n }, 1); + ++idx; + } + } + + const uint32_t cumsum_elem_per_thread = (device->vendor_id == VK_VENDOR_ID_AMD || device->vendor_id == VK_VENDOR_ID_INTEL) ? 2 : 4; + ggml_vk_create_pipeline(device, device->pipeline_cumsum_f32, "cumsum_f32", cumsum_f32_len, cumsum_f32_data, "main", 2, sizeof(vk_op_sum_rows_push_constants), {1, 1, 1}, { 256, device->subgroup_size, cumsum_elem_per_thread }, 1, true, true, device->subgroup_size); + ggml_vk_create_pipeline(device, device->pipeline_cumsum_small_f32, "cumsum_f32", cumsum_f32_len, cumsum_f32_data, "main", 2, sizeof(vk_op_sum_rows_push_constants), {1, 1, 1}, { 128, device->subgroup_size, 1 }, 1, true, true, device->subgroup_size); + ggml_vk_create_pipeline(device, device->pipeline_cumsum_multipass1_f32, "cumsum_multipass1_f32", cumsum_multipass1_f32_len, cumsum_multipass1_f32_data, "main", 3, sizeof(vk_op_sum_rows_push_constants), {256, 1, 1}, { 256, device->subgroup_size }, 1, true, true, device->subgroup_size); + ggml_vk_create_pipeline(device, device->pipeline_cumsum_multipass2_f32, "cumsum_multipass2_f32", cumsum_multipass2_f32_len, cumsum_multipass2_f32_data, "main", 3, sizeof(vk_op_sum_rows_push_constants), {256, 1, 1}, { 256, device->subgroup_size }, 1, true, true, device->subgroup_size); + + ggml_vk_create_pipeline(device, device->pipeline_count_equal_i32, "count_equal_i32", count_equal_i32_len, count_equal_i32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, { device->subgroup_size }, 1); + + ggml_vk_create_pipeline(device, device->pipeline_count_experts, "count_experts", count_experts_len, count_experts_data, "main", 2, sizeof(vk_op_count_experts_push_constants), {1, 1, 1}, {}, 1, true); + + 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, batch_N, block_size }, 1, true); + } + +#define IM2COL(bda) \ + ggml_vk_create_pipeline(device, device->pipeline_im2col_f32, "im2col_f32", im2col_f32 ## bda ## _len, im2col_f32 ## bda ## _data, "main", 2, sizeof(vk_op_im2col_push_constants), {512, 1, 1}, { device->subgroup_size }, 1, true); \ + ggml_vk_create_pipeline(device, device->pipeline_im2col_3d_f32, "im2col_3d_f32", im2col_3d_f32 ## bda ## _len, im2col_3d_f32 ## bda ## _data, "main", 2, sizeof(vk_op_im2col_3d_push_constants), {512, 1, 1}, { 512 }, 1, true); \ + ggml_vk_create_pipeline(device, device->pipeline_im2col_f32_f16, "im2col_f32_f16", im2col_f32_f16 ## bda ## _len, im2col_f32_f16 ## bda ## _data, "main", 2, sizeof(vk_op_im2col_push_constants), {512, 1, 1}, { device->subgroup_size }, 1, true); \ + ggml_vk_create_pipeline(device, device->pipeline_im2col_3d_f32_f16, "im2col_3d_f32_f16", im2col_3d_f32_f16 ## bda ## _len, im2col_3d_f32_f16 ## bda ## _data, "main", 2, sizeof(vk_op_im2col_3d_push_constants), {512, 1, 1}, { 512 }, 1, true); + if (device->shader_int64 && device->buffer_device_address) { + IM2COL(_bda) + } else { + IM2COL() + } + + ggml_vk_create_pipeline(device, device->pipeline_timestep_embedding_f32, "timestep_embedding_f32", timestep_embedding_f32_len, timestep_embedding_f32_data, "main", 2, sizeof(vk_op_timestep_embedding_push_constants), {256, 1, 1}, {}, 1); + + ggml_vk_create_pipeline(device, device->pipeline_conv_transpose_1d_f32, "conv_transpose_1d_f32", conv_transpose_1d_f32_len, conv_transpose_1d_f32_data, "main", 3, sizeof(vk_op_conv_transpose_1d_push_constants), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_col2im_1d_f32, "col2im_1d_f32", col2im_1d_f32_len, col2im_1d_f32_data, "main", 2, sizeof(vk_op_col2im_1d_push_constants), {256, 1, 1}, {}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_col2im_1d_f16, "col2im_1d_f16", col2im_1d_f16_len, col2im_1d_f16_data, "main", 2, sizeof(vk_op_col2im_1d_push_constants), {256, 1, 1}, {}, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_col2im_1d_bf16, "col2im_1d_bf16", col2im_1d_bf16_len, col2im_1d_bf16_data, "main", 2, sizeof(vk_op_col2im_1d_push_constants), {256, 1, 1}, {}, 1, true); + + ggml_vk_create_pipeline(device, device->pipeline_snake_f32, "snake_f32", snake_f32_len, snake_f32_data, "main", 4, sizeof(vk_op_snake_push_constants), {256, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_snake_f16, "snake_f16", snake_f16_len, snake_f16_data, "main", 4, sizeof(vk_op_snake_push_constants), {256, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_snake_bf16, "snake_bf16", snake_bf16_len, snake_bf16_data, "main", 4, sizeof(vk_op_snake_push_constants), {256, 1, 1}, {}, 1); + + ggml_vk_create_pipeline(device, device->pipeline_pool2d_f32, "pool2d_f32", pool2d_f32_len, pool2d_f32_data, "main", 2, sizeof(vk_op_pool2d_push_constants), {512, 1, 1}, {}, 1); + + ggml_vk_create_pipeline(device, device->pipeline_rwkv_wkv6_f32, "rwkv_wkv6_f32", rwkv_wkv6_f32_len, rwkv_wkv6_f32_data, "main", 7, sizeof(vk_op_rwkv_wkv6_push_constants), {1, 1, 1}, {device->subgroup_size}, 1); + + ggml_vk_create_pipeline(device, device->pipeline_rwkv_wkv7_f32, "rwkv_wkv7_f32", rwkv_wkv7_f32_len, rwkv_wkv7_f32_data, "main", 8, sizeof(vk_op_rwkv_wkv7_push_constants), {1, 1, 1}, {device->subgroup_size}, 1); + + { + const uint32_t gdn_sizes[] = {16, 32, 64, 128}; + const char * gdn_names[][2] = { + {"gated_delta_net_f32_d16", "gated_delta_net_f32_d16_kda"}, + {"gated_delta_net_f32_d32", "gated_delta_net_f32_d32_kda"}, + {"gated_delta_net_f32_d64", "gated_delta_net_f32_d64_kda"}, + {"gated_delta_net_f32_d128", "gated_delta_net_f32_d128_kda"}, + }; + for (uint32_t si = 0; si < 4; si++) { + const uint32_t S_V = gdn_sizes[si]; + GGML_ASSERT(is_pow2(S_V)); + + uint32_t lanes_per_column; + if (S_V >= 128u && device->subgroup_clustered) { + lanes_per_column = 8u; + } else { + // Use largest power-of-two that divides both S_V and subgroup_size so that + // (1) S_V % lanes_per_column == 0 and (2) S_V % (subgroup_size / lanes_per_column) == 0. + // This means we don't need extra bounds checking logic in the shader. + lanes_per_column = std::min(S_V, device->subgroup_size); + } + + // gated_delta_net.comp relies on S_V % COLS_PER_WG == 0 and + // S_V % LANES_PER_COLUMN == 0 to avoid bounds checks. + while (lanes_per_column > 1u) { + const bool valid_lanes = (device->subgroup_size % lanes_per_column) == 0 && + (S_V % lanes_per_column) == 0; + const uint32_t cols_per_wg = valid_lanes ? device->subgroup_size / lanes_per_column : 0; + if (valid_lanes && cols_per_wg > 0 && (S_V % cols_per_wg) == 0) { + break; + } + lanes_per_column >>= 1u; + } + + GGML_ASSERT((device->subgroup_size % lanes_per_column) == 0); + GGML_ASSERT((S_V % lanes_per_column) == 0); + GGML_ASSERT((S_V % (device->subgroup_size / lanes_per_column)) == 0); + + const bool need_partial_subgroup_reduce = lanes_per_column != 1u && lanes_per_column < device->subgroup_size; + const bool use_clustered_reduce = device->subgroup_arithmetic && device->subgroup_clustered && need_partial_subgroup_reduce; + const bool use_subgroup_reduce = device->subgroup_arithmetic && !need_partial_subgroup_reduce; + const bool use_subgroup_ops = use_clustered_reduce || use_subgroup_reduce; + size_t gdn_len; + const void * gdn_data; + if (use_clustered_reduce) { + gdn_len = gated_delta_net_f32_len; + gdn_data = (const void *)gated_delta_net_f32_data; + } else if (use_subgroup_reduce) { + gdn_len = gated_delta_net_f32_nocluster_len; + gdn_data = (const void *)gated_delta_net_f32_nocluster_data; + } else { + gdn_len = gated_delta_net_f32_shmem_len; + gdn_data = (const void *)gated_delta_net_f32_shmem_data; + } + + const uint32_t cols_per_wg = device->subgroup_size / lanes_per_column; + const std::array wg_denoms = {1u, 1u, cols_per_wg}; + + for (uint32_t kda = 0; kda < 2; kda++) { + ggml_vk_create_pipeline(device, device->pipeline_gated_delta_net[si][kda], + gdn_names[si][kda], gdn_len, gdn_data, "main", 7, sizeof(vk_op_gated_delta_net_push_constants), + wg_denoms, {S_V, kda, device->subgroup_size, lanes_per_column}, 1, true, use_subgroup_ops, device->subgroup_size); + } + } + } + + if (device->subgroup_arithmetic && device->subgroup_require_full_support) { + ggml_vk_create_pipeline(device, device->pipeline_ssm_scan_f32_d128, "ssm_scan_128_f32", ssm_scan_subgroup_f32_len, ssm_scan_subgroup_f32_data, "main", 8, sizeof(vk_op_ssm_scan_push_constants), {1, 1, 1}, {128, device->subgroup_size}, 1, true, true); + ggml_vk_create_pipeline(device, device->pipeline_ssm_scan_f32_d256, "ssm_scan_256_f32", ssm_scan_subgroup_f32_len, ssm_scan_subgroup_f32_data, "main", 8, sizeof(vk_op_ssm_scan_push_constants), {1, 1, 1}, {256, device->subgroup_size}, 1, true, true); + } else { + ggml_vk_create_pipeline(device, device->pipeline_ssm_scan_f32_d128, "ssm_scan_128_f32", ssm_scan_f32_len, ssm_scan_f32_data, "main", 8, sizeof(vk_op_ssm_scan_push_constants), {1, 1, 1}, {128, device->subgroup_size, 16}, 1, true, true); + ggml_vk_create_pipeline(device, device->pipeline_ssm_scan_f32_d256, "ssm_scan_256_f32", ssm_scan_f32_len, ssm_scan_f32_data, "main", 8, sizeof(vk_op_ssm_scan_push_constants), {1, 1, 1}, {256, device->subgroup_size, 16}, 1, true, true); + } + + ggml_vk_create_pipeline(device, device->pipeline_ssm_conv_f32, "ssm_conv_f32", ssm_conv_f32_len, ssm_conv_f32_data, "main", 4, sizeof(vk_op_ssm_conv_push_constants), {32, 16, 1}, {32, 16, 0, 0}, 1); + ggml_vk_create_pipeline(device, device->pipeline_ssm_conv_silu_f32, "ssm_conv_silu_f32", ssm_conv_f32_len, ssm_conv_f32_data, "main", 4, sizeof(vk_op_ssm_conv_push_constants), {32, 16, 1}, {32, 16, 0, 1}, 1); + ggml_vk_create_pipeline(device, device->pipeline_ssm_conv_bias_silu_f32, "ssm_conv_bias_silu_f32", ssm_conv_f32_len, ssm_conv_f32_data, "main", 4, sizeof(vk_op_ssm_conv_push_constants), {32, 16, 1}, {32, 16, 1, 1}, 1); + + ggml_vk_create_pipeline(device, device->pipeline_opt_step_adamw_f32, "opt_step_adamw_f32", opt_step_adamw_f32_len, opt_step_adamw_f32_data, "main", 5, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + + ggml_vk_create_pipeline(device, device->pipeline_opt_step_sgd_f32, "opt_step_sgd_f32", opt_step_sgd_f32_len, opt_step_sgd_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + + // conv2d, conv_transpose_2d, conv3d + for (uint32_t s = 0; s < CONV_SHAPE_COUNT; ++s) { + // smaller WG for the small-tile fallback gives more concurrent WGs per SM + uint32_t conv2d_WG_SIZE = (s == CONV_SHAPE_64x32) ? 128 : 256; + uint32_t use_collectives = 0; // Enables subgroup ops for preventing the re-calculation of indices. + 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) + if (device->coopmat2) { + conv2d_SHMEM_PAD = 8; // 8 float16_t + } +#endif + + if (device->vendor_id == VK_VENDOR_ID_INTEL) { + conv2d_SHMEM_PAD = 0; + conv2d_UNROLL = false; + } else if (device->vendor_id == VK_VENDOR_ID_AMD) { + conv2d_SHMEM_PAD = device->architecture == vk_device_architecture::AMD_GCN ? 1 : 4; + if (s == CONV_SHAPE_128x128 && device->architecture != vk_device_architecture::AMD_GCN) { + conv2d_UNROLL = false; + } + } + + // Use collectives on pre-Turing NVIDIA GPUs and GCN AMD cards, which had slower integer math. + bool allow_collectives_nv = device->vendor_id != VK_VENDOR_ID_NVIDIA || + device->architecture == vk_device_architecture::NVIDIA_PRE_TURING; + bool allow_collectives_amd = device->vendor_id != VK_VENDOR_ID_AMD || + device->architecture == vk_device_architecture::AMD_GCN; + + if (device->subgroup_shuffle && + device->vendor_id != VK_VENDOR_ID_INTEL && // Do not enable collectives on Intel, see PR 14316. + allow_collectives_nv && + allow_collectives_amd) { + use_collectives = 1; + 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. + } + + // cm1 is used only when cm2 is unavailable; capped at 64x128 (due to shared memory size). + // Requires 16x16x16 f16-acc since that's the fragment shape hard-coded in the shader. + // Subgroup size must be 32 or 64 (to keep WG_SIZE sane) and we need + // subgroup_size_control to force the driver to actually use it. + bool conv2d_use_cm1 = false; +#if defined(VK_KHR_cooperative_matrix) && defined(GGML_VULKAN_COOPMAT_GLSLC_SUPPORT) + conv2d_use_cm1 = !device->coopmat2 && + device->coopmat_support && device->coopmat_support_16x16x16_f16acc && + device->subgroup_size_control && + (device->subgroup_size == 32 || device->subgroup_size == 64) && + s != CONV_SHAPE_128x128; +#endif + + const uint32_t conv2d_cm1_shmem_pad = 8; + + auto shmem_req = [&](uint32_t pad, bool csh_store, bool fp16_shmem) { + const uint32_t elem_size = fp16_shmem ? (uint32_t)sizeof(uint16_t) : (uint32_t)sizeof(float); + const uint32_t csh_elems = csh_store ? conv2d_BS.K * conv2d_BS.NPQ : 0u; + return (conv2d_BS.K * (conv2d_BS.CRS + pad) + conv2d_BS.CRS * (conv2d_BS.NPQ + pad) + csh_elems) * elem_size; + }; + + // 2D, transpose-2D, and 3D conv use the same KxCRS @ CRSxNPQ shmem + // layout. cm1 needs Csh for output, so check before applying cm1 params. + if (conv2d_use_cm1 && device->properties.limits.maxComputeSharedMemorySize < shmem_req(conv2d_cm1_shmem_pad, true, true)) { + conv2d_use_cm1 = false; + } + + uint32_t conv2d_WM = 16, conv2d_WN = 16; // cm1 subgroup tile, ignored otherwise + if (conv2d_use_cm1) { + conv2d_SHMEM_PAD = conv2d_cm1_shmem_pad; + // 16x16x16 fragments; pick WM/WN to keep WG_SIZE at 256 + // (i.e. 8 subgroups for sg=32, 4 subgroups for sg=64). + const bool sg64 = (device->subgroup_size == 64); + switch (s) { + case CONV_SHAPE_64x32: conv2d_WM = sg64 ? 32 : 16; conv2d_WN = 16; break; + case CONV_SHAPE_64x128: conv2d_WM = 32; conv2d_WN = sg64 ? 64 : 32; break; + case CONV_SHAPE_32x256: conv2d_WM = sg64 ? 16 : 32; conv2d_WN = sg64 ? 128 : 32; break; + default: break; + } + const uint32_t warps_M = conv2d_BS.K / conv2d_WM; + const uint32_t warps_N = conv2d_BS.NPQ / conv2d_WN; + conv2d_WG_SIZE = warps_M * warps_N * device->subgroup_size; + } + + // stage cm2 accumulator through shmem for coalesced global stores; + // skipped on 128x128 where the extra Csh footprint hurts occupancy. + // cm1 always uses the staged path. + uint32_t conv2d_csh_store = (device->coopmat2 && s != CONV_SHAPE_128x128) ? 1u : 0u; + if (conv2d_use_cm1) { + conv2d_csh_store = 1; + } + + // shmem is fp16 on cm2/cm1 (matches Csh), fp32 on scalar + const bool conv2d_use_fp16_shmem = device->coopmat2 || conv2d_use_cm1; + + // shrink CRS if the non-cm1 config still doesn't fit + if (device->properties.limits.maxComputeSharedMemorySize < shmem_req(conv2d_SHMEM_PAD, conv2d_csh_store, conv2d_use_fp16_shmem)) { + GGML_ASSERT(!conv2d_use_cm1); + conv2d_BS.CRS = 8; + if (use_collectives) { + conv2d_BS.CRS = std::min(device->subgroup_size, conv2d_BS.CRS); + } + conv2d_csh_store = 0; + } + + 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 }; + + // cm1 needs a fixed subgroup width to match the WG_SIZE we computed + const uint32_t conv2d_required_subgroup_size = conv2d_use_cm1 ? device->subgroup_size : 0; + +#define CREATE_CONV(name, type_suffix, spv_suffix) \ + for (auto &c : device->pipeline_##name##type_suffix[s]) { \ + const vk_conv2d_pipeline_state &state = c.first; \ + std::vector spec_constants_cpy = spec_constants; \ + spec_constants_cpy.push_back(state.s0); \ + spec_constants_cpy.push_back(state.s1); \ + spec_constants_cpy.push_back(state.p0); \ + spec_constants_cpy.push_back(state.p1); \ + spec_constants_cpy.push_back(state.d0); \ + spec_constants_cpy.push_back(state.d1); \ + spec_constants_cpy.push_back(state.KW); \ + spec_constants_cpy.push_back(state.KH); \ + spec_constants_cpy.push_back(state.aligned); \ + spec_constants_cpy.push_back(conv2d_csh_store); \ + spec_constants_cpy.push_back(conv2d_WM); \ + spec_constants_cpy.push_back(conv2d_WN); \ + 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_conv2d_push_constants), wg_denoms, spec_constants_cpy, 1, true, use_collectives || conv2d_required_subgroup_size, conv2d_required_subgroup_size); \ + } +#define CREATE_CONVS(spv_suffix) \ + CREATE_CONV(conv2d, _f32, spv_suffix) \ + CREATE_CONV(conv2d, _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) + } else +#endif +#if defined(VK_KHR_cooperative_matrix) && defined(GGML_VULKAN_COOPMAT_GLSLC_SUPPORT) + if (conv2d_use_cm1) { + CREATE_CONVS(_cm1) + } else +#endif + if (conv2d_UNROLL) { + CREATE_CONVS(_unroll) + } else { + CREATE_CONVS( ) + } +#undef CREATE_CONV +#undef CREATE_CONVS + + std::vector conv3d_spec_constants = { conv2d_WG_SIZE, conv2d_BS.K, conv2d_BS.CRS, conv2d_BS.NPQ, conv2d_TS_K, conv2d_SHMEM_PAD }; +#define CREATE_CONV3D(type_suffix, spv_suffix) \ + for (auto &c : device->pipeline_conv3d##type_suffix[s]) { \ + const vk_conv3d_pipeline_state &state = c.first; \ + std::vector spec_constants_cpy = conv3d_spec_constants; \ + spec_constants_cpy.push_back(state.s0); \ + spec_constants_cpy.push_back(state.s1); \ + spec_constants_cpy.push_back(state.s2); \ + spec_constants_cpy.push_back(state.p0); \ + spec_constants_cpy.push_back(state.p1); \ + spec_constants_cpy.push_back(state.p2); \ + spec_constants_cpy.push_back(state.d0); \ + spec_constants_cpy.push_back(state.d1); \ + spec_constants_cpy.push_back(state.d2); \ + spec_constants_cpy.push_back(state.KW); \ + spec_constants_cpy.push_back(state.KH); \ + spec_constants_cpy.push_back(state.KD); \ + spec_constants_cpy.push_back(state.aligned); \ + spec_constants_cpy.push_back(conv2d_csh_store); \ + spec_constants_cpy.push_back(conv2d_WM); \ + spec_constants_cpy.push_back(conv2d_WN); \ + ggml_vk_create_pipeline( \ + device, c.second, "conv3d" #type_suffix, \ + conv3d##type_suffix##spv_suffix##_len, conv3d##type_suffix##spv_suffix##_data, "main", 3, \ + sizeof(vk_op_conv3d_push_constants), wg_denoms, spec_constants_cpy, 1, true, conv2d_required_subgroup_size != 0, conv2d_required_subgroup_size); \ + } +#if defined(GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT) + if (device->coopmat2) { + CREATE_CONV3D(_f32, _cm2) + CREATE_CONV3D(_f16_f32, _cm2) + } else +#endif +#if defined(VK_KHR_cooperative_matrix) && defined(GGML_VULKAN_COOPMAT_GLSLC_SUPPORT) + if (conv2d_use_cm1) { + CREATE_CONV3D(_f32, _cm1) + CREATE_CONV3D(_f16_f32, _cm1) + } else +#endif + if (conv2d_UNROLL) { + CREATE_CONV3D(_f32, _unroll) + CREATE_CONV3D(_f16_f32, _unroll) + } else { + CREATE_CONV3D(_f32, ) + CREATE_CONV3D(_f16_f32, ) + } +#undef CREATE_CONV3D + } + + ggml_vk_create_pipeline(device, device->pipeline_conv2d_dw_whcn_f32, "conv2d_dw_whcn_f32", conv2d_dw_whcn_f32_len, conv2d_dw_whcn_f32_data, "main", 3, sizeof(vk_op_conv2d_dw_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_conv2d_dw_cwhn_f32, "conv2d_dw_cwhn_f32", conv2d_dw_cwhn_f32_len, conv2d_dw_cwhn_f32_data, "main", 3, sizeof(vk_op_conv2d_dw_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_conv2d_dw_whcn_f16_f32, "conv2d_dw_whcn_f16_f32", conv2d_dw_whcn_f16_f32_len, conv2d_dw_whcn_f16_f32_data, "main", 3, sizeof(vk_op_conv2d_dw_push_constants), {512, 1, 1}, {}, 1); + 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 use_push = 0; use_push < 2; ++use_push) { + for (uint32_t i = 0; i < num_topk_moe_pipelines; ++i) { + ggml_vk_create_pipeline2(device, device->pipeline_topk_moe[i][use_push], "topk_moe_f32_"+std::to_string(i), topk_moe_f32_len, topk_moe_f32_data, "main", 4, sizeof(vk_op_topk_moe_push_constants), {1, 1, 1}, {device->subgroup_size, 1u<subgroup_size); + } + } + + // Drop compile_mutex so other threads can walk while we compile. + compile_lock.unlock(); + + // Compile what we claimed; create_pipeline_func reacquires compile_mutex + // at the end to flip compile_pending/compiled and notify waiters. + if (has_claimed_task) { + auto & task = claimed_task; + ggml_vk_create_pipeline_func(device, task.pipeline, task.spv_size, task.spv_data, + task.entrypoint, task.parameter_count, task.wg_denoms, + task.specialization_constants, task.disable_robustness, + task.require_full_subgroups, task.required_subgroup_size); + } + + // Another thread may be compiling the pipeline we need; block on it here. + if (wait_pipeline) { + std::unique_lock wait_lock(device->compile_mutex); + device->compile_cv.wait(wait_lock, [&] { + return wait_pipeline->compiled.load(); + }); + } +} + +static bool ggml_vk_khr_cooperative_matrix_support(const vk::PhysicalDeviceProperties& props, const vk::PhysicalDeviceDriverProperties& driver_props, vk_device_architecture arch); +static uint32_t ggml_vk_intel_shader_core_count(const vk::PhysicalDevice& vkdev); + +static vk_device ggml_vk_get_device(size_t idx) { + VK_LOG_DEBUG("ggml_vk_get_device(" << idx << ")"); + + if (vk_instance.devices[idx] == nullptr) { + VK_LOG_DEBUG("Initializing new vk_device"); + vk_device device = std::make_shared(); + vk_instance.devices[idx] = device; + + device->memory_logger = std::unique_ptr(new vk_memory_logger()); + + size_t dev_num = vk_instance.device_indices[idx]; + + std::vector physical_devices = vk_instance.instance.enumeratePhysicalDevices(); + + if (dev_num >= physical_devices.size()) { + std::cerr << "ggml_vulkan: Device with index " << dev_num << " does not exist." << std::endl; + throw std::runtime_error("Device not found"); + } + + device->physical_device = physical_devices[dev_num]; + const std::vector ext_props = device->physical_device.enumerateDeviceExtensionProperties(); + + device->architecture = get_device_architecture(device->physical_device); + + const char* GGML_VK_PREFER_HOST_MEMORY = getenv("GGML_VK_PREFER_HOST_MEMORY"); + device->prefer_host_memory = GGML_VK_PREFER_HOST_MEMORY != nullptr; + + const char* GGML_VK_DISABLE_HOST_VISIBLE_VIDMEM = getenv("GGML_VK_DISABLE_HOST_VISIBLE_VIDMEM"); + device->disable_host_visible_vidmem = GGML_VK_DISABLE_HOST_VISIBLE_VIDMEM != nullptr; + + const char* GGML_VK_ALLOW_SYSMEM_FALLBACK = getenv("GGML_VK_ALLOW_SYSMEM_FALLBACK"); + device->allow_sysmem_fallback = GGML_VK_ALLOW_SYSMEM_FALLBACK != nullptr; + + const char* GGML_VK_DISABLE_GRAPH_OPTIMIZE = getenv("GGML_VK_DISABLE_GRAPH_OPTIMIZE"); + device->disable_graph_optimize = GGML_VK_DISABLE_GRAPH_OPTIMIZE != nullptr; + + bool fp16_storage = false; + bool fp16_compute = false; + bool maintenance4_support = false; + bool sm_builtins = false; + bool amd_shader_core_properties2 = false; + bool pipeline_robustness = false; + bool coopmat2_support = false; + bool coopmat2_decode_vector_support = false; + bool pipeline_executable_properties_support = false; + device->coopmat_support = false; + device->integer_dot_product = false; + device->shader_64b_indexing = false; + bool bfloat16_support = false; + bool dot2_f16_support = false; + + for (const auto& properties : ext_props) { + if (strcmp("VK_KHR_maintenance4", properties.extensionName) == 0) { + maintenance4_support = true; + } else if (strcmp("VK_KHR_16bit_storage", properties.extensionName) == 0) { + fp16_storage = true; + } else if (strcmp("VK_KHR_shader_float16_int8", properties.extensionName) == 0) { + fp16_compute = true; + } else if (strcmp("VK_NV_shader_sm_builtins", properties.extensionName) == 0) { + sm_builtins = true; + } else if (strcmp("VK_AMD_shader_core_properties2", properties.extensionName) == 0) { + amd_shader_core_properties2 = true; + } else if (strcmp("VK_EXT_pipeline_robustness", properties.extensionName) == 0) { + pipeline_robustness = true; + } else if (strcmp("VK_EXT_subgroup_size_control", properties.extensionName) == 0) { + device->subgroup_size_control = true; +#if defined(GGML_VULKAN_COOPMAT_GLSLC_SUPPORT) + } else if (strcmp("VK_KHR_cooperative_matrix", properties.extensionName) == 0 && + !getenv("GGML_VK_DISABLE_COOPMAT")) { + device->coopmat_support = true; + device->coopmat_m = 0; + device->coopmat_n = 0; + device->coopmat_k = 0; +#endif +#if defined(GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT) + } else if (strcmp("VK_NV_cooperative_matrix2", properties.extensionName) == 0 && + !getenv("GGML_VK_DISABLE_COOPMAT2")) { + coopmat2_support = true; +#endif + } else if (strcmp(VK_NV_COOPERATIVE_MATRIX_DECODE_VECTOR_EXTENSION_NAME, properties.extensionName) == 0 && + !getenv("GGML_VK_DISABLE_COOPMAT2_DECODE_VECTOR")) { + coopmat2_decode_vector_support = true; +#if defined(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT) + } else if (strcmp("VK_KHR_shader_integer_dot_product", properties.extensionName) == 0 && + !getenv("GGML_VK_DISABLE_INTEGER_DOT_PRODUCT")) { + device->integer_dot_product = true; +#endif +#if defined(GGML_VULKAN_BFLOAT16_GLSLC_SUPPORT) + } else if (strcmp("VK_KHR_shader_bfloat16", properties.extensionName) == 0 && + !getenv("GGML_VK_DISABLE_BFLOAT16")) { + bfloat16_support = true; +#endif + } else if (strcmp("VK_VALVE_shader_mixed_float_dot_product", properties.extensionName) == 0 && + !getenv("GGML_VK_DISABLE_DOT2")) { + dot2_f16_support = true; + } 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; + } else if (strcmp("VK_EXT_external_memory_host", properties.extensionName) == 0) { + device->external_memory_host = true; +#if defined(VK_EXT_shader_64bit_indexing) + } else if (strcmp("VK_EXT_shader_64bit_indexing", properties.extensionName) == 0) { + device->shader_64b_indexing = true; +#endif + } + } + + vk::PhysicalDeviceProperties2 props2; + vk::PhysicalDeviceMaintenance3Properties props3; + vk::PhysicalDeviceMaintenance4Properties props4; + vk::PhysicalDeviceSubgroupProperties subgroup_props; + vk::PhysicalDeviceDriverProperties driver_props; + vk::PhysicalDeviceShaderSMBuiltinsPropertiesNV sm_props; + vk::PhysicalDeviceShaderCoreProperties2AMD amd_shader_core_properties2_props; + vk::PhysicalDeviceVulkan11Properties vk11_props; + vk::PhysicalDeviceVulkan12Properties vk12_props; + vk::PhysicalDeviceSubgroupSizeControlPropertiesEXT subgroup_size_control_props; + vk::PhysicalDeviceShaderIntegerDotProductPropertiesKHR shader_integer_dot_product_props; + vk::PhysicalDeviceExternalMemoryHostPropertiesEXT external_memory_host_props; + + props2.pNext = &props3; + props3.pNext = &subgroup_props; + subgroup_props.pNext = &driver_props; + driver_props.pNext = &vk11_props; + vk11_props.pNext = &vk12_props; + + VkBaseOutStructure * last_struct = (VkBaseOutStructure *)&vk12_props; + + if (maintenance4_support) { + last_struct->pNext = (VkBaseOutStructure *)&props4; + last_struct = (VkBaseOutStructure *)&props4; + } + if (sm_builtins) { + last_struct->pNext = (VkBaseOutStructure *)&sm_props; + last_struct = (VkBaseOutStructure *)&sm_props; + } + if (amd_shader_core_properties2) { + last_struct->pNext = (VkBaseOutStructure *)&amd_shader_core_properties2_props; + last_struct = (VkBaseOutStructure *)&amd_shader_core_properties2_props; + } + if (device->subgroup_size_control) { + last_struct->pNext = (VkBaseOutStructure *)&subgroup_size_control_props; + last_struct = (VkBaseOutStructure *)&subgroup_size_control_props; + } + +#if defined(VK_NV_cooperative_matrix2) + vk::PhysicalDeviceCooperativeMatrix2PropertiesNV coopmat2_props; + if (coopmat2_support) { + last_struct->pNext = (VkBaseOutStructure *)&coopmat2_props; + last_struct = (VkBaseOutStructure *)&coopmat2_props; + } +#endif + + if (device->integer_dot_product) { + last_struct->pNext = (VkBaseOutStructure *)&shader_integer_dot_product_props; + last_struct = (VkBaseOutStructure *)&shader_integer_dot_product_props; + } + + if (device->external_memory_host) { + last_struct->pNext = (VkBaseOutStructure *)&external_memory_host_props; + last_struct = (VkBaseOutStructure *)&external_memory_host_props; + } + + device->physical_device.getProperties2(&props2); + device->properties = props2.properties; + device->vendor_id = device->properties.vendorID; + device->driver_id = driver_props.driverID; + + if (device->driver_id == vk::DriverId::eMoltenvk) { + // Disable external_memory_host until https://github.com/KhronosGroup/MoltenVK/pull/2622 + // is available in the Vulkan SDK. + device->external_memory_host = false; + } + + // Implementing the async backend interfaces seems broken on older Intel HW, + // see https://github.com/ggml-org/llama.cpp/issues/17302. + device->support_async = (device->vendor_id != VK_VENDOR_ID_INTEL || + std::string(device->properties.deviceName.data()).find("(DG1)") == std::string::npos) && + getenv("GGML_VK_DISABLE_ASYNC") == nullptr; + + if (!device->support_async) { + GGML_LOG_DEBUG("ggml_vulkan: WARNING: Async execution disabled on certain Intel devices.\n"); + } + + const char* GGML_VK_FORCE_MAX_ALLOCATION_SIZE = getenv("GGML_VK_FORCE_MAX_ALLOCATION_SIZE"); + + if (GGML_VK_FORCE_MAX_ALLOCATION_SIZE != nullptr) { + device->max_memory_allocation_size = std::stoull(GGML_VK_FORCE_MAX_ALLOCATION_SIZE); + } else if (maintenance4_support) { + device->max_memory_allocation_size = std::min(props3.maxMemoryAllocationSize, props4.maxBufferSize); + } else { + device->max_memory_allocation_size = props3.maxMemoryAllocationSize; + } + + const char* GGML_VK_FORCE_MAX_BUFFER_SIZE = getenv("GGML_VK_FORCE_MAX_BUFFER_SIZE"); + + if (GGML_VK_FORCE_MAX_BUFFER_SIZE != nullptr) { + device->max_buffer_size = std::stoull(GGML_VK_FORCE_MAX_BUFFER_SIZE); + } else if (maintenance4_support) { + device->max_buffer_size = props4.maxBufferSize; + } else { + device->max_buffer_size = device->max_memory_allocation_size; + } + + const char* GGML_VK_SUBALLOCATION_BLOCK_SIZE = getenv("GGML_VK_SUBALLOCATION_BLOCK_SIZE"); + + if (GGML_VK_SUBALLOCATION_BLOCK_SIZE != nullptr) { + device->suballocation_block_size = std::stoull(GGML_VK_SUBALLOCATION_BLOCK_SIZE); + } else { + // Limit batching of allocations to 1GB by default to avoid fragmentation issues + device->suballocation_block_size = 1024*1024*1024; + } + device->suballocation_block_size = std::min(device->suballocation_block_size, device->max_memory_allocation_size); + + device->subgroup_size = subgroup_props.subgroupSize; + device->subgroup_size_log2 = uint32_t(log2f(float(device->subgroup_size))); + device->uma = device->properties.deviceType == vk::PhysicalDeviceType::eIntegratedGpu; + if (sm_builtins) { + device->shader_core_count = sm_props.shaderSMCount; + } else if (amd_shader_core_properties2) { + device->shader_core_count = amd_shader_core_properties2_props.activeComputeUnitCount; + } else if (device->vendor_id == VK_VENDOR_ID_INTEL) { + device->shader_core_count = ggml_vk_intel_shader_core_count(device->physical_device); + } else { + device->shader_core_count = 0; + } + device->float_controls_rte_fp16 = vk12_props.shaderRoundingModeRTEFloat16; + + device->subgroup_basic = (vk11_props.subgroupSupportedStages & vk::ShaderStageFlagBits::eCompute) && + (vk11_props.subgroupSupportedOperations & vk::SubgroupFeatureFlagBits::eBasic); + device->subgroup_arithmetic = (vk11_props.subgroupSupportedStages & vk::ShaderStageFlagBits::eCompute) && + (vk11_props.subgroupSupportedOperations & vk::SubgroupFeatureFlagBits::eArithmetic); +#ifdef __APPLE__ + // Workaround for subgroup arithmetic failing on MoltenVK with AMD GPUs (issue 15846) + if (device->vendor_id == VK_VENDOR_ID_AMD) { + device->subgroup_arithmetic = false; + } +#endif + device->subgroup_shuffle = (vk11_props.subgroupSupportedStages & vk::ShaderStageFlagBits::eCompute) && + (vk11_props.subgroupSupportedOperations & vk::SubgroupFeatureFlagBits::eShuffle); +#ifdef __APPLE__ + if (device->vendor_id == VK_VENDOR_ID_AMD) { + device->subgroup_shuffle = false; + } +#endif + device->subgroup_clustered = (vk11_props.subgroupSupportedStages & vk::ShaderStageFlagBits::eCompute) && + (vk11_props.subgroupSupportedOperations & vk::SubgroupFeatureFlagBits::eClustered); + + device->subgroup_ballot = (vk11_props.subgroupSupportedStages & vk::ShaderStageFlagBits::eCompute) && + (vk11_props.subgroupSupportedOperations & vk::SubgroupFeatureFlagBits::eBallot); + + device->subgroup_vote = (vk11_props.subgroupSupportedStages & vk::ShaderStageFlagBits::eCompute) && + (vk11_props.subgroupSupportedOperations & vk::SubgroupFeatureFlagBits::eVote); + + // Submit at least every 100 nodes, in case there are workloads without as much matmul. + device->max_nodes_per_submit = 100; + const char* GGML_VK_MAX_NODES_PER_SUBMIT = getenv("GGML_VK_MAX_NODES_PER_SUBMIT"); + if (GGML_VK_MAX_NODES_PER_SUBMIT != nullptr) { + uint32_t max_nodes_per_submit = std::stoul(GGML_VK_MAX_NODES_PER_SUBMIT); + device->max_nodes_per_submit = std::max(max_nodes_per_submit, 1u); + } + + const bool force_disable_f16 = getenv("GGML_VK_DISABLE_F16") != nullptr; + + device->fp16 = !force_disable_f16 && fp16_storage && fp16_compute; + + if (!ggml_vk_khr_cooperative_matrix_support(device->properties, driver_props, device->architecture)) { + device->coopmat_support = false; + } + + device->integer_dot_product = device->integer_dot_product && shader_integer_dot_product_props.integerDotProduct4x8BitPackedSignedAccelerated; + + device->min_imported_host_pointer_alignment = external_memory_host_props.minImportedHostPointerAlignment; + + device->max_workgroup_size_log2 = uint32_t(log2f(float(device->properties.limits.maxComputeWorkGroupInvocations))); + + std::vector queue_family_props = device->physical_device.getQueueFamilyProperties(); + + // Try to find a non-graphics compute queue and transfer-focused queues + // Allow overriding avoiding the graphics queue because it can increase performance on RADV + const bool allow_graphics_queue = (getenv("GGML_VK_ALLOW_GRAPHICS_QUEUE") != nullptr); + const vk::QueueFlagBits graphics_flag = allow_graphics_queue ? (vk::QueueFlagBits)0 : vk::QueueFlagBits::eGraphics; + const uint32_t compute_queue_family_index = ggml_vk_find_queue_family_index(queue_family_props, vk::QueueFlagBits::eCompute, graphics_flag, -1, 1); + const uint32_t transfer_queue_family_index = ggml_vk_find_queue_family_index(queue_family_props, vk::QueueFlagBits::eTransfer, vk::QueueFlagBits::eCompute | graphics_flag, compute_queue_family_index, 1); + + const float priorities[] = { 1.0f, 1.0f }; + device->single_queue = compute_queue_family_index == transfer_queue_family_index && queue_family_props[compute_queue_family_index].queueCount == 1; + + std::vector device_queue_create_infos; + if (compute_queue_family_index != transfer_queue_family_index) { + device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), compute_queue_family_index, 1, priorities}); + device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), transfer_queue_family_index, 1, priorities + 1}); + } else if(!device->single_queue) { + device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), compute_queue_family_index, 2, priorities}); + } else { + device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), compute_queue_family_index, 1, priorities}); + } + vk::DeviceCreateInfo device_create_info{}; + std::vector device_extensions; + vk::PhysicalDeviceFeatures device_features = device->physical_device.getFeatures(); + + VkPhysicalDeviceFeatures2 device_features2; + device_features2.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_FEATURES_2; + device_features2.pNext = nullptr; + device_features2.features = (VkPhysicalDeviceFeatures)device_features; + + VkPhysicalDeviceVulkan11Features vk11_features; + vk11_features.pNext = nullptr; + vk11_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_VULKAN_1_1_FEATURES; + device_features2.pNext = &vk11_features; + + VkPhysicalDeviceVulkan12Features vk12_features; + vk12_features.pNext = nullptr; + vk12_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_VULKAN_1_2_FEATURES; + vk11_features.pNext = &vk12_features; + + last_struct = (VkBaseOutStructure *)&vk12_features; + + VkPhysicalDevicePipelineRobustnessFeaturesEXT pl_robustness_features; + pl_robustness_features.pNext = nullptr; + pl_robustness_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_PIPELINE_ROBUSTNESS_FEATURES_EXT; + pl_robustness_features.pipelineRobustness = VK_FALSE; + + if (pipeline_robustness) { + last_struct->pNext = (VkBaseOutStructure *)&pl_robustness_features; + last_struct = (VkBaseOutStructure *)&pl_robustness_features; + 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; + subgroup_size_control_features.computeFullSubgroups = false; + subgroup_size_control_features.subgroupSizeControl = false; + + if (device->subgroup_size_control) { + last_struct->pNext = (VkBaseOutStructure *)&subgroup_size_control_features; + last_struct = (VkBaseOutStructure *)&subgroup_size_control_features; + } + +#if defined(VK_KHR_cooperative_matrix) + VkPhysicalDeviceCooperativeMatrixFeaturesKHR coopmat_features; + coopmat_features.pNext = nullptr; + coopmat_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_COOPERATIVE_MATRIX_FEATURES_KHR; + coopmat_features.cooperativeMatrix = VK_FALSE; + + if (device->coopmat_support) { + last_struct->pNext = (VkBaseOutStructure *)&coopmat_features; + last_struct = (VkBaseOutStructure *)&coopmat_features; + } +#endif + +#if defined(VK_NV_cooperative_matrix2) + VkPhysicalDeviceCooperativeMatrix2FeaturesNV coopmat2_features {}; + coopmat2_features.pNext = nullptr; + coopmat2_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_COOPERATIVE_MATRIX_2_FEATURES_NV; + if (coopmat2_support) { + last_struct->pNext = (VkBaseOutStructure *)&coopmat2_features; + last_struct = (VkBaseOutStructure *)&coopmat2_features; + device_extensions.push_back("VK_NV_cooperative_matrix2"); + } +#endif + + VkPhysicalDeviceCooperativeMatrixDecodeVectorFeaturesNV coopmat2_decode_vector_features {}; + coopmat2_decode_vector_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_COOPERATIVE_MATRIX_DECODE_VECTOR_FEATURES_NV; + if (coopmat2_decode_vector_support) { + last_struct->pNext = (VkBaseOutStructure *)&coopmat2_decode_vector_features; + last_struct = (VkBaseOutStructure *)&coopmat2_decode_vector_features; + device_extensions.push_back(VK_NV_COOPERATIVE_MATRIX_DECODE_VECTOR_EXTENSION_NAME); + } + +#if defined(VK_KHR_shader_bfloat16) + VkPhysicalDeviceShaderBfloat16FeaturesKHR bfloat16_features {}; + bfloat16_features.pNext = nullptr; + bfloat16_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_SHADER_BFLOAT16_FEATURES_KHR; + if (bfloat16_support) { + last_struct->pNext = (VkBaseOutStructure *)&bfloat16_features; + last_struct = (VkBaseOutStructure *)&bfloat16_features; + device_extensions.push_back("VK_KHR_shader_bfloat16"); + } +#endif + + VkPhysicalDeviceMaintenance4Features maint4_features {}; + maint4_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_MAINTENANCE_4_FEATURES; + if (maintenance4_support) { + last_struct->pNext = (VkBaseOutStructure *)&maint4_features; + last_struct = (VkBaseOutStructure *)&maint4_features; + device_extensions.push_back("VK_KHR_maintenance4"); + } + + VkPhysicalDeviceShaderIntegerDotProductFeaturesKHR shader_integer_dot_product_features {}; + shader_integer_dot_product_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_SHADER_INTEGER_DOT_PRODUCT_FEATURES_KHR; + if (device->integer_dot_product) { + last_struct->pNext = (VkBaseOutStructure *)&shader_integer_dot_product_features; + last_struct = (VkBaseOutStructure *)&shader_integer_dot_product_features; + device_extensions.push_back("VK_KHR_shader_integer_dot_product"); + } + + VkPhysicalDeviceShaderMixedFloatDotProductFeaturesVALVE dot2_features {}; + dot2_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_SHADER_MIXED_FLOAT_DOT_PRODUCT_FEATURES_VALVE; + if (dot2_f16_support) { + last_struct->pNext = (VkBaseOutStructure *)&dot2_features; + last_struct = (VkBaseOutStructure *)&dot2_features; + device_extensions.push_back("VK_VALVE_shader_mixed_float_dot_product"); + } + + VkPhysicalDevicePipelineExecutablePropertiesFeaturesKHR pep_features {}; + pep_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_PIPELINE_EXECUTABLE_PROPERTIES_FEATURES_KHR; + if (pipeline_executable_properties_support) { + last_struct->pNext = (VkBaseOutStructure *)&pep_features; + last_struct = (VkBaseOutStructure *)&pep_features; + device_extensions.push_back("VK_KHR_pipeline_executable_properties"); + } + + if (device->external_memory_host) { + device_extensions.push_back("VK_EXT_external_memory_host"); + } + +#if defined(VK_EXT_shader_64bit_indexing) + VkPhysicalDeviceShader64BitIndexingFeaturesEXT shader_64bit_indexing_features {}; + shader_64bit_indexing_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_SHADER_64_BIT_INDEXING_FEATURES_EXT; + if (device->shader_64b_indexing) { + last_struct->pNext = (VkBaseOutStructure *)&shader_64bit_indexing_features; + last_struct = (VkBaseOutStructure *)&shader_64bit_indexing_features; + device_extensions.push_back("VK_EXT_shader_64bit_indexing"); + } +#endif + + vkGetPhysicalDeviceFeatures2(device->physical_device, &device_features2); + + device->pipeline_executable_properties_support = pipeline_executable_properties_support; + + device->fp16 = device->fp16 && vk12_features.shaderFloat16; + +#if defined(VK_KHR_shader_bfloat16) + device->bf16 = bfloat16_support && bfloat16_features.shaderBFloat16Type; +#else + device->bf16 = false; +#endif + + device->dot2_f16 = dot2_f16_support && dot2_features.shaderMixedFloatDotProductFloat16AccFloat32; + + device->pipeline_robustness = pl_robustness_features.pipelineRobustness; + + device->multi_add = vk12_props.shaderRoundingModeRTEFloat16 && + device->properties.limits.maxPushConstantsSize >= sizeof(vk_op_multi_add_push_constants) && + getenv("GGML_VK_DISABLE_MULTI_ADD") == nullptr; + + device->shader_int64 = device_features2.features.shaderInt64; + device->buffer_device_address = vk12_features.bufferDeviceAddress; + device->vulkan_memory_model = vk12_features.vulkanMemoryModel; + + if (device->subgroup_size_control) { + device->subgroup_min_size = subgroup_size_control_props.minSubgroupSize; + device->subgroup_max_size = subgroup_size_control_props.maxSubgroupSize; + device_extensions.push_back("VK_EXT_subgroup_size_control"); + } + + device->subgroup_size_control = device->subgroup_size_control && + (subgroup_size_control_props.requiredSubgroupSizeStages & vk::ShaderStageFlagBits::eCompute) && + subgroup_size_control_features.subgroupSizeControl; + + device->subgroup_require_full_support = subgroup_size_control_features.computeFullSubgroups; + +#if defined(VK_KHR_cooperative_matrix) + device->coopmat_support = device->coopmat_support && coopmat_features.cooperativeMatrix; + device->coopmat1_fa_support = device->coopmat_support && device->subgroup_require_full_support; +#endif + + if (coopmat2_support) { +#if defined(VK_NV_cooperative_matrix2) && defined(GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT) + if (coopmat2_features.cooperativeMatrixWorkgroupScope && + coopmat2_features.cooperativeMatrixFlexibleDimensions && + coopmat2_features.cooperativeMatrixReductions && + coopmat2_features.cooperativeMatrixConversions && + coopmat2_features.cooperativeMatrixPerElementOperations && + coopmat2_features.cooperativeMatrixTensorAddressing && + coopmat2_features.cooperativeMatrixBlockLoads && + vk12_features.bufferDeviceAddress) { + + std::vector flexible_dimensions; + uint32_t count = 0; + + PFN_vkGetPhysicalDeviceCooperativeMatrixFlexibleDimensionsPropertiesNV + _vkGetPhysicalDeviceCooperativeMatrixFlexibleDimensionsPropertiesNV = + (PFN_vkGetPhysicalDeviceCooperativeMatrixFlexibleDimensionsPropertiesNV) + vk_instance.instance.getProcAddr("vkGetPhysicalDeviceCooperativeMatrixFlexibleDimensionsPropertiesNV"); + + _vkGetPhysicalDeviceCooperativeMatrixFlexibleDimensionsPropertiesNV(device->physical_device, &count, nullptr); + + VkCooperativeMatrixFlexibleDimensionsPropertiesNV empty_prop {}; + empty_prop.sType = VK_STRUCTURE_TYPE_COOPERATIVE_MATRIX_FLEXIBLE_DIMENSIONS_PROPERTIES_NV; + flexible_dimensions.resize(count, empty_prop); + + _vkGetPhysicalDeviceCooperativeMatrixFlexibleDimensionsPropertiesNV(device->physical_device, &count, flexible_dimensions.data()); + + bool found_fp16_128 = false, + found_fp16_256 = false, + found_fp32_128 = false, + found_fp32_256 = false; + bool found_bf16_128 = false, + found_bf16_256 = false; + // need to support fp16*fp16 with fp16/fp32 accumulator, for workgroupsize 128 + // with 32x16x16 and 256 with 32x32x16. + for (auto &prop : flexible_dimensions) { + if (prop.saturatingAccumulation == VK_FALSE && + prop.scope == VK_SCOPE_WORKGROUP_KHR) { + + if (prop.AType == VK_COMPONENT_TYPE_FLOAT16_KHR && + prop.BType == VK_COMPONENT_TYPE_FLOAT16_KHR) { + + if (prop.workgroupInvocations == 128 && + prop.MGranularity <= 32 && + prop.NGranularity <= 16 && + prop.KGranularity <= 16) { + if (prop.CType == VK_COMPONENT_TYPE_FLOAT16_KHR && + prop.ResultType == VK_COMPONENT_TYPE_FLOAT16_KHR) { + found_fp16_128 = true; + } + if (prop.CType == VK_COMPONENT_TYPE_FLOAT32_KHR && + prop.ResultType == VK_COMPONENT_TYPE_FLOAT32_KHR) { + found_fp32_128 = true; + } + } + if (prop.workgroupInvocations == 256 && + prop.MGranularity <= 32 && + prop.NGranularity <= 32 && + prop.KGranularity <= 16) { + if (prop.CType == VK_COMPONENT_TYPE_FLOAT16_KHR && + prop.ResultType == VK_COMPONENT_TYPE_FLOAT16_KHR) { + found_fp16_256 = true; + } + if (prop.CType == VK_COMPONENT_TYPE_FLOAT32_KHR && + prop.ResultType == VK_COMPONENT_TYPE_FLOAT32_KHR) { + found_fp32_256 = true; + } + } + } + +#if defined(VK_KHR_shader_bfloat16) && defined(GGML_VULKAN_BFLOAT16_GLSLC_SUPPORT) + if (prop.AType == VK_COMPONENT_TYPE_BFLOAT16_KHR && + prop.BType == VK_COMPONENT_TYPE_BFLOAT16_KHR && + prop.CType == VK_COMPONENT_TYPE_FLOAT32_KHR && + prop.ResultType == VK_COMPONENT_TYPE_FLOAT32_KHR) { + + if (prop.workgroupInvocations == 128 && + prop.MGranularity <= 32 && + prop.NGranularity <= 16 && + prop.KGranularity <= 16) { + found_bf16_128 = true; + } + if (prop.workgroupInvocations == 256 && + prop.MGranularity <= 32 && + prop.NGranularity <= 32 && + prop.KGranularity <= 16) { + found_bf16_256 = true; + } + } +#endif + } + } + if (found_fp16_128 && found_fp16_256 && + found_fp32_128 && found_fp32_256 && + coopmat2_props.cooperativeMatrixFlexibleDimensionsMaxDimension >= 512) { + device->coopmat2 = true; + device->coopmat2_bf16_support = found_bf16_128 && found_bf16_256; + device->coopmat2_decode_vector = coopmat2_decode_vector_support && coopmat2_decode_vector_features.cooperativeMatrixDecodeVector; + } + } +#endif + } + + if (!vk11_features.storageBuffer16BitAccess) { + std::cerr << "ggml_vulkan: device " << GGML_VK_NAME << idx << " does not support 16-bit storage." << std::endl; + throw std::runtime_error("Unsupported device"); + } + + device_extensions.push_back("VK_KHR_16bit_storage"); + +#ifdef GGML_VULKAN_VALIDATE + device_extensions.push_back("VK_KHR_shader_non_semantic_info"); +#endif + + if (device->fp16) { + device_extensions.push_back("VK_KHR_shader_float16_int8"); + } + +#if defined(VK_KHR_cooperative_matrix) + if (device->coopmat_support) { + // Query supported shapes + std::vector cm_props; + + PFN_vkGetPhysicalDeviceCooperativeMatrixPropertiesKHR pfn_vkGetPhysicalDeviceCooperativeMatrixPropertiesKHR = + (PFN_vkGetPhysicalDeviceCooperativeMatrixPropertiesKHR)vkGetInstanceProcAddr(vk_instance.instance, "vkGetPhysicalDeviceCooperativeMatrixPropertiesKHR"); + + uint32_t cm_props_num; + + pfn_vkGetPhysicalDeviceCooperativeMatrixPropertiesKHR(device->physical_device, &cm_props_num, nullptr); + + cm_props.resize(cm_props_num); + + for (auto& prop : cm_props) { + prop.sType = VK_STRUCTURE_TYPE_COOPERATIVE_MATRIX_PROPERTIES_KHR; + } + + pfn_vkGetPhysicalDeviceCooperativeMatrixPropertiesKHR(device->physical_device, &cm_props_num, cm_props.data()); + + VK_LOG_DEBUG("ggml_vulkan: Cooperative Matrix Shapes: " << cm_props.size()); + + for (auto& prop : cm_props) { + VK_LOG_DEBUG("ggml_vulkan: M: " << prop.MSize << " N: " << prop.NSize << " K: " << prop.KSize << " A: " << vk::to_string((vk::ComponentTypeKHR)prop.AType) << " B: " << vk::to_string((vk::ComponentTypeKHR)prop.BType) << " C: " << vk::to_string((vk::ComponentTypeKHR)prop.CType) << " Result: " << vk::to_string((vk::ComponentTypeKHR)prop.ResultType) << " saturatingAccumulation: " << prop.saturatingAccumulation << " scope: " << vk::to_string((vk::ScopeKHR)prop.scope)); + + if ((vk::ComponentTypeKHR)prop.AType == vk::ComponentTypeKHR::eFloat16 && + (vk::ComponentTypeKHR)prop.BType == vk::ComponentTypeKHR::eFloat16 && + (vk::ScopeKHR)prop.scope == vk::ScopeKHR::eSubgroup + ) { + if ((vk::ComponentTypeKHR)prop.CType == vk::ComponentTypeKHR::eFloat32 && + (vk::ComponentTypeKHR)prop.ResultType == vk::ComponentTypeKHR::eFloat32) { + // coopmat sizes not set yet + if (device->coopmat_m == 0) { + device->coopmat_acc_f32_support = true; + device->coopmat_m = prop.MSize; + device->coopmat_n = prop.NSize; + device->coopmat_k = prop.KSize; + } else if (device->coopmat_m == prop.MSize && device->coopmat_n == prop.NSize && device->coopmat_k == prop.KSize) { + // Only enable if shape is identical + device->coopmat_acc_f32_support = true; + } + if (prop.MSize == 16 && prop.NSize == 16 && prop.KSize == 16) { + device->coopmat_support_16x16x16_f32acc = true; + } + } else if ((vk::ComponentTypeKHR)prop.CType == vk::ComponentTypeKHR::eFloat16 && + (vk::ComponentTypeKHR)prop.ResultType == vk::ComponentTypeKHR::eFloat16) { + // coopmat sizes not set yet + if (device->coopmat_m == 0) { + device->coopmat_acc_f16_support = true; + device->coopmat_m = prop.MSize; + device->coopmat_n = prop.NSize; + device->coopmat_k = prop.KSize; + } else if (device->coopmat_m == prop.MSize && device->coopmat_n == prop.NSize && device->coopmat_k == prop.KSize) { + // Only enable if shape is identical + device->coopmat_acc_f16_support = true; + } + if (prop.MSize == 16 && prop.NSize == 16 && prop.KSize == 16) { + device->coopmat_support_16x16x16_f16acc = true; + } + } + } else if ((vk::ComponentTypeKHR)prop.AType == vk::ComponentTypeKHR::eSint8 && + (vk::ComponentTypeKHR)prop.BType == vk::ComponentTypeKHR::eSint8 && + (vk::ComponentTypeKHR)prop.CType == vk::ComponentTypeKHR::eSint32 && + (vk::ComponentTypeKHR)prop.ResultType == vk::ComponentTypeKHR::eSint32 && + (vk::ScopeKHR)prop.scope == vk::ScopeKHR::eSubgroup && + device->coopmat_int_m == 0 + ) { + device->coopmat_int_support = true; + device->coopmat_int_m = prop.MSize; + device->coopmat_int_n = prop.NSize; + device->coopmat_int_k = prop.KSize; + } +#if defined(VK_KHR_shader_bfloat16) && defined(GGML_VULKAN_BFLOAT16_GLSLC_SUPPORT) + if (prop.AType == VK_COMPONENT_TYPE_BFLOAT16_KHR && + prop.BType == VK_COMPONENT_TYPE_BFLOAT16_KHR && + prop.CType == VK_COMPONENT_TYPE_FLOAT32_KHR && + prop.ResultType == VK_COMPONENT_TYPE_FLOAT32_KHR && + (vk::ScopeKHR)prop.scope == vk::ScopeKHR::eSubgroup + ) { + // coopmat sizes not set yet + if (device->coopmat_m == 0) { + device->coopmat_bf16_support = true; + device->coopmat_m = prop.MSize; + device->coopmat_n = prop.NSize; + device->coopmat_k = prop.KSize; + } else if (device->coopmat_m == prop.MSize && device->coopmat_n == prop.NSize && device->coopmat_k == prop.KSize) { + // Only enable if shape is identical + device->coopmat_bf16_support = true; + } + } +#endif + } + + if (device->coopmat_m == 0 || !device->coopmat_acc_f32_support) { + // No suitable matmul mode found + GGML_LOG_DEBUG("ggml_vulkan: WARNING: No suitable matrix core mode found. Disabling matrix cores.\n"); + device->coopmat_support = false; + } + if (getenv("GGML_VK_DISABLE_BFLOAT16")) { + device->coopmat_bf16_support = false; + } + } + + if (device->coopmat_support) { + device_extensions.push_back("VK_KHR_cooperative_matrix"); + } +#if defined(VK_KHR_shader_bfloat16) + if (device->coopmat_bf16_support) { + device_extensions.push_back("VK_KHR_shader_bfloat16"); + } +#endif +#endif + device->name = GGML_VK_NAME + std::to_string(idx); + + device_create_info + .setFlags(vk::DeviceCreateFlags()) + .setQueueCreateInfos(device_queue_create_infos) + .setPEnabledExtensionNames(device_extensions); + device_create_info.setPNext(&device_features2); + device->device = device->physical_device.createDevice(device_create_info); + + // Queues + ggml_vk_create_queue(device, device->compute_queue, compute_queue_family_index, 0, { vk::PipelineStageFlagBits::eComputeShader | vk::PipelineStageFlagBits::eTransfer }, false); + + // Shaders + // Disable matmul tile sizes early if performance low or not supported + for (uint32_t i = 0; i < GGML_TYPE_COUNT; ++i) { + switch (device->vendor_id) { +#ifndef GGML_VULKAN_RUN_TESTS + case VK_VENDOR_ID_AMD: + device->mul_mat_l[i] = device->coopmat_support && device->driver_id != vk::DriverId::eAmdProprietary; + device->mul_mat_m[i] = true; + device->mul_mat_s[i] = true; + device->mul_mat_id_l[i] = false; + device->mul_mat_id_m[i] = true; + device->mul_mat_id_s[i] = true; + break; + case VK_VENDOR_ID_INTEL: { + // Current Windows driver does not expose BF16 support. + // We only want to use l_warptile if coopmat is available + const bool use_l_warptile = (i == GGML_TYPE_BF16) ? (device->coopmat_bf16_support && device->coopmat_support) : device->coopmat_support; + device->mul_mat_l[i] = use_l_warptile; + device->mul_mat_id_l[i] = use_l_warptile; + device->mul_mat_m[i] = true; + device->mul_mat_s[i] = true; + device->mul_mat_id_m[i] = true; + device->mul_mat_id_s[i] = true; + break; + } + case VK_VENDOR_ID_APPLE: + device->mul_mat_l[i] = false; + device->mul_mat_m[i] = true; + device->mul_mat_s[i] = false; + device->mul_mat_id_l[i] = false; + device->mul_mat_id_m[i] = true; + device->mul_mat_id_s[i] = false; + break; + case VK_VENDOR_ID_QUALCOMM: + device->mul_mat_l[i] = false; + device->mul_mat_m[i] = true; + device->mul_mat_s[i] = true; + device->mul_mat_id_l[i] = false; + device->mul_mat_id_m[i] = true; + device->mul_mat_id_s[i] = true; + break; +#endif + default: + device->mul_mat_l[i] = true; + device->mul_mat_m[i] = true; + device->mul_mat_s[i] = true; + device->mul_mat_id_l[i] = true; + device->mul_mat_id_m[i] = true; + device->mul_mat_id_s[i] = true; + break; + } + +#if VK_HEADER_VERSION >= 287 + // Honeykrisp driver for Asahi Linux doesn't report VK_VENDOR_ID_APPLE. + // Check for Honeykrisp driver and force same configuration as the VK_VENDOR_ID_APPLE case. + if (device->driver_id == vk::DriverId::eMesaHoneykrisp) { + device->mul_mat_l[i] = false; + device->mul_mat_m[i] = true; + device->mul_mat_s[i] = false; + device->mul_mat_id_l[i] = false; + device->mul_mat_id_m[i] = true; + device->mul_mat_id_s[i] = false; + } +#endif + + device->mul_mat_l_int[i] = device->mul_mat_l[i]; + device->mul_mat_m_int[i] = device->mul_mat_m[i]; + device->mul_mat_s_int[i] = device->mul_mat_s[i]; + device->mul_mat_id_l_int[i] = device->mul_mat_id_l[i]; + device->mul_mat_id_m_int[i] = device->mul_mat_id_m[i]; + device->mul_mat_id_s_int[i] = device->mul_mat_id_s[i]; + } + + + std::vector dsl_binding; + std::vector dsl_binding_flags; + for (uint32_t i = 0; i < MAX_PARAMETER_COUNT; i++) { + dsl_binding.push_back({i, vk::DescriptorType::eStorageBuffer, 1, vk::ShaderStageFlagBits::eCompute}); + dsl_binding_flags.push_back({}); + } + + vk::DescriptorSetLayoutBindingFlagsCreateInfo dslbfci = { dsl_binding_flags }; + + vk::DescriptorSetLayoutCreateInfo descriptor_set_layout_create_info( + {}, + dsl_binding); + descriptor_set_layout_create_info.setPNext(&dslbfci); + device->dsl = device->device.createDescriptorSetLayout(descriptor_set_layout_create_info); + + ggml_vk_load_shaders(device); + + // Prefer a dedicated transfer queue on AMD dGPUs (non-GCN) when graphics queue use is disabled. + const bool prefers_transfer_queue = + device->vendor_id == VK_VENDOR_ID_AMD && + device->architecture != AMD_GCN && + !device->uma && + !allow_graphics_queue; + + if (!device->single_queue) { + const uint32_t transfer_queue_index = compute_queue_family_index == transfer_queue_family_index ? 1 : 0; + ggml_vk_create_queue(device, device->transfer_queue, transfer_queue_family_index, transfer_queue_index, { vk::PipelineStageFlagBits::eTransfer }, true); + + device->async_use_transfer_queue = prefers_transfer_queue || (getenv("GGML_VK_ASYNC_USE_TRANSFER_QUEUE") != nullptr); + } else { + // TODO: Use pointer or reference to avoid copy + device->transfer_queue.copyFrom(device->compute_queue); + device->transfer_queue.cmd_pool.init(device, &device->transfer_queue); + + device->async_use_transfer_queue = false; + } + + device->buffer_type = { + /* .iface = */ ggml_backend_vk_buffer_type_interface, + /* .device = */ ggml_backend_reg_dev_get(ggml_backend_vk_reg(), idx), + /* .context = */ new ggml_backend_vk_buffer_type_context{ device->name, device }, + }; + + device->fence = device->device.createFence({}); + + device->idx = idx; + + device->disable_fusion = getenv("GGML_VK_DISABLE_FUSION") != nullptr; + + device->add_rms_fusion = !device->disable_fusion && + device->subgroup_arithmetic && + device->vendor_id != VK_VENDOR_ID_INTEL; + device->partials_binding_alignment = + std::max(4u, (uint32_t)device->properties.limits.minStorageBufferOffsetAlignment); + + device->mmvq_mode = 0; + if (getenv("GGML_VK_DISABLE_MMVQ")) { + device->mmvq_mode = -1; + } else if (getenv("GGML_VK_FORCE_MMVQ")) { + device->mmvq_mode = 1; + } + + return device; + } + + return vk_instance.devices[idx]; +} + +static void ggml_vk_print_gpu_info(size_t idx) { + GGML_ASSERT(idx < vk_instance.device_indices.size()); + size_t dev_num = vk_instance.device_indices[idx]; + VK_LOG_DEBUG("ggml_vk_print_gpu_info(" << dev_num << ")"); + GGML_ASSERT(vk_instance_initialized); + + std::vector devices = vk_instance.instance.enumeratePhysicalDevices(); + + if (dev_num >= devices.size()) { + std::cerr << "ggml_vulkan: Device with index " << dev_num << " does not exist." << std::endl; + throw std::runtime_error("Device not found"); + } + + vk::PhysicalDevice physical_device = devices[dev_num]; + std::vector ext_props = physical_device.enumerateDeviceExtensionProperties(); + + bool fp16_storage = false; + bool fp16_compute = false; + bool coopmat_support = false; + bool coopmat2_support = false; + bool coopmat2_decode_vector_support = false; + bool integer_dot_product = false; + bool bfloat16_support = false; + bool dot2_f16_support = false; + + for (auto properties : ext_props) { + if (strcmp("VK_KHR_16bit_storage", properties.extensionName) == 0) { + fp16_storage = true; + } else if (strcmp("VK_KHR_shader_float16_int8", properties.extensionName) == 0) { + fp16_compute = true; +#if defined(GGML_VULKAN_COOPMAT_GLSLC_SUPPORT) + } else if (strcmp("VK_KHR_cooperative_matrix", properties.extensionName) == 0 && + !getenv("GGML_VK_DISABLE_COOPMAT")) { + coopmat_support = true; +#endif +#if defined(GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT) + } else if (strcmp("VK_NV_cooperative_matrix2", properties.extensionName) == 0 && + !getenv("GGML_VK_DISABLE_COOPMAT2")) { + coopmat2_support = true; +#endif + } else if (strcmp(VK_NV_COOPERATIVE_MATRIX_DECODE_VECTOR_EXTENSION_NAME, properties.extensionName) == 0 && + !getenv("GGML_VK_DISABLE_COOPMAT2_DECODE_VECTOR")) { + coopmat2_decode_vector_support = true; +#if defined(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT) + } else if (strcmp("VK_KHR_shader_integer_dot_product", properties.extensionName) == 0 && + !getenv("GGML_VK_DISABLE_INTEGER_DOT_PRODUCT")) { + integer_dot_product = true; +#endif +#if defined(GGML_VULKAN_BFLOAT16_GLSLC_SUPPORT) + } else if (strcmp("VK_KHR_shader_bfloat16", properties.extensionName) == 0 && + !getenv("GGML_VK_DISABLE_BFLOAT16")) { + bfloat16_support = true; +#endif + } else if (strcmp("VK_VALVE_shader_mixed_float_dot_product", properties.extensionName) == 0 && + !getenv("GGML_VK_DISABLE_DOT2")) { + dot2_f16_support = true; + } + } + + const vk_device_architecture device_architecture = get_device_architecture(physical_device); + + const char* GGML_VK_DISABLE_F16 = getenv("GGML_VK_DISABLE_F16"); + bool force_disable_f16 = GGML_VK_DISABLE_F16 != nullptr; + + bool fp16 = !force_disable_f16 && fp16_storage && fp16_compute; + + vk::PhysicalDeviceProperties2 props2; + vk::PhysicalDeviceMaintenance3Properties props3; + vk::PhysicalDeviceSubgroupProperties subgroup_props; + vk::PhysicalDeviceDriverProperties driver_props; + vk::PhysicalDeviceShaderIntegerDotProductPropertiesKHR shader_integer_dot_product_props; + props2.pNext = &props3; + props3.pNext = &subgroup_props; + subgroup_props.pNext = &driver_props; + + // Pointer to the last chain element + VkBaseOutStructure * last_struct = (VkBaseOutStructure *)&driver_props; + + if (integer_dot_product) { + last_struct->pNext = (VkBaseOutStructure *)&shader_integer_dot_product_props; + last_struct = (VkBaseOutStructure *)&shader_integer_dot_product_props; + } + + physical_device.getProperties2(&props2); + + VkPhysicalDeviceFeatures2 device_features2; + device_features2.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_FEATURES_2; + device_features2.pNext = nullptr; + + VkPhysicalDeviceVulkan11Features vk11_features; + vk11_features.pNext = nullptr; + vk11_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_VULKAN_1_1_FEATURES; + device_features2.pNext = &vk11_features; + + VkPhysicalDeviceVulkan12Features vk12_features; + vk12_features.pNext = nullptr; + vk12_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_VULKAN_1_2_FEATURES; + vk11_features.pNext = &vk12_features; + + // Pointer to the last chain element + last_struct = (VkBaseOutStructure *)&vk12_features; + +#if defined(GGML_VULKAN_COOPMAT_GLSLC_SUPPORT) + VkPhysicalDeviceCooperativeMatrixFeaturesKHR coopmat_features; + coopmat_features.pNext = nullptr; + coopmat_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_COOPERATIVE_MATRIX_FEATURES_KHR; + coopmat_features.cooperativeMatrix = VK_FALSE; + + if (coopmat_support) { + last_struct->pNext = (VkBaseOutStructure *)&coopmat_features; + last_struct = (VkBaseOutStructure *)&coopmat_features; + } +#endif + + VkPhysicalDeviceShaderIntegerDotProductFeaturesKHR shader_integer_dot_product_features {}; + shader_integer_dot_product_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_SHADER_INTEGER_DOT_PRODUCT_FEATURES_KHR; + if (integer_dot_product) { + last_struct->pNext = (VkBaseOutStructure *)&shader_integer_dot_product_features; + last_struct = (VkBaseOutStructure *)&shader_integer_dot_product_features; + } + +#if defined(VK_KHR_shader_bfloat16) + VkPhysicalDeviceShaderBfloat16FeaturesKHR bfloat16_features {}; + bfloat16_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_SHADER_BFLOAT16_FEATURES_KHR; + if (bfloat16_support) { + last_struct->pNext = (VkBaseOutStructure *)&bfloat16_features; + last_struct = (VkBaseOutStructure *)&bfloat16_features; + } +#endif + +#if defined(VK_NV_cooperative_matrix2) + VkPhysicalDeviceCooperativeMatrix2FeaturesNV coopmat2_features {}; + coopmat2_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_COOPERATIVE_MATRIX_2_FEATURES_NV; + if (coopmat2_support) { + last_struct->pNext = (VkBaseOutStructure *)&coopmat2_features; + last_struct = (VkBaseOutStructure *)&coopmat2_features; + } +#endif + + VkPhysicalDeviceCooperativeMatrixDecodeVectorFeaturesNV coopmat2_decode_vector_features {}; + coopmat2_decode_vector_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_COOPERATIVE_MATRIX_DECODE_VECTOR_FEATURES_NV; + if (coopmat2_decode_vector_support) { + last_struct->pNext = (VkBaseOutStructure *)&coopmat2_decode_vector_features; + last_struct = (VkBaseOutStructure *)&coopmat2_decode_vector_features; + } + + VkPhysicalDeviceShaderMixedFloatDotProductFeaturesVALVE dot2_features {}; + dot2_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_SHADER_MIXED_FLOAT_DOT_PRODUCT_FEATURES_VALVE; + if (dot2_f16_support) { + last_struct->pNext = (VkBaseOutStructure *)&dot2_features; + last_struct = (VkBaseOutStructure *)&dot2_features; + } + + vkGetPhysicalDeviceFeatures2(physical_device, &device_features2); + + fp16 = fp16 && vk12_features.shaderFloat16; + +#if defined(VK_KHR_shader_bfloat16) + bool bf16 = bfloat16_support && bfloat16_features.shaderBFloat16Type; +#else + bool bf16 = false; +#endif + + uint32_t default_subgroup_size = get_subgroup_size("", device_architecture); + const size_t subgroup_size = (default_subgroup_size != 0) ? default_subgroup_size : subgroup_props.subgroupSize; + const bool uma = props2.properties.deviceType == vk::PhysicalDeviceType::eIntegratedGpu; + + integer_dot_product = integer_dot_product + && shader_integer_dot_product_props.integerDotProduct4x8BitPackedSignedAccelerated + && shader_integer_dot_product_features.shaderIntegerDotProduct; + + coopmat_support = coopmat_support +#if defined(GGML_VULKAN_COOPMAT_GLSLC_SUPPORT) + && coopmat_features.cooperativeMatrix +#endif + && ggml_vk_khr_cooperative_matrix_support(props2.properties, driver_props, device_architecture); + +#if defined(VK_NV_cooperative_matrix2) && defined(GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT) + coopmat2_support = coopmat2_support && + coopmat2_features.cooperativeMatrixWorkgroupScope && + coopmat2_features.cooperativeMatrixFlexibleDimensions && + coopmat2_features.cooperativeMatrixReductions && + coopmat2_features.cooperativeMatrixConversions && + coopmat2_features.cooperativeMatrixPerElementOperations && + coopmat2_features.cooperativeMatrixTensorAddressing && + coopmat2_features.cooperativeMatrixBlockLoads; +#else + coopmat2_support = false; +#endif + + coopmat2_decode_vector_support = coopmat2_decode_vector_support && coopmat2_decode_vector_features.cooperativeMatrixDecodeVector; +#if !defined(GGML_VULKAN_COOPMAT2_DECODE_VECTOR_GLSLC_SUPPORT) + coopmat2_decode_vector_support = false; +#endif + + std::string matrix_cores = coopmat2_support ? (coopmat2_decode_vector_support ? "NV_coopmat2v" : "NV_coopmat2") + : coopmat_support ? "KHR_coopmat" + : "none"; + + bool dot2_f16 = dot2_f16_support && dot2_features.shaderMixedFloatDotProductFloat16AccFloat32; + const char *fp16_str = fp16 ? (dot2_f16 ? "dot2" : "1") : "0"; + + std::string device_name = props2.properties.deviceName.data(); + GGML_LOG_DEBUG("ggml_vulkan: %zu = %s (%s) | uma: %d | fp16: %s | bf16: %d | warp size: %zu | shared memory: %d | int dot: %d | matrix cores: %s\n", + idx, device_name.c_str(), driver_props.driverName.data(), uma, fp16_str, bf16, subgroup_size, + props2.properties.limits.maxComputeSharedMemorySize, integer_dot_product, matrix_cores.c_str()); + + if (props2.properties.deviceType == vk::PhysicalDeviceType::eCpu) { + GGML_LOG_DEBUG("ggml_vulkan: Warning: Device type is CPU. This is probably not the device you want.\n"); + } +} + +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); + +static DispatchLoaderDynamic ggml_vk_default_dispatcher_instance; +DispatchLoaderDynamic & ggml_vk_default_dispatcher() { + return ggml_vk_default_dispatcher_instance; +} + +static void ggml_vk_instance_init() { + if (vk_instance_initialized) { + return; + } + VK_LOG_DEBUG("ggml_vk_instance_init()"); + + // See https://github.com/KhronosGroup/Vulkan-Hpp?tab=readme-ov-file#extensions--per-device-function-pointers- + ggml_vk_default_dispatcher_instance.init(vkGetInstanceProcAddr); + + uint32_t api_version = vk::enumerateInstanceVersion(); + + if (api_version < VK_API_VERSION_1_2) { + std::cerr << "ggml_vulkan: Error: Vulkan 1.2 required." << std::endl; + throw vk::SystemError(vk::Result::eErrorFeatureNotPresent, "Vulkan 1.2 required"); + } + + vk::ApplicationInfo app_info{ "ggml-vulkan", 1, nullptr, 0, api_version }; + + const std::vector instance_extensions = vk::enumerateInstanceExtensionProperties(); + 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 (layer_settings) { + layers.push_back("VK_LAYER_KHRONOS_validation"); + } + std::vector extensions; + if (layer_settings) { + extensions.push_back("VK_EXT_layer_settings"); + } +#ifdef __APPLE__ + if (portability_enumeration_ext) { + extensions.push_back("VK_KHR_portability_enumeration"); + } +#endif + if (debug_utils_ext) { + extensions.push_back("VK_EXT_debug_utils"); + } + 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 + + vk_instance.instance = vk::createInstance(instance_create_info); + vk_instance_initialized = true; + + if (debug_utils_ext) { + vk_instance.debug_utils_support = true; + vk_instance.pfn_vkSetDebugUtilsObjectNameEXT = (PFN_vkSetDebugUtilsObjectNameEXT) vkGetInstanceProcAddr(vk_instance.instance, "vkSetDebugUtilsObjectNameEXT"); + vk_instance.pfn_vkQueueBeginDebugUtilsLabelEXT = (PFN_vkQueueBeginDebugUtilsLabelEXT) vkGetInstanceProcAddr(vk_instance.instance, "vkQueueBeginDebugUtilsLabelEXT"); + vk_instance.pfn_vkQueueEndDebugUtilsLabelEXT = (PFN_vkQueueEndDebugUtilsLabelEXT) vkGetInstanceProcAddr(vk_instance.instance, "vkQueueEndDebugUtilsLabelEXT"); + vk_instance.pfn_vkCmdBeginDebugUtilsLabelEXT = (PFN_vkCmdBeginDebugUtilsLabelEXT) vkGetInstanceProcAddr(vk_instance.instance, "vkCmdBeginDebugUtilsLabelEXT"); + vk_instance.pfn_vkCmdEndDebugUtilsLabelEXT = (PFN_vkCmdEndDebugUtilsLabelEXT) vkGetInstanceProcAddr(vk_instance.instance, "vkCmdEndDebugUtilsLabelEXT"); + vk_instance.pfn_vkCmdInsertDebugUtilsLabelEXT = (PFN_vkCmdInsertDebugUtilsLabelEXT) vkGetInstanceProcAddr(vk_instance.instance, "vkCmdInsertDebugUtilsLabelEXT"); + } + + vk_perf_logger_enabled = getenv("GGML_VK_PERF_LOGGER") != nullptr; + vk_perf_logger_concurrent = getenv("GGML_VK_PERF_LOGGER_CONCURRENT") != nullptr; + vk_enable_sync_logger = getenv("GGML_VK_SYNC_LOGGER") != nullptr; + vk_memory_logger_enabled = getenv("GGML_VK_MEMORY_LOGGER") != nullptr; + const char* GGML_VK_PIPELINE_STATS = getenv("GGML_VK_PIPELINE_STATS"); + if (GGML_VK_PIPELINE_STATS != nullptr) { + vk_pipeline_stats_filter = GGML_VK_PIPELINE_STATS; + } + const char* GGML_VK_PERF_LOGGER_FREQUENCY = getenv("GGML_VK_PERF_LOGGER_FREQUENCY"); + + if (GGML_VK_PERF_LOGGER_FREQUENCY != nullptr) { + vk_perf_logger_frequency = std::stoul(GGML_VK_PERF_LOGGER_FREQUENCY); + } + + // See https://github.com/KhronosGroup/Vulkan-Hpp?tab=readme-ov-file#extensions--per-device-function-pointers- + VULKAN_HPP_DEFAULT_DISPATCHER.init(vk_instance.instance); + + std::vector devices = vk_instance.instance.enumeratePhysicalDevices(); + + // Emulate behavior of CUDA_VISIBLE_DEVICES for Vulkan + char * devices_env = getenv("GGML_VK_VISIBLE_DEVICES"); + if (devices_env != nullptr) { + size_t num_available_devices = devices.size(); + + std::string devices(devices_env); + std::replace(devices.begin(), devices.end(), ',', ' '); + + std::stringstream ss(devices); + size_t tmp; + while (ss >> tmp) { + if(tmp >= num_available_devices) { + std::cerr << "ggml_vulkan: Invalid device index " << tmp << " in GGML_VK_VISIBLE_DEVICES." << std::endl; + throw std::runtime_error("Invalid Vulkan device index"); + } + vk_instance.device_indices.push_back(tmp); + } + } else { + // If no vulkan devices are found, return early + if (devices.empty()) { + GGML_LOG_INFO("ggml_vulkan: No devices found.\n"); + return; + } + + // Default to using all dedicated GPUs + for (size_t i = 0; i < devices.size(); i++) { + vk::PhysicalDeviceProperties2 new_props; + vk::PhysicalDeviceDriverProperties new_driver; + vk::PhysicalDeviceIDProperties new_id; + new_props.pNext = &new_driver; + new_driver.pNext = &new_id; + devices[i].getProperties2(&new_props); + + if ((new_props.properties.deviceType == vk::PhysicalDeviceType::eDiscreteGpu || new_props.properties.deviceType == vk::PhysicalDeviceType::eIntegratedGpu) && ggml_vk_device_is_supported(devices[i])) { + // Check if there are two physical devices corresponding to the same GPU + // This handles the case where the same GPU appears with different drivers (e.g., RADV + AMDVLK on Linux), + // see https://github.com/ggml-org/llama.cpp/pull/7582 for original deduplication. + // MoltenVK on macOS may report the same UUID for distinct GPUs on multi-GPU cards, + // see https://github.com/KhronosGroup/MoltenVK/issues/2683. Skip when both old/new + // driver is MoltenVK + auto old_device = std::find_if( + vk_instance.device_indices.begin(), + vk_instance.device_indices.end(), + [&devices, &new_id, &new_driver](const size_t k){ + vk::PhysicalDeviceProperties2 old_props; + vk::PhysicalDeviceDriverProperties old_driver; + vk::PhysicalDeviceIDProperties old_id; + old_props.pNext = &old_driver; + old_driver.pNext = &old_id; + devices[k].getProperties2(&old_props); + + bool same_uuid = std::equal(std::begin(old_id.deviceUUID), std::end(old_id.deviceUUID), std::begin(new_id.deviceUUID)); + same_uuid = same_uuid || ( + old_id.deviceLUIDValid && new_id.deviceLUIDValid && + std::equal(std::begin(old_id.deviceLUID), std::end(old_id.deviceLUID), std::begin(new_id.deviceLUID)) + ); + bool both_molten_vk = (new_driver.driverID == vk::DriverId::eMoltenvk && old_driver.driverID == vk::DriverId::eMoltenvk); + + return same_uuid && !both_molten_vk; + } + ); + if (old_device == vk_instance.device_indices.end()) { + vk_instance.device_indices.push_back(i); + } else { + // There can be two physical devices corresponding to the same GPU if there are 2 different drivers + // This can cause error when splitting layers aross the devices, need to keep only 1 + VK_LOG_DEBUG("Device " << i << " and device " << *old_device << " have the same deviceUUID"); + + vk::PhysicalDeviceProperties2 old_props; + vk::PhysicalDeviceDriverProperties old_driver; + old_props.pNext = &old_driver; + devices[*old_device].getProperties2(&old_props); + + std::map driver_priorities {}; + int old_priority = std::numeric_limits::max(); + int new_priority = std::numeric_limits::max(); + + // Check https://registry.khronos.org/vulkan/specs/1.3-extensions/man/html/VkDriverId.html for the list of driver id + // Smaller number -> higher priority + switch (old_props.properties.vendorID) { + case VK_VENDOR_ID_AMD: + driver_priorities[vk::DriverId::eMesaRadv] = 1; + driver_priorities[vk::DriverId::eAmdOpenSource] = 2; + driver_priorities[vk::DriverId::eAmdProprietary] = 3; + break; + case VK_VENDOR_ID_INTEL: + driver_priorities[vk::DriverId::eIntelOpenSourceMESA] = 1; + driver_priorities[vk::DriverId::eIntelProprietaryWindows] = 2; + break; + case VK_VENDOR_ID_NVIDIA: + driver_priorities[vk::DriverId::eNvidiaProprietary] = 1; +#if defined(VK_API_VERSION_1_3) && VK_HEADER_VERSION >= 235 + driver_priorities[vk::DriverId::eMesaNvk] = 2; +#endif + break; + case VK_VENDOR_ID_QUALCOMM: + driver_priorities[vk::DriverId::eQualcommProprietary] = 1; + driver_priorities[vk::DriverId::eMesaTurnip] = 2; + break; + } + driver_priorities[vk::DriverId::eMesaDozen] = 100; + + if (driver_priorities.count(old_driver.driverID)) { + old_priority = driver_priorities[old_driver.driverID]; + } + if (driver_priorities.count(new_driver.driverID)) { + new_priority = driver_priorities[new_driver.driverID]; + } + + if (new_priority < old_priority) { + auto r = std::remove(vk_instance.device_indices.begin(), vk_instance.device_indices.end(), *old_device); + vk_instance.device_indices.erase(r, vk_instance.device_indices.end()); + vk_instance.device_indices.push_back(i); + + VK_LOG_DEBUG("Prioritize device " << i << " driver " << new_driver.driverName << " over device " << *old_device << " driver " << old_driver.driverName); + } + else { + VK_LOG_DEBUG("Prioritize device " << *old_device << " driver " << old_driver.driverName << " over device " << i << " driver " << new_driver.driverName << std::endl); + } + } + } + } + + // If no GPUs found, fall back to the first non-CPU device. + // If only CPU devices are available, return without devices. + if (vk_instance.device_indices.empty()) { + for (size_t i = 0; i < devices.size(); i++) { + if (devices[i].getProperties().deviceType != vk::PhysicalDeviceType::eCpu) { + vk_instance.device_indices.push_back(i); + break; + } + } + } + + if (vk_instance.device_indices.empty()) { + GGML_LOG_INFO("ggml_vulkan: No devices found.\n"); + return; + } + } + GGML_LOG_DEBUG("ggml_vulkan: Found %zu Vulkan devices:\n", vk_instance.device_indices.size()); + + for (size_t i = 0; i < vk_instance.device_indices.size(); i++) { + vk::PhysicalDevice vkdev = devices[vk_instance.device_indices[i]]; + std::vector extensionprops = vkdev.enumerateDeviceExtensionProperties(); + + bool membudget_supported = false; + for (const auto & ext : extensionprops) { + if (strcmp(VK_EXT_MEMORY_BUDGET_EXTENSION_NAME, ext.extensionName) == 0) { + membudget_supported = true; + break; + } + } + + vk_instance.device_supports_membudget.push_back(membudget_supported); + + ggml_vk_print_gpu_info(i); + } +} + +static void ggml_vk_init(ggml_backend_vk_context * ctx, size_t idx) { + VK_LOG_DEBUG("ggml_vk_init(" << ctx->name << ", " << idx << ")"); + ggml_vk_instance_init(); + GGML_ASSERT(idx < vk_instance.device_indices.size()); + + ctx->name = GGML_VK_NAME + std::to_string(idx); + + ctx->device = ggml_vk_get_device(idx); + + ctx->semaphore_idx = 0; + ctx->event_idx = 0; + + ctx->prealloc_size_x = 0; + ctx->prealloc_size_y = 0; + ctx->prealloc_size_split_k = 0; + // Fixed size of 1KB, for deterministic behavior + ctx->prealloc_size_add_rms_partials = 1024; + + ctx->fence = ctx->device->device.createFence({}); + ctx->almost_ready_fence = ctx->device->device.createFence({}); + + ctx->compute_cmd_pool.init(ctx->device, &ctx->device->compute_queue); + if (ctx->device->async_use_transfer_queue) { + vk::SemaphoreTypeCreateInfo tci{ vk::SemaphoreType::eTimeline, 0 }; + vk::SemaphoreCreateInfo ci{}; + ci.setPNext(&tci); + ctx->transfer_semaphore.s = ctx->device->device.createSemaphore(ci); + ctx->transfer_semaphore.value = 0; + + ctx->transfer_cmd_pool.init(ctx->device, &ctx->device->transfer_queue); + } + + if (vk_perf_logger_enabled) { + ctx->perf_logger = std::unique_ptr(new vk_perf_logger()); + } + +#ifdef GGML_VULKAN_CHECK_RESULTS + const char* skip_checks = getenv("GGML_VULKAN_SKIP_CHECKS"); + vk_skip_checks = (skip_checks == NULL ? 0 : atoi(skip_checks)); + const char* output_tensor = getenv("GGML_VULKAN_OUTPUT_TENSOR"); + vk_output_tensor = (output_tensor == NULL ? 0 : atoi(output_tensor)); +#endif +} + +static vk_pipeline ggml_vk_get_to_fp16(ggml_backend_vk_context * ctx, ggml_type type) { + VK_LOG_DEBUG("ggml_vk_get_to_fp16()"); + switch (type) { + case GGML_TYPE_F32: + case GGML_TYPE_Q1_0: + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q4_1: + case GGML_TYPE_Q5_0: + case GGML_TYPE_Q5_1: + case GGML_TYPE_Q8_0: + case GGML_TYPE_Q2_K: + case GGML_TYPE_Q3_K: + case GGML_TYPE_Q4_K: + case GGML_TYPE_Q5_K: + case GGML_TYPE_Q6_K: + case GGML_TYPE_IQ1_S: + case GGML_TYPE_IQ1_M: + case GGML_TYPE_IQ2_XXS: + case GGML_TYPE_IQ2_XS: + case GGML_TYPE_IQ2_S: + case GGML_TYPE_IQ3_XXS: + case GGML_TYPE_IQ3_S: + case GGML_TYPE_IQ4_XS: + case GGML_TYPE_IQ4_NL: + case GGML_TYPE_MXFP4: + case GGML_TYPE_NVFP4: + break; + default: + return nullptr; + } + + return ctx->device->pipeline_dequant[type]; +} + +static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_pipeline(ggml_backend_vk_context * ctx, ggml_type src0_type, ggml_type src1_type, ggml_prec prec) { + VK_LOG_DEBUG("ggml_vk_get_mul_mat_mat_pipeline(" << ggml_type_name(src0_type) << ", " << ggml_type_name(src1_type) << ", " << prec << ")"); + if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F32) { + return ctx->device->pipeline_matmul_f32; + } + if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F16) { + return ctx->device->pipeline_matmul_f32_f16; + } + if (src0_type == GGML_TYPE_BF16 && src1_type == GGML_TYPE_BF16) { + return ctx->device->pipeline_matmul_bf16; + } + if (prec == GGML_PREC_DEFAULT && ctx->device->fp16 && !(ctx->device->coopmat_support && !ctx->device->coopmat_acc_f16_support)) { + if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F32) { + return ctx->device->pipeline_matmul_f16_f32.f16acc; + } + if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F16) { + return ctx->device->pipeline_matmul_f16.f16acc; + } + } else { + if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F32) { + return ctx->device->pipeline_matmul_f16_f32.f32acc; + } + if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F16) { + return ctx->device->pipeline_matmul_f16.f32acc; + } + } + + // MMQ + if (src1_type == GGML_TYPE_Q8_1) { + vk_matmul_pipeline pipelines = ctx->device->pipeline_dequant_mul_mat_mat_q8_1[src0_type].f32acc; + + if (pipelines->is_empty()) { + return nullptr; + } + + return pipelines; + } + + if (src1_type != GGML_TYPE_F32 && !ctx->device->coopmat2) { + return nullptr; + } + + switch (src0_type) { + case GGML_TYPE_Q1_0: + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q4_1: + case GGML_TYPE_Q5_0: + case GGML_TYPE_Q5_1: + case GGML_TYPE_Q8_0: + case GGML_TYPE_Q2_K: + case GGML_TYPE_Q3_K: + case GGML_TYPE_Q4_K: + case GGML_TYPE_Q5_K: + case GGML_TYPE_Q6_K: + case GGML_TYPE_IQ1_S: + case GGML_TYPE_IQ1_M: + case GGML_TYPE_IQ2_XXS: + case GGML_TYPE_IQ2_XS: + case GGML_TYPE_IQ2_S: + case GGML_TYPE_IQ3_XXS: + case GGML_TYPE_IQ3_S: + case GGML_TYPE_IQ4_XS: + case GGML_TYPE_IQ4_NL: + case GGML_TYPE_MXFP4: + case GGML_TYPE_NVFP4: + break; + default: + return nullptr; + } + + if (ctx->device->coopmat2) { + assert(src1_type == GGML_TYPE_F16); + return prec == GGML_PREC_DEFAULT ? ctx->device->pipeline_dequant_mul_mat_mat_f16[src0_type].f16acc : ctx->device->pipeline_dequant_mul_mat_mat_f16[src0_type].f32acc; + } + if (ctx->device->coopmat_support) { + return (ctx->device->fp16 && ctx->device->coopmat_acc_f16_support && prec == GGML_PREC_DEFAULT) ? ctx->device->pipeline_dequant_mul_mat_mat[src0_type].f16acc : ctx->device->pipeline_dequant_mul_mat_mat[src0_type].f32acc; + } + return (ctx->device->fp16 && prec == GGML_PREC_DEFAULT) ? ctx->device->pipeline_dequant_mul_mat_mat[src0_type].f16acc : ctx->device->pipeline_dequant_mul_mat_mat[src0_type].f32acc; +} + +static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec(ggml_backend_vk_context * ctx, ggml_type a_type, ggml_type b_type, uint32_t num_cols, uint32_t m, uint32_t k) { + VK_LOG_DEBUG("ggml_vk_get_dequantize_mul_mat_vec()"); + GGML_ASSERT(b_type == GGML_TYPE_F32 || b_type == GGML_TYPE_F16 || b_type == GGML_TYPE_Q8_1); + GGML_ASSERT(num_cols >= 1 && num_cols <= mul_mat_vec_max_cols); + + if (b_type == GGML_TYPE_Q8_1) { + switch (a_type) { + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q4_1: + case GGML_TYPE_Q5_0: + case GGML_TYPE_Q5_1: + case GGML_TYPE_Q8_0: + case GGML_TYPE_MXFP4: + case GGML_TYPE_Q2_K: + case GGML_TYPE_Q3_K: + case GGML_TYPE_Q4_K: + case GGML_TYPE_Q5_K: + case GGML_TYPE_Q6_K: + case GGML_TYPE_IQ1_S: + case GGML_TYPE_IQ1_M: + break; + default: + return nullptr; + } + } + + switch (a_type) { + case GGML_TYPE_F32: + case GGML_TYPE_F16: + case GGML_TYPE_BF16: + case GGML_TYPE_Q1_0: + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q4_1: + case GGML_TYPE_Q5_0: + case GGML_TYPE_Q5_1: + case GGML_TYPE_Q8_0: + case GGML_TYPE_Q2_K: + case GGML_TYPE_Q3_K: + case GGML_TYPE_Q4_K: + case GGML_TYPE_Q5_K: + case GGML_TYPE_Q6_K: + case GGML_TYPE_IQ1_S: + case GGML_TYPE_IQ1_M: + case GGML_TYPE_IQ2_XXS: + case GGML_TYPE_IQ2_XS: + case GGML_TYPE_IQ2_S: + case GGML_TYPE_IQ3_XXS: + case GGML_TYPE_IQ3_S: + case GGML_TYPE_IQ4_XS: + case GGML_TYPE_IQ4_NL: + case GGML_TYPE_MXFP4: + case GGML_TYPE_NVFP4: + break; + default: + return nullptr; + } + + // heuristic to choose workgroup size + uint32_t dmmv_wg = DMMV_WG_SIZE_SUBGROUP; + if ((ctx->device->vendor_id == VK_VENDOR_ID_NVIDIA && ctx->device->architecture != vk_device_architecture::NVIDIA_PRE_TURING) || ctx->device->vendor_id == VK_VENDOR_ID_INTEL) { + // Prefer larger workgroups when M is small, to spread the work out more + // and keep more SMs busy. + // q6_k seems to prefer small workgroup size even for "medium" values of M. + if (a_type == GGML_TYPE_Q6_K) { + if (m < 4096 && k >= 1024) { + dmmv_wg = DMMV_WG_SIZE_LARGE; + } + } else { + if (m <= 8192 && k >= 1024) { + dmmv_wg = DMMV_WG_SIZE_LARGE; + } + } + } + + if (b_type == GGML_TYPE_Q8_1) { + if (ctx->device->vendor_id == VK_VENDOR_ID_INTEL) { + dmmv_wg = DMMV_WG_SIZE_SUBGROUP; + } + return ctx->device->pipeline_dequant_mul_mat_vec_q8_1_f32[dmmv_wg][a_type][num_cols-1]; + } + + return b_type == GGML_TYPE_F32 ? ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[dmmv_wg][a_type][num_cols-1] : ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[dmmv_wg][a_type][num_cols-1]; +} + +static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_id_pipeline(ggml_backend_vk_context * ctx, ggml_type src0_type, ggml_type src1_type, ggml_prec prec) { + VK_LOG_DEBUG("ggml_vk_get_mul_mat_mat_id_pipeline()"); + if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F32) { + return ctx->device->pipeline_matmul_id_f32; + } + if (src0_type == GGML_TYPE_BF16 && src1_type == GGML_TYPE_BF16) { + return ctx->device->pipeline_matmul_id_bf16; + } + if (prec == GGML_PREC_DEFAULT && ctx->device->fp16 && !(ctx->device->coopmat_support && !ctx->device->coopmat_acc_f16_support)) { + if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F32) { + return ctx->device->pipeline_matmul_id_f16_f32.f16acc; + } + if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F16) { + return ctx->device->pipeline_matmul_id_f16.f16acc; + } + } else { + if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F32) { + return ctx->device->pipeline_matmul_id_f16_f32.f32acc; + } + if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F16) { + return ctx->device->pipeline_matmul_id_f16.f32acc; + } + } + + // MMQ + if (src1_type == GGML_TYPE_Q8_1) { + vk_matmul_pipeline pipelines = ctx->device->pipeline_dequant_mul_mat_mat_id_q8_1[src0_type].f32acc; + + if (pipelines->is_empty()) { + return nullptr; + } + + return pipelines; + } + + GGML_ASSERT(src1_type == GGML_TYPE_F32 || (ctx->device->coopmat2 && src1_type == GGML_TYPE_F16)); + + switch (src0_type) { + case GGML_TYPE_Q1_0: + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q4_1: + case GGML_TYPE_Q5_0: + case GGML_TYPE_Q5_1: + case GGML_TYPE_Q8_0: + case GGML_TYPE_Q2_K: + case GGML_TYPE_Q3_K: + case GGML_TYPE_Q4_K: + case GGML_TYPE_Q5_K: + case GGML_TYPE_Q6_K: + case GGML_TYPE_IQ1_S: + case GGML_TYPE_IQ1_M: + case GGML_TYPE_IQ2_XXS: + case GGML_TYPE_IQ2_XS: + case GGML_TYPE_IQ2_S: + case GGML_TYPE_IQ3_XXS: + case GGML_TYPE_IQ3_S: + case GGML_TYPE_IQ4_XS: + case GGML_TYPE_IQ4_NL: + case GGML_TYPE_MXFP4: + case GGML_TYPE_NVFP4: + break; + default: + return nullptr; + } + + vk_matmul_pipeline2& mmp = ctx->device->pipeline_dequant_mul_mat_mat_id[src0_type]; + // XXX TODO 'prec' is not actually allowed in mul_mat_id. + bool prefer_fp16acc = ctx->device->fp16 /*&& prec == GGML_PREC_DEFAULT*/; + bool support_fp16acc = !mmp.f16acc->is_empty(); + bool support_fp32acc = !mmp.f32acc->is_empty(); + + if (support_fp16acc && (prefer_fp16acc || !support_fp32acc)) { + return mmp.f16acc; + } else { + GGML_ASSERT(support_fp32acc); + return mmp.f32acc; + } +} + +static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec_id(ggml_backend_vk_context * ctx, ggml_type a_type, ggml_type b_type, uint32_t m, uint32_t k) { + VK_LOG_DEBUG("ggml_vk_get_dequantize_mul_mat_vec_id()"); + GGML_ASSERT(b_type == GGML_TYPE_F32 || b_type == GGML_TYPE_Q8_1); + + if (b_type == GGML_TYPE_Q8_1) { + switch (a_type) { + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q4_1: + case GGML_TYPE_Q5_0: + case GGML_TYPE_Q5_1: + case GGML_TYPE_Q8_0: + case GGML_TYPE_MXFP4: + case GGML_TYPE_Q2_K: + case GGML_TYPE_Q3_K: + case GGML_TYPE_Q4_K: + case GGML_TYPE_Q5_K: + case GGML_TYPE_Q6_K: + case GGML_TYPE_IQ1_S: + case GGML_TYPE_IQ1_M: + break; + default: + return nullptr; + } + } + + switch (a_type) { + case GGML_TYPE_F32: + case GGML_TYPE_F16: + case GGML_TYPE_BF16: + case GGML_TYPE_Q1_0: + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q4_1: + case GGML_TYPE_Q5_0: + case GGML_TYPE_Q5_1: + case GGML_TYPE_Q8_0: + case GGML_TYPE_Q2_K: + case GGML_TYPE_Q3_K: + case GGML_TYPE_Q4_K: + case GGML_TYPE_Q5_K: + case GGML_TYPE_Q6_K: + case GGML_TYPE_IQ1_S: + case GGML_TYPE_IQ1_M: + case GGML_TYPE_IQ2_XXS: + case GGML_TYPE_IQ2_XS: + case GGML_TYPE_IQ2_S: + case GGML_TYPE_IQ3_XXS: + case GGML_TYPE_IQ3_S: + case GGML_TYPE_IQ4_XS: + case GGML_TYPE_IQ4_NL: + case GGML_TYPE_MXFP4: + case GGML_TYPE_NVFP4: + break; + default: + return nullptr; + } + + // heuristic to choose workgroup size + uint32_t dmmv_wg = DMMV_WG_SIZE_SUBGROUP; + if ((ctx->device->vendor_id == VK_VENDOR_ID_NVIDIA && ctx->device->architecture != vk_device_architecture::NVIDIA_PRE_TURING) || ctx->device->vendor_id == VK_VENDOR_ID_INTEL) { + // Prefer larger workgroups when M is small, to spread the work out more + // and keep more SMs busy. + // q6_k seems to prefer small workgroup size even for "medium" values of M. + if (a_type == GGML_TYPE_Q6_K) { + if (m < 4096 && k >= 1024) { + dmmv_wg = DMMV_WG_SIZE_LARGE; + } + } else { + if (m <= 8192 && k >= 1024) { + dmmv_wg = DMMV_WG_SIZE_LARGE; + } + } + } + + if (b_type == GGML_TYPE_Q8_1) { + if (ctx->device->vendor_id == VK_VENDOR_ID_INTEL) { + dmmv_wg = DMMV_WG_SIZE_SUBGROUP; + } + return ctx->device->pipeline_dequant_mul_mat_vec_id_q8_1_f32[dmmv_wg][a_type]; + } + + return ctx->device->pipeline_dequant_mul_mat_vec_id_f32[dmmv_wg][a_type]; +} + +static void * ggml_vk_host_malloc(vk_device& device, size_t size) { + VK_LOG_MEMORY("ggml_vk_host_malloc(" << size << ")"); + vk_buffer buf = ggml_vk_create_buffer(device, size, + {vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached, + vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent}); + + if(!(buf->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible)) { + fprintf(stderr, "WARNING: failed to allocate %.2f MB of pinned memory\n", + size/1024.0/1024.0); + device->device.freeMemory(buf->device_memory); + device->device.destroyBuffer(buf->buffer); + return nullptr; + } + + std::lock_guard guard(device->pinned_memory_mutex); + device->pinned_memory.push_back(std::make_tuple(buf->ptr, size, buf)); + + return buf->ptr; +} + +static void ggml_vk_host_free(vk_device& device, void* ptr) { + if (ptr == nullptr) { + return; + } + VK_LOG_MEMORY("ggml_vk_host_free(" << ptr << ")"); + std::lock_guard guard(device->pinned_memory_mutex); + + vk_buffer buf; + size_t index; + for (size_t i = 0; i < device->pinned_memory.size(); i++) { + const uint8_t* addr = (const uint8_t*) std::get<0>(device->pinned_memory[i]); + const uint8_t* endr = addr + std::get<1>(device->pinned_memory[i]); + if (ptr >= addr && ptr < endr) { + buf = std::get<2>(device->pinned_memory[i]); + index = i; + break; + } + } + if (buf == nullptr) { + fprintf(stderr, "WARNING: failed to free pinned memory: memory not in map\n"); + return; + } + + ggml_vk_destroy_buffer(buf); + + device->pinned_memory.erase(device->pinned_memory.begin() + index); +} + +static void ggml_vk_host_get(const vk_device& device, const void * ptr, vk_buffer& buf, size_t& buf_offset) { + std::shared_lock guard(device->pinned_memory_mutex); + buf = nullptr; + buf_offset = 0; + for (size_t i = 0; i < device->pinned_memory.size(); i++) { + const uint8_t* addr = (const uint8_t*) std::get<0>(device->pinned_memory[i]); + const uint8_t* endr = addr + std::get<1>(device->pinned_memory[i]); + if (ptr >= addr && ptr < endr) { + buf = std::get<2>(device->pinned_memory[i]); + buf_offset = ((const uint8_t *)ptr) - addr; + break; + } + } +} + +static vk_subbuffer ggml_vk_tensor_subbuffer( + const ggml_backend_vk_context * ctx, const ggml_tensor * tensor, bool allow_misalign = false) { + + vk_buffer buffer = nullptr; + size_t offset = 0; + if (ctx->device->uma) { + ggml_vk_host_get(ctx->device, tensor->data, buffer, offset); + } + if (!buffer) { + auto buf_ctx = (ggml_backend_vk_buffer_context *)tensor->buffer->context; + buffer = buf_ctx->dev_buffer; + offset = vk_tensor_offset(tensor) + tensor->view_offs; + } + GGML_ASSERT(buffer != nullptr); + + size_t size = ggml_nbytes(tensor); + + size_t misalign_bytes = offset & (ctx->device->properties.limits.minStorageBufferOffsetAlignment - 1); + // The shader must support misaligned offsets when indexing into the buffer + GGML_ASSERT(allow_misalign || misalign_bytes == 0); + offset &= ~misalign_bytes; + size += misalign_bytes; + + return vk_subbuffer{buffer, offset, size}; +} + +// Get a command buffer from pool. Create a new one if no reusable buffer is available +static vk_command_buffer* ggml_vk_get_or_create_cmd_buffer(vk_device& device, vk_command_pool& pool) { + for (auto& cmd_buffer : pool.cmd_buffers) { + if (!cmd_buffer.in_use) { + cmd_buffer.use_counter++; + cmd_buffer.in_use = true; + return &cmd_buffer; + } + } + return ggml_vk_create_cmd_buffer(device, pool); +} + +static vk_submission ggml_vk_begin_submission(vk_device& device, vk_command_pool& p, bool one_time = true) { + vk_submission s; + s.buffer = ggml_vk_get_or_create_cmd_buffer(device, p); + if (one_time) { + s.buffer->buf.begin({ vk::CommandBufferUsageFlagBits::eOneTimeSubmit }); + } else { + s.buffer->buf.begin({ vk::CommandBufferUsageFlags{} }); + } + + return s; +} + +template size_t push_constant_size(const T &t) { + static_assert(std::is_class::value, "T must be a struct/class"); + GGML_UNUSED(t); + return sizeof(T); +} +template size_t push_constant_size(const std::vector &t) { + GGML_UNUSED(t); + return sizeof(T) * t.size(); +} +template size_t push_constant_size(const std::array &t) { + GGML_UNUSED(t); + return sizeof(T) * N; +} + +template const T *push_constant_data(const T &t) { + static_assert(std::is_class::value, "T must be a struct/class"); + return &t; +} +template const T *push_constant_data(const std::vector &t) { + return t.data(); +} +template const T *push_constant_data(const std::array &t) { + return t.data(); +} + +template +static void ggml_vk_dispatch_pipeline(ggml_backend_vk_context* ctx, vk_context& subctx, vk_pipeline& pipeline, std::initializer_list const& descriptor_buffer_infos, const T &push_constants, std::array elements) { + const uint32_t wg0 = CEIL_DIV(elements[0], pipeline->wg_denoms[0]); + const uint32_t wg1 = CEIL_DIV(elements[1], pipeline->wg_denoms[1]); + const uint32_t wg2 = CEIL_DIV(elements[2], pipeline->wg_denoms[2]); + VK_LOG_DEBUG("ggml_vk_dispatch_pipeline(" << pipeline->name << ", {"; + for (auto& buffer : descriptor_buffer_infos) { + std::cerr << "(" << buffer.buffer << ", " << buffer.offset << ", " << buffer.range << "), "; + } + std::cerr << "}, (" << wg0 << "," << wg1 << "," << wg2 << "))"); + GGML_ASSERT(wg0 <= ctx->device->properties.limits.maxComputeWorkGroupCount[0] && + wg1 <= ctx->device->properties.limits.maxComputeWorkGroupCount[1] && + wg2 <= ctx->device->properties.limits.maxComputeWorkGroupCount[2]); + GGML_ASSERT(ctx->descriptor_set_idx < ctx->descriptor_sets.size()); + GGML_ASSERT(descriptor_buffer_infos.size() <= MAX_PARAMETER_COUNT); + GGML_ASSERT(pipeline->parameter_count == descriptor_buffer_infos.size()); + GGML_ASSERT(pipeline->push_constant_size == push_constant_size(push_constants)); + + vk::DescriptorSet& descriptor_set = ctx->descriptor_sets[ctx->descriptor_set_idx++]; + vk::WriteDescriptorSet write_descriptor_set{ descriptor_set, 0, 0, pipeline->parameter_count, vk::DescriptorType::eStorageBuffer, nullptr, descriptor_buffer_infos.begin() }; + ctx->device->device.updateDescriptorSets({ write_descriptor_set }, {}); + + subctx->s->buffer->buf.pushConstants(pipeline->layout, vk::ShaderStageFlagBits::eCompute, 0, push_constant_size(push_constants), push_constant_data(push_constants)); + subctx->s->buffer->buf.bindPipeline(vk::PipelineBindPoint::eCompute, pipeline->pipeline); + subctx->s->buffer->buf.bindDescriptorSets(vk::PipelineBindPoint::eCompute, + pipeline->layout, + 0, + { descriptor_set }, + {}); + subctx->s->buffer->buf.dispatch(wg0, wg1, wg2); +} + +static void ggml_vk_ctx_end(vk_context& ctx) { + VK_LOG_DEBUG("ggml_vk_ctx_end(" << ctx << ", " << ctx->seqs.size() << ")"); + if (ctx->s == nullptr) { + return; + } + + ctx->s->buffer->buf.end(); + ctx->s = nullptr; +} + +static void ggml_vk_ctx_begin(vk_device& device, vk_context& subctx) { + VK_LOG_DEBUG("ggml_vk_ctx_begin(" << device->name << ")"); + if (subctx->s != nullptr) { + ggml_vk_ctx_end(subctx); + } + + subctx->seqs.push_back({ ggml_vk_begin_submission(device, *subctx->p) }); + subctx->s = subctx->seqs[subctx->seqs.size() - 1].data(); +} + +static vk_context ggml_vk_get_compute_ctx(ggml_backend_vk_context * ctx) { + vk_context result; + if (!ctx->compute_ctx.expired()) { + result = ctx->compute_ctx.lock(); + } else { + result = ggml_vk_create_context(ctx, ctx->compute_cmd_pool); + + ctx->compute_ctx = result; + ggml_vk_ctx_begin(ctx->device, result); + } + + if (ctx->device->async_use_transfer_queue && ctx->transfer_semaphore_last_submitted < ctx->transfer_semaphore.value) { + result->s->wait_semaphores.push_back(ctx->transfer_semaphore); + ctx->transfer_semaphore_last_submitted = ctx->transfer_semaphore.value; + } + + return result; +} + +// Submit any pending transfer queue work and signal the transfer semaphore. +// The next compute context created via ggml_vk_get_compute_ctx will wait on this semaphore. +// Returns true if work was submitted. +static bool ggml_vk_submit_transfer_ctx(ggml_backend_vk_context * ctx) { + if (!ctx->device->async_use_transfer_queue || ctx->transfer_ctx.expired()) { + return false; + } + + vk_context cpy_ctx = ctx->transfer_ctx.lock(); + ggml_vk_ctx_end(cpy_ctx); + + for (auto& cpy : cpy_ctx->in_memcpys) { + memcpy(cpy.dst, cpy.src, cpy.n); + } + + ctx->transfer_semaphore.value++; + cpy_ctx->seqs.back().back().signal_semaphores.push_back(ctx->transfer_semaphore); + + ggml_vk_submit(cpy_ctx, {}); + ctx->transfer_ctx.reset(); + return true; +} + +static size_t ggml_vk_align_size(size_t width, size_t align) { + VK_LOG_DEBUG("ggml_vk_align_size(" << width << ", " << align << ")"); + return CEIL_DIV(width, align) * align; +} + +static void deferred_memcpy(void * dst, const void * src, size_t size, std::vector* memcpys = nullptr) { + if (memcpys == nullptr) { + memcpy(dst, src, size); + } else { + memcpys->emplace_back(dst, src, size); + } +} + +static void deferred_memset(void * dst, uint32_t val, size_t size, std::vector* memsets = nullptr) { + if (memsets == nullptr) { + memset(dst, val, size); + } else { + memsets->emplace_back(dst, val, size); + } +} + +static void ggml_vk_ensure_sync_staging_buffer(vk_device& device, size_t size) { + if (device->sync_staging == nullptr || device->sync_staging->size < size) { + VK_LOG_MEMORY("ggml_vk_ensure_sync_staging_buffer(" << size << ")"); + ggml_vk_destroy_buffer(device->sync_staging); + device->sync_staging = ggml_vk_create_buffer_check(device, size, + vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached, + vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent); + } +} + +static void ggml_vk_ensure_sync_staging_buffer(ggml_backend_vk_context * ctx, size_t size) { + if (ctx->sync_staging == nullptr || ctx->sync_staging->size < size) { + VK_LOG_MEMORY("ggml_vk_ensure_sync_staging_buffer(" << size << ")"); + ggml_vk_destroy_buffer(ctx->sync_staging); + ctx->sync_staging = ggml_vk_create_buffer_check(ctx->device, size, + vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached, + vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent); + } +} + +static void ggml_vk_buffer_write_nc_async(ggml_backend_vk_context * ctx, vk_context& subctx, vk_buffer& dst, size_t offset, const ggml_tensor * tensor, bool sync_staging = false) { + VK_LOG_DEBUG("ggml_vk_buffer_write_nc_async(" << tensor << ")"); + GGML_ASSERT(!ggml_is_contiguous(tensor)); + // Buffer is already mapped + if(dst->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible) { + std::cerr << "ggml_vulkan: buffer_write_nc_async dst buffer is host_visible. Use synchronous write." << std::endl; + GGML_ABORT("fatal error"); + } + // Check if src is pinned memory + vk_buffer buf = nullptr; + size_t buf_offset = 0; + ggml_vk_host_get(ctx->device, tensor->data, buf, buf_offset); + + const uint64_t ne0 = tensor->ne[0]; + const uint64_t ne1 = tensor->ne[1]; + const uint64_t ne2 = tensor->ne[2]; + const uint64_t ne3 = tensor->ne[3]; + const uint64_t nb0 = tensor->nb[0]; + const uint64_t nb1 = tensor->nb[1]; + const uint64_t nb2 = tensor->nb[2]; + const uint64_t nb3 = tensor->nb[3]; + const ggml_type type = tensor->type; + const uint64_t ts = ggml_type_size(type); + const uint64_t bs = ggml_blck_size(type); + + const uint64_t dstnb0 = ts; + const uint64_t dstnb1 = dstnb0*(ne0/bs); + const uint64_t dstnb2 = dstnb1*ne1; + const uint64_t dstnb3 = dstnb2*ne2; + + const uint64_t ne = ggml_nelements(tensor); + + if (buf != nullptr) { + // Memory is pinned, use as staging buffer + std::vector slices; + + for (uint64_t i3 = 0; i3 < ne3; i3++) { + for (uint64_t i2 = 0; i2 < ne2; i2++) { + // Find longest contiguous slice + if (ne1*nb1 == dstnb2) { + slices.push_back({ buf_offset + i3*nb3 + i2*nb2, offset + i3*dstnb3 + i2*dstnb2, dstnb2 }); + } else { + for (uint64_t i1 = 0; i1 < ne1; i1++) { + if (ne0*nb0/bs == dstnb1) { + slices.push_back({ buf_offset + i3*nb3 + i2*nb2 + i1*nb1, offset + i3*dstnb3 + i2*dstnb2 + i1*dstnb1, dstnb1 }); + } else { + const uint64_t s_off = buf_offset + i3*nb3 + i2*nb2 + i1*nb1; + const uint64_t d_off = offset + i3*dstnb3 + i2*dstnb2 + i1*dstnb1; + for (uint64_t i0 = 0; i0 < ne0; i0++) { + slices.push_back({ s_off + i0*nb0, d_off + i0*dstnb0, dstnb0 }); + } + } + } + } + } + } + + ggml_vk_sync_buffers(ctx, subctx); + subctx->s->buffer->buf.copyBuffer(buf->buffer, dst->buffer, slices); + return; + } + + if (!sync_staging) { + GGML_ABORT("Asynchronous write to non-pinned memory not supported"); + } + + // Staging buffer required + vk_buffer& staging = ctx->device->sync_staging; + const uint64_t copy_size = ts*ne/bs; + ggml_vk_ensure_sync_staging_buffer(ctx->device, copy_size); + VkBufferCopy buf_copy{ 0, offset, copy_size }; + + ggml_vk_sync_buffers(ctx, subctx); + vkCmdCopyBuffer(subctx->s->buffer->buf, (VkBuffer)staging->buffer, (VkBuffer)dst->buffer, 1, &buf_copy); + + for (uint64_t i3 = 0; i3 < ne3; i3++) { + for (uint64_t i2 = 0; i2 < ne2; i2++) { + // Find longest contiguous slice + if (ne1*nb1 == dstnb2) { + deferred_memcpy((uint8_t *)staging->ptr + i3*dstnb3 + i2*dstnb2, (const uint8_t *) tensor->data + buf_offset + i3*nb3 + i2*nb2, dstnb2, &subctx->in_memcpys); + } else { + for (uint64_t i1 = 0; i1 < ne1; i1++) { + if (ne0*nb0/bs == dstnb1) { + deferred_memcpy((uint8_t *)staging->ptr + i3*dstnb3 + i2*dstnb2 + i1*dstnb1, (const uint8_t *) tensor->data + buf_offset + i3*nb3 + i2*nb2 + i1*nb1, dstnb1, &subctx->in_memcpys); + } else { + const uint64_t s_off = buf_offset + i3*nb3 + i2*nb2 + i1*nb1; + const uint64_t d_off = i3*dstnb3 + i2*dstnb2 + i1*dstnb1; + for (uint64_t i0 = 0; i0 < ne0; i0++) { + deferred_memcpy((uint8_t *)staging->ptr + d_off + i0*dstnb0, (const uint8_t *) tensor->data + s_off + i0*nb0, dstnb0, &subctx->in_memcpys); + } + } + } + } + } + } +} + +static bool ggml_vk_buffer_write_2d_async(vk_context subctx, vk_buffer& dst, size_t offset, const void * src, size_t spitch, size_t dpitch, size_t width, size_t height, bool sync_staging = false) { + VK_LOG_DEBUG("ggml_vk_buffer_write_2d_async(" << width << ", " << height << ")"); + // Check if src is pinned memory + vk_buffer buf = nullptr; + size_t buf_offset = 0; + ggml_vk_host_get(dst->device, src, buf, buf_offset); + + if (buf != nullptr) { + // Memory is pinned, use as staging buffer + std::vector slices(1); + if (width == spitch && width == dpitch) { + // Only do single write if stride is equal + slices[0].srcOffset = buf_offset; + slices[0].dstOffset = offset; + slices[0].size = width * height; + } else { + slices.resize(height); + for (size_t i = 0; i < height; i++) { + slices[i].srcOffset = buf_offset + i * spitch; + slices[i].dstOffset = offset + i * dpitch; + slices[i].size = width; + } + } + + ggml_vk_sync_buffers(nullptr, subctx); + subctx->s->buffer->buf.copyBuffer(buf->buffer, dst->buffer, slices); + return true; + } + VK_LOG_DEBUG("STAGING"); + + if (!sync_staging) { + // copy was not handled caller needs to fall back + return false; + } + + // Staging buffer required + const size_t staging_size = width * height; + ggml_vk_ensure_sync_staging_buffer(dst->device, staging_size); + + vk_buffer& staging_buffer = dst->device->sync_staging; + + std::vector slices(1); + if (width == dpitch) { + slices[0].srcOffset = 0; + slices[0].dstOffset = offset; + slices[0].size = staging_size; + } else { + slices.resize(height); + for (size_t i = 0; i < height; i++) { + slices[i].srcOffset = i * width; + slices[i].dstOffset = offset + i * dpitch; + slices[i].size = width; + } + } + + ggml_vk_sync_buffers(nullptr, subctx); + subctx->s->buffer->buf.copyBuffer((VkBuffer)staging_buffer->buffer, (VkBuffer)dst->buffer, slices); + + if (width == spitch) { + deferred_memcpy((uint8_t *)staging_buffer->ptr, src, staging_size, &subctx->in_memcpys); + } else { + for (size_t i = 0; i < height; i++) { + deferred_memcpy((uint8_t *)staging_buffer->ptr + i * width, (const uint8_t *) src + i * spitch, width, &subctx->in_memcpys); + } + } + return true; +} + +static bool ggml_vk_buffer_write_async(vk_context subctx, vk_buffer& dst, size_t offset, const void * src, size_t size, bool sync_staging = false) { + VK_LOG_DEBUG("ggml_vk_buffer_write_async(" << size << ")"); + return ggml_vk_buffer_write_2d_async(subctx, dst, offset, src, size, size, size, 1, sync_staging); +} + +static void ggml_vk_buffer_write_2d(vk_buffer& dst, size_t offset, const void * src, size_t spitch, size_t dpitch, size_t width, size_t height) { + VK_LOG_DEBUG("ggml_vk_buffer_write_2d(" << width << ", " << height << ")"); + // Buffer is already mapped + if(dst->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible) { + GGML_ASSERT(dst->memory_property_flags & vk::MemoryPropertyFlagBits::eHostCoherent); + + if (width == spitch && width == dpitch) { + memcpy((uint8_t *)dst->ptr + offset, src, width * height); + } else { + for (size_t i = 0; i < height; i++) { + memcpy((uint8_t *)dst->ptr + offset + i * dpitch, (const uint8_t *) src + i * spitch, width); + } + } + } else { + std::lock_guard guard(dst->device->mutex); + + vk_context subctx = ggml_vk_create_temporary_context(dst->device->transfer_queue.cmd_pool); + ggml_vk_ctx_begin(dst->device, subctx); + bool ret = ggml_vk_buffer_write_2d_async(subctx, dst, offset, src, spitch, dpitch, width, height, true); + GGML_ASSERT(ret); + ggml_vk_ctx_end(subctx); + + for (auto& cpy : subctx->in_memcpys) { + memcpy(cpy.dst, cpy.src, cpy.n); + } + + for (auto& mset : subctx->memsets) { + memset(mset.dst, mset.val, mset.n); + } + + ggml_vk_submit(subctx, dst->device->fence); + VK_CHECK(dst->device->device.waitForFences({ dst->device->fence }, true, UINT64_MAX), "vk_buffer_write_2d waitForFences"); + dst->device->device.resetFences({ dst->device->fence }); + ggml_vk_queue_command_pools_cleanup(dst->device); + } +} + +static void ggml_vk_buffer_write(vk_buffer& dst, size_t offset, const void * src, size_t size) { + VK_LOG_DEBUG("ggml_vk_buffer_write(" << size << ")"); + ggml_vk_buffer_write_2d(dst, offset, src, size, size, size, 1); +} + +static bool ggml_vk_buffer_read_2d_async(vk_context subctx, vk_buffer& src, size_t offset, void * dst, size_t spitch, size_t dpitch, size_t width, size_t height, bool sync_staging = false) { + VK_LOG_DEBUG("ggml_vk_buffer_read_2d_async(offset=" << offset << ", width=" << width << ", height=" << height << ")"); + GGML_ASSERT(width > 0); + GGML_ASSERT(height > 0); + GGML_ASSERT(src != nullptr); + + // TODO: staging_offset is not used + + // Check if dst is pinned memory + vk_buffer buf = nullptr; + size_t buf_offset = 0; + ggml_vk_host_get(src->device, dst, buf, buf_offset); + + std::vector slices(1); + if (width == spitch && width == dpitch) { + // Only do single write if stride is equal + slices[0].srcOffset = offset; + slices[0].dstOffset = buf_offset; + slices[0].size = width * height; + } else { + slices.resize(height); + for (size_t i = 0; i < height; i++) { + slices[i].srcOffset = offset + i * spitch; + slices[i].dstOffset = buf_offset + i * dpitch; + slices[i].size = width; + } + } + + if (buf != nullptr) { + // Memory is pinned, use as staging buffer + ggml_vk_sync_buffers(nullptr, subctx); + subctx->s->buffer->buf.copyBuffer(src->buffer, buf->buffer, slices); + + return true; + } + VK_LOG_DEBUG("STAGING"); + + if (!sync_staging) { + // copy was not handled caller needs to fall back + return false; + } + + // Fall back to staging buffer + const size_t staging_size = width * height; + ggml_vk_ensure_sync_staging_buffer(src->device, staging_size); + + vk_buffer& staging_buffer = src->device->sync_staging; + + std::vector staging_slices(1); + if (width == spitch) { + staging_slices[0].srcOffset = offset; + staging_slices[0].dstOffset = 0; + staging_slices[0].size = staging_size; + } else { + staging_slices.resize(height); + for (size_t i = 0; i < height; i++) { + staging_slices[i].srcOffset = offset + i * spitch; + staging_slices[i].dstOffset = i * width; + staging_slices[i].size = width; + } + } + + ggml_vk_sync_buffers(nullptr, subctx); + subctx->s->buffer->buf.copyBuffer(src->buffer, staging_buffer->buffer, staging_slices); + + if (width == dpitch) { + deferred_memcpy(dst, staging_buffer->ptr, staging_size, &subctx->out_memcpys); + } else { + for (size_t i = 0; i < height; i++) { + deferred_memcpy((uint8_t *) dst + i * dpitch, (const uint8_t *) staging_buffer->ptr + i * width, width, &subctx->out_memcpys); + } + } + return true; +} + +static bool ggml_vk_buffer_read_async(vk_context subctx, vk_buffer& src, size_t offset, void * dst, size_t size, bool sync_staging = false) { + return ggml_vk_buffer_read_2d_async(subctx, src, offset, dst, size, size, size, 1, sync_staging); +} + +static void ggml_vk_buffer_read_2d(vk_buffer& src, size_t offset, void * dst, size_t spitch, size_t dpitch, size_t width, size_t height) { + VK_LOG_DEBUG("ggml_vk_buffer_read_2d(" << src->buffer << ", " << offset << ", " << width << ", " << height << ")"); + + // If the device is not an UMA device the memory is host-accessible through rebar. While writing + // through PCIe is sufficient fast reading back data from PCIe is slower than going through + // the HW device to host copy path. + if(src->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible && src->device->uma) { + GGML_ASSERT(src->memory_property_flags & vk::MemoryPropertyFlagBits::eHostCoherent); + + std::lock_guard guard(src->device->mutex); + vk_context subctx = ggml_vk_create_temporary_context(src->device->compute_queue.cmd_pool); + ggml_vk_ctx_begin(src->device, subctx); + subctx->s->buffer->buf.pipelineBarrier( + vk::PipelineStageFlagBits::eComputeShader | vk::PipelineStageFlagBits::eTransfer, + vk::PipelineStageFlagBits::eHost, + {}, + { { vk::AccessFlagBits::eShaderWrite | vk::AccessFlagBits::eTransferWrite, + vk::AccessFlagBits::eHostRead } }, + {}, {}); + ggml_vk_ctx_end(subctx); + ggml_vk_submit(subctx, src->device->fence); + VK_CHECK(src->device->device.waitForFences({ src->device->fence }, true, UINT64_MAX), + "vk_buffer_read_2d uma waitForFences"); + src->device->device.resetFences({ src->device->fence }); + ggml_vk_queue_command_pools_cleanup(src->device); + + if (width == spitch && width == dpitch) { + memcpy(dst, (const uint8_t *) src->ptr + offset, width * height); + } else { + for (size_t i = 0; i < height; i++) { + memcpy((uint8_t *) dst + i * dpitch, (const uint8_t *) src->ptr + offset + i * spitch, width); + } + } + } else { + std::lock_guard guard(src->device->mutex); + + vk_context subctx = ggml_vk_create_temporary_context(src->device->transfer_queue.cmd_pool); + ggml_vk_ctx_begin(src->device, subctx); + bool ret = ggml_vk_buffer_read_2d_async(subctx, src, offset, dst, spitch, dpitch, width, height, true); + GGML_ASSERT(ret); + ggml_vk_ctx_end(subctx); + + ggml_vk_submit(subctx, src->device->fence); + VK_CHECK(src->device->device.waitForFences({ src->device->fence }, true, UINT64_MAX), "vk_buffer_read_2d waitForFences"); + src->device->device.resetFences({ src->device->fence }); + ggml_vk_queue_command_pools_cleanup(src->device); + + for (auto& cpy : subctx->out_memcpys) { + memcpy(cpy.dst, cpy.src, cpy.n); + } + } +} + +static void ggml_vk_buffer_read(vk_buffer& src, size_t offset, void * dst, size_t size) { + VK_LOG_DEBUG("ggml_vk_buffer_read(" << src->buffer << ", " << offset << ", " << size << ")"); + ggml_vk_buffer_read_2d(src, offset, dst, size, size, size, 1); +} + +static void ggml_vk_buffer_copy_async(vk_context& ctx, vk_buffer& dst, size_t dst_offset, vk_buffer& src, size_t src_offset, size_t size) { + VK_LOG_DEBUG("ggml_vk_buffer_copy_async(" << size << ")"); + // Make sure both buffers are on same device + GGML_ASSERT(src->device == dst->device); + + VkBufferCopy bc{ src_offset, dst_offset, size }; + + vkCmdCopyBuffer(ctx->s->buffer->buf, (VkBuffer)src->buffer, (VkBuffer)dst->buffer, 1, &bc); +} + +static void ggml_vk_buffer_copy(vk_buffer& dst, size_t dst_offset, vk_buffer& src, size_t src_offset, size_t size) { + if (src->device == dst->device) { + std::lock_guard guard(src->device->mutex); + VK_LOG_DEBUG("ggml_vk_buffer_copy(SINGLE_DEVICE, " << size << ")"); + // Copy within the device + vk_context subctx = ggml_vk_create_temporary_context(src->device->transfer_queue.cmd_pool); + ggml_vk_ctx_begin(src->device, subctx); + ggml_vk_buffer_copy_async(subctx, dst, dst_offset, src, src_offset, size); + ggml_vk_ctx_end(subctx); + ggml_vk_submit(subctx, src->device->fence); + VK_CHECK(src->device->device.waitForFences({ src->device->fence }, true, UINT64_MAX), "vk_buffer_copy waitForFences"); + src->device->device.resetFences({ src->device->fence }); + ggml_vk_queue_command_pools_cleanup(src->device); + } else { + VK_LOG_DEBUG("ggml_vk_buffer_copy(MULTI_DEVICE, " << size << ")"); + // Copy device to device + ggml_vk_ensure_sync_staging_buffer(src->device, size); + + // Copy to src staging buffer + ggml_vk_buffer_copy(src->device->sync_staging, 0, src, src_offset, size); + // Copy to dst buffer + ggml_vk_buffer_write(dst, dst_offset, src->device->sync_staging->ptr, size); + } +} + +static void ggml_vk_buffer_memset_async(vk_context& ctx, vk_buffer& dst, size_t offset, uint32_t c, size_t size) { + VK_LOG_DEBUG("ggml_vk_buffer_memset_async(" << offset << ", " << c << ", " << size << ")"); + + if (dst->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible && + dst->device->uma) { + deferred_memset((uint8_t*)dst->ptr + offset, c, size, &ctx->memsets); + return; + } + + // Fall back to GPU fillBuffer for non-UMA or non-host-visible buffers + ctx->s->buffer->buf.fillBuffer(dst->buffer, offset, size, c); +} + +static void ggml_vk_buffer_memset(vk_buffer& dst, size_t offset, uint32_t c, size_t size) { + VK_LOG_DEBUG("ggml_vk_buffer_memset(" << offset << ", " << c << ", " << size << ")"); + + if (dst->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible && + dst->device->uma) { + memset((uint8_t*)dst->ptr + offset, c, size); + return; + } + + std::lock_guard guard(dst->device->mutex); + vk_context subctx = ggml_vk_create_temporary_context(dst->device->transfer_queue.cmd_pool); + ggml_vk_ctx_begin(dst->device, subctx); + subctx->s->buffer->buf.fillBuffer(dst->buffer, offset, size, c); + ggml_vk_ctx_end(subctx); + + ggml_vk_submit(subctx, dst->device->fence); + VK_CHECK(dst->device->device.waitForFences({ dst->device->fence }, true, UINT64_MAX), "vk_memset waitForFences"); + dst->device->device.resetFences({ dst->device->fence }); + ggml_vk_queue_command_pools_cleanup(dst->device); +} + +static uint32_t ggml_vk_guess_split_k(ggml_backend_vk_context * ctx, uint32_t m, uint32_t n, uint32_t k, bool disable_split_k, const vk_pipeline& pipeline) { + VK_LOG_DEBUG("ggml_vk_guess_split_k(" << m << ", " << n << ", " << k << ", " << disable_split_k << ")"); + + if (disable_split_k) { + return 1; + } + + uint32_t split_k = 1; + if (ctx->device->shader_core_count != 0 && m >= pipeline->wg_denoms[0] && n >= pipeline->wg_denoms[1]) { + // If k is 'large' and the SMs will fill less than halfway, use split_k. + uint32_t m_tiles = CEIL_DIV(m, pipeline->wg_denoms[0]); + uint32_t n_tiles = CEIL_DIV(n, pipeline->wg_denoms[1]); + + if (k >= 2048) { + if (m_tiles * n_tiles <= ctx->device->shader_core_count / 2) { + split_k = ctx->device->shader_core_count / (m_tiles * n_tiles); + } else if (m_tiles * n_tiles <= ctx->device->shader_core_count * 2 / 3) { + split_k = 3; + } + // Cap the split at 8x. Unless k is huge this is a lot of overhead. + split_k = std::min(split_k, 8u); + + // ggml_vk_matmul will align the splits to be a multiple of 256. + // If this rounded up size would cause the last split to be empty, + // then reduce the split count. + while (true) { + if (split_k == 1) { + break; + } + uint32_t k_split = CEIL_DIV(k, split_k); + k_split = ROUNDUP_POW2(k_split, 256); + if (k_split * (split_k - 1) < k) { + break; + } + split_k--; + } + } + } + + return split_k; +} + +static vk_pipeline ggml_vk_guess_matmul_pipeline(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, uint32_t m, uint32_t n, bool aligned, ggml_type src0_type, ggml_type src1_type) { + VK_LOG_DEBUG("ggml_vk_guess_matmul_pipeline(" << m << ", " << n << ", " << aligned << ", " << ggml_type_name(src0_type) << ", " << ggml_type_name(src1_type) << ")"); + + // The q8_1 (integer dot) mmq path uses a different shader with its own + // shared-memory layout, so use the int-specific availability flags. + const bool is_q8_1 = (src1_type == GGML_TYPE_Q8_1); + const bool mm_l = is_q8_1 ? ctx->device->mul_mat_l_int[src0_type] : ctx->device->mul_mat_l[src0_type]; + const bool mm_m = is_q8_1 ? ctx->device->mul_mat_m_int[src0_type] : ctx->device->mul_mat_m[src0_type]; + const bool mm_s = is_q8_1 ? ctx->device->mul_mat_s_int[src0_type] : ctx->device->mul_mat_s[src0_type]; + + if (ctx->device->coopmat2) { + const uint32_t shader_core_count = ctx->device->shader_core_count; + const uint32_t tiles_l = CEIL_DIV(m, mmp->a_l->wg_denoms[0]) * CEIL_DIV(n, mmp->a_l->wg_denoms[1]); + const uint32_t tiles_m = CEIL_DIV(m, mmp->a_m->wg_denoms[0]) * CEIL_DIV(n, mmp->a_m->wg_denoms[1]); + + // Use large shader when the N dimension is greater than the medium shader's tile size + uint32_t crossover_large = mmp->m->wg_denoms[1]; + + // Prefer large over medium if either: + // - medium or large tiles would overfill the GPU + // - large tiles with a split_k==3 fits in the GPU and medium tiles with split_k==2 does not + // (medium with split_k==2 is probably better if it fits - more workgroups running and less split_k overhead) + bool prefer_large = tiles_m > shader_core_count || tiles_l > shader_core_count || + // split_k==3 with large tiles likely better than medium tiles with no split_k. + (tiles_l <= shader_core_count / 3 && tiles_m > shader_core_count / 2); + + if ((mm_l && (n > crossover_large && prefer_large)) || (!mm_m && !mm_s)) { + return aligned ? mmp->a_l : mmp->l; + } + // Use medium shader when the N dimension is greater than the small shader's tile size + uint32_t crossover_medium = mmp->s->wg_denoms[1]; + if ((mm_m && (n > crossover_medium)) || !mm_s) { + return aligned ? mmp->a_m : mmp->m; + } + return aligned ? mmp->a_s : mmp->s; + } + + if ((mm_s && (m <= 32 || n <= 32)) || (!mm_m && !mm_l)) { + return aligned ? mmp->a_s : mmp->s; + } + if ((mm_m && (m <= 64 || n <= 64)) || !mm_l) { + return aligned ? mmp->a_m : mmp->m; + } + return aligned ? mmp->a_l : mmp->l; +} + +static uint32_t ggml_vk_guess_matmul_pipeline_align(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, int m, int n, ggml_type src0_type, ggml_type src1_type) { + VK_LOG_DEBUG("ggml_vk_guess_matmul_pipeline_align(" << m << ", " << n << ", " << ggml_type_name(src0_type) << ", " << ggml_type_name(src1_type) << ")"); + return ggml_vk_guess_matmul_pipeline(ctx, mmp, m, n, true, src0_type, src1_type)->align; +} + +static void ggml_vk_matmul( + ggml_backend_vk_context * ctx, vk_context& subctx, vk_pipeline& pipeline, + vk_subbuffer&& a, vk_subbuffer&& b, vk_subbuffer&& d, vk_subbuffer&& split_k_buffer, + uint32_t m, uint32_t n, uint32_t k, uint32_t stride_a, uint32_t stride_b, uint32_t stride_d, + uint32_t batch_stride_a, uint32_t batch_stride_b, uint32_t batch_stride_d, + uint32_t split_k, uint32_t batch, uint32_t ne02, uint32_t ne12, uint32_t broadcast2, uint32_t broadcast3, + uint32_t padded_n) { + VK_LOG_DEBUG("ggml_vk_matmul(a: (" << a.buffer->buffer << ", " << a.offset << ", " << a.size << "), b: (" << b.buffer->buffer << ", " << b.offset << ", " << b.size << "), d: (" << d.buffer->buffer << ", " << d.offset << ", " << d.size << "), split_k: (" << (split_k_buffer.buffer != nullptr ? split_k_buffer.buffer->buffer : VK_NULL_HANDLE) << ", " << split_k_buffer.offset << ", " << split_k_buffer.size << "), m: " << m << ", n: " << n << ", k: " << k << ", stride_a: " << stride_a << ", stride_b: " << stride_b << ", stride_d: " << stride_d << ", batch_stride_a: " << batch_stride_a << ", batch_stride_b: " << batch_stride_b << ", batch_stride_d: " << batch_stride_d << ", split_k: " << split_k << ", batch: " << batch << ", ne02: " << ne02 << ", ne12: " << ne12 << ", broadcast2: " << broadcast2 << ", broadcast3: " << broadcast3 << ", padded_n: " << padded_n << ")"); + if (split_k == 1) { + ggml_pipeline_request_descriptor_sets(ctx, pipeline, CEIL_DIV(batch, ctx->device->properties.limits.maxComputeWorkGroupCount[2])); + + uint32_t base_work_group_z = 0; + while (base_work_group_z < batch) { + uint32_t groups_z = std::min(batch - base_work_group_z, ctx->device->properties.limits.maxComputeWorkGroupCount[2]); + + const vk_mat_mat_push_constants pc = { m, n, k, stride_a, stride_b, stride_d, batch_stride_a, batch_stride_b, batch_stride_d, base_work_group_z, batch, k, ne02, ne12, broadcast2, broadcast3, padded_n }; + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { a, b, d }, pc, { m, n, groups_z }); + base_work_group_z += groups_z; + } + return; + } + + if (ctx->prealloc_split_k_need_sync) { + ggml_vk_sync_buffers(ctx, subctx); + } + + GGML_ASSERT(batch_stride_d == m * n); + + // Round the split size up to a multiple of 256 (k-quant alignment) + uint32_t k_split = CEIL_DIV(k, split_k); + k_split = ROUNDUP_POW2(k_split, 256); + + ggml_pipeline_request_descriptor_sets(ctx, pipeline, CEIL_DIV(batch, ctx->device->properties.limits.maxComputeWorkGroupCount[2])); + + uint32_t base_work_group_z = 0; + while (base_work_group_z < batch) { + uint32_t groups_z = std::min(batch - base_work_group_z, ctx->device->properties.limits.maxComputeWorkGroupCount[2]); + + const vk_mat_mat_push_constants pc1 = { m, n, k, stride_a, stride_b, stride_d, batch_stride_a, batch_stride_b, batch_stride_d, base_work_group_z, batch, k_split, ne02, ne12, broadcast2, broadcast3, padded_n }; + // Make sure enough workgroups get assigned for split k to work + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { a, b, split_k_buffer }, pc1, { (CEIL_DIV(m, pipeline->wg_denoms[0]) * pipeline->wg_denoms[0]) * split_k, n, groups_z }); + base_work_group_z += groups_z; + } + ggml_vk_sync_buffers(ctx, subctx); + const std::array pc2 = { (uint32_t)(m * n * batch), split_k }; + ggml_vk_dispatch_pipeline(ctx, subctx, ctx->device->pipeline_matmul_split_k_reduce, { split_k_buffer, d }, pc2, { m * n * batch, 1, 1 }); + ctx->prealloc_split_k_need_sync = true; +} + +static vk_pipeline ggml_vk_guess_matmul_id_pipeline(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, uint32_t m, uint32_t n, bool aligned, ggml_type src0_type, ggml_type src1_type) { + VK_LOG_DEBUG("ggml_vk_guess_matmul_id_pipeline(" << m << ", " << n << ", " << aligned << ", " << ggml_type_name(src0_type) << ", " << ggml_type_name(src1_type) << ")"); + + // The q8_1 (integer dot) mmq path uses a different shader with its own + // shared-memory layout, so use the int-specific availability flags. + const bool is_q8_1 = (src1_type == GGML_TYPE_Q8_1); + const bool mm_l = is_q8_1 ? ctx->device->mul_mat_id_l_int[src0_type] : ctx->device->mul_mat_id_l[src0_type]; + const bool mm_m = is_q8_1 ? ctx->device->mul_mat_id_m_int[src0_type] : ctx->device->mul_mat_id_m[src0_type]; + const bool mm_s = is_q8_1 ? ctx->device->mul_mat_id_s_int[src0_type] : ctx->device->mul_mat_id_s[src0_type]; + + if (ctx->device->coopmat2) { + // Use large shader when the N dimension is greater than the medium shader's tile size + uint32_t crossover_large = mmp->m->wg_denoms[1]; + if ((mm_l && (n > crossover_large)) || (!mm_m && !mm_s)) { + return aligned ? mmp->a_l : mmp->l; + } + // Use medium shader when the N dimension is greater than the small shader's tile size + uint32_t crossover_medium = mmp->s->wg_denoms[1]; + if ((mm_m && (n > crossover_medium)) || !mm_s) { + return aligned ? mmp->a_m : mmp->m; + } + return aligned ? mmp->a_s : mmp->s; + } + + if ((mm_s && (m <= 32 || n <= 32)) || (!mm_m && !mm_l)) { + return aligned ? mmp->a_s : mmp->s; + } + if ((mm_m && (m <= 64 || n <= 64)) || !mm_l) { + return aligned ? mmp->a_m : mmp->m; + } + return aligned ? mmp->a_l : mmp->l; +} + +static uint32_t ggml_vk_guess_matmul_id_pipeline_align(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, int m, int n, ggml_type src0_type, ggml_type src1_type) { + VK_LOG_DEBUG("ggml_vk_guess_matmul_pipeline_align(" << m << ", " << n << ", " << ggml_type_name(src0_type) << ", " << ggml_type_name(src1_type) << ")"); + return ggml_vk_guess_matmul_id_pipeline(ctx, mmp, m, n, true, src0_type, src1_type)->align; +} + +static void ggml_vk_matmul_id( + ggml_backend_vk_context * ctx, vk_context& subctx, vk_pipeline& pipeline, + vk_subbuffer&& a, vk_subbuffer&& b, vk_subbuffer&& d, vk_subbuffer&& ids, const vk_subbuffer & expert_count_buf, + uint32_t m, uint32_t n, uint32_t k, uint32_t stride_a, uint32_t stride_b, uint32_t stride_d, + uint32_t batch_stride_a, uint32_t batch_stride_b, uint32_t batch_stride_d, + uint32_t n_as, uint32_t nei0, uint32_t nei1, uint32_t nbi1, uint32_t ne11, + uint32_t padded_n) { + VK_LOG_DEBUG("ggml_vk_matmul_id(a: (" << a.buffer->buffer << ", " << a.offset << ", " << a.size << "), b: (" << b.buffer->buffer << ", " << b.offset << ", " << b.size << "), d: (" << d.buffer->buffer << ", " << d.offset << ", " << d.size << "), ids: (" << ids.buffer->buffer << ", " << ids.offset << ", " << ids.size << "), expert_count: (" << expert_count_buf.buffer->buffer << ", " << expert_count_buf.offset << ", " << expert_count_buf.size << "), " << + "m: " << m << ", n: " << n << ", k: " << k << ", stride_a: " << stride_a << ", stride_b: " << stride_b << ", stride_d: " << stride_d << ", " << + "batch_stride_a: " << batch_stride_a << ", batch_stride_b: " << batch_stride_b << ", batch_stride_d: " << batch_stride_d << ", " << + "n_as: " << n_as << ", nei0: " << nei0 << ", nei1: " << nei1 << ", nbi1: " << nbi1 << ", ne11: " << ne11 << ")"); + const vk_mat_mat_id_push_constants pc = { m, n, k, stride_a, stride_b, stride_d, batch_stride_a, batch_stride_b, batch_stride_d, + nei0, nei1, nbi1, ne11, padded_n }; + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { a, b, d, ids, expert_count_buf }, pc, { m, nei1, n_as }); +} + +static bool ggml_vk_dim01_contiguous(const ggml_tensor * tensor) { + return + tensor->nb[0] == ggml_type_size(tensor->type) && + tensor->nb[1] == (tensor->nb[0]*tensor->ne[0])/ggml_blck_size(tensor->type) && + (tensor->ne[3] == 1 || tensor->nb[3] == tensor->nb[2]*tensor->ne[2]); +} + +static vk_pipeline ggml_vk_get_cpy_pipeline(ggml_backend_vk_context * ctx, const ggml_tensor * src, const ggml_tensor * dst, ggml_type to) { + + // Choose "contiguous copy" shader if src/dst are contiguous + bool contig = ggml_is_contiguous(src) && (!dst || ggml_is_contiguous(dst)); + + // Use optimized "transpose" shader if src dim1 is the innermost dimension. + bool transpose = dst && src->nb[1] == ggml_type_size(to) && ggml_are_same_shape(dst, src); + + if (transpose && src->type == to) { + if (ggml_type_size(to) == 4) { + return ctx->device->pipeline_cpy_transpose_32; + } else if (ggml_type_size(to) == 2) { + return ctx->device->pipeline_cpy_transpose_16; + } + } + + if (src->type == GGML_TYPE_F32 && to == GGML_TYPE_F32) { + if (contig) { + return ctx->device->pipeline_contig_cpy_f32_f32; + } else { + return ctx->device->pipeline_cpy_f32_f32; + } + } + if (src->type == GGML_TYPE_F32 && to == GGML_TYPE_F16) { + if (contig) { + return ctx->device->pipeline_contig_cpy_f32_f16; + } else { + return ctx->device->pipeline_cpy_f32_f16; + } + } + if (src->type == GGML_TYPE_F16 && to == GGML_TYPE_F16) { + if (contig) { + return ctx->device->pipeline_contig_cpy_f16_f16; + } else { + return ctx->device->pipeline_cpy_f16_f16; + } + } + if (src->type == GGML_TYPE_F16 && to == GGML_TYPE_F32) { + if (contig) { + return ctx->device->pipeline_contig_cpy_f16_f32; + } else { + return ctx->device->pipeline_cpy_f16_f32; + } + } + if (src->type == GGML_TYPE_F32 && to == GGML_TYPE_BF16) { + if (contig) { + return ctx->device->pipeline_contig_cpy_f32_bf16; + } else { + return ctx->device->pipeline_cpy_f32_bf16; + } + } + if (src->type == GGML_TYPE_BF16 && to == GGML_TYPE_F32) { + if (contig) { + return ctx->device->pipeline_contig_cpy_bf16_f32; + } else { + return ctx->device->pipeline_cpy_bf16_f32; + } + } + if (src->type == GGML_TYPE_F32 && to == GGML_TYPE_I32) { + if (contig) { + return ctx->device->pipeline_contig_cpy_f32_i32; + } else { + return ctx->device->pipeline_cpy_f32_i32; + } + } + if (src->type == GGML_TYPE_I32 && to == GGML_TYPE_F32) { + if (contig) { + return ctx->device->pipeline_contig_cpy_i32_f32; + } else { + return ctx->device->pipeline_cpy_i32_f32; + } + } + if (src->type == GGML_TYPE_F32) { + switch (to) { + case GGML_TYPE_Q1_0: + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q4_1: + case GGML_TYPE_Q5_0: + case GGML_TYPE_Q5_1: + case GGML_TYPE_Q8_0: + case GGML_TYPE_IQ4_NL: + return ctx->device->pipeline_cpy_f32_quant[to]; + default: + break; + } + } + + if (to == GGML_TYPE_F32) { + switch (src->type) { + case GGML_TYPE_Q1_0: + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q4_1: + case GGML_TYPE_Q5_0: + case GGML_TYPE_Q5_1: + case GGML_TYPE_Q8_0: + case GGML_TYPE_IQ4_NL: + return ctx->device->pipeline_cpy_quant_f32[src->type]; + default: + break; + } + } + + if (src->type == to) { + // Copy two or four bytes at a time, depending on block size. + // For quantized types, we scale by block size/type size. But + // this path is also used for bf16->bf16 for example, where the + // type size must be exactly 2 or 4. + GGML_ASSERT(ggml_is_quantized(to) || ggml_type_size(src->type) == 2 || ggml_type_size(src->type) == 4); + if ((ggml_type_size(src->type) % 4) == 0) { + if (contig) { + return ctx->device->pipeline_contig_cpy_f32_f32; + } else { + return ctx->device->pipeline_cpy_f32_f32; + } + } else { + if (contig) { + return ctx->device->pipeline_contig_cpy_f16_f16; + } else { + return ctx->device->pipeline_cpy_f16_f16; + } + } + } + + std::cerr << "Missing CPY op for types: " << ggml_type_name(src->type) << " " << ggml_type_name(to) << std::endl; + GGML_ABORT("fatal error"); +} + +static void ggml_vk_cpy_to_contiguous(ggml_backend_vk_context * ctx, vk_context& subctx, vk_pipeline pipeline, const ggml_tensor * tensor, const vk_subbuffer & in, const vk_subbuffer & out) { + VK_LOG_DEBUG("ggml_vk_cpy_to_contiguous((" << tensor << ", type=" << tensor->type << ", ne0=" << tensor->ne[0] << ", ne1=" << tensor->ne[1] << ", ne2=" << tensor->ne[2] << ", ne3=" << tensor->ne[3] << ", nb0=" << tensor->nb[0] << ", nb1=" << tensor->nb[1] << ", nb2=" << tensor->nb[2] << ", nb3=" << tensor->nb[3] << "), "; + std::cerr << "buffer in size=" << in.buffer->size << ", buffer out size=" << out.buffer->size << ")"); + + const uint32_t ne = ggml_nelements(tensor); + std::array elements; + + if (ne > 262144) { + elements = { 512, 512, CEIL_DIV(ne, 262144) }; + } else if (ne > 512) { + elements = { 512, CEIL_DIV(ne, 512), 1 }; + } else { + elements = { ne, 1, 1 }; + } + + vk_op_unary_push_constants pc = vk_op_unary_push_constants_init(tensor, tensor, ne); + pc.nb10 = 1; + pc.nb11 = (uint32_t)tensor->ne[0]; + pc.nb12 = (uint32_t)(tensor->ne[0] * tensor->ne[1]); + pc.nb13 = (uint32_t)(tensor->ne[0] * tensor->ne[1] * tensor->ne[2]); + init_pushconst_fastdiv(pc); + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { in, out }, pc, elements); + ggml_vk_sync_buffers(ctx, subctx); +} + +// Copy/convert tensor into a caller-defined dense layout. Destination strides +// are in output elements, not bytes. +static void ggml_vk_cpy_to_strided( + ggml_backend_vk_context * ctx, vk_context& subctx, vk_pipeline pipeline, const ggml_tensor * tensor, + const vk_subbuffer & in, const vk_subbuffer & out, + uint32_t nb10, uint32_t nb11, uint32_t nb12, uint32_t nb13) { + VK_LOG_DEBUG("ggml_vk_cpy_to_strided((" << tensor << ", type=" << tensor->type << ", ne0=" << tensor->ne[0] << ", ne1=" << tensor->ne[1] << ", ne2=" << tensor->ne[2] << ", ne3=" << tensor->ne[3] << ", nb0=" << tensor->nb[0] << ", nb1=" << tensor->nb[1] << ", nb2=" << tensor->nb[2] << ", nb3=" << tensor->nb[3] << "), "; + std::cerr << "dst_nb=(" << nb10 << ", " << nb11 << ", " << nb12 << ", " << nb13 << "), buffer in size=" << in.buffer->size << ", buffer out size=" << out.buffer->size << ")"); + + const uint32_t ne = ggml_nelements(tensor); + std::array elements; + + if (ne > 262144) { + elements = { 512, 512, CEIL_DIV(ne, 262144) }; + } else if (ne > 512) { + elements = { 512, CEIL_DIV(ne, 512), 1 }; + } else { + elements = { ne, 1, 1 }; + } + + vk_op_unary_push_constants pc = vk_op_unary_push_constants_init(tensor, tensor, ne); + pc.nb10 = nb10; + pc.nb11 = nb11; + pc.nb12 = nb12; + pc.nb13 = nb13; + init_pushconst_fastdiv(pc); + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { in, out }, pc, elements); + ggml_vk_sync_buffers(ctx, subctx); +} + +static vk_pipeline ggml_vk_get_quantize_pipeline(ggml_backend_vk_context * ctx, ggml_type type) { + switch(type) { + case GGML_TYPE_Q8_1: + return ctx->device->pipeline_quantize_q8_1_x4; + default: + std::cerr << "Missing quantize pipeline for type: " << ggml_type_name(type) << std::endl; + GGML_ABORT("fatal error"); + } +} + +static void ggml_vk_quantize_q8_1(ggml_backend_vk_context * ctx, vk_context& subctx, const vk_subbuffer & in, const vk_subbuffer & out, uint32_t ne) { + VK_LOG_DEBUG("ggml_vk_quantize_q8_1(" << "buffer in size=" << in.buffer->size << ", buffer out size=" << out.buffer->size << ", " << ne << ")"); + + vk_pipeline pipeline = ggml_vk_get_quantize_pipeline(ctx, GGML_TYPE_Q8_1); + + const uint32_t num_blocks = CEIL_DIV(ne, pipeline->wg_denoms[0]); + // clamp the number of elements to the max workgroup count. The shader will iterate over the total number of blocks. + const uint64_t max_elements = std::min(uint64_t{ctx->device->properties.limits.maxComputeWorkGroupCount[0]} * pipeline->wg_denoms[0], std::numeric_limits::max()); + const uint32_t elements = std::min(ne, static_cast(max_elements)); + + const vk_quantize_q8_1_push_constants pc = { + ne, + num_blocks, + }; + + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { in, out }, pc, { elements, 1, 1 }); + ggml_vk_sync_buffers(ctx, subctx); +} + +static vk_pipeline ggml_vk_get_64b_indexing_pipeline(ggml_backend_vk_context * ctx, vk_pipeline &pipeline) { + GGML_UNUSED(ctx); +#if defined(VK_EXT_shader_64bit_indexing) + vk_pipeline *ptr = &pipeline; + while (*ptr) { + if ((*ptr)->is_64b_indexing) { + return *ptr; + } + ptr = &(*ptr)->next; + } +#endif + return pipeline; +} + +static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool disable_split_k) { + VK_LOG_DEBUG("ggml_vk_mul_mat_q_f16((" << src0 << ", name=" << src0->name << ", type=" << ggml_type_name(src0->type) << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; + std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << ggml_type_name(src1->type) << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; + std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << ggml_type_name(dst->type) << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3]; + std::cerr << "))"); + GGML_ASSERT(ggml_vk_dim01_contiguous(src0) || src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || src0->type == GGML_TYPE_BF16); // NOLINT + GGML_ASSERT(ggml_vk_dim01_contiguous(src1) || src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16); // NOLINT + + const uint64_t ne00 = src0->ne[0]; + const uint64_t ne01 = src0->ne[1]; + const uint64_t ne02 = src0->ne[2]; + const uint64_t ne03 = src0->ne[3]; + + const uint64_t ne10 = src1->ne[0]; + const uint64_t ne11 = src1->ne[1]; + const uint64_t ne12 = src1->ne[2]; + const uint64_t ne13 = src1->ne[3]; + + const uint64_t ne21 = dst->ne[1]; + const uint32_t stride_d = dst->nb[1] / ggml_type_size(dst->type); + const uint32_t stride_batch_d = stride_d*ne21; + + const uint64_t r2 = ne12 / ne02; + const uint64_t r3 = ne13 / ne03; + + ggml_backend_vk_buffer_context * dst_buf_ctx = (ggml_backend_vk_buffer_context *)dst->buffer->context; + ggml_backend_vk_buffer_context * src0_buf_ctx = (ggml_backend_vk_buffer_context *)src0->buffer->context; + ggml_backend_vk_buffer_context * src1_buf_ctx = (ggml_backend_vk_buffer_context *)src1->buffer->context; + + vk_buffer d_Qx = nullptr; + size_t qx_buf_offset = 0; + vk_buffer d_Qy = nullptr; + size_t qy_buf_offset = 0; + + bool src0_uma = false; + bool src1_uma = false; + + if (ctx->device->uma) { + ggml_vk_host_get(ctx->device, src0->data, d_Qx, qx_buf_offset); + ggml_vk_host_get(ctx->device, src1->data, d_Qy, qy_buf_offset); + src0_uma = d_Qx != nullptr; + src1_uma = d_Qy != nullptr; + } + + // Reformat and convert to fp16 if non-contiguous, or for coopmat2 for better perf + const bool x_non_contig = (ctx->device->coopmat2 && src0->type == GGML_TYPE_F32) || + !ggml_vk_dim01_contiguous(src0); + const bool y_non_contig = (ctx->device->coopmat2 && src1->type == GGML_TYPE_F32) || + (src0->type == GGML_TYPE_BF16 && src1->type != GGML_TYPE_BF16) || + !ggml_vk_dim01_contiguous(src1); + + // If src0 is BF16, try to use a BF16 x BF16 multiply + ggml_type f16_type = src0->type == GGML_TYPE_BF16 ? GGML_TYPE_BF16 : GGML_TYPE_F16; + + const bool y_f32_kernel = src1->type == GGML_TYPE_F32 && !y_non_contig; + + bool quantize_y = ctx->device->integer_dot_product && src1->type == GGML_TYPE_F32 && ggml_is_contiguous(src1) && !y_non_contig && (ne11 * ne10) % 4 == 0; + + // Check for mmq first + vk_matmul_pipeline mmp = quantize_y ? ggml_vk_get_mul_mat_mat_pipeline(ctx, src0->type, GGML_TYPE_Q8_1, (ggml_prec)dst->op_params[0]) : nullptr; + + if (mmp == nullptr) { + // Fall back to f16 dequant mul mat + mmp = ggml_vk_get_mul_mat_mat_pipeline(ctx, src0->type, y_non_contig ? f16_type : src1->type, (ggml_prec)dst->op_params[0]); + quantize_y = false; + } + + const bool qx_needs_dequant = mmp == nullptr || x_non_contig; + const bool qy_needs_dequant = !quantize_y && ((src1->type != f16_type && !y_f32_kernel) || y_non_contig); + + if (qx_needs_dequant) { + // Fall back to dequant + f16 mulmat + mmp = ggml_vk_get_mul_mat_mat_pipeline(ctx, f16_type, y_f32_kernel ? GGML_TYPE_F32 : f16_type, (ggml_prec)dst->op_params[0]); + } + + // Not implemented + GGML_ASSERT(y_non_contig || !qy_needs_dequant); // NOLINT + + const ggml_type effective_src1_type = quantize_y ? GGML_TYPE_Q8_1 : (y_f32_kernel ? GGML_TYPE_F32 : src1->type); + + const uint32_t kpad = quantize_y ? 0 : ggml_vk_align_size(ne10, ggml_vk_guess_matmul_pipeline_align(ctx, mmp, ne01, ne11, qx_needs_dequant ? f16_type : src0->type, effective_src1_type)); + const bool aligned = !quantize_y && ne10 == kpad && ne01 > 8 && ne11 > 8; + + vk_pipeline pipeline = ggml_vk_guess_matmul_pipeline(ctx, mmp, ne01, ne11, aligned, qx_needs_dequant ? f16_type : src0->type, effective_src1_type); + + if (ggml_nbytes(src0) > ctx->device->properties.limits.maxStorageBufferRange) { + pipeline = ggml_vk_get_64b_indexing_pipeline(ctx, pipeline); + } + + // Reserve extra storage in the N dimension for the Y matrix, so we can avoid bounds-checking + uint32_t padded_n = qy_needs_dequant ? ROUNDUP_POW2(ne11, pipeline->wg_denoms[1]) : ne11; + const uint64_t x_ne = ggml_nelements(src0); + // 128 elements per Q8_1 x4 block + const uint64_t y_ne = padded_n * ne10 * ne12 * ne13; + const uint64_t d_ne = ggml_nelements(dst); + + const uint32_t split_k = ggml_vk_guess_split_k(ctx, ne01, ne11, ne10, disable_split_k, pipeline); + + const uint64_t qx_sz = ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type); + const uint64_t qy_sz = ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type); + const uint64_t x_sz = !qx_needs_dequant ? qx_sz : sizeof(ggml_fp16_t) * x_ne; + const uint64_t y_sz = quantize_y ? (ggml_vk_align_size(y_ne, 128) * ggml_type_size(GGML_TYPE_Q8_1) / ggml_blck_size(GGML_TYPE_Q8_1)) : (y_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne); + const uint64_t d_sz = sizeof(float) * d_ne; + + vk_pipeline to_fp16_vk_0 = nullptr; + vk_pipeline to_fp16_vk_1 = nullptr; + vk_pipeline to_q8_1 = nullptr; + + if (x_non_contig) { + to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0, nullptr, f16_type); + } else { + to_fp16_vk_0 = ggml_vk_get_to_fp16(ctx, src0->type); + } + if (y_non_contig) { + to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(ctx, src1, nullptr, f16_type); + } else { + to_fp16_vk_1 = ggml_vk_get_to_fp16(ctx, src1->type); + } + GGML_ASSERT(!qx_needs_dequant || to_fp16_vk_0 != nullptr); // NOLINT + GGML_ASSERT(!qy_needs_dequant || to_fp16_vk_1 != nullptr); // NOLINT + + if (quantize_y) { + to_q8_1 = ggml_vk_get_quantize_pipeline(ctx, GGML_TYPE_Q8_1); + } + + { + const uint64_t split_k_size = split_k > 1 ? d_sz * split_k : 0; + if ( + (qx_needs_dequant && x_sz > ctx->device->properties.limits.maxStorageBufferRange) || + (qy_needs_dequant && y_sz > ctx->device->properties.limits.maxStorageBufferRange) || + (split_k > 1 && split_k_size > ctx->device->properties.limits.maxStorageBufferRange)) { + GGML_ABORT("Requested preallocation size is too large"); + } + if (qx_needs_dequant && ctx->prealloc_size_x < x_sz) { + ctx->prealloc_size_x = x_sz; + ggml_vk_preallocate_buffers(ctx, subctx); + } + if ((qy_needs_dequant || quantize_y) && ctx->prealloc_size_y < y_sz) { + ctx->prealloc_size_y = y_sz; + ggml_vk_preallocate_buffers(ctx, subctx); + } + if (split_k > 1 && ctx->prealloc_size_split_k < split_k_size) { + ctx->prealloc_size_split_k = split_k_size; + ggml_vk_preallocate_buffers(ctx, subctx); + } + + // Request descriptor sets + if (qx_needs_dequant) { + ggml_pipeline_request_descriptor_sets(ctx, to_fp16_vk_0, 1); + } + if (qy_needs_dequant) { + ggml_pipeline_request_descriptor_sets(ctx, to_fp16_vk_1, 1); + } + if (quantize_y) { + ggml_pipeline_request_descriptor_sets(ctx, to_q8_1, 1); + } + if (split_k > 1) { + ggml_pipeline_request_descriptor_sets(ctx, ctx->device->pipeline_matmul_split_k_reduce, 1); + } + } + + vk_buffer d_D = dst_buf_ctx->dev_buffer; + const uint64_t d_buf_offset = vk_tensor_offset(dst) + dst->view_offs; + GGML_ASSERT(d_D != nullptr); + GGML_ASSERT(d_D->size >= d_buf_offset + d_sz); + vk_buffer d_X; + uint64_t x_buf_offset = 0; + vk_buffer d_Y; + uint64_t y_buf_offset = 0; + if (!src0_uma) { + d_Qx = src0_buf_ctx->dev_buffer; + qx_buf_offset = vk_tensor_offset(src0) + src0->view_offs; + GGML_ASSERT(d_Qx != nullptr); + } + if (!src1_uma) { + d_Qy = src1_buf_ctx->dev_buffer; + qy_buf_offset = vk_tensor_offset(src1) + src1->view_offs; + GGML_ASSERT(d_Qy != nullptr); + } + if (qx_needs_dequant) { + d_X = ctx->prealloc_x; + GGML_ASSERT(d_X->size >= x_sz); + } else { + d_X = d_Qx; + x_buf_offset = qx_buf_offset; + GGML_ASSERT(qx_sz == x_sz); + } + if (qy_needs_dequant) { + d_Y = ctx->prealloc_y; + GGML_ASSERT(d_Y->size >= y_sz); + } else if (quantize_y) { + d_Y = ctx->prealloc_y; + GGML_ASSERT(d_Y->size >= CEIL_DIV(y_sz, 144) * 144); + } else { + d_Y = d_Qy; + y_buf_offset = qy_buf_offset; + GGML_ASSERT(qy_sz == y_sz); + } + + if (x_non_contig || qx_needs_dequant) { + if (ctx->prealloc_x_need_sync) { + ggml_vk_sync_buffers(ctx, subctx); + } + } + + if (x_non_contig) { + ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_0, src0, ggml_vk_subbuffer(ctx, d_Qx, qx_buf_offset), ggml_vk_subbuffer(ctx, d_X, 0)); + } else if (qx_needs_dequant) { + const std::vector pc = { (uint32_t)ne01, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)(ggml_nelements(src0)) }; + ggml_vk_dispatch_pipeline(ctx, subctx, to_fp16_vk_0, { vk_subbuffer{ d_Qx, qx_buf_offset, qx_sz }, vk_subbuffer{ d_X, 0, x_sz } }, pc, { (uint32_t)(x_ne), 1, 1}); + ggml_vk_sync_buffers(ctx, subctx); + } + if (y_non_contig) { + if (ctx->prealloc_y_last_pipeline_used != to_fp16_vk_1.get() || + ctx->prealloc_y_last_tensor_used != src1 || + ctx->prealloc_y_last_decode_vector_staging) { + if (ctx->prealloc_y_need_sync) { + ggml_vk_sync_buffers(ctx, subctx); + } + ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, ggml_vk_subbuffer(ctx, d_Qy, qy_buf_offset), ggml_vk_subbuffer(ctx, d_Y, 0)); + ctx->prealloc_y_last_pipeline_used = to_fp16_vk_1.get(); + ctx->prealloc_y_last_tensor_used = src1; + ctx->prealloc_y_last_decode_vector_staging = false; + } + } + if (quantize_y) { + if (ctx->prealloc_y_last_pipeline_used != to_q8_1.get() || + ctx->prealloc_y_last_tensor_used != src1 || + ctx->prealloc_y_last_decode_vector_staging) { + if (ctx->prealloc_y_need_sync) { + ggml_vk_sync_buffers(ctx, subctx); + } + ggml_vk_quantize_q8_1(ctx, subctx, ggml_vk_subbuffer(ctx, d_Qy, qy_buf_offset), ggml_vk_subbuffer(ctx, d_Y, 0), y_ne); + ctx->prealloc_y_last_pipeline_used = to_q8_1.get(); + ctx->prealloc_y_last_tensor_used = src1; + ctx->prealloc_y_last_decode_vector_staging = false; + } + } + + uint32_t stride_batch_x = ne00*ne01; + uint32_t stride_batch_y = ne10*ne11; + + if (!ggml_vk_dim01_contiguous(src0) && !qx_needs_dequant) { + stride_batch_x = src0->nb[0] / ggml_type_size(src0->type); + } + + if (!ggml_vk_dim01_contiguous(src1) && !qy_needs_dequant && !quantize_y) { + stride_batch_y = src1->nb[0] / ggml_type_size(src1->type); + } + + // compute + ggml_vk_matmul( + ctx, subctx, pipeline, + { d_X, x_buf_offset, x_sz }, { d_Y, y_buf_offset, y_sz }, + ggml_vk_subbuffer(ctx, d_D, d_buf_offset), { ctx->prealloc_split_k, 0, d_sz * split_k }, + ne01, ne11, ne10, + ne10, ne10, stride_d, stride_batch_x, stride_batch_y, stride_batch_d, + split_k, ne12*ne13, ne02, ne12, r2, r3, padded_n + ); // NOLINT + + if (x_non_contig || qx_needs_dequant) { + ctx->prealloc_x_need_sync = true; + } + if (y_non_contig || quantize_y) { + ctx->prealloc_y_need_sync = true; + } +} + +// Device tuning +static bool ggml_vk_should_use_mmvq(const vk_device& device, uint32_t m, uint32_t n, uint32_t k, ggml_type src0_type) { + if (device->mmvq_mode == 1) { + return true; + } else if (device->mmvq_mode == -1) { + return false; + } + + // q6_k only has 2-byte alignment which makes it somewhat problematic, + // using MMVQ is only a win on Intel. + bool mmvq_q6 = device->vendor_id == VK_VENDOR_ID_INTEL; + if (src0_type == GGML_TYPE_Q6_K && !mmvq_q6) { + return false; + } + + // MMVQ is generally good for batches + if (n > 1) { + return true; + } + + // 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 || src0_type == GGML_TYPE_Q3_K || src0_type == GGML_TYPE_IQ1_S || src0_type == GGML_TYPE_IQ1_M) { + return true; + } + + if (k <= 4096) { + return false; + } + + switch (src0_type) { + case GGML_TYPE_MXFP4: + case GGML_TYPE_Q8_0: + return device->architecture == vk_device_architecture::NVIDIA_PRE_TURING; + default: + return true; + } + case VK_VENDOR_ID_AMD: + if (k < 2048) { + return false; + } + + switch (src0_type) { + case GGML_TYPE_Q8_0: + return device->architecture == vk_device_architecture::AMD_GCN; + default: + return true; + } + case VK_VENDOR_ID_INTEL: + if (device->architecture == vk_device_architecture::INTEL_XE2) { + if (src0_type == GGML_TYPE_Q2_K || src0_type == GGML_TYPE_Q3_K || src0_type == GGML_TYPE_Q6_K) { + return true; + } + } + + if (device->driver_id == vk::DriverId::eIntelProprietaryWindows) { + // Intel Windows proprietary driver MMVQ performance for !Q2/Q3/Q6 is worse than fp16, + // see https://github.com/ggml-org/llama.cpp/issues/17628 and + // https://github.com/ggml-org/llama.cpp/pull/23056 + return false; + } + + if (k < 2048) { + return false; + } + + switch (src0_type) { + // From tests on A770 Linux, may need more tuning + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q5_1: + return false; + default: + return true; + } + default: + return true; + } + + GGML_UNUSED(m); +} + +static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context& subctx, const struct ggml_cgraph * cgraph, int node_idx) { + ggml_tensor * dst = cgraph->nodes[node_idx]; + const ggml_tensor * src0 = dst->src[0]; + const ggml_tensor * src1 = dst->src[1]; + + VK_LOG_DEBUG("ggml_vk_mul_mat_vec_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; + std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; + std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3]; + std::cerr << ")),)"); + GGML_ASSERT(ggml_vk_dim01_contiguous(src0) || src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || src0->type == GGML_TYPE_BF16); // NOLINT + GGML_ASSERT(ggml_vk_dim01_contiguous(src1) || src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16); // NOLINT + + const uint64_t ne00 = src0->ne[0]; + const uint64_t ne01 = src0->ne[1]; + const uint64_t ne02 = src0->ne[2]; + const uint64_t ne03 = src0->ne[3]; + + const uint64_t ne10 = src1->ne[0]; + const uint64_t ne11 = src1->ne[1]; + const uint64_t ne12 = src1->ne[2]; + const uint64_t ne13 = src1->ne[3]; + + const uint64_t ne20 = dst->ne[0]; + const uint64_t ne21 = dst->ne[1]; + // const uint64_t ne22 = dst->ne[2]; + // const uint64_t ne23 = dst->ne[3]; + + const uint64_t r2 = ne12 / ne02; + const uint64_t r3 = ne13 / ne03; + + // batch_n indicates that we need to compute a few vector results, and this assumes + // ne12 and ne13 are 1. It overloads the batch_strides to hold the row strides. + GGML_ASSERT(ne11 == 1 || ne12 * ne13 == 1); + bool batch_n = ne11 > 1; + + const bool x_non_contig = !ggml_vk_dim01_contiguous(src0); + const bool y_non_contig = !ggml_vk_dim01_contiguous(src1); + + const bool f16_f32_kernel = src1->type == GGML_TYPE_F32; + bool quantize_y = ctx->device->integer_dot_product && src1->type == GGML_TYPE_F32 && ggml_is_contiguous(src1) && !y_non_contig && (ne11 * ne10) % 4 == 0 && ggml_vk_should_use_mmvq(ctx->device, ne01, ne11, ne10, src0->type); + + vk_pipeline to_fp16_vk_0 = nullptr; + vk_pipeline to_fp16_vk_1 = nullptr; + if (x_non_contig) { + to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0, nullptr, src0->type); + } + if (y_non_contig) { + to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(ctx, src1, nullptr, src1->type); + } else { + to_fp16_vk_1 = ggml_vk_get_to_fp16(ctx, src1->type); + } + + // Check for mmq first + vk_pipeline dmmv = quantize_y ? ggml_vk_get_dequantize_mul_mat_vec(ctx, src0->type, GGML_TYPE_Q8_1, ne11, ne20, ne00) : nullptr; + vk_pipeline to_q8_1 = nullptr; + + if (dmmv == nullptr) { + // Fall back to f16 dequant mul mat + dmmv = ggml_vk_get_dequantize_mul_mat_vec(ctx, src0->type, src1->type, ne11, ne20, ne00); + quantize_y = false; + } + + if (quantize_y) { + to_q8_1 = ggml_vk_get_quantize_pipeline(ctx, GGML_TYPE_Q8_1); + } + + if (ggml_nbytes(src0) > ctx->device->properties.limits.maxStorageBufferRange) { + dmmv = ggml_vk_get_64b_indexing_pipeline(ctx, dmmv); + } + + const bool qx_needs_dequant = x_non_contig; + const bool qy_needs_dequant = !quantize_y && ((src1->type != GGML_TYPE_F16 && !f16_f32_kernel) || y_non_contig); + + // Not implemented + GGML_ASSERT(y_non_contig || !qy_needs_dequant); // NOLINT + + GGML_ASSERT(!qx_needs_dequant || to_fp16_vk_0 != nullptr); // NOLINT + GGML_ASSERT(!qy_needs_dequant || to_fp16_vk_1 != nullptr); // NOLINT + GGML_ASSERT(dmmv != nullptr); + + const uint64_t x_ne = ggml_nelements(src0); + const uint64_t y_ne = ggml_nelements(src1); + + const uint64_t qx_sz = ggml_vk_align_size(ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type), ctx->device->properties.limits.minStorageBufferOffsetAlignment); + const uint64_t x_sz = x_non_contig ? ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment) : qx_sz; + const uint64_t y_sz = quantize_y ? (ggml_vk_align_size(y_ne, 128) * ggml_type_size(GGML_TYPE_Q8_1) / ggml_blck_size(GGML_TYPE_Q8_1)) : + (f16_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne); + + { + if ( + (qx_needs_dequant && x_sz > ctx->device->properties.limits.maxStorageBufferRange) || + (qy_needs_dequant && y_sz > ctx->device->properties.limits.maxStorageBufferRange)) { + GGML_ABORT("Requested preallocation size is too large"); + } + if (qx_needs_dequant && ctx->prealloc_size_x < x_sz) { + ctx->prealloc_size_x = x_sz; + ggml_vk_preallocate_buffers(ctx, subctx); + } + if ((qy_needs_dequant || quantize_y) && ctx->prealloc_size_y < y_sz) { + ctx->prealloc_size_y = y_sz; + ggml_vk_preallocate_buffers(ctx, subctx); + } + + // Request descriptor sets + if (qx_needs_dequant) { + ggml_pipeline_request_descriptor_sets(ctx, to_fp16_vk_0, 1); + } + if (qy_needs_dequant) { + ggml_pipeline_request_descriptor_sets(ctx, to_fp16_vk_1, 1); + } + if (quantize_y) { + ggml_pipeline_request_descriptor_sets(ctx, to_q8_1, 1); + } + } + + vk_subbuffer d_D = ggml_vk_tensor_subbuffer(ctx, cgraph->nodes[node_idx + ctx->num_additional_fused_ops]); + vk_subbuffer d_Qx = ggml_vk_tensor_subbuffer(ctx, src0); + vk_subbuffer d_Qy = ggml_vk_tensor_subbuffer(ctx, src1); + vk_subbuffer d_X, d_Y; + + if (qx_needs_dequant) { + d_X = { ctx->prealloc_x, 0, ctx->prealloc_x->size }; + } else { + d_X = d_Qx; + GGML_ASSERT(qx_sz == x_sz); + } + if (qy_needs_dequant || quantize_y) { + d_Y = { ctx->prealloc_y, 0, ctx->prealloc_y->size }; + } else { + d_Y = d_Qy; + } + + if (x_non_contig) { + if (ctx->prealloc_x_need_sync) { + ggml_vk_sync_buffers(ctx, subctx); + } + + GGML_ASSERT(x_sz == ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment)); + ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_0, src0, d_Qx, d_X); + } + if (y_non_contig) { + GGML_ASSERT(y_sz == ggml_type_size(src1->type) * y_ne); + if (ctx->prealloc_y_last_pipeline_used != to_fp16_vk_1.get() || + ctx->prealloc_y_last_tensor_used != src1 || + ctx->prealloc_y_last_decode_vector_staging) { + if (ctx->prealloc_y_need_sync) { + ggml_vk_sync_buffers(ctx, subctx); + } + ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, d_Qy, d_Y); + ctx->prealloc_y_last_pipeline_used = to_fp16_vk_1.get(); + ctx->prealloc_y_last_tensor_used = src1; + ctx->prealloc_y_last_decode_vector_staging = false; + } + } + if (quantize_y) { + if (ctx->prealloc_y_last_pipeline_used != to_q8_1.get() || + ctx->prealloc_y_last_tensor_used != src1 || + ctx->prealloc_y_last_decode_vector_staging) { + if (ctx->prealloc_y_need_sync) { + ggml_vk_sync_buffers(ctx, subctx); + } + ggml_vk_quantize_q8_1(ctx, subctx, d_Qy, d_Y, y_ne); + ctx->prealloc_y_last_pipeline_used = to_q8_1.get(); + ctx->prealloc_y_last_tensor_used = src1; + ctx->prealloc_y_last_decode_vector_staging = false; + } + } + + // For batch_n, the A matrix is the same for each batch, and B/D use the row stride as the batch stride + uint32_t stride_batch_x = batch_n ? 0 : ne00*ne01; + uint32_t stride_batch_y = batch_n ? ne10 : (ne10*ne11); + uint32_t stride_batch_d = batch_n ? ne20 : (ne20*ne21); + + if (!ggml_vk_dim01_contiguous(src0) && !qx_needs_dequant) { + stride_batch_x = src0->nb[0] / ggml_type_size(src0->type); + } + + if (!ggml_vk_dim01_contiguous(src1) && !qy_needs_dequant) { + stride_batch_y = src1->nb[0] / ggml_type_size(src1->type); + } + + const uint32_t max_groups_x = ctx->device->properties.limits.maxComputeWorkGroupCount[0]; + + uint32_t groups_x = ne01; + uint32_t groups_z = 1; + + if (ne01 > max_groups_x) { + groups_z = 64; + groups_x = CEIL_DIV(groups_x, groups_z); + } + + uint32_t fusion_flags = 0; + + vk_subbuffer d_F0 = d_D; + if (ctx->num_additional_fused_ops > 0) { + const ggml_tensor * add = cgraph->nodes[node_idx + 1]; + const ggml_tensor * bias = add->src[0] == dst ? add->src[1] : add->src[0]; + + d_F0 = ggml_vk_tensor_subbuffer(ctx, bias); + fusion_flags |= MAT_VEC_FUSION_FLAGS_BIAS0; + } + + vk_subbuffer d_F1 = d_D; + if (ctx->num_additional_fused_ops == 2) { + const ggml_tensor * add = cgraph->nodes[node_idx + 2]; + const ggml_tensor * bias = add->src[0] == cgraph->nodes[node_idx + 1] ? add->src[1] : add->src[0]; + + d_F1 = ggml_vk_tensor_subbuffer(ctx, bias); + fusion_flags |= MAT_VEC_FUSION_FLAGS_BIAS1; + } + + ggml_pipeline_request_descriptor_sets(ctx, dmmv, CEIL_DIV(ne12 * ne13, ctx->device->properties.limits.maxComputeWorkGroupCount[1])); + + uint32_t base_work_group_y = 0; + while (base_work_group_y < ne12 * ne13) { + + uint32_t groups_y = std::min((uint32_t)(ne12 * ne13) - base_work_group_y, ctx->device->properties.limits.maxComputeWorkGroupCount[1]); + const vk_mat_vec_push_constants pc = { + (uint32_t)ne00, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)ne01, + stride_batch_x, stride_batch_y, stride_batch_d, + fusion_flags, base_work_group_y, + (uint32_t)ne02, (uint32_t)ne12, (uint32_t)r2, (uint32_t)r3, + }; + ggml_vk_dispatch_pipeline(ctx, subctx, dmmv, + { + d_X, + d_Y, + d_D, + d_F0, + d_F1, + }, + pc, { groups_x, groups_y, groups_z }); + base_work_group_y += groups_y; + } + + if (x_non_contig) { + ctx->prealloc_x_need_sync = true; + } + if (y_non_contig || quantize_y) { + ctx->prealloc_y_need_sync = true; + } +} + +static void ggml_vk_mul_mat_vec_p021_f16_f32(ggml_backend_vk_context * ctx, vk_context& subctx, const struct ggml_cgraph * cgraph, int node_idx) { + ggml_tensor * dst = cgraph->nodes[node_idx]; + const ggml_tensor * src0 = dst->src[0]; + const ggml_tensor * src1 = dst->src[1]; + VK_LOG_DEBUG("ggml_vk_mul_mat_p021_f16_f32(" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; + std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; + std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3]; + std::cerr << "))"); + GGML_ASSERT(ggml_is_permuted(src0) && ggml_is_permuted(src1)); + GGML_ASSERT(src0->nb[0] <= src0->nb[1] && src0->nb[2] <= src0->nb[3]); // NOLINT + GGML_ASSERT(src1->nb[0] <= src1->nb[1] && src1->nb[2] <= src1->nb[3]); // NOLINT + GGML_ASSERT(src0->type == GGML_TYPE_F16); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + + const uint64_t ne00 = src0->ne[0]; + const uint64_t ne01 = src0->ne[1]; + const uint64_t ne02 = src0->ne[2]; + // const uint64_t ne03 = src0->ne[3]; + + //const uint64_t ne10 = src1->ne[0]; + const uint64_t ne11 = src1->ne[1]; + const uint64_t ne12 = src1->ne[2]; + // const uint64_t ne13 = src1->ne[3]; + + GGML_ASSERT(ne11 == 1); + + // With grouped query attention there are > 1 Q matrices per K, V matrix. + uint32_t gqa_ratio = (uint32_t)ne12 / (uint32_t)ne02; + if (gqa_ratio > 8 || gqa_ratio == 0 || ne12 != ne02 * gqa_ratio) { + gqa_ratio = 1; + } + + vk_pipeline pipeline = ctx->device->pipeline_mul_mat_vec_p021_f16_f32[gqa_ratio - 1]; + + if (ggml_nbytes(src0) > ctx->device->properties.limits.maxStorageBufferRange) { + pipeline = ggml_vk_get_64b_indexing_pipeline(ctx, pipeline); + } + + { + // Request descriptor sets + ggml_pipeline_request_descriptor_sets(ctx, pipeline, 1); + } + + vk_subbuffer d_D = ggml_vk_tensor_subbuffer(ctx, cgraph->nodes[node_idx + ctx->num_additional_fused_ops], true); + vk_subbuffer d_Qx = ggml_vk_tensor_subbuffer(ctx, src0); + vk_subbuffer d_Qy = ggml_vk_tensor_subbuffer(ctx, src1, true); + + vk_subbuffer d_F0 = d_D; + + uint32_t fusion_flags = 0; + + if (ctx->num_additional_fused_ops > 0) { + const ggml_tensor * add = cgraph->nodes[node_idx + 1]; + const ggml_tensor * bias = add->src[0] == dst ? add->src[1] : add->src[0]; + + d_F0 = ggml_vk_tensor_subbuffer(ctx, bias); + fusion_flags |= MAT_VEC_FUSION_FLAGS_BIAS0; + } + + vk_subbuffer d_F1 = d_D; + if (ctx->num_additional_fused_ops > 1) { + const ggml_tensor * bias = cgraph->nodes[node_idx + 2]->src[1]; + + d_F1 = ggml_vk_tensor_subbuffer(ctx, bias); + fusion_flags |= MAT_VEC_FUSION_FLAGS_BIAS1; + } + + // compute + + vk_mat_vec_p021_push_constants pc = { + (uint32_t)ne00, (uint32_t)ne01, (uint32_t)ne02, (uint32_t)ne12, + 0, 0, fusion_flags + }; + + init_pushconst_tensor_offsets(ctx, pc, src0, src1, nullptr, nullptr, cgraph->nodes[node_idx + ctx->num_additional_fused_ops]); + + uint32_t workgroups_z = (uint32_t)ne12; + // When gqa_ratio > 1, each invocation does multiple rows and we can launch fewer workgroups + if (gqa_ratio > 1) { + workgroups_z /= gqa_ratio; + } + + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, + { + d_Qx, + d_Qy, + d_D, + d_F0, + d_F1, + }, pc, { 1, (uint32_t)ne01, workgroups_z }); +} + +static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_context& subctx, const struct ggml_cgraph * cgraph, int node_idx) { + ggml_tensor * dst = cgraph->nodes[node_idx]; + const ggml_tensor * src0 = dst->src[0]; + const ggml_tensor * src1 = dst->src[1]; + VK_LOG_DEBUG("ggml_vk_mul_mat_nc_f16_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; + std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; + std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3]; + std::cerr << "))"); + GGML_ASSERT(!ggml_is_transposed(src0)); + GGML_ASSERT(!ggml_is_transposed(src1)); + GGML_ASSERT(!ggml_is_permuted(src0)); + GGML_ASSERT(src0->type == GGML_TYPE_F16); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + + const uint64_t ne00 = src0->ne[0]; + const uint64_t ne01 = src0->ne[1]; + const uint64_t ne02 = src0->ne[2]; + const uint64_t ne03 = src0->ne[3]; + + const uint64_t nb01 = src0->nb[1]; + const uint64_t nb02 = src0->nb[2]; + + const uint64_t nb12 = src1->nb[2]; + + // const uint64_t ne10 = src1->ne[0]; + const uint64_t ne11 = src1->ne[1]; + const uint64_t ne12 = src1->ne[2]; + // const uint64_t ne13 = src1->ne[3]; + + const uint32_t nb03 = (uint32_t)(src0->nb[3] / sizeof(ggml_fp16_t)); + const uint32_t nb13 = (uint32_t)(src1->nb[3] / sizeof(float)); + const uint32_t nb23 = (uint32_t)(dst->nb[3] / sizeof(float)); + + GGML_ASSERT(ne11 == 1); + GGML_ASSERT(src0->ne[3] == src1->ne[3]); // checked in supports_op + + const uint32_t row_stride_x = nb01 / sizeof(ggml_fp16_t); + const uint32_t channel_stride_x = nb02 / sizeof(ggml_fp16_t); + const uint32_t channel_stride_y = nb12 / sizeof(float); + + vk_pipeline pipeline = ctx->device->pipeline_mul_mat_vec_nc_f16_f32; + if (ggml_nbytes(src0) > ctx->device->properties.limits.maxStorageBufferRange) { + pipeline = ggml_vk_get_64b_indexing_pipeline(ctx, pipeline); + } + + { + // Request descriptor sets + ggml_pipeline_request_descriptor_sets(ctx, pipeline, 1); + } + + vk_subbuffer d_D = ggml_vk_tensor_subbuffer(ctx, cgraph->nodes[node_idx + ctx->num_additional_fused_ops], true); + vk_subbuffer d_Qx = ggml_vk_tensor_subbuffer(ctx, src0); + vk_subbuffer d_Qy = ggml_vk_tensor_subbuffer(ctx, src1, true); + vk_subbuffer d_F0 = d_D; + + uint32_t fusion_flags = 0; + + if (ctx->num_additional_fused_ops > 0) { + const ggml_tensor * add = cgraph->nodes[node_idx + 1]; + const ggml_tensor * bias = add->src[0] == dst ? add->src[1] : add->src[0]; + + d_F0 = ggml_vk_tensor_subbuffer(ctx, bias); + fusion_flags |= MAT_VEC_FUSION_FLAGS_BIAS0; + } + + vk_subbuffer d_F1 = d_D; + if (ctx->num_additional_fused_ops > 1) { + const ggml_tensor * bias = cgraph->nodes[node_idx + 2]->src[1]; + + d_F1 = ggml_vk_tensor_subbuffer(ctx, bias); + fusion_flags |= MAT_VEC_FUSION_FLAGS_BIAS1; + } + + // compute + vk_mat_vec_nc_push_constants pc = { + (uint32_t)ne00, (uint32_t)ne01, + row_stride_x, channel_stride_x, channel_stride_y, + (uint32_t)(ne12 / ne02), (uint32_t)ne12, + 0, 0, + nb03, nb13, nb23, fusion_flags + }; + + init_pushconst_tensor_offsets(ctx, pc, src0, src1, nullptr, nullptr, cgraph->nodes[node_idx + ctx->num_additional_fused_ops]); + + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, + { + d_Qx, + d_Qy, + d_D, + d_F0, + d_F1, + }, pc, { (uint32_t)ne03, (uint32_t)ne01, (uint32_t)ne12 }); +} + +static int ggml_vk_fwht_pipeline_idx(int64_t n) { + switch (n) { + case 64: return 0; + case 128: return 1; + case 256: return 2; + case 512: return 3; + default: return -1; + } +} + +static bool ggml_vk_can_use_fwht(const ggml_backend_vk_context * ctx, const ggml_tensor * src1, const ggml_tensor * dst) { + if (ctx->num_additional_fused_ops != 0) { + return false; + } + + if (ggml_get_op_params_i32(dst, 1) != GGML_HINT_SRC0_IS_HADAMARD) { + return false; + } + + const int idx = ggml_vk_fwht_pipeline_idx(src1->ne[0]); + if (idx < 0 || ctx->device->pipeline_fwht_f32[idx] == nullptr) { + return false; + } + + if (src1->type != GGML_TYPE_F32 || dst->type != GGML_TYPE_F32) { + return false; + } + + if (!ggml_is_contiguous(src1)) { + return false; + } + GGML_ASSERT(ggml_is_contiguous(dst)); + + return true; +} + +static void ggml_vk_fwht(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src, ggml_tensor * dst) { + const int idx = ggml_vk_fwht_pipeline_idx(src->ne[0]); + vk_pipeline pipeline = ctx->device->pipeline_fwht_f32[idx]; + + const uint32_t rows_per_workgroup = 4; + const uint32_t n_rows = (uint32_t)ggml_nrows(src); + const uint32_t max_workgroups_x = ctx->device->properties.limits.maxComputeWorkGroupCount[0]; + + const uint32_t total_workgroups = CEIL_DIV(n_rows, rows_per_workgroup); + const uint32_t workgroups_x = std::min(total_workgroups, max_workgroups_x); + ggml_pipeline_request_descriptor_sets(ctx, pipeline, 1); + + const vk_subbuffer src_buf = ggml_vk_tensor_subbuffer(ctx, src, true); + const vk_subbuffer dst_buf = ggml_vk_tensor_subbuffer(ctx, dst, true); + + vk_op_fwht_push_constants pc = { + n_rows, + 0, + 0, + 1.0f / std::sqrt((float)src->ne[0]), + }; + init_pushconst_tensor_offsets(ctx, pc, src, nullptr, nullptr, nullptr, dst); + + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { src_buf, dst_buf }, pc, { workgroups_x, 1, 1 }); +} + +static void ggml_vk_mul_mat(ggml_backend_vk_context * ctx, vk_context& subctx, const struct ggml_cgraph * cgraph, int node_idx) { + ggml_tensor * dst = cgraph->nodes[node_idx]; + ggml_tensor * src0 = dst->src[0]; + ggml_tensor * src1 = dst->src[1]; + VK_LOG_DEBUG("ggml_vk_mul_mat(" << src0 << ", " << src1 << ", " << dst << ")"); + + // Handle huge A matrix by splitting the M dimensions. This works well for convolution use cases + // where the M dimension is very large. + // Split_k doesn't work with M splitting. + // This only supports batchsize == 1. + const size_t nbytes = ggml_nbytes(src0); + const bool needs_split = dst->ne[2] == 1 && dst->ne[3] == 1 && nbytes > ctx->device->properties.limits.maxStorageBufferRange; + if (needs_split) { + // Choose the number of rows that can fit (and divide by two, to allow for any additional offsets) + const uint32_t M_split = ctx->device->properties.limits.maxStorageBufferRange / (2 * src0->nb[1]); + uint32_t m_offset = 0; + while (m_offset < dst->ne[0]) { + const uint32_t cur_M_size = std::min(M_split, (uint32_t)(dst->ne[0] - m_offset)); + ggml_tensor dst2 = *dst; + ggml_tensor src02 = *src0; + + dst2.view_src = dst->view_src ? dst->view_src : dst; + src02.view_src = src0->view_src ? src0->view_src : src0; + + dst2.view_offs += m_offset * dst->nb[0]; + src02.view_offs += m_offset * src0->nb[1]; + dst2.ne[0] = cur_M_size; + src02.ne[1] = cur_M_size; + + ggml_vk_mul_mat_q_f16(ctx, subctx, &src02, src1, &dst2, true); + + m_offset += cur_M_size; + } + } else if (ggml_vk_can_use_fwht(ctx, src1, dst)) { + ggml_vk_fwht(ctx, subctx, src1, dst); + } else if (src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && dst->ne[1] == 1 && + // detect 0213 permutation, and batch size of 1 + src0->nb[0] <= src0->nb[2] && + src0->nb[2] <= src0->nb[1] && + src0->nb[1] <= src0->nb[3] && + src1->nb[0] <= src1->nb[2] && + src1->nb[2] <= src1->nb[1] && + src1->nb[1] <= src1->nb[3] && + src0->ne[3] == 1 && + src1->ne[3] == 1 && + src0->ne[1] <= ctx->device->properties.limits.maxComputeWorkGroupCount[1] && + src1->ne[2] <= ctx->device->properties.limits.maxComputeWorkGroupCount[2]) { + ggml_vk_mul_mat_vec_p021_f16_f32(ctx, subctx, cgraph, node_idx); + } else if (src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && dst->ne[1] == 1 && + !ggml_is_permuted(src0) && !ggml_is_permuted(src1) && + src0->ne[3] <= ctx->device->properties.limits.maxComputeWorkGroupCount[0] && + src0->ne[1] <= ctx->device->properties.limits.maxComputeWorkGroupCount[1] && + src1->ne[2] <= ctx->device->properties.limits.maxComputeWorkGroupCount[2]) { + ggml_vk_mul_mat_vec_nc_f16_f32(ctx, subctx, cgraph, node_idx); + // mul_mat_vec supports batching ne12*ne13 when ne11==1, or treating ne11 as the batch size (up to four) + // when ne12 and ne13 are one. + } else if ((dst->ne[1] == 1 || (dst->ne[1] <= mul_mat_vec_max_cols && src1->ne[2] * src1->ne[3] == 1)) && + (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || src0->type == GGML_TYPE_BF16 || ggml_is_quantized(src0->type))) { + ggml_vk_mul_mat_vec_q_f16(ctx, subctx, cgraph, node_idx); + } else { + ggml_vk_mul_mat_q_f16(ctx, subctx, src0, src1, dst, false); + } +} + +static void ggml_vk_mul_mat_id_q_f16(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * ids, ggml_tensor * dst) { + VK_LOG_DEBUG("ggml_vk_mul_mat_id_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; + std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; + std::cerr << "), (" << ids << ", name=" << ids->name << ", type=" << ids->type << ", ne0=" << ids->ne[0] << ", ne1=" << ids->ne[1] << ", ne2=" << ids->ne[2] << ", ne3=" << ids->ne[3] << ", nb0=" << ids->nb[0] << ", nb1=" << ids->nb[1] << ", nb2=" << ids->nb[2] << ", nb3=" << ids->nb[3]; + std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)"); + GGML_ASSERT(ggml_vk_dim01_contiguous(src1) || src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16); // NOLINT + GGML_ASSERT(ids->type == GGML_TYPE_I32); + + const uint64_t ne00 = src0->ne[0]; + const uint64_t ne01 = src0->ne[1]; + const uint64_t ne02 = src0->ne[2]; + // const uint64_t ne03 = src0->ne[3]; + + const uint64_t ne10 = src1->ne[0]; + const uint64_t ne11 = src1->ne[1]; + const uint64_t ne12 = src1->ne[2]; + const uint64_t ne13 = src1->ne[3]; + + const uint64_t nei0 = ids->ne[0]; + const uint64_t nei1 = ids->ne[1]; + + const uint32_t nbi0 = ids->nb[0]; + const uint32_t nbi1 = ids->nb[1]; + const uint32_t nbi2 = ids->nb[2]; + + const uint64_t ne20 = dst->ne[0]; + const uint64_t ne21 = dst->ne[1]; + // const uint64_t ne22 = dst->ne[2]; + // const uint64_t ne23 = dst->ne[3]; + + const uint64_t n_as = ne02; + + ggml_backend_vk_buffer_context * dst_buf_ctx = (ggml_backend_vk_buffer_context *)dst->buffer->context; + ggml_backend_vk_buffer_context * src0_buf_ctx = (ggml_backend_vk_buffer_context *)src0->buffer->context; + ggml_backend_vk_buffer_context * src1_buf_ctx = (ggml_backend_vk_buffer_context *)src1->buffer->context; + ggml_backend_vk_buffer_context * ids_buf_ctx = (ggml_backend_vk_buffer_context *)ids->buffer->context; + + vk_buffer d_Qx = nullptr; + size_t qx_buf_offset = 0; + vk_buffer d_Qy = nullptr; + size_t qy_buf_offset = 0; + vk_buffer d_ids = nullptr; + size_t ids_buf_offset = 0; + + bool src0_uma = false; + bool src1_uma = false; + bool ids_uma = false; + + if (ctx->device->uma) { + ggml_vk_host_get(ctx->device, src0->data, d_Qx, qx_buf_offset); + ggml_vk_host_get(ctx->device, src1->data, d_Qy, qy_buf_offset); + ggml_vk_host_get(ctx->device, ids->data, d_ids, ids_buf_offset); + src0_uma = d_Qx != nullptr; + src1_uma = d_Qy != nullptr; + ids_uma = d_ids != nullptr; + } + + // Reformat and convert to fp16 if non-contiguous, or for coopmat2 for better perf + const bool x_non_contig = (ctx->device->coopmat2 && src0->type == GGML_TYPE_F32) || + !ggml_vk_dim01_contiguous(src0); + // If src0 is BF16, try to use a BF16 x BF16 multiply + ggml_type f16_type = src0->type == GGML_TYPE_BF16 ? GGML_TYPE_BF16 : GGML_TYPE_F16; +#if defined(GGML_VULKAN_COOPMAT2_DECODE_VECTOR_GLSLC_SUPPORT) + // B must already be, or be convertible to, the matmul B type used by this path. + const bool y_decode_vector_supported = ctx->device->coopmat2_decode_vector && + (f16_type != GGML_TYPE_BF16 || ctx->device->coopmat2_bf16_support) && + (src1->type == GGML_TYPE_F32 || src1->type == f16_type); + // If B is copied to prealloc_y, we can choose a 4-element-aligned row stride. + const bool y_decode_vector_uses_prealloc = !ggml_vk_dim01_contiguous(src1) || src1->type != f16_type; + // Direct B reads are safe only if row starts and the original buffer offset are 4-element aligned. + const bool y_decode_vector_aligned = + (ne10 % 4 == 0) && + (y_decode_vector_uses_prealloc || get_misalign_bytes(ctx, src1) % (4 * ggml_type_size(src1->type)) == 0); + // Stage B only when decode-vector is available and direct B reads would be misaligned. + const bool y_decode_vector_staging = y_decode_vector_supported && !y_decode_vector_aligned; +#else + const bool y_decode_vector_staging = false; +#endif + const bool y_non_contig = y_decode_vector_staging || + (ctx->device->coopmat2 && src1->type == GGML_TYPE_F32) || + (src0->type == GGML_TYPE_BF16 && src1->type != GGML_TYPE_BF16) || + !ggml_vk_dim01_contiguous(src1); + + const uint32_t y_staged_row_stride = y_decode_vector_staging ? (uint32_t)ggml_vk_align_size(ne10, 4) : (uint32_t)ne10; + + const bool y_f32_kernel = src1->type == GGML_TYPE_F32 && !y_non_contig; + + bool quantize_y = ctx->device->integer_dot_product && src1->type == GGML_TYPE_F32 && ggml_is_contiguous(src1) && !y_non_contig && (ne11 * ne10) % 4 == 0; + + // Check for mmq first + vk_matmul_pipeline mmp = quantize_y ? ggml_vk_get_mul_mat_mat_id_pipeline(ctx, src0->type, GGML_TYPE_Q8_1, (ggml_prec)dst->op_params[0]) : nullptr; + + if (mmp == nullptr) { + // Fall back to f16 dequant mul mat + mmp = ggml_vk_get_mul_mat_mat_id_pipeline(ctx, src0->type, y_non_contig ? f16_type : src1->type, (ggml_prec)dst->op_params[0]); + quantize_y = false; + } + + const bool qx_needs_dequant = mmp == nullptr || x_non_contig; + const bool qy_needs_dequant = !quantize_y && ((src1->type != f16_type && !y_f32_kernel) || y_non_contig); + + if (qx_needs_dequant) { + // Fall back to dequant + f16 mulmat + mmp = ggml_vk_get_mul_mat_mat_id_pipeline(ctx, f16_type, y_f32_kernel ? GGML_TYPE_F32 : f16_type, (ggml_prec)dst->op_params[0]); + } + + // Not implemented + GGML_ASSERT(y_non_contig || !qy_needs_dequant); // NOLINT + + const ggml_type effective_src1_type = quantize_y ? GGML_TYPE_Q8_1 : (y_f32_kernel ? GGML_TYPE_F32 : src1->type); + + const uint32_t kpad = quantize_y ? 0 : ggml_vk_align_size(ne10, ggml_vk_guess_matmul_id_pipeline_align(ctx, mmp, ne01, nei1, qx_needs_dequant ? f16_type : src0->type, effective_src1_type)); + const bool aligned = !quantize_y && ne10 == kpad && ne01 > 8 && nei1 > 8; + + vk_pipeline pipeline = ggml_vk_guess_matmul_id_pipeline(ctx, mmp, ne01, nei1, aligned, qx_needs_dequant ? f16_type : src0->type, effective_src1_type); + + if (ggml_nbytes(src0) > ctx->device->properties.limits.maxStorageBufferRange) { + pipeline = ggml_vk_get_64b_indexing_pipeline(ctx, pipeline); + } + // Reserve extra storage in the N dimension for the Y matrix, so we can avoid bounds-checking + uint32_t padded_n = qy_needs_dequant ? ROUNDUP_POW2(ne11, pipeline->wg_denoms[1]) :ne11; + const uint64_t x_ne = ggml_nelements(src0); + const uint64_t y_ne = (uint64_t)y_staged_row_stride * padded_n * ne12 * ne13; + const uint64_t d_ne = ggml_nelements(dst); + + const uint64_t qx_sz = ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type); + const uint64_t qy_sz = ggml_type_size(src1->type) * ggml_nelements(src1) / ggml_blck_size(src1->type); + const uint64_t x_sz = !qx_needs_dequant ? qx_sz : sizeof(ggml_fp16_t) * x_ne; + const uint64_t y_sz = quantize_y ? (ggml_vk_align_size(y_ne, 128) * ggml_type_size(GGML_TYPE_Q8_1) / ggml_blck_size(GGML_TYPE_Q8_1)) : (y_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne); + const uint64_t ids_sz = nbi2; + const uint64_t d_sz = sizeof(float) * d_ne; + + vk_pipeline to_fp16_vk_0 = nullptr; + vk_pipeline to_fp16_vk_1 = nullptr; + vk_pipeline to_q8_1 = nullptr; + + auto make_y_staged_dst = [&]() { + ggml_tensor y_staged_dst = *src1; + y_staged_dst.type = f16_type; + y_staged_dst.nb[0] = ggml_type_size(f16_type); + y_staged_dst.nb[1] = y_staged_dst.nb[0] * y_staged_row_stride; + y_staged_dst.nb[2] = y_staged_dst.nb[1] * padded_n; + y_staged_dst.nb[3] = y_staged_dst.nb[2] * y_staged_dst.ne[2]; + return y_staged_dst; + }; + + if (x_non_contig) { + to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0, nullptr, f16_type); + } else { + to_fp16_vk_0 = ggml_vk_get_to_fp16(ctx, src0->type); + } + if (y_non_contig) { + ggml_tensor y_staged_dst; + const ggml_tensor * y_staged_dst_ptr = nullptr; + if (y_decode_vector_staging) { + y_staged_dst = make_y_staged_dst(); + y_staged_dst_ptr = &y_staged_dst; + } + + to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(ctx, src1, y_staged_dst_ptr, f16_type); + } else { + to_fp16_vk_1 = ggml_vk_get_to_fp16(ctx, src1->type); + } + GGML_ASSERT(!qx_needs_dequant || to_fp16_vk_0 != nullptr); // NOLINT + GGML_ASSERT(!qy_needs_dequant || to_fp16_vk_1 != nullptr); // NOLINT + + if (quantize_y) { + to_q8_1 = ggml_vk_get_quantize_pipeline(ctx, GGML_TYPE_Q8_1); + } + vk_pipeline count_experts = ctx->device->pipeline_count_experts; + + uint32_t expert_count_size = sizeof(uint32_t) * n_as; + + { + if ( + (qx_needs_dequant && x_sz > ctx->device->properties.limits.maxStorageBufferRange) || + (qy_needs_dequant && y_sz > ctx->device->properties.limits.maxStorageBufferRange)) { + GGML_ABORT("Requested preallocation size is too large"); + } + if (qx_needs_dequant && ctx->prealloc_size_x < x_sz) { + ctx->prealloc_size_x = x_sz; + ggml_vk_preallocate_buffers(ctx, subctx); + } + if ((qy_needs_dequant || quantize_y) && ctx->prealloc_size_y < y_sz) { + ctx->prealloc_size_y = y_sz; + ggml_vk_preallocate_buffers(ctx, subctx); + } + if (ctx->prealloc_size_split_k < expert_count_size) { + ctx->prealloc_size_split_k = expert_count_size; + ggml_vk_preallocate_buffers(ctx, subctx); + } + + // Request descriptor sets + ggml_pipeline_request_descriptor_sets(ctx, pipeline, 1); + if (qx_needs_dequant) { + ggml_pipeline_request_descriptor_sets(ctx, to_fp16_vk_0, 1); + } + if (qy_needs_dequant) { + ggml_pipeline_request_descriptor_sets(ctx, to_fp16_vk_1, 1); + } + if (quantize_y) { + ggml_pipeline_request_descriptor_sets(ctx, to_q8_1, 1); + } + ggml_pipeline_request_descriptor_sets(ctx, count_experts, 1); + } + + vk_buffer d_D = dst_buf_ctx->dev_buffer; + const uint64_t d_buf_offset = vk_tensor_offset(dst) + dst->view_offs; + GGML_ASSERT(d_D != nullptr); + vk_buffer d_X; + uint64_t x_buf_offset = 0; + vk_buffer d_Y; + uint64_t y_buf_offset = 0; + if (!src0_uma) { + d_Qx = src0_buf_ctx->dev_buffer; + qx_buf_offset = vk_tensor_offset(src0) + src0->view_offs; + GGML_ASSERT(d_Qx != nullptr); + } + if (!src1_uma) { + d_Qy = src1_buf_ctx->dev_buffer; + qy_buf_offset = vk_tensor_offset(src1) + src1->view_offs; + GGML_ASSERT(d_Qy != nullptr); + } + if (!ids_uma) { + d_ids = ids_buf_ctx->dev_buffer; + ids_buf_offset = vk_tensor_offset(ids) + ids->view_offs; + GGML_ASSERT(d_ids != nullptr); + } + if (qx_needs_dequant) { + d_X = ctx->prealloc_x; + GGML_ASSERT(d_X->size >= x_sz); + } else { + d_X = d_Qx; + x_buf_offset = qx_buf_offset; + GGML_ASSERT(qx_sz == x_sz); + } + if (qy_needs_dequant) { + d_Y = ctx->prealloc_y; + GGML_ASSERT(d_Y->size >= y_sz); + } else if (quantize_y) { + d_Y = ctx->prealloc_y; + GGML_ASSERT(d_Y->size >= CEIL_DIV(y_sz, 144) * 144); + } else { + d_Y = d_Qy; + y_buf_offset = qy_buf_offset; + GGML_ASSERT(qy_sz == y_sz); + } + + if (x_non_contig || qx_needs_dequant) { + if (ctx->prealloc_x_need_sync) { + ggml_vk_sync_buffers(ctx, subctx); + } + } + // Count how many times each expert is used + vk_subbuffer expert_count_buf = ggml_vk_subbuffer(ctx, ctx->prealloc_split_k, 0); + if (ctx->prealloc_split_k_need_sync) { + ggml_vk_sync_buffers(ctx, subctx); + } + { + const std::vector pc = { (uint32_t)nei0, + (uint32_t)nei1, + (uint32_t)(nbi0 / ggml_type_size(ids->type)), + (uint32_t)(nbi1 / ggml_type_size(ids->type)), + (uint32_t)(get_misalign_bytes(ctx, ids) / ggml_type_size(ids->type)) }; + ggml_vk_dispatch_pipeline(ctx, subctx, count_experts, + { vk_subbuffer{ d_ids, ids_buf_offset, ids_sz }, expert_count_buf }, pc, { (uint32_t)n_as, 1, 1}); + } + + if (x_non_contig) { + ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_0, src0, ggml_vk_subbuffer(ctx, d_Qx, qx_buf_offset), ggml_vk_subbuffer(ctx, d_X, 0)); + } else if (qx_needs_dequant) { + const std::vector pc = { (uint32_t)ne01, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)(ggml_nelements(src0)) }; + ggml_vk_dispatch_pipeline(ctx, subctx, to_fp16_vk_0, + { vk_subbuffer{ d_Qx, qx_buf_offset, qx_sz }, vk_subbuffer{ d_X, 0, x_sz } }, pc, { (uint32_t)x_ne, 1, 1}); + } + if (y_non_contig) { + if (ctx->prealloc_y_last_pipeline_used != to_fp16_vk_1.get() || + ctx->prealloc_y_last_tensor_used != src1 || + ctx->prealloc_y_last_decode_vector_staging != y_decode_vector_staging) { + if (ctx->prealloc_y_need_sync) { + ggml_vk_sync_buffers(ctx, subctx); + } + if (y_decode_vector_staging) { + const ggml_tensor y_staged_dst = make_y_staged_dst(); + const uint32_t y_staged_dst_type_size = ggml_type_size(y_staged_dst.type); + ggml_vk_cpy_to_strided( + ctx, subctx, to_fp16_vk_1, src1, + ggml_vk_subbuffer(ctx, d_Qy, qy_buf_offset), ggml_vk_subbuffer(ctx, d_Y, 0), + (uint32_t)(y_staged_dst.nb[0] / y_staged_dst_type_size), + (uint32_t)(y_staged_dst.nb[1] / y_staged_dst_type_size), + (uint32_t)(y_staged_dst.nb[2] / y_staged_dst_type_size), + (uint32_t)(y_staged_dst.nb[3] / y_staged_dst_type_size)); + } else { + ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, ggml_vk_subbuffer(ctx, d_Qy, qy_buf_offset), ggml_vk_subbuffer(ctx, d_Y, 0)); + } + ctx->prealloc_y_last_pipeline_used = to_fp16_vk_1.get(); + ctx->prealloc_y_last_tensor_used = src1; + ctx->prealloc_y_last_decode_vector_staging = y_decode_vector_staging; + } + } + if (quantize_y) { + if (ctx->prealloc_y_last_pipeline_used != to_q8_1.get() || + ctx->prealloc_y_last_tensor_used != src1 || + ctx->prealloc_y_last_decode_vector_staging) { + if (ctx->prealloc_y_need_sync) { + ggml_vk_sync_buffers(ctx, subctx); + } + ggml_vk_quantize_q8_1(ctx, subctx, ggml_vk_subbuffer(ctx, d_Qy, qy_buf_offset), ggml_vk_subbuffer(ctx, d_Y, 0), y_ne); + ctx->prealloc_y_last_pipeline_used = to_q8_1.get(); + ctx->prealloc_y_last_tensor_used = src1; + ctx->prealloc_y_last_decode_vector_staging = false; + } + } + ggml_vk_sync_buffers(ctx, subctx); + + uint32_t stride_batch_x = ne00*ne01; + uint32_t stride_b_y = y_decode_vector_staging ? y_staged_row_stride : ne10; + uint32_t stride_batch_y = y_decode_vector_staging ? y_staged_row_stride * padded_n : ne10*ne11; + + if (!ggml_vk_dim01_contiguous(src0) && !qx_needs_dequant) { + stride_batch_x = src0->nb[0] / ggml_type_size(src0->type); + } + + if (!ggml_vk_dim01_contiguous(src1) && !qy_needs_dequant && !quantize_y) { + stride_batch_y = src1->nb[0] / ggml_type_size(src1->type); + } + + // compute + ggml_vk_matmul_id( + ctx, subctx, pipeline, + { d_X, x_buf_offset, x_sz }, { d_Y, y_buf_offset, y_sz }, + { d_D, d_buf_offset, d_sz }, { d_ids, ids_buf_offset, ids_sz }, expert_count_buf, + ne01, ne21, ne10, ne10, stride_b_y, ne01, + stride_batch_x, stride_batch_y, ne20*ne21, + n_as, nei0, nei1, nbi1 / ggml_type_size(ids->type), ne11, padded_n + ); // NOLINT + + if (x_non_contig || qx_needs_dequant) { + ctx->prealloc_x_need_sync = true; + } + if (y_non_contig || quantize_y) { + ctx->prealloc_y_need_sync = true; + } + ctx->prealloc_split_k_need_sync = true; +} + +static void ggml_vk_mul_mat_vec_id_q_f16(ggml_backend_vk_context * ctx, vk_context& subctx, const struct ggml_cgraph * cgraph, int node_idx) { + ggml_tensor * dst = cgraph->nodes[node_idx]; + ggml_tensor * src0 = dst->src[0]; + ggml_tensor * src1 = dst->src[1]; + ggml_tensor * ids = dst->src[2]; + VK_LOG_DEBUG("ggml_vk_mul_mat_vec_id_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; + std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; + std::cerr << "), (" << ids << ", name=" << ids->name << ", type=" << ids->type << ", ne0=" << ids->ne[0] << ", ne1=" << ids->ne[1] << ", ne2=" << ids->ne[2] << ", ne3=" << ids->ne[3] << ", nb0=" << ids->nb[0] << ", nb1=" << ids->nb[1] << ", nb2=" << ids->nb[2] << ", nb3=" << ids->nb[3]; + std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3]; + std::cerr << "))"); + GGML_ASSERT(ggml_vk_dim01_contiguous(src0) || src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || src0->type == GGML_TYPE_BF16); // NOLINT + GGML_ASSERT(ggml_vk_dim01_contiguous(src1) || src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16); // NOLINT + GGML_ASSERT(ids->type == GGML_TYPE_I32); + + const uint64_t ne00 = src0->ne[0]; + const uint64_t ne01 = src0->ne[1]; + // const uint64_t ne02 = src0->ne[2]; + // const uint64_t ne03 = src0->ne[3]; + + const uint64_t ne10 = src1->ne[0]; + const uint64_t ne11 = src1->ne[1]; + const uint64_t ne12 = src1->ne[2]; + // const uint64_t ne13 = src1->ne[3]; + + const uint64_t nei0 = ids->ne[0]; + const uint64_t nei1 = ids->ne[1]; + const uint32_t nbi1 = (uint32_t)(ids->nb[1] / sizeof(int)); + + const uint64_t ne20 = dst->ne[0]; + const uint64_t ne21 = dst->ne[1]; + // const uint64_t ne22 = dst->ne[2]; + // const uint64_t ne23 = dst->ne[3]; + + const bool x_non_contig = !ggml_vk_dim01_contiguous(src0); + const bool y_non_contig = !ggml_vk_dim01_contiguous(src1); + + const bool f16_f32_kernel = src1->type == GGML_TYPE_F32; + bool quantize_y = ctx->device->integer_dot_product && src1->type == GGML_TYPE_F32 && ggml_is_contiguous(src1) && !y_non_contig && (ne11 * ne10) % 4 == 0 && ggml_vk_should_use_mmvq(ctx->device, ne01, ne12, ne10, src0->type); + + vk_pipeline to_fp16_vk_0 = nullptr; + vk_pipeline to_fp16_vk_1 = nullptr; + if (x_non_contig) { + to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0, nullptr, src0->type); + } + if (y_non_contig) { + to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(ctx, src1, nullptr, src1->type); + } else { + to_fp16_vk_1 = ggml_vk_get_to_fp16(ctx, src1->type); + } + + // Check for mmq first + vk_pipeline dmmv = quantize_y ? ggml_vk_get_dequantize_mul_mat_vec_id(ctx, src0->type, GGML_TYPE_Q8_1, ne20, ne00) : nullptr; + vk_pipeline to_q8_1 = nullptr; + + if (dmmv == nullptr) { + // Fall back to f16 dequant mul mat + dmmv = ggml_vk_get_dequantize_mul_mat_vec_id(ctx, src0->type, src1->type, ne20, ne00); + quantize_y = false; + } + + if (quantize_y) { + to_q8_1 = ggml_vk_get_quantize_pipeline(ctx, GGML_TYPE_Q8_1); + } + + const bool qx_needs_dequant = x_non_contig; + const bool qy_needs_dequant = !quantize_y && ((src1->type != GGML_TYPE_F16 && !f16_f32_kernel) || y_non_contig); + + if (ggml_nbytes(src0) > ctx->device->properties.limits.maxStorageBufferRange) { + dmmv = ggml_vk_get_64b_indexing_pipeline(ctx, dmmv); + } + + // Not implemented + GGML_ASSERT(y_non_contig || !qy_needs_dequant); // NOLINT + GGML_ASSERT(!qx_needs_dequant || to_fp16_vk_0 != nullptr); // NOLINT + GGML_ASSERT(!qy_needs_dequant || to_fp16_vk_1 != nullptr); // NOLINT + GGML_ASSERT(dmmv != nullptr); + + const uint64_t x_ne = ggml_nelements(src0); + const uint64_t y_ne = ggml_nelements(src1); + + const uint64_t qx_sz = ggml_vk_align_size(ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type), ctx->device->properties.limits.minStorageBufferOffsetAlignment); + const uint64_t x_sz = x_non_contig ? ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment) : qx_sz; + const uint64_t y_sz = quantize_y ? (ggml_vk_align_size(y_ne, 128) * ggml_type_size(GGML_TYPE_Q8_1) / ggml_blck_size(GGML_TYPE_Q8_1)) : + (f16_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne); + + { + if ( + (qx_needs_dequant && x_sz > ctx->device->properties.limits.maxStorageBufferRange) || + (qy_needs_dequant && y_sz > ctx->device->properties.limits.maxStorageBufferRange)) { + GGML_ABORT("Requested preallocation size is too large"); + } + if (qx_needs_dequant && ctx->prealloc_size_x < x_sz) { + ctx->prealloc_size_x = x_sz; + ggml_vk_preallocate_buffers(ctx, subctx); + } + if ((qy_needs_dequant || quantize_y) && ctx->prealloc_size_y < y_sz) { + ctx->prealloc_size_y = y_sz; + ggml_vk_preallocate_buffers(ctx, subctx); + } + + // Request descriptor sets + if (qx_needs_dequant) { + ggml_pipeline_request_descriptor_sets(ctx, to_fp16_vk_0, 1); + } + if (qy_needs_dequant) { + ggml_pipeline_request_descriptor_sets(ctx, to_fp16_vk_1, 1); + } + if (quantize_y) { + ggml_pipeline_request_descriptor_sets(ctx, to_q8_1, 1); + } + ggml_pipeline_request_descriptor_sets(ctx, dmmv, nei1); + } + + vk_subbuffer d_D = ggml_vk_tensor_subbuffer(ctx, cgraph->nodes[node_idx + ctx->num_additional_fused_ops]); + vk_subbuffer d_Qx = ggml_vk_tensor_subbuffer(ctx, src0); + vk_subbuffer d_Qy = ggml_vk_tensor_subbuffer(ctx, src1); + vk_subbuffer d_ids = ggml_vk_tensor_subbuffer(ctx, ids); + vk_subbuffer d_F0 = d_D; + vk_subbuffer d_X, d_Y; + + if (qx_needs_dequant) { + d_X = { ctx->prealloc_x, 0, ctx->prealloc_x->size }; + } else { + d_X = d_Qx; + } + if (qy_needs_dequant || quantize_y) { + d_Y = { ctx->prealloc_y, 0, ctx->prealloc_y->size }; + } else { + d_Y = d_Qy; + } + + if (x_non_contig) { + if (ctx->prealloc_x_need_sync) { + ggml_vk_sync_buffers(ctx, subctx); + } + } + + if (x_non_contig) { + GGML_ASSERT(x_sz == ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment)); + ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_0, src0, d_Qx, d_X); + } + if (y_non_contig) { + GGML_ASSERT(y_sz == ggml_type_size(src1->type) * y_ne); + if (ctx->prealloc_y_last_pipeline_used != to_fp16_vk_1.get() || + ctx->prealloc_y_last_tensor_used != src1 || + ctx->prealloc_y_last_decode_vector_staging) { + if (ctx->prealloc_y_need_sync) { + ggml_vk_sync_buffers(ctx, subctx); + } + ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, d_Qy, d_Y); + ctx->prealloc_y_last_pipeline_used = to_fp16_vk_1.get(); + ctx->prealloc_y_last_tensor_used = src1; + ctx->prealloc_y_last_decode_vector_staging = false; + } + } + if (quantize_y) { + if (ctx->prealloc_y_last_pipeline_used != to_q8_1.get() || + ctx->prealloc_y_last_tensor_used != src1 || + ctx->prealloc_y_last_decode_vector_staging) { + if (ctx->prealloc_y_need_sync) { + ggml_vk_sync_buffers(ctx, subctx); + } + ggml_vk_quantize_q8_1(ctx, subctx, d_Qy, d_Y, y_ne); + ctx->prealloc_y_last_pipeline_used = to_q8_1.get(); + ctx->prealloc_y_last_tensor_used = src1; + ctx->prealloc_y_last_decode_vector_staging = false; + } + } + + uint32_t stride_batch_y = ne10*ne11; + + if (!ggml_vk_dim01_contiguous(src1) && !qy_needs_dequant) { + stride_batch_y = src1->nb[2] / ggml_type_size(src1->type); + } + + const uint32_t max_groups_x = ctx->device->properties.limits.maxComputeWorkGroupCount[0]; + + uint32_t groups_x = ne01; + uint32_t groups_z = 1; + + if (ne01 > max_groups_x) { + groups_z = 64; + groups_x = CEIL_DIV(groups_x, groups_z); + } + + uint32_t fusion_flags = 0; + + if (ctx->num_additional_fused_ops > 0) { + const ggml_tensor * bias = cgraph->nodes[node_idx + 1]->src[1]; + + d_F0 = ggml_vk_tensor_subbuffer(ctx, bias); + + if (cgraph->nodes[node_idx + 1]->op == GGML_OP_MUL) { + fusion_flags |= MAT_VEC_FUSION_FLAGS_SCALE0; + } else { + GGML_ASSERT(cgraph->nodes[node_idx + 1]->op == GGML_OP_ADD_ID); + fusion_flags |= MAT_VEC_FUSION_FLAGS_BIAS0; + } + } + + vk_subbuffer d_F1 = d_D; + if (ctx->num_additional_fused_ops > 1) { + const ggml_tensor * scale = cgraph->nodes[node_idx + 2]->src[1]; + + d_F1 = ggml_vk_tensor_subbuffer(ctx, scale); + fusion_flags |= MAT_VEC_FUSION_FLAGS_SCALE1; + } + + // Loop over the batch dimension + for (uint32_t expert_i1 = 0; expert_i1 < nei1; ++expert_i1) { + const vk_mat_vec_id_push_constants pc = { + (uint32_t)ne00, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)ne01, + (uint32_t)(ne00 * ne01), stride_batch_y, (uint32_t)(ne20 * ne21), + fusion_flags, + (uint32_t)nei0, (uint32_t)ne11, expert_i1, nbi1 + }; + ggml_vk_dispatch_pipeline(ctx, subctx, dmmv, + { + d_X, + d_Y, + d_D, + d_F0, + d_F1, + d_ids, + }, + pc, { groups_x, (uint32_t)nei0, groups_z }); + } + + if (x_non_contig) { + ctx->prealloc_x_need_sync = true; + } + if (y_non_contig || quantize_y) { + ctx->prealloc_y_need_sync = true; + } +} + +static bool ggml_vk_use_mul_mat_vec_id(const struct ggml_cgraph * cgraph, int node_idx) { + ggml_tensor * dst = cgraph->nodes[node_idx]; + ggml_tensor * src0 = dst->src[0]; + ggml_tensor * src2 = dst->src[2]; + return (src2->ne[1] <= 8) && (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)); +} + +static void ggml_vk_mul_mat_id(ggml_backend_vk_context * ctx, vk_context& subctx, const struct ggml_cgraph * cgraph, int node_idx) { + ggml_tensor * dst = cgraph->nodes[node_idx]; + ggml_tensor * src0 = dst->src[0]; + ggml_tensor * src1 = dst->src[1]; + ggml_tensor * src2 = dst->src[2]; + VK_LOG_DEBUG("ggml_vk_mul_mat_id(" << src0 << ", " << src1 << ", " << src2 << ", " << dst << ")"); + if (ggml_vk_use_mul_mat_vec_id(cgraph, node_idx)) { + ggml_vk_mul_mat_vec_id_q_f16(ctx, subctx, cgraph, node_idx); + } else { + ggml_vk_mul_mat_id_q_f16(ctx, subctx, src0, src1, src2, dst); + } +} + +static bool ggml_vk_flash_attn_scalar_shmem_support(const vk_device& device, const vk_fa_tuning_params& params, uint32_t hsk, uint32_t hsv, bool f32acc, ggml_type k_type, ggml_type v_type) { + GGML_UNUSED(f32acc); + GGML_UNUSED(v_type); + // Needs to be kept up to date on shader changes + const uint32_t wg_size = params.workgroup_size; + const uint32_t Br = params.block_rows; + const uint32_t Bc = params.block_cols; + + // BF16 uses the fp32 shader (FLOAT_TYPE=float) + const uint32_t float_type_size = (device->fp16 && k_type != GGML_TYPE_BF16) ? sizeof(ggml_fp16_t) : sizeof(float); + + const bool mmq = ggml_vk_fa_scalar_uses_mmq(device, k_type); + + // tmpsh is overestimated slightly + const uint32_t tmpsh = wg_size * sizeof(float); + const uint32_t tmpshv4 = wg_size * 4 * float_type_size; + + const uint32_t masksh = Bc * (Br + 1) * float_type_size; + + uint32_t Qf, kvsh, kblocksh_size; + if (mmq) { + // block_b_cache: int32_t qs[8] + FLOAT_TYPEV2 ds + const uint32_t block_b_size = 8 * sizeof(int32_t) + 2 * float_type_size; + Qf = Br * (hsk / 32) * block_b_size; + + // kvsh uses D = HSV (K goes through kblocksh instead) + kvsh = params.shmem_staging ? Bc * (hsv / 4 + 1) * 4 * float_type_size : 4 * float_type_size; + + // The mixed MMQ shader uses a superset block_a_cache that fits every + // FA-supported quant: int32_t qs[8] + uint32_t qh + FLOAT_TYPEV2 dm. + // Single-scale types leave dm.y unused; non-Q5_* leave qh unused. + const uint32_t block_a_size = 8 * sizeof(int32_t) + sizeof(uint32_t) + 2 * float_type_size; + kblocksh_size = params.shmem_staging ? Bc * (hsk / 32) * block_a_size : block_a_size; + } else { + Qf = Br * (hsk / 4 + 1) * 4 * float_type_size; + + const uint32_t D = std::max(hsk, hsv); + kvsh = params.shmem_staging ? Bc * (D / 4 + 1) * 4 * float_type_size : 4 * float_type_size; + + kblocksh_size = 0; + } + + const uint32_t total_size = tmpsh + tmpshv4 + masksh + Qf + kvsh + kblocksh_size; + const bool supported = total_size <= device->properties.limits.maxComputeSharedMemorySize; + + VK_LOG_DEBUG("ggml_vk_flash_attn_scalar_shmem_support(HSK=" << hsk << ", HSV=" << hsv << ", mmq=" << mmq << ", total_size=" << total_size << ", supported=" << supported); + + return supported; +} + +static bool ggml_vk_flash_attn_coopmat_shmem_support(const vk_device& device, const vk_fa_tuning_params& params, uint32_t hsk, uint32_t hsv, bool f32acc, ggml_type k_type) { + // Needs to be kept up to date on shader changes + const uint32_t Br = params.block_rows; + const uint32_t Bc = params.block_cols; + + const uint32_t MatBr = 16, MatBc = 16; + + const uint32_t row_split = Bc / MatBc; + + const uint32_t hsk_pad = ROUNDUP_POW2(hsk, 16); + const uint32_t hsv_pad = ROUNDUP_POW2(hsv, 16); + + const uint32_t acctype = f32acc ? 4 : 2; + const uint32_t f16vec4 = 8; + + const uint32_t tmpsh = (Bc / MatBc) * sizeof(float); + + const uint32_t qstride = hsk_pad / 4 + 2; + const uint32_t Qf = Br * qstride * f16vec4; + + const uint32_t psh_stride = Br / 4 + 2; + const uint32_t Psh = Bc * psh_stride * f16vec4; + + const uint32_t sfshstride = (hsk <= 128) ? (Br + 8) : Br; + const uint32_t sfsh = Bc * sfshstride * acctype; + + const uint32_t kvshstride = (params.shmem_staging ? std::max(hsk_pad, hsv_pad) : MatBr) / 4 + 2; + const uint32_t vsh_stride = MatBc / 4 * row_split; + const uint32_t ksh = ((kvshstride >= vsh_stride) ? (Bc * kvshstride) : (Bc * vsh_stride)) * f16vec4; + + // BF16 PVMat accumulator is f32 (no bf16 accumulator support), so pvsh is vec4 (16 bytes) + const uint32_t pvsh_elem_size = (k_type == GGML_TYPE_BF16) ? 16u : f16vec4; + const uint32_t osh_stride = params.row_split * MatBr / 4; + const uint32_t pvsh = MatBc * osh_stride * pvsh_elem_size; + + const uint32_t slope = Br * acctype; + + const uint32_t total_size = tmpsh + Qf + Psh + sfsh + ksh + pvsh + slope; + const bool supported = total_size <= device->properties.limits.maxComputeSharedMemorySize; + + VK_LOG_DEBUG("ggml_vk_flash_attn_coopmat_shmem_support(HSK=" << hsk << ", HSV=" << hsv << ", f32acc=" << f32acc << ", total_size=" << total_size << ", supported=" << supported); + + return supported; +} + +static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * q, const ggml_tensor * k, const ggml_tensor * v, const ggml_tensor * mask, const ggml_tensor * sinks, ggml_tensor * dst) { + VK_LOG_DEBUG("ggml_vk_flash_attn((" << q << ", name=" << q->name << ", type=" << q->type << ", ne0=" << q->ne[0] << ", ne1=" << q->ne[1] << ", ne2=" << q->ne[2] << ", ne3=" << q->ne[3] << ", nb0=" << q->nb[0] << ", nb1=" << q->nb[1] << ", nb2=" << q->nb[2] << ", nb3=" << q->nb[3]; + std::cerr << "), (" << k << ", name=" << k->name << ", type=" << k->type << ", ne0=" << k->ne[0] << ", ne1=" << k->ne[1] << ", ne2=" << k->ne[2] << ", ne3=" << k->ne[3] << ", nb0=" << k->nb[0] << ", nb1=" << k->nb[1] << ", nb2=" << k->nb[2] << ", nb3=" << k->nb[3]; + std::cerr << "), (" << v << ", name=" << v->name << ", type=" << v->type << ", ne0=" << v->ne[0] << ", ne1=" << v->ne[1] << ", ne2=" << v->ne[2] << ", ne3=" << v->ne[3] << ", nb0=" << v->nb[0] << ", nb1=" << v->nb[1] << ", nb2=" << v->nb[2] << ", nb3=" << v->nb[3]; + std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3]; + if (sinks) { + std::cerr << "), (" << sinks << ", name=" << sinks->name << ", type=" << sinks->type << ", ne0=" << sinks->ne[0] << ", ne1=" << sinks->ne[1] << ", ne2=" << sinks->ne[2] << ", ne3=" << sinks->ne[3] << ", nb0=" << sinks->nb[0] << ", nb1=" << sinks->nb[1] << ", nb2=" << sinks->nb[2] << ", nb3=" << sinks->nb[3]; + } + std::cerr << "))"); + + GGML_TENSOR_LOCALS(int64_t, neq, q, ne) + GGML_TENSOR_LOCALS(size_t, nbq, q, nb) + GGML_TENSOR_LOCALS(int64_t, nek, k, ne) + GGML_TENSOR_LOCALS(size_t, nbk, k, nb) + GGML_TENSOR_LOCALS(int64_t, nev, v, ne) + GGML_TENSOR_LOCALS(size_t, nbv, v, nb) + GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) + GGML_TENSOR_LOCALS(size_t, nb, dst, nb) + + const uint32_t nem0 = mask ? mask->ne[0] : 0; + const uint32_t nem1 = mask ? mask->ne[1] : 0; + const uint32_t nem2 = mask ? mask->ne[2] : 0; + const uint32_t nem3 = mask ? mask->ne[3] : 0; + + const uint32_t HSK = nek0; + const uint32_t HSV = nev0; + uint32_t N = neq1; + const uint32_t KV = nek1; + + GGML_ASSERT(ne0 == HSV); + GGML_ASSERT(ne2 == N); + + // input tensor rows must be contiguous + GGML_ASSERT(nbq0 == ggml_type_size(q->type)); + GGML_ASSERT(nbk0 == ggml_type_size(k->type)); + GGML_ASSERT(nbv0 == ggml_type_size(v->type)); + + GGML_ASSERT(neq0 == HSK); + + GGML_ASSERT(neq1 == N); + + GGML_ASSERT(nev1 == nek1); + + // dst cannot be transposed or permuted + GGML_ASSERT(nb0 == sizeof(float)); + GGML_ASSERT(nb0 <= nb1); + GGML_ASSERT(nb1 <= nb2); + GGML_ASSERT(nb2 <= nb3); + + assert(dst->type == GGML_TYPE_F32); + assert(q->type == GGML_TYPE_F32); + uint32_t gqa_ratio = 1; + uint32_t qk_ratio = neq2 / nek2; + uint32_t workgroups_x = (uint32_t)neq1; + uint32_t workgroups_y = (uint32_t)neq2; + uint32_t workgroups_z = (uint32_t)neq3; + + const bool f32acc = !ctx->device->fp16 || dst->op_params[3] == GGML_PREC_F32 || k->type == GGML_TYPE_BF16; + + // For scalar/coopmat1 FA, we can use the "large" size to accommodate qga. + // For coopmat2 FA, we always use the small size (which is still pretty large for gqa). + vk_fa_tuning_params tuning_params = get_fa_tuning_params(ctx->device, HSK, HSV, 512, KV, k->type, v->type, f32acc); + const uint32_t max_gqa = std::min(tuning_params.block_rows, 32u); + + if (N <= 8 && qk_ratio > 1 && qk_ratio <= max_gqa && + qk_ratio * nek2 == neq2 && nek2 == nev2 && nem2 <= 1) { + // grouped query attention - make the N dimension equal to gqa_ratio, reduce + // workgroups proportionally in y dimension. The shader will detect gqa_ratio > 1 + // and change addressing calculations to index Q's dimension 2. + gqa_ratio = qk_ratio; + N = gqa_ratio; + workgroups_y /= gqa_ratio; + } + + tuning_params = get_fa_tuning_params(ctx->device, HSK, HSV, N, KV, k->type, v->type, f32acc); + + const uint32_t q_stride = (uint32_t)(nbq1 / ggml_type_size(q->type)); + uint32_t k_stride = (uint32_t)(nbk1 / ggml_type_size(k->type)); + uint32_t v_stride = (uint32_t)(nbv1 / ggml_type_size(v->type)); + + // For F32, the shader treats it as a block of size 4 (for vec4 loads) + if (k->type == GGML_TYPE_F32) { + k_stride /= 4; + } + if (v->type == GGML_TYPE_F32) { + v_stride /= 4; + } + + const uint32_t alignment = tuning_params.block_cols; + bool aligned = (KV % alignment) == 0 && + // the "aligned" shader variant will forcibly align strides, for performance + (q_stride & 7) == 0 && (k_stride & 7) == 0 && (v_stride & 7) == 0; + + // Need to use the coopmat2 variant that clamps loads when HSK/HSV aren't sufficiently aligned. + if (((HSK | HSV) % 16) != 0 && tuning_params.path == FA_COOPMAT2) { + aligned = false; + } + + float scale = 1.0f; + float max_bias = 0.0f; + float logit_softcap = 0.0f; + + memcpy(&scale, (const float *) dst->op_params + 0, sizeof(float)); + memcpy(&max_bias, (const float *) dst->op_params + 1, sizeof(float)); + memcpy(&logit_softcap, (const float *) dst->op_params + 2, sizeof(float)); + + if (logit_softcap != 0) { + scale /= logit_softcap; + } + + // Only use mask opt when the mask is fairly large. This hasn't been tuned extensively. + bool use_mask_opt = mask && nem1 >= 32 && nem0 * nem1 > 32768 && nem0 >= tuning_params.block_cols * 16 + && (ctx->device->architecture != vk_device_architecture::AMD_GCN || HSK > 256 || HSV > 256); + vk_fa_pipeline_state fa_pipeline_state = get_fa_pipeline_state(ctx->device, tuning_params, HSK, HSV, aligned, f32acc, + mask != nullptr, use_mask_opt, logit_softcap != 0, k->type, v->type); + + vk_pipeline pipeline = nullptr; + + { + std::lock_guard guard(ctx->device->compile_mutex); + auto &pipelines = ctx->device->pipeline_flash_attn_f32_f16; + auto it = pipelines.find(fa_pipeline_state); + if (it != pipelines.end()) { + pipeline = it->second; + } else { + pipelines[fa_pipeline_state] = pipeline = std::make_shared(); + } + } + + assert(pipeline); + // Compile early to initialize wg_denoms. + ggml_pipeline_request_descriptor_sets(ctx, pipeline, 1); + + uint32_t split_kv = KV; + uint32_t split_k = 1; + + // Intel Alchemist prefers more workgroups + const uint32_t shader_core_count_multiplier = (ctx->device->vendor_id == VK_VENDOR_ID_INTEL && ctx->device->architecture != INTEL_XE2) ? 2 : 1; + + // Use a placeholder core count if one isn't available. split_k is a big help for perf. + const uint32_t shader_core_count = ctx->device->shader_core_count ? ctx->device->shader_core_count * shader_core_count_multiplier : 16; + + const uint32_t Br = fa_pipeline_state.Br; + const uint32_t Bc = fa_pipeline_state.Bc; + + GGML_ASSERT(Br == pipeline->wg_denoms[0]); + const uint32_t Tr = CEIL_DIV(N, Br); + + // Try to use split_k when KV is large enough to be worth the overhead. + if (gqa_ratio > 1 && workgroups_x <= Br) { + split_k = shader_core_count * 2 / (workgroups_x * workgroups_y * workgroups_z); + } else if (gqa_ratio <= 1) { + uint32_t total_wgs_no_split = Tr * workgroups_y * workgroups_z; + if (total_wgs_no_split < shader_core_count * 2) { + split_k = shader_core_count * 2 / total_wgs_no_split; + } + } + + if (split_k > 1) { + // Try to evenly split KV into split_k chunks, but it needs to be a multiple + // of "align", so recompute split_k based on that. + split_kv = ROUNDUP_POW2(std::max(1u, KV / split_k), alignment); + split_k = CEIL_DIV(KV, split_kv); + } + + // Reserve space for split_k temporaries. For each split x batch, we need to store the O matrix (D x ne1) + // and the per-row m and L values (ne1 rows). We store all the matrices first, followed by the rows. + // For matrices, the order is (inner to outer) [HSV, ne1, k, ne2, ne3]. + // For L/M, the order is (inner to outer) [ne1, k, ne2, ne3]. + const uint64_t split_k_size = split_k > 1 ? (HSV * ne1 * sizeof(float) + ne1 * sizeof(float) * 2) * split_k * ne2 * ne3 : 0; + if (split_k_size > ctx->device->properties.limits.maxStorageBufferRange) { + GGML_ABORT("Requested preallocation size is too large"); + } + if (ctx->prealloc_size_split_k < split_k_size) { + ctx->prealloc_size_split_k = split_k_size; + ggml_vk_preallocate_buffers(ctx, subctx); + } + + const uint32_t mask_opt_num_dwords = CEIL_DIV(nem0, 16 * Bc); + const uint64_t mask_opt_size = sizeof(uint32_t) * mask_opt_num_dwords * CEIL_DIV(nem1, Br) * nem2 * nem3; + + vk_pipeline pipeline_fa_mask_opt = nullptr; + if (use_mask_opt) { + { + std::lock_guard guard(ctx->device->compile_mutex); + auto &pipelines = ctx->device->pipeline_fa_mask_opt; + auto it = pipelines.find({Br, Bc}); + if (it != pipelines.end()) { + pipeline_fa_mask_opt = it->second; + } else { + pipelines[{Br, Bc}] = pipeline_fa_mask_opt = std::make_shared(); + } + } + assert(pipeline_fa_mask_opt); + ggml_pipeline_request_descriptor_sets(ctx, pipeline_fa_mask_opt, 1); + + if (ctx->prealloc_size_y < mask_opt_size) { + ctx->prealloc_size_y = mask_opt_size; + ggml_vk_preallocate_buffers(ctx, subctx); + } + if (ctx->prealloc_y_need_sync) { + ggml_vk_sync_buffers(ctx, subctx); + } + } + + const uint32_t n_head_kv = neq2; + const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head_kv)); + const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); + const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); + + vk_subbuffer q_buf = ggml_vk_tensor_subbuffer(ctx, q); + vk_subbuffer k_buf = ggml_vk_tensor_subbuffer(ctx, k); + vk_subbuffer v_buf = ggml_vk_tensor_subbuffer(ctx, v); + vk_subbuffer dst_buf = ggml_vk_tensor_subbuffer(ctx, dst); + vk_subbuffer mask_buf = mask ? ggml_vk_tensor_subbuffer(ctx, mask) : q_buf; + vk_subbuffer sinks_buf = sinks ? ggml_vk_tensor_subbuffer(ctx, sinks) : q_buf; + vk_subbuffer mask_opt_buf = use_mask_opt ? ggml_vk_subbuffer(ctx, ctx->prealloc_y, 0) : q_buf; + + uint32_t mask_n_head_log2 = ((sinks != nullptr) << 24) | n_head_log2; + + if (use_mask_opt) + { + const vk_op_flash_attn_mask_opt_push_constants opt_pc = { + nem0, + nem1, + nem2, + (uint32_t)(mask->nb[1] / sizeof(ggml_fp16_t)), + (uint32_t)(mask->nb[2] / sizeof(ggml_fp16_t)), + (uint32_t)(mask->nb[3] / sizeof(ggml_fp16_t)), + mask_opt_num_dwords, + mask_opt_num_dwords * CEIL_DIV(nem1, Br), + mask_opt_num_dwords * CEIL_DIV(nem1, Br) * nem2, + }; + + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline_fa_mask_opt, + { mask_buf, mask_opt_buf }, opt_pc, + { mask_opt_num_dwords, CEIL_DIV(nem1, Br), nem2 * nem3 }); + ggml_vk_sync_buffers(ctx, subctx); + } + + const vk_flash_attn_push_constants pc = { N, KV, + (uint32_t)ne1, (uint32_t)ne2, (uint32_t)ne3, + (uint32_t)neq2, (uint32_t)neq3, + (uint32_t)nek2, (uint32_t)nek3, + (uint32_t)nev2, (uint32_t)nev3, + nem1, nem2, nem3, + q_stride, (uint32_t)nbq2, (uint32_t)nbq3, + k_stride, (uint32_t)nbk2, (uint32_t)nbk3, + v_stride, (uint32_t)nbv2, (uint32_t)nbv3, + scale, max_bias, logit_softcap, + mask_n_head_log2, m0, m1, + gqa_ratio, split_kv, split_k }; + + if (split_k > 1) { + ggml_pipeline_request_descriptor_sets(ctx, ctx->device->pipeline_flash_attn_split_k_reduce, 1); + + if (ctx->prealloc_split_k_need_sync) { + ggml_vk_sync_buffers(ctx, subctx); + } + + // We reuse workgroups_x to mean the number of splits, so we need to + // cancel out the divide by wg_denoms[0]. + uint32_t dispatch_x; + if (gqa_ratio > 1) { + workgroups_x *= pipeline->wg_denoms[0]; + dispatch_x = split_k * workgroups_x; + } else { + dispatch_x = Tr * split_k * pipeline->wg_denoms[0]; + } + + vk_subbuffer split_k_buf = ggml_vk_subbuffer(ctx, ctx->prealloc_split_k, 0); + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, + {q_buf, k_buf, v_buf, mask_buf, sinks_buf, split_k_buf, mask_opt_buf}, + pc, { dispatch_x, workgroups_y, workgroups_z }); + + ggml_vk_sync_buffers(ctx, subctx); + const vk_op_flash_attn_split_k_reduce_push_constants pc2 = { HSV, (uint32_t)ne1, (uint32_t)ne2, (uint32_t)ne3, split_k, (sinks != nullptr) }; + ggml_vk_dispatch_pipeline(ctx, subctx, ctx->device->pipeline_flash_attn_split_k_reduce, + {split_k_buf, sinks_buf, dst_buf}, + pc2, { (uint32_t)ne1, HSV, (uint32_t)(ne2 * ne3) }); + ctx->prealloc_split_k_need_sync = true; + } else { + if (gqa_ratio > 1) { + // When using gqa, we want one actual workgroup per batch, so cancel out wg_denoms + workgroups_x *= pipeline->wg_denoms[0]; + } + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, + {q_buf, k_buf, v_buf, mask_buf, sinks_buf, dst_buf, mask_opt_buf}, + pc, { workgroups_x, workgroups_y, workgroups_z }); + } +} + +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); + }; + + // 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; + + // 128x128 isn't used with cm1 due to shared memory size; fall through to a smaller tile. + bool allow_128x128 = true; +#if defined(VK_KHR_cooperative_matrix) && defined(GGML_VULKAN_COOPMAT_GLSLC_SUPPORT) + if (!ctx->device->coopmat2 && ctx->device->coopmat_support && ctx->device->coopmat_support_16x16x16_f16acc) { + allow_128x128 = false; + } +#endif + + if (allow_128x128 && 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 if (K <= 64 && n_tiles(CONV_SHAPE_64x128) >= shader_core_count * 2) { + return CONV_SHAPE_64x128; + } else if (!allow_128x128 && K > 64 && n_tiles(CONV_SHAPE_64x128) >= shader_core_count * 2) { + // cm1 fallback for large K when 128x128 isn't available + return CONV_SHAPE_64x128; + } 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) { + switch (op) { + case GGML_OP_GET_ROWS: + GGML_ASSERT(src1->type == GGML_TYPE_I32); + if (src0->type == GGML_TYPE_I32) { + // i32 src only supports i32 result + GGML_ASSERT(dst->type == GGML_TYPE_I32); + return ctx->device->pipeline_get_rows[src0->type]; + } + if (dst->type == GGML_TYPE_F16) { + return ctx->device->pipeline_get_rows[src0->type]; + } + if (dst->type == GGML_TYPE_F32) { + return ctx->device->pipeline_get_rows_f32[src0->type]; + } + return nullptr; + case GGML_OP_GET_ROWS_BACK: + if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_I32 && dst->type == GGML_TYPE_F32) { + return ctx->device->pipeline_get_rows_back_f32; + } + return nullptr; + case GGML_OP_ACC: + if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + return ctx->device->pipeline_acc_f32; + } + return nullptr; + case GGML_OP_SET: + if (src0->type == src1->type && src0->type == dst->type && + (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_I32)) { + return ctx->device->pipeline_set_f32; + } + return nullptr; + case GGML_OP_ADD: + case GGML_OP_SUB: + case GGML_OP_MUL: + case GGML_OP_DIV: + if ((src0->type != GGML_TYPE_F32 && src0->type != GGML_TYPE_F16) || + (src1->type != GGML_TYPE_F32 && src1->type != GGML_TYPE_F16) || + (dst->type != GGML_TYPE_F32 && dst->type != GGML_TYPE_F16)) { + return nullptr; + } + switch (op) { + case GGML_OP_ADD: + { + if (ctx->num_additional_fused_ops > 0) { + if (ctx->do_add_rms_partials) { + return ctx->device->pipeline_multi_add_rms[ctx->num_additional_fused_ops]; + } else { + return ctx->device->pipeline_multi_add[ctx->num_additional_fused_ops]; + } + } + if (ctx->do_add_rms_partials) { + auto pipelines = ggml_are_same_shape(src0, src1) ? ctx->device->pipeline_add_rms_norepeat : ctx->device->pipeline_add_rms; + return pipelines[src0->type == GGML_TYPE_F16][src1->type == GGML_TYPE_F16][dst->type == GGML_TYPE_F16]; + } else { + auto pipelines = ggml_are_same_shape(src0, src1) ? ctx->device->pipeline_add_norepeat : ctx->device->pipeline_add; + return pipelines[src0->type == GGML_TYPE_F16][src1->type == GGML_TYPE_F16][dst->type == GGML_TYPE_F16]; + } + } + case GGML_OP_SUB: + { + auto pipelines = ggml_are_same_shape(src0, src1) ? ctx->device->pipeline_sub_norepeat : ctx->device->pipeline_sub; + return pipelines[src0->type == GGML_TYPE_F16][src1->type == GGML_TYPE_F16][dst->type == GGML_TYPE_F16]; + } + case GGML_OP_MUL: + { + auto pipelines = ggml_are_same_shape(src0, src1) ? ctx->device->pipeline_mul_norepeat : ctx->device->pipeline_mul; + return pipelines[src0->type == GGML_TYPE_F16][src1->type == GGML_TYPE_F16][dst->type == GGML_TYPE_F16]; + } + case GGML_OP_DIV: + { + auto pipelines = ggml_are_same_shape(src0, src1) ? ctx->device->pipeline_div_norepeat : ctx->device->pipeline_div; + return pipelines[src0->type == GGML_TYPE_F16][src1->type == GGML_TYPE_F16][dst->type == GGML_TYPE_F16]; + } + default: + break; + } + return nullptr; + case GGML_OP_ADD_ID: + if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && src2->type == GGML_TYPE_I32 && dst->type == GGML_TYPE_F32) { + return ctx->device->pipeline_add_id_f32; + } + return nullptr; + case GGML_OP_CONCAT: { + if (src0->type != src1->type || src0->type != dst->type) { + return nullptr; + } + if (ggml_blck_size(src0->type) != 1) { + return nullptr; + } + const size_t type_size = ggml_type_size(src0->type); + switch (type_size) { + case 1: + return ctx->device->pipeline_concat_i8; + case 2: + return ctx->device->pipeline_concat_i16; + case 4: + return ctx->device->pipeline_concat_i32; + case 8: + return ctx->device->pipeline_concat_i64; + default: + return nullptr; + } + } + case GGML_OP_UPSCALE: + if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + uint32_t mode = (ggml_get_op_params_i32(dst, 0) & (0xFF | GGML_SCALE_FLAG_ANTIALIAS)); + switch (mode) { + case GGML_SCALE_MODE_NEAREST: + return ctx->device->pipeline_upscale_nearest_f32; + case GGML_SCALE_MODE_BILINEAR: + return ctx->device->pipeline_upscale_bilinear_f32; + case GGML_SCALE_MODE_BICUBIC: + return ctx->device->pipeline_upscale_bicubic_f32; + case GGML_SCALE_MODE_BILINEAR | GGML_SCALE_FLAG_ANTIALIAS: + return ctx->device->pipeline_upscale_bilinear_antialias_f32; + default: + return nullptr; + } + } + return nullptr; + case GGML_OP_SCALE: + if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + return ctx->device->pipeline_scale_f32; + } + return nullptr; + case GGML_OP_SQR: + if (src0->type == dst->type && + (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16)) { + return ctx->device->pipeline_sqr[dst->type == GGML_TYPE_F16]; + } + return nullptr; + case GGML_OP_SQRT: + if (src0->type == dst->type && + (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16)) { + return ctx->device->pipeline_sqrt[dst->type == GGML_TYPE_F16]; + } + return nullptr; + case GGML_OP_SIN: + if (src0->type == dst->type && + (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16)) { + return ctx->device->pipeline_sin[dst->type == GGML_TYPE_F16]; + } + return nullptr; + case GGML_OP_COS: + if (src0->type == dst->type && + (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16)) { + return ctx->device->pipeline_cos[dst->type == GGML_TYPE_F16]; + } + return nullptr; + case GGML_OP_LOG: + if (src0->type == dst->type && + (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16)) { + return ctx->device->pipeline_log[dst->type == GGML_TYPE_F16]; + } + return nullptr; + case GGML_OP_TRI: + if (src0->type == dst->type && + (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16)) { + return ctx->device->pipeline_tri[dst->type == GGML_TYPE_F16]; + } + return nullptr; + case GGML_OP_DIAG: + if (src0->type == dst->type && + (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16)) { + return ctx->device->pipeline_diag[dst->type == GGML_TYPE_F16]; + } + return nullptr; + case GGML_OP_CLAMP: + if (src0->type == dst->type && + (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16)) { + return ctx->device->pipeline_clamp[dst->type == GGML_TYPE_F16]; + } + return nullptr; + case GGML_OP_PAD: + if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + return ctx->device->pipeline_pad_f32; + } + return nullptr; + case GGML_OP_ROLL: + if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + return ctx->device->pipeline_roll_f32; + } + return nullptr; + case GGML_OP_REPEAT: + if (ggml_type_size(src0->type) == sizeof(float) && ggml_type_size(dst->type) == sizeof(float)) { + return ctx->device->pipeline_repeat_i32; + } + if (ggml_type_size(src0->type) == 2 && ggml_type_size(dst->type) == 2) { + return ctx->device->pipeline_repeat_i16; + } + return nullptr; + case GGML_OP_REPEAT_BACK: + if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + return ctx->device->pipeline_repeat_back_f32; + } + return nullptr; + case GGML_OP_CPY: + case GGML_OP_CONT: + case GGML_OP_DUP: + return ggml_vk_get_cpy_pipeline(ctx, src0, dst, dst->type); + case GGML_OP_SET_ROWS: + if (src1->type == GGML_TYPE_I64) { + return ctx->device->pipeline_set_rows_i64[dst->type]; + } else { + return ctx->device->pipeline_set_rows_i32[dst->type]; + } + case GGML_OP_SILU_BACK: + if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + return ctx->device->pipeline_silu_back_f32; + } + return nullptr; + case GGML_OP_NORM: + if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + return ctx->device->pipeline_norm_f32; + } + return nullptr; + case GGML_OP_GROUP_NORM: + if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + return ctx->device->pipeline_group_norm_f32; + } + return nullptr; + case GGML_OP_RMS_NORM: + if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + if (ctx->do_add_rms_partials) { + return ctx->num_additional_fused_ops > 0 ? ctx->device->pipeline_rms_norm_mul_partials_f32 : ctx->device->pipeline_rms_norm_partials_f32; + } else { + return ctx->num_additional_fused_ops > 0 ? ctx->device->pipeline_rms_norm_mul_f32 : ctx->device->pipeline_rms_norm_f32; + } + } + return nullptr; + case GGML_OP_RMS_NORM_BACK: + if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + return ctx->device->pipeline_rms_norm_back_f32; + } + return nullptr; + case GGML_OP_L2_NORM: + if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + return ctx->device->pipeline_l2_norm_f32; + } + return nullptr; + case GGML_OP_UNARY: + if ((src0->type != GGML_TYPE_F32 && src0->type != GGML_TYPE_F16) || + (dst->type != GGML_TYPE_F32 && dst->type != GGML_TYPE_F16) || + (src0->type != dst->type)) { + return nullptr; + } + + switch (ggml_get_unary_op(dst)) { + case GGML_UNARY_OP_EXP: + return ctx->device->pipeline_exp[dst->type == GGML_TYPE_F16]; + case GGML_UNARY_OP_EXPM1: + return ctx->device->pipeline_expm1[dst->type == GGML_TYPE_F16]; + case GGML_UNARY_OP_ELU: + return ctx->device->pipeline_elu[dst->type == GGML_TYPE_F16]; + case GGML_UNARY_OP_SILU: + return ctx->device->pipeline_silu[dst->type == GGML_TYPE_F16]; + case GGML_UNARY_OP_GELU: + return ctx->device->pipeline_gelu[dst->type == GGML_TYPE_F16]; + case GGML_UNARY_OP_GELU_ERF: + return ctx->device->pipeline_gelu_erf[dst->type == GGML_TYPE_F16]; + case GGML_UNARY_OP_GELU_QUICK: + return ctx->device->pipeline_gelu_quick[dst->type == GGML_TYPE_F16]; + case GGML_UNARY_OP_RELU: + return ctx->device->pipeline_relu[dst->type == GGML_TYPE_F16]; + case GGML_UNARY_OP_XIELU: + return ctx->device->pipeline_xielu[dst->type == GGML_TYPE_F16]; + case GGML_UNARY_OP_NEG: + return ctx->device->pipeline_neg[dst->type == GGML_TYPE_F16]; + case GGML_UNARY_OP_TANH: + return ctx->device->pipeline_tanh[dst->type == GGML_TYPE_F16]; + case GGML_UNARY_OP_SIGMOID: + return ctx->device->pipeline_sigmoid[dst->type == GGML_TYPE_F16]; + case GGML_UNARY_OP_HARDSIGMOID: + return ctx->device->pipeline_hardsigmoid[dst->type == GGML_TYPE_F16]; + case GGML_UNARY_OP_HARDSWISH: + return ctx->device->pipeline_hardswish[dst->type == GGML_TYPE_F16]; + case GGML_UNARY_OP_ABS: + return ctx->device->pipeline_abs[dst->type == GGML_TYPE_F16]; + case GGML_UNARY_OP_SOFTPLUS: + return ctx->device->pipeline_softplus[dst->type == GGML_TYPE_F16]; + case GGML_UNARY_OP_STEP: + return ctx->device->pipeline_step[dst->type == GGML_TYPE_F16]; + case GGML_UNARY_OP_ROUND: + return ctx->device->pipeline_round[dst->type == GGML_TYPE_F16]; + case GGML_UNARY_OP_CEIL: + return ctx->device->pipeline_ceil[dst->type == GGML_TYPE_F16]; + case GGML_UNARY_OP_FLOOR: + return ctx->device->pipeline_floor[dst->type == GGML_TYPE_F16]; + case GGML_UNARY_OP_TRUNC: + return ctx->device->pipeline_trunc[dst->type == GGML_TYPE_F16]; + case GGML_UNARY_OP_SGN: + return ctx->device->pipeline_sgn[dst->type == GGML_TYPE_F16]; + default: + break; + } + return nullptr; + case GGML_OP_GLU: + if ((src0->type != GGML_TYPE_F32 && src0->type != GGML_TYPE_F16) || + (dst->type != GGML_TYPE_F32 && dst->type != GGML_TYPE_F16) || + (src0->type != dst->type)) { + return nullptr; + } + + switch (ggml_get_glu_op(dst)) { + case GGML_GLU_OP_GEGLU: + return ctx->device->pipeline_geglu[dst->type == GGML_TYPE_F16]; + case GGML_GLU_OP_REGLU: + return ctx->device->pipeline_reglu[dst->type == GGML_TYPE_F16]; + case GGML_GLU_OP_SWIGLU: + return ctx->device->pipeline_swiglu[dst->type == GGML_TYPE_F16]; + case GGML_GLU_OP_SWIGLU_OAI: + return ctx->device->pipeline_swiglu_oai[dst->type == GGML_TYPE_F16]; + case GGML_GLU_OP_GEGLU_ERF: + return ctx->device->pipeline_geglu_erf[dst->type == GGML_TYPE_F16]; + case GGML_GLU_OP_GEGLU_QUICK: + return ctx->device->pipeline_geglu_quick[dst->type == GGML_TYPE_F16]; + default: + break; + } + return nullptr; + case GGML_OP_DIAG_MASK_INF: + if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + return ctx->device->pipeline_diag_mask_inf_f32; + } + return nullptr; + case GGML_OP_SOFT_MAX: + GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16); + GGML_ASSERT(!src2 || src2->type == GGML_TYPE_F32); + + if (ctx->num_additional_fused_ops) { + uint32_t idx = (uint32_t)ceilf(log2f(float(dst->ne[0]))); + GGML_ASSERT(idx < num_topk_moe_pipelines); + // use n_experts from push constant if it's not equal to the power of two spec constant + bool use_push = dst->ne[0] != (1u << idx); + return ctx->device->pipeline_topk_moe[idx][use_push]; + } + + if (src0->type == GGML_TYPE_F32 && (src1 == nullptr || src1->type == GGML_TYPE_F32) && dst->type == GGML_TYPE_F32) { + return src0->ne[0] > 1024 ? ctx->device->pipeline_soft_max_f32_wg512 : ctx->device->pipeline_soft_max_f32; + } + if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F32) { + return src0->ne[0] > 1024 ? ctx->device->pipeline_soft_max_f32_f16_wg512 : ctx->device->pipeline_soft_max_f32_f16; + } + return nullptr; + case GGML_OP_SOFT_MAX_BACK: + if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + return ctx->device->pipeline_soft_max_back_f32; + } + return nullptr; + case GGML_OP_ROPE: + case GGML_OP_ROPE_BACK: + { + const ggml_tensor *rope = ctx->num_additional_fused_ops == 2 ? dst->src[0]->src[0] : dst; + const int mode = ((const int32_t *) rope->op_params)[2]; + const bool is_neox = mode & GGML_ROPE_TYPE_NEOX; + const bool is_mrope = mode & GGML_ROPE_TYPE_MROPE; + const bool is_vision = mode == GGML_ROPE_TYPE_VISION; + + if (is_neox) { + if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + return ctx->device->pipeline_rope_neox_f32; + } + if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F16) { + return ctx->device->pipeline_rope_neox_f32_f16; + } + if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) { + return ctx->device->pipeline_rope_neox_f16; + } + } else if (is_mrope && !is_vision) { + if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + return ctx->device->pipeline_rope_multi_f32; + } + if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F16) { + return ctx->device->pipeline_rope_multi_f32_f16; + } + if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) { + return ctx->device->pipeline_rope_multi_f16; + } + } else if (is_vision) { + if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + return ctx->device->pipeline_rope_vision_f32; + } + if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) { + return ctx->device->pipeline_rope_vision_f16; + } + } else { + if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + return ctx->device->pipeline_rope_norm_f32; + } + if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F16) { + return ctx->device->pipeline_rope_norm_f32_f16; + } + if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) { + return ctx->device->pipeline_rope_norm_f16; + } + } + return nullptr; + } + case GGML_OP_SUM: + case GGML_OP_SUM_ROWS: + case GGML_OP_MEAN: + if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + return ctx->device->pipeline_sum_rows_f32; + } + return nullptr; + case GGML_OP_CUMSUM: + if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + if (src0->ne[0] <= 512) { + return ctx->device->pipeline_cumsum_small_f32; + } else { + return ctx->device->pipeline_cumsum_f32; + } + } + return nullptr; + case GGML_OP_SOLVE_TRI: + if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + + vk_solve_tri_pipeline_state solve_tri_pipeline_state(src0->ne[0], src1->ne[0]); + + vk_pipeline pipeline = nullptr; + + { + std::lock_guard guard(ctx->device->compile_mutex); + auto it = ctx->device->pipeline_solve_tri_f32.find(solve_tri_pipeline_state); + if (it != ctx->device->pipeline_solve_tri_f32.end()) { + pipeline = it->second; + } else { + ctx->device->pipeline_solve_tri_f32[solve_tri_pipeline_state] = pipeline = std::make_shared(); + } + } + + return pipeline; + } + return nullptr; + case GGML_OP_ARGMAX: + if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_I32) { + return ctx->device->pipeline_argmax_f32; + } + return nullptr; + case GGML_OP_COUNT_EQUAL: + if (src0->type == GGML_TYPE_I32 && src1->type == GGML_TYPE_I32 && dst->type == GGML_TYPE_I64) { + return ctx->device->pipeline_count_equal_i32; + } + return nullptr; + case GGML_OP_IM2COL: + if (src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + return ctx->device->pipeline_im2col_f32; + } + if (src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F16) { + return ctx->device->pipeline_im2col_f32_f16; + } + return nullptr; + case GGML_OP_IM2COL_3D: + if (src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + return ctx->device->pipeline_im2col_3d_f32; + } + if (src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F16) { + return ctx->device->pipeline_im2col_3d_f32_f16; + } + return nullptr; + case GGML_OP_TIMESTEP_EMBEDDING: + if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + return ctx->device->pipeline_timestep_embedding_f32; + } + return nullptr; + case GGML_OP_CONV_TRANSPOSE_1D: + if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + return ctx->device->pipeline_conv_transpose_1d_f32; + } + return nullptr; + case GGML_OP_COL2IM_1D: + switch (src0->type) { + case GGML_TYPE_F32: return ctx->device->pipeline_col2im_1d_f32; + case GGML_TYPE_F16: return ctx->device->pipeline_col2im_1d_f16; + case GGML_TYPE_BF16: return ctx->device->pipeline_col2im_1d_bf16; + default: return nullptr; + } + case GGML_OP_POOL_2D: + if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + return ctx->device->pipeline_pool2d_f32; + } + return nullptr; + case GGML_OP_RWKV_WKV6: + if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + return ctx->device->pipeline_rwkv_wkv6_f32; + } + return nullptr; + case GGML_OP_RWKV_WKV7: + if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + return ctx->device->pipeline_rwkv_wkv7_f32; + } + return nullptr; + case GGML_OP_GATED_DELTA_NET: + if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + const uint32_t S_v = dst->src[2]->ne[0]; + const uint32_t kda = (dst->src[3]->ne[0] == (int64_t)S_v) ? 1 : 0; + uint32_t si; + switch (S_v) { + case 16: si = 0; break; + case 32: si = 1; break; + case 64: si = 2; break; + case 128: si = 3; break; + default: return nullptr; + } + return ctx->device->pipeline_gated_delta_net[si][kda]; + } + return nullptr; + case GGML_OP_SSM_SCAN: + if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + const uint32_t d_state = src0->ne[0]; + if (d_state == 128) { + return ctx->device->pipeline_ssm_scan_f32_d128; + } else if (d_state == 256) { + return ctx->device->pipeline_ssm_scan_f32_d256; + } + } + return nullptr; + case GGML_OP_SSM_CONV: + if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + switch (ctx->num_additional_fused_ops) { + case 0: return ctx->device->pipeline_ssm_conv_f32; + case 1: return ctx->device->pipeline_ssm_conv_silu_f32; + case 2: return ctx->device->pipeline_ssm_conv_bias_silu_f32; + default: return nullptr; + } + } + return nullptr; + case GGML_OP_OPT_STEP_ADAMW: + if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + return ctx->device->pipeline_opt_step_adamw_f32; + } + return nullptr; + case GGML_OP_OPT_STEP_SGD: + if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + return ctx->device->pipeline_opt_step_sgd_f32; + } + return nullptr; + case GGML_OP_LEAKY_RELU: + if (src0->type == dst->type && + (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16)) { + return ctx->device->pipeline_leaky_relu[dst->type == GGML_TYPE_F16]; + } + return nullptr; + case GGML_OP_CONV_2D: + case GGML_OP_CONV_TRANSPOSE_2D: + 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; + + // tile-aligned shapes let the shader skip bounds checks + const uint32_t Cin = (uint32_t)src1->ne[2]; + const uint32_t CRS = Cin * KW * KH; + const uint32_t BS_K = vk_conv_block_sizes[shape].K; + const uint32_t BS_CRS = vk_conv_block_sizes[shape].CRS; + const uint32_t BS_NPQ = vk_conv_block_sizes[shape].NPQ; + const uint32_t aligned = ((K % BS_K == 0) && + (CRS % BS_CRS == 0) && + (NPQ % BS_NPQ == 0)) ? 1u : 0u; + + vk_conv2d_pipeline_state conv2d_pipeline_state(s0, s1, p0, p1, d0, d1, KW, KH, aligned); + + std::map *pipelines = nullptr; + if (op == GGML_OP_CONV_2D) { + if (src0->type == GGML_TYPE_F32) { + pipelines = &ctx->device->pipeline_conv2d_f32[shape]; + } else if (src0->type == GGML_TYPE_F16) { + pipelines = &ctx->device->pipeline_conv2d_f16_f32[shape]; + } + } else if (op == GGML_OP_CONV_TRANSPOSE_2D) { + if (src0->type == GGML_TYPE_F32) { + pipelines = &ctx->device->pipeline_conv_transpose_2d_f32[shape]; + } else if (src0->type == GGML_TYPE_F16) { + pipelines = &ctx->device->pipeline_conv_transpose_2d_f16_f32[shape]; + } + } + + vk_pipeline pipeline = nullptr; + + { + std::lock_guard guard(ctx->device->compile_mutex); + auto it = pipelines->find(conv2d_pipeline_state); + if (it != pipelines->end()) { + pipeline = it->second; + } else { + (*pipelines)[conv2d_pipeline_state] = pipeline = std::make_shared(); + } + } + + return pipeline; + } + return nullptr; + case GGML_OP_CONV_2D_DW: + if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + if (ggml_is_contiguous(src1)) { + return ctx->device->pipeline_conv2d_dw_whcn_f32; + } else if (ggml_is_contiguous_channels(src1)) { + return ctx->device->pipeline_conv2d_dw_cwhn_f32; + } + } else if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F32) { + if (ggml_is_contiguous(src1)) { + return ctx->device->pipeline_conv2d_dw_whcn_f16_f32; + } else if (ggml_is_contiguous_channels(src1)) { + return ctx->device->pipeline_conv2d_dw_cwhn_f16_f32; + } + } + return nullptr; + case GGML_OP_CONV_3D: + if (src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + const uint32_t OC = (uint32_t)ggml_get_op_params_i32(dst, 11); + const uint32_t IC = (uint32_t)ggml_get_op_params_i32(dst, 9); + const uint32_t N = (uint32_t)ggml_get_op_params_i32(dst, 10); + const uint32_t NPQ = N * dst->ne[2] * dst->ne[1] * dst->ne[0]; + const vk_conv_shapes shape = ggml_vk_conv_select_shape(ctx, OC, NPQ); + + const uint32_t KW = (uint32_t)src0->ne[0]; + const uint32_t KH = (uint32_t)src0->ne[1]; + const uint32_t KD = (uint32_t)src0->ne[2]; + const uint32_t s0 = (uint32_t)ggml_get_op_params_i32(dst, 0); + const uint32_t s1 = (uint32_t)ggml_get_op_params_i32(dst, 1); + const uint32_t s2 = (uint32_t)ggml_get_op_params_i32(dst, 2); + const uint32_t p0 = (uint32_t)ggml_get_op_params_i32(dst, 3); + const uint32_t p1 = (uint32_t)ggml_get_op_params_i32(dst, 4); + const uint32_t p2 = (uint32_t)ggml_get_op_params_i32(dst, 5); + const uint32_t d0 = (uint32_t)ggml_get_op_params_i32(dst, 6); + const uint32_t d1 = (uint32_t)ggml_get_op_params_i32(dst, 7); + const uint32_t d2 = (uint32_t)ggml_get_op_params_i32(dst, 8); + + const uint32_t CRS = IC * KW * KH * KD; + const uint32_t BS_K = vk_conv_block_sizes[shape].K; + const uint32_t BS_CRS = vk_conv_block_sizes[shape].CRS; + const uint32_t BS_NPQ = vk_conv_block_sizes[shape].NPQ; + const uint32_t aligned = ((OC % BS_K == 0) && + (CRS % BS_CRS == 0) && + (NPQ % BS_NPQ == 0)) ? 1u : 0u; + + vk_conv3d_pipeline_state conv3d_pipeline_state(s0, s1, s2, p0, p1, p2, d0, d1, d2, KW, KH, KD, aligned); + + std::map *pipelines = nullptr; + if (src0->type == GGML_TYPE_F32) { + pipelines = &ctx->device->pipeline_conv3d_f32[shape]; + } else if (src0->type == GGML_TYPE_F16) { + pipelines = &ctx->device->pipeline_conv3d_f16_f32[shape]; + } else { + return nullptr; + } + + vk_pipeline pipeline = nullptr; + + { + std::lock_guard guard(ctx->device->compile_mutex); + auto it = pipelines->find(conv3d_pipeline_state); + if (it != pipelines->end()) { + pipeline = it->second; + } else { + (*pipelines)[conv3d_pipeline_state] = pipeline = std::make_shared(); + } + } + + return pipeline; + } + return nullptr; + case GGML_OP_ADD1: + if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) { + return ctx->device->pipeline_add1_f16_f16; + } + if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F16) { + return ctx->device->pipeline_add1_f16_f32; + } + if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + return ctx->device->pipeline_add1_f32_f32; + } + return nullptr; + case GGML_OP_ARANGE: + if (dst->type == GGML_TYPE_F32) { + return ctx->device->pipeline_arange_f32; + } + return nullptr; + case GGML_OP_FILL: + if (dst->type == GGML_TYPE_F32) { + return ctx->device->pipeline_fill_f32; + } + if (dst->type == GGML_TYPE_F16) { + return ctx->device->pipeline_fill_f16; + } + return nullptr; + default: + return nullptr; + } + + GGML_UNUSED(src2); +} + +template <> void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, vk_op_unary_push_constants &p, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, const ggml_tensor * src3, ggml_tensor * dst) { + const uint32_t a_offset = get_misalign_bytes(ctx, src0) / ggml_type_size(src0->type); + const uint32_t d_offset = get_misalign_bytes(ctx, dst) / ggml_type_size(dst->type); + + p.misalign_offsets = (a_offset << 16) | d_offset; + + GGML_UNUSED(src1); + GGML_UNUSED(src2); + GGML_UNUSED(src3); +} + +template <> void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, vk_op_glu_push_constants &p, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, const ggml_tensor * src3, ggml_tensor * dst) { + const uint32_t a_offset = get_misalign_bytes(ctx, src0) / ggml_type_size(src0->type); + const uint32_t b_offset = src1 ? get_misalign_bytes(ctx, src1) / ggml_type_size(src1->type) : a_offset; + const uint32_t d_offset = get_misalign_bytes(ctx, dst) / ggml_type_size(dst->type); + + GGML_ASSERT(a_offset < (1u << 8)); + GGML_ASSERT(b_offset < (1u << 8)); + GGML_ASSERT(d_offset < (1u << 8)); + + p.misalign_offsets = (a_offset << 16) | (b_offset << 8) | d_offset; + + GGML_UNUSED(src2); + GGML_UNUSED(src3); +} + +template <> void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, vk_op_sum_rows_push_constants &p, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, const ggml_tensor * src3, ggml_tensor * dst) { + const uint32_t a_offset = get_misalign_bytes(ctx, src0) / ggml_type_size(src0->type); + const uint32_t d_offset = get_misalign_bytes(ctx, dst) / ggml_type_size(dst->type); + + p.misalign_offsets = (a_offset << 16) | d_offset; + + GGML_UNUSED(src1); + GGML_UNUSED(src2); + GGML_UNUSED(src3); +} + +template <> void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, vk_op_pad_push_constants &p, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, const ggml_tensor * src3, ggml_tensor * dst) { + const uint32_t a_offset = get_misalign_bytes(ctx, src0) / ggml_type_size(src0->type); + const uint32_t d_offset = get_misalign_bytes(ctx, dst) / ggml_type_size(dst->type); + + p.misalign_offsets = (a_offset << 16) | d_offset; + + GGML_UNUSED(src1); + GGML_UNUSED(src2); + GGML_UNUSED(src3); +} + +template <> void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, vk_op_im2col_3d_push_constants &p, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, const ggml_tensor * src3, ggml_tensor * dst) { + const uint32_t a_offset = get_misalign_bytes(ctx, src1) / ggml_type_size(src1->type); + const uint32_t d_offset = get_misalign_bytes(ctx, dst) / ggml_type_size(dst->type); + + p.misalign_offsets = (a_offset << 16) | d_offset; + + GGML_UNUSED(src0); + GGML_UNUSED(src2); + GGML_UNUSED(src3); +} + +template <> void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, vk_op_binary_push_constants &p, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, const ggml_tensor * src3, ggml_tensor * dst) { + const uint32_t a_offset = get_misalign_bytes(ctx, src0) / ggml_type_size(src0->type); + const uint32_t b_offset = get_misalign_bytes(ctx, src1) / ggml_type_size(src1->type); + const uint32_t d_offset = get_misalign_bytes(ctx, dst) / ggml_type_size(dst->type); + + GGML_ASSERT(dst->op != GGML_OP_GET_ROWS || (a_offset == 0 && b_offset == 0 && d_offset == 0)); + + p.misalign_offsets = (a_offset << 16) | (b_offset << 8) | d_offset; + + GGML_UNUSED(src2); + GGML_UNUSED(src3); +} + +template <> void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, vk_op_upscale_push_constants &p, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, const ggml_tensor * src3, ggml_tensor * dst) { + const uint32_t a_offset = get_misalign_bytes(ctx, src0) / ggml_type_size(src0->type); + const uint32_t d_offset = get_misalign_bytes(ctx, dst) / ggml_type_size(dst->type); + + p.a_offset = a_offset; + p.d_offset = d_offset; + + GGML_UNUSED(src1); + GGML_UNUSED(src2); + GGML_UNUSED(src3); +} + +template <> void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, vk_op_rope_push_constants &p, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, const ggml_tensor * src3, ggml_tensor * dst) { + p.a_offset = get_misalign_bytes(ctx, src0) / ggml_type_size(src0->type); + p.d_offset = get_misalign_bytes(ctx, dst) / ggml_type_size(dst->type); + + GGML_UNUSED(src1); + GGML_UNUSED(src2); + GGML_UNUSED(src3); +} + +template +static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, const ggml_tensor * src3, ggml_tensor * dst, ggml_op op, PC&& pc) { + VK_LOG_DEBUG("ggml_vk_op_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; + if (src1 != nullptr) { + std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; + } + if (src2 != nullptr) { + std::cerr << "), (" << src2 << ", name=" << src2->name << ", type=" << src2->type << ", ne0=" << src2->ne[0] << ", ne1=" << src2->ne[1] << ", ne2=" << src2->ne[2] << ", ne3=" << src2->ne[3] << ", nb0=" << src2->nb[0] << ", nb1=" << src2->nb[1] << ", nb2=" << src2->nb[2] << ", nb3=" << src2->nb[3]; + } + if (src3 != nullptr) { + std::cerr << "), (" << src3 << ", name=" << src3->name << ", type=" << src3->type << ", ne0=" << src3->ne[0] << ", ne1=" << src3->ne[1] << ", ne2=" << src3->ne[2] << ", ne3=" << src3->ne[3] << ", nb0=" << src3->nb[0] << ", nb1=" << src3->nb[1] << ", nb2=" << src3->nb[2] << ", nb3=" << src3->nb[3]; + } + std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3]; + std::cerr << "), " << ggml_op_name(op) << ")"); + GGML_ASSERT(op == GGML_OP_GET_ROWS || op == GGML_OP_CPY || (!ggml_is_quantized(src0->type) && (src1 == nullptr || !ggml_is_quantized(src1->type)))); // NOLINT + GGML_ASSERT(dst->buffer != nullptr); + const uint64_t ne00 = src0->ne[0]; + const uint64_t ne01 = src0->ne[1]; + const uint64_t ne02 = src0->ne[2]; + const uint64_t ne03 = src0->ne[3]; + + const bool use_src1 = src1 != nullptr; + const uint64_t ne10 = use_src1 ? src1->ne[0] : 0; + const uint64_t ne11 = use_src1 ? src1->ne[1] : 0; + const uint64_t ne12 = use_src1 ? src1->ne[2] : 0; + const uint64_t ne13 = use_src1 ? src1->ne[3] : 0; + + const bool use_src2 = src2 != nullptr; + const bool use_src3 = src3 != nullptr; + + init_pushconst_fastdiv(pc); + + vk_pipeline pipeline = ggml_vk_op_get_pipeline(ctx, src0, src1, src2, dst, op); + + if (pipeline == nullptr) { + std::cerr << "ggml_vulkan: Error: Missing op: " << ggml_op_name(op) << " for " << ggml_type_name(src0->type); + if (src1 != nullptr) { + std::cerr << " and " << ggml_type_name(src1->type); + } + std::cerr << " to " << ggml_type_name(dst->type) << std::endl; + GGML_ABORT("fatal error"); + } + + ggml_pipeline_request_descriptor_sets(ctx, pipeline, 1); + + vk_subbuffer src0_buf = ggml_vk_tensor_subbuffer(ctx, src0, true); + vk_subbuffer src1_buf = use_src1 ? ggml_vk_tensor_subbuffer(ctx, src1, true) : vk_subbuffer{}; + vk_subbuffer src2_buf = use_src2 ? ggml_vk_tensor_subbuffer(ctx, src2, true) : vk_subbuffer{}; + vk_subbuffer src3_buf = use_src3 ? ggml_vk_tensor_subbuffer(ctx, src3, true) : vk_subbuffer{}; + vk_subbuffer dst_buf = ggml_vk_tensor_subbuffer(ctx, dst, true); + + // Compute misalignment offset for descriptors and store it in in push constants. + init_pushconst_tensor_offsets(ctx, pc, src0, src1, src2, src3, dst); + + std::array elements; + + switch (op) { + case GGML_OP_NORM: + case GGML_OP_RMS_NORM_BACK: + case GGML_OP_L2_NORM: + case GGML_OP_SOFT_MAX: + case GGML_OP_SOFT_MAX_BACK: + case GGML_OP_SUM_ROWS: + case GGML_OP_CUMSUM: + case GGML_OP_MEAN: + case GGML_OP_ARGMAX: + { + const uint32_t nr = ggml_nrows(src0); + if (nr > 262144) { + elements = { 512, 512, CEIL_DIV(nr, 262144) }; + } else if (nr > 512) { + elements = { 512, CEIL_DIV(nr, 512), 1 }; + } else { + elements = { nr, 1, 1 }; + } + } break; + case GGML_OP_SOLVE_TRI: + { + uint32_t nr = (uint32_t)(ne02 * ne03); + if (nr > 262144) { + elements = { 512, 512, CEIL_DIV(nr, 262144) }; + } else if (nr > 512) { + elements = { 512, CEIL_DIV(nr, 512), 1 }; + } else { + elements = { nr, 1, 1 }; + } + } + break; + case GGML_OP_RMS_NORM: + if (ctx->do_add_rms_partials) { + // Run one element per thread, 128 threads per workgroup + elements = { (uint32_t)CEIL_DIV(ne00, 128), 1, 1 }; + } else { + elements = { (uint32_t)ne01, (uint32_t)ne02, (uint32_t)ne03 }; + } + break; + + case GGML_OP_SUM: + // We use GGML_OP_SUM_ROWS with 1 row. + elements = { 1, 1, 1 }; + break; + case GGML_OP_GROUP_NORM: + { + const uint32_t num_groups = dst->op_params[0]; + elements = { num_groups * (uint32_t)src0->ne[3], 1, 1 }; + } break; + case GGML_OP_DIAG_MASK_INF: + elements = { (uint32_t)ggml_nrows(src0), (uint32_t)ne00, 1 }; + break; + case GGML_OP_ROPE: + case GGML_OP_ROPE_BACK: + { + uint32_t nrows = (uint32_t)ggml_nrows(src0); + uint32_t z = 1; + if (nrows > ctx->device->properties.limits.maxComputeWorkGroupCount[0]) { + z = CEIL_DIV(nrows, 32768); + nrows = 32768; + } + elements = { nrows, (uint32_t)ne00, z }; + + } break; + case GGML_OP_GET_ROWS: + elements = { (uint32_t)ne00, (uint32_t)ne10, (uint32_t)(ne11 * ne12) }; + elements[1] = std::min(elements[1], ctx->device->properties.limits.maxComputeWorkGroupCount[1]); + elements[2] = std::min(elements[2], ctx->device->properties.limits.maxComputeWorkGroupCount[2]); + break; + case GGML_OP_GET_ROWS_BACK: + elements = { (uint32_t)dst->ne[0], (uint32_t)dst->ne[1], 1 }; + elements[1] = std::min(elements[1], ctx->device->properties.limits.maxComputeWorkGroupCount[1]); + break; + case GGML_OP_ARGSORT: + GGML_ASSERT(0); + break; + case GGML_OP_IM2COL: + { + const bool is_2D = dst->op_params[6] == 1; + + const uint32_t IC = src1->ne[is_2D ? 2 : 1]; + + const uint32_t KH = is_2D ? src0->ne[1] : 1; + const uint32_t KW = src0->ne[0]; + + const uint32_t OH = is_2D ? dst->ne[2] : 1; + const uint32_t OW = dst->ne[1]; + + const uint32_t batch = src1->ne[is_2D ? 3 : 2]; + + const uint32_t CHW = IC * KH * KW; + // Cap X workgroups to limit concurrent IC channel reads. + // The shader loops over X to cover the full CHW dimension. + // AMD prefers a lower limit + const uint32_t min_cap = ctx->device->vendor_id == VK_VENDOR_ID_AMD ? 512u : 4096u; + const uint32_t x_elements = std::min(CHW, std::max(min_cap, OW * KH * KW)); + elements = { x_elements, OW, OH * batch }; + elements[1] = std::min(elements[1], ctx->device->properties.limits.maxComputeWorkGroupCount[1]); + elements[2] = std::min(elements[2], ctx->device->properties.limits.maxComputeWorkGroupCount[2]); + } break; + case GGML_OP_IM2COL_3D: + { + const uint32_t IC = ((const uint32_t *)(dst->op_params))[9]; + + const uint32_t N = ne13 / IC; + + const uint32_t KD = ne02; + const uint32_t KH = ne01; + const uint32_t KW = ne00; + + const uint32_t OD = dst->ne[3] / N; + const uint32_t OH = dst->ne[2]; + const uint32_t OW = dst->ne[1]; + + const uint32_t IC_KD_KH_KW = IC*KD*KH*KW; + const uint32_t N_OD_OH = N*OD*OH; + + elements = { IC_KD_KH_KW, OW, N_OD_OH }; + elements[2] = std::min(elements[2], ctx->device->properties.limits.maxComputeWorkGroupCount[2]); + } break; + case GGML_OP_TIMESTEP_EMBEDDING: + { + const uint32_t dim = dst->op_params[0]; + uint32_t half_ceil = (dim + 1) / 2; + elements = { half_ceil, (uint32_t)src0->ne[0], 1 }; + } break; + case GGML_OP_CONV_TRANSPOSE_1D: + { + elements = {uint32_t(src0->ne[1]), 1, 1}; // parallelize in {Cout, 1, 1} + } break; + case GGML_OP_COL2IM_1D: + { + elements = { uint32_t(dst->ne[0]), uint32_t(dst->ne[1]), 1 }; + } break; + case GGML_OP_POOL_2D: + { + const uint32_t N = dst->ne[3]; + const uint32_t OC = dst->ne[2]; + const uint32_t OH = dst->ne[1]; + const uint32_t OW = dst->ne[0]; + elements = { N * OC * OH * OW, 1, 1}; + } break; + case GGML_OP_CONV_2D: + case GGML_OP_CONV_TRANSPOSE_2D: + 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_CONV_3D: + if constexpr (std::is_same_v) { + const uint32_t NPQ = pc.N * pc.OD * pc.OH * pc.OW; + const vk_conv_shapes shape = ggml_vk_conv_select_shape(ctx, pc.OC, NPQ); + const uint32_t NPQ_blocks = CEIL_DIV(NPQ, vk_conv_block_sizes[shape].NPQ); + + elements = { pc.OC, 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_3D"); + } + break; + case GGML_OP_ADD: + case GGML_OP_SUB: + case GGML_OP_DIV: + case GGML_OP_MUL: + case GGML_OP_ADD1: + case GGML_OP_ARANGE: + case GGML_OP_FILL: + case GGML_OP_SCALE: + case GGML_OP_SQR: + case GGML_OP_SQRT: + case GGML_OP_SIN: + case GGML_OP_COS: + case GGML_OP_LOG: + case GGML_OP_TRI: + case GGML_OP_DIAG: + case GGML_OP_CLAMP: + case GGML_OP_LEAKY_RELU: + case GGML_OP_PAD: + case GGML_OP_ROLL: + case GGML_OP_REPEAT: + case GGML_OP_REPEAT_BACK: + case GGML_OP_CPY: + case GGML_OP_CONCAT: + case GGML_OP_UPSCALE: + case GGML_OP_UNARY: + case GGML_OP_GLU: + case GGML_OP_CONV_2D_DW: + { + uint32_t ne = ggml_nelements(dst); + if (op == GGML_OP_CPY && ggml_is_quantized(src0->type) && ggml_is_quantized(dst->type)) { + // Convert from number of logical elements to 2- or 4-byte units. + ne /= ggml_blck_size(src0->type); + if ((ggml_type_size(src0->type) % 4) == 0) { + ne *= ggml_type_size(src0->type) / 4; + } else { + ne *= ggml_type_size(src0->type) / 2; + } + } + // copy_to_quant has block size of 32, and each thread does QUANT_K elements. + // Splitting into 512x512xZ wouldn't work well since each workgroup does 1024 elements. + // So divide by block size here before splitting into 512x512 groups. + if (op == GGML_OP_CPY && !ggml_is_quantized(src0->type) && ggml_is_quantized(dst->type)) { + ne = CEIL_DIV(ne, ggml_blck_size(dst->type)); + } + if (ne > 262144) { + elements = { 512, 512, CEIL_DIV(ne, 262144) }; + } else if (ne > 512) { + elements = { 512, CEIL_DIV(ne, 512), 1 }; + } else { + elements = { ne, 1, 1 }; + } + + if (pipeline == ctx->device->pipeline_cpy_transpose_32 || + pipeline == ctx->device->pipeline_cpy_transpose_16) { + // 32x32 tiles + elements[0] = (uint32_t)CEIL_DIV(dst->ne[0], 32); + elements[1] = (uint32_t)CEIL_DIV(dst->ne[1], 32); + elements[2] = (uint32_t)(dst->ne[2]*dst->ne[3]); + elements[0] = std::min(elements[0], ctx->device->properties.limits.maxComputeWorkGroupCount[0]); + elements[1] = std::min(elements[1], ctx->device->properties.limits.maxComputeWorkGroupCount[1]); + elements[2] = std::min(elements[2], ctx->device->properties.limits.maxComputeWorkGroupCount[2]); + } + } break; + case GGML_OP_ADD_ID: + { + elements = { (uint32_t)ne01, (uint32_t)ne02, 1 }; + } break; + case GGML_OP_SET_ROWS: + { + uint32_t ne = ggml_nelements(src0); + if (ggml_is_quantized(dst->type)) { + // quants run 32 threads each doing QUANT_K elements + ne = CEIL_DIV(ne, 32 * ggml_blck_size(dst->type)); + } else { + // scalar types do one element per thread, running 512 threads + ne = CEIL_DIV(ne, 512); + } + if (ne > 262144) { + elements = { 512, 512, CEIL_DIV(ne, 262144) }; + } else if (ne > 512) { + elements = { 512, CEIL_DIV(ne, 512), 1 }; + } else { + elements = { ne, 1, 1 }; + } + } + break; + case GGML_OP_SSM_CONV: + { + const uint32_t nr = src0->ne[1]; + const uint32_t n_t = dst->ne[1]; + const uint32_t n_s = dst->ne[2]; + elements = { nr, n_t, n_s }; + } + break; + default: + elements = { (uint32_t)ggml_nelements(src0), 1, 1 }; + break; + } + + if (op == GGML_OP_ADD || op == GGML_OP_RMS_NORM) { + vk_subbuffer a_buf = src0_buf; + if (ctx->do_add_rms_partials) { + a_buf = ggml_vk_subbuffer(ctx, ctx->prealloc_add_rms_partials, ctx->prealloc_size_add_rms_partials_offset); + } + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, + { src0_buf, src1_buf, dst_buf, a_buf }, pc, elements); + } else if (op == GGML_OP_GLU) { + // Empty src1 is possible in glu, but the shader needs a buffer + vk_subbuffer subbuf1 = use_src1 ? src1_buf : src0_buf; + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { src0_buf, subbuf1, dst_buf }, pc, elements); + } else if (op == GGML_OP_SOFT_MAX) { + // Empty src1 and src2 is possible in soft_max, but the shader needs a buffer + vk_subbuffer subbuf1 = use_src1 ? src1_buf : src0_buf; + vk_subbuffer subbuf2 = use_src2 ? src2_buf : src0_buf; + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { src0_buf, subbuf1, subbuf2, dst_buf }, pc, elements); + } else if (op == GGML_OP_ROPE || op == GGML_OP_ROPE_BACK) { + // Empty src2 and src3 is possible in rope, but the shader needs a buffer + vk_subbuffer subbuf2 = use_src2 ? src2_buf : src0_buf; + vk_subbuffer subbuf3 = use_src3 ? src3_buf : src0_buf; + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { src0_buf, src1_buf, subbuf2, dst_buf, subbuf3 }, pc, elements); + } else if (op == GGML_OP_IM2COL || op == GGML_OP_IM2COL_3D) { + if (ctx->device->shader_int64 && ctx->device->buffer_device_address) { + // buffer device address path doesn't use dst buffer + dst_buf.size = 1; + } + // im2col uses only src1 and dst buffers + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { src1_buf, dst_buf }, pc, elements); + } else if (op == GGML_OP_COUNT_EQUAL) { + // count_equal assumes that destination buffer is initialized with zeroes + ggml_vk_buffer_memset_async(subctx, dst_buf.buffer, dst_buf.offset, 0, dst_buf.size); + ggml_vk_sync_buffers(ctx, subctx); + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { src0_buf, src1_buf, dst_buf }, pc, elements); + } else if (op == GGML_OP_OPT_STEP_SGD) { + // OPT_STEP_SGD works on src0, it does not need dst + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { src0_buf, src1_buf, src2_buf }, pc, elements); + } else if (use_src3) { + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { src0_buf, src1_buf, src2_buf, src3_buf, dst_buf }, pc, elements); + } else if (use_src2) { + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { src0_buf, src1_buf, src2_buf, dst_buf }, pc, elements); + } else if (use_src1) { + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { src0_buf, src1_buf, dst_buf }, pc, elements); + } else { + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { src0_buf, dst_buf }, pc, elements); + } +} + +static void ggml_vk_get_rows(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + const uint32_t src0_type_size = ggml_type_size(src0->type); + const uint32_t src1_type_size = ggml_type_size(src1->type); + const uint32_t dst_type_size = ggml_type_size(dst->type); + + ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, nullptr, dst, GGML_OP_GET_ROWS, { + (uint32_t)ggml_nelements(src0), + (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size, + (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size, + (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, + 0, + 0.0f, 0.0f, 0, + }); +} + +static void ggml_vk_get_rows_back(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + const uint32_t src0_type_size = ggml_type_size(src0->type); + const uint32_t src1_type_size = ggml_type_size(src1->type); + const uint32_t dst_type_size = ggml_type_size(dst->type); + + ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, nullptr, dst, GGML_OP_GET_ROWS_BACK, { + (uint32_t)ggml_nelements(src0), + (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], (uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size, + (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2], (uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size, + (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, + 0, + 0.0f, 0.0f, 0, + }); +} + +static void ggml_vk_acc(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + const uint32_t src0_type_size = ggml_type_size(src0->type); + const uint32_t src1_type_size = ggml_type_size(src1->type); + const uint32_t dst_type_size = ggml_type_size(dst->type); + + int nb1 = dst->op_params[0] / src0_type_size; // 4 bytes of float32 + int nb2 = dst->op_params[1] / src0_type_size; // 4 bytes of float32 + int nb3 = dst->op_params[2] / src0_type_size; // 4 bytes of float32 + int offset = dst->op_params[3] / src0_type_size; // offset in bytes + + ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, nullptr, dst, dst->op, { + (uint32_t)ggml_nelements(src0), + (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)nb1, (uint32_t)nb2, (uint32_t)nb3, + (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size, + (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t)nb1, (uint32_t)nb2, (uint32_t)nb3, + 0, + 0.0f, 0.0f, offset, + }); +} + +static void ggml_vk_multi_add(ggml_backend_vk_context * ctx, vk_context& subctx, ggml_cgraph * cgraph, int node_idx) { + const ggml_tensor *first_node = cgraph->nodes[node_idx]; + const ggml_tensor *dst = cgraph->nodes[node_idx + ctx->num_additional_fused_ops]; + + // Make a list of all the tensors used by the op. + // Last element of the list is the dest tensor. + const ggml_tensor *tensors[MAX_PARAMETER_COUNT]; + uint32_t num_srcs = ctx->num_additional_fused_ops + 2; + uint32_t num_tensors = num_srcs + 1; + GGML_ASSERT(num_tensors + ctx->do_add_rms_partials <= MAX_PARAMETER_COUNT); + + tensors[0] = first_node->src[0]; + tensors[1] = first_node->src[1]; + for (int32_t i = 0; i < ctx->num_additional_fused_ops; ++i) { + // check whether the previous result is src[0] or src[1] + if (cgraph->nodes[node_idx + i] == cgraph->nodes[node_idx + i + 1]->src[0]) { + tensors[i+2] = cgraph->nodes[node_idx + i + 1]->src[1]; + } else { + tensors[i+2] = cgraph->nodes[node_idx + i + 1]->src[0]; + } + } + tensors[num_srcs] = dst; + + vk_op_multi_add_push_constants pc; + pc.ne20 = (uint32_t)dst->ne[0]; + pc.ne21 = (uint32_t)dst->ne[1]; + pc.ne22 = (uint32_t)dst->ne[2]; + pc.ne23 = (uint32_t)dst->ne[3]; + + for (uint32_t i = 0; i < num_tensors; ++i) { + const ggml_tensor *t = tensors[i]; + pc.nb[i][0] = (uint32_t)t->nb[0] / sizeof(float); + pc.nb[i][1] = (uint32_t)t->nb[1] / sizeof(float); + pc.nb[i][2] = (uint32_t)t->nb[2] / sizeof(float); + pc.nb[i][3] = (uint32_t)t->nb[3] / sizeof(float); + } + pc.rms_partials = ctx->do_add_rms_partials; + + vk_pipeline pipeline = ggml_vk_op_get_pipeline(ctx, tensors[0], tensors[1], nullptr, dst, dst->op); + + if (pipeline == nullptr) { + std::cerr << "ggml_vulkan: Error: Missing multi_add"; + GGML_ABORT("fatal error"); + } + + ggml_pipeline_request_descriptor_sets(ctx, pipeline, 1); + + ggml_backend_vk_buffer_context * buf_ctx[MAX_PARAMETER_COUNT]; + vk_buffer buf[MAX_PARAMETER_COUNT]; + size_t offset[MAX_PARAMETER_COUNT]; + bool uma[MAX_PARAMETER_COUNT]; + + for (uint32_t i = 0; i < num_tensors; ++i) { + buf_ctx[i] = (ggml_backend_vk_buffer_context *)tensors[i]->buffer->context; + buf[i] = nullptr; + offset[i] = 0; + uma[i] = false; + + if (ctx->device->uma) { + ggml_vk_host_get(ctx->device, tensors[i]->data, buf[i], offset[i]); + uma[i] = buf[i] != nullptr; + } + if (!uma[i]) { + buf[i] = buf_ctx[i]->dev_buffer; + offset[i] = vk_tensor_offset(tensors[i]) + tensors[i]->view_offs; + } + GGML_ASSERT(buf[i] != nullptr); + } + // If any remaining descriptors are unused, just point them at src[0] + for (uint32_t i = num_tensors; i < MAX_PARAMETER_COUNT; ++i) { + buf[i] = buf[0]; + offset[i] = 0; + } + if (ctx->do_add_rms_partials) { + buf[num_tensors] = ctx->prealloc_add_rms_partials; + offset[num_tensors] = ctx->prealloc_size_add_rms_partials_offset; + } + + std::array elements; + + uint32_t ne = ggml_nelements(dst); + if (ne > 262144) { + elements = { 512, 512, CEIL_DIV(ne, 262144) }; + } else if (ne > 512) { + elements = { 512, CEIL_DIV(ne, 512), 1 }; + } else { + elements = { ne, 1, 1 }; + } + + static_assert(MAX_PARAMETER_COUNT == 12); + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, + { + ggml_vk_subbuffer(ctx, buf[0], offset[0]), + ggml_vk_subbuffer(ctx, buf[1], offset[1]), + ggml_vk_subbuffer(ctx, buf[2], offset[2]), + ggml_vk_subbuffer(ctx, buf[3], offset[3]), + ggml_vk_subbuffer(ctx, buf[4], offset[4]), + ggml_vk_subbuffer(ctx, buf[5], offset[5]), + ggml_vk_subbuffer(ctx, buf[6], offset[6]), + ggml_vk_subbuffer(ctx, buf[7], offset[7]), + ggml_vk_subbuffer(ctx, buf[8], offset[8]), + ggml_vk_subbuffer(ctx, buf[9], offset[9]), + ggml_vk_subbuffer(ctx, buf[10], offset[10]), + ggml_vk_subbuffer(ctx, buf[11], offset[11]), + }, pc, elements); +} + +static void ggml_vk_add(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + const uint32_t src0_type_size = ggml_type_size(src0->type); + const uint32_t src1_type_size = ggml_type_size(src1->type); + const uint32_t dst_type_size = ggml_type_size(dst->type); + + ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, nullptr, dst, GGML_OP_ADD, { + (uint32_t)ggml_nelements(src0), + (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size, + (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size, + (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, + 0, + 0.0f, 0.0f, ctx->do_add_rms_partials, + }); +} + +static void ggml_vk_sub(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + const uint32_t src0_type_size = ggml_type_size(src0->type); + const uint32_t src1_type_size = ggml_type_size(src1->type); + const uint32_t dst_type_size = ggml_type_size(dst->type); + + ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, nullptr, dst, GGML_OP_SUB, { + (uint32_t)ggml_nelements(src0), + (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size, + (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size, + (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, + 0, + 0.0f, 0.0f, 0, + }); +} + +static void ggml_vk_mul(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + const uint32_t src0_type_size = ggml_type_size(src0->type); + const uint32_t src1_type_size = ggml_type_size(src1->type); + const uint32_t dst_type_size = ggml_type_size(dst->type); + + ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, nullptr, dst, GGML_OP_MUL, { + (uint32_t)ggml_nelements(src0), + (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size, + (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size, + (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, + 0, + 0.0f, 0.0f, 0, + }); +} + +static void ggml_vk_div(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + const uint32_t src0_type_size = ggml_type_size(src0->type); + const uint32_t src1_type_size = ggml_type_size(src1->type); + const uint32_t dst_type_size = ggml_type_size(dst->type); + + ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, nullptr, dst, GGML_OP_DIV, { + (uint32_t)ggml_nelements(src0), + (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size, + (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size, + (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, + 0, + 0.0f, 0.0f, 0, + }); +} + +static void ggml_vk_add_id(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst) { + const uint32_t src0_type_size = ggml_type_size(src0->type); + const uint32_t src1_type_size = ggml_type_size(src1->type); + const uint32_t src2_type_size = ggml_type_size(src2->type); + + ggml_vk_op_f32(ctx, subctx, src0, src1, src2, nullptr, dst, GGML_OP_ADD_ID, { + (uint32_t)dst->ne[0], + (uint32_t)dst->ne[1], + (uint32_t)src0->nb[1] / src0_type_size, + (uint32_t)src0->nb[2] / src0_type_size, + (uint32_t)src1->nb[1] / src1_type_size, + (uint32_t)src2->nb[1] / src2_type_size, + }); +} + +static void ggml_vk_op_f32_wkv(ggml_backend_vk_context * ctx, vk_context& subctx, ggml_tensor * dst, const vk_op_rwkv_wkv6_push_constants&& pc, int version) { + GGML_ASSERT(version == 6 || version == 7); + int num_srcs = version == 6 ? 6 : 7; + + for (int i = 0; i < num_srcs; i++) { + GGML_ASSERT(!ggml_is_quantized(dst->src[i]->type)); + } + + GGML_ASSERT(dst->buffer != nullptr); + + vk_pipeline pipeline = ggml_vk_op_get_pipeline(ctx, dst->src[0], dst->src[1], dst->src[2], dst, dst->op); + GGML_ASSERT(pipeline != nullptr); + + ggml_pipeline_request_descriptor_sets(ctx, pipeline, 1); + + vk_subbuffer dst_buf = ggml_vk_tensor_subbuffer(ctx, dst); + vk_subbuffer src_buf[7] = {}; + for (int i = 0; i < num_srcs; i++) { + src_buf[i] = ggml_vk_tensor_subbuffer(ctx, dst->src[i]); + } + + std::array elements = { + (uint32_t)(pc.B * pc.H), + 1, + 1 + }; + + if (version == 6) { + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, + {src_buf[0], src_buf[1], src_buf[2], src_buf[3], src_buf[4], src_buf[5], dst_buf}, + pc, elements); + } else if (version == 7) { + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, + {src_buf[0], src_buf[1], src_buf[2], src_buf[3], src_buf[4], src_buf[5], src_buf[6], dst_buf}, + pc, elements); + } else { + // shouldn't happen + GGML_ASSERT(false); + } +} + +static void ggml_vk_rwkv_wkv6(ggml_backend_vk_context * ctx, vk_context& subctx, ggml_tensor * dst) { + const size_t seq_length = dst->src[0]->ne[2]; + const size_t n_embed = dst->ne[0]; + const size_t n_heads = dst->src[0]->ne[1]; + const size_t n_seqs = dst->src[5]->ne[1]; + + ggml_vk_op_f32_wkv( + ctx, subctx, dst, + { + (uint32_t)n_seqs, + (uint32_t)seq_length, + (uint32_t)n_embed, + (uint32_t)n_heads, + }, + 6 + ); +} + +static void ggml_vk_rwkv_wkv7(ggml_backend_vk_context * ctx, vk_context& subctx, ggml_tensor * dst) { + const size_t seq_length = dst->src[0]->ne[2]; + const size_t n_embed = dst->ne[0]; + const size_t n_heads = dst->src[0]->ne[1]; + const size_t n_seqs = dst->src[6]->ne[1]; + + ggml_vk_op_f32_wkv( + ctx, subctx, dst, + { + (uint32_t)n_seqs, + (uint32_t)seq_length, + (uint32_t)n_embed, + (uint32_t)n_heads, + }, + 7 + ); +} + +static void ggml_vk_gated_delta_net(ggml_backend_vk_context * ctx, vk_context& subctx, ggml_tensor * dst) { + const ggml_tensor * src_q = dst->src[0]; + const ggml_tensor * src_v = dst->src[2]; + const ggml_tensor * src_beta = dst->src[4]; + + GGML_ASSERT(dst->buffer != nullptr); + + const uint32_t S_v = (uint32_t)src_v->ne[0]; + const uint32_t H = (uint32_t)src_v->ne[1]; + const uint32_t n_tokens = (uint32_t)src_v->ne[2]; + const uint32_t n_seqs = (uint32_t)src_v->ne[3]; + + // K (snapshot slot count) is an op param; state holds s0 only [S_v, S_v, H, n_seqs]. + const uint32_t K = (uint32_t)ggml_get_op_params_i32(dst, 0); + + const uint32_t s_off = S_v * H * n_tokens * n_seqs; + + vk_pipeline pipeline = ggml_vk_op_get_pipeline(ctx, dst->src[0], dst->src[1], dst->src[2], dst, dst->op); + GGML_ASSERT(pipeline != nullptr); + + ggml_pipeline_request_descriptor_sets(ctx, pipeline, 1); + + vk_subbuffer dst_buf = ggml_vk_tensor_subbuffer(ctx, dst); + vk_subbuffer src_buf[6] = {}; + for (int i = 0; i < 6; i++) { + src_buf[i] = ggml_vk_tensor_subbuffer(ctx, dst->src[i]); + } + + const uint32_t sq1 = (uint32_t)(src_q->nb[1] / sizeof(float)); + const uint32_t sq2 = (uint32_t)(src_q->nb[2] / sizeof(float)); + const uint32_t sq3 = (uint32_t)(src_q->nb[3] / sizeof(float)); + const uint32_t sv1 = (uint32_t)(src_v->nb[1] / sizeof(float)); + const uint32_t sv2 = (uint32_t)(src_v->nb[2] / sizeof(float)); + const uint32_t sv3 = (uint32_t)(src_v->nb[3] / sizeof(float)); + const uint32_t sb1 = (uint32_t)(src_beta->nb[1] / sizeof(float)); + const uint32_t sb2 = (uint32_t)(src_beta->nb[2] / sizeof(float)); + const uint32_t sb3 = (uint32_t)(src_beta->nb[3] / sizeof(float)); + + const uint32_t neq1 = (uint32_t)src_q->ne[1]; + const uint32_t rq3 = (uint32_t)(src_v->ne[3] / src_q->ne[3]); + + const float scale = 1.0f / sqrtf((float)S_v); + const vk_op_gated_delta_net_push_constants pc = { + H, n_tokens, n_seqs, s_off, + sq1, sq2, sq3, + sv1, sv2, sv3, + sb1, sb2, sb3, + neq1, rq3, + scale, + K + }; + + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, + {src_buf[0], src_buf[1], src_buf[2], src_buf[3], src_buf[4], src_buf[5], dst_buf}, + pc, { H, n_seqs, S_v }); +} + +static void ggml_vk_ssm_scan(ggml_backend_vk_context * ctx, vk_context& subctx, ggml_tensor * dst) { + const ggml_tensor * src0 = dst->src[0]; + const ggml_tensor * src1 = dst->src[1]; + const ggml_tensor * src2 = dst->src[2]; + const ggml_tensor * src3 = dst->src[3]; + const ggml_tensor * src4 = dst->src[4]; + const ggml_tensor * src5 = dst->src[5]; + + GGML_ASSERT(dst->buffer != nullptr); + + const uint32_t head_dim = src0->ne[1]; + const uint32_t n_head = src1->ne[1]; + const uint32_t n_group = src4->ne[1]; + const uint32_t n_tok = src1->ne[2]; + const uint32_t n_seq = src1->ne[3]; + + bool is_mamba2 = (src3->nb[1] == sizeof(float)); + GGML_ASSERT(is_mamba2); + + vk_pipeline pipeline = ggml_vk_op_get_pipeline(ctx, src0, src1, src2, dst, dst->op); + GGML_ASSERT(pipeline != nullptr); + + ggml_pipeline_request_descriptor_sets(ctx, pipeline, 1); + + const int64_t s_off = ggml_nelements(src1) * sizeof(float); + + const vk_op_ssm_scan_push_constants pc = { + (uint32_t)src0->nb[2], (uint32_t)src0->nb[3], + (uint32_t)src1->nb[2], (uint32_t)src1->nb[3], + (uint32_t)src2->nb[1], (uint32_t)src2->nb[2], + (uint32_t)src3->nb[1], + (uint32_t)src4->nb[2], (uint32_t)src4->nb[3], + (uint32_t)src5->nb[2], (uint32_t)src5->nb[3], + (uint32_t)s_off, + n_head, head_dim, n_group, n_tok + }; + + vk_subbuffer dst_buf = ggml_vk_tensor_subbuffer(ctx, dst); + vk_subbuffer src_buf[7] = {}; + for (int i = 0; i < 7 && dst->src[i] != nullptr; i++) { + src_buf[i] = ggml_vk_tensor_subbuffer(ctx, dst->src[i]); + } + + std::array elements; + + const uint32_t d_state = src0->ne[0]; + uint32_t num_subgroups = d_state / ctx->device->subgroup_size; + const uint32_t num_workgroups_x = CEIL_DIV(n_head * head_dim, num_subgroups); + const uint32_t num_workgroups_y = n_seq; + elements = { num_workgroups_x, num_workgroups_y, 1 }; + + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, + {src_buf[0], src_buf[1], src_buf[2], src_buf[3], src_buf[4], src_buf[5], src_buf[6], dst_buf}, + pc, elements); +} + +static void ggml_vk_ssm_conv(ggml_backend_vk_context * ctx, vk_context& subctx, const struct ggml_cgraph * cgraph, int node_idx) { + ggml_tensor * conv = cgraph->nodes[node_idx]; + const ggml_tensor * src0 = conv->src[0]; + const ggml_tensor * src1 = conv->src[1]; + + // Pick the destination tensor (last node in the fused chain) and the optional bias. + // Fusion modes: 0 = ssm_conv, 1 = ssm_conv+silu, 2 = ssm_conv+add(bias)+silu. + ggml_tensor * dst = conv; + const ggml_tensor * bias = nullptr; + + if (ctx->num_additional_fused_ops == 1) { + dst = cgraph->nodes[node_idx + 1]; // silu + } else if (ctx->num_additional_fused_ops == 2) { + ggml_tensor * add = cgraph->nodes[node_idx + 1]; + bias = (add->src[0] == conv) ? add->src[1] : add->src[0]; + dst = cgraph->nodes[node_idx + 2]; // silu + } + + // The shader always declares 4 bindings; bind src0 as a dummy when bias isn't fused. + const ggml_tensor * src2 = bias ? bias : src0; + + ggml_vk_op_f32(ctx, subctx, src0, src1, src2, nullptr, dst, GGML_OP_SSM_CONV, { + (uint32_t)src0->nb[1], (uint32_t)src0->nb[2], + (uint32_t)src1->nb[1], + (uint32_t)dst->nb[0], (uint32_t)dst->nb[1], (uint32_t)dst->nb[2], + (uint32_t)src1->ne[0], + (uint32_t)src0->ne[0], + (uint32_t)src0->ne[1], + (uint32_t)dst->ne[1], + (uint32_t)dst->ne[2], + }); +} + +static void ggml_vk_op_f32_opt_step_adamw(ggml_backend_vk_context * ctx, vk_context& subctx, ggml_tensor * dst, const vk_op_push_constants&& pc) { + const ggml_tensor * x = dst->src[0]; + const ggml_tensor * g = dst->src[1]; + const ggml_tensor * gm = dst->src[2]; + const ggml_tensor * gv = dst->src[3]; + const ggml_tensor * p = dst->src[4]; + + GGML_ASSERT(x->type == GGML_TYPE_F32); + GGML_ASSERT(g->type == GGML_TYPE_F32); + GGML_ASSERT(gm->type == GGML_TYPE_F32); + GGML_ASSERT(gv->type == GGML_TYPE_F32); + GGML_ASSERT(p->type == GGML_TYPE_F32); + GGML_ASSERT(dst->buffer != nullptr); + GGML_ASSERT(ggml_is_contiguous(x)); + GGML_ASSERT(ggml_is_contiguous(g)); + GGML_ASSERT(ggml_is_contiguous(gm)); + GGML_ASSERT(ggml_is_contiguous(gv)); + GGML_ASSERT(ggml_is_contiguous(p)); + GGML_ASSERT(ggml_are_same_shape(x, g)); + GGML_ASSERT(ggml_are_same_shape(x, gm)); + GGML_ASSERT(ggml_are_same_shape(x, gv)); + GGML_ASSERT(ggml_nelements(p) == 7); + + vk_pipeline pipeline = ggml_vk_op_get_pipeline(ctx, g, gm, gv, dst, GGML_OP_OPT_STEP_ADAMW); + GGML_ASSERT(pipeline != nullptr); + + ggml_pipeline_request_descriptor_sets(ctx, pipeline, 1); + + vk_subbuffer x_buf = ggml_vk_tensor_subbuffer(ctx, x); + vk_subbuffer g_buf = ggml_vk_tensor_subbuffer(ctx, g); + vk_subbuffer gm_buf = ggml_vk_tensor_subbuffer(ctx, gm); + vk_subbuffer gv_buf = ggml_vk_tensor_subbuffer(ctx, gv); + vk_subbuffer p_buf = ggml_vk_tensor_subbuffer(ctx, p); + + std::array elements = { (uint32_t)ggml_nelements(x), 1, 1 }; + + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, + {x_buf, g_buf, gm_buf, gv_buf, p_buf}, + pc, elements); +} + +static void ggml_vk_opt_step_adamw(ggml_backend_vk_context * ctx, vk_context& subctx, ggml_tensor * dst) { + const size_t n = ggml_nelements(dst->src[0]); + + ggml_vk_op_f32_opt_step_adamw( + ctx, subctx, dst, + { (uint32_t)n, 0, 0.0f, 0.0f, 0.0f, 0.0f } + ); +} + +static void ggml_vk_opt_step_sgd(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst) { + const size_t n = ggml_nelements(dst->src[0]); + + ggml_vk_op_f32(ctx, subctx, src0, src1, src2, nullptr, dst, GGML_OP_OPT_STEP_SGD, { (uint32_t)n, 0, 0.0f, 0.0f, 0.0f, 0.0f }); +} + +static void ggml_vk_concat(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + int * op_params = (int *)dst->op_params; + + const uint32_t src0_type_size = ggml_type_size(src0->type); + const uint32_t src1_type_size = ggml_type_size(src1->type); + const uint32_t dst_type_size = ggml_type_size(dst->type); + + ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, nullptr, dst, GGML_OP_CONCAT, { + (uint32_t)ggml_nelements(dst), + (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size, + (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size, + (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, + 0, + 0.0f, 0.0f, op_params[0], + }); +} + +static void ggml_vk_upscale(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) { + const uint32_t src0_type_size = ggml_type_size(src0->type); + const uint32_t mode = (uint32_t)ggml_get_op_params_i32(dst, 0); + + GGML_TENSOR_UNARY_OP_LOCALS + + float sf0 = (float)ne0 / ne00; + float sf1 = (float)ne1 / ne01; + float sf2 = (float)ne2 / ne02; + float sf3 = (float)ne3 / ne03; + float pixel_offset = 0.5f; + + if (mode & GGML_SCALE_FLAG_ALIGN_CORNERS) { + sf0 = ne0 > 1 && ne00 > 1 ? (float)(ne0 - 1) / (ne00 - 1) : sf0; + sf1 = ne1 > 1 && ne01 > 1 ? (float)(ne1 - 1) / (ne01 - 1) : sf1; + pixel_offset = 0.0f; + } + + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_UPSCALE, { + (uint32_t)ggml_nelements(dst), 0, 0, + (uint32_t)ne00, (uint32_t)ne01, + (uint32_t)nb00 / src0_type_size, (uint32_t)nb01 / src0_type_size, (uint32_t)nb02 / src0_type_size, (uint32_t)nb03 / src0_type_size, + (uint32_t)ne0, (uint32_t)ne1, (uint32_t)ne2, (uint32_t)ne3, + sf0, sf1, sf2, sf3, pixel_offset + }); +} + +static void ggml_vk_scale(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) { + vk_op_unary_push_constants p = vk_op_unary_push_constants_init(src0, dst); + p.param1 = ggml_get_op_params_f32(dst, 0); + p.param2 = ggml_get_op_params_f32(dst, 1); + + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_SCALE, std::move(p)); +} + +static void ggml_vk_sqr(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) { + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_SQR, vk_op_unary_push_constants_init(src0, dst)); +} + +static void ggml_vk_sqrt(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) { + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_SQRT, vk_op_unary_push_constants_init(src0, dst)); +} + +static void ggml_vk_add1(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + const uint32_t src0_type_size = ggml_type_size(src0->type); + const uint32_t src1_type_size = ggml_type_size(src1->type); + const uint32_t dst_type_size = ggml_type_size(dst->type); + + ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, nullptr, dst, GGML_OP_ADD1, { + (uint32_t)ggml_nelements(src0), + (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size, + (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size, + (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, + 0, + 0.0f, 0.0f, 0, + }); +} + +static void ggml_vk_arange(ggml_backend_vk_context * ctx, vk_context& subctx, ggml_tensor * dst) { + VK_LOG_DEBUG("ggml_vk_arange(dst=" << dst << ", ne=" << ggml_nelements(dst) << ")"); + + vk_op_push_constants pc = { + (uint32_t)ggml_nelements(dst), + 1, + ggml_get_op_params_f32(dst, 0), + ggml_get_op_params_f32(dst, 2), + 0.0f, 0.0f, + }; + + vk_pipeline pipeline = ggml_vk_op_get_pipeline(ctx, nullptr, nullptr, nullptr, dst, GGML_OP_ARANGE); + GGML_ASSERT(pipeline != nullptr); + + ggml_pipeline_request_descriptor_sets(ctx, pipeline, 1); + vk_subbuffer dst_buf = ggml_vk_tensor_subbuffer(ctx, dst, false); + + std::array elements = { (uint32_t)ggml_nelements(dst), 1, 1 }; + + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { dst_buf }, pc, elements); +} + +static void ggml_vk_fill(ggml_backend_vk_context * ctx, vk_context& subctx, ggml_tensor * dst) { + VK_LOG_DEBUG("ggml_vk_fill(dst=" << dst << ", ne=" << ggml_nelements(dst) << ")"); + + vk_op_push_constants pc = { + (uint32_t)ggml_nelements(dst), + 1, + ggml_get_op_params_f32(dst, 0), + 0.0f, + 0.0f, 0.0f, + }; + + vk_pipeline pipeline = ggml_vk_op_get_pipeline(ctx, nullptr, nullptr, nullptr, dst, GGML_OP_FILL); + GGML_ASSERT(pipeline != nullptr); + + ggml_pipeline_request_descriptor_sets(ctx, pipeline, 1); + vk_subbuffer dst_buf = ggml_vk_tensor_subbuffer(ctx, dst, false); + + std::array elements = { (uint32_t)ggml_nelements(dst), 1, 1 }; + + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { dst_buf }, pc, elements); +} + +static void ggml_vk_sin(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) { + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_SIN, vk_op_unary_push_constants_init(src0, dst)); +} + +static void ggml_vk_cos(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) { + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_COS, vk_op_unary_push_constants_init(src0, dst)); +} + +static void ggml_vk_log(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) { + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_LOG, vk_op_unary_push_constants_init(src0, dst)); +} + +static void ggml_vk_tri(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) { + vk_op_unary_push_constants p = vk_op_unary_push_constants_init(src0, dst); + p.param1 = ggml_get_op_params_f32(dst, 0); + + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_TRI, std::move(p)); +} + +static void ggml_vk_diag(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) { + vk_op_unary_push_constants p = vk_op_unary_push_constants_init(src0, dst, ggml_nelements(dst)); + + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_DIAG, std::move(p)); +} + +static void ggml_vk_clamp(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) { + vk_op_unary_push_constants p = vk_op_unary_push_constants_init(src0, dst); + p.param1 = ggml_get_op_params_f32(dst, 0); + p.param2 = ggml_get_op_params_f32(dst, 1); + + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_CLAMP, std::move(p)); +} + +static void ggml_vk_pad(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) { + vk_op_pad_push_constants p = vk_op_pad_push_constants_init(src0, dst); + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_PAD, std::move(p)); +} + +static void ggml_vk_roll(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) { + const int32_t s0 = ggml_get_op_params_i32(dst, 0); + const int32_t s1 = ggml_get_op_params_i32(dst, 1); + const int32_t s2 = ggml_get_op_params_i32(dst, 2); + const int32_t s3 = ggml_get_op_params_i32(dst, 3); + const uint32_t s01_packed = ((s0 + 0x8000) << 16) | (s1 + 0x8000); + const uint32_t s23_packed = ((s2 + 0x8000) << 16) | (s3 + 0x8000); + + vk_op_unary_push_constants p = vk_op_unary_push_constants_init(src0, dst); + memcpy(&p.param1, &s01_packed, sizeof(float)); + memcpy(&p.param2, &s23_packed, sizeof(float)); + + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_ROLL, std::move(p)); +} + +static void ggml_vk_repeat(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) { + vk_op_unary_push_constants p = vk_op_unary_push_constants_init(src0, dst, ggml_nelements(dst)); + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_REPEAT, std::move(p)); +} + +static void ggml_vk_repeat_back(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) { + vk_op_unary_push_constants p = vk_op_unary_push_constants_init(src0, dst, ggml_nelements(dst)); + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_REPEAT_BACK, std::move(p)); +} + +static void ggml_vk_cpy(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) { + uint32_t ne = (uint32_t)ggml_nelements(src0); + if (ggml_is_quantized(src0->type) && ggml_is_quantized(dst->type)) { + // Convert from number of logical elements to 2- or 4-byte units. + ne /= ggml_blck_size(src0->type); + if ((ggml_type_size(src0->type) % 4) == 0) { + ne *= ggml_type_size(src0->type) / 4; + } else { + ne *= ggml_type_size(src0->type) / 2; + } + } + + vk_op_unary_push_constants p = vk_op_unary_push_constants_init(src0, dst, ne); + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_CPY, std::move(p)); +} + +static void ggml_vk_set_rows(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + const uint32_t src0_type_size = ggml_type_size(src0->type); + const uint32_t src1_type_size = ggml_type_size(src1->type); + const uint32_t dst_type_size = ggml_type_size(dst->type); + + // Skip empty skip_rows operations. For most ops the empty check at the start + // of ggml_vk_build_graph is sufficient, but set_rows can have a nonempty dst + // with empty srcs. + if (ggml_is_empty(src0) || ggml_is_empty(src1)) { + return; + } + + ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, nullptr, dst, GGML_OP_SET_ROWS, { + (uint32_t)ggml_nelements(src0), + (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size, + (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size, + (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, + 0, + 0.0f, 0.0f, 0, + }); +} + +static void ggml_vk_silu_back(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, nullptr, dst, GGML_OP_SILU_BACK, { (uint32_t)ggml_nelements(src0), 0, 0.0f, 0.0f, 0.0f, 0.0f }); +} + +static void ggml_vk_norm(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) { + float * op_params = (float *)dst->op_params; + vk_op_unary_push_constants p = vk_op_unary_push_constants_init(src0, dst); + p.param1 = op_params[0]; + + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_NORM, std::move(p)); +} + +static void ggml_vk_group_norm(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) { + const int * int_op_params = (const int *)dst->op_params; + const float * float_op_params = (const float *)dst->op_params; + + const uint32_t num_groups = int_op_params[0]; + const float eps = float_op_params[1]; + const uint32_t group_size = src0->ne[0] * src0->ne[1] * ((src0->ne[2] + num_groups - 1) / num_groups); + + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_GROUP_NORM, { group_size, 0, eps, 0.0f, 0.0f, 0.0f }); +} + +static uint32_t ggml_vk_rms_num_partials(ggml_backend_vk_context * ctx, const ggml_tensor *node) { + const uint32_t ne = (uint32_t)node->ne[0]; + const uint32_t denom = ctx->device->pipeline_add_rms[0][0][0]->wg_denoms[0]; + const uint32_t num_partials = CEIL_DIV(ne, denom); + return num_partials; +} + +static uint32_t ggml_vk_rms_partials_size(ggml_backend_vk_context * ctx, const ggml_tensor *node) { + const uint32_t num_partials = ggml_vk_rms_num_partials(ctx, node); + const uint32_t num_bytes = ROUNDUP_POW2(num_partials * sizeof(uint32_t), ctx->device->partials_binding_alignment); + return num_bytes; +} + +static vk_op_rope_push_constants ggml_vk_make_rope_constants(const ggml_tensor *dst, const ggml_tensor *src0, const bool has_ff, bool backprop, const uint32_t set_rows_stride) { + const int n_dims = ((const int32_t *) dst->op_params)[1]; + const int mode = ((const int32_t *) dst->op_params)[2]; + // const int n_ctx = ((const int32_t *) dst->op_params)[3]; + const int n_ctx_orig = ((const int32_t *) dst->op_params)[4]; + const float freq_base = ((const float *) dst->op_params)[5]; + const float freq_scale = ((const float *) dst->op_params)[6]; + const float ext_factor = ((const float *) dst->op_params)[7]; + const float attn_factor = ((const float *) dst->op_params)[8]; + const float beta_fast = ((const float *) dst->op_params)[9]; + const float beta_slow = ((const float *) dst->op_params)[10]; + int sections[4] {}; + if (mode & GGML_ROPE_TYPE_MROPE) { + memcpy(sections, (const int32_t *) dst->op_params + 11, sizeof(int)*4); + } + + const bool is_imrope = mode == GGML_ROPE_TYPE_IMROPE; + + float corr_dims[2]; + ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims); + + const float theta_scale = powf(freq_base, -2.0f/n_dims); + + uint32_t nb01 = src0->nb[1] / ggml_type_size(src0->type); + uint32_t nb02 = src0->nb[2] / ggml_type_size(src0->type); + uint32_t nb03 = src0->nb[3] / ggml_type_size(src0->type); + + uint32_t nb11 = dst->nb[1] / ggml_type_size(dst->type); + uint32_t nb12 = dst->nb[2] / ggml_type_size(dst->type); + uint32_t nb13 = dst->nb[3] / ggml_type_size(dst->type); + + vk_op_rope_push_constants rope { + (uint32_t)mode, (uint32_t)ggml_nrows(src0), (uint32_t)n_dims, freq_scale, + freq_base, ext_factor, attn_factor, {corr_dims[0], corr_dims[1]}, theta_scale, has_ff, + { sections[0], sections[1], sections[2], sections[3] }, is_imrope, backprop, set_rows_stride, + + (uint32_t)src0->ne[0], + (uint32_t)src0->ne[1], + (uint32_t)src0->ne[2], + nb01, nb02, nb03, + nb11, nb12, nb13, + 0, 0, // a_offset, d_offset filled in by init_pushconst_tensor_offsets + }; + + return rope; +} + +static void ggml_vk_rms_norm(ggml_backend_vk_context * ctx, vk_context& subctx, const struct ggml_cgraph * cgraph, int node_idx, float * op_params) { + ggml_tensor * dst; + const ggml_tensor * src0; + const ggml_tensor * src1; + + if (ctx->num_additional_fused_ops > 0) { + // fused rms_norm + mul + ggml_tensor *mul = cgraph->nodes[node_idx + 1]; + ggml_tensor *other_src = mul->src[0] == cgraph->nodes[node_idx + 0] ? mul->src[1] : mul->src[0]; + dst = mul; + src0 = cgraph->nodes[node_idx]->src[0]; + src1 = other_src; + } else { + dst = cgraph->nodes[node_idx]; + src0 = src1 = dst->src[0]; + } + + const uint32_t src0_type_size = ggml_type_size(src0->type); + const uint32_t src1_type_size = ggml_type_size(src1->type); + const uint32_t dst_type_size = ggml_type_size(dst->type); + + uint32_t param3 = ctx->do_add_rms_partials ? ggml_vk_rms_num_partials(ctx, dst) : 0; + + vk_op_binary_push_constants bin { + (uint32_t)ggml_nelements(src0), + (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size, + (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size, + (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, + 0, + op_params[0], 0.0f, (int32_t)param3, + }; + + // more than one fused op means rms_norm+mul+rope + if (ctx->num_additional_fused_ops > 1) { + static constexpr uint32_t max_tensors = 7; + const ggml_tensor *tensors[max_tensors] {}; + + ggml_tensor *rms = cgraph->nodes[node_idx + 0]; + ggml_tensor *mul = cgraph->nodes[node_idx + 1]; + ggml_tensor *rope = cgraph->nodes[node_idx + 2]; + + ggml_tensor *other_src = mul->src[0] == rms ? mul->src[1] : mul->src[0]; + + bool do_set_rows = ctx->num_additional_fused_ops == 4; + + tensors[0] = rms->src[0]; + tensors[1] = other_src; + tensors[2] = mul; + tensors[3] = rope->src[1]; // pos + tensors[4] = rope->src[2]; // ff + tensors[5] = cgraph->nodes[node_idx + ctx->num_additional_fused_ops]; // dst + tensors[6] = do_set_rows ? tensors[5]->src[1] : nullptr; + const uint32_t set_rows_stride = do_set_rows ? tensors[5]->nb[1] / ggml_type_size(tensors[5]->type) : 0; + + vk_op_rms_norm_mul_rope_push_constants pc; + pc.bin = bin; + pc.rope = ggml_vk_make_rope_constants(rope, rope->src[0], tensors[4] != nullptr, false, set_rows_stride); + + vk_pipeline pipeline = tensors[5]->type == GGML_TYPE_F16 ? ctx->device->pipeline_rms_norm_mul_rope_f32_f16 : ctx->device->pipeline_rms_norm_mul_rope_f32_f32; + + ggml_pipeline_request_descriptor_sets(ctx, pipeline, 1); + + ggml_backend_vk_buffer_context * buf_ctx[max_tensors]; + vk_buffer buf[max_tensors]; + size_t offset[max_tensors]; + bool uma[max_tensors]; + + for (uint32_t i = 0; i < max_tensors; ++i) { + if (!tensors[i]) { + // If any remaining descriptors are unused, just point them at src[0] + buf[i] = buf[0]; + offset[i] = 0; + continue; + } + buf_ctx[i] = (ggml_backend_vk_buffer_context *)tensors[i]->buffer->context; + buf[i] = nullptr; + offset[i] = 0; + uma[i] = false; + + if (ctx->device->uma) { + ggml_vk_host_get(ctx->device, tensors[i]->data, buf[i], offset[i]); + uma[i] = buf[i] != nullptr; + } + if (!uma[i]) { + buf[i] = buf_ctx[i]->dev_buffer; + offset[i] = vk_tensor_offset(tensors[i]) + tensors[i]->view_offs; + } + GGML_ASSERT(buf[i] != nullptr); + } + + // a_offset is unused (the fused path reads from shared memory), but the rope/set_rows dst can be misaligned. + // Round the binding offset down to the storage buffer alignment; the in-element shift goes in pc.rope.d_offset. + pc.rope.d_offset = get_misalign_bytes(ctx, tensors[5]) / ggml_type_size(tensors[5]->type); + offset[5] &= ~(size_t(ctx->device->properties.limits.minStorageBufferOffsetAlignment) - 1); + + std::array elements; + elements = { (uint32_t)rms->src[0]->ne[1], (uint32_t)rms->src[0]->ne[2], (uint32_t)rms->src[0]->ne[3] }; + + static_assert(max_tensors == 7); + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, + { + ggml_vk_subbuffer(ctx, buf[0], offset[0]), + ggml_vk_subbuffer(ctx, buf[1], offset[1]), + ggml_vk_subbuffer(ctx, buf[2], offset[2]), + ggml_vk_subbuffer(ctx, buf[3], offset[3]), + ggml_vk_subbuffer(ctx, buf[4], offset[4]), + ggml_vk_subbuffer(ctx, buf[5], offset[5]), + ggml_vk_subbuffer(ctx, buf[6], offset[6]), + }, pc, elements); + } else { + ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, nullptr, dst, GGML_OP_RMS_NORM, std::move(bin)); + } + + if (ctx->do_add_rms_partials_offset_calculation) { + ctx->prealloc_size_add_rms_partials_offset += ggml_vk_rms_partials_size(ctx, src0); + ctx->do_add_rms_partials = false; + ctx->do_add_rms_partials_offset_calculation = false; + } +} + +static void ggml_vk_rms_norm_back(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + float * op_params = (float *)dst->op_params; + ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, nullptr, dst, GGML_OP_RMS_NORM_BACK, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0], 0.0f, 0.0f, 0.0f }); +} + +static void ggml_vk_l2_norm(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) { + const float * op_params = (const float *)dst->op_params; + vk_op_unary_push_constants p = vk_op_unary_push_constants_init(src0, dst); + p.param1 = op_params[0]; + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_L2_NORM, std::move(p)); +} + +static void ggml_vk_unary(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) { + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_UNARY, vk_op_unary_push_constants_init(src0, dst)); +} + +static void ggml_vk_xielu(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) { + float * op_params = (float *)dst->op_params; + vk_op_unary_push_constants p = vk_op_unary_push_constants_init(src0, dst); + p.param1 = op_params[1]; + p.param2 = op_params[2]; + p.param3 = op_params[3]; + p.param4 = op_params[4]; + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_UNARY, std::move(p)); +} + +static void ggml_vk_glu(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + const float * op_params_f = (const float *)dst->op_params; + + const bool swapped = (bool)dst->op_params[1]; + const bool split = src1 != nullptr; + const float alpha = op_params_f[2]; + const float limit = op_params_f[3]; + + if (!split) { + GGML_ASSERT(src0->ne[0] / 2 == dst->ne[0]); + } else { + GGML_ASSERT(src0->ne[0] == src1->ne[0]); + GGML_ASSERT(src0->ne[0] == dst->ne[0]); + GGML_ASSERT(src0->type == src1->type); + } + + const uint32_t mode = split ? 2 : (swapped ? 1 : 0); + const uint32_t src0_type_size = ggml_type_size(src0->type); + const uint32_t src1_type_size = split ? ggml_type_size(src1->type) : src0_type_size; + const uint32_t dst_type_size = ggml_type_size(dst->type); + + ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, nullptr, dst, GGML_OP_GLU, + { + (uint32_t)ggml_nelements(dst), + (uint32_t)src0->ne[0], + (uint32_t)dst->ne[0], + mode, + alpha, + limit, + (uint32_t)(src0->nb[0] / src0_type_size), + (uint32_t)(src0->nb[1] / src0_type_size), + (uint32_t)(src0->nb[2] / src0_type_size), + (uint32_t)(src0->nb[3] / src0_type_size), + (uint32_t)((split ? src1->nb[0] : src0->nb[0]) / src1_type_size), + (uint32_t)((split ? src1->nb[1] : src0->nb[1]) / src1_type_size), + (uint32_t)((split ? src1->nb[2] : src0->nb[2]) / src1_type_size), + (uint32_t)((split ? src1->nb[3] : src0->nb[3]) / src1_type_size), + (uint32_t)(dst->nb[0] / dst_type_size), + (uint32_t)(dst->nb[1] / dst_type_size), + (uint32_t)(dst->nb[2] / dst_type_size), + (uint32_t)(dst->nb[3] / dst_type_size), + (uint32_t)dst->ne[1], + (uint32_t)dst->ne[2], + 0, + 0, 0, 0, 0, 0, 0, + }); +} + +static void ggml_vk_diag_mask_inf(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) { + int32_t * op_params = (int32_t *)dst->op_params; + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_DIAG_MASK_INF, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0] }); +} + +static void ggml_vk_soft_max(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst) { + float * op_params = (float *)dst->op_params; + + float scale = op_params[0]; + float max_bias = op_params[1]; + + const uint32_t ncols = (uint32_t)src0->ne[0]; + const uint32_t nrows_x = (uint32_t)ggml_nrows(src0); + const uint32_t nrows_y = (uint32_t)src0->ne[1]; + + const uint32_t ne12 = src1 ? (uint32_t)(src1->ne[2]) : 0u; + const uint32_t ne13 = src1 ? (uint32_t)(src1->ne[3]) : 0u; + const uint32_t nb11 = src1 ? (uint32_t)(src1->nb[1] / src1->nb[0]) : 0u; + const uint32_t nb12 = src1 ? (uint32_t)(src1->nb[2] / src1->nb[0]) : 0u; + const uint32_t nb13 = src1 ? (uint32_t)(src1->nb[3] / src1->nb[0]) : 0u; + + const uint32_t n_head_kv = src0->ne[2]; + const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head_kv)); + + const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); + const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); + + vk_op_soft_max_push_constants pc { + ncols, + src1 != nullptr ? nrows_y : (uint32_t)0, + (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], + ne12, ne13, + nb11, nb12, nb13, + scale, max_bias, + m0, m1, + n_head_log2, + nrows_x, + src2 != nullptr + }; + + if (ncols <= 16384) { + ggml_vk_op_f32(ctx, subctx, src0, src1, src2, nullptr, dst, GGML_OP_SOFT_MAX, std::move(pc)); + } else { + + vk_subbuffer buf_a = ggml_vk_tensor_subbuffer(ctx, src0); + vk_subbuffer buf_b = src1 ? ggml_vk_tensor_subbuffer(ctx, src1) : buf_a; + vk_subbuffer buf_c = src2 ? ggml_vk_tensor_subbuffer(ctx, src2) : buf_a; + vk_subbuffer buf_d = ggml_vk_tensor_subbuffer(ctx, dst); + + uint32_t elems_per_wg = 128 * 4; + uint32_t num_wgs = CEIL_DIV(ncols, elems_per_wg); + size_t tmp_size = num_wgs * nrows_x * sizeof(float); + + if (ctx->prealloc_size_x < tmp_size) { + ctx->prealloc_size_x = tmp_size; + ggml_vk_preallocate_buffers(ctx, subctx); + } + if (ctx->prealloc_size_y < tmp_size) { + ctx->prealloc_size_y = tmp_size; + ggml_vk_preallocate_buffers(ctx, subctx); + } + if (ctx->prealloc_x_need_sync || ctx->prealloc_y_need_sync) { + ggml_vk_sync_buffers(ctx, subctx); + } + + vk_subbuffer buf_x = { ctx->prealloc_x, 0, tmp_size }; + vk_subbuffer buf_y = { ctx->prealloc_y, 0, tmp_size }; + + std::array elements = { num_wgs, nrows_x, 1 }; + + vk_pipeline pipeline1 = src1 && src1->type == GGML_TYPE_F16 ? ctx->device->pipeline_soft_max_large1_f32_f16 : ctx->device->pipeline_soft_max_large1_f32; + vk_pipeline pipeline2 = src1 && src1->type == GGML_TYPE_F16 ? ctx->device->pipeline_soft_max_large2_f32_f16 : ctx->device->pipeline_soft_max_large2_f32; + vk_pipeline pipeline3 = src1 && src1->type == GGML_TYPE_F16 ? ctx->device->pipeline_soft_max_large3_f32_f16 : ctx->device->pipeline_soft_max_large3_f32; + + ggml_pipeline_request_descriptor_sets(ctx, pipeline1, 1); + ggml_pipeline_request_descriptor_sets(ctx, pipeline2, 1); + ggml_pipeline_request_descriptor_sets(ctx, pipeline3, 1); + + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline1, { buf_a, buf_b, buf_c, buf_d, buf_x, buf_y }, pc, elements); + ggml_vk_sync_buffers(ctx, subctx); + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline2, { buf_a, buf_b, buf_c, buf_d, buf_x, buf_y }, pc, elements); + ggml_vk_sync_buffers(ctx, subctx); + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline3, { buf_a, buf_b, buf_c, buf_d, buf_x, buf_y }, pc, elements); + + ctx->prealloc_x_need_sync = true; + ctx->prealloc_y_need_sync = true; + } +} + +static void ggml_vk_soft_max_back(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + float * op_params = (float *)dst->op_params; + ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, nullptr, dst, GGML_OP_SOFT_MAX_BACK, { (uint32_t)src0->ne[0], (uint32_t)ggml_nrows(src0), op_params[0], op_params[1], 0.0f, 0.0f }); +} + +static void ggml_vk_topk_moe(ggml_backend_vk_context * ctx, vk_context& subctx, ggml_cgraph * cgraph, int node_idx) { + topk_moe_mode mode = ctx->fused_topk_moe_mode; + ggml_tensor * logits = cgraph->nodes[node_idx + 0]->src[0]; + ggml_tensor * bias = (mode == TOPK_MOE_SIGMOID_NORM_BIAS) ? cgraph->nodes[node_idx + 2]->src[1] : logits; + ggml_tensor * weights = cgraph->nodes[node_idx + ctx->num_additional_fused_ops]; + ggml_tensor * ids = (mode == TOPK_MOE_SIGMOID_NORM_BIAS) ? cgraph->nodes[node_idx + 4] : + (mode == TOPK_MOE_LATE_SOFTMAX) ? cgraph->nodes[node_idx + 1] : + cgraph->nodes[node_idx + 3]; + + GGML_ASSERT(logits->type == GGML_TYPE_F32); + GGML_ASSERT(bias->type == GGML_TYPE_F32); + GGML_ASSERT(weights->type == GGML_TYPE_F32); + GGML_ASSERT(ids->type == GGML_TYPE_I32); + + const int n_experts = logits->ne[0]; + const int n_rows = logits->ne[1]; + const int n_expert_used = weights->ne[1]; + + GGML_ASSERT(ids->nb[1] / ggml_type_size(ids->type) == (size_t) n_experts); + + vk_pipeline pipeline = ggml_vk_op_get_pipeline(ctx, nullptr, nullptr, nullptr, cgraph->nodes[node_idx], GGML_OP_SOFT_MAX); + + ggml_pipeline_request_descriptor_sets(ctx, pipeline, 1); + + vk_subbuffer logits_buf = ggml_vk_tensor_subbuffer(ctx, logits); + vk_subbuffer bias_buf = ggml_vk_tensor_subbuffer(ctx, bias); + vk_subbuffer weights_buf = ggml_vk_tensor_subbuffer(ctx, weights); + vk_subbuffer ids_buf = ggml_vk_tensor_subbuffer(ctx, ids); + + vk_op_topk_moe_push_constants pc {}; + pc.n_rows = n_rows; + pc.n_experts_push = n_experts; + pc.n_expert_used = n_expert_used; + pc.clamp_min = -std::numeric_limits::infinity(); + pc.clamp_max = std::numeric_limits::infinity(); + if (mode == TOPK_MOE_EARLY_SOFTMAX_NORM) { + ggml_tensor * clamp = cgraph->nodes[node_idx + 7]; + GGML_ASSERT(clamp->op == GGML_OP_CLAMP); + pc.clamp_min = ggml_get_op_params_f32(clamp, 0); + pc.clamp_max = ggml_get_op_params_f32(clamp, 1); + } + if (mode == TOPK_MOE_SIGMOID_NORM_BIAS) { + ggml_tensor * clamp = cgraph->nodes[node_idx + 8]; + GGML_ASSERT(clamp->op == GGML_OP_CLAMP); + pc.clamp_min = ggml_get_op_params_f32(clamp, 0); + pc.clamp_max = ggml_get_op_params_f32(clamp, 1); + } + +#define GATING_FUNC_SOFTMAX 0 +#define GATING_FUNC_SIGMOID 1 +#define GATING_FUNC_SOFTMAX_WEIGHT 2 + + pc.gating_func = mode == TOPK_MOE_SIGMOID_NORM_BIAS ? GATING_FUNC_SIGMOID : + mode == TOPK_MOE_LATE_SOFTMAX ? GATING_FUNC_SOFTMAX_WEIGHT : + GATING_FUNC_SOFTMAX; + pc.has_bias = mode == TOPK_MOE_SIGMOID_NORM_BIAS; + pc.with_norm = mode == TOPK_MOE_EARLY_SOFTMAX_NORM || mode == TOPK_MOE_SIGMOID_NORM_BIAS; + if (ctx->fused_topk_moe_scale) { + GGML_ASSERT(weights->op == GGML_OP_SCALE); + pc.output_scale = ggml_get_op_params_f32(weights, 0); + pc.output_bias = ggml_get_op_params_f32(weights, 1); + } else { + pc.output_scale = 1.0f; + pc.output_bias = 0.0f; + } + + GGML_ASSERT(n_expert_used <= n_experts); + + const uint32_t rows_per_block = 4; + std::array elements = { CEIL_DIV(n_rows, rows_per_block), 1, 1 }; + + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, {logits_buf, bias_buf, weights_buf, ids_buf}, pc, elements); +} + +static void ggml_vk_rope(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_cgraph * cgraph, int node_idx, bool backprop) { + ggml_tensor * dst = cgraph->nodes[node_idx]; + const ggml_tensor * src0 = dst->src[0]; + const ggml_tensor * src1 = dst->src[1]; + const ggml_tensor * src2 = dst->src[2]; + const ggml_tensor * src3 = nullptr; + const int n_dims = ((int32_t *) dst->op_params)[1]; + const int mode = ((int32_t *) dst->op_params)[2]; + // const int n_ctx = ((int32_t *) dst->op_params)[3]; + const int n_ctx_orig = ((int32_t *) dst->op_params)[4]; + const float freq_base = ((float *) dst->op_params)[5]; + const float beta_fast = ((float *) dst->op_params)[9]; + const float beta_slow = ((float *) dst->op_params)[10]; + int sections[4] {}; + if (mode & GGML_ROPE_TYPE_MROPE) { + memcpy(sections, (int32_t *) dst->op_params + 11, sizeof(int)*4); + } + + float corr_dims[2]; + ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims); + + uint32_t set_rows_stride = 0; + // Fused rope + view + set_rows passes the set_rows destination stride in set_rows_stride + // and overrides the dst and sets src3=row_indices + if (ctx->num_additional_fused_ops > 0) { + set_rows_stride = cgraph->nodes[node_idx + 2]->nb[1] / ggml_type_size(cgraph->nodes[node_idx + 2]->type); + src3 = cgraph->nodes[node_idx + 2]->src[1]; + dst = cgraph->nodes[node_idx + 2]; + } + + ggml_vk_op_f32(ctx, subctx, src0, src1, src2, src3, dst, GGML_OP_ROPE, + ggml_vk_make_rope_constants(cgraph->nodes[node_idx], src0, src2 != nullptr, backprop, set_rows_stride)); +} + +static void ggml_vk_argsort(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) { + const uint32_t * op_params = (const uint32_t *)dst->op_params; + + uint32_t ncols = src0->ne[0]; + uint32_t nrows = ggml_nrows(src0); + + uint32_t ncols_pad_log2 = (uint32_t)ceilf(log2f(float(ncols))); + uint32_t ncolsp2 = 1 << ncols_pad_log2; + + vk_op_argsort_push_constants pc { ncols, ncolsp2, ncols_pad_log2, nrows, op_params[0], 0, 0, 0, 0, }; + + // Pick the largest workgroup size <= ncolsp2 + uint32_t pipeline_idx = std::min(ncols_pad_log2, num_argsort_pipelines - 1); + + // Use the "small" argsort shader if the whole sort can be done by a single workgroup. + bool use_small = ncols_pad_log2 <= ctx->device->max_workgroup_size_log2 && + ctx->device->pipeline_argsort_f32[pipeline_idx] != nullptr; + + vk_pipeline pipeline = use_small ? ctx->device->pipeline_argsort_f32[pipeline_idx] + : ctx->device->pipeline_argsort_large_f32[pipeline_idx]; + + vk_subbuffer src0_buf = ggml_vk_tensor_subbuffer(ctx, src0); + vk_subbuffer dst_buf = ggml_vk_tensor_subbuffer(ctx, dst); + vk_subbuffer subbuf1 = dst_buf; + + // Reserve space for ivec2 per element, with rows padded to a power of two + if (!use_small) { + const size_t x_sz = size_t{ncolsp2} * nrows * 2 * sizeof(int); + + if (ctx->prealloc_size_x < x_sz) { + ctx->prealloc_size_x = x_sz; + ggml_vk_preallocate_buffers(ctx, subctx); + } + if (ctx->prealloc_x_need_sync) { + ggml_vk_sync_buffers(ctx, subctx); + } + subbuf1 = { ctx->prealloc_x, 0, ctx->prealloc_x->size }; + } + + std::array elements; + + elements[0] = ncolsp2; + elements[1] = std::min((uint32_t)ggml_nrows(src0), ctx->device->properties.limits.maxComputeWorkGroupCount[1]); + elements[2] = 1; + + // First dispatch initializes tmp_idx and does the first N passes where + // there is only communication between threads in the same workgroup. + { + vk_op_argsort_push_constants pc2 = pc; + pc2.outer_start = 0; + pc2.outer_end = std::min(ncols_pad_log2, ctx->device->max_workgroup_size_log2); + pc2.inner_start = 0; + pc2.inner_end = 100; + ggml_pipeline_request_descriptor_sets(ctx, pipeline, 1); + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { src0_buf, subbuf1, dst_buf }, pc2, elements); + } + if (!use_small) { + ggml_vk_sync_buffers(ctx, subctx); + // Loop over outer/inner passes, synchronizing between each pass. + for (uint32_t outer = ctx->device->max_workgroup_size_log2; outer < ncols_pad_log2; ++outer) { + for (uint32_t inner = 0; inner < outer + 1; ++inner) { + vk_op_argsort_push_constants pc2 = pc; + pc2.outer_start = outer; + pc2.outer_end = outer + 1; + pc2.inner_start = inner; + pc2.inner_end = inner + 1; + // When the inner idx is large enough, there's only communication + // within a workgroup. So the remaining inner iterations can all + // run in the same dispatch. + if (outer - inner < pipeline_idx) { + pc2.inner_end = 100; + inner = outer; + pipeline = ctx->device->pipeline_argsort_large_f32[pipeline_idx]; + } else { + // Smaller workgroup empirically seems to perform better + pipeline = ctx->device->pipeline_argsort_large_f32[pipeline_idx - 2]; + } + ggml_pipeline_request_descriptor_sets(ctx, pipeline, 1); + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { src0_buf, subbuf1, dst_buf }, pc2, elements); + ggml_vk_sync_buffers(ctx, subctx); + } + } + ctx->prealloc_x_need_sync = true; + } +} + +static void ggml_vk_topk(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) { + uint32_t ncols = src0->ne[0]; + uint32_t nrows = ggml_nrows(src0); + uint32_t k = dst->ne[0]; + + vk_op_topk_push_constants pc { ncols, ncols, ncols, k, nrows, 0, 0 }; + + if (ctx->prealloc_x_need_sync) { + ggml_vk_sync_buffers(ctx, subctx); + } + + std::array elements; + elements[1] = std::min(nrows, ctx->device->properties.limits.maxComputeWorkGroupCount[1]); + elements[2] = 1; + + uint32_t num_elements = ncols; + + // Each iteration reduces a workgroup's worth of elements down to the K + // largest elements. Repeat until we have the top K elements. + // Need to do at least one iteration to write out the results. + bool done_one_iter = false; + uint32_t dbl_buf_index = 0; + size_t dbl_buf_size; + while (num_elements > k || !done_one_iter) { + + // Prefer going as small as num_topk_pipelines - 3 for perf reasons. + // But if K is larger, then we need a larger workgroup + uint32_t max_pipeline = num_topk_pipelines - 1; + uint32_t preferred_pipeline = std::max(num_topk_pipelines - 3, (uint32_t)log2f(float(k)) + 2); + max_pipeline = std::min(preferred_pipeline, max_pipeline); + uint32_t min_pipeline = (uint32_t)log2f(float(k)) + 1; + // require full subgroup + min_pipeline = std::max(min_pipeline, ctx->device->subgroup_size_log2); + + uint32_t pipeline_idx = (uint32_t)ceilf(log2f(float(num_elements))); + pipeline_idx = std::min(pipeline_idx, max_pipeline); + pipeline_idx = std::max(pipeline_idx, min_pipeline); + + if (num_elements > (1u << pipeline_idx)) { + // If we could finish on this loop iteration (i.e. a single workgroup) + // then do so. It's better than the overhead of another pass. + for (uint32_t i = pipeline_idx; i < num_topk_pipelines; ++i) { + if (num_elements <= (1u << i)) { + pipeline_idx = i; + break; + } + } + } + + vk_pipeline pipeline = ctx->device->pipeline_topk_f32[pipeline_idx]; + // If the device doesn't support a pipeline this large, use smaller + while (!pipeline) { + pipeline_idx--; + GGML_ASSERT(pipeline_idx >= min_pipeline); + pipeline = ctx->device->pipeline_topk_f32[pipeline_idx]; + } + + vk_op_topk_push_constants pc2 = pc; + pc2.ncols_input = num_elements; + + // Number of elements remaining after this pass + uint32_t num_dst_elements = (num_elements / pipeline->wg_denoms[0]) * k + std::min(k, num_elements % pipeline->wg_denoms[0]); + + pc2.ncols_output = num_dst_elements; + + if (!done_one_iter) { + // Reserve space for ivec2 per element, double buffered + // K per workgroup per row + dbl_buf_size = num_dst_elements * nrows * 2 * sizeof(int); + dbl_buf_size = ROUNDUP_POW2(dbl_buf_size, ctx->device->properties.limits.minStorageBufferOffsetAlignment); + const size_t x_sz = dbl_buf_size * 2; + + if (ctx->prealloc_size_x < x_sz) { + ctx->prealloc_size_x = x_sz; + ggml_vk_preallocate_buffers(ctx, subctx); + } + } + + vk_subbuffer src_buf; + vk_subbuffer dst_buf; + + if (num_elements == ncols) { + pc2.first_pass = 1; + src_buf = ggml_vk_tensor_subbuffer(ctx, src0); + } else { + src_buf = { ctx->prealloc_x, dbl_buf_index * dbl_buf_size, dbl_buf_size }; + } + if (num_dst_elements == k) { + pc2.last_pass = 1; + dst_buf = ggml_vk_tensor_subbuffer(ctx, dst); + } else { + dst_buf = { ctx->prealloc_x, (dbl_buf_index ^ 1) * dbl_buf_size, dbl_buf_size }; + } + + elements[0] = num_elements; + + ggml_pipeline_request_descriptor_sets(ctx, pipeline, 1); + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { src_buf, dst_buf }, pc2, elements); + num_elements = num_dst_elements; + dbl_buf_index ^= 1; + if (num_elements > k) { + ggml_vk_sync_buffers(ctx, subctx); + } + done_one_iter = true; + } + ctx->prealloc_x_need_sync = true; +} + +static void ggml_vk_sum(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) { + vk_op_sum_rows_push_constants p = vk_op_sum_rows_push_constants_init(src0, dst, ggml_nelements(src0)); + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_SUM, p); +} + +static void ggml_vk_sum_rows(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) { + vk_op_sum_rows_push_constants p = vk_op_sum_rows_push_constants_init(src0, dst, src0->ne[0]); + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_SUM_ROWS, p); +} + +static void ggml_vk_mean(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) { + vk_op_sum_rows_push_constants p = vk_op_sum_rows_push_constants_init(src0, dst, src0->ne[0]); + p.weight = 1.0f / (float)src0->ne[0]; + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_MEAN, p); +} + +static void ggml_vk_cumsum(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) { + vk_op_sum_rows_push_constants pc = vk_op_sum_rows_push_constants_init(src0, dst, src0->ne[0]); + // Use the single pass shader when the rows are small or there are enough rows to fill the GPU. + // For fewer, larger rows, use the multipass shader to spread each row across SMs. + if (dst->ne[0] <= 4096 || ggml_nrows(dst) >= ctx->device->shader_core_count) { + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_CUMSUM, pc); + return; + } + + // First pass computes partial sums within a block, and stores the last partial + // to the temp buffer. Second pass sums the block partials from the temp buffer + // and adds that to the result of the first pass. + vk_pipeline pipeline1 = ctx->device->pipeline_cumsum_multipass1_f32; + vk_pipeline pipeline2 = ctx->device->pipeline_cumsum_multipass2_f32; + GGML_ASSERT(pipeline1 != nullptr && pipeline2 != nullptr); + + ggml_pipeline_request_descriptor_sets(ctx, pipeline1, 1); + ggml_pipeline_request_descriptor_sets(ctx, pipeline2, 1); + + std::array elements; + + elements[0] = dst->ne[0]; + elements[1] = (uint32_t)ggml_nrows(dst); + elements[2] = 1; + + size_t temp_size = sizeof(float) * elements[0] * ggml_nrows(dst); + + if (ctx->prealloc_size_split_k < temp_size) { + ctx->prealloc_size_split_k = temp_size; + ggml_vk_preallocate_buffers(ctx, subctx); + } + + vk_subbuffer src_buf = ggml_vk_tensor_subbuffer(ctx, src0); + vk_subbuffer dst_buf = ggml_vk_tensor_subbuffer(ctx, dst); + vk_subbuffer temp_buf = ggml_vk_subbuffer(ctx, ctx->prealloc_split_k, 0); + + if (ctx->prealloc_split_k_need_sync) { + ggml_vk_sync_buffers(ctx, subctx); + } + + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline1, {src_buf, dst_buf, temp_buf}, pc, elements); + ggml_vk_sync_buffers(ctx, subctx); + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline2, {src_buf, dst_buf, temp_buf}, pc, elements); + + ctx->prealloc_split_k_need_sync = true; +} + +static void ggml_vk_argmax(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) { + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_ARGMAX, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], 0.0f, 0.0f, 0.0f, 0.0f }); +} + +static void ggml_vk_count_equal(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, nullptr, dst, GGML_OP_COUNT_EQUAL, { (uint32_t)ggml_nelements(src0), 0, 0.0f, 0.0f, 0.0f, 0.0f }); +} + +static void ggml_vk_solve_tri(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + const uint32_t src0_type_size = ggml_type_size(src0->type); + const uint32_t src1_type_size = ggml_type_size(src1->type); + const uint32_t dst_type_size = ggml_type_size(dst->type); + + ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, nullptr, dst, GGML_OP_SOLVE_TRI, { + (uint32_t)ggml_nelements(src0), + (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size, + (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size, + (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, + 0, + 0.0f, 0.0f, 0, + }); +} + +static void ggml_vk_im2col(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + const int32_t s0 = dst->op_params[0]; + const int32_t s1 = dst->op_params[1]; + const int32_t p0 = dst->op_params[2]; + const int32_t p1 = dst->op_params[3]; + const int32_t d0 = dst->op_params[4]; + const int32_t d1 = dst->op_params[5]; + + const bool is_2D = dst->op_params[6] == 1; + + const uint32_t IC = src1->ne[is_2D ? 2 : 1]; + const uint32_t IH = is_2D ? src1->ne[1] : 1; + const uint32_t IW = src1->ne[0]; + + const uint32_t KH = is_2D ? src0->ne[1] : 1; + const uint32_t KW = src0->ne[0]; + + const uint32_t OH = is_2D ? dst->ne[2] : 1; + const uint32_t OW = dst->ne[1]; + + const uint32_t offset_delta = src1->nb[is_2D ? 2 : 1] / 4; // nb is byte offset, src is type float32 + const uint32_t batch_offset = src1->nb[is_2D ? 3 : 2] / 4; // nb is byte offset, src is type float32 + + const uint32_t batch = src1->ne[is_2D ? 3 : 2]; + + const ggml_backend_vk_buffer_context * d_buf_ctx = (ggml_backend_vk_buffer_context *)dst->buffer->context; + const vk_buffer d_buf = d_buf_ctx->dev_buffer; + + const vk::DeviceAddress dst_addr = d_buf->bda_addr + vk_tensor_offset(dst) + dst->view_offs; + + ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, nullptr, dst, GGML_OP_IM2COL, { + dst_addr, + batch_offset, offset_delta, + IC, IW, IH, OW, OH, KW, KH, + OH * batch, + IC * KH * KW, + s0, s1, p0, p1, d0, d1, batch * IC + }); +} + +static void ggml_vk_im2col_3d(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_TENSOR_BINARY_OP_LOCALS + + const int32_t s0 = ((const int32_t *)(dst->op_params))[0]; + const int32_t s1 = ((const int32_t *)(dst->op_params))[1]; + const int32_t s2 = ((const int32_t *)(dst->op_params))[2]; + const int32_t p0 = ((const int32_t *)(dst->op_params))[3]; + const int32_t p1 = ((const int32_t *)(dst->op_params))[4]; + const int32_t p2 = ((const int32_t *)(dst->op_params))[5]; + const int32_t d0 = ((const int32_t *)(dst->op_params))[6]; + const int32_t d1 = ((const int32_t *)(dst->op_params))[7]; + const int32_t d2 = ((const int32_t *)(dst->op_params))[8]; + const int32_t IC = ((const int32_t *)(dst->op_params))[9]; + + const int64_t N = ne13 / IC; + const int64_t ID = ne12; + const int64_t IH = ne11; + const int64_t IW = ne10; + + const int64_t KD = ne02; + const int64_t KH = ne01; + const int64_t KW = ne00; + + const int64_t OD = ne3 / N; + const int64_t OH = ne2; + const int64_t OW = ne1; + + const ggml_backend_vk_buffer_context * d_buf_ctx = (ggml_backend_vk_buffer_context *)dst->buffer->context; + const vk_buffer d_buf = d_buf_ctx->dev_buffer; + + const vk::DeviceAddress dst_addr = d_buf->bda_addr + vk_tensor_offset(dst) + dst->view_offs; + + vk_op_im2col_3d_push_constants pc {}; + + pc.dst_addr = dst_addr; + pc.nb10 = nb10 / ggml_type_size(src1->type); + pc.nb11 = nb11 / ggml_type_size(src1->type); + pc.nb12 = nb12 / ggml_type_size(src1->type); + pc.nb13 = nb13 / ggml_type_size(src1->type); + pc.s0 = s0; + pc.s1 = s1; + pc.s2 = s2; + pc.p0 = p0; + pc.p1 = p1; + pc.p2 = p2; + pc.d0 = d0; + pc.d1 = d1; + pc.d2 = d2; + pc.IW = IW; + pc.IH = IH; + pc.ID = ID; + pc.IC = IC; + pc.KW = KW; + pc.OH = OH; + pc.KD_KH_KW = KD*KH*KW; + pc.KH_KW = KH*KW; + pc.IC_KD_KH_KW = IC*KD*KH*KW; + pc.N_OD_OH = N*OD*OH; + pc.OD_OH = OD*OH; + pc.OD_OH_OW_IC_KD_KH_KW = OD*OH*OW*IC*KD*KH*KW; + pc.OH_OW_IC_KD_KH_KW = OH*OW*IC*KD*KH*KW; + pc.OW_IC_KD_KH_KW = OW*IC*KD*KH*KW; + + ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, nullptr, dst, GGML_OP_IM2COL_3D, std::move(pc)); +} + +static void ggml_vk_timestep_embedding(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) { + const uint32_t dim = dst->op_params[0]; + const uint32_t max_period = dst->op_params[1]; + const uint32_t nb1 = dst->nb[1] / ggml_type_size(dst->type); + + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_TIMESTEP_EMBEDDING, { + nb1, dim, max_period, + }); +} + +static void ggml_vk_conv_transpose_1d(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + // src0: (K, Cout, Cin, 1) -- kernel + // src1: (L, Cin, 1, 1) -- input + // dst: (*, Cout, 1, 1) + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + GGML_TENSOR_BINARY_OP_LOCALS + + GGML_ASSERT(nb00 == sizeof(float)); + GGML_ASSERT(nb10 == sizeof(float)); + + const int32_t s0 = dst->op_params[0]; + + vk_op_conv_transpose_1d_push_constants p{}; + p.Cout = static_cast(ne01); + p.Cin = static_cast(ne02); + p.K = static_cast(ne00); + p.L = static_cast(ne10); + p.KL = static_cast(ne0); + p.nb01 = static_cast(nb01 / nb00); + p.nb02 = static_cast(nb02 / nb00); + p.nb11 = static_cast(nb11 / nb10); + p.nb1 = static_cast(nb1 / nb0); + p.s0 = static_cast(s0); + + ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, nullptr, dst, GGML_OP_CONV_TRANSPOSE_1D, std::move(p)); +} + +static void ggml_vk_col2im_1d(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) { + // src0: [K_OC, T_in] columns from matmul + // dst: [T_out, OC] + + const int32_t stride = dst->op_params[0]; + const int32_t oc = dst->op_params[1]; + const int32_t p0 = dst->op_params[2]; + + const uint32_t K_OC = static_cast(src0->ne[0]); + const uint32_t T_in = static_cast(src0->ne[1]); + const uint32_t T_out = static_cast(dst->ne[0]); + const uint32_t OC = static_cast(oc); + const uint32_t K = K_OC / OC; + + vk_op_col2im_1d_push_constants p{}; + p.T_out = T_out; + p.OC = OC; + p.K_OC = K_OC; + p.T_in = T_in; + p.K = K; + p.stride = stride; + p.p0 = p0; + + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_COL2IM_1D, std::move(p)); +} + +// Dispatch the fused snake activation: y = x + sin^2(a * x) * inv_b. +// Match the naive mul -> sin -> sqr -> mul -> add chain and run the +// dedicated kernel directly. The pattern is validated by +// ggml_vk_can_fuse_snake before this call. +static void ggml_vk_snake_dispatch_fused(ggml_backend_vk_context * ctx, vk_context& subctx, ggml_cgraph * cgraph, int node_idx) { + const ggml_tensor * mul0 = cgraph->nodes[node_idx + 0]; + const ggml_tensor * sqr = cgraph->nodes[node_idx + 2]; + const ggml_tensor * mul1 = cgraph->nodes[node_idx + 3]; + ggml_tensor * add = cgraph->nodes[node_idx + 4]; + + // x carries the full activation shape, a is the broadcast operand + const ggml_tensor * x = ggml_are_same_shape(mul0, mul0->src[0]) ? mul0->src[0] : mul0->src[1]; + const ggml_tensor * a = (x == mul0->src[0]) ? mul0->src[1] : mul0->src[0]; + + // mul1 reads sqr and inv_b in either operand order + const ggml_tensor * inv_b = (mul1->src[0] == sqr) ? mul1->src[1] : mul1->src[0]; + + vk_pipeline pipeline = nullptr; + switch (x->type) { + case GGML_TYPE_F32: pipeline = ctx->device->pipeline_snake_f32; break; + case GGML_TYPE_F16: pipeline = ctx->device->pipeline_snake_f16; break; + case GGML_TYPE_BF16: pipeline = ctx->device->pipeline_snake_bf16; break; + default: GGML_ABORT("unsupported type"); + } + ggml_pipeline_request_descriptor_sets(ctx, pipeline, 1); + + vk_subbuffer x_buf = ggml_vk_tensor_subbuffer(ctx, x); + vk_subbuffer a_buf = ggml_vk_tensor_subbuffer(ctx, a); + vk_subbuffer inv_b_buf = ggml_vk_tensor_subbuffer(ctx, inv_b); + vk_subbuffer dst_buf = ggml_vk_tensor_subbuffer(ctx, add); + + vk_op_snake_push_constants pc{}; + pc.ne0 = static_cast(x->ne[0]); + pc.ne1 = static_cast(x->ne[1]); + + std::array elements = { pc.ne0, pc.ne1, 1 }; + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { x_buf, a_buf, inv_b_buf, dst_buf }, pc, elements); +} + +static void ggml_vk_pool_2d(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) { + uint32_t op = static_cast(dst->op_params[0]); + const int32_t k1 = dst->op_params[1]; + const int32_t k0 = dst->op_params[2]; + const int32_t s1 = dst->op_params[3]; + const int32_t s0 = dst->op_params[4]; + const int32_t p1 = dst->op_params[5]; + const int32_t p0 = dst->op_params[6]; + + const uint32_t IH = src0->ne[1]; + const uint32_t IW = src0->ne[0]; + + const uint32_t N = dst->ne[3]; + + const uint32_t OC = dst->ne[2]; + const uint32_t OH = dst->ne[1]; + const uint32_t OW = dst->ne[0]; + + const uint32_t parallel_elements = N * OC * OH * OW; + + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_POOL_2D, { + IW, IH, OW, OH, OC, + parallel_elements, + op, + k0, k1, s0, s1, p0, p1, + }); +} + +static void ggml_vk_conv_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)); + + bool transpose = dst->op == GGML_OP_CONV_TRANSPOSE_2D; + + vk_op_conv2d_push_constants p{}; + 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.W = static_cast(ne10); + p.H = static_cast(ne11); + p.OW = static_cast(ne0); + p.OH = static_cast(ne1); + + 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_vk_op_f32(ctx, subctx, src0, src1, nullptr, nullptr, dst, dst->op, std::move(p)); +} + +static void ggml_vk_conv_3d(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_conv3d_push_constants p{}; + p.IC = static_cast(ggml_get_op_params_i32(dst, 9)); + p.N = static_cast(ggml_get_op_params_i32(dst, 10)); + p.OC = static_cast(ggml_get_op_params_i32(dst, 11)); + GGML_ASSERT(src0->ne[3] == (int64_t)p.IC * p.OC); + GGML_ASSERT(src1->ne[3] == (int64_t)p.IC * p.N); + GGML_ASSERT(dst->ne[3] == (int64_t)p.OC * p.N); + + p.IW = static_cast(ne10); + p.IH = static_cast(ne11); + p.ID = static_cast(ne12); + p.OW = static_cast(ne0); + p.OH = static_cast(ne1); + p.OD = static_cast(ne2); + + // the shader clamps src addresses to p.IC * p.N * p.IW * p.IH * p.ID - 1 in uint32, so the + // total input element count must fit in a uint32. + GGML_ASSERT((uint64_t)p.IC * p.N * p.IW * p.IH * p.ID <= 0xFFFFFFFFull); + + 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_vk_op_f32(ctx, subctx, src0, src1, nullptr, nullptr, dst, GGML_OP_CONV_3D, 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) { + vk_op_conv2d_dw_push_constants p{}; + p.ne = ggml_nelements(dst); + p.channels = dst->ne[2]; + p.batches = dst->ne[3]; + p.dst_w = dst->ne[0]; + p.dst_h = dst->ne[1]; + p.src_w = src1->ne[0]; + p.src_h = src1->ne[1]; + p.knl_w = src0->ne[0]; + p.knl_h = src0->ne[1]; + p.stride_x = dst->op_params[0]; + p.stride_y = dst->op_params[1]; + p.pad_x = dst->op_params[2]; + p.pad_y = dst->op_params[3]; + p.dilation_x = dst->op_params[4]; + p.dilation_y = dst->op_params[5]; + + GGML_ASSERT(src0->ne[3] == p.channels); + GGML_ASSERT(src1->ne[3] == p.batches); + + ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, nullptr, dst, GGML_OP_CONV_2D_DW, std::move(p)); +} + +static void ggml_vk_leaky_relu(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) { + const float * op_params = (const float *)dst->op_params; + vk_op_unary_push_constants p = vk_op_unary_push_constants_init(src0, dst); + p.param1 = op_params[0]; + + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_LEAKY_RELU, std::move(p)); +} + +#ifdef GGML_VULKAN_RUN_TESTS +static void ggml_vk_print_matrix_area(const void * data, ggml_type type, int ne0, int ne1, int i0, int i1, int i2) { + if (type != GGML_TYPE_F32 && type != GGML_TYPE_F16) { + return; + } + i0 = std::max(i0, 5); + i1 = std::max(i1, 5); + i2 = std::max(i2, 0); + fprintf(stderr, " "); + for (int idx1 = i1 - 5; idx1 < i1 + 5; idx1++) { + fprintf(stderr, "%7d ", idx1); + } + fprintf(stderr, "\n"); + for (int idx0 = i0 - 5; idx0 < i0 + 5; idx0++) { + fprintf(stderr, "%7d: ", idx0); + for (int idx1 = i1 - 5; idx1 < i1 + 5; idx1++) { + if (idx0 >= 0 && idx0 < ne0 && idx1 >= 0 && idx1 < ne1) { + float val; + if (type == GGML_TYPE_F32) { + val = *((const float *) data + i2*ne1*ne0 + idx1*ne0 + idx0); + } else if (type == GGML_TYPE_F16) { + val = ggml_fp16_to_fp32(*((const ggml_fp16_t *) data + i2*ne1*ne0 + idx1*ne0 + idx0)); + } else { + GGML_ABORT("fatal error"); + } + fprintf(stderr, "% 7.2f ", val); + } else { + fprintf(stderr, " "); + } + } + fprintf(stderr, "\n"); + } +} + +template +static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t n, size_t k, size_t batch, size_t num_it, int split_k, int shader_size) { + VK_LOG_DEBUG("ggml_vk_test_matmul(" << m << ", " << n << ", " << k << ", " << batch << ", " << num_it << ", " << split_k << ", " << shader_size << ")"); + const size_t x_ne = m * k * batch; + const size_t y_ne = k * n * batch; + const size_t d_ne = m * n * batch; + + vk_pipeline p; + std::string shname; + if (shader_size == 0) { + if (std::is_same() && std::is_same()) { + p = ctx->device->pipeline_matmul_f32->a_s; + shname = "F32_ALIGNED_S"; + } else if (std::is_same() && std::is_same()) { + p = ctx->device->pipeline_matmul_f32_f16->a_s; + shname = "F32_F16_ALIGNED_S"; + } else if (std::is_same() && std::is_same()) { + p = ctx->device->pipeline_matmul_f16_f32.f32acc->a_s; + shname = "F16_F32_ALIGNED_S"; + } else if (std::is_same() && std::is_same()) { + p = ctx->device->pipeline_matmul_f16.f32acc->a_s; + shname = "F16_ALIGNED_S"; + } else { + GGML_ABORT("fatal error"); + } + } else if (shader_size == 1) { + if (std::is_same() && std::is_same()) { + p = ctx->device->pipeline_matmul_f32->a_m; + shname = "F32_ALIGNED_M"; + } else if (std::is_same() && std::is_same()) { + p = ctx->device->pipeline_matmul_f32_f16->a_m; + shname = "F32_F16_ALIGNED_M"; + } else if (std::is_same() && std::is_same()) { + p = ctx->device->pipeline_matmul_f16_f32.f32acc->a_m; + shname = "F16_F32_ALIGNED_M"; + } else if (std::is_same() && std::is_same()) { + p = ctx->device->pipeline_matmul_f16.f32acc->a_m; + shname = "F16_ALIGNED_M"; + } else { + GGML_ABORT("fatal error"); + } + } else if (shader_size == 2) { + if (std::is_same() && std::is_same()) { + p = ctx->device->pipeline_matmul_f32->a_l; + shname = "F32_ALIGNED_L"; + } else if (std::is_same() && std::is_same()) { + p = ctx->device->pipeline_matmul_f32_f16->a_l; + shname = "F32_F16_ALIGNED_L"; + } else if (std::is_same() && std::is_same()) { + p = ctx->device->pipeline_matmul_f16_f32.f32acc->a_l; + shname = "F16_F32_ALIGNED_L"; + } else if (std::is_same() && std::is_same()) { + p = ctx->device->pipeline_matmul_f16.f32acc->a_l; + shname = "F16_ALIGNED_L"; + } else { + GGML_ABORT("fatal error"); + } + } else { + GGML_ASSERT(0); + } + + const size_t kpad = ggml_vk_align_size(k, p->align); + + if (k != kpad) { + if (shader_size == 0) { + if (std::is_same() && std::is_same()) { + p = ctx->device->pipeline_matmul_f32->s; + shname = "F32_S"; + } else if (std::is_same() && std::is_same()) { + p = ctx->device->pipeline_matmul_f32_f16->s; + shname = "F32_F16_S"; + } else if (std::is_same() && std::is_same()) { + p = ctx->device->pipeline_matmul_f16_f32.f32acc->s; + shname = "F16_F32_S"; + } else if (std::is_same() && std::is_same()) { + p = ctx->device->pipeline_matmul_f16.f32acc->s; + shname = "F16_S"; + } + } else if (shader_size == 1) { + if (std::is_same() && std::is_same()) { + p = ctx->device->pipeline_matmul_f32->m; + shname = "F32_M"; + } else if (std::is_same() && std::is_same()) { + p = ctx->device->pipeline_matmul_f32_f16->m; + shname = "F32_F16_M"; + } else if (std::is_same() && std::is_same()) { + p = ctx->device->pipeline_matmul_f16_f32.f32acc->m; + shname = "F16_F32_M"; + } else if (std::is_same() && std::is_same()) { + p = ctx->device->pipeline_matmul_f16.f32acc->m; + shname = "F16_M"; + } + } else if (shader_size == 2) { + if (std::is_same() && std::is_same()) { + p = ctx->device->pipeline_matmul_f32->l; + shname = "F32_L"; + } else if (std::is_same() && std::is_same()) { + p = ctx->device->pipeline_matmul_f32_f16->l; + shname = "F32_F16_L"; + } else if (std::is_same() && std::is_same()) { + p = ctx->device->pipeline_matmul_f16_f32.f32acc->l; + shname = "F16_F32_L"; + } else if (std::is_same() && std::is_same()) { + p = ctx->device->pipeline_matmul_f16.f32acc->l; + shname = "F16_L"; + } + } + } + + if (split_k > 1) { + ggml_pipeline_request_descriptor_sets(ctx, ctx->device->pipeline_matmul_split_k_reduce, num_it); + + if (ctx->prealloc_split_k == nullptr || ctx->prealloc_split_k->size < sizeof(float) * d_ne * split_k) { + // Resize buffer + if (ctx->prealloc_split_k != nullptr) { + ggml_vk_destroy_buffer(ctx->prealloc_split_k); + } + ctx->prealloc_split_k = ggml_vk_create_buffer_check(ctx->device, sizeof(float) * d_ne * split_k, {vk::MemoryPropertyFlagBits::eDeviceLocal}); + } + } + + ggml_pipeline_allocate_descriptor_sets(ctx); + + vk_buffer d_X = ggml_vk_create_buffer_check(ctx->device, sizeof(X_TYPE) * x_ne, {vk::MemoryPropertyFlagBits::eDeviceLocal}); + vk_buffer d_Y = ggml_vk_create_buffer_check(ctx->device, sizeof(Y_TYPE) * y_ne, {vk::MemoryPropertyFlagBits::eDeviceLocal}); + vk_buffer d_D = ggml_vk_create_buffer_check(ctx->device, sizeof(float) * d_ne, {vk::MemoryPropertyFlagBits::eDeviceLocal}); + + X_TYPE* x = (X_TYPE *) malloc(sizeof(X_TYPE) * x_ne); + Y_TYPE* y = (Y_TYPE *) malloc(sizeof(Y_TYPE) * y_ne); + float* d = (float *) malloc(sizeof(float) * d_ne); + + for (size_t i = 0; i < x_ne; i++) { + if (std::is_same()) { + x[i] = (rand() / (float)RAND_MAX) * 2.0f - 1.0f; + // x[i] = 1.0f; + // x[i] = i + 1; + // x[i] = (i % k == i / k) ? 1.0f : 0.0f; + } else if (std::is_same()) { + x[i] = ggml_fp32_to_fp16((rand() / (float)RAND_MAX) * 2.0f - 1.0f); + // x[i] = ggml_fp32_to_fp16(1.0f); + // x[i] = ggml_fp32_to_fp16(i + 1); + // x[i] = ggml_fp32_to_fp16((i % k == i / k) ? 1.0f : 0.0f); + } else { + GGML_ABORT("fatal error"); + } + } + for (size_t i = 0; i < y_ne; i++) { + if (std::is_same()) { + y[i] = (rand() / (float)RAND_MAX) * 2.0f - 1.0f; + // y[i] = (i % k == i / k) ? 1.0f : 0.0f; + // y[i] = i + 1; + } else if (std::is_same()) { + y[i] = ggml_fp32_to_fp16((rand() / (float)RAND_MAX) * 2.0f - 1.0f); + // y[i] = ggml_fp32_to_fp16((i % k == i / k) ? 1.0f : 0.0f); + // y[i] = ggml_fp32_to_fp16(i + 1); + } else { + GGML_ABORT("fatal error"); + } + } + + ggml_vk_buffer_write(d_X, 0, x, sizeof(X_TYPE) * k * m * batch); + ggml_vk_buffer_write(d_Y, 0, y, sizeof(Y_TYPE) * k * n * batch); + + vk_context subctx = ggml_vk_create_context(ctx, ctx->compute_cmd_pool); + ggml_vk_ctx_begin(ctx->device, subctx); + for (size_t i = 0; i < num_it; i++) { + ggml_vk_matmul( + ctx, subctx, p, ggml_vk_subbuffer(ctx, d_X), ggml_vk_subbuffer(ctx, d_Y), ggml_vk_subbuffer(ctx, d_D), ggml_vk_subbuffer(ctx, ctx->prealloc_split_k), + m, n, k, + k, k, m, k*m, k*n, m*n, + split_k, batch, batch, batch, 1, 1, n + ); + } + ggml_vk_ctx_end(subctx); + + auto begin = std::chrono::high_resolution_clock::now(); + ggml_vk_submit(subctx, ctx->fence); + VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_matmul waitForFences"); + ctx->device->device.resetFences({ ctx->fence }); + ggml_vk_queue_command_pools_cleanup(ctx->device); + + auto end = std::chrono::high_resolution_clock::now(); + double time = std::chrono::duration_cast(end-begin).count() / 1000.0; + + // copy dst to host + ggml_vk_buffer_read(d_D, 0, d, sizeof(float) * d_ne); + + float * d_chk = (float *) malloc(sizeof(float) * d_ne); + + ggml_init_params iparams = { + /*.mem_size =*/ 1024*1024*1024, + /*.mem_buffer =*/ NULL, + /*.no_alloc =*/ true, + }; + + ggml_context * ggml_ctx = ggml_init(iparams); + + ggml_type src0_type; + ggml_type src1_type; + + if (std::is_same()) { + src0_type = GGML_TYPE_F32; + } else if (std::is_same()) { + src0_type = GGML_TYPE_F16; + } else { + GGML_ABORT("fatal error"); + } + if (std::is_same()) { + src1_type = GGML_TYPE_F32; + } else if (std::is_same()) { + src1_type = GGML_TYPE_F16; + } else { + GGML_ABORT("fatal error"); + } + + ggml_tensor * src0_ggml = ggml_new_tensor_3d(ggml_ctx, src0_type, k, m, batch); + ggml_tensor * src1_ggml = ggml_new_tensor_3d(ggml_ctx, src1_type, k, n, batch); + ggml_tensor * tensor_ggml = ggml_mul_mat(ggml_ctx, src0_ggml, src1_ggml); + + src0_ggml->data = x; + src1_ggml->data = y; + tensor_ggml->data = d_chk; + + ggml_cgraph * cgraph = ggml_new_graph(ggml_ctx); + ggml_build_forward_expand(cgraph, tensor_ggml); + + ggml_graph_compute_with_ctx(ggml_ctx, cgraph, 1); + + ggml_free(ggml_ctx); + + double avg_err = 0.0; + int first_err_n = -1; + int first_err_m = -1; + int first_err_b = -1; + + for (size_t i = 0; i < m*n*batch; i++) { + double err = std::fabs(d[i] - d_chk[i]); + avg_err += err; + + if ((err > 0.05f || std::isnan(err)) && first_err_n == -1) { + first_err_b = i / (m * n); + first_err_n = (i % (m * n)) / m; + first_err_m = (i % (m * n)) % m; + } + } + + avg_err /= m * n; + + double tflops = 2.0*m*n*k*batch*num_it / (time / 1000.0) / (1000.0*1000.0*1000.0*1000.0); + + std::cerr << "TEST " << shname << " m=" << m << " n=" << n << " k=" << k << " batch=" << batch << " split_k=" << split_k << " matmul " << time / num_it << "ms " << tflops << " TFLOPS avg_err=" << avg_err << std::endl; + + if (avg_err > 0.1 || std::isnan(avg_err)) { + std::cerr << "m = " << first_err_m << " n = " << first_err_n << " b = " << first_err_b << std::endl; + std::cerr << "Actual result: " << std::endl << std::endl; + ggml_vk_print_matrix_area(d, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b); + std::cerr << "Expected result: " << std::endl << std::endl; + ggml_vk_print_matrix_area(d_chk, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b); + + if (split_k > 1) { + float * split_k_buf = (float *) malloc(sizeof(float) * d_ne * split_k); + ggml_vk_buffer_read(ctx->prealloc_split_k, 0, split_k_buf, sizeof(float) * d_ne * split_k); + + std::cerr << "d_buf0: " << std::endl << std::endl; + ggml_vk_print_matrix_area(split_k_buf, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b); + + std::cerr << "d_buf1: " << std::endl << std::endl; + ggml_vk_print_matrix_area(split_k_buf + d_ne, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b); + + std::cerr << "d_buf2: " << std::endl << std::endl; + ggml_vk_print_matrix_area(split_k_buf + 2 * d_ne, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b); + + std::cerr << "d_buf3: " << std::endl << std::endl; + ggml_vk_print_matrix_area(split_k_buf + 3 * d_ne, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b); + + free(split_k_buf); + } + } + + free(d_chk); + + ggml_vk_command_pool_cleanup(ctx->device, ctx->compute_cmd_pool); + + ggml_vk_destroy_buffer(d_X); + ggml_vk_destroy_buffer(d_Y); + ggml_vk_destroy_buffer(d_D); + + free(x); + free(y); + free(d); +} + +static void ggml_vk_print_tensor_area(const ggml_tensor * tensor, int i0, int i1, int i2, int i3) { + if (tensor->type != GGML_TYPE_F32 && tensor->type != GGML_TYPE_F16) { + return; + } + i0 = std::max(i0, 5); + i1 = std::max(i1, 5); + i2 = std::max(i2, 0); + i3 = std::max(i3, 0); + fprintf(stderr, " "); + for (int idx1 = i1 - 5; idx1 < i1 + 5; idx1++) { + fprintf(stderr, "%7d ", idx1); + } + fprintf(stderr, "\n"); + for (int idx0 = i0 - 5; idx0 < i0 + 5; idx0++) { + fprintf(stderr, "%7d: ", idx0); + for (int idx1 = i1 - 5; idx1 < i1 + 5; idx1++) { + if (idx0 >= 0 && idx0 < tensor->ne[0] && idx1 >= 0 && idx1 < tensor->ne[1] && i2 >= 0 && i2 < tensor->ne[2] && i3 >= 0 && i3 < tensor->ne[3]) { + float val; + if (tensor->type == GGML_TYPE_F32) { + val = *(float *) ((char *) tensor->data + i3*tensor->nb[3] + i2*tensor->nb[2] + idx1*tensor->nb[1] + idx0*tensor->nb[0]); + } else if (tensor->type == GGML_TYPE_F16) { + val = ggml_fp16_to_fp32(*(ggml_fp16_t *) ((char *) tensor->data + i3*tensor->nb[3] + i2*tensor->nb[2] + idx1*tensor->nb[1] + idx0*tensor->nb[0])); + } else { + GGML_ABORT("fatal error"); + } + fprintf(stderr, "% 7.2f ", val); + } else { + fprintf(stderr, " "); + } + } + fprintf(stderr, "\n"); + } +} + +static void ggml_vk_quantize_data(const float * from, void * to, size_t ne, ggml_type quant) { + ggml_quantize_chunk(quant, from, to, 0, 1, ne, nullptr); +} + +static void ggml_vk_dequantize_data(const void * from, float * to, size_t ne, ggml_type quant) { + if (quant == GGML_TYPE_F32) { + memcpy(to, from, sizeof(float) * ne); + return; + } + + const auto * tt = ggml_get_type_traits(quant); + + ggml_to_float_t dequant_fn = tt->to_float; + + dequant_fn(from, to, ne); +} + +static void ggml_vk_test_dequant(ggml_backend_vk_context * ctx, size_t ne, ggml_type quant) { + VK_LOG_DEBUG("ggml_vk_test_dequant(" << ne << ")"); + const size_t x_sz = sizeof(float) * ne; + const size_t x_sz_f16 = sizeof(ggml_fp16_t) * ne; + const size_t qx_sz = ne * ggml_type_size(quant)/ggml_blck_size(quant); + float * x = (float *) malloc(x_sz); + void * qx = malloc(qx_sz); + vk_buffer qx_buf = ggml_vk_create_buffer_check(ctx->device, qx_sz, {vk::MemoryPropertyFlagBits::eDeviceLocal}); + vk_buffer x_buf = ggml_vk_create_buffer_check(ctx->device, x_sz_f16, {vk::MemoryPropertyFlagBits::eDeviceLocal}); + float * x_ref = (float *) malloc(x_sz); + ggml_fp16_t * x_chk = (ggml_fp16_t *) malloc(x_sz_f16); + + for (size_t i = 0; i < ne; i++) { + x[i] = rand() / (float)RAND_MAX; + } + + vk_pipeline p = ggml_vk_get_to_fp16(ctx, quant); + + ggml_vk_quantize_data(x, qx, ne, quant); + ggml_vk_dequantize_data(qx, x_ref, ne, quant); + + ggml_pipeline_request_descriptor_sets(ctx, p, 1); + + ggml_pipeline_allocate_descriptor_sets(ctx); + + ggml_vk_buffer_write(qx_buf, 0, qx, qx_sz); + + vk_context subctx = ggml_vk_create_context(ctx, ctx->compute_cmd_pool); + ggml_vk_ctx_begin(ctx->device, subctx); + const std::vector pc = { 1, (uint32_t)ne, (uint32_t)ne, (uint32_t)ne, (uint32_t)ne }; + ggml_vk_dispatch_pipeline(ctx, subctx, p, { vk_subbuffer{ qx_buf, 0, qx_sz }, vk_subbuffer{ x_buf, 0, x_sz_f16 } }, pc, { (uint32_t)ne, 1, 1}); + ggml_vk_ctx_end(subctx); + + auto begin = std::chrono::high_resolution_clock::now(); + + ggml_vk_submit(subctx, ctx->fence); + VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_dequant waitForFences"); + ctx->device->device.resetFences({ ctx->fence }); + ggml_vk_queue_command_pools_cleanup(ctx->device); + + auto end = std::chrono::high_resolution_clock::now(); + + double ms_dequant = std::chrono::duration_cast(end-begin).count() / 1000.0; + ggml_vk_buffer_read(x_buf, 0, x_chk, x_sz_f16); + + int first_err = -1; + + double avg_err = 0.0; + for (size_t i = 0; i < ne; i++) { + double error = std::fabs(x_ref[i] - ggml_fp16_to_fp32(x_chk[i])); + avg_err += error; + + if (first_err < 0 && error > 0.05) { + first_err = i; + } + } + + avg_err /= ne; + + std::cerr << "TEST DEQUANT " << ggml_type_name(quant) << " time=" << ms_dequant << "ms avg_err=" << avg_err << std::endl; + + if (avg_err > 0.1) { + std::cerr << "first_error = " << first_err << std::endl; + std::cerr << "Actual result: " << std::endl << std::endl; + for (int i = std::max(0, first_err - 5); i < std::min((int)ne, first_err + 5); i++) { + std::cerr << ggml_fp16_to_fp32(x_chk[i]) << ", "; + } + std::cerr << std::endl << "Expected result: " << std::endl << std::endl; + for (int i = std::max(0, first_err - 5); i < std::min((int)ne, first_err + 5); i++) { + std::cerr << x_ref[i] << ", "; + } + std::cerr << std::endl; + } + + ggml_vk_destroy_buffer(x_buf); + ggml_vk_destroy_buffer(qx_buf); + + free(x); + free(qx); + free(x_ref); + free(x_chk); +} + +// This does not work without ggml q8_1 quantization support +// +// typedef uint16_t ggml_half; +// typedef uint32_t ggml_half2; +// +// #define QK8_1 32 +// typedef struct { +// union { +// struct { +// ggml_half d; // delta +// ggml_half s; // d * sum(qs[i]) +// } GGML_COMMON_AGGR_S; +// ggml_half2 ds; +// } GGML_COMMON_AGGR_U; +// int8_t qs[QK8_1]; // quants +// } block_q8_1; +// +// static void ggml_vk_test_quantize(ggml_backend_vk_context * ctx, size_t ne, ggml_type quant) { +// VK_LOG_DEBUG("ggml_vk_test_quantize(" << ne << ")"); +// GGML_ASSERT(quant == GGML_TYPE_Q8_1); +// +// const size_t x_sz = sizeof(float) * ne; +// const size_t qx_sz = ne * ggml_type_size(quant)/ggml_blck_size(quant); +// float * x = (float *) malloc(x_sz); +// block_q8_1 * qx = (block_q8_1 *)malloc(qx_sz); +// block_q8_1 * qx_res = (block_q8_1 *)malloc(qx_sz); +// vk_buffer x_buf = ggml_vk_create_buffer_check(ctx->device, x_sz, {vk::MemoryPropertyFlagBits::eDeviceLocal}); +// vk_buffer qx_buf = ggml_vk_create_buffer_check(ctx->device, qx_sz, {vk::MemoryPropertyFlagBits::eDeviceLocal}); +// +// for (size_t i = 0; i < ne; i++) { +// x[i] = rand() / (float)RAND_MAX; +// } +// +// vk_pipeline p = ggml_vk_get_quantize_pipeline(ctx, quant); +// +// ggml_pipeline_request_descriptor_sets(ctx, p, 1); +// +// ggml_pipeline_allocate_descriptor_sets(ctx); +// +// ggml_vk_buffer_write(x_buf, 0, x, x_sz); +// +// vk_context subctx = ggml_vk_create_context(ctx, ctx->compute_cmd_pool); +// ggml_vk_ctx_begin(ctx->device, subctx); +// ggml_vk_quantize_q8_1(ctx, subctx, ggml_vk_subbuffer(ctx, x_buf), ggml_vk_subbuffer(ctx, qx_buf), ne); +// ggml_vk_ctx_end(subctx); +// +// auto begin = std::chrono::high_resolution_clock::now(); +// +// ggml_vk_submit(subctx, ctx->fence); +// VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_quantize waitForFences"); +// ctx->device->device.resetFences({ ctx->fence }); +// ggml_vk_queue_command_pools_cleanup(ctx->device); +// +// auto end = std::chrono::high_resolution_clock::now(); +// +// double ms_quant = std::chrono::duration_cast(end-begin).count() / 1000.0; +// ggml_vk_buffer_read(qx_buf, 0, qx, qx_sz); +// +// ggml_vk_quantize_data(x, qx_res, ne, quant); +// +// int first_err = -1; +// +// for (size_t i = 0; i < ne / 32; i++) { +// double error = std::fabs(ggml_fp16_to_fp32(qx_res[i].GGML_COMMON_AGGR_U.GGML_COMMON_AGGR_S.d) - ggml_fp16_to_fp32(qx[i].GGML_COMMON_AGGR_U.GGML_COMMON_AGGR_S.d)); +// +// if (first_err < 0 && error > 0.1) { +// first_err = i; +// } +// +// error = std::fabs(ggml_fp16_to_fp32(qx_res[i].GGML_COMMON_AGGR_U.GGML_COMMON_AGGR_S.s) - ggml_fp16_to_fp32(qx[i].GGML_COMMON_AGGR_U.GGML_COMMON_AGGR_S.s)); +// +// if (first_err < 0 && error > 0.1) { +// first_err = i; +// } +// +// for (size_t j = 0; j < 32; j++) { +// uint64_t error = std::abs(qx_res[i].qs[j] - qx[i].qs[j]); +// +// if (first_err < 0 && error > 1) { +// first_err = i; +// } +// } +// } +// +// std::cerr << "TEST QUANTIZE " << ggml_type_name(quant) << " time=" << ms_quant << "ms " << (first_err == -1 ? "CORRECT" : "INCORRECT") << std::endl; +// +// if (first_err != -1) { +// std::cerr << "first_error = " << first_err << std::endl; +// std::cerr << "Actual result: " << std::endl << std::endl; +// std::cout << "d=" << ggml_fp16_to_fp32(qx[first_err].GGML_COMMON_AGGR_U.GGML_COMMON_AGGR_S.d) << " s=" << ggml_fp16_to_fp32(qx[first_err].GGML_COMMON_AGGR_U.GGML_COMMON_AGGR_S.s) << " "; +// for (size_t j = 0; j < 32; j++) { +// std::cout << " qs" << j << "=" << (uint32_t)qx[first_err].qs[j] << " "; +// } +// std::cerr << std::endl << std::endl << "Expected result: " << std::endl << std::endl; +// std::cout << "d=" << ggml_fp16_to_fp32(qx_res[first_err].GGML_COMMON_AGGR_U.GGML_COMMON_AGGR_S.d) << " s=" << ggml_fp16_to_fp32(qx_res[first_err].GGML_COMMON_AGGR_U.GGML_COMMON_AGGR_S.s) << " "; +// for (size_t j = 0; j < 32; j++) { +// std::cout << " qs" << j << "=" << (uint32_t)qx_res[first_err].qs[j] << " "; +// } +// std::cerr << std::endl; +// } +// +// ggml_vk_destroy_buffer(x_buf); +// ggml_vk_destroy_buffer(qx_buf); +// +// free(x); +// free(qx); +// free(qx_res); +// } + +static void ggml_vk_test_dequant_matmul(ggml_backend_vk_context * ctx, size_t m, size_t n, size_t k, size_t batch, size_t num_it, size_t split_k, size_t shader_size, ggml_type quant, bool mmq = false) { + VK_LOG_DEBUG("ggml_vk_test_dequant_matmul(" << m << ", " << n << ", " << k << ", " << batch << ", " << num_it << ", " << split_k << ", " << ggml_type_name(quant) << ")"); + const size_t x_ne = m * k * batch; + const size_t y_ne = k * n * batch; + const size_t d_ne = m * n * batch; + + vk_matmul_pipeline2 * pipelines; + + if (mmq) { + pipelines = ctx->device->pipeline_dequant_mul_mat_mat_q8_1; + } else { + pipelines = ctx->device->pipeline_dequant_mul_mat_mat; + } + + const bool fp16acc = ctx->device->fp16; + + vk_pipeline p; + std::string shname; + if (shader_size == 0) { + p = fp16acc ? pipelines[quant].f16acc->a_s : pipelines[quant].f32acc->a_s; + shname = std::string(ggml_type_name(quant)) + "_ALIGNED_S"; + } else if (shader_size == 1) { + p = fp16acc ? pipelines[quant].f16acc->a_m : pipelines[quant].f32acc->a_m; + shname = std::string(ggml_type_name(quant)) + "_ALIGNED_M"; + } else if (shader_size == 2) { + p = fp16acc ? pipelines[quant].f16acc->a_l : pipelines[quant].f32acc->a_l; + shname = std::string(ggml_type_name(quant)) + "_ALIGNED_L"; + } else { + GGML_ASSERT(0); + } + + const size_t kpad = mmq ? 0 : ggml_vk_align_size(k, p->align); + + if (mmq || k != kpad) { + if (shader_size == 0) { + p = fp16acc ? pipelines[quant].f16acc->s : pipelines[quant].f32acc->s; + shname = std::string(ggml_type_name(quant)) + "_S"; + } else if (shader_size == 1) { + p = fp16acc ? pipelines[quant].f16acc->m : pipelines[quant].f32acc->m; + shname = std::string(ggml_type_name(quant)) + "_M"; + } else if (shader_size == 2) { + p = fp16acc ? pipelines[quant].f16acc->l : pipelines[quant].f32acc->l; + shname = std::string(ggml_type_name(quant)) + "_L"; + } else { + GGML_ASSERT(0); + } + } + + if (p == nullptr) { + std::cerr << "error: no pipeline for ggml_vk_test_dequant_matmul " << ggml_type_name(quant) << std::endl; + return; + } + + const size_t x_sz = sizeof(float) * x_ne; + const size_t y_sz = sizeof(float) * y_ne; + const size_t qx_sz = x_ne * ggml_type_size(quant)/ggml_blck_size(quant); + const size_t qy_sz = mmq ? y_ne * ggml_type_size(GGML_TYPE_Q8_1)/ggml_blck_size(GGML_TYPE_Q8_1) : y_sz; + const size_t d_sz = sizeof(float) * d_ne; + float * x = (float *) malloc(x_sz); + float * y = (float *) malloc(y_sz); + void * qx = malloc(qx_sz); + vk_buffer qx_buf = ggml_vk_create_buffer_check(ctx->device, qx_sz, {vk::MemoryPropertyFlagBits::eDeviceLocal}); + vk_buffer y_buf = ggml_vk_create_buffer_check(ctx->device, y_sz, {vk::MemoryPropertyFlagBits::eDeviceLocal}); + vk_buffer qy_buf = ggml_vk_create_buffer_check(ctx->device, qy_sz, {vk::MemoryPropertyFlagBits::eDeviceLocal}); + vk_buffer d_buf = ggml_vk_create_buffer_check(ctx->device, d_sz, {vk::MemoryPropertyFlagBits::eDeviceLocal}); + float * d = (float *) malloc(d_sz); + float * d_chk = (float *) malloc(d_sz); + + for (size_t i = 0; i < x_ne; i++) { + x[i] = (rand() / (float)RAND_MAX) * 2.0f - 1.0f; + // x[i] = (i % k == i / k) ? 1.0f : 0.0f; + // x[i] = i % k; + } + + ggml_vk_quantize_data(x, qx, x_ne, quant); + + for (size_t i = 0; i < y_ne; i++) { + y[i] = (rand() / (float)RAND_MAX) * 2.0f - 1.0f; + // y[i] = (i % k == i / k) ? 1.0f : 0.0f; + // y[i] = i % k; + } + + if (split_k > 1) { + ggml_pipeline_request_descriptor_sets(ctx, ctx->device->pipeline_matmul_split_k_reduce, num_it); + + if (ctx->prealloc_split_k == nullptr || ctx->prealloc_split_k->size < sizeof(float) * d_ne * split_k) { + // Resize buffer + if (ctx->prealloc_split_k != nullptr) { + ggml_vk_destroy_buffer(ctx->prealloc_split_k); + } + ctx->prealloc_split_k = ggml_vk_create_buffer_check(ctx->device, sizeof(float) * d_ne * split_k, {vk::MemoryPropertyFlagBits::eDeviceLocal}); + } + } + if (mmq) { + vk_pipeline pipeline_quantize_q8_1 = ggml_vk_get_quantize_pipeline(ctx, GGML_TYPE_Q8_1); + ggml_pipeline_request_descriptor_sets(ctx, pipeline_quantize_q8_1, num_it); + } + + ggml_pipeline_allocate_descriptor_sets(ctx); + + ggml_vk_buffer_write(qx_buf, 0, qx, qx_sz); + ggml_vk_buffer_write(y_buf, 0, y, y_sz); + + vk_context subctx = ggml_vk_create_context(ctx, ctx->compute_cmd_pool); + ggml_vk_ctx_begin(ctx->device, subctx); + if (mmq) { + for (size_t i = 0; i < num_it; i++) { + ggml_vk_quantize_q8_1(ctx, subctx, { y_buf, 0, y_sz }, { qy_buf, 0, qy_sz }, y_ne); + ggml_vk_matmul( + ctx, subctx, p, { qx_buf, 0, qx_sz }, { qy_buf, 0, qy_sz }, { d_buf, 0, d_sz }, { ctx->prealloc_split_k, 0, ctx->prealloc_size_split_k }, + m, n, k, + k, k, m, k*m, k*n, m*n, + split_k, batch, batch, batch, 1, 1, n + ); + } + } else { + for (size_t i = 0; i < num_it; i++) { + ggml_vk_matmul( + ctx, subctx, p, { qx_buf, 0, qx_sz }, { y_buf, 0, y_sz }, { d_buf, 0, d_sz }, { ctx->prealloc_split_k, 0, ctx->prealloc_size_split_k }, + m, n, k, + k, k, m, k*m, k*n, m*n, + split_k, batch, batch, batch, 1, 1, n + ); + } + } + ggml_vk_ctx_end(subctx); + + auto begin = std::chrono::high_resolution_clock::now(); + + ggml_vk_submit(subctx, ctx->fence); + VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_dequant waitForFences"); + ctx->device->device.resetFences({ ctx->fence }); + ggml_vk_queue_command_pools_cleanup(ctx->device); + + auto end = std::chrono::high_resolution_clock::now(); + + double time_ms = std::chrono::duration_cast(end-begin).count() / 1000.0; + ggml_vk_buffer_read(d_buf, 0, d, d_sz); + + ggml_init_params iparams = { + /*.mem_size =*/ 1024*1024*1024, + /*.mem_buffer =*/ NULL, + /*.no_alloc =*/ true, + }; + + ggml_context * ggml_ctx = ggml_init(iparams); + + ggml_tensor * src0_ggml = ggml_new_tensor_3d(ggml_ctx, quant, k, m, batch); + ggml_tensor * src1_ggml = ggml_new_tensor_3d(ggml_ctx, GGML_TYPE_F32, k, n, batch); + ggml_tensor * tensor_ggml = ggml_mul_mat(ggml_ctx, src0_ggml, src1_ggml); + + src0_ggml->data = qx; + src1_ggml->data = y; + tensor_ggml->data = d_chk; + + ggml_cgraph * cgraph = ggml_new_graph(ggml_ctx); + ggml_build_forward_expand(cgraph, tensor_ggml); + + ggml_graph_compute_with_ctx(ggml_ctx, cgraph, 1); + + ggml_free(ggml_ctx); + + double avg_err = 0.0; + int first_err_n = -1; + int first_err_m = -1; + int first_err_b = -1; + + for (size_t i = 0; i < m*n*batch; i++) { + double err = std::fabs(d[i] - d_chk[i]); + avg_err += err; + + if ((err > 0.05f || std::isnan(err)) && first_err_n == -1) { + first_err_b = i / (m * n); + first_err_n = (i % (m * n)) / m; + first_err_m = (i % (m * n)) % m; + } + } + + avg_err /= m * n; + + double tflops = 2.0*m*n*k*batch*num_it / (time_ms / 1000.0) / (1000.0*1000.0*1000.0*1000.0); + + std::cerr << "TEST dequant matmul " << shname; + if (mmq) { + std::cerr << " mmq"; + } + std::cerr << " m=" << m << " n=" << n << " k=" << k << " batch=" << batch << " split_k=" << split_k << " matmul " << time_ms / num_it << "ms " << tflops << " TFLOPS avg_err=" << avg_err << std::endl; + + if (avg_err > 0.01 || std::isnan(avg_err)) { + std::cerr << "m = " << first_err_m << " n = " << first_err_n << " b = " << first_err_b << std::endl; + std::cerr << "Actual result: " << std::endl << std::endl; + ggml_vk_print_matrix_area(d, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b); + std::cerr << std::endl; + std::cerr << "Expected result: " << std::endl << std::endl; + ggml_vk_print_matrix_area(d_chk, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b); + + std::cerr << "src0: " << std::endl << std::endl; + ggml_vk_print_matrix_area(x, GGML_TYPE_F32, k, m, first_err_m, first_err_n, first_err_b); + std::cerr << std::endl; + std::cerr << "src1: " << std::endl << std::endl; + ggml_vk_print_matrix_area(y, GGML_TYPE_F32, k, n, first_err_m, first_err_n, first_err_b); + + if (split_k > 1) { + float * split_k_buf = (float *) malloc(sizeof(float) * d_ne * split_k); + ggml_vk_buffer_read(ctx->prealloc_split_k, 0, split_k_buf, sizeof(float) * d_ne * split_k); + + std::cerr << "d_buf0: " << std::endl << std::endl; + ggml_vk_print_matrix_area(split_k_buf, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b); + + std::cerr << "d_buf1: " << std::endl << std::endl; + ggml_vk_print_matrix_area(split_k_buf + d_ne, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b); + + std::cerr << "d_buf2: " << std::endl << std::endl; + ggml_vk_print_matrix_area(split_k_buf + 2 * d_ne, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b); + + std::cerr << "d_buf3: " << std::endl << std::endl; + ggml_vk_print_matrix_area(split_k_buf + 3 * d_ne, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b); + + free(split_k_buf); + } + } + + ggml_vk_destroy_buffer(qx_buf); + ggml_vk_destroy_buffer(y_buf); + ggml_vk_destroy_buffer(qy_buf); + ggml_vk_destroy_buffer(d_buf); + + free(x); + free(qx); + free(y); + free(d); + free(d_chk); +} +#endif + +static void ggml_vk_preallocate_buffers(ggml_backend_vk_context * ctx, vk_context subctx) { +#if defined(GGML_VULKAN_RUN_TESTS) + const std::vector vals { + 512, 512, 128, + 128, 512, 512, + 4096, 512, 4096, + 11008, 512, 4096, + 4096, 512, 11008, + 32000, 512, 4096, + 8, 8, 8, + 100, 46, 576, + 623, 111, 128, + 100, 46, 558, + 512, 1, 256, + 128, 110, 622, + 511, 511, 127, + 511, 511, 7, + 511, 511, 17, + 49, 49, 128, + 128, 49, 49, + 4096, 49, 4096, + }; + const size_t num_it = 100; + + ggml_vk_test_dequant_matmul(ctx, 4096, 512, 4096, 2, num_it, 1, 0, GGML_TYPE_Q4_0); + ggml_vk_test_dequant_matmul(ctx, 4096, 512, 4096, 2, num_it, 1, 1, GGML_TYPE_Q4_0); + ggml_vk_test_dequant_matmul(ctx, 4096, 512, 4096, 2, num_it, 1, 2, GGML_TYPE_Q4_0); + + ggml_vk_test_dequant_matmul(ctx, 4096, 512, 4096, 2, num_it, 1, 0, GGML_TYPE_Q4_0, true); + ggml_vk_test_dequant_matmul(ctx, 4096, 512, 4096, 2, num_it, 1, 1, GGML_TYPE_Q4_0, true); + ggml_vk_test_dequant_matmul(ctx, 4096, 512, 4096, 2, num_it, 1, 2, GGML_TYPE_Q4_0, true); + + ggml_vk_test_dequant_matmul(ctx, 4096, 512, 4096, 2, num_it, 1, 0, GGML_TYPE_Q8_0); + ggml_vk_test_dequant_matmul(ctx, 4096, 512, 4096, 2, num_it, 1, 1, GGML_TYPE_Q8_0); + ggml_vk_test_dequant_matmul(ctx, 4096, 512, 4096, 2, num_it, 1, 2, GGML_TYPE_Q8_0); + + ggml_vk_test_dequant_matmul(ctx, 4096, 512, 4096, 2, num_it, 1, 0, GGML_TYPE_Q8_0, true); + ggml_vk_test_dequant_matmul(ctx, 4096, 512, 4096, 2, num_it, 1, 1, GGML_TYPE_Q8_0, true); + ggml_vk_test_dequant_matmul(ctx, 4096, 512, 4096, 2, num_it, 1, 2, GGML_TYPE_Q8_0, true); + + abort(); + + for (size_t i = 0; i < vals.size(); i += 3) { + ggml_vk_test_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 1, 0); + ggml_vk_test_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 1, 1); + ggml_vk_test_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 1, 2); + std::cerr << '\n'; + ggml_vk_test_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 2, 0); + ggml_vk_test_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 2, 1); + ggml_vk_test_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 2, 2); + std::cerr << '\n'; + ggml_vk_test_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 4, 0); + ggml_vk_test_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 4, 1); + ggml_vk_test_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 4, 2); + std::cerr << '\n' << std::endl; + + if (vals[i + 2] % 32 == 0) { + ggml_vk_test_dequant_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 1, 0, GGML_TYPE_Q4_0); + ggml_vk_test_dequant_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 1, 1, GGML_TYPE_Q4_0); + ggml_vk_test_dequant_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 1, 2, GGML_TYPE_Q4_0); + std::cerr << '\n'; + ggml_vk_test_dequant_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 2, 0, GGML_TYPE_Q4_0); + ggml_vk_test_dequant_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 2, 1, GGML_TYPE_Q4_0); + ggml_vk_test_dequant_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 2, 2, GGML_TYPE_Q4_0); + std::cerr << '\n'; + ggml_vk_test_dequant_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 4, 0, GGML_TYPE_Q4_0); + ggml_vk_test_dequant_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 4, 1, GGML_TYPE_Q4_0); + ggml_vk_test_dequant_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 4, 2, GGML_TYPE_Q4_0); + std::cerr << '\n' << std::endl; + } + + if (vals[i + 2] % 256 == 0) { + ggml_vk_test_dequant_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 1, 0, GGML_TYPE_Q4_K); + ggml_vk_test_dequant_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 1, 1, GGML_TYPE_Q4_K); + ggml_vk_test_dequant_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 1, 2, GGML_TYPE_Q4_K); + std::cerr << '\n'; + ggml_vk_test_dequant_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 2, 0, GGML_TYPE_Q4_K); + ggml_vk_test_dequant_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 2, 1, GGML_TYPE_Q4_K); + ggml_vk_test_dequant_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 2, 2, GGML_TYPE_Q4_K); + std::cerr << '\n'; + ggml_vk_test_dequant_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 4, 0, GGML_TYPE_Q4_K); + ggml_vk_test_dequant_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 4, 1, GGML_TYPE_Q4_K); + ggml_vk_test_dequant_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 4, 2, GGML_TYPE_Q4_K); + std::cerr << '\n' << std::endl; + } + } + + GGML_ABORT("fatal error"); +#endif + + if (subctx) { + // Submit and wait for any pending work before reallocating the buffers + ggml_vk_ctx_end(subctx); + ggml_vk_submit(subctx, {}); + ctx->submit_pending = true; + ggml_vk_synchronize(ctx); + GGML_ASSERT(ctx->compute_ctx.expired()); + ggml_vk_ctx_begin(ctx->device, subctx); + ctx->compute_ctx = subctx; + } + + if (ctx->prealloc_x == nullptr || (ctx->prealloc_size_x > 0 && ctx->prealloc_x->size < ctx->prealloc_size_x)) { + VK_LOG_MEMORY("ggml_vk_preallocate_buffers(x_size: " << ctx->prealloc_size_x << ")"); + // Resize buffer + if (ctx->prealloc_x != nullptr) { + ggml_vk_destroy_buffer(ctx->prealloc_x); + } + ctx->prealloc_x = ggml_vk_create_buffer_device(ctx->device, ctx->prealloc_size_x); + } + if (ctx->prealloc_y == nullptr || (ctx->prealloc_size_y > 0 && ctx->prealloc_y->size < ctx->prealloc_size_y)) { + VK_LOG_MEMORY("ggml_vk_preallocate_buffers(y_size: " << ctx->prealloc_size_y << ")"); + // Resize buffer + if (ctx->prealloc_y != nullptr) { + ggml_vk_destroy_buffer(ctx->prealloc_y); + } + ctx->prealloc_y = ggml_vk_create_buffer_device(ctx->device, ctx->prealloc_size_y); + ctx->prealloc_y_last_pipeline_used = nullptr; + ctx->prealloc_y_last_tensor_used = nullptr; + ctx->prealloc_y_last_decode_vector_staging = false; + } + if (ctx->prealloc_split_k == nullptr || (ctx->prealloc_size_split_k > 0 && ctx->prealloc_split_k->size < ctx->prealloc_size_split_k)) { + VK_LOG_MEMORY("ggml_vk_preallocate_buffers(split_k_size: " << ctx->prealloc_size_split_k << ")"); + // Resize buffer + if (ctx->prealloc_split_k != nullptr) { + ggml_vk_destroy_buffer(ctx->prealloc_split_k); + } + ctx->prealloc_split_k = ggml_vk_create_buffer_device(ctx->device, ctx->prealloc_size_split_k); + } + if (ctx->prealloc_add_rms_partials == nullptr || (ctx->prealloc_size_add_rms_partials > 0 && ctx->prealloc_add_rms_partials->size < ctx->prealloc_size_add_rms_partials)) { + VK_LOG_MEMORY("ggml_vk_preallocate_buffers(add_partials_size: " << ctx->prealloc_add_rms_partials << ")"); + // Resize buffer + if (ctx->prealloc_add_rms_partials != nullptr) { + ggml_vk_destroy_buffer(ctx->prealloc_add_rms_partials); + } + ctx->prealloc_add_rms_partials = ggml_vk_create_buffer_device(ctx->device, ctx->prealloc_size_add_rms_partials); + } +} + +static void ggml_vk_compute_forward(ggml_backend_vk_context* ctx, ggml_cgraph * cgraph, ggml_tensor* tensor, int tensor_idx, bool almost_ready); + +// Returns true if node has enqueued work into the queue, false otherwise +// If submit is true the current all operations queued so far are being submitted to Vulkan to overlap cmdlist creation and GPU execution. +static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgraph, int node_idx, ggml_tensor *node_begin, int node_idx_begin, bool last_node, bool almost_ready, bool submit){ + ggml_tensor * node = cgraph->nodes[node_idx]; + if (ggml_is_empty(node) || ggml_op_is_empty(node->op) || !node->buffer) { + return false; + } + if ((node->flags & GGML_TENSOR_FLAG_COMPUTE) == 0) { + return false; + } + + VK_LOG_DEBUG("ggml_vk_build_graph(" << node << ", " << ggml_op_name(node->op) << ")"); + ctx->semaphore_idx = 0; + + ggml_tensor * src0 = node->src[0]; + ggml_tensor * src1 = node->src[1]; + ggml_tensor * src2 = node->src[2]; + ggml_tensor * src3 = node->src[3]; + + if (node->op == GGML_OP_ADD) { + int next_node_idx = node_idx + 1 + ctx->num_additional_fused_ops; + if (next_node_idx < cgraph->n_nodes && + cgraph->nodes[next_node_idx]->op == GGML_OP_RMS_NORM && + cgraph->nodes[next_node_idx]->src[0] == cgraph->nodes[next_node_idx - 1] && + ggml_nrows(cgraph->nodes[next_node_idx]) == 1 && + ctx->device->add_rms_fusion) { + uint32_t size = ggml_vk_rms_partials_size(ctx, cgraph->nodes[node_idx]); + ctx->do_add_rms_partials_offset_calculation = true; + if (ctx->prealloc_size_add_rms_partials_offset + size <= ctx->prealloc_size_add_rms_partials) { + ctx->do_add_rms_partials = true; + } + } + } + + vk_context compute_ctx = ggml_vk_get_compute_ctx(ctx); + + { + // This logic detects dependencies between modes in the graph and calls ggml_vk_sync_buffers + // to synchronize them. This handles most "normal" synchronization when computing the graph, and when + // there is no auxiliary memory use, it shouldn't be necessary to call ggml_vk_sync_buffers + // outside of this logic. When a node uses one of the prealloc buffers for something like + // dequantization or split_k, additional synchronization is needed between those passes. + bool need_sync = false; + + // Check whether "node" requires synchronization. The node requires synchronization if it + // overlaps in memory with another unsynchronized node and at least one of them is a write. + // Destination nodes are checked against both the written/read lists. Source nodes are only + // checked against the written list. Two nodes overlap in memory if they come from the same + // buffer and the tensor or view ranges overlap. + auto const &overlaps_unsynced = [&](const ggml_tensor *node, const std::vector &unsynced_nodes) -> bool { + if (unsynced_nodes.size() == 0) { + return false; + } + auto n_base = vk_tensor_offset(node) + node->view_offs; + auto n_size = ggml_nbytes(node); + ggml_backend_vk_buffer_context * a_buf_ctx = (ggml_backend_vk_buffer_context *)node->buffer->context; + vk_buffer a_buf = a_buf_ctx->dev_buffer; + for (auto &other : unsynced_nodes) { + ggml_backend_vk_buffer_context * o_buf_ctx = (ggml_backend_vk_buffer_context *)other->buffer->context; + vk_buffer o_buf = o_buf_ctx->dev_buffer; + if (a_buf == o_buf) { + auto o_base = vk_tensor_offset(other) + other->view_offs; + auto o_size = ggml_nbytes(other); + + if ((o_base <= n_base && n_base < o_base + o_size) || + (n_base <= o_base && o_base < n_base + n_size)) { + return true; + } + } + } + return false; + }; + + // For all fused ops, check if the destination node or any of the source + // nodes require synchronization. + for (int32_t i = 0; i < ctx->num_additional_fused_ops + 1 && !need_sync; ++i) { + const ggml_tensor *cur_node = cgraph->nodes[node_idx + i]; + // If the node actually writes to memory, then check if it needs to sync + if (ctx->fused_ops_write_mask & (1 << i)) { + if (overlaps_unsynced(cur_node, ctx->unsynced_nodes_read) || overlaps_unsynced(cur_node, ctx->unsynced_nodes_written)) { + need_sync = true; + break; + } + } + for (uint32_t j = 0; j < GGML_MAX_SRC; ++j) { + if (!cur_node->src[j]) { + continue; + } + if (overlaps_unsynced(cur_node->src[j], ctx->unsynced_nodes_written)) { + need_sync = true; + break; + } + } + } + + if (need_sync) { + if (vk_enable_sync_logger) { + std::cerr << "sync" << std::endl; + } + ctx->unsynced_nodes_written.clear(); + ctx->unsynced_nodes_read.clear(); + ggml_vk_sync_buffers(ctx, compute_ctx); + + if (vk_perf_logger_enabled && vk_perf_logger_concurrent) { + ctx->query_node_idx[ctx->query_idx] = node_idx; + compute_ctx->s->buffer->buf.writeTimestamp(vk::PipelineStageFlagBits::eAllCommands, ctx->query_pool, ctx->query_idx++); + ggml_vk_sync_buffers(ctx, compute_ctx); + } + } + // Add all fused nodes to the unsynchronized lists. + for (int32_t i = 0; i < ctx->num_additional_fused_ops + 1; ++i) { + const ggml_tensor *cur_node = cgraph->nodes[node_idx + i]; + // Multiple outputs could be written, e.g. in topk_moe. Add them all to the list. + if (ctx->fused_ops_write_mask & (1 << i)) { + ctx->unsynced_nodes_written.push_back(cur_node); + } + for (uint32_t j = 0; j < GGML_MAX_SRC; ++j) { + if (!cur_node->src[j]) { + continue; + } + ctx->unsynced_nodes_read.push_back(cur_node->src[j]); + } + } + } + if (vk_enable_sync_logger) { + for (int i = 0; i < ctx->num_additional_fused_ops + 1; ++i) { + auto *n = cgraph->nodes[node_idx + i]; + std::cerr << node_idx + i << " " << ggml_op_name(n->op) << " " << n->name; + if (n->op == GGML_OP_GLU) { + std::cerr << " " << ggml_glu_op_name(ggml_get_glu_op(n)) << " " << (n->src[1] ? "split" : "single") << " "; + } + if (n->op == GGML_OP_ROPE) { + const int mode = ((const int32_t *) n->op_params)[2]; + std::cerr << " rope mode: " << mode; + } + std::cerr << std::endl; + } + } + + switch (node->op) { + case GGML_OP_REPEAT: + ggml_vk_repeat(ctx, compute_ctx, src0, node); + + break; + case GGML_OP_REPEAT_BACK: + ggml_vk_repeat_back(ctx, compute_ctx, src0, node); + + break; + case GGML_OP_ACC: + case GGML_OP_SET: + ggml_vk_acc(ctx, compute_ctx, src0, src1, node); + + break; + case GGML_OP_GET_ROWS: + ggml_vk_get_rows(ctx, compute_ctx, src0, src1, node); + + break; + case GGML_OP_GET_ROWS_BACK: + ggml_vk_get_rows_back(ctx, compute_ctx, src0, src1, node); + + break; + case GGML_OP_ADD: + if (ctx->num_additional_fused_ops) { + ggml_vk_multi_add(ctx, compute_ctx, cgraph, node_idx); + } else { + ggml_vk_add(ctx, compute_ctx, src0, src1, node); + } + break; + case GGML_OP_SUB: + ggml_vk_sub(ctx, compute_ctx, src0, src1, node); + + break; + case GGML_OP_MUL: + if (ctx->num_additional_fused_ops) { + ggml_vk_snake_dispatch_fused(ctx, compute_ctx, cgraph, node_idx); + } else { + ggml_vk_mul(ctx, compute_ctx, src0, src1, node); + } + + break; + case GGML_OP_DIV: + ggml_vk_div(ctx, compute_ctx, src0, src1, node); + + break; + case GGML_OP_ADD_ID: + ggml_vk_add_id(ctx, compute_ctx, src0, src1, src2, node); + + break; + case GGML_OP_CONCAT: + ggml_vk_concat(ctx, compute_ctx, src0, src1, node); + + break; + case GGML_OP_UPSCALE: + ggml_vk_upscale(ctx, compute_ctx, src0, node); + + break; + case GGML_OP_ADD1: + ggml_vk_add1(ctx, compute_ctx, src0, src1, node); + + break; + case GGML_OP_ARANGE: + ggml_vk_arange(ctx, compute_ctx, node); + + break; + case GGML_OP_FILL: + ggml_vk_fill(ctx, compute_ctx, node); + + break; + case GGML_OP_SCALE: + ggml_vk_scale(ctx, compute_ctx, src0, node); + + break; + case GGML_OP_SQR: + ggml_vk_sqr(ctx, compute_ctx, src0, node); + + break; + case GGML_OP_SQRT: + ggml_vk_sqrt(ctx, compute_ctx, src0, node); + + break; + case GGML_OP_SIN: + ggml_vk_sin(ctx, compute_ctx, src0, node); + + break; + case GGML_OP_COS: + ggml_vk_cos(ctx, compute_ctx, src0, node); + + break; + case GGML_OP_LOG: + ggml_vk_log(ctx, compute_ctx, src0, node); + + break; + case GGML_OP_TRI: + ggml_vk_tri(ctx, compute_ctx, src0, node); + + break; + case GGML_OP_DIAG: + ggml_vk_diag(ctx, compute_ctx, src0, node); + + break; + case GGML_OP_CLAMP: + ggml_vk_clamp(ctx, compute_ctx, src0, node); + + break; + case GGML_OP_PAD: + ggml_vk_pad(ctx, compute_ctx, src0, node); + + break; + case GGML_OP_ROLL: + ggml_vk_roll(ctx, compute_ctx, src0, node); + + break; + case GGML_OP_CPY: + case GGML_OP_CONT: + case GGML_OP_DUP: + ggml_vk_cpy(ctx, compute_ctx, src0, node); + + break; + case GGML_OP_SET_ROWS: + ggml_vk_set_rows(ctx, compute_ctx, src0, src1, node); + + break; + case GGML_OP_SILU_BACK: + ggml_vk_silu_back(ctx, compute_ctx, src0, src1, node); + + break; + case GGML_OP_NORM: + ggml_vk_norm(ctx, compute_ctx, src0, node); + + break; + case GGML_OP_GROUP_NORM: + ggml_vk_group_norm(ctx, compute_ctx, src0, node); + + break; + case GGML_OP_RMS_NORM: + ggml_vk_rms_norm(ctx, compute_ctx, cgraph, node_idx, (float *)node->op_params); + break; + case GGML_OP_RMS_NORM_BACK: + ggml_vk_rms_norm_back(ctx, compute_ctx, src0, src1, node); + + break; + case GGML_OP_L2_NORM: + ggml_vk_l2_norm(ctx, compute_ctx, src0, node); + + break; + case GGML_OP_UNARY: + if (ctx->fused_topk_moe_mode != TOPK_MOE_COUNT) { + ggml_vk_topk_moe(ctx, compute_ctx, cgraph, node_idx); + break; + } + + switch (ggml_get_unary_op(node)) { + case GGML_UNARY_OP_ELU: + case GGML_UNARY_OP_EXP: + case GGML_UNARY_OP_EXPM1: + case GGML_UNARY_OP_SILU: + case GGML_UNARY_OP_GELU: + case GGML_UNARY_OP_GELU_ERF: + case GGML_UNARY_OP_GELU_QUICK: + case GGML_UNARY_OP_RELU: + case GGML_UNARY_OP_NEG: + case GGML_UNARY_OP_TANH: + case GGML_UNARY_OP_SIGMOID: + case GGML_UNARY_OP_HARDSIGMOID: + case GGML_UNARY_OP_HARDSWISH: + case GGML_UNARY_OP_ABS: + case GGML_UNARY_OP_SOFTPLUS: + case GGML_UNARY_OP_STEP: + case GGML_UNARY_OP_ROUND: + case GGML_UNARY_OP_CEIL: + case GGML_UNARY_OP_FLOOR: + case GGML_UNARY_OP_TRUNC: + case GGML_UNARY_OP_SGN: + ggml_vk_unary(ctx, compute_ctx, src0, node); + break; + case GGML_UNARY_OP_XIELU: + ggml_vk_xielu(ctx, compute_ctx, src0, node); + break; + default: + return false; + } + break; + case GGML_OP_GLU: + switch (ggml_get_glu_op(node)) { + case GGML_GLU_OP_GEGLU: + case GGML_GLU_OP_REGLU: + case GGML_GLU_OP_SWIGLU: + case GGML_GLU_OP_SWIGLU_OAI: + case GGML_GLU_OP_GEGLU_ERF: + case GGML_GLU_OP_GEGLU_QUICK: + ggml_vk_glu(ctx, compute_ctx, src0, src1, node); + break; + default: + return false; + } + break; + case GGML_OP_DIAG_MASK_INF: + ggml_vk_diag_mask_inf(ctx, compute_ctx, src0, node); + + break; + case GGML_OP_SOFT_MAX: + if (ctx->fused_topk_moe_mode != TOPK_MOE_COUNT) { + ggml_vk_topk_moe(ctx, compute_ctx, cgraph, node_idx); + } else { + ggml_vk_soft_max(ctx, compute_ctx, src0, src1, src2, node); + } + + break; + case GGML_OP_SOFT_MAX_BACK: + ggml_vk_soft_max_back(ctx, compute_ctx, src0, src1, node); + + break; + case GGML_OP_ROPE: + ggml_vk_rope(ctx, compute_ctx, cgraph, node_idx, false); + + break; + case GGML_OP_ROPE_BACK: + ggml_vk_rope(ctx, compute_ctx, cgraph, node_idx, true); + + break; + case GGML_OP_ARGSORT: + if (ctx->fused_topk_moe_mode != TOPK_MOE_COUNT) { + ggml_vk_topk_moe(ctx, compute_ctx, cgraph, node_idx); + } else { + ggml_vk_argsort(ctx, compute_ctx, src0, node); + } + + break; + case GGML_OP_TOP_K: + ggml_vk_topk(ctx, compute_ctx, src0, node); + + break; + case GGML_OP_SUM: + ggml_vk_sum(ctx, compute_ctx, src0, node); + + break; + case GGML_OP_SUM_ROWS: + ggml_vk_sum_rows(ctx, compute_ctx, src0, node); + + break; + case GGML_OP_CUMSUM: + ggml_vk_cumsum(ctx, compute_ctx, src0, node); + + break; + case GGML_OP_MEAN: + ggml_vk_mean(ctx, compute_ctx, src0, node); + + break; + case GGML_OP_ARGMAX: + ggml_vk_argmax(ctx, compute_ctx, src0, node); + + break; + case GGML_OP_COUNT_EQUAL: + ggml_vk_count_equal(ctx, compute_ctx, src0, src1, node); + + break; + case GGML_OP_SOLVE_TRI: + ggml_vk_solve_tri(ctx, compute_ctx, src0, src1, node); + + break; + case GGML_OP_IM2COL: + ggml_vk_im2col(ctx, compute_ctx, src0, src1, node); + + break; + case GGML_OP_IM2COL_3D: + ggml_vk_im2col_3d(ctx, compute_ctx, src0, src1, node); + + break; + case GGML_OP_TIMESTEP_EMBEDDING: + ggml_vk_timestep_embedding(ctx, compute_ctx, src0, node); + + break; + case GGML_OP_COL2IM_1D: + ggml_vk_col2im_1d(ctx, compute_ctx, src0, node); + + break; + case GGML_OP_CONV_TRANSPOSE_1D: + ggml_vk_conv_transpose_1d(ctx, compute_ctx, src0, src1, node); + + break; + case GGML_OP_POOL_2D: + ggml_vk_pool_2d(ctx, compute_ctx, src0, node); + + break; + case GGML_OP_CONV_2D: + case GGML_OP_CONV_TRANSPOSE_2D: + ggml_vk_conv_2d(ctx, compute_ctx, src0, src1, node); + + break; + case GGML_OP_CONV_3D: + ggml_vk_conv_3d(ctx, compute_ctx, src0, src1, node); + + break; + case GGML_OP_CONV_2D_DW: + ggml_vk_conv_2d_dw(ctx, compute_ctx, src0, src1, node); + + break; + case GGML_OP_LEAKY_RELU: + ggml_vk_leaky_relu(ctx, compute_ctx, src0, node); + + break; + case GGML_OP_MUL_MAT: + ggml_vk_mul_mat(ctx, compute_ctx, cgraph, node_idx); + + break; + case GGML_OP_MUL_MAT_ID: + ggml_vk_mul_mat_id(ctx, compute_ctx, cgraph, node_idx); + + break; + + case GGML_OP_FLASH_ATTN_EXT: + ggml_vk_flash_attn(ctx, compute_ctx, src0, src1, src2, src3, node->src[4], node); + + break; + + case GGML_OP_RWKV_WKV6: + ggml_vk_rwkv_wkv6(ctx, compute_ctx, node); + + break; + + case GGML_OP_RWKV_WKV7: + ggml_vk_rwkv_wkv7(ctx, compute_ctx, node); + + break; + + case GGML_OP_GATED_DELTA_NET: + ggml_vk_gated_delta_net(ctx, compute_ctx, node); + + break; + + case GGML_OP_SSM_SCAN: + ggml_vk_ssm_scan(ctx, compute_ctx, node); + + break; + + case GGML_OP_SSM_CONV: + ggml_vk_ssm_conv(ctx, compute_ctx, cgraph, node_idx); + + break; + + case GGML_OP_OPT_STEP_ADAMW: + ggml_vk_opt_step_adamw(ctx, compute_ctx, node); + + break; + + case GGML_OP_OPT_STEP_SGD: + ggml_vk_opt_step_sgd(ctx, compute_ctx, src0, src1, src2, node); + + break; + default: + return false; + } + + ctx->tensor_ctxs[node_idx] = compute_ctx; + +#if defined(GGML_VULKAN_CHECK_RESULTS) + // Force context reset on each node so that each tensor ends up in its own context + // and can be run and compared to its CPU equivalent separately + last_node = true; +#endif + + if (submit || last_node) { + ggml_vk_ctx_end(compute_ctx); + + // TODO probably it'd be better to pass a exit_node flag to ggml_vk_compute_forward + if (last_node) { + compute_ctx->exit_tensor_idx = node_idx_begin; + } + else { + compute_ctx->exit_tensor_idx = -1; + } + + ctx->compute_ctx.reset(); + + ggml_vk_compute_forward(ctx, cgraph, node_begin, node_idx_begin, almost_ready); + } + return true; +} + +static void ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_cgraph * cgraph, ggml_tensor * tensor, int tensor_idx, bool almost_ready = false) { + GGML_UNUSED(cgraph); + GGML_UNUSED(tensor); + + VK_LOG_DEBUG("ggml_vk_compute_forward(" << tensor << ", name=" << tensor->name << ", op=" << ggml_op_name(tensor->op) << ", type=" << tensor->type << ", ne0=" << tensor->ne[0] << ", ne1=" << tensor->ne[1] << ", ne2=" << tensor->ne[2] << ", ne3=" << tensor->ne[3] << ", nb0=" << tensor->nb[0] << ", nb1=" << tensor->nb[1] << ", nb2=" << tensor->nb[2] << ", nb3=" << tensor->nb[3] << ", view_src=" << tensor->view_src << ", view_offs=" << tensor->view_offs << ")"); + + vk_context subctx = ctx->tensor_ctxs[tensor_idx].lock(); + + // Only run if ctx hasn't been submitted yet + if (!subctx->seqs.empty()) { +#ifdef GGML_VULKAN_CHECK_RESULTS + ggml_vk_check_results_0(ctx, cgraph, tensor_idx); +#endif + + // Do staging buffer copies + for (auto& cpy : subctx->in_memcpys) { + memcpy(cpy.dst, cpy.src, cpy.n); + } + + for (auto& mset : subctx->memsets) { + memset(mset.dst, mset.val, mset.n); + } + + if (almost_ready && !ctx->almost_ready_fence_pending) { + ggml_vk_submit(subctx, ctx->almost_ready_fence); + ctx->almost_ready_fence_pending = true; + } else { + ggml_vk_submit(subctx, {}); + } + ctx->submit_pending = true; + +#ifdef GGML_VULKAN_CHECK_RESULTS + ggml_vk_synchronize(ctx); + ggml_vk_check_results_1(ctx, cgraph, tensor_idx); +#endif + } + + if (tensor_idx == subctx->exit_tensor_idx) { + // Do staging buffer copies + for (auto& cpy : subctx->out_memcpys) { + memcpy(cpy.dst, cpy.src, cpy.n); + } + subctx->in_memcpys.clear(); + subctx->out_memcpys.clear(); + subctx->memsets.clear(); + } +} + +// Clean up after graph processing is done +static void ggml_vk_graph_cleanup(ggml_backend_vk_context * ctx) { + VK_LOG_DEBUG("ggml_vk_graph_cleanup()"); + ctx->prealloc_y_last_pipeline_used = {}; + ctx->prealloc_y_last_tensor_used = nullptr; + ctx->prealloc_y_last_decode_vector_staging = false; + + ctx->unsynced_nodes_written.clear(); + ctx->unsynced_nodes_read.clear(); + ctx->prealloc_x_need_sync = ctx->prealloc_y_need_sync = ctx->prealloc_split_k_need_sync = false; + + ggml_vk_command_pool_cleanup(ctx->device, ctx->compute_cmd_pool); + if (ctx->device->async_use_transfer_queue) { + ggml_vk_command_pool_cleanup(ctx->device, ctx->transfer_cmd_pool); + } + + for (size_t i = 0; i < ctx->gc.semaphores.size(); i++) { + ctx->device->device.destroySemaphore({ ctx->gc.semaphores[i].s }); + } + ctx->gc.semaphores.clear(); + + for (size_t i = 0; i < ctx->gc.tl_semaphores.size(); i++) { + ctx->device->device.destroySemaphore({ ctx->gc.tl_semaphores[i].s }); + } + ctx->gc.tl_semaphores.clear(); + ctx->semaphore_idx = 0; + + ctx->event_idx = 0; + + for (auto& event : ctx->gc.events) { + ctx->device->device.resetEvent(event); + } + + ctx->tensor_ctxs.clear(); + ctx->gc.contexts.clear(); + ctx->pipeline_descriptor_set_requirements = 0; + ctx->descriptor_set_idx = 0; +} + +// Clean up on backend free +static void ggml_vk_cleanup(ggml_backend_vk_context * ctx) { + VK_LOG_DEBUG("ggml_vk_cleanup(" << ctx->name << ")"); + // discard any unsubmitted command buffers + ctx->compute_ctx.reset(); + // wait for any pending command buffers to finish + ggml_vk_synchronize(ctx); + + ggml_vk_graph_cleanup(ctx); + + ggml_vk_destroy_buffer(ctx->prealloc_x); + ggml_vk_destroy_buffer(ctx->prealloc_y); + ggml_vk_destroy_buffer(ctx->prealloc_split_k); + ggml_vk_destroy_buffer(ctx->prealloc_add_rms_partials); + ggml_vk_destroy_buffer(ctx->sync_staging); + + ctx->prealloc_y_last_pipeline_used = nullptr; + ctx->prealloc_y_last_tensor_used = nullptr; + ctx->prealloc_y_last_decode_vector_staging = false; + + ctx->prealloc_size_x = 0; + ctx->prealloc_size_y = 0; + ctx->prealloc_size_split_k = 0; + + for (auto& event : ctx->gc.events) { + ctx->device->device.destroyEvent(event); + } + ctx->gc.events.clear(); + + ctx->device->device.destroyFence(ctx->fence); + ctx->device->device.destroyFence(ctx->almost_ready_fence); + + for (auto& pool : ctx->descriptor_pools) { + ctx->device->device.destroyDescriptorPool(pool); + } + ctx->descriptor_pools.clear(); + ctx->descriptor_sets.clear(); + + ctx->compute_cmd_pool.destroy(ctx->device->device); + if (ctx->device->async_use_transfer_queue) { + ctx->device->device.destroySemaphore(ctx->transfer_semaphore.s); + + ctx->transfer_cmd_pool.destroy(ctx->device->device); + } + if (vk_perf_logger_enabled) { + ctx->perf_logger->print_timings(true); + } +} + +static int ggml_vk_get_device_count() { + ggml_vk_instance_init(); + + return vk_instance.device_indices.size(); +} + +static void ggml_vk_get_device_description(int device, char * description, size_t description_size) { + ggml_vk_instance_init(); + + std::vector devices = vk_instance.instance.enumeratePhysicalDevices(); + + vk::PhysicalDeviceProperties props; + devices[device].getProperties(&props); + + snprintf(description, description_size, "%s", props.deviceName.data()); +} + +// backend interface + +#define UNUSED GGML_UNUSED + +// device backend + +static bool ggml_backend_buffer_is_vk(ggml_backend_buffer_t buffer) { + return buffer->buft->iface.get_name == ggml_backend_vk_buffer_type_name; +} + +static void ggml_backend_vk_buffer_free_buffer(ggml_backend_buffer_t buffer) { + VK_LOG_MEMORY("ggml_backend_vk_buffer_free_buffer()"); + ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context; + ggml_vk_destroy_buffer(ctx->dev_buffer); + delete ctx; +} + +static void * ggml_backend_vk_buffer_get_base(ggml_backend_buffer_t buffer) { + return vk_ptr_base; + + UNUSED(buffer); +} + +static enum ggml_status ggml_backend_vk_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) { + VK_LOG_DEBUG("ggml_backend_vk_buffer_init_tensor(" << buffer << " (" << buffer->context << "), " << tensor << ")"); + if (tensor->view_src != nullptr) { + GGML_ASSERT(tensor->view_src->buffer->buft == buffer->buft); + } + return GGML_STATUS_SUCCESS; +} + +static void ggml_backend_vk_buffer_memset_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) { + VK_LOG_DEBUG("ggml_backend_vk_buffer_memset_tensor(" << buffer << ", " << tensor << ", " << value << ", " << offset << ", " << size << ")"); + ggml_backend_vk_buffer_context * buf_ctx = (ggml_backend_vk_buffer_context *)buffer->context; + vk_buffer buf = buf_ctx->dev_buffer; + + if (size == 0) { + return; + } + + uint32_t val32 = (uint32_t)value * 0x01010101; + ggml_vk_buffer_memset(buf, vk_tensor_offset(tensor) + tensor->view_offs + offset, val32, size); +} + +static void ggml_backend_vk_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { + VK_LOG_DEBUG("ggml_backend_vk_buffer_set_tensor(" << buffer << ", " << tensor << ", " << data << ", " << offset << ", " << size << ")"); + ggml_backend_vk_buffer_context * buf_ctx = (ggml_backend_vk_buffer_context *)buffer->context; + vk_buffer buf = buf_ctx->dev_buffer; + + if (size == 0) { + return; + } + + ggml_vk_buffer_write(buf, vk_tensor_offset(tensor) + tensor->view_offs + offset, data, size); +} + +static void ggml_backend_vk_buffer_set_tensor_2d(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, + size_t size, size_t n_copies, size_t stride_tensor, size_t stride_data) { + VK_LOG_DEBUG("ggml_backend_vk_buffer_set_tensor_2d(" << buffer << ", " << tensor << ", " << data << ", " << offset << ", " << size << ", " << + n_copies << ", " << stride_tensor << ", " << stride_data << ")"); + ggml_backend_vk_buffer_context * buf_ctx = (ggml_backend_vk_buffer_context *)buffer->context; + vk_buffer buf = buf_ctx->dev_buffer; + + if (size == 0) { + return; + } + + ggml_vk_buffer_write_2d(buf, vk_tensor_offset(tensor) + tensor->view_offs + offset, data, stride_data, stride_tensor, size, n_copies); +} + +static void ggml_backend_vk_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { + VK_LOG_DEBUG("ggml_backend_vk_buffer_get_tensor(" << buffer << ", " << tensor << ", " << data << ", " << offset << ", " << size << ")"); + ggml_backend_vk_buffer_context * buf_ctx = (ggml_backend_vk_buffer_context *)buffer->context; + + if (size == 0) { + return; + } + + vk_buffer buf = buf_ctx->dev_buffer; + + ggml_vk_buffer_read(buf, vk_tensor_offset(tensor) + tensor->view_offs + offset, data, size); +} + +static void ggml_backend_vk_buffer_get_tensor_2d(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, + size_t size, size_t n_copies, size_t stride_tensor, size_t stride_data) { + VK_LOG_DEBUG("ggml_backend_vk_buffer_get_tensor_2d(" << buffer << ", " << tensor << ", " << data << ", " << offset << ", " << size << ", " << + n_copies << ", " << stride_tensor << ", " << stride_data << ")"); + ggml_backend_vk_buffer_context * buf_ctx = (ggml_backend_vk_buffer_context *)buffer->context; + + if (size == 0) { + return; + } + + vk_buffer buf = buf_ctx->dev_buffer; + + ggml_vk_buffer_read_2d(buf, vk_tensor_offset(tensor) + tensor->view_offs + offset, data, stride_tensor, stride_data, size, n_copies); +} + +static bool ggml_backend_vk_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * src, ggml_tensor * dst) { + if (ggml_nbytes(src) == 0) { + return true; + } + + if (ggml_backend_buffer_is_vk(src->buffer)) { + ggml_backend_vk_buffer_context * src_buf_ctx = (ggml_backend_vk_buffer_context *)src->buffer->context; + ggml_backend_vk_buffer_context * dst_buf_ctx = (ggml_backend_vk_buffer_context *)dst->buffer->context; + + vk_buffer src_buf = src_buf_ctx->dev_buffer; + vk_buffer dst_buf = dst_buf_ctx->dev_buffer; + + ggml_vk_buffer_copy(dst_buf, vk_tensor_offset(dst) + dst->view_offs, src_buf, vk_tensor_offset(src) + src->view_offs, ggml_nbytes(src)); + + return true; + } + return false; + + UNUSED(buffer); +} + +static void ggml_backend_vk_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { + ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context; + + ggml_vk_buffer_memset(ctx->dev_buffer, 0, value, buffer->size); +} + +static ggml_backend_buffer_i ggml_backend_vk_buffer_interface = { + /* .free_buffer = */ ggml_backend_vk_buffer_free_buffer, + /* .get_base = */ ggml_backend_vk_buffer_get_base, + /* .init_tensor = */ ggml_backend_vk_buffer_init_tensor, + /* .memset_tensor = */ ggml_backend_vk_buffer_memset_tensor, + /* .set_tensor = */ ggml_backend_vk_buffer_set_tensor, + /* .get_tensor = */ ggml_backend_vk_buffer_get_tensor, + /* .set_tensor_2d = */ ggml_backend_vk_buffer_set_tensor_2d, + /* .get_tensor_2d = */ ggml_backend_vk_buffer_get_tensor_2d, + /* .cpy_tensor = */ ggml_backend_vk_buffer_cpy_tensor, + /* .clear = */ ggml_backend_vk_buffer_clear, + /* .reset = */ NULL, +}; + +// vk buffer type +static const char * ggml_backend_vk_buffer_type_name(ggml_backend_buffer_type_t buft) { + ggml_backend_vk_buffer_type_context * ctx = (ggml_backend_vk_buffer_type_context *)buft->context; + + return ctx->name.c_str(); +} + +static ggml_backend_buffer_t ggml_backend_vk_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { + VK_LOG_MEMORY("ggml_backend_vk_buffer_type_alloc_buffer(" << size << ")"); + ggml_backend_vk_buffer_type_context * ctx = (ggml_backend_vk_buffer_type_context *) buft->context; + + vk_buffer dev_buffer = nullptr; + try { + dev_buffer = ggml_vk_create_buffer_device(ctx->device, size); + } catch (const vk::SystemError& e) { + return nullptr; + } + + ggml_backend_vk_buffer_context * bufctx = new ggml_backend_vk_buffer_context(ctx->device, std::move(dev_buffer), ctx->name); + + return ggml_backend_buffer_init(buft, ggml_backend_vk_buffer_interface, bufctx, size); +} + +static size_t ggml_backend_vk_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { + ggml_backend_vk_buffer_type_context * ctx = (ggml_backend_vk_buffer_type_context *) buft->context; + return ctx->device->properties.limits.minStorageBufferOffsetAlignment; +} + +static size_t ggml_backend_vk_buffer_type_get_max_size(ggml_backend_buffer_type_t buft) { + ggml_backend_vk_buffer_type_context * ctx = (ggml_backend_vk_buffer_type_context *) buft->context; + return ctx->device->suballocation_block_size; +} + +static size_t ggml_backend_vk_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) { + return ggml_nbytes(tensor); + + UNUSED(buft); +} + +ggml_backend_buffer_type_t ggml_backend_vk_buffer_type(size_t dev_num) { + ggml_vk_instance_init(); + + VK_LOG_DEBUG("ggml_backend_vk_buffer_type(" << dev_num << ")"); + + vk_device dev = ggml_vk_get_device(dev_num); + + return &dev->buffer_type; +} + +// host buffer type + +static const char * ggml_backend_vk_host_buffer_type_name(ggml_backend_buffer_type_t buft) { + return GGML_VK_NAME "_Host"; + + UNUSED(buft); +} + +static void ggml_backend_vk_host_buffer_free_buffer(ggml_backend_buffer_t buffer) { + VK_LOG_MEMORY("ggml_backend_vk_host_buffer_free_buffer()"); + ggml_vk_host_free(vk_instance.devices[0], buffer->context); +} + +static ggml_backend_buffer_t ggml_backend_vk_host_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { + VK_LOG_MEMORY("ggml_backend_vk_host_buffer_type_alloc_buffer(" << size << ")"); + + size += 32; // Behave like the CPU buffer type + void * ptr = nullptr; + try { + ptr = ggml_vk_host_malloc(vk_instance.devices[0], size); + } catch (vk::SystemError& e) { + GGML_LOG_WARN("ggml_vulkan: Failed to allocate pinned memory (%s)\n", e.what()); + // fallback to cpu buffer + return ggml_backend_buft_alloc_buffer(ggml_backend_cpu_buffer_type(), size); + } + + ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size); + buffer->buft = buft; + buffer->iface.free_buffer = ggml_backend_vk_host_buffer_free_buffer; + + return buffer; + + UNUSED(buft); +} + +static size_t ggml_backend_vk_host_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { + return vk_instance.devices[0]->properties.limits.minMemoryMapAlignment; + + UNUSED(buft); +} + +static size_t ggml_backend_vk_host_buffer_type_get_max_size(ggml_backend_buffer_type_t buft) { + return vk_instance.devices[0]->suballocation_block_size; + + UNUSED(buft); +} + +// Should be changed to return device-specific host buffer type +// but that probably requires changes in llama.cpp +ggml_backend_buffer_type_t ggml_backend_vk_host_buffer_type() { + static struct ggml_backend_buffer_type ggml_backend_vk_buffer_type_host = { + /* .iface = */ { + /* .get_name = */ ggml_backend_vk_host_buffer_type_name, + /* .alloc_buffer = */ ggml_backend_vk_host_buffer_type_alloc_buffer, + /* .get_alignment = */ ggml_backend_vk_host_buffer_type_get_alignment, + /* .get_max_size = */ ggml_backend_vk_host_buffer_type_get_max_size, + /* .get_alloc_size = */ ggml_backend_cpu_buffer_type()->iface.get_alloc_size, + /* .is_host = */ ggml_backend_cpu_buffer_type()->iface.is_host, + }, + /* .device = */ ggml_backend_reg_dev_get(ggml_backend_vk_reg(), 0), + /* .context = */ nullptr, + }; + + // Make sure device 0 is initialized + ggml_vk_instance_init(); + ggml_vk_get_device(0); + + return &ggml_backend_vk_buffer_type_host; +} + + +// backend + +static const char * ggml_backend_vk_name(ggml_backend_t backend) { + ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; + + return ctx->name.c_str(); +} + +static void ggml_backend_vk_free(ggml_backend_t backend) { + ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; + VK_LOG_DEBUG("ggml_backend_vk_free(" << ctx->name << ")"); + + ggml_vk_cleanup(ctx); + + delete ctx; + delete backend; +} + +static ggml_backend_buffer_type_t ggml_backend_vk_get_default_buffer_type(ggml_backend_t backend) { + ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; + + return &ctx->device->buffer_type; +} + +static void ggml_backend_vk_set_tensor_2d_async(ggml_backend_t backend, ggml_tensor * tensor, const void * data, size_t offset, + size_t size, size_t n_copies, size_t stride_tensor, size_t stride_data) { + VK_LOG_DEBUG("ggml_backend_vk_set_tensor_2d_async(" << size << ", " << n_copies << ")"); + ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; + GGML_ASSERT((tensor->buffer->buft == ggml_backend_vk_get_default_buffer_type(backend) || tensor->buffer->buft == ggml_backend_vk_host_buffer_type()) && "unsupported buffer type"); + + if (size == 0) { + return; + } + + ggml_backend_vk_buffer_context * buf_ctx = (ggml_backend_vk_buffer_context *)tensor->buffer->context; + + vk_context cpy_ctx; + + if (ctx->device->async_use_transfer_queue) { + if (ctx->transfer_ctx.expired()) { + cpy_ctx = ggml_vk_create_context(ctx, ctx->transfer_cmd_pool); + ctx->transfer_ctx = cpy_ctx; + ggml_vk_ctx_begin(ctx->device, cpy_ctx); + } else { + cpy_ctx = ctx->transfer_ctx.lock(); + } + } else { + cpy_ctx = ggml_vk_get_compute_ctx(ctx); + } + + vk_buffer buf = buf_ctx->dev_buffer; + + auto dst_offset = vk_tensor_offset(tensor) + tensor->view_offs + offset; + + bool ret = ggml_vk_buffer_write_2d_async(cpy_ctx, buf, dst_offset, data, stride_data, stride_tensor, size, n_copies); + + if (!ret) { + const size_t staging_size = size * n_copies; + ggml_vk_ensure_sync_staging_buffer(ctx, staging_size); + ggml_vk_sync_buffers(nullptr, cpy_ctx); + + std::vector slices(1); + if (size == stride_tensor) { + slices[0].srcOffset = 0; + slices[0].dstOffset = dst_offset; + slices[0].size = staging_size; + } else { + slices.resize(n_copies); + for (size_t i = 0; i < n_copies; i++) { + slices[i].srcOffset = i * size; + slices[i].dstOffset = dst_offset + i * stride_tensor; + slices[i].size = size; + } + } + + cpy_ctx->s->buffer->buf.copyBuffer(ctx->sync_staging->buffer, buf->buffer, slices); + + if (size == stride_data) { + deferred_memcpy(ctx->sync_staging->ptr, data, staging_size, &cpy_ctx->in_memcpys); + } else { + for (size_t i = 0; i < n_copies; i++) { + deferred_memcpy((uint8_t *)ctx->sync_staging->ptr + i * size, (const uint8_t *)data + i * stride_data, size, &cpy_ctx->in_memcpys); + } + } + ggml_vk_synchronize(ctx); + } +} + +static void ggml_backend_vk_set_tensor_async(ggml_backend_t backend, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { + VK_LOG_DEBUG("ggml_backend_vk_set_tensor_async(" << size << ")"); + ggml_backend_vk_set_tensor_2d_async(backend, tensor, data, offset, size, 1, size, size); +} + +static void ggml_backend_vk_get_tensor_2d_async(ggml_backend_t backend, const ggml_tensor * tensor, void * data, size_t offset, + size_t size, size_t n_copies, size_t stride_tensor, size_t stride_data) { + VK_LOG_DEBUG("ggml_backend_vk_get_tensor_2d_async(" << size << ", " << n_copies << ")"); + ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; + GGML_ASSERT((tensor->buffer->buft == ggml_backend_vk_get_default_buffer_type(backend) || tensor->buffer->buft == ggml_backend_vk_host_buffer_type()) && "unsupported buffer type"); + + if (size == 0) { + return; + } + + ggml_backend_vk_buffer_context * buf_ctx = (ggml_backend_vk_buffer_context *)tensor->buffer->context; + + vk_context compute_ctx = ggml_vk_get_compute_ctx(ctx); + + vk_buffer buf = buf_ctx->dev_buffer; + + auto src_offset = vk_tensor_offset(tensor) + tensor->view_offs + offset; + bool ret = ggml_vk_buffer_read_2d_async(compute_ctx, buf, src_offset, data, stride_tensor, stride_data, size, n_copies); + + if (!ret) { + const size_t staging_size = size * n_copies; + ggml_vk_ensure_sync_staging_buffer(ctx, staging_size); + ggml_vk_sync_buffers(nullptr, compute_ctx); + + std::vector slices(1); + if (size == stride_tensor) { + slices[0].srcOffset = src_offset; + slices[0].dstOffset = 0; + slices[0].size = staging_size; + } else { + slices.resize(n_copies); + for (size_t i = 0; i < n_copies; i++) { + slices[i].srcOffset = src_offset + i * stride_tensor; + slices[i].dstOffset = i * size; + slices[i].size = size; + } + } + + compute_ctx->s->buffer->buf.copyBuffer(buf->buffer, ctx->sync_staging->buffer, slices); + + if (size == stride_data) { + deferred_memcpy(data, ctx->sync_staging->ptr, staging_size, &compute_ctx->out_memcpys); + } else { + for (size_t i = 0; i < n_copies; i++) { + deferred_memcpy((uint8_t *)data + i * stride_data, (const uint8_t *)ctx->sync_staging->ptr + i * size, size, &compute_ctx->out_memcpys); + } + } + ggml_vk_synchronize(ctx); + } +} + +static void ggml_backend_vk_get_tensor_async(ggml_backend_t backend, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { + VK_LOG_DEBUG("ggml_backend_vk_get_tensor_async(" << size << ")"); + ggml_backend_vk_get_tensor_2d_async(backend, tensor, data, offset, size, 1, size, size); +} + +static bool ggml_backend_vk_cpy_tensor_async(ggml_backend_t backend_src, ggml_backend_t backend_dst, const ggml_tensor * src, ggml_tensor * dst) { + VK_LOG_DEBUG("ggml_backend_vk_cpy_tensor_async(" << src << " -> " << dst << ", size=" << ggml_nbytes(src) << ")"); + ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend_dst->context; + + // Skip zero-size tensors + if (ggml_nbytes(src) == 0) { + return true; + } + + if (dst->buffer->buft != ggml_backend_vk_get_default_buffer_type(backend_dst)) { + return false; + } + + ggml_backend_vk_buffer_context * dst_buf_ctx = (ggml_backend_vk_buffer_context *)dst->buffer->context; + vk_buffer dst_buf = dst_buf_ctx->dev_buffer; + + if (ggml_backend_buffer_is_vk(src->buffer)) { + ggml_backend_vk_buffer_context * src_buf_ctx = (ggml_backend_vk_buffer_context *)src->buffer->context; + + // Async copy only works within the same device + if (src_buf_ctx->dev_buffer->device != dst_buf->device) { + return false; + } + + vk_context compute_ctx = ggml_vk_get_compute_ctx(ctx); + + ggml_vk_buffer_copy_async(compute_ctx, dst_buf, vk_tensor_offset(dst) + dst->view_offs, + src_buf_ctx->dev_buffer, vk_tensor_offset(src) + src->view_offs, + ggml_nbytes(src)); + return true; + } + + if (ggml_backend_buffer_is_host(src->buffer)) { + vk_buffer pinned_buf = nullptr; + size_t pinned_offset = 0; + ggml_vk_host_get(ctx->device, src->data, pinned_buf, pinned_offset); + if (pinned_buf == nullptr) { + return false; + } + + vk_context cpy_ctx; + if (ctx->device->async_use_transfer_queue) { + if (ctx->transfer_ctx.expired()) { + cpy_ctx = ggml_vk_create_context(ctx, ctx->transfer_cmd_pool); + ctx->transfer_ctx = cpy_ctx; + ggml_vk_ctx_begin(ctx->device, cpy_ctx); + } else { + cpy_ctx = ctx->transfer_ctx.lock(); + } + } else { + cpy_ctx = ggml_vk_get_compute_ctx(ctx); + } + + return ggml_vk_buffer_write_async(cpy_ctx, dst_buf, + vk_tensor_offset(dst) + dst->view_offs, + src->data, ggml_nbytes(src)); + } + + GGML_UNUSED(backend_src); + return false; +} + +static void ggml_vk_synchronize(ggml_backend_vk_context * ctx) { + VK_LOG_DEBUG("ggml_vk_synchronize()"); + + bool do_transfer = !ctx->compute_ctx.expired(); + + if (ggml_vk_submit_transfer_ctx(ctx)) { + ctx->submit_pending = true; + } + + vk_context compute_ctx; + vk_command_buffer* cmd_buf = nullptr; + if (do_transfer) { + compute_ctx = ctx->compute_ctx.lock(); + if (compute_ctx->s) { + cmd_buf = compute_ctx->s->buffer; + } + + ggml_vk_ctx_end(compute_ctx); + + for (auto& cpy : compute_ctx->in_memcpys) { + memcpy(cpy.dst, cpy.src, cpy.n); + } + + ggml_vk_submit(compute_ctx, {}); + ctx->submit_pending = true; + } + + if (ctx->submit_pending) { + if (ctx->device->async_use_transfer_queue && ctx->transfer_semaphore_last_submitted < ctx->transfer_semaphore.value) { + vk::TimelineSemaphoreSubmitInfo tl_info{ + 1, &ctx->transfer_semaphore.value, + 0, nullptr, + }; + vk::PipelineStageFlags stage = ctx->device->transfer_queue.stage_flags; + vk::SubmitInfo si{ + 1, &ctx->transfer_semaphore.s, &stage, + 0, nullptr, + 0, nullptr, + }; + si.setPNext(&tl_info); + std::lock_guard guard(queue_mutex); + ctx->device->compute_queue.queue.submit({ si }, ctx->fence); + ctx->transfer_semaphore_last_submitted = ctx->transfer_semaphore.value; + } else { + std::lock_guard guard(queue_mutex); + ctx->device->compute_queue.queue.submit({}, ctx->fence); + } + ggml_vk_wait_for_fence(ctx); + ctx->submit_pending = false; + if (cmd_buf) { + cmd_buf->in_use = false; + cmd_buf->buf.reset(); + } + } + + if (do_transfer) { + for (auto& cpy : compute_ctx->out_memcpys) { + memcpy(cpy.dst, cpy.src, cpy.n); + } + ctx->compute_ctx.reset(); + } +} + +static void ggml_backend_vk_synchronize(ggml_backend_t backend) { + VK_LOG_DEBUG("ggml_backend_vk_synchronize()"); + ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; + + ggml_vk_synchronize(ctx); + + ggml_vk_graph_cleanup(ctx); +} + +static bool ggml_vk_is_empty(ggml_tensor * node) { + return ggml_is_empty(node) || node->op == GGML_OP_NONE || node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE; +} + +static bool ggml_vk_can_fuse(const ggml_backend_vk_context * ctx, const struct ggml_cgraph * cgraph, int node_idx, std::initializer_list ops) { + if (!ggml_can_fuse(cgraph, node_idx, ops)) { + return false; + } + + if (ops.size() == 2 && ops.begin()[0] == GGML_OP_RMS_NORM && ops.begin()[1] == GGML_OP_MUL) { + // additional constraints specific to this fusion + const ggml_tensor *rms_norm = cgraph->nodes[node_idx]; + const ggml_tensor *mul = cgraph->nodes[node_idx + 1]; + + GGML_ASSERT(rms_norm->src[0]->type == GGML_TYPE_F32); + GGML_ASSERT(rms_norm->type == GGML_TYPE_F32); + // rms_norm only supports f32 + if (mul->src[0]->type != GGML_TYPE_F32 || + mul->src[1]->type != GGML_TYPE_F32 || + mul->type != GGML_TYPE_F32) { + return false; + } + // if rms_norm is the B operand, then we don't handle broadcast + if (rms_norm == mul->src[1] && + !ggml_are_same_shape(mul->src[0], rms_norm)) { + return false; + } + // rms_norm shader assumes contiguous rows + if (!ggml_is_contiguous_rows(mul->src[0]) || !ggml_is_contiguous_rows(mul->src[1])) { + return false; + } + } + auto const &mm_add_ok = [&](const ggml_tensor *mul, const ggml_tensor *add) { + const ggml_tensor *bias = add->src[0] == mul ? add->src[1] : add->src[0]; + + // mat-vec only + if (ggml_nrows(mul) != 1) { + return false; + } + // shaders assume the types match + if (mul->type != bias->type) { + return false; + } + // shaders reuse the D shape for bias + if (!ggml_are_same_shape(mul, bias) || + !ggml_are_same_stride(mul, bias)) { + return false; + } + // unaligned bias isn't handled + if (get_misalign_bytes(ctx, bias) != 0) { + return false; + } + return true; + }; + + if ((ops.size() == 2 || ops.size() == 3) && ops.begin()[0] == GGML_OP_MUL_MAT && ops.begin()[1] == GGML_OP_ADD) { + // additional constraints specific to this fusion + const ggml_tensor *mul = cgraph->nodes[node_idx]; + const ggml_tensor *add = cgraph->nodes[node_idx + 1]; + + if (!mm_add_ok(mul, add)) { + return false; + } + if (ops.size() == 3) { + if (ops.begin()[2] != GGML_OP_ADD) { + return false; + } + if (!mm_add_ok(add, cgraph->nodes[node_idx + 2])) { + return false; + } + } + } + + auto const &mmid_mul_ok = [&](const ggml_tensor *mmid, const ggml_tensor *mul) { + const ggml_tensor *scale = mul->src[1]; + + if (mmid != mul->src[0]) { + return false; + } + // mat-vec only + if (!ggml_vk_use_mul_mat_vec_id(cgraph, node_idx)) { + return false; + } + // shaders assume the types match + if (mmid->type != scale->type) { + return false; + } + // shaders assume the bias is contiguous + if (!ggml_is_contiguous(scale)) { + return false; + } + // unaligned bias isn't handled + if (get_misalign_bytes(ctx, scale) != 0) { + return false; + } + // shader only indexes by expert index + if (scale->ne[0] != 1 || + scale->ne[1] != mul->ne[1] || + scale->ne[2] != 1 || + scale->ne[3] != 1) { + return false; + } + return true; + }; + + if ((ops.size() == 2 || ops.size() == 3) && ops.begin()[0] == GGML_OP_MUL_MAT_ID && ops.begin()[1] == GGML_OP_ADD_ID) { + // additional constraints specific to this fusion + const ggml_tensor *mul = cgraph->nodes[node_idx]; + const ggml_tensor *add = cgraph->nodes[node_idx + 1]; + const ggml_tensor *bias = add->src[1]; + + if (mul != add->src[0]) { + return false; + } + // mat-vec only + if (!ggml_vk_use_mul_mat_vec_id(cgraph, node_idx)) { + return false; + } + // shaders assume the types match + if (mul->type != bias->type) { + return false; + } + // shaders assume the bias is contiguous + if (!ggml_is_contiguous(bias)) { + return false; + } + // the ID tensor must be the same for mul_mat_id and add_id + if (mul->src[2] != add->src[2]) { + return false; + } + // unaligned bias isn't handled + if (get_misalign_bytes(ctx, bias) != 0) { + return false; + } + + if (ops.size() == 3) { + if (ops.begin()[2] != GGML_OP_MUL) { + return false; + } + const ggml_tensor *mul = cgraph->nodes[node_idx + 2]; + return mmid_mul_ok(add, mul); + } + } + + if (ops.size() == 2 && ops.begin()[0] == GGML_OP_MUL_MAT_ID && ops.begin()[1] == GGML_OP_MUL) { + // additional constraints specific to this fusion + const ggml_tensor *mmid = cgraph->nodes[node_idx]; + const ggml_tensor *mul = cgraph->nodes[node_idx + 1]; + + if (!mmid_mul_ok(mmid, mul)) { + return false; + } + } + + return true; +} + +// Match SSM_CONV + UNARY(SILU) or SSM_CONV + ADD + UNARY(SILU). num_extra is 1 or 2. +static bool ggml_vk_can_fuse_ssm_conv(const ggml_backend_vk_context * ctx, const struct ggml_cgraph * cgraph, + int node_idx, int num_extra) { + const ggml_tensor * conv = cgraph->nodes[node_idx]; + if (conv->op != GGML_OP_SSM_CONV) { + return false; + } + + const ggml_tensor * silu = nullptr; + const ggml_tensor * bias = nullptr; + + if (num_extra == 1) { + if (!ggml_can_fuse(cgraph, node_idx, { GGML_OP_SSM_CONV, GGML_OP_UNARY })) { + return false; + } + silu = cgraph->nodes[node_idx + 1]; + } else if (num_extra == 2) { + if (!ggml_can_fuse(cgraph, node_idx, { GGML_OP_SSM_CONV, GGML_OP_ADD, GGML_OP_UNARY })) { + return false; + } + const ggml_tensor * add = cgraph->nodes[node_idx + 1]; + silu = cgraph->nodes[node_idx + 2]; + bias = (add->src[0] == conv) ? add->src[1] : add->src[0]; + + if (bias->type != GGML_TYPE_F32 || !ggml_is_contiguous(bias)) { + return false; + } + // bias must be channel-wise (one element per channel of the conv output) + if (ggml_nelements(bias) != conv->ne[0] || bias->ne[0] != conv->ne[0]) { + return false; + } + if (add->type != GGML_TYPE_F32) { + return false; + } + // The shader doesn't apply per-tensor offsets, so reject misaligned bias. + if (get_misalign_bytes(ctx, bias) != 0) { + return false; + } + } else { + return false; + } + + if (ggml_get_unary_op(silu) != GGML_UNARY_OP_SILU) { + return false; + } + if (conv->type != GGML_TYPE_F32 || silu->type != GGML_TYPE_F32) { + return false; + } + // The shader writes to the fused dst using its own strides, but the push constants don't + // carry a per-tensor offset, so the binding must be naturally aligned. + if (get_misalign_bytes(ctx, silu) != 0) { + return false; + } + return true; +} + +static bool ggml_vk_can_fuse_topk_moe(ggml_backend_vk_context * ctx, const struct ggml_cgraph * cgraph, + int node_idx, topk_moe_mode mode) { + + const ggml_tensor * softmax; + const ggml_tensor * weights; + const ggml_tensor * get_rows; + const ggml_tensor * argsort; + + switch (mode) { + case TOPK_MOE_EARLY_SOFTMAX_NORM: + softmax = cgraph->nodes[node_idx + 0]; + weights = cgraph->nodes[node_idx + 9]; + get_rows = cgraph->nodes[node_idx + 4]; + argsort = cgraph->nodes[node_idx + 2]; + break; + case TOPK_MOE_SIGMOID_NORM_BIAS: + softmax = cgraph->nodes[node_idx + 0]; // really sigmoid + weights = cgraph->nodes[node_idx + 10]; + get_rows = cgraph->nodes[node_idx + 5]; + argsort = cgraph->nodes[node_idx + 3]; + if (ggml_get_unary_op(softmax) != GGML_UNARY_OP_SIGMOID) { + return false; + } + // bias is expected to be 1D + if (ggml_nrows(cgraph->nodes[node_idx + 2]->src[1]) != 1 || + !ggml_is_contiguous(cgraph->nodes[node_idx + 2]->src[1])) { + return false; + } + // sigmoid fusion seems to generate infinities on moltenvk + if (ctx->device->driver_id == vk::DriverId::eMoltenvk) { + return false; + } + break; + case TOPK_MOE_EARLY_SOFTMAX: + softmax = cgraph->nodes[node_idx + 0]; + weights = cgraph->nodes[node_idx + 4]; + get_rows = cgraph->nodes[node_idx + 4]; + argsort = cgraph->nodes[node_idx + 2]; + break; + case TOPK_MOE_LATE_SOFTMAX: + softmax = cgraph->nodes[node_idx + 4]; + weights = cgraph->nodes[node_idx + 5]; + get_rows = cgraph->nodes[node_idx + 2]; + argsort = cgraph->nodes[node_idx + 0]; + break; + default: + return false; + } + + ggml_tensor * probs = get_rows->src[0]; + if (probs->op != GGML_OP_RESHAPE) { + return false; + } + probs = probs->src[0]; + ggml_tensor * selection_probs = argsort->src[0]; + + if (probs != selection_probs && mode != TOPK_MOE_SIGMOID_NORM_BIAS) { + return false; + } + + if (!ggml_is_contiguous(softmax->src[0]) || !ggml_is_contiguous(weights)) { + return false; + } + + if (softmax->op == GGML_OP_SOFT_MAX) { + const float * op_params = (const float *)softmax->op_params; + + float scale = op_params[0]; + float max_bias = op_params[1]; + + if (scale != 1.0f || max_bias != 0.0f) { + return false; + } + + // don't fuse when masks or sinks are present + if (softmax->src[1] || softmax->src[2]) { + return false; + } + } + + const int n_expert = softmax->ne[0]; + if (n_expert > (1 << (num_topk_moe_pipelines-1))) { + return false; + } + + if (!ctx->device->subgroup_arithmetic || + !ctx->device->subgroup_shuffle || + !ctx->device->subgroup_require_full_support || + ctx->device->disable_fusion) { + return false; + } + + return true; +} + +static bool ggml_vk_can_fuse_rope_set_rows(ggml_backend_vk_context * ctx, const struct ggml_cgraph * cgraph, + int node_idx) { + GGML_UNUSED(ctx); + const ggml_tensor *rope = cgraph->nodes[node_idx + 0]; + const ggml_tensor *view = cgraph->nodes[node_idx + 1]; + const ggml_tensor *set_rows = cgraph->nodes[node_idx + 2]; + + // ne3 not tested + if (rope->src[0]->ne[3] != 1) { + return false; + } + + if (set_rows->type != GGML_TYPE_F32 && set_rows->type != GGML_TYPE_F16) { + return false; + } + + if (set_rows->src[1]->type != GGML_TYPE_I64) { + return false; + } + + // The view should flatten two dims of rope into one dim + if (!ggml_is_contiguous(view) || + view->ne[0] != rope->ne[0] * rope->ne[1]) { + return false; + } + + // Only norm/neox/mrope shaders have the fusion code + const int mode = ((const int32_t *) rope->op_params)[2]; + if (mode != GGML_ROPE_TYPE_NORMAL && mode != GGML_ROPE_TYPE_NEOX && mode != GGML_ROPE_TYPE_MROPE) { + return false; + } + + return true; +} + +// Pattern check for the 5-op Snake fusion: mul -> sin -> sqr -> mul -> add. +// Verifies the chain shape, the closure x_in_add == x_in_mul0, and that +// the broadcast operands a and inv_b share a [1, C] layout. +static bool ggml_vk_can_fuse_snake(ggml_backend_vk_context * ctx, const struct ggml_cgraph * cgraph, int node_idx) { + GGML_UNUSED(ctx); + if (!ggml_can_fuse(cgraph, node_idx, snake_pattern)) { + return false; + } + + const ggml_tensor * mul0 = cgraph->nodes[node_idx + 0]; + const ggml_tensor * sin_node = cgraph->nodes[node_idx + 1]; + const ggml_tensor * sqr = cgraph->nodes[node_idx + 2]; + const ggml_tensor * mul1 = cgraph->nodes[node_idx + 3]; + const ggml_tensor * add = cgraph->nodes[node_idx + 4]; + + const ggml_tensor * x = ggml_are_same_shape(mul0, mul0->src[0]) ? mul0->src[0] : mul0->src[1]; + const ggml_tensor * a = (x == mul0->src[0]) ? mul0->src[1] : mul0->src[0]; + + const ggml_tensor * inv_b = (mul1->src[0] == sqr) ? mul1->src[1] : mul1->src[0]; + const ggml_tensor * x_in_add = (add->src[0] == mul1) ? add->src[1] : add->src[0]; + + if (x_in_add != x) { + return false; + } + if (x->type != GGML_TYPE_F32 && x->type != GGML_TYPE_F16 && x->type != GGML_TYPE_BF16) { + return false; + } + // Shader bindings: data_a is A_TYPE so it follows x's precision, while + // data_b and data_c are hardcoded float, so the broadcast operands must + // be F32 regardless of x's type. + if (a->type != GGML_TYPE_F32) return false; + if (inv_b->type != GGML_TYPE_F32) return false; + // Chain intermediates and output share x's precision (single A_TYPE / D_TYPE pipeline). + if (mul0->type != x->type) return false; + if (sin_node->type != x->type) return false; + if (sqr->type != x->type) return false; + if (mul1->type != x->type) return false; + if (add->type != x->type) return false; + if (!ggml_are_same_shape(a, inv_b)) { + return false; + } + if (a->ne[0] != 1 || a->ne[1] != x->ne[1]) { + return false; + } + // Dispatch is 2D over (ne0, ne1), so x and add must be 2D and a / inv_b + // must collapse to [1, C, 1, 1]. Higher dims are not handled by the shader. + if (x->ne[2] != 1 || x->ne[3] != 1) return false; + if (add->ne[2] != 1 || add->ne[3] != 1) return false; + if (a->ne[2] != 1 || a->ne[3] != 1) return false; + if (inv_b->ne[2] != 1 || inv_b->ne[3] != 1) return false; + // Shader uses idx = i0 + i1 * ne0 and reads data_b[i1] / data_c[i1], + // so every operand must be contiguous. + if (!ggml_is_contiguous(x) || !ggml_is_contiguous(add) || + !ggml_is_contiguous(a) || !ggml_is_contiguous(inv_b)) { + return false; + } + return true; +} + +// Check whether the tensors overlap in memory. +// Fusions can potentially overwrite src tensors in ways that are not prevented +// by ggml-alloc. If the fusion src is being applied in a way that's elementwise +// with the destination, then it's OK for them to overlap if they are exactly equal. +static bool ggml_vk_tensors_overlap(const ggml_tensor * a, const ggml_tensor * b, bool elementwise) { + ggml_backend_vk_buffer_context * a_buf_ctx = (ggml_backend_vk_buffer_context *)a->buffer->context; + vk_buffer a_buf = a_buf_ctx->dev_buffer; + ggml_backend_vk_buffer_context * b_buf_ctx = (ggml_backend_vk_buffer_context *)b->buffer->context; + vk_buffer b_buf = b_buf_ctx->dev_buffer; + if (a_buf == b_buf) { + auto a_base = vk_tensor_offset(a) + a->view_offs; + auto a_size = ggml_nbytes(a); + auto b_base = vk_tensor_offset(b) + b->view_offs; + auto b_size = ggml_nbytes(b); + + if (elementwise && a_base == b_base && a_size == b_size) { + return false; + } + + if ((b_base <= a_base && a_base < b_base + b_size) || + (a_base <= b_base && b_base < a_base + a_size)) { + return true; + } + } + return false; +} + +static bool ggml_vk_can_fuse_rms_norm_mul_rope(ggml_backend_vk_context * ctx, const struct ggml_cgraph * cgraph, + int node_idx) { + GGML_UNUSED(ctx); + const ggml_tensor *rms = cgraph->nodes[node_idx + 0]; + const ggml_tensor *mul = cgraph->nodes[node_idx + 1]; + const ggml_tensor *rope = cgraph->nodes[node_idx + 2]; + + const int mode = ((const int32_t *) rope->op_params)[2]; + + // noncontig tensors aren't tested, and don't seem common in practice + if (!ggml_is_contiguous(rms) || + !ggml_is_contiguous(mul) || + !ggml_is_contiguous(rope)) { + return false; + } + + // only norm/neox are handled in the shader + if (mode != GGML_ROPE_TYPE_NEOX && mode != GGML_ROPE_TYPE_NORMAL) { + return false; + } + + // shared memory size for passing data from mul->rope + if (mul->ne[0] > 1024) { + return false; + } + + // conditions for pipeline creation + if (sizeof(vk_op_rms_norm_mul_rope_push_constants) > ctx->device->properties.limits.maxPushConstantsSize) { + return false; + } + + return true; +} + +static uint32_t ggml_vk_fuse_multi_add(ggml_backend_vk_context * ctx, const struct ggml_cgraph * cgraph, int node_idx) { + + const ggml_tensor *first_node = cgraph->nodes[node_idx]; + if (first_node->op != GGML_OP_ADD) { + return 0; + } + + if (!ctx->device->multi_add) { + return 0; + } + + int32_t num_adds = 1; + while (node_idx + num_adds < cgraph->n_nodes && + cgraph->nodes[node_idx + num_adds]->op == GGML_OP_ADD && + num_adds < MAX_FUSED_ADDS) { + num_adds++; + } + + // The shader currently requires same shapes (but different strides are allowed), + // everything f32, and no misalignment + for (int32_t i = 0; i < num_adds; ++i) { + const ggml_tensor *next_node = cgraph->nodes[node_idx + i]; + if (!ggml_are_same_shape(first_node, next_node->src[0]) || + !ggml_are_same_shape(first_node, next_node->src[1]) || + next_node->type != GGML_TYPE_F32 || + next_node->src[0]->type != GGML_TYPE_F32 || + next_node->src[1]->type != GGML_TYPE_F32 || + get_misalign_bytes(ctx, next_node) || + get_misalign_bytes(ctx, next_node->src[0]) || + get_misalign_bytes(ctx, next_node->src[1])) { + num_adds = i; + } + } + + // Verify we can fuse these + ggml_op adds[MAX_FUSED_ADDS]; + for (int32_t i = 0; i < num_adds; ++i) { + adds[i] = GGML_OP_ADD; + } + + // decrease num_adds if they can't all be fused + while (num_adds > 1 && !ggml_can_fuse(cgraph, node_idx, adds, num_adds)) { + num_adds--; + } + + // a single add is not "fused", so just return zero + if (num_adds == 1) { + return 0; + } + return num_adds; +} + +static int32_t find_first_set(uint32_t x) { + int32_t ret = 0; + if (!x) { + return -1; + } + while (!(x & 1)) { + x >>= 1; + ret++; + } + return ret; +} + +static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { + VK_LOG_DEBUG("ggml_backend_vk_graph_compute(" << cgraph->n_nodes << " nodes)"); + ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; + + if (vk_instance.debug_utils_support) { + vk::DebugUtilsLabelEXT dul = {}; + dul.pLabelName = "ggml_backend_vk_graph_compute"; + dul.color = std::array{1.0f, 1.0f, 1.0f, 1.0f}; + vk_instance.pfn_vkQueueBeginDebugUtilsLabelEXT(ctx->device->compute_queue.queue, reinterpret_cast(&dul)); + } + + ctx->prealloc_size_add_rms_partials_offset = 0; + ctx->do_add_rms_partials = false; + ctx->do_add_rms_partials_offset_calculation = false; + + int last_node = cgraph->n_nodes - 1; + + // If the last op in the cgraph isn't backend GPU, the command buffer doesn't get closed properly + while (last_node > 0 && (ggml_vk_is_empty(cgraph->nodes[last_node]) || ((cgraph->nodes[last_node]->flags & GGML_TENSOR_FLAG_COMPUTE) == 0))) { + last_node -= 1; + } + + // Reserve tensor context space for all nodes + ctx->tensor_ctxs.resize(cgraph->n_nodes); + + bool first_node_in_batch = true; // true if next node will be first node in a batch + int submit_node_idx = 0; // index to first node in a batch + + ggml_vk_submit_transfer_ctx(ctx); + + vk_context compute_ctx; + if (vk_perf_logger_enabled) { + // allocate/resize the query pool + if (ctx->num_queries < cgraph->n_nodes + 1) { + if (ctx->query_pool) { + ctx->device->device.destroyQueryPool(ctx->query_pool); + } + vk::QueryPoolCreateInfo query_create_info; + query_create_info.queryType = vk::QueryType::eTimestamp; + query_create_info.queryCount = cgraph->n_nodes + 100; + ctx->query_pool = ctx->device->device.createQueryPool(query_create_info); + ctx->num_queries = query_create_info.queryCount; + ctx->query_fusion_names.resize(ctx->num_queries); + ctx->query_fusion_node_count.resize(ctx->num_queries); + ctx->query_nodes.resize(ctx->num_queries); + ctx->query_node_idx.resize(ctx->num_queries); + } + + ctx->device->device.resetQueryPool(ctx->query_pool, 0, cgraph->n_nodes+1); + std::fill(ctx->query_fusion_names.begin(), ctx->query_fusion_names.end(), nullptr); + std::fill(ctx->query_fusion_node_count.begin(), ctx->query_fusion_node_count.end(), 0); + std::fill(ctx->query_nodes.begin(), ctx->query_nodes.end(), nullptr); + std::fill(ctx->query_node_idx.begin(), ctx->query_node_idx.end(), 0); + + GGML_ASSERT(ctx->compute_ctx.expired()); + compute_ctx = ggml_vk_get_compute_ctx(ctx); + ctx->query_idx = 0; + compute_ctx->s->buffer->buf.writeTimestamp(vk::PipelineStageFlagBits::eAllCommands, ctx->query_pool, ctx->query_idx++); + ggml_vk_sync_buffers(ctx, compute_ctx); + } + + ctx->prealloc_y_last_pipeline_used = nullptr; + ctx->prealloc_y_last_tensor_used = nullptr; + ctx->prealloc_y_last_decode_vector_staging = false; + + if (ctx->prealloc_size_add_rms_partials) { + ggml_vk_preallocate_buffers(ctx, nullptr); + compute_ctx = ggml_vk_get_compute_ctx(ctx); + // initialize partial sums to zero. + ggml_vk_buffer_memset_async(compute_ctx, ctx->prealloc_add_rms_partials, 0, 0, ctx->prealloc_size_add_rms_partials); + ggml_vk_sync_buffers(ctx, compute_ctx); + } + + // Submit after enough work has accumulated, to overlap CPU cmdbuffer generation with GPU execution. + // Estimate the amount of compute work using flops, and submit every 200 GFLOP + // (and scaled down based on total graph flops, so smaller models submit earlier). + // Also submit at least every 100 nodes, in case there are workloads without heavy compute. + uint32_t submitted_nodes = 0; + uint32_t submit_count = 0; + uint64_t batch_flops = 0; + uint64_t total_flops = 0; + uint64_t flops_cap = 200'000'000'000ULL; + + // On weaker AMD GPUs larger submissions can hit a driver timeout, submit more often to avoid this + if (ctx->device->vendor_id == VK_VENDOR_ID_AMD && ctx->device->shader_core_count > 0) { + if (ctx->device->architecture == AMD_GCN && ctx->device->shader_core_count < 32) { + flops_cap = 500'000'000ULL * ctx->device->shader_core_count; + } else if (ctx->device->architecture != AMD_GCN && ctx->device->shader_core_count < 24) { + flops_cap = 2'000'000'000ULL * ctx->device->shader_core_count; + } + } + uint64_t flops_per_submit = std::min(flops_cap, ctx->last_total_flops / 40u); + + for (int i = 0; i < cgraph->n_nodes; i++) { + if (first_node_in_batch) { + submit_node_idx = i; + } + + { + auto node_flops = ggml_vk_get_node_flops(cgraph->nodes[i]); + batch_flops += node_flops; + total_flops += node_flops; + } + + // op_srcs_fused_elementwise indicates whether an op's srcs all contribute to + // the fused result in an elementwise-way. This affects whether the memory for + // the src is allowed to overlap the memory for the destination. + // The array is sized to handle the largest fusion (asserted later). + bool op_srcs_fused_elementwise[12]; + + ctx->fused_topk_moe_mode = TOPK_MOE_COUNT; + ctx->fused_topk_moe_scale = false; + const char *fusion_string {}; + if (!ctx->device->disable_fusion) { + uint32_t num_adds = ggml_vk_fuse_multi_add(ctx, cgraph, i); + if (num_adds) { + ctx->num_additional_fused_ops = num_adds - 1; + fusion_string = "MULTI_ADD"; + std::fill_n(op_srcs_fused_elementwise, ctx->num_additional_fused_ops + 1, true); + } else if (ggml_vk_can_fuse(ctx, cgraph, i, { GGML_OP_MUL_MAT, GGML_OP_ADD, GGML_OP_ADD })) { + ctx->num_additional_fused_ops = 2; + fusion_string = "MUL_MAT_ADD_ADD"; + op_srcs_fused_elementwise[0] = false; + op_srcs_fused_elementwise[1] = true; + op_srcs_fused_elementwise[2] = true; + } else if (ggml_vk_can_fuse(ctx, cgraph, i, { GGML_OP_MUL_MAT, GGML_OP_ADD })) { + ctx->num_additional_fused_ops = 1; + fusion_string = "MUL_MAT_ADD"; + op_srcs_fused_elementwise[0] = false; + op_srcs_fused_elementwise[1] = true; + } else if (ggml_vk_can_fuse(ctx, cgraph, i, { GGML_OP_MUL_MAT_ID, GGML_OP_ADD_ID, GGML_OP_MUL })) { + ctx->num_additional_fused_ops = 2; + fusion_string = "MUL_MAT_ID_ADD_ID_MUL"; + op_srcs_fused_elementwise[0] = false; + op_srcs_fused_elementwise[1] = true; + op_srcs_fused_elementwise[2] = true; + } else if (ggml_vk_can_fuse(ctx, cgraph, i, { GGML_OP_MUL_MAT_ID, GGML_OP_ADD_ID })) { + ctx->num_additional_fused_ops = 1; + fusion_string = "MUL_MAT_ID_ADD_ID"; + op_srcs_fused_elementwise[0] = false; + op_srcs_fused_elementwise[1] = true; + } else if (ggml_vk_can_fuse(ctx, cgraph, i, { GGML_OP_MUL_MAT_ID, GGML_OP_MUL })) { + ctx->num_additional_fused_ops = 1; + fusion_string = "MUL_MAT_ID_MUL"; + op_srcs_fused_elementwise[0] = false; + op_srcs_fused_elementwise[1] = true; + } else if (ggml_can_fuse_subgraph(cgraph, i, { GGML_OP_RMS_NORM, GGML_OP_MUL, GGML_OP_ROPE, GGML_OP_VIEW, GGML_OP_SET_ROWS }, { i + 4 }) && + ggml_check_edges(cgraph, i, rms_norm_mul_rope_view_set_rows_edges) && + ggml_vk_can_fuse_rms_norm_mul_rope(ctx, cgraph, i) && + ggml_vk_can_fuse_rope_set_rows(ctx, cgraph, i + 2)) { + ctx->num_additional_fused_ops = 4; + fusion_string = "RMS_NORM_MUL_ROPE_VIEW_SET_ROWS"; + op_srcs_fused_elementwise[0] = false; + op_srcs_fused_elementwise[1] = false; + op_srcs_fused_elementwise[2] = false; + op_srcs_fused_elementwise[3] = false; + op_srcs_fused_elementwise[4] = false; + } else if (ggml_vk_can_fuse(ctx, cgraph, i, { GGML_OP_RMS_NORM, GGML_OP_MUL, GGML_OP_ROPE })&& + ggml_vk_can_fuse_rms_norm_mul_rope(ctx, cgraph, i)) { + ctx->num_additional_fused_ops = 2; + fusion_string = "RMS_NORM_MUL_ROPE"; + // rope is approximately elementwise - whole rows are done by a single workgroup and it's row-wise + op_srcs_fused_elementwise[0] = false; + op_srcs_fused_elementwise[1] = true; + op_srcs_fused_elementwise[2] = true; + } else if (ggml_vk_can_fuse(ctx, cgraph, i, { GGML_OP_RMS_NORM, GGML_OP_MUL })) { + ctx->num_additional_fused_ops = 1; + fusion_string = "RMS_NORM_MUL"; + // rms_norm is not elementwise, but whole rows must be consumed and the scale factor computed before + // they are overwritten, and one workgroup per row. So close enough. + op_srcs_fused_elementwise[0] = true; + op_srcs_fused_elementwise[1] = true; + } else if (ggml_vk_can_fuse_ssm_conv(ctx, cgraph, i, 2)) { + ctx->num_additional_fused_ops = 2; + fusion_string = "SSM_CONV_BIAS_SILU"; + // ssm_conv reads multiple input tokens per output, so it's not elementwise w.r.t. its srcs. + // The downstream add and silu are elementwise on the conv output. + op_srcs_fused_elementwise[0] = false; + op_srcs_fused_elementwise[1] = true; + op_srcs_fused_elementwise[2] = true; + } else if (ggml_vk_can_fuse_ssm_conv(ctx, cgraph, i, 1)) { + ctx->num_additional_fused_ops = 1; + fusion_string = "SSM_CONV_SILU"; + op_srcs_fused_elementwise[0] = false; + op_srcs_fused_elementwise[1] = true; + } else if (ggml_can_fuse_subgraph(cgraph, i, { GGML_OP_ROPE, GGML_OP_VIEW, GGML_OP_SET_ROWS }, { i + 2 }) && + ggml_check_edges(cgraph, i, rope_view_set_rows_edges) && + ggml_vk_can_fuse_rope_set_rows(ctx, cgraph, i)) { + ctx->num_additional_fused_ops = 2; + fusion_string = "ROPE_VIEW_SET_ROWS"; + op_srcs_fused_elementwise[0] = false; + op_srcs_fused_elementwise[1] = false; + op_srcs_fused_elementwise[2] = false; + } else if (ggml_vk_can_fuse_snake(ctx, cgraph, i)) { + ctx->num_additional_fused_ops = 4; + fusion_string = "SNAKE"; + // elementwise=true: snake.comp is safe under exact aliasing because each + // thread reads data_x[idx] into a register before writing data_d[idx] + // with a data dependency on that register. The overlap check still + // rejects partial overlaps (different base or size). + std::fill_n(op_srcs_fused_elementwise, 5, true); + } else if (ggml_can_fuse_subgraph(cgraph, i, topk_moe_early_softmax_norm, { i + 3, i + 9 }) && + ggml_check_edges(cgraph, i, topk_moe_early_softmax_norm_edges) && + ggml_vk_can_fuse_topk_moe(ctx, cgraph, i, TOPK_MOE_EARLY_SOFTMAX_NORM)) { + ctx->num_additional_fused_ops = topk_moe_early_softmax_norm.size() - 1; + // view of argsort writes to memory + ctx->fused_ops_write_mask |= 1 << 3; + ctx->fused_topk_moe_mode = TOPK_MOE_EARLY_SOFTMAX_NORM; + fusion_string = "TOPK_MOE_EARLY_SOFTMAX_NORM"; + std::fill_n(op_srcs_fused_elementwise, ctx->num_additional_fused_ops + 1, false); + } else if (ggml_can_fuse_subgraph(cgraph, i, topk_moe_sigmoid_norm_bias, { i + 4, i + 10 }) && + ggml_check_edges(cgraph, i, topk_moe_sigmoid_norm_bias_edges) && + ggml_vk_can_fuse_topk_moe(ctx, cgraph, i, TOPK_MOE_SIGMOID_NORM_BIAS)) { + ctx->num_additional_fused_ops = topk_moe_sigmoid_norm_bias.size() - 1; + // view of argsort writes to memory + ctx->fused_ops_write_mask |= 1 << 4; + ctx->fused_topk_moe_mode = TOPK_MOE_SIGMOID_NORM_BIAS; + fusion_string = "TOPK_MOE_SIGMOID_NORM_BIAS"; + std::fill_n(op_srcs_fused_elementwise, ctx->num_additional_fused_ops + 1, false); + } else if (ggml_can_fuse_subgraph(cgraph, i, topk_moe_early_softmax, { i + 3, i + 4 }) && + ggml_check_edges(cgraph, i, topk_moe_early_softmax_edges) && + ggml_vk_can_fuse_topk_moe(ctx, cgraph, i, TOPK_MOE_EARLY_SOFTMAX)) { + ctx->num_additional_fused_ops = topk_moe_early_softmax.size() - 1; + // view of argsort writes to memory + ctx->fused_ops_write_mask |= 1 << 3; + ctx->fused_topk_moe_mode = TOPK_MOE_EARLY_SOFTMAX; + fusion_string = "TOPK_MOE_EARLY_SOFTMAX"; + std::fill_n(op_srcs_fused_elementwise, ctx->num_additional_fused_ops + 1, false); + } else if (ggml_can_fuse_subgraph(cgraph, i, topk_moe_late_softmax, { i + 1, i + 5 }) && + ggml_check_edges(cgraph, i, topk_moe_late_softmax_edges) && + ggml_vk_can_fuse_topk_moe(ctx, cgraph, i, TOPK_MOE_LATE_SOFTMAX)) { + ctx->num_additional_fused_ops = topk_moe_late_softmax.size() - 1; + // view of argsort writes to memory + ctx->fused_ops_write_mask |= 1 << 1; + ctx->fused_topk_moe_mode = TOPK_MOE_LATE_SOFTMAX; + fusion_string = "TOPK_MOE_LATE_SOFTMAX"; + std::fill_n(op_srcs_fused_elementwise, ctx->num_additional_fused_ops + 1, false); + } + if (ctx->fused_topk_moe_mode != TOPK_MOE_COUNT) { + // Look for an additional scale op to fuse - occurs in deepseek2 and nemotron3 nano. + if (ggml_can_fuse_subgraph(cgraph, i + ctx->num_additional_fused_ops - 1, { GGML_OP_DIV, GGML_OP_RESHAPE, GGML_OP_SCALE }, { i + ctx->num_additional_fused_ops + 1 }) || + ggml_can_fuse_subgraph(cgraph, i + ctx->num_additional_fused_ops, { GGML_OP_GET_ROWS, GGML_OP_SCALE }, { i + ctx->num_additional_fused_ops + 1 })) { + ctx->fused_topk_moe_scale = true; + ctx->num_additional_fused_ops++; + op_srcs_fused_elementwise[ctx->num_additional_fused_ops] = false; + } + } + } + GGML_ASSERT(ctx->num_additional_fused_ops < (int)(sizeof(op_srcs_fused_elementwise) / sizeof(op_srcs_fused_elementwise[0]))); + ctx->fused_ops_write_mask |= 1 << ctx->num_additional_fused_ops; + + // Check whether fusion would overwrite src operands while they're still in use. + // If so, disable fusion. + if (ctx->num_additional_fused_ops) { + // There are up to two output nodes - topk_moe has two. + uint32_t bits = ctx->fused_ops_write_mask & ~(1 << ctx->num_additional_fused_ops); + ggml_tensor *output_nodes[2] {}; + output_nodes[0] = cgraph->nodes[i + ctx->num_additional_fused_ops]; + if (bits) { + int output_idx = find_first_set(bits); + GGML_ASSERT(bits == (1u << output_idx)); + output_nodes[1] = cgraph->nodes[i + output_idx]; + } + + bool need_disable = false; + + // topk_moe often overwrites the source, but for a given row all the src values are + // loaded before anything is stored. If there's only one row, this is safe, so treat + // this as a special case. + bool is_topk_moe_single_row = ctx->fused_topk_moe_mode != TOPK_MOE_COUNT && + ggml_nrows(cgraph->nodes[i]->src[0]) == 1; + + if (!is_topk_moe_single_row) { + for (int j = 0; j < 2; ++j) { + ggml_tensor *dst = output_nodes[j]; + if (!dst) { + continue; + } + // Loop over all srcs of all nodes in the fusion. If the src overlaps + // the destination and the src is not an intermediate node that's being + // elided, then disable fusion. + for (int k = 0; k <= ctx->num_additional_fused_ops; ++k) { + for (uint32_t s = 0; s < GGML_MAX_SRC; ++s) { + ggml_tensor *src = cgraph->nodes[i + k]->src[s]; + if (!src || src->op == GGML_OP_NONE) { + continue; + } + if (ggml_vk_tensors_overlap(src, dst, op_srcs_fused_elementwise[k])) { + bool found = false; + for (int n = 0; n < k; ++n) { + if (cgraph->nodes[i + n] == src) { + found = true; + break; + } + } + if (!found) { + need_disable = true; + } + } + } + } + } + } + if (need_disable) { + ctx->num_additional_fused_ops = 0; + ctx->fused_ops_write_mask = 1; + ctx->fused_topk_moe_mode = TOPK_MOE_COUNT; + ctx->fused_topk_moe_scale = false; + } + } + + // Signal the almost_ready fence when the graph is mostly complete (< 20% remaining) + bool almost_ready = (cgraph->n_nodes - i) < cgraph->n_nodes / 5; + bool submit = (submitted_nodes >= ctx->device->max_nodes_per_submit) || + (flops_per_submit != 0 && batch_flops >= flops_per_submit) || + (i + ctx->num_additional_fused_ops >= last_node) || + (almost_ready && !ctx->almost_ready_fence_pending); + + bool enqueued = ggml_vk_build_graph(ctx, cgraph, i, cgraph->nodes[submit_node_idx], submit_node_idx, i + ctx->num_additional_fused_ops >= last_node, almost_ready, submit); + + if (vk_perf_logger_enabled && enqueued) { + compute_ctx = ggml_vk_get_compute_ctx(ctx); + if (!vk_perf_logger_concurrent) { + // track a single node/fusion for the current query + ctx->query_nodes[ctx->query_idx] = cgraph->nodes[i]; + ctx->query_fusion_names[ctx->query_idx] = fusion_string; + compute_ctx->s->buffer->buf.writeTimestamp(vk::PipelineStageFlagBits::eAllCommands, ctx->query_pool, ctx->query_idx++); + ggml_vk_sync_buffers(ctx, compute_ctx); + } else { + // track a fusion string and number of fused ops for the current node_idx + ctx->query_fusion_names[i] = fusion_string; + ctx->query_fusion_node_count[i] = ctx->num_additional_fused_ops; + } + } + + if (enqueued) { + ++submitted_nodes; + +#ifndef GGML_VULKAN_CHECK_RESULTS + if (first_node_in_batch) { + first_node_in_batch = false; + } +#endif + } + + if (submit && enqueued) { + first_node_in_batch = true; + submitted_nodes = 0; + batch_flops = 0; + if (submit_count < 3) { + flops_per_submit *= 2; + } + submit_count++; + } + i += ctx->num_additional_fused_ops; + ctx->num_additional_fused_ops = 0; + ctx->fused_ops_write_mask = 0; + } + + ctx->last_total_flops = total_flops; + + if (vk_perf_logger_enabled) { + // End the command buffer and submit/wait + GGML_ASSERT(!ctx->compute_ctx.expired()); + compute_ctx = ctx->compute_ctx.lock(); + ggml_vk_ctx_end(compute_ctx); + + ggml_vk_submit(compute_ctx, ctx->device->fence); + VK_CHECK(ctx->device->device.waitForFences({ ctx->device->fence }, true, UINT64_MAX), "GGML_VULKAN_PERF waitForFences"); + ctx->device->device.resetFences({ ctx->device->fence }); + ctx->compute_ctx.reset(); + + // Get the results and pass them to the logger + std::vector timestamps(cgraph->n_nodes + 1); + VK_CHECK(ctx->device->device.getQueryPoolResults(ctx->query_pool, 0, ctx->query_idx, (cgraph->n_nodes + 1)*sizeof(uint64_t), timestamps.data(), sizeof(uint64_t), vk::QueryResultFlagBits::e64 | vk::QueryResultFlagBits::eWait), "get timestamp results"); + if (!vk_perf_logger_concurrent) { + // Log each op separately + for (int i = 1; i < ctx->query_idx; i++) { + auto node = ctx->query_nodes[i]; + auto name = ctx->query_fusion_names[i]; + ctx->perf_logger->log_timing(node, name, uint64_t((timestamps[i] - timestamps[i-1]) * ctx->device->properties.limits.timestampPeriod)); + } + } else { + // Log each group of nodes + int prev_node_idx = 0; + for (int i = 1; i < ctx->query_idx; i++) { + auto cur_node_idx = ctx->query_node_idx[i]; + std::vector nodes; + std::vector names; + for (int node_idx = prev_node_idx; node_idx < cur_node_idx; ++node_idx) { + if (ggml_op_is_empty(cgraph->nodes[node_idx]->op)) { + continue; + } + nodes.push_back(cgraph->nodes[node_idx]); + names.push_back(ctx->query_fusion_names[node_idx]); + node_idx += ctx->query_fusion_node_count[node_idx]; + } + prev_node_idx = cur_node_idx; + ctx->perf_logger->log_timing(nodes, names, uint64_t((timestamps[i] - timestamps[i-1]) * ctx->device->properties.limits.timestampPeriod)); + } + } + ctx->perf_logger->print_timings(); + } + + if (!ctx->device->support_async) { + ggml_vk_synchronize(ctx); + } + + return GGML_STATUS_SUCCESS; + + UNUSED(backend); +} + +// Sort the graph for improved parallelism. +static void ggml_vk_graph_optimize(ggml_backend_t backend, struct ggml_cgraph * graph) +{ + VK_LOG_DEBUG("ggml_vk_graph_optimize(" << graph->n_nodes << " nodes)"); + ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; + + if (ctx->device->disable_graph_optimize) { + return; + } + + auto const &is_empty = [](ggml_tensor * node) -> bool { + return node->op == GGML_OP_NONE || node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE; + }; + + auto const &is_src_of = [](const ggml_tensor *dst, const ggml_tensor *src) -> bool { + for (uint32_t s = 0; s < GGML_MAX_SRC; ++s) { + if (dst->src[s] == src) { + return true; + } + } + // implicit dependency if they view the same tensor + const ggml_tensor *dst2 = dst->view_src ? dst->view_src : dst; + const ggml_tensor *src2 = src->view_src ? src->view_src : src; + if (dst2 == src2) { + return true; + } + return false; + }; + + std::vector new_order; + std::vector used(graph->n_nodes, false); + std::set used_node_set; + + int first_unused = 0; + while (first_unused < graph->n_nodes) { + std::vector current_set; + + // Check for fusion patterns and avoid reordering them + auto const &match_pattern = [&](const std::initializer_list &pattern, int start) -> bool { + if (start + (int)pattern.size() <= graph->n_nodes) { + bool is_pattern = true; + for (size_t j = 0; j < pattern.size(); ++j) { + if (graph->nodes[start + j]->op != pattern.begin()[j] || used[start + j]) { + is_pattern = false; + } + } + return is_pattern; + } + return false; + }; + + auto const &keep_pattern = [&](const std::initializer_list &pattern) -> bool { + if (match_pattern(pattern, first_unused)) { + for (size_t j = 0; j < pattern.size(); ++j) { + new_order.push_back(graph->nodes[first_unused + j]); + used_node_set.insert(graph->nodes[first_unused + j]); + used[first_unused + j] = true; + } + while (first_unused < graph->n_nodes && used[first_unused]) { + first_unused++; + } + return true; + } + return false; + }; + + if (keep_pattern(topk_moe_early_softmax_norm)) { + continue; + } + if (keep_pattern(topk_moe_sigmoid_norm_bias)) { + continue; + } + if (keep_pattern(topk_moe_early_softmax)) { + continue; + } + if (keep_pattern(topk_moe_late_softmax)) { + continue; + } + if (keep_pattern(snake_pattern)) { + continue; + } + + // First, grab the next unused node. + current_set.push_back(first_unused); + + // Loop through the next N nodes. Grab any that don't depend on other nodes that + // haven't already been run. Nodes that have already been run have used[i] set + // to true. Allow nodes that depend on the previous node if it's a fusion pattern + // that we support (e.g. RMS_NORM + MUL). + // This first pass only grabs "real" (non-view nodes). Second pass grabs view nodes. + // The goal is to not interleave real and view nodes in a way that breaks fusion. + const int NUM_TO_CHECK = 20; + for (int j = first_unused+1; j < std::min(first_unused + NUM_TO_CHECK, graph->n_nodes); ++j) { + if (used[j]) { + continue; + } + if (is_empty(graph->nodes[j])) { + continue; + } + // Don't pull forward nodes from fusion patterns + if (match_pattern(topk_moe_early_softmax_norm, j) || + match_pattern(topk_moe_sigmoid_norm_bias, j) || + match_pattern(topk_moe_early_softmax, j) || + match_pattern(topk_moe_late_softmax, j) || + match_pattern(snake_pattern, j)) { + continue; + } + bool ok = true; + for (int c = first_unused; c < j; ++c) { + if (!used[c] && + is_src_of(graph->nodes[j], graph->nodes[c]) && + !(j == c+1 && c == current_set.back() && graph->nodes[c]->op == GGML_OP_RMS_NORM && graph->nodes[j]->op == GGML_OP_MUL) && + !(j == c+1 && c == current_set.back() && graph->nodes[c]->op == GGML_OP_MUL_MAT && graph->nodes[j]->op == GGML_OP_ADD) && + !(j == c+1 && c == current_set.back() && graph->nodes[c]->op == GGML_OP_MUL_MAT_ID && graph->nodes[j]->op == GGML_OP_ADD_ID) && + !(j == c+1 && c == current_set.back() && graph->nodes[c]->op == GGML_OP_MUL_MAT_ID && graph->nodes[j]->op == GGML_OP_MUL) && + !(j == c+1 && c == current_set.back() && graph->nodes[c]->op == GGML_OP_ADD && graph->nodes[j]->op == GGML_OP_ADD) && + !(j == c+1 && c == current_set.back() && graph->nodes[c]->op == GGML_OP_SSM_CONV && graph->nodes[j]->op == GGML_OP_ADD) && + !(j == c+1 && c == current_set.back() && graph->nodes[c]->op == GGML_OP_SSM_CONV && graph->nodes[j]->op == GGML_OP_UNARY)) { + ok = false; + break; + } + } + if (ok) { + current_set.push_back(j); + + int rope_idx = j; + + // When we've found RMS_NORM + MUL, try to find a ROPE that uses it + if (j > 0 && + graph->nodes[j]->op == GGML_OP_MUL && + graph->nodes[j-1]->op == GGML_OP_RMS_NORM) { + for (int k = j + 1; k < std::min(j + 15, graph->n_nodes); ++k) { + if (graph->nodes[k]->op == GGML_OP_ROPE && + graph->nodes[k]->src[0] == graph->nodes[j] && + // Check that other srcs are already valid + graph->nodes[k]->src[1]->op == GGML_OP_NONE && + (graph->nodes[k]->src[2] == nullptr || graph->nodes[k]->src[2]->op == GGML_OP_NONE)) { + rope_idx = k; + current_set.push_back(rope_idx); + used[rope_idx] = true; + break; + } + } + } + // Look for ROPE + VIEW + SET_ROWS and make them consecutive + if (graph->nodes[rope_idx]->op == GGML_OP_ROPE) { + int view_idx = -1; + int set_rows_idx = -1; + for (int k = rope_idx+1; k < std::min(rope_idx + 10, graph->n_nodes); ++k) { + if (view_idx == -1 && + graph->nodes[k]->op == GGML_OP_VIEW && + graph->nodes[k]->src[0] == graph->nodes[rope_idx]) { + view_idx = k; + continue; + } + if (view_idx != -1 && + set_rows_idx == -1 && + graph->nodes[k]->op == GGML_OP_SET_ROWS && + graph->nodes[k]->src[0] == graph->nodes[view_idx]) { + set_rows_idx = k; + break; + } + } + if (set_rows_idx != -1) { + current_set.push_back(view_idx); + current_set.push_back(set_rows_idx); + used[view_idx] = true; + used[set_rows_idx] = true; + } + } + // Look for MUL_MAT_ID + ADD_ID + MUL + if (j > 0 && + graph->nodes[j]->op == GGML_OP_ADD_ID && + graph->nodes[j-1]->op == GGML_OP_MUL_MAT_ID) { + for (int k = j + 1; k < std::min(j + 15, graph->n_nodes); ++k) { + if (graph->nodes[k]->op == GGML_OP_MUL && + graph->nodes[k]->src[0] == graph->nodes[j] && + // src1 must either be weights or already processed + (graph->nodes[k]->src[1]->op == GGML_OP_NONE || used_node_set.find(graph->nodes[k]->src[1]) != used_node_set.end())) { + current_set.push_back(k); + used[k] = true; + break; + } + } + } + // Look for MUL_MAT + ADD + ADD + if (j > 0 && + graph->nodes[j]->op == GGML_OP_ADD && + graph->nodes[j-1]->op == GGML_OP_MUL_MAT) { + for (int k = j + 1; k < std::min(j + 15, graph->n_nodes); ++k) { + if (graph->nodes[k]->op == GGML_OP_ADD && + graph->nodes[k]->src[0] == graph->nodes[j] && + // src1 must either be weights or already processed + (graph->nodes[k]->src[1]->op == GGML_OP_NONE || used_node_set.find(graph->nodes[k]->src[1]) != used_node_set.end())) { + current_set.push_back(k); + used[k] = true; + break; + } + } + } + // SSM_CONV + ADD + UNARY: pull the consuming UNARY forward + if (j > 0 && + graph->nodes[j]->op == GGML_OP_ADD && + graph->nodes[j-1]->op == GGML_OP_SSM_CONV) { + for (int k = j + 1; k < std::min(j + 15, graph->n_nodes); ++k) { + if (graph->nodes[k]->op == GGML_OP_UNARY && + graph->nodes[k]->src[0] == graph->nodes[j]) { + current_set.push_back(k); + used[k] = true; + break; + } + } + } + } + } + // Second pass grabs view nodes. + // Skip this if it would break a fusion optimization (don't split up add->rms_norm or add->add). + if (graph->nodes[current_set.back()]->op != GGML_OP_ADD) { + for (int j = first_unused+1; j < std::min(first_unused + NUM_TO_CHECK, graph->n_nodes); ++j) { + if (used[j]) { + continue; + } + if (!is_empty(graph->nodes[j])) { + continue; + } + bool ok = true; + for (int c = first_unused; c < j; ++c) { + bool c_in_current_set = std::find(current_set.begin(), current_set.end(), c) != current_set.end(); + // skip views whose srcs haven't been processed. + if (!used[c] && + is_src_of(graph->nodes[j], graph->nodes[c]) && + !c_in_current_set) { + ok = false; + break; + } + } + if (ok) { + current_set.push_back(j); + } + } + } + + // Push the current set into new_order + for (auto c : current_set) { + new_order.push_back(graph->nodes[c]); + used_node_set.insert(graph->nodes[c]); + used[c] = true; + } + while (first_unused < graph->n_nodes && used[first_unused]) { + first_unused++; + } + } + // Replace the graph with the new order. + for (int i = 0; i < graph->n_nodes; ++i) { + graph->nodes[i] = new_order[i]; + } +} + +static void ggml_backend_vk_event_record(ggml_backend_t backend, ggml_backend_event_t event) { + VK_LOG_DEBUG("ggml_backend_vk_event_record(backend=" << backend << ", event=" << event << ")"); + ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; + vk_event *vkev = (vk_event *)event->context; + + ggml_vk_submit_transfer_ctx(ctx); + + vk_context compute_ctx = ggml_vk_get_compute_ctx(ctx); + auto* cmd_buf = compute_ctx->s->buffer; // retrieve pointer before it gets reset + + if (vkev->has_event) { + // Move existing event into submitted + vkev->events_submitted.push_back(vkev->event); + } + + // Grab the next event and record it, create one if necessary + if (vkev->events_free.empty()) { + vkev->event = ctx->device->device.createEvent({}); + } else { + vkev->event = vkev->events_free.back(); + vkev->events_free.pop_back(); + } + + vkev->has_event = true; + + ggml_vk_set_event(compute_ctx, vkev->event); + + vkev->tl_semaphore.value++; + compute_ctx->s->signal_semaphores.push_back(vkev->tl_semaphore); + ggml_vk_ctx_end(compute_ctx); + + ggml_vk_submit(compute_ctx, {}); + ctx->submit_pending = true; + vkev->cmd_buffer = cmd_buf; + vkev->cmd_buffer_use_counter = cmd_buf->use_counter; + ctx->compute_ctx.reset(); +} + +static void ggml_backend_vk_event_wait(ggml_backend_t backend, ggml_backend_event_t event) { + VK_LOG_DEBUG("ggml_backend_vk_event_wait(backend=" << backend << ", event=" << event << ")"); + ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; + vk_event *vkev = (vk_event *)event->context; + + vk_context compute_ctx = ggml_vk_get_compute_ctx(ctx); + + if (vkev->has_event) { + // Wait for latest event + ggml_vk_wait_events(compute_ctx, { vkev->event }); + } +} + +// TODO: enable async and synchronize +static ggml_backend_i ggml_backend_vk_interface = { + /* .get_name = */ ggml_backend_vk_name, + /* .free = */ ggml_backend_vk_free, + /* .set_tensor_async = */ ggml_backend_vk_set_tensor_async, + /* .get_tensor_async = */ ggml_backend_vk_get_tensor_async, + /* .set_tensor_2d_async = */ ggml_backend_vk_set_tensor_2d_async, + /* .get_tensor_2d_async = */ ggml_backend_vk_get_tensor_2d_async, + /* .cpy_tensor_async = */ ggml_backend_vk_cpy_tensor_async, + /* .synchronize = */ ggml_backend_vk_synchronize, + /* .graph_plan_create = */ NULL, + /* .graph_plan_free = */ NULL, + /* .graph_plan_update = */ NULL, + /* .graph_plan_compute = */ NULL, + /* .graph_compute = */ ggml_backend_vk_graph_compute, + /* .event_record = */ ggml_backend_vk_event_record, + /* .event_wait = */ ggml_backend_vk_event_wait, + /* .graph_optimize = */ ggml_vk_graph_optimize, +}; + +static ggml_guid_t ggml_backend_vk_guid() { + static ggml_guid guid = { 0xb8, 0xf7, 0x4f, 0x86, 0x40, 0x3c, 0xe1, 0x02, 0x91, 0xc8, 0xdd, 0xe9, 0x02, 0x3f, 0xc0, 0x2b }; + return &guid; +} + +ggml_backend_t ggml_backend_vk_init(size_t dev_num) { + VK_LOG_DEBUG("ggml_backend_vk_init(" << dev_num << ")"); + + ggml_backend_vk_context * ctx = new ggml_backend_vk_context; + ggml_vk_init(ctx, dev_num); + + ggml_backend_t vk_backend = new ggml_backend { + /* .guid = */ ggml_backend_vk_guid(), + /* .iface = */ ggml_backend_vk_interface, + /* .device = */ ggml_backend_reg_dev_get(ggml_backend_vk_reg(), dev_num), + /* .context = */ ctx, + }; + + if (!ctx->device->support_async) { + vk_backend->iface.get_tensor_async = nullptr; + } + + return vk_backend; +} + +bool ggml_backend_is_vk(ggml_backend_t backend) { + return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_vk_guid()); +} + +int ggml_backend_vk_get_device_count() { + return ggml_vk_get_device_count(); +} + +void ggml_backend_vk_get_device_description(int device, char * description, size_t description_size) { + GGML_ASSERT(device < (int) vk_instance.device_indices.size()); + int dev_idx = vk_instance.device_indices[device]; + ggml_vk_get_device_description(dev_idx, description, description_size); +} + +void ggml_backend_vk_get_device_memory(int device, size_t * free, size_t * total) { + GGML_ASSERT(device < (int) vk_instance.device_indices.size()); + GGML_ASSERT(device < (int) vk_instance.device_supports_membudget.size()); + + vk::PhysicalDevice vkdev = vk_instance.instance.enumeratePhysicalDevices()[vk_instance.device_indices[device]]; + vk::PhysicalDeviceMemoryBudgetPropertiesEXT budgetprops; + vk::PhysicalDeviceMemoryProperties2 memprops = {}; + const bool membudget_supported = vk_instance.device_supports_membudget[device]; + const bool is_integrated_gpu = vkdev.getProperties().deviceType == vk::PhysicalDeviceType::eIntegratedGpu; + + if (membudget_supported) { + memprops.pNext = &budgetprops; + } + vkdev.getMemoryProperties2(&memprops); + + *total = 0; + *free = 0; + + for (uint32_t i = 0; i < memprops.memoryProperties.memoryHeapCount; ++i) { + const vk::MemoryHeap & heap = memprops.memoryProperties.memoryHeaps[i]; + + if (is_integrated_gpu || (heap.flags & vk::MemoryHeapFlagBits::eDeviceLocal)) { + *total += heap.size; + + if (membudget_supported && i < budgetprops.heapUsage.size()) { + *free += budgetprops.heapBudget[i] - budgetprops.heapUsage[i]; + } else { + *free += heap.size; + } + } + } +} + +static vk::PhysicalDeviceType ggml_backend_vk_get_device_type(int device_idx) { + GGML_ASSERT(device_idx >= 0 && device_idx < (int) vk_instance.device_indices.size()); + + vk::PhysicalDevice device = vk_instance.instance.enumeratePhysicalDevices()[vk_instance.device_indices[device_idx]]; + + vk::PhysicalDeviceProperties2 props = {}; + device.getProperties2(&props); + + return props.properties.deviceType; +} + +static std::string ggml_backend_vk_get_device_pci_id(int device_idx) { + GGML_ASSERT(device_idx >= 0 && device_idx < (int) vk_instance.device_indices.size()); + + vk::PhysicalDevice device = vk_instance.instance.enumeratePhysicalDevices()[vk_instance.device_indices[device_idx]]; + + const std::vector ext_props = device.enumerateDeviceExtensionProperties(); + + bool ext_support = false; + + for (const auto& properties : ext_props) { + if (strcmp("VK_EXT_pci_bus_info", properties.extensionName) == 0) { + ext_support = true; + break; + } + } + + if (!ext_support) { + return ""; + } + + vk::PhysicalDeviceProperties2 props = {}; + vk::PhysicalDevicePCIBusInfoPropertiesEXT pci_bus_info = {}; + + props.pNext = &pci_bus_info; + + device.getProperties2(&props); + + const uint32_t pci_domain = pci_bus_info.pciDomain; + const uint32_t pci_bus = pci_bus_info.pciBus; + const uint32_t pci_device = pci_bus_info.pciDevice; + const uint8_t pci_function = (uint8_t) pci_bus_info.pciFunction; // pci function is between 0 and 7, prevent printf overflow warning + + char pci_bus_id[16] = {}; + snprintf(pci_bus_id, sizeof(pci_bus_id), "%04x:%02x:%02x.%x", pci_domain, pci_bus, pci_device, pci_function); + + return std::string(pci_bus_id); +} + +////////////////////////// + +struct ggml_backend_vk_device_context { + size_t device; + std::string name; + std::string description; + bool is_integrated_gpu; + std::string pci_bus_id; + int op_offload_min_batch_size; +}; + +static const char * ggml_backend_vk_device_get_name(ggml_backend_dev_t dev) { + ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)dev->context; + return ctx->name.c_str(); +} + +static const char * ggml_backend_vk_device_get_description(ggml_backend_dev_t dev) { + ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)dev->context; + return ctx->description.c_str(); +} + +static void ggml_backend_vk_device_get_memory(ggml_backend_dev_t device, size_t * free, size_t * total) { + ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)device->context; + ggml_backend_vk_get_device_memory(ctx->device, free, total); +} + +static ggml_backend_buffer_type_t ggml_backend_vk_device_get_buffer_type(ggml_backend_dev_t dev) { + ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)dev->context; + return ggml_backend_vk_buffer_type(ctx->device); +} + +static ggml_backend_buffer_type_t ggml_backend_vk_device_get_host_buffer_type(ggml_backend_dev_t dev) { + UNUSED(dev); + return ggml_backend_vk_host_buffer_type(); +} + +static enum ggml_backend_dev_type ggml_backend_vk_device_get_type(ggml_backend_dev_t dev) { + ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)dev->context; + + return ctx->is_integrated_gpu ? GGML_BACKEND_DEVICE_TYPE_IGPU : GGML_BACKEND_DEVICE_TYPE_GPU; +} + +static void ggml_backend_vk_device_get_props(ggml_backend_dev_t dev, struct ggml_backend_dev_props * props) { + ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)dev->context; + + props->name = ggml_backend_vk_device_get_name(dev); + props->description = ggml_backend_vk_device_get_description(dev); + props->type = ggml_backend_vk_device_get_type(dev); + props->device_id = ctx->pci_bus_id.empty() ? nullptr : ctx->pci_bus_id.c_str(); + ggml_backend_vk_device_get_memory(dev, &props->memory_free, &props->memory_total); + props->caps = { + /* .async = */ true, + /* .host_buffer = */ true, + /* .buffer_from_host_ptr = */ false, + /* .events = */ true, + }; +} + +static ggml_backend_t ggml_backend_vk_device_init(ggml_backend_dev_t dev, const char * params) { + UNUSED(params); + ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)dev->context; + return ggml_backend_vk_init(ctx->device); +} + +static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggml_tensor * op) { + ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)dev->context; + const vk_device& device = ggml_vk_get_device(ctx->device); + + const bool uses_bda = (op->op == GGML_OP_IM2COL || op->op == GGML_OP_IM2COL_3D) && + device->shader_int64 && device->buffer_device_address; + + auto const & tensor_size_supported = [&](size_t tensor_size) { + if (tensor_size > device->max_buffer_size) { + return false; + } + // For im2col shaders using BDA, maxStorageBufferRange limit doesn't apply. + // If shader64BitIndexing is enabled, maxStorageBufferRange limit doesn't apply. + if (!uses_bda && !device->shader_64b_indexing) { + if (tensor_size > device->properties.limits.maxStorageBufferRange) { + return false; + } + } + return true; + }; + // reject any tensors larger than the max buffer size + for (int i = 0; i < GGML_MAX_SRC; i++) { + if (op->src[i] && !tensor_size_supported(ggml_nbytes(op->src[i]))) { + return false; + } + } + if (!tensor_size_supported(ggml_nbytes(op))) { + return false; + } + + switch (op->op) { + case GGML_OP_UNARY: + switch (ggml_get_unary_op(op)) { + case GGML_UNARY_OP_EXP: + case GGML_UNARY_OP_EXPM1: + case GGML_UNARY_OP_ELU: + case GGML_UNARY_OP_GELU: + case GGML_UNARY_OP_GELU_ERF: + case GGML_UNARY_OP_GELU_QUICK: + case GGML_UNARY_OP_SILU: + case GGML_UNARY_OP_RELU: + case GGML_UNARY_OP_XIELU: + case GGML_UNARY_OP_NEG: + case GGML_UNARY_OP_TANH: + case GGML_UNARY_OP_SIGMOID: + case GGML_UNARY_OP_HARDSIGMOID: + case GGML_UNARY_OP_HARDSWISH: + case GGML_UNARY_OP_ABS: + case GGML_UNARY_OP_SOFTPLUS: + case GGML_UNARY_OP_STEP: + case GGML_UNARY_OP_ROUND: + case GGML_UNARY_OP_CEIL: + case GGML_UNARY_OP_FLOOR: + case GGML_UNARY_OP_TRUNC: + case GGML_UNARY_OP_SGN: + return (op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16) && + (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16) && + (op->src[0]->type == op->type); + default: + return false; + } + case GGML_OP_GLU: + switch (ggml_get_glu_op(op)) { + case GGML_GLU_OP_GEGLU: + case GGML_GLU_OP_REGLU: + case GGML_GLU_OP_SWIGLU: + case GGML_GLU_OP_SWIGLU_OAI: + case GGML_GLU_OP_GEGLU_ERF: + case GGML_GLU_OP_GEGLU_QUICK: + return (op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16) && + (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16) && + (op->src[0]->type == op->type) && + (!op->src[1] || op->src[1]->type == op->src[0]->type); + default: + return false; + } + case GGML_OP_MUL_MAT: + case GGML_OP_MUL_MAT_ID: + { + ggml_type src0_type = op->src[0]->type; + if (op->op == GGML_OP_MUL_MAT_ID) { + if (!device->mul_mat_id_s[src0_type] && !device->mul_mat_id_m[src0_type] && !device->mul_mat_id_l[src0_type]) { + // If there's not enough shared memory for row_ids and the result tile, fallback to CPU + return false; + } + } + switch (src0_type) { + case GGML_TYPE_F32: + case GGML_TYPE_F16: + case GGML_TYPE_BF16: + case GGML_TYPE_Q1_0: + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q4_1: + case GGML_TYPE_Q5_0: + case GGML_TYPE_Q5_1: + case GGML_TYPE_Q8_0: + case GGML_TYPE_Q2_K: + case GGML_TYPE_Q3_K: + case GGML_TYPE_Q4_K: + case GGML_TYPE_Q5_K: + case GGML_TYPE_Q6_K: + case GGML_TYPE_IQ1_S: + case GGML_TYPE_IQ1_M: + case GGML_TYPE_IQ2_XXS: + case GGML_TYPE_IQ2_XS: + case GGML_TYPE_IQ2_S: + case GGML_TYPE_IQ3_XXS: + case GGML_TYPE_IQ3_S: + case GGML_TYPE_IQ4_XS: + case GGML_TYPE_IQ4_NL: + case GGML_TYPE_MXFP4: + case GGML_TYPE_NVFP4: + break; + default: + return false; + } + struct ggml_tensor * a; + struct ggml_tensor * b; + if (op->op == GGML_OP_MUL_MAT) { + a = op->src[0]; + b = op->src[1]; + } else { + a = op->src[2]; + b = op->src[1]; + } + if (a->ne[3] != b->ne[3]) { + return false; + } + if (!(ggml_vk_dim01_contiguous(op->src[0]) || op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16 || op->src[0]->type == GGML_TYPE_BF16) || + !(ggml_vk_dim01_contiguous(op->src[1]) || op->src[1]->type == GGML_TYPE_F32 || op->src[1]->type == GGML_TYPE_F16)) { + return false; + } + if (op->src[0]->type == GGML_TYPE_BF16 && op->src[1]->type == GGML_TYPE_F16) { + // We currently don't have a bf16 x f16 shader, or an fp16->bf16 copy shader. + // So don't support this combination for now. + return false; + } + + return true; + } + case GGML_OP_FLASH_ATTN_EXT: + { + bool coopmat2 = device->coopmat2; + uint32_t HSK = op->src[1]->ne[0]; + uint32_t HSV = op->src[2]->ne[0]; + if ((HSK % 8) != 0 || (HSV % 8) != 0) { + return false; + } + if (op->src[4] && op->src[4]->type != GGML_TYPE_F32) { + return false; + } + if (op->src[0]->type != GGML_TYPE_F32) { + return false; + } + if (op->type != GGML_TYPE_F32) { + return false; + } + if (op->src[3] && op->src[3]->type != GGML_TYPE_F16) { + return false; + } + auto fa_kv_ok = [coopmat2](ggml_type t) { + switch (t) { + case GGML_TYPE_F32: + case GGML_TYPE_F16: + case GGML_TYPE_BF16: + case GGML_TYPE_Q8_0: + case GGML_TYPE_Q5_1: + case GGML_TYPE_Q5_0: + case GGML_TYPE_Q4_1: + case GGML_TYPE_Q4_0: + return true; + case GGML_TYPE_Q1_0: + return coopmat2; + default: + return false; + } + }; + if (!fa_kv_ok(op->src[1]->type) || !fa_kv_ok(op->src[2]->type)) { + return false; + } + if ((op->src[1]->type == GGML_TYPE_BF16) != (op->src[2]->type == GGML_TYPE_BF16)) { + return false; + } + if (!coopmat2 && !(device->subgroup_shuffle && device->subgroup_vote)) { + // scalar/coopmat1 FA uses subgroupShuffle/subgroupAll + return false; + } + return true; + } + case GGML_OP_GET_ROWS: + { + switch (op->src[0]->type) { + case GGML_TYPE_F32: + case GGML_TYPE_F16: + case GGML_TYPE_BF16: + case GGML_TYPE_Q1_0: + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q4_1: + case GGML_TYPE_Q5_0: + case GGML_TYPE_Q5_1: + case GGML_TYPE_Q8_0: + case GGML_TYPE_Q2_K: + case GGML_TYPE_Q3_K: + case GGML_TYPE_Q4_K: + case GGML_TYPE_Q5_K: + case GGML_TYPE_Q6_K: + case GGML_TYPE_IQ1_S: + case GGML_TYPE_IQ1_M: + case GGML_TYPE_IQ2_XXS: + case GGML_TYPE_IQ2_XS: + case GGML_TYPE_IQ2_S: + case GGML_TYPE_IQ3_XXS: + case GGML_TYPE_IQ3_S: + case GGML_TYPE_IQ4_XS: + case GGML_TYPE_IQ4_NL: + case GGML_TYPE_MXFP4: + case GGML_TYPE_NVFP4: + case GGML_TYPE_I32: + return true; + default: + return false; + } + } + case GGML_OP_GET_ROWS_BACK: + return op->type == GGML_TYPE_F32 && op->src[0]->type == GGML_TYPE_F32; + case GGML_OP_SET_ROWS: + { + if (op->src[0]->type == GGML_TYPE_F32) { + switch (op->type) { + case GGML_TYPE_F32: + case GGML_TYPE_F16: + case GGML_TYPE_BF16: + case GGML_TYPE_Q1_0: + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q4_1: + case GGML_TYPE_Q5_0: + case GGML_TYPE_Q5_1: + case GGML_TYPE_Q8_0: + case GGML_TYPE_IQ4_NL: + return true; + default: + return false; + } + } + return false; + } + case GGML_OP_CONT: + case GGML_OP_CPY: + case GGML_OP_DUP: + { + ggml_type src0_type = op->src[0]->type; + ggml_type src1_type = op->src[1] != nullptr ? op->src[1]->type : src0_type; + + if (src0_type == GGML_TYPE_F32) { + switch (src1_type) { + case GGML_TYPE_F32: + case GGML_TYPE_F16: + case GGML_TYPE_BF16: + case GGML_TYPE_Q1_0: + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q4_1: + case GGML_TYPE_Q5_0: + case GGML_TYPE_Q5_1: + case GGML_TYPE_Q8_0: + case GGML_TYPE_IQ4_NL: + return true; + default: + break; + } + } + if (src1_type == GGML_TYPE_F32) { + switch (src0_type) { + case GGML_TYPE_F16: + case GGML_TYPE_BF16: + case GGML_TYPE_Q1_0: + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q4_1: + case GGML_TYPE_Q5_0: + case GGML_TYPE_Q5_1: + case GGML_TYPE_Q8_0: + case GGML_TYPE_IQ4_NL: + return true; + default: + break; + } + } + + if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F16) { + return true; + } + + if ( + (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_I32) || + (src0_type == GGML_TYPE_I32 && src1_type == GGML_TYPE_F32) + ) { + return true; + } + + // We can handle copying from a type to the same type if it's + // either not quantized or is quantized and contiguous. + // We use f16 or f32 shaders to do the copy, + // so the type/block size must be a multiple of 4. + if (src0_type == src1_type && + (!ggml_is_quantized(src0_type) || (ggml_is_contiguous(op->src[0]) && ggml_is_contiguous(op))) && + (ggml_type_size(src0_type) % 2) == 0) { + return true; + } + return false; + } + case GGML_OP_REPEAT: + return ggml_type_size(op->type) == ggml_type_size(op->src[0]->type) && + (ggml_type_size(op->type) == sizeof(float) || ggml_type_size(op->type) == 2); + case GGML_OP_REPEAT_BACK: + return op->type == GGML_TYPE_F32 && op->src[0]->type == GGML_TYPE_F32; + case GGML_OP_ROPE: + return ggml_is_contiguous_rows(op) && ggml_is_contiguous_rows(op->src[0]); + case GGML_OP_ROPE_BACK: + case GGML_OP_NONE: + case GGML_OP_RESHAPE: + case GGML_OP_VIEW: + case GGML_OP_PERMUTE: + case GGML_OP_TRANSPOSE: + case GGML_OP_RMS_NORM: + return true; + case GGML_OP_GROUP_NORM: + return ggml_is_contiguous(op->src[0]); + case GGML_OP_NORM: + case GGML_OP_L2_NORM: + return op->src[0]->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32; + case GGML_OP_ADD: + case GGML_OP_SUB: + case GGML_OP_MUL: + case GGML_OP_DIV: + return (op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16) && + (op->src[1]->type == GGML_TYPE_F32 || op->src[1]->type == GGML_TYPE_F16) && + (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16); + case GGML_OP_ADD_ID: + return op->src[0]->type == GGML_TYPE_F32 && op->src[1]->type == GGML_TYPE_F32 && op->src[2]->type == GGML_TYPE_I32 && + op->type == GGML_TYPE_F32; + case GGML_OP_SILU_BACK: + case GGML_OP_RMS_NORM_BACK: + return ggml_is_contiguous(op->src[0]) && op->src[0]->type == GGML_TYPE_F32; + case GGML_OP_SQR: + case GGML_OP_SQRT: + case GGML_OP_SIN: + case GGML_OP_COS: + case GGML_OP_CLAMP: + case GGML_OP_LEAKY_RELU: + return (op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16) && + op->type == op->src[0]->type; + case GGML_OP_OPT_STEP_ADAMW: + case GGML_OP_OPT_STEP_SGD: + return ggml_is_contiguous(op->src[0]) && op->src[0]->type == GGML_TYPE_F32; + case GGML_OP_LOG: + case GGML_OP_TRI: + case GGML_OP_DIAG: + return (op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16) && + op->type == op->src[0]->type; + case GGML_OP_ARGSORT: + { + if (!ggml_is_contiguous(op) || !ggml_is_contiguous(op->src[0])) { + return false; + } + // pipeline_argsort_large_f32 requires vulkan memory model. + if (device->vulkan_memory_model) { + return true; + } else { + return op->ne[0] <= (1 << device->max_workgroup_size_log2); + } + } + case GGML_OP_TOP_K: + { + if (!ggml_is_contiguous(op) || !ggml_is_contiguous(op->src[0])) { + return false; + } + // We could potentially support larger, using argsort to sort the + // whole thing. Not clear if this is needed. + uint32_t min_pipeline = (uint32_t)log2f(float(op->ne[0])) + 1; + if (min_pipeline >= num_topk_pipelines || + !device->pipeline_topk_f32[min_pipeline]) { + return false; + } + } + return true; + case GGML_OP_UPSCALE: + if (op->op_params[0] & GGML_SCALE_FLAG_ANTIALIAS) { + if ((op->op_params[0] & 0xFF) != GGML_SCALE_MODE_BILINEAR) { + return false; + } + } + return op->src[0]->type == GGML_TYPE_F32; + case GGML_OP_ACC: + return op->src[0]->type == GGML_TYPE_F32 && op->src[1]->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32; + case GGML_OP_SET: + return op->src[0]->type == op->src[1]->type && op->src[0]->type == op->type && + (op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_I32); + case GGML_OP_CONCAT: { + if (op->src[0]->type != op->src[1]->type || op->src[0]->type != op->type) { + return false; + } + const size_t type_size = ggml_type_size(op->type); + return ggml_blck_size(op->type) == 1 && + (type_size == 1 || type_size == 2 || type_size == 4 || type_size == 8); + } + case GGML_OP_ADD1: + return (op->src[0]->type == GGML_TYPE_F32 && op->src[1]->type == GGML_TYPE_F32) + || (op->src[0]->type == GGML_TYPE_F16 && op->src[1]->type == GGML_TYPE_F32) + || (op->src[0]->type == GGML_TYPE_F16 && op->src[1]->type == GGML_TYPE_F16); + case GGML_OP_ARANGE: + return op->type == GGML_TYPE_F32; + case GGML_OP_FILL: + return op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16; + case GGML_OP_SCALE: + return ggml_is_contiguous(op->src[0]) && op->src[0]->type == GGML_TYPE_F32; + case GGML_OP_PAD: + case GGML_OP_ROLL: + return op->src[0]->type == GGML_TYPE_F32; + case GGML_OP_DIAG_MASK_INF: + return ggml_is_contiguous(op->src[0]) && op->src[0]->type == GGML_TYPE_F32; + case GGML_OP_SOFT_MAX: + return ggml_is_contiguous(op->src[0]) && op->src[0]->type == GGML_TYPE_F32 + && (!op->src[1] || (op->src[1]->type == GGML_TYPE_F32 || op->src[1]->type == GGML_TYPE_F16)); + case GGML_OP_SOFT_MAX_BACK: + return ggml_is_contiguous(op->src[0]) && op->src[0]->type == GGML_TYPE_F32 + && ggml_is_contiguous(op->src[1]) && op->src[1]->type == GGML_TYPE_F32; + case GGML_OP_SUM: + case GGML_OP_SUM_ROWS: + case GGML_OP_MEAN: + return op->src[0]->type == GGML_TYPE_F32 && ggml_is_contiguous_rows(op->src[0]); + case GGML_OP_CUMSUM: + { + if (device->subgroup_arithmetic && device->subgroup_require_full_support) { + return op->src[0]->type == GGML_TYPE_F32 && ggml_is_contiguous_rows(op->src[0]); + } + return false; + } + case GGML_OP_SOLVE_TRI: + { + if (op->type != GGML_TYPE_F32 || op->src[0]->type != GGML_TYPE_F32) { + return false; + } + 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 > 1u << device->max_workgroup_size_log2) { + return false; + } + const uint32_t batch_N = device->properties.limits.maxComputeSharedMemorySize / ((N + K) * sizeof(float)); + + if (batch_N == 0) { + return false; + } + return true; + } + case GGML_OP_ARGMAX: + return ggml_is_contiguous(op->src[0]) && op->src[0]->type == GGML_TYPE_F32; + case GGML_OP_COUNT_EQUAL: + return ggml_is_contiguous(op->src[0]) && op->src[0]->type == GGML_TYPE_I32 + && ggml_is_contiguous(op->src[1]) && op->src[1]->type == GGML_TYPE_I32; + case GGML_OP_IM2COL: + return ggml_is_contiguous(op->src[1]) + && op->src[1]->type == GGML_TYPE_F32 + && (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16); + case GGML_OP_IM2COL_3D: + return op->src[1]->type == GGML_TYPE_F32 + && (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16); + case GGML_OP_TIMESTEP_EMBEDDING: + return op->src[0]->type == GGML_TYPE_F32; + case GGML_OP_CONV_2D_DW: + return (op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16) + && op->src[1]->type == GGML_TYPE_F32; + case GGML_OP_POOL_2D: + return ggml_is_contiguous(op->src[0]) && op->src[0]->type == GGML_TYPE_F32; + case GGML_OP_RWKV_WKV6: + case GGML_OP_RWKV_WKV7: + return true; // all inputs are contiguous, see ggml.c + case GGML_OP_GATED_DELTA_NET: + { + const uint32_t S_v = op->src[2]->ne[0]; + if (S_v != 16 && S_v != 32 && S_v != 64 && S_v != 128) { + return false; + } + for (int i = 0; i < 6; i++) { + if (op->src[i] == nullptr || op->src[i]->type != GGML_TYPE_F32) { + return false; + } + } + return op->type == GGML_TYPE_F32; + } + case GGML_OP_SSM_SCAN: + { + for (int i = 0; i < 6; i++) { + if (op->src[i] && ggml_is_quantized(op->src[i]->type)) { + return false; + } + } + if (op->src[6] && op->src[6]->type != GGML_TYPE_I32) { + return false; + } + if (op->src[0]->type != GGML_TYPE_F32 || op->type != GGML_TYPE_F32) { + return false; + } + + const uint32_t d_state = op->src[0]->ne[0]; + const uint32_t head_dim = op->src[0]->ne[1]; + + bool is_mamba2 = (op->src[3] && op->src[3]->nb[1] == sizeof(float)); + if (!is_mamba2) { + return false; + } + + if ((d_state != 128 && d_state != 256) || head_dim % 16 != 0) { + return false; + } + + size_t shmem_size = d_state * sizeof(float); + + if (shmem_size > device->properties.limits.maxComputeSharedMemorySize) { + return false; + } + + if (!device->subgroup_basic) { + return false; + } + + return true; + } + case GGML_OP_SSM_CONV: + return op->src[0]->type == GGML_TYPE_F32; + case GGML_OP_CONV_TRANSPOSE_1D: + return op->src[0]->type == GGML_TYPE_F32 && op->src[1]->type == GGML_TYPE_F32; + case GGML_OP_COL2IM_1D: + return (op->src[0]->type == GGML_TYPE_F32 || + op->src[0]->type == GGML_TYPE_F16 || + op->src[0]->type == GGML_TYPE_BF16) && + op->type == op->src[0]->type && + ggml_is_contiguous(op->src[0]) && + ggml_is_contiguous(op); + case GGML_OP_CONV_2D: + case GGML_OP_CONV_TRANSPOSE_2D: + { + // 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 && + op->type == GGML_TYPE_F32 && + ggml_is_contiguous(op->src[0]) && + ggml_is_contiguous(op->src[1]) && + ggml_is_contiguous(op)); + } + case GGML_OP_CONV_3D: + return (op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16) && + op->src[1]->type == GGML_TYPE_F32 && + op->type == GGML_TYPE_F32 && + ggml_is_contiguous(op->src[0]) && + ggml_is_contiguous(op->src[1]) && + ggml_is_contiguous(op); + default: + return false; + } + + UNUSED(dev); +} + +static bool ggml_backend_vk_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) { + if (buft->iface.get_name != ggml_backend_vk_buffer_type_name) { + return false; + } + + ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)dev->context; + ggml_backend_vk_buffer_type_context * buft_ctx = (ggml_backend_vk_buffer_type_context *)buft->context; + + return buft_ctx->device->idx == ctx->device; +} + +static int64_t ggml_vk_get_op_batch_size(const ggml_tensor * op) { + switch (op->op) { + case GGML_OP_GET_ROWS: + return 0; + case GGML_OP_MUL_MAT: + return op->ne[1]; + case GGML_OP_MUL_MAT_ID: + case GGML_OP_ROPE: + case GGML_OP_ROPE_BACK: + return op->ne[2]; + default: + return ggml_nrows(op); + } +} + +static bool ggml_backend_vk_device_offload_op(ggml_backend_dev_t dev, const ggml_tensor * op) { + ggml_backend_vk_device_context * dev_ctx = (ggml_backend_vk_device_context *)dev->context; + + return ggml_vk_get_op_batch_size(op) >= dev_ctx->op_offload_min_batch_size; +} + +static ggml_backend_event_t ggml_backend_vk_device_event_new(ggml_backend_dev_t dev) { + ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)dev->context; + auto device = ggml_vk_get_device(ctx->device); + + vk_event *vkev = new vk_event; + if (!vkev) { + return nullptr; + } + + // No events initially, they get created on demand + vkev->has_event = false; + + vk::SemaphoreTypeCreateInfo tci{ vk::SemaphoreType::eTimeline, 0 }; + vk::SemaphoreCreateInfo ci{}; + ci.setPNext(&tci); + vkev->tl_semaphore = { device->device.createSemaphore(ci), 0 }; + + return new ggml_backend_event { + /* .device = */ dev, + /* .context = */ vkev, + }; +} + +static void ggml_backend_vk_device_event_free(ggml_backend_dev_t dev, ggml_backend_event_t event) { + ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)dev->context; + auto device = ggml_vk_get_device(ctx->device); + + vk_event *vkev = (vk_event *)event->context; + + device->device.destroySemaphore(vkev->tl_semaphore.s); + for (auto& event : vkev->events_free) { + device->device.destroyEvent(event); + } + for (auto& event : vkev->events_submitted) { + device->device.destroyEvent(event); + } + if (vkev->has_event) { + device->device.destroyEvent(vkev->event); + } + delete vkev; + delete event; +} + +static void ggml_backend_vk_device_event_synchronize(ggml_backend_dev_t dev, ggml_backend_event_t event) { + VK_LOG_DEBUG("ggml_backend_vk_device_event_synchronize(backend=" << dev << ", event=" << event << ")"); + ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)dev->context; + auto device = ggml_vk_get_device(ctx->device); + vk_event *vkev = (vk_event *)event->context; + + // Only do something if the event has actually been used + if (vkev->has_event) { + vk::Semaphore sem = vkev->tl_semaphore.s; + uint64_t val = vkev->tl_semaphore.value; + vk::SemaphoreWaitInfo swi{vk::SemaphoreWaitFlags{}, sem, val}; + VK_CHECK(device->device.waitSemaphores(swi, UINT64_MAX), "event_synchronize"); + + // Reset and move submitted events + for (auto& event : vkev->events_submitted) { + device->device.resetEvent(event); + } + vkev->events_free.insert(vkev->events_free.end(), vkev->events_submitted.begin(), vkev->events_submitted.end()); + vkev->events_submitted.clear(); + + // Finished using current command buffer so we flag for reuse + if (vkev->cmd_buffer) { + // Only flag for reuse if it hasn't been reused already + if (vkev->cmd_buffer_use_counter == vkev->cmd_buffer->use_counter) { + vkev->cmd_buffer->in_use = false; + vkev->cmd_buffer->buf.reset(); + } + vkev->cmd_buffer = nullptr; + } + } +} + +static vk_buffer ggml_vk_buffer_from_host_ptr(vk_device & device, void * ptr, size_t size) { + if (!device->external_memory_host) { + return {}; + } + + uintptr_t uptr = reinterpret_cast(ptr); + if (uptr & (device->min_imported_host_pointer_alignment - 1)) { + return {}; + } + if (size & (device->min_imported_host_pointer_alignment - 1)) { + return {}; + } + + const vk::MemoryPropertyFlags property_flags = vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached; + + vk_buffer buf {}; + try { + buf = ggml_vk_create_buffer(device, size, { property_flags }, ptr); + } catch (vk::SystemError& e) { + GGML_LOG_WARN("ggml_vulkan: Failed ggml_vk_create_buffer (%s)\n", e.what()); + } + + return buf; +} + +static ggml_backend_buffer_t ggml_backend_vk_device_buffer_from_host_ptr(ggml_backend_dev_t dev, void * ptr, size_t size, size_t max_tensor_size) { + VK_LOG_DEBUG("ggml_backend_vk_device_buffer_from_host_ptr(backend=" << dev << ", ptr=" << ptr << ", size=" << size << ")"); + GGML_UNUSED(max_tensor_size); + + ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)dev->context; + auto device = ggml_vk_get_device(ctx->device); + + vk_buffer buf = ggml_vk_buffer_from_host_ptr(device, ptr, size); + + if (!buf) { + return {}; + } + + ggml_backend_vk_buffer_context * bufctx = new ggml_backend_vk_buffer_context(device, std::move(buf), device->name); + + ggml_backend_buffer_t ret = ggml_backend_buffer_init(ggml_backend_vk_device_get_buffer_type(dev), ggml_backend_vk_buffer_interface, bufctx, size); + + return ret; +} + +static const struct ggml_backend_device_i ggml_backend_vk_device_i = { + /* .get_name = */ ggml_backend_vk_device_get_name, + /* .get_description = */ ggml_backend_vk_device_get_description, + /* .get_memory = */ ggml_backend_vk_device_get_memory, + /* .get_type = */ ggml_backend_vk_device_get_type, + /* .get_props = */ ggml_backend_vk_device_get_props, + /* .init_backend = */ ggml_backend_vk_device_init, + /* .get_buffer_type = */ ggml_backend_vk_device_get_buffer_type, + /* .get_host_buffer_type = */ ggml_backend_vk_device_get_host_buffer_type, + /* .buffer_from_host_ptr = */ ggml_backend_vk_device_buffer_from_host_ptr, + /* .supports_op = */ ggml_backend_vk_device_supports_op, + /* .supports_buft = */ ggml_backend_vk_device_supports_buft, + /* .offload_op = */ ggml_backend_vk_device_offload_op, + /* .event_new = */ ggml_backend_vk_device_event_new, + /* .event_free = */ ggml_backend_vk_device_event_free, + /* .event_synchronize = */ ggml_backend_vk_device_event_synchronize, +}; + +static const char * ggml_backend_vk_reg_get_name(ggml_backend_reg_t reg) { + UNUSED(reg); + return GGML_VK_NAME; +} + +static size_t ggml_backend_vk_reg_get_device_count(ggml_backend_reg_t reg) { + UNUSED(reg); + return ggml_backend_vk_get_device_count(); +} + +static ggml_backend_dev_t ggml_backend_vk_reg_get_device(ggml_backend_reg_t reg, size_t device) { + static std::vector devices; + + static bool initialized = false; + + { + static std::mutex mutex; + std::lock_guard lock(mutex); + if (!initialized) { + const int min_batch_size = getenv("GGML_OP_OFFLOAD_MIN_BATCH") ? atoi(getenv("GGML_OP_OFFLOAD_MIN_BATCH")) : 32; + for (int i = 0; i < ggml_backend_vk_get_device_count(); i++) { + ggml_backend_vk_device_context * ctx = new ggml_backend_vk_device_context; + char desc[256]; + ggml_backend_vk_get_device_description(i, desc, sizeof(desc)); + ctx->device = i; + ctx->name = GGML_VK_NAME + std::to_string(i); + ctx->description = desc; + ctx->is_integrated_gpu = ggml_backend_vk_get_device_type(i) == vk::PhysicalDeviceType::eIntegratedGpu; + ctx->pci_bus_id = ggml_backend_vk_get_device_pci_id(i); + ctx->op_offload_min_batch_size = min_batch_size; + devices.push_back(new ggml_backend_device { + /* .iface = */ ggml_backend_vk_device_i, + /* .reg = */ reg, + /* .context = */ ctx, + }); + } + initialized = true; + } + } + + GGML_ASSERT(device < devices.size()); + return devices[device]; +} + +static const struct ggml_backend_reg_i ggml_backend_vk_reg_i = { + /* .get_name = */ ggml_backend_vk_reg_get_name, + /* .get_device_count = */ ggml_backend_vk_reg_get_device_count, + /* .get_device = */ ggml_backend_vk_reg_get_device, + /* .get_proc_address = */ NULL, +}; + +ggml_backend_reg_t ggml_backend_vk_reg() { + static ggml_backend_reg reg = { + /* .api_version = */ GGML_BACKEND_API_VERSION, + /* .iface = */ ggml_backend_vk_reg_i, + /* .context = */ nullptr, + }; + try { + ggml_vk_instance_init(); + return ® + } catch (const vk::SystemError& e) { + VK_LOG_DEBUG("ggml_backend_vk_reg() -> Error: System error: " << e.what()); + return nullptr; + } catch (const std::exception &e) { + VK_LOG_DEBUG("ggml_backend_vk_reg() -> Error: " << e.what()); + return nullptr; + } catch (...) { + VK_LOG_DEBUG("ggml_backend_vk_reg() -> Error: unknown exception during Vulkan init"); + return nullptr; + } +} + +// Extension availability +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_layer_settings", ext.extensionName.data()) == 0) { + return true; + } + } + } + } + + std::cerr << "ggml_vulkan: WARNING: Validation layer or layer extension VK_EXT_layer_settings not found." << std::endl; +#endif + return false; +} +static bool ggml_vk_instance_portability_enumeration_ext_available(const std::vector& instance_extensions) { +#ifdef __APPLE__ + // Check for portability enumeration extension for MoltenVK support + for (const auto& properties : instance_extensions) { + if (strcmp("VK_KHR_portability_enumeration", properties.extensionName) == 0) { + return true; + } + } + std::cerr << "ggml_vulkan: WARNING: Instance extension VK_KHR_portability_enumeration not found." << std::endl; +#endif + return false; + + UNUSED(instance_extensions); +} + +// Extension availability +static bool ggml_vk_instance_debug_utils_ext_available( + const std::vector & instance_extensions) { + // Check for portability enumeration extension for MoltenVK support + for (const auto & properties : instance_extensions) { + if (strcmp("VK_EXT_debug_utils", properties.extensionName) == 0) { + return true; + } + } + + std::cerr << "ggml_vulkan: WARNING: Instance extension VK_EXT_debug_utils not found." << std::endl; + return false; + + UNUSED(instance_extensions); +} + +static bool ggml_vk_device_is_supported(const vk::PhysicalDevice & vkdev) { + VkPhysicalDeviceFeatures2 device_features2; + device_features2.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_FEATURES_2; + + VkPhysicalDeviceVulkan11Features vk11_features; + vk11_features.pNext = nullptr; + vk11_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_VULKAN_1_1_FEATURES; + device_features2.pNext = &vk11_features; + + vkGetPhysicalDeviceFeatures2(vkdev, &device_features2); + + return vk11_features.storageBuffer16BitAccess; +} + +static bool ggml_vk_khr_cooperative_matrix_support(const vk::PhysicalDeviceProperties& props, const vk::PhysicalDeviceDriverProperties& driver_props, vk_device_architecture arch) { + switch (props.vendorID) { + case VK_VENDOR_ID_INTEL: + // Only allowing Xe2/Xe3 GPU and integrated Xe GPUs at the moment since older hardware (ex. Arc A770) has performance regressions. + return (arch == vk_device_architecture::INTEL_XE2) || + (arch == vk_device_architecture::INTEL_XE1 && props.deviceType == vk::PhysicalDeviceType::eIntegratedGpu && driver_props.driverID == vk::DriverId::eIntelProprietaryWindows); + case VK_VENDOR_ID_AMD: + if (driver_props.driverID == vk::DriverId::eAmdProprietary || driver_props.driverID == vk::DriverId::eAmdOpenSource) { + // Workaround for AMD proprietary driver reporting support on all GPUs + return arch == vk_device_architecture::AMD_RDNA3; + } + return true; + default: + return true; + } +} + +static uint32_t ggml_vk_intel_shader_core_count(const vk::PhysicalDevice& vkdev) { + VkPhysicalDeviceProperties2 props = vkdev.getProperties2(); + + if (props.properties.vendorID != VK_VENDOR_ID_INTEL) { + return 0; + } + + const uint32_t device_id = props.properties.deviceID; + + switch (device_id) { + case 0x56A6: // A310 + return 6; + case 0x5693: // A370M + case 0x56A5: // A380 + case 0x56B1: // Pro A40/A50 + return 8; + case 0x5697: // A530M + return 12; + case 0x5692: // A550M + case 0x56B3: // Pro A60 + return 16; + case 0x56A2: // A580 + return 24; + case 0x5691: // A730M + case 0x56A1: // A750 + return 28; + case 0x56A0: // A770 + case 0x5690: // A770M + return 32; + case 0xE212: // Pro B50 + return 16; + case 0xE20C: // B570 + return 18; + case 0xE20B: // B580 + case 0xE211: // Pro B60 + return 20; + case 0xB080: // PTL Xe3 LPG 2x6 (12 subslices) + return 12; + default: + return 0; + } +} + +// checks + +#ifdef GGML_VULKAN_CHECK_RESULTS +static void ggml_vk_print_graph_origin(const ggml_tensor * tensor, std::vector& done, int level = 0) { + if (std::find(done.begin(), done.end(), tensor) != done.end() || level > 10) { + return; + } + for (int j = 0; j < level; j++) { + std::cerr << " "; + } + std::cerr << ggml_op_name(tensor->op) << " gpu=" << (tensor->extra != nullptr) << std::endl; + + done.push_back(tensor); + + for (int i = 0; i < GGML_MAX_SRC; i++) { + if (tensor->src[i] != nullptr) { + ggml_vk_print_graph_origin(tensor->src[i], done, level + 1); + } + } +} + +static void ggml_vk_print_tensor_area(const ggml_tensor * tensor, const void * data, int i0, int i1, int i2, int i3) { + if (tensor->type != GGML_TYPE_F32 && tensor->type != GGML_TYPE_F16 && tensor->type != GGML_TYPE_I32) { + return; + } + i0 = std::max(i0, 5); + i1 = std::max(i1, 5); + i2 = std::max(i2, 0); + i3 = std::max(i3, 0); + fprintf(stderr, " "); + for (int idx1 = i1 - 5; idx1 < i1 + 5; idx1++) { + fprintf(stderr, "%7d ", idx1); + } + fprintf(stderr, "\n"); + for (int idx0 = i0 - 5; idx0 < i0 + 5; idx0++) { + fprintf(stderr, "%7d: ", idx0); + for (int idx1 = i1 - 5; idx1 < i1 + 5; idx1++) { + if (idx0 >= 0 && idx0 < tensor->ne[0] && idx1 >= 0 && idx1 < tensor->ne[1] && i2 >= 0 && i2 < tensor->ne[2] && i3 >= 0 && i3 < tensor->ne[3]) { + float val; + if (tensor->type == GGML_TYPE_F32) { + val = *(const float *) ((const char *) data + i3*tensor->nb[3] + i2*tensor->nb[2] + idx1*tensor->nb[1] + idx0*tensor->nb[0]); + } else if (tensor->type == GGML_TYPE_F16) { + val = ggml_fp16_to_fp32(*(const ggml_fp16_t *) ((const char *) data + i3*tensor->nb[3] + i2*tensor->nb[2] + idx1*tensor->nb[1] + idx0*tensor->nb[0])); + } else if (tensor->type == GGML_TYPE_I32) { + val = *(const int32_t *) ((const char *) data + i3*tensor->nb[3] + i2*tensor->nb[2] + idx1*tensor->nb[1] + idx0*tensor->nb[0]); + } else { + GGML_ABORT("fatal error"); + } + fprintf(stderr, "% 7.2f ", val); + } else { + fprintf(stderr, " "); + } + } + fprintf(stderr, "\n"); + } +} + +static void ggml_vk_print_tensor(const ggml_tensor * tensor, const char * name) { + void * tensor_data = tensor->data; + + const bool is_gpu = tensor->buffer != nullptr && ggml_backend_buffer_is_vk(tensor->buffer); + + if (is_gpu) { + const size_t tensor_size = ggml_nbytes(tensor); + tensor_data = malloc(tensor_size); + + ggml_backend_vk_buffer_context * buf_ctx = (ggml_backend_vk_buffer_context *)tensor->buffer->context; + + vk_buffer buffer_gpu = buf_ctx->dev_buffer; + ggml_vk_buffer_read(buffer_gpu, vk_tensor_offset(tensor) + tensor->view_offs, tensor_data, tensor_size); + } + + std::cerr << "TENSOR CHECK " << name << " (" << tensor->name << "): " << ggml_op_name(tensor->op) << std::endl; + std::cerr << "tensor=" << tensor << " tensor->type: " << ggml_type_name(tensor->type) << " ne0=" << tensor->ne[0] << " nb0=" << tensor->nb[0] << " ne1=" << tensor->ne[1] << " nb1=" << tensor->nb[1] << " ne2=" << tensor->ne[2] << " nb2=" << tensor->nb[2] << " ne3=" << tensor->ne[3] << " nb3=" << tensor->nb[3] << std::endl; + if (tensor->src[0] != nullptr) { + std::cerr << "tensor->src[0]=" << tensor->src[0] << " name=" << tensor->src[0]->name << " op=" << ggml_op_name(tensor->src[0]->op) << " type=" << ggml_type_name(tensor->src[0]->type) << " ne0=" << tensor->src[0]->ne[0] << " nb0=" << tensor->src[0]->nb[0] << " ne1=" << tensor->src[0]->ne[1] << " nb1=" << tensor->src[0]->nb[1] << " ne2=" << tensor->src[0]->ne[2] << " nb2=" << tensor->src[0]->nb[2] << " ne3=" << tensor->src[0]->ne[3] << " nb3=" << tensor->src[0]->nb[3] << std::endl; + } + if (tensor->src[1] != nullptr) { + std::cerr << "tensor->src[1]=" << tensor->src[1] << " name=" << tensor->src[1]->name << " op=" << ggml_op_name(tensor->src[1]->op) << " type=" << ggml_type_name(tensor->src[1]->type) << " ne0=" << tensor->src[1]->ne[0] << " nb0=" << tensor->src[1]->nb[0] << " ne1=" << tensor->src[1]->ne[1] << " nb1=" << tensor->src[1]->nb[1] << " ne2=" << tensor->src[1]->ne[2] << " nb2=" << tensor->src[1]->nb[2] << " ne3=" << tensor->src[1]->ne[3] << " nb3=" << tensor->src[1]->nb[3] << std::endl; + } + std::cerr << std::endl << "Result:" << std::endl; + ggml_vk_print_tensor_area(tensor, tensor_data, 5, 5, 0, 0); + std::cerr << std::endl; + std::vector done; + ggml_vk_print_graph_origin(tensor, done); + + if (is_gpu) { + free(tensor_data); + } +} + +void * comp_result; +size_t comp_size; +size_t comp_nb[GGML_MAX_DIMS]; +size_t check_counter = 0; +static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_cgraph * cgraph, int tensor_idx) { + ggml_tensor * tensor = cgraph->nodes[tensor_idx + ctx->num_additional_fused_ops]; + if (tensor->op == GGML_OP_TRANSPOSE || tensor->op == GGML_OP_SET_ROWS) { + return; + } + + check_counter++; + if (!(vk_output_tensor > 0 && vk_output_tensor == check_counter) && check_counter <= vk_skip_checks) { + return; + } + + VK_LOG_DEBUG("ggml_vk_check_results_0(" << tensor->name << ")"); + + struct ggml_init_params iparams = { + /*.mem_size =*/ 2ul*1024ul*1024ul*1024ul, + /*.mem_buffer =*/ NULL, + /*.no_alloc =*/ false, + }; + + struct ggml_context * ggml_ctx = ggml_init(iparams); + + std::array src_clone = {nullptr, nullptr, nullptr, nullptr, nullptr, nullptr, nullptr, nullptr, nullptr, nullptr}; + const char * srci_name[GGML_MAX_SRC] = {"src0", "src1", "src2", "src3", "src4", "src5", "src6", "src7", "src8", "src9"}; + + std::map cloned_tensors; + std::vector cloned_mallocs; + + struct ggml_tensor * tensor_clone = nullptr; + + for (int f = 0; f < ctx->num_additional_fused_ops + 1; ++f) { + tensor = cgraph->nodes[tensor_idx + f]; + for (int i = 0; i < GGML_MAX_SRC; i++) { + ggml_tensor * srci = tensor->src[i]; + if (srci == nullptr) { + continue; + } + // If a src tensor has been cloned, use that one + auto it = cloned_tensors.find(srci); + if (it != cloned_tensors.end()) { + src_clone[i] = it->second; + continue; + } + ggml_tensor * srci_clone = ggml_dup_tensor(ggml_ctx, srci); + size_t srci_size = ggml_nbytes(srci); + + src_clone[i] = srci_clone; + void *src_buffer = malloc(srci_size); + cloned_mallocs.push_back(src_buffer); + + srci_clone->data = src_buffer; + if (ggml_backend_buffer_is_host(srci->buffer)) { + memcpy(srci_clone->data, srci->data, srci_size); + memcpy(srci_clone->nb, srci->nb, sizeof(size_t) * GGML_MAX_DIMS); + } else if (ggml_backend_buffer_is_vk(srci->buffer)) { + ggml_backend_vk_buffer_context * buf_ctx = (ggml_backend_vk_buffer_context *)srci->buffer->context; + vk_buffer& buffer_gpu = buf_ctx->dev_buffer; + uint64_t offset = vk_tensor_offset(srci) + srci->view_offs; + if (!ggml_is_contiguous(srci) && ggml_vk_dim01_contiguous(srci)) { + for (int i3 = 0; i3 < srci->ne[3]; i3++) { + for (int i2 = 0; i2 < srci->ne[2]; i2++) { + const int idx = i3*srci->ne[2] + i2; + ggml_vk_buffer_read(buffer_gpu, offset + idx * srci->nb[2], ((char *)srci_clone->data + idx * srci_clone->nb[2]), srci->ne[1] * srci->nb[1]); + } + } + + srci_clone->nb[0] = srci->nb[0]; + srci_clone->nb[1] = srci->nb[1]; + for (int i = 2; i < GGML_MAX_DIMS; i++) { + srci_clone->nb[i] = srci_clone->nb[i - 1]*srci_clone->ne[i - 1]; + } + } else { + if (offset + srci_size >= buffer_gpu->size) { + srci_size = buffer_gpu->size - offset; + } + ggml_vk_buffer_read(buffer_gpu, offset, srci_clone->data, srci_size); + memcpy(srci_clone->nb, srci->nb, sizeof(size_t) * GGML_MAX_DIMS); + } + } else { + GGML_ABORT("fatal error"); + } + + if (vk_output_tensor > 0 && vk_output_tensor == check_counter) { + ggml_vk_print_tensor(srci, srci_name[i]); + } + } + + if (tensor->op == GGML_OP_FLASH_ATTN_EXT) { + const float * params = (const float *)tensor->op_params; + tensor_clone = ggml_flash_attn_ext(ggml_ctx, src_clone[0], src_clone[1], src_clone[2], src_clone[3], params[0], params[1], params[2]); + if (src_clone[4]) { + ggml_flash_attn_ext_add_sinks(tensor_clone, src_clone[4]); + } + } else if (tensor->op == GGML_OP_MUL_MAT) { + tensor_clone = ggml_mul_mat(ggml_ctx, src_clone[0], src_clone[1]); + } else if (tensor->op == GGML_OP_MUL_MAT_ID) { + tensor_clone = ggml_mul_mat_id(ggml_ctx, src_clone[0], src_clone[1], src_clone[2]); + } else if (tensor->op == GGML_OP_SUB) { + tensor_clone = ggml_sub(ggml_ctx, src_clone[0], src_clone[1]); + } else if (tensor->op == GGML_OP_MUL) { + tensor_clone = ggml_mul(ggml_ctx, src_clone[0], src_clone[1]); + } else if (tensor->op == GGML_OP_DIV) { + tensor_clone = ggml_div(ggml_ctx, src_clone[0], src_clone[1]); + } else if (tensor->op == GGML_OP_CONCAT) { + tensor_clone = ggml_concat(ggml_ctx, src_clone[0], src_clone[1], *(int *)tensor->op_params); + } else if (tensor->op == GGML_OP_UPSCALE) { + tensor_clone = ggml_interpolate(ggml_ctx, src_clone[0], tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3], (ggml_scale_mode) tensor->op_params[0]); + } else if (tensor->op == GGML_OP_SCALE) { + const float * params = (const float *)tensor->op_params; + tensor_clone = ggml_scale_bias(ggml_ctx, src_clone[0], params[0], params[1]); + } else if (tensor->op == GGML_OP_ADD1) { + tensor_clone = ggml_add1(ggml_ctx, src_clone[0], src_clone[1]); + } else if (tensor->op == GGML_OP_ARANGE) { + const float start = ggml_get_op_params_f32(tensor, 0); + const float stop = ggml_get_op_params_f32(tensor, 1); + const float step = ggml_get_op_params_f32(tensor, 2); + tensor_clone = ggml_arange(ggml_ctx, start, stop, step); + } else if (tensor->op == GGML_OP_FILL) { + const float value = ggml_get_op_params_f32(tensor, 0); + tensor_clone = ggml_fill(ggml_ctx, src_clone[0], value); + } else if (tensor->op == GGML_OP_SQR) { + tensor_clone = ggml_sqr(ggml_ctx, src_clone[0]); + } else if (tensor->op == GGML_OP_SQRT) { + tensor_clone = ggml_sqrt(ggml_ctx, src_clone[0]); + } else if (tensor->op == GGML_OP_SIN) { + tensor_clone = ggml_sin(ggml_ctx, src_clone[0]); + } else if (tensor->op == GGML_OP_COS) { + tensor_clone = ggml_cos(ggml_ctx, src_clone[0]); + } else if (tensor->op == GGML_OP_LOG) { + tensor_clone = ggml_log(ggml_ctx, src_clone[0]); + } else if (tensor->op == GGML_OP_TRI) { + tensor_clone = ggml_tri(ggml_ctx, src_clone[0], (ggml_tri_type)ggml_get_op_params_i32(tensor, 0)); + } else if (tensor->op == GGML_OP_DIAG) { + tensor_clone = ggml_diag(ggml_ctx, src_clone[0]); + } else if (tensor->op == GGML_OP_CLAMP) { + const float * params = (const float *)tensor->op_params; + tensor_clone = ggml_clamp(ggml_ctx, src_clone[0], params[0], params[1]); + } else if (tensor->op == GGML_OP_PAD) { + tensor_clone = ggml_pad_ext(ggml_ctx, src_clone[0], tensor->op_params[0], tensor->op_params[1], tensor->op_params[2], tensor->op_params[3], + tensor->op_params[4], tensor->op_params[5], tensor->op_params[6], tensor->op_params[7]); + } else if (tensor->op == GGML_OP_REPEAT) { + tensor_clone = ggml_repeat(ggml_ctx, src_clone[0], tensor); + } else if (tensor->op == GGML_OP_REPEAT_BACK) { + tensor_clone = ggml_repeat_back(ggml_ctx, src_clone[0], tensor); + } else if (tensor->op == GGML_OP_ADD) { + tensor_clone = ggml_add(ggml_ctx, src_clone[0], src_clone[1]); + } else if (tensor->op == GGML_OP_ACC) { + tensor_clone = ggml_acc(ggml_ctx, src_clone[0], src_clone[1], tensor->op_params[0], tensor->op_params[1], tensor->op_params[2], tensor->op_params[3]); + } else if (tensor->op == GGML_OP_SET) { + tensor_clone = ggml_set(ggml_ctx, src_clone[0], src_clone[1], tensor->op_params[0], tensor->op_params[1], tensor->op_params[2], tensor->op_params[3]); + } else if (tensor->op == GGML_OP_NORM) { + tensor_clone = ggml_norm(ggml_ctx, src_clone[0], *(float *)tensor->op_params); + } else if (tensor->op == GGML_OP_GROUP_NORM) { + const float * float_params = (const float *)tensor->op_params; + tensor_clone = ggml_group_norm(ggml_ctx, src_clone[0], tensor->op_params[0], float_params[1]); + } else if (tensor->op == GGML_OP_RMS_NORM) { + tensor_clone = ggml_rms_norm(ggml_ctx, src_clone[0], *(float *)tensor->op_params); + } else if (tensor->op == GGML_OP_RMS_NORM_BACK) { + const float eps = ((float *) tensor->op_params)[0]; + tensor_clone = ggml_rms_norm_back(ggml_ctx, src_clone[0], src_clone[1], eps); + } else if (tensor->op == GGML_OP_SILU_BACK) { + tensor_clone = ggml_silu_back(ggml_ctx, src_clone[0], src_clone[1]); + } else if (tensor->op == GGML_OP_L2_NORM) { + const float eps = ((float *) tensor->op_params)[0]; + tensor_clone = ggml_l2_norm(ggml_ctx, src_clone[0], eps); + } else if (tensor->op == GGML_OP_SOFT_MAX) { + if (tensor->src[1] != nullptr) { + const float * params = (const float *)tensor->op_params; + tensor_clone = ggml_soft_max_ext(ggml_ctx, src_clone[0], src_clone[1], params[0], params[1]); + } else { + tensor_clone = ggml_soft_max(ggml_ctx, src_clone[0]); + } + } else if (tensor->op == GGML_OP_SOFT_MAX_BACK) { + tensor_clone = ggml_soft_max_ext_back(ggml_ctx, src_clone[0], src_clone[1], ((float *)tensor->op_params)[0], ((float *)tensor->op_params)[1]); + } else if (tensor->op == GGML_OP_DIAG_MASK_INF) { + tensor_clone = ggml_diag_mask_inf(ggml_ctx, src_clone[0], tensor->op_params[0]); + } else if (tensor->op == GGML_OP_ROPE || tensor->op == GGML_OP_ROPE_BACK) { + const int n_dims = ((int32_t *) tensor->op_params)[1]; + const int mode = ((int32_t *) tensor->op_params)[2]; + //const int n_ctx_ggml = ((int32_t *) tensor->op_params)[3]; + const int n_ctx_orig_ggml = ((int32_t *) tensor->op_params)[4]; + const float freq_base = ((float *) tensor->op_params)[5]; + const float freq_scale = ((float *) tensor->op_params)[6]; + const float ext_factor = ((float *) tensor->op_params)[7]; + const float attn_factor = ((float *) tensor->op_params)[8]; + const float beta_fast = ((float *) tensor->op_params)[9]; + const float beta_slow = ((float *) tensor->op_params)[10]; + if (mode & GGML_ROPE_TYPE_MROPE) { + int32_t *sections = ((int32_t *) tensor->op_params) + 11; + if (tensor->op == GGML_OP_ROPE) { + tensor_clone = ggml_rope_multi(ggml_ctx, src_clone[0], src_clone[1], src_clone[2], n_dims, sections, mode, n_ctx_orig_ggml, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); + } else { + tensor_clone = ggml_rope_multi_back(ggml_ctx, src_clone[0], src_clone[1], src_clone[2], n_dims, sections, mode, n_ctx_orig_ggml, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); + } + } else { + if (tensor->op == GGML_OP_ROPE) { + tensor_clone = ggml_rope_ext(ggml_ctx, src_clone[0], src_clone[1], src_clone[2], n_dims, mode, n_ctx_orig_ggml, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); + } else { + tensor_clone = ggml_rope_ext_back(ggml_ctx, src_clone[0], src_clone[1], src_clone[2], n_dims, mode, n_ctx_orig_ggml, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); + } + } + } else if (tensor->op == GGML_OP_UNARY) { + switch (ggml_get_unary_op(tensor)) { + case GGML_UNARY_OP_EXP: + tensor_clone = ggml_exp(ggml_ctx, src_clone[0]); + break; + case GGML_UNARY_OP_EXPM1: + tensor_clone = ggml_expm1(ggml_ctx, src_clone[0]); + break; + case GGML_UNARY_OP_ELU: + tensor_clone = ggml_elu(ggml_ctx, src_clone[0]); + break; + case GGML_UNARY_OP_SILU: + tensor_clone = ggml_silu(ggml_ctx, src_clone[0]); + break; + case GGML_UNARY_OP_GELU: + tensor_clone = ggml_gelu(ggml_ctx, src_clone[0]); + break; + case GGML_UNARY_OP_GELU_ERF: + tensor_clone = ggml_gelu_erf(ggml_ctx, src_clone[0]); + break; + case GGML_UNARY_OP_GELU_QUICK: + tensor_clone = ggml_gelu_quick(ggml_ctx, src_clone[0]); + break; + case GGML_UNARY_OP_RELU: + tensor_clone = ggml_relu(ggml_ctx, src_clone[0]); + break; + case GGML_UNARY_OP_XIELU: + tensor_clone = ggml_xielu(ggml_ctx, src_clone[0], 0, 0, 0, 0); + ggml_set_op_params_f32(tensor_clone, 1, ggml_get_op_params_f32(tensor, 1)); + ggml_set_op_params_f32(tensor_clone, 2, ggml_get_op_params_f32(tensor, 2)); + ggml_set_op_params_f32(tensor_clone, 3, ggml_get_op_params_f32(tensor, 3)); + ggml_set_op_params_f32(tensor_clone, 4, ggml_get_op_params_f32(tensor, 4)); + break; + case GGML_UNARY_OP_NEG: + tensor_clone = ggml_neg(ggml_ctx, src_clone[0]); + break; + case GGML_UNARY_OP_TANH: + tensor_clone = ggml_tanh(ggml_ctx, src_clone[0]); + break; + case GGML_UNARY_OP_SIGMOID: + tensor_clone = ggml_sigmoid(ggml_ctx, src_clone[0]); + break; + case GGML_UNARY_OP_HARDSIGMOID: + tensor_clone = ggml_hardsigmoid(ggml_ctx, src_clone[0]); + break; + case GGML_UNARY_OP_HARDSWISH: + tensor_clone = ggml_hardswish(ggml_ctx, src_clone[0]); + break; + case GGML_UNARY_OP_ABS: + tensor_clone = ggml_abs(ggml_ctx, src_clone[0]); + break; + case GGML_UNARY_OP_SOFTPLUS: + tensor_clone = ggml_softplus(ggml_ctx, src_clone[0]); + break; + case GGML_UNARY_OP_STEP: + tensor_clone = ggml_step(ggml_ctx, src_clone[0]); + break; + case GGML_UNARY_OP_ROUND: + tensor_clone = ggml_round(ggml_ctx, src_clone[0]); + break; + case GGML_UNARY_OP_CEIL: + tensor_clone = ggml_ceil(ggml_ctx, src_clone[0]); + break; + case GGML_UNARY_OP_FLOOR: + tensor_clone = ggml_floor(ggml_ctx, src_clone[0]); + break; + case GGML_UNARY_OP_TRUNC: + tensor_clone = ggml_trunc(ggml_ctx, src_clone[0]); + break; + case GGML_UNARY_OP_SGN: + tensor_clone = ggml_sgn(ggml_ctx, src_clone[0]); + break; + default: + std::cerr << "Missing vk_check_results OP: " << ggml_op_name(tensor->op) << std::endl; + GGML_ABORT("fatal error"); + } + } else if (tensor->op == GGML_OP_GLU) { + if (src_clone[1] == nullptr) { + tensor_clone = ggml_glu(ggml_ctx, src_clone[0], (ggml_glu_op) tensor->op_params[0], tensor->op_params[1]); + } else { + tensor_clone = ggml_glu_split(ggml_ctx, src_clone[0], src_clone[1], (ggml_glu_op) tensor->op_params[0]); + } + ggml_set_op_params_i32(tensor_clone, 2, ggml_get_op_params_i32(tensor, 2)); + ggml_set_op_params_i32(tensor_clone, 3, ggml_get_op_params_i32(tensor, 3)); + } else if (tensor->op == GGML_OP_CPY || tensor->op == GGML_OP_DUP) { + if (tensor->src[1] == nullptr) { + tensor_clone = ggml_dup(ggml_ctx, src_clone[0]); + tensor_clone->type = tensor->type; + } else { + tensor_clone = ggml_cpy(ggml_ctx, src_clone[0], src_clone[1]); + } + } else if (tensor->op == GGML_OP_CONT) { + tensor_clone = ggml_cont_4d(ggml_ctx, src_clone[0], tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3]); + } else if (tensor->op == GGML_OP_RESHAPE) { + tensor_clone = ggml_reshape_4d(ggml_ctx, src_clone[0], tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3]); + } else if (tensor->op == GGML_OP_VIEW) { + tensor_clone = ggml_view_4d(ggml_ctx, src_clone[0], tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3], tensor->nb[1], tensor->nb[2], tensor->nb[3], ((int32_t *) tensor->op_params)[0]); + } else if (tensor->op == GGML_OP_PERMUTE) { + int32_t * params = (int32_t *)tensor->op_params; + tensor_clone = ggml_permute(ggml_ctx, src_clone[0], params[0], params[1], params[2], params[3]); + } else if (tensor->op == GGML_OP_TRANSPOSE) { + tensor_clone = ggml_transpose(ggml_ctx, src_clone[0]); + } else if (tensor->op == GGML_OP_GET_ROWS) { + tensor_clone = ggml_get_rows(ggml_ctx, src_clone[0], src_clone[1]); + } else if (tensor->op == GGML_OP_ARGSORT) { + tensor_clone = ggml_argsort(ggml_ctx, src_clone[0], (ggml_sort_order) *(int *)tensor->op_params); + } else if (tensor->op == GGML_OP_TOP_K) { + tensor_clone = ggml_top_k(ggml_ctx, src_clone[0], tensor->ne[0]); + } else if (tensor->op == GGML_OP_SUM) { + tensor_clone = ggml_sum(ggml_ctx, src_clone[0]); + } else if (tensor->op == GGML_OP_SUM_ROWS) { + tensor_clone = ggml_sum_rows(ggml_ctx, src_clone[0]); + } else if (tensor->op == GGML_OP_CUMSUM) { + tensor_clone = ggml_cumsum(ggml_ctx, src_clone[0]); + } else if (tensor->op == GGML_OP_MEAN) { + tensor_clone = ggml_mean(ggml_ctx, src_clone[0]); + } else if (tensor->op == GGML_OP_ARGMAX) { + tensor_clone = ggml_argmax(ggml_ctx, src_clone[0]); + } else if (tensor->op == GGML_OP_COUNT_EQUAL) { + tensor_clone = ggml_count_equal(ggml_ctx, src_clone[0], src_clone[1]); + } else if (tensor->op == GGML_OP_SOLVE_TRI) { + tensor_clone = ggml_solve_tri(ggml_ctx, src_clone[0], src_clone[1], true, true, false); + } else if (tensor->op == GGML_OP_IM2COL) { + const int32_t s0 = tensor->op_params[0]; + const int32_t s1 = tensor->op_params[1]; + const int32_t p0 = tensor->op_params[2]; + const int32_t p1 = tensor->op_params[3]; + const int32_t d0 = tensor->op_params[4]; + const int32_t d1 = tensor->op_params[5]; + + const bool is_2D = tensor->op_params[6] == 1; + tensor_clone = ggml_im2col(ggml_ctx, src_clone[0], src_clone[1], s0, s1, p0, p1, d0, d1, is_2D, tensor->type); + } else if (tensor->op == GGML_OP_IM2COL_3D) { + const int32_t s0 = tensor->op_params[0]; + const int32_t s1 = tensor->op_params[1]; + const int32_t s2 = tensor->op_params[2]; + const int32_t p0 = tensor->op_params[3]; + const int32_t p1 = tensor->op_params[4]; + const int32_t p2 = tensor->op_params[5]; + const int32_t d0 = tensor->op_params[6]; + const int32_t d1 = tensor->op_params[7]; + const int32_t d2 = tensor->op_params[8]; + const int32_t IC = tensor->op_params[9]; + + tensor_clone = ggml_im2col_3d(ggml_ctx, src_clone[0], src_clone[1], IC, s0, s1, s2, p0, p1, p2, d0, d1, d2, tensor->type); + } else if (tensor->op == GGML_OP_TIMESTEP_EMBEDDING) { + const int32_t dim = tensor->op_params[0]; + const int32_t max_period = tensor->op_params[1]; + tensor_clone = ggml_timestep_embedding(ggml_ctx, src_clone[0], dim, max_period); + } else if (tensor->op == GGML_OP_CONV_TRANSPOSE_1D){ + const int32_t s0 = tensor->op_params[0]; + const int32_t p0 = tensor->op_params[1]; + const int32_t d0 = tensor->op_params[2]; + tensor_clone = ggml_conv_transpose_1d(ggml_ctx, src_clone[0], src_clone[1], s0, p0, d0); + } else if (tensor->op == GGML_OP_COL2IM_1D) { + const int32_t stride = tensor->op_params[0]; + const int32_t oc = tensor->op_params[1]; + const int32_t p0 = tensor->op_params[2]; + tensor_clone = ggml_col2im_1d(ggml_ctx, src_clone[0], stride, oc, p0); + } else if (tensor->op == GGML_OP_POOL_2D) { + enum ggml_op_pool op = static_cast(tensor->op_params[0]); + const int32_t k0 = tensor->op_params[1]; + const int32_t k1 = tensor->op_params[2]; + const int32_t s0 = tensor->op_params[3]; + const int32_t s1 = tensor->op_params[4]; + const int32_t p0 = tensor->op_params[5]; + const int32_t p1 = tensor->op_params[6]; + + tensor_clone = ggml_pool_2d(ggml_ctx, src_clone[0], op, k0, k1, s0, s1, p0, p1); + } else if (tensor->op == GGML_OP_CONV_2D) { + const int32_t s0 = tensor->op_params[0]; + const int32_t s1 = tensor->op_params[1]; + const int32_t p0 = tensor->op_params[2]; + const int32_t p1 = tensor->op_params[3]; + const int32_t d0 = tensor->op_params[4]; + const int32_t d1 = tensor->op_params[5]; + tensor_clone = ggml_conv_2d(ggml_ctx, src_clone[0], src_clone[1], s0, s1, p0, p1, d0, d1); + } else if (tensor->op == GGML_OP_CONV_3D) { + const int32_t s0 = tensor->op_params[0]; + const int32_t s1 = tensor->op_params[1]; + const int32_t s2 = tensor->op_params[2]; + const int32_t p0 = tensor->op_params[3]; + const int32_t p1 = tensor->op_params[4]; + const int32_t p2 = tensor->op_params[5]; + const int32_t d0 = tensor->op_params[6]; + const int32_t d1 = tensor->op_params[7]; + const int32_t d2 = tensor->op_params[8]; + const int32_t IC = tensor->op_params[9]; + const int32_t N = tensor->op_params[10]; + const int32_t OC = tensor->op_params[11]; + tensor_clone = ggml_conv_3d_direct(ggml_ctx, src_clone[0], src_clone[1], s0, s1, s2, p0, p1, p2, d0, d1, d2, IC, N, OC); + } else if (tensor->op == GGML_OP_CONV_2D_DW) { + const int32_t s0 = tensor->op_params[0]; + const int32_t s1 = tensor->op_params[1]; + const int32_t p0 = tensor->op_params[2]; + const int32_t p1 = tensor->op_params[3]; + const int32_t d0 = tensor->op_params[4]; + const int32_t d1 = tensor->op_params[5]; + tensor_clone = ggml_conv_2d_dw_direct(ggml_ctx, src_clone[0], src_clone[1], s0, s1, p0, p1, d0, d1); + } else if (tensor->op == GGML_OP_CONV_TRANSPOSE_2D) { + const int32_t s = tensor->op_params[0]; + tensor_clone = ggml_conv_transpose_2d_p0(ggml_ctx, src_clone[0], src_clone[1], s); + } else if (tensor->op == GGML_OP_LEAKY_RELU) { + const float * op_params = (const float *)tensor->op_params; + tensor_clone = ggml_leaky_relu(ggml_ctx, src_clone[0], op_params[0], false); + } else if (tensor->op == GGML_OP_RWKV_WKV6) { + tensor_clone = ggml_rwkv_wkv6(ggml_ctx, src_clone[0], src_clone[1], + src_clone[2], src_clone[3], src_clone[4], src_clone[5]); + } else if (tensor->op == GGML_OP_RWKV_WKV7) { + tensor_clone = ggml_rwkv_wkv7(ggml_ctx, src_clone[0], src_clone[1], src_clone[2], src_clone[3], + src_clone[4], src_clone[5], src_clone[6]); + } else if (tensor->op == GGML_OP_GATED_DELTA_NET) { + tensor_clone = ggml_gated_delta_net(ggml_ctx, src_clone[0], src_clone[1], + src_clone[2], src_clone[3], src_clone[4], src_clone[5], + ggml_get_op_params_i32(tensor, 0)); + } else if (tensor->op == GGML_OP_OPT_STEP_ADAMW) { + src_clone[0]->flags = tensor->src[0]->flags; + tensor_clone = ggml_opt_step_adamw(ggml_ctx, src_clone[0], src_clone[1], + src_clone[2], src_clone[3], src_clone[4]); + } else if (tensor->op == GGML_OP_OPT_STEP_SGD) { + src_clone[0]->flags = tensor->src[0]->flags; + tensor_clone = ggml_opt_step_sgd(ggml_ctx, src_clone[0], src_clone[1], + src_clone[2]); + } else if (tensor->op == GGML_OP_ADD_ID) { + tensor_clone = ggml_add_id(ggml_ctx, src_clone[0], src_clone[1], src_clone[2]); + } else if (tensor->op == GGML_OP_SSM_SCAN) { + tensor_clone = ggml_ssm_scan(ggml_ctx, src_clone[0], src_clone[1], src_clone[2], + src_clone[3], src_clone[4], src_clone[5], src_clone[6]); + } else if (tensor->op == GGML_OP_SSM_CONV) { + tensor_clone = ggml_ssm_conv(ggml_ctx, src_clone[0], src_clone[1]); + } else if (tensor->op == GGML_OP_ROLL) { + const int32_t s0 = tensor->op_params[0]; + const int32_t s1 = tensor->op_params[1]; + const int32_t s2 = tensor->op_params[2]; + const int32_t s3 = tensor->op_params[3]; + tensor_clone = ggml_roll(ggml_ctx, src_clone[0], s0, s1, s2, s3); + } + else { + std::cerr << "Missing vk_check_results OP: " << ggml_op_name(tensor->op) << std::endl; + GGML_ABORT("fatal error"); + } + cloned_tensors[tensor] = tensor_clone; + } + + ggml_cgraph * cgraph_cpu = ggml_new_graph(ggml_ctx); + ggml_build_forward_expand(cgraph_cpu, tensor_clone); + + ggml_graph_compute_with_ctx(ggml_ctx, cgraph_cpu, 8); + + if (vk_output_tensor > 0 && vk_output_tensor == check_counter) { + ggml_vk_print_tensor(tensor_clone, "tensor_clone"); + } + + comp_size = ggml_nbytes(tensor_clone); + + comp_result = malloc(comp_size); + memcpy(comp_result, tensor_clone->data, comp_size); + memcpy(comp_nb, tensor_clone->nb, sizeof(size_t) * GGML_MAX_DIMS); + + for (auto m : cloned_mallocs) { + free(m); + } + + ggml_free(ggml_ctx); + + VK_LOG_DEBUG("END ggml_vk_check_results_0(" << tensor->name << ")"); +} + +static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_cgraph * cgraph, int tensor_idx) { + ggml_tensor * tensor = cgraph->nodes[tensor_idx + ctx->num_additional_fused_ops]; + if (tensor->op == GGML_OP_TRANSPOSE || tensor->op == GGML_OP_SET_ROWS) { + return; + } + + if (!(vk_output_tensor > 0 && vk_output_tensor == check_counter) && check_counter <= vk_skip_checks) { + return; + } + + VK_LOG_DEBUG("ggml_vk_check_results_1(" << tensor->name << ")"); + + ggml_tensor * src0 = tensor->src[0]; + ggml_tensor * src1 = tensor->src[1]; + ggml_tensor * src2 = tensor->src[2]; + ggml_tensor * src3 = tensor->src[3]; + + void * tensor_data = tensor->data; + + if (ggml_backend_buffer_is_vk(tensor->buffer)) { + size_t tensor_size = ggml_nbytes(tensor); + tensor_data = malloc(tensor_size); + + ggml_backend_vk_buffer_context * buf_ctx = (ggml_backend_vk_buffer_context *)tensor->buffer->context; + + vk_buffer& buffer_gpu = buf_ctx->dev_buffer; + uint64_t offset = vk_tensor_offset(tensor) + tensor->view_offs; + if (offset + tensor_size >= buffer_gpu->size) { + tensor_size = buffer_gpu->size - offset; + } + + ggml_vk_buffer_read(buffer_gpu, offset, tensor_data, tensor_size); + } + + float first_error_result = -1.0f; + float first_error_correct = -1.0f; + std::array first_error = { -1, -1, -1, -1 }; + double avg_err = 0.0; + size_t counter = 0; + + for (int i3 = 0; i3 < tensor->ne[3]; i3++) { + for (int i2 = 0; i2 < tensor->ne[2]; i2++) { + for (int i1 = 0; i1 < tensor->ne[1]; i1++) { + for (int i0 = 0; i0 < tensor->ne[0]; i0++) { + const bool buffer_size_fit = i3*comp_nb[3] + i2*comp_nb[2] + i1*comp_nb[1] + i0*comp_nb[0] < comp_size; + float correct = 0.0f; + float result = 0.0f; + + if (buffer_size_fit) { + if (tensor->type == GGML_TYPE_F32) { + correct = *(float *) ((char *) comp_result + i3*comp_nb[3] + i2*comp_nb[2] + i1*comp_nb[1] + i0*comp_nb[0]); + result = *(float *) ((char *) tensor_data + i3*tensor->nb[3] + i2*tensor->nb[2] + i1*tensor->nb[1] + i0*tensor->nb[0]); + } else if (tensor->type == GGML_TYPE_F16) { + correct = ggml_fp16_to_fp32(*(ggml_fp16_t *) ((char *) comp_result + i3*comp_nb[3] + i2*comp_nb[2] + i1*comp_nb[1] + i0*comp_nb[0])); + result = ggml_fp16_to_fp32(*(ggml_fp16_t *) ((char *) tensor_data + i3*tensor->nb[3] + i2*tensor->nb[2] + i1*tensor->nb[1] + i0*tensor->nb[0])); + } else if (tensor->type == GGML_TYPE_BF16) { + correct = ggml_bf16_to_fp32(*(ggml_bf16_t *) ((char *) comp_result + i3*comp_nb[3] + i2*comp_nb[2] + i1*comp_nb[1] + i0*comp_nb[0])); + result = ggml_bf16_to_fp32(*(ggml_bf16_t *) ((char *) tensor_data + i3*tensor->nb[3] + i2*tensor->nb[2] + i1*tensor->nb[1] + i0*tensor->nb[0])); + } else if (tensor->type == GGML_TYPE_I32) { + correct = *(int32_t *) ((char *) comp_result + i3*comp_nb[3] + i2*comp_nb[2] + i1*comp_nb[1] + i0*comp_nb[0]); + result = *(int32_t *) ((char *) tensor_data + i3*tensor->nb[3] + i2*tensor->nb[2] + i1*tensor->nb[1] + i0*tensor->nb[0]); + } else if (tensor->type == GGML_TYPE_I64) { + correct = *(int64_t *) ((char *) comp_result + i3*comp_nb[3] + i2*comp_nb[2] + i1*comp_nb[1] + i0*comp_nb[0]); + result = *(int64_t *) ((char *) tensor_data + i3*tensor->nb[3] + i2*tensor->nb[2] + i1*tensor->nb[1] + i0*tensor->nb[0]); + } else { + std::cerr << "Results check not implemented for type " << ggml_type_name(tensor->type) << std::endl; + } + } else { + std::cerr << "Missing debug code for type " << ggml_type_name(tensor->type) << std::endl; + GGML_ABORT("fatal error"); + } + + if ((std::isnan(correct) != std::isnan(result)) || (std::isinf(correct) != std::isinf(result)) || !buffer_size_fit) { + std::cerr << "ERROR: Invalid value in " << ggml_op_name(tensor->op) << " i3=" << i3 << " i2=" << i2 << " i1=" << i1 << " i0=" << i0 << " result=" << result << " correct=" << correct << " avg_err=" << (avg_err / counter) << std::endl; + std::cerr << "tensor=" << tensor << " tensor->name=" << tensor->name << " tensor->type: " << ggml_type_name(tensor->type) << " ne0=" << tensor->ne[0] << " nb0=" << tensor->nb[0] << " ne1=" << tensor->ne[1] << " nb1=" << tensor->nb[1] << " ne2=" << tensor->ne[2] << " nb2=" << tensor->nb[2] << " ne3=" << tensor->ne[3] << " nb3=" << tensor->nb[3] << " offset=" << tensor->view_offs << std::endl; + if (src0 != nullptr) { + std::cerr << "src0=" << src0 << " src0->name=" << src0->name << " op=" << ggml_op_name(src0->op) << " type=" << ggml_type_name(src0->type) << " ne0=" << src0->ne[0] << " nb0=" << src0->nb[0] << " ne1=" << src0->ne[1] << " nb1=" << src0->nb[1] << " ne2=" << src0->ne[2] << " nb2=" << src0->nb[2] << " ne3=" << src0->ne[3] << " nb3=" << src0->nb[3] << " offset=" << src0->view_offs << std::endl; + } + if (src1 != nullptr) { + std::cerr << "src1=" << src1 << " src1->name=" << src1->name << " op=" << ggml_op_name(src1->op) << " type=" << ggml_type_name(src1->type) << " ne0=" << src1->ne[0] << " nb0=" << src1->nb[0] << " ne1=" << src1->ne[1] << " nb1=" << src1->nb[1] << " ne2=" << src1->ne[2] << " nb2=" << src1->nb[2] << " ne3=" << src1->ne[3] << " nb3=" << src1->nb[3] << " offset=" << src1->view_offs << std::endl; + } + if (src2 != nullptr) { + std::cerr << "src2=" << src2 << " src2->name=" << src2->name << " op=" << ggml_op_name(src2->op) << " type=" << ggml_type_name(src2->type) << " ne0=" << src2->ne[0] << " nb0=" << src2->nb[0] << " ne1=" << src2->ne[1] << " nb1=" << src2->nb[1] << " ne2=" << src2->ne[2] << " nb2=" << src2->nb[2] << " ne3=" << src2->ne[3] << " nb3=" << src2->nb[3] << " offset=" << src2->view_offs << std::endl; + } + if (src3 != nullptr) { + std::cerr << "src3=" << src3 << " src3->name=" << src3->name << " op=" << ggml_op_name(src3->op) << " type=" << ggml_type_name(src3->type) << " ne0=" << src3->ne[0] << " nb0=" << src3->nb[0] << " ne1=" << src3->ne[1] << " nb1=" << src3->nb[1] << " ne2=" << src3->ne[2] << " nb2=" << src3->nb[2] << " ne3=" << src3->ne[3] << " nb3=" << src3->nb[3] << " offset=" << src3->view_offs << std::endl; + } + std::cerr << "First error: result=" << first_error_result << " correct=" << first_error_correct << " i3=" << first_error[3] << " i2=" << first_error[2] << " i1=" << first_error[1] << " i0=" << first_error[0] << std::endl; + std::cerr << std::endl << "Result:" << std::endl; + ggml_vk_print_tensor_area(tensor, tensor_data, i0, i1, i2, i3); + std::cerr << std::endl << "Correct:" << std::endl; + ggml_vk_print_tensor_area(tensor, comp_result, i0, i1, i2, i3); + std::cerr << std::endl; + std::vector done; + ggml_vk_print_graph_origin(tensor, done); + GGML_ABORT("fatal error"); + } + const double denom = std::fabs(correct) > 1.0f ? (std::fabs(correct) > 1e-8 ? std::fabs(correct) : 1e-8) : 1.0f; + if (first_error[0] == -1 && std::fabs(correct - result) / denom > 0.5) { + first_error[0] = i0; + first_error[1] = i1; + first_error[2] = i2; + first_error[3] = i3; + first_error_result = result; + first_error_correct = correct; + } + + // Special case, value is infinite, avoid NaN result in avg_err + // NaN also appears in results, if both are nan error is 0 + if (!std::isinf(correct) && !std::isinf(result) && !std::isnan(correct) && !std::isnan(result)) { + avg_err += std::fabs(correct - result) / denom; + } + counter++; + } + } + } + } + + avg_err /= counter; + + if (vk_output_tensor > 0 && vk_output_tensor == check_counter) { + std::cerr << "TENSOR CHECK: avg_err=" << avg_err << " in " << ggml_op_name(tensor->op) << " (check " << check_counter << ")" << std::endl; + std::cerr << "tensor=" << tensor << " tensor->name=" << tensor->name << " tensor->type: " << ggml_type_name(tensor->type) << " ne0=" << tensor->ne[0] << " nb0=" << tensor->nb[0] << " ne1=" << tensor->ne[1] << " nb1=" << tensor->nb[1] << " ne2=" << tensor->ne[2] << " nb2=" << tensor->nb[2] << " ne3=" << tensor->ne[3] << " nb3=" << tensor->nb[3] << " offset=" << tensor->view_offs << std::endl; + if (src0 != nullptr) { + std::cerr << "src0=" << src0 << " op=" << ggml_op_name(src0->op) << " type=" << ggml_type_name(src0->type) << " ne0=" << src0->ne[0] << " nb0=" << src0->nb[0] << " ne1=" << src0->ne[1] << " nb1=" << src0->nb[1] << " ne2=" << src0->ne[2] << " nb2=" << src0->nb[2] << " ne3=" << src0->ne[3] << " nb3=" << src0->nb[3] << " offset=" << src0->view_offs << std::endl; + } + if (src1 != nullptr) { + std::cerr << "src1=" << src1 << " op=" << ggml_op_name(src1->op) << " type=" << ggml_type_name(src1->type) << " ne0=" << src1->ne[0] << " nb0=" << src1->nb[0] << " ne1=" << src1->ne[1] << " nb1=" << src1->nb[1] << " ne2=" << src1->ne[2] << " nb2=" << src1->nb[2] << " ne3=" << src1->ne[3] << " nb3=" << src1->nb[3] << " offset=" << src1->view_offs << std::endl; + } + if (src2 != nullptr) { + std::cerr << "src2=" << src2 << " op=" << ggml_op_name(src2->op) << " type=" << ggml_type_name(src2->type) << " ne0=" << src2->ne[0] << " nb0=" << src2->nb[0] << " ne1=" << src2->ne[1] << " nb1=" << src2->nb[1] << " ne2=" << src2->ne[2] << " nb2=" << src2->nb[2] << " ne3=" << src2->ne[3] << " nb3=" << src2->nb[3] << " offset=" << src2->view_offs << std::endl; + } + if (src3 != nullptr) { + std::cerr << "src3=" << src3 << " op=" << ggml_op_name(src3->op) << " type=" << ggml_type_name(src3->type) << " ne0=" << src3->ne[0] << " nb0=" << src3->nb[0] << " ne1=" << src3->ne[1] << " nb1=" << src3->nb[1] << " ne2=" << src3->ne[2] << " nb2=" << src3->nb[2] << " ne3=" << src3->ne[3] << " nb3=" << src3->nb[3] << " offset=" << src3->view_offs << std::endl; + } + std::cerr << "First error: result=" << first_error_result << " correct=" << first_error_correct << " i3=" << first_error[3] << " i2=" << first_error[2] << " i1=" << first_error[1] << " i0=" << first_error[0] << std::endl; + std::cerr << std::endl << "Result:" << std::endl; + ggml_vk_print_tensor_area(tensor, tensor_data, 5, 5, 0, 0); + std::cerr << std::endl << "Correct:" << std::endl; + ggml_vk_print_tensor_area(tensor, comp_result, 5, 5, 0, 0); + std::cerr << std::endl; + std::vector done; + ggml_vk_print_graph_origin(tensor, done); + } + + if (avg_err > 0.01 || std::isnan(avg_err)) { + std::cerr << "ERROR: avg_err=" << avg_err << " in " << ggml_op_name(tensor->op) << " (check " << check_counter << ")" << std::endl; + std::cerr << "tensor=" << tensor << " tensor->name=" << tensor->name << " tensor->type: " << ggml_type_name(tensor->type) << " ne0=" << tensor->ne[0] << " nb0=" << tensor->nb[0] << " ne1=" << tensor->ne[1] << " nb1=" << tensor->nb[1] << " ne2=" << tensor->ne[2] << " nb2=" << tensor->nb[2] << " ne3=" << tensor->ne[3] << " nb3=" << tensor->nb[3] << " offset=" << tensor->view_offs << std::endl; + if (src0 != nullptr) { + std::cerr << "src0=" << src0 << " op=" << ggml_op_name(src0->op) << " type=" << ggml_type_name(src0->type) << " ne0=" << src0->ne[0] << " nb0=" << src0->nb[0] << " ne1=" << src0->ne[1] << " nb1=" << src0->nb[1] << " ne2=" << src0->ne[2] << " nb2=" << src0->nb[2] << " ne3=" << src0->ne[3] << " nb3=" << src0->nb[3] << " offset=" << src0->view_offs << std::endl; + } + if (src1 != nullptr) { + std::cerr << "src1=" << src1 << " op=" << ggml_op_name(src1->op) << " type=" << ggml_type_name(src1->type) << " ne0=" << src1->ne[0] << " nb0=" << src1->nb[0] << " ne1=" << src1->ne[1] << " nb1=" << src1->nb[1] << " ne2=" << src1->ne[2] << " nb2=" << src1->nb[2] << " ne3=" << src1->ne[3] << " nb3=" << src1->nb[3] << " offset=" << src1->view_offs << std::endl; + } + if (src2 != nullptr) { + std::cerr << "src2=" << src2 << " op=" << ggml_op_name(src2->op) << " type=" << ggml_type_name(src2->type) << " ne0=" << src2->ne[0] << " nb0=" << src2->nb[0] << " ne1=" << src2->ne[1] << " nb1=" << src2->nb[1] << " ne2=" << src2->ne[2] << " nb2=" << src2->nb[2] << " ne3=" << src2->ne[3] << " nb3=" << src2->nb[3] << " offset=" << src2->view_offs << std::endl; + } + if (src3 != nullptr) { + std::cerr << "src3=" << src3 << " op=" << ggml_op_name(src3->op) << " type=" << ggml_type_name(src3->type) << " ne0=" << src3->ne[0] << " nb0=" << src3->nb[0] << " ne1=" << src3->ne[1] << " nb1=" << src3->nb[1] << " ne2=" << src3->ne[2] << " nb2=" << src3->nb[2] << " ne3=" << src3->ne[3] << " nb3=" << src3->nb[3] << " offset=" << src3->view_offs << std::endl; + } + std::cerr << "First error: result=" << first_error_result << " correct=" << first_error_correct << " i3=" << first_error[3] << " i2=" << first_error[2] << " i1=" << first_error[1] << " i0=" << first_error[0] << std::endl; + std::cerr << std::endl << "Result:" << std::endl; + ggml_vk_print_tensor_area(tensor, tensor_data, first_error[0], first_error[1], first_error[2], first_error[3]); + std::cerr << std::endl << "Correct:" << std::endl; + ggml_vk_print_tensor_area(tensor, comp_result, first_error[0], first_error[1], first_error[2], first_error[3]); + std::cerr << std::endl; + std::vector done; + ggml_vk_print_graph_origin(tensor, done); + GGML_ABORT("fatal error"); + } else { + std::cerr << check_counter << " " << tensor->name << " op=" << ggml_op_name(tensor->op) << " avg_err=" << avg_err << std::endl; + } + + free(comp_result); + comp_result = nullptr; + comp_size = 0; + + if (ggml_backend_buffer_is_vk(tensor->buffer)) { + free(tensor_data); + } + + VK_LOG_DEBUG("END ggml_vk_check_results_1(" << tensor->name << ")"); +} +#endif + +GGML_BACKEND_DL_IMPL(ggml_backend_vk_reg) diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/CMakeLists.txt b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/CMakeLists.txt new file mode 100644 index 0000000000000000000000000000000000000000..10a9ea21025f51ca0656debc093fecc4e66489ac --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/CMakeLists.txt @@ -0,0 +1,35 @@ +cmake_minimum_required(VERSION 3.19) +project("vulkan-shaders-gen" C CXX) + +find_package (Threads REQUIRED) + +if (GGML_VULKAN_COOPMAT_GLSLC_SUPPORT) + add_compile_definitions(GGML_VULKAN_COOPMAT_GLSLC_SUPPORT) + message(STATUS "Enabling coopmat glslc support") +endif() +if (GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT) + add_compile_definitions(GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT) + message(STATUS "Enabling coopmat2 glslc support") +endif() +if (GGML_VULKAN_COOPMAT2_DECODE_VECTOR_GLSLC_SUPPORT) + add_compile_definitions(GGML_VULKAN_COOPMAT2_DECODE_VECTOR_GLSLC_SUPPORT) + message(STATUS "Enabling coopmat2 decode_vector glslc support") +endif() +if (GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT) + add_compile_definitions(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT) + message(STATUS "Enabling dot glslc support") +endif() +if (GGML_VULKAN_BFLOAT16_GLSLC_SUPPORT) + add_compile_definitions(GGML_VULKAN_BFLOAT16_GLSLC_SUPPORT) + message(STATUS "Enabling bfloat16 glslc support") +endif() +if (GGML_VULKAN_SHADER_DEBUG_INFO) + add_compile_definitions(GGML_VULKAN_SHADER_DEBUG_INFO) + message(STATUS "Enabling shader debug info") +endif() + +set(TARGET vulkan-shaders-gen) +add_executable(${TARGET} vulkan-shaders-gen.cpp) +install(TARGETS ${TARGET} RUNTIME) +target_compile_features(${TARGET} PRIVATE cxx_std_17) +target_link_libraries(vulkan-shaders-gen PUBLIC Threads::Threads) diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/acc.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/acc.comp new file mode 100644 index 0000000000000000000000000000000000000000..6ba3d1d89e0a96421c499b2a9c3791d6c2ee67f2 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/acc.comp @@ -0,0 +1,37 @@ +#version 450 + +#include "types.glsl" +#include "generic_binary_head.glsl" + +// false for SET, true for ACC +layout(constant_id = 1) const bool ACC = true; + +layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; + +void main() { + const uint idx = gl_GlobalInvocationID.x; + if (idx >= p.ne) { + return; + } + + const uint offset = p.param3; + const uint src1_i = idx - offset; + const uint i3 = src1_i / p.nb03; + const uint rem2 = src1_i - i3 * p.nb03; + const uint i2 = rem2 / p.nb02; + const uint rem1 = rem2 - i2 * p.nb02; + const uint i1 = rem1 / p.nb01; + const uint i0 = rem1 % p.nb01; + + uint i00, i01, i02, i03; + + if (i0 < p.ne10 && i1 < p.ne11 && i2 < p.ne12 && i3 < p.ne13) { + if (ACC) { + data_d[get_doffset() + idx] = D_TYPE(FLOAT_TYPE(data_a[get_aoffset() + idx]) + FLOAT_TYPE(data_b[get_boffset() + src1_idx(i0, i1, i2, i3)])); + } else { + data_d[get_doffset() + idx] = D_TYPE(FLOAT_TYPE(data_b[get_boffset() + src1_idx(i0, i1, i2, i3)])); + } + } else { + data_d[get_doffset() + idx] = D_TYPE(FLOAT_TYPE(data_a[get_aoffset() + idx])); + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/add.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/add.comp new file mode 100644 index 0000000000000000000000000000000000000000..3bcfe6908eef599f01476de4835c6e66c880005f --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/add.comp @@ -0,0 +1,69 @@ +#version 450 + +#extension GL_EXT_shader_16bit_storage : require +#if ADD_RMS +#extension GL_KHR_shader_subgroup_arithmetic : enable +#extension GL_KHR_shader_subgroup_basic : enable +#endif + +#include "types.glsl" +#include "generic_binary_head.glsl" + +const uint num_threads = 256; + +layout (binding = 3, std430) buffer PartialBuf {float partial_sums[];}; + +layout(local_size_x = num_threads, local_size_y = 1, local_size_z = 1) in; + +#if ADD_RMS +// XXX TODO this could be sized based on number of subgroups, but that't not considered a constant +shared FLOAT_TYPE sumsh[num_threads]; +#endif + +void main() { + uint idx = get_idx(); + uint orig_idx = idx; + + // num_threads * num_iter must equal 512, to match the wg_denoms and get_idx calculation + const uint num_iter = 2; + + FLOAT_TYPE sum_sq = 0; + + [[unroll]] for (uint i = 0; i < num_iter; ++i) { + if (idx >= p.ne) { + continue; + } + uint i00, i01, i02, i03; + get_indices(idx, i00, i01, i02, i03); + + FLOAT_TYPE sum = FLOAT_TYPE(data_a[get_aoffset() + src0_idx(i00, i01, i02, i03)]) + FLOAT_TYPE(data_b[get_boffset() + src1_idx(i00, i01, i02, i03)]); + sum_sq += sum*sum; + + data_d[get_doffset() + dst_idx(i00, i01, i02, i03)] = D_TYPE(sum); + + idx += num_threads; + } + +#if ADD_RMS + if (p.param3 != 0) { + // reduce the sum within each subgroup, then across subgroups + const uint NumSubgroups = num_threads / gl_SubgroupSize; + sum_sq = subgroupAdd(sum_sq); + if (gl_SubgroupInvocationID == 0) { + sumsh[gl_SubgroupID] = sum_sq; + } + barrier(); + [[unroll]] for (uint s = NumSubgroups / 2; s > 0; s >>= 1) { + if (gl_SubgroupID < s && gl_SubgroupInvocationID == 0) { + sum_sq += sumsh[gl_SubgroupID + s]; + sumsh[gl_SubgroupID] = sum_sq; + } + barrier(); + } + + if (gl_SubgroupID == 0 && gl_SubgroupInvocationID == 0) { + partial_sums[orig_idx / (num_iter * num_threads)] = sum_sq; + } + } +#endif +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/add1.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/add1.comp new file mode 100644 index 0000000000000000000000000000000000000000..db60725d4c5557cf3d5b8425ae6db73ece781498 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/add1.comp @@ -0,0 +1,28 @@ +#version 450 + +#extension GL_EXT_shader_16bit_storage : require + +#include "types.glsl" +#include "generic_binary_head.glsl" + +const uint num_threads = 256; + +layout(local_size_x = num_threads, local_size_y = 1, local_size_z = 1) in; + +void main() { + uint idx = get_idx(); + + const uint num_iter = 2; + + [[unroll]] for (uint i = 0; i < num_iter; ++i) { + if (idx >= p.ne) { + continue; + } + uint i00, i01, i02, i03; + get_indices(idx, i00, i01, i02, i03); + + data_d[get_doffset() + dst_idx(i00, i01, i02, i03)] = D_TYPE(FLOAT_TYPE(data_a[get_aoffset() + src0_idx(i00, i01, i02, i03)]) + FLOAT_TYPE(data_b[get_boffset()])); + + idx += num_threads; + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/add_id.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/add_id.comp new file mode 100644 index 0000000000000000000000000000000000000000..495249d5f6cc0b2264183f3e3cddccaad76124a7 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/add_id.comp @@ -0,0 +1,42 @@ +#version 450 + +#extension GL_EXT_control_flow_attributes : require + +#include "types.glsl" + +layout (push_constant) uniform parameter +{ + uint ne0; + uint ne1; + uint s01; + uint s02; + uint s11; + uint s21; +} p; + +#define BLOCK_SIZE 512 + +layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; +layout (binding = 1) readonly buffer Y {B_TYPE data_b[];}; +layout (binding = 2) readonly buffer Z {int32_t data_c[];}; +layout (binding = 3) writeonly buffer D {D_TYPE data_d[];}; + +void main() { + const uint i1 = gl_WorkGroupID.x; + const uint i2 = gl_WorkGroupID.y; + + const uint i11 = data_c[i1 + i2 * p.s21]; + + const uint s1 = p.ne0; + const uint s2 = p.ne0 * p.ne1; + + const uint d0 = i1 * s1 + i2 * s2; + const uint a0 = i1 * p.s01 + i2 * p.s02; + const uint b0 = i11 * p.s11; + + for (uint i0 = gl_LocalInvocationID.x; i0 < p.ne0; i0 += BLOCK_SIZE) { + data_d[d0 + i0] = data_a[a0 + i0] + data_b[b0 + i0]; + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/arange.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/arange.comp new file mode 100644 index 0000000000000000000000000000000000000000..f4936eeada954837eeab866f014d5ae8e32f20f8 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/arange.comp @@ -0,0 +1,20 @@ +#version 450 + +#include "generic_head.glsl" +#include "types.glsl" + +layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) writeonly buffer D {D_TYPE data_d[];}; + +void main() { + const uint i = gl_GlobalInvocationID.x; + + if (i >= p.KX) { + return; + } + + // p.param1 = start, p.param2 = step + float value = p.param1 + p.param2 * float(i); + data_d[i] = D_TYPE(value); +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/argmax.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/argmax.comp new file mode 100644 index 0000000000000000000000000000000000000000..7c128776710e4c1700007df45aca415977b1e7a0 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/argmax.comp @@ -0,0 +1,60 @@ +#version 450 + +#include "generic_head.glsl" +#include "types.glsl" + +#extension GL_EXT_control_flow_attributes : enable + +#define FLT_MAX 3.402823466e+38F + +layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_d[];}; + +layout (constant_id = 0) const uint BLOCK_SIZE = 32; + +shared FLOAT_TYPE tmpmax[BLOCK_SIZE]; +shared uint tmp[BLOCK_SIZE]; + +void main() { + const uint row = gl_WorkGroupID.z * 262144 + gl_WorkGroupID.y * 512 + gl_WorkGroupID.x; + const uint col = gl_LocalInvocationID.x; + + if (row >= p.KY) { + return; + } + + A_TYPE amax = -FLT_MAX; + uint acol = col; + + if (col < p.KX) { + amax = data_a[row*p.KX + col]; + } + + for (uint i = col + BLOCK_SIZE; i < p.KX; i += BLOCK_SIZE) { + A_TYPE val = data_a[row*p.KX + i]; + if (val > amax) { + amax = val; + acol = i; + } + } + + tmp[col] = acol; + tmpmax[col] = amax; + + barrier(); + [[unroll]] for (int s = int(BLOCK_SIZE) / 2; s > 0; s >>= 1) { + if (col < s && col + s < p.KX) { + if (tmpmax[col] < tmpmax[col + s]) { + tmpmax[col] = tmpmax[col + s]; + tmp[col] = tmp[col + s]; + } + } + barrier(); + } + + if (col == 0) { + data_d[row] = D_TYPE(tmp[0]); + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/argsort.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/argsort.comp new file mode 100644 index 0000000000000000000000000000000000000000..0fc2b9b725350623763717450ae22ce31ff1840f --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/argsort.comp @@ -0,0 +1,86 @@ +#version 450 +#extension GL_EXT_control_flow_attributes : enable + +#include "types.glsl" + +layout(constant_id = 0) const int BLOCK_SIZE = 1024; +layout(constant_id = 1) const int NCOLS_PADDED_LOG2 = 10; +#define ASC 0 + +layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; +layout (binding = 2) writeonly buffer D {int data_d[];}; + +layout (push_constant) uniform parameter { + uint ncols; + uint ncols_padded; + uint ncols_padded_log2; + uint nrows; + uint order; + uint outer_start; + uint outer_end; + uint inner_start; + uint inner_end; +} p; + +shared ivec2 dst_row[BLOCK_SIZE]; + +void argsort(bool needs_bounds_check, const uint row) { + // bitonic sort + const int col = int(gl_LocalInvocationID.x); + + const uint row_offset = row * p.ncols; + + // initialize indices + dst_row[col] = ivec2(col, floatBitsToInt(data_a[row_offset + col])); + barrier(); + + uint num_outer_loop_iters = NCOLS_PADDED_LOG2; + [[unroll]] for (uint k = 2, outer_idx = 0; outer_idx < num_outer_loop_iters; k *= 2, outer_idx++) { + uint num_inner_loop_iters = outer_idx + 1; + [[unroll]] for (uint j = k / 2, inner_idx = 0; inner_idx < num_inner_loop_iters; j /= 2, inner_idx++) { + const int ixj = int(col ^ j); + + int idx_0 = (col & k) == 0 ? col : ixj; + int idx_1 = (col & k) == 0 ? ixj : col; + + ivec2 sh_idx_0 = dst_row[idx_0]; + ivec2 sh_idx_1 = dst_row[idx_1]; + bool idx_0_oob = needs_bounds_check ? sh_idx_0.x >= p.ncols : false; + bool idx_1_oob = needs_bounds_check ? sh_idx_1.x >= p.ncols : false; + + if ((idx_0_oob || + (!idx_1_oob && intBitsToFloat(sh_idx_0.y) > intBitsToFloat(sh_idx_1.y))) && (ixj > col)) { + dst_row[idx_0] = sh_idx_1; + dst_row[idx_1] = sh_idx_0; + } + + barrier(); + } + } + + if (col < p.ncols) { + if (p.order == ASC) { + data_d[row_offset + col] = dst_row[col].x; + } else { + data_d[row_offset + p.ncols - col - 1] = dst_row[col].x; + } + } +} + +void main() { + if (p.ncols == BLOCK_SIZE) { + uint row = gl_WorkGroupID.y; + while (row < p.nrows) { + argsort(false, row); + row += gl_WorkGroupSize.y * gl_NumWorkGroups.y; + } + } else { + uint row = gl_WorkGroupID.y; + while (row < p.nrows) { + argsort(true, row); + row += gl_WorkGroupSize.y * gl_NumWorkGroups.y; + } + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/argsort_large.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/argsort_large.comp new file mode 100644 index 0000000000000000000000000000000000000000..920bac6bb899661042fc78e218d47c67e49c32bc --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/argsort_large.comp @@ -0,0 +1,114 @@ +#version 450 +#extension GL_EXT_control_flow_attributes : enable +#extension GL_KHR_memory_scope_semantics : enable +#pragma use_vulkan_memory_model + +#include "types.glsl" + +layout(constant_id = 0) const int BLOCK_SIZE = 1024; +layout(constant_id = 1) const int WG_UNROLL_FACTOR = 2; +#define ASC 0 + +layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; +layout (binding = 1) workgroupcoherent buffer B {ivec2 tmp_idx[];}; +layout (binding = 2) workgroupcoherent buffer D {int data_d[];}; + +layout (push_constant) uniform parameter { + uint ncols; + uint ncols_padded; + uint ncols_padded_log2; + uint nrows; + uint order; + uint outer_start; + uint outer_end; + uint inner_start; + uint inner_end; +} p; + +void argsort(bool needs_bounds_check, const uint row) { + // bitonic sort + int col = int(gl_GlobalInvocationID.x); + col = (col % BLOCK_SIZE) + (col / BLOCK_SIZE) * BLOCK_SIZE * WG_UNROLL_FACTOR; + + const uint row_offset = row * p.ncols; + uint idx_offset = row * p.ncols_padded; + + bool need_barrier = false; + + // initialize indices + if (p.outer_start == 0 && p.inner_start == 0) { + [[unroll]] for (int u = 0; u < WG_UNROLL_FACTOR; ++u) { + uint c = u*BLOCK_SIZE + col; + if (c < p.ncols_padded) { + ivec2 v = ivec2(c, floatBitsToInt(data_a[row_offset + c])); + tmp_idx[idx_offset + c] = v; + } + } + need_barrier = true; + } + + [[unroll]] for (uint outer_idx = p.outer_start, k = (2 << outer_idx); outer_idx < p.outer_end; k *= 2, outer_idx++) { + uint inner_end = min(p.inner_end, outer_idx + 1); + for (uint j = k >> (p.inner_start + 1), inner_idx = p.inner_start; inner_idx < inner_end; j /= 2, inner_idx++) { + if (need_barrier) { + controlBarrier(gl_ScopeWorkgroup, gl_ScopeWorkgroup, gl_StorageSemanticsBuffer, gl_SemanticsAcquireRelease); + } + need_barrier = true; + [[unroll]] for (int u = 0; u < WG_UNROLL_FACTOR; ++u) { + int c = u*BLOCK_SIZE + col; + const int ixj = int(c ^ j); + + if (ixj < c) { + continue; + } + + int idx_0 = (c & k) == 0 ? c : ixj; + int idx_1 = (c & k) == 0 ? ixj : c; + + ivec2 sh_idx_0 = tmp_idx[idx_offset + idx_0]; + ivec2 sh_idx_1 = tmp_idx[idx_offset + idx_1]; + bool idx_0_oob = needs_bounds_check ? sh_idx_0.x >= p.ncols : false; + bool idx_1_oob = needs_bounds_check ? sh_idx_1.x >= p.ncols : false; + + if ((idx_0_oob || + (!idx_1_oob && intBitsToFloat(sh_idx_0.y) > intBitsToFloat(sh_idx_1.y)))) { + tmp_idx[idx_offset + idx_0] = sh_idx_1; + tmp_idx[idx_offset + idx_1] = sh_idx_0; + } + } + } + } + + if (p.outer_end == p.ncols_padded_log2 && + p.inner_end >= p.ncols_padded_log2 + 1) { + controlBarrier(gl_ScopeWorkgroup, gl_ScopeWorkgroup, gl_StorageSemanticsBuffer, gl_SemanticsAcquireRelease); + [[unroll]] for (int u = 0; u < WG_UNROLL_FACTOR; ++u) { + uint c = u*BLOCK_SIZE + col; + if (c < p.ncols) { + if (p.order == ASC) { + data_d[row_offset + c] = tmp_idx[idx_offset + c].x; + } else { + data_d[row_offset + p.ncols - c - 1] = tmp_idx[idx_offset + c].x; + } + } + } + } +} + +void main() { + if (p.ncols == p.ncols_padded) { + uint row = gl_WorkGroupID.y; + while (row < p.nrows) { + argsort(false, row); + row += gl_WorkGroupSize.y * gl_NumWorkGroups.y; + } + } else { + uint row = gl_WorkGroupID.y; + while (row < p.nrows) { + argsort(true, row); + row += gl_WorkGroupSize.y * gl_NumWorkGroups.y; + } + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/col2im_1d.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/col2im_1d.comp new file mode 100644 index 0000000000000000000000000000000000000000..a23de380f07122921db2bee76706a55430d51c85 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/col2im_1d.comp @@ -0,0 +1,61 @@ +#version 450 + +#include "types.glsl" + +layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; // columns: [K_OC, T_in] +layout (binding = 1) writeonly buffer D {D_TYPE data_d[];}; // output: [T_out, OC] + +layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in; + +layout (push_constant) uniform parameter { + uint32_t T_out; + uint32_t OC; + uint32_t K_OC; + uint32_t T_in; + uint32_t K; + int32_t stride; + int32_t p0; +} p; + +// Load A_TYPE to float +float load_col(uint32_t idx) { +#if defined(DATA_A_BF16) + return bf16_to_fp32(uint32_t(data_a[idx])); +#else + return float(data_a[idx]); +#endif +} + +// Store float as D_TYPE +void store_dst(uint32_t idx, float v) { +#if defined(DATA_A_BF16) + data_d[idx] = D_TYPE(fp32_to_bf16(v)); +#else + data_d[idx] = D_TYPE(v); +#endif +} + +void main() { + const uint32_t t_out = gl_GlobalInvocationID.x; + const uint32_t oc = gl_GlobalInvocationID.y; + if (t_out >= p.T_out || oc >= p.OC) return; + + const int32_t t_abs = int32_t(t_out) + p.p0; // absolute position in uncropped signal + + // Gather: only the ceil(K/stride) columns that scatter into t_abs, no modulo + int32_t t_in_min = (t_abs - int32_t(p.K) + p.stride) / p.stride; + if (t_in_min < 0) t_in_min = 0; + int32_t t_in_max = t_abs / p.stride; + if (t_in_max >= int32_t(p.T_in)) t_in_max = int32_t(p.T_in) - 1; + + float val = 0.0; + for (int32_t t_in = t_in_min; t_in <= t_in_max; t_in++) { + int32_t k = t_abs - t_in * p.stride; + // col layout: [K_OC, T_in], column index = oc * K + k + uint32_t col_idx = (oc * p.K + uint32_t(k)) + uint32_t(t_in) * p.K_OC; + val += load_col(col_idx); + } + + // dst layout: [T_out, OC], element (t_out, oc) = t_out + oc * T_out + store_dst(t_out + oc * p.T_out, val); +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/concat.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/concat.comp new file mode 100644 index 0000000000000000000000000000000000000000..e4046983820aa35907a0b9d8eaf398a4fd9e31a4 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/concat.comp @@ -0,0 +1,41 @@ +#version 450 + +#include "types.glsl" +#include "generic_binary_head.glsl" + +layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; + +void main() { + const uint idx = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x; + const int dim = p.param3; + + if (idx >= p.ne) { + return; + } + + const uint i3 = idx / (p.ne22*p.ne21*p.ne20); + const uint i3_offset = i3 * p.ne22*p.ne21*p.ne20; + const uint i2 = (idx - i3_offset) / (p.ne21*p.ne20); + const uint i2_offset = i2*p.ne21*p.ne20; + const uint i1 = (idx - i3_offset - i2_offset) / p.ne20; + const uint i0 = idx - i3_offset - i2_offset - i1*p.ne20; + + uint o[4] = {0, 0, 0, 0}; + o[dim] = dim == 0 ? p.ne00 : (dim == 1 ? p.ne01 : (dim == 2 ? p.ne02 : p.ne03)); + + const uint src0_idx = i3*p.nb03 + i2*p.nb02 + i1*p.nb01 + i0*p.nb00; + const uint src1_idx = (i3 - o[3])*p.nb13 + (i2 - o[2])*p.nb12 + (i1 - o[1])*p.nb11 + (i0 - o[0])*p.nb10; + const uint dst_idx = i3*p.nb23 + i2*p.nb22 + i1*p.nb21 + i0*p.nb20; + + const bool is_src0 = i0 < p.ne00 && i1 < p.ne01 && i2 < p.ne02 && i3 < p.ne03; + +#ifndef OPTIMIZATION_ERROR_WORKAROUND + data_d[get_doffset() + dst_idx] = D_TYPE(is_src0 ? data_a[get_aoffset() + src0_idx] : data_b[get_boffset() + src1_idx]); +#else + if (is_src0) { + data_d[get_doffset() + dst_idx] = data_a[get_aoffset() + src0_idx]; + } else { + data_d[get_doffset() + dst_idx] = data_b[get_boffset() + src1_idx]; + } +#endif +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/contig_copy.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/contig_copy.comp new file mode 100644 index 0000000000000000000000000000000000000000..b3b182fb084220b395547a6f4d1bce452006413d --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/contig_copy.comp @@ -0,0 +1,53 @@ +#version 450 + +#include "types.glsl" +#include "generic_unary_head.glsl" + +#extension GL_EXT_control_flow_attributes : require + +const uint num_threads = 128; + +layout(local_size_x = num_threads, local_size_y = 1, local_size_z = 1) in; + +void main() { + uint idx = get_idx(); + + // num_threads * num_iter must equal 512, to match the wg_denoms and get_idx calculation + const uint num_iter = 4; + + // fast path for when all four iterations are in-bounds + if (idx + (num_iter-1)*num_threads < p.ne) { + [[unroll]] for (uint i = 0; i < num_iter; ++i) { + +#if defined(DATA_A_BF16) + data_d[get_doffset() + idx] = D_TYPE(bf16_to_fp32(uint32_t(data_a[get_aoffset() + idx]))); +#elif defined(DATA_D_BF16) + float f = float(data_a[get_aoffset() + idx]); + data_d[get_doffset() + idx] = D_TYPE(fp32_to_bf16(f)); +#elif !defined(OPTIMIZATION_ERROR_WORKAROUND) + data_d[get_doffset() + idx] = D_TYPE(data_a[get_aoffset() + idx]); +#else + data_d[get_doffset() + idx] = data_a[get_aoffset() + idx]; +#endif + idx += num_threads; + } + } else { + [[unroll]] for (uint i = 0; i < num_iter; ++i) { + if (idx >= p.ne) { + continue; + } + +#if defined(DATA_A_BF16) + data_d[get_doffset() + idx] = D_TYPE(bf16_to_fp32(uint32_t(data_a[get_aoffset() + idx]))); +#elif defined(DATA_D_BF16) + float f = float(data_a[get_aoffset() + idx]); + data_d[get_doffset() + idx] = D_TYPE(fp32_to_bf16(f)); +#elif !defined(OPTIMIZATION_ERROR_WORKAROUND) + data_d[get_doffset() + idx] = D_TYPE(data_a[get_aoffset() + idx]); +#else + data_d[get_doffset() + idx] = data_a[get_aoffset() + idx]; +#endif + idx += num_threads; + } + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/conv2d_dw.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/conv2d_dw.comp new file mode 100644 index 0000000000000000000000000000000000000000..70a301488eb1d57891746355f76035cb835cbf40 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/conv2d_dw.comp @@ -0,0 +1,105 @@ +#version 450 + +#include "types.glsl" + +layout (push_constant) uniform parameter +{ + uint ne; + uint batches; + uint channels; + uint dst_w; + uint dst_h; + uint src_w; + uint src_h; + uint knl_w; + uint knl_h; + int stride_x; + int stride_y; + int pad_x; + int pad_y; + int dilation_x; + int dilation_y; +} p; + +layout (binding = 0) readonly buffer A {A_TYPE knl_data[];}; +layout (binding = 1) readonly buffer B {B_TYPE src_data[];}; +layout (binding = 2) writeonly buffer D {D_TYPE dst_data[];}; + +layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; + +FLOAT_TYPE conv_2d_dw_whcn(uint idx) { + uint i0 = idx / p.dst_w; + uint dst_x = idx - i0 * p.dst_w; + uint i1 = i0 / p.dst_h; + uint dst_y = i0 - i1 * p.dst_h; + uint n = i1 / p.channels; + uint c = i1 - n * p.channels; + + uint src_i = n * p.channels * p.src_h * p.src_w + c * p.src_h * p.src_w; + uint knl_i = c * p.knl_h * p.knl_w; + + FLOAT_TYPE sum = 0.0; + for (uint knl_y = 0; knl_y < p.knl_h; ++knl_y) { + uint src_y = dst_y * p.stride_y + knl_y * p.dilation_y - p.pad_y; + if (src_y >= p.src_h) { // src_y < 0 will wrap to a large unsigned int + continue; + } + for (uint knl_x = 0; knl_x < p.knl_w; ++knl_x) { + uint src_x = dst_x * p.stride_x + knl_x * p.dilation_x - p.pad_x; + if (src_x >= p.src_w) { // src_x < 0 will wrap to a large unsigned int + continue; + } + FLOAT_TYPE v = FLOAT_TYPE(src_data[src_i + src_y * p.src_w + src_x]); + FLOAT_TYPE k = FLOAT_TYPE(knl_data[knl_i + knl_y * p.knl_w + knl_x]); + sum = fma(v, k, sum); + } + } + return sum; +} + +FLOAT_TYPE conv_2d_dw_cwhn(uint idx) { + uint i0 = idx / p.channels; + uint c = idx - i0 * p.channels; + uint i1 = i0 / p.dst_w; + uint dst_x = i0 - i1 * p.dst_w; + uint n = i1 / p.dst_h; + uint dst_y = i1 - n * p.dst_h; + + uint src_i = n * p.channels * p.src_h * p.src_w; + uint src_row = p.src_w * p.channels; + uint knl_row = p.knl_w * p.channels; + + FLOAT_TYPE sum = 0.0; + for (uint knl_y = 0; knl_y < p.knl_h; ++knl_y) { + uint src_y = dst_y * p.stride_y + knl_y * p.dilation_y - p.pad_y; + if (src_y >= p.src_h) { // src_y < 0 will wrap to a large unsigned int + continue; + } + for (uint knl_x = 0; knl_x < p.knl_w; ++knl_x) { + uint src_x = dst_x * p.stride_x + knl_x * p.dilation_x - p.pad_x; + if (src_x >= p.src_w) { // src_x < 0 will wrap to a large unsigned int + continue; + } + FLOAT_TYPE v = FLOAT_TYPE(src_data[src_i + src_y * src_row + src_x * p.channels + c]); + FLOAT_TYPE k = FLOAT_TYPE(knl_data[ knl_y * knl_row + knl_x * p.channels + c]); + sum = fma(v, k, sum); + } + } + return sum; +} + +void main() { + uint idx = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x; + if (idx >= p.ne) { + return; + } + + FLOAT_TYPE result = +#ifdef WHCN + conv_2d_dw_whcn(idx); +#else + conv_2d_dw_cwhn(idx); +#endif + dst_data[idx] = D_TYPE(result); +} + diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/conv2d_mm.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/conv2d_mm.comp new file mode 100644 index 0000000000000000000000000000000000000000..99400098bf2b708d59b414b1ec1749990d278911 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/conv2d_mm.comp @@ -0,0 +1,480 @@ +#version 450 + +#extension GL_EXT_control_flow_attributes : enable +#ifdef COOPMAT2 +#extension GL_NV_cooperative_matrix2 : enable +#extension GL_EXT_shader_explicit_arithmetic_types_float16 : require +#extension GL_KHR_memory_scope_semantics : enable +#endif + +#ifdef COOPMAT +#extension GL_KHR_cooperative_matrix : enable +#extension GL_KHR_shader_subgroup_basic : enable +#extension GL_EXT_shader_explicit_arithmetic_types_float16 : require +#extension GL_KHR_memory_scope_semantics : enable +#endif + +#ifdef USE_COLLECTIVES +# extension GL_KHR_shader_subgroup_shuffle : enable +#endif + +#include "types.glsl" + +// shape notation: [dim(N), ..., dim(0)] -- stride(dim(j)) >= stride(dim(i)) if i > j +layout(binding = 0) readonly buffer A { + A_TYPE knl_data[]; +}; // src0 - kernel: [KW, KH, Cin, Cout] for conv_2d, [KW, KH, Cout, Cin] for conv_transposed_2d + +layout(binding = 1) readonly buffer B { + B_TYPE src_data[]; +}; // src1 - input: [W, H, Cin, N] -- channel_first format + +layout(binding = 2) writeonly buffer D { + D_TYPE dst_data[]; +}; // dst - result: [OW, OH, Cout, N] + +layout(push_constant) uniform parameter { + // I/O channels, batch size + uint32_t Cout; + uint32_t Cin; + uint32_t N; + + // Tensor spatial sizes: input, output + uint32_t W; + uint32_t H; + uint32_t OW; + uint32_t OH; + + // Strides in elements + 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; + + // fastdiv helper values + uint32_t OWmp; uint32_t OWL; + uint32_t OWOHmp; uint32_t OWOHL; +} + +p; + +layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; +// Blocktile sizes +layout(constant_id = 1) const uint BS_K = 128; +layout(constant_id = 2) const uint BS_CRS = 16; +layout(constant_id = 3) const uint BS_NPQ = 128; +// Thread-tile sizes +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; +// when set, skip bounds checks and address clamps (K/CRS/NPQ are tile-aligned) +layout(constant_id = 15) const uint aligned = 0; +// stage cm2 result through shmem (Csh) for coalesced stores. cm1 always does this. +layout(constant_id = 16) const uint csh_store = 0; + +#ifdef COOPMAT +// cm1 subgroup tile: each subgroup computes a WM x WN region as a grid of +// TM x TN x TK fragments. Requires WM%TM == WN%TN == BS_K%WM == BS_NPQ%WN == +// BS_CRS%TK == 0, and WG_SIZE == (BS_K/WM) * (BS_NPQ/WN) * subgroup_size. +layout(constant_id = 17) const uint WM = 32; +layout(constant_id = 18) const uint WN = 32; +const uint TM = 16; +const uint TN = 16; +const uint TK = 16; +const uint cms_per_row = WM / TM; +const uint cms_per_col = WN / TN; +const uint warps_M = BS_K / WM; +const uint warps_N = BS_NPQ / WN; +#endif + +// without padding, H_idx/W_idx are in bounds by construction (non-TRANSPOSE only) +#ifdef TRANSPOSE +const bool hw_in_bounds = false; +#else +const bool hw_in_bounds = (p0 == 0) && (p1 == 0); +#endif + +// TRANSPOSE stride alignment is trivially satisfied for stride 1 +#ifdef TRANSPOSE +const bool stride_in_bounds = (s0 == 1) && (s1 == 1); +#else +const bool stride_in_bounds = true; +#endif + +uint32_t tid = gl_LocalInvocationID.x; +const uint32_t WG_SIZE = gl_WorkGroupSize.x; + +uint splitWork(uint work_size, uint block_size) { + return (block_size + work_size - 1) / block_size; +} + +uint32_t K = p.Cout; +uint32_t CRS = p.Cin * KH * KW; +uint32_t NPQ = p.N * p.OH * p.OW; + +uint32_t n_elems_out = K * NPQ; + +// Number of blocktiles per input +uint32_t NB_CRS = splitWork(CRS, BS_CRS); + +#if defined(COOPMAT2) || defined(COOPMAT) +#define SHMEM_TYPE float16_t +#else +#define SHMEM_TYPE float +#endif + +const uint32_t Ash_stride = BS_CRS + SHMEM_PAD; +const uint32_t Bsh_stride = BS_NPQ + SHMEM_PAD; + +const uint32_t Ash_numel = BS_K * BS_CRS; +const uint32_t Bsh_numel = BS_CRS * BS_NPQ; + +const uint32_t Ash_len = BS_K * Ash_stride; +const uint32_t Bsh_len = BS_CRS * Bsh_stride; + +shared SHMEM_TYPE Ash[Ash_len]; // K x CRS +shared SHMEM_TYPE Bsh[Bsh_len]; // CRS x NPQ + +#if defined(COOPMAT2) || defined(COOPMAT) +// stage matC through shmem so global stores are row-major (NPQ-contiguous) +const uint32_t Csh_stride = BS_NPQ; +#ifdef COOPMAT +const uint32_t Csh_len = BS_K * Csh_stride; +#else +const uint32_t Csh_len = csh_store != 0 ? BS_K * Csh_stride : 8; // 8 to workaround compiler bug +#endif +shared SHMEM_TYPE Csh[Csh_len]; // K x NPQ +#endif + +// Threadtile sizes +const uint32_t TS_NPQ = BS_K * BS_NPQ / WG_SIZE / TS_K; + +// Number of threadtiles per blocktile +const uint32_t NT_K = BS_K / TS_K; +const uint32_t NT_NPQ = BS_NPQ / TS_NPQ; + +/* +Compute +KxCRS @ CRSxNPQ = K x NPQ +K=Cout +C=Cin +R,S=KH,KW +P,Q=OH,OW +*/ + +uint32_t B_idx_K = gl_WorkGroupID.x; +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; + +uint32_t Ar = tid / BS_CRS; +uint32_t Ac = tid % BS_CRS; +const uint32_t ArpWg = WG_SIZE / BS_CRS; + +uint32_t Br = tid / BS_NPQ; +uint32_t Bc = tid % BS_NPQ; +const uint32_t BrpWg = WG_SIZE / BS_NPQ; + +// see init_fastdiv_values in ggml-vulkan.cpp +uint fastdiv(uint n, uint mp, uint L) { + uint msbs, lsbs; + // msbs = mulhi(n, mp) + umulExtended(n, mp, msbs, lsbs); + return (msbs + n) >> L; +} + +#ifdef COOPMAT2 +#define ACC_TYPE float16_t + +ACC_TYPE perElemOpStore(const in uint32_t r, const in uint32_t c, const in ACC_TYPE elem) +{ + uint32_t K_idx = B_idx_K * BS_K + r; + uint32_t NPQ_idx = B_idx_NPQ * BS_NPQ + c; + uint32_t N_idx = fastdiv(NPQ_idx, p.OWOHmp, p.OWOHL); // divide by p.OH * p.OW; + uint32_t OH_idx = fastdiv(NPQ_idx - N_idx * p.OH * p.OW, p.OWmp, p.OWL); // divide by p.OW; + uint32_t OW_idx = NPQ_idx - N_idx * p.OH * p.OW - OH_idx * p.OW; + uint32_t dst_idx = OW_idx + OH_idx * p.nb1 + K_idx * p.nb2 + N_idx * p.nb3; + if (aligned != 0 || (K_idx < K && NPQ_idx < NPQ)) { + dst_data[dst_idx] = D_TYPE(elem); + } + return elem; +} +#endif + +void main() { + if (B_idx_NPQ * BS_NPQ >= NPQ) { + return; + } + +#ifdef COOPMAT2 + coopmat matC; + matC = coopmat(0.0); +#elif defined(COOPMAT) + coopmat sums[cms_per_row * cms_per_col]; + [[unroll]] for (uint i = 0; i < cms_per_row * cms_per_col; i++) { + sums[i] = coopmat(0.0); + } + const uint warp_r = gl_SubgroupID / warps_N; + const uint warp_c = gl_SubgroupID % warps_N; +#else + float regC[TS_K][TS_NPQ]; + for (uint32_t T_ly = 0; T_ly < TS_K; T_ly++) { + for (uint32_t T_lx = 0; T_lx < TS_NPQ; T_lx++) { + regC[T_ly][T_lx] = 0.0; + } + } +#endif + /* Advance block in CRS dim */ + [[dont_unroll]] for (uint32_t B_idx_CRS = 0; B_idx_CRS < NB_CRS; B_idx_CRS++) { + uint32_t CRS_idx_a; + uint32_t Cin_idx_a; + uint32_t KH_idx_a; + uint32_t KW_idx_a; + +#ifdef USE_COLLECTIVES + uint32_t cached_CRS_idx; + uint32_t cached_Cin_idx; + uint32_t cached_KH_idx; + uint32_t cached_KW_idx; + if (use_collectives == 1) { + cached_CRS_idx = B_idx_CRS * BS_CRS + gl_SubgroupInvocationID; + cached_Cin_idx = cached_CRS_idx / (KW * KH); + uint32_t cached_CRS_remainder = cached_CRS_idx % (KW * KH); + cached_KH_idx = cached_CRS_remainder / KW; + cached_KW_idx = cached_CRS_remainder % KW; + + CRS_idx_a = subgroupShuffle(cached_CRS_idx, Ac); + Cin_idx_a = subgroupShuffle(cached_Cin_idx, Ac); + KH_idx_a = subgroupShuffle(cached_KH_idx, Ac); + KW_idx_a = subgroupShuffle(cached_KW_idx, Ac); + } else { + CRS_idx_a = B_idx_CRS * BS_CRS + Ac; // Global CRS_idx_a (column index of A) + Cin_idx_a = CRS_idx_a / (KW * KH); + uint32_t CRS_remainder = CRS_idx_a % (KW * KH); + KH_idx_a = CRS_remainder / KW; + KW_idx_a = CRS_remainder % KW; + } +#else + CRS_idx_a = B_idx_CRS * BS_CRS + Ac; // Global CRS_idx_a (column index of A) + Cin_idx_a = CRS_idx_a / (KW * KH); + CRS_remainder = CRS_idx_a % (KW * KH); + KH_idx_a = CRS_remainder / KW; + KW_idx_a = CRS_remainder % KW; +#endif + + /* Load kernel to A_block: (BS_K x BS_CRS)*/ + UNROLL for (uint32_t r_offset = 0; r_offset < BS_K; r_offset += ArpWg) { + uint32_t B_ly = r_offset + Ar; + uint32_t B_lx = Ac; + uint32_t K_idx = B_idx_K * BS_K + B_ly; /* Global K_idx (row index of A)*/ +#ifdef TRANSPOSE + uint32_t knl_idx = KW_idx_a + KH_idx_a * p.nb01 + K_idx * p.nb02 + Cin_idx_a * p.nb03; +#else + uint32_t knl_idx = KW_idx_a + KH_idx_a * p.nb01 + Cin_idx_a * p.nb02 + K_idx * p.nb03; +#endif + if (aligned == 0) { + knl_idx = min(knl_idx, K * CRS - 1); + } + float val = knl_data[knl_idx]; + if (aligned == 0 && (K_idx >= K || CRS_idx_a >= CRS)) { + val = 0.0; + } + Ash[B_ly * Ash_stride + B_lx] = SHMEM_TYPE(val); + } + /* Load input to B_block: (BS_CRS x BS_NPQ) */ + UNROLL for (uint32_t r_offset = 0; r_offset < BS_CRS; r_offset += BrpWg) { + uint32_t B_ly = r_offset + Br; /* Row index of B block */ + uint32_t B_lx = Bc; + uint32_t NPQ_idx = B_idx_NPQ * BS_NPQ + B_lx; /* Global NPQ index (column index of B) */ + uint32_t N_idx = fastdiv(NPQ_idx, p.OWOHmp, p.OWOHL); // divide by p.OH * p.OW; + uint32_t NPQ_remainder = NPQ_idx - N_idx * p.OH * p.OW; + uint32_t OH_idx = fastdiv(NPQ_remainder, p.OWmp, p.OWL); // divide by p.OW; + uint32_t OW_idx = NPQ_remainder - OH_idx * p.OW; + + uint32_t CRS_idx_b; + uint32_t Cin_idx_b; + uint32_t KH_idx_b; + uint32_t KW_idx_b; +#ifdef USE_COLLECTIVES + if (use_collectives == 1) { + CRS_idx_b = subgroupShuffle(cached_CRS_idx, r_offset + Br); + Cin_idx_b = subgroupShuffle(cached_Cin_idx, r_offset + Br); + KH_idx_b = subgroupShuffle(cached_KH_idx, r_offset + Br); + KW_idx_b = subgroupShuffle(cached_KW_idx, r_offset + Br); + } else { + CRS_idx_b = B_idx_CRS * BS_CRS + B_ly; /* Global CRS index (row index of B) */ + Cin_idx_b = CRS_idx_b / (KW * KH); + uint32_t CRS_remainder = CRS_idx_b % (KW * KH); + KH_idx_b = CRS_remainder / KW; + KW_idx_b = CRS_remainder % KW; + } +#else + CRS_idx_b = B_idx_CRS * BS_CRS + B_ly; /* Global CRS index (row index of B) */ + Cin_idx_b = CRS_idx_b / (KW * KH); + uint32_t CRS_remainder = CRS_idx_b % (KW * KH); + KH_idx_b = CRS_remainder / KW; + KW_idx_b = CRS_remainder % KW; +#endif + +#ifdef TRANSPOSE + uint32_t H_idx_x_s1 = OH_idx - KH_idx_b * d1 + p1; + uint32_t W_idx_x_s0 = OW_idx - KW_idx_b * d0 + p0; + uint32_t H_idx = H_idx_x_s1 / s1; + uint32_t W_idx = W_idx_x_s0 / s0; +#else + uint32_t H_idx = OH_idx * s1 + KH_idx_b * d1 - p1; + uint32_t W_idx = OW_idx * s0 + KW_idx_b * d0 - p0; +#endif + uint32_t src_idx = W_idx + H_idx * p.nb11 + Cin_idx_b * p.nb12 + N_idx * p.nb13; + // skip clamp when address can't go OOB + if (aligned == 0 || !hw_in_bounds || !stride_in_bounds) { + src_idx = min(max(src_idx, 0), p.Cin * p.N * p.W * p.H - 1); + } + float val = src_data[src_idx]; + bool oob = false; + if (aligned == 0 && (CRS_idx_b >= CRS || NPQ_idx >= NPQ)) { + oob = true; + } + // also catches lower-bound underflow (idx wraps to 0x80000000+) + if (!hw_in_bounds && (H_idx >= p.H || W_idx >= p.W)) { + oob = true; + } +#ifdef TRANSPOSE + if (!stride_in_bounds && + ((H_idx_x_s1 - H_idx * s1 != 0) || (W_idx_x_s0 - W_idx * s0 != 0))) { + oob = true; + } +#endif + if (oob) { + val = 0.0; + } + Bsh[B_ly * Bsh_stride + B_lx] = SHMEM_TYPE(val); + } + barrier(); +#ifdef COOPMAT2 + coopmat matA; + coopmat matB; + + coopMatLoad(matA, Ash, 0, Ash_stride, gl_CooperativeMatrixLayoutRowMajor); + coopMatLoad(matB, Bsh, 0, Bsh_stride, gl_CooperativeMatrixLayoutRowMajor); + matC = coopMatMulAdd(matA, matB, matC); +#elif defined(COOPMAT) + // each subgroup multiplies its grid of fragments per TK-sized CRS chunk + [[unroll]] for (uint k_step = 0; k_step < BS_CRS / TK; k_step++) { + coopmat cache_a[cms_per_row]; + [[unroll]] for (uint cm_row = 0; cm_row < cms_per_row; cm_row++) { + const uint a_off = (warp_r * WM + cm_row * TM) * Ash_stride + k_step * TK; + coopMatLoad(cache_a[cm_row], Ash, a_off, Ash_stride, gl_CooperativeMatrixLayoutRowMajor); + } + [[unroll]] for (uint cm_col = 0; cm_col < cms_per_col; cm_col++) { + coopmat cache_b; + const uint b_off = k_step * TK * Bsh_stride + warp_c * WN + cm_col * TN; + coopMatLoad(cache_b, Bsh, b_off, Bsh_stride, gl_CooperativeMatrixLayoutRowMajor); + [[unroll]] for (uint cm_row = 0; cm_row < cms_per_row; cm_row++) { + sums[cm_col * cms_per_row + cm_row] = coopMatMulAdd(cache_a[cm_row], cache_b, sums[cm_col * cms_per_row + cm_row]); + } + } + } +#else + if (T_y * TS_K < K) { + UNROLL for (uint32_t CRS_lidx = 0; CRS_lidx < BS_CRS; CRS_lidx++) { + float regA[TS_K]; + float regB[TS_NPQ]; + for (uint32_t T_ly = 0; T_ly < TS_K; T_ly++) { + regA[T_ly] = Ash[(T_y * TS_K + T_ly) * Ash_stride + CRS_lidx]; + } + for (uint32_t T_lx = 0; T_lx < TS_NPQ; T_lx++) { + regB[T_lx] = Bsh[CRS_lidx * Bsh_stride + T_x * TS_NPQ + T_lx]; + } + for (uint32_t T_ly = 0; T_ly < TS_K; T_ly++) { + for (uint32_t T_lx = 0; T_lx < TS_NPQ; T_lx++) { + regC[T_ly][T_lx] = fma(regA[T_ly], regB[T_lx], regC[T_ly][T_lx]); + } + } + } + } +#endif + barrier(); + } + /* Save C* */ +#if defined(COOPMAT2) || defined(COOPMAT) + // stage matC into Csh, then write to dst with coalesced NPQ-contiguous stores +#ifdef COOPMAT + const bool use_staged_store = true; +#else + const bool use_staged_store = (csh_store != 0); +#endif + if (use_staged_store) { +#ifdef COOPMAT + // cm1: each subgroup stores its fragment grid into its Csh slot + [[unroll]] for (uint cm_row = 0; cm_row < cms_per_row; cm_row++) { + [[unroll]] for (uint cm_col = 0; cm_col < cms_per_col; cm_col++) { + const uint csh_off = (warp_r * WM + cm_row * TM) * Csh_stride + warp_c * WN + cm_col * TN; + coopMatStore(sums[cm_col * cms_per_row + cm_row], Csh, csh_off, Csh_stride, gl_CooperativeMatrixLayoutRowMajor); + } + } +#else + coopMatStore(matC, Csh, 0, Csh_stride, gl_CooperativeMatrixLayoutRowMajor); +#endif + barrier(); + + // cooperative shmem->global: WG threads spread across BS_NPQ (the + // contiguous direction of dst), each iter covers store_rows_per_iter K-rows + const uint32_t store_rows_per_iter = WG_SIZE / BS_NPQ; + const uint32_t store_iters = BS_K / store_rows_per_iter; + const uint32_t k_thread_offset = tid / BS_NPQ; + const uint32_t npq_thread = tid % BS_NPQ; + [[unroll]] for (uint32_t i = 0; i < store_iters; i++) { + uint32_t k_local = i * store_rows_per_iter + k_thread_offset; + uint32_t K_idx = B_idx_K * BS_K + k_local; + uint32_t NPQ_idx = B_idx_NPQ * BS_NPQ + npq_thread; + uint32_t N_idx = fastdiv(NPQ_idx, p.OWOHmp, p.OWOHL); + uint32_t OH_idx = fastdiv(NPQ_idx - N_idx * p.OH * p.OW, p.OWmp, p.OWL); + uint32_t OW_idx = NPQ_idx - N_idx * p.OH * p.OW - OH_idx * p.OW; + uint32_t dst_idx = OW_idx + OH_idx * p.nb1 + K_idx * p.nb2 + N_idx * p.nb3; + if (aligned != 0 || (K_idx < K && NPQ_idx < NPQ)) { + dst_data[dst_idx] = D_TYPE(Csh[k_local * Csh_stride + npq_thread]); + } + } + } +#ifdef COOPMAT2 + else { + coopMatPerElementNV(matC, matC, perElemOpStore); + } +#endif +#else + if (T_y * TS_K < K) { + for (uint32_t T_ly = 0; T_ly < TS_K; T_ly++) { + for (uint32_t T_lx = 0; T_lx < TS_NPQ; T_lx++) { + uint32_t K_idx = B_idx_K * BS_K + T_y * TS_K + T_ly; + uint32_t NPQ_idx = B_idx_NPQ * BS_NPQ + T_x * TS_NPQ + T_lx; + uint32_t N_idx = fastdiv(NPQ_idx, p.OWOHmp, p.OWOHL); // divide by p.OH * p.OW; + uint32_t OH_idx = fastdiv(NPQ_idx - N_idx * p.OH * p.OW, p.OWmp, p.OWL); // divide by p.OW; + uint32_t OW_idx = NPQ_idx - N_idx * p.OH * p.OW - OH_idx * p.OW; + uint32_t dst_idx = OW_idx + OH_idx * p.nb1 + K_idx * p.nb2 + N_idx * p.nb3; + if (aligned != 0 || (K_idx < K && NPQ_idx < NPQ)) { + dst_data[dst_idx] = regC[T_ly][T_lx]; + } + } + } + } +#endif +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/conv3d_mm.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/conv3d_mm.comp new file mode 100644 index 0000000000000000000000000000000000000000..f66f299f6dae4d1c7ae1139e8fb580292dae77d4 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/conv3d_mm.comp @@ -0,0 +1,431 @@ +#version 450 + +#extension GL_EXT_control_flow_attributes : enable +#ifdef COOPMAT2 +#extension GL_NV_cooperative_matrix2 : enable +#extension GL_EXT_shader_explicit_arithmetic_types_float16 : require +#extension GL_KHR_memory_scope_semantics : enable +#endif + +#ifdef COOPMAT +#extension GL_KHR_cooperative_matrix : enable +#extension GL_KHR_shader_subgroup_basic : enable +#extension GL_EXT_shader_explicit_arithmetic_types_float16 : require +#extension GL_KHR_memory_scope_semantics : enable +#endif + +#include "types.glsl" + +// shape notation: [dim(N), ..., dim(0)] -- stride(dim(j)) >= stride(dim(i)) if i > j +layout(binding = 0) readonly buffer A { + A_TYPE knl_data[]; +}; // src0 - kernel: [KW, KH, KD, IC*OC] + +layout(binding = 1) readonly buffer B { + B_TYPE src_data[]; +}; // src1 - input: [IW, IH, ID, IC*N] -- channel_first format + +layout(binding = 2) writeonly buffer D { + D_TYPE dst_data[]; +}; // dst - result: [OW, OH, OD, OC*N] + +layout(push_constant) uniform parameter { + // I/O channels, batch size + uint32_t OC; + uint32_t IC; + uint32_t N; + + // Tensor spatial sizes: input, output + uint32_t IW; + uint32_t IH; + uint32_t ID; + uint32_t OW; + uint32_t OH; + uint32_t OD; + + // Strides in elements + 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; + + // fastdiv helper values + uint32_t OWmp; uint32_t OWL; + uint32_t OWOHmp; uint32_t OWOHL; + uint32_t OWOHODmp; uint32_t OWOHODL; +} + +p; + +layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; +// Blocktile sizes +layout(constant_id = 1) const uint BS_K = 128; +layout(constant_id = 2) const uint BS_CRS = 16; +layout(constant_id = 3) const uint BS_NPQ = 128; +// Thread-tile sizes +layout(constant_id = 4) const uint TS_K = 8; +layout(constant_id = 5) const uint SHMEM_PAD = 4; +// Stride, padding, dilation +layout(constant_id = 6) const uint s0 = 1; +layout(constant_id = 7) const uint s1 = 1; +layout(constant_id = 8) const uint s2 = 1; +layout(constant_id = 9) const uint p0 = 0; +layout(constant_id = 10) const uint p1 = 0; +layout(constant_id = 11) const uint p2 = 0; +layout(constant_id = 12) const uint d0 = 1; +layout(constant_id = 13) const uint d1 = 1; +layout(constant_id = 14) const uint d2 = 1; +// Kernel spatial sizes +layout(constant_id = 15) const uint KW = 1; +layout(constant_id = 16) const uint KH = 1; +layout(constant_id = 17) const uint KD = 1; +// when set, skip bounds checks and address clamps (K/CRS/NPQ are tile-aligned) +layout(constant_id = 18) const uint aligned = 0; +// stage cm2 result through shmem (Csh) for coalesced stores. cm1 always does this. +layout(constant_id = 19) const uint csh_store = 0; + +#ifdef COOPMAT +// cm1 subgroup tile: each subgroup computes a WM x WN region as a grid of +// TM x TN x TK fragments. Requires WM%TM == WN%TN == BS_K%WM == BS_NPQ%WN == +// BS_CRS%TK == 0, and WG_SIZE == (BS_K/WM) * (BS_NPQ/WN) * subgroup_size. +layout(constant_id = 20) const uint WM = 32; +layout(constant_id = 21) const uint WN = 32; +const uint TM = 16; +const uint TN = 16; +const uint TK = 16; +const uint cms_per_row = WM / TM; +const uint cms_per_col = WN / TN; +const uint warps_M = BS_K / WM; +const uint warps_N = BS_NPQ / WN; +#endif + +// without padding, ID_idx/IH_idx/IW_idx are in bounds by construction +const bool dhw_in_bounds = (p0 == 0) && (p1 == 0) && (p2 == 0); + +uint32_t tid = gl_LocalInvocationID.x; +const uint32_t WG_SIZE = gl_WorkGroupSize.x; + +uint splitWork(uint work_size, uint block_size) { + return (block_size + work_size - 1) / block_size; +} + +uint32_t K = p.OC; +uint32_t CRS = p.IC * KD * KH * KW; +uint32_t NPQ = p.N * p.OD * p.OH * p.OW; + +// Number of blocktiles per input +uint32_t NB_CRS = splitWork(CRS, BS_CRS); + +#if defined(COOPMAT2) || defined(COOPMAT) +#define SHMEM_TYPE float16_t +#else +#define SHMEM_TYPE float +#endif + +const uint32_t Ash_stride = BS_CRS + SHMEM_PAD; +const uint32_t Bsh_stride = BS_NPQ + SHMEM_PAD; + +const uint32_t Ash_len = BS_K * Ash_stride; +const uint32_t Bsh_len = BS_CRS * Bsh_stride; + +shared SHMEM_TYPE Ash[Ash_len]; // K x CRS +shared SHMEM_TYPE Bsh[Bsh_len]; // CRS x NPQ + +#if defined(COOPMAT2) || defined(COOPMAT) +// stage matC through shmem so global stores are row-major (NPQ-contiguous) +const uint32_t Csh_stride = BS_NPQ; +#ifdef COOPMAT +const uint32_t Csh_len = BS_K * Csh_stride; +#else +const uint32_t Csh_len = csh_store != 0 ? BS_K * Csh_stride : 8; // 8 to workaround compiler bug +#endif +shared SHMEM_TYPE Csh[Csh_len]; // K x NPQ +#endif + +// Threadtile sizes +const uint32_t TS_NPQ = BS_K * BS_NPQ / WG_SIZE / TS_K; + +// Number of threadtiles per blocktile +const uint32_t NT_NPQ = BS_NPQ / TS_NPQ; + +/* +Compute +KxCRS @ CRSxNPQ = K x NPQ +K=OC +C=IC +D,R,S=KD,KH,KW +Z,P,Q=OD,OH,OW +*/ + +uint32_t B_idx_K = gl_WorkGroupID.x; +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; + +uint32_t Ar = tid / BS_CRS; +uint32_t Ac = tid % BS_CRS; +const uint32_t ArpWg = WG_SIZE / BS_CRS; + +uint32_t Br = tid / BS_NPQ; +uint32_t Bc = tid % BS_NPQ; +const uint32_t BrpWg = WG_SIZE / BS_NPQ; + +// see init_fastdiv_values in ggml-vulkan.cpp +uint fastdiv(uint n, uint mp, uint L) { + uint msbs, lsbs; + // msbs = mulhi(n, mp) + umulExtended(n, mp, msbs, lsbs); + return (msbs + n) >> L; +} + +void split_crs(uint32_t crs_idx, out uint32_t ic, out uint32_t kd, out uint32_t kh, out uint32_t kw) { + const uint32_t KHKW = KH * KW; + const uint32_t KDKHKW = KD * KHKW; + ic = crs_idx / KDKHKW; + uint32_t rem = crs_idx - ic * KDKHKW; + kd = rem / KHKW; + rem = rem - kd * KHKW; + kh = rem / KW; + kw = rem - kh * KW; +} + +void split_npq(uint32_t npq_idx, out uint32_t n, out uint32_t od, out uint32_t oh, out uint32_t ow) { + const uint32_t OWOH = p.OW * p.OH; + n = fastdiv(npq_idx, p.OWOHODmp, p.OWOHODL); + uint32_t rem = npq_idx - n * p.OD * OWOH; + od = fastdiv(rem, p.OWOHmp, p.OWOHL); + rem = rem - od * OWOH; + oh = fastdiv(rem, p.OWmp, p.OWL); + ow = rem - oh * p.OW; +} + +#ifdef COOPMAT2 +#define ACC_TYPE float16_t + +ACC_TYPE perElemOpStore(const in uint32_t r, const in uint32_t c, const in ACC_TYPE elem) +{ + uint32_t K_idx = B_idx_K * BS_K + r; + uint32_t NPQ_idx = B_idx_NPQ * BS_NPQ + c; + uint32_t N_idx; + uint32_t OD_idx; + uint32_t OH_idx; + uint32_t OW_idx; + split_npq(NPQ_idx, N_idx, OD_idx, OH_idx, OW_idx); + uint32_t dst_idx = OW_idx + OH_idx * p.nb1 + OD_idx * p.nb2 + (N_idx * p.OC + K_idx) * p.nb3; + if (aligned != 0 || (K_idx < K && NPQ_idx < NPQ)) { + dst_data[dst_idx] = D_TYPE(elem); + } + return elem; +} +#endif + +void main() { + if (B_idx_NPQ * BS_NPQ >= NPQ) { + return; + } + +#ifdef COOPMAT2 + coopmat matC; + matC = coopmat(0.0); +#elif defined(COOPMAT) + coopmat sums[cms_per_row * cms_per_col]; + [[unroll]] for (uint i = 0; i < cms_per_row * cms_per_col; i++) { + sums[i] = coopmat(0.0); + } + const uint warp_r = gl_SubgroupID / warps_N; + const uint warp_c = gl_SubgroupID % warps_N; +#else + float regC[TS_K][TS_NPQ]; + for (uint32_t T_ly = 0; T_ly < TS_K; T_ly++) { + for (uint32_t T_lx = 0; T_lx < TS_NPQ; T_lx++) { + regC[T_ly][T_lx] = 0.0; + } + } +#endif + /* Advance block in CRS dim */ + [[dont_unroll]] for (uint32_t B_idx_CRS = 0; B_idx_CRS < NB_CRS; B_idx_CRS++) { + uint32_t CRS_idx_a = B_idx_CRS * BS_CRS + Ac; + uint32_t IC_idx_a; + uint32_t KD_idx_a; + uint32_t KH_idx_a; + uint32_t KW_idx_a; + split_crs(CRS_idx_a, IC_idx_a, KD_idx_a, KH_idx_a, KW_idx_a); + + /* Load kernel to A_block: (BS_K x BS_CRS)*/ + UNROLL for (uint32_t r_offset = 0; r_offset < BS_K; r_offset += ArpWg) { + uint32_t B_ly = r_offset + Ar; + uint32_t B_lx = Ac; + uint32_t K_idx = B_idx_K * BS_K + B_ly; /* Global K_idx (row index of A)*/ + uint32_t knl_idx = KW_idx_a + KH_idx_a * p.nb01 + KD_idx_a * p.nb02 + (K_idx * p.IC + IC_idx_a) * p.nb03; + if (aligned == 0) { + knl_idx = min(knl_idx, K * CRS - 1); + } + float val = knl_data[knl_idx]; + if (aligned == 0 && (K_idx >= K || CRS_idx_a >= CRS)) { + val = 0.0; + } + Ash[B_ly * Ash_stride + B_lx] = SHMEM_TYPE(val); + } + /* Load input to B_block: (BS_CRS x BS_NPQ) */ + UNROLL for (uint32_t r_offset = 0; r_offset < BS_CRS; r_offset += BrpWg) { + uint32_t B_ly = r_offset + Br; /* Row index of B block */ + uint32_t B_lx = Bc; + uint32_t NPQ_idx = B_idx_NPQ * BS_NPQ + B_lx; /* Global NPQ index (column index of B) */ + uint32_t N_idx; + uint32_t OD_idx; + uint32_t OH_idx; + uint32_t OW_idx; + split_npq(NPQ_idx, N_idx, OD_idx, OH_idx, OW_idx); + + uint32_t CRS_idx_b = B_idx_CRS * BS_CRS + B_ly; + uint32_t IC_idx_b; + uint32_t KD_idx_b; + uint32_t KH_idx_b; + uint32_t KW_idx_b; + split_crs(CRS_idx_b, IC_idx_b, KD_idx_b, KH_idx_b, KW_idx_b); + + uint32_t ID_idx = OD_idx * s2 + KD_idx_b * d2 - p2; + uint32_t IH_idx = OH_idx * s1 + KH_idx_b * d1 - p1; + uint32_t IW_idx = OW_idx * s0 + KW_idx_b * d0 - p0; + + uint32_t src_idx = IW_idx + IH_idx * p.nb11 + ID_idx * p.nb12 + (N_idx * p.IC + IC_idx_b) * p.nb13; + // skip clamp when address can't go OOB + if (aligned == 0 || !dhw_in_bounds) { + src_idx = min(src_idx, p.IC * p.N * p.IW * p.IH * p.ID - 1); + } + float val = src_data[src_idx]; + bool oob = false; + if (aligned == 0 && (CRS_idx_b >= CRS || NPQ_idx >= NPQ)) { + oob = true; + } + // also catches lower-bound underflow (idx wraps to 0x80000000+) + if (!dhw_in_bounds && (ID_idx >= p.ID || IH_idx >= p.IH || IW_idx >= p.IW)) { + oob = true; + } + if (oob) { + val = 0.0; + } + Bsh[B_ly * Bsh_stride + B_lx] = SHMEM_TYPE(val); + } + barrier(); +#ifdef COOPMAT2 + coopmat matA; + coopmat matB; + + coopMatLoad(matA, Ash, 0, Ash_stride, gl_CooperativeMatrixLayoutRowMajor); + coopMatLoad(matB, Bsh, 0, Bsh_stride, gl_CooperativeMatrixLayoutRowMajor); + matC = coopMatMulAdd(matA, matB, matC); +#elif defined(COOPMAT) + // each subgroup multiplies its grid of fragments per TK-sized CRS chunk + [[unroll]] for (uint k_step = 0; k_step < BS_CRS / TK; k_step++) { + coopmat cache_a[cms_per_row]; + [[unroll]] for (uint cm_row = 0; cm_row < cms_per_row; cm_row++) { + const uint a_off = (warp_r * WM + cm_row * TM) * Ash_stride + k_step * TK; + coopMatLoad(cache_a[cm_row], Ash, a_off, Ash_stride, gl_CooperativeMatrixLayoutRowMajor); + } + [[unroll]] for (uint cm_col = 0; cm_col < cms_per_col; cm_col++) { + coopmat cache_b; + const uint b_off = k_step * TK * Bsh_stride + warp_c * WN + cm_col * TN; + coopMatLoad(cache_b, Bsh, b_off, Bsh_stride, gl_CooperativeMatrixLayoutRowMajor); + [[unroll]] for (uint cm_row = 0; cm_row < cms_per_row; cm_row++) { + sums[cm_col * cms_per_row + cm_row] = coopMatMulAdd(cache_a[cm_row], cache_b, sums[cm_col * cms_per_row + cm_row]); + } + } + } +#else + if (T_y * TS_K < K) { + UNROLL for (uint32_t CRS_lidx = 0; CRS_lidx < BS_CRS; CRS_lidx++) { + float regA[TS_K]; + float regB[TS_NPQ]; + for (uint32_t T_ly = 0; T_ly < TS_K; T_ly++) { + regA[T_ly] = Ash[(T_y * TS_K + T_ly) * Ash_stride + CRS_lidx]; + } + for (uint32_t T_lx = 0; T_lx < TS_NPQ; T_lx++) { + regB[T_lx] = Bsh[CRS_lidx * Bsh_stride + T_x * TS_NPQ + T_lx]; + } + for (uint32_t T_ly = 0; T_ly < TS_K; T_ly++) { + for (uint32_t T_lx = 0; T_lx < TS_NPQ; T_lx++) { + regC[T_ly][T_lx] = fma(regA[T_ly], regB[T_lx], regC[T_ly][T_lx]); + } + } + } + } +#endif + barrier(); + } + /* Save C* */ +#if defined(COOPMAT2) || defined(COOPMAT) + // stage matC into Csh, then write to dst with coalesced NPQ-contiguous stores +#ifdef COOPMAT + const bool use_staged_store = true; +#else + const bool use_staged_store = (csh_store != 0); +#endif + if (use_staged_store) { +#ifdef COOPMAT + // cm1: each subgroup stores its fragment grid into its Csh slot + [[unroll]] for (uint cm_row = 0; cm_row < cms_per_row; cm_row++) { + [[unroll]] for (uint cm_col = 0; cm_col < cms_per_col; cm_col++) { + const uint csh_off = (warp_r * WM + cm_row * TM) * Csh_stride + warp_c * WN + cm_col * TN; + coopMatStore(sums[cm_col * cms_per_row + cm_row], Csh, csh_off, Csh_stride, gl_CooperativeMatrixLayoutRowMajor); + } + } +#else + coopMatStore(matC, Csh, 0, Csh_stride, gl_CooperativeMatrixLayoutRowMajor); +#endif + barrier(); + + // cooperative shmem->global: WG threads spread across BS_NPQ (the + // contiguous direction of dst), each iter covers store_rows_per_iter K-rows + const uint32_t store_rows_per_iter = WG_SIZE / BS_NPQ; + const uint32_t store_iters = BS_K / store_rows_per_iter; + const uint32_t k_thread_offset = tid / BS_NPQ; + const uint32_t npq_thread = tid % BS_NPQ; + [[unroll]] for (uint32_t i = 0; i < store_iters; i++) { + uint32_t k_local = i * store_rows_per_iter + k_thread_offset; + uint32_t K_idx = B_idx_K * BS_K + k_local; + uint32_t NPQ_idx = B_idx_NPQ * BS_NPQ + npq_thread; + uint32_t N_idx; + uint32_t OD_idx; + uint32_t OH_idx; + uint32_t OW_idx; + split_npq(NPQ_idx, N_idx, OD_idx, OH_idx, OW_idx); + uint32_t dst_idx = OW_idx + OH_idx * p.nb1 + OD_idx * p.nb2 + (N_idx * p.OC + K_idx) * p.nb3; + if (aligned != 0 || (K_idx < K && NPQ_idx < NPQ)) { + dst_data[dst_idx] = D_TYPE(Csh[k_local * Csh_stride + npq_thread]); + } + } + } +#ifdef COOPMAT2 + else { + coopMatPerElementNV(matC, matC, perElemOpStore); + } +#endif +#else + if (T_y * TS_K < K) { + for (uint32_t T_ly = 0; T_ly < TS_K; T_ly++) { + for (uint32_t T_lx = 0; T_lx < TS_NPQ; T_lx++) { + uint32_t K_idx = B_idx_K * BS_K + T_y * TS_K + T_ly; + uint32_t NPQ_idx = B_idx_NPQ * BS_NPQ + T_x * TS_NPQ + T_lx; + uint32_t N_idx; + uint32_t OD_idx; + uint32_t OH_idx; + uint32_t OW_idx; + split_npq(NPQ_idx, N_idx, OD_idx, OH_idx, OW_idx); + uint32_t dst_idx = OW_idx + OH_idx * p.nb1 + OD_idx * p.nb2 + (N_idx * p.OC + K_idx) * p.nb3; + if (aligned != 0 || (K_idx < K && NPQ_idx < NPQ)) { + dst_data[dst_idx] = D_TYPE(regC[T_ly][T_lx]); + } + } + } + } +#endif +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/conv_transpose_1d.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/conv_transpose_1d.comp new file mode 100644 index 0000000000000000000000000000000000000000..5217e18bdd96d485f3b8ffced8c56619c21c8a79 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/conv_transpose_1d.comp @@ -0,0 +1,98 @@ +#version 450 + +#include "types.glsl" + +layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; // src0 - kernel: [K, Cout, Cin] +layout (binding = 1) readonly buffer B {B_TYPE data_b[];}; // src1 - input: [L, Cin] +layout (binding = 2) writeonly buffer D {D_TYPE data_d[];}; // dst - result [KL, Cout] + +layout(local_size_x = 128 , local_size_y = 1, local_size_z = 1) in; + +layout (push_constant) uniform parameter { + uint32_t Cout; + uint32_t Cin; + uint32_t K; + uint32_t L; + uint32_t KL; + + uint32_t nb01; + uint32_t nb02; + uint32_t nb11; + uint32_t nb1; + + int32_t s0; +} p; + + +uint32_t Cout_idx = gl_WorkGroupID.x; +const uint32_t bs = gl_WorkGroupSize.x; +uint32_t tid = gl_LocalInvocationID.x; +// Code is more straightforward if we assume it is bs*s0+K instead of (bs-1)*s0+K. +uint32_t tmp_len = bs*p.s0+p.K; +shared D_TYPE tmp[4096]; + +uint splitWork(uint workSize){ + return (bs + workSize -1) / bs; +} + +void main(){ + for(uint32_t i = 0; i < splitWork(tmp_len); i++){ + uint32_t idx = i*bs+tid; + if(idx < tmp_len){ + tmp[idx] = 0.0; + } + } + + uint32_t L_blocks = splitWork(p.L); + for(uint32_t L_block_id = 0; L_block_id < L_blocks; L_block_id++){ + if(L_block_id > 0){ + barrier(); + // Shift values in tmp to the current processing window + for(int i = 0; i < splitWork(tmp_len); i++){ + uint32_t idx = i*bs+tid; + if(idx >= bs*p.s0 && idx < tmp_len){ + tmp[idx-bs*p.s0] = tmp[idx]; + tmp[idx] = 0.0; + }else if(idx >= p.K && idx < bs*p.s0){ + tmp[idx] = 0.0; + } + } + } + barrier(); + + // Save contributions of the block to tmp + uint32_t L_idx = L_block_id*bs + tid; + for(uint32_t K_idx = 0; K_idx < p.K; K_idx++){ + D_TYPE dp = 0.0; + for(uint32_t Cin_idx = 0; Cin_idx < p.Cin; Cin_idx++){ + A_TYPE elemKrn = data_a[K_idx + Cout_idx * p.nb01 + Cin_idx * p.nb02]; + if(L_idx < p.L){ + B_TYPE elemInp = data_b[L_idx + Cin_idx*p.nb11]; + dp = fma(elemKrn, elemInp, dp); + } + } + tmp[tid*p.s0 + K_idx] += dp; + barrier(); + } + + // Save the computed values except the last block that can have different size + uint32_t KLb_idx = L_block_id*bs*p.s0; + if(L_block_id < L_blocks-1){ + for(uint32_t s0_idx = 0; s0_idx < p.s0; s0_idx++){ + uint32_t sh_idx = p.s0*tid+s0_idx; + uint32_t KL_idx = KLb_idx+sh_idx; + if(KL_idx < p.KL){ + data_d[KL_idx + Cout_idx*p.nb1] = tmp[sh_idx]; + } + } + } + } + + for(uint32_t i = 0; i < splitWork(tmp_len); i++){ + uint32_t idx = i*bs+tid; + uint32_t KL_idx = (L_blocks-1)*bs*p.s0+idx; + if(KL_idx < p.KL){ + data_d[KL_idx + Cout_idx*p.nb1] = tmp[idx]; + } + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/copy.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/copy.comp new file mode 100644 index 0000000000000000000000000000000000000000..d55e13253a8b3001966790b0172ae30bf6fb8274 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/copy.comp @@ -0,0 +1,25 @@ +#version 450 + +#include "types.glsl" +#include "generic_unary_head.glsl" + +layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; + +void main() { + const uint idx = get_idx(); + + if (idx >= p.ne) { + return; + } + +#if defined(DATA_A_BF16) + data_d[get_doffset() + dst_idx(idx)] = D_TYPE(bf16_to_fp32(uint32_t(data_a[get_aoffset() + src0_idx(idx)]))); +#elif defined(DATA_D_BF16) + float f = float(data_a[get_aoffset() + src0_idx(idx)]); + data_d[get_doffset() + dst_idx(idx)] = D_TYPE(fp32_to_bf16(f)); +#elif !defined(OPTIMIZATION_ERROR_WORKAROUND) + data_d[get_doffset() + dst_idx(idx)] = D_TYPE(data_a[get_aoffset() + src0_idx(idx)]); +#else + data_d[get_doffset() + dst_idx(idx)] = data_a[get_aoffset() + src0_idx(idx)]; +#endif +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/copy_from_quant.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/copy_from_quant.comp new file mode 100644 index 0000000000000000000000000000000000000000..6a6921474781f46f77e3dc3b94bf94b04bb16a9b --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/copy_from_quant.comp @@ -0,0 +1,51 @@ +#version 450 + +#include "types.glsl" +#include "generic_unary_head.glsl" +#include "dequant_funcs.glsl" + +#if defined(DATA_A_IQ4_NL) || defined(DATA_A_MXFP4) || defined(DATA_A_NVFP4) +// 16 invocations needed for init_iq_shmem +layout(local_size_x = 16, local_size_y = 1, local_size_z = 1) in; +#else +layout(local_size_x = 1, local_size_y = 1, local_size_z = 1) in; +#endif + +void main() { +#ifdef NEEDS_INIT_IQ_SHMEM + init_iq_shmem(gl_WorkGroupSize); + if (gl_LocalInvocationIndex.x != 0) { + return; + } +#endif + + const uint idx = gl_WorkGroupID.z * 262144 + gl_WorkGroupID.y * 512 + gl_WorkGroupID.x * QUANT_K; + + if (idx >= p.ne) { + return; + } + + uint dst_idx = get_doffset() + dst_idx(idx); + uint src_idx = src0_idx_quant(idx, QUANT_K); + + const uint a_offset = 0; + const uint ib = src_idx; + const vec2 dm = get_dm(ib, a_offset); + + [[unroll]] for (int j = 0; j < QUANT_K; j += 4) { + vec4 v = dequantize4(ib, j / QUANT_R, a_offset); + v = v * dm.x + vec4(dm.y); + +#if QUANT_R == 2 + data_d[dst_idx + j/2 + 0] = v[0]; + data_d[dst_idx + j/2 + QUANT_K/2 + 0] = v[1]; + data_d[dst_idx + j/2 + 1] = v[2]; + data_d[dst_idx + j/2 + QUANT_K/2 + 1] = v[3]; +#else + data_d[dst_idx + j + 0] = v[0]; + data_d[dst_idx + j + 1] = v[1]; + data_d[dst_idx + j + 2] = v[2]; + data_d[dst_idx + j + 3] = v[3]; +#endif + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/copy_to_quant.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/copy_to_quant.comp new file mode 100644 index 0000000000000000000000000000000000000000..710c15296da2a48a3c507938883a528c291659d2 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/copy_to_quant.comp @@ -0,0 +1,320 @@ +#version 450 + +#include "types.glsl" + +#if defined(SET_ROWS) && QUANT_K == 1 +layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; +const uint BLOCK_SIZE = 512; +#else +layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in; +const uint BLOCK_SIZE = 32; +#endif + +layout (binding = 0) readonly buffer S {float data_s[];}; + +#if defined(SET_ROWS) +#include "generic_binary_head.glsl" +layout (binding = 1) readonly buffer C {B_TYPE data_i[];}; +layout (binding = 2) writeonly buffer Q {A_TYPE data_q[];}; + +#if B_SIZE == 64 +#define DATA_I_SWIZZLE .x +#else +#define DATA_I_SWIZZLE +#endif + +#else +#include "generic_unary_head.glsl" +layout (binding = 1) writeonly buffer Q {A_TYPE data_q[];}; +#endif + +#if defined(DATA_A_Q4_0) +void quantize(uint dst_idx, uint src_idx) +{ + float amax = 0.0; + float vmax = 0.0; + + [[unroll]] for (int j = 0; j < QUANT_K_Q4_0; ++j) { + const float v = data_s[src_idx + j]; + if (amax < abs(v)) { + amax = abs(v); + vmax = v; + } + } + + const float d = vmax / -8; + const float id = (d != 0.0) ? 1.0/d : 0.0; + + data_q[dst_idx].d = float16_t(d); + + [[unroll]] for (int j = 0; j < QUANT_K_Q4_0/2; ++j) { + const float x0 = data_s[src_idx + 0 + j]*id; + const float x1 = data_s[src_idx + QUANT_K_Q4_0/2 + j]*id; + + const uint xi0 = min(15, int(x0 + 8.5)); + const uint xi1 = min(15, int(x1 + 8.5)); + + data_q[dst_idx].qs[j] = uint8_t(xi0 | (xi1 << 4)); + } +} +#endif + +#if defined(DATA_A_Q4_1) +void quantize(uint dst_idx, uint src_idx) +{ + float vmin = 1.0/0.0; + float vmax = -vmin; + + [[unroll]] for (int j = 0; j < QUANT_K_Q4_1; ++j) { + const float v = data_s[src_idx + j]; + + if (v < vmin) vmin = v; + if (v > vmax) vmax = v; + } + + const float d = (vmax - vmin) / ((1 << 4) - 1); + const float id = (d != 0.0) ? 1.0/d : 0.0; + + data_q[dst_idx].d = float16_t(d); + data_q[dst_idx].m = float16_t(vmin); + + [[unroll]] for (int j = 0; j < QUANT_K_Q4_1/2; ++j) { + const float x0 = (data_s[src_idx + 0 + j] - vmin)*id; + const float x1 = (data_s[src_idx + QUANT_K_Q4_1/2 + j] - vmin)*id; + + const uint xi0 = min(15, int(x0 + 0.5)); + const uint xi1 = min(15, int(x1 + 0.5)); + + data_q[dst_idx].qs[j] = uint8_t(xi0 | (xi1 << 4)); + } +} +#endif + +#if defined(DATA_A_Q5_0) +void quantize(uint dst_idx, uint src_idx) +{ + float amax = 0.0; + float vmax = 0.0; + + [[unroll]] for (int j = 0; j < QUANT_K_Q5_0; ++j) { + const float v = data_s[src_idx + j]; + if (amax < abs(v)) { + amax = abs(v); + vmax = v; + } + } + + const float d = vmax / -16; + const float id = (d != 0.0) ? 1.0/d : 0.0; + + data_q[dst_idx].d = float16_t(d); + + uint32_t qh = 0; + [[unroll]] for (int j = 0; j < QUANT_K_Q5_0/2; ++j) { + const float x0 = data_s[src_idx + 0 + j]*id; + const float x1 = data_s[src_idx + QUANT_K_Q5_0/2 + j]*id; + + const uint xi0 = min(31, int(x0 + 16.5)); + const uint xi1 = min(31, int(x1 + 16.5)); + + data_q[dst_idx].qs[j] = uint8_t((xi0 & 0xf) | ((xi1 & 0xf) << 4)); + qh |= ((xi0 & 0x10u) >> 4) << (j + 0); + qh |= ((xi1 & 0x10u) >> 4) << (j + QUANT_K_Q5_0/2); + } + data_q[dst_idx].qh[0] = uint16_t(qh & 0xFFFF); + data_q[dst_idx].qh[1] = uint16_t(qh >> 16); +} +#endif + +#if defined(DATA_A_Q5_1) +void quantize(uint dst_idx, uint src_idx) +{ + float min = data_s[src_idx + 0]; + float max = min; + + [[unroll]] for (int j = 1; j < QUANT_K_Q5_1; ++j) { + const float v = data_s[src_idx + j]; + min = v < min ? v : min; + max = v > max ? v : max; + } + + const float d = (max - min) / 31; + const float id = (d != 0) ? 1.0/d : 0.0; + + data_q[dst_idx].d = float16_t(d); + data_q[dst_idx].m = float16_t(min); + + uint32_t qh = 0; + [[unroll]] for (int j = 0; j < QUANT_K_Q5_1/2; ++j) { + const float x0 = (data_s[src_idx + 0 + j] - min)*id; + const float x1 = (data_s[src_idx + QUANT_K_Q5_1/2 + j] - min)*id; + + const uint xi0 = uint(x0 + 0.5); + const uint xi1 = uint(x1 + 0.5); + + data_q[dst_idx].qs[j] = uint8_t((xi0 & 0xf) | ((xi1 & 0xf) << 4)); + qh |= ((xi0 & 0x10u) >> 4) << (j + 0); + qh |= ((xi1 & 0x10u) >> 4) << (j + QUANT_K_Q5_1/2); + } + data_q[dst_idx].qh = qh; +} +#endif + +#if defined(DATA_A_Q8_0) +void quantize(uint dst_idx, uint src_idx) +{ + float amax = 0.0; // absolute max + + [[unroll]] for (int j = 0; j < QUANT_K_Q8_0; j++) { + const float v = data_s[src_idx + j]; + amax = max(amax, abs(v)); + } + + const float d = amax / ((1 << 7) - 1); + const float id = (d != 0.0) ? 1.0/d : 0.0; + + data_q[dst_idx].d = float16_t(d); + + [[unroll]] for (int j = 0; j < QUANT_K_Q8_0; ++j) { + const float x0 = data_s[src_idx + j]*id; + + data_q[dst_idx].qs[j] = int8_t(round(x0)); + } +} +#endif + +#if defined(DATA_A_Q1_0) +void quantize(uint dst_idx, uint src_idx) +{ + float sum_abs = 0.0; + + [[unroll]] for (int j = 0; j < QUANT_K_Q1_0; j++) { + sum_abs += abs(data_s[src_idx + j]); + } + + const float d = sum_abs / QUANT_K_Q1_0; + + data_q[dst_idx].d = float16_t(d); + + [[unroll]] for (int j = 0; j < QUANT_K_Q1_0 / 8; ++j) { + data_q[dst_idx].qs[j] = uint8_t(0); + } + + [[unroll]] for (int j = 0; j < QUANT_K_Q1_0; ++j) { + if (data_s[src_idx + j] >= 0.0) { + data_q[dst_idx].qs[j / 8] |= uint8_t(1 << (j % 8)); + } + } +} +#endif + +#if defined(DATA_A_IQ4_NL) +uint best_index(float x) { + if (x <= kvalues_iq4nl[0]) return 0; + if (x >= kvalues_iq4nl[15]) return 15; + int ml = 0, mu = 15; + while (mu-ml > 1) { + int mav = (ml+mu)/2; + if (x < kvalues_iq4nl[mav]) mu = mav; else ml = mav; + } + return x - kvalues_iq4nl[mu-1] < kvalues_iq4nl[mu] - x ? mu-1 : mu; +} + +void quantize(uint dst_idx, uint src_idx) +{ + float amax = 0.0; + float vmax = 0.0; + + [[unroll]] for (int j = 0; j < QUANT_K_IQ4_NL; ++j) { + const float v = data_s[src_idx + j]; + if (amax < abs(v)) { + amax = abs(v); + vmax = v; + } + } + + float d = vmax / kvalues_iq4nl[0]; + const float id = (d != 0.0) ? 1.0/d : 0.0; + + float sumqx = 0, sumq2 = 0; + [[unroll]] for (int j = 0; j < QUANT_K_IQ4_NL/2; ++j) { + const float x0 = data_s[src_idx + 0 + j]*id; + const float x1 = data_s[src_idx + QUANT_K_IQ4_NL/2 + j]*id; + const uint xi0 = best_index(x0); + const uint xi1 = best_index(x1); + data_q[dst_idx].qs[j] = uint8_t(xi0 | (xi1 << 4)); + const float v0 = kvalues_iq4nl[xi0]; + const float v1 = kvalues_iq4nl[xi1]; + const float w0 = data_s[src_idx + 0 + j]*data_s[src_idx + 0 + j]; + const float w1 = data_s[src_idx + QUANT_K_IQ4_NL/2 + j]*data_s[src_idx + QUANT_K_IQ4_NL/2 + j]; + sumqx += w0*v0*data_s[src_idx + j] + w1*v1*data_s[src_idx + QUANT_K_IQ4_NL/2 + j]; + sumq2 += w0*v0*v0 + w1*v1*v1; + } + + data_q[dst_idx].d = float16_t(sumq2 > 0 ? sumqx/sumq2 : d); + +} +#endif + +#if defined(DATA_A_F32) || defined(DATA_A_F16) +void quantize(uint dst_idx, uint src_idx) +{ + data_q[dst_idx] = A_TYPE(data_s[src_idx]); +} +#endif + +#if defined(DATA_A_BF16) +void quantize(uint dst_idx, uint src_idx) +{ + data_q[dst_idx] = A_TYPE(fp32_to_bf16(data_s[src_idx])); +} +#endif + +#if defined(SET_ROWS) + +void main() { +#ifdef NEEDS_INIT_IQ_SHMEM + init_iq_shmem(gl_WorkGroupSize); +#endif + + const uint idx = ((gl_WorkGroupID.z * 262144 + gl_WorkGroupID.y * 512 + gl_WorkGroupID.x) * BLOCK_SIZE + gl_LocalInvocationID.x) * QUANT_K; + + if (idx >= p.ne) { + return; + } + + uint i00, i01, i02, i03; + get_indices(idx, i00, i01, i02, i03); + + uint i12 = fastmod(i03, p.ne12); + uint i11 = fastmod(i02, p.ne11); + uint i10 = i01; + + uint i1 = data_i[src1_idx(i10, i11, i12, 0) + get_boffset()] DATA_I_SWIZZLE; + + uint src0_idx = src0_idx(i00, i01, i02, i03) + get_aoffset(); + uint dst_idx = dst_idx(i00 / QUANT_K, i1, i02, i03) + get_doffset(); + + quantize(dst_idx, src0_idx); +} + +#else + +void main() { +#ifdef NEEDS_INIT_IQ_SHMEM + init_iq_shmem(gl_WorkGroupSize); +#endif + + const uint idx = (gl_WorkGroupID.z * 262144 + gl_WorkGroupID.y * 512 + gl_WorkGroupID.x * 32 + gl_LocalInvocationID.x) * QUANT_K; + + if (idx >= p.ne) { + return; + } + + uint dst_idx = dst_idx_quant(idx, QUANT_K); + uint src_idx = get_aoffset() + src0_idx(idx); + + quantize(dst_idx, src_idx); +} + +#endif diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/copy_transpose.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/copy_transpose.comp new file mode 100644 index 0000000000000000000000000000000000000000..220ccc9111c0ed074ae3dec759cd8a62599b7627 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/copy_transpose.comp @@ -0,0 +1,67 @@ +#version 450 + +#include "types.glsl" +#include "generic_unary_head.glsl" + +// workgroup does 32x32 tile, but uses 32x8 threads +#define TILE_DIM 32 +layout(local_size_x = 32, local_size_y = 8, local_size_z = 1) in; + +shared uint sh[TILE_DIM][TILE_DIM + 1]; + +void iter(uvec3 wg_id) { + const uint tile_col = wg_id.x; + const uint tile_row = wg_id.y; + + const uint tid_col = gl_LocalInvocationID.x; + const uint tid_row = gl_LocalInvocationID.y; + + const uint i2 = wg_id.z % p.ne12; + const uint i3 = wg_id.z / p.ne12; + const uint i02 = i2; + const uint i03 = i3; + + // The workgroup does TILE_DIM x TILE_DIM, but swaps the LSBs of the + // src coords to make memory accesses contiguous, dst has tid.x in i0, + // src has tid.x in i01 + + [[unroll]] for (uint y = 0; y < 4; ++y) { + const uint i00 = tile_col * TILE_DIM + tid_row + 8 * y; + const uint i01 = tile_row * TILE_DIM + tid_col; + if (i00 < p.ne00 && i01 < p.ne01 && i02 < p.ne02 && i03 < p.ne03) { + const uint src_idx = i00 * p.nb00 + i01 * p.nb01 + i02 * p.nb02 + i03 * p.nb03; + sh[tid_row + 8 * y][tid_col] = uint(data_a[get_aoffset() + src_idx]); + } + } + + barrier(); + + [[unroll]] for (uint y = 0; y < 4; ++y) { + const uint i0 = tile_col * TILE_DIM + tid_col; + const uint i1 = tile_row * TILE_DIM + tid_row + 8 * y; + if (i0 < p.ne10 && i1 < p.ne11 && i2 < p.ne12 && i3 < p.ne13) { + const uint dst_idx = i0 * p.nb10 + i1 * p.nb11 + i2 * p.nb12 + i3 * p.nb13; + // load transposed + data_d[get_doffset() + dst_idx] = D_TYPE(sh[tid_col][tid_row + 8 * y]); + } + } +} + +#define CEIL_DIV(a, b) (((a) + (b) - 1) / (b)) + +void main() { + uint z = gl_WorkGroupID.z; + uint y = gl_WorkGroupID.y; + bool need_barrier = false; + for (uint z = gl_WorkGroupID.z; z < p.ne12 * p.ne13; z += gl_NumWorkGroups.z) { + for (uint y = gl_WorkGroupID.y; y < CEIL_DIV(p.ne11, TILE_DIM); y += gl_NumWorkGroups.y) { + for (uint x = gl_WorkGroupID.x; x < CEIL_DIV(p.ne10, TILE_DIM); x += gl_NumWorkGroups.x) { + if (need_barrier) { + barrier(); + } + need_barrier = true; + iter(uvec3(x, y, z)); + } + } + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/count_equal.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/count_equal.comp new file mode 100644 index 0000000000000000000000000000000000000000..e75df667564a01ef9e64a96702b31db17ae27bc1 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/count_equal.comp @@ -0,0 +1,31 @@ +#version 450 + +#extension GL_EXT_control_flow_attributes : enable + +#include "types.glsl" +#include "generic_head.glsl" + +layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; +layout (binding = 1) readonly buffer Y {B_TYPE data_b[];}; +layout (binding = 2) buffer D {D_TYPE data_d[];}; + +const uint CHUNK_SIZE = 512; + +void main() { + const uint base = gl_WorkGroupID.x * CHUNK_SIZE; + const uint col = gl_LocalInvocationID.x; + + uint count = 0; + [[unroll]] + for (uint i = 0; i < CHUNK_SIZE; i += gl_WorkGroupSize.x) { + const uint idx = base + i + col; + if (idx >= p.KX) { + break; + } + count += uint(data_a[idx] == data_b[idx]); + } + + atomicAdd(data_d[0], D_TYPE(count)); +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/count_experts.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/count_experts.comp new file mode 100644 index 0000000000000000000000000000000000000000..ffc8608691f7cedae53a734d7d08658efc39c84c --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/count_experts.comp @@ -0,0 +1,51 @@ +#version 450 + +#extension GL_EXT_control_flow_attributes : enable + +#include "types.glsl" + +layout (push_constant) uniform parameter +{ + uint32_t ne00; + uint32_t ne01; + uint32_t nb00; + uint32_t nb01; + uint32_t a_offset; +} p; + +#define BLOCK_SIZE 256 + +layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {uint data_a[];}; +layout (binding = 1) writeonly buffer D {uint data_d[];}; + +shared uint vals[BLOCK_SIZE]; + +void main() { + const uint expert_id = gl_WorkGroupID.x; + const uint num_elements = p.ne00 * p.ne01; + const uint tid = gl_LocalInvocationID.x; + + uint count = 0; + for (uint idx = tid; idx < num_elements; idx += BLOCK_SIZE) { + const uint i01 = idx / p.ne00; + const uint i00 = idx % p.ne00; + const uint a = data_a[p.a_offset + i01 * p.nb01 + i00 * p.nb00]; + + count += uint(a == expert_id); + } + + vals[tid] = count; + barrier(); + [[unroll]] for (uint s = BLOCK_SIZE / 2; s > 0; s >>= 1) { + if (tid < s) { + vals[tid] += vals[tid + s]; + } + barrier(); + } + + if (tid == 0) { + data_d[expert_id] = vals[0]; + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/cumsum.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/cumsum.comp new file mode 100644 index 0000000000000000000000000000000000000000..75e3c3b0eb44ae683b706179c12e37c09dce10c6 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/cumsum.comp @@ -0,0 +1,83 @@ +#version 450 + +#include "types.glsl" +#include "sum_rows.glsl" + +#extension GL_EXT_control_flow_attributes : enable +#extension GL_KHR_shader_subgroup_arithmetic : enable +#extension GL_KHR_shader_subgroup_basic : enable + +layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_d[];}; + +layout (constant_id = 0) const uint BLOCK_SIZE = 128; +layout (constant_id = 1) const uint SUBGROUP_SIZE = 32; +layout (constant_id = 2) const uint ELEM_PER_THREAD = 4; + +#define CEIL_DIV(a, b) (((a) + (b) - 1) / (b)) + +shared FLOAT_TYPE partial[BLOCK_SIZE / SUBGROUP_SIZE]; +shared FLOAT_TYPE last_sum; + +void main() { + const uint row = gl_WorkGroupID.z * 262144 + gl_WorkGroupID.y * 512 + gl_WorkGroupID.x; + const uint tid = gl_LocalInvocationID.x; + + const uint i03 = fastdiv(row, p.ne0_12mp, p.ne0_12L); + const uint i03_offset = i03 * p.ne01*p.ne02; + const uint i02 = fastdiv(row - i03_offset, p.ne0_1mp, p.ne0_1L); + const uint i01 = row - i03_offset - i02*p.ne01; + + const uint src_idx = get_aoffset() + i01 * p.nb01 + i02 * p.nb02 + i03 * p.nb03; + const uint dst_idx = get_doffset() + i01 * p.nb11 + i02 * p.nb12 + i03 * p.nb13; + + uint subgroup_id = tid / SUBGROUP_SIZE; + + if (tid == 0) { + last_sum = 0; + } + + uint col = tid * ELEM_PER_THREAD; + uint num_iter = CEIL_DIV(p.n_cols, BLOCK_SIZE * ELEM_PER_THREAD); + for (int i = 0; i < num_iter; ++i) { + FLOAT_TYPE v[ELEM_PER_THREAD]; + FLOAT_TYPE thread_sum = 0; + [[unroll]] for (uint j = 0; j < ELEM_PER_THREAD; ++j) { + if (col + j < p.n_cols) { + thread_sum += FLOAT_TYPE(data_a[src_idx + col + j]); + } + v[j] = thread_sum; + } + + thread_sum = subgroupExclusiveAdd(thread_sum); + [[unroll]] for (uint j = 0; j < ELEM_PER_THREAD; ++j) { + v[j] += thread_sum; + } + // Store the largest partial sum for each subgroup, then add the partials for all + // lower subgroups and the final partial sum from the previous iteration. + if (gl_SubgroupInvocationID == SUBGROUP_SIZE - 1) { + partial[subgroup_id] = v[ELEM_PER_THREAD - 1]; + } + barrier(); + for (int s = 0; s < subgroup_id; ++s) { + [[unroll]] for (uint j = 0; j < ELEM_PER_THREAD; ++j) { + v[j] += partial[s]; + } + } + [[unroll]] for (uint j = 0; j < ELEM_PER_THREAD; ++j) { + v[j] += last_sum; + } + barrier(); + if (tid == BLOCK_SIZE - 1) { + last_sum = v[ELEM_PER_THREAD - 1]; + } + [[unroll]] for (uint j = 0; j < ELEM_PER_THREAD; ++j) { + if (col + j < p.n_cols) { + data_d[dst_idx + col + j] = D_TYPE(v[j]); + } + } + col += BLOCK_SIZE * ELEM_PER_THREAD; + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/cumsum_multipass1.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/cumsum_multipass1.comp new file mode 100644 index 0000000000000000000000000000000000000000..6d39f927fc1ec62970ba7b7d062594ff2fa56f0e --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/cumsum_multipass1.comp @@ -0,0 +1,60 @@ +#version 450 + +#include "types.glsl" +#include "sum_rows.glsl" + +#extension GL_EXT_control_flow_attributes : enable +#extension GL_KHR_shader_subgroup_arithmetic : enable +#extension GL_KHR_shader_subgroup_basic : enable + +layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_d[];}; +layout (binding = 2) writeonly buffer T {D_TYPE data_t[];}; + +layout (constant_id = 0) const uint BLOCK_SIZE = 128; +layout (constant_id = 1) const uint SUBGROUP_SIZE = 32; + +#define CEIL_DIV(a, b) (((a) + (b) - 1) / (b)) + +shared FLOAT_TYPE partial[BLOCK_SIZE / SUBGROUP_SIZE]; + +void main() { + const uint row = gl_WorkGroupID.y; + const uint tid = gl_LocalInvocationID.x; + const uint col = gl_GlobalInvocationID.x; + + const uint i03 = fastdiv(row, p.ne0_12mp, p.ne0_12L); + const uint i03_offset = i03 * p.ne01*p.ne02; + const uint i02 = fastdiv(row - i03_offset, p.ne0_1mp, p.ne0_1L); + const uint i01 = row - i03_offset - i02*p.ne01; + + const uint src_idx = get_aoffset() + i01 * p.nb01 + i02 * p.nb02 + i03 * p.nb03; + const uint dst_idx = get_doffset() + i01 * p.nb11 + i02 * p.nb12 + i03 * p.nb13; + + uint subgroup_id = tid / SUBGROUP_SIZE; + + FLOAT_TYPE v = 0; + if (col < p.n_cols) { + v = FLOAT_TYPE(data_a[src_idx + col]); + } + v = subgroupInclusiveAdd(v); + + // Store the largest partial sum for each subgroup, then add the partials for all + // lower subgroups and the final partial sum from the previous iteration. + if (gl_SubgroupInvocationID == SUBGROUP_SIZE - 1) { + partial[subgroup_id] = v; + } + barrier(); + for (int j = 0; j < subgroup_id; ++j) { + v += partial[j]; + } + barrier(); + if (tid == BLOCK_SIZE - 1) { + data_t[gl_WorkGroupID.x + gl_NumWorkGroups.x * row] = v; + } + if (col < p.n_cols) { + data_d[dst_idx + col] = D_TYPE(v); + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/cumsum_multipass2.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/cumsum_multipass2.comp new file mode 100644 index 0000000000000000000000000000000000000000..e401893466ccf128b124156c477c012e32c7adc5 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/cumsum_multipass2.comp @@ -0,0 +1,66 @@ +#version 450 + +#include "types.glsl" +#include "sum_rows.glsl" + +#extension GL_EXT_control_flow_attributes : enable +#extension GL_KHR_shader_subgroup_arithmetic : enable +#extension GL_KHR_shader_subgroup_basic : enable + +layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; +layout (binding = 1) buffer D {D_TYPE data_d[];}; +layout (binding = 2) readonly buffer T {D_TYPE data_t[];}; + +layout (constant_id = 0) const uint BLOCK_SIZE = 128; +layout (constant_id = 1) const uint SUBGROUP_SIZE = 32; + +#define CEIL_DIV(a, b) (((a) + (b) - 1) / (b)) + +shared FLOAT_TYPE temp[BLOCK_SIZE / SUBGROUP_SIZE]; + +void main() { + const uint row = gl_WorkGroupID.y; + const uint tid = gl_LocalInvocationID.x; + + const uint i03 = fastdiv(row, p.ne0_12mp, p.ne0_12L); + const uint i03_offset = i03 * p.ne01*p.ne02; + const uint i02 = fastdiv(row - i03_offset, p.ne0_1mp, p.ne0_1L); + const uint i01 = row - i03_offset - i02*p.ne01; + + const uint src_idx = get_aoffset() + i01 * p.nb01 + i02 * p.nb02 + i03 * p.nb03; + const uint dst_idx = get_doffset() + i01 * p.nb11 + i02 * p.nb12 + i03 * p.nb13; + + const uint col = gl_GlobalInvocationID.x; + + float v = 0; + // prefetch value we're adding to + if (col < p.n_cols) { + v = data_d[dst_idx + col]; + } + + // compute the sum of all previous blocks + uint c = tid; + float sum = 0; + while (c < gl_WorkGroupID.x) { + sum += data_t[c + gl_NumWorkGroups.x * row]; + c += BLOCK_SIZE; + } + + sum = subgroupAdd(sum); + if (gl_SubgroupInvocationID == 0) { + temp[gl_SubgroupID] = sum; + } + barrier(); + sum = 0; + [[unroll]] for (uint s = 0; s < BLOCK_SIZE / SUBGROUP_SIZE; ++s) { + sum += temp[s]; + } + + // Add the sum to what the first pass computed + if (col < p.n_cols) { + data_d[dst_idx + col] = v + sum; + } +} + diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_f32.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_f32.comp new file mode 100644 index 0000000000000000000000000000000000000000..765afffa80fd7f5705758a797e87c089ad2ac95d --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_f32.comp @@ -0,0 +1,20 @@ +#version 450 + +#include "dequant_head.glsl" + +layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {float data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; + +void main() { + const uint i = gl_GlobalInvocationID.x * 16; + + if (i >= p.nel) { + return; + } + + [[unroll]] for (uint l = 0; l < 16; l++) { + data_b[i + l] = D_TYPE(data_a[i + l]); + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs.glsl b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs.glsl new file mode 100644 index 0000000000000000000000000000000000000000..e67299fdeca04db4fdb973aa9035260ecd857ca3 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs.glsl @@ -0,0 +1,692 @@ +#if !defined(DATA_A_F32) && !defined(DATA_A_F16) +#extension GL_EXT_shader_explicit_arithmetic_types_int8 : require +#endif + +#include "types.glsl" + +#if defined(DATA_A_F32) +FLOAT_TYPE dequantize1(uint ib, uint iqs, uint a_offset) { + return data_a[a_offset + ib]; +} +vec2 dequantize(uint ib, uint iqs, uint a_offset) { + return vec2(data_a[a_offset + ib], data_a[a_offset + ib + 1]); +} +vec4 dequantize4(uint ib, uint iqs, uint a_offset) { + return vec4(data_a[a_offset + ib ], data_a[a_offset + ib + 1], + data_a[a_offset + ib + 2], data_a[a_offset + ib + 3]); +} +vec4 dequantize4_2aligned(uint ib, uint iqs, uint a_offset) { + return vec4(data_a[a_offset + ib ], data_a[a_offset + ib + 1], + data_a[a_offset + ib + 2], data_a[a_offset + ib + 3]); +} + +#endif + +#if defined(DATA_A_F16) +FLOAT_TYPE dequantize1(uint ib, uint iqs, uint a_offset) { + return data_a[a_offset + ib]; +} +vec2 dequantize(uint ib, uint iqs, uint a_offset) { + return vec2(data_a[a_offset + ib], data_a[a_offset + ib + 1]); +} +vec4 dequantize4(uint ib, uint iqs, uint a_offset) { + return vec4(data_a[a_offset + ib ], data_a[a_offset + ib + 1], + data_a[a_offset + ib + 2], data_a[a_offset + ib + 3]); +} +vec4 dequantize4_2aligned(uint ib, uint iqs, uint a_offset) { + const vec2 a = data_a_packed32[(a_offset + ib)/2]; + const vec2 b = data_a_packed32[(a_offset + ib)/2 + 1]; + return vec4(a, b); +} +#endif + +#if defined(DATA_A_BF16) +FLOAT_TYPE dequantize1(uint ib, uint iqs, uint a_offset) { + return bf16_to_fp32(data_a[a_offset + ib]); +} +vec2 dequantize(uint ib, uint iqs, uint a_offset) { + return vec2(bf16_to_fp32(data_a[a_offset + ib]), bf16_to_fp32(data_a[a_offset + ib + 1])); +} +vec4 dequantize4(uint ib, uint iqs, uint a_offset) { + return vec4(bf16_to_fp32(data_a[a_offset + ib ]), bf16_to_fp32(data_a[a_offset + ib + 1]), + bf16_to_fp32(data_a[a_offset + ib + 2]), bf16_to_fp32(data_a[a_offset + ib + 3])); +} +vec4 dequantize4_2aligned(uint ib, uint iqs, uint a_offset) { + const uint a = data_a_packed32[(a_offset + ib)/2]; + const uint b = data_a_packed32[(a_offset + ib)/2 + 1]; + return vec4(uintBitsToFloat((a & 0x0000ffff) << 16), + uintBitsToFloat( a & 0xffff0000), + uintBitsToFloat((b & 0x0000ffff) << 16), + uintBitsToFloat( b & 0xffff0000)); +} +#endif + +#if defined(DATA_A_Q4_0) +vec2 dequantize(uint ib, uint iqs, uint a_offset) { + const uint vui = uint(data_a[a_offset + ib].qs[iqs]); + return (vec2(vui & 0xF, vui >> 4) - 8.0f); +} +vec4 dequantize4(uint ib, uint iqs, uint a_offset) { + const uint vui = uint(data_a_packed16[a_offset + ib].qs[iqs/2]); + return (vec4(vui & 0xF, (vui >> 4) & 0xF, (vui >> 8) & 0xF, vui >> 12) - 8.0f); +} +#endif + +#if defined(DATA_A_Q4_1) +vec2 dequantize(uint ib, uint iqs, uint a_offset) { + const uint vui = uint(data_a[a_offset + ib].qs[iqs]); + return vec2(vui & 0xF, vui >> 4); +} +vec4 dequantize4(uint ib, uint iqs, uint a_offset) { + const uint vui = uint(data_a_packed16[a_offset + ib].qs[iqs/2]); + return vec4(vui & 0xF, (vui >> 4) & 0xF, (vui >> 8) & 0xF, vui >> 12); +} +#endif + +#if defined(DATA_A_Q5_0) +vec2 dequantize(uint ib, uint iqs, uint a_offset) { + const uint uint_qh = uint(data_a[a_offset + ib].qh[1]) << 16 | data_a[a_offset + ib].qh[0]; + const ivec2 qh = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10); + const uint vui = uint(data_a[a_offset + ib].qs[iqs]); + return (vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y) - 16.0f); +} +vec4 dequantize4(uint ib, uint iqs, uint a_offset) { + const uint uint_qh = uint(data_a_packed16[a_offset + ib].qh[1]) << 16 | data_a_packed16[a_offset + ib].qh[0]; + const ivec2 qh0 = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10); + const ivec2 qh1 = ivec2(((uint_qh >> (iqs + 1)) << 4) & 0x10, (uint_qh >> (iqs + 13)) & 0x10); + const uint vui = uint(data_a_packed16[a_offset + ib].qs[iqs/2]); + return (vec4((vui & 0xF) | qh0.x, ((vui >> 4) & 0xF) | qh0.y, ((vui >> 8) & 0xF) | qh1.x, (vui >> 12) | qh1.y) - 16.0f); +} +#endif + +#if defined(DATA_A_Q5_1) +vec2 dequantize(uint ib, uint iqs, uint a_offset) { + const uint uint_qh = data_a[a_offset + ib].qh; + const ivec2 qh = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10); + const uint vui = uint(data_a[a_offset + ib].qs[iqs]); + return vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y); +} +vec4 dequantize4(uint ib, uint iqs, uint a_offset) { + const uint uint_qh = data_a_packed16[a_offset + ib].qh; + const ivec2 qh0 = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10); + const ivec2 qh1 = ivec2(((uint_qh >> (iqs + 1)) << 4) & 0x10, (uint_qh >> (iqs + 13)) & 0x10); + const uint vui = uint(data_a_packed16[a_offset + ib].qs[iqs/2]); + return vec4((vui & 0xF) | qh0.x, ((vui >> 4) & 0xF) | qh0.y, ((vui >> 8) & 0xF) | qh1.x, (vui >> 12) | qh1.y); +} +#endif + +#if defined(DATA_A_Q8_0) +vec2 dequantize(uint ib, uint iqs, uint a_offset) { + return vec2(int(data_a[a_offset + ib].qs[iqs]), int(data_a[a_offset + ib].qs[iqs + 1])); +} +vec4 dequantize4(uint ib, uint iqs, uint a_offset) { + const i8vec2 v0 = unpack8(int32_t(data_a_packed16[a_offset + ib].qs[iqs/2])).xy; // vec4 used due to #12147 + const i8vec2 v1 = unpack8(int32_t(data_a_packed16[a_offset + ib].qs[iqs/2 + 1])).xy; + return vec4(v0.x, v0.y, v1.x, v1.y); +} +#endif + +#if defined(DATA_A_Q1_0) +vec2 dequantize(uint ib, uint iqs, uint a_offset) { + const uint bits = uint(data_a[a_offset + ib].qs[iqs / 8u]) >> (iqs % 8u); + return vec2( + (bits & 1u) != 0u ? 1.0f : -1.0f, + (bits & 2u) != 0u ? 1.0f : -1.0f); +} +vec4 dequantize4(uint ib, uint iqs, uint a_offset) { + const uint bits = uint(data_a[a_offset + ib].qs[iqs / 8u]) >> (iqs % 8u); + return vec4( + (bits & 1u) != 0u ? 1.0f : -1.0f, + (bits & 2u) != 0u ? 1.0f : -1.0f, + (bits & 4u) != 0u ? 1.0f : -1.0f, + (bits & 8u) != 0u ? 1.0f : -1.0f); +} +#endif + +#if defined(DATA_A_IQ1_S) +vec2 dequantize(uint ib, uint iqs, uint a_offset) { + const uint ib32 = iqs / 32; + const uint ib8 = iqs / 8; + const int i8 = int(iqs % 8); + const uint qh = data_a[a_offset + ib].qh[ib32]; + const uint qs = data_a[a_offset + ib].qs[ib8]; + const float dl = float(2 * bitfieldExtract(qh, 12, 3) + 1); + const float delta = ((qh & 0x8000) != 0) ? -IQ1S_DELTA : IQ1S_DELTA; + const uint idxhi = bitfieldExtract(qh, 3 * int(ib8 & 3), 3); + const int16_t grid = int16_t(iq1s_grid[qs | (idxhi << 8)]); + // Signed bitfield extract. + const ivec2 gvec = ivec2( + bitfieldExtract(grid, 2 * (i8), 2), + bitfieldExtract(grid, 2 * (i8 + 1), 2) + ); + return dl * (vec2(gvec) + delta); +} +vec4 dequantize4(uint ib, uint iqs, uint a_offset) { + const uint ib32 = iqs / 32; + const uint ib8 = iqs / 8; + const int i8 = int(iqs % 8); + const uint qh = data_a[a_offset + ib].qh[ib32]; + const uint qs = data_a[a_offset + ib].qs[ib8]; + const float dl = 2 * bitfieldExtract(qh, 12, 3) + 1; + const float delta = ((qh & 0x8000) != 0) ? -IQ1S_DELTA : IQ1S_DELTA; + const int16_t grid = int16_t(iq1s_grid[qs | (bitfieldExtract(qh, 3 * int(ib8 & 3), 3) << 8)]); + // Signed bitfield extract. + const ivec4 gvec = ivec4( + bitfieldExtract(grid, 2 * (i8), 2), + bitfieldExtract(grid, 2 * (i8 + 1), 2), + bitfieldExtract(grid, 2 * (i8 + 2), 2), + bitfieldExtract(grid, 2 * (i8 + 3), 2) + ); + return dl * (vec4(gvec) + delta); +} +#endif + +#if defined(DATA_A_IQ1_M) +vec2 dequantize(uint ib, uint iqs, uint a_offset) { + const uint ib8 = iqs / 8; + const uint ib16 = iqs / 16; + const int i8 = int(iqs % 8); + const uint sc = data_a[a_offset + ib].scales[iqs / 64]; + const uint qs = data_a[a_offset + ib].qs[ib8]; + const uint qh = data_a[a_offset + ib].qh[ib16] >> (4 * (ib8 & 1)); + const float dl = 2 * bitfieldExtract(sc, 3 * int(ib16 & 3), 3) + 1; + const float delta = ((qh & 8) != 0) ? -IQ1M_DELTA : IQ1M_DELTA; + const int16_t grid = int16_t(iq1s_grid[qs | ((qh & 7) << 8)]); + // Signed bitfield extract. + const ivec2 gvec = ivec2( + bitfieldExtract(grid, 2 * (i8), 2), + bitfieldExtract(grid, 2 * (i8 + 1), 2) + ); + return dl * (vec2(gvec) + delta); +} +vec4 dequantize4(uint ib, uint iqs, uint a_offset) { + const uint ib8 = iqs / 8; + const uint ib16 = iqs / 16; + const int i8 = int(iqs % 8); + const uint sc = data_a[a_offset + ib].scales[iqs / 64]; + const uint qs = data_a[a_offset + ib].qs[ib8]; + const uint qh = data_a[a_offset + ib].qh[ib16] >> (4 * (ib8 & 1)); + const float dl = 2 * bitfieldExtract(sc, 3 * int(ib16 & 3), 3) + 1; + const float delta = ((qh & 8) != 0) ? -IQ1M_DELTA : IQ1M_DELTA; + const int16_t grid = int16_t(iq1s_grid[qs | ((qh & 7) << 8)]); + // Signed bitfield extract. + const ivec4 gvec = ivec4( + bitfieldExtract(grid, 2 * (i8), 2), + bitfieldExtract(grid, 2 * (i8 + 1), 2), + bitfieldExtract(grid, 2 * (i8 + 2), 2), + bitfieldExtract(grid, 2 * (i8 + 3), 2) + ); + return dl * (vec4(gvec) + delta); +} +#endif + +#if defined(DATA_A_IQ2_XXS) +vec2 dequantize(uint ib, uint iqs, uint a_offset) { + const uint ib32 = iqs / 32; + const uint ib8 = (iqs / 8) % 4; + const uint qs = data_a[a_offset + ib].qs[8 * ib32 + ib8]; + // Scales are stored as packed 7+7+7+7+4 bits (4 sign tuples and 1 int4 scale) + const uint signs = pack32(u16vec2(data_a_packed16[a_offset + ib].qs[4 * ib32 + 2], + data_a_packed16[a_offset + ib].qs[4 * ib32 + 3])); + const float db = 0.25 * (0.5 + (signs >> 28)); + const uint sign7 = bitfieldExtract(signs, 7 * int(ib8), 7); + // Add parity bit + const uint sign8 = sign7 | (bitCount(sign7) << 7); + const uint sign = sign8 >> (iqs % 8); + const u8vec4 grid = unpack8(iq2xxs_grid[qs][(iqs % 8) / 4] >> (8 * (iqs % 4))); + bool sign0 = (sign & 1) != 0; + bool sign1 = (sign & 2) != 0; + return db * vec2( + grid.x * (sign0 ? -1.0 : 1.0), + grid.y * (sign1 ? -1.0 : 1.0) + ); +} +vec4 dequantize4(uint ib, uint iqs, uint a_offset) { + const uint ib32 = iqs / 32; + const uint ib8 = (iqs / 8) % 4; + const uint qs = data_a[a_offset + ib].qs[8 * ib32 + ib8]; + // Scales are stored as packed 7+7+7+7+4 bits (4 sign tuples and 1 int4 scale) + const uint signs = pack32(u16vec2(data_a_packed16[a_offset + ib].qs[4 * ib32 + 2], + data_a_packed16[a_offset + ib].qs[4 * ib32 + 3])); + const float db = 0.25 * (0.5 + (signs >> 28)); + const uint sign7 = bitfieldExtract(signs, 7 * int(ib8), 7); + // Add parity bit + const uint sign8 = sign7 | (bitCount(sign7) << 7); + const uint sign = sign8 >> (iqs % 8); + const u8vec4 grid = unpack8(iq2xxs_grid[qs][(iqs % 8) / 4] >> (8 * (iqs % 4))); + bool sign0 = (sign & 1) != 0; + bool sign1 = (sign & 2) != 0; + bool sign2 = (sign & 4) != 0; + bool sign3 = (sign & 8) != 0; + return db * vec4( + grid.x * (sign0 ? -1.0 : 1.0), + grid.y * (sign1 ? -1.0 : 1.0), + grid.z * (sign2 ? -1.0 : 1.0), + grid.w * (sign3 ? -1.0 : 1.0) + ); +} +#endif + +#if defined(DATA_A_IQ2_XS) +vec2 dequantize(uint ib, uint iqs, uint a_offset) { + const uint scale = (data_a[a_offset + ib].scales[iqs / 32] >> (4 * ((iqs / 16) & 1))) & 0xf; + const uint qs = data_a[a_offset + ib].qs[iqs / 8]; + const float db = 0.25 * (0.5 + scale); + const uint sign7 = qs >> 9; + // Add parity bit + const uint sign8 = sign7 | (bitCount(sign7) << 7); + const uint sign = sign8 >> (iqs % 8); + const u8vec4 grid = unpack8(iq2xs_grid[qs & 511][(iqs % 8) / 4] >> (8 * (iqs % 4))); + bool sign0 = (sign & 1) != 0; + bool sign1 = (sign & 2) != 0; + return db * vec2( + grid.x * (sign0 ? -1.0 : 1.0), + grid.y * (sign1 ? -1.0 : 1.0) + ); +} +vec4 dequantize4(uint ib, uint iqs, uint a_offset) { + const uint scale = (data_a[a_offset + ib].scales[iqs / 32] >> (4 * ((iqs / 16) & 1))) & 0xf; + const uint qs = data_a[a_offset + ib].qs[iqs / 8]; + const float db = 0.25 * (0.5 + scale); + const uint sign7 = qs >> 9; + // Add parity bit + const uint sign8 = sign7 | (bitCount(sign7) << 7); + const uint sign = sign8 >> (iqs % 8); + const u8vec4 grid = unpack8(iq2xs_grid[qs & 511][(iqs % 8) / 4] >> (8 * (iqs % 4))); + bool sign0 = (sign & 1) != 0; + bool sign1 = (sign & 2) != 0; + bool sign2 = (sign & 4) != 0; + bool sign3 = (sign & 8) != 0; + return db * vec4( + grid.x * (sign0 ? -1.0 : 1.0), + grid.y * (sign1 ? -1.0 : 1.0), + grid.z * (sign2 ? -1.0 : 1.0), + grid.w * (sign3 ? -1.0 : 1.0) + ); +} +#endif + +#if defined(DATA_A_IQ2_S) +vec2 dequantize(uint ib, uint iqs, uint a_offset) { + const uint ib32 = iqs / 32; + const uint ib8 = iqs / 8; + + const uint scale = (data_a[a_offset + ib].scales[ib32] >> (4 * ((iqs / 16) & 1))) & 0xf; + const uint qs = data_a[a_offset + ib].qs[ib8]; + const uint qh = data_a[a_offset + ib].qh[ib32]; + const uint qhshift = 2 * (ib8 % 4); + const uint sign = data_a[a_offset + ib].qs[QUANT_K / 8 + ib8] >> (iqs % 8); + + const float db = 0.25 * (0.5 + scale); + const u8vec4 grid = unpack8(iq2s_grid[qs | ((qh << (8 - qhshift)) & 0x300)][(iqs % 8) / 4]); + bool sign0 = (sign & 1) != 0; + bool sign1 = (sign & 2) != 0; + return db * vec2( + grid[iqs % 4] * (sign0 ? -1.0 : 1.0), + grid[(iqs % 4) + 1] * (sign1 ? -1.0 : 1.0) + ); +} +vec4 dequantize4(uint ib, uint iqs, uint a_offset) { + const uint ib32 = iqs / 32; + const uint ib8 = iqs / 8; + + const uint scale = (data_a[a_offset + ib].scales[ib32] >> (4 * ((iqs / 16) & 1))) & 0xf; + const uint qs = data_a[a_offset + ib].qs[ib8]; + const uint qh = data_a[a_offset + ib].qh[ib32]; + const uint qhshift = 2 * (ib8 % 4); + const uint sign = data_a[a_offset + ib].qs[QUANT_K / 8 + ib8] >> (iqs % 8); + + const float db = 0.25 * (0.5 + scale); + const u8vec4 grid = unpack8(iq2s_grid[qs | ((qh << (8 - qhshift)) & 0x300)][(iqs % 8) / 4]); + bool sign0 = (sign & 1) != 0; + bool sign1 = (sign & 2) != 0; + bool sign2 = (sign & 4) != 0; + bool sign3 = (sign & 8) != 0; + return db * vec4( + grid.x * (sign0 ? -1.0 : 1.0), + grid.y * (sign1 ? -1.0 : 1.0), + grid.z * (sign2 ? -1.0 : 1.0), + grid.w * (sign3 ? -1.0 : 1.0) + ); +} +#endif + +#if defined(DATA_A_IQ3_XXS) +vec2 dequantize(uint ib, uint iqs, uint a_offset) { + const uint ib4 = iqs / 4; + const uint ib32 = iqs / 32; + const uint is = QUANT_K / 4 + 4 * ib32; + const uint qs = data_a[a_offset + ib].qs[ib4]; + // Scales are stored as packed 7+7+7+7+4 bits (4 sign tuples and 1 int4 scale) + const uint signs = pack32(u16vec2(data_a_packed16[a_offset + ib].qs[is / 2], + data_a_packed16[a_offset + ib].qs[is / 2 + 1])); + const float db = 0.5 * (0.5 + (signs >> 28)); + const uint sign7 = bitfieldExtract(signs, 7 * (int(ib4 / 2) % 4), 7); + // Add parity bit + const uint sign8 = sign7 | (bitCount(sign7) << 7); + const uint sign = sign8 >> (iqs % 8); + const u8vec4 grid = unpack8(iq3xxs_grid[qs] >> (8 * (iqs % 4))); + bool sign0 = (sign & 1) != 0; + bool sign1 = (sign & 2) != 0; + return db * vec2( + grid.x * (sign0 ? -1.0 : 1.0), + grid.y * (sign1 ? -1.0 : 1.0) + ); +} +vec4 dequantize4(uint ib, uint iqs, uint a_offset) { + const uint ib4 = iqs / 4; + const uint ib32 = iqs / 32; + const uint is = QUANT_K / 4 + 4 * ib32; + const uint qs = data_a[a_offset + ib].qs[ib4]; + const uint signs = pack32(u16vec2(data_a_packed16[a_offset + ib].qs[is / 2], + data_a_packed16[a_offset + ib].qs[is / 2 + 1])); + const float db = 0.5 * (0.5 + (signs >> 28)); + const uint sign7 = bitfieldExtract(signs, 7 * (int(ib4 / 2) % 4), 7); + // Add parity bit + const uint sign8 = sign7 | (bitCount(sign7) << 7); + const uint sign = sign8 >> (iqs % 8); + const u8vec4 grid = unpack8(iq3xxs_grid[qs]); + bool sign0 = (sign & 1) != 0; + bool sign1 = (sign & 2) != 0; + bool sign2 = (sign & 4) != 0; + bool sign3 = (sign & 8) != 0; + return db * vec4( + grid.x * (sign0 ? -1.0 : 1.0), + grid.y * (sign1 ? -1.0 : 1.0), + grid.z * (sign2 ? -1.0 : 1.0), + grid.w * (sign3 ? -1.0 : 1.0) + ); +} +#endif + +#if defined(DATA_A_IQ3_S) +vec2 dequantize(uint ib, uint iqs, uint a_offset) { + const uint qs = data_a[a_offset + ib].qs[iqs / 4]; + const uint qh = data_a[a_offset + ib].qh[iqs / 32]; + const uint sign = data_a[a_offset + ib].signs[iqs / 8] >> (iqs % 8); + const uint scale = data_a[a_offset + ib].scales[iqs / 64]; + bool sign0 = (sign & 1) != 0; + bool sign1 = (sign & 2) != 0; + const float db = 1 + 2 * ((scale >> (4 * ((iqs / 32) & 1))) & 0xf); + const uint32_t grid = iq3s_grid[qs | ((qh << (8 - ((iqs / 4) % 8))) & 256)] >> (8 * (iqs % 4)); + return db * vec2( + int(grid & 0xFF) * (sign0 ? -1.0 : 1.0), + int((grid >> 8) & 0xFF) * (sign1 ? -1.0 : 1.0) + ); +} +vec4 dequantize4(uint ib, uint iqs, uint a_offset) { + const uint ib4 = iqs / 4; + const uint ib32 = iqs / 32; + const uint qs = data_a[a_offset + ib].qs[ib4]; + const uint qh = data_a[a_offset + ib].qh[ib32]; + const uint sign = data_a[a_offset + ib].signs[iqs / 8] >> (iqs % 8); + const uint scale = data_a[a_offset + ib].scales[ib32 / 2]; + bool sign0 = (sign & 1) != 0; + bool sign1 = (sign & 2) != 0; + bool sign2 = (sign & 4) != 0; + bool sign3 = (sign & 8) != 0; + const float db = 1 + 2 * ((scale >> (4 * (ib32 & 1))) & 0xf); + const uint32_t grid = iq3s_grid[qs | ((qh << (8 - ib4 % 8)) & 256)] >> (8 * (iqs % 4)); + return db * vec4( + int(grid & 0xFF) * (sign0 ? -1.0 : 1.0), + int((grid >> 8) & 0xFF) * (sign1 ? -1.0 : 1.0), + int((grid >> 16) & 0xFF) * (sign2 ? -1.0 : 1.0), + int((grid >> 24) & 0xFF) * (sign3 ? -1.0 : 1.0) + ); +} +#endif + +#if defined(DATA_A_IQ4_XS) +vec2 dequantize(uint ib, uint iqs, uint a_offset) { + const uint ib32 = iqs / 32; + const uint iq = 16 * ib32 + (iqs % 16); + + const uint sl = (data_a[a_offset + ib].scales_l[ib32/2] >> (4 * (ib32 & 1))) & 0xF; + const uint sh = (data_a[a_offset + ib].scales_h >> (2 * ib32)) & 3; + const uint qshift = (iqs & 16) >> 2; + u8vec2 qs = u8vec2(data_a[a_offset + ib].qs[iq], data_a[a_offset + ib].qs[iq + 1]); + qs = (qs >> qshift) & uint8_t(0xF); + + const float dl = float(int(sl | (sh << 4)) - 32); + return dl * vec2(kvalues_iq4nl[qs.x], kvalues_iq4nl[qs.y]); +} +vec4 dequantize4(uint ib, uint iqs, uint a_offset) { + const uint ib32 = iqs / 32; + const uint iq = 16 * ib32 + (iqs % 16); + + const uint sl = (data_a[a_offset + ib].scales_l[ib32/2] >> (4 * (ib32 & 1))) & 0xF; + const uint sh = (data_a[a_offset + ib].scales_h >> (2 * ib32)) & 3; + const uint qshift = (iqs & 16) >> 2; + const u8vec4 qs = unpack8((data_a_packed32[a_offset + ib].qs[iq/4] >> qshift) & 0x0F0F0F0F); + + const float dl = float(int(sl | (sh << 4)) - 32); + return dl * vec4( + kvalues_iq4nl[qs.x], kvalues_iq4nl[qs.y], + kvalues_iq4nl[qs.z], kvalues_iq4nl[qs.w]); +} +#endif + +#if defined(DATA_A_IQ4_NL) +vec2 dequantize(uint ib, uint iqs, uint a_offset) { + const uint vui = uint(data_a[a_offset + ib].qs[iqs]); + return vec2(kvalues_iq4nl[vui & 0xF], kvalues_iq4nl[vui >> 4]); +} +vec4 dequantize4(uint ib, uint iqs, uint a_offset) { + const uint vui = uint(data_a_packed16[a_offset + ib].qs[iqs/2]); + return vec4(kvalues_iq4nl[vui & 0xF], kvalues_iq4nl[(vui >> 4) & 0xF], kvalues_iq4nl[(vui >> 8) & 0xF], kvalues_iq4nl[vui >> 12]); +} +#endif + +#if defined(DATA_A_MXFP4) +vec2 dequantize(uint ib, uint iqs, uint a_offset) { + const uint vui = uint(data_a[a_offset + ib].qs[iqs]); + return vec2(kvalues_mxfp4[vui & 0xF], kvalues_mxfp4[vui >> 4]) * 0.5; +} +vec4 dequantize4(uint ib, uint iqs, uint a_offset) { + vec2 v0 = dequantize(ib, iqs, a_offset); + vec2 v1 = dequantize(ib, iqs + 1, a_offset); + return vec4(v0.x, v0.y, v1.x, v1.y); +} +#endif + +#if defined(DATA_A_NVFP4) +vec2 dequantize(uint ib, uint iqs, uint a_offset) { + const uint sub = iqs >> 4; + const float d = ue4m3_to_fp32(data_a[a_offset + ib].d[sub]); + const uint j = iqs & 7; + const uint shift = (iqs & 8) >> 1; // 0 or 4 + const uint vui0 = uint(data_a[a_offset + ib].qs[sub * 8u + j]); + const uint vui1 = uint(data_a[a_offset + ib].qs[sub * 8u + j + 1]); + const uint qs0 = (vui0 >> shift) & 0xF; + const uint qs1 = (vui1 >> shift) & 0xF; + return vec2(float(kvalues_mxfp4[qs0]), float(kvalues_mxfp4[qs1])) * d * 0.5; +} +vec4 dequantize4(uint ib, uint iqs, uint a_offset) { + const vec2 v0 = dequantize(ib, iqs, a_offset); + const vec2 v1 = dequantize(ib, iqs + 2u, a_offset); + return vec4(v0.x, v0.y, v1.x, v1.y); +} +#endif + +#if defined(DATA_A_F32) || defined(DATA_A_F16) || defined(DATA_A_BF16) +vec2 get_dm(uint ib, uint a_offset) { + return vec2(0, 0); +} +#endif + +#if defined(DATA_A_IQ1_M) +vec2 get_dm(uint ib, uint a_offset) { + const uint16_t[4] scales = data_a[a_offset + ib].scales; + const u16vec4 s = u16vec4(scales[0], scales[1], scales[2], scales[3]) >> 12; + const float d = float(unpackHalf2x16(s.x | (s.y << 4) | (s.z << 8) | (s.w << 12)).x); + return vec2(d, 0); +} +#endif + +#if defined(DATA_A_Q4_0) || defined(DATA_A_Q5_0) || defined(DATA_A_Q8_0) || defined(DATA_A_IQ1_S) || defined(DATA_A_IQ2_XXS) || defined(DATA_A_IQ2_XS) || defined(DATA_A_IQ2_S) || defined(DATA_A_IQ3_XXS) || defined(DATA_A_IQ3_S) || defined(DATA_A_IQ4_XS) || defined(DATA_A_IQ4_NL) +vec2 get_dm(uint ib, uint a_offset) { + return vec2(float(data_a[a_offset + ib].d), 0); +} +#endif + +#if defined(DATA_A_Q1_0) +vec2 get_dm(uint ib, uint a_offset) { + const float d = float(data_a[a_offset + ib].d); + return vec2(d, 0); +} +#endif + +#if defined(DATA_A_MXFP4) +vec2 get_dm(uint ib, uint a_offset) { + return vec2(e8m0_to_fp32(data_a[a_offset + ib].e), 0); +} +#endif + +#if defined(DATA_A_NVFP4) +vec2 get_dm(uint ib, uint a_offset) { + return vec2(1.0, 0.0); +} +#endif + +#if defined(DATA_A_Q4_1) || defined(DATA_A_Q5_1) +vec2 get_dm(uint ib, uint a_offset) { + const vec2 dm = vec2(data_a_packed32[a_offset + ib].dm); + return dm; +} +#endif + +#if defined(DATA_A_Q2_K) +vec2 dequantize(uint ib, uint iqs, uint a_offset) { + iqs /= 2; + const uint qsi = (iqs / 64) * 32 + (iqs % 16) * 2; // 0,2,4..30 + const uint scalesi = iqs / 8; // 0..15 + const uint qsshift = ((iqs % 64) / 16) * 2; // 0,2,4,6 + + const uvec2 qs = uvec2(data_a[a_offset + ib].qs[qsi], data_a[a_offset + ib].qs[qsi + 1]); + const uint scales = data_a[a_offset + ib].scales[scalesi]; + const vec2 dm = vec2(data_a[a_offset + ib].dm); + + return dm.x * float(scales & 0xF) * vec2((qs >> qsshift) & 3) - dm.y * float(scales >> 4); +} +vec2 get_dm(uint ib, uint a_offset) { + return vec2(1, 0); +} +#endif + +#if defined(DATA_A_Q3_K) +vec2 dequantize(uint ib, uint iqs, uint a_offset) { + iqs /= 2; + const uint n = iqs / 64; // 0,1 + const uint qsi = n * 32 + (iqs % 16) * 2; // 0,2,4..62 + const uint hmi = (iqs % 16) * 2; // 0,2,4..30 + const uint j = (iqs % 64) / 4; // 0..3 + const uint is = iqs / 8; // 0..15 + const uint halfsplit = ((iqs % 64) / 16); // 0,1,2,3 + const uint qsshift = halfsplit * 2; // 0,2,4,6 + const uint m = 1 << (4 * n + halfsplit); // 1,2,4,8,16,32,64,128 + + const int8_t us = int8_t(((data_a[a_offset + ib].scales[is % 8] >> (4 * int(is / 8))) & 0xF) + | (((data_a[a_offset + ib].scales[8 + (is % 4)] >> (2 * int(is / 4))) & 3) << 4)); + const float dl = float(data_a[a_offset + ib].d) * float(us - 32); + + return vec2(dl * float(int8_t((data_a[a_offset + ib].qs[qsi ] >> qsshift) & 3) - (((data_a[a_offset + ib].hmask[hmi ] & m) != 0) ? 0 : 4)), + dl * float(int8_t((data_a[a_offset + ib].qs[qsi + 1] >> qsshift) & 3) - (((data_a[a_offset + ib].hmask[hmi + 1] & m) != 0) ? 0 : 4))); +} +vec2 get_dm(uint ib, uint a_offset) { + return vec2(1, 0); +} +#endif + +#if defined(DATA_A_Q4_K) +vec2 dequantize(uint ib, uint iqs, uint a_offset) { + iqs /= 2; + const uint n = iqs / 32; // 0,1,2,3 + const uint b = (iqs % 32) / 16; // 0,1 + const uint is = 2 * n + b; // 0..7 + const uint qsi = n * 32 + (iqs % 16) * 2; // 0,2,4..126 + + const vec2 loadd = vec2(data_a[a_offset + ib].dm); + + const uint scidx0 = (is < 4) ? is : (is + 4); + const uint scidx1 = (is < 4) ? is : (is - 4); + const uint scidxmask1 = (is < 4) ? 0x30 : 0xC0; + const uint scidxshift1 = (is < 4) ? 0 : 2; + const uint mbidx0 = is + 4; + const uint mbidx1 = (is < 4) ? is + 4 : is; + const uint mbidxmask0 = (is < 4) ? 0xF : 0xF0; + const uint mbidxshift0 = (is < 4) ? 0 : 4; + const uint mbidxmask1 = (is < 4) ? 0x30 : 0xC0; + const uint mbidxshift1 = (is < 4) ? 0 : 2; + + const uint8_t sc = uint8_t((data_a[a_offset + ib].scales[scidx0] & 0xF) | ((data_a[a_offset + ib].scales[scidx1] & scidxmask1) >> scidxshift1)); + const uint8_t mbyte = uint8_t((data_a[a_offset + ib].scales[mbidx0] & mbidxmask0) >> mbidxshift0 | ((data_a[a_offset + ib].scales[mbidx1] & mbidxmask1) >> mbidxshift1)); + + const float d = loadd.x * sc; + const float m = -loadd.y * mbyte; + + return vec2(fma(d, float((data_a[a_offset + ib].qs[qsi ] >> (b * 4)) & 0xF), m), + fma(d, float((data_a[a_offset + ib].qs[qsi + 1] >> (b * 4)) & 0xF), m)); +} +vec2 get_dm(uint ib, uint a_offset) { + return vec2(1, 0); +} +#endif + +#if defined(DATA_A_Q5_K) +vec2 dequantize(uint ib, uint iqs, uint a_offset) { + iqs /= 2; + const uint n = iqs / 32; // 0,1,2,3 + const uint b = (iqs % 32) / 16; // 0,1 + const uint is = 2 * n + b; // 0..7 + const uint qsi = n * 32 + (iqs % 16) * 2; // 0,2,4..126 + const uint qhi = (iqs % 16) * 2; // 0,2,4..30 + + const uint8_t hm = uint8_t(1 << (iqs / 16)); + + const vec2 loadd = vec2(data_a[a_offset + ib].dm); + + const uint scidx0 = (is < 4) ? is : (is + 4); + const uint scidx1 = (is < 4) ? is : (is - 4); + const uint scidxmask1 = (is < 4) ? 0x30 : 0xC0; + const uint scidxshift1 = (is < 4) ? 0 : 2; + const uint mbidx0 = is + 4; + const uint mbidx1 = (is < 4) ? is + 4 : is; + const uint mbidxmask0 = (is < 4) ? 0xF : 0xF0; + const uint mbidxshift0 = (is < 4) ? 0 : 4; + const uint mbidxmask1 = (is < 4) ? 0x30 : 0xC0; + const uint mbidxshift1 = (is < 4) ? 0 : 2; + + const uint8_t sc = uint8_t((data_a[a_offset + ib].scales[scidx0] & 0xF) | ((data_a[a_offset + ib].scales[scidx1] & scidxmask1) >> scidxshift1)); + const uint8_t mbyte = uint8_t(((data_a[a_offset + ib].scales[mbidx0] & mbidxmask0) >> mbidxshift0) | ((data_a[a_offset + ib].scales[mbidx1] & mbidxmask1) >> mbidxshift1)); + + const float d = loadd.x * sc; + const float m = -loadd.y * mbyte; + + return vec2(fma(d, float((data_a[a_offset + ib].qs[qsi ] >> (b * 4)) & 0xF) + float((data_a[a_offset + ib].qh[qhi ] & hm) != 0 ? 16 : 0), m), + fma(d, float((data_a[a_offset + ib].qs[qsi + 1] >> (b * 4)) & 0xF) + float((data_a[a_offset + ib].qh[qhi + 1] & hm) != 0 ? 16 : 0), m)); +} +vec2 get_dm(uint ib, uint a_offset) { + return vec2(1, 0); +} +#endif + +#if defined(DATA_A_Q6_K) +vec2 dequantize(uint ib, uint iqs, uint a_offset) { + iqs /= 2; + const uint n = iqs / 64; // 0,1 + const uint b = (iqs % 64) / 32; // 0,1 + const uint is_b = (iqs % 16) / 8; // 0,1 + const uint qhshift = ((iqs % 64) / 16) * 2; // 0,2,4,6 + const uint is = 8 * n + qhshift + is_b; // 0..15 + const uint qsi = n * 64 + (iqs % 32) * 2; // 0,2,4..126 + const uint qhi = n * 32 + (iqs % 16) * 2; // 0,2,4..62 + + const float dscale = float(data_a[a_offset + ib].d) * float(data_a[a_offset + ib].scales[is]); + + return vec2(dscale * float(int8_t(((data_a[a_offset + ib].ql[qsi ] >> (b * 4)) & 0xF) | (((data_a[a_offset + ib].qh[qhi ] >> qhshift) & 3) << 4)) - 32), + dscale * float(int8_t(((data_a[a_offset + ib].ql[qsi + 1] >> (b * 4)) & 0xF) | (((data_a[a_offset + ib].qh[qhi + 1] >> qhshift) & 3) << 4)) - 32)); +} +vec2 get_dm(uint ib, uint a_offset) { + return vec2(1, 0); +} +#endif diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs_cm2.glsl b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs_cm2.glsl new file mode 100644 index 0000000000000000000000000000000000000000..7171cbfa55998e6bf50040c43f19b9d3f9ee347f --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs_cm2.glsl @@ -0,0 +1,1376 @@ + +// Each format defines a scalar dequantFunc plus a V=4 dequantFunc_v +// passed as the optional vector decoder to coopMatLoadTensorNV via +// GL_NV_cooperative_matrix_decode_vector. When the driver doesn't support +// the extension, ggml-vulkan.cpp strips it from the compiled SPIR-V. +#ifdef GL_NV_cooperative_matrix_decode_vector +#extension GL_NV_cooperative_matrix_decode_vector : enable +#endif + +#include "types.glsl" + +layout(buffer_reference, std430, buffer_reference_align = 16) buffer decodeBufF32 { + vec4 block; +}; + +float16_t dequantFuncF32(const in decodeBufF32 bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + const vec4 v = bl.block; + const uint idx = coordInBlock[1]; + const f16vec4 vf16 = f16vec4(v); + return vf16[idx]; +} + +layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufQ1_0 { + block_q1_0 block; +}; + +float16_t dequantFuncQ1_0(const in decodeBufQ1_0 bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + const float16_t d = bl.block.d; + const uint idx = coordInBlock[1]; + const uint bit = (uint(bl.block.qs[(idx & 0x78) >> 3]) >> (idx & 0x7)) & 1u; + return bit != 0u ? d : -d; +} + +f16vec4 dequantFuncQ1_0_v(const in decodeBufQ1_0 bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + const float16_t d = bl.block.d; + const float16_t md = -d; + const uint idx = coordInBlock[1]; + const uint qs_nib = uint(bl.block.qs[idx >> 3]) >> (idx & 0x4u); + return f16vec4( + (qs_nib & 1u) != 0u ? d : md, + (qs_nib & 2u) != 0u ? d : md, + (qs_nib & 4u) != 0u ? d : md, + (qs_nib & 8u) != 0u ? d : md); +} + +layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufQ4_0 { + block_q4_0_packed16 block; +}; + +float16_t dequantFuncQ4_0(const in decodeBufQ4_0 bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + const float16_t d = bl.block.d; + const uint idx = coordInBlock[1]; + const uint shift = (idx & 0x10) >> 2; + uint32_t qs = uint32_t(bl.block.qs[(idx & 0xE) >> 1]); + qs >>= shift; + qs &= 0x0F0F; + qs = unpack8(qs)[idx & 1]; + float16_t ret = (float16_t(qs) - float16_t(8)) * d; + return ret; +} + +f16vec4 dequantFuncQ4_0_v(const in decodeBufQ4_0 bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + const float16_t d = bl.block.d; + const uint idx = coordInBlock[1]; + const uint shift = (idx & 0x10) >> 2; // 0 or 4 + const uint qs_i = (idx & 0xE) >> 1; // even, in {0,2,4,6} + const uint qsw = uint32_t(bl.block.qs[qs_i ]) + | (uint32_t(bl.block.qs[qs_i + 1u]) << 16); + // shift in {0,4}: per-byte mask 0x0F isolates the wanted nibble in each byte. + const uint q4 = (qsw >> shift) & 0x0F0F0F0Fu; + const u8vec4 q = unpack8(q4); + return f16vec4((vec4(q) - vec4(8.0)) * vec4(float(d))); +} + +layout(buffer_reference, std430, buffer_reference_align = 4) buffer decodeBufQ4_1 { + block_q4_1 block; +}; + +layout(buffer_reference, std430, buffer_reference_align = 4) buffer decodeBufQ4_1_packed32 { + block_q4_1_packed32 block; +}; + +float16_t dequantFuncQ4_1(const in decodeBufQ4_1 bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + const float16_t d = bl.block.d; + const float16_t m = bl.block.m; + const uint idx = coordInBlock[1]; + const uint iqs = idx & 0xF; + const uint shift = (idx & 0x10) >> 2; + uint32_t qs = bl.block.qs[iqs]; + qs >>= shift; + qs &= 0xF; + float16_t ret = float16_t(qs) * d + m; + return ret; +} + +f16vec4 dequantFuncQ4_1_v(const in decodeBufQ4_1 bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + decodeBufQ4_1_packed32 bl32 = decodeBufQ4_1_packed32(bl); + const float16_t d = bl.block.d; + const float16_t m = bl.block.m; + const uint idx = coordInBlock[1]; + const uint shift = (idx & 0x10) >> 2; // 0 or 4 + const uint qs_w = (idx & 0xC) >> 2; // iqs / 4 in [0,4) + const uint qsw = uint32_t(bl32.block.qs[qs_w]); + const u8vec4 q = unpack8((qsw >> shift) & 0x0F0F0F0Fu); + return f16vec4(vec4(q) * vec4(float(d)) + vec4(float(m))); +} + +layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufQ5_0 { + block_q5_0 block; +}; + +layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufQ5_0_packed16 { + block_q5_0_packed16 block; +}; + +float16_t dequantFuncQ5_0(const in decodeBufQ5_0 bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + const float16_t d = bl.block.d; + const uint idx = coordInBlock[1]; + const uint iqs = idx & 0xF; + + const uint uint_qh = uint(bl.block.qh[1]) << 16 | bl.block.qh[0]; + const uint qh = ((uint_qh >> idx) << 4) & 0x10; + + const uint shift = (idx & 0x10) >> 2; + uint32_t qs = bl.block.qs[iqs]; + qs >>= shift; + qs &= 0xF; + + float16_t ret = (float16_t(qs | qh) - float16_t(16)) * d; + return ret; +} + +f16vec4 dequantFuncQ5_0_v(const in decodeBufQ5_0 bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + decodeBufQ5_0_packed16 bl16 = decodeBufQ5_0_packed16(bl); + const float16_t d = bl.block.d; + const uint idx = coordInBlock[1]; + const uint shift = (idx & 0x10) >> 2; // 0 or 4 + const uint qs_i = (idx & 0xC) >> 1; // packed16 word index, in {0,2,4,6} + const uint qsw = uint32_t(bl16.block.qs[qs_i ]) + | (uint32_t(bl16.block.qs[qs_i + 1u]) << 16); + const u8vec4 ql = unpack8((qsw >> shift) & 0x0F0F0F0Fu); + + const uint uint_qh = uint(bl16.block.qh[1]) << 16 | uint(bl16.block.qh[0]); + const uint qh_pack = uint_qh >> idx; // bits 0..3 = element idx..idx+3 high bits + const uvec4 qh_high = (uvec4(qh_pack, qh_pack >> 1u, qh_pack >> 2u, qh_pack >> 3u) & uvec4(0x01u)) << 4u; + + return f16vec4((vec4(ql) + vec4(qh_high) - vec4(16.0)) * vec4(float(d))); +} + +layout(buffer_reference, std430, buffer_reference_align = 8) buffer decodeBufQ5_1 { + block_q5_1 block; +}; + +layout(buffer_reference, std430, buffer_reference_align = 8) buffer decodeBufQ5_1_packed32 { + block_q5_1_packed32 block; +}; + +float16_t dequantFuncQ5_1(const in decodeBufQ5_1 bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + const float16_t d = bl.block.d; + const float16_t m = bl.block.m; + const uint idx = coordInBlock[1]; + const uint iqs = idx & 0xF; + + const uint uint_qh = bl.block.qh; + const uint qh = ((uint_qh >> idx) << 4) & 0x10; + + const uint shift = (idx & 0x10) >> 2; + uint32_t qs = bl.block.qs[iqs]; + qs >>= shift; + qs &= 0xF; + + float16_t ret = float16_t(qs | qh) * d + m; + return ret; +} + +f16vec4 dequantFuncQ5_1_v(const in decodeBufQ5_1 bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + decodeBufQ5_1_packed32 bl32 = decodeBufQ5_1_packed32(bl); + const float16_t d = bl.block.d; + const float16_t m = bl.block.m; + const uint idx = coordInBlock[1]; + const uint shift = (idx & 0x10) >> 2; // 0 or 4 + const uint qs_w = (idx & 0xC) >> 2; // iqs / 4 in [0,4) + const uint qsw = uint32_t(bl32.block.qs[qs_w]); + const u8vec4 ql = unpack8((qsw >> shift) & 0x0F0F0F0Fu); + + const uint qh_pack = bl.block.qh >> idx; // bits 0..3 = element idx..idx+3 high bits + const uvec4 qh_high = (uvec4(qh_pack, qh_pack >> 1u, qh_pack >> 2u, qh_pack >> 3u) & uvec4(0x01u)) << 4u; + + return f16vec4((vec4(ql) + vec4(qh_high)) * vec4(float(d)) + vec4(float(m))); +} + +layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufQ8_0 { + block_q8_0_packed16 block; +}; + +float16_t dequantFuncQ8_0(const in decodeBufQ8_0 bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + const float16_t d = bl.block.d; + const uint idx = coordInBlock[1]; + const uint iqs = idx; + + // Load 16b and select the byte for this element + int32_t qs = unpack8(bl.block.qs[(iqs & 0x1E) >> 1])[iqs & 1]; + float16_t ret = float16_t(qs) * d; + return ret; +} + +f16vec4 dequantFuncQ8_0_v(const in decodeBufQ8_0 bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + const float16_t d = bl.block.d; + const uint idx = coordInBlock[1]; + const uint base = idx >> 1u; + const uint w = uint(uint16_t(bl.block.qs[base])) + | (uint(uint16_t(bl.block.qs[base + 1u])) << 16u); + const i8vec4 qi = unpack8(int32_t(w)); + return f16vec4(vec4(qi) * vec4(float(d))); +} + +layout(buffer_reference, std430, buffer_reference_align = 4) buffer decodeBufQ2_K { + block_q2_K block; +}; + +layout(buffer_reference, std430, buffer_reference_align = 16) buffer decodeBufQ2_K_packed16 { + block_q2_K_packed16 block; +}; + +layout(buffer_reference, std430, buffer_reference_align = 4) buffer decodeBufQ2_K_packed32 { + block_q2_K_packed32 block; +}; + +float16_t dequantFuncQ2_K(const in decodeBufQ2_K bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + decodeBufQ2_K_packed16 bl16 = decodeBufQ2_K_packed16(bl); + const f16vec2 dm = bl.block.dm; + const uint idx = coordInBlock[1]; + + const uint scalesi = (idx & 0xF0) >> 4; // 0..15 + const uint qsshift = (idx & 0x60) >> 4; // 0,2,4,6 + + uint qs = uint32_t(bl16.block.qs[((idx & 0x80) >> 3) + ((idx & 0x1E) >> 1)]); + qs = (qs >> qsshift) & 0x0303; + qs = unpack8(qs)[idx & 1]; + + const uint scales = bl.block.scales[scalesi]; + float16_t ret = dm.x * float16_t(scales & 0xF) * float16_t(qs) - dm.y * float16_t(scales >> 4); + return ret; +} + +f16vec4 dequantFuncQ2_K_v(const in decodeBufQ2_K bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + decodeBufQ2_K_packed32 bl32 = decodeBufQ2_K_packed32(bl); + const f16vec2 dm = bl.block.dm; + const uint idx = coordInBlock[1]; + + const uint scalesi = idx >> 4; // 0..15 + const uint qsshift = (idx & 0x60) >> 4; // 0,2,4,6 + + // qs_i (packed16) = ((idx & 0x80) >> 3) + ((idx & 0x1E) >> 1) is even for idx % 4 == 0, + // so qs_w (packed32) = qs_i / 2 = ((idx & 0x80) >> 4) + ((idx & 0x1Cu) >> 2). + const uint qs_w = ((idx & 0x80) >> 4) + ((idx & 0x1Cu) >> 2); + const uint qsw = uint32_t(bl32.block.qs[qs_w]); + const uint qs4 = (qsw >> qsshift) & 0x03030303u; + const u8vec4 qi = unpack8(qs4); + + const uint scales = bl.block.scales[scalesi]; + const float16_t d_sub = dm.x * float16_t(scales & 0xF); + const float16_t m_sub = dm.y * float16_t(scales >> 4); + return f16vec4(vec4(qi) * vec4(float(d_sub)) - vec4(float(m_sub))); +} + +layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufQ3_K { + block_q3_K block; +}; + +layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufQ3_K_packed16 { + block_q3_K_packed16 block; +}; + +float16_t dequantFuncQ3_K(const in decodeBufQ3_K bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + const uint idx = coordInBlock[1]; + const uint iqs = idx; + + const uint n = iqs / 128; // 0,1 + const uint qsi = n * 32 + (iqs % 32); // 0..63 + const uint hmi = (iqs % 32); // 0..31 + const uint j = (iqs % 128) / 8; // 0..15 + const uint is = iqs / 16; // 0..15 + const uint halfsplit = ((iqs % 128) / 32); // 0,1,2,3 + const uint qsshift = halfsplit * 2; // 0,2,4,6 + const uint m = 1 << (4 * n + halfsplit); // 1,2,4,8,16,32,64,128 + + uint32_t scaleidx0 = (is < 8) ? is : (is-8); + uint32_t scaleidx0shift = (is < 8) ? 0 : 4; + uint32_t scaleidx1 = is + 8 - (is/4)*4; + uint32_t scaleidx1shift = (is/4)*2; + + const int8_t us = int8_t(((bl.block.scales[scaleidx0] >> scaleidx0shift) & 0xF) | (((bl.block.scales[scaleidx1] >> scaleidx1shift) & 3) << 4)); + + const float16_t dl = bl.block.d * float16_t(us - 32); + + float16_t ret = dl * float16_t(int8_t((bl.block.qs[qsi ] >> qsshift) & 3) - (((bl.block.hmask[hmi ] & m) != 0) ? 0 : 4)); + + return ret; +} + +f16vec4 dequantFuncQ3_K_v(const in decodeBufQ3_K bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + decodeBufQ3_K_packed16 bl16 = decodeBufQ3_K_packed16(bl); + const uint idx = coordInBlock[1]; + + const uint n = idx >> 7; // 0,1 + const uint is = idx >> 4; // 0..15 + const uint halfsplit = (idx & 0x60) >> 5; // 0,1,2,3 + const uint qsshift = halfsplit << 1; // 0,2,4,6 + const uint hbit = (n << 2) + halfsplit; // 0..7 (bit position in hmask byte) + + uint32_t scaleidx0 = (is < 8) ? is : (is - 8); + uint32_t scaleidx0shift = (is < 8) ? 0u : 4u; + uint32_t scaleidx1 = is + 8 - (is / 4) * 4; + uint32_t scaleidx1shift = (is / 4) * 2; + + const int8_t us = int8_t( + ((bl.block.scales[scaleidx0] >> scaleidx0shift) & 0xF) | + (((bl.block.scales[scaleidx1] >> scaleidx1shift) & 3) << 4)); + const float16_t dl = bl.block.d * float16_t(int(us) - 32); + + // For idx % 4 == 0: (idx & 0x1F) == (idx & 0x1C) is a multiple of 4. + const uint qsi = (n << 5) + (idx & 0x1Cu); + const uint hmi = (idx & 0x1Cu); + + // Two adjacent uint16 packed16 reads, combined into a uint32 in registers. + // After this: byte j of qsw / hmw holds the data for element idx+j. + const uint qsw = uint32_t(bl16.block.qs[qsi >> 1]) + | (uint32_t(bl16.block.qs[(qsi >> 1) + 1u]) << 16); + const uint hmw = uint32_t(bl16.block.hmask[hmi >> 1]) + | (uint32_t(bl16.block.hmask[(hmi >> 1) + 1u]) << 16); + + // qsshift in {0,2,4,6} and hbit in {0..7}: per-byte masks isolate the wanted bits + // with no inter-byte leakage. + const uint ql4 = (qsw >> qsshift) & 0x03030303u; + const uint qh4 = (hmw >> hbit) & 0x01010101u; + + const ivec4 q = ivec4(unpack8(ql4 | (qh4 << 2))) - ivec4(4); + return f16vec4(vec4(q) * vec4(float(dl))); +} + +layout(buffer_reference, std430, buffer_reference_align = 16) buffer decodeBufQ4_K { + block_q4_K block; +}; + +layout(buffer_reference, std430, buffer_reference_align = 16) buffer decodeBufQ4_K_packed16 { + block_q4_K_packed16 block; +}; + +layout(buffer_reference, std430, buffer_reference_align = 16) buffer decodeBufQ4_K_packed32 { + block_q4_K_packed32 block; +}; + +layout(buffer_reference, std430, buffer_reference_align = 16) buffer decodeBufQ4_K_packed128 { + block_q4_K_packed128 block; +}; + +#if defined(IS_MUL_MM2) + +// For Q4_K and Q5_K in the mat-mul shader, we decode a tile's worth of scales +// into shared memory and then process the whole tile using those scales. +// There is a fetch function that loads into private variables and then a store +// function that stores into shared memory. +// Q4_K and Q5_K have the same encoding of scales, so everything is shared except +// the part that fetches from the structure (which has a different block layout). +#if defined(DATA_A_Q4_K) || defined(DATA_A_Q5_K) +const uint shAscales_stride = (BM + 2); +// 1 scale per 32 elements -> 8 scales per block, per row +shared vec2 shAscales[8 * shAscales_stride]; +uvec4 row_v; +#endif + +#if defined(DATA_A_Q4_K) +layout (binding = 0) readonly buffer A_Q4_K_128 {block_q4_K_packed128 data_a_q4_k_packed128[];}; + +void fetch_scalesQ4_K(uint ir_BM, uint pos_a, uint stride_a, uint block_k, uint tid, bool in_bounds) +{ + uint tids_per_row = BLOCK_SIZE / BM; + uint is_per_tid = 8 / tids_per_row; + uint is_start = is_per_tid * (tid % tids_per_row); + uint tid_row = tid / tids_per_row; + + uint row = ir_BM + tid_row; + uint block_index = pos_a + row * stride_a + (block_k / QUANT_K); + if (in_bounds || row < p.M) { + row_v = data_a_q4_k_packed128[block_index].q4k[0]; + } +} +#endif +#if defined(DATA_A_Q5_K) +layout (binding = 0) readonly buffer A_Q5_K_128 {block_q5_K_packed128 data_a_q5_k_packed128[];}; + +void fetch_scalesQ5_K(uint ir_BM, uint pos_a, uint stride_a, uint block_k, uint tid, bool in_bounds) +{ + uint tids_per_row = BLOCK_SIZE / BM; + uint is_per_tid = 8 / tids_per_row; + uint is_start = is_per_tid * (tid % tids_per_row); + uint tid_row = tid / tids_per_row; + + uint row = ir_BM + tid_row; + uint block_index = pos_a + row * stride_a + (block_k / QUANT_K); + if (in_bounds || row < p.M) { + row_v = data_a_q5_k_packed128[block_index].q5k[0]; + } +} +#endif + +#if defined(DATA_A_Q4_K) || defined(DATA_A_Q5_K) +void store_scalesQ4_K(uint tid) +{ + barrier(); + + uint tids_per_row = BLOCK_SIZE / BM; + uint is_per_tid = 8 / tids_per_row; + uint is_start = is_per_tid * (tid % tids_per_row); + uint tid_row = tid / tids_per_row; + + [[unroll]] for (uint idx = 0; idx < is_per_tid; ++idx) { + uint is = idx + is_start; + uvec4 v = row_v; + const vec2 loadd = vec2(unpackFloat2x16(v.x)); + + uint32_t sc; + uint32_t mbyte; + + uint32_t scale0 = v.y; + uint32_t scale4 = v.z; + uint32_t scale8 = v.w; + + uint32_t sc_lo = scale0; + uint32_t mb_lo = scale4; + uint32_t sc_hi = (scale8 & 0x0F0F0F0F) | ((scale0 & 0xC0C0C0C0) >> 2); + uint32_t mb_hi = ((scale8 & 0xF0F0F0F0) >> 4) | ((scale4 & 0xC0C0C0C0) >> 2); + + sc = is < 4 ? sc_lo : sc_hi; + mbyte = is < 4 ? mb_lo : mb_hi; + sc = sc >> (8 * (is & 3)); + mbyte = mbyte >> (8 * (is & 3)); + sc &= 0x3F; + mbyte &= 0x3F; + + const float d = loadd.x * float(sc); + const float m = loadd.y * float(mbyte); + shAscales[is * shAscales_stride + tid_row] = vec2(d,m); + } + + barrier(); +} +#endif + +#endif + +float16_t dequantFuncQ4_K(const in decodeBufQ4_K bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + decodeBufQ4_K_packed16 bl16 = decodeBufQ4_K_packed16(bl); + decodeBufQ4_K_packed128 bl128 = decodeBufQ4_K_packed128(bl); + const uint idx = coordInBlock[1]; + + const uint b = (idx & 0x20) >> 5; // 0,1 + const uint is = (idx & 0xE0) >> 5; // 0..7 + +#if defined(IS_MUL_MM2) && defined(DATA_A_Q4_K) + vec2 v = shAscales[is * shAscales_stride + (blockCoords[0] % BM)]; + float d = v.x; + float m = v.y; +#else + uvec4 v = bl128.block.q4k[0]; + const vec2 loadd = vec2(unpackFloat2x16(v.x)); + + uint32_t sc; + uint32_t mbyte; + + uint32_t scale0 = v.y; + uint32_t scale4 = v.z; + uint32_t scale8 = v.w; + + uint32_t sc_lo = scale0; + uint32_t mb_lo = scale4; + uint32_t sc_hi = (scale8 & 0x0F0F0F0F) | ((scale0 & 0xC0C0C0C0) >> 2); + uint32_t mb_hi = ((scale8 & 0xF0F0F0F0) >> 4) | ((scale4 & 0xC0C0C0C0) >> 2); + + sc = is < 4 ? sc_lo : sc_hi; + mbyte = is < 4 ? mb_lo : mb_hi; + sc = sc >> (8 * (is & 3)); + mbyte = mbyte >> (8 * (is & 3)); + sc &= 0x3F; + mbyte &= 0x3F; + + const float d = loadd.x * float(sc); + const float m = loadd.y * float(mbyte); +#endif + + uint qs = uint32_t(bl16.block.qs[((idx & 0xC0) >> 2) + ((idx & 0x1E) >> 1)]); + qs = (qs >> (b * 4 + 8 * (idx & 1))) & 0xF; + + float ret = d * float(qs) - m; + + return float16_t(ret); +} + +f16vec4 dequantFuncQ4_K_v(const in decodeBufQ4_K bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + decodeBufQ4_K_packed32 bl32 = decodeBufQ4_K_packed32(bl); + decodeBufQ4_K_packed128 bl128 = decodeBufQ4_K_packed128(bl); + const uint idx = coordInBlock[1]; + + const uint is = idx >> 5; // 0..7 + +#if defined(IS_MUL_MM2) && defined(DATA_A_Q4_K) + vec2 v = shAscales[is * shAscales_stride + (blockCoords[0] % BM)]; + float d = v.x; + float m = v.y; +#else + uvec4 v = bl128.block.q4k[0]; + const vec2 loadd = vec2(unpackFloat2x16(v.x)); + + uint32_t sc; + uint32_t mbyte; + + uint32_t scale0 = v.y; + uint32_t scale4 = v.z; + uint32_t scale8 = v.w; + + uint32_t sc_lo = scale0; + uint32_t mb_lo = scale4; + uint32_t sc_hi = (scale8 & 0x0F0F0F0F) | ((scale0 & 0xC0C0C0C0) >> 2); + uint32_t mb_hi = ((scale8 & 0xF0F0F0F0) >> 4) | ((scale4 & 0xC0C0C0C0) >> 2); + + sc = is < 4 ? sc_lo : sc_hi; + mbyte = is < 4 ? mb_lo : mb_hi; + sc = sc >> (8 * (is & 3)); + mbyte = mbyte >> (8 * (is & 3)); + sc &= 0x3F; + mbyte &= 0x3F; + + const float d = loadd.x * float(sc); + const float m = loadd.y * float(mbyte); +#endif + + // idx in [0,256); vector decode uses idx a multiple of 4. packed32 word index: + // (qs_i >> 1) == (idx >> 6) * 8 + ((idx & 0x1E) >> 2). sh is 0 or 4 only, so a + // single (w >> sh) & 0x0F0F0F0F isolates all four nibbles without inter-byte leakage. + const uint sh = (idx & 0x20u) >> 3u; + const uint w = uint32_t(bl32.block.qs[(idx >> 6) * 8u + ((idx & 0x1Eu) >> 2)]); + const u8vec4 q = unpack8((w >> sh) & 0x0F0F0F0Fu); + + return f16vec4(vec4(d) * vec4(q) - vec4(m)); +} + +layout(buffer_reference, std430, buffer_reference_align = 16) buffer decodeBufQ5_K { + block_q5_K block; +}; + +layout(buffer_reference, std430, buffer_reference_align = 16) buffer decodeBufQ5_K_packed16 { + block_q5_K_packed16 block; +}; + +layout(buffer_reference, std430, buffer_reference_align = 16) buffer decodeBufQ5_K_packed128 { + block_q5_K_packed128 block; +}; + +layout(buffer_reference, std430, buffer_reference_align = 16) buffer decodeBufQ5_K_packed32 { + block_q5_K_packed32 block; +}; + +float16_t dequantFuncQ5_K(const in decodeBufQ5_K bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + decodeBufQ5_K_packed16 bl16 = decodeBufQ5_K_packed16(bl); + decodeBufQ5_K_packed128 bl128 = decodeBufQ5_K_packed128(bl); + const uint idx = coordInBlock[1]; + + const uint b = (idx & 0x20) >> 5; // 0,1 + const uint is = (idx & 0xE0) >> 5; // 0..7 + +#if defined(IS_MUL_MM2) && defined(DATA_A_Q5_K) + vec2 v = shAscales[is * shAscales_stride + (blockCoords[0] % BM)]; + float d = v.x; + float m = v.y; +#else + uvec4 v = bl128.block.q5k[0]; + + const f16vec2 loadd = unpackFloat2x16(v.x); + + uint32_t sc; + uint32_t mbyte; + + uint32_t scale0 = v.y; + uint32_t scale4 = v.z; + uint32_t scale8 = v.w; + + uint32_t sc_lo = scale0; + uint32_t mb_lo = scale4; + uint32_t sc_hi = (scale8 & 0x0F0F0F0F) | ((scale0 & 0xC0C0C0C0) >> 2); + uint32_t mb_hi = ((scale8 & 0xF0F0F0F0) >> 4) | ((scale4 & 0xC0C0C0C0) >> 2); + + sc = is < 4 ? sc_lo : sc_hi; + mbyte = is < 4 ? mb_lo : mb_hi; + sc = sc >> (8 * (is & 3)); + mbyte = mbyte >> (8 * (is & 3)); + sc &= 0x3F; + mbyte &= 0x3F; + + const float16_t d = loadd.x * float16_t(sc); + const float16_t m = loadd.y * float16_t(mbyte); +#endif + + uint qh = uint32_t(bl16.block.qh[(idx & 0x1E) >> 1]); + qh = ((qh >> is) & 0x101) << 4; + + uint qs = uint32_t(bl16.block.qs[((idx & 0xC0) >> 2) + ((idx & 0x1E) >> 1)]); + qs = (qs >> (b * 4)) & 0x0F0F; + qs = unpack8(qs | qh)[idx & 1]; + + float ret = d * float(qs) - m; + + return float16_t(ret); +} + +f16vec4 dequantFuncQ5_K_v(const in decodeBufQ5_K bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + decodeBufQ5_K_packed32 bl32 = decodeBufQ5_K_packed32(bl); + decodeBufQ5_K_packed128 bl128 = decodeBufQ5_K_packed128(bl); + const uint idx = coordInBlock[1]; + const uint is = idx >> 5; + +#if defined(IS_MUL_MM2) && defined(DATA_A_Q5_K) + vec2 v = shAscales[is * shAscales_stride + (blockCoords[0] % BM)]; + float d = v.x; + float m = v.y; +#else + uvec4 v = bl128.block.q5k[0]; + + const f16vec2 loadd = unpackFloat2x16(v.x); + + uint32_t sc; + uint32_t mbyte; + + uint32_t scale0 = v.y; + uint32_t scale4 = v.z; + uint32_t scale8 = v.w; + + uint32_t sc_lo = scale0; + uint32_t mb_lo = scale4; + uint32_t sc_hi = (scale8 & 0x0F0F0F0F) | ((scale0 & 0xC0C0C0C0) >> 2); + uint32_t mb_hi = ((scale8 & 0xF0F0F0F0) >> 4) | ((scale4 & 0xC0C0C0C0) >> 2); + + sc = is < 4 ? sc_lo : sc_hi; + mbyte = is < 4 ? mb_lo : mb_hi; + sc = sc >> (8 * (is & 3)); + mbyte = mbyte >> (8 * (is & 3)); + sc &= 0x3F; + mbyte &= 0x3F; + + const float16_t d = loadd.x * float16_t(sc); + const float16_t m = loadd.y * float16_t(mbyte); +#endif + + // sh is 0 or 4; mask 0x0F0F0F0F covers the four nibbles regardless (no inter-byte leakage). + const uint sh = (idx & 0x20u) >> 3u; + const uint qs_w = (idx >> 6) * 8u + ((idx & 0x1Eu) >> 2); + const uint qh_w = (idx & 0x1Eu) >> 2; + + const uint ql4 = (uint32_t(bl32.block.qs[qs_w]) >> sh) & 0x0F0F0F0Fu; + // qh stores bit `is` per element across 4 consecutive bytes; one shift+mask handles all 4. + const uint qh4 = ((uint32_t(bl32.block.qh[qh_w]) >> is) & 0x01010101u) << 4u; + + const u8vec4 qi = unpack8(ql4 | qh4); + return f16vec4(vec4(qi) * vec4(d) - vec4(m)); +} + +layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufQ6_K { + block_q6_K block; +}; + +layout(buffer_reference, std430, buffer_reference_align = 16) buffer decodeBufQ6_K_packed16 { + block_q6_K_packed16 block; +}; + +float16_t dequantFuncQ6_K(const in decodeBufQ6_K bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + decodeBufQ6_K_packed16 bl16 = decodeBufQ6_K_packed16(bl); + const uint idx = coordInBlock[1]; + + const uint b = (idx & 0x40) >> 6; // 0,1 + const uint qhshift = (idx & 0x60) >> 4; // 0,2,4,6 + const uint is = (idx & 0xF0) >> 4; // 0..15 + + const float16_t dscale = bl.block.d * float16_t(bl.block.scales[is]); + + uint ql = uint32_t(bl16.block.ql[((idx & 0x80) >> 2) + ((idx & 0x3E) >> 1)]); + ql = (ql >> (b * 4)) & 0x0F0F; + + uint qh = uint32_t(bl16.block.qh[((idx & 0x80) >> 3) + ((idx & 0x1E) >> 1)]); + qh = ((qh >> qhshift) & 0x0303) << 4; + + int q = unpack8(ql | qh)[idx & 1]; + + float16_t ret = dscale * float16_t(q - 32); + + return ret; +} + +f16vec4 dequantFuncQ6_K_v(const in decodeBufQ6_K bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + decodeBufQ6_K_packed16 bl16 = decodeBufQ6_K_packed16(bl); + const uint idx = coordInBlock[1]; + + const uint b = (idx & 0x40) >> 6; + const uint qhshift = (idx & 0x60) >> 4; // 0,2,4,6 + const uint is = idx >> 4; + const uint sh = b * 4; // 0 or 4 + + const float16_t dscale = bl.block.d * float16_t(bl.block.scales[is]); + + const uint ql_i = ((idx & 0x80) >> 2) + ((idx & 0x3E) >> 1); + const uint qh_i = ((idx & 0x80) >> 3) + ((idx & 0x1E) >> 1); + + // Two adjacent uint16 packed16 reads, combined into a uint32 in registers. + // After this: byte j of qlw / qhw holds the data for element idx+j. + const uint qlw = uint32_t(bl16.block.ql[ql_i ]) | (uint32_t(bl16.block.ql[ql_i + 1]) << 16); + const uint qhw = uint32_t(bl16.block.qh[qh_i ]) | (uint32_t(bl16.block.qh[qh_i + 1]) << 16); + + // sh in {0,4} and qhshift in {0,2,4,6}: per-byte masks 0x0F / 0x03 keep only the + // wanted bits with no inter-byte leakage; place qh's 2 bits at nibble high position. + const uint ql4 = (qlw >> sh) & 0x0F0F0F0Fu; + const uint qh4 = ((qhw >> qhshift) & 0x03030303u) << 4u; + + const ivec4 qi = ivec4(unpack8(ql4 | qh4)); + return f16vec4((vec4(qi) - vec4(32.0f)) * vec4(float(dscale))); +} + +#if defined(DATA_A_IQ1_S) +layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufIQ1_S { + block_iq1_s block; +}; + +float16_t dequantFuncIQ1_S(const in decodeBufIQ1_S bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + const float16_t d = bl.block.d; + const uint idx = coordInBlock[1]; + + const uint ib32 = (idx & 0xE0) >> 5; + const uint ib8 = (idx & 0xF8) >> 3; + + const uint qh = bl.block.qh[ib32]; + const uint qs = bl.block.qs[ib8]; + const float dl = d * float(2 * bitfieldExtract(qh, 12, 3) + 1); + const float delta = ((qh & 0x8000) != 0) ? -IQ1S_DELTA : IQ1S_DELTA; + const uint grid = iq1s_grid[qs | (bitfieldExtract(qh, 3 * int(ib8 & 3), 3) << 8)]; + + float16_t ret = float16_t(dl) * (float16_t(bitfieldExtract(int(grid), 2 * int(idx % 8), 2)) + float16_t(delta)); + return ret; +} + +f16vec4 dequantFuncIQ1_S_v(const in decodeBufIQ1_S bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + const float16_t d = bl.block.d; + const uint idx = coordInBlock[1]; + + const uint ib32 = idx >> 5; + const uint ib8 = idx >> 3; + const int i8b = int(idx & 4); // 0 or 4 + + const uint qh = bl.block.qh[ib32]; + const uint qs = bl.block.qs[ib8]; + const float dl = float(d) * float(2 * bitfieldExtract(qh, 12, 3) + 1); + const float delta = ((qh & 0x8000u) != 0u) ? -IQ1S_DELTA : IQ1S_DELTA; + const uint grid = iq1s_grid[qs | (bitfieldExtract(qh, 3 * int(ib8 & 3), 3) << 8)]; + + const ivec4 q = ivec4( + bitfieldExtract(int(grid), 2 * (i8b + 0), 2), + bitfieldExtract(int(grid), 2 * (i8b + 1), 2), + bitfieldExtract(int(grid), 2 * (i8b + 2), 2), + bitfieldExtract(int(grid), 2 * (i8b + 3), 2)); + return f16vec4((vec4(q) + vec4(delta)) * dl); +} +#endif + +#if defined(DATA_A_IQ1_M) +layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufIQ1_M { + block_iq1_m block; +}; + +layout(buffer_reference, std430, buffer_reference_align = 8) buffer decodeBufIQ1_M_packed64 { + block_iq1_m_packed64 block; +}; + +float16_t dequantFuncIQ1_M(const in decodeBufIQ1_M bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + decodeBufIQ1_M_packed64 bl64 = decodeBufIQ1_M_packed64(bl); + const uint idx = coordInBlock[1]; + + uvec2 scales = unpack32(bl64.block.scales); + const float16_t d = uint16BitsToHalf(uint16_t(((scales.x & 0xF000) >> 12) | ((scales.x & 0xF0000000) >> 24) | ((scales.y & 0xF000) >> 4) | ((scales.y & 0xF0000000) >> 16))); + + const uint ib8 = (idx & 0xF8) >> 3; + const uint ib16 = (idx & 0xF0) >> 4; + const int i8 = int(idx % 8); + const uint sc = bl.block.scales[ib8 / 8]; + const uint qs = bl.block.qs[ib8]; + const uint qh = bl.block.qh[ib16] >> (4 * (ib8 & 1)); + const float dl = 2 * bitfieldExtract(sc, 3 * int(ib16 & 3), 3) + 1; + const float delta = ((qh & 8) != 0) ? -IQ1S_DELTA : IQ1S_DELTA; + const uint grid = iq1s_grid[qs | ((qh & 7) << 8)]; + + float16_t ret = d * float16_t(dl) * (float16_t(bitfieldExtract(int(grid), 2 * i8, 2)) + float16_t(delta)); + return ret; +} + +f16vec4 dequantFuncIQ1_M_v(const in decodeBufIQ1_M bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + decodeBufIQ1_M_packed64 bl64 = decodeBufIQ1_M_packed64(bl); + const uint idx = coordInBlock[1]; + + uvec2 scales = unpack32(bl64.block.scales); + const float16_t d = uint16BitsToHalf(uint16_t(((scales.x & 0xF000) >> 12) | ((scales.x & 0xF0000000) >> 24) | ((scales.y & 0xF000) >> 4) | ((scales.y & 0xF0000000) >> 16))); + + const uint ib8 = idx >> 3; + const uint ib16 = idx >> 4; + const int i8b = int(idx & 4); // 0 or 4 -- i8 base for the V=4 group + + const uint sc = bl.block.scales[ib8 / 8]; + const uint qs = bl.block.qs[ib8]; + const uint qh = bl.block.qh[ib16] >> (4 * (ib8 & 1)); + const float dl = 2.0 * float(bitfieldExtract(sc, 3 * int(ib16 & 3), 3)) + 1.0; + const float delta = ((qh & 8u) != 0u) ? -IQ1S_DELTA : IQ1S_DELTA; + const uint grid = iq1s_grid[qs | ((qh & 7u) << 8)]; + + const ivec4 q = ivec4( + bitfieldExtract(int(grid), 2 * (i8b + 0), 2), + bitfieldExtract(int(grid), 2 * (i8b + 1), 2), + bitfieldExtract(int(grid), 2 * (i8b + 2), 2), + bitfieldExtract(int(grid), 2 * (i8b + 3), 2)); + return f16vec4((vec4(q) + vec4(delta)) * (float(d) * dl)); +} +#endif + +#if defined(DATA_A_IQ2_XXS) +layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufIQ2_XXS { + block_iq2_xxs block; +}; + +layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufIQ2_XXS_packed16 { + block_iq2_xxs_packed16 block; +}; + +float16_t dequantFuncIQ2_XXS(const in decodeBufIQ2_XXS bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + decodeBufIQ2_XXS_packed16 bl16 = decodeBufIQ2_XXS_packed16(bl); + const float16_t d = bl.block.d; + const uint idx = coordInBlock[1]; + + const uint ib32 = (idx & 0xE0) >> 5; // 0..7 + const uint ib8 = (idx & 0x18) >> 3; // 0..3 + const uint iqs = 8 * ib32 + ib8; + + const uint qs = bl.block.qs[iqs]; + const uint signscale = pack32(u16vec2(bl16.block.qs[4*ib32+2], bl16.block.qs[4*ib32+3])); + + const float dscale = float(bl.block.d) * 0.25 * (0.5 + float(signscale >> 28)); + uint sign = bitfieldExtract(signscale, 7 * int(ib8), 7); + sign |= bitCount(sign) << 7; + + uint g2 = iq2xxs_grid[qs][(idx & 4) >> 2]; + g2 >>= (idx & 2) * 8; + const vec2 g = vec2(unpack8(g2)); + + vec2 ret = dscale * g * ((sign & (1 << (idx & 7))) != 0 ? -1.0hf : 1.0hf); + return float16_t(ret[idx & 1]); +} + +f16vec4 dequantFuncIQ2_XXS_v(const in decodeBufIQ2_XXS bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + decodeBufIQ2_XXS_packed16 bl16 = decodeBufIQ2_XXS_packed16(bl); + const uint idx = coordInBlock[1]; + + const uint ib32 = idx >> 5; + const uint ib8 = (idx & 0x18) >> 3; + const uint iqs = 8 * ib32 + ib8; + + const uint qs = bl.block.qs[iqs]; + const uint signscale = pack32(u16vec2(bl16.block.qs[4*ib32+2], bl16.block.qs[4*ib32+3])); + const float dscale = float(bl.block.d) * 0.25 * (0.5 + float(signscale >> 28)); + + uint sign = bitfieldExtract(signscale, 7 * int(ib8), 7); + sign |= bitCount(sign) << 7; + const uint sb = sign >> (idx & 7u); + + const uint g2 = iq2xxs_grid[qs][(idx & 4) >> 2]; + const u8vec4 g = unpack8(g2); + + return f16vec4( + dscale * float(g.x) * ((sb & 1u) != 0u ? -1.0 : 1.0), + dscale * float(g.y) * ((sb & 2u) != 0u ? -1.0 : 1.0), + dscale * float(g.z) * ((sb & 4u) != 0u ? -1.0 : 1.0), + dscale * float(g.w) * ((sb & 8u) != 0u ? -1.0 : 1.0)); +} +#endif + +#if defined(DATA_A_IQ2_XS) +layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufIQ2_XS { + block_iq2_xs block; +}; + +float16_t dequantFuncIQ2_XS(const in decodeBufIQ2_XS bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + const float16_t d = bl.block.d; + const uint idx = coordInBlock[1]; + + const uint is = (idx & 0xE0) >> 5; // 0..8 + const uint sshift = (idx & 0x10) >> 2; // 0,4 + const uint iqs = (idx & 0xF8) >> 3; // 0..63 + + const uint16_t qs = bl.block.qs[iqs]; + const float dscale = float(bl.block.d) * 0.25 * (0.5 + float((bl.block.scales[is] >> sshift) & 0xF)); + + uint sign = uint(qs >> 9); + sign |= bitCount(sign) << 7; + uint g2 = iq2xs_grid[qs & 0x1FF][(idx & 4) >> 2]; + g2 >>= (idx & 2) * 8; + const vec2 g = vec2(unpack8(g2)); + + vec2 ret = dscale * g * ((sign & (1 << (idx & 7))) != 0 ? -1.0hf : 1.0hf); + return float16_t(ret[idx & 1]); +} + +f16vec4 dequantFuncIQ2_XS_v(const in decodeBufIQ2_XS bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + const uint idx = coordInBlock[1]; + + const uint is = idx >> 5; + const uint sshift = (idx & 0x10) >> 2; + const uint iqs = idx >> 3; + + const uint16_t qs = bl.block.qs[iqs]; + const float dscale = float(bl.block.d) * 0.25 * (0.5 + float((bl.block.scales[is] >> sshift) & 0xF)); + + uint sign = uint(qs >> 9); + sign |= bitCount(sign) << 7; + const uint sb = sign >> (idx & 7u); + + const uint g2 = iq2xs_grid[qs & 0x1FF][(idx & 4) >> 2]; + const u8vec4 g = unpack8(g2); + + return f16vec4( + dscale * float(g.x) * ((sb & 1u) != 0u ? -1.0 : 1.0), + dscale * float(g.y) * ((sb & 2u) != 0u ? -1.0 : 1.0), + dscale * float(g.z) * ((sb & 4u) != 0u ? -1.0 : 1.0), + dscale * float(g.w) * ((sb & 8u) != 0u ? -1.0 : 1.0)); +} +#endif + +#if defined(DATA_A_IQ2_S) +layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufIQ2_S { + block_iq2_s block; +}; + +float16_t dequantFuncIQ2_S(const in decodeBufIQ2_S bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + uint idx = coordInBlock[1]; + + const uint ib32 = (idx & 0xE0) >> 5; // 0..7 + const uint ib8 = (idx & 0xF8) >> 3; // 0..31 + const uint qhshift = 2 * (ib8 % 4); + + const uint scale = (bl.block.scales[ib32] >> ((idx & 0x10) >> 2)) & 0xf; + const uint qs = bl.block.qs[ib8]; + const uint qh = bl.block.qh[ib32]; + const uint sign = bl.block.qs[QUANT_K / 8 + ib8] >> (idx & 0x6); + + const float d = float(bl.block.d); + const float db = d * 0.25 * (0.5 + scale); + const ivec2 sign01 = 1 - (2 & ivec2(sign << 1, sign)); + uint g2 = iq2s_grid[qs | ((qh << (8 - qhshift)) & 0x300)][(idx & 4) >> 2]; + g2 >>= (idx & 2) * 8; + const vec2 v = db * vec2(sign01) * vec2(unpack8(g2)); + return float16_t(v[idx & 1]); +} + +f16vec4 dequantFuncIQ2_S_v(const in decodeBufIQ2_S bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + const uint idx = coordInBlock[1]; + + const uint ib32 = idx >> 5; + const uint ib8 = idx >> 3; + const uint qhshift = 2 * (ib8 % 4); + + const uint scale = (bl.block.scales[ib32] >> ((idx & 0x10) >> 2)) & 0xf; + const uint qs = bl.block.qs[ib8]; + const uint qh = bl.block.qh[ib32]; + const uint sb = uint(bl.block.qs[QUANT_K / 8 + ib8]) >> (idx & 0x6u); + + const float d = float(bl.block.d); + const float db = d * 0.25 * (0.5 + scale); + + const uint g2 = iq2s_grid[qs | ((qh << (8 - qhshift)) & 0x300)][(idx & 4) >> 2]; + const u8vec4 g = unpack8(g2); + + return f16vec4( + db * float(g.x) * ((sb & 1u) != 0u ? -1.0 : 1.0), + db * float(g.y) * ((sb & 2u) != 0u ? -1.0 : 1.0), + db * float(g.z) * ((sb & 4u) != 0u ? -1.0 : 1.0), + db * float(g.w) * ((sb & 8u) != 0u ? -1.0 : 1.0)); +} +#endif + +#if defined(DATA_A_IQ3_XXS) +layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufIQ3_XXS { + block_iq3_xxs block; +}; + +layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufIQ3_XXS_packed16 { + block_iq3_xxs_packed16 block; +}; + +float16_t dequantFuncIQ3_XXS(const in decodeBufIQ3_XXS bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + decodeBufIQ3_XXS_packed16 bl16 = decodeBufIQ3_XXS_packed16(bl); + uint idx = coordInBlock[1]; + + const uint iqs = (idx & 0xFC) >> 2; // 0..63 + const uint is = QUANT_K / 4 + ((idx & 0xE0) >> 3);// 8 values + + const float d = float(bl.block.d); + const uint qs = bl.block.qs[iqs]; + const uint signs = pack32(u16vec2( + bl16.block.qs[is/2+0], + bl16.block.qs[is/2+1] + )); + const float db = d * 0.5 * (0.5 + (signs >> 28)); + const uint32_t sign7 = bitfieldExtract(signs, 7 * (int(iqs / 2) % 4), 7); + const uint sign = (sign7 | (bitCount(sign7) << 7)) >> (idx & 0x6); + const ivec2 sign01 = ivec2(1 - (2 & ivec2(sign << 1, sign))); + const uint grid = iq3xxs_grid[qs] >> (16 * ((idx & 2) >> 1)); + const vec2 v = db * vec2(sign01) * vec2(unpack8(grid).xy); + return float16_t(v[idx & 1]); +} + +f16vec4 dequantFuncIQ3_XXS_v(const in decodeBufIQ3_XXS bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + decodeBufIQ3_XXS_packed16 bl16 = decodeBufIQ3_XXS_packed16(bl); + const uint idx = coordInBlock[1]; + + const uint iqs = idx >> 2; + const uint is = QUANT_K / 4 + ((idx & 0xE0) >> 3); + + const float d = float(bl.block.d); + const uint qs = bl.block.qs[iqs]; + const uint signs = pack32(u16vec2(bl16.block.qs[is/2+0], bl16.block.qs[is/2+1])); + const float db = d * 0.5 * (0.5 + (signs >> 28)); + + const uint sign7 = bitfieldExtract(signs, 7 * (int(iqs / 2) % 4), 7); + const uint sb = (sign7 | (bitCount(sign7) << 7)) >> (idx & 0x6u); + + const uint grid = iq3xxs_grid[qs]; + const u8vec4 g = unpack8(grid); + + return f16vec4( + db * float(g.x) * ((sb & 1u) != 0u ? -1.0 : 1.0), + db * float(g.y) * ((sb & 2u) != 0u ? -1.0 : 1.0), + db * float(g.z) * ((sb & 4u) != 0u ? -1.0 : 1.0), + db * float(g.w) * ((sb & 8u) != 0u ? -1.0 : 1.0)); +} +#endif + +#if defined(DATA_A_IQ3_S) +layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufIQ3_S { + block_iq3_s block; +}; + +float16_t dequantFuncIQ3_S(const in decodeBufIQ3_S bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + uint idx = coordInBlock[1]; + + const uint iqs = (idx & 0xFC) >> 2; // 0..63 + const uint iqh = (idx & 0xE0) >> 5; + + const float d = float(bl.block.d); + const uint qs = bl.block.qs[iqs]; + const uint qh = bl.block.qh[iqh]; + const int8_t sign = int8_t(bl.block.signs[iqs / 2] >> (idx & 0x6)); + const uint scale = bl.block.scales[iqs / 16]; + const ivec2 sign01 = ivec2(1 - (2 & ivec2(sign << 1, sign))); + const float db = d * (1 + 2 * ((scale >> (4 * (iqh & 1))) & 0xf)); + const uint32_t grid = iq3s_grid[qs | ((qh << (8 - (iqs % 8))) & 256)] >> ((idx & 2) << 3); + const vec2 v = db * vec2(sign01) * vec2(unpack8(grid).xy); + + return float16_t(v[idx & 1]); +} + +f16vec4 dequantFuncIQ3_S_v(const in decodeBufIQ3_S bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + const uint idx = coordInBlock[1]; + + const uint iqs = idx >> 2; + const uint iqh = idx >> 5; + + const float d = float(bl.block.d); + const uint qs = bl.block.qs[iqs]; + const uint qh = bl.block.qh[iqh]; + const uint sb = uint(bl.block.signs[iqs / 2]) >> (idx & 0x6u); + const uint scale = bl.block.scales[iqs / 16]; + const float db = d * (1 + 2 * ((scale >> (4 * (iqh & 1))) & 0xf)); + + const uint grid = iq3s_grid[qs | ((qh << (8 - (iqs % 8))) & 256)]; + const u8vec4 g = unpack8(grid); + + return f16vec4( + db * float(g.x) * ((sb & 1u) != 0u ? -1.0 : 1.0), + db * float(g.y) * ((sb & 2u) != 0u ? -1.0 : 1.0), + db * float(g.z) * ((sb & 4u) != 0u ? -1.0 : 1.0), + db * float(g.w) * ((sb & 8u) != 0u ? -1.0 : 1.0)); +} +#endif + +#if defined(DATA_A_IQ4_XS) +layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufIQ4_XS { + block_iq4_xs block; +}; + +layout(buffer_reference, std430, buffer_reference_align = 4) buffer decodeBufIQ4_XS_packed32 { + block_iq4_xs_packed32 block; +}; + +float16_t dequantFuncIQ4_XS(const in decodeBufIQ4_XS bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + const float16_t d = bl.block.d; + const uint idx = coordInBlock[1]; + + const uint ib32 = (idx & 0xE0) >> 5; // 0..7 + + const uint sl = (bl.block.scales_l[ib32/2] >> (4 * (ib32 & 1))) & 0xF; + const uint sh = ((bl.block.scales_h) >> (2 * ib32)) & 3; + const uint qshift = (idx & 16) >> 2; + const uint q = (bl.block.qs[16 * ib32 + (idx % 16)] >> qshift) & 0xF; + + float16_t ret = d * float16_t(int(sl | (sh << 4)) - 32) * float16_t(kvalues_iq4nl[q]); + return ret; +} + +f16vec4 dequantFuncIQ4_XS_v(const in decodeBufIQ4_XS bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + decodeBufIQ4_XS_packed32 bl32 = decodeBufIQ4_XS_packed32(bl); + const float16_t d = bl.block.d; + const uint idx = coordInBlock[1]; + + const uint ib32 = idx >> 5; // 0..7 + const uint sl = (bl32.block.scales_l >> (4 * ib32)) & 0xF; + const uint sh = (uint(bl32.block.scales_h) >> (2 * ib32)) & 0x3; + const uint qshift = (idx & 0x10) >> 2; // {0, 4} + const uint qs_w = 4 * ib32 + ((idx & 0xC) >> 2); // iqs / 4, in [0,32) + + const float16_t dl = d * float16_t(int(sl | (sh << 4)) - 32); + + const uint qsw = bl32.block.qs[qs_w]; + const u8vec4 qv = unpack8((qsw >> qshift) & 0x0F0F0F0Fu); + const vec4 ret = vec4( + float(kvalues_iq4nl[qv.x]), + float(kvalues_iq4nl[qv.y]), + float(kvalues_iq4nl[qv.z]), + float(kvalues_iq4nl[qv.w])) * float(dl); + return f16vec4(ret); +} +#endif + +#if defined(DATA_A_IQ4_NL) +layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufIQ4_NL { + block_iq4_nl block; +}; + +layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufIQ4_NL_packed16 { + block_iq4_nl_packed16 block; +}; + +float16_t dequantFuncIQ4_NL(const in decodeBufIQ4_NL bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + const float16_t d = bl.block.d; + const uint idx = coordInBlock[1]; + const uint iqs = idx & 0xF; + const uint shift = (idx & 0x10) >> 2; + uint32_t qs = bl.block.qs[iqs]; + qs >>= shift; + qs &= 0xF; + float16_t ret = float16_t(kvalues_iq4nl[qs]) * d; + return ret; +} + +f16vec4 dequantFuncIQ4_NL_v(const in decodeBufIQ4_NL bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + decodeBufIQ4_NL_packed16 bl16 = decodeBufIQ4_NL_packed16(bl); + const float16_t d = bl.block.d; + const uint idx = coordInBlock[1]; + const uint shift = (idx & 0x10) >> 2; // 0 or 4 + const uint qs_i = (idx & 0xC) >> 1; // packed16 word index, in {0,2,4,6} + const uint qsw = uint32_t(bl16.block.qs[qs_i ]) + | (uint32_t(bl16.block.qs[qs_i + 1u]) << 16); + // shift in {0,4}: per-byte mask 0x0F isolates the wanted nibble in each byte. + const u8vec4 q = unpack8((qsw >> shift) & 0x0F0F0F0Fu); + return f16vec4( + float(d) * float(kvalues_iq4nl[q.x]), + float(d) * float(kvalues_iq4nl[q.y]), + float(d) * float(kvalues_iq4nl[q.z]), + float(d) * float(kvalues_iq4nl[q.w])); +} +#endif + +#if defined(DATA_A_MXFP4) +layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufMXFP4 { + block_mxfp4 block; +}; + +float16_t dequantFuncMXFP4(const in decodeBufMXFP4 bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + const float d = e8m0_to_fp32(bl.block.e); + const uint idx = coordInBlock[1]; + const uint iqs = idx & 0xF; + const uint shift = (idx & 0x10) >> 2; + uint32_t qs = bl.block.qs[iqs]; + qs >>= shift; + qs &= 0xF; + float16_t ret = float16_t(kvalues_mxfp4[qs] * d * 0.5); + return ret; +} + +f16vec4 dequantFuncMXFP4_v(const in decodeBufMXFP4 bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + const float d = e8m0_to_fp32(bl.block.e); + const uint idx = coordInBlock[1]; + const uint iqs = idx & 0xF; + const uint shift = (idx & 0x10) >> 2; + uvec4 qv = uvec4( + uint(bl.block.qs[iqs]), + uint(bl.block.qs[iqs + 1u]), + uint(bl.block.qs[iqs + 2u]), + uint(bl.block.qs[iqs + 3u])); + qv = (qv >> shift) & 0xFu; + const vec4 ret = vec4( + float(kvalues_mxfp4[qv.x]), + float(kvalues_mxfp4[qv.y]), + float(kvalues_mxfp4[qv.z]), + float(kvalues_mxfp4[qv.w])) * d * 0.5f; + return f16vec4(ret); +} +#endif + +#if defined(DATA_A_NVFP4) +layout(buffer_reference, std430, buffer_reference_align = 4) buffer decodeBufNVFP4 { + block_nvfp4 block; +}; + +layout(buffer_reference, std430, buffer_reference_align = 4) buffer decodeBufNVFP4_packed32 { + block_nvfp4_packed32 block; +}; + +float16_t dequantFuncNVFP4(const in decodeBufNVFP4 bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + const uint idx = coordInBlock[1]; + const uint sub = (idx & 0x30) >> 4; + const uint iqs = ((idx & 0x30) >> 1) + (idx & 0x7); + const uint shift = (idx & 0x8) >> 1; + const float d = ue4m3_to_fp32(bl.block.d[sub]); + uint qs = uint(bl.block.qs[iqs]); + qs = (qs >> shift) & 0xF; + return float16_t(kvalues_mxfp4[qs] * d * 0.5); +} + +f16vec4 dequantFuncNVFP4_v(const in decodeBufNVFP4 bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + decodeBufNVFP4_packed32 bl32 = decodeBufNVFP4_packed32(bl); + const uint idx = coordInBlock[1]; + const uint sub = idx >> 4; + const uint qs_w = ((idx & 0x30) >> 3) + ((idx & 0x4u) >> 2); // iqs / 4, in [0,8) + const uint shift = (idx & 0x8) >> 1; + const float d = ue4m3_to_fp32(bl.block.d[sub]); + + const uint qsw = uint32_t(bl32.block.qs[qs_w]); + const u8vec4 qv = unpack8((qsw >> shift) & 0x0F0F0F0Fu); + const vec4 ret = vec4( + float(kvalues_mxfp4[qv.x]), + float(kvalues_mxfp4[qv.y]), + float(kvalues_mxfp4[qv.z]), + float(kvalues_mxfp4[qv.w])) * d * 0.5f; + return f16vec4(ret); +} +#endif + +#if defined(DATA_A_Q1_0) +#define dequantFuncA dequantFuncQ1_0 +#define dequantFuncA_v dequantFuncQ1_0_v +#elif defined(DATA_A_Q4_0) +#define dequantFuncA dequantFuncQ4_0 +#define dequantFuncA_v dequantFuncQ4_0_v +#elif defined(DATA_A_Q4_1) +#define dequantFuncA dequantFuncQ4_1 +#define dequantFuncA_v dequantFuncQ4_1_v +#elif defined(DATA_A_Q5_0) +#define dequantFuncA dequantFuncQ5_0 +#define dequantFuncA_v dequantFuncQ5_0_v +#elif defined(DATA_A_Q5_1) +#define dequantFuncA dequantFuncQ5_1 +#define dequantFuncA_v dequantFuncQ5_1_v +#elif defined(DATA_A_Q8_0) +#define dequantFuncA dequantFuncQ8_0 +#define dequantFuncA_v dequantFuncQ8_0_v +#elif defined(DATA_A_Q2_K) +#define dequantFuncA dequantFuncQ2_K +#define dequantFuncA_v dequantFuncQ2_K_v +#elif defined(DATA_A_Q3_K) +#define dequantFuncA dequantFuncQ3_K +#define dequantFuncA_v dequantFuncQ3_K_v +#elif defined(DATA_A_Q4_K) +#define dequantFuncA dequantFuncQ4_K +#define dequantFuncA_v dequantFuncQ4_K_v +#define fetch_scales fetch_scalesQ4_K +#define store_scales store_scalesQ4_K +#elif defined(DATA_A_Q5_K) +#define dequantFuncA dequantFuncQ5_K +#define dequantFuncA_v dequantFuncQ5_K_v +#define fetch_scales fetch_scalesQ5_K +#define store_scales store_scalesQ4_K +#elif defined(DATA_A_Q6_K) +#define dequantFuncA dequantFuncQ6_K +#define dequantFuncA_v dequantFuncQ6_K_v +#elif defined(DATA_A_IQ1_S) +#define dequantFuncA dequantFuncIQ1_S +#define dequantFuncA_v dequantFuncIQ1_S_v +#elif defined(DATA_A_IQ1_M) +#define dequantFuncA dequantFuncIQ1_M +#define dequantFuncA_v dequantFuncIQ1_M_v +#elif defined(DATA_A_IQ2_XXS) +#define dequantFuncA dequantFuncIQ2_XXS +#define dequantFuncA_v dequantFuncIQ2_XXS_v +#elif defined(DATA_A_IQ2_XS) +#define dequantFuncA dequantFuncIQ2_XS +#define dequantFuncA_v dequantFuncIQ2_XS_v +#elif defined(DATA_A_IQ2_S) +#define dequantFuncA dequantFuncIQ2_S +#define dequantFuncA_v dequantFuncIQ2_S_v +#elif defined(DATA_A_IQ3_XXS) +#define dequantFuncA dequantFuncIQ3_XXS +#define dequantFuncA_v dequantFuncIQ3_XXS_v +#elif defined(DATA_A_IQ3_S) +#define dequantFuncA dequantFuncIQ3_S +#define dequantFuncA_v dequantFuncIQ3_S_v +#elif defined(DATA_A_IQ4_XS) +#define dequantFuncA dequantFuncIQ4_XS +#define dequantFuncA_v dequantFuncIQ4_XS_v +#elif defined(DATA_A_IQ4_NL) +#define dequantFuncA dequantFuncIQ4_NL +#define dequantFuncA_v dequantFuncIQ4_NL_v +#elif defined(DATA_A_MXFP4) +#define dequantFuncA dequantFuncMXFP4 +#define dequantFuncA_v dequantFuncMXFP4_v +#elif defined(DATA_A_NVFP4) +#define dequantFuncA dequantFuncNVFP4 +#define dequantFuncA_v dequantFuncNVFP4_v +#elif defined(DATA_A_F32) +#define dequantFuncA dequantFuncF32 +#endif diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_head.glsl b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_head.glsl new file mode 100644 index 0000000000000000000000000000000000000000..addceafade9b7f2bc10310bc4bce3b7e8275870f --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_head.glsl @@ -0,0 +1,13 @@ +#extension GL_EXT_control_flow_attributes : require +#extension GL_EXT_shader_16bit_storage : require + +layout (push_constant) uniform parameter +{ + uint M; + uint K; + uint stride_a; + uint stride_b; + uint nel; +} p; + +#include "types.glsl" diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq1_m.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq1_m.comp new file mode 100644 index 0000000000000000000000000000000000000000..637c95fa35304dba7b7f521654e2affef5c5faf0 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq1_m.comp @@ -0,0 +1,42 @@ +#version 450 + +#extension GL_EXT_shader_explicit_arithmetic_types_int16 : require + +#include "dequant_head.glsl" + +layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {block_iq1_m data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; + +void main() { + // Each thread handles 1 subblock (32 values with 2 scales) + const uint ib = gl_WorkGroupID.x * 32 + gl_LocalInvocationID.x / 8; + + init_iq_shmem(gl_WorkGroupSize); + + if (ib >= p.nel / 256) { + return; + } + + const uint ib32 = gl_LocalInvocationID.x % 8; + const uint ib64 = ib32 / 2; + const uint b_idx = 256 * ib + 32 * ib32; + + const uint16_t[4] scales = data_a[ib].scales; + const u16vec4 s = u16vec4(scales[0], scales[1], scales[2], scales[3]) >> 12; + const float d = float(unpackHalf2x16(s.x | (s.y << 4) | (s.z << 8) | (s.w << 12)).x); + + const uint sc = data_a[ib].scales[ib64]; + [[unroll]] for (int l = 0; l < 4; ++l) { + const uint ib16 = 2 * ib32 + l / 2; + const float dl = d * (2 * bitfieldExtract(sc, 3 * int(ib16 & 3), 3) + 1); + const uint qh = data_a[ib].qh[ib16] >> (4 * (l & 1)); + const uint qs = data_a[ib].qs[4 * ib32 + l]; + const float delta = ((qh & 8) != 0) ? -IQ1M_DELTA : IQ1M_DELTA; + const int16_t grid = int16_t(iq1s_grid[qs | ((qh & 7) << 8)]); + [[unroll]] for (int j = 0; j < 8; ++j) { + data_b[b_idx + 8 * l + j] = D_TYPE(dl * (bitfieldExtract(grid, 2*j, 2) + delta)); + } + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq1_s.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq1_s.comp new file mode 100644 index 0000000000000000000000000000000000000000..d1cbc5e9d02ef28aafe3450f9f3af32372379c6f --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq1_s.comp @@ -0,0 +1,35 @@ +#version 450 + +#include "dequant_head.glsl" + +layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {block_iq1_s data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; + +void main() { + // Each thread handles 1 subblock (32 values with 2 scales) + const uint ib = gl_WorkGroupID.x * 32 + gl_LocalInvocationID.x / 8; + + init_iq_shmem(gl_WorkGroupSize); + + if (ib >= p.nel / 256) { + return; + } + + const uint ib32 = gl_LocalInvocationID.x % 8; + const uint b_idx = 256 * ib + 32 * ib32; + + uint qh = data_a[ib].qh[ib32]; + const float d = float(data_a[ib].d); + const float dl = d * float(2 * bitfieldExtract(qh, 12, 3) + 1); + const float delta = ((qh & 0x8000) != 0) ? -IQ1S_DELTA : IQ1S_DELTA; + [[unroll]] for (uint l = 0; l < 4; ++l) { + const uint qs = data_a[ib].qs[4 * ib32 + l]; + const uint hi = bitfieldExtract(qh, 3 * int(l), 3); + const int16_t grid = int16_t(iq1s_grid[qs | (hi << 8)]); + [[unroll]] for (int j = 0; j < 8; ++j) { + data_b[b_idx + 8 * l + j] = D_TYPE(dl * (bitfieldExtract(grid, 2*j, 2) + delta)); + } + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq2_s.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq2_s.comp new file mode 100644 index 0000000000000000000000000000000000000000..78490162cd167d4de02c31b62f90dedc8348e3fd --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq2_s.comp @@ -0,0 +1,44 @@ +#version 450 + +#include "dequant_head.glsl" + +layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {block_iq2_s data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; + +void main() { + // Each thread handles 1 subblock (32 values with 2 scales) + const uint ib = gl_WorkGroupID.x * 32 + gl_LocalInvocationID.x / 8; + + init_iq_shmem(gl_WorkGroupSize); + + if (ib >= p.nel / 256) { + return; + } + + const uint ib32 = gl_LocalInvocationID.x % 8; + const uint b_idx = 256 * ib + 32 * ib32; + + const float d = float(data_a[ib].d); + const vec2 scale = vec2(data_a[ib].scales[ib32] & 0xf, data_a[ib].scales[ib32] >> 4); + const vec2 db = d * (0.5 + scale) * 0.25; + + uint qh = data_a[ib].qh[ib32]; + [[unroll]] for (uint l = 0; l < 4; ++l) { + uint qs = data_a[ib].qs[4 * ib32 + l]; + const uint8_t sign = data_a[ib].qs[QUANT_K / 8 + 4 * ib32 + l]; + qs |= (qh << (8 - 2 * l)) & 0x300; + const uvec2 grid = iq2s_grid[qs]; + const u8vec4 grid0 = unpack8(grid.x); + const u8vec4 grid1 = unpack8(grid.y); + data_b[b_idx + 8 * l + 0] = D_TYPE(db[l/2] * grid0.x * ((sign & 1) != 0 ? -1.0 : 1.0)); + data_b[b_idx + 8 * l + 1] = D_TYPE(db[l/2] * grid0.y * ((sign & 2) != 0 ? -1.0 : 1.0)); + data_b[b_idx + 8 * l + 2] = D_TYPE(db[l/2] * grid0.z * ((sign & 4) != 0 ? -1.0 : 1.0)); + data_b[b_idx + 8 * l + 3] = D_TYPE(db[l/2] * grid0.w * ((sign & 8) != 0 ? -1.0 : 1.0)); + data_b[b_idx + 8 * l + 4] = D_TYPE(db[l/2] * grid1.x * ((sign & 16) != 0 ? -1.0 : 1.0)); + data_b[b_idx + 8 * l + 5] = D_TYPE(db[l/2] * grid1.y * ((sign & 32) != 0 ? -1.0 : 1.0)); + data_b[b_idx + 8 * l + 6] = D_TYPE(db[l/2] * grid1.z * ((sign & 64) != 0 ? -1.0 : 1.0)); + data_b[b_idx + 8 * l + 7] = D_TYPE(db[l/2] * grid1.w * ((sign & 128) != 0 ? -1.0 : 1.0)); + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq2_xs.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq2_xs.comp new file mode 100644 index 0000000000000000000000000000000000000000..9b8ce0a7f816fefe695b9c453b31e8c841eeb744 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq2_xs.comp @@ -0,0 +1,43 @@ +#version 450 + +#include "dequant_head.glsl" + +layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {block_iq2_xs data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; + +void main() { + // Each thread handles 1 subblock (32 values with 2 scales) + const uint ib = gl_WorkGroupID.x * 32 + gl_LocalInvocationID.x / 8; + + init_iq_shmem(gl_WorkGroupSize); + + if (ib >= p.nel / 256) { + return; + } + + const uint ib32 = gl_LocalInvocationID.x % 8; + const uint b_idx = 256 * ib + 32 * ib32; + + const float d = float(data_a[ib].d); + const vec2 scale = vec2(data_a[ib].scales[ib32] & 0xf, data_a[ib].scales[ib32] >> 4); + const vec2 db = d * (0.5 + scale) * 0.25; + + [[unroll]] for (uint l = 0; l < 4; ++l) { + uint16_t qs = data_a[ib].qs[4 * ib32 + l]; + const uint sign7 = qs >> 9; + const uint sign8 = sign7 | (bitCount(sign7) << 7); // parity bit + const uvec2 grid = iq2xs_grid[qs & 511]; + const u8vec4 grid0 = unpack8(grid.x); + const u8vec4 grid1 = unpack8(grid.y); + data_b[b_idx + 8 * l + 0] = D_TYPE(db[l/2] * grid0.x * ((sign8 & 1) != 0 ? -1.0 : 1.0)); + data_b[b_idx + 8 * l + 1] = D_TYPE(db[l/2] * grid0.y * ((sign8 & 2) != 0 ? -1.0 : 1.0)); + data_b[b_idx + 8 * l + 2] = D_TYPE(db[l/2] * grid0.z * ((sign8 & 4) != 0 ? -1.0 : 1.0)); + data_b[b_idx + 8 * l + 3] = D_TYPE(db[l/2] * grid0.w * ((sign8 & 8) != 0 ? -1.0 : 1.0)); + data_b[b_idx + 8 * l + 4] = D_TYPE(db[l/2] * grid1.x * ((sign8 & 16) != 0 ? -1.0 : 1.0)); + data_b[b_idx + 8 * l + 5] = D_TYPE(db[l/2] * grid1.y * ((sign8 & 32) != 0 ? -1.0 : 1.0)); + data_b[b_idx + 8 * l + 6] = D_TYPE(db[l/2] * grid1.z * ((sign8 & 64) != 0 ? -1.0 : 1.0)); + data_b[b_idx + 8 * l + 7] = D_TYPE(db[l/2] * grid1.w * ((sign8 & 128) != 0 ? -1.0 : 1.0)); + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq2_xxs.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq2_xxs.comp new file mode 100644 index 0000000000000000000000000000000000000000..aacf07d0f8e715b254ccbbf9a6ad92b5615385ed --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq2_xxs.comp @@ -0,0 +1,49 @@ +#version 450 + +#include "dequant_head.glsl" + +layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {block_iq2_xxs data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; + +void main() { + // Each thread handles 1 scale block (32 values) + // Each block is described by 4 lattice indices, 4x7 sign bits and 4 scale bits + const uint ib = gl_WorkGroupID.x * 32 + gl_LocalInvocationID.x / 8; + + init_iq_shmem(gl_WorkGroupSize); + + if (ib >= p.nel / 256) { + return; + } + + const uint is = gl_LocalInvocationID.x % 8; + const uint b_idx = 256 * ib + 32 * is; + + const float d = float(data_a[ib].d); + uint signscale = pack32(u8vec4( + data_a[ib].qs[8*is + 4], + data_a[ib].qs[8*is + 5], + data_a[ib].qs[8*is + 6], + data_a[ib].qs[8*is + 7] + )); + const float db = d * (0.5 + (signscale >> 28)) * 0.25; + + [[unroll]] for (uint l = 0; l < 4; ++l) { + const uint sign7 = bitfieldExtract(signscale, 7 * int(l), 7); + const uint sign8 = sign7 | (bitCount(sign7) << 7); // parity bit + const uint qs = data_a[ib].qs[8 * is + l]; + const uvec2 grid = iq2xxs_grid[qs]; + const u8vec4 grid0 = unpack8(grid.x); + const u8vec4 grid1 = unpack8(grid.y); + data_b[b_idx + 8 * l + 0] = D_TYPE(db * grid0.x * ((sign8 & 1) != 0 ? -1.0 : 1.0)); + data_b[b_idx + 8 * l + 1] = D_TYPE(db * grid0.y * ((sign8 & 2) != 0 ? -1.0 : 1.0)); + data_b[b_idx + 8 * l + 2] = D_TYPE(db * grid0.z * ((sign8 & 4) != 0 ? -1.0 : 1.0)); + data_b[b_idx + 8 * l + 3] = D_TYPE(db * grid0.w * ((sign8 & 8) != 0 ? -1.0 : 1.0)); + data_b[b_idx + 8 * l + 4] = D_TYPE(db * grid1.x * ((sign8 & 16) != 0 ? -1.0 : 1.0)); + data_b[b_idx + 8 * l + 5] = D_TYPE(db * grid1.y * ((sign8 & 32) != 0 ? -1.0 : 1.0)); + data_b[b_idx + 8 * l + 6] = D_TYPE(db * grid1.z * ((sign8 & 64) != 0 ? -1.0 : 1.0)); + data_b[b_idx + 8 * l + 7] = D_TYPE(db * grid1.w * ((sign8 & 128) != 0 ? -1.0 : 1.0)); + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq3_s.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq3_s.comp new file mode 100644 index 0000000000000000000000000000000000000000..f2c20b1d2c0c282b4b90c4a5b3669d76c64f2574 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq3_s.comp @@ -0,0 +1,40 @@ +#version 450 + +#include "dequant_head.glsl" + +layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {block_iq3_s data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; + +void main() { + // Each thread handles 1 scale nibble. + // Each block contains 4 scale bytes (8 scales) for 256 output values. + const uint ib = gl_WorkGroupID.x * 32 + gl_LocalInvocationID.x / 8; + + init_iq_shmem(gl_WorkGroupSize); + + if (ib >= p.nel / 256) { + return; + } + + const uint is = gl_LocalInvocationID.x % 8; + const uint b_idx = 256 * ib + 32 * is; + + const float d = float(data_a[ib].d); + const float db = d * (1 + 2 * ((data_a[ib].scales[is / 2] >> (4 * (is % 2))) & 0xf)); + + // We must produce 32 values using 4 sign bytes, 1 qh byte, 8 qs bytes. + uint qh = data_a[ib].qh[is]; + [[unroll]] for (uint l = 0; l < 8; ++l) { + const uint iqs = 8 * is + l; + const uint qs = data_a[ib].qs[iqs]; + const uint gidx = qs | ((qh << (8 - l)) & 256); + const uint8_t signs = data_a[ib].signs[iqs / 2] >> (4 * (l & 1)); + const u8vec4 grid = unpack8(iq3s_grid[gidx]); + data_b[b_idx + 4 * l + 0] = D_TYPE(db * grid.x * ((signs & 1) != 0 ? -1.0 : 1.0)); + data_b[b_idx + 4 * l + 1] = D_TYPE(db * grid.y * ((signs & 2) != 0 ? -1.0 : 1.0)); + data_b[b_idx + 4 * l + 2] = D_TYPE(db * grid.z * ((signs & 4) != 0 ? -1.0 : 1.0)); + data_b[b_idx + 4 * l + 3] = D_TYPE(db * grid.w * ((signs & 8) != 0 ? -1.0 : 1.0)); + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq3_xxs.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq3_xxs.comp new file mode 100644 index 0000000000000000000000000000000000000000..671c1f4a0d363170742680dbcb65b8361166ddf8 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq3_xxs.comp @@ -0,0 +1,51 @@ +#version 450 + +#include "dequant_head.glsl" + +layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {block_iq3_xxs data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; + +void main() { + // Each thread handles 1 scale block (32 values) + // 8 threads handle 1 superblock + const uint ib = gl_WorkGroupID.x * 32 + gl_LocalInvocationID.x / 8; + + init_iq_shmem(gl_WorkGroupSize); + + if (ib >= p.nel / 256) { + return; + } + + const uint is = gl_LocalInvocationID.x % 8; + const uint b_idx = 256 * ib + 32 * is; + const uint s_idx = QUANT_K / 4 + 4 * is; + + const float d = float(data_a[ib].d); + uint signscale = pack32(u8vec4( + data_a[ib].qs[s_idx + 0], + data_a[ib].qs[s_idx + 1], + data_a[ib].qs[s_idx + 2], + data_a[ib].qs[s_idx + 3] + )); + const float db = d * (0.5 + (signscale >> 28)) * 0.5; + + [[unroll]] for (uint l = 0; l < 4; ++l) { + const uint sign7 = bitfieldExtract(signscale, 7 * int(l), 7); + // Restore parity bit. + const uint sign8 = sign7 | (bitCount(sign7) << 7); + const uint qs0 = data_a[ib].qs[8 * is + 2 * l]; + const uint qs1 = data_a[ib].qs[8 * is + 2 * l + 1]; + const u8vec4 grid0 = unpack8(iq3xxs_grid[qs0]); + const u8vec4 grid1 = unpack8(iq3xxs_grid[qs1]); + data_b[b_idx + 8 * l + 0] = D_TYPE(db * grid0.x * ((sign8 & 1) != 0 ? -1.0 : 1.0)); + data_b[b_idx + 8 * l + 1] = D_TYPE(db * grid0.y * ((sign8 & 2) != 0 ? -1.0 : 1.0)); + data_b[b_idx + 8 * l + 2] = D_TYPE(db * grid0.z * ((sign8 & 4) != 0 ? -1.0 : 1.0)); + data_b[b_idx + 8 * l + 3] = D_TYPE(db * grid0.w * ((sign8 & 8) != 0 ? -1.0 : 1.0)); + data_b[b_idx + 8 * l + 4] = D_TYPE(db * grid1.x * ((sign8 & 16) != 0 ? -1.0 : 1.0)); + data_b[b_idx + 8 * l + 5] = D_TYPE(db * grid1.y * ((sign8 & 32) != 0 ? -1.0 : 1.0)); + data_b[b_idx + 8 * l + 6] = D_TYPE(db * grid1.z * ((sign8 & 64) != 0 ? -1.0 : 1.0)); + data_b[b_idx + 8 * l + 7] = D_TYPE(db * grid1.w * ((sign8 & 128) != 0 ? -1.0 : 1.0)); + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq4_nl.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq4_nl.comp new file mode 100644 index 0000000000000000000000000000000000000000..8f7833eab2e7065d484ceb0976c759ed5cf1dc27 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq4_nl.comp @@ -0,0 +1,32 @@ +#version 450 + +#include "dequant_head.glsl" + +layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {block_iq4_nl data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; + +void main() { + const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64; + + init_iq_shmem(gl_WorkGroupSize); + + const uint tid = gl_LocalInvocationID.x % 64; + const uint il = tid/32; + const uint ir = tid%32; + const uint ib = 32*i + ir; + if (ib >= p.nel / 32) { + return; + } + + const uint q_idx = 8*il; + const uint b_idx = 1024*i + 32*ir + q_idx; + + const float d = float(data_a[ib].d); + + [[unroll]] for (uint l = 0; l < 8; ++l) { + data_b[b_idx + l + 0] = D_TYPE(d * kvalues_iq4nl[data_a[ib].qs[q_idx + l] & 0xF]); + data_b[b_idx + l + 16] = D_TYPE(d * kvalues_iq4nl[data_a[ib].qs[q_idx + l] >> 4]); + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq4_xs.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq4_xs.comp new file mode 100644 index 0000000000000000000000000000000000000000..a313699775fcdb3a4fd8da3442ec8e8b8bab7e43 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_iq4_xs.comp @@ -0,0 +1,34 @@ +#version 450 + +#include "dequant_head.glsl" + +layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {block_iq4_xs data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; + +void main() { + // Each thread handles 1 subblock (1 scale and 32 quantized values) + const uint ib = gl_WorkGroupID.x * 32 + gl_LocalInvocationID.x / 8; + + init_iq_shmem(gl_WorkGroupSize); + + if (ib >= p.nel / 256) { + return; + } + + const uint ib32 = gl_LocalInvocationID.x % 8; + + const float d = float(data_a[ib].d); + // Scales are 6 bits + const uint scale = ((data_a[ib].scales_l[ib32/2] >> (4 * (ib32 & 1))) & 0xF) + | (((data_a[ib].scales_h >> (2 * ib32)) & 3) << 4); + const float dl = d * (int(scale) - 32); + + const uint b_idx = 256 * ib + 32 * ib32; + const uint q_idx = 16 * ib32; + [[unroll]] for (uint l = 0; l < 16; ++l) { + data_b[b_idx + l + 0] = D_TYPE(dl * kvalues_iq4nl[data_a[ib].qs[q_idx + l] & 0xF]); + data_b[b_idx + l + 16] = D_TYPE(dl * kvalues_iq4nl[data_a[ib].qs[q_idx + l] >> 4]); + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_mxfp4.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_mxfp4.comp new file mode 100644 index 0000000000000000000000000000000000000000..3194ba291f311bd6b9de728f2ce121ff6b0d000b --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_mxfp4.comp @@ -0,0 +1,32 @@ +#version 450 + +#include "dequant_head.glsl" + +layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {block_mxfp4 data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; + +void main() { + const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64; + + init_iq_shmem(gl_WorkGroupSize); + + const uint tid = gl_LocalInvocationID.x % 64; + const uint il = tid/32; + const uint ir = tid%32; + const uint ib = 32*i + ir; + if (ib >= p.nel / 32) { + return; + } + + const uint q_idx = 8*il; + const uint b_idx = 1024*i + 32*ir + q_idx; + + const float d = e8m0_to_fp32(data_a[ib].e); + + [[unroll]] for (uint l = 0; l < 8; ++l) { + data_b[b_idx + l + 0] = D_TYPE(d * 0.5 * float(kvalues_mxfp4[data_a[ib].qs[q_idx + l] & 0xF])); + data_b[b_idx + l + 16] = D_TYPE(d * 0.5 * float(kvalues_mxfp4[data_a[ib].qs[q_idx + l] >> 4])); + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_nvfp4.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_nvfp4.comp new file mode 100644 index 0000000000000000000000000000000000000000..689089160b70ca5a94c4f79cdfce5456da33b407 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_nvfp4.comp @@ -0,0 +1,32 @@ +#version 450 + +#include "dequant_head.glsl" + +layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {block_nvfp4 data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; + +void main() { + const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64; + + init_iq_shmem(gl_WorkGroupSize); + + const uint tid = gl_LocalInvocationID.x % 64; + const uint sub = tid / 16; + const uint ir = tid % 16; + const uint ib = 16 * i + ir; + if (ib >= p.nel / 64) { + return; + } + + const uint q_idx = 8 * sub; + const uint b_idx = 1024 * i + 64 * ir + 16 * sub; + + const float d = ue4m3_to_fp32(data_a[ib].d[sub]); + + [[unroll]] for (uint l = 0; l < 8; ++l) { + data_b[b_idx + l + 0] = D_TYPE(d * 0.5 * float(kvalues_mxfp4[data_a[ib].qs[q_idx + l] & 0xF])); + data_b[b_idx + l + 8] = D_TYPE(d * 0.5 * float(kvalues_mxfp4[data_a[ib].qs[q_idx + l] >> 4])); + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q1_0.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q1_0.comp new file mode 100644 index 0000000000000000000000000000000000000000..ca0bdbc63e0afff65eea45823505114704bfc56f --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q1_0.comp @@ -0,0 +1,29 @@ +#version 450 + +#include "dequant_head.glsl" + +layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {block_q1_0 data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; + +void main() { + const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64; + + const uint tid = gl_LocalInvocationID.x % 64; + const uint il = tid / 4; + const uint ir = tid % 4; + const uint ib = 4*i + ir; + if (ib >= p.nel / 128) { + return; + } + + const uint b_idx = 512*i + 128*ir + 8*il; + + const float d = float(data_a[ib].d); + const uint bits = uint(data_a[ib].qs[il]); + + [[unroll]] for (uint l = 0; l < 8; ++l) { + data_b[b_idx + l] = D_TYPE((bits & (1u << l)) != 0u ? d : -d); + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q2_k.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q2_k.comp new file mode 100644 index 0000000000000000000000000000000000000000..dc05a78348909723e71282b242eaa8de7de69741 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q2_k.comp @@ -0,0 +1,34 @@ +#version 450 + +#include "dequant_head.glsl" + +layout(local_size_x = 64, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; + +void main() { + [[unroll]] for (uint wgy = 0; wgy < 256; wgy++) { + const uint i = gl_WorkGroupID.x * 256 + wgy; + if (i >= p.nel / QUANT_K) { + return; + } + + const uint tid = gl_LocalInvocationID.x; + const uint ip = tid / 32; + const uint il = tid - 32 * ip; + const uint is = 8 * ip + il / 16; + + const uint y_idx = i * QUANT_K + 128 * ip + il; + + const uint ql_idx = 32 * ip + il; + const uint8_t qs = data_a[i].qs[32 * ip + il]; + + FLOAT_TYPE dall = FLOAT_TYPE(data_a[i].dm.x); + FLOAT_TYPE dmin = FLOAT_TYPE(data_a[i].dm.y); + data_b[y_idx + 0] = D_TYPE(dall * FLOAT_TYPE((data_a[i].scales[is+0] & 0xF) * ((qs >> 0) & 3)) - dmin * FLOAT_TYPE(data_a[i].scales[is+0] >> 4)); + data_b[y_idx + 32] = D_TYPE(dall * FLOAT_TYPE((data_a[i].scales[is+2] & 0xF) * ((qs >> 2) & 3)) - dmin * FLOAT_TYPE(data_a[i].scales[is+2] >> 4)); + data_b[y_idx + 64] = D_TYPE(dall * FLOAT_TYPE((data_a[i].scales[is+4] & 0xF) * ((qs >> 4) & 3)) - dmin * FLOAT_TYPE(data_a[i].scales[is+4] >> 4)); + data_b[y_idx + 96] = D_TYPE(dall * FLOAT_TYPE((data_a[i].scales[is+6] & 0xF) * ((qs >> 6) & 3)) - dmin * FLOAT_TYPE(data_a[i].scales[is+6] >> 4)); + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q3_k.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q3_k.comp new file mode 100644 index 0000000000000000000000000000000000000000..0c90be8b4e2540bb0efae9a7dfa43da72cbe2f72 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q3_k.comp @@ -0,0 +1,42 @@ +#version 450 + +#include "dequant_head.glsl" + +layout(local_size_x = 64, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; + +void main() { + [[unroll]] for (uint wgy = 0; wgy < 256; wgy++) { + const uint i = uint(gl_WorkGroupID.x * 256 + wgy); + if (i >= p.nel / QUANT_K) { + return; + } + + const uint r = gl_LocalInvocationID.x / 4; + const uint tid = r / 2; + const uint is0 = r % 2; + const uint l0 = 16 * is0 + 4 * (gl_LocalInvocationID.x % 4); + const uint n = tid / 4; + const uint j = tid - 4*n; + + const uint8_t m = uint8_t(1 << (4*n + j)); + const uint is = 8*n + 2*j + is0; + const uint shift = 2*j; + + const int8_t us = int8_t(is < 4 ? (data_a[i].scales[is-0] & 0xF) | (((data_a[i].scales[is+8] >> 0) & 3) << 4) : + is < 8 ? (data_a[i].scales[is-0] & 0xF) | (((data_a[i].scales[is+4] >> 2) & 3) << 4) : + is < 12 ? (data_a[i].scales[is-8] >> 4) | (((data_a[i].scales[is+0] >> 4) & 3) << 4) : + (data_a[i].scales[is-8] >> 4) | (((data_a[i].scales[is-4] >> 6) & 3) << 4)); + const FLOAT_TYPE d_all = FLOAT_TYPE(data_a[i].d); + const FLOAT_TYPE dl = d_all * FLOAT_TYPE(us - 32); + + const uint y_idx = i * QUANT_K + 128 * n + 32 * j; + const uint qs_idx = 32*n; + + for (uint l = l0; l < l0 + 4; ++l) { + data_b[y_idx + l] = D_TYPE(dl * FLOAT_TYPE(int8_t((data_a[i].qs[qs_idx + l] >> shift) & 3) - (((data_a[i].hmask[l] & m) != 0) ? 0 : 4))); + } + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q4_0.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q4_0.comp new file mode 100644 index 0000000000000000000000000000000000000000..b92b292135b4540f5a94f1c2964cb556554801d6 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q4_0.comp @@ -0,0 +1,30 @@ +#version 450 + +#include "dequant_head.glsl" + +layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {block_q4_0 data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; + +void main() { + const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64; + + const uint tid = gl_LocalInvocationID.x % 64; + const uint il = tid/32; + const uint ir = tid%32; + const uint ib = 32*i + ir; + if (ib >= p.nel / 32) { + return; + } + + const uint q_idx = 8*il; + const uint b_idx = 1024*i + 32*ir + q_idx; + + const float d = float(data_a[ib].d); + + [[unroll]] for (uint l = 0; l < 8; ++l) { + data_b[b_idx + l + 0] = D_TYPE(d * ((data_a[ib].qs[q_idx + l] & 0xF) - 8.0f)); + data_b[b_idx + l + 16] = D_TYPE(d * ((data_a[ib].qs[q_idx + l] >> 4) - 8.0f)); + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q4_1.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q4_1.comp new file mode 100644 index 0000000000000000000000000000000000000000..6b63cbe5833bdde504cfc67d0731cdb9a18f95fb --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q4_1.comp @@ -0,0 +1,32 @@ +#version 450 + +#include "dequant_head.glsl" + +layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {block_q4_1 data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; + +void main() { + const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64; + + const uint tid = gl_LocalInvocationID.x % 64; + const uint il = tid/32; + const uint ir = tid%32; + const uint ib = 32*i + ir; + if (ib >= p.nel / 32) { + return; + } + + const uint b_idx = 1024*i + 32*ir + 8*il; + + const float d = float(data_a[ib].d); + const float m = float(data_a[ib].m); + + const uint q_idx = 8*il; + + [[unroll]] for (uint l = 0; l < 8; ++l) { + data_b[b_idx + l + 0] = D_TYPE(d * (data_a[ib].qs[q_idx + l] & 0xF) + m); + data_b[b_idx + l + 16] = D_TYPE(d * (data_a[ib].qs[q_idx + l] >> 4) + m); + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q4_k.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q4_k.comp new file mode 100644 index 0000000000000000000000000000000000000000..0f23dc0a349f62618c389f278cab8a855ce21bfe --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q4_k.comp @@ -0,0 +1,68 @@ +#version 450 + +#include "dequant_head.glsl" + +layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; + +void main() { + [[unroll]] for (uint wgy = 0; wgy < 256; wgy++) { + const uint ib = gl_WorkGroupID.x * 256 + wgy; + if (ib >= p.nel / QUANT_K) { + return; + } + + const uint tid = gl_LocalInvocationID.x; + const uint il = tid / 8; + const uint ir = tid % 8; + const uint is = 2 * il; + const uint n = 4; + + const FLOAT_TYPE dall = FLOAT_TYPE(data_a[ib].dm.x); + const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[ib].dm.y); + + const uint y_idx = ib * QUANT_K + 64 * il + n * ir; + const uint qs_idx = 32*il + n * ir; + + uint scidx0 = (is < 4) ? is : (is + 4); + uint scidx1 = (is < 4) ? is : (is - 4); + uint scidxmask1 = (is < 4) ? 0x30 : 0xC0; + uint scidxshift1 = (is < 4) ? 0 : 2; + uint mbidx0 = is + 4; + uint mbidx1 = (is < 4) ? is + 4 : is; + uint mbidxmask0 = (is < 4) ? 0xF : 0xF0; + uint mbidxshift0 = (is < 4) ? 0 : 4; + uint mbidxmask1 = (is < 4) ? 0x30 : 0xC0; + uint mbidxshift1 = (is < 4) ? 0 : 2; + + uint8_t sc = uint8_t((data_a[ib].scales[scidx0] & 0xF) | ((data_a[ib].scales[scidx1] & scidxmask1) >> scidxshift1)); + uint8_t mbyte = uint8_t((data_a[ib].scales[mbidx0] & mbidxmask0) >> mbidxshift0 | ((data_a[ib].scales[mbidx1] & mbidxmask1) >> mbidxshift1)); + + const FLOAT_TYPE d1 = dall * sc; + const FLOAT_TYPE m1 = dmin * mbyte; + + scidx0 = (is < 4) ? is + 1 : (is + 5); + scidx1 = (is < 4) ? is + 1 : (is - 3); + scidxmask1 = (is < 4) ? 0x30 : 0xC0; + scidxshift1 = (is < 4) ? 0 : 2; + mbidx0 = is + 5; + mbidx1 = (is < 4) ? is + 5 : is + 1; + mbidxmask0 = (is < 4) ? 0xF : 0xF0; + mbidxshift0 = (is < 4) ? 0 : 4; + mbidxmask1 = (is < 4) ? 0x30 : 0xC0; + mbidxshift1 = (is < 4) ? 0 : 2; + + sc = uint8_t((data_a[ib].scales[scidx0] & 0xF) | ((data_a[ib].scales[scidx1] & scidxmask1) >> scidxshift1)); + mbyte = uint8_t((data_a[ib].scales[mbidx0] & mbidxmask0) >> mbidxshift0 | ((data_a[ib].scales[mbidx1] & mbidxmask1) >> mbidxshift1)); + + const FLOAT_TYPE d2 = dall * sc; + const FLOAT_TYPE m2 = dmin * mbyte; + + [[unroll]] for (uint l = 0; l < n; ++l) { + data_b[y_idx + l ] = D_TYPE(d1 * FLOAT_TYPE(data_a[ib].qs[qs_idx + l] & 0xF) - m1); + data_b[y_idx + l + 32] = D_TYPE(d2 * FLOAT_TYPE(data_a[ib].qs[qs_idx + l] >> 4) - m2); + } + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q5_0.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q5_0.comp new file mode 100644 index 0000000000000000000000000000000000000000..f1b0bac872712c82f787391d89252e080f58b9e7 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q5_0.comp @@ -0,0 +1,34 @@ +#version 450 + +#include "dequant_head.glsl" + +layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {block_q5_0 data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; + +void main() { + const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64; + + const uint tid = gl_LocalInvocationID.x % 64; + const uint il = tid/32; + const uint ir = tid%32; + const uint ib = 32*i + ir; + if (ib >= p.nel / 32) { + return; + } + + const uint b_idx = 1024*i + 32*ir + 8*il; + + const float d = float(data_a[ib].d); + const uint qh = uint(data_a[ib].qh[1]) << 16 | data_a[ib].qh[0]; + + const uint q_idx = 8*il; + + [[unroll]] for (uint l = 0; l < 8; ++l) { + const uint iqs = q_idx + l; + const uint vui = uint(data_a[ib].qs[iqs]); + data_b[b_idx + l + 0] = D_TYPE(d * (((vui & 0xF) | (((qh >> iqs) << 4) & 0x10)) - 16.0f)); + data_b[b_idx + l + 16] = D_TYPE(d * (((vui >> 4) | ((qh >> (iqs + 12)) & 0x10)) - 16.0f)); + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q5_1.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q5_1.comp new file mode 100644 index 0000000000000000000000000000000000000000..c495b31f175426098f6e5bf06d37faca701e60d2 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q5_1.comp @@ -0,0 +1,35 @@ +#version 450 + +#include "dequant_head.glsl" + +layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {block_q5_1 data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; + +void main() { + const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64; + + const uint tid = gl_LocalInvocationID.x % 64; + const uint il = tid/32; + const uint ir = tid%32; + const uint ib = 32*i + ir; + if (ib >= p.nel / 32) { + return; + } + + const uint b_idx = 1024*i + 32*ir + 8*il; + + const float d = float(data_a[ib].d); + const float m = float(data_a[ib].m); + const uint qh = data_a[ib].qh; + + const uint q_idx = 8*il; + + [[unroll]] for (uint l = 0; l < 8; ++l) { + const uint iqs = q_idx + l; + const uint vui = uint(data_a[ib].qs[iqs]); + data_b[b_idx + l + 0] = D_TYPE(d * (((vui & 0xF) | (((qh >> iqs) << 4) & 0x10))) + m); + data_b[b_idx + l + 16] = D_TYPE(d * (((vui >> 4) | ((qh >> (iqs + 12)) & 0x10))) + m); + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q5_k.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q5_k.comp new file mode 100644 index 0000000000000000000000000000000000000000..970469a601cc654dcf848efa86638c675c530ada --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q5_k.comp @@ -0,0 +1,70 @@ +#version 450 + +#include "dequant_head.glsl" + +layout(local_size_x = 64, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; + +void main() { + [[unroll]] for (uint wgy = 0; wgy < 256; wgy++) { + const uint ib = gl_WorkGroupID.x * 256 + wgy; + if (ib >= p.nel / QUANT_K) { + return; + } + + const uint tid = gl_LocalInvocationID.x; + const uint il = tid / 16; + const uint ir = tid % 16; + const uint is = 2 * il; + + const FLOAT_TYPE dall = FLOAT_TYPE(data_a[ib].dm.x); + const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[ib].dm.y); + + const uint y_idx = ib * QUANT_K + 64 * il + 2 * ir; + const uint qs_idx = 32*il + 2 * ir; + const uint qh_idx = 2 * ir; + + uint scidx0 = (is < 4) ? is : (is + 4); + uint scidx1 = (is < 4) ? is : (is - 4); + uint scidxmask1 = (is < 4) ? 0x30 : 0xC0; + uint scidxshift1 = (is < 4) ? 0 : 2; + uint mbidx0 = is + 4; + uint mbidx1 = (is < 4) ? is + 4 : is; + uint mbidxmask0 = (is < 4) ? 0xF : 0xF0; + uint mbidxshift0 = (is < 4) ? 0 : 4; + uint mbidxmask1 = (is < 4) ? 0x30 : 0xC0; + uint mbidxshift1 = (is < 4) ? 0 : 2; + + uint8_t sc = uint8_t((data_a[ib].scales[scidx0] & 0xF) | ((data_a[ib].scales[scidx1] & scidxmask1) >> scidxshift1)); + uint8_t mbyte = uint8_t((data_a[ib].scales[mbidx0] & mbidxmask0) >> mbidxshift0 | ((data_a[ib].scales[mbidx1] & mbidxmask1) >> mbidxshift1)); + + const FLOAT_TYPE d1 = dall * sc; + const FLOAT_TYPE m1 = dmin * mbyte; + + scidx0 = (is < 4) ? is + 1 : (is + 5); + scidx1 = (is < 4) ? is + 1 : (is - 3); + scidxmask1 = (is < 4) ? 0x30 : 0xC0; + scidxshift1 = (is < 4) ? 0 : 2; + mbidx0 = is + 5; + mbidx1 = (is < 4) ? is + 5 : is + 1; + mbidxmask0 = (is < 4) ? 0xF : 0xF0; + mbidxshift0 = (is < 4) ? 0 : 4; + mbidxmask1 = (is < 4) ? 0x30 : 0xC0; + mbidxshift1 = (is < 4) ? 0 : 2; + + sc = uint8_t((data_a[ib].scales[scidx0] & 0xF) | ((data_a[ib].scales[scidx1] & scidxmask1) >> scidxshift1)); + mbyte = uint8_t((data_a[ib].scales[mbidx0] & mbidxmask0) >> mbidxshift0 | ((data_a[ib].scales[mbidx1] & mbidxmask1) >> mbidxshift1)); + + const FLOAT_TYPE d2 = dall * sc; + const FLOAT_TYPE m2 = dmin * mbyte; + + const uint8_t hm1 = uint8_t(1 << (2 * il )); + const uint8_t hm2 = uint8_t(1 << (2 * il + 1)); + data_b[y_idx ] = D_TYPE(d1 * FLOAT_TYPE((data_a[ib].qs[qs_idx ] & 0xF) + (((data_a[ib].qh[qh_idx ] & hm1) != 0) ? 16 : 0)) - m1); + data_b[y_idx + 1] = D_TYPE(d1 * FLOAT_TYPE((data_a[ib].qs[qs_idx + 1] & 0xF) + (((data_a[ib].qh[qh_idx + 1] & hm1) != 0) ? 16 : 0)) - m1); + data_b[y_idx + 32] = D_TYPE(d2 * FLOAT_TYPE((data_a[ib].qs[qs_idx ] >> 4) + (((data_a[ib].qh[qh_idx ] & hm2) != 0) ? 16 : 0)) - m2); + data_b[y_idx + 33] = D_TYPE(d2 * FLOAT_TYPE((data_a[ib].qs[qs_idx + 1] >> 4) + (((data_a[ib].qh[qh_idx + 1] & hm2) != 0) ? 16 : 0)) - m2); + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q6_k.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q6_k.comp new file mode 100644 index 0000000000000000000000000000000000000000..c8d6fcb49fcaf76a5261ba9674f1ea06d95fa2de --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q6_k.comp @@ -0,0 +1,33 @@ +#version 450 + +#include "dequant_head.glsl" + +layout(local_size_x = 64, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; + +void main() { + [[unroll]] for (uint wgy = 0; wgy < 256; wgy++) { + const uint i = gl_WorkGroupID.x * 256 + wgy; + if (i >= p.nel / QUANT_K) { + return; + } + const uint tid = gl_LocalInvocationID.x; + const uint ip = tid / 32; + const uint il = tid - 32 * ip; + const uint is = 8 * ip + il / 16; + + const uint y_idx = i * QUANT_K + 128 * ip + il; + + const uint ql_idx = 64 * ip + il; + const uint8_t qh = data_a[i].qh[32 * ip + il]; + + const FLOAT_TYPE d = FLOAT_TYPE(data_a[i].d); + + data_b[y_idx + 0] = D_TYPE(d * FLOAT_TYPE(data_a[i].scales[is + 0] * (int8_t((data_a[i].ql[ql_idx + 0] & 0xF) | (((qh >> 0) & 3) << 4)) - 32))); + data_b[y_idx + 32] = D_TYPE(d * FLOAT_TYPE(data_a[i].scales[is + 2] * (int8_t((data_a[i].ql[ql_idx + 32] & 0xF) | (((qh >> 2) & 3) << 4)) - 32))); + data_b[y_idx + 64] = D_TYPE(d * FLOAT_TYPE(data_a[i].scales[is + 4] * (int8_t((data_a[i].ql[ql_idx + 0] >> 4) | (((qh >> 4) & 3) << 4)) - 32))); + data_b[y_idx + 96] = D_TYPE(d * FLOAT_TYPE(data_a[i].scales[is + 6] * (int8_t((data_a[i].ql[ql_idx + 32] >> 4) | (((qh >> 6) & 3) << 4)) - 32))); + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q8_0.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q8_0.comp new file mode 100644 index 0000000000000000000000000000000000000000..10844ddf7813b71f69a90cbaea06fa0df363bd88 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dequant_q8_0.comp @@ -0,0 +1,31 @@ +#version 450 + +#include "dequant_head.glsl" + +layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {block_q8_0 data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; + +void main() { + const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64; + + const uint tid = gl_LocalInvocationID.x % 64; + const uint il = tid/32; + const uint ir = tid%32; + const uint ib = 32*i + ir; + if (ib >= p.nel / 32) { + return; + } + + const uint b_idx = 1024*i + 32*ir + 16*il; + + const float d = float(data_a[ib].d); + + const uint q_idx = 16*il; + + [[unroll]] for (uint l = 0; l < 16; l += 2) { + data_b[b_idx + l ] = D_TYPE(d * data_a[ib].qs[q_idx + l ]); + data_b[b_idx + l + 1] = D_TYPE(d * data_a[ib].qs[q_idx + l + 1]); + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/diag.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/diag.comp new file mode 100644 index 0000000000000000000000000000000000000000..249e6b16eef1bfe3ed4ea15c7e5746c78b85688f --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/diag.comp @@ -0,0 +1,28 @@ +#version 450 + +#include "types.glsl" +#include "generic_unary_head.glsl" + +layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; + +void main() { + const uint idx = get_idx(); + + if (idx >= p.ne) { + return; + } + + const uint i13 = fastdiv(idx, p.ne1_012mp, fastdiv_L(p.ne1_Ls, 0)); + const uint i13_offset = i13 * p.ne12*p.ne11*p.ne10; + const uint i12 = fastdiv(idx - i13_offset, p.ne1_01mp, fastdiv_L(p.ne1_Ls, 1)); + const uint i12_offset = i12*p.ne11*p.ne10; + const uint i11 = fastdiv(idx - i13_offset - i12_offset, p.ne1_0mp, fastdiv_L(p.ne1_Ls, 2)); + const uint i10 = idx - i13_offset - i12_offset - i11*p.ne10; + + if (i10 == i11) { + const float val = float(data_a[get_aoffset() + i13*p.nb03 + i12*p.nb02 + 0*p.nb01 + i10*p.nb00]); + data_d[get_doffset() + dst_idx(idx)] = D_TYPE(val); + } else { + data_d[get_doffset() + dst_idx(idx)] = D_TYPE(0); + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/diag_mask_inf.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/diag_mask_inf.comp new file mode 100644 index 0000000000000000000000000000000000000000..9cef8a8ec3d2af502cf24585209296b04c69f1bc --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/diag_mask_inf.comp @@ -0,0 +1,34 @@ +#version 450 + +#extension GL_EXT_shader_16bit_storage : require +#extension GL_EXT_control_flow_attributes : enable + +layout (push_constant) uniform parameter +{ + uint ncols; + uint rows_per_channel; + uint n_past; +} p; + +#include "types.glsl" + +layout(local_size_x = 1, local_size_y = 512, local_size_z = 1) in; + +layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_d[];}; + +void main() { + const uint col = gl_GlobalInvocationID.y; + const uint row = gl_GlobalInvocationID.x; + + if (col >= p.ncols) { + return; + } + + const uint i = row*p.ncols + col; + if (col > p.n_past + row % p.rows_per_channel) { + data_d[i] = D_TYPE(uintBitsToFloat(0xFF800000)); + } else { + data_d[i] = D_TYPE(data_a[i]); + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/div.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/div.comp new file mode 100644 index 0000000000000000000000000000000000000000..572472f8a941cef2db603253ee1cab4077843e99 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/div.comp @@ -0,0 +1,27 @@ +#version 450 + +#include "types.glsl" +#include "generic_binary_head.glsl" + +const uint num_threads = 256; + +layout(local_size_x = num_threads, local_size_y = 1, local_size_z = 1) in; + +void main() { + uint idx = get_idx(); + + // num_threads * num_iter must equal 512, to match the wg_denoms and get_idx calculation + const uint num_iter = 2; + + [[unroll]] for (uint i = 0; i < num_iter; ++i) { + if (idx >= p.ne) { + continue; + } + uint i00, i01, i02, i03; + get_indices(idx, i00, i01, i02, i03); + + data_d[get_doffset() + dst_idx(i00, i01, i02, i03)] = D_TYPE(FLOAT_TYPE(data_a[get_aoffset() + src0_idx(i00, i01, i02, i03)]) / FLOAT_TYPE(data_b[get_boffset() + src1_idx(i00, i01, i02, i03)])); + + idx += num_threads; + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dot_product_funcs.glsl b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dot_product_funcs.glsl new file mode 100644 index 0000000000000000000000000000000000000000..c474bfe09ce99b8a74f3048bc4fdbe4854a9b098 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/dot_product_funcs.glsl @@ -0,0 +1,27 @@ +#ifdef DOT2_F16 +#extension GL_EXT_spirv_intrinsics : require + +spirv_instruction(extensions = ["SPV_VALVE_mixed_float_dot_product"], + capabilities = [6912], id = 6916) +float v_dot2_f32_f16(f16vec2 a, f16vec2 b, float acc); + +ACC_TYPE dot_product(f16vec4 a, f16vec4 b, ACC_TYPE acc) { + return ACC_TYPE(v_dot2_f32_f16(a.zw, b.zw, v_dot2_f32_f16(a.xy, b.xy, float(acc)))); +} + +ACC_TYPE dot_product(f16vec2 a, f16vec2 b, ACC_TYPE acc) { + return ACC_TYPE(v_dot2_f32_f16(a, b, float(acc))); +} + +#else + +ACC_TYPE dot_product(FLOAT_TYPEV4 a, FLOAT_TYPEV4 b, ACC_TYPE acc) { + return fma(ACC_TYPE(a.x), ACC_TYPE(b.x), fma(ACC_TYPE(a.y), ACC_TYPE(b.y), + fma(ACC_TYPE(a.z), ACC_TYPE(b.z), fma(ACC_TYPE(a.w), ACC_TYPE(b.w), acc)))); +} + +ACC_TYPE dot_product(FLOAT_TYPEV2 a, FLOAT_TYPEV2 b, ACC_TYPE acc) { + return fma(ACC_TYPE(a.x), ACC_TYPE(b.x), fma(ACC_TYPE(a.y), ACC_TYPE(b.y), acc)); +} + +#endif diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/feature-tests/bfloat16.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/feature-tests/bfloat16.comp new file mode 100644 index 0000000000000000000000000000000000000000..fd0ba401feb0cbf2ef08daa5e686f3d93620a81a --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/feature-tests/bfloat16.comp @@ -0,0 +1,7 @@ +#version 460 + +#extension GL_EXT_bfloat16 : require + +void main() +{ +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/feature-tests/coopmat.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/feature-tests/coopmat.comp new file mode 100644 index 0000000000000000000000000000000000000000..8c5dd1bd1679c8476ae9815aad549a5e0dfa980f --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/feature-tests/coopmat.comp @@ -0,0 +1,7 @@ +#version 460 + +#extension GL_KHR_cooperative_matrix : require + +void main() +{ +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/feature-tests/coopmat2.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/feature-tests/coopmat2.comp new file mode 100644 index 0000000000000000000000000000000000000000..28eb24e11f871cdb7e1413757181ce3cdb3ed481 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/feature-tests/coopmat2.comp @@ -0,0 +1,7 @@ +#version 460 + +#extension GL_NV_cooperative_matrix2 : require + +void main() +{ +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/feature-tests/coopmat2_decode_vector.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/feature-tests/coopmat2_decode_vector.comp new file mode 100644 index 0000000000000000000000000000000000000000..65e9c678401b93e5329a2bc3d2c0d2e430b12f4f --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/feature-tests/coopmat2_decode_vector.comp @@ -0,0 +1,7 @@ +#version 460 + +#extension GL_NV_cooperative_matrix_decode_vector : require + +void main() +{ +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/feature-tests/integer_dot.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/feature-tests/integer_dot.comp new file mode 100644 index 0000000000000000000000000000000000000000..470e3074d938adb636135cd91eeda75766c20474 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/feature-tests/integer_dot.comp @@ -0,0 +1,7 @@ +#version 460 + +#extension GL_EXT_integer_dot_product : require + +void main() +{ +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/fill.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/fill.comp new file mode 100644 index 0000000000000000000000000000000000000000..a56be76c61c5a078ea6aa094a8a7b9f39cd2c3a6 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/fill.comp @@ -0,0 +1,19 @@ +#version 450 + +#include "generic_head.glsl" +#include "types.glsl" + +layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) writeonly buffer D {D_TYPE data_d[];}; + +void main() { + const uint i = gl_GlobalInvocationID.x; + + if (i >= p.KX) { + return; + } + + // p.param1 = fill value + data_d[i] = D_TYPE(p.param1); +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp new file mode 100644 index 0000000000000000000000000000000000000000..3192130ccf57ae1f3c9a7954f94763f00066d22b --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp @@ -0,0 +1,758 @@ +#version 450 + +#extension GL_EXT_control_flow_attributes : enable +#extension GL_EXT_shader_16bit_storage : require + +#extension GL_EXT_shader_explicit_arithmetic_types_int32 : require + +#ifdef FLOAT16 +#extension GL_EXT_shader_explicit_arithmetic_types_float16 : require +#extension GL_EXT_shader_subgroup_extended_types_float16 : require +#endif + +#ifdef MMQ +#extension GL_EXT_integer_dot_product : require +#extension GL_KHR_shader_subgroup_clustered : require + +#include "mul_mmq_shmem_types.glsl" +#endif + +#extension GL_KHR_shader_subgroup_shuffle : enable +#extension GL_KHR_shader_subgroup_vote : enable + +#include "types.glsl" +#include "dot_product_funcs.glsl" +#include "flash_attn_base.glsl" +#include "flash_attn_dequant.glsl" + +const uint32_t HSK_per_thread = HSK / D_split; +const uint32_t HSV_per_thread = HSV / D_split; + +const uint32_t rows_per_thread = Br / row_split; +const uint32_t cols_per_iter = WorkGroupSize / D_split / row_split; +const uint32_t cols_per_thread = Bc / cols_per_iter; +const uint32_t num_subgroups = SubGroupSize == 0 ? 0 : WorkGroupSize / SubGroupSize; + + +layout (binding = 0) readonly buffer Q {float data_q[];}; +layout (binding = 0) readonly buffer QV4 {vec4 data_qv4[];}; +layout (binding = 1) readonly buffer K {float16_t data_k[];}; +layout (binding = 1) readonly buffer KV4 {f16vec4 data_kv4[];}; +layout (binding = 2) readonly buffer V {float16_t data_v[];}; +layout (binding = 2) readonly buffer VV4 {f16vec4 data_vv4[];}; +layout (binding = 3) readonly buffer M {float16_t data_m[];}; + +// If SubGroupSize is set to 0 then only use shmem reductions +const uint32_t tmpsh_size = (SubGroupSize > 0) ? (row_split == 1 ? num_subgroups * D_split : num_subgroups) : WorkGroupSize; +shared float tmpsh[tmpsh_size]; +shared FLOAT_TYPEV4 tmpshv4[tmpsh_size]; + +const uint32_t masksh_stride = Br + 1; +shared FLOAT_TYPE masksh[Bc * masksh_stride]; + +#ifndef MMQ +const uint32_t qf_stride = HSK / 4 + 1; +shared FLOAT_TYPEV4 Qf[Br * qf_stride]; +#else + +const uint32_t qf_stride = HSK / 32; +shared block_b_cache Qf[Br * qf_stride]; +#endif + +#ifndef MMQ +const uint32_t D = HSK > HSV ? HSK : HSV; +#else +const uint32_t D = HSV; +#endif +const uint32_t kvsh_stride = D / 4 + 1; +shared FLOAT_TYPEV4 kvsh[SHMEM_STAGING != 0 ? Bc * kvsh_stride : 1]; + +#ifdef MMQ + +shared block_a_cache kblocksh[SHMEM_STAGING != 0 ? Bc * qf_stride : 1]; +#endif + +shared vec4 occupancy_limiter[LIMIT_OCCUPANCY_SHMEM > 0 ? LIMIT_OCCUPANCY_SHMEM : 1]; + +#ifdef MMQ +#include "flash_attn_mmq_funcs.glsl" +#endif + +void main() { +#ifdef NEEDS_INIT_IQ_SHMEM + init_iq_shmem(gl_WorkGroupSize); +#endif + + init_indices(); + + const uint32_t tid = gl_LocalInvocationIndex; + const uint32_t threads_per_rowgroup = gl_WorkGroupSize.x / row_split; + const uint32_t row_tid = gl_LocalInvocationIndex / threads_per_rowgroup; + const uint32_t rowgroup_tid = gl_LocalInvocationIndex % threads_per_rowgroup; + const uint32_t d_tid = gl_LocalInvocationIndex % D_split; + const uint32_t col_tid = (gl_LocalInvocationIndex % threads_per_rowgroup) / D_split; + + if (LIMIT_OCCUPANCY_SHMEM > 0) { + // This just exists to avoid the occupancy_limiter array getting optimized out + occupancy_limiter[tid] = vec4(tid); + + barrier(); + + if (occupancy_limiter[tid] == vec4(99999.0)) { + data_ov4[0] = D_TYPEV4(occupancy_limiter[tid]); + } + } + +#define tile_row(r) (row_tid * rows_per_thread + (r)) + + uint32_t q_offset = gqa_iq1*p.nb01 + (iq2*p.nb02 + iq3*p.nb03) / 4; + + [[unroll]] for (uint32_t idx = 0; idx < Br * HSK / 4; idx += gl_WorkGroupSize.x) { + uint32_t d = (idx + tid) % (HSK / 4); + uint32_t r = (idx + tid) / (HSK / 4); + const bool is_in_bounds = r < Br && d < HSK / 4 && i * Br + r < N; +#ifndef MMQ + if (is_in_bounds) { + Qf[r * qf_stride + d] = FLOAT_TYPEV4(data_qv4[q_offset / 4 + (i * Br + r) * q_stride / 4 + d] * p.scale); + } +#else + const uint buf_ib = r * qf_stride + d / 8; + const uint buf_iqs = d % 8; + + FLOAT_TYPEV4 vals = is_in_bounds ? FLOAT_TYPEV4(data_qv4[q_offset / 4 + (i * Br + r) * q_stride / 4 + d] * p.scale) : FLOAT_TYPEV4(0.0f); + const FLOAT_TYPEV4 abs_vals = abs(vals); + + const FLOAT_TYPE thread_max = max(max(abs_vals.x, abs_vals.y), max(abs_vals.z, abs_vals.w)); + const FLOAT_TYPE amax = subgroupClusteredMax(thread_max, 8); + const FLOAT_TYPE qd = amax / FLOAT_TYPE(127.0); + const FLOAT_TYPE qd_inv = qd != FLOAT_TYPE(0.0) ? FLOAT_TYPE(1.0) / qd : FLOAT_TYPE(0.0); + vals = round(vals * qd_inv); + + Qf[buf_ib].qs[buf_iqs] = pack32(i8vec4(vals)); + + // Q8_0 K only needs (qd, _); the asymmetric Q4_*/Q5_* family also stores + // the row-sum scaled by qd, used in k_dot_correction. + if (FaTypeK == FA_TYPE_Q8_0) { + if (buf_iqs == 0) { + Qf[buf_ib].ds = FLOAT_TYPEV2(qd, 0.0); + } + } else { + const FLOAT_TYPE thread_sum = vals.x + vals.y + vals.z + vals.w; + const FLOAT_TYPE sum = subgroupClusteredAdd(thread_sum, 8); + + if (buf_iqs == 0) { + Qf[buf_ib].ds = FLOAT_TYPEV2(qd, sum * qd); + } + } +#endif + } + barrier(); + + FLOAT_TYPEV4 Of[rows_per_thread][HSV_per_thread / 4]; + [[unroll]] for (uint32_t d = 0; d < HSV_per_thread / 4; ++d) { + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + Of[r][d] = FLOAT_TYPEV4(0.0); + } + } + + float Lf[rows_per_thread], Mf[rows_per_thread]; + + // Use -FLT_MAX/2 rather than -inf to reduce the possibility of NaNs, e.g. when computing Mold-M. + const float NEG_FLT_MAX_OVER_2 = uintBitsToFloat(0xFEFFFFFF); + + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + Lf[r] = 0; + Mf[r] = NEG_FLT_MAX_OVER_2; + } + + ACC_TYPE slope[rows_per_thread]; + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + slope[r] = ACC_TYPE(1.0); + } + + // ALiBi + if (p.max_bias > 0.0f) { + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + slope[r] = perElemOpComputeSlope(tile_row(r), col_tid, ACC_TYPE(0), iq2); + } + } + + const uint32_t mo_stride = CEIL_DIV(KV, 16 * Bc); + // mo_offset will point to the tile starting at row i*Br and col 0 + uint32_t mo_offset = mo_stride * i; + + // FaBlockBytesK/V == 2 for f16, 16 for f32, ggml block byte size for quants. + uint32_t k_offset = (ik2*p.nb12 + ik3*p.nb13) / FaBlockBytesK; + uint32_t v_offset = (iv2*p.nb22 + iv3*p.nb23) / FaBlockBytesV; + uint32_t m_offset = gqa_iq1*KV; + if (p.nem2 != 1 || p.nem3 != 1) { + m_offset += ((iq3 % p.nem3) * p.nem2 + (iq2 % p.nem2)) * p.nem1 * KV; + mo_offset += ((iq3 % p.nem3) * p.nem2 + (iq2 % p.nem2)) * CEIL_DIV(p.nem1, Br) * mo_stride; + } + + uint32_t mask_opt = 0; + uint32_t mask_opt_idx = ~0; + uint32_t mask_opt_bits = 0; + + [[dont_unroll]] + for (uint32_t j = start_j; j < end_j; ++j) { + if (MASK_ENABLE) { + if (USE_MASK_OPT && mask_opt_idx != j / 16) { + mask_opt_idx = j / 16; + mask_opt = data_mask_opt[mo_offset + mask_opt_idx]; + } + mask_opt_bits = (mask_opt >> ((j % 16) * 2)) & 0x3; + if (mask_opt_bits == MASK_OPT_ALL_NEG_INF) { + // skip this block + continue; + } + // Only load if the block is not all zeros + if (mask_opt_bits != MASK_OPT_ALL_ZERO) { + bool nem1_bounds_check = !(p.gqa_ratio > 1) && (p.nem1 % Br) != 0; + + float max_mask = NEG_FLT_MAX_OVER_2; + barrier(); + [[unroll]] for (uint32_t idx = 0; idx < Bc * Br; idx += gl_WorkGroupSize.x) { + uint32_t c = (idx + tid) % Bc; + uint32_t r = (idx + tid) / Bc; + if (idx + tid < Bc * Br) { + if ((!KV_bounds_check || j * Bc + c < KV) && (!nem1_bounds_check || i * Br + r < p.nem1)) { + FLOAT_TYPE m = FLOAT_TYPE(data_m[m_offset + (i * Br + r) * m_stride + (j * Bc + c)]); + masksh[c * masksh_stride + r] = m; + max_mask = max(max_mask, float(m)); + } else { + masksh[c * masksh_stride + r] = FLOAT_TYPE(0); + } + } + } + // skip the block if the mask is entirely -inf + bool all_less = subgroupAll(max_mask <= NEG_FLT_MAX_OVER_2); + barrier(); + if (gl_SubgroupInvocationID == 0) { + tmpsh[gl_SubgroupID] = all_less ? NEG_FLT_MAX_OVER_2 : 0.0f; + } + barrier(); + [[unroll]] for (uint s = 0; s < gl_NumSubgroups; ++s) { + max_mask = max(max_mask, tmpsh[s]); + } + if (max_mask <= NEG_FLT_MAX_OVER_2) { + continue; + } + } + } + + ACC_TYPE Sf[rows_per_thread][cols_per_thread]; + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + [[unroll]] for (uint32_t c = 0; c < cols_per_thread; ++c) { + Sf[r][c] = ACC_TYPE(0.0); + } + } + + if (SHMEM_STAGING != 0) { + barrier(); +#ifndef MMQ + [[unroll]] for (uint32_t idx = 0; idx < Bc * HSK / 4; idx += gl_WorkGroupSize.x) { + uint32_t d = (idx + tid) % (HSK / 4); + uint32_t c = (idx + tid) / (HSK / 4); + if (idx + gl_WorkGroupSize.x <= Bc * HSK / 4 || c < Bc) { + FLOAT_TYPEV4 K_Tf = FLOAT_TYPEV4(0); + if (!KV_bounds_check || j * Bc + c < KV) { + if (USE_DECODE_K) { + uint coord = (j * Bc + c) * k_stride * BLOCK_SIZE_K + 4 * d; + uint ib = coord / BLOCK_SIZE_K; + uint iqs = (coord % BLOCK_SIZE_K); + K_Tf = dequantize4(ib, iqs, k_offset, BINDING_IDX_K); + } else { + K_Tf = FLOAT_TYPEV4(data_kv4[k_offset / 4 + (j * Bc + c) * k_stride / 4 + d]); + } + } + + kvsh[c * kvsh_stride + d] = K_Tf; + } + } +#else // MMQ + const uint ints_per_block = 8u / fa_quant_r_mmq(FaTypeK); + const uint quant_iters = Bc * HSK / 32 * ints_per_block; + [[unroll]] for (uint32_t idx = 0; idx < quant_iters; idx += gl_WorkGroupSize.x) { + const uint32_t iqs = (idx + tid) % ints_per_block; + const uint32_t ib = (idx + tid) / ints_per_block; + const uint32_t c = ib / (HSK / 32); + const uint32_t block = ib % (HSK / 32); + if (idx + gl_WorkGroupSize.x <= quant_iters || c < Bc) { + const uint buf_ib = c * qf_stride + block; + if (!KV_bounds_check || j * Bc + c < KV) { + const uint global_ib = (j * Bc + c) * k_stride + block; + k_block_to_shmem(buf_ib, global_ib, iqs, k_offset); + } else { + k_block_to_shmem_zero(buf_ib, iqs); + } + } + } +#endif // MMQ + barrier(); + } + +#ifndef MMQ + // More d iterations means Q register caching becomes relevant + // Few iterations means the additional registers needed are worse than the speed-up from caching + if (HSK_per_thread / 4 > 4) { + [[unroll]] for (uint32_t d = 0; d < HSK_per_thread / 4; ++d) { + FLOAT_TYPEV4 Q_cache[rows_per_thread]; + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + Q_cache[r] = Qf[tile_row(r) * qf_stride + d * D_split + d_tid]; + } + + [[unroll]] for (uint32_t c = 0; c < cols_per_thread; ++c) { + if (KV_bounds_check && j * Bc + c * cols_per_iter + col_tid >= KV) { + continue; + } + + FLOAT_TYPEV4 K_Tf; + if (SHMEM_STAGING != 0) { + K_Tf = kvsh[(c * cols_per_iter + col_tid) * kvsh_stride + (d * D_split + d_tid)]; + } else if (USE_DECODE_K) { + uint coord = (j * Bc + c * cols_per_iter + col_tid) * k_stride * BLOCK_SIZE_K + 4 * (d * D_split + d_tid); + uint ib = coord / BLOCK_SIZE_K; + uint iqs = (coord % BLOCK_SIZE_K); + K_Tf = dequantize4(ib, iqs, k_offset, BINDING_IDX_K); + } else { + K_Tf = FLOAT_TYPEV4(data_kv4[k_offset / 4 + (j * Bc + c * cols_per_iter + col_tid) * k_stride / 4 + d * D_split + d_tid]); + } + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + Sf[r][c] = dot_product(Q_cache[r], K_Tf, Sf[r][c]); + } + } + } + } else { + [[unroll]] for (uint32_t c = 0; c < cols_per_thread; ++c) { + if (KV_bounds_check && j * Bc + c * cols_per_iter + col_tid >= KV) { + continue; + } + + [[unroll]] for (uint32_t d = 0; d < HSK_per_thread / 4; ++d) { + FLOAT_TYPEV4 K_Tf; + if (SHMEM_STAGING != 0) { + K_Tf = kvsh[(c * cols_per_iter + col_tid) * kvsh_stride + (d * D_split + d_tid)]; + } else if (USE_DECODE_K) { + uint coord = (j * Bc + c * cols_per_iter + col_tid) * k_stride * BLOCK_SIZE_K + 4 * (d * D_split + d_tid); + uint ib = coord / BLOCK_SIZE_K; + uint iqs = (coord % BLOCK_SIZE_K); + K_Tf = dequantize4(ib, iqs, k_offset, BINDING_IDX_K); + } else { + K_Tf = FLOAT_TYPEV4(data_kv4[k_offset / 4 + (j * Bc + c * cols_per_iter + col_tid) * k_stride / 4 + d * D_split + d_tid]); + } + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + Sf[r][c] = dot_product(Qf[tile_row(r) * qf_stride + d * D_split + d_tid], K_Tf, Sf[r][c]); + } + } + } + } +#else // MMQ + const uint hsk4 = HSK_per_thread / 4; + const uint d_per_step = (hsk4 % 8 == 0) ? 8 : + (hsk4 % 4 == 0) ? 4 : + (hsk4 % 2 == 0) ? 2 : 1; + + [[unroll]] for (uint32_t c = 0; c < cols_per_thread; ++c) { + if (KV_bounds_check && j * Bc + c * cols_per_iter + col_tid >= KV) { + continue; + } + + [[unroll]] for (uint32_t d_block = 0; d_block < HSK_per_thread / 4; d_block += d_per_step) { + int32_t k_quants[d_per_step]; + ACC_TYPEV2 k_dm; + + // Q4_*/Q5_* take the block-8 fast path when one step covers a full + // block; Q8_0 always goes through the per-int get_k_qs* helpers + // (its qs is byte-packed, not nibble-packed). + const bool block8_fast = (d_per_step == 8) && (FaTypeK != FA_TYPE_Q8_0); + + if (SHMEM_STAGING != 0) { + const uint k_block_idx = (d_tid * (HSK_per_thread / 4) + d_block) / 8; + const uint buf_ib = (c * cols_per_iter + col_tid) * qf_stride + k_block_idx; + k_dm = ACC_TYPEV2(kblocksh[buf_ib].dm); + + if (block8_fast) { + const bool has_qh = (FaTypeK == FA_TYPE_Q5_0) || (FaTypeK == FA_TYPE_Q5_1); + [[unroll]] for (uint32_t d = 0; d < 4; d++) { + uint vui = kblocksh[buf_ib].qs[d]; + k_quants[d ] = int32_t( vui & 0x0F0F0F0F); + k_quants[d + 4] = int32_t((vui >> 4) & 0x0F0F0F0F); + if (has_qh) { + uint qh_lo = (kblocksh[buf_ib].qh >> (d * 4)) & 0xF; + uint qh_hi = (kblocksh[buf_ib].qh >> (d * 4 + 16)) & 0xF; + k_quants[d ] |= int32_t((qh_lo * 0x02040810u) & 0x10101010u); + k_quants[d + 4] |= int32_t((qh_hi * 0x02040810u) & 0x10101010u); + } + } + } else { + [[unroll]] for (uint32_t d = 0; d < d_per_step; d++) { + k_quants[d] = get_k_qs_shmem(buf_ib, (d_tid * (HSK_per_thread / 4) + d_block) % 8 + d); + } + } + } else { + const uint coord = (j * Bc + c * cols_per_iter + col_tid) * k_stride * BLOCK_SIZE_K + 4 * (d_tid * (HSK_per_thread / 4) + d_block); + const uint ib = coord / BLOCK_SIZE_K; + const uint iqs = (coord % BLOCK_SIZE_K); + + k_dm = ACC_TYPEV2(get_k_scale(ib, k_offset)); + + if (block8_fast) { + fa_k_qs_block8 blk = get_k_qs_block8(ib, k_offset); + [[unroll]] for (uint32_t d = 0; d < 8; d++) { + k_quants[d] = blk.qs[d]; + } + } else { + [[unroll]] for (uint32_t d = 0; d < d_per_step; d++) { + k_quants[d] = get_k_qs(ib, iqs + d * 4, k_offset); + } + } + } + + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + const uint qib = tile_row(r) * qf_stride + (d_tid * (HSK_per_thread / 4) + d_block) / 8; + const uint qiqs = (d_tid * (HSK_per_thread / 4) + d_block) % 8; + + int32_t acc = 0; + [[unroll]] for (uint32_t d = 0; d < d_per_step; d++) { + acc += dotPacked4x8EXT(Qf[qib].qs[qiqs + d], k_quants[d]); + } + + Sf[r][c] += ACC_TYPE(acc) * ACC_TYPE(Qf[qib].ds.x) * k_dm.x; + if ((d_tid * (HSK_per_thread / 4) + d_block) % 8 == 0) { + Sf[r][c] += k_dot_correction(qib, k_dm); + } + } + } + } +#endif // MMQ + + [[unroll]] for (uint32_t c = 0; c < cols_per_thread; ++c) { + // Compute sum across the D_split + [[unroll]] for (uint s = D_split / 2; s > 0; s >>= 1) { + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + Sf[r][c] += subgroupShuffleXor(Sf[r][c], s); + } + } + } + + if (LOGIT_SOFTCAP) { + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + [[unroll]] for (uint32_t c = 0; c < cols_per_thread; ++c) { + Sf[r][c] = ACC_TYPE(p.logit_softcap * tanh(Sf[r][c])); + } + } + } + + if (MASK_ENABLE && mask_opt_bits != MASK_OPT_ALL_ZERO) { + [[unroll]] for (uint32_t c = 0; c < cols_per_thread; ++c) { + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + FLOAT_TYPE mvf = masksh[(c * cols_per_iter + col_tid) * masksh_stride + tile_row(r)]; + + Sf[r][c] += slope[r]*mvf; + } + } + } + + float eMf[rows_per_thread]; + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + float rowmaxf = NEG_FLT_MAX_OVER_2; + [[unroll]] for (uint32_t c = 0; c < cols_per_thread; ++c) { + if (KV_bounds_check && j * Bc + c * cols_per_iter + col_tid >= KV) { + continue; + } + rowmaxf = max(rowmaxf, float(Sf[r][c])); + } + rowmaxf += FATTN_KQ_MAX_OFFSET; + float Moldf = Mf[r]; + + // M = max(rowmax, Mold) + // P = e^(S - M) + // eM = e^(Mold - M) + Mf[r] = max(rowmaxf, Moldf); + eMf[r] = exp(Moldf - Mf[r]); + Lf[r] = eMf[r]*Lf[r]; + } + + [[unroll]] for (uint32_t d = 0; d < HSV_per_thread / 4; ++d) { + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + Of[r][d] = FLOAT_TYPE(eMf[r]) * Of[r][d]; + } + } + + if (SHMEM_STAGING != 0) { + barrier(); + [[unroll]] for (uint32_t idx = 0; idx < Bc * HSV / 4; idx += gl_WorkGroupSize.x) { + uint32_t d = (idx + tid) % (HSV / 4); + uint32_t c = (idx + tid) / (HSV / 4); + if (idx + gl_WorkGroupSize.x <= Bc * HSV / 4 || c < Bc) { + FLOAT_TYPEV4 V_Tf = FLOAT_TYPEV4(0); + if (!KV_bounds_check || j * Bc + c < KV) { + if (USE_DECODE_V) { + uint coord = (j * Bc + c) * v_stride * BLOCK_SIZE_V + 4 * d; + uint ib = coord / BLOCK_SIZE_V; + uint iqs = (coord % BLOCK_SIZE_V); + V_Tf = dequantize4(ib, iqs, v_offset, BINDING_IDX_V); + } else { + V_Tf = FLOAT_TYPEV4(data_vv4[v_offset / 4 + (j * Bc + c) * v_stride / 4 + d]); + } + } + + kvsh[c * kvsh_stride + d] = V_Tf; + } + } + barrier(); + } + + [[unroll]] for (uint32_t c = 0; c < cols_per_thread; ++c) { + if (KV_bounds_check && j * Bc + c * cols_per_iter + col_tid >= KV) { + continue; + } + + FLOAT_TYPE Pf[rows_per_thread]; + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + Pf[r] = FLOAT_TYPE(exp(float(Sf[r][c]) - Mf[r])); + Lf[r] += Pf[r]; + } + + [[unroll]] for (uint32_t d = 0; d < HSV_per_thread / 4; ++d) { + FLOAT_TYPEV4 Vf; + if (SHMEM_STAGING != 0) { + Vf = kvsh[(c * cols_per_iter + col_tid) * kvsh_stride + (d * D_split + d_tid)]; + } else if (USE_DECODE_V) { + uint coord = (j * Bc + c * cols_per_iter + col_tid) * v_stride * BLOCK_SIZE_V + 4 * (d * D_split + d_tid); + uint ib = coord / BLOCK_SIZE_V; + uint iqs = (coord % BLOCK_SIZE_V); + Vf = dequantize4(ib, iqs, v_offset, BINDING_IDX_V); + } else { + Vf = FLOAT_TYPEV4(data_vv4[v_offset / 4 + (j * Bc + c * cols_per_iter + col_tid) * v_stride / 4 + d * D_split + d_tid]); + } + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + Of[r][d] += FLOAT_TYPEV4(Pf[r] * Vf); + } + } + } + } + + // prevent race on tmpsh + barrier(); + + // reduce across threads + + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + float rowmaxf = Mf[r]; + + // Compute max across the row + if (SubGroupSize > 0) { + [[unroll]] for (uint s = D_split; s < SubGroupSize; s *= 2) { + rowmaxf = max(rowmaxf, subgroupShuffleXor(rowmaxf, s)); + } + if (row_split == 1) { + // Reduce inside workgroup with shmem + barrier(); + if (gl_SubgroupInvocationID == d_tid) { + tmpsh[gl_SubgroupID * D_split + d_tid] = rowmaxf; + } + barrier(); + rowmaxf = tmpsh[d_tid]; + [[unroll]] for (uint32_t s = 1; s < num_subgroups; ++s) { + rowmaxf = max(rowmaxf, tmpsh[s * D_split + d_tid]); + } + } + } else { + barrier(); + tmpsh[tid] = rowmaxf; + barrier(); + [[unroll]] for (int s = int(threads_per_rowgroup) / 2; s >= D_split; s >>= 1) { + if (rowgroup_tid < s) { + tmpsh[tid] = max(tmpsh[tid], tmpsh[tid ^ s]); + } + barrier(); + } + rowmaxf = tmpsh[row_tid * threads_per_rowgroup + d_tid]; + } + + float Moldf = Mf[r]; + + // M = max(rowmax, Mold) + // eM = e^(Mold - M) + Mf[r] = max(rowmaxf, Moldf); + float eMf = exp(Moldf - Mf[r]); + + Lf[r] = eMf*Lf[r]; + + // Compute sum across the row + if (SubGroupSize > 0) { + [[unroll]] for (uint s = D_split; s < SubGroupSize; s *= 2) { + Lf[r] += subgroupShuffleXor(Lf[r], s); + } + if (row_split == 1) { + barrier(); + if (gl_SubgroupInvocationID == d_tid) { + tmpsh[gl_SubgroupID * D_split + d_tid] = Lf[r]; + } + barrier(); + Lf[r] = tmpsh[d_tid]; + [[unroll]] for (uint32_t s = 1; s < num_subgroups; ++s) { + Lf[r] += tmpsh[s * D_split + d_tid]; + } + } + } else { + barrier(); + tmpsh[tid] = Lf[r]; + barrier(); + [[unroll]] for (int s = int(threads_per_rowgroup) / 2; s >= D_split; s >>= 1) { + if (rowgroup_tid < s) { + tmpsh[tid] = tmpsh[tid] + tmpsh[tid ^ s]; + } + barrier(); + } + Lf[r] = tmpsh[row_tid * threads_per_rowgroup + d_tid]; + } + + [[unroll]] for (uint32_t d = 0; d < HSV_per_thread / 4; ++d) { + Of[r][d] = FLOAT_TYPE(eMf) * Of[r][d]; + + if (SubGroupSize > 0) { + [[unroll]] for (uint s = D_split; s < SubGroupSize; s *= 2) { + if (!OLD_AMD_WINDOWS) { + Of[r][d] += subgroupShuffleXor(Of[r][d], s); + } else { + // Something about f16vec4 subgroupShuffleXor is broken on AMD Windows RDNA2 and below. + // Shuffle full vec4 as workaround. + // See https://github.com/ggml-org/llama.cpp/issues/19881#issuecomment-3958643697 + Of[r][d] += FLOAT_TYPEV4(subgroupShuffleXor(vec4(Of[r][d]), s)); + } + } + if (row_split == 1) { + barrier(); + if (gl_SubgroupInvocationID == d_tid) { + tmpshv4[gl_SubgroupID * D_split + d_tid] = Of[r][d]; + } + barrier(); + Of[r][d] = tmpshv4[d_tid]; + [[unroll]] for (uint32_t s = 1; s < num_subgroups; ++s) { + Of[r][d] += tmpshv4[s * D_split + d_tid]; + } + } + } else { + barrier(); + tmpshv4[tid] = Of[r][d]; + barrier(); + [[unroll]] for (int s = int(threads_per_rowgroup) / 2; s >= D_split; s >>= 1) { + if (rowgroup_tid < s) { + Of[r][d] += tmpshv4[tid ^ s]; + tmpshv4[tid] = Of[r][d]; + } + barrier(); + } + Of[r][d] = tmpshv4[row_tid * threads_per_rowgroup + d_tid]; + } + } + } + + + // If there is split_k, then the split_k resolve shader does the final + // division by L. Store the intermediate O value and per-row m and L values. + if (p.k_num > 1) { + if (p.gqa_ratio > 1) { + // note: O and Q have swapped coord 1,2. + uint32_t o_offset = HSV * p.ne1 * (split_k_index + p.k_num * (gqa_iq1 + p.ne2 * iq3)) / 4; + + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + const uint row = tile_row(r); + if (row < N) { + [[unroll]] for (uint32_t d = 0; d < HSV_per_thread / 4; ++d) { + gqaStore(row, d * D_split + d_tid, Of[r][d], o_offset, iq2, N); + } + } + } + + o_offset = HSV * p.ne1 * p.k_num * p.ne2 * p.ne3 + p.ne1 * 2 * (split_k_index + p.k_num * (gqa_iq1 + p.ne2 * iq3)); + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + const uint row = tile_row(r); + if (row < N) { + perElemOpStoreCol0(row, 0u, ACC_TYPE(Lf[r]), o_offset, iq2, N); + perElemOpStoreCol0(row, 0u, ACC_TYPE(Mf[r]), o_offset + p.ne1, iq2, N); + } + } + } else { + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + const uint row = tile_row(r); + const uint global_row = i * Br + row; + + if (global_row < N) { + uint32_t o_offset = HSV * p.ne1 * (split_k_index + p.k_num * (global_row + p.ne2 * iq3)) / 4; + + [[unroll]] for (uint32_t d = 0; d < HSV_per_thread / 4; ++d) { + data_ov4[o_offset + iq2 * HSV/4 + d * D_split + d_tid] = D_TYPEV4(Of[r][d]); + } + } + + if (global_row < N && d_tid == 0 && col_tid == 0) { + uint32_t lm_offset = HSV * p.ne1 * p.k_num * p.ne2 * p.ne3 + p.ne1 * 2 * (split_k_index + p.k_num * (global_row + p.ne2 * iq3)); + data_o[lm_offset + iq2] = D_TYPE(Lf[r]); + data_o[lm_offset + p.ne1 + iq2] = D_TYPE(Mf[r]); + } + } + } + return; + } + + if ((p.mask_n_head_log2 & SINK_ENABLE_BIT) != 0) { + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + float sink = perElemOpGetSink(tile_row(r), 0u, ACC_TYPE(0), iq2); + + float ms = 1.0f; + float vs = 1.0f; + + if (sink > Mf[r]) { + ms = exp(Mf[r] - sink); + + [[unroll]] for (uint32_t d = 0; d < HSV_per_thread / 4; ++d) { + Of[r][d] *= FLOAT_TYPE(ms); + } + } else { + vs = exp(sink - Mf[r]); + } + + Lf[r] = Lf[r]*ms + vs; + } + } + + float Lfrcp[rows_per_thread]; + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + Lfrcp[r] = (Lf[r] == 0.0) ? 0.0 : (1.0 / Lf[r]); + } + + [[unroll]] for (uint32_t d = 0; d < HSV_per_thread / 4; ++d) { + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + Of[r][d] *= FLOAT_TYPE(Lfrcp[r]); +#if defined(FLOAT_TYPE_MAX) + Of[r][d] = clamp(Of[r][d], -FLOAT_TYPE_MAX, FLOAT_TYPE_MAX); +#endif + } + } + + uint32_t o_offset = (gqa_iq1*p.ne1*HSV + iq3*p.ne2*p.ne1*HSV) / 4; + + if (p.gqa_ratio > 1) { + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + const uint row = tile_row(r); + if (row < N) { + [[unroll]] for (uint32_t d = 0; d < HSV_per_thread / 4; ++d) { + gqaStore(row, d * D_split + d_tid, Of[r][d], o_offset, iq2, N); + } + } + } + } else { + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + const uint row = tile_row(r); + if (i * Br + row < N) { + [[unroll]] for (uint32_t d = 0; d < HSV_per_thread / 4; ++d) { + data_ov4[o_offset + (iq2 * HSV + (i * Br + row) * p.ne1 * HSV) / 4 + d * D_split + d_tid] = D_TYPEV4(Of[r][d]); + } + } + } + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_base.glsl b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_base.glsl new file mode 100644 index 0000000000000000000000000000000000000000..66dcf61021900e8025f22193f2f4406fa9034798 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_base.glsl @@ -0,0 +1,265 @@ + +layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; + +layout (constant_id = 0) const uint32_t WorkGroupSize = 128; +layout (constant_id = 1) const uint32_t Br = 1; +layout (constant_id = 2) const uint32_t Bc = 32; +layout (constant_id = 3) const uint32_t HSK = 32; +layout (constant_id = 4) const uint32_t HSV = 32; +layout (constant_id = 5) const uint32_t Clamp = 0; +layout (constant_id = 6) const uint32_t D_split = 16; +layout (constant_id = 7) const uint32_t row_split = 1; +layout (constant_id = 8) const uint32_t SubGroupSize = 32; +layout (constant_id = 9) const uint32_t SHMEM_STAGING = 0; +layout (constant_id = 10) const uint32_t Flags = 0; +layout (constant_id = 11) const uint32_t LIMIT_OCCUPANCY_SHMEM = 0; +// ggml_type enumerant for K/V +layout (constant_id = 12) const uint32_t FaTypeK = 0; +layout (constant_id = 13) const uint32_t FaTypeV = 0; +// sizeof(decode buffer): quants -> ggml block size; F32 -> 16 (decodeBufF32 vec4). +layout (constant_id = 14) const uint32_t FaBlockBytesK = 2; +layout (constant_id = 15) const uint32_t FaBlockBytesV = 2; + +const bool USE_MASK_OPT = (Flags & 1) != 0; +const bool MASK_ENABLE = (Flags & 2) != 0; +const bool LOGIT_SOFTCAP = (Flags & 4) != 0; +const bool OLD_AMD_WINDOWS = (Flags & 8) != 0; + +// Round up head sizes to a multiple of 16, for coopmat1/coopmat2 paths +const uint32_t HSK_pad = (HSK + 15) & ~15; +const uint32_t HSV_pad = (HSV + 15) & ~15; + +const bool KV_bounds_check = Clamp != 0; + +layout (push_constant) uniform parameter { + uint32_t N; + uint32_t KV; + + uint32_t ne1; + uint32_t ne2; + uint32_t ne3; + + uint32_t neq2; + uint32_t neq3; + uint32_t nek2; + uint32_t nek3; + uint32_t nev2; + uint32_t nev3; + uint32_t nem1; + uint32_t nem2; + uint32_t nem3; + + uint32_t nb01; + uint32_t nb02; + uint32_t nb03; + uint32_t nb11; + uint32_t nb12; + uint32_t nb13; + uint32_t nb21; + uint32_t nb22; + uint32_t nb23; + + float scale; + float max_bias; + float logit_softcap; + + uint32_t mask_n_head_log2; + float m0; + float m1; + + uint32_t gqa_ratio; + uint32_t split_kv; + uint32_t k_num; +} p; + +#define SINK_ENABLE_BIT (1<<24) +#define N_LOG2_MASK 0xFFFF + +layout (binding = 4) readonly buffer S {float data_s[];}; + +layout (binding = 5) writeonly buffer O {D_TYPE data_o[];}; +layout (binding = 5) writeonly buffer OV4 {D_TYPEV4 data_ov4[];}; + +layout (binding = 6) readonly buffer MO {uint32_t data_mask_opt[];}; + +#define MASK_OPT_ALL_NEG_INF 1 +#define MASK_OPT_ALL_ZERO 2 + +#define BINDING_IDX_K 0 +#define BINDING_IDX_V 1 + +// FaTypeK / FaTypeV spec constant values. These mirror enum ggml_type so the +// host can pass the type directly. Keep in sync with ggml.h. +#define FA_TYPE_F32 0u +#define FA_TYPE_F16 1u +#define FA_TYPE_Q4_0 2u +#define FA_TYPE_Q4_1 3u +#define FA_TYPE_Q5_0 6u +#define FA_TYPE_Q5_1 7u +#define FA_TYPE_Q8_0 8u +#define FA_TYPE_BF16 30u +#define FA_TYPE_Q1_0 41u + +#if defined(BFLOAT16) +#define O_TYPE float +#define O_TYPEV4 vec4 +#else +#define O_TYPE FLOAT_TYPE +#define O_TYPEV4 FLOAT_TYPEV4 +#endif + +// Number of matrix elements per buffer block, derived from the K/V type spec +// constant. F32 is treated as a vec4 "block" of 4 floats. F16 uses block size 1 +// and bypasses the dequant path entirely. Quants follow their ggml block sizes. +uint fa_block_elems(uint ty) { + switch (ty) { + case FA_TYPE_F32: return 4u; + case FA_TYPE_F16: return 1u; + case FA_TYPE_Q4_0: return uint(QUANT_K_Q4_0); + case FA_TYPE_Q4_1: return uint(QUANT_K_Q4_1); + case FA_TYPE_Q5_0: return uint(QUANT_K_Q5_0); + case FA_TYPE_Q5_1: return uint(QUANT_K_Q5_1); + case FA_TYPE_Q8_0: return uint(QUANT_K_Q8_0); + case FA_TYPE_BF16: return 1u; + case FA_TYPE_Q1_0: return uint(QUANT_K_Q1_0); // cm2-only, harmless elsewhere + default: return 1u; + } +} + +// QUANT_R_MMQ for FA-eligible K types. Q4_*/Q5_* store two nibbles per byte +// (R==2); Q8_0 stores one byte per element (R==1). Used to derive the number +// of int32s per 32-element block on the MMQ K path: ints_per_block == 8 / R. +uint fa_quant_r_mmq(uint ty) { + switch (ty) { + case FA_TYPE_Q4_0: return uint(QUANT_R_Q4_0); + case FA_TYPE_Q4_1: return uint(QUANT_R_Q4_1); + case FA_TYPE_Q5_0: return uint(QUANT_R_Q5_0); + case FA_TYPE_Q5_1: return uint(QUANT_R_Q5_1); + case FA_TYPE_Q8_0: return uint(QUANT_R_Q8_0); + default: return 1u; + } +} + +// These can't be `const` globals because GLSL forbids function calls in global +// const initializers, even when the spec constants would let the driver fold +// them. Macros expand at the use site and fold after specialization. +#define BLOCK_SIZE_K fa_block_elems(FaTypeK) +#define BLOCK_SIZE_V fa_block_elems(FaTypeV) +// F16 reads f16 elements directly from the binding; everything else routes +// through dequantize4 / the MMQ helpers to unpack from the packed block layout. +#define USE_DECODE_K (FaTypeK != FA_TYPE_F16) +#define USE_DECODE_V (FaTypeV != FA_TYPE_F16) + +#define CEIL_DIV(a, b) (((a) + (b) - 1) / (b)) + + +// Store column zero. This is used to save per-row m and L values for split_k. +ACC_TYPE perElemOpStoreCol0(const in uint32_t r, const in uint32_t c, const in ACC_TYPE elem, const in uint32_t o_offset, const in uint32_t iq2, const in uint32_t N) +{ + if (r < N && c == 0) { + uint32_t offset = iq2 + r; + data_o[o_offset + offset] = D_TYPE(elem); + } + return elem; +} + +// Load the slope matrix, indexed by Q's dimension 2. +ACC_TYPE perElemOpComputeSlope(const in uint32_t r, const in uint32_t c, const in ACC_TYPE elem, const in uint32_t iq2) +{ + const uint32_t h = iq2 + (r % p.gqa_ratio); + + uint32_t n_head_log2 = p.mask_n_head_log2 & N_LOG2_MASK; + + const ACC_TYPE base = ACC_TYPE(h < n_head_log2 ? p.m0 : p.m1); + const int exph = int(h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1); + + return ACC_TYPE(pow(base, ACC_TYPE(exph))); +} + +// Load the sink value, indexed by Q's dimension 2. +ACC_TYPE perElemOpGetSink(const in uint32_t r, const in uint32_t c, const in ACC_TYPE elem, const in uint32_t iq2) +{ + const uint32_t h = iq2 + (r % p.gqa_ratio); + + return ACC_TYPE(data_s[h]); +} + +uint32_t i, N, KV, split_k_index, Tr, start_j, end_j, + gqa_iq1, iq2, iq3, rk2, rk3, rv2, rv3, ik2, ik3, iv2, iv3, + q_stride, k_stride, v_stride, m_stride; + +void init_indices() +{ + N = p.N; + KV = p.KV; + + if (p.k_num > 1) { + if (p.gqa_ratio > 1) { + i = 0; + // batch and split_k share gl_WorkGroupID.x + gqa_iq1 = gl_WorkGroupID.x / p.k_num; + split_k_index = gl_WorkGroupID.x % p.k_num; + } else { + gqa_iq1 = 0; + split_k_index = gl_WorkGroupID.x % p.k_num; + i = gl_WorkGroupID.x / p.k_num; + } + } else if (p.gqa_ratio > 1) { + i = 0; + gqa_iq1 = gl_WorkGroupID.x; + split_k_index = 0; + } else { + i = gl_WorkGroupID.x; + gqa_iq1 = 0; + split_k_index = 0; + } + + Tr = CEIL_DIV(N, Br); + + start_j = split_k_index * p.split_kv / Bc; + end_j = CEIL_DIV(min(KV, (split_k_index + 1) * p.split_kv), Bc); + + // When not using grouped query attention, all rows share the same iq2, equal to gl_WorkGroupID.y. + // When using grouped query attention, each workgroup does gqa_ratio consecutive values of iq2. + iq2 = gl_WorkGroupID.y * p.gqa_ratio; + iq3 = gl_WorkGroupID.z; + + // broadcast factors + rk2 = p.neq2/p.nek2; + rk3 = p.neq3/p.nek3; + + rv2 = p.neq2/p.nev2; + rv3 = p.neq3/p.nev3; + + // k indices + ik3 = iq3 / rk3; + ik2 = iq2 / rk2; + + // v indices + iv3 = iq3 / rv3; + iv2 = iq2 / rv2; + + // nb?1 are already divided by the type size and are in units of elements. + // When using grouped query attention, Q is indexed by iq2, so the stride + // should be nb02 (which is in bytes). + q_stride = p.gqa_ratio > 1 ? (p.nb02 / 4) : p.nb01; + k_stride = p.nb11; + v_stride = p.nb21; + // When using grouped query attention, all rows use the same mask (stride 0). + // "p.gqa_ratio >> 16" is just a roundabout way of writing zero + // that prevents the compiler from folding the "&" through the select + // and breaking the alignment detection. + m_stride = (p.gqa_ratio > 1) ? (p.gqa_ratio >> 16) : KV; +} + +// Bias applied to softmax to stay in fp16 range. +// Based on ggml-cuda issue https://github.com/ggml-org/llama.cpp/issues/18606 +const float FATTN_KQ_MAX_OFFSET = 3.0f*0.6931f; + +// Store the output when doing grouped query attention. +// Rows index by Q's dimension 2, and the first N rows are valid. +void gqaStore(const in uint32_t r, const in uint32_t c, const in O_TYPEV4 elems, const in uint32_t o_offset, const in uint32_t iq2, const in uint32_t N) +{ + uint32_t offset = (iq2 + r) * HSV / 4 + c; + data_ov4[o_offset + offset] = D_TYPEV4(elems); +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm1.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm1.comp new file mode 100644 index 0000000000000000000000000000000000000000..16178e57702420042f90f6d177ca7c3985fd799e --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm1.comp @@ -0,0 +1,645 @@ +#version 450 + +#extension GL_EXT_control_flow_attributes : enable +#extension GL_EXT_shader_16bit_storage : require + +#extension GL_EXT_shader_explicit_arithmetic_types_float16 : require +#extension GL_EXT_shader_explicit_arithmetic_types_int32 : require + +#if defined(BFLOAT16) +#extension GL_EXT_bfloat16 : enable +#endif + +#extension GL_KHR_shader_subgroup_basic : enable +#extension GL_KHR_shader_subgroup_arithmetic : enable +#extension GL_KHR_shader_subgroup_vote : enable +#extension GL_KHR_memory_scope_semantics : enable +#extension GL_KHR_cooperative_matrix : enable + +#include "types.glsl" +#include "flash_attn_base.glsl" +#if !defined(BFLOAT16) +#include "flash_attn_dequant.glsl" +#endif + +// These need to be supported N,M values for a MatBc x MatBr x 16 coopmatmuladd +const uint32_t MatBr = 16; +const uint32_t MatBc = 16; + +const uint32_t rows_per_thread = Br / row_split; +const uint32_t cols_per_iter = gl_WorkGroupSize.x / row_split; +const uint32_t cols_per_thread = Bc / cols_per_iter; + + +layout (binding = 0) readonly buffer Q {float data_q[];}; +layout (binding = 0) readonly buffer QV4 {vec4 data_qv4[];}; +layout (binding = 1) readonly buffer K {FLOAT_TYPE data_k[];}; +layout (binding = 1) readonly buffer KV4 {FLOAT_TYPEV4 data_kv4[];}; +layout (binding = 2) readonly buffer V {FLOAT_TYPE data_v[];}; +layout (binding = 2) readonly buffer VV4 {FLOAT_TYPEV4 data_vv4[];}; +layout (binding = 3) readonly buffer M {float16_t data_m[];}; + +shared float tmpsh[row_split]; + +const uint32_t qstride = HSK_pad / 4 + 2; +shared FLOAT_TYPEV4 Qf[Br * qstride]; + +const uint psh_stride = Br / 4 + 2; +shared FLOAT_TYPEV4 Psh[Bc * psh_stride]; + +// Avoid padding for hsk==256 to make it fit in 48KB shmem. +const uint32_t sfshstride = (HSK <= 128) ? (Br / 4 + 2) : Br / 4; +shared ACC_TYPEV4 sfsh[Bc * sfshstride]; + +const uint32_t D_pad = HSK_pad > HSV_pad ? HSK_pad : HSV_pad; +const uint32_t kvsh_stride = (SHMEM_STAGING != 0 ? D_pad : MatBr) / 4 + 2; +const uint v_cols = MatBc / 4 * row_split; // total cols, 4 vec4s per MatBc * number of subgroups +const uint vsh_stride = v_cols; +shared FLOAT_TYPEV4 kvsh[(kvsh_stride >= vsh_stride) ? (Bc * kvsh_stride) : (Bc * vsh_stride)]; + +const uint32_t osh_stride = row_split * MatBr / 4; +shared O_TYPEV4 pvsh[MatBc * osh_stride]; + +shared ACC_TYPE slope[Br]; + +void main() { +#ifdef NEEDS_INIT_IQ_SHMEM + init_iq_shmem(gl_WorkGroupSize); +#endif + + init_indices(); + + const uint32_t tid = gl_LocalInvocationIndex; + + const uint32_t threads_per_rowgroup = gl_WorkGroupSize.x / row_split; + const uint32_t d_per_thread = (HSV/4 + threads_per_rowgroup - 1) / threads_per_rowgroup; + const uint32_t row_tid = gl_LocalInvocationIndex / threads_per_rowgroup; + const uint32_t col_tid = gl_LocalInvocationIndex % threads_per_rowgroup; + +#define tile_row(r) (row_tid * rows_per_thread + (r)) + + // Zero-initialize shared memory for Q/K when HSK is not a multiple of 16 (HSK_pad > HSK). + if ((HSK % 16) != 0) { + [[unroll]] for (uint i = 0; i < Br * qstride; i += gl_WorkGroupSize.x) { + if (i + tid < Br * qstride) { + Qf[i + tid] = FLOAT_TYPEV4(0); + } + } + barrier(); + } + + uint32_t q_offset = gqa_iq1*p.nb01 + (iq2*p.nb02+iq3*p.nb03) / 4; + + [[unroll]] for (uint32_t idx = 0; idx < Br * HSK / 4; idx += gl_WorkGroupSize.x) { + uint32_t d = (idx + tid) % (HSK / 4); + uint32_t r = (idx + tid) / (HSK / 4); + if (r < Br && d < HSK / 4 && + i * Br + r < N) { + Qf[r * qstride + d] = FLOAT_TYPEV4(data_qv4[q_offset / 4 + (i * Br + r) * q_stride / 4 + d] * p.scale); + } + } + barrier(); + + O_TYPEV4 Of[rows_per_thread][d_per_thread]; + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + [[unroll]] for (uint32_t d = 0; d < d_per_thread; ++d) { + Of[r][d] = O_TYPEV4(0.0); + } + } + + float Lf[rows_per_thread], Mf[rows_per_thread]; + + // Use -FLT_MAX/2 rather than -inf to reduce the possibility of NaNs, e.g. when computing Mold-M. + const float NEG_FLT_MAX_OVER_2 = uintBitsToFloat(0xFEFFFFFF); + + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + Lf[r] = 0; + Mf[r] = NEG_FLT_MAX_OVER_2; + } + + // ALiBi + if (p.max_bias > 0.0f) { + if (tid < Br) { + uint r = tid; + slope[r] = perElemOpComputeSlope(r, col_tid, ACC_TYPE(0), iq2); + } + } else { + if (tid < Br) { + uint r = tid; + slope[r] = ACC_TYPE(1.0); + } + } + + const uint32_t mo_stride = CEIL_DIV(KV, 16 * Bc); + // mo_offset will point to the tile starting at row i*Br and col 0 + uint32_t mo_offset = mo_stride * i; + + // FaBlockBytesK/V == 2 for f16 (sizeof f16) and == 16 for f32 (vec4) and == ggml block size for quants. + uint32_t k_offset = (ik2*p.nb12 + ik3*p.nb13) / FaBlockBytesK; + uint32_t v_offset = (iv2*p.nb22 + iv3*p.nb23) / FaBlockBytesV; + uint32_t m_offset = gqa_iq1*KV; + if (p.nem2 != 1 || p.nem3 != 1) { + m_offset += ((iq3 % p.nem3) * p.nem2 + (iq2 % p.nem2)) * p.nem1 * KV; + mo_offset += ((iq3 % p.nem3) * p.nem2 + (iq2 % p.nem2)) * CEIL_DIV(p.nem1, Br) * mo_stride; + } + + uint32_t mask_opt = 0; + uint32_t mask_opt_idx = ~0; + uint32_t mask_opt_bits = 0; + f16vec4 mask_cache[Bc * Br / 4 / WorkGroupSize]; + + [[dont_unroll]] + for (uint32_t j = start_j; j < end_j; ++j) { + + [[unroll]] for (uint32_t idx = 0; idx < mask_cache.length(); ++idx) { + mask_cache[idx] = f16vec4(0); + } + + if (MASK_ENABLE) { + if (USE_MASK_OPT && mask_opt_idx != j / 16) { + mask_opt_idx = j / 16; + mask_opt = data_mask_opt[mo_offset + mask_opt_idx]; + } + mask_opt_bits = (mask_opt >> ((j % 16) * 2)) & 0x3; + if (mask_opt_bits == MASK_OPT_ALL_NEG_INF) { + // skip this block + continue; + } + // Only load if the block is not all zeros + if (mask_opt_bits != MASK_OPT_ALL_ZERO) { + bool nem1_bounds_check = !(p.gqa_ratio > 1) && (p.nem1 % Br) != 0; + + float max_mask = NEG_FLT_MAX_OVER_2; + [[unroll]] for (uint32_t idx = 0; idx < Bc * Br / 4; idx += gl_WorkGroupSize.x) { + uint32_t c = (idx + tid) / (Br / 4); + uint32_t r = (idx + tid) % (Br / 4); + if (idx + tid < Bc * Br / 4 || idx + gl_WorkGroupSize.x <= Bc * Br / 4) { + if ((!KV_bounds_check || j * Bc + c < KV)) { + f16vec4 m; + if (!nem1_bounds_check || i * Br + r * 4 + 3 < p.nem1) { + m = f16vec4(data_m[m_offset + (i * Br + r * 4 ) * m_stride + (j * Bc + c)], + data_m[m_offset + (i * Br + r * 4 + 1) * m_stride + (j * Bc + c)], + data_m[m_offset + (i * Br + r * 4 + 2) * m_stride + (j * Bc + c)], + data_m[m_offset + (i * Br + r * 4 + 3) * m_stride + (j * Bc + c)]); + max_mask = max(max(max(max(max_mask, float(m[0])), float(m[1])), float(m[2])), float(m[3])); + } else if (i * Br + r * 4 + 2 < p.nem1) { + m = f16vec4(data_m[m_offset + (i * Br + r * 4 ) * m_stride + (j * Bc + c)], + data_m[m_offset + (i * Br + r * 4 + 1) * m_stride + (j * Bc + c)], + data_m[m_offset + (i * Br + r * 4 + 2) * m_stride + (j * Bc + c)], + 0.0); + max_mask = max(max(max(max_mask, float(m[0])), float(m[1])), float(m[2])); + } else if (i * Br + r * 4 + 1 < p.nem1) { + m = f16vec4(data_m[m_offset + (i * Br + r * 4 ) * m_stride + (j * Bc + c)], + data_m[m_offset + (i * Br + r * 4 + 1) * m_stride + (j * Bc + c)], + 0.0, + 0.0); + max_mask = max(max(max_mask, float(m[0])), float(m[1])); + } else if (i * Br + r * 4 < p.nem1) { + m = f16vec4(data_m[m_offset + (i * Br + r * 4 ) * m_stride + (j * Bc + c)], + 0.0, + 0.0, + 0.0); + max_mask = max(max_mask, float(m[0])); + } else { + m = f16vec4(0.0); + } + mask_cache[idx / WorkGroupSize] = m; + } + } + } + // skip the block if the mask is entirely -inf + bool all_less = subgroupAll(max_mask <= NEG_FLT_MAX_OVER_2); + barrier(); + if (gl_SubgroupInvocationID == 0) { + tmpsh[gl_SubgroupID] = all_less ? NEG_FLT_MAX_OVER_2 : 0.0f; + } + barrier(); + [[unroll]] for (uint s = 0; s < gl_NumSubgroups; ++s) { + max_mask = max(max_mask, tmpsh[s]); + } + if (max_mask <= NEG_FLT_MAX_OVER_2) { + continue; + } + } + } + + if (SHMEM_STAGING != 0) { + [[unroll]] for (uint32_t idx = 0; idx < Bc * HSK_pad / 4; idx += gl_WorkGroupSize.x) { + uint32_t d = (idx + tid) % (HSK_pad / 4); + uint32_t c = (idx + tid) / (HSK_pad / 4); + if (idx + gl_WorkGroupSize.x <= Bc * HSK_pad / 4 || c < Bc) { + FLOAT_TYPEV4 K_Tf = FLOAT_TYPEV4(0); + if ((!KV_bounds_check || j * Bc + c < KV) && (HSK == HSK_pad || d < HSK / 4)) { +#if !defined(BFLOAT16) + if (USE_DECODE_K) { + uint coord = (j * Bc + c) * k_stride * BLOCK_SIZE_K + 4 * d; + uint ib = coord / BLOCK_SIZE_K; + uint iqs = (coord % BLOCK_SIZE_K); + K_Tf = dequantize4(ib, iqs, k_offset, BINDING_IDX_K); + } else +#endif + { + K_Tf = FLOAT_TYPEV4(data_kv4[k_offset / 4 + (j * Bc + c) * k_stride / 4 + d]); + } + } + + kvsh[c * kvsh_stride + d] = K_Tf; + } + } + barrier(); + } + + // K * Q^T -> S^T: Bc x HSK_pad * HSK_pad x Br -> Bc x Br + // Bc split across workgroup (four subgroups), loop over HSK in chunks of 16: 16 x 16 * 16 x 16 -> 16 x 16 + // This is written transposed in order to allow for N being 8 if implementations need it + coopmat SfMat = coopmat(0); + coopmat KMat; + coopmat QMat; + + [[unroll]] for (uint32_t d = 0; d < HSK_pad / 16; ++d) { + // If SHMEM_STAGING is set, a Bc * HSK_pad size tile of K is loaded to shmem + // If not, K is loaded directly from global memory if aligned, otherwise + // staged through a Bc * MatBr size staging buffer. + // If K is a quant type, then it is always staged for dequantization. + if (SHMEM_STAGING == 0) { + // For quants we always need to dequant into kvsh; for f16/bf16 we can load + // directly from global memory when alignment / bounds allow it. + const bool stage_k = USE_DECODE_K || KV_bounds_check || d * 16 + 16 > HSK; + if (stage_k) { + barrier(); + [[unroll]] for (uint32_t idx = 0; idx < Bc * MatBr / 4; idx += gl_WorkGroupSize.x) { + uint32_t col_vec = (idx + tid) % (MatBr / 4); + uint32_t row = (idx + tid) / (MatBr / 4); + if (idx + tid < Bc * MatBr / 4) { + FLOAT_TYPEV4 K_Tf = FLOAT_TYPEV4(0); + if ((!KV_bounds_check || j * Bc + row < KV) && (HSK == HSK_pad || d * 16 + col_vec * 4 < HSK)) { +#if !defined(BFLOAT16) + if (USE_DECODE_K) { + uint coord = (j * Bc + row) * k_stride * BLOCK_SIZE_K + d * 16 + col_vec * 4; + uint ib = coord / BLOCK_SIZE_K; + uint iqs = (coord % BLOCK_SIZE_K); + K_Tf = dequantize4(ib, iqs, k_offset, BINDING_IDX_K); + } else +#endif + { + K_Tf = FLOAT_TYPEV4(data_kv4[k_offset / 4 + (j * Bc + row) * k_stride / 4 + d * 16 / 4 + col_vec]); + } + } + + kvsh[row * kvsh_stride + col_vec] = K_Tf; + } + } + barrier(); + } + + if (stage_k) { + uint coord = (gl_SubgroupID * MatBc) * kvsh_stride; + coopMatLoad(KMat, kvsh, coord, kvsh_stride, gl_CooperativeMatrixLayoutRowMajor); + } else { + const uint coord = k_offset / 4 + (j * Bc + gl_SubgroupID * MatBc) * k_stride / 4 + d * 16 / 4; + coopMatLoad(KMat, data_kv4, coord, k_stride / 4, gl_CooperativeMatrixLayoutRowMajor); + } + } else { + uint coord = (gl_SubgroupID * MatBc) * kvsh_stride + d * 16 / 4; + coopMatLoad(KMat, kvsh, coord, kvsh_stride, gl_CooperativeMatrixLayoutRowMajor); + } + + coopMatLoad(QMat, Qf, d * 16 / 4, qstride, gl_CooperativeMatrixLayoutColumnMajor); + + SfMat = coopMatMulAdd(KMat, QMat, SfMat); + } + + uint coord = gl_SubgroupID * MatBc * sfshstride; + coopMatStore(SfMat, sfsh, coord, sfshstride, gl_CooperativeMatrixLayoutRowMajor); + barrier(); + + if (LOGIT_SOFTCAP) { + [[unroll]] for (uint32_t idx = 0; idx < Bc * Br / 4; idx += gl_WorkGroupSize.x) { + uint32_t c = (idx + tid) / (Br / 4); + uint32_t r = (idx + tid) % (Br / 4); + if (idx + tid < Bc * Br / 4 || idx + gl_WorkGroupSize.x <= Bc * Br / 4) { + sfsh[c * sfshstride + r] = ACC_TYPEV4(p.logit_softcap * tanh(sfsh[c * sfshstride + r])); + } + } + barrier(); + } + + if (MASK_ENABLE && mask_opt_bits != MASK_OPT_ALL_ZERO) { + [[unroll]] for (uint32_t idx = 0; idx < Bc * Br / 4; idx += gl_WorkGroupSize.x) { + uint32_t c = (idx + tid) / (Br / 4); + uint32_t r = (idx + tid) % (Br / 4); + if (idx + tid < Bc * Br / 4 || idx + gl_WorkGroupSize.x <= Bc * Br / 4) { + if (!KV_bounds_check || j * Bc + c < KV) { + // Mask nem1 bounds check is handled when loading masks + ACC_TYPEV4 masks = ACC_TYPEV4(mask_cache[idx / WorkGroupSize]); + ACC_TYPEV4 slopes = ACC_TYPEV4(slope[r * 4], slope[r * 4 + 1], slope[r * 4 + 2], slope[r * 4 + 3]); + sfsh[c * sfshstride + r] += slopes * masks; + } + } + } + barrier(); + } + + float eMf[rows_per_thread]; + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + const uint r_vec = tile_row(r) / 4; + const uint r_comp = tile_row(r) % 4; + + float rowmaxf = NEG_FLT_MAX_OVER_2; + [[unroll]] for (uint32_t c = 0; c < cols_per_thread; ++c) { + if (KV_bounds_check && j * Bc + c * cols_per_iter + col_tid >= KV) { + continue; + } + rowmaxf = max(rowmaxf, float(sfsh[r_vec + (c * cols_per_iter + col_tid) * sfshstride][r_comp])); + } + rowmaxf += FATTN_KQ_MAX_OFFSET; + float Moldf = Mf[r]; + + // Compute max across the row + rowmaxf = subgroupMax(rowmaxf); + + // M = max(rowmax, Mold) + // P = e^(S - M) + // eM = e^(Mold - M) + Mf[r] = max(rowmaxf, Moldf); + eMf[r] = exp(Moldf - Mf[r]); + + Lf[r] = eMf[r]*Lf[r]; + } + + [[unroll]] for (uint32_t d0 = 0; d0 < HSV / 4; d0 += threads_per_rowgroup) { + const uint d_local = d0 / threads_per_rowgroup; + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + Of[r][d_local] = O_TYPE(eMf[r]) * Of[r][d_local]; + } + } + + // Calculate and store Pf in Psh + [[unroll]] for (uint32_t c = 0; c < cols_per_thread; ++c) { + const uint col = c * cols_per_iter + col_tid; + + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; r += 4) { + const uint row = tile_row(r); + if (KV_bounds_check && j * Bc + col >= KV) { + Psh[col * psh_stride + row / 4] = FLOAT_TYPEV4(0.0f); + } else { + const vec4 mfvec = vec4(Mf[r], Mf[r + 1], Mf[r + 2], Mf[r + 3]); + const FLOAT_TYPEV4 Pf = FLOAT_TYPEV4(exp(vec4(sfsh[row / 4 + col * sfshstride]) - mfvec)); + [[unroll]] for (uint32_t vec_idx = 0; vec_idx < 4; ++vec_idx) { + Lf[r + vec_idx] += Pf[vec_idx]; + } + Psh[col * psh_stride + row / 4] = Pf; + } + } + } + + if (SHMEM_STAGING != 0) { + [[unroll]] for (uint32_t idx = 0; idx < Bc * HSV_pad / 4; idx += gl_WorkGroupSize.x) { + uint32_t d = (idx + tid) % (HSV_pad / 4); + uint32_t c = (idx + tid) / (HSV_pad / 4); + if (idx + gl_WorkGroupSize.x <= Bc * HSV_pad / 4 || c < Bc) { + FLOAT_TYPEV4 V_Tf = FLOAT_TYPEV4(0); + if ((!KV_bounds_check || j * Bc + c < KV) && (HSV == HSV_pad || d < HSV / 4)) { +#if !defined(BFLOAT16) + if (USE_DECODE_V) { + uint coord = (j * Bc + c) * v_stride * BLOCK_SIZE_V + 4 * d; + uint ib = coord / BLOCK_SIZE_V; + uint iqs = (coord % BLOCK_SIZE_V); + V_Tf = dequantize4(ib, iqs, v_offset, BINDING_IDX_V); + } else +#endif + { + V_Tf = FLOAT_TYPEV4(data_vv4[v_offset / 4 + (j * Bc + c) * v_stride / 4 + d]); + } + } + + kvsh[c * kvsh_stride + d] = V_Tf; + } + } + } + barrier(); + + const uint num_hsv_tiles = (HSV + MatBc * row_split - 1) / (MatBc * row_split); // round up + + // Each subgroup handles HSV/4 columns + [[unroll]] for (uint32_t hsv_tile = 0; hsv_tile < num_hsv_tiles; ++hsv_tile) { + const uint hsv_offset = (hsv_tile * row_split + gl_SubgroupID) * 16; + + coopmat PVMat = coopmat(0); + + // Preload V tiles for [Bc, 16 * num subgroups] + const uint v_rows = Bc; + const uint v_total = v_rows * v_cols; + const uint v_loads_per_thread = v_total / gl_WorkGroupSize.x; + + // If SHMEM_STAGING is set, a Bc * HSV_pad size tile of V is loaded to shmem. + // If not, V is loaded directly from global memory if aligned, otherwise + // staged through a Bc * MatBr size staging buffer. + // If V is a quant type, then it is always staged for dequantization. + if (SHMEM_STAGING == 0) { + // For quants we always preload via kvsh. For f16/bf16 we only preload when + // alignment / bounds force it (otherwise we coopMatLoad direct from data_vv4). + const bool stage_v = USE_DECODE_V || KV_bounds_check; + if (stage_v) { + [[unroll]] for (uint32_t i = 0; i < v_loads_per_thread; ++i) { + const uint idx = i * gl_WorkGroupSize.x + tid; + const uint row = idx / v_cols; + const uint col = idx % v_cols; + + const uint v_row = j * Bc + row; + const uint v_col = hsv_tile * MatBc * row_split + col * 4; + + const uint coord = v_row * v_stride * BLOCK_SIZE_V + v_col; + const uint ib = coord / BLOCK_SIZE_V; + const uint iqs = coord % BLOCK_SIZE_V; + + if (!KV_bounds_check || (v_row < KV && v_col < HSV)) { +#if !defined(BFLOAT16) + if (USE_DECODE_V) { + kvsh[row * vsh_stride + col] = dequantize4(ib, iqs, v_offset, BINDING_IDX_V); + } else +#endif + { + kvsh[row * vsh_stride + col] = data_vv4[(v_offset + v_row * v_stride + v_col) / 4]; + } + } else { + kvsh[row * vsh_stride + col] = FLOAT_TYPEV4(0.0f); + } + } + } + } + barrier(); + + const uint o_offset = gl_SubgroupID * MatBr / 4; + + if (hsv_offset < HSV_pad) { + [[unroll]] for (uint32_t bc_chunk = 0; bc_chunk < Bc / MatBc; ++bc_chunk) { + coopMatLoad(KMat, Psh, bc_chunk * MatBc * psh_stride, psh_stride, gl_CooperativeMatrixLayoutColumnMajor); + + if (SHMEM_STAGING == 0) { + if (!USE_DECODE_V && !KV_bounds_check) { + // F16/BF16 values can be loaded directly from global memory + const uint v_tile_row = j * Bc + bc_chunk * MatBc; + const uint v_tile_offset = v_offset / 4 + v_tile_row * v_stride / 4 + hsv_offset / 4; + coopMatLoad(QMat, data_vv4, v_tile_offset, v_stride / 4, gl_CooperativeMatrixLayoutRowMajor); + } else { + const uint v_tile_offset = bc_chunk * MatBr * v_cols + gl_SubgroupID * (MatBc / 4); + coopMatLoad(QMat, kvsh, v_tile_offset, vsh_stride, gl_CooperativeMatrixLayoutRowMajor); + } + } else { + const uint v_tile_offset = bc_chunk * MatBc * kvsh_stride + (hsv_tile * row_split + gl_SubgroupID) * (MatBc / 4); + coopMatLoad(QMat, kvsh, v_tile_offset, kvsh_stride, gl_CooperativeMatrixLayoutRowMajor); + } + + PVMat = coopMatMulAdd(KMat, QMat, PVMat); + } + + // Store PVMat to pvsh and load into Of + coopMatStore(PVMat, pvsh, o_offset, osh_stride, gl_CooperativeMatrixLayoutRowMajor); + } + + barrier(); + + const uint hsv_per_tile = row_split * MatBc; + const uint hsv_base = hsv_tile * hsv_per_tile; + const uint d_values_per_tile = hsv_per_tile / 4; + + const uint d_start = hsv_tile * d_values_per_tile; + const uint d_end = min(d_start + d_values_per_tile, HSV / 4); + + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + const uint row = tile_row(r); + + [[unroll]] for (uint32_t d_local = 0; d_local < d_per_thread; ++d_local) { + const uint d = d_local * threads_per_rowgroup + col_tid; + const uint hsv_col = 4 * d; + + if (hsv_col >= hsv_base && hsv_col < hsv_base + hsv_per_tile && hsv_col < HSV) { + const uint local_hsv = (hsv_col - hsv_base) / 4; + Of[r][d_local] += pvsh[row * osh_stride + local_hsv]; + } + } + } + } + + barrier(); + } + + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + Lf[r] = subgroupAdd(Lf[r]); + } + + // If there is split_k, then the split_k resolve shader does the final + // division by L. Store the intermediate O value and per-row m and L values. + if (p.k_num > 1) { + if (p.gqa_ratio > 1) { + // note: O and Q have swapped coord 1,2. + uint32_t o_offset = HSV * p.ne1 * (split_k_index + p.k_num * (gqa_iq1 + p.ne2 * iq3)) / 4; + + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + if (tile_row(r) < N) { + [[unroll]] for (uint32_t d0 = 0; d0 < HSV / 4; d0 += threads_per_rowgroup) { + const uint d = d0 + col_tid; + if (d >= HSV/4) break; + const uint d_local = d0 / threads_per_rowgroup; + gqaStore(tile_row(r), d, Of[r][d_local], o_offset, iq2, N); + } + } + } + + o_offset = HSV * p.ne1 * p.k_num * p.ne2 * p.ne3 + p.ne1 * 2 * (split_k_index + p.k_num * (gqa_iq1 + p.ne2 * iq3)); + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + if (tile_row(r) < N) { + perElemOpStoreCol0(tile_row(r), 0u, ACC_TYPE(Lf[r]), o_offset, iq2, N); + perElemOpStoreCol0(tile_row(r), 0u, ACC_TYPE(Mf[r]), o_offset + p.ne1, iq2, N); + } + } + } else { + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + const uint row = tile_row(r); + const uint global_row = i * Br + row; + + if (global_row < N) { + uint32_t o_offset = HSV * p.ne1 * (split_k_index + p.k_num * (global_row + p.ne2 * iq3)) / 4; + + [[unroll]] for (uint32_t d0 = 0; d0 < HSV / 4; d0 += threads_per_rowgroup) { + const uint d = d0 + col_tid; + if (d >= HSV/4) break; + data_ov4[o_offset + iq2 * HSV/4 + d] = D_TYPEV4(Of[r][d/threads_per_rowgroup]); + } + } + + if (global_row < N && col_tid == 0) { + uint32_t lm_offset = HSV * p.ne1 * p.k_num * p.ne2 * p.ne3 + p.ne1 * 2 * (split_k_index + p.k_num * (global_row + p.ne2 * iq3)); + data_o[lm_offset + iq2] = D_TYPE(Lf[r]); + data_o[lm_offset + p.ne1 + iq2] = D_TYPE(Mf[r]); + } + } + } + + return; + } + + if ((p.mask_n_head_log2 & SINK_ENABLE_BIT) != 0) { + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + float sink = perElemOpGetSink(tile_row(r), 0u, ACC_TYPE(0), iq2); + + float ms = 1.0f; + float vs = 1.0f; + + if (sink > Mf[r]) { + ms = exp(Mf[r] - sink); + + [[unroll]] for (uint32_t d0 = 0; d0 < HSV / 4; d0 += threads_per_rowgroup) { + const uint d_local = d0 / threads_per_rowgroup; + Of[r][d_local] *= O_TYPE(ms); + } + } else { + vs = exp(sink - Mf[r]); + } + + Lf[r] = Lf[r]*ms + vs; + } + } + + float Lfrcp[rows_per_thread]; + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + Lfrcp[r] = (Lf[r] == 0.0) ? 0.0 : (1.0 / Lf[r]); + } + + [[unroll]] for (uint32_t d0 = 0; d0 < HSV / 4; d0 += threads_per_rowgroup) { + const uint d_local = d0 / threads_per_rowgroup; + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + Of[r][d_local] *= O_TYPE(Lfrcp[r]); +#if defined(FLOAT_TYPE_MAX) + Of[r][d_local] = clamp(Of[r][d_local], -FLOAT_TYPE_MAX, FLOAT_TYPE_MAX); +#endif + } + } + + uint32_t o_offset = (gqa_iq1*p.ne1*HSV + iq3*p.ne2*p.ne1*HSV) / 4; + + if (p.gqa_ratio > 1) { + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + if (tile_row(r) < N) { + [[unroll]] for (uint32_t d0 = 0; d0 < HSV / 4; d0 += threads_per_rowgroup) { + const uint d = d0 + col_tid; + if (d >= HSV / 4) break; + const uint d_local = d0 / threads_per_rowgroup; + gqaStore(tile_row(r), d, Of[r][d_local], o_offset, iq2, N); + } + } + } + } else { + [[unroll]] for (uint32_t r = 0; r < rows_per_thread; ++r) { + if (i * Br + tile_row(r) < N) { + [[unroll]] for (uint32_t d0 = 0; d0 < HSV / 4; d0 += threads_per_rowgroup) { + const uint d = d0 + col_tid; + if (d >= HSV / 4) break; + const uint d_local = d0 / threads_per_rowgroup; + data_ov4[o_offset + (iq2 * HSV + (i * Br + tile_row(r)) * p.ne1 * HSV) / 4 + d] = D_TYPEV4(Of[r][d_local]); + } + } + } + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp new file mode 100644 index 0000000000000000000000000000000000000000..b9c03fe499d4a5c80ea3a8a4af72871d0a99c1cf --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm2.comp @@ -0,0 +1,481 @@ +#version 450 + +#extension GL_EXT_control_flow_attributes : enable +#extension GL_EXT_shader_16bit_storage : require + +#extension GL_EXT_shader_explicit_arithmetic_types_float16 : require +#extension GL_EXT_shader_explicit_arithmetic_types_int8 : require +#extension GL_EXT_shader_explicit_arithmetic_types_int32 : require +#extension GL_EXT_shader_explicit_arithmetic_types_int16 : require + +#if defined(BFLOAT16) +#extension GL_EXT_bfloat16 : enable +#endif + +#extension GL_KHR_memory_scope_semantics : enable +#extension GL_KHR_cooperative_matrix : enable +#extension GL_NV_cooperative_matrix2 : enable +#ifdef GL_NV_cooperative_matrix_decode_vector +#extension GL_NV_cooperative_matrix_decode_vector : enable +#endif +#extension GL_EXT_buffer_reference : enable +#extension GL_KHR_shader_subgroup_ballot : enable +#extension GL_KHR_shader_subgroup_vote : enable +#extension GL_EXT_null_initializer : enable + +#include "types.glsl" +#include "flash_attn_base.glsl" +#if !defined(BFLOAT16) +#include "dequant_funcs_cm2.glsl" +#endif + +// buffer_reference stride = sizeof(struct) = FaBlockBytesK/V. +layout(buffer_reference, std430, buffer_reference_align = 1) buffer decodeBufFA_K { + uint8_t raw[FaBlockBytesK]; +}; +layout(buffer_reference, std430, buffer_reference_align = 1) buffer decodeBufFA_V { + uint8_t raw[FaBlockBytesV]; +}; + +#if !defined(BFLOAT16) +float16_t faDecodeK(const decodeBufFA_K bl_in, const uint blockCoords[2], const uint coordInBlock[2]) { + switch (FaTypeK) { + case FA_TYPE_F32: return dequantFuncF32 (decodeBufF32 (bl_in), blockCoords, coordInBlock); + case FA_TYPE_Q4_0: return dequantFuncQ4_0(decodeBufQ4_0(bl_in), blockCoords, coordInBlock); + case FA_TYPE_Q4_1: return dequantFuncQ4_1(decodeBufQ4_1(bl_in), blockCoords, coordInBlock); + case FA_TYPE_Q5_0: return dequantFuncQ5_0(decodeBufQ5_0(bl_in), blockCoords, coordInBlock); + case FA_TYPE_Q5_1: return dequantFuncQ5_1(decodeBufQ5_1(bl_in), blockCoords, coordInBlock); + case FA_TYPE_Q8_0: return dequantFuncQ8_0(decodeBufQ8_0(bl_in), blockCoords, coordInBlock); + case FA_TYPE_Q1_0: return dequantFuncQ1_0(decodeBufQ1_0(bl_in), blockCoords, coordInBlock); + default: return float16_t(0); + } +} + +float16_t faDecodeV(const decodeBufFA_V bl_in, const uint blockCoords[2], const uint coordInBlock[2]) { + switch (FaTypeV) { + case FA_TYPE_F32: return dequantFuncF32 (decodeBufF32 (bl_in), blockCoords, coordInBlock); + case FA_TYPE_Q4_0: return dequantFuncQ4_0(decodeBufQ4_0(bl_in), blockCoords, coordInBlock); + case FA_TYPE_Q4_1: return dequantFuncQ4_1(decodeBufQ4_1(bl_in), blockCoords, coordInBlock); + case FA_TYPE_Q5_0: return dequantFuncQ5_0(decodeBufQ5_0(bl_in), blockCoords, coordInBlock); + case FA_TYPE_Q5_1: return dequantFuncQ5_1(decodeBufQ5_1(bl_in), blockCoords, coordInBlock); + case FA_TYPE_Q8_0: return dequantFuncQ8_0(decodeBufQ8_0(bl_in), blockCoords, coordInBlock); + case FA_TYPE_Q1_0: return dequantFuncQ1_0(decodeBufQ1_0(bl_in), blockCoords, coordInBlock); + default: return float16_t(0); + } +} + +// V=4 vector decode for K/V; dispatches to per-format _v decoders. +f16vec4 faDecodeKVector(const decodeBufFA_K bl_in, const uint blockCoords[2], const uint coordInBlock[2]) { + switch (FaTypeK) { + case 0u: return f16vec4(decodeBufF32(bl_in).block); + case 2u: return dequantFuncQ4_0_v(decodeBufQ4_0(bl_in), blockCoords, coordInBlock); + case 3u: return dequantFuncQ4_1_v(decodeBufQ4_1(bl_in), blockCoords, coordInBlock); + case 6u: return dequantFuncQ5_0_v(decodeBufQ5_0(bl_in), blockCoords, coordInBlock); + case 7u: return dequantFuncQ5_1_v(decodeBufQ5_1(bl_in), blockCoords, coordInBlock); + case 8u: return dequantFuncQ8_0_v(decodeBufQ8_0(bl_in), blockCoords, coordInBlock); + case 41u: return dequantFuncQ1_0_v(decodeBufQ1_0(bl_in), blockCoords, coordInBlock); + default: return f16vec4(0); + } +} + +f16vec4 faDecodeVVector(const decodeBufFA_V bl_in, const uint blockCoords[2], const uint coordInBlock[2]) { + switch (FaTypeV) { + case 0u: return f16vec4(decodeBufF32(bl_in).block); + case 2u: return dequantFuncQ4_0_v(decodeBufQ4_0(bl_in), blockCoords, coordInBlock); + case 3u: return dequantFuncQ4_1_v(decodeBufQ4_1(bl_in), blockCoords, coordInBlock); + case 6u: return dequantFuncQ5_0_v(decodeBufQ5_0(bl_in), blockCoords, coordInBlock); + case 7u: return dequantFuncQ5_1_v(decodeBufQ5_1(bl_in), blockCoords, coordInBlock); + case 8u: return dequantFuncQ8_0_v(decodeBufQ8_0(bl_in), blockCoords, coordInBlock); + case 41u: return dequantFuncQ1_0_v(decodeBufQ1_0(bl_in), blockCoords, coordInBlock); + default: return f16vec4(0); + } +} + +#ifdef GL_NV_cooperative_matrix_decode_vector +#define FADECODEK , faDecodeK, faDecodeKVector +#define FADECODEV , faDecodeV, faDecodeVVector +#else +#define FADECODEK , faDecodeK +#define FADECODEV , faDecodeV +#endif +#endif + +layout (binding = 0) readonly buffer Q {uint8_t data_q[];}; +layout (binding = 1) readonly buffer K {uint8_t data_k[];}; +layout (binding = 2) readonly buffer V {uint8_t data_v[];}; +layout (binding = 3) readonly buffer M {uint8_t data_m[];}; + +ACC_TYPE maxReduce(const in ACC_TYPE x, const in ACC_TYPE y) { + return max(x, y); +} + +float16_t maxReduceFp16(const in float16_t x, const in float16_t y) { + return max(x, y); +} + +ACC_TYPE smearReduce(const in ACC_TYPE x, const in ACC_TYPE y) { + return x; +} + +// Replace matrix elements >= numRows or numCols with 'replace' +ACC_TYPE replacePadding(const in uint32_t row, const in uint32_t col, const in ACC_TYPE elem, const in ACC_TYPE replace, const in uint32_t numRows, const in uint32_t numCols) { + if (row >= numRows || col >= numCols) { + return replace; + } + return elem; +} + +ACC_TYPE Exp(const in uint32_t row, const in uint32_t col, const in ACC_TYPE elem) +{ + return exp(elem); +} + +ACC_TYPE Max(const in uint32_t row, const in uint32_t col, const in ACC_TYPE elem0, const in ACC_TYPE elem1) +{ + return max(elem0, elem1); +} + +// Store the output when doing grouped query attention. +// Rows index by Q's dimension 2, and the first N rows are valid. +D_TYPE perElemOpGqaStore(const in uint32_t r, const in uint32_t c, const in D_TYPE elem, const in uint32_t o_offset, const in uint32_t iq2, const in uint32_t N) +{ + if (r < N && c < HSV) { + uint32_t offset = (iq2 + r) * HSV + c; + data_o[o_offset + offset] = D_TYPE(elem); + } + return elem; +} + +// Store O values for non-GQA split_k. Rows are tokens, not heads. +D_TYPE perElemOpNonGqaSplitKStore(const in uint32_t r, const in uint32_t c, const in D_TYPE elem, const in uint32_t unused, const in uint32_t iq2, const in uint32_t N) { + uint32_t global_row = i * Br + r; + if (global_row < N && c < HSV) { + uint32_t o_off = HSV * p.ne1 + * (split_k_index + p.k_num * (global_row + p.ne2 * iq3)); + data_o[o_off + iq2 * HSV + c] = D_TYPE(elem); + } + return elem; +} + +// Store L/M values for non-GQA split_k. +ACC_TYPE perElemOpNonGqaSplitKStoreCol0(const in uint32_t r, const in uint32_t c, const in ACC_TYPE elem, const in uint32_t lm_base, const in uint32_t iq2, const in uint32_t N) { + uint32_t global_row = i * Br + r; + if (global_row < N && c == 0) { + uint32_t lm_off = HSV * p.ne1 * p.k_num * p.ne2 * p.ne3 + + p.ne1 * 2 * (split_k_index + p.k_num * (global_row + p.ne2 * iq3)); + data_o[lm_off + lm_base + iq2] = D_TYPE(elem); + } + return elem; +} + +void main() { + init_indices(); + + tensorLayoutNV<2, gl_CooperativeMatrixClampModeConstantNV> tensorLayoutQ = createTensorLayoutNV(2, gl_CooperativeMatrixClampModeConstantNV); + tensorLayoutNV<2, Clamp> tensorLayoutK = createTensorLayoutNV(2, Clamp); + tensorLayoutNV<2, Clamp> tensorLayoutV = createTensorLayoutNV(2, Clamp); + + tensorViewNV<2, false, 1, 0> tensorViewTranspose = createTensorViewNV(2, false, 1, 0); + + const uint bs_k = fa_block_elems(FaTypeK); + const uint bs_v = fa_block_elems(FaTypeV); + tensorLayoutK = setTensorLayoutBlockSizeNV(tensorLayoutK, 1, bs_k); + tensorLayoutV = setTensorLayoutBlockSizeNV(tensorLayoutV, 1, bs_v); + + tensorLayoutQ = setTensorLayoutDimensionNV(tensorLayoutQ, N, HSK); + tensorLayoutK = setTensorLayoutDimensionNV(tensorLayoutK, KV, HSK); + tensorLayoutV = setTensorLayoutDimensionNV(tensorLayoutV, KV, HSV); + + // hint to the compiler that strides are aligned for the aligned variant of the shader + if (Clamp != gl_CooperativeMatrixClampModeConstantNV) + { + q_stride &= ~7; + if (bs_k == 1u) { + k_stride &= ~7; + } + if (bs_v == 1u) { + v_stride &= ~7; + } + m_stride &= ~7; + } + tensorLayoutQ = setTensorLayoutStrideNV(tensorLayoutQ, q_stride, 1); + tensorLayoutK = setTensorLayoutStrideNV(tensorLayoutK, k_stride, 1); + tensorLayoutV = setTensorLayoutStrideNV(tensorLayoutV, v_stride, 1); + + coopmat Q; + coopmat Qf16; + + uint32_t q_offset = gqa_iq1*p.nb01*4/*sizeof(float)*/ + iq2*p.nb02+iq3*p.nb03; + coopMatLoadTensorNV(Q, data_q, q_offset, sliceTensorLayoutNV(tensorLayoutQ, i * Br, Br, 0, HSK_pad)); + + Q *= Q_TYPE(p.scale); + Qf16 = coopmat(Q); + + coopmat O = coopmat(0); + + coopmat L, M; + + // Use -FLT_MAX/2 rather than -inf to reduce the possibility of NaNs, e.g. when computing Mold-M. + const float NEG_FLT_MAX_OVER_2 = uintBitsToFloat(0xFEFFFFFF); + + L = coopmat(0); +#if defined(ACC_TYPE_MAX) + M = coopmat(-ACC_TYPE_MAX / ACC_TYPE(2)); +#else + M = coopmat(NEG_FLT_MAX_OVER_2); +#endif + + coopmat slopeMat = coopmat(1.0); + + // ALiBi + if (p.max_bias > 0.0f) { + coopMatPerElementNV(slopeMat, slopeMat, perElemOpComputeSlope, iq2); + } + + const uint32_t mo_stride = CEIL_DIV(KV, 16 * Bc); + // mo_offset will point to the tile starting at row i*Br and col 0 + uint32_t mo_offset = mo_stride * i; + + uint32_t m_offset = gqa_iq1*KV * 2 /*sizeof(float16_t)*/; + if (p.nem2 != 1 || p.nem3 != 1) { + m_offset += ((iq3 % p.nem3) * p.nem2 + (iq2 % p.nem2)) * p.nem1 * KV * 2 /*sizeof(float16_t)*/; + mo_offset += ((iq3 % p.nem3) * p.nem2 + (iq2 % p.nem2)) * CEIL_DIV(p.nem1, Br) * mo_stride; + } + + uint32_t mask_opt = 0; + uint32_t mask_opt_idx = ~0; + + [[dont_unroll]] + for (uint32_t j = start_j; j < end_j; ++j) { + + coopmat mv = coopmat(0); + if (MASK_ENABLE) { + + if (USE_MASK_OPT && mask_opt_idx != j / 16) { + mask_opt_idx = j / 16; + mask_opt = data_mask_opt[mo_offset + mask_opt_idx]; + } + uint32_t mask_opt_bits = (mask_opt >> ((j % 16) * 2)) & 0x3; + if (mask_opt_bits == MASK_OPT_ALL_NEG_INF) { + // skip this block + continue; + } + // Only load if the block is not all zeros + if (mask_opt_bits != MASK_OPT_ALL_ZERO) { + bool nem1_bounds_check = !(p.gqa_ratio > 1) && (p.nem1 % Br) != 0; + + if (nem1_bounds_check) { + tensorLayoutNV<2, gl_CooperativeMatrixClampModeConstantNV> tensorLayoutM = createTensorLayoutNV(2, gl_CooperativeMatrixClampModeConstantNV); + tensorLayoutM = setTensorLayoutDimensionNV(tensorLayoutM, p.nem1, KV); + tensorLayoutM = setTensorLayoutStrideNV(tensorLayoutM, m_stride, 1); + tensorLayoutM = setTensorLayoutClampValueNV(tensorLayoutM, 0xfc00); // -inf in float16_t + + coopmat mvmax; + + coopMatLoadTensorNV(mv, data_m, m_offset, sliceTensorLayoutNV(tensorLayoutM, i * Br, Br, j * Bc, Bc)); + // skip the block if the mask is entirely -inf + coopMatReduceNV(mvmax, mv, gl_CooperativeMatrixReduceRowAndColumnNV, maxReduceFp16); + if (mvmax[0] <= NEG_FLT_MAX_OVER_2) { + continue; + } + } else { + tensorLayoutNV<2, Clamp> tensorLayoutM = createTensorLayoutNV(2, Clamp); + // Don't clamp against nem1 when GQA is enabled + uint32_t m_height = p.gqa_ratio > 1 ? ~0 : p.nem1; + tensorLayoutM = setTensorLayoutDimensionNV(tensorLayoutM, m_height, KV); + tensorLayoutM = setTensorLayoutStrideNV(tensorLayoutM, m_stride, 1); + + coopmat mvmax; + + coopMatLoadTensorNV(mv, data_m, m_offset, sliceTensorLayoutNV(tensorLayoutM, i * Br, Br, j * Bc, Bc)); + // skip the block if the mask is entirely -inf + coopMatReduceNV(mvmax, mv, gl_CooperativeMatrixReduceRowAndColumnNV, maxReduceFp16); + if (mvmax[0] <= NEG_FLT_MAX_OVER_2) { + continue; + } + } + } + } + + coopmat S = coopmat(0); + + coopmat K_T; + + uint32_t k_offset = ik2*p.nb12 + ik3*p.nb13; + // F16: bs_k==1 (direct load). F32: bs_k==4 (vec4 / dequantFuncF32). Q4/Q8 family: bs_k==32. Q1_0: bs_k==128. +#if defined(BFLOAT16) + coopMatLoadTensorNV(K_T, data_k, k_offset, sliceTensorLayoutNV(tensorLayoutK, j * Bc, Bc, 0, HSK_pad), tensorViewTranspose); +#else + const bool k_use_decode = (bs_k > 1u); + if (k_use_decode) { + coopMatLoadTensorNV(K_T, data_k, k_offset, sliceTensorLayoutNV(tensorLayoutK, j * Bc, Bc, 0, HSK_pad), tensorViewTranspose FADECODEK); + } else { + coopMatLoadTensorNV(K_T, data_k, k_offset, sliceTensorLayoutNV(tensorLayoutK, j * Bc, Bc, 0, HSK_pad), tensorViewTranspose); + } +#endif + S = coopMatMulAdd(Qf16, K_T, S); + + if (LOGIT_SOFTCAP) { + [[unroll]] + for (int k = 0; k < S.length(); ++k) { + S[k] = ACC_TYPE(p.logit_softcap)*tanh(S[k]); + } + } + + if (MASK_ENABLE) { + S += slopeMat*coopmat(mv); + } + + // Clear padding elements to -inf, so they don't contribute to rowmax + if (Clamp != 0 && + ((j + 1) * Bc > KV || + (i + 1) * Br > N)) { + + uint R = ((i + 1) * Br > N) ? (N % Br) : Br; + uint C = ((j + 1) * Bc > KV) ? (KV % Bc) : Bc; + + coopMatPerElementNV(S, S, replacePadding, ACC_TYPE(NEG_FLT_MAX_OVER_2), R, C); + } + + coopmat rowmax, P, rowsum, eM; + + coopMatReduceNV(rowmax, S, gl_CooperativeMatrixReduceRowNV, maxReduce); + + rowmax += coopmat(FATTN_KQ_MAX_OFFSET); + + coopmat Mold = M; + + // M = max(rowmax, Mold) + // P = e^(S - M) + // eM = e^(Mold - M) + coopMatPerElementNV(M, rowmax, Max, Mold); + coopMatPerElementNV(P, S - M, Exp); + coopMatPerElementNV(eM, Mold - M, Exp); + + // Clear padding elements to 0, so they don't contribute to rowsum + if (Clamp != 0 && + ((j + 1) * Bc > KV || + (i + 1) * Br > N)) { + + uint R = ((i + 1) * Br > N) ? (N % Br) : Br; + uint C = ((j + 1) * Bc > KV) ? (KV % Bc) : Bc; + + coopMatPerElementNV(P, P, replacePadding, ACC_TYPE(0.0), R, C); + } + + coopmat P_A = coopmat(P); + + // compute rowsum by multiplying by matrix of all ones. + coopmat One = coopmat(1.0); + + rowsum = coopmat(0.0); + rowsum = coopMatMulAdd(P_A, One, rowsum); + + coopmat V; + uint32_t v_offset = iv2*p.nb22 + iv3*p.nb23; +#if defined(BFLOAT16) + coopMatLoadTensorNV(V, data_v, v_offset, sliceTensorLayoutNV(tensorLayoutV, j * Bc, Bc, 0, HSV_pad)); +#else + const bool v_use_decode = (bs_v > 1u); + if (v_use_decode) { + coopMatLoadTensorNV(V, data_v, v_offset, sliceTensorLayoutNV(tensorLayoutV, j * Bc, Bc, 0, HSV_pad) FADECODEV); + } else { + coopMatLoadTensorNV(V, data_v, v_offset, sliceTensorLayoutNV(tensorLayoutV, j * Bc, Bc, 0, HSV_pad)); + } +#endif + + L = eM*L + rowsum; + + // This is the "diagonal" matrix in the paper, but since we do componentwise + // multiply rather than matrix multiply it has the diagonal element smeared + // across the row + coopmat eMdiag; + + // resize eM by using smear/reduce + coopMatReduceNV(eMdiag, eM, gl_CooperativeMatrixReduceRowNV, smearReduce); + + O *= coopmat(eMdiag); + O = coopMatMulAdd(P_A, V, O); + } + + // If there is split_k, then the split_k resolve shader does the final + // division by L. Store the intermediate O value and per-row m and L values. + if (p.k_num > 1) { + coopmat O_D = coopmat(O); + + if (p.gqa_ratio > 1) { + // note: O and Q have swapped coord 1,2. + uint32_t o_offset = HSV * p.ne1 * (split_k_index + p.k_num * (gqa_iq1 + p.ne2 * iq3)); + coopMatPerElementNV(O_D, O_D, perElemOpGqaStore, o_offset, iq2, N); + + o_offset = HSV * p.ne1 * p.k_num * p.ne2 * p.ne3 + p.ne1 * 2 * (split_k_index + p.k_num * (gqa_iq1 + p.ne2 * iq3)); + coopMatPerElementNV(L, L, perElemOpStoreCol0, o_offset, iq2, N); + coopMatPerElementNV(M, M, perElemOpStoreCol0, o_offset + p.ne1, iq2, N); + } else { + coopMatPerElementNV(O_D, O_D, perElemOpNonGqaSplitKStore, 0u, iq2, N); + coopMatPerElementNV(L, L, perElemOpNonGqaSplitKStoreCol0, 0u, iq2, N); + coopMatPerElementNV(M, M, perElemOpNonGqaSplitKStoreCol0, p.ne1, iq2, N); + } + return; + } + + coopmat Ldiag; + + // resize L by using smear/reduce + coopMatReduceNV(Ldiag, L, gl_CooperativeMatrixReduceRowNV, smearReduce); + + if ((p.mask_n_head_log2 & SINK_ENABLE_BIT) != 0) { + coopmat S; + coopMatPerElementNV(S, S, perElemOpGetSink, iq2); + + coopmat Mr; + + // resize M by using smear/reduce + coopMatReduceNV(Mr, M, gl_CooperativeMatrixReduceRowNV, smearReduce); + + // O, Ldiag, Mr all have the same type so all element locations match + [[unroll]] for (uint32_t i = 0; i < Ldiag.length(); ++i) { + ACC_TYPE sink = S[i]; + + ACC_TYPE ms = ACC_TYPE(1.0f); + ACC_TYPE vs = ACC_TYPE(1.0f); + + if (sink > Mr[i]) { + ms = exp(Mr[i] - sink); + + O[i] *= O_TYPE(ms); + } else { + vs = exp(sink - Mr[i]); + } + + Ldiag[i] = Ldiag[i]*ms + vs; + } + } + + [[unroll]] + for (int k = 0; k < Ldiag.length(); ++k) { + Ldiag[k] = (Ldiag[k] == 0.0) ? ACC_TYPE(0.0) : (ACC_TYPE(1.0) / Ldiag[k]); + } + + coopmat O_D = coopmat(O); + + O_D = coopmat(Ldiag)*O_D; + +#if defined(ACC_TYPE_MAX) + [[unroll]] for (uint i = 0; i < O_D.length(); ++i) { O_D[i] = clamp(O_D[i], D_TYPE(-ACC_TYPE_MAX), D_TYPE(ACC_TYPE_MAX)); } +#endif + + uint32_t o_offset = gqa_iq1*p.ne1*HSV + iq3*p.ne2*p.ne1*HSV; + + if (p.gqa_ratio > 1) { + coopMatPerElementNV(O_D, O_D, perElemOpGqaStore, o_offset, iq2, N); + } else { + tensorLayoutNV<3, gl_CooperativeMatrixClampModeConstantNV> tensorLayoutD = createTensorLayoutNV(3, gl_CooperativeMatrixClampModeConstantNV); + tensorLayoutD = setTensorLayoutDimensionNV(tensorLayoutD, p.ne2, p.ne1, HSV); + + // permute dimensions + tensorViewNV<3, false, 1, 0, 2> tensorViewPermute = createTensorViewNV(3, false, 1, 0, 2); + + coopMatStoreTensorNV(O_D, data_o, o_offset, sliceTensorLayoutNV(tensorLayoutD, i * Br, Br, iq2, N, 0, HSV_pad), tensorViewPermute); + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_dequant.glsl b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_dequant.glsl new file mode 100644 index 0000000000000000000000000000000000000000..8704479d9600aa9196d8c4a34cd6c7c942203fde --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_dequant.glsl @@ -0,0 +1,131 @@ +// Asymmetric K/V flash attention: aliased SSBO views of bindings 1 (K) and 2 (V) +// covering every supported FA element type, plus an uber dequantize4() that +// switches on FaTypeK / FaTypeV. After spec-constant specialization the driver +// folds away every path except the one matching the K/V type for this pipeline. +// +// Included by flash_attn.comp and flash_attn_cm1.comp. Not included by +// flash_attn_cm2.comp, which has its own buffer_reference-based decode path. +// +// We use macros (rather than per-quant decode functions taking a struct) on +// purpose: the FA shaders don't enable GL_EXT_shader_explicit_arithmetic_types_float16 +// when FLOAT16 isn't defined, which makes float16-containing struct values +// illegal to return from / pass to functions. Macros expand inline where the +// float16 stays in storage and is converted to FLOAT_TYPE at use. + +// F32 is fed as a vec4 "block" (4 floats), matching what dequant_funcs_cm2.glsl +// does for F32 in the cm2 shader. FaBlockBytesK/V == 16 for F32. +layout (binding = 1) readonly buffer K_PACKED_F32 { vec4 data[]; } k_packed_f32; +layout (binding = 2) readonly buffer V_PACKED_F32 { vec4 data[]; } v_packed_f32; + +layout (binding = 1) readonly buffer K_PACKED_Q4_0 { block_q4_0_packed16 data[]; } k_packed_q4_0; +layout (binding = 2) readonly buffer V_PACKED_Q4_0 { block_q4_0_packed16 data[]; } v_packed_q4_0; +layout (binding = 1) readonly buffer K_PACKED_Q4_1 { block_q4_1_packed16 data[]; } k_packed_q4_1; +layout (binding = 2) readonly buffer V_PACKED_Q4_1 { block_q4_1_packed16 data[]; } v_packed_q4_1; +layout (binding = 1) readonly buffer K_PACKED_Q5_0 { block_q5_0_packed16 data[]; } k_packed_q5_0; +layout (binding = 2) readonly buffer V_PACKED_Q5_0 { block_q5_0_packed16 data[]; } v_packed_q5_0; +layout (binding = 1) readonly buffer K_PACKED_Q5_1 { block_q5_1_packed16 data[]; } k_packed_q5_1; +layout (binding = 2) readonly buffer V_PACKED_Q5_1 { block_q5_1_packed16 data[]; } v_packed_q5_1; +layout (binding = 1) readonly buffer K_PACKED_Q8_0 { block_q8_0_packed16 data[]; } k_packed_q8_0; +layout (binding = 2) readonly buffer V_PACKED_Q8_0 { block_q8_0_packed16 data[]; } v_packed_q8_0; + +layout (binding = 1) readonly buffer K_PACKED_BF16 { u16vec4 data[]; } k_packed_bf16; +layout (binding = 2) readonly buffer V_PACKED_BF16 { u16vec4 data[]; } v_packed_bf16; + +// Q4_1 and Q5_1 packed32 views: aliased to the same memory as the packed16 +// views, used by the MMQ K-side hot path for fast 4-uint loads. +layout (binding = 1) readonly buffer K_PACKED_Q4_1_P32 { block_q4_1_packed32 data[]; } k_packed_q4_1_p32; +layout (binding = 1) readonly buffer K_PACKED_Q5_1_P32 { block_q5_1_packed32 data[]; } k_packed_q5_1_p32; + +// Per-quant decode bodies are expanded once for the K view set and once for +// the V view set. The macros take the buffer name as a parameter. +#define FA_DEQUANT4_F32(BUF) \ + return FLOAT_TYPEV4(BUF.data[a_offset + ib]); + +#define FA_DEQUANT4_Q4_0(BUF) { \ + uint vui_lo = uint(BUF.data[a_offset + ib].qs[(iqs & 0xF) / 2 + 0]); \ + uint vui_hi = uint(BUF.data[a_offset + ib].qs[(iqs & 0xF) / 2 + 1]); \ + uint shift = (iqs & 0x10) >> 2; \ + vui_lo >>= shift; \ + vui_hi >>= shift; \ + FLOAT_TYPEV4 nibbles = FLOAT_TYPEV4(vui_lo & 0xF, (vui_lo >> 8) & 0xF, \ + vui_hi & 0xF, (vui_hi >> 8) & 0xF); \ + return FLOAT_TYPE(BUF.data[a_offset + ib].d) * (nibbles - FLOAT_TYPE(8.0f)); \ +} + +#define FA_DEQUANT4_Q4_1(BUF) { \ + uint vui_lo = uint(BUF.data[a_offset + ib].qs[(iqs & 0xF) / 2 + 0]); \ + uint vui_hi = uint(BUF.data[a_offset + ib].qs[(iqs & 0xF) / 2 + 1]); \ + uint shift = (iqs & 0x10) >> 2; \ + vui_lo >>= shift; \ + vui_hi >>= shift; \ + FLOAT_TYPEV4 nibbles = FLOAT_TYPEV4(vui_lo & 0xF, (vui_lo >> 8) & 0xF, \ + vui_hi & 0xF, (vui_hi >> 8) & 0xF); \ + return FLOAT_TYPE(BUF.data[a_offset + ib].d) * nibbles \ + + FLOAT_TYPE(BUF.data[a_offset + ib].m); \ +} + +#define FA_DEQUANT4_Q5_0(BUF) { \ + uint vui_lo = uint(BUF.data[a_offset + ib].qs[(iqs & 0xF) / 2 + 0]); \ + uint vui_hi = uint(BUF.data[a_offset + ib].qs[(iqs & 0xF) / 2 + 1]); \ + uint shift = (iqs & 0x10) >> 2; \ + vui_lo >>= shift; \ + vui_hi >>= shift; \ + uint qh = uint(BUF.data[a_offset + ib].qh[0]) \ + | (uint(BUF.data[a_offset + ib].qh[1]) << 16); \ + FLOAT_TYPEV4 hb = FLOAT_TYPEV4((qh >> iqs) & 1, (qh >> (iqs + 1)) & 1, \ + (qh >> (iqs + 2)) & 1, (qh >> (iqs + 3)) & 1) \ + * FLOAT_TYPE(16.0f); \ + FLOAT_TYPEV4 nibbles = FLOAT_TYPEV4(vui_lo & 0xF, (vui_lo >> 8) & 0xF, \ + vui_hi & 0xF, (vui_hi >> 8) & 0xF); \ + return FLOAT_TYPE(BUF.data[a_offset + ib].d) * (nibbles + hb - FLOAT_TYPE(16.0f)); \ +} + +#define FA_DEQUANT4_Q5_1(BUF) { \ + uint vui_lo = uint(BUF.data[a_offset + ib].qs[(iqs & 0xF) / 2 + 0]); \ + uint vui_hi = uint(BUF.data[a_offset + ib].qs[(iqs & 0xF) / 2 + 1]); \ + uint shift = (iqs & 0x10) >> 2; \ + vui_lo >>= shift; \ + vui_hi >>= shift; \ + uint qh = BUF.data[a_offset + ib].qh; \ + FLOAT_TYPEV4 hb = FLOAT_TYPEV4((qh >> iqs) & 1, (qh >> (iqs + 1)) & 1, \ + (qh >> (iqs + 2)) & 1, (qh >> (iqs + 3)) & 1) \ + * FLOAT_TYPE(16.0f); \ + FLOAT_TYPEV4 nibbles = FLOAT_TYPEV4(vui_lo & 0xF, (vui_lo >> 8) & 0xF, \ + vui_hi & 0xF, (vui_hi >> 8) & 0xF); \ + return FLOAT_TYPE(BUF.data[a_offset + ib].d) * (nibbles + hb) \ + + FLOAT_TYPE(BUF.data[a_offset + ib].m); \ +} + +#define FA_DEQUANT4_Q8_0(BUF) { \ + const i8vec2 v0 = unpack8(int32_t(BUF.data[a_offset + ib].qs[iqs / 2 ])).xy; \ + const i8vec2 v1 = unpack8(int32_t(BUF.data[a_offset + ib].qs[iqs / 2 + 1])).xy; \ + return FLOAT_TYPE(BUF.data[a_offset + ib].d) * FLOAT_TYPEV4(v0.x, v0.y, v1.x, v1.y); \ +} + +#define FA_DEQUANT4_BF16(BUF) \ + return FLOAT_TYPEV4(bf16_to_fp32(uvec4(BUF.data[(a_offset + ib) / 4]))); + +FLOAT_TYPEV4 dequantize4(uint ib, uint iqs, uint a_offset, uint binding_idx) { + if (binding_idx == BINDING_IDX_K) { + switch (FaTypeK) { + case FA_TYPE_F32: FA_DEQUANT4_F32 (k_packed_f32) + case FA_TYPE_Q4_0: FA_DEQUANT4_Q4_0(k_packed_q4_0) + case FA_TYPE_Q4_1: FA_DEQUANT4_Q4_1(k_packed_q4_1) + case FA_TYPE_Q5_0: FA_DEQUANT4_Q5_0(k_packed_q5_0) + case FA_TYPE_Q5_1: FA_DEQUANT4_Q5_1(k_packed_q5_1) + case FA_TYPE_Q8_0: FA_DEQUANT4_Q8_0(k_packed_q8_0) + case FA_TYPE_BF16: FA_DEQUANT4_BF16(k_packed_bf16) + } + } else { + switch (FaTypeV) { + case FA_TYPE_F32: FA_DEQUANT4_F32 (v_packed_f32) + case FA_TYPE_Q4_0: FA_DEQUANT4_Q4_0(v_packed_q4_0) + case FA_TYPE_Q4_1: FA_DEQUANT4_Q4_1(v_packed_q4_1) + case FA_TYPE_Q5_0: FA_DEQUANT4_Q5_0(v_packed_q5_0) + case FA_TYPE_Q5_1: FA_DEQUANT4_Q5_1(v_packed_q5_1) + case FA_TYPE_Q8_0: FA_DEQUANT4_Q8_0(v_packed_q8_0) + case FA_TYPE_BF16: FA_DEQUANT4_BF16(v_packed_bf16) + } + } + return FLOAT_TYPEV4(0); +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_mask_opt.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_mask_opt.comp new file mode 100644 index 0000000000000000000000000000000000000000..0e417708062b75fd6d6a00e3cd749b763cbc69ec --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_mask_opt.comp @@ -0,0 +1,162 @@ +#version 450 + +#extension GL_EXT_control_flow_attributes : enable +#extension GL_EXT_shader_16bit_storage : enable +#extension GL_KHR_shader_subgroup_arithmetic : enable + +layout (constant_id = 0) const uint BLOCK_SIZE = 128; +layout (constant_id = 1) const uint NUM_SUBGROUPS = 4; +layout (constant_id = 2) const uint Br = 32; +layout (constant_id = 3) const uint Bc = 32; + +layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {float16_t data_a[];}; +layout (binding = 0) readonly buffer Av4 {f16vec4 data_av4[];}; +layout (binding = 1) writeonly buffer D {uint data_d[];}; + +layout (push_constant) uniform parameter { + uint nem0; + uint nem1; + uint nem2; + uint nbm1; + uint nbm2; + uint nbm3; + uint nbd1; + uint nbd2; + uint nbd3; +}; + +#define MASK_OPT_ALL_NEG_INF 1 +#define MASK_OPT_ALL_ZERO 2 + +shared float minsh[NUM_SUBGROUPS]; +shared float maxsh[NUM_SUBGROUPS]; + +float FLT_MAX_OVER_2 = uintBitsToFloat(0x7EFFFFFF); + +void loadvec4(inout uint result, const uint i0, const uint i1, const uint i2, const uint i3, const bool need_bounds_check) { + const uint tid = gl_LocalInvocationIndex; + + [[unroll]] for (uint block_x = 0; block_x < 16; ++block_x) { + float min_v = FLT_MAX_OVER_2; + float max_v = -FLT_MAX_OVER_2; + [[unroll]] for (uint i = 0; i < Br * Bc / 4; i += BLOCK_SIZE) { + uint j0 = (i + tid) % (Bc / 4); + uint j1 = (i + tid) / (Bc / 4); + + j0 *= 4; + j0 += (i0 * 16 + block_x) * Bc; + j1 += i1 * Br; + + if (!need_bounds_check || j0 + 3 < nem0) { + vec4 f = vec4(data_av4[(j0 + j1 * nbm1 + i2 * nbm2 + i3 * nbm3) / 4]); + [[unroll]] for (int c = 0; c < 4; ++c) { + min_v = min(min_v, f[c]); + max_v = max(max_v, f[c]); + } + } else { + [[unroll]] for (int c = 0; c < 4; ++c) { + if (j0 + c < nem0) { + float f = float(data_a[j0 + j1 * nbm1 + i2 * nbm2 + i3 * nbm3]); + min_v = min(min_v, f); + max_v = max(max_v, f); + } + } + } + } + min_v = subgroupMin(min_v); + max_v = subgroupMax(max_v); + if (gl_SubgroupInvocationID == 0) { + minsh[gl_SubgroupID] = min_v; + maxsh[gl_SubgroupID] = max_v; + } + barrier(); + if (tid == 0) { + [[unroll]] for (uint i = 0; i < NUM_SUBGROUPS; ++i) { + min_v = min(min_v, minsh[i]); + max_v = max(max_v, maxsh[i]); + } + if (max_v <= -FLT_MAX_OVER_2) { + result |= 1 << (2*block_x); + } + if (min_v == 0.0f && max_v == 0.0f) { + result |= 2 << (2*block_x); + } + } + barrier(); + } +} + +// For each Br x Bc block of the mask (input) buffer, read all values and check +// if it's all -inf or all zero. Write out a two-bit code indicating which it is +// (or zero for neither). Each workgroup processes 16 tiles and writes out a +// 32-bit result mask. +// +// TODO: This is a lot of work per workgroup, might make sense to split this into +// more workgroups in the future. +void main() { + // Each workgroup handles a row + const uint tid = gl_LocalInvocationIndex; + const uint i0 = gl_WorkGroupID.x; + const uint i1 = gl_WorkGroupID.y; + const uint i2 = gl_WorkGroupID.z % nem2; + const uint i3 = gl_WorkGroupID.z / nem2; + + uint result = 0; + + // Fast path for fully in-bounds blocks where we can do f16vec4 loads + if ((nem0 % Bc) == 0 && (nem1 % Br) == 0 && + ((Br * Bc) % (BLOCK_SIZE * 4)) == 0) { + if ((i0 + 1) * 16 * Bc <= nem0) { + loadvec4(result, i0, i1, i2, i3, false); + } else { + loadvec4(result, i0, i1, i2, i3, true); + } + } else { + [[unroll]] for (uint block_x = 0; block_x < 16; ++block_x) { + float min_v = FLT_MAX_OVER_2; + float max_v = -FLT_MAX_OVER_2; + [[unroll]] for (uint i = 0; i < Br * Bc; i += BLOCK_SIZE) { + if ((Br * Bc % BLOCK_SIZE) != 0 && i + tid >= Br * Bc) { + continue; + } + uint j0 = (i + tid) % Bc; + uint j1 = (i + tid) / Bc; + + j0 += (i0 * 16 + block_x) * Bc; + j1 += i1 * Br; + + if (j0 < nem0 && j1 < nem1) { + float f = float(data_a[j0 + j1 * nbm1 + i2 * nbm2 + i3 * nbm3]); + min_v = min(min_v, f); + max_v = max(max_v, f); + } + } + min_v = subgroupMin(min_v); + max_v = subgroupMax(max_v); + if (gl_SubgroupInvocationID == 0) { + minsh[gl_SubgroupID] = min_v; + maxsh[gl_SubgroupID] = max_v; + } + barrier(); + if (tid == 0) { + [[unroll]] for (uint i = 0; i < NUM_SUBGROUPS; ++i) { + min_v = min(min_v, minsh[i]); + max_v = max(max_v, maxsh[i]); + } + if (max_v <= -FLT_MAX_OVER_2) { + result |= 1 << (2*block_x); + } + if (min_v == 0.0f && max_v == 0.0f) { + result |= 2 << (2*block_x); + } + } + barrier(); + } + } + + if (tid == 0) { + data_d[i0 + i1 * nbd1 + i2 * nbd2 + i3 * nbd3] = result; + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_mmq_funcs.glsl b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_mmq_funcs.glsl new file mode 100644 index 0000000000000000000000000000000000000000..6bf10a7cffd25cd647ba456d4fb0519c6616b3a8 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_mmq_funcs.glsl @@ -0,0 +1,203 @@ +// MMQ K-side helpers, asymmetric form. Each function dispatches on FaTypeK and +// reads from the matching aliased K binding declared in flash_attn_dequant.glsl. +// Spec-constant specialization folds the unused paths. + +int32_t get_k_qs(uint ib, uint iqs, uint a_offset) { + switch (FaTypeK) { + case FA_TYPE_Q4_0: { + uint vui = pack32(u16vec2(k_packed_q4_0.data[a_offset + ib].qs[(iqs & 0xF) / 2 + 0], + k_packed_q4_0.data[a_offset + ib].qs[(iqs & 0xF) / 2 + 1])); + uint shift = (iqs & 0x10) >> 2; + vui >>= shift; + return int32_t(vui & 0x0F0F0F0F); + } + case FA_TYPE_Q4_1: { // uses packed32 alias + uint vui = k_packed_q4_1_p32.data[a_offset + ib].qs[(iqs & 0xF) / 4]; + uint shift = (iqs & 0x10) >> 2; + vui >>= shift; + return int32_t(vui & 0x0F0F0F0F); + } + case FA_TYPE_Q5_0: { + uint vui = pack32(u16vec2(k_packed_q5_0.data[a_offset + ib].qs[(iqs & 0xF) / 2 + 0], + k_packed_q5_0.data[a_offset + ib].qs[(iqs & 0xF) / 2 + 1])); + uint qh = pack32(u16vec2(k_packed_q5_0.data[a_offset + ib].qh[0], + k_packed_q5_0.data[a_offset + ib].qh[1])); + uint shift = (iqs & 0x10) >> 2; + vui >>= shift; + uint qh_bits = (qh >> iqs) & 0xF; + return int32_t(vui & 0x0F0F0F0F) | int32_t((qh_bits * 0x02040810u) & 0x10101010u); + } + case FA_TYPE_Q5_1: { // qs via packed32, qh via packed16 + uint vui = k_packed_q5_1_p32.data[a_offset + ib].qs[(iqs & 0xF) / 4]; + uint qh = k_packed_q5_1.data[a_offset + ib].qh; + uint shift = (iqs & 0x10) >> 2; + vui >>= shift; + uint qh_bits = (qh >> iqs) & 0xF; + return int32_t(vui & 0x0F0F0F0F) | int32_t((qh_bits * 0x02040810u) & 0x10101010u); + } + case FA_TYPE_Q8_0: { + return pack32(i16vec2(k_packed_q8_0.data[a_offset + ib].qs[iqs / 2], + k_packed_q8_0.data[a_offset + ib].qs[iqs / 2 + 1])); + } + default: return 0; + } +} + +// Per-block scale/min, packed as (d, m). Single-scale types (Q4_0, Q5_0, Q8_0) +// return (d, 0) so call sites always see the same shape. +FLOAT_TYPEV2 get_k_scale(uint ib, uint a_offset) { + switch (FaTypeK) { + case FA_TYPE_Q4_0: return FLOAT_TYPEV2(FLOAT_TYPE(k_packed_q4_0.data[a_offset + ib].d), 0.0); + case FA_TYPE_Q4_1: return FLOAT_TYPEV2(k_packed_q4_1_p32.data[a_offset + ib].dm); + case FA_TYPE_Q5_0: return FLOAT_TYPEV2(FLOAT_TYPE(k_packed_q5_0.data[a_offset + ib].d), 0.0); + case FA_TYPE_Q5_1: return FLOAT_TYPEV2(k_packed_q5_1_p32.data[a_offset + ib].dm); + case FA_TYPE_Q8_0: return FLOAT_TYPEV2(FLOAT_TYPE(k_packed_q8_0.data[a_offset + ib].d), 0.0); + default: return FLOAT_TYPEV2(0); + } +} + +void k_block_to_shmem(const uint buf_ib, const uint global_ib, const uint iqs, const uint a_offset) { + // kblocksh[].qs is int32_t for the unified MMQ struct; uint sources need + // explicit casts. The bit pattern is what we care about here -- the actual + // signed/unsigned interpretation happens downstream in the dot product. + switch (FaTypeK) { + case FA_TYPE_Q4_0: { + kblocksh[buf_ib].qs[iqs] = int32_t(pack32(u16vec2(k_packed_q4_0.data[a_offset + global_ib].qs[iqs * 2], + k_packed_q4_0.data[a_offset + global_ib].qs[iqs * 2 + 1]))); + break; + } + case FA_TYPE_Q4_1: { + kblocksh[buf_ib].qs[iqs] = int32_t(k_packed_q4_1_p32.data[a_offset + global_ib].qs[iqs]); + break; + } + case FA_TYPE_Q5_0: { + kblocksh[buf_ib].qs[iqs] = int32_t(pack32(u16vec2(k_packed_q5_0.data[a_offset + global_ib].qs[iqs * 2], + k_packed_q5_0.data[a_offset + global_ib].qs[iqs * 2 + 1]))); + if (iqs == 0) { + kblocksh[buf_ib].qh = pack32(u16vec2(k_packed_q5_0.data[a_offset + global_ib].qh[0], + k_packed_q5_0.data[a_offset + global_ib].qh[1])); + } + break; + } + case FA_TYPE_Q5_1: { + kblocksh[buf_ib].qs[iqs] = int32_t(k_packed_q5_1_p32.data[a_offset + global_ib].qs[iqs]); + if (iqs == 0) { + kblocksh[buf_ib].qh = k_packed_q5_1.data[a_offset + global_ib].qh; + } + break; + } + case FA_TYPE_Q8_0: { + kblocksh[buf_ib].qs[iqs] = pack32(i16vec2(k_packed_q8_0.data[a_offset + global_ib].qs[iqs * 2], + k_packed_q8_0.data[a_offset + global_ib].qs[iqs * 2 + 1])); + break; + } + } + + if (iqs == 0) { + // Q4_0/Q5_0/Q8_0 store dm.x = d; Q4_1/Q5_1 store dm = (d, m) pair. + switch (FaTypeK) { + case FA_TYPE_Q4_0: kblocksh[buf_ib].dm = FLOAT_TYPEV2(FLOAT_TYPE(k_packed_q4_0.data[a_offset + global_ib].d), 0.0); break; + case FA_TYPE_Q4_1: kblocksh[buf_ib].dm = FLOAT_TYPEV2(k_packed_q4_1_p32.data[a_offset + global_ib].dm); break; + case FA_TYPE_Q5_0: kblocksh[buf_ib].dm = FLOAT_TYPEV2(FLOAT_TYPE(k_packed_q5_0.data[a_offset + global_ib].d), 0.0); break; + case FA_TYPE_Q5_1: kblocksh[buf_ib].dm = FLOAT_TYPEV2(k_packed_q5_1_p32.data[a_offset + global_ib].dm); break; + case FA_TYPE_Q8_0: kblocksh[buf_ib].dm = FLOAT_TYPEV2(FLOAT_TYPE(k_packed_q8_0.data[a_offset + global_ib].d), 0.0); break; + } + } +} + +// d_per_step==8 hot path: read one full 32-element block worth of nibble-packed +// int32 quants. Equivalent to 8 calls to get_k_qs(ib, d*4, a_offset) but reads +// qh (Q5_*) and runs pack32 (Q4_0/Q5_0) once per block instead of per nibble +// quad. iqs is always 0 in this path (hsk4 % 8 == 0 implies block-aligned). +// Q8_0 takes the generic get_k_qs path because its qs layout (i8 pairs) doesn't +// share this nibble shape. +// +// Returned via a struct so the caller's k_quants array (sized from spec +// constants) doesn't need to match a fixed[8] out-parameter type. +struct fa_k_qs_block8 { + int32_t qs[8]; +}; + +fa_k_qs_block8 get_k_qs_block8(uint ib, uint a_offset) { + fa_k_qs_block8 r; + uint qh = 0; + if (FaTypeK == FA_TYPE_Q5_0) { + qh = pack32(u16vec2(k_packed_q5_0.data[a_offset + ib].qh[0], + k_packed_q5_0.data[a_offset + ib].qh[1])); + } else if (FaTypeK == FA_TYPE_Q5_1) { + qh = k_packed_q5_1.data[a_offset + ib].qh; + } + const bool has_qh = (FaTypeK == FA_TYPE_Q5_0) || (FaTypeK == FA_TYPE_Q5_1); + [[unroll]] for (uint32_t d = 0; d < 4; d++) { + uint vui = 0; + switch (FaTypeK) { + case FA_TYPE_Q4_0: { // packed16 + vui = pack32(u16vec2(k_packed_q4_0.data[a_offset + ib].qs[d * 2 + 0], + k_packed_q4_0.data[a_offset + ib].qs[d * 2 + 1])); + break; + } + case FA_TYPE_Q4_1: { // packed32 alias + vui = k_packed_q4_1_p32.data[a_offset + ib].qs[d]; + break; + } + case FA_TYPE_Q5_0: { // packed16 + vui = pack32(u16vec2(k_packed_q5_0.data[a_offset + ib].qs[d * 2 + 0], + k_packed_q5_0.data[a_offset + ib].qs[d * 2 + 1])); + break; + } + case FA_TYPE_Q5_1: { // packed32 alias + vui = k_packed_q5_1_p32.data[a_offset + ib].qs[d]; + break; + } + } + r.qs[d ] = int32_t( vui & 0x0F0F0F0F); + r.qs[d + 4] = int32_t((vui >> 4) & 0x0F0F0F0F); + if (has_qh) { + uint qh_lo = (qh >> (d * 4)) & 0xFu; + uint qh_hi = (qh >> (d * 4 + 16)) & 0xFu; + r.qs[d ] |= int32_t((qh_lo * 0x02040810u) & 0x10101010u); + r.qs[d + 4] |= int32_t((qh_hi * 0x02040810u) & 0x10101010u); + } + } + return r; +} + +int32_t get_k_qs_shmem(const uint buf_ib, const uint pos) { + switch (FaTypeK) { + case FA_TYPE_Q4_0: + case FA_TYPE_Q4_1: { + uint sub = pos % 4; + uint shift = ((pos % 8) >= 4) ? 4u : 0u; + return int32_t((uint(kblocksh[buf_ib].qs[sub]) >> shift) & 0x0F0F0F0Fu); + } + case FA_TYPE_Q5_0: + case FA_TYPE_Q5_1: { + uint sub = pos % 4; + uint shift = ((pos % 8) >= 4) ? 4u : 0u; + int32_t result = int32_t((uint(kblocksh[buf_ib].qs[sub]) >> shift) & 0x0F0F0F0Fu); + uint qh_bits = (kblocksh[buf_ib].qh >> (pos * 4u)) & 0xFu; + return result | int32_t((qh_bits * 0x02040810u) & 0x10101010u); + } + case FA_TYPE_Q8_0: { + return kblocksh[buf_ib].qs[pos]; + } + default: return 0; + } +} + +ACC_TYPE k_dot_correction(const uint qib, const ACC_TYPEV2 k_dm) { + switch (FaTypeK) { + case FA_TYPE_Q4_0: return -ACC_TYPE(8.0) * ACC_TYPE(Qf[qib].ds.y) * k_dm.x; + case FA_TYPE_Q5_0: return -ACC_TYPE(16.0) * ACC_TYPE(Qf[qib].ds.y) * k_dm.x; + case FA_TYPE_Q4_1: + case FA_TYPE_Q5_1: return ACC_TYPE(Qf[qib].ds.y) * k_dm.y; + default: return ACC_TYPE(0.0); + } +} + +void k_block_to_shmem_zero(const uint buf_ib, const uint iqs) { + kblocksh[buf_ib].qs[iqs] = 0; + if (iqs == 0) { + kblocksh[buf_ib].dm = FLOAT_TYPEV2(0.0f); + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_split_k_reduce.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_split_k_reduce.comp new file mode 100644 index 0000000000000000000000000000000000000000..68917fc0bb02f41d2f56aaa2fd09d41857dd310e --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_split_k_reduce.comp @@ -0,0 +1,121 @@ +#version 450 + +#extension GL_EXT_control_flow_attributes : enable + +layout(constant_id = 0) const uint BLOCK_SIZE = 32; + +layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {float data_a[];}; +layout (binding = 1) readonly buffer B {float data_s[];}; +layout (binding = 2) writeonly buffer D {float data_d[];}; + +layout (push_constant) uniform parameter { + uint D; + uint ne1; + uint ne2; + uint ne3; + uint k_num; + uint sinks; +} p; + +shared float tmpsh[BLOCK_SIZE]; + +void main() { + // Each workgroup handles a row + const uint n = gl_WorkGroupID.x; + const uint tid = gl_LocalInvocationID.x; + const uint i2 = gl_WorkGroupID.z % p.ne2; + const uint i3 = gl_WorkGroupID.z / p.ne2; + + uint D = p.D; + uint k_num = p.k_num; + + uint l_offset = D * p.ne1 * p.ne2 * p.ne3 * k_num + p.ne1 * 2 * (0/*split_k_index*/ + p.k_num * (i2 + p.ne2 * i3)) + n; + uint m_offset = D * p.ne1 * p.ne2 * p.ne3 * k_num + p.ne1 * 2 * (0/*split_k_index*/ + p.k_num * (i2 + p.ne2 * i3)) + p.ne1 + n; + uint lm_stride = p.ne1 * 2; + + // Compute the max m value for the row + float m_max = -1.0/0.0; + for (uint k = 0; k + tid < k_num; k += BLOCK_SIZE) { + float m = data_a[m_offset + (k + tid) * lm_stride]; + m_max = max(m_max, m); + } + + // reduce across the workgroup + tmpsh[tid] = m_max; + barrier(); + [[unroll]] for (uint s = BLOCK_SIZE/2; s > 0; s >>= 1) { + if (tid < s) { + m_max = max(m_max, tmpsh[tid + s]); + tmpsh[tid] = m_max; + } + barrier(); + } + m_max = tmpsh[0]; + + barrier(); + + // Compute L based on m_max + float L = 0; + for (uint k = 0; k + tid < k_num; k += BLOCK_SIZE) { + float l = data_a[l_offset + (k + tid) * lm_stride]; + float m = data_a[m_offset + (k + tid) * lm_stride]; + L += exp(m - m_max) * l; + } + + // reduce across the workgroup + tmpsh[tid] = L; + barrier(); + [[unroll]] for (uint s = BLOCK_SIZE/2; s > 0; s >>= 1) { + if (tid < s) { + L += tmpsh[tid + s]; + tmpsh[tid] = L; + } + barrier(); + } + L = tmpsh[0]; + + float sink; + if (p.sinks != 0) { + sink = data_s[n]; + + float ms = 1.0f; + float vs = 1.0f; + + if (sink > m_max) { + ms = exp(m_max - sink); + } else { + vs = exp(sink - m_max); + } + + L = L*ms + vs; + } + + L = (L == 0.0) ? 0.0 : 1.0 / L; + + // D dimension is split across workgroups in the y dimension + uint d = tid + gl_WorkGroupID.y * BLOCK_SIZE; + // Scale and sum the O contributions based on m_max and store the result to memory + if (d < D) { + float O = 0.0; + [[unroll]] for (uint k = 0; k < k_num; ++k) { + uint o_offset = D * p.ne1 * (k + p.k_num * (i2 + p.ne2 * i3)) + D * n + d; + float m = data_a[m_offset + k * lm_stride]; + O += exp(m - m_max) * data_a[o_offset]; + } + if (p.sinks != 0) { + if (sink > m_max) { + float ms = 1.0f; + ms = exp(m_max - sink); + O *= ms; + } + } + O *= L; + + const float FLT_MAX = uintBitsToFloat(0x7F7FFFFF); + O = clamp(O, -FLT_MAX, FLT_MAX); + + data_d[(i3 * p.ne2 + i2) * p.ne1 * D + D * n + d] = O; + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/fwht.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/fwht.comp new file mode 100644 index 0000000000000000000000000000000000000000..a2069964adbe4e9fd84deb9f9f63da17f8460a12 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/fwht.comp @@ -0,0 +1,115 @@ +#version 450 + +#extension GL_EXT_control_flow_attributes : require +#ifndef FWHT_SHMEM +#extension GL_KHR_shader_subgroup_basic : enable +#extension GL_KHR_shader_subgroup_shuffle : enable +#endif + +layout(constant_id = 0) const uint BLOCK_SIZE = 32; +layout(constant_id = 1) const uint N = 128; + +layout(local_size_x_id = 0, local_size_y = 4, local_size_z = 1) in; + +layout(push_constant) uniform parameter +{ + uint n_rows; + uint src_offset; + uint dst_offset; + float scale; +}; + +layout(binding = 0, std430) readonly buffer A { float data_a[]; }; +layout(binding = 1, std430) writeonly buffer D { float data_d[]; }; + +const uint EL_W = N / BLOCK_SIZE; + +#ifdef FWHT_SHMEM +shared float shmem[4 * N]; +#endif + +void main() { +#ifdef FWHT_SHMEM + const uint tid = gl_LocalInvocationID.x; + const uint shmem_base = gl_LocalInvocationID.y * N; + const uint row_id = gl_LocalInvocationID.y; +#else + const uint tid = gl_SubgroupInvocationID; + const uint row_id = gl_SubgroupID; +#endif + + for (uint base_row = gl_WorkGroupID.x * gl_WorkGroupSize.y; + base_row < n_rows; + base_row += gl_NumWorkGroups.x * gl_WorkGroupSize.y) { + const uint row = base_row + row_id; + const uint row_offset = row * N; + +#ifndef FWHT_SHMEM + if (row >= n_rows) { + continue; + } +#endif + + float reg[EL_W]; + + [[unroll]] + for (uint i = 0; i < EL_W; ++i) { + reg[i] = row < n_rows ? data_a[src_offset + row_offset + i * BLOCK_SIZE + tid] * scale : 0.0; + } + +#ifdef FWHT_SHMEM + [[unroll]] + for (uint h = 1; h < BLOCK_SIZE; h <<= 1) { + [[unroll]] + for (uint i = 0; i < EL_W; ++i) { + shmem[shmem_base + i * BLOCK_SIZE + tid] = reg[i]; + } + barrier(); + [[unroll]] + for (uint j = 0; j < EL_W; ++j) { + const float val = reg[j]; + const float other = shmem[shmem_base + j * BLOCK_SIZE + (tid ^ h)]; + reg[j] = (tid & h) == 0 ? val + other : other - val; + } + barrier(); + } +#else + [[unroll]] + for (uint h = 1; h < BLOCK_SIZE; h <<= 1) { + [[unroll]] + for (uint j = 0; j < EL_W; ++j) { + const float val = reg[j]; + const float val2 = subgroupShuffleXor(val, h); + reg[j] = (tid & h) == 0 ? val + val2 : val2 - val; + } + } +#endif + + [[unroll]] + for (uint h = BLOCK_SIZE; h < N; h <<= 1) { + const uint step = h / BLOCK_SIZE; + [[unroll]] + for (uint j = 0; j < EL_W; j += 2 * step) { + [[unroll]] + for (uint k = 0; k < step; ++k) { + const float x = reg[j + k]; + const float y = reg[j + k + step]; + reg[j + k] = x + y; + reg[j + k + step] = x - y; + } + } + } + +#ifdef FWHT_SHMEM + if (row < n_rows) { +#endif + [[unroll]] + for (uint i = 0; i < EL_W; ++i) { + data_d[dst_offset + row_offset + i * BLOCK_SIZE + tid] = reg[i]; + } +#ifdef FWHT_SHMEM + } + barrier(); +#endif + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/gated_delta_net.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/gated_delta_net.comp new file mode 100644 index 0000000000000000000000000000000000000000..0e384330b9b92d86c6dd4c34b66f789180ee7146 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/gated_delta_net.comp @@ -0,0 +1,189 @@ +#version 450 + +#extension GL_EXT_control_flow_attributes : require +#extension GL_KHR_shader_subgroup_basic : enable +#if USE_SUBGROUP_CLUSTERED +#extension GL_KHR_shader_subgroup_clustered : enable +#endif +#if USE_SUBGROUP_ADD +#extension GL_KHR_shader_subgroup_arithmetic : enable +#endif + +// Caller guarantees valid spec constants: S_V % COLS_PER_WG == 0 and S_V % LANES_PER_COLUMN == 0, +// so no bounds checking is needed. +layout(constant_id = 0) const uint S_V = 128; +layout(constant_id = 1) const uint KDA = 0; +layout(constant_id = 2) const uint SUBGROUP_SIZE = 32; +layout(constant_id = 3) const uint LANES_PER_COLUMN = 32; + +const uint COLS_PER_WG = SUBGROUP_SIZE / LANES_PER_COLUMN; +const uint ROWS_PER_LANE = S_V / LANES_PER_COLUMN; + +layout(local_size_x_id = 2, local_size_y = 1, local_size_z = 1) in; + +layout(push_constant) uniform Parameters { + uint H; + uint n_tokens; + uint n_seqs; + uint s_off; + uint sq1, sq2, sq3; + uint sv1, sv2, sv3; + uint sb1, sb2, sb3; + uint neq1, rq3; + float scale; + uint K; +}; + +layout(binding = 0) readonly buffer QBuf { FLOAT_TYPE data_q[]; }; +layout(binding = 1) readonly buffer KBuf { FLOAT_TYPE data_k[]; }; +layout(binding = 2) readonly buffer VBuf { FLOAT_TYPE data_v[]; }; +layout(binding = 3) readonly buffer GBuf { FLOAT_TYPE data_g[]; }; +layout(binding = 4) readonly buffer BetaBuf { FLOAT_TYPE data_beta[]; }; +layout(binding = 5) readonly buffer StateBuf { FLOAT_TYPE data_state[]; }; +layout(binding = 6) buffer DstBuf { FLOAT_TYPE data_dst[]; }; + +#if !USE_SUBGROUP_ADD && !USE_SUBGROUP_CLUSTERED +shared FLOAT_TYPE temp[SUBGROUP_SIZE]; + +// This does a reduction across groups of LANES_PER_COLUMN +FLOAT_TYPE reduce_add_shmem(FLOAT_TYPE partial) { + const uint lane = gl_SubgroupInvocationID; + temp[lane] = partial; + barrier(); + [[unroll]] for (uint s = LANES_PER_COLUMN / 2u; s > 0; s >>= 1u) { + FLOAT_TYPE other = temp[lane ^ s]; + barrier(); + temp[lane] += other; + barrier(); + } + const FLOAT_TYPE result = temp[lane]; + barrier(); + return result; +} +#endif + +// clusterSize for subgroupClusteredAdd must be a compile-time constant; branch on spec constant +FLOAT_TYPE reduce_partial(FLOAT_TYPE partial) { + switch (LANES_PER_COLUMN) { + case 1u: + return partial; +#if USE_SUBGROUP_CLUSTERED + // Workaround for GLSL requiring a literal constant for the cluster size. + // The branches should all fold away. + case 2u: + return subgroupClusteredAdd(partial, 2u); + case 4u: + return subgroupClusteredAdd(partial, 4u); + case 8u: + return subgroupClusteredAdd(partial, 8u); + case 16u: + return subgroupClusteredAdd(partial, 16u); + case 32u: + return subgroupClusteredAdd(partial, 32u); + case 64u: + return subgroupClusteredAdd(partial, 64u); +#endif + default: +#if USE_SUBGROUP_ADD + return subgroupAdd(partial); +#else + return reduce_add_shmem(partial); +#endif + } +} + +void main() { + const uint head_id = gl_WorkGroupID.x; + const uint seq_id = gl_WorkGroupID.y; + const uint lane = gl_SubgroupInvocationID % LANES_PER_COLUMN; + const uint col = gl_WorkGroupID.z * COLS_PER_WG + (gl_SubgroupInvocationID / LANES_PER_COLUMN); + + const uint iq1 = head_id % neq1; + const uint iq3 = seq_id / rq3; + + const uint state_size = S_V * S_V; + // input state holds s0 only [S_v, S_v, H, n_seqs]: per-seq stride is H*D. + const uint state_in_base = (seq_id * H + head_id) * state_size; + // output state layout per slot: same per-(seq,head) offset as the single-slot case. + const uint state_out_base = (seq_id * H + head_id) * state_size; + const uint state_size_per_snap = state_size * H * n_seqs; + + FLOAT_TYPE s_shard[ROWS_PER_LANE]; + [[unroll]] for (uint r = 0; r < ROWS_PER_LANE; r++) { + s_shard[r] = FLOAT_TYPE(data_state[state_in_base + col * S_V + r * LANES_PER_COLUMN + lane]); + } + + // snapshot slot mapping: slot 0 = most recent state, slot s = s tokens back. + // When n_tokens < K, only slots 0..n_tokens-1 are written; older slots are caller-owned. + + uint attn_off = (seq_id * n_tokens * H + head_id) * S_V; + + for (uint t = 0; t < n_tokens; t++) { + const uint q_off = iq3 * sq3 + t * sq2 + iq1 * sq1; + const uint k_off = q_off; + const uint v_off = seq_id * sv3 + t * sv2 + head_id * sv1; + const uint gb_off = seq_id * sb3 + t * sb2 + head_id * sb1; + const FLOAT_TYPE beta_val = FLOAT_TYPE(data_beta[gb_off]); + + FLOAT_TYPE k_reg[ROWS_PER_LANE]; + FLOAT_TYPE q_reg[ROWS_PER_LANE]; + [[unroll]] for (uint r = 0; r < ROWS_PER_LANE; r++) { + const uint i = r * LANES_PER_COLUMN + lane; + k_reg[r] = FLOAT_TYPE(data_k[k_off + i]); + q_reg[r] = FLOAT_TYPE(data_q[q_off + i]); + } + + FLOAT_TYPE g_exp[ROWS_PER_LANE]; + if (KDA == 0) { + const FLOAT_TYPE g_val = exp(FLOAT_TYPE(data_g[gb_off])); + [[unroll]] for (uint r = 0; r < ROWS_PER_LANE; r++) { + g_exp[r] = g_val; + } + } else { + const uint g_base = gb_off * S_V; + [[unroll]] for (uint r = 0; r < ROWS_PER_LANE; r++) { + const uint i = r * LANES_PER_COLUMN + lane; + g_exp[r] = exp(FLOAT_TYPE(data_g[g_base + i])); + } + } + + const FLOAT_TYPE v_val = FLOAT_TYPE(data_v[v_off + col]); + + FLOAT_TYPE kv_shard = 0.0; + [[unroll]] for (uint r = 0; r < ROWS_PER_LANE; r++) { + kv_shard += g_exp[r] * s_shard[r] * k_reg[r]; + } + FLOAT_TYPE kv_col = reduce_partial(kv_shard); + + FLOAT_TYPE delta_col = (v_val - kv_col) * beta_val; + + FLOAT_TYPE attn_partial = 0.0; + [[unroll]] for (uint r = 0; r < ROWS_PER_LANE; r++) { + s_shard[r] = g_exp[r] * s_shard[r] + k_reg[r] * delta_col; + attn_partial += s_shard[r] * q_reg[r]; + } + FLOAT_TYPE attn_col = reduce_partial(attn_partial); + + if (lane == 0) { + data_dst[attn_off + col] = attn_col * scale; + } + + attn_off += S_V * H; + + if (K > 1u) { + const int target_slot = int(n_tokens) - 1 - int(t); + if (target_slot >= 0 && target_slot < int(K)) { + const uint slot_base = s_off + uint(target_slot) * state_size_per_snap + state_out_base; + [[unroll]] for (uint r = 0; r < ROWS_PER_LANE; r++) { + data_dst[slot_base + col * S_V + r * LANES_PER_COLUMN + lane] = s_shard[r]; + } + } + } + } + + if (K == 1u) { + [[unroll]] for (uint r = 0; r < ROWS_PER_LANE; r++) { + data_dst[s_off + state_out_base + col * S_V + r * LANES_PER_COLUMN + lane] = s_shard[r]; + } + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/geglu.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/geglu.comp new file mode 100644 index 0000000000000000000000000000000000000000..e017b503688fdc08d500521a5c91fdee799012d5 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/geglu.comp @@ -0,0 +1,13 @@ +#version 450 + +#include "glu_head.glsl" + +const float GELU_COEF_A = 0.044715f; +const float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f; + +float op(float a, float b) { + const float val = SQRT_2_OVER_PI*a*(1.0f + GELU_COEF_A*a*a); + return 0.5f*a*(2.0f - 2.0f / (exp(2 * val) + 1)) * b; +} + +#include "glu_main.glsl" diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/geglu_erf.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/geglu_erf.comp new file mode 100644 index 0000000000000000000000000000000000000000..759a1848fa1d6a9ee8ca6fdf08d5d5c984e8c6f7 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/geglu_erf.comp @@ -0,0 +1,27 @@ +#version 450 + +#include "glu_head.glsl" + +// based on Abramowitz and Stegun formula 7.1.26 or similar Hastings' approximation +// ref: https://www.johndcook.com/blog/python_erf/ +const float p_erf = 0.3275911f; +const float a1_erf = 0.254829592f; +const float a2_erf = -0.284496736f; +const float a3_erf = 1.421413741f; +const float a4_erf = -1.453152027f; +const float a5_erf = 1.061405429f; + +const float SQRT_2_INV = 0.70710678118654752440084436210484f; + +float op(float a, float b) { + const float a_div_sqr2 = a * SQRT_2_INV; + const float sign_x = sign(a_div_sqr2); + const float x = abs(a_div_sqr2); + const float t = 1.0f / (1.0f + p_erf * x); + const float y = 1.0f - (((((a5_erf * t + a4_erf) * t) + a3_erf) * t + a2_erf) * t + a1_erf) * t * exp(-x * x); + const float erf_approx = sign_x * y; + + return 0.5f * a * (1.0f + erf_approx) * b; +} + +#include "glu_main.glsl" diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/geglu_quick.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/geglu_quick.comp new file mode 100644 index 0000000000000000000000000000000000000000..c4032ab21d00cae97ab78d7d6b10ef972b6a7106 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/geglu_quick.comp @@ -0,0 +1,11 @@ +#version 450 + +#include "glu_head.glsl" + +const float GELU_QUICK_COEF = -1.702f; + +float op(float a, float b) { + return a * (1.0f / (1.0f + exp(GELU_QUICK_COEF * a))) * b; +} + +#include "glu_main.glsl" diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/generic_binary_head.glsl b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/generic_binary_head.glsl new file mode 100644 index 0000000000000000000000000000000000000000..dc657f3c70844789e636ed330f60043f5f9f59db --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/generic_binary_head.glsl @@ -0,0 +1,65 @@ +#extension GL_EXT_shader_16bit_storage : require +#extension GL_EXT_control_flow_attributes : require + +#include "utils.glsl" +#if RMS_NORM_ROPE_FUSION +#include "rope_params.glsl" +#endif + +layout (push_constant) uniform parameter +{ + uint ne; + uint ne00; uint ne01; uint ne02; uint ne03; uint nb00; uint nb01; uint nb02; uint nb03; + uint ne10; uint ne11; uint ne12; uint ne13; uint nb10; uint nb11; uint nb12; uint nb13; + uint ne20; uint ne21; uint ne22; uint ne23; uint nb20; uint nb21; uint nb22; uint nb23; + uint misalign_offsets; + float param1; float param2; int param3; +#if RMS_NORM_ROPE_FUSION + rope_params rope; +#endif +} p; + +#if !RMS_NORM_ROPE_FUSION +layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; +#if defined(A_TYPE_PACKED16) +layout (binding = 0) readonly buffer A_PACKED16 {A_TYPE_PACKED16 data_a_packed16[];}; +#endif +#if defined(A_TYPE_PACKED32) +layout (binding = 0) readonly buffer A_PACKED32 {A_TYPE_PACKED32 data_a_packed32[];}; +#endif + +layout (binding = 1) readonly buffer B {B_TYPE data_b[];}; +layout (binding = 2) writeonly buffer D {D_TYPE data_d[];}; +#endif + +// true if src0/src1 are the same shape and the indices can be reused without additional modulus +layout(constant_id = 0) const bool norepeat = false; + +uint get_idx() { + return gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x; +} + +uint get_aoffset() { return p.misalign_offsets >> 16; } +uint get_boffset() { return (p.misalign_offsets >> 8) & 0xFF; } +uint get_doffset() { return p.misalign_offsets & 0xFF; } + + +void get_indices(uint idx, out uint i00, out uint i01, out uint i02, out uint i03) { + get_indices(idx, i00, i01, i02, i03, p.ne00, p.ne01, p.ne02, p.ne03); +} + +uint src0_idx(uint i00, uint i01, uint i02, uint i03) { + return i03*p.nb03 + i02*p.nb02 + i01*p.nb01 + i00*p.nb00; +} + +uint src1_idx(uint i00, uint i01, uint i02, uint i03) { + if (norepeat) { + return i03*p.nb13 + i02*p.nb12 + i01*p.nb11 + i00*p.nb10; + } else { + return fastmod(i03, p.ne13)*p.nb13 + fastmod(i02, p.ne12)*p.nb12 + fastmod(i01, p.ne11)*p.nb11 + fastmod(i00, p.ne10)*p.nb10; + } +} + +uint dst_idx(uint i00, uint i01, uint i02, uint i03) { + return i03*p.nb23 + i02*p.nb22 + i01*p.nb21 + i00*p.nb20; +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/generic_head.glsl b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/generic_head.glsl new file mode 100644 index 0000000000000000000000000000000000000000..3797901f0435e19181bd2b17f562776c20f24616 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/generic_head.glsl @@ -0,0 +1,11 @@ +#extension GL_EXT_shader_16bit_storage : require + +layout (push_constant) uniform parameter +{ + uint KX; + uint KY; + float param1; + float param2; + float param3; + float param4; +} p; diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/generic_unary_head.glsl b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/generic_unary_head.glsl new file mode 100644 index 0000000000000000000000000000000000000000..9d4176f3f967e5ba7988213dcd524e1051ecccc2 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/generic_unary_head.glsl @@ -0,0 +1,85 @@ +#extension GL_EXT_shader_16bit_storage : require +#extension GL_EXT_control_flow_attributes : require + +layout (push_constant) uniform parameter +{ + uint ne; + uint ne00; uint ne01; uint ne02; uint ne03; uint nb00; uint nb01; uint nb02; uint nb03; + uint ne10; uint ne11; uint ne12; uint ne13; uint nb10; uint nb11; uint nb12; uint nb13; + uint misalign_offsets; + float param1; float param2; float param3; float param4; + + // The three L values are packed as bytes to keep this layout under the 128B + // push constant limit while still leaving room for four float parameters. + uint ne0_012mp; uint ne0_01mp; uint ne0_0mp; uint ne0_Ls; + uint ne1_012mp; uint ne1_01mp; uint ne1_0mp; uint ne1_Ls; +} p; + +layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; +#if defined(A_TYPE_PACKED16) +layout (binding = 0) readonly buffer A_PACKED16 {A_TYPE_PACKED16 data_a_packed16[];}; +#endif +#if defined(A_TYPE_PACKED32) +layout (binding = 0) readonly buffer A_PACKED32 {A_TYPE_PACKED32 data_a_packed32[];}; +#endif + +layout (binding = 1) writeonly buffer D {D_TYPE data_d[];}; + +uint get_idx() { + return gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x; +} + +uint get_aoffset() { return p.misalign_offsets >> 16; } +uint get_doffset() { return p.misalign_offsets & 0xFFFF; } + +// see init_fastdiv_values in ggml-vulkan.cpp +uint fastdiv(uint n, uint mp, uint L) { + uint msbs, lsbs; + // msbs = mulhi(n, mp) + umulExtended(n, mp, msbs, lsbs); + return (msbs + n) >> L; +} + +uint fastdiv_L(uint packed, uint slot) { + return (packed >> (slot * 8)) & 0x3Fu; +} + +uint src0_idx(uint idx) { + const uint i03 = fastdiv(idx, p.ne0_012mp, fastdiv_L(p.ne0_Ls, 0)); + const uint i03_offset = i03 * p.ne02*p.ne01*p.ne00; + const uint i02 = fastdiv(idx - i03_offset, p.ne0_01mp, fastdiv_L(p.ne0_Ls, 1)); + const uint i02_offset = i02*p.ne01*p.ne00; + const uint i01 = fastdiv(idx - i03_offset - i02_offset, p.ne0_0mp, fastdiv_L(p.ne0_Ls, 2)); + const uint i00 = idx - i03_offset - i02_offset - i01*p.ne00; + return i03*p.nb03 + i02*p.nb02 + i01*p.nb01 + i00*p.nb00; +} + +uint dst_idx(uint idx) { + const uint i13 = fastdiv(idx, p.ne1_012mp, fastdiv_L(p.ne1_Ls, 0)); + const uint i13_offset = i13 * p.ne12*p.ne11*p.ne10; + const uint i12 = fastdiv(idx - i13_offset, p.ne1_01mp, fastdiv_L(p.ne1_Ls, 1)); + const uint i12_offset = i12*p.ne11*p.ne10; + const uint i11 = fastdiv(idx - i13_offset - i12_offset, p.ne1_0mp, fastdiv_L(p.ne1_Ls, 2)); + const uint i10 = idx - i13_offset - i12_offset - i11*p.ne10; + return i13*p.nb13 + i12*p.nb12 + i11*p.nb11 + i10*p.nb10; +} + +uint src0_idx_quant(uint idx, uint qk) { + const uint i03 = fastdiv(idx, p.ne0_012mp, fastdiv_L(p.ne0_Ls, 0)); + const uint i03_offset = i03 * p.ne02*p.ne01*p.ne00; + const uint i02 = fastdiv(idx - i03_offset, p.ne0_01mp, fastdiv_L(p.ne0_Ls, 1)); + const uint i02_offset = i02*p.ne01*p.ne00; + const uint i01 = fastdiv(idx - i03_offset - i02_offset, p.ne0_0mp, fastdiv_L(p.ne0_Ls, 2)); + const uint i00 = idx - i03_offset - i02_offset - i01*p.ne00; + return i03*p.nb03 + i02*p.nb02 + i01*p.nb01 + (i00/qk)*p.nb00; +} + +uint dst_idx_quant(uint idx, uint qk) { + const uint i13 = fastdiv(idx, p.ne1_012mp, fastdiv_L(p.ne1_Ls, 0)); + const uint i13_offset = i13 * p.ne12*p.ne11*p.ne10; + const uint i12 = fastdiv(idx - i13_offset, p.ne1_01mp, fastdiv_L(p.ne1_Ls, 1)); + const uint i12_offset = i12*p.ne11*p.ne10; + const uint i11 = fastdiv(idx - i13_offset - i12_offset, p.ne1_0mp, fastdiv_L(p.ne1_Ls, 2)); + const uint i10 = idx - i13_offset - i12_offset - i11*p.ne10; + return i13*p.nb13 + i12*p.nb12 + i11*p.nb11 + (i10/qk)*p.nb10; +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/get_rows.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/get_rows.comp new file mode 100644 index 0000000000000000000000000000000000000000..e88bdd057eaad7b6d50ce999e43553aac15fbd28 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/get_rows.comp @@ -0,0 +1,42 @@ +#version 450 + +#include "types.glsl" +#include "generic_binary_head.glsl" + +layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; + +void main() { + const uint i00 = gl_GlobalInvocationID.x; + + if (i00 >= p.ne00) { + return; + } + + uint gid_z = gl_GlobalInvocationID.z; + while (gid_z < p.ne11 * p.ne12) { + uint gid_y = gl_GlobalInvocationID.y; + while (gid_y < p.ne10) { + const uint i10 = gid_y; + const uint i11 = gid_z / p.ne12; + const uint i12 = gid_z % p.ne12; + + const uint i01 = data_b[get_boffset() + i10*p.nb10 + i11*p.nb11 + i12*p.nb12]; + + const uint a_offset = get_aoffset() + i01*p.nb01 + i11*p.nb02 + i12*p.nb03; + const uint d_offset = get_doffset() + i10*p.nb21 + i11*p.nb22 + i12*p.nb23; + +#if defined(DATA_A_BF16) + TEMP_TYPE v = TEMP_TYPE(bf16_to_fp32(data_a[a_offset + i00])); +#else + TEMP_TYPE v = TEMP_TYPE(data_a[a_offset + i00]); +#endif +#ifndef OPTIMIZATION_ERROR_WORKAROUND + data_d[d_offset + i00] = D_TYPE(v); +#else + data_d[d_offset + i00] = D_TYPE(v); +#endif + gid_y += gl_WorkGroupSize.y * gl_NumWorkGroups.y; + } + gid_z += gl_WorkGroupSize.z * gl_NumWorkGroups.z; + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/get_rows_back.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/get_rows_back.comp new file mode 100644 index 0000000000000000000000000000000000000000..7e3d8a28197271713a8ae3af967ac10f09d70049 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/get_rows_back.comp @@ -0,0 +1,25 @@ +#version 450 + +#include "types.glsl" +#include "generic_binary_head.glsl" + +layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in; + +void main() { + const uint col = gl_GlobalInvocationID.x; + + if (col >= p.ne20) { + return; + } + + for (uint row = gl_GlobalInvocationID.y; row < p.ne21; row += gl_WorkGroupSize.y * gl_NumWorkGroups.y) { + float sum = 0.0f; + for (uint i = 0; i < p.ne10; ++i) { + if (data_b[get_boffset() + i*p.nb10] == int(row)) { + sum += data_a[get_aoffset() + i*p.nb01 + col*p.nb00]; + } + } + + data_d[get_doffset() + row*p.nb21 + col*p.nb20] = sum; + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/get_rows_quant.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/get_rows_quant.comp new file mode 100644 index 0000000000000000000000000000000000000000..9dba437edbee56e23e8ac844fe30e28f54725efd --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/get_rows_quant.comp @@ -0,0 +1,51 @@ +#version 450 + +#extension GL_EXT_control_flow_attributes : enable + +#include "types.glsl" +#include "generic_binary_head.glsl" +#include "dequant_funcs.glsl" + +layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; + +void main() { + const uint i00 = (gl_GlobalInvocationID.x)*2; + +#ifdef NEEDS_INIT_IQ_SHMEM + init_iq_shmem(gl_WorkGroupSize); +#endif + + if (i00 >= p.ne00) { + return; + } + + uint gid_z = gl_GlobalInvocationID.z; + while (gid_z < p.ne11 * p.ne12) { + uint gid_y = gl_GlobalInvocationID.y; + while (gid_y < p.ne10) { + const uint i10 = gid_y; + const uint i11 = gid_z / p.ne12; + const uint i12 = gid_z % p.ne12; + + const uint i01 = data_b[i10*p.nb10 + i11*p.nb11 + i12*p.nb12]; + + const uint a_offset = i01*p.nb01 + i11*p.nb02 + i12*p.nb03; + const uint d_offset = i10*p.nb21 + i11*p.nb22 + i12*p.nb23; + + const uint ib = a_offset + i00/QUANT_K; // block index + const uint iqs = (i00%QUANT_K)/QUANT_R; // quant index + const uint iybs = i00 - i00%QUANT_K; // dst block start index + const uint y_offset = QUANT_R == 1 ? 1 : QUANT_K/2; + + vec2 v = dequantize(ib, iqs, 0); + const vec2 dm = get_dm(ib, 0); + v = v * dm.x + dm.y; + + data_d[d_offset + iybs + iqs ] = D_TYPE(v.x); + data_d[d_offset + iybs + iqs + y_offset] = D_TYPE(v.y); + + gid_y += gl_WorkGroupSize.y * gl_NumWorkGroups.y; + } + gid_z += gl_WorkGroupSize.z * gl_NumWorkGroups.z; + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/glu_head.glsl b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/glu_head.glsl new file mode 100644 index 0000000000000000000000000000000000000000..c3cae736f977f32f991bd13d0b1c79992b3002f8 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/glu_head.glsl @@ -0,0 +1,47 @@ +#extension GL_EXT_shader_16bit_storage : require + + +layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; +layout (binding = 1) readonly buffer B {A_TYPE data_b[];}; +layout (binding = 2) writeonly buffer D {D_TYPE data_d[];}; + +layout (push_constant) uniform parameter +{ + uint N; + uint ne00; + uint ne20; + uint mode; + float alpha; + float limit; + uint nb00; + uint nb01; + uint nb02; + uint nb03; + uint nb10; + uint nb11; + uint nb12; + uint nb13; + uint nb20; + uint nb21; + uint nb22; + uint nb23; + uint ne21; + uint ne22; + uint misalign_offsets; + uint ne2_012mp; uint ne2_012L; + uint ne2_01mp; uint ne2_01L; + uint ne2_0mp; uint ne2_0L; +} p; + +uint get_aoffset() { return p.misalign_offsets >> 16; } +uint get_boffset() { return (p.misalign_offsets >> 8) & 0xFF; } +uint get_doffset() { return p.misalign_offsets & 0xFF; } + +// see init_fastdiv_values in ggml-vulkan.cpp +uint fastdiv(uint n, uint mp, uint L) { + uint msbs, lsbs; + umulExtended(n, mp, msbs, lsbs); + return (msbs + n) >> L; +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/glu_main.glsl b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/glu_main.glsl new file mode 100644 index 0000000000000000000000000000000000000000..14c5e7a54a9783904748f45c90221708232d814f --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/glu_main.glsl @@ -0,0 +1,35 @@ +void main() { + const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x; + + if (i >= p.N) { + return; + } + + const uint i23 = fastdiv(i, p.ne2_012mp, p.ne2_012L); + const uint i23_offset = i23 * p.ne22*p.ne21*p.ne20; + const uint i22 = fastdiv(i - i23_offset, p.ne2_01mp, p.ne2_01L); + const uint i22_offset = i22*p.ne21*p.ne20; + const uint i21 = fastdiv(i - i23_offset - i22_offset, p.ne2_0mp, p.ne2_0L); + const uint i20 = i - i23_offset - i22_offset - i21*p.ne20; + + const uint src_idx_a = get_aoffset() + i23 * p.nb03 + i22 * p.nb02 + i21 * p.nb01 + i20 * p.nb00; + const uint src_idx_b = get_boffset() + i23 * p.nb13 + i22 * p.nb12 + i21 * p.nb11 + i20 * p.nb10; + const uint dst_idx = get_doffset() + i23 * p.nb23 + i22 * p.nb22 + i21 * p.nb21 + i20 * p.nb20; + + if (p.mode == 0) { + // Default + const uint offset = (p.ne00 / 2) * p.nb00; + const uint idx = src_idx_a; + + data_d[dst_idx] = D_TYPE(op(float(data_a[idx]), float(data_a[idx + offset]))); + } else if (p.mode == 1) { + // Swapped + const uint offset = (p.ne00 / 2) * p.nb00; + const uint idx = src_idx_a; + + data_d[dst_idx] = D_TYPE(op(float(data_a[idx + offset]), float(data_a[idx]))); + } else { + // Split + data_d[dst_idx] = D_TYPE(op(float(data_a[src_idx_a]), float(data_b[src_idx_b]))); + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/group_norm.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/group_norm.comp new file mode 100644 index 0000000000000000000000000000000000000000..bdf97dbb5dc9f6105bac96626398d49d5a38d0df --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/group_norm.comp @@ -0,0 +1,66 @@ +#version 450 + +#include "generic_head.glsl" +#include "types.glsl" + +#extension GL_EXT_control_flow_attributes : enable +#define BLOCK_SIZE 512 + +layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_d[];}; + +shared float tmp[BLOCK_SIZE]; + +void main() { + const uint group_size = p.KX; + const float eps = p.param1; + + const uint tid = gl_LocalInvocationID.x; + const uint start = gl_WorkGroupID.x * group_size + tid; + const uint end = (gl_WorkGroupID.x + 1) * group_size; + + tmp[tid] = 0.0f; + + // Calculate mean + [[unroll]] for (uint col = start; col < end; col += BLOCK_SIZE) { + tmp[tid] += float(data_a[col]); + } + + // tmp up partial tmps and write back result + barrier(); + [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) { + if (tid < s) { + tmp[tid] += tmp[tid + s]; + } + barrier(); + } + + const float mean = tmp[0] / group_size; + barrier(); + tmp[tid] = 0.0f; + + // Calculate variance + [[unroll]] for (uint col = start; col < end; col += BLOCK_SIZE) { + const float xi = float(data_a[col]) - mean; + data_d[col] = D_TYPE(xi); + tmp[tid] += xi * xi; + } + + // sum up partial sums and write back result + barrier(); + [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) { + if (tid < s) { + tmp[tid] += tmp[tid + s]; + } + barrier(); + } + + const float variance = tmp[0] / group_size; + const float scale = inversesqrt(variance + eps); + + [[unroll]] for (uint col = start; col < end; col += BLOCK_SIZE) { + data_d[col] *= D_TYPE(scale); + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/im2col.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/im2col.comp new file mode 100644 index 0000000000000000000000000000000000000000..f4130d223b133713cc5cd1f87bf212bd21735870 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/im2col.comp @@ -0,0 +1,138 @@ +#version 450 + +#extension GL_EXT_shader_16bit_storage : require +#extension GL_EXT_control_flow_attributes : require + +#include "types.glsl" + +layout (push_constant) uniform parameter +{ + BDA_STORAGE_T dst_addr; + uint batch_offset; uint offset_delta; + uint IC; + uint IW; uint IH; + uint OW; uint OH; + uint KW; uint KH; + uint OH_batch; + uint CHW; + int s0; int s1; + int p0; int p1; + int d0; int d1; + uint batch_IC; +} p; + +layout(constant_id = 0) const uint BLOCK_SIZE = 32; + +const uint NUM_ITER = 512 / BLOCK_SIZE; + +layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_d[];}; + +#if BDA +layout (buffer_reference) buffer D_ptr {D_TYPE d;}; +#endif + +void im2col(const uint ow, const uint z_idx) { + const uint oh = z_idx % p.OH; + const uint batch_idx = z_idx / p.OH; + + const uint gidx = gl_LocalInvocationID.x; + const uint src_batch = batch_idx * p.batch_offset; + const BDA_OFFSET_T dst_row = ((BDA_OFFSET_T(batch_idx) * p.OH + oh) * p.OW + ow) * p.CHW; + + const uint KHKW = p.KH * p.KW; + + // Precompute base input coordinates + const int base_iw = int(ow * p.s0) - p.p0; + const int base_ih = int(oh * p.s1) - p.p1; + + // Precompute step deltas + const uint delta_ic = BLOCK_SIZE / KHKW; + const uint delta_rem = BLOCK_SIZE % KHKW; + + const uint delta_ky = delta_rem / p.KW; + const uint delta_kx = delta_rem % p.KW; + + const uint delta_ic_offset = delta_ic * p.offset_delta; + + // If using BDA mode, precompute the base pointer and step size +#if BDA + const BDA_STORAGE_T base_dst_addr = p.dst_addr + D_SIZE * dst_row; + const uint bda_step = D_SIZE * BLOCK_SIZE; +#endif + + uint wg_x = gl_WorkGroupID.x; + do { + const uint wg_offset = wg_x * 512; + + uint chw_idx = wg_offset + gidx; + + uint ic = chw_idx / KHKW; + uint rem = chw_idx % KHKW; + + uint ky = rem / p.KW; + uint kx = rem % p.KW; + + uint ic_offset = src_batch + ic * p.offset_delta; + + // Initialize running pointer/index for the destination buffer +#if BDA + BDA_STORAGE_T current_dst_addr = base_dst_addr + D_SIZE * chw_idx; +#else + uint current_dst_idx = dst_row + chw_idx; +#endif + + [[unroll]] for (uint i = 0; i < NUM_ITER; ++i) { + if (chw_idx >= p.CHW) { + return; + } + + const int iiw = base_iw + int(kx * p.d0); + const int iih = base_ih + int(ky * p.d1); + + A_TYPE val = A_TYPE(0); + if (uint(iih) < p.IH && uint(iiw) < p.IW) { + val = data_a[ic_offset + uint(iih) * p.IW + uint(iiw)]; + } + +#if BDA + D_ptr(current_dst_addr).d = D_TYPE(val); + current_dst_addr += bda_step; +#else + data_d[current_dst_idx] = D_TYPE(val); + current_dst_idx += BLOCK_SIZE; +#endif + + chw_idx += BLOCK_SIZE; + ic_offset += delta_ic_offset; + kx += delta_kx; + ky += delta_ky; + + // Handle X axis wrap + uint kx_wrap = uint(kx >= p.KW); + kx -= kx_wrap * p.KW; + ky += kx_wrap; + + // Handle Y axis wrap + uint ky_wrap = uint(ky >= p.KH); + ky -= ky_wrap * p.KH; + ic_offset += ky_wrap * p.offset_delta; + } + + wg_x += gl_NumWorkGroups.x; + } while (wg_x * 512 < p.CHW); +} + +void main() { + uint ow = gl_GlobalInvocationID.y; + while (ow < p.OW) { + uint z = gl_GlobalInvocationID.z; + while (z < p.OH_batch) { + im2col(ow, z); + z += gl_NumWorkGroups.z; + } + ow += gl_NumWorkGroups.y; + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/im2col_3d.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/im2col_3d.comp new file mode 100644 index 0000000000000000000000000000000000000000..93f61fd85435fab92490a738323255880f9d4317 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/im2col_3d.comp @@ -0,0 +1,124 @@ +#version 450 + +#extension GL_EXT_shader_16bit_storage : require +#extension GL_EXT_control_flow_attributes : require +#extension GL_EXT_shader_explicit_arithmetic_types_int32 : require + +#include "types.glsl" + +layout (push_constant) uniform parameter +{ + BDA_STORAGE_T dst_addr; + uint32_t nb10; + uint32_t nb11; + uint32_t nb12; + uint32_t nb13; + uint32_t s0; + uint32_t s1; + uint32_t s2; + uint32_t p0; + uint32_t p1; + uint32_t p2; + uint32_t d0; + uint32_t d1; + uint32_t d2; + uint32_t IW; + uint32_t IH; + uint32_t ID; + uint32_t IC; + uint32_t KW; + uint32_t OH; + uint32_t KD_KH_KW; + uint32_t KH_KW; + uint32_t IC_KD_KH_KW; + uint32_t N_OD_OH; + uint32_t OD_OH; + uint32_t OD_OH_OW_IC_KD_KH_KW; + uint32_t OH_OW_IC_KD_KH_KW; + uint32_t OW_IC_KD_KH_KW; + uint32_t misalign_offsets; +} p; + +uint get_aoffset() { return p.misalign_offsets >> 16; } +uint get_doffset() { return p.misalign_offsets & 0xFFFF; } + +layout(constant_id = 0) const uint BLOCK_SIZE = 32; + +layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_d[];}; + +#if BDA +layout (buffer_reference) buffer D_ptr {D_TYPE d;}; +#endif + +void main() { + const uint32_t i = gl_GlobalInvocationID.x; + + uint32_t nb10 = p.nb10; + uint32_t nb11 = p.nb11; + uint32_t nb12 = p.nb12; + uint32_t nb13 = p.nb13; + uint32_t s0 = p.s0; + uint32_t s1 = p.s1; + uint32_t s2 = p.s2; + uint32_t p0 = p.p0; + uint32_t p1 = p.p1; + uint32_t p2 = p.p2; + uint32_t d0 = p.d0; + uint32_t d1 = p.d1; + uint32_t d2 = p.d2; + uint32_t IW = p.IW; + uint32_t IH = p.IH; + uint32_t ID = p.ID; + uint32_t IC = p.IC; + uint32_t KW = p.KW; + uint32_t OH = p.OH; + uint32_t KD_KH_KW = p.KD_KH_KW; + uint32_t KH_KW = p.KH_KW; + uint32_t IC_KD_KH_KW = p.IC_KD_KH_KW; + uint32_t N_OD_OH = p.N_OD_OH; + uint32_t OD_OH = p.OD_OH; + uint32_t OD_OH_OW_IC_KD_KH_KW = p.OD_OH_OW_IC_KD_KH_KW; + uint32_t OH_OW_IC_KD_KH_KW = p.OH_OW_IC_KD_KH_KW; + uint32_t OW_IC_KD_KH_KW = p.OW_IC_KD_KH_KW; + + if (i >= IC_KD_KH_KW) { + return; + } + + const uint32_t iic = i / KD_KH_KW; + const uint32_t ikd = (i - iic * KD_KH_KW) / KH_KW; + const uint32_t ikh = (i - iic * KD_KH_KW - ikd * KH_KW) / KW; + const uint32_t ikw = i % KW; + + const uint32_t iow = gl_GlobalInvocationID.y; + for (uint32_t iz = gl_GlobalInvocationID.z; iz < N_OD_OH; iz += gl_NumWorkGroups.z) { + const uint32_t in_ = iz / OD_OH; + const uint32_t iod = (iz - in_*OD_OH) / OH; + const uint32_t ioh = iz % OH; + + const uint32_t iiw = iow * s0 + ikw * d0 - p0; + const uint32_t iih = ioh * s1 + ikh * d1 - p1; + const uint32_t iid = iod * s2 + ikd * d2 - p2; + + const BDA_OFFSET_T offset_dst = BDA_OFFSET_T(in_)*OD_OH_OW_IC_KD_KH_KW + BDA_OFFSET_T(iod)*OH_OW_IC_KD_KH_KW + BDA_OFFSET_T(ioh)*OW_IC_KD_KH_KW + BDA_OFFSET_T(iow)*IC_KD_KH_KW + iic*KD_KH_KW + ikd * KH_KW + ikh*KW + ikw; + + const uint32_t offset_src = (in_*IC + iic)*nb13 + iid*nb12 + iih*nb11 + iiw*nb10; +#if BDA + D_ptr dst_addr = D_ptr(p.dst_addr + D_SIZE * offset_dst); + if (iih >= IH || iiw >= IW || iid >= ID) { + dst_addr.d = D_TYPE(0.0f); + } else { + dst_addr.d = D_TYPE(data_a[offset_src + get_aoffset()]); + } +#else + if (iih >= IH || iiw >= IW || iid >= ID) { + data_d[offset_dst + get_doffset()] = D_TYPE(0.0f); + } else { + data_d[offset_dst + get_doffset()] = D_TYPE(data_a[offset_src + get_aoffset()]); + } +#endif + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/l2_norm.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/l2_norm.comp new file mode 100644 index 0000000000000000000000000000000000000000..9039ed1ded3b5894f4726cdca18acfd976a26e07 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/l2_norm.comp @@ -0,0 +1,41 @@ +#version 450 + +#include "generic_unary_head.glsl" +#include "types.glsl" + +#extension GL_EXT_control_flow_attributes : enable +#define BLOCK_SIZE 512 + +layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in; + +shared FLOAT_TYPE sum[BLOCK_SIZE]; + +void main() { + const uint row = gl_WorkGroupID.z * 262144 + gl_WorkGroupID.y * 512 + gl_WorkGroupID.x; + const uint tid = gl_LocalInvocationID.x; + + const uint a_base = get_aoffset() + src0_idx(row * p.ne00); + const uint d_base = get_doffset() + dst_idx(row * p.ne10); + + sum[tid] = FLOAT_TYPE(0.0f); // partial sum for thread in warp + + [[unroll]] for (uint i0 = tid; i0 < p.ne00; i0 += BLOCK_SIZE) { + const FLOAT_TYPE xi = FLOAT_TYPE(data_a[a_base + i0*p.nb00]); + sum[tid] += xi * xi; + } + + // sum up partial sums and write back result + barrier(); + [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) { + if (tid < s) { + sum[tid] += sum[tid + s]; + } + barrier(); + } + + const FLOAT_TYPE scale = 1.0f / max(sqrt(sum[0]), FLOAT_TYPE(p.param1)); + + [[unroll]] for (uint i0 = tid; i0 < p.ne00; i0 += BLOCK_SIZE) { + data_d[d_base + i0*p.nb10] = D_TYPE(scale * FLOAT_TYPE(data_a[a_base + i0*p.nb00])); + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/log.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/log.comp new file mode 100644 index 0000000000000000000000000000000000000000..3cda6a63c45a1ff41aa015e9d8ad67088cb554e9 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/log.comp @@ -0,0 +1,17 @@ +#version 450 + +#include "types.glsl" +#include "generic_unary_head.glsl" + +layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; + +void main() { + const uint idx = get_idx(); + + if (idx >= p.ne) { + return; + } + + const float val = float(data_a[get_aoffset() + src0_idx(idx)]); + data_d[get_doffset() + dst_idx(idx)] = D_TYPE(log(val)); +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul.comp new file mode 100644 index 0000000000000000000000000000000000000000..02ef1eace169f3502730f184ad05917b9c42eb49 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul.comp @@ -0,0 +1,27 @@ +#version 450 + +#include "types.glsl" +#include "generic_binary_head.glsl" + +const uint num_threads = 256; + +layout(local_size_x = num_threads, local_size_y = 1, local_size_z = 1) in; + +void main() { + uint idx = get_idx(); + + // num_threads * num_iter must equal 512, to match the wg_denoms and get_idx calculation + const uint num_iter = 2; + + [[unroll]] for (uint i = 0; i < num_iter; ++i) { + if (idx >= p.ne) { + continue; + } + uint i00, i01, i02, i03; + get_indices(idx, i00, i01, i02, i03); + + data_d[get_doffset() + dst_idx(i00, i01, i02, i03)] = D_TYPE(FLOAT_TYPE(data_a[get_aoffset() + src0_idx(i00, i01, i02, i03)]) * FLOAT_TYPE(data_b[get_boffset() + src1_idx(i00, i01, i02, i03)])); + + idx += num_threads; + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_split_k_reduce.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_split_k_reduce.comp new file mode 100644 index 0000000000000000000000000000000000000000..4c64fd47af718d5e43224648e2f5a7b527739723 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_split_k_reduce.comp @@ -0,0 +1,48 @@ +#version 450 + +#extension GL_EXT_control_flow_attributes : enable + +layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {float data_a[];}; +layout (binding = 0) readonly buffer A4 {vec4 data_a4[];}; +layout (binding = 1) writeonly buffer D {float data_d[];}; +layout (binding = 1) writeonly buffer D4 {vec4 data_d4[];}; + +layout (push_constant) uniform parameter { + uint ne; + uint k_num; +} p; + +void main() { + // Each invocation handles four consecutive components + const uint idx = gl_GlobalInvocationID.x * 4; + + if (idx >= p.ne) { + return; + } + + // Check if all four components are in bounds and aligned, + // then use vector loads + if (idx + 3 < p.ne && (p.ne % 4) == 0) { + vec4 result = vec4(0.0f); + + [[unroll]] for (uint i = 0; i < p.k_num; i++) { + result += data_a4[(i * p.ne + idx) / 4]; + } + + data_d4[idx / 4] = result; + } else { + [[unroll]] for (uint j = 0; j < 4; ++j) { + if (idx + j < p.ne) { + float result = 0.0f; + + [[unroll]] for (uint i = 0; i < p.k_num; i++) { + result += data_a[i * p.ne + idx + j]; + } + + data_d[idx + j] = result; + } + } + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec.comp new file mode 100644 index 0000000000000000000000000000000000000000..5a9d0e778fdf3866ac77d576cd218d307507afae --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec.comp @@ -0,0 +1,264 @@ +#version 450 + +#extension GL_EXT_shader_explicit_arithmetic_types_int32 : require + +#include "mul_mat_vec_base.glsl" +#include "dequant_funcs.glsl" + +layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; + +#if !defined(DATA_A_F32) && !defined(DATA_A_F16) && !defined(DATA_A_BF16) +#define K_PER_ITER 8 +#else +#define K_PER_ITER 4 +#endif + + +uint a_offset, b_offset, d_offset, y_offset; + +vec4 load_b(const uint j, const uint iybs, const uint iqs, const bool lastiter, out bool OOB_y, out bool OOB_z, out bool OOB_w) { + // Check if the latter elements are OOB, and don't fetch B or accumulate it. + OOB_y = lastiter && (iybs + iqs + y_offset >= p.ncols); + OOB_z = lastiter && (iybs + iqs + y_offset*2 >= p.ncols); + OOB_w = lastiter && (iybs + iqs + y_offset*3 >= p.ncols); + + if (!OOB_w) { + return vec4(FLOAT_TYPE(data_b[j*p.batch_stride_b + b_offset + iybs + iqs]), + FLOAT_TYPE(data_b[j*p.batch_stride_b + b_offset + iybs + iqs + y_offset]), + FLOAT_TYPE(data_b[j*p.batch_stride_b + b_offset + iybs + iqs + y_offset*2]), + FLOAT_TYPE(data_b[j*p.batch_stride_b + b_offset + iybs + iqs + y_offset*3])); + } else if (!OOB_z) { + return vec4(FLOAT_TYPE(data_b[j*p.batch_stride_b + b_offset + iybs + iqs]), + FLOAT_TYPE(data_b[j*p.batch_stride_b + b_offset + iybs + iqs + y_offset]), + FLOAT_TYPE(data_b[j*p.batch_stride_b + b_offset + iybs + iqs + y_offset*2]), + 0); + } else if (!OOB_y) { + return vec4(FLOAT_TYPE(data_b[j*p.batch_stride_b + b_offset + iybs + iqs]), + FLOAT_TYPE(data_b[j*p.batch_stride_b + b_offset + iybs + iqs + y_offset]), + 0, 0); + } else { + return vec4(FLOAT_TYPE(data_b[j*p.batch_stride_b + b_offset + iybs + iqs]), + 0, 0, 0); + } +} + +void iter(inout FLOAT_TYPE temp[NUM_COLS][NUM_ROWS], const uint first_row, const uint num_rows, const uint tid, const uint i, bool lastiter) +{ + [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) { + const uint col = i*BLOCK_SIZE + K_PER_ITER*tid; + const uint iqs = (col%QUANT_K)/QUANT_R; // quant index + const uint iybs = col - col%QUANT_K; // y block start index + +#if K_PER_ITER == 8 +#if QUANT_R == 2 + // Note that we end up fetching bogus elements here, but its fine as they'll be + // within an accessible block. + const vec4 bv02 = vec4(data_b_v4[(j*p.batch_stride_b + b_offset + iybs + iqs) / 4]); + const vec4 bv13 = vec4(data_b_v4[(j*p.batch_stride_b + b_offset + iybs + iqs + y_offset) / 4]); + const vec4 bv0 = vec4(bv02.x, bv13.x, bv02.y, bv13.y); + const vec4 bv1 = vec4(bv02.z, bv13.z, bv02.w, bv13.w); +#else + const vec4 bv0 = vec4(data_b_v4[(j*p.batch_stride_b + b_offset + iybs + iqs) / 4]); + const vec4 bv1 = vec4(data_b_v4[(j*p.batch_stride_b + b_offset + iybs + iqs) / 4 + 1]); +#endif +#else + bool OOB_y; + bool OOB_z; + bool OOB_w; + + const vec4 b = load_b(j, iybs, iqs, lastiter, OOB_y, OOB_z, OOB_w); +#endif + uint ibi = first_row*p.ncols; + [[unroll]] for (uint n = 0; n < num_rows; ++n) { + const uint ib = (ibi + col)/QUANT_K; // block index + ibi += p.ncols; + +#if K_PER_ITER == 8 + vec4 v = dequantize4(ib, iqs, a_offset); + vec4 v2 = dequantize4(ib, iqs+(4/QUANT_R), a_offset); + + const vec2 dm = get_dm(ib, a_offset); + if (dm.y != 0) { // quant has min component + v = v * dm.x + dm.y; + v2 = v2 * dm.x + dm.y; + } + + // matrix multiplication + FLOAT_TYPE rowtmp = dot(bv0, v); + rowtmp += dot(bv1, v2); + + if (dm.y == 0) + rowtmp *= dm.x; + + temp[j][n] += rowtmp; +#else + if (!OOB_w) { + const vec4 v = dequantize4(ib, iqs, a_offset); + temp[j][n] += dot(v, b); + } else if (!OOB_z) { + const vec2 v0 = dequantize(ib, iqs, a_offset); + const FLOAT_TYPE v1 = dequantize1(ib + 2/QUANT_R, iqs, a_offset); + const vec3 v = vec3(v0.x, v0.y, v1); + const vec3 b0 = vec3(b.x, b.y, b.z); + temp[j][n] += dot(v, b0); + } else if (!OOB_y) { + const vec2 v0 = dequantize(ib, iqs, a_offset); + const vec2 b0 = vec2(b.x, b.y); + temp[j][n] += dot(v0, b0); + } else { + const FLOAT_TYPE v = dequantize1(ib, iqs, a_offset); + temp[j][n] = fma(v, b.x, temp[j][n]); + } +#endif + } + } +} + +#if defined(DATA_A_F32) || defined(DATA_A_F16) || defined(DATA_A_BF16) +void iter_aligned_nonquant(inout FLOAT_TYPE temp[NUM_COLS][NUM_ROWS], const uint first_row, const uint num_rows, const uint tid, const uint i) +{ + [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) { + const uint col = i*BLOCK_SIZE + K_PER_ITER*tid; + const uint iqs = 0; // quant index + const uint iybs = col; // y block start index + + const vec4 b = data_b_v4[(j*p.batch_stride_b + b_offset + iybs + iqs) / 4]; + + uint ibi = first_row*p.ncols; + [[unroll]] for (uint n = 0; n < num_rows; ++n) { + const uint ib = (ibi + col)/QUANT_K; // block index + ibi += p.ncols; + + const vec4 v = dequantize4_2aligned(ib, iqs, a_offset); + + // matrix multiplication + temp[j][n] += dot(v, b); + } + } +} +#endif + +void compute_outputs(const uint32_t first_row, const uint32_t num_rows) { + const uint tid = gl_LocalInvocationID.x; + + get_offsets(a_offset, b_offset, d_offset); + const bool is_aligned_nonquant = + p.batch_stride_b % 4 == 0 && b_offset % 4 == 0 && + p.ncols % 4 == 0 && BLOCK_SIZE % 4 == 0 && + K_PER_ITER == 4; + + y_offset = QUANT_R == 1 ? 1 : QUANT_K/2; + + FLOAT_TYPE temp[NUM_COLS][NUM_ROWS]; + + [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) { + [[unroll]] for (uint i = 0; i < NUM_ROWS; ++i) { + temp[j][i] = FLOAT_TYPE(0); + } + } + + uint num_iters = p.ncols / (K_PER_ITER * BLOCK_SIZE); + if (num_iters * K_PER_ITER * BLOCK_SIZE + K_PER_ITER*tid < p.ncols) { + num_iters++; + } + int unroll_count = 4; + uint unrolled_iters = num_iters & ~(unroll_count - 1); + + uint i = 0; + +#if K_PER_ITER == 4 + // If the K dimension is odd, we need lastiter==true on the last iteration + // so OOB is computed correctly. Skip some unrolling to make that happen. + if ((p.ncols & 3) != 0 && + unrolled_iters == num_iters && + unrolled_iters > 0) { + unrolled_iters -= unroll_count; + } + if (is_aligned_nonquant) { + while (i < unrolled_iters) { + // Manually partially unroll the loop + [[unroll]] for (uint k = 0; k < unroll_count; ++k) { + iter_aligned_nonquant(temp, first_row, num_rows, tid, i*K_PER_ITER); + i++; + } + } + } else { +#endif + while (i < unrolled_iters) { + // Manually partially unroll the loop + [[unroll]] for (uint k = 0; k < unroll_count; ++k) { + iter(temp, first_row, num_rows, tid, i*K_PER_ITER, false); + i++; + } + } +#if K_PER_ITER == 4 + } +#endif + + unroll_count = 2; + unrolled_iters = num_iters & ~(unroll_count - 1); + +#if K_PER_ITER == 4 + if ((p.ncols & 3) != 0 && + unrolled_iters == num_iters && + unrolled_iters > 0) { + unrolled_iters -= unroll_count; + } + + if (is_aligned_nonquant) { + while (i < unrolled_iters && is_aligned_nonquant) { + // Manually partially unroll the loop + [[unroll]] for (uint k = 0; k < unroll_count; ++k) { + iter_aligned_nonquant(temp, first_row, num_rows, tid, i*K_PER_ITER); + i++; + } + } + } else { +#endif + while (i < unrolled_iters) { + // Manually partially unroll the loop + [[unroll]] for (uint k = 0; k < unroll_count; ++k) { + iter(temp, first_row, num_rows, tid, i*K_PER_ITER, false); + i++; + } + } +#if K_PER_ITER == 4 + } +#endif + +#if K_PER_ITER == 4 + if (is_aligned_nonquant) { + while (i < num_iters) { + iter_aligned_nonquant(temp, first_row, num_rows, tid, i*K_PER_ITER); + i++; + } + } else { +#endif + while (i < num_iters) { + iter(temp, first_row, num_rows, tid, i*K_PER_ITER, true); + i++; + } +#if K_PER_ITER == 4 + } +#endif + + reduce_result(temp, d_offset, first_row, num_rows, tid); +} + +void main() { + const uint first_row = NUM_ROWS * (gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z); + +#ifdef NEEDS_INIT_IQ_SHMEM + init_iq_shmem(gl_WorkGroupSize); +#endif + + // do NUM_ROWS at a time, unless there aren't enough remaining rows + if (first_row + NUM_ROWS <= p.stride_d) { + compute_outputs(first_row, NUM_ROWS); + } else { + if (first_row >= p.stride_d) { + return; + } + compute_outputs(first_row, min(NUM_ROWS, p.stride_d - first_row)); + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_base.glsl b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_base.glsl new file mode 100644 index 0000000000000000000000000000000000000000..4aeda68c7f2dbc21755ecd9be5f7d7b150861e45 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_base.glsl @@ -0,0 +1,230 @@ +#extension GL_EXT_control_flow_attributes : enable +#extension GL_EXT_shader_16bit_storage : require +#extension GL_EXT_shader_8bit_storage : require + +#if USE_SUBGROUP_ADD || USE_SUBGROUP_ADD_NO_SHMEM +#extension GL_KHR_shader_subgroup_basic : require +#extension GL_KHR_shader_subgroup_arithmetic : require +#endif + +#ifdef MUL_MAT_ID +#define EXPERT_COUNT 8 +#endif + +#include "mul_mat_vec_iface.glsl" + +layout (push_constant) uniform parameter +{ + uint ncols; + uint stride_a; + uint stride_b; + uint stride_d; + + uint batch_stride_a; + uint batch_stride_b; + uint batch_stride_d; + + uint fusion_flags; + +#ifdef MUL_MAT_ID + uint nei0; + uint ne11; + uint expert_i1; + uint nbi1; +#else + uint base_work_group_y; + uint ne02; + uint ne12; + uint broadcast2; + uint broadcast3; +#endif +} p; + +#ifdef MUL_MAT_ID +uint expert_id; +#endif + +void get_offsets(out uint a_offset, out uint b_offset, out uint d_offset) { +#ifdef MUL_MAT_ID + const uint expert_i0 = gl_WorkGroupID.y; +#else + const uint batch_idx = gl_WorkGroupID.y + p.base_work_group_y; +#endif + +#ifndef MUL_MAT_ID + uint batch_idx_a = 0; + if (batch_idx != 0) { + const uint i13 = batch_idx / p.ne12; + const uint i12 = batch_idx % p.ne12; + + const uint i03 = i13 / p.broadcast3; + const uint i02 = i12 / p.broadcast2; + + batch_idx_a = i03 * p.ne02 + i02; + } +#else + expert_id = data_ids[expert_i0 + p.expert_i1 * p.nbi1]; +#endif + + a_offset = +#ifdef MUL_MAT_ID + expert_id * (p.batch_stride_a / QUANT_K); +#else + batch_idx_a * (p.batch_stride_a / QUANT_K); +#endif + b_offset = +#ifdef MUL_MAT_ID + (expert_i0 % p.ne11) * p.stride_b + p.expert_i1 * p.batch_stride_b; +#else + batch_idx * p.batch_stride_b; +#endif + d_offset = +#ifdef MUL_MAT_ID + expert_i0 * p.stride_d + p.expert_i1 * p.batch_stride_d; +#else + batch_idx * p.batch_stride_d; +#endif +} + +layout (constant_id = 0) const uint BLOCK_SIZE = 32; +layout (constant_id = 1) const uint NUM_ROWS = 1; +layout (constant_id = 2) const uint NUM_COLS = 1; + +#ifdef USE_SUBGROUP_ADD_NO_SHMEM +void reduce_result(inout FLOAT_TYPE temp[NUM_COLS][NUM_ROWS], const in uint32_t d_offset, const in uint32_t first_row, const in uint32_t num_rows, const in uint32_t tid) { + [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) { + [[unroll]] for (uint n = 0; n < num_rows; ++n) { + temp[j][n] = subgroupAdd(temp[j][n]); + } + } + + if (tid == 0) { + [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) { + [[unroll]] for (uint n = 0; n < num_rows; ++n) { +#ifdef MUL_MAT_ID + if ((p.fusion_flags & MAT_VEC_FUSION_FLAGS_BIAS0) != 0) { + temp[j][n] += FLOAT_TYPE(data_fuse0[expert_id*p.stride_d + first_row + n]); + } + if ((p.fusion_flags & MAT_VEC_FUSION_FLAGS_SCALE0) != 0) { + const uint expert_i0 = gl_GlobalInvocationID.y; + temp[j][n] *= FLOAT_TYPE(data_fuse0[expert_i0]); + } + if ((p.fusion_flags & MAT_VEC_FUSION_FLAGS_SCALE1) != 0) { + const uint expert_i0 = gl_GlobalInvocationID.y; + temp[j][n] *= FLOAT_TYPE(data_fuse1[expert_i0]); + } +#else + if ((p.fusion_flags & MAT_VEC_FUSION_FLAGS_BIAS0) != 0) { + temp[j][n] += FLOAT_TYPE(data_fuse0[j*p.batch_stride_d + d_offset + first_row + n]); + } + if ((p.fusion_flags & MAT_VEC_FUSION_FLAGS_BIAS1) != 0) { + temp[j][n] += FLOAT_TYPE(data_fuse1[j*p.batch_stride_d + d_offset + first_row + n]); + } +#endif + data_d[j*p.batch_stride_d + d_offset + first_row + n] = D_TYPE(temp[j][n]); + } + } + } +} +#else +shared FLOAT_TYPE tmpsh[NUM_COLS][NUM_ROWS][BLOCK_SIZE]; + +void reduce_result(FLOAT_TYPE temp[NUM_COLS][NUM_ROWS], const in uint32_t d_offset, const in uint32_t first_row, const in uint32_t num_rows, const in uint32_t tid) { + // subgroupAdd is probably faster on devices that support it, + // particularly when the workgroup has more than one subgroup +#if USE_SUBGROUP_ADD + // sum up partial sums within a subgroup + [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) { + [[unroll]] for (uint n = 0; n < num_rows; ++n) { + temp[j][n] = subgroupAdd(temp[j][n]); + } + } + + // Go through shared memory to sum partials across subgroups + if (gl_SubgroupInvocationID == 0) { + [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) { + [[unroll]] for (uint n = 0; n < num_rows; ++n) { + tmpsh[j][n][gl_SubgroupID] = temp[j][n]; + } + } + } + barrier(); + if (tid == 0) { + [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) { + [[unroll]] for (uint n = 0; n < num_rows; ++n) { + temp[j][n] = FLOAT_TYPE(0); + [[unroll]] for (uint s = 0; s < gl_NumSubgroups; ++s) { + temp[j][n] += tmpsh[j][n][s]; + } +#ifdef MUL_MAT_ID + if ((p.fusion_flags & MAT_VEC_FUSION_FLAGS_BIAS0) != 0) { + temp[j][n] += FLOAT_TYPE(data_fuse0[expert_id*p.stride_d + first_row + n]); + } + if ((p.fusion_flags & MAT_VEC_FUSION_FLAGS_SCALE0) != 0) { + const uint expert_i0 = gl_GlobalInvocationID.y; + temp[j][n] *= FLOAT_TYPE(data_fuse0[expert_i0]); + } + if ((p.fusion_flags & MAT_VEC_FUSION_FLAGS_SCALE1) != 0) { + const uint expert_i0 = gl_GlobalInvocationID.y; + temp[j][n] *= FLOAT_TYPE(data_fuse1[expert_i0]); + } +#else + if ((p.fusion_flags & MAT_VEC_FUSION_FLAGS_BIAS0) != 0) { + temp[j][n] += FLOAT_TYPE(data_fuse0[j*p.batch_stride_d + d_offset + first_row + n]); + } + if ((p.fusion_flags & MAT_VEC_FUSION_FLAGS_BIAS1) != 0) { + temp[j][n] += FLOAT_TYPE(data_fuse1[j*p.batch_stride_d + d_offset + first_row + n]); + } +#endif + data_d[j*p.batch_stride_d + d_offset + first_row + n] = D_TYPE(temp[j][n]); + } + } + } +#else + // sum up partial sums and write back result + [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) { + [[unroll]] for (uint n = 0; n < num_rows; ++n) { + tmpsh[j][n][tid] = temp[j][n]; + } + } + barrier(); + [[unroll]] for (uint s = BLOCK_SIZE/2; s > 0; s >>= 1) { + if (tid < s) { + [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) { + [[unroll]] for (uint n = 0; n < num_rows; ++n) { + tmpsh[j][n][tid] += tmpsh[j][n][tid + s]; + } + } + } + barrier(); + } + if (tid == 0) { + [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) { + [[unroll]] for (uint n = 0; n < num_rows; ++n) { +#ifdef MUL_MAT_ID + if ((p.fusion_flags & MAT_VEC_FUSION_FLAGS_BIAS0) != 0) { + tmpsh[j][n][0] += FLOAT_TYPE(data_fuse0[expert_id*p.stride_d + first_row + n]); + } + if ((p.fusion_flags & MAT_VEC_FUSION_FLAGS_SCALE0) != 0) { + const uint expert_i0 = gl_GlobalInvocationID.y; + tmpsh[j][n][0] *= FLOAT_TYPE(data_fuse0[expert_i0]); + } + if ((p.fusion_flags & MAT_VEC_FUSION_FLAGS_SCALE1) != 0) { + const uint expert_i0 = gl_GlobalInvocationID.y; + tmpsh[j][n][0] *= FLOAT_TYPE(data_fuse1[expert_i0]); + } +#else + if ((p.fusion_flags & MAT_VEC_FUSION_FLAGS_BIAS0) != 0) { + tmpsh[j][n][0] += FLOAT_TYPE(data_fuse0[j*p.batch_stride_d + d_offset + first_row + n]); + } + if ((p.fusion_flags & MAT_VEC_FUSION_FLAGS_BIAS1) != 0) { + tmpsh[j][n][0] += FLOAT_TYPE(data_fuse1[j*p.batch_stride_d + d_offset + first_row + n]); + } +#endif + data_d[j*p.batch_stride_d + d_offset + first_row + n] = D_TYPE(tmpsh[j][n][0]); + } + } + } +#endif +} +#endif diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_iface.glsl b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_iface.glsl new file mode 100644 index 0000000000000000000000000000000000000000..e8d053cdd4325189f0b3def989b05dcdc73c7bdd --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_iface.glsl @@ -0,0 +1,35 @@ +#include "types.glsl" + +#define MAT_VEC_FUSION_FLAGS_BIAS0 0x1 +#define MAT_VEC_FUSION_FLAGS_BIAS1 0x2 +#define MAT_VEC_FUSION_FLAGS_SCALE0 0x4 +#define MAT_VEC_FUSION_FLAGS_SCALE1 0x8 + +layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; +#if defined(A_TYPEV4) +layout (binding = 0) readonly buffer AV4 {A_TYPEV4 data_a_v4[];}; +#endif +#if defined(A_TYPE_PACKED16) +layout (binding = 0) readonly buffer A_PACKED16 {A_TYPE_PACKED16 data_a_packed16[];}; +#endif +#if defined(A_TYPE_PACKED32) +layout (binding = 0) readonly buffer A_PACKED32 {A_TYPE_PACKED32 data_a_packed32[];}; +#endif + +layout (binding = 1) readonly buffer B {B_TYPE data_b[];}; +#ifdef B_TYPEV2 +layout (binding = 1) readonly buffer BV2 {B_TYPEV2 data_b_v2[];}; +#endif +#ifdef B_TYPEV4 +layout (binding = 1) readonly buffer BV4 {B_TYPEV4 data_b_v4[];}; +#endif + +layout (binding = 2) writeonly buffer D {D_TYPE data_d[];}; + +layout (binding = 3) readonly buffer Fuse0 {D_TYPE data_fuse0[];}; +layout (binding = 4) readonly buffer Fuse1 {D_TYPE data_fuse1[];}; + +#ifdef MUL_MAT_ID +layout (binding = 5) readonly buffer IDS {int data_ids[];}; +#endif + diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_iq1_m.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_iq1_m.comp new file mode 100644 index 0000000000000000000000000000000000000000..3ea24a76cec006fa80bdde5c0ff57925d671d068 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_iq1_m.comp @@ -0,0 +1,132 @@ +#version 450 +#extension GL_EXT_shader_explicit_arithmetic_types_int32 : require + +#include "mul_mat_vec_base.glsl" + +layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; + +FLOAT_TYPE temp[NUM_COLS][NUM_ROWS]; + +void calc_superblock(const uint a_offset, const uint b_offset, const uint ib32, const uint i, + const uint num_blocks_per_row, const uint first_row, const uint num_rows) { + // Compute starting index in matrix B for this superblock + const uint y_idx = i * QUANT_K + 32 * ib32; + uint ibi = a_offset + first_row * num_blocks_per_row + i; + + // Precompute indices for quantization lookup tables + const uint qh_base = 2 * ib32; + const uint qs_base = 4 * ib32; + const uint sc_index = ib32 / 2; + const uint sc_shift = 6 * (ib32 & 1); + + // Loop over rows in the superblock + [[unroll]] for (uint n = 0; n < num_rows; ++n) { + // Load per-block scales and shift for quantization + const uint16_t[4] scales = data_a[ibi].scales; + const u16vec4 s = u16vec4(scales[0], scales[1], scales[2], scales[3]) >> 12; + const float d = float(unpackHalf2x16(s.x | (s.y << 4) | (s.z << 8) | (s.w << 12)).x); + const uint sc = data_a[ibi].scales[sc_index] >> sc_shift; + + // Temporary caches for decoding + FLOAT_TYPE dl_cache[4]; + uint16_t gvf_cache[4]; + float delta_cache[4]; + + // Precompute the multiplier and lookup values for 4 sub-blocks + [[unroll]] for (uint l = 0; l < 4; ++l) { + dl_cache[l] = FLOAT_TYPE(d * (2 * bitfieldExtract(sc, 3 * int(l / 2), 3) + 1)); + const uint qh = data_a[ibi].qh[qh_base + l / 2] >> (4 * (l & 1)); + const uint qs = data_a[ibi].qs[qs_base + l]; + gvf_cache[l] = iq1s_grid[qs | ((qh & 7) << 8)]; + delta_cache[l] = ((qh & 8) != 0) ? -IQ1M_DELTA : IQ1M_DELTA; + } + + // Loop over columns of the output + [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) { + // Compute base index for matrix B + const uint base_b_idx = (j * p.batch_stride_b + b_offset + y_idx) / 4; + vec4 b_vals[8]; + + // Load 8 vec4 values from matrix B + [[unroll]] for (int idx = 0; idx < 8; ++idx) { + b_vals[idx] = vec4(data_b_v4[base_b_idx + idx]); + } + + FLOAT_TYPE col_sum = FLOAT_TYPE(0.0); + + // Loop over sub-blocks + [[unroll]] for (uint l = 0; l < 4; ++l) { + const uint16_t grid = gvf_cache[l]; + const float dl = dl_cache[l]; + + // Decode 8 2-bit fbits from gvf_cache + float f0 = float(bitfieldExtract(grid, 0, 2)); + float f1 = float(bitfieldExtract(grid, 2, 2)); + float f2 = float(bitfieldExtract(grid, 4, 2)); + float f3 = float(bitfieldExtract(grid, 6, 2)); + float f4 = float(bitfieldExtract(grid, 8, 2)); + float f5 = float(bitfieldExtract(grid, 10, 2)); + float f6 = float(bitfieldExtract(grid, 12, 2)); + float f7 = float(bitfieldExtract(grid, 14, 2)); + + // Pack into vec4 for vectorized FMA + const vec4 fbits_v0 = vec4(f0, f1, f2, f3); + const vec4 fbits_v1 = vec4(f4, f5, f6, f7); + const vec4 delta_v = vec4(delta_cache[l]); + + // Vectorized fused multiply-add + vec4 sum_v = fma(b_vals[2*l + 0], fbits_v0 + delta_v, vec4(0.0)); + sum_v = fma(b_vals[2*l + 1], fbits_v1 + delta_v, sum_v); + + // Horizontal add to get scalar sum + FLOAT_TYPE sum = sum_v.x + sum_v.y + sum_v.z + sum_v.w; + + // Accumulate to column sum + col_sum = fma(dl, sum, col_sum); + } + // Write result to temporary buffer + temp[j][n] += col_sum; + } + ibi += num_blocks_per_row; + } +} + +void compute_outputs(const uint32_t first_row, const uint32_t num_rows) { + uint a_offset, b_offset, d_offset; + get_offsets(a_offset, b_offset, d_offset); + + const uint num_blocks_per_row = p.ncols / QUANT_K; + + // 8 threads are used to process each block + const uint blocks_per_wg = gl_WorkGroupSize.x/8; + const uint tid = gl_LocalInvocationID.x; + const uint itid = tid % 8; // 0...7 + const uint ix = tid / 8; + + [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) { + [[unroll]] for (uint i = 0; i < NUM_ROWS; ++i) { + temp[j][i] = FLOAT_TYPE(0); + } + } + + [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += blocks_per_wg) + calc_superblock(a_offset, b_offset, itid, i, num_blocks_per_row, first_row, num_rows); + + reduce_result(temp, d_offset, first_row, num_rows, tid); +} + +void main() { + const uint first_row = NUM_ROWS * (gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z); + + init_iq_shmem(gl_WorkGroupSize); + + // do NUM_ROWS at a time, unless there aren't enough remaining rows + if (first_row + NUM_ROWS <= p.stride_d) { + compute_outputs(first_row, NUM_ROWS); + } else { + if (first_row >= p.stride_d) { + return; + } + compute_outputs(first_row, p.stride_d - first_row); + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_iq1_s.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_iq1_s.comp new file mode 100644 index 0000000000000000000000000000000000000000..fd953c8faddcd57c80e6b548fc5a2303e36bb7f0 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_iq1_s.comp @@ -0,0 +1,95 @@ +#version 450 +#extension GL_EXT_shader_explicit_arithmetic_types_int32 : require + +#include "mul_mat_vec_base.glsl" + +layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; + +FLOAT_TYPE temp[NUM_COLS][NUM_ROWS]; + +void calc_superblock(const uint a_offset, const uint b_offset, const uint ib32, const uint i, + const uint num_blocks_per_row, const uint first_row, const uint num_rows) { + const uint y_idx_base = i * QUANT_K + 32 * ib32; + [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) { + const uint base_b_idx = (j * p.batch_stride_b + b_offset + y_idx_base) / 4; + [[unroll]] for (uint l = 0; l < 4; ++l) { + const vec4 b_val_0 = vec4(data_b_v4[base_b_idx + 2 * l]); + const vec4 b_val_1 = vec4(data_b_v4[base_b_idx + 2 * l + 1]); + + // index for data_a + uint ibi = a_offset + first_row * num_blocks_per_row + i; + + [[unroll]] for (uint n = 0; n < num_rows; ++n) { + const float d = float(data_a[ibi].d); + const uint qh = data_a[ibi].qh[ib32]; + + const float dl = d * float(2 * bitfieldExtract(qh, 12, 3) + 1); + const uint qs = data_a[ibi].qs[4 * ib32 + l]; + const uint idxhi = bitfieldExtract(qh, 3 * int(l), 3); + const uint16_t grid = uint16_t(iq1s_grid[qs | (idxhi << 8)]); + + const float delta_val = ((qh & 0x8000) != 0) ? -IQ1S_DELTA : IQ1S_DELTA; + const vec4 delta_v = vec4(delta_val); + const vec4 fbits0 = vec4( + float(bitfieldExtract(grid, 0, 2)), + float(bitfieldExtract(grid, 2, 2)), + float(bitfieldExtract(grid, 4, 2)), + float(bitfieldExtract(grid, 6, 2)) + ); + const vec4 fbits1 = vec4( + float(bitfieldExtract(grid, 8, 2)), + float(bitfieldExtract(grid, 10, 2)), + float(bitfieldExtract(grid, 12, 2)), + float(bitfieldExtract(grid, 14, 2)) + ); + + vec4 sum_v = fma(b_val_0, fbits0 + delta_v, vec4(0.0)); + sum_v = fma(b_val_1, fbits1 + delta_v, sum_v); + FLOAT_TYPE sum = dot(sum_v, vec4(1.0)); + + temp[j][n] = fma(dl, sum, temp[j][n]); + ibi += num_blocks_per_row; + } + } + } +} + +void compute_outputs(const uint32_t first_row, const uint32_t num_rows) { + uint a_offset, b_offset, d_offset; + get_offsets(a_offset, b_offset, d_offset); + + const uint num_blocks_per_row = p.ncols / QUANT_K; + + // 8 threads are used to process each block + const uint blocks_per_wg = gl_WorkGroupSize.x/8; + const uint tid = gl_LocalInvocationID.x; + const uint itid = tid % 8; // 0...7 + const uint ix = tid / 8; + + [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) { + [[unroll]] for (uint i = 0; i < NUM_ROWS; ++i) { + temp[j][i] = FLOAT_TYPE(0); + } + } + + [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += blocks_per_wg) + calc_superblock(a_offset, b_offset, itid, i, num_blocks_per_row, first_row, num_rows); + + reduce_result(temp, d_offset, first_row, num_rows, tid); +} + +void main() { + const uint first_row = NUM_ROWS * (gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z); + + init_iq_shmem(gl_WorkGroupSize); + + // do NUM_ROWS at a time, unless there aren't enough remaining rows + if (first_row + NUM_ROWS <= p.stride_d) { + compute_outputs(first_row, NUM_ROWS); + } else { + if (first_row >= p.stride_d) { + return; + } + compute_outputs(first_row, p.stride_d - first_row); + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_iq2_s.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_iq2_s.comp new file mode 100644 index 0000000000000000000000000000000000000000..b4f6d1d6b648dc95126d2f40a8396d92a6c9d264 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_iq2_s.comp @@ -0,0 +1,90 @@ +#version 450 +#extension GL_EXT_shader_explicit_arithmetic_types_int32 : require + +#include "mul_mat_vec_base.glsl" + +layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; + +FLOAT_TYPE temp[NUM_COLS][NUM_ROWS]; + +void calc_superblock(const uint a_offset, const uint b_offset, const uint itid, const uint i, const uint num_blocks_per_row, const uint first_row, const uint num_rows) { + const uint y_idx = i * QUANT_K + 16 * itid; + const uint nibble_shift = 4 * (itid & 1); + const uint ib32 = itid / 2; // 0..7 + + uint ibi = a_offset + first_row * num_blocks_per_row + i; + [[unroll]] for (uint n = 0; n < num_rows; ++n) { + const float d = float(data_a[ibi].d); + const uint scale = (data_a[ibi].scales[ib32] >> nibble_shift) & 0xF; + const float db = d * (0.5 + scale) * 0.25; + + const uint qh = data_a[ibi].qh[ib32]; + const u8vec2 qs16 = unpack8(uint32_t(data_a_packed16[ibi].qs[itid])).xy; // vec4 used due to #12147 + const u8vec2 sign16 = unpack8(uint32_t(data_a_packed16[ibi].qs[QUANT_K / 16 + itid])).xy; + [[unroll]] for (uint l = 0; l < 2; ++l) { + const uint8_t sign = sign16[l]; + const uint qs = qs16[l] | ((qh << (8 - nibble_shift - 2 * l)) & 0x300); + const uvec2 grid = iq2s_grid[qs]; + const vec4 grid0 = vec4(unpack8(grid.x)); + const vec4 grid1 = vec4(unpack8(grid.y)); + + [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) { + vec4 b0 = vec4(data_b_v4[(j*p.batch_stride_b + b_offset + y_idx) / 4 + 2*l + 0]); + vec4 b4 = vec4(data_b_v4[(j*p.batch_stride_b + b_offset + y_idx) / 4 + 2*l + 1]); + + FLOAT_TYPE sum = + fma(FLOAT_TYPE(b0.x), FLOAT_TYPE((sign & 1) != 0 ? -grid0.x : grid0.x), + fma(FLOAT_TYPE(b0.y), FLOAT_TYPE((sign & 2) != 0 ? -grid0.y : grid0.y), + fma(FLOAT_TYPE(b0.z), FLOAT_TYPE((sign & 4) != 0 ? -grid0.z : grid0.z), + fma(FLOAT_TYPE(b0.w), FLOAT_TYPE((sign & 8) != 0 ? -grid0.w : grid0.w), + fma(FLOAT_TYPE(b4.x), FLOAT_TYPE((sign & 16) != 0 ? -grid1.x : grid1.x), + fma(FLOAT_TYPE(b4.y), FLOAT_TYPE((sign & 32) != 0 ? -grid1.y : grid1.y), + fma(FLOAT_TYPE(b4.z), FLOAT_TYPE((sign & 64) != 0 ? -grid1.z : grid1.z), + fma(FLOAT_TYPE(b4.w), FLOAT_TYPE((sign & 128) != 0 ? -grid1.w : grid1.w), + FLOAT_TYPE(0.0))))))))); + temp[j][n] = fma(db, sum, temp[j][n]); + } + } + ibi += num_blocks_per_row; + } +} + +void compute_outputs(const uint32_t first_row, const uint32_t num_rows) { + uint a_offset, b_offset, d_offset; + get_offsets(a_offset, b_offset, d_offset); + + const uint num_blocks_per_row = p.ncols / QUANT_K; + + // 16 threads are used to process each block + const uint blocks_per_wg = gl_WorkGroupSize.x/16; + const uint tid = gl_LocalInvocationID.x; + const uint itid = tid % 16; // 0...15 + const uint ix = tid / 16; + + [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) { + [[unroll]] for (uint i = 0; i < NUM_ROWS; ++i) { + temp[j][i] = FLOAT_TYPE(0); + } + } + + [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += blocks_per_wg) + calc_superblock(a_offset, b_offset, itid, i, num_blocks_per_row, first_row, num_rows); + + reduce_result(temp, d_offset, first_row, num_rows, tid); +} + +void main() { + const uint first_row = NUM_ROWS * (gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z); + + init_iq_shmem(gl_WorkGroupSize); + + // do NUM_ROWS at a time, unless there aren't enough remaining rows + if (first_row + NUM_ROWS <= p.stride_d) { + compute_outputs(first_row, NUM_ROWS); + } else { + if (first_row >= p.stride_d) { + return; + } + compute_outputs(first_row, p.stride_d - first_row); + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_iq2_xs.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_iq2_xs.comp new file mode 100644 index 0000000000000000000000000000000000000000..d8dafe5f709d891ad76cba57ff388555bdbedeef --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_iq2_xs.comp @@ -0,0 +1,105 @@ +#version 450 +#extension GL_EXT_shader_explicit_arithmetic_types_int32 : require + +#include "mul_mat_vec_base.glsl" + +layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; + +FLOAT_TYPE temp[NUM_COLS][NUM_ROWS]; + +void calc_superblock(const uint a_offset, const uint b_offset, const uint itid, const uint i, const uint num_blocks_per_row, const uint first_row, const uint num_rows) { + const uint y_idx = i * QUANT_K + 16 * itid; + const uint nibble_shift = 4 * (itid & 1); + const uint ib32 = itid / 2; // 0..7 + uint ibi = a_offset + first_row * num_blocks_per_row + i; + // Precompute db multiplication factors + float db_vals[NUM_ROWS]; + [[unroll]] for (uint n = 0; n < num_rows; ++n) { + const float d = float(data_a[ibi].d); + const uint scale_raw = data_a[ibi].scales[ib32]; + const uint scale = (scale_raw >> nibble_shift) & 0xF; + // Merge constant calculations d * (0.5 + scale) * 0.25 = d*0.125 + d*scale*0.25 + db_vals[n] = d * (0.125f + float(scale) * 0.25f); + ibi += num_blocks_per_row; + } + ibi = a_offset + first_row * num_blocks_per_row + i; + [[unroll]] for (uint n = 0; n < num_rows; ++n) { + // Preload grid and sign data for all l values + vec4 grid0_vals[2], grid1_vals[2]; + uint sign_vals[2], sign7_vals[2]; + [[unroll]] for (uint l = 0; l < 2; ++l) { + const uint qs = data_a[ibi].qs[2 * itid + l]; + sign_vals[l] = qs >> 9; + sign7_vals[l] = bitCount(sign_vals[l]); + const uvec2 grid_data = iq2xs_grid[qs & 511]; + grid0_vals[l] = vec4(unpack8(grid_data.x)); + grid1_vals[l] = vec4(unpack8(grid_data.y)); + } + // Preload B data for all j columns (reduce repeated index calculations) + [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) { + FLOAT_TYPE sum = FLOAT_TYPE(0.0); + [[unroll]] for (uint l = 0; l < 2; ++l) { + const uint sign = sign_vals[l]; + const uint sign7 = sign7_vals[l]; + const vec4 grid0 = grid0_vals[l]; + const vec4 grid1 = grid1_vals[l]; + // Precompute indices + const uint b_idx = (j * p.batch_stride_b + b_offset + y_idx) / 4 + 2 * l; + const vec4 b0 = vec4(data_b_v4[b_idx + 0]); + const vec4 b4 = vec4(data_b_v4[b_idx + 1]); + sum += + fma(FLOAT_TYPE(b0.x), FLOAT_TYPE((sign & 1) != 0 ? -grid0.x : grid0.x), + fma(FLOAT_TYPE(b0.y), FLOAT_TYPE((sign & 2) != 0 ? -grid0.y : grid0.y), + fma(FLOAT_TYPE(b0.z), FLOAT_TYPE((sign & 4) != 0 ? -grid0.z : grid0.z), + fma(FLOAT_TYPE(b0.w), FLOAT_TYPE((sign & 8) != 0 ? -grid0.w : grid0.w), + fma(FLOAT_TYPE(b4.x), FLOAT_TYPE((sign & 16) != 0 ? -grid1.x : grid1.x), + fma(FLOAT_TYPE(b4.y), FLOAT_TYPE((sign & 32) != 0 ? -grid1.y : grid1.y), + fma(FLOAT_TYPE(b4.z), FLOAT_TYPE((sign & 64) != 0 ? -grid1.z : grid1.z), + fma(FLOAT_TYPE(b4.w), FLOAT_TYPE((sign7 & 1) != 0 ? -grid1.w : grid1.w), + FLOAT_TYPE(0.0))))))))); + } + temp[j][n] = fma(FLOAT_TYPE(db_vals[n]), sum, temp[j][n]); + } + ibi += num_blocks_per_row; + } +} + +void compute_outputs(const uint32_t first_row, const uint32_t num_rows) { + uint a_offset, b_offset, d_offset; + get_offsets(a_offset, b_offset, d_offset); + + const uint num_blocks_per_row = p.ncols / QUANT_K; + + // 16 threads are used to process each block + const uint blocks_per_wg = gl_WorkGroupSize.x/16; + const uint tid = gl_LocalInvocationID.x; + const uint itid = tid % 16; // 0...15 + const uint ix = tid / 16; + + [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) { + [[unroll]] for (uint i = 0; i < NUM_ROWS; ++i) { + temp[j][i] = FLOAT_TYPE(0); + } + } + + [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += blocks_per_wg) + calc_superblock(a_offset, b_offset, itid, i, num_blocks_per_row, first_row, num_rows); + + reduce_result(temp, d_offset, first_row, num_rows, tid); +} + +void main() { + const uint first_row = NUM_ROWS * (gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z); + + init_iq_shmem(gl_WorkGroupSize); + + // do NUM_ROWS at a time, unless there aren't enough remaining rows + if (first_row + NUM_ROWS <= p.stride_d) { + compute_outputs(first_row, NUM_ROWS); + } else { + if (first_row >= p.stride_d) { + return; + } + compute_outputs(first_row, p.stride_d - first_row); + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_iq2_xxs.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_iq2_xxs.comp new file mode 100644 index 0000000000000000000000000000000000000000..f75dcf8331d93b4e1c1b7a073f462441b2d9d5ee --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_iq2_xxs.comp @@ -0,0 +1,87 @@ +#version 450 +#extension GL_EXT_shader_explicit_arithmetic_types_int32 : require + +#include "mul_mat_vec_base.glsl" + +layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; + +FLOAT_TYPE temp[NUM_COLS][NUM_ROWS]; + +void calc_superblock(const uint a_offset, const uint b_offset, const uint itid, const uint i, const uint num_blocks_per_row, const uint first_row, const uint num_rows) { + const uint y_idx = i * QUANT_K + 16 * itid; + const uint ib32 = itid / 2; // 0..7 + + uint ibi = a_offset + first_row * num_blocks_per_row + i; + [[unroll]] for (uint n = 0; n < num_rows; ++n) { + const float d = float(data_a[ibi].d); + const uint signscale = pack32(u16vec2( + data_a_packed16[ibi].qs[4 * ib32 + 2], + data_a_packed16[ibi].qs[4 * ib32 + 3])); + const float db = d * 0.25 * (0.5 + (signscale >> 28)); + [[unroll]] for (uint l = 0; l < 2; ++l) { + const uint qs = data_a[ibi].qs[8 * ib32 + 2 * (itid & 1) + l]; + const uint sign = bitfieldExtract(signscale, 7 * int(2 * (itid & 1) + l), 7); + const uint sign7 = bitCount(sign); + const vec4 grid0 = vec4(unpack8(iq2xxs_grid[qs].x)); + const vec4 grid1 = vec4(unpack8(iq2xxs_grid[qs].y)); + + [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) { + const vec4 b0 = vec4(data_b_v4[(j*p.batch_stride_b + b_offset + y_idx) / 4 + 2*l + 0]); + const vec4 b4 = vec4(data_b_v4[(j*p.batch_stride_b + b_offset + y_idx) / 4 + 2*l + 1]); + + FLOAT_TYPE sum = + fma(FLOAT_TYPE(b0.x), FLOAT_TYPE((sign & 1) != 0 ? -grid0.x : grid0.x), + fma(FLOAT_TYPE(b0.y), FLOAT_TYPE((sign & 2) != 0 ? -grid0.y : grid0.y), + fma(FLOAT_TYPE(b0.z), FLOAT_TYPE((sign & 4) != 0 ? -grid0.z : grid0.z), + fma(FLOAT_TYPE(b0.w), FLOAT_TYPE((sign & 8) != 0 ? -grid0.w : grid0.w), + fma(FLOAT_TYPE(b4.x), FLOAT_TYPE((sign & 16) != 0 ? -grid1.x : grid1.x), + fma(FLOAT_TYPE(b4.y), FLOAT_TYPE((sign & 32) != 0 ? -grid1.y : grid1.y), + fma(FLOAT_TYPE(b4.z), FLOAT_TYPE((sign & 64) != 0 ? -grid1.z : grid1.z), + fma(FLOAT_TYPE(b4.w), FLOAT_TYPE((sign7 & 1) != 0 ? -grid1.w : grid1.w), + FLOAT_TYPE(0.0))))))))); + temp[j][n] = fma(db, sum, temp[j][n]); + } + } + ibi += num_blocks_per_row; + } +} + +void compute_outputs(const uint32_t first_row, const uint32_t num_rows) { + uint a_offset, b_offset, d_offset; + get_offsets(a_offset, b_offset, d_offset); + + const uint num_blocks_per_row = p.ncols / QUANT_K; + + // 16 threads are used to process each block + const uint blocks_per_wg = gl_WorkGroupSize.x/16; + const uint tid = gl_LocalInvocationID.x; + const uint itid = tid % 16; // 0...15 + const uint ix = tid / 16; + + [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) { + [[unroll]] for (uint i = 0; i < NUM_ROWS; ++i) { + temp[j][i] = FLOAT_TYPE(0); + } + } + + [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += blocks_per_wg) + calc_superblock(a_offset, b_offset, itid, i, num_blocks_per_row, first_row, num_rows); + + reduce_result(temp, d_offset, first_row, num_rows, tid); +} + +void main() { + const uint first_row = NUM_ROWS * (gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z); + + init_iq_shmem(gl_WorkGroupSize); + + // do NUM_ROWS at a time, unless there aren't enough remaining rows + if (first_row + NUM_ROWS <= p.stride_d) { + compute_outputs(first_row, NUM_ROWS); + } else { + if (first_row >= p.stride_d) { + return; + } + compute_outputs(first_row, p.stride_d - first_row); + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_iq3_s.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_iq3_s.comp new file mode 100644 index 0000000000000000000000000000000000000000..5cdf2a89d0fd2c6888ebdf68c64bde0a00cf018d --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_iq3_s.comp @@ -0,0 +1,90 @@ +#version 450 +#extension GL_EXT_shader_explicit_arithmetic_types_int32 : require + +#include "mul_mat_vec_base.glsl" + +layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; + +FLOAT_TYPE temp[NUM_COLS][NUM_ROWS]; + +void calc_superblock(const uint a_offset, const uint b_offset, const uint ib32, const uint i, const uint num_blocks_per_row, const uint first_row, const uint num_rows) { + const uint y_idx = i * QUANT_K + 32 * ib32; + + uint ibi = a_offset + first_row * num_blocks_per_row + i; + [[unroll]] for (uint n = 0; n < num_rows; ++n) { + const float d = float(data_a[ibi].d); + const uint scale = (data_a[ibi].scales[ib32/2] >> (4 * (ib32 & 1))) & 0xF; + const float dscale = d * (1 + 2 * scale); + const uint qh = data_a[ibi].qh[ib32]; + FLOAT_TYPE sum[NUM_COLS]; + [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) { + sum[j] = 0.0; + } + [[unroll]] for (uint l = 0; l < 4; ++l) { + const u8vec2 qs = unpack8(uint32_t(data_a_packed16[ibi].qs[4 * ib32 + l])).xy; // vec4 used due to #12147 + const uint sign = data_a[ibi].signs[4 * ib32 + l]; + const vec4 grid0 = vec4(unpack8(iq3s_grid[qs.x | ((qh << (8 - 2*l)) & 0x100)])); + const vec4 grid1 = vec4(unpack8(iq3s_grid[qs.y | ((qh << (7 - 2*l)) & 0x100)])); + + [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) { + const vec4 b0 = vec4(data_b_v4[(j*p.batch_stride_b + b_offset + y_idx) / 4 + 2*l + 0]); + const vec4 b4 = vec4(data_b_v4[(j*p.batch_stride_b + b_offset + y_idx) / 4 + 2*l + 1]); + + sum[j] = + fma(FLOAT_TYPE(b0.x), FLOAT_TYPE((sign & 1) != 0 ? -grid0.x : grid0.x), + fma(FLOAT_TYPE(b0.y), FLOAT_TYPE((sign & 2) != 0 ? -grid0.y : grid0.y), + fma(FLOAT_TYPE(b0.z), FLOAT_TYPE((sign & 4) != 0 ? -grid0.z : grid0.z), + fma(FLOAT_TYPE(b0.w), FLOAT_TYPE((sign & 8) != 0 ? -grid0.w : grid0.w), + fma(FLOAT_TYPE(b4.x), FLOAT_TYPE((sign & 16) != 0 ? -grid1.x : grid1.x), + fma(FLOAT_TYPE(b4.y), FLOAT_TYPE((sign & 32) != 0 ? -grid1.y : grid1.y), + fma(FLOAT_TYPE(b4.z), FLOAT_TYPE((sign & 64) != 0 ? -grid1.z : grid1.z), + fma(FLOAT_TYPE(b4.w), FLOAT_TYPE((sign & 128) != 0 ? -grid1.w : grid1.w), + sum[j])))))))); + } + } + [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) { + temp[j][n] = fma(dscale, sum[j], temp[j][n]); + } + ibi += num_blocks_per_row; + } +} + +void compute_outputs(const uint32_t first_row, const uint32_t num_rows) { + uint a_offset, b_offset, d_offset; + get_offsets(a_offset, b_offset, d_offset); + + const uint num_blocks_per_row = p.ncols / QUANT_K; + + // 8 threads are used to process each block + const uint blocks_per_wg = gl_WorkGroupSize.x/8; + const uint tid = gl_LocalInvocationID.x; + const uint itid = tid % 8; // 0...7 + const uint ix = tid / 8; + + [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) { + [[unroll]] for (uint i = 0; i < NUM_ROWS; ++i) { + temp[j][i] = FLOAT_TYPE(0); + } + } + + [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += blocks_per_wg) + calc_superblock(a_offset, b_offset, itid, i, num_blocks_per_row, first_row, num_rows); + + reduce_result(temp, d_offset, first_row, num_rows, tid); +} + +void main() { + const uint first_row = NUM_ROWS * (gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z); + + init_iq_shmem(gl_WorkGroupSize); + + // do NUM_ROWS at a time, unless there aren't enough remaining rows + if (first_row + NUM_ROWS <= p.stride_d) { + compute_outputs(first_row, NUM_ROWS); + } else { + if (first_row >= p.stride_d) { + return; + } + compute_outputs(first_row, p.stride_d - first_row); + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_iq3_xxs.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_iq3_xxs.comp new file mode 100644 index 0000000000000000000000000000000000000000..a88898109ab6ee4da4da4b141db8e304e2e35ae9 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_iq3_xxs.comp @@ -0,0 +1,88 @@ +#version 450 +#extension GL_EXT_shader_explicit_arithmetic_types_int32 : require + +#include "mul_mat_vec_base.glsl" + +layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; + +FLOAT_TYPE temp[NUM_COLS][NUM_ROWS]; + +void calc_superblock(const uint a_offset, const uint b_offset, const uint itid, const uint i, const uint num_blocks_per_row, const uint first_row, const uint num_rows) { + const uint y_idx = i * QUANT_K + 16 * itid; + const uint ib32 = itid / 2; // 0..7 + + uint ibi = a_offset + first_row * num_blocks_per_row + i; + [[unroll]] for (uint n = 0; n < num_rows; ++n) { + const float d = float(data_a[ibi].d); + const uint signscale = pack32(u16vec2( + data_a_packed16[ibi].qs[QUANT_K / 8 + 2 * ib32], + data_a_packed16[ibi].qs[QUANT_K / 8 + 2 * ib32 + 1])); + const float db = d * 0.5 * (0.5 + (signscale >> 28)); + [[unroll]] for (uint l = 0; l < 2; ++l) { + const uint qs0 = data_a[ibi].qs[8 * ib32 + 4 * (itid & 1) + 2 * l]; + const uint qs1 = data_a[ibi].qs[8 * ib32 + 4 * (itid & 1) + 2 * l + 1]; + const uint sign = bitfieldExtract(signscale, 7 * int(2 * (itid & 1) + l), 7); + const uint sign7 = bitCount(sign); + const vec4 grid0 = vec4(unpack8(iq3xxs_grid[qs0])); + const vec4 grid1 = vec4(unpack8(iq3xxs_grid[qs1])); + + [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) { + const vec4 b0 = vec4(data_b_v4[(j*p.batch_stride_b + b_offset + y_idx) / 4 + 2*l + 0]); + const vec4 b4 = vec4(data_b_v4[(j*p.batch_stride_b + b_offset + y_idx) / 4 + 2*l + 1]); + + FLOAT_TYPE sum = + fma(FLOAT_TYPE(b0.x), FLOAT_TYPE((sign & 1) != 0 ? -grid0.x : grid0.x), + fma(FLOAT_TYPE(b0.y), FLOAT_TYPE((sign & 2) != 0 ? -grid0.y : grid0.y), + fma(FLOAT_TYPE(b0.z), FLOAT_TYPE((sign & 4) != 0 ? -grid0.z : grid0.z), + fma(FLOAT_TYPE(b0.w), FLOAT_TYPE((sign & 8) != 0 ? -grid0.w : grid0.w), + fma(FLOAT_TYPE(b4.x), FLOAT_TYPE((sign & 16) != 0 ? -grid1.x : grid1.x), + fma(FLOAT_TYPE(b4.y), FLOAT_TYPE((sign & 32) != 0 ? -grid1.y : grid1.y), + fma(FLOAT_TYPE(b4.z), FLOAT_TYPE((sign & 64) != 0 ? -grid1.z : grid1.z), + fma(FLOAT_TYPE(b4.w), FLOAT_TYPE((sign7 & 1) != 0 ? -grid1.w : grid1.w), + FLOAT_TYPE(0.0))))))))); + temp[j][n] = fma(db, sum, temp[j][n]); + } + } + ibi += num_blocks_per_row; + } +} + +void compute_outputs(const uint32_t first_row, const uint32_t num_rows) { + uint a_offset, b_offset, d_offset; + get_offsets(a_offset, b_offset, d_offset); + + const uint num_blocks_per_row = p.ncols / QUANT_K; + + // 16 threads are used to process each block + const uint blocks_per_wg = gl_WorkGroupSize.x/16; + const uint tid = gl_LocalInvocationID.x; + const uint itid = tid % 16; // 0...15 + const uint ix = tid / 16; + + [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) { + [[unroll]] for (uint i = 0; i < NUM_ROWS; ++i) { + temp[j][i] = FLOAT_TYPE(0); + } + } + + [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += blocks_per_wg) + calc_superblock(a_offset, b_offset, itid, i, num_blocks_per_row, first_row, num_rows); + + reduce_result(temp, d_offset, first_row, num_rows, tid); +} + +void main() { + const uint first_row = NUM_ROWS * (gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z); + + init_iq_shmem(gl_WorkGroupSize); + + // do NUM_ROWS at a time, unless there aren't enough remaining rows + if (first_row + NUM_ROWS <= p.stride_d) { + compute_outputs(first_row, NUM_ROWS); + } else { + if (first_row >= p.stride_d) { + return; + } + compute_outputs(first_row, p.stride_d - first_row); + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_nc.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_nc.comp new file mode 100644 index 0000000000000000000000000000000000000000..beea5296225e95ad9604d559e40e0563f75eb83f --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_nc.comp @@ -0,0 +1,124 @@ +#version 450 + +#extension GL_EXT_control_flow_attributes : enable +#extension GL_EXT_shader_16bit_storage : require + +#define BLOCK_SIZE 32 +#define FLOAT_TYPE float + +layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in; + +#include "mul_mat_vec_iface.glsl" + +layout (push_constant) uniform parameter +{ + uint ncols_x; + uint nrows_x; + uint row_stride_x; + uint channel_stride_x; + uint channel_stride_y; + uint channel_x_divisor; + uint ne12; + uint b_offset; + uint d_offset; + uint nb03; + uint nb13; + uint nb23; + uint fusion_flags; +} p; + +shared FLOAT_TYPE tmp[BLOCK_SIZE]; + +void main() { + const uint tid = gl_LocalInvocationID.x; + const uint row_x = gl_GlobalInvocationID.y; + const uint channel = gl_GlobalInvocationID.z; + const uint i3 = gl_WorkGroupID.x; + const uint channel_x = channel / p.channel_x_divisor; + const uint channel_y = channel % p.ne12; + + const uint nrows_y = p.ncols_x; + const uint nrows_dst = p.nrows_x; + const uint row_dst = row_x; + + const uint idst = i3*p.nb23 + channel*nrows_dst + row_dst; + + FLOAT_TYPE temp = 0.0f; + + // Detect alignment for vector loads + bool is_aligned = (p.ncols_x % 4) == 0 && (p.row_stride_x % 4) == 0 && (p.channel_stride_x % 4) == 0; + + for (uint col_x0 = 0; col_x0 < p.ncols_x;) { + + // Unroll 2x and do vec4 loads if aligned + const uint unroll_count = 2; + if (col_x0 + unroll_count * 4 * BLOCK_SIZE <= p.ncols_x && is_aligned) { + [[unroll]] for (uint i = 0; i < unroll_count; ++i) { + const uint col_x = col_x0 + 4*tid; + + const uint row_y = col_x; + + const uint ix = i3*p.nb03 + channel_x*p.channel_stride_x + row_x*p.row_stride_x + col_x; + const uint iy = i3*p.nb13 + channel_y*p.channel_stride_y + row_y; + + const vec4 av4 = vec4(data_a_v4[ix / 4]); + const vec4 bv4 = vec4(data_b_v4[iy / 4]); + + temp += dot(av4, bv4); + + col_x0 += 4*BLOCK_SIZE; + } + // do vec4 loads if aligned + } else if (col_x0 + 4*BLOCK_SIZE <= p.ncols_x && is_aligned) { + const uint col_x = col_x0 + 4*tid; + + const uint row_y = col_x; + + const uint ix = i3*p.nb03 + channel_x*p.channel_stride_x + row_x*p.row_stride_x + col_x; + const uint iy = i3*p.nb13 + channel_y*p.channel_stride_y + row_y; + + const vec4 av4 = vec4(data_a_v4[ix / 4]); + const vec4 bv4 = vec4(data_b_v4[iy / 4]); + + temp += dot(av4, bv4); + + col_x0 += 4*BLOCK_SIZE; + } else { + const uint col_x = col_x0 + tid; + if (col_x >= p.ncols_x) { + break; + } + + const uint row_y = col_x; + + const uint ix = i3*p.nb03 + channel_x*p.channel_stride_x + row_x*p.row_stride_x + col_x; + const uint iy = i3*p.nb13 + channel_y*p.channel_stride_y + row_y; + + const FLOAT_TYPE xi = FLOAT_TYPE(data_a[ix]); + + temp = fma(xi, FLOAT_TYPE(data_b[iy]), temp); + col_x0 += BLOCK_SIZE; + } + } + + tmp[tid] = temp; + + // sum up partial sums and write back result + barrier(); + [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) { + if (tid < s) { + tmp[tid] += tmp[tid + s]; + } + barrier(); + } + + if (tid == 0) { + if ((p.fusion_flags & MAT_VEC_FUSION_FLAGS_BIAS0) != 0) { + tmp[0] += FLOAT_TYPE(data_fuse0[idst]); + } + if ((p.fusion_flags & MAT_VEC_FUSION_FLAGS_BIAS1) != 0) { + tmp[0] += FLOAT_TYPE(data_fuse1[idst]); + } + data_d[idst] = tmp[0]; + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_p021.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_p021.comp new file mode 100644 index 0000000000000000000000000000000000000000..32628c6e97d439af1ffdeaf68d282003d5235143 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_p021.comp @@ -0,0 +1,156 @@ +#version 450 + +#extension GL_EXT_control_flow_attributes : enable +#extension GL_EXT_shader_16bit_storage : require +#if USE_SUBGROUP_ADD +#extension GL_KHR_shader_subgroup_arithmetic : enable +#endif + +#define FLOAT_TYPE float + +layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; + +#include "mul_mat_vec_iface.glsl" + +layout(constant_id = 0) const int BLOCK_SIZE = 32; +// gqa_ratio is in the range [1,8] +layout(constant_id = 1) const uint gqa_ratio = 1; + +layout (push_constant) uniform parameter +{ + uint ncols_x; + uint nrows_x; + uint nchannels_x; + uint nchannels_y; + uint b_offset; + uint d_offset; + uint fusion_flags; +} p; + +#if !USE_SUBGROUP_ADD +shared FLOAT_TYPE tmp[8][BLOCK_SIZE]; +#endif + +void main() { + const uint tid = gl_LocalInvocationID.x; + const uint row_x = gl_GlobalInvocationID.y; + + uint channel, channel_x; + + // When gqa_ratio > 1, each invocation does multiple rows. + // The row in the A matrix is starting from channel / gqa_ratio and the + // rows in the B matrix are [channel, channel+gqa_ratio). + // When gpa_ratio is 1, each invocation does one row. + if (gqa_ratio > 1) { + channel_x = gl_GlobalInvocationID.z; + channel = channel_x * gqa_ratio; + } else { + channel = gl_GlobalInvocationID.z; + channel_x = channel / (p.nchannels_y / p.nchannels_x);; + } + + const uint nrows_y = p.ncols_x; + const uint nrows_dst = p.nrows_x; + const uint row_dst = row_x; + + FLOAT_TYPE temp[8]; + [[unroll]] for (uint i = 0; i < 8; ++i) { + temp[i] = FLOAT_TYPE(0.0f); + } + + // Detect alignment for vector loads + bool is_aligned = (p.ncols_x % 4) == 0 && (p.nchannels_x % 4) == 0 && (nrows_y % 4) == 0; + + for (uint col_x0 = 0; col_x0 < p.ncols_x; col_x0 += BLOCK_SIZE) { + + // Use vec4 loads if aligned + if (col_x0 + 4*BLOCK_SIZE <= p.ncols_x && is_aligned) { + + uint col_x = col_x0 + 4*tid; + const uint row_y = col_x; + + // x is transposed and permuted + const uint ix = row_x*p.nchannels_x*p.ncols_x + channel_x*p.ncols_x + col_x; + const vec4 av4 = vec4(data_a_v4[ix / 4]); + + [[unroll]] for (uint c = 0; c < gqa_ratio; ++c) { + // y is not transposed but permuted + const uint iy = (channel + c)*nrows_y + row_y; + + vec4 bv4 = data_b_v4[iy / 4]; + temp[c] += dot(av4, bv4); + } + + col_x0 += 3*BLOCK_SIZE; + } else { + const uint col_x = col_x0 + tid; + + if (col_x >= p.ncols_x) { + break; + } + + // x is transposed and permuted + const uint ix = row_x*p.nchannels_x*p.ncols_x + channel_x*p.ncols_x + col_x; + const FLOAT_TYPE xi = FLOAT_TYPE(data_a[ix]); + + const uint row_y = col_x; + + [[unroll]] for (uint c = 0; c < gqa_ratio; ++c) { + // y is not transposed but permuted + const uint iy = (channel + c)*nrows_y + row_y; + + temp[c] = fma(xi, FLOAT_TYPE(data_b[iy]), temp[c]); + } + } + } + +#if USE_SUBGROUP_ADD + // reduce vec4 at a time + vec4 t = vec4(temp[0], temp[1], temp[2], temp[3]); + t = subgroupAdd(t); + temp[0] = t[0]; + temp[1] = t[1]; + temp[2] = t[2]; + temp[3] = t[3]; + if (gqa_ratio > 4) { + t = vec4(temp[4], temp[5], temp[6], temp[7]); + t = subgroupAdd(t); + temp[4] = t[0]; + temp[5] = t[1]; + temp[6] = t[2]; + temp[7] = t[3]; + } +#else + [[unroll]] for (uint c = 0; c < gqa_ratio; ++c) { + tmp[c][tid] = temp[c]; + } + // sum up partial sums and write back result + barrier(); + [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) { + if (tid < s) { + [[unroll]] for (uint c = 0; c < gqa_ratio; ++c) { + temp[c] += tmp[c][tid + s]; + tmp[c][tid] = temp[c]; + } + } + barrier(); + } + [[unroll]] for (uint c = 0; c < gqa_ratio; ++c) { + temp[c] = tmp[c][tid]; + } +#endif + + if (tid == 0) { + [[unroll]] for (uint c = 0; c < gqa_ratio; ++c) { + // dst is not transposed and not permuted + const uint idst = (channel + c)*nrows_dst + row_dst; + if ((p.fusion_flags & MAT_VEC_FUSION_FLAGS_BIAS0) != 0) { + temp[c] += FLOAT_TYPE(data_fuse0[idst]); + } + if ((p.fusion_flags & MAT_VEC_FUSION_FLAGS_BIAS1) != 0) { + temp[c] += FLOAT_TYPE(data_fuse1[idst]); + } + data_d[idst] = temp[c]; + } + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q2_k.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q2_k.comp new file mode 100644 index 0000000000000000000000000000000000000000..975cec8013f22a44a030fa00363484f838afb9c2 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q2_k.comp @@ -0,0 +1,128 @@ +#version 450 +#extension GL_EXT_shader_explicit_arithmetic_types_int32 : require + +#include "mul_mat_vec_base.glsl" + +layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; + +shared FLOAT_TYPE sccache1[2][BLOCK_SIZE/16][16]; +shared FLOAT_TYPE sccache2[2][BLOCK_SIZE/16][16]; + +FLOAT_TYPE temp[NUM_COLS][NUM_ROWS]; +uint csel = 0; + +void calc_superblock(const uint a_offset, const uint b_offset, const uint itid, const uint v_im, const uint ix, const uint q_offset, const uint y_offset, const uint i, const uint num_blocks_per_row, const uint first_row, const uint num_rows, const bool all_threads) { + const uint y_idx = i * QUANT_K + y_offset; + + [[unroll]] for (uint n = 0; n < num_rows; ++n) { + const uint ib0 = a_offset + (first_row+n)*num_blocks_per_row; + csel ^= 1; + + if (!all_threads) { // when we don't have enough blocks to use all threads + if (i < num_blocks_per_row) { + const uint32_t scale = uint32_t(data_a[ib0 + i].scales[itid]); + sccache1[csel][ix][itid] = FLOAT_TYPE(scale & 0xF); + sccache2[csel][ix][itid] = FLOAT_TYPE((scale >> 4) & 0xF); + } + barrier(); + + if (i >= num_blocks_per_row) + continue; + } else { + const uint32_t scale = uint32_t(data_a[ib0 + i].scales[itid]); + sccache1[csel][ix][itid] = FLOAT_TYPE(scale & 0xF); + sccache2[csel][ix][itid] = FLOAT_TYPE((scale >> 4) & 0xF); + barrier(); + } + + const uint32_t qs_u32 = uint32_t(data_a_packed16[ib0 + i].qs[q_offset / 2]) | (uint32_t(data_a_packed16[ib0 + i].qs[q_offset / 2 + 8]) << 16); + const vec4 qs_u32_0 = vec4(unpack8(qs_u32 & 0x03030303)); + const vec4 qs_u32_2 = vec4(unpack8((qs_u32 >> 2) & 0x03030303)); + const vec4 qs_u32_4 = vec4(unpack8((qs_u32 >> 4) & 0x03030303)); + const vec4 qs_u32_6 = vec4(unpack8((qs_u32 >> 6) & 0x03030303)); + + const FLOAT_TYPEV2 dm = vec2(data_a[ib0 + i].dm); + + [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) { + vec2 b0 = vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 0]); + vec2 b16 = vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 8]); + vec2 b32 = vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 16]); + vec2 b48 = vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 24]); + vec2 b64 = vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 32]); + vec2 b80 = vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 40]); + vec2 b96 = vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 48]); + vec2 b112 = vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 56]); + + FLOAT_TYPE sum1 = FLOAT_TYPE(0.0); + FLOAT_TYPE sum2 = FLOAT_TYPE(0.0); + [[unroll]] for (int l = 0; l < 2; ++l) { + sum1 = fma(FLOAT_TYPE(b0[l]), sccache1[csel][ix][ 8*v_im] * qs_u32_0[l ], + fma(FLOAT_TYPE(b16[l]), sccache1[csel][ix][1 + 8*v_im] * qs_u32_0[l+2], + fma(FLOAT_TYPE(b32[l]), sccache1[csel][ix][2 + 8*v_im] * qs_u32_2[l ], + fma(FLOAT_TYPE(b48[l]), sccache1[csel][ix][3 + 8*v_im] * qs_u32_2[l+2], + fma(FLOAT_TYPE(b64[l]), sccache1[csel][ix][4 + 8*v_im] * qs_u32_4[l ], + fma(FLOAT_TYPE(b80[l]), sccache1[csel][ix][5 + 8*v_im] * qs_u32_4[l+2], + fma(FLOAT_TYPE(b96[l]), sccache1[csel][ix][6 + 8*v_im] * qs_u32_6[l ], + fma(FLOAT_TYPE(b112[l]), sccache1[csel][ix][7 + 8*v_im] * qs_u32_6[l+2], sum1)))))))); + sum2 = fma(FLOAT_TYPE(b0[l]), sccache2[csel][ix][ 8*v_im], + fma(FLOAT_TYPE(b16[l]), sccache2[csel][ix][1 + 8*v_im], + fma(FLOAT_TYPE(b32[l]), sccache2[csel][ix][2 + 8*v_im], + fma(FLOAT_TYPE(b48[l]), sccache2[csel][ix][3 + 8*v_im], + fma(FLOAT_TYPE(b64[l]), sccache2[csel][ix][4 + 8*v_im], + fma(FLOAT_TYPE(b80[l]), sccache2[csel][ix][5 + 8*v_im], + fma(FLOAT_TYPE(b96[l]), sccache2[csel][ix][6 + 8*v_im], + fma(FLOAT_TYPE(b112[l]), sccache2[csel][ix][7 + 8*v_im], sum2)))))))); + } + temp[j][n] = fma(dm.x, sum1, fma(-dm.y, sum2, temp[j][n])); + } + } +} + +void compute_outputs(const uint32_t first_row, const uint32_t num_rows) { + uint a_offset, b_offset, d_offset; + get_offsets(a_offset, b_offset, d_offset); + + const uint num_blocks_per_row = p.ncols / QUANT_K; + + // 16 threads are used to process each block + const uint it_size = gl_WorkGroupSize.x/16; + const uint tid = gl_LocalInvocationID.x; + const uint itid = tid%16; // 0...15 + const uint ix = tid/16; + + const uint v_im = itid/8; // 0 or 1. 0 computes 0..., 1 computes 128... + const uint v_in = itid - 8*v_im; // 0...7 + + const uint l0 = 2*v_in; // 0...15 + const uint q_offset = 32*v_im + l0; + const uint y_offset = 128*v_im + l0; + + [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) { + [[unroll]] for (uint i = 0; i < NUM_ROWS; ++i) { + temp[j][i] = FLOAT_TYPE(0); + } + } + + const uint nbr_par_th = num_blocks_per_row%it_size; + const uint nbr_all_th = num_blocks_per_row - nbr_par_th; + uint i0 = 0; + [[unroll]] for (; i0 < nbr_all_th; i0 += it_size) + calc_superblock(a_offset, b_offset, itid, v_im, ix, q_offset, y_offset, i0 + ix, num_blocks_per_row, first_row, num_rows, true); + calc_superblock(a_offset, b_offset, itid, v_im, ix, q_offset, y_offset, i0 + ix, num_blocks_per_row, first_row, num_rows, false); + + reduce_result(temp, d_offset, first_row, num_rows, tid); +} + +void main() { + const uint first_row = NUM_ROWS * (gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z); + + // do NUM_ROWS at a time, unless there aren't enough remaining rows + if (first_row + NUM_ROWS <= p.stride_d) { + compute_outputs(first_row, NUM_ROWS); + } else { + if (first_row >= p.stride_d) { + return; + } + compute_outputs(first_row, p.stride_d - first_row); + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q3_k.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q3_k.comp new file mode 100644 index 0000000000000000000000000000000000000000..93e48b790122a922f9c0d7d76022b459d2cf2ccb --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q3_k.comp @@ -0,0 +1,132 @@ +#version 450 +#extension GL_EXT_shader_explicit_arithmetic_types_int32 : require + +#include "mul_mat_vec_base.glsl" + +layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; + +shared FLOAT_TYPE sccache[2][BLOCK_SIZE/16][2][8]; + +FLOAT_TYPE temp[NUM_COLS][NUM_ROWS]; +uint csel = 0; + +void calc_superblock(const uint a_offset, const uint b_offset, const uint ix, const uint itid8, const uint v_im, const uint v_im4, const uint v_in, const uint32_t hm_m[4], const uint q_offset, const uint y_offset, const uint s_shift, const uint i, const uint num_blocks_per_row, const uint first_row, const uint num_rows, const bool all_threads) { + const uint y_idx = i * QUANT_K + y_offset; + + [[unroll]] for (uint n = 0; n < num_rows; ++n) { + const uint ib0 = a_offset + (first_row+n)*num_blocks_per_row; + csel ^= 1; + + if (!all_threads) { // when we don't have enough blocks to use all threads + if (i < num_blocks_per_row) + sccache[csel][ix][v_im][itid8] = FLOAT_TYPE(int8_t(((data_a[ib0+i].scales[itid8] >> v_im4) & 0xF) | (((data_a[ib0+i].scales[itid8%4+8] >> s_shift) & 3) << 4)) - 32); + barrier(); + + if (i >= num_blocks_per_row) + continue; + } + + const uint32_t hmk = ~(uint32_t(data_a_packed16[ib0 + i].hmask[v_in]) | (uint32_t(data_a_packed16[ib0 + i].hmask[v_in + 8]) << 16)); + const vec4 hmk_0 = vec4(unpack8(((hmk & hm_m[0]) >> ( v_im4)) << 2)); + const vec4 hmk_1 = vec4(unpack8(((hmk & hm_m[1]) >> (1 + v_im4)) << 2)); + const vec4 hmk_2 = vec4(unpack8(((hmk & hm_m[2]) >> (2 + v_im4)) << 2)); + const vec4 hmk_3 = vec4(unpack8(((hmk & hm_m[3]) >> (3 + v_im4)) << 2)); + + // 0, 1, 16, 17 + uint32_t qs_u32 = uint32_t(data_a[ib0 + i].qs[q_offset]) | (uint32_t(data_a[ib0 + i].qs[q_offset + 1]) << 8); + qs_u32 |= (uint32_t(data_a[ib0 + i].qs[q_offset + 16]) | (uint32_t(data_a[ib0 + i].qs[q_offset + 17]) << 8)) << 16; + const vec4 qs_u32_0 = vec4(unpack8(qs_u32 & 0x03030303)); + const vec4 qs_u32_2 = vec4(unpack8((qs_u32 >> 2) & 0x03030303)); + const vec4 qs_u32_4 = vec4(unpack8((qs_u32 >> 4) & 0x03030303)); + const vec4 qs_u32_6 = vec4(unpack8((qs_u32 >> 6) & 0x03030303)); + + if (all_threads) { + sccache[csel][ix][v_im][itid8] = FLOAT_TYPE(int8_t(((data_a[ib0+i].scales[itid8] >> v_im4) & 0xF) | (((data_a[ib0+i].scales[itid8%4+8] >> s_shift) & 3) << 4)) - 32); + barrier(); + } + + const FLOAT_TYPE d = FLOAT_TYPE(data_a[ib0 + i].d); + + [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) { + vec2 b0 = vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 0]); + vec2 b16 = vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 8]); + vec2 b32 = vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 16]); + vec2 b48 = vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 24]); + vec2 b64 = vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 32]); + vec2 b80 = vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 40]); + vec2 b96 = vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 48]); + vec2 b112 = vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 56]); + + FLOAT_TYPE sum = FLOAT_TYPE(0.0); + [[unroll]] for (int l = 0; l < 2; ++l) { + sum = fma(FLOAT_TYPE( b0[l]) * sccache[csel][ix][v_im][0], qs_u32_0[l ] - hmk_0[l ], + fma(FLOAT_TYPE( b16[l]) * sccache[csel][ix][v_im][1], qs_u32_0[l+2] - hmk_0[l+2], + fma(FLOAT_TYPE( b32[l]) * sccache[csel][ix][v_im][2], qs_u32_2[l ] - hmk_1[l ], + fma(FLOAT_TYPE( b48[l]) * sccache[csel][ix][v_im][3], qs_u32_2[l+2] - hmk_1[l+2], + fma(FLOAT_TYPE( b64[l]) * sccache[csel][ix][v_im][4], qs_u32_4[l ] - hmk_2[l ], + fma(FLOAT_TYPE( b80[l]) * sccache[csel][ix][v_im][5], qs_u32_4[l+2] - hmk_2[l+2], + fma(FLOAT_TYPE( b96[l]) * sccache[csel][ix][v_im][6], qs_u32_6[l ] - hmk_3[l ], + fma(FLOAT_TYPE(b112[l]) * sccache[csel][ix][v_im][7], qs_u32_6[l+2] - hmk_3[l+2], sum)))))))); + } + temp[j][n] = fma(d, sum, temp[j][n]); + } + } +} + +void compute_outputs(const uint32_t first_row, const uint32_t num_rows) { + uint a_offset, b_offset, d_offset; + get_offsets(a_offset, b_offset, d_offset); + + const uint num_blocks_per_row = p.ncols / QUANT_K; + + // 16 threads are used to process each block + const uint it_size = gl_WorkGroupSize.x/16; + const uint tid = gl_LocalInvocationID.x; + const uint itid = tid%16; // 0...15 + const uint ix = tid/16; + const uint itid8 = itid%8; + + const uint v_im = itid/8; // 0 or 1. 0 computes 0..., 1 computes 128... + const uint v_im4 = v_im*4; + const uint v_in = itid - 8*v_im; // 0...7 + + const uint32_t m = 0x01010101 << (4 * v_im); + uint32_t hm_m[4]; + [[unroll]] for (uint j = 0; j < 4; ++j) + hm_m[j] = m << j; + + const uint l0 = 2*v_in; // 0...15 + const uint q_offset = 32*v_im + l0; + const uint y_offset = 128*v_im + l0; + + [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) { + [[unroll]] for (uint i = 0; i < NUM_ROWS; ++i) { + temp[j][i] = FLOAT_TYPE(0); + } + } + + const uint s_shift = v_im4 + 2*(itid8/4); + + const uint nbr_par_th = num_blocks_per_row%it_size; + const uint nbr_all_th = num_blocks_per_row - nbr_par_th; + uint i0 = 0; + [[unroll]] for (; i0 < nbr_all_th; i0 += it_size) + calc_superblock(a_offset, b_offset, ix, itid8, v_im, v_im4, v_in, hm_m, q_offset, y_offset, s_shift, i0 + ix, num_blocks_per_row, first_row, num_rows, true); + calc_superblock(a_offset, b_offset, ix, itid8, v_im, v_im4, v_in, hm_m, q_offset, y_offset, s_shift, i0 + ix, num_blocks_per_row, first_row, num_rows, false); + + reduce_result(temp, d_offset, first_row, num_rows, tid); +} + +void main() { + const uint first_row = NUM_ROWS * (gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z); + + // do NUM_ROWS at a time, unless there aren't enough remaining rows + if (first_row + NUM_ROWS <= p.stride_d) { + compute_outputs(first_row, NUM_ROWS); + } else { + if (first_row >= p.stride_d) { + return; + } + compute_outputs(first_row, p.stride_d - first_row); + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q4_k.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q4_k.comp new file mode 100644 index 0000000000000000000000000000000000000000..93fbacc6282c49358dfdac807f0358a2d64107aa --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q4_k.comp @@ -0,0 +1,134 @@ +#version 450 + +#extension GL_EXT_shader_explicit_arithmetic_types_int32 : require + +#include "mul_mat_vec_base.glsl" + +layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; + +FLOAT_TYPE temp[NUM_COLS][NUM_ROWS]; + +void calc_superblock(const uint a_offset, const uint b_offset, const uint v_im, const uint q_offset, const uint y_offset, const uint i, const uint num_blocks_per_row, const uint first_row, const uint num_rows) { + const uint y1_idx = i * QUANT_K + y_offset; + const uint y2_idx = y1_idx + 128; + + [[unroll]] for (uint n = 0; n < num_rows; ++n) { + const uint ib0 = a_offset + (first_row+n)*num_blocks_per_row; + const FLOAT_TYPEV2 dm = FLOAT_TYPEV2(data_a[ib0 + i].dm); + + const uint32_t scale0_u32 = data_a_packed16[ib0 + i].scales[v_im ]; + const uint32_t scale4_u32 = data_a_packed16[ib0 + i].scales[v_im + 2]; + const uint32_t scale8_u32 = data_a_packed16[ib0 + i].scales[v_im + 4]; + + const uint32_t scale_0_4_l = (scale4_u32 << 16) | scale0_u32; + const uint32_t scale_0_4_h = (scale_0_4_l & 0xC0C0C0C0) >> 2; + const vec4 scale_0_4_l_f = vec4(unpack8(scale_0_4_l & 0x3F3F3F3F)); + const vec4 scale8_f = vec4(unpack8((((scale8_u32 << 12) | scale8_u32) & 0x0F0F0F0F) | scale_0_4_h)); + + const FLOAT_TYPE sc0 = scale_0_4_l_f.x; + const FLOAT_TYPE sc1 = scale_0_4_l_f.y; + const FLOAT_TYPE sc2 = scale_0_4_l_f.z; + const FLOAT_TYPE sc3 = scale_0_4_l_f.w; + const FLOAT_TYPE sc4 = scale8_f.x; + const FLOAT_TYPE sc5 = scale8_f.y; + const FLOAT_TYPE sc6 = scale8_f.z; + const FLOAT_TYPE sc7 = scale8_f.w; + + const uint32_t qs0_u32 = data_a_packed32[ib0 + i].qs[q_offset / 4]; + const uint32_t qs64_u32 = data_a_packed32[ib0 + i].qs[q_offset / 4 + 16]; + + const uint32_t qs0_u32_lo4 = qs0_u32 & 0x0F0F0F0F; + const uint32_t qs0_u32_hi4 = (qs0_u32 >> 4) & 0x0F0F0F0F; + const uint32_t qs64_u32_lo4 = qs64_u32 & 0x0F0F0F0F; + const uint32_t qs64_u32_hi4 = (qs64_u32 >> 4) & 0x0F0F0F0F; + + const vec4 qs0_lo4 = vec4(unpack8(qs0_u32_lo4)); + const vec4 qs64_lo4 = vec4(unpack8(qs64_u32_lo4)); + const vec4 qs0_hi4 = vec4(unpack8(qs0_u32_hi4)); + const vec4 qs64_hi4 = vec4(unpack8(qs64_u32_hi4)); + + const FLOAT_TYPE q4_0 = qs0_lo4.x; + const FLOAT_TYPE q4_1 = qs0_lo4.y; + const FLOAT_TYPE q4_2 = qs0_lo4.z; + const FLOAT_TYPE q4_3 = qs0_lo4.w; + const FLOAT_TYPE q4_4 = qs0_hi4.x; + const FLOAT_TYPE q4_5 = qs0_hi4.y; + const FLOAT_TYPE q4_6 = qs0_hi4.z; + const FLOAT_TYPE q4_7 = qs0_hi4.w; + const FLOAT_TYPE q4_8 = qs64_lo4.x; + const FLOAT_TYPE q4_9 = qs64_lo4.y; + const FLOAT_TYPE q4_10 = qs64_lo4.z; + const FLOAT_TYPE q4_11 = qs64_lo4.w; + const FLOAT_TYPE q4_12 = qs64_hi4.x; + const FLOAT_TYPE q4_13 = qs64_hi4.y; + const FLOAT_TYPE q4_14 = qs64_hi4.z; + const FLOAT_TYPE q4_15 = qs64_hi4.w; + + [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) { + vec4 by10 = vec4(data_b_v4[(j*p.batch_stride_b + b_offset + y1_idx) / 4 ]); + vec4 by132 = vec4(data_b_v4[(j*p.batch_stride_b + b_offset + y1_idx) / 4 + 8]); + vec4 by20 = vec4(data_b_v4[(j*p.batch_stride_b + b_offset + y2_idx) / 4 ]); + vec4 by232 = vec4(data_b_v4[(j*p.batch_stride_b + b_offset + y2_idx) / 4 + 8]); + + const FLOAT_TYPE sx = fma(FLOAT_TYPE(by10.x), q4_0, fma(FLOAT_TYPE(by10.y), q4_1, fma(FLOAT_TYPE(by10.z), q4_2, FLOAT_TYPE(by10.w) * q4_3))); + const FLOAT_TYPE sy = fma(FLOAT_TYPE(by132.x), q4_4, fma(FLOAT_TYPE(by132.y), q4_5, fma(FLOAT_TYPE(by132.z), q4_6, FLOAT_TYPE(by132.w) * q4_7))); + const FLOAT_TYPE sz = fma(FLOAT_TYPE(by20.x), q4_8, fma(FLOAT_TYPE(by20.y), q4_9, fma(FLOAT_TYPE(by20.z), q4_10, FLOAT_TYPE(by20.w) * q4_11))); + const FLOAT_TYPE sw = fma(FLOAT_TYPE(by232.x), q4_12, fma(FLOAT_TYPE(by232.y), q4_13, fma(FLOAT_TYPE(by232.z), q4_14, FLOAT_TYPE(by232.w) * q4_15))); + const FLOAT_TYPE smin = + fma(FLOAT_TYPE(by10.x), sc2, fma(FLOAT_TYPE(by132.x), sc3, fma(FLOAT_TYPE(by20.x), sc6, fma(FLOAT_TYPE(by232.x), sc7, + fma(FLOAT_TYPE(by10.y), sc2, fma(FLOAT_TYPE(by132.y), sc3, fma(FLOAT_TYPE(by20.y), sc6, fma(FLOAT_TYPE(by232.y), sc7, + fma(FLOAT_TYPE(by10.z), sc2, fma(FLOAT_TYPE(by132.z), sc3, fma(FLOAT_TYPE(by20.z), sc6, fma(FLOAT_TYPE(by232.z), sc7, + fma(FLOAT_TYPE(by10.w), sc2, fma(FLOAT_TYPE(by132.w), sc3, fma(FLOAT_TYPE(by20.w), sc6, FLOAT_TYPE(by232.w) * sc7))))))))))))))); + temp[j][n] = fma(dm.x, fma(sx, sc0, fma(sy, sc1, fma(sz, sc4, sw * sc5))), fma(-dm.y, smin, temp[j][n])); + } + } +} + +void compute_outputs(const uint32_t first_row, const uint32_t num_rows) { + uint a_offset, b_offset, d_offset; + get_offsets(a_offset, b_offset, d_offset); + + const uint num_blocks_per_row = p.ncols / QUANT_K; + + // 16 threads are used to process each block + const uint it_size = gl_WorkGroupSize.x/16; + const uint tid = gl_LocalInvocationID.x; + const uint itid = tid%16; // 0...15 + const uint ix = tid/16; + + const uint il = itid/4; // 0...3 + const uint ir = itid - 4*il; // 0...3 + const uint n = 4; + + const uint v_im = il / 2; // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224 + const uint v_in = il % 2; + + const uint l0 = n * (2 * ir + v_in); // 0...15 + const uint q_offset = 32*v_im + l0; + const uint y_offset = 64*v_im + l0; + + [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) { + [[unroll]] for (uint i = 0; i < NUM_ROWS; ++i) { + temp[j][i] = FLOAT_TYPE(0); + } + } + + [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += it_size) + calc_superblock(a_offset, b_offset, v_im, q_offset, y_offset, i, num_blocks_per_row, first_row, num_rows); + + reduce_result(temp, d_offset, first_row, num_rows, tid); +} + +void main() { + const uint first_row = NUM_ROWS * (gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z); + + // do NUM_ROWS at a time, unless there aren't enough remaining rows + if (first_row + NUM_ROWS <= p.stride_d) { + compute_outputs(first_row, NUM_ROWS); + } else { + if (first_row >= p.stride_d) { + return; + } + compute_outputs(first_row, p.stride_d - first_row); + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q5_k.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q5_k.comp new file mode 100644 index 0000000000000000000000000000000000000000..54d7e1bcdca6703856ab3a9d8250e20d213f023b --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q5_k.comp @@ -0,0 +1,165 @@ +#version 450 + +#extension GL_EXT_shader_explicit_arithmetic_types_int32 : require + +#include "mul_mat_vec_base.glsl" + +layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; + +FLOAT_TYPE temp[NUM_COLS][NUM_ROWS]; + +void calc_superblock(const uint a_offset, const uint b_offset, const uint v_im, const uint l0, const uint q_offset, const uint y_offset, const uint i, const uint num_blocks_per_row, const uint first_row, const uint num_rows) { + const uint y1_idx = i * QUANT_K + y_offset; + const uint y2_idx = y1_idx + 128; + + [[unroll]] for (uint n = 0; n < num_rows; ++n) { + const uint ib0 = a_offset + (first_row+n)*num_blocks_per_row; + const FLOAT_TYPEV2 dm = FLOAT_TYPEV2(data_a[ib0 + i].dm); + + const uint32_t scale0_u32 = data_a_packed16[ib0 + i].scales[v_im ]; + const uint32_t scale4_u32 = data_a_packed16[ib0 + i].scales[v_im + 2]; + const uint32_t scale8_u32 = data_a_packed16[ib0 + i].scales[v_im + 4]; + + const uint32_t scale_0_4_l = (scale4_u32 << 16) | scale0_u32; + const uint32_t scale_0_4_h = (scale_0_4_l & 0xC0C0C0C0) >> 2; + const vec4 scale_0_4_l_f = vec4(unpack8(scale_0_4_l & 0x3F3F3F3F)); + const vec4 scale8_f = vec4(unpack8((((scale8_u32 << 12) | scale8_u32) & 0x0F0F0F0F) | scale_0_4_h)); + + const FLOAT_TYPE sc0 = scale_0_4_l_f.x; + const FLOAT_TYPE sc1 = scale_0_4_l_f.y; + const FLOAT_TYPE sc2 = scale_0_4_l_f.z; + const FLOAT_TYPE sc3 = scale_0_4_l_f.w; + const FLOAT_TYPE sc4 = scale8_f.x; + const FLOAT_TYPE sc5 = scale8_f.y; + const FLOAT_TYPE sc6 = scale8_f.z; + const FLOAT_TYPE sc7 = scale8_f.w; + + const uint32_t qs0_16_u32 = uint32_t(data_a_packed16[ib0 + i].qs[q_offset / 2]) | (uint32_t(data_a_packed16[ib0 + i].qs[q_offset / 2 + 8]) << 16); + const uint32_t qs64_80_u32 = uint32_t(data_a_packed16[ib0 + i].qs[q_offset / 2 + 32]) | (uint32_t(data_a_packed16[ib0 + i].qs[q_offset / 2 + 40]) << 16); + + uint32_t qs0_16_u32_lo4 = qs0_16_u32 & 0x0F0F0F0F; + uint32_t qs0_16_u32_hi4 = (qs0_16_u32 >> 4) & 0x0F0F0F0F; + uint32_t qs64_80_u32_lo4 = qs64_80_u32 & 0x0F0F0F0F; + uint32_t qs64_80_u32_hi4 = (qs64_80_u32 >> 4) & 0x0F0F0F0F; + + const uint32_t qh = pack32(u16vec2(data_a_packed16[ib0 + i].qh[l0 / 2], data_a_packed16[ib0 + i].qh[l0 / 2 + 8])); + + const uint32_t qs0_16_lo4_offset16 = ((qh >> (2*v_im)) & 0x01010101) << 4; + const uint32_t qs0_16_hi4_offset16 = ((qh >> (2*v_im)) & 0x02020202) << 3; + const uint32_t qs64_80_lo4_offset16 = ((qh >> (2*v_im)) & 0x10101010); + const uint32_t qs64_80_hi4_offset16 = ((qh >> (2*v_im)) & 0x20202020) >> 1; + + qs0_16_u32_lo4 += qs0_16_lo4_offset16; + qs0_16_u32_hi4 += qs0_16_hi4_offset16; + qs64_80_u32_lo4 += qs64_80_lo4_offset16; + qs64_80_u32_hi4 += qs64_80_hi4_offset16; + + const vec4 qs0_16_lo4 = vec4(unpack8(qs0_16_u32_lo4)); + const vec4 qs64_80_lo4 = vec4(unpack8(qs64_80_u32_lo4)); + const vec4 qs0_16_hi4 = vec4(unpack8(qs0_16_u32_hi4)); + const vec4 qs64_80_hi4 = vec4(unpack8(qs64_80_u32_hi4)); + + const FLOAT_TYPE q4_0 = qs0_16_lo4.x; + const FLOAT_TYPE q4_1 = qs0_16_lo4.y; + const FLOAT_TYPE q4_2 = qs0_16_lo4.z; + const FLOAT_TYPE q4_3 = qs0_16_lo4.w; + const FLOAT_TYPE q4_4 = qs0_16_hi4.x; + const FLOAT_TYPE q4_5 = qs0_16_hi4.y; + const FLOAT_TYPE q4_6 = qs0_16_hi4.z; + const FLOAT_TYPE q4_7 = qs0_16_hi4.w; + const FLOAT_TYPE q4_8 = qs64_80_lo4.x; + const FLOAT_TYPE q4_9 = qs64_80_lo4.y; + const FLOAT_TYPE q4_10 = qs64_80_lo4.z; + const FLOAT_TYPE q4_11 = qs64_80_lo4.w; + const FLOAT_TYPE q4_12 = qs64_80_hi4.x; + const FLOAT_TYPE q4_13 = qs64_80_hi4.y; + const FLOAT_TYPE q4_14 = qs64_80_hi4.z; + const FLOAT_TYPE q4_15 = qs64_80_hi4.w; + + [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) { + vec2 by10 = vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y1_idx) / 2 ]); + vec2 by116 = vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y1_idx) / 2 + 8]); + vec2 by132 = vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y1_idx) / 2 + 16]); + vec2 by148 = vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y1_idx) / 2 + 24]); + vec2 by20 = vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y2_idx) / 2 ]); + vec2 by216 = vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y2_idx) / 2 + 8]); + vec2 by232 = vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y2_idx) / 2 + 16]); + vec2 by248 = vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y2_idx) / 2 + 24]); + + const FLOAT_TYPE sx = + fma(FLOAT_TYPE(by10.x), q4_0, + fma(FLOAT_TYPE(by10.y), q4_1, + fma(FLOAT_TYPE(by116.x), q4_2, + FLOAT_TYPE(by116.y) * q4_3))); + const FLOAT_TYPE sy = + fma(FLOAT_TYPE(by132.x), q4_4, + fma(FLOAT_TYPE(by132.y), q4_5, + fma(FLOAT_TYPE(by148.x), q4_6, + FLOAT_TYPE(by148.y) * q4_7))); + const FLOAT_TYPE sz = + fma(FLOAT_TYPE(by20.x), q4_8, + fma(FLOAT_TYPE(by20.y), q4_9, + fma(FLOAT_TYPE(by216.x), q4_10, + FLOAT_TYPE(by216.y) * q4_11))); + const FLOAT_TYPE sw = + fma(FLOAT_TYPE(by232.x), q4_12, + fma(FLOAT_TYPE(by232.y), q4_13, + fma(FLOAT_TYPE(by248.x), q4_14, + FLOAT_TYPE(by248.y) * q4_15))); + const FLOAT_TYPE smin = + fma(FLOAT_TYPE(by10.x) + FLOAT_TYPE(by10.y) + FLOAT_TYPE(by116.x) + FLOAT_TYPE(by116.y), sc2, + fma(FLOAT_TYPE(by132.x) + FLOAT_TYPE(by132.y) + FLOAT_TYPE(by148.x) + FLOAT_TYPE(by148.y), sc3, + fma(FLOAT_TYPE(by20.x) + FLOAT_TYPE(by20.y) + FLOAT_TYPE(by216.x) + FLOAT_TYPE(by216.y), sc6, + (FLOAT_TYPE(by232.x) + FLOAT_TYPE(by232.y) + FLOAT_TYPE(by248.x) + FLOAT_TYPE(by248.y)) * sc7))); + temp[j][n] = fma(dm.x, fma(sx, sc0, fma(sy, sc1, fma(sz, sc4, sw * sc5))), fma(-dm.y, smin, temp[j][n])); + } + } +} + +void compute_outputs(const uint32_t first_row, const uint32_t num_rows) { + uint a_offset, b_offset, d_offset; + get_offsets(a_offset, b_offset, d_offset); + + const uint num_blocks_per_row = p.ncols / QUANT_K; + + // 16 threads are used to process each block + const uint it_size = gl_WorkGroupSize.x/16; + const uint tid = gl_LocalInvocationID.x; + const uint itid = tid%16; // 0...15 + const uint ix = tid/16; + + const uint il = itid/4; // 0...3 + const uint ir = itid - 4*il; // 0...3 + + const uint v_im = il / 2; // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224 + const uint v_in = il % 2; + + const uint l0 = 4*ir + 2*v_in; // 0...15 + const uint q_offset = 32*v_im + l0; + const uint y_offset = 64*v_im + l0; + + [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) { + [[unroll]] for (uint i = 0; i < NUM_ROWS; ++i) { + temp[j][i] = FLOAT_TYPE(0); + } + } + + [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += it_size) + calc_superblock(a_offset, b_offset, v_im, l0, q_offset, y_offset, i, num_blocks_per_row, first_row, num_rows); + + reduce_result(temp, d_offset, first_row, num_rows, tid); +} + +void main() { + const uint first_row = NUM_ROWS * (gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z); + + // do NUM_ROWS at a time, unless there aren't enough remaining rows + if (first_row + NUM_ROWS <= p.stride_d) { + compute_outputs(first_row, NUM_ROWS); + } else { + if (first_row >= p.stride_d) { + return; + } + compute_outputs(first_row, p.stride_d - first_row); + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q6_k.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q6_k.comp new file mode 100644 index 0000000000000000000000000000000000000000..3e89d91cbb0476f82bf04899a6d90863f9cc737b --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_q6_k.comp @@ -0,0 +1,130 @@ +#version 450 + +#extension GL_EXT_shader_explicit_arithmetic_types_int32 : require + +#include "mul_mat_vec_base.glsl" + +layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; + +shared FLOAT_TYPE sccache[2][BLOCK_SIZE/16][16]; + +FLOAT_TYPE temp[NUM_COLS][NUM_ROWS]; +uint csel = 0; + +void calc_superblock(const uint a_offset, const uint b_offset, const uint itid, const uint ix, const uint ql_offset, const uint qh_offset, const uint s_offset, const uint y_offset, const uint i, const uint num_blocks_per_row, const uint first_row, const uint num_rows, const bool all_threads) { + const uint y_idx = i * QUANT_K + y_offset; + + [[unroll]] for (uint n = 0; n < num_rows; ++n) { + const uint ib0 = a_offset + (first_row+n)*num_blocks_per_row; + csel ^= 1; + + if (!all_threads) { // when we don't have enough blocks to use all threads + if (i < num_blocks_per_row) + sccache[csel][ix][itid] = FLOAT_TYPE(data_a[ib0 + i].scales[itid]); + barrier(); + + if (i >= num_blocks_per_row) + continue; + } + + const uint32_t ql0_u32 = uint32_t(data_a_packed16[ib0 + i].ql[ql_offset / 2]) | (uint32_t(data_a_packed16[ib0 + i].ql[ql_offset / 2 + 1]) << 16); + const uint32_t ql32_u32 = uint32_t(data_a_packed16[ib0 + i].ql[ql_offset / 2 + 16]) | (uint32_t(data_a_packed16[ib0 + i].ql[ql_offset / 2 + 17]) << 16); + + const uint32_t ql0_u32_lo4 = ql0_u32 & 0x0F0F0F0F; + const uint32_t ql0_u32_hi4 = (ql0_u32 >> 4) & 0x0F0F0F0F; + const uint32_t ql32_u32_lo4 = ql32_u32 & 0x0F0F0F0F; + const uint32_t ql32_u32_hi4 = (ql32_u32 >> 4) & 0x0F0F0F0F; + + const uint32_t qh_u32 = uint32_t(data_a_packed16[ib0 + i].qh[qh_offset / 2]) | (uint32_t(data_a_packed16[ib0 + i].qh[qh_offset / 2 + 1]) << 16); + const uint32_t qh0_u32 = (qh_u32 & 0x03030303) << 4; + const uint32_t qh2_u32 = (qh_u32 & 0x0C0C0C0C) << 2; + const uint32_t qh4_u32 = (qh_u32 & 0x30303030); + const uint32_t qh6_u32 = (qh_u32 & 0xC0C0C0C0) >> 2; + + const uint32_t q0_u32 = ql0_u32_lo4 | qh0_u32; + const uint32_t q1_u32 = ql32_u32_lo4 | qh2_u32; + const uint32_t q2_u32 = ql0_u32_hi4 | qh4_u32; + const uint32_t q3_u32 = ql32_u32_hi4 | qh6_u32; + + const vec4 q0 = vec4(unpack8(q0_u32)) - 32; + const vec4 q1 = vec4(unpack8(q1_u32)) - 32; + const vec4 q2 = vec4(unpack8(q2_u32)) - 32; + const vec4 q3 = vec4(unpack8(q3_u32)) - 32; + + if (all_threads) { + sccache[csel][ix][itid] = FLOAT_TYPE(data_a[ib0 + i].scales[itid]); + barrier(); + } + + const FLOAT_TYPE d = FLOAT_TYPE(data_a[ib0 + i].d); + + [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) { + vec4 by0 = vec4(data_b_v4[(j*p.batch_stride_b + b_offset + y_idx) / 4 ]); + vec4 by32 = vec4(data_b_v4[(j*p.batch_stride_b + b_offset + y_idx) / 4 + 8]); + vec4 by64 = vec4(data_b_v4[(j*p.batch_stride_b + b_offset + y_idx) / 4 + 16]); + vec4 by96 = vec4(data_b_v4[(j*p.batch_stride_b + b_offset + y_idx) / 4 + 24]); + + FLOAT_TYPE sum[4] = {0, 0, 0, 0}; + [[unroll]] for (uint l = 0; l < 4; ++l) { + sum[0] = fma(FLOAT_TYPE(by0[l]), q0[l], sum[0]); + sum[1] = fma(FLOAT_TYPE(by32[l]), q1[l], sum[1]); + sum[2] = fma(FLOAT_TYPE(by64[l]), q2[l], sum[2]); + sum[3] = fma(FLOAT_TYPE(by96[l]), q3[l], sum[3]); + } + temp[j][n] = fma(fma(sum[0], sccache[csel][ix][s_offset], fma(sum[1], sccache[csel][ix][s_offset + 2], fma(sum[2], sccache[csel][ix][s_offset + 4], sum[3] * sccache[csel][ix][s_offset + 6]))), d, temp[j][n]); + } + } +} + +void compute_outputs(const uint first_row, const uint num_rows) { + uint a_offset, b_offset, d_offset; + get_offsets(a_offset, b_offset, d_offset); + + const uint num_blocks_per_row = p.ncols / QUANT_K; + + // 16 threads are used to process each block + const uint it_size = gl_WorkGroupSize.x/16; + const uint tid = gl_LocalInvocationID.x; + const uint itid = tid%16; // 0...15 + const uint ix = tid/16; + + const uint v_im = itid/8; // 0 or 1. 0 computes 0..., 1 computes 128... + const uint v_in = itid - 8*v_im; // 0...7 + + const uint l0 = 4 * v_in; // 0, 4, 8, ..., 28 + const uint is = v_in / 4; + + const uint ql_offset = 64*v_im + l0; + const uint qh_offset = 32*v_im + l0; + const uint s_offset = 8*v_im + is; + const uint y_offset = 128*v_im + l0; + + [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) { + [[unroll]] for (uint i = 0; i < NUM_ROWS; ++i) { + temp[j][i] = FLOAT_TYPE(0); + } + } + + const uint nbr_par_th = num_blocks_per_row%it_size; + const uint nbr_all_th = num_blocks_per_row - nbr_par_th; + uint i0 = 0; + [[unroll]] for (; i0 < nbr_all_th; i0 += it_size) + calc_superblock(a_offset, b_offset, itid, ix, ql_offset, qh_offset, s_offset, y_offset, i0 + ix, num_blocks_per_row, first_row, num_rows, true); + calc_superblock(a_offset, b_offset, itid, ix, ql_offset, qh_offset, s_offset, y_offset, i0 + ix, num_blocks_per_row, first_row, num_rows, false); + + reduce_result(temp, d_offset, first_row, num_rows, tid); +} + +void main() { + const uint first_row = NUM_ROWS * (gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z); + + // do NUM_ROWS at a time, unless there aren't enough remaining rows + if (first_row + NUM_ROWS <= p.stride_d) { + compute_outputs(first_row, NUM_ROWS); + } else { + if (first_row >= p.stride_d) { + return; + } + compute_outputs(first_row, p.stride_d - first_row); + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vecq.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vecq.comp new file mode 100644 index 0000000000000000000000000000000000000000..7bbee577fb74032d29cc356d41e26a6f1d67a927 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vecq.comp @@ -0,0 +1,141 @@ +#version 450 + +#extension GL_EXT_shader_explicit_arithmetic_types_int32 : require +#extension GL_EXT_integer_dot_product : require + +#define MMQ +#define NEEDS_IQ1S_GRID_GPU +#define B_TYPE block_q8_1_x4 + +#include "mul_mat_vec_base.glsl" + +layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; + +#if defined(DATA_A_QUANT_LEGACY) || defined(DATA_A_MXFP4) +#define K_PER_ITER 8 +#elif defined(DATA_A_QUANT_K) +#define K_PER_ITER 16 +#elif defined(DATA_A_IQ1_S) || defined(DATA_A_IQ1_M) +#define K_PER_ITER 32 +#else +#error unimplemented +#endif + +uint a_offset, b_offset, d_offset; + +int32_t cache_b_qs[K_PER_ITER / 4]; +vec2 cache_b_ds; + +#include "mul_mat_vecq_funcs.glsl" + +void iter(inout FLOAT_TYPE temp[NUM_COLS][NUM_ROWS], const uint first_row, const uint num_rows, const uint col, const uint b_qs_idx) { + [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) { + // Preload data_b block + const uint b_block_idx = (j*p.batch_stride_b + col) / QUANT_K_Q8_1 + b_offset; + const uint b_block_idx_outer = b_block_idx / 4; + const uint b_block_idx_inner = b_block_idx % 4; + cache_b_ds = vec2(data_b[b_block_idx_outer].ds[b_block_idx_inner]); + +#if QUANT_R == 2 + // Assumes K_PER_ITER == 8 + cache_b_qs[0] = data_b[b_block_idx_outer].qs[b_block_idx_inner * 8 + b_qs_idx]; + cache_b_qs[1] = data_b[b_block_idx_outer].qs[b_block_idx_inner * 8 + b_qs_idx + 4]; +#else +#if K_PER_ITER == 8 + cache_b_qs[0] = data_b[b_block_idx_outer].qs[b_block_idx_inner * 8 + b_qs_idx * 2]; + cache_b_qs[1] = data_b[b_block_idx_outer].qs[b_block_idx_inner * 8 + b_qs_idx * 2 + 1]; +#elif K_PER_ITER == 16 + cache_b_qs[0] = data_b[b_block_idx_outer].qs[b_block_idx_inner * 8 + b_qs_idx * 4 ]; + cache_b_qs[1] = data_b[b_block_idx_outer].qs[b_block_idx_inner * 8 + b_qs_idx * 4 + 1]; + cache_b_qs[2] = data_b[b_block_idx_outer].qs[b_block_idx_inner * 8 + b_qs_idx * 4 + 2]; + cache_b_qs[3] = data_b[b_block_idx_outer].qs[b_block_idx_inner * 8 + b_qs_idx * 4 + 3]; +#elif K_PER_ITER == 32 + cache_b_qs[0] = data_b[b_block_idx_outer].qs[b_block_idx_inner * 8 ]; + cache_b_qs[1] = data_b[b_block_idx_outer].qs[b_block_idx_inner * 8 + 1]; + cache_b_qs[2] = data_b[b_block_idx_outer].qs[b_block_idx_inner * 8 + 2]; + cache_b_qs[3] = data_b[b_block_idx_outer].qs[b_block_idx_inner * 8 + 3]; + cache_b_qs[4] = data_b[b_block_idx_outer].qs[b_block_idx_inner * 8 + 4]; + cache_b_qs[5] = data_b[b_block_idx_outer].qs[b_block_idx_inner * 8 + 5]; + cache_b_qs[6] = data_b[b_block_idx_outer].qs[b_block_idx_inner * 8 + 6]; + cache_b_qs[7] = data_b[b_block_idx_outer].qs[b_block_idx_inner * 8 + 7]; +#else +#error unimplemented +#endif +#endif + + uint ibi = first_row*p.ncols; + [[unroll]] for (uint n = 0; n < num_rows; ++n) { + const uint a_block_idx = (ibi + col)/QUANT_K_Q8_1 + a_offset; + ibi += p.ncols; + + temp[j][n] += mmvq_dot_product(a_block_idx, b_qs_idx); + } + } +} + +void compute_outputs(const uint32_t first_row, const uint32_t num_rows) { + const uint tid = gl_LocalInvocationID.x; + + get_offsets(a_offset, b_offset, d_offset); + a_offset *= QUANT_K / QUANT_K_Q8_1; + b_offset /= QUANT_K_Q8_1; + + FLOAT_TYPE temp[NUM_COLS][NUM_ROWS]; + + [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) { + [[unroll]] for (uint n = 0; n < num_rows; ++n) { + temp[j][n] = FLOAT_TYPE(0.0f); + } + } + + const uint col_stride = K_PER_ITER * BLOCK_SIZE; + uint num_iters = p.ncols / col_stride; + if (num_iters * col_stride + K_PER_ITER * tid < p.ncols) { + num_iters++; + } + + const uint b_qs_idx = tid % (32 / K_PER_ITER); + uint col = tid * K_PER_ITER; + while (num_iters >= 4) { + // Manually partially unroll the loop + [[unroll]] for (uint k = 0; k < 4; ++k) { + iter(temp, first_row, num_rows, col, b_qs_idx); + col += col_stride; + } + + num_iters -= 4; + } + + if (num_iters >= 2) { + // Manually partially unroll the loop + iter(temp, first_row, num_rows, col, b_qs_idx); + col += col_stride; + iter(temp, first_row, num_rows, col, b_qs_idx); + col += col_stride; + num_iters -= 2; + } + + if (num_iters > 0) { + iter(temp, first_row, num_rows, col, b_qs_idx); + } + + reduce_result(temp, d_offset, first_row, num_rows, tid); +} + +void main() { + const uint first_row = NUM_ROWS * (gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z); + +#ifdef NEEDS_INIT_IQ_SHMEM + init_iq_shmem(gl_WorkGroupSize); +#endif + + // do NUM_ROWS at a time, unless there aren't enough remaining rows + if (first_row + NUM_ROWS <= p.stride_d) { + compute_outputs(first_row, NUM_ROWS); + } else { + if (first_row >= p.stride_d) { + return; + } + compute_outputs(first_row, p.stride_d - first_row); + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vecq_funcs.glsl b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vecq_funcs.glsl new file mode 100644 index 0000000000000000000000000000000000000000..73cf9c799554134ab3d60c5ae8dcffa6f65a2888 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vecq_funcs.glsl @@ -0,0 +1,527 @@ +#extension GL_EXT_shader_explicit_arithmetic_types_int32 : require +#extension GL_EXT_shader_explicit_arithmetic_types_int16 : require +#extension GL_EXT_shader_explicit_arithmetic_types_int8 : require + +#include "types.glsl" + +#if defined(DATA_A_Q4_0) || defined(DATA_A_Q5_0) || defined(DATA_A_Q8_0) || defined(DATA_A_IQ1_S) || defined(DATA_A_IQ2_XXS) || defined(DATA_A_IQ2_XS) || defined(DATA_A_IQ2_S) || defined(DATA_A_IQ3_XXS) || defined(DATA_A_IQ3_S) || defined(DATA_A_IQ4_XS) || defined(DATA_A_IQ4_NL) +FLOAT_TYPE get_dm(uint ib) { + return FLOAT_TYPE(data_a[ib].d); +} +#endif + +#if defined(DATA_A_Q4_1) || defined(DATA_A_Q5_1) +FLOAT_TYPEV2 get_dm(uint ib) { + return FLOAT_TYPEV2(data_a_packed32[ib].dm); +} +#endif + +#if defined(DATA_A_MXFP4) +FLOAT_TYPE get_dm(uint ib) { + return FLOAT_TYPE(e8m0_to_fp32(data_a[ib].e)); +} +#endif + +#if defined(DATA_A_Q2_K) +FLOAT_TYPEV2 get_dm(uint ib) { + const uint ib_k = ib / 8; + return FLOAT_TYPEV2(data_a_packed32[ib_k].dm); +} +#endif + +// Each iqs value maps to a 32-bit integer +#if defined(DATA_A_Q4_0) +// 2-byte loads for Q4_0 blocks (18 bytes) +i32vec2 repack(uint ib, uint iqs) { + const u16vec2 quants = u16vec2(data_a_packed16[ib].qs[iqs * 2 ], + data_a_packed16[ib].qs[iqs * 2 + 1]); + const uint32_t vui = pack32(quants); + return i32vec2( vui & 0x0F0F0F0F, + (vui >> 4) & 0x0F0F0F0F); +} + +FLOAT_TYPE mul_q8_1(const int32_t q_sum, const float da, const vec2 dsb, const int32_t sum_divisor) { + return FLOAT_TYPE(da * (float(q_sum) * dsb.x - (8 / sum_divisor) * dsb.y)); +} +#endif + +#if defined(DATA_A_Q4_1) +// 4-byte loads for Q4_1 blocks (20 bytes) +i32vec2 repack(uint ib, uint iqs) { + const uint32_t vui = data_a_packed32[ib].qs[iqs]; + return i32vec2( vui & 0x0F0F0F0F, + (vui >> 4) & 0x0F0F0F0F); +} + +FLOAT_TYPE mul_q8_1(const int32_t q_sum, const vec2 dma, const vec2 dsb, const int32_t sum_divisor) { + return FLOAT_TYPE(float(q_sum) * dma.x * dsb.x + dma.y * dsb.y / sum_divisor); +} +#endif + +#if defined(DATA_A_Q5_0) +// 2-byte loads for Q5_0 blocks (22 bytes) +i32vec2 repack(uint ib, uint iqs) { + const u16vec2 quants = u16vec2(data_a_packed16[ib].qs[iqs * 2 ], + data_a_packed16[ib].qs[iqs * 2 + 1]); + const uint32_t vui = pack32(quants); + const int32_t qh = int32_t((uint32_t(data_a_packed16[ib].qh[1]) << 16 | data_a_packed16[ib].qh[0]) >> (4 * iqs)); + const int32_t v0 = int32_t(vui & 0x0F0F0F0F) + | ((qh & 0xF) * 0x02040810) & 0x10101010; // (0,1,2,3) -> (4,12,20,28) + + const int32_t v1 = int32_t((vui >> 4) & 0x0F0F0F0F) + | (((qh >> 16) & 0xF) * 0x02040810) & 0x10101010; // (16,17,18,19) -> (4,12,20,28) + + return i32vec2(v0, v1); +} + +FLOAT_TYPE mul_q8_1(const int32_t q_sum, const float da, const vec2 dsb, const int32_t sum_divisor) { + return FLOAT_TYPE(da * (float(q_sum) * dsb.x - (16 / sum_divisor) * dsb.y)); +} +#endif + +#if defined(DATA_A_Q5_1) +// 4-byte loads for Q5_1 blocks (24 bytes) +i32vec2 repack(uint ib, uint iqs) { + const u16vec2 quants = u16vec2(data_a_packed16[ib].qs[iqs * 2 ], + data_a_packed16[ib].qs[iqs * 2 + 1]); + const uint32_t vui = pack32(quants); + const int32_t qh = int32_t(data_a_packed32[ib].qh >> (4 * iqs)); + const int32_t v0 = int32_t(vui & 0x0F0F0F0F) + | ((qh & 0xF) * 0x02040810) & 0x10101010; // (0,1,2,3) -> (4,12,20,28) + + const int32_t v1 = int32_t((vui >> 4) & 0x0F0F0F0F) + | (((qh >> 16) & 0xF) * 0x02040810) & 0x10101010; // (16,17,18,19) -> (4,12,20,28) + + return i32vec2(v0, v1); +} + +FLOAT_TYPE mul_q8_1(const int32_t q_sum, const vec2 dma, const vec2 dsb, const int32_t sum_divisor) { + return FLOAT_TYPE(float(q_sum) * dma.x * dsb.x + dma.y * dsb.y / sum_divisor); +} +#endif + +#if defined(DATA_A_Q8_0) +// 2-byte loads for Q8_0 blocks (34 bytes) +int32_t repack(uint ib, uint iqs) { + return pack32(i16vec2(data_a_packed16[ib].qs[iqs * 2 ], + data_a_packed16[ib].qs[iqs * 2 + 1])); +} + +FLOAT_TYPE mul_q8_1(const int32_t q_sum, const float da, const vec2 dsb, const int32_t sum_divisor) { + return FLOAT_TYPE(float(q_sum) * da * dsb.x); +} +#endif + +#if defined(DATA_A_MXFP4) +// 1-byte loads for mxfp4 blocks (17 bytes) +i32vec2 repack(uint ib, uint iqs) { + const uint32_t qs = pack32(u8vec4(data_a[ib].qs[iqs * 4 ], + data_a[ib].qs[iqs * 4 + 1], + data_a[ib].qs[iqs * 4 + 2], + data_a[ib].qs[iqs * 4 + 3])); + + const u8vec4 i_a0 = unpack8( qs & 0x0F0F0F0F); + const u8vec4 i_a1 = unpack8((qs >> 4) & 0x0F0F0F0F); + + return i32vec2(pack32(i8vec4(kvalues_mxfp4[i_a0.x], kvalues_mxfp4[i_a0.y], kvalues_mxfp4[i_a0.z], kvalues_mxfp4[i_a0.w])), + pack32(i8vec4(kvalues_mxfp4[i_a1.x], kvalues_mxfp4[i_a1.y], kvalues_mxfp4[i_a1.z], kvalues_mxfp4[i_a1.w]))); +} + +FLOAT_TYPE mul_q8_1(const int32_t q_sum, const float da, const vec2 dsb, const int32_t sum_divisor) { + return FLOAT_TYPE(da * dsb.x * float(q_sum) * 0.5); +} +#endif + +#if defined(DATA_A_QUANT_LEGACY) || defined(DATA_A_MXFP4) +FLOAT_TYPE mmvq_dot_product(const uint ib_a, const uint iqs) { + int32_t q_sum = 0; +#if QUANT_R == 2 + const i32vec2 data_a_qs = repack(ib_a, iqs); + q_sum += dotPacked4x8EXT(data_a_qs.x, + cache_b_qs[0]); + q_sum += dotPacked4x8EXT(data_a_qs.y, + cache_b_qs[1]); +#else + int32_t data_a_qs = repack(ib_a, iqs * 2); + q_sum += dotPacked4x8EXT(data_a_qs, + cache_b_qs[0]); + data_a_qs = repack(ib_a, iqs * 2 + 1); + q_sum += dotPacked4x8EXT(data_a_qs, + cache_b_qs[1]); +#endif + + // 2 quants per call => divide sums by 8/2 = 4 + return mul_q8_1(q_sum, get_dm(ib_a), cache_b_ds, 4); +} +#endif + +#if defined(DATA_A_Q2_K) +// 4-byte loads for Q2_K blocks (84 bytes) +i32vec4 repack4(uint ib, uint iqs) { + const uint ib_k = ib / 8; + const uint iqs_k = (ib % 8) * 8 + iqs; + + const uint qs_idx = (iqs_k / 32) * 8 + (iqs_k % 8); + const uint qs_shift = ((iqs_k % 32) / 8) * 2; + + return i32vec4((data_a_packed32[ib_k].qs[qs_idx ] >> qs_shift) & 0x03030303, + (data_a_packed32[ib_k].qs[qs_idx + 1] >> qs_shift) & 0x03030303, + (data_a_packed32[ib_k].qs[qs_idx + 2] >> qs_shift) & 0x03030303, + (data_a_packed32[ib_k].qs[qs_idx + 3] >> qs_shift) & 0x03030303); +} + +uint8_t get_scale(uint ib, uint iqs) { + const uint ib_k = ib / 8; + const uint iqs_k = (ib % 8) * 8 + iqs; + + return data_a[ib_k].scales[iqs_k / 4]; +} + +FLOAT_TYPE mmvq_dot_product(const uint ib_a, const uint iqs) { + int32_t sum_d = 0; + int32_t sum_m = 0; + + const i32vec4 qs_a = repack4(ib_a, iqs * 4); + const uint8_t scale = get_scale(ib_a, iqs * 4); + const vec2 dm = vec2(get_dm(ib_a)); + const int32_t scale_m = int32_t(scale >> 4) * 0x01010101; // Duplicate 8-bit value across 32-bits. + + sum_d += dotPacked4x8EXT(qs_a.x, cache_b_qs[0]) * (scale & 0xF); + sum_m += dotPacked4x8EXT(scale_m, cache_b_qs[0]); + + sum_d += dotPacked4x8EXT(qs_a.y, cache_b_qs[1]) * (scale & 0xF); + sum_m += dotPacked4x8EXT(scale_m, cache_b_qs[1]); + + sum_d += dotPacked4x8EXT(qs_a.z, cache_b_qs[2]) * (scale & 0xF); + sum_m += dotPacked4x8EXT(scale_m, cache_b_qs[2]); + + sum_d += dotPacked4x8EXT(qs_a.w, cache_b_qs[3]) * (scale & 0xF); + sum_m += dotPacked4x8EXT(scale_m, cache_b_qs[3]); + + return FLOAT_TYPE(float(cache_b_ds.x) * (float(dm.x) * float(sum_d) - float(dm.y) * float(sum_m))); +} +#endif + +#if defined(DATA_A_Q3_K) +// 2-byte loads for Q3_K blocks (110 bytes) +i32vec4 repack4(uint ib, uint iqs) { + const uint ib_k = ib / 8; + const uint iqs_k = (ib % 8) * 8 + iqs; + + const uint qs_idx = (iqs_k / 32) * 8 + (iqs_k % 8); + const uint qs_shift = ((iqs_k % 32) / 8) * 2; + const uint hm_shift = iqs_k / 8; + + const uvec4 qs = uvec4( uint32_t(data_a_packed16[ib_k].qs[qs_idx * 2 ]) | + (uint32_t(data_a_packed16[ib_k].qs[qs_idx * 2 + 1]) << 16), + uint32_t(data_a_packed16[ib_k].qs[qs_idx * 2 + 2]) | + (uint32_t(data_a_packed16[ib_k].qs[qs_idx * 2 + 3]) << 16), + uint32_t(data_a_packed16[ib_k].qs[qs_idx * 2 + 4]) | + (uint32_t(data_a_packed16[ib_k].qs[qs_idx * 2 + 5]) << 16), + uint32_t(data_a_packed16[ib_k].qs[qs_idx * 2 + 6]) | + (uint32_t(data_a_packed16[ib_k].qs[qs_idx * 2 + 7]) << 16)); + + const uvec4 hmask = uvec4( uint32_t(data_a_packed16[ib_k].hmask[iqs * 2 ]) | + (uint32_t(data_a_packed16[ib_k].hmask[iqs * 2 + 1]) << 16), + uint32_t(data_a_packed16[ib_k].hmask[iqs * 2 + 2]) | + (uint32_t(data_a_packed16[ib_k].hmask[iqs * 2 + 3]) << 16), + uint32_t(data_a_packed16[ib_k].hmask[iqs * 2 + 4]) | + (uint32_t(data_a_packed16[ib_k].hmask[iqs * 2 + 5]) << 16), + uint32_t(data_a_packed16[ib_k].hmask[iqs * 2 + 6]) | + (uint32_t(data_a_packed16[ib_k].hmask[iqs * 2 + 7]) << 16)); + + // bitwise OR to add 4 if hmask is set, subtract later + const uint vals0 = (( qs.x >> qs_shift) & 0x03030303) | + (((hmask.x >> hm_shift) & 0x01010101) << 2); + const uint vals1 = (( qs.y >> qs_shift) & 0x03030303) | + (((hmask.y >> hm_shift) & 0x01010101) << 2); + const uint vals2 = (( qs.z >> qs_shift) & 0x03030303) | + (((hmask.z >> hm_shift) & 0x01010101) << 2); + const uint vals3 = (( qs.w >> qs_shift) & 0x03030303) | + (((hmask.w >> hm_shift) & 0x01010101) << 2); + + // Subtract 4 by twiddling bits rather than using re-packing as mesa + // compiles repacking poorly. + return i32vec4(int32_t(((vals0 ^ 0x80808080) - 0x04040404) ^ 0x80808080), + int32_t(((vals1 ^ 0x80808080) - 0x04040404) ^ 0x80808080), + int32_t(((vals2 ^ 0x80808080) - 0x04040404) ^ 0x80808080), + int32_t(((vals3 ^ 0x80808080) - 0x04040404) ^ 0x80808080)); +} + +float get_d_scale(uint ib, uint iqs) { + const uint ib_k = ib / 8; + const uint iqs_k = (ib % 8) * 8 + iqs; + const uint is = iqs_k / 4; + + const int8_t scale = int8_t(((data_a[ib_k].scales[is % 8 ] >> (4 * (is / 8))) & 0x0F0F) | + (((data_a[ib_k].scales[8 + (is % 4)] >> (2 * (is / 4))) & 0x0303) << 4)); + return float(data_a[ib_k].d) * float(scale - 32); +} + +FLOAT_TYPE mmvq_dot_product(const uint ib_a, const uint iqs) { + int32_t q_sum = 0; + + const i32vec4 qs_a = repack4(ib_a, iqs * 4); + const float d_scale = get_d_scale(ib_a, iqs * 4); + + q_sum += dotPacked4x8EXT(qs_a.x, cache_b_qs[0]); + q_sum += dotPacked4x8EXT(qs_a.y, cache_b_qs[1]); + q_sum += dotPacked4x8EXT(qs_a.z, cache_b_qs[2]); + q_sum += dotPacked4x8EXT(qs_a.w, cache_b_qs[3]); + + return FLOAT_TYPE(float(cache_b_ds.x) * d_scale * float(q_sum)); +} +#endif + +#if defined(DATA_A_Q4_K) || defined(DATA_A_Q5_K) +// 4-byte loads for Q4_K blocks (144 bytes) and Q5_K blocks (176 bytes) +i32vec4 repack4(uint ib, uint iqs) { + const uint ib_k = ib / 8; + const uint iqs_k = (ib % 8) * 8 + iqs; + + const uint qs_idx = (iqs_k / 16) * 8 + (iqs_k % 8); + const uint qs_shift = ((iqs_k % 16) / 8) * 4; + +#if defined(DATA_A_Q4_K) + const uint32_t vals0 = (data_a_packed32[ib_k].qs[qs_idx ] >> qs_shift) & 0x0F0F0F0F; + const uint32_t vals1 = (data_a_packed32[ib_k].qs[qs_idx + 1] >> qs_shift) & 0x0F0F0F0F; + const uint32_t vals2 = (data_a_packed32[ib_k].qs[qs_idx + 2] >> qs_shift) & 0x0F0F0F0F; + const uint32_t vals3 = (data_a_packed32[ib_k].qs[qs_idx + 3] >> qs_shift) & 0x0F0F0F0F; + + return i32vec4(vals0, vals1, vals2, vals3); +#else // defined(DATA_A_Q5_K) + const uint qh_idx = iqs; + const uint qh_shift = iqs_k / 8; + + return i32vec4(((data_a_packed32[ib_k].qs[qs_idx ] >> qs_shift) & 0x0F0F0F0F) | + (((data_a_packed32[ib_k].qh[qh_idx ] >> qh_shift) & 0x01010101) << 4), + ((data_a_packed32[ib_k].qs[qs_idx + 1] >> qs_shift) & 0x0F0F0F0F) | + (((data_a_packed32[ib_k].qh[qh_idx + 1] >> qh_shift) & 0x01010101) << 4), + ((data_a_packed32[ib_k].qs[qs_idx + 2] >> qs_shift) & 0x0F0F0F0F) | + (((data_a_packed32[ib_k].qh[qh_idx + 2] >> qh_shift) & 0x01010101) << 4), + ((data_a_packed32[ib_k].qs[qs_idx + 3] >> qs_shift) & 0x0F0F0F0F) | + (((data_a_packed32[ib_k].qh[qh_idx + 3] >> qh_shift) & 0x01010101) << 4)); +#endif +} + +vec2 get_dm_scale(uint ib, uint iqs) { + const uint ib_k = ib / 8; + const uint iqs_k = (ib % 8) * 8 + iqs; + const uint is = iqs_k / 8; + + const uvec3 scales = uvec3(data_a_packed32[ib_k].scales[0], + data_a_packed32[ib_k].scales[1], + data_a_packed32[ib_k].scales[2]); + const uint scalesoffs = (is & 3) * 8; + + const uint scidx0 = (is < 4) ? 0 : 2; + const uint scidxshift0 = scalesoffs; + const uint scidxshift1 = (is < 4) ? scalesoffs : scalesoffs + 2; + const uint mbidx0 = (is < 4) ? 1 : 2; + const uint mbidxshift0 = (is < 4) ? scalesoffs : scalesoffs + 4; + const uint mbidxshift1 = (is < 4) ? scalesoffs : scalesoffs + 2; + + const uint8_t sc = uint8_t(((scales[scidx0] >> scidxshift0) & 0xF) | ((scales[0] >> scidxshift1) & 0x30)); + const uint8_t mbyte = uint8_t(((scales[mbidx0] >> mbidxshift0) & 0xF) | ((scales[1] >> mbidxshift1) & 0x30)); + u8vec2 scale_dm = u8vec2(sc, mbyte); + + return FLOAT_TYPEV2(data_a_packed32[ib_k].dm) * FLOAT_TYPEV2(scale_dm); +} + +FLOAT_TYPE mmvq_dot_product(const uint ib_a, const uint iqs) { + int32_t q_sum = 0; + + const i32vec4 qs_a = repack4(ib_a, iqs * 4); + const vec2 dm_scale = get_dm_scale(ib_a, iqs * 4); + + q_sum += dotPacked4x8EXT(qs_a.x, cache_b_qs[0]); + q_sum += dotPacked4x8EXT(qs_a.y, cache_b_qs[1]); + q_sum += dotPacked4x8EXT(qs_a.z, cache_b_qs[2]); + q_sum += dotPacked4x8EXT(qs_a.w, cache_b_qs[3]); + + return FLOAT_TYPE(float(cache_b_ds.x) * float(dm_scale.x) * float(q_sum) - float(dm_scale.y) * float(cache_b_ds.y / 2)); +} +#endif + +#if defined(DATA_A_Q6_K) +// 2-byte loads for Q6_K blocks (210 bytes) +i32vec4 repack4(uint ib, uint iqs) { + const uint ib_k = ib / 8; + const uint iqs_k = (ib % 8) * 8 + iqs; + + const uint ql_idx = (iqs_k / 32) * 16 + iqs_k % 16; + const uint ql_shift = ((iqs_k % 32) / 16) * 4; + + const uint qh_idx = (iqs_k / 32) * 8 + iqs; + const uint qh_shift = ((iqs_k % 32) / 8) * 2; + + const uvec4 ql = uvec4( uint32_t(data_a_packed16[ib_k].ql[ql_idx * 2 ]) | + (uint32_t(data_a_packed16[ib_k].ql[ql_idx * 2 + 1]) << 16), + uint32_t(data_a_packed16[ib_k].ql[ql_idx * 2 + 2]) | + (uint32_t(data_a_packed16[ib_k].ql[ql_idx * 2 + 3]) << 16), + uint32_t(data_a_packed16[ib_k].ql[ql_idx * 2 + 4]) | + (uint32_t(data_a_packed16[ib_k].ql[ql_idx * 2 + 5]) << 16), + uint32_t(data_a_packed16[ib_k].ql[ql_idx * 2 + 6]) | + (uint32_t(data_a_packed16[ib_k].ql[ql_idx * 2 + 7]) << 16)); + + const uvec4 qh = uvec4( uint32_t(data_a_packed16[ib_k].qh[qh_idx * 2 ]) | + (uint32_t(data_a_packed16[ib_k].qh[qh_idx * 2 + 1]) << 16), + uint32_t(data_a_packed16[ib_k].qh[qh_idx * 2 + 2]) | + (uint32_t(data_a_packed16[ib_k].qh[qh_idx * 2 + 3]) << 16), + uint32_t(data_a_packed16[ib_k].qh[qh_idx * 2 + 4]) | + (uint32_t(data_a_packed16[ib_k].qh[qh_idx * 2 + 5]) << 16), + uint32_t(data_a_packed16[ib_k].qh[qh_idx * 2 + 6]) | + (uint32_t(data_a_packed16[ib_k].qh[qh_idx * 2 + 7]) << 16)); + + const uint vals0 = (( ql.x >> ql_shift) & 0x0F0F0F0F) | + (((qh.x >> qh_shift) & 0x03030303) << 4); + const uint vals1 = (( ql.y >> ql_shift) & 0x0F0F0F0F) | + (((qh.y >> qh_shift) & 0x03030303) << 4); + const uint vals2 = (( ql.z >> ql_shift) & 0x0F0F0F0F) | + (((qh.z >> qh_shift) & 0x03030303) << 4); + const uint vals3 = (( ql.w >> ql_shift) & 0x0F0F0F0F) | + (((qh.w >> qh_shift) & 0x03030303) << 4); + + // Subtract 32 by twiddling bits rather than using re-packing as mesa + // compiles repacking poorly. + return i32vec4(int32_t(((vals0 ^ 0x80808080) - 0x20202020) ^ 0x80808080), + int32_t(((vals1 ^ 0x80808080) - 0x20202020) ^ 0x80808080), + int32_t(((vals2 ^ 0x80808080) - 0x20202020) ^ 0x80808080), + int32_t(((vals3 ^ 0x80808080) - 0x20202020) ^ 0x80808080)); +} + +float get_d_scale(uint ib, uint iqs) { + const uint ib_k = ib / 8; + const uint iqs_k = (ib % 8) * 8 + iqs; + return float(data_a[ib_k].d) * float(data_a[ib_k].scales[iqs_k / 4]); +} + +FLOAT_TYPE mmvq_dot_product(const uint ib_a, const uint iqs) { + int32_t q_sum = 0; + + const i32vec4 qs_a = repack4(ib_a, iqs * 4); + const float d_scale = get_d_scale(ib_a, iqs * 4); + + q_sum += dotPacked4x8EXT(qs_a.x, cache_b_qs[0]); + q_sum += dotPacked4x8EXT(qs_a.y, cache_b_qs[1]); + q_sum += dotPacked4x8EXT(qs_a.z, cache_b_qs[2]); + q_sum += dotPacked4x8EXT(qs_a.w, cache_b_qs[3]); + + return FLOAT_TYPE(float(cache_b_ds.x) * float(d_scale) * float(q_sum)); +} +#endif + +#if defined(DATA_A_IQ1_S) +void repack8(uint ib, uint iqs, out i32vec4 out0, out i32vec4 out1) { + const uint ib32 = iqs / 32; + + const uint qh = data_a[ib].qh[ib32]; + + const uint qs16_0 = data_a_packed16[ib].qs[(4 * ib32 + 0) / 2]; + const uint qs16_1 = data_a_packed16[ib].qs[(4 * ib32 + 2) / 2]; + + const uint qs0 = qs16_0 & 0xFF; + const uint qs1 = qs16_0 >> 8; + const uint qs2 = qs16_1 & 0xFF; + const uint qs3 = qs16_1 >> 8; + + const uint hi0 = bitfieldExtract(qh, 3 * int(0), 3); + const uint hi1 = bitfieldExtract(qh, 3 * int(1), 3); + const uint hi2 = bitfieldExtract(qh, 3 * int(2), 3); + const uint hi3 = bitfieldExtract(qh, 3 * int(3), 3); + + const int32_t grid0 = int32_t(iq1s_grid_gpu[qs0 | (hi0 << 8)]); + const int32_t grid1 = int32_t(iq1s_grid_gpu[qs1 | (hi1 << 8)]); + const int32_t grid2 = int32_t(iq1s_grid_gpu[qs2 | (hi2 << 8)]); + const int32_t grid3 = int32_t(iq1s_grid_gpu[qs3 | (hi3 << 8)]); + + out0 = i32vec4((grid0 >> 0) & 0x0F0F0F0F, + (grid0 >> 4) & 0x0F0F0F0F, + (grid1 >> 0) & 0x0F0F0F0F, + (grid1 >> 4) & 0x0F0F0F0F); + out1 = i32vec4((grid2 >> 0) & 0x0F0F0F0F, + (grid2 >> 4) & 0x0F0F0F0F, + (grid3 >> 0) & 0x0F0F0F0F, + (grid3 >> 4) & 0x0F0F0F0F); +} + +vec2 get_dm(uint ib, uint iqs) { + const uint ib32 = iqs / 32; + + const uint qh = data_a[ib].qh[ib32]; + const float delta = ((qh & 0x8000) != 0) ? -IQ1S_DELTA : IQ1S_DELTA; + + const float d = float(data_a[ib].d); + const float dl = d * float(2 * bitfieldExtract(qh, 12, 3) + 1); + + // the -1 cancels out the bias in iq1s_grid_gpu + return FLOAT_TYPEV2(dl, dl * (delta - 1)); +} + +FLOAT_TYPE mmvq_dot_product(const uint ib_a, const uint iqs) { + int32_t q_sum = 0; + + const uint ib_k = ib_a / 8; + const uint iqs_k = (ib_a % 8) * 32 + iqs * 32; + + i32vec4 qs_a0; + i32vec4 qs_a1; + repack8(ib_k, iqs_k, qs_a0, qs_a1); + + const vec2 dm = get_dm(ib_k, iqs_k); + + q_sum += dotPacked4x8EXT(qs_a0.x, cache_b_qs[0]); + q_sum += dotPacked4x8EXT(qs_a0.y, cache_b_qs[1]); + q_sum += dotPacked4x8EXT(qs_a0.z, cache_b_qs[2]); + q_sum += dotPacked4x8EXT(qs_a0.w, cache_b_qs[3]); + q_sum += dotPacked4x8EXT(qs_a1.x, cache_b_qs[4]); + q_sum += dotPacked4x8EXT(qs_a1.y, cache_b_qs[5]); + q_sum += dotPacked4x8EXT(qs_a1.z, cache_b_qs[6]); + q_sum += dotPacked4x8EXT(qs_a1.w, cache_b_qs[7]); + + return FLOAT_TYPE(float(cache_b_ds.x) * float(dm.x) * float(q_sum) + float(dm.y) * float(cache_b_ds.y)); +} +#endif + +#if defined(DATA_A_IQ1_M) +FLOAT_TYPE mmvq_dot_product(const uint ib_a, const uint iqs) { + const uint ib_k = ib_a / 8; + const uint iqs_k = (ib_a % 8) * 32 + iqs * 32; + + const uint ib32 = iqs_k / 32; + const uint ib64 = ib32 / 2; + + const uint16_t[4] scales = data_a[ib_k].scales; + const u16vec4 s = u16vec4(scales[0], scales[1], scales[2], scales[3]) >> 12; + const float d = float(unpackHalf2x16(s.x | (s.y << 4) | (s.z << 8) | (s.w << 12)).x); + + const uint qs32 = data_a_packed32[ib_k].qs[ib32]; + const uint qh16 = data_a_packed16[ib_k].qh[ib32]; + + float sum = 0; + const uint sc = data_a[ib_k].scales[ib64]; + [[unroll]] for (int l = 0; l < 4; ++l) { + const uint ib16 = 2 * ib32 + l / 2; + const float dl = d * (2 * bitfieldExtract(sc, 3 * int(ib16 & 3), 3) + 1); + const uint qh = qh16 >> (4 * l); + const uint qs = (qs32 >> (8 * l)) & 0xFF; + const float delta = ((qh & 8) != 0) ? -IQ1M_DELTA : IQ1M_DELTA; + + const int32_t grid = int32_t(iq1s_grid_gpu[qs | ((qh & 7) << 8)]); + + int32_t q_sum = 0; + q_sum += dotPacked4x8EXT((grid >> 0) & 0x0F0F0F0F, cache_b_qs[2 * l + 0]); + q_sum += dotPacked4x8EXT((grid >> 4) & 0x0F0F0F0F, cache_b_qs[2 * l + 1]); + + int32_t y_sum = 0; + y_sum += dotPacked4x8EXT(int(0x01010101), cache_b_qs[2 * l + 0]); + y_sum += dotPacked4x8EXT(int(0x01010101), cache_b_qs[2 * l + 1]); + + // the -1 cancels out the bias in iq1s_grid_gpu + sum += dl * (q_sum + y_sum * (delta - 1)); + } + sum *= float(cache_b_ds.x); + + return sum; +} +#endif diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm.comp new file mode 100644 index 0000000000000000000000000000000000000000..57c0410e455568497e04080f44e1bf69539d3927 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm.comp @@ -0,0 +1,466 @@ +#version 450 + +#extension GL_EXT_control_flow_attributes : enable +#extension GL_EXT_shader_16bit_storage : require + +#ifdef FLOAT16 +#extension GL_EXT_shader_explicit_arithmetic_types_float16 : require +#endif +#if defined(DATA_A_IQ1_M) +#extension GL_EXT_shader_explicit_arithmetic_types_int16 : require +#endif + +#if defined(DATA_A_BF16) && defined(COOPMAT) +#extension GL_EXT_bfloat16 : enable +#endif + +#ifdef COOPMAT +#extension GL_KHR_cooperative_matrix : enable +#extension GL_KHR_memory_scope_semantics : enable +#endif + +#if defined(COOPMAT) || defined(MUL_MAT_ID_USE_SUBGROUPS) +#extension GL_KHR_shader_subgroup_basic : enable +#extension GL_KHR_shader_subgroup_ballot : enable +#endif + +#ifdef MUL_MAT_ID +#extension GL_EXT_shader_explicit_arithmetic_types_int16 : require +#endif + +#include "types.glsl" +#include "dot_product_funcs.glsl" + +#ifndef LOAD_VEC_A +#define LOAD_VEC_A 1 +#endif +#ifndef LOAD_VEC_B +#define LOAD_VEC_B 1 +#endif + +layout (constant_id = 11) const uint ALIGNED = 0; + +#if !defined(TO_FLOAT_TYPE) +#define TO_FLOAT_TYPE FLOAT_TYPE +#endif + +layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; +#if defined(DATA_A_F32) +layout (binding = 0) readonly buffer A_SCALAR {float data_a_scalar[];}; +#elif defined(DATA_A_F16) +layout (binding = 0) readonly buffer A_SCALAR {float16_t data_a_scalar[];}; +#elif defined(DATA_A_BF16) +layout (binding = 0) readonly buffer A_SCALAR {uint16_t data_a_scalar[];}; +#endif +#if defined(A_TYPE_PACKED16) +layout (binding = 0) readonly buffer A_PACKED16 {A_TYPE_PACKED16 data_a_packed16[];}; +#endif +#if defined(A_TYPE_PACKED32) +layout (binding = 0) readonly buffer A_PACKED32 {A_TYPE_PACKED32 data_a_packed32[];}; +#endif + +layout (binding = 1) readonly buffer B {B_TYPE data_b[];}; +layout (binding = 1) readonly buffer B_SCALAR {B_TYPE_SCALAR data_b_scalar[];}; +layout (binding = 2) writeonly buffer D {D_TYPE data_d[];}; + +#ifdef MUL_MAT_ID +layout (binding = 3) readonly buffer IDS {int data_ids[];}; +layout (binding = 4) readonly buffer Counts {int data_expert_count[];}; +#endif + +layout (push_constant) uniform parameter +{ + uint M; + uint N; + uint K; + uint stride_a; + uint stride_b; + uint stride_d; + + uint batch_stride_a; + uint batch_stride_b; + uint batch_stride_d; + +#ifdef MUL_MAT_ID + uint nei0; + uint nei1; + uint nbi1; + uint ne11; +#else + uint base_work_group_z; + uint num_batches; + uint k_split; + uint ne02; + uint ne12; + uint broadcast2; + uint broadcast3; +#endif +} p; + +layout (constant_id = 0) const uint BLOCK_SIZE = 64; +layout (constant_id = 1) const uint BM = 64; +layout (constant_id = 2) const uint BN = 64; +layout (constant_id = 4) const uint WM = 32; +layout (constant_id = 5) const uint WN = 32; +layout (constant_id = 6) const uint WMITER = 2; +layout (constant_id = 7) const uint TM = 4; +layout (constant_id = 8) const uint TN = 2; +layout (constant_id = 9) const uint TK = 1; // Only needed for coopmat +layout (constant_id = 10) const uint WARP = 32; + +#if defined(DATA_A_F32) || defined(DATA_A_F16) +#define BK 32 +#define BK_STEP 4 +#else +layout (constant_id = 3) const uint BK = 16; // Assumed to be 32 if working with a quant +#define BK_STEP 2 +#endif + +#ifdef COOPMAT +#define SHMEM_STRIDE (BK / 2 + 4) +#else +#define SHMEM_STRIDE (BK / 2 + 1) +#endif + +shared FLOAT_TYPEV2 buf_a[BM * SHMEM_STRIDE]; +shared FLOAT_TYPEV2 buf_b[BN * SHMEM_STRIDE]; + +#define NUM_WARPS (BLOCK_SIZE / WARP) + +#ifdef COOPMAT +shared ACC_TYPE coopmat_stage[TM * TN * NUM_WARPS]; +#endif + +#include "mul_mm_id_funcs.glsl" +#include "mul_mm_funcs.glsl" + +void main() { + const uint ic = gl_WorkGroupID.y; + +#ifdef MUL_MAT_ID + const uint expert_idx = gl_WorkGroupID.z; + if (ic * BN >= data_expert_count[expert_idx]) { + return; + } +#endif +#ifdef NEEDS_INIT_IQ_SHMEM + init_iq_shmem(gl_WorkGroupSize); +#endif + +#ifndef MUL_MAT_ID + const uint batch_idx = gl_WorkGroupID.z + p.base_work_group_z; + + const uint i13 = batch_idx / p.ne12; + const uint i12 = batch_idx % p.ne12; + + const uint i03 = i13 / p.broadcast3; + const uint i02 = i12 / p.broadcast2; + + const uint batch_idx_a = i03 * p.ne02 + i02; +#endif + + const uint blocks_m = (p.M + BM - 1) / BM; + const uint ir = gl_WorkGroupID.x % blocks_m; + const uint ik = gl_WorkGroupID.x / blocks_m; + + const uint WNITER = (WM * WN) / (WARP * TM * TN * WMITER); + const uint WSUBM = WM / WMITER; + const uint WSUBN = WN / WNITER; + +#ifdef COOPMAT + const uint warp_i = gl_SubgroupID; + + const uint tiw = gl_SubgroupInvocationID; + + const uint cms_per_row = WM / TM; + const uint cms_per_col = WN / TN; + + const uint storestride = WARP / TM; + const uint store_r = tiw % TM; + const uint store_c = tiw / TM; +#else + const uint warp_i = gl_LocalInvocationID.x / WARP; + + const uint tiw = gl_LocalInvocationID.x % WARP; + + const uint tiwr = tiw % (WSUBM / TM); + const uint tiwc = tiw / (WSUBM / TM); +#endif + + const uint warp_r = warp_i % (BM / WM); + const uint warp_c = warp_i / (BM / WM); + +#if defined(DATA_A_F32) || defined(DATA_A_F16) || defined(DATA_A_BF16) + const uint LOAD_VEC_A_EFF = (ALIGNED != 0) ? LOAD_VEC_A : 1; + const uint LOAD_VEC_BATCH_A = (ALIGNED != 0) ? 1 : 2; +#else + const uint LOAD_VEC_A_EFF = LOAD_VEC_A; + const uint LOAD_VEC_BATCH_A = 1; +#endif + const uint LOAD_VEC_B_EFF = (ALIGNED != 0) ? LOAD_VEC_B : 1; + const uint LOAD_VEC_BATCH_B = (ALIGNED != 0) ? 1 : 2; + + const uint loadr_a = gl_LocalInvocationID.x % (BK / LOAD_VEC_A_EFF / LOAD_VEC_BATCH_A); + const uint loadc_a = gl_LocalInvocationID.x / (BK / LOAD_VEC_A_EFF / LOAD_VEC_BATCH_A); + const uint loadr_b = gl_LocalInvocationID.x % (BK / LOAD_VEC_B_EFF / LOAD_VEC_BATCH_B); + const uint loadc_b = gl_LocalInvocationID.x / (BK / LOAD_VEC_B_EFF / LOAD_VEC_BATCH_B); + + const uint loadstride_a = gl_WorkGroupSize.x * LOAD_VEC_A_EFF * LOAD_VEC_BATCH_A / BK; + const uint loadstride_b = gl_WorkGroupSize.x * LOAD_VEC_B_EFF * LOAD_VEC_BATCH_B / BK; + +#ifdef MUL_MAT_ID +#ifdef MUL_MAT_ID_USE_SUBGROUPS + if (bitCount(p.nei0) == 1) { + load_row_ids(expert_idx, true, ic); + } else { + load_row_ids(expert_idx, false, ic); + } +#else + _ne1 = 0; + for (uint ii1 = 0; ii1 < p.nei1 && _ne1 < (ic + 1) * BN; ii1++) { + for (uint ii0 = 0; ii0 < p.nei0 && _ne1 < (ic + 1) * BN; ii0++) { + if (data_ids[ii1*p.nbi1 + ii0] == expert_idx) { + if (_ne1 >= ic * BN) { + row_ids[_ne1 - ic * BN] = u16vec2(ii0, ii1); + } + _ne1++; + } + } + } + + barrier(); +#endif + + // Workgroup has no work + if (ic * BN >= _ne1) return; +#endif + +#ifdef MUL_MAT_ID + const uint start_k = 0; + const uint end_k = p.K; +#else + const uint start_k = ik * p.k_split; + const uint end_k = min(p.K, (ik + 1) * p.k_split); +#endif + + uint pos_a = +#ifdef MUL_MAT_ID + expert_idx * (p.batch_stride_a / LOAD_VEC_A_EFF) + +#else + batch_idx_a * (p.batch_stride_a / LOAD_VEC_A_EFF) + +#endif + (ir * BM * p.stride_a + start_k) / LOAD_VEC_A_EFF; +#ifdef MUL_MAT_ID + uint pos_b = 0; +#else + uint pos_b = (batch_idx * p.batch_stride_b + ic * BN * p.stride_b + start_k) / LOAD_VEC_B_EFF; +#endif + +#ifdef COOPMAT + coopmat cache_a; + coopmat cache_b; + coopmat sums[cms_per_row * cms_per_col]; + + [[unroll]] for (uint i = 0; i < cms_per_row * cms_per_col; i++) { + sums[i] = coopmat(0.0f); + } +#else + ACC_TYPEV2 sums[WMITER * TM * WNITER * TN/2]; +#if defined(DATA_A_F32) || defined(DATA_A_F16) + FLOAT_TYPEV4 cache_a[WMITER * TM]; + FLOAT_TYPEV4 cache_b; +#else + FLOAT_TYPEV2 cache_a[WMITER * TM]; + FLOAT_TYPEV2 cache_b; +#endif + + [[unroll]] for (uint i = 0; i < WMITER*TM*WNITER*TN/2; i++) { + sums[i] = ACC_TYPEV2(0.0f, 0.0f); + } +#endif + + for (uint block = start_k; block < end_k; block += BK) { + [[unroll]] for (uint l = 0; l < BM; l += loadstride_a) { + load_a_to_shmem(pos_a, loadr_a, loadc_a + l, ir * BM + loadc_a + l, block, end_k); + } + [[unroll]] for (uint l = 0; l < BN; l += loadstride_b) { +#if !defined(MUL_MAT_ID) + load_b_to_shmem(pos_b, loadr_b, loadc_b + l, ic * BN + loadc_b + l, block, end_k); +#else + load_b_to_shmem(pos_b, loadr_b, loadc_b + l, ic, _ne1, block, end_k); +#endif + } + + barrier(); + + pos_a += BK / LOAD_VEC_A_EFF; + pos_b += BK / LOAD_VEC_B_EFF; + +#ifdef COOPMAT + [[unroll]] for (uint i = 0; i < BK; i += TK) { + [[unroll]] for (uint cm_row = 0; cm_row < cms_per_row; cm_row++) { + // Load from shared into cache + coopMatLoad(cache_a, buf_a, (warp_r * WM + cm_row * TM) * SHMEM_STRIDE + i / 2, SHMEM_STRIDE, gl_CooperativeMatrixLayoutRowMajor); + + [[unroll]] for (uint cm_col = 0; cm_col < cms_per_col; cm_col++) { + coopMatLoad(cache_b, buf_b, (warp_c * WN + cm_col * TN) * SHMEM_STRIDE + i / 2, SHMEM_STRIDE, gl_CooperativeMatrixLayoutColumnMajor); + + sums[cm_col * cms_per_row + cm_row] = coopMatMulAdd(cache_a, cache_b, sums[cm_col * cms_per_row + cm_row]); + } + } + } +#else + [[unroll]] for (uint i = 0; i < BK / BK_STEP; i++) { + // Load from shared into cache + [[unroll]] for (uint wsir = 0; wsir < WMITER; wsir++) { + [[unroll]] for (uint j = 0; j < TM; j++) { + #if defined(DATA_A_F32) || defined(DATA_A_F16) + cache_a[wsir * TM + j].xy = buf_a[(warp_r * WM + wsir * WSUBM + tiwr * TM + j) * SHMEM_STRIDE + 2 * i ]; + cache_a[wsir * TM + j].zw = buf_a[(warp_r * WM + wsir * WSUBM + tiwr * TM + j) * SHMEM_STRIDE + 2 * i + 1]; + #else + cache_a[wsir * TM + j] = buf_a[(warp_r * WM + wsir * WSUBM + tiwr * TM + j) * SHMEM_STRIDE + i]; + #endif + } + } + + [[unroll]] for (uint wsic = 0; wsic < WNITER; wsic++) { + [[unroll]] for (uint cc = 0; cc < TN; cc++) { + #if defined(DATA_A_F32) || defined(DATA_A_F16) + cache_b.xy = buf_b[(warp_c * WN + wsic * WSUBN + tiwc * TN + cc) * SHMEM_STRIDE + 2 * i ]; + cache_b.zw = buf_b[(warp_c * WN + wsic * WSUBN + tiwc * TN + cc) * SHMEM_STRIDE + 2 * i + 1]; + #else + cache_b = buf_b[(warp_c * WN + wsic * WSUBN + tiwc * TN + cc) * SHMEM_STRIDE + i]; + #endif + + [[unroll]] for (uint wsir = 0; wsir < WMITER; wsir++) { + [[unroll]] for (uint cr = 0; cr < TM / 2; cr++) { + // [WNITER][TN][WMITER][TM / 2] -> [wsic][cc][wsir][cr] + const uint sums_idx = (wsic * TN + cc) * WMITER * (TM / 2) + wsir * (TM / 2) + cr; + sums[sums_idx].x = dot_product(cache_a[wsir * TM + 2 * cr ], cache_b, sums[sums_idx].x); + sums[sums_idx].y = dot_product(cache_a[wsir * TM + 2 * cr + 1], cache_b, sums[sums_idx].y); + } + } + } + } + + } +#endif + + barrier(); + } + +#if defined(ACC_TYPE_MAX) +#ifdef COOPMAT + [[unroll]] for (uint j = 0; j < cms_per_row * cms_per_col; j++) { + [[unroll]] for (uint i = 0; i < sums[j].length(); ++i) { + sums[j][i] = clamp(sums[j][i], -ACC_TYPE_MAX, ACC_TYPE_MAX); + } + } +#else + [[unroll]] for (uint i = 0; i < WMITER*TM*WNITER*TN/2; i++) { + sums[i].x = clamp(sums[i].x, -ACC_TYPE_MAX, ACC_TYPE_MAX); + sums[i].y = clamp(sums[i].y, -ACC_TYPE_MAX, ACC_TYPE_MAX); + } +#endif +#endif + + const uint dr = ir * BM + warp_r * WM; + const uint dc = ic * BN + warp_c * WN; + +#ifndef MUL_MAT_ID + const uint offsets = batch_idx * p.batch_stride_d + ik * p.batch_stride_d * p.num_batches; +#endif + +#ifdef COOPMAT +#ifdef MUL_MAT_ID + [[unroll]] for (uint cm_row = 0; cm_row < cms_per_row; cm_row++) { + [[unroll]] for (uint cm_col = 0; cm_col < cms_per_col; cm_col++) { + coopMatStore(sums[cm_col * cms_per_row + cm_row], coopmat_stage, warp_i * TM * TN, TM, gl_CooperativeMatrixLayoutColumnMajor); + + barrier(); + [[unroll]] for (uint col = 0; col < TN; col += storestride) { + const uint row_i = dc + cm_col * TN + col + store_c; + if (row_i >= _ne1) break; + + const u16vec2 row_idx = row_ids[row_i - ic * BN]; + + if (dr + cm_row * TM + store_r < p.M) { + data_d[row_idx.y * p.batch_stride_d + row_idx.x * p.stride_d + dr + cm_row * TM + store_r] = D_TYPE(coopmat_stage[warp_i * TM * TN + (col + store_c) * TM + store_r]); + } + } + barrier(); + } + } +#else + const bool is_aligned = p.stride_d % 4 == 0; // Assumption: D_TYPE == float + + [[unroll]] for (uint cm_row = 0; cm_row < cms_per_row; cm_row++) { + [[unroll]] for (uint cm_col = 0; cm_col < cms_per_col; cm_col++) { + const bool is_in_bounds = dr + (cm_row + 1) * TM <= p.M && dc + (cm_col + 1) * TN <= p.N; + + if (is_aligned && is_in_bounds) { + // Full coopMat is within bounds and stride_d is aligned with 16B + coopmat cm_dtype = coopmat(sums[cm_col * cms_per_row + cm_row]); + coopMatStore(cm_dtype, data_d, offsets + (dc + cm_col * TN) * p.stride_d + dr + cm_row * TM, p.stride_d, gl_CooperativeMatrixLayoutColumnMajor); + } else if (is_in_bounds) { + // Full coopMat is within bounds, but stride_d is not aligned + coopMatStore(sums[cm_col * cms_per_row + cm_row], coopmat_stage, warp_i * TM * TN, TM, gl_CooperativeMatrixLayoutColumnMajor); + + controlBarrier(gl_ScopeSubgroup, gl_ScopeSubgroup, gl_StorageSemanticsShared, gl_SemanticsAcquireRelease); + [[unroll]] for (uint col = 0; col < TN; col += storestride) { + data_d[offsets + (dc + cm_col * TN + col + store_c) * p.stride_d + dr + cm_row * TM + store_r] = D_TYPE(coopmat_stage[warp_i * TM * TN + (col + store_c) * TM + store_r]); + } + controlBarrier(gl_ScopeSubgroup, gl_ScopeSubgroup, gl_StorageSemanticsShared, gl_SemanticsAcquireRelease); + } else if (dr + cm_row * TM < p.M && dc + cm_col * TN < p.N) { + // Partial coopMat is within bounds + coopMatStore(sums[cm_col * cms_per_row + cm_row], coopmat_stage, warp_i * TM * TN, TM, gl_CooperativeMatrixLayoutColumnMajor); + + controlBarrier(gl_ScopeSubgroup, gl_ScopeSubgroup, gl_StorageSemanticsShared, gl_SemanticsAcquireRelease); + [[unroll]] for (uint col = 0; col < TN; col += storestride) { + if (dr + cm_row * TM + store_r < p.M && dc + cm_col * TN + col + store_c < p.N) { + data_d[offsets + (dc + cm_col * TN + col + store_c) * p.stride_d + dr + cm_row * TM + store_r] = D_TYPE(coopmat_stage[warp_i * TM * TN + (col + store_c) * TM + store_r]); + } + } + controlBarrier(gl_ScopeSubgroup, gl_ScopeSubgroup, gl_StorageSemanticsShared, gl_SemanticsAcquireRelease); + } + } + } +#endif // MUL_MAT_ID +#else + [[unroll]] for (uint wsic = 0; wsic < WNITER; wsic++) { + [[unroll]] for (uint wsir = 0; wsir < WMITER; wsir++) { + + const uint dr_warp = dr + wsir * WSUBM + tiwr * TM; + const uint dc_warp = dc + wsic * WSUBN + tiwc * TN; + [[unroll]] for (uint cc = 0; cc < TN; cc++) { +#ifdef MUL_MAT_ID + const uint row_i = dc_warp + cc; + if (row_i >= _ne1) break; + + const u16vec2 row_idx = row_ids[row_i - ic * BN]; +#endif // MUL_MAT_ID + [[unroll]] for (uint cr = 0; cr < TM / 2; cr++) { + const uint sums_idx = (wsic * TN + cc) * WMITER * (TM / 2) + wsir * (TM / 2) + cr; +#ifdef MUL_MAT_ID + if (dr_warp + 2 * cr < p.M) { + data_d[row_idx.y * p.batch_stride_d + row_idx.x * p.stride_d + dr_warp + 2 * cr] = D_TYPE(sums[sums_idx].x); + } + if (dr_warp + 2 * cr + 1 < p.M) { + data_d[row_idx.y * p.batch_stride_d + row_idx.x * p.stride_d + dr_warp + 2 * cr + 1] = D_TYPE(sums[sums_idx].y); + } +#else + if (dr_warp + 2 * cr < p.M && dc_warp + cc < p.N) { + data_d[offsets + (dc_warp + cc) * p.stride_d + dr_warp + 2 * cr] = D_TYPE(sums[sums_idx].x); + } + if (dr_warp + 2 * cr + 1 < p.M && dc_warp + cc < p.N) { + data_d[offsets + (dc_warp + cc) * p.stride_d + dr_warp + 2 * cr + 1] = D_TYPE(sums[sums_idx].y); + } +#endif // MUL_MAT_ID + } + } + } + } +#endif // COOPMAT +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_cm2.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_cm2.comp new file mode 100644 index 0000000000000000000000000000000000000000..a2e15f6f5cede930f61caa6e4d4bf76f7ca77fe8 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_cm2.comp @@ -0,0 +1,658 @@ +#version 450 + +#extension GL_EXT_control_flow_attributes : enable +#extension GL_EXT_shader_16bit_storage : require + +#extension GL_EXT_shader_explicit_arithmetic_types_float16 : require +#extension GL_EXT_shader_explicit_arithmetic_types_int8 : require +#extension GL_EXT_shader_explicit_arithmetic_types_int32 : require +#extension GL_EXT_shader_explicit_arithmetic_types_int16 : require + +#extension GL_KHR_memory_scope_semantics : enable +#extension GL_KHR_cooperative_matrix : enable +#extension GL_NV_cooperative_matrix2 : enable +#ifdef GGML_VULKAN_COOPMAT2_DECODE_VECTOR +#extension GL_NV_cooperative_matrix_decode_vector : enable +#endif +#extension GL_EXT_buffer_reference : enable +#extension GL_KHR_shader_subgroup_ballot : enable +#extension GL_KHR_shader_subgroup_vote : enable +#ifdef DATA_A_BF16 +#extension GL_EXT_bfloat16 : enable +#endif + +#include "types.glsl" +#include "utils.glsl" + +layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; + +#define IS_MUL_MM2 1 + +layout (constant_id = 0) const uint BLOCK_SIZE = 256; +layout (constant_id = 1) const uint BM = 64; +layout (constant_id = 2) const uint BN = 64; +layout (constant_id = 3) const uint BK = 16; // Assumed to be 32 if working with a quant + +layout (constant_id = 4) const bool enable_smaller_matrices = false; +const uint BNover2 = enable_smaller_matrices ? (BN / 2) : BN; +const uint BNover4 = enable_smaller_matrices ? (BN / 4) : BN; +layout (constant_id = 5) const uint ALIGNED = 0; + +layout (push_constant) uniform parameter +{ + uint M; + uint N; + uint K; + uint stride_a; + uint stride_b; + uint stride_d; + + uint batch_stride_a; + uint batch_stride_b; + uint batch_stride_d; + +#ifdef MUL_MAT_ID + uint nei0; + uint nei1; + uint nbi1; + uint ne11; +#else + uint base_work_group_z; + uint num_batches; + uint k_split; + uint ne02; + uint ne12; + uint broadcast2; + uint broadcast3; +#endif + // N dimension for the B matrix can be >= p.N + uint padded_N; +} p; + + +layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; +layout (binding = 1) readonly buffer B {B_TYPE data_b[];}; +layout (binding = 2) writeonly buffer D {D_TYPE data_d[];}; +#if defined(MUL_MAT_ID) && defined(GGML_VULKAN_COOPMAT2_DECODE_VECTOR) +layout (binding = 1) readonly buffer B4 {B_TYPEV4 data_b_v4[];}; +#endif + +#if QUANT_K > 1 +#include "dequant_funcs_cm2.glsl" +#if defined(dequantFuncA_v) && defined(GGML_VULKAN_COOPMAT2_DECODE_VECTOR) +#define DECODEFUNCA , dequantFuncA, dequantFuncA_v +#else +#define DECODEFUNCA , dequantFuncA +#endif +#else +#define DECODEFUNCA +#endif + +#if !defined(fetch_scales) +#define fetch_scales(a, b, c, d, e, f) +#endif +#if !defined(store_scales) +#define store_scales(a) +#endif + +#if defined(DATA_A_BF16) +#define MAT_TYPE bfloat16_t +#else +#define MAT_TYPE FLOAT_TYPE +#endif + +#ifdef MUL_MAT_ID +layout (binding = 3) readonly buffer IDS {int data_ids[];}; +layout (binding = 4) readonly buffer Counts {int data_expert_count[];}; + +shared u16vec4 row_ids[BN]; + +layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufB { + B_TYPE b[]; +}; + +uint _ne1; +layout (constant_id = 6) const uint subgroup_size = 32; +shared uvec4 ballots_sh[BLOCK_SIZE / subgroup_size]; + +B_TYPE decodeFuncB(const in decodeBufB bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + const uint row_i = blockCoords[0]; + + const u16vec4 row_idx = row_ids[row_i]; +#if defined(GGML_VULKAN_COOPMAT2_DECODE_VECTOR) + // The decode-vector path gives B a K-dimension tensor-layout block size of BK. + const uint k = blockCoords[1] * BK + coordInBlock[1]; +#else + const uint k = blockCoords[1]; +#endif + B_TYPE ret = data_b[row_idx.y * p.batch_stride_b + row_idx.x * p.stride_b + k]; + + return ret; +} + +#if defined(GGML_VULKAN_COOPMAT2_DECODE_VECTOR) +B_TYPEV4 decodeFuncB_v(const in decodeBufB bl, const in uint blockCoords[2], const in uint coordInBlock[2]) +{ + const uint row_i = blockCoords[0]; + + const u16vec4 row_idx = row_ids[row_i]; + const uint k = blockCoords[1] * BK + coordInBlock[1]; + const uint base = row_idx.y * p.batch_stride_b + row_idx.x * p.stride_b + k; + + return data_b_v4[base >> 2]; +} +#define DECODEFUNCB , decodeFuncB, decodeFuncB_v +#else +#define DECODEFUNCB , decodeFuncB +#endif + +D_TYPE perElemOpD(const in uint32_t r, const in uint32_t c, const in D_TYPE elem, const in uint32_t ir, const in uint32_t ic) +{ + uint dr = ir * BM + r; + uint dc = ic * BN + c; + + if (dr < p.M && dc < _ne1) { + uint row_i = c; + const u16vec4 row_idx = row_ids[row_i]; + data_d[row_idx.y * p.batch_stride_d + row_idx.z * p.stride_d + dr] = elem; + } + return elem; +} + +void load_row_ids(uint expert_idx, bool nei0_is_pow2, uint ic) { + _ne1 = 0; + uint num_elements = p.nei1 * p.nei0; + uint nei0shift = findLSB(p.nei0); + + uint ids[16]; + uint iter = 0; + + uint expert_count = data_expert_count[expert_idx]; + + for (uint j = 0; j < num_elements; j += BLOCK_SIZE) { + // prefetch up to 16 elements + if (iter == 0) { + [[unroll]] for (uint k = 0; k < 16; ++k) { + uint i = j + gl_LocalInvocationIndex + k*BLOCK_SIZE; + bool in_range = i < num_elements; + uint ii1; + if (nei0_is_pow2) { + ii1 = i >> nei0shift; + } else { + ii1 = i / p.nei0; + } + uint ii0 = i - ii1 * p.nei0; + ids[k] = in_range ? data_ids[ii1*p.nbi1 + ii0] : 0; + } + } + uint i = j + gl_LocalInvocationIndex; + bool in_range = i < num_elements; + uint ii1; + if (nei0_is_pow2) { + ii1 = i >> nei0shift; + } else { + ii1 = i / p.nei0; + } + uint ii0 = i - ii1 * p.nei0; + uint id = ids[iter++]; + uvec4 ballot = subgroupBallot(in_range && id == expert_idx); + + if (gl_SubgroupInvocationID == 0) { + ballots_sh[gl_SubgroupID] = ballot; + } + barrier(); + + uint subgroup_base = 0; + uint total = 0; + for (uint k = 0; k < gl_NumSubgroups; ++k) { + if (k == gl_SubgroupID) { + subgroup_base = total; + } + total += subgroupBallotBitCount(ballots_sh[k]); + } + barrier(); + + uint idx = subgroup_base + subgroupBallotExclusiveBitCount(ballot); + if (in_range && id == expert_idx && _ne1 + idx >= ic * BN && _ne1 + idx < (ic + 1) * BN) { + row_ids[_ne1 + idx - ic * BN] = u16vec4(fastmod(ii0, p.ne11), ii1, ii0, 0); + } + _ne1 += total; + iter &= 15; + if (_ne1 >= (ic + 1) * BN || _ne1 == expert_count) { + break; + } + } + barrier(); +} +#endif + +void main() { + const uint tid = gl_LocalInvocationIndex; + const uint ic = gl_WorkGroupID.y; + +#ifdef MUL_MAT_ID + const uint expert_idx = gl_WorkGroupID.z; + if (ic * BN >= data_expert_count[expert_idx]) { + return; + } + // initialize to row 0 so we don't need to bounds check + if (tid < BN) { + row_ids[tid] = u16vec4(0); + } +#if !defined(NEEDS_INIT_IQ_SHMEM) + barrier(); +#endif +#endif + +#ifdef NEEDS_INIT_IQ_SHMEM + init_iq_shmem(gl_WorkGroupSize); +#endif + +#ifndef MUL_MAT_ID + const uint batch_idx = gl_WorkGroupID.z + p.base_work_group_z; + + const uint i13 = batch_idx / p.ne12; + const uint i12 = batch_idx % p.ne12; + + const uint i03 = i13 / p.broadcast3; + const uint i02 = i12 / p.broadcast2; + + const uint batch_idx_a = i03 * p.ne02 + i02; +#endif + + const uint blocks_m = (p.M + BM - 1) / BM; + const uint ir = gl_WorkGroupID.x % blocks_m; + const uint ik = gl_WorkGroupID.x / blocks_m; + +#ifdef MUL_MAT_ID + if (bitCount(p.nei0) == 1) { + load_row_ids(expert_idx, true, ic); + } else { + load_row_ids(expert_idx, false, ic); + } + + // Workgroup has no work + if (ic * BN >= _ne1) return; +#endif + +#ifdef MUL_MAT_ID + uint start_k = 0; + const uint end_k = p.K; +#else + uint start_k = ik * p.k_split; + const uint end_k = min(p.K, (ik + 1) * p.k_split); +#endif + +#ifdef MUL_MAT_ID + uint pos_a = expert_idx * (p.batch_stride_a / QUANT_K); + uint pos_b = 0; +#else + uint pos_a = batch_idx_a * (p.batch_stride_a / QUANT_K); + uint pos_b = batch_idx * p.batch_stride_b; + uint pos_d = batch_idx * p.batch_stride_d + ik * p.batch_stride_d * p.num_batches; +#endif + + uint stride_a = p.stride_a / QUANT_K; + uint stride_b = p.stride_b; + + // Hint to the compiler that values are aligned (want 16B alignment). + // Quants are always block-aligned, no alignment needed. + if (ALIGNED != 0) { +#if QUANT_K == 1 + stride_a &= ~7; +#endif + stride_b &= ~7; + } + + // Create layouts for both clamped and unclamped accesses + tensorLayoutNV<2> tensorLayoutA = createTensorLayoutNV(2); + tensorLayoutNV<2, gl_CooperativeMatrixClampModeConstantNV> tensorLayoutAClamp = createTensorLayoutNV(2, gl_CooperativeMatrixClampModeConstantNV); + tensorLayoutNV<2> tensorLayoutB = createTensorLayoutNV(2); + tensorLayoutNV<2, gl_CooperativeMatrixClampModeConstantNV> tensorLayoutBClamp = createTensorLayoutNV(2, gl_CooperativeMatrixClampModeConstantNV); + tensorLayoutNV<2, gl_CooperativeMatrixClampModeConstantNV> tensorLayoutD = createTensorLayoutNV(2, gl_CooperativeMatrixClampModeConstantNV); + +#if QUANT_K > 1 + tensorLayoutA = setTensorLayoutBlockSizeNV(tensorLayoutA, 1, QUANT_K); + tensorLayoutAClamp = setTensorLayoutBlockSizeNV(tensorLayoutAClamp, 1, QUANT_K); +#endif +#if defined(MUL_MAT_ID) && defined(GGML_VULKAN_COOPMAT2_DECODE_VECTOR) + tensorLayoutB = setTensorLayoutBlockSizeNV(tensorLayoutB, 1, BK); +#endif + + // Use end_k rather than p.K as the dimension because that's what + // we need to bound check against when using split_k. + // Bounds check B against padded_N, but bounds check D against N. + tensorLayoutA = setTensorLayoutDimensionNV(tensorLayoutA, p.M, end_k); + tensorLayoutB = setTensorLayoutDimensionNV(tensorLayoutB, p.padded_N, end_k); + tensorLayoutD = setTensorLayoutDimensionNV(tensorLayoutD, p.N, p.M); + tensorLayoutAClamp = setTensorLayoutDimensionNV(tensorLayoutAClamp, p.M, end_k); + tensorLayoutBClamp = setTensorLayoutDimensionNV(tensorLayoutBClamp, p.padded_N, end_k); + + tensorLayoutD = setTensorLayoutStrideNV(tensorLayoutD, p.stride_d, 1); + + tensorViewNV<2, false, 1, 0> tensorViewTranspose = createTensorViewNV(2, false, 1, 0); + +#if !defined(MUL_MAT_ID) + + const uint START_ALIGN_K = 256; + // For Qi_K (block size 256), unroll whole 256 element tiles. + // For legacy quants (block size 32), unroll 8x. + const uint UNROLL_K = (QUANT_K == 256) ? 256 : (BK * 8); + const uint unroll_count = UNROLL_K / BK; + + // Detect a fast path where all loads are entirely in bounds and no clamping is required + if ((ir + 1) * BM <= p.M && (ic + 1) * BN <= p.padded_N && (start_k % START_ALIGN_K) == 0 && (end_k % BK) == 0 && +#if QUANT_K == 1 + (stride_a % 8) == 0 && +#endif + (stride_b % 8) == 0) { + // Hint to the compiler that values are aligned (want 16B alignment) + start_k &= ~(START_ALIGN_K-1); + stride_b &= ~7; +#if QUANT_K == 1 + stride_a &= ~7; +#endif + + tensorLayoutA = setTensorLayoutStrideNV(tensorLayoutA, stride_a, 1); + tensorLayoutB = setTensorLayoutStrideNV(tensorLayoutB, stride_b, 1); + + uint k_iters = (end_k - start_k) / UNROLL_K; + uint block_k = start_k; + + // fetch scale values for a tile of quants. These will be copied into shared memory. + // The fetches and stores are pipelined to hide the latency. + fetch_scales(ir * BM, pos_a, stride_a, start_k, tid, true); + + if (enable_smaller_matrices && ic * BN + BNover4 >= p.N) { + coopmat sum = coopmat(0.0); + for (uint i = 0; i < k_iters; ++i) { + + store_scales(tid); + if (block_k + UNROLL_K < end_k) { + fetch_scales(ir * BM, pos_a, stride_a, block_k + UNROLL_K, tid, true); + } + + // Manually partial unroll + [[unroll]] for (uint j = 0; j < unroll_count; ++j) { + coopmat mat_a; + coopmat mat_b; + + coopMatLoadTensorNV(mat_a, data_a, pos_a, sliceTensorLayoutNV(tensorLayoutA, ir * BM, BM, block_k, BK) DECODEFUNCA); + coopMatLoadTensorNV(mat_b, data_b, pos_b, sliceTensorLayoutNV(tensorLayoutB, ic * BN, BNover4, block_k, BK), tensorViewTranspose); + + sum = coopMatMulAdd(mat_a, mat_b, sum); + block_k += BK; + } + } + // Do any remaining iterations that were not unrolled + if (block_k < end_k) { + store_scales(tid); + } + while (block_k < end_k) { + coopmat mat_a; + coopmat mat_b; + + coopMatLoadTensorNV(mat_a, data_a, pos_a, sliceTensorLayoutNV(tensorLayoutA, ir * BM, BM, block_k, BK) DECODEFUNCA); + coopMatLoadTensorNV(mat_b, data_b, pos_b, sliceTensorLayoutNV(tensorLayoutB, ic * BN, BNover4, block_k, BK), tensorViewTranspose); + + sum = coopMatMulAdd(mat_a, mat_b, sum); + block_k += BK; + } +#if defined(ACC_TYPE_MAX) + [[unroll]] for (uint i = 0; i < sum.length(); ++i) { sum[i] = clamp(sum[i], -ACC_TYPE_MAX, ACC_TYPE_MAX); } +#endif + + coopmat mat_d = coopmat(sum); + + coopMatStoreTensorNV(mat_d, data_d, pos_d, sliceTensorLayoutNV(tensorLayoutD, ic * BN, BNover4, ir * BM, BM), tensorViewTranspose); + return; + } else if (enable_smaller_matrices && ic * BN + BNover2 >= p.N) { + coopmat sum = coopmat(0.0); + for (uint i = 0; i < k_iters; ++i) { + + store_scales(tid); + if (block_k + UNROLL_K < end_k) { + fetch_scales(ir * BM, pos_a, stride_a, block_k + UNROLL_K, tid, true); + } + + // Manually partial unroll + [[unroll]] for (uint j = 0; j < unroll_count; ++j) { + coopmat mat_a; + coopmat mat_b; + + coopMatLoadTensorNV(mat_a, data_a, pos_a, sliceTensorLayoutNV(tensorLayoutA, ir * BM, BM, block_k, BK) DECODEFUNCA); + coopMatLoadTensorNV(mat_b, data_b, pos_b, sliceTensorLayoutNV(tensorLayoutB, ic * BN, BNover2, block_k, BK), tensorViewTranspose); + + sum = coopMatMulAdd(mat_a, mat_b, sum); + block_k += BK; + } + } + // Do any remaining iterations that were not unrolled + if (block_k < end_k) { + store_scales(tid); + } + while (block_k < end_k) { + coopmat mat_a; + coopmat mat_b; + + coopMatLoadTensorNV(mat_a, data_a, pos_a, sliceTensorLayoutNV(tensorLayoutA, ir * BM, BM, block_k, BK) DECODEFUNCA); + coopMatLoadTensorNV(mat_b, data_b, pos_b, sliceTensorLayoutNV(tensorLayoutB, ic * BN, BNover2, block_k, BK), tensorViewTranspose); + + sum = coopMatMulAdd(mat_a, mat_b, sum); + block_k += BK; + } +#if defined(ACC_TYPE_MAX) + [[unroll]] for (uint i = 0; i < sum.length(); ++i) { sum[i] = clamp(sum[i], -ACC_TYPE_MAX, ACC_TYPE_MAX); } +#endif + + coopmat mat_d = coopmat(sum); + + coopMatStoreTensorNV(mat_d, data_d, pos_d, sliceTensorLayoutNV(tensorLayoutD, ic * BN, BNover2, ir * BM, BM), tensorViewTranspose); + return; + } else { + coopmat sum = coopmat(0.0); + + for (uint i = 0; i < k_iters; ++i) { + + store_scales(tid); + if (block_k + UNROLL_K < end_k) { + fetch_scales(ir * BM, pos_a, stride_a, block_k + UNROLL_K, tid, true); + } + + // Manually partial unroll + [[unroll]] for (uint j = 0; j < unroll_count; ++j) { + coopmat mat_a; + coopmat mat_b; + + coopMatLoadTensorNV(mat_a, data_a, pos_a, sliceTensorLayoutNV(tensorLayoutA, ir * BM, BM, block_k, BK) DECODEFUNCA); + coopMatLoadTensorNV(mat_b, data_b, pos_b, sliceTensorLayoutNV(tensorLayoutB, ic * BN, BN, block_k, BK), tensorViewTranspose); + + sum = coopMatMulAdd(mat_a, mat_b, sum); + block_k += BK; + } + } + // Do any remaining iterations that were not unrolled + if (block_k < end_k) { + store_scales(tid); + } + while (block_k < end_k) { + coopmat mat_a; + coopmat mat_b; + + coopMatLoadTensorNV(mat_a, data_a, pos_a, sliceTensorLayoutNV(tensorLayoutA, ir * BM, BM, block_k, BK) DECODEFUNCA); + coopMatLoadTensorNV(mat_b, data_b, pos_b, sliceTensorLayoutNV(tensorLayoutB, ic * BN, BN, block_k, BK), tensorViewTranspose); + + sum = coopMatMulAdd(mat_a, mat_b, sum); + block_k += BK; + } +#if defined(ACC_TYPE_MAX) + [[unroll]] for (uint i = 0; i < sum.length(); ++i) { sum[i] = clamp(sum[i], -ACC_TYPE_MAX, ACC_TYPE_MAX); } +#endif + + coopmat mat_d = coopmat(sum); + + coopMatStoreTensorNV(mat_d, data_d, pos_d, sliceTensorLayoutNV(tensorLayoutD, ic * BN, BN, ir * BM, BM), tensorViewTranspose); + return; + } + } else +#endif // !defined(MUL_MAT_ID) + { + tensorLayoutA = setTensorLayoutStrideNV(tensorLayoutA, stride_a, 1); + + tensorLayoutAClamp = setTensorLayoutStrideNV(tensorLayoutAClamp, stride_a, 1); + + tensorLayoutB = setTensorLayoutStrideNV(tensorLayoutB, stride_b, 1); + + tensorLayoutBClamp = setTensorLayoutStrideNV(tensorLayoutBClamp, stride_b, 1); + + uint k_iters = (end_k - start_k + BK - 1) / BK; + + fetch_scales(ir * BM, pos_a, stride_a, start_k, tid, false); + store_scales(tid); + +#ifdef MUL_MAT_ID + if (enable_smaller_matrices && ic * BN + BNover4 >= _ne1) { + coopmat sum; + sum = coopmat(0.0); + + [[dont_unroll]] + for (uint block_k = start_k, i = 0; i < k_iters; block_k += BK, ++i) { + + if ((block_k % QUANT_K) == 0) { + store_scales(tid); + } + if (block_k + BK < end_k && ((block_k + BK) % QUANT_K) == 0) { + fetch_scales(ir * BM, pos_a, stride_a, block_k + BK, tid, false); + } + + if ((ir + 1) * BM <= p.M && block_k + BK <= end_k) { + coopmat mat_a; + coopmat mat_b; + + coopMatLoadTensorNV(mat_a, data_a, pos_a, sliceTensorLayoutNV(tensorLayoutA, ir * BM, BM, block_k, BK) DECODEFUNCA); + coopMatLoadTensorNV(mat_b, data_b, pos_b, sliceTensorLayoutNV(tensorLayoutB, 0, BNover4, block_k, BK), tensorViewTranspose DECODEFUNCB); + + sum = coopMatMulAdd(mat_a, mat_b, sum); + } else { + coopmat mat_a; + coopmat mat_b; + + coopMatLoadTensorNV(mat_a, data_a, pos_a, sliceTensorLayoutNV(tensorLayoutAClamp, ir * BM, BM, block_k, BK) DECODEFUNCA); + coopMatLoadTensorNV(mat_b, data_b, pos_b, sliceTensorLayoutNV(tensorLayoutB, 0, BNover4, block_k, BK), tensorViewTranspose DECODEFUNCB); + + sum = coopMatMulAdd(mat_a, mat_b, sum); + } + } +#if defined(ACC_TYPE_MAX) + [[unroll]] for (uint i = 0; i < sum.length(); ++i) { sum[i] = clamp(sum[i], -ACC_TYPE_MAX, ACC_TYPE_MAX); } +#endif + + // Convert from ACC_TYPE to D_TYPE + coopmat mat_d; + mat_d = coopmat(sum); + + // Call callback to store each element, remapping row through shared memory + coopMatPerElementNV(mat_d, mat_d, perElemOpD, ir, ic); + return; + } + if (enable_smaller_matrices && ic * BN + BNover2 >= _ne1) { + coopmat sum; + sum = coopmat(0.0); + + [[dont_unroll]] + for (uint block_k = start_k, i = 0; i < k_iters; block_k += BK, ++i) { + + if ((block_k % QUANT_K) == 0) { + store_scales(tid); + } + if (block_k + BK < end_k && ((block_k + BK) % QUANT_K) == 0) { + fetch_scales(ir * BM, pos_a, stride_a, block_k + BK, tid, false); + } + + if ((ir + 1) * BM <= p.M && block_k + BK <= end_k) { + coopmat mat_a; + coopmat mat_b; + + coopMatLoadTensorNV(mat_a, data_a, pos_a, sliceTensorLayoutNV(tensorLayoutA, ir * BM, BM, block_k, BK) DECODEFUNCA); + coopMatLoadTensorNV(mat_b, data_b, pos_b, sliceTensorLayoutNV(tensorLayoutB, 0, BNover2, block_k, BK), tensorViewTranspose DECODEFUNCB); + + sum = coopMatMulAdd(mat_a, mat_b, sum); + } else { + coopmat mat_a; + coopmat mat_b; + + coopMatLoadTensorNV(mat_a, data_a, pos_a, sliceTensorLayoutNV(tensorLayoutAClamp, ir * BM, BM, block_k, BK) DECODEFUNCA); + coopMatLoadTensorNV(mat_b, data_b, pos_b, sliceTensorLayoutNV(tensorLayoutB, 0, BNover2, block_k, BK), tensorViewTranspose DECODEFUNCB); + + sum = coopMatMulAdd(mat_a, mat_b, sum); + } + } +#if defined(ACC_TYPE_MAX) + [[unroll]] for (uint i = 0; i < sum.length(); ++i) { sum[i] = clamp(sum[i], -ACC_TYPE_MAX, ACC_TYPE_MAX); } +#endif + + // Convert from ACC_TYPE to D_TYPE + coopmat mat_d; + mat_d = coopmat(sum); + + // Call callback to store each element, remapping row through shared memory + coopMatPerElementNV(mat_d, mat_d, perElemOpD, ir, ic); + return; + } +#endif + coopmat sum; + sum = coopmat(0.0); + + [[dont_unroll]] + for (uint block_k = start_k, i = 0; i < k_iters; block_k += BK, ++i) { + + if ((block_k % QUANT_K) == 0) { + store_scales(tid); + } + if (block_k + BK < end_k && ((block_k + BK) % QUANT_K) == 0) { + fetch_scales(ir * BM, pos_a, stride_a, block_k + BK, tid, false); + } + + if ((ir + 1) * BM <= p.M && block_k + BK <= end_k) { + coopmat mat_a; + coopmat mat_b; + + coopMatLoadTensorNV(mat_a, data_a, pos_a, sliceTensorLayoutNV(tensorLayoutA, ir * BM, BM, block_k, BK) DECODEFUNCA); +#ifdef MUL_MAT_ID + coopMatLoadTensorNV(mat_b, data_b, pos_b, sliceTensorLayoutNV(tensorLayoutB, 0, BN, block_k, BK), tensorViewTranspose DECODEFUNCB); +#else + coopMatLoadTensorNV(mat_b, data_b, pos_b, sliceTensorLayoutNV(tensorLayoutBClamp, ic * BN, BN, block_k, BK), tensorViewTranspose); +#endif + + sum = coopMatMulAdd(mat_a, mat_b, sum); + } else { + coopmat mat_a; + coopmat mat_b; + + coopMatLoadTensorNV(mat_a, data_a, pos_a, sliceTensorLayoutNV(tensorLayoutAClamp, ir * BM, BM, block_k, BK) DECODEFUNCA); +#ifdef MUL_MAT_ID + coopMatLoadTensorNV(mat_b, data_b, pos_b, sliceTensorLayoutNV(tensorLayoutB, 0, BN, block_k, BK), tensorViewTranspose DECODEFUNCB); +#else + coopMatLoadTensorNV(mat_b, data_b, pos_b, sliceTensorLayoutNV(tensorLayoutBClamp, ic * BN, BN, block_k, BK), tensorViewTranspose); +#endif + + sum = coopMatMulAdd(mat_a, mat_b, sum); + } + } +#if defined(ACC_TYPE_MAX) + [[unroll]] for (uint i = 0; i < sum.length(); ++i) { sum[i] = clamp(sum[i], -ACC_TYPE_MAX, ACC_TYPE_MAX); } +#endif + + // Convert from ACC_TYPE to D_TYPE + coopmat mat_d; + mat_d = coopmat(sum); + +#ifdef MUL_MAT_ID + // Call callback to store each element, remapping row through shared memory + coopMatPerElementNV(mat_d, mat_d, perElemOpD, ir, ic); +#else + coopMatStoreTensorNV(mat_d, data_d, pos_d, sliceTensorLayoutNV(tensorLayoutD, ic * BN, BN, ir * BM, BM), tensorViewTranspose); +#endif + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_funcs.glsl b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_funcs.glsl new file mode 100644 index 0000000000000000000000000000000000000000..56a8a0f187f94f790dbb9facfa7f6f466ef0e58a --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_funcs.glsl @@ -0,0 +1,617 @@ +void load_a_to_shmem(const uint pos_a, const uint row, const uint col, const uint idx_m, const uint block, const uint end_k) { +#if defined(DATA_A_F32) || defined(DATA_A_F16) +#if LOAD_VEC_A == 8 + if (ALIGNED != 0) { + const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row; + const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 2; + FLOAT_TYPEV8 aa = FLOAT_TYPEV8(data_a[idx]); + buf_a[buf_idx ] = aa[0].xy; + buf_a[buf_idx + 1] = aa[0].zw; + buf_a[buf_idx + 2] = aa[1].xy; + buf_a[buf_idx + 3] = aa[1].zw; + return; + } +#elif LOAD_VEC_A == 4 + if (ALIGNED != 0) { + const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row; + const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 2; + FLOAT_TYPEV4 aa = FLOAT_TYPEV4(data_a[idx]); + buf_a[buf_idx ] = aa.xy; + buf_a[buf_idx + 1] = aa.zw; + return; + } +#endif + const uint idx = pos_a + col * p.stride_a + row * 2; + const uint buf_idx = col * SHMEM_STRIDE + row; + if (idx_m < p.M && block + row * 2 + 1 < end_k) { + buf_a[buf_idx] = FLOAT_TYPEV2(data_a_scalar[idx], + data_a_scalar[idx + 1]); + } else if (idx_m < p.M && block + row * 2 < end_k) { + buf_a[buf_idx] = FLOAT_TYPEV2(data_a_scalar[idx], 0.0f); + } else { + buf_a[buf_idx] = FLOAT_TYPEV2(0.0f); + } +#elif defined(DATA_A_BF16) +#if LOAD_VEC_A == 4 + if (ALIGNED != 0) { + const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row; + const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 2; + FLOAT_TYPEV4 aa = FLOAT_TYPEV4(TO_FLOAT_TYPE(data_a[idx])); + buf_a[buf_idx ] = aa.xy; + buf_a[buf_idx + 1] = aa.zw; + return; + } +#endif + const uint idx = pos_a + col * p.stride_a + row * 2; + const uint buf_idx = col * SHMEM_STRIDE + row; + if (idx_m < p.M && block + row * 2 + 1 < end_k) { + buf_a[buf_idx] = FLOAT_TYPEV2(TO_FLOAT_TYPE(data_a_scalar[idx]), + TO_FLOAT_TYPE(data_a_scalar[idx + 1])); + } else if (idx_m < p.M && block + row * 2 < end_k) { + buf_a[buf_idx] = FLOAT_TYPEV2(TO_FLOAT_TYPE(data_a_scalar[idx]), 0.0f); + } else { + buf_a[buf_idx] = FLOAT_TYPEV2(0.0f); + } +#elif defined(DATA_A_Q4_0) + const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row; + const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 4; + + const uint ib = idx / 4; + const uint iqs = idx & 0x03; + + const float d = float(data_a_packed16[ib].d); + const uint vui = uint(data_a_packed16[ib].qs[2*iqs]) | (uint(data_a_packed16[ib].qs[2*iqs + 1]) << 16); + const vec4 v0 = (vec4(unpack8(vui & 0x0F0F0F0F)) - 8.0f) * d; + const vec4 v1 = (vec4(unpack8((vui >> 4) & 0x0F0F0F0F)) - 8.0f) * d; + + buf_a[buf_idx ] = FLOAT_TYPEV2(v0.xy); + buf_a[buf_idx + 1] = FLOAT_TYPEV2(v0.zw); + buf_a[buf_idx + 8] = FLOAT_TYPEV2(v1.xy); + buf_a[buf_idx + 9] = FLOAT_TYPEV2(v1.zw); +#elif defined(DATA_A_Q4_1) + const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row; + const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 4; + + const uint ib = idx / 4; + const uint iqs = idx & 0x03; + + const vec2 dm = vec2(data_a_packed32[ib].dm); + const uint vui = data_a_packed32[ib].qs[iqs]; + const vec4 v0 = vec4(unpack8(vui & 0x0F0F0F0F)) * dm.x + dm.y; + const vec4 v1 = vec4(unpack8((vui >> 4) & 0x0F0F0F0F)) * dm.x + dm.y; + + buf_a[buf_idx ] = FLOAT_TYPEV2(v0.xy); + buf_a[buf_idx + 1 ] = FLOAT_TYPEV2(v0.zw); + buf_a[buf_idx + 8 ] = FLOAT_TYPEV2(v1.xy); + buf_a[buf_idx + 9 ] = FLOAT_TYPEV2(v1.zw); +#elif defined(DATA_A_Q5_0) + const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row; + const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 4; + + const uint ib = idx / 8; + const uint iqs = idx & 0x07; + + const float d = float(data_a_packed16[ib].d); + const uint uint_qh = uint(data_a_packed16[ib].qh[1]) << 16 | uint(data_a_packed16[ib].qh[0]); + const ivec2 qh0 = ivec2(((uint_qh >> 2*iqs) << 4) & 0x10, (uint_qh >> (2*iqs + 12)) & 0x10); + const ivec2 qh1 = ivec2(((uint_qh >> (2*iqs + 1)) << 4) & 0x10, (uint_qh >> (2*iqs + 13)) & 0x10); + + const uint vui = uint(data_a_packed16[ib].qs[iqs]); + const vec4 v = (vec4((vui & 0xF) | qh0.x, ((vui >> 4) & 0xF) | qh0.y, ((vui >> 8) & 0xF) | qh1.x, (vui >> 12) | qh1.y) - 16.0f) * d; + + buf_a[buf_idx ] = FLOAT_TYPEV2(v.xz); + buf_a[buf_idx + 8] = FLOAT_TYPEV2(v.yw); +#elif defined(DATA_A_Q5_1) + const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row; + const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 4; + + const uint ib = idx / 4; + const uint iqs = idx & 0x03; + + const vec2 dm = vec2(data_a_packed32[ib].dm); + const uint uint_qh = data_a_packed32[ib].qh; + const uvec2 qh0 = uvec2(((uint_qh >> 4*iqs) << 4) & 0x10, (uint_qh >> (4*iqs + 12)) & 0x10); + const uvec2 qh1 = uvec2(((uint_qh >> (4*iqs + 1)) << 4) & 0x10, (uint_qh >> (4*iqs + 13)) & 0x10); + const uvec2 qh2 = uvec2(((uint_qh >> (4*iqs + 2)) << 4) & 0x10, (uint_qh >> (4*iqs + 14)) & 0x10); + const uvec2 qh3 = uvec2(((uint_qh >> (4*iqs + 3)) << 4) & 0x10, (uint_qh >> (4*iqs + 15)) & 0x10); + + const uint vui = data_a_packed32[ib].qs[iqs]; + const vec4 v0 = vec4((vui & 0xF) | qh0.x, ((vui >> 4) & 0xF) | qh0.y, ((vui >> 8) & 0xF) | qh1.x, ((vui >> 12) & 0xF) | qh1.y) * dm.x + dm.y; + const vec4 v1 = vec4(((vui >> 16) & 0xF) | qh2.x, ((vui >> 20) & 0xF) | qh2.y, ((vui >> 24) & 0xF) | qh3.x, ((vui >> 28) & 0xF) | qh3.y) * dm.x + dm.y; + + buf_a[buf_idx ] = FLOAT_TYPEV2(v0.xz); + buf_a[buf_idx + 1] = FLOAT_TYPEV2(v1.xz); + buf_a[buf_idx + 8] = FLOAT_TYPEV2(v0.yw); + buf_a[buf_idx + 9] = FLOAT_TYPEV2(v1.yw); +#elif defined(DATA_A_Q8_0) + const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row; + const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 2; + + const uint ib = idx / 8; + const uint iqs = idx & 0x07; + + const float d = float(data_a_packed16[ib].d); + const i8vec2 v0 = unpack8(int32_t(data_a_packed16[ib].qs[2*iqs])).xy; // vec4 used due to #12147 + const i8vec2 v1 = unpack8(int32_t(data_a_packed16[ib].qs[2*iqs + 1])).xy; + const vec4 v = vec4(v0.x, v0.y, v1.x, v1.y) * d; + + buf_a[buf_idx ] = FLOAT_TYPEV2(v.xy); + buf_a[buf_idx + 1] = FLOAT_TYPEV2(v.zw); +#elif defined(DATA_A_Q1_0) + const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row; + const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 2; + + const uint ib = idx / 16; + const uint iqs = idx & 0xfu; + + const float d = float(data_a[ib].d); + const uint bits = uint(data_a[ib].qs[iqs]); + + buf_a[buf_idx ] = FLOAT_TYPEV2((bits & 0x01u) != 0u ? d : -d, (bits & 0x02u) != 0u ? d : -d); + buf_a[buf_idx + 1] = FLOAT_TYPEV2((bits & 0x04u) != 0u ? d : -d, (bits & 0x08u) != 0u ? d : -d); + buf_a[buf_idx + 2] = FLOAT_TYPEV2((bits & 0x10u) != 0u ? d : -d, (bits & 0x20u) != 0u ? d : -d); + buf_a[buf_idx + 3] = FLOAT_TYPEV2((bits & 0x40u) != 0u ? d : -d, (bits & 0x80u) != 0u ? d : -d); +#elif defined(DATA_A_Q2_K) + const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row; + const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 2; + + const uint ib = idx / 64; // 4 values per idx + const uint iqs = (idx % 64) * 2; // 0,2,4..126 + + const uint qsi = (iqs / 64) * 16 + (iqs % 16); // 0..15 + const uint scalesi = iqs / 8; // 0..15 + const uint qsshift = ((iqs % 64) / 16) * 2; // 0,2,4,6 + + const vec4 qs = vec4(unpack8((data_a_packed32[ib].qs[qsi / 2] >> qsshift) & 0x03030303)); + const uint scales = data_a[ib].scales[scalesi]; + const vec2 dm = vec2(data_a[ib].dm); + + const vec4 v = dm.x * float(scales & 0xF) * qs - dm.y * float(scales >> 4); + + buf_a[buf_idx ] = FLOAT_TYPEV2(v.xy); + buf_a[buf_idx + 1] = FLOAT_TYPEV2(v.zw); +#elif defined(DATA_A_Q3_K) + const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row; + const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 2; + + const uint ib = idx / 128; // 2 values per idx + const uint iqs = idx % 128; // 0..127 + + const uint n = iqs / 64; // 0,1 + const uint qsi = n * 32 + (iqs % 16) * 2; // 0,2,4..62 + const uint hmi = (iqs % 16) * 2; // 0,2,4..30 + const uint j = (iqs % 64) / 4; // 0..3 + const uint is = iqs / 8; // 0..15 + const uint halfsplit = ((iqs % 64) / 16); // 0,1,2,3 + const uint qsshift = halfsplit * 2; // 0,2,4,6 + + const int8_t us = int8_t(((data_a[ib].scales[is % 8] >> (4 * int(is / 8))) & 0xF) + | (((data_a[ib].scales[8 + (is % 4)] >> (2 * int(is / 4))) & 3) << 4)); + const float dl = float(data_a[ib].d) * float(us - 32); + + const vec2 qs = vec2(unpack8((uint(data_a_packed16[ib].qs[qsi / 2]) >> qsshift) & 0x0303).xy); + const vec2 hm = vec2(unpack8(((uint(data_a_packed16[ib].hmask[hmi / 2]) >> (4 * n + halfsplit)) & 0x0101 ^ 0x0101) << 2).xy); + + buf_a[buf_idx] = FLOAT_TYPEV2(dl * (qs.x - hm.x), + dl * (qs.y - hm.y)); +#elif defined(DATA_A_Q4_K) + const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row; + const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 2; + + const uint ib = idx / 64; // 4 values per idx + const uint iqs = (idx % 64) * 2; // 0,2,4..126 + + const uint n = iqs / 32; // 0,1,2,3 + const uint b = (iqs % 32) / 16; // 0,1 + const uint is = 2 * n + b; // 0..7 + const uint qsi = n * 32 + (iqs % 16) * 2; // 0,2,4..126 + + const vec2 loadd = vec2(data_a[ib].dm); + + const uvec3 scales = uvec3(data_a_packed32[ib].scales[0], + data_a_packed32[ib].scales[1], + data_a_packed32[ib].scales[2]); + const uint scalesoffs = (is & 3) * 8; + + const uint scidx0 = (is < 4) ? 0 : 2; + const uint scidxshift0 = scalesoffs; + const uint scidxshift1 = (is < 4) ? scalesoffs : scalesoffs + 2; + const uint mbidx0 = (is < 4) ? 1 : 2; + const uint mbidxshift0 = (is < 4) ? scalesoffs : scalesoffs + 4; + const uint mbidxshift1 = (is < 4) ? scalesoffs : scalesoffs + 2; + + const uint8_t sc = uint8_t(((scales[scidx0] >> scidxshift0) & 0xF) | ((scales[0] >> scidxshift1) & 0x30)); + const uint8_t mbyte = uint8_t(((scales[mbidx0] >> mbidxshift0) & 0xF) | ((scales[1] >> mbidxshift1) & 0x30)); + + const float d = loadd.x * sc; + const float m = -loadd.y * mbyte; + + const vec4 q = vec4(unpack8((data_a_packed32[ib].qs[qsi / 4] >> (b * 4)) & 0x0F0F0F0F)); + + buf_a[buf_idx ] = FLOAT_TYPEV2(fma(d, q.x, m), fma(d, q.y, m)); + buf_a[buf_idx + 1] = FLOAT_TYPEV2(fma(d, q.z, m), fma(d, q.w, m)); +#elif defined(DATA_A_Q5_K) + const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row; + const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 2; + + const uint ib = idx / 64; // 4 values per idx + const uint iqs = (idx % 64) * 2; // 0,2,4..126 + + const uint n = iqs / 32; // 0,1,2,3 + const uint b = (iqs % 32) / 16; // 0,1 + const uint is = 2 * n + b; // 0..7 + const uint qsi = n * 32 + (iqs % 16) * 2; // 0,2,4..126 + const uint qhi = (iqs % 16) * 2; // 0,2,4..30 + + const vec2 loadd = vec2(data_a[ib].dm); + + const uvec3 scales = uvec3(data_a_packed32[ib].scales[0], + data_a_packed32[ib].scales[1], + data_a_packed32[ib].scales[2]); + const uint scalesoffs = (is & 3) * 8; + + const uint scidx0 = (is < 4) ? 0 : 2; + const uint scidxshift0 = scalesoffs; + const uint scidxshift1 = (is < 4) ? scalesoffs : scalesoffs + 2; + const uint mbidx0 = (is < 4) ? 1 : 2; + const uint mbidxshift0 = (is < 4) ? scalesoffs : scalesoffs + 4; + const uint mbidxshift1 = (is < 4) ? scalesoffs : scalesoffs + 2; + + const uint8_t sc = uint8_t(((scales[scidx0] >> scidxshift0) & 0xF) | ((scales[0] >> scidxshift1) & 0x30)); + const uint8_t mbyte = uint8_t(((scales[mbidx0] >> mbidxshift0) & 0xF) | ((scales[1] >> mbidxshift1) & 0x30)); + + const float d = loadd.x * sc; + const float m = -loadd.y * mbyte; + + const uint qs = (data_a_packed32[ib].qs[qsi / 4] >> (b * 4)) & 0x0F0F0F0F; + const uint qh = ((data_a_packed32[ib].qh[qhi / 4] >> (iqs / 16)) & 0x01010101) << 4; + const vec4 q = vec4(unpack8(qs | qh)); + + buf_a[buf_idx ] = FLOAT_TYPEV2(fma(d, q.x, m), fma(d, q.y, m)); + buf_a[buf_idx + 1] = FLOAT_TYPEV2(fma(d, q.z, m), fma(d, q.w, m)); +#elif defined(DATA_A_Q6_K) + const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row; + const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 2; + + const uint ib = idx / 128; // 2 values per idx + const uint iqs = idx % 128; // 0..127 + + const uint n = iqs / 64; // 0,1 + const uint b = ((iqs % 64) / 32) * 4; // 0,4 + const uint is_b = (iqs % 16) / 8; // 0,1 + const uint qhshift = ((iqs % 64) / 16) * 2; // 0,2,4,6 + const uint is = 8 * n + qhshift + is_b; // 0..15 + const uint qsi = n * 32 + (iqs % 32); // 0..63 + const uint qhi = n * 16 + (iqs % 16); // 0..31 + + const float dscale = float(data_a[ib].d) * float(data_a[ib].scales[is]); + + const uint ql = (uint(data_a_packed16[ib].ql[qsi]) >> b) & 0x0F0F; + const uint qh = (uint(data_a_packed16[ib].qh[qhi]) >> qhshift) & 0x0303; + const vec2 q = (vec2(unpack8(ql | (qh << 4)).xy) - 32) * dscale; + + buf_a[buf_idx] = FLOAT_TYPEV2(q.x, q.y); +#elif defined(DATA_A_IQ1_S) + const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row; + const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 2; + + const uint ib = idx / 32; // 8 values per idx + const uint ib32 = (idx % 32) / 4; // 0..7 + const uint ib8 = idx % 32; + + const float d = float(data_a[ib].d); + const uint qh = data_a[ib].qh[ib32]; + const uint qs = data_a[ib].qs[ib8]; + const float dl = d * (2 * bitfieldExtract(qh, 12, 3) + 1); + const float delta = ((qh & 0x8000) != 0) ? -IQ1S_DELTA : IQ1S_DELTA; + const int16_t grid = int16_t(iq1s_grid[qs | (bitfieldExtract(qh, 3 * int(ib8 & 3), 3) << 8)]); + + [[unroll]] for (int k = 0; k < 4; ++k) { + buf_a[buf_idx + k] = FLOAT_TYPEV2(dl * (bitfieldExtract(grid, 4 * k , 2) + delta), + dl * (bitfieldExtract(grid, 4 * k + 2, 2) + delta)); + } +#elif defined(DATA_A_IQ1_M) + const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row; + const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 2; + + const uint ib = idx / 32; // 8 values per idx + const uint ib8 = idx % 32; + const uint ib16 = ib8 / 2; + + const uint16_t[4] scales = data_a[ib].scales; + const u16vec4 s = u16vec4(scales[0], scales[1], scales[2], scales[3]) >> 12; + const float d = float(unpackHalf2x16(s.x | (s.y << 4) | (s.z << 8) | (s.w << 12)).x); + const uint sc = scales[ib8 / 8]; + const uint qs = data_a[ib].qs[ib8]; + const uint qh = data_a[ib].qh[ib16] >> (4 * (ib8 & 1)); + const float dl = d * (2 * bitfieldExtract(sc, 3 * int(ib16 & 3), 3) + 1); + const float delta = ((qh & 8) != 0) ? -IQ1M_DELTA : IQ1M_DELTA; + const int16_t grid = int16_t(iq1s_grid[qs | ((qh & 7) << 8)]); + + [[unroll]] for (int k = 0; k < 4; ++k) { + buf_a[buf_idx + k] = FLOAT_TYPEV2(dl * (bitfieldExtract(grid, 4 * k , 2) + delta), + dl * (bitfieldExtract(grid, 4 * k + 2, 2) + delta)); + } +#elif defined(DATA_A_IQ2_XXS) + const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row; + const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 2; + + const uint ib = idx / 32; // 8 values per idx + const uint ib32 = (idx % 32) / 4; // 0..7 + const uint ib8 = idx % 4; + + const float d = float(data_a[ib].d); + const uint qs = data_a[ib].qs[8 * ib32 + ib8]; + const uint signs = pack32(u8vec4( + data_a[ib].qs[8*ib32 + 4], + data_a[ib].qs[8*ib32 + 5], + data_a[ib].qs[8*ib32 + 6], + data_a[ib].qs[8*ib32 + 7] + )); + const FLOAT_TYPE db = FLOAT_TYPE(d * 0.25 * (0.5 + (signs >> 28))); + const uint32_t sign7 = bitfieldExtract(signs, 7 * int(ib8), 7); + const uint sign = sign7 | (bitCount(sign7) << 7); + const uvec2 grid = iq2xxs_grid[qs]; + const vec4 grid0 = vec4(unpack8(grid.x)); + const vec4 grid1 = vec4(unpack8(grid.y)); + + buf_a[buf_idx ] = db * FLOAT_TYPEV2((sign & 1) != 0 ? -grid0.x : grid0.x, + (sign & 2) != 0 ? -grid0.y : grid0.y); + buf_a[buf_idx + 1] = db * FLOAT_TYPEV2((sign & 4) != 0 ? -grid0.z : grid0.z, + (sign & 8) != 0 ? -grid0.w : grid0.w); + buf_a[buf_idx + 2] = db * FLOAT_TYPEV2((sign & 16) != 0 ? -grid1.x : grid1.x, + (sign & 32) != 0 ? -grid1.y : grid1.y); + buf_a[buf_idx + 3] = db * FLOAT_TYPEV2((sign & 64) != 0 ? -grid1.z : grid1.z, + (sign & 128) != 0 ? -grid1.w : grid1.w); +#elif defined(DATA_A_IQ2_XS) + const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row; + const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 2; + + const uint ib = idx / 32; // 8 values per idx + const uint ib32 = (idx % 32) / 4; // 0..7 + const uint ib8 = idx % 4; // 0..3 + + const float d = float(data_a[ib].d); + const uint scale = (data_a[ib].scales[ib32] >> (2 * (ib8 & 2))) & 0xf; + const FLOAT_TYPE db = FLOAT_TYPE(d * 0.25 * (0.5 + scale)); + const uint qs = data_a[ib].qs[4 * ib32 + ib8]; + const uint sign7 = qs >> 9; + const uint sign = sign7 | (bitCount(sign7) << 7); + const uvec2 grid = iq2xs_grid[qs & 511]; + const vec4 grid0 = vec4(unpack8(grid.x)); + const vec4 grid1 = vec4(unpack8(grid.y)); + + buf_a[buf_idx ] = db * FLOAT_TYPEV2((sign & 1) != 0 ? -grid0.x : grid0.x, + (sign & 2) != 0 ? -grid0.y : grid0.y); + buf_a[buf_idx + 1] = db * FLOAT_TYPEV2((sign & 4) != 0 ? -grid0.z : grid0.z, + (sign & 8) != 0 ? -grid0.w : grid0.w); + buf_a[buf_idx + 2] = db * FLOAT_TYPEV2((sign & 16) != 0 ? -grid1.x : grid1.x, + (sign & 32) != 0 ? -grid1.y : grid1.y); + buf_a[buf_idx + 3] = db * FLOAT_TYPEV2((sign & 64) != 0 ? -grid1.z : grid1.z, + (sign & 128) != 0 ? -grid1.w : grid1.w); +#elif defined(DATA_A_IQ2_S) + const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row; + const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 2; + + const uint ib = idx / 32; // 8 values per idx + const uint ib8 = idx % 32; // 0..31 + const uint ib32 = ib8 / 4; // 0..7 + + const uint scale = (data_a[ib].scales[ib32] >> (2 * (ib8 & 2))) & 0xf; + const uint qs = data_a[ib].qs[ib8]; + const uint qh = data_a[ib].qh[ib32]; + const uint qhshift = 2 * (ib8 % 4); + const uint sign = data_a[ib].qs[QUANT_K / 8 + ib8]; + + const float d = float(data_a[ib].d); + const FLOAT_TYPE db = FLOAT_TYPE(d * 0.25 * (0.5 + scale)); + const uvec2 grid = iq2s_grid[qs | ((qh << (8 - qhshift)) & 0x300)]; + const vec4 grid0 = vec4(unpack8(grid.x)); + const vec4 grid1 = vec4(unpack8(grid.y)); + + buf_a[buf_idx ] = db * FLOAT_TYPEV2((sign & 1) != 0 ? -grid0.x : grid0.x, + (sign & 2) != 0 ? -grid0.y : grid0.y); + buf_a[buf_idx + 1] = db * FLOAT_TYPEV2((sign & 4) != 0 ? -grid0.z : grid0.z, + (sign & 8) != 0 ? -grid0.w : grid0.w); + buf_a[buf_idx + 2] = db * FLOAT_TYPEV2((sign & 16) != 0 ? -grid1.x : grid1.x, + (sign & 32) != 0 ? -grid1.y : grid1.y); + buf_a[buf_idx + 3] = db * FLOAT_TYPEV2((sign & 64) != 0 ? -grid1.z : grid1.z, + (sign & 128) != 0 ? -grid1.w : grid1.w); +#elif defined(DATA_A_IQ3_XXS) + const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row; + const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 2; + + const uint ib = idx / 64; // 4 values per idx + const uint iqs = idx % 64; // 0..63 + const uint is = QUANT_K / 4 + 4 * (iqs / 8); // 8 values + + const float d = float(data_a[ib].d); + const uint qs = data_a[ib].qs[iqs]; + const uint signs = pack32(u16vec2( + data_a_packed16[ib].qs[is/2], + data_a_packed16[ib].qs[is/2+1] + )); + const float db = d * 0.5 * (0.5 + (signs >> 28)); + const uint32_t sign7 = bitfieldExtract(signs, 7 * (int(iqs / 2) % 4), 7); + const uint sign = (sign7 | (bitCount(sign7) << 7)) >> (4 * (idx % 2)); + const uint grid = iq3xxs_grid[qs]; + const vec4 v = db * vec4(unpack8(grid)); + + buf_a[buf_idx ] = FLOAT_TYPEV2((sign & 1) != 0 ? -v.x : v.x, + (sign & 2) != 0 ? -v.y : v.y); + buf_a[buf_idx + 1] = FLOAT_TYPEV2((sign & 4) != 0 ? -v.z : v.z, + (sign & 8) != 0 ? -v.w : v.w); +#elif defined(DATA_A_IQ3_S) + const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row; + const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 2; + + const uint ib = idx / 64; // 4 values per idx + const uint iqs = idx % 64; // 0..63 + const uint iqh = iqs / 8; + + const float d = float(data_a[ib].d); + const uint qs = data_a[ib].qs[iqs]; + const uint qh = data_a[ib].qh[iqh]; + const int8_t sign = int8_t(data_a[ib].signs[iqs / 2] >> (4 * (idx % 2))); + const uint scale = data_a[ib].scales[iqs / 16]; + const i8vec2 sign01 = i8vec2(1 - (2 & i8vec2(sign << 1, sign))); + const float db = d * (1 + 2 * ((scale >> (4 * (iqh & 1))) & 0xf)); + const uint32_t grid = iq3s_grid[qs | ((qh << (8 - (iqs % 8))) & 256)]; + const vec4 v = db * vec4(unpack8(grid)); + + buf_a[buf_idx ] = FLOAT_TYPEV2((sign & 1) != 0 ? -v.x : v.x, + (sign & 2) != 0 ? -v.y : v.y); + buf_a[buf_idx + 1] = FLOAT_TYPEV2((sign & 4) != 0 ? -v.z : v.z, + (sign & 8) != 0 ? -v.w : v.w); +#elif defined(DATA_A_IQ4_XS) + const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row; + const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 2; + + const uint ib = idx / 64; // 4 values per idx + const uint ib32 = (idx % 64) / 8; // 0..7 + const uint iq = 4 * ib32 + (idx % 4); + + const uint sl = (data_a[ib].scales_l[ib32/2] >> (4 * (ib32 & 1))) & 0xF; + const uint sh = ((data_a[ib].scales_h) >> (2 * ib32)) & 3; + const uint qshift = idx & 4; + u8vec4 qs = unpack8((uint(data_a_packed32[ib].qs[iq]) >> qshift) & 0x0F0F0F0F); + + const float d = float(data_a[ib].d); + const vec4 v = d * float(int(sl | (sh << 4)) - 32) * vec4(kvalues_iq4nl[qs.x], kvalues_iq4nl[qs.y], kvalues_iq4nl[qs.z], kvalues_iq4nl[qs.w]); + + buf_a[buf_idx ] = FLOAT_TYPEV2(v.xy); + buf_a[buf_idx + 1] = FLOAT_TYPEV2(v.zw); +#elif defined(DATA_A_IQ4_NL) + const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row; + const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 4; + + const uint ib = idx / 8; + const uint iqs = idx & 0x07; + + const FLOAT_TYPE d = FLOAT_TYPE(data_a_packed16[ib].d); + const uint vui = uint(data_a_packed16[ib].qs[iqs]); + + buf_a[buf_idx ] = d * FLOAT_TYPEV2(kvalues_iq4nl[vui & 0xF], + kvalues_iq4nl[bitfieldExtract(vui, 8, 4)]); + buf_a[buf_idx + 8] = d * FLOAT_TYPEV2(kvalues_iq4nl[bitfieldExtract(vui, 4, 4)], + kvalues_iq4nl[vui >> 12]); +#elif defined(DATA_A_MXFP4) + const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row; + const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 4; + + const uint ib = idx / 8; + const uint iqs = (idx & 0x07) * 2; + + const float d = e8m0_to_fp32(data_a[ib].e) * 0.5; + const uint vui = uint(data_a[ib].qs[iqs]); + const uint vui2 = uint(data_a[ib].qs[iqs+1]); + + buf_a[buf_idx ] = FLOAT_TYPEV2(kvalues_mxfp4[vui & 0xF] * d, + kvalues_mxfp4[vui2 & 0xF] * d); + buf_a[buf_idx + 8] = FLOAT_TYPEV2(kvalues_mxfp4[vui >> 4] * d, + kvalues_mxfp4[vui2 >> 4] * d); +#elif defined(DATA_A_NVFP4) + const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row; + // lo and hi nibbles are 8 elements apart, which doesn't quite line up with + // how the thread mapping and buf_idx calculation works for other types. + const uint buf_idx = col * SHMEM_STRIDE + (row & 3) + (row & ~3) * 2; + + const uint ib = idx / 16u; + const uint sub = (idx & 0xC) >> 2; + const uint iqs = (idx & 0xF) * 2; + const float d = ue4m3_to_fp32(data_a[ib].d[sub]) * 0.5; + const uint vui = uint(data_a[ib].qs[iqs]); + const uint vui2 = uint(data_a[ib].qs[iqs+1]); + + buf_a[buf_idx ] = FLOAT_TYPEV2(kvalues_mxfp4[vui & 0xF] * d, + kvalues_mxfp4[vui2 & 0xF] * d); + buf_a[buf_idx + 4] = FLOAT_TYPEV2(kvalues_mxfp4[vui >> 4] * d, + kvalues_mxfp4[vui2 >> 4] * d); +#endif +} + +#if !defined(MUL_MAT_ID) +void load_b_to_shmem(const uint pos_b, const uint row, const uint col, const uint idx_n, const uint block, const uint end_k) { +#if LOAD_VEC_B == 8 + if (ALIGNED != 0) { + // Not supported for b_type bf16 because bf16mat2x4 does not exist + const uint idx = pos_b + col * p.stride_b / LOAD_VEC_B + row; + const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_B / 2; + FLOAT_TYPEV8 bb = FLOAT_TYPEV8(data_b[idx]); + buf_b[buf_idx + 0] = bb[0].xy; + buf_b[buf_idx + 1] = bb[0].zw; + buf_b[buf_idx + 2] = bb[1].xy; + buf_b[buf_idx + 3] = bb[1].zw; + return; + } +#elif LOAD_VEC_B == 4 + if (ALIGNED != 0) { + const uint idx = pos_b + col * p.stride_b / LOAD_VEC_B + row; + const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_B / 2; +#if defined(DATA_B_BF16) + FLOAT_TYPEV4 bb = FLOAT_TYPEV4(TO_FLOAT_TYPE(data_b[idx])); +#else + FLOAT_TYPEV4 bb = FLOAT_TYPEV4(data_b[idx]); +#endif + buf_b[buf_idx + 0] = bb.xy; + buf_b[buf_idx + 1] = bb.zw; + return; + } +#endif + const uint idx = pos_b + col * p.stride_b + row * 2; + const uint buf_idx = col * SHMEM_STRIDE + row; + if (idx_n < p.N && block + row * 2 + 1 < end_k) { + buf_b[buf_idx] = FLOAT_TYPEV2(TO_FLOAT_TYPE(data_b_scalar[idx]), + TO_FLOAT_TYPE(data_b_scalar[idx + 1])); + } else if (idx_n < p.N && block + row * 2 < end_k) { + buf_b[buf_idx] = FLOAT_TYPEV2(TO_FLOAT_TYPE(data_b_scalar[idx]), 0.0f); + } else { + buf_b[buf_idx] = FLOAT_TYPEV2(0.0f); + } +} +#else +void load_b_to_shmem(const uint pos_b, const uint row, const uint col, const uint ic, const uint _ne1, const uint block, const uint end_k) { +#if LOAD_VEC_B == 8 + if (ALIGNED != 0) { + // Not supported for b_type bf16 because bf16mat2x4 does not exist + const u16vec2 row_idx = row_ids[col]; + const uint idx = pos_b + row_idx.y * p.batch_stride_b / LOAD_VEC_B + (row_idx.x % p.ne11) * p.stride_b / LOAD_VEC_B + row; + const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_B / 2; + FLOAT_TYPEV8 bb = FLOAT_TYPEV8(data_b[idx]); + buf_b[buf_idx + 0] = bb[0].xy; + buf_b[buf_idx + 1] = bb[0].zw; + buf_b[buf_idx + 2] = bb[1].xy; + buf_b[buf_idx + 3] = bb[1].zw; + return; + } +#elif LOAD_VEC_B == 4 + if (ALIGNED != 0) { + const u16vec2 row_idx = row_ids[col]; + const uint idx = pos_b + row_idx.y * p.batch_stride_b / LOAD_VEC_B + (row_idx.x % p.ne11) * p.stride_b / LOAD_VEC_B + row; + const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_B / 2; +#if defined(DATA_B_BF16) + FLOAT_TYPEV4 bb = FLOAT_TYPEV4(TO_FLOAT_TYPE(data_b[idx])); +#else + FLOAT_TYPEV4 bb = FLOAT_TYPEV4(data_b[idx]); +#endif + buf_b[buf_idx + 0] = bb.xy; + buf_b[buf_idx + 1] = bb.zw; + return; + } +#endif + const uint row_i = ic * BN + col; + const uint buf_idx = col * SHMEM_STRIDE + row; + if (row_i < _ne1 && block + row * 2 + 1 < end_k) { + const u16vec2 row_idx = row_ids[col]; + const uint idx = pos_b + row_idx.y * p.batch_stride_b + (row_idx.x % p.ne11) * p.stride_b + row * 2; + buf_b[buf_idx] = FLOAT_TYPEV2(TO_FLOAT_TYPE(data_b_scalar[idx]), + TO_FLOAT_TYPE(data_b_scalar[idx + 1])); + } else if (row_i < _ne1 && block + row * 2 < end_k) { + const u16vec2 row_idx = row_ids[col]; + const uint idx = pos_b + row_idx.y * p.batch_stride_b + (row_idx.x % p.ne11) * p.stride_b + row * 2; + buf_b[buf_idx] = FLOAT_TYPEV2(TO_FLOAT_TYPE(data_b_scalar[idx]), 0.0f); + } else { + buf_b[buf_idx] = FLOAT_TYPEV2(0.0f); + } +} +#endif diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_id_funcs.glsl b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_id_funcs.glsl new file mode 100644 index 0000000000000000000000000000000000000000..26c5c12a49a226085921d1019eb0776e4a6a109a --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_id_funcs.glsl @@ -0,0 +1,74 @@ +#ifdef MUL_MAT_ID +shared u16vec2 row_ids[BN]; +uint _ne1; + +#ifdef MUL_MAT_ID_USE_SUBGROUPS +shared uvec4 ballots_sh[NUM_WARPS]; + +void load_row_ids(uint expert_idx, bool nei0_is_pow2, uint ic) { + _ne1 = 0; + uint num_elements = p.nei1 * p.nei0; + uint nei0shift = findLSB(p.nei0); + + uint ids[16]; + uint iter = 0; + + uint expert_count = data_expert_count[expert_idx]; + + for (uint j = 0; j < num_elements; j += BLOCK_SIZE) { + // prefetch up to 16 elements + if (iter == 0) { + [[unroll]] for (uint k = 0; k < 16; ++k) { + uint i = j + gl_LocalInvocationIndex + k*BLOCK_SIZE; + bool in_range = i < num_elements; + uint ii1; + if (nei0_is_pow2) { + ii1 = i >> nei0shift; + } else { + ii1 = i / p.nei0; + } + uint ii0 = i - ii1 * p.nei0; + ids[k] = in_range ? data_ids[ii1*p.nbi1 + ii0] : 0; + } + } + uint i = j + gl_LocalInvocationIndex; + bool in_range = i < num_elements; + uint ii1; + if (nei0_is_pow2) { + ii1 = i >> nei0shift; + } else { + ii1 = i / p.nei0; + } + uint ii0 = i - ii1 * p.nei0; + uint id = ids[iter++]; + uvec4 ballot = subgroupBallot(in_range && id == expert_idx); + + if (gl_SubgroupInvocationID == 0) { + ballots_sh[gl_SubgroupID] = ballot; + } + barrier(); + + uint subgroup_base = 0; + uint total = 0; + for (uint k = 0; k < gl_NumSubgroups; ++k) { + if (k == gl_SubgroupID) { + subgroup_base = total; + } + total += subgroupBallotBitCount(ballots_sh[k]); + } + barrier(); + + uint idx = subgroup_base + subgroupBallotExclusiveBitCount(ballot); + if (in_range && id == expert_idx && _ne1 + idx >= ic * BN && _ne1 + idx < (ic + 1) * BN) { + row_ids[_ne1 + idx - ic * BN] = u16vec2(ii0, ii1); + } + _ne1 += total; + iter &= 15; + if (_ne1 >= (ic + 1) * BN || _ne1 == expert_count) { + break; + } + } + barrier(); +} +#endif // MUL_MAT_ID_USE_SUBGROUPS +#endif // MUL_MAT_ID diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mmq.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mmq.comp new file mode 100644 index 0000000000000000000000000000000000000000..aae1c2e8ae9fc51543b9cd54c5fc89f011712fad --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mmq.comp @@ -0,0 +1,311 @@ +#version 450 + +#extension GL_EXT_control_flow_attributes : enable +#extension GL_EXT_shader_16bit_storage : require +#extension GL_EXT_shader_explicit_arithmetic_types_int8 : require + +#extension GL_EXT_integer_dot_product : require + +#ifdef FLOAT16 +#extension GL_EXT_shader_explicit_arithmetic_types_float16 : require +#endif + +#if defined(MUL_MAT_ID_USE_SUBGROUPS) +#extension GL_KHR_shader_subgroup_basic : enable +#extension GL_KHR_shader_subgroup_ballot : enable +#endif + +#ifdef MUL_MAT_ID +#extension GL_EXT_shader_explicit_arithmetic_types_int16 : require +#endif + +#include "types.glsl" + +layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; +#if defined(A_TYPE_PACKED16) +layout (binding = 0) readonly buffer A_PACKED16 {A_TYPE_PACKED16 data_a_packed16[];}; +#endif +#if defined(A_TYPE_PACKED32) +layout (binding = 0) readonly buffer A_PACKED32 {A_TYPE_PACKED32 data_a_packed32[];}; +#endif +layout (binding = 1) readonly buffer B {block_q8_1_x4_packed128 data_b[];}; +layout (binding = 2) writeonly buffer D {D_TYPE data_d[];}; + +#ifdef MUL_MAT_ID +layout (binding = 3) readonly buffer IDS {int data_ids[];}; +layout (binding = 4) readonly buffer Counts {int data_expert_count[];}; +#endif + +layout (push_constant) uniform parameter +{ + uint M; + uint N; + uint K; + uint stride_a; + uint stride_b; + uint stride_d; + + uint batch_stride_a; + uint batch_stride_b; + uint batch_stride_d; + +#ifdef MUL_MAT_ID + uint nei0; + uint nei1; + uint nbi1; + uint ne11; +#else + uint base_work_group_z; + uint num_batches; + uint k_split; + uint ne02; + uint ne12; + uint broadcast2; + uint broadcast3; +#endif +} p; + +layout (constant_id = 0) const uint BLOCK_SIZE = 64; +layout (constant_id = 1) const uint BM = 64; +layout (constant_id = 2) const uint BN = 64; +// layout (constant_id = 3) const uint BK = 32; +layout (constant_id = 4) const uint WM = 32; +layout (constant_id = 5) const uint WN = 32; +layout (constant_id = 6) const uint WMITER = 2; +layout (constant_id = 7) const uint TM = 4; +layout (constant_id = 8) const uint TN = 2; +layout (constant_id = 9) const uint TK = 1; // Only needed for coopmat +layout (constant_id = 10) const uint WARP = 32; + +#define BK 32 + +#include "mul_mmq_shmem_types.glsl" + +#ifdef MUL_MAT_ID +#define BK_STEP 1 +#else +#ifndef BK_STEP +#define BK_STEP 4 +#endif +#endif + +// Shared memory cache +shared block_a_cache buf_a[BM * BK_STEP]; +shared block_b_cache buf_b[BN * BK_STEP]; +// Register cache +block_a_cache cache_a[WMITER * TM]; +block_b_cache cache_b; + +#define LOAD_VEC_A (4 * QUANT_R_MMQ) +#define LOAD_VEC_B 16 + +#define NUM_WARPS (BLOCK_SIZE / WARP) + +#include "mul_mm_id_funcs.glsl" +#include "mul_mmq_funcs.glsl" + +void main() { + const uint ic = gl_WorkGroupID.y; + +#ifdef MUL_MAT_ID + const uint expert_idx = gl_WorkGroupID.z; + if (ic * BN >= data_expert_count[expert_idx]) { + return; + } +#endif +#ifdef NEEDS_INIT_IQ_SHMEM + init_iq_shmem(gl_WorkGroupSize); +#endif + +#ifndef MUL_MAT_ID + const uint batch_idx = gl_WorkGroupID.z + p.base_work_group_z; + + const uint i13 = batch_idx / p.ne12; + const uint i12 = batch_idx % p.ne12; + + const uint i03 = i13 / p.broadcast3; + const uint i02 = i12 / p.broadcast2; + + const uint batch_idx_a = i03 * p.ne02 + i02; +#endif + + const uint blocks_m = (p.M + BM - 1) / BM; + const uint ir = gl_WorkGroupID.x % blocks_m; + const uint ik = gl_WorkGroupID.x / blocks_m; + + const uint WNITER = (WM * WN) / (WARP * TM * TN * WMITER); + const uint WSUBM = WM / WMITER; + const uint WSUBN = WN / WNITER; + const uint warp_i = gl_LocalInvocationID.x / WARP; + + const uint tiw = gl_LocalInvocationID.x % WARP; + + const uint tiwr = tiw % (WSUBM / TM); + const uint tiwc = tiw / (WSUBM / TM); + + const uint warp_r = warp_i % (BM / WM); + const uint warp_c = warp_i / (BM / WM); + + const uint loadr_a = gl_LocalInvocationID.x % (BK / LOAD_VEC_A); + const uint loadc_a = gl_LocalInvocationID.x / (BK / LOAD_VEC_A); + const uint loadr_b = gl_LocalInvocationID.x % (BK / LOAD_VEC_B); + const uint loadc_b = gl_LocalInvocationID.x / (BK / LOAD_VEC_B); + + const uint loadstride_a = BLOCK_SIZE * LOAD_VEC_A / BK; + const uint loadstride_b = BLOCK_SIZE * LOAD_VEC_B / BK; + +#ifdef MUL_MAT_ID +#ifdef MUL_MAT_ID_USE_SUBGROUPS + if (bitCount(p.nei0) == 1) { + load_row_ids(expert_idx, true, ic); + } else { + load_row_ids(expert_idx, false, ic); + } +#else + _ne1 = 0; + for (uint ii1 = 0; ii1 < p.nei1 && _ne1 < (ic + 1) * BN; ii1++) { + for (uint ii0 = 0; ii0 < p.nei0 && _ne1 < (ic + 1) * BN; ii0++) { + if (data_ids[ii1*p.nbi1 + ii0] == expert_idx) { + if (_ne1 >= ic * BN) { + row_ids[_ne1 - ic * BN] = u16vec2(ii0, ii1); + } + _ne1++; + } + } + } + + barrier(); +#endif + + // Workgroup has no work + if (ic * BN >= _ne1) return; +#endif + +#ifdef MUL_MAT_ID + const uint start_k = 0; + const uint end_k = p.K; +#else + const uint start_k = ik * p.k_split; + const uint end_k = min(p.K, (ik + 1) * p.k_split); +#endif + + uint pos_a_ib = +#ifdef MUL_MAT_ID + expert_idx * (p.batch_stride_a / BK) + +#else + batch_idx_a * (p.batch_stride_a / BK) + +#endif + (ir * BM * p.stride_a + start_k) / BK; +#ifdef MUL_MAT_ID + uint pos_b_ib = 0; +#else + uint pos_b_ib = (batch_idx * p.batch_stride_b + ic * BN * p.stride_b + start_k) / BK; +#endif + + ACC_TYPE sums[WMITER * TM * WNITER * TN]; + + [[unroll]] for (uint i = 0; i < WMITER*TM*WNITER*TN; i++) { + sums[i] = ACC_TYPE(0.0f); + } + + for (uint block = start_k; block < end_k; block += BK * BK_STEP) { + [[unroll]] for (uint l = 0; loadc_a + l < BM; l += loadstride_a) { + const uint buf_ib = loadc_a + l; + const uint ib = pos_a_ib + buf_ib * p.stride_a / BK; + const uint iqs = loadr_a; + + [[unroll]] for (uint k_step = 0; k_step < BK_STEP; k_step++) { + if (block + k_step * BK < end_k) { + block_a_to_shmem(k_step * BM + buf_ib, ib + k_step, iqs); + } + } + } + [[unroll]] for (uint l = 0; loadc_b + l < BN; l += loadstride_b) { + const uint buf_ib = loadc_b + l; + +#ifdef MUL_MAT_ID + const u16vec2 row_idx = row_ids[buf_ib]; + const uint ib = pos_b_ib + row_idx.y * p.batch_stride_b / BK + (row_idx.x % p.ne11) * p.stride_b / BK; +#else + const uint ib = pos_b_ib + buf_ib * p.stride_b / BK; +#endif + const uint iqs = loadr_b; + + [[unroll]] for (uint k_step = 0; k_step < BK_STEP; k_step++) { + block_b_to_shmem(k_step * BN + buf_ib, ib + k_step, iqs, block + k_step * BK < end_k); + } + } + + barrier(); + + pos_a_ib += BK_STEP; + pos_b_ib += BK_STEP; + + for (uint k_step = 0; k_step < BK_STEP; k_step++) { + // Load from shared into cache + [[unroll]] for (uint wsir = 0; wsir < WMITER; wsir++) { + [[unroll]] for (uint cr = 0; cr < TM; cr++) { + const uint reg_ib = wsir * TM + cr; + const uint buf_ib = warp_r * WM + wsir * WSUBM + tiwr * TM + cr; + + block_a_to_registers(reg_ib, k_step * BM + buf_ib); + } + } + + [[unroll]] for (uint wsic = 0; wsic < WNITER; wsic++) { + [[unroll]] for (uint cc = 0; cc < TN; cc++) { + const uint ib = k_step * BN + warp_c * WN + wsic * WSUBN + tiwc * TN + cc; + block_b_to_registers(ib); + + [[unroll]] for (uint wsir = 0; wsir < WMITER; wsir++) { + [[unroll]] for (uint cr = 0; cr < TM; cr++) { + const uint cache_a_idx = wsir * TM + cr; + const uint sums_idx = (wsic * TN + cc) * (WMITER * TM) + wsir * TM + cr; + + sums[sums_idx] += mmq_dot_product(cache_a_idx); + } + } + } + } + } + + barrier(); + } + + const uint dr = ir * BM + warp_r * WM; + const uint dc = ic * BN + warp_c * WN; + +#ifndef MUL_MAT_ID + const uint offsets = batch_idx * p.batch_stride_d + ik * p.batch_stride_d * p.num_batches; +#endif + + [[unroll]] for (uint wsic = 0; wsic < WNITER; wsic++) { + [[unroll]] for (uint wsir = 0; wsir < WMITER; wsir++) { + + const uint dr_warp = dr + wsir * WSUBM + tiwr * TM; + const uint dc_warp = dc + wsic * WSUBN + tiwc * TN; + [[unroll]] for (uint cc = 0; cc < TN; cc++) { +#ifdef MUL_MAT_ID + const uint row_i = dc_warp + cc; + if (row_i >= _ne1) break; + + const u16vec2 row_idx = row_ids[row_i - ic * BN]; +#endif // MUL_MAT_ID + [[unroll]] for (uint cr = 0; cr < TM; cr++) { + const uint sums_idx = (wsic * TN + cc) * WMITER * TM + wsir * TM + cr; +#ifdef MUL_MAT_ID + if (dr_warp + cr < p.M) { + data_d[row_idx.y * p.batch_stride_d + row_idx.x * p.stride_d + dr_warp + cr] = D_TYPE(sums[sums_idx].x); + } +#else + if (dr_warp + cr < p.M && dc_warp + cc < p.N) { + data_d[offsets + (dc_warp + cc) * p.stride_d + dr_warp + cr] = D_TYPE(sums[sums_idx].x); + } +#endif // MUL_MAT_ID + } + } + } + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mmq_funcs.glsl b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mmq_funcs.glsl new file mode 100644 index 0000000000000000000000000000000000000000..59931b04b9413b7d028b689adc43acf254d3d1c5 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mmq_funcs.glsl @@ -0,0 +1,454 @@ +#extension GL_EXT_shader_explicit_arithmetic_types_int32 : require +#extension GL_EXT_shader_explicit_arithmetic_types_int16 : require +#extension GL_EXT_shader_explicit_arithmetic_types_int8 : require + +#include "types.glsl" + +// Each iqs value maps to a 32-bit integer + +#if defined(DATA_A_Q4_0) || defined(DATA_A_Q4_1) +// 2-byte loads for Q4_0 blocks (18 bytes) +// 4-byte loads for Q4_1 blocks (20 bytes) +void block_a_to_shmem(const uint buf_ib, const uint ib, const uint iqs) { +#ifdef DATA_A_Q4_0 + buf_a[buf_ib].qs[iqs] = pack32(u16vec2(data_a_packed16[ib].qs[iqs * 2], + data_a_packed16[ib].qs[iqs * 2 + 1])); + + if (iqs == 0) { + buf_a[buf_ib].dm = FLOAT_TYPE(data_a_packed16[ib].d); + } +#else // DATA_A_Q4_1 + buf_a[buf_ib].qs[iqs] = data_a_packed32[ib].qs[iqs]; + + if (iqs == 0) { + buf_a[buf_ib].dm = FLOAT_TYPEV2(data_a_packed32[ib].dm); + } +#endif +} + +void block_a_to_registers(const uint reg_ib, const uint buf_ib) { + cache_a[reg_ib].dm = buf_a[buf_ib].dm; + + [[unroll]] for (uint iqs = 0; iqs < 4; iqs++) { + cache_a[reg_ib].qs[iqs] = buf_a[buf_ib].qs[iqs]; + } +} + +ACC_TYPE mmq_dot_product(const uint ib_a) { + int32_t q_sum = 0; + [[unroll]] for (uint iqs = 0; iqs < 4; iqs++) { + const uint32_t vui = cache_a[ib_a].qs[iqs]; + const i32vec2 qs_a = i32vec2( vui & 0x0F0F0F0F, + (vui >> 4) & 0x0F0F0F0F); + + const int32_t qs_b0 = cache_b.qs[iqs]; + const int32_t qs_b1 = cache_b.qs[iqs + 4]; + + q_sum += dotPacked4x8EXT(qs_a.x, qs_b0); + q_sum += dotPacked4x8EXT(qs_a.y, qs_b1); + } + +#ifdef DATA_A_Q4_0 + return ACC_TYPE(float(cache_a[ib_a].dm) * (float(q_sum) * float(cache_b.ds.x) - 8.0 * float(cache_b.ds.y))); +#else // DATA_A_Q4_1 + return ACC_TYPE(float(q_sum) * float(cache_a[ib_a].dm.x) * float(cache_b.ds.x) + float(cache_a[ib_a].dm.y) * float(cache_b.ds.y)); +#endif +} +#endif + +#if defined(DATA_A_Q5_0) || defined(DATA_A_Q5_1) +// 2-byte loads for Q5_0 blocks (22 bytes) +// 4-byte loads for Q5_1 blocks (24 bytes) +void block_a_to_shmem(const uint buf_ib, const uint ib, const uint iqs) { +#ifdef DATA_A_Q5_0 + buf_a[buf_ib].qs[iqs] = pack32(u16vec2(data_a_packed16[ib].qs[iqs * 2], + data_a_packed16[ib].qs[iqs * 2 + 1])); + + if (iqs == 0) { + buf_a[buf_ib].dm = FLOAT_TYPE(data_a_packed16[ib].d); + buf_a[buf_ib].qh = pack32(u16vec2(data_a_packed16[ib].qh[0], data_a_packed16[ib].qh[1])); + } +#else // DATA_A_Q5_1 + buf_a[buf_ib].qs[iqs] = data_a_packed32[ib].qs[iqs]; + + if (iqs == 0) { + buf_a[buf_ib].dm = FLOAT_TYPEV2(data_a_packed32[ib].dm); + buf_a[buf_ib].qh = data_a_packed32[ib].qh; + } +#endif +} + +void block_a_to_registers(const uint reg_ib, const uint buf_ib) { + cache_a[reg_ib].dm = buf_a[buf_ib].dm; + cache_a[reg_ib].qh = buf_a[buf_ib].qh; + + [[unroll]] for (uint iqs = 0; iqs < 4; iqs++) { + cache_a[reg_ib].qs[iqs] = buf_a[buf_ib].qs[iqs]; + } +} + +ACC_TYPE mmq_dot_product(const uint ib_a) { + int32_t q_sum = 0; + [[unroll]] for (uint iqs = 0; iqs < 4; iqs++) { + const uint32_t vui = cache_a[ib_a].qs[iqs]; + const int32_t qh = int32_t(cache_a[ib_a].qh >> (4 * iqs)); + const int32_t qs_a0 = int32_t(vui & 0x0F0F0F0F) + | ((qh & 0xF) * 0x02040810) & 0x10101010; // (0,1,2,3) -> (4,12,20,28) + const int32_t qs_a1 = int32_t((vui >> 4) & 0x0F0F0F0F) + | (((qh >> 16) & 0xF) * 0x02040810) & 0x10101010; // (16,17,18,19) -> (4,12,20,28) + + const int32_t qs_b0 = cache_b.qs[iqs]; + const int32_t qs_b1 = cache_b.qs[iqs + 4]; + + q_sum += dotPacked4x8EXT(qs_a0, qs_b0); + q_sum += dotPacked4x8EXT(qs_a1, qs_b1); + } + +#ifdef DATA_A_Q5_0 + return ACC_TYPE(float(cache_a[ib_a].dm) * (float(q_sum) * float(cache_b.ds.x) - 16.0 * float(cache_b.ds.y))); +#else // DATA_A_Q5_1 + return ACC_TYPE(float(q_sum) * float(cache_a[ib_a].dm.x) * float(cache_b.ds.x) + float(cache_a[ib_a].dm.y) * float(cache_b.ds.y)); +#endif +} +#endif + +#if defined(DATA_A_Q8_0) +// 2-byte loads for Q8_0 blocks (34 bytes) +void block_a_to_shmem(const uint buf_ib, const uint ib, const uint iqs) { + buf_a[buf_ib].qs[iqs] = pack32(i16vec2(data_a_packed16[ib].qs[iqs * 2], + data_a_packed16[ib].qs[iqs * 2 + 1])); + + if (iqs == 0) { + buf_a[buf_ib].dm = FLOAT_TYPE(data_a_packed16[ib].d); + } +} + +void block_a_to_registers(const uint reg_ib, const uint buf_ib) { + cache_a[reg_ib].dm = buf_a[buf_ib].dm; + + [[unroll]] for (uint iqs = 0; iqs < 8; iqs++) { + cache_a[reg_ib].qs[iqs] = buf_a[buf_ib].qs[iqs]; + } +} + +ACC_TYPE mmq_dot_product(const uint ib_a) { + int32_t q_sum = 0; + [[unroll]] for (uint iqs = 0; iqs < 8; iqs++) { + const int32_t qs_a = cache_a[ib_a].qs[iqs]; + const int32_t qs_b = cache_b.qs[iqs]; + + q_sum += dotPacked4x8EXT(qs_a, qs_b); + } + + return ACC_TYPE(float(q_sum) * float(cache_a[ib_a].dm) * float(cache_b.ds.x)); +} +#endif + +#if defined(DATA_A_MXFP4) +// 1-byte loads for mxfp4 blocks (17 bytes) +void block_a_to_shmem(const uint buf_ib, const uint ib, const uint iqs) { + const uint32_t qs = pack32(u8vec4(data_a[ib].qs[iqs * 4 ], + data_a[ib].qs[iqs * 4 + 1], + data_a[ib].qs[iqs * 4 + 2], + data_a[ib].qs[iqs * 4 + 3])); + + const u8vec4 i_a0 = unpack8( qs & 0x0F0F0F0F); + const u8vec4 i_a1 = unpack8((qs >> 4) & 0x0F0F0F0F); + + buf_a[buf_ib].qs[iqs ] = pack32(i8vec4(kvalues_mxfp4[i_a0.x], kvalues_mxfp4[i_a0.y], kvalues_mxfp4[i_a0.z], kvalues_mxfp4[i_a0.w])); + buf_a[buf_ib].qs[iqs + 4] = pack32(i8vec4(kvalues_mxfp4[i_a1.x], kvalues_mxfp4[i_a1.y], kvalues_mxfp4[i_a1.z], kvalues_mxfp4[i_a1.w])); + + if (iqs == 0) { + buf_a[buf_ib].d = FLOAT_TYPE(e8m0_to_fp32(data_a[ib].e) * 0.5); + } +} + +void block_a_to_registers(const uint reg_ib, const uint buf_ib) { + cache_a[reg_ib].d = buf_a[buf_ib].d; + + [[unroll]] for (uint iqs = 0; iqs < 8; iqs++) { + cache_a[reg_ib].qs[iqs] = buf_a[buf_ib].qs[iqs]; + } +} + +ACC_TYPE mmq_dot_product(const uint ib_a) { + int32_t q_sum = 0; + [[unroll]] for (uint iqs = 0; iqs < 8; iqs++) { + const int32_t qs_a = cache_a[ib_a].qs[iqs]; + + q_sum += dotPacked4x8EXT(qs_a, cache_b.qs[iqs]); + } + + return ACC_TYPE(float(cache_a[ib_a].d) * float(cache_b.ds.x) * float(q_sum)); +} +#endif + +// For k-quants, ib and iqs still assume 32-wide blocks, but k-quants are 256-wide +// iqs still refers to a 32-bit integer, meaning 0..7 for 32-wide quants +#if defined(DATA_A_Q2_K) +// 4-byte loads for Q2_K blocks (84 bytes) +void block_a_to_shmem(const uint buf_ib, const uint ib, const uint iqs) { + const uint ib_k = ib / 8; + const uint iqs_k = (ib % 8) * 8 + iqs * QUANT_R_MMQ; + + const uint qs_idx = (iqs_k / 32) * 8 + (iqs_k % 8); + const uint qs_shift = ((iqs_k % 32) / 8) * 2; + + // Repack 4x4 quants into one int + const uint32_t vals0 = (data_a_packed32[ib_k].qs[qs_idx ] >> qs_shift) & 0x03030303; + const uint32_t vals1 = (data_a_packed32[ib_k].qs[qs_idx + 1] >> qs_shift) & 0x03030303; + const uint32_t vals2 = (data_a_packed32[ib_k].qs[qs_idx + 2] >> qs_shift) & 0x03030303; + const uint32_t vals3 = (data_a_packed32[ib_k].qs[qs_idx + 3] >> qs_shift) & 0x03030303; + + buf_a[buf_ib].qs[iqs] = vals0 | (vals1 << 2) | (vals2 << 4) | (vals3 << 6); + + if (iqs == 0) { + buf_a[buf_ib].dm = FLOAT_TYPEV2(data_a_packed32[ib_k].dm); + buf_a[buf_ib].scales = unpack8(uint32_t(data_a_packed16[ib_k].scales[iqs_k / 8])).xy; // vec4 used due to #12147 + } +} + +void block_a_to_registers(const uint reg_ib, const uint buf_ib) { + cache_a[reg_ib].dm = buf_a[buf_ib].dm; + cache_a[reg_ib].scales = buf_a[buf_ib].scales; + + [[unroll]] for (uint iqs = 0; iqs < 2; iqs++) { + cache_a[reg_ib].qs[iqs] = buf_a[buf_ib].qs[iqs]; + } +} + +ACC_TYPE mmq_dot_product(const uint ib_a) { + int32_t sum_d = 0; + int32_t sum_m = 0; + + [[unroll]] for (uint iqs = 0; iqs < 8; iqs++) { + const uint8_t scale = cache_a[ib_a].scales[iqs / 4]; + const int32_t scale_m = int32_t(scale >> 4) * 0x01010101; // Duplicate 8-bit value across 32-bits. + const int32_t qs_a = int32_t((cache_a[ib_a].qs[iqs / 4] >> ((iqs % 4) * 2)) & 0x03030303); + + sum_d += dotPacked4x8EXT(qs_a, cache_b.qs[iqs]) * (scale & 0xF); + sum_m += dotPacked4x8EXT(scale_m, cache_b.qs[iqs]); + } + + return ACC_TYPE(float(cache_b.ds.x) * (float(cache_a[ib_a].dm.x) * float(sum_d) - float(cache_a[ib_a].dm.y) * float(sum_m))); +} +#endif + +#if defined(DATA_A_Q3_K) +// 2-byte loads for Q3_K blocks (110 bytes) +void block_a_to_shmem(const uint buf_ib, const uint ib, const uint iqs) { + const uint ib_k = ib / 8; + const uint hm_idx = iqs * QUANT_R_MMQ; + const uint iqs_k = (ib % 8) * 8 + hm_idx; + + const uint qs_idx = (iqs_k / 32) * 8 + (iqs_k % 8); + const uint qs_shift = ((iqs_k % 32) / 8) * 2; + const uint hm_shift = iqs_k / 8; + + // Repack 2x4 quants into one int + // Add the 3rd bit instead of subtracting it to allow packing the quants + // vec4 for unpack8 used due to #12147 + const i8vec2 vals00 = unpack8(int32_t(int16_t((data_a_packed16[ib_k].qs[qs_idx * 2 ] >> qs_shift) & uint16_t(0x0303)))).xy | + unpack8(int32_t(int16_t(((data_a_packed16[ib_k].hmask[hm_idx * 2 ] >> hm_shift) & uint16_t(0x0101))) << 2)).xy; + const i8vec2 vals01 = unpack8(int32_t(int16_t((data_a_packed16[ib_k].qs[qs_idx * 2 + 1 ] >> qs_shift) & uint16_t(0x0303)))).xy | + unpack8(int32_t(int16_t(((data_a_packed16[ib_k].hmask[hm_idx * 2 + 1] >> hm_shift) & uint16_t(0x0101))) << 2)).xy; + const i8vec2 vals10 = unpack8(int32_t(int16_t((data_a_packed16[ib_k].qs[qs_idx * 2 + 2 ] >> qs_shift) & uint16_t(0x0303)))).xy | + unpack8(int32_t(int16_t(((data_a_packed16[ib_k].hmask[hm_idx * 2 + 2] >> hm_shift) & uint16_t(0x0101))) << 2)).xy; + const i8vec2 vals11 = unpack8(int32_t(int16_t((data_a_packed16[ib_k].qs[qs_idx * 2 + 3 ] >> qs_shift) & uint16_t(0x0303)))).xy | + unpack8(int32_t(int16_t(((data_a_packed16[ib_k].hmask[hm_idx * 2 + 3] >> hm_shift) & uint16_t(0x0101))) << 2)).xy; + buf_a[buf_ib].qs[iqs] = pack32(u8vec4(vals00.x, vals00.y, vals01.x, vals01.y)) | + (pack32(u8vec4(vals10.x, vals10.y, vals11.x, vals11.y)) << 4); + + if (iqs == 0) { + const uint is = iqs_k / 4; + const i8vec2 scales = i8vec2(unpack8(uint32_t(((data_a_packed16[ib_k].scales[(is % 8 ) / 2] >> (4 * (is / 8))) & 0x0F0F) | + (((data_a_packed16[ib_k].scales[(8 + (is % 4)) / 2] >> (2 * (is / 4))) & 0x0303) << 4))).xy); // vec4 used due to #12147 + + buf_a[buf_ib].d_scales = FLOAT_TYPEV2(float(data_a_packed16[ib_k].d) * vec2(scales - 32)); + } +} + +void block_a_to_registers(const uint reg_ib, const uint buf_ib) { + cache_a[reg_ib].d_scales = buf_a[buf_ib].d_scales; + + [[unroll]] for (uint iqs = 0; iqs < 4; iqs++) { + cache_a[reg_ib].qs[iqs] = buf_a[buf_ib].qs[iqs]; + } +} + +ACC_TYPE mmq_dot_product(const uint ib_a) { + float result = 0.0; + int32_t q_sum = 0; + + [[unroll]] for (uint iqs = 0; iqs < 4; iqs++) { + // Subtract 4 from the quants to correct the 3rd bit offset + const int32_t qs_a = pack32(unpack8(int32_t((cache_a[ib_a].qs[iqs / 2] >> ((iqs % 2) * 4)) & 0x0F0F0F0F)) - int8_t(4)); + + q_sum += dotPacked4x8EXT(qs_a, cache_b.qs[iqs]); + } + result += float(cache_a[ib_a].d_scales[0]) * float(q_sum); + q_sum = 0; + + [[unroll]] for (uint iqs = 4; iqs < 8; iqs++) { + const int32_t qs_a = pack32(unpack8(int32_t((cache_a[ib_a].qs[iqs / 2] >> ((iqs % 2) * 4)) & 0x0F0F0F0F)) - int8_t(4)); + + q_sum += dotPacked4x8EXT(qs_a, cache_b.qs[iqs]); + } + result += float(cache_a[ib_a].d_scales[1]) * float(q_sum); + + return ACC_TYPE(float(cache_b.ds.x) * result); +} +#endif + +#if defined(DATA_A_Q4_K) || defined(DATA_A_Q5_K) +// 4-byte loads for Q4_K blocks (144 bytes) and Q5_K blocks (176 bytes) +void block_a_to_shmem(const uint buf_ib, const uint ib, const uint iqs) { + const uint ib_k = ib / 8; + const uint iqs_k = (ib % 8) * 8 + iqs * QUANT_R_MMQ; + + const uint qs_idx = (iqs_k / 16) * 8 + (iqs_k % 8); + const uint qs_shift = ((iqs_k % 16) / 8) * 4; + + // Repack 2x4 quants into one int +#if defined(DATA_A_Q4_K) + const uint32_t vals0 = (data_a_packed32[ib_k].qs[qs_idx ] >> qs_shift) & 0x0F0F0F0F; + const uint32_t vals1 = (data_a_packed32[ib_k].qs[qs_idx + 1] >> qs_shift) & 0x0F0F0F0F; + + buf_a[buf_ib].qs[iqs] = vals0 | (vals1 << 4); +#else // defined(DATA_A_Q5_K) + const uint qh_idx = iqs * QUANT_R_MMQ; + const uint qh_shift = iqs_k / 8; + + buf_a[buf_ib].qs[iqs] = int32_t(((data_a_packed32[ib_k].qs[qs_idx] >> qs_shift) & 0x0F0F0F0F) | + (((data_a_packed32[ib_k].qh[qh_idx] >> qh_shift) & 0x01010101) << 4)); +#endif + + if (iqs == 0) { + // Scale index + const uint is = iqs_k / 8; + u8vec2 scale_dm; + if (is < 4) { + scale_dm = u8vec2(data_a[ib_k].scales[is] & 0x3F, data_a[ib_k].scales[is + 4] & 0x3F); + } else { + scale_dm = u8vec2((data_a[ib_k].scales[is+4] & 0xF) | ((data_a[ib_k].scales[is-4] & 0xC0) >> 2), + (data_a[ib_k].scales[is+4] >> 4) | ((data_a[ib_k].scales[is ] & 0xC0) >> 2)); + } + + buf_a[buf_ib].dm = FLOAT_TYPEV2(vec2(data_a_packed32[ib_k].dm) * vec2(scale_dm)); + } +} + +void block_a_to_registers(const uint reg_ib, const uint buf_ib) { + cache_a[reg_ib].dm = buf_a[buf_ib].dm; + + [[unroll]] for (uint iqs = 0; iqs < 8 / QUANT_R_MMQ; iqs++) { + cache_a[reg_ib].qs[iqs] = buf_a[buf_ib].qs[iqs]; + } +} + +ACC_TYPE mmq_dot_product(const uint ib_a) { + int32_t q_sum = 0; + + [[unroll]] for (uint iqs = 0; iqs < 8; iqs++) { +#if defined(DATA_A_Q4_K) + const int32_t qs_a = int32_t((cache_a[ib_a].qs[iqs / 2] >> ((iqs % 2) * 4)) & 0x0F0F0F0F); +#else // defined(DATA_A_Q5_K) + const int32_t qs_a = cache_a[ib_a].qs[iqs]; +#endif + + q_sum += dotPacked4x8EXT(qs_a, cache_b.qs[iqs]); + } + + return ACC_TYPE(float(cache_b.ds.x) * float(cache_a[ib_a].dm.x) * float(q_sum) - float(cache_a[ib_a].dm.y) * float(cache_b.ds.y)); +} +#endif + +#if defined(DATA_A_Q6_K) +// 2-byte loads for Q6_K blocks (210 bytes) +void block_a_to_shmem(const uint buf_ib, const uint ib, const uint iqs) { + const uint ib_k = ib / 8; + const uint iqs_k = (ib % 8) * 8 + iqs; + + const uint ql_idx = (iqs_k / 32) * 16 + iqs_k % 16; + const uint ql_shift = ((iqs_k % 32) / 16) * 4; + + const uint qh_idx = (iqs_k / 32) * 8 + iqs; + const uint qh_shift = ((iqs_k % 32) / 8) * 2; + + const i8vec2 vals00 = (unpack8(int32_t((data_a_packed16[ib_k].ql[ql_idx * 2 ] >> ql_shift) & uint16_t(0x0F0F))).xy | + unpack8(int32_t(((data_a_packed16[ib_k].qh[qh_idx * 2 ] >> qh_shift) & uint16_t(0x0303)) << 4)).xy) - int8_t(32); + const i8vec2 vals01 = (unpack8(int32_t((data_a_packed16[ib_k].ql[ql_idx * 2 + 1] >> ql_shift) & uint16_t(0x0F0F))).xy | + unpack8(int32_t(((data_a_packed16[ib_k].qh[qh_idx * 2 + 1] >> qh_shift) & uint16_t(0x0303)) << 4)).xy) - int8_t(32); + buf_a[buf_ib].qs[iqs] = pack32(i8vec4(vals00.x, vals00.y, vals01.x, vals01.y)); + + if (iqs == 0) { + const uint is = iqs_k / 4; + const i8vec2 scales = unpack8(int32_t(data_a_packed16[ib_k].scales[is / 2])).xy; + + buf_a[buf_ib].d_scales = FLOAT_TYPEV2(float(data_a_packed16[ib_k].d) * vec2(scales)); + } +} + +void block_a_to_registers(const uint reg_ib, const uint buf_ib) { + cache_a[reg_ib].d_scales = buf_a[buf_ib].d_scales; + + [[unroll]] for (uint iqs = 0; iqs < 8; iqs++) { + cache_a[reg_ib].qs[iqs] = buf_a[buf_ib].qs[iqs]; + } +} + +ACC_TYPE mmq_dot_product(const uint ib_a) { + float result = 0.0; + int32_t q_sum = 0; + + [[unroll]] for (uint iqs = 0; iqs < 4; iqs++) { + const int32_t qs_a = cache_a[ib_a].qs[iqs]; + + q_sum += dotPacked4x8EXT(qs_a, cache_b.qs[iqs]); + } + result += float(cache_a[ib_a].d_scales[0]) * float(q_sum); + q_sum = 0; + + [[unroll]] for (uint iqs = 4; iqs < 8; iqs++) { + const int32_t qs_a = cache_a[ib_a].qs[iqs]; + + q_sum += dotPacked4x8EXT(qs_a, cache_b.qs[iqs]); + } + result += float(cache_a[ib_a].d_scales[1]) * float(q_sum); + + return ACC_TYPE(float(cache_b.ds.x) * result); +} +#endif + +void block_b_to_shmem(const uint buf_ib, const uint ib, const uint iqs, const bool is_in_bounds) { + if (is_in_bounds) { + const uint ib_outer = ib / 4; + const uint ib_inner = ib % 4; + + if (iqs == 0) { + buf_b[buf_ib].ds = FLOAT_TYPEV2(data_b[ib_outer].ds[ib_inner]); + } + + const ivec4 values = data_b[ib_outer].qs[ib_inner * 2 + iqs]; + buf_b[buf_ib].qs[iqs * 4 ] = values.x; + buf_b[buf_ib].qs[iqs * 4 + 1] = values.y; + buf_b[buf_ib].qs[iqs * 4 + 2] = values.z; + buf_b[buf_ib].qs[iqs * 4 + 3] = values.w; + } else { + if (iqs == 0) { + buf_b[buf_ib].ds = FLOAT_TYPEV2(0.0f); + } + + buf_b[buf_ib].qs[iqs * 4 ] = 0; + buf_b[buf_ib].qs[iqs * 4 + 1] = 0; + buf_b[buf_ib].qs[iqs * 4 + 2] = 0; + buf_b[buf_ib].qs[iqs * 4 + 3] = 0; + } +} + +void block_b_to_registers(const uint ib) { + cache_b.ds = buf_b[ib].ds; + [[unroll]] for (uint iqs = 0; iqs < BK / 4; iqs++) { + cache_b.qs[iqs] = buf_b[ib].qs[iqs]; + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mmq_shmem_types.glsl b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mmq_shmem_types.glsl new file mode 100644 index 0000000000000000000000000000000000000000..79c933f40cf2de48d94559ddc40dbcf17933e14c --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/mul_mmq_shmem_types.glsl @@ -0,0 +1,93 @@ +#if defined(FA_MMQ_MIXED) +// Mixed-K flash attention MMQ: superset cache that fits Q4_0/Q4_1/Q5_0/Q5_1/Q8_0. +// Q4_*/Q5_* only use qs[0..3] and (for Q5_*) qh. Q8_0 uses qs[0..7]. Single-scale +// types (Q4_0/Q5_0/Q8_0) leave dm.y unused. +struct block_a_cache { + int32_t qs[8]; + uint32_t qh; + FLOAT_TYPEV2 dm; +}; +#elif defined(DATA_A_Q4_0) +#define QUANT_R_MMQ 2 +struct block_a_cache { + uint32_t qs[16/4]; + FLOAT_TYPE dm; +}; +#elif defined(DATA_A_Q4_1) +#define QUANT_R_MMQ 2 +struct block_a_cache { + uint32_t qs[16/4]; + FLOAT_TYPEV2 dm; +}; +#elif defined(DATA_A_Q5_0) +#define QUANT_R_MMQ 2 +struct block_a_cache { + uint32_t qs[16/4]; + uint32_t qh; + FLOAT_TYPE dm; +}; +#elif defined(DATA_A_Q5_1) +#define QUANT_R_MMQ 2 +struct block_a_cache { + uint32_t qs[16/4]; + uint32_t qh; + FLOAT_TYPEV2 dm; +}; +#elif defined(DATA_A_Q8_0) +#define QUANT_R_MMQ 1 +// AMD likes 4, Intel likes 1 and Nvidia likes 2 +// #define BK_STEP 1 +struct block_a_cache { + int32_t qs[32/4]; + FLOAT_TYPE dm; +}; +#elif defined(DATA_A_IQ4_NL) +#define QUANT_R_MMQ 2 +struct block_a_cache { + int32_t qs[8]; + FLOAT_TYPE dm; +}; +#elif defined(DATA_A_MXFP4) +#define QUANT_R_MMQ 2 +struct block_a_cache { + int32_t qs[8]; + FLOAT_TYPE d; +}; +#elif defined(DATA_A_Q2_K) +#define QUANT_R_MMQ 4 +struct block_a_cache { + uint32_t qs[2]; + u8vec2 scales; + FLOAT_TYPEV2 dm; +}; +#elif defined(DATA_A_Q3_K) +#define QUANT_R_MMQ 2 +struct block_a_cache { + uint32_t qs[4]; + FLOAT_TYPEV2 d_scales; +}; +#elif defined(DATA_A_Q4_K) +#define QUANT_R_MMQ 2 +struct block_a_cache { + uint32_t qs[4]; + FLOAT_TYPEV2 dm; +}; +#elif defined(DATA_A_Q5_K) +#define QUANT_R_MMQ 1 +struct block_a_cache { + int32_t qs[8]; + FLOAT_TYPEV2 dm; +}; +#elif defined(DATA_A_Q6_K) +#define QUANT_R_MMQ 1 +struct block_a_cache { + int32_t qs[8]; + FLOAT_TYPEV2 d_scales; +}; +#endif + +struct block_b_cache +{ + int32_t qs[8]; + FLOAT_TYPEV2 ds; +}; diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/multi_add.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/multi_add.comp new file mode 100644 index 0000000000000000000000000000000000000000..26d194e9e8de97552e24f11ca15e2227e145c012 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/multi_add.comp @@ -0,0 +1,194 @@ +#version 450 + +#extension GL_EXT_shader_16bit_storage : require +#extension GL_EXT_nonuniform_qualifier : enable +#extension GL_EXT_control_flow_attributes : require +#if ADD_RMS +#extension GL_KHR_shader_subgroup_arithmetic : enable +#extension GL_KHR_shader_subgroup_basic : enable +#endif + +#include "types.glsl" +#include "utils.glsl" + +layout (push_constant) uniform parameter2 +{ + // shape for dst + uint ne20; uint ne21; uint ne22; uint ne23; + + // strides for srcs+dst + uint nb[12][4]; + + uint rms_partials; +} p; + +// No readonly/writeonly decorations. Workaround for MoltenVK Bug, see https://github.com/ggml-org/llama.cpp/issues/15498 +layout (binding = 0) buffer A0 {A_TYPE data_a[];} a0; +layout (binding = 1) buffer A1 {A_TYPE data_a[];} a1; +layout (binding = 2) buffer A2 {A_TYPE data_a[];} a2; +layout (binding = 3) buffer A3 {A_TYPE data_a[];} a3; +layout (binding = 4) buffer A4 {A_TYPE data_a[];} a4; +layout (binding = 5) buffer A5 {A_TYPE data_a[];} a5; +layout (binding = 6) buffer A6 {A_TYPE data_a[];} a6; +layout (binding = 7) buffer A7 {A_TYPE data_a[];} a7; +layout (binding = 8) buffer A8 {A_TYPE data_a[];} a8; +layout (binding = 9) buffer A9 {A_TYPE data_a[];} a9; +layout (binding = 10) buffer A10 {A_TYPE data_a[];} a10; +layout (binding = 11) buffer A11 {A_TYPE data_a[];} a11; +layout (binding = 0) buffer D0 {D_TYPE data_d[];} d0; +layout (binding = 1) buffer D1 {D_TYPE data_d[];} d1; +layout (binding = 2) buffer D2 {D_TYPE data_d[];} d2; +layout (binding = 3) buffer D3 {D_TYPE data_d[];} d3; +layout (binding = 4) buffer D4 {D_TYPE data_d[];} d4; +layout (binding = 5) buffer D5 {D_TYPE data_d[];} d5; +layout (binding = 6) buffer D6 {D_TYPE data_d[];} d6; +layout (binding = 7) buffer D7 {D_TYPE data_d[];} d7; +layout (binding = 8) buffer D8 {D_TYPE data_d[];} d8; +layout (binding = 9) buffer D9 {D_TYPE data_d[];} d9; +layout (binding = 10) buffer D10 {D_TYPE data_d[];} d10; +layout (binding = 11) buffer D11 {D_TYPE data_d[];} d11; +layout (binding = 0, std430) buffer PartialBuf0 {float partial_sums[];} partials0; +layout (binding = 1, std430) buffer PartialBuf1 {float partial_sums[];} partials1; +layout (binding = 2, std430) buffer PartialBuf2 {float partial_sums[];} partials2; +layout (binding = 3, std430) buffer PartialBuf3 {float partial_sums[];} partials3; +layout (binding = 4, std430) buffer PartialBuf4 {float partial_sums[];} partials4; +layout (binding = 5, std430) buffer PartialBuf5 {float partial_sums[];} partials5; +layout (binding = 6, std430) buffer PartialBuf6 {float partial_sums[];} partials6; +layout (binding = 7, std430) buffer PartialBuf7 {float partial_sums[];} partials7; +layout (binding = 8, std430) buffer PartialBuf8 {float partial_sums[];} partials8; +layout (binding = 9, std430) buffer PartialBuf9 {float partial_sums[];} partials9; +layout (binding = 10, std430) buffer PartialBuf10 {float partial_sums[];} partials10; +layout (binding = 11, std430) buffer PartialBuf11 {float partial_sums[];} partials11; + +layout(constant_id = 0) const uint num_srcs = 2; + +FLOAT_TYPE load_a(uint b, uint i) { + switch (b) { + case 0: return FLOAT_TYPE(a0.data_a[i]); + case 1: return FLOAT_TYPE(a1.data_a[i]); + case 2: return FLOAT_TYPE(a2.data_a[i]); + case 3: return FLOAT_TYPE(a3.data_a[i]); + case 4: return FLOAT_TYPE(a4.data_a[i]); + case 5: return FLOAT_TYPE(a5.data_a[i]); + case 6: return FLOAT_TYPE(a6.data_a[i]); + case 7: return FLOAT_TYPE(a7.data_a[i]); + case 8: return FLOAT_TYPE(a8.data_a[i]); + case 9: return FLOAT_TYPE(a9.data_a[i]); + case 10: return FLOAT_TYPE(a10.data_a[i]); + case 11: return FLOAT_TYPE(a11.data_a[i]); + default: return FLOAT_TYPE(0); + } +} + +void store_d(uint b, uint i, FLOAT_TYPE v) { + switch (b) { + case 0: d0.data_d[i] = D_TYPE(v); break; + case 1: d1.data_d[i] = D_TYPE(v); break; + case 2: d2.data_d[i] = D_TYPE(v); break; + case 3: d3.data_d[i] = D_TYPE(v); break; + case 4: d4.data_d[i] = D_TYPE(v); break; + case 5: d5.data_d[i] = D_TYPE(v); break; + case 6: d6.data_d[i] = D_TYPE(v); break; + case 7: d7.data_d[i] = D_TYPE(v); break; + case 8: d8.data_d[i] = D_TYPE(v); break; + case 9: d9.data_d[i] = D_TYPE(v); break; + case 10: d10.data_d[i] = D_TYPE(v); break; + case 11: d11.data_d[i] = D_TYPE(v); break; + default: break; + } +} + +void store_partial(uint b, uint i, float v) { + switch (b) { + case 0: partials0.partial_sums[i] = v; break; + case 1: partials1.partial_sums[i] = v; break; + case 2: partials2.partial_sums[i] = v; break; + case 3: partials3.partial_sums[i] = v; break; + case 4: partials4.partial_sums[i] = v; break; + case 5: partials5.partial_sums[i] = v; break; + case 6: partials6.partial_sums[i] = v; break; + case 7: partials7.partial_sums[i] = v; break; + case 8: partials8.partial_sums[i] = v; break; + case 9: partials9.partial_sums[i] = v; break; + case 10: partials10.partial_sums[i] = v; break; + case 11: partials11.partial_sums[i] = v; break; + default: break; + } +} + +uint src_idx(uint s, uint i00, uint i01, uint i02, uint i03) { + return i03*p.nb[s][3] + i02*p.nb[s][2] + i01*p.nb[s][1] + i00*p.nb[s][0]; +} + +uint dst_idx(uint i00, uint i01, uint i02, uint i03) { + uint nb20 = p.nb[num_srcs][0]; + uint nb21 = p.nb[num_srcs][1]; + uint nb22 = p.nb[num_srcs][2]; + uint nb23 = p.nb[num_srcs][3]; + return i03*nb23 + i02*nb22 + i01*nb21 + i00*nb20; +} + +uint get_idx() { + return gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x; +} + +const uint num_threads = 256; + +layout(local_size_x = num_threads, local_size_y = 1, local_size_z = 1) in; + +#if ADD_RMS +// XXX TODO this could be sized based on number of subgroups, but that't not considered a constant +shared FLOAT_TYPE sumsh[num_threads]; +#endif + +void main() { + uint idx = get_idx(); + uint orig_idx = idx; + + uint ne = p.ne20 * p.ne21 * p.ne22 * p.ne23; + + // num_threads * num_iter must equal 512, to match the wg_denoms and get_idx calculation + const uint num_iter = 2; + + FLOAT_TYPE sum_sq = 0; + + [[unroll]] for (uint i = 0; i < num_iter; ++i) { + if (idx >= ne) { + continue; + } + uint i00, i01, i02, i03; + get_indices(idx, i00, i01, i02, i03, p.ne20, p.ne21, p.ne22, p.ne23); + + FLOAT_TYPE sum = FLOAT_TYPE(0); + [[unroll]] for (uint s = 0; s < num_srcs; ++s) { + sum += load_a(s, src_idx(s, i00, i01, i02, i03)); + } + sum_sq += sum*sum; + store_d(num_srcs, dst_idx(i00, i01, i02, i03), sum); + + idx += num_threads; + } + +#if ADD_RMS + if (p.rms_partials != 0) { + // reduce the sum within each subgroup, then across subgroups + const uint NumSubgroups = num_threads / gl_SubgroupSize; + sum_sq = subgroupAdd(sum_sq); + if (gl_SubgroupInvocationID == 0) { + sumsh[gl_SubgroupID] = sum_sq; + } + barrier(); + [[unroll]] for (uint s = NumSubgroups / 2; s > 0; s >>= 1) { + if (gl_SubgroupID < s && gl_SubgroupInvocationID == 0) { + sum_sq += sumsh[gl_SubgroupID + s]; + sumsh[gl_SubgroupID] = sum_sq; + } + barrier(); + } + + if (gl_SubgroupID == 0 && gl_SubgroupInvocationID == 0) { + store_partial(num_srcs + 1, orig_idx / (num_iter * num_threads), sum_sq); + } + } +#endif +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/norm.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/norm.comp new file mode 100644 index 0000000000000000000000000000000000000000..792012d57e856de7f89fa4213a6958d890b9369b --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/norm.comp @@ -0,0 +1,44 @@ +#version 450 + +#include "types.glsl" +#include "generic_unary_head.glsl" + +#extension GL_EXT_control_flow_attributes : enable +#define BLOCK_SIZE 512 + +layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in; + +shared vec2 sum[BLOCK_SIZE]; + +void main() { + const uint row = gl_WorkGroupID.z * 262144 + gl_WorkGroupID.y * 512 + gl_WorkGroupID.x; + const uint tid = gl_LocalInvocationID.x; + + const uint a_base = get_aoffset() + src0_idx(row * p.ne00); + const uint d_base = get_doffset() + dst_idx(row * p.ne10); + + sum[tid] = vec2(0.0f, 0.0f); + + [[unroll]] for (uint i0 = tid; i0 < p.ne00; i0 += BLOCK_SIZE) { + const float xi = float(data_a[a_base + i0*p.nb00]); + sum[tid].x += xi; + sum[tid].y += xi * xi; + } + + // sum up partial sums and write back result + barrier(); + [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) { + if (tid < s) { + sum[tid] += sum[tid + s]; + } + barrier(); + } + + const float mean = sum[0].x / p.ne00; + const float var = sum[0].y / p.ne00 - mean * mean; + const float inv_std = inversesqrt(var + p.param1); + + [[unroll]] for (uint i0 = tid; i0 < p.ne00; i0 += BLOCK_SIZE) { + data_d[d_base + i0*p.nb10] = D_TYPE((float(data_a[a_base + i0*p.nb00]) - mean) * inv_std); + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/opt_step_adamw.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/opt_step_adamw.comp new file mode 100644 index 0000000000000000000000000000000000000000..1f05f922cc2a4b4f04f93359d2078bf9d6166964 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/opt_step_adamw.comp @@ -0,0 +1,42 @@ +#version 450 + +#include "generic_head.glsl" +#include "types.glsl" + +#extension GL_EXT_control_flow_attributes : enable + +layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) buffer X {A_TYPE x[];}; +layout (binding = 1) readonly buffer G {A_TYPE grad[];}; +layout (binding = 2) buffer GM {A_TYPE gradm[];}; +layout (binding = 3) buffer GV {A_TYPE gradv[];}; +layout (binding = 4) readonly buffer P {float params[7];}; + +void main() { + const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x; + + if (i >= p.KX) { + return; + } + + const float alpha = params[0]; + const float beta1 = params[1]; + const float beta2 = params[2]; + const float eps = params[3]; + const float wd = params[4]; + const float beta1h = params[5]; + const float beta2h = params[6]; + + const float gi = grad[i]; + const float gmi = gradm[i]*beta1 + gi*(1.0f - beta1); + const float gvi = gradv[i]*beta2 + gi*gi*(1.0f - beta2); + + gradm[i] = gmi; + gradv[i] = gvi; + + const float mh = gmi*beta1h; + const float vh = sqrt(gvi*beta2h) + eps; + + x[i] = x[i]*(1.0f - alpha*wd) - alpha*mh/vh; +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/opt_step_sgd.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/opt_step_sgd.comp new file mode 100644 index 0000000000000000000000000000000000000000..1251f9cc641b490e9f5024c7c71b443fbb4dc678 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/opt_step_sgd.comp @@ -0,0 +1,22 @@ +#version 450 + +#include "generic_head.glsl" + +layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) buffer X {A_TYPE data_x[];}; +layout (binding = 1) readonly buffer G {A_TYPE data_grad[];}; +layout (binding = 2) readonly buffer P {float data_params[2];}; + +void main() { + const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x; + + if (i >= p.KX) { + return; + } + + const float alpha = data_params[0]; + const float keep = 1.f - alpha * data_params[1]; + + data_x[i] = data_x[i] * keep - alpha * data_grad[i]; +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/pad.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/pad.comp new file mode 100644 index 0000000000000000000000000000000000000000..5abd2f6fc693176bcbbb732a8c48d46029b39c48 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/pad.comp @@ -0,0 +1,64 @@ +#version 450 + +#include "types.glsl" + +layout (push_constant) uniform parameter +{ + uint ne; + uint ne00; uint ne01; uint ne02; uint ne03; uint nb00; uint nb01; uint nb02; uint nb03; + uint ne10; uint ne11; uint ne12; uint ne13; uint nb10; uint nb11; uint nb12; uint nb13; + uint misalign_offsets; + uint circular; + + uint lp0; uint rp0; + uint lp1; uint rp1; + uint lp2; uint rp2; + uint lp3; uint rp3; +} p; + +uint get_aoffset() { return p.misalign_offsets >> 16; } +uint get_doffset() { return p.misalign_offsets & 0xFFFF; } + +uint wrap_around(int coord, uint size) { + return (uint(coord + int(size))) % size; // add size to avoid issues with negative +} + +layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_d[];}; + +layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; + +void main() { + const uint idx = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x; + + if (idx >= p.ne) { + return; + } + + const uint i3 = idx / (p.ne12*p.ne11*p.ne10); + const uint i3_offset = i3 * p.ne12*p.ne11*p.ne10; + const uint i2 = (idx - i3_offset) / (p.ne11*p.ne10); + const uint i2_offset = i2*p.ne11*p.ne10; + const uint i1 = (idx - i3_offset - i2_offset) / p.ne10; + const uint i0 = idx - i3_offset - i2_offset - i1*p.ne10; + + const uint src0_idx = (i3 - p.lp3)*p.nb03 + (i2 - p.lp2)*p.nb02 + (i1 - p.lp1)*p.nb01 + (i0 - p.lp0)*p.nb00; + const uint dst_idx = i3*p.nb13 + i2*p.nb12 + i1*p.nb11 + i0*p.nb10; + + if (p.circular != 0u) { + const uint ci0 = wrap_around(int(i0) - int(p.lp0), p.ne00); + const uint ci1 = wrap_around(int(i1) - int(p.lp1), p.ne01); + const uint ci2 = wrap_around(int(i2) - int(p.lp2), p.ne02); + const uint ci3 = wrap_around(int(i3) - int(p.lp3), p.ne03); + const uint circular_src_idx = ci3*p.nb03 + ci2*p.nb02 + ci1*p.nb01 + ci0*p.nb00; + data_d[get_doffset() + dst_idx] = D_TYPE(data_a[get_aoffset() + circular_src_idx]); + } else { + const bool is_src0 = i0 >= p.lp0 && i0 < p.ne10 - p.rp0 && + i1 >= p.lp1 && i1 < p.ne11 - p.rp1 && + i2 >= p.lp2 && i2 < p.ne12 - p.rp2 && + i3 >= p.lp3 && i3 < p.ne13 - p.rp3; + data_d[get_doffset() + dst_idx] = D_TYPE(is_src0 ? data_a[get_aoffset() + src0_idx] : 0.0f); + } + + +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/pool2d.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/pool2d.comp new file mode 100644 index 0000000000000000000000000000000000000000..d9d7166e3617cc173a4c042ee6416de38329e5ce --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/pool2d.comp @@ -0,0 +1,74 @@ +#version 450 + +#include "types.glsl" + +#extension GL_EXT_shader_16bit_storage : require + +layout(push_constant) uniform parameter { + uint IW; uint IH; + uint OW; uint OH; + uint OC; + uint pelements; + uint op; + int k0; int k1; + int s0; int s1; + int p0; int p1; +} p; + +#define BLOCK_SIZE 512 +#define FLT_MAX 3.402823466e+38F +#define OP_POOL_MAX 0u +#define OP_POOL_AVG 1u + +layout (local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in; + +layout(binding = 0) readonly buffer X {A_TYPE data_a[];}; +layout(binding = 1) writeonly buffer D {D_TYPE data_d[];}; + +void main() { + const uint idx = gl_GlobalInvocationID.x; + if (idx >= p.pelements) { + return; + } + + const uint O_HW = p.OW * p.OH; + + const uint nc = idx / O_HW; + const uint cur_oh = (idx % O_HW) / p.OW; + const uint cur_ow = (idx % O_HW) % p.OW; + + const int start_h = int(cur_oh) * p.s0 - p.p0; + const uint bh = max(start_h, 0); + const uint eh = min(start_h + p.k0, p.IH); + + const int start_w = int(cur_ow) * p.s1 - p.p1; + const uint bw = max(start_w, 0); + const uint ew = min(start_w + p.k1, p.IW); + + const float scale = 1.0 / float(p.k0 * p.k1); + float res; + + if (p.op == OP_POOL_AVG) { + res = 0.0; + } else if (p.op == OP_POOL_MAX) { + res = -FLT_MAX; + } else { + return; + } + + #pragma unroll + for (uint i = bh; i < eh; i++) { + #pragma unroll + for (uint j = bw; j < ew; j++) { + const float cur = D_TYPE(data_a[nc * p.IH * p.IW + i * p.IW + j]); + + if (p.op == OP_POOL_AVG) { + res += cur * scale; + } else if (p.op == OP_POOL_MAX) { + res = max(res, cur); + } + } + } + + data_d[nc * O_HW + cur_oh * p.OW + cur_ow] = res; +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/quantize_q8_1.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/quantize_q8_1.comp new file mode 100644 index 0000000000000000000000000000000000000000..7ea29a07e3748efc74ed7eea55bb10cb4e397c3f --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/quantize_q8_1.comp @@ -0,0 +1,127 @@ +#version 450 + +#extension GL_EXT_control_flow_attributes : require +#extension GL_EXT_shader_16bit_storage : require + +#ifdef USE_SUBGROUPS +#extension GL_KHR_shader_subgroup_basic : require +#extension GL_KHR_shader_subgroup_clustered : require + +#define INVOCATION_ID gl_SubgroupInvocationID.x +#else +#define INVOCATION_ID gl_LocalInvocationID.x +#endif + +layout (push_constant) uniform parameter +{ + uint ne; + uint num_blocks; +} p; + +#include "types.glsl" + +layout(constant_id = 0) const uint GROUP_SIZE = 32; +layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {vec4 data_a[];}; +#ifndef QBLOCK_X4 +layout (binding = 1) writeonly buffer D {block_q8_1_packed32 data_b[];}; +#else +layout (binding = 1) writeonly buffer D {block_q8_1_x4 data_b[];}; +#endif + +#ifndef USE_SUBGROUPS +shared float shmem[GROUP_SIZE]; +#endif + +void quantize(const uint wgid) { + const uint tid = INVOCATION_ID; + + // Each thread handles a vec4, so 8 threads handle a block + const uint blocks_per_group = GROUP_SIZE / 8; + + const uint block_in_wg = tid / 8; + + const uint ib = wgid * blocks_per_group + block_in_wg; + const uint iqs = tid % 8; + +#ifdef QBLOCK_X4 + const uint ibx4_outer = ib / 4; + const uint ibx4_inner = ib % 4; + + const uint required_x4_blocks = (p.ne + 127) / 128; + if (ibx4_outer >= required_x4_blocks) { + return; + } +#endif + + const uint a_idx = ib * 8 + iqs; + + vec4 vals = a_idx < p.ne / 4 ? data_a[a_idx] : vec4(0.0f); + const vec4 abs_vals = abs(vals); + + // Find absolute max for each block + const float thread_max = max(max(abs_vals.x, abs_vals.y), max(abs_vals.z, abs_vals.w)); +#ifndef USE_SUBGROUPS + shmem[tid] = thread_max; + barrier(); + [[unroll]] for (uint s = 4; s > 0; s >>= 1) { + if (iqs < s) { + shmem[tid] = max(shmem[tid], shmem[tid + s]); + } + barrier(); + } + + const float amax = shmem[block_in_wg * 8]; +#else + const float amax = subgroupClusteredMax(thread_max, 8); +#endif + + const float d = amax / 127.0; + const float d_inv = d != 0.0 ? 1.0 / d : 0.0; + vals = round(vals * d_inv); + +#ifndef QBLOCK_X4 + data_b[ib].qs[iqs] = pack32(i8vec4(round(vals))); +#else + data_b[ibx4_outer].qs[ibx4_inner * 8 + iqs] = pack32(i8vec4(round(vals))); +#endif + +#ifndef USE_SUBGROUPS + barrier(); +#endif + + // Calculate the sum for each block + const float thread_sum = vals.x + vals.y + vals.z + vals.w; +#ifndef USE_SUBGROUPS + shmem[tid] = thread_sum; + barrier(); + [[unroll]] for (uint s = 4; s > 0; s >>= 1) { + if (iqs < s) { + shmem[tid] += shmem[tid + s]; + } + barrier(); + } +#else + const float sum = subgroupClusteredAdd(thread_sum, 8); +#endif + if (iqs == 0) { +#ifndef USE_SUBGROUPS + const float sum = shmem[tid]; +#endif + +#ifndef QBLOCK_X4 + data_b[ib].ds = f16vec2(vec2(d, sum * d)); +#else + data_b[ibx4_outer].ds[ibx4_inner] = f16vec2(vec2(d, sum * d)); +#endif + } +} + +void main() { + uint wgid = gl_WorkGroupID.x; + while (wgid < p.num_blocks) { + quantize(wgid); + wgid += gl_NumWorkGroups.x; + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/reglu.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/reglu.comp new file mode 100644 index 0000000000000000000000000000000000000000..86be2669a16e7eff7da43e29eac194a69b79d6f0 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/reglu.comp @@ -0,0 +1,9 @@ +#version 450 + +#include "glu_head.glsl" + +float op(float a, float b) { + return max(a, 0.0f) * b; +} + +#include "glu_main.glsl" diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/repeat.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/repeat.comp new file mode 100644 index 0000000000000000000000000000000000000000..8f4b9a8684ed9d4eb1077587b27906ca9a495f61 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/repeat.comp @@ -0,0 +1,26 @@ +#version 450 + +#include "types.glsl" +#include "generic_unary_head.glsl" + +layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; + +uint src0_idx_mod(uint idx) { + const uint i13 = idx / (p.ne12*p.ne11*p.ne10); + const uint i13_offset = i13 * p.ne12*p.ne11*p.ne10; + const uint i12 = (idx - i13_offset) / (p.ne11*p.ne10); + const uint i12_offset = i12*p.ne11*p.ne10; + const uint i11 = (idx - i13_offset - i12_offset) / p.ne10; + const uint i10 = idx - i13_offset - i12_offset - i11*p.ne10; + return (i13 % p.ne03)*p.nb03 + (i12 % p.ne02)*p.nb02 + (i11 % p.ne01)*p.nb01 + (i10 % p.ne00)*p.nb00; +} + +void main() { + const uint idx = get_idx(); + + if (idx >= p.ne) { + return; + } + + data_d[get_doffset() + dst_idx(idx)] = D_TYPE(data_a[get_aoffset() + src0_idx_mod(idx)]); +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/repeat_back.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/repeat_back.comp new file mode 100644 index 0000000000000000000000000000000000000000..10f334d4229fa69f2669404a010f08a84bb8e12a --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/repeat_back.comp @@ -0,0 +1,37 @@ +#version 450 + +#include "types.glsl" +#include "generic_unary_head.glsl" + +layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; + +void main() { + const uint idx = get_idx(); + + if (idx >= p.ne) { + return; + } + + // Destination multi-index (inlined dst_idx) + const uint i13 = fastdiv(idx, p.ne1_012mp, fastdiv_L(p.ne1_Ls, 0)); + const uint i13_offset = i13 * p.ne12*p.ne11*p.ne10; + const uint i12 = fastdiv(idx - i13_offset, p.ne1_01mp, fastdiv_L(p.ne1_Ls, 1)); + const uint i12_offset = i12*p.ne11*p.ne10; + const uint i11 = fastdiv(idx - i13_offset - i12_offset, p.ne1_0mp, fastdiv_L(p.ne1_Ls, 2)); + const uint i10 = idx - i13_offset - i12_offset - i11*p.ne10; + const uint d_idx = i13*p.nb13 + i12*p.nb12 + i11*p.nb11 + i10*p.nb10; + + // Accumulate from sources + A_TYPE acc = A_TYPE(0); + for (uint i3 = i13; i3 < p.ne03; i3 += p.ne13) { + for (uint i2 = i12; i2 < p.ne02; i2 += p.ne12) { + for (uint i1 = i11; i1 < p.ne01; i1 += p.ne11) { + for (uint i0 = i10; i0 < p.ne00; i0 += p.ne10) { + acc += data_a[i3*p.nb03 + i2*p.nb02 + i1*p.nb01 + i0*p.nb00]; + } + } + } + } + + data_d[get_doffset() + d_idx] = D_TYPE(acc); +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/rms_norm.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/rms_norm.comp new file mode 100644 index 0000000000000000000000000000000000000000..55b89f19a7a894c65dc7fd41eaa0b592b2a6e71a --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/rms_norm.comp @@ -0,0 +1,150 @@ +#version 450 + +#include "generic_binary_head.glsl" +#include "types.glsl" + +#if RMS_NORM_ROPE_FUSION + +layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; +layout (binding = 1) readonly buffer B {B_TYPE data_b[];}; + +// data is passed from rms_norm -> rope through shared memory. +// rms_norm calls this data_d, rope calls this rope_data_a. +// Binding 2 is not used +shared FLOAT_TYPE rope_data_a[1024]; +#define data_d rope_data_a + +layout (binding = 3) readonly buffer R_Y {int rope_data_pos[];}; +layout (binding = 4) readonly buffer R_Z {float rope_data_ff[];}; +layout (binding = 5) writeonly buffer R_D {ROPE_D_TYPE rope_data_d[];}; +layout (binding = 6) readonly buffer R_I {uvec2 rope_data_i[];}; // indices for set_rows + +#include "rope_params.glsl" +#include "rope_funcs.glsl" + +#define GGML_ROPE_TYPE_NORMAL 0 +#define GGML_ROPE_TYPE_NEOX 2 +#define GGML_ROPE_TYPE_MROPE 8 +#define GGML_ROPE_TYPE_VISION 24 + +#endif + +#extension GL_EXT_control_flow_attributes : enable +#define BLOCK_SIZE 512 + +layout (constant_id = 1) const bool do_multiply = false; + +layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in; + +shared FLOAT_TYPE sumsh[BLOCK_SIZE]; + +void rms_norm(uint num_iters) { + const uint ncols = p.ne00; + const uint nrows = gl_NumWorkGroups.x; + const uint nchannels = gl_NumWorkGroups.y; + + const uint row = gl_WorkGroupID.x; + const uint channel = gl_WorkGroupID.y; + const uint samp = gl_WorkGroupID.z; + const uint tid = gl_LocalInvocationID.x; + + const uint stride_row = p.nb01; + const uint stride_channel = p.nb02; + const uint stride_sample = p.nb03; + + uint32_t a_offset = samp*stride_sample + channel*stride_channel + row*stride_row + get_aoffset(); + uint32_t b_offset = src1_idx(0, row, channel, samp) + get_boffset(); +#if RMS_NORM_ROPE_FUSION + // Per-row offset in shared memory + uint32_t d_offset = 0; +#else + uint32_t d_offset = ((samp*nchannels + channel)*nrows + row)*ncols + get_doffset(); +#endif + FLOAT_TYPE sum = FLOAT_TYPE(0.0f); // partial sum for thread in warp + + [[unroll]] for (uint col = tid, idx = 0; idx < num_iters; col += BLOCK_SIZE, ++idx) { + FLOAT_TYPE xi = FLOAT_TYPE(0); + if (col < ncols) { + xi = FLOAT_TYPE(data_a[a_offset + col]); + } + sum += xi * xi; + } + + sumsh[tid] = sum; + // sum up partial sums and write back result + barrier(); + [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) { + if (tid < s) { + sum += sumsh[tid + s]; + sumsh[tid] = sum; + } + barrier(); + } + sum = sumsh[0]; + + const FLOAT_TYPE mean = sum / FLOAT_TYPE(ncols); + const FLOAT_TYPE scale = inversesqrt(mean + FLOAT_TYPE(p.param1)); + + if (do_multiply) { + if (ncols > p.ne10) { + [[unroll]] for (uint col = tid, idx = 0; idx < num_iters; col += BLOCK_SIZE, ++idx) { + if (col >= ncols) { + continue; + } + data_d[d_offset + col] = D_TYPE(scale * FLOAT_TYPE(data_a[a_offset + col]) * FLOAT_TYPE(data_b[b_offset + fastmod(col, p.ne10)])); + } + } else { + [[unroll]] for (uint col = tid, idx = 0; idx < num_iters; col += BLOCK_SIZE, ++idx) { + if (col >= ncols) { + continue; + } + data_d[d_offset + col] = D_TYPE(scale * FLOAT_TYPE(data_a[a_offset + col]) * FLOAT_TYPE(data_b[b_offset + col])); + } + } + } else { + [[unroll]] for (uint col = tid, idx = 0; idx < num_iters; col += BLOCK_SIZE, ++idx) { + if (col >= ncols) { + continue; + } + data_d[d_offset + col] = D_TYPE(scale * FLOAT_TYPE(data_a[a_offset + col])); + } + } +#if RMS_NORM_ROPE_FUSION + barrier(); + rope_params rp = p.rope; + for (uint t = 2*tid; t < ncols; t += 2*BLOCK_SIZE) { + if (rp.rope_mode == GGML_ROPE_TYPE_NEOX) { + rope_neox(t, row, channel, samp, rp); + } else if (rp.rope_mode == GGML_ROPE_TYPE_NORMAL) { + rope_norm(t, row, channel, samp, rp); + } + } +#endif +} + +void main() { + // instantiate the rms_norm function for several different + // dimensions, to allow loop unrolling + uint num_blocks = (p.ne00 + BLOCK_SIZE - 1) / BLOCK_SIZE; + if (num_blocks > 32) { + rms_norm(num_blocks); + } else if (num_blocks > 16) { + rms_norm(32); + } 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) { + rms_norm(4); + } else if (num_blocks == 3) { + rms_norm(3); + } else if (num_blocks == 2) { + rms_norm(2); + } else if (num_blocks == 1) { + rms_norm(1); + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/rms_norm_back.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/rms_norm_back.comp new file mode 100644 index 0000000000000000000000000000000000000000..87707fc1494dd570dae28844884f126a6d9eb05a --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/rms_norm_back.comp @@ -0,0 +1,55 @@ +#version 450 + +#include "generic_head.glsl" +#include "types.glsl" + +#extension GL_EXT_control_flow_attributes : enable +#define BLOCK_SIZE 512 + +layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer G {A_TYPE data_a[];}; +layout (binding = 1) readonly buffer X {B_TYPE data_b[];}; +layout (binding = 2) writeonly buffer D {D_TYPE data_d[];}; + +shared FLOAT_TYPE sum_xx[BLOCK_SIZE]; +shared FLOAT_TYPE sum_xg[BLOCK_SIZE]; + +void main() { + const uint row = gl_WorkGroupID.z * 262144 + gl_WorkGroupID.y * 512 + gl_WorkGroupID.x; + const uint tid = gl_LocalInvocationID.x; + + // Compute derivative of x[i]/norm(x) = g[i]/norm(x) - x[i] dot(x,g)/KX / norm(x)^1.5 + + // partial sums for thread in warp + sum_xx[tid] = FLOAT_TYPE(0.0f); + sum_xg[tid] = FLOAT_TYPE(0.0f); + + [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) { + const FLOAT_TYPE gi = FLOAT_TYPE(data_a[row*p.KX + col]); + const FLOAT_TYPE xi = FLOAT_TYPE(data_b[row*p.KX + col]); + sum_xx[tid] += xi * xi; + sum_xg[tid] += xi * gi; + } + + // sum up partial sums and write back result + barrier(); + [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) { + if (tid < s) { + sum_xx[tid] += sum_xx[tid + s]; + sum_xg[tid] += sum_xg[tid + s]; + } + barrier(); + } + + const FLOAT_TYPE eps = FLOAT_TYPE(p.param1); + const FLOAT_TYPE mean = sum_xx[0] / FLOAT_TYPE(p.KX); + const FLOAT_TYPE scale_g = inversesqrt(mean + eps); + const FLOAT_TYPE scale_x = -scale_g * sum_xg[0] / (sum_xx[0] + FLOAT_TYPE(p.KX) * eps); + + [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) { + data_d[row*p.KX + col] = D_TYPE( + scale_g * FLOAT_TYPE(data_a[row*p.KX + col]) + + scale_x * FLOAT_TYPE(data_b[row*p.KX + col])); + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/rms_norm_partials.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/rms_norm_partials.comp new file mode 100644 index 0000000000000000000000000000000000000000..4618b2c7e8a1ed8fbee746f1b0c29ac158ec05a8 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/rms_norm_partials.comp @@ -0,0 +1,65 @@ +#version 450 + +#include "generic_binary_head.glsl" +#include "types.glsl" + +#extension GL_EXT_control_flow_attributes : enable +#extension GL_KHR_shader_subgroup_arithmetic : enable +#extension GL_KHR_shader_subgroup_basic : enable + +#define BLOCK_SIZE 128 + +layout (constant_id = 1) const bool do_multiply = false; + +layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 3, std430) readonly buffer PartialsBuf {float partial_sums[];}; + +shared FLOAT_TYPE sumsh[BLOCK_SIZE]; + +void main() { + const uint ncols = p.ne00; + const uint nrows = gl_NumWorkGroups.x; + const uint nchannels = gl_NumWorkGroups.y; + + const uint row = 0; + const uint channel = gl_WorkGroupID.y; + const uint samp = gl_WorkGroupID.z; + // The work is split across multiple workgroups in the x dimension. Each invocation + // processes one element + const uint tid = gl_GlobalInvocationID.x; + + const uint stride_row = p.nb01; + const uint stride_channel = p.nb02; + const uint stride_sample = p.nb03; + + uint32_t a_offset = samp*stride_sample + channel*stride_channel + row*stride_row + get_aoffset(); + uint32_t b_offset = src1_idx(0, row, channel, samp) + get_boffset(); + uint32_t d_offset = ((samp*nchannels + channel)*nrows + row)*ncols + get_doffset(); + + FLOAT_TYPE sum = FLOAT_TYPE(0.0f); // partial sum for thread in warp + + uint32_t num_partials = p.param3; + for (uint32_t i = gl_SubgroupInvocationID; i < num_partials; i += gl_SubgroupSize) { + sum += partial_sums[i]; + } + sum = subgroupAdd(sum); + + uint col = tid; + if (col >= ncols) { + return; + } + + const FLOAT_TYPE mean = sum / FLOAT_TYPE(ncols); + const FLOAT_TYPE scale = inversesqrt(mean + FLOAT_TYPE(p.param1)); + + if (do_multiply) { + if (ncols > p.ne10) { + data_d[d_offset + col] = D_TYPE(scale * FLOAT_TYPE(data_a[a_offset + col]) * FLOAT_TYPE(data_b[b_offset + fastmod(col, p.ne10)])); + } else { + data_d[d_offset + col] = D_TYPE(scale * FLOAT_TYPE(data_a[a_offset + col]) * FLOAT_TYPE(data_b[b_offset + col])); + } + } else { + data_d[d_offset + col] = D_TYPE(scale * FLOAT_TYPE(data_a[a_offset + col])); + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/roll.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/roll.comp new file mode 100644 index 0000000000000000000000000000000000000000..dae811ad98bae68b0ca499d85a2b28fef5eb2099 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/roll.comp @@ -0,0 +1,46 @@ +#version 450 + +#include "types.glsl" +#include "generic_unary_head.glsl" + +layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; + +uint wrap_idx(int i, uint ne) { + if (i < 0) { + return i + ne; + } else if (i >= ne) { + return i - ne; + } + return i; +} + +void main() { + const uint idx = get_idx(); + if (idx >= p.ne) { + return; + } + + const uint i3 = fastdiv(idx, p.ne1_012mp, fastdiv_L(p.ne1_Ls, 0)); + const uint i3_offset = i3 * p.ne12*p.ne11*p.ne10; + const uint i2 = fastdiv(idx - i3_offset, p.ne1_01mp, fastdiv_L(p.ne1_Ls, 1)); + const uint i2_offset = i2*p.ne11*p.ne10; + const uint i1 = fastdiv(idx - i3_offset - i2_offset, p.ne1_0mp, fastdiv_L(p.ne1_Ls, 2)); + const uint i0 = idx - i3_offset - i2_offset - i1*p.ne10; + + const uint p1 = floatBitsToUint(p.param1); + const uint p2 = floatBitsToUint(p.param2); + const int s0 = int(p1 >> 16) - 0x8000; + const int s1 = int(p1 & 0xFFFF) - 0x8000; + const int s2 = int(p2 >> 16) - 0x8000; + const int s3 = int(p2 & 0xFFFF) - 0x8000; + + const uint i00 = wrap_idx(int(i0) - s0, p.ne10); + const uint i01 = wrap_idx(int(i1) - s1, p.ne11); + const uint i02 = wrap_idx(int(i2) - s2, p.ne12); + const uint i03 = wrap_idx(int(i3) - s3, p.ne13); + + const uint a_idx = i03*p.nb03 + i02*p.nb02 + i01*p.nb01 + i00*p.nb00; + const uint d_idx = i3 *p.nb13 + i2 *p.nb12 + i1 *p.nb11 + i0 *p.nb10; + + data_d[get_doffset() + d_idx] = D_TYPE(data_a[get_aoffset() + a_idx]); +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/rope_funcs.glsl b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/rope_funcs.glsl new file mode 100644 index 0000000000000000000000000000000000000000..033587931402c0ae247422d544e01abb44cc324a --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/rope_funcs.glsl @@ -0,0 +1,210 @@ + +float rope_yarn_ramp(const float low, const float high, const uint i0) { + const float y = (i0 / 2 - low) / max(0.001f, high - low); + return 1.0f - min(1.0f, max(0.0f, y)); +} + +uint rope_a_coord(const uint i0, const uint i01, const uint i02, const uint i03, rope_params p) { +#if RMS_NORM_ROPE_FUSION + // Per-row offset in shared memory + const uint ix = i0; +#else + const uint ix = p.a_offset + i03*p.nb03 + i02*p.nb02 + i01*p.nb01 + i0; +#endif + return ix; +} + +void rope_yarn(const float theta_extrap, const uint i0, out float cos_theta, out float sin_theta, rope_params p) { + float mscale = p.attn_factor; + // Get n-d rotational scaling corrected for extrapolation + float theta_interp = p.freq_scale * theta_extrap; + float theta = theta_interp; + if (p.ext_factor != 0.0f) { + float ramp_mix = rope_yarn_ramp(p.corr_dims[0], p.corr_dims[1], i0) * p.ext_factor; + theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix; + + // Get n-d magnitude scaling corrected for interpolation + mscale *= 1.0f + 0.1f * log(1.0f / p.freq_scale); + } + // Backprogagation uses inverted rotation + if (p.is_back != 0) { + theta = -theta; + } + cos_theta = cos(theta) * mscale; + sin_theta = sin(theta) * mscale; +} + +void rope_norm(const uint i0, const uint i1, const uint i2, const uint i3, rope_params p) { + if (i0 >= p.ne00) { + return; + } + + uint idst = i0 + i1 * p.nb11 + i2 * p.nb12 + i3 * p.nb13; + const uint ix = rope_a_coord(i0, i1, i2, i3, p); + + // Fusion optimization: ROPE + VIEW + SET_ROWS. + // The rope output is viewed as a 1D tensor and offset based on a row index in rope_data_i. + if (p.set_rows_stride != 0) { + idst = i1*p.nb11 + i0; + idst += rope_data_i[i2].x * p.set_rows_stride; + } + idst += p.d_offset; + + if (i0 >= p.n_dims) { + rope_data_d[idst + 0] = ROPE_D_TYPE(rope_data_a[ix + 0]); + rope_data_d[idst + 1] = ROPE_D_TYPE(rope_data_a[ix + 1]); + + return; + } + + const float theta_base = rope_data_pos[i2] * pow(p.theta_scale, i0/2.0f); + + const float freq_factor = p.has_ff != 0 ? rope_data_ff[i0/2] : 1.0f; + + float cos_theta, sin_theta; + rope_yarn(theta_base / freq_factor, i0, cos_theta, sin_theta, p); + + const float x0 = float(rope_data_a[ix + 0]); + const float x1 = float(rope_data_a[ix + 1]); + + rope_data_d[idst + 0] = ROPE_D_TYPE(x0*cos_theta - x1*sin_theta); + rope_data_d[idst + 1] = ROPE_D_TYPE(x0*sin_theta + x1*cos_theta); +} + +void rope_neox(const uint i0, const uint i1, const uint i2, const uint i3, rope_params p) { + if (i0 >= p.ne00) { + return; + } + + uint idst = i0/2 + i1 * p.nb11 + i2 * p.nb12 + i3 * p.nb13; + const uint ix = rope_a_coord(i0/2, i1, i2, i3, p); + + // Fusion optimization: ROPE + VIEW + SET_ROWS. + // The rope output is viewed as a 1D tensor and offset based on a row index in rope_data_i. + if (p.set_rows_stride != 0) { + idst = i1*p.nb11 + i0/2; + idst += rope_data_i[i2].x * p.set_rows_stride; + } + idst += p.d_offset; + + if (i0 >= p.n_dims) { + rope_data_d[idst + i0/2 + 0] = ROPE_D_TYPE(rope_data_a[ix + i0/2 + 0]); + rope_data_d[idst + i0/2 + 1] = ROPE_D_TYPE(rope_data_a[ix + i0/2 + 1]); + + return; + } + + const float theta_base = rope_data_pos[i2] * pow(p.theta_scale, i0/2.0f); + + const float freq_factor = p.has_ff != 0 ? rope_data_ff[i0/2] : 1.0f; + + float cos_theta, sin_theta; + rope_yarn(theta_base / freq_factor, i0, cos_theta, sin_theta, p); + + const float x0 = float(rope_data_a[ix + 0]); + const float x1 = float(rope_data_a[ix + p.n_dims/2]); + + rope_data_d[idst + 0] = ROPE_D_TYPE(x0*cos_theta - x1*sin_theta); + rope_data_d[idst + p.n_dims/2] = ROPE_D_TYPE(x0*sin_theta + x1*cos_theta); +} + + +void rope_multi(const uint i0, const uint i1, const uint i2, const uint i3, rope_params p) { + if (i0 >= p.ne00) { + return; + } + + uint idst = i0/2 + i1 * p.nb11 + i2 * p.nb12 + i3 * p.nb13; + const uint ix = rope_a_coord(i0/2, i1, i2, i3, p); + + // Fusion optimization: ROPE + VIEW + SET_ROWS. + // The rope output is viewed as a 1D tensor and offset based on a row index in rope_data_i. + if (p.set_rows_stride != 0) { + idst = i1*p.nb11 + i0/2; + idst += rope_data_i[i2].x * p.set_rows_stride; + } + idst += p.d_offset; + + if (i0 >= p.n_dims) { + rope_data_d[idst + i0/2 + 0] = ROPE_D_TYPE(rope_data_a[ix + i0/2 + 0]); + rope_data_d[idst + i0/2 + 1] = ROPE_D_TYPE(rope_data_a[ix + i0/2 + 1]); + + return; + } + + const int sect_dims = p.sections[0] + p.sections[1] + p.sections[2] + p.sections[3]; + const int sec_w = p.sections[1] + p.sections[0]; + const uint sector = (i0 / 2) % sect_dims; + + float theta_base = 0.0; + if (p.is_imrope != 0) { + if (sector % 3 == 1 && sector < 3 * p.sections[1]) { + theta_base = rope_data_pos[i2 + p.ne02 * 1]*pow(p.theta_scale, i0/2.0f); + } else if (sector % 3 == 2 && sector < 3 * p.sections[2]) { + theta_base = rope_data_pos[i2 + p.ne02 * 2]*pow(p.theta_scale, i0/2.0f); + } else if (sector % 3 == 0 && sector < 3 * p.sections[0]) { + theta_base = rope_data_pos[i2]*pow(p.theta_scale, i0/2.0f); + } else { + theta_base = rope_data_pos[i2 + p.ne02 * 3]*pow(p.theta_scale, i0/2.0f); + } + } else { + if (sector < p.sections[0]) { + theta_base = rope_data_pos[i2]*pow(p.theta_scale, i0/2.0f); + } + else if (sector >= p.sections[0] && sector < sec_w) { + theta_base = rope_data_pos[i2 + p.ne02 * 1]*pow(p.theta_scale, i0/2.0f); + } + else if (sector >= sec_w && sector < sec_w + p.sections[2]) { + theta_base = rope_data_pos[i2 + p.ne02 * 2]*pow(p.theta_scale, i0/2.0f); + } + else if (sector >= sec_w + p.sections[2]) { + theta_base = rope_data_pos[i2 + p.ne02 * 3]*pow(p.theta_scale, i0/2.0f); + } + } + + const float freq_factor = p.has_ff != 0 ? rope_data_ff[i0/2] : 1.0f; + + float cos_theta, sin_theta; + rope_yarn(theta_base / freq_factor, i0, cos_theta, sin_theta, p); + + const float x0 = float(rope_data_a[ix + 0]); + const float x1 = float(rope_data_a[ix + p.n_dims/2]); + + rope_data_d[idst + 0] = ROPE_D_TYPE(x0*cos_theta - x1*sin_theta); + rope_data_d[idst + p.n_dims/2] = ROPE_D_TYPE(x0*sin_theta + x1*cos_theta); +} + +void rope_vision(const uint i0, const uint i1, const uint i2, const uint i3, rope_params p) { + if (i0 >= p.ne00) { + return; + } + + const uint idst = p.d_offset + i0/2 + i1 * p.nb11 + i2 * p.nb12 + i3 * p.nb13; + const uint ix = rope_a_coord(i0/2, i1, i2, i3, p); + + const int sect_dims = p.sections[0] + p.sections[1]; + const int sec_w = p.sections[1] + p.sections[0]; + const uint sector = (i0 / 2) % sect_dims; + + float theta_base = 0.0; + if (sector < p.sections[0]) { + const uint p0 = sector; + theta_base = rope_data_pos[i2]*pow(p.theta_scale, p0); + } + else if (sector >= p.sections[0] && sector < sec_w) { + const uint p0 = sector - p.sections[0]; + theta_base = rope_data_pos[i2 + p.ne02]*pow(p.theta_scale, p0); + } + + const float freq_factor = p.has_ff != 0 ? rope_data_ff[i0/2] : 1.0f; + + float cos_theta, sin_theta; + rope_yarn(theta_base / freq_factor, i0, cos_theta, sin_theta, p); + + const float x0 = float(rope_data_a[ix + 0]); + const float x1 = float(rope_data_a[ix + p.n_dims]); + + rope_data_d[idst + 0] = ROPE_D_TYPE(x0*cos_theta - x1*sin_theta); + rope_data_d[idst + p.n_dims] = ROPE_D_TYPE(x0*sin_theta + x1*cos_theta); +} + diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/rope_head.glsl b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/rope_head.glsl new file mode 100644 index 0000000000000000000000000000000000000000..51a127bcd87c0a9664bb502c4a7a22eab03fdc82 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/rope_head.glsl @@ -0,0 +1,19 @@ +#include "types.glsl" + +#extension GL_EXT_shader_16bit_storage : require + +#include "rope_params.glsl" + +layout(local_size_x = 1, local_size_y = 256, local_size_z = 1) in; + +layout (binding = 0) readonly buffer X {A_TYPE rope_data_a[];}; +layout (binding = 1) readonly buffer Y {int rope_data_pos[];}; +layout (binding = 2) readonly buffer Z {float rope_data_ff[];}; +layout (binding = 3) writeonly buffer D {ROPE_D_TYPE rope_data_d[];}; +layout (binding = 4) readonly buffer I {uvec2 rope_data_i[];}; // indices for set_rows + + +layout (push_constant) uniform parameter { + rope_params pc; +}; + diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/rope_multi.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/rope_multi.comp new file mode 100644 index 0000000000000000000000000000000000000000..1528fbeeaecfb04c909ebeec1f5a5512bff344cf --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/rope_multi.comp @@ -0,0 +1,17 @@ +#version 450 + +#include "rope_head.glsl" +#include "rope_funcs.glsl" + +void main() { + const uint i0 = 2*gl_GlobalInvocationID.y; + const uint row = gl_GlobalInvocationID.x + 32768 * gl_GlobalInvocationID.z; + if (row >= pc.nrows) { + return; + } + const uint i3 = row / (pc.ne01*pc.ne02); + const uint i2 = (row - i3 * pc.ne01*pc.ne02) / pc.ne01; + const uint i1 = (row - i3 * pc.ne01*pc.ne02 - i2 * pc.ne01); + + rope_multi(i0, i1, i2, i3, pc); +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/rope_neox.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/rope_neox.comp new file mode 100644 index 0000000000000000000000000000000000000000..ad0896095db62c734ec79c756f22305f36245300 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/rope_neox.comp @@ -0,0 +1,17 @@ +#version 450 + +#include "rope_head.glsl" +#include "rope_funcs.glsl" + +void main() { + const uint i0 = 2*gl_GlobalInvocationID.y; + const uint row = gl_GlobalInvocationID.x + 32768 * gl_GlobalInvocationID.z; + if (row >= pc.nrows) { + return; + } + const uint i3 = row / (pc.ne01*pc.ne02); + const uint i2 = (row - i3 * pc.ne01*pc.ne02) / pc.ne01; + const uint i1 = (row - i3 * pc.ne01*pc.ne02 - i2 * pc.ne01); + + rope_neox(i0, i1, i2, i3, pc); +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/rope_norm.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/rope_norm.comp new file mode 100644 index 0000000000000000000000000000000000000000..11220817df09311a3fb40027cc44874f75b8f8d9 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/rope_norm.comp @@ -0,0 +1,17 @@ +#version 450 + +#include "rope_head.glsl" +#include "rope_funcs.glsl" + +void main() { + const uint i0 = 2*gl_GlobalInvocationID.y; + const uint row = gl_GlobalInvocationID.x + 32768 * gl_GlobalInvocationID.z; + if (row >= pc.nrows) { + return; + } + const uint i3 = row / (pc.ne01*pc.ne02); + const uint i2 = (row - i3 * pc.ne01*pc.ne02) / pc.ne01; + const uint i1 = (row - i3 * pc.ne01*pc.ne02 - i2 * pc.ne01); + + rope_norm(i0, i1, i2, i3, pc); +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/rope_params.glsl b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/rope_params.glsl new file mode 100644 index 0000000000000000000000000000000000000000..3602485b943f32840a9389f83b4c236d0561772a --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/rope_params.glsl @@ -0,0 +1,34 @@ +#if !defined(GGML_ROPE_PARAMS) +#define GGML_ROPE_PARAMS + +struct rope_params { + uint rope_mode; + uint nrows; + uint n_dims; + float freq_scale; + float freq_base; + float ext_factor; + float attn_factor; + float corr_dims[2]; + float theta_scale; + uint has_ff; + int sections[4]; + uint is_imrope; + uint is_back; + uint set_rows_stride; + + uint ne00; + uint ne01; + uint ne02; + uint nb01; + uint nb02; + uint nb03; + uint nb11; + uint nb12; + uint nb13; + + uint a_offset; + uint d_offset; +}; + +#endif // !defined(GGML_ROPE_PARAMS) diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/rope_vision.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/rope_vision.comp new file mode 100644 index 0000000000000000000000000000000000000000..ca71efb2f5586e715e1f9b5040b459fa3594e6d6 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/rope_vision.comp @@ -0,0 +1,17 @@ +#version 450 + +#include "rope_head.glsl" +#include "rope_funcs.glsl" + +void main() { + const uint i0 = 2*gl_GlobalInvocationID.y; + const uint row = gl_GlobalInvocationID.x + 32768 * gl_GlobalInvocationID.z; + if (row >= pc.nrows) { + return; + } + const uint i3 = row / (pc.ne01*pc.ne02); + const uint i2 = (row - i3 * pc.ne01*pc.ne02) / pc.ne01; + const uint i1 = (row - i3 * pc.ne01*pc.ne02 - i2 * pc.ne01); + + rope_vision(i0, i1, i2, i3, pc); +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/scale.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/scale.comp new file mode 100644 index 0000000000000000000000000000000000000000..35ec726a01c62a6c2319be4c31e6a212540d67dc --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/scale.comp @@ -0,0 +1,24 @@ +#version 450 + +#include "types.glsl" +#include "generic_unary_head.glsl" + +const uint num_threads = 128; + +layout(local_size_x = num_threads, local_size_y = 1, local_size_z = 1) in; + +void main() { + uint idx = get_idx(); + + // num_threads * num_iter must equal 512, to match the wg_denoms and get_idx calculation + const uint num_iter = 4; + + [[unroll]] for (uint i = 0; i < num_iter; ++i) { + if (idx >= p.ne) { + continue; + } + + data_d[get_doffset() + idx] = D_TYPE(FLOAT_TYPE(data_a[get_aoffset() + idx]) * FLOAT_TYPE(p.param1) + FLOAT_TYPE(p.param2)); + idx += num_threads; + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/silu_back.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/silu_back.comp new file mode 100644 index 0000000000000000000000000000000000000000..e5d949ff180bbd5f2d890183b3ebcad39373b7f4 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/silu_back.comp @@ -0,0 +1,26 @@ +#version 450 + +#include "generic_head.glsl" +#include "types.glsl" + +#extension GL_EXT_control_flow_attributes : enable + +layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer G {A_TYPE data_g[];}; +layout (binding = 1) readonly buffer X {B_TYPE data_x[];}; +layout (binding = 2) writeonly buffer D {D_TYPE data_d[];}; + +void main() { + const uint i = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x; + + if (i >= p.KX) { + return; + } + + // Compute derivative of SiLU(x): 1/(1+exp(-x)) - x*exp(-x)/(1+exp(-x))^2 + + const float xi = float(data_x[i]); + const float s = 1.0f / (1.0f + exp(-xi)); + data_d[i] = D_TYPE(data_g[i] * (s + xi * s * (1 - s))); +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/snake.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/snake.comp new file mode 100644 index 0000000000000000000000000000000000000000..8585538cbb06877e4a7b1b669ad1385b6f037494 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/snake.comp @@ -0,0 +1,49 @@ +#version 450 + +#include "types.glsl" + +// Fused snake activation: y = x + sin(b * x)^2 * c +// data_a [ne0, ne1] per element activation x (A_TYPE) +// data_b [1, ne1] per channel multiplier (float) +// data_c [1, ne1] per channel inverse scale (float, precomputed as 1 / freq) +// data_d [ne0, ne1] output y (D_TYPE) +layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; +layout (binding = 1) readonly buffer B {float data_b[];}; +layout (binding = 2) readonly buffer C {float data_c[];}; +layout (binding = 3) writeonly buffer D {D_TYPE data_d[];}; + +layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in; + +layout (push_constant) uniform parameter { + uint32_t ne0; + uint32_t ne1; +} p; + +// Load A_TYPE to float +float load_val(uint32_t idx) { +#if defined(DATA_A_BF16) + return bf16_to_fp32(uint32_t(data_a[idx])); +#else + return float(data_a[idx]); +#endif +} + +// Store float as D_TYPE +void store_val(uint32_t idx, float v) { +#if defined(DATA_D_BF16) + data_d[idx] = D_TYPE(fp32_to_bf16(v)); +#else + data_d[idx] = D_TYPE(v); +#endif +} + +void main() { + const uint32_t i0 = gl_GlobalInvocationID.x; + const uint32_t i1 = gl_GlobalInvocationID.y; + if (i0 >= p.ne0 || i1 >= p.ne1) return; + + const uint32_t idx = i0 + i1 * p.ne0; + const float xi = load_val(idx); + const float s = sin(data_b[i1] * xi); + store_val(idx, xi + s * s * data_c[i1]); +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/soft_max.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/soft_max.comp new file mode 100644 index 0000000000000000000000000000000000000000..dca0d896bc2ec5c5bee9ef5d244f87a975a0b98b --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/soft_max.comp @@ -0,0 +1,195 @@ +#version 450 + +#extension GL_EXT_control_flow_attributes : enable + +layout (push_constant) uniform parameter +{ + uint KX; + uint KY; + uint ne00; + uint ne01; + uint ne02; + uint ne12; + uint ne13; + uint nb11; + uint nb12; + uint nb13; + float scale; + float max_bias; + float m0; + float m1; + uint n_head_log2; + uint nrows_x; + uint has_sinks; +} p; + +#include "types.glsl" + +layout(constant_id = 0) const uint BLOCK_SIZE = 32; +layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; +layout (binding = 1) readonly buffer Y {B_TYPE data_b[];}; +layout (binding = 2) readonly buffer Z {float data_c[];}; +layout (binding = 3) buffer D {D_TYPE data_d[];}; + +shared FLOAT_TYPE vals[BLOCK_SIZE]; + +// num_iters is the number of BLOCK_SIZE loop iterations we need to iterate +// over all the columns. The main function tries to pass a constant here, +// as if it were a template function, to allow unrolling. +void soft_max(uint num_iters) { + const uint tid = gl_LocalInvocationID.x; + const uint rowx = gl_WorkGroupID.z * 262144 + gl_WorkGroupID.y * 512 + gl_WorkGroupID.x; + + const uint32_t i03 = rowx / (p.ne01 * p.ne02); + const uint32_t i02 = (rowx - i03 * p.ne01 * p.ne02) / p.ne01; + const uint32_t i01 = rowx % p.ne01; + + uint rowy_start = 0; + if (p.KY > 0) { + rowy_start = i01 * p.nb11 + (i02 % p.ne12) * p.nb12 + (i03 % p.ne13) * p.nb13; + } + + if (rowx >= p.nrows_x) { + return; + } + + float slope = 1.0f; + + // ALiBi + if (p.max_bias > 0.0f) { + const uint h = (rowx / p.ne01) % p.ne02; // head index + + const float base = h < p.n_head_log2 ? p.m0 : p.m1; + const uint exp = h < p.n_head_log2 ? h + 1 : 2*(h - p.n_head_log2) + 1; + + slope = pow(base, exp); + } + + // Find max + FLOAT_TYPE max_val = p.has_sinks == 0 ? uintBitsToFloat(0xFF800000) : data_c[i02]; + + // Cache values while we compute the max, so we don't need to read them + // again when we're ready to compute exp(x-max). + const uint DATA_CACHE_SIZE = 16; + FLOAT_TYPE data_cache[DATA_CACHE_SIZE]; + + [[unroll]] for (uint col0 = 0, idx = 0; idx < num_iters; col0 += BLOCK_SIZE, ++idx) { + const uint col = col0 + tid; + + FLOAT_TYPE a = FLOAT_TYPE(0); + if (col < p.KX) { + a = data_a[rowx * p.KX + col]; + } + + FLOAT_TYPE b = FLOAT_TYPE(0); + if (p.KY > 0 && col < p.KX) { + b = data_b[rowy_start + col]; + } + + FLOAT_TYPE v = a * p.scale + slope * b; + + if (col < p.KX) { + max_val = max(max_val, v); + } + + if (idx < DATA_CACHE_SIZE) { + data_cache[idx] = v; + } + } + + // reduce across the workgroup + vals[tid] = max_val; + barrier(); + [[unroll]] for (uint s = BLOCK_SIZE / 2; s > 0; s >>= 1) { + if (tid < s) { + vals[tid] = max(vals[tid], vals[tid + s]); + } + barrier(); + } + + max_val = vals[0]; + barrier(); + + FLOAT_TYPE sum = FLOAT_TYPE(0.0f); + + // Compute sum{exp(x - max)} + [[unroll]] for (uint col0 = 0, idx = 0; idx < num_iters; col0 += BLOCK_SIZE, ++idx) { + const uint col = col0 + tid; + + if (col >= p.KX) { + break; + } + + // compute exp(a*scale+b*slope), add it to sum, and cache the new value + // in data_cache if possible. + const uint i = rowx * p.KX + col; + FLOAT_TYPE val; + if (idx < DATA_CACHE_SIZE) { + val = exp(data_cache[idx] - max_val); + } else { + val = exp(FLOAT_TYPE(data_a[i]) * p.scale + (p.KY > 0 ? slope * FLOAT_TYPE(data_b[rowy_start + col]) : FLOAT_TYPE(0.0f)) - max_val); + } + sum += val; + if (idx < DATA_CACHE_SIZE) { + data_cache[idx] = val; + } else { + data_d[i] = D_TYPE(val); + } + } + + // reduce across the workgroup + vals[tid] = sum; + barrier(); + [[unroll]] for (uint s = BLOCK_SIZE / 2; s > 0; s >>= 1) { + if (tid < s) { + vals[tid] += vals[tid + s]; + } + barrier(); + } + sum = vals[0]; + + if (p.has_sinks != 0) { + sum += FLOAT_TYPE(exp(FLOAT_TYPE(data_c[i02]) - max_val)); + } + + FLOAT_TYPE rcpdivisor = 1.0/sum; + + [[unroll]] for (uint col0 = 0, idx = 0; idx < num_iters; col0 += BLOCK_SIZE, ++idx) { + const uint col = col0 + tid; + + if (col >= p.KX) { + continue; + } + + if (idx < DATA_CACHE_SIZE) { + data_d[rowx*p.KX + col] = D_TYPE(data_cache[idx] * rcpdivisor); + } else { + data_d[rowx*p.KX + col] *= D_TYPE(rcpdivisor); + } + } +} + +void main() { + // instantiate the soft_max function for several different + // dimensions, to allow loop unrolling + uint num_blocks = (p.KX + BLOCK_SIZE - 1) / BLOCK_SIZE; + if (num_blocks > 32) { + soft_max(num_blocks); + } else if (num_blocks > 16) { + soft_max(32); + } else if (num_blocks > 8) { + soft_max(16); + } else if (num_blocks > 4) { + soft_max(8); + } else if (num_blocks == 4) { + soft_max(4); + } else if (num_blocks == 3) { + soft_max(3); + } else if (num_blocks == 2) { + soft_max(2); + } else if (num_blocks == 1) { + soft_max(1); + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/soft_max_back.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/soft_max_back.comp new file mode 100644 index 0000000000000000000000000000000000000000..d873332eeb8e72234a1ece88552ac0b313fb8445 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/soft_max_back.comp @@ -0,0 +1,54 @@ +#version 450 + +#extension GL_EXT_control_flow_attributes : enable + +#include "generic_head.glsl" +#include "types.glsl" + +layout(constant_id = 0) const uint BLOCK_SIZE = 32; +layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; + +// In this shader Y = softmax(X) and X is not provided as input. + +layout (binding = 0) readonly buffer G {A_TYPE data_g[];}; +layout (binding = 1) readonly buffer Y {B_TYPE data_y[];}; +layout (binding = 2) buffer D {D_TYPE data_d[];}; + +shared FLOAT_TYPE sum_yg[BLOCK_SIZE]; + +void main() { + const uint row = gl_WorkGroupID.z * 262144 + gl_WorkGroupID.y * 512 + gl_WorkGroupID.x; + const uint tid = gl_LocalInvocationID.x; + + if (row >= p.KY) { + return; + } + + FLOAT_TYPE scale = p.param1; + + // partial sums for thread in warp + sum_yg[tid] = FLOAT_TYPE(0.0f); + + [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) { + const FLOAT_TYPE gi = FLOAT_TYPE(data_g[row*p.KX + col]); + const FLOAT_TYPE yi = FLOAT_TYPE(data_y[row*p.KX + col]); + sum_yg[tid] += yi * gi; + } + + // sum up partial sums and write back result + barrier(); + [[unroll]] for (uint s = BLOCK_SIZE / 2; s > 0; s >>= 1) { + if (tid < s) { + sum_yg[tid] += sum_yg[tid + s]; + } + barrier(); + } + + const FLOAT_TYPE dot_yg = sum_yg[0]; + + [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) { + data_d[row*p.KX + col] = D_TYPE(scale + * (FLOAT_TYPE(data_g[row*p.KX + col]) - dot_yg) + * FLOAT_TYPE(data_y[row*p.KX + col])); + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/soft_max_large1.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/soft_max_large1.comp new file mode 100644 index 0000000000000000000000000000000000000000..39c466391224dbf3feb6aceea1310d589233526b --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/soft_max_large1.comp @@ -0,0 +1,62 @@ +#version 450 + +#include "soft_max_large_common.glsl" + +void main() { + const uint tid = gl_LocalInvocationID.x; + const uint rowx = gl_WorkGroupID.y; + const uint wg_start = gl_WorkGroupID.x * BLOCK_SIZE * num_iters; + + const uint32_t i03 = rowx / (p.ne01 * p.ne02); + const uint32_t i02 = (rowx - i03 * p.ne01 * p.ne02) / p.ne01; + const uint32_t i01 = rowx % p.ne01; + + uint rowy_start = 0; + if (p.KY > 0) { + rowy_start = i01 * p.nb11 + (i02 % p.ne12) * p.nb12 + (i03 % p.ne13) * p.nb13; + } + + if (rowx >= p.nrows_x) { + return; + } + + float slope = get_slope(rowx); + + // Find max + FLOAT_TYPE max_val = p.has_sinks == 0 ? uintBitsToFloat(0xFF800000) : data_c[i02]; + + [[unroll]] for (uint col0 = wg_start, idx = 0; idx < num_iters; col0 += BLOCK_SIZE, ++idx) { + const uint col = col0 + tid; + + FLOAT_TYPE a = FLOAT_TYPE(0); + if (col < p.KX) { + a = data_a[rowx * p.KX + col]; + } + + FLOAT_TYPE b = FLOAT_TYPE(0); + if (p.KY > 0 && col < p.KX) { + b = data_b[rowy_start + col]; + } + + FLOAT_TYPE v = a * p.scale + slope * b; + + if (col < p.KX) { + max_val = max(max_val, v); + } + } + + // reduce across the workgroup + vals[tid] = max_val; + barrier(); + [[unroll]] for (uint s = BLOCK_SIZE / 2; s > 0; s >>= 1) { + if (tid < s) { + vals[tid] = max(vals[tid], vals[tid + s]); + } + barrier(); + } + + if (tid == 0) { + max_val = vals[0]; + data_m[rowx * gl_NumWorkGroups.x + gl_WorkGroupID.x] = max_val; + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/soft_max_large2.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/soft_max_large2.comp new file mode 100644 index 0000000000000000000000000000000000000000..69524f5f756110030c6a5784e07b0d42fc059d2d --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/soft_max_large2.comp @@ -0,0 +1,79 @@ +#version 450 + +#include "soft_max_large_common.glsl" + +void main() { + const uint tid = gl_LocalInvocationID.x; + const uint rowx = gl_WorkGroupID.y; + const uint wg_start = gl_WorkGroupID.x * BLOCK_SIZE * num_iters; + + const uint32_t i03 = rowx / (p.ne01 * p.ne02); + const uint32_t i02 = (rowx - i03 * p.ne01 * p.ne02) / p.ne01; + const uint32_t i01 = rowx % p.ne01; + + uint rowy_start = 0; + if (p.KY > 0) { + rowy_start = i01 * p.nb11 + (i02 % p.ne12) * p.nb12 + (i03 % p.ne13) * p.nb13; + } + + if (rowx >= p.nrows_x) { + return; + } + + float slope = get_slope(rowx); + + // Find max + FLOAT_TYPE max_val = p.has_sinks == 0 ? uintBitsToFloat(0xFF800000) : data_c[i02]; + + [[unroll]] for (uint i = 0; i < gl_NumWorkGroups.x; i += BLOCK_SIZE) { + if (i + tid < gl_NumWorkGroups.x) { + max_val = max(max_val, data_m[rowx * gl_NumWorkGroups.x + i + tid]); + } + } + + // reduce across the workgroup + vals[tid] = max_val; + barrier(); + [[unroll]] for (uint s = BLOCK_SIZE / 2; s > 0; s >>= 1) { + if (tid < s) { + vals[tid] = max(max_val, vals[tid + s]); + } + barrier(); + } + + max_val = vals[0]; + barrier(); + + FLOAT_TYPE sum = FLOAT_TYPE(0.0f); + + // Compute sum{exp(x - max)} + [[unroll]] for (uint col0 = wg_start, idx = 0; idx < num_iters; col0 += BLOCK_SIZE, ++idx) { + const uint col = col0 + tid; + + if (col >= p.KX) { + break; + } + + // compute exp(a*scale+b*slope), add it to sum + const uint i = rowx * p.KX + col; + FLOAT_TYPE val; + val = exp(FLOAT_TYPE(data_a[i]) * p.scale + (p.KY > 0 ? slope * FLOAT_TYPE(data_b[rowy_start + col]) : FLOAT_TYPE(0.0f)) - max_val); + sum += val; + data_d[i] = D_TYPE(val); + } + + // reduce across the workgroup + vals[tid] = sum; + barrier(); + [[unroll]] for (uint s = BLOCK_SIZE / 2; s > 0; s >>= 1) { + if (tid < s) { + vals[tid] += vals[tid + s]; + } + barrier(); + } + + if (tid == 0) { + sum = vals[0]; + data_s[rowx * gl_NumWorkGroups.x + gl_WorkGroupID.x] = sum; + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/soft_max_large3.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/soft_max_large3.comp new file mode 100644 index 0000000000000000000000000000000000000000..06efd7d9fb43644ca3bd81dc058bd13ec420de3f --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/soft_max_large3.comp @@ -0,0 +1,65 @@ +#version 450 + +#include "soft_max_large_common.glsl" + +shared FLOAT_TYPE sumsh[BLOCK_SIZE]; + +void main() { + const uint tid = gl_LocalInvocationID.x; + const uint rowx = gl_WorkGroupID.y; + const uint wg_start = gl_WorkGroupID.x * BLOCK_SIZE * num_iters; + + const uint32_t i03 = rowx / (p.ne01 * p.ne02); + const uint32_t i02 = (rowx - i03 * p.ne01 * p.ne02) / p.ne01; + const uint32_t i01 = rowx % p.ne01; + + uint rowy_start = 0; + if (p.KY > 0) { + rowy_start = i01 * p.nb11 + (i02 % p.ne12) * p.nb12 + (i03 % p.ne13) * p.nb13; + } + + if (rowx >= p.nrows_x) { + return; + } + + FLOAT_TYPE max_val = p.has_sinks == 0 ? uintBitsToFloat(0xFF800000) : data_c[i02]; + FLOAT_TYPE sum = FLOAT_TYPE(0.0f); + + [[unroll]] for (uint i = 0; i < gl_NumWorkGroups.x; i += BLOCK_SIZE) { + if (i + tid < gl_NumWorkGroups.x) { + max_val = max(max_val, data_m[rowx * gl_NumWorkGroups.x + i + tid]); + sum += data_s[rowx * gl_NumWorkGroups.x + i + tid]; + } + } + + // reduce across the workgroup + vals[tid] = max_val; + sumsh[tid] = sum; + barrier(); + [[unroll]] for (uint s = BLOCK_SIZE / 2; s > 0; s >>= 1) { + if (tid < s) { + vals[tid] = max(max_val, vals[tid + s]); + sumsh[tid] += sumsh[tid + s]; + } + barrier(); + } + + max_val = vals[0]; + sum = sumsh[0]; + + if (p.has_sinks != 0) { + sum += FLOAT_TYPE(exp(FLOAT_TYPE(data_c[i02]) - max_val)); + } + + FLOAT_TYPE rcpdivisor = 1.0/sum; + + [[unroll]] for (uint col0 = wg_start, idx = 0; idx < num_iters; col0 += BLOCK_SIZE, ++idx) { + const uint col = col0 + tid; + + if (col >= p.KX) { + continue; + } + + data_d[rowx*p.KX + col] *= D_TYPE(rcpdivisor); + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/soft_max_large_common.glsl b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/soft_max_large_common.glsl new file mode 100644 index 0000000000000000000000000000000000000000..6636d1f8deaa9b23ddc84ab6c72eb4627dcdd0fd --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/soft_max_large_common.glsl @@ -0,0 +1,53 @@ +#extension GL_EXT_control_flow_attributes : enable + +layout (push_constant) uniform parameter +{ + uint KX; + uint KY; + uint ne00; + uint ne01; + uint ne02; + uint ne12; + uint ne13; + uint nb11; + uint nb12; + uint nb13; + float scale; + float max_bias; + float m0; + float m1; + uint n_head_log2; + uint nrows_x; + uint has_sinks; +} p; + +#include "types.glsl" + +layout(constant_id = 0) const uint BLOCK_SIZE = 128; +layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; +layout(constant_id = 1) const uint num_iters = 4; + +layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; +layout (binding = 1) readonly buffer Y {B_TYPE data_b[];}; +layout (binding = 2) readonly buffer Z {float data_c[];}; +layout (binding = 3) buffer D {D_TYPE data_d[];}; +layout (binding = 4) buffer M {float data_m[];}; +layout (binding = 5) buffer S {float data_s[];}; + +shared FLOAT_TYPE vals[BLOCK_SIZE]; + +float get_slope(uint rowx) { + float slope = 1.0f; + + // ALiBi + if (p.max_bias > 0.0f) { + const uint h = (rowx / p.ne01) % p.ne02; // head index + + const float base = h < p.n_head_log2 ? p.m0 : p.m1; + const uint exp = h < p.n_head_log2 ? h + 1 : 2*(h - p.n_head_log2) + 1; + + slope = pow(base, exp); + } + + return slope; +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/solve_tri.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/solve_tri.comp new file mode 100644 index 0000000000000000000000000000000000000000..3b65145032ccfd5f5f3c5d3e261c33e09a22140a --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/solve_tri.comp @@ -0,0 +1,81 @@ +#version 450 + +#include "types.glsl" +#include "generic_binary_head.glsl" + +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_id = 4, local_size_y = 1, local_size_z = 1) in; + +uint a_base, b_base, x_base; + +FLOAT_TYPE get_a(uint r, uint c) { + return FLOAT_TYPE(data_a[a_base + r * p.nb01 + c * p.nb00]); +} + +FLOAT_TYPE get_b(uint r, uint c) { + return FLOAT_TYPE(data_b[b_base + r * p.nb11 + c * p.nb10]); +} + +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[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; + const uint tid = gl_LocalInvocationID.x; + + if (batch >= p.ne02 * p.ne03) { + return; + } + + const uint i3 = batch / p.ne22; + const uint i2 = batch % p.ne22; + a_base = get_aoffset() + i2 * p.nb02 + i3 * p.nb03; + b_base = get_boffset() + i2 * p.nb12 + i3 * p.nb13; + x_base = get_doffset() + i2 * p.nb22 + i3 * p.nb23; + + FLOAT_TYPE X[N]; + + // 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); + } + } + // 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/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/ssm_conv.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/ssm_conv.comp new file mode 100644 index 0000000000000000000000000000000000000000..4cd9b8da3596a44a172a57e77c61678be6871ef0 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/ssm_conv.comp @@ -0,0 +1,60 @@ +#version 450 + +#extension GL_EXT_control_flow_attributes : require + +#include "types.glsl" + +layout(constant_id = 0) const uint BLOCK_SIZE = 32; +layout(constant_id = 1) const uint TOKENS_PER_WG = 16; +layout(constant_id = 2) const bool APPLY_BIAS = false; +layout(constant_id = 3) const bool APPLY_SILU = false; + +layout(local_size_x_id = 0, local_size_y_id = 1, local_size_z = 1) in; + +layout(binding = 0) readonly buffer Src0 { float src0[]; }; +layout(binding = 1) readonly buffer Src1 { float src1[]; }; +layout(binding = 2) readonly buffer Bias { float bias[]; }; +layout(binding = 3) buffer Dst { float dst[]; }; + +layout(push_constant) uniform PushConstants { + uint nb01; uint nb02; + uint nb11; + uint dst_nb0; uint dst_nb1; uint dst_nb2; + uint nc; uint ncs; uint nr; uint n_t; uint n_s; +}; + +void main() { + const uint i1 = gl_GlobalInvocationID.x; + const uint i2 = gl_WorkGroupID.y * TOKENS_PER_WG + gl_LocalInvocationID.y; + const uint i3 = gl_WorkGroupID.z; + + if (i1 >= nr || i2 >= n_t || i3 >= n_s) { + return; + } + + const uint src0_base = i3 * (nb02 / 4) + i2 + i1 * (nb01 / 4); + const uint src1_base = i1 * (nb11 / 4); + + float sum = 0.0; + + if (nc == 4) { + sum = dot( + vec4(src0[src0_base], src0[src0_base + 1], src0[src0_base + 2], src0[src0_base + 3]), + vec4(src1[src1_base], src1[src1_base + 1], src1[src1_base + 2], src1[src1_base + 3]) + ); + } else { + [[unroll]] for (uint i0 = 0; i0 < nc; i0++) { + sum += src0[src0_base + i0] * src1[src1_base + i0]; + } + } + + if (APPLY_BIAS) { + sum += bias[i1]; + } + if (APPLY_SILU) { + sum = sum / (1.0f + exp(-sum)); + } + + const uint dst_idx = i3 * (dst_nb2 / 4) + i2 * (dst_nb1 / 4) + i1; + dst[dst_idx] = sum; +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/ssm_scan.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/ssm_scan.comp new file mode 100644 index 0000000000000000000000000000000000000000..c7416206dbdaed3ea3bb185d02d9a398a8a3ce70 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/ssm_scan.comp @@ -0,0 +1,124 @@ +#version 450 + +#extension GL_EXT_control_flow_attributes : require +#extension GL_KHR_shader_subgroup_basic : enable +#if USE_SUBGROUP_ADD +#extension GL_KHR_shader_subgroup_arithmetic : enable +#endif + +#include "types.glsl" + +layout(constant_id = 0) const uint D_STATE = 128; +layout(constant_id = 1) const uint SUBGROUP_SIZE = 32; + +const uint32_t c_factor = D_STATE / SUBGROUP_SIZE; + +layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; + +layout(binding = 0) readonly buffer Src0 { float s0[]; }; +layout(binding = 1) readonly buffer Src1 { float x[]; }; +layout(binding = 2) readonly buffer Src2 { float dt[]; }; +layout(binding = 3) readonly buffer Src3 { float A[]; }; +layout(binding = 4) readonly buffer Src4 { float B[]; }; +layout(binding = 5) readonly buffer Src5 { float C[]; }; +layout(binding = 6) readonly buffer Src6 { int ids[]; }; +layout(binding = 7) buffer Dst { float d[]; }; + +layout(push_constant) uniform PushConstants { + uint nb02; uint nb03; uint nb12; uint nb13; + uint nb21; uint nb22; uint nb31; + uint nb42; uint nb43; uint nb52; uint nb53; + uint s_off; + uint n_head; + uint d_head; + uint n_group; + uint n_tok; +}; + +float softplus(float x) { + if (x <= 20.0) { + return log(1.0 + exp(x)); + } else { + return x; + } +} + +#if !USE_SUBGROUP_ADD +shared float temp[D_STATE]; +#endif + +void main() { + const uint subgroup = gl_SubgroupID; + const uint lane = gl_SubgroupInvocationID; + const uint tid = gl_SubgroupID * SUBGROUP_SIZE + lane; + const uint subgroup_idx = gl_WorkGroupID.x * c_factor + subgroup; + + const uint head_idx = subgroup_idx / d_head; + const uint head_off = (subgroup_idx % d_head) * 4; + const uint seq_idx = gl_WorkGroupID.y; + + const uint group_off = (head_idx / (n_head / n_group)) * D_STATE * 4; + const uint s0_base_idx = (uint(ids[seq_idx]) * nb03 + head_idx * nb02 + head_off * D_STATE) / 4; + const uint x_base_idx = (seq_idx * nb13 + subgroup_idx * 4) / 4; + const uint dt_base_idx = (seq_idx * nb22 + head_idx * 4) / 4; + const uint A_base_idx = (head_idx * nb31) / 4; + const uint B_base_idx = (seq_idx * nb43 + group_off) / 4; + const uint C_base_idx = (seq_idx * nb53 + group_off) / 4; + const uint y_base_idx = seq_idx * n_tok * n_head * d_head + subgroup_idx; + const uint s_base_idx = (s_off + seq_idx * nb03 + head_idx * nb02 + head_off * D_STATE) / 4; + + const uint stride_x = nb12 / 4; + const uint stride_dt = nb21 / 4; + const uint stride_B = nb42 / 4; + const uint stride_C = nb52 / 4; + const uint stride_y = n_head * d_head; + + float state[c_factor]; + + [[unroll]] for (uint j = 0; j < c_factor; j++) { + state[j] = s0[s0_base_idx + SUBGROUP_SIZE * j + lane]; + } + + float a = A[A_base_idx]; + + for (uint i = 0; i < n_tok; i++) { + float dt_soft_plus = softplus(dt[dt_base_idx + i * stride_dt]); + + float state_sum = 0.0f; + + const float dA = exp(dt_soft_plus * a); + const float x_dt = x[x_base_idx + i * stride_x] * dt_soft_plus; + [[unroll]] for (uint j = 0; j < c_factor; j++) { + float B_val = B[B_base_idx + i * stride_B + SUBGROUP_SIZE * j + lane]; + float C_val = C[C_base_idx + i * stride_C + SUBGROUP_SIZE * j + lane]; + state[j] = (state[j] * dA) + (B_val * x_dt); + state_sum += state[j] * C_val; + } + +#if USE_SUBGROUP_ADD + state_sum = subgroupAdd(state_sum); +#else + temp[tid] = state_sum; + barrier(); + [[unroll]] for (uint s = SUBGROUP_SIZE / 2; s > 0; s >>= 1) { + if (lane < s) { + temp[tid] += temp[tid + s]; + } + barrier(); + } + // get the value from lane 0 + state_sum = temp[subgroup * SUBGROUP_SIZE]; + barrier(); +#endif + + if (lane == 0) { + d[y_base_idx + i * stride_y] = state_sum; + } + } + + // write back the state + [[unroll]] + for (int j = 0; j < c_factor; j++) { + d[s_base_idx + SUBGROUP_SIZE * j + lane] = state[j]; + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/sub.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/sub.comp new file mode 100644 index 0000000000000000000000000000000000000000..bc924b520a74c01167bf8068faf1d96031cf8185 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/sub.comp @@ -0,0 +1,29 @@ +#version 450 + +#extension GL_EXT_shader_16bit_storage : require + +#include "types.glsl" +#include "generic_binary_head.glsl" + +const uint num_threads = 256; + +layout(local_size_x = num_threads, local_size_y = 1, local_size_z = 1) in; + +void main() { + uint idx = get_idx(); + + // num_threads * num_iter must equal 512, to match the wg_denoms and get_idx calculation + const uint num_iter = 2; + + [[unroll]] for (uint i = 0; i < num_iter; ++i) { + if (idx >= p.ne) { + continue; + } + uint i00, i01, i02, i03; + get_indices(idx, i00, i01, i02, i03); + + data_d[get_doffset() + dst_idx(i00, i01, i02, i03)] = D_TYPE(FLOAT_TYPE(data_a[get_aoffset() + src0_idx(i00, i01, i02, i03)]) - FLOAT_TYPE(data_b[get_boffset() + src1_idx(i00, i01, i02, i03)])); + + idx += num_threads; + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/sum_rows.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/sum_rows.comp new file mode 100644 index 0000000000000000000000000000000000000000..13ba2e99dcccd948b72cf1091856a18ac31e7f09 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/sum_rows.comp @@ -0,0 +1,47 @@ +#version 450 + +#include "types.glsl" +#include "sum_rows.glsl" + +#extension GL_EXT_control_flow_attributes : enable + +layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_d[];}; + +layout (constant_id = 0) const uint BLOCK_SIZE = 32; + +shared FLOAT_TYPE tmp[BLOCK_SIZE]; + +void main() { + const uint row = gl_WorkGroupID.z * 262144 + gl_WorkGroupID.y * 512 + gl_WorkGroupID.x; + const uint col = gl_LocalInvocationID.x; + const float weight = p.weight; + + const uint i03 = fastdiv(row, p.ne0_12mp, p.ne0_12L); + const uint i03_offset = i03 * p.ne01*p.ne02; + const uint i02 = fastdiv(row - i03_offset, p.ne0_1mp, p.ne0_1L); + const uint i01 = row - i03_offset - i02*p.ne01; + + const uint src_idx = get_aoffset() + i01 * p.nb01 + i02 * p.nb02 + i03 * p.nb03; + const uint dst_idx = get_doffset() + i01 * p.nb11 + i02 * p.nb12 + i03 * p.nb13; + + tmp[col] = FLOAT_TYPE(0.0); + + for (uint i = col; i < p.n_cols; i += BLOCK_SIZE) { + tmp[col] += FLOAT_TYPE(data_a[src_idx + i]); + } + + barrier(); + [[unroll]] for (int s = int(BLOCK_SIZE) / 2; s > 0; s >>= 1) { + if (col < s) { + tmp[col] += tmp[col + s]; + } + barrier(); + } + + if (col == 0) { + data_d[dst_idx] = D_TYPE(tmp[0] * weight); + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/sum_rows.glsl b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/sum_rows.glsl new file mode 100644 index 0000000000000000000000000000000000000000..2b841baa6bf28d73af68d61e4f38bbab1780f3b8 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/sum_rows.glsl @@ -0,0 +1,25 @@ + +// vk_op_sum_rows_push_constants +layout (push_constant) uniform parameter +{ + uint n_cols; + uint ne01, ne02; + uint nb01, nb02, nb03; + uint nb11, nb12, nb13; + float weight; + uint misalign_offsets; + uint ne0_12mp, ne0_12L; + uint ne0_1mp, ne0_1L; +} p; + +uint get_aoffset() { return p.misalign_offsets >> 16; } +uint get_doffset() { return p.misalign_offsets & 0xFFFF; } + +// see init_fastdiv_values in ggml-vulkan.cpp +uint fastdiv(uint n, uint mp, uint L) { + uint msbs, lsbs; + // msbs = mulhi(n, mp) + umulExtended(n, mp, msbs, lsbs); + return (msbs + n) >> L; +} + diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/swiglu.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/swiglu.comp new file mode 100644 index 0000000000000000000000000000000000000000..4fee433a126605daeab44e0d512667d5932e511c --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/swiglu.comp @@ -0,0 +1,9 @@ +#version 450 + +#include "glu_head.glsl" + +float op(float a, float b) { + return a / (1.0f + exp(-a)) * b; +} + +#include "glu_main.glsl" diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/swiglu_oai.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/swiglu_oai.comp new file mode 100644 index 0000000000000000000000000000000000000000..bda9dea21c1841ef3f0cfb736c211aa151adc91b --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/swiglu_oai.comp @@ -0,0 +1,14 @@ +#version 450 + +#include "glu_head.glsl" + +float op(float a, float b) { + float xi = min(a, p.limit); + float gi = max(min(b, p.limit), -p.limit); + + float out_glu = xi / (1.0f + exp(-xi * p.alpha)); + out_glu = out_glu * (1.0f + gi); + return out_glu; +} + +#include "glu_main.glsl" diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/timestep_embedding.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/timestep_embedding.comp new file mode 100644 index 0000000000000000000000000000000000000000..16055654573471fdeaac606f7a1e8ac5d9128736 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/timestep_embedding.comp @@ -0,0 +1,42 @@ +#version 450 + +#extension GL_EXT_shader_16bit_storage : require + +layout (push_constant) uniform parameter +{ + uint nb1; + uint dim; + uint max_period; +} p; + +#include "types.glsl" + +#extension GL_EXT_control_flow_attributes : enable +#define BLOCK_SIZE 256 + +layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_d[];}; + +void main() { + const uint i = gl_WorkGroupID.y; + const uint j = gl_GlobalInvocationID.x; + const uint d_offset = i * p.nb1; + + const uint half_dim = p.dim / 2; + + if (p.dim % 2 != 0 && j == half_dim) { + data_d[d_offset + 2 * half_dim] = 0.f; + } + + if (j >= half_dim) { + return; + } + + const float timestep = float(data_a[i]); + const float freq = float(exp(-log(p.max_period) * j / half_dim)); + const float arg = timestep * freq; + data_d[d_offset + j] = D_TYPE(cos(arg)); + data_d[d_offset + j + half_dim] = D_TYPE(sin(arg)); +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/topk_argsort.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/topk_argsort.comp new file mode 100644 index 0000000000000000000000000000000000000000..49d4ab8e7c096366639b2a1b848d22d77f93b8ae --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/topk_argsort.comp @@ -0,0 +1,118 @@ +#version 450 +#extension GL_EXT_control_flow_attributes : enable + +#include "types.glsl" + +layout(constant_id = 0) const int BLOCK_SIZE = 1024; +layout(constant_id = 1) const int NCOLS_PADDED_LOG2 = 10; + +layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; + +// Input can either be the source (A) or intermediate values (S). +// Similarly, output can be either destination (D) or intermediate values (S). +layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; +layout (binding = 0) readonly buffer S {ivec2 data_s[];}; +layout (binding = 1) writeonly buffer D {int data_d[];}; +layout (binding = 1) writeonly buffer T {ivec2 data_t[];}; + +layout (push_constant) uniform parameter { + uint orig_ncols; + uint ncols_input; + uint ncols_output; + uint k; + uint nrows; + uint first_pass; + uint last_pass; +} p; + +// pairs of (gid, value) +shared ivec2 dst_row[BLOCK_SIZE]; + +void topk(bool needs_bounds_check, const uint row) { + const int col = int(gl_LocalInvocationID.x); + + // initialize indices + if (gl_GlobalInvocationID.x < p.ncols_input) { + if (p.first_pass != 0) { + const uint row_offset = row * p.ncols_input; + dst_row[col] = ivec2(gl_GlobalInvocationID.x, floatBitsToInt(data_a[row_offset + gl_GlobalInvocationID.x])); + } else { + const uint row_offset = row * p.ncols_input; + dst_row[col] = data_s[row_offset + gl_GlobalInvocationID.x]; + } + } else { + dst_row[col] = ivec2(p.orig_ncols, 0); + } + barrier(); + + if (p.k == 1) { + // Fast path for single output - just do a max reduction + [[unroll]] for (int s = BLOCK_SIZE / 2; s >= 1; s /= 2) { + if (col < s) { + ivec2 a = dst_row[col]; + ivec2 b = dst_row[col + s]; + if (a.x >= p.orig_ncols || + b.x < p.orig_ncols && b.y > a.y) { + dst_row[col] = b; + } + } + barrier(); + } + } else { + // bitonic sort on this group of elements + uint num_outer_loop_iters = NCOLS_PADDED_LOG2; + for (uint k = 2, outer_idx = 0; outer_idx < num_outer_loop_iters; k *= 2, outer_idx++) { + uint num_inner_loop_iters = outer_idx + 1; + for (uint j = k / 2, inner_idx = 0; inner_idx < num_inner_loop_iters; j /= 2, inner_idx++) { + const int ixj = int(col ^ j); + + int idx_0 = (col & k) == 0 ? col : ixj; + int idx_1 = (col & k) == 0 ? ixj : col; + + ivec2 sh_idx_0 = dst_row[idx_0]; + ivec2 sh_idx_1 = dst_row[idx_1]; + bool idx_0_oob = needs_bounds_check ? sh_idx_0.x >= p.orig_ncols : false; + bool idx_1_oob = needs_bounds_check ? sh_idx_1.x >= p.orig_ncols : false; + + if ((idx_0_oob || + (!idx_1_oob && intBitsToFloat(sh_idx_0.y) < intBitsToFloat(sh_idx_1.y))) && (ixj > col)) { + dst_row[idx_0] = sh_idx_1; + dst_row[idx_1] = sh_idx_0; + } + + barrier(); + } + } + } + + if (col < p.k) { + if (p.last_pass != 0) { + if (gl_GlobalInvocationID.x < p.ncols_input) { + const uint row_offset = row * p.k; + data_d[row_offset + col] = dst_row[col].x; + } + } else { + if (gl_WorkGroupID.x * p.k + col < p.ncols_output) { + const uint row_offset = row * p.ncols_output + gl_WorkGroupID.x * p.k; + data_t[row_offset + col] = dst_row[col]; + } + } + } +} + +void main() { + // Fast path for fully occupied workgroups + if ((p.ncols_input % BLOCK_SIZE) == 0) { + uint row = gl_WorkGroupID.y; + while (row < p.nrows) { + topk(false, row); + row += gl_WorkGroupSize.y * gl_NumWorkGroups.y; + } + } else { + uint row = gl_WorkGroupID.y; + while (row < p.nrows) { + topk(true, row); + row += gl_WorkGroupSize.y * gl_NumWorkGroups.y; + } + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/topk_moe.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/topk_moe.comp new file mode 100644 index 0000000000000000000000000000000000000000..ef2f202ec9b6a005b7c862bbc4beba7a6f85f222 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/topk_moe.comp @@ -0,0 +1,213 @@ +#version 450 + +#extension GL_EXT_control_flow_attributes : require +#extension GL_KHR_shader_subgroup_basic : enable +#extension GL_KHR_shader_subgroup_arithmetic : enable +#extension GL_KHR_shader_subgroup_shuffle : enable + +#include "types.glsl" + +#define GATING_FUNC_SOFTMAX 0 +#define GATING_FUNC_SIGMOID 1 +#define GATING_FUNC_SOFTMAX_WEIGHT 2 + +layout (push_constant) uniform parameter +{ + uint n_rows; + uint n_experts_push; + uint n_expert_used; + float clamp_min; + float clamp_max; + uint gating_func; + uint has_bias; + uint with_norm; + float output_scale; + float output_bias; +}; + +layout(local_size_x_id = 0, local_size_y = 4, local_size_z = 1) in; + +layout(constant_id = 0) const uint WARP_SIZE = 32; +layout(constant_id = 1) const uint n_experts_spec = 512; +layout(constant_id = 2) const bool nexperts_use_push = false; + +uint n_experts = nexperts_use_push ? n_experts_push : n_experts_spec; + +#define CEIL_DIV(a, b) (((a) + (b) - 1) / (b)) + +const uint experts_per_thread = CEIL_DIV(n_experts_spec, WARP_SIZE); + +layout (binding = 0, std430) readonly buffer Logits {float logits[];}; +layout (binding = 1, std430) readonly buffer BiasProbs {float bias[];}; +layout (binding = 2, std430) writeonly buffer Weights {float weights[];}; +layout (binding = 3, std430) writeonly buffer Ids {uint ids[];}; + +const float INFINITY = 1.0 / 0.0; + +// Warp-local softmax used for both the pre-top-k logits and the post-top-k delayed path. +void softmax_warp_inplace(inout float vals[experts_per_thread], const uint limit, const uint lane, const bool use_limit) { + float max_val = -INFINITY; + + [[unroll]] + for (int i = 0; i < experts_per_thread; i++) { + const uint idx = lane + i * WARP_SIZE; + const bool is_active = !use_limit || (idx < limit); + if (is_active) { + max_val = max(max_val, vals[i]); + } + } + + max_val = subgroupMax(max_val); + + float sum = 0.f; + + [[unroll]] + for (int i = 0; i < experts_per_thread; i++) { + const uint idx = lane + i * WARP_SIZE; + const bool is_active = !use_limit || (idx < limit); + if (is_active) { + const float val = exp(vals[i] - max_val); + vals[i] = val; + sum += val; + } else { + vals[i] = 0.f; + } + } + + sum = subgroupAdd(sum); + + const float inv_sum = 1.0f / sum; + + [[unroll]] + for (int i = 0; i < experts_per_thread; i++) { + const uint idx = lane + i * WARP_SIZE; + const bool is_active = !use_limit || (idx < limit); + if (is_active) { + vals[i] *= inv_sum; + } + } +} + +void main() { + const uint row = gl_WorkGroupID.x * gl_WorkGroupSize.y + gl_SubgroupID; + if (row >= n_rows) { + return; + } + + const uint logits_offset = n_experts * row; + const uint bias_offset = 0; // 1D + const uint weights_offset = n_expert_used * row; + const uint ids_offset = n_experts * row; + const uint lane = gl_SubgroupInvocationID; + + float probs[experts_per_thread]; + [[unroll]] + for (int i = 0; i < experts_per_thread; i++) { + probs[i] = -INFINITY; + } + + [[unroll]] + for (uint i = 0; i < n_experts; i += WARP_SIZE) { + const uint expert = i + lane; + probs[i / WARP_SIZE] = (n_experts % WARP_SIZE == 0 || expert < n_experts) ? logits[logits_offset + expert] : -INFINITY; + } + + if (gating_func == GATING_FUNC_SOFTMAX) { + softmax_warp_inplace(probs, n_experts, lane, nexperts_use_push); + } else if (gating_func == GATING_FUNC_SIGMOID) { + [[unroll]] + for (uint i = 0; i < n_experts; i += WARP_SIZE) { + const uint expert = i + lane; + probs[i / WARP_SIZE] = (n_experts % WARP_SIZE == 0 || expert < n_experts) ? 1.f / (1.f + exp(-probs[i / WARP_SIZE])) : -INFINITY; + } + } + + float selection_probs[experts_per_thread]; + if (has_bias != 0) { + [[unroll]] + for (uint i = 0; i < n_experts; i += WARP_SIZE) { + const uint expert = i + lane; + selection_probs[i / WARP_SIZE] = (n_experts % WARP_SIZE == 0 || expert < n_experts) ? probs[i / WARP_SIZE] + bias[bias_offset + expert] : -INFINITY; + } + } else { + [[unroll]] + for (int i = 0; i < experts_per_thread; i++) { + selection_probs[i] = probs[i]; + } + } + + // at this point, each thread holds a portion of softmax, + // we do the argmax reduce over n_expert_used, each time marking + // the expert weight as -inf to exclude from the next iteration + + float wt_sum = 0.f; + + float output_weights[experts_per_thread]; + + [[unroll]] + for (int i = 0; i < experts_per_thread; i++) { + output_weights[i] = 0.f; + } + + for (int k = 0; k < n_expert_used; k++) { + float max_val = probs[0]; + float max_val_s = selection_probs[0]; + uint max_expert = lane; + + [[unroll]] + for (uint i = WARP_SIZE; i < n_experts; i += WARP_SIZE) { + const uint expert = i + lane; + if ((n_experts % WARP_SIZE == 0 || expert < n_experts) && selection_probs[i / WARP_SIZE] > max_val_s) { + max_val = probs[i / WARP_SIZE]; + max_val_s = selection_probs[i / WARP_SIZE]; + max_expert = expert; + } + } + + [[unroll]] + for (uint mask = WARP_SIZE / 2; mask > 0; mask /= 2) { + const float val = subgroupShuffleXor(max_val, mask); + const float val_s = subgroupShuffleXor(max_val_s, mask); + const uint expert = subgroupShuffleXor(max_expert, mask); + if (val_s > max_val_s || (val_s == max_val_s && expert < max_expert)) { + max_val = val; + max_val_s = val_s; + max_expert = expert; + } + } + + if ((k & (WARP_SIZE - 1)) == lane) { + output_weights[k / WARP_SIZE] = max_val; + } + + if ((max_expert & (WARP_SIZE - 1)) == lane) { + selection_probs[max_expert / WARP_SIZE] = -INFINITY; + + ids[ids_offset + k] = max_expert; + wt_sum += max_val; + } + } + + if (with_norm != 0) { + wt_sum = subgroupAdd(wt_sum); + wt_sum = clamp(wt_sum, clamp_min, clamp_max); + const float inv_sum = 1.0f / wt_sum; + + [[unroll]] + for (uint i = 0; i < experts_per_thread; ++i) { + output_weights[i] *= inv_sum; + } + } + + if (gating_func == GATING_FUNC_SOFTMAX_WEIGHT) { + 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 + lane; + if (idx < n_expert_used) { + weights[weights_offset + idx] = output_scale * output_weights[i] + output_bias; + } + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/topk_nary_search.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/topk_nary_search.comp new file mode 100644 index 0000000000000000000000000000000000000000..0b757f38e1868e473ed829b2e08487cd4d214e8c --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/topk_nary_search.comp @@ -0,0 +1,246 @@ +#version 450 +#extension GL_EXT_control_flow_attributes : enable +#extension GL_EXT_debug_printf : enable +#extension GL_KHR_shader_subgroup_basic : enable +#extension GL_KHR_shader_subgroup_ballot : enable +#extension GL_KHR_shader_subgroup_arithmetic : enable +#extension GL_KHR_shader_subgroup_shuffle : enable + +#include "types.glsl" + +layout(constant_id = 0) const int BLOCK_SIZE = 1024; +layout(constant_id = 1) const int SUBGROUP_SIZE = 32; +layout(constant_id = 2) const int SUBGROUP_SIZE_LOG2 = 5; + +layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; + +// Input can either be the source (A) or intermediate values (S). +// Similarly, output can be either destination (D) or intermediate values (S). +layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; +layout (binding = 0) readonly buffer S {ivec2 data_s[];}; +layout (binding = 1) writeonly buffer D {int data_d[];}; +layout (binding = 1) writeonly buffer T {ivec2 data_t[];}; + +layout (push_constant) uniform parameter { + uint orig_ncols; + uint ncols_input; + uint ncols_output; + uint k; + uint nrows; + uint first_pass; + uint last_pass; +} p; + +// pairs of (gid, value) +shared ivec2 dst_row[BLOCK_SIZE]; + +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. +// +0.0 -> 0x80000000, -0.0 -> 0x7FFFFFFF are in the correct places. +uint f2ui(float x) { + uint y = floatBitsToUint(x); + if ((y & 0x80000000) != 0) { + y ^= ~0; + } else { + y |= 0x80000000; + } + return y; +} + +void topk(const uint row) { + const int tid = int(gl_LocalInvocationID.x); + + // initialize indices + if (gl_GlobalInvocationID.x < p.ncols_input) { + if (p.first_pass != 0) { + const uint row_offset = row * p.ncols_input; + dst_row[tid] = ivec2(gl_GlobalInvocationID.x, floatBitsToInt(data_a[row_offset + gl_GlobalInvocationID.x])); + } else { + const uint row_offset = row * p.ncols_input; + dst_row[tid] = data_s[row_offset + gl_GlobalInvocationID.x]; + } + } else { + dst_row[tid] = ivec2(p.orig_ncols, 0xFF800000); // -inf + } + barrier(); + + if (p.k == 1) { + // Fast path for single output - just do a max reduction + [[unroll]] for (int s = BLOCK_SIZE / 2; s >= 1; s /= 2) { + if (tid < s) { + ivec2 a = dst_row[tid]; + ivec2 b = dst_row[tid + s]; + if (a.x >= p.orig_ncols || + b.x < p.orig_ncols && b.y > a.y) { + dst_row[tid] = b; + } + } + barrier(); + } + } else { + // Do an N-ary search to find the K-th largest value. + // We remap the float values to be comparable as unsigned integers, + // and split the range into 2^N smaller ranges where N is the + // subgroup size. Count how many values are in each range, if the K-th + // largest value is in the middle of one of thee ranges then repeat + // and split again. + + // Mask is the current set of bits we're searching. Shift is the LSB index. + int shift = 32 - SUBGROUP_SIZE_LOG2; + uint mask = ((1 << SUBGROUP_SIZE_LOG2) - 1) << shift; + + // The current range. + uint range_min = 0; + uint range_max = 0xFF800000; + // How many are above the current range, and how many we need to find. + uint total = 0; + uint limit = min(p.k, p.ncols_input - gl_WorkGroupID.x * BLOCK_SIZE); + + while (mask != 0) { + barrier(); + // Initialize bucket counts to zero. + if (tid < SUBGROUP_SIZE) { + counts[tid] = 0; + } + barrier(); + // Count how many values are in each bucket. + if (tid < p.ncols_input) { + float y = intBitsToFloat(dst_row[tid].y); + uint fy = f2ui(y); + if (fy >= range_min && fy < range_max) { + uint bucket = (fy & mask) >> shift; + atomicAdd(counts[bucket], 1); + } + } + barrier(); + + // On the first subgroup, do a scan to count (from the top down) how + // many elements are in the top N buckets. Find the index of the first + // that is over the limit. Copy it to the other invocations through + // shared memory. + if (tid < SUBGROUP_SIZE) { + uint partial_sum = counts[SUBGROUP_SIZE - 1 - tid]; + partial_sum = subgroupInclusiveAdd(partial_sum) + total; + uint t = subgroupBallotFindLSB(subgroupBallot(partial_sum >= limit)); + if (tid == t) { + sh_min_idx = int(SUBGROUP_SIZE - 1 - t); + sh_total = partial_sum; + } + } + barrier(); + int min_idx = sh_min_idx; + total = sh_total; + + // Update the range, and break if we've found the K-th largest. + range_max = range_min + ((min_idx + 1) << shift); + range_min = range_min + (min_idx << shift); + + if (total == p.k) { + break; + } + total -= counts[min_idx]; + mask >>= SUBGROUP_SIZE_LOG2; + shift -= SUBGROUP_SIZE_LOG2; + if (shift < 0) { + shift = 0; + } + } + + ivec2 v = dst_row[tid]; + + // 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. + // 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 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(); + } + + if (tid < p.k) { + if (p.last_pass != 0) { + if (gl_GlobalInvocationID.x < p.ncols_input) { + const uint row_offset = row * p.k; + data_d[row_offset + tid] = dst_row[tid].x; + } + } else { + if (gl_WorkGroupID.x * p.k + tid < p.ncols_output) { + const uint row_offset = row * p.ncols_output + gl_WorkGroupID.x * p.k; + data_t[row_offset + tid] = dst_row[tid]; + } + } + } +} + +void main() { + uint row = gl_WorkGroupID.y; + while (row < p.nrows) { + topk(row); + row += gl_WorkGroupSize.y * gl_NumWorkGroups.y; + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/tri.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/tri.comp new file mode 100644 index 0000000000000000000000000000000000000000..9def5dbc9a06cb1fca734e7a523c2b59a5f3151b --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/tri.comp @@ -0,0 +1,42 @@ +#version 450 + +#include "types.glsl" +#include "generic_unary_head.glsl" + +#define GGML_TRI_TYPE_UPPER_DIAG 0 +#define GGML_TRI_TYPE_UPPER 1 +#define GGML_TRI_TYPE_LOWER_DIAG 2 +#define GGML_TRI_TYPE_LOWER 3 + +layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; + +void main() { + const uint idx = get_idx(); + + if (idx >= p.ne) { + return; + } + + const uint i03 = fastdiv(idx, p.ne0_012mp, fastdiv_L(p.ne0_Ls, 0)); + const uint i03_offset = i03 * p.ne02*p.ne01*p.ne00; + const uint i02 = fastdiv(idx - i03_offset, p.ne0_01mp, fastdiv_L(p.ne0_Ls, 1)); + const uint i02_offset = i02*p.ne01*p.ne00; + const uint i01 = fastdiv(idx - i03_offset - i02_offset, p.ne0_0mp, fastdiv_L(p.ne0_Ls, 2)); + const uint i00 = idx - i03_offset - i02_offset - i01*p.ne00; + + int param = floatBitsToInt(p.param1); + bool pass = false; + switch (param) { + case GGML_TRI_TYPE_UPPER_DIAG: pass = i00 >= i01; break; + case GGML_TRI_TYPE_UPPER: pass = i00 > i01; break; + case GGML_TRI_TYPE_LOWER_DIAG: pass = i00 <= i01; break; + case GGML_TRI_TYPE_LOWER: pass = i00 < i01; break; + } + + if (pass) { + const float val = float(data_a[get_aoffset() + src0_idx(idx)]); + data_d[get_doffset() + dst_idx(idx)] = D_TYPE(val); + } else { + data_d[get_doffset() + dst_idx(idx)] = D_TYPE(0); + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/types.glsl b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/types.glsl new file mode 100644 index 0000000000000000000000000000000000000000..8c6b20c6889460b8c31180f7c20f5da99d5b1e34 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/types.glsl @@ -0,0 +1,1861 @@ +#if !defined(GGML_TYPES_COMP) +#define GGML_TYPES_COMP + +#extension GL_EXT_shader_explicit_arithmetic_types_int64 : require +#extension GL_EXT_shader_explicit_arithmetic_types_int32 : require +#extension GL_EXT_shader_explicit_arithmetic_types_int16 : require +#extension GL_EXT_shader_explicit_arithmetic_types_int8 : require +#extension GL_EXT_shader_16bit_storage : require + +#if defined(DATA_A_F32) +#define QUANT_K 1 +#define QUANT_R 1 + +#if LOAD_VEC_A == 4 +#define A_TYPE vec4 +#elif LOAD_VEC_A == 8 +#define A_TYPE mat2x4 +#else +#define A_TYPE float +#endif +#endif + +#if defined(DATA_A_F16) +#define QUANT_K 1 +#define QUANT_R 1 + +#if LOAD_VEC_A == 4 +#define A_TYPE f16vec4 +#elif LOAD_VEC_A == 8 +#define A_TYPE f16mat2x4 +#else +#define A_TYPE float16_t +#endif +#define A_TYPE_PACKED32 f16vec2 +#endif + +#if defined(DATA_A_BF16) +#define QUANT_K 1 +#define QUANT_R 1 + +#if LOAD_VEC_A == 4 +#define A_TYPE u16vec4 +#elif LOAD_VEC_A == 8 +#error unsupported +#else +#define A_TYPE uint16_t +#endif +#define A_TYPE_PACKED32 uint32_t +#endif + +#define QUANT_K_Q4_0 32 +#define QUANT_R_Q4_0 2 + +struct block_q4_0 +{ + float16_t d; + uint8_t qs[16]; +}; +struct block_q4_0_packed16 +{ + float16_t d; + uint16_t qs[16/2]; +}; + +#if defined(DATA_A_Q4_0) +#define QUANT_K QUANT_K_Q4_0 +#define QUANT_R QUANT_R_Q4_0 +#define QUANT_AUXF 1 +#define A_TYPE block_q4_0 +#define A_TYPE_PACKED16 block_q4_0_packed16 +#define DATA_A_QUANT_LEGACY +#endif + +#define QUANT_K_Q4_1 32 +#define QUANT_R_Q4_1 2 + +struct block_q4_1 +{ + float16_t d; + float16_t m; + uint8_t qs[16]; +}; + +struct block_q4_1_packed16 +{ + float16_t d; + float16_t m; + uint16_t qs[16/2]; +}; + +struct block_q4_1_packed32 +{ + f16vec2 dm; + uint32_t qs[16/4]; +}; + +#if defined(DATA_A_Q4_1) +#define QUANT_K QUANT_K_Q4_1 +#define QUANT_R QUANT_R_Q4_1 +#define QUANT_AUXF 2 +#define A_TYPE block_q4_1 +#define A_TYPE_PACKED16 block_q4_1_packed16 +#define A_TYPE_PACKED32 block_q4_1_packed32 +#define DATA_A_QUANT_LEGACY +#endif + +#define QUANT_K_Q5_0 32 +#define QUANT_R_Q5_0 2 + +struct block_q5_0 +{ + float16_t d; + uint16_t qh[2]; + uint8_t qs[16]; +}; + +struct block_q5_0_packed16 +{ + float16_t d; + uint16_t qh[2]; + uint16_t qs[16/2]; +}; + +#if defined(DATA_A_Q5_0) +#define QUANT_K QUANT_K_Q5_0 +#define QUANT_R QUANT_R_Q5_0 +#define QUANT_AUXF 1 +#define A_TYPE block_q5_0 +#define A_TYPE_PACKED16 block_q5_0_packed16 +#define DATA_A_QUANT_LEGACY +#endif + +#define QUANT_K_Q5_1 32 +#define QUANT_R_Q5_1 2 + +struct block_q5_1 +{ + float16_t d; + float16_t m; + uint qh; + uint8_t qs[16]; +}; + +struct block_q5_1_packed16 +{ + float16_t d; + float16_t m; + uint qh; + uint16_t qs[16/2]; +}; + +struct block_q5_1_packed32 +{ + f16vec2 dm; + uint qh; + uint32_t qs[16/4]; +}; + +#if defined(DATA_A_Q5_1) +#define QUANT_K QUANT_K_Q5_1 +#define QUANT_R QUANT_R_Q5_1 +#define QUANT_AUXF 2 +#define A_TYPE block_q5_1 +#define A_TYPE_PACKED16 block_q5_1_packed16 +#define A_TYPE_PACKED32 block_q5_1_packed32 +#define DATA_A_QUANT_LEGACY +#endif + +#define QUANT_K_Q8_0 32 +#define QUANT_R_Q8_0 1 + +struct block_q8_0 +{ + float16_t d; + int8_t qs[32]; +}; + +struct block_q8_0_packed16 +{ + float16_t d; + int16_t qs[32/2]; +}; + +#if defined(DATA_A_Q8_0) +#define QUANT_K QUANT_K_Q8_0 +#define QUANT_R QUANT_R_Q8_0 +#define QUANT_AUXF 1 +#define A_TYPE block_q8_0 +#define A_TYPE_PACKED16 block_q8_0_packed16 +#define DATA_A_QUANT_LEGACY +#endif + +#define QUANT_K_Q1_0 128 +#define QUANT_R_Q1_0 1 + +struct block_q1_0 +{ + float16_t d; + uint8_t qs[QUANT_K_Q1_0 / 8]; +}; + +#if defined(DATA_A_Q1_0) +#define QUANT_K QUANT_K_Q1_0 +#define QUANT_R QUANT_R_Q1_0 +#define QUANT_AUXF 1 +#define A_TYPE block_q1_0 +#endif + +#define QUANT_K_Q8_1 32 +#define QUANT_R_Q8_1 1 + +struct block_q8_1 +{ + f16vec2 ds; + int8_t qs[32]; +}; + +struct block_q8_1_packed16 +{ + f16vec2 ds; + int16_t qs[16]; +}; + +struct block_q8_1_packed32 +{ + f16vec2 ds; + int32_t qs[8]; +}; + +// 4 blocks in one to allow 16-byte/128-bit alignment and loads +struct block_q8_1_x4 +{ + f16vec2 ds[4]; + int32_t qs[32]; +}; + +struct block_q8_1_x4_packed128 +{ + f16vec2 ds[4]; + ivec4 qs[8]; +}; + +// K-quants +#define QUANT_K_Q2_K 256 + +struct block_q2_K +{ + uint8_t scales[QUANT_K_Q2_K/16]; + uint8_t qs[QUANT_K_Q2_K/4]; + f16vec2 dm; +}; + +struct block_q2_K_packed16 +{ + uint16_t scales[QUANT_K_Q2_K/16/2]; + uint16_t qs[QUANT_K_Q2_K/4/2]; + f16vec2 dm; +}; + +struct block_q2_K_packed32 +{ + uint32_t scales[QUANT_K_Q2_K/16/4]; + uint32_t qs[QUANT_K_Q2_K/4/4]; + f16vec2 dm; +}; + +#if defined(DATA_A_Q2_K) +#define QUANT_K QUANT_K_Q2_K +#define QUANT_R 1 +#define A_TYPE block_q2_K +#define A_TYPE_PACKED16 block_q2_K_packed16 +#define A_TYPE_PACKED32 block_q2_K_packed32 +#define SCALES_PER_32 2 +#define DATA_A_QUANT_K +#endif + +#define QUANT_K_Q3_K 256 + +struct block_q3_K +{ + uint8_t hmask[QUANT_K_Q3_K/8]; + uint8_t qs[QUANT_K_Q3_K/4]; + uint8_t scales[12]; + float16_t d; +}; + +struct block_q3_K_packed16 +{ + uint16_t hmask[QUANT_K_Q3_K/8/2]; + uint16_t qs[QUANT_K_Q3_K/4/2]; + uint16_t scales[12/2]; + float16_t d; +}; + +#if defined(DATA_A_Q3_K) +#define QUANT_K QUANT_K_Q3_K +#define QUANT_R 1 +#define A_TYPE block_q3_K +#define A_TYPE_PACKED16 block_q3_K_packed16 +#define DATA_A_QUANT_K +#endif + +#define QUANT_K_Q4_K 256 + +struct block_q4_K +{ + f16vec2 dm; + uint8_t scales[3*QUANT_K_Q4_K/64]; + uint8_t qs[QUANT_K_Q4_K/2]; +}; + +struct block_q4_K_packed16 +{ + f16vec2 dm; + uint16_t scales[3*QUANT_K_Q4_K/64/2]; + uint16_t qs[QUANT_K_Q4_K/2/2]; +}; + +struct block_q4_K_packed32 +{ + f16vec2 dm; + uint32_t scales[3*QUANT_K_Q4_K/64/4]; + uint32_t qs[QUANT_K_Q4_K/2/4]; +}; + +struct block_q4_K_packed128 +{ + uvec4 q4k[9]; +}; + +#if defined(DATA_A_Q4_K) +#define QUANT_K QUANT_K_Q4_K +#define QUANT_R 1 +#define A_TYPE block_q4_K +#define A_TYPE_PACKED16 block_q4_K_packed16 +#define A_TYPE_PACKED32 block_q4_K_packed32 +#define DATA_A_QUANT_K +#endif + +#define QUANT_K_Q5_K 256 + +struct block_q5_K +{ + f16vec2 dm; + uint8_t scales[12]; + uint8_t qh[QUANT_K_Q5_K/8]; + uint8_t qs[QUANT_K_Q5_K/2]; +}; + +struct block_q5_K_packed16 +{ + f16vec2 dm; + uint16_t scales[12/2]; + uint16_t qh[QUANT_K_Q5_K/8/2]; + uint16_t qs[QUANT_K_Q5_K/2/2]; +}; + +struct block_q5_K_packed32 +{ + f16vec2 dm; + uint32_t scales[12/4]; + uint32_t qh[QUANT_K_Q5_K/8/4]; + uint32_t qs[QUANT_K_Q5_K/2/4]; +}; + +struct block_q5_K_packed128 +{ + uvec4 q5k[11]; +}; + +#if defined(DATA_A_Q5_K) +#define QUANT_K QUANT_K_Q5_K +#define QUANT_R 1 +#define A_TYPE block_q5_K +#define A_TYPE_PACKED16 block_q5_K_packed16 +#define A_TYPE_PACKED32 block_q5_K_packed32 +#define DATA_A_QUANT_K +#endif + +#define QUANT_K_Q6_K 256 + +struct block_q6_K +{ + uint8_t ql[QUANT_K_Q6_K/2]; + uint8_t qh[QUANT_K_Q6_K/4]; + int8_t scales[QUANT_K_Q6_K/16]; + float16_t d; +}; + +struct block_q6_K_packed16 +{ + uint16_t ql[QUANT_K_Q6_K/2/2]; + uint16_t qh[QUANT_K_Q6_K/4/2]; + int16_t scales[QUANT_K_Q6_K/16/2]; + float16_t d; +}; + +#if defined(DATA_A_Q6_K) +#define QUANT_K QUANT_K_Q6_K +#define QUANT_R 1 +#define A_TYPE block_q6_K +#define A_TYPE_PACKED16 block_q6_K_packed16 +#define DATA_A_QUANT_K +#endif + +// IQuants + +#define QUANT_K_IQ1_S 256 +#define QUANT_R_IQ1_S 1 + +struct block_iq1_s { + float16_t d; + uint8_t qs[QUANT_K_IQ1_S/8]; + uint16_t qh[QUANT_K_IQ1_S/32]; +}; + +struct block_iq1_s_packed16 { + float16_t d; + uint16_t qs[QUANT_K_IQ1_S/8/2]; + uint16_t qh[QUANT_K_IQ1_S/32]; +}; + +#define QUANT_K_IQ1_M 256 +#define QUANT_R_IQ1_M 1 + +struct block_iq1_m { + uint8_t qs[QUANT_K_IQ1_M/8]; + uint8_t qh[QUANT_K_IQ1_M/16]; + uint16_t scales[QUANT_K_IQ1_M/64]; +}; + +struct block_iq1_m_packed16 { + uint16_t qs[QUANT_K_IQ1_M/8/2]; + uint16_t qh[QUANT_K_IQ1_M/16/2]; + uint16_t scales[QUANT_K_IQ1_M/64]; +}; + +struct block_iq1_m_packed32 { + uint32_t qs[QUANT_K_IQ1_M/8/4]; + uint32_t qh[QUANT_K_IQ1_M/16/4]; + uint32_t scales[QUANT_K_IQ1_M/64/2]; +}; + +struct block_iq1_m_packed64 { + uint64_t qs[QUANT_K_IQ1_M/8/8]; + uint64_t qh[QUANT_K_IQ1_M/16/8]; + uint64_t scales; +}; + +#if defined(DATA_A_IQ1_S) +#define QUANT_K QUANT_K_IQ1_S +#define QUANT_R QUANT_R_IQ1_S +#define A_TYPE block_iq1_s +#define A_TYPE_PACKED16 block_iq1_s_packed16 +#endif + +#if defined(DATA_A_IQ1_M) +#define QUANT_K QUANT_K_IQ1_M +#define QUANT_R QUANT_R_IQ1_M +#define A_TYPE block_iq1_m +#define A_TYPE_PACKED16 block_iq1_m_packed16 +#define A_TYPE_PACKED32 block_iq1_m_packed32 +#endif + +#if defined(DATA_A_IQ1_S) || defined(DATA_A_IQ1_M) +#define IQ1S_DELTA 0.125f +#define IQ1M_DELTA 0.125f + +// Packed IQ1S grid where every 2 vec8 are encoded on 32 bits (2 bits per coordinate). +const uint[1024] iq1s_grid_const = { + 0xfffdffff, 0xfff7fff0, 0xffccfff5, 0xffdfffc0, 0xffd7ffdd, 0xff30ffd5, 0xff03ff0c, 0xff10ff01, + 0xff7dff7f, 0xff75ff77, 0xff5fff40, 0xff57ff5d, 0xfcf3ff55, 0xfcccfcf0, 0xfcc1fcc3, 0xfcc5fcc4, + 0xfc3cfcd0, 0xfc34fc31, 0xfc00fc0d, 0xfc1cfc05, 0xfc11fc13, 0xfc70fc17, 0xfc43fc4c, 0xfc50fc41, + 0xfdfdfdff, 0xfdf5fdf7, 0xfddffdc0, 0xfdd7fddd, 0xfd30fdd5, 0xfd04fd0c, 0xfd14fd13, 0xfd7dfd7f, + 0xfd75fd77, 0xfd40fd4c, 0xfd5ffd44, 0xfd57fd5d, 0xf3ccfd55, 0xf3c1f3c3, 0xf33cf3d0, 0xf300f334, + 0xf313f305, 0xf34cf310, 0xf350f344, 0xf0f3f0fc, 0xf0f1f0f0, 0xf0c7f0c0, 0xf0d4f0c5, 0xf030f03f, + 0xf00ff035, 0xf003f00c, 0xf001f000, 0xf01ff004, 0xf010f01d, 0xf015f017, 0xf04cf07c, 0xf047f040, + 0xf05cf045, 0xf050f053, 0xf054f051, 0xf1c4f1c3, 0xf133f13c, 0xf10df10f, 0xf107f100, 0xf11cf11f, + 0xf114f111, 0xf14cf170, 0xf144f143, 0xf7fdf7ff, 0xf7f5f7f7, 0xf7dff7c0, 0xf7d7f7dd, 0xf730f7d5, + 0xf701f70c, 0xf77ff710, 0xf777f77d, 0xf740f775, 0xf75df75f, 0xf755f757, 0xf4ccf4f0, 0xf4c4f4c3, + 0xf4d0f4d3, 0xf40ff43c, 0xf400f40c, 0xf413f41c, 0xf44cf414, 0xf441f443, 0xf450f444, 0xf5fdf5ff, + 0xf5f5f5f7, 0xf5dff5c0, 0xf5d7f5dd, 0xf530f5d5, 0xf504f50c, 0xf510f51c, 0xf57df57f, 0xf577f570, + 0xf540f575, 0xf55df55f, 0xf555f557, 0xcfcccfcf, 0xcfc4cfc3, 0xcfd0cfd3, 0xcf33cf3c, 0xcf00cf0f, + 0xcf1ccf07, 0xcf10cf13, 0xcf4ccf14, 0xcf41cf43, 0xcf50cf5c, 0xccf3ccfc, 0xccf4ccf1, 0xcccdcccf, + 0xccc7ccc0, 0xccd3ccdc, 0xcc30ccd4, 0xcc0fcc35, 0xcc0dcc0c, 0xcc00cc03, 0xcc04cc01, 0xcc10cc1f, + 0xcc4dcc73, 0xcc5ccc40, 0xcdcccc53, 0xcdc1cdc3, 0xcd3fcdd0, 0xcd34cd31, 0xcd00cd0d, 0xcd05cd07, + 0xcd11cd13, 0xcd4ccd70, 0xcd41cd43, 0xc3fccd50, 0xc3f4c3f1, 0xc3c0c3c3, 0xc3c4c3c7, 0xc3d1c3dc, + 0xc330c33c, 0xc337c331, 0xc30cc335, 0xc300c303, 0xc304c301, 0xc310c31d, 0xc373c317, 0xc34fc374, + 0xc340c343, 0xc344c347, 0xc35cc345, 0xc350c353, 0xc0fdc354, 0xc0f5c0f0, 0xc0c3c0cc, 0xc0c1c0c0, + 0xc0dfc0c4, 0xc0d0c0dd, 0xc0d5c0d7, 0xc033c03c, 0xc031c030, 0xc00dc00c, 0xc000c003, 0xc004c001, + 0xc01cc005, 0xc010c013, 0xc014c011, 0xc07dc07f, 0xc070c073, 0xc075c077, 0xc04cc04f, 0xc040c043, + 0xc044c041, 0xc05fc045, 0xc050c05d, 0xc1f3c1fc, 0xc1f1c1f0, 0xc1c1c1c0, 0xc1c5c1c7, 0xc1d1c1dc, + 0xc13dc13f, 0xc130c133, 0xc135c137, 0xc100c10c, 0xc107c101, 0xc11cc104, 0xc110c113, 0xc114c117, + 0xc171c115, 0xc14dc175, 0xc153c140, 0xc7ccc154, 0xc7d0c7c1, 0xc733c73c, 0xc734c731, 0xc700c70f, + 0xc705c707, 0xc71cc71f, 0xc711c713, 0xc770c714, 0xc743c74c, 0xc4cfc750, 0xc4c0c4cd, 0xc4dcc4c5, + 0xc43dc4d0, 0xc430c433, 0xc40cc437, 0xc400c403, 0xc404c401, 0xc41fc405, 0xc415c410, 0xc44cc474, + 0xc440c44d, 0xc45cc447, 0xc454c451, 0xc5c1c5f4, 0xc5d1c5d3, 0xc531c533, 0xc50fc534, 0xc500c50d, + 0xc51cc507, 0xc514c511, 0xc54cc570, 0xc545c541, 0xdffddfff, 0xdff5dff7, 0xdfdfdfc0, 0xdfd0dfdd, + 0xdfd5dfd7, 0xdf0cdf30, 0xdf1cdf04, 0xdf7fdf10, 0xdf77df7d, 0xdf40df75, 0xdf5ddf5f, 0xdf57df50, + 0xdcf0df55, 0xdcc3dccc, 0xdcd0dcc4, 0xdc33dc3d, 0xdc00dc34, 0xdc05dc07, 0xdc13dc1c, 0xdc11dc10, + 0xdc4fdc70, 0xdc44dc41, 0xddfcdc50, 0xddf5ddf7, 0xddc0ddcc, 0xdddddddf, 0xddd5ddd7, 0xdd0cdd30, + 0xdd04dd01, 0xdd7cdd10, 0xdd75dd77, 0xdd40dd4c, 0xdd5ddd5f, 0xdd55dd57, 0xd3c3d3f0, 0xd3c4d3c1, + 0xd333d3d0, 0xd331d330, 0xd30dd334, 0xd307d300, 0xd311d305, 0xd34cd370, 0xd344d343, 0xd350d35c, + 0xd0c0d0f4, 0xd0d4d0dc, 0xd030d03f, 0xd00cd037, 0xd000d003, 0xd01dd004, 0xd017d010, 0xd04fd074, + 0xd040d043, 0xd045d047, 0xd053d05c, 0xd054d051, 0xd1cfd1f0, 0xd1c4d1cd, 0xd13cd1d0, 0xd100d134, + 0xd11cd11f, 0xd173d114, 0xd14fd171, 0xd7ffd145, 0xd7f7d7fd, 0xd7c0d7f5, 0xd7ddd7df, 0xd7d5d7d7, + 0xd70cd730, 0xd710d703, 0xd77dd77f, 0xd775d777, 0xd75dd75f, 0xd755d757, 0xd4ccd4f4, 0xd4c4d4c3, + 0xd431d4d0, 0xd40dd434, 0xd41cd400, 0xd411d413, 0xd470d414, 0xd441d44f, 0xd453d444, 0xd5ffd450, + 0xd5f7d5fd, 0xd5dfd5f5, 0xd5d7d5dd, 0xd530d5d5, 0xd501d50c, 0xd510d504, 0xd57dd57f, 0xd575d577, + 0xd55fd540, 0xd557d55d, 0x3ff0d555, 0x3fc13fcc, 0x3f343fd0, 0x3f003f0d, 0x3f053f07, 0x3f133f1c, + 0x3f433f11, 0x3f5c3f44, 0x3cff3f51, 0x3cf33cfc, 0x3cf43cf1, 0x3cc03ccd, 0x3cc73cc1, 0x3cdc3cc5, + 0x3cd43cd1, 0x3c373c30, 0x3c0c3c35, 0x3c003c03, 0x3c043c01, 0x3c103c05, 0x3c153c17, 0x3c733c7c, + 0x3c4f3c71, 0x3c403c4d, 0x3c5c3c5f, 0x3df03c5d, 0x3dc33dcc, 0x3dd03dc1, 0x3d0d3d3c, 0x3d053d00, + 0x3d143d13, 0x3d433d74, 0x33fc3d50, 0x33c433c0, 0x333033d4, 0x33353337, 0x3303330c, 0x33013300, + 0x331d331c, 0x33173310, 0x337c3315, 0x33743371, 0x334d334f, 0x335f3340, 0x3354335c, 0x30fd30fc, + 0x30f530f0, 0x30c330cc, 0x30c130c0, 0x30df30c4, 0x30d530d0, 0x3033303c, 0x30313030, 0x300f3034, + 0x3003300c, 0x30013000, 0x30043007, 0x3013301c, 0x30113010, 0x307d3014, 0x30703073, 0x304c3077, + 0x30403043, 0x30443041, 0x30503045, 0x30553057, 0x31f031fc, 0x31c331f4, 0x31c731c0, 0x31dc31c5, + 0x31d431d3, 0x313d313f, 0x31373130, 0x310c310f, 0x3100310d, 0x31043101, 0x3110311d, 0x317c3117, + 0x31753170, 0x31403143, 0x3153315c, 0x37f03151, 0x37c037cc, 0x37d037c5, 0x3734373d, 0x3700370f, + 0x371c3707, 0x37113713, 0x37703714, 0x3743374c, 0x37443741, 0x34fc3750, 0x34f134f0, 0x34cf34f5, + 0x34c034c3, 0x34dc34c7, 0x34d134d3, 0x3430343f, 0x340c3435, 0x3403340d, 0x34013400, 0x341f3404, + 0x3410341d, 0x34153411, 0x34743471, 0x3440344d, 0x34473441, 0x3453345c, 0x34543451, 0x353335c1, + 0x35343531, 0x35073500, 0x35133505, 0x35433514, 0x0ffc3550, 0x0ff00ff3, 0x0ff40ff1, 0x0fc00fcd, + 0x0fdc0fc5, 0x0fd40fd3, 0x0f300f3f, 0x0f0c0f37, 0x0f000f03, 0x0f040f01, 0x0f170f10, 0x0f740f71, + 0x0f470f40, 0x0f5c0f5f, 0x0f540f51, 0x0cf70cf0, 0x0cf50cf4, 0x0cc30ccc, 0x0cc10cc0, 0x0cc40cc7, + 0x0cd00cdf, 0x0cd70cd1, 0x0c3c0cd5, 0x0c300c33, 0x0c340c31, 0x0c0c0c0f, 0x0c030c0d, 0x0c010c00, + 0x0c040c07, 0x0c1c0c05, 0x0c100c13, 0x0c140c11, 0x0c700c7d, 0x0c430c4c, 0x0c410c40, 0x0c5f0c44, + 0x0c550c50, 0x0df10dfc, 0x0dc00dcd, 0x0ddc0dc5, 0x0d3d0dd3, 0x0d350d30, 0x0d030d0c, 0x0d010d00, + 0x0d1d0d04, 0x0d700d10, 0x0d4d0d4f, 0x0d440d40, 0x0d530d45, 0x03f003f3, 0x03c303cc, 0x03c103c0, + 0x03c403c7, 0x03d003dc, 0x03d503d7, 0x0333033c, 0x03310330, 0x03350334, 0x030c030f, 0x03000303, + 0x03070301, 0x03050304, 0x031d031c, 0x03100313, 0x03140311, 0x0377037f, 0x034c0375, 0x03400343, + 0x03440341, 0x0353035c, 0x03550350, 0x00fd00fc, 0x00f000f3, 0x00f400f1, 0x00cc00cf, 0x00c300cd, + 0x00c100c0, 0x00c500c4, 0x00d300dc, 0x00d100d0, 0x003f00d4, 0x003d003c, 0x00300033, 0x00370031, + 0x000f0034, 0x000d000c, 0x00000003, 0x00070001, 0x00050004, 0x001c001f, 0x00100013, 0x00170011, + 0x00150014, 0x0073007c, 0x00740070, 0x004f0075, 0x0043004c, 0x00410040, 0x00440047, 0x0053005c, + 0x00510050, 0x01ff0054, 0x01fd01fc, 0x01f101f3, 0x01f401f7, 0x01c301cc, 0x01c701c0, 0x01df01c4, + 0x01dd01dc, 0x01d001d3, 0x01d701d1, 0x013c01d4, 0x01310130, 0x01340137, 0x010f0135, 0x010d010c, + 0x01000103, 0x01070101, 0x01050104, 0x0113011c, 0x01140110, 0x0170017d, 0x01770171, 0x01750174, + 0x0140014c, 0x015d0145, 0x01510150, 0x01540157, 0x07f007f3, 0x07f407f1, 0x07c007cf, 0x07dc07c7, + 0x073007d5, 0x07350737, 0x0703070c, 0x07010700, 0x07040707, 0x071d071f, 0x07100713, 0x0774077d, + 0x074d074f, 0x07470740, 0x0754075c, 0x04fd04fc, 0x04f504f0, 0x04c304cc, 0x04c104c0, 0x04d004c4, + 0x0433043c, 0x04310430, 0x040f0434, 0x040d040c, 0x04000403, 0x04070401, 0x04050404, 0x0413041c, + 0x04110410, 0x047c0414, 0x04740470, 0x0443044c, 0x04410440, 0x04440447, 0x05f30450, 0x05c005f7, + 0x05df05c5, 0x05d105d0, 0x053005d4, 0x05340537, 0x0500050c, 0x05070501, 0x051d0504, 0x05170510, + 0x057c0515, 0x054d0575, 0x05410540, 0x05450547, 0x1ff0055c, 0x1fc11fc3, 0x1fd01fc4, 0x1f0f1f33, + 0x1f011f00, 0x1f051f07, 0x1f131f1c, 0x1f141f11, 0x1f411f7c, 0x1cfc1f50, 0x1cf11cf3, 0x1ccd1cf4, + 0x1cdc1cc0, 0x1cd11cdd, 0x1c301cd4, 0x1c0c1c34, 0x1c011c00, 0x1c101c04, 0x1c151c11, 0x1c751c73, + 0x1c401c4d, 0x1c511c5c, 0x1dcc1c54, 0x1dc41dc1, 0x1d3c1d3f, 0x1d001d31, 0x1d071d01, 0x1d701d1f, + 0x1d411d4c, 0x13cc1d50, 0x13c013cd, 0x13c513c1, 0x13d113dc, 0x133f13d4, 0x1330133d, 0x13351337, + 0x1303130c, 0x13011300, 0x13051304, 0x131d131f, 0x13731310, 0x13741370, 0x134d134f, 0x13401343, + 0x13471341, 0x135c1345, 0x13541353, 0x10f710f0, 0x10cc10f5, 0x10c110c0, 0x103310c4, 0x10311030, + 0x100f1034, 0x1003100c, 0x10011000, 0x101c1004, 0x10101013, 0x10141011, 0x10741071, 0x104c1075, + 0x10411040, 0x10451044, 0x1050105d, 0x10571051, 0x11f411fd, 0x11df11c0, 0x11d711d1, 0x113f11d4, + 0x11371130, 0x110c1135, 0x11001103, 0x11071101, 0x111f1105, 0x11171110, 0x117d117f, 0x11751170, + 0x11411143, 0x11441147, 0x1153115f, 0x11551151, 0x17c417c1, 0x173c17d0, 0x1700170d, 0x171c1705, + 0x17701714, 0x1747174c, 0x14fc1751, 0x14cf14f3, 0x14dc14c0, 0x14d114d3, 0x143f14d4, 0x1430143c, + 0x14371431, 0x1403140c, 0x14011400, 0x141f1404, 0x14151410, 0x1473147d, 0x14401475, 0x1453145c, + 0x14541450, 0x15c115cc, 0x153c15c7, 0x15341533, 0x1500150f, 0x15051507, 0x15101513, 0x15711514, + 0x15471543, 0x15511545, 0x7ffd7fff, 0x7ff57ff7, 0x7fdd7fdf, 0x7fd57fd7, 0x7f0f7f30, 0x7f037f0c, + 0x7f047f01, 0x7f7f7f10, 0x7f777f7d, 0x7f407f75, 0x7f5d7f5f, 0x7f557f57, 0x7ccc7cf0, 0x7cc17cc3, + 0x7cd07cc4, 0x7c337c3c, 0x7c0f7c34, 0x7c007c0d, 0x7c077c01, 0x7c137c04, 0x7c147c11, 0x7c747c70, + 0x7c417c43, 0x7c507c44, 0x7dfd7dff, 0x7df57df7, 0x7ddf7dc0, 0x7dd77ddd, 0x7d0c7dd5, 0x7d047d03, + 0x7d7f7d10, 0x7d777d7d, 0x7d407d75, 0x7d5d7d5f, 0x7d557d57, 0x73c473c3, 0x7333733c, 0x7300730c, + 0x731c7305, 0x73147313, 0x73447343, 0x70f470fc, 0x70c070cd, 0x70d170c5, 0x703f70d4, 0x7030703c, + 0x700c7037, 0x70007003, 0x70047001, 0x70107005, 0x70177011, 0x707c7015, 0x70717073, 0x704f7074, + 0x7040704d, 0x70517047, 0x71c171cc, 0x71d071c4, 0x7133713c, 0x71357134, 0x7100710f, 0x71057104, + 0x7111711c, 0x71707115, 0x7145714c, 0x77ff7153, 0x77f777fd, 0x77c077f5, 0x77dd77df, 0x77d577d7, + 0x7730773c, 0x7703770c, 0x77107704, 0x777f7714, 0x7777777d, 0x77407775, 0x775d775f, 0x77557757, + 0x74f174f0, 0x74c374cc, 0x74d074c1, 0x7433743c, 0x74347431, 0x740d740f, 0x74057400, 0x7413741c, + 0x74417470, 0x74507444, 0x75fd75ff, 0x75f575f7, 0x75df75c0, 0x75d775dd, 0x753075d5, 0x7503750c, + 0x757f7501, 0x7577757d, 0x75407575, 0x755d755f, 0x75557557, 0x4fcc4ff0, 0x4fc74fc1, 0x4fd04fc4, + 0x4f314f3c, 0x4f004f34, 0x4f054f07, 0x4f154f14, 0x4f4c4f70, 0x4f414f43, 0x4f504f44, 0x4cf34cfc, + 0x4cf44cf1, 0x4cc04ccf, 0x4cc54cc7, 0x4cd34cdc, 0x4cd44cd1, 0x4c304c3f, 0x4c0c4c0f, 0x4c004c03, + 0x4c044c01, 0x4c104c1d, 0x4c714c73, 0x4c404c4d, 0x4c5c4c47, 0x4c514c53, 0x4df04c54, 0x4dc34dcc, + 0x4dd04dc4, 0x4d314d33, 0x4d0f4d34, 0x4d004d0d, 0x4d114d07, 0x4d704d14, 0x4d414d43, 0x43fc4d54, + 0x43f143f3, 0x43c043cf, 0x43d143c7, 0x4335433f, 0x4303430c, 0x43014300, 0x43044307, 0x431c431f, + 0x4310431d, 0x43714373, 0x4343434d, 0x43474340, 0x4354435c, 0x40f040ff, 0x40f540f7, 0x40cc40cf, + 0x40c040c3, 0x40c440c1, 0x40d040dc, 0x40d540d4, 0x4033403c, 0x40314030, 0x400f4034, 0x400d400c, + 0x40004003, 0x40074001, 0x40054004, 0x4013401c, 0x40114010, 0x407c4014, 0x40774070, 0x404d404c, + 0x40404043, 0x40444041, 0x405f4045, 0x4050405d, 0x40554057, 0x41f341fc, 0x41c041cf, 0x41df41c4, + 0x41d441d1, 0x41374130, 0x410c4134, 0x4100410d, 0x41044101, 0x41174110, 0x4173417d, 0x41754174, + 0x4143414d, 0x41534140, 0x41544151, 0x47c147f0, 0x47d047c4, 0x4731473c, 0x470d470f, 0x47014700, + 0x47134705, 0x47704710, 0x4741474c, 0x47504744, 0x44f144f3, 0x44cf44f4, 0x44c044cd, 0x44c544c7, + 0x44dc44df, 0x44d144d3, 0x443d443f, 0x44374430, 0x440c4435, 0x44004403, 0x44044401, 0x4410441d, + 0x44154411, 0x4473447c, 0x444d444f, 0x44454440, 0x4451445c, 0x45c045f0, 0x453345d0, 0x45344531, + 0x4500450f, 0x451c4507, 0x454c4570, 0x45404543, 0x5fff4541, 0x5ff75ffd, 0x5fc05ff5, 0x5fdd5fdf, + 0x5fd55fd7, 0x5f0c5f30, 0x5f015f03, 0x5f7f5f04, 0x5f775f7d, 0x5f405f75, 0x5f5d5f5f, 0x5f555f57, + 0x5cf45cf0, 0x5cc35ccc, 0x5cc45cc1, 0x5c315cc5, 0x5c0c5c34, 0x5c075c00, 0x5c1c5c05, 0x5c705c13, + 0x5c4d5c4f, 0x5c445c41, 0x5df75dfd, 0x5dcf5df5, 0x5ddd5dc4, 0x5dd55dd7, 0x5d0c5d30, 0x5d045d01, + 0x5d7f5d10, 0x5d775d7d, 0x5d405d75, 0x5d5d5d5f, 0x5d555d57, 0x53d053c4, 0x5333533c, 0x5303530f, + 0x53075300, 0x531c5305, 0x53115310, 0x53145317, 0x50f15370, 0x50cf50f4, 0x50c050cd, 0x50d150c7, + 0x503d50d4, 0x500c5030, 0x50005003, 0x50045001, 0x50155010, 0x5073507c, 0x50715070, 0x504d5074, + 0x50475040, 0x51cc51f0, 0x51c551c1, 0x51d051dc, 0x51315133, 0x510d5135, 0x51015100, 0x511f5107, + 0x5171511d, 0x5140514f, 0x51445141, 0x5153515c, 0x57ff5151, 0x57f757fd, 0x57df57f5, 0x57d757dd, + 0x570c57d5, 0x57015703, 0x577f5704, 0x5777577d, 0x57405775, 0x575d575f, 0x57555757, 0x54c354f0, + 0x54dc54c4, 0x543c54d0, 0x5400540f, 0x541c5405, 0x54145411, 0x5441544f, 0x55fd55ff, 0x55f555f7, + 0x55dd55df, 0x55d555d7, 0x5503550c, 0x557f5501, 0x5577557d, 0x55405575, 0x555d555f, 0x55555557 +}; + +#if defined(NEEDS_IQ1S_GRID_GPU) +// Same content as iq1s_grid_const except each 2-bit value is expanded to 4-bit +// and has 1 added to it (allows packed values to be extracted with & 0x0F0F0F0F +// and 0xF0F0F0F0). This is only used by the q8_1/int-dot vector path. +const uint32_t[2048] iq1s_grid_gpu_const = { + 0x00000000, 0x00000002, 0x00000101, 0x00000200, 0x00000202, 0x00010001, 0x00010101, 0x00020000, + 0x00020002, 0x00020200, 0x00020202, 0x01000101, 0x01010001, 0x01010100, 0x01010102, 0x01020101, + 0x02000000, 0x02000002, 0x02000200, 0x02000202, 0x02010101, 0x02020000, 0x02020002, 0x02020200, + 0x02020202, 0x00000110, 0x00000111, 0x00010011, 0x00010110, 0x00010112, 0x00010211, 0x00010212, + 0x00020111, 0x01000011, 0x01000112, 0x01000211, 0x01010012, 0x01010111, 0x01010212, 0x01020011, + 0x01020110, 0x01020112, 0x01020210, 0x02000111, 0x02010011, 0x02010110, 0x02010112, 0x02020111, + 0x00000020, 0x00000022, 0x00000220, 0x00000222, 0x00010121, 0x00020020, 0x00020022, 0x00020220, + 0x00020222, 0x01000121, 0x01010021, 0x01010221, 0x01020120, 0x01020221, 0x02000020, 0x02000022, + 0x02000220, 0x02000222, 0x02010021, 0x02010121, 0x02010221, 0x02020020, 0x02020022, 0x02020220, + 0x02020222, 0x00011001, 0x00011100, 0x00011102, 0x00021101, 0x01001001, 0x01001201, 0x01011101, + 0x01011202, 0x01021100, 0x01021101, 0x02011001, 0x02011201, 0x02021101, 0x00001011, 0x00001110, + 0x00001111, 0x00001112, 0x00011111, 0x00011210, 0x00011212, 0x00021211, 0x01001010, 0x01001111, + 0x01001212, 0x01011010, 0x01011011, 0x01011110, 0x01011111, 0x01011112, 0x01011211, 0x01021010, + 0x01021012, 0x01021111, 0x01021210, 0x01021212, 0x02001011, 0x02011011, 0x02011111, 0x02011210, + 0x02011212, 0x02021011, 0x02021110, 0x02021111, 0x02021112, 0x02021211, 0x00011120, 0x00011221, + 0x01001021, 0x01001120, 0x01011020, 0x01011022, 0x01011121, 0x01011220, 0x01021020, 0x01021021, + 0x01021122, 0x01021221, 0x02001121, 0x02011021, 0x02011120, 0x02011221, 0x00002000, 0x00002002, + 0x00002200, 0x00002202, 0x00012101, 0x00022000, 0x00022002, 0x00022200, 0x00022202, 0x01002101, + 0x01012001, 0x01012102, 0x01022101, 0x02002000, 0x02002002, 0x02002200, 0x02002202, 0x02012101, + 0x02022000, 0x02022002, 0x02022200, 0x02022202, 0x00002111, 0x00012011, 0x00012110, 0x00012211, + 0x00022110, 0x00022111, 0x01002011, 0x01012010, 0x01012011, 0x01012111, 0x01022011, 0x01022110, + 0x01022211, 0x02012011, 0x02012110, 0x02012112, 0x02012211, 0x02022111, 0x00002020, 0x00002022, + 0x00002220, 0x00002222, 0x00012121, 0x00022020, 0x00022022, 0x00022220, 0x00022222, 0x01002121, + 0x01012021, 0x01012221, 0x01022021, 0x01022121, 0x02002020, 0x02002022, 0x02002121, 0x02002220, + 0x02002222, 0x02012121, 0x02022020, 0x02022022, 0x02022220, 0x02022222, 0x00110000, 0x00110001, + 0x00110100, 0x00110201, 0x00120100, 0x00120101, 0x01100001, 0x01100100, 0x01110000, 0x01110101, + 0x01110200, 0x01120001, 0x01120100, 0x01120101, 0x01120201, 0x02110001, 0x02110100, 0x02110102, + 0x02120001, 0x02120101, 0x00100011, 0x00100110, 0x00100112, 0x00100211, 0x00110010, 0x00110012, + 0x00110111, 0x00110210, 0x00120011, 0x00120110, 0x00120211, 0x01100111, 0x01100212, 0x01110010, + 0x01110011, 0x01110012, 0x01110110, 0x01110111, 0x01110112, 0x01110211, 0x01120010, 0x01120111, + 0x02100110, 0x02110012, 0x02110111, 0x02120011, 0x02120110, 0x00110021, 0x00110120, 0x00110122, + 0x00120121, 0x01100020, 0x01100122, 0x01100221, 0x01110022, 0x01110121, 0x01110220, 0x01110222, + 0x01120120, 0x01120122, 0x02100121, 0x02110021, 0x02110120, 0x02110122, 0x02120121, 0x00101001, + 0x00101102, 0x00101201, 0x00111100, 0x00111101, 0x00111200, 0x00111201, 0x00121001, 0x00121102, + 0x01101001, 0x01101101, 0x01101102, 0x01101200, 0x01101202, 0x01111001, 0x01111100, 0x01111101, + 0x01111102, 0x01111201, 0x01121002, 0x01121101, 0x01121200, 0x02101100, 0x02101201, 0x02111000, + 0x02111100, 0x02111101, 0x02111200, 0x02111201, 0x02111202, 0x02121001, 0x02121100, 0x02121101, + 0x02121201, 0x00101012, 0x00101111, 0x00101212, 0x00111011, 0x00111110, 0x00111111, 0x00111112, + 0x00111211, 0x00121010, 0x00121012, 0x00121111, 0x00121210, 0x00121212, 0x01101011, 0x01101110, + 0x01101111, 0x01101112, 0x01111011, 0x01111012, 0x01111110, 0x01111111, 0x01111112, 0x01111211, + 0x01111212, 0x01121011, 0x01121110, 0x01121111, 0x01121112, 0x01121211, 0x02101010, 0x02101012, + 0x02101110, 0x02101111, 0x02101210, 0x02101212, 0x02111010, 0x02111011, 0x02111110, 0x02111111, + 0x02111112, 0x02111211, 0x02111212, 0x02121010, 0x02121012, 0x02121111, 0x00101021, 0x00101120, + 0x00101121, 0x00101122, 0x00111121, 0x00111122, 0x00111220, 0x00111222, 0x00121021, 0x00121122, + 0x01101020, 0x01101022, 0x01101120, 0x01101121, 0x01101220, 0x01101222, 0x01111021, 0x01111121, + 0x01111122, 0x01111220, 0x01111221, 0x01121021, 0x01121120, 0x01121121, 0x01121220, 0x01121221, + 0x01121222, 0x02101122, 0x02101222, 0x02111022, 0x02111121, 0x02121120, 0x02121221, 0x00112001, + 0x00112102, 0x00122101, 0x01102001, 0x01102100, 0x01102102, 0x01102201, 0x01112000, 0x01112101, + 0x01112200, 0x01112202, 0x01122000, 0x01122001, 0x01122100, 0x01122102, 0x01122201, 0x02102101, + 0x02112001, 0x02112100, 0x02122101, 0x00112010, 0x00112012, 0x00112111, 0x00112212, 0x00122011, + 0x00122111, 0x01102012, 0x01102110, 0x01102111, 0x01102210, 0x01112011, 0x01112110, 0x01112111, + 0x01112112, 0x01112211, 0x01112212, 0x01122010, 0x01122111, 0x01122212, 0x02102211, 0x02112011, + 0x02112012, 0x02112111, 0x02112210, 0x02122011, 0x02122112, 0x02122211, 0x00102221, 0x00112122, + 0x00122120, 0x00122122, 0x01102120, 0x01102122, 0x01102221, 0x01112020, 0x01112022, 0x01112121, + 0x01112220, 0x01122021, 0x01122122, 0x01122221, 0x02102121, 0x02112021, 0x02112122, 0x02112222, + 0x00200000, 0x00200002, 0x00200200, 0x00200202, 0x00210101, 0x00220000, 0x00220002, 0x00220101, + 0x00220200, 0x00220202, 0x01200101, 0x01210001, 0x01210201, 0x01220001, 0x01220101, 0x02200000, + 0x02200002, 0x02200200, 0x02200202, 0x02210101, 0x02220000, 0x02220002, 0x02220101, 0x02220200, + 0x02220202, 0x00200111, 0x00210011, 0x00210110, 0x00210211, 0x00220111, 0x01200012, 0x01200110, + 0x01200211, 0x01210111, 0x01210210, 0x01210212, 0x01220011, 0x01220110, 0x01220111, 0x01220112, + 0x02200111, 0x02210010, 0x02210112, 0x02210211, 0x02220111, 0x00200021, 0x00200220, 0x00200222, + 0x00210021, 0x00210121, 0x00220020, 0x00220022, 0x00220220, 0x00220222, 0x01200121, 0x01210021, + 0x01210122, 0x01210221, 0x01220121, 0x02200021, 0x02200220, 0x02200222, 0x02210021, 0x02210121, + 0x02220020, 0x02220022, 0x02220220, 0x02220222, 0x00201101, 0x00211100, 0x00211102, 0x00211201, + 0x00221101, 0x01201100, 0x01201101, 0x01201102, 0x01201201, 0x01211002, 0x01211101, 0x01211200, + 0x01211202, 0x01221102, 0x02201101, 0x02211001, 0x02211100, 0x02211201, 0x02221001, 0x02221101, + 0x00201211, 0x00211111, 0x00221011, 0x00221211, 0x01201010, 0x01201111, 0x01201210, 0x01211011, + 0x01211110, 0x01211111, 0x01211211, 0x01221012, 0x01221111, 0x01221210, 0x02201211, 0x02211010, + 0x02211110, 0x02211111, 0x02211210, 0x02211212, 0x02221011, 0x02221110, 0x02221112, 0x02221211, + 0x00201121, 0x00211020, 0x00211022, 0x00211221, 0x00221121, 0x01201021, 0x01201221, 0x01211121, + 0x01221020, 0x01221021, 0x01221221, 0x02201120, 0x02201122, 0x02211020, 0x02211222, 0x00202000, + 0x00202002, 0x00202200, 0x00202202, 0x00212101, 0x00222000, 0x00222002, 0x00222200, 0x00222202, + 0x01202101, 0x01212001, 0x01212100, 0x01222101, 0x02202000, 0x02202002, 0x02202200, 0x02202202, + 0x02222000, 0x02222002, 0x02222200, 0x02222202, 0x00202211, 0x00212011, 0x00212110, 0x00212211, + 0x00222111, 0x01202112, 0x01202211, 0x01212012, 0x01212111, 0x01222011, 0x01222110, 0x01222112, + 0x01222211, 0x02202111, 0x02212010, 0x02212112, 0x02212211, 0x02222110, 0x02222111, 0x00202020, + 0x00202022, 0x00202220, 0x00202222, 0x00222020, 0x00222022, 0x00222220, 0x00222222, 0x01202121, + 0x01212021, 0x01212122, 0x01212221, 0x01222121, 0x02202020, 0x02202022, 0x02202220, 0x02202222, + 0x02212121, 0x02222020, 0x02222022, 0x02222220, 0x02222222, 0x10000101, 0x10010001, 0x10010102, + 0x10020101, 0x11000201, 0x11010002, 0x11010101, 0x11010200, 0x11010202, 0x11020001, 0x11020100, + 0x11020102, 0x12010100, 0x12010201, 0x12020001, 0x12020102, 0x10000010, 0x10000011, 0x10000110, + 0x10000112, 0x10000211, 0x10010012, 0x10010111, 0x10010112, 0x10010210, 0x10010212, 0x10020011, + 0x10020112, 0x10020211, 0x11000111, 0x11000210, 0x11000212, 0x11010011, 0x11010110, 0x11010111, + 0x11010112, 0x11010211, 0x11010212, 0x11020111, 0x11020210, 0x11020212, 0x12000011, 0x12000110, + 0x12000112, 0x12010010, 0x12010012, 0x12010111, 0x12020010, 0x12020011, 0x12020012, 0x10000121, + 0x10010021, 0x10010120, 0x10010122, 0x10020121, 0x11000021, 0x11010022, 0x11010121, 0x11010222, + 0x11020120, 0x11020221, 0x12000221, 0x12010120, 0x12020121, 0x10001001, 0x10011101, 0x10011201, + 0x10021201, 0x11001101, 0x11001200, 0x11001202, 0x11011001, 0x11011100, 0x11011101, 0x11011102, + 0x11021001, 0x11021002, 0x11021101, 0x11021200, 0x11021202, 0x12001001, 0x12001102, 0x12001201, + 0x12011000, 0x12011002, 0x12011101, 0x12021000, 0x12021001, 0x12021201, 0x10001011, 0x10001012, + 0x10001111, 0x10001212, 0x10011011, 0x10011110, 0x10011111, 0x10011112, 0x10011211, 0x10021010, + 0x10021111, 0x10021212, 0x11001011, 0x11001110, 0x11001111, 0x11001112, 0x11001211, 0x11011010, + 0x11011011, 0x11011110, 0x11011111, 0x11011112, 0x11011210, 0x11011211, 0x11021011, 0x11021110, + 0x11021111, 0x11021112, 0x11021211, 0x12001012, 0x12001110, 0x12001111, 0x12001210, 0x12011011, + 0x12011110, 0x12011111, 0x12011112, 0x12011211, 0x12011212, 0x12021111, 0x12021210, 0x12021212, + 0x10001021, 0x10001121, 0x10001221, 0x10011120, 0x10011121, 0x10011220, 0x10011222, 0x10021021, + 0x10021120, 0x10021221, 0x11001020, 0x11001022, 0x11001121, 0x11001220, 0x11011020, 0x11011021, + 0x11011022, 0x11011121, 0x11011122, 0x11011221, 0x11021022, 0x11021121, 0x11021220, 0x12001021, + 0x12001121, 0x12001222, 0x12011120, 0x12011121, 0x12021021, 0x12021120, 0x12021122, 0x10002101, + 0x10012001, 0x10012101, 0x10012202, 0x10022101, 0x11002002, 0x11002201, 0x11012000, 0x11012101, + 0x11012200, 0x11022001, 0x11022100, 0x11022102, 0x11022201, 0x12002101, 0x12012001, 0x12012100, + 0x12012102, 0x12012201, 0x12022101, 0x10002011, 0x10002111, 0x10002112, 0x10002212, 0x10012010, + 0x10012110, 0x10012111, 0x10012210, 0x10022011, 0x10022110, 0x10022112, 0x11002010, 0x11002111, + 0x11002212, 0x11012011, 0x11012012, 0x11012110, 0x11012111, 0x11012112, 0x11012211, 0x11022010, + 0x11022012, 0x11022111, 0x11022112, 0x11022212, 0x12002112, 0x12002211, 0x12012012, 0x12012111, + 0x12012112, 0x12012210, 0x12022011, 0x12022110, 0x12022112, 0x12022211, 0x10012122, 0x11002120, + 0x11002122, 0x11002221, 0x11012121, 0x11012220, 0x11012222, 0x11022120, 0x11022221, 0x12012120, + 0x12022121, 0x10100001, 0x10100100, 0x10100101, 0x10100102, 0x10100201, 0x10110002, 0x10110101, + 0x10110202, 0x10120001, 0x10120100, 0x10120201, 0x11100000, 0x11100101, 0x11100200, 0x11110001, + 0x11110100, 0x11110101, 0x11110102, 0x11110201, 0x11120101, 0x11120200, 0x12100102, 0x12100201, + 0x12110101, 0x12110200, 0x12120000, 0x12120001, 0x12120102, 0x12120201, 0x10100111, 0x10100210, + 0x10100211, 0x10100212, 0x10110011, 0x10110110, 0x10110111, 0x10110112, 0x10110210, 0x10110211, + 0x10120010, 0x10120111, 0x10120112, 0x10120210, 0x10120212, 0x11100011, 0x11100110, 0x11100111, + 0x11100112, 0x11100211, 0x11110010, 0x11110011, 0x11110012, 0x11110110, 0x11110111, 0x11110112, + 0x11110210, 0x11110211, 0x11110212, 0x11120011, 0x11120110, 0x11120111, 0x11120112, 0x11120211, + 0x12100012, 0x12100111, 0x12110011, 0x12110110, 0x12110111, 0x12110112, 0x12110211, 0x12120010, + 0x12120111, 0x12120212, 0x10100021, 0x10100122, 0x10110022, 0x10110121, 0x10110222, 0x10120021, + 0x10120120, 0x11100022, 0x11100121, 0x11100222, 0x11110021, 0x11110120, 0x11110121, 0x11110122, + 0x11110221, 0x11120022, 0x11120121, 0x12100121, 0x12110020, 0x12110022, 0x12110121, 0x12110221, + 0x12110222, 0x12120120, 0x10101100, 0x10101101, 0x10111001, 0x10111100, 0x10111101, 0x10111102, + 0x10111200, 0x10111201, 0x10121001, 0x10121101, 0x10121200, 0x10121202, 0x11101001, 0x11101100, + 0x11101101, 0x11101102, 0x11101201, 0x11101202, 0x11111000, 0x11111001, 0x11111100, 0x11111101, + 0x11111102, 0x11111200, 0x11111201, 0x11111202, 0x11121001, 0x11121002, 0x11121100, 0x11121101, + 0x11121102, 0x11121201, 0x12101000, 0x12101200, 0x12101202, 0x12111001, 0x12111100, 0x12111101, + 0x12111102, 0x12111201, 0x12121001, 0x12121100, 0x12121101, 0x12121202, 0x10101011, 0x10101012, + 0x10101110, 0x10101111, 0x10101112, 0x10101211, 0x10111010, 0x10111011, 0x10111012, 0x10111110, + 0x10111111, 0x10111112, 0x10111211, 0x10111212, 0x10121011, 0x10121110, 0x10121111, 0x10121112, + 0x10121211, 0x11101010, 0x11101011, 0x11101012, 0x11101110, 0x11101111, 0x11101112, 0x11101210, + 0x11101211, 0x11111010, 0x11111011, 0x11111012, 0x11111110, 0x11111111, 0x11111112, 0x11111210, + 0x11111211, 0x11111212, 0x11121010, 0x11121011, 0x11121110, 0x11121111, 0x11121112, 0x11121210, + 0x11121211, 0x11121212, 0x12101011, 0x12101110, 0x12101111, 0x12101211, 0x12101212, 0x12111010, + 0x12111011, 0x12111110, 0x12111111, 0x12111112, 0x12111210, 0x12111211, 0x12121011, 0x12121110, + 0x12121111, 0x12121112, 0x12121211, 0x10101020, 0x10101021, 0x10101022, 0x10101120, 0x10101122, + 0x10101220, 0x10101221, 0x10111021, 0x10111120, 0x10111121, 0x10111220, 0x10111221, 0x10121020, + 0x10121021, 0x10121022, 0x10121120, 0x10121121, 0x10121122, 0x10121220, 0x10121221, 0x11101021, + 0x11101121, 0x11101122, 0x11101220, 0x11101221, 0x11101222, 0x11111020, 0x11111021, 0x11111022, + 0x11111120, 0x11111121, 0x11111122, 0x11111220, 0x11111221, 0x11111222, 0x11121021, 0x11121120, + 0x11121121, 0x11121221, 0x12101022, 0x12101121, 0x12101122, 0x12101220, 0x12101221, 0x12101222, + 0x12111021, 0x12111121, 0x12111222, 0x12121022, 0x12121121, 0x12121122, 0x12121220, 0x12121221, + 0x10102100, 0x10102101, 0x10102102, 0x10102201, 0x10112000, 0x10112101, 0x10112200, 0x10122001, + 0x10122202, 0x11102101, 0x11102200, 0x11102202, 0x11112001, 0x11112100, 0x11112101, 0x11112102, + 0x11112200, 0x11112201, 0x11122000, 0x11122002, 0x11122100, 0x11122101, 0x12102002, 0x12102201, + 0x12112000, 0x12112002, 0x12112101, 0x12112200, 0x12122001, 0x12122201, 0x10102011, 0x10102012, + 0x10102111, 0x10102212, 0x10112011, 0x10112110, 0x10112111, 0x10112112, 0x10112211, 0x10122111, + 0x11102011, 0x11102110, 0x11102111, 0x11102112, 0x11102211, 0x11112010, 0x11112011, 0x11112012, + 0x11112110, 0x11112111, 0x11112112, 0x11112210, 0x11112211, 0x11112212, 0x11122011, 0x11122110, + 0x11122111, 0x11122112, 0x11122211, 0x12102011, 0x12102111, 0x12102211, 0x12112011, 0x12112110, + 0x12112111, 0x12112112, 0x12112210, 0x12112211, 0x12122111, 0x10102120, 0x10102220, 0x10112121, + 0x10112222, 0x10122020, 0x10122121, 0x10122122, 0x10122221, 0x11102121, 0x11102220, 0x11102221, + 0x11112021, 0x11112121, 0x11112122, 0x11112220, 0x11112221, 0x11122022, 0x11122121, 0x11122220, + 0x11122222, 0x12102021, 0x12102222, 0x12112022, 0x12112121, 0x12112122, 0x12112220, 0x12112222, + 0x12122021, 0x10200101, 0x10210100, 0x10210102, 0x10210201, 0x10220101, 0x11200100, 0x11210000, + 0x11210101, 0x11210102, 0x11210200, 0x11210202, 0x11220001, 0x11220100, 0x11220102, 0x11220201, + 0x12200001, 0x12210102, 0x12220101, 0x10200011, 0x10200110, 0x10200112, 0x10200211, 0x10210012, + 0x10210111, 0x10220011, 0x10220012, 0x10220112, 0x10220211, 0x11200111, 0x11200211, 0x11210011, + 0x11210111, 0x11210112, 0x11210211, 0x11220111, 0x11220112, 0x11220212, 0x12200110, 0x12200212, + 0x12210012, 0x12210111, 0x12220011, 0x12220112, 0x12220211, 0x10210021, 0x10210122, 0x10210221, + 0x11200020, 0x11200021, 0x11200122, 0x11210121, 0x11210122, 0x11210220, 0x11220020, 0x12200121, + 0x12210021, 0x12210122, 0x12220121, 0x10211001, 0x10211002, 0x10211101, 0x10211102, 0x10211202, + 0x10221001, 0x10221102, 0x10221201, 0x11201000, 0x11201002, 0x11201101, 0x11201200, 0x11201202, + 0x11211001, 0x11211100, 0x11211101, 0x11211102, 0x11211201, 0x11211202, 0x11221000, 0x11221002, + 0x11221101, 0x12201100, 0x12201101, 0x12201201, 0x12211000, 0x12211002, 0x12211100, 0x12211101, + 0x12211102, 0x12211200, 0x12211202, 0x12221001, 0x12221100, 0x12221201, 0x10201111, 0x10201210, + 0x10201212, 0x10211011, 0x10211111, 0x10211112, 0x10211211, 0x11201110, 0x11201111, 0x11201112, + 0x11201211, 0x11211010, 0x11211011, 0x11211110, 0x11211111, 0x11211112, 0x11211211, 0x11221011, + 0x11221110, 0x11221111, 0x11221112, 0x11221211, 0x12201112, 0x12201211, 0x12201212, 0x12211011, + 0x12211111, 0x12211112, 0x12211211, 0x12211212, 0x12221012, 0x12221111, 0x12221112, 0x12221210, + 0x10201022, 0x10201221, 0x10211121, 0x10221020, 0x10221122, 0x10221220, 0x10221221, 0x11201020, + 0x11201121, 0x11201220, 0x11201222, 0x11211021, 0x11211120, 0x11211121, 0x11211122, 0x11211220, + 0x11211222, 0x11221020, 0x11221121, 0x11221220, 0x12201020, 0x12201022, 0x12201121, 0x12201222, + 0x12211120, 0x12211122, 0x12211220, 0x12211221, 0x12221020, 0x12221120, 0x12221122, 0x12221222, + 0x10212102, 0x10212201, 0x10222101, 0x11202001, 0x11212002, 0x11212101, 0x11212202, 0x11222001, + 0x11222201, 0x12202101, 0x12212001, 0x12212200, 0x12222102, 0x10202011, 0x10202110, 0x10212010, + 0x10212111, 0x10222011, 0x10222110, 0x10222112, 0x10222211, 0x11202010, 0x11202011, 0x11202111, + 0x11202112, 0x11202210, 0x11212011, 0x11212110, 0x11212111, 0x11212112, 0x11212211, 0x11222010, + 0x11222111, 0x11222212, 0x12202012, 0x12202110, 0x12202212, 0x12212111, 0x12222011, 0x12222110, + 0x12222111, 0x12222211, 0x10212021, 0x10212122, 0x10212220, 0x11202021, 0x11202120, 0x11202221, + 0x11212020, 0x11212121, 0x11212220, 0x11212222, 0x11222120, 0x11222121, 0x11222221, 0x12202122, + 0x12212120, 0x12212220, 0x12212222, 0x12222122, 0x20000000, 0x20000002, 0x20000200, 0x20000202, + 0x20020000, 0x20020002, 0x20020200, 0x20020202, 0x21000101, 0x21010000, 0x21010001, 0x21010100, + 0x21010102, 0x21010201, 0x21020101, 0x22000000, 0x22000002, 0x22000200, 0x22000202, 0x22010101, + 0x22020000, 0x22020002, 0x22020200, 0x22020202, 0x20000111, 0x20010011, 0x20010110, 0x20010112, + 0x20010211, 0x20020111, 0x21000011, 0x21000110, 0x21000211, 0x21010010, 0x21010012, 0x21010111, + 0x21010112, 0x21010210, 0x21010211, 0x21020110, 0x21020112, 0x21020211, 0x22000111, 0x22000211, + 0x22010110, 0x22010112, 0x22010211, 0x22020111, 0x20000020, 0x20000022, 0x20000220, 0x20000222, + 0x20010121, 0x20020020, 0x20020022, 0x20020220, 0x20020222, 0x21010021, 0x21010120, 0x21010221, + 0x21020121, 0x22000020, 0x22000022, 0x22000220, 0x22000222, 0x22010121, 0x22020020, 0x22020022, + 0x22020220, 0x22020222, 0x20011100, 0x20011201, 0x21001001, 0x21001100, 0x21011001, 0x21011101, + 0x21011202, 0x21021001, 0x21021100, 0x21021201, 0x22011100, 0x22011201, 0x20001011, 0x20001211, + 0x20011012, 0x20011111, 0x20011212, 0x20021112, 0x20021211, 0x21001010, 0x21001011, 0x21001111, + 0x21001210, 0x21011011, 0x21011110, 0x21011111, 0x21011112, 0x21011211, 0x21011212, 0x21021111, + 0x21021112, 0x21021210, 0x21021212, 0x22001011, 0x22001110, 0x22001112, 0x22001211, 0x22011010, + 0x22011012, 0x22011111, 0x22011210, 0x22021112, 0x20011021, 0x20011122, 0x20011221, 0x20021121, + 0x21001021, 0x21001120, 0x21001221, 0x21001222, 0x21011020, 0x21011121, 0x21011221, 0x21011222, + 0x21021021, 0x21021122, 0x21021222, 0x22001121, 0x22011021, 0x22011222, 0x22021120, 0x20002000, + 0x20002002, 0x20002200, 0x20002202, 0x20012101, 0x20022000, 0x20022002, 0x20022200, 0x20022202, + 0x21002001, 0x21002101, 0x21012001, 0x21012100, 0x21012201, 0x21022101, 0x21022201, 0x22002000, + 0x22002002, 0x22002200, 0x22002202, 0x22012101, 0x22022000, 0x22022002, 0x22022200, 0x22022202, + 0x20002111, 0x20002112, 0x20012011, 0x20012110, 0x20012112, 0x20022111, 0x21002011, 0x21002110, + 0x21002112, 0x21002211, 0x21012010, 0x21012012, 0x21012111, 0x21012212, 0x21022011, 0x21022110, + 0x22002111, 0x22012112, 0x22012211, 0x22022111, 0x20002020, 0x20002022, 0x20002220, 0x20002222, + 0x20012121, 0x20022020, 0x20022022, 0x20022220, 0x20022222, 0x21002121, 0x21012021, 0x21012120, + 0x21012122, 0x22002020, 0x22002022, 0x22002220, 0x22002222, 0x22012121, 0x22022020, 0x22022022, + 0x22022220, 0x22022222, 0x20100101, 0x20110001, 0x20110102, 0x20110200, 0x20110201, 0x20120101, + 0x21100001, 0x21100102, 0x21100201, 0x21110101, 0x21110200, 0x21110202, 0x21120201, 0x21120202, + 0x22100101, 0x22110001, 0x22110100, 0x22110102, 0x22110201, 0x22120101, 0x20100011, 0x20100110, + 0x20100112, 0x20100211, 0x20110010, 0x20110111, 0x20110210, 0x20110212, 0x20120011, 0x20120110, + 0x20120112, 0x20120211, 0x21100010, 0x21100111, 0x21110010, 0x21110011, 0x21110110, 0x21110111, + 0x21110112, 0x21110211, 0x21120012, 0x21120111, 0x22100110, 0x22100112, 0x22110012, 0x22110111, + 0x22110210, 0x22120011, 0x22120110, 0x22120112, 0x22120211, 0x20100121, 0x20110021, 0x20110120, + 0x20110221, 0x20120121, 0x21100120, 0x21100122, 0x21100221, 0x21110020, 0x21110022, 0x21110121, + 0x21110220, 0x21120122, 0x21120221, 0x22100121, 0x22110120, 0x22110122, 0x22120221, 0x20101001, + 0x20101100, 0x20101102, 0x20111000, 0x20111101, 0x20111200, 0x20121102, 0x21101000, 0x21101202, + 0x21111001, 0x21111100, 0x21111101, 0x21111102, 0x21111200, 0x21111201, 0x21121000, 0x21121001, + 0x21121002, 0x21121101, 0x22101100, 0x22101102, 0x22111002, 0x22111100, 0x22111101, 0x22111200, + 0x22121001, 0x22121201, 0x20101010, 0x20101111, 0x20101210, 0x20101212, 0x20111010, 0x20111011, + 0x20111110, 0x20111111, 0x20111112, 0x20111211, 0x20121011, 0x20121111, 0x20121211, 0x20121212, + 0x21101011, 0x21101110, 0x21101111, 0x21101112, 0x21101211, 0x21111010, 0x21111011, 0x21111012, + 0x21111110, 0x21111111, 0x21111112, 0x21111210, 0x21111211, 0x21111212, 0x21121011, 0x21121110, + 0x21121111, 0x21121112, 0x21121211, 0x22101011, 0x22101111, 0x22101210, 0x22111011, 0x22111012, + 0x22111110, 0x22111111, 0x22111112, 0x22111211, 0x22111212, 0x22121010, 0x22121012, 0x22121111, + 0x22121210, 0x22121212, 0x20101021, 0x20101120, 0x20111020, 0x20111121, 0x20111221, 0x20121020, + 0x20121122, 0x20121221, 0x21101121, 0x21101220, 0x21101221, 0x21111021, 0x21111022, 0x21111121, + 0x21111122, 0x21111221, 0x21121121, 0x21121220, 0x22101022, 0x22101120, 0x22101221, 0x22101222, + 0x22111022, 0x22111120, 0x22111121, 0x22121120, 0x22121122, 0x22121221, 0x20102101, 0x20112102, + 0x20112201, 0x20122101, 0x21102001, 0x21102102, 0x21112000, 0x21112002, 0x21112101, 0x21112102, + 0x21112202, 0x21122100, 0x21122101, 0x22102101, 0x22112001, 0x22112102, 0x22112201, 0x22122101, + 0x20102110, 0x20102112, 0x20102211, 0x20112010, 0x20112012, 0x20112111, 0x20112210, 0x20112212, + 0x20122010, 0x20122011, 0x20122110, 0x20122112, 0x21102010, 0x21102012, 0x21102111, 0x21102210, + 0x21102212, 0x21112011, 0x21112110, 0x21112111, 0x21112112, 0x21112211, 0x21122012, 0x21122111, + 0x21122112, 0x21122212, 0x22102011, 0x22102110, 0x22112010, 0x22112012, 0x22112111, 0x22112212, + 0x22122011, 0x22122112, 0x20102121, 0x20112121, 0x20122121, 0x21102120, 0x21102122, 0x21102221, + 0x21112020, 0x21112121, 0x21112220, 0x21122021, 0x22102121, 0x22112021, 0x22112120, 0x22112121, + 0x22112122, 0x20200000, 0x20200002, 0x20200200, 0x20200202, 0x20210101, 0x20220000, 0x20220002, + 0x20220200, 0x20220202, 0x21200101, 0x21210001, 0x21210100, 0x21210102, 0x21210201, 0x22200000, + 0x22200002, 0x22200200, 0x22200202, 0x22210101, 0x22220000, 0x22220002, 0x22220200, 0x22220202, + 0x20200111, 0x20200211, 0x20210011, 0x20210110, 0x20210112, 0x20210211, 0x20210212, 0x21200112, + 0x21200211, 0x21210011, 0x21210111, 0x21210210, 0x21210212, 0x21220011, 0x21220110, 0x22200111, + 0x22210010, 0x22210012, 0x22210112, 0x22210211, 0x20200022, 0x20200220, 0x20200222, 0x20210020, + 0x20210221, 0x20220022, 0x20220220, 0x20220222, 0x21200121, 0x21210021, 0x21210122, 0x21210221, + 0x21220121, 0x22200020, 0x22200022, 0x22200220, 0x22200222, 0x22210121, 0x22220020, 0x22220022, + 0x22220220, 0x22220222, 0x20211201, 0x20221101, 0x21201001, 0x21201100, 0x21211000, 0x21211100, + 0x21211101, 0x21211200, 0x21211202, 0x21221001, 0x21221101, 0x21221102, 0x21221200, 0x21221201, + 0x22201101, 0x20201112, 0x20201211, 0x20211010, 0x20211012, 0x20211111, 0x20211210, 0x20221112, + 0x20221211, 0x21201012, 0x21201111, 0x21211011, 0x21211110, 0x21211111, 0x21211112, 0x21211211, + 0x21221111, 0x21221212, 0x22201011, 0x22201110, 0x22201111, 0x22201112, 0x22201211, 0x22211012, + 0x22211111, 0x22211210, 0x20201121, 0x20211021, 0x20211122, 0x20211222, 0x20221021, 0x20221121, + 0x21201120, 0x21201122, 0x21201222, 0x21211022, 0x21211121, 0x21211122, 0x21211220, 0x21221020, + 0x21221022, 0x22201122, 0x22211020, 0x22211121, 0x22211122, 0x22211221, 0x22221021, 0x22221120, + 0x22221122, 0x20202000, 0x20202002, 0x20202200, 0x20202202, 0x20222000, 0x20222002, 0x20222200, + 0x20222202, 0x21212001, 0x21212100, 0x21212102, 0x21212201, 0x22202000, 0x22202002, 0x22202200, + 0x22202202, 0x22212101, 0x22222000, 0x22222002, 0x22222200, 0x22222202, 0x20202111, 0x20212110, + 0x20212211, 0x20222011, 0x20222111, 0x21202011, 0x21212010, 0x21212111, 0x21212212, 0x21222011, + 0x21222112, 0x21222211, 0x22212010, 0x22212112, 0x20202020, 0x20202022, 0x20202220, 0x20202222, + 0x20222020, 0x20222022, 0x20222220, 0x20222222, 0x21212021, 0x21212120, 0x21212122, 0x22202020, + 0x22202022, 0x22202220, 0x22202222, 0x22212121, 0x22222020, 0x22222022, 0x22222220, 0x22222222, +}; +#endif + +shared uint16_t iq1s_grid[2048]; +#if defined(NEEDS_IQ1S_GRID_GPU) +shared uint32_t iq1s_grid_gpu[2048]; +#endif + +#define NEEDS_INIT_IQ_SHMEM +void init_iq_shmem(uvec3 wgsize) +{ + // copy the table into shared memory and sync + [[unroll]] for (uint i = 0; i < iq1s_grid_const.length(); i += wgsize.x) { + uint idx = i + gl_LocalInvocationIndex.x; + if (iq1s_grid_const.length() % wgsize.x == 0 || idx < iq1s_grid_const.length()) { + u16vec2 g = unpack16(iq1s_grid_const[idx]); + iq1s_grid[2*idx+0] = g.x; + iq1s_grid[2*idx+1] = g.y; + } + } +#if defined(NEEDS_IQ1S_GRID_GPU) + [[unroll]] for (uint i = 0; i < iq1s_grid_gpu_const.length(); i += wgsize.x) { + uint idx = i + gl_LocalInvocationIndex.x; + if (iq1s_grid_gpu_const.length() % wgsize.x == 0 || idx < iq1s_grid_gpu_const.length()) { + iq1s_grid_gpu[idx] = iq1s_grid_gpu_const[idx]; + } + } +#endif + barrier(); +} +#endif + +#define QUANT_K_IQ2_XXS 256 +#define QUANT_R_IQ2_XXS 1 + +struct block_iq2_xxs +{ + float16_t d; + uint8_t qs[QUANT_K_IQ2_XXS/4]; +}; + +struct block_iq2_xxs_packed16 +{ + float16_t d; + uint16_t qs[QUANT_K_IQ2_XXS/8]; +}; + +#if defined(DATA_A_IQ2_XXS) + +const uvec2[256] iq2xxs_grid_const = { + uvec2(0x08080808, 0x08080808), uvec2(0x0808082b, 0x08080808), uvec2(0x08081919, 0x08080808), uvec2(0x08082b08, 0x08080808), + uvec2(0x08082b2b, 0x08080808), uvec2(0x08190819, 0x08080808), uvec2(0x08191908, 0x08080808), uvec2(0x082b0808, 0x08080808), + uvec2(0x082b082b, 0x08080808), uvec2(0x082b2b08, 0x08080808), uvec2(0x082b2b2b, 0x08080808), uvec2(0x19080819, 0x08080808), + uvec2(0x19081908, 0x08080808), uvec2(0x19190808, 0x08080808), uvec2(0x19192b08, 0x08080808), uvec2(0x192b0819, 0x08080808), + uvec2(0x192b1908, 0x08080808), uvec2(0x2b080808, 0x08080808), uvec2(0x2b08082b, 0x08080808), uvec2(0x2b082b2b, 0x08080808), + uvec2(0x2b2b082b, 0x08080808), uvec2(0x08080819, 0x08080819), uvec2(0x08081908, 0x08080819), uvec2(0x08190808, 0x08080819), + uvec2(0x08191919, 0x08080819), uvec2(0x19080808, 0x08080819), uvec2(0x2b081908, 0x08080819), uvec2(0x2b192b08, 0x08080819), + uvec2(0x08080808, 0x0808082b), uvec2(0x0808082b, 0x0808082b), uvec2(0x082b082b, 0x0808082b), uvec2(0x2b08082b, 0x0808082b), + uvec2(0x08080819, 0x08081908), uvec2(0x08081908, 0x08081908), uvec2(0x08190808, 0x08081908), uvec2(0x082b0819, 0x08081908), + uvec2(0x082b1908, 0x08081908), uvec2(0x19080808, 0x08081908), uvec2(0x1908082b, 0x08081908), uvec2(0x19082b08, 0x08081908), + uvec2(0x192b0808, 0x08081908), uvec2(0x2b080819, 0x08081908), uvec2(0x2b081908, 0x08081908), uvec2(0x2b190808, 0x08081908), + uvec2(0x2b2b1908, 0x08081908), uvec2(0x08080808, 0x08081919), uvec2(0x0808082b, 0x08081919), uvec2(0x08082b08, 0x08081919), + uvec2(0x082b0808, 0x08081919), uvec2(0x1908192b, 0x08081919), uvec2(0x192b2b19, 0x08081919), uvec2(0x2b080808, 0x08081919), + uvec2(0x2b190819, 0x08081919), uvec2(0x08082b19, 0x0808192b), uvec2(0x08190808, 0x0808192b), uvec2(0x19080808, 0x0808192b), + uvec2(0x2b081908, 0x0808192b), uvec2(0x2b2b1908, 0x0808192b), uvec2(0x08080808, 0x08082b08), uvec2(0x08081919, 0x08082b08), + uvec2(0x08082b08, 0x08082b08), uvec2(0x08191908, 0x08082b08), uvec2(0x082b2b08, 0x08082b08), uvec2(0x19080819, 0x08082b08), + uvec2(0x19081908, 0x08082b08), uvec2(0x19190808, 0x08082b08), uvec2(0x1919082b, 0x08082b08), uvec2(0x2b082b08, 0x08082b08), + uvec2(0x08081908, 0x08082b19), uvec2(0x19080808, 0x08082b19), uvec2(0x0808082b, 0x08082b2b), uvec2(0x08191908, 0x08082b2b), + uvec2(0x08080819, 0x08190808), uvec2(0x08081908, 0x08190808), uvec2(0x08190808, 0x08190808), uvec2(0x082b0819, 0x08190808), + uvec2(0x19080808, 0x08190808), uvec2(0x192b0808, 0x08190808), uvec2(0x2b081908, 0x08190808), uvec2(0x2b190808, 0x08190808), + uvec2(0x2b191919, 0x08190808), uvec2(0x08080808, 0x08190819), uvec2(0x08082b08, 0x08190819), uvec2(0x082b0808, 0x08190819), + uvec2(0x19190808, 0x08190819), uvec2(0x19192b2b, 0x08190819), uvec2(0x2b080808, 0x08190819), uvec2(0x082b1908, 0x0819082b), + uvec2(0x19081919, 0x0819082b), uvec2(0x08080808, 0x08191908), uvec2(0x08082b08, 0x08191908), uvec2(0x082b0808, 0x08191908), + uvec2(0x082b1919, 0x08191908), uvec2(0x19082b19, 0x08191908), uvec2(0x2b080808, 0x08191908), uvec2(0x08192b08, 0x08191919), + uvec2(0x192b082b, 0x08191919), uvec2(0x08080808, 0x0819192b), uvec2(0x0819192b, 0x0819192b), uvec2(0x08080819, 0x08192b08), + uvec2(0x08081908, 0x08192b08), uvec2(0x08190808, 0x08192b08), uvec2(0x19080808, 0x08192b08), uvec2(0x2b080819, 0x08192b08), + uvec2(0x08080808, 0x08192b19), uvec2(0x08081919, 0x08192b19), uvec2(0x2b2b0808, 0x08192b19), uvec2(0x19190819, 0x08192b2b), + uvec2(0x08080808, 0x082b0808), uvec2(0x0808082b, 0x082b0808), uvec2(0x08082b2b, 0x082b0808), uvec2(0x19081908, 0x082b0808), + uvec2(0x192b0819, 0x082b0808), uvec2(0x2b080808, 0x082b0808), uvec2(0x2b08082b, 0x082b0808), uvec2(0x082b2b19, 0x082b0819), + uvec2(0x19082b08, 0x082b0819), uvec2(0x08080808, 0x082b082b), uvec2(0x0808082b, 0x082b082b), uvec2(0x08080819, 0x082b1908), + uvec2(0x08081908, 0x082b1908), uvec2(0x08190808, 0x082b1908), uvec2(0x19080808, 0x082b1908), uvec2(0x1919192b, 0x082b1908), + uvec2(0x08080808, 0x082b1919), uvec2(0x19080819, 0x082b1919), uvec2(0x192b1908, 0x082b1919), uvec2(0x2b190808, 0x082b192b), + uvec2(0x08082b08, 0x082b2b08), uvec2(0x082b0808, 0x082b2b08), uvec2(0x2b191908, 0x082b2b08), uvec2(0x19081908, 0x082b2b2b), + uvec2(0x08080819, 0x19080808), uvec2(0x08081908, 0x19080808), uvec2(0x08190808, 0x19080808), uvec2(0x08192b08, 0x19080808), + uvec2(0x082b0819, 0x19080808), uvec2(0x082b1908, 0x19080808), uvec2(0x19080808, 0x19080808), uvec2(0x19082b08, 0x19080808), + uvec2(0x1919192b, 0x19080808), uvec2(0x192b0808, 0x19080808), uvec2(0x2b080819, 0x19080808), uvec2(0x2b081908, 0x19080808), + uvec2(0x2b190808, 0x19080808), uvec2(0x08080808, 0x19080819), uvec2(0x082b0808, 0x19080819), uvec2(0x192b0819, 0x19080819), + uvec2(0x2b080808, 0x19080819), uvec2(0x2b081919, 0x19080819), uvec2(0x08080819, 0x1908082b), uvec2(0x08190808, 0x1908082b), + uvec2(0x19082b08, 0x1908082b), uvec2(0x1919192b, 0x1908082b), uvec2(0x192b2b08, 0x1908082b), uvec2(0x08080808, 0x19081908), + uvec2(0x08082b08, 0x19081908), uvec2(0x082b0808, 0x19081908), uvec2(0x2b080808, 0x19081908), uvec2(0x2b192b19, 0x19081908), + uvec2(0x0819082b, 0x19081919), uvec2(0x082b1908, 0x19081919), uvec2(0x08080808, 0x1908192b), uvec2(0x08080819, 0x19082b08), + uvec2(0x08081908, 0x19082b08), uvec2(0x08190808, 0x19082b08), uvec2(0x19080808, 0x19082b08), uvec2(0x19081919, 0x19082b08), + uvec2(0x08080808, 0x19082b19), uvec2(0x19192b08, 0x19082b19), uvec2(0x192b0819, 0x19082b19), uvec2(0x2b08082b, 0x19082b19), + uvec2(0x19081919, 0x19082b2b), uvec2(0x2b190808, 0x19082b2b), uvec2(0x08080808, 0x19190808), uvec2(0x08082b08, 0x19190808), + uvec2(0x08190819, 0x19190808), uvec2(0x08192b19, 0x19190808), uvec2(0x082b0808, 0x19190808), uvec2(0x2b080808, 0x19190808), + uvec2(0x2b082b08, 0x19190808), uvec2(0x08081908, 0x19190819), uvec2(0x1908082b, 0x19190819), uvec2(0x2b2b1908, 0x19190819), + uvec2(0x2b190819, 0x1919082b), uvec2(0x2b190808, 0x19191908), uvec2(0x2b19082b, 0x19191908), uvec2(0x08082b2b, 0x19191919), + uvec2(0x08080819, 0x1919192b), uvec2(0x19191908, 0x1919192b), uvec2(0x08080808, 0x19192b08), uvec2(0x08190819, 0x19192b08), + uvec2(0x08192b19, 0x19192b08), uvec2(0x192b1908, 0x19192b08), uvec2(0x19080808, 0x19192b19), uvec2(0x08082b08, 0x19192b2b), + uvec2(0x08081908, 0x192b0808), uvec2(0x08190808, 0x192b0808), uvec2(0x19080808, 0x192b0808), uvec2(0x192b2b08, 0x192b0808), + uvec2(0x08080808, 0x192b0819), uvec2(0x19191919, 0x192b0819), uvec2(0x08192b08, 0x192b082b), uvec2(0x192b0808, 0x192b082b), + uvec2(0x08080808, 0x192b1908), uvec2(0x08081919, 0x192b1908), uvec2(0x08190808, 0x192b1919), uvec2(0x0819082b, 0x192b1919), + uvec2(0x2b081908, 0x192b1919), uvec2(0x1908082b, 0x192b2b08), uvec2(0x08080808, 0x2b080808), uvec2(0x0808082b, 0x2b080808), + uvec2(0x08082b2b, 0x2b080808), uvec2(0x19080819, 0x2b080808), uvec2(0x2b08082b, 0x2b080808), uvec2(0x08081908, 0x2b080819), + uvec2(0x08192b08, 0x2b080819), uvec2(0x19080808, 0x2b080819), uvec2(0x08190819, 0x2b08082b), uvec2(0x08080819, 0x2b081908), + uvec2(0x08081908, 0x2b081908), uvec2(0x08190808, 0x2b081908), uvec2(0x08191919, 0x2b081908), uvec2(0x19080808, 0x2b081908), + uvec2(0x192b0808, 0x2b081908), uvec2(0x08080808, 0x2b081919), uvec2(0x1908192b, 0x2b081919), uvec2(0x2b191908, 0x2b081919), + uvec2(0x08082b19, 0x2b08192b), uvec2(0x19080808, 0x2b08192b), uvec2(0x192b0808, 0x2b08192b), uvec2(0x0808082b, 0x2b082b08), + uvec2(0x08081908, 0x2b082b19), uvec2(0x08190819, 0x2b082b2b), uvec2(0x08081908, 0x2b190808), uvec2(0x08190808, 0x2b190808), + uvec2(0x082b1908, 0x2b190808), uvec2(0x19080808, 0x2b190808), uvec2(0x2b2b0819, 0x2b190808), uvec2(0x0819192b, 0x2b190819), + uvec2(0x2b080808, 0x2b190819), uvec2(0x19081919, 0x2b19082b), uvec2(0x08080808, 0x2b191908), uvec2(0x082b082b, 0x2b191908), + uvec2(0x19081908, 0x2b191908), uvec2(0x19190819, 0x2b191919), uvec2(0x2b080819, 0x2b192b08), uvec2(0x082b0808, 0x2b192b19), + uvec2(0x0808082b, 0x2b2b0808), uvec2(0x19190808, 0x2b2b0808), uvec2(0x2b081919, 0x2b2b0808), uvec2(0x08082b19, 0x2b2b0819), + uvec2(0x08080808, 0x2b2b082b), uvec2(0x08192b08, 0x2b2b1908), uvec2(0x19190808, 0x2b2b2b08), uvec2(0x08081908, 0x2b2b2b19) +}; + +shared uvec2 iq2xxs_grid[256]; + +#define NEEDS_INIT_IQ_SHMEM +void init_iq_shmem(uvec3 wgsize) +{ + // copy the table into shared memory and sync + [[unroll]] for (uint i = 0; i < iq2xxs_grid.length(); i += wgsize.x) { + if (iq2xxs_grid_const.length() % wgsize.x == 0 || i + gl_LocalInvocationIndex.x < iq2xxs_grid_const.length()) { + iq2xxs_grid[i + gl_LocalInvocationIndex.x] = iq2xxs_grid_const[i + gl_LocalInvocationIndex.x]; + } + } + barrier(); +} + +#define QUANT_K QUANT_K_IQ2_XXS +#define QUANT_R QUANT_R_IQ2_XXS +#define A_TYPE block_iq2_xxs +#define A_TYPE_PACKED16 block_iq2_xxs_packed16 +#endif + +#define QUANT_K_IQ2_XS 256 +#define QUANT_R_IQ2_XS 1 + +struct block_iq2_xs +{ + float16_t d; + uint16_t qs[QUANT_K_IQ2_XS/8]; + uint8_t scales[QUANT_K_IQ2_XS/32]; +}; + +struct block_iq2_xs_packed16 +{ + float16_t d; + uint16_t qs[QUANT_K_IQ2_XS/8]; + uint16_t scales[QUANT_K_IQ2_XS/64]; +}; + +#if defined(DATA_A_IQ2_XS) + +const uvec2 iq2xs_grid_const[512] = { + uvec2(0x08080808, 0x08080808), uvec2(0x0808082b, 0x08080808), uvec2(0x08081919, 0x08080808), uvec2(0x08082b08, 0x08080808), + uvec2(0x08082b2b, 0x08080808), uvec2(0x08190819, 0x08080808), uvec2(0x08191908, 0x08080808), uvec2(0x0819192b, 0x08080808), + uvec2(0x08192b19, 0x08080808), uvec2(0x082b0808, 0x08080808), uvec2(0x082b082b, 0x08080808), uvec2(0x082b1919, 0x08080808), + uvec2(0x082b2b08, 0x08080808), uvec2(0x19080819, 0x08080808), uvec2(0x19081908, 0x08080808), uvec2(0x1908192b, 0x08080808), + uvec2(0x19082b19, 0x08080808), uvec2(0x19190808, 0x08080808), uvec2(0x1919082b, 0x08080808), uvec2(0x19191919, 0x08080808), + uvec2(0x19192b08, 0x08080808), uvec2(0x192b0819, 0x08080808), uvec2(0x192b1908, 0x08080808), uvec2(0x2b080808, 0x08080808), + uvec2(0x2b08082b, 0x08080808), uvec2(0x2b081919, 0x08080808), uvec2(0x2b082b08, 0x08080808), uvec2(0x2b190819, 0x08080808), + uvec2(0x2b191908, 0x08080808), uvec2(0x2b192b19, 0x08080808), uvec2(0x2b2b0808, 0x08080808), uvec2(0x08080819, 0x08080819), + uvec2(0x08081908, 0x08080819), uvec2(0x0808192b, 0x08080819), uvec2(0x08082b19, 0x08080819), uvec2(0x08190808, 0x08080819), + uvec2(0x0819082b, 0x08080819), uvec2(0x08191919, 0x08080819), uvec2(0x08192b08, 0x08080819), uvec2(0x08192b2b, 0x08080819), + uvec2(0x082b0819, 0x08080819), uvec2(0x082b1908, 0x08080819), uvec2(0x19080808, 0x08080819), uvec2(0x1908082b, 0x08080819), + uvec2(0x19081919, 0x08080819), uvec2(0x19082b08, 0x08080819), uvec2(0x19190819, 0x08080819), uvec2(0x19191908, 0x08080819), + uvec2(0x192b0808, 0x08080819), uvec2(0x192b2b08, 0x08080819), uvec2(0x2b080819, 0x08080819), uvec2(0x2b081908, 0x08080819), + uvec2(0x2b190808, 0x08080819), uvec2(0x08080808, 0x0808082b), uvec2(0x0808082b, 0x0808082b), uvec2(0x08081919, 0x0808082b), + uvec2(0x08082b08, 0x0808082b), uvec2(0x08190819, 0x0808082b), uvec2(0x08191908, 0x0808082b), uvec2(0x082b0808, 0x0808082b), + uvec2(0x19080819, 0x0808082b), uvec2(0x19081908, 0x0808082b), uvec2(0x19190808, 0x0808082b), uvec2(0x19191919, 0x0808082b), + uvec2(0x2b080808, 0x0808082b), uvec2(0x2b082b2b, 0x0808082b), uvec2(0x08080819, 0x08081908), uvec2(0x08081908, 0x08081908), + uvec2(0x0808192b, 0x08081908), uvec2(0x08082b19, 0x08081908), uvec2(0x08190808, 0x08081908), uvec2(0x0819082b, 0x08081908), + uvec2(0x08191919, 0x08081908), uvec2(0x08192b08, 0x08081908), uvec2(0x082b0819, 0x08081908), uvec2(0x082b1908, 0x08081908), + uvec2(0x19080808, 0x08081908), uvec2(0x1908082b, 0x08081908), uvec2(0x19081919, 0x08081908), uvec2(0x19082b08, 0x08081908), + uvec2(0x19190819, 0x08081908), uvec2(0x19191908, 0x08081908), uvec2(0x1919192b, 0x08081908), uvec2(0x192b0808, 0x08081908), + uvec2(0x2b080819, 0x08081908), uvec2(0x2b081908, 0x08081908), uvec2(0x2b190808, 0x08081908), uvec2(0x08080808, 0x08081919), + uvec2(0x0808082b, 0x08081919), uvec2(0x08081919, 0x08081919), uvec2(0x08082b08, 0x08081919), uvec2(0x08190819, 0x08081919), + uvec2(0x08191908, 0x08081919), uvec2(0x082b0808, 0x08081919), uvec2(0x19080819, 0x08081919), uvec2(0x19081908, 0x08081919), + uvec2(0x19190808, 0x08081919), uvec2(0x192b0819, 0x08081919), uvec2(0x2b080808, 0x08081919), uvec2(0x08080819, 0x0808192b), + uvec2(0x08081908, 0x0808192b), uvec2(0x08190808, 0x0808192b), uvec2(0x082b192b, 0x0808192b), uvec2(0x19080808, 0x0808192b), + uvec2(0x1908082b, 0x0808192b), uvec2(0x2b081908, 0x0808192b), uvec2(0x08080808, 0x08082b08), uvec2(0x0808082b, 0x08082b08), + uvec2(0x08081919, 0x08082b08), uvec2(0x08082b08, 0x08082b08), uvec2(0x08082b2b, 0x08082b08), uvec2(0x08190819, 0x08082b08), + uvec2(0x08191908, 0x08082b08), uvec2(0x082b0808, 0x08082b08), uvec2(0x082b1919, 0x08082b08), uvec2(0x19080819, 0x08082b08), + uvec2(0x19081908, 0x08082b08), uvec2(0x19190808, 0x08082b08), uvec2(0x19192b08, 0x08082b08), uvec2(0x2b080808, 0x08082b08), + uvec2(0x2b2b0808, 0x08082b08), uvec2(0x2b2b2b2b, 0x08082b08), uvec2(0x08080819, 0x08082b19), uvec2(0x08081908, 0x08082b19), + uvec2(0x08190808, 0x08082b19), uvec2(0x19080808, 0x08082b19), uvec2(0x2b080819, 0x08082b19), uvec2(0x2b082b19, 0x08082b19), + uvec2(0x08080808, 0x08082b2b), uvec2(0x082b0808, 0x08082b2b), uvec2(0x082b2b08, 0x08082b2b), uvec2(0x2b19192b, 0x08082b2b), + uvec2(0x2b2b0808, 0x08082b2b), uvec2(0x08080819, 0x08190808), uvec2(0x08081908, 0x08190808), uvec2(0x0808192b, 0x08190808), + uvec2(0x08082b19, 0x08190808), uvec2(0x08190808, 0x08190808), uvec2(0x0819082b, 0x08190808), uvec2(0x08191919, 0x08190808), + uvec2(0x08192b08, 0x08190808), uvec2(0x082b0819, 0x08190808), uvec2(0x082b1908, 0x08190808), uvec2(0x19080808, 0x08190808), + uvec2(0x1908082b, 0x08190808), uvec2(0x19081919, 0x08190808), uvec2(0x19082b08, 0x08190808), uvec2(0x19190819, 0x08190808), + uvec2(0x19191908, 0x08190808), uvec2(0x192b0808, 0x08190808), uvec2(0x192b2b2b, 0x08190808), uvec2(0x2b080819, 0x08190808), + uvec2(0x2b081908, 0x08190808), uvec2(0x2b190808, 0x08190808), uvec2(0x08080808, 0x08190819), uvec2(0x0808082b, 0x08190819), + uvec2(0x08081919, 0x08190819), uvec2(0x08082b08, 0x08190819), uvec2(0x08190819, 0x08190819), uvec2(0x08191908, 0x08190819), + uvec2(0x082b0808, 0x08190819), uvec2(0x19080819, 0x08190819), uvec2(0x19081908, 0x08190819), uvec2(0x19190808, 0x08190819), + uvec2(0x2b080808, 0x08190819), uvec2(0x2b191908, 0x08190819), uvec2(0x2b19192b, 0x08190819), uvec2(0x08080819, 0x0819082b), + uvec2(0x08081908, 0x0819082b), uvec2(0x0808192b, 0x0819082b), uvec2(0x08190808, 0x0819082b), uvec2(0x19080808, 0x0819082b), + uvec2(0x192b0808, 0x0819082b), uvec2(0x08080808, 0x08191908), uvec2(0x0808082b, 0x08191908), uvec2(0x08081919, 0x08191908), + uvec2(0x08082b08, 0x08191908), uvec2(0x08190819, 0x08191908), uvec2(0x08191908, 0x08191908), uvec2(0x082b0808, 0x08191908), + uvec2(0x19080819, 0x08191908), uvec2(0x19081908, 0x08191908), uvec2(0x19082b19, 0x08191908), uvec2(0x19190808, 0x08191908), + uvec2(0x192b1908, 0x08191908), uvec2(0x2b080808, 0x08191908), uvec2(0x08080819, 0x08191919), uvec2(0x08081908, 0x08191919), + uvec2(0x08190808, 0x08191919), uvec2(0x19080808, 0x08191919), uvec2(0x08080808, 0x0819192b), uvec2(0x08191908, 0x0819192b), + uvec2(0x19082b19, 0x0819192b), uvec2(0x08080819, 0x08192b08), uvec2(0x08081908, 0x08192b08), uvec2(0x08190808, 0x08192b08), + uvec2(0x0819082b, 0x08192b08), uvec2(0x19080808, 0x08192b08), uvec2(0x19191908, 0x08192b08), uvec2(0x2b08192b, 0x08192b08), + uvec2(0x08080808, 0x08192b19), uvec2(0x08081919, 0x08192b19), uvec2(0x192b192b, 0x08192b19), uvec2(0x19190819, 0x08192b2b), + uvec2(0x2b2b2b19, 0x08192b2b), uvec2(0x08080808, 0x082b0808), uvec2(0x0808082b, 0x082b0808), uvec2(0x08081919, 0x082b0808), + uvec2(0x08082b08, 0x082b0808), uvec2(0x08082b2b, 0x082b0808), uvec2(0x08190819, 0x082b0808), uvec2(0x08191908, 0x082b0808), + uvec2(0x082b0808, 0x082b0808), uvec2(0x19080819, 0x082b0808), uvec2(0x19081908, 0x082b0808), uvec2(0x19190808, 0x082b0808), + uvec2(0x2b080808, 0x082b0808), uvec2(0x2b2b0808, 0x082b0808), uvec2(0x08080819, 0x082b0819), uvec2(0x08081908, 0x082b0819), + uvec2(0x08190808, 0x082b0819), uvec2(0x19080808, 0x082b0819), uvec2(0x19082b08, 0x082b0819), uvec2(0x192b1919, 0x082b0819), + uvec2(0x08080808, 0x082b082b), uvec2(0x082b082b, 0x082b082b), uvec2(0x2b080808, 0x082b082b), uvec2(0x2b2b2b08, 0x082b082b), + uvec2(0x08080819, 0x082b1908), uvec2(0x08081908, 0x082b1908), uvec2(0x08190808, 0x082b1908), uvec2(0x082b2b19, 0x082b1908), + uvec2(0x19080808, 0x082b1908), uvec2(0x08080808, 0x082b1919), uvec2(0x19080819, 0x082b1919), uvec2(0x1919082b, 0x082b1919), + uvec2(0x2b192b19, 0x082b1919), uvec2(0x08080819, 0x082b192b), uvec2(0x08192b2b, 0x082b192b), uvec2(0x2b2b192b, 0x082b192b), + uvec2(0x08080808, 0x082b2b08), uvec2(0x08082b08, 0x082b2b08), uvec2(0x08082b2b, 0x082b2b08), uvec2(0x082b0808, 0x082b2b08), + uvec2(0x19191919, 0x082b2b08), uvec2(0x2b082b08, 0x082b2b08), uvec2(0x2b2b082b, 0x082b2b08), uvec2(0x192b2b08, 0x082b2b19), + uvec2(0x2b190808, 0x082b2b19), uvec2(0x08082b08, 0x082b2b2b), uvec2(0x082b0808, 0x082b2b2b), uvec2(0x2b08082b, 0x082b2b2b), + uvec2(0x2b082b08, 0x082b2b2b), uvec2(0x2b082b2b, 0x082b2b2b), uvec2(0x08080819, 0x19080808), uvec2(0x08081908, 0x19080808), + uvec2(0x0808192b, 0x19080808), uvec2(0x08082b19, 0x19080808), uvec2(0x08190808, 0x19080808), uvec2(0x0819082b, 0x19080808), + uvec2(0x08191919, 0x19080808), uvec2(0x08192b08, 0x19080808), uvec2(0x082b0819, 0x19080808), uvec2(0x082b1908, 0x19080808), + uvec2(0x19080808, 0x19080808), uvec2(0x1908082b, 0x19080808), uvec2(0x19081919, 0x19080808), uvec2(0x19082b08, 0x19080808), + uvec2(0x19082b2b, 0x19080808), uvec2(0x19190819, 0x19080808), uvec2(0x19191908, 0x19080808), uvec2(0x192b0808, 0x19080808), + uvec2(0x192b1919, 0x19080808), uvec2(0x2b080819, 0x19080808), uvec2(0x2b081908, 0x19080808), uvec2(0x2b190808, 0x19080808), + uvec2(0x08080808, 0x19080819), uvec2(0x0808082b, 0x19080819), uvec2(0x08081919, 0x19080819), uvec2(0x08082b08, 0x19080819), + uvec2(0x08190819, 0x19080819), uvec2(0x08191908, 0x19080819), uvec2(0x082b0808, 0x19080819), uvec2(0x19080819, 0x19080819), + uvec2(0x19081908, 0x19080819), uvec2(0x19190808, 0x19080819), uvec2(0x2b080808, 0x19080819), uvec2(0x2b081919, 0x19080819), + uvec2(0x2b2b082b, 0x19080819), uvec2(0x08080819, 0x1908082b), uvec2(0x08081908, 0x1908082b), uvec2(0x08190808, 0x1908082b), + uvec2(0x0819082b, 0x1908082b), uvec2(0x082b2b19, 0x1908082b), uvec2(0x19080808, 0x1908082b), uvec2(0x08080808, 0x19081908), + uvec2(0x0808082b, 0x19081908), uvec2(0x08081919, 0x19081908), uvec2(0x08082b08, 0x19081908), uvec2(0x08190819, 0x19081908), + uvec2(0x08191908, 0x19081908), uvec2(0x08192b19, 0x19081908), uvec2(0x082b0808, 0x19081908), uvec2(0x19080819, 0x19081908), + uvec2(0x19081908, 0x19081908), uvec2(0x19190808, 0x19081908), uvec2(0x2b080808, 0x19081908), uvec2(0x2b191908, 0x19081908), + uvec2(0x08080819, 0x19081919), uvec2(0x08081908, 0x19081919), uvec2(0x08190808, 0x19081919), uvec2(0x082b1908, 0x19081919), + uvec2(0x19080808, 0x19081919), uvec2(0x2b192b2b, 0x19081919), uvec2(0x08080808, 0x1908192b), uvec2(0x08082b2b, 0x1908192b), + uvec2(0x19081908, 0x1908192b), uvec2(0x19190808, 0x1908192b), uvec2(0x08080819, 0x19082b08), uvec2(0x08081908, 0x19082b08), + uvec2(0x08190808, 0x19082b08), uvec2(0x19080808, 0x19082b08), uvec2(0x19081919, 0x19082b08), uvec2(0x19191908, 0x19082b08), + uvec2(0x192b082b, 0x19082b08), uvec2(0x08080808, 0x19082b19), uvec2(0x08190819, 0x19082b19), uvec2(0x19081908, 0x19082b19), + uvec2(0x19190808, 0x19082b19), uvec2(0x192b2b19, 0x19082b19), uvec2(0x08081908, 0x19082b2b), uvec2(0x08080808, 0x19190808), + uvec2(0x0808082b, 0x19190808), uvec2(0x08081919, 0x19190808), uvec2(0x08082b08, 0x19190808), uvec2(0x08190819, 0x19190808), + uvec2(0x08191908, 0x19190808), uvec2(0x082b0808, 0x19190808), uvec2(0x082b2b08, 0x19190808), uvec2(0x19080819, 0x19190808), + uvec2(0x19081908, 0x19190808), uvec2(0x19190808, 0x19190808), uvec2(0x2b080808, 0x19190808), uvec2(0x08080819, 0x19190819), + uvec2(0x08081908, 0x19190819), uvec2(0x08190808, 0x19190819), uvec2(0x08191919, 0x19190819), uvec2(0x19080808, 0x19190819), + uvec2(0x1908082b, 0x19190819), uvec2(0x08080808, 0x1919082b), uvec2(0x19081908, 0x1919082b), uvec2(0x2b2b2b2b, 0x1919082b), + uvec2(0x08080819, 0x19191908), uvec2(0x08081908, 0x19191908), uvec2(0x08190808, 0x19191908), uvec2(0x082b0819, 0x19191908), + uvec2(0x19080808, 0x19191908), uvec2(0x192b0808, 0x19191908), uvec2(0x2b080819, 0x19191908), uvec2(0x2b2b0819, 0x19191908), + uvec2(0x08080808, 0x19191919), uvec2(0x08082b08, 0x19191919), uvec2(0x2b080808, 0x19191919), uvec2(0x2b082b08, 0x19191919), + uvec2(0x082b0819, 0x1919192b), uvec2(0x192b2b08, 0x1919192b), uvec2(0x2b2b0819, 0x1919192b), uvec2(0x08080808, 0x19192b08), + uvec2(0x08191908, 0x19192b08), uvec2(0x19080819, 0x19192b08), uvec2(0x19190808, 0x19192b08), uvec2(0x2b192b19, 0x19192b08), + uvec2(0x08192b2b, 0x19192b19), uvec2(0x19080808, 0x19192b19), uvec2(0x1908082b, 0x19192b19), uvec2(0x2b081919, 0x19192b2b), + uvec2(0x08080819, 0x192b0808), uvec2(0x08081908, 0x192b0808), uvec2(0x08190808, 0x192b0808), uvec2(0x19080808, 0x192b0808), + uvec2(0x19191908, 0x192b0808), uvec2(0x192b082b, 0x192b0808), uvec2(0x2b08192b, 0x192b0808), uvec2(0x2b2b2b19, 0x192b0808), + uvec2(0x08080808, 0x192b0819), uvec2(0x082b1908, 0x192b082b), uvec2(0x19082b2b, 0x192b082b), uvec2(0x2b19082b, 0x192b082b), + uvec2(0x08080808, 0x192b1908), uvec2(0x0819192b, 0x192b1908), uvec2(0x08190808, 0x192b1919), uvec2(0x19080808, 0x192b1919), + uvec2(0x19081919, 0x192b1919), uvec2(0x2b2b1908, 0x192b1919), uvec2(0x08080819, 0x192b2b08), uvec2(0x192b2b2b, 0x192b2b08), + uvec2(0x082b1919, 0x192b2b19), uvec2(0x0808192b, 0x192b2b2b), uvec2(0x19191908, 0x192b2b2b), uvec2(0x192b082b, 0x192b2b2b), + uvec2(0x08080808, 0x2b080808), uvec2(0x0808082b, 0x2b080808), uvec2(0x08081919, 0x2b080808), uvec2(0x08082b08, 0x2b080808), + uvec2(0x08190819, 0x2b080808), uvec2(0x08191908, 0x2b080808), uvec2(0x082b0808, 0x2b080808), uvec2(0x082b2b2b, 0x2b080808), + uvec2(0x19080819, 0x2b080808), uvec2(0x19081908, 0x2b080808), uvec2(0x19190808, 0x2b080808), uvec2(0x2b080808, 0x2b080808), + uvec2(0x2b08082b, 0x2b080808), uvec2(0x2b2b2b08, 0x2b080808), uvec2(0x2b2b2b2b, 0x2b080808), uvec2(0x08080819, 0x2b080819), + uvec2(0x08081908, 0x2b080819), uvec2(0x0808192b, 0x2b080819), uvec2(0x08190808, 0x2b080819), uvec2(0x19080808, 0x2b080819), + uvec2(0x19190819, 0x2b080819), uvec2(0x19192b19, 0x2b080819), uvec2(0x08080808, 0x2b08082b), uvec2(0x082b0808, 0x2b08082b), + uvec2(0x2b080808, 0x2b08082b), uvec2(0x2b08082b, 0x2b08082b), uvec2(0x2b2b0808, 0x2b08082b), uvec2(0x2b2b2b08, 0x2b08082b), + uvec2(0x08080819, 0x2b081908), uvec2(0x08081908, 0x2b081908), uvec2(0x08190808, 0x2b081908), uvec2(0x0819082b, 0x2b081908), + uvec2(0x08191919, 0x2b081908), uvec2(0x19080808, 0x2b081908), uvec2(0x192b0808, 0x2b081908), uvec2(0x2b082b19, 0x2b081908), + uvec2(0x08080808, 0x2b081919), uvec2(0x19081908, 0x2b081919), uvec2(0x2b2b1919, 0x2b081919), uvec2(0x08192b08, 0x2b08192b), + uvec2(0x192b2b2b, 0x2b08192b), uvec2(0x08080808, 0x2b082b08), uvec2(0x08082b08, 0x2b082b08), uvec2(0x082b1919, 0x2b082b08), + uvec2(0x19192b2b, 0x2b082b08), uvec2(0x2b080808, 0x2b082b08), uvec2(0x2b08082b, 0x2b082b08), uvec2(0x2b2b2b08, 0x2b082b08), + uvec2(0x0808192b, 0x2b082b19), uvec2(0x082b082b, 0x2b082b2b), uvec2(0x2b080808, 0x2b082b2b), uvec2(0x2b082b08, 0x2b082b2b), + uvec2(0x2b19192b, 0x2b082b2b), uvec2(0x2b2b2b08, 0x2b082b2b), uvec2(0x08080819, 0x2b190808), uvec2(0x08081908, 0x2b190808), + uvec2(0x08190808, 0x2b190808), uvec2(0x19080808, 0x2b190808), uvec2(0x1919192b, 0x2b190808), uvec2(0x2b081908, 0x2b190808), + uvec2(0x08080808, 0x2b190819), uvec2(0x082b082b, 0x2b190819), uvec2(0x192b1908, 0x2b190819), uvec2(0x1919192b, 0x2b19082b), + uvec2(0x2b082b19, 0x2b19082b), uvec2(0x08080808, 0x2b191908), uvec2(0x08081919, 0x2b191908), uvec2(0x19081908, 0x2b191908), + uvec2(0x19190808, 0x2b191908), uvec2(0x19192b08, 0x2b191908), uvec2(0x082b2b19, 0x2b191919), uvec2(0x2b190808, 0x2b191919), + uvec2(0x2b19082b, 0x2b191919), uvec2(0x19080819, 0x2b19192b), uvec2(0x19190819, 0x2b192b08), uvec2(0x2b2b192b, 0x2b192b08), + uvec2(0x19082b19, 0x2b192b19), uvec2(0x08191919, 0x2b192b2b), uvec2(0x192b0808, 0x2b192b2b), uvec2(0x08080808, 0x2b2b0808), + uvec2(0x0808082b, 0x2b2b0808), uvec2(0x08082b08, 0x2b2b0808), uvec2(0x08082b2b, 0x2b2b0808), uvec2(0x082b0808, 0x2b2b0808), + uvec2(0x082b2b2b, 0x2b2b0808), uvec2(0x2b2b0808, 0x2b2b0808), uvec2(0x19190819, 0x2b2b0819), uvec2(0x19192b19, 0x2b2b0819), + uvec2(0x2b2b192b, 0x2b2b0819), uvec2(0x08080808, 0x2b2b082b), uvec2(0x0808082b, 0x2b2b082b), uvec2(0x08082b08, 0x2b2b082b), + uvec2(0x082b2b2b, 0x2b2b082b), uvec2(0x2b080808, 0x2b2b082b), uvec2(0x2b2b0808, 0x2b2b082b), uvec2(0x19080808, 0x2b2b1908), + uvec2(0x2b191919, 0x2b2b1908), uvec2(0x192b1919, 0x2b2b192b), uvec2(0x2b192b08, 0x2b2b192b), uvec2(0x08082b2b, 0x2b2b2b08), + uvec2(0x082b0808, 0x2b2b2b08), uvec2(0x082b082b, 0x2b2b2b08), uvec2(0x082b2b08, 0x2b2b2b08), uvec2(0x2b2b0808, 0x2b2b2b08), + uvec2(0x2b2b2b08, 0x2b2b2b08), uvec2(0x08081908, 0x2b2b2b19), uvec2(0x2b081908, 0x2b2b2b19), uvec2(0x2b08192b, 0x2b2b2b19), + uvec2(0x082b2b08, 0x2b2b2b2b), uvec2(0x082b2b2b, 0x2b2b2b2b), uvec2(0x2b190819, 0x2b2b2b2b), uvec2(0x2b2b2b2b, 0x2b2b2b2b), +}; + +shared uvec2 iq2xs_grid[512]; + +#define NEEDS_INIT_IQ_SHMEM +void init_iq_shmem(uvec3 wgsize) +{ + // copy the table into shared memory and sync + [[unroll]] for (uint i = 0; i < iq2xs_grid.length(); i += wgsize.x) { + if (iq2xs_grid.length() % wgsize.x == 0 || i + gl_LocalInvocationIndex.x < iq2xs_grid_const.length()) { + iq2xs_grid[i + gl_LocalInvocationIndex.x] = iq2xs_grid_const[i + gl_LocalInvocationIndex.x]; + } + } + barrier(); +} + +#define QUANT_K QUANT_K_IQ2_XS +#define QUANT_R QUANT_R_IQ2_XS +#define A_TYPE block_iq2_xs +#define A_TYPE_PACKED16 block_iq2_xs_packed16 +#endif + +#define QUANT_K_IQ2_S 256 +#define QUANT_R_IQ2_S 1 + +struct block_iq2_s +{ + float16_t d; + uint8_t qs[QUANT_K_IQ2_S/4]; + uint8_t qh[QUANT_K_IQ2_S/32]; + uint8_t scales[QUANT_K_IQ2_S/32]; +}; + +struct block_iq2_s_packed16 +{ + float16_t d; + uint16_t qs[QUANT_K_IQ2_S/8]; + uint16_t qh[QUANT_K_IQ2_S/64]; + uint16_t scales[QUANT_K_IQ2_S/64]; +}; + +#if defined(DATA_A_IQ2_S) + +const uvec2 iq2s_grid_const[1024] = { + uvec2(0x08080808, 0x08080808), uvec2(0x0808082b, 0x08080808), uvec2(0x08081919, 0x08080808), uvec2(0x08082b08, 0x08080808), + uvec2(0x08082b2b, 0x08080808), uvec2(0x08190819, 0x08080808), uvec2(0x08191908, 0x08080808), uvec2(0x0819192b, 0x08080808), + uvec2(0x08192b19, 0x08080808), uvec2(0x082b0808, 0x08080808), uvec2(0x082b082b, 0x08080808), uvec2(0x082b1919, 0x08080808), + uvec2(0x082b2b08, 0x08080808), uvec2(0x19080819, 0x08080808), uvec2(0x19081908, 0x08080808), uvec2(0x1908192b, 0x08080808), + uvec2(0x19082b19, 0x08080808), uvec2(0x19190808, 0x08080808), uvec2(0x1919082b, 0x08080808), uvec2(0x19191919, 0x08080808), + uvec2(0x19192b08, 0x08080808), uvec2(0x192b0819, 0x08080808), uvec2(0x192b1908, 0x08080808), uvec2(0x192b192b, 0x08080808), + uvec2(0x192b2b19, 0x08080808), uvec2(0x2b080808, 0x08080808), uvec2(0x2b08082b, 0x08080808), uvec2(0x2b081919, 0x08080808), + uvec2(0x2b082b08, 0x08080808), uvec2(0x2b190819, 0x08080808), uvec2(0x2b191908, 0x08080808), uvec2(0x2b2b0808, 0x08080808), + uvec2(0x2b2b1919, 0x08080808), uvec2(0x2b2b2b2b, 0x08080808), uvec2(0x08080819, 0x08080819), uvec2(0x08081908, 0x08080819), + uvec2(0x0808192b, 0x08080819), uvec2(0x08082b19, 0x08080819), uvec2(0x08190808, 0x08080819), uvec2(0x0819082b, 0x08080819), + uvec2(0x08191919, 0x08080819), uvec2(0x08192b08, 0x08080819), uvec2(0x082b0819, 0x08080819), uvec2(0x082b1908, 0x08080819), + uvec2(0x19080808, 0x08080819), uvec2(0x1908082b, 0x08080819), uvec2(0x19081919, 0x08080819), uvec2(0x19082b08, 0x08080819), + uvec2(0x19190819, 0x08080819), uvec2(0x19191908, 0x08080819), uvec2(0x1919192b, 0x08080819), uvec2(0x19192b19, 0x08080819), + uvec2(0x192b0808, 0x08080819), uvec2(0x192b1919, 0x08080819), uvec2(0x192b2b08, 0x08080819), uvec2(0x2b080819, 0x08080819), + uvec2(0x2b081908, 0x08080819), uvec2(0x2b190808, 0x08080819), uvec2(0x2b19082b, 0x08080819), uvec2(0x2b191919, 0x08080819), + uvec2(0x2b2b0819, 0x08080819), uvec2(0x2b2b1908, 0x08080819), uvec2(0x08080808, 0x0808082b), uvec2(0x0808082b, 0x0808082b), + uvec2(0x08081919, 0x0808082b), uvec2(0x08082b08, 0x0808082b), uvec2(0x08190819, 0x0808082b), uvec2(0x08191908, 0x0808082b), + uvec2(0x082b0808, 0x0808082b), uvec2(0x082b2b2b, 0x0808082b), uvec2(0x19080819, 0x0808082b), uvec2(0x19081908, 0x0808082b), + uvec2(0x1908192b, 0x0808082b), uvec2(0x19082b19, 0x0808082b), uvec2(0x19190808, 0x0808082b), uvec2(0x19191919, 0x0808082b), + uvec2(0x2b080808, 0x0808082b), uvec2(0x2b081919, 0x0808082b), uvec2(0x2b082b2b, 0x0808082b), uvec2(0x2b191908, 0x0808082b), + uvec2(0x2b2b082b, 0x0808082b), uvec2(0x08080819, 0x08081908), uvec2(0x08081908, 0x08081908), uvec2(0x0808192b, 0x08081908), + uvec2(0x08082b19, 0x08081908), uvec2(0x08190808, 0x08081908), uvec2(0x0819082b, 0x08081908), uvec2(0x08191919, 0x08081908), + uvec2(0x08192b08, 0x08081908), uvec2(0x082b0819, 0x08081908), uvec2(0x082b1908, 0x08081908), uvec2(0x082b192b, 0x08081908), + uvec2(0x082b2b19, 0x08081908), uvec2(0x19080808, 0x08081908), uvec2(0x1908082b, 0x08081908), uvec2(0x19081919, 0x08081908), + uvec2(0x19082b08, 0x08081908), uvec2(0x19082b2b, 0x08081908), uvec2(0x19190819, 0x08081908), uvec2(0x19191908, 0x08081908), + uvec2(0x1919192b, 0x08081908), uvec2(0x19192b19, 0x08081908), uvec2(0x192b0808, 0x08081908), uvec2(0x192b082b, 0x08081908), + uvec2(0x192b1919, 0x08081908), uvec2(0x2b080819, 0x08081908), uvec2(0x2b081908, 0x08081908), uvec2(0x2b08192b, 0x08081908), + uvec2(0x2b082b19, 0x08081908), uvec2(0x2b190808, 0x08081908), uvec2(0x2b191919, 0x08081908), uvec2(0x2b192b08, 0x08081908), + uvec2(0x2b2b0819, 0x08081908), uvec2(0x2b2b1908, 0x08081908), uvec2(0x08080808, 0x08081919), uvec2(0x0808082b, 0x08081919), + uvec2(0x08081919, 0x08081919), uvec2(0x08082b08, 0x08081919), uvec2(0x08082b2b, 0x08081919), uvec2(0x08190819, 0x08081919), + uvec2(0x08191908, 0x08081919), uvec2(0x0819192b, 0x08081919), uvec2(0x08192b19, 0x08081919), uvec2(0x082b0808, 0x08081919), + uvec2(0x082b1919, 0x08081919), uvec2(0x082b2b08, 0x08081919), uvec2(0x19080819, 0x08081919), uvec2(0x19081908, 0x08081919), + uvec2(0x1908192b, 0x08081919), uvec2(0x19082b19, 0x08081919), uvec2(0x19190808, 0x08081919), uvec2(0x1919082b, 0x08081919), + uvec2(0x19191919, 0x08081919), uvec2(0x19192b08, 0x08081919), uvec2(0x192b0819, 0x08081919), uvec2(0x192b1908, 0x08081919), + uvec2(0x2b080808, 0x08081919), uvec2(0x2b08082b, 0x08081919), uvec2(0x2b081919, 0x08081919), uvec2(0x2b082b08, 0x08081919), + uvec2(0x2b190819, 0x08081919), uvec2(0x2b191908, 0x08081919), uvec2(0x2b2b0808, 0x08081919), uvec2(0x08080819, 0x0808192b), + uvec2(0x08081908, 0x0808192b), uvec2(0x0808192b, 0x0808192b), uvec2(0x08082b19, 0x0808192b), uvec2(0x08190808, 0x0808192b), + uvec2(0x08191919, 0x0808192b), uvec2(0x19080808, 0x0808192b), uvec2(0x19081919, 0x0808192b), uvec2(0x19082b08, 0x0808192b), + uvec2(0x19190819, 0x0808192b), uvec2(0x19191908, 0x0808192b), uvec2(0x192b0808, 0x0808192b), uvec2(0x2b080819, 0x0808192b), + uvec2(0x2b081908, 0x0808192b), uvec2(0x2b190808, 0x0808192b), uvec2(0x08080808, 0x08082b08), uvec2(0x0808082b, 0x08082b08), + uvec2(0x08081919, 0x08082b08), uvec2(0x08082b08, 0x08082b08), uvec2(0x08190819, 0x08082b08), uvec2(0x08191908, 0x08082b08), + uvec2(0x0819192b, 0x08082b08), uvec2(0x08192b19, 0x08082b08), uvec2(0x082b0808, 0x08082b08), uvec2(0x082b1919, 0x08082b08), + uvec2(0x082b2b2b, 0x08082b08), uvec2(0x19080819, 0x08082b08), uvec2(0x19081908, 0x08082b08), uvec2(0x1908192b, 0x08082b08), + uvec2(0x19082b19, 0x08082b08), uvec2(0x19190808, 0x08082b08), uvec2(0x1919082b, 0x08082b08), uvec2(0x19191919, 0x08082b08), + uvec2(0x19192b08, 0x08082b08), uvec2(0x192b0819, 0x08082b08), uvec2(0x192b1908, 0x08082b08), uvec2(0x2b080808, 0x08082b08), + uvec2(0x2b081919, 0x08082b08), uvec2(0x2b191908, 0x08082b08), uvec2(0x2b2b2b2b, 0x08082b08), uvec2(0x08080819, 0x08082b19), + uvec2(0x08081908, 0x08082b19), uvec2(0x08190808, 0x08082b19), uvec2(0x0819082b, 0x08082b19), uvec2(0x08191919, 0x08082b19), + uvec2(0x08192b08, 0x08082b19), uvec2(0x082b0819, 0x08082b19), uvec2(0x19080808, 0x08082b19), uvec2(0x19081919, 0x08082b19), + uvec2(0x19082b08, 0x08082b19), uvec2(0x19190819, 0x08082b19), uvec2(0x19191908, 0x08082b19), uvec2(0x192b0808, 0x08082b19), + uvec2(0x2b080819, 0x08082b19), uvec2(0x2b190808, 0x08082b19), uvec2(0x08080808, 0x08082b2b), uvec2(0x08190819, 0x08082b2b), + uvec2(0x08191908, 0x08082b2b), uvec2(0x082b082b, 0x08082b2b), uvec2(0x082b2b08, 0x08082b2b), uvec2(0x082b2b2b, 0x08082b2b), + uvec2(0x19190808, 0x08082b2b), uvec2(0x2b192b19, 0x08082b2b), uvec2(0x08080819, 0x08190808), uvec2(0x08081908, 0x08190808), + uvec2(0x0808192b, 0x08190808), uvec2(0x08082b19, 0x08190808), uvec2(0x08190808, 0x08190808), uvec2(0x0819082b, 0x08190808), + uvec2(0x08191919, 0x08190808), uvec2(0x08192b08, 0x08190808), uvec2(0x082b0819, 0x08190808), uvec2(0x082b1908, 0x08190808), + uvec2(0x082b192b, 0x08190808), uvec2(0x19080808, 0x08190808), uvec2(0x1908082b, 0x08190808), uvec2(0x19081919, 0x08190808), + uvec2(0x19082b08, 0x08190808), uvec2(0x19190819, 0x08190808), uvec2(0x19191908, 0x08190808), uvec2(0x1919192b, 0x08190808), + uvec2(0x19192b19, 0x08190808), uvec2(0x192b0808, 0x08190808), uvec2(0x192b082b, 0x08190808), uvec2(0x192b1919, 0x08190808), + uvec2(0x192b2b08, 0x08190808), uvec2(0x2b080819, 0x08190808), uvec2(0x2b081908, 0x08190808), uvec2(0x2b08192b, 0x08190808), + uvec2(0x2b190808, 0x08190808), uvec2(0x2b191919, 0x08190808), uvec2(0x2b192b08, 0x08190808), uvec2(0x2b2b0819, 0x08190808), + uvec2(0x2b2b1908, 0x08190808), uvec2(0x08080808, 0x08190819), uvec2(0x0808082b, 0x08190819), uvec2(0x08081919, 0x08190819), + uvec2(0x08082b08, 0x08190819), uvec2(0x08082b2b, 0x08190819), uvec2(0x08190819, 0x08190819), uvec2(0x08191908, 0x08190819), + uvec2(0x0819192b, 0x08190819), uvec2(0x08192b19, 0x08190819), uvec2(0x082b0808, 0x08190819), uvec2(0x082b082b, 0x08190819), + uvec2(0x082b1919, 0x08190819), uvec2(0x082b2b08, 0x08190819), uvec2(0x19080819, 0x08190819), uvec2(0x19081908, 0x08190819), + uvec2(0x1908192b, 0x08190819), uvec2(0x19082b19, 0x08190819), uvec2(0x19190808, 0x08190819), uvec2(0x1919082b, 0x08190819), + uvec2(0x19191919, 0x08190819), uvec2(0x19192b08, 0x08190819), uvec2(0x192b0819, 0x08190819), uvec2(0x192b1908, 0x08190819), + uvec2(0x2b080808, 0x08190819), uvec2(0x2b08082b, 0x08190819), uvec2(0x2b081919, 0x08190819), uvec2(0x2b082b08, 0x08190819), + uvec2(0x2b190819, 0x08190819), uvec2(0x2b191908, 0x08190819), uvec2(0x08080819, 0x0819082b), uvec2(0x08081908, 0x0819082b), + uvec2(0x08082b19, 0x0819082b), uvec2(0x08190808, 0x0819082b), uvec2(0x08191919, 0x0819082b), uvec2(0x082b0819, 0x0819082b), + uvec2(0x082b1908, 0x0819082b), uvec2(0x19080808, 0x0819082b), uvec2(0x19081919, 0x0819082b), uvec2(0x19190819, 0x0819082b), + uvec2(0x19191908, 0x0819082b), uvec2(0x2b080819, 0x0819082b), uvec2(0x2b081908, 0x0819082b), uvec2(0x2b190808, 0x0819082b), + uvec2(0x08080808, 0x08191908), uvec2(0x0808082b, 0x08191908), uvec2(0x08081919, 0x08191908), uvec2(0x08082b08, 0x08191908), + uvec2(0x08190819, 0x08191908), uvec2(0x08191908, 0x08191908), uvec2(0x0819192b, 0x08191908), uvec2(0x08192b19, 0x08191908), + uvec2(0x082b0808, 0x08191908), uvec2(0x082b1919, 0x08191908), uvec2(0x082b2b08, 0x08191908), uvec2(0x19080819, 0x08191908), + uvec2(0x19081908, 0x08191908), uvec2(0x1908192b, 0x08191908), uvec2(0x19082b19, 0x08191908), uvec2(0x19190808, 0x08191908), + uvec2(0x1919082b, 0x08191908), uvec2(0x19191919, 0x08191908), uvec2(0x19192b08, 0x08191908), uvec2(0x192b0819, 0x08191908), + uvec2(0x192b1908, 0x08191908), uvec2(0x2b080808, 0x08191908), uvec2(0x2b08082b, 0x08191908), uvec2(0x2b081919, 0x08191908), + uvec2(0x2b082b08, 0x08191908), uvec2(0x2b190819, 0x08191908), uvec2(0x2b191908, 0x08191908), uvec2(0x2b2b0808, 0x08191908), + uvec2(0x08080819, 0x08191919), uvec2(0x08081908, 0x08191919), uvec2(0x0808192b, 0x08191919), uvec2(0x08082b19, 0x08191919), + uvec2(0x08190808, 0x08191919), uvec2(0x0819082b, 0x08191919), uvec2(0x08191919, 0x08191919), uvec2(0x08192b08, 0x08191919), + uvec2(0x082b0819, 0x08191919), uvec2(0x082b1908, 0x08191919), uvec2(0x19080808, 0x08191919), uvec2(0x1908082b, 0x08191919), + uvec2(0x19081919, 0x08191919), uvec2(0x19082b08, 0x08191919), uvec2(0x19190819, 0x08191919), uvec2(0x19191908, 0x08191919), + uvec2(0x192b0808, 0x08191919), uvec2(0x2b080819, 0x08191919), uvec2(0x2b081908, 0x08191919), uvec2(0x2b190808, 0x08191919), + uvec2(0x08080808, 0x0819192b), uvec2(0x08081919, 0x0819192b), uvec2(0x08082b08, 0x0819192b), uvec2(0x08190819, 0x0819192b), + uvec2(0x08191908, 0x0819192b), uvec2(0x082b0808, 0x0819192b), uvec2(0x19080819, 0x0819192b), uvec2(0x19081908, 0x0819192b), + uvec2(0x19190808, 0x0819192b), uvec2(0x2b080808, 0x0819192b), uvec2(0x2b2b2b2b, 0x0819192b), uvec2(0x08080819, 0x08192b08), + uvec2(0x08081908, 0x08192b08), uvec2(0x0808192b, 0x08192b08), uvec2(0x08082b19, 0x08192b08), uvec2(0x08190808, 0x08192b08), + uvec2(0x08191919, 0x08192b08), uvec2(0x08192b08, 0x08192b08), uvec2(0x082b0819, 0x08192b08), uvec2(0x19080808, 0x08192b08), + uvec2(0x1908082b, 0x08192b08), uvec2(0x19081919, 0x08192b08), uvec2(0x19082b08, 0x08192b08), uvec2(0x19190819, 0x08192b08), + uvec2(0x19191908, 0x08192b08), uvec2(0x192b0808, 0x08192b08), uvec2(0x2b080819, 0x08192b08), uvec2(0x2b081908, 0x08192b08), + uvec2(0x08080808, 0x08192b19), uvec2(0x0808082b, 0x08192b19), uvec2(0x08081919, 0x08192b19), uvec2(0x08082b08, 0x08192b19), + uvec2(0x08190819, 0x08192b19), uvec2(0x08191908, 0x08192b19), uvec2(0x082b0808, 0x08192b19), uvec2(0x19080819, 0x08192b19), + uvec2(0x19081908, 0x08192b19), uvec2(0x19190808, 0x08192b19), uvec2(0x192b2b19, 0x08192b19), uvec2(0x2b2b082b, 0x08192b19), + uvec2(0x08081908, 0x08192b2b), uvec2(0x08190808, 0x08192b2b), uvec2(0x19080808, 0x08192b2b), uvec2(0x1919192b, 0x08192b2b), + uvec2(0x08080808, 0x082b0808), uvec2(0x0808082b, 0x082b0808), uvec2(0x08081919, 0x082b0808), uvec2(0x08082b08, 0x082b0808), + uvec2(0x08190819, 0x082b0808), uvec2(0x08191908, 0x082b0808), uvec2(0x0819192b, 0x082b0808), uvec2(0x08192b19, 0x082b0808), + uvec2(0x082b0808, 0x082b0808), uvec2(0x082b1919, 0x082b0808), uvec2(0x082b2b2b, 0x082b0808), uvec2(0x19080819, 0x082b0808), + uvec2(0x19081908, 0x082b0808), uvec2(0x19190808, 0x082b0808), uvec2(0x1919082b, 0x082b0808), uvec2(0x19191919, 0x082b0808), + uvec2(0x192b1908, 0x082b0808), uvec2(0x2b080808, 0x082b0808), uvec2(0x2b082b2b, 0x082b0808), uvec2(0x2b191908, 0x082b0808), + uvec2(0x2b2b2b2b, 0x082b0808), uvec2(0x08080819, 0x082b0819), uvec2(0x08081908, 0x082b0819), uvec2(0x08190808, 0x082b0819), + uvec2(0x0819082b, 0x082b0819), uvec2(0x08191919, 0x082b0819), uvec2(0x082b0819, 0x082b0819), uvec2(0x19080808, 0x082b0819), + uvec2(0x1908082b, 0x082b0819), uvec2(0x19081919, 0x082b0819), uvec2(0x19190819, 0x082b0819), uvec2(0x19191908, 0x082b0819), + uvec2(0x192b0808, 0x082b0819), uvec2(0x2b080819, 0x082b0819), uvec2(0x2b081908, 0x082b0819), uvec2(0x2b190808, 0x082b0819), + uvec2(0x08080808, 0x082b082b), uvec2(0x08082b2b, 0x082b082b), uvec2(0x082b082b, 0x082b082b), uvec2(0x082b2b08, 0x082b082b), + uvec2(0x082b2b2b, 0x082b082b), uvec2(0x19081908, 0x082b082b), uvec2(0x19190808, 0x082b082b), uvec2(0x2b082b08, 0x082b082b), + uvec2(0x2b082b2b, 0x082b082b), uvec2(0x2b2b2b08, 0x082b082b), uvec2(0x08080819, 0x082b1908), uvec2(0x08081908, 0x082b1908), + uvec2(0x0808192b, 0x082b1908), uvec2(0x08082b19, 0x082b1908), uvec2(0x08190808, 0x082b1908), uvec2(0x08191919, 0x082b1908), + uvec2(0x08192b08, 0x082b1908), uvec2(0x082b0819, 0x082b1908), uvec2(0x082b1908, 0x082b1908), uvec2(0x19080808, 0x082b1908), + uvec2(0x1908082b, 0x082b1908), uvec2(0x19081919, 0x082b1908), uvec2(0x19082b08, 0x082b1908), uvec2(0x19190819, 0x082b1908), + uvec2(0x19191908, 0x082b1908), uvec2(0x192b0808, 0x082b1908), uvec2(0x2b080819, 0x082b1908), uvec2(0x2b081908, 0x082b1908), + uvec2(0x2b190808, 0x082b1908), uvec2(0x08080808, 0x082b1919), uvec2(0x08081919, 0x082b1919), uvec2(0x08082b08, 0x082b1919), + uvec2(0x08190819, 0x082b1919), uvec2(0x08191908, 0x082b1919), uvec2(0x082b0808, 0x082b1919), uvec2(0x19080819, 0x082b1919), + uvec2(0x19081908, 0x082b1919), uvec2(0x19190808, 0x082b1919), uvec2(0x192b192b, 0x082b1919), uvec2(0x2b080808, 0x082b1919), + uvec2(0x08080819, 0x082b192b), uvec2(0x08081908, 0x082b192b), uvec2(0x08190808, 0x082b192b), uvec2(0x19080808, 0x082b192b), + uvec2(0x19192b19, 0x082b192b), uvec2(0x08080808, 0x082b2b08), uvec2(0x08081919, 0x082b2b08), uvec2(0x08190819, 0x082b2b08), + uvec2(0x08191908, 0x082b2b08), uvec2(0x19080819, 0x082b2b08), uvec2(0x19081908, 0x082b2b08), uvec2(0x19190808, 0x082b2b08), + uvec2(0x2b082b2b, 0x082b2b08), uvec2(0x2b2b2b2b, 0x082b2b08), uvec2(0x08080819, 0x082b2b19), uvec2(0x08081908, 0x082b2b19), + uvec2(0x08190808, 0x082b2b19), uvec2(0x2b191919, 0x082b2b19), uvec2(0x08082b2b, 0x082b2b2b), uvec2(0x082b082b, 0x082b2b2b), + uvec2(0x192b1908, 0x082b2b2b), uvec2(0x2b082b08, 0x082b2b2b), uvec2(0x2b082b2b, 0x082b2b2b), uvec2(0x08080819, 0x19080808), + uvec2(0x08081908, 0x19080808), uvec2(0x0808192b, 0x19080808), uvec2(0x08082b19, 0x19080808), uvec2(0x08190808, 0x19080808), + uvec2(0x0819082b, 0x19080808), uvec2(0x08191919, 0x19080808), uvec2(0x08192b08, 0x19080808), uvec2(0x08192b2b, 0x19080808), + uvec2(0x082b0819, 0x19080808), uvec2(0x082b1908, 0x19080808), uvec2(0x082b192b, 0x19080808), uvec2(0x19080808, 0x19080808), + uvec2(0x1908082b, 0x19080808), uvec2(0x19081919, 0x19080808), uvec2(0x19082b08, 0x19080808), uvec2(0x19082b2b, 0x19080808), + uvec2(0x19190819, 0x19080808), uvec2(0x19191908, 0x19080808), uvec2(0x1919192b, 0x19080808), uvec2(0x19192b19, 0x19080808), + uvec2(0x192b0808, 0x19080808), uvec2(0x192b082b, 0x19080808), uvec2(0x192b1919, 0x19080808), uvec2(0x2b080819, 0x19080808), + uvec2(0x2b081908, 0x19080808), uvec2(0x2b190808, 0x19080808), uvec2(0x2b191919, 0x19080808), uvec2(0x2b192b08, 0x19080808), + uvec2(0x2b2b0819, 0x19080808), uvec2(0x2b2b1908, 0x19080808), uvec2(0x08080808, 0x19080819), uvec2(0x0808082b, 0x19080819), + uvec2(0x08081919, 0x19080819), uvec2(0x08082b08, 0x19080819), uvec2(0x08190819, 0x19080819), uvec2(0x08191908, 0x19080819), + uvec2(0x0819192b, 0x19080819), uvec2(0x08192b19, 0x19080819), uvec2(0x082b0808, 0x19080819), uvec2(0x082b082b, 0x19080819), + uvec2(0x082b1919, 0x19080819), uvec2(0x19080819, 0x19080819), uvec2(0x19081908, 0x19080819), uvec2(0x1908192b, 0x19080819), + uvec2(0x19082b19, 0x19080819), uvec2(0x19190808, 0x19080819), uvec2(0x1919082b, 0x19080819), uvec2(0x19191919, 0x19080819), + uvec2(0x19192b08, 0x19080819), uvec2(0x192b0819, 0x19080819), uvec2(0x192b1908, 0x19080819), uvec2(0x2b080808, 0x19080819), + uvec2(0x2b08082b, 0x19080819), uvec2(0x2b081919, 0x19080819), uvec2(0x2b082b08, 0x19080819), uvec2(0x2b190819, 0x19080819), + uvec2(0x2b191908, 0x19080819), uvec2(0x2b2b0808, 0x19080819), uvec2(0x08080819, 0x1908082b), uvec2(0x08081908, 0x1908082b), + uvec2(0x08190808, 0x1908082b), uvec2(0x0819082b, 0x1908082b), uvec2(0x08191919, 0x1908082b), uvec2(0x08192b08, 0x1908082b), + uvec2(0x082b1908, 0x1908082b), uvec2(0x19080808, 0x1908082b), uvec2(0x19081919, 0x1908082b), uvec2(0x19082b08, 0x1908082b), + uvec2(0x19190819, 0x1908082b), uvec2(0x19191908, 0x1908082b), uvec2(0x192b0808, 0x1908082b), uvec2(0x2b080819, 0x1908082b), + uvec2(0x2b081908, 0x1908082b), uvec2(0x08080808, 0x19081908), uvec2(0x0808082b, 0x19081908), uvec2(0x08081919, 0x19081908), + uvec2(0x08082b08, 0x19081908), uvec2(0x08082b2b, 0x19081908), uvec2(0x08190819, 0x19081908), uvec2(0x08191908, 0x19081908), + uvec2(0x0819192b, 0x19081908), uvec2(0x08192b19, 0x19081908), uvec2(0x082b0808, 0x19081908), uvec2(0x082b082b, 0x19081908), + uvec2(0x082b1919, 0x19081908), uvec2(0x082b2b08, 0x19081908), uvec2(0x19080819, 0x19081908), uvec2(0x19081908, 0x19081908), + uvec2(0x1908192b, 0x19081908), uvec2(0x19082b19, 0x19081908), uvec2(0x19190808, 0x19081908), uvec2(0x1919082b, 0x19081908), + uvec2(0x19191919, 0x19081908), uvec2(0x19192b08, 0x19081908), uvec2(0x192b0819, 0x19081908), uvec2(0x192b1908, 0x19081908), + uvec2(0x2b080808, 0x19081908), uvec2(0x2b08082b, 0x19081908), uvec2(0x2b081919, 0x19081908), uvec2(0x2b082b08, 0x19081908), + uvec2(0x2b190819, 0x19081908), uvec2(0x2b191908, 0x19081908), uvec2(0x2b2b0808, 0x19081908), uvec2(0x08080819, 0x19081919), + uvec2(0x08081908, 0x19081919), uvec2(0x0808192b, 0x19081919), uvec2(0x08082b19, 0x19081919), uvec2(0x08190808, 0x19081919), + uvec2(0x0819082b, 0x19081919), uvec2(0x08191919, 0x19081919), uvec2(0x08192b08, 0x19081919), uvec2(0x082b0819, 0x19081919), + uvec2(0x082b1908, 0x19081919), uvec2(0x19080808, 0x19081919), uvec2(0x1908082b, 0x19081919), uvec2(0x19081919, 0x19081919), + uvec2(0x19082b08, 0x19081919), uvec2(0x19190819, 0x19081919), uvec2(0x19191908, 0x19081919), uvec2(0x192b0808, 0x19081919), + uvec2(0x192b2b2b, 0x19081919), uvec2(0x2b080819, 0x19081919), uvec2(0x2b081908, 0x19081919), uvec2(0x2b190808, 0x19081919), + uvec2(0x08080808, 0x1908192b), uvec2(0x0808082b, 0x1908192b), uvec2(0x08081919, 0x1908192b), uvec2(0x08082b08, 0x1908192b), + uvec2(0x08190819, 0x1908192b), uvec2(0x08191908, 0x1908192b), uvec2(0x082b0808, 0x1908192b), uvec2(0x19080819, 0x1908192b), + uvec2(0x19081908, 0x1908192b), uvec2(0x19190808, 0x1908192b), uvec2(0x2b080808, 0x1908192b), uvec2(0x2b2b1919, 0x1908192b), + uvec2(0x08080819, 0x19082b08), uvec2(0x08081908, 0x19082b08), uvec2(0x08082b19, 0x19082b08), uvec2(0x08190808, 0x19082b08), + uvec2(0x0819082b, 0x19082b08), uvec2(0x08191919, 0x19082b08), uvec2(0x08192b08, 0x19082b08), uvec2(0x082b0819, 0x19082b08), + uvec2(0x082b1908, 0x19082b08), uvec2(0x19080808, 0x19082b08), uvec2(0x1908082b, 0x19082b08), uvec2(0x19081919, 0x19082b08), + uvec2(0x19082b08, 0x19082b08), uvec2(0x19190819, 0x19082b08), uvec2(0x19191908, 0x19082b08), uvec2(0x192b0808, 0x19082b08), + uvec2(0x2b081908, 0x19082b08), uvec2(0x2b190808, 0x19082b08), uvec2(0x08080808, 0x19082b19), uvec2(0x0808082b, 0x19082b19), + uvec2(0x08081919, 0x19082b19), uvec2(0x08082b08, 0x19082b19), uvec2(0x08190819, 0x19082b19), uvec2(0x08191908, 0x19082b19), + uvec2(0x082b0808, 0x19082b19), uvec2(0x19080819, 0x19082b19), uvec2(0x19081908, 0x19082b19), uvec2(0x19190808, 0x19082b19), + uvec2(0x2b080808, 0x19082b19), uvec2(0x2b19192b, 0x19082b19), uvec2(0x08080819, 0x19082b2b), uvec2(0x08081908, 0x19082b2b), + uvec2(0x08190808, 0x19082b2b), uvec2(0x19080808, 0x19082b2b), uvec2(0x08080808, 0x19190808), uvec2(0x0808082b, 0x19190808), + uvec2(0x08081919, 0x19190808), uvec2(0x08082b08, 0x19190808), uvec2(0x08190819, 0x19190808), uvec2(0x08191908, 0x19190808), + uvec2(0x0819192b, 0x19190808), uvec2(0x08192b19, 0x19190808), uvec2(0x082b0808, 0x19190808), uvec2(0x082b082b, 0x19190808), + uvec2(0x082b1919, 0x19190808), uvec2(0x082b2b08, 0x19190808), uvec2(0x19080819, 0x19190808), uvec2(0x19081908, 0x19190808), + uvec2(0x1908192b, 0x19190808), uvec2(0x19082b19, 0x19190808), uvec2(0x19190808, 0x19190808), uvec2(0x1919082b, 0x19190808), + uvec2(0x19191919, 0x19190808), uvec2(0x19192b08, 0x19190808), uvec2(0x192b0819, 0x19190808), uvec2(0x192b1908, 0x19190808), + uvec2(0x2b080808, 0x19190808), uvec2(0x2b08082b, 0x19190808), uvec2(0x2b081919, 0x19190808), uvec2(0x2b082b08, 0x19190808), + uvec2(0x2b190819, 0x19190808), uvec2(0x2b191908, 0x19190808), uvec2(0x08080819, 0x19190819), uvec2(0x08081908, 0x19190819), + uvec2(0x0808192b, 0x19190819), uvec2(0x08082b19, 0x19190819), uvec2(0x08190808, 0x19190819), uvec2(0x0819082b, 0x19190819), + uvec2(0x08191919, 0x19190819), uvec2(0x08192b08, 0x19190819), uvec2(0x082b0819, 0x19190819), uvec2(0x082b1908, 0x19190819), + uvec2(0x19080808, 0x19190819), uvec2(0x1908082b, 0x19190819), uvec2(0x19081919, 0x19190819), uvec2(0x19082b08, 0x19190819), + uvec2(0x19190819, 0x19190819), uvec2(0x19191908, 0x19190819), uvec2(0x192b0808, 0x19190819), uvec2(0x2b080819, 0x19190819), + uvec2(0x2b081908, 0x19190819), uvec2(0x2b190808, 0x19190819), uvec2(0x08080808, 0x1919082b), uvec2(0x08081919, 0x1919082b), + uvec2(0x08082b08, 0x1919082b), uvec2(0x08190819, 0x1919082b), uvec2(0x08191908, 0x1919082b), uvec2(0x082b0808, 0x1919082b), + uvec2(0x19080819, 0x1919082b), uvec2(0x19081908, 0x1919082b), uvec2(0x19190808, 0x1919082b), uvec2(0x192b2b19, 0x1919082b), + uvec2(0x2b080808, 0x1919082b), uvec2(0x08080819, 0x19191908), uvec2(0x08081908, 0x19191908), uvec2(0x0808192b, 0x19191908), + uvec2(0x08082b19, 0x19191908), uvec2(0x08190808, 0x19191908), uvec2(0x0819082b, 0x19191908), uvec2(0x08191919, 0x19191908), + uvec2(0x08192b08, 0x19191908), uvec2(0x082b0819, 0x19191908), uvec2(0x082b1908, 0x19191908), uvec2(0x19080808, 0x19191908), + uvec2(0x1908082b, 0x19191908), uvec2(0x19081919, 0x19191908), uvec2(0x19082b08, 0x19191908), uvec2(0x19190819, 0x19191908), + uvec2(0x19191908, 0x19191908), uvec2(0x192b0808, 0x19191908), uvec2(0x2b080819, 0x19191908), uvec2(0x2b081908, 0x19191908), + uvec2(0x2b190808, 0x19191908), uvec2(0x08080808, 0x19191919), uvec2(0x0808082b, 0x19191919), uvec2(0x08081919, 0x19191919), + uvec2(0x08082b08, 0x19191919), uvec2(0x08190819, 0x19191919), uvec2(0x08191908, 0x19191919), uvec2(0x082b0808, 0x19191919), + uvec2(0x19080819, 0x19191919), uvec2(0x19081908, 0x19191919), uvec2(0x19190808, 0x19191919), uvec2(0x2b080808, 0x19191919), + uvec2(0x08080819, 0x1919192b), uvec2(0x08081908, 0x1919192b), uvec2(0x08190808, 0x1919192b), uvec2(0x082b192b, 0x1919192b), + uvec2(0x19080808, 0x1919192b), uvec2(0x08080808, 0x19192b08), uvec2(0x0808082b, 0x19192b08), uvec2(0x08081919, 0x19192b08), + uvec2(0x08082b08, 0x19192b08), uvec2(0x08190819, 0x19192b08), uvec2(0x08191908, 0x19192b08), uvec2(0x082b0808, 0x19192b08), + uvec2(0x19080819, 0x19192b08), uvec2(0x19081908, 0x19192b08), uvec2(0x19190808, 0x19192b08), uvec2(0x19192b2b, 0x19192b08), + uvec2(0x2b080808, 0x19192b08), uvec2(0x08080819, 0x19192b19), uvec2(0x08081908, 0x19192b19), uvec2(0x08190808, 0x19192b19), + uvec2(0x19080808, 0x19192b19), uvec2(0x08080808, 0x19192b2b), uvec2(0x08192b19, 0x19192b2b), uvec2(0x2b081919, 0x19192b2b), + uvec2(0x2b2b2b08, 0x19192b2b), uvec2(0x08080819, 0x192b0808), uvec2(0x08081908, 0x192b0808), uvec2(0x0808192b, 0x192b0808), + uvec2(0x08190808, 0x192b0808), uvec2(0x0819082b, 0x192b0808), uvec2(0x08191919, 0x192b0808), uvec2(0x08192b08, 0x192b0808), + uvec2(0x082b0819, 0x192b0808), uvec2(0x082b1908, 0x192b0808), uvec2(0x19080808, 0x192b0808), uvec2(0x19081919, 0x192b0808), + uvec2(0x19082b08, 0x192b0808), uvec2(0x19190819, 0x192b0808), uvec2(0x19191908, 0x192b0808), uvec2(0x192b0808, 0x192b0808), + uvec2(0x2b081908, 0x192b0808), uvec2(0x2b190808, 0x192b0808), uvec2(0x08080808, 0x192b0819), uvec2(0x0808082b, 0x192b0819), + uvec2(0x08081919, 0x192b0819), uvec2(0x08082b08, 0x192b0819), uvec2(0x08190819, 0x192b0819), uvec2(0x08191908, 0x192b0819), + uvec2(0x082b0808, 0x192b0819), uvec2(0x19080819, 0x192b0819), uvec2(0x19081908, 0x192b0819), uvec2(0x19190808, 0x192b0819), + uvec2(0x2b080808, 0x192b0819), uvec2(0x2b192b19, 0x192b0819), uvec2(0x08081908, 0x192b082b), uvec2(0x08190808, 0x192b082b), + uvec2(0x19080808, 0x192b082b), uvec2(0x1919192b, 0x192b082b), uvec2(0x2b2b0819, 0x192b082b), uvec2(0x08080808, 0x192b1908), + uvec2(0x08081919, 0x192b1908), uvec2(0x08082b08, 0x192b1908), uvec2(0x08190819, 0x192b1908), uvec2(0x08191908, 0x192b1908), + uvec2(0x082b0808, 0x192b1908), uvec2(0x19080819, 0x192b1908), uvec2(0x19081908, 0x192b1908), uvec2(0x19190808, 0x192b1908), + uvec2(0x2b080808, 0x192b1908), uvec2(0x08080819, 0x192b1919), uvec2(0x08081908, 0x192b1919), uvec2(0x08190808, 0x192b1919), + uvec2(0x19080808, 0x192b1919), uvec2(0x19082b2b, 0x192b1919), uvec2(0x192b2b08, 0x192b1919), uvec2(0x2b19082b, 0x192b1919), + uvec2(0x08080808, 0x192b192b), uvec2(0x2b191908, 0x192b192b), uvec2(0x08080819, 0x192b2b08), uvec2(0x08081908, 0x192b2b08), + uvec2(0x08190808, 0x192b2b08), uvec2(0x192b1919, 0x192b2b08), uvec2(0x2b192b08, 0x192b2b08), uvec2(0x08080808, 0x192b2b19), + uvec2(0x082b2b2b, 0x192b2b19), uvec2(0x1908082b, 0x192b2b2b), uvec2(0x2b2b0819, 0x192b2b2b), uvec2(0x08080808, 0x2b080808), + uvec2(0x0808082b, 0x2b080808), uvec2(0x08081919, 0x2b080808), uvec2(0x08082b08, 0x2b080808), uvec2(0x08190819, 0x2b080808), + uvec2(0x08191908, 0x2b080808), uvec2(0x08192b19, 0x2b080808), uvec2(0x082b0808, 0x2b080808), uvec2(0x082b1919, 0x2b080808), + uvec2(0x19080819, 0x2b080808), uvec2(0x19081908, 0x2b080808), uvec2(0x19190808, 0x2b080808), uvec2(0x1919082b, 0x2b080808), + uvec2(0x19191919, 0x2b080808), uvec2(0x19192b08, 0x2b080808), uvec2(0x192b0819, 0x2b080808), uvec2(0x2b080808, 0x2b080808), + uvec2(0x2b081919, 0x2b080808), uvec2(0x2b190819, 0x2b080808), uvec2(0x2b191908, 0x2b080808), uvec2(0x08080819, 0x2b080819), + uvec2(0x08081908, 0x2b080819), uvec2(0x08082b19, 0x2b080819), uvec2(0x08190808, 0x2b080819), uvec2(0x0819082b, 0x2b080819), + uvec2(0x08191919, 0x2b080819), uvec2(0x08192b08, 0x2b080819), uvec2(0x082b0819, 0x2b080819), uvec2(0x082b1908, 0x2b080819), + uvec2(0x19080808, 0x2b080819), uvec2(0x1908082b, 0x2b080819), uvec2(0x19081919, 0x2b080819), uvec2(0x19082b08, 0x2b080819), + uvec2(0x19190819, 0x2b080819), uvec2(0x19191908, 0x2b080819), uvec2(0x2b080819, 0x2b080819), uvec2(0x2b081908, 0x2b080819), + uvec2(0x2b190808, 0x2b080819), uvec2(0x2b2b2b19, 0x2b080819), uvec2(0x08080808, 0x2b08082b), uvec2(0x08081919, 0x2b08082b), + uvec2(0x08082b2b, 0x2b08082b), uvec2(0x08190819, 0x2b08082b), uvec2(0x08191908, 0x2b08082b), uvec2(0x19080819, 0x2b08082b), + uvec2(0x19081908, 0x2b08082b), uvec2(0x19190808, 0x2b08082b), uvec2(0x08080819, 0x2b081908), uvec2(0x08081908, 0x2b081908), + uvec2(0x0808192b, 0x2b081908), uvec2(0x08082b19, 0x2b081908), uvec2(0x08190808, 0x2b081908), uvec2(0x0819082b, 0x2b081908), + uvec2(0x08191919, 0x2b081908), uvec2(0x08192b08, 0x2b081908), uvec2(0x082b0819, 0x2b081908), uvec2(0x19080808, 0x2b081908), + uvec2(0x1908082b, 0x2b081908), uvec2(0x19081919, 0x2b081908), uvec2(0x19082b08, 0x2b081908), uvec2(0x19190819, 0x2b081908), + uvec2(0x19191908, 0x2b081908), uvec2(0x192b0808, 0x2b081908), uvec2(0x2b080819, 0x2b081908), uvec2(0x2b081908, 0x2b081908), + uvec2(0x2b190808, 0x2b081908), uvec2(0x08080808, 0x2b081919), uvec2(0x0808082b, 0x2b081919), uvec2(0x08081919, 0x2b081919), + uvec2(0x08082b08, 0x2b081919), uvec2(0x08190819, 0x2b081919), uvec2(0x08191908, 0x2b081919), uvec2(0x082b0808, 0x2b081919), + uvec2(0x19080819, 0x2b081919), uvec2(0x19081908, 0x2b081919), uvec2(0x19190808, 0x2b081919), uvec2(0x2b080808, 0x2b081919), + uvec2(0x2b082b2b, 0x2b081919), uvec2(0x08080819, 0x2b08192b), uvec2(0x08081908, 0x2b08192b), uvec2(0x08190808, 0x2b08192b), + uvec2(0x082b2b19, 0x2b08192b), uvec2(0x19080808, 0x2b08192b), uvec2(0x08080808, 0x2b082b08), uvec2(0x08081919, 0x2b082b08), + uvec2(0x08190819, 0x2b082b08), uvec2(0x08191908, 0x2b082b08), uvec2(0x19080819, 0x2b082b08), uvec2(0x19081908, 0x2b082b08), + uvec2(0x19190808, 0x2b082b08), uvec2(0x2b2b082b, 0x2b082b08), uvec2(0x08080819, 0x2b082b19), uvec2(0x08081908, 0x2b082b19), + uvec2(0x19080808, 0x2b082b19), uvec2(0x192b1919, 0x2b082b19), uvec2(0x082b082b, 0x2b082b2b), uvec2(0x19192b08, 0x2b082b2b), + uvec2(0x19192b2b, 0x2b082b2b), uvec2(0x2b08082b, 0x2b082b2b), uvec2(0x2b2b082b, 0x2b082b2b), uvec2(0x08080819, 0x2b190808), + uvec2(0x08081908, 0x2b190808), uvec2(0x08082b19, 0x2b190808), uvec2(0x08190808, 0x2b190808), uvec2(0x0819082b, 0x2b190808), + uvec2(0x08191919, 0x2b190808), uvec2(0x08192b08, 0x2b190808), uvec2(0x082b1908, 0x2b190808), uvec2(0x19080808, 0x2b190808), + uvec2(0x1908082b, 0x2b190808), uvec2(0x19081919, 0x2b190808), uvec2(0x19082b08, 0x2b190808), uvec2(0x19190819, 0x2b190808), + uvec2(0x19191908, 0x2b190808), uvec2(0x192b0808, 0x2b190808), uvec2(0x2b080819, 0x2b190808), uvec2(0x2b081908, 0x2b190808), + uvec2(0x2b190808, 0x2b190808), uvec2(0x08080808, 0x2b190819), uvec2(0x08081919, 0x2b190819), uvec2(0x08190819, 0x2b190819), + uvec2(0x08191908, 0x2b190819), uvec2(0x19080819, 0x2b190819), uvec2(0x19081908, 0x2b190819), uvec2(0x19190808, 0x2b190819), + uvec2(0x19192b2b, 0x2b190819), uvec2(0x08080819, 0x2b19082b), uvec2(0x08081908, 0x2b19082b), uvec2(0x08190808, 0x2b19082b), + uvec2(0x19080808, 0x2b19082b), uvec2(0x2b2b192b, 0x2b19082b), uvec2(0x08080808, 0x2b191908), uvec2(0x0808082b, 0x2b191908), + uvec2(0x08081919, 0x2b191908), uvec2(0x08082b08, 0x2b191908), uvec2(0x08190819, 0x2b191908), uvec2(0x08191908, 0x2b191908), + uvec2(0x082b0808, 0x2b191908), uvec2(0x19080819, 0x2b191908), uvec2(0x19081908, 0x2b191908), uvec2(0x19190808, 0x2b191908), + uvec2(0x2b080808, 0x2b191908), uvec2(0x2b19192b, 0x2b191908), uvec2(0x08080819, 0x2b191919), uvec2(0x08081908, 0x2b191919), + uvec2(0x08190808, 0x2b191919), uvec2(0x19080808, 0x2b191919), uvec2(0x2b192b08, 0x2b191919), uvec2(0x2b2b0819, 0x2b191919), + uvec2(0x08080808, 0x2b19192b), uvec2(0x1908192b, 0x2b19192b), uvec2(0x192b1908, 0x2b19192b), uvec2(0x08080819, 0x2b192b08), + uvec2(0x08081908, 0x2b192b08), uvec2(0x08190808, 0x2b192b08), uvec2(0x082b192b, 0x2b192b08), uvec2(0x19080808, 0x2b192b08), + uvec2(0x2b2b2b19, 0x2b192b08), uvec2(0x08080808, 0x2b192b19), uvec2(0x19082b19, 0x2b192b19), uvec2(0x1919082b, 0x2b192b19), + uvec2(0x2b190808, 0x2b192b2b), uvec2(0x08080808, 0x2b2b0808), uvec2(0x08081919, 0x2b2b0808), uvec2(0x08082b2b, 0x2b2b0808), + uvec2(0x08191908, 0x2b2b0808), uvec2(0x082b082b, 0x2b2b0808), uvec2(0x082b2b2b, 0x2b2b0808), uvec2(0x19080819, 0x2b2b0808), + uvec2(0x19081908, 0x2b2b0808), uvec2(0x19190808, 0x2b2b0808), uvec2(0x2b2b082b, 0x2b2b0808), uvec2(0x2b2b2b2b, 0x2b2b0808), + uvec2(0x19080808, 0x2b2b0819), uvec2(0x192b1919, 0x2b2b0819), uvec2(0x0808082b, 0x2b2b082b), uvec2(0x08082b2b, 0x2b2b082b), + uvec2(0x082b082b, 0x2b2b082b), uvec2(0x082b2b08, 0x2b2b082b), uvec2(0x082b2b2b, 0x2b2b082b), uvec2(0x2b08082b, 0x2b2b082b), + uvec2(0x2b082b08, 0x2b2b082b), uvec2(0x2b082b2b, 0x2b2b082b), uvec2(0x2b2b2b08, 0x2b2b082b), uvec2(0x08080819, 0x2b2b1908), + uvec2(0x08081908, 0x2b2b1908), uvec2(0x08190808, 0x2b2b1908), uvec2(0x19080808, 0x2b2b1908), uvec2(0x2b082b19, 0x2b2b1908), + uvec2(0x2b2b1908, 0x2b2b1908), uvec2(0x08080808, 0x2b2b1919), uvec2(0x08192b19, 0x2b2b1919), uvec2(0x19190819, 0x2b2b192b), + uvec2(0x08082b2b, 0x2b2b2b08), uvec2(0x082b2b08, 0x2b2b2b08), uvec2(0x2b2b082b, 0x2b2b2b08), uvec2(0x19191908, 0x2b2b2b19), + uvec2(0x2b08192b, 0x2b2b2b19), uvec2(0x08082b08, 0x2b2b2b2b), uvec2(0x08082b2b, 0x2b2b2b2b), uvec2(0x082b0808, 0x2b2b2b2b), + uvec2(0x082b082b, 0x2b2b2b2b), uvec2(0x082b2b08, 0x2b2b2b2b), uvec2(0x2b082b08, 0x2b2b2b2b), uvec2(0x2b2b2b2b, 0x2b2b2b2b) +}; + +shared uvec2 iq2s_grid[1024]; + +#define NEEDS_INIT_IQ_SHMEM +void init_iq_shmem(uvec3 wgsize) +{ + // copy the table into shared memory and sync + [[unroll]] for (uint i = 0; i < iq2s_grid.length(); i += wgsize.x) { + if (iq2s_grid.length() % wgsize.x == 0 || i + gl_LocalInvocationIndex.x < iq2s_grid_const.length()) { + iq2s_grid[i + gl_LocalInvocationIndex.x] = iq2s_grid_const[i + gl_LocalInvocationIndex.x]; + } + } + barrier(); +} + +#define QUANT_K QUANT_K_IQ2_S +#define QUANT_R QUANT_R_IQ2_S +#define A_TYPE block_iq2_s +#define A_TYPE_PACKED16 block_iq2_s_packed16 +#endif + +#define QUANT_K_IQ3_XXS 256 +#define QUANT_R_IQ3_XXS 1 + +struct block_iq3_xxs +{ + float16_t d; + uint8_t qs[QUANT_K_IQ3_XXS/4 + QUANT_K_IQ3_XXS/8]; +}; + +struct block_iq3_xxs_packed16 +{ + float16_t d; + uint16_t qs[QUANT_K_IQ3_XXS/8 + QUANT_K_IQ3_XXS/16]; +}; + +#if defined(DATA_A_IQ3_XXS) + +const uint32_t iq3xxs_grid_const[256] = { + 0x04040404, 0x04040414, 0x04040424, 0x04040c0c, 0x04040c1c, 0x04040c3e, 0x04041404, 0x04041414, + 0x04041c0c, 0x04042414, 0x04043e1c, 0x04043e2c, 0x040c040c, 0x040c041c, 0x040c0c04, 0x040c0c14, + 0x040c140c, 0x040c142c, 0x040c1c04, 0x040c1c14, 0x040c240c, 0x040c2c24, 0x040c3e04, 0x04140404, + 0x04140414, 0x04140424, 0x04140c0c, 0x04141404, 0x04141414, 0x04141c0c, 0x04141c1c, 0x04141c3e, + 0x04142c0c, 0x04142c3e, 0x04143e2c, 0x041c040c, 0x041c043e, 0x041c0c04, 0x041c0c14, 0x041c142c, + 0x041c3e04, 0x04240c1c, 0x04241c3e, 0x04242424, 0x04242c3e, 0x04243e1c, 0x04243e2c, 0x042c040c, + 0x042c043e, 0x042c1c14, 0x042c2c14, 0x04341c2c, 0x04343424, 0x043e0c04, 0x043e0c24, 0x043e0c34, + 0x043e241c, 0x043e340c, 0x0c04040c, 0x0c04041c, 0x0c040c04, 0x0c040c14, 0x0c04140c, 0x0c04141c, + 0x0c041c04, 0x0c041c14, 0x0c041c24, 0x0c04243e, 0x0c042c04, 0x0c0c0404, 0x0c0c0414, 0x0c0c0c0c, + 0x0c0c1404, 0x0c0c1414, 0x0c14040c, 0x0c14041c, 0x0c140c04, 0x0c140c14, 0x0c14140c, 0x0c141c04, + 0x0c143e14, 0x0c1c0404, 0x0c1c0414, 0x0c1c1404, 0x0c1c1c0c, 0x0c1c2434, 0x0c1c3434, 0x0c24040c, + 0x0c24042c, 0x0c242c04, 0x0c2c1404, 0x0c2c1424, 0x0c2c2434, 0x0c2c3e0c, 0x0c34042c, 0x0c3e1414, + 0x0c3e2404, 0x14040404, 0x14040414, 0x14040c0c, 0x14040c1c, 0x14041404, 0x14041414, 0x14041434, + 0x14041c0c, 0x14042414, 0x140c040c, 0x140c041c, 0x140c042c, 0x140c0c04, 0x140c0c14, 0x140c140c, + 0x140c1c04, 0x140c341c, 0x140c343e, 0x140c3e04, 0x14140404, 0x14140414, 0x14140c0c, 0x14140c3e, + 0x14141404, 0x14141414, 0x14141c3e, 0x14142404, 0x14142c2c, 0x141c040c, 0x141c0c04, 0x141c0c24, + 0x141c3e04, 0x141c3e24, 0x14241c2c, 0x14242c1c, 0x142c041c, 0x142c143e, 0x142c240c, 0x142c3e24, + 0x143e040c, 0x143e041c, 0x143e0c34, 0x143e242c, 0x1c04040c, 0x1c040c04, 0x1c040c14, 0x1c04140c, + 0x1c04141c, 0x1c042c04, 0x1c04342c, 0x1c043e14, 0x1c0c0404, 0x1c0c0414, 0x1c0c1404, 0x1c0c1c0c, + 0x1c0c2424, 0x1c0c2434, 0x1c14040c, 0x1c14041c, 0x1c140c04, 0x1c14142c, 0x1c142c14, 0x1c143e14, + 0x1c1c0c0c, 0x1c1c1c1c, 0x1c241c04, 0x1c24243e, 0x1c243e14, 0x1c2c0404, 0x1c2c0434, 0x1c2c1414, + 0x1c2c2c2c, 0x1c340c24, 0x1c341c34, 0x1c34341c, 0x1c3e1c1c, 0x1c3e3404, 0x24040424, 0x24040c3e, + 0x24041c2c, 0x24041c3e, 0x24042c1c, 0x24042c3e, 0x240c3e24, 0x24141404, 0x24141c3e, 0x24142404, + 0x24143404, 0x24143434, 0x241c043e, 0x241c242c, 0x24240424, 0x24242c0c, 0x24243424, 0x242c142c, + 0x242c241c, 0x242c3e04, 0x243e042c, 0x243e0c04, 0x243e0c14, 0x243e1c04, 0x2c040c14, 0x2c04240c, + 0x2c043e04, 0x2c0c0404, 0x2c0c0434, 0x2c0c1434, 0x2c0c2c2c, 0x2c140c24, 0x2c141c14, 0x2c143e14, + 0x2c1c0414, 0x2c1c2c1c, 0x2c240c04, 0x2c24141c, 0x2c24143e, 0x2c243e14, 0x2c2c0414, 0x2c2c1c0c, + 0x2c342c04, 0x2c3e1424, 0x2c3e2414, 0x34041424, 0x34042424, 0x34042434, 0x34043424, 0x340c140c, + 0x340c340c, 0x34140c3e, 0x34143424, 0x341c1c04, 0x341c1c34, 0x34242424, 0x342c042c, 0x342c2c14, + 0x34341c1c, 0x343e041c, 0x343e140c, 0x3e04041c, 0x3e04042c, 0x3e04043e, 0x3e040c04, 0x3e041c14, + 0x3e042c14, 0x3e0c1434, 0x3e0c2404, 0x3e140c14, 0x3e14242c, 0x3e142c14, 0x3e1c0404, 0x3e1c0c2c, + 0x3e1c1c1c, 0x3e1c3404, 0x3e24140c, 0x3e24240c, 0x3e2c0404, 0x3e2c0414, 0x3e2c1424, 0x3e341c04, +}; + +shared uint32_t iq3xxs_grid[256]; + +#define NEEDS_INIT_IQ_SHMEM +void init_iq_shmem(uvec3 wgsize) +{ + // copy the table into shared memory and sync + [[unroll]] for (uint i = 0; i < iq3xxs_grid.length(); i += wgsize.x) { + if (iq3xxs_grid.length() % wgsize.x == 0 || i + gl_LocalInvocationIndex.x < iq3xxs_grid.length()) { + iq3xxs_grid[i + gl_LocalInvocationIndex.x] = iq3xxs_grid_const[i + gl_LocalInvocationIndex.x]; + } + } + barrier(); +} + +#define QUANT_K QUANT_K_IQ3_XXS +#define QUANT_R QUANT_R_IQ3_XXS +#define A_TYPE block_iq3_xxs +#define A_TYPE_PACKED16 block_iq3_xxs_packed16 +#endif + +#define QUANT_K_IQ3_S 256 +#define QUANT_R_IQ3_S 1 + +struct block_iq3_s +{ + float16_t d; + uint8_t qs[QUANT_K_IQ3_S/4]; + uint8_t qh[QUANT_K_IQ3_S/32]; + uint8_t signs[QUANT_K_IQ3_S/8]; + uint8_t scales[QUANT_K_IQ3_S/64]; +}; + +struct block_iq3_s_packed16 +{ + float16_t d; + uint16_t qs[QUANT_K_IQ3_S/4/2]; + uint16_t qh[QUANT_K_IQ3_S/32/2]; + uint16_t signs[QUANT_K_IQ3_S/8/2]; + uint16_t scales[QUANT_K_IQ3_S/64/2]; +}; + +#if defined(DATA_A_IQ3_S) + +const uint32_t iq3s_grid_const[512] = { + 0x01010101, 0x01010103, 0x01010105, 0x0101010b, 0x0101010f, 0x01010301, 0x01010303, 0x01010305, + 0x01010309, 0x0101030d, 0x01010501, 0x01010503, 0x0101050b, 0x01010707, 0x01010901, 0x01010905, + 0x0101090b, 0x0101090f, 0x01010b03, 0x01010b07, 0x01010d01, 0x01010d05, 0x01010f03, 0x01010f09, + 0x01010f0f, 0x01030101, 0x01030103, 0x01030105, 0x01030109, 0x01030301, 0x01030303, 0x0103030b, + 0x01030501, 0x01030507, 0x0103050f, 0x01030703, 0x0103070b, 0x01030909, 0x01030d03, 0x01030d0b, + 0x01030f05, 0x01050101, 0x01050103, 0x0105010b, 0x0105010f, 0x01050301, 0x01050307, 0x0105030d, + 0x01050503, 0x0105050b, 0x01050701, 0x01050709, 0x01050905, 0x0105090b, 0x0105090f, 0x01050b03, + 0x01050b07, 0x01050f01, 0x01050f07, 0x01070107, 0x01070303, 0x0107030b, 0x01070501, 0x01070505, + 0x01070703, 0x01070707, 0x0107070d, 0x01070909, 0x01070b01, 0x01070b05, 0x01070d0f, 0x01070f03, + 0x01070f0b, 0x01090101, 0x01090307, 0x0109030f, 0x01090503, 0x01090509, 0x01090705, 0x01090901, + 0x01090907, 0x01090b03, 0x01090f01, 0x010b0105, 0x010b0109, 0x010b0501, 0x010b0505, 0x010b050d, + 0x010b0707, 0x010b0903, 0x010b090b, 0x010b090f, 0x010b0d0d, 0x010b0f07, 0x010d010d, 0x010d0303, + 0x010d0307, 0x010d0703, 0x010d0b05, 0x010d0f03, 0x010f0101, 0x010f0105, 0x010f0109, 0x010f0501, + 0x010f0505, 0x010f050d, 0x010f0707, 0x010f0b01, 0x010f0b09, 0x03010101, 0x03010103, 0x03010105, + 0x03010109, 0x03010301, 0x03010303, 0x03010307, 0x0301030b, 0x0301030f, 0x03010501, 0x03010505, + 0x03010703, 0x03010709, 0x0301070d, 0x03010b09, 0x03010b0d, 0x03010d03, 0x03010f05, 0x03030101, + 0x03030103, 0x03030107, 0x0303010d, 0x03030301, 0x03030309, 0x03030503, 0x03030701, 0x03030707, + 0x03030903, 0x03030b01, 0x03030b05, 0x03030f01, 0x03030f0d, 0x03050101, 0x03050305, 0x0305030b, + 0x0305030f, 0x03050501, 0x03050509, 0x03050705, 0x03050901, 0x03050907, 0x03050b0b, 0x03050d01, + 0x03050f05, 0x03070103, 0x03070109, 0x0307010f, 0x03070301, 0x03070307, 0x03070503, 0x0307050f, + 0x03070701, 0x03070709, 0x03070903, 0x03070d05, 0x03070f01, 0x03090107, 0x0309010b, 0x03090305, + 0x03090309, 0x03090703, 0x03090707, 0x03090905, 0x0309090d, 0x03090b01, 0x03090b09, 0x030b0103, + 0x030b0301, 0x030b0307, 0x030b0503, 0x030b0701, 0x030b0705, 0x030b0b03, 0x030d0501, 0x030d0509, + 0x030d050f, 0x030d0909, 0x030d090d, 0x030f0103, 0x030f0107, 0x030f0301, 0x030f0305, 0x030f0503, + 0x030f070b, 0x030f0903, 0x030f0d05, 0x030f0f01, 0x05010101, 0x05010103, 0x05010107, 0x0501010b, + 0x0501010f, 0x05010301, 0x05010305, 0x05010309, 0x0501030d, 0x05010503, 0x05010507, 0x0501050f, + 0x05010701, 0x05010705, 0x05010903, 0x05010907, 0x0501090b, 0x05010b01, 0x05010b05, 0x05010d0f, + 0x05010f01, 0x05010f07, 0x05010f0b, 0x05030101, 0x05030105, 0x05030301, 0x05030307, 0x0503030f, + 0x05030505, 0x0503050b, 0x05030703, 0x05030709, 0x05030905, 0x05030b03, 0x05050103, 0x05050109, + 0x0505010f, 0x05050503, 0x05050507, 0x05050701, 0x0505070f, 0x05050903, 0x05050b07, 0x05050b0f, + 0x05050f03, 0x05050f09, 0x05070101, 0x05070105, 0x0507010b, 0x05070303, 0x05070505, 0x05070509, + 0x05070703, 0x05070707, 0x05070905, 0x05070b01, 0x05070d0d, 0x05090103, 0x0509010f, 0x05090501, + 0x05090507, 0x05090705, 0x0509070b, 0x05090903, 0x05090f05, 0x05090f0b, 0x050b0109, 0x050b0303, + 0x050b0505, 0x050b070f, 0x050b0901, 0x050b0b07, 0x050b0f01, 0x050d0101, 0x050d0105, 0x050d010f, + 0x050d0503, 0x050d0b0b, 0x050d0d03, 0x050f010b, 0x050f0303, 0x050f050d, 0x050f0701, 0x050f0907, + 0x050f0b01, 0x07010105, 0x07010303, 0x07010307, 0x0701030b, 0x0701030f, 0x07010505, 0x07010703, + 0x07010707, 0x0701070b, 0x07010905, 0x07010909, 0x0701090f, 0x07010b03, 0x07010d07, 0x07010f03, + 0x07030103, 0x07030107, 0x0703010b, 0x07030309, 0x07030503, 0x07030507, 0x07030901, 0x07030d01, + 0x07030f05, 0x07030f0d, 0x07050101, 0x07050305, 0x07050501, 0x07050705, 0x07050709, 0x07050b01, + 0x07070103, 0x07070301, 0x07070309, 0x07070503, 0x07070507, 0x0707050f, 0x07070701, 0x07070903, + 0x07070907, 0x0707090f, 0x07070b0b, 0x07070f07, 0x07090107, 0x07090303, 0x0709030d, 0x07090505, + 0x07090703, 0x07090b05, 0x07090d01, 0x07090d09, 0x070b0103, 0x070b0301, 0x070b0305, 0x070b050b, + 0x070b0705, 0x070b0909, 0x070b0b0d, 0x070b0f07, 0x070d030d, 0x070d0903, 0x070f0103, 0x070f0107, + 0x070f0501, 0x070f0505, 0x070f070b, 0x09010101, 0x09010109, 0x09010305, 0x09010501, 0x09010509, + 0x0901050f, 0x09010705, 0x09010903, 0x09010b01, 0x09010f01, 0x09030105, 0x0903010f, 0x09030303, + 0x09030307, 0x09030505, 0x09030701, 0x0903070b, 0x09030907, 0x09030b03, 0x09030b0b, 0x09050103, + 0x09050107, 0x09050301, 0x0905030b, 0x09050503, 0x09050707, 0x09050901, 0x09050b0f, 0x09050d05, + 0x09050f01, 0x09070109, 0x09070303, 0x09070307, 0x09070501, 0x09070505, 0x09070703, 0x0907070b, + 0x09090101, 0x09090105, 0x09090509, 0x0909070f, 0x09090901, 0x09090f03, 0x090b010b, 0x090b010f, + 0x090b0503, 0x090b0d05, 0x090d0307, 0x090d0709, 0x090d0d01, 0x090f0301, 0x090f030b, 0x090f0701, + 0x090f0907, 0x090f0b03, 0x0b010105, 0x0b010301, 0x0b010309, 0x0b010505, 0x0b010901, 0x0b010909, + 0x0b01090f, 0x0b010b05, 0x0b010d0d, 0x0b010f09, 0x0b030103, 0x0b030107, 0x0b03010b, 0x0b030305, + 0x0b030503, 0x0b030705, 0x0b030f05, 0x0b050101, 0x0b050303, 0x0b050507, 0x0b050701, 0x0b05070d, + 0x0b050b07, 0x0b070105, 0x0b07010f, 0x0b070301, 0x0b07050f, 0x0b070909, 0x0b070b03, 0x0b070d0b, + 0x0b070f07, 0x0b090103, 0x0b090109, 0x0b090501, 0x0b090705, 0x0b09090d, 0x0b0b0305, 0x0b0b050d, + 0x0b0b0b03, 0x0b0b0b07, 0x0b0d0905, 0x0b0f0105, 0x0b0f0109, 0x0b0f0505, 0x0d010303, 0x0d010307, + 0x0d01030b, 0x0d010703, 0x0d010707, 0x0d010d01, 0x0d030101, 0x0d030501, 0x0d03050f, 0x0d030d09, + 0x0d050305, 0x0d050709, 0x0d050905, 0x0d050b0b, 0x0d050d05, 0x0d050f01, 0x0d070101, 0x0d070309, + 0x0d070503, 0x0d070901, 0x0d09050b, 0x0d090907, 0x0d090d05, 0x0d0b0101, 0x0d0b0107, 0x0d0b0709, + 0x0d0b0d01, 0x0d0d010b, 0x0d0d0901, 0x0d0f0303, 0x0d0f0307, 0x0f010101, 0x0f010109, 0x0f01010f, + 0x0f010501, 0x0f010505, 0x0f01070d, 0x0f010901, 0x0f010b09, 0x0f010d05, 0x0f030105, 0x0f030303, + 0x0f030509, 0x0f030907, 0x0f03090b, 0x0f050103, 0x0f050109, 0x0f050301, 0x0f05030d, 0x0f050503, + 0x0f050701, 0x0f050b03, 0x0f070105, 0x0f070705, 0x0f07070b, 0x0f070b07, 0x0f090103, 0x0f09010b, + 0x0f090307, 0x0f090501, 0x0f090b01, 0x0f0b0505, 0x0f0b0905, 0x0f0d0105, 0x0f0d0703, 0x0f0f0101, +}; + +shared uint32_t iq3s_grid[512]; + +#define NEEDS_INIT_IQ_SHMEM +void init_iq_shmem(uvec3 wgsize) +{ + // copy the table into shared memory and sync + [[unroll]] for (uint i = 0; i < iq3s_grid.length(); i += wgsize.x) { + if (iq3s_grid.length() % wgsize.x == 0 || i + gl_LocalInvocationIndex.x < iq3s_grid.length()) { + iq3s_grid[i + gl_LocalInvocationIndex.x] = iq3s_grid_const[i + gl_LocalInvocationIndex.x]; + } + } + barrier(); +} + +#define QUANT_K QUANT_K_IQ3_S +#define QUANT_R QUANT_R_IQ3_S +#define A_TYPE block_iq3_s +#define A_TYPE_PACKED16 block_iq3_s_packed16 +#endif + +#define QUANT_K_IQ4_XS 256 +#define QUANT_R_IQ4_XS 1 + +struct block_iq4_xs +{ + float16_t d; + uint16_t scales_h; + uint8_t scales_l[QUANT_K_IQ4_XS/64]; + uint8_t qs[QUANT_K_IQ4_XS/2]; +}; + +struct block_iq4_xs_packed16 +{ + float16_t d; + uint16_t scales_h; + uint16_t scales_l[QUANT_K_IQ4_XS/128]; + uint16_t qs[QUANT_K_IQ4_XS/4]; +}; + +struct block_iq4_xs_packed32 +{ + float16_t d; + uint16_t scales_h; + uint32_t scales_l; + uint32_t qs[QUANT_K_IQ4_XS/8]; +}; + +#if defined(DATA_A_IQ4_XS) +#define QUANT_K QUANT_K_IQ4_XS +#define QUANT_R QUANT_R_IQ4_XS +#define A_TYPE block_iq4_xs +#define A_TYPE_PACKED16 block_iq4_xs_packed16 +#define A_TYPE_PACKED32 block_iq4_xs_packed32 +#endif + +#define QUANT_K_IQ4_NL 32 +#define QUANT_R_IQ4_NL 2 + +struct block_iq4_nl +{ + float16_t d; + uint8_t qs[QUANT_K_IQ4_NL/2]; +}; + +struct block_iq4_nl_packed16 +{ + float16_t d; + uint16_t qs[QUANT_K_IQ4_NL/2/2]; +}; + +#if defined(DATA_A_IQ4_NL) +#define QUANT_K QUANT_K_IQ4_NL +#define QUANT_R QUANT_R_IQ4_NL +#define QUANT_AUXF 1 +#define A_TYPE block_iq4_nl +#define A_TYPE_PACKED16 block_iq4_nl_packed16 +#endif + +#define QUANT_K_MXFP4 32 +#define QUANT_R_MXFP4 2 + +struct block_mxfp4 +{ + uint8_t e; + uint8_t qs[QUANT_K_MXFP4/2]; +}; + +#if defined(DATA_A_MXFP4) +#define QUANT_K QUANT_K_MXFP4 +#define QUANT_R QUANT_R_MXFP4 +#define QUANT_AUXF 1 +#define A_TYPE block_mxfp4 +#endif + +#define QUANT_K_NVFP4 64 +#define QUANT_R_NVFP4 1 + +struct block_nvfp4 +{ + uint8_t d[QUANT_K_NVFP4 / 16]; + uint8_t qs[QUANT_K_NVFP4 / 2]; +}; + +struct block_nvfp4_packed32 +{ + uint32_t d[QUANT_K_NVFP4 / 16 / 4]; + uint32_t qs[QUANT_K_NVFP4 / 2 / 4]; +}; + +#if defined(DATA_A_NVFP4) +#define QUANT_K QUANT_K_NVFP4 +#define QUANT_R QUANT_R_NVFP4 +#define QUANT_AUXF 1 +#define A_TYPE block_nvfp4 +#define A_TYPE_PACKED32 block_nvfp4_packed32 +#endif + +#if defined(DATA_A_IQ4_NL) || defined(DATA_A_IQ4_XS) +const int8_t kvalues_iq4nl_const[16] = { + int8_t(-127), int8_t(-104), int8_t(-83), int8_t(-65), int8_t(-49), int8_t(-35), int8_t(-22), int8_t(-10), + int8_t(1), int8_t(13), int8_t(25), int8_t(38), int8_t(53), int8_t(69), int8_t(89), int8_t(113) +}; + +shared FLOAT_TYPE kvalues_iq4nl[16]; + +#define NEEDS_INIT_IQ_SHMEM +void init_iq_shmem(uvec3 wgsize) +{ + // copy the table into shared memory and sync + for (uint i = gl_LocalInvocationIndex.x; i < kvalues_iq4nl.length(); i += wgsize.x) { + kvalues_iq4nl[i] = FLOAT_TYPE(kvalues_iq4nl_const[i]); + } + barrier(); +} +#endif + +#if defined(DATA_A_MXFP4) || defined(DATA_A_NVFP4) +const int8_t kvalues_mxfp4_const[16] = { + int8_t(0), int8_t(1), int8_t(2), int8_t(3), int8_t(4), int8_t(6), int8_t(8), int8_t(12), + int8_t(0), int8_t(-1), int8_t(-2), int8_t(-3), int8_t(-4), int8_t(-6), int8_t(-8), int8_t(-12), +}; + +shared int8_t kvalues_mxfp4[16]; + +#if defined(DATA_A_NVFP4) +// UE4M3 scale in NVFP4 blocks use only 7 bits; sign (bit 7) is always zero. +shared float ue4m3_fp32_lut[128]; + +float ue4m3_to_fp32_build(uint u) { + if (u == 0u || u == 127u) { + return 0.0; + } + const uint exp = (u >> 3) & 15u; + const uint man = u & 7u; + if (exp == 0u) { + return float(man) * (1.0 / 512.0); + } + const uint bits = (exp + 120u) << 23 | (man << 20); + return uintBitsToFloat(bits); +} +#endif + +#define NEEDS_INIT_IQ_SHMEM +void init_iq_shmem(uvec3 wgsize) +{ + // copy the table into shared memory and sync + for (uint i = gl_LocalInvocationIndex.x; i < kvalues_mxfp4.length(); i += wgsize.x) { + kvalues_mxfp4[i] = kvalues_mxfp4_const[i]; + } +#if defined(DATA_A_NVFP4) + for (uint i = gl_LocalInvocationIndex.x; i < 128u; i += wgsize.x) { + ue4m3_fp32_lut[i] = ue4m3_to_fp32_build(i); + } +#endif + barrier(); +} +#endif + +// returns the bfloat value in the low 16b. +// See ggml_compute_fp32_to_bf16 +uint32_t fp32_to_bf16(float f) +{ + uint32_t u = floatBitsToUint(f); + u = (u + (0x7fff + ((u >> 16) & 1))) >> 16; + return u; +} + +float bf16_to_fp32(uint32_t u) +{ + return uintBitsToFloat(u << 16); +} + +vec4 bf16_to_fp32(uvec4 u) +{ + return vec4(bf16_to_fp32(u.x), bf16_to_fp32(u.y), bf16_to_fp32(u.z), bf16_to_fp32(u.w)); +} + +float e8m0_to_fp32(uint8_t x) { + uint32_t bits; + + if (x == 0) { + bits = 0x00400000; + } else { + bits = x; + bits = bits << 23; + } + + return uintBitsToFloat(bits); +} + +#if defined(DATA_A_NVFP4) +float ue4m3_to_fp32(uint8_t x) { + return ue4m3_fp32_lut[uint(x)]; +} +#endif + +#if BDA + +#extension GL_EXT_buffer_reference : enable +#extension GL_EXT_shader_explicit_arithmetic_types_int64 : enable + +#define BDA_STORAGE_T uint64_t +#define BDA_OFFSET_T uint64_t + +#else + +#define BDA_STORAGE_T uvec2 +#define BDA_OFFSET_T uint + +#endif + +#endif // !defined(GGML_TYPES_COMP) diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/unary.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/unary.comp new file mode 100644 index 0000000000000000000000000000000000000000..5ee5275d278228b96a93fef659f1977c4ed09252 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/unary.comp @@ -0,0 +1,168 @@ +#version 450 + +#include "types.glsl" +#include "generic_unary_head.glsl" + +layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; + +float op_abs(float x) { + return abs(x); +} + +float op_sgn(float x) { + return sign(x); +} + +float op_neg(float x) { + return -x; +} + +float op_sqr(float x) { + return x * x; +} + +float op_sqrt(float x) { + return sqrt(x); +} + +float op_sin(float x) { + return sin(x); +} + +float op_cos(float x) { + return cos(x); +} + +float op_clamp(float x) { + return clamp(x, p.param1, p.param2); +} + +float op_leaky_relu(float x) { + return max(x, 0.0f) + min(x, 0.0f) * p.param1; +} + +float op_step(float x) { + return x > 0.0f ? 1.0f : 0.0f; +} + +float op_tanh(float x) { + return 1.0f - 2.0f / (exp(2.0f*x) + 1.0f); +} + +float op_elu(float x) { + return x < 0.0f ? exp(x) - 1.0f : x; +} + +float op_relu(float x) { + return max(x, 0.0f); +} + +float op_sigmoid(float x) { + return 1.0f / (1.0f + exp(-x)); +} + +float op_gelu(float x) { + const float GELU_COEF_A = 0.044715f; + const float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f; + const float val = SQRT_2_OVER_PI*x*(1.0f + GELU_COEF_A*x*x); + return 0.5f*x*(2.0f - 2.0f / (exp(2.0f * val) + 1.0f)); +} + +float op_gelu_quick(float x) { + const float GELU_QUICK_COEF = -1.702f; + return x * (1.0f / (1.0f + exp(GELU_QUICK_COEF * x))); +} + +float op_silu(float x) { + return x / (1.0f + exp(-x)); +} + +float op_hardswish(float x) { + return x * min(1.0f, max(0.0f, (x + 3.0f) / 6.0f)); +} + +float op_hardsigmoid(float x) { + return min(1.0f, max(0.0f, (x + 3.0f) / 6.0f)); +} + +float op_exp(float x) { + return exp(x); +} + +float op_expm1(float x) { + // exp(x) - 1 loses many ulps to cancellation near zero. Use a degree-6 + // Taylor expansion for |x| <= 1/4: the omitted x^7/5040 term is < 1.3e-8, + // about 0.5 ulp at expm1(0.25), and a host-side f32 model stays within + // 2 ulps over the interval. The first native exp(x)-1 values outside the + // cutoff are about 1 ulp for +0.25 and 2 ulps for -0.25. + if (abs(x) <= 0.25f) { + return x * (1.0f + x * (0.5f + x * ((1.0f/6.0f) + x * ((1.0f/24.0f) + x * ((1.0f/120.0f) + x * (1.0f/720.0f)))))); + } + return exp(x) - 1.0f; +} + +float op_softplus(float x) { + return (x > 20.0f) ? x : log(1.0f + exp(x)); +} + +float op_gelu_erf(float a) { + // based on Abramowitz and Stegun formula 7.1.26 or similar Hastings' approximation + const float p_erf = 0.3275911f; + const float a1_erf = 0.254829592f; + const float a2_erf = -0.284496736f; + const float a3_erf = 1.421413741f; + const float a4_erf = -1.453152027f; + const float a5_erf = 1.061405429f; + + const float SQRT_2_INV = 0.70710678118654752440084436210484f; + const float a_div_sqr2 = a * SQRT_2_INV; + const float sign_x = sign(a_div_sqr2); + const float x = abs(a_div_sqr2); + const float t = 1.0f / (1.0f + p_erf * x); + const float y = 1.0f - (((((a5_erf * t + a4_erf) * t) + a3_erf) * t + a2_erf) * t + a1_erf) * t * exp(-x * x); + return 0.5f * a * (1.0f + sign_x * y); +} + +float op_xielu(float x) { + const float alpha_n = p.param1; + const float alpha_p = p.param2; + const float beta = p.param3; + const float eps = p.param4; + + if (x > 0.0f) { + return alpha_p * x * x + beta * x; + } + + const float min_x_eps = min(x, eps); + return (op_expm1(min_x_eps) - x) * alpha_n + beta * x; +} + +float op_floor(float x) { + return floor(x); +} + +float op_ceil(float x) { + return ceil(x); +} + +float op_round(float x) { + // Round halfway cases away from zero as roundf does. + return x >= 0.0f ? floor(x + 0.5f) : ceil(x - 0.5f); +} + +float op_trunc(float x) { + return trunc(x); +} + +void main() { + const uint idx = get_idx(); + + if (idx >= p.ne) { + return; + } + + const uint a_idx = get_aoffset() + src0_idx(idx); + const uint d_idx = get_doffset() + dst_idx(idx); + + data_d[d_idx] = D_TYPE(OP(float(data_a[a_idx]))); +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/upscale.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/upscale.comp new file mode 100644 index 0000000000000000000000000000000000000000..f7d12a8dda6ca74911398c777f076bcbb9fb7585 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/upscale.comp @@ -0,0 +1,178 @@ +#version 450 + +layout (push_constant) uniform parameter +{ + uint ne; uint a_offset; uint d_offset; + uint ne00; uint ne01; + uint nb00; uint nb01; uint nb02; uint nb03; + uint ne10; uint ne11; uint ne12; uint ne13; + float sf0; float sf1; float sf2; float sf3; + float pixel_offset; +} p; + +#include "types.glsl" + +layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_d[];}; + +// from ggml.h: enum ggml_scale_mode, enum ggml_scale_flag +#define NEAREST 0 +#define BILINEAR 1 +#define BICUBIC 2 +#define BILINEAR_ANTIALIAS 513 + +layout (constant_id = 0) const uint scale_mode = 0; + +float fetch_nearest(uint i10, uint i11, uint i12, uint i13) { + const uint i00 = uint(i10 / p.sf0); + const uint i01 = uint(i11 / p.sf1); + const uint i02 = uint(i12 / p.sf2); + const uint i03 = uint(i13 / p.sf3); + + return data_a[p.a_offset + i03 * p.nb03 + i02 * p.nb02 + i01 * p.nb01 + i00 * p.nb00]; +} + +float fetch_bilinear(ivec2 c0, ivec2 c1, vec2 d, uint i12, uint i13) { + const uint i02 = uint(i12 / p.sf2); + const uint i03 = uint(i13 / p.sf3); + const uint base = p.a_offset + i03 * p.nb03 + i02 * p.nb02; + + const float v00 = data_a[base + c0.y * p.nb01 + c0.x * p.nb00]; + const float v01 = data_a[base + c0.y * p.nb01 + c1.x * p.nb00]; + const float v10 = data_a[base + c1.y * p.nb01 + c0.x * p.nb00]; + const float v11 = data_a[base + c1.y * p.nb01 + c1.x * p.nb00]; + + return + v00 * (1.0-d.x) * (1.0-d.y) + + v01 * d.x * (1.0-d.y) + + v10 * (1.0-d.x) * d.y + + v11 * d.x * d.y; +} + +float interpolate_bilinear(uint i10, uint i11, uint i12, uint i13) { + const ivec2 ne0 = ivec2(p.ne00, p.ne01); + + const vec2 c = (vec2(i10, i11) + p.pixel_offset) / vec2(p.sf0, p.sf1) - p.pixel_offset; + const vec2 c0f = floor(c); + const vec2 d = c - c0f; + const ivec2 c0 = max(ivec2(c0f), 0); + const ivec2 c1 = min(ivec2(c0f + 1), ne0 - 1); + + return fetch_bilinear(c0, c1, d, i12, i13); +} + +float triangle_filter(float x) { + return max(1.0f - abs(x), 0.0f); +} + +float interpolate_bilinear_antialias(uint i10, uint i11, uint i12, uint i13) { + const float support1 = max(1.0f, 1.0f / p.sf1); + const float invscale1 = 1.0f / support1; + const float support0 = max(1.0f, 1.0f / p.sf0); + const float invscale0 = 1.0f / support0; + + const uint i02 = uint(i12 / p.sf2); + const uint i03 = uint(i13 / p.sf3); + + const float y = (float(i11) + p.pixel_offset) / p.sf1; + const float x = (float(i10) + p.pixel_offset) / p.sf0; + + // the range of source pixels that contribute + const int x_min = max(int(x - support0 + p.pixel_offset), 0); + const int x_max = min(int(x + support0 + p.pixel_offset), int(p.ne00)); + const int y_min = max(int(y - support1 + p.pixel_offset), 0); + const int y_max = min(int(y + support1 + p.pixel_offset), int(p.ne01)); + + // bilinear filter with antialiasing + float val = 0.0f; + float total_weight = 0.0f; + + for (int sy = y_min; sy < y_max; sy++) { + const float weight_y = triangle_filter((sy - y + p.pixel_offset) * invscale1); + + for (int sx = x_min; sx < x_max; sx++) { + const float weight_x = triangle_filter((sx - x + p.pixel_offset) * invscale0); + const float weight = weight_x * weight_y; + + if (weight <= 0.0f) { + continue; + } + + const float pixel = data_a[p.a_offset + i03 * p.nb03 + i02 * p.nb02 + sy * p.nb01 + sx * p.nb00]; + val += pixel * weight; + total_weight += weight; + } + } + + if (total_weight > 0.0f) { + val /= total_weight; + } + + return val; +} + +// Bicubic interpolation with alpha = -0.75 +// https://en.wikipedia.org/wiki/Bicubic_interpolation#Bicubic_convolution_algorithm +const vec4 bcoeffs1 = vec4( 1.25, -2.25, 0.0, 1.0); +const vec4 bcoeffs2 = vec4(-0.75, 3.75, -6.0, 3.0); +vec4 powers(float x) { return vec4(x*x*x, x*x, x, 1); } + +float bicubic(float p0, float p1, float p2, float p3, float x) { + return p0 * dot(bcoeffs2, powers(x + 1)) + + p1 * dot(bcoeffs1, powers(x )) + + p2 * dot(bcoeffs1, powers(1 - x)) + + p3 * dot(bcoeffs2, powers(2 - x)); +} + +#define FETCH(a,b) data_a[base + clamp(i.x+(a), 0, res.x) * p.nb00 + clamp(i.y+(b), 0, res.y) * p.nb01] + +float interpolate_bicubic(uint i10, uint i11, uint i12, uint i13) { + const ivec2 res = ivec2(p.ne00 - 1, p.ne01 - 1); + + const vec2 coord = (vec2(i10, i11) + p.pixel_offset) / vec2(p.sf0, p.sf1) - p.pixel_offset; + const vec2 d = fract(coord); + const ivec2 i = ivec2(floor(coord)); + + const uint i02 = uint(i12 / p.sf2); + const uint i03 = uint(i13 / p.sf3); + const uint base = p.a_offset + i03 * p.nb03 + i02 * p.nb02; + + return bicubic( + bicubic(FETCH(-1,-1), FETCH(0,-1), FETCH(1,-1), FETCH(2,-1), d.x), + bicubic(FETCH(-1, 0), FETCH(0, 0), FETCH(1, 0), FETCH(2, 0), d.x), + bicubic(FETCH(-1, 1), FETCH(0, 1), FETCH(1, 1), FETCH(2, 1), d.x), + bicubic(FETCH(-1, 2), FETCH(0, 2), FETCH(1, 2), FETCH(2, 2), d.x), d.y); +} + +void main() { + const uint idx = gl_GlobalInvocationID.z * 262144 + gl_GlobalInvocationID.y * 512 + gl_GlobalInvocationID.x; + + if (idx >= p.ne) { + return; + } + + const uint i10 = idx % p.ne10; + const uint i11 = (idx / p.ne10) % p.ne11; + const uint i12 = (idx / (p.ne10 * p.ne11)) % p.ne12; + const uint i13 = (idx / (p.ne10 * p.ne11 * p.ne12)) % p.ne13; + + float result; + switch (scale_mode) { + case NEAREST: + result = fetch_nearest(i10, i11, i12, i13); + break; + case BILINEAR: + result = interpolate_bilinear(i10, i11, i12, i13); + break; + case BICUBIC: + result = interpolate_bicubic(i10, i11, i12, i13); + break; + case BILINEAR_ANTIALIAS: + result = interpolate_bilinear_antialias(i10, i11, i12, i13); + break; + } + + data_d[p.d_offset + idx] = D_TYPE(result); +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/utils.glsl b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/utils.glsl new file mode 100644 index 0000000000000000000000000000000000000000..dc4a1e6d96bab0329603c1f28ea98ef43d7fa5a2 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/utils.glsl @@ -0,0 +1,25 @@ +#ifndef UTILS_COMP +#define UTILS_COMP + +// mod and div are expensive and coordinates/dimensions are often power of 2 or equal to 1 +uint fastmod(uint a, uint b) { + if ((b & (b-1)) == 0) { + return a & (b-1); + } + return a % b; +} + +uint fastdiv(uint a, uint b) { + return (a < b) ? 0 : (a / b); +} + +void get_indices(uint idx, out uint i00, out uint i01, out uint i02, out uint i03, uint ne00, uint ne01, uint ne02, uint ne03) { + i03 = fastdiv(idx, (ne02*ne01*ne00)); + const uint i03_offset = i03 * ne02*ne01*ne00; + i02 = fastdiv((idx - i03_offset), (ne01*ne00)); + const uint i02_offset = i02*ne01*ne00; + i01 = (idx - i03_offset - i02_offset) / ne00; + i00 = idx - i03_offset - i02_offset - i01*ne00; +} + +#endif // UTILS_COMP diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp new file mode 100644 index 0000000000000000000000000000000000000000..1925582ffedb39a367b32c2c8e3eef965825b684 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp @@ -0,0 +1,1293 @@ +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#ifdef _WIN32 + #define NOMINMAX + #include + #include // For _mkdir on Windows +#else + #include + #include + #include +#endif + +#define ASYNCIO_CONCURRENCY 64 + +std::mutex lock; +std::vector> shader_fnames; +// Set when any shader subprocess fails (non-zero exit / stderr / launch failure) so the +// build is stopped instead of silently producing a broken libggml-vulkan. (issue #24393) +static std::atomic compile_failed{false}; +std::locale c_locale("C"); + +std::string GLSLC = "glslc"; +std::string input_filepath = ""; +std::string output_dir = "/tmp"; +std::string target_hpp = ""; +std::string target_cpp = ""; + +const std::vector type_names = { + "f32", + "f16", + "q1_0", + "q4_0", + "q4_1", + "q5_0", + "q5_1", + "q8_0", + "q2_k", + "q3_k", + "q4_k", + "q5_k", + "q6_k", + "iq1_s", + "iq1_m", + "iq2_xxs", + "iq2_xs", + "iq2_s", + "iq3_xxs", + "iq3_s", + "iq4_xs", + "iq4_nl", + "mxfp4", + "nvfp4", + "bf16", +}; + +enum MatMulIdType { + NONE, + DEFAULT, + SUBGROUP, +}; + +namespace { + +int execute_command(std::vector& command, std::string& stdout_str, std::string& stderr_str) { +#ifdef _WIN32 + HANDLE stdout_read, stdout_write; + HANDLE stderr_read, stderr_write; + SECURITY_ATTRIBUTES sa = { sizeof(SECURITY_ATTRIBUTES), NULL, TRUE }; + + if (!CreatePipe(&stdout_read, &stdout_write, &sa, 0) || + !SetHandleInformation(stdout_read, HANDLE_FLAG_INHERIT, 0)) { + throw std::runtime_error("Failed to create stdout pipe"); + } + + if (!CreatePipe(&stderr_read, &stderr_write, &sa, 0) || + !SetHandleInformation(stderr_read, HANDLE_FLAG_INHERIT, 0)) { + throw std::runtime_error("Failed to create stderr pipe"); + } + + PROCESS_INFORMATION pi; + STARTUPINFOA si = {}; + si.cb = sizeof(STARTUPINFOA); + si.dwFlags = STARTF_USESTDHANDLES; + si.hStdOutput = stdout_write; + si.hStdError = stderr_write; + + std::string cmd; + for (const auto& part : command) { + cmd += part + " "; + } + + if (!CreateProcessA(NULL, cmd.data(), NULL, NULL, TRUE, 0, NULL, NULL, &si, &pi)) { + throw std::runtime_error("Failed to create process"); + } + + CloseHandle(stdout_write); + CloseHandle(stderr_write); + + std::array buffer; + DWORD bytes_read; + + while (ReadFile(stdout_read, buffer.data(), (DWORD)buffer.size(), &bytes_read, NULL) && bytes_read > 0) { + stdout_str.append(buffer.data(), bytes_read); + } + + while (ReadFile(stderr_read, buffer.data(), (DWORD)buffer.size(), &bytes_read, NULL) && bytes_read > 0) { + stderr_str.append(buffer.data(), bytes_read); + } + + CloseHandle(stdout_read); + CloseHandle(stderr_read); + WaitForSingleObject(pi.hProcess, INFINITE); + DWORD exit_code = 1; + GetExitCodeProcess(pi.hProcess, &exit_code); + CloseHandle(pi.hProcess); + CloseHandle(pi.hThread); + return (int)exit_code; +#else + int stdout_pipe[2]; + int stderr_pipe[2]; + + if (pipe(stdout_pipe) != 0 || pipe(stderr_pipe) != 0) { + throw std::runtime_error("Failed to create pipes"); + } + + pid_t pid = fork(); + if (pid < 0) { + std::cerr << strerror(errno) << "\n"; + throw std::runtime_error("Failed to fork process"); + } + + std::vector argv; + for (std::string& part : command) { + argv.push_back(part.data()); + } + argv.push_back(nullptr); + + if (pid == 0) { + close(stdout_pipe[0]); + close(stderr_pipe[0]); + dup2(stdout_pipe[1], STDOUT_FILENO); + dup2(stderr_pipe[1], STDERR_FILENO); + close(stdout_pipe[1]); + close(stderr_pipe[1]); + execvp(argv[0], argv.data()); + _exit(EXIT_FAILURE); + } else { + close(stdout_pipe[1]); + close(stderr_pipe[1]); + + std::array buffer; + ssize_t bytes_read; + + while ((bytes_read = read(stdout_pipe[0], buffer.data(), buffer.size())) > 0) { + stdout_str.append(buffer.data(), bytes_read); + } + + while ((bytes_read = read(stderr_pipe[0], buffer.data(), buffer.size())) > 0) { + stderr_str.append(buffer.data(), bytes_read); + } + + close(stdout_pipe[0]); + close(stderr_pipe[0]); + int status = 0; + waitpid(pid, &status, 0); + return WIFEXITED(status) ? WEXITSTATUS(status) : -1; + } +#endif +} + +bool directory_exists(const std::string& path) { + struct stat info; + if (stat(path.c_str(), &info) != 0) { + return false; // Path doesn't exist or can't be accessed + } + return (info.st_mode & S_IFDIR) != 0; // Check if it is a directory +} + +bool create_directory(const std::string& path) { +#ifdef _WIN32 + return _mkdir(path.c_str()) == 0 || errno == EEXIST; // EEXIST means the directory already exists +#else + return mkdir(path.c_str(), 0755) == 0 || errno == EEXIST; // 0755 is the directory permissions +#endif +} + +std::string to_uppercase(const std::string& input) { + std::string result = input; + for (char& c : result) { + c = std::toupper(c); + } + return result; +} + +bool string_starts_with(const std::string& str, const std::string& prefix) { + if (prefix.size() > str.size()) { + return false; + } + return std::equal(prefix.begin(), prefix.end(), str.begin()); +} + +bool string_ends_with(const std::string& str, const std::string& suffix) { + if (suffix.size() > str.size()) { + return false; + } + return std::equal(suffix.rbegin(), suffix.rend(), str.rbegin()); +} + +bool is_quantized_type(const std::string& type_name) { + return type_name != "f32" && type_name != "f16" && type_name != "bf16"; +} + +bool is_legacy_quant(const std::string& type_name) { + return type_name == "q4_0" || type_name == "q4_1" || type_name == "q5_0" || type_name == "q5_1" || type_name == "q8_0"; +} + +bool is_k_quant(const std::string& type_name) { + return string_ends_with(type_name, "_k"); +} + +bool is_iq_quant(const std::string& type_name) { + return string_starts_with(type_name, "iq"); +} + +static const char path_separator = '/'; + +std::string join_paths(const std::string& path1, const std::string& path2) { + return path1 + path_separator + path2; +} + +std::string basename(const std::string &path) { + return path.substr(path.find_last_of("/\\") + 1); +} + +std::stringstream make_generic_stringstream() { + std::stringstream ss; + ss.imbue(c_locale); + return ss; +} + +std::string read_binary_file(const std::string& path, bool may_not_exist = false) { + FILE* f = fopen(path.c_str(), "rb"); + if (!f) { + if (!may_not_exist) { + std::cerr << "Error opening file: " << path << " (" << strerror(errno) << ")\n"; + } + return {}; + } + + fseek(f, 0, SEEK_END); + size_t size = ftell(f); + fseek(f, 0, SEEK_SET); + + std::string data(size, '\0'); + size_t read_size = fread(data.data(), 1, size, f); + fclose(f); + if (read_size != size) { + std::cerr << "Error reading file: " << path << " (" << strerror(errno) << ")\n"; + return {}; + } + + return data; +} + +void write_binary_file(const std::string& path, const std::string& content) { + FILE* f = fopen(path.c_str(), "wb"); + if (!f) { + std::cerr << "Error opening file for writing: " << path << " (" << strerror(errno) << ")\n"; + return; + } + + size_t write_size = fwrite(content.data(), 1, content.size(), f); + fclose(f); + if (write_size != content.size()) { + std::cerr << "Error writing file: " << path << " (" << strerror(errno) << ")\n"; + return; + } +} + +void write_file_if_changed(const std::string& path, const std::string& content) { + std::string existing = read_binary_file(path, true); + if (existing != content) { + write_binary_file(path, content); + } +} + + +// variables to track number of compiles in progress +static uint32_t compile_count = 0; +static std::mutex compile_count_mutex; +static std::condition_variable compile_count_cond; +static bool generate_dep_file = true; + +void decrement_compile_count(uint32_t * count) { + if (count) { + std::lock_guard guard(compile_count_mutex); + assert(compile_count > 0); + compile_count--; + compile_count_cond.notify_all(); + } +} + +using compile_count_guard = std::unique_ptr; + +compile_count_guard acquire_compile_slot() { + // wait until fewer than N compiles are in progress. + // 16 is an arbitrary limit, the goal is to avoid "failed to create pipe" errors. + uint32_t N = std::max(1u, std::min(16u, std::thread::hardware_concurrency())); + std::unique_lock guard(compile_count_mutex); + compile_count_cond.wait(guard, [N] { return compile_count < N; }); + compile_count++; + return compile_count_guard(&compile_count, &decrement_compile_count); +} + +void string_to_spv_func(std::string name, std::string in_path, std::string out_path, std::map defines, bool coopmat, bool dep_file, compile_count_guard slot) { + std::string target_env = (name.find("_cm2") != std::string::npos) ? "--target-env=vulkan1.3" : "--target-env=vulkan1.2"; + + #ifdef _WIN32 + std::vector cmd = {GLSLC, "-fshader-stage=compute", target_env, "\"" + in_path + "\"", "-o", "\"" + out_path + "\""}; + #else + std::vector cmd = {GLSLC, "-fshader-stage=compute", target_env, in_path, "-o", out_path}; + #endif + + // disable spirv-opt for coopmat shaders for https://github.com/ggml-org/llama.cpp/issues/10734 + // disable spirv-opt for bf16 shaders for https://github.com/ggml-org/llama.cpp/issues/15344 + // disable spirv-opt for rope shaders for https://github.com/ggml-org/llama.cpp/issues/16860 + // disable spirv-opt for dot2 shaders (spirv-opt doesn't recognize SPV_VALVE_mixed_float_dot_product capability) + if (!coopmat && name.find("bf16") == std::string::npos && name.find("rope") == std::string::npos && name.find("_dot2") == std::string::npos) { + cmd.push_back("-O"); + } + + if (dep_file) { + cmd.push_back("-MD"); + cmd.push_back("-MF"); +#ifdef _WIN32 + cmd.push_back("\"" + target_cpp + ".d\""); +#else + cmd.push_back(target_cpp + ".d"); +#endif + } + + #ifdef GGML_VULKAN_SHADER_DEBUG_INFO + cmd.push_back("-g"); + #endif + + for (const auto& define : defines) { + cmd.push_back("-D" + define.first + "=" + define.second); + } + + std::string command; + for (const auto& part : cmd) { + command += part + " "; + } + + std::string stdout_str, stderr_str; + try { + // std::cout << "Executing command: "; + // for (const auto& part : cmd) { + // std::cout << part << " "; + // } + // std::cout << std::endl; + + int exit_code = execute_command(cmd, stdout_str, stderr_str); + if (exit_code != 0 || !stderr_str.empty()) { + std::cerr << "cannot compile " << name << " (exit code " << exit_code << ")\n\n"; + for (const auto& part : cmd) { + std::cerr << part << " "; + } + std::cerr << "\n\n" << stderr_str << std::endl; + compile_failed = true; + return; + } + + if (dep_file) { + // replace .spv output path with the embed .cpp path which is used as output in CMakeLists.txt + std::string dep = read_binary_file(target_cpp + ".d", true); + if (!dep.empty()) { + size_t pos = dep.find(out_path); + if (pos != std::string::npos) { + dep.replace(pos, out_path.length(), target_cpp); + } + write_binary_file(target_cpp + ".d", dep); + } + } + + std::lock_guard guard(lock); + shader_fnames.push_back(std::make_pair(name, out_path)); + } catch (const std::exception& e) { + std::cerr << "Error executing command for " << name << ": " << e.what() << std::endl; + compile_failed = true; + } +} + +std::map merge_maps(const std::map& a, const std::map& b) { + std::map result = a; + result.insert(b.begin(), b.end()); + return result; +} + +static std::deque> compiles; +void string_to_spv(std::string name, const std::string& source, const std::map& defines, bool fp16 = true, bool coopmat = false, bool coopmat2 = false, bool f16acc = false, const std::string& suffix = "") { + name = name + (f16acc ? "_f16acc" : "") + (coopmat ? "_cm1" : "") + (coopmat2 ? "_cm2" : (fp16 ? "" : "_fp32")) + suffix; + std::string out_path = join_paths(output_dir, name + ".spv"); + + if (input_filepath == "") { + // No input source to compile, only generate header for all shaders + shader_fnames.push_back(std::pair(name, out_path)); + return; + } else if (basename(input_filepath) != source) { + // Only compile shader variants matching the input filename + return; + } + + compile_count_guard slot = acquire_compile_slot(); + compiles.push_back(std::async( + string_to_spv_func, name, input_filepath, out_path, defines, coopmat, generate_dep_file, std::move(slot))); + // Don't write the same dep file from multiple processes + generate_dep_file = false; + + // Clean up completed futures - don't accumulate virtual memory for completed threads' stacks. + while (!compiles.empty() && compiles.front().wait_for(std::chrono::seconds(0)) == std::future_status::ready) { + compiles.pop_front(); + } +} + +void matmul_shaders(bool fp16, MatMulIdType matmul_id_type, bool coopmat, bool coopmat2, bool f16acc, bool dot2 = false) { + std::string load_vec = coopmat2 ? "1" : fp16 ? "8" : "4"; + std::string aligned_b_type_f32 = coopmat2 ? "float" : fp16 ? "mat2x4" : "vec4"; + std::string aligned_b_type_f16 = coopmat2 ? "float16_t" : fp16 ? "f16mat2x4" : "f16vec4"; + std::string dot2_sfx = dot2 ? "_dot2" : ""; + + std::map base_dict; + std::string shader_name = "matmul"; + + if (matmul_id_type == MatMulIdType::DEFAULT) { + base_dict["MUL_MAT_ID"] = "1"; + shader_name = "matmul_id"; + } else if (matmul_id_type == MatMulIdType::SUBGROUP) { + base_dict["MUL_MAT_ID"] = "1"; + base_dict["MUL_MAT_ID_USE_SUBGROUPS"] = "1"; + shader_name = "matmul_id_subgroup"; + } + + if (fp16) { + base_dict["FLOAT16"] = "1"; + } + + base_dict["ACC_TYPE" ] = f16acc ? "float16_t" : "float"; + base_dict["ACC_TYPEV2"] = f16acc ? "f16vec2" : "vec2"; + if (f16acc) { + base_dict["ACC_TYPE_MAX"] = "float16_t(65504.0)"; + } + + if (coopmat) { + base_dict["COOPMAT"] = "1"; + } +#if defined(GGML_VULKAN_COOPMAT2_DECODE_VECTOR_GLSLC_SUPPORT) + if (coopmat2) { + base_dict["GGML_VULKAN_COOPMAT2_DECODE_VECTOR"] = "1"; + } +#endif + + if (dot2) { + base_dict["DOT2_F16"] = "1"; + } + + const std::string source_name = coopmat2 ? "mul_mm_cm2.comp" : "mul_mm.comp"; + + auto const &FLOAT_TYPE = [&](int vec, const std::string &t) -> std::string { + switch (vec) { + case 1: + if (t == "bf16") { + // scalar path promotes to float + if (!coopmat && !coopmat2) { + return "float"; + } + return "bfloat16_t"; + } + if (coopmat2 || fp16) { + return "float16_t"; + } + return "float"; + case 2: + if (t == "bf16") { + // scalar path promotes to float + if (!coopmat && !coopmat2) { + return "vec2"; + } + return "bf16vec2"; + } + if (coopmat2 || fp16) { + return "f16vec2"; + } + return "vec2"; + case 4: + if (t == "bf16") { + // scalar path promotes to float + if (!coopmat && !coopmat2) { + return "vec4"; + } + return "bf16vec4"; + } + if (coopmat2 || fp16) { + return "f16vec4"; + } + return "vec4"; + case 8: + if (t == "bf16") { + // scalar path promotes to float + if (!coopmat && !coopmat2) { + return "mat2x4"; + } + throw std::runtime_error("bf16 vec8 not supported"); + } + if (coopmat2 || fp16) { + return "f16mat2x4"; + } + return "mat2x4"; + default: + throw std::runtime_error("invalid vector size"); + } + }; + + const std::map float_type_dict_f16 = { + {"FLOAT_TYPE", FLOAT_TYPE(1, "f16")}, + {"FLOAT_TYPEV2", FLOAT_TYPE(2, "f16")}, + {"FLOAT_TYPEV4", FLOAT_TYPE(4, "f16")}, + {"FLOAT_TYPEV8", FLOAT_TYPE(8, "f16")}, + }; + + // Shaders with f16 B_TYPE + string_to_spv(shader_name + "_f32_f16" + dot2_sfx, source_name, merge_maps(merge_maps(base_dict, float_type_dict_f16), {{"DATA_A_F32", "1"}, {"LOAD_VEC_A", load_vec}, {"LOAD_VEC_B", load_vec}, {"B_TYPE", aligned_b_type_f16}, {"B_TYPE_SCALAR", "float16_t"}, {"B_TYPEV4", "f16vec4"}, {"D_TYPE", "float"}}), fp16, coopmat, coopmat2, f16acc); + + string_to_spv(shader_name + "_f16" + dot2_sfx, source_name, merge_maps(merge_maps(base_dict, float_type_dict_f16), {{"DATA_A_F16", "1"}, {"LOAD_VEC_A", load_vec}, {"LOAD_VEC_B", load_vec}, {"B_TYPE", aligned_b_type_f16}, {"B_TYPE_SCALAR", "float16_t"}, {"B_TYPEV4", "f16vec4"}, {"D_TYPE", "float"}}), fp16, coopmat, coopmat2, f16acc); + + // bf16 + { + // For aligned matmul loads + std::string load_vec_a = coopmat2 ? "1" : "4"; + + // scalar path promotes to float + std::string to_float_type = (coopmat || coopmat2) ? "uintBitsToBFloat16EXT" : "bf16_to_fp32"; + + const std::map float_type_dict_bf16 = { + {"FLOAT_TYPE", FLOAT_TYPE(1, "bf16")}, + {"FLOAT_TYPEV2", FLOAT_TYPE(2, "bf16")}, + {"FLOAT_TYPEV4", FLOAT_TYPE(4, "bf16")}, + }; + + // If bfloat16 is not supported, then only compile the scalar (promote to fp32) shader +#if !defined(GGML_VULKAN_BFLOAT16_GLSLC_SUPPORT) + if (!(coopmat || coopmat2)) +#endif + { + if (!dot2) { + string_to_spv(shader_name + "_bf16", source_name, merge_maps(merge_maps(base_dict, float_type_dict_bf16), {{"TO_FLOAT_TYPE", to_float_type}, {"DATA_A_BF16", "1"}, {"LOAD_VEC_A", load_vec_a}, {"LOAD_VEC_B", "4"}, {"B_TYPE", coopmat2 ? "bfloat16_t" : "u16vec4"}, {"B_TYPE_SCALAR", coopmat2 ? "bfloat16_t" : "uint16_t"}, {"B_TYPEV4", "bf16vec4"}, {"D_TYPE", "float"}, {"B_IS_FLOAT", "1"}, {"DATA_B_BF16", "1"}}), fp16, coopmat, coopmat2, f16acc); + } + } + } + + for (const auto& tname : type_names) { + std::string load_vec_quant = "2"; + if ((tname == "q1_0") || (tname == "q4_0") || (tname == "q4_1") || (tname == "q5_1") || (tname == "iq1_s") || (tname == "iq1_m") || (tname == "iq2_xxs") || (tname == "iq2_xs") || (tname == "iq2_s")) + load_vec_quant = "8"; + else if ((tname == "q5_0") || (tname == "q8_0") || (tname == "q2_k") || (tname == "q4_k") || (tname == "q5_k") || (tname == "iq3_xxs") || (tname == "iq3_s") || (tname == "iq4_xs") || (tname == "iq4_nl") || (tname == "mxfp4") || (tname == "nvfp4")) + load_vec_quant = "4"; + + if (tname == "bf16") { + continue; + } + + std::string data_a_key = "DATA_A_" + to_uppercase(tname); + // For aligned matmul loads + std::string load_vec_a = (coopmat2 || tname == "f32" || tname == "f16" || tname == "bf16") ? load_vec : load_vec_quant; + + const std::map float_type_dict = { + {"FLOAT_TYPE", FLOAT_TYPE(1, tname)}, + {"FLOAT_TYPEV2", FLOAT_TYPE(2, tname)}, + {"FLOAT_TYPEV4", FLOAT_TYPE(4, tname)}, + {"FLOAT_TYPEV8", FLOAT_TYPE(8, tname)}, + }; + + // don't generate f32 variants for coopmat2 + if (!coopmat2) { + string_to_spv(shader_name + "_" + tname + "_f32" + dot2_sfx, source_name, merge_maps(merge_maps(base_dict, float_type_dict), {{data_a_key, "1"}, {"LOAD_VEC_A", load_vec_a}, {"LOAD_VEC_B", load_vec}, {"B_TYPE", aligned_b_type_f32}, {"B_TYPE_SCALAR", "float"}, {"B_TYPEV4", "vec4"}, {"D_TYPE", "float"}}), fp16, coopmat, coopmat2, f16acc); + } + + if (tname != "f16" && tname != "f32") { + string_to_spv(shader_name + "_" + tname + "_f16" + dot2_sfx, source_name, merge_maps(merge_maps(base_dict, float_type_dict), {{data_a_key, "1"}, {"LOAD_VEC_A", load_vec_a}, {"LOAD_VEC_B", load_vec}, {"B_TYPE", aligned_b_type_f16}, {"B_TYPE_SCALAR", "float16_t"}, {"B_TYPEV4", "f16vec4"}, {"D_TYPE", "float"}}), fp16, coopmat, coopmat2, f16acc); + } + +#if defined(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT) + // Integer dot mmq performs better with f32 accumulators (different shader, skip for dot2) + if (!f16acc && !coopmat && !coopmat2 && !dot2 && (is_legacy_quant(tname) || is_k_quant(tname) || tname == "mxfp4")) { + string_to_spv(shader_name + "_" + tname + "_q8_1", "mul_mmq.comp", merge_maps(merge_maps(base_dict, float_type_dict), {{data_a_key, "1"}, {"D_TYPE", "float"},}), fp16, coopmat, coopmat2, f16acc); + } +#endif + } +} + +void process_shaders() { + // matmul + for (const MatMulIdType& matmul_id_type : {MatMulIdType::NONE, MatMulIdType::DEFAULT, MatMulIdType::SUBGROUP}) { + // No coopmats + // fp32 + matmul_shaders(false, matmul_id_type, false, false, false); + + // fp16, fp32acc and fp16acc + matmul_shaders(true, matmul_id_type, false, false, false); + matmul_shaders(true, matmul_id_type, false, false, true); + + // dot2 variants (scalar fp16 only) + matmul_shaders(true, matmul_id_type, false, false, false, true); + matmul_shaders(true, matmul_id_type, false, false, true, true); + + if (matmul_id_type != MatMulIdType::DEFAULT) { +#if defined(GGML_VULKAN_COOPMAT_GLSLC_SUPPORT) + // Coopmat, fp32acc and fp16acc + matmul_shaders(true, matmul_id_type, true, false, false); + matmul_shaders(true, matmul_id_type, true, false, true); +#endif + +#if defined(GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT) + // Coopmat2, fp32acc and fp16acc + matmul_shaders(true, matmul_id_type, false, true, false); + matmul_shaders(true, matmul_id_type, false, true, true); +#endif + } + } + + for (const bool& fp16 : {false, true}) { + std::map base_dict; + if (fp16) { + base_dict = {{"FLOAT_TYPE", "float16_t"}, {"FLOAT_TYPEV2", "f16vec2"}, {"FLOAT_TYPEV4", "f16vec4"}, {"FLOAT16", "1"}, {"FLOAT_TYPE_MAX", "float16_t(65504.0)"}}; + } else { + base_dict = {{"FLOAT_TYPE", "float"}, {"FLOAT_TYPEV2", "vec2"}, {"FLOAT_TYPEV4", "vec4"}}; + } + + // flash attention + for (const bool& f16acc : {false, true}) { + std::map fa_base_dict = base_dict; + fa_base_dict["ACC_TYPE"] = fp16 && f16acc ? "float16_t" : "float"; + fa_base_dict["ACC_TYPEV2"] = fp16 && f16acc ? "f16vec2" : "vec2"; + fa_base_dict["ACC_TYPEV4"] = fp16 && f16acc ? "f16vec4" : "vec4"; + if (fp16 && f16acc) { + fa_base_dict["ACC_TYPE_MAX"] = "float16_t(65504.0)"; + } + + if (fp16) { +#if defined(GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT) + string_to_spv("flash_attn_f32_f16", "flash_attn_cm2.comp", + merge_maps(fa_base_dict, {{"Q_TYPE", "float"}, {"D_TYPE", "float"}, {"D_TYPEV4", "vec4"}}), fp16, false, true, f16acc); +#endif + +#if defined(GGML_VULKAN_COOPMAT_GLSLC_SUPPORT) + string_to_spv("flash_attn_f32_f16", "flash_attn_cm1.comp", + merge_maps(fa_base_dict, {{"Q_TYPE", "float"}, {"D_TYPE", "float"}, {"D_TYPEV4", "vec4"}, {"COOPMAT", "1"}}), fp16, true, false, f16acc); +#endif + } + + string_to_spv("flash_attn_f32_f16", "flash_attn.comp", + merge_maps(fa_base_dict, {{"Q_TYPE", "float"}, {"D_TYPE", "float"}, {"D_TYPEV4", "vec4"}}), fp16, false, false, f16acc); + + if (fp16) { + string_to_spv("flash_attn_f32_f16_dot2", "flash_attn.comp", + merge_maps(fa_base_dict, {{"Q_TYPE", "float"}, {"D_TYPE", "float"}, {"D_TYPEV4", "vec4"}, {"DOT2_F16", "1"}}), fp16, false, false, f16acc); + } + +#if defined(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT) + string_to_spv("flash_attn_f32_f16", "flash_attn.comp", + merge_maps(fa_base_dict, {{"Q_TYPE", "float"}, {"D_TYPE", "float"}, {"D_TYPEV4", "vec4"}, {"MMQ", "1"}, {"FA_MMQ_MIXED", "1"}}), fp16, false, false, f16acc, "_int8"); +#endif + } + } + + const std::map fa_bf16_dict = { + {"FLOAT_TYPE", "bfloat16_t"}, + {"FLOAT_TYPEV2", "bf16vec2"}, + {"FLOAT_TYPEV4", "bf16vec4"}, + {"ACC_TYPE", "float"}, + {"ACC_TYPEV2", "vec2"}, + {"ACC_TYPEV4", "vec4"}, + {"BFLOAT16", "1"}, + }; + +#if defined(GGML_VULKAN_BFLOAT16_GLSLC_SUPPORT) && defined(GGML_VULKAN_COOPMAT_GLSLC_SUPPORT) + string_to_spv("flash_attn_f32_f16_bf16", "flash_attn_cm1.comp", + merge_maps(fa_bf16_dict, {{"Q_TYPE", "float"}, {"D_TYPE", "float"}, {"D_TYPEV4", "vec4"}, {"COOPMAT", "1"}}), + true, true, false, false); +#endif + +#if defined(GGML_VULKAN_BFLOAT16_GLSLC_SUPPORT) && defined(GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT) + string_to_spv("flash_attn_f32_f16_bf16", "flash_attn_cm2.comp", + merge_maps(fa_bf16_dict, {{"Q_TYPE", "float"}, {"D_TYPE", "float"}, {"D_TYPEV4", "vec4"}}), + true, false, true, false); +#endif + + std::map base_dict = {{"FLOAT_TYPE", "float"}, {"FLOAT_TYPEV2", "vec2"}}; + + for (const auto& tname : type_names) { + // mul mat vec + std::string data_a_key = "DATA_A_" + to_uppercase(tname); + std::string shader = (string_ends_with(tname, "_k") || string_starts_with(tname, "iq1_") || string_starts_with(tname, "iq2_") || string_starts_with(tname, "iq3_")) ? "mul_mat_vec_" + tname + ".comp" : "mul_mat_vec.comp"; + + string_to_spv("mul_mat_vec_" + tname + "_f32_f32", shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "float"}, {"B_TYPEV2", "vec2"}, {"B_TYPEV4", "vec4"}, {"D_TYPE", "float"}})); + string_to_spv("mul_mat_vec_" + tname + "_f16_f32", shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "float16_t"}, {"B_TYPEV2", "f16vec2"}, {"B_TYPEV4", "f16vec4"}, {"D_TYPE", "float"}})); + + string_to_spv("mul_mat_vec_" + tname + "_f32_f32_subgroup", shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "float"}, {"B_TYPEV2", "vec2"}, {"B_TYPEV4", "vec4"}, {"D_TYPE", "float"}, {"USE_SUBGROUP_ADD", "1"}})); + string_to_spv("mul_mat_vec_" + tname + "_f16_f32_subgroup", shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "float16_t"}, {"B_TYPEV2", "f16vec2"}, {"B_TYPEV4", "f16vec4"}, {"D_TYPE", "float"}, {"USE_SUBGROUP_ADD", "1"}})); + + string_to_spv("mul_mat_vec_" + tname + "_f32_f32_subgroup_no_shmem", shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "float"}, {"B_TYPEV2", "vec2"}, {"B_TYPEV4", "vec4"}, {"D_TYPE", "float"}, {"USE_SUBGROUP_ADD_NO_SHMEM", "1"}})); + string_to_spv("mul_mat_vec_" + tname + "_f16_f32_subgroup_no_shmem", shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "float16_t"}, {"B_TYPEV2", "f16vec2"}, {"B_TYPEV4", "f16vec4"}, {"D_TYPE", "float"}, {"USE_SUBGROUP_ADD_NO_SHMEM", "1"}})); + + string_to_spv("mul_mat_vec_id_" + tname + "_f32_f32", shader, merge_maps(base_dict, {{"MUL_MAT_ID", "1"}, {data_a_key, "1"}, {"B_TYPE", "float"}, {"B_TYPEV2", "vec2"}, {"B_TYPEV4", "vec4"}, {"D_TYPE", "float"}})); + string_to_spv("mul_mat_vec_id_" + tname + "_f32_f32_subgroup", shader, merge_maps(base_dict, {{"MUL_MAT_ID", "1"}, {data_a_key, "1"}, {"B_TYPE", "float"}, {"B_TYPEV2", "vec2"}, {"B_TYPEV4", "vec4"}, {"D_TYPE", "float"}, {"USE_SUBGROUP_ADD", "1"}})); + string_to_spv("mul_mat_vec_id_" + tname + "_f32_f32_subgroup_no_shmem", shader, merge_maps(base_dict, {{"MUL_MAT_ID", "1"}, {data_a_key, "1"}, {"B_TYPE", "float"}, {"B_TYPEV2", "vec2"}, {"B_TYPEV4", "vec4"}, {"D_TYPE", "float"}, {"USE_SUBGROUP_ADD_NO_SHMEM", "1"}})); + + // mul mat vec with integer dot product +#if defined(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT) + if (is_legacy_quant(tname) || tname == "mxfp4" || is_k_quant(tname) || tname == "iq1_s" || tname == "iq1_m") { + string_to_spv("mul_mat_vec_" + tname + "_q8_1_f32", "mul_mat_vecq.comp", merge_maps(base_dict, {{data_a_key, "1"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}, {"FLOAT_TYPEV2", "vec2"}, {"ACC_TYPE", "float"}})); + string_to_spv("mul_mat_vec_" + tname + "_q8_1_f32_subgroup", "mul_mat_vecq.comp", merge_maps(base_dict, {{data_a_key, "1"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}, {"FLOAT_TYPEV2", "vec2"}, {"ACC_TYPE", "float"}, {"USE_SUBGROUP_ADD", "1"}})); + string_to_spv("mul_mat_vec_" + tname + "_q8_1_f32_subgroup_no_shmem", "mul_mat_vecq.comp", merge_maps(base_dict, {{data_a_key, "1"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}, {"FLOAT_TYPEV2", "vec2"}, {"ACC_TYPE", "float"}, {"USE_SUBGROUP_ADD_NO_SHMEM", "1"}})); + + string_to_spv("mul_mat_vec_id_" + tname + "_q8_1_f32", "mul_mat_vecq.comp", merge_maps(base_dict, {{"MUL_MAT_ID", "1"}, {data_a_key, "1"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}, {"FLOAT_TYPEV2", "vec2"}, {"ACC_TYPE", "float"}})); + string_to_spv("mul_mat_vec_id_" + tname + "_q8_1_f32_subgroup", "mul_mat_vecq.comp", merge_maps(base_dict, {{"MUL_MAT_ID", "1"}, {data_a_key, "1"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}, {"FLOAT_TYPEV2", "vec2"}, {"ACC_TYPE", "float"}, {"USE_SUBGROUP_ADD", "1"}})); + string_to_spv("mul_mat_vec_id_" + tname + "_q8_1_f32_subgroup_no_shmem", "mul_mat_vecq.comp", merge_maps(base_dict, {{"MUL_MAT_ID", "1"}, {data_a_key, "1"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}, {"FLOAT_TYPEV2", "vec2"}, {"ACC_TYPE", "float"}, {"USE_SUBGROUP_ADD_NO_SHMEM", "1"}})); + } +#endif + + // Dequant shaders + if (tname != "f16" && tname != "bf16") { + string_to_spv("dequant_" + tname, "dequant_" + tname + ".comp", merge_maps(base_dict, {{data_a_key, "1"}, {"D_TYPE", "float16_t"}})); + } + + shader = (tname == "f32" || tname == "f16" || tname == "bf16") ? "get_rows.comp" : "get_rows_quant.comp"; + + if (tname == "f16") { + string_to_spv("get_rows_" + tname, shader, merge_maps(base_dict, {{"TEMP_TYPE", "FLOAT_TYPE"}, {data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float16_t"}, {"OPTIMIZATION_ERROR_WORKAROUND", "1"}})); + } else { + string_to_spv("get_rows_" + tname, shader, merge_maps(base_dict, {{"TEMP_TYPE", "FLOAT_TYPE"}, {data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float16_t"}})); + } + string_to_spv("get_rows_" + tname + "_f32", shader, merge_maps(base_dict, {{"TEMP_TYPE", "FLOAT_TYPE"}, {data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float"}})); + } + + string_to_spv("get_rows_i32", "get_rows.comp", {{"TEMP_TYPE", "uint"}, {"A_TYPE", "uint"}, {"B_TYPE", "int"}, {"D_TYPE", "uint"}}); + + string_to_spv("mul_mat_vec_p021_f16_f32_subgroup_add", "mul_mat_vec_p021.comp", {{"A_TYPE", "float16_t"}, {"A_TYPEV4", "f16vec4"}, {"B_TYPE", "float"}, {"B_TYPEV4", "vec4"}, {"D_TYPE", "float"}, {"USE_SUBGROUP_ADD", "1"}}); + string_to_spv("mul_mat_vec_p021_f16_f32", "mul_mat_vec_p021.comp", {{"A_TYPE", "float16_t"}, {"A_TYPEV4", "f16vec4"}, {"B_TYPE", "float"}, {"B_TYPEV4", "vec4"}, {"D_TYPE", "float"}}); + string_to_spv("mul_mat_vec_nc_f16_f32", "mul_mat_vec_nc.comp", {{"A_TYPE", "float16_t"}, {"A_TYPEV4", "f16vec4"}, {"B_TYPE", "float"}, {"B_TYPEV4", "vec4"}, {"D_TYPE", "float"}}); + + // Norms + string_to_spv("norm_f32", "norm.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}})); + string_to_spv("group_norm_f32", "group_norm.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}})); + string_to_spv("rms_norm_f32", "rms_norm.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}})); + string_to_spv("rms_norm_partials_f32", "rms_norm_partials.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}})); + string_to_spv("rms_norm_mul_rope_f32_f32", "rms_norm.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"ROPE_D_TYPE", "float"}, {"RMS_NORM_ROPE_FUSION", "1"}})); + string_to_spv("rms_norm_mul_rope_f32_f16", "rms_norm.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"ROPE_D_TYPE", "float16_t"}, {"RMS_NORM_ROPE_FUSION", "1"}})); + string_to_spv("rms_norm_back_f32", "rms_norm_back.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}})); + string_to_spv("l2_norm_f32", "l2_norm.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}})); + + string_to_spv("cpy_f32_f32", "copy.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); + string_to_spv("cpy_f32_f16", "copy.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float16_t"}}); + string_to_spv("cpy_f16_f16", "copy.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"OPTIMIZATION_ERROR_WORKAROUND", "1"}}); + string_to_spv("cpy_f16_f32", "copy.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float"}, {"OPTIMIZATION_ERROR_WORKAROUND", "1"}}); + string_to_spv("cpy_f32_bf16","copy.comp", {{"A_TYPE", "float"}, {"D_TYPE", "uint16_t"}, {"DATA_D_BF16", "1"}}); + string_to_spv("cpy_bf16_f32","copy.comp", {{"A_TYPE", "uint16_t"}, {"D_TYPE", "float"}, {"DATA_A_BF16", "1"}}); + string_to_spv("contig_cpy_f32_f32", "contig_copy.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); + string_to_spv("contig_cpy_f32_i32", "contig_copy.comp", {{"A_TYPE", "float"}, {"D_TYPE", "int"}}); + string_to_spv("contig_cpy_i32_f32", "contig_copy.comp", {{"A_TYPE", "int"}, {"D_TYPE", "float"}}); + string_to_spv("contig_cpy_f32_f16", "contig_copy.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float16_t"}}); + string_to_spv("contig_cpy_f16_f16", "contig_copy.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"OPTIMIZATION_ERROR_WORKAROUND", "1"}}); + string_to_spv("contig_cpy_f16_f32", "contig_copy.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float"}, {"OPTIMIZATION_ERROR_WORKAROUND", "1"}}); + string_to_spv("contig_cpy_f32_bf16","contig_copy.comp",{{"A_TYPE", "float"}, {"D_TYPE", "uint16_t"}, {"DATA_D_BF16", "1"}}); + string_to_spv("contig_cpy_bf16_f32","contig_copy.comp",{{"A_TYPE", "uint16_t"}, {"D_TYPE", "float"}, {"DATA_A_BF16", "1"}}); + string_to_spv("cpy_f32_i32", "copy.comp", {{"A_TYPE", "float"}, {"D_TYPE", "int"}}); + string_to_spv("cpy_i32_f32", "copy.comp", {{"A_TYPE", "int"}, {"D_TYPE", "float"}}); + + string_to_spv("cpy_transpose_16", "copy_transpose.comp", {{"A_TYPE", "uint16_t"}, {"D_TYPE", "uint16_t"}}); + string_to_spv("cpy_transpose_32", "copy_transpose.comp", {{"A_TYPE", "uint"}, {"D_TYPE", "uint"}}); + + for (std::string t : {"q1_0", "q4_0", "q4_1", "q5_0", "q5_1", "q8_0", "iq4_nl"}) { + string_to_spv("cpy_f32_" + t, "copy_to_quant.comp", {{"DATA_A_" + to_uppercase(t), "1"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); + string_to_spv("cpy_" + t + "_f32", "copy_from_quant.comp", {{"DATA_A_" + to_uppercase(t), "1"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); + } + + for (std::string t : {"f32", "f16", "bf16", "q1_0", "q4_0", "q4_1", "q5_0", "q5_1", "q8_0", "iq4_nl"}) { + string_to_spv("set_rows_" + t + "_i32", "copy_to_quant.comp", {{"SET_ROWS", "1"}, {"DATA_A_" + to_uppercase(t), "1"}, {"B_TYPE", "uint"}, {"B_SIZE", "32"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); + string_to_spv("set_rows_" + t + "_i64", "copy_to_quant.comp", {{"SET_ROWS", "1"}, {"DATA_A_" + to_uppercase(t), "1"}, {"B_TYPE", "uvec2"}, {"B_SIZE", "64"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); + } + + auto get_type_str = [](bool f16) { + return f16 ? "float16_t" : "float"; + }; + auto get_suffix = [](bool src0_f16, bool src1_f16, bool dst_f16) { + std::string s; + s += std::string(src0_f16 ? "_f16" : "_f32"); + s += std::string(src1_f16 ? "_f16" : "_f32"); + s += std::string(dst_f16 ? "_f16" : "_f32"); + return s; + }; + for (std::string op : {"add", "sub", "mul", "div", "add_rms", }) { + for (auto src0_f16 : {false, true}) { + for (auto src1_f16 : {false, true}) { + for (auto dst_f16 : {false, true}) { + auto source = op == "add_rms" ? std::string("add") : op; + auto name = op + get_suffix(src0_f16, src1_f16, dst_f16); + auto add_rms = op == "add_rms" ? "1" : "0"; + string_to_spv(name.c_str(), source + ".comp", {{"A_TYPE", get_type_str(src0_f16)}, {"B_TYPE", get_type_str(src1_f16)}, {"D_TYPE", get_type_str(dst_f16)}, {"FLOAT_TYPE", "float"}, {"ADD_RMS" , add_rms}}); + } + } + } + } + + string_to_spv("sub_f32", "sub.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); + + string_to_spv("acc_f32", "acc.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); + + string_to_spv("split_k_reduce", "mul_mat_split_k_reduce.comp", {}); + string_to_spv("fa_split_k_reduce", "flash_attn_split_k_reduce.comp", {}); + + string_to_spv("fa_mask_opt", "flash_attn_mask_opt.comp", {}); + + string_to_spv("quantize_q8_1", "quantize_q8_1.comp", {}); + string_to_spv("quantize_q8_1_subgroup", "quantize_q8_1.comp", {{"USE_SUBGROUPS", "1"}}); + + string_to_spv("quantize_q8_1_x4", "quantize_q8_1.comp", {{"QBLOCK_X4", "1"}}); + string_to_spv("quantize_q8_1_x4_subgroup", "quantize_q8_1.comp", {{"QBLOCK_X4", "1"}, {"USE_SUBGROUPS", "1"}}); + + string_to_spv("mul_f32", "mul.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); + + string_to_spv("div_f32", "div.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); + + string_to_spv("repeat_i32", "repeat.comp", {{"A_TYPE", "int32_t"}, {"D_TYPE", "int32_t"}}); + string_to_spv("repeat_back_f32", "repeat_back.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); + string_to_spv("get_rows_back_f32", "get_rows_back.comp", {{"A_TYPE", "float"}, {"B_TYPE", "int"}, {"D_TYPE", "float"}}); + + string_to_spv("repeat_i16", "repeat.comp", {{"A_TYPE", "int16_t"}, {"D_TYPE", "int16_t"}}); + + string_to_spv("scale_f32", "scale.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); + + string_to_spv("pad_f32", "pad.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); + + string_to_spv("concat_i8", "concat.comp", {{"A_TYPE", "uint8_t"}, {"B_TYPE", "uint8_t"}, {"D_TYPE", "uint8_t"}}); + string_to_spv("concat_i16", "concat.comp", {{"A_TYPE", "uint16_t"}, {"B_TYPE", "uint16_t"}, {"D_TYPE", "uint16_t"}}); + string_to_spv("concat_i32", "concat.comp", {{"A_TYPE", "uint"}, {"B_TYPE", "uint"}, {"D_TYPE", "uint"}}); + string_to_spv("concat_i64", "concat.comp", {{"A_TYPE", "uvec2"}, {"B_TYPE", "uvec2"}, {"D_TYPE", "uvec2"}}); + + string_to_spv("upscale_f32", "upscale.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}); + + string_to_spv("exp_f16", "unary.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"OP", "op_exp"}}); + string_to_spv("exp_f32", "unary.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"OP", "op_exp"}}); + string_to_spv("expm1_f16", "unary.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"OP", "op_expm1"}}); + string_to_spv("expm1_f32", "unary.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"OP", "op_expm1"}}); + + string_to_spv("log_f16", "log.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}}); + string_to_spv("log_f32", "log.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); + string_to_spv("gelu_f16", "unary.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"OP", "op_gelu"}}); + string_to_spv("gelu_f32", "unary.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"OP", "op_gelu"}}); + string_to_spv("gelu_erf_f16", "unary.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"OP", "op_gelu_erf"}}); + string_to_spv("gelu_erf_f32", "unary.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"OP", "op_gelu_erf"}}); + string_to_spv("gelu_quick_f16", "unary.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"OP", "op_gelu_quick"}}); + string_to_spv("gelu_quick_f32", "unary.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"OP", "op_gelu_quick"}}); + string_to_spv("silu_f16", "unary.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"OP", "op_silu"}}); + string_to_spv("silu_f32", "unary.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"OP", "op_silu"}}); + string_to_spv("relu_f16", "unary.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"OP", "op_relu"}}); + string_to_spv("relu_f32", "unary.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"OP", "op_relu"}}); + string_to_spv("sqr_f16", "unary.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"OP", "op_sqr"}}); + string_to_spv("sqr_f32", "unary.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"OP", "op_sqr"}}); + string_to_spv("sqrt_f16", "unary.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"OP", "op_sqrt"}}); + string_to_spv("sqrt_f32", "unary.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"OP", "op_sqrt"}}); + string_to_spv("sin_f16", "unary.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"OP", "op_sin"}}); + string_to_spv("sin_f32", "unary.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"OP", "op_sin"}}); + string_to_spv("cos_f16", "unary.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"OP", "op_cos"}}); + string_to_spv("cos_f32", "unary.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"OP", "op_cos"}}); + string_to_spv("clamp_f16", "unary.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"OP", "op_clamp"}}); + string_to_spv("clamp_f32", "unary.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"OP", "op_clamp"}}); + string_to_spv("leaky_relu_f16", "unary.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"OP", "op_leaky_relu"}}); + string_to_spv("leaky_relu_f32", "unary.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"OP", "op_leaky_relu"}}); + string_to_spv("neg_f16", "unary.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"OP", "op_neg"}}); + string_to_spv("neg_f32", "unary.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"OP", "op_neg"}}); + string_to_spv("tanh_f16", "unary.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"OP", "op_tanh"}}); + string_to_spv("tanh_f32", "unary.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"OP", "op_tanh"}}); + string_to_spv("sigmoid_f16", "unary.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"OP", "op_sigmoid"}}); + string_to_spv("sigmoid_f32", "unary.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"OP", "op_sigmoid"}}); + string_to_spv("hardsigmoid_f16","unary.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"OP", "op_hardsigmoid"}}); + string_to_spv("hardsigmoid_f32","unary.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"OP", "op_hardsigmoid"}}); + string_to_spv("hardswish_f16", "unary.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"OP", "op_hardswish"}}); + string_to_spv("hardswish_f32", "unary.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"OP", "op_hardswish"}}); + string_to_spv("abs_f16", "unary.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"OP", "op_abs"}}); + string_to_spv("abs_f32", "unary.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"OP", "op_abs"}}); + string_to_spv("elu_f16", "unary.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"OP", "op_elu"}}); + string_to_spv("elu_f32", "unary.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"OP", "op_elu"}}); + string_to_spv("xielu_f16", "unary.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"OP", "op_xielu"}}); + string_to_spv("xielu_f32", "unary.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"OP", "op_xielu"}}); + string_to_spv("sgn_f16", "unary.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"OP", "op_sgn"}}); + string_to_spv("sgn_f32", "unary.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"OP", "op_sgn"}}); + + string_to_spv("tri_f16", "tri.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}}); + string_to_spv("tri_f32", "tri.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); + string_to_spv("diag_f16", "diag.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}}); + string_to_spv("diag_f32", "diag.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); + + string_to_spv("softplus_f16", "unary.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"OP", "op_softplus"}}); + string_to_spv("softplus_f32", "unary.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"OP", "op_softplus"}}); + + string_to_spv("add1_f16_f16", "add1.comp", {{"A_TYPE", "float16_t"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"FLOAT_TYPE", "float"}}); + string_to_spv("add1_f16_f32", "add1.comp", {{"A_TYPE", "float16_t"}, {"B_TYPE", "float"}, {"D_TYPE", "float16_t"}, {"FLOAT_TYPE", "float"}}); + string_to_spv("add1_f32_f32", "add1.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); + string_to_spv("arange_f32", "arange.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); + string_to_spv("fill_f32", "fill.comp", {{"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}}); + string_to_spv("fill_f16", "fill.comp", {{"D_TYPE", "float16_t"}, {"FLOAT_TYPE", "float"}}); + string_to_spv("step_f16", "unary.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"OP", "op_step"}}); + string_to_spv("step_f32", "unary.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"OP", "op_step"}}); + string_to_spv("round_f16", "unary.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"OP", "op_round"}}); + string_to_spv("round_f32", "unary.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"OP", "op_round"}}); + string_to_spv("ceil_f16", "unary.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"OP", "op_ceil"}}); + string_to_spv("ceil_f32", "unary.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"OP", "op_ceil"}}); + string_to_spv("floor_f16", "unary.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"OP", "op_floor"}}); + string_to_spv("floor_f32", "unary.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"OP", "op_floor"}}); + string_to_spv("trunc_f16", "unary.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"OP", "op_trunc"}}); + string_to_spv("trunc_f32", "unary.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"OP", "op_trunc"}}); + + string_to_spv("geglu_f16", "geglu.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}}); + string_to_spv("geglu_f32", "geglu.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); + string_to_spv("reglu_f16", "reglu.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}}); + string_to_spv("reglu_f32", "reglu.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); + string_to_spv("swiglu_f16", "swiglu.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}}); + string_to_spv("swiglu_f32", "swiglu.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); + string_to_spv("swiglu_oai_f16", "swiglu_oai.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}}); + string_to_spv("swiglu_oai_f32", "swiglu_oai.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); + string_to_spv("geglu_erf_f16", "geglu_erf.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}}); + string_to_spv("geglu_erf_f32", "geglu_erf.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); + string_to_spv("geglu_quick_f16","geglu_quick.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}}); + string_to_spv("geglu_quick_f32","geglu_quick.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); + + string_to_spv("silu_back_f32", "silu_back.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}); + + string_to_spv("diag_mask_inf_f32", "diag_mask_inf.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); + + string_to_spv("soft_max_f32", "soft_max.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}})); + string_to_spv("soft_max_f32_f16", "soft_max.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}})); + string_to_spv("soft_max_back_f32", "soft_max_back.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}})); + + string_to_spv("soft_max_large1_f32", "soft_max_large1.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}})); + string_to_spv("soft_max_large2_f32", "soft_max_large2.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}})); + string_to_spv("soft_max_large3_f32", "soft_max_large3.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}})); + string_to_spv("soft_max_large1_f32_f16", "soft_max_large1.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}})); + string_to_spv("soft_max_large2_f32_f16", "soft_max_large2.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}})); + string_to_spv("soft_max_large3_f32_f16", "soft_max_large3.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}})); + + string_to_spv("rope_norm_f32", "rope_norm.comp", {{"A_TYPE", "float"}, {"ROPE_D_TYPE", "float"}}); + string_to_spv("rope_norm_f16", "rope_norm.comp", {{"A_TYPE", "float16_t"}, {"ROPE_D_TYPE", "float16_t"}}); + string_to_spv("rope_norm_f32_f16", "rope_norm.comp", {{"A_TYPE", "float"}, {"ROPE_D_TYPE", "float16_t"}}); + + string_to_spv("rope_neox_f32", "rope_neox.comp", {{"A_TYPE", "float"}, {"ROPE_D_TYPE", "float"}}); + string_to_spv("rope_neox_f16", "rope_neox.comp", {{"A_TYPE", "float16_t"}, {"ROPE_D_TYPE", "float16_t"}}); + string_to_spv("rope_neox_f32_f16", "rope_neox.comp", {{"A_TYPE", "float"}, {"ROPE_D_TYPE", "float16_t"}}); + + string_to_spv("rope_multi_f32", "rope_multi.comp", {{"A_TYPE", "float"}, {"ROPE_D_TYPE", "float"}}); + string_to_spv("rope_multi_f16", "rope_multi.comp", {{"A_TYPE", "float16_t"}, {"ROPE_D_TYPE", "float16_t"}}); + string_to_spv("rope_multi_f32_f16", "rope_multi.comp", {{"A_TYPE", "float"}, {"ROPE_D_TYPE", "float16_t"}}); + + string_to_spv("rope_vision_f32", "rope_vision.comp", {{"A_TYPE", "float"}, {"ROPE_D_TYPE", "float"}}); + string_to_spv("rope_vision_f16", "rope_vision.comp", {{"A_TYPE", "float16_t"}, {"ROPE_D_TYPE", "float16_t"}}); + + string_to_spv("argsort_f32", "argsort.comp", {{"A_TYPE", "float"}}); + string_to_spv("argsort_large_f32", "argsort_large.comp", {{"A_TYPE", "float"}}); + + string_to_spv("topk_argsort_f32", "topk_argsort.comp", {{"A_TYPE", "float"}}); + string_to_spv("topk_nary_search_f32", "topk_nary_search.comp", {{"A_TYPE", "float"}}); + + string_to_spv("argmax_f32", "argmax.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "int"}})); + string_to_spv("sum_rows_f32", "sum_rows.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}})); + string_to_spv("fwht_f32", "fwht.comp", {}); + string_to_spv("fwht_shmem_f32", "fwht.comp", {{"FWHT_SHMEM", "1"}}); + string_to_spv("count_equal_i32", "count_equal.comp", merge_maps(base_dict, {{"A_TYPE", "int"}, {"B_TYPE", "int"}, {"D_TYPE", "int"}})); + string_to_spv("cumsum_f32", "cumsum.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}})); + string_to_spv("cumsum_multipass1_f32", "cumsum_multipass1.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}})); + string_to_spv("cumsum_multipass2_f32", "cumsum_multipass2.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}})); + + string_to_spv("count_experts", "count_experts.comp", merge_maps(base_dict, {{"A_TYPE", "uint"}, {"D_TYPE", "uint"}})); + + for (std::string dim_str : {"", "_3d"}) { + for (bool bda : {false, true}) { + std::string bda_str = bda ? "_bda" : ""; + std::string bda_def = bda ? "1" : "0"; + string_to_spv("im2col" + dim_str + "_f32" + bda_str, "im2col" + dim_str + ".comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}, {"D_SIZE", "4"}, {"BDA", bda_def}})); + string_to_spv("im2col" + dim_str + "_f32_f16" + bda_str, "im2col" + dim_str + ".comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float16_t"}, {"D_SIZE", "2"}, {"BDA", bda_def}})); + } + } + + string_to_spv("timestep_embedding_f32", "timestep_embedding.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}})); + + string_to_spv("conv_transpose_1d_f32", "conv_transpose_1d.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}); + string_to_spv("col2im_1d_f32", "col2im_1d.comp", {{"DATA_A_F32", "1"}, {"A_TYPE", "float"}, {"D_TYPE", "float"}}); + string_to_spv("col2im_1d_f16", "col2im_1d.comp", {{"DATA_A_F16", "1"}, {"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}}); + string_to_spv("col2im_1d_bf16", "col2im_1d.comp", {{"DATA_A_BF16", "1"}, {"A_TYPE", "uint16_t"}, {"D_TYPE", "uint16_t"}}); + + string_to_spv("snake_f32", "snake.comp", {{"DATA_A_F32", "1"}, {"A_TYPE", "float"}, {"D_TYPE", "float"}}); + string_to_spv("snake_f16", "snake.comp", {{"DATA_A_F16", "1"}, {"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}}); + string_to_spv("snake_bf16", "snake.comp", {{"DATA_A_BF16", "1"}, {"DATA_D_BF16", "1"}, {"A_TYPE", "uint16_t"}, {"D_TYPE", "uint16_t"}}); + + string_to_spv("pool2d_f32", "pool2d.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}})); + + string_to_spv("rwkv_wkv6_f32", "wkv6.comp", merge_maps(base_dict, {{"A_TYPE", "float"}})); + + string_to_spv("rwkv_wkv7_f32", "wkv7.comp", merge_maps(base_dict, {{"A_TYPE", "float"}})); + + string_to_spv("gated_delta_net_f32", "gated_delta_net.comp", merge_maps(base_dict, {{"FLOAT_TYPE", "float"}, {"USE_SUBGROUP_ADD", "1"}, {"USE_SUBGROUP_CLUSTERED", "1"}})); + string_to_spv("gated_delta_net_f32_nocluster", "gated_delta_net.comp", merge_maps(base_dict, {{"FLOAT_TYPE", "float"}, {"USE_SUBGROUP_ADD", "1"}, {"USE_SUBGROUP_CLUSTERED", "0"}})); + string_to_spv("gated_delta_net_f32_shmem", "gated_delta_net.comp", merge_maps(base_dict, {{"FLOAT_TYPE", "float"}, {"USE_SUBGROUP_ADD", "0"}, {"USE_SUBGROUP_CLUSTERED", "0"}})); + + string_to_spv("opt_step_adamw_f32", "opt_step_adamw.comp", merge_maps(base_dict, {{"A_TYPE", "float"}})); + string_to_spv("opt_step_sgd_f32", "opt_step_sgd.comp", merge_maps(base_dict, {{"A_TYPE", "float"}})); + + string_to_spv("solve_tri_f32", "solve_tri.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}})); + + for (auto transpose : {false, true}) { + for (auto unroll : {false, true}) { + for (auto a_f16 : {false, true}) { + std::map defines = { + {"A_TYPE", a_f16 ? "float16_t" : "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, + {"USE_COLLECTIVES", "1"}, {"UNROLL", unroll ? "[[unroll]]" : ""}, + }; + if (transpose) defines["TRANSPOSE"] = "1"; + std::string name = std::string(transpose ? "conv_transpose_2d": "conv2d") + + (a_f16 ? "_f16" : "") + "_f32"; + string_to_spv(name + (unroll ? "_unroll" : ""), "conv2d_mm.comp", defines); +#if defined(GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT) + if (unroll) { + auto cm2_defines = defines; + cm2_defines["COOPMAT2"] = "1"; + string_to_spv(name, "conv2d_mm.comp", cm2_defines, true, false, true); + } +#endif +#if defined(GGML_VULKAN_COOPMAT_GLSLC_SUPPORT) + if (unroll) { + auto cm1_defines = defines; + cm1_defines["COOPMAT"] = "1"; + string_to_spv(name, "conv2d_mm.comp", cm1_defines, true, true, false); + } +#endif + } + } + } + + for (auto unroll : {false, true}) { + for (auto a_f16 : {false, true}) { + std::map defines = { + {"A_TYPE", a_f16 ? "float16_t" : "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, + {"UNROLL", unroll ? "[[unroll]]" : ""}, + }; + std::string name = std::string("conv3d") + (a_f16 ? "_f16" : "") + "_f32"; + string_to_spv(name + (unroll ? "_unroll" : ""), "conv3d_mm.comp", defines); +#if defined(GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT) + if (unroll) { + auto cm2_defines = defines; + cm2_defines["COOPMAT2"] = "1"; + string_to_spv(name, "conv3d_mm.comp", cm2_defines, true, false, true); + } +#endif +#if defined(GGML_VULKAN_COOPMAT_GLSLC_SUPPORT) + if (unroll) { + auto cm1_defines = defines; + cm1_defines["COOPMAT"] = "1"; + string_to_spv(name, "conv3d_mm.comp", cm1_defines, true, true, false); + } +#endif + } + } + + string_to_spv("conv2d_dw_whcn_f32", "conv2d_dw.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"WHCN", "1"}})); + string_to_spv("conv2d_dw_cwhn_f32", "conv2d_dw.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"CWHN", "1"}})); + string_to_spv("conv2d_dw_whcn_f16_f32", "conv2d_dw.comp", merge_maps(base_dict, {{"A_TYPE", "float16_t"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"WHCN", "1"}})); + string_to_spv("conv2d_dw_cwhn_f16_f32", "conv2d_dw.comp", merge_maps(base_dict, {{"A_TYPE", "float16_t"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"CWHN", "1"}})); + + string_to_spv("roll_f32", "roll.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}})); + + string_to_spv("add_id_f32", "add_id.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}})); + + string_to_spv("multi_add_f32", "multi_add.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}, {"ADD_RMS" , "0"}}); + string_to_spv("multi_add_rms_f32", "multi_add.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}, {"ADD_RMS" , "1"}}); + + string_to_spv("ssm_scan_f32", "ssm_scan.comp", {{"A_TYPE", "float"}}); + string_to_spv("ssm_scan_subgroup_f32", "ssm_scan.comp", {{"A_TYPE", "float"}, {"USE_SUBGROUP_ADD", "1"}}); + + string_to_spv("ssm_conv_f32", "ssm_conv.comp", {{"A_TYPE", "float"}}); + + string_to_spv("topk_moe_f32", "topk_moe.comp", {}); + + for (auto &c : compiles) { + c.wait(); + } +} + +void write_output_files() { + std::stringstream hdr = make_generic_stringstream(); + std::stringstream src = make_generic_stringstream(); + + hdr << "#include \n\n"; + src << "#include \"" << basename(target_hpp) << "\"\n\n"; + + std::sort(shader_fnames.begin(), shader_fnames.end()); + for (const auto& pair : shader_fnames) { + const std::string& name = pair.first; + #ifdef _WIN32 + std::string path = pair.second; + std::replace(path.begin(), path.end(), '/', '\\' ); + #else + const std::string& path = pair.second; + #endif + + hdr << "extern const uint64_t " << name << "_len;\n"; + hdr << "extern const unsigned char " << name << "_data[];\n\n"; + + if (input_filepath != "") { + std::string data = read_binary_file(path); + if (data.empty()) { + continue; + } + + src << "const uint64_t " << name << "_len = " << data.size() << ";\n"; + src << "const unsigned char " << name << "_data[" << data.size() << "] = {\n" << std::hex; + auto bytes = reinterpret_cast(data.data()); + for (size_t i = 0; i < data.size(); ++i) { + src << "0x" << static_cast(bytes[i]) << ","; + if ((i + 1) % 12 == 0) src << "\n"; + } + src << std::dec << "\n};\n\n"; + } + } + + std::string suffixes[2] = {"_f32", "_f16"}; + for (std::string op : {"add", "sub", "mul", "div", "add_rms"}) { + hdr << "extern const void * " << op << "_data[2][2][2];\n"; + hdr << "extern const uint64_t " << op << "_len[2][2][2];\n"; + + std::string op_file = op == "add_rms" ? "add.comp" : std::string(op) + ".comp"; + if (basename(input_filepath) != op_file) { + continue; + } + std::stringstream data = make_generic_stringstream(); + std::stringstream len = make_generic_stringstream(); + data << "const void * " << op << "_data[2][2][2] = "; + len << "const uint64_t " << op << "_len[2][2][2] = "; + for (uint32_t t0 = 0; t0 < 2; ++t0) { + if (t0 == 0) { + data << "{"; + len << "{"; + } + for (uint32_t t1 = 0; t1 < 2; ++t1) { + if (t1 == 0) { + data << "{"; + len << "{"; + } + for (uint32_t t2 = 0; t2 < 2; ++t2) { + if (t2 == 0) { + data << "{"; + len << "{"; + } + data << op << suffixes[t0] << suffixes[t1] << suffixes[t2]; + len << op << suffixes[t0] << suffixes[t1] << suffixes[t2]; + data << "_data,"; + len << "_len,"; + if (t2 == 1) { + data << "}, "; + len << "}, "; + } + } + if (t1 == 1) { + data << "}, "; + len << "}, "; + } + } + if (t0 == 1) { + data << "};\n"; + len << "};\n"; + } + } + src << data.str(); + src << len.str(); + } + + std::vector btypes = {"f16", "f32"}; + +#if defined(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT) + btypes.push_back("q8_1"); +#endif + + for (const std::string& btype : btypes) { + for (const auto& tname : type_names) { + if (btype == "q8_1" && !is_legacy_quant(tname) && tname != "mxfp4" && !is_k_quant(tname) && tname != "iq1_s" && tname != "iq1_m") { + continue; + } + hdr << "extern const void * arr_dmmv_" << tname << "_" << btype << "_f32_data[3];\n"; + hdr << "extern const uint64_t arr_dmmv_" << tname << "_" << btype << "_f32_len[3];\n"; + if (basename(input_filepath) == "mul_mat_vec.comp") { + src << "const void * arr_dmmv_" << tname << "_" << btype << "_f32_data[3] = {mul_mat_vec_" << tname << "_" << btype << "_f32_data, mul_mat_vec_" << tname << "_" << btype << "_f32_subgroup_data, mul_mat_vec_" << tname << "_" << btype << "_f32_subgroup_no_shmem_data};\n"; + src << "const uint64_t arr_dmmv_" << tname << "_" << btype << "_f32_len[3] = {mul_mat_vec_" << tname << "_" << btype << "_f32_len, mul_mat_vec_" << tname << "_" << btype << "_f32_subgroup_len, mul_mat_vec_" << tname << "_" << btype << "_f32_subgroup_no_shmem_len};\n"; + } + + if (btype == "f16") { + continue; + } + hdr << "extern const void * arr_dmmv_id_" << tname << "_" << btype << "_f32_data[3];\n"; + hdr << "extern const uint64_t arr_dmmv_id_" << tname << "_" << btype << "_f32_len[3];\n"; + if (basename(input_filepath) == "mul_mat_vec.comp") { + src << "const void * arr_dmmv_id_" << tname << "_" << btype << "_f32_data[3] = {mul_mat_vec_id_" << tname << "_" << btype << "_f32_data, mul_mat_vec_id_" << tname << "_" << btype << "_f32_subgroup_data, mul_mat_vec_id_" << tname << "_" << btype << "_f32_subgroup_no_shmem_data};\n"; + src << "const uint64_t arr_dmmv_id_" << tname << "_" << btype << "_f32_len[3] = {mul_mat_vec_id_" << tname << "_" << btype << "_f32_len, mul_mat_vec_id_" << tname << "_" << btype << "_f32_subgroup_len, mul_mat_vec_id_" << tname << "_" << btype << "_f32_subgroup_no_shmem_len};\n"; + } + } + } + + if (input_filepath == "") { + write_file_if_changed(target_hpp, hdr.str()); + } + if (target_cpp != "") { + write_binary_file(target_cpp, src.str()); + } +} + +} // namespace + +int main(int argc, char** argv) { + std::map args; + for (int i = 1; i < argc; ++i) { + std::string arg = argv[i]; + if (arg.rfind("--", 0) == 0) { + if (i + 1 < argc && argv[i + 1][0] != '-') { + args[arg] = argv[i + 1]; + ++i; + } else { + args[arg] = ""; + } + } + } + + if (args.find("--glslc") != args.end()) { + GLSLC = args["--glslc"]; // Path to glslc + } + if (args.find("--source") != args.end()) { + input_filepath = args["--source"]; // The shader source file to compile + } + if (args.find("--output-dir") != args.end()) { + output_dir = args["--output-dir"]; // Directory for containing SPIR-V output + } + if (args.find("--target-hpp") != args.end()) { + target_hpp = args["--target-hpp"]; // Path to generated header file + } + if (args.find("--target-cpp") != args.end()) { + target_cpp = args["--target-cpp"]; // Path to generated cpp file + } + + if (!directory_exists(output_dir)) { + if (!create_directory(output_dir)) { + std::cerr << "Error creating output directory: " << output_dir << "\n"; + return EXIT_FAILURE; + } + } + + process_shaders(); + + if (compile_failed) { + std::cerr << "vulkan-shaders-gen: one or more shaders failed to compile" << std::endl; + return EXIT_FAILURE; + } + + write_output_files(); + + return EXIT_SUCCESS; +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/wkv6.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/wkv6.comp new file mode 100644 index 0000000000000000000000000000000000000000..35cc6c45f90a572eece67491e06475d1fa33a91c --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/wkv6.comp @@ -0,0 +1,87 @@ +#version 450 + +#extension GL_EXT_control_flow_attributes : require + +#define BLOCK_SIZE 64 +layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in; + +layout(push_constant) uniform Parameters { + uint B; + uint T; + uint C; + uint H; +}; + +layout(binding = 0) readonly buffer KBuf { A_TYPE k[]; }; +layout(binding = 1) readonly buffer VBuf { A_TYPE v[]; }; +layout(binding = 2) readonly buffer RBuf { A_TYPE r[]; }; +layout(binding = 3) readonly buffer TimeFBuf { A_TYPE tf[]; }; +layout(binding = 4) readonly buffer TimeDBuf { A_TYPE td[]; }; +layout(binding = 5) readonly buffer StateBuf { A_TYPE state_in[]; }; +layout(binding = 6) buffer DstBuf { A_TYPE dst[]; }; + +shared A_TYPE _k[BLOCK_SIZE], _r[BLOCK_SIZE], _tf[BLOCK_SIZE], _td[BLOCK_SIZE]; + +void main() { + const uint head_size = BLOCK_SIZE; + const uint batch_id = gl_WorkGroupID.x / H; + const uint head_id = gl_WorkGroupID.x % H; + const uint tid = gl_LocalInvocationID.x; + + const uint state_size = C * head_size; + const uint n_seq_tokens = T / B; + + if (batch_id >= B || head_id >= H) { + return; + } + + A_TYPE state[BLOCK_SIZE]; + [[unroll]] for (uint i = 0; i < head_size; i++) { + state[i] = state_in[batch_id * state_size + head_id * head_size * head_size + + i * head_size + tid]; + } + + barrier(); + _tf[tid] = tf[head_id * head_size + tid]; + barrier(); + + const uint start_t = batch_id * n_seq_tokens * C + head_id * head_size + tid; + const uint end_t = (batch_id + 1) * n_seq_tokens * C + head_id * head_size + tid; + + for (uint t = start_t; t < end_t; t += C) { + barrier(); + _k[tid] = k[t]; + _r[tid] = r[t]; + _td[tid] = td[t]; + barrier(); + + const A_TYPE v_val = v[t]; + A_TYPE y = 0.0; + + [[unroll]] for (uint j = 0; j < head_size; j += 4) { + vec4 k_vec = vec4(_k[j], _k[j+1], _k[j+2], _k[j+3]); + vec4 r_vec = vec4(_r[j], _r[j+1], _r[j+2], _r[j+3]); + vec4 tf_vec = vec4(_tf[j], _tf[j+1], _tf[j+2], _tf[j+3]); + vec4 td_vec = vec4(_td[j], _td[j+1], _td[j+2], _td[j+3]); + vec4 s_vec = vec4(state[j], state[j+1], state[j+2], state[j+3]); + + vec4 kv = k_vec * v_val; + + vec4 temp = tf_vec * kv + s_vec; + y += dot(r_vec, temp); + + s_vec = s_vec * td_vec + kv; + state[j] = s_vec.x; + state[j+1] = s_vec.y; + state[j+2] = s_vec.z; + state[j+3] = s_vec.w; + } + + dst[t] = y; + } + + [[unroll]] for (uint i = 0; i < head_size; i++) { + dst[T * C + batch_id * state_size + head_id * head_size * head_size + + i * head_size + tid] = state[i]; + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/wkv7.comp b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/wkv7.comp new file mode 100644 index 0000000000000000000000000000000000000000..88c1c02b32b8c3fa7a027821a1c9b7857164772e --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-vulkan/vulkan-shaders/wkv7.comp @@ -0,0 +1,91 @@ +#version 450 + +#extension GL_EXT_control_flow_attributes : require + +#define BLOCK_SIZE 64 +layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in; + +layout(push_constant) uniform Parameters { + uint B; + uint T; + uint C; + uint H; +}; + +layout(binding = 0) readonly buffer RBuf { A_TYPE r[]; }; +layout(binding = 1) readonly buffer WBuf { A_TYPE w[]; }; +layout(binding = 2) readonly buffer KBuf { A_TYPE k[]; }; +layout(binding = 3) readonly buffer VBuf { A_TYPE v[]; }; +layout(binding = 4) readonly buffer ABuf { A_TYPE a[]; }; +layout(binding = 5) readonly buffer BBuf { A_TYPE b[]; }; +layout(binding = 6) readonly buffer StateBuf { A_TYPE state_in[]; }; +layout(binding = 7) buffer DstBuf { A_TYPE dst[]; }; + +shared A_TYPE _r[BLOCK_SIZE], _w[BLOCK_SIZE], _k[BLOCK_SIZE], _a[BLOCK_SIZE], _b[BLOCK_SIZE]; + +void main() { + const uint head_size = BLOCK_SIZE; + const uint batch_id = gl_WorkGroupID.x / H; + const uint head_id = gl_WorkGroupID.x % H; + const uint tid = gl_LocalInvocationID.x; + + const uint state_size = C * head_size; + const uint n_seq_tokens = T / B; + + if (batch_id >= B || head_id >= H) { + return; + } + + A_TYPE state[BLOCK_SIZE]; + [[unroll]] for (uint i = 0; i < head_size; i++) { + state[i] = state_in[batch_id * state_size + head_id * head_size * head_size + + tid * head_size + i]; + } + + const uint start_t = batch_id * n_seq_tokens * C + head_id * head_size + tid; + const uint end_t = (batch_id + 1) * n_seq_tokens * C + head_id * head_size + tid; + + for (uint t = start_t; t < end_t; t += C) { + barrier(); + _r[tid] = r[t]; + _w[tid] = w[t]; + _k[tid] = k[t]; + _a[tid] = a[t]; + _b[tid] = b[t]; + barrier(); + + A_TYPE sa = 0.0; + [[unroll]] for (uint j = 0; j < head_size; j += 4) { + vec4 s_vec = vec4(state[j], state[j+1], state[j+2], state[j+3]); + vec4 a_vec = vec4(_a[j], _a[j+1], _a[j+2], _a[j+3]); + sa += dot(s_vec, a_vec); + } + + const A_TYPE v_val = v[t]; + A_TYPE y = 0.0; + + [[unroll]] for (uint j = 0; j < head_size; j += 4) { + vec4 r_vec = vec4(_r[j], _r[j+1], _r[j+2], _r[j+3]); + vec4 w_vec = vec4(_w[j], _w[j+1], _w[j+2], _w[j+3]); + vec4 k_vec = vec4(_k[j], _k[j+1], _k[j+2], _k[j+3]); + vec4 b_vec = vec4(_b[j], _b[j+1], _b[j+2], _b[j+3]); + vec4 s_vec = vec4(state[j], state[j+1], state[j+2], state[j+3]); + + vec4 kv = k_vec * v_val; + s_vec = s_vec * w_vec + kv + sa * b_vec; + y += dot(r_vec, s_vec); + + state[j] = s_vec.x; + state[j+1] = s_vec.y; + state[j+2] = s_vec.z; + state[j+3] = s_vec.w; + } + + dst[t] = y; + } + + [[unroll]] for (uint i = 0; i < head_size; i++) { + dst[T * C + batch_id * state_size + head_id * head_size * head_size + + tid * head_size + i] = state[i]; + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-webgpu/CMakeLists.txt b/backend/llama.cpp/ggml/src/ggml-webgpu/CMakeLists.txt new file mode 100644 index 0000000000000000000000000000000000000000..1503a1ef8ba6f05d7e5897028c1d2b080af889e2 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-webgpu/CMakeLists.txt @@ -0,0 +1,83 @@ +cmake_minimum_required(VERSION 3.13) + +find_package(Python3 REQUIRED) + +# Shader locations +set(SHADER_DIR "${CMAKE_CURRENT_SOURCE_DIR}/wgsl-shaders") +set(SHADER_OUTPUT_DIR "${CMAKE_CURRENT_BINARY_DIR}/generated") +set(SHADER_HEADER "${SHADER_OUTPUT_DIR}/ggml-wgsl-shaders.hpp") +file(MAKE_DIRECTORY ${SHADER_OUTPUT_DIR}) + +message(STATUS "Shader output dir: ${SHADER_OUTPUT_DIR}") + +# Find all WGSL sources +file(GLOB WGSL_SHADER_FILES + "${SHADER_DIR}/*.wgsl" + "${SHADER_DIR}/*.tmpl" +) + +# Generate the header using a Python script +add_custom_command( + OUTPUT ${SHADER_HEADER} + COMMAND ${CMAKE_COMMAND} -E echo "Embedding WGSL shaders to ggml-wgsl-shaders.hpp" + COMMAND ${CMAKE_COMMAND} -E make_directory ${SHADER_OUTPUT_DIR} + COMMAND ${CMAKE_COMMAND} -E env PYTHONIOENCODING=utf-8 + ${Python3_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/wgsl-shaders/embed_wgsl.py + --input_dir "${SHADER_DIR}" + --output_file "${SHADER_HEADER}" + DEPENDS ${WGSL_SHADER_FILES} ${CMAKE_CURRENT_SOURCE_DIR}/wgsl-shaders/embed_wgsl.py + VERBATIM +) + +add_custom_target(generate_shaders DEPENDS ${SHADER_HEADER}) + +ggml_add_backend_library(ggml-webgpu + ggml-webgpu.cpp + ${SHADER_HEADER} + ../../include/ggml-webgpu.h +) + +add_dependencies(ggml-webgpu generate_shaders) + +if(EMSCRIPTEN) + set(EMDAWNWEBGPU_DIR "" CACHE PATH "Path to emdawnwebgpu_pkg") + + if(NOT EMDAWNWEBGPU_DIR) + # default built-in port + target_compile_options(ggml-webgpu PRIVATE "--use-port=emdawnwebgpu") + target_link_options(ggml-webgpu INTERFACE "--use-port=emdawnwebgpu") + else() + # custom port + target_compile_options(ggml-webgpu PRIVATE "--use-port=${EMDAWNWEBGPU_DIR}/emdawnwebgpu.port.py") + target_link_options(ggml-webgpu INTERFACE "--use-port=${EMDAWNWEBGPU_DIR}/emdawnwebgpu.port.py") + endif() + + if (GGML_WEBGPU_JSPI) + target_compile_options(ggml-webgpu PRIVATE "-fwasm-exceptions") + target_link_options(ggml-webgpu INTERFACE "-sJSPI" "-fwasm-exceptions") + else() + target_compile_options(ggml-webgpu PRIVATE "-fexceptions") + target_link_options(ggml-webgpu INTERFACE "-sASYNCIFY" "-exceptions") + endif() +else() + find_package(Dawn REQUIRED) + set(DawnWebGPU_TARGET dawn::webgpu_dawn) +endif() + +if (GGML_WEBGPU_DEBUG) + target_compile_definitions(ggml-webgpu PRIVATE GGML_WEBGPU_DEBUG=1) + if(EMSCRIPTEN) + target_link_options(ggml-webgpu INTERFACE "-sASSERTIONS=2") + endif() +endif() + +if (GGML_WEBGPU_CPU_PROFILE) + target_compile_definitions(ggml-webgpu PRIVATE GGML_WEBGPU_CPU_PROFILE=1) +endif() + +if (GGML_WEBGPU_GPU_PROFILE) + target_compile_definitions(ggml-webgpu PRIVATE GGML_WEBGPU_GPU_PROFILE=1) +endif() + +target_include_directories(ggml-webgpu PRIVATE ${SHADER_OUTPUT_DIR}) +target_link_libraries(ggml-webgpu PRIVATE ${DawnWebGPU_TARGET}) diff --git a/backend/llama.cpp/ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp b/backend/llama.cpp/ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp new file mode 100644 index 0000000000000000000000000000000000000000..d7692363a1dedf93119da6c9c351e9f5b78306bd --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-webgpu/ggml-webgpu-shader-lib.hpp @@ -0,0 +1,3300 @@ +#ifndef GGML_WEBGPU_SHADER_LIB_HPP +#define GGML_WEBGPU_SHADER_LIB_HPP + +#include "ggml-impl.h" +#include "ggml-wgsl-shaders.hpp" +#include "ggml.h" +#include "pre_wgsl.hpp" + +#include + +#include +#include +#include +#include +#include + +#define GGML_WEBGPU_F16_SIZE_BYTES 2 +#define GGML_WEBGPU_F32_SIZE_BYTES 4 +#define GGML_WEBGPU_I32_SIZE_BYTES 4 +#define GGML_WEBGPU_FLASH_ATTN_PREFERRED_KV_SG_TILES 8u +#define GGML_WEBGPU_FLASH_ATTN_VEC_MAX_SEQ_LEN 20u +#define GGML_WEBGPU_FLASH_ATTN_VEC_MAX_KV_TILE 32u +#define GGML_WEBGPU_FLASH_ATTN_TILE_MAX_KV_TILE 64u +#define GGML_WEBGPU_FLASH_ATTN_PREFERRED_WG_SIZE 128u +// Matches GGML_PAD(..., 256) in src/llama-context.cpp for KV cache sizing. +#define GGML_WEBGPU_KV_SEQ_PAD 256u + +#define GGML_WEBGPU_ARGSORT_MERGE_MAX_WG_SIZE 512u + +// Matrix multiplication parameters + +// Register tiling parameters +#define WEBGPU_MUL_MAT_TILE_M 4 +#define WEBGPU_MUL_MAT_TILE_N 4 +#define WEBGPU_MUL_MAT_WG_SIZE_M 8 +#define WEBGPU_MUL_MAT_WG_SIZE_N 8 +#define WEBGPU_MUL_MAT_REG_TILE_K_FLOAT 8 +#define WEBGPU_MUL_MAT_REG_TILE_K_QUANT 32 + +// Subgroup matrix parameters +// The number of subgroups in the M dimension +#define WEBGPU_MUL_MAT_SUBGROUP_M 2 +// The number of subgroups in the N dimension +#define WEBGPU_MUL_MAT_SUBGROUP_N 4 +// The number of subgroup matrices each subgroup accumulates over +#define WEBGPU_MUL_MAT_SUBGROUP_MATRIX_M 4 +#define WEBGPU_MUL_MAT_SUBGROUP_MATRIX_N 2 +#define WEBGPU_MUL_MAT_SUBGROUP_TILE_K_FLOAT 32 +#define WEBGPU_MUL_MAT_SUBGROUP_TILE_K_QUANT 32 + +// Matrix-vector multiplication parameters +#define WEBGPU_MUL_MAT_VEC_WG_SIZE 256 + +#define WEBGPU_MUL_MAT_VEC_FLOAT_OUTPUTS_PER_WG 4 +#define WEBGPU_MUL_MAT_VEC_LEGACY_Q_OUTPUTS_PER_WG 4 +#define WEBGPU_MUL_MAT_VEC_K_Q_OUTPUTS_PER_WG 4 + +// default size for reg-tile matrix multiplication +#define WEBGPU_MUL_MAT_WG_SIZE 256 + +// Same hash combine function as in boost +template inline void ggml_webgpu_hash_combine(size_t & seed, const T & value) { + seed ^= std::hash{}(value) + 0x9e3779b9 + (seed << 6) + (seed >> 2); +} + +// Calculates base address of a tensor ignoring the fake base pointer +inline uintptr_t ggml_webgpu_tensor_addr(const ggml_tensor * tensor) { + const ggml_tensor * base_tensor = tensor->view_src ? tensor->view_src : tensor; + return (uintptr_t) base_tensor->data + tensor->view_offs; +} + +inline bool ggml_webgpu_tensor_equal(const ggml_tensor * a, const ggml_tensor * b) { + return a->buffer == b->buffer && ggml_webgpu_tensor_addr(a) == ggml_webgpu_tensor_addr(b); +} + +inline bool ggml_webgpu_tensor_overlap(const ggml_tensor * a, const ggml_tensor * b) { + return a->buffer == b->buffer && ggml_webgpu_tensor_addr(a) < ggml_webgpu_tensor_addr(b) + ggml_nbytes(b) && + ggml_webgpu_tensor_addr(b) < ggml_webgpu_tensor_addr(a) + ggml_nbytes(a); +} + +struct ggml_webgpu_shader_lib_context { + ggml_tensor * src0; + ggml_tensor * src1; + ggml_tensor * src2; + ggml_tensor * src3; + ggml_tensor * src4; + ggml_tensor * src5; + ggml_tensor * dst; + + uint32_t max_wg_size; + size_t wg_mem_limit_bytes = 0; + bool supports_subgroups = false; + bool supports_subgroup_matrix = false; + uint32_t sg_mat_m = 0; + uint32_t sg_mat_n = 0; + uint32_t sg_mat_k = 0; + uint32_t min_subgroup_size = 0; + uint32_t max_subgroup_size = 0; + bool supports_dot_product = false; + std::string vendor; +}; + +struct webgpu_pipeline { + wgpu::ComputePipeline pipeline; + std::string name; + std::shared_ptr context = nullptr; +}; + +struct ggml_webgpu_generic_shader_decisions { + uint32_t wg_size = 0; + bool inplace = false; +}; + +struct ggml_webgpu_binary_shader_decisions { + uint32_t wg_size = 0; + bool inplace = false; + bool overlap = false; + bool src_overlap = false; +}; + +struct ggml_webgpu_processed_shader { + std::string wgsl; + std::string variant; + std::shared_ptr decisions; +}; + +struct ggml_webgpu_ssm_conv_shader_decisions { + uint32_t block_size; + uint32_t tokens_per_wg; +}; + +struct ggml_webgpu_ssm_scan_pipeline_key { + int type; + int d_state; + bool xbc_overlap; + + bool operator==(const ggml_webgpu_ssm_scan_pipeline_key & other) const { + return type == other.type && d_state == other.d_state && xbc_overlap == other.xbc_overlap; + } +}; + +struct ggml_webgpu_ssm_scan_pipeline_key_hash { + size_t operator()(const ggml_webgpu_ssm_scan_pipeline_key & key) const { + size_t seed = 0; + ggml_webgpu_hash_combine(seed, key.type); + ggml_webgpu_hash_combine(seed, key.d_state); + ggml_webgpu_hash_combine(seed, key.xbc_overlap); + return seed; + } +}; + +struct ggml_webgpu_ssm_scan_shader_decisions { + uint32_t wg_size; + uint32_t tokens_per_tile; + bool xbc_overlap = false; +}; + +/** Argsort **/ + +struct ggml_webgpu_argsort_shader_lib_context { + uint32_t max_wg_size; + size_t wg_mem_limit_bytes; + int32_t order; +}; + +/** Set Rows **/ + +struct ggml_webgpu_set_rows_pipeline_key { + int dst_type; + int vec4; + int i64_idx; + int pair_blocks; + + bool operator==(const ggml_webgpu_set_rows_pipeline_key & other) const { + return dst_type == other.dst_type && vec4 == other.vec4 && i64_idx == other.i64_idx && + pair_blocks == other.pair_blocks; + } +}; + +struct ggml_webgpu_set_rows_pipeline_key_hash { + size_t operator()(const ggml_webgpu_set_rows_pipeline_key & key) const { + size_t seed = 0; + ggml_webgpu_hash_combine(seed, key.dst_type); + ggml_webgpu_hash_combine(seed, key.vec4); + ggml_webgpu_hash_combine(seed, key.i64_idx); + ggml_webgpu_hash_combine(seed, key.pair_blocks); + return seed; + } +}; + +struct ggml_webgpu_set_rows_shader_decisions { + bool vec4; + bool i64_idx; + bool pair_blocks; + uint32_t wg_size; +}; + +/** Set **/ + +struct ggml_webgpu_set_pipeline_key { + ggml_type type; + bool inplace; + + bool operator==(const ggml_webgpu_set_pipeline_key & other) const { + return type == other.type && inplace == other.inplace; + } +}; + +struct ggml_webgpu_set_pipeline_key_hash { + size_t operator()(const ggml_webgpu_set_pipeline_key & key) const { + size_t seed = 0; + ggml_webgpu_hash_combine(seed, key.type); + ggml_webgpu_hash_combine(seed, key.inplace); + return seed; + } +}; + +/** Get Rows **/ + +struct ggml_webgpu_get_rows_pipeline_key { + ggml_type src_type; + int vectorized; + + bool operator==(const ggml_webgpu_get_rows_pipeline_key & other) const { + return src_type == other.src_type && vectorized == other.vectorized; + } +}; + +struct ggml_webgpu_get_rows_pipeline_key_hash { + size_t operator()(const ggml_webgpu_get_rows_pipeline_key & key) const { + size_t seed = 0; + ggml_webgpu_hash_combine(seed, key.src_type); + ggml_webgpu_hash_combine(seed, key.vectorized); + return seed; + } +}; + +/** Row Norm **/ + +struct ggml_webgpu_row_norm_pipeline_key { + ggml_op op; + ggml_type src_type; + ggml_type dst_type; + bool inplace; + + bool operator==(const ggml_webgpu_row_norm_pipeline_key & other) const { + return op == other.op && src_type == other.src_type && dst_type == other.dst_type && inplace == other.inplace; + } +}; + +struct ggml_webgpu_row_norm_pipeline_key_hash { + size_t operator()(const ggml_webgpu_row_norm_pipeline_key & key) const { + size_t seed = 0; + ggml_webgpu_hash_combine(seed, key.op); + ggml_webgpu_hash_combine(seed, key.src_type); + ggml_webgpu_hash_combine(seed, key.dst_type); + ggml_webgpu_hash_combine(seed, key.inplace); + return seed; + } +}; + +/** RMS_NORM + MUL **/ + +struct ggml_webgpu_rms_norm_mul_pipeline_key { + bool inplace; // rn_src == dst + bool overlap; // mul_src == dst + bool src_overlap; // rn_src == mul_src + + bool operator==(const ggml_webgpu_rms_norm_mul_pipeline_key & other) const { + return inplace == other.inplace && overlap == other.overlap && src_overlap == other.src_overlap; + } +}; + +struct ggml_webgpu_rms_norm_mul_pipeline_key_hash { + size_t operator()(const ggml_webgpu_rms_norm_mul_pipeline_key & key) const { + size_t seed = 0; + ggml_webgpu_hash_combine(seed, key.inplace); + ggml_webgpu_hash_combine(seed, key.overlap); + ggml_webgpu_hash_combine(seed, key.src_overlap); + return seed; + } +}; + +struct ggml_webgpu_rms_norm_mul_shader_decisions { + uint32_t wg_size = 0; + bool inplace = false; + bool overlap = false; + bool src_overlap = false; +}; + +/** Pad **/ +struct ggml_webgpu_pad_pipeline_key { + bool circular; + + bool operator==(const ggml_webgpu_pad_pipeline_key & other) const { return circular == other.circular; } +}; + +struct ggml_webgpu_pad_pipeline_key_hash { + size_t operator()(const ggml_webgpu_pad_pipeline_key & key) const { + size_t seed = 0; + ggml_webgpu_hash_combine(seed, key.circular); + return seed; + } +}; + +/** Solve Tri **/ +struct ggml_webgpu_solve_tri_pipeline_key { + int type; + int n; + int k; + + bool operator==(const ggml_webgpu_solve_tri_pipeline_key & other) const { + return type == other.type && n == other.n && k == other.k; + } +}; + +struct ggml_webgpu_solve_tri_pipeline_key_hash { + size_t operator()(const ggml_webgpu_solve_tri_pipeline_key & key) const { + size_t seed = 0; + ggml_webgpu_hash_combine(seed, key.type); + ggml_webgpu_hash_combine(seed, key.n); + ggml_webgpu_hash_combine(seed, key.k); + return seed; + } +}; + +/** SSM Conv **/ +struct ggml_webgpu_ssm_conv_pipeline_key { + int type; + int vectorized; + + bool operator==(const ggml_webgpu_ssm_conv_pipeline_key & other) const { + return type == other.type && vectorized == other.vectorized; + } +}; + +/** CONV 2D */ +struct ggml_webgpu_conv2d_pipeline_key { + ggml_type weight_type; + ggml_type input_type; + ggml_type output_type; + + bool operator==(const ggml_webgpu_conv2d_pipeline_key & other) const { + return weight_type == other.weight_type && input_type == other.input_type && output_type == other.output_type; + } +}; + +struct ggml_webgpu_conv2d_pipeline_key_hash { + size_t operator()(const ggml_webgpu_conv2d_pipeline_key & key) const { + size_t seed = 0; + ggml_webgpu_hash_combine(seed, key.weight_type); + ggml_webgpu_hash_combine(seed, key.input_type); + ggml_webgpu_hash_combine(seed, key.output_type); + return seed; + } +}; + +/** Im2Col **/ +struct ggml_webgpu_im2col_pipeline_key { + ggml_type input_type; + ggml_type output_type; + + bool operator==(const ggml_webgpu_im2col_pipeline_key & other) const { + return input_type == other.input_type && output_type == other.output_type; + } +}; + +struct ggml_webgpu_im2col_pipeline_key_hash { + size_t operator()(const ggml_webgpu_im2col_pipeline_key & key) const { + size_t seed = 0; + ggml_webgpu_hash_combine(seed, key.input_type); + ggml_webgpu_hash_combine(seed, key.output_type); + return seed; + } +}; + +/** Gated Delta Net **/ +struct ggml_webgpu_gated_delta_net_pipeline_key { + int type; + int s_v; + int kda; + + bool operator==(const ggml_webgpu_gated_delta_net_pipeline_key & other) const { + return type == other.type && s_v == other.s_v && kda == other.kda; + } +}; + +struct ggml_webgpu_gated_delta_net_pipeline_key_hash { + size_t operator()(const ggml_webgpu_gated_delta_net_pipeline_key & key) const { + size_t seed = 0; + ggml_webgpu_hash_combine(seed, key.type); + ggml_webgpu_hash_combine(seed, key.s_v); + ggml_webgpu_hash_combine(seed, key.kda); + return seed; + } +}; + +struct ggml_webgpu_ssm_conv_pipeline_key_hash { + size_t operator()(const ggml_webgpu_ssm_conv_pipeline_key & key) const { + size_t seed = 0; + ggml_webgpu_hash_combine(seed, key.type); + ggml_webgpu_hash_combine(seed, key.vectorized); + return seed; + } +}; + +/** Scale **/ + +struct ggml_webgpu_scale_pipeline_key { + int inplace; + + bool operator==(const ggml_webgpu_scale_pipeline_key & other) const { return inplace == other.inplace; } +}; + +struct ggml_webgpu_scale_pipeline_key_hash { + size_t operator()(const ggml_webgpu_scale_pipeline_key & key) const { + size_t seed = 0; + ggml_webgpu_hash_combine(seed, key.inplace); + return seed; + } +}; + +/** Upscale **/ + +struct ggml_webgpu_upscale_pipeline_key { + ggml_type input_type; + ggml_type output_type; + uint32_t base_mode; + bool antialias; + + bool operator==(const ggml_webgpu_upscale_pipeline_key & other) const { + return input_type == other.input_type && output_type == other.output_type && base_mode == other.base_mode && + antialias == other.antialias; + } +}; + +struct ggml_webgpu_upscale_pipeline_key_hash { + size_t operator()(const ggml_webgpu_upscale_pipeline_key & key) const { + size_t seed = 0; + ggml_webgpu_hash_combine(seed, key.input_type); + ggml_webgpu_hash_combine(seed, key.output_type); + ggml_webgpu_hash_combine(seed, key.base_mode); + ggml_webgpu_hash_combine(seed, key.antialias); + return seed; + } +}; + +/** Concat **/ + +struct ggml_webgpu_concat_pipeline_key { + int type; + bool src_overlap; + + bool operator==(const ggml_webgpu_concat_pipeline_key & other) const { + return type == other.type && src_overlap == other.src_overlap; + } +}; + +struct ggml_webgpu_concat_pipeline_key_hash { + size_t operator()(const ggml_webgpu_concat_pipeline_key & key) const { + size_t seed = 0; + ggml_webgpu_hash_combine(seed, key.type); + ggml_webgpu_hash_combine(seed, key.src_overlap); + return seed; + } +}; + +/** Repeat **/ + +struct ggml_webgpu_repeat_pipeline_key { + int type; + + bool operator==(const ggml_webgpu_repeat_pipeline_key & other) const { return type == other.type; } +}; + +struct ggml_webgpu_repeat_pipeline_key_hash { + size_t operator()(const ggml_webgpu_repeat_pipeline_key & key) const { + size_t seed = 0; + ggml_webgpu_hash_combine(seed, key.type); + return seed; + } +}; + +/** Binary **/ + +struct ggml_webgpu_binary_pipeline_key { + int type; + int op; + bool inplace; + bool overlap; + bool src_overlap; + + bool operator==(const ggml_webgpu_binary_pipeline_key & other) const { + return type == other.type && op == other.op && inplace == other.inplace && overlap == other.overlap && + src_overlap == other.src_overlap; + } +}; + +struct ggml_webgpu_binary_pipeline_key_hash { + size_t operator()(const ggml_webgpu_binary_pipeline_key & key) const { + size_t seed = 0; + ggml_webgpu_hash_combine(seed, key.type); + ggml_webgpu_hash_combine(seed, key.op); + ggml_webgpu_hash_combine(seed, key.inplace); + ggml_webgpu_hash_combine(seed, key.overlap); + ggml_webgpu_hash_combine(seed, key.src_overlap); + return seed; + } +}; + +/* Add_Id */ + +struct ggml_webgpu_add_id_pipeline_key { + bool inplace; + + bool operator==(const ggml_webgpu_add_id_pipeline_key & other) const { return inplace == other.inplace; } +}; + +struct ggml_webgpu_add_id_pipeline_key_hash { + size_t operator()(const ggml_webgpu_add_id_pipeline_key & key) const { + size_t seed = 0; + ggml_webgpu_hash_combine(seed, key.inplace); + return seed; + } +}; + +/** Unary **/ + +struct ggml_webgpu_unary_pipeline_key { + int type; + int op; + bool is_unary; // many unary operators fall under the GGML_OP_UNARY umbrella + bool inplace; + ggml_tri_type ttype; // only used for GGML_OP_TRI + + bool operator==(const ggml_webgpu_unary_pipeline_key & other) const { + return type == other.type && op == other.op && is_unary == other.is_unary && inplace == other.inplace && + ttype == other.ttype; + } +}; + +struct ggml_webgpu_unary_pipeline_key_hash { + size_t operator()(const ggml_webgpu_unary_pipeline_key & key) const { + size_t seed = 0; + ggml_webgpu_hash_combine(seed, key.type); + ggml_webgpu_hash_combine(seed, key.op); + ggml_webgpu_hash_combine(seed, key.is_unary); + ggml_webgpu_hash_combine(seed, key.inplace); + ggml_webgpu_hash_combine(seed, key.ttype); + return seed; + } +}; + +/** FlashAttention */ + +struct ggml_webgpu_flash_attn_common_pipeline_key { + ggml_type q_type; + ggml_type k_type; + ggml_type v_type; + ggml_type dst_type; + uint32_t head_dim_qk; + uint32_t head_dim_v; + bool kv_direct; + bool kv_overlap; + bool has_mask; + bool has_sinks; + bool uses_logit_softcap; + + bool operator==(const ggml_webgpu_flash_attn_common_pipeline_key & other) const { + return q_type == other.q_type && k_type == other.k_type && v_type == other.v_type && + dst_type == other.dst_type && head_dim_qk == other.head_dim_qk && head_dim_v == other.head_dim_v && + kv_direct == other.kv_direct && kv_overlap == other.kv_overlap && has_mask == other.has_mask && + has_sinks == other.has_sinks && uses_logit_softcap == other.uses_logit_softcap; + } +}; + +inline void ggml_webgpu_flash_attn_hash_common_pipeline_key(size_t & seed, + const ggml_webgpu_flash_attn_common_pipeline_key & key) { + ggml_webgpu_hash_combine(seed, key.q_type); + ggml_webgpu_hash_combine(seed, key.k_type); + ggml_webgpu_hash_combine(seed, key.v_type); + ggml_webgpu_hash_combine(seed, key.dst_type); + ggml_webgpu_hash_combine(seed, key.head_dim_qk); + ggml_webgpu_hash_combine(seed, key.head_dim_v); + ggml_webgpu_hash_combine(seed, key.kv_direct); + ggml_webgpu_hash_combine(seed, key.kv_overlap); + ggml_webgpu_hash_combine(seed, key.has_mask); + ggml_webgpu_hash_combine(seed, key.has_sinks); + ggml_webgpu_hash_combine(seed, key.uses_logit_softcap); +} + +struct ggml_webgpu_flash_attn_vec_pipeline_key { + ggml_webgpu_flash_attn_common_pipeline_key common; + + bool operator==(const ggml_webgpu_flash_attn_vec_pipeline_key & other) const { return common == other.common; } +}; + +struct ggml_webgpu_flash_attn_vec_pipeline_key_hash { + size_t operator()(const ggml_webgpu_flash_attn_vec_pipeline_key & key) const { + size_t seed = 0; + ggml_webgpu_flash_attn_hash_common_pipeline_key(seed, key.common); + return seed; + } +}; + +struct ggml_webgpu_flash_attn_pipeline_key { + ggml_webgpu_flash_attn_common_pipeline_key common; + bool use_sg_matrix; + + bool operator==(const ggml_webgpu_flash_attn_pipeline_key & other) const { + return common == other.common && use_sg_matrix == other.use_sg_matrix; + } +}; + +struct ggml_webgpu_flash_attn_pipeline_key_hash { + size_t operator()(const ggml_webgpu_flash_attn_pipeline_key & key) const { + size_t seed = 0; + ggml_webgpu_flash_attn_hash_common_pipeline_key(seed, key.common); + ggml_webgpu_hash_combine(seed, key.use_sg_matrix); + return seed; + } +}; + +struct ggml_webgpu_flash_attn_vec_decisions { + uint32_t kv_tile = 0; + uint32_t wg_size = 0; +}; + +struct ggml_webgpu_flash_attn_decisions { + bool use_sg_matrix = false; + uint32_t q_tile = 0; + uint32_t kv_tile = 0; + uint32_t wg_size = 0; +}; + +inline constexpr uint32_t GGML_WEBGPU_FLASH_ATTN_TILE_KV_VEC_WIDTH = 4u; +inline constexpr uint32_t GGML_WEBGPU_FLASH_ATTN_TILE_Q_TILE = 4u; + +inline size_t ggml_webgpu_flash_attn_tensor_offset(const ggml_tensor * tensor) { + constexpr uintptr_t ptr_base_addr = 0x1000u; + const ggml_tensor * base = tensor->view_src != nullptr ? tensor->view_src : tensor; + return reinterpret_cast(base->data) - ptr_base_addr + tensor->view_offs; +} + +inline bool ggml_webgpu_flash_attn_float_vec4_aligned(const ggml_tensor * K, size_t storage_offset_alignment) { + const uint32_t offset_elems = + (uint32_t) ((ggml_webgpu_flash_attn_tensor_offset(K) & (storage_offset_alignment - 1)) / + ggml_type_size(K->type)); + return offset_elems % GGML_WEBGPU_FLASH_ATTN_TILE_KV_VEC_WIDTH == 0u; +} + +inline bool ggml_webgpu_flash_attn_float_vec4_aligned(const ggml_tensor * K, + const ggml_tensor * V, + size_t storage_offset_alignment) { + return ggml_webgpu_flash_attn_float_vec4_aligned(K, storage_offset_alignment) && + ggml_webgpu_flash_attn_float_vec4_aligned(V, storage_offset_alignment); +} + +inline bool ggml_webgpu_flash_attn_kv_direct(const ggml_tensor * Q, + const ggml_tensor * K, + const ggml_tensor * V, + uint32_t kv_direct_align) { + return K->type == GGML_TYPE_F16 && V->type == GGML_TYPE_F16 && (Q->ne[0] % kv_direct_align == 0) && + (K->ne[1] % GGML_WEBGPU_KV_SEQ_PAD == 0); +} + +inline ggml_webgpu_flash_attn_common_pipeline_key ggml_webgpu_flash_attn_make_common_pipeline_key( + const ggml_webgpu_shader_lib_context & context, + uint32_t kv_direct_align) { + ggml_webgpu_flash_attn_common_pipeline_key key = {}; + key.q_type = context.src0->type; + key.k_type = context.src1->type; + key.v_type = context.src2->type; + key.dst_type = context.dst->type; + key.head_dim_qk = (uint32_t) context.src0->ne[0]; + key.head_dim_v = (uint32_t) context.src2->ne[0]; + key.kv_direct = ggml_webgpu_flash_attn_kv_direct(context.src0, context.src1, context.src2, kv_direct_align); + key.kv_overlap = ggml_webgpu_tensor_overlap(context.src1, context.src2); + key.has_mask = context.src3 != nullptr; + key.has_sinks = context.src4 != nullptr; + key.uses_logit_softcap = ggml_get_op_params_f32(context.dst, 2) != 0.0f; + return key; +} + +inline std::vector ggml_webgpu_flash_attn_common_defines( + const ggml_webgpu_flash_attn_common_pipeline_key & key, + std::string & variant, + uint32_t q_tile, + uint32_t kv_tile, + uint32_t wg_size) { + std::vector defines; + + switch (key.k_type) { + case GGML_TYPE_F32: + defines.push_back("K_F32"); + break; + case GGML_TYPE_F16: + defines.push_back("K_F16"); + break; + case GGML_TYPE_Q4_0: + defines.push_back("K_Q4_0"); + break; + case GGML_TYPE_Q8_0: + defines.push_back("K_Q8_0"); + break; + default: + GGML_ABORT("Unsupported K type for flash attention shader"); + } + variant += std::string("_k") + ggml_type_name(key.k_type); + + switch (key.v_type) { + case GGML_TYPE_F32: + defines.push_back("V_F32"); + break; + case GGML_TYPE_F16: + defines.push_back("V_F16"); + break; + case GGML_TYPE_Q4_0: + defines.push_back("V_Q4_0"); + break; + case GGML_TYPE_Q8_0: + defines.push_back("V_Q8_0"); + break; + default: + GGML_ABORT("Unsupported V type for flash attention shader"); + } + variant += std::string("_v") + ggml_type_name(key.v_type); + + switch (key.q_type) { + case GGML_TYPE_F32: + defines.push_back("Q_F32"); + break; + case GGML_TYPE_F16: + defines.push_back("Q_F16"); + break; + default: + GGML_ABORT("Unsupported Q type for flash attention shader"); + } + variant += std::string("_q") + ggml_type_name(key.q_type); + + switch (key.dst_type) { + case GGML_TYPE_F32: + defines.push_back("DST_F32"); + break; + case GGML_TYPE_F16: + defines.push_back("DST_F16"); + break; + default: + GGML_ABORT("Unsupported dst type for flash attention shader"); + } + variant += std::string("_dst") + ggml_type_name(key.dst_type); + + if (key.has_mask) { + defines.push_back("MASK"); + variant += "_mask"; + } + if (key.has_sinks) { + defines.push_back("SINKS"); + variant += "_sinks"; + } + if (key.uses_logit_softcap) { + defines.push_back("LOGIT_SOFTCAP"); + variant += "_lgsc"; + } + if (key.kv_direct) { + defines.push_back("KV_DIRECT"); + variant += "_kvdirect"; + } + if (key.kv_overlap) { + defines.push_back("KV_OVERLAP"); + variant += "_kv_overlap"; + } + + defines.push_back(std::string("HEAD_DIM_QK=") + std::to_string(key.head_dim_qk)); + variant += std::string("_hsqk") + std::to_string(key.head_dim_qk); + + defines.push_back(std::string("HEAD_DIM_V=") + std::to_string(key.head_dim_v)); + variant += std::string("_hsv") + std::to_string(key.head_dim_v); + + defines.push_back(std::string("Q_TILE=") + std::to_string(q_tile)); + defines.push_back(std::string("KV_TILE=") + std::to_string(kv_tile)); + defines.push_back(std::string("WG_SIZE=") + std::to_string(wg_size)); + + if (ggml_is_quantized(key.k_type) || ggml_is_quantized(key.v_type)) { + defines.push_back("U32_DEQUANT_HELPERS"); + } + + return defines; +} + +struct ggml_webgpu_flash_attn_vec_reduce_pipeline_key { + uint32_t head_dim_v; + uint32_t wg_size; + ggml_type dst_type; +}; + +struct ggml_webgpu_flash_attn_vec_reduce_pipeline_key_hash { + size_t operator()(const ggml_webgpu_flash_attn_vec_reduce_pipeline_key & key) const { + size_t seed = 0; + ggml_webgpu_hash_combine(seed, key.head_dim_v); + ggml_webgpu_hash_combine(seed, key.wg_size); + ggml_webgpu_hash_combine(seed, key.dst_type); + return seed; + } +}; + +inline bool operator==(const ggml_webgpu_flash_attn_vec_reduce_pipeline_key & lhs, + const ggml_webgpu_flash_attn_vec_reduce_pipeline_key & rhs) { + return lhs.head_dim_v == rhs.head_dim_v && lhs.wg_size == rhs.wg_size && lhs.dst_type == rhs.dst_type; +} + +struct ggml_webgpu_flash_attn_blk_pipeline_key { + uint32_t kv_tile; + + bool operator==(const ggml_webgpu_flash_attn_blk_pipeline_key & other) const { return kv_tile == other.kv_tile; } +}; + +struct ggml_webgpu_flash_attn_blk_pipeline_key_hash { + size_t operator()(const ggml_webgpu_flash_attn_blk_pipeline_key & key) const { + size_t seed = 0; + ggml_webgpu_hash_combine(seed, key.kv_tile); + return seed; + } +}; + +// Note: this will slightly overestimate memory usage for vec path +// since row_max and exp_sum shmem are not needed. +inline size_t ggml_webgpu_flash_attn_wg_mem_bytes(uint32_t q_tile, + uint32_t kv_tile, + uint32_t head_dim_qk, + uint32_t head_dim_v, + bool has_mask, + bool kv_direct) { + const uint32_t max_head_dim = std::max(head_dim_qk, head_dim_v); + size_t f16_elems = 0; + size_t f32_elems = 0; + + f32_elems += q_tile * head_dim_qk; // q_shmem + if (!kv_direct) { + f32_elems += kv_tile * max_head_dim; // kv_shmem + } + f32_elems += q_tile * head_dim_v; // o_shmem + if (has_mask) { + f32_elems += q_tile * kv_tile; // mask_shmem + } + f32_elems += q_tile * kv_tile; // inter_shmem + f32_elems += q_tile; // row_max_shmem + f32_elems += q_tile; // exp_sum_shmem + return f16_elems * GGML_WEBGPU_F16_SIZE_BYTES + f32_elems * GGML_WEBGPU_F32_SIZE_BYTES; +} + +inline uint32_t ggml_webgpu_flash_attn_max_kv_tile(size_t limit_bytes, + uint32_t q_tile, + uint32_t kv_granularity, + uint32_t head_dim_qk, + uint32_t head_dim_v, + bool has_mask, + bool kv_direct) { + const size_t base_q_bytes = + ggml_webgpu_flash_attn_wg_mem_bytes(q_tile, 0, head_dim_qk, head_dim_v, has_mask, kv_direct); + if (limit_bytes <= base_q_bytes) { + return 0; + } + const size_t one_kv_bytes = + ggml_webgpu_flash_attn_wg_mem_bytes(q_tile, 1, head_dim_qk, head_dim_v, has_mask, kv_direct); + const size_t bytes_per_kv = one_kv_bytes - base_q_bytes; + if (bytes_per_kv == 0) { + return 0; + } + const size_t max_kv_tile = (limit_bytes - base_q_bytes) / bytes_per_kv; + return (uint32_t) ((max_kv_tile / kv_granularity) * kv_granularity); +} + +inline uint32_t ggml_webgpu_flash_attn_get_vec_kv_tile(size_t wg_mem_limit_bytes, + uint32_t head_dim_qk, + uint32_t head_dim_v, + bool has_mask, + bool kv_direct) { + const uint32_t max_kv_tile = + ggml_webgpu_flash_attn_max_kv_tile(wg_mem_limit_bytes, 1u, 1u, head_dim_qk, head_dim_v, has_mask, kv_direct); + GGML_ASSERT(max_kv_tile > 0); + + uint32_t kv_tile = std::min(GGML_WEBGPU_FLASH_ATTN_VEC_MAX_KV_TILE, max_kv_tile); + if (kv_direct) { + kv_tile = std::min(kv_tile, GGML_WEBGPU_KV_SEQ_PAD); + while (GGML_WEBGPU_KV_SEQ_PAD % kv_tile != 0) { + kv_tile -= 1u; + } + } + + return kv_tile; +} + +inline bool ggml_webgpu_flash_attn_can_use_subgroup_matrix_path(bool supports_subgroup_matrix, + uint32_t sg_mat_k, + uint32_t sg_mat_n, + const ggml_tensor * Q, + const ggml_tensor * V) { + return supports_subgroup_matrix && Q->ne[0] % sg_mat_k == 0 && V->ne[0] % sg_mat_n == 0; +} + +/** Matrix Multiplication **/ + +struct ggml_webgpu_mul_mat_vec_pipeline_key { + ggml_type src0_type; + ggml_type src1_type; + int vectorized; + uint32_t num_cols; + bool use_mmvq; + + bool operator==(const ggml_webgpu_mul_mat_vec_pipeline_key & other) const { + return src0_type == other.src0_type && src1_type == other.src1_type && vectorized == other.vectorized && + num_cols == other.num_cols && use_mmvq == other.use_mmvq; + } +}; + +struct ggml_webgpu_mul_mat_vec_pipeline_key_hash { + size_t operator()(const ggml_webgpu_mul_mat_vec_pipeline_key & key) const { + size_t seed = 0; + ggml_webgpu_hash_combine(seed, key.src0_type); + ggml_webgpu_hash_combine(seed, key.src1_type); + ggml_webgpu_hash_combine(seed, key.vectorized); + ggml_webgpu_hash_combine(seed, key.num_cols); + ggml_webgpu_hash_combine(seed, key.use_mmvq); + return seed; + } +}; + +struct ggml_webgpu_mul_mat_vec_shader_decisions { + uint32_t wg_size; + uint32_t outputs_per_wg; + uint32_t vec_size; +}; + +struct ggml_webgpu_quantize_q8_pipeline_key { + ggml_type src0_type; + + bool operator==(const ggml_webgpu_quantize_q8_pipeline_key & other) const { return src0_type == other.src0_type; } +}; + +struct ggml_webgpu_quantize_q8_pipeline_key_hash { + size_t operator()(const ggml_webgpu_quantize_q8_pipeline_key & key) const { + size_t seed = 0; + ggml_webgpu_hash_combine(seed, key.src0_type); + return seed; + } +}; + +struct ggml_webgpu_mul_mat_pipeline_key { + ggml_type src0_type; + ggml_type src1_type; + int vectorized; + int use_subgroup_matrix; + + bool operator==(const ggml_webgpu_mul_mat_pipeline_key & other) const { + return src0_type == other.src0_type && src1_type == other.src1_type && vectorized == other.vectorized && + use_subgroup_matrix == other.use_subgroup_matrix; + } +}; + +struct ggml_webgpu_mul_mat_pipeline_key_hash { + size_t operator()(const ggml_webgpu_mul_mat_pipeline_key & key) const { + size_t seed = 0; + ggml_webgpu_hash_combine(seed, key.src0_type); + ggml_webgpu_hash_combine(seed, key.src1_type); + ggml_webgpu_hash_combine(seed, key.vectorized); + ggml_webgpu_hash_combine(seed, key.use_subgroup_matrix); + return seed; + } +}; + +struct ggml_webgpu_mul_mat_shader_decisions { + uint32_t tile_k; + uint32_t wg_size_m; + uint32_t wg_size_n; + uint32_t wg_size; + uint32_t outputs_per_wg; + int use_subgroup_matrix; + + uint32_t tile_m; + uint32_t tile_n; + + // Subgroup matrix parameters + uint32_t subgroup_m; + uint32_t subgroup_n; + uint32_t subgroup_matrix_m; + uint32_t subgroup_matrix_n; + + uint32_t mul_mat_wg_size; +}; + +/** MUL_MAT_ID **/ + +struct ggml_webgpu_mul_mat_id_pipeline_key { + ggml_type src0_type; + ggml_type src1_type; + uint32_t n_experts; + uint32_t num_cols; + int vectorized; + + bool operator==(const ggml_webgpu_mul_mat_id_pipeline_key & other) const { + return src0_type == other.src0_type && src1_type == other.src1_type && n_experts == other.n_experts && + num_cols == other.num_cols && vectorized == other.vectorized; + } +}; + +struct ggml_webgpu_mul_mat_id_pipeline_key_hash { + size_t operator()(const ggml_webgpu_mul_mat_id_pipeline_key & key) const { + size_t seed = 0; + ggml_webgpu_hash_combine(seed, key.src0_type); + ggml_webgpu_hash_combine(seed, key.src1_type); + ggml_webgpu_hash_combine(seed, key.n_experts); + ggml_webgpu_hash_combine(seed, key.num_cols); + ggml_webgpu_hash_combine(seed, key.vectorized); + return seed; + } +}; + +/** Cpy **/ + +struct ggml_webgpu_cpy_pipeline_key { + ggml_type src_type; + ggml_type dst_type; + + bool operator==(const ggml_webgpu_cpy_pipeline_key & other) const { + return src_type == other.src_type && dst_type == other.dst_type; + } +}; + +struct ggml_webgpu_cpy_pipeline_key_hash { + size_t operator()(const ggml_webgpu_cpy_pipeline_key & key) const { + size_t seed = 0; + ggml_webgpu_hash_combine(seed, key.src_type); + ggml_webgpu_hash_combine(seed, key.dst_type); + return seed; + } +}; + +/** Glu **/ + +struct ggml_webgpu_glu_pipeline_key { + ggml_glu_op glu_op; + ggml_type type; + bool split; + + bool operator==(const ggml_webgpu_glu_pipeline_key & other) const { + return glu_op == other.glu_op && type == other.type && split == other.split; + } +}; + +struct ggml_webgpu_glu_pipeline_key_hash { + size_t operator()(const ggml_webgpu_glu_pipeline_key & key) const { + size_t seed = 0; + ggml_webgpu_hash_combine(seed, key.glu_op); + ggml_webgpu_hash_combine(seed, key.type); + ggml_webgpu_hash_combine(seed, key.split); + return seed; + } +}; + +/** Rope **/ + +struct ggml_webgpu_rope_pipeline_key { + ggml_type type; + bool inplace; + bool has_ff; + + bool operator==(const ggml_webgpu_rope_pipeline_key & other) const { + return type == other.type && inplace == other.inplace && has_ff == other.has_ff; + } +}; + +struct ggml_webgpu_rope_pipeline_key_hash { + size_t operator()(const ggml_webgpu_rope_pipeline_key & key) const { + size_t seed = 0; + ggml_webgpu_hash_combine(seed, key.type); + ggml_webgpu_hash_combine(seed, key.inplace); + ggml_webgpu_hash_combine(seed, key.has_ff); + return seed; + } +}; + +/** SoftMax **/ + +struct ggml_webgpu_soft_max_pipeline_key { + ggml_type mask_type; + bool has_mask; + bool has_sink; + bool inplace; + + bool operator==(const ggml_webgpu_soft_max_pipeline_key & other) const { + return mask_type == other.mask_type && has_mask == other.has_mask && has_sink == other.has_sink && + inplace == other.inplace; + } +}; + +struct ggml_webgpu_soft_max_pipeline_key_hash { + size_t operator()(const ggml_webgpu_soft_max_pipeline_key & key) const { + size_t seed = 0; + ggml_webgpu_hash_combine(seed, key.mask_type); + ggml_webgpu_hash_combine(seed, key.has_mask); + ggml_webgpu_hash_combine(seed, key.has_sink); + ggml_webgpu_hash_combine(seed, key.inplace); + return seed; + } +}; + +/** MMVQ **/ + +inline bool ggml_webgpu_can_use_mmvq(const ggml_tensor * src0, + const ggml_tensor * src1, + bool supports_dot_product, + const std::string & vendor) { + if (src1->ne[1] <= 4) { + bool supports_dp4a = vendor == "amd" || vendor == "intel" || vendor == "nvidia"; + if (supports_dp4a && supports_dot_product) { + switch (src1->type) { + case GGML_TYPE_F32: + switch (src0->type) { + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q4_1: + case GGML_TYPE_Q8_0: + case GGML_TYPE_Q2_K: + case GGML_TYPE_Q4_K: + return src0->ne[0] % 4 == 0; + default: + break; + } + break; + default: + break; + } + } + } + return false; +} + +class ggml_webgpu_shader_lib { + wgpu::Device device; + pre_wgsl::Preprocessor preprocessor; + + std::unordered_map sum_rows_pipelines; // key is fixed, no variants yet + std::unordered_map argmax_pipelines; // key is vec4 + std::unordered_map argsort_pipelines; // key is order + std::unordered_map argsort_merge_pipelines; // key is order + std::unordered_map cumsum_pipelines; // key is fixed, no variants yet + std::unordered_map + row_norm_pipelines; // op/inplace + + std::unordered_map + get_rows_pipelines; // src_type, vectorized + std::unordered_map + unary_pipelines; // type/op/inplace + std::unordered_map + scale_pipelines; // inplace + std::unordered_map + solve_tri_pipelines; // type + std::unordered_map + ssm_conv_pipelines; // type/vectorized + std::unordered_map + ssm_scan_pipelines; // type/d_state + std::unordered_map + gated_delta_net_pipelines; // type/S_v/kda + std::unordered_map + pad_pipelines; // circular/non-circular + std::unordered_map + binary_pipelines; // type/op/inplace/overlap/src_overlap + std::unordered_map + add_id_pipelines; // inplace + std::unordered_map + concat_pipelines; // type + std::unordered_map + repeat_pipelines; // type + std::unordered_map + flash_attn_vec_pipelines; + std::unordered_map + flash_attn_pipelines; + std::unordered_map + flash_attn_vec_reduce_pipelines; + std::unordered_map + flash_attn_blk_pipelines; + std::unordered_map + mul_mat_vec_pipelines; // fast mat-vec (n==1) + std::unordered_map + mul_mat_fast_pipelines; // fast mat-mat (reg-tile or subgroup) + std::unordered_map + quantize_q8_pipelines; + std::unordered_map mul_mat_id_gather_pipelines; // key is fixed + std::unordered_map + mul_mat_id_pipelines; // src0_type/src1_type + std::unordered_map + mul_mat_id_vec_pipelines; // src0_type/src1_type + + std::unordered_map + set_rows_pipelines; + std::unordered_map set_pipelines; + std::unordered_map cpy_pipelines; + std::unordered_map glu_pipelines; + std::unordered_map + rope_pipelines; + std::unordered_map + soft_max_pipelines; + std::unordered_map + conv2d_pipelines; + std::unordered_map + im2col_pipelines; + + std::unordered_map + rms_norm_mul_pipelines; + std::unordered_map + upscale_pipelines; + + public: + ggml_webgpu_shader_lib(wgpu::Device device) { this->device = device; } + + webgpu_pipeline get_sum_rows_pipeline(const ggml_webgpu_shader_lib_context & context) { + auto it = sum_rows_pipelines.find(1); + if (it != sum_rows_pipelines.end()) { + return it->second; + } + std::vector defines; + defines.push_back(std::string("WG_SIZE=") + std::to_string(context.max_wg_size)); + + auto processed = preprocessor.preprocess(wgsl_sum_rows, defines); + sum_rows_pipelines[1] = ggml_webgpu_create_pipeline(device, processed, "sum_rows"); + return sum_rows_pipelines[1]; + } + + webgpu_pipeline get_row_norm_pipeline(const ggml_webgpu_shader_lib_context & context) { + ggml_webgpu_row_norm_pipeline_key key = {}; + key.op = context.dst->op; + key.src_type = context.src0->type; + key.dst_type = context.dst->type; + key.inplace = ggml_webgpu_tensor_equal(context.src0, context.dst); + + auto it = row_norm_pipelines.find(key); + if (it != row_norm_pipelines.end()) { + return it->second; + } + std::vector defines; + std::string variant; + + switch (key.op) { + case GGML_OP_RMS_NORM: + defines.push_back("RMS_NORM"); + variant = "rms_norm"; + break; + case GGML_OP_NORM: + defines.push_back("NORM"); + variant = "norm"; + break; + case GGML_OP_L2_NORM: + defines.push_back("L2_NORM"); + variant = "l2_norm"; + break; + default: + GGML_ABORT("Unsupported op for row_norm shader"); + } + + if (key.inplace) { + defines.push_back("INPLACE"); + variant += "_inplace"; + } + + if (key.src_type == GGML_TYPE_F32) { + defines.push_back("SRC_F32"); + variant += "_src_f32"; + } else if (key.src_type == GGML_TYPE_F16) { + defines.push_back("SRC_F16"); + variant += "_src_f16"; + } + + if (key.dst_type == GGML_TYPE_F32) { + defines.push_back("DST_F32"); + variant += "_dst_f32"; + } else if (key.dst_type == GGML_TYPE_F16) { + defines.push_back("DST_F16"); + variant += "_dst_f16"; + } + + const uint32_t row_norm_wg_size = 128u; + uint32_t wg_size = std::min(context.max_wg_size, row_norm_wg_size); + defines.push_back(std::string("WG_SIZE=") + std::to_string(wg_size)); + auto processed = preprocessor.preprocess(wgsl_row_norm, defines); + auto decisions = std::make_shared(); + decisions->wg_size = wg_size; + decisions->inplace = key.inplace; + row_norm_pipelines[key] = ggml_webgpu_create_pipeline(device, processed, variant); + row_norm_pipelines[key].context = decisions; + return row_norm_pipelines[key]; + } + + webgpu_pipeline get_argmax_pipeline(const ggml_webgpu_shader_lib_context & context) { + bool vec4 = context.src0->ne[0] % 4 == 0; + + auto it = argmax_pipelines.find(vec4); + if (it != argmax_pipelines.end()) { + return it->second; + } + std::string variant = "argmax"; + std::vector defines; + defines.push_back(std::string("WG_SIZE=") + std::to_string(context.max_wg_size)); + if (vec4) { + defines.push_back("VEC4"); + variant += "_vec4"; + } + + auto processed = preprocessor.preprocess(wgsl_argmax, defines); + argmax_pipelines[vec4] = ggml_webgpu_create_pipeline(device, processed, variant); + return argmax_pipelines.at(vec4); + } + + webgpu_pipeline get_set_rows_pipeline(const ggml_webgpu_shader_lib_context & context) { + const bool quantized = ggml_is_quantized(context.dst->type); + ggml_webgpu_set_rows_pipeline_key key = {}; + key.dst_type = context.dst->type; + key.vec4 = + (context.dst->type == GGML_TYPE_F32 || context.dst->type == GGML_TYPE_F16) && context.src0->ne[0] % 4 == 0; + key.i64_idx = context.src1->type == GGML_TYPE_I64; + key.pair_blocks = quantized && ((context.src0->ne[0] / ggml_blck_size(context.dst->type)) % 2 == 0); + + auto it = set_rows_pipelines.find(key); + if (it != set_rows_pipelines.end()) { + return it->second; + } + + std::vector defines; + std::string variant = "set_rows"; + + switch (context.dst->type) { + case GGML_TYPE_F32: + defines.push_back("DST_F32"); + variant += "_dstf32"; + break; + case GGML_TYPE_F16: + defines.push_back("DST_F16"); + variant += "_dstf16"; + break; + case GGML_TYPE_Q8_0: + defines.push_back("DST_Q8_0"); + variant += "_dstq8_0"; + break; + case GGML_TYPE_Q4_0: + defines.push_back("DST_Q4_0"); + variant += "_dstq4_0"; + break; + default: + GGML_ABORT("Unsupported dst type for set_rows shader"); + } + + if (key.vec4) { + defines.push_back("VEC4"); + variant += "_vec4"; + } + if (key.i64_idx) { + defines.push_back("I64_IDX"); + variant += "_i64idx"; + } + if (key.pair_blocks) { + defines.push_back("PAIR_BLOCKS"); + variant += "_pair_blocks"; + } + + defines.push_back(std::string("WG_SIZE=") + std::to_string(context.max_wg_size)); + + const auto & shader_source = quantized ? wgsl_set_rows_quant : wgsl_set_rows; + auto processed = preprocessor.preprocess(shader_source, defines); + auto decisions = std::make_shared(); + decisions->vec4 = key.vec4; + decisions->i64_idx = key.i64_idx; + decisions->pair_blocks = key.pair_blocks; + decisions->wg_size = context.max_wg_size; + set_rows_pipelines[key] = ggml_webgpu_create_pipeline(device, processed, variant); + set_rows_pipelines[key].context = decisions; + return set_rows_pipelines[key]; + } + + webgpu_pipeline get_set_pipeline(const ggml_webgpu_shader_lib_context & context) { + ggml_webgpu_set_pipeline_key key = {}; + key.type = context.dst->type; + key.inplace = ggml_webgpu_tensor_equal(context.src0, context.dst); + + auto it = set_pipelines.find(key); + if (it != set_pipelines.end()) { + return it->second; + } + + std::vector defines; + std::string variant = "set"; + + switch (key.type) { + case GGML_TYPE_F32: + defines.push_back("TYPE_F32"); + variant += "_f32"; + break; + case GGML_TYPE_I32: + defines.push_back("TYPE_I32"); + variant += "_i32"; + break; + default: + GGML_ABORT("Unsupported type for set shader"); + } + + if (key.inplace) { + defines.push_back("INPLACE"); + variant += "_inplace"; + } + + defines.push_back(std::string("WG_SIZE=") + std::to_string(context.max_wg_size)); + + auto processed = preprocessor.preprocess(wgsl_set, defines); + auto decisions = std::make_shared(); + decisions->wg_size = context.max_wg_size; + decisions->inplace = key.inplace; + webgpu_pipeline pipeline = ggml_webgpu_create_pipeline(device, processed, variant); + pipeline.context = decisions; + set_pipelines[key] = pipeline; + return set_pipelines[key]; + } + + webgpu_pipeline get_cumsum_pipeline(const ggml_webgpu_shader_lib_context & context) { + auto it = cumsum_pipelines.find(1); + if (it != cumsum_pipelines.end()) { + return it->second; + } + + std::vector defines; + defines.push_back(std::string("WG_SIZE=") + std::to_string(context.max_wg_size)); + + auto processed = preprocessor.preprocess(wgsl_cumsum, defines); + cumsum_pipelines[1] = ggml_webgpu_create_pipeline(device, processed, "cumsum"); + return cumsum_pipelines[1]; + } + + webgpu_pipeline get_argsort_pipeline(const ggml_webgpu_shader_lib_context & context) { + bool is_top_k = context.dst->op == GGML_OP_TOP_K; + // ascending order is 0, descending order is 1 + const int32_t order = + is_top_k ? (int32_t) GGML_SORT_ORDER_DESC : (int32_t) ggml_get_op_params_i32(context.dst, 0); + + auto it = argsort_pipelines.find(order); + if (it != argsort_pipelines.end()) { + return it->second; + } + + std::vector defines; + std::string variant = "argsort"; + defines.push_back(std::string("ORDER=") + std::to_string(order)); + variant += std::string("_order") + std::to_string(order); + uint32_t wg_size = 1; + while (wg_size * 2 <= context.max_wg_size && + wg_size * GGML_WEBGPU_I32_SIZE_BYTES <= context.wg_mem_limit_bytes / 2) { + wg_size *= 2; + } + defines.push_back(std::string("WG_SIZE=") + std::to_string(wg_size)); + auto processed = preprocessor.preprocess(wgsl_argsort, defines); + auto decisions = std::make_shared(); + decisions->wg_size = wg_size; + argsort_pipelines[order] = ggml_webgpu_create_pipeline(device, processed, variant); + argsort_pipelines[order].context = decisions; + return argsort_pipelines[order]; + } + + webgpu_pipeline get_argsort_merge_pipeline(const ggml_webgpu_shader_lib_context & context) { + bool is_top_k = context.dst->op == GGML_OP_TOP_K; + // ascending order is 0, descending order is 1 + const int32_t order = + is_top_k ? (int32_t) GGML_SORT_ORDER_DESC : (int32_t) ggml_get_op_params_i32(context.dst, 0); + + auto it = argsort_merge_pipelines.find(order); + if (it != argsort_merge_pipelines.end()) { + return it->second; + } + + std::vector defines; + std::string variant = "argsort_merge"; + defines.push_back(std::string("ORDER=") + std::to_string(order)); + variant += std::string("_order") + std::to_string(order); + uint32_t wg_size = std::min(GGML_WEBGPU_ARGSORT_MERGE_MAX_WG_SIZE, context.max_wg_size); + defines.push_back(std::string("WG_SIZE=") + std::to_string(wg_size)); + + auto processed = preprocessor.preprocess(wgsl_argsort_merge, defines); + argsort_merge_pipelines[order] = ggml_webgpu_create_pipeline(device, processed, variant); + return argsort_merge_pipelines[order]; + } + + webgpu_pipeline get_get_rows_pipeline(const ggml_webgpu_shader_lib_context & context) { + const bool vectorized = context.src0->type == GGML_TYPE_F32 && context.dst->ne[0] % 4 == 0; + ggml_webgpu_get_rows_pipeline_key key = {}; + key.src_type = context.src0->type; + key.vectorized = (int) vectorized; + + auto it = get_rows_pipelines.find(key); + if (it != get_rows_pipelines.end()) { + return it->second; + } + + std::vector defines; + std::string variant = "get_rows"; + + const struct ggml_type_traits * type_traits = ggml_get_type_traits(key.src_type); + const char * type_str = type_traits->type_name; + + switch (key.src_type) { + case GGML_TYPE_F32: + defines.push_back("FLOAT_PARALLEL"); + if (key.vectorized) { + defines.push_back("F32_VEC"); + defines.push_back("SRC_TYPE=vec4"); + defines.push_back("DST_TYPE=vec4"); + defines.push_back("BLOCK_SIZE=4u"); + } else { + defines.push_back("F32"); + defines.push_back("SRC_TYPE=f32"); + defines.push_back("DST_TYPE=f32"); + defines.push_back("BLOCK_SIZE=1u"); + } + variant += "_f32"; + break; + case GGML_TYPE_F16: + defines.push_back("FLOAT_PARALLEL"); + defines.push_back("F16"); + defines.push_back("SRC_TYPE=f16"); + defines.push_back("DST_TYPE=f32"); + defines.push_back("BLOCK_SIZE=1u"); + variant += "_f16"; + break; + case GGML_TYPE_I32: + defines.push_back("FLOAT_PARALLEL"); + defines.push_back("I32"); + defines.push_back("SRC_TYPE=i32"); + defines.push_back("DST_TYPE=i32"); + defines.push_back("BLOCK_SIZE=1u"); + variant += "_i32"; + break; + default: + { + std::string type_upper = type_str; + std::transform(type_upper.begin(), type_upper.end(), type_upper.begin(), ::toupper); + + switch (key.src_type) { + case GGML_TYPE_Q1_0: + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q5_0: + case GGML_TYPE_Q8_0: + case GGML_TYPE_Q3_K: + case GGML_TYPE_Q6_K: + case GGML_TYPE_IQ2_XXS: + case GGML_TYPE_IQ2_XS: + case GGML_TYPE_IQ2_S: + case GGML_TYPE_IQ3_XXS: + case GGML_TYPE_IQ3_S: + case GGML_TYPE_IQ1_S: + case GGML_TYPE_IQ4_NL: + case GGML_TYPE_MXFP4: + case GGML_TYPE_NVFP4: + { + // Quantized types using u32 buffers for portability. + defines.push_back("SRC_TYPE=u32"); + defines.push_back("U32_DEQUANT_HELPERS"); + break; + } + default: + { + defines.push_back(std::string("SRC_TYPE=") + type_str); + } + } + + defines.push_back("BYTE_HELPERS"); + defines.push_back(type_upper + "_T"); + defines.push_back(type_upper); + defines.push_back(type_upper + "_SCALE_MIN"); + defines.push_back(type_upper + "_TABLES"); + defines.push_back(type_upper + "_GRID"); + defines.push_back(type_upper + "_LUT"); + + variant += "_"; + variant += type_str; + + defines.push_back("DST_TYPE=f32"); + + if (key.src_type == GGML_TYPE_Q1_0) { + defines.push_back("BLOCK_SIZE=128u"); + } else if ((key.src_type >= GGML_TYPE_Q4_0 && key.src_type <= GGML_TYPE_Q8_1) || + key.src_type == GGML_TYPE_IQ4_NL || key.src_type == GGML_TYPE_MXFP4) { + defines.push_back("BLOCK_SIZE=32u"); + } else if (key.src_type == GGML_TYPE_NVFP4) { + defines.push_back("BLOCK_SIZE=64u"); + } else if (key.src_type >= GGML_TYPE_Q2_K) { + defines.push_back("BLOCK_SIZE=256u"); + } else { + defines.push_back("BLOCK_SIZE=1u"); + } + break; + } + } + + if (key.vectorized) { + variant += "_vec"; + } + + defines.push_back("WG_SIZE=" + std::to_string(context.max_wg_size)); + + auto processed = preprocessor.preprocess(wgsl_get_rows, defines); + auto decisions = std::make_shared(); + decisions->wg_size = context.max_wg_size; + webgpu_pipeline pipeline = ggml_webgpu_create_pipeline(device, processed, variant); + pipeline.context = decisions; + get_rows_pipelines[key] = pipeline; + return get_rows_pipelines[key]; + } + + webgpu_pipeline get_scale_pipeline(const ggml_webgpu_shader_lib_context & context) { + ggml_webgpu_scale_pipeline_key key = {}; + key.inplace = ggml_webgpu_tensor_equal(context.src0, context.dst); + + auto it = scale_pipelines.find(key); + if (it != scale_pipelines.end()) { + return it->second; + } + + std::vector defines; + std::string variant = "scale"; + + if (key.inplace) { + defines.push_back("INPLACE"); + variant += "_inplace"; + } + + defines.push_back(std::string("WG_SIZE=") + std::to_string(context.max_wg_size)); + + auto processed = preprocessor.preprocess(wgsl_scale, defines); + auto decisions = std::make_shared(); + decisions->wg_size = context.max_wg_size; + decisions->inplace = key.inplace; + webgpu_pipeline pipeline = ggml_webgpu_create_pipeline(device, processed, variant); + pipeline.context = decisions; + scale_pipelines[key] = pipeline; + return scale_pipelines[key]; + } + + webgpu_pipeline get_solve_tri_pipeline(const ggml_webgpu_shader_lib_context & context) { + ggml_webgpu_solve_tri_pipeline_key key = {}; + key.type = context.dst->type; + key.n = (int) context.src0->ne[0]; + key.k = (int) context.src1->ne[0]; + + auto it = solve_tri_pipelines.find(key); + if (it != solve_tri_pipelines.end()) { + return it->second; + } + + std::vector defines; + std::string variant = "solve_tri"; + + switch (key.type) { + case GGML_TYPE_F32: + variant += "_f32"; + break; + default: + GGML_ABORT("Unsupported type for solve_tri shader"); + } + + const uint32_t wg_size = std::min((uint32_t) key.n, context.max_wg_size); + const uint32_t k_tile = wg_size; + const uint32_t bytes_per_row = ((uint32_t) key.n + wg_size) * GGML_WEBGPU_F32_SIZE_BYTES; + const uint32_t batch_n = (uint32_t) (context.wg_mem_limit_bytes / bytes_per_row); + + defines.push_back(std::string("N=") + std::to_string(key.n)); + defines.push_back(std::string("WG_SIZE=") + std::to_string(wg_size)); + defines.push_back(std::string("K_TILE=") + std::to_string(k_tile)); + defines.push_back(std::string("BATCH_N=") + std::to_string(batch_n)); + + auto processed = preprocessor.preprocess(wgsl_solve_tri, defines); + auto decisions = std::make_shared(); + decisions->wg_size = wg_size; + webgpu_pipeline pipeline = ggml_webgpu_create_pipeline(device, processed, variant); + pipeline.context = decisions; + solve_tri_pipelines[key] = pipeline; + return solve_tri_pipelines[key]; + } + + webgpu_pipeline get_ssm_conv_pipeline(const ggml_webgpu_shader_lib_context & context) { + ggml_webgpu_ssm_conv_pipeline_key key = {}; + key.type = context.dst->type; + key.vectorized = context.src1->ne[0] == 4; + + auto it = ssm_conv_pipelines.find(key); + if (it != ssm_conv_pipelines.end()) { + return it->second; + } + + std::vector defines; + std::string variant = "ssm_conv"; + + switch (key.type) { + case GGML_TYPE_F32: + variant += "_f32"; + break; + default: + GGML_ABORT("Unsupported type for ssm_conv shader"); + } + + if (key.vectorized) { + defines.push_back("VECTORIZED"); + variant += "_vec4"; + } + + constexpr uint32_t block_size = 32u; + constexpr uint32_t tokens_per_wg = 8u; + + defines.push_back("BLOCK_SIZE=" + std::to_string(block_size) + "u"); + defines.push_back("TOKENS_PER_WG=" + std::to_string(tokens_per_wg) + "u"); + + auto processed = preprocessor.preprocess(wgsl_ssm_conv, defines); + auto decisions = std::make_shared(); + decisions->block_size = block_size; + decisions->tokens_per_wg = tokens_per_wg; + webgpu_pipeline pipeline = ggml_webgpu_create_pipeline(device, processed, variant); + pipeline.context = decisions; + ssm_conv_pipelines[key] = pipeline; + return ssm_conv_pipelines[key]; + } + + webgpu_pipeline get_ssm_scan_pipeline(const ggml_webgpu_shader_lib_context & context) { + ggml_webgpu_ssm_scan_pipeline_key key = {}; + key.type = context.dst->type; + key.d_state = (int) context.src0->ne[0]; + key.xbc_overlap = ggml_webgpu_tensor_overlap(context.src1, context.src4) && + ggml_webgpu_tensor_overlap(context.src1, context.src5); + + auto it = ssm_scan_pipelines.find(key); + if (it != ssm_scan_pipelines.end()) { + return it->second; + } + + std::vector defines; + std::string variant = "ssm_scan"; + + switch (key.type) { + case GGML_TYPE_F32: + variant += "_f32"; + break; + default: + GGML_ABORT("Unsupported type for ssm_scan shader"); + } + + const uint32_t wg_size = (uint32_t) key.d_state; + + constexpr uint32_t tokens_per_tile = 4u; + + defines.push_back("WG_SIZE=" + std::to_string(wg_size) + "u"); + defines.push_back("TOKENS_PER_TILE=" + std::to_string(tokens_per_tile) + "u"); + + if (context.supports_subgroups) { + defines.push_back("USE_SUBGROUP_REDUCTION"); + variant += "_sg_reduce"; + } else { + variant += "_wg_reduce"; + } + + if (key.xbc_overlap) { + defines.push_back("XBC_OVERLAP"); + } + + variant += "_d" + std::to_string(key.d_state); + + auto processed = preprocessor.preprocess(wgsl_ssm_scan, defines); + auto decisions = std::make_shared(); + decisions->wg_size = wg_size; + decisions->tokens_per_tile = tokens_per_tile; + decisions->xbc_overlap = key.xbc_overlap; + webgpu_pipeline pipeline = ggml_webgpu_create_pipeline(device, processed, variant); + pipeline.context = decisions; + ssm_scan_pipelines[key] = pipeline; + return ssm_scan_pipelines[key]; + } + + webgpu_pipeline get_gated_delta_net_pipeline(const ggml_webgpu_shader_lib_context & context) { + ggml_webgpu_gated_delta_net_pipeline_key key = {}; + key.type = context.dst->type; + key.s_v = (int) context.src2->ne[0]; + key.kda = context.src3->ne[0] == context.src2->ne[0]; + + auto it = gated_delta_net_pipelines.find(key); + if (it != gated_delta_net_pipelines.end()) { + return it->second; + } + + std::vector defines; + std::string variant = "gated_delta_net"; + + switch (key.type) { + case GGML_TYPE_F32: + variant += "_f32"; + break; + default: + GGML_ABORT("Unsupported type for gated_delta_net shader"); + } + + if (key.kda) { + defines.push_back("KDA"); + variant += "_kda"; + } + + defines.push_back("S_V=" + std::to_string(key.s_v) + "u"); + defines.push_back("WG_SIZE=" + std::to_string(key.s_v) + "u"); + + auto processed = preprocessor.preprocess(wgsl_gated_delta_net, defines); + webgpu_pipeline pipeline = ggml_webgpu_create_pipeline(device, processed, variant); + gated_delta_net_pipelines[key] = pipeline; + return gated_delta_net_pipelines[key]; + } + + webgpu_pipeline get_pad_pipeline(const ggml_webgpu_shader_lib_context & context) { + ggml_webgpu_pad_pipeline_key key = {}; + key.circular = ggml_get_op_params_i32(context.dst, 8) != 0; + + auto it = pad_pipelines.find(key); + if (it != pad_pipelines.end()) { + return it->second; + } + + std::vector defines; + std::string variant = "pad"; + + if (key.circular) { + defines.push_back("CIRCULAR"); + variant += "_circular"; + } + + defines.push_back(std::string("WG_SIZE=") + std::to_string(context.max_wg_size)); + + auto processed = preprocessor.preprocess(wgsl_pad, defines); + auto decisions = std::make_shared(); + decisions->wg_size = context.max_wg_size; + webgpu_pipeline pipeline = ggml_webgpu_create_pipeline(device, processed, variant); + pipeline.context = decisions; + pad_pipelines[key] = pipeline; + return pad_pipelines[key]; + } + + webgpu_pipeline get_quantize_q8_pipeline(const ggml_webgpu_shader_lib_context & context) { + ggml_webgpu_quantize_q8_pipeline_key key = {}; + key.src0_type = context.src0->type; + + auto it = quantize_q8_pipelines.find(key); + if (it != quantize_q8_pipelines.end()) { + return it->second; + } + const char * shader_src = wgsl_quantize_q8; + std::vector defines; + std::string variant = "quantize_q8"; + + uint32_t wg_size = WEBGPU_MUL_MAT_VEC_WG_SIZE; + + defines.push_back("SRC1_INNER_TYPE=f32"); + defines.push_back(std::string("WG_SIZE=") + std::to_string(wg_size)); + + const struct ggml_type_traits * src0_traits = ggml_get_type_traits(context.src0->type); + std::string src0_name = src0_traits->type_name; + std::string type_upper = src0_name; + variant += "_" + src0_name; + std::transform(type_upper.begin(), type_upper.end(), type_upper.begin(), ::toupper); + + defines.push_back("MUL_ACC_" + type_upper); + defines.push_back("Q8_1_T"); + + defines.push_back(context.supports_subgroups ? "USE_SUBGROUP_REDUCTION" : "USE_WORKGROUP_REDUCTION"); + variant += context.supports_subgroups ? "_sg_reduce" : "_wg_reduce"; + + auto processed = preprocessor.preprocess(shader_src, defines); + auto decisions = std::make_shared(); + decisions->wg_size = wg_size; + webgpu_pipeline pipeline = ggml_webgpu_create_pipeline(device, processed, variant); + pipeline.context = decisions; + quantize_q8_pipelines[key] = pipeline; + return quantize_q8_pipelines[key]; + } + + webgpu_pipeline get_mul_mat_vec_pipeline(const ggml_webgpu_shader_lib_context & context) { + ggml_webgpu_mul_mat_vec_pipeline_key key = {}; + key.src0_type = context.src0->type; + key.src1_type = context.src1->type; + key.vectorized = (context.src0->ne[0] % 4 == 0 && + (context.src0->type == GGML_TYPE_F32 || context.src0->type == GGML_TYPE_F16)) ? + 1 : + 0; + key.num_cols = context.dst->ne[1]; + key.use_mmvq = + ggml_webgpu_can_use_mmvq(context.src0, context.src1, context.supports_dot_product, context.vendor); + + auto it = mul_mat_vec_pipelines.find(key); + if (it != mul_mat_vec_pipelines.end()) { + return it->second; + } + + std::vector defines; + std::string variant = "mul_mat_vec"; + const char * shader_src = wgsl_mul_mat_vec; + + // src0 type (matrix row) + switch (context.src0->type) { + case GGML_TYPE_F32: + defines.push_back("SRC0_INNER_TYPE=f32"); + defines.push_back("MUL_ACC_FLOAT"); + variant += "_f32"; + break; + case GGML_TYPE_F16: + defines.push_back("SRC0_INNER_TYPE=f16"); + defines.push_back("MUL_ACC_FLOAT"); + variant += "_f16"; + break; + default: + { + // Quantized types: use helpers but accumulate in f16 + const struct ggml_type_traits * src0_traits = ggml_get_type_traits(context.src0->type); + std::string src0_name = src0_traits->type_name; + std::string type_upper = src0_name; + variant += "_" + src0_name; + std::transform(type_upper.begin(), type_upper.end(), type_upper.begin(), ::toupper); + + defines.push_back("BYTE_HELPERS"); + defines.push_back("MUL_ACC_" + type_upper); + defines.push_back("U32_DEQUANT_HELPERS"); + defines.push_back("SRC0_INNER_TYPE=u32"); + switch (context.src0->type) { + case GGML_TYPE_Q8_0: + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q4_1: + if (key.use_mmvq) { + defines.push_back("LEGACY_QUANTS"); + } + break; + case GGML_TYPE_Q2_K: + case GGML_TYPE_Q4_K: + if (key.use_mmvq) { + defines.push_back("K_QUANTS"); + } + break; + case GGML_TYPE_IQ1_S: + case GGML_TYPE_IQ1_M: + case GGML_TYPE_IQ2_S: + case GGML_TYPE_IQ3_S: + case GGML_TYPE_IQ4_NL: + case GGML_TYPE_IQ4_XS: + defines.push_back(type_upper + "_GRID"); + break; + case GGML_TYPE_IQ2_XXS: + case GGML_TYPE_IQ2_XS: + case GGML_TYPE_IQ3_XXS: + defines.push_back(type_upper + "_GRID"); + defines.push_back(type_upper + "_TABLES"); + break; + case GGML_TYPE_MXFP4: + case GGML_TYPE_NVFP4: + defines.push_back(type_upper + "_LUT"); + break; + default: + break; + } + break; + } + } + + // src1 type (vector) + switch (context.src1->type) { + case GGML_TYPE_F32: + defines.push_back("SRC1_INNER_TYPE=f32"); + variant += "_f32"; + break; + case GGML_TYPE_F16: + defines.push_back("SRC1_INNER_TYPE=f16"); + variant += "_f16"; + break; + default: + GGML_ABORT("Unsupported src1 type for mul_mat_vec shader"); + } + + // VEC/SCALAR controls + defines.push_back(key.vectorized ? "VEC" : "SCALAR"); + + uint32_t wg_size = WEBGPU_MUL_MAT_VEC_WG_SIZE; + uint32_t outputs_per_wg = WEBGPU_MUL_MAT_VEC_FLOAT_OUTPUTS_PER_WG; + + if (key.src0_type == GGML_TYPE_Q1_0) { + outputs_per_wg = WEBGPU_MUL_MAT_VEC_LEGACY_Q_OUTPUTS_PER_WG; + } else if (key.src0_type >= GGML_TYPE_Q2_K) { + outputs_per_wg = WEBGPU_MUL_MAT_VEC_K_Q_OUTPUTS_PER_WG; + } else if (key.src0_type >= GGML_TYPE_Q4_0) { + outputs_per_wg = WEBGPU_MUL_MAT_VEC_LEGACY_Q_OUTPUTS_PER_WG; + } + + if (key.use_mmvq) { + defines.push_back("MMVQ"); + defines.push_back("Q8_1_T"); + } + + defines.push_back(std::string("WG_SIZE=") + std::to_string(wg_size)); + defines.push_back(std::string("OUTPUTS_PER_WG=") + std::to_string(outputs_per_wg)); + defines.push_back(context.supports_subgroups ? "USE_SUBGROUP_REDUCTION" : "USE_WORKGROUP_REDUCTION"); + variant += context.supports_subgroups ? "_sg_reduce" : "_wg_reduce"; + if (key.vectorized) { + variant += "_vectorized"; + } + defines.push_back(std::string("NUM_COLS=") + std::to_string(key.num_cols)); + + auto processed = preprocessor.preprocess(shader_src, defines); + auto decisions = std::make_shared(); + decisions->wg_size = wg_size; + decisions->outputs_per_wg = outputs_per_wg; + decisions->vec_size = key.vectorized ? 4 : 1; + + webgpu_pipeline pipeline = ggml_webgpu_create_pipeline(device, processed, variant); + pipeline.context = decisions; + mul_mat_vec_pipelines[key] = pipeline; + return mul_mat_vec_pipelines[key]; + } + + webgpu_pipeline get_mul_mat_fast_pipeline(const ggml_webgpu_shader_lib_context & context) { + ggml_webgpu_mul_mat_pipeline_key key = {}; + key.src0_type = context.src0->type; + key.src1_type = context.src1->type; + key.vectorized = (context.src0->ne[0] % 4 == 0 && context.dst->ne[0] % 4 == 0 && + (context.src0->type == GGML_TYPE_F32 || context.src0->type == GGML_TYPE_F16)) ? + 1 : + 0; + key.use_subgroup_matrix = context.supports_subgroup_matrix; + + auto it = mul_mat_fast_pipelines.find(key); + if (it != mul_mat_fast_pipelines.end()) { + return it->second; + } + + const char * shader_src = key.use_subgroup_matrix ? wgsl_mul_mat_subgroup_matrix : wgsl_mul_mat_reg_tile; + std::vector defines; + std::string variant = key.use_subgroup_matrix ? "mul_mat_subgroup_matrix" : "mul_mat_reg_tile"; + + // src1 type + switch (context.src1->type) { + case GGML_TYPE_F32: + defines.push_back("SRC1_INNER_TYPE=f32"); + break; + case GGML_TYPE_F16: + defines.push_back("SRC1_INNER_TYPE=f16"); + break; + default: + GGML_ABORT("Unsupported src1 type for mul_mat fast shader"); + } + + // src0 type + const struct ggml_type_traits * src0_traits = ggml_get_type_traits(context.src0->type); + const char * src0_name = src0_traits->type_name; + + switch (context.src0->type) { + case GGML_TYPE_F32: + defines.push_back("SRC0_INNER_TYPE=f32"); + defines.push_back("FLOAT"); + defines.push_back("MUL_ACC_FLOAT"); + defines.push_back("INIT_SRC0_SHMEM_FLOAT"); + defines.push_back("INIT_SRC1_SHMEM_FLOAT"); + variant += "_f32"; + break; + case GGML_TYPE_F16: + defines.push_back("SRC0_INNER_TYPE=f16"); + defines.push_back("FLOAT"); + defines.push_back("MUL_ACC_FLOAT"); + defines.push_back("INIT_SRC0_SHMEM_FLOAT"); + defines.push_back("INIT_SRC1_SHMEM_FLOAT"); + variant += "_f16"; + break; + default: + { + std::string type_upper = src0_name; + std::transform(type_upper.begin(), type_upper.end(), type_upper.begin(), ::toupper); + + defines.push_back("BYTE_HELPERS"); + defines.push_back("MUL_ACC_" + type_upper); + defines.push_back("INIT_SRC0_SHMEM_" + type_upper); + defines.push_back("INIT_SRC1_SHMEM_FLOAT"); + defines.push_back("U32_DEQUANT_HELPERS"); + defines.push_back("SRC0_INNER_TYPE=u32"); + + switch (context.src0->type) { + case GGML_TYPE_IQ1_S: + case GGML_TYPE_IQ1_M: + case GGML_TYPE_IQ4_NL: + case GGML_TYPE_IQ4_XS: + defines.push_back(type_upper + "_GRID"); + break; + case GGML_TYPE_IQ2_XXS: + case GGML_TYPE_IQ2_XS: + case GGML_TYPE_IQ2_S: + case GGML_TYPE_IQ3_XXS: + case GGML_TYPE_IQ3_S: + defines.push_back(type_upper + "_GRID"); + defines.push_back(type_upper + "_TABLES"); + break; + case GGML_TYPE_MXFP4: + case GGML_TYPE_NVFP4: + defines.push_back(type_upper + "_LUT"); + break; + default: + break; + } + + variant += std::string("_") + src0_name; + break; + } + } + + // VEC/SCALAR controls + defines.push_back(key.vectorized ? "VEC" : "SCALAR"); + + const bool is_quant = ggml_is_quantized(context.src0->type); + + uint32_t tile_k; + if (key.use_subgroup_matrix) { + tile_k = is_quant ? WEBGPU_MUL_MAT_SUBGROUP_TILE_K_QUANT : WEBGPU_MUL_MAT_SUBGROUP_TILE_K_FLOAT; + } else { + tile_k = is_quant ? WEBGPU_MUL_MAT_REG_TILE_K_QUANT : WEBGPU_MUL_MAT_REG_TILE_K_FLOAT; + } + + // Tiles + defines.push_back("TILE_M=" + std::to_string(WEBGPU_MUL_MAT_TILE_M) + "u"); + defines.push_back("TILE_N=" + std::to_string(WEBGPU_MUL_MAT_TILE_N) + "u"); + + // Subgroup matrix specifics + if (key.use_subgroup_matrix) { + defines.push_back("TILE_K=" + std::to_string(tile_k) + "u"); + defines.push_back("MAX_SUBGROUP_SIZE=" + std::to_string(context.max_subgroup_size) + "u"); + defines.push_back("SUBGROUP_M=" + std::to_string(WEBGPU_MUL_MAT_SUBGROUP_M) + "u"); + defines.push_back("SUBGROUP_N=" + std::to_string(WEBGPU_MUL_MAT_SUBGROUP_N) + "u"); + defines.push_back("SUBGROUP_MATRIX_M=" + std::to_string(WEBGPU_MUL_MAT_SUBGROUP_MATRIX_M) + "u"); + defines.push_back("SUBGROUP_MATRIX_N=" + std::to_string(WEBGPU_MUL_MAT_SUBGROUP_MATRIX_N) + "u"); + defines.push_back("SUBGROUP_MATRIX_M_SIZE=" + std::to_string(context.sg_mat_m) + "u"); + defines.push_back("SUBGROUP_MATRIX_N_SIZE=" + std::to_string(context.sg_mat_n) + "u"); + defines.push_back("SUBGROUP_MATRIX_K_SIZE=" + std::to_string(context.sg_mat_k) + "u"); + } + + // variant suffix for src1 type + variant += std::string("_") + (context.src1->type == GGML_TYPE_F32 ? "f32" : "f16"); + if (key.vectorized) { + variant += "_vectorized"; + } + + if (!key.use_subgroup_matrix) { + defines.push_back("WORKGROUP_SIZE_M=" + std::to_string(WEBGPU_MUL_MAT_WG_SIZE_M) + "u"); + defines.push_back("WORKGROUP_SIZE_N=" + std::to_string(WEBGPU_MUL_MAT_WG_SIZE_N) + "u"); + defines.push_back("TILE_K=" + std::to_string(tile_k) + "u"); + } + + auto processed = preprocessor.preprocess(shader_src, defines); + + auto decisions = std::make_shared(); + decisions->tile_k = tile_k; + decisions->tile_m = WEBGPU_MUL_MAT_TILE_M; + decisions->tile_n = WEBGPU_MUL_MAT_TILE_N; + decisions->use_subgroup_matrix = key.use_subgroup_matrix; + if (key.use_subgroup_matrix) { + decisions->subgroup_m = WEBGPU_MUL_MAT_SUBGROUP_M; + decisions->subgroup_n = WEBGPU_MUL_MAT_SUBGROUP_N; + decisions->subgroup_matrix_m = WEBGPU_MUL_MAT_SUBGROUP_MATRIX_M; + decisions->subgroup_matrix_n = WEBGPU_MUL_MAT_SUBGROUP_MATRIX_N; + decisions->wg_size = context.max_subgroup_size; + } else { + decisions->wg_size_m = WEBGPU_MUL_MAT_WG_SIZE_M; + decisions->wg_size_n = WEBGPU_MUL_MAT_WG_SIZE_N; + decisions->wg_size = WEBGPU_MUL_MAT_WG_SIZE_M * WEBGPU_MUL_MAT_WG_SIZE_N; + decisions->mul_mat_wg_size = WEBGPU_MUL_MAT_WG_SIZE; + } + + webgpu_pipeline pipeline = ggml_webgpu_create_pipeline(device, processed, variant); + pipeline.context = decisions; + mul_mat_fast_pipelines[key] = pipeline; + return mul_mat_fast_pipelines[key]; + } + + webgpu_pipeline get_mul_mat_id_gather_pipeline(const ggml_webgpu_shader_lib_context & context) { + auto it = mul_mat_id_gather_pipelines.find(1); + if (it != mul_mat_id_gather_pipelines.end()) { + return it->second; + } + std::vector defines; + defines.push_back(std::string("WG_SIZE=") + std::to_string(context.max_wg_size)); + + auto processed = preprocessor.preprocess(wgsl_mul_mat_id_gather, defines); + auto decisions = std::make_shared(); + decisions->wg_size = context.max_wg_size; + + webgpu_pipeline pipeline = ggml_webgpu_create_pipeline(device, processed, "mul_mat_id_gather"); + pipeline.context = decisions; + mul_mat_id_gather_pipelines[1] = pipeline; + return pipeline; + } + + webgpu_pipeline get_mul_mat_id_pipeline(const ggml_webgpu_shader_lib_context & context) { + ggml_webgpu_mul_mat_id_pipeline_key key = {}; + key.src0_type = context.src0->type; + key.src1_type = context.src1->type; + key.n_experts = context.src0->ne[2]; + key.vectorized = (context.src0->ne[0] % 4 == 0 && context.src0->ne[1] % 4 == 0 && + (context.src0->type == GGML_TYPE_F32 || context.src0->type == GGML_TYPE_F16)) ? + 1 : + 0; + + auto it = mul_mat_id_pipelines.find(key); + if (it != mul_mat_id_pipelines.end()) { + return it->second; + } + + std::vector defines; + std::string variant = "mul_mat_id"; + defines.push_back("MUL_MAT_ID"); + + // src1 type + switch (context.src1->type) { + case GGML_TYPE_F32: + defines.push_back("SRC1_INNER_TYPE=f32"); + break; + case GGML_TYPE_F16: + defines.push_back("SRC1_INNER_TYPE=f16"); + break; + default: + GGML_ABORT("Unsupported src1 type for mul_mat fast shader"); + } + + // src0 type + const struct ggml_type_traits * src0_traits = ggml_get_type_traits(context.src0->type); + const char * src0_name = src0_traits->type_name; + + switch (context.src0->type) { + case GGML_TYPE_F32: + defines.push_back("SRC0_INNER_TYPE=f32"); + defines.push_back("INIT_SRC0_SHMEM_FLOAT"); + defines.push_back("INIT_SRC1_SHMEM_FLOAT"); + variant += "_f32"; + break; + case GGML_TYPE_F16: + defines.push_back("SRC0_INNER_TYPE=f16"); + defines.push_back("INIT_SRC0_SHMEM_FLOAT"); + defines.push_back("INIT_SRC1_SHMEM_FLOAT"); + variant += "_f16"; + break; + default: + { + std::string type_upper = src0_name; + std::transform(type_upper.begin(), type_upper.end(), type_upper.begin(), ::toupper); + + defines.push_back("BYTE_HELPERS"); + defines.push_back("INIT_SRC0_SHMEM_" + type_upper); + defines.push_back("INIT_SRC1_SHMEM_FLOAT"); + defines.push_back("U32_DEQUANT_HELPERS"); + defines.push_back("SRC0_INNER_TYPE=u32"); + + switch (context.src0->type) { + case GGML_TYPE_IQ1_S: + case GGML_TYPE_IQ1_M: + case GGML_TYPE_IQ4_NL: + case GGML_TYPE_IQ4_XS: + defines.push_back(type_upper + "_GRID"); + break; + case GGML_TYPE_IQ2_XXS: + case GGML_TYPE_IQ2_XS: + case GGML_TYPE_IQ2_S: + case GGML_TYPE_IQ3_XXS: + case GGML_TYPE_IQ3_S: + defines.push_back(type_upper + "_GRID"); + defines.push_back(type_upper + "_TABLES"); + break; + case GGML_TYPE_MXFP4: + case GGML_TYPE_NVFP4: + defines.push_back(type_upper + "_LUT"); + break; + default: + break; + } + + variant += std::string("_") + src0_name; + break; + } + } + + // VEC/SCALAR controls + defines.push_back(key.vectorized ? "VEC" : "SCALAR"); + + // mul_mat_id is register-tile only. + const uint32_t tile_k = + ggml_is_quantized(context.src0->type) ? WEBGPU_MUL_MAT_REG_TILE_K_QUANT : WEBGPU_MUL_MAT_REG_TILE_K_FLOAT; + + // Tiles + defines.push_back("TILE_M=" + std::to_string(WEBGPU_MUL_MAT_TILE_M) + "u"); + defines.push_back("TILE_N=" + std::to_string(WEBGPU_MUL_MAT_TILE_N) + "u"); + defines.push_back("TILE_K=" + std::to_string(tile_k) + "u"); + + defines.push_back("WORKGROUP_SIZE_M=" + std::to_string(WEBGPU_MUL_MAT_WG_SIZE_M) + "u"); + defines.push_back("WORKGROUP_SIZE_N=" + std::to_string(WEBGPU_MUL_MAT_WG_SIZE_N) + "u"); + + // variant suffix for src1 type + variant += std::string("_") + (context.src1->type == GGML_TYPE_F32 ? "f32" : "f16"); + if (key.vectorized) { + variant += "_vectorized"; + } + + auto processed = preprocessor.preprocess(wgsl_mul_mat_id, defines); + + auto decisions = std::make_shared(); + decisions->tile_k = tile_k; + decisions->tile_m = WEBGPU_MUL_MAT_TILE_M; + decisions->tile_n = WEBGPU_MUL_MAT_TILE_N; + decisions->wg_size_m = WEBGPU_MUL_MAT_WG_SIZE_M; + decisions->wg_size_n = WEBGPU_MUL_MAT_WG_SIZE_N; + decisions->wg_size = WEBGPU_MUL_MAT_WG_SIZE_M * WEBGPU_MUL_MAT_WG_SIZE_N; + + webgpu_pipeline pipeline = ggml_webgpu_create_pipeline(device, processed, variant); + pipeline.context = decisions; + mul_mat_id_pipelines[key] = pipeline; + return mul_mat_id_pipelines[key]; + } + + webgpu_pipeline get_mul_mat_id_vec_pipeline(const ggml_webgpu_shader_lib_context & context) { + ggml_webgpu_mul_mat_id_pipeline_key key = {}; + key.src0_type = context.src0->type; + key.src1_type = context.src1->type; + key.n_experts = context.src0->ne[2]; + key.vectorized = (context.src0->ne[0] % 4 == 0 && + (context.src0->type == GGML_TYPE_F32 || context.src0->type == GGML_TYPE_F16)) ? + 1 : + 0; + + auto it = mul_mat_id_vec_pipelines.find(key); + if (it != mul_mat_id_vec_pipelines.end()) { + return it->second; + } + + std::vector defines; + std::string variant = "mul_mat_id_vec"; + const char * shader_src = wgsl_mul_mat_id_vec; + + // src1 type + switch (context.src1->type) { + case GGML_TYPE_F32: + defines.push_back("SRC1_INNER_TYPE=f32"); + break; + case GGML_TYPE_F16: + defines.push_back("SRC1_INNER_TYPE=f16"); + break; + default: + GGML_ABORT("Unsupported src1 type for mul_mat fast shader"); + } + + // src0 type + switch (context.src0->type) { + case GGML_TYPE_F32: + defines.push_back("SRC0_INNER_TYPE=f32"); + defines.push_back("MUL_ACC_FLOAT"); + variant += "_f32"; + break; + case GGML_TYPE_F16: + defines.push_back("SRC0_INNER_TYPE=f16"); + defines.push_back("MUL_ACC_FLOAT"); + variant += "_f16"; + break; + default: + { + // Quantized types: use helpers but accumulate in f16 + const struct ggml_type_traits * src0_traits = ggml_get_type_traits(context.src0->type); + std::string src0_name = src0_traits->type_name; + std::string type_upper = src0_name; + variant += "_" + src0_name; + std::transform(type_upper.begin(), type_upper.end(), type_upper.begin(), ::toupper); + + defines.push_back("BYTE_HELPERS"); + defines.push_back("MUL_ACC_" + type_upper); + defines.push_back("U32_DEQUANT_HELPERS"); + defines.push_back("SRC0_INNER_TYPE=u32"); + switch (context.src0->type) { + case GGML_TYPE_IQ1_S: + case GGML_TYPE_IQ1_M: + case GGML_TYPE_IQ2_S: + case GGML_TYPE_IQ3_S: + case GGML_TYPE_IQ4_NL: + case GGML_TYPE_IQ4_XS: + defines.push_back(type_upper + "_GRID"); + break; + case GGML_TYPE_IQ2_XXS: + case GGML_TYPE_IQ2_XS: + case GGML_TYPE_IQ3_XXS: + defines.push_back(type_upper + "_GRID"); + defines.push_back(type_upper + "_TABLES"); + break; + case GGML_TYPE_MXFP4: + case GGML_TYPE_NVFP4: + defines.push_back(type_upper + "_LUT"); + break; + default: + break; + } + break; + } + } + + // VEC/SCALAR controls + defines.push_back(key.vectorized ? "VEC" : "SCALAR"); + + uint32_t wg_size = WEBGPU_MUL_MAT_VEC_WG_SIZE; + uint32_t outputs_per_wg = WEBGPU_MUL_MAT_VEC_FLOAT_OUTPUTS_PER_WG; + + if (key.src0_type == GGML_TYPE_Q1_0) { + outputs_per_wg = WEBGPU_MUL_MAT_VEC_LEGACY_Q_OUTPUTS_PER_WG; + } else if (key.src0_type >= GGML_TYPE_Q2_K) { + outputs_per_wg = WEBGPU_MUL_MAT_VEC_K_Q_OUTPUTS_PER_WG; + } else if (key.src0_type >= GGML_TYPE_Q4_0) { + outputs_per_wg = WEBGPU_MUL_MAT_VEC_LEGACY_Q_OUTPUTS_PER_WG; + } + + // variant suffix for src1 type + variant += std::string("_") + (context.src1->type == GGML_TYPE_F32 ? "f32" : "f16"); + + defines.push_back(std::string("WG_SIZE=") + std::to_string(wg_size)); + defines.push_back(std::string("OUTPUTS_PER_WG=") + std::to_string(outputs_per_wg)); + defines.push_back(context.supports_subgroups ? "USE_SUBGROUP_REDUCTION" : "USE_WORKGROUP_REDUCTION"); + variant += context.supports_subgroups ? "_sg_reduce" : "_wg_reduce"; + if (key.vectorized) { + variant += "_vectorized"; + } + defines.push_back(std::string("NUM_COLS=1")); + + defines.push_back(std::string("N_EXPERTS=") + std::to_string(key.n_experts)); + + auto processed = preprocessor.preprocess(shader_src, defines); + + auto decisions = std::make_shared(); + decisions->wg_size = wg_size; + decisions->outputs_per_wg = outputs_per_wg; + + webgpu_pipeline pipeline = ggml_webgpu_create_pipeline(device, processed, variant); + pipeline.context = decisions; + mul_mat_id_vec_pipelines[key] = pipeline; + return mul_mat_id_vec_pipelines[key]; + } + + webgpu_pipeline get_unary_pipeline(const ggml_webgpu_shader_lib_context & context) { + const bool is_unary = context.dst->op == GGML_OP_UNARY; + const int op = is_unary ? (int) ggml_get_unary_op(context.dst) : context.dst->op; + ggml_webgpu_unary_pipeline_key key = {}; + key.type = context.dst->type; + key.op = op; + key.is_unary = is_unary; + key.inplace = ggml_webgpu_tensor_equal(context.src0, context.dst) || context.dst->op == GGML_OP_FILL; + key.ttype = (ggml_tri_type) ggml_get_op_params_i32(context.dst, 0); + + auto it = unary_pipelines.find(key); + if (it != unary_pipelines.end()) { + return it->second; + } + + std::vector defines; + std::string variant = + key.is_unary ? ggml_unary_op_name((ggml_unary_op) key.op) : ggml_op_name((ggml_op) key.op); + defines.push_back(variant); + + switch (key.type) { + case GGML_TYPE_F32: + defines.push_back("TYPE_F32"); + variant += "_f32"; + break; + case GGML_TYPE_F16: + defines.push_back("TYPE_F16"); + variant += "_f16"; + break; + default: + GGML_ABORT("Unsupported type for unary shader"); + } + + if (key.inplace) { + defines.push_back("INPLACE"); + variant += "_inplace"; + } + + if (op == GGML_OP_TRI) { + switch (key.ttype) { + case GGML_TRI_TYPE_LOWER: + defines.push_back("TRI_TYPE_LOWER"); + variant += "_tri_type_lower"; + break; + case GGML_TRI_TYPE_LOWER_DIAG: + defines.push_back("TRI_TYPE_LOWER_DIAG"); + variant += "_tri_type_lower_diag"; + break; + case GGML_TRI_TYPE_UPPER: + defines.push_back("TRI_TYPE_UPPER"); + variant += "_tri_type_upper"; + break; + case GGML_TRI_TYPE_UPPER_DIAG: + defines.push_back("TRI_TYPE_UPPER_DIAG"); + variant += "_tri_upper_diag"; + break; + default: + GGML_ABORT("Unsupported ggml_tri_type for unary shader"); + } + } + + defines.push_back(std::string("WG_SIZE=") + std::to_string(context.max_wg_size)); + + auto processed = preprocessor.preprocess(wgsl_unary, defines); + auto decisions = std::make_shared(); + decisions->wg_size = context.max_wg_size; + decisions->inplace = key.inplace; + webgpu_pipeline pipeline = ggml_webgpu_create_pipeline(device, processed, variant); + pipeline.context = decisions; + unary_pipelines[key] = pipeline; + return unary_pipelines[key]; + } + + webgpu_pipeline get_rms_norm_mul_pipeline(const ggml_webgpu_shader_lib_context & context) { + ggml_webgpu_rms_norm_mul_pipeline_key key = {}; + key.inplace = ggml_webgpu_tensor_equal(context.src0, context.dst); + key.overlap = ggml_webgpu_tensor_equal(context.src1, context.dst); + key.src_overlap = ggml_webgpu_tensor_overlap(context.src0, context.src1); + + auto it = rms_norm_mul_pipelines.find(key); + if (it != rms_norm_mul_pipelines.end()) { + return it->second; + } + + std::vector defines; + std::string op_name = "RMS_NORM_MUL"; + std::string variant = op_name; + + if (key.inplace) { + defines.push_back("INPLACE"); + variant += "_inplace"; + } else if (key.overlap) { + defines.push_back("OVERLAP"); + variant += "_overlap"; + } else if (key.src_overlap) { + defines.push_back("SRC_OVERLAP"); + variant += "_src_overlap"; + } + + defines.push_back(std::string("WG_SIZE=") + std::to_string(context.max_wg_size)); + + auto processed = preprocessor.preprocess(wgsl_rms_norm_mul, defines); + auto pipeline_decisions = std::make_shared(); + pipeline_decisions->wg_size = context.max_wg_size; + pipeline_decisions->inplace = key.inplace; + pipeline_decisions->overlap = key.overlap; + pipeline_decisions->src_overlap = key.src_overlap; + webgpu_pipeline pipeline = ggml_webgpu_create_pipeline(device, processed, variant); + pipeline.context = pipeline_decisions; + rms_norm_mul_pipelines[key] = pipeline; + return rms_norm_mul_pipelines[key]; + } + + webgpu_pipeline get_binary_pipeline(const ggml_webgpu_shader_lib_context & context) { + ggml_webgpu_binary_pipeline_key key = {}; + key.type = context.dst->type; + key.op = context.dst->op; + key.inplace = ggml_webgpu_tensor_equal(context.src0, context.dst); + key.overlap = ggml_webgpu_tensor_equal(context.src1, context.dst); + key.src_overlap = ggml_webgpu_tensor_overlap(context.src0, context.src1); + + auto it = binary_pipelines.find(key); + if (it != binary_pipelines.end()) { + return it->second; + } + + std::vector defines; + std::string op_name = ggml_op_name((ggml_op) key.op); + std::string variant = op_name; + + defines.push_back(std::string("OP_") + op_name); + + switch (key.type) { + case GGML_TYPE_F32: + defines.push_back("TYPE_F32"); + variant += "_f32"; + break; + case GGML_TYPE_F16: + defines.push_back("TYPE_F16"); + variant += "_f16"; + break; + default: + GGML_ABORT("Unsupported type for binary shader"); + } + + if (key.inplace) { + defines.push_back("INPLACE"); + variant += "_inplace"; + } else if (key.overlap) { + defines.push_back("OVERLAP"); + variant += "_overlap"; + } else if (key.src_overlap) { + defines.push_back("SRC_OVERLAP"); + variant += "_src_overlap"; + } + + defines.push_back(std::string("WG_SIZE=") + std::to_string(context.max_wg_size)); + + auto processed = preprocessor.preprocess(wgsl_binary, defines); + auto pipeline_decisions = std::make_shared(); + pipeline_decisions->wg_size = context.max_wg_size; + pipeline_decisions->inplace = key.inplace; + pipeline_decisions->overlap = key.overlap; + pipeline_decisions->src_overlap = key.src_overlap; + + webgpu_pipeline pipeline = ggml_webgpu_create_pipeline(device, processed, variant); + pipeline.context = pipeline_decisions; + binary_pipelines[key] = pipeline; + return binary_pipelines[key]; + } + + webgpu_pipeline get_add_id_pipeline(const ggml_webgpu_shader_lib_context & context) { + ggml_webgpu_add_id_pipeline_key key = {}; + key.inplace = ggml_webgpu_tensor_equal(context.src0, context.dst); + + auto it = add_id_pipelines.find(key); + if (it != add_id_pipelines.end()) { + return it->second; + } + + std::vector defines; + std::string variant = "add_id"; + const char * shader_src = wgsl_add_id; + + if (key.inplace) { + defines.push_back("INPLACE"); + variant += "_inplace"; + } + + defines.push_back(std::string("WG_SIZE=") + std::to_string(context.max_wg_size)); + + auto processed = preprocessor.preprocess(shader_src, defines); + auto pipeline_decisions = std::make_shared(); + pipeline_decisions->wg_size = context.max_wg_size; + pipeline_decisions->inplace = key.inplace; + + webgpu_pipeline pipeline = ggml_webgpu_create_pipeline(device, processed, variant); + pipeline.context = pipeline_decisions; + add_id_pipelines[key] = pipeline; + return pipeline; + } + + webgpu_pipeline get_concat_pipeline(const ggml_webgpu_shader_lib_context & context) { + ggml_webgpu_concat_pipeline_key key = {}; + key.type = context.dst->type; + key.src_overlap = ggml_webgpu_tensor_overlap(context.src0, context.src1); + + auto it = concat_pipelines.find(key); + if (it != concat_pipelines.end()) { + return it->second; + } + + std::vector defines; + std::string variant = "concat"; + + switch (key.type) { + case GGML_TYPE_F32: + defines.push_back("TYPE_F32"); + variant += "_f32"; + break; + case GGML_TYPE_I32: + defines.push_back("TYPE_I32"); + variant += "_i32"; + break; + default: + GGML_ABORT("Unsupported type for concat shader"); + } + + if (key.src_overlap) { + defines.push_back("SRC_OVERLAP"); + variant += "_src_overlap"; + } + + defines.push_back(std::string("WG_SIZE=") + std::to_string(context.max_wg_size)); + + auto processed = preprocessor.preprocess(wgsl_concat, defines); + auto decisions = std::make_shared(); + decisions->wg_size = context.max_wg_size; + decisions->src_overlap = key.src_overlap; + webgpu_pipeline pipeline = ggml_webgpu_create_pipeline(device, processed, variant); + pipeline.context = decisions; + concat_pipelines[key] = pipeline; + return concat_pipelines[key]; + } + + webgpu_pipeline get_repeat_pipeline(const ggml_webgpu_shader_lib_context & context) { + ggml_webgpu_repeat_pipeline_key key = {}; + key.type = context.dst->type; + + auto it = repeat_pipelines.find(key); + if (it != repeat_pipelines.end()) { + return it->second; + } + + std::vector defines; + std::string variant = "repeat"; + + switch (key.type) { + case GGML_TYPE_F32: + defines.push_back("TYPE_F32"); + variant += "_f32"; + break; + case GGML_TYPE_I32: + defines.push_back("TYPE_I32"); + variant += "_i32"; + break; + case GGML_TYPE_I16: + defines.push_back("TYPE_I16"); + variant += "_i16"; + break; + default: + GGML_ABORT("Unsupported type for repeat shader"); + } + + defines.push_back(std::string("WG_SIZE=") + std::to_string(context.max_wg_size)); + + auto processed = preprocessor.preprocess(wgsl_repeat, defines); + auto decisions = std::make_shared(); + decisions->wg_size = context.max_wg_size; + webgpu_pipeline pipeline = ggml_webgpu_create_pipeline(device, processed, variant); + pipeline.context = decisions; + repeat_pipelines[key] = pipeline; + return repeat_pipelines[key]; + } + + webgpu_pipeline get_flash_attn_pipeline(const ggml_webgpu_shader_lib_context & context) { + const bool can_use_subgroup_matrix = ggml_webgpu_flash_attn_can_use_subgroup_matrix_path( + context.supports_subgroup_matrix, context.sg_mat_k, context.sg_mat_n, context.src0, context.src2); + ggml_webgpu_flash_attn_decisions decisions = {}; + decisions.use_sg_matrix = can_use_subgroup_matrix; + decisions.q_tile = decisions.use_sg_matrix ? context.sg_mat_m : GGML_WEBGPU_FLASH_ATTN_TILE_Q_TILE; + + ggml_webgpu_flash_attn_pipeline_key key = {}; + key.common = + ggml_webgpu_flash_attn_make_common_pipeline_key(context, decisions.use_sg_matrix ? context.sg_mat_k : 1u); + key.common.kv_direct = decisions.use_sg_matrix && key.common.kv_direct; + key.use_sg_matrix = decisions.use_sg_matrix; + + const uint32_t max_kv_tile = ggml_webgpu_flash_attn_max_kv_tile( + context.wg_mem_limit_bytes, decisions.q_tile, decisions.use_sg_matrix ? context.sg_mat_n : 1u, + key.common.head_dim_qk, key.common.head_dim_v, key.common.has_mask, key.common.kv_direct); + GGML_ASSERT(max_kv_tile > 0); + + decisions.kv_tile = decisions.use_sg_matrix ? + std::min(max_kv_tile, context.sg_mat_n * GGML_WEBGPU_FLASH_ATTN_PREFERRED_KV_SG_TILES) : + std::min(GGML_WEBGPU_FLASH_ATTN_TILE_MAX_KV_TILE, max_kv_tile); + decisions.wg_size = + decisions.use_sg_matrix ? + std::max(context.max_subgroup_size, GGML_WEBGPU_FLASH_ATTN_PREFERRED_WG_SIZE) : + std::min(context.max_wg_size, std::max(GGML_WEBGPU_FLASH_ATTN_PREFERRED_WG_SIZE, + GGML_WEBGPU_FLASH_ATTN_TILE_Q_TILE * context.max_subgroup_size)); + + if (key.common.kv_direct) { + decisions.kv_tile = std::min(decisions.kv_tile, GGML_WEBGPU_KV_SEQ_PAD); + while (GGML_WEBGPU_KV_SEQ_PAD % decisions.kv_tile != 0) { + decisions.kv_tile -= decisions.use_sg_matrix ? context.sg_mat_n : context.min_subgroup_size; + } + } + + auto it = flash_attn_pipelines.find(key); + if (it != flash_attn_pipelines.end()) { + return it->second; + } + + std::string variant = decisions.use_sg_matrix ? "flash_attn" : "flash_attn_tile"; + std::vector defines = ggml_webgpu_flash_attn_common_defines(key.common, variant, decisions.q_tile, + decisions.kv_tile, decisions.wg_size); + const char * shader_src = nullptr; + if (!key.use_sg_matrix) { + shader_src = wgsl_flash_attn_tile; + defines.push_back("MIN_SUBGROUP_SIZE=" + std::to_string(context.min_subgroup_size) + "u"); + defines.push_back("MAX_SUBGROUP_SIZE=" + std::to_string(context.max_subgroup_size) + "u"); + variant += "_tile_sg" + std::to_string(context.min_subgroup_size) + "_" + + std::to_string(context.max_subgroup_size); + } else { + shader_src = wgsl_flash_attn; + defines.push_back(std::string("SG_MAT_M=") + std::to_string(context.sg_mat_m)); + defines.push_back(std::string("SG_MAT_N=") + std::to_string(context.sg_mat_n)); + defines.push_back(std::string("SG_MAT_K=") + std::to_string(context.sg_mat_k)); + } + auto pipeline_decisions = std::make_shared(decisions); + webgpu_pipeline pipeline = + ggml_webgpu_create_pipeline(device, preprocessor.preprocess(shader_src, defines), variant); + pipeline.context = pipeline_decisions; + flash_attn_pipelines[key] = pipeline; + return flash_attn_pipelines[key]; + } + + webgpu_pipeline get_flash_attn_vec_pipeline(const ggml_webgpu_shader_lib_context & context) { + ggml_webgpu_flash_attn_vec_pipeline_key key = {}; + key.common = ggml_webgpu_flash_attn_make_common_pipeline_key(context, GGML_WEBGPU_FLASH_ATTN_TILE_KV_VEC_WIDTH); + + auto it = flash_attn_vec_pipelines.find(key); + if (it != flash_attn_vec_pipelines.end()) { + return it->second; + } + + ggml_webgpu_flash_attn_vec_decisions decisions = {}; + decisions.kv_tile = + ggml_webgpu_flash_attn_get_vec_kv_tile(context.wg_mem_limit_bytes, key.common.head_dim_qk, + key.common.head_dim_v, key.common.has_mask, key.common.kv_direct); + decisions.wg_size = context.max_subgroup_size; + + std::string variant = "flash_attn_vec"; + std::vector defines = + ggml_webgpu_flash_attn_common_defines(key.common, variant, 1u, decisions.kv_tile, decisions.wg_size); + if (key.common.has_mask) { + defines.push_back("BLK"); + variant.resize(variant.size() - (sizeof("_mask") - 1)); + variant += "_mask_blk"; + } + + uint32_t d_split = context.min_subgroup_size; + if (key.common.k_type == GGML_TYPE_F16 && key.common.v_type == GGML_TYPE_F16) { + const uint32_t D = key.common.head_dim_qk | key.common.head_dim_v; + const uint32_t D_lsb = D & (~(D - 1u)); + d_split = std::min(std::min(context.min_subgroup_size, 4u), std::max(D_lsb / 4u, 1u)); + } + + defines.push_back(std::string("D_SPLIT=") + std::to_string(d_split)); + variant += "_dsplit" + std::to_string(d_split); + + auto pipeline_decisions = std::make_shared(decisions); + webgpu_pipeline pipeline = + ggml_webgpu_create_pipeline(device, preprocessor.preprocess(wgsl_flash_attn_vec_split, defines), variant); + pipeline.context = pipeline_decisions; + flash_attn_vec_pipelines[key] = pipeline; + return flash_attn_vec_pipelines[key]; + } + + webgpu_pipeline get_flash_attn_blk_pipeline(const ggml_webgpu_shader_lib_context & context, uint32_t kv_tile) { + ggml_webgpu_flash_attn_blk_pipeline_key key = {}; + key.kv_tile = kv_tile; + auto it = flash_attn_blk_pipelines.find(key); + if (it != flash_attn_blk_pipelines.end()) { + return it->second; + } + + std::vector defines; + std::string variant = "flash_attn_vec_blk"; + + defines.push_back(std::string("KV_TILE=") + std::to_string(key.kv_tile)); + variant += std::string("_kvt") + std::to_string(key.kv_tile); + + uint32_t wg_size = 1; + while ((wg_size << 1) <= context.max_wg_size) { + wg_size <<= 1; + } + defines.push_back(std::string("WG_SIZE=") + std::to_string(wg_size)); + variant += std::string("_wg") + std::to_string(wg_size); + + webgpu_pipeline pipeline = + ggml_webgpu_create_pipeline(device, preprocessor.preprocess(wgsl_flash_attn_vec_blk, defines), variant); + flash_attn_blk_pipelines[key] = pipeline; + return flash_attn_blk_pipelines[key]; + } + + webgpu_pipeline get_flash_attn_vec_reduce_pipeline(const ggml_webgpu_shader_lib_context & context) { + ggml_webgpu_flash_attn_vec_reduce_pipeline_key key = {}; + key.head_dim_v = (uint32_t) context.src2->ne[0]; + key.dst_type = context.dst->type; + key.wg_size = context.max_wg_size; + auto it = flash_attn_vec_reduce_pipelines.find(key); + if (it != flash_attn_vec_reduce_pipelines.end()) { + return it->second; + } + + std::vector defines; + std::string variant = "flash_attn_vec_reduce"; + + switch (key.dst_type) { + case GGML_TYPE_F32: + defines.push_back("DST_F32"); + break; + case GGML_TYPE_F16: + defines.push_back("DST_F16"); + break; + default: + GGML_ABORT("Unsupported dst type for flash attention vec reduce shader"); + } + variant += std::string("_dst") + ggml_type_name(key.dst_type); + + defines.push_back(std::string("HEAD_DIM_V=") + std::to_string(key.head_dim_v)); + variant += std::string("_hsv") + std::to_string(key.head_dim_v); + + defines.push_back(std::string("WG_SIZE=") + std::to_string(context.max_wg_size)); + variant += std::string("_wg") + std::to_string(context.max_wg_size); + + webgpu_pipeline pipeline = + ggml_webgpu_create_pipeline(device, preprocessor.preprocess(wgsl_flash_attn_vec_reduce, defines), variant); + flash_attn_vec_reduce_pipelines[key] = pipeline; + return flash_attn_vec_reduce_pipelines[key]; + } + + webgpu_pipeline get_cpy_pipeline(const ggml_webgpu_shader_lib_context & context) { + ggml_webgpu_cpy_pipeline_key key = {}; + key.src_type = context.src0->type; + key.dst_type = context.dst->type; + + auto it = cpy_pipelines.find(key); + if (it != cpy_pipelines.end()) { + return it->second; + } + + std::vector defines; + std::string variant = "cpy"; + + switch (key.src_type) { + case GGML_TYPE_F32: + defines.push_back("SRC_F32"); + variant += "_f32"; + break; + case GGML_TYPE_F16: + defines.push_back("SRC_F16"); + variant += "_f16"; + break; + default: + GGML_ABORT("Unsupported src type for cpy shader"); + } + + switch (key.dst_type) { + case GGML_TYPE_F32: + defines.push_back("DST_F32"); + variant += "_f32"; + break; + case GGML_TYPE_F16: + defines.push_back("DST_F16"); + variant += "_f16"; + break; + case GGML_TYPE_I32: + defines.push_back("DST_I32"); + variant += "_i32"; + break; + default: + GGML_ABORT("Unsupported dst type for cpy shader"); + } + + defines.push_back(std::string("WG_SIZE=") + std::to_string(context.max_wg_size)); + + auto processed = preprocessor.preprocess(wgsl_cpy, defines); + auto decisions = std::make_shared(); + decisions->wg_size = context.max_wg_size; + webgpu_pipeline pipeline = ggml_webgpu_create_pipeline(device, processed, variant); + pipeline.context = decisions; + cpy_pipelines[key] = pipeline; + return cpy_pipelines[key]; + } + + webgpu_pipeline get_glu_pipeline(const ggml_webgpu_shader_lib_context & context) { + ggml_webgpu_glu_pipeline_key key = {}; + key.glu_op = ggml_get_glu_op(context.dst); + key.type = context.dst->type; + key.split = (context.src1 != nullptr); + + auto it = glu_pipelines.find(key); + if (it != glu_pipelines.end()) { + return it->second; + } + + std::vector defines; + std::string variant = "glu"; + + switch (key.glu_op) { + case GGML_GLU_OP_REGLU: + defines.push_back("OP_REGLU"); + variant += "_reglu"; + break; + case GGML_GLU_OP_GEGLU: + defines.push_back("OP_GEGLU"); + variant += "_geglu"; + break; + case GGML_GLU_OP_SWIGLU: + defines.push_back("OP_SWIGLU"); + variant += "_swiglu"; + break; + case GGML_GLU_OP_SWIGLU_OAI: + defines.push_back("OP_SWIGLU_OAI"); + variant += "_swiglu_oai"; + break; + case GGML_GLU_OP_GEGLU_ERF: + defines.push_back("OP_GEGLU_ERF"); + variant += "_geglu_erf"; + break; + case GGML_GLU_OP_GEGLU_QUICK: + defines.push_back("OP_GEGLU_QUICK"); + variant += "_geglu_quick"; + break; + default: + GGML_ABORT("Unsupported GLU op"); + } + switch (key.type) { + case GGML_TYPE_F32: + defines.push_back("TYPE_F32"); + variant += "_f32"; + break; + case GGML_TYPE_F16: + defines.push_back("TYPE_F16"); + variant += "_f16"; + break; + default: + GGML_ABORT("Unsupported type for GLU shader"); + } + + if (key.split) { + variant += "_split"; + } else { + defines.push_back("NO_SPLIT"); + } + + defines.push_back(std::string("WG_SIZE=") + std::to_string(context.max_wg_size)); + + auto processed = preprocessor.preprocess(wgsl_glu, defines); + auto decisions = std::make_shared(); + decisions->wg_size = context.max_wg_size; + webgpu_pipeline pipeline = ggml_webgpu_create_pipeline(device, processed, variant); + pipeline.context = decisions; + glu_pipelines[key] = pipeline; + return glu_pipelines[key]; + } + + webgpu_pipeline get_rope_pipeline(const ggml_webgpu_shader_lib_context & context) { + ggml_webgpu_rope_pipeline_key key = {}; + key.type = context.dst->type; + key.inplace = ggml_webgpu_tensor_equal(context.src0, context.dst); + key.has_ff = (context.src2 != nullptr); + + auto it = rope_pipelines.find(key); + if (it != rope_pipelines.end()) { + return it->second; + } + + std::vector defines; + std::string variant = "rope"; + + switch (key.type) { + case GGML_TYPE_F32: + defines.push_back("TYPE_F32"); + variant += "_f32"; + break; + case GGML_TYPE_F16: + defines.push_back("TYPE_F16"); + variant += "_f16"; + break; + default: + GGML_ABORT("Unsupported type for ROPE shader"); + } + + if (key.inplace) { + defines.push_back("INPLACE"); + variant += "_inplace"; + } + + if (key.has_ff) { + defines.push_back("FF_FUNC"); + variant += "_ff"; + } + + defines.push_back(std::string("WG_SIZE=") + std::to_string(context.max_wg_size)); + + auto processed = preprocessor.preprocess(wgsl_rope, defines); + auto decisions = std::make_shared(); + decisions->wg_size = context.max_wg_size; + decisions->inplace = key.inplace; + webgpu_pipeline pipeline = ggml_webgpu_create_pipeline(device, processed, variant); + pipeline.context = decisions; + rope_pipelines[key] = pipeline; + return rope_pipelines[key]; + } + + webgpu_pipeline get_soft_max_pipeline(const ggml_webgpu_shader_lib_context & context) { + ggml_webgpu_soft_max_pipeline_key key = {}; + key.mask_type = context.src1 ? context.src1->type : GGML_TYPE_F32; + key.has_mask = (context.src1 != nullptr); + key.has_sink = (context.src2 != nullptr); + key.inplace = ggml_webgpu_tensor_equal(context.src0, context.dst); + + auto it = soft_max_pipelines.find(key); + if (it != soft_max_pipelines.end()) { + return it->second; + } + + std::vector defines; + std::string variant = "soft_max"; + + if (key.has_mask) { + defines.push_back("HAS_MASK"); + switch (key.mask_type) { + case GGML_TYPE_F32: + defines.push_back("MASK_F32"); + variant += "_mask_f32"; + break; + case GGML_TYPE_F16: + defines.push_back("MASK_F16"); + variant += "_mask_f16"; + break; + default: + GGML_ABORT("Unsupported type for SOFT_MAX shader"); + } + } + + if (key.has_sink) { + defines.push_back("HAS_SINK"); + variant += "_sink"; + } + + if (key.inplace) { + defines.push_back("INPLACE"); + variant += "_inplace"; + } + + defines.push_back(std::string("WG_SIZE=") + std::to_string(context.max_wg_size)); + + auto processed = preprocessor.preprocess(wgsl_soft_max, defines); + auto decisions = std::make_shared(); + decisions->wg_size = context.max_wg_size; + decisions->inplace = key.inplace; + webgpu_pipeline pipeline = ggml_webgpu_create_pipeline(device, processed, variant); + pipeline.context = decisions; + soft_max_pipelines[key] = pipeline; + return soft_max_pipelines[key]; + } + + webgpu_pipeline get_conv2d_pipeline(const ggml_webgpu_shader_lib_context & context) { + ggml_webgpu_conv2d_pipeline_key key = {}; + key.weight_type = context.src0->type; + key.input_type = context.src1->type; + key.output_type = context.dst->type; + + auto it = conv2d_pipelines.find(key); + if (it != conv2d_pipelines.end()) { + return it->second; + } + + std::vector defines; + std::string variant = "conv_2d"; + + auto push_type_defines = [&](const char * prefix, ggml_type type) { + std::string s_prefix = prefix; + if (type == GGML_TYPE_F32) { + defines.push_back(s_prefix + "_F32"); + } else if (type == GGML_TYPE_F16) { + defines.push_back(s_prefix + "_F16"); + } else { + GGML_ABORT("Unsupported type for CONV_2D shader"); + } + }; + + push_type_defines("WEIGHT", key.weight_type); + push_type_defines("INPUT", key.input_type); + push_type_defines("OUTPUT", key.output_type); + + defines.push_back(std::string("WG_SIZE=") + std::to_string(context.max_wg_size)); + + auto processed = preprocessor.preprocess(wgsl_conv2d, defines); + auto decisions = std::make_shared(); + decisions->wg_size = context.max_wg_size; + webgpu_pipeline pipeline = ggml_webgpu_create_pipeline(device, processed, variant); + pipeline.context = decisions; + conv2d_pipelines[key] = pipeline; + return conv2d_pipelines[key]; + } + + webgpu_pipeline get_im2col_pipeline(const ggml_webgpu_shader_lib_context & context) { + ggml_webgpu_im2col_pipeline_key key = {}; + key.input_type = context.src1->type; + key.output_type = context.dst->type; + + auto it = im2col_pipelines.find(key); + if (it != im2col_pipelines.end()) { + return it->second; + } + + std::vector defines; + std::string variant = "im2col"; + + auto push_type_defines = [&](const char * prefix, ggml_type type) { + std::string s_prefix = prefix; + if (type == GGML_TYPE_F32) { + defines.push_back(s_prefix + "_F32"); + } else if (type == GGML_TYPE_F16) { + defines.push_back(s_prefix + "_F16"); + } else { + GGML_ABORT("Unsupported type for IM2COL shader"); + } + }; + + push_type_defines("INPUT", key.input_type); + push_type_defines("OUTPUT", key.output_type); + + defines.push_back(std::string("WG_SIZE=") + std::to_string(context.max_wg_size)); + + auto processed = preprocessor.preprocess(wgsl_im2col, defines); + auto decisions = std::make_shared(); + decisions->wg_size = context.max_wg_size; + webgpu_pipeline pipeline = ggml_webgpu_create_pipeline(device, processed, variant); + pipeline.context = decisions; + im2col_pipelines[key] = pipeline; + return im2col_pipelines[key]; + } + + webgpu_pipeline get_upscale_pipeline(const ggml_webgpu_shader_lib_context & context) { + const uint32_t mode_flags = (uint32_t) ggml_get_op_params_i32(context.dst, 0); + const uint32_t base_mode = mode_flags & 0xFFu; + const bool antialias = (mode_flags & GGML_SCALE_FLAG_ANTIALIAS) != 0u; + + ggml_webgpu_upscale_pipeline_key key = {}; + key.input_type = context.src0->type; + key.output_type = context.dst->type; + key.base_mode = base_mode; + key.antialias = antialias; + + auto it = upscale_pipelines.find(key); + if (it != upscale_pipelines.end()) { + return it->second; + } + + std::vector defines; + std::string variant = "upscale"; + + if (key.input_type == GGML_TYPE_F16) { + defines.push_back("SRC_F16"); + variant += "_src_f16"; + } else { + variant += "_src_f32"; + } + + if (key.output_type == GGML_TYPE_F16) { + defines.push_back("DST_F16"); + variant += "_dst_f16"; + } else { + variant += "_dst_f32"; + } + + switch (base_mode) { + case GGML_SCALE_MODE_NEAREST: + defines.push_back("NEAREST"); + variant += "_nearest"; + break; + case GGML_SCALE_MODE_BILINEAR: + defines.push_back("BILINEAR"); + variant += "_bilinear"; + break; + case GGML_SCALE_MODE_BICUBIC: + defines.push_back("BICUBIC"); + variant += "_bicubic"; + break; + default: + GGML_ABORT("Unsupported upscale mode"); + } + + if (antialias) { + defines.push_back("ANTIALIAS"); + variant += "_aa"; + } + + defines.push_back(std::string("WG_SIZE=") + std::to_string(context.max_wg_size)); + + auto processed = preprocessor.preprocess(wgsl_upscale, defines); + auto decisions = std::make_shared(); + decisions->wg_size = context.max_wg_size; + webgpu_pipeline pipeline = ggml_webgpu_create_pipeline(device, processed, variant); + pipeline.context = decisions; + upscale_pipelines[key] = pipeline; + return upscale_pipelines[key]; + } + + private: + static webgpu_pipeline ggml_webgpu_create_pipeline(wgpu::Device & device, + std::string shader_code, + std::string label) { + wgpu::ShaderSourceWGSL shader_source; + shader_source.code = shader_code.c_str(); + + 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.c_str(); + 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 + return { device.CreateComputePipeline(&pipeline_desc), label }; + } +}; + +#endif // GGML_WEBGPU_SHADER_LIB_HPP diff --git a/backend/llama.cpp/ggml/src/ggml-webgpu/ggml-webgpu.cpp b/backend/llama.cpp/ggml/src/ggml-webgpu/ggml-webgpu.cpp new file mode 100644 index 0000000000000000000000000000000000000000..29025e9ba4e391f0ee21376d40a306ed8cf10c8f --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-webgpu/ggml-webgpu.cpp @@ -0,0 +1,4578 @@ +/* + WebGPU backend implementation. + Note: Use ClangFormat to format this file. +*/ + +#include "ggml-webgpu.h" + +#include "ggml-backend-impl.h" +#include "ggml-impl.h" +#include "ggml-webgpu-shader-lib.hpp" +#include "ggml.h" + +#ifdef __EMSCRIPTEN__ +# include +#endif + +#include + +#include +#include +#include +#ifdef GGML_WEBGPU_GPU_PROFILE +# include +#endif +#if defined(GGML_WEBGPU_DEBUG) || defined(GGML_WEBGPU_CPU_PROFILE) || defined(GGML_WEBGPU_GPU_PROFILE) +# include +#endif +#include +#include +#include +#include +#include +#include + +#define ROUNDUP_POW2(x, pow2) (((x) + ((pow2) - 1)) & ~((pow2) - 1)) +#define CEIL_DIV(M, N) (((M) + (N) - 1) / (N)) + +// Return a rectangular grid of workgroups with minimal over-provisioned workgroups. +// Assumes that the total number of workgroups does not exceed max_per_dim^2. +static inline void compute_2d_workgroups(uint32_t total_wg, uint32_t max_per_dim, uint32_t & wg_x, uint32_t & wg_y) { + wg_y = std::max(1u, CEIL_DIV(total_wg, max_per_dim)); + wg_x = CEIL_DIV(total_wg, wg_y); +} + +static inline uint32_t ggml_webgpu_u32_from_f32(float value) { + uint32_t bits; + memcpy(&bits, &value, sizeof(bits)); + return bits; +} + +#ifdef GGML_WEBGPU_DEBUG +# define WEBGPU_LOG_DEBUG(msg) std::cout << msg << std::endl +# define WEBGPU_DEBUG_BUF_ELEMS 512 +#else +# define WEBGPU_LOG_DEBUG(msg) ((void) 0) +#endif // GGML_WEBGPU_DEBUG + +#ifdef GGML_WEBGPU_CPU_PROFILE +// total timing (aggregated) +# define WEBGPU_CPU_PROFILE_TOTAL_START(id) auto cpu_total_start_##id = std::chrono::high_resolution_clock::now(); + +# define WEBGPU_CPU_PROFILE_TOTAL_END(id, ctx) \ + auto cpu_total_end_##id = std::chrono::high_resolution_clock::now(); \ + double cpu_total_time_##id = \ + std::chrono::duration(cpu_total_end_##id - cpu_total_start_##id).count(); \ + (ctx)->cpu_time_ms[#id] += cpu_total_time_##id; +// fine-grained timing (not included in totals) +# define WEBGPU_CPU_PROFILE_DETAIL_START(id) auto cpu_detail_start_##id = std::chrono::high_resolution_clock::now(); + +# define WEBGPU_CPU_PROFILE_DETAIL_END(id, ctx) \ + auto cpu_detail_end_##id = std::chrono::high_resolution_clock::now(); \ + double cpu_detail_time_##id = \ + std::chrono::duration(cpu_detail_end_##id - cpu_detail_start_##id).count(); \ + (ctx)->cpu_detail_ms[#id] += cpu_detail_time_##id; +#else +# define WEBGPU_CPU_PROFILE_TOTAL_START(id) +# define WEBGPU_CPU_PROFILE_TOTAL_END(id, ctx) +# define WEBGPU_CPU_PROFILE_DETAIL_START(id) +# define WEBGPU_CPU_PROFILE_DETAIL_END(id, ctx) +#endif // GGML_WEBGPU_CPU_PROFILE + +#ifdef GGML_WEBGPU_GPU_PROFILE +# define WEBGPU_MAX_PROFILE_QUERY_COUNT 4096u +# define WEBGPU_TIMESTAMP_QUERY_BUF_SIZE_BYTES (WEBGPU_MAX_PROFILE_QUERY_COUNT * sizeof(uint64_t)) +#endif + +/* Constants */ + +#define WEBGPU_DEFAULT_COMMAND_SUBMIT_BATCH_SIZE 64u +#define WEBGPU_NUM_PARAM_SLOT_SAFETY_MARGIN 10u +#define WEBGPU_RUNTIME_WAIT_TIMEOUT_MS 30000u +#define WEBGPU_RUNTIME_WAIT_TIMEOUT_NS (WEBGPU_RUNTIME_WAIT_TIMEOUT_MS * 1e6) +#define WEBGPU_PARAMS_BUF_SIZE_BYTES 128 // enough for 32 parameters +#define WEBGPU_SET_ROWS_ERROR_BUF_SIZE_BYTES 4 +#define WEBGPU_STORAGE_BUF_BINDING_MULT 4 // a storage buffer binding size must be a multiple of 4 + +/* End Constants */ + +// This is a "fake" base pointer, since WebGPU buffers do not have pointers to +// their locations. +static void * const webgpu_ptr_base = (void *) (uintptr_t) 0x1000; // NOLINT + +static size_t ggml_webgpu_tensor_offset(const ggml_tensor * tensor) { + const ggml_tensor * base_tensor = tensor->view_src ? tensor->view_src : tensor; + return (size_t) ((uintptr_t) base_tensor->data - (uintptr_t) webgpu_ptr_base) + tensor->view_offs; +} + +/* Struct definitions */ + +// Forward reference +static void ggml_webgpu_create_buffer(wgpu::Device & device, + wgpu::Buffer & buffer, + size_t size, + wgpu::BufferUsage usage, + const char * label); + +// Slot-based parameter arena for compute graph encoding. Each encoded kernel +// gets a unique uniform-buffer slice within the current batch, and the slot +// cursor is reset immediately after that batch is submitted. +struct webgpu_param_arena { + wgpu::Buffer buffer; + size_t slot_stride = 0; + size_t slot_size = 0; + uint32_t slot_count = 0; + uint32_t next_slot = 0; + + void init(wgpu::Device device, size_t slot_size, uint32_t slot_count, size_t alignment) { + this->slot_stride = ROUNDUP_POW2(slot_size, alignment); + this->slot_size = slot_size; + this->slot_count = slot_count; + this->next_slot = 0; + + ggml_webgpu_create_buffer(device, buffer, this->slot_stride * slot_count, + wgpu::BufferUsage::CopyDst | wgpu::BufferUsage::Uniform, "ggml_webgpu_param_arena"); + } + + size_t alloc_slot(size_t size) { + GGML_ASSERT(size <= slot_size); + if (next_slot >= slot_count) { + GGML_ABORT("ggml_webgpu: parameter arena exhausted while encoding a batch"); + } + + return slot_stride * next_slot++; + } + + void reset() { next_slot = 0; } + + void cleanup() { + if (buffer) { + buffer.Destroy(); + buffer = nullptr; + } + } + + ~webgpu_param_arena() { this->cleanup(); } +}; + +struct webgpu_encoded_op { + uint32_t num_kernels = 0; +#ifdef GGML_WEBGPU_GPU_PROFILE + std::vector pipeline_names; +#endif +}; + +struct webgpu_dispatch_desc { + webgpu_pipeline pipeline; + std::vector params; + std::vector bind_group_entries; + std::pair workgroups = { 1, 1 }; +}; + +struct webgpu_capabilities { + wgpu::Limits limits; + bool supports_subgroups = false; + bool supports_subgroup_matrix = false; + bool supports_dot_product = false; + + uint32_t sg_mat_m = 0; + uint32_t sg_mat_n = 0; + uint32_t sg_mat_k = 0; + + uint32_t subgroup_size = 0; + uint32_t min_subgroup_size = 0; + uint32_t max_subgroup_size = 0; + size_t memset_bytes_per_thread; +}; + +// Stores global webgpu members +struct webgpu_global_context_struct { + wgpu::Instance instance; + wgpu::Adapter adapter; + wgpu::Device device; + wgpu::Queue queue; + uint32_t command_submit_batch_size = WEBGPU_DEFAULT_COMMAND_SUBMIT_BATCH_SIZE; + uint32_t max_inflight_batches = UINT32_MAX; + + webgpu_capabilities capabilities; + // Shared buffer to move data from device to host + wgpu::Buffer get_tensor_staging_buf; + // Global mutex for get_tensor + std::recursive_mutex mutex; + + wgpu::Buffer memset_params_buf; + webgpu_pipeline memset_pipeline; + + std::string vendor; + + // TODO: We should rework the CPU profiling time handling to make it more useful. ref: https://github.com/ggml-org/llama.cpp/pull/22050 +#ifdef GGML_WEBGPU_CPU_PROFILE + // Profiling: labeled CPU time in ms (total) + std::unordered_map cpu_time_ms; + // Profiling: detailed CPU time in ms + std::unordered_map cpu_detail_ms; +#endif + +#ifdef GGML_WEBGPU_DEBUG + wgpu::Buffer debug_host_buf; + wgpu::Buffer debug_dev_buf; +#endif + + ~webgpu_global_context_struct() { + if (this->get_tensor_staging_buf) { + this->get_tensor_staging_buf.Destroy(); + this->get_tensor_staging_buf = nullptr; + } + if (this->memset_params_buf) { + this->memset_params_buf.Destroy(); + this->memset_params_buf = nullptr; + } +#ifdef GGML_WEBGPU_DEBUG + if (this->debug_host_buf) { + this->debug_host_buf.Destroy(); + this->debug_host_buf = nullptr; + } + if (this->debug_dev_buf) { + this->debug_dev_buf.Destroy(); + this->debug_dev_buf = nullptr; + } +#endif + } +}; + +typedef std::shared_ptr webgpu_global_context; + +// All the base objects needed to run operations on a WebGPU device +struct webgpu_context_struct { + // Points to global instances owned by ggml_backend_webgpu_reg_context + webgpu_global_context global_ctx; + + std::unique_ptr shader_lib; + + webgpu_param_arena param_arena; + wgpu::Buffer set_rows_dev_error_buf; + wgpu::Buffer set_rows_host_error_buf; + wgpu::CommandEncoder active_command_encoder; + wgpu::ComputePassEncoder active_compute_pass; + bool batch_compute_passes = true; + + size_t memset_bytes_per_thread; + +#ifdef GGML_WEBGPU_GPU_PROFILE + // Profiling: per-shader GPU time in ms + std::unordered_map shader_gpu_time_ms; + wgpu::Buffer profile_timestamp_dev_buf; + wgpu::Buffer profile_timestamp_host_buf; + wgpu::QuerySet profile_timestamp_query_set; + uint32_t profile_timestamp_query_count = 0; +#endif + + ~webgpu_context_struct() { +#ifdef GGML_WEBGPU_GPU_PROFILE + if (this->profile_timestamp_host_buf) { + this->profile_timestamp_host_buf.Destroy(); + this->profile_timestamp_host_buf = nullptr; + } + if (this->profile_timestamp_dev_buf) { + this->profile_timestamp_dev_buf.Destroy(); + this->profile_timestamp_dev_buf = nullptr; + } + if (this->profile_timestamp_query_set) { + this->profile_timestamp_query_set.Destroy(); + this->profile_timestamp_query_set = nullptr; + } +#endif + if (this->set_rows_host_error_buf) { + this->set_rows_host_error_buf.Destroy(); + this->set_rows_host_error_buf = nullptr; + } + if (this->set_rows_dev_error_buf) { + this->set_rows_dev_error_buf.Destroy(); + this->set_rows_dev_error_buf = nullptr; + } + } +}; + +typedef std::shared_ptr webgpu_context; + +// Metadata required for the ggml backend registration/discovery interface +struct ggml_backend_webgpu_reg_context { + // Since the Instance is a global entrypoint into the WebGPU API, it lives here + webgpu_global_context webgpu_global_ctx; + size_t device_count; + const char * name; +}; + +// Per-device struct for the global logical device interface +struct ggml_backend_webgpu_device_context { + webgpu_global_context webgpu_global_ctx; + std::string device_name; + std::string device_desc; +}; + +// Per-thread data required to actually run WebGPU operations in a backend instance +struct ggml_backend_webgpu_context { + webgpu_context webgpu_ctx; + std::string name; +}; + +// Per-thread data related to buffers +struct ggml_backend_webgpu_buffer_context { + wgpu::Buffer buffer; + std::string label; + webgpu_global_context global_ctx; + + ggml_backend_webgpu_buffer_context(wgpu::Buffer buf, std::string lbl, webgpu_global_context global_ctx_) : + buffer(std::move(buf)), + label(std::move(lbl)), + global_ctx(std::move(global_ctx_)) {} +}; + +/* WebGPU object initializations */ + +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; + + 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(); + } + return { device.CreateComputePipeline(&pipeline_desc), label }; +} + +static void ggml_webgpu_create_buffer(wgpu::Device & device, + wgpu::Buffer & buffer, + size_t size, + wgpu::BufferUsage usage, + const char * label) { + wgpu::BufferDescriptor buffer_desc; + buffer_desc.size = size; + buffer_desc.usage = usage; + buffer_desc.label = label; + buffer_desc.mappedAtCreation = false; + + // TODO: error handling + buffer = device.CreateBuffer(&buffer_desc); +} + +static wgpu::Buffer ggml_webgpu_tensor_buf(const ggml_tensor * tensor) { + ggml_backend_webgpu_buffer_context * ctx = (ggml_backend_webgpu_buffer_context *) tensor->buffer->context; + return ctx->buffer; +} + +static size_t ggml_webgpu_tensor_misalignment(webgpu_context & ctx, const ggml_tensor * t) { + size_t offset = ggml_webgpu_tensor_offset(t); + return offset & (ctx->global_ctx->capabilities.limits.minStorageBufferOffsetAlignment - 1); +} + +static size_t ggml_webgpu_tensor_align_offset(webgpu_context & ctx, const ggml_tensor * t) { + size_t offset = ggml_webgpu_tensor_offset(t); + return offset & ~(ctx->global_ctx->capabilities.limits.minStorageBufferOffsetAlignment - 1); +} + +static size_t ggml_webgpu_tensor_binding_size(webgpu_context & ctx, ggml_tensor * t) { + return ROUNDUP_POW2(ggml_nbytes(t) + ggml_webgpu_tensor_misalignment(ctx, t), WEBGPU_STORAGE_BUF_BINDING_MULT); +} + +struct ggml_webgpu_merged_binding_range { + size_t offset; + size_t size; +}; + +static ggml_webgpu_merged_binding_range ggml_webgpu_tensor_merged_binding_range( + webgpu_context & ctx, + std::initializer_list tensors) { + size_t merged_offset = SIZE_MAX; + size_t merged_end = 0; + + for (ggml_tensor * tensor : tensors) { + const size_t bind_offset = ggml_webgpu_tensor_align_offset(ctx, tensor); + const size_t bind_end = bind_offset + ggml_webgpu_tensor_binding_size(ctx, tensor); + + merged_offset = std::min(merged_offset, bind_offset); + merged_end = std::max(merged_end, bind_end); + } + + return { merged_offset, merged_end - merged_offset }; +} + +static uint32_t ggml_webgpu_tensor_merged_element_offset(const ggml_tensor * tensor, + const ggml_webgpu_merged_binding_range & merged_range) { + return (uint32_t) ((ggml_webgpu_tensor_offset(tensor) - merged_range.offset) / ggml_type_size(tensor->type)); +} + +static wgpu::BindGroupEntry ggml_webgpu_make_bind_group_entry(uint32_t binding, + wgpu::Buffer buffer, + uint64_t offset, + uint64_t size) { + wgpu::BindGroupEntry entry = {}; + entry.binding = binding; + entry.buffer = std::move(buffer); + entry.offset = offset; + entry.size = size; + return entry; +} + +static wgpu::BindGroupEntry ggml_webgpu_make_tensor_bind_group_entry(webgpu_context & ctx, + uint32_t binding, + ggml_tensor * tensor) { + return ggml_webgpu_make_bind_group_entry(binding, ggml_webgpu_tensor_buf(tensor), + ggml_webgpu_tensor_align_offset(ctx, tensor), + ggml_webgpu_tensor_binding_size(ctx, tensor)); +} + +/** End WebGPU object initializations */ + +/** WebGPU Actions */ + +template +static void ggml_backend_webgpu_check_wait_status(wgpu::WaitStatus wait_status, + T callback_status, + T success_status, + const char * wait_name, + const char * failure_name, + const char * callback_message) { + if (wait_status == wgpu::WaitStatus::TimedOut) { + GGML_ABORT("ggml_webgpu: %s timed out after %u ms\n", wait_name, WEBGPU_RUNTIME_WAIT_TIMEOUT_MS); + } + if (wait_status == wgpu::WaitStatus::Error) { + GGML_ABORT("ggml_webgpu: %s failed\n", wait_name); + } + if (callback_status != success_status) { + GGML_ABORT("ggml_webgpu: %s failed with status %d: %s\n", failure_name, static_cast(callback_status), + callback_message); + } +} + +// TODO: these next two functions may want tuning across different platforms and workloads, +static uint32_t ggml_backend_webgpu_get_max_inflight_batches() { + return UINT32_MAX; +} + +static uint32_t ggml_backend_webgpu_get_command_submit_batch_size() { + return WEBGPU_DEFAULT_COMMAND_SUBMIT_BATCH_SIZE; +} + +static void ggml_backend_webgpu_wait_queue(webgpu_global_context & ctx) { + wgpu::QueueWorkDoneStatus callback_status = wgpu::QueueWorkDoneStatus::Error; + std::string callback_message; + + const wgpu::WaitStatus wait_status = ctx->instance.WaitAny( + ctx->queue.OnSubmittedWorkDone( + wgpu::CallbackMode::AllowSpontaneous, + [&callback_status, &callback_message](wgpu::QueueWorkDoneStatus status, wgpu::StringView message) { + callback_status = status; + callback_message = std::string(message); + }), + WEBGPU_RUNTIME_WAIT_TIMEOUT_NS); + + ggml_backend_webgpu_check_wait_status(wait_status, callback_status, wgpu::QueueWorkDoneStatus::Success, + "Queue wait", "Queue work", callback_message.c_str()); +} + +static void ggml_backend_webgpu_map_buffer(webgpu_global_context & ctx, + wgpu::Buffer & buffer, + wgpu::MapMode mode, + size_t offset, + size_t size) { + wgpu::MapAsyncStatus callback_status = wgpu::MapAsyncStatus::Error; + std::string callback_message; + + const wgpu::WaitStatus wait_status = ctx->instance.WaitAny( + buffer.MapAsync(mode, offset, size, wgpu::CallbackMode::AllowSpontaneous, + [&callback_status, &callback_message](wgpu::MapAsyncStatus status, wgpu::StringView message) { + callback_status = status; + callback_message = std::string(message); + }), + WEBGPU_RUNTIME_WAIT_TIMEOUT_NS); + + ggml_backend_webgpu_check_wait_status(wait_status, callback_status, wgpu::MapAsyncStatus::Success, + "Buffer map wait", "Buffer map", callback_message.c_str()); +} + +static void ggml_backend_webgpu_submit_commands(webgpu_context & ctx, + const wgpu::CommandBuffer commands, + uint32_t & num_inflight_batches) { + if (num_inflight_batches >= ctx->global_ctx->max_inflight_batches) { + ggml_backend_webgpu_wait_queue(ctx->global_ctx); + num_inflight_batches = 0; + } + + ctx->global_ctx->queue.Submit(1, &commands); + num_inflight_batches++; +} + +#ifdef GGML_WEBGPU_DEBUG +// This function adds debugging information to shaders, as WebGPU does not support printing directly. +// To use, add a bind group entry to the setup for the shader you are debugging, add the buffer and +// debug statements in the shader, and then call this function after encoding the commands and submitting them. +static void ggml_backend_webgpu_debug(webgpu_global_context & ctx) { + wgpu::CommandEncoder encoder = ctx->device.CreateCommandEncoder(); + encoder.CopyBufferToBuffer(ctx->debug_dev_buf, 0, ctx->debug_host_buf, 0, ctx->debug_host_buf.GetSize()); + wgpu::CommandBuffer commands = encoder.Finish(); + ctx->queue.Submit(1, &commands); + ggml_backend_webgpu_map_buffer(ctx, ctx->debug_host_buf, wgpu::MapMode::Read, 0, ctx->debug_host_buf.GetSize()); + const float * debug_data = (const float *) ctx->debug_host_buf.GetConstMappedRange(); + std::cout << "debug[0]: " << debug_data[0] << "\n"; + ctx->debug_host_buf.Unmap(); +} +#endif + +static webgpu_encoded_op ggml_backend_webgpu_build_multi(webgpu_context & ctx, + const std::vector & dispatches) { + webgpu_encoded_op result = {}; + std::vector bind_groups; + std::vector param_offsets; + result.num_kernels = dispatches.size(); + + for (size_t i = 0; i < dispatches.size(); i++) { + const webgpu_dispatch_desc & dispatch = dispatches[i]; + const size_t param_size = dispatch.params.size() * sizeof(uint32_t); + const size_t param_offset = ctx->param_arena.alloc_slot(param_size); + + std::vector entries = dispatch.bind_group_entries; + uint32_t params_binding_num = entries.size(); + entries.push_back(ggml_webgpu_make_bind_group_entry(params_binding_num, ctx->param_arena.buffer, param_offset, + ctx->param_arena.slot_size)); + + wgpu::BindGroupDescriptor bind_group_desc; + bind_group_desc.layout = dispatch.pipeline.pipeline.GetBindGroupLayout(0); + bind_group_desc.entryCount = entries.size(); + bind_group_desc.entries = entries.data(); + bind_group_desc.label = dispatch.pipeline.name.c_str(); + bind_groups.push_back(ctx->global_ctx->device.CreateBindGroup(&bind_group_desc)); + param_offsets.push_back(param_offset); + } + + for (size_t i = 0; i < param_offsets.size(); i++) { + ctx->global_ctx->queue.WriteBuffer(ctx->param_arena.buffer, param_offsets[i], dispatches[i].params.data(), + dispatches[i].params.size() * sizeof(uint32_t)); + } + +#ifdef GGML_WEBGPU_GPU_PROFILE + for (size_t i = 0; i < dispatches.size(); i++) { + GGML_ASSERT(ctx->profile_timestamp_query_count + 2 <= WEBGPU_MAX_PROFILE_QUERY_COUNT); + const uint32_t query_begin = ctx->profile_timestamp_query_count++; + const uint32_t query_end = ctx->profile_timestamp_query_count++; + + wgpu::PassTimestampWrites ts_writes = {}; + ts_writes.querySet = ctx->profile_timestamp_query_set; + ts_writes.beginningOfPassWriteIndex = query_begin; + ts_writes.endOfPassWriteIndex = query_end; + wgpu::ComputePassDescriptor pass_desc = {}; + pass_desc.timestampWrites = &ts_writes; + + wgpu::ComputePassEncoder pass = ctx->active_command_encoder.BeginComputePass(&pass_desc); + + pass.SetPipeline(dispatches[i].pipeline.pipeline); + pass.SetBindGroup(0, bind_groups[i]); + pass.DispatchWorkgroups(dispatches[i].workgroups.first, dispatches[i].workgroups.second, 1); + pass.End(); + result.pipeline_names.push_back(dispatches[i].pipeline.name); + } +#else + for (size_t i = 0; i < dispatches.size(); i++) { + if (ctx->batch_compute_passes) { + ctx->active_compute_pass.SetPipeline(dispatches[i].pipeline.pipeline); + ctx->active_compute_pass.SetBindGroup(0, bind_groups[i]); + ctx->active_compute_pass.DispatchWorkgroups(dispatches[i].workgroups.first, dispatches[i].workgroups.second, + 1); + } else { + wgpu::ComputePassEncoder pass = ctx->active_command_encoder.BeginComputePass(); + pass.SetPipeline(dispatches[i].pipeline.pipeline); + pass.SetBindGroup(0, bind_groups[i]); + pass.DispatchWorkgroups(dispatches[i].workgroups.first, dispatches[i].workgroups.second, 1); + pass.End(); + } + } +#endif + + return result; +} + +static webgpu_encoded_op ggml_backend_webgpu_build(webgpu_context & ctx, + webgpu_pipeline & pipeline, + std::vector params, + std::vector bind_group_entries, + uint32_t wg_x, + uint32_t wg_y = 1) { + return ggml_backend_webgpu_build_multi( + ctx, { + { pipeline, std::move(params), std::move(bind_group_entries), { wg_x, wg_y } }, + }); +} + +static void ggml_backend_webgpu_buffer_memset(webgpu_global_context & ctx, + wgpu::Buffer & buf, + uint32_t value, + size_t offset, + size_t size) { + std::vector params = { (uint32_t) offset, (uint32_t) size, value }; + std::vector entries = { ggml_webgpu_make_bind_group_entry(0, buf, 0, buf.GetSize()) }; + size_t bytes_per_wg = + ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup * ctx->capabilities.memset_bytes_per_thread; + uint32_t wg_x = CEIL_DIV(size + 3, bytes_per_wg); + + ctx->queue.WriteBuffer(ctx->memset_params_buf, 0, params.data(), params.size() * sizeof(uint32_t)); + + wgpu::BindGroupEntry params_entry = {}; + params_entry.binding = 1; + params_entry.buffer = ctx->memset_params_buf; + params_entry.offset = 0; + params_entry.size = WEBGPU_PARAMS_BUF_SIZE_BYTES; + entries.push_back(params_entry); + + wgpu::BindGroupDescriptor bind_group_desc; + bind_group_desc.layout = ctx->memset_pipeline.pipeline.GetBindGroupLayout(0); + bind_group_desc.entryCount = entries.size(); + bind_group_desc.entries = entries.data(); + bind_group_desc.label = ctx->memset_pipeline.name.c_str(); + wgpu::BindGroup bind_group = ctx->device.CreateBindGroup(&bind_group_desc); + + wgpu::CommandEncoder encoder = ctx->device.CreateCommandEncoder(); + wgpu::ComputePassEncoder pass = encoder.BeginComputePass(); + pass.SetPipeline(ctx->memset_pipeline.pipeline); + pass.SetBindGroup(0, bind_group); + pass.DispatchWorkgroups(wg_x, 1, 1); + pass.End(); + + wgpu::CommandBuffer command = encoder.Finish(); + std::vector commands = { command }; + ctx->queue.Submit(commands.size(), commands.data()); +} + +/** End WebGPU Actions */ + +/** GGML Backend Interface */ + +static const char * ggml_backend_webgpu_name(ggml_backend_t backend) { + ggml_backend_webgpu_context * ctx = (ggml_backend_webgpu_context *) backend->context; + return ctx->name.c_str(); +} + +static void ggml_backend_webgpu_free(ggml_backend_t backend) { + ggml_backend_webgpu_context * ctx = (ggml_backend_webgpu_context *) backend->context; + WEBGPU_LOG_DEBUG("ggml_backend_webgpu_free(" << ctx->name << ")"); + +#ifdef GGML_WEBGPU_CPU_PROFILE + std::cout << "\n[ggml_webgpu cpu profiling summary]\n"; + double total_cpu = 0.0; + for (const auto & kv : ctx->webgpu_ctx->global_ctx->cpu_time_ms) { + total_cpu += kv.second; + } + std::cout << "ggml_webgpu: total cpu time: " << total_cpu << " ms\n"; + std::cout << "ggml_webgpu: cpu breakdown:\n"; + for (const auto & kv : ctx->webgpu_ctx->global_ctx->cpu_time_ms) { + double pct = (total_cpu > 0.0) ? (kv.second / total_cpu * 100.0) : 0.0; + std::cout << "ggml_webgpu: " << kv.first << ": " << kv.second << " ms (" << pct << "%)\n"; + } + if (ctx->webgpu_ctx->global_ctx->cpu_detail_ms.size() > 0) { + std::cout << "ggml_webgpu: cpu detailed breakdown:\n"; + } + for (const auto & kv : ctx->webgpu_ctx->global_ctx->cpu_detail_ms) { + double pct = (total_cpu > 0.0) ? (kv.second / total_cpu * 100.0) : 0.0; + std::cout << "ggml_webgpu: " << kv.first << ": " << kv.second << " ms (" << pct << "%)\n"; + } +#endif + +#ifdef GGML_WEBGPU_GPU_PROFILE + std::cout << "\n[ggml_webgpu gpu profiling summary]\n"; + double total_gpu = 0.0; + for (const auto & kv : ctx->webgpu_ctx->shader_gpu_time_ms) { + total_gpu += kv.second; + } + std::cout << "ggml_webgpu: total gpu time (all shaders): " << total_gpu << " ms\n"; + std::cout << "\nggml_webgpu: gpu breakdown:\n"; + for (const auto & kv : ctx->webgpu_ctx->shader_gpu_time_ms) { + double pct = (total_gpu > 0.0) ? (kv.second / total_gpu * 100.0) : 0.0; + std::cout << "ggml_webgpu: " << kv.first << ": " << kv.second << " ms (" << std::fixed << std::setprecision(2) + << pct << "%)\n"; + } +#endif + +#if defined(GGML_WEBGPU_CPU_PROFILE) && defined(GGML_WEBGPU_GPU_PROFILE) + std::cout << "ggml_webgpu: gpu/cpu ratio: " << (total_cpu > 0.0 ? total_gpu / total_cpu : 0.0) << "\n"; +#endif + + delete ctx; + delete backend; +} + +static webgpu_encoded_op ggml_webgpu_cpy(webgpu_context & ctx, ggml_tensor * src, ggml_tensor * dst) { + ggml_webgpu_shader_lib_context shader_lib_ctx = {}; + shader_lib_ctx.src0 = src; + shader_lib_ctx.dst = dst; + shader_lib_ctx.max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup; + + webgpu_pipeline pipeline = ctx->shader_lib->get_cpy_pipeline(shader_lib_ctx); + + auto * decisions = static_cast(pipeline.context.get()); + + uint32_t ne = (uint32_t) ggml_nelements(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] + }; + + std::vector entries = { + ggml_webgpu_make_tensor_bind_group_entry(ctx, 0, src), + ggml_webgpu_make_tensor_bind_group_entry(ctx, 1, dst), + }; + + uint32_t wg_x; + uint32_t wg_y; + uint32_t total_wg = CEIL_DIV(ne, decisions->wg_size); + compute_2d_workgroups(total_wg, ctx->global_ctx->capabilities.limits.maxComputeWorkgroupsPerDimension, wg_x, wg_y); + return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x, wg_y); +} + +static webgpu_encoded_op ggml_webgpu_set(webgpu_context & ctx, + ggml_tensor * src0, + ggml_tensor * src1, + ggml_tensor * dst) { + ggml_webgpu_shader_lib_context shader_lib_ctx = {}; + shader_lib_ctx.src0 = src0; + shader_lib_ctx.src1 = src1; + shader_lib_ctx.dst = dst; + shader_lib_ctx.max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup; + + webgpu_pipeline pipeline = ctx->shader_lib->get_set_pipeline(shader_lib_ctx); + + auto * decisions = static_cast(pipeline.context.get()); + const bool inplace = decisions->inplace; + + const uint32_t ne = inplace ? (uint32_t) ggml_nelements(src1) : (uint32_t) ggml_nelements(dst); + const uint32_t dst_type_size = (uint32_t) ggml_type_size(dst->type); + + std::vector params = { + ne, + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src0) / ggml_type_size(src0->type)), + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src1) / ggml_type_size(src1->type)), + (uint32_t) (((const int32_t *) dst->op_params)[3] / dst_type_size), + + (uint32_t) (src1->nb[0] / ggml_type_size(src1->type)), + (uint32_t) (src1->nb[1] / ggml_type_size(src1->type)), + (uint32_t) (src1->nb[2] / ggml_type_size(src1->type)), + (uint32_t) (src1->nb[3] / ggml_type_size(src1->type)), + + 1u, + (uint32_t) (((const int32_t *) dst->op_params)[0] / dst_type_size), + (uint32_t) (((const int32_t *) dst->op_params)[1] / dst_type_size), + (uint32_t) (((const int32_t *) dst->op_params)[2] / dst_type_size), + + (uint32_t) src1->ne[0], + (uint32_t) src1->ne[1], + (uint32_t) src1->ne[2], + (uint32_t) src1->ne[3], + }; + + std::vector entries; + uint32_t binding_index = 0; + if (!inplace) { + entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 0, src0)); + binding_index++; + } + entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, binding_index, src1)); + entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, binding_index + 1, dst)); + + uint32_t wg_x = CEIL_DIV(ne, decisions->wg_size); + return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x); +} + +static webgpu_encoded_op ggml_webgpu_pad(webgpu_context & ctx, ggml_tensor * src, ggml_tensor * dst) { + ggml_webgpu_shader_lib_context shader_lib_ctx = {}; + shader_lib_ctx.src0 = src; + shader_lib_ctx.dst = dst; + shader_lib_ctx.max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup; + + webgpu_pipeline pipeline = ctx->shader_lib->get_pad_pipeline(shader_lib_ctx); + + auto * decisions = static_cast(pipeline.context.get()); + + const uint32_t ne = (uint32_t) ggml_nelements(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)), + // Strides (in elements) + (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)), + // Shapes + (uint32_t) src->ne[0], + (uint32_t) src->ne[1], + (uint32_t) src->ne[2], + (uint32_t) src->ne[3], + (uint32_t) dst->ne[0], + (uint32_t) dst->ne[1], + (uint32_t) dst->ne[2], + (uint32_t) dst->ne[3], + // Pad sizes + (uint32_t) ggml_get_op_params_i32(dst, 0), + (uint32_t) ggml_get_op_params_i32(dst, 1), + (uint32_t) ggml_get_op_params_i32(dst, 2), + (uint32_t) ggml_get_op_params_i32(dst, 3), + (uint32_t) ggml_get_op_params_i32(dst, 4), + (uint32_t) ggml_get_op_params_i32(dst, 5), + (uint32_t) ggml_get_op_params_i32(dst, 6), + (uint32_t) ggml_get_op_params_i32(dst, 7), + }; + + std::vector entries = { + ggml_webgpu_make_tensor_bind_group_entry(ctx, 0, src), + ggml_webgpu_make_tensor_bind_group_entry(ctx, 1, dst), + }; + + uint32_t wg_x = CEIL_DIV(ne, decisions->wg_size); + return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x); +} + +static webgpu_encoded_op ggml_webgpu_solve_tri(webgpu_context & ctx, + ggml_tensor * src0, + ggml_tensor * src1, + ggml_tensor * dst) { + ggml_webgpu_shader_lib_context shader_lib_ctx = {}; + shader_lib_ctx.src0 = src0; + shader_lib_ctx.src1 = src1; + shader_lib_ctx.dst = dst; + shader_lib_ctx.max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup; + shader_lib_ctx.wg_mem_limit_bytes = ctx->global_ctx->capabilities.limits.maxComputeWorkgroupStorageSize; + + webgpu_pipeline pipeline = ctx->shader_lib->get_solve_tri_pipeline(shader_lib_ctx); + + auto * decisions = static_cast(pipeline.context.get()); + + std::vector params = { + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src0) / ggml_type_size(src0->type)), + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src1) / ggml_type_size(src1->type)), + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)), + + (uint32_t) (src0->nb[0] / ggml_type_size(src0->type)), + (uint32_t) (src0->nb[1] / ggml_type_size(src0->type)), + (uint32_t) (src0->nb[2] / ggml_type_size(src0->type)), + (uint32_t) (src0->nb[3] / ggml_type_size(src0->type)), + + (uint32_t) (src1->nb[0] / ggml_type_size(src1->type)), + (uint32_t) (src1->nb[1] / ggml_type_size(src1->type)), + (uint32_t) (src1->nb[2] / ggml_type_size(src1->type)), + (uint32_t) (src1->nb[3] / ggml_type_size(src1->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)), + + (uint32_t) src1->ne[0], + (uint32_t) dst->ne[2], + (uint32_t) dst->ne[3], + }; + + std::vector entries = { + ggml_webgpu_make_tensor_bind_group_entry(ctx, 0, src0), + ggml_webgpu_make_tensor_bind_group_entry(ctx, 1, src1), + ggml_webgpu_make_tensor_bind_group_entry(ctx, 2, dst), + }; + + const uint32_t wg_x = CEIL_DIV((uint32_t) src1->ne[0], decisions->wg_size); + const uint32_t wg_y = (uint32_t) (dst->ne[2] * dst->ne[3]); + return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x, wg_y); +} + +static webgpu_encoded_op ggml_webgpu_conv_2d(webgpu_context & ctx, + ggml_tensor * src0, + ggml_tensor * src1, + ggml_tensor * dst) { + const int32_t s0 = ggml_get_op_params_i32(dst, 0); + const int32_t s1 = ggml_get_op_params_i32(dst, 1); + const int32_t p0 = ggml_get_op_params_i32(dst, 2); + const int32_t p1 = ggml_get_op_params_i32(dst, 3); + const int32_t d0 = ggml_get_op_params_i32(dst, 4); + const int32_t d1 = ggml_get_op_params_i32(dst, 5); + + std::vector params = { + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src0) / ggml_type_size(src0->type)), + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src1) / ggml_type_size(src1->type)), + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)), + + (uint32_t) (src0->nb[0] / ggml_type_size(src0->type)), + (uint32_t) (src0->nb[1] / ggml_type_size(src0->type)), + (uint32_t) (src0->nb[2] / ggml_type_size(src0->type)), + (uint32_t) (src0->nb[3] / ggml_type_size(src0->type)), + + (uint32_t) (src1->nb[0] / ggml_type_size(src1->type)), + (uint32_t) (src1->nb[1] / ggml_type_size(src1->type)), + (uint32_t) (src1->nb[2] / ggml_type_size(src1->type)), + (uint32_t) (src1->nb[3] / ggml_type_size(src1->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)), + + (uint32_t) src0->ne[0], + (uint32_t) src0->ne[1], + (uint32_t) src0->ne[2], + + (uint32_t) src1->ne[0], + (uint32_t) src1->ne[1], + + (uint32_t) dst->ne[0], + (uint32_t) dst->ne[1], + (uint32_t) dst->ne[2], + (uint32_t) dst->ne[3], + + (uint32_t) s0, + (uint32_t) s1, + (uint32_t) p0, + (uint32_t) p1, + (uint32_t) d0, + (uint32_t) d1, + }; + + std::vector entries = { + ggml_webgpu_make_tensor_bind_group_entry(ctx, 0, src0), + ggml_webgpu_make_tensor_bind_group_entry(ctx, 1, src1), + ggml_webgpu_make_tensor_bind_group_entry(ctx, 2, dst), + }; + + ggml_webgpu_shader_lib_context shader_lib_ctx = {}; + shader_lib_ctx.src0 = src0; + shader_lib_ctx.src1 = src1; + shader_lib_ctx.dst = dst; + shader_lib_ctx.max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup; + + webgpu_pipeline pipeline = ctx->shader_lib->get_conv2d_pipeline(shader_lib_ctx); + + auto * decisions = static_cast(pipeline.context.get()); + + uint32_t wg_x; + uint32_t wg_y; + uint32_t total_wg = CEIL_DIV((uint32_t) ggml_nelements(dst), decisions->wg_size); + compute_2d_workgroups(total_wg, ctx->global_ctx->capabilities.limits.maxComputeWorkgroupsPerDimension, wg_x, wg_y); + + return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x, wg_y); +} + +static webgpu_encoded_op ggml_webgpu_im2col(webgpu_context & ctx, + ggml_tensor * src0, + ggml_tensor * src1, + ggml_tensor * dst) { + const int32_t s0 = ggml_get_op_params_i32(dst, 0); + const int32_t s1 = ggml_get_op_params_i32(dst, 1); + const int32_t p0 = ggml_get_op_params_i32(dst, 2); + const int32_t p1 = ggml_get_op_params_i32(dst, 3); + const int32_t d0 = ggml_get_op_params_i32(dst, 4); + const int32_t d1 = ggml_get_op_params_i32(dst, 5); + const bool is_2D = ggml_get_op_params_i32(dst, 6) == 1; + + const uint32_t KW = src0->ne[0]; + const uint32_t KH = is_2D ? src0->ne[1] : 1; + const uint32_t IC = is_2D ? src0->ne[2] : src0->ne[1]; + + const uint32_t IW = src1->ne[0]; + const uint32_t IH = is_2D ? src1->ne[1] : 1; + const uint32_t N = is_2D ? src1->ne[3] : src1->ne[2]; + + const uint32_t OW = dst->ne[1]; + const uint32_t OH = is_2D ? dst->ne[2] : 1; + + const uint32_t si0 = (uint32_t) (src1->nb[0] / ggml_type_size(src1->type)); + const uint32_t si1 = is_2D ? (uint32_t) (src1->nb[1] / ggml_type_size(src1->type)) : 0; + const uint32_t si2 = is_2D ? (uint32_t) (src1->nb[2] / ggml_type_size(src1->type)) : + (uint32_t) (src1->nb[1] / ggml_type_size(src1->type)); + const uint32_t si3 = is_2D ? (uint32_t) (src1->nb[3] / ggml_type_size(src1->type)) : + (uint32_t) (src1->nb[2] / ggml_type_size(src1->type)); + + const uint32_t so0 = (uint32_t) (dst->nb[0] / ggml_type_size(dst->type)); + const uint32_t so1 = (uint32_t) (dst->nb[1] / ggml_type_size(dst->type)); + const uint32_t so2 = is_2D ? (uint32_t) (dst->nb[2] / ggml_type_size(dst->type)) : 0; + const uint32_t so3 = is_2D ? (uint32_t) (dst->nb[3] / ggml_type_size(dst->type)) : + (uint32_t) (dst->nb[2] / ggml_type_size(dst->type)); + + std::vector params = { + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src1) / ggml_type_size(src1->type)), + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)), + + si0, + si1, + si2, + si3, + so0, + so1, + so2, + so3, + + KW, + KH, + IC, + + IW, + IH, + N, + + OW, + OH, + + (uint32_t) s0, + (uint32_t) s1, + (uint32_t) p0, + (uint32_t) p1, + (uint32_t) d0, + (uint32_t) d1, + }; + + std::vector entries = { + ggml_webgpu_make_tensor_bind_group_entry(ctx, 0, src1), + ggml_webgpu_make_tensor_bind_group_entry(ctx, 1, dst), + }; + + ggml_webgpu_shader_lib_context shader_lib_ctx = {}; + shader_lib_ctx.src0 = src0; + shader_lib_ctx.src1 = src1; + shader_lib_ctx.dst = dst; + shader_lib_ctx.max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup; + + webgpu_pipeline pipeline = ctx->shader_lib->get_im2col_pipeline(shader_lib_ctx); + + auto * decisions = static_cast(pipeline.context.get()); + + uint32_t wg_x; + uint32_t wg_y; + uint32_t total_wg = CEIL_DIV((uint32_t) ggml_nelements(dst), decisions->wg_size); + compute_2d_workgroups(total_wg, ctx->global_ctx->capabilities.limits.maxComputeWorkgroupsPerDimension, wg_x, wg_y); + + return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x, wg_y); +} + +static webgpu_encoded_op ggml_webgpu_ssm_conv(webgpu_context & ctx, + ggml_tensor * src0, + ggml_tensor * src1, + ggml_tensor * dst) { + ggml_webgpu_shader_lib_context shader_lib_ctx = {}; + shader_lib_ctx.src0 = src0; + shader_lib_ctx.src1 = src1; + shader_lib_ctx.dst = dst; + shader_lib_ctx.max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup; + + webgpu_pipeline pipeline = ctx->shader_lib->get_ssm_conv_pipeline(shader_lib_ctx); + auto * decisions = static_cast(pipeline.context.get()); + + const uint32_t token_tiles = CEIL_DIV((uint32_t) dst->ne[1], decisions->tokens_per_wg); + + std::vector params = { + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src0) / ggml_type_size(src0->type)), + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src1) / ggml_type_size(src1->type)), + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)), + + (uint32_t) (src0->nb[1] / ggml_type_size(src0->type)), + (uint32_t) (src0->nb[2] / ggml_type_size(src0->type)), + (uint32_t) (src1->nb[1] / ggml_type_size(src1->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) src1->ne[0], + (uint32_t) src0->ne[1], + (uint32_t) dst->ne[1], + (uint32_t) dst->ne[2], + token_tiles, + }; + + std::vector entries = { + ggml_webgpu_make_tensor_bind_group_entry(ctx, 0, src0), + ggml_webgpu_make_tensor_bind_group_entry(ctx, 1, src1), + ggml_webgpu_make_tensor_bind_group_entry(ctx, 2, dst), + }; + + const uint32_t wg_x = CEIL_DIV((uint32_t) src0->ne[1], decisions->block_size); + const uint32_t wg_y = token_tiles * (uint32_t) dst->ne[2]; + return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x, wg_y); +} + +static webgpu_encoded_op ggml_webgpu_ssm_scan(webgpu_context & ctx, + ggml_tensor * src0, + ggml_tensor * src1, + ggml_tensor * src2, + ggml_tensor * src3, + ggml_tensor * src4, + ggml_tensor * src5, + ggml_tensor * src6, + ggml_tensor * dst) { + ggml_webgpu_shader_lib_context shader_lib_ctx = {}; + shader_lib_ctx.src0 = src0; + shader_lib_ctx.src1 = src1; + shader_lib_ctx.src4 = src4; + shader_lib_ctx.src5 = src5; + shader_lib_ctx.dst = dst; + shader_lib_ctx.max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup; + shader_lib_ctx.supports_subgroups = ctx->global_ctx->capabilities.supports_subgroups; + + webgpu_pipeline pipeline = ctx->shader_lib->get_ssm_scan_pipeline(shader_lib_ctx); + auto * decisions = static_cast(pipeline.context.get()); + const bool xbc_overlap = decisions->xbc_overlap; + + uint32_t offset_x = (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src1) / ggml_type_size(src1->type)); + uint32_t offset_B = (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src4) / ggml_type_size(src4->type)); + uint32_t offset_C = (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src5) / ggml_type_size(src5->type)); + size_t xbc_bind_offset = 0; + size_t xbc_bind_size = 0; + if (xbc_overlap) { + const ggml_webgpu_merged_binding_range merged_range = + ggml_webgpu_tensor_merged_binding_range(ctx, { src1, src4, src5 }); + xbc_bind_offset = merged_range.offset; + xbc_bind_size = merged_range.size; + offset_x = ggml_webgpu_tensor_merged_element_offset(src1, merged_range); + offset_B = ggml_webgpu_tensor_merged_element_offset(src4, merged_range); + offset_C = ggml_webgpu_tensor_merged_element_offset(src5, merged_range); + } + + std::vector params = { + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src0) / ggml_type_size(src0->type)), + offset_x, + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src2) / ggml_type_size(src2->type)), + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src3) / ggml_type_size(src3->type)), + offset_B, + offset_C, + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src6) / ggml_type_size(src6->type)), + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)), + + (uint32_t) (src0->nb[1] / ggml_type_size(src0->type)), + (uint32_t) (src0->nb[2] / ggml_type_size(src0->type)), + (uint32_t) (src0->nb[3] / ggml_type_size(src0->type)), + + (uint32_t) (src1->nb[1] / ggml_type_size(src1->type)), + (uint32_t) (src1->nb[2] / ggml_type_size(src1->type)), + (uint32_t) (src1->nb[3] / ggml_type_size(src1->type)), + + (uint32_t) (src2->nb[1] / ggml_type_size(src2->type)), + (uint32_t) (src2->nb[2] / ggml_type_size(src2->type)), + + (uint32_t) src3->ne[0], + (uint32_t) (src3->nb[1] / ggml_type_size(src3->type)), + + (uint32_t) (src4->nb[1] / ggml_type_size(src4->type)), + (uint32_t) (src4->nb[2] / ggml_type_size(src4->type)), + (uint32_t) (src4->nb[3] / ggml_type_size(src4->type)), + + (uint32_t) (src5->nb[1] / ggml_type_size(src5->type)), + (uint32_t) (src5->nb[2] / ggml_type_size(src5->type)), + (uint32_t) (src5->nb[3] / ggml_type_size(src5->type)), + + (uint32_t) src0->ne[0], + (uint32_t) src0->ne[1], + (uint32_t) src0->ne[2], + (uint32_t) src4->ne[1], + (uint32_t) src1->ne[2], + (uint32_t) src1->ne[3], + (uint32_t) ggml_nelements(src1), + }; + + std::vector entries = { + ggml_webgpu_make_tensor_bind_group_entry(ctx, 0, src0), + }; + if (xbc_overlap) { + entries.push_back( + ggml_webgpu_make_bind_group_entry(1, ggml_webgpu_tensor_buf(src1), xbc_bind_offset, xbc_bind_size)); + entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 2, src2)); + entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 3, src3)); + entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 4, src6)); + entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 5, dst)); + } else { + entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 1, src1)); + entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 2, src2)); + entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 3, src3)); + entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 4, src4)); + entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 5, src5)); + entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 6, src6)); + entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 7, dst)); + } + + const uint32_t total_wg = (uint32_t) (src0->ne[1] * src0->ne[2] * src1->ne[3]); + const uint32_t max_wg_per_dim = ctx->global_ctx->capabilities.limits.maxComputeWorkgroupsPerDimension; + uint32_t wg_x; + uint32_t wg_y; + compute_2d_workgroups(total_wg, max_wg_per_dim, wg_x, wg_y); + + return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x, wg_y); +} + +static webgpu_encoded_op ggml_webgpu_gated_delta_net(webgpu_context & ctx, + ggml_tensor * src0, + ggml_tensor * src1, + ggml_tensor * src2, + ggml_tensor * src3, + ggml_tensor * src4, + ggml_tensor * src5, + ggml_tensor * dst) { + ggml_webgpu_shader_lib_context shader_lib_ctx = {}; + shader_lib_ctx.src0 = src0; + shader_lib_ctx.src1 = src1; + shader_lib_ctx.src2 = src2; + shader_lib_ctx.src3 = src3; + shader_lib_ctx.src4 = src4; + shader_lib_ctx.dst = dst; + shader_lib_ctx.max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup; + + webgpu_pipeline pipeline = ctx->shader_lib->get_gated_delta_net_pipeline(shader_lib_ctx); + + const uint32_t s_v = (uint32_t) src2->ne[0]; + const uint32_t h = (uint32_t) src2->ne[1]; + const uint32_t n_tokens = (uint32_t) src2->ne[2]; + const uint32_t n_seqs = (uint32_t) src2->ne[3]; + const uint32_t K = (uint32_t) ggml_get_op_params_i32(dst, 0); + const float scale = 1.0f / sqrtf((float) s_v); + uint32_t scale_u32; + memcpy(&scale_u32, &scale, sizeof(scale_u32)); + + std::vector params = { + h, + n_tokens, + n_seqs, + s_v * h * n_tokens * n_seqs, + + (uint32_t) (src0->nb[1] / ggml_type_size(src0->type)), + (uint32_t) (src0->nb[2] / ggml_type_size(src0->type)), + (uint32_t) (src0->nb[3] / ggml_type_size(src0->type)), + + (uint32_t) (src2->nb[1] / ggml_type_size(src2->type)), + (uint32_t) (src2->nb[2] / ggml_type_size(src2->type)), + (uint32_t) (src2->nb[3] / ggml_type_size(src2->type)), + + (uint32_t) (src4->nb[1] / ggml_type_size(src4->type)), + (uint32_t) (src4->nb[2] / ggml_type_size(src4->type)), + (uint32_t) (src4->nb[3] / ggml_type_size(src4->type)), + + (uint32_t) src0->ne[1], + (uint32_t) (src2->ne[3] / src0->ne[3]), + K, + scale_u32, + }; + + std::vector entries = { + ggml_webgpu_make_tensor_bind_group_entry(ctx, 0, src0), ggml_webgpu_make_tensor_bind_group_entry(ctx, 1, src1), + ggml_webgpu_make_tensor_bind_group_entry(ctx, 2, src2), ggml_webgpu_make_tensor_bind_group_entry(ctx, 3, src3), + ggml_webgpu_make_tensor_bind_group_entry(ctx, 4, src4), ggml_webgpu_make_tensor_bind_group_entry(ctx, 5, src5), + ggml_webgpu_make_tensor_bind_group_entry(ctx, 6, dst), + }; + + return ggml_backend_webgpu_build(ctx, pipeline, params, entries, h, n_seqs); +} + +static std::optional ggml_webgpu_set_rows(webgpu_context & ctx, + ggml_tensor * src, + ggml_tensor * idx, + ggml_tensor * dst) { + // For set rows specifically, we need to check if src and idx are empty + // tensors. + if (ggml_is_empty(src) || ggml_is_empty(idx)) { + return std::nullopt; + } + + ggml_webgpu_shader_lib_context shader_lib_ctx = {}; + shader_lib_ctx.src0 = src; + shader_lib_ctx.src1 = idx; + shader_lib_ctx.dst = dst; + shader_lib_ctx.max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup; + + webgpu_pipeline pipeline = ctx->shader_lib->get_set_rows_pipeline(shader_lib_ctx); + + auto * decisions = static_cast(pipeline.context.get()); + + std::vector params = { + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src) / ggml_type_size(src->type)), + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, idx) / ggml_type_size(idx->type)), + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)), + // Convert byte-strides to element-strides + (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) (idx->nb[0] / ggml_type_size(idx->type)), + (uint32_t) (idx->nb[1] / ggml_type_size(idx->type)), (uint32_t) (idx->nb[2] / ggml_type_size(idx->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)), + // Shape of src + (uint32_t) src->ne[0], (uint32_t) src->ne[1], (uint32_t) src->ne[2], (uint32_t) src->ne[3], + // Shape of idx + (uint32_t) (idx->ne[1]), (uint32_t) (idx->ne[2]) + }; + + std::vector entries = { + ggml_webgpu_make_tensor_bind_group_entry(ctx, 0, src), + ggml_webgpu_make_tensor_bind_group_entry(ctx, 1, idx), + ggml_webgpu_make_tensor_bind_group_entry(ctx, 2, dst), + }; + + if (decisions->i64_idx) { + entries.push_back(ggml_webgpu_make_bind_group_entry(3, ctx->set_rows_dev_error_buf, 0, + ctx->set_rows_dev_error_buf.GetSize())); + } + + uint32_t threads; + if (ggml_is_quantized(dst->type)) { + const uint32_t blocks_per_row = src->ne[0] / ggml_blck_size(dst->type); + threads = + (src->ne[1] * src->ne[2] * src->ne[3]) * (decisions->pair_blocks ? (blocks_per_row / 2) : blocks_per_row); + } else if (decisions->vec4) { + threads = (src->ne[1] * src->ne[2] * src->ne[3]) * (src->ne[0] / 4); + } else { + threads = src->ne[0] * src->ne[1] * src->ne[2] * src->ne[3]; + } + uint32_t wg_x = CEIL_DIV(threads, decisions->wg_size); + return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x, 1); +} + +// Workgroup size is a common constant +static std::vector ggml_webgpu_wg_size_entry(uint32_t wg_size) { + std::vector constants(1); + constants[0].key = "wg_size"; + constants[0].value = wg_size; + return constants; +} + +static webgpu_encoded_op ggml_webgpu_get_rows(webgpu_context & ctx, + ggml_tensor * src, + ggml_tensor * idx, + ggml_tensor * dst) { + const bool float_parallel = src->type == GGML_TYPE_F32 || src->type == GGML_TYPE_F16 || src->type == GGML_TYPE_I32; + + ggml_webgpu_shader_lib_context shader_lib_ctx = {}; + shader_lib_ctx.src0 = src; + shader_lib_ctx.src1 = nullptr; + shader_lib_ctx.dst = dst; + shader_lib_ctx.max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup; + + webgpu_pipeline pipeline = ctx->shader_lib->get_get_rows_pipeline(shader_lib_ctx); + auto * decisions = static_cast(pipeline.context.get()); + + std::vector params = { (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src) / ggml_type_size(src->type)), + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, idx) / ggml_type_size(idx->type)), + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->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) (idx->nb[0] / ggml_type_size(idx->type)), + (uint32_t) (idx->nb[1] / ggml_type_size(idx->type)), + (uint32_t) (idx->nb[2] / ggml_type_size(idx->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)), + (uint32_t) dst->ne[0], + (uint32_t) dst->ne[1], + (uint32_t) dst->ne[2], + (uint32_t) dst->ne[3], + (uint32_t) (idx->ne[1]), + (uint32_t) (idx->ne[2]) }; + + std::vector entries = { ggml_webgpu_make_tensor_bind_group_entry(ctx, 0, src), + ggml_webgpu_make_tensor_bind_group_entry(ctx, 1, idx), + ggml_webgpu_make_tensor_bind_group_entry(ctx, 2, dst) }; + + uint32_t blocks_per_row = (uint32_t) (dst->ne[0] / (src->type == GGML_TYPE_F32 && dst->ne[0] % 4 == 0 ? 4 : 1)); + uint32_t total_rows = (uint32_t) (dst->ne[1] * dst->ne[2] * dst->ne[3]); + uint32_t total_threads = float_parallel ? blocks_per_row * total_rows : total_rows; + uint32_t wg_x = CEIL_DIV(total_threads, decisions->wg_size); + + return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x); +} + +static void ggml_webgpu_quantize_q8_dispatch(webgpu_context & ctx, + ggml_tensor * src0, + ggml_tensor * src1, + ggml_tensor * dst, + std::vector & dispatches) { + ggml_webgpu_shader_lib_context shader_lib_ctx = {}; + + shader_lib_ctx.src0 = src0; + shader_lib_ctx.src1 = src1; + shader_lib_ctx.dst = dst; + shader_lib_ctx.max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup; + shader_lib_ctx.supports_subgroups = ctx->global_ctx->capabilities.supports_subgroups; + + webgpu_pipeline qq8_pipeline = ctx->shader_lib->get_quantize_q8_pipeline(shader_lib_ctx); + + // quantize_q8 pipeline + const size_t dst_offset = ggml_webgpu_tensor_offset(dst); + const size_t q8_src1_align_offset = ROUNDUP_POW2( + dst_offset + ggml_nbytes(dst), ctx->global_ctx->capabilities.limits.minStorageBufferOffsetAlignment); + const size_t q8_src1_binding_size = ROUNDUP_POW2( + src1->ne[3] * src1->ne[2] * src1->ne[1] * (36 /* sizeof(q8_1) */ * (src1->ne[0] / /* block_size */ 32)), + WEBGPU_STORAGE_BUF_BINDING_MULT); + + std::vector q8_params = { + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src1) / ggml_type_size(src1->type)), + (uint32_t) (src1->nb[1] / ggml_type_size(src1->type)), + (uint32_t) (src1->nb[2] / ggml_type_size(src1->type)), + (uint32_t) (src1->nb[3] / ggml_type_size(src1->type)), + (uint32_t) src1->ne[0], + (uint32_t) src1->ne[1], + (uint32_t) src1->ne[2], + (uint32_t) src1->ne[3], + }; + + std::vector q8_entries = { + ggml_webgpu_make_tensor_bind_group_entry(ctx, 0, src1), + ggml_webgpu_make_bind_group_entry(1, ggml_webgpu_tensor_buf(dst), q8_src1_align_offset, q8_src1_binding_size) + }; + + auto q8_decisions = static_cast(qq8_pipeline.context.get()); + + uint32_t q8_wg_size = q8_decisions->wg_size; + uint32_t q8_wg_x = 1; + uint32_t q8_wg_y = 1; + const uint32_t wg_per_vec = (src0->ne[0] / 4 + (q8_wg_size - 1)) / q8_wg_size; + const uint32_t q8_total_wg = src1->ne[1] * src1->ne[2] * src1->ne[3] * wg_per_vec; + const uint32_t max_wg_per_dim = ctx->global_ctx->capabilities.limits.maxComputeWorkgroupsPerDimension; + compute_2d_workgroups(q8_total_wg, max_wg_per_dim, q8_wg_x, q8_wg_y); + + dispatches.push_back({ + qq8_pipeline, std::move(q8_params), std::move(q8_entries), { q8_wg_x, q8_wg_y } + }); +} + +static webgpu_encoded_op ggml_webgpu_mul_mat(webgpu_context & ctx, + ggml_tensor * src0, + ggml_tensor * src1, + ggml_tensor * dst) { + // Determine if this is a mat-vec operation + bool use_mat_vec = (dst->ne[1] <= 4); + + // use MMVQ path for mat-vec + bool use_mmvq = ggml_webgpu_can_use_mmvq(src0, src1, ctx->global_ctx->capabilities.supports_dot_product, + ctx->global_ctx->vendor); + + ggml_webgpu_shader_lib_context shader_lib_ctx = {}; + + shader_lib_ctx.src0 = src0; + shader_lib_ctx.src1 = src1; + shader_lib_ctx.dst = dst; + shader_lib_ctx.max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup; + shader_lib_ctx.supports_subgroups = ctx->global_ctx->capabilities.supports_subgroups; + shader_lib_ctx.supports_subgroup_matrix = ctx->global_ctx->capabilities.supports_subgroup_matrix; + shader_lib_ctx.sg_mat_m = ctx->global_ctx->capabilities.sg_mat_m; + shader_lib_ctx.sg_mat_n = ctx->global_ctx->capabilities.sg_mat_n; + shader_lib_ctx.sg_mat_k = ctx->global_ctx->capabilities.sg_mat_k; + shader_lib_ctx.min_subgroup_size = ctx->global_ctx->capabilities.min_subgroup_size; + shader_lib_ctx.max_subgroup_size = ctx->global_ctx->capabilities.max_subgroup_size; + shader_lib_ctx.supports_dot_product = ctx->global_ctx->capabilities.supports_dot_product; + shader_lib_ctx.vendor = ctx->global_ctx->vendor; + + // Get or create pipeline + webgpu_pipeline pipeline; + std::vector dispatches; + + if (use_mat_vec) { + if (use_mmvq) { + ggml_webgpu_quantize_q8_dispatch(ctx, src0, src1, dst, dispatches); + } + pipeline = ctx->shader_lib->get_mul_mat_vec_pipeline(shader_lib_ctx); + } else { + pipeline = ctx->shader_lib->get_mul_mat_fast_pipeline(shader_lib_ctx); + } + + // Build params + std::vector params = { + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src0) / ggml_type_size(src0->type)), + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src1) / ggml_type_size(src1->type)), + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)), + (uint32_t) dst->ne[0], + (uint32_t) dst->ne[1], + (uint32_t) src0->ne[0], + (uint32_t) (src0->nb[1] / ggml_type_size(src0->type)), + (uint32_t) (src1->nb[1] / ggml_type_size(src1->type)), + (uint32_t) (src0->nb[2] / ggml_type_size(src0->type)), + (uint32_t) (src1->nb[2] / ggml_type_size(src1->type)), + (uint32_t) (src0->nb[3] / ggml_type_size(src0->type)), + (uint32_t) (src1->nb[3] / ggml_type_size(src1->type)), + (uint32_t) src0->ne[2], + (uint32_t) src0->ne[3], + (uint32_t) (src1->ne[2] / src0->ne[2]), + (uint32_t) (src1->ne[3] / src0->ne[3]) + }; + + // Build bind group entries + std::vector entries = {}; + + entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 0, src0)); + if (use_mmvq) { + auto & mmvq_qq8_entry = dispatches[0].bind_group_entries[1]; + entries.push_back(ggml_webgpu_make_bind_group_entry(1, ggml_webgpu_tensor_buf(dst), mmvq_qq8_entry.offset, + mmvq_qq8_entry.size)); + } else { + entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 1, src1)); + } + entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 2, dst)); + + // Calculate workgroup dimensions + uint32_t wg_x = 1; + uint32_t wg_y = 1; + const uint32_t max_wg_per_dim = ctx->global_ctx->capabilities.limits.maxComputeWorkgroupsPerDimension; + + if (use_mat_vec) { + auto * decisions = static_cast(pipeline.context.get()); + + uint32_t batches = dst->ne[2] * dst->ne[3]; + uint32_t output_groups = CEIL_DIV(dst->ne[0], decisions->outputs_per_wg); + uint32_t total_wg = output_groups * batches; + compute_2d_workgroups(total_wg, max_wg_per_dim, wg_x, wg_y); + } else { + auto * decisions = static_cast(pipeline.context.get()); + + // Fast-path tiled/subgroup calculations + uint32_t wg_m; + uint32_t wg_n; + if (decisions->use_subgroup_matrix) { + uint32_t wg_m_sg_tile = + decisions->subgroup_m * decisions->subgroup_matrix_m * ctx->global_ctx->capabilities.sg_mat_m; + wg_m = CEIL_DIV(dst->ne[0], wg_m_sg_tile); + uint32_t wg_n_sg_tile = + decisions->subgroup_n * decisions->subgroup_matrix_n * ctx->global_ctx->capabilities.sg_mat_n; + wg_n = CEIL_DIV(dst->ne[1], wg_n_sg_tile); + } else { + uint32_t tile_m_s = decisions->tile_m * decisions->wg_size_m; + uint32_t tile_n_s = decisions->tile_n * decisions->wg_size_n; + wg_m = CEIL_DIV(dst->ne[0], tile_m_s); + wg_n = CEIL_DIV(dst->ne[1], tile_n_s); + } + uint32_t total_wg = wg_m * wg_n * dst->ne[2] * dst->ne[3]; + compute_2d_workgroups(total_wg, max_wg_per_dim, wg_x, wg_y); + } + + dispatches.push_back({ + pipeline, std::move(params), std::move(entries), { wg_x, wg_y } + }); + + return ggml_backend_webgpu_build_multi(ctx, dispatches); +} + +static webgpu_encoded_op ggml_webgpu_mul_mat_id_vec(webgpu_context & ctx, + ggml_tensor * src0, + ggml_tensor * src1, + ggml_tensor * src2, + ggml_tensor * dst) { + const uint32_t param_n_expert = (uint32_t) src0->ne[2]; + const uint32_t param_n_expert_used = (uint32_t) dst->ne[1]; + + ggml_webgpu_shader_lib_context shader_lib_ctx = {}; + shader_lib_ctx.src0 = src0; + shader_lib_ctx.src1 = src1; + shader_lib_ctx.src2 = src2; + shader_lib_ctx.dst = dst; + shader_lib_ctx.supports_subgroups = ctx->global_ctx->capabilities.supports_subgroups; + shader_lib_ctx.max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup; + + webgpu_pipeline pipeline = ctx->shader_lib->get_mul_mat_id_vec_pipeline(shader_lib_ctx); + + std::vector params = { + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src0) / ggml_type_size(src0->type)), + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src1) / ggml_type_size(src1->type)), + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src2) / ggml_type_size(src2->type)), + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)), + (uint32_t) src0->ne[0], + (uint32_t) src0->ne[1], + param_n_expert, + param_n_expert_used, + (uint32_t) src1->ne[1], + (uint32_t) (src0->nb[1] / ggml_type_size(src0->type)), + (uint32_t) (src1->nb[1] / ggml_type_size(src1->type)), + (uint32_t) (src0->nb[2] / ggml_type_size(src0->type)), + (uint32_t) (src1->nb[2] / ggml_type_size(src1->type)), + }; + + std::vector entries = { + ggml_webgpu_make_bind_group_entry(0, ggml_webgpu_tensor_buf(src0), ggml_webgpu_tensor_align_offset(ctx, src0), + ggml_webgpu_tensor_binding_size(ctx, src0)), + ggml_webgpu_make_bind_group_entry(1, ggml_webgpu_tensor_buf(src1), ggml_webgpu_tensor_align_offset(ctx, src1), + ggml_webgpu_tensor_binding_size(ctx, src1)), + ggml_webgpu_make_bind_group_entry(2, ggml_webgpu_tensor_buf(src2), ggml_webgpu_tensor_align_offset(ctx, src2), + ggml_webgpu_tensor_binding_size(ctx, src2)), + ggml_webgpu_make_bind_group_entry(3, ggml_webgpu_tensor_buf(dst), ggml_webgpu_tensor_align_offset(ctx, dst), + ggml_webgpu_tensor_binding_size(ctx, dst)), + }; + + uint32_t wg_x = 1; + uint32_t wg_y = 1; + + auto * decisions = static_cast(pipeline.context.get()); + + const uint32_t max_wg_per_dim = ctx->global_ctx->capabilities.limits.maxComputeWorkgroupsPerDimension; + uint32_t output_groups = CEIL_DIV(dst->ne[0], decisions->outputs_per_wg); + uint32_t total_wg = output_groups * param_n_expert_used; + compute_2d_workgroups(total_wg, max_wg_per_dim, wg_x, wg_y); + + return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x, wg_y); +} + +static webgpu_encoded_op ggml_webgpu_mul_mat_id(webgpu_context & ctx, + ggml_tensor * src0, + ggml_tensor * src1, + ggml_tensor * src2, + ggml_tensor * dst) { + // we can use mat-vec fast path + if (dst->ne[2] == 1) { + return ggml_webgpu_mul_mat_id_vec(ctx, src0, src1, src2, dst); + } + + ggml_webgpu_shader_lib_context shader_lib_ctx = {}; + shader_lib_ctx.src0 = src0; + shader_lib_ctx.src1 = src1; + shader_lib_ctx.src2 = src2; + shader_lib_ctx.dst = dst; + shader_lib_ctx.max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup; + + // Get or create pipeline + webgpu_pipeline gather_pipeline; + webgpu_pipeline main_pipeline; + + std::vector dispatches; + + gather_pipeline = ctx->shader_lib->get_mul_mat_id_gather_pipeline(shader_lib_ctx); + main_pipeline = ctx->shader_lib->get_mul_mat_id_pipeline(shader_lib_ctx); + + const uint32_t param_n_expert = (uint32_t) src0->ne[2]; + const uint32_t param_n_expert_used = (uint32_t) dst->ne[1]; + const uint32_t param_n_tokens = (uint32_t) dst->ne[2]; + + // params for mul_mat_id_gather.wgsl + std::vector gather_params = { + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src2) / ggml_type_size(src2->type)), + param_n_expert, + param_n_expert_used, + param_n_tokens, + (uint32_t) (src2->nb[1] / ggml_type_size(src2->type)), + }; + + const size_t dst_offset = ggml_webgpu_tensor_offset(dst); + const size_t gathered_buf_nbytes = src0->ne[2] * src1->ne[2] * sizeof(uint32_t); + + const size_t gathered_expert_used_align_offset = ROUNDUP_POW2( + dst_offset + ggml_nbytes(dst), ctx->global_ctx->capabilities.limits.minStorageBufferOffsetAlignment); + const size_t gathered_tokens_align_offset = + ROUNDUP_POW2(gathered_expert_used_align_offset + gathered_buf_nbytes, + ctx->global_ctx->capabilities.limits.minStorageBufferOffsetAlignment); + const size_t gathered_count_ids_align_offset = + ROUNDUP_POW2(gathered_tokens_align_offset + gathered_buf_nbytes, + ctx->global_ctx->capabilities.limits.minStorageBufferOffsetAlignment); + + const size_t gathered_binding_size = ROUNDUP_POW2(gathered_buf_nbytes, WEBGPU_STORAGE_BUF_BINDING_MULT); + const size_t gathered_count_ids_binding_size = + ROUNDUP_POW2(src0->ne[2] * sizeof(uint32_t), WEBGPU_STORAGE_BUF_BINDING_MULT); + + // bind group entries for mul_mat_id_gather.wgsl + std::vector gather_entries = { + ggml_webgpu_make_bind_group_entry(0, ggml_webgpu_tensor_buf(src2), ggml_webgpu_tensor_align_offset(ctx, src2), + ggml_webgpu_tensor_binding_size(ctx, src2)), + ggml_webgpu_make_bind_group_entry(1, ggml_webgpu_tensor_buf(dst), gathered_expert_used_align_offset, + gathered_binding_size), + ggml_webgpu_make_bind_group_entry(2, ggml_webgpu_tensor_buf(dst), gathered_tokens_align_offset, + gathered_binding_size), + ggml_webgpu_make_bind_group_entry(3, ggml_webgpu_tensor_buf(dst), gathered_count_ids_align_offset, + gathered_count_ids_binding_size), + }; + + // n_expert is much less than maxComputeWorkgroupsPerDimension (e.g., n_exeprt=256 at Qwen3.5-35B-A3B) + const uint32_t gather_wg_x = param_n_expert; + + dispatches.push_back({ + gather_pipeline, std::move(gather_params), std::move(gather_entries), { gather_wg_x, 1 } + }); + + // params for mul_mat_id.wgsl + std::vector main_params = { + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src0) / ggml_type_size(src0->type)), + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src1) / ggml_type_size(src1->type)), + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)), + (uint32_t) src0->ne[0], + (uint32_t) src0->ne[1], + param_n_expert, + param_n_expert_used, + param_n_tokens, + (uint32_t) src1->ne[1], + (uint32_t) (src0->nb[1] / ggml_type_size(src0->type)), + (uint32_t) (src1->nb[1] / ggml_type_size(src1->type)), + (uint32_t) (src0->nb[2] / ggml_type_size(src0->type)), + (uint32_t) (src1->nb[2] / ggml_type_size(src1->type)), + }; + + // bind group entries for mul_mat_id.wgsl + std::vector main_entries = { + ggml_webgpu_make_bind_group_entry(0, ggml_webgpu_tensor_buf(src0), ggml_webgpu_tensor_align_offset(ctx, src0), + ggml_webgpu_tensor_binding_size(ctx, src0)), + ggml_webgpu_make_bind_group_entry(1, ggml_webgpu_tensor_buf(src1), ggml_webgpu_tensor_align_offset(ctx, src1), + ggml_webgpu_tensor_binding_size(ctx, src1)), + ggml_webgpu_make_bind_group_entry(2, ggml_webgpu_tensor_buf(dst), ggml_webgpu_tensor_align_offset(ctx, dst), + ggml_webgpu_tensor_binding_size(ctx, dst)), + ggml_webgpu_make_bind_group_entry(3, ggml_webgpu_tensor_buf(dst), gathered_expert_used_align_offset, + gathered_binding_size), + ggml_webgpu_make_bind_group_entry(4, ggml_webgpu_tensor_buf(dst), gathered_tokens_align_offset, + gathered_binding_size), + ggml_webgpu_make_bind_group_entry(5, ggml_webgpu_tensor_buf(dst), gathered_count_ids_align_offset, + gathered_count_ids_binding_size), + }; + + // Calculate workgroup dimensions + uint32_t wg_x = 1; + uint32_t wg_y = 1; + + auto * main_decisions = static_cast(main_pipeline.context.get()); + + uint32_t wg_m; + + uint32_t tile_m_s = main_decisions->tile_m * main_decisions->wg_size_m; + uint32_t tile_n_s = main_decisions->tile_n * main_decisions->wg_size_n; + wg_m = CEIL_DIV(dst->ne[0], tile_m_s); + uint32_t total_gathered = dst->ne[1] * dst->ne[2]; + uint32_t max_active_experts = std::min((uint32_t) src0->ne[2], total_gathered); + uint32_t max_wg_n = CEIL_DIV(total_gathered, tile_n_s) + max_active_experts; + uint32_t total_wg = wg_m * max_wg_n; + + compute_2d_workgroups(total_wg, ctx->global_ctx->capabilities.limits.maxComputeWorkgroupsPerDimension, wg_x, wg_y); + + dispatches.push_back({ + main_pipeline, std::move(main_params), std::move(main_entries), { wg_x, wg_y } + }); + + return ggml_backend_webgpu_build_multi(ctx, dispatches); +} + +struct ggml_webgpu_flash_attn_op { + ggml_webgpu_shader_lib_context shader_lib_ctx = {}; + std::vector params; + std::vector entries; + size_t kv_bind_offset = 0; + size_t kv_bind_size = 0; + bool has_mask = false; + bool has_sinks = false; + bool kv_overlap = false; +}; + +static bool ggml_webgpu_flash_attn_use_vec_path(const webgpu_global_context & global_ctx, + const ggml_tensor * Q, + const ggml_tensor * K, + const ggml_tensor * V) { + const size_t storage_offset_alignment = global_ctx->capabilities.limits.minStorageBufferOffsetAlignment; + const bool k_float_vec4_aligned = (K->type != GGML_TYPE_F16 && K->type != GGML_TYPE_F32) || + ggml_webgpu_flash_attn_float_vec4_aligned(K, storage_offset_alignment); + const bool v_float_vec4_aligned = (V->type != GGML_TYPE_F16 && V->type != GGML_TYPE_F32) || + ggml_webgpu_flash_attn_float_vec4_aligned(V, storage_offset_alignment); + const bool k_vec_type_supported = + K->type == GGML_TYPE_F32 || K->type == GGML_TYPE_F16 || K->type == GGML_TYPE_Q4_0 || K->type == GGML_TYPE_Q8_0; + const bool v_vec_type_supported = + V->type == GGML_TYPE_F32 || V->type == GGML_TYPE_F16 || V->type == GGML_TYPE_Q4_0 || V->type == GGML_TYPE_Q8_0; + const uint32_t k_vec_head_align = (K->type == GGML_TYPE_F32 || K->type == GGML_TYPE_F16) ? + GGML_WEBGPU_FLASH_ATTN_TILE_KV_VEC_WIDTH : + (uint32_t) ggml_blck_size(K->type); + const uint32_t v_vec_head_align = (V->type == GGML_TYPE_F32 || V->type == GGML_TYPE_F16) ? + GGML_WEBGPU_FLASH_ATTN_TILE_KV_VEC_WIDTH : + (uint32_t) ggml_blck_size(V->type); + const bool kv_vec_head_dims_aligned = Q->ne[0] % k_vec_head_align == 0 && V->ne[0] % v_vec_head_align == 0; + + return global_ctx->capabilities.supports_subgroups && (Q->ne[1] < GGML_WEBGPU_FLASH_ATTN_VEC_MAX_SEQ_LEN) && + kv_vec_head_dims_aligned && k_vec_type_supported && v_vec_type_supported && k_float_vec4_aligned && + v_float_vec4_aligned; +} + +static ggml_webgpu_flash_attn_op ggml_webgpu_flash_attn_prepare(webgpu_context & ctx, + ggml_tensor * Q, + ggml_tensor * K, + ggml_tensor * V, + ggml_tensor * mask, + ggml_tensor * sinks, + ggml_tensor * dst) { + float scale = ggml_get_op_params_f32(dst, 0); + float max_bias = ggml_get_op_params_f32(dst, 1); + float logit_softcap = ggml_get_op_params_f32(dst, 2); + if (logit_softcap != 0.0f) { + scale /= logit_softcap; + } + float n_head_log2 = float(1u << (uint32_t) floor(log2(Q->ne[2]))); + float m0 = powf(2.0f, -(max_bias) / n_head_log2); + float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); + + ggml_webgpu_flash_attn_op op = {}; + op.shader_lib_ctx.src0 = Q; + op.shader_lib_ctx.src1 = K; + op.shader_lib_ctx.src2 = V; + op.shader_lib_ctx.src3 = mask; + op.shader_lib_ctx.src4 = sinks; + op.shader_lib_ctx.dst = dst; + op.shader_lib_ctx.supports_subgroups = ctx->global_ctx->capabilities.supports_subgroups; + op.shader_lib_ctx.supports_subgroup_matrix = ctx->global_ctx->capabilities.supports_subgroup_matrix; + op.shader_lib_ctx.max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup; + op.shader_lib_ctx.wg_mem_limit_bytes = ctx->global_ctx->capabilities.limits.maxComputeWorkgroupStorageSize; + op.shader_lib_ctx.sg_mat_m = ctx->global_ctx->capabilities.sg_mat_m; + op.shader_lib_ctx.sg_mat_n = ctx->global_ctx->capabilities.sg_mat_n; + op.shader_lib_ctx.sg_mat_k = ctx->global_ctx->capabilities.sg_mat_k; + op.shader_lib_ctx.min_subgroup_size = ctx->global_ctx->capabilities.min_subgroup_size; + op.shader_lib_ctx.max_subgroup_size = ctx->global_ctx->capabilities.max_subgroup_size; + + op.has_mask = mask != nullptr; + op.has_sinks = sinks != nullptr; + op.kv_overlap = ggml_webgpu_tensor_overlap(K, V); + + uint32_t offset_k = (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, K) / ggml_type_size(K->type)); + uint32_t offset_v = (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, V) / ggml_type_size(V->type)); + if (op.kv_overlap) { + const ggml_webgpu_merged_binding_range merged_range = ggml_webgpu_tensor_merged_binding_range(ctx, { K, V }); + op.kv_bind_offset = merged_range.offset; + op.kv_bind_size = merged_range.size; + offset_k = ggml_webgpu_tensor_merged_element_offset(K, merged_range); + offset_v = ggml_webgpu_tensor_merged_element_offset(V, merged_range); + } + + op.params = { + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, Q) / ggml_type_size(Q->type)), + offset_k, + offset_v, + op.has_mask ? (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, mask) / ggml_type_size(mask->type)) : 0, + op.has_sinks ? (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, sinks) / ggml_type_size(sinks->type)) : 0, + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)), + (uint32_t) Q->ne[2], // number of heads + (uint32_t) Q->ne[1], // sequence length (Q) + (uint32_t) K->ne[1], // sequence length (K/V) + (uint32_t) (Q->nb[1] / ggml_type_size(Q->type)), // stride (elements/blocks) of Q in dimension 1 + (uint32_t) (Q->nb[2] / ggml_type_size(Q->type)), // stride (elements/blocks) of Q in dimension 2 + (uint32_t) (Q->nb[3] / ggml_type_size(Q->type)), // stride (elements/blocks) of Q in dimension 3 + (uint32_t) (K->nb[1] / ggml_type_size(K->type)), // stride (elements/blocks) of K in dimension 1 + (uint32_t) (K->nb[2] / ggml_type_size(K->type)), // stride (elements/blocks) of K in dimension 2 + (uint32_t) (K->nb[3] / ggml_type_size(K->type)), // stride (elements/blocks) of K in dimension 3 + (uint32_t) (V->nb[1] / ggml_type_size(V->type)), // stride (elements/blocks) of V in dimension 1 + (uint32_t) (V->nb[2] / ggml_type_size(V->type)), // stride (elements/blocks) of V in dimension 2 + (uint32_t) (V->nb[3] / ggml_type_size(V->type)), // stride (elements/blocks) of V in dimension 3 + op.has_mask ? (uint32_t) (mask->nb[3] / ggml_type_size(mask->type)) : 0, // stride of mask dim 3 + (uint32_t) (Q->ne[2] / K->ne[2]), // repeat factor for K/V in dim 2 (MHA/MQA/GQA) + ggml_webgpu_u32_from_f32(scale), // scale (possibly adjusted for logit softcap) + ggml_webgpu_u32_from_f32(max_bias), + ggml_webgpu_u32_from_f32(logit_softcap), + ggml_webgpu_u32_from_f32(n_head_log2), + ggml_webgpu_u32_from_f32(m0), + ggml_webgpu_u32_from_f32(m1) + }; + op.entries = { + ggml_webgpu_make_tensor_bind_group_entry(ctx, 0, Q), + }; + if (op.kv_overlap) { + op.entries.push_back( + ggml_webgpu_make_bind_group_entry(1, ggml_webgpu_tensor_buf(K), op.kv_bind_offset, op.kv_bind_size)); + } else { + op.entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 1, K)); + op.entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 2, V)); + } + uint32_t binding_index = op.kv_overlap ? 2u : 3u; + if (op.has_mask) { + op.entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, binding_index++, mask)); + } + if (op.has_sinks) { + op.entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, binding_index++, sinks)); + } + op.entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, binding_index++, dst)); + + return op; +} + +static uint32_t ggml_webgpu_flash_attn_vec_nwg(uint32_t vec_nwg_cap, uint32_t kv_tile, uint32_t seq_len_kv) { + uint32_t nwg = 1u; + const uint64_t kv_span = (uint64_t) kv_tile; + while ((2u * nwg * kv_span) < (uint64_t) seq_len_kv && nwg < vec_nwg_cap) { + nwg <<= 1; + } + return std::min(nwg, vec_nwg_cap); +} + +static webgpu_encoded_op ggml_webgpu_flash_attn_direct(webgpu_context & ctx, const ggml_webgpu_flash_attn_op & op) { + webgpu_pipeline pipeline = ctx->shader_lib->get_flash_attn_pipeline(op.shader_lib_ctx); + auto * decisions = static_cast(pipeline.context.get()); + uint32_t wg_per_head = CEIL_DIV(op.shader_lib_ctx.src0->ne[1], decisions->q_tile); + uint32_t wg_x = wg_per_head * op.shader_lib_ctx.src0->ne[2] * op.shader_lib_ctx.src0->ne[3]; + return ggml_backend_webgpu_build(ctx, pipeline, op.params, op.entries, wg_x); +} + +static webgpu_encoded_op ggml_webgpu_flash_attn_vec(webgpu_context & ctx, + ggml_tensor * Q, + ggml_tensor * K, + ggml_tensor * V, + ggml_tensor * mask, + ggml_tensor * sinks, + ggml_tensor * dst, + ggml_webgpu_flash_attn_op op) { + webgpu_pipeline pipeline = ctx->shader_lib->get_flash_attn_vec_pipeline(op.shader_lib_ctx); + auto * decisions = static_cast(pipeline.context.get()); + + wgpu::Buffer blk_buf = {}; + uint64_t blk_size_bytes = 0; + uint32_t blk_nblk0 = 0; + uint32_t blk_nblk1 = 0; + uint32_t blk_batch_count = 0; + + const uint32_t vec_nwg_cap = ctx->global_ctx->capabilities.min_subgroup_size; + uint32_t nwg = ggml_webgpu_flash_attn_vec_nwg(vec_nwg_cap, decisions->kv_tile, (uint32_t) K->ne[1]); + const uint64_t nrows = (uint64_t) Q->ne[1] * Q->ne[2] * Q->ne[3]; + const bool use_vec_reduce = nwg > 1u; + GGML_ASSERT(nrows <= UINT32_MAX); + + uint64_t tmp_stats_base = 0; + uint64_t tmp_size_bytes = 0; + wgpu::Buffer tmp_buf = {}; + uint64_t tmp_bind_offset = 0; + uint64_t tmp_bind_size = 0; + const size_t align_bytes = ctx->global_ctx->capabilities.limits.minStorageBufferOffsetAlignment; + const size_t dst_offset = ggml_webgpu_tensor_offset(dst); + size_t scratch_offset = ROUNDUP_POW2(dst_offset + ggml_nbytes(dst), align_bytes); + + if (use_vec_reduce) { + const uint64_t tmp_data_elems = nrows * (uint64_t) V->ne[0] * nwg; + const uint64_t tmp_stats_elems = nrows * 2u * nwg; + tmp_stats_base = tmp_data_elems; + tmp_size_bytes = + ROUNDUP_POW2((tmp_data_elems + tmp_stats_elems) * sizeof(float), WEBGPU_STORAGE_BUF_BINDING_MULT); + GGML_ASSERT(tmp_stats_base <= UINT32_MAX); + tmp_buf = ggml_webgpu_tensor_buf(dst); + tmp_bind_offset = scratch_offset; + tmp_bind_size = tmp_size_bytes; + scratch_offset = ROUNDUP_POW2(scratch_offset + tmp_size_bytes, align_bytes); + } else { + // nwg==1 writes final dst directly in vec-split; bind tmp to a tiny non-overlapping scratch region. + tmp_size_bytes = WEBGPU_STORAGE_BUF_BINDING_MULT; + tmp_buf = ggml_webgpu_tensor_buf(dst); + tmp_bind_offset = scratch_offset; + tmp_bind_size = tmp_size_bytes; + scratch_offset = ROUNDUP_POW2(scratch_offset + tmp_size_bytes, align_bytes); + } + + webgpu_pipeline blk_pipeline; + std::vector blk_params; + std::vector blk_entries; + if (op.has_mask) { + blk_nblk0 = CEIL_DIV((uint32_t) K->ne[1], decisions->kv_tile); + blk_nblk1 = (uint32_t) Q->ne[1]; + blk_buf = ggml_webgpu_tensor_buf(dst); + const uint32_t stride_mask3 = (uint32_t) (mask->nb[3] / ggml_type_size(mask->type)); + blk_batch_count = stride_mask3 > 0 ? (uint32_t) Q->ne[3] : 1u; + const uint64_t blk_elems = (uint64_t) blk_nblk0 * blk_nblk1 * blk_batch_count; + blk_size_bytes = ROUNDUP_POW2(blk_elems * sizeof(uint32_t), WEBGPU_STORAGE_BUF_BINDING_MULT); + const ggml_webgpu_shader_lib_context blk_shader_ctx = op.shader_lib_ctx; + blk_pipeline = ctx->shader_lib->get_flash_attn_blk_pipeline(blk_shader_ctx, decisions->kv_tile); + + blk_params = { + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, mask) / ggml_type_size(mask->type)), // offset_mask + (uint32_t) Q->ne[1], // seq_len_q + (uint32_t) K->ne[1], // seq_len_kv + stride_mask3, // stride_mask3 + blk_nblk0, // nblk0 + blk_nblk1, // nblk1 + }; + blk_entries = { + ggml_webgpu_make_bind_group_entry(0, ggml_webgpu_tensor_buf(mask), + ggml_webgpu_tensor_align_offset(ctx, mask), + ggml_webgpu_tensor_binding_size(ctx, mask)), + ggml_webgpu_make_bind_group_entry(1, blk_buf, scratch_offset, blk_size_bytes), + }; + scratch_offset = ROUNDUP_POW2(scratch_offset + blk_size_bytes, align_bytes); + } + + std::vector split_params = op.params; + if (op.has_mask) { + split_params.push_back(0u); // blk_base + split_params.push_back(blk_nblk0); // blk_nblk0 + split_params.push_back(blk_nblk1); // blk_nblk1 + } + split_params.push_back(0u); // tmp_data_base + split_params.push_back((uint32_t) tmp_stats_base); // tmp_stats_base + split_params.push_back(nwg); // nwg + + std::vector split_entries = { + ggml_webgpu_make_bind_group_entry(0, ggml_webgpu_tensor_buf(Q), ggml_webgpu_tensor_align_offset(ctx, Q), + ggml_webgpu_tensor_binding_size(ctx, Q)), + }; + if (op.kv_overlap) { + split_entries.push_back( + ggml_webgpu_make_bind_group_entry(1, ggml_webgpu_tensor_buf(K), op.kv_bind_offset, op.kv_bind_size)); + } else { + split_entries.push_back(ggml_webgpu_make_bind_group_entry(1, ggml_webgpu_tensor_buf(K), + ggml_webgpu_tensor_align_offset(ctx, K), + ggml_webgpu_tensor_binding_size(ctx, K))); + split_entries.push_back(ggml_webgpu_make_bind_group_entry(2, ggml_webgpu_tensor_buf(V), + ggml_webgpu_tensor_align_offset(ctx, V), + ggml_webgpu_tensor_binding_size(ctx, V))); + } + uint32_t split_binding_index = op.kv_overlap ? 2u : 3u; + if (op.has_mask) { + split_entries.push_back(ggml_webgpu_make_bind_group_entry(split_binding_index++, ggml_webgpu_tensor_buf(mask), + ggml_webgpu_tensor_align_offset(ctx, mask), + ggml_webgpu_tensor_binding_size(ctx, mask))); + } + if (op.has_sinks) { + split_entries.push_back(ggml_webgpu_make_bind_group_entry(split_binding_index++, ggml_webgpu_tensor_buf(sinks), + ggml_webgpu_tensor_align_offset(ctx, sinks), + ggml_webgpu_tensor_binding_size(ctx, sinks))); + } + if (op.has_mask) { + split_entries.push_back( + ggml_webgpu_make_bind_group_entry(split_binding_index++, blk_buf, blk_entries[1].offset, blk_size_bytes)); + } + split_entries.push_back( + ggml_webgpu_make_bind_group_entry(split_binding_index++, tmp_buf, tmp_bind_offset, tmp_bind_size)); + split_entries.push_back(ggml_webgpu_make_bind_group_entry(split_binding_index++, ggml_webgpu_tensor_buf(dst), + ggml_webgpu_tensor_align_offset(ctx, dst), + ggml_webgpu_tensor_binding_size(ctx, dst))); + + webgpu_pipeline reduce_pipeline; + std::vector reduce_params; + std::vector reduce_entries; + if (use_vec_reduce) { + const uint32_t reduce_sg_size = ctx->global_ctx->capabilities.max_subgroup_size; + const uint32_t reduce_wg_size = std::max( + reduce_sg_size, + (uint32_t) std::min((uint64_t) nwg * reduce_sg_size, + ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup)); + ggml_webgpu_shader_lib_context reduce_shader_ctx = op.shader_lib_ctx; + reduce_shader_ctx.max_wg_size = reduce_wg_size; + reduce_pipeline = ctx->shader_lib->get_flash_attn_vec_reduce_pipeline(reduce_shader_ctx); + + reduce_params = { + (uint32_t) nrows, // nrows + (uint32_t) Q->ne[1], // seq_len_q + (uint32_t) Q->ne[2], // n_heads + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)), // offset_dst + nwg, // nwg + 0u, // tmp_data_base + (uint32_t) tmp_stats_base, // tmp_stats_base + }; + + reduce_entries = { + ggml_webgpu_make_bind_group_entry(0, tmp_buf, tmp_bind_offset, tmp_size_bytes), + ggml_webgpu_make_bind_group_entry(1, ggml_webgpu_tensor_buf(dst), ggml_webgpu_tensor_align_offset(ctx, dst), + ggml_webgpu_tensor_binding_size(ctx, dst)), + }; + } + + uint32_t wg_x = Q->ne[1] * Q->ne[2] * Q->ne[3]; + const uint64_t split_wg_total = (uint64_t) wg_x * nwg; + GGML_ASSERT(split_wg_total <= UINT32_MAX); + + std::vector dispatches; + + if (op.has_mask) { + dispatches.push_back({ + blk_pipeline, std::move(blk_params), std::move(blk_entries), { blk_nblk0, blk_nblk1 * blk_batch_count } + }); + } + dispatches.push_back({ + pipeline, std::move(split_params), std::move(split_entries), { (uint32_t) split_wg_total, 1u } + }); + if (use_vec_reduce) { + dispatches.push_back({ + reduce_pipeline, std::move(reduce_params), std::move(reduce_entries), { (uint32_t) nrows, 1u } + }); + } + + return ggml_backend_webgpu_build_multi(ctx, dispatches); +} + +static webgpu_encoded_op ggml_webgpu_flash_attn(webgpu_context & ctx, + ggml_tensor * Q, + ggml_tensor * K, + ggml_tensor * V, + ggml_tensor * mask, + ggml_tensor * sinks, + ggml_tensor * dst) { + ggml_webgpu_flash_attn_op op = ggml_webgpu_flash_attn_prepare(ctx, Q, K, V, mask, sinks, dst); + if (ggml_webgpu_flash_attn_use_vec_path(ctx->global_ctx, Q, K, V)) { + return ggml_webgpu_flash_attn_vec(ctx, Q, K, V, mask, sinks, dst, std::move(op)); + } + return ggml_webgpu_flash_attn_direct(ctx, op); +} + +static webgpu_encoded_op ggml_webgpu_unary_op(webgpu_context & ctx, ggml_tensor * src, ggml_tensor * dst) { + bool is_unary = dst->op == GGML_OP_UNARY; + + ggml_webgpu_shader_lib_context shader_lib_ctx = {}; + shader_lib_ctx.src0 = src; + shader_lib_ctx.src1 = nullptr; + shader_lib_ctx.dst = dst; + shader_lib_ctx.max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup; + + webgpu_pipeline pipeline = ctx->shader_lib->get_unary_pipeline(shader_lib_ctx); + + auto * decisions = static_cast(pipeline.context.get()); + const bool inplace = decisions->inplace; + + uint32_t ne = (uint32_t) ggml_nelements(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)), + (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) src->ne[0], + (uint32_t) src->ne[1], + (uint32_t) src->ne[2] }; + + ggml_tensor * effective_src = src; + if (is_unary) { + ggml_unary_op unary_op = ggml_get_unary_op(dst); + 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(ggml_webgpu_u32_from_f32(alpha_n)); + params.push_back(ggml_webgpu_u32_from_f32(alpha_p)); + params.push_back(ggml_webgpu_u32_from_f32(beta)); + params.push_back(ggml_webgpu_u32_from_f32(eps)); + break; + } + default: + break; + } + } else if (dst->op == GGML_OP_CLAMP) { + float clamp_min = ggml_get_op_params_f32(dst, 0); + float clamp_max = ggml_get_op_params_f32(dst, 1); + params.push_back(ggml_webgpu_u32_from_f32(clamp_min)); + params.push_back(ggml_webgpu_u32_from_f32(clamp_max)); + } else if (dst->op == GGML_OP_FILL) { + float fill_val = ggml_get_op_params_f32(dst, 0); + params.push_back(ggml_webgpu_u32_from_f32(fill_val)); + effective_src = dst; // fill simply fills dst + } + + std::vector entries = { + ggml_webgpu_make_tensor_bind_group_entry(ctx, 0, effective_src), + }; + if (!inplace) { + entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 1, dst)); + } + + uint32_t wg_x, wg_y; + uint32_t total_wg = CEIL_DIV(ggml_nelements(dst), decisions->wg_size); + compute_2d_workgroups(total_wg, ctx->global_ctx->capabilities.limits.maxComputeWorkgroupsPerDimension, wg_x, wg_y); + return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x, wg_y); +} + +static webgpu_encoded_op ggml_webgpu_binary_op(webgpu_context & ctx, + ggml_tensor * src0, + ggml_tensor * src1, + ggml_tensor * dst) { + ggml_webgpu_shader_lib_context shader_lib_ctx = {}; + shader_lib_ctx.src0 = src0; + shader_lib_ctx.src1 = src1; + shader_lib_ctx.dst = dst; + shader_lib_ctx.max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup; + + webgpu_pipeline pipeline = ctx->shader_lib->get_binary_pipeline(shader_lib_ctx); + auto * decisions = static_cast(pipeline.context.get()); + + uint32_t ne = (uint32_t) ggml_nelements(dst); + + size_t src0_webgpu_tensor_align_offset = ggml_webgpu_tensor_align_offset(ctx, src0); + size_t src1_webgpu_tensor_align_offset = ggml_webgpu_tensor_align_offset(ctx, src1); + + uint32_t offset_src0 = (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src0) / ggml_type_size(src0->type)); + uint32_t offset_src1 = (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src1) / ggml_type_size(src1->type)); + size_t merged_offset = 0; + size_t merged_size = 0; + if (decisions->src_overlap) { + const ggml_webgpu_merged_binding_range merged_range = + ggml_webgpu_tensor_merged_binding_range(ctx, { src0, src1 }); + merged_offset = merged_range.offset; + merged_size = merged_range.size; + offset_src0 = ggml_webgpu_tensor_merged_element_offset(src0, merged_range); + offset_src1 = ggml_webgpu_tensor_merged_element_offset(src1, merged_range); + } + + std::vector params = { + ne, + offset_src0, + offset_src1, + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)), + (uint32_t) (src0->nb[0] / ggml_type_size(src0->type)), + (uint32_t) (src0->nb[1] / ggml_type_size(src0->type)), + (uint32_t) (src0->nb[2] / ggml_type_size(src0->type)), + (uint32_t) (src0->nb[3] / ggml_type_size(src0->type)), + (uint32_t) (src1->nb[0] / ggml_type_size(src1->type)), + (uint32_t) (src1->nb[1] / ggml_type_size(src1->type)), + (uint32_t) (src1->nb[2] / ggml_type_size(src1->type)), + (uint32_t) (src1->nb[3] / ggml_type_size(src1->type)), + (uint32_t) src0->ne[0], + (uint32_t) src0->ne[1], + (uint32_t) src0->ne[2], + (uint32_t) src1->ne[0], + (uint32_t) src1->ne[1], + (uint32_t) src1->ne[2], + (uint32_t) src1->ne[3], + }; + + std::vector entries; + + if (decisions->src_overlap) { + entries.push_back( + ggml_webgpu_make_bind_group_entry(0, ggml_webgpu_tensor_buf(src0), merged_offset, merged_size)); + entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 1, dst)); + } else { + entries.push_back(ggml_webgpu_make_bind_group_entry(0, ggml_webgpu_tensor_buf(src0), + src0_webgpu_tensor_align_offset, + ggml_webgpu_tensor_binding_size(ctx, src0))); + entries.push_back(ggml_webgpu_make_bind_group_entry(1, ggml_webgpu_tensor_buf(src1), + src1_webgpu_tensor_align_offset, + ggml_webgpu_tensor_binding_size(ctx, src1))); + if (!decisions->inplace && !decisions->overlap) { + entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 2, dst)); + } + } + + uint32_t wg_x, wg_y; + uint32_t total_wg = CEIL_DIV(ggml_nelements(dst), decisions->wg_size); + compute_2d_workgroups(total_wg, ctx->global_ctx->capabilities.limits.maxComputeWorkgroupsPerDimension, wg_x, wg_y); + return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x, wg_y); +} + +static webgpu_encoded_op ggml_webgpu_add_id(webgpu_context & ctx, + ggml_tensor * src0, + ggml_tensor * src1, + ggml_tensor * src2, + ggml_tensor * dst) { + ggml_webgpu_shader_lib_context shader_lib_ctx = {}; + shader_lib_ctx.src0 = src0; + shader_lib_ctx.src1 = src1; + shader_lib_ctx.src2 = src2; + shader_lib_ctx.dst = dst; + shader_lib_ctx.max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup; + + webgpu_pipeline pipeline = ctx->shader_lib->get_add_id_pipeline(shader_lib_ctx); + + auto * decisions = static_cast(pipeline.context.get()); + + std::vector params = { + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src0) / ggml_type_size(src0->type)), + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src1) / ggml_type_size(src1->type)), + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src2) / ggml_type_size(src2->type)), + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)), + (uint32_t) (src0->nb[1] / ggml_type_size(src0->type)), + (uint32_t) (src0->nb[2] / ggml_type_size(src0->type)), + (uint32_t) (src1->nb[1] / ggml_type_size(src1->type)), + (uint32_t) (src2->nb[0] / ggml_type_size(src2->type)), + (uint32_t) (src2->nb[1] / ggml_type_size(src2->type)), + (uint32_t) dst->ne[0], + (uint32_t) dst->ne[1], + (uint32_t) dst->ne[2], + }; + + std::vector entries; + + entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 0, src0)); + entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 1, src1)); + entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 2, src2)); + + if (!decisions->inplace) { + entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 3, dst)); + } + + uint32_t wg_x = 1; + uint32_t wg_y = 1; + uint32_t total_wg = ggml_nrows(dst); + const uint32_t max_wg_per_dim = ctx->global_ctx->capabilities.limits.maxComputeWorkgroupsPerDimension; + compute_2d_workgroups(total_wg, max_wg_per_dim, wg_x, wg_y); + + return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x, wg_y); +} + +static webgpu_encoded_op ggml_webgpu_concat(webgpu_context & ctx, + ggml_tensor * src0, + ggml_tensor * src1, + ggml_tensor * dst) { + uint32_t ne = (uint32_t) ggml_nelements(dst); + uint32_t dim = (uint32_t) dst->op_params[0]; + + ggml_webgpu_shader_lib_context shader_lib_ctx = {}; + shader_lib_ctx.src0 = src0; + shader_lib_ctx.src1 = src1; + shader_lib_ctx.dst = dst; + shader_lib_ctx.max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup; + + webgpu_pipeline pipeline = ctx->shader_lib->get_concat_pipeline(shader_lib_ctx); + auto * decisions = static_cast(pipeline.context.get()); + + uint32_t offset_src0 = (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src0) / ggml_type_size(src0->type)); + uint32_t offset_src1 = (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src1) / ggml_type_size(src1->type)); + size_t merged_offset = 0; + size_t merged_size = 0; + if (decisions->src_overlap) { + const ggml_webgpu_merged_binding_range merged_range = + ggml_webgpu_tensor_merged_binding_range(ctx, { src0, src1 }); + merged_offset = merged_range.offset; + merged_size = merged_range.size; + offset_src0 = ggml_webgpu_tensor_merged_element_offset(src0, merged_range); + offset_src1 = ggml_webgpu_tensor_merged_element_offset(src1, merged_range); + } + + std::vector params = { ne, + offset_src0, + offset_src1, + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)), + (uint32_t) (src0->nb[0] / ggml_type_size(src0->type)), + (uint32_t) (src0->nb[1] / ggml_type_size(src0->type)), + (uint32_t) (src0->nb[2] / ggml_type_size(src0->type)), + (uint32_t) (src0->nb[3] / ggml_type_size(src0->type)), + (uint32_t) (src1->nb[0] / ggml_type_size(src1->type)), + (uint32_t) (src1->nb[1] / ggml_type_size(src1->type)), + (uint32_t) (src1->nb[2] / ggml_type_size(src1->type)), + (uint32_t) (src1->nb[3] / ggml_type_size(src1->type)), + (uint32_t) dst->ne[0], + (uint32_t) dst->ne[1], + (uint32_t) dst->ne[2], + (uint32_t) dst->ne[3], + dim, + (uint32_t) src0->ne[dim] }; + + std::vector entries = {}; + if (decisions->src_overlap) { + entries.push_back( + ggml_webgpu_make_bind_group_entry(0, ggml_webgpu_tensor_buf(src0), merged_offset, merged_size)); + entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 1, dst)); + } else { + entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 0, src0)); + entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 1, src1)); + entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 2, dst)); + } + + uint32_t wg_x = CEIL_DIV(ne, decisions->wg_size); + return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x); +} + +static webgpu_encoded_op ggml_webgpu_repeat(webgpu_context & ctx, ggml_tensor * src0, ggml_tensor * dst) { + uint32_t ne = (uint32_t) ggml_nelements(dst); + + std::vector params = { ne, + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src0) / + ggml_type_size(src0->type)), + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)), + (uint32_t) (src0->nb[0] / ggml_type_size(src0->type)), + (uint32_t) (src0->nb[1] / ggml_type_size(src0->type)), + (uint32_t) (src0->nb[2] / ggml_type_size(src0->type)), + (uint32_t) (src0->nb[3] / ggml_type_size(src0->type)), + (uint32_t) (src0->ne[0]), + (uint32_t) (src0->ne[1]), + (uint32_t) (src0->ne[2]), + (uint32_t) (src0->ne[3]), + (uint32_t) (dst->ne[0]), + (uint32_t) (dst->ne[1]), + (uint32_t) (dst->ne[2]) }; + + std::vector entries = { + ggml_webgpu_make_tensor_bind_group_entry(ctx, 0, src0), + ggml_webgpu_make_tensor_bind_group_entry(ctx, 1, dst), + }; + + ggml_webgpu_shader_lib_context shader_lib_ctx = {}; + shader_lib_ctx.src0 = src0; + shader_lib_ctx.dst = dst; + shader_lib_ctx.max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup; + + webgpu_pipeline pipeline = ctx->shader_lib->get_repeat_pipeline(shader_lib_ctx); + auto * decisions = static_cast(pipeline.context.get()); + uint32_t wg_x = CEIL_DIV(ne, decisions->wg_size); + return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x); +} + +static std::optional ggml_webgpu_rms_norm_mul(webgpu_context & ctx, + ggml_tensor * rn_src, + ggml_tensor * rn_dst, + ggml_tensor * mul_src0, + ggml_tensor * mul_src1, + ggml_tensor * dst) { + ggml_tensor * mul_src; + + if (ggml_webgpu_tensor_equal(rn_dst, mul_src0)) { + mul_src = mul_src1; + } else if (ggml_webgpu_tensor_equal(rn_dst, mul_src1)) { + mul_src = mul_src0; + } else { + GGML_ABORT("rms_norm must be equal to the one of mul_src0 and mul_src1"); + } + + uint32_t offset_rn_src = (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, rn_src) / ggml_type_size(rn_src->type)); + uint32_t offset_mul_src = + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, mul_src) / ggml_type_size(mul_src->type)); + size_t merged_offset = 0; + size_t merged_size = 0; + + std::vector params = { + offset_rn_src, + offset_mul_src, + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)), + (uint32_t) (rn_src->nb[1] / ggml_type_size(rn_src->type)), + (uint32_t) (rn_src->nb[2] / ggml_type_size(rn_src->type)), + (uint32_t) (rn_src->nb[3] / ggml_type_size(rn_src->type)), + (uint32_t) (mul_src->nb[1] / ggml_type_size(mul_src->type)), + (uint32_t) (mul_src->nb[2] / ggml_type_size(mul_src->type)), + (uint32_t) (mul_src->nb[3] / ggml_type_size(mul_src->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)), + (uint32_t) mul_src->ne[0], + (uint32_t) mul_src->ne[1], + (uint32_t) mul_src->ne[2], + (uint32_t) mul_src->ne[3], + (uint32_t) dst->ne[0], + (uint32_t) dst->ne[1], + (uint32_t) dst->ne[2], + (uint32_t) dst->ne[3], + ggml_webgpu_u32_from_f32(ggml_get_op_params_f32(rn_dst, 0)) // epsilon, treated as f32 in the shader + }; + + std::vector entries; + + ggml_webgpu_shader_lib_context shader_lib_ctx = {}; + shader_lib_ctx.src0 = rn_src; + shader_lib_ctx.src1 = mul_src; + shader_lib_ctx.dst = dst; + shader_lib_ctx.max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup; + + webgpu_pipeline pipeline = ctx->shader_lib->get_rms_norm_mul_pipeline(shader_lib_ctx); + auto * decisions = static_cast(pipeline.context.get()); + + if (decisions->src_overlap) { + const ggml_webgpu_merged_binding_range merged_range = + ggml_webgpu_tensor_merged_binding_range(ctx, { rn_src, mul_src }); + merged_offset = merged_range.offset; + merged_size = merged_range.size; + offset_rn_src = ggml_webgpu_tensor_merged_element_offset(rn_src, merged_range); + offset_mul_src = ggml_webgpu_tensor_merged_element_offset(mul_src, merged_range); + params[0] = offset_rn_src; + params[1] = offset_mul_src; + } + + if (decisions->inplace || decisions->overlap) { + entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 0, rn_src)); + entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 1, mul_src)); + } else if (decisions->src_overlap) { + entries.push_back( + ggml_webgpu_make_bind_group_entry(0, ggml_webgpu_tensor_buf(rn_src), merged_offset, merged_size)); + entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 1, dst)); + } else { + entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 0, rn_src)); + entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 1, mul_src)); + entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 2, dst)); + } + + return ggml_backend_webgpu_build(ctx, pipeline, params, entries, ggml_nrows(dst)); +} + +static webgpu_encoded_op ggml_webgpu_row_norm(webgpu_context & ctx, ggml_tensor * src, ggml_tensor * dst) { + std::vector params = { + (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)), + (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[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)), + (uint32_t) src->ne[0], + (uint32_t) src->ne[1], + (uint32_t) src->ne[2], + (uint32_t) src->ne[3], + ggml_webgpu_u32_from_f32(ggml_get_op_params_f32(dst, 0)) // epsilon, treated as f32 in the shader + }; + + ggml_webgpu_shader_lib_context shader_lib_ctx = {}; + shader_lib_ctx.src0 = src; + shader_lib_ctx.dst = dst; + shader_lib_ctx.max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup; + + webgpu_pipeline pipeline = ctx->shader_lib->get_row_norm_pipeline(shader_lib_ctx); + auto * decisions = static_cast(pipeline.context.get()); + + std::vector entries = { ggml_webgpu_make_tensor_bind_group_entry(ctx, 0, src) }; + if (!decisions->inplace) { + entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 1, dst)); + } + return ggml_backend_webgpu_build(ctx, pipeline, params, entries, ggml_nrows(src)); +} + +static webgpu_encoded_op ggml_webgpu_rope(webgpu_context & ctx, + ggml_tensor * src0, + ggml_tensor * src1, + ggml_tensor * src2, + ggml_tensor * dst) { + ggml_webgpu_shader_lib_context shader_lib_ctx = {}; + shader_lib_ctx.src0 = src0; + shader_lib_ctx.src1 = src1; + shader_lib_ctx.src2 = src2; + shader_lib_ctx.dst = dst; + shader_lib_ctx.max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup; + + webgpu_pipeline pipeline = ctx->shader_lib->get_rope_pipeline(shader_lib_ctx); + + auto * decisions = static_cast(pipeline.context.get()); + + const bool inplace = decisions->inplace; + const int has_freq_factor = (src2 != nullptr); + + const int n_dims = ((int32_t *) dst->op_params)[1]; + const int mode = ((int32_t *) dst->op_params)[2]; + const int n_ctx_orig = ((int32_t *) dst->op_params)[4]; + + float freq_base; + float freq_scale; + float ext_factor; + float attn_factor; + float beta_fast; + float beta_slow; + memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float)); + memcpy(&freq_scale, (int32_t *) dst->op_params + 6, sizeof(float)); + memcpy(&ext_factor, (int32_t *) dst->op_params + 7, sizeof(float)); + memcpy(&attn_factor, (int32_t *) dst->op_params + 8, sizeof(float)); + memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float)); + memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float)); + + int sections[4]; + memcpy(sections, (int32_t *) dst->op_params + 11, 4 * sizeof(int)); + + float theta_scale = powf(freq_base, -2.0f / n_dims); + + float corr_dims[2]; + ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims); + + std::vector params = { + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src0) / ggml_type_size(src0->type)), + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src1) / ggml_type_size(src1->type)), + src2 != nullptr ? (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src2) / ggml_type_size(src2->type)) : 0, + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)), + (uint32_t) (src0->nb[1] / ggml_type_size(src0->type)), + (uint32_t) (src0->nb[2] / ggml_type_size(src0->type)), + (uint32_t) (src0->nb[3] / ggml_type_size(src0->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)), + (uint32_t) ggml_nelements(src0) / 2, + (uint32_t) src0->ne[0], + (uint32_t) src0->ne[1], + (uint32_t) src0->ne[2], + (uint32_t) n_dims, + (uint32_t) mode, + ggml_webgpu_u32_from_f32(theta_scale), + ggml_webgpu_u32_from_f32(attn_factor), + ggml_webgpu_u32_from_f32(freq_scale), + ggml_webgpu_u32_from_f32(ext_factor), + ggml_webgpu_u32_from_f32(corr_dims[0]), + ggml_webgpu_u32_from_f32(corr_dims[1]), + (uint32_t) sections[0], + (uint32_t) sections[1], + (uint32_t) sections[2], + (uint32_t) sections[3] + }; + + std::vector entries = { ggml_webgpu_make_tensor_bind_group_entry(ctx, 0, src0), + ggml_webgpu_make_tensor_bind_group_entry(ctx, 1, src1) }; + uint32_t dst_binding = 2; + if (has_freq_factor) { + dst_binding = 3; + entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 2, src2)); + } + if (!inplace) { + entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, dst_binding, dst)); + } + + uint32_t wg_x = CEIL_DIV(ggml_nelements(dst), decisions->wg_size); + return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x); +} + +static webgpu_encoded_op ggml_webgpu_glu(webgpu_context & ctx, + ggml_tensor * src0, + ggml_tensor * src1, + ggml_tensor * dst) { + ggml_webgpu_shader_lib_context shader_lib_ctx = {}; + shader_lib_ctx.src0 = src0; + shader_lib_ctx.src1 = src1; + shader_lib_ctx.dst = dst; + shader_lib_ctx.max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup; + + webgpu_pipeline pipeline = ctx->shader_lib->get_glu_pipeline(shader_lib_ctx); + + auto * decisions = static_cast(pipeline.context.get()); + + const int split = (src1 != nullptr); + + std::vector params = { + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src0) / ggml_type_size(src0->type)), + src1 != nullptr ? (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src1) / ggml_type_size(src1->type)) : 0, + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)), + (uint32_t) (src0->nb[1] / ggml_type_size(src0->type)), + (uint32_t) (src0->nb[2] / ggml_type_size(src0->type)), + (uint32_t) (src0->nb[3] / ggml_type_size(src0->type)), + src1 != nullptr ? (uint32_t) (src1->nb[1] / ggml_type_size(src1->type)) : + (uint32_t) (src0->nb[1] / ggml_type_size(src0->type)), + src1 != nullptr ? (uint32_t) (src1->nb[2] / ggml_type_size(src1->type)) : + (uint32_t) (src0->nb[2] / ggml_type_size(src0->type)), + src1 != nullptr ? (uint32_t) (src1->nb[3] / ggml_type_size(src1->type)) : + (uint32_t) (src0->nb[3] / ggml_type_size(src0->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)), + (uint32_t) ggml_nelements(dst), + (uint32_t) dst->ne[0], + (uint32_t) dst->ne[1], + (uint32_t) dst->ne[2], + (uint32_t) ((int32_t *) dst->op_params)[1], // swapped + ggml_webgpu_u32_from_f32(ggml_get_op_params_f32(dst, 2)), // alpha, for swiglu_oai + ggml_webgpu_u32_from_f32(ggml_get_op_params_f32(dst, 3)), // limit, for swiglu_oai + }; + + std::vector entries = { + ggml_webgpu_make_tensor_bind_group_entry(ctx, 0, src0), + }; + uint32_t dst_binding = 1; + if (split) { + dst_binding = 2; + entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 1, src1)); + } + entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, dst_binding, dst)); + + uint32_t wg_x = CEIL_DIV(ggml_nelements(dst), decisions->wg_size); + return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x); +} + +static webgpu_encoded_op ggml_webgpu_scale(webgpu_context & ctx, ggml_tensor * src, ggml_tensor * dst) { + ggml_webgpu_shader_lib_context shader_lib_ctx = {}; + shader_lib_ctx.src0 = src; + shader_lib_ctx.src1 = nullptr; + shader_lib_ctx.dst = dst; + shader_lib_ctx.max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup; + + webgpu_pipeline pipeline = ctx->shader_lib->get_scale_pipeline(shader_lib_ctx); + auto * decisions = static_cast(pipeline.context.get()); + + // params unchanged + std::vector params = { + (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)), + (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[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)), + (uint32_t) ggml_nelements(dst), + (uint32_t) src->ne[0], + (uint32_t) src->ne[1], + (uint32_t) src->ne[2], + ggml_webgpu_u32_from_f32(ggml_get_op_params_f32(dst, 0)), // scale + ggml_webgpu_u32_from_f32(ggml_get_op_params_f32(dst, 1)) // bias + }; + + // bindgroups unchanged + std::vector entries = { ggml_webgpu_make_tensor_bind_group_entry(ctx, 0, src) }; + + if (!decisions->inplace) { + entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, 1, dst)); + } + + uint32_t wg_x, wg_y; + uint32_t total_wg = CEIL_DIV(ggml_nelements(dst), decisions->wg_size); + compute_2d_workgroups(total_wg, ctx->global_ctx->capabilities.limits.maxComputeWorkgroupsPerDimension, wg_x, wg_y); + return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x, wg_y); +} + +static webgpu_encoded_op ggml_webgpu_soft_max(webgpu_context & ctx, + ggml_tensor * src0, + ggml_tensor * src1, + ggml_tensor * src2, + ggml_tensor * dst) { + ggml_webgpu_shader_lib_context shader_lib_ctx = {}; + shader_lib_ctx.src0 = src0; + shader_lib_ctx.src1 = src1; + shader_lib_ctx.src2 = src2; + shader_lib_ctx.dst = dst; + shader_lib_ctx.max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup; + + webgpu_pipeline pipeline = ctx->shader_lib->get_soft_max_pipeline(shader_lib_ctx); + auto * decisions = static_cast(pipeline.context.get()); + + const bool inplace = decisions->inplace; + const int has_mask = (src1 != nullptr); + const int has_sink = (src2 != nullptr); + float max_bias = ggml_get_op_params_f32(dst, 1); + float n_head_log2 = float(1u << (uint32_t) floor(log2(src0->ne[2]))); + float m0 = powf(2.0f, -(max_bias) / n_head_log2); + float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); + + std::vector params = { + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src0) / ggml_type_size(src0->type)), + has_mask ? (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src1) / ggml_type_size(src1->type)) : 0, + has_sink ? (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src2) / ggml_type_size(src2->type)) : 0, + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)), + (uint32_t) (src0->nb[1] / ggml_type_size(src0->type)), + (uint32_t) (src0->nb[2] / ggml_type_size(src0->type)), + (uint32_t) (src0->nb[3] / ggml_type_size(src0->type)), + has_mask ? (uint32_t) (src1->nb[1] / ggml_type_size(src1->type)) : 0, + has_mask ? (uint32_t) (src1->nb[2] / ggml_type_size(src1->type)) : 0, + has_mask ? (uint32_t) (src1->nb[3] / ggml_type_size(src1->type)) : 0, + (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)), + (uint32_t) ggml_nelements(dst), + (uint32_t) src0->ne[0], + (uint32_t) src0->ne[1], + (uint32_t) src0->ne[2], + has_mask ? (uint32_t) src1->ne[2] : 0, + has_mask ? (uint32_t) src1->ne[3] : 0, + ggml_webgpu_u32_from_f32(ggml_get_op_params_f32(dst, 0)), // scale + ggml_webgpu_u32_from_f32(max_bias), + ggml_webgpu_u32_from_f32(n_head_log2), + ggml_webgpu_u32_from_f32(m0), + ggml_webgpu_u32_from_f32(m1) + }; + + std::vector entries = { ggml_webgpu_make_bind_group_entry( + 0, ggml_webgpu_tensor_buf(src0), ggml_webgpu_tensor_align_offset(ctx, src0), + ggml_webgpu_tensor_binding_size(ctx, src0)) }; + uint32_t binding_num = 1; + if (has_mask) { + entries.push_back(ggml_webgpu_make_bind_group_entry(binding_num, ggml_webgpu_tensor_buf(src1), + ggml_webgpu_tensor_align_offset(ctx, src1), + ggml_webgpu_tensor_binding_size(ctx, src1))); + binding_num++; + } + if (has_sink) { + entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, binding_num, src2)); + binding_num++; + } + if (!inplace) { + entries.push_back(ggml_webgpu_make_tensor_bind_group_entry(ctx, binding_num, dst)); + } + + return ggml_backend_webgpu_build(ctx, pipeline, params, entries, ggml_nrows(dst)); +} + +static webgpu_encoded_op ggml_webgpu_argmax(webgpu_context & ctx, ggml_tensor * src, ggml_tensor * dst) { + std::vector params = { (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)), + (uint32_t) src->ne[0] }; + + std::vector entries = { ggml_webgpu_make_tensor_bind_group_entry(ctx, 0, src), + ggml_webgpu_make_tensor_bind_group_entry(ctx, 1, dst) }; + + ggml_webgpu_shader_lib_context shader_lib_ctx = {}; + shader_lib_ctx.src0 = src; + shader_lib_ctx.dst = dst; + shader_lib_ctx.max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup; + + webgpu_pipeline pipeline = ctx->shader_lib->get_argmax_pipeline(shader_lib_ctx); + uint32_t wg_x = ggml_nelements(dst); + return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x); +} + +static webgpu_encoded_op ggml_webgpu_argsort(webgpu_context & ctx, ggml_tensor * src, ggml_tensor * dst) { + bool is_top_k = dst->op == GGML_OP_TOP_K; + + ggml_webgpu_shader_lib_context shader_lib_ctx = {}; + shader_lib_ctx.src0 = src; + shader_lib_ctx.src1 = nullptr; + shader_lib_ctx.dst = dst; + shader_lib_ctx.max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup; + shader_lib_ctx.wg_mem_limit_bytes = ctx->global_ctx->capabilities.limits.maxComputeWorkgroupStorageSize; + + webgpu_pipeline argsort_pipeline = ctx->shader_lib->get_argsort_pipeline(shader_lib_ctx); + auto * argsort_decisions = static_cast(argsort_pipeline.context.get()); + + webgpu_pipeline argsort_merge_pipeline = ctx->shader_lib->get_argsort_merge_pipeline(shader_lib_ctx); + + const uint32_t src_ne0 = (uint32_t) src->ne[0]; + const uint32_t nrows = (uint32_t) ggml_nrows(src); + const uint32_t npr = CEIL_DIV(src_ne0, argsort_decisions->wg_size); + const uint32_t block_size = + is_top_k ? std::min(argsort_decisions->wg_size, (uint32_t) dst->ne[0]) : argsort_decisions->wg_size; + uint32_t out_ne0 = src_ne0; + if (is_top_k) { + if (npr > 1) { + const uint32_t last_tile = src_ne0 - (npr - 1) * argsort_decisions->wg_size; + out_ne0 = (npr - 1) * block_size + std::min(last_tile, block_size); + } else { + out_ne0 = block_size; + } + } + + uint32_t merge_len = block_size; + uint32_t merge_passes = 0; + while (merge_len < out_ne0) { + merge_len <<= 1; + merge_passes++; + } + + const bool start_in_tmp = (merge_passes % 2) == 1; + + const size_t dst_offset = ggml_webgpu_tensor_offset(dst); + const size_t idx_nbytes = out_ne0 * ggml_nrows(dst) * sizeof(int32_t); + const size_t tmp_offset = + ROUNDUP_POW2(dst_offset + idx_nbytes, ctx->global_ctx->capabilities.limits.minStorageBufferOffsetAlignment); + const size_t tmp_binding_size = ROUNDUP_POW2(idx_nbytes, WEBGPU_STORAGE_BUF_BINDING_MULT); + const size_t dst_binding_size = + ROUNDUP_POW2(idx_nbytes + ggml_webgpu_tensor_misalignment(ctx, dst), WEBGPU_STORAGE_BUF_BINDING_MULT); + + const uint32_t offset_src = (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src) / ggml_type_size(src->type)); + const uint32_t offset_dst = (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)); + const uint32_t offset_tmp = 0; + const uint32_t stride_src1 = (uint32_t) (src->nb[1] / ggml_type_size(src->type)); + const uint32_t stride_src2 = (uint32_t) (src->nb[2] / ggml_type_size(src->type)); + const uint32_t stride_src3 = (uint32_t) (src->nb[3] / ggml_type_size(src->type)); + const uint32_t stride_idx1 = out_ne0; + const uint32_t stride_idx2 = out_ne0 * (uint32_t) dst->ne[1]; + const uint32_t stride_idx3 = stride_idx2 * (uint32_t) dst->ne[2]; + + std::vector dispatches; + + const uint32_t init_offset = start_in_tmp ? offset_tmp : offset_dst; + const size_t init_align_offset = start_in_tmp ? tmp_offset : ggml_webgpu_tensor_align_offset(ctx, dst); + const size_t init_binding_size = start_in_tmp ? tmp_binding_size : dst_binding_size; + + std::vector init_params = { + offset_src, init_offset, stride_src1, stride_src2, stride_src3, stride_idx1, + stride_idx2, stride_idx3, src_ne0, (uint32_t) src->ne[1], (uint32_t) src->ne[2], out_ne0, + block_size, npr, nrows + }; + + uint32_t wg_x_init; + uint32_t wg_y_init; + const uint32_t total_wg_init = npr * nrows; + const uint32_t max_wg_per_dim = ctx->global_ctx->capabilities.limits.maxComputeWorkgroupsPerDimension; + compute_2d_workgroups(total_wg_init, max_wg_per_dim, wg_x_init, wg_y_init); + + std::vector init_entries = { + ggml_webgpu_make_tensor_bind_group_entry(ctx, 0, src), + ggml_webgpu_make_bind_group_entry(1, ggml_webgpu_tensor_buf(dst), init_align_offset, init_binding_size) + }; + + dispatches.push_back({ + argsort_pipeline, std::move(init_params), std::move(init_entries), { wg_x_init, wg_y_init } + }); + + if (merge_passes == 0) { + return ggml_backend_webgpu_build_multi(ctx, dispatches); + } + + bool in_is_tmp = start_in_tmp; + uint32_t len = block_size; + while (len < out_ne0) { + const uint32_t nm = CEIL_DIV(out_ne0, 2 * len); + + const bool out_is_tmp = !in_is_tmp; + const uint32_t offset_in = in_is_tmp ? offset_tmp : offset_dst; + const uint32_t offset_out = out_is_tmp ? offset_tmp : offset_dst; + const size_t align_in = in_is_tmp ? tmp_offset : ggml_webgpu_tensor_align_offset(ctx, dst); + const size_t align_out = out_is_tmp ? tmp_offset : ggml_webgpu_tensor_align_offset(ctx, dst); + const size_t size_in = in_is_tmp ? tmp_binding_size : dst_binding_size; + const size_t size_out = out_is_tmp ? tmp_binding_size : dst_binding_size; + const uint32_t top_k_out = (is_top_k && nm == 1) ? (uint32_t) dst->ne[0] : out_ne0; + const uint32_t stride_out1 = top_k_out; + const uint32_t stride_out2 = top_k_out * (uint32_t) dst->ne[1]; + const uint32_t stride_out3 = stride_out2 * (uint32_t) dst->ne[2]; + + std::vector merge_params = { offset_src, + offset_in, + offset_out, + stride_src1, + stride_src2, + stride_src3, + stride_idx1, + stride_idx2, + stride_idx3, + stride_out1, + stride_out2, + stride_out3, + out_ne0, + (uint32_t) src->ne[1], + (uint32_t) src->ne[2], + top_k_out, + len, + nm, + nrows }; + + std::vector merge_entries = { + ggml_webgpu_make_tensor_bind_group_entry(ctx, 0, src), + ggml_webgpu_make_bind_group_entry(1, ggml_webgpu_tensor_buf(dst), align_in, size_in), + ggml_webgpu_make_bind_group_entry(2, ggml_webgpu_tensor_buf(dst), align_out, size_out) + }; + + uint32_t wg_x_merge; + uint32_t wg_y_merge; + const uint32_t total_wg_merge = nm * nrows; + compute_2d_workgroups(total_wg_merge, max_wg_per_dim, wg_x_merge, wg_y_merge); + + dispatches.push_back({ + argsort_merge_pipeline, std::move(merge_params), std::move(merge_entries), { wg_x_merge, wg_y_merge } + }); + + len <<= 1; + in_is_tmp = !in_is_tmp; + } + + return ggml_backend_webgpu_build_multi(ctx, dispatches); +} + +static webgpu_encoded_op ggml_webgpu_cumsum(webgpu_context & ctx, ggml_tensor * src, ggml_tensor * dst) { + std::vector params = { (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)), + (uint32_t) src->ne[0] }; + + std::vector entries = { ggml_webgpu_make_tensor_bind_group_entry(ctx, 0, src), + ggml_webgpu_make_tensor_bind_group_entry(ctx, 1, dst) }; + + ggml_webgpu_shader_lib_context shader_lib_ctx = {}; + shader_lib_ctx.src0 = src; + shader_lib_ctx.src1 = nullptr; + shader_lib_ctx.dst = dst; + shader_lib_ctx.max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup; + + webgpu_pipeline pipeline = ctx->shader_lib->get_cumsum_pipeline(shader_lib_ctx); + uint32_t wg_x = ggml_nrows(dst); + return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x); +} + +static webgpu_encoded_op ggml_webgpu_sum_rows(webgpu_context & ctx, ggml_tensor * src, ggml_tensor * dst) { + bool total_sum = dst->op == GGML_OP_SUM; + std::vector params = { (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)), + total_sum ? 0 : (uint32_t) (src->nb[1] / ggml_type_size(src->type)), + total_sum ? 0 : (uint32_t) (src->nb[2] / ggml_type_size(src->type)), + total_sum ? 0 : (uint32_t) (src->nb[3] / ggml_type_size(src->type)), + total_sum ? static_cast(ggml_nelements(src)) : (uint32_t) src->ne[0], + total_sum ? 1 : (uint32_t) src->ne[1], + total_sum ? 1 : (uint32_t) src->ne[2] }; + + std::vector entries = { ggml_webgpu_make_tensor_bind_group_entry(ctx, 0, src), + ggml_webgpu_make_tensor_bind_group_entry(ctx, 1, dst) }; + + ggml_webgpu_shader_lib_context shader_lib_ctx = {}; + shader_lib_ctx.src0 = src; + shader_lib_ctx.dst = dst; + shader_lib_ctx.max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup; + + webgpu_pipeline pipeline = ctx->shader_lib->get_sum_rows_pipeline(shader_lib_ctx); + + uint32_t wg_x = total_sum ? 1 : ggml_nrows(dst); + return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x); +} + +static bool ggml_webgpu_can_fuse_rms_norm_mul(const struct ggml_cgraph * cgraph, int node_idx) { + if (!ggml_can_fuse(cgraph, node_idx, { GGML_OP_RMS_NORM, GGML_OP_MUL })) { + return false; + } + + // additional constraints specific to this fusion + const ggml_tensor * rms_norm = cgraph->nodes[node_idx]; + const ggml_tensor * mul = cgraph->nodes[node_idx + 1]; + + GGML_ASSERT(rms_norm->src[0]->type == GGML_TYPE_F32); + GGML_ASSERT(rms_norm->type == GGML_TYPE_F32); + // rms_norm only supports f32 + if (mul->src[0]->type != GGML_TYPE_F32 || mul->src[1]->type != GGML_TYPE_F32 || mul->type != GGML_TYPE_F32) { + return false; + } + // if rms_norm is the B operand, then we don't handle broadcast + if (rms_norm == mul->src[1] && !ggml_are_same_shape(mul->src[0], rms_norm)) { + return false; + } + // rms_norm shader assumes contiguous rows + if (!ggml_is_contiguous_rows(mul->src[0]) || !ggml_is_contiguous_rows(mul->src[1])) { + return false; + } + + return true; +} + +static webgpu_encoded_op ggml_webgpu_upscale(webgpu_context ctx, ggml_tensor * src, ggml_tensor * dst) { + const uint32_t mode_flags = (uint32_t) ggml_get_op_params_i32(dst, 0); + std::vector params = { (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)), + + (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)), + + (uint32_t) src->ne[0], + (uint32_t) src->ne[1], + (uint32_t) src->ne[2], + (uint32_t) src->ne[3], + + (uint32_t) dst->ne[0], + (uint32_t) dst->ne[1], + (uint32_t) dst->ne[2], + (uint32_t) dst->ne[3], + + mode_flags }; + + std::vector entries = { ggml_webgpu_make_tensor_bind_group_entry(ctx, 0, src), + ggml_webgpu_make_tensor_bind_group_entry(ctx, 1, dst) }; + + ggml_webgpu_shader_lib_context shader_lib_ctx = {}; + shader_lib_ctx.src0 = src; + shader_lib_ctx.dst = dst; + shader_lib_ctx.max_wg_size = ctx->global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup; + + webgpu_pipeline pipeline = ctx->shader_lib->get_upscale_pipeline(shader_lib_ctx); + auto * decisions = static_cast(pipeline.context.get()); + + uint32_t wg_x; + uint32_t wg_y; + uint32_t total_wg = CEIL_DIV((uint32_t) ggml_nelements(dst), decisions->wg_size); + compute_2d_workgroups(total_wg, ctx->global_ctx->capabilities.limits.maxComputeWorkgroupsPerDimension, wg_x, wg_y); + + return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x, wg_y); +} + +// Returns the encoded command, or std::nullopt if the operation is a no-op +static std::optional ggml_webgpu_encode(webgpu_context ctx, + ggml_cgraph * cgraph, + int node_idx, + int & num_encoded_ops) { + ggml_tensor ** nodes = cgraph->nodes; + ggml_tensor * node = nodes[node_idx]; + + if (ggml_is_empty(node)) { + return std::nullopt; + } + if ((node->flags & GGML_TENSOR_FLAG_COMPUTE) == 0) { + return std::nullopt; + } + WEBGPU_LOG_DEBUG("ggml_webgpu_encode(" << node << ", " << ggml_op_name(node->op) << ")"); + + ggml_tensor * src0 = node->src[0]; + ggml_tensor * src1 = node->src[1]; + ggml_tensor * src2 = node->src[2]; + + switch (node->op) { + // no-ops + case GGML_OP_NONE: + case GGML_OP_VIEW: + case GGML_OP_PERMUTE: + case GGML_OP_TRANSPOSE: + case GGML_OP_RESHAPE: + return std::nullopt; + case GGML_OP_CPY: + case GGML_OP_CONT: + return ggml_webgpu_cpy(ctx, src0, node); + case GGML_OP_SET: + return ggml_webgpu_set(ctx, src0, src1, node); + case GGML_OP_SET_ROWS: + return ggml_webgpu_set_rows(ctx, src0, src1, node); + case GGML_OP_GET_ROWS: + return ggml_webgpu_get_rows(ctx, src0, src1, node); + case GGML_OP_MUL_MAT: + return ggml_webgpu_mul_mat(ctx, src0, src1, node); + case GGML_OP_MUL_MAT_ID: + return ggml_webgpu_mul_mat_id(ctx, src0, src1, src2, node); + case GGML_OP_FLASH_ATTN_EXT: + return ggml_webgpu_flash_attn(ctx, src0, src1, src2, node->src[3], node->src[4], node); + case GGML_OP_ADD: + case GGML_OP_SUB: + case GGML_OP_MUL: + case GGML_OP_DIV: + return ggml_webgpu_binary_op(ctx, src0, src1, node); + case GGML_OP_ADD_ID: + return ggml_webgpu_add_id(ctx, src0, src1, src2, node); + case GGML_OP_CONCAT: + return ggml_webgpu_concat(ctx, src0, src1, node); + case GGML_OP_REPEAT: + return ggml_webgpu_repeat(ctx, src0, node); + case GGML_OP_RMS_NORM: + if (ggml_webgpu_can_fuse_rms_norm_mul(cgraph, node_idx)) { + num_encoded_ops = 2; + ggml_tensor * mul_node = nodes[node_idx + 1]; + return ggml_webgpu_rms_norm_mul(ctx, src0, node, mul_node->src[0], mul_node->src[1], mul_node); + } else { + return ggml_webgpu_row_norm(ctx, src0, node); + } + case GGML_OP_NORM: + case GGML_OP_L2_NORM: + return ggml_webgpu_row_norm(ctx, src0, node); + case GGML_OP_ROPE: + return ggml_webgpu_rope(ctx, src0, src1, src2, node); + case GGML_OP_GLU: + return ggml_webgpu_glu(ctx, src0, src1, node); + case GGML_OP_SCALE: + 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: + case GGML_OP_CLAMP: + case GGML_OP_FILL: + case GGML_OP_LOG: + case GGML_OP_SQR: + case GGML_OP_SQRT: + case GGML_OP_SIN: + case GGML_OP_COS: + case GGML_OP_DIAG: + case GGML_OP_TRI: + return ggml_webgpu_unary_op(ctx, src0, node); + case GGML_OP_SOLVE_TRI: + return ggml_webgpu_solve_tri(ctx, src0, src1, node); + case GGML_OP_SSM_CONV: + return ggml_webgpu_ssm_conv(ctx, src0, src1, node); + case GGML_OP_SSM_SCAN: + return ggml_webgpu_ssm_scan(ctx, src0, src1, src2, node->src[3], node->src[4], node->src[5], node->src[6], + node); + case GGML_OP_GATED_DELTA_NET: + return ggml_webgpu_gated_delta_net(ctx, src0, src1, src2, node->src[3], node->src[4], node->src[5], node); + case GGML_OP_PAD: + return ggml_webgpu_pad(ctx, src0, node); + case GGML_OP_ARGMAX: + return ggml_webgpu_argmax(ctx, src0, node); + case GGML_OP_ARGSORT: + case GGML_OP_TOP_K: + // we reuse the same argsort implementation for top_k + return ggml_webgpu_argsort(ctx, src0, node); + case GGML_OP_CUMSUM: + return ggml_webgpu_cumsum(ctx, src0, node); + case GGML_OP_SUM: + case GGML_OP_SUM_ROWS: + return ggml_webgpu_sum_rows(ctx, src0, node); + case GGML_OP_CONV_2D: + return ggml_webgpu_conv_2d(ctx, src0, src1, node); + case GGML_OP_IM2COL: + return ggml_webgpu_im2col(ctx, src0, src1, node); + case GGML_OP_UPSCALE: + return ggml_webgpu_upscale(ctx, src0, node); + default: + return std::nullopt; + } +} + +#ifdef GGML_WEBGPU_GPU_PROFILE +static void ggml_backend_webgpu_collect_profile_results(webgpu_context & ctx, + const std::vector & pipeline_names, + uint32_t & num_inflight_batches) { + if (pipeline_names.empty()) { + return; + } + + wgpu::CommandEncoder encoder = ctx->global_ctx->device.CreateCommandEncoder(); + encoder.ResolveQuerySet(ctx->profile_timestamp_query_set, 0, ctx->profile_timestamp_query_count, + ctx->profile_timestamp_dev_buf, 0); + encoder.CopyBufferToBuffer(ctx->profile_timestamp_dev_buf, 0, ctx->profile_timestamp_host_buf, 0, + ctx->profile_timestamp_query_count * sizeof(uint64_t)); + + wgpu::CommandBuffer profile_commands = encoder.Finish(); + ggml_backend_webgpu_submit_commands(ctx, profile_commands, num_inflight_batches); + + const size_t mapped_size = ctx->profile_timestamp_query_count * sizeof(uint64_t); + GGML_ASSERT(ctx->profile_timestamp_query_count == 2 * pipeline_names.size()); + + ggml_backend_webgpu_map_buffer(ctx->global_ctx, ctx->profile_timestamp_host_buf, wgpu::MapMode::Read, 0, + mapped_size); + const uint64_t * ts_data = (const uint64_t *) ctx->profile_timestamp_host_buf.GetConstMappedRange(0, mapped_size); + + for (size_t i = 0; i < pipeline_names.size(); ++i) { + // WebGPU timestamps are in ns; convert to ms. + const double elapsed_ms = double(ts_data[2 * i + 1] - ts_data[2 * i]) * 1e-6; + ctx->shader_gpu_time_ms[pipeline_names[i]] += elapsed_ms; + } + + ctx->profile_timestamp_host_buf.Unmap(); +} +#endif + +// Don't bother checking set_rows index overflow for now, since practically the WebGPU doesn't need to support +// models that would require it right now. +static void ggml_backend_webgpu_check_set_rows(webgpu_context & ctx, uint32_t & num_inflight_batches) { +#ifdef GGML_WEBGPU_CHECK_SET_ROWS + wgpu::CommandEncoder encoder = ctx->global_ctx->device.CreateCommandEncoder(); + encoder.CopyBufferToBuffer(ctx->set_rows_dev_error_buf, 0, ctx->set_rows_host_error_buf, 0, + ctx->set_rows_host_error_buf.GetSize()); + wgpu::CommandBuffer commands = encoder.Finish(); + ggml_backend_webgpu_submit_commands(ctx, commands, num_inflight_batches); + ggml_backend_webgpu_map_buffer(ctx->global_ctx, ctx->set_rows_host_error_buf, wgpu::MapMode::Read, 0, + ctx->set_rows_host_error_buf.GetSize()); + const uint32_t * error_data = (const uint32_t *) ctx->set_rows_host_error_buf.GetConstMappedRange(); + if (*error_data) { + GGML_ABORT("ggml_webgpu: SET_ROWS index > 2^32, unsupported."); + } + ctx->set_rows_host_error_buf.Unmap(); +#else + GGML_UNUSED(ctx); + GGML_UNUSED(num_inflight_batches); +#endif +} + +static ggml_status ggml_backend_webgpu_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { + WEBGPU_LOG_DEBUG("ggml_backend_webgpu_graph_compute(" << cgraph->n_nodes << " nodes)"); + + ggml_backend_webgpu_context * backend_ctx = (ggml_backend_webgpu_context *) backend->context; + webgpu_context ctx = backend_ctx->webgpu_ctx; + + WEBGPU_CPU_PROFILE_TOTAL_START(graph_compute); + + std::vector commands; + + uint32_t num_batched_kernels = 0; + uint32_t num_inflight_batches = 0; + bool contains_set_rows = false; + int num_encoded_ops = 1; + int node_idx = 0; + +#ifdef GGML_WEBGPU_GPU_PROFILE + ctx->profile_timestamp_query_count = 0; + std::vector profile_pipeline_names; +#endif + + ctx->active_command_encoder = ctx->global_ctx->device.CreateCommandEncoder(); + if (ctx->batch_compute_passes) { + ctx->active_compute_pass = ctx->active_command_encoder.BeginComputePass(); + } + + while (node_idx < cgraph->n_nodes) { + if (cgraph->nodes[node_idx]->op == GGML_OP_SET_ROWS) { + contains_set_rows = true; + } + if (auto cmd = ggml_webgpu_encode(ctx, cgraph, node_idx, num_encoded_ops)) { + commands.push_back(*cmd); + num_batched_kernels += cmd.value().num_kernels; +#ifdef GGML_WEBGPU_GPU_PROFILE + profile_pipeline_names.insert(profile_pipeline_names.end(), cmd->pipeline_names.begin(), + cmd->pipeline_names.end()); +#endif + } + + if (num_batched_kernels >= ctx->global_ctx->command_submit_batch_size) { + if (ctx->active_compute_pass) { + ctx->active_compute_pass.End(); + } + num_batched_kernels = 0; + wgpu::CommandBuffer batch_commands = ctx->active_command_encoder.Finish(); + ggml_backend_webgpu_submit_commands(ctx, batch_commands, num_inflight_batches); + + // reset state for next batch + ctx->active_command_encoder = ctx->global_ctx->device.CreateCommandEncoder(); + if (ctx->batch_compute_passes) { + ctx->active_compute_pass = ctx->active_command_encoder.BeginComputePass(); + } + ctx->param_arena.reset(); + commands.clear(); +#ifdef GGML_WEBGPU_GPU_PROFILE + // flush before the next batch can overflow the QuerySet + if (ctx->profile_timestamp_query_count + 2 * ctx->global_ctx->command_submit_batch_size >= + WEBGPU_MAX_PROFILE_QUERY_COUNT) { + ggml_backend_webgpu_collect_profile_results(ctx, profile_pipeline_names, num_inflight_batches); + // reset profile timestamp state + ctx->profile_timestamp_query_count = 0; + profile_pipeline_names.clear(); + } +#endif + } + + node_idx += num_encoded_ops; + num_encoded_ops = 1; + } + + if (ctx->active_compute_pass) { + ctx->active_compute_pass.End(); + ctx->active_compute_pass = nullptr; + } + + if (num_batched_kernels > 0) { + wgpu::CommandBuffer batch_commands = ctx->active_command_encoder.Finish(); + ggml_backend_webgpu_submit_commands(ctx, batch_commands, num_inflight_batches); + ctx->param_arena.reset(); + commands.clear(); + } + ctx->active_command_encoder = nullptr; + +#ifdef GGML_WEBGPU_GPU_PROFILE + ggml_backend_webgpu_collect_profile_results(ctx, profile_pipeline_names, num_inflight_batches); +#endif + + if (contains_set_rows) { + ggml_backend_webgpu_check_set_rows(ctx, num_inflight_batches); + } + + WEBGPU_CPU_PROFILE_TOTAL_END(graph_compute, ctx->global_ctx); + return GGML_STATUS_SUCCESS; +} + +struct ggml_backend_webgpu_event_context { + webgpu_global_context global_ctx; + wgpu::Future future; + bool recorded = false; +}; + +static ggml_backend_event_t ggml_backend_webgpu_device_event_new(ggml_backend_dev_t device) { + ggml_backend_webgpu_device_context * dev_ctx = (ggml_backend_webgpu_device_context *) device->context; + + auto * event_ctx = new ggml_backend_webgpu_event_context(); + event_ctx->global_ctx = dev_ctx->webgpu_global_ctx; + + auto * event = new ggml_backend_event; + event->device = device; + event->context = event_ctx; + return event; +} + +static void ggml_backend_webgpu_device_event_free(ggml_backend_dev_t dev, ggml_backend_event_t event) { + GGML_UNUSED(dev); + delete static_cast(event->context); + delete event; +} + +static void ggml_backend_webgpu_device_event_synchronize(ggml_backend_dev_t dev, ggml_backend_event_t event) { + GGML_UNUSED(dev); + ggml_backend_webgpu_event_context * event_ctx = (ggml_backend_webgpu_event_context *) event->context; + if (!event_ctx->recorded) { + return; + } + wgpu::WaitStatus status = + event_ctx->global_ctx->instance.WaitAny(event_ctx->future, WEBGPU_RUNTIME_WAIT_TIMEOUT_NS); + if (status == wgpu::WaitStatus::TimedOut) { + GGML_ABORT("ggml_webgpu: event_synchronize timed out after %u ms\n", WEBGPU_RUNTIME_WAIT_TIMEOUT_MS); + } + event_ctx->recorded = false; +} + +static void ggml_backend_webgpu_event_record(ggml_backend_t backend, ggml_backend_event_t event) { + ggml_backend_webgpu_context * backend_ctx = (ggml_backend_webgpu_context *) backend->context; + ggml_backend_webgpu_event_context * event_ctx = (ggml_backend_webgpu_event_context *) event->context; + + event_ctx->future = backend_ctx->webgpu_ctx->global_ctx->queue.OnSubmittedWorkDone( + wgpu::CallbackMode::AllowSpontaneous, [](wgpu::QueueWorkDoneStatus, wgpu::StringView) {}); + event_ctx->recorded = true; +} + +static void ggml_backend_webgpu_event_wait(ggml_backend_t backend, ggml_backend_event_t event) { + GGML_UNUSED(backend); + ggml_backend_webgpu_device_event_synchronize(nullptr, event); +} + +static void ggml_backend_webgpu_set_tensor_async(ggml_backend_t backend, + ggml_tensor * tensor, + const void * data, + size_t offset, + size_t size) { + GGML_UNUSED(backend); + auto * buf_ctx = (ggml_backend_webgpu_buffer_context *) tensor->buffer->context; + size_t total_offset = ggml_webgpu_tensor_offset(tensor) + offset; + + // Write aligned portion + buf_ctx->global_ctx->queue.WriteBuffer(buf_ctx->buffer, total_offset, data, (size / 4) * 4); + + if (size % 4 != 0) { + // If size is not a multiple of 4, we need to memset the remaining bytes + size_t remaining_size = size % 4; + + // pack the remaining bytes into a uint32_t + uint32_t val32 = 0; + + for (size_t i = 0; i < remaining_size; i++) { + ((uint8_t *) &val32)[i] = ((const uint8_t *) data)[size - remaining_size + i]; + } + // memset the remaining bytes + ggml_backend_webgpu_buffer_memset(buf_ctx->global_ctx, buf_ctx->buffer, val32, + total_offset + (size - remaining_size), remaining_size); + } +} + +static void ggml_backend_webgpu_synchronize(ggml_backend_t backend) { + ggml_backend_webgpu_context * backend_ctx = (ggml_backend_webgpu_context *) backend->context; + ggml_backend_webgpu_wait_queue(backend_ctx->webgpu_ctx->global_ctx); +} + +static ggml_backend_i ggml_backend_webgpu_i = { + /* .get_name = */ ggml_backend_webgpu_name, + /* .free = */ ggml_backend_webgpu_free, + /* .set_tensor_async = */ ggml_backend_webgpu_set_tensor_async, + /* .get_tensor_async = */ NULL, + /* .set_tensor_2d_async = */ NULL, + /* .get_tensor_2d_async = */ NULL, + /* .cpy_tensor_async = */ NULL, + /* .synchronize = */ ggml_backend_webgpu_synchronize, + /* .graph_plan_create = */ NULL, + /* .graph_plan_free = */ NULL, + /* .graph_plan_update = */ NULL, + /* .graph_plan_compute = */ NULL, + /* .graph_compute = */ ggml_backend_webgpu_graph_compute, + /* .event_record = */ ggml_backend_webgpu_event_record, + /* .event_wait = */ ggml_backend_webgpu_event_wait, + /* .graph_optimize = */ NULL, +}; + +/* End GGML Backend Interface */ + +/* GGML Backend Buffer Interface */ + +static void ggml_backend_webgpu_buffer_free_buffer(ggml_backend_buffer_t buffer) { + ggml_backend_webgpu_buffer_context * ctx = static_cast(buffer->context); + if (ctx != nullptr && ctx->buffer != nullptr) { + ctx->buffer.Destroy(); + delete ctx; + } +} + +// Returns the "fake" base pointer. +static void * ggml_backend_webgpu_buffer_get_base(ggml_backend_buffer_t buffer) { + GGML_UNUSED(buffer); + return webgpu_ptr_base; +} + +static void ggml_backend_webgpu_buffer_memset_tensor(ggml_backend_buffer_t buffer, + ggml_tensor * tensor, + uint8_t value, + size_t offset, + size_t size) { + if (size == 0) { + WEBGPU_LOG_DEBUG( + "ggml_backend_webgpu_buffer_memset_tensor: size is zero, " + "nothing to do."); + return; + } + + WEBGPU_CPU_PROFILE_TOTAL_START(memset_tensor); + + ggml_backend_webgpu_buffer_context * buf_ctx = (ggml_backend_webgpu_buffer_context *) buffer->context; + + WEBGPU_LOG_DEBUG("ggml_backend_webgpu_buffer_memset_tensor(" << buf_ctx->label << ", " << tensor << ", " << value + << ", " << offset << ", " << size << ")"); + + size_t total_offset = ggml_webgpu_tensor_offset(tensor) + offset; + + // This is a trick to set all bytes of a u32 to the same 1 byte value. + uint32_t val32 = (uint32_t) value * 0x01010101; + ggml_backend_webgpu_buffer_memset(buf_ctx->global_ctx, buf_ctx->buffer, val32, total_offset, size); + WEBGPU_CPU_PROFILE_TOTAL_END(memset_tensor, buf_ctx->global_ctx); +} + +static void ggml_backend_webgpu_buffer_set_tensor(ggml_backend_buffer_t buffer, + ggml_tensor * tensor, + const void * data, + size_t offset, + size_t size) { + WEBGPU_CPU_PROFILE_TOTAL_START(set_tensor); + ggml_backend_webgpu_buffer_context * buf_ctx = (ggml_backend_webgpu_buffer_context *) buffer->context; + + WEBGPU_LOG_DEBUG("ggml_backend_webgpu_buffer_set_tensor(" << buf_ctx->label << ", " << tensor << ", " << data + << ", " << offset << ", " << size << ")"); + + size_t total_offset = ggml_webgpu_tensor_offset(tensor) + offset; + + buf_ctx->global_ctx->queue.WriteBuffer(buf_ctx->buffer, total_offset, data, (size / 4) * 4); + + if (size % 4 != 0) { + // If size is not a multiple of 4, we need to memset the remaining bytes + size_t remaining_size = size % 4; + + // pack the remaining bytes into a uint32_t + uint32_t val32 = 0; + + for (size_t i = 0; i < remaining_size; i++) { + ((uint8_t *) &val32)[i] = ((const uint8_t *) data)[size - remaining_size + i]; + } + // memset the remaining bytes + ggml_backend_webgpu_buffer_memset(buf_ctx->global_ctx, buf_ctx->buffer, val32, + total_offset + (size - remaining_size), remaining_size); + } + WEBGPU_CPU_PROFILE_TOTAL_END(set_tensor, buf_ctx->global_ctx); +} + +static void ggml_backend_webgpu_buffer_get_tensor(ggml_backend_buffer_t buffer, + const ggml_tensor * tensor, + void * data, + size_t offset, + size_t size) { + WEBGPU_CPU_PROFILE_TOTAL_START(get_tensor); + ggml_backend_webgpu_buffer_context * buf_ctx = (ggml_backend_webgpu_buffer_context *) buffer->context; + WEBGPU_LOG_DEBUG("ggml_backend_webgpu_buffer_get_tensor(" << buf_ctx->label << ", " << tensor << ", " << data + << ", " << offset << ", " << size << ")"); + wgpu::Device device = buf_ctx->global_ctx->device; + + size_t total_offset = ggml_webgpu_tensor_offset(tensor) + offset; + + size_t final_size = size; + if (size % 4 != 0) { + // If size is not a multiple of 4, we need to round it up to the next + // multiple of 4 + final_size = size + (4 - (size % 4)); + } + + std::lock_guard lock(buf_ctx->global_ctx->mutex); + + if (buf_ctx->global_ctx->get_tensor_staging_buf == nullptr || + buf_ctx->global_ctx->get_tensor_staging_buf.GetSize() < final_size) { + // Create a new staging buffer if it doesn't exist or is too small + if (buf_ctx->global_ctx->get_tensor_staging_buf) { + buf_ctx->global_ctx->get_tensor_staging_buf.Destroy(); + } + ggml_webgpu_create_buffer(device, buf_ctx->global_ctx->get_tensor_staging_buf, final_size, + wgpu::BufferUsage::CopyDst | wgpu::BufferUsage::MapRead, "get_tensor_staging_buf"); + } + + // Copy the data from the buffer to the staging buffer + wgpu::CommandEncoder encoder = device.CreateCommandEncoder(); + encoder.CopyBufferToBuffer(buf_ctx->buffer, total_offset, buf_ctx->global_ctx->get_tensor_staging_buf, 0, + final_size); + wgpu::CommandBuffer commands = encoder.Finish(); + + // Submit the command buffer to the queue + buf_ctx->global_ctx->queue.Submit(1, &commands); + + // Map the staging buffer to read the data + ggml_backend_webgpu_map_buffer(buf_ctx->global_ctx, buf_ctx->global_ctx->get_tensor_staging_buf, + wgpu::MapMode::Read, 0, final_size); + // Must specify size here since the staging buffer might be larger than the tensor size + const void * mapped_range = buf_ctx->global_ctx->get_tensor_staging_buf.GetConstMappedRange(0, final_size); + + // Copy the data from the mapped range to the output buffer + std::memcpy(data, mapped_range, size); + buf_ctx->global_ctx->get_tensor_staging_buf.Unmap(); + WEBGPU_CPU_PROFILE_TOTAL_END(get_tensor, buf_ctx->global_ctx); +} + +static void ggml_backend_webgpu_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { + WEBGPU_LOG_DEBUG("ggml_backend_webgpu_buffer_clear(" << buffer << ", " << (uint32_t) value << ")"); + WEBGPU_CPU_PROFILE_TOTAL_START(clear); + ggml_backend_webgpu_buffer_context * buf_ctx = (ggml_backend_webgpu_buffer_context *) buffer->context; + ggml_backend_webgpu_buffer_memset(buf_ctx->global_ctx, buf_ctx->buffer, value, 0, buffer->size); + WEBGPU_CPU_PROFILE_TOTAL_END(clear, buf_ctx->global_ctx); +} + +static ggml_backend_buffer_i ggml_backend_webgpu_buffer_interface = { + /* .free_buffer = */ ggml_backend_webgpu_buffer_free_buffer, + /* .get_base = */ ggml_backend_webgpu_buffer_get_base, + /* .init_tensor = */ NULL, // TODO: optional, needed? + /* .memset_tensor = */ ggml_backend_webgpu_buffer_memset_tensor, + /* .set_tensor = */ ggml_backend_webgpu_buffer_set_tensor, + /* .get_tensor = */ ggml_backend_webgpu_buffer_get_tensor, + /* .set_tensor_2d = */ NULL, + /* .get_tensor_2d = */ NULL, + /* .cpy_tensor = */ NULL, // TODO: optional, implement this + /* .clear = */ ggml_backend_webgpu_buffer_clear, + /* .reset = */ NULL, // TODO: optional, think it coordinates with + // .init_tensor +}; + +/* End GGML Backend Buffer Interface */ + +/* GGML Backend Buffer Type Interface */ + +static const char * ggml_backend_webgpu_buffer_type_get_name(ggml_backend_buffer_type_t buft) { + ggml_backend_webgpu_device_context * ctx = static_cast(buft->device->context); + return ctx->device_name.c_str(); +} + +static ggml_backend_buffer_t ggml_backend_webgpu_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, + size_t size) { + static std::atomic buffer_count; + int buffer_id = buffer_count++; + std::string buf_name = "tensor_buf" + std::to_string(buffer_id); + WEBGPU_LOG_DEBUG("ggml_backend_webgpu_buffer_type_alloc_buffer_" << buffer_id << ": " << size << " bytes"); + + ggml_backend_webgpu_device_context * ctx = static_cast(buft->device->context); + wgpu::Buffer buf; + ggml_webgpu_create_buffer(ctx->webgpu_global_ctx->device, buf, ROUNDUP_POW2(size, WEBGPU_STORAGE_BUF_BINDING_MULT), + wgpu::BufferUsage::Storage | wgpu::BufferUsage::CopySrc | wgpu::BufferUsage::CopyDst, + buf_name.c_str()); + + ggml_backend_webgpu_buffer_context * buf_ctx = + new ggml_backend_webgpu_buffer_context(buf, buf_name, ctx->webgpu_global_ctx); + + return ggml_backend_buffer_init(buft, ggml_backend_webgpu_buffer_interface, buf_ctx, size); +} + +static size_t ggml_backend_webgpu_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { + ggml_backend_webgpu_device_context * dev_ctx = + static_cast(buft->device->context); + return dev_ctx->webgpu_global_ctx->capabilities.limits.minStorageBufferOffsetAlignment; +} + +// maxBufferSize might be larger, but you can't bind more than +// maxStorageBufferBindingSize to a single binding. +static size_t ggml_backend_webgpu_buffer_type_get_max_size(ggml_backend_buffer_type_t buft) { + ggml_backend_webgpu_device_context * dev_ctx = + static_cast(buft->device->context); + return dev_ctx->webgpu_global_ctx->capabilities.limits.maxStorageBufferBindingSize; +} + +static size_t ggml_backend_webgpu_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, + const ggml_tensor * tensor) { + ggml_backend_webgpu_device_context * ctx = static_cast(buft->device->context); + size_t res = ggml_nbytes(tensor); + switch (tensor->op) { + case GGML_OP_ARGSORT: + res = ROUNDUP_POW2(res * 2 + ctx->webgpu_global_ctx->capabilities.limits.minStorageBufferOffsetAlignment, + WEBGPU_STORAGE_BUF_BINDING_MULT); + break; + case GGML_OP_TOP_K: + { + const ggml_tensor * src0 = tensor->src[0]; + if (src0) { + const size_t full = sizeof(int32_t) * ggml_nelements(src0); + res = ROUNDUP_POW2( + full * 2 + ctx->webgpu_global_ctx->capabilities.limits.minStorageBufferOffsetAlignment, + WEBGPU_STORAGE_BUF_BINDING_MULT); + } + } + break; + case GGML_OP_FLASH_ATTN_EXT: + { + const ggml_tensor * Q = tensor->src[0]; + const ggml_tensor * K = tensor->src[1]; + const ggml_tensor * V = tensor->src[2]; + const ggml_tensor * mask = tensor->src[3]; + const auto & capabilities = ctx->webgpu_global_ctx->capabilities; + if (ggml_webgpu_flash_attn_use_vec_path(ctx->webgpu_global_ctx, Q, K, V)) { + const bool kv_direct = + ggml_webgpu_flash_attn_kv_direct(Q, K, V, GGML_WEBGPU_FLASH_ATTN_TILE_KV_VEC_WIDTH); + const uint32_t kv_tile = ggml_webgpu_flash_attn_get_vec_kv_tile( + capabilities.limits.maxComputeWorkgroupStorageSize, (uint32_t) Q->ne[0], (uint32_t) V->ne[0], + mask != nullptr, kv_direct); + + const uint32_t vec_nwg_cap = capabilities.min_subgroup_size; + uint32_t nwg = ggml_webgpu_flash_attn_vec_nwg(vec_nwg_cap, kv_tile, (uint32_t) K->ne[1]); + + const size_t align = capabilities.limits.minStorageBufferOffsetAlignment; + const uint64_t nrows = (uint64_t) Q->ne[1] * Q->ne[2] * Q->ne[3]; + if (nwg > 1u) { + const uint64_t tmp_data_elems = nrows * (uint64_t) V->ne[0] * nwg; + const uint64_t tmp_stats_elems = nrows * 2u * nwg; + const size_t tmp_size_bytes = ROUNDUP_POW2((tmp_data_elems + tmp_stats_elems) * sizeof(float), + WEBGPU_STORAGE_BUF_BINDING_MULT); + res += tmp_size_bytes + align; + } else { + res += WEBGPU_STORAGE_BUF_BINDING_MULT + align; + } + if (mask != nullptr) { + const uint32_t blk_nblk0 = CEIL_DIV((uint32_t) K->ne[1], kv_tile); + const uint32_t blk_nblk1 = CEIL_DIV((uint32_t) Q->ne[1], 1u); + const uint32_t stride_mask3 = (uint32_t) (mask->nb[3] / ggml_type_size(mask->type)); + const uint32_t blk_batch_count = stride_mask3 > 0 ? (uint32_t) Q->ne[3] : 1u; + const uint64_t blk_elems = (uint64_t) blk_nblk0 * blk_nblk1 * blk_batch_count; + const size_t blk_size_bytes = + ROUNDUP_POW2(blk_elems * sizeof(uint32_t), WEBGPU_STORAGE_BUF_BINDING_MULT); + res += blk_size_bytes + align; + } + res = ROUNDUP_POW2(res, WEBGPU_STORAGE_BUF_BINDING_MULT); + } + } + break; + case GGML_OP_MUL_MAT: + { + const ggml_tensor * src0 = tensor->src[0]; + const ggml_tensor * src1 = tensor->src[1]; + bool use_mmvq = + ggml_webgpu_can_use_mmvq(src0, src1, ctx->webgpu_global_ctx->capabilities.supports_dot_product, + ctx->webgpu_global_ctx->vendor); + if (use_mmvq) { + const size_t q8_src1_size = src1->ne[3] * src1->ne[2] * src1->ne[1] * + (36 /* sizeof(q8_1) */ * (src1->ne[0] / /* block_size */ 32)); + res = ROUNDUP_POW2(res + q8_src1_size + + ctx->webgpu_global_ctx->capabilities.limits.minStorageBufferOffsetAlignment, + WEBGPU_STORAGE_BUF_BINDING_MULT); + } + } + break; + case GGML_OP_MUL_MAT_ID: + { + const ggml_tensor * src0 = tensor->src[0]; + const ggml_tensor * src1 = tensor->src[1]; + if (src0 && src1) { + const size_t gathered_size = sizeof(uint32_t) * tensor->src[0]->ne[2] * tensor->src[1]->ne[2]; + const size_t gathered_count_ids_size = sizeof(uint32_t) * tensor->src[0]->ne[2]; + res = ROUNDUP_POW2( + res + gathered_size * 2 + gathered_count_ids_size + + ctx->webgpu_global_ctx->capabilities.limits.minStorageBufferOffsetAlignment * 3, + WEBGPU_STORAGE_BUF_BINDING_MULT); + } + } + break; + default: + break; + } + return res; +} + +/* End GGML Backend Buffer Type Interface */ + +/* GGML Backend Device Interface */ + +static const char * ggml_backend_webgpu_device_get_name(ggml_backend_dev_t dev) { + ggml_backend_webgpu_device_context * ctx = static_cast(dev->context); + return ctx->device_name.c_str(); +} + +static const char * ggml_backend_webgpu_device_get_description(ggml_backend_dev_t dev) { + ggml_backend_webgpu_device_context * ctx = static_cast(dev->context); + return ctx->device_desc.c_str(); +} + +static void ggml_backend_webgpu_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) { + ggml_backend_webgpu_device_context * ctx = static_cast(dev->context); + // TODO: for now, return maxBufferSize as both free and total memory + // Track https://github.com/gpuweb/gpuweb/issues/5505 for updates. + uint64_t max_buffer_size = ctx->webgpu_global_ctx->capabilities.limits.maxBufferSize; + // If we're on a 32-bit system, clamp to UINTPTR_MAX +#if UINTPTR_MAX < UINT64_MAX + uint64_t max_ptr_size = static_cast(UINTPTR_MAX); + if (max_buffer_size > max_ptr_size) { + max_buffer_size = max_ptr_size; + } +#endif + *free = static_cast(max_buffer_size); + *total = static_cast(max_buffer_size); +} + +static enum ggml_backend_dev_type ggml_backend_webgpu_device_get_type(ggml_backend_dev_t dev) { + GGML_UNUSED(dev); + return GGML_BACKEND_DEVICE_TYPE_GPU; +} + +static void ggml_backend_webgpu_device_get_props(ggml_backend_dev_t dev, struct ggml_backend_dev_props * props) { + props->name = ggml_backend_webgpu_device_get_name(dev); + props->description = ggml_backend_webgpu_device_get_description(dev); + props->type = ggml_backend_webgpu_device_get_type(dev); + ggml_backend_webgpu_device_get_memory(dev, &props->memory_free, &props->memory_total); + props->caps = { + /* .async = */ false, + /* .host_buffer = */ false, + /* .buffer_from_host_ptr = */ false, + /* .events = */ false, + }; +} + +static ggml_guid_t ggml_backend_webgpu_guid(void) { + static ggml_guid guid = { 0x67, 0xc7, 0xa4, 0xb1, 0x78, 0x74, 0x4f, 0x51, + 0x9d, 0x65, 0x44, 0x6d, 0xe4, 0x1b, 0x82, 0x9a }; + return &guid; +} + +static void ggml_webgpu_init_memset_pipeline(webgpu_global_context & ctx) { + // we use the maximum workgroup size for the memset pipeline + size_t max_threads = ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup * + ctx->capabilities.limits.maxComputeWorkgroupsPerDimension; + // Size the bytes_per_thread so that the largest buffer size can be handled + ctx->capabilities.memset_bytes_per_thread = + CEIL_DIV(ctx->capabilities.limits.maxStorageBufferBindingSize, max_threads); + std::vector constants(2); + constants[0].key = "wg_size"; + constants[0].value = ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup; + constants[1].key = "bytes_per_thread"; + constants[1].value = ctx->capabilities.memset_bytes_per_thread; + ctx->memset_pipeline = ggml_webgpu_create_pipeline(ctx->device, wgsl_memset, "memset", constants); +} + +static void ggml_backend_webgpu_request_adapter(wgpu::Instance & instance, wgpu::Adapter & adapter) { + wgpu::RequestAdapterOptions options = {}; + +#ifndef __EMSCRIPTEN__ + // TODO: track need for these toggles: https://issues.chromium.org/issues/42251215 + const char * const adapterEnabledToggles[] = { "vulkan_enable_f16_on_nvidia", "use_vulkan_memory_model" }; + wgpu::DawnTogglesDescriptor adapterTogglesDesc; + adapterTogglesDesc.enabledToggles = adapterEnabledToggles; + adapterTogglesDesc.enabledToggleCount = 2; + options.nextInChain = &adapterTogglesDesc; +#endif + + instance.WaitAny(instance.RequestAdapter( + &options, wgpu::CallbackMode::AllowSpontaneous, + [&adapter](wgpu::RequestAdapterStatus status, wgpu::Adapter _adapter, const char * message) { + if (status != wgpu::RequestAdapterStatus::Success) { + GGML_LOG_ERROR("ggml_webgpu: Failed to get an adapter: %s\n", message); + return; + } + adapter = std::move(_adapter); + }), + UINT64_MAX); +} + +static void create_webgpu_device(ggml_backend_webgpu_reg_context * ctx) { + ggml_backend_webgpu_request_adapter(ctx->webgpu_global_ctx->instance, ctx->webgpu_global_ctx->adapter); + GGML_ASSERT(ctx->webgpu_global_ctx->adapter != nullptr); + + ctx->webgpu_global_ctx->adapter.GetLimits(&ctx->webgpu_global_ctx->capabilities.limits); + + wgpu::AdapterInfo info{}; +#ifndef __EMSCRIPTEN__ + wgpu::AdapterPropertiesSubgroupMatrixConfigs subgroup_matrix_configs{}; + if (ctx->webgpu_global_ctx->adapter.HasFeature(wgpu::FeatureName::ChromiumExperimentalSubgroupMatrix)) { + info.nextInChain = &subgroup_matrix_configs; + } +#endif + ctx->webgpu_global_ctx->adapter.GetInfo(&info); + ctx->webgpu_global_ctx->command_submit_batch_size = ggml_backend_webgpu_get_command_submit_batch_size(); + ctx->webgpu_global_ctx->max_inflight_batches = ggml_backend_webgpu_get_max_inflight_batches(); + ctx->webgpu_global_ctx->vendor = info.vendor; + ctx->webgpu_global_ctx->capabilities.supports_subgroups = + ctx->webgpu_global_ctx->adapter.HasFeature(wgpu::FeatureName::Subgroups); + // for dot4I8packed + ctx->webgpu_global_ctx->capabilities.supports_dot_product = ctx->webgpu_global_ctx->instance.HasWGSLLanguageFeature( + wgpu::WGSLLanguageFeatureName::Packed4x8IntegerDotProduct); + + bool valid_subgroup_matrix_config = false; +#ifndef __EMSCRIPTEN__ + // Accept f16 subgroup matrix configurations (square or non-square). + // NVIDIA GPUs typically report square configs (e.g. 16x16x16), + // while Intel Xe2 GPUs report non-square configs (e.g. 8x16x16). + // The shaders are already parameterized to handle any M/N/K dimensions. + if (ctx->webgpu_global_ctx->adapter.HasFeature(wgpu::FeatureName::ChromiumExperimentalSubgroupMatrix)) { + for (size_t i = 0; i < subgroup_matrix_configs.configCount; i++) { + const wgpu::SubgroupMatrixConfig config = subgroup_matrix_configs.configs[i]; + if (config.componentType == wgpu::SubgroupMatrixComponentType::F16 && + config.resultComponentType == wgpu::SubgroupMatrixComponentType::F16) { + ctx->webgpu_global_ctx->capabilities.sg_mat_m = config.M; + ctx->webgpu_global_ctx->capabilities.sg_mat_n = config.N; + ctx->webgpu_global_ctx->capabilities.sg_mat_k = config.K; + valid_subgroup_matrix_config = true; + break; + } + } + } +#endif + ctx->webgpu_global_ctx->capabilities.supports_subgroup_matrix = valid_subgroup_matrix_config; + + // Runtime subgroup size can be any supported size in this range. Shaders + // that allocate per-lane register arrays must size them for the minimum. + ctx->webgpu_global_ctx->capabilities.min_subgroup_size = info.subgroupMinSize; + ctx->webgpu_global_ctx->capabilities.max_subgroup_size = info.subgroupMaxSize; + // Initialize device + std::vector required_features = { wgpu::FeatureName::ShaderF16 }; + +#ifndef __EMSCRIPTEN__ + required_features.push_back(wgpu::FeatureName::ImplicitDeviceSynchronization); + if (ctx->webgpu_global_ctx->capabilities.supports_subgroup_matrix) { + required_features.push_back(wgpu::FeatureName::ChromiumExperimentalSubgroupMatrix); + } +#endif + + if (ctx->webgpu_global_ctx->capabilities.supports_subgroups) { + required_features.push_back(wgpu::FeatureName::Subgroups); + } + +#ifdef GGML_WEBGPU_GPU_PROFILE + required_features.push_back(wgpu::FeatureName::TimestampQuery); +#endif + + wgpu::DeviceDescriptor dev_desc; + dev_desc.requiredLimits = &ctx->webgpu_global_ctx->capabilities.limits; + dev_desc.requiredFeatures = required_features.data(); + dev_desc.requiredFeatureCount = required_features.size(); + dev_desc.SetDeviceLostCallback( + wgpu::CallbackMode::AllowSpontaneous, + [](const wgpu::Device & device, wgpu::DeviceLostReason reason, wgpu::StringView message) { + if (reason == wgpu::DeviceLostReason::Destroyed) { + return; + } + GGML_UNUSED(device); + GGML_LOG_ERROR("ggml_webgpu: Device lost! Reason: %d, Message: %s\n", static_cast(reason), + std::string(message).c_str()); + }); + dev_desc.SetUncapturedErrorCallback( + [](const wgpu::Device & device, wgpu::ErrorType reason, wgpu::StringView message) { + GGML_UNUSED(device); + GGML_ABORT("ggml_webgpu: Device error! Reason: %d, Message: %s\n", static_cast(reason), + std::string(message).c_str()); + }); + +#ifndef __EMSCRIPTEN__ + // Enable Dawn-specific toggles to increase native performance + // TODO: Maybe WebGPU needs a "fast" mode where you can request compilers skip adding checks like these, + // only for native performance? + const char * const deviceEnabledToggles[] = { "disable_robustness", "disable_workgroup_init", + "disable_polyfills_on_integer_div_and_mod" }; + const char * const deviceDisabledToggles[] = { "timestamp_quantization" }; + wgpu::DawnTogglesDescriptor deviceTogglesDesc; + deviceTogglesDesc.enabledToggles = deviceEnabledToggles; + deviceTogglesDesc.enabledToggleCount = 3; + deviceTogglesDesc.disabledToggles = deviceDisabledToggles; + deviceTogglesDesc.disabledToggleCount = 1; + + dev_desc.nextInChain = &deviceTogglesDesc; +#endif + + ctx->webgpu_global_ctx->instance.WaitAny( + ctx->webgpu_global_ctx->adapter.RequestDevice( + &dev_desc, wgpu::CallbackMode::AllowSpontaneous, + [ctx](wgpu::RequestDeviceStatus status, wgpu::Device device, wgpu::StringView message) { + if (status != wgpu::RequestDeviceStatus::Success) { + GGML_LOG_ERROR("ggml_webgpu: Failed to get a device: %s\n", std::string(message).c_str()); + return; + } + ctx->webgpu_global_ctx->device = std::move(device); + }), + UINT64_MAX); + GGML_ASSERT(ctx->webgpu_global_ctx->device != nullptr); + + ggml_webgpu_init_memset_pipeline(ctx->webgpu_global_ctx); + ggml_webgpu_create_buffer(ctx->webgpu_global_ctx->device, ctx->webgpu_global_ctx->memset_params_buf, + WEBGPU_PARAMS_BUF_SIZE_BYTES, wgpu::BufferUsage::CopyDst | wgpu::BufferUsage::Uniform, + "memset_params_buf"); + ctx->webgpu_global_ctx->queue = ctx->webgpu_global_ctx->device.GetQueue(); + + GGML_LOG_INFO( + "ggml_webgpu: adapter_info: vendor_id: %u | vendor: %s | architecture: %s | device_id: %u | name: %s | " + "device_desc: %s\n", + info.vendorID, std::string(info.vendor).c_str(), std::string(info.architecture).c_str(), info.deviceID, + std::string(info.device).c_str(), std::string(info.description).c_str()); +} + +static webgpu_context initialize_webgpu_context(ggml_backend_dev_t dev) { + ggml_backend_webgpu_device_context * dev_ctx = (ggml_backend_webgpu_device_context *) dev->context; + webgpu_context webgpu_ctx = std::make_shared(); + webgpu_ctx->global_ctx = dev_ctx->webgpu_global_ctx; + webgpu_ctx->shader_lib = std::make_unique(dev_ctx->webgpu_global_ctx->device); + webgpu_ctx->param_arena.init( + webgpu_ctx->global_ctx->device, WEBGPU_PARAMS_BUF_SIZE_BYTES, + webgpu_ctx->global_ctx->command_submit_batch_size + WEBGPU_NUM_PARAM_SLOT_SAFETY_MARGIN, + webgpu_ctx->global_ctx->capabilities.limits.minUniformBufferOffsetAlignment); + ggml_webgpu_create_buffer(webgpu_ctx->global_ctx->device, webgpu_ctx->set_rows_dev_error_buf, + WEBGPU_SET_ROWS_ERROR_BUF_SIZE_BYTES, + wgpu::BufferUsage::Storage | wgpu::BufferUsage::CopySrc, "set_rows_dev_error_buf"); + ggml_webgpu_create_buffer(webgpu_ctx->global_ctx->device, webgpu_ctx->set_rows_host_error_buf, + WEBGPU_SET_ROWS_ERROR_BUF_SIZE_BYTES, + wgpu::BufferUsage::CopyDst | wgpu::BufferUsage::MapRead, "set_rows_host_error_buf"); + +#ifdef GGML_WEBGPU_GPU_PROFILE + webgpu_ctx->batch_compute_passes = false; + ggml_webgpu_create_buffer( + webgpu_ctx->global_ctx->device, webgpu_ctx->profile_timestamp_dev_buf, WEBGPU_TIMESTAMP_QUERY_BUF_SIZE_BYTES, + wgpu::BufferUsage::QueryResolve | wgpu::BufferUsage::CopySrc, "profile_timestamp_dev_buf"); + ggml_webgpu_create_buffer(webgpu_ctx->global_ctx->device, webgpu_ctx->profile_timestamp_host_buf, + WEBGPU_TIMESTAMP_QUERY_BUF_SIZE_BYTES, + wgpu::BufferUsage::CopyDst | wgpu::BufferUsage::MapRead, "profile_timestamp_host_buf"); + wgpu::QuerySetDescriptor query_set_desc = {}; + query_set_desc.type = wgpu::QueryType::Timestamp; + query_set_desc.count = WEBGPU_MAX_PROFILE_QUERY_COUNT; + webgpu_ctx->profile_timestamp_query_set = webgpu_ctx->global_ctx->device.CreateQuerySet(&query_set_desc); +#endif + +#ifdef GGML_WEBGPU_DEBUG + // Initialize debug buffers + ggml_webgpu_create_buffer(webgpu_ctx->global_ctx->device, webgpu_ctx->global_ctx->debug_host_buf, + WEBGPU_DEBUG_BUF_ELEMS * sizeof(uint32_t), + wgpu::BufferUsage::CopyDst | wgpu::BufferUsage::MapRead, "debug_host_buf"); + ggml_webgpu_create_buffer(webgpu_ctx->global_ctx->device, webgpu_ctx->global_ctx->debug_dev_buf, + WEBGPU_DEBUG_BUF_ELEMS * sizeof(uint32_t), + wgpu::BufferUsage::Storage | wgpu::BufferUsage::CopySrc, "debug_dev_buf"); +#endif + return webgpu_ctx; +} + +static ggml_backend_t ggml_backend_webgpu_backend_init(ggml_backend_dev_t dev, const char * params) { + GGML_UNUSED(params); + + WEBGPU_LOG_DEBUG("ggml_backend_webgpu_backend_init()"); + + ggml_backend_webgpu_device_context * dev_ctx = static_cast(dev->context); + + auto * backend_ctx = new ggml_backend_webgpu_context(); + backend_ctx->name = GGML_WEBGPU_NAME + std::string(": ") + dev_ctx->device_name; + backend_ctx->webgpu_ctx = initialize_webgpu_context(dev); + + // See GGML Backend Interface section + auto * backend = new ggml_backend(); + *backend = { + /* .guid = */ ggml_backend_webgpu_guid(), + /* .interface = */ ggml_backend_webgpu_i, + /* .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, + /* .alloc_buffer = */ ggml_backend_webgpu_buffer_type_alloc_buffer, + /* .get_alignment = */ ggml_backend_webgpu_buffer_type_get_alignment, + /* .get_max_size = */ ggml_backend_webgpu_buffer_type_get_max_size, + /* .get_alloc_size = */ ggml_backend_webgpu_buffer_type_get_alloc_size, + /* .is_host = */ NULL, // defaults to false + }, + /* .device = */ + dev, + /* .context = */ NULL + }; + + return &ggml_backend_webgpu_buffer_type; +} + +static bool ggml_backend_webgpu_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) { + GGML_UNUSED(dev); + return buft->iface.get_name == ggml_backend_webgpu_buffer_type_get_name; +} + +static bool ggml_webgpu_supported_qtype(ggml_type type) { + switch (type) { + case GGML_TYPE_Q1_0: + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q4_1: + case GGML_TYPE_Q5_0: + case GGML_TYPE_Q5_1: + case GGML_TYPE_Q8_0: + case GGML_TYPE_Q2_K: + case GGML_TYPE_Q3_K: + case GGML_TYPE_Q4_K: + case GGML_TYPE_Q5_K: + case GGML_TYPE_Q6_K: + case GGML_TYPE_IQ2_XXS: + case GGML_TYPE_IQ2_XS: + case GGML_TYPE_IQ2_S: + case GGML_TYPE_IQ3_XXS: + case GGML_TYPE_IQ3_S: + case GGML_TYPE_IQ1_S: + case GGML_TYPE_IQ1_M: + case GGML_TYPE_IQ4_NL: + case GGML_TYPE_IQ4_XS: + case GGML_TYPE_MXFP4: + case GGML_TYPE_NVFP4: + return true; + default: + return false; + } +} + +static bool ggml_backend_webgpu_device_supports_op(ggml_backend_dev_t dev, const ggml_tensor * op) { + ggml_backend_webgpu_device_context * ctx = static_cast(dev->context); + + ggml_tensor * src0 = op->src[0]; + ggml_tensor * src1 = op->src[1]; + ggml_tensor * src2 = op->src[2]; + + // on smaller devices (or CI), tensors may be larger than the max storage buffer size + if (ggml_nbytes(op) > ctx->webgpu_global_ctx->capabilities.limits.maxStorageBufferBindingSize || + (src0 != nullptr && + ggml_nbytes(src0) > ctx->webgpu_global_ctx->capabilities.limits.maxStorageBufferBindingSize) || + (src1 != nullptr && + ggml_nbytes(src1) > ctx->webgpu_global_ctx->capabilities.limits.maxStorageBufferBindingSize)) { + return false; + } + + bool supports_op = false; + switch (op->op) { + case GGML_OP_NONE: + case GGML_OP_VIEW: + case GGML_OP_PERMUTE: + case GGML_OP_TRANSPOSE: + case GGML_OP_RESHAPE: + supports_op = true; + break; + case GGML_OP_ADD: + case GGML_OP_SUB: + case GGML_OP_MUL: + case GGML_OP_DIV: + supports_op = (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16) && (src0->type == op->type) && + (src1->type == op->type); + break; + case GGML_OP_ADD_ID: + supports_op = src0->type == GGML_TYPE_F32; + break; + case GGML_OP_CONCAT: + supports_op = (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_I32); + break; + case GGML_OP_REPEAT: + supports_op = (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_I32 || src0->type == GGML_TYPE_I16); + break; + case GGML_OP_CPY: + case GGML_OP_CONT: + supports_op = ((op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16) && + (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16)) || + (op->type == GGML_TYPE_I32 && src0->type == GGML_TYPE_F32); + break; + case GGML_OP_SET: + supports_op = src0->type == src1->type && src0->type == op->type && + (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_I32); + break; + case GGML_OP_SET_ROWS: + supports_op = ((op->type == GGML_TYPE_F16 || op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_Q8_0 || + op->type == GGML_TYPE_Q4_0) && + src0->type == GGML_TYPE_F32 && (src1->type == GGML_TYPE_I64 || src1->type == GGML_TYPE_I32)); + break; + case GGML_OP_GET_ROWS: + if (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || ggml_webgpu_supported_qtype(src0->type)) { + supports_op = (op->type == GGML_TYPE_F32); + } else if (src0->type == GGML_TYPE_I32) { + supports_op = op->type == GGML_TYPE_I32; + } + break; + case GGML_OP_MUL_MAT: + { + switch (src1->type) { + case GGML_TYPE_F16: + supports_op |= (src0->type == GGML_TYPE_F16); + break; + case GGML_TYPE_F32: + switch (src0->type) { + case GGML_TYPE_F32: + case GGML_TYPE_F16: + case GGML_TYPE_Q1_0: + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q4_1: + case GGML_TYPE_Q5_0: + case GGML_TYPE_Q5_1: + case GGML_TYPE_Q8_0: + case GGML_TYPE_Q2_K: + case GGML_TYPE_Q3_K: + case GGML_TYPE_Q4_K: + case GGML_TYPE_Q5_K: + case GGML_TYPE_Q6_K: + case GGML_TYPE_IQ2_XXS: + case GGML_TYPE_IQ2_XS: + case GGML_TYPE_IQ2_S: + case GGML_TYPE_IQ3_XXS: + case GGML_TYPE_IQ3_S: + case GGML_TYPE_IQ1_S: + case GGML_TYPE_IQ1_M: + case GGML_TYPE_IQ4_NL: + case GGML_TYPE_IQ4_XS: + case GGML_TYPE_MXFP4: + case GGML_TYPE_NVFP4: + supports_op = true; + break; + default: + break; + } + default: + break; + } + break; + } + case GGML_OP_MUL_MAT_ID: + switch (src1->type) { + case GGML_TYPE_F16: + supports_op |= (src0->type == GGML_TYPE_F16); + break; + case GGML_TYPE_F32: + switch (src0->type) { + case GGML_TYPE_F32: + case GGML_TYPE_F16: + case GGML_TYPE_Q1_0: + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q4_1: + case GGML_TYPE_Q5_0: + case GGML_TYPE_Q5_1: + case GGML_TYPE_Q8_0: + case GGML_TYPE_Q2_K: + case GGML_TYPE_Q3_K: + case GGML_TYPE_Q4_K: + case GGML_TYPE_Q5_K: + case GGML_TYPE_Q6_K: + case GGML_TYPE_IQ1_S: + case GGML_TYPE_IQ1_M: + case GGML_TYPE_IQ2_XXS: + case GGML_TYPE_IQ2_XS: + case GGML_TYPE_IQ2_S: + case GGML_TYPE_IQ3_XXS: + case GGML_TYPE_IQ3_S: + case GGML_TYPE_IQ4_NL: + case GGML_TYPE_IQ4_XS: + case GGML_TYPE_MXFP4: + case GGML_TYPE_NVFP4: + supports_op = true; + break; + default: + break; + } + break; + default: + break; + } + break; + case GGML_OP_FLASH_ATTN_EXT: + { + // conservative support checks for whether the more resource-intensive shader paths + // can be used, to avoid cases where flash_attn is assigned to the CPU later on + supports_op = src0->type == GGML_TYPE_F32 && + (src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16 || + src1->type == GGML_TYPE_Q4_0 || src1->type == GGML_TYPE_Q8_0) && + (src2->type == GGML_TYPE_F32 || src2->type == GGML_TYPE_F16 || + src2->type == GGML_TYPE_Q4_0 || src2->type == GGML_TYPE_Q8_0) && + op->type == GGML_TYPE_F32; + if (!supports_op) { + break; + } + if (ggml_webgpu_tensor_overlap(src1, src2) && src1->type != src2->type && + !ggml_is_quantized(src1->type) && !ggml_is_quantized(src2->type)) { + supports_op = false; + break; + } + const auto & capabilities = ctx->webgpu_global_ctx->capabilities; + const size_t storage_offset_alignment = capabilities.limits.minStorageBufferOffsetAlignment; + + // subgroup matrix path requirements + const bool use_subgroup_matrix = ggml_webgpu_flash_attn_can_use_subgroup_matrix_path( + capabilities.supports_subgroup_matrix, capabilities.sg_mat_k, capabilities.sg_mat_n, src0, src2); + + // tile path requirements + const bool float_vec4_aligned = + ((src1->type != GGML_TYPE_F16 && src1->type != GGML_TYPE_F32) || + ggml_webgpu_flash_attn_float_vec4_aligned(src1, storage_offset_alignment)) && + ((src2->type != GGML_TYPE_F16 && src2->type != GGML_TYPE_F32) || + ggml_webgpu_flash_attn_float_vec4_aligned(src2, storage_offset_alignment)); + const uint32_t k_tile_head_align = (src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16) ? + GGML_WEBGPU_FLASH_ATTN_TILE_KV_VEC_WIDTH : + (uint32_t) ggml_blck_size(src1->type); + const uint32_t v_tile_head_align = (src2->type == GGML_TYPE_F32 || src2->type == GGML_TYPE_F16) ? + GGML_WEBGPU_FLASH_ATTN_TILE_KV_VEC_WIDTH : + (uint32_t) ggml_blck_size(src2->type); + const bool tile_kv_head_dims_aligned = + src0->ne[0] % k_tile_head_align == 0 && src2->ne[0] % v_tile_head_align == 0; + const bool tile_can_dispatch_all_q_rows = + capabilities.limits.maxComputeInvocationsPerWorkgroup >= + GGML_WEBGPU_FLASH_ATTN_TILE_Q_TILE * capabilities.max_subgroup_size; + const bool use_tile = !use_subgroup_matrix && capabilities.supports_subgroups && float_vec4_aligned && + tile_kv_head_dims_aligned && tile_can_dispatch_all_q_rows; + + if (!use_subgroup_matrix && !use_tile) { + supports_op = false; + break; + } + const uint32_t q_tile = + use_subgroup_matrix ? capabilities.sg_mat_m : GGML_WEBGPU_FLASH_ATTN_TILE_Q_TILE; + const uint32_t kv_granularity = use_subgroup_matrix ? capabilities.sg_mat_n : 1u; + const bool kv_direct = use_subgroup_matrix ? + ggml_webgpu_flash_attn_kv_direct(src0, src1, src2, capabilities.sg_mat_k) : + false; + const uint32_t max_kv_tile = ggml_webgpu_flash_attn_max_kv_tile( + capabilities.limits.maxComputeWorkgroupStorageSize, q_tile, kv_granularity, (uint32_t) src0->ne[0], + (uint32_t) src2->ne[0], op->src[3] != nullptr, kv_direct); + supports_op = max_kv_tile > 0; + break; + } + case GGML_OP_RMS_NORM: + case GGML_OP_NORM: + case GGML_OP_L2_NORM: + supports_op = (op->type == GGML_TYPE_F32 && src0->type == GGML_TYPE_F32) && ggml_is_contiguous_rows(src0); + break; + case GGML_OP_ROPE: + supports_op = op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16; + break; + case GGML_OP_GLU: + switch (ggml_get_glu_op(op)) { + case GGML_GLU_OP_REGLU: + case GGML_GLU_OP_GEGLU: + case GGML_GLU_OP_SWIGLU: + case GGML_GLU_OP_GEGLU_ERF: + case GGML_GLU_OP_GEGLU_QUICK: + supports_op = op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16; + break; + case GGML_GLU_OP_SWIGLU_OAI: + supports_op = op->type == GGML_TYPE_F32; + break; + default: + break; + } + break; + case GGML_OP_SCALE: + supports_op = op->type == GGML_TYPE_F32; + break; + 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_SOFTPLUS: + case GGML_UNARY_OP_EXPM1: + case GGML_UNARY_OP_FLOOR: + case GGML_UNARY_OP_CEIL: + case GGML_UNARY_OP_ROUND: + case GGML_UNARY_OP_TRUNC: + case GGML_UNARY_OP_XIELU: + supports_op = + (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16) && (src0->type == op->type); + break; + default: + break; + } + } + break; + case GGML_OP_TRI: + supports_op = op->type == GGML_TYPE_F32 && src0->type == GGML_TYPE_F32; + break; + case GGML_OP_DIAG: + supports_op = op->type == GGML_TYPE_F32 && src0->type == GGML_TYPE_F32; + break; + case GGML_OP_SOLVE_TRI: + supports_op = op->type == GGML_TYPE_F32 && src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32; + break; + case GGML_OP_CONV_2D: + supports_op = (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16) && + (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16) && + (src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16); + break; + case GGML_OP_IM2COL: + supports_op = (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16) && + (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16); + break; + case GGML_OP_SSM_CONV: + supports_op = op->type == GGML_TYPE_F32; + break; + case GGML_OP_SSM_SCAN: + supports_op = op->type == GGML_TYPE_F32 && + src0->ne[0] <= ctx->webgpu_global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup; + break; + case GGML_OP_GATED_DELTA_NET: + { + const uint32_t s_v = (uint32_t) src2->ne[0]; + supports_op = op->type == GGML_TYPE_F32 && src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && + src2->type == GGML_TYPE_F32 && op->src[3]->type == GGML_TYPE_F32 && + op->src[4]->type == GGML_TYPE_F32 && op->src[5]->type == GGML_TYPE_F32 && + s_v <= ctx->webgpu_global_ctx->capabilities.limits.maxComputeInvocationsPerWorkgroup; + } + break; + case GGML_OP_CLAMP: + supports_op = (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16) && (src0->type == op->type); + break; + case GGML_OP_FILL: + supports_op = op->type == GGML_TYPE_F32 && src0->type == GGML_TYPE_F32; + break; + case GGML_OP_LOG: + supports_op = (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16) && (src0->type == op->type); + break; + case GGML_OP_SQR: + supports_op = (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16) && (src0->type == op->type); + break; + case GGML_OP_SQRT: + supports_op = (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16) && (src0->type == op->type); + break; + case GGML_OP_SIN: + supports_op = (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16) && (src0->type == op->type); + break; + case GGML_OP_COS: + supports_op = (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16) && (src0->type == op->type); + break; + case GGML_OP_PAD: + supports_op = op->type == GGML_TYPE_F32 && src0->type == GGML_TYPE_F32; + break; + case GGML_OP_ARGMAX: + supports_op = op->type == GGML_TYPE_I32 && src0->type == GGML_TYPE_F32; + break; + case GGML_OP_ARGSORT: + supports_op = op->type == GGML_TYPE_I32 && src0->type == GGML_TYPE_F32 && ggml_is_contiguous_rows(src0); + break; + case GGML_OP_TOP_K: + supports_op = op->type == GGML_TYPE_I32 && src0->type == GGML_TYPE_F32 && ggml_is_contiguous_rows(src0); + break; + case GGML_OP_CUMSUM: + supports_op = op->type == GGML_TYPE_F32 && src0->type == op->type; + break; + case GGML_OP_SUM: + case GGML_OP_SUM_ROWS: + supports_op = op->type == GGML_TYPE_F32 && src0->type == op->type && ggml_is_contiguous_rows(src0); + break; + case GGML_OP_UPSCALE: + supports_op = (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16) && + (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16); + break; + default: + break; + } + if (ggml_nbytes(op) > ctx->webgpu_global_ctx->capabilities.limits.maxStorageBufferBindingSize || + (src0 != nullptr && + ggml_nbytes(src0) > ctx->webgpu_global_ctx->capabilities.limits.maxStorageBufferBindingSize) || + (src1 != nullptr && + ggml_nbytes(src1) > ctx->webgpu_global_ctx->capabilities.limits.maxStorageBufferBindingSize) || + (src2 != nullptr && + ggml_nbytes(src2) > ctx->webgpu_global_ctx->capabilities.limits.maxStorageBufferBindingSize)) { + supports_op = false; + WEBGPU_LOG_DEBUG("ggml_webgpu op not supported due to size: "); + } + + if (!supports_op) { + WEBGPU_LOG_DEBUG("ggml_webgpu op not supported: " + << ggml_op_name(op->op) << " with types dst: " << ggml_type_name(op->type) + << ", src0: " << (op->src[0] ? ggml_type_name(op->src[0]->type) : "null") + << ", src1: " << (op->src[1] ? ggml_type_name(op->src[1]->type) : "null")); + } else { + WEBGPU_LOG_DEBUG("ggml_webgpu op supported: " + << ggml_op_name(op->op) << " with types dst: " << ggml_type_name(op->type) + << ", src0: " << (op->src[0] ? ggml_type_name(op->src[0]->type) : "null") + << ", src1: " << (op->src[1] ? ggml_type_name(op->src[1]->type) : "null")); + } + return supports_op; +} + +static struct ggml_backend_device_i ggml_backend_webgpu_device_i = { + /* .get_name = */ ggml_backend_webgpu_device_get_name, + /* .get_description = */ ggml_backend_webgpu_device_get_description, + /* .get_memory = */ ggml_backend_webgpu_device_get_memory, + /* .get_type = */ ggml_backend_webgpu_device_get_type, + /* .get_props = */ ggml_backend_webgpu_device_get_props, + /* .init_backend = */ ggml_backend_webgpu_backend_init, + /* .get_buffer_type = */ ggml_backend_webgpu_device_get_buffer_type, + /* .get_host_buffer_type = */ NULL, + /* .buffer_from_host_ptr = */ NULL, + /* .supports_op = */ ggml_backend_webgpu_device_supports_op, + /* .supports_buft = */ ggml_backend_webgpu_device_supports_buft, + /* .offload_op = */ NULL, + /* .event_new = */ ggml_backend_webgpu_device_event_new, + /* .event_free = */ ggml_backend_webgpu_device_event_free, + /* .event_synchronize = */ ggml_backend_webgpu_device_event_synchronize, +}; + +/* End GGML Backend Device Interface */ + +/* GGML Backend Registration Interface */ + +static const char * ggml_backend_webgpu_reg_get_name(ggml_backend_reg_t reg) { + ggml_backend_webgpu_reg_context * ctx = static_cast(reg->context); + return ctx->name; +} + +static size_t ggml_backend_webgpu_reg_get_device_count(ggml_backend_reg_t reg) { + ggml_backend_webgpu_reg_context * ctx = static_cast(reg->context); + return ctx->device_count; +} + +// Only one device is supported for now +static ggml_backend_dev_t ggml_backend_webgpu_reg_get_device(ggml_backend_reg_t reg, size_t index) { + GGML_ASSERT(index == 0); + WEBGPU_LOG_DEBUG("ggml_backend_reg_get_device()"); + + WEBGPU_CPU_PROFILE_TOTAL_START(reg_get_device); + + ggml_backend_webgpu_reg_context * reg_ctx = static_cast(reg->context); + + create_webgpu_device(reg_ctx); + + static ggml_backend_webgpu_device_context device_ctx; + device_ctx.device_name = GGML_WEBGPU_NAME; + device_ctx.device_desc = GGML_WEBGPU_NAME; + device_ctx.webgpu_global_ctx = reg_ctx->webgpu_global_ctx; + // See GGML Backend Device Interface section + static ggml_backend_device device = { + /* .iface = */ ggml_backend_webgpu_device_i, + /* .reg = */ reg, + /* .context = */ &device_ctx, + }; + + WEBGPU_CPU_PROFILE_TOTAL_END(reg_get_device, reg_ctx->webgpu_global_ctx); + return &device; +} + +static const struct ggml_backend_reg_i ggml_backend_webgpu_reg_i = { + /* .get_name = */ ggml_backend_webgpu_reg_get_name, + /* .get_device_count = */ ggml_backend_webgpu_reg_get_device_count, + /* .get_device = */ ggml_backend_webgpu_reg_get_device, + /* .get_proc_address = */ NULL, +}; + +/* End GGML Backend Registration Interface */ + +ggml_backend_reg_t ggml_backend_webgpu_reg() { + WEBGPU_LOG_DEBUG("ggml_backend_webgpu_reg()"); + + // Intentionally leak the global registry context to avoid crashing inside + // Dawn/Vulkan static teardown during process exit. + static ggml_backend_webgpu_reg_context * ctx = new ggml_backend_webgpu_reg_context(); + + static ggml_backend_reg reg = { + /* .api_version = */ GGML_BACKEND_API_VERSION, + /* .iface = */ ggml_backend_webgpu_reg_i, + /* .context = */ ctx, + }; + + ctx->name = GGML_WEBGPU_NAME; + ctx->device_count = 0; + + // Keep one Dawn/WebGPU instance alive for the lifetime of the static backend + // registry. Recreating it on repeated registry lookups can invalidate + // adapter/device references that are still held by the backend/device layer. + if (ctx->webgpu_global_ctx != nullptr && ctx->webgpu_global_ctx->instance != nullptr) { + return ® + } + + wgpu::InstanceDescriptor instance_descriptor{}; + std::vector instance_features = { wgpu::InstanceFeatureName::TimedWaitAny }; + instance_descriptor.requiredFeatures = instance_features.data(); + instance_descriptor.requiredFeatureCount = instance_features.size(); + +#ifndef __EMSCRIPTEN__ + const char * const instanceEnabledToggles[] = { "allow_unsafe_apis" }; + wgpu::DawnTogglesDescriptor instanceTogglesDesc; + instanceTogglesDesc.enabledToggles = instanceEnabledToggles; + instanceTogglesDesc.enabledToggleCount = 1; + instance_descriptor.nextInChain = &instanceTogglesDesc; +#endif + + wgpu::Instance inst = wgpu::CreateInstance(&instance_descriptor); + ctx->webgpu_global_ctx = webgpu_global_context(new webgpu_global_context_struct()); + ctx->webgpu_global_ctx->instance = std::move(inst); + + // Probe for adapter support + wgpu::Adapter adapter; + if (ctx->webgpu_global_ctx->instance != nullptr) { + ggml_backend_webgpu_request_adapter(ctx->webgpu_global_ctx->instance, adapter); + } + + // WebGPU backend requires f16 support and, on native, implicit device synchronization. + if (adapter != nullptr && adapter.HasFeature(wgpu::FeatureName::ShaderF16) +#ifndef __EMSCRIPTEN__ + && adapter.HasFeature(wgpu::FeatureName::ImplicitDeviceSynchronization) +#endif + ) { + ctx->device_count = 1; + } + + return ® +} + +ggml_backend_t ggml_backend_webgpu_init(void) { + ggml_backend_reg_t reg = ggml_backend_webgpu_reg(); + if (ggml_backend_reg_dev_count(reg) == 0) { + return nullptr; + } + ggml_backend_dev_t dev = ggml_backend_reg_dev_get(reg, 0); + return ggml_backend_webgpu_backend_init(dev, nullptr); +} + +GGML_BACKEND_DL_IMPL(ggml_backend_webgpu_reg) diff --git a/backend/llama.cpp/ggml/src/ggml-webgpu/pre_wgsl.hpp b/backend/llama.cpp/ggml/src/ggml-webgpu/pre_wgsl.hpp new file mode 100644 index 0000000000000000000000000000000000000000..fb41a961d745683428c9661448c49476dce847a6 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-webgpu/pre_wgsl.hpp @@ -0,0 +1,808 @@ +#ifndef PRE_WGSL_HPP +#define PRE_WGSL_HPP + +#include +#include +#include +#include +#include +#include +#include +#include +#include + +namespace pre_wgsl { + +//============================================================== +// Options +//============================================================== +struct Options { + std::string include_path = "."; + std::vector macros; +}; + +//============================================================== +// Utility: trim +//============================================================== +static std::string trim(const std::string & s) { + size_t a = 0; + while (a < s.size() && std::isspace((unsigned char) s[a])) { + a++; + } + size_t b = s.size(); + while (b > a && std::isspace((unsigned char) s[b - 1])) { + b--; + } + return s.substr(a, b - a); +} + +static std::string trim_value(std::istream & is) { + std::ostringstream ss; + ss << is.rdbuf(); + return trim(ss.str()); +} + +static bool isIdentChar(char c) { + return std::isalnum(static_cast(c)) || c == '_'; +} + +static bool endsWithContinuation(const std::string & line) { + size_t i = line.size(); + while (i > 0 && std::isspace((unsigned char) line[i - 1])) { + i--; + } + return i > 0 && line[i - 1] == '\\'; +} + +static void stripContinuation(std::string & line) { + size_t i = line.size(); + while (i > 0 && std::isspace((unsigned char) line[i - 1])) { + i--; + } + if (i > 0 && line[i - 1] == '\\') { + line.erase(i - 1); + } +} + +static std::string expandMacrosRecursiveInternal(const std::string & line, + const std::unordered_map & macros, + std::unordered_set & visiting); + +static std::string expandMacroValue(const std::string & name, + const std::unordered_map & macros, + std::unordered_set & visiting) { + if (visiting.count(name)) { + throw std::runtime_error("Recursive macro: " + name); + } + visiting.insert(name); + + auto it = macros.find(name); + if (it == macros.end()) { + visiting.erase(name); + return name; + } + + const std::string & value = it->second; + if (value.empty()) { + visiting.erase(name); + return ""; + } + + std::string expanded = expandMacrosRecursiveInternal(value, macros, visiting); + visiting.erase(name); + return expanded; +} + +static std::string expandMacrosRecursiveInternal(const std::string & line, + const std::unordered_map & macros, + std::unordered_set & visiting) { + std::string result; + result.reserve(line.size()); + + size_t i = 0; + while (i < line.size()) { + if (isIdentChar(line[i])) { + size_t start = i; + while (i < line.size() && isIdentChar(line[i])) { + i++; + } + std::string token = line.substr(start, i - start); + + auto it = macros.find(token); + if (it != macros.end()) { + result += expandMacroValue(token, macros, visiting); + } else { + result += token; + } + } else { + result += line[i]; + i++; + } + } + + return result; +} + +static std::string expandMacrosRecursive(const std::string & line, + const std::unordered_map & macros) { + std::unordered_set visiting; + return expandMacrosRecursiveInternal(line, macros, visiting); +} + +//============================================================== +// Tokenizer for expressions in #if/#elif +//============================================================== +class ExprLexer { + public: + enum Kind { END, IDENT, NUMBER, OP, LPAREN, RPAREN }; + + struct Tok { + Kind kind; + std::string text; + }; + + explicit ExprLexer(std::string_view sv) : src(sv), pos(0) {} + + Tok next() { + skipWS(); + if (pos >= src.size()) { + return { END, "" }; + } + + char c = src[pos]; + + // number + if (std::isdigit((unsigned char) c)) { + size_t start = pos; + while (pos < src.size() && std::isdigit((unsigned char) src[pos])) { + pos++; + } + return { NUMBER, std::string(src.substr(start, pos - start)) }; + } + + // identifier + if (std::isalpha((unsigned char) c) || c == '_') { + size_t start = pos; + while (pos < src.size() && (std::isalnum((unsigned char) src[pos]) || src[pos] == '_')) { + pos++; + } + return { IDENT, std::string(src.substr(start, pos - start)) }; + } + + if (c == '(') { + pos++; + return { LPAREN, "(" }; + } + if (c == ')') { + pos++; + return { RPAREN, ")" }; + } + + // multi-char operators + static const char * two_ops[] = { "==", "!=", "<=", ">=", "&&", "||", "<<", ">>" }; + for (auto op : two_ops) { + if (src.substr(pos, 2) == op) { + pos += 2; + return { OP, std::string(op) }; + } + } + + // single-char operators + if (std::string("+-*/%<>!").find(c) != std::string::npos) { + pos++; + return { OP, std::string(1, c) }; + } + + // unexpected + pos++; + return { END, "" }; + } + + private: + std::string_view src; + size_t pos; + + void skipWS() { + while (pos < src.size() && std::isspace((unsigned char) src[pos])) { + pos++; + } + } +}; + +//============================================================== +// Expression Parser (recursive descent) +//============================================================== +class ExprParser { + public: + ExprParser(std::string_view expr, + const std::unordered_map & macros, + std::unordered_set & visiting) : + lex(expr), + macros(macros), + visiting(visiting) { + advance(); + } + + int parse() { return parseLogicalOr(); } + + private: + ExprLexer lex; + ExprLexer::Tok tok; + const std::unordered_map & macros; + std::unordered_set & visiting; + + void advance() { tok = lex.next(); } + + bool acceptOp(const std::string & s) { + if (tok.kind == ExprLexer::OP && tok.text == s) { + advance(); + return true; + } + return false; + } + + bool acceptKind(ExprLexer::Kind k) { + if (tok.kind == k) { + advance(); + return true; + } + return false; + } + + int parseLogicalOr() { + int v = parseLogicalAnd(); + while (acceptOp("||")) { + int rhs = parseLogicalAnd(); + v = (v || rhs); + } + return v; + } + + int parseLogicalAnd() { + int v = parseEquality(); + while (acceptOp("&&")) { + int rhs = parseEquality(); + v = (v && rhs); + } + return v; + } + + int parseEquality() { + int v = parseRelational(); + for (;;) { + if (acceptOp("==")) { + int rhs = parseRelational(); + v = (v == rhs); + } else if (acceptOp("!=")) { + int rhs = parseRelational(); + v = (v != rhs); + } else { + break; + } + } + return v; + } + + int parseRelational() { + int v = parseShift(); + for (;;) { + if (acceptOp("<")) { + int rhs = parseShift(); + v = (v < rhs); + } else if (acceptOp(">")) { + int rhs = parseShift(); + v = (v > rhs); + } else if (acceptOp("<=")) { + int rhs = parseShift(); + v = (v <= rhs); + } else if (acceptOp(">=")) { + int rhs = parseShift(); + v = (v >= rhs); + } else { + break; + } + } + return v; + } + + int parseShift() { + int v = parseAdd(); + for (;;) { + if (acceptOp("<<")) { + int rhs = parseAdd(); + v = (v << rhs); + } else if (acceptOp(">>")) { + int rhs = parseAdd(); + v = (v >> rhs); + } else { + break; + } + } + return v; + } + + int parseAdd() { + int v = parseMult(); + for (;;) { + if (acceptOp("+")) { + int rhs = parseMult(); + v = (v + rhs); + } else if (acceptOp("-")) { + int rhs = parseMult(); + v = (v - rhs); + } else { + break; + } + } + return v; + } + + int parseMult() { + int v = parseUnary(); + for (;;) { + if (acceptOp("*")) { + int rhs = parseUnary(); + v = (v * rhs); + } else if (acceptOp("/")) { + int rhs = parseUnary(); + v = (rhs == 0 ? 0 : v / rhs); + } else if (acceptOp("%")) { + int rhs = parseUnary(); + v = (rhs == 0 ? 0 : v % rhs); + } else { + break; + } + } + return v; + } + + int parseUnary() { + if (acceptOp("!")) { + return !parseUnary(); + } + if (acceptOp("-")) { + return -parseUnary(); + } + if (acceptOp("+")) { + return +parseUnary(); + } + return parsePrimary(); + } + + int parsePrimary() { + // '(' expr ')' + if (acceptKind(ExprLexer::LPAREN)) { + int v = parse(); + if (!acceptKind(ExprLexer::RPAREN)) { + throw std::runtime_error("missing ')'"); + } + return v; + } + + // number + if (tok.kind == ExprLexer::NUMBER) { + int v = std::stoi(tok.text); + advance(); + return v; + } + + // defined(identifier) + if (tok.kind == ExprLexer::IDENT && tok.text == "defined") { + advance(); + if (acceptKind(ExprLexer::LPAREN)) { + if (tok.kind != ExprLexer::IDENT) { + throw std::runtime_error("expected identifier in defined()"); + } + std::string name = tok.text; + advance(); + if (!acceptKind(ExprLexer::RPAREN)) { + throw std::runtime_error("missing ) in defined()"); + } + return macros.count(name) ? 1 : 0; + } else { + // defined NAME + if (tok.kind != ExprLexer::IDENT) { + throw std::runtime_error("expected identifier in defined NAME"); + } + std::string name = tok.text; + advance(); + return macros.count(name) ? 1 : 0; + } + } + + // identifier -> treat as integer, if defined use its value else 0 + if (tok.kind == ExprLexer::IDENT) { + std::string name = tok.text; + advance(); + auto it = macros.find(name); + if (it == macros.end()) { + return 0; + } + if (it->second.empty()) { + return 1; + } + return evalMacroExpression(name, it->second); + } + + // unexpected + return 0; + } + + int evalMacroExpression(const std::string & name, const std::string & value) { + if (visiting.count(name)) { + throw std::runtime_error("Recursive macro: " + name); + } + + visiting.insert(name); + ExprParser ep(value, macros, visiting); + int v = ep.parse(); + visiting.erase(name); + return v; + } +}; + +//============================================================== +// Preprocessor +//============================================================== +class Preprocessor { + public: + explicit Preprocessor(Options opts = {}) : opts_(std::move(opts)) { + // Treat empty include path as current directory + if (opts_.include_path.empty()) { + opts_.include_path = "."; + } + parseMacroDefinitions(opts_.macros); + } + + std::string preprocess_file(const std::string & filename, const std::vector & additional_macros = {}) { + std::unordered_map macros; + std::unordered_set predefined; + std::unordered_set include_stack; + buildMacros(additional_macros, macros, predefined); + + std::string result = processFile(filename, macros, predefined, include_stack, DirectiveMode::All); + return result; + } + + std::string preprocess(const std::string & contents, const std::vector & additional_macros = {}) { + std::unordered_map macros; + std::unordered_set predefined; + std::unordered_set include_stack; + buildMacros(additional_macros, macros, predefined); + + std::string result = processString(contents, macros, predefined, include_stack, DirectiveMode::All); + return result; + } + + std::string preprocess_includes_file(const std::string & filename) { + std::unordered_map macros; + std::unordered_set predefined; + std::unordered_set include_stack; + std::string result = processFile(filename, macros, predefined, include_stack, DirectiveMode::IncludesOnly); + return result; + } + + std::string preprocess_includes(const std::string & contents) { + std::unordered_map macros; + std::unordered_set predefined; + std::unordered_set include_stack; + std::string result = processString(contents, macros, predefined, include_stack, DirectiveMode::IncludesOnly); + return result; + } + + private: + Options opts_; + std::unordered_map global_macros; + + enum class DirectiveMode { All, IncludesOnly }; + + struct Cond { + bool parent_active; + bool active; + bool taken; + }; + + //---------------------------------------------------------- + // Parse macro definitions into global_macros + //---------------------------------------------------------- + void parseMacroDefinitions(const std::vector & macro_defs) { + for (const auto & def : macro_defs) { + size_t eq_pos = def.find('='); + if (eq_pos != std::string::npos) { + // Format: NAME=VALUE + std::string name = trim(def.substr(0, eq_pos)); + std::string value = trim(def.substr(eq_pos + 1)); + global_macros[name] = value; + } else { + // Format: NAME + std::string name = trim(def); + global_macros[name] = ""; + } + } + } + + //---------------------------------------------------------- + // Build combined macro map and predefined set for a preprocessing operation + //---------------------------------------------------------- + void buildMacros(const std::vector & additional_macros, + std::unordered_map & macros, + std::unordered_set & predefined) { + macros = global_macros; + predefined.clear(); + + for (const auto & [name, value] : global_macros) { + predefined.insert(name); + } + + for (const auto & def : additional_macros) { + size_t eq_pos = def.find('='); + std::string name, value; + if (eq_pos != std::string::npos) { + name = trim(def.substr(0, eq_pos)); + value = trim(def.substr(eq_pos + 1)); + } else { + name = trim(def); + value = ""; + } + + // Add to macros map (will override global if same name) + macros[name] = value; + predefined.insert(name); + } + } + + //---------------------------------------------------------- + // Helpers + //---------------------------------------------------------- + std::string loadFile(const std::string & fname) { + std::ifstream f(fname); + if (!f.is_open()) { + throw std::runtime_error("Could not open file: " + fname); + } + std::stringstream ss; + ss << f.rdbuf(); + return ss.str(); + } + + bool condActive(const std::vector & cond) const { + if (cond.empty()) { + return true; + } + return cond.back().active; + } + + //---------------------------------------------------------- + // Process a file + //---------------------------------------------------------- + std::string processFile(const std::string & name, + std::unordered_map & macros, + const std::unordered_set & predefined_macros, + std::unordered_set & include_stack, + DirectiveMode mode) { + if (include_stack.count(name)) { + throw std::runtime_error("Recursive include: " + name); + } + + include_stack.insert(name); + std::string shader_code = loadFile(name); + std::string out = processString(shader_code, macros, predefined_macros, include_stack, mode); + include_stack.erase(name); + return out; + } + + std::string processIncludeFile(const std::string & fname, + std::unordered_map & macros, + const std::unordered_set & predefined_macros, + std::unordered_set & include_stack, + DirectiveMode mode) { + std::string full_path = opts_.include_path + "/" + fname; + return processFile(full_path, macros, predefined_macros, include_stack, mode); + } + + //---------------------------------------------------------- + // Process text + //---------------------------------------------------------- + std::string processString(const std::string & shader_code, + std::unordered_map & macros, + const std::unordered_set & predefined_macros, + std::unordered_set & include_stack, + DirectiveMode mode) { + std::vector cond; // Conditional stack for this shader + std::stringstream out; + std::istringstream in(shader_code); + std::string line; + + while (std::getline(in, line)) { + std::string logical = line; + std::string t = trim(logical); + if (!t.empty() && t[0] == '#') { + while (endsWithContinuation(logical)) { + stripContinuation(logical); + if (!std::getline(in, line)) { + break; + } + logical += "\n"; + logical += line; + } + t = trim(logical); + } + + if (!t.empty() && t[0] == '#') { + bool handled = handleDirective(t, out, macros, predefined_macros, cond, include_stack, mode); + if (mode == DirectiveMode::IncludesOnly && !handled) { + out << logical << "\n"; + } + } else { + if (mode == DirectiveMode::IncludesOnly) { + out << logical << "\n"; + } else if (condActive(cond)) { + // Expand macros in the line before outputting + std::string expanded = expandMacrosRecursive(logical, macros); + out << expanded << "\n"; + } + } + } + + if (mode == DirectiveMode::All && !cond.empty()) { + throw std::runtime_error("Unclosed #if directive"); + } + + return out.str(); + } + + //---------------------------------------------------------- + // Directive handler + //---------------------------------------------------------- + bool handleDirective(const std::string & t, + std::stringstream & out, + std::unordered_map & macros, + const std::unordered_set & predefined_macros, + std::vector & cond, + std::unordered_set & include_stack, + DirectiveMode mode) { + // split into tokens + std::string body = t.substr(1); + std::istringstream iss(body); + std::string cmd; + iss >> cmd; + + if (cmd == "include") { + if (mode == DirectiveMode::All && !condActive(cond)) { + return true; + } + std::string file; + iss >> file; + if (file.size() >= 2 && file.front() == '"' && file.back() == '"') { + file = file.substr(1, file.size() - 2); + } + out << processIncludeFile(file, macros, predefined_macros, include_stack, mode); + return true; + } + + if (mode == DirectiveMode::IncludesOnly) { + return false; + } + + if (cmd == "define") { + if (!condActive(cond)) { + return true; + } + std::string name; + iss >> name; + // Don't override predefined macros from options + if (predefined_macros.count(name)) { + return true; + } + std::string value = trim_value(iss); + macros[name] = value; + return true; + } + + if (cmd == "undef") { + if (!condActive(cond)) { + return true; + } + std::string name; + iss >> name; + // Don't undef predefined macros from options + if (predefined_macros.count(name)) { + return true; + } + macros.erase(name); + return true; + } + + if (cmd == "ifdef") { + std::string name; + iss >> name; + bool p = condActive(cond); + bool v = macros.count(name); + cond.push_back({ p, p && v, p && v }); + return true; + } + + if (cmd == "ifndef") { + std::string name; + iss >> name; + bool p = condActive(cond); + bool v = !macros.count(name); + cond.push_back({ p, p && v, p && v }); + return true; + } + + if (cmd == "if") { + std::string expr = trim_value(iss); + bool p = condActive(cond); + bool v = false; + if (p) { + std::unordered_set visiting; + ExprParser ep(expr, macros, visiting); + v = ep.parse() != 0; + } + cond.push_back({ p, p && v, p && v }); + return true; + } + + if (cmd == "elif") { + std::string expr = trim_value(iss); + + if (cond.empty()) { + throw std::runtime_error("#elif without #if"); + } + + Cond & c = cond.back(); + if (!c.parent_active) { + c.active = false; + return true; + } + + if (c.taken) { + c.active = false; + return true; + } + + std::unordered_set visiting; + ExprParser ep(expr, macros, visiting); + bool v = ep.parse() != 0; + c.active = v; + if (v) { + c.taken = true; + } + return true; + } + + if (cmd == "else") { + if (cond.empty()) { + throw std::runtime_error("#else without #if"); + } + + Cond & c = cond.back(); + if (!c.parent_active) { + c.active = false; + return true; + } + if (c.taken) { + c.active = false; + } else { + c.active = true; + c.taken = true; + } + return true; + } + + if (cmd == "endif") { + if (cond.empty()) { + throw std::runtime_error("#endif without #if"); + } + cond.pop_back(); + return true; + } + + // Unknown directive + throw std::runtime_error("Unknown directive: #" + cmd); + } +}; + +} // namespace pre_wgsl + +#endif // PRE_WGSL_HPP diff --git a/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/add_id.wgsl b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/add_id.wgsl new file mode 100644 index 0000000000000000000000000000000000000000..2573926cb893977c57bdd16db6fb590b9b34c878 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/add_id.wgsl @@ -0,0 +1,64 @@ +struct Params { + offset_src0: u32, + offset_src1: u32, + offset_ids: u32, + offset_dst: u32, + + nb01: u32, + nb02: u32, + nb11: u32, + nb20: u32, + nb21: u32, + + ne0: u32, + ne1: u32, + ne2: u32, +}; + +@group(0) @binding(0) var src0: array; // [n_embd, n_experts_used, n_token] +@group(0) @binding(1) var src1: array; // [n_embd, n_experts] +@group(0) @binding(2) var ids: array; // [n_experts_used, n_token] + +#ifdef INPLACE + +@group(0) @binding(3) +var params: Params; + +#else + +@group(0) @binding(3) +var dst: array; + +@group(0) @binding(4) +var params: Params; + +#endif + +@compute @workgroup_size(WG_SIZE) +fn main(@builtin(workgroup_id) wg_id: vec3, + @builtin(num_workgroups) num_wg: vec3, + @builtin(local_invocation_id) local_id: vec3) { + + let wg_linear = wg_id.x + wg_id.y * num_wg.x; + + if (wg_linear < params.ne1 * params.ne2) { + let thread_id = local_id.x; + let i2 = wg_linear / params.ne1; + let i1 = wg_linear % params.ne1; + + let i11 = u32(ids[params.offset_ids + i1 * params.nb20 + i2 * params.nb21]); + + let src0_row = params.offset_src0 + i1 * params.nb01 + i2 * params.nb02; + let src1_row = params.offset_src1 + i11 * params.nb11; + let dst_row = params.offset_dst + i1 * params.ne0 + i2 * (params.ne0 * params.ne1); + + for (var i = thread_id;i < params.ne0; i += WG_SIZE) { +#ifdef INPLACE + src0[src0_row + i] = src0[src0_row + i] + src1[src1_row + i]; +#else + dst[dst_row + i] = src0[src0_row + i] + src1[src1_row + i]; +#endif + } + } + +} diff --git a/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/argmax.wgsl b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/argmax.wgsl new file mode 100644 index 0000000000000000000000000000000000000000..ca5bfcc4d4c9636a9186f9867c28a7ec7ab97b93 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/argmax.wgsl @@ -0,0 +1,72 @@ +@group(0) @binding(0) +#ifdef VEC4 +var src: array>; +#define VEC_SIZE 4 +#else +var src: array; +#define VEC_SIZE 1 +#endif + +@group(0) @binding(1) +var dst: array; + +struct Params { + offset_src: u32, // in elements + offset_dst: u32, // in elements + ne0: u32, +}; + +@group(0) @binding(2) +var params: Params; + +const FLOAT_MIN: f32 = -1.0e9; + +struct Pair { + value: f32, + index: i32 +}; + +var shared_max: array; + +@compute @workgroup_size(WG_SIZE) +fn main(@builtin(workgroup_id) wid: vec3, + @builtin(local_invocation_id) lid: vec3) { + let row_idx = params.offset_src + wid.x * params.ne0; + var local_pair = Pair(FLOAT_MIN, -1); +#ifdef VEC4 + for (var col = lid.x; col < params.ne0/VEC_SIZE; col += WG_SIZE) { + let vec_val = src[row_idx / VEC_SIZE + col]; + for (var v = 0u; v < VEC_SIZE; v++) { + let val = vec_val[v]; + if (val >= local_pair.value) { + local_pair = Pair(val, i32(col * VEC_SIZE + v)); + } + } + } +#else + for (var col = lid.x; col < params.ne0; col += WG_SIZE) { + if (src[row_idx + col] >= local_pair.value) { + local_pair = Pair(src[row_idx + col], i32(col)); + } + } +#endif + shared_max[lid.x] = local_pair; + workgroupBarrier(); + var offset: u32 = WG_SIZE >> 1; + while (offset > 0) { + if (lid.x < offset) { + let a = shared_max[lid.x]; + let b = shared_max[lid.x + offset]; + if (b.value > a.value) { + shared_max[lid.x] = b; + } else if (b.value == a.value && b.index > a.index) { + shared_max[lid.x] = b; + } + } + workgroupBarrier(); + offset >>= 1; + } + if (lid.x == 0u) { + dst[params.offset_dst + wid.x] = shared_max[0].index; + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/argsort.wgsl b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/argsort.wgsl new file mode 100644 index 0000000000000000000000000000000000000000..46ed19fc7752f644818c35c4a9345896ab9a02cb --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/argsort.wgsl @@ -0,0 +1,106 @@ +@group(0) @binding(0) +var src: array; + +@group(0) @binding(1) +var dst: array; + +struct Params { + offset_src: u32, // in elements + offset_dst: u32, // in elements + + stride_src1: u32, + stride_src2: u32, + stride_src3: u32, + + stride_dst1: u32, + stride_dst2: u32, + stride_dst3: u32, + + // src/dst dimensions + src_ne0: u32, + ne1: u32, + ne2: u32, + + ne0: u32, + top_k: u32, + + npr: u32, // tiles per row + nrows: u32 +}; + +@group(0) @binding(2) +var params: Params; + +var shmem_idx: array; + +#if ORDER == 0 +#define EXTREME_VALUE 1e30 +#define SWAP_COMPARE_UP > +#define SWAP_COMPARE_DOWN < +#else +#define EXTREME_VALUE -1e30 +#define SWAP_COMPARE_UP < +#define SWAP_COMPARE_DOWN > +#endif + +@compute @workgroup_size(WG_SIZE) +fn main(@builtin(workgroup_id) wid: vec3, + @builtin(num_workgroups) num_wg: vec3, + @builtin(local_invocation_id) lid: vec3) { + let linear = wid.x + wid.y * num_wg.x; + // guard against overprovisioned workgroups + if (linear >= params.npr * params.nrows) { + return; + } + let tile = linear % params.npr; + var row = linear / params.npr; + let i3 = row / (params.ne2 * params.ne1); + row = row % (params.ne2 * params.ne1); + let i2 = row / params.ne1; + let i1 = row % params.ne1; + + let row_base = params.offset_src + + i1 * params.stride_src1 + + i2 * params.stride_src2 + + i3 * params.stride_src3; + + let tile_base = tile * WG_SIZE; + let idx = tile_base + lid.x; + shmem_idx[lid.x] = select(params.src_ne0, idx, idx < params.src_ne0); + workgroupBarrier(); + + var k = 2u; + while (k <= WG_SIZE) { + var j = k >> 1; + while (j > 0) { + let ixj = lid.x ^ j; + if (ixj > lid.x) { + let dir_up = (lid.x & k) == 0; + let a_idx = shmem_idx[lid.x]; + let b_idx = shmem_idx[ixj]; + let a_val = select(EXTREME_VALUE, src[row_base + a_idx], a_idx < params.src_ne0); + let b_val = select(EXTREME_VALUE, src[row_base + b_idx], b_idx < params.src_ne0); + let should_swap = select( + (a_val SWAP_COMPARE_DOWN b_val), + (a_val SWAP_COMPARE_UP b_val), + dir_up); + if (should_swap) { + shmem_idx[lid.x] = b_idx; + shmem_idx[ixj] = a_idx; + } + } + workgroupBarrier(); + j >>= 1; + } + k <<= 1; + } + + let out_idx = tile * params.top_k + lid.x; + if (out_idx < params.ne0 && lid.x < params.top_k) { + let row_dst = params.offset_dst + + i1 * params.stride_dst1 + + i2 * params.stride_dst2 + + i3 * params.stride_dst3; + dst[row_dst + out_idx] = i32(shmem_idx[lid.x]); + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/argsort_merge.wgsl b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/argsort_merge.wgsl new file mode 100644 index 0000000000000000000000000000000000000000..9a77f6eca740df8e9e75c1b78e813aaf6eed312f --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/argsort_merge.wgsl @@ -0,0 +1,134 @@ +@group(0) @binding(0) +var src: array; + +@group(0) @binding(1) +var idx_in: array; + +@group(0) @binding(2) +var idx_out: array; + +struct Params { + offset_src: u32, // in elements + offset_in: u32, // in elements + offset_out: u32, // in elements + + stride_src1: u32, + stride_src2: u32, + stride_src3: u32, + + stride_idx1: u32, + stride_idx2: u32, + stride_idx3: u32, + + stride_out1: u32, + stride_out2: u32, + stride_out3: u32, + + ne0: u32, + ne1: u32, + ne2: u32, + + top_k: u32, + + len: u32, + nm: u32, + nrows: u32 +}; + +@group(0) @binding(3) +var params: Params; + +fn take_left(a_idx: i32, b_idx: i32, row_base: u32) -> bool { + let a_val = src[row_base + u32(a_idx)]; + let b_val = src[row_base + u32(b_idx)]; +#if ORDER == 0 + return a_val <= b_val; +#else + return a_val >= b_val; +#endif +} + +@compute @workgroup_size(WG_SIZE) +fn main(@builtin(workgroup_id) wid: vec3, + @builtin(num_workgroups) num_wg: vec3, + @builtin(local_invocation_id) lid: vec3) { + let linear = wid.x + wid.y * num_wg.x; + // guard against overprovisioned workgroups + if (linear >= params.nm * params.nrows) { + return; + } + + let start = (linear % params.nm) * params.len * 2; + let len0 = min(params.len, params.ne0 - start); + let rem1 = select(0, params.ne0 - (start + params.len), params.ne0 > (start + params.len)); + let len1 = min(params.len, rem1); + let total = len0 + len1; + let chunk = (total + WG_SIZE - 1u) / WG_SIZE; + let k0 = lid.x * chunk; + let k1 = min(min(k0 + chunk, total), params.top_k); + // guard against overprovisioned threads + if (k0 >= params.top_k || k0 >= total) { + return; + } + + var row = linear / params.nm; + let i3 = row / (params.ne2 * params.ne1); + row = row % (params.ne2 * params.ne1); + let i2 = row / params.ne1; + let i1 = row % params.ne1; + + let row_src = params.offset_src + + i1 * params.stride_src1 + + i2 * params.stride_src2 + + i3 * params.stride_src3; + + let row_in = params.offset_in + + i1 * params.stride_idx1 + + i2 * params.stride_idx2 + + i3 * params.stride_idx3; + + let row_out = params.offset_out + + i1 * params.stride_out1 + + i2 * params.stride_out2 + + i3 * params.stride_out3; + + + var low: u32 = select(0, k0 - len1, k0 > len1); + var high: u32 = min(k0, len0); + + while (low < high) { + let mid = (low + high) >> 1; + let idx0 = idx_in[row_in + start + mid]; + let idx1 = idx_in[row_in + start + params.len + (k0 - mid - 1)]; + if (take_left(idx0, idx1, row_src)) { + low = mid + 1; + } else { + high = mid; + } + } + + var i = low; + var j = k0 - i; + var k = k0; + while (k < k1) { + var take_l = false; + if (i >= len0) { + take_l = false; + } else if (j >= len1) { + take_l = true; + } else { + let idx0 = idx_in[row_in + start + i]; + let idx1 = idx_in[row_in + start + params.len + j]; + take_l = take_left(idx0, idx1, row_src); + } + + let out_idx = select( + idx_in[row_in + start + params.len + j], + idx_in[row_in + start + i], + take_l); + idx_out[row_out + start + k] = out_idx; + i = select(i, i + 1, take_l); + j = select(j + 1, j, take_l); + k += 1; + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/binary.wgsl b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/binary.wgsl new file mode 100644 index 0000000000000000000000000000000000000000..f262c4a8f6a1ab3bf7e115255fc23b67db481731 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/binary.wgsl @@ -0,0 +1,142 @@ +enable f16; + +struct Params { + ne: u32, + + // offsets in elements + offset_src0: u32, + offset_src1: u32, + offset_dst: u32, + + stride_src0_0: u32, + stride_src0_1: u32, + stride_src0_2: u32, + stride_src0_3: u32, + + stride_src1_0: u32, + stride_src1_1: u32, + stride_src1_2: u32, + stride_src1_3: u32, + + a_ne0: u32, + a_ne1: u32, + a_ne2: u32, + + b_ne0: u32, + b_ne1: u32, + b_ne2: u32, + b_ne3: u32, +}; + +fn src0_index(_i: u32) -> u32 { + var i = _i; + let a_i3 = i / (params.a_ne2 * params.a_ne1 * params.a_ne0); + i = i % (params.a_ne2 * params.a_ne1 * params.a_ne0); + let a_i2 = i / (params.a_ne1 * params.a_ne0); + i = i % (params.a_ne1 * params.a_ne0); + let a_i1 = i / params.a_ne0; + let a_i0 = i % params.a_ne0; + + return a_i0 * params.stride_src0_0 + + a_i1 * params.stride_src0_1 + + a_i2 * params.stride_src0_2 + + a_i3 * params.stride_src0_3; +} + +fn src1_index(_i: u32) -> u32 { + var i = _i; + let a_i3 = i / (params.a_ne2 * params.a_ne1 * params.a_ne0); + i = i % (params.a_ne2 * params.a_ne1 * params.a_ne0); + let a_i2 = i / (params.a_ne1 * params.a_ne0); + i = i % (params.a_ne1 * params.a_ne0); + let a_i1 = i / params.a_ne0; + let a_i0 = i % params.a_ne0; + + // handle repetition of b + // index loops back to the beginning and repeats after elements are exhausted = modulo + let b_i0 = a_i0 % params.b_ne0; + let b_i1 = a_i1 % params.b_ne1; + let b_i2 = a_i2 % params.b_ne2; + let b_i3 = a_i3 % params.b_ne3; + + // compute index for position in b's flat array + return b_i0 * params.stride_src1_0 + + b_i1 * params.stride_src1_1 + + b_i2 * params.stride_src1_2 + + b_i3 * params.stride_src1_3; +} + +#ifdef TYPE_F32 +#define DataType f32 +#endif +#ifdef TYPE_F16 +#define DataType f16 +#endif + +#ifdef SRC_OVERLAP +@group(0) @binding(0) +var merged_src: array; + +@group(0) @binding(1) +var dst: array; + +@group(0) @binding(2) +var params: Params; +#else +@group(0) @binding(0) +var src0: array; + +@group(0) @binding(1) +var src1 : array; +#if defined(INPLACE) || defined(OVERLAP) +@group(0) @binding(2) +var params: Params; + +#else +@group(0) @binding(2) +var dst: array; + +@group(0) @binding(3) +var params: Params; +#endif +#endif + +fn op(a: DataType, b: DataType) -> DataType { +#ifdef OP_ADD + return a + b; +#elif defined(OP_SUB) + return a - b; +#elif defined(OP_MUL) + return a * b; +#elif defined(OP_DIV) + return a / b; +#endif +} + +fn update(dst_i: u32, src0_i: u32, src1_i: u32) { +#ifdef SRC_OVERLAP + let result = op(merged_src[src0_i], merged_src[src1_i]); +#else + let result = op(src0[src0_i], src1[src1_i]); +#endif + +#ifdef INPLACE + src0[src0_i] = result; +#elif defined(OVERLAP) + src1[src1_i] = result; +#else + dst[dst_i] = result; +#endif +} + +@compute @workgroup_size(WG_SIZE) +fn main(@builtin(global_invocation_id) gid: vec3, + @builtin(num_workgroups) num_wg: vec3) { + let threads_per_group = u32(WG_SIZE); + let i = gid.x + (num_wg.x * threads_per_group) * gid.y; + if (i < params.ne) { + let src0_i = params.offset_src0 + src0_index(i); + let src1_i = params.offset_src1 + src1_index(i); + update(params.offset_dst + i, src0_i, src1_i); + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/common_decls.tmpl b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/common_decls.tmpl new file mode 100644 index 0000000000000000000000000000000000000000..6634fbd65782110b3c66ba8e7a043a6a07bc95b8 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/common_decls.tmpl @@ -0,0 +1,918 @@ +#ifdef BYTE_HELPERS +fn get_byte(value: u32, index: u32) -> u32 { + return (value >> (index * 8)) & 0xFF; +} + +fn get_byte_i32(value: u32, index: u32) -> i32 { + return bitcast(((value >> (index * 8)) & 0xFF) << 24) >> 24; +} +#endif + +#ifdef U32_DEQUANT_HELPERS +#ifdef DECLARE_BYTE_LOADERS_SRC +fn load_u16_at_src(byte_offset: u32) -> u32 { + let word = src[byte_offset / 4u]; + let shift = (byte_offset & 0x2u) * 8u; + return (word >> shift) & 0xFFFFu; +} + +fn load_u32_at_src(byte_offset: u32) -> u32 { + let word_idx = byte_offset / 4u; + let shift = (byte_offset & 0x3u) * 8u; + let lo = src[word_idx]; + let hi = src[word_idx + 1u]; + let shifted = (lo >> shift) | (hi << (32u - shift)); + return select(shifted, lo, shift == 0u); +} + +fn load_f16_at_src(byte_offset: u32) -> f16 { + let packed = unpack2x16float(load_u16_at_src(byte_offset)); + return f16(packed[0]); +} + +fn load_f16_as_f32_at_src(byte_offset: u32) -> f32 { + let word = src[byte_offset / 4u]; + let shift = (byte_offset & 0x2u) * 8u; + let d_bits = (word >> shift) & 0xFFFFu; + return unpack2x16float(d_bits)[0]; +} +#endif + +#ifdef DECLARE_BYTE_LOADERS_SRC0 +fn load_u16_at_src0(byte_offset: u32) -> u32 { + let word = src0[byte_offset / 4u]; + let shift = (byte_offset & 0x2u) * 8u; + return (word >> shift) & 0xFFFFu; +} + +// Always reads the 4-byte-aligned word containing byte_offset. +// Caller extracts the 16-bit half it needs via & 0xFFFFu or >> 16u. +// this is used in k-quants for better performance +fn load_u32_at_src0_aligned(byte_offset: u32) -> u32 { + return src0[(byte_offset & ~3u) / 4u]; +} + +fn load_u32_at_src0(byte_offset: u32) -> u32 { + let word_idx = byte_offset / 4u; + let shift = (byte_offset & 0x3u) * 8u; + let lo = src0[word_idx]; + let hi = src0[word_idx + 1u]; + let shifted = (lo >> shift) | (hi << (32u - shift)); + return select(shifted, lo, shift == 0u); +} + +fn load_f16_at_src0(byte_offset: u32) -> f16 { + let packed = unpack2x16float(load_u16_at_src0(byte_offset)); + return f16(packed[0]); +} + +fn load_f16_as_f32_at_src0(byte_offset: u32) -> f32 { + let word = src0[byte_offset / 4u]; + let shift = (byte_offset & 0x2u) * 8u; + let d_bits = (word >> shift) & 0xFFFFu; + return unpack2x16float(d_bits)[0]; +} +#endif +#endif + + + +#ifdef Q4_1_T +struct q4_1 { + d: f16, + m: f16, + qs: array +}; +#endif + + +#ifdef Q5_1_T +struct q5_1 { + d: f16, + m: f16, + qh: u32, + qs: array +}; +#endif + +#ifdef Q8_1_T +struct q8_1 { + d: f16, + s: f16, // d * sum(qs[i]) + qs: array +}; +#endif + +#ifdef Q2_K_T +struct q2_K { + scales: array, + qs: array, + d: f16, + dmin: f16 +}; +#endif + + +#if defined(Q4_K_SCALE_MIN) || defined(Q5_K_SCALE_MIN) +fn get_scale_min(is: u32, scales: array) -> vec2 { + if (is < 4) { + let sc_byte = get_byte(scales[is / 4], is % 4); + let min_byte = get_byte(scales[(is + 4) / 4], is % 4); + return vec2(f32(sc_byte & 63), f32(min_byte & 63)); + } else { + let sc_min_lo = get_byte(scales[(is + 4) / 4], (is + 4) % 4); + let sc_hi = get_byte(scales[(is - 4) / 4], (is - 4) % 4); + let min_hi = get_byte(scales[is / 4], is % 4); + let sc = (sc_min_lo & 0xF) | ((sc_hi >> 6) << 4); + let m = (sc_min_lo >> 4) | ((min_hi >> 6) << 4); + return vec2(f32(sc), f32(m)); + } +} +#endif +#ifdef Q4_K_T +struct q4_K { + d: f16, + dmin: f16, + scales: array, + qs: array +}; +#endif + +#ifdef Q5_K_T +struct q5_K { + d: f16, + dmin: f16, + scales: array, + qh: array, + qs: array +}; +#endif + +#ifdef IQ1_M_T +struct iq1_m { + qs: array, + qh: array, + scales: array +}; +#endif + +#ifdef IQ4_XS_T +struct iq4_xs { + d_scales_h: u32, + scales_l: u32, + qs: array +}; +#endif + +#if defined(IQ2_XXS_TABLES) || defined(IQ2_XS_TABLES) || defined(IQ2_S_TABLES) || defined(IQ3_XXS_TABLES) || defined(IQ3_S_TABLES) +const kmask_iq2xs : array = array( + 0x08040201u, // 1, 2, 4, 8 + 0x80402010u // 16, 32, 64, 128 +); + +const ksigns_iq2xs: array = array( + 0x03828100,0x87060584,0x8b0a0988,0x0f8e8d0c, + 0x93121190,0x17969514,0x1b9a9918,0x9f1e1d9c, + 0xa32221a0,0x27a6a524,0x2baaa928,0xaf2e2dac, + 0x33b2b130,0xb73635b4,0xbb3a39b8,0x3fbebd3c, + 0xc34241c0,0x47c6c544,0x4bcac948,0xcf4e4dcc, + 0x53d2d150,0xd75655d4,0xdb5a59d8,0x5fdedd5c, + 0x63e2e160,0xe76665e4,0xeb6a69e8,0x6feeed6c, + 0xf37271f0,0x77f6f574,0x7bfaf978,0xff7e7dfc +); +#endif + +#ifdef IQ2_XXS_GRID +const iq2xxs_grid = array( + 0x08080808, 0x08080808, 0x0808082b, 0x08080808, 0x08081919, 0x08080808, 0x08082b08, 0x08080808, + 0x08082b2b, 0x08080808, 0x08190819, 0x08080808, 0x08191908, 0x08080808, 0x082b0808, 0x08080808, + 0x082b082b, 0x08080808, 0x082b2b08, 0x08080808, 0x082b2b2b, 0x08080808, 0x19080819, 0x08080808, + 0x19081908, 0x08080808, 0x19190808, 0x08080808, 0x19192b08, 0x08080808, 0x192b0819, 0x08080808, + 0x192b1908, 0x08080808, 0x2b080808, 0x08080808, 0x2b08082b, 0x08080808, 0x2b082b2b, 0x08080808, + 0x2b2b082b, 0x08080808, 0x08080819, 0x08080819, 0x08081908, 0x08080819, 0x08190808, 0x08080819, + 0x08191919, 0x08080819, 0x19080808, 0x08080819, 0x2b081908, 0x08080819, 0x2b192b08, 0x08080819, + 0x08080808, 0x0808082b, 0x0808082b, 0x0808082b, 0x082b082b, 0x0808082b, 0x2b08082b, 0x0808082b, + 0x08080819, 0x08081908, 0x08081908, 0x08081908, 0x08190808, 0x08081908, 0x082b0819, 0x08081908, + 0x082b1908, 0x08081908, 0x19080808, 0x08081908, 0x1908082b, 0x08081908, 0x19082b08, 0x08081908, + 0x192b0808, 0x08081908, 0x2b080819, 0x08081908, 0x2b081908, 0x08081908, 0x2b190808, 0x08081908, + 0x2b2b1908, 0x08081908, 0x08080808, 0x08081919, 0x0808082b, 0x08081919, 0x08082b08, 0x08081919, + 0x082b0808, 0x08081919, 0x1908192b, 0x08081919, 0x192b2b19, 0x08081919, 0x2b080808, 0x08081919, + 0x2b190819, 0x08081919, 0x08082b19, 0x0808192b, 0x08190808, 0x0808192b, 0x19080808, 0x0808192b, + 0x2b081908, 0x0808192b, 0x2b2b1908, 0x0808192b, 0x08080808, 0x08082b08, 0x08081919, 0x08082b08, + 0x08082b08, 0x08082b08, 0x08191908, 0x08082b08, 0x082b2b08, 0x08082b08, 0x19080819, 0x08082b08, + 0x19081908, 0x08082b08, 0x19190808, 0x08082b08, 0x1919082b, 0x08082b08, 0x2b082b08, 0x08082b08, + 0x08081908, 0x08082b19, 0x19080808, 0x08082b19, 0x0808082b, 0x08082b2b, 0x08191908, 0x08082b2b, + 0x08080819, 0x08190808, 0x08081908, 0x08190808, 0x08190808, 0x08190808, 0x082b0819, 0x08190808, + 0x19080808, 0x08190808, 0x192b0808, 0x08190808, 0x2b081908, 0x08190808, 0x2b190808, 0x08190808, + 0x2b191919, 0x08190808, 0x08080808, 0x08190819, 0x08082b08, 0x08190819, 0x082b0808, 0x08190819, + 0x19190808, 0x08190819, 0x19192b2b, 0x08190819, 0x2b080808, 0x08190819, 0x082b1908, 0x0819082b, + 0x19081919, 0x0819082b, 0x08080808, 0x08191908, 0x08082b08, 0x08191908, 0x082b0808, 0x08191908, + 0x082b1919, 0x08191908, 0x19082b19, 0x08191908, 0x2b080808, 0x08191908, 0x08192b08, 0x08191919, + 0x192b082b, 0x08191919, 0x08080808, 0x0819192b, 0x0819192b, 0x0819192b, 0x08080819, 0x08192b08, + 0x08081908, 0x08192b08, 0x08190808, 0x08192b08, 0x19080808, 0x08192b08, 0x2b080819, 0x08192b08, + 0x08080808, 0x08192b19, 0x08081919, 0x08192b19, 0x2b2b0808, 0x08192b19, 0x19190819, 0x08192b2b, + 0x08080808, 0x082b0808, 0x0808082b, 0x082b0808, 0x08082b2b, 0x082b0808, 0x19081908, 0x082b0808, + 0x192b0819, 0x082b0808, 0x2b080808, 0x082b0808, 0x2b08082b, 0x082b0808, 0x082b2b19, 0x082b0819, + 0x19082b08, 0x082b0819, 0x08080808, 0x082b082b, 0x0808082b, 0x082b082b, 0x08080819, 0x082b1908, + 0x08081908, 0x082b1908, 0x08190808, 0x082b1908, 0x19080808, 0x082b1908, 0x1919192b, 0x082b1908, + 0x08080808, 0x082b1919, 0x19080819, 0x082b1919, 0x192b1908, 0x082b1919, 0x2b190808, 0x082b192b, + 0x08082b08, 0x082b2b08, 0x082b0808, 0x082b2b08, 0x2b191908, 0x082b2b08, 0x19081908, 0x082b2b2b, + 0x08080819, 0x19080808, 0x08081908, 0x19080808, 0x08190808, 0x19080808, 0x08192b08, 0x19080808, + 0x082b0819, 0x19080808, 0x082b1908, 0x19080808, 0x19080808, 0x19080808, 0x19082b08, 0x19080808, + 0x1919192b, 0x19080808, 0x192b0808, 0x19080808, 0x2b080819, 0x19080808, 0x2b081908, 0x19080808, + 0x2b190808, 0x19080808, 0x08080808, 0x19080819, 0x082b0808, 0x19080819, 0x192b0819, 0x19080819, + 0x2b080808, 0x19080819, 0x2b081919, 0x19080819, 0x08080819, 0x1908082b, 0x08190808, 0x1908082b, + 0x19082b08, 0x1908082b, 0x1919192b, 0x1908082b, 0x192b2b08, 0x1908082b, 0x08080808, 0x19081908, + 0x08082b08, 0x19081908, 0x082b0808, 0x19081908, 0x2b080808, 0x19081908, 0x2b192b19, 0x19081908, + 0x0819082b, 0x19081919, 0x082b1908, 0x19081919, 0x08080808, 0x1908192b, 0x08080819, 0x19082b08, + 0x08081908, 0x19082b08, 0x08190808, 0x19082b08, 0x19080808, 0x19082b08, 0x19081919, 0x19082b08, + 0x08080808, 0x19082b19, 0x19192b08, 0x19082b19, 0x192b0819, 0x19082b19, 0x2b08082b, 0x19082b19, + 0x19081919, 0x19082b2b, 0x2b190808, 0x19082b2b, 0x08080808, 0x19190808, 0x08082b08, 0x19190808, + 0x08190819, 0x19190808, 0x08192b19, 0x19190808, 0x082b0808, 0x19190808, 0x2b080808, 0x19190808, + 0x2b082b08, 0x19190808, 0x08081908, 0x19190819, 0x1908082b, 0x19190819, 0x2b2b1908, 0x19190819, + 0x2b190819, 0x1919082b, 0x2b190808, 0x19191908, 0x2b19082b, 0x19191908, 0x08082b2b, 0x19191919, + 0x08080819, 0x1919192b, 0x19191908, 0x1919192b, 0x08080808, 0x19192b08, 0x08190819, 0x19192b08, + 0x08192b19, 0x19192b08, 0x192b1908, 0x19192b08, 0x19080808, 0x19192b19, 0x08082b08, 0x19192b2b, + 0x08081908, 0x192b0808, 0x08190808, 0x192b0808, 0x19080808, 0x192b0808, 0x192b2b08, 0x192b0808, + 0x08080808, 0x192b0819, 0x19191919, 0x192b0819, 0x08192b08, 0x192b082b, 0x192b0808, 0x192b082b, + 0x08080808, 0x192b1908, 0x08081919, 0x192b1908, 0x08190808, 0x192b1919, 0x0819082b, 0x192b1919, + 0x2b081908, 0x192b1919, 0x1908082b, 0x192b2b08, 0x08080808, 0x2b080808, 0x0808082b, 0x2b080808, + 0x08082b2b, 0x2b080808, 0x19080819, 0x2b080808, 0x2b08082b, 0x2b080808, 0x08081908, 0x2b080819, + 0x08192b08, 0x2b080819, 0x19080808, 0x2b080819, 0x08190819, 0x2b08082b, 0x08080819, 0x2b081908, + 0x08081908, 0x2b081908, 0x08190808, 0x2b081908, 0x08191919, 0x2b081908, 0x19080808, 0x2b081908, + 0x192b0808, 0x2b081908, 0x08080808, 0x2b081919, 0x1908192b, 0x2b081919, 0x2b191908, 0x2b081919, + 0x08082b19, 0x2b08192b, 0x19080808, 0x2b08192b, 0x192b0808, 0x2b08192b, 0x0808082b, 0x2b082b08, + 0x08081908, 0x2b082b19, 0x08190819, 0x2b082b2b, 0x08081908, 0x2b190808, 0x08190808, 0x2b190808, + 0x082b1908, 0x2b190808, 0x19080808, 0x2b190808, 0x2b2b0819, 0x2b190808, 0x0819192b, 0x2b190819, + 0x2b080808, 0x2b190819, 0x19081919, 0x2b19082b, 0x08080808, 0x2b191908, 0x082b082b, 0x2b191908, + 0x19081908, 0x2b191908, 0x19190819, 0x2b191919, 0x2b080819, 0x2b192b08, 0x082b0808, 0x2b192b19, + 0x0808082b, 0x2b2b0808, 0x19190808, 0x2b2b0808, 0x2b081919, 0x2b2b0808, 0x08082b19, 0x2b2b0819, + 0x08080808, 0x2b2b082b, 0x08192b08, 0x2b2b1908, 0x19190808, 0x2b2b2b08, 0x08081908, 0x2b2b2b19 +); +#endif + +#ifdef IQ2_XS_GRID +const iq2xs_grid = array( + 0x08080808, 0x08080808, 0x0808082b, 0x08080808, 0x08081919, 0x08080808, 0x08082b08, 0x08080808, + 0x08082b2b, 0x08080808, 0x08190819, 0x08080808, 0x08191908, 0x08080808, 0x0819192b, 0x08080808, + 0x08192b19, 0x08080808, 0x082b0808, 0x08080808, 0x082b082b, 0x08080808, 0x082b1919, 0x08080808, + 0x082b2b08, 0x08080808, 0x19080819, 0x08080808, 0x19081908, 0x08080808, 0x1908192b, 0x08080808, + 0x19082b19, 0x08080808, 0x19190808, 0x08080808, 0x1919082b, 0x08080808, 0x19191919, 0x08080808, + 0x19192b08, 0x08080808, 0x192b0819, 0x08080808, 0x192b1908, 0x08080808, 0x2b080808, 0x08080808, + 0x2b08082b, 0x08080808, 0x2b081919, 0x08080808, 0x2b082b08, 0x08080808, 0x2b190819, 0x08080808, + 0x2b191908, 0x08080808, 0x2b192b19, 0x08080808, 0x2b2b0808, 0x08080808, 0x08080819, 0x08080819, + 0x08081908, 0x08080819, 0x0808192b, 0x08080819, 0x08082b19, 0x08080819, 0x08190808, 0x08080819, + 0x0819082b, 0x08080819, 0x08191919, 0x08080819, 0x08192b08, 0x08080819, 0x08192b2b, 0x08080819, + 0x082b0819, 0x08080819, 0x082b1908, 0x08080819, 0x19080808, 0x08080819, 0x1908082b, 0x08080819, + 0x19081919, 0x08080819, 0x19082b08, 0x08080819, 0x19190819, 0x08080819, 0x19191908, 0x08080819, + 0x192b0808, 0x08080819, 0x192b2b08, 0x08080819, 0x2b080819, 0x08080819, 0x2b081908, 0x08080819, + 0x2b190808, 0x08080819, 0x08080808, 0x0808082b, 0x0808082b, 0x0808082b, 0x08081919, 0x0808082b, + 0x08082b08, 0x0808082b, 0x08190819, 0x0808082b, 0x08191908, 0x0808082b, 0x082b0808, 0x0808082b, + 0x19080819, 0x0808082b, 0x19081908, 0x0808082b, 0x19190808, 0x0808082b, 0x19191919, 0x0808082b, + 0x2b080808, 0x0808082b, 0x2b082b2b, 0x0808082b, 0x08080819, 0x08081908, 0x08081908, 0x08081908, + 0x0808192b, 0x08081908, 0x08082b19, 0x08081908, 0x08190808, 0x08081908, 0x0819082b, 0x08081908, + 0x08191919, 0x08081908, 0x08192b08, 0x08081908, 0x082b0819, 0x08081908, 0x082b1908, 0x08081908, + 0x19080808, 0x08081908, 0x1908082b, 0x08081908, 0x19081919, 0x08081908, 0x19082b08, 0x08081908, + 0x19190819, 0x08081908, 0x19191908, 0x08081908, 0x1919192b, 0x08081908, 0x192b0808, 0x08081908, + 0x2b080819, 0x08081908, 0x2b081908, 0x08081908, 0x2b190808, 0x08081908, 0x08080808, 0x08081919, + 0x0808082b, 0x08081919, 0x08081919, 0x08081919, 0x08082b08, 0x08081919, 0x08190819, 0x08081919, + 0x08191908, 0x08081919, 0x082b0808, 0x08081919, 0x19080819, 0x08081919, 0x19081908, 0x08081919, + 0x19190808, 0x08081919, 0x192b0819, 0x08081919, 0x2b080808, 0x08081919, 0x08080819, 0x0808192b, + 0x08081908, 0x0808192b, 0x08190808, 0x0808192b, 0x082b192b, 0x0808192b, 0x19080808, 0x0808192b, + 0x1908082b, 0x0808192b, 0x2b081908, 0x0808192b, 0x08080808, 0x08082b08, 0x0808082b, 0x08082b08, + 0x08081919, 0x08082b08, 0x08082b08, 0x08082b08, 0x08082b2b, 0x08082b08, 0x08190819, 0x08082b08, + 0x08191908, 0x08082b08, 0x082b0808, 0x08082b08, 0x082b1919, 0x08082b08, 0x19080819, 0x08082b08, + 0x19081908, 0x08082b08, 0x19190808, 0x08082b08, 0x19192b08, 0x08082b08, 0x2b080808, 0x08082b08, + 0x2b2b0808, 0x08082b08, 0x2b2b2b2b, 0x08082b08, 0x08080819, 0x08082b19, 0x08081908, 0x08082b19, + 0x08190808, 0x08082b19, 0x19080808, 0x08082b19, 0x2b080819, 0x08082b19, 0x2b082b19, 0x08082b19, + 0x08080808, 0x08082b2b, 0x082b0808, 0x08082b2b, 0x082b2b08, 0x08082b2b, 0x2b19192b, 0x08082b2b, + 0x2b2b0808, 0x08082b2b, 0x08080819, 0x08190808, 0x08081908, 0x08190808, 0x0808192b, 0x08190808, + 0x08082b19, 0x08190808, 0x08190808, 0x08190808, 0x0819082b, 0x08190808, 0x08191919, 0x08190808, + 0x08192b08, 0x08190808, 0x082b0819, 0x08190808, 0x082b1908, 0x08190808, 0x19080808, 0x08190808, + 0x1908082b, 0x08190808, 0x19081919, 0x08190808, 0x19082b08, 0x08190808, 0x19190819, 0x08190808, + 0x19191908, 0x08190808, 0x192b0808, 0x08190808, 0x192b2b2b, 0x08190808, 0x2b080819, 0x08190808, + 0x2b081908, 0x08190808, 0x2b190808, 0x08190808, 0x08080808, 0x08190819, 0x0808082b, 0x08190819, + 0x08081919, 0x08190819, 0x08082b08, 0x08190819, 0x08190819, 0x08190819, 0x08191908, 0x08190819, + 0x082b0808, 0x08190819, 0x19080819, 0x08190819, 0x19081908, 0x08190819, 0x19190808, 0x08190819, + 0x2b080808, 0x08190819, 0x2b191908, 0x08190819, 0x2b19192b, 0x08190819, 0x08080819, 0x0819082b, + 0x08081908, 0x0819082b, 0x0808192b, 0x0819082b, 0x08190808, 0x0819082b, 0x19080808, 0x0819082b, + 0x192b0808, 0x0819082b, 0x08080808, 0x08191908, 0x0808082b, 0x08191908, 0x08081919, 0x08191908, + 0x08082b08, 0x08191908, 0x08190819, 0x08191908, 0x08191908, 0x08191908, 0x082b0808, 0x08191908, + 0x19080819, 0x08191908, 0x19081908, 0x08191908, 0x19082b19, 0x08191908, 0x19190808, 0x08191908, + 0x192b1908, 0x08191908, 0x2b080808, 0x08191908, 0x08080819, 0x08191919, 0x08081908, 0x08191919, + 0x08190808, 0x08191919, 0x19080808, 0x08191919, 0x08080808, 0x0819192b, 0x08191908, 0x0819192b, + 0x19082b19, 0x0819192b, 0x08080819, 0x08192b08, 0x08081908, 0x08192b08, 0x08190808, 0x08192b08, + 0x0819082b, 0x08192b08, 0x19080808, 0x08192b08, 0x19191908, 0x08192b08, 0x2b08192b, 0x08192b08, + 0x08080808, 0x08192b19, 0x08081919, 0x08192b19, 0x192b192b, 0x08192b19, 0x19190819, 0x08192b2b, + 0x2b2b2b19, 0x08192b2b, 0x08080808, 0x082b0808, 0x0808082b, 0x082b0808, 0x08081919, 0x082b0808, + 0x08082b08, 0x082b0808, 0x08082b2b, 0x082b0808, 0x08190819, 0x082b0808, 0x08191908, 0x082b0808, + 0x082b0808, 0x082b0808, 0x19080819, 0x082b0808, 0x19081908, 0x082b0808, 0x19190808, 0x082b0808, + 0x2b080808, 0x082b0808, 0x2b2b0808, 0x082b0808, 0x08080819, 0x082b0819, 0x08081908, 0x082b0819, + 0x08190808, 0x082b0819, 0x19080808, 0x082b0819, 0x19082b08, 0x082b0819, 0x192b1919, 0x082b0819, + 0x08080808, 0x082b082b, 0x082b082b, 0x082b082b, 0x2b080808, 0x082b082b, 0x2b2b2b08, 0x082b082b, + 0x08080819, 0x082b1908, 0x08081908, 0x082b1908, 0x08190808, 0x082b1908, 0x082b2b19, 0x082b1908, + 0x19080808, 0x082b1908, 0x08080808, 0x082b1919, 0x19080819, 0x082b1919, 0x1919082b, 0x082b1919, + 0x2b192b19, 0x082b1919, 0x08080819, 0x082b192b, 0x08192b2b, 0x082b192b, 0x2b2b192b, 0x082b192b, + 0x08080808, 0x082b2b08, 0x08082b08, 0x082b2b08, 0x08082b2b, 0x082b2b08, 0x082b0808, 0x082b2b08, + 0x19191919, 0x082b2b08, 0x2b082b08, 0x082b2b08, 0x2b2b082b, 0x082b2b08, 0x192b2b08, 0x082b2b19, + 0x2b190808, 0x082b2b19, 0x08082b08, 0x082b2b2b, 0x082b0808, 0x082b2b2b, 0x2b08082b, 0x082b2b2b, + 0x2b082b08, 0x082b2b2b, 0x2b082b2b, 0x082b2b2b, 0x08080819, 0x19080808, 0x08081908, 0x19080808, + 0x0808192b, 0x19080808, 0x08082b19, 0x19080808, 0x08190808, 0x19080808, 0x0819082b, 0x19080808, + 0x08191919, 0x19080808, 0x08192b08, 0x19080808, 0x082b0819, 0x19080808, 0x082b1908, 0x19080808, + 0x19080808, 0x19080808, 0x1908082b, 0x19080808, 0x19081919, 0x19080808, 0x19082b08, 0x19080808, + 0x19082b2b, 0x19080808, 0x19190819, 0x19080808, 0x19191908, 0x19080808, 0x192b0808, 0x19080808, + 0x192b1919, 0x19080808, 0x2b080819, 0x19080808, 0x2b081908, 0x19080808, 0x2b190808, 0x19080808, + 0x08080808, 0x19080819, 0x0808082b, 0x19080819, 0x08081919, 0x19080819, 0x08082b08, 0x19080819, + 0x08190819, 0x19080819, 0x08191908, 0x19080819, 0x082b0808, 0x19080819, 0x19080819, 0x19080819, + 0x19081908, 0x19080819, 0x19190808, 0x19080819, 0x2b080808, 0x19080819, 0x2b081919, 0x19080819, + 0x2b2b082b, 0x19080819, 0x08080819, 0x1908082b, 0x08081908, 0x1908082b, 0x08190808, 0x1908082b, + 0x0819082b, 0x1908082b, 0x082b2b19, 0x1908082b, 0x19080808, 0x1908082b, 0x08080808, 0x19081908, + 0x0808082b, 0x19081908, 0x08081919, 0x19081908, 0x08082b08, 0x19081908, 0x08190819, 0x19081908, + 0x08191908, 0x19081908, 0x08192b19, 0x19081908, 0x082b0808, 0x19081908, 0x19080819, 0x19081908, + 0x19081908, 0x19081908, 0x19190808, 0x19081908, 0x2b080808, 0x19081908, 0x2b191908, 0x19081908, + 0x08080819, 0x19081919, 0x08081908, 0x19081919, 0x08190808, 0x19081919, 0x082b1908, 0x19081919, + 0x19080808, 0x19081919, 0x2b192b2b, 0x19081919, 0x08080808, 0x1908192b, 0x08082b2b, 0x1908192b, + 0x19081908, 0x1908192b, 0x19190808, 0x1908192b, 0x08080819, 0x19082b08, 0x08081908, 0x19082b08, + 0x08190808, 0x19082b08, 0x19080808, 0x19082b08, 0x19081919, 0x19082b08, 0x19191908, 0x19082b08, + 0x192b082b, 0x19082b08, 0x08080808, 0x19082b19, 0x08190819, 0x19082b19, 0x19081908, 0x19082b19, + 0x19190808, 0x19082b19, 0x192b2b19, 0x19082b19, 0x08081908, 0x19082b2b, 0x08080808, 0x19190808, + 0x0808082b, 0x19190808, 0x08081919, 0x19190808, 0x08082b08, 0x19190808, 0x08190819, 0x19190808, + 0x08191908, 0x19190808, 0x082b0808, 0x19190808, 0x082b2b08, 0x19190808, 0x19080819, 0x19190808, + 0x19081908, 0x19190808, 0x19190808, 0x19190808, 0x2b080808, 0x19190808, 0x08080819, 0x19190819, + 0x08081908, 0x19190819, 0x08190808, 0x19190819, 0x08191919, 0x19190819, 0x19080808, 0x19190819, + 0x1908082b, 0x19190819, 0x08080808, 0x1919082b, 0x19081908, 0x1919082b, 0x2b2b2b2b, 0x1919082b, + 0x08080819, 0x19191908, 0x08081908, 0x19191908, 0x08190808, 0x19191908, 0x082b0819, 0x19191908, + 0x19080808, 0x19191908, 0x192b0808, 0x19191908, 0x2b080819, 0x19191908, 0x2b2b0819, 0x19191908, + 0x08080808, 0x19191919, 0x08082b08, 0x19191919, 0x2b080808, 0x19191919, 0x2b082b08, 0x19191919, + 0x082b0819, 0x1919192b, 0x192b2b08, 0x1919192b, 0x2b2b0819, 0x1919192b, 0x08080808, 0x19192b08, + 0x08191908, 0x19192b08, 0x19080819, 0x19192b08, 0x19190808, 0x19192b08, 0x2b192b19, 0x19192b08, + 0x08192b2b, 0x19192b19, 0x19080808, 0x19192b19, 0x1908082b, 0x19192b19, 0x2b081919, 0x19192b2b, + 0x08080819, 0x192b0808, 0x08081908, 0x192b0808, 0x08190808, 0x192b0808, 0x19080808, 0x192b0808, + 0x19191908, 0x192b0808, 0x192b082b, 0x192b0808, 0x2b08192b, 0x192b0808, 0x2b2b2b19, 0x192b0808, + 0x08080808, 0x192b0819, 0x082b1908, 0x192b082b, 0x19082b2b, 0x192b082b, 0x2b19082b, 0x192b082b, + 0x08080808, 0x192b1908, 0x0819192b, 0x192b1908, 0x08190808, 0x192b1919, 0x19080808, 0x192b1919, + 0x19081919, 0x192b1919, 0x2b2b1908, 0x192b1919, 0x08080819, 0x192b2b08, 0x192b2b2b, 0x192b2b08, + 0x082b1919, 0x192b2b19, 0x0808192b, 0x192b2b2b, 0x19191908, 0x192b2b2b, 0x192b082b, 0x192b2b2b, + 0x08080808, 0x2b080808, 0x0808082b, 0x2b080808, 0x08081919, 0x2b080808, 0x08082b08, 0x2b080808, + 0x08190819, 0x2b080808, 0x08191908, 0x2b080808, 0x082b0808, 0x2b080808, 0x082b2b2b, 0x2b080808, + 0x19080819, 0x2b080808, 0x19081908, 0x2b080808, 0x19190808, 0x2b080808, 0x2b080808, 0x2b080808, + 0x2b08082b, 0x2b080808, 0x2b2b2b08, 0x2b080808, 0x2b2b2b2b, 0x2b080808, 0x08080819, 0x2b080819, + 0x08081908, 0x2b080819, 0x0808192b, 0x2b080819, 0x08190808, 0x2b080819, 0x19080808, 0x2b080819, + 0x19190819, 0x2b080819, 0x19192b19, 0x2b080819, 0x08080808, 0x2b08082b, 0x082b0808, 0x2b08082b, + 0x2b080808, 0x2b08082b, 0x2b08082b, 0x2b08082b, 0x2b2b0808, 0x2b08082b, 0x2b2b2b08, 0x2b08082b, + 0x08080819, 0x2b081908, 0x08081908, 0x2b081908, 0x08190808, 0x2b081908, 0x0819082b, 0x2b081908, + 0x08191919, 0x2b081908, 0x19080808, 0x2b081908, 0x192b0808, 0x2b081908, 0x2b082b19, 0x2b081908, + 0x08080808, 0x2b081919, 0x19081908, 0x2b081919, 0x2b2b1919, 0x2b081919, 0x08192b08, 0x2b08192b, + 0x192b2b2b, 0x2b08192b, 0x08080808, 0x2b082b08, 0x08082b08, 0x2b082b08, 0x082b1919, 0x2b082b08, + 0x19192b2b, 0x2b082b08, 0x2b080808, 0x2b082b08, 0x2b08082b, 0x2b082b08, 0x2b2b2b08, 0x2b082b08, + 0x0808192b, 0x2b082b19, 0x082b082b, 0x2b082b2b, 0x2b080808, 0x2b082b2b, 0x2b082b08, 0x2b082b2b, + 0x2b19192b, 0x2b082b2b, 0x2b2b2b08, 0x2b082b2b, 0x08080819, 0x2b190808, 0x08081908, 0x2b190808, + 0x08190808, 0x2b190808, 0x19080808, 0x2b190808, 0x1919192b, 0x2b190808, 0x2b081908, 0x2b190808, + 0x08080808, 0x2b190819, 0x082b082b, 0x2b190819, 0x192b1908, 0x2b190819, 0x1919192b, 0x2b19082b, + 0x2b082b19, 0x2b19082b, 0x08080808, 0x2b191908, 0x08081919, 0x2b191908, 0x19081908, 0x2b191908, + 0x19190808, 0x2b191908, 0x19192b08, 0x2b191908, 0x082b2b19, 0x2b191919, 0x2b190808, 0x2b191919, + 0x2b19082b, 0x2b191919, 0x19080819, 0x2b19192b, 0x19190819, 0x2b192b08, 0x2b2b192b, 0x2b192b08, + 0x19082b19, 0x2b192b19, 0x08191919, 0x2b192b2b, 0x192b0808, 0x2b192b2b, 0x08080808, 0x2b2b0808, + 0x0808082b, 0x2b2b0808, 0x08082b08, 0x2b2b0808, 0x08082b2b, 0x2b2b0808, 0x082b0808, 0x2b2b0808, + 0x082b2b2b, 0x2b2b0808, 0x2b2b0808, 0x2b2b0808, 0x19190819, 0x2b2b0819, 0x19192b19, 0x2b2b0819, + 0x2b2b192b, 0x2b2b0819, 0x08080808, 0x2b2b082b, 0x0808082b, 0x2b2b082b, 0x08082b08, 0x2b2b082b, + 0x082b2b2b, 0x2b2b082b, 0x2b080808, 0x2b2b082b, 0x2b2b0808, 0x2b2b082b, 0x19080808, 0x2b2b1908, + 0x2b191919, 0x2b2b1908, 0x192b1919, 0x2b2b192b, 0x2b192b08, 0x2b2b192b, 0x08082b2b, 0x2b2b2b08, + 0x082b0808, 0x2b2b2b08, 0x082b082b, 0x2b2b2b08, 0x082b2b08, 0x2b2b2b08, 0x2b2b0808, 0x2b2b2b08, + 0x2b2b2b08, 0x2b2b2b08, 0x08081908, 0x2b2b2b19, 0x2b081908, 0x2b2b2b19, 0x2b08192b, 0x2b2b2b19, + 0x082b2b08, 0x2b2b2b2b, 0x082b2b2b, 0x2b2b2b2b, 0x2b190819, 0x2b2b2b2b, 0x2b2b2b2b, 0x2b2b2b2b +); +#endif + +#ifdef IQ2_S_GRID +const iq2s_grid = array( + 0x08080808, 0x08080808, 0x0808082b, 0x08080808, 0x08081919, 0x08080808, 0x08082b08, 0x08080808, + 0x08082b2b, 0x08080808, 0x08190819, 0x08080808, 0x08191908, 0x08080808, 0x0819192b, 0x08080808, + 0x08192b19, 0x08080808, 0x082b0808, 0x08080808, 0x082b082b, 0x08080808, 0x082b1919, 0x08080808, + 0x082b2b08, 0x08080808, 0x19080819, 0x08080808, 0x19081908, 0x08080808, 0x1908192b, 0x08080808, + 0x19082b19, 0x08080808, 0x19190808, 0x08080808, 0x1919082b, 0x08080808, 0x19191919, 0x08080808, + 0x19192b08, 0x08080808, 0x192b0819, 0x08080808, 0x192b1908, 0x08080808, 0x192b192b, 0x08080808, + 0x192b2b19, 0x08080808, 0x2b080808, 0x08080808, 0x2b08082b, 0x08080808, 0x2b081919, 0x08080808, + 0x2b082b08, 0x08080808, 0x2b190819, 0x08080808, 0x2b191908, 0x08080808, 0x2b2b0808, 0x08080808, + 0x2b2b1919, 0x08080808, 0x2b2b2b2b, 0x08080808, 0x08080819, 0x08080819, 0x08081908, 0x08080819, + 0x0808192b, 0x08080819, 0x08082b19, 0x08080819, 0x08190808, 0x08080819, 0x0819082b, 0x08080819, + 0x08191919, 0x08080819, 0x08192b08, 0x08080819, 0x082b0819, 0x08080819, 0x082b1908, 0x08080819, + 0x19080808, 0x08080819, 0x1908082b, 0x08080819, 0x19081919, 0x08080819, 0x19082b08, 0x08080819, + 0x19190819, 0x08080819, 0x19191908, 0x08080819, 0x1919192b, 0x08080819, 0x19192b19, 0x08080819, + 0x192b0808, 0x08080819, 0x192b1919, 0x08080819, 0x192b2b08, 0x08080819, 0x2b080819, 0x08080819, + 0x2b081908, 0x08080819, 0x2b190808, 0x08080819, 0x2b19082b, 0x08080819, 0x2b191919, 0x08080819, + 0x2b2b0819, 0x08080819, 0x2b2b1908, 0x08080819, 0x08080808, 0x0808082b, 0x0808082b, 0x0808082b, + 0x08081919, 0x0808082b, 0x08082b08, 0x0808082b, 0x08190819, 0x0808082b, 0x08191908, 0x0808082b, + 0x082b0808, 0x0808082b, 0x082b2b2b, 0x0808082b, 0x19080819, 0x0808082b, 0x19081908, 0x0808082b, + 0x1908192b, 0x0808082b, 0x19082b19, 0x0808082b, 0x19190808, 0x0808082b, 0x19191919, 0x0808082b, + 0x2b080808, 0x0808082b, 0x2b081919, 0x0808082b, 0x2b082b2b, 0x0808082b, 0x2b191908, 0x0808082b, + 0x2b2b082b, 0x0808082b, 0x08080819, 0x08081908, 0x08081908, 0x08081908, 0x0808192b, 0x08081908, + 0x08082b19, 0x08081908, 0x08190808, 0x08081908, 0x0819082b, 0x08081908, 0x08191919, 0x08081908, + 0x08192b08, 0x08081908, 0x082b0819, 0x08081908, 0x082b1908, 0x08081908, 0x082b192b, 0x08081908, + 0x082b2b19, 0x08081908, 0x19080808, 0x08081908, 0x1908082b, 0x08081908, 0x19081919, 0x08081908, + 0x19082b08, 0x08081908, 0x19082b2b, 0x08081908, 0x19190819, 0x08081908, 0x19191908, 0x08081908, + 0x1919192b, 0x08081908, 0x19192b19, 0x08081908, 0x192b0808, 0x08081908, 0x192b082b, 0x08081908, + 0x192b1919, 0x08081908, 0x2b080819, 0x08081908, 0x2b081908, 0x08081908, 0x2b08192b, 0x08081908, + 0x2b082b19, 0x08081908, 0x2b190808, 0x08081908, 0x2b191919, 0x08081908, 0x2b192b08, 0x08081908, + 0x2b2b0819, 0x08081908, 0x2b2b1908, 0x08081908, 0x08080808, 0x08081919, 0x0808082b, 0x08081919, + 0x08081919, 0x08081919, 0x08082b08, 0x08081919, 0x08082b2b, 0x08081919, 0x08190819, 0x08081919, + 0x08191908, 0x08081919, 0x0819192b, 0x08081919, 0x08192b19, 0x08081919, 0x082b0808, 0x08081919, + 0x082b1919, 0x08081919, 0x082b2b08, 0x08081919, 0x19080819, 0x08081919, 0x19081908, 0x08081919, + 0x1908192b, 0x08081919, 0x19082b19, 0x08081919, 0x19190808, 0x08081919, 0x1919082b, 0x08081919, + 0x19191919, 0x08081919, 0x19192b08, 0x08081919, 0x192b0819, 0x08081919, 0x192b1908, 0x08081919, + 0x2b080808, 0x08081919, 0x2b08082b, 0x08081919, 0x2b081919, 0x08081919, 0x2b082b08, 0x08081919, + 0x2b190819, 0x08081919, 0x2b191908, 0x08081919, 0x2b2b0808, 0x08081919, 0x08080819, 0x0808192b, + 0x08081908, 0x0808192b, 0x0808192b, 0x0808192b, 0x08082b19, 0x0808192b, 0x08190808, 0x0808192b, + 0x08191919, 0x0808192b, 0x19080808, 0x0808192b, 0x19081919, 0x0808192b, 0x19082b08, 0x0808192b, + 0x19190819, 0x0808192b, 0x19191908, 0x0808192b, 0x192b0808, 0x0808192b, 0x2b080819, 0x0808192b, + 0x2b081908, 0x0808192b, 0x2b190808, 0x0808192b, 0x08080808, 0x08082b08, 0x0808082b, 0x08082b08, + 0x08081919, 0x08082b08, 0x08082b08, 0x08082b08, 0x08190819, 0x08082b08, 0x08191908, 0x08082b08, + 0x0819192b, 0x08082b08, 0x08192b19, 0x08082b08, 0x082b0808, 0x08082b08, 0x082b1919, 0x08082b08, + 0x082b2b2b, 0x08082b08, 0x19080819, 0x08082b08, 0x19081908, 0x08082b08, 0x1908192b, 0x08082b08, + 0x19082b19, 0x08082b08, 0x19190808, 0x08082b08, 0x1919082b, 0x08082b08, 0x19191919, 0x08082b08, + 0x19192b08, 0x08082b08, 0x192b0819, 0x08082b08, 0x192b1908, 0x08082b08, 0x2b080808, 0x08082b08, + 0x2b081919, 0x08082b08, 0x2b191908, 0x08082b08, 0x2b2b2b2b, 0x08082b08, 0x08080819, 0x08082b19, + 0x08081908, 0x08082b19, 0x08190808, 0x08082b19, 0x0819082b, 0x08082b19, 0x08191919, 0x08082b19, + 0x08192b08, 0x08082b19, 0x082b0819, 0x08082b19, 0x19080808, 0x08082b19, 0x19081919, 0x08082b19, + 0x19082b08, 0x08082b19, 0x19190819, 0x08082b19, 0x19191908, 0x08082b19, 0x192b0808, 0x08082b19, + 0x2b080819, 0x08082b19, 0x2b190808, 0x08082b19, 0x08080808, 0x08082b2b, 0x08190819, 0x08082b2b, + 0x08191908, 0x08082b2b, 0x082b082b, 0x08082b2b, 0x082b2b08, 0x08082b2b, 0x082b2b2b, 0x08082b2b, + 0x19190808, 0x08082b2b, 0x2b192b19, 0x08082b2b, 0x08080819, 0x08190808, 0x08081908, 0x08190808, + 0x0808192b, 0x08190808, 0x08082b19, 0x08190808, 0x08190808, 0x08190808, 0x0819082b, 0x08190808, + 0x08191919, 0x08190808, 0x08192b08, 0x08190808, 0x082b0819, 0x08190808, 0x082b1908, 0x08190808, + 0x082b192b, 0x08190808, 0x19080808, 0x08190808, 0x1908082b, 0x08190808, 0x19081919, 0x08190808, + 0x19082b08, 0x08190808, 0x19190819, 0x08190808, 0x19191908, 0x08190808, 0x1919192b, 0x08190808, + 0x19192b19, 0x08190808, 0x192b0808, 0x08190808, 0x192b082b, 0x08190808, 0x192b1919, 0x08190808, + 0x192b2b08, 0x08190808, 0x2b080819, 0x08190808, 0x2b081908, 0x08190808, 0x2b08192b, 0x08190808, + 0x2b190808, 0x08190808, 0x2b191919, 0x08190808, 0x2b192b08, 0x08190808, 0x2b2b0819, 0x08190808, + 0x2b2b1908, 0x08190808, 0x08080808, 0x08190819, 0x0808082b, 0x08190819, 0x08081919, 0x08190819, + 0x08082b08, 0x08190819, 0x08082b2b, 0x08190819, 0x08190819, 0x08190819, 0x08191908, 0x08190819, + 0x0819192b, 0x08190819, 0x08192b19, 0x08190819, 0x082b0808, 0x08190819, 0x082b082b, 0x08190819, + 0x082b1919, 0x08190819, 0x082b2b08, 0x08190819, 0x19080819, 0x08190819, 0x19081908, 0x08190819, + 0x1908192b, 0x08190819, 0x19082b19, 0x08190819, 0x19190808, 0x08190819, 0x1919082b, 0x08190819, + 0x19191919, 0x08190819, 0x19192b08, 0x08190819, 0x192b0819, 0x08190819, 0x192b1908, 0x08190819, + 0x2b080808, 0x08190819, 0x2b08082b, 0x08190819, 0x2b081919, 0x08190819, 0x2b082b08, 0x08190819, + 0x2b190819, 0x08190819, 0x2b191908, 0x08190819, 0x08080819, 0x0819082b, 0x08081908, 0x0819082b, + 0x08082b19, 0x0819082b, 0x08190808, 0x0819082b, 0x08191919, 0x0819082b, 0x082b0819, 0x0819082b, + 0x082b1908, 0x0819082b, 0x19080808, 0x0819082b, 0x19081919, 0x0819082b, 0x19190819, 0x0819082b, + 0x19191908, 0x0819082b, 0x2b080819, 0x0819082b, 0x2b081908, 0x0819082b, 0x2b190808, 0x0819082b, + 0x08080808, 0x08191908, 0x0808082b, 0x08191908, 0x08081919, 0x08191908, 0x08082b08, 0x08191908, + 0x08190819, 0x08191908, 0x08191908, 0x08191908, 0x0819192b, 0x08191908, 0x08192b19, 0x08191908, + 0x082b0808, 0x08191908, 0x082b1919, 0x08191908, 0x082b2b08, 0x08191908, 0x19080819, 0x08191908, + 0x19081908, 0x08191908, 0x1908192b, 0x08191908, 0x19082b19, 0x08191908, 0x19190808, 0x08191908, + 0x1919082b, 0x08191908, 0x19191919, 0x08191908, 0x19192b08, 0x08191908, 0x192b0819, 0x08191908, + 0x192b1908, 0x08191908, 0x2b080808, 0x08191908, 0x2b08082b, 0x08191908, 0x2b081919, 0x08191908, + 0x2b082b08, 0x08191908, 0x2b190819, 0x08191908, 0x2b191908, 0x08191908, 0x2b2b0808, 0x08191908, + 0x08080819, 0x08191919, 0x08081908, 0x08191919, 0x0808192b, 0x08191919, 0x08082b19, 0x08191919, + 0x08190808, 0x08191919, 0x0819082b, 0x08191919, 0x08191919, 0x08191919, 0x08192b08, 0x08191919, + 0x082b0819, 0x08191919, 0x082b1908, 0x08191919, 0x19080808, 0x08191919, 0x1908082b, 0x08191919, + 0x19081919, 0x08191919, 0x19082b08, 0x08191919, 0x19190819, 0x08191919, 0x19191908, 0x08191919, + 0x192b0808, 0x08191919, 0x2b080819, 0x08191919, 0x2b081908, 0x08191919, 0x2b190808, 0x08191919, + 0x08080808, 0x0819192b, 0x08081919, 0x0819192b, 0x08082b08, 0x0819192b, 0x08190819, 0x0819192b, + 0x08191908, 0x0819192b, 0x082b0808, 0x0819192b, 0x19080819, 0x0819192b, 0x19081908, 0x0819192b, + 0x19190808, 0x0819192b, 0x2b080808, 0x0819192b, 0x2b2b2b2b, 0x0819192b, 0x08080819, 0x08192b08, + 0x08081908, 0x08192b08, 0x0808192b, 0x08192b08, 0x08082b19, 0x08192b08, 0x08190808, 0x08192b08, + 0x08191919, 0x08192b08, 0x08192b08, 0x08192b08, 0x082b0819, 0x08192b08, 0x19080808, 0x08192b08, + 0x1908082b, 0x08192b08, 0x19081919, 0x08192b08, 0x19082b08, 0x08192b08, 0x19190819, 0x08192b08, + 0x19191908, 0x08192b08, 0x192b0808, 0x08192b08, 0x2b080819, 0x08192b08, 0x2b081908, 0x08192b08, + 0x08080808, 0x08192b19, 0x0808082b, 0x08192b19, 0x08081919, 0x08192b19, 0x08082b08, 0x08192b19, + 0x08190819, 0x08192b19, 0x08191908, 0x08192b19, 0x082b0808, 0x08192b19, 0x19080819, 0x08192b19, + 0x19081908, 0x08192b19, 0x19190808, 0x08192b19, 0x192b2b19, 0x08192b19, 0x2b2b082b, 0x08192b19, + 0x08081908, 0x08192b2b, 0x08190808, 0x08192b2b, 0x19080808, 0x08192b2b, 0x1919192b, 0x08192b2b, + 0x08080808, 0x082b0808, 0x0808082b, 0x082b0808, 0x08081919, 0x082b0808, 0x08082b08, 0x082b0808, + 0x08190819, 0x082b0808, 0x08191908, 0x082b0808, 0x0819192b, 0x082b0808, 0x08192b19, 0x082b0808, + 0x082b0808, 0x082b0808, 0x082b1919, 0x082b0808, 0x082b2b2b, 0x082b0808, 0x19080819, 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0x192b082b, 0x1919192b, 0x192b082b, 0x2b2b0819, 0x192b082b, 0x08080808, 0x192b1908, + 0x08081919, 0x192b1908, 0x08082b08, 0x192b1908, 0x08190819, 0x192b1908, 0x08191908, 0x192b1908, + 0x082b0808, 0x192b1908, 0x19080819, 0x192b1908, 0x19081908, 0x192b1908, 0x19190808, 0x192b1908, + 0x2b080808, 0x192b1908, 0x08080819, 0x192b1919, 0x08081908, 0x192b1919, 0x08190808, 0x192b1919, + 0x19080808, 0x192b1919, 0x19082b2b, 0x192b1919, 0x192b2b08, 0x192b1919, 0x2b19082b, 0x192b1919, + 0x08080808, 0x192b192b, 0x2b191908, 0x192b192b, 0x08080819, 0x192b2b08, 0x08081908, 0x192b2b08, + 0x08190808, 0x192b2b08, 0x192b1919, 0x192b2b08, 0x2b192b08, 0x192b2b08, 0x08080808, 0x192b2b19, + 0x082b2b2b, 0x192b2b19, 0x1908082b, 0x192b2b2b, 0x2b2b0819, 0x192b2b2b, 0x08080808, 0x2b080808, + 0x0808082b, 0x2b080808, 0x08081919, 0x2b080808, 0x08082b08, 0x2b080808, 0x08190819, 0x2b080808, + 0x08191908, 0x2b080808, 0x08192b19, 0x2b080808, 0x082b0808, 0x2b080808, 0x082b1919, 0x2b080808, + 0x19080819, 0x2b080808, 0x19081908, 0x2b080808, 0x19190808, 0x2b080808, 0x1919082b, 0x2b080808, + 0x19191919, 0x2b080808, 0x19192b08, 0x2b080808, 0x192b0819, 0x2b080808, 0x2b080808, 0x2b080808, + 0x2b081919, 0x2b080808, 0x2b190819, 0x2b080808, 0x2b191908, 0x2b080808, 0x08080819, 0x2b080819, + 0x08081908, 0x2b080819, 0x08082b19, 0x2b080819, 0x08190808, 0x2b080819, 0x0819082b, 0x2b080819, + 0x08191919, 0x2b080819, 0x08192b08, 0x2b080819, 0x082b0819, 0x2b080819, 0x082b1908, 0x2b080819, + 0x19080808, 0x2b080819, 0x1908082b, 0x2b080819, 0x19081919, 0x2b080819, 0x19082b08, 0x2b080819, + 0x19190819, 0x2b080819, 0x19191908, 0x2b080819, 0x2b080819, 0x2b080819, 0x2b081908, 0x2b080819, + 0x2b190808, 0x2b080819, 0x2b2b2b19, 0x2b080819, 0x08080808, 0x2b08082b, 0x08081919, 0x2b08082b, + 0x08082b2b, 0x2b08082b, 0x08190819, 0x2b08082b, 0x08191908, 0x2b08082b, 0x19080819, 0x2b08082b, + 0x19081908, 0x2b08082b, 0x19190808, 0x2b08082b, 0x08080819, 0x2b081908, 0x08081908, 0x2b081908, + 0x0808192b, 0x2b081908, 0x08082b19, 0x2b081908, 0x08190808, 0x2b081908, 0x0819082b, 0x2b081908, + 0x08191919, 0x2b081908, 0x08192b08, 0x2b081908, 0x082b0819, 0x2b081908, 0x19080808, 0x2b081908, + 0x1908082b, 0x2b081908, 0x19081919, 0x2b081908, 0x19082b08, 0x2b081908, 0x19190819, 0x2b081908, + 0x19191908, 0x2b081908, 0x192b0808, 0x2b081908, 0x2b080819, 0x2b081908, 0x2b081908, 0x2b081908, + 0x2b190808, 0x2b081908, 0x08080808, 0x2b081919, 0x0808082b, 0x2b081919, 0x08081919, 0x2b081919, + 0x08082b08, 0x2b081919, 0x08190819, 0x2b081919, 0x08191908, 0x2b081919, 0x082b0808, 0x2b081919, + 0x19080819, 0x2b081919, 0x19081908, 0x2b081919, 0x19190808, 0x2b081919, 0x2b080808, 0x2b081919, + 0x2b082b2b, 0x2b081919, 0x08080819, 0x2b08192b, 0x08081908, 0x2b08192b, 0x08190808, 0x2b08192b, + 0x082b2b19, 0x2b08192b, 0x19080808, 0x2b08192b, 0x08080808, 0x2b082b08, 0x08081919, 0x2b082b08, + 0x08190819, 0x2b082b08, 0x08191908, 0x2b082b08, 0x19080819, 0x2b082b08, 0x19081908, 0x2b082b08, + 0x19190808, 0x2b082b08, 0x2b2b082b, 0x2b082b08, 0x08080819, 0x2b082b19, 0x08081908, 0x2b082b19, + 0x19080808, 0x2b082b19, 0x192b1919, 0x2b082b19, 0x082b082b, 0x2b082b2b, 0x19192b08, 0x2b082b2b, + 0x19192b2b, 0x2b082b2b, 0x2b08082b, 0x2b082b2b, 0x2b2b082b, 0x2b082b2b, 0x08080819, 0x2b190808, + 0x08081908, 0x2b190808, 0x08082b19, 0x2b190808, 0x08190808, 0x2b190808, 0x0819082b, 0x2b190808, + 0x08191919, 0x2b190808, 0x08192b08, 0x2b190808, 0x082b1908, 0x2b190808, 0x19080808, 0x2b190808, + 0x1908082b, 0x2b190808, 0x19081919, 0x2b190808, 0x19082b08, 0x2b190808, 0x19190819, 0x2b190808, + 0x19191908, 0x2b190808, 0x192b0808, 0x2b190808, 0x2b080819, 0x2b190808, 0x2b081908, 0x2b190808, + 0x2b190808, 0x2b190808, 0x08080808, 0x2b190819, 0x08081919, 0x2b190819, 0x08190819, 0x2b190819, + 0x08191908, 0x2b190819, 0x19080819, 0x2b190819, 0x19081908, 0x2b190819, 0x19190808, 0x2b190819, + 0x19192b2b, 0x2b190819, 0x08080819, 0x2b19082b, 0x08081908, 0x2b19082b, 0x08190808, 0x2b19082b, + 0x19080808, 0x2b19082b, 0x2b2b192b, 0x2b19082b, 0x08080808, 0x2b191908, 0x0808082b, 0x2b191908, + 0x08081919, 0x2b191908, 0x08082b08, 0x2b191908, 0x08190819, 0x2b191908, 0x08191908, 0x2b191908, + 0x082b0808, 0x2b191908, 0x19080819, 0x2b191908, 0x19081908, 0x2b191908, 0x19190808, 0x2b191908, + 0x2b080808, 0x2b191908, 0x2b19192b, 0x2b191908, 0x08080819, 0x2b191919, 0x08081908, 0x2b191919, + 0x08190808, 0x2b191919, 0x19080808, 0x2b191919, 0x2b192b08, 0x2b191919, 0x2b2b0819, 0x2b191919, + 0x08080808, 0x2b19192b, 0x1908192b, 0x2b19192b, 0x192b1908, 0x2b19192b, 0x08080819, 0x2b192b08, + 0x08081908, 0x2b192b08, 0x08190808, 0x2b192b08, 0x082b192b, 0x2b192b08, 0x19080808, 0x2b192b08, + 0x2b2b2b19, 0x2b192b08, 0x08080808, 0x2b192b19, 0x19082b19, 0x2b192b19, 0x1919082b, 0x2b192b19, + 0x2b190808, 0x2b192b2b, 0x08080808, 0x2b2b0808, 0x08081919, 0x2b2b0808, 0x08082b2b, 0x2b2b0808, + 0x08191908, 0x2b2b0808, 0x082b082b, 0x2b2b0808, 0x082b2b2b, 0x2b2b0808, 0x19080819, 0x2b2b0808, + 0x19081908, 0x2b2b0808, 0x19190808, 0x2b2b0808, 0x2b2b082b, 0x2b2b0808, 0x2b2b2b2b, 0x2b2b0808, + 0x19080808, 0x2b2b0819, 0x192b1919, 0x2b2b0819, 0x0808082b, 0x2b2b082b, 0x08082b2b, 0x2b2b082b, + 0x082b082b, 0x2b2b082b, 0x082b2b08, 0x2b2b082b, 0x082b2b2b, 0x2b2b082b, 0x2b08082b, 0x2b2b082b, + 0x2b082b08, 0x2b2b082b, 0x2b082b2b, 0x2b2b082b, 0x2b2b2b08, 0x2b2b082b, 0x08080819, 0x2b2b1908, + 0x08081908, 0x2b2b1908, 0x08190808, 0x2b2b1908, 0x19080808, 0x2b2b1908, 0x2b082b19, 0x2b2b1908, + 0x2b2b1908, 0x2b2b1908, 0x08080808, 0x2b2b1919, 0x08192b19, 0x2b2b1919, 0x19190819, 0x2b2b192b, + 0x08082b2b, 0x2b2b2b08, 0x082b2b08, 0x2b2b2b08, 0x2b2b082b, 0x2b2b2b08, 0x19191908, 0x2b2b2b19, + 0x2b08192b, 0x2b2b2b19, 0x08082b08, 0x2b2b2b2b, 0x08082b2b, 0x2b2b2b2b, 0x082b0808, 0x2b2b2b2b, + 0x082b082b, 0x2b2b2b2b, 0x082b2b08, 0x2b2b2b2b, 0x2b082b08, 0x2b2b2b2b, 0x2b2b2b2b, 0x2b2b2b2b +); +#endif + +#ifdef IQ3_XXS_GRID +const iq3xxs_grid = array( + 0x04040404, 0x04040414, 0x04040424, 0x04040c0c, 0x04040c1c, 0x04040c3e, 0x04041404, 0x04041414, + 0x04041c0c, 0x04042414, 0x04043e1c, 0x04043e2c, 0x040c040c, 0x040c041c, 0x040c0c04, 0x040c0c14, + 0x040c140c, 0x040c142c, 0x040c1c04, 0x040c1c14, 0x040c240c, 0x040c2c24, 0x040c3e04, 0x04140404, + 0x04140414, 0x04140424, 0x04140c0c, 0x04141404, 0x04141414, 0x04141c0c, 0x04141c1c, 0x04141c3e, + 0x04142c0c, 0x04142c3e, 0x04143e2c, 0x041c040c, 0x041c043e, 0x041c0c04, 0x041c0c14, 0x041c142c, + 0x041c3e04, 0x04240c1c, 0x04241c3e, 0x04242424, 0x04242c3e, 0x04243e1c, 0x04243e2c, 0x042c040c, + 0x042c043e, 0x042c1c14, 0x042c2c14, 0x04341c2c, 0x04343424, 0x043e0c04, 0x043e0c24, 0x043e0c34, + 0x043e241c, 0x043e340c, 0x0c04040c, 0x0c04041c, 0x0c040c04, 0x0c040c14, 0x0c04140c, 0x0c04141c, + 0x0c041c04, 0x0c041c14, 0x0c041c24, 0x0c04243e, 0x0c042c04, 0x0c0c0404, 0x0c0c0414, 0x0c0c0c0c, + 0x0c0c1404, 0x0c0c1414, 0x0c14040c, 0x0c14041c, 0x0c140c04, 0x0c140c14, 0x0c14140c, 0x0c141c04, + 0x0c143e14, 0x0c1c0404, 0x0c1c0414, 0x0c1c1404, 0x0c1c1c0c, 0x0c1c2434, 0x0c1c3434, 0x0c24040c, + 0x0c24042c, 0x0c242c04, 0x0c2c1404, 0x0c2c1424, 0x0c2c2434, 0x0c2c3e0c, 0x0c34042c, 0x0c3e1414, + 0x0c3e2404, 0x14040404, 0x14040414, 0x14040c0c, 0x14040c1c, 0x14041404, 0x14041414, 0x14041434, + 0x14041c0c, 0x14042414, 0x140c040c, 0x140c041c, 0x140c042c, 0x140c0c04, 0x140c0c14, 0x140c140c, + 0x140c1c04, 0x140c341c, 0x140c343e, 0x140c3e04, 0x14140404, 0x14140414, 0x14140c0c, 0x14140c3e, + 0x14141404, 0x14141414, 0x14141c3e, 0x14142404, 0x14142c2c, 0x141c040c, 0x141c0c04, 0x141c0c24, + 0x141c3e04, 0x141c3e24, 0x14241c2c, 0x14242c1c, 0x142c041c, 0x142c143e, 0x142c240c, 0x142c3e24, + 0x143e040c, 0x143e041c, 0x143e0c34, 0x143e242c, 0x1c04040c, 0x1c040c04, 0x1c040c14, 0x1c04140c, + 0x1c04141c, 0x1c042c04, 0x1c04342c, 0x1c043e14, 0x1c0c0404, 0x1c0c0414, 0x1c0c1404, 0x1c0c1c0c, + 0x1c0c2424, 0x1c0c2434, 0x1c14040c, 0x1c14041c, 0x1c140c04, 0x1c14142c, 0x1c142c14, 0x1c143e14, + 0x1c1c0c0c, 0x1c1c1c1c, 0x1c241c04, 0x1c24243e, 0x1c243e14, 0x1c2c0404, 0x1c2c0434, 0x1c2c1414, + 0x1c2c2c2c, 0x1c340c24, 0x1c341c34, 0x1c34341c, 0x1c3e1c1c, 0x1c3e3404, 0x24040424, 0x24040c3e, + 0x24041c2c, 0x24041c3e, 0x24042c1c, 0x24042c3e, 0x240c3e24, 0x24141404, 0x24141c3e, 0x24142404, + 0x24143404, 0x24143434, 0x241c043e, 0x241c242c, 0x24240424, 0x24242c0c, 0x24243424, 0x242c142c, + 0x242c241c, 0x242c3e04, 0x243e042c, 0x243e0c04, 0x243e0c14, 0x243e1c04, 0x2c040c14, 0x2c04240c, + 0x2c043e04, 0x2c0c0404, 0x2c0c0434, 0x2c0c1434, 0x2c0c2c2c, 0x2c140c24, 0x2c141c14, 0x2c143e14, + 0x2c1c0414, 0x2c1c2c1c, 0x2c240c04, 0x2c24141c, 0x2c24143e, 0x2c243e14, 0x2c2c0414, 0x2c2c1c0c, + 0x2c342c04, 0x2c3e1424, 0x2c3e2414, 0x34041424, 0x34042424, 0x34042434, 0x34043424, 0x340c140c, + 0x340c340c, 0x34140c3e, 0x34143424, 0x341c1c04, 0x341c1c34, 0x34242424, 0x342c042c, 0x342c2c14, + 0x34341c1c, 0x343e041c, 0x343e140c, 0x3e04041c, 0x3e04042c, 0x3e04043e, 0x3e040c04, 0x3e041c14, + 0x3e042c14, 0x3e0c1434, 0x3e0c2404, 0x3e140c14, 0x3e14242c, 0x3e142c14, 0x3e1c0404, 0x3e1c0c2c, + 0x3e1c1c1c, 0x3e1c3404, 0x3e24140c, 0x3e24240c, 0x3e2c0404, 0x3e2c0414, 0x3e2c1424, 0x3e341c04 +); +#endif + +#ifdef IQ3_S_GRID +const iq3s_grid = array( + 0x01010101, 0x01010103, 0x01010105, 0x0101010b, 0x0101010f, 0x01010301, 0x01010303, 0x01010305, + 0x01010309, 0x0101030d, 0x01010501, 0x01010503, 0x0101050b, 0x01010707, 0x01010901, 0x01010905, + 0x0101090b, 0x0101090f, 0x01010b03, 0x01010b07, 0x01010d01, 0x01010d05, 0x01010f03, 0x01010f09, + 0x01010f0f, 0x01030101, 0x01030103, 0x01030105, 0x01030109, 0x01030301, 0x01030303, 0x0103030b, + 0x01030501, 0x01030507, 0x0103050f, 0x01030703, 0x0103070b, 0x01030909, 0x01030d03, 0x01030d0b, + 0x01030f05, 0x01050101, 0x01050103, 0x0105010b, 0x0105010f, 0x01050301, 0x01050307, 0x0105030d, + 0x01050503, 0x0105050b, 0x01050701, 0x01050709, 0x01050905, 0x0105090b, 0x0105090f, 0x01050b03, + 0x01050b07, 0x01050f01, 0x01050f07, 0x01070107, 0x01070303, 0x0107030b, 0x01070501, 0x01070505, + 0x01070703, 0x01070707, 0x0107070d, 0x01070909, 0x01070b01, 0x01070b05, 0x01070d0f, 0x01070f03, + 0x01070f0b, 0x01090101, 0x01090307, 0x0109030f, 0x01090503, 0x01090509, 0x01090705, 0x01090901, + 0x01090907, 0x01090b03, 0x01090f01, 0x010b0105, 0x010b0109, 0x010b0501, 0x010b0505, 0x010b050d, + 0x010b0707, 0x010b0903, 0x010b090b, 0x010b090f, 0x010b0d0d, 0x010b0f07, 0x010d010d, 0x010d0303, + 0x010d0307, 0x010d0703, 0x010d0b05, 0x010d0f03, 0x010f0101, 0x010f0105, 0x010f0109, 0x010f0501, + 0x010f0505, 0x010f050d, 0x010f0707, 0x010f0b01, 0x010f0b09, 0x03010101, 0x03010103, 0x03010105, + 0x03010109, 0x03010301, 0x03010303, 0x03010307, 0x0301030b, 0x0301030f, 0x03010501, 0x03010505, + 0x03010703, 0x03010709, 0x0301070d, 0x03010b09, 0x03010b0d, 0x03010d03, 0x03010f05, 0x03030101, + 0x03030103, 0x03030107, 0x0303010d, 0x03030301, 0x03030309, 0x03030503, 0x03030701, 0x03030707, + 0x03030903, 0x03030b01, 0x03030b05, 0x03030f01, 0x03030f0d, 0x03050101, 0x03050305, 0x0305030b, + 0x0305030f, 0x03050501, 0x03050509, 0x03050705, 0x03050901, 0x03050907, 0x03050b0b, 0x03050d01, + 0x03050f05, 0x03070103, 0x03070109, 0x0307010f, 0x03070301, 0x03070307, 0x03070503, 0x0307050f, + 0x03070701, 0x03070709, 0x03070903, 0x03070d05, 0x03070f01, 0x03090107, 0x0309010b, 0x03090305, + 0x03090309, 0x03090703, 0x03090707, 0x03090905, 0x0309090d, 0x03090b01, 0x03090b09, 0x030b0103, + 0x030b0301, 0x030b0307, 0x030b0503, 0x030b0701, 0x030b0705, 0x030b0b03, 0x030d0501, 0x030d0509, + 0x030d050f, 0x030d0909, 0x030d090d, 0x030f0103, 0x030f0107, 0x030f0301, 0x030f0305, 0x030f0503, + 0x030f070b, 0x030f0903, 0x030f0d05, 0x030f0f01, 0x05010101, 0x05010103, 0x05010107, 0x0501010b, + 0x0501010f, 0x05010301, 0x05010305, 0x05010309, 0x0501030d, 0x05010503, 0x05010507, 0x0501050f, + 0x05010701, 0x05010705, 0x05010903, 0x05010907, 0x0501090b, 0x05010b01, 0x05010b05, 0x05010d0f, + 0x05010f01, 0x05010f07, 0x05010f0b, 0x05030101, 0x05030105, 0x05030301, 0x05030307, 0x0503030f, + 0x05030505, 0x0503050b, 0x05030703, 0x05030709, 0x05030905, 0x05030b03, 0x05050103, 0x05050109, + 0x0505010f, 0x05050503, 0x05050507, 0x05050701, 0x0505070f, 0x05050903, 0x05050b07, 0x05050b0f, + 0x05050f03, 0x05050f09, 0x05070101, 0x05070105, 0x0507010b, 0x05070303, 0x05070505, 0x05070509, + 0x05070703, 0x05070707, 0x05070905, 0x05070b01, 0x05070d0d, 0x05090103, 0x0509010f, 0x05090501, + 0x05090507, 0x05090705, 0x0509070b, 0x05090903, 0x05090f05, 0x05090f0b, 0x050b0109, 0x050b0303, + 0x050b0505, 0x050b070f, 0x050b0901, 0x050b0b07, 0x050b0f01, 0x050d0101, 0x050d0105, 0x050d010f, + 0x050d0503, 0x050d0b0b, 0x050d0d03, 0x050f010b, 0x050f0303, 0x050f050d, 0x050f0701, 0x050f0907, + 0x050f0b01, 0x07010105, 0x07010303, 0x07010307, 0x0701030b, 0x0701030f, 0x07010505, 0x07010703, + 0x07010707, 0x0701070b, 0x07010905, 0x07010909, 0x0701090f, 0x07010b03, 0x07010d07, 0x07010f03, + 0x07030103, 0x07030107, 0x0703010b, 0x07030309, 0x07030503, 0x07030507, 0x07030901, 0x07030d01, + 0x07030f05, 0x07030f0d, 0x07050101, 0x07050305, 0x07050501, 0x07050705, 0x07050709, 0x07050b01, + 0x07070103, 0x07070301, 0x07070309, 0x07070503, 0x07070507, 0x0707050f, 0x07070701, 0x07070903, + 0x07070907, 0x0707090f, 0x07070b0b, 0x07070f07, 0x07090107, 0x07090303, 0x0709030d, 0x07090505, + 0x07090703, 0x07090b05, 0x07090d01, 0x07090d09, 0x070b0103, 0x070b0301, 0x070b0305, 0x070b050b, + 0x070b0705, 0x070b0909, 0x070b0b0d, 0x070b0f07, 0x070d030d, 0x070d0903, 0x070f0103, 0x070f0107, + 0x070f0501, 0x070f0505, 0x070f070b, 0x09010101, 0x09010109, 0x09010305, 0x09010501, 0x09010509, + 0x0901050f, 0x09010705, 0x09010903, 0x09010b01, 0x09010f01, 0x09030105, 0x0903010f, 0x09030303, + 0x09030307, 0x09030505, 0x09030701, 0x0903070b, 0x09030907, 0x09030b03, 0x09030b0b, 0x09050103, + 0x09050107, 0x09050301, 0x0905030b, 0x09050503, 0x09050707, 0x09050901, 0x09050b0f, 0x09050d05, + 0x09050f01, 0x09070109, 0x09070303, 0x09070307, 0x09070501, 0x09070505, 0x09070703, 0x0907070b, + 0x09090101, 0x09090105, 0x09090509, 0x0909070f, 0x09090901, 0x09090f03, 0x090b010b, 0x090b010f, + 0x090b0503, 0x090b0d05, 0x090d0307, 0x090d0709, 0x090d0d01, 0x090f0301, 0x090f030b, 0x090f0701, + 0x090f0907, 0x090f0b03, 0x0b010105, 0x0b010301, 0x0b010309, 0x0b010505, 0x0b010901, 0x0b010909, + 0x0b01090f, 0x0b010b05, 0x0b010d0d, 0x0b010f09, 0x0b030103, 0x0b030107, 0x0b03010b, 0x0b030305, + 0x0b030503, 0x0b030705, 0x0b030f05, 0x0b050101, 0x0b050303, 0x0b050507, 0x0b050701, 0x0b05070d, + 0x0b050b07, 0x0b070105, 0x0b07010f, 0x0b070301, 0x0b07050f, 0x0b070909, 0x0b070b03, 0x0b070d0b, + 0x0b070f07, 0x0b090103, 0x0b090109, 0x0b090501, 0x0b090705, 0x0b09090d, 0x0b0b0305, 0x0b0b050d, + 0x0b0b0b03, 0x0b0b0b07, 0x0b0d0905, 0x0b0f0105, 0x0b0f0109, 0x0b0f0505, 0x0d010303, 0x0d010307, + 0x0d01030b, 0x0d010703, 0x0d010707, 0x0d010d01, 0x0d030101, 0x0d030501, 0x0d03050f, 0x0d030d09, + 0x0d050305, 0x0d050709, 0x0d050905, 0x0d050b0b, 0x0d050d05, 0x0d050f01, 0x0d070101, 0x0d070309, + 0x0d070503, 0x0d070901, 0x0d09050b, 0x0d090907, 0x0d090d05, 0x0d0b0101, 0x0d0b0107, 0x0d0b0709, + 0x0d0b0d01, 0x0d0d010b, 0x0d0d0901, 0x0d0f0303, 0x0d0f0307, 0x0f010101, 0x0f010109, 0x0f01010f, + 0x0f010501, 0x0f010505, 0x0f01070d, 0x0f010901, 0x0f010b09, 0x0f010d05, 0x0f030105, 0x0f030303, + 0x0f030509, 0x0f030907, 0x0f03090b, 0x0f050103, 0x0f050109, 0x0f050301, 0x0f05030d, 0x0f050503, + 0x0f050701, 0x0f050b03, 0x0f070105, 0x0f070705, 0x0f07070b, 0x0f070b07, 0x0f090103, 0x0f09010b, + 0x0f090307, 0x0f090501, 0x0f090b01, 0x0f0b0505, 0x0f0b0905, 0x0f0d0105, 0x0f0d0703, 0x0f0f0101 +); +#endif + +#if defined(IQ1_S_GRID) || defined(IQ1_M_GRID) + +const IQ1_DELTA: f32 = 0.125; + +const iq1_grid = array( + 0xfffdffff, 0xfff7fff0, 0xffccfff5, 0xffdfffc0, 0xffd7ffdd, 0xff30ffd5, 0xff03ff0c, 0xff10ff01, + 0xff7dff7f, 0xff75ff77, 0xff5fff40, 0xff57ff5d, 0xfcf3ff55, 0xfcccfcf0, 0xfcc1fcc3, 0xfcc5fcc4, + 0xfc3cfcd0, 0xfc34fc31, 0xfc00fc0d, 0xfc1cfc05, 0xfc11fc13, 0xfc70fc17, 0xfc43fc4c, 0xfc50fc41, + 0xfdfdfdff, 0xfdf5fdf7, 0xfddffdc0, 0xfdd7fddd, 0xfd30fdd5, 0xfd04fd0c, 0xfd14fd13, 0xfd7dfd7f, + 0xfd75fd77, 0xfd40fd4c, 0xfd5ffd44, 0xfd57fd5d, 0xf3ccfd55, 0xf3c1f3c3, 0xf33cf3d0, 0xf300f334, + 0xf313f305, 0xf34cf310, 0xf350f344, 0xf0f3f0fc, 0xf0f1f0f0, 0xf0c7f0c0, 0xf0d4f0c5, 0xf030f03f, + 0xf00ff035, 0xf003f00c, 0xf001f000, 0xf01ff004, 0xf010f01d, 0xf015f017, 0xf04cf07c, 0xf047f040, + 0xf05cf045, 0xf050f053, 0xf054f051, 0xf1c4f1c3, 0xf133f13c, 0xf10df10f, 0xf107f100, 0xf11cf11f, + 0xf114f111, 0xf14cf170, 0xf144f143, 0xf7fdf7ff, 0xf7f5f7f7, 0xf7dff7c0, 0xf7d7f7dd, 0xf730f7d5, + 0xf701f70c, 0xf77ff710, 0xf777f77d, 0xf740f775, 0xf75df75f, 0xf755f757, 0xf4ccf4f0, 0xf4c4f4c3, + 0xf4d0f4d3, 0xf40ff43c, 0xf400f40c, 0xf413f41c, 0xf44cf414, 0xf441f443, 0xf450f444, 0xf5fdf5ff, + 0xf5f5f5f7, 0xf5dff5c0, 0xf5d7f5dd, 0xf530f5d5, 0xf504f50c, 0xf510f51c, 0xf57df57f, 0xf577f570, + 0xf540f575, 0xf55df55f, 0xf555f557, 0xcfcccfcf, 0xcfc4cfc3, 0xcfd0cfd3, 0xcf33cf3c, 0xcf00cf0f, + 0xcf1ccf07, 0xcf10cf13, 0xcf4ccf14, 0xcf41cf43, 0xcf50cf5c, 0xccf3ccfc, 0xccf4ccf1, 0xcccdcccf, + 0xccc7ccc0, 0xccd3ccdc, 0xcc30ccd4, 0xcc0fcc35, 0xcc0dcc0c, 0xcc00cc03, 0xcc04cc01, 0xcc10cc1f, + 0xcc4dcc73, 0xcc5ccc40, 0xcdcccc53, 0xcdc1cdc3, 0xcd3fcdd0, 0xcd34cd31, 0xcd00cd0d, 0xcd05cd07, + 0xcd11cd13, 0xcd4ccd70, 0xcd41cd43, 0xc3fccd50, 0xc3f4c3f1, 0xc3c0c3c3, 0xc3c4c3c7, 0xc3d1c3dc, + 0xc330c33c, 0xc337c331, 0xc30cc335, 0xc300c303, 0xc304c301, 0xc310c31d, 0xc373c317, 0xc34fc374, + 0xc340c343, 0xc344c347, 0xc35cc345, 0xc350c353, 0xc0fdc354, 0xc0f5c0f0, 0xc0c3c0cc, 0xc0c1c0c0, + 0xc0dfc0c4, 0xc0d0c0dd, 0xc0d5c0d7, 0xc033c03c, 0xc031c030, 0xc00dc00c, 0xc000c003, 0xc004c001, + 0xc01cc005, 0xc010c013, 0xc014c011, 0xc07dc07f, 0xc070c073, 0xc075c077, 0xc04cc04f, 0xc040c043, + 0xc044c041, 0xc05fc045, 0xc050c05d, 0xc1f3c1fc, 0xc1f1c1f0, 0xc1c1c1c0, 0xc1c5c1c7, 0xc1d1c1dc, + 0xc13dc13f, 0xc130c133, 0xc135c137, 0xc100c10c, 0xc107c101, 0xc11cc104, 0xc110c113, 0xc114c117, + 0xc171c115, 0xc14dc175, 0xc153c140, 0xc7ccc154, 0xc7d0c7c1, 0xc733c73c, 0xc734c731, 0xc700c70f, + 0xc705c707, 0xc71cc71f, 0xc711c713, 0xc770c714, 0xc743c74c, 0xc4cfc750, 0xc4c0c4cd, 0xc4dcc4c5, + 0xc43dc4d0, 0xc430c433, 0xc40cc437, 0xc400c403, 0xc404c401, 0xc41fc405, 0xc415c410, 0xc44cc474, + 0xc440c44d, 0xc45cc447, 0xc454c451, 0xc5c1c5f4, 0xc5d1c5d3, 0xc531c533, 0xc50fc534, 0xc500c50d, + 0xc51cc507, 0xc514c511, 0xc54cc570, 0xc545c541, 0xdffddfff, 0xdff5dff7, 0xdfdfdfc0, 0xdfd0dfdd, + 0xdfd5dfd7, 0xdf0cdf30, 0xdf1cdf04, 0xdf7fdf10, 0xdf77df7d, 0xdf40df75, 0xdf5ddf5f, 0xdf57df50, + 0xdcf0df55, 0xdcc3dccc, 0xdcd0dcc4, 0xdc33dc3d, 0xdc00dc34, 0xdc05dc07, 0xdc13dc1c, 0xdc11dc10, + 0xdc4fdc70, 0xdc44dc41, 0xddfcdc50, 0xddf5ddf7, 0xddc0ddcc, 0xdddddddf, 0xddd5ddd7, 0xdd0cdd30, + 0xdd04dd01, 0xdd7cdd10, 0xdd75dd77, 0xdd40dd4c, 0xdd5ddd5f, 0xdd55dd57, 0xd3c3d3f0, 0xd3c4d3c1, + 0xd333d3d0, 0xd331d330, 0xd30dd334, 0xd307d300, 0xd311d305, 0xd34cd370, 0xd344d343, 0xd350d35c, + 0xd0c0d0f4, 0xd0d4d0dc, 0xd030d03f, 0xd00cd037, 0xd000d003, 0xd01dd004, 0xd017d010, 0xd04fd074, + 0xd040d043, 0xd045d047, 0xd053d05c, 0xd054d051, 0xd1cfd1f0, 0xd1c4d1cd, 0xd13cd1d0, 0xd100d134, + 0xd11cd11f, 0xd173d114, 0xd14fd171, 0xd7ffd145, 0xd7f7d7fd, 0xd7c0d7f5, 0xd7ddd7df, 0xd7d5d7d7, + 0xd70cd730, 0xd710d703, 0xd77dd77f, 0xd775d777, 0xd75dd75f, 0xd755d757, 0xd4ccd4f4, 0xd4c4d4c3, + 0xd431d4d0, 0xd40dd434, 0xd41cd400, 0xd411d413, 0xd470d414, 0xd441d44f, 0xd453d444, 0xd5ffd450, + 0xd5f7d5fd, 0xd5dfd5f5, 0xd5d7d5dd, 0xd530d5d5, 0xd501d50c, 0xd510d504, 0xd57dd57f, 0xd575d577, + 0xd55fd540, 0xd557d55d, 0x3ff0d555, 0x3fc13fcc, 0x3f343fd0, 0x3f003f0d, 0x3f053f07, 0x3f133f1c, + 0x3f433f11, 0x3f5c3f44, 0x3cff3f51, 0x3cf33cfc, 0x3cf43cf1, 0x3cc03ccd, 0x3cc73cc1, 0x3cdc3cc5, + 0x3cd43cd1, 0x3c373c30, 0x3c0c3c35, 0x3c003c03, 0x3c043c01, 0x3c103c05, 0x3c153c17, 0x3c733c7c, + 0x3c4f3c71, 0x3c403c4d, 0x3c5c3c5f, 0x3df03c5d, 0x3dc33dcc, 0x3dd03dc1, 0x3d0d3d3c, 0x3d053d00, + 0x3d143d13, 0x3d433d74, 0x33fc3d50, 0x33c433c0, 0x333033d4, 0x33353337, 0x3303330c, 0x33013300, + 0x331d331c, 0x33173310, 0x337c3315, 0x33743371, 0x334d334f, 0x335f3340, 0x3354335c, 0x30fd30fc, + 0x30f530f0, 0x30c330cc, 0x30c130c0, 0x30df30c4, 0x30d530d0, 0x3033303c, 0x30313030, 0x300f3034, + 0x3003300c, 0x30013000, 0x30043007, 0x3013301c, 0x30113010, 0x307d3014, 0x30703073, 0x304c3077, + 0x30403043, 0x30443041, 0x30503045, 0x30553057, 0x31f031fc, 0x31c331f4, 0x31c731c0, 0x31dc31c5, + 0x31d431d3, 0x313d313f, 0x31373130, 0x310c310f, 0x3100310d, 0x31043101, 0x3110311d, 0x317c3117, + 0x31753170, 0x31403143, 0x3153315c, 0x37f03151, 0x37c037cc, 0x37d037c5, 0x3734373d, 0x3700370f, + 0x371c3707, 0x37113713, 0x37703714, 0x3743374c, 0x37443741, 0x34fc3750, 0x34f134f0, 0x34cf34f5, + 0x34c034c3, 0x34dc34c7, 0x34d134d3, 0x3430343f, 0x340c3435, 0x3403340d, 0x34013400, 0x341f3404, + 0x3410341d, 0x34153411, 0x34743471, 0x3440344d, 0x34473441, 0x3453345c, 0x34543451, 0x353335c1, + 0x35343531, 0x35073500, 0x35133505, 0x35433514, 0x0ffc3550, 0x0ff00ff3, 0x0ff40ff1, 0x0fc00fcd, + 0x0fdc0fc5, 0x0fd40fd3, 0x0f300f3f, 0x0f0c0f37, 0x0f000f03, 0x0f040f01, 0x0f170f10, 0x0f740f71, + 0x0f470f40, 0x0f5c0f5f, 0x0f540f51, 0x0cf70cf0, 0x0cf50cf4, 0x0cc30ccc, 0x0cc10cc0, 0x0cc40cc7, + 0x0cd00cdf, 0x0cd70cd1, 0x0c3c0cd5, 0x0c300c33, 0x0c340c31, 0x0c0c0c0f, 0x0c030c0d, 0x0c010c00, + 0x0c040c07, 0x0c1c0c05, 0x0c100c13, 0x0c140c11, 0x0c700c7d, 0x0c430c4c, 0x0c410c40, 0x0c5f0c44, + 0x0c550c50, 0x0df10dfc, 0x0dc00dcd, 0x0ddc0dc5, 0x0d3d0dd3, 0x0d350d30, 0x0d030d0c, 0x0d010d00, + 0x0d1d0d04, 0x0d700d10, 0x0d4d0d4f, 0x0d440d40, 0x0d530d45, 0x03f003f3, 0x03c303cc, 0x03c103c0, + 0x03c403c7, 0x03d003dc, 0x03d503d7, 0x0333033c, 0x03310330, 0x03350334, 0x030c030f, 0x03000303, + 0x03070301, 0x03050304, 0x031d031c, 0x03100313, 0x03140311, 0x0377037f, 0x034c0375, 0x03400343, + 0x03440341, 0x0353035c, 0x03550350, 0x00fd00fc, 0x00f000f3, 0x00f400f1, 0x00cc00cf, 0x00c300cd, + 0x00c100c0, 0x00c500c4, 0x00d300dc, 0x00d100d0, 0x003f00d4, 0x003d003c, 0x00300033, 0x00370031, + 0x000f0034, 0x000d000c, 0x00000003, 0x00070001, 0x00050004, 0x001c001f, 0x00100013, 0x00170011, + 0x00150014, 0x0073007c, 0x00740070, 0x004f0075, 0x0043004c, 0x00410040, 0x00440047, 0x0053005c, + 0x00510050, 0x01ff0054, 0x01fd01fc, 0x01f101f3, 0x01f401f7, 0x01c301cc, 0x01c701c0, 0x01df01c4, + 0x01dd01dc, 0x01d001d3, 0x01d701d1, 0x013c01d4, 0x01310130, 0x01340137, 0x010f0135, 0x010d010c, + 0x01000103, 0x01070101, 0x01050104, 0x0113011c, 0x01140110, 0x0170017d, 0x01770171, 0x01750174, + 0x0140014c, 0x015d0145, 0x01510150, 0x01540157, 0x07f007f3, 0x07f407f1, 0x07c007cf, 0x07dc07c7, + 0x073007d5, 0x07350737, 0x0703070c, 0x07010700, 0x07040707, 0x071d071f, 0x07100713, 0x0774077d, + 0x074d074f, 0x07470740, 0x0754075c, 0x04fd04fc, 0x04f504f0, 0x04c304cc, 0x04c104c0, 0x04d004c4, + 0x0433043c, 0x04310430, 0x040f0434, 0x040d040c, 0x04000403, 0x04070401, 0x04050404, 0x0413041c, + 0x04110410, 0x047c0414, 0x04740470, 0x0443044c, 0x04410440, 0x04440447, 0x05f30450, 0x05c005f7, + 0x05df05c5, 0x05d105d0, 0x053005d4, 0x05340537, 0x0500050c, 0x05070501, 0x051d0504, 0x05170510, + 0x057c0515, 0x054d0575, 0x05410540, 0x05450547, 0x1ff0055c, 0x1fc11fc3, 0x1fd01fc4, 0x1f0f1f33, + 0x1f011f00, 0x1f051f07, 0x1f131f1c, 0x1f141f11, 0x1f411f7c, 0x1cfc1f50, 0x1cf11cf3, 0x1ccd1cf4, + 0x1cdc1cc0, 0x1cd11cdd, 0x1c301cd4, 0x1c0c1c34, 0x1c011c00, 0x1c101c04, 0x1c151c11, 0x1c751c73, + 0x1c401c4d, 0x1c511c5c, 0x1dcc1c54, 0x1dc41dc1, 0x1d3c1d3f, 0x1d001d31, 0x1d071d01, 0x1d701d1f, + 0x1d411d4c, 0x13cc1d50, 0x13c013cd, 0x13c513c1, 0x13d113dc, 0x133f13d4, 0x1330133d, 0x13351337, + 0x1303130c, 0x13011300, 0x13051304, 0x131d131f, 0x13731310, 0x13741370, 0x134d134f, 0x13401343, + 0x13471341, 0x135c1345, 0x13541353, 0x10f710f0, 0x10cc10f5, 0x10c110c0, 0x103310c4, 0x10311030, + 0x100f1034, 0x1003100c, 0x10011000, 0x101c1004, 0x10101013, 0x10141011, 0x10741071, 0x104c1075, + 0x10411040, 0x10451044, 0x1050105d, 0x10571051, 0x11f411fd, 0x11df11c0, 0x11d711d1, 0x113f11d4, + 0x11371130, 0x110c1135, 0x11001103, 0x11071101, 0x111f1105, 0x11171110, 0x117d117f, 0x11751170, + 0x11411143, 0x11441147, 0x1153115f, 0x11551151, 0x17c417c1, 0x173c17d0, 0x1700170d, 0x171c1705, + 0x17701714, 0x1747174c, 0x14fc1751, 0x14cf14f3, 0x14dc14c0, 0x14d114d3, 0x143f14d4, 0x1430143c, + 0x14371431, 0x1403140c, 0x14011400, 0x141f1404, 0x14151410, 0x1473147d, 0x14401475, 0x1453145c, + 0x14541450, 0x15c115cc, 0x153c15c7, 0x15341533, 0x1500150f, 0x15051507, 0x15101513, 0x15711514, + 0x15471543, 0x15511545, 0x7ffd7fff, 0x7ff57ff7, 0x7fdd7fdf, 0x7fd57fd7, 0x7f0f7f30, 0x7f037f0c, + 0x7f047f01, 0x7f7f7f10, 0x7f777f7d, 0x7f407f75, 0x7f5d7f5f, 0x7f557f57, 0x7ccc7cf0, 0x7cc17cc3, + 0x7cd07cc4, 0x7c337c3c, 0x7c0f7c34, 0x7c007c0d, 0x7c077c01, 0x7c137c04, 0x7c147c11, 0x7c747c70, + 0x7c417c43, 0x7c507c44, 0x7dfd7dff, 0x7df57df7, 0x7ddf7dc0, 0x7dd77ddd, 0x7d0c7dd5, 0x7d047d03, + 0x7d7f7d10, 0x7d777d7d, 0x7d407d75, 0x7d5d7d5f, 0x7d557d57, 0x73c473c3, 0x7333733c, 0x7300730c, + 0x731c7305, 0x73147313, 0x73447343, 0x70f470fc, 0x70c070cd, 0x70d170c5, 0x703f70d4, 0x7030703c, + 0x700c7037, 0x70007003, 0x70047001, 0x70107005, 0x70177011, 0x707c7015, 0x70717073, 0x704f7074, + 0x7040704d, 0x70517047, 0x71c171cc, 0x71d071c4, 0x7133713c, 0x71357134, 0x7100710f, 0x71057104, + 0x7111711c, 0x71707115, 0x7145714c, 0x77ff7153, 0x77f777fd, 0x77c077f5, 0x77dd77df, 0x77d577d7, + 0x7730773c, 0x7703770c, 0x77107704, 0x777f7714, 0x7777777d, 0x77407775, 0x775d775f, 0x77557757, + 0x74f174f0, 0x74c374cc, 0x74d074c1, 0x7433743c, 0x74347431, 0x740d740f, 0x74057400, 0x7413741c, + 0x74417470, 0x74507444, 0x75fd75ff, 0x75f575f7, 0x75df75c0, 0x75d775dd, 0x753075d5, 0x7503750c, + 0x757f7501, 0x7577757d, 0x75407575, 0x755d755f, 0x75557557, 0x4fcc4ff0, 0x4fc74fc1, 0x4fd04fc4, + 0x4f314f3c, 0x4f004f34, 0x4f054f07, 0x4f154f14, 0x4f4c4f70, 0x4f414f43, 0x4f504f44, 0x4cf34cfc, + 0x4cf44cf1, 0x4cc04ccf, 0x4cc54cc7, 0x4cd34cdc, 0x4cd44cd1, 0x4c304c3f, 0x4c0c4c0f, 0x4c004c03, + 0x4c044c01, 0x4c104c1d, 0x4c714c73, 0x4c404c4d, 0x4c5c4c47, 0x4c514c53, 0x4df04c54, 0x4dc34dcc, + 0x4dd04dc4, 0x4d314d33, 0x4d0f4d34, 0x4d004d0d, 0x4d114d07, 0x4d704d14, 0x4d414d43, 0x43fc4d54, + 0x43f143f3, 0x43c043cf, 0x43d143c7, 0x4335433f, 0x4303430c, 0x43014300, 0x43044307, 0x431c431f, + 0x4310431d, 0x43714373, 0x4343434d, 0x43474340, 0x4354435c, 0x40f040ff, 0x40f540f7, 0x40cc40cf, + 0x40c040c3, 0x40c440c1, 0x40d040dc, 0x40d540d4, 0x4033403c, 0x40314030, 0x400f4034, 0x400d400c, + 0x40004003, 0x40074001, 0x40054004, 0x4013401c, 0x40114010, 0x407c4014, 0x40774070, 0x404d404c, + 0x40404043, 0x40444041, 0x405f4045, 0x4050405d, 0x40554057, 0x41f341fc, 0x41c041cf, 0x41df41c4, + 0x41d441d1, 0x41374130, 0x410c4134, 0x4100410d, 0x41044101, 0x41174110, 0x4173417d, 0x41754174, + 0x4143414d, 0x41534140, 0x41544151, 0x47c147f0, 0x47d047c4, 0x4731473c, 0x470d470f, 0x47014700, + 0x47134705, 0x47704710, 0x4741474c, 0x47504744, 0x44f144f3, 0x44cf44f4, 0x44c044cd, 0x44c544c7, + 0x44dc44df, 0x44d144d3, 0x443d443f, 0x44374430, 0x440c4435, 0x44004403, 0x44044401, 0x4410441d, + 0x44154411, 0x4473447c, 0x444d444f, 0x44454440, 0x4451445c, 0x45c045f0, 0x453345d0, 0x45344531, + 0x4500450f, 0x451c4507, 0x454c4570, 0x45404543, 0x5fff4541, 0x5ff75ffd, 0x5fc05ff5, 0x5fdd5fdf, + 0x5fd55fd7, 0x5f0c5f30, 0x5f015f03, 0x5f7f5f04, 0x5f775f7d, 0x5f405f75, 0x5f5d5f5f, 0x5f555f57, + 0x5cf45cf0, 0x5cc35ccc, 0x5cc45cc1, 0x5c315cc5, 0x5c0c5c34, 0x5c075c00, 0x5c1c5c05, 0x5c705c13, + 0x5c4d5c4f, 0x5c445c41, 0x5df75dfd, 0x5dcf5df5, 0x5ddd5dc4, 0x5dd55dd7, 0x5d0c5d30, 0x5d045d01, + 0x5d7f5d10, 0x5d775d7d, 0x5d405d75, 0x5d5d5d5f, 0x5d555d57, 0x53d053c4, 0x5333533c, 0x5303530f, + 0x53075300, 0x531c5305, 0x53115310, 0x53145317, 0x50f15370, 0x50cf50f4, 0x50c050cd, 0x50d150c7, + 0x503d50d4, 0x500c5030, 0x50005003, 0x50045001, 0x50155010, 0x5073507c, 0x50715070, 0x504d5074, + 0x50475040, 0x51cc51f0, 0x51c551c1, 0x51d051dc, 0x51315133, 0x510d5135, 0x51015100, 0x511f5107, + 0x5171511d, 0x5140514f, 0x51445141, 0x5153515c, 0x57ff5151, 0x57f757fd, 0x57df57f5, 0x57d757dd, + 0x570c57d5, 0x57015703, 0x577f5704, 0x5777577d, 0x57405775, 0x575d575f, 0x57555757, 0x54c354f0, + 0x54dc54c4, 0x543c54d0, 0x5400540f, 0x541c5405, 0x54145411, 0x5441544f, 0x55fd55ff, 0x55f555f7, + 0x55dd55df, 0x55d555d7, 0x5503550c, 0x557f5501, 0x5577557d, 0x55405575, 0x555d555f, 0x55555557 +); + +#endif + +#if defined(IQ4_NL_GRID) || defined(IQ4_XS_GRID) + +const kvalues_iq4nl = array( + -127, -104, -83, -65, -49, -35, -22, -10, 1, 13, 25, 38, 53, 69, 89, 113 +); + +#endif + +#if defined(MXFP4_LUT) || defined(NVFP4_LUT) +const kvalues_mxfp4 = array( + 0, 1, 2, 3, 4, 6, 8, 12, 0, -1, -2, -3, -4, -6, -8, -12 +); +#endif // MXFP4_LUT || NVFP4_LUT + +#ifdef NVFP4_LUT +fn ue4m3_to_fp32(u: u32) -> f32 { + if (u == 0u || u == 127u) { + return 0.0; + } + let exp = (u >> 3u) & 15u; + let man = u & 7u; + if (exp == 0u) { + return f32(man) * (1.0 / 512.0); + } + let bits = ((exp + 120u) << 23u) | (man << 20u); + return bitcast(bits); +} +#endif // NVFP4_LUT diff --git a/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/concat.wgsl b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/concat.wgsl new file mode 100644 index 0000000000000000000000000000000000000000..eb901bf054717096e2201731ad3947c6391e41d1 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/concat.wgsl @@ -0,0 +1,93 @@ +struct Params { + ne: u32, + + offset_src0: u32, + offset_src1: u32, + offset_dst: u32, + + stride_src0_0: u32, + stride_src0_1: u32, + stride_src0_2: u32, + stride_src0_3: u32, + + stride_src1_0: u32, + stride_src1_1: u32, + stride_src1_2: u32, + stride_src1_3: u32, + + ne0: u32, + ne1: u32, + ne2: u32, + ne3: u32, + + dim: u32, + src0_nedim: u32 +}; + +#ifdef TYPE_F32 +#define DataType f32 +#endif +#ifdef TYPE_I32 +#define DataType i32 +#endif + +#ifdef SRC_OVERLAP +@group(0) @binding(0) +var merged_src: array; + +@group(0) @binding(1) +var dst: array; + +@group(0) @binding(2) +var params: Params; +#else +@group(0) @binding(0) +var src0: array; + +@group(0) @binding(1) +var src1 : array; + +@group(0) @binding(2) +var dst: array; + +@group(0) @binding(3) +var params: Params; +#endif +@compute @workgroup_size(WG_SIZE) +fn main(@builtin(global_invocation_id) gid: vec3) { + + if (gid.x < params.ne) { + var i = gid.x; + let i3 = i / (params.ne2 * params.ne1 * params.ne0); + i = i % (params.ne2 * params.ne1 * params.ne0); + let i2 = i / (params.ne1 * params.ne0); + i = i % (params.ne1 * params.ne0); + let i1 = i / params.ne0; + let i0 = i % params.ne0; + + var ni = array(i0, i1, i2, i3); + + if (ni[params.dim] < params.src0_nedim) { + let src_i = ni[0] * params.stride_src0_0 + + ni[1] * params.stride_src0_1 + + ni[2] * params.stride_src0_2 + + ni[3] * params.stride_src0_3; +#ifdef SRC_OVERLAP + dst[params.offset_dst + gid.x] = merged_src[params.offset_src0 + src_i]; +#else + dst[params.offset_dst + gid.x] = src0[params.offset_src0 + src_i]; +#endif + } else { + ni[params.dim] -= params.src0_nedim; + let src_i = ni[0] * params.stride_src1_0 + + ni[1] * params.stride_src1_1 + + ni[2] * params.stride_src1_2 + + ni[3] * params.stride_src1_3; +#ifdef SRC_OVERLAP + dst[params.offset_dst + gid.x] = merged_src[params.offset_src1 + src_i]; +#else + dst[params.offset_dst + gid.x] = src1[params.offset_src1 + src_i]; +#endif + } + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/conv2d.wgsl b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/conv2d.wgsl new file mode 100644 index 0000000000000000000000000000000000000000..9eb131dc22184e346efc9580bd4ddf9629ac726c --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/conv2d.wgsl @@ -0,0 +1,165 @@ +#include "common_decls.tmpl" +enable f16; + +@group(0) @binding(0) +#if defined(WEIGHT_F32) +var weights: array; +#elif defined(WEIGHT_F16) +var weights: array; +#endif + +@group(0) @binding(1) +#if defined(INPUT_F32) +var input: array; +#elif defined(INPUT_F16) +var input: array; +#endif + +@group(0) @binding(2) +#if defined(OUTPUT_F32) +var output: array; +#elif defined(OUTPUT_F16) +var output: array; +#endif + +struct Params { + offset_w: u32, + offset_i: u32, + offset_o: u32, + + // element strides + sw0: u32, sw1: u32, sw2: u32, sw3: u32, + si0: u32, si1: u32, si2: u32, si3: u32, + so0: u32, so1: u32, so2: u32, so3: u32, + + // kernel dimensions + KW: u32, KH: u32, IC: u32, + // input dimensions + IW: u32, IH: u32, + // output dimensions + OW: u32, OH: u32, OC_out: u32, N_out: u32, + + // stride + s0: u32, s1: u32, + // padding + p0: u32, p1: u32, + // dilation + d0: u32, d1: u32, +}; + +@group(0) @binding(3) +var params: Params; + +fn load_weight(idx: u32) -> f32 { + #if defined(WEIGHT_F32) + return weights[idx]; + #elif defined(WEIGHT_F16) + return f32(weights[idx]); + #endif +} + +fn load_input(idx: u32) -> f32 { + #if defined(INPUT_F32) + return input[idx]; + #elif defined(INPUT_F16) + return f32(input[idx]); + #endif +} + +fn store_output(idx: u32, val: f32) { + #if defined(OUTPUT_F32) + output[idx] = val; + #elif defined(OUTPUT_F16) + output[idx] = f16(val); + #endif +} + +fn ceil_div_u32(x: u32, y: u32) -> u32 { + return (x + y - 1) / y; +} + +// returns the first valid kernel index k such that base + k * step >= 0 +fn first_valid_k(base: i32, step: u32) -> u32 { + if (base >= 0) { + return 0; + } + + return ceil_div_u32(u32(-base), step); +} + +// returns the first invalid kernel index k such that base + k * step >= limit so valid k are in [0, end_valid_k) +fn end_valid_k(base: i32, step: u32, limit: u32, k_max: u32) -> u32 { + let remaining = i32(limit) - base; + if (remaining <= 0) { + return 0; + } + + return min(k_max, ceil_div_u32(u32(remaining), step)); +} + +@compute @workgroup_size(WG_SIZE) +fn main( + @builtin(global_invocation_id) gid: vec3, + @builtin(num_workgroups) num_wg: vec3 +) { + + let threads_per_group = u32(WG_SIZE); + let i_out = gid.x + (num_wg.x * threads_per_group) * gid.y; + let n_out = params.OW * params.OH * params.OC_out * params.N_out; + + var sum: f32 = 0.0; + if (i_out >= n_out) { + return; + } + + // Kernel layout: [KW, KH, IC, ..] + // Input layout: [IW, IH, .., ..] + // Output layout: [OW, OH, OC, N] + + var i = i_out; + let n = i / (params.OC_out * params.OH * params.OW); + i = i % (params.OC_out * params.OH * params.OW); + let oc = i / (params.OH * params.OW); + i = i % (params.OH * params.OW); + let oh = i / params.OW; + let ow = i % params.OW; + + let ow_base = i32(ow * params.s0) - i32(params.p0); + let oh_base = i32(oh * params.s1) - i32(params.p1); + + // clip the valid kernel window once + let kw_begin = first_valid_k(ow_base, params.d0); + let kw_end = end_valid_k(ow_base, params.d0, params.IW, params.KW); + let kh_begin = first_valid_k(oh_base, params.d1); + let kh_end = end_valid_k(oh_base, params.d1, params.IH, params.KH); + + // entire receptive field is out of bounds + if (kw_begin >= kw_end || kh_begin >= kh_end) { + let out_idx = params.offset_o + ow * params.so0 + oh * params.so1 + oc * params.so2 + n * params.so3; + store_output(out_idx, 0.0); + return; + } + + let weight_oc_base = params.offset_w + oc * params.sw3; + let input_n_base = params.offset_i + n * params.si3; + + for (var ic: u32 = 0; ic < params.IC; ic += 1) { + let w_base_ic = ic * params.sw2 + weight_oc_base; + let in_base = ic * params.si2 + input_n_base; + + for (var kh: u32 = kh_begin; kh < kh_end; kh += 1) { + let ih = u32(oh_base + i32(kh * params.d1)); + let w_row_base = w_base_ic + kh * params.sw1; + let in_row_base = in_base + ih * params.si1; + for (var kw: u32 = kw_begin; kw < kw_end; kw += 1) { + let iw = u32(ow_base + i32(kw * params.d0)); + let w_idx = w_row_base + kw * params.sw0; + let in_idx = in_row_base + iw * params.si0; + sum += load_weight(w_idx) * load_input(in_idx); + } + } + } + + let out_idx = params.offset_o + ow * params.so0 + oh * params.so1 + oc * params.so2 + n * params.so3; + store_output(out_idx, sum); +} diff --git a/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/cpy.wgsl b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/cpy.wgsl new file mode 100644 index 0000000000000000000000000000000000000000..67f1dc0928f861892d39643906d2c47758bf5ad1 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/cpy.wgsl @@ -0,0 +1,82 @@ +enable f16; + +#ifdef SRC_F32 +#define SRC_TYPE f32 +#elif defined(SRC_F16) +#define SRC_TYPE f16 +#endif + +#ifdef DST_F32 +#define DST_TYPE f32 +#elif defined(DST_F16) +#define DST_TYPE f16 +#elif defined(DST_I32) +#define DST_TYPE i32 +#endif + +@group(0) @binding(0) +var src: array; + +@group(0) @binding(1) +var dst: array; + +struct Params{ + ne: u32, + offset_src: u32, + offset_dst: u32, + + stride_src0: u32, + stride_src1: u32, + stride_src2: u32, + stride_src3: u32, + + + stride_dst0: u32, + stride_dst1: u32, + stride_dst2: u32, + stride_dst3: u32, + + src_ne0: u32, + src_ne1: u32, + src_ne2: u32, + + dst_ne0: u32, + dst_ne1: u32, + dst_ne2: u32 +}; + +@group(0) @binding(2) +var params: Params; + +@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; + + dst[params.offset_dst + dst_idx] = DST_TYPE((src[params.offset_src + src_idx])); +} diff --git a/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/cumsum.wgsl b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/cumsum.wgsl new file mode 100644 index 0000000000000000000000000000000000000000..e622552c421eb06a77ef1f6201d8035c43d2dd71 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/cumsum.wgsl @@ -0,0 +1,66 @@ +@group(0) @binding(0) +var src: array; + +@group(0) @binding(1) +var dst: array; + +struct Params { + offset_src: u32, // in elements + offset_dst: u32, // in elements + ne0: u32, +}; + +@group(0) @binding(2) +var params: Params; + +var shared_sum: array; + +@compute @workgroup_size(WG_SIZE) +fn main(@builtin(workgroup_id) wid: vec3, + @builtin(local_invocation_id) lid: vec3) { + let row_idx = params.offset_src + wid.x * params.ne0; + let elems = (params.ne0 + WG_SIZE - 1) / WG_SIZE; + var local_sum: f32 = 0.0; + for (var col = lid.x * elems; col < (lid.x + 1) * elems && col < params.ne0; col ++) { + local_sum += src[row_idx + col]; + } + shared_sum[lid.x] = local_sum; + workgroupBarrier(); + + // upsweep + var offset = 1u; + while (offset < WG_SIZE) { + let idx = (lid.x + 1) * offset * 2 - 1; + if (idx < WG_SIZE) { + shared_sum[idx] = shared_sum[idx] + shared_sum[idx - offset]; + } + workgroupBarrier(); + offset <<= 1; + } + + // set last to 0 for exclusive sum + if (lid.x == 0) { + shared_sum[WG_SIZE - 1] = 0.0; + } + workgroupBarrier(); + + // downsweep + offset = WG_SIZE >> 1; + while (offset > 0) { + let idx = (lid.x + 1) * offset * 2 - 1; + if (idx < WG_SIZE) { + let t = shared_sum[idx - offset]; + shared_sum[idx - offset] = shared_sum[idx]; + shared_sum[idx] = shared_sum[idx] + t; + } + workgroupBarrier(); + offset = offset >> 1; + } + + // shared_sum[lid] is exclusive prefix sum up to this thread. + var running_sum = shared_sum[lid.x]; + for (var col = lid.x * elems; col < (lid.x + 1) * elems && col < params.ne0; col ++) { + running_sum += src[row_idx + col]; + dst[params.offset_dst + wid.x * params.ne0 + col] = running_sum; + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/embed_wgsl.py b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/embed_wgsl.py new file mode 100644 index 0000000000000000000000000000000000000000..79a3a9597abe1f424304c1f15c00edef63fe2af0 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/embed_wgsl.py @@ -0,0 +1,89 @@ +import os +import re +import argparse + + +def expand_includes(shader, input_dir): + """ + Replace #include "file" lines in the text with the contents of that file. + Searches for files relative to input_dir. + """ + include_pattern = re.compile(r'^\s*#include\s+"([^"]+)"\s*$', re.MULTILINE) + + def replacer(match): + fname = match.group(1) + file_path = os.path.join(input_dir, fname) + if not os.path.exists(file_path): + raise FileNotFoundError(f"Included file not found: {file_path}") + with open(file_path, "r", encoding="utf-8") as f: + included_code = f.read() + # Recursively expand includes inside the included file + return expand_includes(included_code, input_dir) + + return include_pattern.sub(replacer, shader) + + +def chunk_shader(shader_code, max_chunk_len=60000): + """Split shader_code into safe raw-string sized chunks.""" + return [shader_code[i : i + max_chunk_len] for i in range(0, len(shader_code), max_chunk_len)] + + +def raw_delim(shader_code): + """Pick a raw-string delimiter that does not appear in the shader.""" + delim = "wgsl" + while f"){delim}\"" in shader_code: + delim += "_x" + return delim + + +def write_shader(shader_name, shader_code, output_dir, outfile, input_dir): + shader_code = expand_includes(shader_code, input_dir) + + if output_dir: + wgsl_filename = os.path.join(output_dir, f"{shader_name}.wgsl") + with open(wgsl_filename, "w", encoding="utf-8") as f_out: + f_out.write(shader_code) + + delim = raw_delim(shader_code) + chunks = chunk_shader(shader_code) + + if len(chunks) == 1: + outfile.write(f'const char* wgsl_{shader_name} = R"{delim}({shader_code}){delim}";\n\n') + else: + for idx, chunk in enumerate(chunks): + outfile.write(f'static const char wgsl_{shader_name}_part{idx}[] = R"{delim}({chunk}){delim}";\n\n') + outfile.write(f'static const std::string& wgsl_{shader_name}_str() {{\n') + outfile.write(' static const std::string s = []{\n') + outfile.write(' std::string tmp;\n') + outfile.write(f' tmp.reserve({len(shader_code)});\n') + for idx in range(len(chunks)): + outfile.write(f' tmp.append(wgsl_{shader_name}_part{idx});\n') + outfile.write(' return tmp;\n') + outfile.write(' }();\n') + outfile.write(' return s;\n') + outfile.write('}\n') + outfile.write(f'const char* wgsl_{shader_name} = wgsl_{shader_name}_str().c_str();\n\n') + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--input_dir", required=True) + parser.add_argument("--output_file", required=True) + args = parser.parse_args() + + with open(args.output_file, "w", encoding="utf-8") as out: + out.write("// Auto-generated shader embedding\n") + out.write("#include \n\n") + for fname in sorted(os.listdir(args.input_dir)): + if fname.endswith(".wgsl"): + shader_path = os.path.join(args.input_dir, fname) + shader_name = fname.replace(".wgsl", "") + + with open(shader_path, "r", encoding="utf-8") as f: + shader_code = f.read() + + write_shader(shader_name, shader_code, None, out, args.input_dir) + + +if __name__ == "__main__": + main() diff --git a/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn.wgsl b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn.wgsl new file mode 100644 index 0000000000000000000000000000000000000000..9767ca3d754387c2ecaeb18993641644ad021cbf --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn.wgsl @@ -0,0 +1,559 @@ +diagnostic(off, chromium.subgroup_matrix_uniformity); +diagnostic(off, subgroup_uniformity); +enable f16; +enable subgroups; +enable chromium_experimental_subgroup_matrix; + +#define BYTE_HELPERS +#include "common_decls.tmpl" + +#ifdef K_F32 +#define K_TYPE f32 +#elif defined(K_Q4_0) || defined(K_Q8_0) +#define K_TYPE u32 +#else +#define K_TYPE f16 +#endif + +#ifdef V_F32 +#define V_TYPE f32 +#elif defined(V_Q4_0) || defined(V_Q8_0) +#define V_TYPE u32 +#else +#define V_TYPE f16 +#endif + +// Default values +#define HEAD_DIM_QK 64 +#define HEAD_DIM_V 64 + +// The number of rows/columns/k in a subgroup matrix. MxK * KxN = MxN +// Note that the "K" here does not correspond to the K in attention's Q/K/V, it's just the common dimension. +#define SG_MAT_M 8 +#define SG_MAT_N 8 +#define SG_MAT_K 8 + +// Each workgroup processes one subgroup matrix of Q rows +#define Q_TILE SG_MAT_M +#define KV_TILE 16 +#define WG_SIZE 64 + +// Number of subgroup-matrix-width blocks that span the KV tile. SG_MAT_N must divide KV_TILE. +#define KV_BLOCKS (KV_TILE / SG_MAT_N) + +struct Params { + offset_q: u32, + offset_k: u32, + offset_v: u32, + offset_mask: u32, + offset_sinks: u32, + offset_dst: u32, + + // shapes of Q/K/V + n_heads: u32, + seq_len_q: u32, + seq_len_kv: u32, + + // strides (in elements) + stride_q1: u32, + stride_q2: u32, + stride_q3: u32, + stride_k1: u32, + stride_k2: u32, + stride_k3: u32, + stride_v1: u32, + stride_v2: u32, + stride_v3: u32, + stride_mask3: u32, + + // repeat factors for K/V, e.g., MHA vs. MQA vs. GQA + q_per_kv: u32, + + // softmax params + scale: f32, + max_bias: f32, + logit_softcap: f32, + n_head_log2: f32, + m0: f32, + m1: f32, +}; + +@group(0) @binding(0) var Q: array; +#ifdef KV_OVERLAP +@group(0) @binding(1) var K: array; +#define V K +#else +@group(0) @binding(1) var K: array; +@group(0) @binding(2) var V: array; +#endif + +#if defined(MASK) && defined(SINKS) +#ifdef KV_OVERLAP +@group(0) @binding(2) var mask: array; +@group(0) @binding(3) var sinks: array; +#define DST_BINDING 4 +#define PARAMS_BINDING 5 +#else +@group(0) @binding(3) var mask: array; +@group(0) @binding(4) var sinks: array; +#define DST_BINDING 5 +#define PARAMS_BINDING 6 +#endif +#elif defined(MASK) +#ifdef KV_OVERLAP +@group(0) @binding(2) var mask: array; +#define DST_BINDING 3 +#define PARAMS_BINDING 4 +#else +@group(0) @binding(3) var mask: array; +#define DST_BINDING 4 +#define PARAMS_BINDING 5 +#endif +#elif defined(SINKS) +#ifdef KV_OVERLAP +@group(0) @binding(2) var sinks: array; +#define DST_BINDING 3 +#define PARAMS_BINDING 4 +#else +@group(0) @binding(3) var sinks: array; +#define DST_BINDING 4 +#define PARAMS_BINDING 5 +#endif +#else +#ifdef KV_OVERLAP +#define DST_BINDING 2 +#define PARAMS_BINDING 3 +#else +#define DST_BINDING 3 +#define PARAMS_BINDING 4 +#endif +#endif + +@group(0) @binding(DST_BINDING) var dst: array>; +@group(0) @binding(PARAMS_BINDING) var params: Params; + +// Just a very small float value. +const FLOAT_MIN: f32 = -1.0e9; + +// The number of Q rows processed per workgroup +var q_shmem: array; + +#ifndef KV_DIRECT +const kv_shmem_size = KV_TILE * max(HEAD_DIM_QK, HEAD_DIM_V); +// we can reuse the same shmem for K and V since we only need one at a time +var kv_shmem: array; +#endif + +var o_shmem: array; // output shmem + +#ifdef MASK +// storage for mask values +var mask_shmem: array; +#endif + +// storage for output of Q*K^T scores for online softmax (S matrix from paper) +// also storage for diagonal matrix during online softmax (P matrix from paper) +// note that we reuse the same storage for both since we only need one at a time +var inter_shmem: array; + +// Storage for row max and exp sum during online softmax +var row_max_shmem: array; +var exp_sum_shmem: array; + +fn calc_softmax_term(kv_idx: u32, q_tile_row: u32, slope: f32) -> f32 { + var v = select(FLOAT_MIN, + f32(inter_shmem[kv_idx + q_tile_row * KV_TILE]) * params.scale, + kv_idx < KV_TILE); +#ifdef LOGIT_SOFTCAP + v = params.logit_softcap * tanh(v); +#endif +#ifdef MASK + let mask_val = select(0.0, f32(mask_shmem[q_tile_row * KV_TILE + kv_idx]), kv_idx < KV_TILE); + let mask_term = slope * mask_val; + v += mask_term; +#endif + return v; +} + +fn load_f32x4(buf: ptr>, read_write>, scalar_index: u32) -> vec4 { + return (*buf)[scalar_index >> 2u]; +} + +fn load_kx4(buf: ptr>, read_write>, scalar_index: u32) -> vec4 { + return (*buf)[scalar_index >> 2u]; +} + +#ifndef KV_DIRECT +#define QUANT_SHMEM kv_shmem +#define QUANT_OUT_TYPE f16 +#include "quant_inner_loops.tmpl" +#include "flash_attn_quant_staging.tmpl" + +#if !defined(K_Q4_0) && !defined(K_Q8_0) +fn load_k_tile_block(local_x: u32, kv_count: u32, kv_tile: u32, k_head_offset: u32) { + for (var elem_idx = local_x; elem_idx < KV_TILE * HEAD_DIM_QK; elem_idx += WG_SIZE) { + let k_row = elem_idx / HEAD_DIM_QK; + let k_col = elem_idx % HEAD_DIM_QK; + let global_k_row = kv_tile + k_row; + let global_k_row_offset = k_head_offset + global_k_row * params.stride_k1; + kv_shmem[elem_idx] = f16(select( + 0.0, + K[global_k_row_offset + k_col], + global_k_row < params.seq_len_kv && k_col < HEAD_DIM_QK)); + } +} +#endif + +#if !defined(V_Q4_0) && !defined(V_Q8_0) +fn load_v_tile_block(local_x: u32, kv_count: u32, kv_tile: u32, v_head_offset: u32) { + for (var elem_idx = local_x; elem_idx < KV_TILE * HEAD_DIM_V; elem_idx += WG_SIZE) { + let v_row = elem_idx / HEAD_DIM_V; + let v_col = elem_idx % HEAD_DIM_V; + let global_v_row = kv_tile + v_row; + let global_v_row_offset = v_head_offset + global_v_row * params.stride_v1; + kv_shmem[elem_idx] = f16(select( + 0.0, + V[global_v_row_offset + v_col], + global_v_row < params.seq_len_kv && v_col < HEAD_DIM_V)); + } +} +#endif +#endif + +@compute @workgroup_size(WG_SIZE) +fn main(@builtin(workgroup_id) wg_id: vec3, + @builtin(local_invocation_id) local_id: vec3, + @builtin(subgroup_id) subgroup_id: u32, + @builtin(subgroup_size) subgroup_size: u32, + @builtin(num_subgroups) num_subgroups: u32, + @builtin(subgroup_invocation_id) sg_inv_id: u32) { + + // initialize row max for online softmax + for (var i = local_id.x; i < Q_TILE; i += WG_SIZE) { + row_max_shmem[i] = FLOAT_MIN; + exp_sum_shmem[i] = 0.0; + } + + for (var i = local_id.x; i < Q_TILE * HEAD_DIM_V; i += WG_SIZE) { + o_shmem[i] = 0.0; + } + + // workgroups per head/batch + let wg_per_head = (params.seq_len_q + Q_TILE - 1u) / Q_TILE; + let wg_per_batch = wg_per_head * params.n_heads; + + let dst2_stride = HEAD_DIM_V * params.n_heads; + let dst3_stride = dst2_stride * params.seq_len_q; + + // batch index + let batch_idx = wg_id.x / wg_per_batch; + let q_batch_offset = params.offset_q + batch_idx * params.stride_q3; + let k_batch_offset = params.offset_k + batch_idx * params.stride_k3; + let v_batch_offset = params.offset_v + batch_idx * params.stride_v3; + let dst_batch_offset = params.offset_dst + batch_idx * dst3_stride; + let wg_in_batch = wg_id.x % wg_per_batch; + + // head index + let head_idx = wg_in_batch / wg_per_head; + let q_head_offset = q_batch_offset + head_idx * params.stride_q2; + let k_head_idx = head_idx / params.q_per_kv; + let v_head_idx = k_head_idx; + let k_head_offset = k_batch_offset + k_head_idx * params.stride_k2; + let v_head_offset = v_batch_offset + v_head_idx * params.stride_v2; + + // starting Q row for this workgroup + let wg_in_head = wg_in_batch % wg_per_head; + let q_row_start = wg_in_head * Q_TILE; + +#ifdef MASK + // mask offset + let mask_global_offset = params.offset_mask + batch_idx * params.stride_mask3 + q_row_start * params.seq_len_kv; +#endif + + // note that the output is permuted, the layout is [head_dim_v, n_heads, seq_len_q, batch_size] + let dst_global_offset = dst_batch_offset + q_row_start * dst2_stride + head_idx * HEAD_DIM_V; + + let head = f32(head_idx); + let slope = select(1.0, select(pow(params.m1, 2.0 * (head - params.n_head_log2) + 1.0), pow(params.m0, head + 1.0), head < params.n_head_log2), params.max_bias > 0); + + // load q tile into shared memory + for (var elem_idx = local_id.x; elem_idx < Q_TILE * HEAD_DIM_QK; elem_idx += WG_SIZE) { + let q_row = elem_idx / HEAD_DIM_QK; + let q_col = elem_idx % HEAD_DIM_QK; + let head_q_row = q_row_start + q_row; + let global_q_row_offset = q_head_offset + head_q_row * params.stride_q1; + q_shmem[elem_idx] = f16(select( + 0.0, + Q[global_q_row_offset + q_col], + head_q_row < params.seq_len_q && q_col < HEAD_DIM_QK)); + } + + for (var kv_tile = 0u; kv_tile < params.seq_len_kv; kv_tile += KV_TILE) { + let kv_count = min(KV_TILE, params.seq_len_kv - kv_tile); + // clear inter_shmem to ensure zero-initialized accumulators + for (var elem_idx = local_id.x; elem_idx < Q_TILE * KV_TILE; elem_idx += WG_SIZE) { + inter_shmem[elem_idx] = 0.0; + } + + // load k tile into shared memory +#ifndef KV_DIRECT + load_k_tile_block(local_id.x, kv_count, kv_tile, k_head_offset); +#endif + + workgroupBarrier(); + + // accumulate q block * k block into registers across the entire KV tile + // TODO: this loop seems to be the current largest bottleneck + // this bracket exists to scope the lifetime of variables, reducing register pressure + { +#ifdef KV_DIRECT + let k_block_row = kv_tile + subgroup_id * SG_MAT_N; + var k_global_offset = k_head_offset + k_block_row * params.stride_k1; +#else + var k_block_offset = subgroup_id * SG_MAT_N * HEAD_DIM_QK; +#endif + for (var kv_block = subgroup_id; kv_block < KV_BLOCKS; kv_block += num_subgroups) { + let inter_offset = kv_block * SG_MAT_N; + var acc: subgroup_matrix_result = subgroupMatrixLoad>(&inter_shmem, inter_offset, false, KV_TILE); + + var q_cur = subgroupMatrixLoad>(&q_shmem, 0u, false, HEAD_DIM_QK); + +#ifdef KV_DIRECT + var k_cur = subgroupMatrixLoad>(&K, k_global_offset + 0u, true, params.stride_k1); +#else + var k_cur = subgroupMatrixLoad>(&kv_shmem, k_block_offset + 0u, true, HEAD_DIM_QK); +#endif + + var t: u32 = 1u; + for (; t + 1u < HEAD_DIM_QK / SG_MAT_K; t += 2u) { + let h0 = t * SG_MAT_K; + var q0 = subgroupMatrixLoad>(&q_shmem, h0, false, HEAD_DIM_QK); +#ifdef KV_DIRECT + var k0 = subgroupMatrixLoad>(&K, k_global_offset + h0, true, params.stride_k1); +#else + var k0 = subgroupMatrixLoad>(&kv_shmem, k_block_offset + h0, true, HEAD_DIM_QK); +#endif + acc = subgroupMatrixMultiplyAccumulate(q_cur, k_cur, acc); + q_cur = q0; + k_cur = k0; + + let h1 = (t + 1u) * SG_MAT_K; + var q1g = subgroupMatrixLoad>(&q_shmem, h1, false, HEAD_DIM_QK); +#ifdef KV_DIRECT + var k1g = subgroupMatrixLoad>(&K, k_global_offset + h1, true, params.stride_k1); +#else + var k1g = subgroupMatrixLoad>(&kv_shmem, k_block_offset + h1, true, HEAD_DIM_QK); +#endif + acc = subgroupMatrixMultiplyAccumulate(q_cur, k_cur, acc); + q_cur = q1g; + k_cur = k1g; + } + + // handle odd tail + if (t < HEAD_DIM_QK / SG_MAT_K) { + let h = t * SG_MAT_K; + var qn = subgroupMatrixLoad>(&q_shmem, h, false, HEAD_DIM_QK); +#ifdef KV_DIRECT + var kn = subgroupMatrixLoad>(&K, k_global_offset + h, true, params.stride_k1); +#else + var kn = subgroupMatrixLoad>(&kv_shmem, k_block_offset + h, true, HEAD_DIM_QK); +#endif + acc = subgroupMatrixMultiplyAccumulate(q_cur, k_cur, acc); + q_cur = qn; + k_cur = kn; + } + + acc = subgroupMatrixMultiplyAccumulate(q_cur, k_cur, acc); + +#ifdef KV_DIRECT + k_global_offset += num_subgroups * SG_MAT_N * params.stride_k1; +#else + k_block_offset += num_subgroups * SG_MAT_N * HEAD_DIM_QK; +#endif + subgroupMatrixStore(&inter_shmem, inter_offset, acc, false, KV_TILE); + } + } + + +#ifdef MASK + // load mask tile into shared memory for this KV block + // TODO: optimize and skip if mask is -INF for the entire tile + for (var elem_idx = local_id.x; elem_idx < Q_TILE * KV_TILE; elem_idx += WG_SIZE) { + let mask_row = elem_idx / KV_TILE; + let mask_col = elem_idx % KV_TILE; + let global_q_row = q_row_start + mask_row; + let global_k_col = kv_tile + mask_col; + let mask_in_bounds = global_q_row < params.seq_len_q && global_k_col < params.seq_len_kv; + let mask_idx = mask_global_offset + mask_row * params.seq_len_kv + global_k_col; + mask_shmem[elem_idx] = select(0.0, mask[mask_idx], mask_in_bounds); + } +#endif + + workgroupBarrier(); + + // online softmax + for (var q_tile_row = subgroup_id; q_tile_row < Q_TILE; q_tile_row += num_subgroups) { + let global_q_row = q_row_start + q_tile_row; + if (global_q_row >= params.seq_len_q) { + break; + } + + // initialize running max for this row + var prev_max = row_max_shmem[q_tile_row]; + var final_max = prev_max; + // pass 1: compute final max across the full KV tile in chunks + for (var kv_offset = 0u; kv_offset < KV_TILE; kv_offset += subgroup_size) { + let kv_idx = kv_offset + sg_inv_id; + let softmax_term = calc_softmax_term(kv_idx, q_tile_row, slope); + final_max = subgroupMax(max(final_max, softmax_term)); + } + + var total_exp_term: f32 = 0.0; + // pass 2: compute exp sum and write P using final_max + for (var kv_offset = 0u; kv_offset < KV_TILE; kv_offset += subgroup_size) { + let kv_idx = kv_offset + sg_inv_id; + let softmax_term = calc_softmax_term(kv_idx, q_tile_row, slope); + let cur_p = select(0.0, + exp(softmax_term - final_max), + kv_tile + kv_idx < params.seq_len_kv && kv_idx < KV_TILE); + total_exp_term += subgroupAdd(cur_p); + if (kv_idx < KV_TILE) { + inter_shmem[kv_idx + q_tile_row * KV_TILE] = f16(cur_p); + } + } + + let cur_exp = exp(prev_max - final_max); + + if (sg_inv_id == 0) { + row_max_shmem[q_tile_row] = final_max; + exp_sum_shmem[q_tile_row] = exp_sum_shmem[q_tile_row] * cur_exp + total_exp_term; + } + + for (var elem_idx = sg_inv_id; elem_idx < HEAD_DIM_V; elem_idx += subgroup_size) { + let idx = q_tile_row * HEAD_DIM_V + elem_idx; + o_shmem[idx] = f16(f32(o_shmem[idx]) * cur_exp); + } + } + + // load v tile into shared memory +#ifndef KV_DIRECT + load_v_tile_block(local_id.x, kv_count, kv_tile, v_head_offset); +#endif + + workgroupBarrier(); + + // we have P (Q_TILE x KV_TILE) in inter_shmem and V (KV_TILE x head_dim_v) in kv_shmem + // we want to compute O += P * V across the full KV tile + for (var head_dim_block = subgroup_id * SG_MAT_N; + head_dim_block < HEAD_DIM_V; + head_dim_block += num_subgroups * SG_MAT_N) { + // load O submatrix from shared memory + var o_sg_mat: subgroup_matrix_result = subgroupMatrixLoad>( + &o_shmem, + head_dim_block, + false, + HEAD_DIM_V + ); + for (var kv_block = 0u; kv_block < KV_BLOCKS; kv_block++) { + let p_offset = kv_block * SG_MAT_N; + var p_sg_mat: subgroup_matrix_left = subgroupMatrixLoad>( + &inter_shmem, + p_offset, + false, + KV_TILE + ); + + // load V submatrix from global or shared memory +#ifdef KV_DIRECT + let v_block_row = kv_tile + kv_block * SG_MAT_N; + let v_global_offset = v_head_offset + v_block_row * params.stride_v1 + head_dim_block; + var v_sg_mat: subgroup_matrix_right = subgroupMatrixLoad>( + &V, + v_global_offset, + false, + params.stride_v1 + ); +#else + let v_block_offset = kv_block * SG_MAT_N * HEAD_DIM_V; + var v_sg_mat: subgroup_matrix_right = subgroupMatrixLoad>( + &kv_shmem, + v_block_offset + head_dim_block, + false, + HEAD_DIM_V + ); +#endif + // O += P * V + o_sg_mat = subgroupMatrixMultiplyAccumulate(p_sg_mat, v_sg_mat, o_sg_mat); + } + // store O back to shared memory + subgroupMatrixStore(&o_shmem, head_dim_block, o_sg_mat, false, HEAD_DIM_V); + } + workgroupBarrier(); + } + +#ifdef SINKS + // add sinks (applied once after processing all KV tiles) + for (var q_tile_row = subgroup_id; + q_tile_row < Q_TILE; + q_tile_row += num_subgroups) { + // no need to process rows beyond seq_len_q + let global_q_row = q_row_start + q_tile_row; + if (global_q_row >= params.seq_len_q) { + break; + } + + var prev_max = row_max_shmem[q_tile_row]; + + // for non-sink threads, exp(FLOAT_MIN) effectively zeroes out their contribution to the sum + let sink_val = select(FLOAT_MIN, sinks[params.offset_sinks + head_idx], sg_inv_id == 0); + let new_max = subgroupMax(max(prev_max, sink_val)); + let max_exp = exp(prev_max - new_max); + let sink_exp = exp(sink_val - new_max); + + let sink_exp_sum = subgroupAdd(sink_exp); + + if (sg_inv_id == 0) { + exp_sum_shmem[q_tile_row] = exp_sum_shmem[q_tile_row] * max_exp + sink_exp_sum; + } + + for (var elem_idx = sg_inv_id; elem_idx < HEAD_DIM_V; elem_idx += subgroup_size) { + let idx = q_tile_row * HEAD_DIM_V + elem_idx; + let val = f32(o_shmem[idx]) * max_exp; + o_shmem[idx] = f16(val); + } + } + workgroupBarrier(); +#endif + for (var q_tile_row = subgroup_id; + q_tile_row < Q_TILE; + q_tile_row += num_subgroups) { + + let global_q_row = q_row_start + q_tile_row; + if (global_q_row >= params.seq_len_q) { break; } + + let exp_sum = exp_sum_shmem[q_tile_row]; + let scale = select(0.0, 1.0 / exp_sum, exp_sum != 0.0); + + let row_base: u32 = dst_global_offset + q_tile_row * dst2_stride; + + for (var elem_base = sg_inv_id * 4u; + elem_base < HEAD_DIM_V; + elem_base += subgroup_size * 4u) { + + let i0 = q_tile_row * HEAD_DIM_V + (elem_base + 0u); + let i1 = q_tile_row * HEAD_DIM_V + (elem_base + 1u); + let i2 = q_tile_row * HEAD_DIM_V + (elem_base + 2u); + let i3 = q_tile_row * HEAD_DIM_V + (elem_base + 3u); + + let v = vec4( + f32(o_shmem[i0]) * scale, + f32(o_shmem[i1]) * scale, + f32(o_shmem[i2]) * scale, + f32(o_shmem[i3]) * scale + ); + + let dst_vec_index: u32 = (row_base + elem_base) >> 2u; + dst[dst_vec_index] = v; + } + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn_quant_staging.tmpl b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn_quant_staging.tmpl new file mode 100644 index 0000000000000000000000000000000000000000..8f41eb7bfdbc5313d04ef62dc924b3c5e4483990 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn_quant_staging.tmpl @@ -0,0 +1,124 @@ +#define BLOCK_SIZE 32 +#define BLOCKS_K ((HEAD_DIM_QK + BLOCK_SIZE - 1) / BLOCK_SIZE) +#define BLOCKS_V ((HEAD_DIM_V + BLOCK_SIZE - 1) / BLOCK_SIZE) + +#if defined(K_Q4_0) +#define K_NQ 16 +#define K_BLOCK_SIZE_BYTES 18u +#define K_BYTES_PER_THREAD 8u +#define K_BYTES_PER_INNER_LOOP 4u +#elif defined(K_Q8_0) +#define K_NQ 16 +#define K_BLOCK_SIZE_BYTES 34u +#define K_BYTES_PER_THREAD 16u +#define K_BYTES_PER_INNER_LOOP 4u +#endif + +#if defined(V_Q4_0) +#define V_NQ 16 +#define V_BLOCK_SIZE_BYTES 18u +#define V_BYTES_PER_THREAD 8u +#define V_BYTES_PER_INNER_LOOP 4u +#elif defined(V_Q8_0) +#define V_NQ 16 +#define V_BLOCK_SIZE_BYTES 34u +#define V_BYTES_PER_THREAD 16u +#define V_BYTES_PER_INNER_LOOP 4u +#endif + +#if defined(K_Q4_0) || defined(K_Q8_0) +fn load_k_u16_at(byte_offset: u32) -> u32 { + let word = K[byte_offset / 4u]; + let shift = (byte_offset & 2u) * 8u; + return (word >> shift) & 0xFFFFu; +} + +fn load_k_u32_at(byte_offset: u32) -> u32 { + let word_idx = byte_offset / 4u; + let shift = (byte_offset & 3u) * 8u; + let lo = K[word_idx]; + if (shift == 0u) { + return lo; + } + let hi = K[word_idx + 1u]; + return (lo >> shift) | (hi << (32u - shift)); +} +#endif + +#if defined(V_Q4_0) || defined(V_Q8_0) +fn load_v_u16_at(byte_offset: u32) -> u32 { + let word = V[byte_offset / 4u]; + let shift = (byte_offset & 2u) * 8u; + return (word >> shift) & 0xFFFFu; +} + +fn load_v_u32_at(byte_offset: u32) -> u32 { + let word_idx = byte_offset / 4u; + let shift = (byte_offset & 3u) * 8u; + let lo = V[word_idx]; + if (shift == 0u) { + return lo; + } + let hi = V[word_idx + 1u]; + return (lo >> shift) | (hi << (32u - shift)); +} +#endif + +fn f16_from_u16(bits: u32) -> f16 { + let packed = unpack2x16float(bits); + return f16(packed[0]); +} + +#if defined(K_Q4_0) || defined(K_Q8_0) +fn load_k_tile_block(local_x: u32, kv_count: u32, kv_tile: u32, k_head_offset: u32) { + for (var elem_idx = local_x * K_NQ; elem_idx < kv_count * HEAD_DIM_QK; elem_idx += WG_SIZE * K_NQ) { + let blck_idx = elem_idx / BLOCK_SIZE; + let block_offset = (elem_idx % BLOCK_SIZE) / K_NQ; + let k_row = blck_idx / BLOCKS_K; + let global_k_row = kv_tile + k_row; + let block_k = blck_idx % BLOCKS_K; + let row_offset = k_row * HEAD_DIM_QK; + let global_block_idx = k_head_offset + global_k_row * params.stride_k1 + block_k; + let block_byte_base = global_block_idx * K_BLOCK_SIZE_BYTES; + let d = f16_from_u16(load_k_u16_at(block_byte_base)); + let thread_byte_offset = block_offset * K_BYTES_PER_THREAD; + let shmem_idx = row_offset + block_k * BLOCK_SIZE + thread_byte_offset; + for (var j = 0u; j < K_BYTES_PER_THREAD / K_BYTES_PER_INNER_LOOP; j += 1u) { + let q_byte_offset = block_byte_base + 2u + thread_byte_offset + j * K_BYTES_PER_INNER_LOOP; + let q_packed = load_k_u32_at(q_byte_offset); +#if defined(K_Q4_0) + dequant_q4_0_packed_to_shmem(q_packed, d, shmem_idx + j * K_BYTES_PER_INNER_LOOP); +#elif defined(K_Q8_0) + dequant_q8_0_packed_to_shmem(q_packed, d, shmem_idx + j * K_BYTES_PER_INNER_LOOP); +#endif + } + } +} +#endif + +#if defined(V_Q4_0) || defined(V_Q8_0) +fn load_v_tile_block(local_x: u32, kv_count: u32, kv_tile: u32, v_head_offset: u32) { + for (var elem_idx = local_x * V_NQ; elem_idx < kv_count * HEAD_DIM_V; elem_idx += WG_SIZE * V_NQ) { + let blck_idx = elem_idx / BLOCK_SIZE; + let block_offset = (elem_idx % BLOCK_SIZE) / V_NQ; + let v_row = blck_idx / BLOCKS_V; + let global_v_row = kv_tile + v_row; + let block_k = blck_idx % BLOCKS_V; + let row_offset = v_row * HEAD_DIM_V; + let global_block_idx = v_head_offset + global_v_row * params.stride_v1 + block_k; + let block_byte_base = global_block_idx * V_BLOCK_SIZE_BYTES; + let d = f16_from_u16(load_v_u16_at(block_byte_base)); + let thread_byte_offset = block_offset * V_BYTES_PER_THREAD; + let shmem_idx = row_offset + block_k * BLOCK_SIZE + thread_byte_offset; + for (var j = 0u; j < V_BYTES_PER_THREAD / V_BYTES_PER_INNER_LOOP; j += 1u) { + let q_byte_offset = block_byte_base + 2u + thread_byte_offset + j * V_BYTES_PER_INNER_LOOP; + let q_packed = load_v_u32_at(q_byte_offset); +#if defined(V_Q4_0) + dequant_q4_0_packed_to_shmem(q_packed, d, shmem_idx + j * V_BYTES_PER_INNER_LOOP); +#elif defined(V_Q8_0) + dequant_q8_0_packed_to_shmem(q_packed, d, shmem_idx + j * V_BYTES_PER_INNER_LOOP); +#endif + } + } +} +#endif diff --git a/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn_tile.wgsl b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn_tile.wgsl new file mode 100644 index 0000000000000000000000000000000000000000..e68934113fc1f48af93355961395fee8496f6bc7 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn_tile.wgsl @@ -0,0 +1,397 @@ +enable f16; +enable subgroups; + +#define BYTE_HELPERS +#include "common_decls.tmpl" + +#ifdef Q_F16 +#define Q_TYPE f16 +#else +#define Q_TYPE f32 +#endif + +#ifdef K_F32 +#define K_TYPE f32 +#elif defined(K_Q4_0) || defined(K_Q8_0) +#define K_TYPE u32 +#else +#define K_TYPE f16 +#endif + +#ifdef V_F32 +#define V_TYPE f32 +#elif defined(V_Q4_0) || defined(V_Q8_0) +#define V_TYPE u32 +#else +#define V_TYPE f16 +#endif + +#ifdef DST_F16 +#define DST_TYPE f16 +#else +#define DST_TYPE f32 +#endif + +#define HEAD_DIM_QK 64 +#define HEAD_DIM_V 64 +#define Q_TILE 4 +#define KV_TILE 64 +#define WG_SIZE 128 +#ifndef MIN_SUBGROUP_SIZE +#define MIN_SUBGROUP_SIZE MAX_SUBGROUP_SIZE +#endif + +struct Params { + offset_q: u32, + offset_k: u32, + offset_v: u32, + offset_mask: u32, + offset_sinks: u32, + offset_dst: u32, + + n_heads: u32, + seq_len_q: u32, + seq_len_kv: u32, + + stride_q1: u32, + stride_q2: u32, + stride_q3: u32, + stride_k1: u32, + stride_k2: u32, + stride_k3: u32, + stride_v1: u32, + stride_v2: u32, + stride_v3: u32, + stride_mask3: u32, + + q_per_kv: u32, + + scale: f32, + max_bias: f32, + logit_softcap: f32, + n_head_log2: f32, + m0: f32, + m1: f32, +}; + +@group(0) @binding(0) var Q: array; +#ifdef KV_OVERLAP +#if defined(K_Q4_0) || defined(K_Q8_0) +@group(0) @binding(1) var K: array; +#else +@group(0) @binding(1) var K: array>; +#endif +#define V K +#else +#if defined(K_Q4_0) || defined(K_Q8_0) +@group(0) @binding(1) var K: array; +#else +@group(0) @binding(1) var K: array>; +#endif +#if defined(V_Q4_0) || defined(V_Q8_0) +@group(0) @binding(2) var V: array; +#else +@group(0) @binding(2) var V: array>; +#endif +#endif + +#if defined(MASK) && defined(SINKS) +#ifdef KV_OVERLAP +@group(0) @binding(2) var mask: array; +@group(0) @binding(3) var sinks: array; +#define DST_BINDING 4 +#define PARAMS_BINDING 5 +#else +@group(0) @binding(3) var mask: array; +@group(0) @binding(4) var sinks: array; +#define DST_BINDING 5 +#define PARAMS_BINDING 6 +#endif +#elif defined(MASK) +#ifdef KV_OVERLAP +@group(0) @binding(2) var mask: array; +#define DST_BINDING 3 +#define PARAMS_BINDING 4 +#else +@group(0) @binding(3) var mask: array; +#define DST_BINDING 4 +#define PARAMS_BINDING 5 +#endif +#elif defined(SINKS) +#ifdef KV_OVERLAP +@group(0) @binding(2) var sinks: array; +#define DST_BINDING 3 +#define PARAMS_BINDING 4 +#else +@group(0) @binding(3) var sinks: array; +#define DST_BINDING 4 +#define PARAMS_BINDING 5 +#endif +#else +#ifdef KV_OVERLAP +#define DST_BINDING 2 +#define PARAMS_BINDING 3 +#else +#define DST_BINDING 3 +#define PARAMS_BINDING 4 +#endif +#endif + +@group(0) @binding(DST_BINDING) var dst: array>; +@group(0) @binding(PARAMS_BINDING) var params: Params; + +const FLOAT_MIN: f32 = -1.0e9; +const Q_CHUNKS: u32 = HEAD_DIM_QK / 4u; +const V_CHUNKS: u32 = HEAD_DIM_V / 4u; +const SCORE_REGS_PER_LANE: u32 = (KV_TILE + MIN_SUBGROUP_SIZE - 1u) / MIN_SUBGROUP_SIZE; +const OUT_REGS_PER_LANE: u32 = (V_CHUNKS + MIN_SUBGROUP_SIZE - 1u) / MIN_SUBGROUP_SIZE; +const kv_shmem_size = KV_TILE * max(HEAD_DIM_QK, HEAD_DIM_V); + +var q_shmem: array; +var kv_shmem: array; +var p_shmem: array; + +#define QUANT_SHMEM kv_shmem +#define QUANT_OUT_TYPE f16 +#include "quant_inner_loops.tmpl" +#include "flash_attn_quant_staging.tmpl" + +#if !defined(K_Q4_0) && !defined(K_Q8_0) +fn load_k_tile_block(local_x: u32, kv_count: u32, kv_tile: u32, k_head_offset: u32) { + for (var vec_idx_local = local_x; vec_idx_local < kv_count * Q_CHUNKS; vec_idx_local += WG_SIZE) { + let kv_local = vec_idx_local / Q_CHUNKS; + let chunk = vec_idx_local % Q_CHUNKS; + let global_k_row = kv_tile + kv_local; + let k_vec_index = (k_head_offset + global_k_row * params.stride_k1 + chunk * 4u) >> 2u; + let k4 = K[k_vec_index]; + let kv_off = kv_local * HEAD_DIM_QK + chunk * 4u; + kv_shmem[kv_off + 0u] = f16(k4.x); + kv_shmem[kv_off + 1u] = f16(k4.y); + kv_shmem[kv_off + 2u] = f16(k4.z); + kv_shmem[kv_off + 3u] = f16(k4.w); + } +} +#endif + +#if !defined(V_Q4_0) && !defined(V_Q8_0) +fn load_v_tile_block(local_x: u32, kv_count: u32, kv_tile: u32, v_head_offset: u32) { + for (var vec_idx_local = local_x; vec_idx_local < kv_count * V_CHUNKS; vec_idx_local += WG_SIZE) { + let kv_local = vec_idx_local / V_CHUNKS; + let chunk = vec_idx_local % V_CHUNKS; + let global_v_row = kv_tile + kv_local; + let v_vec_index = (v_head_offset + global_v_row * params.stride_v1 + chunk * 4u) >> 2u; + let v4 = V[v_vec_index]; + let kv_off = kv_local * HEAD_DIM_V + chunk * 4u; + kv_shmem[kv_off + 0u] = f16(v4.x); + kv_shmem[kv_off + 1u] = f16(v4.y); + kv_shmem[kv_off + 2u] = f16(v4.z); + kv_shmem[kv_off + 3u] = f16(v4.w); + } +} +#endif + +@compute @workgroup_size(WG_SIZE) +fn main(@builtin(workgroup_id) wg_id: vec3, + @builtin(local_invocation_id) local_id: vec3, + @builtin(subgroup_id) subgroup_id: u32, + @builtin(subgroup_size) subgroup_size: u32, + @builtin(num_subgroups) num_subgroups: u32, + @builtin(subgroup_invocation_id) sg_inv_id: u32) { + if (subgroup_size == 0u || num_subgroups < Q_TILE) { + return; + } + + let wg_per_head = (params.seq_len_q + Q_TILE - 1u) / Q_TILE; + let wg_per_batch = wg_per_head * params.n_heads; + + let dst2_stride = HEAD_DIM_V * params.n_heads; + let dst3_stride = dst2_stride * params.seq_len_q; + + let batch_idx = wg_id.x / wg_per_batch; + let q_batch_offset = params.offset_q + batch_idx * params.stride_q3; + let k_batch_offset = params.offset_k + batch_idx * params.stride_k3; + let v_batch_offset = params.offset_v + batch_idx * params.stride_v3; + let dst_batch_offset = params.offset_dst + batch_idx * dst3_stride; + let wg_in_batch = wg_id.x % wg_per_batch; + + let head_idx = wg_in_batch / wg_per_head; + let q_head_offset = q_batch_offset + head_idx * params.stride_q2; + let k_head_idx = head_idx / params.q_per_kv; + let v_head_offset = v_batch_offset + k_head_idx * params.stride_v2; + let k_head_offset = k_batch_offset + k_head_idx * params.stride_k2; + + let wg_in_head = wg_in_batch % wg_per_head; + let q_row_start = wg_in_head * Q_TILE; + let global_q_row = q_row_start + subgroup_id; + let row_active = subgroup_id < Q_TILE && global_q_row < params.seq_len_q; + +#ifdef MASK + let mask_global_offset = params.offset_mask + batch_idx * params.stride_mask3 + q_row_start * params.seq_len_kv; +#endif + + let dst_global_offset = dst_batch_offset + q_row_start * dst2_stride + head_idx * HEAD_DIM_V; + + let head = f32(head_idx); + let slope = select(1.0, + select(pow(params.m1, 2.0 * (head - params.n_head_log2) + 1.0), + pow(params.m0, head + 1.0), + head < params.n_head_log2), + params.max_bias > 0.0); + + for (var elem_idx = local_id.x; elem_idx < Q_TILE * HEAD_DIM_QK; elem_idx += WG_SIZE) { + let q_tile_row = elem_idx / HEAD_DIM_QK; + let q_col = elem_idx % HEAD_DIM_QK; + let head_q_row = q_row_start + q_tile_row; + let global_q_row_offset = q_head_offset + head_q_row * params.stride_q1; + q_shmem[elem_idx] = select( + 0.0, + Q_TYPE(Q[global_q_row_offset + q_col]) * params.scale, + head_q_row < params.seq_len_q); + } + + workgroupBarrier(); + + var row_max = FLOAT_MIN; + var exp_sum = 0.0; + var out_regs: array, OUT_REGS_PER_LANE>; + for (var reg_idx = 0u; reg_idx < OUT_REGS_PER_LANE; reg_idx += 1u) { + out_regs[reg_idx] = vec4(0.0); + } + + let q_base = subgroup_id * HEAD_DIM_QK; + let subgroup_p_offset = subgroup_id * KV_TILE; + + for (var kv_tile = 0u; kv_tile < params.seq_len_kv; kv_tile += KV_TILE) { + let kv_count = min(KV_TILE, params.seq_len_kv - kv_tile); + let score_slots = min(SCORE_REGS_PER_LANE, (kv_count + subgroup_size - 1u) / subgroup_size); + let out_slots = min(OUT_REGS_PER_LANE, (V_CHUNKS + subgroup_size - 1u) / subgroup_size); + var local_scores: array; + for (var slot = 0u; slot < SCORE_REGS_PER_LANE; slot += 1u) { + local_scores[slot] = FLOAT_MIN; + } + +#ifndef KV_DIRECT + load_k_tile_block(local_id.x, kv_count, kv_tile, k_head_offset); +#endif + + workgroupBarrier(); + + var local_max = FLOAT_MIN; + if (row_active) { + for (var slot = 0u; slot < score_slots; slot += 1u) { + let kv_local = sg_inv_id + slot * subgroup_size; + if (kv_local >= kv_count) { + continue; + } + + let global_k_row = kv_tile + kv_local; + var dot_val = 0.0; + for (var chunk = 0u; chunk < Q_CHUNKS; chunk += 1u) { + let q_off = q_base + chunk * 4u; + let qv = vec4( + q_shmem[q_off + 0u], + q_shmem[q_off + 1u], + q_shmem[q_off + 2u], + q_shmem[q_off + 3u]); + let kv_off = kv_local * HEAD_DIM_QK + chunk * 4u; + let kv = vec4( + kv_shmem[kv_off + 0u], + kv_shmem[kv_off + 1u], + kv_shmem[kv_off + 2u], + kv_shmem[kv_off + 3u]); + dot_val += dot(vec4(qv), vec4(kv)); + } +#ifdef LOGIT_SOFTCAP + dot_val = params.logit_softcap * tanh(dot_val); +#endif +#ifdef MASK + let mask_idx = mask_global_offset + subgroup_id * params.seq_len_kv + global_k_row; + dot_val += slope * f32(mask[mask_idx]); +#endif + local_scores[slot] = dot_val; + local_max = max(local_max, dot_val); + } + } + + let tile_max = subgroupMax(local_max); + let new_max = max(row_max, tile_max); + let cur_exp = exp(row_max - new_max); + exp_sum *= cur_exp; + for (var reg_idx = 0u; reg_idx < OUT_REGS_PER_LANE; reg_idx += 1u) { + out_regs[reg_idx] *= cur_exp; + } + + var local_sum = 0.0; + for (var slot = 0u; slot < score_slots; slot += 1u) { + let kv_local = sg_inv_id + slot * subgroup_size; + if (row_active && kv_local < kv_count) { + let p = exp(local_scores[slot] - new_max); + p_shmem[subgroup_p_offset + kv_local] = f16(p); + local_sum += p; + } + } + + workgroupBarrier(); + +#ifndef KV_DIRECT + load_v_tile_block(local_id.x, kv_count, kv_tile, v_head_offset); +#endif + + workgroupBarrier(); + + let tile_sum = subgroupAdd(local_sum); + exp_sum += tile_sum; + row_max = new_max; + + if (row_active) { + for (var reg_idx = 0u; reg_idx < out_slots; reg_idx += 1u) { + let chunk = sg_inv_id + reg_idx * subgroup_size; + if (chunk >= V_CHUNKS) { + continue; + } + + var acc = out_regs[reg_idx]; + for (var kv_local = 0u; kv_local < kv_count; kv_local += 1u) { + let p = f32(p_shmem[subgroup_p_offset + kv_local]); + let kv_off = kv_local * HEAD_DIM_V + chunk * 4u; + let v4 = vec4( + kv_shmem[kv_off + 0u], + kv_shmem[kv_off + 1u], + kv_shmem[kv_off + 2u], + kv_shmem[kv_off + 3u]); + acc += p * vec4(v4); + } + out_regs[reg_idx] = acc; + } + } + + workgroupBarrier(); + } + +#ifdef SINKS + if (row_active) { + let sink_score = sinks[params.offset_sinks + head_idx]; + let sink_max = max(row_max, sink_score); + let sink_scale = exp(row_max - sink_max); + for (var reg_idx = 0u; reg_idx < OUT_REGS_PER_LANE; reg_idx += 1u) { + out_regs[reg_idx] *= sink_scale; + } + exp_sum = exp_sum * sink_scale + exp(sink_score - sink_max); + row_max = sink_max; + } +#endif + + if (row_active) { + let inv_exp_sum = select(0.0, 1.0 / exp_sum, exp_sum != 0.0); + let row_base = dst_global_offset + subgroup_id * dst2_stride; + let out_slots = min(OUT_REGS_PER_LANE, (V_CHUNKS + subgroup_size - 1u) / subgroup_size); + for (var reg_idx = 0u; reg_idx < out_slots; reg_idx += 1u) { + let chunk = sg_inv_id + reg_idx * subgroup_size; + if (chunk >= V_CHUNKS) { + continue; + } + let dst_vec_index = (row_base + chunk * 4u) >> 2u; + dst[dst_vec_index] = vec4(out_regs[reg_idx] * inv_exp_sum); + } + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn_vec_blk.wgsl b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn_vec_blk.wgsl new file mode 100644 index 0000000000000000000000000000000000000000..b4f7c16c35dbc37bef3340bf8a47bbe5ae87643a --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn_vec_blk.wgsl @@ -0,0 +1,101 @@ +diagnostic(off, subgroup_uniformity); +enable f16; + +#define KV_TILE 32 +#define WG_SIZE 32 + +struct Params { + offset_mask: u32, + seq_len_q: u32, + seq_len_kv: u32, + stride_mask3: u32, + // Number of KV blocks and Q blocks per batch. + // nblk0 = ceil(seq_len_kv / KV_TILE), nblk1 = seq_len_q. + nblk0: u32, + nblk1: u32, +}; + +@group(0) @binding(0) var mask: array; +@group(0) @binding(1) var blk: array; +@group(0) @binding(2) var params: Params; + +const MASK_MIN: f32 = -65504.0; +const MASK_MAX: f32 = 65504.0; +var wg_min: array; +var wg_max: array; +var wg_any: array; + +@compute @workgroup_size(WG_SIZE) +fn main(@builtin(workgroup_id) wg_id: vec3, + @builtin(local_invocation_id) local_id: vec3) { + // Dispatch mapping: + // - x indexes KV blocks + // - y flattens (batch_idx, q_blk) as y = batch_idx * nblk1 + q_blk + let kv_blk = wg_id.x; + let y = wg_id.y; + let q_blk = y % params.nblk1; + let batch_idx = y / params.nblk1; + if (kv_blk >= params.nblk0) { + return; + } + + let q_start = q_blk; + let k_start = kv_blk * KV_TILE; + + let mask_batch = select(0u, batch_idx, params.stride_mask3 > 0u); + let mask_batch_base = params.offset_mask + mask_batch * params.stride_mask3; + + // We keep min/max to classify: + // - fully masked (max <= MASK_MIN) + // - all-zero mask (min == 0 && max == 0) + // - mixed/general mask + var local_min = MASK_MAX; + var local_max = -MASK_MAX; + var local_any = 0u; + + let q_row = q_start; + if (q_row < params.seq_len_q) { + let row_base = mask_batch_base + q_row * params.seq_len_kv; + for (var k_rel = local_id.x; k_rel < KV_TILE; k_rel += WG_SIZE) { + let k_col = k_start + k_rel; + if (k_col >= params.seq_len_kv) { + continue; + } + let mv = f32(mask[row_base + k_col]); + local_min = min(local_min, mv); + local_max = max(local_max, mv); + local_any = 1u; + } + } + + wg_min[local_id.x] = local_min; + wg_max[local_id.x] = local_max; + wg_any[local_id.x] = local_any; + workgroupBarrier(); + + // Thread 0 writes one state per block. + if (local_id.x == 0u) { + var mmin = wg_min[0]; + var mmax = wg_max[0]; + var many = wg_any[0]; + for (var i = 1u; i < WG_SIZE; i += 1u) { + mmin = min(mmin, wg_min[i]); + mmax = max(mmax, wg_max[i]); + many = max(many, wg_any[i]); + } + + var state = 0u; + if (many != 0u) { + if (mmax <= MASK_MIN) { + state = 0u; + } else if (mmin == 0.0 && mmax == 0.0) { + state = 2u; + } else { + state = 1u; + } + } + + let blk_idx = (batch_idx * params.nblk1 + q_blk) * params.nblk0 + kv_blk; + blk[blk_idx] = state; + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn_vec_reduce.wgsl b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn_vec_reduce.wgsl new file mode 100644 index 0000000000000000000000000000000000000000..1091d744073ab33cb58f726bb737b9a1cd3e4218 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn_vec_reduce.wgsl @@ -0,0 +1,84 @@ +diagnostic(off, subgroup_uniformity); +enable f16; +enable subgroups; + +#ifdef DST_F16 +#define DST_TYPE f16 +#else +#define DST_TYPE f32 +#endif + +// Default values +#define HEAD_DIM_V 64 +#define WG_SIZE 128 + +struct Params { + nrows: u32, + seq_len_q: u32, + n_heads: u32, + offset_dst: u32, + nwg: u32, + tmp_data_base: u32, + tmp_stats_base: u32, +}; + +@group(0) @binding(0) var tmp: array; +@group(0) @binding(1) var dst: array>; +@group(0) @binding(2) var params: Params; + +const FLOAT_MIN: f32 = -1.0e9; + +@compute @workgroup_size(WG_SIZE) +fn main(@builtin(workgroup_id) wg_id: vec3, + @builtin(subgroup_id) subgroup_id: u32, + @builtin(num_subgroups) num_subgroups: u32, + @builtin(subgroup_size) subgroup_size: u32, + @builtin(subgroup_invocation_id) sg_inv_id: u32) { + let rid = wg_id.x; + if (rid >= params.nrows) { + return; + } + + let rows_per_batch = params.n_heads * params.seq_len_q; + let batch_idx = rid / rows_per_batch; + let rem = rid % rows_per_batch; + let head_idx = rem / params.seq_len_q; + let q_row = rem % params.seq_len_q; + + let dst2_stride = HEAD_DIM_V * params.n_heads; + let dst3_stride = dst2_stride * params.seq_len_q; + let row_base = params.offset_dst + batch_idx * dst3_stride + q_row * dst2_stride + head_idx * HEAD_DIM_V; + + let thread = sg_inv_id; + if (params.nwg > subgroup_size) { + return; + } + + let stats_base = params.tmp_stats_base + rid * (2u * params.nwg); + let active_thread = thread < params.nwg; + let si = select(0.0, tmp[stats_base + 2u * thread + 0u], active_thread); + let mi = select(FLOAT_MIN, tmp[stats_base + 2u * thread + 1u], active_thread); + let m = subgroupMax(mi); + let ms = select(0.0, exp(mi - m), active_thread); + let s = subgroupAdd(si * ms); + let inv_s = select(0.0, 1.0 / s, s != 0.0); + + let row_tmp_base = params.tmp_data_base + rid * (HEAD_DIM_V * params.nwg); + for (var elem_base = subgroup_id * 4u; elem_base < HEAD_DIM_V; elem_base += num_subgroups * 4u) { + var weighted = vec4(0.0, 0.0, 0.0, 0.0); + if (active_thread) { + let src = row_tmp_base + thread * HEAD_DIM_V + elem_base; + weighted = vec4(tmp[src + 0u], tmp[src + 1u], tmp[src + 2u], tmp[src + 3u]) * ms; + } + + let sum_x = subgroupAdd(weighted.x); + let sum_y = subgroupAdd(weighted.y); + let sum_z = subgroupAdd(weighted.z); + let sum_w = subgroupAdd(weighted.w); + + if (thread == 0u) { + let dst_vec_index = (row_base + elem_base) >> 2u; + dst[dst_vec_index] = vec4(vec4(sum_x, sum_y, sum_z, sum_w) * inv_s); + } + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn_vec_split.wgsl b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn_vec_split.wgsl new file mode 100644 index 0000000000000000000000000000000000000000..d5127624196b9d844e9190c7771fce9c137d07ab --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/flash_attn_vec_split.wgsl @@ -0,0 +1,619 @@ +diagnostic(off, subgroup_uniformity); +enable f16; +enable subgroups; + +#define BYTE_HELPERS +#include "common_decls.tmpl" + +#ifdef K_F32 +#define K_TYPE f32 +#elif defined(K_Q4_0) || defined(K_Q8_0) +#define K_TYPE u32 +#else +#define K_TYPE f16 +#endif + +#ifdef V_F32 +#define V_TYPE f32 +#elif defined(V_Q4_0) || defined(V_Q8_0) +#define V_TYPE u32 +#else +#define V_TYPE f16 +#endif + +#ifdef Q_F16 +#define Q_TYPE f16 +#else +#define Q_TYPE f32 +#endif + +#ifdef DST_F16 +#define DST_TYPE f16 +#else +#define DST_TYPE f32 +#endif + +#define HEAD_DIM_QK 64 +#define HEAD_DIM_V 64 + +#define KV_GRANULARITY 8 +#define KV_TILE 16 +#define WG_SIZE 64 + +#define KV_BLOCKS (KV_TILE / KV_GRANULARITY) + +struct Params { + offset_q: u32, + offset_k: u32, + offset_v: u32, + offset_mask: u32, + offset_sinks: u32, + offset_dst: u32, + + // shapes of Q/K/V + n_heads: u32, + seq_len_q: u32, + seq_len_kv: u32, + + // strides (in elements) + stride_q1: u32, + stride_q2: u32, + stride_q3: u32, + stride_k1: u32, + stride_k2: u32, + stride_k3: u32, + stride_v1: u32, + stride_v2: u32, + stride_v3: u32, + stride_mask3: u32, + + // repeat factors for K/V, e.g., MHA vs. MQA vs. GQA + q_per_kv: u32, + + // softmax params + scale: f32, + max_bias: f32, + logit_softcap: f32, + n_head_log2: f32, + m0: f32, + m1: f32, + +#ifdef BLK + blk_base: u32, + blk_nblk0: u32, + blk_nblk1: u32, +#endif + + tmp_data_base: u32, + tmp_stats_base: u32, + nwg: u32, +}; + +@group(0) @binding(0) var Q: array; +#ifdef KV_OVERLAP +#if defined(K_Q4_0) || defined(K_Q8_0) +@group(0) @binding(1) var K: array; +#else +@group(0) @binding(1) var K: array>; +#endif +#define V K +#else +#if defined(K_Q4_0) || defined(K_Q8_0) +@group(0) @binding(1) var K: array; +#else +@group(0) @binding(1) var K: array>; +#endif +#if defined(V_Q4_0) || defined(V_Q8_0) +@group(0) @binding(2) var V: array; +#else +@group(0) @binding(2) var V: array>; +#endif +#endif +#if defined(MASK) && defined(SINKS) +#ifdef KV_OVERLAP +@group(0) @binding(2) var mask: array; +@group(0) @binding(3) var sinks: array; +#ifdef BLK +#define BLK_BINDING 4 +#define TMP_BINDING 5 +#define DST_BINDING 6 +#define PARAMS_BINDING 7 +#else +#define TMP_BINDING 4 +#define DST_BINDING 5 +#define PARAMS_BINDING 6 +#endif +#else +@group(0) @binding(3) var mask: array; +@group(0) @binding(4) var sinks: array; +#ifdef BLK +#define BLK_BINDING 5 +#define TMP_BINDING 6 +#define DST_BINDING 7 +#define PARAMS_BINDING 8 +#else +#define TMP_BINDING 5 +#define DST_BINDING 6 +#define PARAMS_BINDING 7 +#endif +#endif +#elif defined(MASK) +#ifdef KV_OVERLAP +@group(0) @binding(2) var mask: array; +#ifdef BLK +#define BLK_BINDING 3 +#define TMP_BINDING 4 +#define DST_BINDING 5 +#define PARAMS_BINDING 6 +#else +#define TMP_BINDING 3 +#define DST_BINDING 4 +#define PARAMS_BINDING 5 +#endif +#else +@group(0) @binding(3) var mask: array; +#ifdef BLK +#define BLK_BINDING 4 +#define TMP_BINDING 5 +#define DST_BINDING 6 +#define PARAMS_BINDING 7 +#else +#define TMP_BINDING 4 +#define DST_BINDING 5 +#define PARAMS_BINDING 6 +#endif +#endif +#elif defined(SINKS) +#ifdef KV_OVERLAP +@group(0) @binding(2) var sinks: array; +#define TMP_BINDING 3 +#define DST_BINDING 4 +#define PARAMS_BINDING 5 +#else +@group(0) @binding(3) var sinks: array; +#define TMP_BINDING 4 +#define DST_BINDING 5 +#define PARAMS_BINDING 6 +#endif +#else +#ifdef KV_OVERLAP +#define TMP_BINDING 2 +#define DST_BINDING 3 +#define PARAMS_BINDING 4 +#else +#define TMP_BINDING 3 +#define DST_BINDING 4 +#define PARAMS_BINDING 5 +#endif +#endif + +#ifdef BLK +@group(0) @binding(BLK_BINDING) var blk: array; +#endif +@group(0) @binding(TMP_BINDING) var tmp: array; +@group(0) @binding(DST_BINDING) var dst: array>; +@group(0) @binding(PARAMS_BINDING) var params: Params; + +// Just a very small float value. +const FLOAT_MIN: f32 = -1.0e9; + +var q_shmem: array; + +#ifndef KV_DIRECT +const kv_shmem_size = KV_TILE * max(HEAD_DIM_QK, HEAD_DIM_V); +// we can reuse the same shmem for K and V since we only need one at a time +var kv_shmem: array; +#endif + +var o_shmem: array; + +#ifdef MASK +// storage for mask values +var mask_shmem: array; +#endif + +// note that we reuse the same storage for both since we only need one at a time +var inter_shmem: array; + +// Storage for row max and exp sum during online softmax +fn calc_softmax_term(kv_idx: u32, slope: f32, has_bias: bool, apply_mask: bool) -> f32 { + var v = select(FLOAT_MIN, + inter_shmem[kv_idx] * params.scale, + kv_idx < KV_TILE); +#ifdef LOGIT_SOFTCAP + v = params.logit_softcap * tanh(v); +#endif +#ifdef MASK + if (apply_mask) { + var mask_val = select(0.0, mask_shmem[kv_idx], kv_idx < KV_TILE); + v += select(mask_val, slope * mask_val, has_bias); + } +#endif + return v; +} + +#ifndef KV_DIRECT +#define QUANT_SHMEM kv_shmem +#define QUANT_OUT_TYPE f32 +#include "quant_inner_loops.tmpl" +#include "flash_attn_quant_staging.tmpl" + +#if !defined(K_Q4_0) && !defined(K_Q8_0) +fn load_k_tile_block(local_x: u32, kv_count: u32, kv_tile: u32, k_head_offset: u32) { + for (var elem_idx = local_x * 4u; elem_idx < KV_TILE * HEAD_DIM_QK; elem_idx += WG_SIZE * 4u) { + let k_row = elem_idx / HEAD_DIM_QK; + let k_col = elem_idx % HEAD_DIM_QK; + let global_k_row = kv_tile + k_row; + let global_k_row_offset = k_head_offset + global_k_row * params.stride_k1; + let in_bounds = global_k_row < params.seq_len_kv && (k_col + 3u) < HEAD_DIM_QK; + let vec_idx = (global_k_row_offset + k_col) >> 2u; + let k4 = select(vec4(0.0), K[vec_idx], in_bounds); + kv_shmem[elem_idx + 0u] = f32(k4.x); + kv_shmem[elem_idx + 1u] = f32(k4.y); + kv_shmem[elem_idx + 2u] = f32(k4.z); + kv_shmem[elem_idx + 3u] = f32(k4.w); + } +} +#endif + +#if !defined(V_Q4_0) && !defined(V_Q8_0) +fn load_v_tile_block(local_x: u32, kv_count: u32, kv_tile: u32, v_head_offset: u32) { + for (var elem_idx = local_x * 4u; elem_idx < KV_TILE * HEAD_DIM_V; elem_idx += WG_SIZE * 4u) { + let v_row = elem_idx / HEAD_DIM_V; + let v_col = elem_idx % HEAD_DIM_V; + let global_v_row = kv_tile + v_row; + let global_v_row_offset = v_head_offset + global_v_row * params.stride_v1; + let in_bounds = global_v_row < params.seq_len_kv && (v_col + 3u) < HEAD_DIM_V; + let vec_idx = (global_v_row_offset + v_col) >> 2u; + let v4 = select(vec4(0.0), V[vec_idx], in_bounds); + kv_shmem[elem_idx + 0u] = f32(v4.x); + kv_shmem[elem_idx + 1u] = f32(v4.y); + kv_shmem[elem_idx + 2u] = f32(v4.z); + kv_shmem[elem_idx + 3u] = f32(v4.w); + } +} +#endif +#endif + +@compute @workgroup_size(WG_SIZE) +fn main(@builtin(workgroup_id) wg_id: vec3, + @builtin(local_invocation_id) local_id: vec3, + @builtin(subgroup_id) subgroup_id: u32, + @builtin(subgroup_size) subgroup_size: u32, + @builtin(num_subgroups) num_subgroups: u32, + @builtin(subgroup_invocation_id) sg_inv_id: u32) { + // Vec path processes exactly one query row per workgroup, so subgroup 0 can + // keep the running softmax state in private storage. + var row_max = FLOAT_MIN; + var exp_sum = 0.0; + + for (var i = local_id.x; i < HEAD_DIM_V; i += WG_SIZE) { + o_shmem[i] = 0.0; + } + + // workgroups per head/batch + let wg_per_head = params.seq_len_q; + let wg_per_batch = wg_per_head * params.n_heads; + + let dst2_stride = HEAD_DIM_V * params.n_heads; + let dst3_stride = dst2_stride * params.seq_len_q; + + let iwg = wg_id.x % params.nwg; + let base_wg_id = wg_id.x / params.nwg; + + // batch index + let batch_idx = base_wg_id / wg_per_batch; + let q_batch_offset = params.offset_q + batch_idx * params.stride_q3; + let k_batch_offset = params.offset_k + batch_idx * params.stride_k3; + let v_batch_offset = params.offset_v + batch_idx * params.stride_v3; + let wg_in_batch = base_wg_id % wg_per_batch; + + // head index + let head_idx = wg_in_batch / wg_per_head; + let q_head_offset = q_batch_offset + head_idx * params.stride_q2; + let k_head_idx = head_idx / params.q_per_kv; + let v_head_idx = k_head_idx; + let k_head_offset = k_batch_offset + k_head_idx * params.stride_k2; + let v_head_offset = v_batch_offset + v_head_idx * params.stride_v2; + + // Vec path handles one Q row per workgroup. + let wg_in_head = wg_in_batch % wg_per_head; + let q_row_start = wg_in_head; + +#ifdef MASK + // mask offset + let mask_global_offset = params.offset_mask + batch_idx * params.stride_mask3 + q_row_start * params.seq_len_kv; +#endif + + let head = f32(head_idx); + let has_bias = params.max_bias > 0.0; + let slope = select(1.0, select(pow(params.m1, 2.0 * (head - params.n_head_log2) + 1.0), pow(params.m0, head + 1.0), head < params.n_head_log2), has_bias); + + // load the single Q row into shared memory + for (var elem_idx = local_id.x; elem_idx < HEAD_DIM_QK; elem_idx += WG_SIZE) { + let global_q_row_offset = q_head_offset + q_row_start * params.stride_q1; + q_shmem[elem_idx] = select( + 0.0, + f32(Q[global_q_row_offset + elem_idx]), + q_row_start < params.seq_len_q); + } + + for (var kv_tile = iwg * KV_TILE; kv_tile < params.seq_len_kv; kv_tile += KV_TILE * params.nwg) { + let kv_count = min(KV_TILE, params.seq_len_kv - kv_tile); +#ifdef BLK + let q_blk = q_row_start; + let kv_blk = kv_tile / KV_TILE; + let blk_batch = select(0u, batch_idx, params.stride_mask3 > 0u); + let blk_idx = params.blk_base + (blk_batch * params.blk_nblk1 + q_blk) * params.blk_nblk0 + kv_blk; + let blk_state_local = blk[blk_idx]; +#else + let blk_state_local = 1u; +#endif + let blk_state = blk_state_local; + let skip_tile = blk_state == 0u; + for (var elem_idx = local_id.x; elem_idx < KV_TILE; elem_idx += WG_SIZE) { + inter_shmem[elem_idx] = 0.0; + } + + // load k tile into shared memory +#ifndef KV_DIRECT + load_k_tile_block(local_id.x, kv_count, kv_tile, k_head_offset); +#endif + + workgroupBarrier(); + + // accumulate q block * k block into registers across the entire KV tile + if (!skip_tile) { + let num_of_threads:u32 = D_SPLIT; + let tx = sg_inv_id % num_of_threads; + let ty = sg_inv_id / num_of_threads; + if (subgroup_id == 0u && q_row_start < params.seq_len_q) { + for (var kv_base : u32 = 0u; kv_base < KV_TILE; kv_base += subgroup_size / D_SPLIT) { + let kv_idx = kv_base + ty; + var partial_sum: f32 = 0.0; + let kv_valid = kv_idx < KV_TILE && (kv_tile + kv_idx) < params.seq_len_kv; + if (kv_valid) { + for (var i = tx; i < (HEAD_DIM_QK / 4u); i += num_of_threads) { + let q_off = i * 4u; + + let qv = vec4( + q_shmem[q_off + 0u], + q_shmem[q_off + 1u], + q_shmem[q_off + 2u], + q_shmem[q_off + 3u]); +#ifdef KV_DIRECT + let idx = k_head_offset + (kv_tile + kv_idx) * params.stride_k1 + (i * 4u); + let kv = vec4(K[idx >> 2u]); +#else + let idx = kv_idx * HEAD_DIM_QK + (i * 4u); + let kv = vec4( + kv_shmem[idx + 0u], + kv_shmem[idx + 1u], + kv_shmem[idx + 2u], + kv_shmem[idx + 3u]); +#endif + partial_sum += dot(qv, kv); + } + } + var sum = partial_sum; + // Reduce over tx threads (NL) for this ty stripe. + var tx_delta = num_of_threads >> 1u; + loop { + if (tx_delta == 0u) { + break; + } + let sh = subgroupShuffleDown(sum, tx_delta); + if (tx < tx_delta) { + sum += sh; + } + tx_delta >>= 1u; + } + + let sum_bcast = subgroupShuffle(sum, num_of_threads * ty); + if (tx == 0u && kv_valid) { + inter_shmem[kv_idx] = sum_bcast; + } + } + } + } + + +#ifdef MASK + let apply_mask = !skip_tile && (blk_state != 2u); + if (apply_mask) { + // load mask tile into shared memory for this KV block + for (var elem_idx = local_id.x; elem_idx < KV_TILE; elem_idx += WG_SIZE) { + let global_k_col = kv_tile + elem_idx; + let mask_in_bounds = q_row_start < params.seq_len_q && global_k_col < params.seq_len_kv; + let mask_idx = mask_global_offset + global_k_col; + mask_shmem[elem_idx] = select(0.0f, f32(mask[mask_idx]), mask_in_bounds); + } + } +#else + let apply_mask = false; +#endif + + workgroupBarrier(); + + // online softmax + if (!skip_tile && subgroup_id == 0u && q_row_start < params.seq_len_q) { + var prev_max = row_max; + var final_max = prev_max; + // pass 1: compute final max across the full KV tile in chunks + for (var kv_offset = 0u; kv_offset < KV_TILE; kv_offset += subgroup_size) { + let kv_idx = kv_offset + sg_inv_id; + let kv_valid = kv_tile + kv_idx < params.seq_len_kv && kv_idx < KV_TILE; + let softmax_term = select(FLOAT_MIN, + calc_softmax_term(kv_idx, slope, has_bias, apply_mask), + kv_valid); + final_max = subgroupMax(max(final_max, softmax_term)); + } + + var total_exp_term: f32 = 0.0; + // pass 2: compute exp sum and write P using final_max + for (var kv_offset = 0u; kv_offset < KV_TILE; kv_offset += subgroup_size) { + let kv_idx = kv_offset + sg_inv_id; + let softmax_term = calc_softmax_term(kv_idx, slope, has_bias, apply_mask); + let cur_p = select(0.0, + exp(softmax_term - final_max), + kv_tile + kv_idx < params.seq_len_kv && kv_idx < KV_TILE); + total_exp_term += subgroupAdd(cur_p); + if (kv_idx < KV_TILE) { + inter_shmem[kv_idx] = cur_p; + } + } + + let cur_exp = exp(prev_max - final_max); + + row_max = final_max; + exp_sum = exp_sum * cur_exp + total_exp_term; + + for (var elem_idx = sg_inv_id; elem_idx < HEAD_DIM_V; elem_idx += subgroup_size) { + o_shmem[elem_idx] = o_shmem[elem_idx] * cur_exp; + } + } + + // load v tile into shared memory +#ifndef KV_DIRECT + load_v_tile_block(local_id.x, kv_count, kv_tile, v_head_offset); +#endif + + workgroupBarrier(); + + if (!skip_tile) { + // we have P (KV_TILE) in inter_shmem and V (KV_TILE x head_dim_v) in kv_shmem + // we want to compute O += P * V across the full KV tile + let ne_threads : u32 = subgroup_size / D_SPLIT; + let nl_threads = max(1u, subgroup_size / ne_threads); + let tx_pv = sg_inv_id % nl_threads; + let ty_pv = sg_inv_id / nl_threads; + if (subgroup_id == 0u && q_row_start < params.seq_len_q) { + for (var vec_col = tx_pv; vec_col < (HEAD_DIM_V / 4u); vec_col += nl_threads) { + var lo = vec4(0.0, 0.0, 0.0, 0.0); + for (var cc = 0u; cc * ne_threads < KV_TILE; cc += 1u) { + let kv_idx = cc * ne_threads + ty_pv; + if (kv_idx >= KV_TILE) { + continue; + } + let v_row = kv_tile + kv_idx; + if (v_row >= params.seq_len_kv) { + continue; + } + + let p = inter_shmem[kv_idx]; +#ifdef KV_DIRECT + let v_idx = v_head_offset + v_row * params.stride_v1 + vec_col * 4u; + let v4 = vec4(V[v_idx >> 2u]); +#else + let v_idx = kv_idx * HEAD_DIM_V + vec_col * 4u; + let v4 = vec4( + kv_shmem[v_idx + 0u], + kv_shmem[v_idx + 1u], + kv_shmem[v_idx + 2u], + kv_shmem[v_idx + 3u]); +#endif + lo += p * v4; + } + + var lo_x = lo.x; + var lo_y = lo.y; + var lo_z = lo.z; + var lo_w = lo.w; + // Reduce over ty threads (NE) for this tx thread. + var ty_delta = ne_threads >> 1u; + loop { + if (ty_delta == 0u) { + break; + } + let thread_delta = ty_delta * nl_threads; + let shx = subgroupShuffleDown(lo_x, thread_delta); + let shy = subgroupShuffleDown(lo_y, thread_delta); + let shz = subgroupShuffleDown(lo_z, thread_delta); + let shw = subgroupShuffleDown(lo_w, thread_delta); + if (ty_pv < ty_delta) { + lo_x += shx; + lo_y += shy; + lo_z += shz; + lo_w += shw; + } + ty_delta >>= 1u; + } + + if (ty_pv == 0u) { + let elem_base = vec_col * 4u; + o_shmem[elem_base + 0u] = o_shmem[elem_base + 0u] + lo_x; + o_shmem[elem_base + 1u] = o_shmem[elem_base + 1u] + lo_y; + o_shmem[elem_base + 2u] = o_shmem[elem_base + 2u] + lo_z; + o_shmem[elem_base + 3u] = o_shmem[elem_base + 3u] + lo_w; + } + } + } + } + + workgroupBarrier(); + } + + +#ifdef SINKS + // Sinks are global terms and must be applied exactly once across split workgroups. + if (iwg == 0u && subgroup_id == 0u && q_row_start < params.seq_len_q) { + var prev_max = row_max; + + // for non-sink threads, exp(FLOAT_MIN) effectively zeroes out their contribution to the sum + let sink_val = select(FLOAT_MIN, sinks[params.offset_sinks + head_idx], sg_inv_id == 0u); + let new_max = subgroupMax(max(prev_max, sink_val)); + let max_exp = exp(prev_max - new_max); + let sink_exp = exp(sink_val - new_max); + + let sink_exp_sum = subgroupAdd(sink_exp); + + row_max = new_max; + exp_sum = exp_sum * max_exp + sink_exp_sum; + + for (var elem_idx = sg_inv_id; elem_idx < HEAD_DIM_V; elem_idx += subgroup_size) { + o_shmem[elem_idx] = o_shmem[elem_idx] * max_exp; + } + } + workgroupBarrier(); +#endif + let rows_per_batch = params.n_heads * params.seq_len_q; + if (subgroup_id == 0u && q_row_start < params.seq_len_q) { + if (params.nwg == 1u) { + let scale = select(0.0, 1.0 / exp_sum, exp_sum != 0.0); + let row_base: u32 = params.offset_dst + batch_idx * dst3_stride + q_row_start * dst2_stride + + head_idx * HEAD_DIM_V; + + for (var elem_base = sg_inv_id * 4u; elem_base < HEAD_DIM_V; elem_base += subgroup_size * 4u) { + let v = vec4( + f32(o_shmem[elem_base + 0u]) * scale, + f32(o_shmem[elem_base + 1u]) * scale, + f32(o_shmem[elem_base + 2u]) * scale, + f32(o_shmem[elem_base + 3u]) * scale + ); + + let dst_vec_index: u32 = (row_base + elem_base) >> 2u; + dst[dst_vec_index] = vec4(v); + } + } else { + let rid = batch_idx * rows_per_batch + head_idx * params.seq_len_q + q_row_start; + let tmp_row_data_base = params.tmp_data_base + rid * (HEAD_DIM_V * params.nwg) + iwg * HEAD_DIM_V; + let tmp_row_stats_base = params.tmp_stats_base + rid * (2u * params.nwg) + 2u * iwg; + + for (var elem_base = sg_inv_id * 4u; + elem_base < HEAD_DIM_V; + elem_base += subgroup_size * 4u) { + + let tbase = tmp_row_data_base + elem_base; + tmp[tbase + 0u] = f32(o_shmem[elem_base + 0u]); + tmp[tbase + 1u] = f32(o_shmem[elem_base + 1u]); + tmp[tbase + 2u] = f32(o_shmem[elem_base + 2u]); + tmp[tbase + 3u] = f32(o_shmem[elem_base + 3u]); + } + + if (sg_inv_id == 0u) { + tmp[tmp_row_stats_base + 0u] = exp_sum; + tmp[tmp_row_stats_base + 1u] = row_max; + } + } + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/gated_delta_net.wgsl b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/gated_delta_net.wgsl new file mode 100644 index 0000000000000000000000000000000000000000..7d7b3475549321ad1ece9934dc9c6bac834a22a2 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/gated_delta_net.wgsl @@ -0,0 +1,149 @@ +@group(0) @binding(0) +var src_q: array; + +@group(0) @binding(1) +var src_k: array; + +@group(0) @binding(2) +var src_v: array; + +@group(0) @binding(3) +var src_g: array; + +@group(0) @binding(4) +var src_beta: array; + +@group(0) @binding(5) +var src_state: array; + +@group(0) @binding(6) +var dst: array; + +struct Params { + h: u32, + n_tokens: u32, + n_seqs: u32, + s_off: u32, + + sq1: u32, + sq2: u32, + sq3: u32, + + sv1: u32, + sv2: u32, + sv3: u32, + + sb1: u32, + sb2: u32, + sb3: u32, + + neq1: u32, + rq3: u32, + K: u32, + scale: f32, +}; + +@group(0) @binding(7) +var params: Params; + +var sh_k: array; +var sh_q: array; +var sh_g: array; + +@compute @workgroup_size(WG_SIZE) +fn main( + @builtin(workgroup_id) workgroup_id: vec3, + @builtin(local_invocation_id) local_id: vec3 +) { + let head_id = workgroup_id.x; + let seq_id = workgroup_id.y; + let col = local_id.x; + + let iq1 = head_id % params.neq1; + let iq3 = seq_id / params.rq3; + + let state_size = S_V * S_V; + // input state holds s0 only [S_v, S_v, H, n_seqs]: per-seq stride is H*D. + let state_in_base = (seq_id * params.h + head_id) * state_size; + let state_out_base = (seq_id * params.h + head_id) * state_size; + let state_size_per_snap = state_size * params.h * params.n_seqs; + + var state: array; + for (var i = 0u; i < S_V; i++) { + state[i] = src_state[state_in_base + col * S_V + i]; + } + + var attn_off = (seq_id * params.n_tokens * params.h + head_id) * S_V; + + for (var t = 0u; t < params.n_tokens; t++) { + let q_off = iq3 * params.sq3 + t * params.sq2 + iq1 * params.sq1; + let k_off = q_off; + let v_off = seq_id * params.sv3 + t * params.sv2 + head_id * params.sv1; + let gb_off = seq_id * params.sb3 + t * params.sb2 + head_id * params.sb1; + + sh_q[col] = src_q[q_off + col]; + sh_k[col] = src_k[k_off + col]; + +#ifdef KDA + let g_base = gb_off * S_V; + sh_g[col] = exp(src_g[g_base + col]); +#endif + + workgroupBarrier(); + + let v_val = src_v[v_off + col]; + let beta_val = src_beta[gb_off]; + + var kv_col = 0.0; + var delta_col = 0.0; + var attn_col = 0.0; + +#ifdef KDA + for (var i = 0u; i < S_V; i++) { + kv_col += (sh_g[i] * state[i]) * sh_k[i]; + } + + delta_col = (v_val - kv_col) * beta_val; + + for (var i = 0u; i < S_V; i++) { + state[i] = sh_g[i] * state[i] + sh_k[i] * delta_col; + attn_col += state[i] * sh_q[i]; + } +#else + let g_val = exp(src_g[gb_off]); + + for (var i = 0u; i < S_V; i++) { + kv_col += state[i] * sh_k[i]; + } + + delta_col = (v_val - g_val * kv_col) * beta_val; + + for (var i = 0u; i < S_V; i++) { + state[i] = g_val * state[i] + sh_k[i] * delta_col; + attn_col += state[i] * sh_q[i]; + } +#endif + + dst[attn_off + col] = attn_col * params.scale; + attn_off += S_V * params.h; + + if (params.K > 1u) { + // snapshot slot mapping: slot 0 = most recent state, slot s = s tokens back. + let target_slot = i32(params.n_tokens) - 1 - i32(t); + if (target_slot >= 0 && target_slot < i32(params.K)) { + let slot_base = params.s_off + u32(target_slot) * state_size_per_snap + state_out_base; + for (var i = 0u; i < S_V; i++) { + dst[slot_base + col * S_V + i] = state[i]; + } + } + } + + workgroupBarrier(); + } + + if (params.K == 1u) { + for (var i = 0u; i < S_V; i++) { + dst[params.s_off + state_out_base + col * S_V + i] = state[i]; + } + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/get_rows.wgsl b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/get_rows.wgsl new file mode 100644 index 0000000000000000000000000000000000000000..487edb3275f3080a88ab24fef5e7783cd7d72bea --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/get_rows.wgsl @@ -0,0 +1,794 @@ +enable f16; +#define DECLARE_BYTE_LOADERS_SRC +#include "common_decls.tmpl" + + +#ifdef F32_VEC +fn copy_elements(src_base: u32, dst_base: u32, offset: u32) { + dst[(dst_base / 4) + offset] = src[(src_base / 4) + offset]; +} +#endif + +#ifdef F32 +fn copy_elements(src_base: u32, dst_base: u32, offset: u32) { + dst[dst_base + offset] = src[src_base + offset]; +} +#endif + +#ifdef F16 +fn copy_elements(src_base: u32, dst_base: u32, offset: u32) { + dst[dst_base + offset] = f32(src[src_base + offset]); +} +#endif + +#ifdef I32 +fn copy_elements(src_base: u32, dst_base: u32, offset: u32) { + dst[dst_base + offset] = src[src_base + offset]; +} +#endif + +#ifdef Q1_0 +fn copy_elements(src_base: u32, dst_base: u32, offset: u32) { + let block_byte_base = (src_base + offset) * 18; + let d = load_f16_as_f32_at_src(block_byte_base); + for (var j: u32 = 0u; j < 4u; j++) { + let q_packed = load_u32_at_src(block_byte_base + 2u + j * 4u); + let dst_base128 = dst_base + offset * 128u + j * 32u; + for (var k: u32 = 0; k < 4u; k++) { + let q_byte = get_byte(q_packed, k); + for (var bit: u32 = 0; bit < 8u; bit++) { + let w = select(-d, d, ((q_byte >> bit) & 1u) != 0u); + dst[dst_base128 + k * 8u + bit] = w; + } + } + } +} +#endif + +#ifdef Q4_0 +fn copy_elements(src_base: u32, dst_base: u32, offset: u32) { + let block_byte_base = (src_base + offset) * 18; // Block stride: 18 bytes + let d = load_f16_as_f32_at_src(block_byte_base); + for (var j: u32 = 0u; j < 4; j++) { + let q_byte_offset = block_byte_base + 2 + j * 4; + let q_packed = load_u32_at_src(q_byte_offset); + for (var k: u32 = 0; k < 4; k++) { + let q_byte = get_byte(q_packed, k); + let q_hi = (f32((q_byte >> 4) & 0xF) - 8.0) * d; + let q_lo = (f32(q_byte & 0xFu) - 8.0) * d; + let dst_offset = dst_base + offset * 32 + j * 4 + k; + dst[dst_offset] = q_lo; + dst[dst_offset + 16u] = q_hi; + } + } +} +#endif + +#ifdef Q4_1 +fn copy_elements(src_base: u32, dst_base: u32, offset: u32) { + let block_q4_1 = src[src_base + offset]; + let d = f32(block_q4_1.d); + let m = f32(block_q4_1.m); + for (var j: u32 = 0; j < 4; j++) { + let q_packed = block_q4_1.qs[j]; + for (var k: u32 = 0; k < 4; k++) { + let q_byte = get_byte(q_packed, k); + let q_hi = f32((q_byte >> 4) & 0xF) * d + m; + let q_lo = f32(q_byte & 0xF) * d + m; + let dst_offset = dst_base + offset * 32 + j * 4 + k; + dst[dst_offset] = q_lo; + dst[dst_offset + 16] = q_hi; + } + } +} +#endif + +#ifdef Q5_0 +fn copy_elements(src_base: u32, dst_base: u32, offset: u32) { + let block_byte_base = (src_base + offset) * 22; // Block stride: 22 bytes + let d = load_f16_as_f32_at_src(block_byte_base); + let qh_packed = load_u32_at_src(block_byte_base + 2); + for (var j: u32 = 0; j < 4; j++) { + let q_byte_offset = block_byte_base + 6 + j * 4; + let q_packed = load_u32_at_src(q_byte_offset); + + for (var k: u32 = 0; k < 4; k++) { + let q_byte = get_byte(q_packed, k); + + let qh_hi = (qh_packed >> (j * 4 + k + 12)) & 0x10; + let q_hi = (f32(((q_byte >> 4) & 0xF) | qh_hi) - 16.0) * d; + + let qh_lo = ((qh_packed >> (j * 4 + k)) << 4) & 0x10; + let q_lo = (f32((q_byte & 0xF) | qh_lo) - 16.0) * d; + + let dst_offset = dst_base + offset * 32 + j * 4 + k; + dst[dst_offset] = q_lo; + dst[dst_offset + 16] = q_hi; + } + } +} +#endif + +#ifdef Q5_1 +fn copy_elements(src_base: u32, dst_base: u32, offset: u32) { + let block_q5_1 = src[src_base + offset]; + let d = f32(block_q5_1.d); + let m = f32(block_q5_1.m); + for (var j: u32 = 0; j < 4; j++) { + let q_packed = block_q5_1.qs[j]; + for (var k: u32 = 0; k < 4; k++) { + let q_byte = get_byte(q_packed, k); + let qh_hi = (block_q5_1.qh >> (j * 4 + k + 12)) & 0x10; + let q_hi = f32(((q_byte >> 4) & 0xF) | qh_hi) * d + m; + let qh_lo = ((block_q5_1.qh >> (j * 4 + k)) << 4) & 0x10; + let q_lo = f32((q_byte & 0xF) | qh_lo) * d + m; + let dst_offset = dst_base + offset * 32 + j * 4 + k; + dst[dst_offset] = q_lo; + dst[dst_offset + 16] = q_hi; + } + } +} +#endif + +#ifdef Q8_0 +fn copy_elements(src_base: u32, dst_base: u32, offset: u32) { + let block_byte_base = (src_base + offset) * 34; // Block stride: 34 bytes + let d = load_f16_as_f32_at_src(block_byte_base); + for (var j: u32 = 0u; j < 8u; j++) { + let q_byte_offset = block_byte_base + 2u + j * 4u; + let q_packed = load_u32_at_src(q_byte_offset); + for (var k: u32 = 0u; k < 4u; k++) { + let q_byte = get_byte_i32(q_packed, k); + let q_val = f32(q_byte) * d; + let dst_offset = dst_base + offset * 32u + j * 4u + k; + dst[dst_offset] = q_val; + } + } +} +#endif + +#ifdef Q2_K +fn copy_elements(src_base: u32, dst_base: u32, offset: u32) { + let block = src[src_base + offset]; + let d = f32(block.d); + let m = f32(block.dmin); + var dst_i = dst_base + offset * 256; + var is: u32 = 0; + // 2 halves of the block (128 elements each) + for (var q_b_idx: u32 = 0; q_b_idx < 64; q_b_idx += 32) { + // 4 groups (each group has 2 blocks of 16 elements) + for (var shift: u32 = 0; shift < 8; shift += 2) { + // 2 blocks + for (var k: u32 = 0; k < 32; k += 16) { + let sc = get_byte(block.scales[is / 4], is % 4); + is++; + let dl = d * f32(sc & 0xF); + let ml = m * f32(sc >> 4); + for (var l: u32 = 0u; l < 16; l++) { + let q_idx = q_b_idx + k + l; + let q_byte = get_byte(block.qs[q_idx / 4], q_idx % 4); + let qs_val = (q_byte >> shift) & 3; + dst[dst_i] = (f32(qs_val) * dl - ml); + dst_i++; + } + } + } + } +} +#endif + +#ifdef Q3_K +fn copy_elements(src_base: u32, dst_base: u32, offset: u32) { + let block_byte_base = (src_base + offset) * 110; // Block stride: 110 bytes + + // Bytes 108-109: f16 scale 'd' + let d = load_f16_as_f32_at_src(block_byte_base + 108); + + // Bytes 96-107: 12 bytes of scales (3 u32s) + let kmask1: u32 = 0x03030303; + let kmask2: u32 = 0x0f0f0f0f; + + var scale_vals: array; + scale_vals[0] = load_u32_at_src(block_byte_base + 96); + scale_vals[1] = load_u32_at_src(block_byte_base + 100); + scale_vals[2] = load_u32_at_src(block_byte_base + 104); + + var tmp: u32 = scale_vals[2]; + scale_vals[2] = ((scale_vals[0] >> 4) & kmask2) | (((tmp >> 4) & kmask1) << 4); + scale_vals[3] = ((scale_vals[1] >> 4) & kmask2) | (((tmp >> 6) & kmask1) << 4); + scale_vals[0] = (scale_vals[0] & kmask2) | ((tmp & kmask1) << 4); + scale_vals[1] = (scale_vals[1] & kmask2) | (((tmp >> 2) & kmask1) << 4); + + // Bytes 0-31: 32 bytes of hmask (8 u32s) + var hmask_vals: array; + for (var i: u32 = 0; i < 8; i++) { + hmask_vals[i] = load_u32_at_src(block_byte_base + i * 4); + } + + // Bytes 32-95: 64 bytes of qs (16 u32s) + var qs_vals: array; + for (var i: u32 = 0u; i < 16; i++) { + qs_vals[i] = load_u32_at_src(block_byte_base + 32 + i * 4); + } + + var dst_i = dst_base + offset * 256; + var is: u32 = 0; + var m: u32 = 1; + + // 2 halves of the block (128 elements each) + for (var q_b_idx: u32 = 0; q_b_idx < 64; q_b_idx += 32) { + // 4 groups (each group has 2 blocks of 16 elements) + for (var shift: u32 = 0; shift < 8; shift += 2) { + // 2 blocks + for (var k: u32 = 0; k < 32; k += 16) { + let sc = get_byte(scale_vals[is / 4], is % 4); + is++; + let dl = d * (f32(sc) - 32.0); + + for (var l: u32 = 0; l < 16; l++) { + let q_idx = q_b_idx + k + l; + let hm_idx = k + l; + let q_byte = get_byte(qs_vals[q_idx / 4], q_idx % 4); + let hmask_byte = get_byte(hmask_vals[hm_idx / 4], hm_idx % 4); + + let hm = select(4.0, 0.0, (hmask_byte & m) != 0); + let qs_val = (q_byte >> shift) & 3; + dst[dst_i] = (f32(qs_val) - hm) * dl; + dst_i++; + } + } + m <<= 1; + } + } +} +#endif + +#ifdef Q4_K +// 8 blocks of 32 elements each +fn copy_elements(src_base: u32, dst_base: u32, offset: u32) { + let block = src[src_base + offset]; + let d = f32(block.d); + let m = f32(block.dmin); + var dst_i = dst_base + offset * 256; + var is: u32 = 0; + // 2 blocks each iteration + for (var q_b_idx: u32 = 0; q_b_idx < 128; q_b_idx += 32) { + for (var shift: u32 = 0; shift < 8; shift += 4) { + let scale_min = get_scale_min(is, block.scales); + is++; + let dl = d * scale_min.x; + let ml = m * scale_min.y; + for (var l: u32 = 0; l < 32; l++) { + let q_idx = q_b_idx + l; + let q_byte = get_byte(block.qs[q_idx / 4], q_idx % 4); + let qs_val = (q_byte >> shift) & 0xF; + dst[dst_i] = (f32(qs_val) * dl - ml); + dst_i++; + } + } + } +} +#endif + +#ifdef Q5_K +fn copy_elements(src_base: u32, dst_base: u32, offset: u32) { + let block = src[src_base + offset]; + let d = f32(block.d); + let m = f32(block.dmin); + var dst_i = dst_base + offset * 256; + var is: u32 = 0; + var u: u32 = 1; + // 2 blocks each iteration + for (var q_b_idx: u32 = 0; q_b_idx < 128; q_b_idx += 32) { + for (var shift: u32 = 0; shift < 8; shift += 4) { + let scale_min = get_scale_min(is, block.scales); + is++; + let dl = d * scale_min.x; + let ml = m * scale_min.y; + for (var l: u32 = 0; l < 32; l++) { + let q_idx = q_b_idx + l; + let q_byte = get_byte(block.qs[q_idx / 4], q_idx % 4); + let qh_byte = get_byte(block.qh[l / 4], l % 4); + let qs_val = (q_byte >> shift) & 0xF; + let qh_val = select(0.0, 16.0, (qh_byte & u) != 0); + dst[dst_i] = (f32(qs_val) + qh_val) * dl - ml; + dst_i++; + } + u <<= 1; + } + } +} +#endif + +#ifdef Q6_K +// 16 blocks of 16 elements each +fn copy_elements(src_base: u32, dst_base: u32, offset: u32) { + let block_byte_base = (src_base + offset) * 210; // Block stride: 210 bytes + + // Bytes 208-209: f16 scale 'd' + let d = load_f16_as_f32_at_src(block_byte_base + 208); + + // Bytes 0-127: 128 bytes of ql (32 u32s) + var ql_vals: array; + for (var i: u32 = 0; i < 32; i++) { + ql_vals[i] = load_u32_at_src(block_byte_base + i * 4); + } + + // Bytes 128-191: 64 bytes of qh (16 u32s) + var qh_vals: array; + for (var i: u32 = 0; i < 16u; i++) { + qh_vals[i] = load_u32_at_src(block_byte_base + 128 + i * 4u); + } + + // Bytes 192-207: 16 bytes of scales (4 u32s) + var scale_vals: array; + for (var i: u32 = 0; i < 4; i++) { + scale_vals[i] = load_u32_at_src(block_byte_base + 192 + i * 4); + } + + var dst_i = dst_base + offset * 256; + var qh_b_idx: u32 = 0; + var sc_b_idx: u32 = 0; + for (var ql_b_idx: u32 = 0; ql_b_idx < 128; ql_b_idx += 64) { + for (var l: u32 = 0; l < 32; l++) { + let ql13_b = get_byte(ql_vals[(ql_b_idx + l) / 4], (ql_b_idx + l) % 4); + let ql24_b = get_byte(ql_vals[(ql_b_idx + l + 32) / 4], (ql_b_idx + l + 32) % 4); + let qh_b = get_byte(qh_vals[(qh_b_idx + l) / 4], (qh_b_idx + l) % 4); + + let q1 = f32((ql13_b & 0xF) | ((qh_b & 3) << 4)) - 32.0; + let q2 = f32((ql24_b & 0xF) | (((qh_b >> 2) & 3) << 4)) - 32.0; + let q3 = f32((ql13_b >> 4) | (((qh_b >> 4) & 3) << 4)) - 32.0; + let q4 = f32((ql24_b >> 4) | (((qh_b >> 6) & 3) << 4)) - 32.0; + + let is = l/16; + let is1 = sc_b_idx + is; + let sc1 = get_byte_i32(scale_vals[is1 / 4], is1 % 4); + let is2 = sc_b_idx + is + 2; + let sc2 = get_byte_i32(scale_vals[is2 / 4], is2 % 4); + let is3 = sc_b_idx + is + 4; + let sc3 = get_byte_i32(scale_vals[is3 / 4], is3 % 4); + let is4 = sc_b_idx + is + 6; + let sc4 = get_byte_i32(scale_vals[is4 / 4], is4 % 4); + + dst[dst_i + l] = (q1 * f32(sc1)) * d; + dst[dst_i + l + 32] = (q2 * f32(sc2)) * d; + dst[dst_i + l + 64] = (q3 * f32(sc3)) * d; + dst[dst_i + l + 96] = (q4 * f32(sc4)) * d; + } + dst_i += 128; + qh_b_idx += 32; + sc_b_idx += 8; + } +} +#endif + +#ifdef IQ2_XXS +fn copy_elements(src_base: u32, dst_base: u32, offset: u32) { + let block_byte_base = (src_base + offset) * 66; // Block stride: 66 bytes + let d = load_f16_as_f32_at_src(block_byte_base); + var dst_i = dst_base + offset * 256; + for (var ib: u32 = 0; ib < 32; ib += 4) { + let aux0_offset = block_byte_base + 2 + ib * 2; + let aux1_offset = block_byte_base + 2 + (ib + 2) * 2; + let aux0 = load_u32_at_src(aux0_offset); + let aux1 = load_u32_at_src(aux1_offset); + let db = d * (0.5 + f32(aux1 >> 28)) * 0.25; + for (var l: u32 = 0; l < 4; l++) { + let ig = get_byte(aux0, l) * 8; + let is = (aux1 >> (7 * l)) & 127; + let signs = get_byte(ksigns_iq2xs[is / 4], is % 4); + for (var j: u32 = 0; j < 8; j++) { + let g = get_byte(iq2xxs_grid[(ig + j) / 4], (ig + j) % 4); + let m = select(1.0, -1.0, (get_byte(kmask_iq2xs[j / 4], j % 4) & signs) != 0); + dst[dst_i] = db * f32(g) * m; + dst_i++; + } + } + } +} +#endif + + + +#ifdef IQ2_XS +fn copy_elements(src_base: u32, dst_base: u32, offset: u32) { + let block_byte_base = (src_base + offset) * 74; // Block stride: 74 bytes + let d = load_f16_as_f32_at_src(block_byte_base); + var dst_i = dst_base + offset * 256; + + var scale_vals = array( + load_u32_at_src(block_byte_base + 66), + load_u32_at_src(block_byte_base + 70) + ); + + for (var ib: u32 = 0; ib < 32; ib += 4) { + let s = get_byte(scale_vals[ib / 16], (ib % 16) / 4); + let db = array( + d * (0.5 + f32(s & 0xF)) * 0.25, + d * (0.5 + f32(s >> 4)) * 0.25 + ); + for (var l: u32 = 0; l < 4; l++) { + let qs_offset = block_byte_base + 2 + (ib + l) * 2; + let qs_val = load_u32_at_src(qs_offset) & 0xFFFF; + let ig = (qs_val & 511) * 8; + let is = qs_val >> 9; + let signs = get_byte(ksigns_iq2xs[is / 4], is % 4); + let dl = db[l/2]; + for (var j: u32 = 0; j < 8; j++) { + let g = get_byte(iq2xs_grid[(ig + j) / 4], (ig + j) % 4); + let m = select(1.0, -1.0, (get_byte(kmask_iq2xs[j / 4], j % 4) & signs) != 0); + dst[dst_i] = dl * f32(g) * m; + dst_i++; + } + } + } +} +#endif + +#ifdef IQ2_S +fn copy_elements(src_base: u32, dst_base: u32, offset: u32) { + let block_byte_base = (src_base + offset) * 82; // Block stride: 82 bytes + let d = load_f16_as_f32_at_src(block_byte_base); + var dst_i = dst_base + offset * 256; + + var qs_vals : array; + for (var i: u32 = 0; i < 16; i++) { + qs_vals[i] = load_u32_at_src(block_byte_base + 2 + i * 4); + } + + var qh_vals: array; + qh_vals[0] = load_u32_at_src(block_byte_base + 66); + qh_vals[1] = load_u32_at_src(block_byte_base + 70); + + var scale_vals: array; + scale_vals[0] = load_u32_at_src(block_byte_base + 74); + scale_vals[1] = load_u32_at_src(block_byte_base + 78); + + for (var ib: u32 = 0; ib < 8; ib ++) { + let s = get_byte(scale_vals[ib / 4], ib % 4); + let db = array( + d * (0.5 + f32(s & 0xF)) * 0.25, + d * (0.5 + f32(s >> 4)) * 0.25 + ); + let qs_w = qs_vals[ib]; + for (var l: u32 = 0; l < 4; l++) { + let qh_b = (get_byte(qh_vals[ib / 4], ib % 4) << (8 - 2 * l)) & 0x300; + let ig = (get_byte(qs_w, l) | qh_b) * 8; + let signs = get_byte(qs_vals[ib + 8], l); + let dl = db[l/2]; + for (var j: u32 = 0; j < 8; j++) { + let g = get_byte(iq2s_grid[(ig + j) / 4], (ig + j) % 4); + let m = select(1.0, -1.0, (get_byte(kmask_iq2xs[j / 4], j % 4) & signs) != 0); + dst[dst_i] = dl * f32(g) * m; + dst_i++; + } + } + } +} +#endif + +#ifdef IQ3_XXS +fn copy_elements(src_base: u32, dst_base: u32, offset: u32) { + let block_byte_base = (src_base + offset) * 98; // Block stride: 98 bytes + let d = load_f16_as_f32_at_src(block_byte_base); + var dst_i = dst_base + offset * 256; + for (var ib: u32 = 0; ib < 16; ib += 2) { + let sc_sign_offset = block_byte_base + 2 + (ib + 32) * 2; + let sc_sign = load_u32_at_src(sc_sign_offset); + let db = d * (0.5 + f32(sc_sign >> 28)) * 0.5; + for (var l: u32 = 0; l < 4; l++) { + let is = (sc_sign >> (7 * l)) & 127; + let signs = get_byte(ksigns_iq2xs[is / 4], is % 4); + let ig_val = load_u32_at_src(block_byte_base + 2 + (ib * 2 + l) * 2) & 0xFFFF; + let ig1 = get_byte(ig_val, 0); + let ig2 = get_byte(ig_val, 1); + for (var j: u32 = 0; j < 4; j++) { + let g1 = get_byte(iq3xxs_grid[ig1], j); + let g2 = get_byte(iq3xxs_grid[ig2], j); + let m1 = select(1.0, -1.0, (get_byte(kmask_iq2xs[0], j) & signs) != 0); + let m2 = select(1.0, -1.0, (get_byte(kmask_iq2xs[1], j) & signs) != 0); + dst[dst_i] = db * f32(g1) * m1; + dst[dst_i + 4] = db * f32(g2) * m2; + dst_i++; + } + dst_i += 4; + } + } +} +#endif + +#ifdef IQ3_S +fn copy_elements(src_base: u32, dst_base: u32, offset: u32) { + let block_byte_base = (src_base + offset) * 110; // Block stride: 110 bytes + let d = load_f16_as_f32_at_src(block_byte_base); + var dst_i = dst_base + offset * 256; + + var qh_vals = array( + load_u32_at_src(block_byte_base + 66), + load_u32_at_src(block_byte_base + 70) + ); + + var sign_vals: array; + for (var i: u32 = 0; i < 8; i++) { + sign_vals[i] = load_u32_at_src(block_byte_base + 74 + i * 4); + } + + var scale_vals = load_u32_at_src(block_byte_base + 106); + + for (var ib: u32 = 0; ib < 4; ib++) { + let s = get_byte(scale_vals, ib); + let db = array( + d * (1.0 + 2.0 * f32(s & 0xF)), + d * (1.0 + 2.0 * f32(s >> 4)) + ); + for (var k: u32 = 0; k < 2; k++) { + let dl = db[k]; + let qh_byte = get_byte(qh_vals[ib / 2], (ib % 2) * 2 + k); + let sign_w = sign_vals[ib * 2 + k]; + for (var l: u32 = 0; l < 4; l++) { + let signs = get_byte(sign_w, l); + let ig_val = load_u32_at_src(block_byte_base + 2 + (ib * 8 + k * 4 + l) * 2) & 0xFFFF; + let ig1 = get_byte(ig_val, 0) | ((qh_byte << ((8 - (2 * l)))) & 256); + let ig2 = get_byte(ig_val, 1) | ((qh_byte << ((7 - (2 * l)))) & 256); + for (var j: u32 = 0; j < 4; j++) { + let g1 = get_byte(iq3s_grid[ig1], j); + let g2 = get_byte(iq3s_grid[ig2], j); + let m1 = select(1.0, -1.0, (get_byte(kmask_iq2xs[0], j) & signs) != 0); + let m2 = select(1.0, -1.0, (get_byte(kmask_iq2xs[1], j) & signs) != 0); + dst[dst_i] = dl * f32(g1) * m1; + dst[dst_i + 4] = dl * f32(g2) * m2; + dst_i++; + } + dst_i += 4; + } + } + } +} +#endif + +#ifdef IQ1_S +fn copy_elements(src_base: u32, dst_base: u32, offset: u32) { + let block_byte_base = (src_base + offset) * 50; // Block stride: 50 bytes + let d = load_f16_as_f32_at_src(block_byte_base); + var dst_i = dst_base + offset * 256; + for (var ib: u32 = 0; ib < 8; ib++) { + let qh = load_u32_at_src(block_byte_base + 34 + ib * 2) & 0xFFFF; + let dl = d * (2.0 * f32((qh >> 12) & 7) + 1.0); + let delta = select(IQ1_DELTA, -IQ1_DELTA, (qh & 0x8000) != 0); + let qs_w = load_u32_at_src(block_byte_base + 2 + ib * 4); + for (var l: u32 = 0; l < 4; l++) { + let ig = (get_byte(qs_w, l) | (((qh >> (3 * l)) & 7) << 8)) * 8; + for (var j: u32 = 0; j < 8; j++) { + let gw = iq1_grid[(ig + j) / 16]; + let g = (gw >> (((ig + j) % 16) * 2)) & 3; + let gs = bitcast(g << 30) >> 30; + dst[dst_i] = dl * (f32(gs) + delta); + dst_i++; + } + } + } +} +#endif + +#ifdef IQ1_M +fn copy_elements(src_base: u32, dst_base: u32, offset: u32) { + let block = src[src_base + offset]; + + let scale = ((block.scales[0] >> 12) & 0xF) | ((block.scales[0] >> 24) & 0x00F0) | ((block.scales[1] >> 4) & 0x0F00) | ((block.scales[1] >> 16) & 0xF000); + let d = f32(bitcast>(scale).x); + var dst_i = dst_base + offset * 256; + for (var ib: u32 = 0; ib < 8; ib++) { + let sw = (block.scales[ib / 4] >> (16 * ((ib / 2) % 2))) & 0xFFFF; + let s1 : u32 = (sw >> (6 * (ib % 2))) & 0x7; + let s2 : u32 = (sw >> (6 * (ib % 2) + 3)) & 0x7; + var dl = array( + d * f32(2 * s1 + 1), + d * f32(2 * s2 + 1) + ); + + let qh = block.qh[ib / 2] >> (16 * (ib % 2)); + var idx = array( + get_byte(block.qs[ib], 0) | ((qh << 8) & 0x700), + get_byte(block.qs[ib], 1) | ((qh << 4) & 0x700), + get_byte(block.qs[ib], 2) | ((qh) & 0x700), + get_byte(block.qs[ib], 3) | ((qh >> 4) & 0x700) + ); + var delta = array( + select(IQ1_DELTA, -IQ1_DELTA, (qh & 0x08) != 0), + select(IQ1_DELTA, -IQ1_DELTA, (qh & 0x80) != 0), + select(IQ1_DELTA, -IQ1_DELTA, ((qh >> 8) & 0x08) != 0), + select(IQ1_DELTA, -IQ1_DELTA, ((qh >> 8) & 0x80) != 0) + ); + for (var l: u32 = 0; l < 4; l++) { + let ig = idx[l] * 8; + for (var j: u32 = 0; j < 8; j++) { + let gw = iq1_grid[(ig + j) / 16]; + let g = (gw >> (((ig + j) % 16) * 2)) & 3; + let gs = bitcast(g << 30) >> 30; + dst[dst_i] = dl[l/2] * (f32(gs) + delta[l]); + dst_i++; + } + } + } +} +#endif + +#ifdef IQ4_NL +fn copy_elements(src_base: u32, dst_base: u32, offset: u32) { + let block_byte_base = (src_base + offset) * 18; // Block stride: 18 bytes + let d = load_f16_as_f32_at_src(block_byte_base); + var dst_i = dst_base + offset * 32; + var qs: array; + for (var i: u32 = 0; i < 4; i++) { + qs[i] = load_u32_at_src(block_byte_base + 2 + i * 4); + } + for (var j: u32 = 0; j < 16; j++) { + let qsb = get_byte(qs[j / 4], j % 4); + dst[dst_i] = d * f32(kvalues_iq4nl[qsb & 0xF]); + dst[dst_i + 16] = d * f32(kvalues_iq4nl[qsb >> 4]); + dst_i++; + } +} +#endif + +#ifdef IQ4_XS +fn copy_elements(src_base: u32, dst_base: u32, offset: u32) { + let block = src[src_base + offset]; + let d = unpack2x16float(block.d_scales_h)[0]; + let scales_h = block.d_scales_h >> 16; + var dst_i = dst_base + offset * 256; + for (var ib: u32 = 0; ib < 8; ib++) { + let ls = ((get_byte(block.scales_l, ib / 2) >> (4 * (ib % 2))) & 0xF) | (((scales_h >> (2 * ib)) & 3) << 4); + let dl = d * (f32(ls) - 32.0); + for (var j: u32 = 0; j < 16; j++) { + let iqs = ib * 16 + j; + let qsb = get_byte(block.qs[iqs / 4], iqs % 4); + dst[dst_i] = dl * f32(kvalues_iq4nl[qsb & 0xF]); + dst[dst_i + 16] = dl * f32(kvalues_iq4nl[qsb >> 4]); + dst_i++; + } + dst_i += 16; + } +} +#endif + +#ifdef MXFP4 +fn copy_elements(src_base: u32, dst_base: u32, offset: u32) { + let block_byte_base = (src_base + offset) * 17; + let eu8 = get_byte(load_u32_at_src(block_byte_base), 0); + let d = ldexp(1.0, i32(eu8) - 128); + for (var j: u32 = 0u; j < 4; j++) { + let q_byte_offset = block_byte_base + 1 + j * 4; + let q_packed = load_u32_at_src(q_byte_offset); + for (var k: u32 = 0; k < 4; k++) { + let q_byte = get_byte(q_packed, k); + let q_hi = f32(kvalues_mxfp4[(q_byte >> 4) & 0xF]) * d; + let q_lo = f32(kvalues_mxfp4[q_byte & 0xFu]) * d; + let dst_offset = dst_base + offset * 32 + j * 4 + k; + dst[dst_offset] = q_lo; + dst[dst_offset + 16u] = q_hi; + } + } +} +#endif + +#ifdef NVFP4 +fn copy_elements(src_base: u32, dst_base: u32, offset: u32) { + let block_byte_base = (src_base + offset) * 36; + let d_word = load_u32_at_src(block_byte_base); + for (var sub: u32 = 0u; sub < 4; sub++) { + let d = ue4m3_to_fp32(get_byte(d_word, sub)) * 0.5; + for (var j: u32 = 0u; j < 2; j++) { + let q_packed = load_u32_at_src(block_byte_base + 4 + sub * 8 + j * 4); + for (var k: u32 = 0; k < 4; k++) { + let q_byte = get_byte(q_packed, k); + let q_lo = f32(kvalues_mxfp4[q_byte & 0xFu]) * d; + let q_hi = f32(kvalues_mxfp4[(q_byte >> 4) & 0xF]) * d; + let dst_offset = dst_base + offset * 64 + sub * 16 + j * 4 + k; + dst[dst_offset] = q_lo; + dst[dst_offset + 8u] = q_hi; + } + } + } +} +#endif + + +@group(0) @binding(0) +var src: array; + +@group(0) @binding(1) +var idx: array; + +@group(0) @binding(2) +var dst: array; + +struct Params { + offset_src: u32, // in elements + offset_idx: u32, // in elements + offset_dst: u32, // in elements + + // Strides (in elements) + stride_src1: u32, + stride_src2: u32, + stride_src3: u32, + + stride_idx0: u32, + stride_idx1: u32, + stride_idx2: u32, + + stride_dst1: u32, + stride_dst2: u32, + stride_dst3: u32, + + // Shape of dst + ne0: u32, + n_rows: u32, + ne2: u32, + ne3: u32, + + // Shape of idx + idx1: u32, + idx2: u32, +}; + +@group(0) @binding(3) +var params: Params; + +@compute @workgroup_size(WG_SIZE) +fn main(@builtin(global_invocation_id) gid: vec3) { +#ifdef FLOAT_PARALLEL + let blocks_per_row = params.ne0 / BLOCK_SIZE; + let row_count = params.n_rows * params.ne2 * params.ne3; + + if (gid.x >= blocks_per_row * row_count) { + return; + } + + let block_idx = gid.x % blocks_per_row; + var row_idx = gid.x / blocks_per_row; + let i_dst3 = row_idx / (params.ne2 * params.n_rows); + + row_idx = row_idx % (params.ne2 * params.n_rows); + let i_dst2 = row_idx / params.n_rows; + let i_dst1 = row_idx % params.n_rows; + + let i_idx2 = i_dst3 % params.idx2; + let i_idx1 = i_dst2 % params.idx1; + let i_idx0 = i_dst1; + + let i_idx = params.offset_idx + i_idx0 * params.stride_idx0 + i_idx1 * params.stride_idx1 + i_idx2 * params.stride_idx2; + + let idx_val = u32(idx[i_idx]); + + let i_src_row = params.offset_src + idx_val * params.stride_src1 + i_dst2 * params.stride_src2 + i_dst3 * params.stride_src3; + let i_dst_row = params.offset_dst + i_dst1 * params.stride_dst1 + i_dst2 * params.stride_dst2 + i_dst3 * params.stride_dst3; + + copy_elements(i_src_row, i_dst_row, block_idx); +#else + if (gid.x >= params.n_rows * params.ne2 * params.ne3) { + return; + } + var i = gid.x; + let i_dst3 = i / (params.ne2 * params.n_rows); + + i = i % (params.ne2 * params.n_rows); + let i_dst2 = i / params.n_rows; + let i_dst1 = i % params.n_rows; + + let i_idx2 = i_dst3 % params.idx2; + let i_idx1 = i_dst2 % params.idx1; + let i_idx0 = i_dst1; + + let i_idx = params.offset_idx + i_idx0 * params.stride_idx0 + i_idx1 * params.stride_idx1 + i_idx2 * params.stride_idx2; + + let idx_val = u32(idx[i_idx]); + + let i_src_row = params.offset_src + idx_val * params.stride_src1 + i_dst2 * params.stride_src2 + i_dst3 * params.stride_src3; + let i_dst_row = params.offset_dst + i_dst1 * params.stride_dst1 + i_dst2 * params.stride_dst2 + i_dst3 * params.stride_dst3; + + for (var i: u32 = 0; i < params.ne0/BLOCK_SIZE; i++) { + copy_elements(i_src_row, i_dst_row, i); + } +#endif +} diff --git a/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/glu.wgsl b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/glu.wgsl new file mode 100644 index 0000000000000000000000000000000000000000..e6d7608cec5de0b1d746095e45baaa8bfa1c6247 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/glu.wgsl @@ -0,0 +1,155 @@ +enable f16; + +#ifdef TYPE_F32 +#define DataType f32 +#endif +#ifdef TYPE_F16 +#define DataType f16 +#endif + +#ifdef OP_REGLU +fn op(a: DataType, b: DataType) -> DataType { + return max(a, 0) * b; +} +#endif + +#ifdef OP_GEGLU +const SQRT_2_OVER_PI: DataType = 0.79788456080286535587989211986876; +const GELU_COEF_A: DataType = 0.044715; + +fn op(a: DataType, b: DataType) -> DataType { + let val = SQRT_2_OVER_PI * a * (1.0 + GELU_COEF_A * a * a); + return 0.5 * a * (2.0 - 2.0/ (exp(2* val) + 1)) * b; +} +#endif + +#ifdef OP_SWIGLU +fn op(a: DataType, b: DataType) -> DataType { + return a / (1.0 + exp(-a)) * b; +} +#endif +#ifdef OP_SWIGLU_OAI +fn op(a: f32, b: f32) -> f32 { + let xi = min(a, params.limit); + let gi = max(min(b, params.limit), -params.limit); + var out_glu = xi / (1.0 + exp(-xi * params.alpha)); + out_glu = out_glu * (1.0 + gi); + return out_glu; +} +#endif +#ifdef OP_GEGLU_ERF +const p_erf: DataType = 0.3275911; +const a1_erf: DataType = 0.254829592; +const a2_erf: DataType = -0.284496736; +const a3_erf: DataType = 1.421413741; +const a4_erf: DataType = -1.453152027; +const a5_erf: DataType = 1.061405429; +const SQRT_2_INV: DataType = 0.7071067811865476; + +fn op(a: DataType, b: DataType) -> DataType { + let a_div_sqr2 = a * SQRT_2_INV; + let sign_x = sign(a_div_sqr2); + let x = abs(a_div_sqr2); + let t = 1.0 / (1.0 + p_erf * x); + let y = 1.0 - (((((a5_erf * t + a4_erf) * t + a3_erf) * t + a2_erf) * t + a1_erf) * t * exp(-x * x)); + let erf_approx = sign_x * y; + return 0.5 * a * (1.0 + erf_approx) * b; +} +#endif +#ifdef OP_GEGLU_QUICK +const GELU_QUICK_COEF: DataType = -1.702; + +fn op(a: DataType, b: DataType) -> DataType { + return a * (1.0 / (1.0 + exp(GELU_QUICK_COEF * a))) * b; +} +#endif + +struct Params { + offset_src0: u32, + offset_src1: u32, + offset_dst: u32, + + // Strides (in elements) + stride_src01: u32, + stride_src02: u32, + stride_src03: u32, + + stride_src11: u32, + stride_src12: u32, + stride_src13: u32, + + stride_dst1: u32, + stride_dst2: u32, + stride_dst3: u32, + + // shape of dst + ne: u32, + ne0: u32, + ne1: u32, + ne2: u32, + + swapped: u32, + alpha: f32, + limit: f32, +} + +@group(0) @binding(0) +var src0: array; + +#ifdef NO_SPLIT +@group(0) @binding(1) +var dst: array; + +@group(0) @binding(2) +var params: Params; + +fn a_value(base: u32) -> DataType { + let offset: u32 = select(0, params.ne0, params.swapped != 0); + return src0[base + offset]; +} + +fn b_value(base: u32) -> DataType { + let offset: u32 = select(params.ne0, 0, params.swapped != 0); + return src0[base + offset]; +} + +#else +@group(0) @binding(1) +var src1: array; + +@group(0) @binding(2) +var dst: array; + +@group(0) @binding(3) +var params: Params; + +fn a_value(base: u32) -> DataType { + return src0[base]; +} + +fn b_value(base: u32) -> DataType { + return src1[base]; +} + +#endif + +@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.ne2 * params.ne1 * params.ne0); + i = i % (params.ne2 * params.ne1 * params.ne0); + let i2 = i / (params.ne1 * params.ne0); + i = i % (params.ne1 * params.ne0); + let i1 = i / params.ne0; + let i0 = i % params.ne0; + + let i_a = params.offset_src0 + i3 * params.stride_src03 + i2 * params.stride_src02 + i1 * params.stride_src01 + i0; + let i_b = params.offset_src1 + i3 * params.stride_src13 + i2 * params.stride_src12 + i1 * params.stride_src11 + i0; + let i_dst = params.offset_dst + i3 * params.stride_dst3 + i2 * params.stride_dst2 + i1 * params.stride_dst1 + i0; + + dst[i_dst] = op(a_value(i_a), b_value(i_b)); +} diff --git a/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/im2col.wgsl b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/im2col.wgsl new file mode 100644 index 0000000000000000000000000000000000000000..386ebab879fbd08b47e1ceb2cf18930402dbb30c --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/im2col.wgsl @@ -0,0 +1,101 @@ +#include "common_decls.tmpl" +enable f16; + +@group(0) @binding(0) +#if defined(INPUT_F32) +var input: array; +#elif defined(INPUT_F16) +var input: array; +#endif + +@group(0) @binding(1) +#if defined(OUTPUT_F32) +var output: array; +#elif defined(OUTPUT_F16) +var output: array; +#endif + +struct Params { + offset_i: u32, + offset_o: u32, + + // element strides + si0: u32, si1: u32, si2: u32, si3: u32, + so0: u32, so1: u32, so2: u32, so3: u32, + + KW: u32, KH: u32, IC: u32, + IW: u32, IH: u32, N: u32, + OW: u32, OH: u32, + + // stride + s0: u32, s1: u32, + // padding + p0: u32, p1: u32, + // dilation + d0: u32, d1: u32, +} + +@group(0) @binding(2) +var params: Params; + +fn load_input(idx: u32) -> f32 { + #if defined(INPUT_F32) + return input[idx]; + #elif defined(INPUT_F16) + return f32(input[idx]); + #endif +} + +fn store_output(idx: u32, val: f32) { + #if defined(OUTPUT_F32) + output[idx] = val; + #elif defined(OUTPUT_F16) + output[idx] = f16(val); + #endif +} + +@compute @workgroup_size(WG_SIZE) +fn main( + @builtin(global_invocation_id) gid: vec3, + @builtin(num_workgroups) num_wg: vec3 +) { + + let threads_per_group = u32(WG_SIZE); + let i_out = gid.x + (num_wg.x * threads_per_group) * gid.y; + let K = params.KW * params.KH * params.IC; + let M = params.OW * params.OH; + let total = K * M * params.N; + + if (i_out >= total) { + return; + } + + // decode (k, m, n) + var i = i_out; + let n = i / (K * M); + i = i % (K * M); + let m = i / K; + let k = i % K; + + // decode (oh, ow) + let oh = m / params.OW; + let ow = m % params.OW; + + // decode (kw, kh, ic) + let kw = k % params.KW; + let tmp = k / params.KW; + let kh = tmp % params.KH; + let ic = tmp / params.KH; + + let iw_i32 = i32(ow * params.s0 + kw * params.d0) - i32(params.p0); + let ih_i32 = i32(oh * params.s1 + kh * params.d1) - i32(params.p1); + + if (iw_i32 >= 0 && iw_i32 < i32(params.IW) && ih_i32 >= 0 && ih_i32 < i32(params.IH)) { + let iw = u32(iw_i32); + let ih = u32(ih_i32); + let in_idx = params.offset_i + iw * params.si0 + ih * params.si1 + ic * params.si2 + n * params.si3; + store_output(params.offset_o + k * params.so0 + ow * params.so1 + oh * params.so2 + n * params.so3, load_input(in_idx)); + } else { + store_output(params.offset_o + k * params.so0 + ow * params.so1 + oh * params.so2 + n * params.so3, 0.0); + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/memset.wgsl b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/memset.wgsl new file mode 100644 index 0000000000000000000000000000000000000000..194d2d6f58c778619016fe07cf8ececea21a5cc0 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/memset.wgsl @@ -0,0 +1,40 @@ +@group(0) @binding(0) +var output_buffer: array; + +struct Params { + offset: u32, // in bytes + size: u32, // in bytes + value: u32, // 4 8-bit values, which are either repeating (memset_tensor) or may be separate (cleaning up unaligned set_tensor operations) +}; + +@group(0) @binding(1) +var params: Params; + +override wg_size: u32; +override bytes_per_thread: u32; + +@compute @workgroup_size(wg_size) +fn main(@builtin(global_invocation_id) gid: vec3) { + let i = gid.x * bytes_per_thread; + let start = params.offset; + let end = params.offset + params.size; + + for (var j: u32 = 0u; j < bytes_per_thread; j += 4) { + let byte_index = start + i + j; + if (byte_index + 4 <= end) { + output_buffer[byte_index >> 2] = params.value; + } else { + // Handle tail (unaligned) + for (var k: u32 = 0; k < 4; k++) { + let idx = byte_index + k; + if (idx < end) { + let word_idx = idx >> 2; + let bit_offset = (idx & 3) * 8u; + let mask = ~(0xffu << bit_offset); + let existing = output_buffer[word_idx]; + output_buffer[word_idx] = (existing & mask) | (params.value & (0xffu << bit_offset)); + } + } + } + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_decls.tmpl b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_decls.tmpl new file mode 100644 index 0000000000000000000000000000000000000000..13996ab5157541f1fa2add017bde1edcf5e2d747 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_decls.tmpl @@ -0,0 +1,1045 @@ +#ifdef VEC +#define VEC_SIZE 4 +#define SHMEM_TYPE vec4 +#define DST_TYPE vec4 +#define SRC0_TYPE vec4 +#define SRC1_TYPE vec4 + +fn store_shmem(val: vec4, idx: u32) { + shmem[idx] = val.x; + shmem[idx + 1] = val.y; + shmem[idx + 2] = val.z; + shmem[idx + 3] = val.w; +} +#endif // VEC + +#ifdef SCALAR +#define VEC_SIZE 1 +#define SHMEM_TYPE f16 +#define DST_TYPE f32 +#define SRC0_TYPE SRC0_INNER_TYPE +#define SRC1_TYPE SRC1_INNER_TYPE + +fn store_shmem(val: f16, idx: u32) { + shmem[idx] = val; +} +#endif // SCALAR + +#define QUANT_SHMEM shmem +#define QUANT_OUT_TYPE f16 +#include "quant_inner_loops.tmpl" + +#ifdef INIT_SRC0_SHMEM_FLOAT +fn init_shmem_src0(thread_id: u32, batch_offset: u32, offset_m: u32, k_outer: u32) { + for (var elem_idx = thread_id * VEC_SIZE; elem_idx < TILE_SRC0_SHMEM; elem_idx += TOTAL_WORKGROUP_SIZE * VEC_SIZE) { + let tile_m = elem_idx / TILE_K; + let tile_k = elem_idx % TILE_K; + let global_m = offset_m + tile_m; + let global_k = k_outer + tile_k; + let src0_idx = batch_offset + global_m * params.stride_01 + global_k; + let src0_val = select( // taking a slight performance hit to avoid oob + SRC0_TYPE(0.0), + src0[src0_idx/VEC_SIZE], + global_m < params.m && global_k < params.k); + store_shmem(SHMEM_TYPE(src0_val), elem_idx); + } +} +#endif // INIT_SRC0_SHMEM_FLOAT + +#ifndef MUL_MAT_ID +#ifdef INIT_SRC1_SHMEM_FLOAT +fn init_shmem_src1(thread_id: u32, batch_offset: u32, offset_n: u32, k_outer: u32) { + for (var elem_idx = thread_id * VEC_SIZE; elem_idx < TILE_SRC1_SHMEM; elem_idx += TOTAL_WORKGROUP_SIZE * VEC_SIZE) { + let tile_n = elem_idx / TILE_K; + let tile_k = elem_idx % TILE_K; + let global_n = offset_n + tile_n; + let global_k = k_outer + tile_k; + let src1_idx = batch_offset + global_n * params.stride_11 + global_k; + let src1_val = select( + SRC1_TYPE(0.0), + src1[src1_idx/VEC_SIZE], + global_n < params.n && global_k < params.k); + store_shmem(SHMEM_TYPE(src1_val), TILE_SRC0_SHMEM + elem_idx); + } +} +#endif // INIT_SRC1_SHMEM_FLOAT +#endif + +#ifdef INIT_SRC0_SHMEM_Q1_0 +const BLOCK_SIZE = 128u; +const BLOCK_SIZE_BYTES = 18u; +const NQ = 8u; // 8 weights (1 byte of qs) per thread per iteration + +fn init_shmem_src0(thread_id: u32, batch_offset: u32, offset_m: u32, k_outer: u32) { + for (var i = thread_id * NQ; i < TILE_SRC0_SHMEM; i += TOTAL_WORKGROUP_SIZE * NQ) { + let tile_m = i / TILE_K; + let tile_k_start = i % TILE_K; + let global_m = offset_m + tile_m; + let global_k_start = k_outer + tile_k_start; + + if (global_m >= params.m) { + break; + } + + let block_k = global_k_start / BLOCK_SIZE; + let byte_in_block = (global_k_start % BLOCK_SIZE) / 8u; + let src0_idx = batch_offset + global_m * params.stride_01 + block_k; + let block_byte_base = src0_idx * BLOCK_SIZE_BYTES; + let d = load_f16_at_src0(block_byte_base); + let q_byte = load_u32_at_src0(block_byte_base + 2u + byte_in_block) & 0xFFu; + + for (var bit = 0u; bit < NQ; bit++) { + let global_k = global_k_start + bit; + if (global_k < params.k) { + shmem[i + bit] = select(-d, d, ((q_byte >> bit) & 1u) != 0u); + } + } + } +} +#endif // INIT_SRC0_SHMEM_Q1_0 + +// legacy-quants +#if defined(INIT_SRC0_SHMEM_Q4_0) || defined(INIT_SRC0_SHMEM_Q4_1) || defined(INIT_SRC0_SHMEM_Q5_0) || defined(INIT_SRC0_SHMEM_Q5_1) || defined(INIT_SRC0_SHMEM_Q8_0) || defined(INIT_SRC0_SHMEM_Q8_1) || defined(INIT_SRC0_SHMEM_MXFP4) +const BLOCK_SIZE = 32u; +// the number of blocks per k-tile. Note that this currently only works if TILE_K is a multiple of BLOCK_SIZE, which may need to be rethought for larger quantized types. +override BLOCKS_K = TILE_K/BLOCK_SIZE; +const NQ = 16u; +#if defined(INIT_SRC0_SHMEM_Q8_0) || defined(INIT_SRC0_SHMEM_Q8_1) +const BYTES_PER_THREAD = 16u; // NQ(16) weights use 16 bytes of q +#else +const BYTES_PER_THREAD = 8u; // NQ(16) weights use 8 bytes of q +#endif +const BYTES_PER_INNER_LOOP = 4u; // == sizeof(q_packed) + +fn init_shmem_src0(thread_id: u32, batch_offset: u32, offset_m: u32, k_outer: u32) { + for (var i = thread_id * NQ; i < TILE_SRC0_SHMEM; i += TOTAL_WORKGROUP_SIZE * NQ) { + let block_idx = i / BLOCK_SIZE; + let block_offset = (i % BLOCK_SIZE) / NQ; + let shmem_idx = block_idx * BLOCK_SIZE + block_offset * BYTES_PER_THREAD; + + let tile_m = block_idx / BLOCKS_K; + let global_m = offset_m + tile_m; + let block_k = block_idx % BLOCKS_K; + let global_block_k = k_outer / BLOCK_SIZE + block_k; + + if (global_m < params.m && global_block_k < params.k / BLOCK_SIZE) { + let src0_idx = batch_offset + global_m * params.stride_01 + global_block_k; + +#if defined(INIT_SRC0_SHMEM_Q4_0) + let block_byte_base = src0_idx * 18u; // BLOCK_SIZE_BYTES = 18u; + let d = load_f16_at_src0(block_byte_base); + + // load NQ(16) weights + for (var j = 0u; j < BYTES_PER_THREAD / BYTES_PER_INNER_LOOP; j += 1) { + let q_byte_offset = block_byte_base + 2u + block_offset * BYTES_PER_THREAD + j * BYTES_PER_INNER_LOOP; + let q_packed = load_u32_at_src0(q_byte_offset); + dequant_q4_0_packed_to_shmem(q_packed, d, shmem_idx + j * BYTES_PER_INNER_LOOP); + } +#endif // INIT_SRC0_SHMEM_Q4_0 + +#if defined(INIT_SRC0_SHMEM_Q4_1) + let block_byte_base = src0_idx * 20u; // BLOCK_SIZE_BYTES = 20u; + let dm = unpack2x16float(load_u32_at_src0_aligned(block_byte_base)); + let d = f16(dm[0]); + let m = f16(dm[1]); + + // load NQ(16) weights + for (var j = 0u; j < BYTES_PER_THREAD / BYTES_PER_INNER_LOOP; j += 1) { + let q_byte_offset = block_byte_base + 4u + block_offset * BYTES_PER_THREAD + j * BYTES_PER_INNER_LOOP; + let q_packed = load_u32_at_src0(q_byte_offset); + + for (var k = 0u; k < BYTES_PER_INNER_LOOP; k++) { + let q_byte = get_byte(q_packed, k); + let q_lo = f16(q_byte & 0xF) * d + m; + let q_hi = f16((q_byte >> 4) & 0xF) * d + m; + shmem[shmem_idx + j * BYTES_PER_INNER_LOOP + k] = q_lo; + shmem[shmem_idx + j * BYTES_PER_INNER_LOOP + k + 16u] = q_hi; + } + } +#endif // INIT_SRC0_SHMEM_Q4_1 + +#if defined(INIT_SRC0_SHMEM_Q5_0) + let block_byte_base = src0_idx * 22u; // BLOCK_SIZE_BYTES = 22u; + + let d = load_f16_at_src0(block_byte_base); + let qh_packed = load_u32_at_src0(block_byte_base + 2u); + + // load NQ(16) weights + for (var j = 0u; j < BYTES_PER_THREAD / BYTES_PER_INNER_LOOP; j += 1) { + let q_byte_offset = block_byte_base + 6u + block_offset * BYTES_PER_THREAD + j * BYTES_PER_INNER_LOOP; + let q_packed = load_u32_at_src0(q_byte_offset); + + for (var k = 0u; k < BYTES_PER_INNER_LOOP; k++) { + let q_byte = get_byte(q_packed, k); + + let byte_idx = block_offset * BYTES_PER_THREAD + j * BYTES_PER_INNER_LOOP + k; + let qh_hi = (qh_packed >> (byte_idx + 12u)) & 0x10; + let q_hi = (f16(((q_byte >> 4) & 0xF) | qh_hi) - 16.0) * d; + let qh_lo = ((qh_packed >> byte_idx) << 4) & 0x10; + let q_lo = (f16((q_byte & 0xF) | qh_lo) - 16.0) * d; + shmem[shmem_idx + j * BYTES_PER_INNER_LOOP + k] = q_lo; + shmem[shmem_idx + j * BYTES_PER_INNER_LOOP + k + 16u] = q_hi; + } + } +#endif // INIT_SRC0_SHMEM_Q5_0 + +#if defined(INIT_SRC0_SHMEM_Q5_1) + let block_byte_base = src0_idx * 24u; // BLOCK_SIZE_BYTES = 24u; + + let dm = unpack2x16float(load_u32_at_src0_aligned(block_byte_base)); + let d = f16(dm[0]); + let m = f16(dm[1]); + let qh_packed = load_u32_at_src0_aligned(block_byte_base + 4u); + + // load NQ(16) weights + for (var j = 0u; j < BYTES_PER_THREAD / BYTES_PER_INNER_LOOP; j += 1) { + let q_byte_offset = block_byte_base + 8u + block_offset * BYTES_PER_THREAD + j * BYTES_PER_INNER_LOOP; + let q_packed = load_u32_at_src0_aligned(q_byte_offset); + + for (var k = 0u; k < BYTES_PER_INNER_LOOP; k++) { + let q_byte = get_byte(q_packed, k); + + let byte_idx = block_offset * BYTES_PER_THREAD + j * BYTES_PER_INNER_LOOP + k; + let qh_hi = (qh_packed >> (byte_idx + 12u)) & 0x10; + let q_hi = f16(((q_byte >> 4) & 0xF) | qh_hi) * d + m; + let qh_lo = ((qh_packed >> byte_idx) << 4) & 0x10; + let q_lo = f16((q_byte & 0xF) | qh_lo) * d + m; + shmem[shmem_idx + j * BYTES_PER_INNER_LOOP + k] = q_lo; + shmem[shmem_idx + j * BYTES_PER_INNER_LOOP + k + 16u] = q_hi; + } + } +#endif // INIT_SRC0_SHMEM_Q5_1 + +#if defined(INIT_SRC0_SHMEM_Q8_0) + let block_byte_base = src0_idx * 34u; // BLOCK_SIZE_BYTES = 34u; + let d = load_f16_at_src0(block_byte_base); + + // load NQ(16) weights + for (var j = 0u; j < BYTES_PER_THREAD / BYTES_PER_INNER_LOOP; j += 1) { + let q_byte_offset = block_byte_base + 2u + block_offset * BYTES_PER_THREAD + j * BYTES_PER_INNER_LOOP; + let q_packed = load_u32_at_src0(q_byte_offset); + dequant_q8_0_packed_to_shmem(q_packed, d, shmem_idx + j * BYTES_PER_INNER_LOOP); + } +#endif // INIT_SRC0_SHMEM_Q8_0 + +#if defined(INIT_SRC0_SHMEM_Q8_1) + let block_byte_base = src0_idx * 36u; // BLOCK_SIZE_BYTES = 36u; + let dm = unpack2x16float(load_u32_at_src0_aligned(block_byte_base)); + let d = f16(dm[0]); + let m = f16(dm[1]); + + // load NQ(16) weights + for (var j = 0u; j < BYTES_PER_THREAD / BYTES_PER_INNER_LOOP; j += 1) { + let q_byte_offset = block_byte_base + 4u + block_offset * BYTES_PER_THREAD + j * BYTES_PER_INNER_LOOP; + let q_packed = load_u32_at_src0(q_byte_offset); + for (var k = 0u; k < BYTES_PER_INNER_LOOP; k++) { + let q_byte = get_byte_i32(q_packed, k); + let q_val = f16(q_byte) * d + m; + shmem[shmem_idx + j * BYTES_PER_INNER_LOOP + k] = q_val; + } + } +#endif // INIT_SRC0_SHMEM_Q8_1 + +#if defined(INIT_SRC0_SHMEM_MXFP4) + let block_byte_base = src0_idx * 17u; // BLOCK_SIZE_BYTES = 17u; + let eu8 = get_byte(load_u32_at_src0_aligned(block_byte_base), block_byte_base & 3u); + let e = ldexp(1.0, i32(eu8) - 128); + + // load NQ(16) weights + for (var j = 0u; j < BYTES_PER_THREAD / BYTES_PER_INNER_LOOP; j += 1) { + let q_byte_offset = block_byte_base + 1u + block_offset * BYTES_PER_THREAD + j * BYTES_PER_INNER_LOOP; + let q_packed = load_u32_at_src0(q_byte_offset); + for (var k = 0u; k < BYTES_PER_INNER_LOOP; k++) { + let q_byte = get_byte(q_packed, k); + let q_hi = f32(kvalues_mxfp4[(q_byte >> 4) & 0xF]) * e; + let q_lo = f32(kvalues_mxfp4[q_byte & 0xF]) * e; + shmem[shmem_idx + j * BYTES_PER_INNER_LOOP + k] = f16(q_lo); + shmem[shmem_idx + j * BYTES_PER_INNER_LOOP + k + 16u] = f16(q_hi); + } + } +#endif // INIT_SRC0_SHMEM_MXFP4 + } + } +} +#endif // legacy-quants + +#if defined(INIT_SRC0_SHMEM_NVFP4) +const BLOCK_SIZE = 64u; +const BLOCK_SIZE_BYTES = 36u; +const SUB_BLOCK_SIZE = 16u; // elements sharing one UE4M3 scale +const NQ = 16u; +const BYTES_PER_THREAD = 8u; +const BYTES_PER_INNER_LOOP = 4u; + +fn init_shmem_src0(thread_id: u32, batch_offset: u32, offset_m: u32, k_outer: u32) { + for (var i = thread_id * NQ; i < TILE_SRC0_SHMEM; i += TOTAL_WORKGROUP_SIZE * NQ) { + let tile_m = i / TILE_K; + let tile_k_start = i % TILE_K; + let global_m = offset_m + tile_m; + let global_k_start = k_outer + tile_k_start; + + if (global_m >= params.m) { + break; + } + + let block_k = global_k_start / BLOCK_SIZE; + let sub_block = (global_k_start % BLOCK_SIZE) / SUB_BLOCK_SIZE; + let src0_idx = batch_offset + global_m * params.stride_01 + block_k; + + let block_byte_base = src0_idx * BLOCK_SIZE_BYTES; + let d_byte_base = block_byte_base; + let qs_byte_base = block_byte_base + 4u; + + let d = ue4m3_to_fp32(get_byte(load_u32_at_src0_aligned(d_byte_base), sub_block)) * 0.5; + + for (var j = 0u; j < BYTES_PER_THREAD / BYTES_PER_INNER_LOOP; j++) { + let q_packed = load_u32_at_src0_aligned(qs_byte_base + sub_block * 8u + j * 4u); + for (var k = 0u; k < BYTES_PER_INNER_LOOP; k++) { + let q_byte = get_byte(q_packed, k); + shmem[i + j * BYTES_PER_INNER_LOOP + k] = f16(f32(kvalues_mxfp4[q_byte & 0xF]) * d); + shmem[i + j * BYTES_PER_INNER_LOOP + k + 8u] = f16(f32(kvalues_mxfp4[(q_byte >> 4) & 0xF]) * d); + } + } + } +} +#endif // INIT_SRC0_SHMEM_NVFP4 + +// k-quants +#if defined(INIT_SRC0_SHMEM_Q2_K) || defined(INIT_SRC0_SHMEM_Q3_K) || defined(INIT_SRC0_SHMEM_Q4_K) || defined(INIT_SRC0_SHMEM_Q5_K) || defined(INIT_SRC0_SHMEM_Q6_K) +const BLOCK_SIZE = 256u; +const NQ = 4u; + +fn store_shmem_kquants(val: vec4, idx: u32) { + shmem[idx] = val.x; + shmem[idx + 1] = val.y; + shmem[idx + 2] = val.z; + shmem[idx + 3] = val.w; +} + +fn load_byte_at_src0_aligned(byte_offset: u32) -> u32 { + return get_byte(load_u32_at_src0_aligned(byte_offset), byte_offset % 4u); +} + +fn init_shmem_src0(thread_id: u32, batch_offset: u32, offset_m: u32, k_outer: u32) { + for (var elem_idx = thread_id * NQ; elem_idx < TILE_SRC0_SHMEM; elem_idx += TOTAL_WORKGROUP_SIZE * NQ) { + let tile_m = elem_idx / TILE_K; + let tile_k = elem_idx % TILE_K; + + let global_m = offset_m + tile_m; + let global_k = k_outer + tile_k; + + if (global_m >= params.m || global_k >= params.k) { + store_shmem_kquants(vec4(f16(0.0), f16(0.0), f16(0.0), f16(0.0)), elem_idx); + continue; + } + + let block_k = global_k / BLOCK_SIZE; + let k_in_block = global_k % BLOCK_SIZE; // k_in_block % 4 == 0; + + let src0_idx = batch_offset + global_m * params.stride_01 + block_k; + +#if defined(INIT_SRC0_SHMEM_Q2_K) + let block_byte_base = src0_idx * 84u; // BLOCK_SIZE_BYTES = 84u; + let scales_byte_base = block_byte_base; + let qs_byte_base = block_byte_base + 16u; + let dm_byte_base = block_byte_base + 80u; + + let d_packed = unpack2x16float(load_u32_at_src0_aligned(dm_byte_base)); + let d = f16(d_packed[0]); + let dmin = f16(d_packed[1]); + + let chunk = k_in_block / 128u; + let pos_in_chunk = k_in_block % 32u; + let sub_block = k_in_block / 16u; + let shift_phase = (k_in_block % 128u) / 32u; + + // whole 2 bits (4 elems) + let qs_word = load_u32_at_src0_aligned(qs_byte_base + 32u * chunk + 1u * pos_in_chunk); + let qs_vec4 = vec4( + f16((qs_word >> (2u * shift_phase + 0u)) & 0x3u), + f16((qs_word >> (2u * shift_phase + 8u)) & 0x3u), + f16((qs_word >> (2u * shift_phase + 16u)) & 0x3u), + f16((qs_word >> (2u * shift_phase + 24u)) & 0x3u), + ); + + let scale = load_byte_at_src0_aligned(scales_byte_base + sub_block); + + let dl = d * f16(scale & 0xFu); + let ml = dmin * f16(scale >> 4u); + + store_shmem_kquants(qs_vec4 * dl - ml, elem_idx); +#endif // INIT_SRC0_SHMEM_Q2_K + +#if defined(INIT_SRC0_SHMEM_Q3_K) + let block_byte_base = src0_idx * 110u; // BLOCK_SIZE_BYTES = 110u; + let hmask_byte_base = block_byte_base + 0u; + let qs_byte_base = block_byte_base + 32u; + let scales_byte_base = block_byte_base + 96u; + + let d_all = load_f16_at_src0(block_byte_base + 108u); + + let chunk = k_in_block / 128u; + let pos_in_chunk = k_in_block % 32u; + let sub_block = k_in_block / 16u; + let shift_phase = (k_in_block % 128u) / 32u; + + let hmask_block = pos_in_chunk; + let hmask_shift_phase = k_in_block / 32u; + + // low 2 bits (4 elems) + let q_lo2_word = load_u32_at_src0(qs_byte_base + 32u * chunk + 1u * hmask_block); + let q_lo2_vec4 = vec4( + f16((q_lo2_word >> (2u * shift_phase + 0u)) & 3u), + f16((q_lo2_word >> (2u * shift_phase + 8u)) & 3u), + f16((q_lo2_word >> (2u * shift_phase + 16u)) & 3u), + f16((q_lo2_word >> (2u * shift_phase + 24u)) & 3u) + ); + + // high 1 bit (4 elems) + let q_hi1_word = load_u32_at_src0(hmask_byte_base + pos_in_chunk); + let q_hi1_vec4 = vec4( + f16(select(4.0, 0.0, ((q_hi1_word >> (1u * hmask_shift_phase + 0u)) & 1u) == 1u)), + f16(select(4.0, 0.0, ((q_hi1_word >> (1u * hmask_shift_phase + 8u)) & 1u) == 1u)), + f16(select(4.0, 0.0, ((q_hi1_word >> (1u * hmask_shift_phase + 16u)) & 1u) == 1u)), + f16(select(4.0, 0.0, ((q_hi1_word >> (1u * hmask_shift_phase + 24u)) & 1u) == 1u)) + ); + + let q_vec4 = q_lo2_vec4 - q_hi1_vec4; + + let scale_low4 = (load_byte_at_src0_aligned(scales_byte_base + (sub_block % 8u)) >> (4u * (sub_block / 8u))) & 0xFu; + let scale_hi2 = (load_byte_at_src0_aligned(scales_byte_base + 8u + (sub_block % 4u)) >> (2u * (sub_block / 4u))) & 3u; + let dl = d_all * (f16((scale_hi2 << 4u) | scale_low4) - 32.0); + + store_shmem_kquants(dl * q_vec4, elem_idx); +#endif // INIT_SRC0_SHMEM_Q3_K + +#if defined(INIT_SRC0_SHMEM_Q4_K) + let block_byte_base = src0_idx * 144u; // BLOCK_SIZE_BYTES = 144u; + let dm_byte_base = block_byte_base + 0u; + let scale_byte_base = block_byte_base + 4u; + let qs_byte_base = block_byte_base + 16u; + + let dm = unpack2x16float(load_u32_at_src0_aligned(dm_byte_base)); + let d = f16(dm[0]); + let dmin = f16(dm[1]); + + let chunk = k_in_block / 64u; + let pos_in_chunk = (k_in_block % 64u) % 32u; + let sub_block = k_in_block / 32u; + let shift_phase = sub_block & 1u; + + // whole 4 bits (4 elems) + let qs_word = load_u32_at_src0_aligned(qs_byte_base + 32u * chunk + 1u * pos_in_chunk); + let qs_vec4 = vec4( + f16((qs_word >> (4u * shift_phase + 0u)) & 0xFu), + f16((qs_word >> (4u * shift_phase + 8u)) & 0xFu), + f16((qs_word >> (4u * shift_phase + 16u)) & 0xFu), + f16((qs_word >> (4u * shift_phase + 24u)) & 0xFu) + ); + + var sc: u32; + var mn: u32; + + if (sub_block < 4u) { + let sc_byte = get_byte(load_u32_at_src0_aligned(scale_byte_base), sub_block % 4u); + let min_byte = get_byte(load_u32_at_src0_aligned(scale_byte_base + 4), sub_block % 4u); + sc = sc_byte & 63u; + mn = min_byte & 63u; + } else { + let sc_min_lo = get_byte(load_u32_at_src0_aligned(scale_byte_base + 8), (sub_block + 4u) % 4u); + let sc_hi = get_byte(load_u32_at_src0_aligned(scale_byte_base), (sub_block - 4u) % 4u); + let min_hi = get_byte(load_u32_at_src0_aligned(scale_byte_base + 4), sub_block % 4u); + sc = (sc_min_lo & 0xFu) | ((sc_hi >> 6u) << 4u); + mn = (sc_min_lo >> 4u) | ((min_hi >> 6u) << 4u); + } + + let dl = d * f16(sc); + let ml = dmin * f16(mn); + + store_shmem_kquants(dl * qs_vec4 - vec4(ml, ml, ml, ml), elem_idx); +#endif // INIT_SRC0_SHMEM_Q4_K + +#if defined(INIT_SRC0_SHMEM_Q5_K) + let block_byte_base = src0_idx * 176u; // BLOCK_SIZE_BYTES = 176u; + let dm_byte_base = block_byte_base + 0u; + let scale_byte_base = block_byte_base + 4u; + let qh_byte_base = block_byte_base + 16u; + let qs_byte_base = block_byte_base + 48u; + + let dm = unpack2x16float(load_u32_at_src0_aligned(dm_byte_base)); + let d = f16(dm[0]); + let dmin = f16(dm[1]); + + let chunk = k_in_block / 64u; + let pos_in_chunk = (k_in_block % 64u) % 32u; + let sub_block = k_in_block / 32u; + let shift_phase = sub_block & 1u; + + let qh_block = k_in_block % 32u; + let qh_shift_phase = sub_block; + + // low 4 bits (4 elems) + let qs_word = load_u32_at_src0_aligned(qs_byte_base + 32u * chunk + 1u * pos_in_chunk); + let qs_lo4_vec4 = vec4( + f16((qs_word >> (4u * shift_phase + 0u)) & 0xFu), + f16((qs_word >> (4u * shift_phase + 8u)) & 0xFu), + f16((qs_word >> (4u * shift_phase + 16u)) & 0xFu), + f16((qs_word >> (4u * shift_phase + 24u)) & 0xFu) + ); + + // high 1 bit (4 elems) + let qh_word = load_u32_at_src0_aligned(qh_byte_base + qh_block); + let qh_vec4 = vec4( + f16(select(0.0, 16.0, ((qh_word >> (1u * qh_shift_phase + 0u)) & 1u) == 1u)), + f16(select(0.0, 16.0, ((qh_word >> (1u * qh_shift_phase + 8u)) & 1u) == 1u)), + f16(select(0.0, 16.0, ((qh_word >> (1u * qh_shift_phase + 16u)) & 1u) == 1u)), + f16(select(0.0, 16.0, ((qh_word >> (1u * qh_shift_phase + 24u)) & 1u) == 1u)) + ); + + var sc: u32; + var mn: u32; + + if (sub_block < 4u) { + let sc_byte = get_byte(load_u32_at_src0_aligned(scale_byte_base), sub_block % 4u); + let min_byte = get_byte(load_u32_at_src0_aligned(scale_byte_base + 4), sub_block % 4u); + sc = sc_byte & 63u; + mn = min_byte & 63u; + } else { + let sc_min_lo = get_byte(load_u32_at_src0_aligned(scale_byte_base + 8), (sub_block + 4u) % 4u); + let sc_hi = get_byte(load_u32_at_src0_aligned(scale_byte_base), (sub_block - 4u) % 4u); + let min_hi = get_byte(load_u32_at_src0_aligned(scale_byte_base + 4), sub_block % 4u); + sc = (sc_min_lo & 0xFu) | ((sc_hi >> 6u) << 4u); + mn = (sc_min_lo >> 4u) | ((min_hi >> 6u) << 4u); + } + + let dl = d * f16(sc); + let ml = dmin * f16(mn); + + store_shmem_kquants((qh_vec4 + qs_lo4_vec4) * dl - vec4(ml, ml, ml, ml), elem_idx); +#endif // INIT_SRC0_SHMEM_Q5_K + +#if defined(INIT_SRC0_SHMEM_Q6_K) + let block_byte_base = src0_idx * 210u; // BLOCK_SIZE_BYTES = 210u; + let ql_byte_base = block_byte_base; + let qh_byte_base = block_byte_base + 128u; + let scales_byte_base = block_byte_base + 192u; + let d_byte_base = block_byte_base + 208u; + + let d = load_f16_at_src0(d_byte_base); + + let chunk = k_in_block / 128u; + let ql_pos_in_chunk = (k_in_block % 128u) % 64u; + let qh_pos_in_chunk = (k_in_block % 128u) % 32u; + let sub_block = k_in_block / 16u; + let ql_shift_phase = (k_in_block % 128u) / 64u; + let qh_shift_phase = (k_in_block % 128u) / 32u; + + // low 4 bits (4 elems) + let ql_word = load_u32_at_src0(ql_byte_base + 64u * chunk + 1u * ql_pos_in_chunk); + let ql_lo4_vec4 = vec4( + (ql_word >> (4u * ql_shift_phase + 0u)) & 0xFu, + (ql_word >> (4u * ql_shift_phase + 8u)) & 0xFu, + (ql_word >> (4u * ql_shift_phase + 16u)) & 0xFu, + (ql_word >> (4u * ql_shift_phase + 24u)) & 0xFu + ); + + // hi 2 bits (4 elems) + let qh_word = load_u32_at_src0(qh_byte_base + 32u * chunk + 1u * qh_pos_in_chunk); + let qh_hi2_vec4 = vec4( + ((qh_word >> (2u * qh_shift_phase + 0u)) & 0x3u) << 4u, + ((qh_word >> (2u * qh_shift_phase + 8u)) & 0x3u) << 4u, + ((qh_word >> (2u * qh_shift_phase + 16u)) & 0x3u) << 4u, + ((qh_word >> (2u * qh_shift_phase + 24u)) & 0x3u) << 4u, + ); + + let q_vec4 = vec4(qh_hi2_vec4 | ql_lo4_vec4) - vec4(32.0, 32.0, 32.0, 32.0); + + let scale_byte = scales_byte_base + 1u * sub_block; + let scale_word = load_u32_at_src0_aligned(scale_byte); + let scale = get_byte_i32(scale_word, scale_byte & 3u); + + store_shmem_kquants(d * q_vec4 * f16(scale), elem_idx); +#endif // INIT_SRC0_SHMEM_Q6_K + } +} +#endif // k-quants + +#if defined(INIT_SRC0_SHMEM_IQ4_NL) +const BLOCK_SIZE = 32u; +const BLOCK_SIZE_BYTES = 18u; +const NQ = 4u; + +fn init_shmem_src0(thread_id: u32, batch_offset: u32, offset_m: u32, k_outer: u32) { + for (var elem_idx = thread_id * NQ; elem_idx < TILE_SRC0_SHMEM; elem_idx += NQ * TOTAL_WORKGROUP_SIZE) { + let tile_m = elem_idx / TILE_K; + let tile_k = elem_idx % TILE_K; + let global_m = offset_m + tile_m; + let global_k = k_outer + tile_k; + + if (global_m >= params.m || global_k >= params.k) { + shmem[elem_idx] = f16(0.0); + continue; + } + + let block_k = global_k / BLOCK_SIZE; + let k_in_block = global_k % BLOCK_SIZE; // k_in_block % 4 == 0; + + let src0_idx = batch_offset + global_m * params.stride_01 + block_k; + + let block_byte_base = src0_idx * BLOCK_SIZE_BYTES; + let d_byte_base = block_byte_base + 0u; + let qs_byte_base = block_byte_base + 2u; + + let d = load_f16_at_src0(d_byte_base); + + let id_qtr = (k_in_block % 16u) / 4u; + let shift_phase = k_in_block / 16u; + + let qs_u32 = load_u32_at_src0(qs_byte_base + 4u * id_qtr); + + shmem[elem_idx + 0u] = d * f16(kvalues_iq4nl[(qs_u32 >> ( 0u + 4u * shift_phase)) & 0xFu]); + shmem[elem_idx + 1u] = d * f16(kvalues_iq4nl[(qs_u32 >> ( 8u + 4u * shift_phase)) & 0xFu]); + shmem[elem_idx + 2u] = d * f16(kvalues_iq4nl[(qs_u32 >> (16u + 4u * shift_phase)) & 0xFu]); + shmem[elem_idx + 3u] = d * f16(kvalues_iq4nl[(qs_u32 >> (24u + 4u * shift_phase)) & 0xFu]); + } +} +#endif // INIT_SRC0_SHMEM_IQ4_NL + +// i-quants (super block size: 256) +#if defined(INIT_SRC0_SHMEM_IQ4_XS) || defined(INIT_SRC0_SHMEM_IQ1_S) || defined(INIT_SRC0_SHMEM_IQ1_M) || defined(INIT_SRC0_SHMEM_IQ2_XXS) \ +|| defined(INIT_SRC0_SHMEM_IQ2_XS) || defined(INIT_SRC0_SHMEM_IQ2_S) || defined(INIT_SRC0_SHMEM_IQ3_XXS) || defined(INIT_SRC0_SHMEM_IQ3_S) +const BLOCK_SIZE = 256u; +const NQ = 16u; + +fn store_shmem_iquants(val: vec4, idx: u32) { + shmem[idx] = val.x; + shmem[idx + 1] = val.y; + shmem[idx + 2] = val.z; + shmem[idx + 3] = val.w; +} + +fn load_byte_at_src0_aligned(byte_offset: u32) -> u32 { + return get_byte(load_u32_at_src0_aligned(byte_offset), byte_offset % 4u); +} + +#if defined(INIT_SRC0_SHMEM_IQ1_M) || defined(INIT_SRC0_SHMEM_IQ1_S) +fn create_iq_gw4(dl: f32, gw: u32, shift_base: u32, delta: f32) -> vec4 { + return vec4( + f16(dl * (f32((bitcast(((gw >> (shift_base + 0u)) & 3u) << 30u) >> 30u)) + delta)), + f16(dl * (f32((bitcast(((gw >> (shift_base + 2u)) & 3u) << 30u) >> 30u)) + delta)), + f16(dl * (f32((bitcast(((gw >> (shift_base + 4u)) & 3u) << 30u) >> 30u)) + delta)), + f16(dl * (f32((bitcast(((gw >> (shift_base + 6u)) & 3u) << 30u) >> 30u)) + delta)), + ); +} +#endif + +#if defined(INIT_SRC0_SHMEM_IQ4_XS) +fn create_iq_gw4(dl: f16, qs_u32: u32, shift_phase: u32) -> vec4 { + return vec4( + dl * f16(kvalues_iq4nl[(qs_u32 >> (4 * shift_phase + 0u)) & 0xFu]), + dl * f16(kvalues_iq4nl[(qs_u32 >> (4 * shift_phase + 8u)) & 0xFu]), + dl * f16(kvalues_iq4nl[(qs_u32 >> (4 * shift_phase + 16u)) & 0xFu]), + dl * f16(kvalues_iq4nl[(qs_u32 >> (4 * shift_phase + 24u)) & 0xFu]), + ); +} +#endif + +#if defined(INIT_SRC0_SHMEM_IQ2_XXS) +fn create_iq_gw4(ig: u32, grid_phase: u32) -> vec4 { + return vec4( + f32(get_byte(iq2xxs_grid[(ig + grid_phase + 0u) / 4u], (ig + grid_phase + 0u) % 4u)), + f32(get_byte(iq2xxs_grid[(ig + grid_phase + 1u) / 4u], (ig + grid_phase + 1u) % 4u)), + f32(get_byte(iq2xxs_grid[(ig + grid_phase + 2u) / 4u], (ig + grid_phase + 2u) % 4u)), + f32(get_byte(iq2xxs_grid[(ig + grid_phase + 3u) / 4u], (ig + grid_phase + 3u) % 4u)), + ); +} +#endif + +#if defined(INIT_SRC0_SHMEM_IQ2_XS) +fn create_iq_gw4(ig: u32, grid_phase: u32) -> vec4 { + return vec4( + f32(get_byte(iq2xs_grid[(ig + grid_phase + 0u) / 4u], (ig + grid_phase + 0u) % 4u)), + f32(get_byte(iq2xs_grid[(ig + grid_phase + 1u) / 4u], (ig + grid_phase + 1u) % 4u)), + f32(get_byte(iq2xs_grid[(ig + grid_phase + 2u) / 4u], (ig + grid_phase + 2u) % 4u)), + f32(get_byte(iq2xs_grid[(ig + grid_phase + 3u) / 4u], (ig + grid_phase + 3u) % 4u)), + ); +} +#endif + +#if defined(INIT_SRC0_SHMEM_IQ2_S) +fn create_iq_gw4(ig: u32, grid_phase: u32) -> vec4 { + return vec4( + f32(get_byte(iq2s_grid[(ig + grid_phase + 0u) / 4u], (ig + grid_phase + 0u) % 4u)), + f32(get_byte(iq2s_grid[(ig + grid_phase + 1u) / 4u], (ig + grid_phase + 1u) % 4u)), + f32(get_byte(iq2s_grid[(ig + grid_phase + 2u) / 4u], (ig + grid_phase + 2u) % 4u)), + f32(get_byte(iq2s_grid[(ig + grid_phase + 3u) / 4u], (ig + grid_phase + 3u) % 4u)), + ); +} +#endif + +#if defined(INIT_SRC0_SHMEM_IQ3_XXS) +fn create_iq_gw4(ig: u32) -> vec4 { + return vec4( + f32(get_byte(iq3xxs_grid[ig], 0)), + f32(get_byte(iq3xxs_grid[ig], 1)), + f32(get_byte(iq3xxs_grid[ig], 2)), + f32(get_byte(iq3xxs_grid[ig], 3)), + ); +} +#endif + +#if defined(INIT_SRC0_SHMEM_IQ3_S) +fn create_iq_gw4(ig: u32) -> vec4 { + return vec4( + f32(get_byte(iq3s_grid[ig], 0)), + f32(get_byte(iq3s_grid[ig], 1)), + f32(get_byte(iq3s_grid[ig], 2)), + f32(get_byte(iq3s_grid[ig], 3)), + ); +} +#endif + +#if defined(INIT_SRC0_SHMEM_IQ2_XXS) || defined(INIT_SRC0_SHMEM_IQ2_XS) || defined(INIT_SRC0_SHMEM_IQ2_S) \ +|| defined(INIT_SRC0_SHMEM_IQ3_XXS) || defined(INIT_SRC0_SHMEM_IQ3_S) +fn create_iq2_m4(signs: u32, mask_phase: u32) -> vec4 { + return vec4( + select(1.0, -1.0, (get_byte(kmask_iq2xs[mask_phase], 0) & signs) != 0u), + select(1.0, -1.0, (get_byte(kmask_iq2xs[mask_phase], 1) & signs) != 0u), + select(1.0, -1.0, (get_byte(kmask_iq2xs[mask_phase], 2) & signs) != 0u), + select(1.0, -1.0, (get_byte(kmask_iq2xs[mask_phase], 3) & signs) != 0u), + ); +} +#endif + +fn init_shmem_src0(thread_id: u32, batch_offset: u32, offset_m: u32, k_outer: u32) { + for (var elem_idx = thread_id * NQ; elem_idx < TILE_SRC0_SHMEM; elem_idx += NQ * TOTAL_WORKGROUP_SIZE) { + let tile_m = elem_idx / TILE_K; + let tile_k = elem_idx % TILE_K; + let global_m = offset_m + tile_m; + let global_k = k_outer + tile_k; + + if (global_m >= params.m || global_k >= params.k) { + let zero_vec4 = vec4(f16(0.0), f16(0.0), f16(0.0), f16(0.0)); + store_shmem_iquants(zero_vec4, elem_idx + 0u); + store_shmem_iquants(zero_vec4, elem_idx + 4u); + store_shmem_iquants(zero_vec4, elem_idx + 8u); + store_shmem_iquants(zero_vec4, elem_idx + 12u); + continue; + } + + let block_k = global_k / BLOCK_SIZE; + let k_in_block = global_k % BLOCK_SIZE; // k_in_block % 16 == 0; + + let src0_idx = batch_offset + global_m * params.stride_01 + block_k; + +#if defined(INIT_SRC0_SHMEM_IQ4_XS) + let block_byte_base = src0_idx * 136u; // BLOCK_SIZE_BYTES = 136u; + let d_byte_base = block_byte_base + 0u; + let scales_l_byte_base = block_byte_base + 4u; + let qs_byte_base = block_byte_base + 8u; + + let d_scales_h = load_u32_at_src0_aligned(d_byte_base); + let d = bitcast>(d_scales_h).x; + let scales_h = d_scales_h >> 16u; + + let sub_block = k_in_block / 32u; + let phase = (k_in_block / NQ) % 2u; + + let scales_l_u32 = load_u32_at_src0_aligned(scales_l_byte_base); + let ls_lo = (get_byte(scales_l_u32, sub_block / 2u) >> (4u * (sub_block % 2u))) & 0xFu; + let ls_hi = ((scales_h >> (2u * sub_block)) & 3u) << 4u; + let dl = d * f16(i32(ls_lo | ls_hi) - 32); + + let qs_0_3_u32 = load_u32_at_src0_aligned(qs_byte_base + 16u * sub_block + 0u); + let qs_4_7_u32 = load_u32_at_src0_aligned(qs_byte_base + 16u * sub_block + 4u); + let qs_8_11_u32 = load_u32_at_src0_aligned(qs_byte_base + 16u * sub_block + 8u); + let qs_12_15_u32 = load_u32_at_src0_aligned(qs_byte_base + 16u * sub_block + 12u); + + store_shmem_iquants(create_iq_gw4(dl, qs_0_3_u32, phase), elem_idx + 0u); + store_shmem_iquants(create_iq_gw4(dl, qs_4_7_u32, phase), elem_idx + 4u); + store_shmem_iquants(create_iq_gw4(dl, qs_8_11_u32, phase), elem_idx + 8u); + store_shmem_iquants(create_iq_gw4(dl, qs_12_15_u32, phase), elem_idx + 12u); +#endif // INIT_SRC0_SHMEM_IQ4_XS + +#if defined(INIT_SRC0_SHMEM_IQ1_S) + let block_byte_base = src0_idx * 50u; // BLOCK_SIZE_BYTES = 50u; + let d_byte_base = block_byte_base + 0u; + let qs_byte_base = block_byte_base + 2u; + let qh_byte_base = block_byte_base + 34u; + + let d = load_f16_as_f32_at_src0(d_byte_base); + + let sub_block = k_in_block / 32u; + let phase = (k_in_block / NQ) % 2u; + + let qh_u16 = load_u32_at_src0(qh_byte_base + sub_block * 2u) & 0xFFFFu; + let qs_u16 = load_u32_at_src0(qs_byte_base + sub_block * 4u + phase * 2u) & 0xFFFFu; + + let dl = d * (2.0 * f32((qh_u16 >> 12u) & 7u) + 1.0); + let delta = select(IQ1_DELTA, -IQ1_DELTA, (qh_u16 & 0x8000u) != 0u); + + let gp0_grid_id = ((qs_u16 & 0xFFu) | (((qh_u16 >> (phase * 6u)) & 7u) << 8u)) * 8u; + let gp1_grid_id = (((qs_u16 >> 8) & 0xFFu) | (((qh_u16 >> (phase * 6u + 3u)) & 7u) << 8u)) * 8u; + + let gp0_gw = iq1_grid[(gp0_grid_id) / 16u]; + let gp1_gw = iq1_grid[(gp1_grid_id) / 16u]; + + let gp0_shift_base = (gp0_grid_id % 16u) * 2u; + let gp1_shift_base = (gp1_grid_id % 16u) * 2u; + + store_shmem_iquants(create_iq_gw4(dl, gp0_gw, gp0_shift_base + 0u, delta), elem_idx + 0u); + store_shmem_iquants(create_iq_gw4(dl, gp0_gw, gp0_shift_base + 8u, delta), elem_idx + 4u); + store_shmem_iquants(create_iq_gw4(dl, gp1_gw, gp1_shift_base + 0u, delta), elem_idx + 8u); + store_shmem_iquants(create_iq_gw4(dl, gp1_gw, gp1_shift_base + 8u, delta), elem_idx + 12u); +#endif // INIT_SRC0_SHMEM_IQ1_S + +#if defined(INIT_SRC0_SHMEM_IQ1_M) + let block_byte_base = src0_idx * 56u; // BLOCK_SIZE_BYTES = 56u; + let qs_byte_base = block_byte_base + 0u; + let qh_byte_base = block_byte_base + 32u; + let scales_byte_base = block_byte_base + 48u; + + let scales0 = load_u32_at_src0_aligned(scales_byte_base); + let scales1 = load_u32_at_src0_aligned(scales_byte_base + 4u); + let scale_packed = ((scales0 >> 12u) & 0xFu) | + ((scales0 >> 24u) & 0x00F0u) | + ((scales1 >> 4u) & 0x0F00u) | + ((scales1 >> 16u) & 0xF000u); + let d = f32(bitcast>(scale_packed).x); + + let sub_block = k_in_block / 32u; + let phase = (k_in_block / NQ) % 2u; + + let scale_u32 = select(scales0, scales1, sub_block >= 4u); + let scale_u3 = (scale_u32 >> (16u * ((sub_block / 2u) % 2u) + 6u * (sub_block % 2u) + 3u * phase)) & 0x7u; + let dl = d * f32(2u * scale_u3 + 1u); + + let qh_u8 = (load_u32_at_src0_aligned(qh_byte_base + 4u * (sub_block / 2u)) >> (16u * (sub_block % 2u) + 8u * phase)) & 0xFFu; + let qs_u16 = (load_u32_at_src0_aligned(qs_byte_base + 4u * sub_block) >> (16u * phase)) & 0xFFFFu; + + let gp0_grid_id = ((qs_u16 & 0xFFu) | ((qh_u8 & 7u) << 8u)) * 8u; + let gp0_delta = select(IQ1_DELTA, -IQ1_DELTA, (qh_u8 & 0x8u) != 0u); + + let gp1_grid_id = (((qs_u16 >> 8u) & 0xFFu) | (((qh_u8 >> 4u) & 7u) << 8u)) * 8u; + let gp1_delta = select(IQ1_DELTA, -IQ1_DELTA, (qh_u8 & 0x80u) != 0u); + + let gp0_gw = iq1_grid[(gp0_grid_id) / 16u]; + let gp1_gw = iq1_grid[(gp1_grid_id) / 16u]; + + let gp0_shift_base = (gp0_grid_id % 16u) * 2u; + let gp1_shift_base = (gp1_grid_id % 16u) * 2u; + + store_shmem_iquants(create_iq_gw4(dl, gp0_gw, gp0_shift_base + 0u, gp0_delta), elem_idx + 0u); + store_shmem_iquants(create_iq_gw4(dl, gp0_gw, gp0_shift_base + 8u, gp0_delta), elem_idx + 4u); + store_shmem_iquants(create_iq_gw4(dl, gp1_gw, gp1_shift_base + 0u, gp1_delta), elem_idx + 8u); + store_shmem_iquants(create_iq_gw4(dl, gp1_gw, gp1_shift_base + 8u, gp1_delta), elem_idx + 12u); +#endif // INIT_SRC0_SHMEM_IQ1_M + +#if defined(INIT_SRC0_SHMEM_IQ2_XXS) + let block_byte_base = src0_idx * 66u; // BLOCK_SIZE_BYTES = 66u; + let d_byte_base = block_byte_base + 0u; + let qs_byte_base = block_byte_base + 2u; + + let d = load_f16_as_f32_at_src0(d_byte_base); + + let sub_block = k_in_block / 32u; + let phase = (k_in_block / NQ) % 2u; + + let aux0 = load_u32_at_src0(qs_byte_base + 8u * sub_block + 0u); + let aux1 = load_u32_at_src0(qs_byte_base + 8u * sub_block + 4u); + let db = d * (0.5 + f32(aux1 >> 28u)) * 0.25; + + let gp0_ig = get_byte(aux0, 2u * phase + 0u) * 8u; + let gp1_ig = get_byte(aux0, 2u * phase + 1u) * 8u; + + let gp0_is = (aux1 >> (14u * phase + 0u)) & 127u; + let gp1_is = (aux1 >> (14u * phase + 7u)) & 127u; + + let gp0_signs = get_byte(ksigns_iq2xs[gp0_is / 4u], gp0_is % 4u); + let gp1_signs = get_byte(ksigns_iq2xs[gp1_is / 4u], gp1_is % 4u); + + let m_0_3_val4 = create_iq2_m4(gp0_signs, 0); + let m_4_7_val4 = create_iq2_m4(gp0_signs, 1); + let m_8_11_val4 = create_iq2_m4(gp1_signs, 0); + let m_12_15_val4 = create_iq2_m4(gp1_signs, 1); + + let gw_0_3_val4 = create_iq_gw4(gp0_ig, 0); + let gw_4_7_val4 = create_iq_gw4(gp0_ig, 4); + let gw_8_11_val4 = create_iq_gw4(gp1_ig, 0); + let gw_12_15_val4 = create_iq_gw4(gp1_ig, 4); + + store_shmem_iquants(vec4(db * m_0_3_val4 * gw_0_3_val4), elem_idx + 0u); + store_shmem_iquants(vec4(db * m_4_7_val4 * gw_4_7_val4), elem_idx + 4u); + store_shmem_iquants(vec4(db * m_8_11_val4 * gw_8_11_val4), elem_idx + 8u); + store_shmem_iquants(vec4(db * m_12_15_val4 * gw_12_15_val4), elem_idx + 12u); +#endif // INIT_SRC0_SHMEM_IQ2_XXS + +#if defined(INIT_SRC0_SHMEM_IQ2_XS) + let block_byte_base = src0_idx * 74u; // BLOCK_SIZE_BYTES = 74u; + let d_byte_base = block_byte_base + 0u; + let qs_byte_base = block_byte_base + 2u; + let scales_byte_base = block_byte_base + 66u; + + let d = load_f16_as_f32_at_src0(d_byte_base); + + let sub_block = k_in_block / 32u; + let phase = (k_in_block / NQ) % 2u; + + let scale = (load_byte_at_src0_aligned(scales_byte_base + 1u * sub_block) >> (4u * phase)) & 0xFu; + let db = d * (0.5 + f32(scale)) * 0.25; + + let qs_u32 = load_u32_at_src0(qs_byte_base + 8u * sub_block + 4u * phase); + + let gp0_ig = (qs_u32 & 0x1FFu) * 8u; + let gp1_ig = ((qs_u32 >> 16u) & 0x1FFu) * 8u; + + let gp0_is = (qs_u32 >> 9u) & 0x7Fu; + let gp1_is = (qs_u32 >> 25u) & 0x7Fu; + + let gp0_signs = get_byte(ksigns_iq2xs[gp0_is / 4u], gp0_is % 4u); + let gp1_signs = get_byte(ksigns_iq2xs[gp1_is / 4u], gp1_is % 4u); + + let m_0_3_val4 = create_iq2_m4(gp0_signs, 0); + let m_4_7_val4 = create_iq2_m4(gp0_signs, 1); + let m_8_11_val4 = create_iq2_m4(gp1_signs, 0); + let m_12_15_val4 = create_iq2_m4(gp1_signs, 1); + + let gw_0_3_val4 = create_iq_gw4(gp0_ig, 0); + let gw_4_7_val4 = create_iq_gw4(gp0_ig, 4); + let gw_8_11_val4 = create_iq_gw4(gp1_ig, 0); + let gw_12_15_val4 = create_iq_gw4(gp1_ig, 4); + + store_shmem_iquants(vec4(db * m_0_3_val4 * gw_0_3_val4), elem_idx + 0u); + store_shmem_iquants(vec4(db * m_4_7_val4 * gw_4_7_val4), elem_idx + 4u); + store_shmem_iquants(vec4(db * m_8_11_val4 * gw_8_11_val4), elem_idx + 8u); + store_shmem_iquants(vec4(db * m_12_15_val4 * gw_12_15_val4), elem_idx + 12u); +#endif // INIT_SRC0_SHMEM_IQ2_XS + +#if defined(INIT_SRC0_SHMEM_IQ2_S) + let block_byte_base = src0_idx * 82u; // BLOCK_SIZE_BYTES = 82u; + let d_byte_base = block_byte_base + 0u; + let qs_byte_base = block_byte_base + 2u; + let qh_byte_base = block_byte_base + 66u; + let scales_byte_base = block_byte_base + 74u; + + let d = load_f16_as_f32_at_src0(d_byte_base); + + let sub_block = k_in_block / 32u; + let phase = (k_in_block / NQ) % 2u; + + let scale = (load_byte_at_src0_aligned(scales_byte_base + 1u * sub_block) >> (4u * phase)) & 0xFu; + let db = d * (0.5 + f32(scale)) * 0.25; + + let qs_u16 = load_u32_at_src0(qs_byte_base + 4u * sub_block + 2u * phase) & 0xFFFFu; + let signs_u16 = load_u32_at_src0(qs_byte_base + 32u + 4u * sub_block + 2u * phase) & 0xFFFFu; + let qh_u4 = (load_byte_at_src0_aligned(qh_byte_base + 1u * sub_block) >> (4u * phase)) & 0xFu; + + let gp0_ig = ((qs_u16 & 0xFFu) | ((qh_u4 & 0x3u) << 8u)) * 8u; + let gp1_ig = (((qs_u16 >> 8u) & 0xFFu) | ((qh_u4 & 0xCu) << 6u)) * 8u; + + let gp0_signs = get_byte(signs_u16, 0); + let gp1_signs = get_byte(signs_u16, 1); + + let m_0_3_val4 = create_iq2_m4(gp0_signs, 0); + let m_4_7_val4 = create_iq2_m4(gp0_signs, 1); + let m_8_11_val4 = create_iq2_m4(gp1_signs, 0); + let m_12_15_val4 = create_iq2_m4(gp1_signs, 1); + + let gw_0_3_val4 = create_iq_gw4(gp0_ig, 0); + let gw_4_7_val4 = create_iq_gw4(gp0_ig, 4); + let gw_8_11_val4 = create_iq_gw4(gp1_ig, 0); + let gw_12_15_val4 = create_iq_gw4(gp1_ig, 4); + + store_shmem_iquants(vec4(db * m_0_3_val4 * gw_0_3_val4), elem_idx + 0u); + store_shmem_iquants(vec4(db * m_4_7_val4 * gw_4_7_val4), elem_idx + 4u); + store_shmem_iquants(vec4(db * m_8_11_val4 * gw_8_11_val4), elem_idx + 8u); + store_shmem_iquants(vec4(db * m_12_15_val4 * gw_12_15_val4), elem_idx + 12u); +#endif // INIT_SRC0_SHMEM_IQ2_S + +#if defined(INIT_SRC0_SHMEM_IQ3_XXS) + let block_byte_base = src0_idx * 98u; // BLOCK_SIZE_BYTES = 98u; + let d_byte_base = block_byte_base + 0u; + let qs_byte_base = block_byte_base + 2u; + + let d = load_f16_as_f32_at_src0(d_byte_base); + + let sub_block = k_in_block / 32u; + let phase = (k_in_block / NQ) % 2u; + + let qs_u32 = load_u32_at_src0(qs_byte_base + 8u * sub_block + 4u * phase); + let sign_u32 = load_u32_at_src0(qs_byte_base + 64u + 4u * sub_block); + let db = d * (0.5 + f32(sign_u32 >> 28u)) * 0.5; + + let ig_0_3 = get_byte(qs_u32, 0); + let ig_4_7 = get_byte(qs_u32, 1); + let ig_8_11 = get_byte(qs_u32, 2); + let ig_12_15 = get_byte(qs_u32, 3); + + let gp0_is = (sign_u32 >> (14u * phase + 0u)) & 0x7Fu; + let gp1_is = (sign_u32 >> (14u * phase + 7u)) & 0x7Fu; + + let gp0_signs = get_byte(ksigns_iq2xs[gp0_is / 4u], gp0_is % 4u); + let gp1_signs = get_byte(ksigns_iq2xs[gp1_is / 4u], gp1_is % 4u); + + let m_0_3_val4 = create_iq2_m4(gp0_signs, 0); + let m_4_7_val4 = create_iq2_m4(gp0_signs, 1); + let m_8_11_val4 = create_iq2_m4(gp1_signs, 0); + let m_12_15_val4 = create_iq2_m4(gp1_signs, 1); + + let gw_0_3_val4 = create_iq_gw4(ig_0_3); + let gw_4_7_val4 = create_iq_gw4(ig_4_7); + let gw_8_11_val4 = create_iq_gw4(ig_8_11); + let gw_12_15_val4 = create_iq_gw4(ig_12_15); + + store_shmem_iquants(vec4(db * m_0_3_val4 * gw_0_3_val4), elem_idx + 0u); + store_shmem_iquants(vec4(db * m_4_7_val4 * gw_4_7_val4), elem_idx + 4u); + store_shmem_iquants(vec4(db * m_8_11_val4 * gw_8_11_val4), elem_idx + 8u); + store_shmem_iquants(vec4(db * m_12_15_val4 * gw_12_15_val4), elem_idx + 12u); +#endif // INIT_SRC0_SHMEM_IQ3_XXS + +#if defined(INIT_SRC0_SHMEM_IQ3_S) + let block_byte_base = src0_idx * 110u; // BLOCK_SIZE_BYTES = 110u; + let d_byte_base = block_byte_base + 0u; + let qs_byte_base = block_byte_base + 2u; + let qh_byte_base = block_byte_base + 66u; + let signs_byte_base = block_byte_base + 74u; + let scales_byte_base = block_byte_base + 106u; + + let d = load_f16_as_f32_at_src0(d_byte_base); + + let sub_block = k_in_block / 32u; + let phase = (k_in_block / NQ) % 2u; + + let scale = (load_byte_at_src0_aligned(scales_byte_base + 1u * (sub_block / 2u)) >> (4u * (sub_block % 2u))) & 0xFu; + let db = d * (1.0 + 2.0 * f32(scale)); + + let qs_u32 = load_u32_at_src0(qs_byte_base + 8u * sub_block + 4u * phase); + let qh_u4 = (load_byte_at_src0_aligned(qh_byte_base + 1u * sub_block) >> (4u * phase)) & 0xFu; + let signs_u16 = (load_u32_at_src0(signs_byte_base + 4u * sub_block + 2u * phase)) & 0xFFFFu; + + let ig_0_3 = ((qs_u32 >> 0u) & 0xFFu) | ((qh_u4 & 0x1u) << 8u); + let ig_4_7 = ((qs_u32 >> 8u) & 0xFFu) | ((qh_u4 & 0x2u) << 7u); + let ig_8_11 = ((qs_u32 >> 16u) & 0xFFu) | ((qh_u4 & 0x4u) << 6u); + let ig_12_15 = ((qs_u32 >> 24u) & 0xFFu) | ((qh_u4 & 0x8u) << 5u); + + let gp0_signs = get_byte(signs_u16, 0); + let gp1_signs = get_byte(signs_u16, 1); + + let m_0_3_val4 = create_iq2_m4(gp0_signs, 0); + let m_4_7_val4 = create_iq2_m4(gp0_signs, 1); + let m_8_11_val4 = create_iq2_m4(gp1_signs, 0); + let m_12_15_val4 = create_iq2_m4(gp1_signs, 1); + + let gw_0_3_val4 = create_iq_gw4(ig_0_3); + let gw_4_7_val4 = create_iq_gw4(ig_4_7); + let gw_8_11_val4 = create_iq_gw4(ig_8_11); + let gw_12_15_val4 = create_iq_gw4(ig_12_15); + + store_shmem_iquants(vec4(db * m_0_3_val4 * gw_0_3_val4), elem_idx + 0u); + store_shmem_iquants(vec4(db * m_4_7_val4 * gw_4_7_val4), elem_idx + 4u); + store_shmem_iquants(vec4(db * m_8_11_val4 * gw_8_11_val4), elem_idx + 8u); + store_shmem_iquants(vec4(db * m_12_15_val4 * gw_12_15_val4), elem_idx + 12u); +#endif // INIT_SRC0_SHMEM_IQ3_S + } +} +#endif // i-quants (super block size: 256) diff --git a/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_id.wgsl b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_id.wgsl new file mode 100644 index 0000000000000000000000000000000000000000..91039ff2546fd772875b4aea0576cdfc9b430fda --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_id.wgsl @@ -0,0 +1,195 @@ +enable f16; + +#define DECLARE_BYTE_LOADERS_SRC0 +#include "common_decls.tmpl" + +#include "mul_mat_decls.tmpl" + +#ifdef VEC +fn store_val(acc: array, TILE_N>, tn: u32, tm: u32) -> vec4 { + return vec4(f32(acc[tn][tm]), f32(acc[tn][tm + 1]), f32(acc[tn][tm + 2]), f32(acc[tn][tm + 3])); +} +#endif + +#ifdef SCALAR +fn store_val(acc: array, TILE_N>, tn: u32, tm: u32) -> f32 { + return f32(acc[tn][tm]); +} +#endif + +struct MulMatIdParams { + offset_src0: u32, + offset_src1: u32, + offset_dst: u32, + + k: u32, + m: u32, + n_expert: u32, + n_expert_used: u32, + n_tokens: u32, + b_ne1: u32, + + stride_01: u32, + stride_11: u32, + stride_02: u32, + stride_12: u32, +}; + +@group(0) @binding(0) var src0: array; // [cols, rows, n_expert] +@group(0) @binding(1) var src1: array; // [cols, b_ne1, n_tokens] +@group(0) @binding(2) var dst: array; // [rows, n_expert_used, n_tokens] +@group(0) @binding(3) var global_gathered_expert_used: array; // [n_expert][n_tokens] +@group(0) @binding(4) var global_gathered_tokens: array; // [n_expert][n_tokens] +@group(0) @binding(5) var gathered_count_ids: array; // [n_expert] + +@group(0) @binding(6) var params: MulMatIdParams; + +fn get_local_n(thread_id: u32) -> u32 { + return thread_id / WORKGROUP_SIZE_M; +} +fn get_local_m(thread_id: u32) -> u32 { + return thread_id % WORKGROUP_SIZE_M; +} + +const TOTAL_WORKGROUP_SIZE = WORKGROUP_SIZE_M * WORKGROUP_SIZE_N; +const TILE_SRC0_SHMEM = TILE_K * WORKGROUP_SIZE_M * TILE_M; +const TILE_SRC1_SHMEM = TILE_K * WORKGROUP_SIZE_N * TILE_N; + +var shmem: array; +var gathered_expert_used: array; +var gathered_tokens: array; + +#ifdef INIT_SRC1_SHMEM_FLOAT +fn init_shmem_id_src1(thread_id: u32, offset_src1: u32, rest_token_n: u32, k_outer: u32) { + for (var elem_idx = thread_id * VEC_SIZE; elem_idx < TILE_SRC1_SHMEM; elem_idx += TOTAL_WORKGROUP_SIZE * VEC_SIZE) { + let tile_n = elem_idx / TILE_K; + let tile_k = elem_idx % TILE_K; + if (tile_n < rest_token_n) { + let global_src10 = k_outer + tile_k; + let expert_used_idx = gathered_expert_used[tile_n] % params.b_ne1; + let token_idx = gathered_tokens[tile_n]; + let src1_idx = offset_src1 + token_idx * params.stride_12 + expert_used_idx * params.stride_11 + global_src10; + let src1_val = select( + SRC1_TYPE(0.0), + src1[src1_idx/VEC_SIZE], + global_src10 < params.k); + store_shmem(SHMEM_TYPE(src1_val), TILE_SRC0_SHMEM + elem_idx); + } else { + store_shmem(SHMEM_TYPE(0.0), TILE_SRC0_SHMEM + elem_idx); + } + } +} +#endif // INIT_SRC1_SHMEM_FLOAT + +@compute @workgroup_size(TOTAL_WORKGROUP_SIZE) +fn main(@builtin(workgroup_id) wg_id: vec3, + @builtin(local_invocation_id) local_id: vec3, + @builtin(num_workgroups) num_wg: vec3) { + + let thread_id = local_id.x; + let local_m = get_local_m(thread_id); + let local_n = get_local_n(thread_id); + + var expert_idx:u32 = 0xFFFFFFFFu; + var wg_in_batch:u32 = 0; + var wg_sum:u32 = 0; + let wg_m_count = (params.m + WORKGROUP_SIZE_M * TILE_M - 1u) / (WORKGROUP_SIZE_M * TILE_M); + let wg_linear = wg_id.y * num_wg.x + wg_id.x; + + for (var i = 0u;i < params.n_expert;i += 1) { + let wg_n_count = (gathered_count_ids[i] + WORKGROUP_SIZE_N * TILE_N - 1u) / (WORKGROUP_SIZE_N * TILE_N); + let wg_per_matrix = wg_m_count * wg_n_count; + if (wg_sum <= wg_linear && wg_linear < wg_sum + wg_per_matrix) { + expert_idx = i; + wg_in_batch = wg_linear - wg_sum; + break; + } + wg_sum += wg_per_matrix; + } + + let is_valid = expert_idx != 0xFFFFFFFFu; + + var wg_m: u32 = 0; + var wg_n: u32 = 0; + var offset_wg_m: u32 = 0; + var offset_wg_n: u32 = 0; + var rest_token_n: u32 = 0; + var src0_batch_offset: u32 = 0; + + wg_m = wg_in_batch % wg_m_count; + wg_n = wg_in_batch / wg_m_count; + + offset_wg_m = wg_m * WORKGROUP_SIZE_M * TILE_M; + offset_wg_n = wg_n * WORKGROUP_SIZE_N * TILE_N; + + if (is_valid) { + rest_token_n = gathered_count_ids[expert_idx] - offset_wg_n; + let global_gathered_base = expert_idx * params.n_tokens + offset_wg_n; + for (var i = thread_id; i < TILE_N * WORKGROUP_SIZE_N && offset_wg_n + i < gathered_count_ids[expert_idx]; i += TOTAL_WORKGROUP_SIZE) { + gathered_expert_used[i] = global_gathered_expert_used[global_gathered_base + i]; + gathered_tokens[i] = global_gathered_tokens[global_gathered_base + i]; + } + src0_batch_offset = params.offset_src0 + expert_idx * params.stride_02; + } + + workgroupBarrier(); + + let output_row_base = offset_wg_m + local_m * TILE_M; + let output_col_base = offset_wg_n + local_n * TILE_N; + + let dst2_stride = params.m * params.n_expert_used; + let dst1_stride = params.m; + + var acc: array, TILE_N>; + + for (var k_outer = 0u; k_outer < params.k; k_outer += TILE_K) { + + if (is_valid) { + init_shmem_src0(thread_id, src0_batch_offset, offset_wg_m, k_outer); + init_shmem_id_src1(thread_id, params.offset_src1, rest_token_n, k_outer); + } + + workgroupBarrier(); + + if (is_valid) { + let k_end = min(TILE_K, params.k - k_outer); + + for (var k_inner = 0u; k_inner < k_end; k_inner++) { + var src0_tile: array; + for (var tm = 0u; tm < TILE_M; tm++) { + let src0_m = local_m * TILE_M + tm; + let src0_idx = k_inner + src0_m * TILE_K; + src0_tile[tm] = shmem[src0_idx]; + } + for (var tn = 0u; tn < TILE_N; tn++) { + let src1_n = local_n * TILE_N + tn; + let src1_idx = src1_n * TILE_K + k_inner; + let src1_val = shmem[TILE_SRC0_SHMEM + src1_idx]; + for (var tm = 0u; tm < TILE_M; tm++) { + acc[tn][tm] += src0_tile[tm] * src1_val; + } + } + } + } + + workgroupBarrier(); + } + + if (is_valid) { + for (var tn = 0u; tn < TILE_N; tn++) { + let n_idx = output_col_base + tn; + if (n_idx < gathered_count_ids[expert_idx]) { + let dst1_idx = gathered_expert_used[n_idx - offset_wg_n]; + let dst2_idx = gathered_tokens[n_idx - offset_wg_n]; + let dst12_offset = params.offset_dst + dst2_idx * dst2_stride + dst1_idx * dst1_stride; + for (var tm = 0u; tm < TILE_M; tm += VEC_SIZE) { + let global_row = output_row_base + tm; + if (global_row < params.m) { + let dst_idx = dst12_offset + global_row; + dst[dst_idx/VEC_SIZE] = store_val(acc, tn, tm); + } + } + } + } + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_id_gather.wgsl b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_id_gather.wgsl new file mode 100644 index 0000000000000000000000000000000000000000..581e922709de756dce0444310131e5d7b054fd4e --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_id_gather.wgsl @@ -0,0 +1,52 @@ +enable f16; + +struct MulMatIdGatherParams { + offset_ids: u32, + + n_expert: u32, + n_expert_used: u32, + n_tokens: u32, + + stride_ids_1: u32, +}; + +@group(0) @binding(0) var ids: array; // [n_expert_used, n_tokens] +@group(0) @binding(1) var global_gathered_expert_used: array; // [n_expert][n_tokens] +@group(0) @binding(2) var global_gathered_tokens: array; // [n_expert][n_tokens] +@group(0) @binding(3) var gathered_count_ids: array; // [n_expert] + +@group(0) @binding(4) var params: MulMatIdGatherParams; + +var count:atomic; + +@compute @workgroup_size(WG_SIZE) +fn main(@builtin(workgroup_id) wg_id: vec3, + @builtin(local_invocation_id) local_id: vec3) { + + let thread_id = local_id.x; + let own_expert = wg_id.x; // the expert assigned to this workgroup + + if (thread_id == 0u) { + atomicStore(&count, 0); + } + + workgroupBarrier(); + + for (var i = thread_id;i < params.n_expert_used * params.n_tokens;i += WG_SIZE) { + let row = i / params.n_expert_used; + let col = i % params.n_expert_used; + let expert = u32(ids[params.offset_ids + row * params.stride_ids_1 + col]); + if (own_expert == expert) { + let pos = atomicAdd(&count, 1u); + let gathered_id = own_expert * params.n_tokens + pos; + global_gathered_expert_used[gathered_id] = col; + global_gathered_tokens[gathered_id] = row; + } + } + + workgroupBarrier(); + + if (thread_id == 0u) { + gathered_count_ids[own_expert] = atomicLoad(&count); + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_id_vec.wgsl b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_id_vec.wgsl new file mode 100644 index 0000000000000000000000000000000000000000..78ae955e6badeb464549e04b42fb5e15d4cc09e5 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_id_vec.wgsl @@ -0,0 +1,154 @@ +#ifdef USE_SUBGROUP_REDUCTION +enable subgroups; +#endif +enable f16; + +#define DECLARE_BYTE_LOADERS_SRC0 +#include "common_decls.tmpl" + +#include "mul_mat_vec_acc.tmpl" + +struct MulMatIdVecParams { + offset_src0: u32, + offset_src1: u32, + offset_ids: u32, + offset_dst: u32, + + k: u32, + m: u32, + n_expert: u32, + n_expert_used: u32, + b_ne1: u32, + + stride_01: u32, + stride_11: u32, + stride_02: u32, + stride_12: u32, +}; + +@group(0) @binding(0) var src0: array; // [cols, rows, n_expert] +@group(0) @binding(1) var src1: array; // [cols, b_ne1, n_tokens(1)] +@group(0) @binding(2) var ids: array; // [n_experd_used, n_tokens(1)] +@group(0) @binding(3) var dst: array; // [rows, n_expert_used, n_tokens(1)] + +// "mul_mat_vec_acc.tmpl" requires params.k, params.m, params.stride_01 +@group(0) @binding(4) var params: MulMatIdVecParams; + +// Flattened as [row][thread] to keep each row's reduction contiguous in memory. +var partial_sums: array; + +fn partial_index(row: u32, thread: u32) -> u32 { + return row * WG_SIZE + thread; +} + +var gathered_count_ids: array; +var gathered_expert_used: array; + +@compute @workgroup_size(WG_SIZE) +fn main( + @builtin(local_invocation_id) local_id: vec3, + @builtin(workgroup_id) wg_id: vec3, + @builtin(num_workgroups) num_wg: vec3 +#ifdef USE_SUBGROUP_REDUCTION + , @builtin(subgroup_id) subgroup_id: u32, + @builtin(subgroup_invocation_id) subgroup_invocation_id: u32, + @builtin(num_subgroups) num_subgroups: u32, + @builtin(subgroup_size) subgroup_size: u32 +#endif +) { + + let thread_id = local_id.x; + + for (var i = thread_id;i < params.n_expert;i += WG_SIZE) { + gathered_count_ids[i] = 0; + } + + workgroupBarrier(); + + // gather the selected experts for the target token. + for (var col = thread_id;col < params.n_expert_used;col += WG_SIZE) { + let expert = ids[params.offset_ids + col]; + gathered_count_ids[expert] = 1; + gathered_expert_used[expert] = col; + } + + workgroupBarrier(); + + let output_groups:u32 = (params.m + OUTPUTS_PER_WG - 1u) / OUTPUTS_PER_WG; + let wg_linear = wg_id.y * num_wg.x + wg_id.x; + + var own_expert:u32 = 0; + var wg_in_batch:u32 = 0; + var wg_sum:u32 = 0; + + for (var i = 0u;i < params.n_expert;i += 1) { + let wg_vec_count = gathered_count_ids[i]; // 1 or 0 + let wg_per_matrix = output_groups * wg_vec_count; + if (wg_sum <= wg_linear && wg_linear < wg_sum + wg_per_matrix) { + own_expert = i; + wg_in_batch = wg_linear - wg_sum; + break; + } + wg_sum += wg_per_matrix; + } + + let row_base = (wg_linear % output_groups) * OUTPUTS_PER_WG; + let dst1_stride = params.m; + + let src0_batch_offset = params.offset_src0 + own_expert * params.stride_02; + let src1_idx_base = params.offset_src1 + (gathered_expert_used[own_expert] % params.b_ne1) * params.stride_11; + let dst_idx_base = params.offset_dst + gathered_expert_used[own_expert] * dst1_stride + row_base; + + let acc = accumulate_vec_dot(thread_id, row_base, src0_batch_offset, src1_idx_base); + +#ifdef USE_SUBGROUP_REDUCTION + for (var row = 0u; row < OUTPUTS_PER_WG; row++) { + let subgroup_total = subgroupAdd(acc[0][row]); + if (subgroup_invocation_id == 0u) { + partial_sums[partial_index(row, subgroup_id)] = subgroup_total; + } + } + + workgroupBarrier(); + + for (var row = subgroup_id; (row < OUTPUTS_PER_WG) && (row_base + row < params.m); row += num_subgroups) { + let output_row = row_base + row; + var row_acc = 0.0f; + for (var k = subgroup_invocation_id; k < num_subgroups; k += subgroup_size) { + row_acc += partial_sums[partial_index(row, k)]; + } + let row_total = subgroupAdd(row_acc); + if (subgroup_invocation_id == 0) { + dst[dst_idx_base + row] = row_total; + } + } +#endif + +#ifdef USE_WORKGROUP_REDUCTION + for (var row = 0u; row < OUTPUTS_PER_WG; row++) { + partial_sums[partial_index(row, thread_id)] = acc[0][row]; + } + + workgroupBarrier(); + + var stride:u32 = WG_SIZE / 2u; + + while (stride > 0) { + if (thread_id < stride) { + for (var row = 0u; row < OUTPUTS_PER_WG; row++) { + partial_sums[partial_index(row, thread_id)] += partial_sums[partial_index(row, thread_id + stride)]; + } + } + + workgroupBarrier(); + stride = stride / 2; + } + + if (thread_id < OUTPUTS_PER_WG) { + let output_row = row_base + thread_id; + if (output_row < params.m) { + dst[dst_idx_base + thread_id] = partial_sums[partial_index(thread_id, 0)]; + } + } +#endif +} diff --git a/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_reg_tile.wgsl b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_reg_tile.wgsl new file mode 100644 index 0000000000000000000000000000000000000000..98bbdeb83ba7fb217aa95205de7adfccf9907be8 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_reg_tile.wgsl @@ -0,0 +1,149 @@ +enable f16; + +#define DECLARE_BYTE_LOADERS_SRC0 +#include "common_decls.tmpl" + +#include "mul_mat_decls.tmpl" + +#ifdef VEC +fn store_val(acc: array, TILE_M>, tn: u32, tm: u32) -> vec4 { + return vec4(acc[tm][tn], acc[tm + 1][tn], acc[tm + 2][tn], acc[tm + 3][tn]); +} +#endif + +#ifdef SCALAR +fn store_val(acc: array, TILE_M>, tn: u32, tm: u32) -> f32 { + return acc[tm][tn]; +} +#endif + +struct MulMatParams { + offset_src0: u32, + offset_src1: u32, + offset_dst: u32, + m: u32, + n: u32, + k: u32, + stride_01: u32, + stride_11: u32, + stride_02: u32, + stride_12: u32, + stride_03: u32, + stride_13: u32, + bs02: u32, + bs03: u32, + broadcast2: u32, + broadcast3: u32 +}; + +@group(0) @binding(0) var src0: array; // M rows, K columns +@group(0) @binding(1) var src1: array; // K rows, N columns (transposed) +@group(0) @binding(2) var dst: array; // M rows, N columns (transposed) + +@group(0) @binding(3) var params: MulMatParams; + +fn get_local_n(thread_id: u32) -> u32 { + return thread_id / WORKGROUP_SIZE_M; +} +fn get_local_m(thread_id: u32) -> u32 { + return thread_id % WORKGROUP_SIZE_M; +} + +const TOTAL_WORKGROUP_SIZE = WORKGROUP_SIZE_M * WORKGROUP_SIZE_N; +const TILE_SRC0_SHMEM = TILE_K * WORKGROUP_SIZE_M * TILE_M; +const TILE_SRC1_SHMEM = TILE_K * WORKGROUP_SIZE_N * TILE_N; + +var shmem: array; + +@compute @workgroup_size(TOTAL_WORKGROUP_SIZE) +fn main(@builtin(workgroup_id) wg_id: vec3, + @builtin(local_invocation_id) local_id: vec3, + @builtin(num_workgroups) num_wg: vec3) { + + let thread_id = local_id.x; + let local_m = get_local_m(thread_id); + let local_n = get_local_n(thread_id); + + let wg_n_count = (params.n + WORKGROUP_SIZE_N * TILE_N - 1u) / (WORKGROUP_SIZE_N * TILE_N); + let wg_m_count = (params.m + WORKGROUP_SIZE_M * TILE_M - 1u) / (WORKGROUP_SIZE_M * TILE_M); + let wg_per_matrix = wg_m_count * wg_n_count; + + let wg_linear = wg_id.y * num_wg.x + wg_id.x; + + let batch_idx = wg_linear / wg_per_matrix; + + let total_batches = params.bs02 * params.broadcast2 * params.bs03 * params.broadcast3; + if (batch_idx >= total_batches) { + return; + } + + let wg_in_batch = wg_linear % wg_per_matrix; + let wg_m = wg_in_batch % wg_m_count; + let wg_n = wg_in_batch / wg_m_count; + + let output_row_base = wg_m * WORKGROUP_SIZE_M * TILE_M + local_m * TILE_M; + let output_col_base = wg_n * WORKGROUP_SIZE_N * TILE_N + local_n * TILE_N; + + let dst2_stride = params.m * params.n; + let dst3_stride = dst2_stride * params.bs02 * params.broadcast2; + + let dst3_idx = batch_idx / (params.bs02 * params.broadcast2); + let src03_idx = dst3_idx / params.broadcast3; + let src13_idx = dst3_idx; + let dst2_idx = batch_idx % (params.bs02 * params.broadcast2); + let src02_idx = dst2_idx / params.broadcast2; + let src12_idx = dst2_idx; + + let src0_batch_offset = params.offset_src0 + src03_idx * params.stride_03 + src02_idx * params.stride_02; + let src1_batch_offset = params.offset_src1 + src13_idx * params.stride_13 + src12_idx * params.stride_12; + + let offset_m = wg_m * WORKGROUP_SIZE_M * TILE_M; + let offset_n = wg_n * WORKGROUP_SIZE_N * TILE_N; + + var acc: array, TILE_M>; + + for (var k_outer = 0u; k_outer < params.k; k_outer += TILE_K) { + + // see mul_mat_decls.tmpl + init_shmem_src0(thread_id, src0_batch_offset, offset_m, k_outer); + init_shmem_src1(thread_id, src1_batch_offset, offset_n, k_outer); + + workgroupBarrier(); + + let k_end = min(TILE_K, params.k - k_outer); + + for (var k_inner = 0u; k_inner < k_end; k_inner++) { + var src0_tile: array; + for (var tm = 0u; tm < TILE_M; tm++) { + let src0_m = local_m * TILE_M + tm; + let src0_idx = k_inner + src0_m * TILE_K; + src0_tile[tm] = shmem[src0_idx]; + } + for (var tn = 0u; tn < TILE_N; tn++) { + let src1_n = local_n * TILE_N + tn; + let src1_idx = src1_n * TILE_K + k_inner; + let src1_val = shmem[TILE_SRC0_SHMEM + src1_idx]; + for (var tm = 0u; tm < TILE_M; tm++) { + acc[tm][tn] += f32(src0_tile[tm]) * f32(src1_val); + } + } + } + + workgroupBarrier(); + } + + let dst_batch_offset = params.offset_dst + dst3_idx * dst3_stride + dst2_idx * dst2_stride; + + for (var tn = 0u; tn < TILE_N; tn++) { + let global_col = output_col_base + tn; + if (global_col < params.n) { + for (var tm = 0u; tm < TILE_M; tm += VEC_SIZE) { + let global_row = output_row_base + tm; + if (global_row < params.m) { + let dst_idx = dst_batch_offset + global_col * params.m + global_row; + dst[dst_idx/VEC_SIZE] = store_val(acc, tn, tm); + } + } + } + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_subgroup_matrix.wgsl b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_subgroup_matrix.wgsl new file mode 100644 index 0000000000000000000000000000000000000000..d86a72ce6e0b43d63003e2b28d19dedb91315058 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_subgroup_matrix.wgsl @@ -0,0 +1,200 @@ +diagnostic(off, chromium.subgroup_matrix_uniformity); +enable f16; +enable subgroups; +enable chromium_experimental_subgroup_matrix; + +#define DECLARE_BYTE_LOADERS_SRC0 +#include "common_decls.tmpl" + +#include "mul_mat_decls.tmpl" + +// TODO: this shader path does not work with some models like qwen2.5 on Metal devices, f16 accumulation causes NaNs. +// See https://github.com/ggml-org/llama.cpp/issues/21602 + +#ifdef VEC +fn store_dst(shmem_idx: u32, dst_idx: u32) { + dst[dst_idx] = vec4( + f32(shmem[shmem_idx]), + f32(shmem[shmem_idx + 1]), + f32(shmem[shmem_idx + 2]), + f32(shmem[shmem_idx + 3]) + ); +} +#endif + +#ifdef SCALAR +fn store_dst(shmem_idx: u32, dst_idx: u32) { + dst[dst_idx] = f32(shmem[shmem_idx]); +} +#endif + +struct MulMatParams { + offset_src0: u32, + offset_src1: u32, + offset_dst: u32, + m: u32, + n: u32, + k: u32, + stride_01: u32, + stride_11: u32, + stride_02: u32, + stride_12: u32, + stride_03: u32, + stride_13: u32, + bs02: u32, + bs03: u32, + broadcast2: u32, + broadcast3: u32 +}; + +// SRC0_TYPE and SRC1_TYPE are defined in mul_mat_decls, which is included +@group(0) @binding(0) var src0: array; // M rows, K columns +@group(0) @binding(1) var src1: array; // K rows, N columns (transposed) +@group(0) @binding(2) var dst: array; // M rows, N columns (transposed) + +@group(0) @binding(3) var params: MulMatParams; + +const WG_M_SG_TILE_SIZE = SUBGROUP_M * SUBGROUP_MATRIX_M * SUBGROUP_MATRIX_M_SIZE; +const WG_N_SG_TILE_SIZE = SUBGROUP_N * SUBGROUP_MATRIX_N * SUBGROUP_MATRIX_N_SIZE; + +// For portability we assume the max subgroup size, meaning some subgroups will be masked out if the +// runtime subgroup size is smaller. +const EXPECTED_SUBGROUPS = SUBGROUP_M * SUBGROUP_N; +const TOTAL_WORKGROUP_SIZE = SUBGROUP_M * SUBGROUP_N * MAX_SUBGROUP_SIZE; +const TILE_SRC0_SHMEM = TILE_K * SUBGROUP_M * SUBGROUP_MATRIX_M * SUBGROUP_MATRIX_M_SIZE; +const TILE_SRC1_SHMEM = TILE_K * SUBGROUP_N * SUBGROUP_MATRIX_N * SUBGROUP_MATRIX_N_SIZE; + +const SG_MAT_ACCUM_SHMEM = SUBGROUP_M * SUBGROUP_MATRIX_M * SUBGROUP_N * SUBGROUP_MATRIX_N * SUBGROUP_MATRIX_M_SIZE * SUBGROUP_MATRIX_N_SIZE; + +// We reuse shmem for accumulation matrices +const SHMEM_SIZE = max(TILE_SRC0_SHMEM + TILE_SRC1_SHMEM, SG_MAT_ACCUM_SHMEM); + +var shmem: array; + +@compute @workgroup_size(TOTAL_WORKGROUP_SIZE) +fn main(@builtin(workgroup_id) wg_id: vec3, + @builtin(local_invocation_id) local_id: vec3, + @builtin(subgroup_id) subgroup_id: u32, + @builtin(num_workgroups) num_wg: vec3) { + + let thread_id = local_id.x; + let subgroup_m = subgroup_id % SUBGROUP_M; + let subgroup_n = subgroup_id / SUBGROUP_M; + + let wg_m_count = (params.m + WG_M_SG_TILE_SIZE - 1) / WG_M_SG_TILE_SIZE; + let wg_n_count = (params.n + WG_N_SG_TILE_SIZE - 1) / WG_N_SG_TILE_SIZE; + let wg_per_matrix = wg_m_count * wg_n_count; + + let wg_linear = wg_id.y * num_wg.x + wg_id.x; + + let batch_idx = wg_linear / wg_per_matrix; + + let total_batches = params.bs02 * params.broadcast2 * params.bs03 * params.broadcast3; + if (batch_idx >= total_batches) { + return; + } + + let wg_in_batch = wg_linear % wg_per_matrix; + let wg_m = wg_in_batch % wg_m_count; + let wg_n = wg_in_batch / wg_m_count; + + let dst2_stride = params.m * params.n; + let dst3_stride = dst2_stride * params.bs02 * params.broadcast2; + + let dst3_idx = batch_idx / (params.bs02 * params.broadcast2); + let src03_idx = dst3_idx / params.broadcast3; + let src13_idx = dst3_idx; + let dst2_idx = batch_idx % (params.bs02 * params.broadcast2); + let src02_idx = dst2_idx / params.broadcast2; + let src12_idx = dst2_idx; + + let src0_batch_offset = params.offset_src0 + src03_idx * params.stride_03 + src02_idx * params.stride_02; + let src1_batch_offset = params.offset_src1 + src13_idx * params.stride_13 + src12_idx * params.stride_12; + + let offset_m = wg_m * SUBGROUP_M * SUBGROUP_MATRIX_M * SUBGROUP_MATRIX_M_SIZE; + let offset_n = wg_n * SUBGROUP_N * SUBGROUP_MATRIX_N * SUBGROUP_MATRIX_N_SIZE; + + var acc_sg_mat : array, SUBGROUP_MATRIX_N>, SUBGROUP_MATRIX_M>; + + for (var k_outer = 0u; k_outer < params.k; k_outer += TILE_K) { + + // see mul_mat_decls.tmpl + init_shmem_src0(thread_id, src0_batch_offset, offset_m, k_outer); + init_shmem_src1(thread_id, src1_batch_offset, offset_n, k_outer); + + workgroupBarrier(); + + if (subgroup_id < EXPECTED_SUBGROUPS) { + + for (var k_inner = 0u; k_inner < TILE_K; k_inner += SUBGROUP_MATRIX_K_SIZE) { + + let src0_shmem_idx_base = subgroup_m * SUBGROUP_MATRIX_M * SUBGROUP_MATRIX_M_SIZE * TILE_K + k_inner; + var src0_sg_mats: array, SUBGROUP_MATRIX_M>; + for (var m = 0u; m < SUBGROUP_MATRIX_M; m++) { + src0_sg_mats[m] = subgroupMatrixLoad>( + &shmem, + src0_shmem_idx_base + m * SUBGROUP_MATRIX_M_SIZE * TILE_K, + false, + TILE_K + ); + } + + let src1_shmem_idx_base = TILE_SRC0_SHMEM + subgroup_n * SUBGROUP_MATRIX_N * SUBGROUP_MATRIX_N_SIZE * TILE_K + k_inner; + for (var n = 0u; n < SUBGROUP_MATRIX_N; n++) { + let src1_sg_mat = subgroupMatrixLoad>( + &shmem, + src1_shmem_idx_base + n * SUBGROUP_MATRIX_N_SIZE * TILE_K, + true, + TILE_K + ); + for (var m = 0u; m < SUBGROUP_MATRIX_M; m++) { + acc_sg_mat[m][n] = subgroupMatrixMultiplyAccumulate(src0_sg_mats[m], src1_sg_mat, acc_sg_mat[m][n]); + } + } + } + } + + workgroupBarrier(); + } + + let dst_batch_offset = params.offset_dst + dst3_idx * dst3_stride + dst2_idx * dst2_stride; + + // Stage the subgroup matrix tiles into shared memory + // This uses WG_M_SG_TILE_SIZE as the stride (number of columns in the workgroup tile). + let WG_TILE_STRIDE = WG_M_SG_TILE_SIZE; + let tile_row_base_local = subgroup_n * SUBGROUP_MATRIX_N * SUBGROUP_MATRIX_N_SIZE; + let tile_col_base_local = subgroup_m * SUBGROUP_MATRIX_M * SUBGROUP_MATRIX_M_SIZE; + + if (subgroup_id < EXPECTED_SUBGROUPS) { // 2-5% performance hit :( + for (var n = 0u; n < SUBGROUP_MATRIX_N; n++) { + for (var m = 0u; m < SUBGROUP_MATRIX_M; m++) { + let local_row = tile_row_base_local + n * SUBGROUP_MATRIX_N_SIZE; + let local_col = tile_col_base_local + m * SUBGROUP_MATRIX_M_SIZE; + let out_base = local_row * WG_TILE_STRIDE + local_col; + subgroupMatrixStore(&shmem, out_base, acc_sg_mat[m][n], true, WG_TILE_STRIDE); + } + } + } + + workgroupBarrier(); + + // Cooperative write: iterate over the entire workgroup tile + let tile_rows = WG_N_SG_TILE_SIZE; + let tile_cols = WG_M_SG_TILE_SIZE; + let total_tile_elems = tile_rows * tile_cols; + let tile_dst_row_base = wg_m * SUBGROUP_M * SUBGROUP_MATRIX_M * SUBGROUP_MATRIX_M_SIZE; + let tile_dst_col_base = wg_n * SUBGROUP_N * SUBGROUP_MATRIX_N * SUBGROUP_MATRIX_N_SIZE; + + for (var idx = thread_id * VEC_SIZE; idx < total_tile_elems; idx += TOTAL_WORKGROUP_SIZE * VEC_SIZE) { + let local_row = idx % WG_TILE_STRIDE; + let local_col = idx / WG_TILE_STRIDE; + + let global_row = tile_dst_row_base + local_row; + let global_col = tile_dst_col_base + local_col; + + if (global_col < params.n && global_row < params.m) { + let dst_idx = dst_batch_offset + global_col * params.m + global_row; + store_dst(idx, dst_idx/VEC_SIZE); + } + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec.wgsl b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec.wgsl new file mode 100644 index 0000000000000000000000000000000000000000..ebdf09513e229863ea52ce6f4fbe91497310c844 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec.wgsl @@ -0,0 +1,157 @@ +#ifdef USE_SUBGROUP_REDUCTION +enable subgroups; +#endif +enable f16; + +#ifdef MMVQ +requires packed_4x8_integer_dot_product; +#endif + +#define DECLARE_BYTE_LOADERS_SRC0 +#include "common_decls.tmpl" + +#ifdef MMVQ +#include "mul_mat_vec_q_acc.tmpl" +#else +#include "mul_mat_vec_acc.tmpl" +#endif + +struct MulMatParams { + offset_src0: u32, + offset_src1: u32, + offset_dst: u32, + m: u32, + n: u32, + k: u32, + stride_01: u32, + stride_11: u32, + stride_02: u32, + stride_12: u32, + stride_03: u32, + stride_13: u32, + bs02: u32, + bs03: u32, + broadcast2: u32, + broadcast3: u32 +}; + +@group(0) @binding(0) var src0: array; + +#ifdef MMVQ +@group(0) @binding(1) var src1q: array; +#else +@group(0) @binding(1) var src1: array; +#endif + +@group(0) @binding(2) var dst: array; +// "mul_mat_vec_acc.tmpl" requires params.k, params.m, params.stride_01 +@group(0) @binding(3) var params: MulMatParams; + +// Flattened as [row][thread] to keep each row's reduction contiguous in memory. +var partial_sums: array; + +fn partial_index(row: u32, thread: u32) -> u32 { + return row * WG_SIZE + thread; +} + +@compute @workgroup_size(WG_SIZE) +fn main( + @builtin(local_invocation_id) local_id: vec3, + @builtin(workgroup_id) wg_id: vec3, + @builtin(num_workgroups) num_wg: vec3 +#ifdef USE_SUBGROUP_REDUCTION + , @builtin(subgroup_id) subgroup_id: u32, + @builtin(subgroup_invocation_id) subgroup_invocation_id: u32, + @builtin(num_subgroups) num_subgroups: u32, + @builtin(subgroup_size) subgroup_size: u32 +#endif +) { + let thread_id = local_id.x; + + let total_batches = params.bs02 * params.broadcast2 * params.bs03 * params.broadcast3; + let wg_linear = wg_id.y * num_wg.x + wg_id.x; + let output_groups = (params.m + OUTPUTS_PER_WG - 1u) / OUTPUTS_PER_WG; + let batch_idx = wg_linear / output_groups; + if (batch_idx >= total_batches) { + return; + } + + let row_base = (wg_linear % output_groups) * OUTPUTS_PER_WG; + + let dst2_stride = params.m * params.n; + let dst2_idx = batch_idx % (params.bs02 * params.broadcast2); + let dst3_stride = dst2_stride * params.bs02 * params.broadcast2; + let dst3_idx = batch_idx / (params.bs02 * params.broadcast2); + let src03_idx = dst3_idx / params.broadcast3; + let src13_idx = dst3_idx; + let src02_idx = dst2_idx / params.broadcast2; + let src12_idx = dst2_idx; + + let src0_batch_offset = params.offset_src0 + src03_idx * params.stride_03 + src02_idx * params.stride_02; + let dst_idx_base = params.offset_dst + dst3_idx * dst3_stride + dst2_idx * dst2_stride + row_base; + +#ifdef MMVQ + let src1q_idx_base = (src13_idx * params.bs02 * params.broadcast2 + src12_idx) * params.n * (params.k / 32u); + let acc = accumulate_vec_q_dot(thread_id, row_base, src0_batch_offset, src1q_idx_base); +#else + let src1_idx_base = params.offset_src1 + src13_idx * params.stride_13 + src12_idx * params.stride_12; + let acc = accumulate_vec_dot(thread_id, row_base, src0_batch_offset, src1_idx_base); +#endif + + for (var col = 0u;col < NUM_COLS;col += 1) { + +#ifdef USE_SUBGROUP_REDUCTION + for (var row = 0u; row < OUTPUTS_PER_WG; row++) { + let subgroup_total = subgroupAdd(acc[col][row]); + if (subgroup_invocation_id == 0u) { + partial_sums[partial_index(row, subgroup_id)] = subgroup_total; + } + } + + workgroupBarrier(); + + for (var row = subgroup_id; (row < OUTPUTS_PER_WG) && (row_base + row < params.m); row += num_subgroups) { + let output_row = row_base + row; + var row_acc = 0.0f; + for (var k = subgroup_invocation_id; k < num_subgroups; k += subgroup_size) { + row_acc += partial_sums[partial_index(row, k)]; + } + let row_total = subgroupAdd(row_acc); + if (subgroup_invocation_id == 0) { + dst[dst_idx_base + col * params.m + row] = row_total; + } + } +#endif + +#ifdef USE_WORKGROUP_REDUCTION + for (var row = 0u; row < OUTPUTS_PER_WG; row++) { + partial_sums[partial_index(row, thread_id)] = acc[col][row]; + } + + workgroupBarrier(); + + var stride = WG_SIZE / 2u; + + while (stride > 0) { + if (thread_id < stride) { + for (var row = 0u; row < OUTPUTS_PER_WG; row++) { + partial_sums[partial_index(row, thread_id)] += partial_sums[partial_index(row, thread_id + stride)]; + } + } + + workgroupBarrier(); + stride = stride / 2; + } + + if (thread_id < OUTPUTS_PER_WG) { + let output_row = row_base + thread_id; + if (output_row < params.m) { + dst[dst_idx_base + col * params.m + thread_id] = partial_sums[partial_index(thread_id, 0)]; + } + } +#endif + + workgroupBarrier(); + + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec_acc.tmpl b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec_acc.tmpl new file mode 100644 index 0000000000000000000000000000000000000000..8fd0d1907cfcaede078c836f030cd663d544569e --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec_acc.tmpl @@ -0,0 +1,1553 @@ +#ifdef U32_DEQUANT_HELPERS +#define SRC0_TYPE u32 + +fn byte_of(v: u32, b: u32) -> u32 { + return (v >> (b * 8u)) & 0xFFu; +} + +fn sbyte_of(v: u32, b: u32) -> i32 { + let raw = i32((v >> (b * 8u)) & 0xFFu); + return select(raw, raw - 256, raw >= 128); +} +#endif + +#ifdef VEC +#define VEC_SIZE 4u +#define SRC0_TYPE vec4 +#define SRC1_TYPE vec4 + +fn inner_dot(src0_val: SRC0_TYPE, src1_val: SRC1_TYPE) -> f32 { + return f32(dot(SRC1_TYPE(src0_val), src1_val)); +} +#endif + +#ifdef SCALAR +#define VEC_SIZE 1u +#define SRC0_TYPE SRC0_INNER_TYPE +#define SRC1_TYPE SRC1_INNER_TYPE + +fn inner_dot(src0_val: SRC0_TYPE, src1_val: SRC1_TYPE) -> f32 { + return f32(src0_val) * f32(src1_val); +} +#endif + +#ifdef MUL_ACC_FLOAT +fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array, NUM_COLS> { + var acc: array, NUM_COLS>; + + let k_vec = params.k / VEC_SIZE; + let src1_idx_base_vec = src1_idx_base / VEC_SIZE; + + // Each thread walks K, loads from the vector, and updates + // a small block of output rows held in registers. + for (var k = thread_id; k < k_vec; k += WG_SIZE) { + var x_vals: array; + for (var col = 0u;col < NUM_COLS;col += 1) { + x_vals[col] = src1[src1_idx_base_vec + col * (params.stride_11 / VEC_SIZE) + k]; + } + for (var row = 0u; row < OUTPUTS_PER_WG; row++) { + let output_row = row_base + row; + if (output_row < params.m) { + let src0_idx = (src0_batch_offset + output_row * params.stride_01) / VEC_SIZE + k; + let w = src0[src0_idx]; + for (var col = 0u;col < NUM_COLS;col += 1) { + acc[col][row] += inner_dot(w, x_vals[col]); + } + } + } + } + + return acc; +} +#endif + +#ifdef MUL_ACC_Q1_0 +#define BLOCK_SIZE 128 +#define BLOCK_SIZE_BYTES 18 +#define THREADS_PER_BLOCK 16 +#define ELEMS_PER_THREAD (BLOCK_SIZE/THREADS_PER_BLOCK) +fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array, NUM_COLS> { + var acc: array, NUM_COLS>; + + let num_blocks = params.k / BLOCK_SIZE; + let thread_within_block = thread_id % THREADS_PER_BLOCK; + for (var block = thread_id / THREADS_PER_BLOCK; block < num_blocks; block += WG_SIZE / THREADS_PER_BLOCK) { + let x_base = src1_idx_base + block * BLOCK_SIZE + thread_within_block * ELEMS_PER_THREAD; + var x_block: array, NUM_COLS>; + for (var col = 0u; col < NUM_COLS;col += 1) { + for (var i = 0u; i < ELEMS_PER_THREAD; i++) { + x_block[col][i] = f32(src1[x_base + col * params.stride_11 + i]); + } + } + for (var row = 0u; row < OUTPUTS_PER_WG; row++) { + let output_row = row_base + row; + if (output_row < params.m) { + let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES; + let d = f32(load_f16_at_src0(block_byte_base)); + let q_byte = load_u32_at_src0(block_byte_base + 2u + thread_within_block) & 0xFFu; + for (var col = 0u;col < NUM_COLS;col += 1) { + var row_sum = 0.0; + for (var bit = 0u; bit < 8u; bit++) { + let w = select(-d, d, ((q_byte >> bit) & 1u) != 0u); + row_sum += w * x_block[col][bit]; + } + acc[col][row] += row_sum; + } + } + } + } + + return acc; +} +#endif + +#ifdef MUL_ACC_Q4_0 +#define BLOCK_SIZE 32 +#define BLOCK_SIZE_BYTES 18 +#define THREADS_PER_BLOCK 4 +#define ELEMS_PER_THREAD (BLOCK_SIZE/THREADS_PER_BLOCK) +fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array, NUM_COLS> { + var acc: array, NUM_COLS>; + + let num_blocks = params.k / BLOCK_SIZE; + let thread_within_block = thread_id % 4; + for (var block = thread_id/THREADS_PER_BLOCK; block < num_blocks; block += WG_SIZE/THREADS_PER_BLOCK) { + let x_base = src1_idx_base + block * BLOCK_SIZE + thread_within_block * 4; + var x_block: array, NUM_COLS>; + for (var col = 0u; col < NUM_COLS;col += 1) { + for (var i = 0u; i < ELEMS_PER_THREAD / 2; i++) { + x_block[col][i] = f32(src1[x_base + col * params.stride_11 + i]); + x_block[col][i + 4] = f32(src1[x_base + col * params.stride_11 + i + 16]); + } + } + for (var row = 0u; row < OUTPUTS_PER_WG; row++) { + let output_row = row_base + row; + if (output_row < params.m) { + let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES; + let d = f32(load_f16_at_src0(block_byte_base)); + let q_packed = load_u32_at_src0(block_byte_base + 2u + 4u * thread_within_block); + for (var col = 0u;col < NUM_COLS;col += 1) { + var row_sum = 0.0; + for (var byte_idx = 0u; byte_idx < 4u; byte_idx++) { + let q_byte = get_byte(q_packed, byte_idx); + let q_lo = (f32(q_byte & 0xFu) - 8.0) * d; + let q_hi = (f32((q_byte >> 4u) & 0xFu) - 8.0) * d; + row_sum += q_lo * x_block[col][byte_idx]; + row_sum += q_hi * x_block[col][byte_idx + 4u]; + } + acc[col][row] += row_sum; + } + } + } + } + + return acc; +} +#endif + +#ifdef MUL_ACC_Q4_1 +#define BLOCK_SIZE 32 +#define BLOCK_SIZE_BYTES 20 +#define THREADS_PER_BLOCK 4 +#define ELEMS_PER_THREAD (BLOCK_SIZE/THREADS_PER_BLOCK) +fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array, NUM_COLS> { + var acc: array, NUM_COLS>; + + let num_blocks = params.k / BLOCK_SIZE; + let thread_within_block = thread_id % THREADS_PER_BLOCK; + for (var block = thread_id / THREADS_PER_BLOCK; block < num_blocks; block += WG_SIZE / THREADS_PER_BLOCK) { + let x_base = src1_idx_base + block * BLOCK_SIZE + thread_within_block * 4; + var x_block: array, NUM_COLS>; + for (var col = 0u; col < NUM_COLS;col += 1) { + for (var i = 0u; i < ELEMS_PER_THREAD / 2; i++) { + x_block[col][i] = f32(src1[x_base + col * params.stride_11 + i]); + x_block[col][i + 4] = f32(src1[x_base + col * params.stride_11 + i + 16]); + } + } + for (var row = 0u; row < OUTPUTS_PER_WG; row++) { + let output_row = row_base + row; + if (output_row < params.m) { + let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES; + let d = f32(load_f16_at_src0(block_byte_base)); + let m = f32(load_f16_at_src0(block_byte_base + 2u)); + let q_packed = load_u32_at_src0(block_byte_base + 4u + 4u * thread_within_block); + for (var col = 0u;col < NUM_COLS;col += 1) { + var row_sum = 0.0; + for (var byte_idx = 0u; byte_idx < 4u; byte_idx++) { + let q_byte = get_byte(q_packed, byte_idx); + let q_lo = f32(q_byte & 0xFu) * d + m; + let q_hi = f32((q_byte >> 4u) & 0xFu) * d + m; + row_sum += q_lo * x_block[col][byte_idx]; + row_sum += q_hi * x_block[col][byte_idx + 4u]; + } + acc[col][row] += row_sum; + } + } + } + } + + return acc; +} +#endif + +#ifdef MUL_ACC_Q5_0 +#define BLOCK_SIZE 32 +#define BLOCK_SIZE_BYTES 22 +#define THREADS_PER_BLOCK 4 +#define ELEMS_PER_THREAD (BLOCK_SIZE/THREADS_PER_BLOCK) +fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array, NUM_COLS> { + var acc: array, NUM_COLS>; + + let num_blocks = params.k / BLOCK_SIZE; + let thread_within_block = thread_id % THREADS_PER_BLOCK; + for (var block = thread_id / THREADS_PER_BLOCK; block < num_blocks; block += WG_SIZE / THREADS_PER_BLOCK) { + let x_base = src1_idx_base + block * BLOCK_SIZE + thread_within_block * 4; + var x_block: array, NUM_COLS>; + for (var col = 0u; col < NUM_COLS;col += 1) { + for (var i = 0u; i < ELEMS_PER_THREAD / 2; i++) { + x_block[col][i] = f32(src1[x_base + col * params.stride_11 + i]); + x_block[col][i + 4] = f32(src1[x_base + col * params.stride_11 + i + 16]); + } + } + for (var row = 0u; row < OUTPUTS_PER_WG; row++) { + let output_row = row_base + row; + if (output_row < params.m) { + let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES; + let d = f32(load_f16_at_src0(block_byte_base)); + let qh_packed = load_u32_at_src0(block_byte_base + 2u); + let q_packed = load_u32_at_src0(block_byte_base + 6u + 4u * thread_within_block); + let qh_shift = thread_within_block * 4u; + for (var col = 0u;col < NUM_COLS;col += 1) { + var row_sum = 0.0; + for (var byte_idx = 0u; byte_idx < 4u; byte_idx++) { + let q_byte = get_byte(q_packed, byte_idx); + let qh_lo = ((qh_packed >> (qh_shift + byte_idx)) << 4u) & 0x10u; + let qh_hi = (qh_packed >> (qh_shift + byte_idx + 12u)) & 0x10u; + let q_lo = (f32((q_byte & 0xFu) | qh_lo) - 16.0) * d; + let q_hi = (f32(((q_byte >> 4u) & 0xFu) | qh_hi) - 16.0) * d; + row_sum += q_lo * x_block[col][byte_idx]; + row_sum += q_hi * x_block[col][byte_idx + 4u]; + } + acc[col][row] += row_sum; + } + } + } + } + + return acc; +} +#endif + +#ifdef MUL_ACC_Q5_1 +#define BLOCK_SIZE 32 +#define BLOCK_SIZE_BYTES 24 +#define THREADS_PER_BLOCK 4 +#define ELEMS_PER_THREAD (BLOCK_SIZE/THREADS_PER_BLOCK) +fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array, NUM_COLS> { + var acc: array, NUM_COLS>; + + let num_blocks = params.k / BLOCK_SIZE; + let thread_within_block = thread_id % THREADS_PER_BLOCK; + for (var block = thread_id / THREADS_PER_BLOCK; block < num_blocks; block += WG_SIZE / THREADS_PER_BLOCK) { + let x_base = src1_idx_base + block * BLOCK_SIZE + thread_within_block * 4; + var x_block: array, NUM_COLS>; + for (var col = 0u; col < NUM_COLS;col += 1) { + for (var i = 0u; i < ELEMS_PER_THREAD / 2; i++) { + x_block[col][i] = f32(src1[x_base + col * params.stride_11 + i]); + x_block[col][i + 4] = f32(src1[x_base + col * params.stride_11 + i + 16]); + } + } + for (var row = 0u; row < OUTPUTS_PER_WG; row++) { + let output_row = row_base + row; + if (output_row < params.m) { + let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES; + let d = f32(load_f16_at_src0(block_byte_base)); + let m = f32(load_f16_at_src0(block_byte_base + 2u)); + let qh_packed = load_u32_at_src0(block_byte_base + 4u); + let q_packed = load_u32_at_src0(block_byte_base + 8u + 4u * thread_within_block); + let qh_shift = thread_within_block * 4u; + for (var col = 0u;col < NUM_COLS;col += 1) { + var row_sum = 0.0; + for (var byte_idx = 0u; byte_idx < 4u; byte_idx++) { + let q_byte = get_byte(q_packed, byte_idx); + let qh_lo = ((qh_packed >> (qh_shift + byte_idx)) << 4u) & 0x10u; + let qh_hi = (qh_packed >> (qh_shift + byte_idx + 12u)) & 0x10u; + let q_lo = f32((q_byte & 0xFu) | qh_lo) * d + m; + let q_hi = f32(((q_byte >> 4u) & 0xFu) | qh_hi) * d + m; + row_sum += q_lo * x_block[col][byte_idx]; + row_sum += q_hi * x_block[col][byte_idx + 4u]; + } + acc[col][row] += row_sum; + } + } + } + } + + return acc; +} +#endif + +#ifdef MUL_ACC_Q8_0 +#define BLOCK_SIZE 32 +#define BLOCK_SIZE_BYTES 34 +#define THREADS_PER_BLOCK 4 +#define ELEMS_PER_THREAD (BLOCK_SIZE/THREADS_PER_BLOCK) +fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array, NUM_COLS> { + var acc: array, NUM_COLS>; + + let num_blocks = params.k / BLOCK_SIZE; + let thread_within_block = thread_id % THREADS_PER_BLOCK; + for (var block = thread_id / THREADS_PER_BLOCK; block < num_blocks; block += WG_SIZE / THREADS_PER_BLOCK) { + let x_base = src1_idx_base + block * BLOCK_SIZE + thread_within_block * ELEMS_PER_THREAD; + var x_block: array, NUM_COLS>; + for (var col = 0u; col < NUM_COLS;col += 1) { + for (var i = 0u; i < ELEMS_PER_THREAD; i++) { + x_block[col][i] = f32(src1[x_base + col * params.stride_11 + i]); + } + } + for (var row = 0u; row < OUTPUTS_PER_WG; row++) { + let output_row = row_base + row; + if (output_row < params.m) { + let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES; + let d = f32(load_f16_at_src0(block_byte_base)); + var q_packed: array; + for (var packed_idx = 0u; packed_idx < ELEMS_PER_THREAD / 4u; packed_idx++) { + q_packed[packed_idx] = load_u32_at_src0(block_byte_base + 2u + 4u * (thread_within_block * 2u + packed_idx)); + } + for (var col = 0u;col < NUM_COLS;col += 1) { + var row_sum = 0.0; + for (var packed_idx = 0u; packed_idx < ELEMS_PER_THREAD / 4u; packed_idx++) { + for (var byte_idx = 0u; byte_idx < 4u; byte_idx++) { + let q_val = f32(get_byte_i32(q_packed[packed_idx], byte_idx)) * d; + row_sum += q_val * x_block[col][packed_idx * 4u + byte_idx]; + } + } + acc[col][row] += row_sum; + } + } + } + } + + return acc; +} +#endif + +#ifdef MUL_ACC_Q8_1 +#define BLOCK_SIZE 32 +#define BLOCK_SIZE_BYTES 36 +#define THREADS_PER_BLOCK 4 +#define ELEMS_PER_THREAD (BLOCK_SIZE/THREADS_PER_BLOCK) +fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array, NUM_COLS> { + var acc: array, NUM_COLS>; + + let num_blocks = params.k / BLOCK_SIZE; + let thread_within_block = thread_id % THREADS_PER_BLOCK; + for (var block = thread_id / THREADS_PER_BLOCK; block < num_blocks; block += WG_SIZE / THREADS_PER_BLOCK) { + let x_base = src1_idx_base + block * BLOCK_SIZE + thread_within_block * ELEMS_PER_THREAD; + var x_block: array, NUM_COLS>; + for (var col = 0u; col < NUM_COLS;col += 1) { + for (var i = 0u; i < ELEMS_PER_THREAD; i++) { + x_block[col][i] = f32(src1[x_base + col * params.stride_11 + i]); + } + } + for (var row = 0u; row < OUTPUTS_PER_WG; row++) { + let output_row = row_base + row; + if (output_row < params.m) { + let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES; + let d = f32(load_f16_at_src0(block_byte_base)); + let m = f32(load_f16_at_src0(block_byte_base + 2u)); + var q_packed: array; + for (var packed_idx = 0u; packed_idx < ELEMS_PER_THREAD / 4u; packed_idx++) { + q_packed[packed_idx] = load_u32_at_src0(block_byte_base + 4u + 4u * (thread_within_block * 2u + packed_idx)); + } + for (var col = 0u;col < NUM_COLS;col += 1) { + var row_sum = 0.0; + for (var packed_idx = 0u; packed_idx < ELEMS_PER_THREAD / 4u; packed_idx++) { + for (var byte_idx = 0u; byte_idx < 4u; byte_idx++) { + let q_val = f32(get_byte_i32(q_packed[packed_idx], byte_idx)) * d + m; + row_sum += q_val * x_block[col][packed_idx * 4u + byte_idx]; + } + } + acc[col][row] += row_sum; + } + } + } + } + + return acc; +} +#endif + +#ifdef MUL_ACC_Q2_K +#define BLOCK_SIZE 256 +#define BLOCK_SIZE_BYTES 84 +#define THREADS_PER_BLOCK 16 +fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array, NUM_COLS> { + var acc: array, NUM_COLS>; + + let tid = thread_id % THREADS_PER_BLOCK; + let block_group = thread_id / THREADS_PER_BLOCK; + let num_block_groups: u32 = WG_SIZE / THREADS_PER_BLOCK; + + let lane = tid / 2u; + let phase = tid % 2u; + let iq = lane / 4u; + let ir = lane % 4u; + let is = ir / 2u; + + let y_offset = 128u * iq + 8u * ir + 4u * phase; + let sc0_byte = 8u * iq + is; + let sc2_byte = 8u * iq + is + 2u; + let sc4_byte = 8u * iq + is + 4u; + let sc6_byte = 8u * iq + is + 6u; + let qs_byte = 16u + (16u * iq + 4u * ir) * 2u + 4u * phase; + + let num_blocks = params.k / BLOCK_SIZE; + + for (var block = block_group; block < num_blocks; block += num_block_groups) { + let x_base = src1_idx_base + block * BLOCK_SIZE + y_offset; + var x_block: array, NUM_COLS>; + for (var col = 0u; col < NUM_COLS;col += 1) { + for (var i = 0u; i < 4u; i++) { + x_block[col][i] = f32(src1[x_base + col * params.stride_11 + i]); + x_block[col][i + 4u] = f32(src1[x_base + col * params.stride_11 + 32u + i]); + x_block[col][i + 8u] = f32(src1[x_base + col * params.stride_11 + 64u + i]); + x_block[col][i + 12u] = f32(src1[x_base + col * params.stride_11 + 96u + i]); + } + } + for (var row = 0u; row < OUTPUTS_PER_WG; row++) { + let output_row = row_base + row; + if (output_row < params.m) { + let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES; + + let dall = f32(load_f16_at_src0(block_byte_base + 80u)); + let dmin = f32(load_f16_at_src0(block_byte_base + 82u)) * (1.0 / 16.0); + + let sc0 = byte_of(load_u32_at_src0_aligned(block_byte_base + sc0_byte), sc0_byte & 3u); + let sc2 = byte_of(load_u32_at_src0_aligned(block_byte_base + sc2_byte), sc2_byte & 3u); + let sc4 = byte_of(load_u32_at_src0_aligned(block_byte_base + sc4_byte), sc4_byte & 3u); + let sc6 = byte_of(load_u32_at_src0_aligned(block_byte_base + sc6_byte), sc6_byte & 3u); + + let q_u32 = load_u32_at_src0_aligned(block_byte_base + qs_byte); + let qs0 = q_u32 & 0xFFFFu; + let qs1 = q_u32 >> 16u; + + for (var col = 0u;col < NUM_COLS;col += 1) { + var sumy = vec4(0.0, 0.0, 0.0, 0.0); + var acc1 = vec4(0.0, 0.0, 0.0, 0.0); + var acc2 = vec4(0.0, 0.0, 0.0, 0.0); + + sumy[0] = x_block[col][0] + x_block[col][1] + x_block[col][2] + x_block[col][3]; + sumy[1] = x_block[col][4] + x_block[col][5] + x_block[col][6] + x_block[col][7]; + sumy[2] = x_block[col][8] + x_block[col][9] + x_block[col][10] + x_block[col][11]; + sumy[3] = x_block[col][12] + x_block[col][13] + x_block[col][14] + x_block[col][15]; + + acc1[0] = x_block[col][0] * f32(qs0 & 0x0003u) + x_block[col][2] * f32(qs1 & 0x0003u); + acc2[0] = x_block[col][1] * f32(qs0 & 0x0300u) + x_block[col][3] * f32(qs1 & 0x0300u); + acc1[1] = x_block[col][4] * f32(qs0 & 0x000Cu) + x_block[col][6] * f32(qs1 & 0x000Cu); + acc2[1] = x_block[col][5] * f32(qs0 & 0x0C00u) + x_block[col][7] * f32(qs1 & 0x0C00u); + acc1[2] = x_block[col][8] * f32(qs0 & 0x0030u) + x_block[col][10] * f32(qs1 & 0x0030u); + acc2[2] = x_block[col][9] * f32(qs0 & 0x3000u) + x_block[col][11] * f32(qs1 & 0x3000u); + acc1[3] = x_block[col][12] * f32(qs0 & 0x00C0u) + x_block[col][14] * f32(qs1 & 0x00C0u); + acc2[3] = x_block[col][13] * f32(qs0 & 0xC000u) + x_block[col][15] * f32(qs1 & 0xC000u); + + acc[col][row] += dall * ((acc1[0] + (1.0/256.0) * acc2[0]) * f32(sc0 & 0xFu) + + (acc1[1] + (1.0/256.0) * acc2[1]) * f32(sc2 & 0xFu) / 4.0 + + (acc1[2] + (1.0/256.0) * acc2[2]) * f32(sc4 & 0xFu) / 16.0 + + (acc1[3] + (1.0/256.0) * acc2[3]) * f32(sc6 & 0xFu) / 64.0) + - dmin * (sumy[0] * f32(sc0 & 0xF0u) + sumy[1] * f32(sc2 & 0xF0u) + + sumy[2] * f32(sc4 & 0xF0u) + sumy[3] * f32(sc6 & 0xF0u)); + } + } + } + } + + return acc; +} +#endif + +#ifdef MUL_ACC_Q3_K +#define BLOCK_SIZE 256 +#define BLOCK_SIZE_BYTES 110 +#define THREADS_PER_BLOCK 16 +fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array, NUM_COLS> { + var acc: array, NUM_COLS>; + + let tid = thread_id % THREADS_PER_BLOCK; + let block_group = thread_id / THREADS_PER_BLOCK; + let num_block_groups: u32 = WG_SIZE / THREADS_PER_BLOCK; + + let lane = tid / 2u; + let phase = tid % 2u; + let ip = lane / 4u; + let il = 2u * ((lane % 4u) / 2u); + let ir = lane % 2u; + let l0 = 8u * ir; + + let q_byte = 32u + 32u * ip + l0 + 16u * phase; + let h_byte = l0 + 16u * phase; + let y_offset = 128u * ip + 32u * il + l0 + 16u * phase; + + let s_shift1 = 4u * ip; + let s_shift2 = s_shift1 + il; + + let v1 = select(64.0, 4.0, il == 0u); + let v2 = 4.0 * v1; + let shift = 2u * il; + + var qm0: u32; var qm1: u32; var qm2: u32; var qm3: u32; + if (il == 0u) { + qm0 = 0x0003u; qm1 = 0x0300u; qm2 = 0x000Cu; qm3 = 0x0C00u; + } else { + qm0 = 0x0030u; qm1 = 0x3000u; qm2 = 0x00C0u; qm3 = 0xC000u; + } + + let mm_idx = 2u * ip + il / 2u; + var hm0: u32; var hm1: u32; var hm2: u32; var hm3: u32; + switch (mm_idx) { + case 0u: { hm0=0x0001u; hm1=0x0100u; hm2=0x0002u; hm3=0x0200u; } + case 1u: { hm0=0x0004u; hm1=0x0400u; hm2=0x0008u; hm3=0x0800u; } + case 2u: { hm0=0x0010u; hm1=0x1000u; hm2=0x0020u; hm3=0x2000u; } + default: { hm0=0x0040u; hm1=0x4000u; hm2=0x0080u; hm3=0x8000u; } + } + + let num_blocks = params.k / BLOCK_SIZE; + + for (var block = block_group; block < num_blocks; block += num_block_groups) { + let x_base = src1_idx_base + block * BLOCK_SIZE + y_offset; + var x_block: array, NUM_COLS>; + for (var col = 0u; col < NUM_COLS;col += 1) { + for (var i = 0u; i < 8u; i++) { + x_block[col][i] = f32(src1[x_base + col * params.stride_11 + i]); + x_block[col][i + 8u] = f32(src1[x_base + col * params.stride_11 + 32u + i]); + } + } + for (var row = 0u; row < OUTPUTS_PER_WG; row++) { + let output_row = row_base + row; + if (output_row < params.m) { + let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES; + + let d = f32(load_f16_at_src0(block_byte_base + 108u)); + let a_base = 96u; + let a_il0 = load_u16_at_src0(block_byte_base + a_base + il * 2u); + let a_il1 = load_u16_at_src0(block_byte_base + a_base + (il + 1u) * 2u); + let a_4 = load_u16_at_src0(block_byte_base + a_base + 8u); + let a_5 = load_u16_at_src0(block_byte_base + a_base + 10u); + + var scales32 = a_4 | (a_5 << 16u); + let aux32 = ((scales32 >> s_shift2) << 4u) & 0x30303030u; + scales32 = a_il0 | (a_il1 << 16u); + scales32 = ((scales32 >> s_shift1) & 0x0F0F0F0Fu) | aux32; + + let scale0 = f32(i32(byte_of(scales32, phase + 0u)) - 32); + let scale1 = f32(i32(byte_of(scales32, phase + 2u)) - 32); + + let q_u32_0 = load_u32_at_src0(block_byte_base + q_byte + 0u); + let q_u32_1 = load_u32_at_src0(block_byte_base + q_byte + 4u); + let h_u32_0 = load_u32_at_src0(block_byte_base + h_byte + 0u); + let h_u32_1 = load_u32_at_src0(block_byte_base + h_byte + 4u); + + for (var col = 0u;col < NUM_COLS;col += 1) { + var s1 = 0.0; var s2 = 0.0; var s3 = 0.0; + var s4 = 0.0; var s5 = 0.0; var s6 = 0.0; + + for (var l = 0u; l < 8u; l += 2u) { + let q_u32 = select(q_u32_0, q_u32_1, l >= 4u); + let qs = select(q_u32 & 0xFFFFu, q_u32 >> 16u, (l & 2u) != 0u); + let h_u32 = select(h_u32_0, h_u32_1, l >= 4u); + let hv = select(h_u32 & 0xFFFFu, h_u32 >> 16u, (l & 2u) != 0u); + + s1 += x_block[col][l + 0u] * f32(qs & qm0); + s2 += x_block[col][l + 1u] * f32(qs & qm1); + s3 += select(0.0, x_block[col][l + 0u], (hv & hm0) == 0u) + + select(0.0, x_block[col][l + 1u], (hv & hm1) == 0u); + s4 += x_block[col][l + 8u] * f32(qs & qm2); + s5 += x_block[col][l + 9u] * f32(qs & qm3); + s6 += select(0.0, x_block[col][l + 8u], (hv & hm2) == 0u) + + select(0.0, x_block[col][l + 9u], (hv & hm3) == 0u); + } + + let d1 = d * (s1 + (1.0/256.0) * s2 - s3 * v1); + let d2 = d * (s4 + (1.0/256.0) * s5 - s6 * v2); + acc[col][row] += (d1 * scale0 + 0.25 * d2 * scale1) / f32(1u << shift); + } + } + } + } + + return acc; +} +#endif + +#ifdef MUL_ACC_Q4_K +#define BLOCK_SIZE 256 +#define BLOCK_SIZE_BYTES 144 +#define THREADS_PER_BLOCK 16 +fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array, NUM_COLS> { + var acc: array, NUM_COLS>; + + let tid = thread_id % THREADS_PER_BLOCK; + let block_group = thread_id / THREADS_PER_BLOCK; + let num_block_groups: u32 = WG_SIZE / THREADS_PER_BLOCK; + + let il = tid / 4u; + let ir = tid % 4u; + let im = il / 2u; + let in = il % 2u; + let l0 = 4u * (2u * ir + in); + + let y_offset = 64u * im + l0; + let q_offset = 32u * im + l0; + let sc0_byte = 4u + im * 2u; + let sc2_byte = 4u + (im + 2u) * 2u; + let sc4_byte = 4u + (im + 4u) * 2u; + + let num_blocks = params.k / BLOCK_SIZE; + + for (var block = block_group; block < num_blocks; block += num_block_groups) { + let x_base = src1_idx_base + block * BLOCK_SIZE + y_offset; + var x_block: array, NUM_COLS>; + for (var col = 0u; col < NUM_COLS;col += 1) { + let col_base = x_base + col * params.stride_11; + for (var i = 0u; i < 4u; i++) { + x_block[col][i] = f32(src1[col_base + i]); + x_block[col][i + 4u] = f32(src1[col_base + 32u + i]); + x_block[col][i + 8u] = f32(src1[col_base + 128u + i]); + x_block[col][i + 12u] = f32(src1[col_base + 160u + i]); + } + } + + for (var row = 0u; row < OUTPUTS_PER_WG; row++) { + let output_row = row_base + row; + if (output_row < params.m) { + let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES; + + let d = f32(load_f16_at_src0(block_byte_base + 0u)); + let dmin = f32(load_f16_at_src0(block_byte_base + 2u)); + + let sc0_u32 = load_u32_at_src0_aligned(block_byte_base + sc0_byte); + let sc0 = select(sc0_u32 & 0xFFFFu, sc0_u32 >> 16u, (sc0_byte & 2u) != 0u); + let sc2_u32 = load_u32_at_src0_aligned(block_byte_base + sc2_byte); + let sc2 = select(sc2_u32 & 0xFFFFu, sc2_u32 >> 16u, (sc2_byte & 2u) != 0u); + let sc4_u32 = load_u32_at_src0_aligned(block_byte_base + sc4_byte); + let sc4 = select(sc4_u32 & 0xFFFFu, sc4_u32 >> 16u, (sc4_byte & 2u) != 0u); + + let sc16_0 = sc0 & 0x3F3Fu; + let sc16_1 = sc2 & 0x3F3Fu; + let sc16_2 = (sc4 & 0x0F0Fu) | ((sc0 & 0xC0C0u) >> 2u); + let sc16_3 = ((sc4 >> 4u) & 0x0F0Fu) | ((sc2 & 0xC0C0u) >> 2u); + + let scale0 = f32(sc16_0 & 0xFFu); + let scale1 = f32((sc16_0 >> 8u) & 0xFFu); + let min0 = f32(sc16_1 & 0xFFu); + let min1 = f32((sc16_1 >> 8u) & 0xFFu); + let scale2 = f32(sc16_2 & 0xFFu); + let scale3 = f32((sc16_2 >> 8u) & 0xFFu); + let min2 = f32(sc16_3 & 0xFFu); + let min3 = f32((sc16_3 >> 8u) & 0xFFu); + + let q1_u32 = load_u32_at_src0_aligned(block_byte_base + 16u + q_offset); + let q2_u32 = load_u32_at_src0_aligned(block_byte_base + 80u + q_offset); + + for (var col = 0u;col < NUM_COLS;col += 1) { + var dot = vec4(0.0, 0.0, 0.0, 0.0); + var sumx = vec4(0.0, 0.0, 0.0, 0.0); + for (var i = 0u; i < 4u; i++) { + let q1b = byte_of(q1_u32, i); + let q2b = byte_of(q2_u32, i); + dot[0] += x_block[col][i] * f32(q1b & 0x0Fu); + dot[1] += x_block[col][i + 4u] * f32(q1b >> 4u); + dot[2] += x_block[col][i + 8u] * f32(q2b & 0x0Fu); + dot[3] += x_block[col][i + 12u] * f32(q2b >> 4u); + sumx[0] += x_block[col][i]; + sumx[1] += x_block[col][i + 4u]; + sumx[2] += x_block[col][i + 8u]; + sumx[3] += x_block[col][i + 12u]; + } + + acc[col][row] += d * (dot[0] * scale0 + dot[1] * scale1 + dot[2] * scale2 + dot[3] * scale3) + - dmin * (sumx[0] * min0 + sumx[1] * min1 + sumx[2] * min2 + sumx[3] * min3); + } + } + } + } + + return acc; +} +#endif + +#ifdef MUL_ACC_Q5_K +#define BLOCK_SIZE 256 +#define BLOCK_SIZE_BYTES 176 +#define THREADS_PER_BLOCK 16 +fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array, NUM_COLS> { + var acc: array, NUM_COLS>; + + let tid = thread_id % THREADS_PER_BLOCK; + let block_group = thread_id / THREADS_PER_BLOCK; + let num_block_groups: u32 = WG_SIZE / THREADS_PER_BLOCK; + + let il = tid / 4u; + let ir = tid % 4u; + let im = il / 2u; + let in = il % 2u; + let l0 = 4u * (2u * ir + in); + + let y_offset = 64u * im + l0; + let q_offset = 48u + 32u * im + l0; + let qh_offset = 16u + 8u * ir + 4u * in; + let sc0_byte = 4u + im * 2u; + let sc2_byte = 4u + (im + 2u) * 2u; + let sc4_byte = 4u + (im + 4u) * 2u; + + let hm1 = 1u << (2u * im); + let hm2 = hm1 << 1u; + let hm3 = hm1 << 4u; + let hm4 = hm2 << 4u; + + let num_blocks = params.k / BLOCK_SIZE; + + for (var block = block_group; block < num_blocks; block += num_block_groups) { + let x_base = src1_idx_base + block * BLOCK_SIZE + y_offset; + var x_block: array, NUM_COLS>; + for (var col = 0u; col < NUM_COLS;col += 1) { + let col_base = x_base + col * params.stride_11; + for (var i = 0u; i < 4u; i++) { + x_block[col][i] = f32(src1[col_base + i]); + x_block[col][i + 4u] = f32(src1[col_base + 32u + i]); + x_block[col][i + 8u] = f32(src1[col_base + 128u + i]); + x_block[col][i + 12u] = f32(src1[col_base + 160u + i]); + } + } + for (var row = 0u; row < OUTPUTS_PER_WG; row++) { + let output_row = row_base + row; + if (output_row < params.m) { + let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES; + + let d = f32(load_f16_at_src0(block_byte_base + 0u)); + let dmin = f32(load_f16_at_src0(block_byte_base + 2u)); + + let sc0_u32 = load_u32_at_src0_aligned(block_byte_base + sc0_byte); + let sc0 = select(sc0_u32 & 0xFFFFu, sc0_u32 >> 16u, (sc0_byte & 2u) != 0u); + let sc2_u32 = load_u32_at_src0_aligned(block_byte_base + sc2_byte); + let sc2 = select(sc2_u32 & 0xFFFFu, sc2_u32 >> 16u, (sc2_byte & 2u) != 0u); + let sc4_u32 = load_u32_at_src0_aligned(block_byte_base + sc4_byte); + let sc4 = select(sc4_u32 & 0xFFFFu, sc4_u32 >> 16u, (sc4_byte & 2u) != 0u); + + let sc16_0 = sc0 & 0x3F3Fu; + let sc16_1 = sc2 & 0x3F3Fu; + let sc16_2 = (sc4 & 0x0F0Fu) | ((sc0 & 0xC0C0u) >> 2u); + let sc16_3 = ((sc4 >> 4u) & 0x0F0Fu) | ((sc2 & 0xC0C0u) >> 2u); + + let f0 = f32(sc16_0 & 0xFFu); + let f1 = f32((sc16_0 >> 8u) & 0xFFu); + let m0 = f32(sc16_1 & 0xFFu); + let m1 = f32((sc16_1 >> 8u) & 0xFFu); + let f4 = f32(sc16_2 & 0xFFu); + let f5 = f32((sc16_2 >> 8u) & 0xFFu); + let m4 = f32(sc16_3 & 0xFFu); + let m5 = f32((sc16_3 >> 8u) & 0xFFu); + + let q1_u32 = load_u32_at_src0_aligned(block_byte_base + q_offset); + let q2_u32 = load_u32_at_src0_aligned(block_byte_base + q_offset + 64u); + let qh_u32 = load_u32_at_src0_aligned(block_byte_base + qh_offset); + + for (var col = 0u;col < NUM_COLS;col += 1) { + var vals = vec4(0.0, 0.0, 0.0, 0.0); + var sumy = vec4(0.0, 0.0, 0.0, 0.0); + for (var i = 0u; i < 4u; i++) { + let q1b = byte_of(q1_u32, i); + let q2b = byte_of(q2_u32, i); + let qhb = byte_of(qh_u32, i); + + let yl0 = x_block[col][i]; + let yl8 = x_block[col][i + 4u]; + let yh0 = x_block[col][i + 8u]; + let yh8 = x_block[col][i + 12u]; + + sumy[0] += yl0; + sumy[1] += yl8; + sumy[2] += yh0; + sumy[3] += yh8; + + let q0 = f32((q1b & 0x0Fu) | select(0u, 0x10u, (qhb & hm1) != 0u)); + let q1 = f32((q1b >> 4u) | select(0u, 0x10u, (qhb & hm2) != 0u)); + let q2 = f32((q2b & 0x0Fu) | select(0u, 0x10u, (qhb & hm3) != 0u)); + let q3 = f32((q2b >> 4u) | select(0u, 0x10u, (qhb & hm4) != 0u)); + + vals[0] += yl0 * q0; + vals[1] += yl8 * q1; + vals[2] += yh0 * q2; + vals[3] += yh8 * q3; + } + + acc[col][row] += d * (f0 * vals[0] + f1 * vals[1] + f4 * vals[2] + f5 * vals[3]) + - dmin * (sumy[0] * m0 + sumy[1] * m1 + + sumy[2] * m4 + sumy[3] * m5); + } + } + } + } + + return acc; +} +#endif + +#ifdef MUL_ACC_Q6_K +#define BLOCK_SIZE 256 +#define BLOCK_SIZE_BYTES 210 +#define THREADS_PER_BLOCK 16 +fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array, NUM_COLS> { + var acc: array, NUM_COLS>; + + let tid = thread_id % THREADS_PER_BLOCK; + let block_group = thread_id / THREADS_PER_BLOCK; + let num_block_groups: u32 = WG_SIZE / THREADS_PER_BLOCK; + + let ip = tid / 8u; + let il = tid % 8u; + let l0 = 4u * il; + let is = 8u * ip + l0 / 16u; + + let y_offset = 128u * ip + l0; + let q_offset_l = 64u * ip + l0; + let q_offset_h = 32u * ip + l0; + + let num_blocks = params.k / BLOCK_SIZE; + let sc_base_byte = 192u + (is & ~3u); + let sc_byte_pos = is & 3u; + + for (var block = block_group; block < num_blocks; block += num_block_groups) { + let x_base = src1_idx_base + block * BLOCK_SIZE + y_offset; + var x_block: array, NUM_COLS>; + for (var col = 0u; col < NUM_COLS;col += 1) { + let col_base = x_base + col * params.stride_11; + for (var l = 0u; l < 4u; l++) { + x_block[col][l] = f32(src1[col_base + l]); + x_block[col][l + 4u] = f32(src1[col_base + 32u + l]); + x_block[col][l + 8u] = f32(src1[col_base + 64u + l]); + x_block[col][l + 12u] = f32(src1[col_base + 96u + l]); + } + } + for (var row = 0u; row < OUTPUTS_PER_WG; row++) { + let output_row = row_base + row; + if (output_row < params.m) { + let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES; + + let d = f32(load_f16_at_src0(block_byte_base + 208u)); + let ql1_u32 = load_u32_at_src0(block_byte_base + q_offset_l); + let ql2_u32 = load_u32_at_src0(block_byte_base + q_offset_l + 32u); + let qh_u32 = load_u32_at_src0(block_byte_base + 128u + q_offset_h); + let sc_u32_0 = load_u32_at_src0(block_byte_base + sc_base_byte); + let sc_u32_1 = load_u32_at_src0(block_byte_base + sc_base_byte + 4u); + + let sc0 = sbyte_of(sc_u32_0, sc_byte_pos); + let sc2 = sbyte_of(sc_u32_0, sc_byte_pos + 2u); + let sc4 = sbyte_of(sc_u32_1, sc_byte_pos); + let sc6 = sbyte_of(sc_u32_1, sc_byte_pos + 2u); + + for (var col = 0u;col < NUM_COLS;col += 1) { + var sums = vec4(0.0, 0.0, 0.0, 0.0); + + for (var l = 0u; l < 4u; l++) { + let q1b = byte_of(ql1_u32, l); + let q2b = byte_of(ql2_u32, l); + let qhb = byte_of(qh_u32, l); + + let dq0 = f32(i32((q1b & 0x0Fu) | ((qhb & 0x03u) << 4u)) - 32); + let dq1 = f32(i32((q2b & 0x0Fu) | ((qhb & 0x0Cu) << 2u)) - 32); + let dq2 = f32(i32((q1b >> 4u) | (qhb & 0x30u)) - 32); + let dq3 = f32(i32((q2b >> 4u) | ((qhb & 0xC0u) >> 2u)) - 32); + + sums[0] += x_block[col][l] * dq0; + sums[1] += x_block[col][l + 4u] * dq1; + sums[2] += x_block[col][l + 8u] * dq2; + sums[3] += x_block[col][l + 12u] * dq3; + } + + acc[col][row] += d * (sums[0] * f32(sc0) + sums[1] * f32(sc2) + + sums[2] * f32(sc4) + sums[3] * f32(sc6)); + } + } + } + } + + return acc; +} +#endif + +#ifdef MUL_ACC_IQ1_S +#define BLOCK_SIZE 256 +#define BLOCK_SIZE_BYTES 50 +#define THREADS_PER_BLOCK 16 +fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array, NUM_COLS> { + var acc: array, NUM_COLS>; + + let tid = thread_id % THREADS_PER_BLOCK; + let block_group = thread_id / THREADS_PER_BLOCK; + let num_block_groups: u32 = WG_SIZE / THREADS_PER_BLOCK; + + let sub_blk = tid / 2u; + let half = tid % 2u; + let slot0 = half * 2u; + let y_offset = sub_blk * 32u + slot0 * 8u; + + let num_blocks = params.k / BLOCK_SIZE; + + for (var block = block_group; block < num_blocks; block += num_block_groups) { + let x_base = src1_idx_base + block * BLOCK_SIZE + y_offset; + var x_block: array, NUM_COLS>; + for (var col = 0u; col < NUM_COLS;col += 1) { + for (var i = 0u; i < 16u; i++) { + x_block[col][i] = f32(src1[x_base + col * params.stride_11 + i]); + } + } + for (var row = 0u; row < OUTPUTS_PER_WG; row++) { + let output_row = row_base + row; + if (output_row < params.m) { + let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES; + + let d = f32(load_f16_at_src0(block_byte_base)); + let qh = load_u32_at_src0(block_byte_base + 34u + sub_blk * 2u) & 0xFFFFu; + let dl = d * f32(2u * ((qh >> 12u) & 7u) + 1u); + let delta = select(IQ1_DELTA, -IQ1_DELTA, (qh & 0x8000u) != 0u); + let qs_w = load_u32_at_src0(block_byte_base + 2u + sub_blk * 4u); + + for (var col = 0u;col < NUM_COLS;col += 1) { + var row_sum = 0.0; + for (var ll = 0u; ll < 2u; ll++) { + let l = slot0 + ll; + let qs_byte = get_byte(qs_w, l); + let ig = (qs_byte | (((qh >> (3u * l)) & 7u) << 8u)) * 8u; + let gw = iq1_grid[ig / 16u]; + let bit_base = (ig % 16u) * 2u; + for (var j = 0u; j < 8u; j++) { + let g = (gw >> (bit_base + j * 2u)) & 3u; + let gs = select(f32(g), f32(g) - 4.0, (g & 2u) != 0u); + row_sum += dl * (gs + delta) * x_block[col][ll * 8u + j]; + } + } + acc[col][row] += row_sum; + } + } + } + } + + return acc; +} +#endif + +#ifdef MUL_ACC_IQ1_M +#define BLOCK_SIZE 256 +#define BLOCK_SIZE_BYTES 56 +#define THREADS_PER_BLOCK 16 +fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array, NUM_COLS> { + var acc: array, NUM_COLS>; + + let tid = thread_id % THREADS_PER_BLOCK; + let block_group = thread_id / THREADS_PER_BLOCK; + let num_block_groups: u32 = WG_SIZE / THREADS_PER_BLOCK; + + let sub_blk = tid / 2u; + let half = tid % 2u; + let slot0 = half * 2u; + let y_offset = sub_blk * 32u + slot0 * 8u; + + let num_blocks = params.k / BLOCK_SIZE; + + for (var block = block_group; block < num_blocks; block += num_block_groups) { + let x_base = src1_idx_base + block * BLOCK_SIZE + y_offset; + var x_block: array, NUM_COLS>; + for (var col = 0u; col < NUM_COLS;col += 1) { + for (var i = 0u; i < 16u; i++) { + x_block[col][i] = f32(src1[x_base + col * params.stride_11 + i]); + } + } + for (var row = 0u; row < OUTPUTS_PER_WG; row++) { + let output_row = row_base + row; + if (output_row < params.m) { + let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES; + + let sc_lo = load_u32_at_src0(block_byte_base + 48u); + let sc_hi = load_u32_at_src0(block_byte_base + 52u); + let sc0 = sc_lo & 0xFFFFu; + let sc1 = (sc_lo >> 16u) & 0xFFFFu; + let sc2 = sc_hi & 0xFFFFu; + let sc3 = (sc_hi >> 16u) & 0xFFFFu; + let d_bits = (sc0 >> 12u) | ((sc1 >> 8u) & 0xF0u) | ((sc2 >> 4u) & 0xF00u) | (sc3 & 0xF000u); + let d = f32(bitcast>(d_bits)[0]); + + let sc_u16 = select(select(sc2, sc3, sub_blk >= 6u), + select(sc0, sc1, sub_blk >= 2u), + sub_blk < 4u); + + let qs_w = load_u32_at_src0(block_byte_base + sub_blk * 4u); + let qh = load_u32_at_src0(block_byte_base + 32u + sub_blk * 2u) & 0xFFFFu; + let qh_lo = qh & 0xFFu; + let qh_hi = (qh >> 8u) & 0xFFu; + + for (var col = 0u;col < NUM_COLS;col += 1) { + var row_sum = 0.0; + for (var ll = 0u; ll < 2u; ll++) { + let l = slot0 + ll; + let bit_off = 6u * (sub_blk % 2u) + 3u * (l / 2u); + let sub_scale = (sc_u16 >> bit_off) & 0x7u; + let dl = d * f32(2u * sub_scale + 1u); + let qh_byte = select(qh_lo, qh_hi, l >= 2u); + let ll2 = l % 2u; + let grid_idx = get_byte(qs_w, l) | (((qh_byte >> (4u * ll2)) & 7u) << 8u); + let delta = select(IQ1_DELTA, -IQ1_DELTA, ((qh_byte >> (3u + 4u * ll2)) & 1u) != 0u); + let ig = grid_idx * 8u; + let gw = iq1_grid[ig / 16u]; + let bit_base = (ig % 16u) * 2u; + for (var j = 0u; j < 8u; j++) { + let g = (gw >> (bit_base + j * 2u)) & 3u; + let gs = select(f32(g), f32(g) - 4.0, (g & 2u) != 0u); + row_sum += dl * (gs + delta) * x_block[col][ll * 8u + j]; + } + } + acc[col][row] += row_sum; + } + } + } + } + + return acc; +} +#endif + +#ifdef MUL_ACC_IQ2_XXS +#define BLOCK_SIZE 256 +#define BLOCK_SIZE_BYTES 66 +#define THREADS_PER_BLOCK 16 +fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array, NUM_COLS> { + var acc: array, NUM_COLS>; + + let tid = thread_id % THREADS_PER_BLOCK; + let block_group = thread_id / THREADS_PER_BLOCK; + let num_block_groups: u32 = WG_SIZE / THREADS_PER_BLOCK; + + let sub_blk = tid / 2u; + let half = tid % 2u; + let slot0 = half * 2u; + let y_offset = sub_blk * 32u + slot0 * 8u; + + let num_blocks = params.k / BLOCK_SIZE; + + for (var block = block_group; block < num_blocks; block += num_block_groups) { + let x_base = src1_idx_base + block * BLOCK_SIZE + y_offset; + var x_block: array, NUM_COLS>; + for (var col = 0u; col < NUM_COLS;col += 1) { + for (var i = 0u; i < 16u; i++) { + x_block[col][i] = f32(src1[x_base + col * params.stride_11 + i]); + } + } + for (var row = 0u; row < OUTPUTS_PER_WG; row++) { + let output_row = row_base + row; + if (output_row < params.m) { + let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES; + let d = f32(load_f16_at_src0(block_byte_base)); + let aux_lo = load_u32_at_src0(block_byte_base + 2u + sub_blk * 8u); + let aux_hi = load_u32_at_src0(block_byte_base + 2u + sub_blk * 8u + 4u); + let ls = aux_hi >> 28u; + let db = d * (0.5 + f32(ls)) * 0.25; + + for (var col = 0u;col < NUM_COLS;col += 1) { + var row_sum = 0.0; + for (var ll = 0u; ll < 2u; ll++) { + let l = slot0 + ll; + let grid_idx = (aux_lo >> (8u * l)) & 0xFFu; + let signs_idx = (aux_hi >> (7u * l)) & 0x7Fu; + let signs = (ksigns_iq2xs[signs_idx / 4u] >> ((signs_idx % 4u) * 8u)) & 0xFFu; + let gw_lo = iq2xxs_grid[grid_idx * 2u]; + let gw_hi = iq2xxs_grid[grid_idx * 2u + 1u]; + for (var j = 0u; j < 8u; j++) { + let gw = select(gw_hi, gw_lo, j < 4u); + let b = f32((gw >> ((j & 3u) * 8u)) & 0xFFu); + let s = select(1.0, -1.0, ((signs >> j) & 1u) != 0u); + row_sum += db * b * s * x_block[col][ll * 8u + j]; + } + } + acc[col][row] += row_sum; + } + } + } + } + + return acc; +} +#endif + +#ifdef MUL_ACC_IQ2_XS +#define BLOCK_SIZE 256 +#define BLOCK_SIZE_BYTES 74 +#define THREADS_PER_BLOCK 16 +fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array, NUM_COLS> { + var acc: array, NUM_COLS>; + + let tid = thread_id % THREADS_PER_BLOCK; + let block_group = thread_id / THREADS_PER_BLOCK; + let num_block_groups: u32 = WG_SIZE / THREADS_PER_BLOCK; + + let sub_blk = tid / 2u; + let half = tid % 2u; + let slot0 = half * 2u; + let y_offset = sub_blk * 32u + slot0 * 8u; + + let num_blocks = params.k / BLOCK_SIZE; + + for (var block = block_group; block < num_blocks; block += num_block_groups) { + let x_base = src1_idx_base + block * BLOCK_SIZE + y_offset; + var x_block: array, NUM_COLS>; + for (var col = 0u; col < NUM_COLS;col += 1) { + for (var i = 0u; i < 16u; i++) { + x_block[col][i] = f32(src1[x_base + col * params.stride_11 + i]); + } + } + for (var row = 0u; row < OUTPUTS_PER_WG; row++) { + let output_row = row_base + row; + if (output_row < params.m) { + let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES; + let d = f32(load_f16_at_src0(block_byte_base)); + let qs_lo = load_u32_at_src0(block_byte_base + 2u + sub_blk * 8u); + let qs_hi = load_u32_at_src0(block_byte_base + 2u + sub_blk * 8u + 4u); + let scales_word = load_u32_at_src0(block_byte_base + 66u + (sub_blk / 4u) * 4u); + let scales_byte = get_byte(scales_word, sub_blk % 4u); + + for (var col = 0u;col < NUM_COLS;col += 1) { + var row_sum = 0.0; + for (var ll = 0u; ll < 2u; ll++) { + let l = slot0 + ll; + let qs_word = select(qs_hi, qs_lo, l < 2u); + let half2 = (l % 2u) * 16u; + let qs_val = (qs_word >> half2) & 0xFFFFu; + let grid_idx = qs_val & 0x1FFu; + let signs_idx = (qs_val >> 9u) & 0x7Fu; + let sub_scale = (scales_byte >> (4u * (l / 2u))) & 0xFu; + let db = d * (0.5 + f32(sub_scale)) * 0.25; + let signs = (ksigns_iq2xs[signs_idx / 4u] >> ((signs_idx % 4u) * 8u)) & 0xFFu; + let gw_lo = iq2xs_grid[grid_idx * 2u]; + let gw_hi = iq2xs_grid[grid_idx * 2u + 1u]; + for (var j = 0u; j < 8u; j++) { + let gw = select(gw_hi, gw_lo, j < 4u); + let b = f32((gw >> ((j & 3u) * 8u)) & 0xFFu); + let s = select(1.0, -1.0, ((signs >> j) & 1u) != 0u); + row_sum += db * b * s * x_block[col][ll * 8u + j]; + } + } + acc[col][row] += row_sum; + } + } + } + } + + return acc; +} +#endif + +#ifdef MUL_ACC_IQ2_S +#define BLOCK_SIZE 256 +#define BLOCK_SIZE_BYTES 82 +#define THREADS_PER_BLOCK 16 +fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array, NUM_COLS> { + var acc: array, NUM_COLS>; + + let tid = thread_id % THREADS_PER_BLOCK; + let block_group = thread_id / THREADS_PER_BLOCK; + let num_block_groups: u32 = WG_SIZE / THREADS_PER_BLOCK; + + let sub_blk = tid / 2u; + let half = tid % 2u; + let slot0 = half * 2u; + let y_offset = sub_blk * 32u + slot0 * 8u; + + let num_blocks = params.k / BLOCK_SIZE; + + for (var block = block_group; block < num_blocks; block += num_block_groups) { + let x_base = src1_idx_base + block * BLOCK_SIZE + y_offset; + var x_block: array, NUM_COLS>; + for (var col = 0u; col < NUM_COLS;col += 1) { + for (var i = 0u; i < 16u; i++) { + x_block[col][i] = f32(src1[x_base + col * params.stride_11 + i]); + } + } + for (var row = 0u; row < OUTPUTS_PER_WG; row++) { + let output_row = row_base + row; + if (output_row < params.m) { + let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES; + let d = f32(load_f16_at_src0(block_byte_base)); + let qs_w = load_u32_at_src0(block_byte_base + 2u + sub_blk * 4u); + let sg_w = load_u32_at_src0(block_byte_base + 34u + sub_blk * 4u); + let qh_word = load_u32_at_src0(block_byte_base + 66u + (sub_blk / 4u) * 4u); + let qh_byte = get_byte(qh_word, sub_blk % 4u); + let sc_word = load_u32_at_src0(block_byte_base + 74u + (sub_blk / 4u) * 4u); + let scales_byte = get_byte(sc_word, sub_blk % 4u); + + for (var col = 0u;col < NUM_COLS;col += 1) { + var row_sum = 0.0; + for (var ll = 0u; ll < 2u; ll++) { + let l = slot0 + ll; + let qs_byte = get_byte(qs_w, l); + let sign_byte = get_byte(sg_w, l); + let grid_idx = qs_byte | (((qh_byte >> (2u * l)) & 3u) << 8u); + let sub_scale = (scales_byte >> (4u * (l / 2u))) & 0xFu; + let db = d * (0.5 + f32(sub_scale)) * 0.25; + let gw_lo = iq2s_grid[grid_idx * 2u]; + let gw_hi = iq2s_grid[grid_idx * 2u + 1u]; + for (var j = 0u; j < 8u; j++) { + let gw = select(gw_hi, gw_lo, j < 4u); + let b = f32((gw >> ((j & 3u) * 8u)) & 0xFFu); + let s = select(1.0, -1.0, ((sign_byte >> j) & 1u) != 0u); + row_sum += db * b * s * x_block[col][ll * 8u + j]; + } + } + acc[col][row] += row_sum; + } + } + } + } + + return acc; +} +#endif + +#ifdef MUL_ACC_IQ3_XXS +#define BLOCK_SIZE 256 +#define BLOCK_SIZE_BYTES 98 +#define THREADS_PER_BLOCK 16 +fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array, NUM_COLS> { + var acc: array, NUM_COLS>; + + let tid = thread_id % THREADS_PER_BLOCK; + let block_group = thread_id / THREADS_PER_BLOCK; + let num_block_groups: u32 = WG_SIZE / THREADS_PER_BLOCK; + + let sub_blk = tid / 2u; + let half = tid % 2u; + let slot0 = half * 2u; + let y_offset = sub_blk * 32u + slot0 * 8u; + + let num_blocks = params.k / BLOCK_SIZE; + + for (var block = block_group; block < num_blocks; block += num_block_groups) { + let x_base = src1_idx_base + block * BLOCK_SIZE + y_offset; + var x_block: array, NUM_COLS>; + for (var col = 0u; col < NUM_COLS;col += 1) { + for (var i = 0u; i < 16u; i++) { + x_block[col][i] = f32(src1[x_base + col * params.stride_11 + i]); + } + } + for (var row = 0u; row < OUTPUTS_PER_WG; row++) { + let output_row = row_base + row; + if (output_row < params.m) { + let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES; + let d = f32(load_f16_at_src0(block_byte_base)); + let qs_lo = load_u32_at_src0(block_byte_base + 2u + sub_blk * 8u); + let qs_hi = load_u32_at_src0(block_byte_base + 2u + sub_blk * 8u + 4u); + let aux = load_u32_at_src0(block_byte_base + 66u + sub_blk * 4u); + let ls = aux >> 28u; + let db = d * (0.5 + f32(ls)) * 0.5; + + for (var col = 0u;col < NUM_COLS;col += 1) { + var row_sum = 0.0; + for (var ll = 0u; ll < 2u; ll++) { + let l = slot0 + ll; + let qs_word = select(qs_hi, qs_lo, l < 2u); + let byte_pos = (l % 2u) * 2u; + let grid_idx_0 = (qs_word >> (byte_pos * 8u)) & 0xFFu; + let grid_idx_1 = (qs_word >> ((byte_pos + 1u) * 8u)) & 0xFFu; + let signs_idx = (aux >> (7u * l)) & 0x7Fu; + let signs = (ksigns_iq2xs[signs_idx / 4u] >> ((signs_idx % 4u) * 8u)) & 0xFFu; + let grid1 = iq3xxs_grid[grid_idx_0]; + let grid2 = iq3xxs_grid[grid_idx_1]; + for (var j = 0u; j < 4u; j++) { + let b1 = f32((grid1 >> (j * 8u)) & 0xFFu); + let b2 = f32((grid2 >> (j * 8u)) & 0xFFu); + let s1 = select(1.0, -1.0, ((signs >> j) & 1u) != 0u); + let s2 = select(1.0, -1.0, ((signs >> (j + 4u)) & 1u) != 0u); + row_sum += db * b1 * s1 * x_block[col][ll * 8u + j]; + row_sum += db * b2 * s2 * x_block[col][ll * 8u + j + 4u]; + } + } + acc[col][row] += row_sum; + } + } + } + } + + return acc; +} +#endif + +#ifdef MUL_ACC_IQ3_S +#define BLOCK_SIZE 256 +#define BLOCK_SIZE_BYTES 110 +#define THREADS_PER_BLOCK 16 +fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array, NUM_COLS> { + var acc: array, NUM_COLS>; + + let tid = thread_id % THREADS_PER_BLOCK; + let block_group = thread_id / THREADS_PER_BLOCK; + let num_block_groups: u32 = WG_SIZE / THREADS_PER_BLOCK; + + let sub_blk = tid / 2u; + let half = tid % 2u; + let slot0 = half * 2u; + let y_offset = sub_blk * 32u + slot0 * 8u; + + let num_blocks = params.k / BLOCK_SIZE; + + for (var block = block_group; block < num_blocks; block += num_block_groups) { + let x_base = src1_idx_base + block * BLOCK_SIZE + y_offset; + var x_block: array, NUM_COLS>; + for (var col = 0u; col < NUM_COLS;col += 1) { + for (var i = 0u; i < 16u; i++) { + x_block[col][i] = f32(src1[x_base + col * params.stride_11 + i]); + } + } + for (var row = 0u; row < OUTPUTS_PER_WG; row++) { + let output_row = row_base + row; + if (output_row < params.m) { + let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES; + let d = f32(load_f16_at_src0(block_byte_base)); + let qs_lo = load_u32_at_src0(block_byte_base + 2u + sub_blk * 8u); + let qs_hi = load_u32_at_src0(block_byte_base + 2u + sub_blk * 8u + 4u); + let qh_word = load_u32_at_src0(block_byte_base + 66u + (sub_blk / 4u) * 4u); + let qh_byte = get_byte(qh_word, sub_blk % 4u); + let sg_w = load_u32_at_src0(block_byte_base + 74u + sub_blk * 4u); + let sc_word = load_u32_at_src0(block_byte_base + 106u); + let scales_byte = get_byte(sc_word, sub_blk / 2u); + let sub_scale = (scales_byte >> (4u * (sub_blk % 2u))) & 0xFu; + let db = d * (1.0 + 2.0 * f32(sub_scale)); + + for (var col = 0u;col < NUM_COLS;col += 1) { + var row_sum = 0.0; + for (var ll = 0u; ll < 2u; ll++) { + let l = slot0 + ll; + let qs_word = select(qs_hi, qs_lo, l < 2u); + let byte_pos = (l % 2u) * 2u; + let qs0 = (qs_word >> (byte_pos * 8u)) & 0xFFu; + let qs1 = (qs_word >> ((byte_pos + 1u) * 8u)) & 0xFFu; + let grid_idx_1 = qs0 | (((qh_byte >> (2u * l)) & 1u) << 8u); + let grid_idx_2 = qs1 | (((qh_byte >> (2u * l + 1u)) & 1u) << 8u); + let sign_byte = get_byte(sg_w, l); + let grid1 = iq3s_grid[grid_idx_1]; + let grid2 = iq3s_grid[grid_idx_2]; + for (var j = 0u; j < 4u; j++) { + let b1 = f32((grid1 >> (j * 8u)) & 0xFFu); + let b2 = f32((grid2 >> (j * 8u)) & 0xFFu); + let s1 = select(1.0, -1.0, ((sign_byte >> j) & 1u) != 0u); + let s2 = select(1.0, -1.0, ((sign_byte >> (j + 4u)) & 1u) != 0u); + row_sum += db * b1 * s1 * x_block[col][ll * 8u + j]; + row_sum += db * b2 * s2 * x_block[col][ll * 8u + j + 4u]; + } + } + acc[col][row] += row_sum; + } + } + } + } + + return acc; +} +#endif + +#ifdef MUL_ACC_IQ4_NL +#define BLOCK_SIZE 32 +#define BLOCK_SIZE_BYTES 18 +#define THREADS_PER_BLOCK 4 +#define ELEMS_PER_THREAD (BLOCK_SIZE/THREADS_PER_BLOCK) +fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array, NUM_COLS> { + var acc: array, NUM_COLS>; + + let num_blocks = params.k / BLOCK_SIZE; + let thread_within_block = thread_id % THREADS_PER_BLOCK; + for (var block = thread_id / THREADS_PER_BLOCK; block < num_blocks; block += WG_SIZE / THREADS_PER_BLOCK) { + let x_base = src1_idx_base + block * BLOCK_SIZE + thread_within_block * 4u; + var x_block: array, NUM_COLS>; + for (var col = 0u; col < NUM_COLS;col += 1) { + for (var i = 0u; i < ELEMS_PER_THREAD / 2u; i++) { + x_block[col][i] = f32(src1[x_base + col * params.stride_11 + i]); + x_block[col][i + 4u] = f32(src1[x_base + col * params.stride_11 + i + 16u]); + } + } + for (var row = 0u; row < OUTPUTS_PER_WG; row++) { + let output_row = row_base + row; + if (output_row < params.m) { + let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES; + let d = f32(load_f16_at_src0(block_byte_base)); + let q_packed = load_u32_at_src0(block_byte_base + 2u + 4u * thread_within_block); + for (var col = 0u;col < NUM_COLS;col += 1) { + var row_sum = 0.0; + for (var byte_idx = 0u; byte_idx < 4u; byte_idx++) { + let q_byte = get_byte(q_packed, byte_idx); + let q_lo = f32(kvalues_iq4nl[q_byte & 0xFu]) * d; + let q_hi = f32(kvalues_iq4nl[(q_byte >> 4u) & 0xFu]) * d; + row_sum += q_lo * x_block[col][byte_idx]; + row_sum += q_hi * x_block[col][byte_idx + 4u]; + } + acc[col][row] += row_sum; + } + } + } + } + + return acc; +} +#endif + +#ifdef MUL_ACC_IQ4_XS +#define BLOCK_SIZE 256 +#define BLOCK_SIZE_BYTES 136 +#define THREADS_PER_BLOCK 16 +fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array, NUM_COLS> { + var acc: array, NUM_COLS>; + + let tid = thread_id % THREADS_PER_BLOCK; + let block_group = thread_id / THREADS_PER_BLOCK; + let num_block_groups: u32 = WG_SIZE / THREADS_PER_BLOCK; + + let sub_blk = tid / 2u; + let half = tid % 2u; + let y_offset = sub_blk * 32u + half * 16u; + + let num_blocks = params.k / BLOCK_SIZE; + + for (var block = block_group; block < num_blocks; block += num_block_groups) { + let x_base = src1_idx_base + block * BLOCK_SIZE + y_offset; + var x_block: array, NUM_COLS>; + for (var col = 0u; col < NUM_COLS;col += 1) { + for (var i = 0u; i < 16u; i++) { + x_block[col][i] = f32(src1[x_base + col * params.stride_11 + i]); + } + } + for (var row = 0u; row < OUTPUTS_PER_WG; row++) { + let output_row = row_base + row; + if (output_row < params.m) { + let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES; + let d = f32(load_f16_at_src0(block_byte_base)); + let scales_h = load_u16_at_src0(block_byte_base + 2u); + let scales_l_word = load_u32_at_src0(block_byte_base + 4u); + let sl_byte = get_byte(scales_l_word, sub_blk / 2u); + let sl = (sl_byte >> (4u * (sub_blk % 2u))) & 0xFu; + let sh_bits = (scales_h >> (2u * sub_blk)) & 3u; + let ls = i32(sl | (sh_bits << 4u)); + let dl = d * f32(ls - 32); + + let qs_byte_off = 8u + sub_blk * 16u; + let q_w0 = load_u32_at_src0(block_byte_base + qs_byte_off); + let q_w1 = load_u32_at_src0(block_byte_base + qs_byte_off + 4u); + let q_w2 = load_u32_at_src0(block_byte_base + qs_byte_off + 8u); + let q_w3 = load_u32_at_src0(block_byte_base + qs_byte_off + 12u); + + for (var col = 0u;col < NUM_COLS;col += 1) { + var row_sum = 0.0; + for (var i = 0u; i < 16u; i++) { + let q_word = select( + select(q_w0, q_w1, i >= 4u), + select(q_w2, q_w3, i >= 12u), + i >= 8u); + let q_byte = get_byte(q_word, i % 4u); + let nib = select(q_byte & 0xFu, (q_byte >> 4u) & 0xFu, half == 1u); + row_sum += f32(kvalues_iq4nl[nib]) * dl * x_block[col][i]; + } + acc[col][row] += row_sum; + } + } + } + } + + return acc; +} +#endif + +#ifdef MUL_ACC_MXFP4 +#define BLOCK_SIZE 32 +#define BLOCK_SIZE_BYTES 17 +#define THREADS_PER_BLOCK 4 +#define ELEMS_PER_THREAD (BLOCK_SIZE/THREADS_PER_BLOCK) +fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array, NUM_COLS> { + var acc: array, NUM_COLS>; + + let num_blocks = params.k / BLOCK_SIZE; + let thread_within_block = thread_id % 4; + for (var block = thread_id/THREADS_PER_BLOCK; block < num_blocks; block += WG_SIZE/THREADS_PER_BLOCK) { + let x_base = src1_idx_base + block * BLOCK_SIZE + thread_within_block * 4; + var x_block: array, NUM_COLS>; + for (var col = 0u; col < NUM_COLS;col += 1) { + for (var i = 0u; i < ELEMS_PER_THREAD / 2; i++) { + x_block[col][i] = f32(src1[x_base + col * params.stride_11 + i]); + x_block[col][i + 4] = f32(src1[x_base + col * params.stride_11 + i + 16]); + } + } + for (var row = 0u; row < OUTPUTS_PER_WG; row++) { + let output_row = row_base + row; + if (output_row < params.m) { + let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES; + let eu8 = get_byte(load_u32_at_src0(block_byte_base), 0); + let e = ldexp(1.0, i32(eu8) - 128); + let q_packed = load_u32_at_src0(block_byte_base + 1u + 4u * thread_within_block); + for (var col = 0u;col < NUM_COLS;col += 1) { + var row_sum = 0.0; + for (var byte_idx = 0u; byte_idx < 4u; byte_idx++) { + let q_byte = get_byte(q_packed, byte_idx); + let q_lo = f32(kvalues_mxfp4[q_byte & 0xFu]) * e; + let q_hi = f32(kvalues_mxfp4[(q_byte >> 4u) & 0xFu]) * e; + row_sum += q_lo * x_block[col][byte_idx]; + row_sum += q_hi * x_block[col][byte_idx + 4u]; + } + acc[col][row] += row_sum; + } + } + } + } + + return acc; +} +#endif + +#ifdef MUL_ACC_NVFP4 +#define BLOCK_SIZE 64 +#define BLOCK_SIZE_BYTES 36 +#define THREADS_PER_BLOCK 4 +#define ELEMS_PER_THREAD (BLOCK_SIZE/THREADS_PER_BLOCK) +fn accumulate_vec_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1_idx_base: u32) -> array, NUM_COLS> { + var acc: array, NUM_COLS>; + + let num_blocks = params.k / BLOCK_SIZE; + let sub = thread_id % THREADS_PER_BLOCK; + for (var block = thread_id/THREADS_PER_BLOCK; block < num_blocks; block += WG_SIZE/THREADS_PER_BLOCK) { + let x_base = src1_idx_base + block * BLOCK_SIZE + sub * ELEMS_PER_THREAD; + var x_block: array, NUM_COLS>; + for (var col = 0u; col < NUM_COLS;col += 1) { + for (var i = 0u; i < ELEMS_PER_THREAD / 2; i++) { + x_block[col][i] = f32(src1[x_base + col * params.stride_11 + i]); + x_block[col][i + 8] = f32(src1[x_base + col * params.stride_11 + i + 8]); + } + } + for (var row = 0u; row < OUTPUTS_PER_WG; row++) { + let output_row = row_base + row; + if (output_row < params.m) { + let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES; + let d = ue4m3_to_fp32(get_byte(load_u32_at_src0_aligned(block_byte_base), sub)) * 0.5; + let q_w0 = load_u32_at_src0_aligned(block_byte_base + 4u + 8u * sub); + let q_w1 = load_u32_at_src0_aligned(block_byte_base + 8u + 8u * sub); + for (var col = 0u;col < NUM_COLS;col += 1) { + var row_sum = 0.0; + for (var l = 0u; l < 8u; l++) { + let q_word = select(q_w0, q_w1, l >= 4u); + let q_byte = get_byte(q_word, l % 4u); + let q_lo = f32(kvalues_mxfp4[q_byte & 0xFu]) * d; + let q_hi = f32(kvalues_mxfp4[(q_byte >> 4u) & 0xFu]) * d; + row_sum += q_lo * x_block[col][l]; + row_sum += q_hi * x_block[col][l + 8u]; + } + acc[col][row] += row_sum; + } + } + } + } + + return acc; +} +#endif diff --git a/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec_q_acc.tmpl b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec_q_acc.tmpl new file mode 100644 index 0000000000000000000000000000000000000000..6ccaf61a6a07d27f9425acef4ac7d2b63a4e96f4 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/mul_mat_vec_q_acc.tmpl @@ -0,0 +1,297 @@ +#ifdef U32_DEQUANT_HELPERS +#define SRC0_TYPE u32 + +fn byte_of(v: u32, b: u32) -> u32 { + return (v >> (b * 8u)) & 0xFFu; +} + +fn sbyte_of(v: u32, b: u32) -> i32 { + let raw = i32((v >> (b * 8u)) & 0xFFu); + return select(raw, raw - 256, raw >= 128); +} +#endif + +#define SRC0_TYPE SRC0_INNER_TYPE +#define SRC1_TYPE SRC1_INNER_TYPE + +#ifdef LEGACY_QUANTS +#define BLOCK_SIZE 32 +#define THREADS_PER_BLOCK 4 +#elif K_QUANTS +#define BLOCK_SIZE 256 +#define THREADS_PER_BLOCK 16 +#endif + +#define ELEMS_PER_THREAD (BLOCK_SIZE/THREADS_PER_BLOCK) +#define Q8_BLOCK_SIZE 32 + +#ifdef MUL_ACC_Q4_0 +#define BLOCK_SIZE_BYTES 18 +#define B_DS_TYPE vec2 +fn repack_a(block_byte_base: u32, inner_id: u32) -> vec2 { + let qs_packed = load_u32_at_src0(block_byte_base + 2u + 4u * inner_id); + + return vec2( + qs_packed & 0x0F0F0F0Fu, + (qs_packed >> 4u) & 0x0F0F0F0Fu + ); +} +fn repack_b_qs(block:u32, inner_id: u32) -> vec2 { + return vec2( + src1q[block].qs[inner_id], + src1q[block].qs[inner_id + 4u], + ); +} +fn repack_b_dm(block: u32) -> B_DS_TYPE { + return B_DS_TYPE( + f32(src1q[block].d), + f32(src1q[block].s) + ); +} +fn get_dm(block_byte_base: u32) -> f32 { + return f32(load_f16_at_src0(block_byte_base)); +} +#endif // MUL_ACC_Q4_0 + +#ifdef MUL_ACC_Q4_1 +#define BLOCK_SIZE_BYTES 20 +#define B_DS_TYPE vec2 +fn repack_a(block_byte_base: u32, inner_id: u32) -> vec2 { + let qs_packed = load_u32_at_src0(block_byte_base + 4u + 4u * inner_id); + + return vec2( + qs_packed & 0x0F0F0F0Fu, + (qs_packed >> 4u) & 0x0F0F0F0Fu + ); +} +fn repack_b_qs(block:u32, inner_id: u32) -> vec2 { + return vec2( + src1q[block].qs[inner_id], + src1q[block].qs[inner_id + 4u], + ); +} +fn repack_b_dm(block: u32) -> B_DS_TYPE { + return B_DS_TYPE( + f32(src1q[block].d), + f32(src1q[block].s) + ); +} +fn get_dm(block_byte_base: u32) -> vec2 { + return vec2( + f32(load_f16_at_src0(block_byte_base)), + f32(load_f16_at_src0(block_byte_base + 2u)) + ); +} +#endif // MUL_ACC_Q4_1 + +#ifdef MUL_ACC_Q8_0 +#define BLOCK_SIZE_BYTES 34 +#define B_DS_TYPE f32 +fn repack_a(block_byte_base: u32, inner_id: u32) -> vec2 { + return vec2( + load_u32_at_src0(block_byte_base + 2u + 4u * (inner_id * 2u)), + load_u32_at_src0(block_byte_base + 2u + 4u * (inner_id * 2u + 1)) + ); +} +fn repack_b_qs(block:u32, inner_id: u32) -> vec2 { + return vec2( + src1q[block].qs[inner_id * 2u], + src1q[block].qs[inner_id * 2u + 1], + ); +} +fn repack_b_dm(block: u32) -> B_DS_TYPE { + return B_DS_TYPE(src1q[block].d); +} +fn get_dm(block_byte_base: u32) -> f32 { + return f32(load_f16_at_src0(block_byte_base)); +} +#endif // MUL_ACC_Q8_0 + +#if defined(LEGACY_QUANTS) +fn accumulate_vec_q_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1q_idx_base: u32) -> array, NUM_COLS> { + var acc: array, NUM_COLS>; + + let num_blocks = params.k / BLOCK_SIZE; + + for (var block = thread_id / THREADS_PER_BLOCK; block < num_blocks; block += WG_SIZE / THREADS_PER_BLOCK) { + let inner_id = thread_id % THREADS_PER_BLOCK; + for (var row = 0u; row < OUTPUTS_PER_WG; row++) { + let output_row = row_base + row; + if (output_row < params.m) { + let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES; + let a_repacked = repack_a(block_byte_base, inner_id); + let da = get_dm(block_byte_base); + for (var col = 0u;col < NUM_COLS;col += 1) { + let src1q_idx = src1q_idx_base + col * (params.k / Q8_BLOCK_SIZE) + block; + let b_repacked = repack_b_qs(src1q_idx, inner_id); + let b_ds = repack_b_dm(src1q_idx); + + let row_sum = dot4I8Packed(a_repacked[0], b_repacked[0]) + dot4I8Packed(a_repacked[1], b_repacked[1]); + +#if defined(MUL_ACC_Q4_0) + acc[col][row] += f32(row_sum) * (da * b_ds.x) - 8.0 * da * b_ds.y / THREADS_PER_BLOCK; +#endif // MUL_ACC_Q4_0 + +#if defined(MUL_ACC_Q4_1) + acc[col][row] += f32(row_sum) * (da.x * b_ds.x) + da.y * b_ds.y / THREADS_PER_BLOCK; +#endif // MUL_ACC_Q4_1 + +#if defined(MUL_ACC_Q8_0) + acc[col][row] += f32(row_sum) * (da * b_ds); +#endif // MUL_ACC_Q8_0 + } + } + } + } + + return acc; +} +#endif // LEGACY_QUANTS + +#ifdef MUL_ACC_Q2_K +#define BLOCK_SIZE_BYTES 84 +#define B_DS_TYPE f32 +fn repack_a(block_byte_base: u32, tid: u32) -> vec4 { + let ih2 = tid / 8u; + let phase = tid % 2u; + let iq4_idx = 2u * ih2 + phase; + let qs_byte_base = block_byte_base + 16u + 16u * iq4_idx; + let qs_shift = tid & 6u; + return vec4( + (load_u32_at_src0_aligned(qs_byte_base) >> qs_shift) & 0x03030303u, + (load_u32_at_src0_aligned(qs_byte_base + 4u) >> qs_shift) & 0x03030303u, + (load_u32_at_src0_aligned(qs_byte_base + 8u) >> qs_shift) & 0x03030303u, + (load_u32_at_src0_aligned(qs_byte_base + 12u) >> qs_shift) & 0x03030303u, + ); +} +fn repack_b_qs(q8_block_idx: u32, tid: u32) -> vec4 { + let phase = tid % 2u; + return vec4( + src1q[q8_block_idx].qs[4u * phase], + src1q[q8_block_idx].qs[4u * phase + 1u], + src1q[q8_block_idx].qs[4u * phase + 2u], + src1q[q8_block_idx].qs[4u * phase + 3u], + ); +} +fn repack_b_dm(q8_block_idx: u32) -> B_DS_TYPE { + return B_DS_TYPE(src1q[q8_block_idx].d); +} +fn get_dm(block_byte_base: u32) -> vec2 { + return vec2( + f32(load_f16_at_src0(block_byte_base + 80u)), + f32(load_f16_at_src0(block_byte_base + 82u)), + ); +} +fn get_scale_min(block_byte_base: u32, tid: u32) -> vec2 { + let scale_byte = block_byte_base + tid; + let scale = byte_of(load_u32_at_src0_aligned(scale_byte), scale_byte & 3u); + return vec2(f32(scale & 0xFu), f32(scale >> 4u)); +} +#endif // MUL_ACC_Q2_K + +#ifdef MUL_ACC_Q4_K +#define BLOCK_SIZE_BYTES 144 +#define B_DS_TYPE vec2 +fn repack_a(block_byte_base: u32, tid: u32) -> vec4 { + let iq4 = tid / 4u; + let phase = tid % 2u; + let nibble = (tid >> 1u) % 2u; + let q_qs_byte_base = block_byte_base + 16u + 32u * iq4 + 16u * phase; + let qs_shift = 4u * nibble; + return vec4( + (load_u32_at_src0_aligned(q_qs_byte_base) >> qs_shift) & 0x0F0F0F0Fu, + (load_u32_at_src0_aligned(q_qs_byte_base + 4u) >> qs_shift) & 0x0F0F0F0Fu, + (load_u32_at_src0_aligned(q_qs_byte_base + 8u) >> qs_shift) & 0x0F0F0F0Fu, + (load_u32_at_src0_aligned(q_qs_byte_base + 12u) >> qs_shift) & 0x0F0F0F0Fu, + ); +} +fn repack_b_qs(q8_block_idx: u32, tid: u32) -> vec4 { + let phase = tid % 2u; + return vec4( + src1q[q8_block_idx].qs[4u * phase], + src1q[q8_block_idx].qs[4u * phase + 1u], + src1q[q8_block_idx].qs[4u * phase + 2u], + src1q[q8_block_idx].qs[4u * phase + 3u], + ); +} +fn repack_b_dm(q8_block_idx: u32) -> B_DS_TYPE { + return B_DS_TYPE( + f32(src1q[q8_block_idx].d), + f32(src1q[q8_block_idx].s), + ); +} +fn get_dm(block_byte_base: u32) -> vec2 { + return vec2( + f32(load_f16_at_src0(block_byte_base + 0u)), + f32(load_f16_at_src0(block_byte_base + 2u)), + ); +} +fn get_scale_min(block_byte_base: u32, tid: u32) -> vec2 { + let sc_m_idx = tid / 2u; + let scales_byte_base = block_byte_base + 4u; + let scales0_3 = load_u32_at_src0_aligned(scales_byte_base); + let scales4_7 = load_u32_at_src0_aligned(scales_byte_base + 4u); + let scales8_11 = load_u32_at_src0_aligned(scales_byte_base + 8u); + + let byte_idx = sc_m_idx & 3u; + let is_high = sc_m_idx >= 4u; + + let sc_low = byte_of(scales0_3, byte_idx) & 0x3Fu; + let sc_high = (byte_of(scales8_11, byte_idx) & 0x0Fu) | ((byte_of(scales0_3, byte_idx) & 0xC0u) >> 2u); + let scale = f32(select(sc_low, sc_high, is_high)); + + let mn_low = byte_of(scales4_7, byte_idx) & 0x3Fu; + let mn_high = (byte_of(scales8_11, byte_idx) >> 4u) | ((byte_of(scales4_7, byte_idx) & 0xC0u) >> 2u); + let min_val = f32(select(mn_low, mn_high, is_high)); + + return vec2(scale, min_val); +} +#endif // MUL_ACC_Q4_K + +#ifdef K_QUANTS +fn accumulate_vec_q_dot(thread_id: u32, row_base: u32, src0_batch_offset: u32, src1q_idx_base: u32) -> array, NUM_COLS> { + var acc: array, NUM_COLS>; + + let tid = thread_id % THREADS_PER_BLOCK; + + for (var block = thread_id / THREADS_PER_BLOCK; block < params.k / BLOCK_SIZE; block += WG_SIZE / THREADS_PER_BLOCK) { + for (var row = 0u; row < OUTPUTS_PER_WG; row++) { + let output_row = row_base + row; + if (output_row < params.m) { + let block_byte_base = (src0_batch_offset + output_row * params.stride_01 + block) * BLOCK_SIZE_BYTES; + let a_repacked = repack_a(block_byte_base, tid); + let dm = get_dm(block_byte_base); + let scale_min = get_scale_min(block_byte_base, tid); + for (var col = 0u;col < NUM_COLS;col += 1) { + let src1q_idx = src1q_idx_base + col * (params.k / Q8_BLOCK_SIZE) + (block * BLOCK_SIZE + ELEMS_PER_THREAD * tid) / Q8_BLOCK_SIZE; + let b_repacked = repack_b_qs(src1q_idx, tid); + let b_ds = repack_b_dm(src1q_idx); + +#if defined(MUL_ACC_Q2_K) + let scale_q = i32(scale_min.x); + let scale_m_i8x4 = u32(scale_min.y) * 0x01010101u; + + let row_sum_d = (dot4I8Packed(b_repacked[0], a_repacked[0]) + dot4I8Packed(b_repacked[1], a_repacked[1]) + + dot4I8Packed(b_repacked[2], a_repacked[2]) + dot4I8Packed(b_repacked[3], a_repacked[3])) * scale_q; + let row_sum_m = dot4I8Packed(b_repacked[0], scale_m_i8x4) + dot4I8Packed(b_repacked[1], scale_m_i8x4) + + dot4I8Packed(b_repacked[2], scale_m_i8x4) + dot4I8Packed(b_repacked[3], scale_m_i8x4); + + acc[col][row] += b_ds * (dm.x * f32(row_sum_d) - dm.y * f32(row_sum_m)); +#endif // MUL_ACC_Q2_K + +#if defined(MUL_ACC_Q4_K) + let row_sum = dot4I8Packed(a_repacked[0], b_repacked[0]) + dot4I8Packed(a_repacked[1], b_repacked[1]) + + dot4I8Packed(a_repacked[2], b_repacked[2]) + dot4I8Packed(a_repacked[3], b_repacked[3]); + + // Each thread covers half of the Q8_1 block, so add only b_ds.y/2. + acc[col][row] += b_ds.x * dm.x * scale_min.x * f32(row_sum) - dm.y * scale_min.y * (b_ds.y / (Q8_BLOCK_SIZE / ELEMS_PER_THREAD)); +#endif // MUL_ACC_Q4_K + + } + } + } + } + + return acc; +} +#endif // K_QUANTS diff --git a/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/pad.wgsl b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/pad.wgsl new file mode 100644 index 0000000000000000000000000000000000000000..ea63b9a731ce9ba915fd7847a91336f7bf367cc4 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/pad.wgsl @@ -0,0 +1,86 @@ +@group(0) @binding(0) +var src: array; + +@group(0) @binding(1) +var dst: array; + +struct Params { + ne: u32, // total number of elements + offset_src: u32, // in elements + offset_dst: u32, // in elements + + // Strides (in elements) + stride_src0: u32, + stride_src1: u32, + stride_src2: u32, + stride_src3: u32, + + // Logical shapes + src_ne0: u32, + src_ne1: u32, + src_ne2: u32, + src_ne3: u32, + + dst_ne0: u32, + dst_ne1: u32, + dst_ne2: u32, + dst_ne3: u32, + + // Pad sizes (in elements) + lp0: u32, + rp0: u32, + lp1: u32, + rp1: u32, + lp2: u32, + rp2: u32, + lp3: u32, + rp3: u32, +}; + +@group(0) @binding(2) +var params: Params; + +fn wrap_around(idx: i32, n: u32) -> u32 { + return u32(idx + i32(n)) % n; +} + +@compute @workgroup_size(WG_SIZE) +fn main(@builtin(global_invocation_id) gid: vec3) { + if (gid.x >= params.ne) { + return; + } + + var i = gid.x; + let dst_plane = params.dst_ne2 * params.dst_ne1 * params.dst_ne0; + let i3 = i / dst_plane; + i = i % dst_plane; + let i2 = i / (params.dst_ne1 * params.dst_ne0); + i = i % (params.dst_ne1 * params.dst_ne0); + let i1 = i / params.dst_ne0; + let i0 = i % params.dst_ne0; + + var value: f32 = 0.0; + +#ifdef CIRCULAR + let ci0 = wrap_around(i32(i0) - i32(params.lp0), params.src_ne0); + let ci1 = wrap_around(i32(i1) - i32(params.lp1), params.src_ne1); + let ci2 = wrap_around(i32(i2) - i32(params.lp2), params.src_ne2); + let ci3 = wrap_around(i32(i3) - i32(params.lp3), params.src_ne3); + let circular_src_idx = ci0 * params.stride_src0 + ci1 * params.stride_src1 + + ci2 * params.stride_src2 + ci3 * params.stride_src3; + value = src[params.offset_src + circular_src_idx]; +#else + let is_src = + (i0 >= params.lp0 && i0 < params.dst_ne0 - params.rp0) && + (i1 >= params.lp1 && i1 < params.dst_ne1 - params.rp1) && + (i2 >= params.lp2 && i2 < params.dst_ne2 - params.rp2) && + (i3 >= params.lp3 && i3 < params.dst_ne3 - params.rp3); + if (is_src) { + let src_idx = (i0 - params.lp0) * params.stride_src0 + (i1 - params.lp1) * params.stride_src1 + + (i2 - params.lp2) * params.stride_src2 + (i3 - params.lp3) * params.stride_src3; + value = src[params.offset_src + src_idx]; + } +#endif + + dst[params.offset_dst + gid.x] = value; +} diff --git a/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/quant_inner_loops.tmpl b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/quant_inner_loops.tmpl new file mode 100644 index 0000000000000000000000000000000000000000..d1da46084347a597c61cf08f7519e4311115d750 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/quant_inner_loops.tmpl @@ -0,0 +1,21 @@ +#ifdef U32_DEQUANT_HELPERS +fn dequant_q4_0_packed_to_shmem(q_packed: u32, d: f16, dst_idx: u32) { + let scale = QUANT_OUT_TYPE(d); + for (var k = 0u; k < 4u; k++) { + let q_byte = get_byte(q_packed, k); + let q_hi = (QUANT_OUT_TYPE((q_byte >> 4) & 0xFu) - QUANT_OUT_TYPE(8.0)) * scale; + let q_lo = (QUANT_OUT_TYPE(q_byte & 0xFu) - QUANT_OUT_TYPE(8.0)) * scale; + QUANT_SHMEM[dst_idx + k] = q_lo; + QUANT_SHMEM[dst_idx + k + 16u] = q_hi; + } +} + +fn dequant_q8_0_packed_to_shmem(q_packed: u32, d: f16, dst_idx: u32) { + let scale = QUANT_OUT_TYPE(d); + for (var k = 0u; k < 4u; k++) { + let q_byte = get_byte_i32(q_packed, k); + let q_val = QUANT_OUT_TYPE(q_byte) * scale; + QUANT_SHMEM[dst_idx + k] = q_val; + } +} +#endif diff --git a/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/quantize_q8.wgsl b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/quantize_q8.wgsl new file mode 100644 index 0000000000000000000000000000000000000000..847b27ffada8b788967233aa45f9d4fd5341c32d --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/quantize_q8.wgsl @@ -0,0 +1,178 @@ +#ifdef USE_SUBGROUP_REDUCTION +enable subgroups; +#endif +enable f16; + +requires packed_4x8_integer_dot_product; + +#include "common_decls.tmpl" + +struct Params { + offset_src1: u32, + stride_11: u32, + stride_12: u32, + stride_13: u32, + ne0: u32, + ne1: u32, + ne2: u32, + ne3: u32, +}; + +#define SRC1_TYPE vec4 + +@group(0) @binding(0) var src1: array; +@group(0) @binding(1) var src1q: array; + +@group(0) @binding(2) var params: Params; + +#ifdef USE_SUBGROUP_REDUCTION +fn cluster_max_8(v: f32) -> f32 { + var r = v; + r = max(r, subgroupShuffleXor(r, 1u)); + r = max(r, subgroupShuffleXor(r, 2u)); + r = max(r, subgroupShuffleXor(r, 4u)); + return r; +} + +#if defined(MUL_ACC_Q4_0) || defined(MUL_ACC_Q4_1) || defined(MUL_ACC_Q4_K) +fn cluster_add_i4x8(v: i32) -> i32 { + var r= v; + r += subgroupShuffleXor(r, 1u); + r += subgroupShuffleXor(r, 2u); + r += subgroupShuffleXor(r, 4u); + return r; +} +#endif +#endif + +#ifdef USE_WORKGROUP_REDUCTION +#define CLUSTER_SIZE 8 + +var partial_amaxs: array, WG_SIZE / CLUSTER_SIZE>; +var partial_sums: array, WG_SIZE / CLUSTER_SIZE>; +#endif + +@compute @workgroup_size(WG_SIZE) +fn main( + @builtin(local_invocation_id) local_id: vec3, + @builtin(workgroup_id) wg_id: vec3, + @builtin(num_workgroups) num_wg: vec3 +) { + let thread_id = local_id.x; + let ne0_vec4 = params.ne0 / 4u; + + let wg_per_vec = (ne0_vec4 + (WG_SIZE - 1u)) / WG_SIZE; + let total_batches = wg_per_vec * params.ne1 * params.ne2 * params.ne3; + + let wg_linear = wg_id.y * num_wg.x + wg_id.x; + if (wg_linear >= total_batches) { + return; + } + + let vec_idx = wg_linear / wg_per_vec; + let src13_idx = vec_idx / (params.ne2 * params.ne1); + let vec_ne12_num = vec_idx % (params.ne2 * params.ne1); + let src12_idx = vec_ne12_num / params.ne1; + let src11_idx = vec_ne12_num % params.ne1; + let src1_idx_base = params.offset_src1 + src13_idx * params.stride_13 + src12_idx * params.stride_12 + src11_idx * params.stride_11; + let src1_idx_vec4_base = src1_idx_base / 4u; + + let blocks_per_row = params.ne0 / 32u; + let blocks_per_wg = (WG_SIZE * 4u) / 32u; + let src1q_idx_base = ((src13_idx * params.ne2 + src12_idx) * params.ne1 + src11_idx) * blocks_per_row; + let src11_wg_idx = wg_linear % wg_per_vec; + let src1q_idx = src1q_idx_base + src11_wg_idx * blocks_per_wg + thread_id / 8u; + let qs_idx = thread_id % 8u; + + // reduction + var q4 = vec4(0.0); + var q4_quants = 0u; + var thread_amax = 0.0; + + let src11_vec4_idx = src11_wg_idx * WG_SIZE + thread_id; + let is_valid = src11_vec4_idx < ne0_vec4; + +#ifdef USE_SUBGROUP_REDUCTION + + var d = 0.0; + + if (is_valid) { + q4 = src1[src1_idx_vec4_base + src11_vec4_idx]; + let abs_q4 = abs(q4); + thread_amax = max(max(abs_q4[0u], abs_q4[1u]), max(abs_q4[2], abs_q4[3])); + } + + d = cluster_max_8(thread_amax) / 127.0; + + if (is_valid) { + let id = select(0.0, 1.0 / d, d > 0.0); + q4_quants = pack4xI8(vec4(round(q4 * id))); + if (qs_idx == 0u) { + src1q[src1q_idx].d = f16(d); + } + src1q[src1q_idx].qs[qs_idx] = q4_quants; + } + +#if defined(MUL_ACC_Q4_0) || defined(MUL_ACC_Q4_1) || defined(MUL_ACC_Q4_K) + let q4_quants_sum = dot4I8Packed(q4_quants, 0x01010101u); + let s = f16(d * f32(cluster_add_i4x8(q4_quants_sum))); + + if (is_valid) { + if (qs_idx == 0u) { + src1q[src1q_idx].s = s; + } + } +#endif +#endif + +#ifdef USE_WORKGROUP_REDUCTION + + var d = 0.0; + let cluster_id = thread_id / 8u; + + if (is_valid) { + q4 = src1[src1_idx_vec4_base + src11_vec4_idx]; + let abs_q4 = abs(q4); + thread_amax = max(max(abs_q4[0], abs_q4[1]), max(abs_q4[2], abs_q4[3])); + partial_amaxs[cluster_id][qs_idx] = thread_amax; + } + + workgroupBarrier(); + + if (is_valid) { + let amax = max( + max( + max(partial_amaxs[cluster_id][0], partial_amaxs[cluster_id][1]), max(partial_amaxs[cluster_id][2], partial_amaxs[cluster_id][3])), + max( + max(partial_amaxs[cluster_id][4], partial_amaxs[cluster_id][5]), max(partial_amaxs[cluster_id][6], partial_amaxs[cluster_id][7])) + ); + + d = amax / 127.0; + let id = select(0.0f, 1.0f / d, d > 0.0f); + + q4_quants = pack4xI8(vec4(round(q4 * id))); + src1q[src1q_idx].qs[qs_idx] = q4_quants; + + if (qs_idx == 0u) { + src1q[src1q_idx].d = f16(d); + } + } + +#if defined(MUL_ACC_Q4_0) || defined(MUL_ACC_Q4_1) || defined(MUL_ACC_Q4_K) + + partial_sums[cluster_id][qs_idx] = dot4I8Packed(q4_quants, 0x01010101u); + + workgroupBarrier(); + + if (is_valid) { + if (qs_idx == 0u) { + let s = d * f32(partial_sums[cluster_id][0] + partial_sums[cluster_id][1] + partial_sums[cluster_id][2] + partial_sums[cluster_id][3] + + partial_sums[cluster_id][4] + partial_sums[cluster_id][5] + partial_sums[cluster_id][6] + partial_sums[cluster_id][7]); + src1q[src1q_idx].s = f16(s); + } + } + +#endif +#endif + +} diff --git a/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/repeat.wgsl b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/repeat.wgsl new file mode 100644 index 0000000000000000000000000000000000000000..6e2a1a8b614d6dbab806ad79d2ffd7810a949652 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/repeat.wgsl @@ -0,0 +1,67 @@ +enable f16; + +struct Params { + ne: u32, + + offset_src0: u32, + offset_dst: u32, + + stride_src0_0: u32, + stride_src0_1: u32, + stride_src0_2: u32, + stride_src0_3: u32, + + a_ne0: u32, + a_ne1: u32, + a_ne2: u32, + a_ne3: u32, + + ne0: u32, + ne1: u32, + ne2: u32, +}; + +#ifdef TYPE_F32 +#define DataType f32 +#endif +#ifdef TYPE_I32 +#define DataType i32 +#endif +#ifdef TYPE_I16 +// same size (16-bit) is sufficient for repeat +#define DataType f16 +#endif + +@group(0) @binding(0) +var src0: array; + +@group(0) @binding(1) +var dst: array; + +@group(0) @binding(2) +var params: Params; + +@compute @workgroup_size(WG_SIZE) +fn main(@builtin(global_invocation_id) gid: vec3) { + if (gid.x < params.ne) { + var i = gid.x; + let i3 = i / (params.ne2 * params.ne1 * params.ne0); + i = i % (params.ne2 * params.ne1 * params.ne0); + let i2 = i / (params.ne1 * params.ne0); + i = i % (params.ne1 * params.ne0); + let i1 = i / params.ne0; + let i0 = i % params.ne0; + + let a_i0 = i0 % params.a_ne0; + let a_i1 = i1 % params.a_ne1; + let a_i2 = i2 % params.a_ne2; + let a_i3 = i3 % params.a_ne3; + + let a_index = a_i0 * params.stride_src0_0 + + a_i1 * params.stride_src0_1 + + a_i2 * params.stride_src0_2 + + a_i3 * params.stride_src0_3; + + dst[params.offset_dst + gid.x] = src0[params.offset_src0 + a_index]; + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/rms_norm_mul.wgsl b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/rms_norm_mul.wgsl new file mode 100644 index 0000000000000000000000000000000000000000..fd20a4e54c932a616f5e054acb2d1b354493c266 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/rms_norm_mul.wgsl @@ -0,0 +1,152 @@ +#ifdef OVERLAP + +@group(0) @binding(0) +var rn_src: array; + +@group(0) @binding(1) +var mul_src: array; + +@group(0) @binding(2) +var params: Params; + +fn update(rn_src_offset: u32, dst_offset: u32, scale: f32, mul_src_offset: u32) { + mul_src[dst_offset] = scale * rn_src[rn_src_offset] * mul_src[mul_src_offset]; +} + +#elif INPLACE + +@group(0) @binding(0) +var rn_src: array; + +@group(0) @binding(1) +var mul_src: array; + +@group(0) @binding(2) +var params: Params; + +fn update(rn_src_offset: u32, dst_offset: u32, scale: f32, mul_src_offset: u32) { + rn_src[dst_offset] = scale * rn_src[rn_src_offset] * mul_src[mul_src_offset]; +} + +#elif SRC_OVERLAP + +@group(0) @binding(0) +var merged_src: array; + +@group(0) @binding(1) +var dst: array; + +@group(0) @binding(2) +var params: Params; + +fn update(rn_src_offset: u32, dst_offset: u32, scale: f32, mul_src_offset: u32) { + dst[dst_offset] = scale * merged_src[rn_src_offset] * merged_src[mul_src_offset]; +} + +#else + +@group(0) @binding(0) +var rn_src: array; + +@group(0) @binding(1) +var mul_src: array; + +@group(0) @binding(2) +var dst: array; + +@group(0) @binding(3) +var params: Params; + +fn update(rn_src_offset: u32, dst_offset: u32, scale: f32, mul_src_offset: u32) { + dst[dst_offset] = scale * rn_src[rn_src_offset] * mul_src[mul_src_offset]; +} + +#endif + +struct Params { + offset_rn_src: u32, + offset_mul_src: u32, + offset_dst: u32, + + stride_rn_src1: u32, + stride_rn_src2: u32, + stride_rn_src3: u32, + + stride_mul_src1: u32, + stride_mul_src2: u32, + stride_mul_src3: u32, + + stride_dst1: u32, + stride_dst2: u32, + stride_dst3: u32, + + mul_src_ne0: u32, + mul_src_ne1: u32, + mul_src_ne2: u32, + mul_src_ne3: u32, + + ne0: u32, + ne1: u32, + ne2: u32, + ne3: u32, + + eps: f32 +}; + +var scratch: array; + +@compute @workgroup_size(WG_SIZE) +fn main(@builtin(workgroup_id) wid: vec3, + @builtin(local_invocation_id) lid: vec3) { + + // one thread per row + var i = wid.x; + let i3 = i / (params.ne2 * params.ne1); + i = i % (params.ne2 * params.ne1); + let i2 = i / params.ne1; + let i1 = i % params.ne1; + let i_rn_src_row = params.offset_rn_src + i3 * params.stride_rn_src3 + i2 * params.stride_rn_src2 + i1 * params.stride_rn_src1; + let i_mul_src_row = params.offset_mul_src + (i3 % params.mul_src_ne3) * params.stride_mul_src3 + (i2 % params.mul_src_ne2) * params.stride_mul_src2 + (i1 % params.mul_src_ne1) * params.stride_mul_src1; + let i_dst_row = params.offset_dst + i3 * params.stride_dst3 + i2 * params.stride_dst2 + i1 * params.stride_dst1; + + let elems = (params.ne0 + WG_SIZE - 1) / WG_SIZE; + + var sum = 0.0f; + var col = lid.x; + for (var j: u32 = 0; j < elems; j++) { + if (col >= params.ne0) { + break; + } +#ifdef SRC_OVERLAP + sum += pow(merged_src[i_rn_src_row + col], 2.0); +#else + sum += pow(rn_src[i_rn_src_row + col], 2.0); +#endif + col += WG_SIZE; + } + + scratch[lid.x] = sum; + + workgroupBarrier(); + + var offset: u32 = WG_SIZE / 2; + while (offset > 0) { + if (lid.x < offset) { + scratch[lid.x] += scratch[lid.x + offset]; + } + offset = offset / 2; + workgroupBarrier(); + } + sum = scratch[0]; + + let scale = 1.0/sqrt(sum/f32(params.ne0) + params.eps); + + col = lid.x; + for (var j: u32 = 0; j < elems; j++) { + if (col >= params.ne0) { + break; + } + update(i_rn_src_row + col, i_dst_row + col, scale, i_mul_src_row + col % params.mul_src_ne0); + col += WG_SIZE; + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/rope.wgsl b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/rope.wgsl new file mode 100644 index 0000000000000000000000000000000000000000..1c874e14240e501e9a02da943c366446de6346ed --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/rope.wgsl @@ -0,0 +1,224 @@ +enable f16; + +#ifdef TYPE_F32 +#define DataType f32 +#endif +#ifdef TYPE_F16 +#define DataType f16 +#endif + +struct Params { + offset_src0: u32, + offset_src1: u32, + offset_src2: u32, + offset_dst: u32, + + // Strides (in elements) + stride_src01: u32, + stride_src02: u32, + stride_src03: u32, + + stride_dst1: u32, + stride_dst2: u32, + stride_dst3: u32, + + n_threads: u32, + ne0: u32, + ne1: u32, + ne2: u32, + + n_dims: u32, + mode: u32, + theta_scale: f32, + attn_factor: f32, + freq_scale: f32, + ext_factor: f32, + corr_dim0: f32, + corr_dim1: f32, + sections0: u32, + sections1: u32, + sections2: u32, + sections3: u32 +}; + +@group(0) @binding(0) +var src0: array; +@group(0) @binding(1) +var src1: array; + +#ifdef INPLACE + +#ifdef FF_FUNC + +@group(0) @binding(2) +var src2: array; + +@group(0) @binding(3) +var params: Params; + +#else + +@group(0) @binding(2) +var params: Params; + +#endif + +#else + +#ifdef FF_FUNC +@group(0) @binding(2) +var src2: array; + +@group(0) @binding(3) +var dst: array; + +@group(0) @binding(4) +var params: Params; + +#else +@group(0) @binding(2) +var dst: array; + +@group(0) @binding(3) +var params: Params; +#endif +#endif + +#ifdef FF_FUNC +fn freq_factor(i: u32) -> f32 { + return src2[params.offset_src2 + i/2]; +} + +#else +fn freq_factor(i: u32) -> f32 { + return 1.0f; +} +#endif +#ifdef INPLACE +fn rotate(i_dst0: u32, i_dst1: u32, out0: f32, out1: f32) { + src0[i_dst0] = DataType(out0); + src0[i_dst1] = DataType(out1); +} +#else +fn rotate(i_dst0: u32, i_dst1: u32, out0: f32, out1: f32) { + dst[i_dst0] = DataType(out0); + dst[i_dst1] = DataType(out1); +} +#endif + +fn rope_yarn_ramp(low: f32, high: f32, i: u32) -> f32 { + let y = (f32(i / 2) - low) / max(0.001f, high - low); + return 1.0f - min(1.0f, max(0.0f, y)); +} + +// returns vector of (cos_theta, sin_theta) +// TODO: check performance of instantiating once on the CPU and passed as buffer, since it's repeated per-row +fn rope_yarn(theta_extrap: f32, i: u32) -> vec2 { + var mscale = params.attn_factor; + var theta = params.freq_scale * theta_extrap; + if (params.ext_factor != 0.0f) { + let ramp_mix = rope_yarn_ramp(params.corr_dim0, params.corr_dim1, i) * params.ext_factor; + theta = theta * (1 - ramp_mix) + theta_extrap * ramp_mix; + mscale *= 1.0f + 0.1f * log(1.0f / params.freq_scale); + } + return vec2(cos(theta) * mscale, sin(theta) * mscale); +} + +fn pair_base(i0: u32, div_2: bool) -> u32 { + if (div_2) { + return i0 / 2; + } else { + return i0; + } +} + +fn pair_offset(is_neox: bool, is_mrope: bool, is_vision: bool) -> u32 { + if (is_vision) { + return params.n_dims; + } else if (is_neox || is_mrope) { + return params.n_dims / 2; + } else { + return 1; + } +} + +@compute @workgroup_size(WG_SIZE) +fn main(@builtin(global_invocation_id) gid: vec3) { + // two elements per n_threads + if (gid.x >= params.n_threads) { + return; + } + + let is_neox = bool(params.mode & 2); + let is_mrope = bool(params.mode & 8); + let is_imrope = params.mode == 40; + let is_vision = params.mode == 24; + + var i = gid.x * 2; // start index for this thread + let i3 = i / (params.ne2 * params.ne1 * params.ne0); + i = i % (params.ne2 * params.ne1 * params.ne0); + let i2 = i / (params.ne1 * params.ne0); + i = i % (params.ne1 * params.ne0); + let i1 = i / params.ne0; + let i0 = i % params.ne0; + + let i_src_row = params.offset_src0 + i3 * params.stride_src03 + i2 * params.stride_src02 + i1 * params.stride_src01; + let i_dst_row = params.offset_dst + i3 * params.stride_dst3 + i2 * params.stride_dst2 + i1 * params.stride_dst1; + + if (i0 >= params.n_dims && !is_vision) { + let i_src = i_src_row + i0; + let i_dst = i_dst_row + i0; + rotate(i_dst, i_dst + 1, f32(src0[i_src]), f32(src0[i_src + 1])); + return; + } + + var theta_base_mult: u32 = 0; + var theta_scale_pwr: u32 = i0 / 2; + if (is_mrope) { + let sect_dims = params.sections0 + params.sections1 + params.sections2 + params.sections3; + let sec_w = params.sections1 + params.sections0; + let sec_e = params.sections2 + sec_w; + let sector = (i0 / 2) % sect_dims; + if (is_imrope) { + if (sector % 3 == 1 && sector < 3 * params.sections1) { + theta_base_mult = 1; + } else if (sector % 3 == 2 && sector < 3 * params.sections2) { + theta_base_mult = 2; + } else if (sector % 3 == 0 && sector < 3 * params.sections0) { + theta_base_mult = 0; + } else { + theta_base_mult = 3; + } + } else { + if (sector >= params.sections0 && sector < sec_w) { + theta_base_mult = 1; + if (is_vision) { + theta_scale_pwr = sector - params.sections0; + } + } else if (sector >= sec_w && sector < sec_e) { + theta_base_mult = 2; + if (is_vision) { + theta_scale_pwr = sector - sec_w; + } + } else if (sector >= sec_e) { + if (is_vision) { + theta_scale_pwr = sector - sec_e; + theta_scale_pwr = (i0 / 2) % sec_e; + } + theta_base_mult = 3; + } else if (is_vision) { + theta_scale_pwr = sector; + } + } + } + let theta_base = f32(src1[params.offset_src1 + i2 + params.ne2 * theta_base_mult]) * pow(params.theta_scale, f32(theta_scale_pwr)); + let thetas = rope_yarn(theta_base/freq_factor(i0), i0); + + let i_src = i_src_row + pair_base(i0, is_neox || is_mrope || is_vision); + let i_dst = i_dst_row + pair_base(i0, is_neox || is_mrope || is_vision); + + let x0 = f32(src0[i_src]); + let x1 = f32(src0[i_src + pair_offset(is_neox, is_mrope, is_vision)]); + rotate(i_dst, i_dst + pair_offset(is_neox, is_mrope, is_vision), x0 * thetas.x - x1 * thetas.y, x0 * thetas.y + x1 * thetas.x); + +} diff --git a/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/row_norm.wgsl b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/row_norm.wgsl new file mode 100644 index 0000000000000000000000000000000000000000..5eaf5e7bbe5de0af86103e67118926d7189cc810 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/row_norm.wgsl @@ -0,0 +1,153 @@ +#if defined(SRC_F16) || defined(DST_F16) +enable f16; +#endif + +#ifdef SRC_F16 +#define SRC_TYPE f16 +#else +#define SRC_TYPE f32 +#endif + +#ifdef DST_F16 +#define DST_TYPE f16 +#else +#define DST_TYPE f32 +#endif + +struct Params { + offset_src: u32, // in elements + offset_dst: u32, // in elements + + // Strides (in elements) + stride_src1: u32, + stride_src2: u32, + stride_src3: u32, + + stride_dst1: u32, + stride_dst2: u32, + stride_dst3: u32, + + // Shape of src/dst + ne0: u32, + ne1: u32, + ne2: u32, + ne3: u32, + + eps: f32 +}; + +@group(0) @binding(0) +var src: array; + +#ifdef INPLACE +@group(0) @binding(1) +var params: Params; +#else +@group(0) @binding(1) +var dst: array; + +@group(0) @binding(2) +var params: Params; +#endif + +var scratch: array; + +@compute @workgroup_size(WG_SIZE) +fn main(@builtin(workgroup_id) wid: vec3, + @builtin(local_invocation_id) lid: vec3) { + + // one thread per row + var i = wid.x; + let i3 = i / (params.ne2 * params.ne1); + i = i % (params.ne2 * params.ne1); + let i2 = i / params.ne1; + let i1 = i % params.ne1; + let i_src_row = params.offset_src + i3 * params.stride_src3 + i2 * params.stride_src2 + i1 * params.stride_src1; + let i_dst_row = params.offset_dst + i3 * params.stride_dst3 + i2 * params.stride_dst2 + i1 * params.stride_dst1; + + let elems = (params.ne0 + WG_SIZE - 1) / WG_SIZE; + + var sum = 0.0f; + var col = lid.x; + for (var j: u32 = 0; j < elems; j++) { + if (col >= params.ne0) { + break; + } + let v = f32(src[i_src_row + col]); +#ifdef NORM + sum += v; +#else + sum += v * v; +#endif + col += WG_SIZE; + } + + scratch[lid.x] = sum; + workgroupBarrier(); + + var offset: u32 = WG_SIZE / 2u; + while (offset > 0) { + if (lid.x < offset) { + scratch[lid.x] += scratch[lid.x + offset]; + } + offset /= 2u; + workgroupBarrier(); + } + sum = scratch[0]; + +#ifdef NORM + let mean = sum / f32(params.ne0); + var sq_sum = 0.0f; + col = lid.x; + for (var j: u32 = 0; j < elems; j++) { + if (col >= params.ne0) { + break; + } + let v = f32(src[i_src_row + col]); + let d = v - mean; + sq_sum += d * d; + col += WG_SIZE; + } + + workgroupBarrier(); + scratch[lid.x] = sq_sum; + workgroupBarrier(); + offset = WG_SIZE / 2u; + while (offset > 0) { + if (lid.x < offset) { + scratch[lid.x] += scratch[lid.x + offset]; + } + offset /= 2u; + workgroupBarrier(); + } + + let variance = scratch[0] / f32(params.ne0); + let scale = 1.0 / sqrt(variance + params.eps); +#elif defined(RMS_NORM) + let scale = 1.0/sqrt(sum/f32(params.ne0) + params.eps); +#elif defined(L2_NORM) + let scale = 1.0/max(sqrt(sum), params.eps); +#endif + +#ifdef NORM + let mean_val = mean; +#else + let mean_val = 0.0f; +#endif + + col = lid.x; + for (var j: u32 = 0; j < elems; j++) { + if (col >= params.ne0) { + break; + } + let i_src = i_src_row + col; + let i_dst = i_dst_row + col; + let v = src[i_src]; +#ifdef INPLACE + src[i_dst] = scale * (v - mean_val); +#else + dst[i_dst] = scale * (v - mean_val); +#endif + col += WG_SIZE; + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/scale.wgsl b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/scale.wgsl new file mode 100644 index 0000000000000000000000000000000000000000..6c76ed69e45f9e79c2edbd4232c4e669f23a9c54 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/scale.wgsl @@ -0,0 +1,65 @@ +#ifdef INPLACE +@group(0) @binding(1) +var params: Params; + +fn store_scale(val: f32, offset: u32) { + src[offset] = val; +} +#else +@group(0) @binding(1) +var dst: array; + +@group(0) @binding(2) +var params: Params; + +fn store_scale(val: f32, offset: u32) { + dst[offset] = val; +} +#endif + +struct Params { + offset_src: u32, + offset_dst: u32, + + // Strides (in elements) + stride_src1: u32, + stride_src2: u32, + stride_src3: u32, + + stride_dst1: u32, + stride_dst2: u32, + stride_dst3: u32, + + ne: u32, + ne0: u32, + ne1: u32, + ne2: u32, + + scale: f32, + bias: f32 +}; + +@group(0) @binding(0) +var src: array; + +@compute @workgroup_size(WG_SIZE) +fn main( + @builtin(global_invocation_id) gid: vec3, + @builtin(num_workgroups) num_wg: vec3) { + let threads_per_group = u32(WG_SIZE); + var i = gid.x + (num_wg.x * threads_per_group) * gid.y; + if (i >= params.ne) { + return; + } + let i3 = i / (params.ne2 * params.ne1 * params.ne0); + i = i % (params.ne2 * params.ne1 * params.ne0); + let i2 = i / (params.ne1 * params.ne0); + i = i % (params.ne1 * params.ne0); + let i1 = i / params.ne0; + let i0 = i % params.ne0; + + let i_src = params.offset_src + i3 * params.stride_src3 + i2 * params.stride_src2 + i1 * params.stride_src1 + i0; + let i_dst = params.offset_dst + i3 * params.stride_dst3 + i2 * params.stride_dst2 + i1 * params.stride_dst1 + i0; + + store_scale(src[i_src] * params.scale + params.bias, i_dst); +} diff --git a/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/set.wgsl b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/set.wgsl new file mode 100644 index 0000000000000000000000000000000000000000..0a7ae9bdb2cd5016f5d9d2ec6b30666ea5e43cf6 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/set.wgsl @@ -0,0 +1,109 @@ +#ifdef TYPE_I32 +#define TYPE i32 +#else +#define TYPE f32 +#endif + +#ifndef INPLACE +@group(0) @binding(0) +var src0: array; +#define SRC1_BINDING 1 +#else +#define SRC1_BINDING 0 +#endif + +#define DST_BINDING SRC1_BINDING + 1 +#define PARAMS_BINDING SRC1_BINDING + 2 + +@group(0) @binding(SRC1_BINDING) +var src1: array; + +@group(0) @binding(DST_BINDING) +var dst: array; + +struct Params { + ne: u32, + offset_src0: u32, + offset_src1: u32, + offset_view: u32, + + stride_src10: u32, + stride_src11: u32, + stride_src12: u32, + stride_src13: u32, + + stride_dst10: u32, + stride_dst11: u32, + stride_dst12: u32, + stride_dst13: u32, + + src1_ne0: u32, + src1_ne1: u32, + src1_ne2: u32, + src1_ne3: u32, +}; + +@group(0) @binding(PARAMS_BINDING) +var params: Params; + +fn decode_src1_coords(idx: u32) -> vec4 { + var i = idx; + let plane = params.src1_ne2 * params.src1_ne1 * params.src1_ne0; + let i3 = i / plane; + i = i % plane; + let row = params.src1_ne1 * params.src1_ne0; + let i2 = i / row; + i = i % row; + let i1 = i / params.src1_ne0; + let i0 = i % params.src1_ne0; + return vec4(i0, i1, i2, i3); +} + +fn decode_view_coords(rel: u32) -> vec4 { + let i3 = rel / params.stride_dst13; + let rem3 = rel % params.stride_dst13; + let i2 = rem3 / params.stride_dst12; + let rem2 = rem3 % params.stride_dst12; + let i1 = rem2 / params.stride_dst11; + let i0 = rem2 % params.stride_dst11; + return vec4(i0, i1, i2, i3); +} + +fn view_rel_from_coords(coords: vec4) -> u32 { + return coords.x * params.stride_dst10 + coords.y * params.stride_dst11 + + coords.z * params.stride_dst12 + coords.w * params.stride_dst13; +} + +fn src1_idx_from_coords(coords: vec4) -> u32 { + return coords.x * params.stride_src10 + coords.y * params.stride_src11 + + coords.z * params.stride_src12 + coords.w * params.stride_src13; +} + +fn in_set_view(rel: u32, coords: vec4) -> bool { + return view_rel_from_coords(coords) == rel; +} + +@compute @workgroup_size(WG_SIZE) +fn main(@builtin(global_invocation_id) gid: vec3) { + if (gid.x >= params.ne) { + return; + } + +#ifdef INPLACE + let coords = decode_src1_coords(gid.x); + + let src1_idx = params.offset_src1 + src1_idx_from_coords(coords); + let dst_idx = params.offset_view + view_rel_from_coords(coords); + + dst[dst_idx] = src1[src1_idx]; +#else + let rel = select(params.ne, gid.x - params.offset_view, gid.x >= params.offset_view); + let coords = decode_view_coords(rel); + + if (rel < params.stride_dst13 * params.src1_ne3 && in_set_view(rel, coords)) { + dst[gid.x] = src1[params.offset_src1 + src1_idx_from_coords(coords)]; + } else { + dst[gid.x] = src0[params.offset_src0 + gid.x]; + } +#endif +} diff --git a/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/set_rows.wgsl b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/set_rows.wgsl new file mode 100644 index 0000000000000000000000000000000000000000..09f2f0eddb3424b8e4b855209ae69bce4fc46144 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/set_rows.wgsl @@ -0,0 +1,108 @@ +enable f16; + +#ifdef DST_F32 +#define DST_INNER_TYPE f32 +#else +#define DST_INNER_TYPE f16 +#endif + +#ifdef VEC4 +#define SRC_TYPE vec4 +#define DST_TYPE vec4 +#define VEC_SIZE 4 +#else +#define SRC_TYPE f32 +#define DST_TYPE DST_INNER_TYPE +#define VEC_SIZE 1 +#endif + +@group(0) @binding(0) +var src: array; + +@group(0) @binding(1) +var idx: array; + +@group(0) @binding(2) +var dst: array; + +#ifdef I64_IDX +@group(0) @binding(3) +var error: atomic; +#define PARAMS_BINDING 4 +#else +#define PARAMS_BINDING 3 +#endif + +struct Params { + offset_src: u32, // in elements + offset_idx: u32, // in elements + offset_dst: u32, // in elements + + // Strides (in elements) + stride_src1: u32, + stride_src2: u32, + stride_src3: u32, + + stride_idx0: u32, + stride_idx1: u32, + stride_idx2: u32, + + stride_dst1: u32, + stride_dst2: u32, + stride_dst3: u32, + + // Shape of src + ne0: u32, + n_rows: u32, + ne2: u32, + ne3: u32, + + // Shape of idx + idx1: u32, + idx2: u32, +}; + +@group(0) @binding(PARAMS_BINDING) +var params: Params; + +@compute @workgroup_size(WG_SIZE) +fn main(@builtin(global_invocation_id) gid: vec3) { + if (gid.x >= (params.ne3 * params.ne2 * params.n_rows * params.ne0) / VEC_SIZE) { + return; + } + + let elems_per_row = params.ne0 / VEC_SIZE; + var i = gid.x / elems_per_row; + + let i_src3 = i / (params.ne2 * params.n_rows); + + i = i % (params.ne2 * params.n_rows); + let i_src2 = i / params.n_rows; + let i_src1 = i % params.n_rows; + + let i_idx2 = i_src3 % params.idx2; + let i_idx1 = i_src2 % params.idx1; + let i_idx0 = i_src1; + +#ifdef I64_IDX + let idx_high = (params.offset_idx + i_idx0 * params.stride_idx0 + i_idx1 * params.stride_idx1 + i_idx2 * params.stride_idx2) * 2; + + let idx_val = idx[idx_high]; + let idx_low_val = idx[idx_high + 1]; + + if (idx_low_val != 0) { + // Upper bits of index are not zero, output will be incorrect + atomicStore(&error, 1); + return; + } +#else + let idx_i = params.offset_idx + i_idx0 * params.stride_idx0 + i_idx1 * params.stride_idx1 + i_idx2 * params.stride_idx2; + let idx_val = idx[idx_i]; +#endif + + let i_dst_row = params.offset_dst + idx_val * params.stride_dst1 + i_src2 * params.stride_dst2 + i_src3 * params.stride_dst3; + let i_src_row = params.offset_src + i_src1 * params.stride_src1 + i_src2 * params.stride_src2 + i_src3 * params.stride_src3; + + let col_idx = gid.x % elems_per_row; + dst[i_dst_row / VEC_SIZE + col_idx] = DST_TYPE(src[i_src_row / VEC_SIZE + col_idx]); +} diff --git a/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/set_rows_quant.wgsl b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/set_rows_quant.wgsl new file mode 100644 index 0000000000000000000000000000000000000000..876e65b6ae13b589e329d13b4d90924bfe87aca6 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/set_rows_quant.wgsl @@ -0,0 +1,224 @@ +#ifdef DST_Q8_0 +#define BLOCK_SIZE 32u +#define BLOCK_BYTES 34u +#define QS_WORDS 8u +#elif defined(DST_Q4_0) +#define BLOCK_SIZE 32u +#define BLOCK_BYTES 18u +#define QS_WORDS 4u +#endif + +@group(0) @binding(0) +var src: array; + +@group(0) @binding(1) +var idx: array; + +@group(0) @binding(2) +#ifdef PAIR_BLOCKS +var dst: array; +#else +var dst: array>; +#endif + +#ifdef I64_IDX +@group(0) @binding(3) +var error: atomic; +#define PARAMS_BINDING 4 +#else +#define PARAMS_BINDING 3 +#endif + +struct Params { + offset_src: u32, // in elements + offset_idx: u32, // in elements + offset_dst: u32, // in blocks + + // Strides (in elements / blocks) + stride_src1: u32, + stride_src2: u32, + stride_src3: u32, + + stride_idx0: u32, + stride_idx1: u32, + stride_idx2: u32, + + stride_dst1: u32, + stride_dst2: u32, + stride_dst3: u32, + + // Shape of src + ne0: u32, + n_rows: u32, + ne2: u32, + ne3: u32, + + // Shape of idx + idx1: u32, + idx2: u32, +}; + +@group(0) @binding(PARAMS_BINDING) +var params: Params; + +// if the quantization type is unaligned and there are an odd number of blocks per row, we need to store atomically +#ifndef PAIR_BLOCKS +fn merge_store_dst_word(word_idx: u32, mask: u32, bits: u32) { + loop { + let old = atomicLoad(&dst[word_idx]); + let merged = (old & ~mask) | (bits & mask); + let result = atomicCompareExchangeWeak(&dst[word_idx], old, merged); + if (result.exchanged) { + return; + } + } +} +#else +fn merge_store_dst_word(word_idx: u32, mask: u32, bits: u32) { + let old = dst[word_idx]; + dst[word_idx] = (old & ~mask) | (bits & mask); +} +#endif + +fn store_u16(dst_word_idx: u32, block_byte_offset: u32, byte_offset: u32, value: u32) { + let total_byte_offset = block_byte_offset + byte_offset; + let word_idx = dst_word_idx + total_byte_offset / 4u; + let shift = (total_byte_offset & 2u) * 8u; + let mask = 0xFFFFu << shift; + merge_store_dst_word(word_idx, mask, (value & 0xFFFFu) << shift); +} + +fn store_u32(dst_word_idx: u32, block_byte_offset: u32, byte_offset: u32, value: u32) { + let total_byte_offset = block_byte_offset + byte_offset; + let word_idx = dst_word_idx + total_byte_offset / 4u; + let shift = (total_byte_offset & 3u) * 8u; + + if (shift == 0u) { +#ifdef PAIR_BLOCKS + dst[word_idx] = value; +#else + atomicStore(&dst[word_idx], value); +#endif + return; + } + + let lo_mask = 0xFFFFFFFFu << shift; + let hi_mask = (1u << shift) - 1u; + merge_store_dst_word(word_idx, lo_mask, value << shift); + merge_store_dst_word(word_idx + 1u, hi_mask, value >> (32u - shift)); +} + +fn quantize_block_params(src_block: u32) -> vec2 { +#ifdef DST_Q8_0 + var amax = 0.0; + for (var j: u32 = 0u; j < BLOCK_SIZE; j++) { + amax = max(amax, abs(src[src_block + j])); + } + + let d = amax / 127.0; + let id = select(0.0, 1.0 / d, d > 0.0); + return vec2(d, id); +#elif defined(DST_Q4_0) + var amax = 0.0; + var max_val = 0.0; + for (var j: u32 = 0u; j < BLOCK_SIZE; j++) { + let v = src[src_block + j]; + let av = abs(v); + if (amax < av) { + amax = av; + max_val = v; + } + } + + let d = max_val / -8.0; + let id = select(0.0, 1.0 / d, d != 0.0); + return vec2(d, id); +#endif +} + +fn quantize_block_word(src_block: u32, j: u32, id: f32) -> u32 { +#ifdef DST_Q8_0 + let base = src_block + j * 4u; + return (u32(i32(round(src[base + 0u] * id)) & 0xFF) << 0u) | + (u32(i32(round(src[base + 1u] * id)) & 0xFF) << 8u) | + (u32(i32(round(src[base + 2u] * id)) & 0xFF) << 16u) | + (u32(i32(round(src[base + 3u] * id)) & 0xFF) << 24u); +#elif defined(DST_Q4_0) + var packed_q = 0u; + for (var k: u32 = 0u; k < 4u; k++) { + let x0 = src[src_block + j * 4u + k] * id; + let x1 = src[src_block + 16u + j * 4u + k] * id; + let q0 = u32(clamp(i32(x0 + 8.5), 0, 15)); + let q1 = u32(clamp(i32(x1 + 8.5), 0, 15)); + packed_q |= (q0 & 0xFu) << (8u * k); + packed_q |= (q1 & 0xFu) << (8u * k + 4u); + } + return packed_q; +#endif +} + +fn quantize_block(src_block: u32, dst_word_idx: u32, block_byte_offset: u32) { + let params = quantize_block_params(src_block); + let d = params.x; + let id = params.y; + let packed_d = pack2x16float(vec2(d, 0.0)) & 0xFFFFu; + store_u16(dst_word_idx, block_byte_offset, 0u, packed_d); + + for (var j: u32 = 0u; j < QS_WORDS; j++) { + store_u32(dst_word_idx, block_byte_offset, 2u + j * 4u, quantize_block_word(src_block, j, id)); + } +} + +@compute @workgroup_size(WG_SIZE) +fn main(@builtin(global_invocation_id) gid: vec3) { + let blocks_per_row = params.ne0 / BLOCK_SIZE; +#ifdef PAIR_BLOCKS + let blocks_per_invocation = 2u; +#else + let blocks_per_invocation = 1u; +#endif + let invocations_per_row = blocks_per_row / blocks_per_invocation; + let total_invocations = params.ne3 * params.ne2 * params.n_rows * invocations_per_row; + if (gid.x >= total_invocations) { + return; + } + + var i = gid.x / invocations_per_row; + let block_in_row = (gid.x % invocations_per_row) * blocks_per_invocation; + + let i_src3 = i / (params.ne2 * params.n_rows); + i = i % (params.ne2 * params.n_rows); + let i_src2 = i / params.n_rows; + let i_src1 = i % params.n_rows; + + let i_idx2 = i_src3 % params.idx2; + let i_idx1 = i_src2 % params.idx1; + let i_idx0 = i_src1; + +#ifdef I64_IDX + let idx_high = (params.offset_idx + i_idx0 * params.stride_idx0 + i_idx1 * params.stride_idx1 + i_idx2 * params.stride_idx2) * 2u; + let idx_val = idx[idx_high]; + let idx_low_val = idx[idx_high + 1u]; + + if (idx_low_val != 0u) { + atomicStore(&error, 1u); + return; + } +#else + let idx_i = params.offset_idx + i_idx0 * params.stride_idx0 + i_idx1 * params.stride_idx1 + i_idx2 * params.stride_idx2; + let idx_val = idx[idx_i]; +#endif + + let dst_row_blocks = params.offset_dst + idx_val * params.stride_dst1 + i_src2 * params.stride_dst2 + i_src3 * params.stride_dst3; + let src_row = params.offset_src + i_src1 * params.stride_src1 + i_src2 * params.stride_src2 + i_src3 * params.stride_src3; + let src_block = src_row + block_in_row * BLOCK_SIZE; + let dst_block_byte = (dst_row_blocks + block_in_row) * BLOCK_BYTES; + + let dst_word_idx = dst_block_byte / 4u; +#ifdef PAIR_BLOCKS + quantize_block(src_block, dst_word_idx, 0u); + quantize_block(src_block + BLOCK_SIZE, dst_word_idx, BLOCK_BYTES); +#else + quantize_block(src_block, dst_word_idx, dst_block_byte & 3u); +#endif +} diff --git a/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/soft_max.wgsl b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/soft_max.wgsl new file mode 100644 index 0000000000000000000000000000000000000000..10edf1360489f4a50516be2a52507bf89012b8ea --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/soft_max.wgsl @@ -0,0 +1,245 @@ +enable f16; + +#ifdef MASK_F32 +#define MaskType f32 +#endif +#ifdef MASK_F16 +#define MaskType f16 +#endif + +struct Params { + offset_src0: u32, + offset_src1: u32, + offset_sinks: u32, + offset_dst: u32, + + // Strides (in elements) + stride_src01: u32, + stride_src02: u32, + stride_src03: u32, + + stride_src11: u32, + stride_src12: u32, + stride_src13: u32, + + stride_dst1: u32, + stride_dst2: u32, + stride_dst3: u32, + + // shape of src0/dst + ne: u32, + ne0: u32, + ne1: u32, + ne2: u32, + + // shape of src1 + ne12: u32, + ne13: u32, + + scale: f32, + max_bias: f32, + n_head_log2: f32, + m0: f32, + m1: f32, +}; + +@group(0) @binding(0) +var src: array; + +#ifdef HAS_MASK +#ifdef HAS_SINK +@group(0) @binding(1) +var mask: array; +@group(0) @binding(2) +var sinks: array; + +#ifdef INPLACE +@group(0) @binding(3) +var params: Params; + +#else +@group(0) @binding(3) +var dst: array; +@group(0) @binding(4) +var params: Params; +#endif + +#else +@group(0) @binding(1) +var mask: array; + +#ifdef INPLACE +@group(0) @binding(2) +var params: Params; + +#else +@group(0) @binding(2) +var dst: array; +@group(0) @binding(3) +var params: Params; +#endif +#endif + +#else +#ifdef HAS_SINK +@group(0) @binding(1) +var sinks: array; + +#ifdef INPLACE +@group(0) @binding(2) +var params: Params; + +#else +@group(0) @binding(2) +var dst: array; +@group(0) @binding(3) +var params: Params; +#endif + +#else +#ifdef INPLACE +@group(0) @binding(1) +var params: Params; +#else +@group(0) @binding(1) +var dst: array; +@group(0) @binding(2) +var params: Params; +#endif +#endif +#endif + +#ifdef INPLACE +fn inter_value(i: u32) -> f32 { + return src[i]; +} +fn update(i: u32, val: f32) { + src[i] = val; +} + +#else +fn inter_value(i: u32) -> f32 { + return dst[i]; +} +fn update(i: u32, val: f32) { + dst[i] = val; +} +#endif + +#ifdef HAS_MASK +fn mask_val(i: u32) -> f32 { + return f32(mask[i]); +} + +#else +fn mask_val(i: u32) -> f32 { + return 0.0; +} +#endif + +#ifdef HAS_SINK +fn lower_max_bound(i2: u32) -> f32 { + return sinks[params.offset_sinks + i2]; +} +fn add_sinks(val: f32, i2: u32, max_val: f32) -> f32 { + return val + exp(sinks[params.offset_sinks + i2] - max_val); +} +#else +fn lower_max_bound(i2: u32) -> f32 { + return -1e30; +} +fn add_sinks(val: f32, i2: u32, max_val: f32) -> f32 { + return val; +} +#endif + +const CACHE_SIZE: u32 = 16; +var scratch: array; + +@compute @workgroup_size(WG_SIZE) +fn main(@builtin(workgroup_id) wid: vec3, + @builtin(local_invocation_id) lid: vec3) { + + var i = wid.x; + let i3 = i / (params.ne2 * params.ne1); + i = i % (params.ne2 * params.ne1); + let i2 = i / params.ne1; + let i1 = i % params.ne1; + let i_src0_row = params.offset_src0 + i3 * params.stride_src03 + i2 * params.stride_src02 + i1 * params.stride_src01; + let i_src1_row = params.offset_src1 + (i3 % params.ne13) * params.stride_src13 + (i2 % params.ne12) * params.stride_src12 + i1 * params.stride_src11; + let i_dst_row = params.offset_dst + i3 * params.stride_dst3 + i2 * params.stride_dst2 + i1 * params.stride_dst1; + let elems = (params.ne0 + WG_SIZE - 1) / WG_SIZE; + + let head = f32(i2); + let slope = select(1, select(pow(params.m1, 2 * (head - params.n_head_log2) + 1), pow(params.m0, head + 1), head < params.n_head_log2), params.max_bias > 0); + + var cache: array; + + var max_val = lower_max_bound(i2); + var col = lid.x; + for (var j: u32 = 0; j < elems; j++) { + if (col >= params.ne0) { + break; + } + let val = src[i_src0_row + col] * params.scale + slope * mask_val(i_src1_row + col); + max_val = max(max_val, val); + if (col < CACHE_SIZE) { + cache[col] = val; + } + col += WG_SIZE; + } + + scratch[lid.x] = max_val; + workgroupBarrier(); + var offset: u32 = WG_SIZE / 2; + while (offset > 0) { + if (lid.x < offset) { + scratch[lid.x] = max(scratch[lid.x], scratch[lid.x + offset]); + } + offset = offset / 2; + workgroupBarrier(); + } + let row_max = scratch[0]; + workgroupBarrier(); + + var sum = 0.0f; + col = lid.x; + for (var j: u32 = 0; j < elems; j++) { + if (col >= params.ne0) { + break; + } + let val = select(src[i_src0_row + col] * params.scale + slope * mask_val(i_src1_row + col), + cache[col], col < CACHE_SIZE); + let ex = exp(val - row_max); + sum += ex; + if (col < CACHE_SIZE) { + cache[col] = ex; + } else { + update(i_dst_row + col, ex); + } + col += WG_SIZE; + } + + scratch[lid.x] = sum; + workgroupBarrier(); + offset = WG_SIZE / 2; + while (offset > 0) { + if (lid.x < offset) { + scratch[lid.x] += scratch[lid.x + offset]; + } + offset = offset / 2; + workgroupBarrier(); + } + let row_sum = add_sinks(scratch[0], i2, row_max); + + let sum_recip = 1.0 / row_sum; + col = lid.x; + for (var j: u32 = 0; j < elems; j++) { + if (col >= params.ne0) { + break; + } + update(i_dst_row + col, select(inter_value(i_dst_row + col), cache[col], col < CACHE_SIZE) * sum_recip); + col += WG_SIZE; + } +} + diff --git a/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/solve_tri.wgsl b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/solve_tri.wgsl new file mode 100644 index 0000000000000000000000000000000000000000..9d5d902cb1e240ed077ebfc9282478b722890f5f --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/solve_tri.wgsl @@ -0,0 +1,121 @@ +@group(0) @binding(0) +var src0: array; + +@group(0) @binding(1) +var src1: array; + +@group(0) @binding(2) +var dst: array; + +struct Params { + offset_src0: u32, + offset_src1: u32, + offset_dst: u32, + + stride_src00: u32, + stride_src01: u32, + stride_src02: u32, + stride_src03: u32, + + stride_src10: u32, + stride_src11: u32, + stride_src12: u32, + stride_src13: u32, + + stride_dst0: u32, + stride_dst1: u32, + stride_dst2: u32, + stride_dst3: u32, + + k: u32, + ne2: u32, + ne3: u32, +}; + +@group(0) @binding(3) +var params: Params; + +var shA: array; +var shB: array; + +fn src0_idx(row: u32, col: u32, i2: u32, i3: u32) -> u32 { + return params.offset_src0 + + col * params.stride_src00 + + row * params.stride_src01 + + i2 * params.stride_src02 + + i3 * params.stride_src03; +} + +fn src1_idx(row: u32, col: u32, i2: u32, i3: u32) -> u32 { + return params.offset_src1 + + col * params.stride_src10 + + row * params.stride_src11 + + i2 * params.stride_src12 + + i3 * params.stride_src13; +} + +fn dst_idx(row: u32, col: u32, i2: u32, i3: u32) -> u32 { + return params.offset_dst + + col * params.stride_dst0 + + row * params.stride_dst1 + + i2 * params.stride_dst2 + + i3 * params.stride_dst3; +} + +@compute @workgroup_size(WG_SIZE) +fn main( + @builtin(workgroup_id) workgroup_id: vec3, + @builtin(local_invocation_id) local_id: vec3 +) { + let batch = workgroup_id.y; + let col = workgroup_id.x * WG_SIZE + local_id.x; + let i3 = batch / params.ne2; + let i2 = batch % params.ne2; + let active_lane = local_id.x < K_TILE; + let active_col = active_lane && col < params.k; + + var X: array; + + for (var row_base = 0u; row_base < N; row_base += BATCH_N) { + let cur_n = min(BATCH_N, N - row_base); + + for (var i = local_id.x; i < cur_n * N; i += WG_SIZE) { + let tile_row = i / N; + let tile_col = i % N; + shA[i] = src0[src0_idx(row_base + tile_row, tile_col, i2, i3)]; + } + + for (var i = local_id.x; i < cur_n * K_TILE; i += WG_SIZE) { + let tile_row = i / K_TILE; + let tile_col = i % K_TILE; + let global_col = workgroup_id.x * WG_SIZE + tile_col; + let sh_idx = tile_row * K_TILE + tile_col; + + if (global_col < params.k) { + shB[sh_idx] = src1[src1_idx(row_base + tile_row, global_col, i2, i3)]; + } else { + shB[sh_idx] = 0.0; + } + } + + workgroupBarrier(); + + if (active_col) { + for (var row_offset = 0u; row_offset < cur_n; row_offset++) { + let r = row_base + row_offset; + var b = shB[row_offset * K_TILE + local_id.x]; + let a_row = row_offset * N; + + for (var t = 0u; t < r; t++) { + b -= shA[a_row + t] * X[t]; + } + + let x = b / shA[a_row + r]; + X[r] = x; + dst[dst_idx(r, col, i2, i3)] = x; + } + } + + workgroupBarrier(); + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/ssm_conv.wgsl b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/ssm_conv.wgsl new file mode 100644 index 0000000000000000000000000000000000000000..11511305ed83cabdf51073500cb08b85a088f759 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/ssm_conv.wgsl @@ -0,0 +1,65 @@ +@group(0) @binding(0) +var src0: array; + +@group(0) @binding(1) +var src1: array; + +@group(0) @binding(2) +var dst: array; + +struct Params { + offset_src0: u32, + offset_src1: u32, + offset_dst: u32, + + stride_src01: u32, + stride_src02: u32, + stride_src11: u32, + + stride_dst0: u32, + stride_dst1: u32, + stride_dst2: u32, + + nc: u32, + nr: u32, + n_t: u32, + n_s: u32, + token_tiles: u32, +}; + +@group(0) @binding(3) +var params: Params; + +@compute @workgroup_size(BLOCK_SIZE, TOKENS_PER_WG) +fn main(@builtin(global_invocation_id) gid: vec3) { + let i1 = gid.x; + let tile_y = gid.y / TOKENS_PER_WG; + let local_token = gid.y % TOKENS_PER_WG; + let i3 = tile_y / params.token_tiles; + let token_tile = tile_y % params.token_tiles; + let i2 = token_tile * TOKENS_PER_WG + local_token; + + if (i1 >= params.nr || i2 >= params.n_t || i3 >= params.n_s) { + return; + } + + let src0_base = params.offset_src0 + i3 * params.stride_src02 + i2 + i1 * params.stride_src01; + let src1_base = params.offset_src1 + i1 * params.stride_src11; + + var sum = 0.0; + +#ifdef VECTORIZED + sum = + src0[src0_base + 0u] * src1[src1_base + 0u] + + src0[src0_base + 1u] * src1[src1_base + 1u] + + src0[src0_base + 2u] * src1[src1_base + 2u] + + src0[src0_base + 3u] * src1[src1_base + 3u]; +#else + for (var i0 = 0u; i0 < params.nc; i0++) { + sum += src0[src0_base + i0] * src1[src1_base + i0]; + } +#endif + + let dst_idx = params.offset_dst + i3 * params.stride_dst2 + i2 * params.stride_dst1 + i1 * params.stride_dst0; + dst[dst_idx] = sum; +} diff --git a/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/ssm_scan.wgsl b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/ssm_scan.wgsl new file mode 100644 index 0000000000000000000000000000000000000000..05761dec353ad232cc3506216416999c681dfb15 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/ssm_scan.wgsl @@ -0,0 +1,193 @@ +#ifdef USE_SUBGROUP_REDUCTION +enable subgroups; +#endif + +struct Params { + offset_s: u32, + offset_x: u32, + offset_dt: u32, + offset_A: u32, + offset_B: u32, + offset_C: u32, + offset_ids: u32, + offset_dst: u32, + + stride_s1: u32, + stride_s2: u32, + stride_s3: u32, + + stride_x1: u32, + stride_x2: u32, + stride_x3: u32, + + stride_dt1: u32, + stride_dt2: u32, + + a_ne0: u32, + stride_A1: u32, + + stride_B1: u32, + stride_B2: u32, + stride_B3: u32, + + stride_C1: u32, + stride_C2: u32, + stride_C3: u32, + + d_state: u32, + d_inner: u32, + n_head: u32, + n_group: u32, + n_seq_tokens: u32, + n_seqs: u32, + + y_elems: u32, +}; + +@group(0) @binding(0) var s_in: array; +#ifdef XBC_OVERLAP +@group(0) @binding(1) var x_B_C_merged: array; +@group(0) @binding(2) var dt: array; +@group(0) @binding(3) var A: array; +@group(0) @binding(4) var ids: array; +@group(0) @binding(5) var dst: array; +@group(0) @binding(6) var params: Params; +#else +@group(0) @binding(1) var x: array; +@group(0) @binding(2) var dt: array; +@group(0) @binding(3) var A: array; +@group(0) @binding(4) var B: array; +@group(0) @binding(5) var C: array; +@group(0) @binding(6) var ids: array; +@group(0) @binding(7) var dst: array; +@group(0) @binding(8) var params: Params; +#endif + +var shared_x_dt: array; +var shared_dtsp: array; +var shared_reduce: array; + +fn reduce_base(token_in_tile: u32) -> u32 { + return token_in_tile * WG_SIZE; +} + +@compute @workgroup_size(WG_SIZE) +fn main( + @builtin(local_invocation_id) local_id: vec3, + @builtin(workgroup_id) wg_id: vec3, + @builtin(num_workgroups) num_wg: vec3 +#ifdef USE_SUBGROUP_REDUCTION + , @builtin(subgroup_id) subgroup_id: u32, + @builtin(subgroup_invocation_id) subgroup_invocation_id: u32, + @builtin(num_subgroups) num_subgroups: u32 +#endif +) { + let tid = local_id.x; + let wg_linear = wg_id.y * num_wg.x + wg_id.x; + + let i1 = wg_linear % params.d_inner; + let head_seq = wg_linear / params.d_inner; + let ir = head_seq % params.n_head; + let i3 = head_seq / params.n_head; + + let state_slot = u32(ids[params.offset_ids + i3]); + let g = ir / (params.n_head / params.n_group); + + let s_idx = params.offset_s + tid + i1 * params.stride_s1 + ir * params.stride_s2 + state_slot * params.stride_s3; + var s_prev = s_in[s_idx]; + + let A0 = A[params.offset_A + (tid % params.a_ne0) + ir * params.stride_A1]; + + for (var token_base = 0u; token_base < params.n_seq_tokens; token_base += TOKENS_PER_TILE) { + if (tid < TOKENS_PER_TILE) { + let token = token_base + tid; + if (token < params.n_seq_tokens) { + let x_idx = params.offset_x + i1 + ir * params.stride_x1 + token * params.stride_x2 + i3 * params.stride_x3; + let dt_idx = params.offset_dt + ir + token * params.stride_dt1 + i3 * params.stride_dt2; + let dt0 = dt[dt_idx]; + let dtsp = select(log(1.0 + exp(dt0)), dt0, dt0 > 20.0); + shared_dtsp[tid] = dtsp; +#ifdef XBC_OVERLAP + shared_x_dt[tid] = x_B_C_merged[x_idx] * dtsp; +#else + shared_x_dt[tid] = x[x_idx] * dtsp; +#endif + } + } + + workgroupBarrier(); + + for (var token_in_tile = 0u; token_in_tile < TOKENS_PER_TILE; token_in_tile++) { + let token = token_base + token_in_tile; + if (token >= params.n_seq_tokens) { + break; + } + + let x_dt = shared_x_dt[token_in_tile]; + let dA = exp(shared_dtsp[token_in_tile] * A0); + let reduce_idx = reduce_base(token_in_tile) + tid; + + let b_idx = params.offset_B + tid + g * params.stride_B1 + token * params.stride_B2 + i3 * params.stride_B3; + let c_idx = params.offset_C + tid + g * params.stride_C1 + token * params.stride_C2 + i3 * params.stride_C3; +#ifdef XBC_OVERLAP + let s = s_prev * dA + x_B_C_merged[b_idx] * x_dt; +#else + let s = s_prev * dA + B[b_idx] * x_dt; +#endif + s_prev = s; + +#ifdef USE_SUBGROUP_REDUCTION +#ifdef XBC_OVERLAP + let subgroup_partial = subgroupAdd(s * x_B_C_merged[c_idx]); +#else + let subgroup_partial = subgroupAdd(s * C[c_idx]); +#endif + if (subgroup_invocation_id == 0u) { + shared_reduce[reduce_idx - tid + subgroup_id] = subgroup_partial; + } +#else +#ifdef XBC_OVERLAP + shared_reduce[reduce_idx] = s * x_B_C_merged[c_idx]; +#else + shared_reduce[reduce_idx] = s * C[c_idx]; +#endif +#endif + + workgroupBarrier(); + +#ifdef USE_SUBGROUP_REDUCTION + if (tid == 0u) { + var sum = 0.0; + for (var sg = 0u; sg < num_subgroups; sg++) { + sum += shared_reduce[reduce_base(token_in_tile) + sg]; + } + let y_idx = + params.offset_dst + i1 + ir * params.d_inner + token * (params.n_head * params.d_inner) + + i3 * (params.n_seq_tokens * params.n_head * params.d_inner); + dst[y_idx] = sum; + } +#else + for (var stride = WG_SIZE / 2u; stride > 0u; stride >>= 1u) { + if (tid < stride) { + shared_reduce[reduce_idx] += shared_reduce[reduce_idx + stride]; + } + workgroupBarrier(); + } + + if (tid == 0u) { + let y_idx = + params.offset_dst + i1 + ir * params.d_inner + token * (params.n_head * params.d_inner) + + i3 * (params.n_seq_tokens * params.n_head * params.d_inner); + dst[y_idx] = shared_reduce[reduce_base(token_in_tile)]; + } +#endif + + workgroupBarrier(); + } + } + + let state_idx = + params.offset_dst + params.y_elems + tid + i1 * params.d_state + ir * (params.d_state * params.d_inner) + + i3 * (params.d_state * params.d_inner * params.n_head); + dst[state_idx] = s_prev; +} diff --git a/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/sum_rows.wgsl b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/sum_rows.wgsl new file mode 100644 index 0000000000000000000000000000000000000000..6ea2de9b7c6103ddfbf763380c8d6dc0f97fd218 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/sum_rows.wgsl @@ -0,0 +1,55 @@ +@group(0) @binding(0) +var src: array; + +@group(0) @binding(1) +var dst: array; + +struct Params { + offset_src: u32, // in elements + offset_dst: u32, // in elements + + // Strides (in elements) + stride_src1: u32, + stride_src2: u32, + stride_src3: u32, + + ne0: u32, + ne1: u32, + ne2: u32 +}; + +@group(0) @binding(2) +var params: Params; + +var shared_sum: array; + +@compute @workgroup_size(WG_SIZE) +fn main(@builtin(workgroup_id) wid: vec3, + @builtin(local_invocation_id) lid: vec3) { + + var i = wid.x; + let i3 = i / (params.ne2 * params.ne1); + i = i % (params.ne2 * params.ne1); + let i2 = i / params.ne1; + let i1 = i % params.ne1; + let i_src_row = params.offset_src + i3 * params.stride_src3 + i2 * params.stride_src2 + i1 * params.stride_src1; + var local_sum: f32 = 0.0; + for (var col = lid.x; col < params.ne0; col += WG_SIZE) { + local_sum += src[i_src_row + col]; + } + shared_sum[lid.x] = local_sum; + workgroupBarrier(); + // reduce within workgroup + var offset: u32 = WG_SIZE >> 1; + while (offset > 0) { + if (lid.x < offset) { + shared_sum[lid.x] = shared_sum[lid.x] + shared_sum[lid.x + offset]; + } + workgroupBarrier(); + offset >>= 1; + } + + if (lid.x == 0) { + dst[params.offset_dst + wid.x] = shared_sum[0]; + } +} diff --git a/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/unary.wgsl b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/unary.wgsl new file mode 100644 index 0000000000000000000000000000000000000000..cb342c4726361ec43e2fec53782f3d30f4ac7294 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/unary.wgsl @@ -0,0 +1,213 @@ +#ifdef TYPE_F16 +enable f16; +#define TYPE f16 +#else +#define TYPE f32 +#endif + +@group(0) @binding(0) +var src: array; + +#ifndef INPLACE +@group(0) @binding(1) +var dst: array; +#define PARAMS_BINDING 2 +#else +#define PARAMS_BINDING 1 +#endif + +struct Params { + ne: u32, // total number of elements + offset_src: u32, // in elements + offset_dst: u32, // in elements + + // Strides (in elements) + stride_src0: u32, + stride_src1: u32, + stride_src2: u32, + stride_src3: u32, + + // Logical shapes + ne0: u32, + ne1: u32, + ne2: u32, +#ifdef CLAMP + clamp_min: f32, + clamp_max: f32, +#endif +#ifdef FILL + fill_val: f32, +#endif +#ifdef XIELU + alpha_n: f32, + alpha_p: f32, + beta: f32, + eps: f32, +#endif + +}; + +@group(0) @binding(PARAMS_BINDING) +var params: Params; + +fn erf_approx(x: TYPE) -> TYPE { + let x_f32 = f32(x); + let s = select(-1.0, 1.0, x_f32 >= 0.0); + let ax = abs(x_f32); + + let t = 1.0 / (1.0 + 0.3275911 * ax); + + let y = 1.0 - + (((((1.061405429 * t - 1.453152027) * t + 1.421413741) * t + - 0.284496736) * t + 0.254829592) * t) * + exp(-ax * ax); + + return TYPE(s * y); +} + +@compute @workgroup_size(WG_SIZE) +fn main(@builtin(global_invocation_id) gid: vec3, + @builtin(num_workgroups) num_wg: vec3) { + let threads_per_group = u32(WG_SIZE); + let flat_i = gid.x + (num_wg.x * threads_per_group) * gid.y; + if (flat_i >= params.ne) { + return; + } + var i = flat_i; + let ne2 = params.ne2; +#ifdef DIAG + let ne1 = params.ne0; +#else + let ne1 = params.ne1; +#endif + let ne0 = params.ne0; + + let i3 = i / (ne2 * ne1 * ne0); + i = i % (ne2 * ne1 * ne0); + let i2 = i / (ne1 * ne0); + i = i % (ne1 * ne0); + let i1 = i / ne0; + let i0 = i % ne0; + + let src_idx = i0 * params.stride_src0 + i1 * params.stride_src1 + i2 * params.stride_src2 + i3 * params.stride_src3; + +#ifdef ABS + let res = abs(src[params.offset_src + src_idx]); +#endif +#ifdef SGN + let res = select(TYPE(select(0.0, -1.0, src[params.offset_src + src_idx] < 0.0)), TYPE(1.0), src[params.offset_src + src_idx] > 0.0); +#endif +#ifdef NEG + let res = -src[params.offset_src + src_idx]; +#endif +#ifdef STEP + let res = TYPE(select(0.0, 1.0, src[params.offset_src + src_idx] > 0.0)); +#endif +#ifdef TANH + let res = tanh(clamp(src[params.offset_src + src_idx], -9.010913, 9.010913)); +#endif +#ifdef RELU + let res = select(0.0, src[params.offset_src + src_idx], src[params.offset_src + src_idx] > 0.0); +#endif +#ifdef ELU + let res = select(exp(src[params.offset_src + src_idx]) - 1.0, src[params.offset_src + src_idx], src[params.offset_src + src_idx] > 0.0); +#endif +#ifdef HARDSIGMOID + let res = min(1.0, max(0.0, (src[params.offset_src + src_idx] + 3.0) / 6.0)); +#endif +#ifdef SIGMOID + let res = 1.0 / (1.0 + exp(-src[params.offset_src + src_idx])); +#endif +#ifdef SILU + let res = src[params.offset_src + src_idx] / (1.0 + exp(-src[params.offset_src + src_idx])); +#endif +#ifdef EXP + let src_f32 = f32(src[params.offset_src + src_idx]); + let res = TYPE(exp(src_f32)); +#endif +#ifdef LOG + let res = TYPE(log(f32(src[params.offset_src + src_idx]))); +#endif +#ifdef CLAMP + let res = clamp(src[params.offset_src + src_idx], TYPE(params.clamp_min), TYPE(params.clamp_max)); +#endif +#ifdef FILL + let res = TYPE(params.fill_val); +#endif +#ifdef HARDSWISH + let res = src[params.offset_src + src_idx] * min(1.0, max(0.0, (src[params.offset_src + src_idx] + 3.0) / 6.0)); +#endif +#ifdef GELU + let res = 0.5 * src[params.offset_src + src_idx] * (1.0 + tanh(clamp(0.7978845608028654 * (src[params.offset_src + src_idx] + 0.044715 * src[params.offset_src + src_idx] * src[params.offset_src + src_idx] * src[params.offset_src + src_idx]), -9.010913, 9.010913))); +#endif +#ifdef GELU_QUICK + let res = src[params.offset_src + src_idx] * (1.0 / (1.0 + exp(clamp(-1.702 * src[params.offset_src + src_idx], -80.0, 80.0)))); +#endif +#ifdef GELU_ERF + let res = 0.5 * src[params.offset_src + src_idx] * (1.0 + erf_approx(src[params.offset_src + src_idx] * 0.7071067811865476)); +#endif +#ifdef XIELU + let val = f32(src[params.offset_src + src_idx]); + let res = + TYPE(select( + ((exp(min(val, params.eps)) - 1.0) - val) * params.alpha_n + params.beta * val, + params.alpha_p * val * val + params.beta * val, + val > 0.0)); +#endif +#ifdef SOFTPLUS + let src_f32 = f32(src[params.offset_src + src_idx]); + let res = TYPE(select(log(1.0 + exp(src_f32)), src_f32, src_f32 > 20.0)); +#endif +#ifdef EXPM1 + let src_f32 = f32(src[params.offset_src + src_idx]); + let res = TYPE(exp(src_f32) - 1.0); +#endif +#ifdef FLOOR + let res = floor(src[params.offset_src + src_idx]); +#endif +#ifdef CEIL + let res = ceil(src[params.offset_src + src_idx]); +#endif +#ifdef ROUND + let src_f32 = f32(src[params.offset_src + src_idx]); + let result = select(ceil(src_f32 - 0.5), floor(src_f32 + 0.5), src_f32 >= 0.0); + let res = TYPE(result); +#endif +#ifdef TRUNC + let res = trunc(src[params.offset_src + src_idx]); +#endif +#ifdef SQR + let res = src[params.offset_src + src_idx] * src[params.offset_src + src_idx]; +#endif +#ifdef SQRT + let res = TYPE(sqrt(f32(src[params.offset_src + src_idx]))); +#endif +#ifdef SIN + let res_f32 = sin(f32(src[params.offset_src + src_idx])); + let res = TYPE(res_f32); +#endif +#ifdef COS + let res_f32 = cos(f32(src[params.offset_src + src_idx])); + let res = TYPE(res_f32); +#endif +#ifdef DIAG + let res = select(0.0, src[params.offset_src + i0 + i2 * params.stride_src2 + i3 * params.stride_src3], i0 == i1); +#endif +#ifdef TRI +#ifdef TRI_TYPE_LOWER + let res = select(0.0, src[params.offset_src + src_idx], i0 < i1); +#elif TRI_TYPE_LOWER_DIAG + let res = select(0.0, src[params.offset_src + src_idx], i0 <= i1); +#elif TRI_TYPE_UPPER + let res = select(0.0, src[params.offset_src + src_idx], i0 > i1); +#elif TRI_TYPE_UPPER_DIAG + let res = select(0.0, src[params.offset_src + src_idx], i0 >= i1); +#endif +#endif + +#ifdef INPLACE + src[params.offset_src + src_idx] = res; +#else + dst[params.offset_dst + flat_i] = res; +#endif +} diff --git a/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/upscale.wgsl b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/upscale.wgsl new file mode 100644 index 0000000000000000000000000000000000000000..e9ef8822644cf7a9a50eb03c5a2b63c3d51265ff --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-webgpu/wgsl-shaders/upscale.wgsl @@ -0,0 +1,240 @@ +#if defined(SRC_F16) || defined(DST_F16) +enable f16; +#endif + +#ifdef SRC_F16 +#define SRC_TYPE f16 +#else +#define SRC_TYPE f32 +#endif + +#ifdef DST_F16 +#define DST_TYPE f16 +#else +#define DST_TYPE f32 +#endif + +@group(0) @binding(0) +var input: array; + +@group(0) @binding(1) +var output: array; + +struct Params { + offset_i: u32, + offset_o: u32, + + // element strides + si0: u32, si1: u32, si2: u32, si3: u32, + so0: u32, so1: u32, so2: u32, so3: u32, + + src_w: u32, + src_h: u32, + src_z: u32, + src_n: u32, + + dst_w: u32, + dst_h: u32, + dst_z: u32, + dst_n: u32, + + mode_flags: u32, +}; + +@group(0) @binding(2) +var params: Params; + +const GGML_SCALE_FLAG_ALIGN_CORNERS: u32 = 1u << 8u; + +fn get_clamped_input(x: i32, y: i32, z: u32, n: u32) -> f32 { + let cx = u32(clamp(x, 0, i32(params.src_w) - 1)); + let cy = u32(clamp(y, 0, i32(params.src_h) - 1)); + let i = params.offset_i + cx * params.si0 + cy * params.si1 + z * params.si2 + n * params.si3; + return f32(input[i]); +} + +fn cubic_weight(t: f32, a: f32) -> f32 { + let at = abs(t); + if (at <= 1.0) { + return (a + 2.0) * at * at * at - (a + 3.0) * at * at + 1.0; + } else if (at <= 2.0) { + return a * at * at * at - 5.0 * a * at * at + 8.0 * a * at - 4.0 * a; + } else { + return 0.0; + } +} + +@compute @workgroup_size(WG_SIZE) +fn main( + @builtin(global_invocation_id) gid: vec3, + @builtin(num_workgroups) num_wg: vec3 +) { + + let i_out = gid.x + (num_wg.x * u32(WG_SIZE)) * gid.y; + let total = params.dst_w * params.dst_h * params.dst_z * params.dst_n; + + if (i_out >= total) { + return; + } + + // decode (x, y, z, n) + var i = i_out; + let x_dst = i % params.dst_w; + i = i / params.dst_w; + let y_dst = i % params.dst_h; + i = i / params.dst_h; + let z_dst = i % params.dst_z; + let n_dst = i / params.dst_z; + + // scale factors + var sf0 = f32(params.dst_w) / f32(params.src_w); + var sf1 = f32(params.dst_h) / f32(params.src_h); + var sf2 = f32(params.dst_z) / f32(params.src_z); + var sf3 = f32(params.dst_n) / f32(params.src_n); + + let align_corners = (params.mode_flags & GGML_SCALE_FLAG_ALIGN_CORNERS) != 0; + + // pixel_offset: 0.5 for half-pixel-center (default), 0.0 for align_corners + var pixel_offset = 0.5; + if (align_corners) { + pixel_offset = 0.0; + if (params.dst_w > 1 && params.src_w > 1) { + sf0 = f32(params.dst_w - 1) / f32(params.src_w - 1); + } + if (params.dst_h > 1 && params.src_h > 1) { + sf1 = f32(params.dst_h - 1) / f32(params.src_h - 1); + } + } + + let z_src = min(params.src_z - 1, u32(floor(f32(z_dst) / sf2))); + let n_src = min(params.src_n - 1, u32(floor(f32(n_dst) / sf3))); + + var result = 0.0; + +#if defined(NEAREST) + + let x_src = min(params.src_w - 1, u32(floor(f32(x_dst) / sf0))); + let y_src = min(params.src_h - 1, u32(floor(f32(y_dst) / sf1))); + + result = get_clamped_input(i32(x_src), i32(y_src), z_src, n_src); + +#elif defined(BILINEAR) + +#if defined(ANTIALIAS) + + // Antialiased bilinear: triangle filter over a variable support region. + let support0 = max(1.0f / sf0, 1.0f); + let support1 = max(1.0f / sf1, 1.0f); + let invscale0 = 1.0 / support0; + let invscale1 = 1.0 / support1; + + let fx = (f32(x_dst) + pixel_offset) / sf0; + let fy = (f32(y_dst) + pixel_offset) / sf1; + + let x_min = max(i32(fx - support0 + pixel_offset), 0); + let y_min = max(i32(fy - support1 + pixel_offset), 0); + let x_max = min(i32(fx + support0 + pixel_offset), i32(params.src_w)); + let y_max = min(i32(fy + support1 + pixel_offset), i32(params.src_h)); + + var weighted_sum = 0.0; + var total_weight = 0.0; + + for (var x = x_min; x < x_max; x += 1) { + let wx = max(1.0 - abs(f32(x) - fx + pixel_offset) * invscale0, 0.0); + for (var y = y_min; y < y_max; y += 1) { + let wy = max(1.0 - abs(f32(y) - fy + pixel_offset) * invscale1, 0.0); + let w = wx * wy; + if (w > 0.0) { + weighted_sum += get_clamped_input(x, y, z_src, n_src) * w; + total_weight += w; + } + } + } + + if (total_weight > 0.0) { + result = weighted_sum / total_weight; + } + +#else + + let fx = (f32(x_dst) + pixel_offset) / sf0 - pixel_offset; + let fy = (f32(y_dst) + pixel_offset) / sf1 - pixel_offset; + let x0 = i32(floor(fx)); + let y0 = i32(floor(fy)); + let dx = clamp(fx - f32(x0), 0.0, 1.0); + let dy = clamp(fy - f32(y0), 0.0, 1.0); + let a = get_clamped_input(x0, y0, z_src, n_src); + let b = get_clamped_input(x0 + 1, y0, z_src, n_src); + let c = get_clamped_input(x0, y0 + 1, z_src, n_src); + let d = get_clamped_input(x0 + 1, y0 + 1, z_src, n_src); + + let wa = (1.0 - dx) * (1.0 - dy); + let wb = dx * (1.0 - dy); + let wc = (1.0 - dx) * dy; + let wd = dx * dy; + + result = a * wa + b * wb + c * wc + d * wd; + +#endif + +#elif defined(BICUBIC) + + // bicubic convolution with alpha = -0.75 (PyTorch default) + let alpha = -0.75; + let fx = (f32(x_dst) + pixel_offset) / sf0 - pixel_offset; + let fy = (f32(y_dst) + pixel_offset) / sf1 - pixel_offset; + + let x0 = i32(floor(fx)); + let y0 = i32(floor(fy)); + let dx = fx - f32(x0); + let dy = fy - f32(y0); + + // horizontal weights for offsets -1, 0, 1, 2 + let wx0 = cubic_weight(dx + 1.0, alpha); + let wx1 = cubic_weight(dx, alpha); + let wx2 = cubic_weight(1.0 - dx, alpha); + let wx3 = cubic_weight(2.0 - dx, alpha); + + // vertical weights for offsets -1, 0, 1, 2 + let wy0 = cubic_weight(dy + 1.0, alpha); + let wy1 = cubic_weight(dy, alpha); + let wy2 = cubic_weight(1.0 - dy, alpha); + let wy3 = cubic_weight(2.0 - dy, alpha); + + // intermediate horizontal interpolation for 4x4 grid of pixels + // x0-1, x0, x0+1, x0+2, y0-1 + let p0 = get_clamped_input(x0 - 1, y0 - 1, z_src, n_src); + let p1 = get_clamped_input(x0, y0 - 1, z_src, n_src); + let p2 = get_clamped_input(x0 + 1, y0 - 1, z_src, n_src); + let p3 = get_clamped_input(x0 + 2, y0 - 1, z_src, n_src); + let row0 = p0 * wx0 + p1 * wx1 + p2 * wx2 + p3 * wx3; + + // x0-1, x0, x0+1, x0+2, y0 + let q0 = get_clamped_input(x0 - 1, y0, z_src, n_src); + let q1 = get_clamped_input(x0, y0, z_src, n_src); + let q2 = get_clamped_input(x0 + 1, y0, z_src, n_src); + let q3 = get_clamped_input(x0 + 2, y0, z_src, n_src); + let row1 = q0 * wx0 + q1 * wx1 + q2 * wx2 + q3 * wx3; + + // x0-1, x0, x0+1, x0+2, y0+1 + let r0 = get_clamped_input(x0 - 1, y0 + 1, z_src, n_src); + let r1 = get_clamped_input(x0, y0 + 1, z_src, n_src); + let r2 = get_clamped_input(x0 + 1, y0 + 1, z_src, n_src); + let r3 = get_clamped_input(x0 + 2, y0 + 1, z_src, n_src); + let row2 = r0 * wx0 + r1 * wx1 + r2 * wx2 + r3 * wx3; + + // x0-1, x0, x0+1, x0+2, y0+2 + let s0 = get_clamped_input(x0 - 1, y0 + 2, z_src, n_src); + let s1 = get_clamped_input(x0, y0 + 2, z_src, n_src); + let s2 = get_clamped_input(x0 + 1, y0 + 2, z_src, n_src); + let s3 = get_clamped_input(x0 + 2, y0 + 2, z_src, n_src); + let row3 = s0 * wx0 + s1 * wx1 + s2 * wx2 + s3 * wx3; + + // final vertical interpolation + result = row0 * wy0 + row1 * wy1 + row2 * wy2 + row3 * wy3; + +#endif + + let dst_idx = params.offset_o + x_dst * params.so0 + y_dst * params.so1 + z_dst * params.so2 + n_dst * params.so3; + output[dst_idx] = DST_TYPE(result); +} diff --git a/backend/llama.cpp/ggml/src/ggml-zdnn/.gitignore b/backend/llama.cpp/ggml/src/ggml-zdnn/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..8322c0f8e6409be8cb72af02a702e0ce5211968e --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-zdnn/.gitignore @@ -0,0 +1 @@ +zdnn.h diff --git a/backend/llama.cpp/ggml/src/ggml-zdnn/CMakeLists.txt b/backend/llama.cpp/ggml/src/ggml-zdnn/CMakeLists.txt new file mode 100644 index 0000000000000000000000000000000000000000..0a723ce4de28646f62c0cc06e66273d0092f381f --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-zdnn/CMakeLists.txt @@ -0,0 +1,36 @@ +if (DEFINED ZDNN_ROOT) + message(STATUS "zdnn: using ZDNN_ROOT override: ${ZDNN_ROOT}") + set(ZDNN_HINT "${ZDNN_ROOT}") +else() + set(ZDNN_HINT "") +endif() + +find_path(ZDNN_INCLUDE + NAMES zdnn.h + HINTS ${ZDNN_HINT} /usr /usr/local + PATH_SUFFIXES include) +if (ZDNN_INCLUDE) + message(STATUS "zdnn: found include: ${ZDNN_INCLUDE}") +else() + message(FATAL_ERROR "zdnn: include directory not found, please set ZDNN_ROOT to the proper path if necessary") +endif() + +find_library(ZDNN_LIB + NAMES zdnn + HINTS ${ZDNN_HINT} /usr /usr/local + PATH_SUFFIXES lib lib64) +if (ZDNN_LIB) + message(STATUS "zdnn: found library: ${ZDNN_LIB}") +else() + message(FATAL_ERROR "zdnn: library not found, please set ZDNN_ROOT to the proper path if necessary") +endif() + +file(GLOB GGML_SOURCES_ZDNN "*.c" "*.cpp") +file(GLOB GGML_HEADERS_ZDNN "*.h" "*.hpp") + +ggml_add_backend_library(ggml-zdnn ${GGML_HEADERS_ZDNN} ${GGML_SOURCES_ZDNN}) +target_link_libraries(ggml-zdnn PRIVATE ${ZDNN_LIB}) +target_include_directories(ggml-zdnn PRIVATE ${ZDNN_INCLUDE}) +target_link_directories(ggml-zdnn PRIVATE ${ZDNN_LIB}) + +target_compile_definitions(ggml-zdnn PRIVATE GGML_USE_ZDNN) diff --git a/backend/llama.cpp/ggml/src/ggml-zdnn/common.hpp b/backend/llama.cpp/ggml/src/ggml-zdnn/common.hpp new file mode 100644 index 0000000000000000000000000000000000000000..2462ded55b7fc0d924ffc69b77fcf07bd7e52c17 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-zdnn/common.hpp @@ -0,0 +1,59 @@ +#ifndef GGML_ZDNN_COMMON_HPP +#define GGML_ZDNN_COMMON_HPP + +#include "ggml.h" +#include "ggml-impl.h" + +#include "zdnn.h" + +#include +#include + +#define GGML_ZDNN_NAME "zDNN" +#define GGML_ZDNN_VERSION ZDNN_VERNUM + +#define ZDNN_CHECK(stmt) \ + do { \ + zdnn_status status = (stmt); \ + GGML_ASSERT(status == ZDNN_OK); \ + } while (0); + +struct ggml_backend_zdnn_device_context { + int zdnn_device; + int zdnn_device_ref_count; + + bool has_parmblkformat_0; + bool has_parmblkformat_1; // checks for z17 + + size_t max_size; + + char name[128]; +}; + +struct ggml_backend_zdnn_context { + int device; + ggml_cgraph * gf; +}; + +struct ggml_backend_zdnn_buffer { + void * data; + ggml_backend_zdnn_buffer * extra; // for bias, etc. + size_t size; + + zdnn_tensor_desc pre_tfm_desc; + zdnn_tensor_desc tfm_desc; + zdnn_ztensor ztensor; + + char name[GGML_MAX_NAME]; +}; + +struct ggml_backend_zdnn_buffer_context { + void * all_data; + size_t all_size; + bool owned; + + int n_buffers; + std::vector> buffers; +}; + +#endif // GGML_ZDNN_COMMON_HPP diff --git a/backend/llama.cpp/ggml/src/ggml-zdnn/ggml-zdnn.cpp b/backend/llama.cpp/ggml/src/ggml-zdnn/ggml-zdnn.cpp new file mode 100644 index 0000000000000000000000000000000000000000..639b818d128e6e5b7c72b3751687f4314ffe9790 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-zdnn/ggml-zdnn.cpp @@ -0,0 +1,637 @@ +#include "ggml-zdnn.h" +#include "ggml-impl.h" +#include "ggml-backend-impl.h" + +#include "ggml-zdnn/common.hpp" +#include "ggml-zdnn/mmf.hpp" +#include "ggml-zdnn/utils.hpp" +#include "ggml.h" + +#include +#include +#include // raise(SIGTRAP) +#include + +static void ggml_zdnn_compute_forward_mul_mat( + const ggml_backend_zdnn_context * ctx, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; // weights + const ggml_tensor * src1 = dst->src[1]; // inputs + + // TODO: implement support for quantized types + // we currently only support f32, f16, and bf16 + ggml_zdnn_mul_mat_f(ctx, src0, src1, dst); +} + +static bool ggml_zdnn_compute_forward( + ggml_backend_zdnn_context * ctx, + ggml_tensor * dst) { + + switch (dst->op) { + case GGML_OP_MUL_MAT: + { + ggml_zdnn_compute_forward_mul_mat(ctx, dst); + } break; + + default: + return false; + } + + return true; +} + +static enum ggml_status ggml_zdnn_graph_compute(ggml_backend_t backend, ggml_cgraph * gf) { + ggml_backend_zdnn_context * ctx = ( ggml_backend_zdnn_context *)backend->context; + ggml_backend_zdnn_device_context * ctx_dev = (ggml_backend_zdnn_device_context *)backend->device->context; + + ctx->gf = gf; + for (int i = 0; i < gf->n_nodes; i++) { + ggml_tensor * node = gf->nodes[i]; + + if (ggml_is_empty(node) + || node->op == GGML_OP_NONE + || node->op == GGML_OP_RESHAPE + || node->op == GGML_OP_VIEW + || node->op == GGML_OP_PERMUTE + || node->op == GGML_OP_TRANSPOSE) { + continue; + } + + if ((node->flags & GGML_TENSOR_FLAG_COMPUTE) == 0) { + continue; + } + + bool ok = ggml_zdnn_compute_forward(ctx, node); + if (!ok) { + GGML_LOG_ERROR("%s: unsupported op %s (%s)\n", + __func__, node->name, ggml_op_name(node->op)); + } + + GGML_ASSERT(ok); + } + + return GGML_STATUS_SUCCESS; + + GGML_UNUSED(ctx_dev); +} + +static bool ggml_zdnn_supports_op(const ggml_backend_zdnn_device_context * ctx_dev, const ggml_tensor * op) { + switch (op->op) { + case GGML_OP_NONE: + case GGML_OP_RESHAPE: + case GGML_OP_VIEW: + case GGML_OP_TRANSPOSE: + case GGML_OP_PERMUTE: + return true; + + case GGML_OP_MUL_MAT: + { + const ggml_tensor * weights = op->src[0]; + const ggml_tensor * inputs = op->src[1]; + + const int64_t ne10 = inputs->ne[0]; + const int64_t ne0 = op->ne[0]; + const int64_t ne1 = op->ne[1]; + + const int64_t max_batch = ctx_dev->max_size; + + if (!ggml_is_matrix(weights) || !ggml_is_matrix(inputs) || + !ggml_is_contiguous(weights) || !ggml_is_contiguous(inputs) || + weights->view_src != nullptr || inputs->view_src != nullptr || + ne0 > max_batch || ne1 > max_batch || ne10 > max_batch) { + return false; + } + + switch (weights->type) { + case GGML_TYPE_F32: + case GGML_TYPE_F16: + case GGML_TYPE_BF16: + return true; + default: + return false; + } + } break; + + default: + return false; + } +} + +//////////////////////////////////////////////////////////////////////////////// + +// +// globals +// + +// initialised in ggml_backend_zdnn_reg +static ggml_backend_reg g_ggml_backend_zdnn_reg; +static ggml_backend_device g_ggml_backend_zdnn_device; + +static ggml_backend_zdnn_device_context g_ggml_ctx_dev_main = { + /* .zdnn_device = */ 0, + /* .zdnn_device_ref_count = */ 0, + /* .has_parmblkformat_0 = */ false, + /* .has_parmblkformat_1 = */ false, + /* .max_size = */ 0, + /* .name = */ "", +}; + +static int ggml_backend_zdnn_device_acq(ggml_backend_zdnn_device_context * ctx) { + assert(ctx != NULL); + + if (ctx->zdnn_device == 0) { + ctx->zdnn_device = 1; + } + + if (ctx->zdnn_device >= 1) { + ctx->has_parmblkformat_0 = zdnn_is_nnpa_parmblk_fmt_installed(1, NNPA_PARMBLKFORMAT_0); + ctx->has_parmblkformat_1 = zdnn_is_nnpa_parmblk_fmt_installed(1, NNPA_PARMBLKFORMAT_1); + ctx->max_size = zdnn_get_nnpa_max_dim_idx_size(); + strncpy(ctx->name, GGML_ZDNN_NAME, sizeof(ctx->name) - 1); + } + + ctx->zdnn_device_ref_count++; + return ctx->zdnn_device; +} + +static void ggml_backend_zdnn_device_rel(ggml_backend_zdnn_device_context * ctx) { + assert(ctx != NULL); + assert(ctx->zdnn_device_ref_count > 0); + + ctx->zdnn_device_ref_count--; + if (ctx->zdnn_device_ref_count == 0) { + if (ctx->zdnn_device >= 0) { + ctx->zdnn_device = 0; + } + } +} + +static ggml_backend_zdnn_context * ggml_zdnn_init(ggml_backend_dev_t dev) { + GGML_LOG_INFO("%s: allocating\n", __func__); + GGML_LOG_INFO("%s: found 1 device\n", __func__); + + #ifdef STATIC_LIB + zdnn_init(); + #endif + + ggml_backend_zdnn_context * ctx = new ggml_backend_zdnn_context(); + ggml_backend_zdnn_device_context * ctx_dev = (ggml_backend_zdnn_device_context *)dev->context; + + int device = 1; + GGML_LOG_INFO("%s: picking default device: %s\n", __func__, ctx_dev->name); + + ctx->device = device; + GGML_LOG_INFO("%s: NNPA name: %s\n", __func__, ctx_dev->name); + GGML_LOG_INFO("%s: NNPA_PARMBLKFORMAT_0 = %s\n", __func__, ctx_dev->has_parmblkformat_0 ? "true" : "false"); + GGML_LOG_INFO("%s: NNPA_PARMBLKFORMAT_1 = %s\n", __func__, ctx_dev->has_parmblkformat_1 ? "true" : "false"); + + ctx->gf = nullptr; + + return ctx; +} + +static void ggml_zdnn_free(ggml_backend_zdnn_context * ctx) { + GGML_LOG_INFO("%s: deallocating\n", __func__); + delete ctx; +} + +// +// backend interface +// + +static void ggml_backend_zdnn_buffer_free_buffer(ggml_backend_buffer_t buffer) { + ggml_backend_zdnn_buffer_context * ctx = (ggml_backend_zdnn_buffer_context *)buffer->context; + + for (const auto & buf_ptr : ctx->buffers) { + ggml_backend_zdnn_buffer * buf = buf_ptr.get(); + + // Free any extra buffer allocated for the tensor. E.g., bias for GGML_OP_MUL_MAT + if (buf->extra != nullptr) free(buf->extra->data); + if (buf->ztensor.buffer_size > 0) ZDNN_CHECK(zdnn_free_ztensor_buffer(&buf->ztensor)); + } + + delete ctx; +} + +static void * ggml_backend_zdnn_buffer_get_base(ggml_backend_buffer_t buffer) { + ggml_backend_zdnn_buffer_context * ctx = (ggml_backend_zdnn_buffer_context *)buffer->context; + return ctx->all_data; +} + +static enum ggml_status ggml_backend_zdnn_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) { + if (tensor->view_src != NULL) { + assert(tensor->view_src->buffer->buft == buffer->buft); + return GGML_STATUS_SUCCESS; + } + + ggml_backend_zdnn_buffer_context * ctx = (ggml_backend_zdnn_buffer_context *)buffer->context; + + const int64_t tsize = ggml_nbytes(tensor); + int buffer_idx = ctx->n_buffers; + + std::unique_ptr zdnn_buffer = std::make_unique(); + zdnn_buffer->data = tensor->data; + zdnn_buffer->size = tsize; + zdnn_buffer->extra = nullptr; + snprintf(zdnn_buffer->name, GGML_MAX_NAME, "%s", tensor->name); + + ggml_zdnn_init_tensor(zdnn_buffer.get(), tensor); + tensor->extra = zdnn_buffer.get(); + + switch (tensor->op) { + case GGML_OP_MUL_MAT: + { + std::unique_ptr zdnn_bias_buffer = std::make_unique(); + zdnn_bias_buffer->data = (void *)calloc(tensor->ne[0], ggml_element_size(tensor)); + zdnn_bias_buffer->size = ggml_element_size(tensor) * tensor->ne[0]; + snprintf(zdnn_bias_buffer->name, GGML_MAX_NAME, "%.*s (bias)", + GGML_MAX_NAME - (int)sizeof(" (bias)"), tensor->name); + + const int64_t bias_dim[GGML_MAX_DIMS] = { 1, 1, 1, tensor->ne[0] }; + ggml_zdnn_create_tensor(zdnn_bias_buffer->pre_tfm_desc, + zdnn_bias_buffer->tfm_desc, + zdnn_bias_buffer->ztensor, + tensor, bias_dim, ZDNN_1D); + + ggml_zdnn_load_tensor(zdnn_bias_buffer->ztensor, zdnn_bias_buffer->data); + zdnn_buffer->extra = zdnn_bias_buffer.get(); + + ctx->buffers.push_back(std::move(zdnn_bias_buffer)); + ctx->n_buffers++; + } break; + default: + break; + } + + ctx->buffers.push_back(std::move(zdnn_buffer)); + ctx->n_buffers++; + + // GGML_LOG_INFO("%s: initialised tensor '%s' in buffer %d, size = %8.2f MiB\n", + // __func__, tensor->name, buffer_idx, tsize); + + return GGML_STATUS_SUCCESS; + + GGML_UNUSED(buffer_idx); +} + +static void ggml_backend_zdnn_buffer_memset_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) { + memset((char *)tensor->data + offset, value, size); + + GGML_UNUSED(buffer); +} + +static void ggml_backend_zdnn_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { + memcpy((char *)tensor->data + offset, data, size); + + ggml_backend_zdnn_buffer * extra = (ggml_backend_zdnn_buffer *)tensor->extra; + + // Fixes the LLAMA_SET_ROWS bug + // see: https://github.com/ggml-org/llama.cpp/issues/15414 + if (tensor->buffer->usage == GGML_BACKEND_BUFFER_USAGE_COMPUTE && extra->ztensor.is_transformed) zdnn_reset_ztensor(&extra->ztensor); + if (extra->ztensor.is_transformed == false) ggml_zdnn_load_tensor(extra->ztensor, tensor->data); + + GGML_UNUSED(buffer); +} + +static void ggml_backend_zdnn_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { + memcpy(data, (const char *)tensor->data + offset, size); + + GGML_UNUSED(buffer); +} + +static void ggml_backend_zdnn_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { + ggml_backend_zdnn_buffer_context * ctx = (ggml_backend_zdnn_buffer_context *)buffer->context; + + memset(ctx->all_data, value, ctx->all_size); +} + +static ggml_backend_buffer_i ggml_backend_zdnn_buffer_i = { + /* .free_buffer = */ ggml_backend_zdnn_buffer_free_buffer, + /* .get_base = */ ggml_backend_zdnn_buffer_get_base, + /* .init_tensor = */ ggml_backend_zdnn_buffer_init_tensor, + /* .memset_tensor = */ ggml_backend_zdnn_buffer_memset_tensor, + /* .set_tensor = */ ggml_backend_zdnn_buffer_set_tensor, + /* .get_tensor = */ ggml_backend_zdnn_buffer_get_tensor, + /* .set_tensor_2d = */ NULL, + /* .get_tensor_2d = */ NULL, + /* .cpy_tensor = */ NULL, + /* .clear = */ ggml_backend_zdnn_buffer_clear, + /* .reset = */ NULL, +}; + +// +// default buffer type +// + +static const char * ggml_backend_zdnn_buffer_type_get_name(ggml_backend_buffer_type_t buft) { + return GGML_ZDNN_NAME; + + GGML_UNUSED(buft); +} + +static ggml_backend_buffer_t ggml_backend_zdnn_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { + ggml_backend_zdnn_buffer_context * ctx = new ggml_backend_zdnn_buffer_context(); + + const size_t size_page = sysconf(_SC_PAGESIZE); + + size_t size_aligned = size; + if ((size_aligned % size_page) != 0) { + size_aligned += size_page - (size_aligned % size_page); + } + + ggml_backend_zdnn_device_context * ctx_dev = (ggml_backend_zdnn_device_context *)buft->device->context; + + GGML_ASSERT(ctx_dev->zdnn_device >= 0); + int device = ctx_dev->zdnn_device; GGML_UNUSED(device); + + ctx->all_data = ggml_aligned_malloc(size_aligned); + ctx->all_size = size_aligned; + ctx->owned = true; + ctx->n_buffers = 1; + + if (ctx->all_data != NULL) { + std::unique_ptr zdnn_buffer = std::make_unique(); + zdnn_buffer->data = ctx->all_data; + zdnn_buffer->size = size_aligned; + ctx->buffers.push_back(std::move(zdnn_buffer)); + } + + if (size_aligned > 0 && (ctx->all_data == NULL)) { + GGML_LOG_ERROR("%s: error: failed to allocate buffer, size = %8.2f\n", + __func__, size_aligned / 1024.0 / 1024.0); + delete ctx; + return NULL; + } + + return ggml_backend_buffer_init(buft, ggml_backend_zdnn_buffer_i, ctx, size); +} + +static size_t ggml_backend_zdnn_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { + return 256; + + GGML_UNUSED(buft); +} + +static bool ggml_backend_zdnn_buffer_type_is_host(ggml_backend_buffer_type_t buft) { + /* while it resides in host memory, additional transformation is needed */ + return false; + + GGML_UNUSED(buft); +} + +ggml_backend_buffer_type_t ggml_backend_zdnn_buffer_type(void) { + static ggml_backend_buffer_type ggml_backend_buffer_type_zdnn = { + /* .iface = */ { + /* .get_name = */ ggml_backend_zdnn_buffer_type_get_name, + /* .alloc_buffer = */ ggml_backend_zdnn_buffer_type_alloc_buffer, + /* .get_alignment = */ ggml_backend_zdnn_buffer_type_get_alignment, + /* .get_max_size = */ NULL, + /* .get_alloc_size = */ NULL, // defaults to ggml_nbytes + /* .is_host = */ ggml_backend_zdnn_buffer_type_is_host, + }, + /* .device = */ &g_ggml_backend_zdnn_device, + /* .context = */ NULL, + }; + + return &ggml_backend_buffer_type_zdnn; +} + +// +// backend +// + +static const char * ggml_backend_zdnn_name(ggml_backend_t backend) { + return GGML_ZDNN_NAME; + + GGML_UNUSED(backend); +} + +static void ggml_backend_zdnn_free(ggml_backend_t backend) { + ggml_backend_zdnn_context * ctx = (ggml_backend_zdnn_context *)backend->context; + + ggml_zdnn_free(ctx); + free(backend); +} + +static enum ggml_status ggml_backend_zdnn_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { + return ggml_zdnn_graph_compute(backend, cgraph); +} + +static ggml_backend_i ggml_backend_zdnn_i = { + /* .get_name = */ ggml_backend_zdnn_name, + /* .free = */ ggml_backend_zdnn_free, + /* .set_tensor_async = */ NULL, + /* .get_tensor_async = */ NULL, + /* .set_tensor_2d_async = */ NULL, + /* .get_tensor_2d_async = */ NULL, + /* .cpy_tensor_async = */ NULL, + /* .synchronize = */ NULL, + /* .graph_plan_create = */ NULL, + /* .graph_plan_free = */ NULL, + /* .graph_plan_update = */ NULL, + /* .graph_plan_compute = */ NULL, + /* .graph_compute = */ ggml_backend_zdnn_graph_compute, + /* .event_record = */ NULL, + /* .event_wait = */ NULL, + /* .graph_optimize = */ NULL, +}; + +static ggml_guid_t ggml_backend_zdnn_guid(void) { + static const char * guid_str = "IBM-ZDNN-ACCELER"; + return reinterpret_cast((void *)guid_str); +} + +bool ggml_backend_is_zdnn(ggml_backend_t backend) { + return backend != NULL && + ggml_guid_matches(backend->guid, ggml_backend_zdnn_guid()); + + GGML_UNUSED(backend); +} + +// +// backend device +// + +static const char * ggml_backend_zdnn_device_get_name(ggml_backend_dev_t dev) { + return GGML_ZDNN_NAME; + + GGML_UNUSED(dev); +} + +static const char * ggml_backend_zdnn_device_get_description(ggml_backend_dev_t dev) { + return "IBM Z Neural Network Processing Assist (NNPA)"; + + GGML_UNUSED(dev); +} + +static void ggml_backend_zdnn_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) { + *free = 0; + *total = 0; + + GGML_UNUSED(dev); +} + +static enum ggml_backend_dev_type ggml_backend_zdnn_device_get_type(ggml_backend_dev_t dev) { + return GGML_BACKEND_DEVICE_TYPE_ACCEL; + + GGML_UNUSED(dev); +} + +static void ggml_backend_zdnn_device_get_props(ggml_backend_dev_t dev, ggml_backend_dev_props * props) { + props->name = ggml_backend_zdnn_device_get_name(dev); + props->description = ggml_backend_zdnn_device_get_description(dev); + props->type = ggml_backend_zdnn_device_get_type(dev); + ggml_backend_zdnn_device_get_memory(dev, &props->memory_free, &props->memory_total); + props->caps = (ggml_backend_dev_caps) { + /* .async = */ false, + /* .host_buffer = */ false, + /* .buffer_from_host_ptr = */ false, + /* .events = */ false + }; +} + +static ggml_backend_t ggml_backend_zdnn_device_init(ggml_backend_dev_t dev, const char * params) { + ggml_backend_zdnn_context * ctx = ggml_zdnn_init(dev); + if (ctx == NULL) { + GGML_LOG_ERROR("%s: error: failed to allocate context\n", __func__); + return NULL; + } + + ggml_backend_t backend = (ggml_backend *)malloc(sizeof(ggml_backend)); + *backend = (ggml_backend) { + /* .guid = */ ggml_backend_zdnn_guid(), + /* .iface = */ ggml_backend_zdnn_i, + /* .device = */ dev, + /* .context = */ ctx + }; + + return backend; + + GGML_UNUSED(params); +} + +static ggml_backend_buffer_type_t ggml_backend_zdnn_device_get_buffer_type(ggml_backend_dev_t dev) { + return ggml_backend_zdnn_buffer_type(); + + GGML_UNUSED(dev); +} + +static bool ggml_backend_zdnn_device_supports_op(ggml_backend_dev_t dev, const ggml_tensor * op) { + ggml_backend_zdnn_device_context * ctx_dev = (ggml_backend_zdnn_device_context *) dev->context; + + return ggml_zdnn_supports_op(ctx_dev, op); +} + +static bool ggml_backend_zdnn_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) { + return + buft->iface.get_name == ggml_backend_zdnn_buffer_type_get_name; + + GGML_UNUSED(dev); +} + +static ggml_backend_device_i ggml_backend_zdnn_device_i = { + /* .get_name = */ ggml_backend_zdnn_device_get_name, + /* .get_description = */ ggml_backend_zdnn_device_get_description, + /* .get_memory = */ ggml_backend_zdnn_device_get_memory, + /* .get_type = */ ggml_backend_zdnn_device_get_type, + /* .get_props = */ ggml_backend_zdnn_device_get_props, + /* .init_backend = */ ggml_backend_zdnn_device_init, + /* .get_buffer_type = */ ggml_backend_zdnn_device_get_buffer_type, + /* .get_host_buffer_type = */ NULL, + /* .buffer_from_host_ptr = */ NULL, + /* .supports_op = */ ggml_backend_zdnn_device_supports_op, + /* .supports_buft = */ ggml_backend_zdnn_device_supports_buft, + /* .offload_op = */ NULL, + /* .event_new = */ NULL, + /* .event_free = */ NULL, + /* .event_synchronize = */ NULL, +}; + +// +// backend registry +// + +static const char * ggml_backend_zdnn_reg_get_name(ggml_backend_reg_t reg) { + return GGML_ZDNN_NAME; + + GGML_UNUSED(reg); +} + +static size_t ggml_backend_zdnn_reg_device_count(ggml_backend_reg_t reg) { + if (!zdnn_is_nnpa_installed()) { + return 0; + } + return 1; + + GGML_UNUSED(reg); +} + +static ggml_backend_dev_t ggml_backend_zdnn_reg_device_get(ggml_backend_reg_t reg, size_t index) { + GGML_ASSERT(index == 0); + + return &g_ggml_backend_zdnn_device; + + GGML_UNUSED(reg); + GGML_UNUSED(index); +} + +static ggml_backend_feature g_ggml_backend_zdnn_features[] = { + { "NNPA", zdnn_is_nnpa_installed() ? "1" : "0" }, + { "NNPA_PARMBLKFORMAT_0", zdnn_is_nnpa_parmblk_fmt_installed(1, NNPA_PARMBLKFORMAT_0) ? "1" : "0" }, + { "NNPA_PARMBLKFORMAT_1", zdnn_is_nnpa_parmblk_fmt_installed(1, NNPA_PARMBLKFORMAT_1) ? "1" : "0" }, + { NULL, NULL }, +}; + +static ggml_backend_feature * ggml_backend_zdnn_get_features(ggml_backend_reg_t reg) { + return g_ggml_backend_zdnn_features; + + GGML_UNUSED(reg); +} + +static void * ggml_backend_zdnn_get_proc_address(ggml_backend_reg_t reg, const char * name) { + if (strcmp(name, "ggml_backend_get_features") == 0) { + return (void *) ggml_backend_zdnn_get_features; + } + + return NULL; + + GGML_UNUSED(reg); +} + +static ggml_backend_reg_i ggml_backend_zdnn_reg_i = { + /* .get_name = */ ggml_backend_zdnn_reg_get_name, + /* .get_device_count = */ ggml_backend_zdnn_reg_device_count, + /* .get_device = */ ggml_backend_zdnn_reg_device_get, + /* .get_proc_address = */ ggml_backend_zdnn_get_proc_address +}; + +static void ggml_zdnn_cleanup(void) { + ggml_backend_zdnn_device_rel(&g_ggml_ctx_dev_main); +} + +// TODO: make thread-safe +ggml_backend_reg_t ggml_backend_zdnn_reg(void) { + ggml_backend_zdnn_device_acq(&g_ggml_ctx_dev_main); + + // register cleanup callback + atexit(ggml_zdnn_cleanup); + + { + g_ggml_backend_zdnn_reg = (ggml_backend_reg) { + /* .api_version = */ GGML_ZDNN_VERSION, + /* .iface = */ ggml_backend_zdnn_reg_i, + /* .context = */ NULL + }; + + g_ggml_backend_zdnn_device = (ggml_backend_device) { + /* .iface = */ ggml_backend_zdnn_device_i, + /* .reg = */ &g_ggml_backend_zdnn_reg, + /* .context = */ &g_ggml_ctx_dev_main + }; + + return &g_ggml_backend_zdnn_reg; + } +} + +GGML_BACKEND_DL_IMPL(ggml_backend_zdnn_reg) diff --git a/backend/llama.cpp/ggml/src/ggml-zdnn/mmf.cpp b/backend/llama.cpp/ggml/src/ggml-zdnn/mmf.cpp new file mode 100644 index 0000000000000000000000000000000000000000..3ac9cf3c931e339edcddac01d37fac01c514f0d0 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-zdnn/mmf.cpp @@ -0,0 +1,80 @@ +#include "ggml.h" +#include "mmf.hpp" + +void ggml_zdnn_mul_mat_f( + const ggml_backend_zdnn_context * ctx, + const ggml_tensor * src0, + const ggml_tensor * src1, + ggml_tensor * dst) { + GGML_TENSOR_BINARY_OP_LOCALS; + + const enum ggml_type type = src0->type; + + GGML_ASSERT(ne0 == ne01); + GGML_ASSERT(ne1 == ne11); + GGML_ASSERT(ne2 == ne12); + GGML_ASSERT(ne3 == ne13); + + // we don't support permuted src0 or src1 + GGML_ASSERT(nb00 == ggml_type_size(type)); + GGML_ASSERT(nb10 == ggml_type_size(src1->type)); + + // dst cannot be transposed or permuted + GGML_ASSERT(nb0 == sizeof(float)); + GGML_ASSERT(nb0 <= nb1); + GGML_ASSERT(nb1 <= nb2); + GGML_ASSERT(nb2 <= nb3); + + const ggml_tensor * weights = src0; + const ggml_tensor * inputs = src1; + ggml_tensor * output = dst; + + ggml_backend_zdnn_buffer * weights_extra = (ggml_backend_zdnn_buffer *)weights->extra; + ggml_backend_zdnn_buffer * inputs_extra = (ggml_backend_zdnn_buffer *)inputs->extra; + ggml_backend_zdnn_buffer * output_extra = (ggml_backend_zdnn_buffer *)output->extra; + ggml_backend_zdnn_buffer * bias_extra = (ggml_backend_zdnn_buffer *)output_extra->extra; + + const int64_t weights_rows = ne01; + const int64_t weights_cols = ne00; + const int64_t inputs_rows = ne11; + const int64_t inputs_cols = ne10; + + assert(inputs_cols == weights_cols); + + const int64_t output_rows = ne1; + const int64_t output_cols = ne0; + + // GGML_LOG_INFO("%s: tensor '%s' tensor dimensions: [%ld, %ld, %ld, %ld] pre_tfm_desc dimensions: [%ld, %ld, %ld, %ld]\n", + // __func__, weights_extra->name, + // weights->ne[3], weights->ne[2], weights->ne[1], weights->ne[0], + // weights_extra->pre_tfm_desc.dim1, + // weights_extra->pre_tfm_desc.dim2, + // weights_extra->pre_tfm_desc.dim3, + // weights_extra->pre_tfm_desc.dim4); + + // GGML_LOG_INFO("%s: tensor '%s' tensor dimensions: [%ld, %ld, %ld, %ld] pre_tfm_desc dimensions: [%ld, %ld, %ld, %ld]\n", + // __func__, inputs_extra->name, + // inputs->ne[3], inputs->ne[2], inputs->ne[1], inputs->ne[0], + // inputs_extra->pre_tfm_desc.dim1, + // inputs_extra->pre_tfm_desc.dim2, + // inputs_extra->pre_tfm_desc.dim3, + // inputs_extra->pre_tfm_desc.dim4); + + GGML_ASSERT(weights_extra->pre_tfm_desc.dim1 == weights->ne[0] && "weights_extra->pre_tfm_desc.dim1 must match weights->ne[0]"); + GGML_ASSERT(weights_extra->pre_tfm_desc.dim2 == weights->ne[1] && "weights_extra->pre_tfm_desc.dim2 must match weights->ne[1]"); + GGML_ASSERT(inputs_extra->pre_tfm_desc.dim1 == inputs->ne[0] && "inputs_extra->pre_tfm_desc.dim1 must match inputs->ne[0]"); + GGML_ASSERT(inputs_extra->pre_tfm_desc.dim2 == inputs->ne[1] && "inputs_extra->pre_tfm_desc.dim2 must match inputs->ne[1]"); + + ZDNN_CHECK(zdnn_matmul_transpose_op(&inputs_extra->ztensor, &weights_extra->ztensor, &bias_extra->ztensor, + false, true, MATMUL_OP_ADDITION, &output_extra->ztensor)); + // TODO: Remove in the future as we are currently DLF16 -> FP32 then in the next op, FP32 -> DLF16 again. Inefficient. + ZDNN_CHECK(zdnn_transform_origtensor(&output_extra->ztensor, output->data)); + + GGML_UNUSED(ctx); + GGML_UNUSED(weights_rows); + GGML_UNUSED(weights_cols); + GGML_UNUSED(inputs_rows); + GGML_UNUSED(inputs_cols); + GGML_UNUSED(output_rows); + GGML_UNUSED(output_cols); +} diff --git a/backend/llama.cpp/ggml/src/ggml-zdnn/mmf.hpp b/backend/llama.cpp/ggml/src/ggml-zdnn/mmf.hpp new file mode 100644 index 0000000000000000000000000000000000000000..a12f1b8f8a0ee430d75e69c94626e8ee82b0aa5e --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-zdnn/mmf.hpp @@ -0,0 +1,12 @@ +#ifndef GGML_ZDNN_MMF_HPP +#define GGML_ZDNN_MMF_HPP + +#include "common.hpp" + +void ggml_zdnn_mul_mat_f( + const ggml_backend_zdnn_context * ctx, + const ggml_tensor * src0, + const ggml_tensor * src1, + ggml_tensor * dst); + +#endif // GGML_ZDNN_MMF_HPP diff --git a/backend/llama.cpp/ggml/src/ggml-zdnn/utils.cpp b/backend/llama.cpp/ggml/src/ggml-zdnn/utils.cpp new file mode 100644 index 0000000000000000000000000000000000000000..2977cb0fe3bdf904c2591bd2bdbfd18c9a859e76 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-zdnn/utils.cpp @@ -0,0 +1,79 @@ +#include "ggml.h" +#include "utils.hpp" + +zdnn_data_types ggml_zdnn_type_mapping(ggml_type type) { + switch (type) { + case GGML_TYPE_F32: + return FP32; + case GGML_TYPE_F16: + return FP16; + case GGML_TYPE_BF16: + return BFLOAT; + case GGML_TYPE_Q8_0: + return INT8; + case GGML_TYPE_I8: + return INT8; + case GGML_TYPE_I32: + return INT32; + default: + GGML_ABORT("%s: fatal: unable to determine zTensor data type", + __func__); + break; + } +} + +void ggml_zdnn_create_tensor(zdnn_tensor_desc & pre_tfm_desc, + zdnn_tensor_desc & tfm_desc, + zdnn_ztensor & ztensor, + const ggml_tensor * src, + const int64_t * ne, + const zdnn_data_layouts layout) { + zdnn_init_pre_transformed_desc( + layout, + ggml_zdnn_type_mapping(src->type), + &pre_tfm_desc, + ne[3], ne[2], ne[1], ne[0] + ); + + ZDNN_CHECK(zdnn_generate_transformed_desc(&pre_tfm_desc, &tfm_desc)); + ZDNN_CHECK(zdnn_init_ztensor_with_malloc(&pre_tfm_desc, &tfm_desc, &ztensor)); +} + +void ggml_zdnn_load_tensor(zdnn_ztensor & ztensor, void * buffer) { + ZDNN_CHECK(zdnn_transform_ztensor(&ztensor, buffer)); +} + +void ggml_zdnn_init_tensor(ggml_backend_zdnn_buffer * buffer, const ggml_tensor * tensor) { + switch (tensor->op) { + case GGML_OP_MUL_MAT: + { + zdnn_init_pre_transformed_desc( + ZDNN_2D, + ggml_zdnn_type_mapping(tensor->type), + &buffer->pre_tfm_desc, + tensor->ne[1], tensor->ne[0] + ); + } break; + + default: + { + // For 4D tensors, GGML uses NCHW layout. However, because zDNN + // automatically transforms everything to NHWC, we will use it + // directly to avoid the performance penalty changing the + // layout and reshaping the tensor. + zdnn_init_pre_transformed_desc( + ZDNN_NHWC, + ggml_zdnn_type_mapping(tensor->type), + &buffer->pre_tfm_desc, + tensor->ne[3], tensor->ne[2], tensor->ne[1], tensor->ne[0] + ); + + // TODO: Consider adding a ggml check. + // TODO: If tensor = 4D, use ZDNN_NCHW by default. + // TODO: If tensor = 2D, use ZDNN_NHWC by default. + } break; + } + + ZDNN_CHECK(zdnn_generate_transformed_desc(&buffer->pre_tfm_desc, &buffer->tfm_desc)); + ZDNN_CHECK(zdnn_init_ztensor_with_malloc(&buffer->pre_tfm_desc, &buffer->tfm_desc, &buffer->ztensor)); +} diff --git a/backend/llama.cpp/ggml/src/ggml-zdnn/utils.hpp b/backend/llama.cpp/ggml/src/ggml-zdnn/utils.hpp new file mode 100644 index 0000000000000000000000000000000000000000..c1e2028edbca7c8a51edecffe5c76055fb4f51d7 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-zdnn/utils.hpp @@ -0,0 +1,19 @@ +#ifndef GGML_ZDNN_UTILITIES_HPP +#define GGML_ZDNN_UTILITIES_HPP + +#include "common.hpp" + +zdnn_data_types ggml_zdnn_type_mapping(ggml_type type); + +void ggml_zdnn_create_tensor(zdnn_tensor_desc & pre_tfm_desc, + zdnn_tensor_desc & tfm_desc, + zdnn_ztensor & ztensor, + const ggml_tensor * src, + const int64_t * ne, + const zdnn_data_layouts layout); + +void ggml_zdnn_load_tensor(zdnn_ztensor & ztensor, void * buffer); + +void ggml_zdnn_init_tensor(ggml_backend_zdnn_buffer * buffer, const ggml_tensor * tensor); + +#endif // GGML_ZDNN_UTILITIES_HPP diff --git a/backend/llama.cpp/ggml/src/ggml-zendnn/CMakeLists.txt b/backend/llama.cpp/ggml/src/ggml-zendnn/CMakeLists.txt new file mode 100644 index 0000000000000000000000000000000000000000..e4ba9cfbd0fcae92db141732036ea79d1843c5c8 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-zendnn/CMakeLists.txt @@ -0,0 +1,91 @@ +ggml_add_backend_library(ggml-zendnn + ggml-zendnn.cpp) + +if (NOT DEFINED ZENDNN_ROOT OR ZENDNN_ROOT STREQUAL "") + set(ZENDNN_ROOT "$ENV{ZENDNN_ROOT}") +endif() + +if (BUILD_SHARED_LIBS) + set(ZENDNN_SHARED_LIB ON) + set(ZENDNN_ARCHIVE_LIB OFF) +else() + set(ZENDNN_SHARED_LIB OFF) + set(ZENDNN_ARCHIVE_LIB ON) +endif() + +# Download and build ZenDNN if not provided +if (NOT ZENDNN_ROOT OR ZENDNN_ROOT STREQUAL "" OR ZENDNN_ROOT STREQUAL "OFF") + message(STATUS "ZENDNN_ROOT not set. Automatically downloading and building ZenDNN...") + message(STATUS "This will take several minutes on first build...") + + include(ExternalProject) + + set(ZENDNN_PREFIX ${CMAKE_BINARY_DIR}/_deps/zendnn-prefix) + set(ZENDNN_SOURCE_DIR ${ZENDNN_PREFIX}/src/zendnn) + set(ZENDNN_BUILD_DIR ${ZENDNN_PREFIX}/build) + set(ZENDNN_INSTALL_DIR ${ZENDNN_BUILD_DIR}/install) + + ExternalProject_Add( + zendnn + GIT_REPOSITORY https://github.com/amd/ZenDNN.git + GIT_TAG 253b94ce0d7e9284c265fefb485714944caff9d3 # ZenDNN-2026-WW19 + PREFIX ${ZENDNN_PREFIX} + SOURCE_DIR ${ZENDNN_SOURCE_DIR} + BINARY_DIR ${ZENDNN_BUILD_DIR} + CMAKE_ARGS + -DCMAKE_BUILD_TYPE=Release + -DCMAKE_INSTALL_PREFIX=${ZENDNN_INSTALL_DIR} + -DZENDNNL_BUILD_EXAMPLES=OFF + -DZENDNNL_BUILD_DOXYGEN=OFF + -DZENDNNL_BUILD_GTEST=OFF + -DZENDNNL_BUILD_BENCHDNN=OFF + -DZENDNNL_DEPENDS_FBGEMM=OFF + -DZENDNNL_LIB_BUILD_ARCHIVE=${ZENDNN_ARCHIVE_LIB} + -DZENDNNL_LIB_BUILD_SHARED=${ZENDNN_SHARED_LIB} + -DZENDNNL_DEPENDS_AOCLDLP=ON + -DZENDNNL_DEPENDS_ONEDNN=ON + -DZENDNNL_DEPENDS_LIBXSMM=ON + BUILD_COMMAND ${CMAKE_COMMAND} --build ${ZENDNN_BUILD_DIR} --target zendnnl + INSTALL_COMMAND ${CMAKE_COMMAND} --build ${ZENDNN_BUILD_DIR} --target install + BUILD_ALWAYS OFF + LOG_DOWNLOAD ON + LOG_CONFIGURE ON + LOG_BUILD ON + LOG_INSTALL ON + ) + + add_dependencies(ggml-zendnn zendnn) + set(ZENDNN_ROOT ${ZENDNN_INSTALL_DIR}) + message(STATUS "ZenDNN will be built to: ${ZENDNN_ROOT}") +else() + message(STATUS "Using custom ZenDNN installation at: ${ZENDNN_ROOT}") +endif() + +target_include_directories(ggml-zendnn PRIVATE + ${ZENDNN_ROOT}/zendnnl/include + ${ZENDNN_ROOT}/deps/json/include + ${ZENDNN_ROOT}/deps/aoclutils/include + ${ZENDNN_ROOT}/deps/aocldlp/include + ${ZENDNN_ROOT}/deps/onednn/include + ${ZENDNN_ROOT}/deps/libxsmm/include) + +if (ZENDNN_SHARED_LIB) + target_link_directories(ggml-zendnn PRIVATE ${ZENDNN_ROOT}/zendnnl/lib) + target_link_libraries(ggml-zendnn PRIVATE zendnnl) +elseif (ZENDNN_ARCHIVE_LIB) + target_link_libraries(ggml-zendnn PRIVATE + ${ZENDNN_ROOT}/zendnnl/lib/libzendnnl_archive.a + ${ZENDNN_ROOT}/deps/aoclutils/${CMAKE_INSTALL_LIBDIR}/libaoclutils.a + ${ZENDNN_ROOT}/deps/aoclutils/${CMAKE_INSTALL_LIBDIR}/libau_cpuid.a + ${ZENDNN_ROOT}/deps/aocldlp/lib/libaocl-dlp.a + ${ZENDNN_ROOT}/deps/onednn/${CMAKE_INSTALL_LIBDIR}/libdnnl.a + ${ZENDNN_ROOT}/deps/libxsmm/lib/libxsmm.a + ${ZENDNN_ROOT}/deps/libxsmm/lib/libxsmmext.a + ${ZENDNN_ROOT}/deps/libxsmm/lib/libxsmmnoblas.a) +endif() + +target_link_libraries(ggml-zendnn PRIVATE m pthread) + +if (GGML_OPENMP) + target_link_libraries(ggml-zendnn PRIVATE OpenMP::OpenMP_CXX) +endif() diff --git a/backend/llama.cpp/ggml/src/ggml-zendnn/ggml-zendnn.cpp b/backend/llama.cpp/ggml/src/ggml-zendnn/ggml-zendnn.cpp new file mode 100644 index 0000000000000000000000000000000000000000..3c33dcb11a03162727162e831a34259becb41450 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml-zendnn/ggml-zendnn.cpp @@ -0,0 +1,703 @@ +#include "ggml-zendnn.h" + +#include "ggml-backend-impl.h" +#include "ggml-impl.h" + +#define GGML_COMMON_DECL_CPP +#include "ggml-common.h" + +#include "zendnnl.hpp" + +#include + + +struct ggml_backend_zendnn_context { + int n_threads = GGML_DEFAULT_N_THREADS; + std::unique_ptr work_data; + size_t work_size = 0; +}; + +template +zendnnl::common::data_type_t ggml_to_zendnn_type() { + if constexpr (std::is_same_v) { + return zendnnl::common::data_type_t::f32; + } else if constexpr (std::is_same_v) { + return zendnnl::common::data_type_t::bf16; + } else if constexpr (std::is_same_v) { + return zendnnl::common::data_type_t::s8; + } else { + return zendnnl::common::data_type_t::none; + } +} + +/** + * ZenDNN matmul: computes C = B * A. + * + * - A: weights, shape (k, m), column-major (each column is a weight vector for one output). + * - B: input, shape (n, k), row-major (each row is an input sample). + * - C: output, shape (n, m), row-major. + * + * Dimensions: + * m = output features (columns of C, columns of A) + * n = batch size (rows of C, rows of B) + * k = inner dimension (columns of B, rows of A) + */ +template +static bool ggml_zendnn_matmul(ggml_backend_zendnn_context * ctx, int64_t m, int64_t n, int64_t k, + const TA * A, int64_t lda, const TB * B, int64_t ldb, TC * C, + int64_t ldc) { + + zendnnl::lowoha::matmul::matmul_params params; + params.dtypes.src = ggml_to_zendnn_type(); + params.dtypes.wei = ggml_to_zendnn_type(); + params.dtypes.dst = ggml_to_zendnn_type(); + params.num_threads = ctx->n_threads; + + zendnnl::lowoha::matmul::matmul_batch_params_t batch_params; + + if constexpr (std::is_same_v) { + params.dtypes.compute = zendnnl::common::data_type_t::s8; + const int64_t num_groups = k / QK8_0; + params.dynamic_quant = true; + params.quant_params.src_scale.buff = nullptr; + params.quant_params.src_scale.dt = zendnnl::common::data_type_t::bf16; + params.quant_params.src_scale.dims = {n, num_groups}; + params.packing.pack_format_b = 1; + } + + zendnnl::error_handling::status_t status = zendnnl::lowoha::matmul::matmul_direct( + 'r', false, true, // row-major, don't transpose B, transpose A (because it's column-major) + n, // M: rows of B and C + m, // N: cols of A^T and C + k, // K: cols of B, rows of A + 1.0f, // alpha + B, ldb, // src: B[n,k] + A, lda, // weight: A[k,m] column-major (transposed) + nullptr, // bias + 0.0f, // beta + C, ldc, // output C[n,m] + true, // is_weights_const + batch_params, // batch_params + params // params + ); + + if (status != zendnnl::error_handling::status_t::success) { + GGML_LOG_ERROR("%s, ZenDNN matmul failed: status=%d\n", __func__, static_cast(status)); + return false; + } + return true; +} + +static bool ggml_zendnn_gemm(ggml_backend_zendnn_context * ctx, int64_t m, int64_t n, int64_t k, + const void * A, int64_t lda, const void * B, int64_t ldb, void * C, + int64_t ldc, int Atype, int Btype, int Ctype) { + + assert(m >= 0); + assert(n >= 0); + assert(k >= 0); + assert(lda >= k); + assert(ldb >= k); + assert(ldc >= m); + + // categorize types + switch (Atype) { + case GGML_TYPE_F32: + if (Btype != GGML_TYPE_F32 || Ctype != GGML_TYPE_F32) + return false; + return ggml_zendnn_matmul( + ctx, m, n, k, + (const float *)A, lda, + (const float *)B, ldb, + (float *)C, ldc); + case GGML_TYPE_BF16: + if (Btype != GGML_TYPE_BF16) + return false; + if (Ctype == GGML_TYPE_BF16) + return ggml_zendnn_matmul( + ctx, m, n, k, + (const ggml_bf16_t *)A, lda, + (const ggml_bf16_t *)B, ldb, + (ggml_bf16_t *)C, ldc); + if (Ctype == GGML_TYPE_F32) + return ggml_zendnn_matmul( + ctx, m, n, k, + (const ggml_bf16_t *)A, lda, + (const ggml_bf16_t *)B, ldb, + (float *)C, ldc); + return false; + case GGML_TYPE_Q8_0: + if (Btype != GGML_TYPE_F32 || Ctype != GGML_TYPE_F32) + return false; + return ggml_zendnn_matmul( + ctx, m, n, k, + (const block_q8_0 *)A, lda, + (const float *)B, ldb, + (float *)C, ldc); + default: + return false; // unsupported type + } +} + +static void ggml_zendnn_compute_forward_mul_mat( + ggml_backend_zendnn_context * ctx, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; // weights + const ggml_tensor * src1 = dst->src[1]; // inputs + + GGML_TENSOR_BINARY_OP_LOCALS + + ggml_type const vec_dot_type = src0->type; + ggml_from_float_t const from_float = ggml_get_type_traits(vec_dot_type)->from_float_ref; + + GGML_ASSERT(ne0 == ne01); + GGML_ASSERT(ne1 == ne11); + GGML_ASSERT(ne2 == ne12); + GGML_ASSERT(ne3 == ne13); + + // we don't support permuted src0 or src1 + GGML_ASSERT(nb00 == ggml_type_size(src0->type)); + GGML_ASSERT(nb10 == ggml_type_size(src1->type)); + + // dst cannot be transposed or permuted + GGML_ASSERT(nb0 == sizeof(float)); + GGML_ASSERT(nb0 <= nb1); + GGML_ASSERT(nb1 <= nb2); + GGML_ASSERT(nb2 <= nb3); + + // broadcast factors + const int64_t r2 = ne12/ne02; + const int64_t r3 = ne13/ne03; + + void * work_data = ctx->work_data.get(); + + // ZenDNN requires FP32 for dynamic quantization, so conversion is skipped + if (src1->type != vec_dot_type && src0->type != GGML_TYPE_Q8_0) { + const size_t nbw1 = ggml_row_size(vec_dot_type, ne10); + const size_t nbw2 = nbw1 * ne11; + const size_t nbw3 = nbw2 * ne12; + const size_t desired_wsize = ne13 * nbw3; + if (ctx->work_size < desired_wsize) { + ctx->work_data.reset(new char[desired_wsize]); + ctx->work_size = desired_wsize; + } + work_data = ctx->work_data.get(); + + // #pragma omp parallel for num_threads(ctx->n_threads) + #pragma omp parallel for collapse(3) num_threads(ctx->n_threads) schedule(static) + for (int64_t i13 = 0; i13 < ne13; ++i13) { + for (int64_t i12 = 0; i12 < ne12; ++i12) { + for (int64_t i11 = 0; i11 < ne11; ++i11) { + const float * src1_f32 = (float *)((char *)src1->data + i11*nb11 + i12*nb12 + i13*nb13); + void * src1_conv = (char *)work_data + i11*nbw1 + i12*nbw2 + i13*nbw3; + from_float(src1_f32, src1_conv, ne10); + } + } + } + } + + for (int64_t i13 = 0; i13 < ne13; i13++) { + for (int64_t i12 = 0; i12 < ne12; i12++) { + const void* wdata = (src1->type == vec_dot_type || src0->type == GGML_TYPE_Q8_0) ? src1->data : work_data; + const size_t row_size = ggml_row_size(vec_dot_type, ne10); + if (!ggml_zendnn_gemm(ctx, + ne01, // m + ne11, // n + ne10, // k + static_cast(src0->data) + (i12/r2)*nb02 + (i13/r3)*nb03, + ne00, // lda + static_cast(wdata) + (i12*ne11 + i13*ne12*ne11)*row_size, + ne10, // ldb + static_cast(dst->data) + i12*nb2 + i13*nb3, + ne01, // ldc + src0->type, + src0->type == GGML_TYPE_Q8_0 ? GGML_TYPE_F32 : vec_dot_type, + dst->type)) + GGML_ABORT("%s: ZenDNN gemm failed\n", __func__); + } + } +} + +struct mmid_row_mapping { + int32_t i1; + int32_t i2; +}; + +static void ggml_zendnn_compute_forward_mul_mat_id( + ggml_backend_zendnn_context * ctx, + ggml_tensor * dst) { + + const ggml_tensor * src0 = dst->src[0]; // expert weights + const ggml_tensor * src1 = dst->src[1]; // inputs + const ggml_tensor * ids = dst->src[2]; // expert ids + + GGML_TENSOR_BINARY_OP_LOCALS + + // exit for no tokens to process + if (ne2 == 0 || ne11 == 0) { + return; + } + + ggml_type const vec_dot_type = src0->type; + ggml_from_float_t const from_float = ggml_get_type_traits(vec_dot_type)->from_float_ref; + + // we don't support permuted src0 or src1 + GGML_ASSERT(nb00 == ggml_type_size(src0->type)); + GGML_ASSERT(nb10 == ggml_type_size(src1->type)); + + // dst cannot be transposed or permuted + GGML_ASSERT(nb0 == sizeof(float)); + GGML_ASSERT(nb0 <= nb1); + GGML_ASSERT(nb1 <= nb2); + GGML_ASSERT(nb2 <= nb3); + + GGML_ASSERT(ne03 == 1); + GGML_ASSERT(ne13 == 1); + GGML_ASSERT(ne3 == 1); + + // row groups + const int n_ids = ids->ne[0]; // n_expert_used + const int n_as = ne02; // n_experts + + std::vector matrix_row_counts(n_as, 0); + std::vector> matrix_rows(n_as); + + int64_t max_rows = 0; + // group rows by expert (preprocessing step) + for (int64_t iid1 = 0; iid1 < ids->ne[1]; ++iid1) { + for (int id = 0; id < n_ids; ++id) { + const int32_t i02 = *(const int32_t *)((const char *)ids->data + iid1*ids->nb[1] + id*ids->nb[0]); + + GGML_ASSERT(i02 >= 0 && i02 < n_as); + + matrix_rows[i02].push_back({id, iid1}); + matrix_row_counts[i02]++; + if (matrix_row_counts[i02] > max_rows) { + max_rows = matrix_row_counts[i02]; + } + } + } + + if (max_rows == 0) { + return; // no rows to process + } + + const size_t row_size = ggml_row_size(vec_dot_type, ne10); + + // size for converting src1 rows to vec_dot_type if needed + const size_t nbw1 = row_size; + const size_t nbw2 = nbw1 * ne11; + const size_t nbw3 = nbw2 * ne12; + const size_t src1_conv_size = (src1->type != vec_dot_type && src0->type != GGML_TYPE_Q8_0) ? ne13 * nbw3 : 0; + + // For Q8_0, src1 is always F32; the gather buffer must hold F32 rows (ne10*4 bytes), + // not Q8_0-encoded rows (row_size ≈ ne10/32*34 bytes) — they differ by ~4x. + const size_t f32_row_size = (size_t)ne10 * sizeof(float); + const size_t gather_row_size = (src0->type == GGML_TYPE_Q8_0) ? f32_row_size : row_size; + + // size for MoE gather/scatter buffers + const size_t wdata_cur_size = max_rows * gather_row_size; + const size_t dst_cur_size = max_rows * ggml_row_size(dst->type, ne01); + + // allocate single buffer for all needs + const size_t total_size = src1_conv_size + wdata_cur_size + dst_cur_size; + if (ctx->work_size < total_size) { + ctx->work_data.reset(new char[total_size]); + ctx->work_size = total_size; + } + + // partition the buffer + char * work_data = ctx->work_data.get(); + char * wdata_cur = work_data + src1_conv_size; + char * dst_cur = wdata_cur + wdata_cur_size; + + // ZenDNN requires FP32 for dynamic quantization, so conversion is skipped + if (src1->type != vec_dot_type && src0->type != GGML_TYPE_Q8_0) { + GGML_ASSERT(src1->type == GGML_TYPE_F32); + + #pragma omp parallel for collapse(3) num_threads(ctx->n_threads) schedule(static) + for (int64_t i13 = 0; i13 < ne13; ++i13) { + for (int64_t i12 = 0; i12 < ne12; ++i12) { + for (int64_t i11 = 0; i11 < ne11; ++i11) { + const float * src1_f32 = (float *)((char *)src1->data + i11*nb11 + i12*nb12 + i13*nb13); + void * src1_conv = (char *)work_data + i11*nbw1 + i12*nbw2 + i13*nbw3; + from_float(src1_f32, src1_conv, ne10); + } + } + } + } + + const void * wdata = (src1->type == vec_dot_type || src0->type == GGML_TYPE_Q8_0) ? src1->data : work_data; + + // process each expert with gather -> gemm -> scatter pattern + for (int64_t cur_a = 0; cur_a < n_as; ++cur_a) { + const int64_t cne1 = matrix_row_counts[cur_a]; + + if (cne1 == 0) { + continue; + } + + const char * src0_cur = (const char *) src0->data + cur_a*nb02; + + // gather input rows for this expert + #pragma omp parallel for num_threads(ctx->n_threads) schedule(static) + for (int64_t ir1 = 0; ir1 < cne1; ++ir1) { + const mmid_row_mapping & row_mapping = matrix_rows[cur_a][ir1]; + const int64_t id = row_mapping.i1; + const int64_t i11 = id % ne11; + const int64_t i12 = row_mapping.i2; + + std::memcpy( + wdata_cur + ir1 * gather_row_size, + (const char *) wdata + (i11 + i12*ne11) * gather_row_size, + gather_row_size + ); + } + + // batched gemm for all tokens in this expert + if (!ggml_zendnn_gemm(ctx, + ne01, // m + cne1, // n + ne10, // k + src0_cur, + ne00, // lda + wdata_cur, + ne10, // ldb + dst_cur, + ne01, // ldc + src0->type, + src0->type == GGML_TYPE_Q8_0 ? GGML_TYPE_F32 : vec_dot_type, + dst->type)) { + GGML_ABORT("%s: ZenDNN gemm failed\n", __func__); + } + + // scatter output rows to destination + #pragma omp parallel for num_threads(ctx->n_threads) schedule(static) + for (int64_t ir1 = 0; ir1 < cne1; ++ir1) { + const mmid_row_mapping & row_mapping = matrix_rows[cur_a][ir1]; + const int64_t id = row_mapping.i1; + const int64_t i1 = id; + const int64_t i2 = row_mapping.i2; + + std::memcpy( + (char *) dst->data + i1*nb1 + i2*nb2, + dst_cur + ir1 * ggml_row_size(dst->type, ne01), + ggml_row_size(dst->type, ne01) + ); + } + } +} + +// backend interface + +static const char * ggml_backend_zendnn_get_name(ggml_backend_t backend) { + return "ZenDNN"; + + GGML_UNUSED(backend); +} + +static void ggml_backend_zendnn_free(ggml_backend_t backend) { + ggml_backend_zendnn_context * ctx = (ggml_backend_zendnn_context *)backend->context; + delete ctx; + delete backend; +} + +static ggml_status ggml_backend_zendnn_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { + ggml_backend_zendnn_context * ctx = (ggml_backend_zendnn_context *)backend->context; + + for (int i = 0; i < cgraph->n_nodes; i++) { + struct ggml_tensor * node = cgraph->nodes[i]; + + if ((node->flags & GGML_TENSOR_FLAG_COMPUTE) == 0) { + continue; + } + + switch (node->op) { + case GGML_OP_MUL_MAT: + ggml_zendnn_compute_forward_mul_mat(ctx, node); + break; + case GGML_OP_MUL_MAT_ID: + ggml_zendnn_compute_forward_mul_mat_id(ctx, node); + break; + case GGML_OP_NONE: + case GGML_OP_RESHAPE: + case GGML_OP_VIEW: + case GGML_OP_PERMUTE: + case GGML_OP_TRANSPOSE: + break; + + default: + GGML_ABORT("%s: unsupported op %s\n", __func__, ggml_op_desc(node)); + } + } + + return GGML_STATUS_SUCCESS; + + GGML_UNUSED(backend); +} + +static struct ggml_backend_i ggml_backend_zendnn_i = { + /* .get_name = */ ggml_backend_zendnn_get_name, + /* .free = */ ggml_backend_zendnn_free, + /* .set_tensor_async = */ NULL, + /* .get_tensor_async = */ NULL, + /* .set_tensor_2d_async = */ NULL, + /* .get_tensor_2d_async = */ NULL, + /* .cpy_tensor_async = */ NULL, + /* .synchronize = */ NULL, + /* .graph_plan_create = */ NULL, + /* .graph_plan_free = */ NULL, + /* .graph_plan_update = */ NULL, + /* .graph_plan_compute = */ NULL, + /* .graph_compute = */ ggml_backend_zendnn_graph_compute, + /* .event_record = */ NULL, + /* .event_wait = */ NULL, + /* .graph_optimize = */ NULL, +}; + +static ggml_guid_t ggml_backend_zendnn_guid(void) { + static const char * guid_str = "AMD-ZENDNN-ACCEL"; + return reinterpret_cast(const_cast(guid_str)); +} + +ggml_backend_t ggml_backend_zendnn_init(void) { + ggml_backend_zendnn_context * ctx = new ggml_backend_zendnn_context; + + ggml_backend_t backend = new ggml_backend { + /* .guid = */ ggml_backend_zendnn_guid(), + /* .iface = */ ggml_backend_zendnn_i, + /* .device = */ ggml_backend_reg_dev_get(ggml_backend_zendnn_reg(), 0), + /* .context = */ ctx, + }; + + return backend; +} + +bool ggml_backend_is_zendnn(ggml_backend_t backend) { + return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_zendnn_guid()); +} + +void ggml_backend_zendnn_set_n_threads(ggml_backend_t backend_zendnn, int n_threads) { + GGML_ASSERT(ggml_backend_is_zendnn(backend_zendnn)); + + ggml_backend_zendnn_context * ctx = (ggml_backend_zendnn_context *)backend_zendnn->context; + ctx->n_threads = n_threads; +} + +// device interface +static const char * ggml_backend_zendnn_device_get_name(ggml_backend_dev_t dev) { + return "ZenDNN"; + + GGML_UNUSED(dev); +} +/** + * ZenDNN is AMD's performance library providing optimized primitives and implementations + * for deep learning workloads on AMD CPUs. It targets improved performance for common + * neural network operations on AMD architectures. For more information, see: + * https://www.amd.com/en/developer/zendnn.html + */ +static const char * ggml_backend_zendnn_device_get_description(ggml_backend_dev_t dev) { + return "ZenDNN: AMD optimized primitives backend for GGML (optimized for AMD CPUs)"; + + GGML_UNUSED(dev); +} + +static void ggml_backend_zendnn_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) { + *free = 0; + *total = 0; + + GGML_UNUSED(dev); +} + +static enum ggml_backend_dev_type ggml_backend_zendnn_device_get_type(ggml_backend_dev_t dev) { + return GGML_BACKEND_DEVICE_TYPE_ACCEL; + + GGML_UNUSED(dev); +} + +static void ggml_backend_zendnn_device_get_props(ggml_backend_dev_t dev, struct ggml_backend_dev_props * props) { + props->name = ggml_backend_zendnn_device_get_name(dev); + props->description = ggml_backend_zendnn_device_get_description(dev); + props->type = ggml_backend_zendnn_device_get_type(dev); + ggml_backend_zendnn_device_get_memory(dev, &props->memory_free, &props->memory_total); + props->caps = { + /* .async = */ false, + /* .host_buffer = */ false, + /* .buffer_from_host_ptr = */ true, + /* .events = */ false + }; +} + +static ggml_backend_t ggml_backend_zendnn_device_init_backend(ggml_backend_dev_t dev, const char * params) { + ggml_backend_t backend = ggml_backend_zendnn_init(); + if (backend == NULL) { + GGML_LOG_ERROR("%s: error: failed to initialize ZenDNN backend\n", __func__); + return NULL; + } + + return backend; + + GGML_UNUSED(dev); + GGML_UNUSED(params); +} + +static ggml_backend_buffer_type_t ggml_backend_zendnn_device_get_buffer_type(ggml_backend_dev_t dev) { + return ggml_backend_cpu_buffer_type(); + + GGML_UNUSED(dev); +} + +static ggml_backend_buffer_t ggml_backend_zendnn_device_buffer_from_host_ptr(ggml_backend_dev_t dev, void * ptr, size_t size, size_t max_tensor_size) { + return ggml_backend_cpu_buffer_from_ptr(ptr, size); + + GGML_UNUSED(dev); + GGML_UNUSED(max_tensor_size); +} + +static bool ggml_zendnn_adaptive_fallback_enabled() { + static const bool enabled = std::getenv("GGML_ZENDNN_ADAPTIVE_FALLBACK") == nullptr || + std::atoi(std::getenv("GGML_ZENDNN_ADAPTIVE_FALLBACK")) != 0; + return enabled; +} + +static bool ggml_backend_zendnn_device_supports_op(ggml_backend_dev_t dev, const struct ggml_tensor * op) { + switch (op->op) { + case GGML_OP_NONE: + case GGML_OP_RESHAPE: + case GGML_OP_VIEW: + case GGML_OP_PERMUTE: + case GGML_OP_TRANSPOSE: + return true; + + case GGML_OP_MUL_MAT: + case GGML_OP_MUL_MAT_ID: + { + const ggml_tensor * weights = op->src[0]; + const ggml_tensor * inputs = op->src[1]; + + const int64_t ne10 = inputs->ne[0]; + const int64_t ne0 = op->ne[0]; + const int64_t ne1 = op->ne[1]; + const int64_t min_batch = 1; + + if(!ggml_is_contiguous(weights) || !ggml_is_contiguous(inputs)) { + return false; + } + + if (ggml_zendnn_adaptive_fallback_enabled()) { + const int64_t K = inputs->ne[0]; + const int64_t N = (inputs->ne[1]*inputs->ne[2]*inputs->ne[3]); + const int64_t M = weights->ne[1]; + if(K <= 256 || N <= 128 || M <= 96) { + return false; + } + } + else if (ne0 < min_batch || ne1 < min_batch || ne10 < min_batch) { + return false; + } + + // MUL_MAT_ID performs best with a moderate number of experts due to its + // gather + batched matmul + scatter approach. Future versions will leverage + // ZenDNN's grouped_gemm for better scalability with larger expert counts: + // https://github.com/amd/ZenDNN/blob/main/docs/operator/lowoha_group_gemm_operator.md + if (op->op == GGML_OP_MUL_MAT_ID) { + const int64_t n_experts = weights->ne[2]; + const int64_t max_experts = 32; + if (n_experts > max_experts) { + return false; + } + } + switch (weights->type) { + case GGML_TYPE_F32: + case GGML_TYPE_BF16: + case GGML_TYPE_Q8_0: + return true; + default: + return false; + } + } break; + + default: + return false; + } + + GGML_UNUSED(dev); +} + +static bool ggml_backend_zendnn_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) { + return ggml_backend_buft_is_host(buft); + + GGML_UNUSED(dev); +} + +static const struct ggml_backend_device_i ggml_backend_zendnn_device_i = { + /* .get_name = */ ggml_backend_zendnn_device_get_name, + /* .get_description = */ ggml_backend_zendnn_device_get_description, + /* .get_memory = */ ggml_backend_zendnn_device_get_memory, + /* .get_type = */ ggml_backend_zendnn_device_get_type, + /* .get_props = */ ggml_backend_zendnn_device_get_props, + /* .init_backend = */ ggml_backend_zendnn_device_init_backend, + /* .get_buffer_type = */ ggml_backend_zendnn_device_get_buffer_type, + /* .get_host_buffer_type = */ NULL, + /* .buffer_from_host_ptr = */ ggml_backend_zendnn_device_buffer_from_host_ptr, + /* .supports_op = */ ggml_backend_zendnn_device_supports_op, + /* .supports_buft = */ ggml_backend_zendnn_device_supports_buft, + /* .offload_op = */ NULL, + /* .event_new = */ NULL, + /* .event_free = */ NULL, + /* .event_synchronize = */ NULL, +}; + +// backend reg interface +static const char * ggml_backend_zendnn_reg_get_name(ggml_backend_reg_t reg) { + return "ZenDNN"; + + GGML_UNUSED(reg); +} + +static size_t ggml_backend_zendnn_reg_get_device_count(ggml_backend_reg_t reg) { + return 1; + + GGML_UNUSED(reg); +} + +static ggml_backend_dev_t ggml_backend_zendnn_reg_get_device(ggml_backend_reg_t reg, size_t index) { + GGML_ASSERT(index == 0); + + static ggml_backend_device ggml_backend_zendnn_device = { + /* .iface = */ ggml_backend_zendnn_device_i, + /* .reg = */ reg, + /* .context = */ nullptr, + }; + + return &ggml_backend_zendnn_device; +} + +static void * ggml_backend_zendnn_get_proc_address(ggml_backend_reg_t reg, const char * name) { + if (std::strcmp(name, "ggml_backend_set_n_threads") == 0) { + return (void *) ggml_backend_zendnn_set_n_threads; + } + return NULL; + + GGML_UNUSED(reg); + GGML_UNUSED(name); +} + +static const struct ggml_backend_reg_i ggml_backend_zendnn_reg_i = { + /* .get_name = */ ggml_backend_zendnn_reg_get_name, + /* .get_device_count = */ ggml_backend_zendnn_reg_get_device_count, + /* .get_device = */ ggml_backend_zendnn_reg_get_device, + /* .get_proc_address = */ ggml_backend_zendnn_get_proc_address, +}; + +ggml_backend_reg_t ggml_backend_zendnn_reg(void) { + static struct ggml_backend_reg ggml_backend_zendnn_reg = { + /* .api_version = */ GGML_BACKEND_API_VERSION, + /* .iface = */ ggml_backend_zendnn_reg_i, + /* .context = */ NULL, + }; + + return &ggml_backend_zendnn_reg; +} + +GGML_BACKEND_DL_IMPL(ggml_backend_zendnn_reg) diff --git a/backend/llama.cpp/ggml/src/ggml.c b/backend/llama.cpp/ggml/src/ggml.c new file mode 100644 index 0000000000000000000000000000000000000000..5d880499169996ce6d9af771f92462063748f7eb --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml.c @@ -0,0 +1,7878 @@ +#define _CRT_SECURE_NO_DEPRECATE // Disables "unsafe" warnings on Windows +#define _USE_MATH_DEFINES // For M_PI on MSVC + +#include "ggml-backend.h" +#include "ggml-impl.h" +#include "ggml-threading.h" +#include "ggml-cpu.h" +#include "ggml.h" + +// FIXME: required here for quantization functions +#include "ggml-quants.h" + +#ifdef GGML_USE_CPU_HBM +#include +#endif + +#if defined(_MSC_VER) || defined(__MINGW32__) +#include // using malloc.h with MSC/MINGW +#elif !defined(__FreeBSD__) && !defined(__NetBSD__) && !defined(__OpenBSD__) +#include +#endif + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#if defined(__gnu_linux__) +#include +#endif + +#if defined(__APPLE__) +#include +#include +#include +#endif + +#if defined(_WIN32) +#define WIN32_LEAN_AND_MEAN +#ifndef NOMINMAX + #define NOMINMAX +#endif +#include +#endif + +#define UNUSED GGML_UNUSED + +uint64_t ggml_graph_next_uid(void) { +#ifdef _MSC_VER +#if defined(_WIN32) + static volatile LONG counter = 1; + return (uint64_t) InterlockedIncrement(&counter) - 1; +#else + static volatile long long counter = 1; + return (uint64_t) _InterlockedIncrement64(&counter) - 1; +#endif +#else + static uint64_t counter = 1; + return __atomic_fetch_add(&counter, 1, __ATOMIC_RELAXED); +#endif +} + +// Needed for ggml_fp32_to_bf16_row() +#if defined(__AVX512BF16__) +#if defined(_MSC_VER) +#define m512i(p) p +#else +#include +#define m512i(p) (__m512i)(p) +#endif // defined(_MSC_VER) +#endif // defined(__AVX512BF16__) + +#if defined(__linux__) || \ + defined(__FreeBSD__) || defined(__NetBSD__) || defined(__OpenBSD__) || \ + (defined(__APPLE__) && !TARGET_OS_TV && !TARGET_OS_WATCH) + +#include +#include +#include +#include +#if defined(__linux__) +#include +#endif + +#if defined(__ANDROID__) +#include +#include +#include + +struct backtrace_state { + void ** current; + void ** end; +}; + +static _Unwind_Reason_Code unwind_callback(struct _Unwind_Context* context, void* arg) { + struct backtrace_state * state = (struct backtrace_state *)arg; + uintptr_t pc = _Unwind_GetIP(context); + if (pc) { + if (state->current == state->end) { + return _URC_END_OF_STACK; + } else { + *state->current++ = (void*)pc; + } + } + return _URC_NO_REASON; +} + +static void ggml_print_backtrace_symbols(void) { + const int max = 100; + void* buffer[max]; + + struct backtrace_state state = {buffer, buffer + max}; + _Unwind_Backtrace(unwind_callback, &state); + + int count = state.current - buffer; + + for (int idx = 0; idx < count; ++idx) { + const void * addr = buffer[idx]; + const char * symbol = ""; + + Dl_info info; + if (dladdr(addr, &info) && info.dli_sname) { + symbol = info.dli_sname; + } + + fprintf(stderr, "%d: %p %s\n", idx, addr, symbol); + } +} +#elif defined(__linux__) && defined(__GLIBC__) +#include +static void ggml_print_backtrace_symbols(void) { + void * trace[100]; + int nptrs = backtrace(trace, sizeof(trace)/sizeof(trace[0])); + backtrace_symbols_fd(trace, nptrs, STDERR_FILENO); +} +#elif defined(__APPLE__) +#include +static void ggml_print_backtrace_symbols(void) { + void * trace[100]; + int nptrs = backtrace(trace, sizeof(trace)/sizeof(trace[0])); + backtrace_symbols_fd(trace, nptrs, STDERR_FILENO); +} +#else +static void ggml_print_backtrace_symbols(void) { + // platform not supported +} +#endif + +void ggml_print_backtrace(void) { + const char * GGML_NO_BACKTRACE = getenv("GGML_NO_BACKTRACE"); + if (GGML_NO_BACKTRACE) { + return; + } +#if defined(__APPLE__) + // On macOS, fork+debugger attachment is problematic due to: + // 1. libdispatch "poisons" forked child processes + // 2. lldb has issues attaching to parent from forked child + // Use simple backtrace() instead to avoid Terminal.app crashes + const char * GGML_BACKTRACE_LLDB = getenv("GGML_BACKTRACE_LLDB"); + if (!GGML_BACKTRACE_LLDB) { + fprintf(stderr, "WARNING: Using native backtrace. Set GGML_BACKTRACE_LLDB for more info.\n"); + fprintf(stderr, "WARNING: GGML_BACKTRACE_LLDB may cause native MacOS Terminal.app to crash.\n"); + fprintf(stderr, "See: https://github.com/ggml-org/llama.cpp/pull/17869\n"); + ggml_print_backtrace_symbols(); + return; + } +#endif +#if defined(__linux__) + FILE * f = fopen("/proc/self/status", "r"); + size_t size = 0; + char * line = NULL; + ssize_t length = 0; + while ((length = getline(&line, &size, f)) > 0) { + if (!strncmp(line, "TracerPid:", sizeof("TracerPid:") - 1) && + (length != sizeof("TracerPid:\t0\n") - 1 || line[length - 2] != '0')) { + // Already being debugged, and the breakpoint is the later abort() + free(line); + fclose(f); + return; + } + } + free(line); + fclose(f); + int lock[2] = { -1, -1 }; + (void) !pipe(lock); // Don't start gdb until after PR_SET_PTRACER +#endif + const int parent_pid = getpid(); + const int child_pid = fork(); + if (child_pid < 0) { // error +#if defined(__linux__) + close(lock[1]); + close(lock[0]); +#endif + return; + } else if (child_pid == 0) { // child + char attach[32]; + snprintf(attach, sizeof(attach), "attach %d", parent_pid); +#if defined(__linux__) + close(lock[1]); + (void) !read(lock[0], lock, 1); + close(lock[0]); +#endif + // try gdb + execlp("gdb", "gdb", "--batch", + "-ex", "set style enabled on", + "-ex", attach, + "-ex", "bt -frame-info source-and-location", + "-ex", "detach", + "-ex", "quit", + (char *) NULL); + // try lldb + execlp("lldb", "lldb", "--batch", + "-o", "bt", + "-o", "quit", + "-p", &attach[sizeof("attach ") - 1], + (char *) NULL); + // gdb failed, fallback to backtrace_symbols + ggml_print_backtrace_symbols(); + _Exit(0); + } else { // parent +#if defined(__linux__) + prctl(PR_SET_PTRACER, child_pid); + close(lock[1]); + close(lock[0]); +#endif + waitpid(child_pid, NULL, 0); + } +} +#else +void ggml_print_backtrace(void) { + // platform not supported +} +#endif + +static ggml_abort_callback_t g_abort_callback = NULL; + +// Set the abort callback (passing null will restore original abort functionality: printing a message to stdout) +GGML_API ggml_abort_callback_t ggml_set_abort_callback(ggml_abort_callback_t callback) { + ggml_abort_callback_t ret_val = g_abort_callback; + g_abort_callback = callback; + return ret_val; +} + +void ggml_abort(const char * file, int line, const char * fmt, ...) { + fflush(stdout); + + char message[2048]; + int offset = snprintf(message, sizeof(message), "%s:%d: ", file, line); + + va_list args; + va_start(args, fmt); + vsnprintf(message + offset, sizeof(message) - offset, fmt, args); + va_end(args); + + if (g_abort_callback) { + g_abort_callback(message); + } else { + // default: print error and backtrace to stderr + fprintf(stderr, "%s\n", message); + ggml_print_backtrace(); + } + + abort(); +} + +// ggml_print_backtrace is registered with std::set_terminate by ggml.cpp + +// +// logging +// + +struct ggml_logger_state { + ggml_log_callback log_callback; + void * log_callback_user_data; +}; +static struct ggml_logger_state g_logger_state = {ggml_log_callback_default, NULL}; + +static void ggml_log_internal_v(enum ggml_log_level level, const char * format, va_list args) { + if (format == NULL) { + return; + } + va_list args_copy; + va_copy(args_copy, args); + char buffer[128]; + int len = vsnprintf(buffer, 128, format, args); + if (len < 128) { + g_logger_state.log_callback(level, buffer, g_logger_state.log_callback_user_data); + } else { + char * buffer2 = (char *) calloc(len + 1, sizeof(char)); + vsnprintf(buffer2, len + 1, format, args_copy); + buffer2[len] = 0; + g_logger_state.log_callback(level, buffer2, g_logger_state.log_callback_user_data); + free(buffer2); + } + va_end(args_copy); +} + +void ggml_log_internal(enum ggml_log_level level, const char * format, ...) { + va_list args; + va_start(args, format); + ggml_log_internal_v(level, format, args); + va_end(args); +} + +void ggml_log_callback_default(enum ggml_log_level level, const char * text, void * user_data) { + (void) level; + (void) user_data; + fputs(text, stderr); + fflush(stderr); +} + +// +// end of logging block +// + +#ifdef GGML_USE_ACCELERATE +// uncomment to use vDSP for soft max computation +// note: not sure if it is actually faster +//#define GGML_SOFT_MAX_ACCELERATE +#endif + + +void * ggml_aligned_malloc(size_t size) { +#if defined(__s390x__) + const int alignment = 256; +#else + const int alignment = 64; +#endif + +#if defined(_MSC_VER) || defined(__MINGW32__) + return _aligned_malloc(size, alignment); +#else + if (size == 0) { + GGML_LOG_WARN("Behavior may be unexpected when allocating 0 bytes for ggml_aligned_malloc!\n"); + return NULL; + } + void * aligned_memory = NULL; + #ifdef GGML_USE_CPU_HBM + int result = hbw_posix_memalign(&aligned_memory, alignment, size); + #elif TARGET_OS_OSX + GGML_UNUSED(alignment); + kern_return_t alloc_status = vm_allocate((vm_map_t) mach_task_self(), (vm_address_t *) &aligned_memory, size, VM_FLAGS_ANYWHERE); + int result = EFAULT; + switch (alloc_status) { + case KERN_SUCCESS: + result = 0; + break; + case KERN_INVALID_ADDRESS: + result = EINVAL; + break; + case KERN_NO_SPACE: + result = ENOMEM; + break; + default: + result = EFAULT; + break; + } + #else + int result = posix_memalign(&aligned_memory, alignment, size); + #endif + if (result != 0) { + // Handle allocation failure + const char *error_desc = "unknown allocation error"; + switch (result) { + case EINVAL: + error_desc = "invalid alignment value"; + break; + case ENOMEM: + error_desc = "insufficient memory"; + break; + } + GGML_LOG_ERROR("%s: %s (attempted to allocate %6.2f MB)\n", __func__, error_desc, size/(1024.0*1024.0)); + return NULL; + } + return aligned_memory; +#endif +} + +void ggml_aligned_free(void * ptr, size_t size) { + GGML_UNUSED(size); +#if defined(_MSC_VER) || defined(__MINGW32__) + _aligned_free(ptr); +#elif GGML_USE_CPU_HBM + if (ptr != NULL) { + hbw_free(ptr); + } +#elif TARGET_OS_OSX + if (ptr != NULL) { + vm_deallocate((vm_map_t)mach_task_self(), (vm_address_t)ptr, size); + } +#else + free(ptr); +#endif +} + + +inline static void * ggml_malloc(size_t size) { + if (size == 0) { + GGML_LOG_WARN("Behavior may be unexpected when allocating 0 bytes for ggml_malloc!\n"); + return NULL; + } + void * result = malloc(size); + if (result == NULL) { + GGML_LOG_ERROR("%s: failed to allocate %6.2f MB\n", __func__, size/(1024.0*1024.0)); + GGML_ABORT("fatal error"); + } + return result; +} + +// calloc +inline static void * ggml_calloc(size_t num, size_t size) { + if (num == 0 || size == 0) { + GGML_LOG_WARN("Behavior may be unexpected when allocating 0 bytes for ggml_calloc!\n"); + return NULL; + } + void * result = calloc(num, size); + if (result == NULL) { + GGML_LOG_ERROR("%s: failed to allocate %6.2f MB\n", __func__, size/(1024.0*1024.0)); + GGML_ABORT("fatal error"); + } + return result; +} + +#define GGML_MALLOC(size) ggml_malloc(size) +#define GGML_CALLOC(num, size) ggml_calloc(num, size) + +#define GGML_FREE(ptr) free(ptr) + +const char * ggml_status_to_string(enum ggml_status status) { + switch (status) { + case GGML_STATUS_ALLOC_FAILED: return "GGML status: error (failed to allocate memory)"; + case GGML_STATUS_FAILED: return "GGML status: error (operation failed)"; + case GGML_STATUS_SUCCESS: return "GGML status: success"; + case GGML_STATUS_ABORTED: return "GGML status: warning (operation aborted)"; + } + + return "GGML status: unknown"; +} + +float ggml_fp16_to_fp32(ggml_fp16_t x) { +#define ggml_fp16_to_fp32 do_not_use__ggml_fp16_to_fp32__in_ggml + return GGML_FP16_TO_FP32(x); +} + +ggml_fp16_t ggml_fp32_to_fp16(float x) { +#define ggml_fp32_to_fp16 do_not_use__ggml_fp32_to_fp16__in_ggml + return GGML_FP32_TO_FP16(x); +} + +float ggml_bf16_to_fp32(ggml_bf16_t x) { +#define ggml_bf16_to_fp32 do_not_use__ggml_bf16_to_fp32__in_ggml + return GGML_BF16_TO_FP32(x); // it just left shifts +} + +ggml_bf16_t ggml_fp32_to_bf16(float x) { +#define ggml_fp32_to_bf16 do_not_use__ggml_fp32_to_bf16__in_ggml + return GGML_FP32_TO_BF16(x); +} + +void ggml_fp16_to_fp32_row(const ggml_fp16_t * x, float * y, int64_t n) { + for (int64_t i = 0; i < n; i++) { + y[i] = GGML_FP16_TO_FP32(x[i]); + } +} + +void ggml_fp32_to_fp16_row(const float * x, ggml_fp16_t * y, int64_t n) { + int i = 0; + for (; i < n; ++i) { + y[i] = GGML_FP32_TO_FP16(x[i]); + } +} + +void ggml_bf16_to_fp32_row(const ggml_bf16_t * x, float * y, int64_t n) { + int i = 0; + for (; i < n; ++i) { + y[i] = GGML_BF16_TO_FP32(x[i]); + } +} + +void ggml_fp32_to_bf16_row_ref(const float * x, ggml_bf16_t * y, int64_t n) { + for (int i = 0; i < n; i++) { + y[i] = ggml_compute_fp32_to_bf16(x[i]); + } +} + +void ggml_fp32_to_bf16_row(const float * x, ggml_bf16_t * y, int64_t n) { + int i = 0; +#if defined(__AVX512BF16__) + // subnormals are flushed to zero on this platform + for (; i + 32 <= n; i += 32) { + _mm512_storeu_si512( + (__m512i *)(y + i), + m512i(_mm512_cvtne2ps_pbh(_mm512_loadu_ps(x + i + 16), + _mm512_loadu_ps(x + i)))); + } +#endif + for (; i < n; i++) { + y[i] = GGML_FP32_TO_BF16(x[i]); + } +} + +bool ggml_guid_matches(ggml_guid_t guid_a, ggml_guid_t guid_b) { + return memcmp(guid_a, guid_b, sizeof(ggml_guid)) == 0; +} + +const char * ggml_version(void) { + return GGML_VERSION; +} + +const char * ggml_commit(void) { + return GGML_COMMIT; +} + +// +// timing +// + +#if defined(_MSC_VER) || defined(__MINGW32__) +static int64_t timer_freq, timer_start; +static BOOL CALLBACK ggml_time_init_once(PINIT_ONCE once, PVOID param, PVOID *ctx) { + UNUSED(once); + UNUSED(param); + UNUSED(ctx); + + LARGE_INTEGER t; + QueryPerformanceFrequency(&t); + timer_freq = t.QuadPart; + + // The multiplication by 1000 or 1000000 below can cause an overflow if timer_freq + // and the uptime is high enough. + // We subtract the program start time to reduce the likelihood of that happening. + QueryPerformanceCounter(&t); + timer_start = t.QuadPart; + + return TRUE; +} +void ggml_time_init(void) { + static INIT_ONCE once = INIT_ONCE_STATIC_INIT; + InitOnceExecuteOnce(&once, ggml_time_init_once, NULL, NULL); +} +int64_t ggml_time_ms(void) { + LARGE_INTEGER t; + QueryPerformanceCounter(&t); + return ((t.QuadPart-timer_start) * 1000) / timer_freq; +} +int64_t ggml_time_us(void) { + LARGE_INTEGER t; + QueryPerformanceCounter(&t); + return ((t.QuadPart-timer_start) * 1000000) / timer_freq; +} +#else +void ggml_time_init(void) {} +int64_t ggml_time_ms(void) { + struct timespec ts; + clock_gettime(CLOCK_MONOTONIC, &ts); + return (int64_t)ts.tv_sec*1000 + (int64_t)ts.tv_nsec/1000000; +} + +int64_t ggml_time_us(void) { + struct timespec ts; + clock_gettime(CLOCK_MONOTONIC, &ts); + return (int64_t)ts.tv_sec*1000000 + (int64_t)ts.tv_nsec/1000; +} +#endif + +int64_t ggml_cycles(void) { + return clock(); +} + +int64_t ggml_cycles_per_ms(void) { + return CLOCKS_PER_SEC/1000; +} + +// +// cross-platform UTF-8 file paths +// + +#ifdef _WIN32 +static wchar_t * ggml_mbstowcs(const char * mbs) { + int wlen = MultiByteToWideChar(CP_UTF8, 0, mbs, -1, NULL, 0); + if (!wlen) { + errno = EINVAL; + return NULL; + } + + wchar_t * wbuf = GGML_MALLOC(wlen * sizeof(wchar_t)); + wlen = MultiByteToWideChar(CP_UTF8, 0, mbs, -1, wbuf, wlen); + if (!wlen) { + GGML_FREE(wbuf); + errno = EINVAL; + return NULL; + } + + return wbuf; +} +#endif + +FILE * ggml_fopen(const char * fname, const char * mode) { +#ifdef _WIN32 + FILE * file = NULL; + + // convert fname (UTF-8) + wchar_t * wfname = ggml_mbstowcs(fname); + if (wfname) { + // convert mode (UTF-8) + wchar_t * wmode = ggml_mbstowcs(mode); + if (wmode) { + // open file + file = _wfopen(wfname, wmode); + GGML_FREE(wmode); + } + + GGML_FREE(wfname); + } + + return file; +#else + return fopen(fname, mode); +#endif + +} + +static const struct ggml_type_traits type_traits[GGML_TYPE_COUNT] = { + [GGML_TYPE_I8] = { + .type_name = "i8", + .blck_size = 1, + .type_size = sizeof(int8_t), + .is_quantized = false, + }, + [GGML_TYPE_I16] = { + .type_name = "i16", + .blck_size = 1, + .type_size = sizeof(int16_t), + .is_quantized = false, + }, + [GGML_TYPE_I32] = { + .type_name = "i32", + .blck_size = 1, + .type_size = sizeof(int32_t), + .is_quantized = false, + }, + [GGML_TYPE_I64] = { + .type_name = "i64", + .blck_size = 1, + .type_size = sizeof(int64_t), + .is_quantized = false, + }, + [GGML_TYPE_F64] = { + .type_name = "f64", + .blck_size = 1, + .type_size = sizeof(double), + .is_quantized = false, + }, + [GGML_TYPE_F32] = { + .type_name = "f32", + .blck_size = 1, + .type_size = sizeof(float), + .is_quantized = false, + }, + [GGML_TYPE_F16] = { + .type_name = "f16", + .blck_size = 1, + .type_size = sizeof(ggml_fp16_t), + .is_quantized = false, + .to_float = (ggml_to_float_t) ggml_fp16_to_fp32_row, + .from_float_ref = (ggml_from_float_t) ggml_fp32_to_fp16_row, + }, + [GGML_TYPE_Q1_0] = { + .type_name = "q1_0", + .blck_size = QK1_0, + .type_size = sizeof(block_q1_0), + .is_quantized = true, + .to_float = (ggml_to_float_t) dequantize_row_q1_0, + .from_float_ref = (ggml_from_float_t) quantize_row_q1_0_ref, + }, + [GGML_TYPE_Q2_0] = { + .type_name = "q2_0", + .blck_size = QK2_0, + .type_size = sizeof(block_q2_0), + .is_quantized = true, + .to_float = (ggml_to_float_t) dequantize_row_q2_0, + .from_float_ref = (ggml_from_float_t) quantize_row_q2_0_ref, + }, + [GGML_TYPE_Q4_0] = { + .type_name = "q4_0", + .blck_size = QK4_0, + .type_size = sizeof(block_q4_0), + .is_quantized = true, + .to_float = (ggml_to_float_t) dequantize_row_q4_0, + .from_float_ref = (ggml_from_float_t) quantize_row_q4_0_ref, + }, + [GGML_TYPE_Q4_1] = { + .type_name = "q4_1", + .blck_size = QK4_1, + .type_size = sizeof(block_q4_1), + .is_quantized = true, + .to_float = (ggml_to_float_t) dequantize_row_q4_1, + .from_float_ref = (ggml_from_float_t) quantize_row_q4_1_ref, + }, + [4] = { // GGML_TYPE_Q4_2 + .type_name = "DEPRECATED", + .blck_size = 0, + .type_size = 0, + .is_quantized = false, + }, + [5] = { // GGML_TYPE_Q4_3 + .type_name = "DEPRECATED", + .blck_size = 0, + .type_size = 0, + .is_quantized = false, + }, + [GGML_TYPE_Q5_0] = { + .type_name = "q5_0", + .blck_size = QK5_0, + .type_size = sizeof(block_q5_0), + .is_quantized = true, + .to_float = (ggml_to_float_t) dequantize_row_q5_0, + .from_float_ref = (ggml_from_float_t) quantize_row_q5_0_ref, + }, + [GGML_TYPE_Q5_1] = { + .type_name = "q5_1", + .blck_size = QK5_1, + .type_size = sizeof(block_q5_1), + .is_quantized = true, + .to_float = (ggml_to_float_t) dequantize_row_q5_1, + .from_float_ref = (ggml_from_float_t) quantize_row_q5_1_ref, + }, + [GGML_TYPE_Q8_0] = { + .type_name = "q8_0", + .blck_size = QK8_0, + .type_size = sizeof(block_q8_0), + .is_quantized = true, + .to_float = (ggml_to_float_t) dequantize_row_q8_0, + .from_float_ref = (ggml_from_float_t) quantize_row_q8_0_ref, + }, + [GGML_TYPE_Q8_1] = { + .type_name = "q8_1", + .blck_size = QK8_1, + .type_size = sizeof(block_q8_1), + .is_quantized = true, + .from_float_ref = (ggml_from_float_t) quantize_row_q8_1_ref, + }, + [GGML_TYPE_MXFP4] = { + .type_name = "mxfp4", + .blck_size = QK_MXFP4, + .type_size = sizeof(block_mxfp4), + .is_quantized = true, + .to_float = (ggml_to_float_t) dequantize_row_mxfp4, + .from_float_ref = (ggml_from_float_t)quantize_row_mxfp4_ref, + }, + [GGML_TYPE_NVFP4] = { + .type_name = "nvfp4", + .blck_size = QK_NVFP4, + .type_size = sizeof(block_nvfp4), + .is_quantized = true, + .to_float = (ggml_to_float_t) dequantize_row_nvfp4, + .from_float_ref = (ggml_from_float_t)quantize_row_nvfp4_ref, + }, + [GGML_TYPE_Q2_K] = { + .type_name = "q2_K", + .blck_size = QK_K, + .type_size = sizeof(block_q2_K), + .is_quantized = true, + .to_float = (ggml_to_float_t) dequantize_row_q2_K, + .from_float_ref = (ggml_from_float_t) quantize_row_q2_K_ref, + }, + [GGML_TYPE_Q3_K] = { + .type_name = "q3_K", + .blck_size = QK_K, + .type_size = sizeof(block_q3_K), + .is_quantized = true, + .to_float = (ggml_to_float_t) dequantize_row_q3_K, + .from_float_ref = (ggml_from_float_t) quantize_row_q3_K_ref, + }, + [GGML_TYPE_Q4_K] = { + .type_name = "q4_K", + .blck_size = QK_K, + .type_size = sizeof(block_q4_K), + .is_quantized = true, + .to_float = (ggml_to_float_t) dequantize_row_q4_K, + .from_float_ref = (ggml_from_float_t) quantize_row_q4_K_ref, + }, + [GGML_TYPE_Q5_K] = { + .type_name = "q5_K", + .blck_size = QK_K, + .type_size = sizeof(block_q5_K), + .is_quantized = true, + .to_float = (ggml_to_float_t) dequantize_row_q5_K, + .from_float_ref = (ggml_from_float_t) quantize_row_q5_K_ref, + }, + [GGML_TYPE_Q6_K] = { + .type_name = "q6_K", + .blck_size = QK_K, + .type_size = sizeof(block_q6_K), + .is_quantized = true, + .to_float = (ggml_to_float_t) dequantize_row_q6_K, + .from_float_ref = (ggml_from_float_t) quantize_row_q6_K_ref, + }, + [GGML_TYPE_IQ2_XXS] = { + .type_name = "iq2_xxs", + .blck_size = QK_K, + .type_size = sizeof(block_iq2_xxs), + .is_quantized = true, + .to_float = (ggml_to_float_t) dequantize_row_iq2_xxs, + .from_float_ref = NULL, + }, + [GGML_TYPE_IQ2_XS] = { + .type_name = "iq2_xs", + .blck_size = QK_K, + .type_size = sizeof(block_iq2_xs), + .is_quantized = true, + .to_float = (ggml_to_float_t) dequantize_row_iq2_xs, + .from_float_ref = NULL, + }, + [GGML_TYPE_IQ3_XXS] = { + .type_name = "iq3_xxs", + .blck_size = QK_K, + .type_size = sizeof(block_iq3_xxs), + .is_quantized = true, + .to_float = (ggml_to_float_t) dequantize_row_iq3_xxs, + .from_float_ref = (ggml_from_float_t)quantize_row_iq3_xxs_ref, + }, + [GGML_TYPE_IQ3_S] = { + .type_name = "iq3_s", + .blck_size = QK_K, + .type_size = sizeof(block_iq3_s), + .is_quantized = true, + .to_float = (ggml_to_float_t) dequantize_row_iq3_s, + .from_float_ref = (ggml_from_float_t)quantize_row_iq3_s_ref, + }, + [GGML_TYPE_IQ2_S] = { + .type_name = "iq2_s", + .blck_size = QK_K, + .type_size = sizeof(block_iq2_s), + .is_quantized = true, + .to_float = (ggml_to_float_t) dequantize_row_iq2_s, + .from_float_ref = (ggml_from_float_t)quantize_row_iq2_s_ref, + }, + [GGML_TYPE_IQ1_S] = { + .type_name = "iq1_s", + .blck_size = QK_K, + .type_size = sizeof(block_iq1_s), + .is_quantized = true, + .to_float = (ggml_to_float_t) dequantize_row_iq1_s, + .from_float_ref = NULL, + }, + [GGML_TYPE_IQ1_M] = { + .type_name = "iq1_m", + .blck_size = QK_K, + .type_size = sizeof(block_iq1_m), + .is_quantized = true, + .to_float = (ggml_to_float_t) dequantize_row_iq1_m, + .from_float_ref = NULL, + }, + [GGML_TYPE_IQ4_NL] = { + .type_name = "iq4_nl", + .blck_size = QK4_NL, + .type_size = sizeof(block_iq4_nl), + .is_quantized = true, + .to_float = (ggml_to_float_t) dequantize_row_iq4_nl, + .from_float_ref = (ggml_from_float_t)quantize_row_iq4_nl_ref, + }, + [GGML_TYPE_IQ4_XS] = { + .type_name = "iq4_xs", + .blck_size = QK_K, + .type_size = sizeof(block_iq4_xs), + .is_quantized = true, + .to_float = (ggml_to_float_t) dequantize_row_iq4_xs, + .from_float_ref = (ggml_from_float_t)quantize_row_iq4_xs_ref, + }, + [GGML_TYPE_Q8_K] = { + .type_name = "q8_K", + .blck_size = QK_K, + .type_size = sizeof(block_q8_K), + .is_quantized = true, + }, + [GGML_TYPE_BF16] = { + .type_name = "bf16", + .blck_size = 1, + .type_size = sizeof(ggml_bf16_t), + .is_quantized = false, + .to_float = (ggml_to_float_t) ggml_bf16_to_fp32_row, + .from_float_ref = (ggml_from_float_t) ggml_fp32_to_bf16_row_ref, + }, + [31] = { // GGML_TYPE_Q4_0_4_4 + .type_name = "TYPE_Q4_0_4_4 REMOVED, use Q4_0 with runtime repacking", + .blck_size = 0, + .type_size = 0, + .is_quantized = false, + }, + [32] = { // GGML_TYPE_Q4_0_4_8 + .type_name = "TYPE_Q4_0_4_8 REMOVED, use Q4_0 with runtime repacking", + .blck_size = 0, + .type_size = 0, + .is_quantized = false, + }, + [33] = { // GGML_TYPE_Q4_0_8_8 + .type_name = "TYPE_Q4_0_8_8 REMOVED, use Q4_0 with runtime repacking", + .blck_size = 0, + .type_size = 0, + .is_quantized = false, + }, + [GGML_TYPE_TQ1_0] = { + .type_name = "tq1_0", + .blck_size = QK_K, + .type_size = sizeof(block_tq1_0), + .is_quantized = true, + .to_float = (ggml_to_float_t) dequantize_row_tq1_0, + .from_float_ref = (ggml_from_float_t) quantize_row_tq1_0_ref, + }, + [GGML_TYPE_TQ2_0] = { + .type_name = "tq2_0", + .blck_size = QK_K, + .type_size = sizeof(block_tq2_0), + .is_quantized = true, + .to_float = (ggml_to_float_t) dequantize_row_tq2_0, + .from_float_ref = (ggml_from_float_t) quantize_row_tq2_0_ref, + }, + [36] = { // GGML_TYPE_IQ4_NL_4_4 + .type_name = "TYPE_IQ4_NL_4_4 REMOVED, use IQ4_NL with runtime repacking", + .blck_size = 0, + .type_size = 0, + .is_quantized = false, + }, + [37] = { // GGML_TYPE_IQ4_NL_4_8 + .type_name = "TYPE_IQ4_NL_4_8 REMOVED, use IQ4_NL with runtime repacking", + .blck_size = 0, + .type_size = 0, + .is_quantized = false, + }, + [38] = { // GGML_TYPE_IQ4_NL_8_8 + .type_name = "TYPE_IQ4_NL_8_8 REMOVED, use IQ4_NL with runtime repacking", + .blck_size = 0, + .type_size = 0, + .is_quantized = false, + }, +}; + +const struct ggml_type_traits * ggml_get_type_traits(enum ggml_type type) { + assert(type >= 0); + assert(type < GGML_TYPE_COUNT); + return &type_traits[type]; +} + +// +// ggml object +// + +struct ggml_object { + size_t offs; + size_t size; + + struct ggml_object * next; + + enum ggml_object_type type; + + char padding[4]; +}; + +static const size_t GGML_OBJECT_SIZE = sizeof(struct ggml_object); + +// +// ggml context +// + +struct ggml_context { + size_t mem_size; + void * mem_buffer; + bool mem_buffer_owned; + bool no_alloc; + + int n_objects; + + struct ggml_object * objects_begin; + struct ggml_object * objects_end; +}; + +// +// data types +// + +static const char * GGML_OP_NAME[GGML_OP_COUNT] = { + "NONE", + + "DUP", + "ADD", + "ADD_ID", + "ADD1", + "ACC", + "SUB", + "MUL", + "DIV", + "SQR", + "SQRT", + "LOG", + "SIN", + "COS", + "SUM", + "SUM_ROWS", + "CUMSUM", + "MEAN", + "ARGMAX", + "COUNT_EQUAL", + "REPEAT", + "REPEAT_BACK", + "CONCAT", + "SILU_BACK", + "NORM", + "RMS_NORM", + "RMS_NORM_BACK", + "GROUP_NORM", + "L2_NORM", + + "MUL_MAT", + "MUL_MAT_ID", + "OUT_PROD", + + "SCALE", + "SET", + "CPY", + "CONT", + "RESHAPE", + "VIEW", + "PERMUTE", + "TRANSPOSE", + "GET_ROWS", + "GET_ROWS_BACK", + "SET_ROWS", + "DIAG", + "DIAG_MASK_INF", + "DIAG_MASK_ZERO", + "SOFT_MAX", + "SOFT_MAX_BACK", + "ROPE", + "ROPE_BACK", + "CLAMP", + "CONV_TRANSPOSE_1D", + "IM2COL", + "IM2COL_BACK", + "IM2COL_3D", + "COL2IM_1D", + "CONV_2D", + "CONV_3D", + "CONV_2D_DW", + "CONV_TRANSPOSE_2D", + "POOL_1D", + "POOL_2D", + "POOL_2D_BACK", + "UPSCALE", + "PAD", + "PAD_REFLECT_1D", + "ROLL", + "ARANGE", + "TIMESTEP_EMBEDDING", + "ARGSORT", + "TOP_K", + "LEAKY_RELU", + "TRI", + "FILL", + + "FLASH_ATTN_EXT", + "FLASH_ATTN_BACK", + "SSM_CONV", + "SSM_SCAN", + "WIN_PART", + "WIN_UNPART", + "GET_REL_POS", + "ADD_REL_POS", + "RWKV_WKV6", + "GATED_LINEAR_ATTN", + "RWKV_WKV7", + "SOLVE_TRI", + "GATED_DELTA_NET", + "LIGHTNING_INDEXER", + + "UNARY", + + "MAP_CUSTOM1", + "MAP_CUSTOM2", + "MAP_CUSTOM3", + + "CUSTOM", + + "CROSS_ENTROPY_LOSS", + "CROSS_ENTROPY_LOSS_BACK", + "OPT_STEP_ADAMW", + "OPT_STEP_SGD", + + "GLU", +}; + +static_assert(GGML_OP_COUNT == 98, "GGML_OP_COUNT != 98"); + +static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { + "none", + + "x", + "x+y", + "x[i]+y", + "x+y", + "view(x,nb,offset)+=y->x", + "x-y", + "x*y", + "x/y", + "x^2", + "√x", + "log(x)", + "sin(x)", + "cos(x)", + "Σx", + "Σx_k", + "cumsum(x)", + "Σx/n", + "argmax(x)", + "count_equal(x)", + "repeat(x)", + "repeat_back(x)", + "concat(x, y)", + "silu_back(x)", + "norm(x)", + "rms_norm(x)", + "rms_norm_back(x)", + "group_norm(x)", + "l2_norm(x)", + + "X*Y", + "X[i]*Y", + "X*Y", + + "x*v", + "y-\\>view(x)", + "x-\\>y", + "cont(x)", + "reshape(x)", + "view(x)", + "permute(x)", + "transpose(x)", + "get_rows(x)", + "get_rows_back(x)", + "set_rows(x)", + "diag(x)", + "diag_mask_inf(x)", + "diag_mask_zero(x)", + "soft_max(x)", + "soft_max_back(x)", + "rope(x)", + "rope_back(x)", + "clamp(x)", + "conv_transpose_1d(x)", + "im2col(x)", + "im2col_back(x)", + "im2col_3d(x)", + "col2im_1d(x)", + "conv_2d(x)", + "conv_3d(x)", + "conv_2d_dw(x)", + "conv_transpose_2d(x)", + "pool_1d(x)", + "pool_2d(x)", + "pool_2d_back(x)", + "upscale(x)", + "pad(x)", + "pad_reflect_1d(x)", + "roll(x)", + "arange(start, stop, step)", + "timestep_embedding(timesteps, dim, max_period)", + "argsort(x)", + "top_k(x)", + "leaky_relu(x)", + "tri(x)", + "fill(x, c)", + + "flash_attn_ext(x)", + "flash_attn_back(x)", + "ssm_conv(x)", + "ssm_scan(x)", + "win_part(x)", + "win_unpart(x)", + "get_rel_pos(x)", + "add_rel_pos(x)", + "rwkv_wkv6(k, v, r, tf, td, s)", + "gated_linear_attn(k, v, q, gate, s)", + "rwkv_wkv7(r, w, k, v, a, b, s)", + "A X = B, A triangular, solve X", + "gated_delta_net(q, k, v, g, beta, s)", + "lightning_indexer(q, k, weights, mask)", + + "unary(x)", + + "map_custom(x)", + "map_custom(x,y)", + "map_custom(x,y,z)", + + "custom(x)", + + "cross_entropy_loss(x,y)", + "cross_entropy_loss_back(x,y)", + "adamw(x)", + "sgd(x)", + + "glu(x)", +}; + +static_assert(GGML_OP_COUNT == 98, "GGML_OP_COUNT != 98"); + +static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2"); + +static const char * GGML_UNARY_OP_NAME[GGML_UNARY_OP_COUNT] = { + "ABS", + "SGN", + "NEG", + "STEP", + "TANH", + "ELU", + "RELU", + "SIGMOID", + "GELU", + "GELU_QUICK", + "SILU", + "HARDSWISH", + "HARDSIGMOID", + "EXP", + "EXPM1", + "SOFTPLUS", + "GELU_ERF", + "XIELU", + "FLOOR", + "CEIL", + "ROUND", + "TRUNC", +}; + +static_assert(GGML_UNARY_OP_COUNT == 22, "GGML_UNARY_OP_COUNT != 22"); + +static const char * GGML_GLU_OP_NAME[GGML_GLU_OP_COUNT] = { + "REGLU", + "GEGLU", + "SWIGLU", + "SWIGLU_OAI", + "GEGLU_ERF", + "GEGLU_QUICK", +}; + +static_assert(GGML_GLU_OP_COUNT == 6, "GGML_GLU_OP_COUNT != 6"); + + +static_assert(sizeof(struct ggml_object)%GGML_MEM_ALIGN == 0, "ggml_object size must be a multiple of GGML_MEM_ALIGN"); +static_assert(sizeof(struct ggml_tensor)%GGML_MEM_ALIGN == 0, "ggml_tensor size must be a multiple of GGML_MEM_ALIGN"); + + +//////////////////////////////////////////////////////////////////////////////// + +void ggml_print_object(const struct ggml_object * obj) { + GGML_LOG_INFO(" - ggml_object: type = %d, offset = %zu, size = %zu, next = %p\n", + obj->type, obj->offs, obj->size, (const void *) obj->next); +} + +void ggml_print_objects(const struct ggml_context * ctx) { + struct ggml_object * obj = ctx->objects_begin; + + GGML_LOG_INFO("%s: objects in context %p:\n", __func__, (const void *) ctx); + + while (obj != NULL) { + ggml_print_object(obj); + obj = obj->next; + } + + GGML_LOG_INFO("%s: --- end ---\n", __func__); +} + +int64_t ggml_nelements(const struct ggml_tensor * tensor) { + static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); + + return tensor->ne[0]*tensor->ne[1]*tensor->ne[2]*tensor->ne[3]; +} + +int64_t ggml_nrows(const struct ggml_tensor * tensor) { + static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); + + return tensor->ne[1]*tensor->ne[2]*tensor->ne[3]; +} + +size_t ggml_nbytes(const struct ggml_tensor * tensor) { + for (int i = 0; i < GGML_MAX_DIMS; ++i) { + if (tensor->ne[i] <= 0) { + return 0; + } + } + + size_t nbytes; + const size_t blck_size = ggml_blck_size(tensor->type); + if (blck_size == 1) { + nbytes = ggml_type_size(tensor->type); + for (int i = 0; i < GGML_MAX_DIMS; ++i) { + nbytes += (tensor->ne[i] - 1)*tensor->nb[i]; + } + } + else { + nbytes = tensor->ne[0]*tensor->nb[0]/blck_size; + for (int i = 1; i < GGML_MAX_DIMS; ++i) { + nbytes += (tensor->ne[i] - 1)*tensor->nb[i]; + } + } + + return nbytes; +} + +size_t ggml_nbytes_pad(const struct ggml_tensor * tensor) { + return GGML_PAD(ggml_nbytes(tensor), GGML_MEM_ALIGN); +} + +int64_t ggml_blck_size(enum ggml_type type) { + assert(type >= 0); + assert(type < GGML_TYPE_COUNT); + return type_traits[type].blck_size; +} + +size_t ggml_type_size(enum ggml_type type) { + assert(type >= 0); + assert(type < GGML_TYPE_COUNT); + return type_traits[type].type_size; +} + +size_t ggml_row_size(enum ggml_type type, int64_t ne) { + assert(type >= 0); + assert(type < GGML_TYPE_COUNT); + assert(ne % ggml_blck_size(type) == 0); + return ggml_type_size(type)*ne/ggml_blck_size(type); +} + +double ggml_type_sizef(enum ggml_type type) { + assert(type >= 0); + assert(type < GGML_TYPE_COUNT); + return ((double)(type_traits[type].type_size))/type_traits[type].blck_size; +} + +const char * ggml_type_name(enum ggml_type type) { + assert(type >= 0); + assert(type < GGML_TYPE_COUNT); + return type_traits[type].type_name; +} + +bool ggml_is_quantized(enum ggml_type type) { + assert(type >= 0); + assert(type < GGML_TYPE_COUNT); + return type_traits[type].is_quantized; +} + +const char * ggml_op_name(enum ggml_op op) { + return GGML_OP_NAME[op]; +} + +const char * ggml_op_symbol(enum ggml_op op) { + return GGML_OP_SYMBOL[op]; +} + +const char * ggml_unary_op_name(enum ggml_unary_op op) { + return GGML_UNARY_OP_NAME[op]; +} + +const char * ggml_glu_op_name(enum ggml_glu_op op) { + return GGML_GLU_OP_NAME[op]; +} + +const char * ggml_op_desc(const struct ggml_tensor * t) { + if (t->op == GGML_OP_UNARY) { + enum ggml_unary_op uop = ggml_get_unary_op(t); + return ggml_unary_op_name(uop); + } + if (t->op == GGML_OP_GLU) { + enum ggml_glu_op gop = ggml_get_glu_op(t); + return ggml_glu_op_name(gop); + } + return ggml_op_name(t->op); +} + +size_t ggml_element_size(const struct ggml_tensor * tensor) { + return ggml_type_size(tensor->type); +} + +bool ggml_is_scalar(const struct ggml_tensor * tensor) { + static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); + + return tensor->ne[0] == 1 && tensor->ne[1] == 1 && tensor->ne[2] == 1 && tensor->ne[3] == 1; +} + +bool ggml_is_vector(const struct ggml_tensor * tensor) { + static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); + + return tensor->ne[1] == 1 && tensor->ne[2] == 1 && tensor->ne[3] == 1; +} + +bool ggml_is_matrix(const struct ggml_tensor * tensor) { + static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); + + return tensor->ne[2] == 1 && tensor->ne[3] == 1; +} + +bool ggml_is_3d(const struct ggml_tensor * tensor) { + return tensor->ne[3] == 1; +} + +int ggml_n_dims(const struct ggml_tensor * tensor) { + for (int i = GGML_MAX_DIMS - 1; i >= 1; --i) { + if (tensor->ne[i] > 1) { + return i + 1; + } + } + return 1; +} + +enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) { + enum ggml_type wtype = GGML_TYPE_COUNT; + + switch (ftype) { + case GGML_FTYPE_ALL_F32: wtype = GGML_TYPE_F32; break; + case GGML_FTYPE_MOSTLY_F16: wtype = GGML_TYPE_F16; break; + case GGML_FTYPE_MOSTLY_BF16: wtype = GGML_TYPE_BF16; break; + case GGML_FTYPE_MOSTLY_Q4_0: wtype = GGML_TYPE_Q4_0; break; + case GGML_FTYPE_MOSTLY_Q4_1: wtype = GGML_TYPE_Q4_1; break; + case GGML_FTYPE_MOSTLY_Q1_0: wtype = GGML_TYPE_Q1_0; break; + case GGML_FTYPE_MOSTLY_Q2_0: wtype = GGML_TYPE_Q2_0; break; + case GGML_FTYPE_MOSTLY_Q5_0: wtype = GGML_TYPE_Q5_0; break; + case GGML_FTYPE_MOSTLY_Q5_1: wtype = GGML_TYPE_Q5_1; break; + case GGML_FTYPE_MOSTLY_Q8_0: wtype = GGML_TYPE_Q8_0; break; + case GGML_FTYPE_MOSTLY_MXFP4: wtype = GGML_TYPE_MXFP4; break; + case GGML_FTYPE_MOSTLY_NVFP4: wtype = GGML_TYPE_NVFP4; break; + case GGML_FTYPE_MOSTLY_Q2_K: wtype = GGML_TYPE_Q2_K; break; + case GGML_FTYPE_MOSTLY_Q3_K: wtype = GGML_TYPE_Q3_K; break; + case GGML_FTYPE_MOSTLY_Q4_K: wtype = GGML_TYPE_Q4_K; break; + case GGML_FTYPE_MOSTLY_Q5_K: wtype = GGML_TYPE_Q5_K; break; + case GGML_FTYPE_MOSTLY_Q6_K: wtype = GGML_TYPE_Q6_K; break; + case GGML_FTYPE_MOSTLY_IQ2_XXS: wtype = GGML_TYPE_IQ2_XXS; break; + case GGML_FTYPE_MOSTLY_IQ2_XS: wtype = GGML_TYPE_IQ2_XS; break; + case GGML_FTYPE_MOSTLY_IQ3_XXS: wtype = GGML_TYPE_IQ3_XXS; break; + case GGML_FTYPE_MOSTLY_IQ1_S: wtype = GGML_TYPE_IQ1_S; break; + case GGML_FTYPE_MOSTLY_IQ1_M: wtype = GGML_TYPE_IQ1_M; break; + case GGML_FTYPE_MOSTLY_IQ4_NL: wtype = GGML_TYPE_IQ4_NL; break; + case GGML_FTYPE_MOSTLY_IQ4_XS: wtype = GGML_TYPE_IQ4_XS; break; + case GGML_FTYPE_MOSTLY_IQ3_S: wtype = GGML_TYPE_IQ3_S; break; + case GGML_FTYPE_MOSTLY_IQ2_S: wtype = GGML_TYPE_IQ2_S; break; + case GGML_FTYPE_UNKNOWN: wtype = GGML_TYPE_COUNT; break; + case GGML_FTYPE_MOSTLY_Q4_1_SOME_F16: wtype = GGML_TYPE_COUNT; break; + } + + GGML_ASSERT(wtype != GGML_TYPE_COUNT); + + return wtype; +} + +size_t ggml_tensor_overhead(void) { + return GGML_OBJECT_SIZE + GGML_TENSOR_SIZE; +} + +bool ggml_is_transposed(const struct ggml_tensor * tensor) { + return tensor->nb[0] > tensor->nb[1]; +} + +static bool ggml_is_contiguous_n(const struct ggml_tensor * tensor, int n) { + size_t next_nb = ggml_type_size(tensor->type); + if (tensor->ne[0] != ggml_blck_size(tensor->type) && tensor->nb[0] != next_nb) { + return false; + } + next_nb *= tensor->ne[0]/ggml_blck_size(tensor->type); + for (int i = 1; i < GGML_MAX_DIMS; i++) { + if (i > n) { + if (tensor->ne[i] != 1 && tensor->nb[i] != next_nb) { + return false; + } + next_nb *= tensor->ne[i]; + } else { + // this dimension does not need to be contiguous + next_nb = tensor->ne[i]*tensor->nb[i]; + } + } + return true; +} + +bool ggml_is_contiguous(const struct ggml_tensor * tensor) { + return ggml_is_contiguous_0(tensor); +} + +bool ggml_is_contiguous_0(const struct ggml_tensor * tensor) { + return ggml_is_contiguous_n(tensor, 0); +} + +bool ggml_is_contiguous_1(const struct ggml_tensor * tensor) { + return ggml_is_contiguous_n(tensor, 1); +} + +bool ggml_is_contiguous_2(const struct ggml_tensor * tensor) { + return ggml_is_contiguous_n(tensor, 2); +} + +bool ggml_is_contiguously_allocated(const struct ggml_tensor * tensor) { + return ggml_nbytes(tensor) == ggml_nelements(tensor) * ggml_type_size(tensor->type)/ggml_blck_size(tensor->type); +} + +bool ggml_is_permuted(const struct ggml_tensor * tensor) { + static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); + + return tensor->nb[0] > tensor->nb[1] || tensor->nb[1] > tensor->nb[2] || tensor->nb[2] > tensor->nb[3]; +} + +bool ggml_is_contiguous_channels(const struct ggml_tensor * tensor) { + return + tensor->nb[0] > tensor->nb[2] && + tensor->nb[1] > tensor->nb[0] && + tensor->nb[2] == ggml_type_size(tensor->type); +} + +bool ggml_is_contiguous_rows(const struct ggml_tensor * tensor) { + return + tensor->ne[0] == ggml_blck_size(tensor->type) || + tensor->nb[0] == ggml_type_size(tensor->type); +} + +static inline bool ggml_is_padded_1d(const struct ggml_tensor * tensor) { + static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); + + return + tensor->nb[0] == ggml_type_size(tensor->type) && + tensor->nb[2] == tensor->nb[1]*tensor->ne[1] && + tensor->nb[3] == tensor->nb[2]*tensor->ne[2]; +} + +bool ggml_is_empty(const struct ggml_tensor * tensor) { + for (int i = 0; i < GGML_MAX_DIMS; ++i) { + if (tensor->ne[i] == 0) { + // empty if any dimension has no elements + return true; + } + } + return false; +} + +bool ggml_are_same_shape(const struct ggml_tensor * t0, const struct ggml_tensor * t1) { + static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); + + return + (t0->ne[0] == t1->ne[0]) && + (t0->ne[1] == t1->ne[1]) && + (t0->ne[2] == t1->ne[2]) && + (t0->ne[3] == t1->ne[3]); +} + +bool ggml_are_same_stride(const struct ggml_tensor * t0, const struct ggml_tensor * t1) { + static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); + + return + (t0->nb[0] == t1->nb[0]) && + (t0->nb[1] == t1->nb[1]) && + (t0->nb[2] == t1->nb[2]) && + (t0->nb[3] == t1->nb[3]); +} + +bool ggml_is_view(const struct ggml_tensor * t) { + return ggml_impl_is_view(t); +} + +// check if t1 can be represented as a repetition of t0 +bool ggml_can_repeat(const struct ggml_tensor * t0, const struct ggml_tensor * t1) { + static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); + + return ggml_is_empty(t0) ? ggml_is_empty(t1) : + (t1->ne[0]%t0->ne[0] == 0) && + (t1->ne[1]%t0->ne[1] == 0) && + (t1->ne[2]%t0->ne[2] == 0) && + (t1->ne[3]%t0->ne[3] == 0); +} + +static inline bool ggml_can_repeat_rows(const struct ggml_tensor * t0, const struct ggml_tensor * t1) { + static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); + + return (t0->ne[0] == t1->ne[0]) && ggml_can_repeat(t0, t1); +} + +// assert that pointer is aligned to GGML_MEM_ALIGN +#define GGML_ASSERT_ALIGNED(ptr) \ + GGML_ASSERT(((uintptr_t) (ptr))%GGML_MEM_ALIGN == 0) + +//////////////////////////////////////////////////////////////////////////////// + +struct ggml_context * ggml_init(struct ggml_init_params params) { + static bool is_first_call = true; + + ggml_critical_section_start(); + + if (is_first_call) { + // initialize time system (required on Windows) + ggml_time_init(); + + is_first_call = false; + } + + ggml_critical_section_end(); + + struct ggml_context * ctx = GGML_MALLOC(sizeof(struct ggml_context)); + + // allow to call ggml_init with 0 size + if (params.mem_size == 0) { + params.mem_size = GGML_MEM_ALIGN; + } + + const size_t mem_size = params.mem_buffer ? params.mem_size : GGML_PAD(params.mem_size, GGML_MEM_ALIGN); + + *ctx = (struct ggml_context) { + /*.mem_size =*/ mem_size, + /*.mem_buffer =*/ params.mem_buffer ? params.mem_buffer : ggml_aligned_malloc(mem_size), + /*.mem_buffer_owned =*/ params.mem_buffer ? false : true, + /*.no_alloc =*/ params.no_alloc, + /*.n_objects =*/ 0, + /*.objects_begin =*/ NULL, + /*.objects_end =*/ NULL, + }; + + GGML_ASSERT(ctx->mem_buffer != NULL); + + GGML_ASSERT_ALIGNED(ctx->mem_buffer); + + GGML_PRINT_DEBUG("%s: context initialized\n", __func__); + + return ctx; +} + +void ggml_reset(struct ggml_context * ctx) { + if (ctx == NULL) { + return; + } + + ctx->n_objects = 0; + ctx->objects_begin = NULL; + ctx->objects_end = NULL; +} + +void ggml_free(struct ggml_context * ctx) { + if (ctx == NULL) { + return; + } + + if (ctx->mem_buffer_owned) { + ggml_aligned_free(ctx->mem_buffer, ctx->mem_size); + } + + GGML_FREE(ctx); +} + +size_t ggml_used_mem(const struct ggml_context * ctx) { + return ctx->objects_end == NULL ? 0 : ctx->objects_end->offs + ctx->objects_end->size; +} + +bool ggml_get_no_alloc(struct ggml_context * ctx) { + return ctx->no_alloc; +} + +void ggml_set_no_alloc(struct ggml_context * ctx, bool no_alloc) { + ctx->no_alloc = no_alloc; +} + +void * ggml_get_mem_buffer(const struct ggml_context * ctx) { + return ctx->mem_buffer; +} + +size_t ggml_get_mem_size(const struct ggml_context * ctx) { + return ctx->mem_size; +} + +size_t ggml_get_max_tensor_size(const struct ggml_context * ctx) { + size_t max_size = 0; + + for (struct ggml_tensor * tensor = ggml_get_first_tensor(ctx); tensor != NULL; tensor = ggml_get_next_tensor(ctx, tensor)) { + size_t bytes = ggml_nbytes(tensor); + max_size = MAX(max_size, bytes); + } + + return max_size; +} + +//////////////////////////////////////////////////////////////////////////////// + +static struct ggml_object * ggml_new_object(struct ggml_context * ctx, enum ggml_object_type type, size_t size) { + // always insert objects at the end of the context's memory pool + struct ggml_object * obj_cur = ctx->objects_end; + + const size_t cur_offs = obj_cur == NULL ? 0 : obj_cur->offs; + const size_t cur_size = obj_cur == NULL ? 0 : obj_cur->size; + const size_t cur_end = cur_offs + cur_size; + + // align to GGML_MEM_ALIGN + GGML_ASSERT(size <= SIZE_MAX - (GGML_MEM_ALIGN - 1)); + size_t size_needed = GGML_PAD(size, GGML_MEM_ALIGN); + + char * const mem_buffer = ctx->mem_buffer; + struct ggml_object * const obj_new = (struct ggml_object *)(mem_buffer + cur_end); + + // integer overflow checks + if (cur_end > SIZE_MAX - size_needed) { + GGML_LOG_WARN("%s: overflow detected in cur_end (%zu) + size_needed (%zu)\n", __func__, cur_end, size_needed); + return NULL; + } + if (cur_end + size_needed > SIZE_MAX - GGML_OBJECT_SIZE) { + GGML_LOG_WARN("%s: overflow detected in cur_end (%zu) + size_needed (%zu) + GGML_OBJECT_SIZE (%zu)\n", __func__, + cur_end, size_needed, (size_t) GGML_OBJECT_SIZE); + return NULL; + } + + if (cur_end + size_needed + GGML_OBJECT_SIZE > ctx->mem_size) { + GGML_LOG_WARN("%s: not enough space in the context's memory pool (needed %zu, available %zu)\n", + __func__, cur_end + size_needed + GGML_OBJECT_SIZE, ctx->mem_size); +#ifndef NDEBUG + GGML_ABORT("not enough space in the context's memory pool"); +#endif + return NULL; + } + + *obj_new = (struct ggml_object) { + .offs = cur_end + GGML_OBJECT_SIZE, + .size = size_needed, + .next = NULL, + .type = type, + }; + + GGML_ASSERT_ALIGNED(mem_buffer + obj_new->offs); + + if (obj_cur != NULL) { + obj_cur->next = obj_new; + } else { + // this is the first object in this context + ctx->objects_begin = obj_new; + } + + ctx->objects_end = obj_new; + + //printf("%s: inserted new object at %zu, size = %zu\n", __func__, cur_end, obj_new->size); + + return obj_new; +} + +static struct ggml_tensor * ggml_new_tensor_impl( + struct ggml_context * ctx, + enum ggml_type type, + int n_dims, + const int64_t * ne, + struct ggml_tensor * view_src, + size_t view_offs) { + + GGML_ASSERT(type >= 0 && type < GGML_TYPE_COUNT); + GGML_ASSERT(n_dims >= 1 && n_dims <= GGML_MAX_DIMS); + + // find the base tensor and absolute offset + if (view_src != NULL && view_src->view_src != NULL) { + view_offs += view_src->view_offs; + view_src = view_src->view_src; + } + + size_t data_size = ggml_row_size(type, ne[0]); + for (int i = 1; i < n_dims; i++) { + data_size *= ne[i]; + } + + GGML_ASSERT(view_src == NULL || data_size == 0 || data_size + view_offs <= ggml_nbytes(view_src)); + + void * data = view_src != NULL ? view_src->data : NULL; + if (data != NULL) { + data = (char *) data + view_offs; + } + + size_t obj_alloc_size = 0; + + if (view_src == NULL && !ctx->no_alloc) { + // allocate tensor data in the context's memory pool + obj_alloc_size = data_size; + } + + GGML_ASSERT(GGML_TENSOR_SIZE <= SIZE_MAX - obj_alloc_size); + + struct ggml_object * const obj_new = ggml_new_object(ctx, GGML_OBJECT_TYPE_TENSOR, GGML_TENSOR_SIZE + obj_alloc_size); + GGML_ASSERT(obj_new); + + struct ggml_tensor * const result = (struct ggml_tensor *)((char *)ctx->mem_buffer + obj_new->offs); + + *result = (struct ggml_tensor) { + /*.type =*/ type, + /*.buffer =*/ NULL, + /*.ne =*/ { 1, 1, 1, 1 }, + /*.nb =*/ { 0, 0, 0, 0 }, + /*.op =*/ GGML_OP_NONE, + /*.op_params =*/ { 0 }, + /*.flags =*/ 0, + /*.src =*/ { NULL }, + /*.view_src =*/ view_src, + /*.view_offs =*/ view_offs, + /*.data =*/ obj_alloc_size > 0 ? (void *)(result + 1) : data, + /*.name =*/ { 0 }, + /*.extra =*/ NULL, + /*.padding =*/ { 0 }, + }; + + // TODO: this should not be needed as long as we don't rely on aligned SIMD loads + //GGML_ASSERT_ALIGNED(result->data); + + for (int i = 0; i < n_dims; i++) { + result->ne[i] = ne[i]; + } + + result->nb[0] = ggml_type_size(type); + result->nb[1] = result->nb[0]*(result->ne[0]/ggml_blck_size(type)); + for (int i = 2; i < GGML_MAX_DIMS; i++) { + result->nb[i] = result->nb[i - 1]*result->ne[i - 1]; + } + + ctx->n_objects++; + + return result; +} + +struct ggml_tensor * ggml_new_tensor( + struct ggml_context * ctx, + enum ggml_type type, + int n_dims, + const int64_t * ne) { + return ggml_new_tensor_impl(ctx, type, n_dims, ne, NULL, 0); +} + +struct ggml_tensor * ggml_new_tensor_1d( + struct ggml_context * ctx, + enum ggml_type type, + int64_t ne0) { + return ggml_new_tensor(ctx, type, 1, &ne0); +} + +struct ggml_tensor * ggml_new_tensor_2d( + struct ggml_context * ctx, + enum ggml_type type, + int64_t ne0, + int64_t ne1) { + const int64_t ne[2] = { ne0, ne1 }; + return ggml_new_tensor(ctx, type, 2, ne); +} + +struct ggml_tensor * ggml_new_tensor_3d( + struct ggml_context * ctx, + enum ggml_type type, + int64_t ne0, + int64_t ne1, + int64_t ne2) { + const int64_t ne[3] = { ne0, ne1, ne2 }; + return ggml_new_tensor(ctx, type, 3, ne); +} + +struct ggml_tensor * ggml_new_tensor_4d( + struct ggml_context * ctx, + enum ggml_type type, + int64_t ne0, + int64_t ne1, + int64_t ne2, + int64_t ne3) { + const int64_t ne[4] = { ne0, ne1, ne2, ne3 }; + return ggml_new_tensor(ctx, type, 4, ne); +} + +void * ggml_new_buffer(struct ggml_context * ctx, size_t nbytes) { + struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_TYPE_WORK_BUFFER, nbytes); + + return (uint8_t *)ctx->mem_buffer + obj->offs; +} + +struct ggml_tensor * ggml_dup_tensor(struct ggml_context * ctx, const struct ggml_tensor * src) { + return ggml_new_tensor(ctx, src->type, GGML_MAX_DIMS, src->ne); +} + +void ggml_unravel_index(const struct ggml_tensor * tensor, int64_t i, int64_t * i0, int64_t * i1, int64_t * i2, int64_t * i3) { + const int64_t ne2 = tensor->ne[2]; + const int64_t ne1 = tensor->ne[1]; + const int64_t ne0 = tensor->ne[0]; + + const int64_t i3_ = (i/(ne2*ne1*ne0)); + const int64_t i2_ = (i - i3_*ne2*ne1*ne0)/(ne1*ne0); + const int64_t i1_ = (i - i3_*ne2*ne1*ne0 - i2_*ne1*ne0)/ne0; + const int64_t i0_ = (i - i3_*ne2*ne1*ne0 - i2_*ne1*ne0 - i1_*ne0); + + if (i0) { + * i0 = i0_; + } + if (i1) { + * i1 = i1_; + } + if (i2) { + * i2 = i2_; + } + if (i3) { + * i3 = i3_; + } +} + +void * ggml_get_data(const struct ggml_tensor * tensor) { + return tensor->data; +} + +float * ggml_get_data_f32(const struct ggml_tensor * tensor) { + assert(tensor->type == GGML_TYPE_F32); + return (float *)(tensor->data); +} + +enum ggml_unary_op ggml_get_unary_op(const struct ggml_tensor * tensor) { + GGML_ASSERT(tensor->op == GGML_OP_UNARY); + return (enum ggml_unary_op) ggml_get_op_params_i32(tensor, 0); +} + +enum ggml_glu_op ggml_get_glu_op(const struct ggml_tensor * tensor) { + GGML_ASSERT(tensor->op == GGML_OP_GLU); + return (enum ggml_glu_op) ggml_get_op_params_i32(tensor, 0); +} + +const char * ggml_get_name(const struct ggml_tensor * tensor) { + return tensor->name; +} + +struct ggml_tensor * ggml_set_name(struct ggml_tensor * tensor, const char * name) { + size_t i; + for (i = 0; i < sizeof(tensor->name) - 1 && name[i] != '\0'; i++) { + tensor->name[i] = name[i]; + } + tensor->name[i] = '\0'; + return tensor; +} + +struct ggml_tensor * ggml_format_name(struct ggml_tensor * tensor, const char * fmt, ...) { + va_list args; + va_start(args, fmt); + vsnprintf(tensor->name, sizeof(tensor->name), fmt, args); + va_end(args); + return tensor; +} + +struct ggml_tensor * ggml_view_tensor( + struct ggml_context * ctx, + struct ggml_tensor * src) { + struct ggml_tensor * result = ggml_new_tensor_impl(ctx, src->type, GGML_MAX_DIMS, src->ne, src, 0); + ggml_format_name(result, "%s (view)", src->name); + + for (int i = 0; i < GGML_MAX_DIMS; i++) { + result->nb[i] = src->nb[i]; + } + + return result; +} + +struct ggml_tensor * ggml_get_first_tensor(const struct ggml_context * ctx) { + struct ggml_object * obj = ctx->objects_begin; + + char * const mem_buffer = ctx->mem_buffer; + + while (obj != NULL) { + if (obj->type == GGML_OBJECT_TYPE_TENSOR) { + return (struct ggml_tensor *)(mem_buffer + obj->offs); + } + + obj = obj->next; + } + + return NULL; +} + +struct ggml_tensor * ggml_get_next_tensor(const struct ggml_context * ctx, struct ggml_tensor * tensor) { + struct ggml_object * obj = (struct ggml_object *) ((char *)tensor - GGML_OBJECT_SIZE); + obj = obj->next; + + char * const mem_buffer = ctx->mem_buffer; + + while (obj != NULL) { + if (obj->type == GGML_OBJECT_TYPE_TENSOR) { + return (struct ggml_tensor *)(mem_buffer + obj->offs); + } + + obj = obj->next; + } + + return NULL; +} + +struct ggml_tensor * ggml_get_tensor(struct ggml_context * ctx, const char * name) { + struct ggml_object * obj = ctx->objects_begin; + + char * const mem_buffer = ctx->mem_buffer; + + while (obj != NULL) { + if (obj->type == GGML_OBJECT_TYPE_TENSOR) { + struct ggml_tensor * cur = (struct ggml_tensor *)(mem_buffer + obj->offs); + if (strcmp(cur->name, name) == 0) { + return cur; + } + } + + obj = obj->next; + } + + return NULL; +} + +//////////////////////////////////////////////////////////////////////////////// + +// ggml_dup + +static struct ggml_tensor * ggml_dup_impl( + struct ggml_context * ctx, + struct ggml_tensor * a, + bool inplace) { + struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); + + result->op = GGML_OP_DUP; + result->src[0] = a; + + return result; +} + +struct ggml_tensor * ggml_dup( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_dup_impl(ctx, a, false); +} + +struct ggml_tensor * ggml_dup_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_dup_impl(ctx, a, true); +} + +// ggml_add + +static struct ggml_tensor * ggml_add_impl( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + bool inplace) { + GGML_ASSERT(ggml_can_repeat(b, a)); + + struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); + + result->op = GGML_OP_ADD; + result->src[0] = a; + result->src[1] = b; + + return result; +} + +struct ggml_tensor * ggml_add( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b) { + return ggml_add_impl(ctx, a, b, false); +} + +struct ggml_tensor * ggml_add_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b) { + return ggml_add_impl(ctx, a, b, true); +} + +// ggml_add_cast + +static struct ggml_tensor * ggml_add_cast_impl( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + enum ggml_type type) { + // TODO: support less-strict constraint + // GGML_ASSERT(ggml_can_repeat(b, a)); + GGML_ASSERT(ggml_can_repeat_rows(b, a)); + + // currently only supported for quantized input and f16 + GGML_ASSERT(ggml_is_quantized(a->type) || + a->type == GGML_TYPE_F16 || + a->type == GGML_TYPE_BF16); + + struct ggml_tensor * result = ggml_new_tensor(ctx, type, GGML_MAX_DIMS, a->ne); + + result->op = GGML_OP_ADD; + result->src[0] = a; + result->src[1] = b; + + return result; +} + +struct ggml_tensor * ggml_add_cast( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + enum ggml_type type) { + return ggml_add_cast_impl(ctx, a, b, type); +} + +struct ggml_tensor * ggml_add_id( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + struct ggml_tensor * ids) { + + GGML_ASSERT(a->ne[0] == b->ne[0]); + GGML_ASSERT(a->ne[1] == ids->ne[0]); + GGML_ASSERT(a->ne[2] == ids->ne[1]); + GGML_ASSERT(ids->type == GGML_TYPE_I32); + + struct ggml_tensor * result = ggml_dup_tensor(ctx, a); + + result->op = GGML_OP_ADD_ID; + result->src[0] = a; + result->src[1] = b; + result->src[2] = ids; + + return result; +} + +// ggml_add1 + +static struct ggml_tensor * ggml_add1_impl( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + bool inplace) { + GGML_ASSERT(ggml_is_scalar(b)); + GGML_ASSERT(ggml_is_padded_1d(a)); + + struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); + + result->op = GGML_OP_ADD1; + result->src[0] = a; + result->src[1] = b; + + return result; +} + +struct ggml_tensor * ggml_add1( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b) { + return ggml_add1_impl(ctx, a, b, false); +} + +struct ggml_tensor * ggml_add1_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b) { + return ggml_add1_impl(ctx, a, b, true); +} + +// ggml_acc + +static struct ggml_tensor * ggml_acc_impl( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + size_t nb1, + size_t nb2, + size_t nb3, + size_t offset, + bool inplace) { + GGML_ASSERT(ggml_nelements(b) <= ggml_nelements(a)); + GGML_ASSERT(ggml_is_contiguous(a)); + GGML_ASSERT(a->type == GGML_TYPE_F32); + GGML_ASSERT(b->type == GGML_TYPE_F32); + + struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); + + int32_t params[] = { nb1, nb2, nb3, offset, inplace ? 1 : 0 }; + ggml_set_op_params(result, params, sizeof(params)); + + result->op = GGML_OP_ACC; + result->src[0] = a; + result->src[1] = b; + + return result; +} + +struct ggml_tensor * ggml_acc( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + size_t nb1, + size_t nb2, + size_t nb3, + size_t offset) { + return ggml_acc_impl(ctx, a, b, nb1, nb2, nb3, offset, false); +} + +struct ggml_tensor * ggml_acc_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + size_t nb1, + size_t nb2, + size_t nb3, + size_t offset) { + return ggml_acc_impl(ctx, a, b, nb1, nb2, nb3, offset, true); +} + +// ggml_sub + +static struct ggml_tensor * ggml_sub_impl( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + bool inplace) { + GGML_ASSERT(ggml_can_repeat(b, a)); + + struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); + + result->op = GGML_OP_SUB; + result->src[0] = a; + result->src[1] = b; + + return result; +} + +struct ggml_tensor * ggml_sub( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b) { + return ggml_sub_impl(ctx, a, b, false); +} + +struct ggml_tensor * ggml_sub_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b) { + return ggml_sub_impl(ctx, a, b, true); +} + +// ggml_mul + +static struct ggml_tensor * ggml_mul_impl( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + bool inplace) { + GGML_ASSERT(ggml_can_repeat(b, a)); + + struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); + + result->op = GGML_OP_MUL; + result->src[0] = a; + result->src[1] = b; + + return result; +} + +struct ggml_tensor * ggml_mul( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b) { + return ggml_mul_impl(ctx, a, b, false); +} + +struct ggml_tensor * ggml_mul_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b) { + return ggml_mul_impl(ctx, a, b, true); +} + +// ggml_div + +static struct ggml_tensor * ggml_div_impl( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + bool inplace) { + GGML_ASSERT(ggml_can_repeat(b, a)); + + struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); + + result->op = GGML_OP_DIV; + result->src[0] = a; + result->src[1] = b; + + return result; +} + +struct ggml_tensor * ggml_div( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b) { + return ggml_div_impl(ctx, a, b, false); +} + +struct ggml_tensor * ggml_div_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b) { + return ggml_div_impl(ctx, a, b, true); +} + +// ggml_sqr + +static struct ggml_tensor * ggml_sqr_impl( + struct ggml_context * ctx, + struct ggml_tensor * a, + bool inplace) { + struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); + + result->op = GGML_OP_SQR; + result->src[0] = a; + + return result; +} + +struct ggml_tensor * ggml_sqr( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_sqr_impl(ctx, a, false); +} + +struct ggml_tensor * ggml_sqr_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_sqr_impl(ctx, a, true); +} + +// ggml_sqrt + +static struct ggml_tensor * ggml_sqrt_impl( + struct ggml_context * ctx, + struct ggml_tensor * a, + bool inplace) { + struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); + + result->op = GGML_OP_SQRT; + result->src[0] = a; + + return result; +} + +struct ggml_tensor * ggml_sqrt( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_sqrt_impl(ctx, a, false); +} + +struct ggml_tensor * ggml_sqrt_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_sqrt_impl(ctx, a, true); +} + +// ggml_log + +static struct ggml_tensor * ggml_log_impl( + struct ggml_context * ctx, + struct ggml_tensor * a, + bool inplace) { + struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); + + result->op = GGML_OP_LOG; + result->src[0] = a; + + return result; +} + +struct ggml_tensor * ggml_log( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_log_impl(ctx, a, false); +} + +struct ggml_tensor * ggml_log_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_log_impl(ctx, a, true); +} + +struct ggml_tensor * ggml_expm1( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_unary(ctx, a, GGML_UNARY_OP_EXPM1); +} + +struct ggml_tensor * ggml_expm1_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_EXPM1); +} + +struct ggml_tensor * ggml_softplus( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_unary(ctx, a, GGML_UNARY_OP_SOFTPLUS); +} + +struct ggml_tensor * ggml_softplus_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_SOFTPLUS); +} + +// ggml_sin + +static struct ggml_tensor * ggml_sin_impl( + struct ggml_context * ctx, + struct ggml_tensor * a, + bool inplace) { + struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); + + result->op = GGML_OP_SIN; + result->src[0] = a; + + return result; +} + +struct ggml_tensor * ggml_sin( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_sin_impl(ctx, a, false); +} + +struct ggml_tensor * ggml_sin_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_sin_impl(ctx, a, true); +} + +// ggml_cos + +static struct ggml_tensor * ggml_cos_impl( + struct ggml_context * ctx, + struct ggml_tensor * a, + bool inplace) { + struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); + + result->op = GGML_OP_COS; + result->src[0] = a; + + return result; +} + +struct ggml_tensor * ggml_cos( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_cos_impl(ctx, a, false); +} + +struct ggml_tensor * ggml_cos_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_cos_impl(ctx, a, true); +} + +// ggml_sum + +struct ggml_tensor * ggml_sum( + struct ggml_context * ctx, + struct ggml_tensor * a) { + struct ggml_tensor * result = ggml_new_tensor_1d(ctx, a->type, 1); + + result->op = GGML_OP_SUM; + result->src[0] = a; + + return result; +} + +// ggml_sum_rows + +struct ggml_tensor * ggml_sum_rows( + struct ggml_context * ctx, + struct ggml_tensor * a) { + int64_t ne[GGML_MAX_DIMS] = { 1 }; + for (int i = 1; i < GGML_MAX_DIMS; ++i) { + ne[i] = a->ne[i]; + } + + struct ggml_tensor * result = ggml_new_tensor(ctx, a->type, GGML_MAX_DIMS, ne); + + result->op = GGML_OP_SUM_ROWS; + result->src[0] = a; + + return result; +} + +// ggml_cumsum + +struct ggml_tensor * ggml_cumsum( + struct ggml_context * ctx, + struct ggml_tensor * a) { + GGML_ASSERT(a->type == GGML_TYPE_F32); + + struct ggml_tensor * result = ggml_dup_tensor(ctx, a); + + result->op = GGML_OP_CUMSUM; + result->src[0] = a; + + return result; +} + +// ggml_mean + +struct ggml_tensor * ggml_mean( + struct ggml_context * ctx, + struct ggml_tensor * a) { + int64_t ne[4] = { 1, a->ne[1], a->ne[2], a->ne[3] }; + struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); + + result->op = GGML_OP_MEAN; + result->src[0] = a; + + return result; +} + +// ggml_argmax + +struct ggml_tensor * ggml_argmax( + struct ggml_context * ctx, + struct ggml_tensor * a) { + GGML_ASSERT(ggml_is_matrix(a)); + GGML_ASSERT(a->ne[0] <= INT32_MAX); + + struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, a->ne[1]); + + result->op = GGML_OP_ARGMAX; + result->src[0] = a; + + return result; +} + +// ggml_count_equal + +struct ggml_tensor * ggml_count_equal( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b) { + GGML_ASSERT(ggml_are_same_shape(a, b)); + + struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_I64, 1); + + result->op = GGML_OP_COUNT_EQUAL; + result->src[0] = a; + result->src[1] = b; + + return result; +} + +// ggml_repeat + +struct ggml_tensor * ggml_repeat( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b) { + GGML_ASSERT(ggml_can_repeat(a, b)); + + struct ggml_tensor * result = ggml_new_tensor(ctx, a->type, GGML_MAX_DIMS, b->ne); + + result->op = GGML_OP_REPEAT; + result->src[0] = a; + + return result; +} + +struct ggml_tensor * ggml_repeat_4d( + struct ggml_context * ctx, + struct ggml_tensor * a, + int64_t ne0, int64_t ne1, int64_t ne2, int64_t ne3) { + const bool can_repeat = ggml_is_empty(a) || ( + (ne0 % a->ne[0] == 0) && + (ne1 % a->ne[1] == 0) && + (ne2 % a->ne[2] == 0) && + (ne3 % a->ne[3] == 0) + ); + GGML_ASSERT(can_repeat); + + struct ggml_tensor * result = ggml_new_tensor_4d(ctx, a->type, ne0, ne1, ne2, ne3); + + result->op = GGML_OP_REPEAT; + result->src[0] = a; + + return result; +} + +// ggml_repeat_back + +struct ggml_tensor * ggml_repeat_back( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b) { + GGML_ASSERT(ggml_can_repeat(b, a)); + + struct ggml_tensor * result = ggml_new_tensor(ctx, a->type, GGML_MAX_DIMS, b->ne); + + result->op = GGML_OP_REPEAT_BACK; + result->src[0] = a; + + return result; +} + +// ggml_concat + +struct ggml_tensor * ggml_concat( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + int dim) { + GGML_ASSERT(dim >= 0 && dim < GGML_MAX_DIMS); + GGML_ASSERT(a->type == b->type); + + int64_t ne[GGML_MAX_DIMS]; + for (int d = 0; d < GGML_MAX_DIMS; ++d) { + if (d == dim) { + ne[d] = a->ne[d] + b->ne[d]; + continue; + } + GGML_ASSERT(a->ne[d] == b->ne[d]); + ne[d] = a->ne[d]; + } + + struct ggml_tensor * result = ggml_new_tensor(ctx, a->type, GGML_MAX_DIMS, ne); + + ggml_set_op_params_i32(result, 0, dim); + + result->op = GGML_OP_CONCAT; + result->src[0] = a; + result->src[1] = b; + + return result; +} + +// ggml_abs + +struct ggml_tensor * ggml_abs( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_unary(ctx, a, GGML_UNARY_OP_ABS); +} + +struct ggml_tensor * ggml_abs_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_ABS); +} + +// ggml_sgn + +struct ggml_tensor * ggml_sgn( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_unary(ctx, a, GGML_UNARY_OP_SGN); +} + +struct ggml_tensor * ggml_sgn_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_SGN); +} + +// ggml_neg + +struct ggml_tensor * ggml_neg( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_unary(ctx, a, GGML_UNARY_OP_NEG); +} + +struct ggml_tensor * ggml_neg_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_NEG); +} + +// ggml_step + +struct ggml_tensor * ggml_step( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_unary(ctx, a, GGML_UNARY_OP_STEP); +} + +struct ggml_tensor * ggml_step_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_STEP); +} + +// ggml_tanh + +struct ggml_tensor * ggml_tanh( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_unary(ctx, a, GGML_UNARY_OP_TANH); +} + +struct ggml_tensor * ggml_tanh_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_TANH); +} + +// ggml_elu + +struct ggml_tensor * ggml_elu( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_unary(ctx, a, GGML_UNARY_OP_ELU); +} + +struct ggml_tensor * ggml_elu_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_ELU); +} + +// ggml_relu + +struct ggml_tensor * ggml_relu( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_unary(ctx, a, GGML_UNARY_OP_RELU); +} + +struct ggml_tensor * ggml_relu_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_RELU); +} + +// ggml_leaky_relu + +struct ggml_tensor * ggml_leaky_relu( + struct ggml_context * ctx, + struct ggml_tensor * a, + float negative_slope, + bool inplace) { + struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); + + ggml_set_op_params(result, &negative_slope, sizeof(negative_slope)); + + result->op = GGML_OP_LEAKY_RELU; + result->src[0] = a; + + return result; +} + +// ggml_sigmoid + +struct ggml_tensor * ggml_sigmoid( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_unary(ctx, a, GGML_UNARY_OP_SIGMOID); +} + +struct ggml_tensor * ggml_sigmoid_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_SIGMOID); +} + +// ggml_gelu + +struct ggml_tensor * ggml_gelu( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_unary(ctx, a, GGML_UNARY_OP_GELU); +} + +struct ggml_tensor * ggml_gelu_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_GELU); +} + +// ggml_gelu_erf + +struct ggml_tensor * ggml_gelu_erf( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_unary(ctx, a, GGML_UNARY_OP_GELU_ERF); +} + +struct ggml_tensor * ggml_gelu_erf_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_GELU_ERF); +} + +// ggml_gelu_quick + +struct ggml_tensor * ggml_gelu_quick( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_unary(ctx, a, GGML_UNARY_OP_GELU_QUICK); +} + +struct ggml_tensor * ggml_gelu_quick_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_GELU_QUICK); +} + +// ggml_silu + +struct ggml_tensor * ggml_silu( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_unary(ctx, a, GGML_UNARY_OP_SILU); +} + +struct ggml_tensor * ggml_silu_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_SILU); +} + +// ggml_xielu + +struct ggml_tensor * ggml_xielu( + struct ggml_context * ctx, + struct ggml_tensor * a, + float alpha_n, + float alpha_p, + float beta, + float eps) { + struct ggml_tensor * result = ggml_dup_tensor(ctx, a); + + ggml_set_op_params_i32(result, 0, (int32_t) GGML_UNARY_OP_XIELU); + ggml_set_op_params_f32(result, 1, beta + ggml_compute_softplus_f32(alpha_n)); + ggml_set_op_params_f32(result, 2, ggml_compute_softplus_f32(alpha_p)); + ggml_set_op_params_f32(result, 3, beta); + ggml_set_op_params_f32(result, 4, eps); + + result->op = GGML_OP_UNARY; + result->src[0] = a; + + return result; +} + +// ggml_silu_back + +struct ggml_tensor * ggml_silu_back( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b) { + struct ggml_tensor * result = ggml_dup_tensor(ctx, a); + + result->op = GGML_OP_SILU_BACK; + result->src[0] = a; + result->src[1] = b; + + return result; +} + +// ggml hardswish + +struct ggml_tensor * ggml_hardswish( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_unary(ctx, a, GGML_UNARY_OP_HARDSWISH); +} + +// ggml hardsigmoid + +struct ggml_tensor * ggml_hardsigmoid( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_unary(ctx, a, GGML_UNARY_OP_HARDSIGMOID); +} + +// ggml exp + +struct ggml_tensor * ggml_exp( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_unary(ctx, a, GGML_UNARY_OP_EXP); +} + +struct ggml_tensor * ggml_exp_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_EXP); +} + +// ggml_glu + +static struct ggml_tensor * ggml_glu_impl( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + enum ggml_glu_op op, + bool swapped) { + GGML_ASSERT(ggml_is_contiguous_1(a)); + + if (b) { + GGML_ASSERT(ggml_is_contiguous_1(b)); + GGML_ASSERT(ggml_are_same_shape(a, b)); + GGML_ASSERT(a->type == b->type); + } + + int64_t ne[GGML_MAX_DIMS] = { a->ne[0] / 2 }; for (int i = 1; i < GGML_MAX_DIMS; i++) ne[i] = a->ne[i]; + struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, GGML_MAX_DIMS, b ? a->ne : ne, NULL, 0); + + ggml_set_op_params_i32(result, 0, (int32_t) op); + ggml_set_op_params_i32(result, 1, (int32_t) swapped); + + result->op = GGML_OP_GLU; + result->src[0] = a; + result->src[1] = b; + + return result; +} + +// ggml_floor + +struct ggml_tensor * ggml_floor( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_unary(ctx, a, GGML_UNARY_OP_FLOOR); +} + +struct ggml_tensor * ggml_floor_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_FLOOR); +} + +// ggml_ceil + +struct ggml_tensor * ggml_ceil( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_unary(ctx, a, GGML_UNARY_OP_CEIL); +} + +struct ggml_tensor * ggml_ceil_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_CEIL); +} + +//ggml_round + +struct ggml_tensor * ggml_round( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_unary(ctx, a, GGML_UNARY_OP_ROUND); +} + +struct ggml_tensor * ggml_round_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_ROUND); +} + +//ggml_trunc + +struct ggml_tensor * ggml_trunc( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_unary(ctx, a, GGML_UNARY_OP_TRUNC); +} + +struct ggml_tensor * ggml_trunc_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_TRUNC); +} + +struct ggml_tensor * ggml_glu( + struct ggml_context * ctx, + struct ggml_tensor * a, + enum ggml_glu_op op, + bool swapped) { + return ggml_glu_impl(ctx, a, NULL, op, swapped); +} + +struct ggml_tensor * ggml_glu_split( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + enum ggml_glu_op op) { + return ggml_glu_impl(ctx, a, b, op, false); +} + +// ggml_reglu + +struct ggml_tensor * ggml_reglu( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_glu_impl(ctx, a, NULL, GGML_GLU_OP_REGLU, false); +} + +struct ggml_tensor * ggml_reglu_swapped( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_glu_impl(ctx, a, NULL, GGML_GLU_OP_REGLU, true); +} + +struct ggml_tensor * ggml_reglu_split( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b) { + return ggml_glu_impl(ctx, a, b, GGML_GLU_OP_REGLU, false); +} + +// ggml_geglu + +struct ggml_tensor * ggml_geglu( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_glu_impl(ctx, a, NULL, GGML_GLU_OP_GEGLU, false); +} + +struct ggml_tensor * ggml_geglu_swapped( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_glu_impl(ctx, a, NULL, GGML_GLU_OP_GEGLU, true); +} + +struct ggml_tensor * ggml_geglu_split( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b) { + return ggml_glu_impl(ctx, a, b, GGML_GLU_OP_GEGLU, false); +} + +// ggml_swiglu + +struct ggml_tensor * ggml_swiglu( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_glu_impl(ctx, a, NULL, GGML_GLU_OP_SWIGLU, false); +} + +struct ggml_tensor * ggml_swiglu_swapped( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_glu_impl(ctx, a, NULL, GGML_GLU_OP_SWIGLU, true); +} + +struct ggml_tensor * ggml_swiglu_split( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b) { + return ggml_glu_impl(ctx, a, b, GGML_GLU_OP_SWIGLU, false); +} + +// ggml_geglu_erf + +struct ggml_tensor * ggml_geglu_erf( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_glu_impl(ctx, a, NULL, GGML_GLU_OP_GEGLU_ERF, false); +} + +struct ggml_tensor * ggml_geglu_erf_swapped( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_glu_impl(ctx, a, NULL, GGML_GLU_OP_GEGLU_ERF, true); +} + +struct ggml_tensor * ggml_geglu_erf_split( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b) { + return ggml_glu_impl(ctx, a, b, GGML_GLU_OP_GEGLU_ERF, false); +} + +// ggml_geglu_quick + +struct ggml_tensor * ggml_geglu_quick( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_glu_impl(ctx, a, NULL, GGML_GLU_OP_GEGLU_QUICK, false); +} + +struct ggml_tensor * ggml_geglu_quick_swapped( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_glu_impl(ctx, a, NULL, GGML_GLU_OP_GEGLU_QUICK, true); +} + +struct ggml_tensor * ggml_geglu_quick_split( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b) { + return ggml_glu_impl(ctx, a, b, GGML_GLU_OP_GEGLU_QUICK, false); +} + +struct ggml_tensor * ggml_swiglu_oai( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + float alpha, + float limit) { + struct ggml_tensor * result = ggml_glu_impl(ctx, a, b, GGML_GLU_OP_SWIGLU_OAI, false); + ggml_set_op_params_f32(result, 2, alpha); + ggml_set_op_params_f32(result, 3, limit); + + return result; +} + +// ggml_norm + +static struct ggml_tensor * ggml_norm_impl( + struct ggml_context * ctx, + struct ggml_tensor * a, + float eps, + bool inplace) { + struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); + + ggml_set_op_params(result, &eps, sizeof(eps)); + + result->op = GGML_OP_NORM; + result->src[0] = a; + + return result; +} + +struct ggml_tensor * ggml_norm( + struct ggml_context * ctx, + struct ggml_tensor * a, + float eps) { + return ggml_norm_impl(ctx, a, eps, false); +} + +struct ggml_tensor * ggml_norm_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a, + float eps) { + return ggml_norm_impl(ctx, a, eps, true); +} + +// ggml_rms_norm + +static struct ggml_tensor * ggml_rms_norm_impl( + struct ggml_context * ctx, + struct ggml_tensor * a, + float eps, + bool inplace) { + struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); + + ggml_set_op_params(result, &eps, sizeof(eps)); + + result->op = GGML_OP_RMS_NORM; + result->src[0] = a; + + return result; +} + +struct ggml_tensor * ggml_rms_norm( + struct ggml_context * ctx, + struct ggml_tensor * a, + float eps) { + return ggml_rms_norm_impl(ctx, a, eps, false); +} + +struct ggml_tensor * ggml_rms_norm_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a, + float eps) { + return ggml_rms_norm_impl(ctx, a, eps, true); +} + +// ggml_rms_norm_back + +struct ggml_tensor * ggml_rms_norm_back( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + float eps) { + struct ggml_tensor * result = ggml_dup_tensor(ctx, a); + + ggml_set_op_params(result, &eps, sizeof(eps)); + + result->op = GGML_OP_RMS_NORM_BACK; + result->src[0] = a; + result->src[1] = b; + + return result; +} + +// ggml_group_norm + +static struct ggml_tensor * ggml_group_norm_impl( + struct ggml_context * ctx, + struct ggml_tensor * a, + int n_groups, + float eps, + bool inplace) { + struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); + + ggml_set_op_params_i32(result, 0, n_groups); + ggml_set_op_params_f32(result, 1, eps); + + result->op = GGML_OP_GROUP_NORM; + result->src[0] = a; + + return result; +} + +struct ggml_tensor * ggml_group_norm( + struct ggml_context * ctx, + struct ggml_tensor * a, + int n_groups, + float eps) { + return ggml_group_norm_impl(ctx, a, n_groups, eps, false); +} + +struct ggml_tensor * ggml_group_norm_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a, + int n_groups, + float eps) { + return ggml_group_norm_impl(ctx, a, n_groups, eps, true); +} + +// ggml_l2_norm + +static struct ggml_tensor * ggml_l2_norm_impl( + struct ggml_context * ctx, + struct ggml_tensor * a, + float eps, + bool inplace) { + struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); + + ggml_set_op_params_f32(result, 0, eps); + + result->op = GGML_OP_L2_NORM; + result->src[0] = a; + + return result; +} + +struct ggml_tensor * ggml_l2_norm( + struct ggml_context * ctx, + struct ggml_tensor * a, + float eps) { + return ggml_l2_norm_impl(ctx, a, eps, false); +} + +struct ggml_tensor * ggml_l2_norm_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a, + float eps) { + return ggml_l2_norm_impl(ctx, a, eps, true); +} + +// ggml_mul_mat + +static inline bool ggml_can_mul_mat(const struct ggml_tensor * t0, const struct ggml_tensor * t1) { + static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); + + return (t0->ne[0] == t1->ne[0]) && + (t1->ne[2]%t0->ne[2] == 0) && // verify t0 is broadcastable + (t1->ne[3]%t0->ne[3] == 0); +} + +struct ggml_tensor * ggml_mul_mat( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b) { + GGML_ASSERT(ggml_can_mul_mat(a, b)); + GGML_ASSERT(!ggml_is_transposed(a)); + + const int64_t ne[4] = { a->ne[1], b->ne[1], b->ne[2], b->ne[3] }; + struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); + + result->op = GGML_OP_MUL_MAT; + result->src[0] = a; + result->src[1] = b; + + return result; +} + +void ggml_mul_mat_set_prec( + struct ggml_tensor * a, + enum ggml_prec prec) { + GGML_ASSERT(a->op == GGML_OP_MUL_MAT); + + const int32_t prec_i32 = (int32_t) prec; + + ggml_set_op_params_i32(a, 0, prec_i32); +} + +void ggml_mul_mat_set_hint( + struct ggml_tensor * a, + enum ggml_op_hint hint) { + GGML_ASSERT(a->op == GGML_OP_MUL_MAT); + + const int32_t hint_i32 = (int32_t) hint; + + ggml_set_op_params_i32(a, 1, hint_i32); +} + +// ggml_mul_mat_id + +/* + c = ggml_mul_mat_id(ctx, as, b, ids); + + as -> [cols, rows, n_expert] + b -> [cols, n_expert_used, n_tokens] + ids -> [n_expert_used, n_tokens] (i32) + c -> [rows, n_expert_used, n_tokens] + + in b, n_expert_used can be broadcasted to match the n_expert_used of ids + + c ~= as[:,:,i] @ b[:,i%r,t], i = ids[e,t] for all e,t in ids +*/ +struct ggml_tensor * ggml_mul_mat_id( + struct ggml_context * ctx, + struct ggml_tensor * as, + struct ggml_tensor * b, + struct ggml_tensor * ids) { + GGML_ASSERT(!ggml_is_transposed(as)); + GGML_ASSERT(ids->type == GGML_TYPE_I32); + + GGML_ASSERT(as->ne[3] == 1); // as is 3d (one matrix per expert) + GGML_ASSERT(b->ne[3] == 1); // b is 3d + GGML_ASSERT(ids->ne[2] == 1 && ids->ne[3] == 1); // ids is 2d + GGML_ASSERT(ids->ne[1] == b->ne[2]); // must have an expert list per b row + GGML_ASSERT(as->ne[0] == b->ne[0]); // can_mul_mat + GGML_ASSERT(ids->ne[0] % b->ne[1] == 0); // can broadcast + + const int64_t ne[4] = { as->ne[1], ids->ne[0], b->ne[2], 1 }; + struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); + + result->op = GGML_OP_MUL_MAT_ID; + result->src[0] = as; + result->src[1] = b; + result->src[2] = ids; + + return result; +} + +// ggml_out_prod + +static inline bool ggml_can_out_prod(const struct ggml_tensor * t0, const struct ggml_tensor * t1) { + static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); + + return (t0->ne[1] == t1->ne[1]) && + (t1->ne[2]%t0->ne[2] == 0) && // verify t0 is broadcastable + (t1->ne[3]%t0->ne[3] == 0); +} + +struct ggml_tensor * ggml_out_prod( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b) { + GGML_ASSERT(ggml_can_out_prod(a, b)); + GGML_ASSERT(!ggml_is_transposed(a)); + + // a is broadcastable to b for ne[2] and ne[3] -> use b->ne[2] and b->ne[3] + const int64_t ne[4] = { a->ne[0], b->ne[0], b->ne[2], b->ne[3] }; + struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); + + result->op = GGML_OP_OUT_PROD; + result->src[0] = a; + result->src[1] = b; + + return result; +} + +// ggml_scale + +static struct ggml_tensor * ggml_scale_impl( + struct ggml_context * ctx, + struct ggml_tensor * a, + float s, + float b, + bool inplace) { + GGML_ASSERT(ggml_is_padded_1d(a)); + + struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); + + float params[2] = { s, b }; + ggml_set_op_params(result, ¶ms, sizeof(params)); + + result->op = GGML_OP_SCALE; + result->src[0] = a; + + return result; +} + +struct ggml_tensor * ggml_scale( + struct ggml_context * ctx, + struct ggml_tensor * a, + float s) { + return ggml_scale_impl(ctx, a, s, 0.0, false); +} + +struct ggml_tensor * ggml_scale_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a, + float s) { + return ggml_scale_impl(ctx, a, s, 0.0, true); +} + +struct ggml_tensor * ggml_scale_bias( + struct ggml_context * ctx, + struct ggml_tensor * a, + float s, + float b) { + return ggml_scale_impl(ctx, a, s, b, false); +} + +struct ggml_tensor * ggml_scale_bias_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a, + float s, + float b) { + return ggml_scale_impl(ctx, a, s, b, true); +} + +// ggml_set + +static struct ggml_tensor * ggml_set_impl( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + size_t nb1, + size_t nb2, + size_t nb3, + size_t offset, + bool inplace) { + GGML_ASSERT(ggml_nelements(a) >= ggml_nelements(b)); + + // make a view of the destination + struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); + + GGML_ASSERT(offset < (size_t)(1 << 30)); + int32_t params[] = { nb1, nb2, nb3, offset, inplace ? 1 : 0 }; + ggml_set_op_params(result, params, sizeof(params)); + + result->op = GGML_OP_SET; + result->src[0] = a; + result->src[1] = b; + + return result; +} + +struct ggml_tensor * ggml_set( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + size_t nb1, + size_t nb2, + size_t nb3, + size_t offset) { + return ggml_set_impl(ctx, a, b, nb1, nb2, nb3, offset, false); +} + +struct ggml_tensor * ggml_set_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + size_t nb1, + size_t nb2, + size_t nb3, + size_t offset) { + return ggml_set_impl(ctx, a, b, nb1, nb2, nb3, offset, true); +} + +struct ggml_tensor * ggml_set_1d( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + size_t offset) { + return ggml_set_impl(ctx, a, b, a->nb[1], a->nb[2], a->nb[3], offset, false); +} + +struct ggml_tensor * ggml_set_1d_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + size_t offset) { + return ggml_set_impl(ctx, a, b, a->nb[1], a->nb[2], a->nb[3], offset, true); +} + +struct ggml_tensor * ggml_set_2d( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + size_t nb1, + size_t offset) { + return ggml_set_impl(ctx, a, b, nb1, a->nb[2], a->nb[3], offset, false); +} + +struct ggml_tensor * ggml_set_2d_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + size_t nb1, + size_t offset) { + return ggml_set_impl(ctx, a, b, nb1, a->nb[2], a->nb[3], offset, true); +} + +// ggml_cpy + +static struct ggml_tensor * ggml_cpy_impl( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b) { + GGML_ASSERT(ggml_nelements(a) == ggml_nelements(b)); + + // make a view of the destination + struct ggml_tensor * result = ggml_view_tensor(ctx, b); + if (strlen(b->name) > 0) { + ggml_format_name(result, "%s (copy of %s)", b->name, a->name); + } else { + ggml_format_name(result, "%s (copy)", a->name); + } + + result->op = GGML_OP_CPY; + result->src[0] = a; + result->src[1] = b; + + return result; +} + +struct ggml_tensor * ggml_cpy( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b) { + return ggml_cpy_impl(ctx, a, b); +} + +struct ggml_tensor * ggml_cast( + struct ggml_context * ctx, + struct ggml_tensor * a, + enum ggml_type type) { + struct ggml_tensor * result = ggml_new_tensor(ctx, type, GGML_MAX_DIMS, a->ne); + ggml_format_name(result, "%s (copy)", a->name); + + result->op = GGML_OP_CPY; + result->src[0] = a; + result->src[1] = result; // note: this self-reference might seem redundant, but it's actually needed by some + // backends for consistency with ggml_cpy_impl() above + + return result; +} + +// ggml_cont + +static struct ggml_tensor * ggml_cont_impl( + struct ggml_context * ctx, + struct ggml_tensor * a) { + struct ggml_tensor * result = ggml_dup_tensor(ctx, a); + ggml_format_name(result, "%s (cont)", a->name); + + result->op = GGML_OP_CONT; + result->src[0] = a; + + return result; +} + +struct ggml_tensor * ggml_cont( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_cont_impl(ctx, a); +} + +// make contiguous, with new shape +GGML_API struct ggml_tensor * ggml_cont_1d( + struct ggml_context * ctx, + struct ggml_tensor * a, + int64_t ne0) { + return ggml_cont_4d(ctx, a, ne0, 1, 1, 1); +} + +GGML_API struct ggml_tensor * ggml_cont_2d( + struct ggml_context * ctx, + struct ggml_tensor * a, + int64_t ne0, + int64_t ne1) { + return ggml_cont_4d(ctx, a, ne0, ne1, 1, 1); +} + +GGML_API struct ggml_tensor * ggml_cont_3d( + struct ggml_context * ctx, + struct ggml_tensor * a, + int64_t ne0, + int64_t ne1, + int64_t ne2) { + return ggml_cont_4d(ctx, a, ne0, ne1, ne2, 1); +} + +struct ggml_tensor * ggml_cont_4d( + struct ggml_context * ctx, + struct ggml_tensor * a, + int64_t ne0, + int64_t ne1, + int64_t ne2, + int64_t ne3) { + GGML_ASSERT(ggml_nelements(a) == (ne0*ne1*ne2*ne3)); + + struct ggml_tensor * result = ggml_new_tensor_4d(ctx, a->type, ne0, ne1, ne2, ne3); + ggml_format_name(result, "%s (cont)", a->name); + + result->op = GGML_OP_CONT; + result->src[0] = a; + + return result; +} + +// ggml_reshape + +struct ggml_tensor * ggml_reshape( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b) { + GGML_ASSERT(ggml_is_contiguous(a)); + // as only the shape of b is relevant, and not its memory layout, b is allowed to be non contiguous. + GGML_ASSERT(ggml_nelements(a) == ggml_nelements(b)); + + struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, GGML_MAX_DIMS, b->ne, a, 0); + ggml_format_name(result, "%s (reshaped)", a->name); + + result->op = GGML_OP_RESHAPE; + result->src[0] = a; + + return result; +} + +struct ggml_tensor * ggml_reshape_1d( + struct ggml_context * ctx, + struct ggml_tensor * a, + int64_t ne0) { + GGML_ASSERT(ggml_is_contiguous(a)); + GGML_ASSERT(ggml_nelements(a) == ne0); + + const int64_t ne[1] = { ne0 }; + struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 1, ne, a, 0); + ggml_format_name(result, "%s (reshaped)", a->name); + + result->op = GGML_OP_RESHAPE; + result->src[0] = a; + + return result; +} + +struct ggml_tensor * ggml_reshape_2d( + struct ggml_context * ctx, + struct ggml_tensor * a, + int64_t ne0, + int64_t ne1) { + GGML_ASSERT(ggml_is_contiguous(a)); + GGML_ASSERT(ggml_nelements(a) == ne0*ne1); + + const int64_t ne[2] = { ne0, ne1 }; + struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 2, ne, a, 0); + ggml_format_name(result, "%s (reshaped)", a->name); + + result->op = GGML_OP_RESHAPE; + result->src[0] = a; + + return result; +} + +struct ggml_tensor * ggml_reshape_3d( + struct ggml_context * ctx, + struct ggml_tensor * a, + int64_t ne0, + int64_t ne1, + int64_t ne2) { + GGML_ASSERT(ggml_is_contiguous(a)); + GGML_ASSERT(ggml_nelements(a) == ne0*ne1*ne2); + + const int64_t ne[3] = { ne0, ne1, ne2 }; + struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 3, ne, a, 0); + ggml_format_name(result, "%s (reshaped)", a->name); + + result->op = GGML_OP_RESHAPE; + result->src[0] = a; + + return result; +} + +struct ggml_tensor * ggml_reshape_4d( + struct ggml_context * ctx, + struct ggml_tensor * a, + int64_t ne0, + int64_t ne1, + int64_t ne2, + int64_t ne3) { + GGML_ASSERT(ggml_is_contiguous(a)); + GGML_ASSERT(ggml_nelements(a) == ne0*ne1*ne2*ne3); + + const int64_t ne[4] = { ne0, ne1, ne2, ne3 }; + struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 4, ne, a, 0); + ggml_format_name(result, "%s (reshaped)", a->name); + + result->op = GGML_OP_RESHAPE; + result->src[0] = a; + + return result; +} + +static struct ggml_tensor * ggml_view_impl( + struct ggml_context * ctx, + struct ggml_tensor * a, + int n_dims, + const int64_t * ne, + size_t offset) { + struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, n_dims, ne, a, offset); + ggml_format_name(result, "%s (view)", a->name); + + ggml_set_op_params(result, &offset, sizeof(offset)); + + result->op = GGML_OP_VIEW; + result->src[0] = a; + + return result; +} + +// ggml_view_1d + +struct ggml_tensor * ggml_view_1d( + struct ggml_context * ctx, + struct ggml_tensor * a, + int64_t ne0, + size_t offset) { + struct ggml_tensor * result = ggml_view_impl(ctx, a, 1, &ne0, offset); + + return result; +} + +// ggml_view_2d + +struct ggml_tensor * ggml_view_2d( + struct ggml_context * ctx, + struct ggml_tensor * a, + int64_t ne0, + int64_t ne1, + size_t nb1, + size_t offset) { + const int64_t ne[2] = { ne0, ne1 }; + + struct ggml_tensor * result = ggml_view_impl(ctx, a, 2, ne, offset); + + result->nb[1] = nb1; + result->nb[2] = result->nb[1]*ne1; + result->nb[3] = result->nb[2]; + + return result; +} + +// ggml_view_3d + +struct ggml_tensor * ggml_view_3d( + struct ggml_context * ctx, + struct ggml_tensor * a, + int64_t ne0, + int64_t ne1, + int64_t ne2, + size_t nb1, + size_t nb2, + size_t offset) { + const int64_t ne[3] = { ne0, ne1, ne2 }; + + struct ggml_tensor * result = ggml_view_impl(ctx, a, 3, ne, offset); + + result->nb[1] = nb1; + result->nb[2] = nb2; + result->nb[3] = result->nb[2]*ne2; + + return result; +} + +// ggml_view_4d + +struct ggml_tensor * ggml_view_4d( + struct ggml_context * ctx, + struct ggml_tensor * a, + int64_t ne0, + int64_t ne1, + int64_t ne2, + int64_t ne3, + size_t nb1, + size_t nb2, + size_t nb3, + size_t offset) { + const int64_t ne[4] = { ne0, ne1, ne2, ne3 }; + + struct ggml_tensor * result = ggml_view_impl(ctx, a, 4, ne, offset); + + result->nb[1] = nb1; + result->nb[2] = nb2; + result->nb[3] = nb3; + + return result; +} + +// ggml_permute + +struct ggml_tensor * ggml_permute( + struct ggml_context * ctx, + struct ggml_tensor * a, + int axis0, + int axis1, + int axis2, + int axis3) { + GGML_ASSERT(axis0 >= 0 && axis0 < GGML_MAX_DIMS); + GGML_ASSERT(axis1 >= 0 && axis1 < GGML_MAX_DIMS); + GGML_ASSERT(axis2 >= 0 && axis2 < GGML_MAX_DIMS); + GGML_ASSERT(axis3 >= 0 && axis3 < GGML_MAX_DIMS); + + GGML_ASSERT(axis0 != axis1); + GGML_ASSERT(axis0 != axis2); + GGML_ASSERT(axis0 != axis3); + GGML_ASSERT(axis1 != axis2); + GGML_ASSERT(axis1 != axis3); + GGML_ASSERT(axis2 != axis3); + + struct ggml_tensor * result = ggml_view_tensor(ctx, a); + ggml_format_name(result, "%s (permuted)", a->name); + + int ne[GGML_MAX_DIMS]; + int nb[GGML_MAX_DIMS]; + + ne[axis0] = a->ne[0]; + ne[axis1] = a->ne[1]; + ne[axis2] = a->ne[2]; + ne[axis3] = a->ne[3]; + + nb[axis0] = a->nb[0]; + nb[axis1] = a->nb[1]; + nb[axis2] = a->nb[2]; + nb[axis3] = a->nb[3]; + + result->ne[0] = ne[0]; + result->ne[1] = ne[1]; + result->ne[2] = ne[2]; + result->ne[3] = ne[3]; + + result->nb[0] = nb[0]; + result->nb[1] = nb[1]; + result->nb[2] = nb[2]; + result->nb[3] = nb[3]; + + result->op = GGML_OP_PERMUTE; + result->src[0] = a; + + int32_t params[] = { axis0, axis1, axis2, axis3 }; + ggml_set_op_params(result, params, sizeof(params)); + + return result; +} + +// ggml_transpose + +struct ggml_tensor * ggml_transpose( + struct ggml_context * ctx, + struct ggml_tensor * a) { + struct ggml_tensor * result = ggml_view_tensor(ctx, a); + ggml_format_name(result, "%s (transposed)", a->name); + + result->ne[0] = a->ne[1]; + result->ne[1] = a->ne[0]; + + result->nb[0] = a->nb[1]; + result->nb[1] = a->nb[0]; + + result->op = GGML_OP_TRANSPOSE; + result->src[0] = a; + + return result; +} + +// ggml_get_rows + +struct ggml_tensor * ggml_get_rows( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b) { + GGML_ASSERT(a->ne[2] == b->ne[1]); + GGML_ASSERT(a->ne[3] == b->ne[2]); + GGML_ASSERT(b->ne[3] == 1); + GGML_ASSERT(b->type == GGML_TYPE_I32); + + // TODO: implement non F32 return + enum ggml_type type = GGML_TYPE_F32; + if (a->type == GGML_TYPE_I32) { + type = a->type; + } + struct ggml_tensor * result = ggml_new_tensor_4d(ctx, type, a->ne[0], b->ne[0], b->ne[1], b->ne[2]); + + result->op = GGML_OP_GET_ROWS; + result->src[0] = a; + result->src[1] = b; + + return result; +} + +// ggml_get_rows_back + +struct ggml_tensor * ggml_get_rows_back( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + struct ggml_tensor * c) { + GGML_ASSERT(ggml_is_matrix(a) && ggml_is_vector(b) && b->type == GGML_TYPE_I32); + GGML_ASSERT(ggml_is_matrix(c) && (a->ne[0] == c->ne[0])); + + // TODO: implement non F32 return + //struct ggml_tensor * result = ggml_new_tensor_2d(ctx, a->type, a->ne[0], b->ne[0]); + struct ggml_tensor * result = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, c->ne[0], c->ne[1]); + + result->op = GGML_OP_GET_ROWS_BACK; + result->src[0] = a; + result->src[1] = b; + + return result; +} + +// ggml_set_rows + +struct ggml_tensor * ggml_set_rows( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + struct ggml_tensor * c) { + GGML_ASSERT(a->ne[0] == b->ne[0]); + GGML_ASSERT(a->ne[2] == b->ne[2]); + GGML_ASSERT(a->ne[3] == b->ne[3]); + GGML_ASSERT(b->ne[1] == c->ne[0]); + GGML_ASSERT(b->ne[2] % c->ne[1] == 0); + GGML_ASSERT(b->ne[3] % c->ne[2] == 0); + GGML_ASSERT(c->ne[3] == 1); + GGML_ASSERT(b->type == GGML_TYPE_F32 || b->type == GGML_TYPE_F16); + GGML_ASSERT(c->type == GGML_TYPE_I64 || c->type == GGML_TYPE_I32); + + GGML_ASSERT(ggml_is_contiguous_rows(a)); + GGML_ASSERT(ggml_is_contiguous_rows(b)); + + struct ggml_tensor * result = ggml_view_tensor(ctx, a); + + result->op = GGML_OP_SET_ROWS; + result->src[0] = b; + result->src[1] = c; + result->src[2] = a; // note: order is weird due to legacy reasons (https://github.com/ggml-org/llama.cpp/pull/16063#discussion_r2385795931) + + return result; +} + +// ggml_diag + +struct ggml_tensor * ggml_diag( + struct ggml_context * ctx, + struct ggml_tensor * a) { + GGML_ASSERT(a->ne[1] == 1); + + const int64_t ne[4] = { a->ne[0], a->ne[0], a->ne[2], a->ne[3] }; + struct ggml_tensor * result = ggml_new_tensor(ctx, a->type, 4, ne); + + result->op = GGML_OP_DIAG; + result->src[0] = a; + + return result; +} + +// ggml_diag_mask_inf + +static struct ggml_tensor * ggml_diag_mask_inf_impl( + struct ggml_context * ctx, + struct ggml_tensor * a, + int n_past, + bool inplace) { + struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); + + int32_t params[] = { n_past }; + ggml_set_op_params(result, params, sizeof(params)); + + result->op = GGML_OP_DIAG_MASK_INF; + result->src[0] = a; + + return result; +} + +struct ggml_tensor * ggml_diag_mask_inf( + struct ggml_context * ctx, + struct ggml_tensor * a, + int n_past) { + return ggml_diag_mask_inf_impl(ctx, a, n_past, false); +} + +struct ggml_tensor * ggml_diag_mask_inf_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a, + int n_past) { + return ggml_diag_mask_inf_impl(ctx, a, n_past, true); +} + +// ggml_diag_mask_zero + +static struct ggml_tensor * ggml_diag_mask_zero_impl( + struct ggml_context * ctx, + struct ggml_tensor * a, + int n_past, + bool inplace) { + struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); + + int32_t params[] = { n_past }; + ggml_set_op_params(result, params, sizeof(params)); + + result->op = GGML_OP_DIAG_MASK_ZERO; + result->src[0] = a; + + return result; +} + +struct ggml_tensor * ggml_diag_mask_zero( + struct ggml_context * ctx, + struct ggml_tensor * a, + int n_past) { + return ggml_diag_mask_zero_impl(ctx, a, n_past, false); +} + +struct ggml_tensor * ggml_diag_mask_zero_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a, + int n_past) { + return ggml_diag_mask_zero_impl(ctx, a, n_past, true); +} + +// ggml_soft_max + +static struct ggml_tensor * ggml_soft_max_impl( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * mask, + float scale, + float max_bias, + bool inplace) { + GGML_ASSERT(ggml_is_contiguous(a)); + + if (mask) { + GGML_ASSERT(mask->type == GGML_TYPE_F16 || mask->type == GGML_TYPE_F32); + GGML_ASSERT(ggml_is_contiguous(mask)); + GGML_ASSERT(mask->ne[0] == a->ne[0]); + GGML_ASSERT(mask->ne[1] >= a->ne[1]); + GGML_ASSERT(a->ne[2]%mask->ne[2] == 0); + GGML_ASSERT(a->ne[3]%mask->ne[3] == 0); + } + + if (max_bias > 0.0f) { + GGML_ASSERT(mask); + } + + struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); + + float params[] = { scale, max_bias }; + ggml_set_op_params(result, params, sizeof(params)); + + result->op = GGML_OP_SOFT_MAX; + result->src[0] = a; + result->src[1] = mask; + + return result; +} + +struct ggml_tensor * ggml_soft_max( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_soft_max_impl(ctx, a, NULL, 1.0f, 0.0f, false); +} + +struct ggml_tensor * ggml_soft_max_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_soft_max_impl(ctx, a, NULL, 1.0f, 0.0f, true); +} + +struct ggml_tensor * ggml_soft_max_ext( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * mask, + float scale, + float max_bias) { + return ggml_soft_max_impl(ctx, a, mask, scale, max_bias, false); +} + +struct ggml_tensor * ggml_soft_max_ext_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * mask, + float scale, + float max_bias) { + return ggml_soft_max_impl(ctx, a, mask, scale, max_bias, true); +} + +void ggml_soft_max_add_sinks( + struct ggml_tensor * a, + struct ggml_tensor * sinks) { + if (!sinks) { + a->src[2] = NULL; + return; + } + + GGML_ASSERT(a->op == GGML_OP_SOFT_MAX); + GGML_ASSERT(a->src[2] == NULL); + GGML_ASSERT(a->src[0]->ne[2] == sinks->ne[0]); + GGML_ASSERT(sinks->type == GGML_TYPE_F32); + + a->src[2] = sinks; +} + +// ggml_soft_max_ext_back + +static struct ggml_tensor * ggml_soft_max_ext_back_impl( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + float scale, + float max_bias, + bool inplace) { + struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); + + result->op = GGML_OP_SOFT_MAX_BACK; + result->src[0] = a; + result->src[1] = b; + + memcpy((float *) result->op_params + 0, &scale, sizeof(float)); + memcpy((float *) result->op_params + 1, &max_bias, sizeof(float)); + + return result; +} + +struct ggml_tensor * ggml_soft_max_ext_back( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + float scale, + float max_bias) { + return ggml_soft_max_ext_back_impl(ctx, a, b, scale, max_bias, false); +} + +struct ggml_tensor * ggml_soft_max_ext_back_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + float scale, + float max_bias) { + return ggml_soft_max_ext_back_impl(ctx, a, b, scale, max_bias, true); +} + +// ggml_rope + +static struct ggml_tensor * ggml_rope_impl( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + struct ggml_tensor * c, + int n_dims, + int sections[GGML_MROPE_SECTIONS], + int mode, + int n_ctx_orig, + float freq_base, + float freq_scale, + float ext_factor, + float attn_factor, + float beta_fast, + float beta_slow, + bool inplace) { + GGML_ASSERT((mode & 1) == 0 && "mode & 1 == 1 is no longer supported"); + + GGML_ASSERT(ggml_is_vector(b)); + GGML_ASSERT(b->type == GGML_TYPE_I32); + + bool mrope_used = mode & GGML_ROPE_TYPE_MROPE; + if (mrope_used) { + GGML_ASSERT(a->ne[2] * 4 == b->ne[0]); // mrope expecting 4 position ids per token + } else { + GGML_ASSERT(a->ne[2] == b->ne[0]); + } + + if (c) { + GGML_ASSERT(c->type == GGML_TYPE_F32); + GGML_ASSERT(c->ne[0] >= n_dims / 2); + } + + struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); + + int32_t params[15] = { /*n_past*/ 0, n_dims, mode, /*n_ctx*/ 0, n_ctx_orig }; + memcpy(params + 5, &freq_base, sizeof(float)); + memcpy(params + 6, &freq_scale, sizeof(float)); + memcpy(params + 7, &ext_factor, sizeof(float)); + memcpy(params + 8, &attn_factor, sizeof(float)); + memcpy(params + 9, &beta_fast, sizeof(float)); + memcpy(params + 10, &beta_slow, sizeof(float)); + if (mrope_used && sections) { + memcpy(params + 11, sections, sizeof(int32_t) * GGML_MROPE_SECTIONS); + } else { + memset(params + 11, 0, sizeof(int32_t) * GGML_MROPE_SECTIONS); + } + ggml_set_op_params(result, params, sizeof(params)); + + result->op = GGML_OP_ROPE; + result->src[0] = a; + result->src[1] = b; + result->src[2] = c; + + return result; +} + +struct ggml_tensor * ggml_rope( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + int n_dims, + int mode) { + return ggml_rope_impl( + ctx, a, b, NULL, n_dims, NULL, mode, 0, 10000.0f, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f, false + ); +} + +struct ggml_tensor * ggml_rope_multi( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + struct ggml_tensor * c, + int n_dims, + int sections[GGML_MROPE_SECTIONS], + int mode, + int n_ctx_orig, + float freq_base, + float freq_scale, + float ext_factor, + float attn_factor, + float beta_fast, + float beta_slow) { + return ggml_rope_impl( + ctx, a, b, c, n_dims, sections, mode, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow, false + ); +} + +struct ggml_tensor * ggml_rope_multi_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + struct ggml_tensor * c, + int n_dims, + int sections[GGML_MROPE_SECTIONS], + int mode, + int n_ctx_orig, + float freq_base, + float freq_scale, + float ext_factor, + float attn_factor, + float beta_fast, + float beta_slow) { + return ggml_rope_impl( + ctx, a, b, c, n_dims, sections, mode, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow, true + ); +} + +struct ggml_tensor * ggml_rope_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + int n_dims, + int mode) { + return ggml_rope_impl( + ctx, a, b, NULL, n_dims, NULL, mode, 0, 10000.0f, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f, true + ); +} + +struct ggml_tensor * ggml_rope_ext( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + struct ggml_tensor * c, + int n_dims, + int mode, + int n_ctx_orig, + float freq_base, + float freq_scale, + float ext_factor, + float attn_factor, + float beta_fast, + float beta_slow) { + return ggml_rope_impl( + ctx, a, b, c, n_dims, NULL, mode, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow, false + ); +} + +struct ggml_tensor * ggml_rope_ext_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + struct ggml_tensor * c, + int n_dims, + int mode, + int n_ctx_orig, + float freq_base, + float freq_scale, + float ext_factor, + float attn_factor, + float beta_fast, + float beta_slow) { + return ggml_rope_impl( + ctx, a, b, c, n_dims, NULL, mode, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow, true + ); +} + +struct ggml_tensor * ggml_rope_custom( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + int n_dims, + int mode, + int n_ctx_orig, + float freq_base, + float freq_scale, + float ext_factor, + float attn_factor, + float beta_fast, + float beta_slow) { + return ggml_rope_impl( + ctx, a, b, NULL, n_dims, NULL, mode, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow, false + ); +} + +struct ggml_tensor * ggml_rope_custom_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + int n_dims, + int mode, + int n_ctx_orig, + float freq_base, + float freq_scale, + float ext_factor, + float attn_factor, + float beta_fast, + float beta_slow) { + return ggml_rope_impl( + ctx, a, b, NULL, n_dims, NULL, mode, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow, true + ); +} + +// Apparently solving `n_rot = 2pi * x * base^((2 * max_pos_emb) / n_dims)` for x, we get +// `corr_dim(n_rot) = n_dims * log(max_pos_emb / (n_rot * 2pi)) / (2 * log(base))` +static float ggml_rope_yarn_corr_dim(int n_dims, int n_ctx_orig, float n_rot, float base) { + return n_dims * logf(n_ctx_orig / (n_rot * 2 * (float)M_PI)) / (2 * logf(base)); +} + +void ggml_rope_yarn_corr_dims( + int n_dims, int n_ctx_orig, float freq_base, float beta_fast, float beta_slow, float dims[2] +) { + // start and end correction dims + float start = floorf(ggml_rope_yarn_corr_dim(n_dims, n_ctx_orig, beta_fast, freq_base)); + float end = ceilf(ggml_rope_yarn_corr_dim(n_dims, n_ctx_orig, beta_slow, freq_base)); + dims[0] = MAX(0, start); + dims[1] = MIN(n_dims - 1, end); +} + +// ggml_rope_back + +struct ggml_tensor * ggml_rope_ext_back( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + struct ggml_tensor * c, + int n_dims, + int mode, + int n_ctx_orig, + float freq_base, + float freq_scale, + float ext_factor, + float attn_factor, + float beta_fast, + float beta_slow) { + struct ggml_tensor * result = ggml_rope_ext( + ctx, a, b, c, n_dims, mode, n_ctx_orig, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); + result->op = GGML_OP_ROPE_BACK; + return result; +} + +struct ggml_tensor * ggml_rope_multi_back( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + struct ggml_tensor * c, + int n_dims, + int sections[4], + int mode, + int n_ctx_orig, + float freq_base, + float freq_scale, + float ext_factor, + float attn_factor, + float beta_fast, + float beta_slow) { + struct ggml_tensor * result = ggml_rope_multi( + ctx, a, b, c, n_dims, sections, mode, n_ctx_orig, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); + result->op = GGML_OP_ROPE_BACK; + return result; +} +// ggml_clamp + +struct ggml_tensor * ggml_clamp( + struct ggml_context * ctx, + struct ggml_tensor * a, + float min, + float max) { + // TODO: when implement backward, fix this: + struct ggml_tensor * result = ggml_view_tensor(ctx, a); + + float params[] = { min, max }; + ggml_set_op_params(result, params, sizeof(params)); + + result->op = GGML_OP_CLAMP; + result->src[0] = a; + + return result; +} + +static int64_t ggml_calc_conv_output_size(int64_t ins, int64_t ks, int s, int p, int d) { + return (ins + 2 * p - d * (ks - 1) - 1) / s + 1; +} + +// im2col: [N, IC, IH, IW] => [N, OH, OW, IC*KH*KW] +// a: [OC，IC, KH, KW] +// b: [N, IC, IH, IW] +// result: [N, OH, OW, IC*KH*KW] +struct ggml_tensor * ggml_im2col( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + int s0, + int s1, + int p0, + int p1, + int d0, + int d1, + bool is_2D, + enum ggml_type dst_type) { + if (is_2D) { + GGML_ASSERT(a->ne[2] == b->ne[2]); + } else { + //GGML_ASSERT(b->ne[1] % a->ne[1] == 0); + GGML_ASSERT(b->ne[1] == a->ne[1]); + GGML_ASSERT(b->ne[3] == 1); + } + + const int64_t OH = is_2D ? ggml_calc_conv_output_size(b->ne[1], a->ne[1], s1, p1, d1) : 0; + const int64_t OW = ggml_calc_conv_output_size(b->ne[0], a->ne[0], s0, p0, d0); + + GGML_ASSERT((!is_2D || OH > 0) && "b too small compared to a"); + GGML_ASSERT((OW > 0) && "b too small compared to a"); + + const int64_t ne[4] = { + is_2D ? (a->ne[2] * a->ne[1] * a->ne[0]) : a->ne[1] * a->ne[0], + OW, + is_2D ? OH : b->ne[2], + is_2D ? b->ne[3] : 1, + }; + + struct ggml_tensor * result = ggml_new_tensor(ctx, dst_type, 4, ne); + int32_t params[] = { s0, s1, p0, p1, d0, d1, (is_2D ? 1 : 0) }; + ggml_set_op_params(result, params, sizeof(params)); + + result->op = GGML_OP_IM2COL; + result->src[0] = a; + result->src[1] = b; + + return result; +} + +struct ggml_tensor * ggml_im2col_back( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + int64_t * ne, + int s0, + int s1, + int p0, + int p1, + int d0, + int d1, + bool is_2D) { + struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); + int32_t params[] = { s0, s1, p0, p1, d0, d1, (is_2D ? 1 : 0) }; + ggml_set_op_params(result, params, sizeof(params)); + + result->op = GGML_OP_IM2COL_BACK; + result->src[0] = a; + result->src[1] = b; + + return result; +} + +// ggml_conv_1d + +struct ggml_tensor * ggml_conv_1d( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + int s0, + int p0, + int d0) { + struct ggml_tensor * im2col = ggml_im2col(ctx, a, b, s0, 0, p0, 0, d0, 0, false, GGML_TYPE_F16); // [N, OL, IC * K] + + struct ggml_tensor * result = + ggml_mul_mat(ctx, + ggml_reshape_2d(ctx, im2col, im2col->ne[0], (im2col->ne[2] * im2col->ne[1])), // [N, OL, IC * K] => [N*OL, IC * K] + ggml_reshape_2d(ctx, a, (a->ne[0] * a->ne[1]), a->ne[2])); // [OC，IC, K] => [OC, IC * K] + + result = ggml_reshape_3d(ctx, result, im2col->ne[1], a->ne[2], im2col->ne[2]); // [N, OC, OL] + + return result; +} + +// ggml_conv_1d_ph + +struct ggml_tensor* ggml_conv_1d_ph( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + int s, + int d) { + return ggml_conv_1d(ctx, a, b, s, a->ne[0] / 2, d); +} + +// ggml_conv_1d_dw + +struct ggml_tensor * ggml_conv_1d_dw( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + int s0, + int p0, + int d0) { + struct ggml_tensor * new_b = ggml_reshape_4d(ctx, b, b->ne[0], 1, b->ne[1], b->ne[2]); + + struct ggml_tensor * im2col = ggml_im2col(ctx, a, new_b, s0, 0, p0, 0, d0, 0, false, GGML_TYPE_F16); + + struct ggml_tensor * result = ggml_mul_mat(ctx, im2col, a); + + result = ggml_reshape_3d(ctx, result, result->ne[0], result->ne[2], 1); + + return result; +} + +// ggml_conv_1d_dw_ph + +struct ggml_tensor * ggml_conv_1d_dw_ph( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + int s0, + int d0) { + return ggml_conv_1d_dw(ctx, a, b, s0, a->ne[0] / 2, d0); +} + +// ggml_col2im_1d + +struct ggml_tensor * ggml_col2im_1d( + struct ggml_context * ctx, + struct ggml_tensor * a, + int s0, + int oc, + int p0) { + GGML_ASSERT(ggml_is_matrix(a)); + GGML_ASSERT(ggml_is_contiguous(a)); + GGML_ASSERT(a->type == GGML_TYPE_F32 || a->type == GGML_TYPE_F16 || a->type == GGML_TYPE_BF16); + GGML_ASSERT(s0 > 0); + GGML_ASSERT(oc > 0); + GGML_ASSERT(p0 >= 0); + + const int64_t K_OC = a->ne[0]; + const int64_t T_in = a->ne[1]; + const int64_t K = K_OC / oc; + const int64_t T_out = (T_in - 1) * s0 + K - 2 * p0; + + GGML_ASSERT(K_OC == K * oc); // a->ne[0] must be a whole number of oc blocks + GGML_ASSERT(K > 0 && T_out > 0); + + const int64_t ne[4] = { T_out, oc, 1, 1 }; + struct ggml_tensor * result = ggml_new_tensor(ctx, a->type, 2, ne); + + int32_t params[] = { s0, (int32_t)oc, (int32_t)p0 }; + ggml_set_op_params(result, params, sizeof(params)); + + result->op = GGML_OP_COL2IM_1D; + result->src[0] = a; + + return result; +} + +// ggml_conv_transpose_1d + +static int64_t ggml_calc_conv_transpose_1d_output_size(int64_t ins, int64_t ks, int s, int p, int d) { + return (ins - 1) * s - 2 * p + d * (ks - 1) + 1; +} + +GGML_API struct ggml_tensor * ggml_conv_transpose_1d( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + int s0, + int p0, + int d0) { + GGML_ASSERT(ggml_is_matrix(b)); + GGML_ASSERT(a->ne[2] == b->ne[1]); + GGML_ASSERT(a->ne[3] == 1); + + GGML_ASSERT(p0 == 0); + GGML_ASSERT(d0 == 1); + + const int64_t ne[4] = { + ggml_calc_conv_transpose_1d_output_size(b->ne[0], a->ne[0], s0, 0 /*p0*/, 1 /*d0*/), + a->ne[1], b->ne[2], 1, + }; + struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); + + int32_t params[] = { s0, p0, d0 }; + ggml_set_op_params(result, params, sizeof(params)); + + result->op = GGML_OP_CONV_TRANSPOSE_1D; + result->src[0] = a; + result->src[1] = b; + + return result; +} + +// ggml_conv_2d + +// a: [OC，IC, KH, KW] +// b: [N, IC, IH, IW] +// result: [N, OC, OH, OW] +struct ggml_tensor * ggml_conv_2d( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + int s0, + int s1, + int p0, + int p1, + int d0, + int d1) { + struct ggml_tensor * im2col = ggml_im2col(ctx, a, b, s0, s1, p0, p1, d0, d1, true, a->type); // [N, OH, OW, IC * KH * KW] + + struct ggml_tensor * result = + ggml_mul_mat(ctx, + ggml_reshape_2d(ctx, im2col, im2col->ne[0], im2col->ne[3] * im2col->ne[2] * im2col->ne[1]), // [N, OH, OW, IC * KH * KW] => [N*OH*OW, IC * KH * KW] + ggml_reshape_2d(ctx, a, (a->ne[0] * a->ne[1] * a->ne[2]), a->ne[3])); // [OC，IC, KH, KW] => [OC, IC * KH * KW] + + result = ggml_reshape_4d(ctx, result, im2col->ne[1], im2col->ne[2], im2col->ne[3], a->ne[3]); // [OC, N, OH, OW] + result = ggml_cont(ctx, ggml_permute(ctx, result, 0, 1, 3, 2)); // [N, OC, OH, OW] + + + return result; +} + +// a: [OC*IC, KD, KH, KW] +// b: [N*IC, ID, IH, IW] +// result: [N*OD, OH, OW, IC * KD * KH * KW] +struct ggml_tensor * ggml_im2col_3d( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + int64_t IC, + int s0, // stride width + int s1, // stride height + int s2, // stride depth + int p0, // padding width + int p1, // padding height + int p2, // padding depth + int d0, // dilation width + int d1, // dilation height + int d2, // dilation depth + enum ggml_type dst_type) { + const int64_t N = b->ne[3] / IC; + const int64_t ID = b->ne[2]; + const int64_t IH = b->ne[1]; + const int64_t IW = b->ne[0]; + + const int64_t OC = a->ne[3] / IC; + UNUSED(OC); + const int64_t KD = a->ne[2]; + const int64_t KH = a->ne[1]; + const int64_t KW = a->ne[0]; + const int64_t OD = ggml_calc_conv_output_size(ID, KD, s2, p2, d2); + const int64_t OH = ggml_calc_conv_output_size(IH, KH, s1, p1, d1); + const int64_t OW = ggml_calc_conv_output_size(IW, KW, s0, p0, d0); + + GGML_ASSERT((OD > 0) && "b too small compared to a"); + GGML_ASSERT((OH > 0) && "b too small compared to a"); + GGML_ASSERT((OW > 0) && "b too small compared to a"); + + + const int64_t ne[4] = {KW*KH*KD*IC, OW, OH, OD*N}; + + struct ggml_tensor * result = ggml_new_tensor(ctx, dst_type, 4, ne); + int32_t params[] = { s0, s1, s2, p0, p1, p2, d0, d1, d2, (int32_t)IC}; + ggml_set_op_params(result, params, sizeof(params)); + + result->op = GGML_OP_IM2COL_3D; + result->src[0] = a; + result->src[1] = b; + + return result; +} + +// a: [OC*IC, KD, KH, KW] +// b: [N*IC, ID, IH, IW] +// result: [N*OC, OD, OH, OW] +struct ggml_tensor * ggml_conv_3d( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + int64_t IC, + int s0, // stride width + int s1, // stride height + int s2, // stride depth + int p0, // padding width + int p1, // padding height + int p2, // padding depth + int d0, // dilation width + int d1, // dilation height + int d2 // dilation depth + ) { + struct ggml_tensor * im2col = ggml_im2col_3d(ctx, a, b, IC, s0, s1, s2, p0, p1, p2, d0, d1, d2, a->type); // [N*OD, OH, OW, IC * KD * KH * KW] + + int64_t OC = a->ne[3] / IC; + int64_t N = b->ne[3] / IC; + struct ggml_tensor * result = + ggml_mul_mat(ctx, + ggml_reshape_2d(ctx, im2col, im2col->ne[0], im2col->ne[3] * im2col->ne[2] * im2col->ne[1]), // [N*OD, OH, OW, IC * KD * KH * KW] => [N*OD*OH*OW, IC * KD * KH * KW] + ggml_reshape_2d(ctx, a, (a->ne[0] * a->ne[1] * a->ne[2] * IC), OC)); // [OC*IC, KD, KH, KW] => [OC, IC * KD * KH * KW] + + int64_t OD = im2col->ne[3] / N; + result = ggml_reshape_4d(ctx, result, im2col->ne[1]*im2col->ne[2], OD, N, OC); // [OC, N*OD*OH*OW] => [OC, N, OD, OH*OW] + result = ggml_cont(ctx, ggml_permute(ctx, result, 0, 1, 3, 2)); // [N, OC, OD, OH*OW] + result = ggml_reshape_4d(ctx, result, im2col->ne[1], im2col->ne[2], OD, OC * N); // [N*OC, OD, OH, OW] + + return result; +} + +// ggml_conv_2d_sk_p0 + +struct ggml_tensor * ggml_conv_2d_sk_p0( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b) { + return ggml_conv_2d(ctx, a, b, a->ne[0], a->ne[1], 0, 0, 1, 1); +} + +// ggml_conv_2d_s1_ph + +struct ggml_tensor * ggml_conv_2d_s1_ph( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b) { + return ggml_conv_2d(ctx, a, b, 1, 1, a->ne[0] / 2, a->ne[1] / 2, 1, 1); +} + +// ggml_conv_2d_dw + +struct ggml_tensor * ggml_conv_2d_dw( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + int s0, + int s1, + int p0, + int p1, + int d0, + int d1) { + struct ggml_tensor * new_a = ggml_reshape_4d(ctx, a, a->ne[0], a->ne[1], 1, a->ne[2] * a->ne[3]); + struct ggml_tensor * im2col = ggml_im2col(ctx, new_a, + ggml_reshape_4d(ctx, b, b->ne[0], b->ne[1], 1, b->ne[2] * b->ne[3]), + s0, s1, p0, p1, d0, d1, true, GGML_TYPE_F16); // [N * IC, OH, OW, KH * KW] + struct ggml_tensor * new_b = ggml_reshape_4d(ctx, im2col, im2col->ne[0], im2col->ne[2] * im2col->ne[1], b->ne[2], b->ne[3]); // [N * IC, OH, OW, KH * KW] => [N, IC, OH * OW, KH * KW] + + new_a = ggml_reshape_4d(ctx, new_a, (new_a->ne[0] * new_a->ne[1]), new_a->ne[2], new_a->ne[3], 1); // [OC，1, KH, KW] => [1, OC, 1, KH * KW] + struct ggml_tensor * result = ggml_mul_mat(ctx, new_a, new_b); + result = ggml_reshape_4d(ctx, result, im2col->ne[1], im2col->ne[2], b->ne[2], b->ne[3]); // [N, OC, OH, OW] + + return result; +} + +// ggml_conv_2d_dw_direct + +struct ggml_tensor * ggml_conv_2d_dw_direct( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + int stride0, + int stride1, + int pad0, + int pad1, + int dilation0, + int dilation1) { + GGML_ASSERT(a->ne[2] == 1); + GGML_ASSERT(a->ne[3] == b->ne[2]); + int64_t ne[4]; + ne[0] = ggml_calc_conv_output_size(b->ne[0], a->ne[0], stride0, pad0, dilation0); + ne[1] = ggml_calc_conv_output_size(b->ne[1], a->ne[1], stride1, pad1, dilation1); + ne[2] = b->ne[2]; + ne[3] = b->ne[3]; + + struct ggml_tensor * result = ggml_new_tensor(ctx, b->type, 4, ne); + + if (ggml_is_contiguous_channels(b)) { + // Result will be permuted the same way as input (CWHN order) + const int64_t type_size = ggml_type_size(result->type); + GGML_ASSERT(ggml_blck_size(result->type) == 1); + result->nb[0] = result->ne[2] * type_size; + result->nb[1] = result->ne[0] * result->nb[0]; + result->nb[2] = type_size; + } + + int32_t params[] = { stride0, stride1, pad0, pad1, dilation0, dilation1 }; + ggml_set_op_params(result, params, sizeof(params)); + + result->op = GGML_OP_CONV_2D_DW; + result->src[0] = a; + result->src[1] = b; + return result; +} + +// ggml_conv_2d_direct + +struct ggml_tensor * ggml_conv_2d_direct( + struct ggml_context * ctx, + struct ggml_tensor * a, // convolution kernel [KW, KH, IC, OC] + struct ggml_tensor * b, // input data [W, H, C, N] + int s0, // stride dimension 0 + int s1, // stride dimension 1 + int p0, // padding dimension 0 + int p1, // padding dimension 1 + int d0, // dilation dimension 0 + int d1) {// dilation dimension 1 + + GGML_ASSERT(a->ne[2] == b->ne[2]); + //GGML_ASSERT(a->type == b->type); + + int64_t ne[4]; + ne[0] = ggml_calc_conv_output_size(b->ne[0], a->ne[0], s0, p0, d0); + ne[1] = ggml_calc_conv_output_size(b->ne[1], a->ne[1], s1, p1, d1); + ne[2] = a->ne[3]; + ne[3] = b->ne[3]; + + struct ggml_tensor * result = ggml_new_tensor(ctx, b->type, 4, ne); + + ggml_set_op_params_i32(result, 0, s0); + ggml_set_op_params_i32(result, 1, s1); + ggml_set_op_params_i32(result, 2, p0); + ggml_set_op_params_i32(result, 3, p1); + ggml_set_op_params_i32(result, 4, d0); + ggml_set_op_params_i32(result, 5, d1); + + result->op = GGML_OP_CONV_2D; + result->src[0] = a; + result->src[1] = b; + + return result; +} + +// ggml_conv_3d_direct + +struct ggml_tensor * ggml_conv_3d_direct( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + int s0, + int s1, + int s2, + int p0, + int p1, + int p2, + int d0, + int d1, + int d2, + int c, + int n, + int oc) { + + GGML_ASSERT(a->ne[3] == (int64_t) c * oc); + GGML_ASSERT(b->ne[3] == (int64_t) c * n); + + int64_t ne[4]; + ne[0] = ggml_calc_conv_output_size(b->ne[0], a->ne[0], s0, p0, d0); + ne[1] = ggml_calc_conv_output_size(b->ne[1], a->ne[1], s1, p1, d1); + ne[2] = ggml_calc_conv_output_size(b->ne[2], a->ne[2], s2, p2, d2); + ne[3] = (int64_t) oc * n; + + struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); + + ggml_set_op_params_i32(result, 0, s0); + ggml_set_op_params_i32(result, 1, s1); + ggml_set_op_params_i32(result, 2, s2); + ggml_set_op_params_i32(result, 3, p0); + ggml_set_op_params_i32(result, 4, p1); + ggml_set_op_params_i32(result, 5, p2); + ggml_set_op_params_i32(result, 6, d0); + ggml_set_op_params_i32(result, 7, d1); + ggml_set_op_params_i32(result, 8, d2); + ggml_set_op_params_i32(result, 9, c); + ggml_set_op_params_i32(result, 10, n); + ggml_set_op_params_i32(result, 11, oc); + + result->op = GGML_OP_CONV_3D; + result->src[0] = a; + result->src[1] = b; + + return result; +} + +// ggml_conv_transpose_2d_p0 + +static int64_t ggml_calc_conv_transpose_output_size(int64_t ins, int64_t ks, int s, int p) { + return (ins - 1) * s - 2 * p + ks; +} + +struct ggml_tensor * ggml_conv_transpose_2d_p0( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + int stride) { + GGML_ASSERT(a->ne[3] == b->ne[2]); + + const int64_t ne[4] = { + ggml_calc_conv_transpose_output_size(b->ne[0], a->ne[0], stride, 0 /*p0*/), + ggml_calc_conv_transpose_output_size(b->ne[1], a->ne[1], stride, 0 /*p1*/), + a->ne[2], b->ne[3], + }; + + struct ggml_tensor* result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); + + ggml_set_op_params_i32(result, 0, stride); + + result->op = GGML_OP_CONV_TRANSPOSE_2D; + result->src[0] = a; + result->src[1] = b; + + return result; +} + +// ggml_pool_* + +static int64_t ggml_calc_pool_output_size(int64_t ins, int ks, int s, float p) { + return (ins + 2 * p - ks) / s + 1; +} + +// ggml_pool_1d + +struct ggml_tensor * ggml_pool_1d( + struct ggml_context * ctx, + struct ggml_tensor * a, + enum ggml_op_pool op, + int k0, + int s0, + int p0) { + const int64_t ne[4] = { + ggml_calc_pool_output_size(a->ne[0], k0, s0, p0), + a->ne[1], + a->ne[2], + a->ne[3], + }; + GGML_ASSERT(ne[0] > 0); + + struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); + + int32_t params[] = { op, k0, s0, p0 }; + ggml_set_op_params(result, params, sizeof(params)); + + result->op = GGML_OP_POOL_1D; + result->src[0] = a; + + return result; +} + +// ggml_pool_2d + +struct ggml_tensor * ggml_pool_2d( + struct ggml_context * ctx, + struct ggml_tensor * a, + enum ggml_op_pool op, + int k0, + int k1, + int s0, + int s1, + float p0, + float p1) { + struct ggml_tensor * result; + const int64_t ne[4] = { + ggml_calc_pool_output_size(a->ne[0], k0, s0, p0), + ggml_calc_pool_output_size(a->ne[1], k1, s1, p1), + a->ne[2], + a->ne[3], + }; + GGML_ASSERT(ne[0] > 0); + GGML_ASSERT(ne[1] > 0); + + result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); + + int32_t params[] = { op, k0, k1, s0, s1, p0, p1 }; + ggml_set_op_params(result, params, sizeof(params)); + + result->op = GGML_OP_POOL_2D; + result->src[0] = a; + + return result; +} + +struct ggml_tensor * ggml_pool_2d_back( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * af, + enum ggml_op_pool op, + int k0, + int k1, + int s0, + int s1, + float p0, + float p1) { + struct ggml_tensor * result; + result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, af->ne); + + int32_t params[] = { op, k0, k1, s0, s1, p0, p1 }; + ggml_set_op_params(result, params, sizeof(params)); + + result->op = GGML_OP_POOL_2D_BACK; + result->src[0] = a; + result->src[1] = af; + + return result; +} + +// ggml_upscale / ggml_interpolate + +static struct ggml_tensor * ggml_interpolate_impl( + struct ggml_context * ctx, + struct ggml_tensor * a, + int64_t ne0, + int64_t ne1, + int64_t ne2, + int64_t ne3, + uint32_t mode) { + GGML_ASSERT((mode & 0xFF) < GGML_SCALE_MODE_COUNT); + // TODO: implement antialias for modes other than bilinear + GGML_ASSERT(!(mode & GGML_SCALE_FLAG_ANTIALIAS) || (mode & 0xFF) == GGML_SCALE_MODE_BILINEAR); + GGML_ASSERT(a->type == GGML_TYPE_F32); + + struct ggml_tensor * result = ggml_new_tensor_4d(ctx, a->type, ne0, ne1, ne2, ne3); + + ggml_set_op_params_i32(result, 0, (int32_t)mode); + + result->op = GGML_OP_UPSCALE; + result->src[0] = a; + + return result; +} + +struct ggml_tensor * ggml_upscale( + struct ggml_context * ctx, + struct ggml_tensor * a, + int scale_factor, + enum ggml_scale_mode mode) { + GGML_ASSERT(scale_factor > 1); + return ggml_interpolate_impl(ctx, a, a->ne[0] * scale_factor, a->ne[1] * scale_factor, a->ne[2], a->ne[3], mode); +} + +struct ggml_tensor * ggml_upscale_ext( + struct ggml_context * ctx, + struct ggml_tensor * a, + int ne0, + int ne1, + int ne2, + int ne3, + enum ggml_scale_mode mode) { + return ggml_interpolate_impl(ctx, a, ne0, ne1, ne2, ne3, mode); +} + +struct ggml_tensor * ggml_interpolate( + struct ggml_context * ctx, + struct ggml_tensor * a, + int64_t ne0, + int64_t ne1, + int64_t ne2, + int64_t ne3, + uint32_t mode) { + return ggml_interpolate_impl(ctx, a, ne0, ne1, ne2, ne3, mode); +} + +// ggml_pad + +struct ggml_tensor * ggml_pad( + struct ggml_context * ctx, + struct ggml_tensor * a, + int p0, + int p1, + int p2, + int p3) { + return ggml_pad_ext(ctx, a, 0, p0, 0, p1, 0, p2, 0, p3); +} + +// ggml_pad_circular + +struct ggml_tensor * ggml_pad_circular( + struct ggml_context * ctx, + struct ggml_tensor * a, + int p0, + int p1, + int p2, + int p3) { + return ggml_pad_ext_circular(ctx, a, 0, p0, 0, p1, 0, p2, 0, p3); +} + +struct ggml_tensor * ggml_pad_ext( + struct ggml_context * ctx, + struct ggml_tensor * a, + int lp0, + int rp0, + int lp1, + int rp1, + int lp2, + int rp2, + int lp3, + int rp3 + ) { + struct ggml_tensor * result = ggml_new_tensor_4d(ctx, a->type, + a->ne[0] + lp0 + rp0, + a->ne[1] + lp1 + rp1, + a->ne[2] + lp2 + rp2, + a->ne[3] + lp3 + rp3); + + ggml_set_op_params_i32(result, 0, lp0); + ggml_set_op_params_i32(result, 1, rp0); + ggml_set_op_params_i32(result, 2, lp1); + ggml_set_op_params_i32(result, 3, rp1); + ggml_set_op_params_i32(result, 4, lp2); + ggml_set_op_params_i32(result, 5, rp2); + ggml_set_op_params_i32(result, 6, lp3); + ggml_set_op_params_i32(result, 7, rp3); + ggml_set_op_params_i32(result, 8, 0); // not circular by default + + + result->op = GGML_OP_PAD; + result->src[0] = a; + + return result; +} + +// ggml_pad_ext_circular + +struct ggml_tensor * ggml_pad_ext_circular( + struct ggml_context * ctx, + struct ggml_tensor * a, + int lp0, + int rp0, + int lp1, + int rp1, + int lp2, + int rp2, + int lp3, + int rp3 + ) { + struct ggml_tensor * result = ggml_pad_ext(ctx, a, lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3); + ggml_set_op_params_i32(result, 8, 1); // circular + return result; +} + +// ggml_pad_reflect_1d + +struct ggml_tensor * ggml_pad_reflect_1d( + struct ggml_context * ctx, + struct ggml_tensor * a, + int p0, + int p1) { + GGML_ASSERT(p0 >= 0); + GGML_ASSERT(p1 >= 0); + + GGML_ASSERT(p0 < a->ne[0]); // padding length on each size must be less than the + GGML_ASSERT(p1 < a->ne[0]); // existing length of the dimension being padded + + GGML_ASSERT(ggml_is_contiguous(a)); + GGML_ASSERT(a->type == GGML_TYPE_F32); + + struct ggml_tensor * result = ggml_new_tensor_4d(ctx, a->type, + a->ne[0] + p0 + p1, + a->ne[1], + a->ne[2], + a->ne[3]); + + int32_t params[] = { p0, p1 }; + ggml_set_op_params(result, params, sizeof(params)); + + result->op = GGML_OP_PAD_REFLECT_1D; + result->src[0] = a; + + return result; +} + +// ggml_roll + +struct ggml_tensor * ggml_roll( + struct ggml_context * ctx, + struct ggml_tensor * a, + int shift0, + int shift1, + int shift2, + int shift3) { + GGML_ASSERT(a->nb[0] == ggml_type_size(a->type)); + GGML_ASSERT(abs(shift0) < a->ne[0]); + GGML_ASSERT(abs(shift1) < a->ne[1]); + GGML_ASSERT(abs(shift2) < a->ne[2]); + GGML_ASSERT(abs(shift3) < a->ne[3]); + + struct ggml_tensor * result = ggml_dup_tensor(ctx, a); + + ggml_set_op_params_i32(result, 0, shift0); + ggml_set_op_params_i32(result, 1, shift1); + ggml_set_op_params_i32(result, 2, shift2); + ggml_set_op_params_i32(result, 3, shift3); + + result->op = GGML_OP_ROLL; + result->src[0] = a; + + return result; +} + +// ggml_timestep_embedding + +struct ggml_tensor * ggml_timestep_embedding( + struct ggml_context * ctx, + struct ggml_tensor * timesteps, + int dim, + int max_period) { + + struct ggml_tensor * result = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, dim, timesteps->ne[0]); + + ggml_set_op_params_i32(result, 0, dim); + ggml_set_op_params_i32(result, 1, max_period); + + result->op = GGML_OP_TIMESTEP_EMBEDDING; + result->src[0] = timesteps; + + return result; +} + +// ggml_tri + +struct ggml_tensor * ggml_tri( + struct ggml_context * ctx, + struct ggml_tensor * a, + enum ggml_tri_type type) { + GGML_ASSERT(a->type == GGML_TYPE_F32); + + GGML_ASSERT(ggml_is_contiguous(a)); + GGML_ASSERT(a->ne[0] == a->ne[1]); + + struct ggml_tensor * result = ggml_dup_tensor(ctx, a); + + ggml_set_op_params_i32(result, 0, type); + + result->op = GGML_OP_TRI; + result->src[0] = a; + + return result; +} + +// ggml_fill + +static struct ggml_tensor * ggml_fill_impl( + struct ggml_context * ctx, + struct ggml_tensor * a, + float c, + bool inplace) { + GGML_ASSERT(a->type == GGML_TYPE_F32 || a->type == GGML_TYPE_F16); + GGML_ASSERT(ggml_is_contiguous(a)); + + struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); + + ggml_set_op_params_f32(result, 0, c); + + result->op = GGML_OP_FILL; + result->src[0] = a; + + return result; +} + +struct ggml_tensor * ggml_fill( + struct ggml_context * ctx, + struct ggml_tensor * a, + float c) { + return ggml_fill_impl(ctx, a, c, false); +} + +struct ggml_tensor * ggml_fill_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a, + float c) { + return ggml_fill_impl(ctx, a, c, true); +} + +// ggml_argsort + +struct ggml_tensor * ggml_argsort( + struct ggml_context * ctx, + struct ggml_tensor * a, + enum ggml_sort_order order) { + GGML_ASSERT(a->ne[0] <= INT32_MAX); + + struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_I32, GGML_MAX_DIMS, a->ne); + + ggml_set_op_params_i32(result, 0, (int32_t) order); + + result->op = GGML_OP_ARGSORT; + result->src[0] = a; + + return result; +} + +// ggml_argsort_top_k + +struct ggml_tensor * ggml_argsort_top_k( + struct ggml_context * ctx, + struct ggml_tensor * a, + int k) { + GGML_ASSERT(a->ne[0] >= k); + + struct ggml_tensor * result = ggml_argsort(ctx, a, GGML_SORT_ORDER_DESC); + + result = ggml_view_4d(ctx, result, + k, result->ne[1], result->ne[2], result->ne[3], + result->nb[1], result->nb[2], result->nb[3], + 0); + + return result; +} + +// ggml_top_k + +struct ggml_tensor * ggml_top_k( + struct ggml_context * ctx, + struct ggml_tensor * a, + int k) { + GGML_ASSERT(a->ne[0] >= k); + + struct ggml_tensor * result = ggml_new_tensor_4d(ctx, GGML_TYPE_I32, k, a->ne[1], a->ne[2], a->ne[3]); + + result->op = GGML_OP_TOP_K; + result->src[0] = a; + + return result; +} + +// ggml_arange + +struct ggml_tensor * ggml_arange( + struct ggml_context * ctx, + float start, + float stop, + float step) { + GGML_ASSERT(stop > start); + + const int64_t steps = (int64_t) ceilf((stop - start) / step); + + struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, steps); + + ggml_set_op_params_f32(result, 0, start); + ggml_set_op_params_f32(result, 1, stop); + ggml_set_op_params_f32(result, 2, step); + + result->op = GGML_OP_ARANGE; + + return result; +} + +// ggml_flash_attn_ext + +struct ggml_tensor * ggml_flash_attn_ext( + struct ggml_context * ctx, + struct ggml_tensor * q, + struct ggml_tensor * k, + struct ggml_tensor * v, + struct ggml_tensor * mask, + float scale, + float max_bias, + float logit_softcap) { + GGML_ASSERT(ggml_can_mul_mat(k, q)); + // TODO: check if vT can be multiplied by (k*qT) + + GGML_ASSERT(q->ne[3] == k->ne[3]); + GGML_ASSERT(q->ne[3] == v->ne[3]); + + if (mask) { + GGML_ASSERT(mask->type == GGML_TYPE_F16); + GGML_ASSERT(ggml_is_contiguous(mask)); + //GGML_ASSERT(ggml_can_repeat_rows(mask, qk)); + + GGML_ASSERT(q->ne[2] % mask->ne[2] == 0); + GGML_ASSERT(q->ne[3] % mask->ne[3] == 0); + } + + if (max_bias > 0.0f) { + GGML_ASSERT(mask); + } + + // permute(0, 2, 1, 3) + int64_t ne[4] = { v->ne[0], q->ne[2], q->ne[1], q->ne[3] }; + struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); + + float params[] = { scale, max_bias, logit_softcap }; + ggml_set_op_params(result, params, sizeof(params)); + + result->op = GGML_OP_FLASH_ATTN_EXT; + result->src[0] = q; + result->src[1] = k; + result->src[2] = v; + result->src[3] = mask; + + return result; +} + +void ggml_flash_attn_ext_set_prec( + struct ggml_tensor * a, + enum ggml_prec prec) { + GGML_ASSERT(a->op == GGML_OP_FLASH_ATTN_EXT); + + const int32_t prec_i32 = (int32_t) prec; + + ggml_set_op_params_i32(a, 3, prec_i32); // scale is on first pos, max_bias on second +} + +enum ggml_prec ggml_flash_attn_ext_get_prec( + const struct ggml_tensor * a) { + GGML_ASSERT(a->op == GGML_OP_FLASH_ATTN_EXT); + + const int32_t prec_i32 = ggml_get_op_params_i32(a, 3); + + return (enum ggml_prec) prec_i32; +} + +void ggml_flash_attn_ext_add_sinks( + struct ggml_tensor * a, + struct ggml_tensor * sinks) { + if (!sinks) { + a->src[4] = NULL; + return; + } + + GGML_ASSERT(a->op == GGML_OP_FLASH_ATTN_EXT); + GGML_ASSERT(a->src[4] == NULL); + GGML_ASSERT(a->src[0]->ne[2] == sinks->ne[0]); + GGML_ASSERT(sinks->type == GGML_TYPE_F32); + + a->src[4] = sinks; +} + +// ggml_flash_attn_back + +struct ggml_tensor * ggml_flash_attn_back( + struct ggml_context * ctx, + struct ggml_tensor * q, + struct ggml_tensor * k, + struct ggml_tensor * v, + struct ggml_tensor * d, + bool masked) { + GGML_ABORT("TODO: adapt to ggml_flash_attn_ext() changes"); + + GGML_ASSERT(ggml_can_mul_mat(k, q)); + // TODO: check if vT can be multiplied by (k*qT) + + // d shape [D,N,ne2,ne3] + // q shape [D,N,ne2,ne3] + // k shape [D,M,kvne2,ne3] + // v shape [M,D,kvne2,ne3] + + const int64_t D = q->ne[0]; + const int64_t N = q->ne[1]; + const int64_t M = k->ne[1]; + const int64_t ne2 = q->ne[2]; + const int64_t ne3 = q->ne[3]; + const int64_t kvne2 = k->ne[2]; + + GGML_ASSERT(k->ne[0] == D); + GGML_ASSERT(v->ne[0] == M); + GGML_ASSERT(v->ne[1] == D); + GGML_ASSERT(d->ne[0] == D); + GGML_ASSERT(d->ne[1] == N); + GGML_ASSERT(k->ne[2] == kvne2); + GGML_ASSERT(k->ne[3] == ne3); + GGML_ASSERT(v->ne[2] == kvne2); + GGML_ASSERT(v->ne[3] == ne3); + GGML_ASSERT(d->ne[2] == ne2); + GGML_ASSERT(d->ne[3] == ne3); + + GGML_ASSERT(ne2 % kvne2 == 0); + + // store gradients of q, k and v as continuous tensors concatenated in result. + // note: v and gradv are actually transposed, i.e. v->ne[0] != D. + const int64_t elem_q = ggml_nelements(q); + const int64_t elem_k = ggml_nelements(k); + const int64_t elem_v = ggml_nelements(v); + + enum ggml_type result_type = GGML_TYPE_F32; + GGML_ASSERT(ggml_blck_size(result_type) == 1); + const size_t tsize = ggml_type_size(result_type); + + const size_t offs_q = 0; + const size_t offs_k = offs_q + GGML_PAD(elem_q * tsize, GGML_MEM_ALIGN); + const size_t offs_v = offs_k + GGML_PAD(elem_k * tsize, GGML_MEM_ALIGN); + const size_t end = offs_v + GGML_PAD(elem_v * tsize, GGML_MEM_ALIGN); + + const size_t nelements = (end + tsize - 1)/tsize; + + struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nelements); + + int32_t masked_i = masked ? 1 : 0; + ggml_set_op_params(result, &masked_i, sizeof(masked_i)); + + result->op = GGML_OP_FLASH_ATTN_BACK; + result->src[0] = q; + result->src[1] = k; + result->src[2] = v; + result->src[3] = d; + + return result; +} + +// ggml_ssm_conv + +struct ggml_tensor * ggml_ssm_conv( + struct ggml_context * ctx, + struct ggml_tensor * sx, + struct ggml_tensor * c) { + GGML_ASSERT(ggml_is_3d(sx)); + GGML_ASSERT(ggml_is_matrix(c)); + + const int64_t d_conv = c->ne[0]; + const int64_t d_inner = c->ne[1]; + const int64_t n_t = sx->ne[0] - d_conv + 1; // tokens per sequence + const int64_t n_s = sx->ne[2]; + + // TODO: maybe support other strides than 1? + GGML_ASSERT(sx->ne[0] == d_conv - 1 + n_t); + GGML_ASSERT(sx->ne[1] == d_inner); + GGML_ASSERT(n_t >= 0); + + struct ggml_tensor * result = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, d_inner, n_t, n_s); + + result->op = GGML_OP_SSM_CONV; + result->src[0] = sx; + result->src[1] = c; + + return result; +} + +// ggml_ssm_scan + +struct ggml_tensor * ggml_ssm_scan( + struct ggml_context * ctx, + struct ggml_tensor * s, + struct ggml_tensor * x, + struct ggml_tensor * dt, + struct ggml_tensor * A, + struct ggml_tensor * B, + struct ggml_tensor * C, + struct ggml_tensor * ids) { + GGML_ASSERT(ggml_is_contiguous(s)); + GGML_ASSERT(ggml_is_contiguous(dt)); + GGML_ASSERT(ggml_is_contiguous(A)); + GGML_ASSERT(x->nb[0] == ggml_type_size(x->type)); + GGML_ASSERT(B->nb[0] == ggml_type_size(B->type)); + GGML_ASSERT(C->nb[0] == ggml_type_size(C->type)); + GGML_ASSERT(x->nb[1] == x->ne[0]*x->nb[0]); + GGML_ASSERT(B->nb[1] == B->ne[0]*B->nb[0]); + GGML_ASSERT(C->nb[1] == C->ne[0]*C->nb[0]); + GGML_ASSERT(ggml_are_same_shape(B, C)); + GGML_ASSERT(ids->type == GGML_TYPE_I32); + + { + const int64_t d_state = s->ne[0]; + const int64_t head_dim = x->ne[0]; + const int64_t n_head = x->ne[1]; + const int64_t n_seq_tokens = x->ne[2]; + const int64_t n_seqs = x->ne[3]; + + GGML_ASSERT(dt->ne[0] == n_head); + GGML_ASSERT(dt->ne[1] == n_seq_tokens); + GGML_ASSERT(dt->ne[2] == n_seqs); + GGML_ASSERT(ggml_is_3d(dt)); + GGML_ASSERT(s->ne[1] == head_dim); + GGML_ASSERT(s->ne[2] == n_head); + GGML_ASSERT(B->ne[0] == d_state); + GGML_ASSERT(B->ne[2] == n_seq_tokens); + GGML_ASSERT(B->ne[3] == n_seqs); + GGML_ASSERT(ids->ne[0] == n_seqs); + GGML_ASSERT(ggml_is_vector(ids)); + GGML_ASSERT(A->ne[1] == n_head); + GGML_ASSERT(ggml_is_matrix(A)); + + if (A->ne[0] != 1) { + // Mamba-1 has more granular decay factors + GGML_ASSERT(A->ne[0] == d_state); + } + } + + // concatenated y + ssm_states + struct ggml_tensor * result = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, ggml_nelements(x) + s->ne[0]*s->ne[1]*s->ne[2]*ids->ne[0]); + + result->op = GGML_OP_SSM_SCAN; + result->src[0] = s; + result->src[1] = x; + result->src[2] = dt; + result->src[3] = A; + result->src[4] = B; + result->src[5] = C; + result->src[6] = ids; + + return result; +} + +// ggml_win_part + +struct ggml_tensor * ggml_win_part( + struct ggml_context * ctx, + struct ggml_tensor * a, + int w) { + GGML_ASSERT(a->ne[3] == 1); + GGML_ASSERT(a->type == GGML_TYPE_F32); + + // padding + const int px = (w - a->ne[1]%w)%w; + const int py = (w - a->ne[2]%w)%w; + + const int npx = (px + a->ne[1])/w; + const int npy = (py + a->ne[2])/w; + const int np = npx*npy; + + const int64_t ne[4] = { a->ne[0], w, w, np, }; + struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); + + int32_t params[] = { npx, npy, w }; + ggml_set_op_params(result, params, sizeof(params)); + + result->op = GGML_OP_WIN_PART; + result->src[0] = a; + + return result; +} + +// ggml_win_unpart + +struct ggml_tensor * ggml_win_unpart( + struct ggml_context * ctx, + struct ggml_tensor * a, + int w0, + int h0, + int w) { + GGML_ASSERT(a->type == GGML_TYPE_F32); + + const int64_t ne[4] = { a->ne[0], w0, h0, 1, }; + struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 3, ne); + + int32_t params[] = { w }; + ggml_set_op_params(result, params, sizeof(params)); + + result->op = GGML_OP_WIN_UNPART; + result->src[0] = a; + + return result; +} + +// ggml_get_rel_pos + +struct ggml_tensor * ggml_get_rel_pos( + struct ggml_context * ctx, + struct ggml_tensor * a, + int qh, + int kh) { + GGML_ASSERT(qh == kh); + GGML_ASSERT(2*MAX(qh, kh) - 1 == a->ne[1]); + + const int64_t ne[4] = { a->ne[0], kh, qh, 1, }; + struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F16, 3, ne); + + result->op = GGML_OP_GET_REL_POS; + result->src[0] = a; + + return result; +} + +// ggml_add_rel_pos + +static struct ggml_tensor * ggml_add_rel_pos_impl( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * pw, + struct ggml_tensor * ph, + bool inplace) { + GGML_ASSERT(ggml_are_same_shape(pw, ph)); + GGML_ASSERT(ggml_is_contiguous(a)); + GGML_ASSERT(ggml_is_contiguous(pw)); + GGML_ASSERT(ggml_is_contiguous(ph)); + GGML_ASSERT(ph->type == GGML_TYPE_F32); + GGML_ASSERT(pw->type == GGML_TYPE_F32); + GGML_ASSERT(pw->ne[3] == a->ne[2]); + GGML_ASSERT(pw->ne[0]*pw->ne[0] == a->ne[0]); + GGML_ASSERT(pw->ne[1]*pw->ne[2] == a->ne[1]); + + struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); + ggml_set_op_params_i32(result, 0, inplace ? 1 : 0); + + result->op = GGML_OP_ADD_REL_POS; + result->src[0] = a; + result->src[1] = pw; + result->src[2] = ph; + + return result; +} + +struct ggml_tensor * ggml_add_rel_pos( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * pw, + struct ggml_tensor * ph) { + return ggml_add_rel_pos_impl(ctx, a, pw, ph, false); +} + +struct ggml_tensor * ggml_add_rel_pos_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * pw, + struct ggml_tensor * ph) { + return ggml_add_rel_pos_impl(ctx, a, pw, ph, true); +} + +// ggml_rwkv_wkv6 + +struct ggml_tensor * ggml_rwkv_wkv6( + struct ggml_context * ctx, + struct ggml_tensor * k, + struct ggml_tensor * v, + struct ggml_tensor * r, + struct ggml_tensor * tf, + struct ggml_tensor * td, + struct ggml_tensor * state) { + GGML_ASSERT(ggml_is_contiguous(k)); + GGML_ASSERT(ggml_is_contiguous(v)); + GGML_ASSERT(ggml_is_contiguous(r)); + GGML_ASSERT(ggml_is_contiguous(tf)); + GGML_ASSERT(ggml_is_contiguous(td)); + GGML_ASSERT(ggml_is_contiguous(state)); + + const int64_t S = k->ne[0]; + const int64_t H = k->ne[1]; + const int64_t n_tokens = k->ne[2]; + const int64_t n_seqs = state->ne[1]; + { + GGML_ASSERT(v->ne[0] == S && v->ne[1] == H && v->ne[2] == n_tokens); + GGML_ASSERT(r->ne[0] == S && r->ne[1] == H && r->ne[2] == n_tokens); + GGML_ASSERT(td->ne[0] == S && td->ne[1] == H && td->ne[2] == n_tokens); + GGML_ASSERT(ggml_nelements(state) == S * S * H * n_seqs); + } + + // concat output and new_state + const int64_t ne[4] = { S * H, n_tokens + S * n_seqs, 1, 1 }; + struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); + + result->op = GGML_OP_RWKV_WKV6; + result->src[0] = k; + result->src[1] = v; + result->src[2] = r; + result->src[3] = tf; + result->src[4] = td; + result->src[5] = state; + + return result; +} + +// ggml_gated_linear_attn + +struct ggml_tensor * ggml_gated_linear_attn( + struct ggml_context * ctx, + struct ggml_tensor * k, + struct ggml_tensor * v, + struct ggml_tensor * q, + struct ggml_tensor * g, + struct ggml_tensor * state, + float scale) { + GGML_ASSERT(ggml_is_contiguous(k)); + GGML_ASSERT(ggml_is_contiguous(v)); + GGML_ASSERT(ggml_is_contiguous(q)); + GGML_ASSERT(ggml_is_contiguous(g)); + GGML_ASSERT(ggml_is_contiguous(state)); + + const int64_t S = k->ne[0]; + const int64_t H = k->ne[1]; + const int64_t n_tokens = k->ne[2]; + const int64_t n_seqs = state->ne[1]; + { + GGML_ASSERT(v->ne[0] == S && v->ne[1] == H && v->ne[2] == n_tokens); + GGML_ASSERT(q->ne[0] == S && q->ne[1] == H && q->ne[2] == n_tokens); + GGML_ASSERT(g->ne[0] == S && g->ne[1] == H && g->ne[2] == n_tokens); + GGML_ASSERT(ggml_nelements(state) == S * S * H * n_seqs); + } + + // concat output and new_state + const int64_t ne[4] = { S * H, n_tokens + S * n_seqs, 1, 1 }; + struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); + + ggml_set_op_params_f32(result, 0, scale); + + result->op = GGML_OP_GATED_LINEAR_ATTN; + result->src[0] = k; + result->src[1] = v; + result->src[2] = q; + result->src[3] = g; + result->src[4] = state; + + return result; +} + +// ggml_rwkv_wkv7 + +struct ggml_tensor * ggml_rwkv_wkv7( + struct ggml_context * ctx, + struct ggml_tensor * r, + struct ggml_tensor * w, + struct ggml_tensor * k, + struct ggml_tensor * v, + struct ggml_tensor * a, + struct ggml_tensor * b, + struct ggml_tensor * state) { + GGML_ASSERT(ggml_is_contiguous(r)); + GGML_ASSERT(ggml_is_contiguous(w)); + GGML_ASSERT(ggml_is_contiguous(k)); + GGML_ASSERT(ggml_is_contiguous(v)); + GGML_ASSERT(ggml_is_contiguous(a)); + GGML_ASSERT(ggml_is_contiguous(b)); + GGML_ASSERT(ggml_is_contiguous(state)); + + const int64_t S = k->ne[0]; + const int64_t H = k->ne[1]; + const int64_t n_tokens = k->ne[2]; + const int64_t n_seqs = state->ne[1]; + { + GGML_ASSERT(w->ne[0] == S && w->ne[1] == H && w->ne[2] == n_tokens); + GGML_ASSERT(k->ne[0] == S && k->ne[1] == H && k->ne[2] == n_tokens); + GGML_ASSERT(v->ne[0] == S && v->ne[1] == H && v->ne[2] == n_tokens); + GGML_ASSERT(a->ne[0] == S && a->ne[1] == H && a->ne[2] == n_tokens); + GGML_ASSERT(b->ne[0] == S && b->ne[1] == H && b->ne[2] == n_tokens); + GGML_ASSERT(ggml_nelements(state) == S * S * H * n_seqs); + } + + // concat output and new_state + const int64_t ne[4] = { S * H, n_tokens + S * n_seqs, 1, 1 }; + struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); + + result->op = GGML_OP_RWKV_WKV7; + result->src[0] = r; + result->src[1] = w; + result->src[2] = k; + result->src[3] = v; + result->src[4] = a; + result->src[5] = b; + result->src[6] = state; + + return result; +} + +// ggml_unary + +static struct ggml_tensor * ggml_unary_impl( + struct ggml_context * ctx, + struct ggml_tensor * a, + enum ggml_unary_op op, + bool inplace) { + GGML_ASSERT(ggml_is_contiguous_rows(a)); + + struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); + + ggml_set_op_params_i32(result, 0, (int32_t) op); + + result->op = GGML_OP_UNARY; + result->src[0] = a; + + return result; +} + +struct ggml_tensor * ggml_unary( + struct ggml_context * ctx, + struct ggml_tensor * a, + enum ggml_unary_op op) { + return ggml_unary_impl(ctx, a, op, false); +} + +struct ggml_tensor * ggml_unary_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a, + enum ggml_unary_op op) { + return ggml_unary_impl(ctx, a, op, true); +} + +// ggml_map_custom1 + +static struct ggml_tensor * ggml_map_custom1_impl( + struct ggml_context * ctx, + struct ggml_tensor * a, + const ggml_custom1_op_t fun, + int n_tasks, + void * userdata, + bool inplace) { + GGML_ASSERT(n_tasks == GGML_N_TASKS_MAX || n_tasks > 0); + + struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); + + struct ggml_map_custom1_op_params params = { + /*.fun =*/ fun, + /*.n_tasks =*/ n_tasks, + /*.userdata =*/ userdata + }; + ggml_set_op_params(result, ¶ms, sizeof(params)); + + result->op = GGML_OP_MAP_CUSTOM1; + result->src[0] = a; + + return result; +} + +struct ggml_tensor * ggml_map_custom1( + struct ggml_context * ctx, + struct ggml_tensor * a, + const ggml_custom1_op_t fun, + int n_tasks, + void * userdata) { + return ggml_map_custom1_impl(ctx, a, fun, n_tasks, userdata, false); +} + +struct ggml_tensor * ggml_map_custom1_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a, + const ggml_custom1_op_t fun, + int n_tasks, + void * userdata) { + return ggml_map_custom1_impl(ctx, a, fun, n_tasks, userdata, true); +} + +// ggml_map_custom2 + +static struct ggml_tensor * ggml_map_custom2_impl( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + const ggml_custom2_op_t fun, + int n_tasks, + void * userdata, + bool inplace) { + GGML_ASSERT(n_tasks == GGML_N_TASKS_MAX || n_tasks > 0); + + struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); + + struct ggml_map_custom2_op_params params = { + /*.fun =*/ fun, + /*.n_tasks =*/ n_tasks, + /*.userdata =*/ userdata + }; + ggml_set_op_params(result, ¶ms, sizeof(params)); + + result->op = GGML_OP_MAP_CUSTOM2; + result->src[0] = a; + result->src[1] = b; + + return result; +} + +struct ggml_tensor * ggml_map_custom2( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + const ggml_custom2_op_t fun, + int n_tasks, + void * userdata) { + return ggml_map_custom2_impl(ctx, a, b, fun, n_tasks, userdata, false); +} + +struct ggml_tensor * ggml_map_custom2_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + const ggml_custom2_op_t fun, + int n_tasks, + void * userdata) { + return ggml_map_custom2_impl(ctx, a, b, fun, n_tasks, userdata, true); +} + +// ggml_map_custom3 + +static struct ggml_tensor * ggml_map_custom3_impl( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + struct ggml_tensor * c, + const ggml_custom3_op_t fun, + int n_tasks, + void * userdata, + bool inplace) { + GGML_ASSERT(n_tasks == GGML_N_TASKS_MAX || n_tasks > 0); + + struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); + + struct ggml_map_custom3_op_params params = { + /*.fun =*/ fun, + /*.n_tasks =*/ n_tasks, + /*.userdata =*/ userdata + }; + ggml_set_op_params(result, ¶ms, sizeof(params)); + + result->op = GGML_OP_MAP_CUSTOM3; + result->src[0] = a; + result->src[1] = b; + result->src[2] = c; + + return result; +} + +struct ggml_tensor * ggml_map_custom3( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + struct ggml_tensor * c, + const ggml_custom3_op_t fun, + int n_tasks, + void * userdata) { + return ggml_map_custom3_impl(ctx, a, b, c, fun, n_tasks, userdata, false); +} + +struct ggml_tensor * ggml_map_custom3_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + struct ggml_tensor * c, + const ggml_custom3_op_t fun, + int n_tasks, + void * userdata) { + return ggml_map_custom3_impl(ctx, a, b, c, fun, n_tasks, userdata, true); +} + +struct ggml_tensor * ggml_custom_4d( + struct ggml_context * ctx, + enum ggml_type type, + int64_t ne0, + int64_t ne1, + int64_t ne2, + int64_t ne3, + struct ggml_tensor ** args, + int n_args, + ggml_custom_op_t fun, + int n_tasks, + void * userdata) { + + GGML_ASSERT(n_args < GGML_MAX_SRC); + + struct ggml_tensor * result = ggml_new_tensor_4d(ctx, type, ne0, ne1, ne2, ne3); + + struct ggml_custom_op_params params = { + /*.fun =*/ fun, + /*.n_tasks =*/ n_tasks, + /*.userdata =*/ userdata + }; + ggml_set_op_params(result, ¶ms, sizeof(params)); + + result->op = GGML_OP_CUSTOM; + for (int i = 0; i < n_args; i++) { + result->src[i] = args[i]; + } + + return result; +} + +struct ggml_tensor * ggml_custom_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor ** args, + int n_args, + ggml_custom_op_t fun, + int n_tasks, + void * userdata) { + + GGML_ASSERT(n_args < GGML_MAX_SRC - 1); + + struct ggml_tensor * result = ggml_view_tensor(ctx, a); + + struct ggml_custom_op_params params = { + /*.fun =*/ fun, + /*.n_tasks =*/ n_tasks, + /*.userdata =*/ userdata + }; + ggml_set_op_params(result, ¶ms, sizeof(params)); + + result->op = GGML_OP_CUSTOM; + result->src[0] = a; + for (int i = 0; i < n_args; i++) { + result->src[i + 1] = args[i]; + } + + return result; +} +// ggml_cross_entropy_loss + +struct ggml_tensor * ggml_cross_entropy_loss( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b) { + GGML_ASSERT(ggml_are_same_shape(a, b)); + + struct ggml_tensor * result = ggml_new_tensor_1d(ctx, a->type, 1); + + result->op = GGML_OP_CROSS_ENTROPY_LOSS; + result->src[0] = a; + result->src[1] = b; + + return result; +} + +// ggml_cross_entropy_loss_back + +struct ggml_tensor * ggml_cross_entropy_loss_back( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + struct ggml_tensor * c) { + GGML_ASSERT(ggml_is_scalar(a)); + GGML_ASSERT(ggml_are_same_shape(b, c)); + + struct ggml_tensor * result = ggml_dup_tensor(ctx, b); + + result->op = GGML_OP_CROSS_ENTROPY_LOSS_BACK; + result->src[0] = a; + result->src[1] = b; + result->src[2] = c; + + return result; +} + +// opt_step_adamw + +struct ggml_tensor * ggml_opt_step_adamw( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * grad, + struct ggml_tensor * m, + struct ggml_tensor * v, + struct ggml_tensor * adamw_params) { + GGML_ASSERT(a->flags & GGML_TENSOR_FLAG_PARAM); + GGML_ASSERT(ggml_are_same_shape(a, grad)); + GGML_ASSERT(ggml_are_same_shape(a, m)); + GGML_ASSERT(ggml_are_same_shape(a, v)); + GGML_ASSERT(adamw_params->type == GGML_TYPE_F32); + GGML_ASSERT(ggml_nelements(adamw_params) == 7); + + struct ggml_tensor * result = ggml_view_tensor(ctx, a); + + result->op = GGML_OP_OPT_STEP_ADAMW; + result->src[0] = a; + result->src[1] = grad; + result->src[2] = m; + result->src[3] = v; + result->src[4] = adamw_params; + + return result; +} + +// opt_step_sgd + +struct ggml_tensor * ggml_opt_step_sgd( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * grad, + struct ggml_tensor * params) { + GGML_ASSERT(a->flags & GGML_TENSOR_FLAG_PARAM); + GGML_ASSERT(ggml_are_same_shape(a, grad)); + GGML_ASSERT(params->type == GGML_TYPE_F32); + GGML_ASSERT(ggml_nelements(params) == 2); + + struct ggml_tensor * result = ggml_view_tensor(ctx, a); + + result->op = GGML_OP_OPT_STEP_SGD; + result->src[0] = a; + result->src[1] = grad; + result->src[2] = params; + + return result; +} + +// solve_tri + +struct ggml_tensor * ggml_solve_tri( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + bool left, + bool lower, + bool uni) { + GGML_ASSERT(a->type == GGML_TYPE_F32); + GGML_ASSERT(b->type == GGML_TYPE_F32); + + // A must be square and lower diagonal + GGML_ASSERT(a->ne[0] == a->ne[1]); + // B must have same outer dimension as A + GGML_ASSERT(a->ne[1] == b->ne[1]); + + // batch dimensions must be equal + GGML_ASSERT(a->ne[2] == b->ne[2]); + GGML_ASSERT(a->ne[3] == b->ne[3]); + + GGML_ASSERT(ggml_is_contiguous(a)); + GGML_ASSERT(ggml_is_contiguous(b)); + + GGML_ASSERT(lower && left && !uni); // TODO: support other variants + + struct ggml_tensor * result = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, b->ne[0], b->ne[1], b->ne[2], b->ne[3]); + + result->op = GGML_OP_SOLVE_TRI; + result->src[0] = a; + result->src[1] = b; + + return result; +} + +// ggml_gated_delta_net + +struct ggml_tensor * ggml_gated_delta_net( + struct ggml_context * ctx, + struct ggml_tensor * q, + struct ggml_tensor * k, + struct ggml_tensor * v, + struct ggml_tensor * g, + struct ggml_tensor * beta, + struct ggml_tensor * state, + int64_t K) { + GGML_ASSERT(ggml_is_contiguous_rows(q)); + GGML_ASSERT(ggml_is_contiguous_rows(k)); + GGML_ASSERT(ggml_is_contiguous_rows(v)); + GGML_ASSERT(ggml_is_contiguous(g)); + GGML_ASSERT(ggml_is_contiguous(beta)); + GGML_ASSERT(ggml_is_contiguous(state)); + + GGML_ASSERT(q->type == GGML_TYPE_F32); + GGML_ASSERT(k->type == GGML_TYPE_F32); + GGML_ASSERT(v->type == GGML_TYPE_F32); + GGML_ASSERT(g->type == GGML_TYPE_F32); + GGML_ASSERT(beta->type == GGML_TYPE_F32); + GGML_ASSERT(state->type == GGML_TYPE_F32); + + const int64_t S_v = v->ne[0]; + const int64_t H = v->ne[1]; + const int64_t n_tokens = v->ne[2]; + const int64_t n_seqs = v->ne[3]; + + // gate: scalar [1, H, T, B] or vector [S_v, H, T, B] (KDA) + GGML_ASSERT(g->ne[0] == 1 || g->ne[0] == S_v); + GGML_ASSERT(beta->ne[0] == 1); + + // state holds the initial state s0 only: [S_v, S_v, H, n_seqs]. K (snapshot slot count) is an op param. + GGML_ASSERT(state->ne[0] == S_v); + GGML_ASSERT(state->ne[1] == S_v); + GGML_ASSERT(state->ne[2] == H); + GGML_ASSERT(state->ne[3] == n_seqs); + GGML_ASSERT(K >= 1); + const int64_t state_rows = K * S_v * n_seqs; + const int64_t ne[4] = { S_v * H, n_tokens * n_seqs + state_rows, 1, 1 }; + struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); + + ggml_set_op_params_i32(result, 0, (int32_t) K); + + result->op = GGML_OP_GATED_DELTA_NET; + result->src[0] = q; + result->src[1] = k; + result->src[2] = v; + result->src[3] = g; + result->src[4] = beta; + result->src[5] = state; + + return result; +} + +// ggml_lightning_indexer + +struct ggml_tensor * ggml_lightning_indexer( + struct ggml_context * ctx, + struct ggml_tensor * q, + struct ggml_tensor * k, + struct ggml_tensor * weights, + struct ggml_tensor * mask) { + + GGML_ASSERT( q->type == GGML_TYPE_F32); + GGML_ASSERT( weights->type == GGML_TYPE_F32); + GGML_ASSERT( mask->type == GGML_TYPE_F16); + GGML_ASSERT( q->ne[0] == k->ne[0]); + GGML_ASSERT( mask->ne[0] == k->ne[2]); + GGML_ASSERT( q->ne[1] == weights->ne[0]); + GGML_ASSERT( k->ne[1] == 1); + GGML_ASSERT( mask->ne[1] == q->ne[2]); + GGML_ASSERT( q->ne[2] == weights->ne[1]); + GGML_ASSERT(weights->ne[2] == 1); + GGML_ASSERT( mask->ne[2] == 1); + GGML_ASSERT( q->ne[3] == k->ne[3]); + GGML_ASSERT( k->ne[3] == weights->ne[3]); + GGML_ASSERT(weights->ne[3] % mask->ne[3] == 0); + + int64_t ne[4] = { k->ne[2], q->ne[2], 1, q->ne[3] }; + struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); + + result->op = GGML_OP_LIGHTNING_INDEXER; + result->src[0] = q; + result->src[1] = k; + result->src[2] = weights; + result->src[3] = mask; + + return result; +} + +//////////////////////////////////////////////////////////////////////////////// + +struct ggml_hash_set ggml_hash_set_new(size_t size) { + size = ggml_hash_size(size); + struct ggml_hash_set result; + result.size = size; + result.keys = GGML_MALLOC(sizeof(struct ggml_tensor *) * size); + result.used = GGML_CALLOC(ggml_bitset_size(size), sizeof(ggml_bitset_t)); + return result; +} + +void ggml_hash_set_reset(struct ggml_hash_set * hash_set) { + memset(hash_set->used, 0, sizeof(ggml_bitset_t) * ggml_bitset_size(hash_set->size)); +} + +void ggml_hash_set_free(struct ggml_hash_set * hash_set) { + GGML_FREE(hash_set->used); + GGML_FREE(hash_set->keys); +} + +size_t ggml_hash_size(size_t min_sz) { + // next primes after powers of two + static const size_t primes[] = { + 2, 3, 5, 11, 17, 37, 67, 131, 257, 521, 1031, + 2053, 4099, 8209, 16411, 32771, 65537, 131101, + 262147, 524309, 1048583, 2097169, 4194319, 8388617, + 16777259, 33554467, 67108879, 134217757, 268435459, + 536870923, 1073741827, 2147483659 + }; + static const size_t n_primes = sizeof(primes)/sizeof(primes[0]); + + // find the smallest prime that is larger or equal than min_sz + size_t l = 0; + size_t r = n_primes; + while (l < r) { + size_t m = (l + r)/2; + if (primes[m] < min_sz) { + l = m + 1; + } else { + r = m; + } + } + size_t sz = l < n_primes ? primes[l] : min_sz | 1; + return sz; +} + +struct hash_map { + struct ggml_hash_set set; + struct ggml_tensor ** vals; +}; + +static struct hash_map * ggml_new_hash_map(size_t size) { + struct hash_map * result = GGML_MALLOC(sizeof(struct hash_map)); + result->set = ggml_hash_set_new(size); + result->vals = GGML_CALLOC(result->set.size, sizeof(struct ggml_tensor *)); + return result; +} + +static void ggml_hash_map_free(struct hash_map * map) { + ggml_hash_set_free(&map->set); + GGML_FREE(map->vals); + GGML_FREE(map); +} + +// utility functions to change gradients +// isrc is the index of tensor in cgraph->visited_has_set.keys +// the corresponding gradient (accumulators) are also at position isrc +// if tensor has a gradient accumulator, modify that accumulator in-place +// else if there is no gradient for tensor, set the corresponding value +// else, just add/subtract/etc. the gradients + +static void ggml_add_or_set( + struct ggml_context * ctx, + struct ggml_cgraph * cgraph, + size_t isrc, + struct ggml_tensor * tensor) { + struct ggml_tensor * src = cgraph->visited_hash_set.keys[isrc]; + GGML_ASSERT(src); + if (cgraph->grads[isrc]) { + cgraph->grads[isrc] = ggml_add_impl(ctx, cgraph->grads[isrc], tensor, /*inplace =*/ cgraph->grad_accs[isrc]); + } else { + cgraph->grads[isrc] = tensor; + } + ggml_format_name(cgraph->grads[isrc], "grad for %s", src->name); + ggml_build_forward_expand(cgraph, cgraph->grads[isrc]); +} + +static void ggml_acc_or_set( + struct ggml_context * ctx, + struct ggml_cgraph * cgraph, + size_t isrc, + struct ggml_tensor * tensor, + const size_t nb1, + const size_t nb2, + const size_t nb3, + const size_t offset) { + struct ggml_tensor * src = cgraph->visited_hash_set.keys[isrc]; + GGML_ASSERT(src); + if (cgraph->grads[isrc]) { + cgraph->grads[isrc] = ggml_acc_impl(ctx, cgraph->grads[isrc], tensor, nb1, nb2, nb3, offset, cgraph->grad_accs[isrc]); + } else { + struct ggml_tensor * a_zero = ggml_scale(ctx, src, 0.0f); // FIXME this is going to produce NaN if a contains inf/NaN + cgraph->grads[isrc] = ggml_acc_impl(ctx, a_zero, tensor, nb1, nb2, nb3, offset, false); + } + ggml_format_name(cgraph->grads[isrc], "grad for %s", cgraph->visited_hash_set.keys[isrc]->name); + ggml_build_forward_expand(cgraph, cgraph->grads[isrc]); +} + +static void ggml_add1_or_set( + struct ggml_context * ctx, + struct ggml_cgraph * cgraph, + size_t isrc, + struct ggml_tensor * tensor) { + struct ggml_tensor * src = cgraph->visited_hash_set.keys[isrc]; + GGML_ASSERT(src); + if (cgraph->grads[isrc]) { + cgraph->grads[isrc] = ggml_add1_impl(ctx, cgraph->grads[isrc], tensor, cgraph->grad_accs[isrc]); + } else { + cgraph->grads[isrc] = ggml_repeat(ctx, tensor, src); + } + ggml_format_name(cgraph->grads[isrc], "grad for %s", src->name); + ggml_build_forward_expand(cgraph, cgraph->grads[isrc]); +} + +static void ggml_sub_or_set( + struct ggml_context * ctx, + struct ggml_cgraph * cgraph, + size_t isrc, + struct ggml_tensor * tensor) { + struct ggml_tensor * src = cgraph->visited_hash_set.keys[isrc]; + GGML_ASSERT(src); + if (cgraph->grads[isrc]) { + cgraph->grads[isrc] = ggml_sub_impl(ctx, cgraph->grads[isrc], tensor, cgraph->grad_accs[isrc]); + } else { + cgraph->grads[isrc] = ggml_neg(ctx, tensor); + } + ggml_format_name(cgraph->grads[isrc], "grad for %s", src->name); + ggml_build_forward_expand(cgraph, cgraph->grads[isrc]); +} + +static void ggml_compute_backward( + struct ggml_context * ctx, struct ggml_cgraph * cgraph, int i, const bool * grads_needed) { + struct ggml_tensor * tensor = cgraph->nodes[i]; + struct ggml_tensor * grad = ggml_graph_get_grad(cgraph, tensor); + + if (!grad) { + return; + } + + struct ggml_tensor * src0 = tensor->src[0]; + struct ggml_tensor * src1 = tensor->src[1]; + struct ggml_tensor * src2 = tensor->src[2]; + struct ggml_hash_set * hash_set = &cgraph->visited_hash_set; + const size_t isrc0 = src0 ? ggml_hash_find(hash_set, src0) : (size_t) -1; + const size_t isrc1 = src1 ? ggml_hash_find(hash_set, src1) : (size_t) -1; + const size_t isrc2 = src2 ? ggml_hash_find(hash_set, src2) : (size_t) -1; + const bool src0_needs_grads = src0 && isrc0 != GGML_HASHSET_FULL && ggml_bitset_get(hash_set->used, isrc0) && grads_needed[isrc0]; + const bool src1_needs_grads = src1 && isrc1 != GGML_HASHSET_FULL && ggml_bitset_get(hash_set->used, isrc1) && grads_needed[isrc1]; + const bool src2_needs_grads = src2 && isrc2 != GGML_HASHSET_FULL && ggml_bitset_get(hash_set->used, isrc2) && grads_needed[isrc2]; + + switch (tensor->op) { + case GGML_OP_DUP: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, grad); + } + } break; + case GGML_OP_ADD: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, grad); + } + if (src1_needs_grads) { + struct ggml_tensor * tmp = grad; + if (!ggml_are_same_shape(src0, src1)) { + tmp = ggml_repeat_back(ctx, tmp, src1); + } + ggml_add_or_set(ctx, cgraph, isrc1, tmp); + } + } break; + case GGML_OP_ADD1: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, grad); + } + if (src1_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc1, ggml_mean(ctx, grad)); // TODO: should probably be sum instead of mean + } + } break; + case GGML_OP_ACC: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, grad); + } + if (src1_needs_grads) { + const size_t nb1 = ((int32_t *) tensor->op_params)[0]; + const size_t nb2 = ((int32_t *) tensor->op_params)[1]; + const size_t nb3 = ((int32_t *) tensor->op_params)[2]; + const size_t offset = ((int32_t *) tensor->op_params)[3]; + + struct ggml_tensor * tensor_grad_view = ggml_view_4d(ctx, + grad, src1->ne[0], src1->ne[1], src1->ne[2], src1->ne[3], + nb1, nb2, nb3, offset); + + ggml_add_or_set(ctx, cgraph, isrc1, ggml_reshape(ctx, ggml_cont(ctx, tensor_grad_view), src1)); + } + } break; + case GGML_OP_SUB: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, grad); + } + if (src1_needs_grads) { + ggml_sub_or_set(ctx, cgraph, isrc1, grad); + } + } break; + case GGML_OP_MUL: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, ggml_mul(ctx, grad, src1)); + } + if (src1_needs_grads) { + struct ggml_tensor * tmp = ggml_mul(ctx, src0, grad); + if (!ggml_are_same_shape(src0, src1)) { + tmp = ggml_repeat_back(ctx, tmp, src1); + } + ggml_add_or_set(ctx, cgraph, isrc1, tmp); + } + } break; + case GGML_OP_DIV: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, ggml_div(ctx, grad, src1)); + } + if (src1_needs_grads) { + ggml_sub_or_set(ctx, cgraph, isrc1, ggml_mul(ctx, grad, ggml_div(ctx, tensor, src1))); + } + } break; + case GGML_OP_SQR: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, ggml_scale(ctx, ggml_mul(ctx, src0, grad), 2.0f)); + } + } break; + case GGML_OP_SQRT: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, ggml_scale(ctx, ggml_div(ctx, grad, tensor), 0.5f)); + } + } break; + case GGML_OP_LOG: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, ggml_div(ctx, grad, src0)); + } + } break; + case GGML_OP_SIN: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, ggml_mul(ctx, grad, ggml_cos(ctx, src0))); + } + } break; + case GGML_OP_COS: { + if (src0_needs_grads) { + ggml_sub_or_set(ctx, cgraph, isrc0, ggml_mul(ctx, grad, ggml_sin(ctx, src0))); + } + } break; + case GGML_OP_SUM: { + if (src0_needs_grads) { + ggml_add1_or_set(ctx, cgraph, isrc0, grad); + } + } break; + case GGML_OP_SUM_ROWS: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, ggml_repeat(ctx, grad, src0)); + } + } break; + case GGML_OP_MEAN: { + if (src0_needs_grads) { + ggml_add1_or_set(ctx, cgraph, isrc0, ggml_scale_impl(ctx, grad, 1.0f/src0->ne[0], 0.0, false)); + } + } break; + case GGML_OP_REPEAT: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, ggml_repeat_back(ctx, grad, src0)); + } + } break; + case GGML_OP_REPEAT_BACK: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, ggml_repeat(ctx, grad, src0)); + } + } break; + case GGML_OP_RMS_NORM: { + if (src0_needs_grads) { + float eps; + memcpy(&eps, tensor->op_params, sizeof(float)); + ggml_add_or_set(ctx, cgraph, isrc0, ggml_rms_norm_back(ctx, grad, src0, eps)); + } + } break; + case GGML_OP_MUL_MAT: { + // https://cs231n.github.io/optimization-2/#staged + // # forward pass + // s0 = np.random.randn(5, 10) + // s1 = np.random.randn(10, 3) + // t = s0.dot(s1) + + // # now suppose we had the gradient on t from above in the circuit + // dt = np.random.randn(*t.shape) # same shape as t + // ds0 = dt.dot(s1.T) #.T gives the transpose of the matrix + // ds1 = t.T.dot(dt) + + // tensor.shape [m,p,qq,rr] + // src0.shape [n,m,q1,r1] + // src1.shape [n,p,qq,rr] + + if (src0_needs_grads) { + GGML_ASSERT(grad->ne[2] == src1->ne[2]); + GGML_ASSERT(grad->ne[3] == src1->ne[3]); + struct ggml_tensor * tmp = + ggml_out_prod(ctx, // [n,m,qq,rr] + src1, // [n,p,qq,rr] + grad); // [m,p,qq,rr] + if (!ggml_are_same_shape(tmp, src0)) { + GGML_ASSERT(tmp->ne[0] == src0->ne[0]); + GGML_ASSERT(tmp->ne[1] == src0->ne[1]); + GGML_ASSERT(tmp->ne[3] == 1); + + const int64_t nr2 = tmp->ne[2] / src0->ne[2]; + const size_t nb2 = tmp->nb[2] * nr2; + const size_t nb3 = tmp->nb[2]; + + tmp = ggml_view_4d(ctx, tmp, src0->ne[0], src0->ne[1], src0->ne[2], nr2, tmp->nb[1], nb2, nb3, 0); + tmp = ggml_repeat_back(ctx, tmp, src0); + } + ggml_add_or_set(ctx, cgraph, isrc0, tmp); + } + if (src1_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc1, + // ggml_mul_mat(ctx, // [n,p,qq,rr] + // ggml_cont(ctx, // [m,n,q1,r1] + // ggml_transpose(ctx, src0)), // [m,n,q1,r1] + // grad), // [m,p,qq,rr] + + // when src0 is bigger than tensor->grad (this is mostly the case in llama), + // avoid transpose of src0, rather transpose smaller tensor->grad + // and then use ggml_out_prod + ggml_out_prod(ctx, // [n,p,qq,rr] + src0, // [n,m,q1,r1] + ggml_transpose(ctx, // [p,m,qq,rr] + grad))); // [m,p,qq,rr] + } + } break; + case GGML_OP_SCALE: { + if (src0_needs_grads) { + float s; + memcpy(&s, tensor->op_params, sizeof(float)); + ggml_add_or_set(ctx, cgraph, isrc0, ggml_scale_impl(ctx, grad, s, 0.0, false)); + } + } break; + case GGML_OP_SET: { + const size_t nb1 = ((const int32_t *) tensor->op_params)[0]; + const size_t nb2 = ((const int32_t *) tensor->op_params)[1]; + const size_t nb3 = ((const int32_t *) tensor->op_params)[2]; + const size_t offset = ((const int32_t *) tensor->op_params)[3]; + + struct ggml_tensor * tensor_grad_view = NULL; + + if (src0_needs_grads || src1_needs_grads) { + GGML_ASSERT(src0->type == tensor->type); + GGML_ASSERT(!cgraph->grads[isrc0] || cgraph->grads[isrc0]->type == grad->type); + GGML_ASSERT(!cgraph->grads[isrc1] || !src1_needs_grads || cgraph->grads[isrc1]->type == grad->type); + + tensor_grad_view = ggml_view_4d(ctx, + grad, src1->ne[0], src1->ne[1], src1->ne[2], src1->ne[3], + nb1, nb2, nb3, offset); + } + + if (src0_needs_grads) { + struct ggml_tensor * tmp = ggml_neg(ctx, tensor_grad_view); + ggml_add_or_set(ctx, cgraph, isrc0, ggml_acc_impl(ctx, grad, tmp, nb1, nb2, nb3, offset, false)); + } + + if (src1_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc1, ggml_reshape(ctx, ggml_cont(ctx, tensor_grad_view), src1)); + } + } break; + case GGML_OP_CPY: { + // cpy overwrites value of src1 by src0 and returns view(src1) + // the overwriting is mathematically equivalent to: + // tensor = src0 * 1 + src1 * 0 + if (src0_needs_grads) { + // dsrc0 = dtensor * 1 + ggml_add_or_set(ctx, cgraph, isrc0, ggml_reshape(ctx, grad, src0)); + } + if (src1_needs_grads) { + // dsrc1 = dtensor * 0 -> noop + } + } break; + case GGML_OP_CONT: { + // same as cpy + if (src0_needs_grads) { + GGML_ASSERT(!cgraph->grads[isrc0] || ggml_is_contiguous(cgraph->grads[isrc0])); + GGML_ASSERT(ggml_is_contiguous(grad)); + GGML_ASSERT(ggml_nelements(tensor) == ggml_nelements(src0)); + ggml_add_or_set(ctx, cgraph, isrc0, + ggml_are_same_shape(tensor, src0) ? grad : ggml_reshape(ctx, grad, src0)); + } + } break; + case GGML_OP_RESHAPE: { + if (src0_needs_grads) { + struct ggml_tensor * grad_cont = ggml_is_contiguous(grad) ? grad : ggml_cont(ctx, grad); + ggml_add_or_set(ctx, cgraph, isrc0, ggml_reshape(ctx, grad_cont, src0)); + } + } break; + case GGML_OP_VIEW: { + if (src0_needs_grads) { + size_t offset; + + memcpy(&offset, tensor->op_params, sizeof(offset)); + + size_t nb1 = tensor->nb[1]; + size_t nb2 = tensor->nb[2]; + size_t nb3 = tensor->nb[3]; + + if (cgraph->grads[isrc0] && src0->type != cgraph->grads[isrc0]->type) { + // gradient is typically F32, but src0 could be other type + size_t ng = ggml_element_size(cgraph->grads[isrc0]); + size_t n0 = ggml_element_size(src0); + GGML_ASSERT(offset % n0 == 0); + GGML_ASSERT(nb1 % n0 == 0); + GGML_ASSERT(nb2 % n0 == 0); + GGML_ASSERT(nb3 % n0 == 0); + offset = (offset / n0) * ng; + nb1 = (nb1 / n0) * ng; + nb2 = (nb2 / n0) * ng; + nb3 = (nb3 / n0) * ng; + } + + ggml_acc_or_set(ctx, cgraph, isrc0, grad, nb1, nb2, nb3, offset); + } + } break; + case GGML_OP_PERMUTE: { + if (src0_needs_grads) { + const int32_t * axes = (const int32_t *) tensor->op_params; + const int axis0 = axes[0] & 0x3; + const int axis1 = axes[1] & 0x3; + const int axis2 = axes[2] & 0x3; + const int axis3 = axes[3] & 0x3; + int axb[4] = {0,0,0,0}; // axes backward + axb[axis0] = 0; + axb[axis1] = 1; + axb[axis2] = 2; + axb[axis3] = 3; + ggml_add_or_set(ctx, cgraph, isrc0, ggml_permute(ctx, grad, axb[0], axb[1], axb[2], axb[3])); + } + } break; + case GGML_OP_TRANSPOSE: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, ggml_transpose(ctx, grad)); + } + } break; + case GGML_OP_GET_ROWS: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, ggml_get_rows_back(ctx, grad, src1, src0)); + } + if (src1_needs_grads) { + // noop + } + } break; + case GGML_OP_DIAG_MASK_INF: { + if (src0_needs_grads) { + /* ggml_diag_mask_inf_impl() shouldn't be here */ + /* ref: https://github.com/ggml-org/llama.cpp/pull/4203#discussion_r1412377992 */ + const int n_past = ((const int32_t *) tensor->op_params)[0]; + ggml_add_or_set(ctx, cgraph, isrc0, ggml_diag_mask_zero_impl(ctx, grad, n_past, false)); + } + } break; + case GGML_OP_DIAG_MASK_ZERO: { + if (src0_needs_grads) { + const int n_past = ((const int32_t *) tensor->op_params)[0]; + ggml_add_or_set(ctx, cgraph, isrc0, ggml_diag_mask_zero_impl(ctx, grad, n_past, false)); + } + } break; + case GGML_OP_SOFT_MAX: { + if (src0_needs_grads) { + float scale = 1.0f; + float max_bias = 0.0f; + + memcpy(&scale, (const float *) tensor->op_params + 0, sizeof(float)); + memcpy(&max_bias, (const float *) tensor->op_params + 1, sizeof(float)); + + ggml_add_or_set(ctx, cgraph, isrc0, ggml_soft_max_ext_back(ctx, grad, tensor, scale, max_bias)); + } + GGML_ASSERT((!src1 || !src1_needs_grads) && "backward pass for softmax mask not implemented"); + } break; + case GGML_OP_ROPE: { + if (src0_needs_grads) { + //const int n_past = ((int32_t *) tensor->op_params)[0]; + const int n_dims = ((const int32_t *) tensor->op_params)[1]; + const int mode = ((const int32_t *) tensor->op_params)[2]; + //const int n_ctx = ((int32_t *) tensor->op_params)[3]; + const int n_ctx_orig = ((const int32_t *) tensor->op_params)[4]; + float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; + int sections[4] = {0, 0, 0, 0}; + + memcpy(&freq_base, (const float *) tensor->op_params + 5, sizeof(float)); + memcpy(&freq_scale, (const float *) tensor->op_params + 6, sizeof(float)); + memcpy(&ext_factor, (const float *) tensor->op_params + 7, sizeof(float)); + memcpy(&attn_factor, (const float *) tensor->op_params + 8, sizeof(float)); + memcpy(&beta_fast, (const float *) tensor->op_params + 9, sizeof(float)); + memcpy(&beta_slow, (const float *) tensor->op_params + 10, sizeof(float)); + memcpy(§ions, tensor->op_params + 11, sizeof(sections)); + + struct ggml_tensor * rope_back = grad->ne[2] == src1->ne[0] ? + ggml_rope_ext_back(ctx, grad, src1, src2, n_dims, + mode, n_ctx_orig, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow) : + ggml_rope_multi_back(ctx, grad, src1, src2, n_dims, sections, + mode, n_ctx_orig, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); + ggml_add_or_set(ctx, cgraph, isrc0, rope_back); + } + GGML_ASSERT((!src2 || !src2_needs_grads) && "gradients for freq factors not implemented"); + } break; + case GGML_OP_IM2COL: { + if (src1_needs_grads) { + const int32_t s0 = ggml_get_op_params_i32(tensor, 0); + const int32_t s1 = ggml_get_op_params_i32(tensor, 1); + const int32_t p0 = ggml_get_op_params_i32(tensor, 2); + const int32_t p1 = ggml_get_op_params_i32(tensor, 3); + const int32_t d0 = ggml_get_op_params_i32(tensor, 4); + const int32_t d1 = ggml_get_op_params_i32(tensor, 5); + const bool is_2D = ggml_get_op_params_i32(tensor, 6) == 1; + + ggml_add_or_set(ctx, cgraph, isrc1, ggml_im2col_back(ctx, grad, src0, src1->ne, s0, s1, p0, p1, d0, d1, is_2D)); + } + } break; + case GGML_OP_POOL_2D: { + if (src0_needs_grads) { + const enum ggml_op_pool op = ggml_get_op_params_i32(tensor, 0); + const int32_t k0 = ggml_get_op_params_i32(tensor, 1); + const int32_t k1 = ggml_get_op_params_i32(tensor, 2); + const int32_t s0 = ggml_get_op_params_i32(tensor, 3); + const int32_t s1 = ggml_get_op_params_i32(tensor, 4); + const int32_t p0 = ggml_get_op_params_i32(tensor, 5); + const int32_t p1 = ggml_get_op_params_i32(tensor, 6); + + ggml_add_or_set(ctx, cgraph, isrc0, ggml_pool_2d_back(ctx, grad, src0, op, k0, k1, s0, s1, p0, p1)); + } + } break; + case GGML_OP_WIN_PART: + case GGML_OP_WIN_UNPART: + case GGML_OP_UNARY: { + switch (ggml_get_unary_op(tensor)) { + case GGML_UNARY_OP_ABS: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, ggml_mul(ctx, ggml_sgn(ctx, src0), grad)); + } + } break; + case GGML_UNARY_OP_SGN: { + // noop + } break; + case GGML_UNARY_OP_NEG: { + if (src0_needs_grads) { + ggml_sub_or_set(ctx, cgraph, isrc0, grad); + } + } break; + case GGML_UNARY_OP_STEP: { + // noop + } break; + case GGML_UNARY_OP_RELU: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, ggml_mul(ctx, ggml_step(ctx, src0), grad)); + } + } break; + case GGML_UNARY_OP_SILU: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, ggml_silu_back(ctx, grad, src0)); + } + } break; + case GGML_UNARY_OP_EXP: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, ggml_mul(ctx, tensor, grad)); + } + } break; + case GGML_UNARY_OP_EXPM1: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, ggml_mul(ctx, grad, ggml_exp(ctx, src0))); + } + } break; + case GGML_UNARY_OP_SOFTPLUS: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, ggml_mul(ctx, grad, ggml_sigmoid(ctx, src0))); + } + } break; + default: { + fprintf(stderr, "%s: unsupported unary op for backward pass: %s\n", + __func__, ggml_unary_op_name(ggml_get_unary_op(tensor))); + GGML_ABORT("fatal error"); + } //break; + } + } break; + case GGML_OP_CROSS_ENTROPY_LOSS: { + if (src0_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc0, ggml_cross_entropy_loss_back(ctx, grad, src0, src1)); + } + GGML_ASSERT(!src1_needs_grads && "backward pass for labels not implemented"); + } break; + case GGML_OP_GLU: { + switch (ggml_get_glu_op(tensor)) { + case GGML_GLU_OP_SWIGLU: { + if (src0_needs_grads) { + GGML_ASSERT(src1 && "backward pass only implemented for split swiglu"); + ggml_add_or_set(ctx, cgraph, isrc0, ggml_silu_back(ctx, ggml_mul(ctx, grad, src1), src0)); + } + if (src1_needs_grads) { + ggml_add_or_set(ctx, cgraph, isrc1, ggml_mul(ctx, ggml_silu(ctx, src0), grad)); + } + } break; + default: { + GGML_ABORT("unsupported glu op for backward pass: %s", ggml_glu_op_name(ggml_get_glu_op(tensor))); + } //break; + } + } break; + case GGML_OP_NONE: { + // noop + } break; + case GGML_OP_COUNT: + default: { + GGML_ABORT("%s: unsupported ggml op for backward pass: %s\n", __func__, ggml_op_name(tensor->op)); + } //break; + } + + GGML_ASSERT(!src0_needs_grads || ggml_are_same_shape(src0, cgraph->grads[isrc0])); + GGML_ASSERT(!src1_needs_grads || ggml_are_same_shape(src1, cgraph->grads[isrc1])); + GGML_ASSERT(!src2_needs_grads || ggml_are_same_shape(src2, cgraph->grads[isrc2])); +} + +static size_t ggml_visit_parents_graph(struct ggml_cgraph * cgraph, struct ggml_tensor * node, bool compute) { + if (node->op != GGML_OP_NONE && compute) { + node->flags |= GGML_TENSOR_FLAG_COMPUTE; + } + + const size_t node_hash_pos = ggml_hash_find(&cgraph->visited_hash_set, node); + GGML_ASSERT(node_hash_pos != GGML_HASHSET_FULL); + + if (ggml_bitset_get(cgraph->visited_hash_set.used, node_hash_pos)) { + // already visited + + if (compute) { + // update the compute flag regardless + for (int i = 0; i < GGML_MAX_SRC; ++i) { + struct ggml_tensor * src = node->src[i]; + if (src && ((src->flags & GGML_TENSOR_FLAG_COMPUTE) == 0)) { + ggml_visit_parents_graph(cgraph, src, true); + } + } + } + + return node_hash_pos; + } + + // This is the first time we see this node in the current graph. + cgraph->visited_hash_set.keys[node_hash_pos] = node; + ggml_bitset_set(cgraph->visited_hash_set.used, node_hash_pos); + cgraph->use_counts[node_hash_pos] = 0; + + for (int i = 0; i < GGML_MAX_SRC; ++i) { + const int k = + (cgraph->order == GGML_CGRAPH_EVAL_ORDER_LEFT_TO_RIGHT) ? i : + (cgraph->order == GGML_CGRAPH_EVAL_ORDER_RIGHT_TO_LEFT) ? (GGML_MAX_SRC-1-i) : + /* unknown order, just fall back to using i */ i; + + struct ggml_tensor * src = node->src[k]; + if (src) { + const size_t src_hash_pos = ggml_visit_parents_graph(cgraph, src, compute); + + // Update the use count for this operand. + cgraph->use_counts[src_hash_pos]++; + } + } + + if (node->op == GGML_OP_NONE && !(node->flags & GGML_TENSOR_FLAG_PARAM)) { + // reached a leaf node, not part of the gradient graph (e.g. a constant) + GGML_ASSERT(cgraph->n_leafs < cgraph->size); + + if (strlen(node->name) == 0) { + ggml_format_name(node, "leaf_%d", cgraph->n_leafs); + } + + cgraph->leafs[cgraph->n_leafs] = node; + cgraph->n_leafs++; + } else { + GGML_ASSERT(cgraph->n_nodes < cgraph->size); + + if (strlen(node->name) == 0) { + ggml_format_name(node, "node_%d", cgraph->n_nodes); + } + + cgraph->nodes[cgraph->n_nodes] = node; + cgraph->n_nodes++; + } + + return node_hash_pos; +} + +static void ggml_build_forward_impl(struct ggml_cgraph * cgraph, struct ggml_tensor * tensor, bool expand, bool compute) { + if (!expand) { + // TODO: this branch isn't accessible anymore, maybe move this to ggml_build_forward_expand + ggml_graph_clear(cgraph); + } + + const int n_old = cgraph->n_nodes; + + ggml_visit_parents_graph(cgraph, tensor, compute); + + const int n_new = cgraph->n_nodes - n_old; + GGML_PRINT_DEBUG("%s: visited %d new nodes\n", __func__, n_new); + + if (n_new > 0) { + // the last added node should always be starting point + GGML_ASSERT(cgraph->nodes[cgraph->n_nodes - 1] == tensor); + } +} + +struct ggml_tensor * ggml_build_forward_select( + struct ggml_cgraph * cgraph, + struct ggml_tensor ** tensors, + int n_tensors, + int idx) { + GGML_ASSERT(idx >= 0 && idx < n_tensors); + + for (int i = 0; i < n_tensors; i++) { + ggml_build_forward_impl(cgraph, tensors[i], true, i == idx ? true : false); + } + + return tensors[idx]; +} + +void ggml_build_forward_expand(struct ggml_cgraph * cgraph, struct ggml_tensor * tensor) { + ggml_build_forward_impl(cgraph, tensor, true, true); +} + +void ggml_build_backward_expand( + struct ggml_context * ctx, + struct ggml_cgraph * cgraph, + struct ggml_tensor ** grad_accs) { + GGML_ASSERT(cgraph->n_nodes > 0); + GGML_ASSERT(cgraph->grads); + GGML_ASSERT(cgraph->grad_accs); + + const int n_nodes_f = cgraph->n_nodes; + + memset(cgraph->grads, 0, cgraph->visited_hash_set.size*sizeof(struct ggml_tensor *)); + memset(cgraph->grad_accs, 0, cgraph->visited_hash_set.size*sizeof(struct ggml_tensor *)); + bool * grads_needed = calloc(cgraph->visited_hash_set.size, sizeof(bool)); + + { + bool any_params = false; + bool any_loss = false; + for (int i = 0; i < n_nodes_f; ++i) { + struct ggml_tensor * node = cgraph->nodes[i]; + any_params = any_params || (node->flags & GGML_TENSOR_FLAG_PARAM); + any_loss = any_loss || (node->flags & GGML_TENSOR_FLAG_LOSS); + } + GGML_ASSERT(any_params && "no trainable parameters found, did you forget to call ggml_set_param?"); + GGML_ASSERT(any_loss && "no training loss found, did you forget to call ggml_set_loss?"); + } + + for (int i = 0; i < n_nodes_f; ++i) { + struct ggml_tensor * node = cgraph->nodes[i]; + + if (node->type == GGML_TYPE_I32) { + continue; + } + + bool node_needs_grad = (node->flags & GGML_TENSOR_FLAG_PARAM) || (node->flags & GGML_TENSOR_FLAG_LOSS); + bool ignore_src[GGML_MAX_SRC] = {false}; + switch (node->op) { + // gradients in node->src[0] for one reason or another have no effect on output gradients + case GGML_OP_IM2COL: // only used for its shape + case GGML_OP_IM2COL_BACK: // same as IM2COL + ignore_src[0] = true; + break; + case GGML_OP_UNARY: { + const enum ggml_unary_op uop = ggml_get_unary_op(node); + // SGN and STEP unary ops are piecewise constant + if (uop == GGML_UNARY_OP_SGN || uop == GGML_UNARY_OP_STEP) { + ignore_src[0] = true; + } + } break; + + // gradients in node->src[1] for one reason or another have no effect on output gradients + case GGML_OP_CPY: // gradients in CPY target are irrelevant + case GGML_OP_GET_ROWS: // row indices not differentiable + case GGML_OP_GET_ROWS_BACK: // same as for GET_ROWS + case GGML_OP_ROPE: // positions not differentiable + ignore_src[1] = true; + break; + + default: + break; + } + for (int j = 0; j < GGML_MAX_SRC; ++j) { + if (!node->src[j] || ignore_src[j] || !grads_needed[ggml_hash_find(&cgraph->visited_hash_set, node->src[j])]) { + continue; + } + GGML_ASSERT(node->src[j]->type == GGML_TYPE_F32 || node->src[j]->type == GGML_TYPE_F16); + node_needs_grad = true; + break; + } + if (!node_needs_grad) { + continue; + } + + // inplace operations are currently not supported + GGML_ASSERT(!node->view_src || node->op == GGML_OP_CPY || node->op == GGML_OP_VIEW || + node->op == GGML_OP_RESHAPE || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_TRANSPOSE); + + const size_t ihash = ggml_hash_find(&cgraph->visited_hash_set, node); + GGML_ASSERT(ihash != GGML_HASHSET_FULL); + GGML_ASSERT(ggml_bitset_get(cgraph->visited_hash_set.used, ihash)); + if (grad_accs && grad_accs[i]) { + cgraph->grad_accs[ihash] = grad_accs[i]; + cgraph->grads[ihash] = cgraph->grad_accs[ihash]; + } else if (node->flags & GGML_TENSOR_FLAG_LOSS) { + // loss tensors always need a gradient accumulator + cgraph->grad_accs[ihash] = ggml_new_tensor(ctx, GGML_TYPE_F32, GGML_MAX_DIMS, node->ne); + cgraph->grads[ihash] = cgraph->grad_accs[ihash]; + } + grads_needed[ihash] = true; + } + + for (int i = n_nodes_f - 1; i >= 0; --i) { + // inplace operations to add gradients are not created by ggml_compute_backward except for gradient accumulation + // use allocator to automatically make inplace operations + ggml_compute_backward(ctx, cgraph, i, grads_needed); + } + + free(grads_needed); +} + +static void * incr_ptr_aligned(void ** p, size_t size, size_t align) { + void * ptr = *p; + ptr = (void *) GGML_PAD((uintptr_t) ptr, align); + *p = (void *) ((char *) ptr + size); + return ptr; +} + +static size_t ggml_graph_nbytes(size_t size, bool grads) { + size_t hash_size = ggml_hash_size(size * 2); + void * p = 0; + incr_ptr_aligned(&p, sizeof(struct ggml_cgraph), 1); + incr_ptr_aligned(&p, size * sizeof(struct ggml_tensor *), sizeof(struct ggml_tensor *)); // nodes + incr_ptr_aligned(&p, size * sizeof(struct ggml_tensor *), sizeof(struct ggml_tensor *)); // leafs + incr_ptr_aligned(&p, hash_size * sizeof(int32_t), sizeof(int32_t)); // use_counts + incr_ptr_aligned(&p, hash_size * sizeof(struct ggml_tensor *), sizeof(struct ggml_tensor *)); // hash keys + if (grads) { + incr_ptr_aligned(&p, hash_size * sizeof(struct ggml_tensor *), sizeof(struct ggml_tensor *)); // grads + incr_ptr_aligned(&p, hash_size * sizeof(struct ggml_tensor *), sizeof(struct ggml_tensor *)); // grad_accs + } + incr_ptr_aligned(&p, ggml_bitset_size(hash_size) * sizeof(ggml_bitset_t), sizeof(ggml_bitset_t)); + + size_t nbytes = (size_t) p; + return nbytes; +} + +size_t ggml_graph_overhead_custom(size_t size, bool grads) { + return GGML_OBJECT_SIZE + GGML_PAD(ggml_graph_nbytes(size, grads), GGML_MEM_ALIGN); +} + +size_t ggml_graph_overhead(void) { + return ggml_graph_overhead_custom(GGML_DEFAULT_GRAPH_SIZE, false); +} + +struct ggml_cgraph * ggml_new_graph_custom(struct ggml_context * ctx, size_t size, bool grads) { + const size_t obj_size = ggml_graph_nbytes(size, grads); + struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_TYPE_GRAPH, obj_size); + struct ggml_cgraph * cgraph = (struct ggml_cgraph *) ((char *) ctx->mem_buffer + obj->offs); + + // the size of the hash table is doubled since it needs to hold both nodes and leafs + size_t hash_size = ggml_hash_size(size * 2); + + void * p = cgraph + 1; + + struct ggml_tensor ** nodes_ptr = incr_ptr_aligned(&p, size * sizeof(struct ggml_tensor *), sizeof(struct ggml_tensor *)); + struct ggml_tensor ** leafs_ptr = incr_ptr_aligned(&p, size * sizeof(struct ggml_tensor *), sizeof(struct ggml_tensor *)); + int32_t * use_counts_ptr = incr_ptr_aligned(&p, hash_size * sizeof(int32_t), sizeof(int32_t)); + struct ggml_tensor ** hash_keys_ptr = incr_ptr_aligned(&p, hash_size * sizeof(struct ggml_tensor *), sizeof(struct ggml_tensor *)); + struct ggml_tensor ** grads_ptr = grads ? incr_ptr_aligned(&p, hash_size * sizeof(struct ggml_tensor *), sizeof(struct ggml_tensor *)) : NULL; + struct ggml_tensor ** grad_accs_ptr = grads ? incr_ptr_aligned(&p, hash_size * sizeof(struct ggml_tensor *), sizeof(struct ggml_tensor *)) : NULL; + + ggml_bitset_t * hash_used = incr_ptr_aligned(&p, ggml_bitset_size(hash_size) * sizeof(ggml_bitset_t), sizeof(ggml_bitset_t)); + + // check that we allocated the correct amount of memory + assert(obj_size == (size_t)((char *)p - (char *)cgraph)); + + *cgraph = (struct ggml_cgraph) { + /*.size =*/ size, + /*.n_nodes =*/ 0, + /*.n_leafs =*/ 0, + /*.nodes =*/ nodes_ptr, + /*.grads =*/ grads_ptr, + /*.grad_accs =*/ grad_accs_ptr, + /*.leafs =*/ leafs_ptr, + /*.use_counts =*/ use_counts_ptr, + /*.hash_table =*/ { hash_size, hash_used, hash_keys_ptr }, + /*.order =*/ GGML_CGRAPH_EVAL_ORDER_LEFT_TO_RIGHT, + /*.uid =*/ 0, + }; + + ggml_hash_set_reset(&cgraph->visited_hash_set); + if (grads) { + memset(cgraph->grads, 0, hash_size*sizeof(struct ggml_tensor *)); + memset(cgraph->grad_accs, 0, hash_size*sizeof(struct ggml_tensor *)); + } + + return cgraph; +} + +struct ggml_cgraph * ggml_new_graph(struct ggml_context * ctx) { + return ggml_new_graph_custom(ctx, GGML_DEFAULT_GRAPH_SIZE, false); +} + +struct ggml_cgraph ggml_graph_view(struct ggml_cgraph * cgraph0, int i0, int i1) { + struct ggml_cgraph cgraph = { + /*.size =*/ 0, + /*.n_nodes =*/ i1 - i0, + /*.n_leafs =*/ 0, + /*.nodes =*/ cgraph0->nodes + i0, + /*.grads =*/ NULL, // gradients would need visited_hash_set + /*.grad_accs =*/ NULL, + /*.leafs =*/ NULL, + /*.use_counts =*/ cgraph0->use_counts, + /*.visited_hash_set =*/ cgraph0->visited_hash_set, + /*.order =*/ cgraph0->order, + /*.uid =*/ 0 + }; + + return cgraph; +} + +void ggml_graph_cpy(struct ggml_cgraph * src, struct ggml_cgraph * dst) { + GGML_ASSERT(dst->size >= src->n_leafs); + GGML_ASSERT(dst->size >= src->n_nodes); + GGML_ASSERT(dst->visited_hash_set.size >= src->visited_hash_set.size); + + dst->n_leafs = src->n_leafs; + dst->n_nodes = src->n_nodes; + dst->order = src->order; + + for (int i = 0; i < src->n_leafs; ++i) { + dst->leafs[i] = src->leafs[i]; + } + + for (int i = 0; i < src->n_nodes; ++i) { + dst->nodes[i] = src->nodes[i]; + } + + for (size_t i = 0; i < src->visited_hash_set.size; ++i) { + // copy all hashset keys (tensors) that are in use + if (ggml_bitset_get(src->visited_hash_set.used, i)) { + size_t new_hash_pos = ggml_hash_insert(&dst->visited_hash_set, src->visited_hash_set.keys[i]); + dst->use_counts[new_hash_pos] = src->use_counts[i]; + } + } + + if (dst->grads) { + memset(dst->grads, 0, dst->visited_hash_set.size*sizeof(struct ggml_tensor *)); + memset(dst->grad_accs, 0, dst->visited_hash_set.size*sizeof(struct ggml_tensor *)); + } + if (src->grads) { + GGML_ASSERT(dst->grads != NULL); + GGML_ASSERT(dst->grad_accs != NULL); + for (int i = 0; i < src->n_nodes; ++i) { + const size_t igrad_src = ggml_hash_find(&src->visited_hash_set, src->nodes[i]); + const size_t igrad_dst = ggml_hash_find(&dst->visited_hash_set, dst->nodes[i]); + + GGML_ASSERT(igrad_src != GGML_HASHSET_FULL); + GGML_ASSERT(ggml_bitset_get(src->visited_hash_set.used, igrad_src)); + GGML_ASSERT(igrad_dst != GGML_HASHSET_FULL); + GGML_ASSERT(ggml_bitset_get(dst->visited_hash_set.used, igrad_dst)); + + dst->grads[igrad_dst] = src->grads[igrad_src]; + dst->grad_accs[igrad_dst] = src->grad_accs[igrad_src]; + } + } +} + +struct ggml_cgraph * ggml_graph_dup(struct ggml_context * ctx, struct ggml_cgraph * cgraph, bool force_grads) { + struct ggml_cgraph * result = ggml_new_graph_custom(ctx, cgraph->size, cgraph->grads || force_grads); + ggml_graph_cpy(cgraph, result); + return result; +} + +struct ggml_tensor * ggml_set_zero(struct ggml_tensor * tensor) { + if (ggml_is_empty(tensor)) { + return tensor; + } + if (tensor->buffer) { + ggml_backend_tensor_memset(tensor, 0, 0, ggml_nbytes(tensor)); + } else { + GGML_ASSERT(tensor->data); + memset(tensor->data, 0, ggml_nbytes(tensor)); + } + return tensor; +} + +void ggml_graph_reset(struct ggml_cgraph * cgraph) { + if (!cgraph) { + return; + } + GGML_ASSERT(cgraph->grads != NULL); + + for (int i = 0; i < cgraph->n_nodes; i++) { + struct ggml_tensor * node = cgraph->nodes[i]; + struct ggml_tensor * grad_acc = ggml_graph_get_grad_acc(cgraph, node); + + if (node->op == GGML_OP_OPT_STEP_ADAMW) { + // clear momenta + ggml_set_zero(node->src[2]); + ggml_set_zero(node->src[3]); + } + + // initial gradients of loss should be 1, 0 otherwise + if (grad_acc) { + if (node->flags & GGML_TENSOR_FLAG_LOSS) { + GGML_ASSERT(grad_acc->type == GGML_TYPE_F32); + GGML_ASSERT(ggml_is_scalar(grad_acc)); + + const float onef = 1.0f; + if (grad_acc->buffer) { + ggml_backend_tensor_set(grad_acc, &onef, 0, sizeof(float)); + } else { + GGML_ASSERT(grad_acc->data); + *((float *) grad_acc->data) = onef; + } + } else { + ggml_set_zero(grad_acc); + } + } + } +} + +void ggml_graph_clear(struct ggml_cgraph * cgraph) { + cgraph->n_leafs = 0; + cgraph->n_nodes = 0; + ggml_hash_set_reset(&cgraph->visited_hash_set); +} + +int ggml_graph_size(struct ggml_cgraph * cgraph) { + return cgraph->size; +} + +struct ggml_tensor * ggml_graph_node(struct ggml_cgraph * cgraph, int i) { + if (i < 0) { + GGML_ASSERT(cgraph->n_nodes + i >= 0); + return cgraph->nodes[cgraph->n_nodes + i]; + } + + GGML_ASSERT(i < cgraph->n_nodes); + return cgraph->nodes[i]; +} + +struct ggml_tensor ** ggml_graph_nodes(struct ggml_cgraph * cgraph) { + return cgraph->nodes; +} + +int ggml_graph_n_nodes(struct ggml_cgraph * cgraph) { + return cgraph->n_nodes; +} + +void ggml_graph_add_node(struct ggml_cgraph * cgraph, struct ggml_tensor * tensor) { + GGML_ASSERT(cgraph->size > cgraph->n_nodes); + cgraph->nodes[cgraph->n_nodes] = tensor; + cgraph->n_nodes++; +} + +struct ggml_tensor * ggml_graph_get_tensor(const struct ggml_cgraph * cgraph, const char * name) { + for (int i = 0; i < cgraph->n_leafs; i++) { + struct ggml_tensor * leaf = cgraph->leafs[i]; + + if (strcmp(leaf->name, name) == 0) { + return leaf; + } + } + + for (int i = 0; i < cgraph->n_nodes; i++) { + struct ggml_tensor * node = cgraph->nodes[i]; + + if (strcmp(node->name, name) == 0) { + return node; + } + } + + return NULL; +} + +struct ggml_tensor * ggml_graph_get_grad(const struct ggml_cgraph * cgraph, const struct ggml_tensor * node) { + const size_t igrad = ggml_hash_find(&cgraph->visited_hash_set, node); + return igrad != GGML_HASHSET_FULL && ggml_bitset_get(cgraph->visited_hash_set.used, igrad) && cgraph->grads ? cgraph->grads[igrad] : NULL; +} + +struct ggml_tensor * ggml_graph_get_grad_acc(const struct ggml_cgraph * cgraph, const struct ggml_tensor * node) { + const size_t igrad = ggml_hash_find(&cgraph->visited_hash_set, node); + return igrad != GGML_HASHSET_FULL && ggml_bitset_get(cgraph->visited_hash_set.used, igrad) && cgraph->grad_accs ? cgraph->grad_accs[igrad] : NULL; +} + +void ggml_graph_print(const struct ggml_cgraph * cgraph) { + GGML_LOG_INFO("=== GRAPH ===\n"); + + GGML_LOG_INFO("n_nodes = %d\n", cgraph->n_nodes); + for (int i = 0; i < cgraph->n_nodes; i++) { + struct ggml_tensor * node = cgraph->nodes[i]; + + GGML_LOG_INFO(" - %3d: [ %5" PRId64 ", %5" PRId64 ", %5" PRId64 "] %16s %s\n", + i, + node->ne[0], node->ne[1], node->ne[2], + ggml_op_name(node->op), (node->flags & GGML_TENSOR_FLAG_PARAM) ? "x" : + ggml_graph_get_grad(cgraph, node) ? "g" : " "); + } + + GGML_LOG_INFO("n_leafs = %d\n", cgraph->n_leafs); + for (int i = 0; i < cgraph->n_leafs; i++) { + struct ggml_tensor * node = cgraph->leafs[i]; + + GGML_LOG_INFO(" - %3d: [ %5" PRId64 ", %5" PRId64 "] %8s %16s\n", + i, + node->ne[0], node->ne[1], + ggml_op_name(node->op), + ggml_get_name(node)); + } + + GGML_LOG_INFO("========================================\n"); +} + +static int ggml_node_list_find_tensor(const struct ggml_cgraph * cgraph, + const int * idxs, + int count, + const struct ggml_tensor * tensor) { + GGML_ASSERT(cgraph && idxs); + for (int i = 0; i < count; ++i) { + const int node_idx = idxs[i]; + + if (node_idx >= cgraph->n_nodes) { + return -1; + } + if (cgraph->nodes[node_idx] == tensor) { + return i; + } + } + return -1; +} + +static bool ggml_is_constant(const struct ggml_tensor * tensor) { + return tensor->buffer != NULL && ggml_backend_buffer_get_usage(tensor->buffer) == GGML_BACKEND_BUFFER_USAGE_WEIGHTS && (tensor->flags & GGML_TENSOR_FLAG_PARAM) == 0; +} + +bool ggml_can_fuse_subgraph_ext(const struct ggml_cgraph * cgraph, + const int * node_idxs, + int count, + const enum ggml_op * ops, + const int * outputs, + int num_outputs) { + GGML_ASSERT(outputs && num_outputs > 0); + + for (int i = 0; i < count; ++i) { + if (node_idxs[i] >= cgraph->n_nodes) { + return false; + } + + const struct ggml_tensor * node = cgraph->nodes[node_idxs[i]]; + + if (node->op != ops[i]) { + return false; + } + + if ((node->flags & GGML_TENSOR_FLAG_COMPUTE) == 0) { + return false; + } + + if (ggml_node_list_find_tensor(cgraph, outputs, num_outputs, node) != -1) { + continue; + } + + if (node->flags & GGML_TENSOR_FLAG_OUTPUT) { + return false; + } + + int subgraph_uses = 0; + for (int j = i + 1; j < count; ++j) { + const struct ggml_tensor * other_node = cgraph->nodes[node_idxs[j]]; + for (int src_idx = 0; src_idx < GGML_MAX_SRC; src_idx++) { + if (other_node->src[src_idx] == node) { + subgraph_uses++; + } + } + } + + if (subgraph_uses != ggml_node_get_use_count(cgraph, node_idxs[i])) { + return false; + } + + // if node is a view, check if the view_src and all its parent view_srcs are within the subgraph. + // external view sources are allowed only for weight tensors, which are constant for this graph execution. + struct ggml_tensor * view_src = node->view_src; + while (view_src) { + if (ggml_node_list_find_tensor(cgraph, node_idxs, count, view_src) == -1 && !ggml_is_constant(view_src)) { + return false; + } + view_src = view_src->view_src; + } + } + + return true; +} + +// check if node is part of the graph +static bool ggml_graph_find(const struct ggml_cgraph * cgraph, const struct ggml_tensor * node) { + if (cgraph == NULL) { + return true; + } + + for (int i = 0; i < cgraph->n_nodes; i++) { + if (cgraph->nodes[i] == node) { + return true; + } + } + + return false; +} + +static struct ggml_tensor * ggml_graph_get_parent(const struct ggml_cgraph * cgraph, const struct ggml_tensor * node) { + for (int i = 0; i < cgraph->n_nodes; i++) { + struct ggml_tensor * parent = cgraph->nodes[i]; + struct ggml_tensor * grad = ggml_graph_get_grad(cgraph, parent); + + if (grad == node) { + return parent; + } + } + + return NULL; +} + +static void ggml_graph_dump_dot_node_edge(FILE * fp, const struct ggml_cgraph * gb, struct ggml_tensor * node, struct ggml_tensor * parent, const char * label) { + struct ggml_tensor * gparent = ggml_graph_get_parent(gb, node); + struct ggml_tensor * gparent0 = ggml_graph_get_parent(gb, parent); + fprintf(fp, " \"%p\" -> \"%p\" [ arrowhead = %s; style = %s; label = \"%s\"; ]\n", + gparent0 ? (void *) gparent0 : (void *) parent, + gparent ? (void *) gparent : (void *) node, + gparent ? "empty" : "vee", + gparent ? "dashed" : "solid", + label); +} + +static void ggml_graph_dump_dot_leaf_edge(FILE * fp, struct ggml_tensor * node, struct ggml_tensor * parent, const char * label) { + fprintf(fp, " \"%p\" -> \"%p\" [ label = \"%s\"; ]\n", + (void *) parent, + (void *) node, + label); +} + +void ggml_graph_dump_dot(const struct ggml_cgraph * gb, const struct ggml_cgraph * cgraph, const char * filename) { + char color[16]; + + FILE * fp = ggml_fopen(filename, "w"); + GGML_ASSERT(fp); + + fprintf(fp, "digraph G {\n"); + fprintf(fp, " newrank = true;\n"); + fprintf(fp, " rankdir = TB;\n"); + + for (int i = 0; i < gb->n_nodes; i++) { + struct ggml_tensor * node = gb->nodes[i]; + struct ggml_tensor * grad = ggml_graph_get_grad(gb, node); + + if (ggml_graph_get_parent(gb, node) != NULL) { + continue; + } + + if (node->flags & GGML_TENSOR_FLAG_PARAM) { + snprintf(color, sizeof(color), "yellow"); + } else if (grad) { + if (ggml_graph_find(cgraph, node)) { + snprintf(color, sizeof(color), "green"); + } else { + snprintf(color, sizeof(color), "lightblue"); + } + } else { + snprintf(color, sizeof(color), "white"); + } + + fprintf(fp, " \"%p\" [ " + "style = filled; fillcolor = %s; shape = record; " + "label=\"", + (void *) node, color); + + if (strlen(node->name) > 0) { + fprintf(fp, "%s (%s)|", node->name, ggml_type_name(node->type)); + } else { + fprintf(fp, "(%s)|", ggml_type_name(node->type)); + } + + if (ggml_is_matrix(node)) { + fprintf(fp, "%d [%" PRId64 ", %" PRId64 "] | %s", i, node->ne[0], node->ne[1], ggml_op_symbol(node->op)); + } else { + fprintf(fp, "%d [%" PRId64 ", %" PRId64 ", %" PRId64 "] | %s", i, node->ne[0], node->ne[1], node->ne[2], ggml_op_symbol(node->op)); + } + + if (grad) { + fprintf(fp, " | %s\"; ]\n", ggml_op_symbol(grad->op)); + } else { + fprintf(fp, "\"; ]\n"); + } + } + + for (int i = 0; i < gb->n_leafs; i++) { + struct ggml_tensor * node = gb->leafs[i]; + + snprintf(color, sizeof(color), "pink"); + + fprintf(fp, " \"%p\" [ " + "style = filled; fillcolor = %s; shape = record; " + "label=\"", + (void *) node, color); + + if (strlen(node->name) > 0) { + fprintf(fp, "%s (%s)|", node->name, ggml_type_name(node->type)); + } else { + fprintf(fp, "(%s)|", ggml_type_name(node->type)); + } + + fprintf(fp, "CONST %d [%" PRId64 ", %" PRId64 "]", i, node->ne[0], node->ne[1]); + if (ggml_nelements(node) < 5 && node->data != NULL) { + fprintf(fp, " | ("); + for (int j = 0; j < ggml_nelements(node); j++) { + // FIXME: use ggml-backend to obtain the tensor data + //if (node->type == GGML_TYPE_I8 || node->type == GGML_TYPE_I16 || node->type == GGML_TYPE_I32) { + // fprintf(fp, "%d", ggml_get_i32_1d(node, j)); + //} + //else if (node->type == GGML_TYPE_F32 || + // node->type == GGML_TYPE_F16 || + // node->type == GGML_TYPE_BF16) { + // fprintf(fp, "%.1e", (double)ggml_get_f32_1d(node, j)); + //} + //else + { + fprintf(fp, "#"); + } + if (j < ggml_nelements(node) - 1) { + fprintf(fp, ", "); + } + } + fprintf(fp, ")"); + } + fprintf(fp, "\"; ]\n"); + } + + for (int i = 0; i < gb->n_nodes; i++) { + struct ggml_tensor * node = gb->nodes[i]; + + for (int j = 0; j < GGML_MAX_SRC; j++) { + if (node->src[j]) { + char label[16]; + snprintf(label, sizeof(label), "src %d", j); + ggml_graph_dump_dot_node_edge(fp, gb, node, node->src[j], label); + } + } + } + + for (int i = 0; i < gb->n_leafs; i++) { + struct ggml_tensor * node = gb->leafs[i]; + + for (int j = 0; j < GGML_MAX_SRC; j++) { + if (node->src[j]) { + char label[16]; + snprintf(label, sizeof(label), "src %d", j); + ggml_graph_dump_dot_leaf_edge(fp, node, node->src[j], label); + } + } + } + + fprintf(fp, "}\n"); + + fclose(fp); + + GGML_LOG_INFO("%s: dot -Tpng %s -o %s.png && open %s.png\n", __func__, filename, filename, filename); +} + +//////////////////////////////////////////////////////////////////////////////// + +void ggml_set_input(struct ggml_tensor * tensor) { + tensor->flags |= GGML_TENSOR_FLAG_INPUT; +} + +void ggml_set_output(struct ggml_tensor * tensor) { + tensor->flags |= GGML_TENSOR_FLAG_OUTPUT; +} + +void ggml_set_param(struct ggml_tensor * tensor) { + GGML_ASSERT(tensor->op == GGML_OP_NONE); + tensor->flags |= GGML_TENSOR_FLAG_PARAM; +} + +void ggml_set_loss(struct ggml_tensor * tensor) { + GGML_ASSERT(ggml_is_scalar(tensor)); + GGML_ASSERT(tensor->type == GGML_TYPE_F32); + tensor->flags |= GGML_TENSOR_FLAG_LOSS; +} + +//////////////////////////////////////////////////////////////////////////////// + +void ggml_quantize_init(enum ggml_type type) { + ggml_critical_section_start(); + + switch (type) { + case GGML_TYPE_IQ2_XXS: + case GGML_TYPE_IQ2_XS: + case GGML_TYPE_IQ2_S: + case GGML_TYPE_IQ1_S: + case GGML_TYPE_IQ1_M: iq2xs_init_impl(type); break; + case GGML_TYPE_IQ3_XXS: iq3xs_init_impl(256); break; + case GGML_TYPE_IQ3_S: iq3xs_init_impl(512); break; + default: // nothing + break; + } + + ggml_critical_section_end(); +} + +void ggml_quantize_free(void) { + ggml_critical_section_start(); + + iq2xs_free_impl(GGML_TYPE_IQ2_XXS); + iq2xs_free_impl(GGML_TYPE_IQ2_XS); + iq2xs_free_impl(GGML_TYPE_IQ2_S); + iq2xs_free_impl(GGML_TYPE_IQ1_S); + iq2xs_free_impl(GGML_TYPE_IQ1_M); + iq3xs_free_impl(256); + iq3xs_free_impl(512); + + ggml_critical_section_end(); +} + +bool ggml_quantize_requires_imatrix(enum ggml_type type) { + return + type == GGML_TYPE_IQ2_XXS || + type == GGML_TYPE_IQ2_XS || + type == GGML_TYPE_IQ1_S;// || + //type == GGML_TYPE_IQ1_M; +} + +size_t ggml_quantize_chunk( + enum ggml_type type, + const float * src, + void * dst, + int64_t start, + int64_t nrows, + int64_t n_per_row, + const float * imatrix) { + const int64_t n = nrows * n_per_row; + + if (ggml_quantize_requires_imatrix(type)) { + GGML_ASSERT(imatrix != NULL); + } + + GGML_ASSERT(start % type_traits[type].blck_size == 0); + GGML_ASSERT(start % n_per_row == 0); + + ggml_quantize_init(type); // this is noop if already initialized + + const size_t start_row = start / n_per_row; + const size_t row_size = ggml_row_size(type, n_per_row); + + size_t result = 0; + + switch (type) { + case GGML_TYPE_Q1_0: result = quantize_q1_0 (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; + case GGML_TYPE_Q2_0: result = quantize_q2_0 (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; + case GGML_TYPE_Q4_0: result = quantize_q4_0 (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; + case GGML_TYPE_Q4_1: result = quantize_q4_1 (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; + case GGML_TYPE_Q5_0: result = quantize_q5_0 (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; + case GGML_TYPE_Q5_1: result = quantize_q5_1 (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; + case GGML_TYPE_Q8_0: result = quantize_q8_0 (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; + case GGML_TYPE_MXFP4: result = quantize_mxfp4 (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; + case GGML_TYPE_NVFP4: result = quantize_nvfp4 (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; + case GGML_TYPE_Q2_K: result = quantize_q2_K (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; + case GGML_TYPE_Q3_K: result = quantize_q3_K (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; + case GGML_TYPE_Q4_K: result = quantize_q4_K (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; + case GGML_TYPE_Q5_K: result = quantize_q5_K (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; + case GGML_TYPE_Q6_K: result = quantize_q6_K (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; + case GGML_TYPE_TQ1_0: result = quantize_tq1_0 (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; + case GGML_TYPE_TQ2_0: result = quantize_tq2_0 (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; + case GGML_TYPE_IQ2_XXS: result = quantize_iq2_xxs(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; + case GGML_TYPE_IQ2_XS: result = quantize_iq2_xs (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; + case GGML_TYPE_IQ3_XXS: result = quantize_iq3_xxs(src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; + case GGML_TYPE_IQ3_S: result = quantize_iq3_s (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; + case GGML_TYPE_IQ2_S: result = quantize_iq2_s (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; + case GGML_TYPE_IQ1_S: result = quantize_iq1_s (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; + case GGML_TYPE_IQ1_M: result = quantize_iq1_m (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; + case GGML_TYPE_IQ4_NL: result = quantize_iq4_nl (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; + case GGML_TYPE_IQ4_XS: result = quantize_iq4_xs (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; + case GGML_TYPE_F16: + { + size_t elemsize = sizeof(ggml_fp16_t); + ggml_fp32_to_fp16_row(src + start, (ggml_fp16_t *)dst + start, n); + result = n * elemsize; + } break; + case GGML_TYPE_BF16: + { + size_t elemsize = sizeof(ggml_bf16_t); + ggml_fp32_to_bf16_row_ref(src + start, (ggml_bf16_t *)dst + start, n); + result = n * elemsize; + } break; + case GGML_TYPE_F32: + { + size_t elemsize = sizeof(float); + result = n * elemsize; + memcpy((uint8_t *)dst + start * elemsize, src + start, result); + } break; + default: + assert(false); + } + + GGML_ASSERT(result == nrows * row_size); + + return result; +} + +//////////////////////////////////////////////////////////////////////////////// + +void ggml_log_get(ggml_log_callback * log_callback, void ** user_data) { + *log_callback = g_logger_state.log_callback; + *user_data = g_logger_state.log_callback_user_data; +} + +void ggml_log_set(ggml_log_callback log_callback, void * user_data) { + g_logger_state.log_callback = log_callback ? log_callback : ggml_log_callback_default; + g_logger_state.log_callback_user_data = user_data; +} + +void ggml_threadpool_params_init(struct ggml_threadpool_params * p, int n_threads) { + p->n_threads = n_threads; + p->prio = 0; // default priority (usually means normal or inherited) + p->poll = 50; // hybrid-polling enabled + p->strict_cpu = false; // no strict placement (all threads share same cpumask) + p->paused = false; // threads are ready to go + memset(p->cpumask, 0, GGML_MAX_N_THREADS); // all-zero means use the default affinity (usually inherited) +} + +struct ggml_threadpool_params ggml_threadpool_params_default(int n_threads) { + struct ggml_threadpool_params p; + ggml_threadpool_params_init(&p, n_threads); + return p; +} + +bool ggml_threadpool_params_match(const struct ggml_threadpool_params * p0, const struct ggml_threadpool_params * p1) { + if (p0->n_threads != p1->n_threads ) return false; + if (p0->prio != p1->prio ) return false; + if (p0->poll != p1->poll ) return false; + if (p0->strict_cpu != p1->strict_cpu ) return false; + return memcmp(p0->cpumask, p1->cpumask, GGML_MAX_N_THREADS) == 0; +} diff --git a/backend/llama.cpp/ggml/src/ggml.cpp b/backend/llama.cpp/ggml/src/ggml.cpp new file mode 100644 index 0000000000000000000000000000000000000000..0d388d45536d1fb6fd39ee35a5b8e0583e212b54 --- /dev/null +++ b/backend/llama.cpp/ggml/src/ggml.cpp @@ -0,0 +1,26 @@ +#include "ggml-impl.h" + +#include +#include + +static std::terminate_handler previous_terminate_handler; + +GGML_NORETURN static void ggml_uncaught_exception() { + ggml_print_backtrace(); + if (previous_terminate_handler) { + previous_terminate_handler(); + } + abort(); // unreachable unless previous_terminate_handler was nullptr +} + +static bool ggml_uncaught_exception_init = []{ + const char * GGML_NO_BACKTRACE = getenv("GGML_NO_BACKTRACE"); + if (GGML_NO_BACKTRACE) { + return false; + } + const auto prev{std::get_terminate()}; + GGML_ASSERT(prev != ggml_uncaught_exception); + previous_terminate_handler = prev; + std::set_terminate(ggml_uncaught_exception); + return true; +}(); diff --git a/backend/llama.cpp/ggml/src/gguf.cpp b/backend/llama.cpp/ggml/src/gguf.cpp new file mode 100644 index 0000000000000000000000000000000000000000..c3ffa1a13435bd531c259b6106a3a6763e4f2df9 --- /dev/null +++ b/backend/llama.cpp/ggml/src/gguf.cpp @@ -0,0 +1,1692 @@ +#include "ggml.h" +#include "ggml-backend.h" +#include "ggml-impl.h" +#include "gguf.h" + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#define GGUF_MAX_STRING_LENGTH (1024*1024*1024) +#define GGUF_MAX_ARRAY_ELEMENTS (1024*1024*1024) + +#ifdef _WIN32 +# define gguf_ftell _ftelli64 +# define gguf_fseek _fseeki64 +#else +# define gguf_ftell ftello +# define gguf_fseek fseeko +#endif + +template +struct type_to_gguf_type; + +template <> +struct type_to_gguf_type { + static constexpr enum gguf_type value = GGUF_TYPE_UINT8; +}; + +template <> +struct type_to_gguf_type { + static constexpr enum gguf_type value = GGUF_TYPE_INT8; +}; + +template <> +struct type_to_gguf_type { + static constexpr enum gguf_type value = GGUF_TYPE_UINT16; +}; + +template <> +struct type_to_gguf_type { + static constexpr enum gguf_type value = GGUF_TYPE_INT16; +}; + +template <> +struct type_to_gguf_type { + static constexpr enum gguf_type value = GGUF_TYPE_UINT32; +}; + +template <> +struct type_to_gguf_type { + static constexpr enum gguf_type value = GGUF_TYPE_INT32; +}; + +template <> +struct type_to_gguf_type { + static constexpr enum gguf_type value = GGUF_TYPE_FLOAT32; +}; + +template <> +struct type_to_gguf_type { + static constexpr enum gguf_type value = GGUF_TYPE_BOOL; +}; + +template <> +struct type_to_gguf_type { + static constexpr enum gguf_type value = GGUF_TYPE_STRING; +}; + +template <> +struct type_to_gguf_type { + static constexpr enum gguf_type value = GGUF_TYPE_UINT64; +}; + +template <> +struct type_to_gguf_type { + static constexpr enum gguf_type value = GGUF_TYPE_INT64; +}; + +template <> +struct type_to_gguf_type { + static constexpr enum gguf_type value = GGUF_TYPE_FLOAT64; +}; + +static const std::map GGUF_TYPE_SIZE = { + {GGUF_TYPE_UINT8, sizeof(uint8_t)}, + {GGUF_TYPE_INT8, sizeof(int8_t)}, + {GGUF_TYPE_UINT16, sizeof(uint16_t)}, + {GGUF_TYPE_INT16, sizeof(int16_t)}, + {GGUF_TYPE_UINT32, sizeof(uint32_t)}, + {GGUF_TYPE_INT32, sizeof(int32_t)}, + {GGUF_TYPE_FLOAT32, sizeof(float)}, + {GGUF_TYPE_BOOL, sizeof(int8_t)}, + {GGUF_TYPE_STRING, 0}, // undefined + {GGUF_TYPE_ARRAY, 0}, // undefined + {GGUF_TYPE_UINT64, sizeof(uint64_t)}, + {GGUF_TYPE_INT64, sizeof(int64_t)}, + {GGUF_TYPE_FLOAT64, sizeof(double)}, +}; +static_assert(GGUF_TYPE_COUNT == 13, "GGUF_TYPE_COUNT != 13"); + +static const std::map GGUF_TYPE_NAME = { + {GGUF_TYPE_UINT8, "u8"}, + {GGUF_TYPE_INT8, "i8"}, + {GGUF_TYPE_UINT16, "u16"}, + {GGUF_TYPE_INT16, "i16"}, + {GGUF_TYPE_UINT32, "u32"}, + {GGUF_TYPE_INT32, "i32"}, + {GGUF_TYPE_FLOAT32, "f32"}, + {GGUF_TYPE_BOOL, "bool"}, + {GGUF_TYPE_STRING, "str"}, + {GGUF_TYPE_ARRAY, "arr"}, + {GGUF_TYPE_UINT64, "u64"}, + {GGUF_TYPE_INT64, "i64"}, + {GGUF_TYPE_FLOAT64, "f64"}, +}; +static_assert(GGUF_TYPE_COUNT == 13, "GGUF_TYPE_COUNT != 13"); + +size_t gguf_type_size(enum gguf_type type) { + auto it = GGUF_TYPE_SIZE.find(type); + return it == GGUF_TYPE_SIZE.end() ? 0 : it->second; +} + +struct gguf_kv { + std::string key; + + bool is_array; + enum gguf_type type; + + std::vector data; + std::vector data_string; + + template + gguf_kv(const std::string & key, const T value) + : key(key), is_array(false), type(type_to_gguf_type::value) { + GGML_ASSERT(!key.empty()); + data.resize(sizeof(T)); + memcpy(data.data(), &value, sizeof(T)); + } + + template + gguf_kv(const std::string & key, const std::vector & value) + : key(key), is_array(true), type(type_to_gguf_type::value) { + GGML_ASSERT(!key.empty()); + data.resize(value.size()*sizeof(T)); + for (size_t i = 0; i < value.size(); ++i) { + const T tmp = value[i]; + memcpy(data.data() + i*sizeof(T), &tmp, sizeof(T)); + } + } + + gguf_kv(const std::string & key, const std::string & value) + : key(key), is_array(false), type(GGUF_TYPE_STRING) { + GGML_ASSERT(!key.empty()); + data_string.push_back(value); + } + + gguf_kv(const std::string & key, const std::vector & value) + : key(key), is_array(true), type(GGUF_TYPE_STRING) { + GGML_ASSERT(!key.empty()); + data_string = value; + } + + const std::string & get_key() const { + return key; + } + + const enum gguf_type & get_type() const { + return type; + } + + size_t get_ne() const { + if (type == GGUF_TYPE_STRING) { + const size_t ne = data_string.size(); + GGML_ASSERT(is_array || ne == 1); + return ne; + } + const size_t type_size = gguf_type_size(type); + GGML_ASSERT(data.size() % type_size == 0); + const size_t ne = data.size() / type_size; + GGML_ASSERT(is_array || ne == 1); + return ne; + } + + template + const T & get_val(const size_t i = 0) const { + GGML_ASSERT(type_to_gguf_type::value == type); + if constexpr (std::is_same::value) { + GGML_ASSERT(data_string.size() >= i+1); + return data_string[i]; + } + const size_t type_size = gguf_type_size(type); + GGML_ASSERT(data.size() % type_size == 0); + GGML_ASSERT(data.size() >= (i+1)*type_size); + return reinterpret_cast(data.data())[i]; + } + + void cast(const enum gguf_type new_type) { + const size_t new_type_size = gguf_type_size(new_type); + GGML_ASSERT(data.size() % new_type_size == 0); + type = new_type; + } +}; + +struct gguf_tensor_info { + struct ggml_tensor t; // for holding the equivalent info + uint64_t offset; // offset from start of `data`, must be a multiple of `ALIGNMENT` +}; + +struct gguf_context { + uint32_t version = GGUF_VERSION; + + std::vector kv; + std::vector info; + + size_t alignment = GGUF_DEFAULT_ALIGNMENT; + size_t offset = 0; // offset of `data` from beginning of file + size_t size = 0; // size of `data` in bytes + + void * data = nullptr; +}; + +struct gguf_reader { + gguf_reader( + gguf_reader_callback_t callback, + void * userdata, + size_t max_chunk_read, + uint64_t data_offset = 0, + uint64_t nbytes_remain = 0) + : callback(callback), + userdata(userdata), + max_chunk_read(max_chunk_read), + data_offset(data_offset), + nbytes_remain(nbytes_remain) { + GGML_ASSERT(max_chunk_read > 0); + } + + // helper for remaining bytes in a file + static uint64_t file_remain(FILE * file) { + const int64_t cur = gguf_ftell(file); + if (cur < 0) { + return 0; + } + if (gguf_fseek(file, 0, SEEK_END) != 0) { + gguf_fseek(file, cur, SEEK_SET); + + return 0; + } + const int64_t end = gguf_ftell(file); + if (end < 0) { + gguf_fseek(file, cur, SEEK_SET); + + return 0; + } + gguf_fseek(file, cur, SEEK_SET); + return static_cast(end - cur); + } + + template + bool read(T & dst) const { + const size_t size = sizeof(dst); + if (size > nbytes_remain) { + return false; + } + return read_raw(&dst, size) == size; + } + + template + bool read(std::vector & dst, const size_t n) const { + if (n > GGUF_MAX_ARRAY_ELEMENTS) { + return false; + } + if constexpr (std::is_same::value) { + // strings are prefixed with their length, so we need to account for that + if (n > SIZE_MAX / sizeof(uint64_t)) { + return false; + } + if (nbytes_remain < n * sizeof(uint64_t)) { + return false; + } + } else { + if (n > SIZE_MAX / sizeof(T)) { + return false; + } + if (nbytes_remain < n * sizeof(T)) { + return false; + } + } + dst.resize(n); + for (size_t i = 0; i < dst.size(); ++i) { + if constexpr (std::is_same::value) { + bool tmp; + if (!read(tmp)) { + return false; + } + dst[i] = tmp; + } else { + if (!read(dst[i])) { + return false; + } + } + } + return true; + } + + bool read(bool & dst) const { + int8_t tmp = -1; + if (!read(tmp)) { + return false; + } + dst = tmp != 0; + return true; + } + + bool read(enum ggml_type & dst) const { + int32_t tmp = -1; + if (!read(tmp)) { + return false; + } + dst = ggml_type(tmp); + return true; + } + + bool read(enum gguf_type & dst) const { + int32_t tmp = -1; + if (!read(tmp)) { + return false; + } + dst = gguf_type(tmp); + return true; + } + + bool read(std::string & dst) const { + uint64_t size = 0; + if (!read(size)) { + return false; + } + if (size > GGUF_MAX_STRING_LENGTH) { + GGML_LOG_ERROR("%s: string length %" PRIu64 " exceeds maximum %" PRIu64 "\n", __func__, size, (uint64_t) GGUF_MAX_STRING_LENGTH); + return false; + } + if (size > nbytes_remain) { + GGML_LOG_ERROR("%s: string length %" PRIu64 " exceeds remaining file size %" PRIu64 " bytes\n", __func__, size, nbytes_remain); + return false; + } + dst.resize(static_cast(size)); + return read_raw(dst.data(), static_cast(size)) == size; + } + + bool read(void * dst, const size_t size) const { + if (size > nbytes_remain) { + return false; + } + return read_raw(dst, size) == size; + } + + uint64_t tell() const { + return data_offset; + } + + bool seek(uint64_t absolute_offset) const { + const uint64_t end_offset = uint64_t(data_offset) + nbytes_remain; + if (absolute_offset > end_offset) { + return false; + } + + data_offset = absolute_offset; + nbytes_remain = end_offset - absolute_offset; + + return true; + } + +private: + size_t read_raw(void * dst, size_t size) const { + if (callback == nullptr || size == 0) { + return 0; + } + + uint8_t * data = static_cast(dst); + size_t total_nread = 0; + bool reached_eof = false; + + while (total_nread < size) { + const size_t chunk_size = std::min(max_chunk_read, size - total_nread); + if (data_offset + total_nread < data_offset) { + break; + } + const size_t nread = callback(userdata, static_cast(data + total_nread), data_offset + total_nread, chunk_size); + total_nread += nread; + if (nread != chunk_size) { + reached_eof = true; + break; + } + } + + data_offset += total_nread; + GGML_ASSERT(total_nread <= nbytes_remain); + nbytes_remain -= total_nread; + + if (reached_eof) { + nbytes_remain = 0; + } + + return total_nread; + } + + gguf_reader_callback_t callback = nullptr; + void * userdata = nullptr; + size_t max_chunk_read = 0; + mutable uint64_t data_offset = 0; + mutable uint64_t nbytes_remain = 0; +}; + +struct gguf_context * gguf_init_empty(void) { + return new gguf_context; +} + +template +bool gguf_read_emplace_helper(const struct gguf_reader & gr, std::vector & kv, const std::string & key, const bool is_array, const size_t n) { + if (is_array) { + std::vector value; + try { + if (!gr.read(value, n)) { + return false; + } + } catch (std::length_error &) { + GGML_LOG_ERROR("%s: encountered length_error while reading value for key '%s'\n", __func__, key.c_str()); + return false; + } catch (std::bad_alloc &) { + GGML_LOG_ERROR("%s: encountered bad_alloc error while reading value for key '%s'\n", __func__, key.c_str()); + return false; + } + kv.emplace_back(key, value); + } else { + T value; + if (!gr.read(value)) { + return false; + } + kv.emplace_back(key, value); + } + return true; +} + +static struct gguf_context * gguf_init_from_reader(const struct gguf_reader & gr, struct gguf_init_params params) { + struct gguf_context * ctx = new gguf_context; + + bool ok = true; + + // file magic + { + std::vector magic; + ok = ok && gr.read(magic, 4); + + if (!ok) { + GGML_LOG_ERROR("%s: failed to read magic\n", __func__); + gguf_free(ctx); + return nullptr; + } + + for (uint32_t i = 0; i < magic.size(); i++) { + if (magic[i] != GGUF_MAGIC[i]) { + char c0 = isprint(magic[0]) ? magic[0] : '?'; + char c1 = isprint(magic[1]) ? magic[1] : '?'; + char c2 = isprint(magic[2]) ? magic[2] : '?'; + char c3 = isprint(magic[3]) ? magic[3] : '?'; + GGML_LOG_ERROR("%s: invalid magic characters: '%c%c%c%c', expected 'GGUF'\n", __func__, c0, c1, c2, c3); + gguf_free(ctx); + return nullptr; + } + } + } + + // header + int64_t n_kv = 0; + int64_t n_tensors = 0; + + if (ok && gr.read(ctx->version)) { + if (ok && ctx->version == 0) { + GGML_LOG_ERROR("%s: bad GGUF version: %" PRIu32 "\n", __func__, ctx->version); + ok = false; + } + + /* + * bit layout is different when reading non-native endian models. + * assuming that the GGUF version is 3, the non-native endian model + * would read it as 0x30000000. we can use the AND operation against + * the last 4 hexadecimal digits to check if the model is the same + * endianness as the host system. + */ + if (ok && (ctx->version & 0x0000FFFF) == 0x00000000) { + GGML_LOG_ERROR("%s: failed to load model: this GGUF file version %" PRIu32 " is extremely large, is there a mismatch between the host and model endianness?\n", __func__, ctx->version); + ok = false; + } + + if (ok && ctx->version == 1) { + GGML_LOG_ERROR("%s: GGUFv1 is no longer supported, please use a more up-to-date version\n", __func__); + ok = false; + } + if (ok && ctx->version > GGUF_VERSION) { + GGML_LOG_ERROR("%s: this GGUF file is version %" PRIu32 " but this software only supports up to version %d\n", + __func__, ctx->version, GGUF_VERSION); + ok = false; + } + } else { + ok = false; + } + + if (ok && gr.read(n_tensors)) { + static_assert(sizeof(size_t) <= 8 && sizeof(gguf_tensor_info) >= 2, "int64_t insufficient for indexing"); + if (n_tensors < 0 || n_tensors > int64_t(SIZE_MAX/sizeof(gguf_tensor_info))) { + GGML_LOG_ERROR("%s: number of tensors is %" PRIi64 " but must be in [0, %zu]\n", + __func__, n_tensors, SIZE_MAX/sizeof(gguf_tensor_info)); + ok = false; + } + } else { + ok = false; + } + + if (ok && gr.read(n_kv)) { + static_assert(sizeof(size_t) <= 8 && sizeof(gguf_tensor_info) >= 2, "int64_t insufficient for indexing"); + if (n_kv < 0 || n_kv > int64_t(SIZE_MAX/sizeof(gguf_kv))) { + GGML_LOG_ERROR("%s: number of key value pairs is %" PRIi64 " but must be in [0, %zu]\n", + __func__, n_kv, SIZE_MAX/sizeof(gguf_kv)); + ok = false; + } + } else { + ok = false; + } + + if (!ok) { + GGML_LOG_ERROR("%s: failed to read header\n", __func__); + gguf_free(ctx); + return nullptr; + } + + // KV pairs + { + for (int64_t i = 0; ok && i < n_kv; ++i) { + std::string key; + gguf_type type = gguf_type(-1); + bool is_array = false; + uint64_t n = 1; + + try { + ok = ok && gr.read(key); + } catch (std::length_error &) { + GGML_LOG_ERROR("%s: encountered length_error while reading key %" PRIi64 "\n", __func__, i); + ok = false; + } catch (std::bad_alloc &) { + GGML_LOG_ERROR("%s: encountered bad_alloc error while reading key %" PRIi64 "\n", __func__, i); + ok = false; + } + if (ok && key.empty()) { + GGML_LOG_ERROR("%s: key %" PRIi64 " is empty\n", __func__, i); + ok = false; + } + for (size_t j = 0; ok && j < ctx->kv.size(); ++j) { + if (key == ctx->kv[j].key) { + GGML_LOG_ERROR("%s: duplicate key '%s' for tensors %zu and %" PRIi64 " \n", __func__, key.c_str(), j, i); + ok = false; + } + } + if (!ok) { + break; + } + + ok = ok && gr.read(type); + if (type == GGUF_TYPE_ARRAY) { + is_array = true; + ok = ok && gr.read(type); + ok = ok && gr.read(n); + } + if (!ok) { + break; + } + + switch (type) { + case GGUF_TYPE_UINT8: ok = ok && gguf_read_emplace_helper (gr, ctx->kv, key, is_array, n); break; + case GGUF_TYPE_INT8: ok = ok && gguf_read_emplace_helper (gr, ctx->kv, key, is_array, n); break; + case GGUF_TYPE_UINT16: ok = ok && gguf_read_emplace_helper (gr, ctx->kv, key, is_array, n); break; + case GGUF_TYPE_INT16: ok = ok && gguf_read_emplace_helper (gr, ctx->kv, key, is_array, n); break; + case GGUF_TYPE_UINT32: ok = ok && gguf_read_emplace_helper (gr, ctx->kv, key, is_array, n); break; + case GGUF_TYPE_INT32: ok = ok && gguf_read_emplace_helper (gr, ctx->kv, key, is_array, n); break; + case GGUF_TYPE_FLOAT32: ok = ok && gguf_read_emplace_helper (gr, ctx->kv, key, is_array, n); break; + case GGUF_TYPE_BOOL: ok = ok && gguf_read_emplace_helper (gr, ctx->kv, key, is_array, n); break; + case GGUF_TYPE_STRING: ok = ok && gguf_read_emplace_helper(gr, ctx->kv, key, is_array, n); break; + case GGUF_TYPE_UINT64: ok = ok && gguf_read_emplace_helper (gr, ctx->kv, key, is_array, n); break; + case GGUF_TYPE_INT64: ok = ok && gguf_read_emplace_helper (gr, ctx->kv, key, is_array, n); break; + case GGUF_TYPE_FLOAT64: ok = ok && gguf_read_emplace_helper (gr, ctx->kv, key, is_array, n); break; + case GGUF_TYPE_ARRAY: + default: + { + GGML_LOG_ERROR("%s: key '%s' has invalid GGUF type %d\n", __func__, key.c_str(), type); + ok = false; + } break; + } + } + + if (!ok) { + GGML_LOG_ERROR("%s: failed to read key-value pairs\n", __func__); + gguf_free(ctx); + return nullptr; + } + GGML_ASSERT(int64_t(ctx->kv.size()) == n_kv); + + const int alignment_idx = gguf_find_key(ctx, GGUF_KEY_GENERAL_ALIGNMENT); + ctx->alignment = alignment_idx == -1 ? GGUF_DEFAULT_ALIGNMENT : gguf_get_val_u32(ctx, alignment_idx); + + if (ctx->alignment == 0 || (ctx->alignment & (ctx->alignment - 1)) != 0) { + GGML_LOG_ERROR("%s: alignment %zu is not a power of 2\n", __func__, ctx->alignment); + gguf_free(ctx); + return nullptr; + } + } + + // read the tensor info + for (int64_t i = 0; ok && i < n_tensors; ++i) { + struct gguf_tensor_info info; + + // tensor name + { + std::string name; + try { + ok = ok && gr.read(name); + } catch (std::length_error &) { + GGML_LOG_ERROR("%s: encountered length_error while reading tensor name %" PRIi64 "\n", __func__, i); + ok = false; + } catch (std::bad_alloc &) { + GGML_LOG_ERROR("%s: encountered bad_alloc error while reading tensor name %" PRIi64 "\n", __func__, i); + ok = false; + } + if (name.length() >= GGML_MAX_NAME) { + GGML_LOG_ERROR("%s: tensor name %" PRIi64 " is too long: %zu >= %d\n", __func__, i, name.length(), GGML_MAX_NAME); + ok = false; + break; + } + ggml_set_name(&info.t, name.c_str()); + + // make sure there are no duplicate tensor names + for (int64_t j = 0; ok && j < i; ++j) { + if (strcmp(info.t.name, ctx->info[j].t.name) == 0) { + GGML_LOG_ERROR("%s: duplicate tensor name '%s' for tensors %" PRIi64 " and %" PRIi64 "\n", __func__, info.t.name, j, i); + ok = false; + break; + } + } + } + if (!ok) { + break; + } + + // tensor shape + { + uint32_t n_dims = 0; + ok = ok && gr.read(n_dims); + if (n_dims > GGML_MAX_DIMS) { + GGML_LOG_ERROR("%s: tensor '%s' has invalid number of dimensions: %" PRIu32 " > %" PRIu32 "\n", + __func__, info.t.name, n_dims, GGML_MAX_DIMS); + ok = false; + break; + } + for (uint32_t j = 0; ok && j < GGML_MAX_DIMS; ++j) { + info.t.ne[j] = 1; + if (j < n_dims) { + ok = ok && gr.read(info.t.ne[j]); + } + + // check that all ne are non-negative + if (info.t.ne[j] < 0) { + GGML_LOG_ERROR("%s: tensor '%s' dimension %" PRIu32 " has invalid number of elements: %" PRIi64 " < 0\n", + __func__, info.t.name, j, info.t.ne[j]); + ok = false; + break; + } + } + + // check that the total number of elements is representable + if (ok && ((INT64_MAX/info.t.ne[1] <= info.t.ne[0]) || + (INT64_MAX/info.t.ne[2] <= info.t.ne[0]*info.t.ne[1]) || + (INT64_MAX/info.t.ne[3] <= info.t.ne[0]*info.t.ne[1]*info.t.ne[2]))) { + + GGML_LOG_ERROR("%s: total number of elements in tensor '%s' with shape " + "(%" PRIi64 ", %" PRIi64 ", %" PRIi64 ", %" PRIi64 ") is >= %" PRIi64 "\n", + __func__, info.t.name, info.t.ne[0], info.t.ne[1], info.t.ne[2], info.t.ne[3], INT64_MAX); + ok = false; + break; + } + } + if (!ok) { + break; + } + + // tensor type + { + ok = ok && gr.read(info.t.type); + + // check that tensor type is within defined range + if (info.t.type < 0 || info.t.type >= GGML_TYPE_COUNT) { + GGML_LOG_ERROR("%s: tensor '%s' has invalid ggml type %d. should be in [0, %d)\n", + __func__, info.t.name, info.t.type, GGML_TYPE_COUNT); + ok = false; + break; + } + const size_t type_size = ggml_type_size(info.t.type); + const int64_t blck_size = ggml_blck_size(info.t.type); + + // check that row size is divisible by block size + if (blck_size == 0 || info.t.ne[0] % blck_size != 0) { + GGML_LOG_ERROR("%s: tensor '%s' of type %d (%s) has %" PRId64 " elements per row, " + "not a multiple of block size (%" PRId64 ")\n", + __func__, info.t.name, (int) info.t.type, ggml_type_name(info.t.type), info.t.ne[0], blck_size); + ok = false; + break; + } + + // check that the size of the tensor in bytes is representable + if (ok && uint64_t(ggml_nelements(&info.t)/ggml_blck_size(info.t.type)) > SIZE_MAX/ggml_type_size(info.t.type)) { + GGML_LOG_ERROR("%s: tensor '%s' with shape (%" PRIi64 ", %" PRIi64 ", %" PRIi64 ", %" PRIi64 ") has a size in bytes > %zu\n", + __func__, info.t.name, info.t.ne[0], info.t.ne[1], info.t.ne[2], info.t.ne[3], SIZE_MAX); + ok = false; + break; + } + + // calculate byte offsets given the tensor shape and type + info.t.nb[0] = type_size; + info.t.nb[1] = info.t.nb[0]*(info.t.ne[0]/blck_size); + for (int j = 2; j < GGML_MAX_DIMS; ++j) { + info.t.nb[j] = info.t.nb[j - 1]*info.t.ne[j - 1]; + } + } + if (!ok) { + break; + } + + // tensor data offset within buffer + ok = ok && gr.read(info.offset); + + ctx->info.push_back(info); + } + + if (!ok) { + GGML_LOG_ERROR("%s: failed to read tensor info\n", __func__); + gguf_free(ctx); + return nullptr; + } + GGML_ASSERT(int64_t(ctx->info.size()) == n_tensors); + + // we require the data section to be aligned, so take into account any padding + if (n_tensors > 0 && !gr.seek(GGML_PAD(gr.tell(), ctx->alignment))) { + GGML_LOG_ERROR("%s: failed to seek to beginning of data section\n", __func__); + gguf_free(ctx); + return nullptr; + } + + // store the current file offset - this is where the data section starts + ctx->offset = gr.tell(); + + // compute the total size of the data section, taking into account the alignment + { + ctx->size = 0; + for (size_t i = 0; i < ctx->info.size(); ++i) { + const gguf_tensor_info & ti = ctx->info[i]; + if (ti.offset != ctx->size) { + GGML_LOG_ERROR("%s: tensor '%s' has offset %" PRIu64 ", expected %zu\n", + __func__, ti.t.name, ti.offset, ctx->size); + GGML_LOG_ERROR("%s: failed to read tensor data\n", __func__); + gguf_free(ctx); + return nullptr; + } + size_t padded_size = GGML_PAD(ggml_nbytes(&ti.t), ctx->alignment); + if (SIZE_MAX - ctx->size < padded_size) { + GGML_LOG_ERROR("%s: tensor '%s' size overflow, cannot accumulate size %zu + %zu\n", + __func__, ti.t.name, ctx->size, padded_size); + gguf_free(ctx); + return nullptr; + } + ctx->size += padded_size; + } + } + + // load the tensor data only if requested + if (params.ctx != nullptr) { + // if the provided gguf_context is no_alloc, then we create "empty" tensors and do not read the binary blob + // otherwise, we load the binary blob into the created ggml_context as well, and point the "data" members of + // the ggml_tensor structs to the appropriate locations in the binary blob + + // compute the exact size needed for the new ggml_context + size_t mem_size = 0; + if (params.no_alloc) { + if (n_tensors != 0 && SIZE_MAX / n_tensors < ggml_tensor_overhead()) { + GGML_LOG_ERROR("%s: memory size overflow while allocating ggml context\n", __func__); + gguf_free(ctx); + return nullptr; + } + + const size_t overhead = n_tensors * ggml_tensor_overhead(); + + mem_size = overhead; + } else { + if ((n_tensors + 1) != 0 && SIZE_MAX / (n_tensors + 1) < ggml_tensor_overhead()) { + GGML_LOG_ERROR("%s: memory size overflow while allocating ggml context\n", __func__); + gguf_free(ctx); + return nullptr; + } + + const size_t overhead = (n_tensors + 1) * ggml_tensor_overhead(); + + if (SIZE_MAX - overhead < ctx->size) { + GGML_LOG_ERROR("%s: memory size overflow while allocating ggml context\n", __func__); + gguf_free(ctx); + return nullptr; + } + + mem_size = overhead + ctx->size; + } + + struct ggml_init_params pdata = { + /*mem_size =*/ mem_size, + /*mem_buffer =*/ nullptr, + /*no_alloc =*/ params.no_alloc, + }; + + *params.ctx = ggml_init(pdata); + if (*params.ctx == nullptr) { + GGML_LOG_ERROR("%s: failed to initialize ggml context for storing tensors\n", __func__); + gguf_free(ctx); + return nullptr; + } + + struct ggml_context * ctx_data = *params.ctx; + + struct ggml_tensor * data = nullptr; + + if (!params.no_alloc) { + data = ggml_new_tensor_1d(ctx_data, GGML_TYPE_I8, ctx->size); + + ok = ok && data != nullptr; + + if (ok) { + ggml_set_name(data, "GGUF tensor data binary blob"); + } + + // read the binary blob with the tensor data + ok = ok && gr.read(data->data, ctx->size); + + if (!ok) { + GGML_LOG_ERROR("%s: failed to read tensor data binary blob\n", __func__); + ggml_free(ctx_data); + *params.ctx = nullptr; + gguf_free(ctx); + return nullptr; + } + + ctx->data = data->data; + } + + ggml_set_no_alloc(ctx_data, true); + + // create the tensors + for (size_t i = 0; i < ctx->info.size(); ++i) { + const struct gguf_tensor_info & info = ctx->info[i]; + + struct ggml_tensor * cur = ggml_new_tensor(ctx_data, info.t.type, GGML_MAX_DIMS, info.t.ne); + + ok = ok && cur != nullptr; + + if (!ok) { + break; + } + + ggml_set_name(cur, info.t.name); + + // point the data member to the appropriate location in the binary blob using the tensor info + if (!params.no_alloc) { + cur->data = (char *) data->data + info.offset; + } + } + + if (!ok) { + GGML_LOG_ERROR("%s: failed to create tensors\n", __func__); + ggml_free(ctx_data); + *params.ctx = nullptr; + gguf_free(ctx); + return nullptr; + } + + ggml_set_no_alloc(ctx_data, params.no_alloc); + } + + return ctx; +} + +struct gguf_context * gguf_init_from_callback(gguf_reader_callback_t callback, void * userdata, size_t max_chunk_read, uint64_t max_expected_size, struct gguf_init_params params) { + if (callback == nullptr) { + return nullptr; + } + + const struct gguf_reader gr(callback, userdata, max_chunk_read == 0 ? SIZE_MAX : max_chunk_read, 0, max_expected_size); + return gguf_init_from_reader(gr, params); +} + +struct gguf_file_reader { + FILE * file; + uint64_t offset; +}; + +static size_t gguf_file_reader_callback(void * userdata, void * output, uint64_t offset, size_t len) { + GGML_ASSERT(len > 0); + + gguf_file_reader & reader = *static_cast(userdata); + + if (reader.offset != offset) { + if (offset > INT64_MAX || gguf_fseek(reader.file, static_cast(offset), SEEK_SET) != 0) { + return 0; + } + + reader.offset = offset; + } + + const size_t nread = fread(static_cast(output), 1, len, reader.file); + reader.offset += nread; + return nread; +} + +struct gguf_context * gguf_init_from_file_ptr(FILE * file, struct gguf_init_params params) { + if (!file) { + return nullptr; + } + + const int64_t cur = gguf_ftell(file); + if (cur < 0) { + return nullptr; + } + + gguf_file_reader reader = { + /*.file = */ file, + /*.offset = */ static_cast(cur), + }; + const struct gguf_reader gr(gguf_file_reader_callback, &reader, SIZE_MAX, reader.offset, gguf_reader::file_remain(file)); + return gguf_init_from_reader(gr, params); +} + +struct gguf_buffer_reader { + const uint8_t * data; + size_t size; +}; + +static size_t gguf_buffer_reader_callback(void * userdata, void * output, uint64_t offset, size_t len) { + GGML_ASSERT(len > 0); + + const gguf_buffer_reader & reader = *static_cast(userdata); + + if (offset > reader.size || len > reader.size - offset) { + return 0; + } + + const size_t data_offset = static_cast(offset); + const size_t nread = std::min(len, reader.size - data_offset); + memcpy(static_cast(output), reader.data + data_offset, nread); + return nread; +} + +struct gguf_context * gguf_init_from_buffer(const void * data, size_t size, struct gguf_init_params params) { + if (data == nullptr || size == 0) { + return nullptr; + } + + gguf_buffer_reader reader = { + /*.data = */ static_cast(data), + /*.size = */ size, + }; + const struct gguf_reader gr(gguf_buffer_reader_callback, &reader, SIZE_MAX, 0, size); + return gguf_init_from_reader(gr, params); +} + +struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_params params) { + FILE * file = ggml_fopen(fname, "rb"); + + if (!file) { + GGML_LOG_ERROR("%s: failed to open GGUF file '%s' (%s)\n", __func__, fname, strerror(errno)); + return nullptr; + } + + struct gguf_context * result = gguf_init_from_file_ptr(file, params); + fclose(file); + return result; +} + +void gguf_free(struct gguf_context * ctx) { + if (ctx == nullptr) { + return; + } + delete ctx; +} + +const char * gguf_type_name(enum gguf_type type) { + auto it = GGUF_TYPE_NAME.find(type); + return it == GGUF_TYPE_NAME.end() ? nullptr : it->second; +} + +uint32_t gguf_get_version(const struct gguf_context * ctx) { + return ctx->version; +} + +size_t gguf_get_alignment(const struct gguf_context * ctx) { + return ctx->alignment; +} + +size_t gguf_get_data_offset(const struct gguf_context * ctx) { + return ctx->offset; +} + +int64_t gguf_get_n_kv(const struct gguf_context * ctx) { + return ctx->kv.size(); +} + +int64_t gguf_find_key(const struct gguf_context * ctx, const char * key) { + // return -1 if key not found + int64_t keyfound = -1; + + const int64_t n_kv = gguf_get_n_kv(ctx); + + for (int64_t i = 0; i < n_kv; ++i) { + if (strcmp(key, gguf_get_key(ctx, i)) == 0) { + keyfound = i; + break; + } + } + + return keyfound; +} + +const char * gguf_get_key(const struct gguf_context * ctx, int64_t key_id) { + GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx)); + return ctx->kv[key_id].get_key().c_str(); +} + +enum gguf_type gguf_get_kv_type(const struct gguf_context * ctx, int64_t key_id) { + GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx)); + return ctx->kv[key_id].is_array ? GGUF_TYPE_ARRAY : ctx->kv[key_id].get_type(); +} + +enum gguf_type gguf_get_arr_type(const struct gguf_context * ctx, int64_t key_id) { + GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx)); + GGML_ASSERT(ctx->kv[key_id].is_array); + return ctx->kv[key_id].get_type(); +} + +const void * gguf_get_arr_data(const struct gguf_context * ctx, int64_t key_id) { + GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx)); + GGML_ASSERT(ctx->kv[key_id].get_type() != GGUF_TYPE_STRING); + return ctx->kv[key_id].data.data(); +} + +const char * gguf_get_arr_str(const struct gguf_context * ctx, int64_t key_id, size_t i) { + GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx)); + GGML_ASSERT(ctx->kv[key_id].get_type() == GGUF_TYPE_STRING); + return ctx->kv[key_id].data_string[i].c_str(); +} + +size_t gguf_get_arr_n(const struct gguf_context * ctx, int64_t key_id) { + GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx)); + + if (ctx->kv[key_id].type == GGUF_TYPE_STRING) { + return ctx->kv[key_id].data_string.size(); + } + + const size_t type_size = gguf_type_size(ctx->kv[key_id].type); + GGML_ASSERT(ctx->kv[key_id].data.size() % type_size == 0); + return ctx->kv[key_id].data.size() / type_size; +} + +uint8_t gguf_get_val_u8(const struct gguf_context * ctx, int64_t key_id) { + GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx)); + GGML_ASSERT(ctx->kv[key_id].get_ne() == 1); + return ctx->kv[key_id].get_val(); +} + +int8_t gguf_get_val_i8(const struct gguf_context * ctx, int64_t key_id) { + GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx)); + GGML_ASSERT(ctx->kv[key_id].get_ne() == 1); + return ctx->kv[key_id].get_val(); +} + +uint16_t gguf_get_val_u16(const struct gguf_context * ctx, int64_t key_id) { + GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx)); + GGML_ASSERT(ctx->kv[key_id].get_ne() == 1); + return ctx->kv[key_id].get_val(); +} + +int16_t gguf_get_val_i16(const struct gguf_context * ctx, int64_t key_id) { + GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx)); + GGML_ASSERT(ctx->kv[key_id].get_ne() == 1); + return ctx->kv[key_id].get_val(); +} + +uint32_t gguf_get_val_u32(const struct gguf_context * ctx, int64_t key_id) { + GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx)); + GGML_ASSERT(ctx->kv[key_id].get_ne() == 1); + return ctx->kv[key_id].get_val(); +} + +int32_t gguf_get_val_i32(const struct gguf_context * ctx, int64_t key_id) { + GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx)); + GGML_ASSERT(ctx->kv[key_id].get_ne() == 1); + return ctx->kv[key_id].get_val(); +} + +float gguf_get_val_f32(const struct gguf_context * ctx, int64_t key_id) { + GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx)); + GGML_ASSERT(ctx->kv[key_id].get_ne() == 1); + return ctx->kv[key_id].get_val(); +} + +uint64_t gguf_get_val_u64(const struct gguf_context * ctx, int64_t key_id) { + GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx)); + GGML_ASSERT(ctx->kv[key_id].get_ne() == 1); + return ctx->kv[key_id].get_val(); +} + +int64_t gguf_get_val_i64(const struct gguf_context * ctx, int64_t key_id) { + GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx)); + GGML_ASSERT(ctx->kv[key_id].get_ne() == 1); + return ctx->kv[key_id].get_val(); +} + +double gguf_get_val_f64(const struct gguf_context * ctx, int64_t key_id) { + GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx)); + GGML_ASSERT(ctx->kv[key_id].get_ne() == 1); + return ctx->kv[key_id].get_val(); +} + +bool gguf_get_val_bool(const struct gguf_context * ctx, int64_t key_id) { + GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx)); + GGML_ASSERT(ctx->kv[key_id].get_ne() == 1); + return ctx->kv[key_id].get_val(); +} + +const char * gguf_get_val_str(const struct gguf_context * ctx, int64_t key_id) { + GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx)); + GGML_ASSERT(ctx->kv[key_id].get_ne() == 1); + return ctx->kv[key_id].get_val().c_str(); +} + +const void * gguf_get_val_data(const struct gguf_context * ctx, int64_t key_id) { + GGML_ASSERT(key_id >= 0 && key_id < gguf_get_n_kv(ctx)); + GGML_ASSERT(ctx->kv[key_id].get_ne() == 1); + GGML_ASSERT(ctx->kv[key_id].get_type() != GGUF_TYPE_STRING); + return ctx->kv[key_id].data.data(); +} + +int64_t gguf_get_n_tensors(const struct gguf_context * ctx) { + return ctx->info.size(); +} + +int64_t gguf_find_tensor(const struct gguf_context * ctx, const char * name) { + // return -1 if tensor not found + int64_t tensor_id = -1; + + const int64_t n_tensors = gguf_get_n_tensors(ctx); + + for (int64_t i = 0; i < n_tensors; ++i) { + if (strcmp(name, gguf_get_tensor_name(ctx, i)) == 0) { + tensor_id = i; + break; + } + } + + return tensor_id; +} + +size_t gguf_get_tensor_offset(const struct gguf_context * ctx, int64_t tensor_id) { + GGML_ASSERT(tensor_id >= 0 && tensor_id < gguf_get_n_tensors(ctx)); + return ctx->info[tensor_id].offset; +} + +const char * gguf_get_tensor_name(const struct gguf_context * ctx, int64_t tensor_id) { + GGML_ASSERT(tensor_id >= 0 && tensor_id < gguf_get_n_tensors(ctx)); + return ctx->info[tensor_id].t.name; +} + +enum ggml_type gguf_get_tensor_type(const struct gguf_context * ctx, int64_t tensor_id) { + GGML_ASSERT(tensor_id >= 0 && tensor_id < gguf_get_n_tensors(ctx)); + return ctx->info[tensor_id].t.type; +} + +size_t gguf_get_tensor_size(const struct gguf_context * ctx, int64_t tensor_id) { + GGML_ASSERT(tensor_id >= 0 && tensor_id < gguf_get_n_tensors(ctx)); + return ggml_nbytes(&ctx->info[tensor_id].t); +} + +int64_t gguf_remove_key(struct gguf_context * ctx, const char * key) { + const int64_t key_id = gguf_find_key(ctx, key); + if (key_id >= 0) { + ctx->kv.erase(ctx->kv.begin() + key_id); + } + return key_id; +} + +template +static void gguf_check_reserved_keys(const std::string & key, const T val) { + if (key == GGUF_KEY_GENERAL_ALIGNMENT) { + if constexpr (std::is_same::value) { + GGML_ASSERT(val > 0 && (val & (val - 1)) == 0 && GGUF_KEY_GENERAL_ALIGNMENT " must be power of 2"); + } else { + GGML_UNUSED(val); + GGML_ABORT(GGUF_KEY_GENERAL_ALIGNMENT " must be type u32"); + } + } +} + +void gguf_set_val_u8(struct gguf_context * ctx, const char * key, uint8_t val) { + gguf_check_reserved_keys(key, val); + gguf_remove_key(ctx, key); + ctx->kv.emplace_back(key, val); +} + +void gguf_set_val_i8(struct gguf_context * ctx, const char * key, int8_t val) { + gguf_check_reserved_keys(key, val); + gguf_remove_key(ctx, key); + ctx->kv.emplace_back(key, val); +} + +void gguf_set_val_u16(struct gguf_context * ctx, const char * key, uint16_t val) { + gguf_check_reserved_keys(key, val); + gguf_remove_key(ctx, key); + ctx->kv.emplace_back(key, val); +} + +void gguf_set_val_i16(struct gguf_context * ctx, const char * key, int16_t val) { + gguf_check_reserved_keys(key, val); + gguf_remove_key(ctx, key); + ctx->kv.emplace_back(key, val); +} + +void gguf_set_val_u32(struct gguf_context * ctx, const char * key, uint32_t val) { + gguf_check_reserved_keys(key, val); + gguf_remove_key(ctx, key); + ctx->kv.emplace_back(key, val); +} + +void gguf_set_val_i32(struct gguf_context * ctx, const char * key, int32_t val) { + gguf_check_reserved_keys(key, val); + gguf_remove_key(ctx, key); + ctx->kv.emplace_back(key, val); +} + +void gguf_set_val_f32(struct gguf_context * ctx, const char * key, float val) { + gguf_check_reserved_keys(key, val); + gguf_remove_key(ctx, key); + ctx->kv.emplace_back(key, val); +} + +void gguf_set_val_u64(struct gguf_context * ctx, const char * key, uint64_t val) { + gguf_check_reserved_keys(key, val); + gguf_remove_key(ctx, key); + ctx->kv.emplace_back(key, val); +} + +void gguf_set_val_i64(struct gguf_context * ctx, const char * key, int64_t val) { + gguf_check_reserved_keys(key, val); + gguf_remove_key(ctx, key); + ctx->kv.emplace_back(key, val); +} + +void gguf_set_val_f64(struct gguf_context * ctx, const char * key, double val) { + gguf_check_reserved_keys(key, val); + gguf_remove_key(ctx, key); + ctx->kv.emplace_back(key, val); +} + +void gguf_set_val_bool(struct gguf_context * ctx, const char * key, bool val) { + gguf_check_reserved_keys(key, val); + gguf_remove_key(ctx, key); + ctx->kv.emplace_back(key, val); +} + +void gguf_set_val_str(struct gguf_context * ctx, const char * key, const char * val) { + gguf_check_reserved_keys(key, val); + gguf_remove_key(ctx, key); + ctx->kv.emplace_back(key, std::string(val)); +} + +void gguf_set_arr_data(struct gguf_context * ctx, const char * key, enum gguf_type type, const void * data, size_t n) { + gguf_check_reserved_keys(key, data); + gguf_remove_key(ctx, key); + + const size_t nbytes = n*gguf_type_size(type); + std::vector tmp(nbytes); + if (!tmp.empty()) { + memcpy(tmp.data(), data, nbytes); + } + ctx->kv.emplace_back(key, tmp); + ctx->kv.back().cast(type); +} + +void gguf_set_arr_str(struct gguf_context * ctx, const char * key, const char ** data, size_t n) { + gguf_check_reserved_keys(key, data); + gguf_remove_key(ctx, key); + + std::vector tmp(n); + for (size_t i = 0; i < n; ++i) { + tmp[i] = data[i]; + } + ctx->kv.emplace_back(key, tmp); +} + +// set or add KV pairs from another context +void gguf_set_kv(struct gguf_context * ctx, const struct gguf_context * src) { + const int64_t n_kv = gguf_get_n_kv(src); + for (int64_t i = 0; i < n_kv; ++i) { + const struct gguf_kv & kv = src->kv[i]; + + if (!kv.is_array) { + switch (kv.get_type()) { + case GGUF_TYPE_UINT8: gguf_set_val_u8 (ctx, kv.get_key().c_str(), kv.get_val()); break; + case GGUF_TYPE_INT8: gguf_set_val_i8 (ctx, kv.get_key().c_str(), kv.get_val()); break; + case GGUF_TYPE_UINT16: gguf_set_val_u16 (ctx, kv.get_key().c_str(), kv.get_val()); break; + case GGUF_TYPE_INT16: gguf_set_val_i16 (ctx, kv.get_key().c_str(), kv.get_val()); break; + case GGUF_TYPE_UINT32: gguf_set_val_u32 (ctx, kv.get_key().c_str(), kv.get_val()); break; + case GGUF_TYPE_INT32: gguf_set_val_i32 (ctx, kv.get_key().c_str(), kv.get_val()); break; + case GGUF_TYPE_FLOAT32: gguf_set_val_f32 (ctx, kv.get_key().c_str(), kv.get_val()); break; + case GGUF_TYPE_UINT64: gguf_set_val_u64 (ctx, kv.get_key().c_str(), kv.get_val()); break; + case GGUF_TYPE_INT64: gguf_set_val_i64 (ctx, kv.get_key().c_str(), kv.get_val()); break; + case GGUF_TYPE_FLOAT64: gguf_set_val_f64 (ctx, kv.get_key().c_str(), kv.get_val()); break; + case GGUF_TYPE_BOOL: gguf_set_val_bool(ctx, kv.get_key().c_str(), kv.get_val()); break; + case GGUF_TYPE_STRING: gguf_set_val_str (ctx, kv.get_key().c_str(), kv.get_val().c_str()); break; + case GGUF_TYPE_ARRAY: + default: GGML_ABORT("invalid type"); + } + continue; + } + + const size_t ne = kv.get_ne(); + + switch (kv.get_type()) { + case GGUF_TYPE_UINT8: + case GGUF_TYPE_INT8: + case GGUF_TYPE_UINT16: + case GGUF_TYPE_INT16: + case GGUF_TYPE_UINT32: + case GGUF_TYPE_INT32: + case GGUF_TYPE_FLOAT32: + case GGUF_TYPE_UINT64: + case GGUF_TYPE_INT64: + case GGUF_TYPE_FLOAT64: + case GGUF_TYPE_BOOL: { + gguf_set_arr_data(ctx, kv.get_key().c_str(), kv.get_type(), kv.data.data(), ne); + } break; + case GGUF_TYPE_STRING: { + std::vector tmp(ne); + for (size_t j = 0; j < ne; ++j) { + tmp[j] = kv.data_string[j].c_str(); + } + gguf_set_arr_str(ctx, kv.get_key().c_str(), tmp.data(), ne); + } break; + case GGUF_TYPE_ARRAY: + default: GGML_ABORT("invalid type"); + } + } +} + +void gguf_add_tensor( + struct gguf_context * ctx, + const struct ggml_tensor * tensor) { + GGML_ASSERT(tensor); + if (gguf_find_tensor(ctx, tensor->name) != -1) { + GGML_ABORT("duplicate tensor name: %s", tensor->name); + } + + struct gguf_tensor_info ti; + ti.t = *tensor; + ti.offset = ctx->info.empty() ? 0 : + ctx->info.back().offset + GGML_PAD(ggml_nbytes(&ctx->info.back().t), ctx->alignment); + ctx->info.push_back(ti); +} + +void gguf_set_tensor_type(struct gguf_context * ctx, const char * name, enum ggml_type type) { + const int64_t tensor_id = gguf_find_tensor(ctx, name); + if (tensor_id < 0) { + GGML_ABORT("tensor not found: %s", name); + } + struct ggml_tensor * tensor = &ctx->info[tensor_id].t; + const size_t type_size = ggml_type_size(type); + const int64_t blck_size = ggml_blck_size(type); + + tensor->type = type; + GGML_ASSERT(tensor->ne[0] % blck_size == 0 && "tensor row size not divisible by block size of new type"); + + tensor->nb[0] = type_size; + tensor->nb[1] = tensor->nb[0]*(tensor->ne[0]/blck_size); + for (int i = 2; i < GGML_MAX_DIMS; i++) { + tensor->nb[i] = tensor->nb[i - 1]*tensor->ne[i - 1]; + } + + // update offsets + const int64_t n_tensors = gguf_get_n_tensors(ctx); + for (int64_t i = tensor_id + 1; i < n_tensors; ++i) { + ctx->info[i].offset = ctx->info[i - 1].offset + GGML_PAD(ggml_nbytes(&ctx->info[i - 1].t), ctx->alignment); + } +} + +void gguf_set_tensor_data(struct gguf_context * ctx, const char * name, const void * data) { + const int64_t tensor_id = gguf_find_tensor(ctx, name); + if (tensor_id < 0) { + GGML_ABORT("tensor not found: %s", name); + } + + ctx->info[tensor_id].t.data = (void *)(uintptr_t)data; // double cast suppresses warning about casting away const +} + +struct gguf_writer_base { + size_t written_bytes {0u}; + + ~gguf_writer_base(void) = default; + + // we bet on devirtualization + virtual void write(int8_t val) = 0; + virtual void write(const std::vector & val) = 0; + virtual void write_tensor_data(const struct gguf_tensor_info & info, size_t offset_data, size_t alignment) = 0; + + template + void write(const T & val) { + for (size_t i = 0; i < sizeof(val); ++i) { + write(reinterpret_cast(&val)[i]); + } + } + + void write(const bool & val) { + const int8_t val8 = val ? 1 : 0; + write(val8); + } + + void write(const std::string & val) { + { + const uint64_t n = val.length(); + write(n); + } + for (size_t i = 0; i < val.length(); ++i) { + write((val.data())[i]); + } + } + + void write(const char * val) { + write(std::string(val)); + } + + void write(const enum ggml_type & val) { + write(int32_t(val)); + } + + void write(const enum gguf_type & val) { + write(int32_t(val)); + } + + void write(const struct gguf_kv & kv) { + const uint64_t ne = kv.get_ne(); + + write(kv.get_key()); + + if (kv.is_array) { + write(GGUF_TYPE_ARRAY); + write(kv.get_type()); + write(ne); + } else { + write(kv.get_type()); + } + + switch (kv.get_type()) { + case GGUF_TYPE_UINT8: + case GGUF_TYPE_INT8: + case GGUF_TYPE_UINT16: + case GGUF_TYPE_INT16: + case GGUF_TYPE_UINT32: + case GGUF_TYPE_INT32: + case GGUF_TYPE_FLOAT32: + case GGUF_TYPE_UINT64: + case GGUF_TYPE_INT64: + case GGUF_TYPE_FLOAT64: { + write(kv.data); + } break; + case GGUF_TYPE_BOOL: { + for (size_t i = 0; i < ne; ++i) { + write(kv.get_val(i)); + } + } break; + case GGUF_TYPE_STRING: { + for (size_t i = 0; i < ne; ++i) { + write(kv.get_val(i)); + } + } break; + case GGUF_TYPE_ARRAY: + default: GGML_ABORT("invalid type"); + } + } + + void write_tensor_meta(const struct gguf_tensor_info & info) { + write(info.t.name); + + const uint32_t n_dims = ggml_n_dims(&info.t); + write(n_dims); + + for (uint32_t j = 0; j < n_dims; ++j) { + write(info.t.ne[j]); + } + write(info.t.type); + write(info.offset); + } + + void pad(const size_t alignment) { + while (written_bytes % alignment != 0) { + const int8_t zero = 0; + write(zero); + } + } +}; + +// vector buffer based writer +struct gguf_writer_buf final : public gguf_writer_base { + std::vector & buf; + + gguf_writer_buf(std::vector & buf) : buf(buf) {} + + using gguf_writer_base::write; + + void write(const int8_t val) override { + buf.push_back(val); + written_bytes++; + } + + void write(const std::vector & val) override { + buf.insert(buf.end(), val.begin(), val.end()); + written_bytes += val.size(); + } + + void write_tensor_data(const struct gguf_tensor_info & info, const size_t offset_data, const size_t alignment) override { + GGML_ASSERT(buf.size() - offset_data == info.offset); + + GGML_ASSERT(ggml_is_contiguous(&info.t)); + const size_t offset = buf.size(); + const size_t nbytes = ggml_nbytes(&info.t); + + buf.resize(offset + nbytes); + if (info.t.buffer) { + ggml_backend_tensor_get(&info.t, buf.data() + offset, 0, nbytes); + } else { + GGML_ASSERT(info.t.data); + memcpy(buf.data() + offset, info.t.data, nbytes); + } + written_bytes += nbytes; + + pad(alignment); + } +}; + +// file based writer +struct gguf_writer_file final : public gguf_writer_base { + FILE * file; + + gguf_writer_file(FILE* file) : file(file) {} + + using gguf_writer_base::write; + + void write(const int8_t val) override { + const auto real_val = static_cast(val); + const auto ret = fputc(real_val, file); + written_bytes++; + if (ret != real_val) { + throw std::runtime_error("unexpected fputc result '" + std::to_string(ret) + "' instead of '" + std::to_string((int)real_val) + "'"); + } + } + + void write(const std::vector & val) override { + const auto ret = fwrite(val.data(), 1, val.size(), file); + written_bytes += val.size(); + if (ret != val.size()) { + throw std::runtime_error("unexpected fwrite number of bytes written, '" + std::to_string(ret) + "' instead of '" + std::to_string(val.size()) + "'"); + } + } + + void write_tensor_data(const struct gguf_tensor_info & info, const size_t offset_data, const size_t alignment) override { + GGML_ASSERT(written_bytes - offset_data == info.offset); + + GGML_ASSERT(ggml_is_contiguous(&info.t)); + const size_t nbytes = ggml_nbytes(&info.t); + + std::vector buf(nbytes); + if (info.t.buffer) { + ggml_backend_tensor_get(&info.t, buf.data(), 0, nbytes); + } else { + GGML_ASSERT(info.t.data); + memcpy(buf.data(), info.t.data, nbytes); + } + write(buf); + + pad(alignment); + } +}; + +template +static void gguf_write_out(const struct gguf_context * ctx, writer_t & gw, bool only_meta) { + const int64_t n_kv = gguf_get_n_kv(ctx); + const int64_t n_tensors = gguf_get_n_tensors(ctx); + + // write header + gw.write(GGUF_MAGIC[0]); + gw.write(GGUF_MAGIC[1]); + gw.write(GGUF_MAGIC[2]); + gw.write(GGUF_MAGIC[3]); + gw.write(ctx->version); + gw.write(n_tensors); + gw.write(n_kv); + + // write key-value pairs + for (int64_t i = 0; i < n_kv; ++i) { + gw.write(ctx->kv[i]); + } + + // write tensor info + for (int64_t i = 0; i < n_tensors; ++i) { + gw.write_tensor_meta(ctx->info[i]); + } + + // we require the data section to be aligned + gw.pad(ctx->alignment); + + if (only_meta) { + return; + } + + const size_t offset_data = gw.written_bytes; + + // write tensor data + for (int64_t i = 0; i < n_tensors; ++i) { + gw.write_tensor_data(ctx->info[i], offset_data, ctx->alignment); + } +} + +void gguf_write_to_buf(const struct gguf_context * ctx, std::vector & buf, bool only_meta) { + gguf_writer_buf gw(buf); + gguf_write_out(ctx, gw, only_meta); +} + +bool gguf_write_to_file_ptr(const struct gguf_context * ctx, FILE * file, bool only_meta) { + GGML_ASSERT(file); + + try { + gguf_writer_file gw(file); + gguf_write_out(ctx, gw, only_meta); + } catch (const std::runtime_error& ex) { + GGML_LOG_ERROR("%s: failed to write GGUF data: %s\n", __func__, ex.what()); + return false; + } + return true; +} + +bool gguf_write_to_file(const struct gguf_context * ctx, const char * fname, bool only_meta) { + FILE * file = ggml_fopen(fname, "wb"); + + if (!file) { + GGML_LOG_ERROR("%s: failed to open file '%s' for writing GGUF data\n", __func__, fname); + return false; + } + + const bool success = gguf_write_to_file_ptr(ctx, file, only_meta); + if (!success) { + GGML_LOG_ERROR("%s: failed to write GGUF data into '%s'\n", __func__, fname); + } + + fclose(file); + return success; +} + +size_t gguf_get_meta_size(const struct gguf_context * ctx) { + // only return size + std::vector buf; + gguf_write_to_buf(ctx, buf, /*only_meta =*/ true); + return buf.size(); +} + +void gguf_get_meta_data(const struct gguf_context * ctx, void * data) { + std::vector buf; + gguf_write_to_buf(ctx, buf, /*only_meta =*/ true); + memcpy(data, buf.data(), buf.size()); +} diff --git a/backend/llama.cpp/gguf-py/LICENSE b/backend/llama.cpp/gguf-py/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..76f67efdc6470081b512a8db5bf2b1d4962d9c3c --- /dev/null +++ b/backend/llama.cpp/gguf-py/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023 Georgi Gerganov + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/backend/llama.cpp/gguf-py/README.md b/backend/llama.cpp/gguf-py/README.md new file mode 100644 index 0000000000000000000000000000000000000000..ca7e09c68184f28b23a35ec7d611ff6ca117433c --- /dev/null +++ b/backend/llama.cpp/gguf-py/README.md @@ -0,0 +1,99 @@ +## gguf + +This is a Python package for writing binary files in the [GGUF](https://github.com/ggml-org/ggml/pull/302) +(GGML Universal File) format. + +See [convert_hf_to_gguf.py](https://github.com/ggml-org/llama.cpp/blob/master/convert_hf_to_gguf.py) +as an example for its usage. + +## Installation +```sh +pip install gguf +``` + +Optionally, you can install gguf with the extra 'gui' to enable the visual GGUF editor. +```sh +pip install gguf[gui] +``` + +## API Examples/Simple Tools + +[examples/writer.py](https://github.com/ggml-org/llama.cpp/blob/master/gguf-py/examples/writer.py) — Generates `example.gguf` in the current directory to demonstrate generating a GGUF file. Note that this file cannot be used as a model. + +[examples/reader.py](https://github.com/ggml-org/llama.cpp/blob/master/gguf-py/examples/reader.py) — Extracts and displays key-value pairs and tensor details from a GGUF file in a readable format. + +[gguf/scripts/gguf_dump.py](https://github.com/ggml-org/llama.cpp/blob/master/gguf-py/gguf/scripts/gguf_dump.py) — Dumps a GGUF file's metadata to the console. + +[gguf/scripts/gguf_set_metadata.py](https://github.com/ggml-org/llama.cpp/blob/master/gguf-py/gguf/scripts/gguf_set_metadata.py) — Allows changing simple metadata values in a GGUF file by key. + +[gguf/scripts/gguf_convert_endian.py](https://github.com/ggml-org/llama.cpp/blob/master/gguf-py/gguf/scripts/gguf_convert_endian.py) — Allows converting the endianness of GGUF files. + +[gguf/scripts/gguf_new_metadata.py](https://github.com/ggml-org/llama.cpp/blob/master/gguf-py/gguf/scripts/gguf_new_metadata.py) — Copies a GGUF file with added/modified/removed metadata values. + +[gguf/scripts/gguf_editor_gui.py](https://github.com/ggml-org/llama.cpp/blob/master/gguf-py/gguf/scripts/gguf_editor_gui.py) — Allows for viewing, editing, adding, or removing metadata values within a GGUF file as well as viewing its tensors with a Qt interface. + +## Development +Maintainers who participate in development of this package are advised to install it in editable mode: + +```sh +cd /path/to/llama.cpp/gguf-py + +pip install --editable . +``` + +**Note**: This may require to upgrade your Pip installation, with a message saying that editable installation currently requires `setup.py`. +In this case, upgrade Pip to the latest: + +```sh +pip install --upgrade pip +``` + +## Automatic publishing with CI + +There's a GitHub workflow to make a release automatically upon creation of tags in a specified format. + +1. Bump the version in `pyproject.toml`. +2. Create a tag named `gguf-vx.x.x` where `x.x.x` is the semantic version number. + +```sh +git tag -a gguf-v1.0.0 -m "Version 1.0 release" +``` + +3. Push the tags. + +```sh +git push origin --tags +``` + +## Manual publishing +If you want to publish the package manually for any reason, you need to have `twine` and `build` installed: + +```sh +pip install build twine +``` + +Then, follow these steps to release a new version: + +1. Bump the version in `pyproject.toml`. +2. Build the package: + +```sh +python -m build +``` + +3. Upload the generated distribution archives: + +```sh +python -m twine upload dist/* +``` + +## Run Unit Tests + +From root of this repository you can run this command to run all the unit tests + +```bash +python -m unittest discover ./gguf-py -v +``` + +## TODO +- [ ] Include conversion scripts as command line entry points in this package. diff --git a/backend/llama.cpp/gguf-py/examples/reader.py b/backend/llama.cpp/gguf-py/examples/reader.py new file mode 100644 index 0000000000000000000000000000000000000000..703b782b5fa6672020492918fa498a77f0e96dfa --- /dev/null +++ b/backend/llama.cpp/gguf-py/examples/reader.py @@ -0,0 +1,49 @@ +#!/usr/bin/env python3 +import logging +import sys +from pathlib import Path + +logger = logging.getLogger("reader") + +# Necessary to load the local gguf package +sys.path.insert(0, str(Path(__file__).parent.parent)) + +from gguf.gguf_reader import GGUFReader + + +def read_gguf_file(gguf_file_path): + """ + Reads and prints key-value pairs and tensor information from a GGUF file in an improved format. + + Parameters: + - gguf_file_path: Path to the GGUF file. + """ + + reader = GGUFReader(gguf_file_path) + + # List all key-value pairs in a columnized format + print("Key-Value Pairs:") # noqa: NP100 + max_key_length = max(len(key) for key in reader.fields.keys()) + for key, field in reader.fields.items(): + value = field.parts[field.data[0]] + print(f"{key:{max_key_length}} : {value}") # noqa: NP100 + print("----") # noqa: NP100 + + # List all tensors + print("Tensors:") # noqa: NP100 + tensor_info_format = "{:<30} | Shape: {:<15} | Size: {:<12} | Quantization: {}" + print(tensor_info_format.format("Tensor Name", "Shape", "Size", "Quantization")) # noqa: NP100 + print("-" * 80) # noqa: NP100 + for tensor in reader.tensors: + shape_str = "x".join(map(str, tensor.shape)) + size_str = str(tensor.n_elements) + quantization_str = tensor.tensor_type.name + print(tensor_info_format.format(tensor.name, shape_str, size_str, quantization_str)) # noqa: NP100 + + +if __name__ == '__main__': + if len(sys.argv) < 2: + logger.info("Usage: reader.py ") + sys.exit(1) + gguf_file_path = sys.argv[1] + read_gguf_file(gguf_file_path) diff --git a/backend/llama.cpp/gguf-py/examples/writer.py b/backend/llama.cpp/gguf-py/examples/writer.py new file mode 100644 index 0000000000000000000000000000000000000000..731873a7d666ca5a58dfb95a42bb809fe92fe747 --- /dev/null +++ b/backend/llama.cpp/gguf-py/examples/writer.py @@ -0,0 +1,39 @@ +#!/usr/bin/env python3 +import sys +from pathlib import Path + +import numpy as np + +# Necessary to load the local gguf package +sys.path.insert(0, str(Path(__file__).parent.parent)) + +from gguf import GGUFWriter # noqa: E402 + + +# Example usage: +def writer_example() -> None: + # Example usage with a file + gguf_writer = GGUFWriter("example.gguf", "llama") + + gguf_writer.add_block_count(12) + gguf_writer.add_uint32("answer", 42) # Write a 32-bit integer + gguf_writer.add_float32("answer_in_float", 42.0) # Write a 32-bit float + gguf_writer.add_custom_alignment(64) + + tensor1 = np.ones((32,), dtype=np.float32) * 100.0 + tensor2 = np.ones((64,), dtype=np.float32) * 101.0 + tensor3 = np.ones((96,), dtype=np.float32) * 102.0 + + gguf_writer.add_tensor("tensor1", tensor1) + gguf_writer.add_tensor("tensor2", tensor2) + gguf_writer.add_tensor("tensor3", tensor3) + + gguf_writer.write_header_to_file() + gguf_writer.write_kv_data_to_file() + gguf_writer.write_tensors_to_file() + + gguf_writer.close() + + +if __name__ == '__main__': + writer_example() diff --git a/backend/llama.cpp/gguf-py/gguf/__init__.py b/backend/llama.cpp/gguf-py/gguf/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..243defc4c1ca42d3713017d8902592f54ac849cd --- /dev/null +++ b/backend/llama.cpp/gguf-py/gguf/__init__.py @@ -0,0 +1,9 @@ +from .constants import * +from .lazy import * +from .gguf_reader import * +from .gguf_writer import * +from .quants import * +from .tensor_mapping import * +from .vocab import * +from .utility import * +from .metadata import * diff --git a/backend/llama.cpp/gguf-py/gguf/__pycache__/__init__.cpython-313.pyc b/backend/llama.cpp/gguf-py/gguf/__pycache__/__init__.cpython-313.pyc new file mode 100644 index 0000000000000000000000000000000000000000..6e0187ffc5ef886f892eec6c1e850115e496dd81 Binary files /dev/null and b/backend/llama.cpp/gguf-py/gguf/__pycache__/__init__.cpython-313.pyc differ diff --git a/backend/llama.cpp/gguf-py/gguf/__pycache__/constants.cpython-313.pyc b/backend/llama.cpp/gguf-py/gguf/__pycache__/constants.cpython-313.pyc new file mode 100644 index 0000000000000000000000000000000000000000..854e52b41cc6cee28d90e23da9dce27595a3414c --- /dev/null +++ b/backend/llama.cpp/gguf-py/gguf/__pycache__/constants.cpython-313.pyc @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9eb0f13ff116ee1cb36a90366ebfcb7f4b33fb8ec7e6777c935846f14092c77a +size 195987 diff --git 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"general.quantization_version" + ALIGNMENT = "general.alignment" + FILE_TYPE = "general.file_type" + + # Recommended Sampler Parameters + SAMPLING_SEQUENCE = "general.sampling.sequence" + SAMPLING_TOP_K = "general.sampling.top_k" + SAMPLING_TOP_P = "general.sampling.top_p" + SAMPLING_MIN_P = "general.sampling.min_p" + SAMPLING_XTC_PROBABILITY = "general.sampling.xtc_probability" + SAMPLING_XTC_THRESHOLD = "general.sampling.xtc_threshold" + SAMPLING_TEMP = "general.sampling.temp" + SAMPLING_PENALTY_LAST_N = "general.sampling.penalty_last_n" + SAMPLING_PENALTY_REPEAT = "general.sampling.penalty_repeat" + SAMPLING_MIROSTAT = "general.sampling.mirostat" + SAMPLING_MIROSTAT_TAU = "general.sampling.mirostat_tau" + SAMPLING_MIROSTAT_ETA = "general.sampling.mirostat_eta" + + # Authorship Metadata + NAME = "general.name" + AUTHOR = "general.author" + VERSION = "general.version" + ORGANIZATION = "general.organization" + + FINETUNE = "general.finetune" + BASENAME = "general.basename" + + DESCRIPTION = "general.description" + QUANTIZED_BY = "general.quantized_by" + + SIZE_LABEL = "general.size_label" + + # Licensing details + LICENSE = "general.license" + LICENSE_NAME = "general.license.name" + LICENSE_LINK = "general.license.link" + + # Typically represents the converted GGUF repo (Unless native) + URL = "general.url" # Model Website/Paper + DOI = "general.doi" + UUID = "general.uuid" + REPO_URL = "general.repo_url" # Model Source Repository (git/svn/etc...) + + # Model Source during conversion + SOURCE_URL = "general.source.url" # Model Website/Paper + SOURCE_DOI = "general.source.doi" + SOURCE_UUID = "general.source.uuid" + SOURCE_REPO_URL = "general.source.repo_url" # Model Source Repository (git/svn/etc...) + + # Base Model Source. There can be more than one source if it's a merged + # model like with 'Mistral-7B-Merge-14-v0.1'. This will assist in + # tracing linage of models as it is finetuned or merged over time. + BASE_MODEL_COUNT = "general.base_model.count" + BASE_MODEL_NAME = "general.base_model.{id}.name" + BASE_MODEL_AUTHOR = "general.base_model.{id}.author" + BASE_MODEL_VERSION = "general.base_model.{id}.version" + BASE_MODEL_ORGANIZATION = "general.base_model.{id}.organization" + BASE_MODEL_DESCRIPTION = "general.base_model.{id}.description" + BASE_MODEL_URL = "general.base_model.{id}.url" # Model Website/Paper + BASE_MODEL_DOI = "general.base_model.{id}.doi" + BASE_MODEL_UUID = "general.base_model.{id}.uuid" + BASE_MODEL_REPO_URL = "general.base_model.{id}.repo_url" # Model Source Repository (git/svn/etc...) + + # Dataset Source + DATASET_COUNT = "general.dataset.count" + DATASET_NAME = "general.dataset.{id}.name" + DATASET_AUTHOR = "general.dataset.{id}.author" + DATASET_VERSION = "general.dataset.{id}.version" + DATASET_ORGANIZATION = "general.dataset.{id}.organization" + DATASET_DESCRIPTION = "general.dataset.{id}.description" + DATASET_URL = "general.dataset.{id}.url" # Model Website/Paper + DATASET_DOI = "general.dataset.{id}.doi" + DATASET_UUID = "general.dataset.{id}.uuid" + DATASET_REPO_URL = "general.dataset.{id}.repo_url" # Model Source Repository (git/svn/etc...) + + # Array based KV stores + TAGS = "general.tags" + LANGUAGES = "general.languages" + + class LLM: + VOCAB_SIZE = "{arch}.vocab_size" + CONTEXT_LENGTH = "{arch}.context_length" + EMBEDDING_LENGTH = "{arch}.embedding_length" + EMBEDDING_LENGTH_OUT = "{arch}.embedding_length_out" + FEATURES_LENGTH = "{arch}.features_length" + BLOCK_COUNT = "{arch}.block_count" + LEADING_DENSE_BLOCK_COUNT = "{arch}.leading_dense_block_count" + FEED_FORWARD_LENGTH = "{arch}.feed_forward_length" + EXPERT_FEED_FORWARD_LENGTH = "{arch}.expert_feed_forward_length" + EXPERT_SHARED_FEED_FORWARD_LENGTH = "{arch}.expert_shared_feed_forward_length" + EXPERT_CHUNK_FEED_FORWARD_LENGTH = "{arch}.expert_chunk_feed_forward_length" + USE_PARALLEL_RESIDUAL = "{arch}.use_parallel_residual" + TENSOR_DATA_LAYOUT = "{arch}.tensor_data_layout" + EXPERT_COUNT = "{arch}.expert_count" + EXPERT_USED_COUNT = "{arch}.expert_used_count" + EXPERT_SHARED_COUNT = "{arch}.expert_shared_count" + EXPERT_GROUP_COUNT = "{arch}.expert_group_count" + EXPERT_GROUP_USED_COUNT = "{arch}.expert_group_used_count" + EXPERT_WEIGHTS_SCALE = "{arch}.expert_weights_scale" + EXPERT_WEIGHTS_NORM = "{arch}.expert_weights_norm" + EXPERT_GATING_FUNC = "{arch}.expert_gating_func" + EXPERT_GROUP_SCALE = "{arch}.expert_group_scale" + EXPERTS_PER_GROUP = "{arch}.experts_per_group" + MOE_EVERY_N_LAYERS = "{arch}.moe_every_n_layers" + MOE_LATENT_SIZE = "{arch}.moe_latent_size" + NEXTN_PREDICT_LAYERS = "{arch}.nextn_predict_layers" + NUM_DEEPSTACK_LAYERS = "{arch}.n_deepstack_layers" + DEEPSTACK_MAPPING = "{arch}.deepstack_mapping" + POOLING_TYPE = "{arch}.pooling_type" + LOGIT_SCALE = "{arch}.logit_scale" + DECODER_START_TOKEN_ID = "{arch}.decoder_start_token_id" + DECODER_BLOCK_COUNT = "{arch}.decoder_block_count" + ATTN_LOGIT_SOFTCAPPING = "{arch}.attn_logit_softcapping" + ROUTER_LOGIT_SOFTCAPPING = "{arch}.router_logit_softcapping" + FINAL_LOGIT_SOFTCAPPING = "{arch}.final_logit_softcapping" + SWIN_NORM = "{arch}.swin_norm" + RESCALE_EVERY_N_LAYERS = "{arch}.rescale_every_n_layers" + TIME_MIX_EXTRA_DIM = "{arch}.time_mix_extra_dim" + TIME_DECAY_EXTRA_DIM = "{arch}.time_decay_extra_dim" + RESIDUAL_SCALE = "{arch}.residual_scale" + EMBEDDING_SCALE = "{arch}.embedding_scale" + TOKEN_SHIFT_COUNT = "{arch}.token_shift_count" + INTERLEAVE_MOE_LAYER_STEP = "{arch}.interleave_moe_layer_step" + FULL_ATTENTION_INTERVAL = "{arch}.full_attention_interval" + HASH_LAYER_COUNT = "{arch}.hash_layer_count" + ACTIVATION_SPARSITY_SCALE = "{arch}.activation_sparsity_scale" + ALTUP_ACTIVE_IDX = "{arch}.altup.active_idx" + ALTUP_NUM_INPUTS = "{arch}.altup.num_inputs" + EMBD_LENGTH_PER_LAYER_INP = "{arch}.embedding_length_per_layer_input" + SWIGLU_CLAMP_EXP = "{arch}.swiglu_clamp_exp" + SWIGLU_CLAMP_SHEXP = "{arch}.swiglu_clamp_shexp" + HIDDEN_ACT = "{arch}.hidden_activation" + DENSE_FEAT_IN_SIZE = "{arch}.{dense}_feat_in" + DENSE_FEAT_OUT_SIZE = "{arch}.{dense}_feat_out" + TARGET_LAYERS = "{arch}.target_layers" + TARGET_HIDDEN_SIZE = "{arch}.target_hidden_size" + BLOCK_SIZE = "{arch}.block_size" + NORM_BEFORE_RESIDUAL = "{arch}.norm_before_residual" + + class Attention: + HEAD_COUNT = "{arch}.attention.head_count" + HEAD_COUNT_KV = "{arch}.attention.head_count_kv" + MAX_ALIBI_BIAS = "{arch}.attention.max_alibi_bias" + CLAMP_KQV = "{arch}.attention.clamp_kqv" + KEY_LENGTH = "{arch}.attention.key_length" + VALUE_LENGTH = "{arch}.attention.value_length" + LAYERNORM_EPS = "{arch}.attention.layer_norm_epsilon" + LAYERNORM_RMS_EPS = "{arch}.attention.layer_norm_rms_epsilon" + GROUPNORM_EPS = "{arch}.attention.group_norm_epsilon" + GROUPNORM_GROUPS = "{arch}.attention.group_norm_groups" + CAUSAL = "{arch}.attention.causal" + Q_LORA_RANK = "{arch}.attention.q_lora_rank" + KV_LORA_RANK = "{arch}.attention.kv_lora_rank" + DECAY_LORA_RANK = "{arch}.attention.decay_lora_rank" + ICLR_LORA_RANK = "{arch}.attention.iclr_lora_rank" + VALUE_RESIDUAL_MIX_LORA_RANK = "{arch}.attention.value_residual_mix_lora_rank" + GATE_LORA_RANK = "{arch}.attention.gate_lora_rank" + REL_BUCKETS_COUNT = "{arch}.attention.relative_buckets_count" + SLIDING_WINDOW = "{arch}.attention.sliding_window" + SCALE = "{arch}.attention.scale" + OUTPUT_GROUP_COUNT = "{arch}.attention.output_group_count" + OUTPUT_LORA_RANK = "{arch}.attention.output_lora_rank" + OUTPUT_SCALE = "{arch}.attention.output_scale" + VALUE_SCALE = "{arch}.attention.value_scale" + COMPRESS_RATIOS = "{arch}.attention.compress_ratios" + COMPRESS_ROPE_FREQ_BASE = "{arch}.attention.compress_rope_freq_base" + TEMPERATURE_LENGTH = "{arch}.attention.temperature_length" + KEY_LENGTH_MLA = "{arch}.attention.key_length_mla" + VALUE_LENGTH_MLA = "{arch}.attention.value_length_mla" + KEY_LENGTH_SWA = "{arch}.attention.key_length_swa" + VALUE_LENGTH_SWA = "{arch}.attention.value_length_swa" + SHARED_KV_LAYERS = "{arch}.attention.shared_kv_layers" + SLIDING_WINDOW_PATTERN = "{arch}.attention.sliding_window_pattern" + TEMPERATURE_SCALE = "{arch}.attention.temperature_scale" + + class Indexer: + HEAD_COUNT = "{arch}.attention.indexer.head_count" + KEY_LENGTH = "{arch}.attention.indexer.key_length" + TOP_K = "{arch}.attention.indexer.top_k" + + class HyperConnection: + COUNT = "{arch}.hyper_connection.count" + SINKHORN_ITERATIONS = "{arch}.hyper_connection.sinkhorn_iterations" + EPSILON = "{arch}.hyper_connection.epsilon" + + class Rope: + DIMENSION_COUNT = "{arch}.rope.dimension_count" + DIMENSION_COUNT_SWA = "{arch}.rope.dimension_count_swa" + DIMENSION_SECTIONS = "{arch}.rope.dimension_sections" + FREQ_BASE = "{arch}.rope.freq_base" + FREQ_BASE_SWA = "{arch}.rope.freq_base_swa" + SCALING_TYPE = "{arch}.rope.scaling.type" + SCALING_FACTOR = "{arch}.rope.scaling.factor" + SCALING_ALPHA = "{arch}.rope.scaling.alpha" + SCALING_ATTN_FACTOR = "{arch}.rope.scaling.attn_factor" + SCALING_ORIG_CTX_LEN = "{arch}.rope.scaling.original_context_length" + SCALING_FINETUNED = "{arch}.rope.scaling.finetuned" + SCALING_YARN_LOG_MUL = "{arch}.rope.scaling.yarn_log_multiplier" + SCALING_YARN_EXT_FACTOR = "{arch}.rope.scaling.yarn_ext_factor" + SCALING_YARN_ATTN_FACTOR = "{arch}.rope.scaling.yarn_attn_factor" + SCALING_YARN_BETA_FAST = "{arch}.rope.scaling.yarn_beta_fast" + SCALING_YARN_BETA_SLOW = "{arch}.rope.scaling.yarn_beta_slow" + + class Split: + LLM_KV_SPLIT_NO = "split.no" + LLM_KV_SPLIT_COUNT = "split.count" + LLM_KV_SPLIT_TENSORS_COUNT = "split.tensors.count" + + class SSM: + CONV_KERNEL = "{arch}.ssm.conv_kernel" + INNER_SIZE = "{arch}.ssm.inner_size" + STATE_SIZE = "{arch}.ssm.state_size" + TIME_STEP_RANK = "{arch}.ssm.time_step_rank" + GROUP_COUNT = "{arch}.ssm.group_count" + DT_B_C_RMS = "{arch}.ssm.dt_b_c_rms" + + class KDA: + HEAD_DIM = "{arch}.kda.head_dim" + + class WKV: + HEAD_SIZE = "{arch}.wkv.head_size" + + class PosNet: + EMBEDDING_LENGTH = "{arch}.posnet.embedding_length" + BLOCK_COUNT = "{arch}.posnet.block_count" + + class ConvNext: + EMBEDDING_LENGTH = "{arch}.convnext.embedding_length" + BLOCK_COUNT = "{arch}.convnext.block_count" + + class Classifier: + OUTPUT_LABELS = "{arch}.classifier.output_labels" + + class ShortConv: + L_CACHE = "{arch}.shortconv.l_cache" + + class Tokenizer: + MODEL = "tokenizer.ggml.model" + PRE = "tokenizer.ggml.pre" + LIST = "tokenizer.ggml.tokens" + TOKEN_TYPE = "tokenizer.ggml.token_type" + TOKEN_TYPE_COUNT = "tokenizer.ggml.token_type_count" # for BERT-style token types + SCORES = "tokenizer.ggml.scores" + MERGES = "tokenizer.ggml.merges" + BOS_ID = "tokenizer.ggml.bos_token_id" + EOS_ID = "tokenizer.ggml.eos_token_id" + EOT_ID = "tokenizer.ggml.eot_token_id" + EOM_ID = "tokenizer.ggml.eom_token_id" + UNK_ID = "tokenizer.ggml.unknown_token_id" + SEP_ID = "tokenizer.ggml.seperator_token_id" + PAD_ID = "tokenizer.ggml.padding_token_id" + MASK_ID = "tokenizer.ggml.mask_token_id" + ADD_BOS = "tokenizer.ggml.add_bos_token" + ADD_EOS = "tokenizer.ggml.add_eos_token" + ADD_SEP = "tokenizer.ggml.add_sep_token" + ADD_PREFIX = "tokenizer.ggml.add_space_prefix" + REMOVE_EXTRA_WS = "tokenizer.ggml.remove_extra_whitespaces" + PRECOMPILED_CHARSMAP = "tokenizer.ggml.precompiled_charsmap" + SUPPRESS_TOKENS = "tokenizer.ggml.suppress_tokens" + HF_JSON = "tokenizer.huggingface.json" + RWKV = "tokenizer.rwkv.world" + CHAT_TEMPLATE = "tokenizer.chat_template" + CHAT_TEMPLATE_N = "tokenizer.chat_template.{name}" + CHAT_TEMPLATES = "tokenizer.chat_templates" + # Normalizer constants + NORMALIZER_LOWERCASE = "tokenizer.ggml.normalizer.lowercase" + NORMALIZER_STRIP_ACCENTS = "tokenizer.ggml.normalizer.strip_accents" + # FIM/Infill special tokens constants + FIM_PRE_ID = "tokenizer.ggml.fim_pre_token_id" + FIM_SUF_ID = "tokenizer.ggml.fim_suf_token_id" + FIM_MID_ID = "tokenizer.ggml.fim_mid_token_id" + FIM_PAD_ID = "tokenizer.ggml.fim_pad_token_id" + FIM_REP_ID = "tokenizer.ggml.fim_rep_token_id" + FIM_SEP_ID = "tokenizer.ggml.fim_sep_token_id" + # deprecated: + PREFIX_ID = "tokenizer.ggml.prefix_token_id" + SUFFIX_ID = "tokenizer.ggml.suffix_token_id" + MIDDLE_ID = "tokenizer.ggml.middle_token_id" + + class Adapter: + TYPE = "adapter.type" + LORA_ALPHA = "adapter.lora.alpha" + LORA_TASK_NAME = "adapter.lora.task_name" + LORA_PROMPT_PREFIX = "adapter.lora.prompt_prefix" + ALORA_INVOCATION_TOKENS = "adapter.alora.invocation_tokens" + + class IMatrix: + CHUNK_COUNT = "imatrix.chunk_count" + CHUNK_SIZE = "imatrix.chunk_size" + DATASETS = "imatrix.datasets" + + class Clip: + PROJECTOR_TYPE = "clip.projector_type" + HAS_VISION_ENCODER = "clip.has_vision_encoder" + HAS_AUDIO_ENCODER = "clip.has_audio_encoder" + HAS_LLAVA_PROJECTOR = "clip.has_llava_projector" + + class ClipVision: + PROJECTOR_TYPE = "clip.vision.projector_type" # for mixed modality models + IMAGE_SIZE = "clip.vision.image_size" + IMAGE_MIN_PIXELS = "clip.vision.image_min_pixels" + IMAGE_MAX_PIXELS = "clip.vision.image_max_pixels" + PREPROC_MIN_TILES = "clip.vision.preproc_min_tiles" + PREPROC_MAX_TILES = "clip.vision.preproc_max_tiles" + PREPROC_IMAGE_SIZE = "clip.vision.preproc_image_size" + PATCH_SIZE = "clip.vision.patch_size" + EMBEDDING_LENGTH = "clip.vision.embedding_length" + FEED_FORWARD_LENGTH = "clip.vision.feed_forward_length" + PROJECTION_DIM = "clip.vision.projection_dim" + BLOCK_COUNT = "clip.vision.block_count" + IMAGE_MEAN = "clip.vision.image_mean" + IMAGE_STD = "clip.vision.image_std" + SPATIAL_MERGE_SIZE = "clip.vision.spatial_merge_size" + USE_GELU = "clip.use_gelu" + USE_SILU = "clip.use_silu" + N_WA_PATTERN = "clip.vision.n_wa_pattern" # used by qwen2.5vl + WA_LAYER_INDEXES = "clip.vision.wa_layer_indexes" # used by youtuvl + WA_PATTERN_MODE = "clip.vision.wa_pattern_mode" # used by mimovl, per-layer -1/0/1 + IS_DEEPSTACK_LAYERS = "clip.vision.is_deepstack_layers" + WINDOW_SIZE = "clip.vision.window_size" + FEATURE_LAYERS = "clip.vision.feature_layer" # Granite4 Vision + IMAGE_GRID_PINPOINTS = "clip.vision.image_grid_pinpoints" # Granite4 Vision + + class Attention: + HEAD_COUNT = "clip.vision.attention.head_count" + HEAD_COUNT_KV = "clip.vision.attention.head_count_kv" # used by mimovl (GQA) + LAYERNORM_EPS = "clip.vision.attention.layer_norm_epsilon" + + class Projector: + SCALE_FACTOR = "clip.vision.projector.scale_factor" + QUERY_SIDE = "clip.vision.projector.query_side" + WINDOW_SIDE = "clip.vision.projector.window_side" + SPATIAL_OFFSETS = "clip.vision.projector.spatial_offsets" + + class SAM: + BLOCK_COUNT = "clip.vision.sam.block_count" + EMBEDDING_LENGTH = "clip.vision.sam.embedding_length" + HEAD_COUNT = "clip.vision.sam.head_count" + + class ClipAudio: + PROJECTOR_TYPE = "clip.audio.projector_type" # for mixed modality models + NUM_MEL_BINS = "clip.audio.num_mel_bins" + EMBEDDING_LENGTH = "clip.audio.embedding_length" + FEED_FORWARD_LENGTH = "clip.audio.feed_forward_length" + PROJECTION_DIM = "clip.audio.projection_dim" + BLOCK_COUNT = "clip.audio.block_count" + CHUNK_SIZE = "clip.audio.chunk_size" + CONV_KERNEL_SIZE = "clip.audio.conv_kernel_size" + MAX_POS_EMB = "clip.audio.max_pos_emb" + FEATURE_LAYERS = "clip.audio.feature_layer" # Granite Speech Plus + + class Attention: + HEAD_COUNT = "clip.audio.attention.head_count" + LAYERNORM_EPS = "clip.audio.attention.layer_norm_epsilon" + + class Projector: + STACK_FACTOR = "clip.audio.projector.stack_factor" + WINDOW_SIZE = "clip.audio.projector.window_size" + DOWNSAMPLE_RATE = "clip.audio.projector.downsample_rate" + HEAD_COUNT = "clip.audio.projector.head_count" + + class Diffusion: + SHIFT_LOGITS = "diffusion.shift_logits" + + class xIELU: + ALPHA_P = "xielu.alpha_p" + ALPHA_N = "xielu.alpha_n" + BETA = "xielu.beta" + EPS = "xielu.eps" + + +# +# recommended mapping of model tensor names for storage in gguf +# + + +class GGUFType: + MODEL = "model" + ADAPTER = "adapter" + IMATRIX = "imatrix" + MMPROJ = "mmproj" # dummy, unused for now + + +class MODEL_ARCH(IntEnum): + MMPROJ = auto() # dummy arch for clip.cpp + LLAMA = auto() + LLAMA4 = auto() + DECI = auto() + FALCON = auto() + FALCON_H1 = auto() + BAICHUAN = auto() + GROK = auto() + GPT2 = auto() + GPTJ = auto() + GPTNEOX = auto() + MPT = auto() + STARCODER = auto() + REFACT = auto() + BERT = auto() + MODERN_BERT = auto() + NOMIC_BERT = auto() + NOMIC_BERT_MOE = auto() + NEO_BERT = auto() + JINA_BERT_V2 = auto() + JINA_BERT_V3 = auto() + EUROBERT = auto() + BLOOM = auto() + STABLELM = auto() + QWEN = auto() + QWEN2 = auto() + QWEN2MOE = auto() + QWEN2VL = auto() + QWEN3 = auto() + QWEN3MOE = auto() + QWEN3NEXT = auto() + QWEN3VL = auto() + QWEN3VLMOE = auto() + QWEN35 = auto() + QWEN35MOE = auto() + PHI2 = auto() + PHI3 = auto() + PHIMOE = auto() + PLAMO = auto() + PLAMO2 = auto() + PLAMO3 = auto() + CODESHELL = auto() + ORION = auto() + INTERNLM2 = auto() + MINICPM = auto() + MINICPM3 = auto() + GEMMA = auto() + GEMMA2 = auto() + GEMMA3 = auto() + GEMMA3N = auto() + GEMMA4 = auto() + GEMMA4_ASSISTANT = auto() + GEMMA_EMBEDDING = auto() + STARCODER2 = auto() + RWKV6 = auto() + RWKV6QWEN2 = auto() + RWKV7 = auto() + ARWKV7 = auto() + MAMBA = auto() + MAMBA2 = auto() + JAMBA = auto() + XVERSE = auto() + COMMAND_R = auto() + COHERE2 = auto() + COHERE2MOE = auto() + DBRX = auto() + OLMO = auto() + OLMO2 = auto() + OLMOE = auto() + OPENELM = auto() + ARCTIC = auto() + DEEPSEEK = auto() + DEEPSEEK2 = auto() + DEEPSEEK2OCR = auto() + DEEPSEEK32 = auto() + DEEPSEEK4 = auto() + CHATGLM = auto() + GLM4 = auto() + GLM4_MOE = auto() + GLM_DSA = auto() + BITNET = auto() + T5 = auto() + T5ENCODER = auto() + JAIS = auto() + JAIS2 = auto() + NEMOTRON = auto() + NEMOTRON_H = auto() + NEMOTRON_H_MOE = auto() + EXAONE = auto() + EXAONE4 = auto() + EXAONE_MOE = auto() + GRANITE = auto() + GRANITE_MOE = auto() + GRANITE_HYBRID = auto() + CHAMELEON = auto() + WAVTOKENIZER_DEC = auto() + PLM = auto() + BAILINGMOE = auto() + BAILINGMOE2 = auto() + DOTS1 = auto() + ARCEE = auto() + AFMOE = auto() + ERNIE4_5 = auto() + ERNIE4_5_MOE = auto() + HUNYUAN_MOE = auto() + HUNYUAN_DENSE = auto() + HUNYUAN_VL = auto() + SMOLLM3 = auto() + GPT_OSS = auto() + LFM2 = auto() + LFM2MOE = auto() + DREAM = auto() + SMALLTHINKER = auto() + LLADA = auto() + LLADA_MOE = auto() + SEED_OSS = auto() + GROVEMOE = auto() + APERTUS = auto() + COGVLM = auto() + MINIMAXM2 = auto() + RND1 = auto() + PANGU_EMBED = auto() + MISTRAL3 = auto() + EAGLE3 = auto() + DFLASH = auto() + MISTRAL4 = auto() + PADDLEOCR = auto() + MIMO2 = auto() + STEP35 = auto() + LLAMA_EMBED = auto() + MAINCODER = auto() + KIMI_LINEAR = auto() + TALKIE = auto() + MELLUM = auto() + + +class VISION_PROJECTOR_TYPE(IntEnum): + MLP = auto() + LDP = auto() + LDPV2 = auto() + RESAMPLER = auto() + GLM_EDGE = auto() + MERGER = auto() + GEMMA3N = auto() + GEMMA3 = auto() + QWEN3VL = auto() + STEP3VL = auto() + COGVLM = auto() + + +class MODEL_TENSOR(IntEnum): + TOKEN_EMBD = auto() + TOKEN_EMBD_NORM = auto() + MASKED_EMBD_CENTROIDS= auto() + MASKED_EMBD_ORDERING = auto() + TOKEN_TYPES = auto() + POS_EMBD = auto() + OUTPUT = auto() + DENSE_2_OUT = auto() # embeddinggemma 2_Dense + DENSE_3_OUT = auto() # embeddinggemma 3_Dense + OUTPUT_NORM = auto() + HC_HEAD_FN = auto() + HC_HEAD_BASE = auto() + HC_HEAD_SCALE = auto() + ROPE_FREQS = auto() + ROPE_FACTORS_LONG = auto() + ROPE_FACTORS_SHORT = auto() + ATTN_Q = auto() + ATTN_K = auto() + ATTN_V = auto() + ATTN_QKV = auto() + ATTN_OUT = auto() + ATTN_NORM = auto() + ATTN_NORM_2 = auto() + ATTN_OUT_NORM = auto() + ATTN_POST_NORM = auto() + ATTN_ROT_EMBD = auto() + ATTN_SINKS = auto() + ATTN_GATE = auto() + FFN_GATE_INP = auto() + FFN_GATE_INP_SHEXP = auto() + FFN_NORM = auto() + FFN_PRE_NORM = auto() # alias of FFN_NORM + FFN_PRE_NORM_2 = auto() # gemma4 + FFN_POST_NORM = auto() + FFN_POST_NORM_1 = auto() # gemma4 + FFN_POST_NORM_2 = auto() # gemma4 + FFN_GATE = auto() + FFN_DOWN = auto() + FFN_UP = auto() + FFN_ACT = auto() + FFN_NORM_EXP = auto() + FFN_GATE_EXP = auto() + FFN_DOWN_EXP = auto() + FFN_UP_EXP = auto() + FFN_GATE_UP_EXP = auto() + FFN_GATE_SHEXP = auto() + FFN_DOWN_SHEXP = auto() + FFN_UP_SHEXP = auto() + FFN_GATE_CHEXP = auto() + FFN_DOWN_CHEXP = auto() + FFN_UP_CHEXP = auto() + FFN_EXP_PROBS_B = auto() + FFN_GATE_TID2EID = auto() + MOE_LATENT_DOWN = auto() # nemotron 3 super + MOE_LATENT_UP = auto() # nemotron 3 super + ATTN_Q_NORM = auto() + ATTN_K_NORM = auto() + LAYER_OUT_NORM = auto() + LAYER_OUT_SCALE = auto() + PER_LAYER_TOKEN_EMBD = auto() # gemma3n + PER_LAYER_MODEL_PROJ = auto() # gemma3n + PER_LAYER_INP_GATE = auto() # gemma3n + PER_LAYER_PROJ = auto() # gemma3n + PER_LAYER_PROJ_NORM = auto() # gemma3n + PER_LAYER_POST_NORM = auto() # gemma3n + ALTUP_PROJ = auto() # gemma3n + ALTUP_UNEMBD_PROJ = auto() # gemma3n + ALTUP_CORRECT_COEF = auto() # gemma3n + ALTUP_CORRECT_SCALE = auto() # gemma3n + ALTUP_PREDICT_COEF = auto() # gemma3n + ALTUP_ROUTER = auto() # gemma3n + ALTUP_ROUTER_NORM = auto() # gemma3n + LAUREL_L = auto() # gemma3n + LAUREL_R = auto() # gemma3n + LAUREL_POST_NORM = auto() # gemma3n + SSM_IN = auto() + SSM_CONV1D = auto() + SSM_X = auto() + SSM_DT = auto() + SSM_DT_NORM = auto() + SSM_A = auto() + SSM_B_NORM = auto() + SSM_C_NORM = auto() + SSM_D = auto() + SSM_NORM = auto() + SSM_OUT = auto() + SSM_ALPHA = auto() # qwen3.5 + SSM_BETA_ALPHA = auto() # qwen3next + SSM_CONV1D_Q = auto() # Kimi Linear + SSM_CONV1D_K = auto() # Kimi Linear + SSM_CONV1D_V = auto() # Kimi Linear + SSM_F_A = auto() # Kimi Linear + SSM_F_B = auto() # Kimi Linear + SSM_BETA = auto() # Kimi Linear qwen3.5 + SSM_G_A = auto() # Kimi Linear + SSM_G_B = auto() # Kimi Linear + TIME_MIX_W0 = auto() + TIME_MIX_W1 = auto() + TIME_MIX_W2 = auto() + TIME_MIX_A0 = auto() + TIME_MIX_A1 = auto() + TIME_MIX_A2 = auto() + TIME_MIX_V0 = auto() + TIME_MIX_V1 = auto() + TIME_MIX_V2 = auto() + TIME_MIX_G1 = auto() + TIME_MIX_G2 = auto() + TIME_MIX_K_K = auto() + TIME_MIX_K_A = auto() + TIME_MIX_R_K = auto() + TIME_MIX_LERP_X = auto() + TIME_MIX_LERP_K = auto() + TIME_MIX_LERP_V = auto() + TIME_MIX_LERP_R = auto() + TIME_MIX_LERP_G = auto() + TIME_MIX_LERP_FUSED = auto() + TIME_MIX_LERP_W = auto() + TIME_MIX_FIRST = auto() + TIME_MIX_DECAY = auto() + TIME_MIX_DECAY_W1 = auto() + TIME_MIX_DECAY_W2 = auto() + TIME_MIX_KEY = auto() + TIME_MIX_VALUE = auto() + TIME_MIX_RECEPTANCE = auto() + TIME_MIX_GATE = auto() + TIME_MIX_LN = auto() + TIME_MIX_OUTPUT = auto() + CHANNEL_MIX_LERP_K = auto() + CHANNEL_MIX_LERP_R = auto() + CHANNEL_MIX_KEY = auto() + CHANNEL_MIX_RECEPTANCE = auto() + CHANNEL_MIX_VALUE = auto() + ATTN_Q_A = auto() + ATTN_Q_B = auto() + ATTN_KV_A_MQA = auto() + ATTN_KV_B = auto() + ATTN_K_B = auto() + ATTN_V_B = auto() + ATTN_Q_A_NORM = auto() + ATTN_KV_A_NORM = auto() + ATTN_KV = auto() + ATTN_KV_NORM = auto() + ATTN_OUT_A = auto() + ATTN_OUT_B = auto() + HC_ATTN_FN = auto() + HC_ATTN_BASE = auto() + HC_ATTN_SCALE = auto() + HC_FFN_FN = auto() + HC_FFN_BASE = auto() + HC_FFN_SCALE = auto() + ATTN_COMPRESSOR_WKV = auto() + ATTN_COMPRESSOR_WGATE = auto() + ATTN_COMPRESSOR_APE = auto() + ATTN_COMPRESSOR_NORM = auto() + FFN_SUB_NORM = auto() + ATTN_SUB_NORM = auto() + DEC_ATTN_NORM = auto() + DEC_ATTN_Q = auto() + DEC_ATTN_K = auto() + DEC_ATTN_V = auto() + DEC_ATTN_OUT = auto() + DEC_ATTN_REL_B = auto() + DEC_CROSS_ATTN_NORM = auto() + DEC_CROSS_ATTN_Q = auto() + DEC_CROSS_ATTN_K = auto() + DEC_CROSS_ATTN_V = auto() + DEC_CROSS_ATTN_OUT = auto() + DEC_CROSS_ATTN_REL_B = auto() + DEC_FFN_NORM = auto() + DEC_FFN_GATE = auto() + DEC_FFN_DOWN = auto() + DEC_FFN_UP = auto() + DEC_OUTPUT_NORM = auto() + ENC_ATTN_NORM = auto() + ENC_ATTN_Q = auto() + ENC_ATTN_K = auto() + ENC_ATTN_V = auto() + ENC_ATTN_OUT = auto() + ENC_ATTN_REL_B = auto() + ENC_FFN_NORM = auto() + ENC_FFN_GATE = auto() + ENC_FFN_DOWN = auto() + ENC_FFN_UP = auto() + ENC_OUTPUT_NORM = auto() + CLS = auto() # classifier + CLS_OUT = auto() # classifier output projection + CLS_NORM = auto() + CONV1D = auto() + CONVNEXT_DW = auto() + CONVNEXT_NORM = auto() + CONVNEXT_PW1 = auto() + CONVNEXT_PW2 = auto() + CONVNEXT_GAMMA = auto() + POSNET_CONV1 = auto() + POSNET_CONV2 = auto() + POSNET_NORM = auto() + POSNET_NORM1 = auto() + POSNET_NORM2 = auto() + POSNET_ATTN_NORM = auto() + POSNET_ATTN_Q = auto() + POSNET_ATTN_K = auto() + POSNET_ATTN_V = auto() + POSNET_ATTN_OUT = auto() + SHORTCONV_CONV = auto() + SHORTCONV_INPROJ = auto() + SHORTCONV_OUTPROJ = auto() + VISEXP_ATTN_QKV = auto() + VISEXP_ATTN_OUT = auto() + VISEXP_GATE = auto() + VISEXP_DOWN = auto() + VISEXP_UP = auto() + INDEXER_K_NORM = auto() + INDEXER_PROJ = auto() + INDEXER_ATTN_K = auto() + INDEXER_ATTN_Q_B = auto() + INDEXER_COMPRESSOR_WKV = auto() + INDEXER_COMPRESSOR_WGATE = auto() + INDEXER_COMPRESSOR_APE = auto() + INDEXER_COMPRESSOR_NORM = auto() + # vision + V_MMPROJ = auto() + V_MMPROJ_FC = auto() + V_MMPROJ_MLP = auto() + V_MMPROJ_PEG = auto() + V_ENC_EMBD_CLS = auto() + V_ENC_EMBD_PATCH = auto() + V_ENC_EMBD_NORM = auto() + V_ENC_EMBD_PATCH_NORM = auto() # allow multiple norms in the same embd, e.g. for gemma4u + V_ENC_EMBD_POS = auto() + V_ENC_INPUT_NORM = auto() + V_ENC_ATTN_QKV = auto() + V_ENC_ATTN_Q = auto() + V_ENC_ATTN_Q_NORM = auto() + V_ENC_ATTN_K = auto() + V_ENC_ATTN_K_NORM = auto() + V_ENC_ATTN_V = auto() + V_ENC_ATTN_O = auto() + V_ENC_ATTN_O_NORM = auto() + V_ENC_ATTN_SINKS = auto() # mimovl + V_ENC_POST_ATTN_NORM = auto() + V_ENC_FFN_UP = auto() + V_ENC_FFN_GATE = auto() + V_ENC_FFN_DOWN = auto() + V_ENC_ATTN_POST_NORM = auto() # gemma4 + V_ENC_FFN_POST_NORM = auto() + V_LAYER_SCALE_1 = auto() + V_LAYER_SCALE_2 = auto() + V_LAYER_OUT_SCALE = auto() + V_PRE_NORM = auto() + V_POST_NORM = auto() + V_MM_PRE_NORM = auto() # hunyuanvl + V_MM_POST_NORM = auto() + V_MM_INP_NORM = auto() + V_MM_INP_PROJ = auto() # gemma3 + V_MM_SOFT_EMB_NORM = auto() # gemma3 + V_MM_EMBEDDING = auto() # gemma3n + V_MM_HARD_EMB_NORM = auto() # gemma3n + V_ENC_CONV_STEM = auto() # gemma3n + V_ENC_CONV_STEM_NORM = auto() # gemma3n + V_ENC_MSFA_EXP = auto() # gemma3n + V_ENC_MSFA_EXP_NORM = auto() # gemma3n + V_ENC_MSFA_PROJ = auto() # gemma3n + V_ENC_MSFA_PROJ_NORM = auto() # gemma3n + V_ENC_MSFA_NORM = auto() # gemma3n + V_RESMPL_POS_EMBD_K = auto() # minicpmv + V_RESMPL_ATTN_Q = auto() # minicpmv + V_RESMPL_ATTN_K = auto() # minicpmv + V_RESMPL_ATTN_V = auto() # minicpmv + V_RESMPL_ATTN_OUT = auto() # minicpmv + V_RESMPL_KV = auto() # minicpmv + V_RESMPL_KV_NORM = auto() # minicpmv + V_RESMPL_POST_NORM = auto() # minicpmv + V_RESMPL_Q_NORM = auto() # minicpmv + V_RESMPL_PROJ = auto() # minicpmv + V_RESMPL_QUERY = auto() # minicpmv + V_TOK_EMBD_IMG_BREAK = auto() # pixtral + V_MM_PATCH_MERGER = auto() # mistral small 3.1 + V_DS_NORM = auto() # qwen3vl + V_DS_FC1 = auto() # qwen3vl + V_DS_FC2 = auto() # qwen3vl + V_MERGER_LN1 = auto() # minicpmv4_6 + V_MERGER_ATTN_Q = auto() # minicpmv4_6 + V_MERGER_ATTN_K = auto() # minicpmv4_6 + V_MERGER_ATTN_V = auto() # minicpmv4_6 + V_MERGER_ATTN_O = auto() # minicpmv4_6 + V_MERGER_DS_LN = auto() # minicpmv4_6 + V_MERGER_DS_UP = auto() # minicpmv4_6 + V_MERGER_DS_DOWN = auto() # minicpmv4_6 + V_MM_POST_FC_NORM = auto() # cogvlm + V_MM_UP = auto() # cogvlm + V_MM_DOWN = auto() # cogvlm + V_MM_GATE = auto() # cogvlm + V_TOK_BOI = auto() # cogvlm + V_TOK_EOI = auto() # cogvlm + V_TOK_IMG_BEGIN = auto() # hunyuanvl + V_TOK_IMG_END = auto() # hunyuanvl + V_STD_BIAS = auto() # gemma4 + V_STD_SCALE = auto() # gemma4 + V_SAM_POS_EMBD = auto() # Deepseek-OCR + V_SAM_PATCH_EMBD = auto() # Deepseek-OCR + V_SAM_PRE_NORM = auto() # Deepseek-OCR + V_SAM_POST_NORM = auto() # Deepseek-OCR + V_SAM_ATTN_POS_H = auto() # Deepseek-OCR + V_SAM_ATTN_POS_W = auto() # Deepseek-OCR + V_SAM_ATTN_QKV = auto() # Deepseek-OCR + V_SAM_ATTN_OUT = auto() # Deepseek-OCR + V_SAM_MLP_LIN_1 = auto() # Deepseek-OCR + V_SAM_MLP_LIN_2 = auto() # Deepseek-OCR + V_SAM_NECK = auto() # Deepseek-OCR + V_SAM_NET_2 = auto() # Deepseek-OCR + V_SAM_NET_3 = auto() # Deepseek-OCR + V_ENC_EMBD_IMGNL = auto() # Deepseek-OCR + V_ENC_EMBD_VSEP = auto() # Deepseek-OCR + V_RESMPL_QUERY_768 = auto() # Deepseek-OCR-2 + V_RESMPL_QUERY_1024 = auto() # Deepseek-OCR-2 + + # qformer projector (vision) - Granite4 Vision + V_QF_PROJ_QUERY = auto() + V_QF_PROJ_NORM = auto() + V_QF_PROJ_LINEAR = auto() + V_QF_SELF_ATTN_Q = auto() + V_QF_SELF_ATTN_K = auto() + V_QF_SELF_ATTN_V = auto() + V_QF_SELF_ATTN_O = auto() + V_QF_SELF_ATTN_NORM = auto() + V_QF_CROSS_ATTN_Q = auto() + V_QF_CROSS_ATTN_K = auto() + V_QF_CROSS_ATTN_V = auto() + V_QF_CROSS_ATTN_O = auto() + V_QF_CROSS_ATTN_NORM = auto() + V_QF_FFN_UP = auto() + V_QF_FFN_DOWN = auto() + V_QF_FFN_NORM = auto() + V_PROJ_NORM = auto() + # multi-projector (bid => projector id) - Granite4 vision + V_MULTI_PROJ_IMG_POS = auto() + V_MULTI_PROJ_QUERY = auto() + V_MULTI_PROJ_NORM = auto() + V_MULTI_PROJ_LINEAR = auto() + V_MULTI_PROJ_POST_NORM = auto() + + # audio (mtmd) + A_ENC_EMBD_POS = auto() + A_ENC_EMBD_NORM = auto() + A_ENC_EMBD_TO_LOGITS = auto() # lfm2 + A_ENC_INP_PROJ = auto() # gemma4 + A_ENC_CONV1D = auto() + A_ENC_CONV1D_NORM = auto() # gemma3n + A_ENC_CONV2D = auto() + A_ENC_CONV_OUT = auto() + A_PRE_NORM = auto() + A_POST_NORM = auto() + A_ENC_LAYER_PRE_NORM = auto() # gemma3n + A_ENC_ATTN_Q = auto() + A_ENC_ATTN_K = auto() + A_ENC_ATTN_V = auto() + A_ENC_ATTN_POST_NORM = auto() + A_ENC_ATTN_PRE_NORM = auto() + A_ENC_ATTN_K_REL = auto() # gemma4 + A_ENC_PER_DIM_SCALE = auto() # gemma3n + A_ENC_INPUT_NORM = auto() + A_ENC_OUTPUT = auto() # TODO @ngxson: rename to ATTN_OUT + A_ENC_OUTPUT_NORM = auto() # TODO @ngxson: rename to ATTN_OUT + A_ENC_FFN_UP = auto() + A_ENC_FFN_NORM = auto() + A_ENC_FFN_POST_NORM = auto() # gemma3n + A_ENC_FFN_SCALE = auto() # gemma3n + A_ENC_FFN_GATE = auto() + A_ENC_FFN_DOWN = auto() + A_ENC_FFN_UP_1 = auto() # lfm2, gemma3n + A_ENC_FFN_NORM_1 = auto() # lfm2, gemma3n (pre-norm) + A_ENC_FFN_POST_NORM_1 = auto() # gemma3n + A_ENC_FFN_SCALE_1 = auto() # gemma3n + A_ENC_FFN_GATE_1 = auto() # lfm2, gemma3n + A_ENC_FFN_DOWN_1 = auto() # lfm2, gemma3n + A_MMPROJ = auto() + A_MMPROJ_FC = auto() + A_MM_NORM_PRE = auto() + A_MM_NORM_MID = auto() + A_MM_EMBEDDING = auto() # gemma3n + A_MM_HARD_EMB_NORM = auto() # gemma3n + A_MM_SOFT_EMB_NORM = auto() # gemma3n + A_MM_INP_PROJ = auto() # gemma3n + A_PER_DIM_K_SCALE = auto() # gemma4 + A_PER_DIM_SCALE = auto() # gemma4 + # nextn/mtp + NEXTN_PROJ_PRE = auto() + NEXTN_PROJ_POST = auto() + NEXTN_EH_PROJ = auto() + NEXTN_EMBED_TOKENS = auto() + NEXTN_ENORM = auto() + NEXTN_HNORM = auto() + NEXTN_SHARED_HEAD_HEAD = auto() + NEXTN_SHARED_HEAD_NORM = auto() + # eagle3 + FC = auto() # feature fusion layer + D2T = auto() # draft to target vocabulary mapping + # lfm2 audio + A_ENC_NORM_CONV = auto() + A_ENC_LINEAR_POS = auto() + A_ENC_POS_BIAS_U = auto() + A_ENC_POS_BIAS_V = auto() + A_ENC_OUT = auto() + A_ENC_CONV_DW = auto() # SSM conv + A_ENC_CONV_NORM = auto() # SSM conv + A_ENC_CONV_PW1 = auto() + A_ENC_CONV_PW2 = auto() + A_CTC_OUT = auto() + A_CTC_OUT_MID = auto() + A_ENC_ATTN_REL_POS_EMB = auto() + # audio qformer projector + A_QF_PROJ_QUERY = auto() + A_QF_PROJ_NORM = auto() + A_QF_PROJ_LINEAR = auto() + A_QF_SELF_ATTN_Q = auto() + A_QF_SELF_ATTN_K = auto() + A_QF_SELF_ATTN_V = auto() + A_QF_SELF_ATTN_O = auto() + A_QF_SELF_ATTN_NORM = auto() + A_QF_CROSS_ATTN_Q = auto() + A_QF_CROSS_ATTN_K = auto() + A_QF_CROSS_ATTN_V = auto() + A_QF_CROSS_ATTN_O = auto() + A_QF_CROSS_ATTN_NORM = auto() + A_QF_FFN_UP = auto() + A_QF_FFN_DOWN = auto() + A_QF_FFN_NORM = auto() + + +MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = { + MODEL_ARCH.MMPROJ: "clip", # dummy arch for clip.cpp + MODEL_ARCH.LLAMA: "llama", + MODEL_ARCH.LLAMA4: "llama4", + MODEL_ARCH.DECI: "deci", + MODEL_ARCH.FALCON: "falcon", + MODEL_ARCH.BAICHUAN: "baichuan", + MODEL_ARCH.GROK: "grok", + MODEL_ARCH.GPT2: "gpt2", + MODEL_ARCH.GPTJ: "gptj", + MODEL_ARCH.GPTNEOX: "gptneox", + MODEL_ARCH.MPT: "mpt", + MODEL_ARCH.STARCODER: "starcoder", + MODEL_ARCH.REFACT: "refact", + MODEL_ARCH.BERT: "bert", + MODEL_ARCH.MODERN_BERT: "modern-bert", + MODEL_ARCH.NOMIC_BERT: "nomic-bert", + MODEL_ARCH.NOMIC_BERT_MOE: "nomic-bert-moe", + MODEL_ARCH.NEO_BERT: "neo-bert", + MODEL_ARCH.JINA_BERT_V2: "jina-bert-v2", + MODEL_ARCH.JINA_BERT_V3: "jina-bert-v3", + MODEL_ARCH.EUROBERT: "eurobert", + MODEL_ARCH.BLOOM: "bloom", + MODEL_ARCH.STABLELM: "stablelm", + MODEL_ARCH.QWEN: "qwen", + MODEL_ARCH.QWEN2: "qwen2", + MODEL_ARCH.QWEN2MOE: "qwen2moe", + MODEL_ARCH.QWEN2VL: "qwen2vl", + MODEL_ARCH.QWEN3: "qwen3", + MODEL_ARCH.QWEN3MOE: "qwen3moe", + MODEL_ARCH.QWEN3NEXT: "qwen3next", + MODEL_ARCH.QWEN3VL: "qwen3vl", + MODEL_ARCH.QWEN3VLMOE: "qwen3vlmoe", + MODEL_ARCH.QWEN35: "qwen35", + MODEL_ARCH.QWEN35MOE: "qwen35moe", + MODEL_ARCH.PHI2: "phi2", + MODEL_ARCH.PHI3: "phi3", + MODEL_ARCH.PHIMOE: "phimoe", + MODEL_ARCH.PLAMO: "plamo", + MODEL_ARCH.PLAMO2: "plamo2", + MODEL_ARCH.PLAMO3: "plamo3", + MODEL_ARCH.CODESHELL: "codeshell", + MODEL_ARCH.ORION: "orion", + MODEL_ARCH.INTERNLM2: "internlm2", + MODEL_ARCH.MINICPM: "minicpm", + MODEL_ARCH.MINICPM3: "minicpm3", + MODEL_ARCH.GEMMA: "gemma", + MODEL_ARCH.GEMMA2: "gemma2", + MODEL_ARCH.GEMMA3: "gemma3", + MODEL_ARCH.GEMMA3N: "gemma3n", + MODEL_ARCH.GEMMA4: "gemma4", + MODEL_ARCH.GEMMA4_ASSISTANT: "gemma4-assistant", + MODEL_ARCH.GEMMA_EMBEDDING: "gemma-embedding", + MODEL_ARCH.STARCODER2: "starcoder2", + MODEL_ARCH.RWKV6: "rwkv6", + MODEL_ARCH.RWKV6QWEN2: "rwkv6qwen2", + MODEL_ARCH.RWKV7: "rwkv7", + MODEL_ARCH.ARWKV7: "arwkv7", + MODEL_ARCH.MAMBA: "mamba", + MODEL_ARCH.MAMBA2: "mamba2", + MODEL_ARCH.JAMBA: "jamba", + MODEL_ARCH.XVERSE: "xverse", + MODEL_ARCH.COMMAND_R: "command-r", + MODEL_ARCH.COHERE2: "cohere2", + MODEL_ARCH.COHERE2MOE: "cohere2moe", + MODEL_ARCH.DBRX: "dbrx", + MODEL_ARCH.OLMO: "olmo", + MODEL_ARCH.OLMO2: "olmo2", + MODEL_ARCH.OLMOE: "olmoe", + MODEL_ARCH.OPENELM: "openelm", + MODEL_ARCH.ARCTIC: "arctic", + MODEL_ARCH.DEEPSEEK: "deepseek", + MODEL_ARCH.DEEPSEEK2: "deepseek2", + MODEL_ARCH.DEEPSEEK2OCR: "deepseek2-ocr", + MODEL_ARCH.DEEPSEEK32: "deepseek32", + MODEL_ARCH.DEEPSEEK4: "deepseek4", + MODEL_ARCH.CHATGLM: "chatglm", + MODEL_ARCH.GLM4: "glm4", + MODEL_ARCH.GLM4_MOE: "glm4moe", + MODEL_ARCH.GLM_DSA: "glm-dsa", + MODEL_ARCH.BITNET: "bitnet", + MODEL_ARCH.T5: "t5", + MODEL_ARCH.T5ENCODER: "t5encoder", + MODEL_ARCH.JAIS: "jais", + MODEL_ARCH.JAIS2: "jais2", + MODEL_ARCH.NEMOTRON: "nemotron", + MODEL_ARCH.NEMOTRON_H: "nemotron_h", + MODEL_ARCH.NEMOTRON_H_MOE: "nemotron_h_moe", + MODEL_ARCH.EXAONE: "exaone", + MODEL_ARCH.EXAONE4: "exaone4", + MODEL_ARCH.EXAONE_MOE: "exaone-moe", + MODEL_ARCH.GRANITE: "granite", + MODEL_ARCH.GRANITE_MOE: "granitemoe", + MODEL_ARCH.GRANITE_HYBRID: "granitehybrid", + MODEL_ARCH.CHAMELEON: "chameleon", + MODEL_ARCH.WAVTOKENIZER_DEC: "wavtokenizer-dec", + MODEL_ARCH.PLM: "plm", + MODEL_ARCH.BAILINGMOE: "bailingmoe", + MODEL_ARCH.BAILINGMOE2: "bailingmoe2", + MODEL_ARCH.DOTS1: "dots1", + MODEL_ARCH.ARCEE: "arcee", + MODEL_ARCH.AFMOE: "afmoe", + MODEL_ARCH.ERNIE4_5: "ernie4_5", + MODEL_ARCH.ERNIE4_5_MOE: "ernie4_5-moe", + MODEL_ARCH.FALCON_H1: "falcon-h1", + MODEL_ARCH.HUNYUAN_MOE: "hunyuan-moe", + MODEL_ARCH.HUNYUAN_DENSE: "hunyuan-dense", + MODEL_ARCH.HUNYUAN_VL: "hunyuan_vl", + MODEL_ARCH.SMOLLM3: "smollm3", + MODEL_ARCH.GPT_OSS: "gpt-oss", + MODEL_ARCH.LFM2: "lfm2", + MODEL_ARCH.LFM2MOE: "lfm2moe", + MODEL_ARCH.DREAM: "dream", + MODEL_ARCH.SMALLTHINKER: "smallthinker", + MODEL_ARCH.LLADA: "llada", + MODEL_ARCH.LLADA_MOE: "llada-moe", + MODEL_ARCH.SEED_OSS: "seed_oss", + MODEL_ARCH.GROVEMOE: "grovemoe", + MODEL_ARCH.APERTUS: "apertus", + MODEL_ARCH.MINIMAXM2: "minimax-m2", + MODEL_ARCH.COGVLM: "cogvlm", + MODEL_ARCH.RND1: "rnd1", + MODEL_ARCH.PANGU_EMBED: "pangu-embedded", + MODEL_ARCH.MISTRAL3: "mistral3", + MODEL_ARCH.EAGLE3: "eagle3", + MODEL_ARCH.DFLASH: "dflash", + MODEL_ARCH.MISTRAL4: "mistral4", + MODEL_ARCH.PADDLEOCR: "paddleocr", + MODEL_ARCH.MIMO2: "mimo2", + MODEL_ARCH.STEP35: "step35", + MODEL_ARCH.LLAMA_EMBED: "llama-embed", + MODEL_ARCH.MAINCODER: "maincoder", + MODEL_ARCH.KIMI_LINEAR: "kimi-linear", + MODEL_ARCH.TALKIE: "talkie", + MODEL_ARCH.MELLUM: "mellum", +} + +VISION_PROJECTOR_TYPE_NAMES: dict[VISION_PROJECTOR_TYPE, str] = { + VISION_PROJECTOR_TYPE.MLP: "mlp", + VISION_PROJECTOR_TYPE.LDP: "ldp", + VISION_PROJECTOR_TYPE.LDPV2: "ldpv2", + VISION_PROJECTOR_TYPE.RESAMPLER: "resampler", + VISION_PROJECTOR_TYPE.GLM_EDGE: "adapter", + VISION_PROJECTOR_TYPE.MERGER: "qwen2vl_merger", + VISION_PROJECTOR_TYPE.GEMMA3: "gemma3", + VISION_PROJECTOR_TYPE.QWEN3VL: "qwen3vl_merger", + VISION_PROJECTOR_TYPE.STEP3VL: "step3vl", +} + +TENSOR_NAMES: dict[MODEL_TENSOR, str] = { + MODEL_TENSOR.TOKEN_EMBD: "token_embd", + MODEL_TENSOR.TOKEN_EMBD_NORM: "token_embd_norm", + MODEL_TENSOR.TOKEN_TYPES: "token_types", + MODEL_TENSOR.MASKED_EMBD_CENTROIDS: "masked_embd_centroids", + MODEL_TENSOR.MASKED_EMBD_ORDERING: "masked_embd_ordering", + MODEL_TENSOR.POS_EMBD: "position_embd", + MODEL_TENSOR.OUTPUT_NORM: "output_norm", + MODEL_TENSOR.OUTPUT: "output", + MODEL_TENSOR.DENSE_2_OUT: "dense_2", # embeddinggemma 2_Dense + MODEL_TENSOR.DENSE_3_OUT: "dense_3", # embeddinggemma 2_Dense + MODEL_TENSOR.HC_HEAD_FN: "output_hc_fn", + MODEL_TENSOR.HC_HEAD_BASE: "output_hc_base", + MODEL_TENSOR.HC_HEAD_SCALE: "output_hc_scale", + MODEL_TENSOR.ROPE_FREQS: "rope_freqs", + MODEL_TENSOR.ROPE_FACTORS_LONG: "rope_factors_long", + MODEL_TENSOR.ROPE_FACTORS_SHORT: "rope_factors_short", + MODEL_TENSOR.ATTN_NORM: "blk.{bid}.attn_norm", + MODEL_TENSOR.ATTN_NORM_2: "blk.{bid}.attn_norm_2", + MODEL_TENSOR.ATTN_QKV: "blk.{bid}.attn_qkv", + MODEL_TENSOR.ATTN_Q: "blk.{bid}.attn_q", + MODEL_TENSOR.ATTN_K: "blk.{bid}.attn_k", + MODEL_TENSOR.ATTN_V: "blk.{bid}.attn_v", + MODEL_TENSOR.ATTN_OUT: "blk.{bid}.attn_output", + MODEL_TENSOR.ATTN_ROT_EMBD: "blk.{bid}.attn_rot_embd", + MODEL_TENSOR.ATTN_SINKS: "blk.{bid}.attn_sinks", + MODEL_TENSOR.ATTN_GATE: "blk.{bid}.attn_gate", + MODEL_TENSOR.ATTN_Q_NORM: "blk.{bid}.attn_q_norm", + MODEL_TENSOR.ATTN_K_NORM: "blk.{bid}.attn_k_norm", + MODEL_TENSOR.ATTN_OUT_NORM: "blk.{bid}.attn_output_norm", + MODEL_TENSOR.ATTN_POST_NORM: "blk.{bid}.post_attention_norm", + MODEL_TENSOR.FFN_GATE_INP: "blk.{bid}.ffn_gate_inp", + MODEL_TENSOR.FFN_GATE_INP_SHEXP: "blk.{bid}.ffn_gate_inp_shexp", + MODEL_TENSOR.FFN_NORM: "blk.{bid}.ffn_norm", + MODEL_TENSOR.FFN_PRE_NORM: "blk.{bid}.ffn_norm", + MODEL_TENSOR.FFN_POST_NORM: "blk.{bid}.post_ffw_norm", + MODEL_TENSOR.FFN_PRE_NORM_2: "blk.{bid}.pre_ffw_norm_2", # gemma4 + MODEL_TENSOR.FFN_POST_NORM_1: "blk.{bid}.post_ffw_norm_1", # gemma4 + MODEL_TENSOR.FFN_POST_NORM_2: "blk.{bid}.post_ffw_norm_2", # gemma4 + MODEL_TENSOR.FFN_GATE: "blk.{bid}.ffn_gate", + MODEL_TENSOR.FFN_DOWN: "blk.{bid}.ffn_down", + MODEL_TENSOR.FFN_UP: "blk.{bid}.ffn_up", + MODEL_TENSOR.FFN_GATE_SHEXP: "blk.{bid}.ffn_gate_shexp", + MODEL_TENSOR.FFN_DOWN_SHEXP: "blk.{bid}.ffn_down_shexp", + MODEL_TENSOR.FFN_UP_SHEXP: "blk.{bid}.ffn_up_shexp", + MODEL_TENSOR.FFN_GATE_CHEXP: "blk.{bid}.ffn_gate_chexps", + MODEL_TENSOR.FFN_DOWN_CHEXP: "blk.{bid}.ffn_down_chexps", + MODEL_TENSOR.FFN_UP_CHEXP: "blk.{bid}.ffn_up_chexps", + MODEL_TENSOR.FFN_ACT: "blk.{bid}.ffn", + MODEL_TENSOR.FFN_NORM_EXP: "blk.{bid}.ffn_norm_exps", + MODEL_TENSOR.FFN_GATE_EXP: "blk.{bid}.ffn_gate_exps", + MODEL_TENSOR.FFN_DOWN_EXP: "blk.{bid}.ffn_down_exps", + MODEL_TENSOR.FFN_UP_EXP: "blk.{bid}.ffn_up_exps", + MODEL_TENSOR.FFN_GATE_UP_EXP: "blk.{bid}.ffn_gate_up_exps", + MODEL_TENSOR.FFN_EXP_PROBS_B: "blk.{bid}.exp_probs_b", + MODEL_TENSOR.FFN_GATE_TID2EID: "blk.{bid}.ffn_gate_tid2eid", + MODEL_TENSOR.MOE_LATENT_DOWN: "blk.{bid}.ffn_latent_down", # nemotron 3 super + MODEL_TENSOR.MOE_LATENT_UP: "blk.{bid}.ffn_latent_up", # nemotron 3 super + MODEL_TENSOR.LAYER_OUT_NORM: "blk.{bid}.layer_output_norm", + MODEL_TENSOR.LAYER_OUT_SCALE: "blk.{bid}.layer_output_scale", + MODEL_TENSOR.PER_LAYER_TOKEN_EMBD: "per_layer_token_embd", # gemma3n + MODEL_TENSOR.PER_LAYER_MODEL_PROJ: "per_layer_model_proj", # gemma3n + MODEL_TENSOR.PER_LAYER_PROJ_NORM: "per_layer_proj_norm", # gemma3n + MODEL_TENSOR.ALTUP_UNEMBD_PROJ: "altup_unembd_proj", # gemma3n + MODEL_TENSOR.ALTUP_PROJ: "altup_proj", # gemma3n + MODEL_TENSOR.PER_LAYER_INP_GATE: "blk.{bid}.inp_gate", # gemma3n + MODEL_TENSOR.PER_LAYER_PROJ: "blk.{bid}.proj", # gemma3n + MODEL_TENSOR.PER_LAYER_POST_NORM: "blk.{bid}.post_norm", # gemma3n + MODEL_TENSOR.ALTUP_CORRECT_COEF: "blk.{bid}.altup_correct_coef", # gemma3n + MODEL_TENSOR.ALTUP_CORRECT_SCALE: "blk.{bid}.altup_correct_scale", # gemma3n + MODEL_TENSOR.ALTUP_PREDICT_COEF: "blk.{bid}.altup_predict_coef", # gemma3n + MODEL_TENSOR.ALTUP_ROUTER: "blk.{bid}.altup_router", # gemma3n + MODEL_TENSOR.ALTUP_ROUTER_NORM: "blk.{bid}.altup_router_norm", # gemma3n + MODEL_TENSOR.LAUREL_L: "blk.{bid}.laurel_l", # gemma3n + MODEL_TENSOR.LAUREL_R: "blk.{bid}.laurel_r", # gemma3n + MODEL_TENSOR.LAUREL_POST_NORM: "blk.{bid}.laurel_post_norm", # gemma3n + MODEL_TENSOR.SSM_IN: "blk.{bid}.ssm_in", + MODEL_TENSOR.SSM_CONV1D: "blk.{bid}.ssm_conv1d", + MODEL_TENSOR.SSM_X: "blk.{bid}.ssm_x", + MODEL_TENSOR.SSM_DT: "blk.{bid}.ssm_dt", + MODEL_TENSOR.SSM_DT_NORM: "blk.{bid}.ssm_dt_norm", + MODEL_TENSOR.SSM_A: "blk.{bid}.ssm_a", + MODEL_TENSOR.SSM_B_NORM: "blk.{bid}.ssm_b_norm", + MODEL_TENSOR.SSM_C_NORM: "blk.{bid}.ssm_c_norm", + MODEL_TENSOR.SSM_D: "blk.{bid}.ssm_d", + MODEL_TENSOR.SSM_NORM: "blk.{bid}.ssm_norm", + MODEL_TENSOR.SSM_OUT: "blk.{bid}.ssm_out", + MODEL_TENSOR.SSM_ALPHA: "blk.{bid}.ssm_alpha", # qwen3.5 + MODEL_TENSOR.SSM_BETA_ALPHA: "blk.{bid}.ssm_ba", + MODEL_TENSOR.SSM_CONV1D_Q: "blk.{bid}.ssm_conv1d_q", # Kimi Linear + MODEL_TENSOR.SSM_CONV1D_K: "blk.{bid}.ssm_conv1d_k", # Kimi Linear + MODEL_TENSOR.SSM_CONV1D_V: "blk.{bid}.ssm_conv1d_v", # Kimi Linear + MODEL_TENSOR.SSM_F_A: "blk.{bid}.ssm_f_a", # Kimi Linear + MODEL_TENSOR.SSM_F_B: "blk.{bid}.ssm_f_b", # Kimi Linear + MODEL_TENSOR.SSM_BETA: "blk.{bid}.ssm_beta", # Kimi Linear qwen3.5 + MODEL_TENSOR.SSM_G_A: "blk.{bid}.ssm_g_a", # Kimi Linear + MODEL_TENSOR.SSM_G_B: "blk.{bid}.ssm_g_b", # Kimi Linear + MODEL_TENSOR.TIME_MIX_W0: "blk.{bid}.time_mix_w0", + MODEL_TENSOR.TIME_MIX_W1: "blk.{bid}.time_mix_w1", + MODEL_TENSOR.TIME_MIX_W2: "blk.{bid}.time_mix_w2", + MODEL_TENSOR.TIME_MIX_A0: "blk.{bid}.time_mix_a0", + MODEL_TENSOR.TIME_MIX_A1: "blk.{bid}.time_mix_a1", + MODEL_TENSOR.TIME_MIX_A2: "blk.{bid}.time_mix_a2", + MODEL_TENSOR.TIME_MIX_V0: "blk.{bid}.time_mix_v0", + MODEL_TENSOR.TIME_MIX_V1: "blk.{bid}.time_mix_v1", + MODEL_TENSOR.TIME_MIX_V2: "blk.{bid}.time_mix_v2", + MODEL_TENSOR.TIME_MIX_G1: "blk.{bid}.time_mix_g1", + MODEL_TENSOR.TIME_MIX_G2: "blk.{bid}.time_mix_g2", + MODEL_TENSOR.TIME_MIX_K_K: "blk.{bid}.time_mix_k_k", + MODEL_TENSOR.TIME_MIX_K_A: "blk.{bid}.time_mix_k_a", + MODEL_TENSOR.TIME_MIX_R_K: "blk.{bid}.time_mix_r_k", + MODEL_TENSOR.TIME_MIX_LERP_X: "blk.{bid}.time_mix_lerp_x", + MODEL_TENSOR.TIME_MIX_LERP_K: "blk.{bid}.time_mix_lerp_k", + MODEL_TENSOR.TIME_MIX_LERP_V: "blk.{bid}.time_mix_lerp_v", + MODEL_TENSOR.TIME_MIX_LERP_R: "blk.{bid}.time_mix_lerp_r", + MODEL_TENSOR.TIME_MIX_LERP_G: "blk.{bid}.time_mix_lerp_g", + MODEL_TENSOR.TIME_MIX_LERP_FUSED: "blk.{bid}.time_mix_lerp_fused", + MODEL_TENSOR.TIME_MIX_LERP_W: "blk.{bid}.time_mix_lerp_w", + MODEL_TENSOR.TIME_MIX_FIRST: "blk.{bid}.time_mix_first", + MODEL_TENSOR.TIME_MIX_DECAY: "blk.{bid}.time_mix_decay", + MODEL_TENSOR.TIME_MIX_DECAY_W1: "blk.{bid}.time_mix_decay_w1", + MODEL_TENSOR.TIME_MIX_DECAY_W2: "blk.{bid}.time_mix_decay_w2", + MODEL_TENSOR.TIME_MIX_KEY: "blk.{bid}.time_mix_key", + MODEL_TENSOR.TIME_MIX_VALUE: "blk.{bid}.time_mix_value", + MODEL_TENSOR.TIME_MIX_RECEPTANCE: "blk.{bid}.time_mix_receptance", + MODEL_TENSOR.TIME_MIX_GATE: "blk.{bid}.time_mix_gate", + MODEL_TENSOR.TIME_MIX_LN: "blk.{bid}.time_mix_ln", + MODEL_TENSOR.TIME_MIX_OUTPUT: "blk.{bid}.time_mix_output", + MODEL_TENSOR.CHANNEL_MIX_LERP_K: "blk.{bid}.channel_mix_lerp_k", + MODEL_TENSOR.CHANNEL_MIX_LERP_R: "blk.{bid}.channel_mix_lerp_r", + MODEL_TENSOR.CHANNEL_MIX_KEY: "blk.{bid}.channel_mix_key", + MODEL_TENSOR.CHANNEL_MIX_RECEPTANCE: "blk.{bid}.channel_mix_receptance", + MODEL_TENSOR.CHANNEL_MIX_VALUE: "blk.{bid}.channel_mix_value", + MODEL_TENSOR.ATTN_Q_A: "blk.{bid}.attn_q_a", + MODEL_TENSOR.ATTN_Q_B: "blk.{bid}.attn_q_b", + MODEL_TENSOR.ATTN_KV_A_MQA: "blk.{bid}.attn_kv_a_mqa", + MODEL_TENSOR.ATTN_KV_B: "blk.{bid}.attn_kv_b", + MODEL_TENSOR.ATTN_K_B: "blk.{bid}.attn_k_b", + MODEL_TENSOR.ATTN_V_B: "blk.{bid}.attn_v_b", + MODEL_TENSOR.ATTN_Q_A_NORM: "blk.{bid}.attn_q_a_norm", + MODEL_TENSOR.ATTN_KV_A_NORM: "blk.{bid}.attn_kv_a_norm", + MODEL_TENSOR.ATTN_KV: "blk.{bid}.attn_kv", + MODEL_TENSOR.ATTN_KV_NORM: "blk.{bid}.attn_kv_a_norm", + MODEL_TENSOR.ATTN_OUT_A: "blk.{bid}.attn_output_a", + MODEL_TENSOR.ATTN_OUT_B: "blk.{bid}.attn_output_b", + MODEL_TENSOR.HC_ATTN_FN: "blk.{bid}.hc_attn_fn", + MODEL_TENSOR.HC_ATTN_BASE: "blk.{bid}.hc_attn_base", + MODEL_TENSOR.HC_ATTN_SCALE: "blk.{bid}.hc_attn_scale", + MODEL_TENSOR.HC_FFN_FN: "blk.{bid}.hc_ffn_fn", + MODEL_TENSOR.HC_FFN_BASE: "blk.{bid}.hc_ffn_base", + MODEL_TENSOR.HC_FFN_SCALE: "blk.{bid}.hc_ffn_scale", + MODEL_TENSOR.ATTN_COMPRESSOR_WKV: "blk.{bid}.attn_compressor_kv", + MODEL_TENSOR.ATTN_COMPRESSOR_WGATE: "blk.{bid}.attn_compressor_gate", + MODEL_TENSOR.ATTN_COMPRESSOR_APE: "blk.{bid}.attn_compressor_ape", + MODEL_TENSOR.ATTN_COMPRESSOR_NORM: "blk.{bid}.attn_compressor_norm", + MODEL_TENSOR.ATTN_SUB_NORM: "blk.{bid}.attn_sub_norm", + MODEL_TENSOR.FFN_SUB_NORM: "blk.{bid}.ffn_sub_norm", + MODEL_TENSOR.DEC_ATTN_NORM: "dec.blk.{bid}.attn_norm", + MODEL_TENSOR.DEC_ATTN_Q: "dec.blk.{bid}.attn_q", + MODEL_TENSOR.DEC_ATTN_K: "dec.blk.{bid}.attn_k", + MODEL_TENSOR.DEC_ATTN_V: "dec.blk.{bid}.attn_v", + MODEL_TENSOR.DEC_ATTN_OUT: "dec.blk.{bid}.attn_o", + MODEL_TENSOR.DEC_ATTN_REL_B: "dec.blk.{bid}.attn_rel_b", + MODEL_TENSOR.DEC_CROSS_ATTN_NORM: "dec.blk.{bid}.cross_attn_norm", + MODEL_TENSOR.DEC_CROSS_ATTN_Q: "dec.blk.{bid}.cross_attn_q", + MODEL_TENSOR.DEC_CROSS_ATTN_K: "dec.blk.{bid}.cross_attn_k", + MODEL_TENSOR.DEC_CROSS_ATTN_V: "dec.blk.{bid}.cross_attn_v", + MODEL_TENSOR.DEC_CROSS_ATTN_OUT: "dec.blk.{bid}.cross_attn_o", + MODEL_TENSOR.DEC_CROSS_ATTN_REL_B: "dec.blk.{bid}.cross_attn_rel_b", + MODEL_TENSOR.DEC_FFN_NORM: "dec.blk.{bid}.ffn_norm", + MODEL_TENSOR.DEC_FFN_GATE: "dec.blk.{bid}.ffn_gate", + MODEL_TENSOR.DEC_FFN_DOWN: "dec.blk.{bid}.ffn_down", + MODEL_TENSOR.DEC_FFN_UP: "dec.blk.{bid}.ffn_up", + MODEL_TENSOR.DEC_OUTPUT_NORM: "dec.output_norm", + MODEL_TENSOR.ENC_ATTN_NORM: "enc.blk.{bid}.attn_norm", + MODEL_TENSOR.ENC_ATTN_Q: "enc.blk.{bid}.attn_q", + MODEL_TENSOR.ENC_ATTN_K: "enc.blk.{bid}.attn_k", + MODEL_TENSOR.ENC_ATTN_V: "enc.blk.{bid}.attn_v", + MODEL_TENSOR.ENC_ATTN_OUT: "enc.blk.{bid}.attn_o", + MODEL_TENSOR.ENC_ATTN_REL_B: "enc.blk.{bid}.attn_rel_b", + MODEL_TENSOR.ENC_FFN_NORM: "enc.blk.{bid}.ffn_norm", + MODEL_TENSOR.ENC_FFN_GATE: "enc.blk.{bid}.ffn_gate", + MODEL_TENSOR.ENC_FFN_DOWN: "enc.blk.{bid}.ffn_down", + MODEL_TENSOR.ENC_FFN_UP: "enc.blk.{bid}.ffn_up", + MODEL_TENSOR.ENC_OUTPUT_NORM: "enc.output_norm", + MODEL_TENSOR.CLS: "cls", + MODEL_TENSOR.CLS_OUT: "cls.output", + MODEL_TENSOR.CLS_NORM: "cls.norm", + MODEL_TENSOR.CONV1D: "conv1d", + MODEL_TENSOR.CONVNEXT_DW: "convnext.{bid}.dw", + MODEL_TENSOR.CONVNEXT_NORM: "convnext.{bid}.norm", + MODEL_TENSOR.CONVNEXT_PW1: "convnext.{bid}.pw1", + MODEL_TENSOR.CONVNEXT_PW2: "convnext.{bid}.pw2", + MODEL_TENSOR.CONVNEXT_GAMMA: "convnext.{bid}.gamma", + MODEL_TENSOR.POSNET_CONV1: "posnet.{bid}.conv1", + MODEL_TENSOR.POSNET_CONV2: "posnet.{bid}.conv2", + MODEL_TENSOR.POSNET_NORM: "posnet.{bid}.norm", + MODEL_TENSOR.POSNET_NORM1: "posnet.{bid}.norm1", + MODEL_TENSOR.POSNET_NORM2: "posnet.{bid}.norm2", + MODEL_TENSOR.POSNET_ATTN_NORM: "posnet.{bid}.attn_norm", + MODEL_TENSOR.POSNET_ATTN_Q: "posnet.{bid}.attn_q", + MODEL_TENSOR.POSNET_ATTN_K: "posnet.{bid}.attn_k", + MODEL_TENSOR.POSNET_ATTN_V: "posnet.{bid}.attn_v", + MODEL_TENSOR.POSNET_ATTN_OUT: "posnet.{bid}.attn_output", + MODEL_TENSOR.SHORTCONV_CONV: "blk.{bid}.shortconv.conv", + MODEL_TENSOR.SHORTCONV_INPROJ: "blk.{bid}.shortconv.in_proj", + MODEL_TENSOR.SHORTCONV_OUTPROJ: "blk.{bid}.shortconv.out_proj", + MODEL_TENSOR.VISEXP_ATTN_QKV: "blk.{bid}.vis_attn_qkv", + MODEL_TENSOR.VISEXP_ATTN_OUT: "blk.{bid}.vis_attn_output", + MODEL_TENSOR.VISEXP_GATE: "blk.{bid}.vis_gate", + MODEL_TENSOR.VISEXP_DOWN: "blk.{bid}.vis_down", + MODEL_TENSOR.VISEXP_UP: "blk.{bid}.vis_up", + MODEL_TENSOR.INDEXER_K_NORM: "blk.{bid}.indexer.k_norm", + MODEL_TENSOR.INDEXER_PROJ: "blk.{bid}.indexer.proj", + MODEL_TENSOR.INDEXER_ATTN_K: "blk.{bid}.indexer.attn_k", + MODEL_TENSOR.INDEXER_ATTN_Q_B: "blk.{bid}.indexer.attn_q_b", + MODEL_TENSOR.INDEXER_COMPRESSOR_WKV: "blk.{bid}.indexer_compressor_kv", + MODEL_TENSOR.INDEXER_COMPRESSOR_WGATE: "blk.{bid}.indexer_compressor_gate", + MODEL_TENSOR.INDEXER_COMPRESSOR_APE: "blk.{bid}.indexer_compressor_ape", + MODEL_TENSOR.INDEXER_COMPRESSOR_NORM: "blk.{bid}.indexer_compressor_norm", + # vision + MODEL_TENSOR.V_MMPROJ: "mm.{bid}", + MODEL_TENSOR.V_MMPROJ_FC: "mm.model.fc", + MODEL_TENSOR.V_MMPROJ_MLP: "mm.model.mlp.{bid}", + MODEL_TENSOR.V_MMPROJ_PEG: "mm.model.peg.{bid}", + MODEL_TENSOR.V_ENC_EMBD_CLS: "v.class_embd", + MODEL_TENSOR.V_ENC_EMBD_PATCH: "v.patch_embd", + MODEL_TENSOR.V_ENC_EMBD_NORM: "v.norm_embd", + MODEL_TENSOR.V_ENC_EMBD_PATCH_NORM: "v.patch_norm.{bid}", + MODEL_TENSOR.V_ENC_EMBD_POS: "v.position_embd", + MODEL_TENSOR.V_ENC_ATTN_QKV: "v.blk.{bid}.attn_qkv", + MODEL_TENSOR.V_ENC_ATTN_Q: "v.blk.{bid}.attn_q", + MODEL_TENSOR.V_ENC_ATTN_Q_NORM: "v.blk.{bid}.attn_q_norm", + MODEL_TENSOR.V_ENC_ATTN_K: "v.blk.{bid}.attn_k", + MODEL_TENSOR.V_ENC_ATTN_K_NORM: "v.blk.{bid}.attn_k_norm", + MODEL_TENSOR.V_ENC_ATTN_V: "v.blk.{bid}.attn_v", + MODEL_TENSOR.V_ENC_INPUT_NORM: "v.blk.{bid}.ln1", + MODEL_TENSOR.V_ENC_ATTN_O: "v.blk.{bid}.attn_out", + MODEL_TENSOR.V_ENC_ATTN_O_NORM: "v.blk.{bid}.attn_out_norm", + MODEL_TENSOR.V_ENC_ATTN_SINKS: "v.blk.{bid}.attn_sinks", + MODEL_TENSOR.V_ENC_POST_ATTN_NORM: "v.blk.{bid}.ln2", + MODEL_TENSOR.V_ENC_FFN_UP: "v.blk.{bid}.ffn_up", + MODEL_TENSOR.V_ENC_FFN_GATE: "v.blk.{bid}.ffn_gate", + MODEL_TENSOR.V_ENC_FFN_DOWN: "v.blk.{bid}.ffn_down", + MODEL_TENSOR.V_ENC_ATTN_POST_NORM: "v.blk.{bid}.attn_post_norm", + MODEL_TENSOR.V_ENC_FFN_POST_NORM: "v.blk.{bid}.ffn_post_norm", + MODEL_TENSOR.V_LAYER_SCALE_1: "v.blk.{bid}.ls1", + MODEL_TENSOR.V_LAYER_SCALE_2: "v.blk.{bid}.ls2", + MODEL_TENSOR.V_LAYER_OUT_SCALE: "v.blk.{bid}.out_scale", + MODEL_TENSOR.V_PRE_NORM: "v.pre_ln", + MODEL_TENSOR.V_POST_NORM: "v.post_ln", + MODEL_TENSOR.V_MM_POST_NORM: "mm.post_norm", + MODEL_TENSOR.V_MM_INP_PROJ: "mm.input_projection", + MODEL_TENSOR.V_MM_INP_NORM: "mm.input_norm", + MODEL_TENSOR.V_MM_SOFT_EMB_NORM: "mm.soft_emb_norm", # gemma3n + MODEL_TENSOR.V_MM_EMBEDDING: "mm.embedding", # gemma3n + MODEL_TENSOR.V_MM_HARD_EMB_NORM: "mm.hard_emb_norm", # gemma3n + MODEL_TENSOR.V_ENC_CONV_STEM: "v.conv_stem.conv", # gemma3n + MODEL_TENSOR.V_ENC_CONV_STEM_NORM: "v.conv_stem.bn", # gemma3n + MODEL_TENSOR.V_ENC_MSFA_EXP: "v.msfa.ffn.pw_exp.conv", # gemma3n + MODEL_TENSOR.V_ENC_MSFA_EXP_NORM: "v.msfa.ffn.pw_exp.bn", # gemma3n + MODEL_TENSOR.V_ENC_MSFA_PROJ: "v.msfa.ffn.pw_proj.conv", # gemma3n + MODEL_TENSOR.V_ENC_MSFA_PROJ_NORM: "v.msfa.ffn.pw_proj.bn", # gemma3n + MODEL_TENSOR.V_ENC_MSFA_NORM: "v.msfa.norm", # gemma3n + MODEL_TENSOR.V_RESMPL_POS_EMBD_K: "resampler.pos_embd_k", + MODEL_TENSOR.V_RESMPL_ATTN_Q: "resampler.attn.q", + MODEL_TENSOR.V_RESMPL_ATTN_K: "resampler.attn.k", + MODEL_TENSOR.V_RESMPL_ATTN_V: "resampler.attn.v", + MODEL_TENSOR.V_RESMPL_ATTN_OUT: "resampler.attn.out", + MODEL_TENSOR.V_RESMPL_KV: "resampler.kv", + MODEL_TENSOR.V_RESMPL_KV_NORM: "resampler.ln_kv", + MODEL_TENSOR.V_RESMPL_POST_NORM: "resampler.ln_post", + MODEL_TENSOR.V_RESMPL_Q_NORM: "resampler.ln_q", + MODEL_TENSOR.V_RESMPL_PROJ: "resampler.proj", + MODEL_TENSOR.V_RESMPL_QUERY: "resampler.query", + MODEL_TENSOR.V_TOK_EMBD_IMG_BREAK: "v.token_embd.img_break", # pixtral + MODEL_TENSOR.V_MM_PATCH_MERGER: "mm.patch_merger", # mistral small 3.1 + MODEL_TENSOR.V_DS_NORM: "v.deepstack.{bid}.norm", + MODEL_TENSOR.V_DS_FC1: "v.deepstack.{bid}.fc1", + MODEL_TENSOR.V_DS_FC2: "v.deepstack.{bid}.fc2", + MODEL_TENSOR.V_MERGER_LN1: "v.vit_merger.ln1", + MODEL_TENSOR.V_MERGER_ATTN_Q: "v.vit_merger.attn_q", + MODEL_TENSOR.V_MERGER_ATTN_K: "v.vit_merger.attn_k", + MODEL_TENSOR.V_MERGER_ATTN_V: "v.vit_merger.attn_v", + MODEL_TENSOR.V_MERGER_ATTN_O: "v.vit_merger.attn_out", + MODEL_TENSOR.V_MERGER_DS_LN: "v.vit_merger.ds_ln", + MODEL_TENSOR.V_MERGER_DS_UP: "v.vit_merger.ds_ffn_up", + MODEL_TENSOR.V_MERGER_DS_DOWN: "v.vit_merger.ds_ffn_down", + MODEL_TENSOR.V_MM_POST_FC_NORM: "mm.post_fc_norm", # cogvlm + MODEL_TENSOR.V_MM_UP: "mm.up", + MODEL_TENSOR.V_MM_DOWN: "mm.down", + MODEL_TENSOR.V_MM_GATE: "mm.gate", + MODEL_TENSOR.V_TOK_BOI: "v.boi", + MODEL_TENSOR.V_TOK_EOI: "v.eoi", + MODEL_TENSOR.V_MM_PRE_NORM: "mm.pre_norm", + MODEL_TENSOR.V_TOK_IMG_BEGIN: "mm.image_begin", + MODEL_TENSOR.V_TOK_IMG_END: "mm.image_end", + MODEL_TENSOR.V_STD_BIAS: "v.std_bias", # gemma4 + MODEL_TENSOR.V_STD_SCALE: "v.std_scale", # gemma4 + # DeepSeek-OCR SAM + MODEL_TENSOR.V_SAM_POS_EMBD: "v.sam.pos_embd", + MODEL_TENSOR.V_SAM_PATCH_EMBD: "v.sam.patch_embd", + MODEL_TENSOR.V_SAM_PRE_NORM: "v.sam.blk.{bid}.pre_ln", + MODEL_TENSOR.V_SAM_POST_NORM: "v.sam.blk.{bid}.post_ln", + MODEL_TENSOR.V_SAM_ATTN_POS_H: "v.sam.blk.{bid}.attn.pos_h", + MODEL_TENSOR.V_SAM_ATTN_POS_W: "v.sam.blk.{bid}.attn.pos_w", + MODEL_TENSOR.V_SAM_ATTN_QKV: "v.sam.blk.{bid}.attn.qkv", + MODEL_TENSOR.V_SAM_ATTN_OUT: "v.sam.blk.{bid}.attn.out", + MODEL_TENSOR.V_SAM_MLP_LIN_1: "v.sam.blk.{bid}.mlp.lin1", + MODEL_TENSOR.V_SAM_MLP_LIN_2: "v.sam.blk.{bid}.mlp.lin2", + MODEL_TENSOR.V_SAM_NECK: "v.sam.neck.{bid}", + MODEL_TENSOR.V_SAM_NET_2: "v.sam.net_2", + MODEL_TENSOR.V_SAM_NET_3: "v.sam.net_3", + MODEL_TENSOR.V_ENC_EMBD_IMGNL: "v.image_newline", # Deepseek-OCR, Granite4Vision + MODEL_TENSOR.V_ENC_EMBD_VSEP: "v.view_seperator", # Deepseek-OCR + MODEL_TENSOR.V_RESMPL_QUERY_768: "v.resample_query_768", # Deepseek-OCR-2 qwen2 + MODEL_TENSOR.V_RESMPL_QUERY_1024: "v.resample_query_1024", # Deepseek-OCR-2 qwen2 + # Granite4 Vision + # qformer layers (bid => proj_id) + # NOTE: Names align with A_QF_* + MODEL_TENSOR.V_QF_SELF_ATTN_Q: "v.proj_blk.{bid}.self_attn_q", + MODEL_TENSOR.V_QF_SELF_ATTN_K: "v.proj_blk.{bid}.self_attn_k", + MODEL_TENSOR.V_QF_SELF_ATTN_V: "v.proj_blk.{bid}.self_attn_v", + MODEL_TENSOR.V_QF_SELF_ATTN_O: "v.proj_blk.{bid}.self_attn_out", + MODEL_TENSOR.V_QF_SELF_ATTN_NORM: "v.proj_blk.{bid}.self_attn_norm", + MODEL_TENSOR.V_QF_CROSS_ATTN_Q: "v.proj_blk.{bid}.cross_attn_q", + MODEL_TENSOR.V_QF_CROSS_ATTN_K: "v.proj_blk.{bid}.cross_attn_k", + MODEL_TENSOR.V_QF_CROSS_ATTN_V: "v.proj_blk.{bid}.cross_attn_v", + MODEL_TENSOR.V_QF_CROSS_ATTN_O: "v.proj_blk.{bid}.cross_attn_out", + MODEL_TENSOR.V_QF_CROSS_ATTN_NORM: "v.proj_blk.{bid}.cross_attn_norm", + MODEL_TENSOR.V_QF_FFN_UP: "v.proj_blk.{bid}.ffn_up", + MODEL_TENSOR.V_QF_FFN_DOWN: "v.proj_blk.{bid}.ffn_down", + MODEL_TENSOR.V_QF_FFN_NORM: "v.proj_blk.{bid}.ffn_norm", + # multi-projector (bid => projector ID) + MODEL_TENSOR.V_MULTI_PROJ_IMG_POS: "v.proj_blk.{bid}.img_pos", + MODEL_TENSOR.V_MULTI_PROJ_QUERY: "v.proj_blk.{bid}.query", + MODEL_TENSOR.V_MULTI_PROJ_NORM: "v.proj_blk.{bid}.norm", + MODEL_TENSOR.V_MULTI_PROJ_LINEAR: "v.proj_blk.{bid}.linear", + MODEL_TENSOR.V_MULTI_PROJ_POST_NORM: "v.proj_blk.{bid}.post_norm", + + # audio (mtmd) + # note: all audio tensor names must use prefix "a." or "mm.a." + MODEL_TENSOR.A_ENC_EMBD_POS: "a.position_embd", + MODEL_TENSOR.A_ENC_EMBD_NORM: "a.position_embd_norm", + MODEL_TENSOR.A_ENC_EMBD_TO_LOGITS: "a.embd_to_logits", + MODEL_TENSOR.A_ENC_INP_PROJ: "a.input_projection", + MODEL_TENSOR.A_ENC_CONV1D: "a.conv1d.{bid}", + MODEL_TENSOR.A_ENC_CONV2D: "a.conv2d.{bid}", + MODEL_TENSOR.A_ENC_CONV_OUT: "a.conv_out", + MODEL_TENSOR.A_ENC_CONV1D_NORM: "a.conv1d.{bid}.norm", + MODEL_TENSOR.A_PRE_NORM: "a.pre_ln", + MODEL_TENSOR.A_POST_NORM: "a.post_ln", + MODEL_TENSOR.A_ENC_LAYER_PRE_NORM: "a.blk.{bid}.layer_pre_norm", + MODEL_TENSOR.A_ENC_ATTN_Q: "a.blk.{bid}.attn_q", + MODEL_TENSOR.A_ENC_ATTN_K: "a.blk.{bid}.attn_k", + MODEL_TENSOR.A_ENC_ATTN_V: "a.blk.{bid}.attn_v", + MODEL_TENSOR.A_ENC_ATTN_POST_NORM: "a.blk.{bid}.attn_post_norm", + MODEL_TENSOR.A_ENC_ATTN_PRE_NORM: "a.blk.{bid}.attn_pre_norm", + MODEL_TENSOR.A_ENC_ATTN_K_REL: "a.blk.{bid}.attn_k_rel", + MODEL_TENSOR.A_ENC_PER_DIM_SCALE: "a.blk.{bid}.per_dim_scale", + MODEL_TENSOR.A_ENC_INPUT_NORM: "a.blk.{bid}.ln1", + MODEL_TENSOR.A_ENC_OUTPUT: "a.blk.{bid}.attn_out", + MODEL_TENSOR.A_ENC_OUTPUT_NORM: "a.blk.{bid}.ln2", + MODEL_TENSOR.A_ENC_FFN_NORM: "a.blk.{bid}.ffn_norm", + MODEL_TENSOR.A_ENC_FFN_POST_NORM: "a.blk.{bid}.ffn_post_norm", + MODEL_TENSOR.A_ENC_FFN_SCALE: "a.blk.{bid}.ffn_scale", + MODEL_TENSOR.A_ENC_FFN_UP: "a.blk.{bid}.ffn_up", + MODEL_TENSOR.A_ENC_FFN_GATE: "a.blk.{bid}.ffn_gate", + MODEL_TENSOR.A_ENC_FFN_DOWN: "a.blk.{bid}.ffn_down", + MODEL_TENSOR.A_ENC_FFN_NORM_1: "a.blk.{bid}.ffn_norm_1", + MODEL_TENSOR.A_ENC_FFN_POST_NORM_1: "a.blk.{bid}.ffn_post_norm_1", + MODEL_TENSOR.A_ENC_FFN_SCALE_1: "a.blk.{bid}.ffn_scale_1", + MODEL_TENSOR.A_ENC_FFN_UP_1: "a.blk.{bid}.ffn_up_1", + MODEL_TENSOR.A_ENC_FFN_GATE_1: "a.blk.{bid}.ffn_gate_1", + MODEL_TENSOR.A_ENC_FFN_DOWN_1: "a.blk.{bid}.ffn_down_1", + MODEL_TENSOR.A_MMPROJ: "mm.a.mlp.{bid}", + MODEL_TENSOR.A_MMPROJ_FC: "mm.a.fc", + MODEL_TENSOR.A_MM_NORM_PRE: "mm.a.norm_pre", + MODEL_TENSOR.A_MM_NORM_MID: "mm.a.norm_mid", + MODEL_TENSOR.A_MM_INP_PROJ: "mm.a.input_projection", # gemma3n + MODEL_TENSOR.A_MM_SOFT_EMB_NORM: "mm.a.soft_emb_norm", # gemma3n + MODEL_TENSOR.A_MM_EMBEDDING: "mm.a.embedding", # gemma3n + MODEL_TENSOR.A_MM_HARD_EMB_NORM: "mm.a.hard_emb_norm", # gemma3n + MODEL_TENSOR.A_PER_DIM_K_SCALE: "a.blk.{bid}.per_dim_k_scale", # gemma4 + MODEL_TENSOR.A_PER_DIM_SCALE: "a.blk.{bid}.per_dim_scale", # gemma4 + # lfm2 audio + MODEL_TENSOR.A_ENC_NORM_CONV: "a.blk.{bid}.norm_conv", + MODEL_TENSOR.A_ENC_LINEAR_POS: "a.blk.{bid}.linear_pos", + MODEL_TENSOR.A_ENC_POS_BIAS_U: "a.blk.{bid}.pos_bias_u", + MODEL_TENSOR.A_ENC_POS_BIAS_V: "a.blk.{bid}.pos_bias_v", + MODEL_TENSOR.A_ENC_OUT: "a.pre_encode.out", + MODEL_TENSOR.A_ENC_CONV_DW: "a.blk.{bid}.conv_dw", + MODEL_TENSOR.A_ENC_CONV_NORM: "a.blk.{bid}.conv_norm", + MODEL_TENSOR.A_ENC_CONV_PW1: "a.blk.{bid}.conv_pw1", + MODEL_TENSOR.A_ENC_CONV_PW2: "a.blk.{bid}.conv_pw2", + MODEL_TENSOR.A_CTC_OUT: "a.enc_ctc_out", + MODEL_TENSOR.A_CTC_OUT_MID: "a.enc_ctc_out_mid", + MODEL_TENSOR.A_ENC_ATTN_REL_POS_EMB: "a.blk.{bid}.attn_rel_pos_emb", + # qformer projector + MODEL_TENSOR.A_QF_PROJ_QUERY: "a.proj_query", + MODEL_TENSOR.A_QF_PROJ_NORM: "a.proj_norm", + MODEL_TENSOR.A_QF_PROJ_LINEAR: "a.proj_linear", + MODEL_TENSOR.A_QF_SELF_ATTN_Q: "a.proj_blk.{bid}.self_attn_q", + MODEL_TENSOR.A_QF_SELF_ATTN_K: "a.proj_blk.{bid}.self_attn_k", + MODEL_TENSOR.A_QF_SELF_ATTN_V: "a.proj_blk.{bid}.self_attn_v", + MODEL_TENSOR.A_QF_SELF_ATTN_O: "a.proj_blk.{bid}.self_attn_out", + MODEL_TENSOR.A_QF_SELF_ATTN_NORM: "a.proj_blk.{bid}.self_attn_norm", + MODEL_TENSOR.A_QF_CROSS_ATTN_Q: "a.proj_blk.{bid}.cross_attn_q", + MODEL_TENSOR.A_QF_CROSS_ATTN_K: "a.proj_blk.{bid}.cross_attn_k", + MODEL_TENSOR.A_QF_CROSS_ATTN_V: "a.proj_blk.{bid}.cross_attn_v", + MODEL_TENSOR.A_QF_CROSS_ATTN_O: "a.proj_blk.{bid}.cross_attn_out", + MODEL_TENSOR.A_QF_CROSS_ATTN_NORM: "a.proj_blk.{bid}.cross_attn_norm", + MODEL_TENSOR.A_QF_FFN_UP: "a.proj_blk.{bid}.ffn_up", + MODEL_TENSOR.A_QF_FFN_DOWN: "a.proj_blk.{bid}.ffn_down", + MODEL_TENSOR.A_QF_FFN_NORM: "a.proj_blk.{bid}.ffn_norm", + # NextN/MTP + MODEL_TENSOR.NEXTN_PROJ_PRE: "nextn.pre_projection", + MODEL_TENSOR.NEXTN_PROJ_POST: "nextn.post_projection", + MODEL_TENSOR.NEXTN_EH_PROJ: "blk.{bid}.nextn.eh_proj", + MODEL_TENSOR.NEXTN_EMBED_TOKENS: "blk.{bid}.nextn.embed_tokens", + MODEL_TENSOR.NEXTN_ENORM: "blk.{bid}.nextn.enorm", + MODEL_TENSOR.NEXTN_HNORM: "blk.{bid}.nextn.hnorm", + MODEL_TENSOR.NEXTN_SHARED_HEAD_HEAD: "blk.{bid}.nextn.shared_head_head", + MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM: "blk.{bid}.nextn.shared_head_norm", + MODEL_TENSOR.FC: "fc", + MODEL_TENSOR.D2T: "d2t", +} + +MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { + MODEL_ARCH.MMPROJ: [ + MODEL_TENSOR.V_MMPROJ, + MODEL_TENSOR.V_MMPROJ_FC, + MODEL_TENSOR.V_MMPROJ_MLP, + MODEL_TENSOR.V_MMPROJ_PEG, + MODEL_TENSOR.V_ENC_EMBD_CLS, + MODEL_TENSOR.V_ENC_EMBD_PATCH, + MODEL_TENSOR.V_ENC_EMBD_NORM, + MODEL_TENSOR.V_ENC_EMBD_PATCH_NORM, + MODEL_TENSOR.V_ENC_EMBD_POS, + MODEL_TENSOR.V_ENC_EMBD_IMGNL, + MODEL_TENSOR.V_ENC_EMBD_VSEP, + MODEL_TENSOR.V_ENC_INPUT_NORM, + MODEL_TENSOR.V_ENC_ATTN_QKV, + MODEL_TENSOR.V_ENC_ATTN_Q, + MODEL_TENSOR.V_ENC_ATTN_Q_NORM, + MODEL_TENSOR.V_ENC_ATTN_K, + MODEL_TENSOR.V_ENC_ATTN_K_NORM, + MODEL_TENSOR.V_ENC_ATTN_V, + MODEL_TENSOR.V_ENC_ATTN_O, + MODEL_TENSOR.V_ENC_ATTN_O_NORM, + MODEL_TENSOR.V_ENC_ATTN_SINKS, + MODEL_TENSOR.V_ENC_POST_ATTN_NORM, + MODEL_TENSOR.V_ENC_FFN_UP, + MODEL_TENSOR.V_ENC_FFN_GATE, + MODEL_TENSOR.V_ENC_FFN_DOWN, + MODEL_TENSOR.V_ENC_ATTN_POST_NORM, + MODEL_TENSOR.V_ENC_FFN_POST_NORM, + MODEL_TENSOR.V_LAYER_SCALE_1, + MODEL_TENSOR.V_LAYER_SCALE_2, + MODEL_TENSOR.V_LAYER_OUT_SCALE, + MODEL_TENSOR.V_PRE_NORM, + MODEL_TENSOR.V_POST_NORM, + MODEL_TENSOR.V_MM_POST_NORM, + MODEL_TENSOR.V_MM_INP_PROJ, + MODEL_TENSOR.V_MM_INP_NORM, + MODEL_TENSOR.V_MM_SOFT_EMB_NORM, + MODEL_TENSOR.V_MM_EMBEDDING, + MODEL_TENSOR.V_MM_HARD_EMB_NORM, + MODEL_TENSOR.V_ENC_CONV_STEM, + MODEL_TENSOR.V_ENC_CONV_STEM_NORM, + MODEL_TENSOR.V_ENC_MSFA_EXP, + MODEL_TENSOR.V_ENC_MSFA_EXP_NORM, + MODEL_TENSOR.V_ENC_MSFA_PROJ, + MODEL_TENSOR.V_ENC_MSFA_PROJ_NORM, + MODEL_TENSOR.V_ENC_MSFA_NORM, + MODEL_TENSOR.V_RESMPL_POS_EMBD_K, + MODEL_TENSOR.V_RESMPL_ATTN_Q, + MODEL_TENSOR.V_RESMPL_ATTN_K, + MODEL_TENSOR.V_RESMPL_ATTN_V, + MODEL_TENSOR.V_RESMPL_ATTN_OUT, + MODEL_TENSOR.V_RESMPL_KV, + MODEL_TENSOR.V_RESMPL_KV_NORM, + MODEL_TENSOR.V_RESMPL_POST_NORM, + MODEL_TENSOR.V_RESMPL_Q_NORM, + MODEL_TENSOR.V_RESMPL_PROJ, + MODEL_TENSOR.V_RESMPL_QUERY, + MODEL_TENSOR.V_TOK_EMBD_IMG_BREAK, + MODEL_TENSOR.V_MM_PATCH_MERGER, + MODEL_TENSOR.V_DS_NORM, + MODEL_TENSOR.V_DS_FC1, + MODEL_TENSOR.V_DS_FC2, + MODEL_TENSOR.V_MERGER_LN1, + MODEL_TENSOR.V_MERGER_ATTN_Q, + MODEL_TENSOR.V_MERGER_ATTN_K, + MODEL_TENSOR.V_MERGER_ATTN_V, + MODEL_TENSOR.V_MERGER_ATTN_O, + MODEL_TENSOR.V_MERGER_DS_LN, + MODEL_TENSOR.V_MERGER_DS_UP, + MODEL_TENSOR.V_MERGER_DS_DOWN, + MODEL_TENSOR.V_MM_POST_FC_NORM, + MODEL_TENSOR.V_MM_UP, + MODEL_TENSOR.V_MM_DOWN, + MODEL_TENSOR.V_MM_GATE, + MODEL_TENSOR.V_TOK_BOI, + MODEL_TENSOR.V_TOK_EOI, + MODEL_TENSOR.V_MM_PRE_NORM, + MODEL_TENSOR.V_TOK_IMG_BEGIN, + MODEL_TENSOR.V_TOK_IMG_END, + MODEL_TENSOR.V_STD_BIAS, + MODEL_TENSOR.V_STD_SCALE, + MODEL_TENSOR.V_SAM_POS_EMBD, + MODEL_TENSOR.V_SAM_PATCH_EMBD, + MODEL_TENSOR.V_SAM_PRE_NORM, + MODEL_TENSOR.V_SAM_POST_NORM, + MODEL_TENSOR.V_SAM_ATTN_POS_H, + MODEL_TENSOR.V_SAM_ATTN_POS_W, + MODEL_TENSOR.V_SAM_ATTN_QKV, + MODEL_TENSOR.V_SAM_ATTN_OUT, + MODEL_TENSOR.V_SAM_MLP_LIN_1, + MODEL_TENSOR.V_SAM_MLP_LIN_2, + MODEL_TENSOR.V_SAM_NECK, + MODEL_TENSOR.V_SAM_NET_2, + MODEL_TENSOR.V_SAM_NET_3, + MODEL_TENSOR.V_RESMPL_QUERY_768, + MODEL_TENSOR.V_RESMPL_QUERY_1024, + MODEL_TENSOR.V_PROJ_NORM, + MODEL_TENSOR.V_QF_PROJ_QUERY, + MODEL_TENSOR.V_QF_PROJ_NORM, + MODEL_TENSOR.V_QF_PROJ_LINEAR, + MODEL_TENSOR.V_QF_SELF_ATTN_Q, + MODEL_TENSOR.V_QF_SELF_ATTN_K, + MODEL_TENSOR.V_QF_SELF_ATTN_V, + MODEL_TENSOR.V_QF_SELF_ATTN_O, + MODEL_TENSOR.V_QF_SELF_ATTN_NORM, + MODEL_TENSOR.V_QF_CROSS_ATTN_Q, + MODEL_TENSOR.V_QF_CROSS_ATTN_K, + MODEL_TENSOR.V_QF_CROSS_ATTN_V, + MODEL_TENSOR.V_QF_CROSS_ATTN_O, + MODEL_TENSOR.V_QF_CROSS_ATTN_NORM, + MODEL_TENSOR.V_QF_FFN_UP, + MODEL_TENSOR.V_QF_FFN_DOWN, + MODEL_TENSOR.V_QF_FFN_NORM, + MODEL_TENSOR.V_QF_PROJ_NORM, + MODEL_TENSOR.V_MULTI_PROJ_IMG_POS, + MODEL_TENSOR.V_MULTI_PROJ_QUERY, + MODEL_TENSOR.V_MULTI_PROJ_LINEAR, + MODEL_TENSOR.V_MULTI_PROJ_NORM, + MODEL_TENSOR.V_MULTI_PROJ_POST_NORM, + # audio + MODEL_TENSOR.A_ENC_EMBD_POS, + MODEL_TENSOR.A_ENC_EMBD_NORM, + MODEL_TENSOR.A_ENC_EMBD_TO_LOGITS, + MODEL_TENSOR.A_ENC_INP_PROJ, + MODEL_TENSOR.A_ENC_CONV1D, + MODEL_TENSOR.A_ENC_CONV2D, + MODEL_TENSOR.A_ENC_CONV_OUT, + MODEL_TENSOR.A_ENC_CONV1D_NORM, + MODEL_TENSOR.A_PRE_NORM, + MODEL_TENSOR.A_POST_NORM, + MODEL_TENSOR.A_ENC_LAYER_PRE_NORM, + MODEL_TENSOR.A_ENC_ATTN_Q, + MODEL_TENSOR.A_ENC_ATTN_K, + MODEL_TENSOR.A_ENC_ATTN_V, + MODEL_TENSOR.A_ENC_ATTN_POST_NORM, + MODEL_TENSOR.A_ENC_ATTN_PRE_NORM, + MODEL_TENSOR.A_ENC_ATTN_K_REL, + MODEL_TENSOR.A_ENC_PER_DIM_SCALE, + MODEL_TENSOR.A_ENC_INPUT_NORM, + MODEL_TENSOR.A_ENC_OUTPUT, + MODEL_TENSOR.A_ENC_OUTPUT_NORM, + MODEL_TENSOR.A_ENC_FFN_NORM, + MODEL_TENSOR.A_ENC_FFN_POST_NORM, + MODEL_TENSOR.A_ENC_FFN_SCALE, + MODEL_TENSOR.A_ENC_FFN_UP, + MODEL_TENSOR.A_ENC_FFN_GATE, + MODEL_TENSOR.A_ENC_FFN_DOWN, + MODEL_TENSOR.A_ENC_FFN_NORM_1, + MODEL_TENSOR.A_ENC_FFN_POST_NORM_1, + MODEL_TENSOR.A_ENC_FFN_SCALE_1, + MODEL_TENSOR.A_ENC_FFN_UP_1, + MODEL_TENSOR.A_ENC_FFN_GATE_1, + MODEL_TENSOR.A_ENC_FFN_DOWN_1, + MODEL_TENSOR.A_MMPROJ, + MODEL_TENSOR.A_MMPROJ_FC, + MODEL_TENSOR.A_MM_NORM_PRE, + MODEL_TENSOR.A_MM_NORM_MID, + MODEL_TENSOR.A_ENC_NORM_CONV, + MODEL_TENSOR.A_ENC_LINEAR_POS, + MODEL_TENSOR.A_ENC_POS_BIAS_U, + MODEL_TENSOR.A_ENC_POS_BIAS_V, + MODEL_TENSOR.A_ENC_OUT, + MODEL_TENSOR.A_ENC_CONV_DW, + MODEL_TENSOR.A_ENC_CONV_NORM, + MODEL_TENSOR.A_ENC_CONV_PW1, + MODEL_TENSOR.A_ENC_CONV_PW2, + MODEL_TENSOR.A_MM_INP_PROJ, + MODEL_TENSOR.A_MM_SOFT_EMB_NORM, + MODEL_TENSOR.A_MM_EMBEDDING, + MODEL_TENSOR.A_MM_HARD_EMB_NORM, + MODEL_TENSOR.A_PER_DIM_K_SCALE, + MODEL_TENSOR.A_PER_DIM_SCALE, + MODEL_TENSOR.A_CTC_OUT, + MODEL_TENSOR.A_CTC_OUT_MID, + MODEL_TENSOR.A_ENC_ATTN_REL_POS_EMB, + # qformer projector + MODEL_TENSOR.A_QF_PROJ_QUERY, + MODEL_TENSOR.A_QF_PROJ_NORM, + MODEL_TENSOR.A_QF_PROJ_LINEAR, + MODEL_TENSOR.A_QF_SELF_ATTN_Q, + MODEL_TENSOR.A_QF_SELF_ATTN_K, + MODEL_TENSOR.A_QF_SELF_ATTN_V, + MODEL_TENSOR.A_QF_SELF_ATTN_O, + MODEL_TENSOR.A_QF_SELF_ATTN_NORM, + MODEL_TENSOR.A_QF_CROSS_ATTN_Q, + MODEL_TENSOR.A_QF_CROSS_ATTN_K, + MODEL_TENSOR.A_QF_CROSS_ATTN_V, + MODEL_TENSOR.A_QF_CROSS_ATTN_O, + MODEL_TENSOR.A_QF_CROSS_ATTN_NORM, + MODEL_TENSOR.A_QF_FFN_UP, + MODEL_TENSOR.A_QF_FFN_DOWN, + MODEL_TENSOR.A_QF_FFN_NORM, + ], + MODEL_ARCH.LLAMA: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + ], + MODEL_ARCH.LLAMA4: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + MODEL_TENSOR.FFN_GATE_SHEXP, + MODEL_TENSOR.FFN_DOWN_SHEXP, + MODEL_TENSOR.FFN_UP_SHEXP, + ], + MODEL_ARCH.DECI: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + ], + MODEL_ARCH.GROK: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.ATTN_OUT_NORM, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + MODEL_TENSOR.FFN_POST_NORM, + MODEL_TENSOR.LAYER_OUT_NORM, + ], + MODEL_ARCH.GPTNEOX: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.FALCON: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_NORM_2, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.BAICHUAN: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.STARCODER: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.POS_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.BERT: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.TOKEN_EMBD_NORM, + MODEL_TENSOR.TOKEN_TYPES, + MODEL_TENSOR.POS_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.ATTN_OUT_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.LAYER_OUT_NORM, + MODEL_TENSOR.CLS, + MODEL_TENSOR.CLS_OUT, + ], + MODEL_ARCH.MODERN_BERT: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.TOKEN_EMBD_NORM, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.CLS, + MODEL_TENSOR.CLS_OUT, + MODEL_TENSOR.CLS_NORM, + ], + MODEL_ARCH.NOMIC_BERT: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.TOKEN_EMBD_NORM, + MODEL_TENSOR.TOKEN_TYPES, + MODEL_TENSOR.POS_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.ATTN_OUT_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.LAYER_OUT_NORM, + ], + MODEL_ARCH.NOMIC_BERT_MOE: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.TOKEN_EMBD_NORM, + MODEL_TENSOR.TOKEN_TYPES, + MODEL_TENSOR.POS_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.ATTN_OUT_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + MODEL_TENSOR.LAYER_OUT_NORM, + ], + MODEL_ARCH.NEO_BERT: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.ENC_OUTPUT_NORM, + MODEL_TENSOR.CLS, + MODEL_TENSOR.CLS_OUT, + ], + MODEL_ARCH.JINA_BERT_V2: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.TOKEN_EMBD_NORM, + MODEL_TENSOR.TOKEN_TYPES, + MODEL_TENSOR.ATTN_NORM_2, + MODEL_TENSOR.ATTN_OUT_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.LAYER_OUT_NORM, + MODEL_TENSOR.CLS, + ], + MODEL_ARCH.JINA_BERT_V3: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.TOKEN_EMBD_NORM, + MODEL_TENSOR.TOKEN_TYPES, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.ATTN_OUT_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.LAYER_OUT_NORM, + ], + MODEL_ARCH.EUROBERT: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_DOWN, + ], + MODEL_ARCH.MPT: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_ACT, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.POS_EMBD, + ], + MODEL_ARCH.GPTJ: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.REFACT: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.BLOOM: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.TOKEN_EMBD_NORM, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.STABLELM: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K_NORM, + ], + MODEL_ARCH.QWEN: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.QWEN2: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.DREAM: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.LLADA: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.QWEN2VL: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.QWEN2MOE: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + MODEL_TENSOR.FFN_GATE_INP_SHEXP, + MODEL_TENSOR.FFN_GATE_SHEXP, + MODEL_TENSOR.FFN_DOWN_SHEXP, + MODEL_TENSOR.FFN_UP_SHEXP, + ], + MODEL_ARCH.QWEN3: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.QWEN3MOE: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + ], + MODEL_ARCH.QWEN3NEXT: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_POST_NORM, + MODEL_TENSOR.ATTN_GATE, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_GATE_INP_SHEXP, + MODEL_TENSOR.FFN_UP_SHEXP, + MODEL_TENSOR.FFN_DOWN_SHEXP, + MODEL_TENSOR.FFN_GATE_SHEXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_GATE_UP_EXP, + MODEL_TENSOR.SSM_A, + MODEL_TENSOR.SSM_CONV1D, + MODEL_TENSOR.SSM_DT, + MODEL_TENSOR.SSM_NORM, + MODEL_TENSOR.SSM_IN, + MODEL_TENSOR.SSM_BETA_ALPHA, + MODEL_TENSOR.SSM_OUT + ], + MODEL_ARCH.QWEN3VL: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.QWEN3VLMOE: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + ], + MODEL_ARCH.QWEN35: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_POST_NORM, + MODEL_TENSOR.ATTN_GATE, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.SSM_A, + MODEL_TENSOR.SSM_CONV1D, + MODEL_TENSOR.SSM_DT, + MODEL_TENSOR.SSM_NORM, + MODEL_TENSOR.SSM_BETA, + MODEL_TENSOR.SSM_ALPHA, + MODEL_TENSOR.SSM_OUT, + # NextN/MTP tensors - preserved but unused + MODEL_TENSOR.NEXTN_EH_PROJ, + MODEL_TENSOR.NEXTN_EMBED_TOKENS, + MODEL_TENSOR.NEXTN_ENORM, + MODEL_TENSOR.NEXTN_HNORM, + MODEL_TENSOR.NEXTN_SHARED_HEAD_HEAD, + MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM, + ], + MODEL_ARCH.QWEN35MOE: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_POST_NORM, + MODEL_TENSOR.ATTN_GATE, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_GATE_INP_SHEXP, + MODEL_TENSOR.FFN_UP_SHEXP, + MODEL_TENSOR.FFN_DOWN_SHEXP, + MODEL_TENSOR.FFN_GATE_SHEXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_GATE_UP_EXP, + MODEL_TENSOR.SSM_A, + MODEL_TENSOR.SSM_CONV1D, + MODEL_TENSOR.SSM_DT, + MODEL_TENSOR.SSM_NORM, + MODEL_TENSOR.SSM_BETA, + MODEL_TENSOR.SSM_ALPHA, + MODEL_TENSOR.SSM_OUT, + # NextN/MTP tensors - preserved but unused + MODEL_TENSOR.NEXTN_EH_PROJ, + MODEL_TENSOR.NEXTN_EMBED_TOKENS, + MODEL_TENSOR.NEXTN_ENORM, + MODEL_TENSOR.NEXTN_HNORM, + MODEL_TENSOR.NEXTN_SHARED_HEAD_HEAD, + MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM, + ], + MODEL_ARCH.PLAMO: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.PLAMO2: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.ATTN_POST_NORM, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_POST_NORM, + MODEL_TENSOR.SSM_IN, + MODEL_TENSOR.SSM_CONV1D, + MODEL_TENSOR.SSM_X, + MODEL_TENSOR.SSM_DT, + MODEL_TENSOR.SSM_A, + MODEL_TENSOR.SSM_D, + MODEL_TENSOR.SSM_OUT, + MODEL_TENSOR.SSM_DT_NORM, + MODEL_TENSOR.SSM_B_NORM, + MODEL_TENSOR.SSM_C_NORM, + ], + MODEL_ARCH.PLAMO3: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_POST_NORM, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_POST_NORM, + ], + MODEL_ARCH.GPT2: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.POS_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.PHI2: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.PHI3: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FACTORS_LONG, + MODEL_TENSOR.ROPE_FACTORS_SHORT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.PHIMOE: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FACTORS_LONG, + MODEL_TENSOR.ROPE_FACTORS_SHORT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + ], + MODEL_ARCH.CODESHELL: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.POS_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.ORION: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.INTERNLM2: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.MINICPM: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ROPE_FACTORS_LONG, + MODEL_TENSOR.ROPE_FACTORS_SHORT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + ], + MODEL_ARCH.MINICPM3: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FACTORS_LONG, + MODEL_TENSOR.ROPE_FACTORS_SHORT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q_A, + MODEL_TENSOR.ATTN_Q_B, + MODEL_TENSOR.ATTN_KV_A_MQA, + MODEL_TENSOR.ATTN_KV_B, + MODEL_TENSOR.ATTN_Q_A_NORM, + MODEL_TENSOR.ATTN_KV_A_NORM, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.GEMMA: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_NORM, + ], + MODEL_ARCH.GEMMA2: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_POST_NORM, + MODEL_TENSOR.FFN_PRE_NORM, + MODEL_TENSOR.FFN_POST_NORM, + ], + MODEL_ARCH.GEMMA3: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_POST_NORM, + MODEL_TENSOR.FFN_PRE_NORM, + MODEL_TENSOR.FFN_POST_NORM, + ], + MODEL_ARCH.GEMMA3N: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_POST_NORM, + MODEL_TENSOR.FFN_PRE_NORM, + MODEL_TENSOR.FFN_POST_NORM, + # altup / laurel + MODEL_TENSOR.PER_LAYER_TOKEN_EMBD, + MODEL_TENSOR.PER_LAYER_MODEL_PROJ, + MODEL_TENSOR.PER_LAYER_INP_GATE, + MODEL_TENSOR.PER_LAYER_PROJ, + MODEL_TENSOR.PER_LAYER_PROJ_NORM, + MODEL_TENSOR.PER_LAYER_POST_NORM, + MODEL_TENSOR.ALTUP_PROJ, + MODEL_TENSOR.ALTUP_UNEMBD_PROJ, + MODEL_TENSOR.ALTUP_CORRECT_COEF, + MODEL_TENSOR.ALTUP_CORRECT_SCALE, + MODEL_TENSOR.ALTUP_PREDICT_COEF, + MODEL_TENSOR.ALTUP_ROUTER, + MODEL_TENSOR.ALTUP_ROUTER_NORM, + MODEL_TENSOR.LAUREL_L, + MODEL_TENSOR.LAUREL_R, + MODEL_TENSOR.LAUREL_POST_NORM, + ], + MODEL_ARCH.GEMMA4: [ + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_GATE_UP_EXP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_UP_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_POST_NORM, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_PRE_NORM, + MODEL_TENSOR.FFN_PRE_NORM_2, + MODEL_TENSOR.FFN_POST_NORM, + MODEL_TENSOR.FFN_POST_NORM_1, + MODEL_TENSOR.FFN_POST_NORM_2, + MODEL_TENSOR.LAYER_OUT_SCALE, + MODEL_TENSOR.PER_LAYER_TOKEN_EMBD, + MODEL_TENSOR.PER_LAYER_MODEL_PROJ, + MODEL_TENSOR.PER_LAYER_INP_GATE, + MODEL_TENSOR.PER_LAYER_PROJ, + MODEL_TENSOR.PER_LAYER_PROJ_NORM, + MODEL_TENSOR.PER_LAYER_POST_NORM, + ], + MODEL_ARCH.GEMMA4_ASSISTANT: [ + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.MASKED_EMBD_CENTROIDS, + MODEL_TENSOR.MASKED_EMBD_ORDERING, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.NEXTN_PROJ_PRE, + MODEL_TENSOR.NEXTN_PROJ_POST, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_POST_NORM, + MODEL_TENSOR.FFN_PRE_NORM, + MODEL_TENSOR.FFN_POST_NORM, + MODEL_TENSOR.LAYER_OUT_SCALE, + ], + MODEL_ARCH.GEMMA_EMBEDDING: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.DENSE_2_OUT, + MODEL_TENSOR.DENSE_3_OUT, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_POST_NORM, + MODEL_TENSOR.FFN_PRE_NORM, + MODEL_TENSOR.FFN_POST_NORM, + ], + MODEL_ARCH.STARCODER2: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.RWKV6: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.TOKEN_EMBD_NORM, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_NORM_2, + MODEL_TENSOR.TIME_MIX_W1, + MODEL_TENSOR.TIME_MIX_W2, + MODEL_TENSOR.TIME_MIX_LERP_X, + MODEL_TENSOR.TIME_MIX_LERP_K, + MODEL_TENSOR.TIME_MIX_LERP_V, + MODEL_TENSOR.TIME_MIX_LERP_R, + MODEL_TENSOR.TIME_MIX_LERP_G, + MODEL_TENSOR.TIME_MIX_LERP_W, + MODEL_TENSOR.TIME_MIX_LERP_FUSED, + MODEL_TENSOR.TIME_MIX_FIRST, + MODEL_TENSOR.TIME_MIX_DECAY, + MODEL_TENSOR.TIME_MIX_DECAY_W1, + MODEL_TENSOR.TIME_MIX_DECAY_W2, + MODEL_TENSOR.TIME_MIX_KEY, + MODEL_TENSOR.TIME_MIX_VALUE, + MODEL_TENSOR.TIME_MIX_RECEPTANCE, + MODEL_TENSOR.TIME_MIX_GATE, + MODEL_TENSOR.TIME_MIX_LN, + MODEL_TENSOR.TIME_MIX_OUTPUT, + MODEL_TENSOR.CHANNEL_MIX_LERP_K, + MODEL_TENSOR.CHANNEL_MIX_LERP_R, + MODEL_TENSOR.CHANNEL_MIX_KEY, + MODEL_TENSOR.CHANNEL_MIX_RECEPTANCE, + MODEL_TENSOR.CHANNEL_MIX_VALUE, + ], + MODEL_ARCH.RWKV6QWEN2: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.TIME_MIX_W1, + MODEL_TENSOR.TIME_MIX_W2, + MODEL_TENSOR.TIME_MIX_LERP_X, + MODEL_TENSOR.TIME_MIX_LERP_K, + MODEL_TENSOR.TIME_MIX_LERP_V, + MODEL_TENSOR.TIME_MIX_LERP_R, + MODEL_TENSOR.TIME_MIX_LERP_G, + MODEL_TENSOR.TIME_MIX_LERP_W, + MODEL_TENSOR.TIME_MIX_LERP_FUSED, + MODEL_TENSOR.TIME_MIX_FIRST, + MODEL_TENSOR.TIME_MIX_DECAY, + MODEL_TENSOR.TIME_MIX_DECAY_W1, + MODEL_TENSOR.TIME_MIX_DECAY_W2, + MODEL_TENSOR.TIME_MIX_KEY, + MODEL_TENSOR.TIME_MIX_VALUE, + MODEL_TENSOR.TIME_MIX_RECEPTANCE, + MODEL_TENSOR.TIME_MIX_GATE, + MODEL_TENSOR.TIME_MIX_LN, + MODEL_TENSOR.TIME_MIX_OUTPUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.RWKV7: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.TOKEN_EMBD_NORM, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_NORM_2, + MODEL_TENSOR.TIME_MIX_LERP_FUSED, + MODEL_TENSOR.TIME_MIX_W0, + MODEL_TENSOR.TIME_MIX_W1, + MODEL_TENSOR.TIME_MIX_W2, + MODEL_TENSOR.TIME_MIX_A0, + MODEL_TENSOR.TIME_MIX_A1, + MODEL_TENSOR.TIME_MIX_A2, + MODEL_TENSOR.TIME_MIX_V0, + MODEL_TENSOR.TIME_MIX_V1, + MODEL_TENSOR.TIME_MIX_V2, + MODEL_TENSOR.TIME_MIX_G1, + MODEL_TENSOR.TIME_MIX_G2, + MODEL_TENSOR.TIME_MIX_K_K, + MODEL_TENSOR.TIME_MIX_K_A, + MODEL_TENSOR.TIME_MIX_R_K, + MODEL_TENSOR.TIME_MIX_KEY, + MODEL_TENSOR.TIME_MIX_VALUE, + MODEL_TENSOR.TIME_MIX_RECEPTANCE, + MODEL_TENSOR.TIME_MIX_LN, + MODEL_TENSOR.TIME_MIX_OUTPUT, + MODEL_TENSOR.CHANNEL_MIX_LERP_K, + MODEL_TENSOR.CHANNEL_MIX_KEY, + MODEL_TENSOR.CHANNEL_MIX_VALUE, + ], + MODEL_ARCH.ARWKV7: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.TOKEN_EMBD_NORM, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.TIME_MIX_LERP_FUSED, + MODEL_TENSOR.TIME_MIX_W0, + MODEL_TENSOR.TIME_MIX_W1, + MODEL_TENSOR.TIME_MIX_W2, + MODEL_TENSOR.TIME_MIX_A0, + MODEL_TENSOR.TIME_MIX_A1, + MODEL_TENSOR.TIME_MIX_A2, + MODEL_TENSOR.TIME_MIX_V0, + MODEL_TENSOR.TIME_MIX_V1, + MODEL_TENSOR.TIME_MIX_V2, + MODEL_TENSOR.TIME_MIX_G1, + MODEL_TENSOR.TIME_MIX_G2, + MODEL_TENSOR.TIME_MIX_K_K, + MODEL_TENSOR.TIME_MIX_K_A, + MODEL_TENSOR.TIME_MIX_R_K, + MODEL_TENSOR.TIME_MIX_KEY, + MODEL_TENSOR.TIME_MIX_VALUE, + MODEL_TENSOR.TIME_MIX_RECEPTANCE, + MODEL_TENSOR.TIME_MIX_LN, + MODEL_TENSOR.TIME_MIX_OUTPUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.MAMBA: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.SSM_IN, + MODEL_TENSOR.SSM_CONV1D, + MODEL_TENSOR.SSM_X, + MODEL_TENSOR.SSM_DT, + MODEL_TENSOR.SSM_A, + MODEL_TENSOR.SSM_D, + MODEL_TENSOR.SSM_OUT, + ], + MODEL_ARCH.MAMBA2: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.SSM_IN, + MODEL_TENSOR.SSM_CONV1D, + MODEL_TENSOR.SSM_DT, + MODEL_TENSOR.SSM_A, + MODEL_TENSOR.SSM_D, + MODEL_TENSOR.SSM_NORM, + MODEL_TENSOR.SSM_OUT, + ], + MODEL_ARCH.JAMBA: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.SSM_IN, + MODEL_TENSOR.SSM_CONV1D, + MODEL_TENSOR.SSM_X, + MODEL_TENSOR.SSM_DT, + MODEL_TENSOR.SSM_DT_NORM, + MODEL_TENSOR.SSM_A, + MODEL_TENSOR.SSM_B_NORM, + MODEL_TENSOR.SSM_C_NORM, + MODEL_TENSOR.SSM_D, + MODEL_TENSOR.SSM_OUT, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + ], + MODEL_ARCH.XVERSE: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.COMMAND_R: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.ATTN_Q_NORM, + ], + MODEL_ARCH.COHERE2: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.COHERE2MOE: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_GATE_UP_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + MODEL_TENSOR.FFN_GATE_SHEXP, + MODEL_TENSOR.FFN_DOWN_SHEXP, + MODEL_TENSOR.FFN_UP_SHEXP, + MODEL_TENSOR.NEXTN_EH_PROJ, + MODEL_TENSOR.NEXTN_EMBED_TOKENS, + MODEL_TENSOR.NEXTN_ENORM, + MODEL_TENSOR.NEXTN_HNORM, + MODEL_TENSOR.NEXTN_SHARED_HEAD_HEAD, + MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM, + ], + MODEL_ARCH.DBRX: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_OUT_NORM, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + ], + MODEL_ARCH.OLMO: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.OLMO2: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_POST_NORM, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.FFN_POST_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.SEED_OSS: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_POST_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + ], + MODEL_ARCH.OLMOE: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_UP_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + ], + MODEL_ARCH.OPENELM: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.ARCTIC: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_NORM_EXP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + ], + MODEL_ARCH.DEEPSEEK: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + MODEL_TENSOR.FFN_GATE_SHEXP, + MODEL_TENSOR.FFN_DOWN_SHEXP, + MODEL_TENSOR.FFN_UP_SHEXP, + ], + MODEL_ARCH.DEEPSEEK2: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_Q_A, + MODEL_TENSOR.ATTN_Q_B, + MODEL_TENSOR.ATTN_KV_A_MQA, + MODEL_TENSOR.ATTN_KV_B, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_K_B, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_V_B, + MODEL_TENSOR.ATTN_Q_A_NORM, + MODEL_TENSOR.ATTN_KV_A_NORM, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + MODEL_TENSOR.FFN_GATE_SHEXP, + MODEL_TENSOR.FFN_DOWN_SHEXP, + MODEL_TENSOR.FFN_UP_SHEXP, + MODEL_TENSOR.FFN_EXP_PROBS_B, + ], + MODEL_ARCH.DEEPSEEK2OCR: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_Q_A, + MODEL_TENSOR.ATTN_Q_B, + MODEL_TENSOR.ATTN_KV_A_MQA, + MODEL_TENSOR.ATTN_KV_B, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_K_B, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_V_B, + MODEL_TENSOR.ATTN_Q_A_NORM, + MODEL_TENSOR.ATTN_KV_A_NORM, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + MODEL_TENSOR.FFN_GATE_UP_EXP, + MODEL_TENSOR.FFN_GATE_SHEXP, + MODEL_TENSOR.FFN_DOWN_SHEXP, + MODEL_TENSOR.FFN_UP_SHEXP, + MODEL_TENSOR.FFN_EXP_PROBS_B, + ], + MODEL_ARCH.DEEPSEEK32: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_Q_A, + MODEL_TENSOR.ATTN_Q_B, + MODEL_TENSOR.ATTN_KV_A_MQA, + MODEL_TENSOR.ATTN_K_B, + MODEL_TENSOR.ATTN_V_B, + MODEL_TENSOR.ATTN_Q_A_NORM, + MODEL_TENSOR.ATTN_KV_A_NORM, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + MODEL_TENSOR.FFN_GATE_SHEXP, + MODEL_TENSOR.FFN_DOWN_SHEXP, + MODEL_TENSOR.FFN_UP_SHEXP, + MODEL_TENSOR.FFN_EXP_PROBS_B, + MODEL_TENSOR.INDEXER_K_NORM, + MODEL_TENSOR.INDEXER_PROJ, + MODEL_TENSOR.INDEXER_ATTN_K, + MODEL_TENSOR.INDEXER_ATTN_Q_B, + # NextN/MTP tensors - preserved but unused + MODEL_TENSOR.NEXTN_EH_PROJ, + MODEL_TENSOR.NEXTN_EMBED_TOKENS, + MODEL_TENSOR.NEXTN_ENORM, + MODEL_TENSOR.NEXTN_HNORM, + MODEL_TENSOR.NEXTN_SHARED_HEAD_HEAD, + MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM, + ], + MODEL_ARCH.DEEPSEEK4: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.HC_HEAD_FN, + MODEL_TENSOR.HC_HEAD_BASE, + MODEL_TENSOR.HC_HEAD_SCALE, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_SINKS, + MODEL_TENSOR.ATTN_Q_A, + MODEL_TENSOR.ATTN_Q_B, + MODEL_TENSOR.ATTN_Q_A_NORM, + MODEL_TENSOR.ATTN_KV, + MODEL_TENSOR.ATTN_KV_NORM, + MODEL_TENSOR.ATTN_OUT_A, + MODEL_TENSOR.ATTN_OUT_B, + MODEL_TENSOR.HC_ATTN_FN, + MODEL_TENSOR.HC_ATTN_BASE, + MODEL_TENSOR.HC_ATTN_SCALE, + MODEL_TENSOR.HC_FFN_FN, + MODEL_TENSOR.HC_FFN_BASE, + MODEL_TENSOR.HC_FFN_SCALE, + MODEL_TENSOR.ATTN_COMPRESSOR_WKV, + MODEL_TENSOR.ATTN_COMPRESSOR_WGATE, + MODEL_TENSOR.ATTN_COMPRESSOR_APE, + MODEL_TENSOR.ATTN_COMPRESSOR_NORM, + MODEL_TENSOR.INDEXER_PROJ, + MODEL_TENSOR.INDEXER_ATTN_Q_B, + MODEL_TENSOR.INDEXER_COMPRESSOR_WKV, + MODEL_TENSOR.INDEXER_COMPRESSOR_WGATE, + MODEL_TENSOR.INDEXER_COMPRESSOR_APE, + MODEL_TENSOR.INDEXER_COMPRESSOR_NORM, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_GATE_TID2EID, + MODEL_TENSOR.FFN_EXP_PROBS_B, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + MODEL_TENSOR.FFN_GATE_SHEXP, + MODEL_TENSOR.FFN_DOWN_SHEXP, + MODEL_TENSOR.FFN_UP_SHEXP, + ], + MODEL_ARCH.ERNIE4_5_MOE: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + MODEL_TENSOR.FFN_GATE_SHEXP, + MODEL_TENSOR.FFN_DOWN_SHEXP, + MODEL_TENSOR.FFN_UP_SHEXP, + MODEL_TENSOR.FFN_EXP_PROBS_B, + ], + MODEL_ARCH.PLM: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_KV_A_MQA, + MODEL_TENSOR.ATTN_KV_A_NORM, + MODEL_TENSOR.ATTN_KV_B, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_DOWN, + ], + MODEL_ARCH.CHATGLM : [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.GLM4 : [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.ATTN_POST_NORM, + MODEL_TENSOR.FFN_POST_NORM, + # NextN/MTP tensors - preserved but unused + MODEL_TENSOR.NEXTN_EH_PROJ, + MODEL_TENSOR.NEXTN_EMBED_TOKENS, + MODEL_TENSOR.NEXTN_ENORM, + MODEL_TENSOR.NEXTN_HNORM, + MODEL_TENSOR.NEXTN_SHARED_HEAD_HEAD, + MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM, + ], + MODEL_ARCH.GLM4_MOE: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_POST_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + MODEL_TENSOR.FFN_GATE_SHEXP, + MODEL_TENSOR.FFN_DOWN_SHEXP, + MODEL_TENSOR.FFN_UP_SHEXP, + MODEL_TENSOR.FFN_EXP_PROBS_B, + # NextN/MTP tensors - preserved but unused + MODEL_TENSOR.NEXTN_EH_PROJ, + MODEL_TENSOR.NEXTN_EMBED_TOKENS, + MODEL_TENSOR.NEXTN_ENORM, + MODEL_TENSOR.NEXTN_HNORM, + MODEL_TENSOR.NEXTN_SHARED_HEAD_HEAD, + MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM, + ], + MODEL_ARCH.GLM_DSA: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_Q_A, + MODEL_TENSOR.ATTN_Q_B, + MODEL_TENSOR.ATTN_KV_A_MQA, + MODEL_TENSOR.ATTN_KV_B, + MODEL_TENSOR.ATTN_K_B, + MODEL_TENSOR.ATTN_V_B, + MODEL_TENSOR.ATTN_Q_A_NORM, + MODEL_TENSOR.ATTN_KV_A_NORM, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + MODEL_TENSOR.FFN_GATE_SHEXP, + MODEL_TENSOR.FFN_DOWN_SHEXP, + MODEL_TENSOR.FFN_UP_SHEXP, + MODEL_TENSOR.FFN_EXP_PROBS_B, + MODEL_TENSOR.INDEXER_K_NORM, + MODEL_TENSOR.INDEXER_PROJ, + MODEL_TENSOR.INDEXER_ATTN_K, + MODEL_TENSOR.INDEXER_ATTN_Q_B, + # NextN/MTP tensors - preserved but unused + MODEL_TENSOR.NEXTN_EH_PROJ, + MODEL_TENSOR.NEXTN_EMBED_TOKENS, + MODEL_TENSOR.NEXTN_ENORM, + MODEL_TENSOR.NEXTN_HNORM, + MODEL_TENSOR.NEXTN_SHARED_HEAD_HEAD, + MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM, + ], + MODEL_ARCH.BITNET: [ + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.ATTN_SUB_NORM, + MODEL_TENSOR.FFN_SUB_NORM, + ], + MODEL_ARCH.T5: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.DEC_ATTN_NORM, + MODEL_TENSOR.DEC_ATTN_Q, + MODEL_TENSOR.DEC_ATTN_K, + MODEL_TENSOR.DEC_ATTN_V, + MODEL_TENSOR.DEC_ATTN_OUT, + MODEL_TENSOR.DEC_ATTN_REL_B, + MODEL_TENSOR.DEC_CROSS_ATTN_NORM, + MODEL_TENSOR.DEC_CROSS_ATTN_Q, + MODEL_TENSOR.DEC_CROSS_ATTN_K, + MODEL_TENSOR.DEC_CROSS_ATTN_V, + MODEL_TENSOR.DEC_CROSS_ATTN_OUT, + MODEL_TENSOR.DEC_CROSS_ATTN_REL_B, + MODEL_TENSOR.DEC_FFN_NORM, + MODEL_TENSOR.DEC_FFN_GATE, + MODEL_TENSOR.DEC_FFN_DOWN, + MODEL_TENSOR.DEC_FFN_UP, + MODEL_TENSOR.DEC_OUTPUT_NORM, + MODEL_TENSOR.ENC_ATTN_NORM, + MODEL_TENSOR.ENC_ATTN_Q, + MODEL_TENSOR.ENC_ATTN_K, + MODEL_TENSOR.ENC_ATTN_V, + MODEL_TENSOR.ENC_ATTN_OUT, + MODEL_TENSOR.ENC_ATTN_REL_B, + MODEL_TENSOR.ENC_FFN_NORM, + MODEL_TENSOR.ENC_FFN_GATE, + MODEL_TENSOR.ENC_FFN_DOWN, + MODEL_TENSOR.ENC_FFN_UP, + MODEL_TENSOR.ENC_OUTPUT_NORM, + ], + MODEL_ARCH.T5ENCODER: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ENC_ATTN_NORM, + MODEL_TENSOR.ENC_ATTN_Q, + MODEL_TENSOR.ENC_ATTN_K, + MODEL_TENSOR.ENC_ATTN_V, + MODEL_TENSOR.ENC_ATTN_OUT, + MODEL_TENSOR.ENC_ATTN_REL_B, + MODEL_TENSOR.ENC_FFN_NORM, + MODEL_TENSOR.ENC_FFN_GATE, + MODEL_TENSOR.ENC_FFN_DOWN, + MODEL_TENSOR.ENC_FFN_UP, + MODEL_TENSOR.ENC_OUTPUT_NORM, + ], + MODEL_ARCH.JAIS: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.JAIS2: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.NEMOTRON: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.NEMOTRON_H: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.SSM_IN, + MODEL_TENSOR.SSM_CONV1D, + MODEL_TENSOR.SSM_DT, + MODEL_TENSOR.SSM_A, + MODEL_TENSOR.SSM_D, + MODEL_TENSOR.SSM_NORM, + MODEL_TENSOR.SSM_OUT, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.NEMOTRON_H_MOE: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.SSM_IN, + MODEL_TENSOR.SSM_CONV1D, + MODEL_TENSOR.SSM_DT, + MODEL_TENSOR.SSM_A, + MODEL_TENSOR.SSM_D, + MODEL_TENSOR.SSM_NORM, + MODEL_TENSOR.SSM_OUT, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + # experts + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_UP_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + # expert latent + MODEL_TENSOR.MOE_LATENT_DOWN, + MODEL_TENSOR.MOE_LATENT_UP, + # shared expert + MODEL_TENSOR.FFN_DOWN_SHEXP, + MODEL_TENSOR.FFN_UP_SHEXP, + MODEL_TENSOR.FFN_EXP_PROBS_B, + ], + MODEL_ARCH.EXAONE: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.EXAONE4: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_POST_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_POST_NORM, + # NextN/MTP tensors - preserved but unused + MODEL_TENSOR.NEXTN_EH_PROJ, + MODEL_TENSOR.NEXTN_EMBED_TOKENS, + MODEL_TENSOR.NEXTN_ENORM, + MODEL_TENSOR.NEXTN_HNORM, + MODEL_TENSOR.NEXTN_SHARED_HEAD_HEAD, + MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM, + ], + MODEL_ARCH.EXAONE_MOE: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + MODEL_TENSOR.FFN_GATE_SHEXP, + MODEL_TENSOR.FFN_DOWN_SHEXP, + MODEL_TENSOR.FFN_UP_SHEXP, + MODEL_TENSOR.FFN_EXP_PROBS_B, + # NextN/MTP tensors - preserved but unused + MODEL_TENSOR.NEXTN_EH_PROJ, + MODEL_TENSOR.NEXTN_EMBED_TOKENS, + MODEL_TENSOR.NEXTN_ENORM, + MODEL_TENSOR.NEXTN_HNORM, + MODEL_TENSOR.NEXTN_SHARED_HEAD_HEAD, + MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM, + ], + MODEL_ARCH.GRANITE: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.GRANITE_MOE: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + MODEL_TENSOR.FFN_GATE_SHEXP, + MODEL_TENSOR.FFN_UP_SHEXP, + MODEL_TENSOR.FFN_DOWN_SHEXP, + ], + MODEL_ARCH.GRANITE_HYBRID: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.SSM_IN, + MODEL_TENSOR.SSM_CONV1D, + MODEL_TENSOR.SSM_DT, + MODEL_TENSOR.SSM_A, + MODEL_TENSOR.SSM_D, + MODEL_TENSOR.SSM_NORM, + MODEL_TENSOR.SSM_OUT, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + # MoE + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + MODEL_TENSOR.FFN_GATE_SHEXP, + MODEL_TENSOR.FFN_UP_SHEXP, + MODEL_TENSOR.FFN_DOWN_SHEXP, + # Dense + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.CHAMELEON: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.WAVTOKENIZER_DEC: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.TOKEN_EMBD_NORM, + MODEL_TENSOR.CONV1D, + MODEL_TENSOR.CONVNEXT_DW, + MODEL_TENSOR.CONVNEXT_NORM, + MODEL_TENSOR.CONVNEXT_PW1, + MODEL_TENSOR.CONVNEXT_PW2, + MODEL_TENSOR.CONVNEXT_GAMMA, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.POSNET_CONV1, + MODEL_TENSOR.POSNET_CONV2, + MODEL_TENSOR.POSNET_NORM, + MODEL_TENSOR.POSNET_NORM1, + MODEL_TENSOR.POSNET_NORM2, + MODEL_TENSOR.POSNET_ATTN_NORM, + MODEL_TENSOR.POSNET_ATTN_Q, + MODEL_TENSOR.POSNET_ATTN_K, + MODEL_TENSOR.POSNET_ATTN_V, + MODEL_TENSOR.POSNET_ATTN_OUT, + ], + MODEL_ARCH.BAILINGMOE: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + MODEL_TENSOR.FFN_GATE_SHEXP, + MODEL_TENSOR.FFN_DOWN_SHEXP, + MODEL_TENSOR.FFN_UP_SHEXP, + ], + MODEL_ARCH.BAILINGMOE2: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_EXP_PROBS_B, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + MODEL_TENSOR.FFN_GATE_SHEXP, + MODEL_TENSOR.FFN_DOWN_SHEXP, + MODEL_TENSOR.FFN_UP_SHEXP, + MODEL_TENSOR.NEXTN_EH_PROJ, + MODEL_TENSOR.NEXTN_EMBED_TOKENS, + MODEL_TENSOR.NEXTN_ENORM, + MODEL_TENSOR.NEXTN_HNORM, + MODEL_TENSOR.NEXTN_SHARED_HEAD_HEAD, + MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM, + MODEL_TENSOR.LAYER_OUT_NORM, + ], + MODEL_ARCH.DOTS1: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_EXP_PROBS_B, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_GATE_SHEXP, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_DOWN_SHEXP, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_UP_EXP, + MODEL_TENSOR.FFN_UP_SHEXP, + ], + MODEL_ARCH.ARCEE: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.AFMOE: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_POST_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.ATTN_GATE, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + MODEL_TENSOR.FFN_GATE_SHEXP, + MODEL_TENSOR.FFN_UP_SHEXP, + MODEL_TENSOR.FFN_DOWN_SHEXP, + MODEL_TENSOR.FFN_PRE_NORM, + MODEL_TENSOR.FFN_POST_NORM, + MODEL_TENSOR.FFN_EXP_PROBS_B, + ], + MODEL_ARCH.ERNIE4_5: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.PADDLEOCR: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.FALCON_H1: [ + # Token embedding + MODEL_TENSOR.TOKEN_EMBD, + + # Input layernorm + MODEL_TENSOR.ATTN_NORM, + + # Attention components + MODEL_TENSOR.ATTN_Q, # Query projection + MODEL_TENSOR.ATTN_K, # Key projection + MODEL_TENSOR.ATTN_V, # Value projection + MODEL_TENSOR.ATTN_OUT, # Output projection + + # SSM components (Mamba2 specific) + MODEL_TENSOR.SSM_IN, # Input projection for SSM + MODEL_TENSOR.SSM_CONV1D, # Convolution layer + MODEL_TENSOR.SSM_DT, # Delta time projection + MODEL_TENSOR.SSM_A, # A parameter (log form) + MODEL_TENSOR.SSM_D, # D parameter + MODEL_TENSOR.SSM_NORM, # Normalization in SSM + MODEL_TENSOR.SSM_OUT, # Output projection + + # Pre-feedforward layernorm + MODEL_TENSOR.FFN_PRE_NORM, + + # Feed-forward network components + MODEL_TENSOR.FFN_GATE, # Gate projection (SwiGLU) + MODEL_TENSOR.FFN_DOWN, # Down projection + MODEL_TENSOR.FFN_UP, # Up projection + + # Post-feedforward layernorm + MODEL_TENSOR.OUTPUT_NORM, # Final layer norm + MODEL_TENSOR.OUTPUT, # Output projection (lm_head) + ], + MODEL_ARCH.HUNYUAN_MOE: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + MODEL_TENSOR.FFN_GATE_SHEXP, + MODEL_TENSOR.FFN_DOWN_SHEXP, + MODEL_TENSOR.FFN_UP_SHEXP, + ], + MODEL_ARCH.HUNYUAN_DENSE: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.HUNYUAN_VL: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.SMOLLM3: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.GPT_OSS: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_POST_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_SINKS, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + ], + MODEL_ARCH.LFM2: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.TOKEN_EMBD_NORM, + MODEL_TENSOR.SHORTCONV_CONV, + MODEL_TENSOR.SHORTCONV_INPROJ, + MODEL_TENSOR.SHORTCONV_OUTPROJ, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.ATTN_NORM, # operator_norm + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.DENSE_2_OUT, # LFM2-ColBert-350M + ], + MODEL_ARCH.LFM2MOE: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.TOKEN_EMBD_NORM, + MODEL_TENSOR.SHORTCONV_CONV, + MODEL_TENSOR.SHORTCONV_INPROJ, + MODEL_TENSOR.SHORTCONV_OUTPROJ, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.ATTN_NORM, # operator_norm + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + MODEL_TENSOR.FFN_EXP_PROBS_B, + ], + MODEL_ARCH.SMALLTHINKER: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + ], + MODEL_ARCH.APERTUS: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.LLADA_MOE: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_UP_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + ], + MODEL_ARCH.GROVEMOE: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + MODEL_TENSOR.FFN_GATE_CHEXP, + MODEL_TENSOR.FFN_DOWN_CHEXP, + MODEL_TENSOR.FFN_UP_CHEXP, + ], + MODEL_ARCH.MINIMAXM2: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + MODEL_TENSOR.FFN_EXP_PROBS_B, + ], + MODEL_ARCH.COGVLM: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.VISEXP_ATTN_QKV, + MODEL_TENSOR.VISEXP_ATTN_OUT, + MODEL_TENSOR.VISEXP_GATE, + MODEL_TENSOR.VISEXP_UP, + MODEL_TENSOR.VISEXP_DOWN, + ], + MODEL_ARCH.RND1: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + ], + MODEL_ARCH.PANGU_EMBED: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.MISTRAL3: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + ], + MODEL_ARCH.EAGLE3: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_NORM_2, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FC, + MODEL_TENSOR.D2T, + ], + MODEL_ARCH.DFLASH: [ + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FC, + MODEL_TENSOR.ENC_OUTPUT_NORM, + ], + MODEL_ARCH.MISTRAL4: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_Q_A, + MODEL_TENSOR.ATTN_Q_B, + MODEL_TENSOR.ATTN_KV_A_MQA, + MODEL_TENSOR.ATTN_KV_B, + MODEL_TENSOR.ATTN_K_B, + MODEL_TENSOR.ATTN_V_B, + MODEL_TENSOR.ATTN_Q_A_NORM, + MODEL_TENSOR.ATTN_KV_A_NORM, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + MODEL_TENSOR.FFN_GATE_UP_EXP, + MODEL_TENSOR.FFN_GATE_SHEXP, + MODEL_TENSOR.FFN_DOWN_SHEXP, + MODEL_TENSOR.FFN_UP_SHEXP, + MODEL_TENSOR.FFN_EXP_PROBS_B, + ], + MODEL_ARCH.MIMO2: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_SINKS, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + MODEL_TENSOR.FFN_EXP_PROBS_B, + MODEL_TENSOR.LAYER_OUT_NORM, + MODEL_TENSOR.NEXTN_EH_PROJ, + MODEL_TENSOR.NEXTN_ENORM, + MODEL_TENSOR.NEXTN_HNORM, + ], + MODEL_ARCH.STEP35: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_GATE, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + MODEL_TENSOR.FFN_UP_SHEXP, + MODEL_TENSOR.FFN_GATE_SHEXP, + MODEL_TENSOR.FFN_DOWN_SHEXP, + MODEL_TENSOR.FFN_EXP_PROBS_B, + # NextN/MTP tensors (Step3p5 draft head) + MODEL_TENSOR.NEXTN_EH_PROJ, + MODEL_TENSOR.NEXTN_EMBED_TOKENS, + MODEL_TENSOR.NEXTN_ENORM, + MODEL_TENSOR.NEXTN_HNORM, + MODEL_TENSOR.NEXTN_SHARED_HEAD_HEAD, + MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM, + ], + MODEL_ARCH.LLAMA_EMBED: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + ], + MODEL_ARCH.MAINCODER: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.KIMI_LINEAR: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_Q_A, + MODEL_TENSOR.ATTN_Q_B, + MODEL_TENSOR.ATTN_KV_A_MQA, + MODEL_TENSOR.ATTN_KV_B, + MODEL_TENSOR.ATTN_K_B, + MODEL_TENSOR.ATTN_V_B, + MODEL_TENSOR.ATTN_Q_A_NORM, + MODEL_TENSOR.ATTN_KV_A_NORM, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + MODEL_TENSOR.SSM_CONV1D_Q, + MODEL_TENSOR.SSM_CONV1D_K, + MODEL_TENSOR.SSM_CONV1D_V, + MODEL_TENSOR.SSM_F_A, + MODEL_TENSOR.SSM_F_B, + MODEL_TENSOR.SSM_BETA, + MODEL_TENSOR.SSM_A, + MODEL_TENSOR.SSM_G_A, + MODEL_TENSOR.SSM_G_B, + MODEL_TENSOR.SSM_DT, + MODEL_TENSOR.SSM_NORM, + MODEL_TENSOR.FFN_EXP_PROBS_B, + MODEL_TENSOR.FFN_GATE_SHEXP, + MODEL_TENSOR.FFN_DOWN_SHEXP, + MODEL_TENSOR.FFN_UP_SHEXP, + ], + MODEL_ARCH.TALKIE: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.LAYER_OUT_SCALE, + ], + MODEL_ARCH.MELLUM: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + ], + # TODO +} + +# tensors that will not be serialized +MODEL_TENSOR_SKIP: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { + MODEL_ARCH.LLAMA: [ + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_ROT_EMBD, + ], + MODEL_ARCH.DECI: [ + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_ROT_EMBD, + ], + MODEL_ARCH.BAICHUAN: [ + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_ROT_EMBD, + ], + MODEL_ARCH.QWEN: [ + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_ROT_EMBD, + ], + MODEL_ARCH.CODESHELL: [ + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_ROT_EMBD, + ], + MODEL_ARCH.ORION: [ + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_ROT_EMBD, + ], + MODEL_ARCH.STARCODER2: [ + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_ROT_EMBD, + ], + MODEL_ARCH.XVERSE: [ + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_ROT_EMBD, + ], + MODEL_ARCH.DEEPSEEK: [ + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_ROT_EMBD, + ], + MODEL_ARCH.DEEPSEEK2: [ + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_ROT_EMBD, + ], + MODEL_ARCH.DEEPSEEK2OCR: [ + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_ROT_EMBD, + ], + MODEL_ARCH.DEEPSEEK32: [ + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_ROT_EMBD, + ], + MODEL_ARCH.CHATGLM: [ + MODEL_TENSOR.ROPE_FREQS, + ], + MODEL_ARCH.NEMOTRON: [ + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_ROT_EMBD, + ], + MODEL_ARCH.BAILINGMOE: [ + MODEL_TENSOR.ROPE_FREQS, + ], + MODEL_ARCH.PANGU_EMBED: [ + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_ROT_EMBD, + ], +} + +# +# types +# + + +class TokenType(IntEnum): + NORMAL = 1 + UNKNOWN = 2 + CONTROL = 3 + USER_DEFINED = 4 + UNUSED = 5 + BYTE = 6 + + +class RopeScalingType(Enum): + NONE = 'none' + LINEAR = 'linear' + YARN = 'yarn' + LONGROPE = 'longrope' + + +class PoolingType(IntEnum): + NONE = 0 + MEAN = 1 + CLS = 2 + LAST = 3 + RANK = 4 + + +class GGMLQuantizationType(IntEnum): + F32 = 0 + F16 = 1 + Q4_0 = 2 + Q4_1 = 3 + Q5_0 = 6 + Q5_1 = 7 + Q8_0 = 8 + Q8_1 = 9 + Q2_K = 10 + Q3_K = 11 + Q4_K = 12 + Q5_K = 13 + Q6_K = 14 + Q8_K = 15 + IQ2_XXS = 16 + IQ2_XS = 17 + IQ3_XXS = 18 + IQ1_S = 19 + IQ4_NL = 20 + IQ3_S = 21 + IQ2_S = 22 + IQ4_XS = 23 + I8 = 24 + I16 = 25 + I32 = 26 + I64 = 27 + F64 = 28 + IQ1_M = 29 + BF16 = 30 + TQ1_0 = 34 + TQ2_0 = 35 + MXFP4 = 39 + NVFP4 = 40 + Q1_0 = 41 + Q2_0 = 42 + + +class ExpertGatingFuncType(IntEnum): + SOFTMAX = 1 + SIGMOID = 2 + SQRTSOFTPLUS = 4 + + +# TODO: add GGMLFileType from ggml_ftype in ggml.h + + +# from llama_ftype in llama.h +# ALL VALUES SHOULD BE THE SAME HERE AS THEY ARE OVER THERE. +class LlamaFileType(IntEnum): + ALL_F32 = 0 + MOSTLY_F16 = 1 # except 1d tensors + MOSTLY_Q4_0 = 2 # except 1d tensors + MOSTLY_Q4_1 = 3 # except 1d tensors + # MOSTLY_Q4_1_SOME_F16 = 4 # tok_embeddings.weight and output.weight are F16 + # MOSTLY_Q4_2 = 5 # support has been removed + # MOSTLY_Q4_3 = 6 # support has been removed + MOSTLY_Q8_0 = 7 # except 1d tensors + MOSTLY_Q5_0 = 8 # except 1d tensors + MOSTLY_Q5_1 = 9 # except 1d tensors + MOSTLY_Q2_K = 10 # except 1d tensors + MOSTLY_Q3_K_S = 11 # except 1d tensors + MOSTLY_Q3_K_M = 12 # except 1d tensors + MOSTLY_Q3_K_L = 13 # except 1d tensors + MOSTLY_Q4_K_S = 14 # except 1d tensors + MOSTLY_Q4_K_M = 15 # except 1d tensors + MOSTLY_Q5_K_S = 16 # except 1d tensors + MOSTLY_Q5_K_M = 17 # except 1d tensors + MOSTLY_Q6_K = 18 # except 1d tensors + MOSTLY_IQ2_XXS = 19 # except 1d tensors + MOSTLY_IQ2_XS = 20 # except 1d tensors + MOSTLY_Q2_K_S = 21 # except 1d tensors + MOSTLY_IQ3_XS = 22 # except 1d tensors + MOSTLY_IQ3_XXS = 23 # except 1d tensors + MOSTLY_IQ1_S = 24 # except 1d tensors + MOSTLY_IQ4_NL = 25 # except 1d tensors + MOSTLY_IQ3_S = 26 # except 1d tensors + MOSTLY_IQ3_M = 27 # except 1d tensors + MOSTLY_IQ2_S = 28 # except 1d tensors + MOSTLY_IQ2_M = 29 # except 1d tensors + MOSTLY_IQ4_XS = 30 # except 1d tensors + MOSTLY_IQ1_M = 31 # except 1d tensors + MOSTLY_BF16 = 32 # except 1d tensors + # MOSTLY_Q4_0_4_4 = 33 # removed from gguf files, use Q4_0 and runtime repack + # MOSTLY_Q4_0_4_8 = 34 # removed from gguf files, use Q4_0 and runtime repack + # MOSTLY_Q4_0_8_8 = 35 # removed from gguf files, use Q4_0 and runtime repack + MOSTLY_TQ1_0 = 36 # except 1d tensors + MOSTLY_TQ2_0 = 37 # except 1d tensors + MOSTLY_MXFP4_MOE = 38 # except 1d tensors + MOSTLY_NVFP4 = 39 # except 1d tensors + MOSTLY_Q1_0 = 40 # except 1d tensors + MOSTLY_Q2_0 = 41 # except 1d tensors + + GUESSED = 1024 # not specified in the model file + + +class GGUFEndian(IntEnum): + LITTLE = 0 + BIG = 1 + + +class GGUFValueType(IntEnum): + UINT8 = 0 + INT8 = 1 + UINT16 = 2 + INT16 = 3 + UINT32 = 4 + INT32 = 5 + FLOAT32 = 6 + BOOL = 7 + STRING = 8 + ARRAY = 9 + UINT64 = 10 + INT64 = 11 + FLOAT64 = 12 + + @staticmethod + def get_type(val: Any) -> GGUFValueType: + if isinstance(val, (str, bytes, bytearray)): + return GGUFValueType.STRING + elif isinstance(val, list): + return GGUFValueType.ARRAY + elif isinstance(val, float): + return GGUFValueType.FLOAT32 + elif isinstance(val, bool): + return GGUFValueType.BOOL + elif isinstance(val, int): + return GGUFValueType.INT32 + # TODO: need help with 64-bit types in Python + else: + raise ValueError(f"Unknown type: {type(val)}") + + +class VisionProjectorType: + GEMMA3 = "gemma3" + GEMMA3NV = "gemma3nv" + GEMMA3NA = "gemma3na" + GEMMA4V = "gemma4v" + GEMMA4A = "gemma4a" + GEMMA4UV = "gemma4uv" # "unified" variant + GEMMA4UA = "gemma4ua" # "unified" variant + PHI4 = "phi4" + IDEFICS3 = "idefics3" + PIXTRAL = "pixtral" + LLAMA4 = "llama4" + QWEN2VL = "qwen2vl_merger" + QWEN25VL = "qwen2.5vl_merger" + EXAONE4_5 = "exaone4_5" + QWEN3VL = "qwen3vl_merger" + STEP3VL = "step3vl" + ULTRAVOX = "ultravox" + INTERNVL = "internvl" + QWEN2A = "qwen2a" # audio + QWEN3A = "qwen3a" # audio + GLMA = "glma" # audio + QWEN25O = "qwen2.5o" # omni + VOXTRAL = "voxtral" + MERALION = "meralion" # audio: Whisper + gated MLP adaptor + LFM2 = "lfm2" + KIMIVL = "kimivl" + PADDLEOCR = "paddleocr" + KIMIK25 = "kimik25" + LIGHTONOCR = "lightonocr" + COGVLM = "cogvlm" + JANUS_PRO = "janus_pro" + DOTSOCR = "dots_ocr" + DEEPSEEKOCR = "deepseekocr" + DEEPSEEKOCR2 = "deepseekocr2" + LFM2A = "lfm2a" # audio + MUSIC_FLAMINGO = "musicflamingo" # audio + GLM4V = "glm4v" + YOUTUVL = "youtuvl" + NEMOTRON_V2_VL = "nemotron_v2_vl" + HUNYUANVL = "hunyuanvl" + MINICPMV4_6 = "minicpmv4_6" + GRANITE_SPEECH = "granite_speech" # audio + MIMOVL = "mimovl" + GRANITE4_VISION = "granite4_vision" + + +# Items here are (block size, type size) +QK_K = 256 +GGML_QUANT_SIZES: dict[GGMLQuantizationType, tuple[int, int]] = { + GGMLQuantizationType.F32: (1, 4), + GGMLQuantizationType.F16: (1, 2), + GGMLQuantizationType.Q4_0: (32, 2 + 16), + GGMLQuantizationType.Q4_1: (32, 2 + 2 + 16), + GGMLQuantizationType.Q5_0: (32, 2 + 4 + 16), + GGMLQuantizationType.Q5_1: (32, 2 + 2 + 4 + 16), + GGMLQuantizationType.Q8_0: (32, 2 + 32), + GGMLQuantizationType.Q8_1: (32, 4 + 4 + 32), + GGMLQuantizationType.Q2_K: (256, 2 + 2 + QK_K // 16 + QK_K // 4), + GGMLQuantizationType.Q3_K: (256, 2 + QK_K // 4 + QK_K // 8 + 12), + GGMLQuantizationType.Q4_K: (256, 2 + 2 + QK_K // 2 + 12), + GGMLQuantizationType.Q5_K: (256, 2 + 2 + QK_K // 2 + QK_K // 8 + 12), + GGMLQuantizationType.Q6_K: (256, 2 + QK_K // 2 + QK_K // 4 + QK_K // 16), + GGMLQuantizationType.Q8_K: (256, 4 + QK_K + QK_K // 8), + GGMLQuantizationType.IQ2_XXS: (256, 2 + QK_K // 4), + GGMLQuantizationType.IQ2_XS: (256, 2 + QK_K // 4 + QK_K // 32), + GGMLQuantizationType.IQ3_XXS: (256, 2 + QK_K // 4 + QK_K // 8), + GGMLQuantizationType.IQ1_S: (256, 2 + QK_K // 8 + QK_K // 16), + GGMLQuantizationType.IQ4_NL: (32, 2 + 16), + GGMLQuantizationType.IQ3_S: (256, 2 + QK_K // 4 + QK_K // 8 + QK_K // 32 + 4), + GGMLQuantizationType.IQ2_S: (256, 2 + QK_K // 4 + QK_K // 16), + GGMLQuantizationType.IQ4_XS: (256, 2 + 2 + QK_K // 2 + QK_K // 64), + GGMLQuantizationType.I8: (1, 1), + GGMLQuantizationType.I16: (1, 2), + GGMLQuantizationType.I32: (1, 4), + GGMLQuantizationType.I64: (1, 8), + GGMLQuantizationType.F64: (1, 8), + GGMLQuantizationType.IQ1_M: (256, QK_K // 8 + QK_K // 16 + QK_K // 32), + GGMLQuantizationType.BF16: (1, 2), + GGMLQuantizationType.TQ1_0: (256, 2 + 4 * 13), + GGMLQuantizationType.TQ2_0: (256, 2 + 64), + GGMLQuantizationType.MXFP4: (32, 1 + 16), + GGMLQuantizationType.NVFP4: (64, 4 + 32), + GGMLQuantizationType.Q1_0: (128, 2 + 16), + GGMLQuantizationType.Q2_0: (64, 2 + 16), +} + + +# Aliases for backward compatibility. + +# general +KEY_GENERAL_ARCHITECTURE = Keys.General.ARCHITECTURE +KEY_GENERAL_QUANTIZATION_VERSION = Keys.General.QUANTIZATION_VERSION +KEY_GENERAL_ALIGNMENT = Keys.General.ALIGNMENT +KEY_GENERAL_NAME = Keys.General.NAME +KEY_GENERAL_AUTHOR = Keys.General.AUTHOR +KEY_GENERAL_URL = Keys.General.URL +KEY_GENERAL_DESCRIPTION = Keys.General.DESCRIPTION +KEY_GENERAL_LICENSE = Keys.General.LICENSE +KEY_GENERAL_SOURCE_URL = Keys.General.SOURCE_URL +KEY_GENERAL_FILE_TYPE = Keys.General.FILE_TYPE + +# LLM +KEY_VOCAB_SIZE = Keys.LLM.VOCAB_SIZE +KEY_CONTEXT_LENGTH = Keys.LLM.CONTEXT_LENGTH +KEY_EMBEDDING_LENGTH = Keys.LLM.EMBEDDING_LENGTH +KEY_BLOCK_COUNT = Keys.LLM.BLOCK_COUNT +KEY_FEED_FORWARD_LENGTH = Keys.LLM.FEED_FORWARD_LENGTH +KEY_USE_PARALLEL_RESIDUAL = Keys.LLM.USE_PARALLEL_RESIDUAL +KEY_TENSOR_DATA_LAYOUT = Keys.LLM.TENSOR_DATA_LAYOUT + +# attention +KEY_ATTENTION_HEAD_COUNT = Keys.Attention.HEAD_COUNT +KEY_ATTENTION_HEAD_COUNT_KV = Keys.Attention.HEAD_COUNT_KV +KEY_ATTENTION_MAX_ALIBI_BIAS = Keys.Attention.MAX_ALIBI_BIAS +KEY_ATTENTION_CLAMP_KQV = Keys.Attention.CLAMP_KQV +KEY_ATTENTION_LAYERNORM_EPS = Keys.Attention.LAYERNORM_EPS +KEY_ATTENTION_LAYERNORM_RMS_EPS = Keys.Attention.LAYERNORM_RMS_EPS + +# RoPE +KEY_ROPE_DIMENSION_COUNT = Keys.Rope.DIMENSION_COUNT +KEY_ROPE_FREQ_BASE = Keys.Rope.FREQ_BASE +KEY_ROPE_SCALING_TYPE = Keys.Rope.SCALING_TYPE +KEY_ROPE_SCALING_FACTOR = Keys.Rope.SCALING_FACTOR +KEY_ROPE_SCALING_ORIG_CTX_LEN = Keys.Rope.SCALING_ORIG_CTX_LEN +KEY_ROPE_SCALING_FINETUNED = Keys.Rope.SCALING_FINETUNED + +# SSM +KEY_SSM_CONV_KERNEL = Keys.SSM.CONV_KERNEL +KEY_SSM_INNER_SIZE = Keys.SSM.INNER_SIZE +KEY_SSM_STATE_SIZE = Keys.SSM.STATE_SIZE +KEY_SSM_TIME_STEP_RANK = Keys.SSM.TIME_STEP_RANK +KEY_SSM_GROUP_COUNT = Keys.SSM.GROUP_COUNT +KEY_SSM_DT_B_C_RMS = Keys.SSM.DT_B_C_RMS + +# KDA +KEY_KDA_HEAD_DIM = Keys.KDA.HEAD_DIM + +# tokenization +KEY_TOKENIZER_MODEL = Keys.Tokenizer.MODEL +KEY_TOKENIZER_PRE = Keys.Tokenizer.PRE +KEY_TOKENIZER_LIST = Keys.Tokenizer.LIST +KEY_TOKENIZER_TOKEN_TYPE = Keys.Tokenizer.TOKEN_TYPE +KEY_TOKENIZER_SCORES = Keys.Tokenizer.SCORES +KEY_TOKENIZER_MERGES = Keys.Tokenizer.MERGES +KEY_TOKENIZER_BOS_ID = Keys.Tokenizer.BOS_ID +KEY_TOKENIZER_EOS_ID = Keys.Tokenizer.EOS_ID +KEY_TOKENIZER_EOT_ID = Keys.Tokenizer.EOT_ID +KEY_TOKENIZER_EOM_ID = Keys.Tokenizer.EOM_ID +KEY_TOKENIZER_UNK_ID = Keys.Tokenizer.UNK_ID +KEY_TOKENIZER_SEP_ID = Keys.Tokenizer.SEP_ID +KEY_TOKENIZER_PAD_ID = Keys.Tokenizer.PAD_ID +KEY_TOKENIZER_MASK_ID = Keys.Tokenizer.MASK_ID +KEY_TOKENIZER_HF_JSON = Keys.Tokenizer.HF_JSON +KEY_TOKENIZER_RWKV = Keys.Tokenizer.RWKV + +KEY_TOKENIZER_FIM_PRE_ID = Keys.Tokenizer.FIM_PRE_ID +KEY_TOKENIZER_FIM_SUF_ID = Keys.Tokenizer.FIM_SUF_ID +KEY_TOKENIZER_FIM_MID_ID = Keys.Tokenizer.FIM_MID_ID +KEY_TOKENIZER_FIM_PAD_ID = Keys.Tokenizer.FIM_PAD_ID +KEY_TOKENIZER_FIM_REP_ID = Keys.Tokenizer.FIM_REP_ID +KEY_TOKENIZER_FIM_SEP_ID = Keys.Tokenizer.FIM_SEP_ID + +# deprecated +KEY_TOKENIZER_PREFIX_ID = Keys.Tokenizer.PREFIX_ID +KEY_TOKENIZER_SUFFIX_ID = Keys.Tokenizer.SUFFIX_ID +KEY_TOKENIZER_MIDDLE_ID = Keys.Tokenizer.MIDDLE_ID diff --git a/backend/llama.cpp/gguf-py/gguf/gguf.py b/backend/llama.cpp/gguf-py/gguf/gguf.py new file mode 100644 index 0000000000000000000000000000000000000000..651a81eb828248728f854c85c1a437b52892f275 --- /dev/null +++ b/backend/llama.cpp/gguf-py/gguf/gguf.py @@ -0,0 +1,15 @@ +# This file left for compatibility. If you want to use the GGUF API from Python +# then don't import gguf/gguf.py directly. If you're looking for examples, see the +# examples/ directory for gguf-py + +import importlib +import sys +from pathlib import Path + +sys.path.insert(0, str(Path(__file__).parent.parent)) + +# Compatibility for people trying to import gguf/gguf.py directly instead of as a package. +importlib.invalidate_caches() +import gguf # noqa: E402 + +importlib.reload(gguf) diff --git a/backend/llama.cpp/gguf-py/gguf/gguf_reader.py b/backend/llama.cpp/gguf-py/gguf/gguf_reader.py new file mode 100644 index 0000000000000000000000000000000000000000..0a1b85f50641b1abf7f597cdad133100f7884a3b --- /dev/null +++ b/backend/llama.cpp/gguf-py/gguf/gguf_reader.py @@ -0,0 +1,370 @@ +# +# GGUF file reading/modification support. For API usage information, +# please see the files scripts/ for some fairly simple examples. +# +from __future__ import annotations + +import logging +import os +import sys +from collections import OrderedDict +from typing import Any, Literal, NamedTuple, TypeVar, Union + +import numpy as np +import numpy.typing as npt + +from .quants import quant_shape_to_byte_shape + +if __name__ == "__main__": + from pathlib import Path + + # Allow running file in package as a script. + sys.path.insert(0, str(Path(__file__).parent.parent)) + +from gguf.constants import ( + GGML_QUANT_SIZES, + GGUF_DEFAULT_ALIGNMENT, + GGUF_MAGIC, + GGUF_VERSION, + GGMLQuantizationType, + GGUFValueType, + GGUFEndian, +) + +logger = logging.getLogger(__name__) + +READER_SUPPORTED_VERSIONS = [2, GGUF_VERSION] + + +class ReaderField(NamedTuple): + # Offset to start of this field. + offset: int + + # Name of the field (not necessarily from file data). + name: str + + # Data parts. Some types have multiple components, such as strings + # that consist of a length followed by the string data. + parts: list[npt.NDArray[Any]] = [] + + # Indexes into parts that we can call the actual data. For example + # an array of strings will be populated with indexes to the actual + # string data. + data: list[int] = [-1] + + types: list[GGUFValueType] = [] + + def contents(self, index_or_slice: int | slice = slice(None)) -> Any: + if self.types: + to_string = lambda x: str(x.tobytes(), encoding='utf-8') # noqa: E731 + main_type = self.types[0] + + if main_type == GGUFValueType.ARRAY: + sub_type = self.types[-1] + + if sub_type == GGUFValueType.STRING: + indices = self.data[index_or_slice] + + if isinstance(index_or_slice, int): + return to_string(self.parts[indices]) # type: ignore + else: + return [to_string(self.parts[idx]) for idx in indices] # type: ignore + else: + # FIXME: When/if _get_field_parts() support multi-dimensional arrays, this must do so too + + # Check if it's unsafe to perform slice optimization on data + # if any(True for idx in self.data if len(self.parts[idx]) != 1): + # optim_slice = slice(None) + # else: + # optim_slice = index_or_slice + # index_or_slice = slice(None) + + # if isinstance(optim_slice, int): + # return self.parts[self.data[optim_slice]].tolist()[0] + # else: + # return [pv for idx in self.data[optim_slice] for pv in self.parts[idx].tolist()][index_or_slice] + + if isinstance(index_or_slice, int): + return self.parts[self.data[index_or_slice]].tolist()[0] + else: + return [pv for idx in self.data[index_or_slice] for pv in self.parts[idx].tolist()] + + if main_type == GGUFValueType.STRING: + return to_string(self.parts[-1]) + else: + return self.parts[-1].tolist()[0] + + return None + + +class ReaderTensor(NamedTuple): + name: str + tensor_type: GGMLQuantizationType + shape: npt.NDArray[np.uint32] + n_elements: int + n_bytes: int + data_offset: int + data: npt.NDArray[Any] + field: ReaderField + + +class GGUFReader: + # I - same as host, S - swapped + byte_order: Literal['I', 'S'] = 'I' + alignment: int = GGUF_DEFAULT_ALIGNMENT + data_offset: int + + # Note: Internal helper, API may change. + gguf_scalar_to_np: dict[GGUFValueType, type[np.generic]] = { + GGUFValueType.UINT8: np.uint8, + GGUFValueType.INT8: np.int8, + GGUFValueType.UINT16: np.uint16, + GGUFValueType.INT16: np.int16, + GGUFValueType.UINT32: np.uint32, + GGUFValueType.INT32: np.int32, + GGUFValueType.FLOAT32: np.float32, + GGUFValueType.UINT64: np.uint64, + GGUFValueType.INT64: np.int64, + GGUFValueType.FLOAT64: np.float64, + GGUFValueType.BOOL: np.bool_, + } + + def __init__(self, path: os.PathLike[str] | str, mode: Literal['r', 'r+', 'c'] = 'r'): + self.data = np.memmap(path, mode = mode) + offs = 0 + + # Check for GGUF magic + if self._get(offs, np.uint32, override_order = '<')[0] != GGUF_MAGIC: + raise ValueError('GGUF magic invalid') + offs += 4 + + # Check GGUF version + temp_version = self._get(offs, np.uint32) + if temp_version[0] & 65535 == 0: + # If we get 0 here that means it's (probably) a GGUF file created for + # the opposite byte order of the machine this script is running on. + self.byte_order = 'S' + temp_version = temp_version.view(temp_version.dtype.newbyteorder(self.byte_order)) + version = temp_version[0] + if version not in READER_SUPPORTED_VERSIONS: + raise ValueError(f'Sorry, file appears to be version {version} which we cannot handle') + if sys.byteorder == "little": + # Host is little endian + host_endian = GGUFEndian.LITTLE + swapped_endian = GGUFEndian.BIG + else: + # Sorry PDP or other weird systems that don't use BE or LE. + host_endian = GGUFEndian.BIG + swapped_endian = GGUFEndian.LITTLE + self.endianess = swapped_endian if self.byte_order == "S" else host_endian + self.fields: OrderedDict[str, ReaderField] = OrderedDict() + self.tensors: list[ReaderTensor] = [] + offs += self._push_field(ReaderField(offs, 'GGUF.version', [temp_version], [0], [GGUFValueType.UINT32])) + + # Check tensor count and kv count + temp_counts = self._get(offs, np.uint64, 2) + offs += self._push_field(ReaderField(offs, 'GGUF.tensor_count', [temp_counts[:1]], [0], [GGUFValueType.UINT64])) + offs += self._push_field(ReaderField(offs, 'GGUF.kv_count', [temp_counts[1:]], [0], [GGUFValueType.UINT64])) + tensor_count, kv_count = temp_counts + offs = self._build_fields(offs, kv_count) + + # Build Tensor Info Fields + offs, tensors_fields = self._build_tensor_info(offs, tensor_count) + new_align = self.fields.get('general.alignment') + if new_align is not None: + if new_align.types != [GGUFValueType.UINT32]: + raise ValueError('Bad type for general.alignment field') + self.alignment = new_align.parts[-1][0] + # Ensure alignment is a non-zero power of two + if self.alignment == 0 or (self.alignment & (self.alignment - 1)) != 0: + raise ValueError('Invalid alignment: must be a non-zero power of two') + padding = offs % self.alignment + if padding != 0: + offs += self.alignment - padding + self.data_offset = offs + self._build_tensors(offs, tensors_fields) + + _DT = TypeVar('_DT', bound = npt.DTypeLike) + + # Fetch a key/value metadata field by key. + def get_field(self, key: str) -> Union[ReaderField, None]: + return self.fields.get(key, None) + + # Fetch a tensor from the list by index. + def get_tensor(self, idx: int) -> ReaderTensor: + return self.tensors[idx] + + def _get( + self, offset: int, dtype: npt.DTypeLike, count: int = 1, override_order: None | Literal['I', 'S', '<'] = None, + ) -> npt.NDArray[Any]: + count = int(count) + itemsize = int(np.empty([], dtype = dtype).itemsize) + end_offs = offset + itemsize * count + arr = self.data[offset:end_offs].view(dtype=dtype)[:count] + return arr.view(arr.dtype.newbyteorder(self.byte_order if override_order is None else override_order)) + + def _push_field(self, field: ReaderField, skip_sum: bool = False) -> int: + if field.name in self.fields: + # TODO: add option to make this a warning and accept duplicate keys like below + raise KeyError(f'Duplicate {field.name} already in list at offset {field.offset}') + + # logger.warning(f'Duplicate key {field.name} at offset {field.offset}') + # self.fields[field.name + '_{}'.format(field.offset)] = field + else: + self.fields[field.name] = field + return 0 if skip_sum else sum(int(part.nbytes) for part in field.parts) + + def _get_str(self, offset: int) -> tuple[npt.NDArray[np.uint64], npt.NDArray[np.uint8]]: + slen = self._get(offset, np.uint64) + return slen, self._get(offset + 8, np.uint8, slen[0]) + + def _get_field_parts( + self, orig_offs: int, raw_type: int, + ) -> tuple[int, list[npt.NDArray[Any]], list[int], list[GGUFValueType]]: + offs = orig_offs + types: list[GGUFValueType] = [] + gtype = GGUFValueType(raw_type) + types.append(gtype) + # Handle strings. + if gtype == GGUFValueType.STRING: + sparts: list[npt.NDArray[Any]] = list(self._get_str(offs)) + size = sum(int(part.nbytes) for part in sparts) + return size, sparts, [1], types + # Check if it's a simple scalar type. + nptype = self.gguf_scalar_to_np.get(gtype) + if nptype is not None: + val = self._get(offs, nptype) + return int(val.nbytes), [val], [0], types + # Handle arrays. + if gtype == GGUFValueType.ARRAY: + raw_itype = self._get(offs, np.uint32) + offs += int(raw_itype.nbytes) + alen = self._get(offs, np.uint64) + offs += int(alen.nbytes) + aparts: list[npt.NDArray[Any]] = [raw_itype, alen] + data_idxs: list[int] = [] + # FIXME: Handle multi-dimensional arrays properly instead of flattening + for idx in range(alen[0]): + curr_size, curr_parts, curr_idxs, curr_types = self._get_field_parts(offs, raw_itype[0]) + if idx == 0: + types += curr_types + idxs_offs = len(aparts) + aparts += curr_parts + data_idxs += (idx + idxs_offs for idx in curr_idxs) + offs += curr_size + return offs - orig_offs, aparts, data_idxs, types + # We can't deal with this one. + raise ValueError(f'Unknown/unhandled field type {gtype}') + + def _get_tensor_info_field(self, orig_offs: int) -> ReaderField: + offs = orig_offs + + # Get Tensor Name + name_len, name_data = self._get_str(offs) + offs += int(name_len.nbytes + name_data.nbytes) + + # Get Tensor Dimensions Count + n_dims = self._get(offs, np.uint32) + offs += int(n_dims.nbytes) + + # Get Tensor Dimension Array + dims = self._get(offs, np.uint64, n_dims[0]) + offs += int(dims.nbytes) + + # Get Tensor Encoding Scheme Type + raw_dtype = self._get(offs, np.uint32) + offs += int(raw_dtype.nbytes) + + # Get Tensor Offset + offset_tensor = self._get(offs, np.uint64) + offs += int(offset_tensor.nbytes) + + return ReaderField( + orig_offs, + str(bytes(name_data), encoding = 'utf-8'), + [name_len, name_data, n_dims, dims, raw_dtype, offset_tensor], + [1, 3, 4, 5], + ) + + def _build_fields(self, offs: int, count: int) -> int: + for _ in range(count): + orig_offs = offs + kv_klen, kv_kdata = self._get_str(offs) + offs += int(kv_klen.nbytes + kv_kdata.nbytes) + raw_kv_type = self._get(offs, np.uint32) + offs += int(raw_kv_type.nbytes) + parts: list[npt.NDArray[Any]] = [kv_klen, kv_kdata, raw_kv_type] + idxs_offs = len(parts) + field_size, field_parts, field_idxs, field_types = self._get_field_parts(offs, raw_kv_type[0]) + parts += field_parts + self._push_field(ReaderField( + orig_offs, + str(bytes(kv_kdata), encoding = 'utf-8'), + parts, + [idx + idxs_offs for idx in field_idxs], + field_types, + ), skip_sum = True) + offs += field_size + return offs + + def _build_tensor_info(self, offs: int, count: int) -> tuple[int, list[ReaderField]]: + tensor_fields = [] + for _ in range(count): + field = self._get_tensor_info_field(offs) + offs += sum(int(part.nbytes) for part in field.parts) + tensor_fields.append(field) + return offs, tensor_fields + + def _build_tensors(self, start_offs: int, fields: list[ReaderField]) -> None: + tensors = [] + tensor_names = set() # keep track of name to prevent duplicated tensors + for field in fields: + _name_len, name_data, _n_dims, dims, raw_dtype, offset_tensor = field.parts + # check if there's any tensor having same name already in the list + tensor_name = str(bytes(name_data), encoding = 'utf-8') + if tensor_name in tensor_names: + raise ValueError(f'Found duplicated tensor with name {tensor_name}') + tensor_names.add(tensor_name) + ggml_type = GGMLQuantizationType(raw_dtype[0]) + n_elems = int(np.prod(dims)) + np_dims = tuple(reversed(dims.tolist())) + block_size, type_size = GGML_QUANT_SIZES[ggml_type] + n_bytes = n_elems * type_size // block_size + data_offs = int(start_offs + offset_tensor[0]) + item_type: npt.DTypeLike + if ggml_type == GGMLQuantizationType.F16: + item_count = n_elems + item_type = np.float16 + elif ggml_type == GGMLQuantizationType.F32: + item_count = n_elems + item_type = np.float32 + elif ggml_type == GGMLQuantizationType.F64: + item_count = n_elems + item_type = np.float64 + elif ggml_type == GGMLQuantizationType.I8: + item_count = n_elems + item_type = np.int8 + elif ggml_type == GGMLQuantizationType.I16: + item_count = n_elems + item_type = np.int16 + elif ggml_type == GGMLQuantizationType.I32: + item_count = n_elems + item_type = np.int32 + elif ggml_type == GGMLQuantizationType.I64: + item_count = n_elems + item_type = np.int64 + else: + item_count = n_bytes + item_type = np.uint8 + np_dims = quant_shape_to_byte_shape(np_dims, ggml_type) + tensors.append(ReaderTensor( + name = tensor_name, + tensor_type = ggml_type, + shape = dims, + n_elements = n_elems, + n_bytes = n_bytes, + data_offset = data_offs, + data = self._get(data_offs, item_type, item_count).reshape(np_dims), + field = field, + )) + self.tensors = tensors diff --git a/backend/llama.cpp/gguf-py/gguf/gguf_writer.py b/backend/llama.cpp/gguf-py/gguf/gguf_writer.py new file mode 100644 index 0000000000000000000000000000000000000000..1e277f0687c5addfec6c9057a3263d1031c5bfd0 --- /dev/null +++ b/backend/llama.cpp/gguf-py/gguf/gguf_writer.py @@ -0,0 +1,1431 @@ +from __future__ import annotations + +import logging +import os +import shutil +import struct +import sys +import tempfile +from dataclasses import dataclass +from enum import Enum, auto +from math import prod +from pathlib import Path +from io import BufferedWriter +from typing import IO, Any, Sequence, Mapping +from string import ascii_letters, digits + +import numpy as np + +from .constants import ( + GGUF_DEFAULT_ALIGNMENT, + GGUF_MAGIC, + GGUF_VERSION, + GGMLQuantizationType, + GGUFEndian, + GGUFValueType, + Keys, + RopeScalingType, + PoolingType, + TokenType, + ExpertGatingFuncType, +) + +from .quants import quant_shape_from_byte_shape + +logger = logging.getLogger(__name__) + + +SHARD_NAME_FORMAT = "{:s}-{:05d}-of-{:05d}.gguf" + + +@dataclass +class TensorInfo: + shape: Sequence[int] + dtype: GGMLQuantizationType + nbytes: int + tensor: np.ndarray[Any, Any] | None = None + + +@dataclass +class GGUFValue: + value: Any + type: GGUFValueType + sub_type: GGUFValueType | None = None + + +class WriterState(Enum): + NO_FILE = auto() + EMPTY = auto() + HEADER = auto() + KV_DATA = auto() + TI_DATA = auto() + WEIGHTS = auto() + + +class GGUFWriter: + fout: list[BufferedWriter] | None + path: Path | None + temp_file: tempfile.SpooledTemporaryFile[bytes] | None + tensors: list[dict[str, TensorInfo]] + kv_data: list[dict[str, GGUFValue]] + state: WriterState + _simple_value_packing = { + GGUFValueType.UINT8: "B", + GGUFValueType.INT8: "b", + GGUFValueType.UINT16: "H", + GGUFValueType.INT16: "h", + GGUFValueType.UINT32: "I", + GGUFValueType.INT32: "i", + GGUFValueType.FLOAT32: "f", + GGUFValueType.UINT64: "Q", + GGUFValueType.INT64: "q", + GGUFValueType.FLOAT64: "d", + GGUFValueType.BOOL: "?", + } + + def __init__( + self, path: os.PathLike[str] | str | None, arch: str, use_temp_file: bool = False, endianess: GGUFEndian = GGUFEndian.LITTLE, + split_max_tensors: int = 0, split_max_size: int = 0, dry_run: bool = False, small_first_shard: bool = False + ): + self.fout = None + self.path = Path(path) if path else None + self.arch = arch + self.endianess = endianess + self.data_alignment = GGUF_DEFAULT_ALIGNMENT + self.use_temp_file = use_temp_file + self.temp_file = None + self.tensors = [{}] + self.kv_data = [{}] + self.split_max_tensors = split_max_tensors + self.split_max_size = split_max_size + self.dry_run = dry_run + self.small_first_shard = small_first_shard + logger.info("gguf: This GGUF file is for {0} Endian only".format( + "Big" if self.endianess == GGUFEndian.BIG else "Little", + )) + self.state = WriterState.NO_FILE + + if self.small_first_shard: + self.tensors.append({}) + + self.add_architecture() + + def get_total_parameter_count(self) -> tuple[int, int, int, int]: + total_params = 0 + shared_params = 0 + expert_params = 0 + + expert_sum = 0 + n_expert_tensors = 0 + + last_lora_a: tuple[str, TensorInfo] | None = None + + for tensors in self.tensors: + for name, info in tensors.items(): + + shape = info.shape + + if name.endswith(".lora_a"): + last_lora_a = (name, info) + continue + elif name.endswith(".lora_b"): + if last_lora_a is None or last_lora_a[0] != name[:-1] + "a": + # Bail when the LoRA pair can't be found trivially + logger.warning("can't measure LoRA size correctly, tensor order is unusual") + return 0, 0, 0, 0 + else: + shape = (*shape[:-1], last_lora_a[1].shape[-1]) + + size = prod(shape) + + if "_exps." in name: + if len(shape) >= 3: + expert_count = shape[-2 if ".bias" in name else -3] + expert_params += (size // expert_count) + expert_sum += expert_count + n_expert_tensors += 1 + else: + shared_params += size + else: + shared_params += size + + total_params += size + + # Hopefully this should work even for variable-expert-count models + expert_count = (expert_sum // n_expert_tensors) if n_expert_tensors > 0 else 0 + + # Negate the total to signal it's likely not exact + if last_lora_a is not None: + total_params = -total_params + + # NOTE: keep the output in the same order as accepted by 'size_label' in gguf-py/gguf/utility.py + return total_params, shared_params, expert_params, expert_count + + def format_shard_names(self, path: Path) -> list[Path]: + if len(self.tensors) == 1: + return [path] + return [path.with_name(SHARD_NAME_FORMAT.format(path.stem, i + 1, len(self.tensors))) for i in range(len(self.tensors))] + + def open_output_file(self, path: Path | None = None) -> None: + if self.state is WriterState.EMPTY and self.fout is not None and (path is None or path == self.path): + # allow calling this multiple times as long as the path is the same + return + + if self.state is not WriterState.NO_FILE: + raise ValueError(f'Expected output file to be not yet opened, got {self.state}') + + if path is not None: + self.path = path + + if self.path is not None: + filenames = self.print_plan() + self.fout = [open(filename, "wb") for filename in filenames] + self.state = WriterState.EMPTY + + def print_plan(self) -> list[Path]: + logger.info("Writing the following files:") + assert self.path is not None + filenames = self.format_shard_names(self.path) + assert len(filenames) == len(self.tensors) + for name, tensors in zip(filenames, self.tensors): + logger.info(f"{name}: n_tensors = {len(tensors)}, total_size = {GGUFWriter.format_n_bytes_to_str(sum(ti.nbytes for ti in tensors.values()))}") + + if self.dry_run: + logger.info("Dry run, not writing files") + for name in filenames: + print(name) # noqa: NP100 + exit() + + return filenames + + def add_shard_kv_data(self) -> None: + if len(self.tensors) == 1: + return + + total_tensors = sum(len(t) for t in self.tensors) + assert self.fout is not None + total_splits = len(self.fout) + self.kv_data.extend({} for _ in range(len(self.kv_data), total_splits)) + for i, kv_data in enumerate(self.kv_data): + kv_data[Keys.Split.LLM_KV_SPLIT_NO] = GGUFValue(i, GGUFValueType.UINT16) + kv_data[Keys.Split.LLM_KV_SPLIT_COUNT] = GGUFValue(total_splits, GGUFValueType.UINT16) + kv_data[Keys.Split.LLM_KV_SPLIT_TENSORS_COUNT] = GGUFValue(total_tensors, GGUFValueType.INT32) + + def write_header_to_file(self, path: Path | None = None) -> None: + if len(self.tensors) == 1 and (self.split_max_tensors != 0 or self.split_max_size != 0): + logger.warning("Model fails split requirements, not splitting") + + self.open_output_file(path) + + if self.state is not WriterState.EMPTY: + raise ValueError(f'Expected output file to be empty, got {self.state}') + + assert self.fout is not None + assert len(self.fout) == len(self.tensors) + assert len(self.kv_data) == 1 + + self.add_shard_kv_data() + + for fout, tensors, kv_data in zip(self.fout, self.tensors, self.kv_data): + fout.write(self._pack(" None: + if self.state is not WriterState.HEADER: + raise ValueError(f'Expected output file to contain the header, got {self.state}') + assert self.fout is not None + + for fout, kv_data in zip(self.fout, self.kv_data): + kv_bytes = bytearray() + + for key, val in kv_data.items(): + kv_bytes += self._pack_val(key, GGUFValueType.STRING, add_vtype=False) + kv_bytes += self._pack_val(val.value, val.type, add_vtype=True, sub_type=val.sub_type) + + fout.write(kv_bytes) + + self.flush() + self.state = WriterState.KV_DATA + + def write_ti_data_to_file(self) -> None: + if self.state is not WriterState.KV_DATA: + raise ValueError(f'Expected output file to contain KV data, got {self.state}') + assert self.fout is not None + + for fout, tensors in zip(self.fout, self.tensors): + ti_data = bytearray() + offset_tensor = 0 + + for name, ti in tensors.items(): + ti_data += self._pack_val(name, GGUFValueType.STRING, add_vtype=False) + n_dims = len(ti.shape) + ti_data += self._pack("I", n_dims) + for j in range(n_dims): + ti_data += self._pack("Q", ti.shape[n_dims - 1 - j]) + ti_data += self._pack("I", ti.dtype) + ti_data += self._pack("Q", offset_tensor) + offset_tensor += GGUFWriter.ggml_pad(ti.nbytes, self.data_alignment) + + fout.write(ti_data) + fout.flush() + self.state = WriterState.TI_DATA + + def add_key_value(self, key: str, val: Any, vtype: GGUFValueType, sub_type: GGUFValueType | None = None) -> None: + if any(key in kv_data for kv_data in self.kv_data): + logger.warning(f'Duplicated key name {key!r}, overwriting it with new value {val!r} of type {vtype.name}') + + self.kv_data[0][key] = GGUFValue(value=val, type=vtype, sub_type=sub_type) + + def add_uint8(self, key: str, val: int) -> None: + self.add_key_value(key,val, GGUFValueType.UINT8) + + def add_int8(self, key: str, val: int) -> None: + self.add_key_value(key, val, GGUFValueType.INT8) + + def add_uint16(self, key: str, val: int) -> None: + self.add_key_value(key, val, GGUFValueType.UINT16) + + def add_int16(self, key: str, val: int) -> None: + self.add_key_value(key, val, GGUFValueType.INT16) + + def add_uint32(self, key: str, val: int) -> None: + self.add_key_value(key, val, GGUFValueType.UINT32) + + def add_int32(self, key: str, val: int) -> None: + self.add_key_value(key, val, GGUFValueType.INT32) + + def add_float32(self, key: str, val: float) -> None: + self.add_key_value(key, val, GGUFValueType.FLOAT32) + + def add_uint64(self, key: str, val: int) -> None: + self.add_key_value(key, val, GGUFValueType.UINT64) + + def add_int64(self, key: str, val: int) -> None: + self.add_key_value(key, val, GGUFValueType.INT64) + + def add_float64(self, key: str, val: float) -> None: + self.add_key_value(key, val, GGUFValueType.FLOAT64) + + def add_bool(self, key: str, val: bool) -> None: + self.add_key_value(key, val, GGUFValueType.BOOL) + + def add_string(self, key: str, val: str) -> None: + if not val: + return + self.add_key_value(key, val, GGUFValueType.STRING) + + def add_array(self, key: str, val: Sequence[Any]) -> None: + if len(val) == 0: + return + self.add_key_value(key, val, GGUFValueType.ARRAY) + + @staticmethod + def ggml_pad(x: int, n: int) -> int: + return ((x + n - 1) // n) * n + + def add_tensor_info( + self, name: str, tensor_shape: Sequence[int], tensor_dtype: np.dtype, + tensor_nbytes: int, raw_dtype: GGMLQuantizationType | None = None, + ) -> None: + if self.state is not WriterState.NO_FILE: + raise ValueError(f'Expected output file to be not yet opened, got {self.state}') + + if any(name in tensors for tensors in self.tensors): + raise ValueError(f'Duplicated tensor name {name!r}') + + if raw_dtype is None: + if tensor_dtype == np.float16: + dtype = GGMLQuantizationType.F16 + elif tensor_dtype == np.float32: + dtype = GGMLQuantizationType.F32 + elif tensor_dtype == np.float64: + dtype = GGMLQuantizationType.F64 + elif tensor_dtype == np.int8: + dtype = GGMLQuantizationType.I8 + elif tensor_dtype == np.int16: + dtype = GGMLQuantizationType.I16 + elif tensor_dtype == np.int32: + dtype = GGMLQuantizationType.I32 + elif tensor_dtype == np.int64: + dtype = GGMLQuantizationType.I64 + else: + raise ValueError("Only F16, F32, F64, I8, I16, I32, I64 tensors are supported for now") + else: + dtype = raw_dtype + if tensor_dtype == np.uint8: + tensor_shape = quant_shape_from_byte_shape(tensor_shape, raw_dtype) + + # make sure there is at least one tensor before splitting + if len(self.tensors[-1]) > 0: + if ( # split when over tensor limit + self.split_max_tensors != 0 + and len(self.tensors[-1]) >= self.split_max_tensors + ) or ( # split when over size limit + self.split_max_size != 0 + and sum(ti.nbytes for ti in self.tensors[-1].values()) + tensor_nbytes > self.split_max_size + ): + self.tensors.append({}) + + self.tensors[-1][name] = TensorInfo(shape=tensor_shape, dtype=dtype, nbytes=tensor_nbytes) + + def add_tensor( + self, name: str, tensor: np.ndarray[Any, Any], raw_shape: Sequence[int] | None = None, + raw_dtype: GGMLQuantizationType | None = None, tensor_endianess: GGUFEndian | None = None + ) -> None: + # if tensor endianness is not passed, assume it's native to system + if tensor_endianess is None: + tensor_endianess = GGUFEndian.BIG if sys.byteorder == 'big' else GGUFEndian.LITTLE + + if tensor_endianess != self.endianess: + # Don't byteswap inplace since lazy copies cannot handle it + tensor = tensor.byteswap(inplace=False) + if self.use_temp_file and self.temp_file is None: + fp = tempfile.SpooledTemporaryFile(mode="w+b", max_size=256 * 1024 * 1024) + fp.seek(0) + self.temp_file = fp + + shape: Sequence[int] = raw_shape if raw_shape is not None else tensor.shape + self.add_tensor_info(name, shape, tensor.dtype, tensor.nbytes, raw_dtype=raw_dtype) + + if self.temp_file is None: + self.tensors[-1][name].tensor = tensor + return + + tensor.tofile(self.temp_file) + self.write_padding(self.temp_file, tensor.nbytes) + + def write_padding(self, fp: IO[bytes], n: int, align: int | None = None) -> None: + pad = GGUFWriter.ggml_pad(n, align if align is not None else self.data_alignment) - n + if pad != 0: + fp.write(bytes([0] * pad)) + + def write_tensor_data(self, tensor: np.ndarray[Any, Any], tensor_endianess: GGUFEndian | None = None) -> None: + if self.state is not WriterState.TI_DATA and self.state is not WriterState.WEIGHTS: + raise ValueError(f'Expected output file to contain tensor info or weights, got {self.state}') + assert self.fout is not None + + # if tensor endianness is not passed, assume it's native to system + if tensor_endianess is None: + tensor_endianess = GGUFEndian.BIG if sys.byteorder == 'big' else GGUFEndian.LITTLE + + if tensor_endianess != self.endianess: + # Don't byteswap inplace since lazy copies cannot handle it + tensor = tensor.byteswap(inplace=False) + + file_id = -1 + for i, tensors in enumerate(self.tensors): + if len(tensors) > 0: + file_id = i + break + + fout = self.fout[file_id] + + # pop the first tensor info + first_tensor_name = next(iter(self.tensors[file_id])) + ti = self.tensors[file_id].pop(first_tensor_name) + assert ti.nbytes == tensor.nbytes + + self.write_padding(fout, fout.tell()) + tensor.tofile(fout) + self.write_padding(fout, tensor.nbytes) + + self.state = WriterState.WEIGHTS + + def write_tensors_to_file(self, *, progress: bool = False) -> None: + self.write_ti_data_to_file() + + assert self.fout is not None + + for fout in self.fout: + self.write_padding(fout, fout.tell()) + + if self.temp_file is None: + shard_bar = None + bar = None + + if progress: + from tqdm import tqdm + + total_bytes = sum(ti.nbytes for t in self.tensors for ti in t.values()) + + if len(self.fout) > 1: + shard_bar = tqdm(desc=f"Shard (0/{len(self.fout)})", total=None, unit="byte", unit_scale=True) + bar = tqdm(desc="Writing", total=total_bytes, unit="byte", unit_scale=True) + + for i, (fout, tensors) in enumerate(zip(self.fout, self.tensors)): + if shard_bar is not None: + shard_bar.set_description(f"Shard ({i + 1}/{len(self.fout)})") + total = sum(ti.nbytes for ti in tensors.values()) + shard_bar.reset(total=(total if total > 0 else None)) + + # relying on the fact that Python dicts preserve insertion order (since 3.7) + for ti in tensors.values(): + assert ti.tensor is not None # can only iterate once over the tensors + assert ti.tensor.nbytes == ti.nbytes + ti.tensor.tofile(fout) + if shard_bar is not None: + shard_bar.update(ti.nbytes) + if bar is not None: + bar.update(ti.nbytes) + self.write_padding(fout, ti.nbytes) + ti.tensor = None + else: + self.temp_file.seek(0) + + shutil.copyfileobj(self.temp_file, self.fout[0 if not self.small_first_shard else 1]) + self.flush() + self.temp_file.close() + + self.state = WriterState.WEIGHTS + + def flush(self) -> None: + assert self.fout is not None + for fout in self.fout: + fout.flush() + + def close(self) -> None: + if self.fout is not None: + for fout in self.fout: + fout.close() + self.fout = None + + def add_type(self, type_name: str) -> None: + self.add_string(Keys.General.TYPE, type_name) + + def add_architecture(self) -> None: + self.add_string(Keys.General.ARCHITECTURE, self.arch) + + def add_quantization_version(self, quantization_version: int) -> None: + self.add_uint32(Keys.General.QUANTIZATION_VERSION, quantization_version) + + def add_custom_alignment(self, alignment: int) -> None: + if alignment <= 0 or (alignment & (alignment - 1)) != 0: + raise ValueError('Invalid alignment: must be a non-zero power of two') + self.data_alignment = alignment + self.add_uint32(Keys.General.ALIGNMENT, alignment) + + def add_file_type(self, ftype: int) -> None: + self.add_uint32(Keys.General.FILE_TYPE, ftype) + + def add_sampling_sequence(self, sequence: str) -> None: + self.add_string(Keys.General.SAMPLING_SEQUENCE, sequence) + + def add_sampling_top_k(self, top_k: int) -> None: + self.add_int32(Keys.General.SAMPLING_TOP_K, top_k) + + def add_sampling_top_p(self, top_p: float) -> None: + self.add_float32(Keys.General.SAMPLING_TOP_P, top_p) + + def add_sampling_min_p(self, min_p: float) -> None: + self.add_float32(Keys.General.SAMPLING_MIN_P, min_p) + + def add_sampling_xtc_probability(self, xtc_probability: float) -> None: + self.add_float32(Keys.General.SAMPLING_XTC_PROBABILITY, xtc_probability) + + def add_sampling_xtc_threshold(self, xtc_threshold: float) -> None: + self.add_float32(Keys.General.SAMPLING_XTC_THRESHOLD, xtc_threshold) + + def add_sampling_temp(self, temp: float) -> None: + self.add_float32(Keys.General.SAMPLING_TEMP, temp) + + def add_sampling_penalty_last_n(self, penalty_last_n: int) -> None: + self.add_int32(Keys.General.SAMPLING_PENALTY_LAST_N, penalty_last_n) + + def add_sampling_penalty_repeat(self, penalty_repeat: float) -> None: + self.add_float32(Keys.General.SAMPLING_PENALTY_REPEAT, penalty_repeat) + + def add_sampling_mirostat(self, mirostat: int) -> None: + self.add_int32(Keys.General.SAMPLING_MIROSTAT, mirostat) + + def add_sampling_mirostat_tau(self, mirostat_tau: float) -> None: + self.add_float32(Keys.General.SAMPLING_MIROSTAT_TAU, mirostat_tau) + + def add_sampling_mirostat_eta(self, mirostat_eta: float) -> None: + self.add_float32(Keys.General.SAMPLING_MIROSTAT_ETA, mirostat_eta) + + def add_name(self, name: str) -> None: + self.add_string(Keys.General.NAME, name) + + def add_author(self, author: str) -> None: + self.add_string(Keys.General.AUTHOR, author) + + def add_version(self, version: str) -> None: + self.add_string(Keys.General.VERSION, version) + + def add_organization(self, organization: str) -> None: + self.add_string(Keys.General.ORGANIZATION, organization) + + def add_finetune(self, finetune: str) -> None: + self.add_string(Keys.General.FINETUNE, finetune) + + def add_basename(self, basename: str) -> None: + self.add_string(Keys.General.BASENAME, basename) + + def add_description(self, description: str) -> None: + self.add_string(Keys.General.DESCRIPTION, description) + + def add_quantized_by(self, quantized: str) -> None: + self.add_string(Keys.General.QUANTIZED_BY, quantized) + + def add_size_label(self, size_label: str) -> None: + self.add_string(Keys.General.SIZE_LABEL, size_label) + + def add_license(self, license: str) -> None: + self.add_string(Keys.General.LICENSE, license) + + def add_license_name(self, license: str) -> None: + self.add_string(Keys.General.LICENSE_NAME, license) + + def add_license_link(self, license: str) -> None: + self.add_string(Keys.General.LICENSE_LINK, license) + + def add_url(self, url: str) -> None: + self.add_string(Keys.General.URL, url) + + def add_doi(self, doi: str) -> None: + self.add_string(Keys.General.DOI, doi) + + def add_uuid(self, uuid: str) -> None: + self.add_string(Keys.General.UUID, uuid) + + def add_repo_url(self, repo_url: str) -> None: + self.add_string(Keys.General.REPO_URL, repo_url) + + def add_source_url(self, url: str) -> None: + self.add_string(Keys.General.SOURCE_URL, url) + + def add_source_doi(self, doi: str) -> None: + self.add_string(Keys.General.SOURCE_DOI, doi) + + def add_source_uuid(self, uuid: str) -> None: + self.add_string(Keys.General.SOURCE_UUID, uuid) + + def add_source_repo_url(self, repo_url: str) -> None: + self.add_string(Keys.General.SOURCE_REPO_URL, repo_url) + + def add_base_model_count(self, source_count: int) -> None: + self.add_uint32(Keys.General.BASE_MODEL_COUNT, source_count) + + def add_base_model_name(self, source_id: int, name: str) -> None: + self.add_string(Keys.General.BASE_MODEL_NAME.format(id=source_id), name) + + def add_base_model_author(self, source_id: int, author: str) -> None: + self.add_string(Keys.General.BASE_MODEL_AUTHOR.format(id=source_id), author) + + def add_base_model_version(self, source_id: int, version: str) -> None: + self.add_string(Keys.General.BASE_MODEL_VERSION.format(id=source_id), version) + + def add_base_model_organization(self, source_id: int, organization: str) -> None: + self.add_string(Keys.General.BASE_MODEL_ORGANIZATION.format(id=source_id), organization) + + def add_base_model_description(self, source_id: int, description: str) -> None: + self.add_string(Keys.General.BASE_MODEL_DESCRIPTION.format(id=source_id), description) + + def add_base_model_url(self, source_id: int, url: str) -> None: + self.add_string(Keys.General.BASE_MODEL_URL.format(id=source_id), url) + + def add_base_model_doi(self, source_id: int, doi: str) -> None: + self.add_string(Keys.General.BASE_MODEL_DOI.format(id=source_id), doi) + + def add_base_model_uuid(self, source_id: int, uuid: str) -> None: + self.add_string(Keys.General.BASE_MODEL_UUID.format(id=source_id), uuid) + + def add_base_model_repo_url(self, source_id: int, repo_url: str) -> None: + self.add_string(Keys.General.BASE_MODEL_REPO_URL.format(id=source_id), repo_url) + + def add_dataset_count(self, source_count: int) -> None: + self.add_uint32(Keys.General.DATASET_COUNT, source_count) + + def add_dataset_name(self, source_id: int, name: str) -> None: + self.add_string(Keys.General.DATASET_NAME.format(id=source_id), name) + + def add_dataset_author(self, source_id: int, author: str) -> None: + self.add_string(Keys.General.DATASET_AUTHOR.format(id=source_id), author) + + def add_dataset_version(self, source_id: int, version: str) -> None: + self.add_string(Keys.General.DATASET_VERSION.format(id=source_id), version) + + def add_dataset_organization(self, source_id: int, organization: str) -> None: + self.add_string(Keys.General.DATASET_ORGANIZATION.format(id=source_id), organization) + + def add_dataset_description(self, source_id: int, description: str) -> None: + self.add_string(Keys.General.DATASET_DESCRIPTION.format(id=source_id), description) + + def add_dataset_url(self, source_id: int, url: str) -> None: + self.add_string(Keys.General.DATASET_URL.format(id=source_id), url) + + def add_dataset_doi(self, source_id: int, doi: str) -> None: + self.add_string(Keys.General.DATASET_DOI.format(id=source_id), doi) + + def add_dataset_uuid(self, source_id: int, uuid: str) -> None: + self.add_string(Keys.General.DATASET_UUID.format(id=source_id), uuid) + + def add_dataset_repo_url(self, source_id: int, repo_url: str) -> None: + self.add_string(Keys.General.DATASET_REPO_URL.format(id=source_id), repo_url) + + def add_tags(self, tags: Sequence[str]) -> None: + self.add_array(Keys.General.TAGS, tags) + + def add_languages(self, languages: Sequence[str]) -> None: + self.add_array(Keys.General.LANGUAGES, languages) + + def add_tensor_data_layout(self, layout: str) -> None: + self.add_string(Keys.LLM.TENSOR_DATA_LAYOUT.format(arch=self.arch), layout) + + def add_vocab_size(self, size: int) -> None: + self.add_uint32(Keys.LLM.VOCAB_SIZE.format(arch=self.arch), size) + + def add_context_length(self, length: int) -> None: + self.add_uint32(Keys.LLM.CONTEXT_LENGTH.format(arch=self.arch), length) + + def add_embedding_length(self, length: int) -> None: + self.add_uint32(Keys.LLM.EMBEDDING_LENGTH.format(arch=self.arch), length) + + def add_embedding_length_out(self, length: int) -> None: + self.add_uint32(Keys.LLM.EMBEDDING_LENGTH_OUT.format(arch=self.arch), length) + + def add_features_length(self, length: int) -> None: + self.add_uint32(Keys.LLM.FEATURES_LENGTH.format(arch=self.arch), length) + + def add_posnet_embedding_length(self, length: int) -> None: + self.add_uint32(Keys.PosNet.EMBEDDING_LENGTH.format(arch=self.arch), length) + + def add_posnet_block_count(self, length: int) -> None: + self.add_uint32(Keys.PosNet.BLOCK_COUNT.format(arch=self.arch), length) + + def add_convnext_embedding_length(self, length: int) -> None: + self.add_uint32(Keys.ConvNext.EMBEDDING_LENGTH.format(arch=self.arch), length) + + def add_convnext_block_count(self, length: int) -> None: + self.add_uint32(Keys.ConvNext.BLOCK_COUNT.format(arch=self.arch), length) + + def add_shortconv_l_cache(self, length: int) -> None: + self.add_uint32(Keys.ShortConv.L_CACHE.format(arch=self.arch), length) + + def add_block_count(self, length: int) -> None: + self.add_uint32(Keys.LLM.BLOCK_COUNT.format(arch=self.arch), length) + + def add_leading_dense_block_count(self, length: int) -> None: + self.add_uint32(Keys.LLM.LEADING_DENSE_BLOCK_COUNT.format(arch=self.arch), length) + + def add_full_attention_interval(self, interval: int) -> None: + self.add_uint32(Keys.LLM.FULL_ATTENTION_INTERVAL.format(arch=self.arch), interval) + + def add_hash_layer_count(self, count: int) -> None: + self.add_uint32(Keys.LLM.HASH_LAYER_COUNT.format(arch=self.arch), count) + + def add_feed_forward_length(self, length: int | Sequence[int]) -> None: + if isinstance(length, int): + self.add_uint32(Keys.LLM.FEED_FORWARD_LENGTH.format(arch=self.arch), length) + else: + self.add_array(Keys.LLM.FEED_FORWARD_LENGTH.format(arch=self.arch), length) + + def add_expert_feed_forward_length(self, length: int) -> None: + self.add_uint32(Keys.LLM.EXPERT_FEED_FORWARD_LENGTH.format(arch=self.arch), length) + + def add_expert_shared_feed_forward_length(self, length: int) -> None: + self.add_uint32(Keys.LLM.EXPERT_SHARED_FEED_FORWARD_LENGTH.format(arch=self.arch), length) + + def add_expert_chunk_feed_forward_length(self, length: int) -> None: + self.add_uint32(Keys.LLM.EXPERT_CHUNK_FEED_FORWARD_LENGTH.format(arch=self.arch), length) + + def add_parallel_residual(self, use: bool) -> None: + self.add_bool(Keys.LLM.USE_PARALLEL_RESIDUAL.format(arch=self.arch), use) + + def add_decoder_start_token_id(self, id: int) -> None: + self.add_uint32(Keys.LLM.DECODER_START_TOKEN_ID.format(arch=self.arch), id) + + def add_decoder_block_count(self, value: int) -> None: + self.add_uint32(Keys.LLM.DECODER_BLOCK_COUNT.format(arch=self.arch), value) + + def add_embedding_length_per_layer_input(self, value: int) -> None: + self.add_uint32(Keys.LLM.EMBD_LENGTH_PER_LAYER_INP.format(arch=self.arch), value) + + def add_altup_active_idx(self, val: int) -> None: + self.add_uint32(Keys.LLM.ALTUP_ACTIVE_IDX.format(arch=self.arch), val) + + def add_altup_num_inputs(self, val: int) -> None: + self.add_uint32(Keys.LLM.ALTUP_NUM_INPUTS.format(arch=self.arch), val) + + def add_activation_sparsity_scale(self, values: Sequence[float]) -> None: + self.add_array(Keys.LLM.ACTIVATION_SPARSITY_SCALE.format(arch=self.arch), values) + + def add_head_count(self, count: int | Sequence[int]) -> None: + if isinstance(count, int): + self.add_uint32(Keys.Attention.HEAD_COUNT.format(arch=self.arch), count) + else: + self.add_array(Keys.Attention.HEAD_COUNT.format(arch=self.arch), count) + + def add_head_count_kv(self, count: int | Sequence[int]) -> None: + if isinstance(count, int): + self.add_uint32(Keys.Attention.HEAD_COUNT_KV.format(arch=self.arch), count) + else: + self.add_array(Keys.Attention.HEAD_COUNT_KV.format(arch=self.arch), count) + + def add_key_length(self, length: int) -> None: + self.add_uint32(Keys.Attention.KEY_LENGTH.format(arch=self.arch), length) + + def add_value_length(self, length: int) -> None: + self.add_uint32(Keys.Attention.VALUE_LENGTH.format(arch=self.arch), length) + + def add_key_length_mla(self, length: int) -> None: + self.add_uint32(Keys.Attention.KEY_LENGTH_MLA.format(arch=self.arch), length) + + def add_value_length_mla(self, length: int) -> None: + self.add_uint32(Keys.Attention.VALUE_LENGTH_MLA.format(arch=self.arch), length) + + def add_key_length_swa(self, length: int) -> None: + self.add_uint32(Keys.Attention.KEY_LENGTH_SWA.format(arch=self.arch), length) + + def add_value_length_swa(self, length: int) -> None: + self.add_uint32(Keys.Attention.VALUE_LENGTH_SWA.format(arch=self.arch), length) + + def add_indexer_head_count(self, count: int) -> None: + self.add_uint32(Keys.Attention.Indexer.HEAD_COUNT.format(arch=self.arch), count) + + def add_indexer_key_length(self, length: int) -> None: + self.add_uint32(Keys.Attention.Indexer.KEY_LENGTH.format(arch=self.arch), length) + + def add_indexer_top_k(self, top_k: int) -> None: + self.add_uint32(Keys.Attention.Indexer.TOP_K.format(arch=self.arch), top_k) + + def add_max_alibi_bias(self, bias: float) -> None: + self.add_float32(Keys.Attention.MAX_ALIBI_BIAS.format(arch=self.arch), bias) + + def add_clamp_kqv(self, value: float) -> None: + self.add_float32(Keys.Attention.CLAMP_KQV.format(arch=self.arch), value) + + def add_shared_kv_layers(self, value: int) -> None: + self.add_uint32(Keys.Attention.SHARED_KV_LAYERS.format(arch=self.arch), value) + + # if input is array, true means SWA and false means full_attention for each layer + def add_sliding_window_pattern(self, value: int | Sequence[bool]) -> None: + key = Keys.Attention.SLIDING_WINDOW_PATTERN.format(arch=self.arch) + if isinstance(value, int): + self.add_uint32(key, value) + else: + self.add_array(key, value) + + def add_dense_features_dims(self, dense:str, in_f:int, out_f:int) -> None: + self.add_uint32(Keys.LLM.DENSE_FEAT_IN_SIZE.format(arch=self.arch, dense=dense), in_f) + self.add_uint32(Keys.LLM.DENSE_FEAT_OUT_SIZE.format(arch=self.arch, dense=dense), out_f) + + def add_logit_scale(self, value: float) -> None: + self.add_float32(Keys.LLM.LOGIT_SCALE.format(arch=self.arch), value) + + def add_attn_logit_softcapping(self, value: float) -> None: + self.add_float32(Keys.LLM.ATTN_LOGIT_SOFTCAPPING.format(arch=self.arch), value) + + def add_router_logit_softcapping(self, value: float) -> None: + self.add_float32(Keys.LLM.ROUTER_LOGIT_SOFTCAPPING.format(arch=self.arch), value) + + def add_final_logit_softcapping(self, value: float) -> None: + self.add_float32(Keys.LLM.FINAL_LOGIT_SOFTCAPPING.format(arch=self.arch), value) + + def add_expert_count(self, count: int) -> None: + self.add_uint32(Keys.LLM.EXPERT_COUNT.format(arch=self.arch), count) + + def add_expert_used_count(self, count: int) -> None: + self.add_uint32(Keys.LLM.EXPERT_USED_COUNT.format(arch=self.arch), count) + + def add_expert_shared_count(self, count: int) -> None: + self.add_uint32(Keys.LLM.EXPERT_SHARED_COUNT.format(arch=self.arch), count) + + def add_expert_group_count(self, count: int) -> None: + self.add_uint32(Keys.LLM.EXPERT_GROUP_COUNT.format(arch=self.arch), count) + + def add_expert_group_used_count(self, count: int) -> None: + self.add_uint32(Keys.LLM.EXPERT_GROUP_USED_COUNT.format(arch=self.arch), count) + + def add_expert_weights_scale(self, value: float) -> None: + self.add_float32(Keys.LLM.EXPERT_WEIGHTS_SCALE.format(arch=self.arch), value) + + def add_expert_weights_norm(self, value: bool) -> None: + self.add_bool(Keys.LLM.EXPERT_WEIGHTS_NORM.format(arch=self.arch), value) + + def add_expert_gating_func(self, value: ExpertGatingFuncType) -> None: + self.add_uint32(Keys.LLM.EXPERT_GATING_FUNC.format(arch=self.arch), value.value) + + def add_swiglu_clamp_exp(self, values: Sequence[float]) -> None: + self.add_array(Keys.LLM.SWIGLU_CLAMP_EXP.format(arch=self.arch), values) + + def add_swiglu_clamp_shexp(self, values: Sequence[float]) -> None: + self.add_array(Keys.LLM.SWIGLU_CLAMP_SHEXP.format(arch=self.arch), values) + + def add_hidden_act(self, value: str) -> None: + self.add_string(Keys.LLM.HIDDEN_ACT.format(arch=self.arch), value) + + def add_expert_group_scale(self, value: float) -> None: + self.add_float32(Keys.LLM.EXPERT_GROUP_SCALE.format(arch=self.arch), value) + + def add_experts_per_group(self, count: int) -> None: + self.add_uint32(Keys.LLM.EXPERTS_PER_GROUP.format(arch=self.arch), count) + + def add_moe_every_n_layers(self, value: int) -> None: + self.add_uint32(Keys.LLM.MOE_EVERY_N_LAYERS.format(arch=self.arch), value) + + def add_moe_latent_size(self, value: int) -> None: + self.add_uint32(Keys.LLM.MOE_LATENT_SIZE.format(arch=self.arch), value) + + def add_nextn_predict_layers(self, count: int) -> None: + self.add_uint32(Keys.LLM.NEXTN_PREDICT_LAYERS.format(arch=self.arch), count) + + def add_swin_norm(self, value: bool) -> None: + self.add_bool(Keys.LLM.SWIN_NORM.format(arch=self.arch), value) + + def add_rescale_every_n_layers(self, count: int) -> None: + self.add_uint32(Keys.LLM.RESCALE_EVERY_N_LAYERS.format(arch=self.arch), count) + + def add_time_mix_extra_dim(self, dim: int) -> None: + self.add_uint32(Keys.LLM.TIME_MIX_EXTRA_DIM.format(arch=self.arch), dim) + + def add_time_decay_extra_dim(self, dim: int) -> None: + self.add_uint32(Keys.LLM.TIME_DECAY_EXTRA_DIM.format(arch=self.arch), dim) + + def add_residual_scale(self, value: float) -> None: + self.add_float32(Keys.LLM.RESIDUAL_SCALE.format(arch=self.arch), value) + + def add_embedding_scale(self, value: float) -> None: + self.add_float32(Keys.LLM.EMBEDDING_SCALE.format(arch=self.arch), value) + + def add_wkv_head_size(self, size: int) -> None: + self.add_uint32(Keys.WKV.HEAD_SIZE.format(arch=self.arch), size) + + def add_token_shift_count(self, count: int) -> None: + self.add_uint32(Keys.LLM.TOKEN_SHIFT_COUNT.format(arch=self.arch), count) + + def add_interleave_moe_layer_step(self, value: int) -> None: + self.add_uint32(Keys.LLM.INTERLEAVE_MOE_LAYER_STEP.format(arch=self.arch), value) + + def add_layer_norm_eps(self, value: float) -> None: + self.add_float32(Keys.Attention.LAYERNORM_EPS.format(arch=self.arch), value) + + def add_layer_norm_rms_eps(self, value: float) -> None: + self.add_float32(Keys.Attention.LAYERNORM_RMS_EPS.format(arch=self.arch), value) + + def add_group_norm_eps(self, value: float) -> None: + self.add_float32(Keys.Attention.GROUPNORM_EPS.format(arch=self.arch), value) + + def add_group_norm_groups(self, value: int) -> None: + self.add_uint32(Keys.Attention.GROUPNORM_GROUPS.format(arch=self.arch), value) + + def add_causal_attention(self, value: bool) -> None: + self.add_bool(Keys.Attention.CAUSAL.format(arch=self.arch), value) + + def add_q_lora_rank(self, length: int) -> None: + self.add_uint32(Keys.Attention.Q_LORA_RANK.format(arch=self.arch), length) + + def add_kv_lora_rank(self, length: int) -> None: + self.add_uint32(Keys.Attention.KV_LORA_RANK.format(arch=self.arch), length) + + def add_decay_lora_rank(self, length: int) -> None: + self.add_uint32(Keys.Attention.DECAY_LORA_RANK.format(arch=self.arch), length) + + def add_iclr_lora_rank(self, length: int) -> None: + self.add_uint32(Keys.Attention.ICLR_LORA_RANK.format(arch=self.arch), length) + + def add_value_residual_mix_lora_rank(self, length: int) -> None: + self.add_uint32(Keys.Attention.VALUE_RESIDUAL_MIX_LORA_RANK.format(arch=self.arch), length) + + def add_rope_freq_base_swa(self, value: float) -> None: + self.add_float32(Keys.Rope.FREQ_BASE_SWA.format(arch=self.arch), value) + + def add_gate_lora_rank(self, length: int) -> None: + self.add_uint32(Keys.Attention.GATE_LORA_RANK.format(arch=self.arch), length) + + def add_relative_attn_buckets_count(self, value: int) -> None: + self.add_uint32(Keys.Attention.REL_BUCKETS_COUNT.format(arch=self.arch), value) + + def add_sliding_window(self, value: int) -> None: + self.add_uint32(Keys.Attention.SLIDING_WINDOW.format(arch=self.arch), value) + + def add_block_size(self, value: int) -> None: + self.add_uint32(Keys.LLM.BLOCK_SIZE.format(arch=self.arch), value) + + def add_target_layers(self, value: Sequence[int]) -> None: + self.add_array(Keys.LLM.TARGET_LAYERS.format(arch=self.arch), value) + + def add_target_hidden_size(self, value: int) -> None: + self.add_uint32(Keys.LLM.TARGET_HIDDEN_SIZE.format(arch=self.arch), value) + + def add_norm_before_residual(self, value: bool) -> None: + self.add_bool(Keys.LLM.NORM_BEFORE_RESIDUAL.format(arch=self.arch), value) + + def add_attention_output_group_count(self, count: int) -> None: + self.add_uint32(Keys.Attention.OUTPUT_GROUP_COUNT.format(arch=self.arch), count) + + def add_attention_output_lora_rank(self, length: int) -> None: + self.add_uint32(Keys.Attention.OUTPUT_LORA_RANK.format(arch=self.arch), length) + + def add_attention_compress_ratios(self, values: Sequence[int]) -> None: + self.add_array(Keys.Attention.COMPRESS_RATIOS.format(arch=self.arch), values) + + def add_attention_compress_rope_freq_base(self, value: float) -> None: + self.add_float32(Keys.Attention.COMPRESS_ROPE_FREQ_BASE.format(arch=self.arch), value) + + def add_hyper_connection_count(self, count: int) -> None: + self.add_uint32(Keys.HyperConnection.COUNT.format(arch=self.arch), count) + + def add_hyper_connection_sinkhorn_iterations(self, count: int) -> None: + self.add_uint32(Keys.HyperConnection.SINKHORN_ITERATIONS.format(arch=self.arch), count) + + def add_hyper_connection_epsilon(self, value: float) -> None: + self.add_float32(Keys.HyperConnection.EPSILON.format(arch=self.arch), value) + + def add_attention_scale(self, value: float) -> None: + self.add_float32(Keys.Attention.SCALE.format(arch=self.arch), value) + + def add_attn_output_scale(self, value: float) -> None: + self.add_float32(Keys.Attention.OUTPUT_SCALE.format(arch=self.arch), value) + + def add_attn_value_scale(self, value: float) -> None: + self.add_float32(Keys.Attention.VALUE_SCALE.format(arch=self.arch), value) + + def add_attn_temperature_length(self, value: int) -> None: + self.add_uint32(Keys.Attention.TEMPERATURE_LENGTH.format(arch=self.arch), value) + + def add_attn_temperature_scale(self, value: float) -> None: + self.add_float32(Keys.Attention.TEMPERATURE_SCALE.format(arch=self.arch), value) + + def add_pooling_type(self, value: PoolingType) -> None: + self.add_uint32(Keys.LLM.POOLING_TYPE.format(arch=self.arch), value.value) + + def add_num_deepstack_layers(self, count: int) -> None: + """Add scalar deepstack layer count (qwen3vl format)""" + self.add_uint32(Keys.LLM.NUM_DEEPSTACK_LAYERS.format(arch=self.arch), count) + + def add_deepstack_mapping(self, layers: Sequence[int]) -> None: + """Add per-layer deepstack projector indices (Granite4 Vision format)""" + self.add_array(Keys.LLM.DEEPSTACK_MAPPING.format(arch=self.arch), list(layers)) + + def add_rope_dimension_count(self, count: int) -> None: + self.add_uint32(Keys.Rope.DIMENSION_COUNT.format(arch=self.arch), count) + + def add_rope_dimension_count_swa(self, count: int) -> None: + self.add_uint32(Keys.Rope.DIMENSION_COUNT_SWA.format(arch=self.arch), count) + + def add_rope_dimension_sections(self, dims: Sequence[int]) -> None: + self.add_array(Keys.Rope.DIMENSION_SECTIONS.format(arch=self.arch), dims) + + def add_rope_freq_base(self, value: float) -> None: + self.add_float32(Keys.Rope.FREQ_BASE.format(arch=self.arch), value) + + def add_rope_scaling_type(self, value: RopeScalingType) -> None: + self.add_string(Keys.Rope.SCALING_TYPE.format(arch=self.arch), value.value) + + def add_rope_scaling_factor(self, value: float) -> None: + self.add_float32(Keys.Rope.SCALING_FACTOR.format(arch=self.arch), value) + + def add_rope_scaling_alpha(self, value: float) -> None: + self.add_float32(Keys.Rope.SCALING_ALPHA.format(arch=self.arch), value) + + def add_rope_scaling_attn_factors(self, value: float) -> None: + self.add_float32(Keys.Rope.SCALING_ATTN_FACTOR.format(arch=self.arch), value) + + def add_rope_scaling_orig_ctx_len(self, value: int) -> None: + self.add_uint32(Keys.Rope.SCALING_ORIG_CTX_LEN.format(arch=self.arch), value) + + def add_rope_scaling_finetuned(self, value: bool) -> None: + self.add_bool(Keys.Rope.SCALING_FINETUNED.format(arch=self.arch), value) + + def add_rope_scaling_yarn_log_mul(self, value: float) -> None: + self.add_float32(Keys.Rope.SCALING_YARN_LOG_MUL.format(arch=self.arch), value) + + def add_rope_scaling_yarn_ext_factor(self, value: float) -> None: + self.add_float32(Keys.Rope.SCALING_YARN_EXT_FACTOR.format(arch=self.arch), value) + + def add_rope_scaling_yarn_attn_factor(self, value: float) -> None: + self.add_float32(Keys.Rope.SCALING_YARN_ATTN_FACTOR.format(arch=self.arch), value) + + def add_rope_scaling_yarn_beta_fast(self, value: float) -> None: + self.add_float32(Keys.Rope.SCALING_YARN_BETA_FAST.format(arch=self.arch), value) + + def add_rope_scaling_yarn_beta_slow(self, value: float) -> None: + self.add_float32(Keys.Rope.SCALING_YARN_BETA_SLOW.format(arch=self.arch), value) + + def add_ssm_conv_kernel(self, value: int) -> None: + self.add_uint32(Keys.SSM.CONV_KERNEL.format(arch=self.arch), value) + + def add_ssm_inner_size(self, value: int) -> None: + self.add_uint32(Keys.SSM.INNER_SIZE.format(arch=self.arch), value) + + def add_ssm_state_size(self, value: int) -> None: + self.add_uint32(Keys.SSM.STATE_SIZE.format(arch=self.arch), value) + + def add_ssm_time_step_rank(self, value: int) -> None: + self.add_uint32(Keys.SSM.TIME_STEP_RANK.format(arch=self.arch), value) + + def add_ssm_group_count(self, value: int) -> None: + self.add_uint32(Keys.SSM.GROUP_COUNT.format(arch=self.arch), value) + + def add_ssm_dt_b_c_rms(self, value: bool) -> None: + self.add_bool(Keys.SSM.DT_B_C_RMS.format(arch=self.arch), value) + + def add_kda_head_dim(self, value: int) -> None: + self.add_uint32(Keys.KDA.HEAD_DIM.format(arch=self.arch), value) + + def add_tokenizer_model(self, model: str) -> None: + self.add_string(Keys.Tokenizer.MODEL, model) + + def add_tokenizer_pre(self, pre: str) -> None: + self.add_string(Keys.Tokenizer.PRE, pre) + + def add_token_list(self, tokens: Sequence[str] | Sequence[bytes] | Sequence[bytearray]) -> None: + self.add_array(Keys.Tokenizer.LIST, tokens) + + def add_token_merges(self, merges: Sequence[str] | Sequence[bytes] | Sequence[bytearray]) -> None: + self.add_array(Keys.Tokenizer.MERGES, merges) + + def add_token_types(self, types: Sequence[TokenType] | Sequence[int]) -> None: + self.add_array(Keys.Tokenizer.TOKEN_TYPE, types) + + def add_token_type_count(self, value: int) -> None: + self.add_uint32(Keys.Tokenizer.TOKEN_TYPE_COUNT, value) + + def add_token_scores(self, scores: Sequence[float]) -> None: + self.add_array(Keys.Tokenizer.SCORES, scores) + + def add_bos_token_id(self, id: int) -> None: + self.add_uint32(Keys.Tokenizer.BOS_ID, id) + + def add_eos_token_id(self, id: int) -> None: + self.add_uint32(Keys.Tokenizer.EOS_ID, id) + + def add_unk_token_id(self, id: int) -> None: + self.add_uint32(Keys.Tokenizer.UNK_ID, id) + + def add_sep_token_id(self, id: int) -> None: + self.add_uint32(Keys.Tokenizer.SEP_ID, id) + + def add_pad_token_id(self, id: int) -> None: + self.add_uint32(Keys.Tokenizer.PAD_ID, id) + + def add_mask_token_id(self, id: int) -> None: + self.add_uint32(Keys.Tokenizer.MASK_ID, id) + + def add_add_bos_token(self, value: bool) -> None: + self.add_bool(Keys.Tokenizer.ADD_BOS, value) + + def add_add_eos_token(self, value: bool) -> None: + self.add_bool(Keys.Tokenizer.ADD_EOS, value) + + def add_add_sep_token(self, value: bool) -> None: + self.add_bool(Keys.Tokenizer.ADD_SEP, value) + + def add_add_space_prefix(self, value: bool) -> None: + self.add_bool(Keys.Tokenizer.ADD_PREFIX, value) + + def add_remove_extra_whitespaces(self, value: bool) -> None: + self.add_bool(Keys.Tokenizer.REMOVE_EXTRA_WS, value) + + def add_precompiled_charsmap(self, charsmap: bytes) -> None: + self.add_array(Keys.Tokenizer.PRECOMPILED_CHARSMAP, charsmap) + + def add_chat_template(self, value: str | Sequence[Mapping[str, str]]) -> None: + if not isinstance(value, str): + template_default = None + template_names = set() + + for choice in value: + name = choice.get('name', '') + template = choice.get('template') + + # Allowing non-alphanumerical characters in template name is probably not a good idea, so filter it + name = ''.join((c if c in ascii_letters + digits else '_' for c in name)) + + if name and template is not None: + if name == 'default': + template_default = template + else: + template_names.add(name) + self.add_string(Keys.Tokenizer.CHAT_TEMPLATE_N.format(name=name), template) + + if template_names: + self.add_array(Keys.Tokenizer.CHAT_TEMPLATES, list(template_names)) + + if template_default is None: + return + + value = template_default + + self.add_string(Keys.Tokenizer.CHAT_TEMPLATE, value) + + def add_suppress_tokens(self, tokens: Sequence[int]) -> None: + self.add_array(Keys.Tokenizer.SUPPRESS_TOKENS, tokens) + + def add_normalizer_lowercase(self, value: bool) -> None: + self.add_bool(Keys.Tokenizer.NORMALIZER_LOWERCASE, value) + + def add_normalizer_strip_accents(self, value: bool) -> None: + self.add_bool(Keys.Tokenizer.NORMALIZER_STRIP_ACCENTS, value) + + def add_eot_token_id(self, id: int) -> None: + self.add_uint32(Keys.Tokenizer.EOT_ID, id) + + def add_eom_token_id(self, id: int) -> None: + self.add_uint32(Keys.Tokenizer.EOM_ID, id) + + def add_classifier_output_labels(self, labels: Sequence[str]) -> None: + self.add_array(Keys.Classifier.OUTPUT_LABELS.format(arch=self.arch), labels) + + # for vision models + + def add_clip_has_vision_encoder(self, value: bool) -> None: + self.add_bool(Keys.Clip.HAS_VISION_ENCODER, value) + + def add_clip_has_audio_encoder(self, value: bool) -> None: + self.add_bool(Keys.Clip.HAS_AUDIO_ENCODER, value) + + def add_clip_projector_type(self, value: str) -> None: + self.add_string(Keys.Clip.PROJECTOR_TYPE, value) + + def add_clip_vision_projector_type(self, value: str) -> None: + self.add_string(Keys.ClipVision.PROJECTOR_TYPE, value) + + def add_vision_projection_dim(self, value: int) -> None: + self.add_uint32(Keys.ClipVision.PROJECTION_DIM, value) + + def add_vision_patch_size(self, value: int) -> None: + self.add_uint32(Keys.ClipVision.PATCH_SIZE, value) + + def add_vision_embedding_length(self, value: int) -> None: + self.add_uint32(Keys.ClipVision.EMBEDDING_LENGTH, value) + + def add_vision_feed_forward_length(self, value: int) -> None: + self.add_uint32(Keys.ClipVision.FEED_FORWARD_LENGTH, value) + + def add_vision_block_count(self, value: int) -> None: + self.add_uint32(Keys.ClipVision.BLOCK_COUNT, value) + + def add_vision_head_count(self, value: int) -> None: + self.add_uint32(Keys.ClipVision.Attention.HEAD_COUNT, value) + + def add_vision_head_count_kv(self, value: int) -> None: + self.add_uint32(Keys.ClipVision.Attention.HEAD_COUNT_KV, value) + + def add_vision_attention_layernorm_eps(self, value: float) -> None: + self.add_float32(Keys.ClipVision.Attention.LAYERNORM_EPS, value) + + def add_vision_image_size(self, value: int) -> None: + self.add_uint32(Keys.ClipVision.IMAGE_SIZE, value) + + def add_vision_max_pixels(self, value: int) -> None: + self.add_uint32(Keys.ClipVision.IMAGE_MAX_PIXELS, value) + + def add_vision_min_pixels(self, value: int) -> None: + self.add_uint32(Keys.ClipVision.IMAGE_MIN_PIXELS, value) + + def add_vision_preproc_max_tiles(self, value: int) -> None: + self.add_uint32(Keys.ClipVision.PREPROC_MAX_TILES, value) + + def add_vision_preproc_min_tiles(self, value: int) -> None: + self.add_uint32(Keys.ClipVision.PREPROC_MIN_TILES, value) + + def add_vision_preproc_image_size(self, value: int) -> None: + self.add_uint32(Keys.ClipVision.PREPROC_IMAGE_SIZE, value) + + def add_vision_projector_query_side(self, value: int) -> None: + self.add_uint32(Keys.ClipVision.Projector.QUERY_SIDE, value) + + def add_vision_projector_window_side(self, value: int) -> None: + self.add_uint32(Keys.ClipVision.Projector.WINDOW_SIDE, value) + + def add_vision_spatial_offsets(self, layers: Sequence[int]) -> None: + self.add_array(Keys.ClipVision.Projector.SPATIAL_OFFSETS, layers) + + def add_vision_image_mean(self, values: Sequence[float]) -> None: + self.add_array(Keys.ClipVision.IMAGE_MEAN, values) + + def add_vision_image_std(self, values: Sequence[float]) -> None: + self.add_array(Keys.ClipVision.IMAGE_STD, values) + + def add_vision_spatial_merge_size(self, value: int) -> None: + self.add_uint32(Keys.ClipVision.SPATIAL_MERGE_SIZE, value) + + def add_vision_use_gelu(self, value: bool) -> None: + self.add_bool(Keys.ClipVision.USE_GELU, value) + + def add_vision_use_silu(self, value: bool) -> None: + self.add_bool(Keys.ClipVision.USE_SILU, value) + + def add_vision_projector_scale_factor(self, value: int) -> None: + self.add_uint32(Keys.ClipVision.Projector.SCALE_FACTOR, value) + + def add_vision_n_wa_pattern(self, value: int) -> None: + """Add window attention pattern interval for vision models. + + This defines the pattern interval for window attention vs full attention layers. + For example, if n_wa_pattern=4, then layers 3, 7, 11, ... use full attention, + while other layers use window attention. + + Used by models like Qwen2.5-VL where full attention layers follow a regular pattern. + """ + self.add_uint32(Keys.ClipVision.N_WA_PATTERN, value) + + def add_vision_wa_layer_indexes(self, layers: Sequence[int]) -> None: + """Add explicit layer indexes that use full attention in vision models. + + This specifies the exact layer indices (0-based) that should use full attention + instead of window attention. All other layers will use window attention. + + Args: + layers: List of layer indices that use full attention (e.g., [3, 7, 11, 15]) + + Used by models like YoutuVL where full attention layers are explicitly specified + rather than following a regular pattern. + + Difference from add_vision_n_wa_pattern: + - n_wa_pattern: Defines a regular interval pattern (every Nth layer uses full attention) + - wa_layer_indexes: Explicitly lists which layers use full attention (irregular pattern) + """ + self.add_array(Keys.ClipVision.WA_LAYER_INDEXES, layers) + + def add_vision_is_deepstack_layers(self, layers: Sequence[bool]) -> None: + self.add_array(Keys.ClipVision.IS_DEEPSTACK_LAYERS, layers) + + def add_vision_wa_pattern_mode(self, modes: Sequence[int]) -> None: + self.add_array(Keys.ClipVision.WA_PATTERN_MODE, modes) + + def add_vision_window_size(self, value: int) -> None: + self.add_uint32(Keys.ClipVision.WINDOW_SIZE, value) + + def add_vision_feature_layers(self, layers: Sequence[int]) -> None: + self.add_array(Keys.ClipVision.FEATURE_LAYERS, layers) + + def add_vision_image_grid_pinpoints(self, layers: Sequence[Sequence[int]]) -> None: + self.add_array(Keys.ClipVision.IMAGE_GRID_PINPOINTS, layers) + + def add_vision_sam_layers_count(self, value: int) -> None: + self.add_uint32(Keys.ClipVision.SAM.BLOCK_COUNT, value) + + def add_vision_sam_embedding_length(self, value: int) -> None: + self.add_uint32(Keys.ClipVision.SAM.EMBEDDING_LENGTH, value) + + def add_vision_sam_head_count(self, value: int) -> None: + self.add_uint32(Keys.ClipVision.SAM.HEAD_COUNT, value) + + # audio models + + def add_clip_audio_projector_type(self, value: str) -> None: + self.add_string(Keys.ClipAudio.PROJECTOR_TYPE, value) + + def add_audio_projection_dim(self, value: int) -> None: + self.add_uint32(Keys.ClipAudio.PROJECTION_DIM, value) + + def add_audio_embedding_length(self, value: int) -> None: + self.add_uint32(Keys.ClipAudio.EMBEDDING_LENGTH, value) + + def add_audio_feed_forward_length(self, value: int) -> None: + self.add_uint32(Keys.ClipAudio.FEED_FORWARD_LENGTH, value) + + def add_audio_block_count(self, value: int) -> None: + self.add_uint32(Keys.ClipAudio.BLOCK_COUNT, value) + + def add_audio_head_count(self, value: int) -> None: + self.add_uint32(Keys.ClipAudio.Attention.HEAD_COUNT, value) + + def add_audio_attention_layernorm_eps(self, value: float) -> None: + self.add_float32(Keys.ClipAudio.Attention.LAYERNORM_EPS, value) + + def add_audio_num_mel_bins(self, value: int) -> None: + self.add_uint32(Keys.ClipAudio.NUM_MEL_BINS, value) + + def add_audio_stack_factor(self, value: int) -> None: + self.add_uint32(Keys.ClipAudio.Projector.STACK_FACTOR, value) + + def add_audio_chunk_size(self, value: int) -> None: + self.add_uint32(Keys.ClipAudio.CHUNK_SIZE, value) + + def add_audio_conv_kernel_size(self, value: int) -> None: + self.add_uint32(Keys.ClipAudio.CONV_KERNEL_SIZE, value) + + def add_audio_max_pos_emb(self, value: int) -> None: + self.add_uint32(Keys.ClipAudio.MAX_POS_EMB, value) + + def add_audio_feature_layers(self, layers: Sequence[int]) -> None: + self.add_array(Keys.ClipAudio.FEATURE_LAYERS, layers) + + def add_audio_projector_window_size(self, value: int) -> None: + self.add_uint32(Keys.ClipAudio.Projector.WINDOW_SIZE, value) + + def add_audio_projector_downsample_rate(self, value: int) -> None: + self.add_uint32(Keys.ClipAudio.Projector.DOWNSAMPLE_RATE, value) + + def add_audio_projector_head_count(self, value: int) -> None: + self.add_uint32(Keys.ClipAudio.Projector.HEAD_COUNT, value) + + def add_xielu_alpha_p(self, values: Sequence[float]): + self.add_array(Keys.xIELU.ALPHA_P, values) + + def add_xielu_alpha_n(self, values: Sequence[float]): + self.add_array(Keys.xIELU.ALPHA_N, values) + + def add_xielu_beta(self, values: Sequence[float]): + self.add_array(Keys.xIELU.BETA, values) + + def add_xielu_eps(self, values: Sequence[float]): + self.add_array(Keys.xIELU.EPS, values) + + # diffusion models + + def add_diffusion_shift_logits(self, value: bool) -> None: + self.add_bool(Keys.Diffusion.SHIFT_LOGITS, value) + + def _pack(self, fmt: str, value: Any, skip_pack_prefix: bool = False) -> bytes: + pack_prefix = '' + if not skip_pack_prefix: + pack_prefix = '<' if self.endianess == GGUFEndian.LITTLE else '>' + return struct.pack(f'{pack_prefix}{fmt}', value) + + def _pack_val(self, val: Any, vtype: GGUFValueType, add_vtype: bool, sub_type: GGUFValueType | None = None) -> bytes: + kv_data = bytearray() + + if add_vtype: + kv_data += self._pack("I", vtype) + + pack_fmt = self._simple_value_packing.get(vtype) + if pack_fmt is not None: + kv_data += self._pack(pack_fmt, val, skip_pack_prefix = vtype == GGUFValueType.BOOL) + elif vtype == GGUFValueType.STRING: + encoded_val = val.encode("utf-8") if isinstance(val, str) else val + kv_data += self._pack("Q", len(encoded_val)) + kv_data += encoded_val + elif vtype == GGUFValueType.ARRAY: + + if not isinstance(val, Sequence): + raise ValueError("Invalid GGUF metadata array, expecting sequence") + + if len(val) == 0: + raise ValueError("Invalid GGUF metadata array. Empty array") + + if sub_type is not None: + ltype = sub_type + elif isinstance(val, bytes): + ltype = GGUFValueType.UINT8 + else: + ltype = GGUFValueType.get_type(val[0]) + if not all(GGUFValueType.get_type(i) is ltype for i in val[1:]): + raise ValueError("All items in a GGUF array should be of the same type") + kv_data += self._pack("I", ltype) + kv_data += self._pack("Q", len(val)) + for item in val: + kv_data += self._pack_val(item, ltype, add_vtype=False) + else: + raise ValueError("Invalid GGUF metadata value type or value") + + return bytes(kv_data) + + @staticmethod + def format_n_bytes_to_str(num: int) -> str: + if num == 0: + return "negligible - metadata only" + fnum = float(num) + for unit in ("", "K", "M", "G"): + if abs(fnum) < 1000.0: + return f"{fnum:3.1f}{unit}" + fnum /= 1000.0 + return f"{fnum:.1f}T - over 1TB, split recommended" diff --git a/backend/llama.cpp/gguf-py/gguf/lazy.py b/backend/llama.cpp/gguf-py/gguf/lazy.py new file mode 100644 index 0000000000000000000000000000000000000000..acbc79258a31d5bfe7fa3dd9b0bb5309e0b829f2 --- /dev/null +++ b/backend/llama.cpp/gguf-py/gguf/lazy.py @@ -0,0 +1,228 @@ +from __future__ import annotations +from abc import ABC, ABCMeta, abstractmethod + +import logging +from typing import Any, Callable + +import numpy as np +from numpy.typing import DTypeLike + + +logger = logging.getLogger(__name__) + + +class LazyMeta(ABCMeta): + + def __new__(cls, name: str, bases: tuple[type, ...], namespace: dict[str, Any], **kwargs): + def __getattr__(self, name: str) -> Any: + meta_attr = getattr(self._meta, name) + if callable(meta_attr): + return type(self)._wrap_fn( + (lambda s, *args, **kwargs: getattr(s, name)(*args, **kwargs)), + use_self=self, + ) + elif isinstance(meta_attr, self._tensor_type): + # e.g. self.T with torch.Tensor should still be wrapped + return type(self)._wrap_fn(lambda s: getattr(s, name))(self) + else: + # no need to wrap non-tensor properties, + # and they likely don't depend on the actual contents of the tensor + return meta_attr + + namespace["__getattr__"] = __getattr__ + + # need to make a builder for the wrapped wrapper to copy the name, + # or else it fails with very cryptic error messages, + # because somehow the same string would end up in every closures + def mk_wrap(op_name: str, *, meta_noop: bool = False): + # need to wrap the wrapper to get self + def wrapped_special_op(self, *args, **kwargs): + return type(self)._wrap_fn( + getattr(type(self)._tensor_type, op_name), + meta_noop=meta_noop, + )(self, *args, **kwargs) + return wrapped_special_op + + # special methods bypass __getattr__, so they need to be added manually + # ref: https://docs.python.org/3/reference/datamodel.html#special-lookup + # NOTE: doing this from a metaclass is very convenient + # TODO: make this even more comprehensive + for binary_op in ( + "lt", "le", "eq", "ne", "ge", "gt", + "add", "and", "floordiv", "lshift", "mod", "mul", "matmul", + "or", "pow", "rshift", "sub", "truediv", "xor", + "iadd", "iand", "ifloordiv", "ilshift", "imod", "imul", "ior", "irshift", "isub", "ixor", + "radd", "rand", "rfloordiv", "rmul", "ror", "rpow", "rsub", "rtruediv", "rxor", + ): + attr_name = f"__{binary_op}__" + # evaluation on the meta tensor is needed in case there's broadcasting + namespace[attr_name] = mk_wrap(attr_name, meta_noop=False) + + for unary_op in ("not", "abs", "invert", "neg", "pos"): + attr_name = f"__{unary_op}__" + # the result of these operators usually has the same shape and dtype as the input, + # so evaluation on the meta tensor can be skipped. + namespace[attr_name] = mk_wrap(attr_name, meta_noop=True) + + for special_op in ( + "getitem", "setitem", "len", + ): + attr_name = f"__{special_op}__" + namespace[attr_name] = mk_wrap(attr_name, meta_noop=False) + + return super().__new__(cls, name, bases, namespace, **kwargs) + + +# Tree of lazy tensors +class LazyBase(ABC, metaclass=LazyMeta): + _tensor_type: type + _meta: Any + _data: Any | None + _args: tuple + _kwargs: dict[str, Any] + _func: Callable[[Any], Any] | None + + def __init__(self, *, meta: Any, data: Any | None = None, args: tuple = (), kwargs: dict[str, Any] | None = None, func: Callable[[Any], Any] | None = None): + super().__init__() + self._meta = meta + self._data = data + self._args = args + self._kwargs = kwargs if kwargs is not None else {} + self._func = func + assert self._func is not None or self._data is not None + + def __init_subclass__(cls) -> None: + if "_tensor_type" not in cls.__dict__: + raise TypeError(f"property '_tensor_type' must be defined for {cls!r}") + return super().__init_subclass__() + + @staticmethod + def _recurse_apply(o: Any, fn: Callable[[Any], Any]) -> Any: + # TODO: dict and set + if isinstance(o, (list, tuple)): + L = [] + for item in o: + L.append(LazyBase._recurse_apply(item, fn)) + if isinstance(o, tuple): + L = tuple(L) + return L + elif isinstance(o, LazyBase): + return fn(o) + else: + return o + + @classmethod + def _wrap_fn(cls, fn: Callable, *, use_self: LazyBase | None = None, meta_noop: bool | DTypeLike | tuple[DTypeLike, Callable[[tuple[int, ...]], tuple[int, ...]]] = False) -> Callable[[Any], Any]: + def wrapped_fn(*args, **kwargs): + if kwargs is None: + kwargs = {} + args = ((use_self,) if use_self is not None else ()) + args + + meta_args = LazyBase._recurse_apply(args, lambda t: t._meta) + # TODO: maybe handle tensors in kwargs too + + if isinstance(meta_noop, bool) and not meta_noop: + try: + res = fn(*meta_args, **kwargs) + except NotImplementedError: + # running some operations on PyTorch's Meta tensors can cause this exception + res = None + else: + # some operators don't need to actually run on the meta tensors + assert len(args) > 0 + res = args[0] + assert isinstance(res, cls) + res = res._meta + # allow operations to override the dtype and shape + if meta_noop is not True: + if isinstance(meta_noop, tuple): + dtype, shape = meta_noop + assert callable(shape) + res = cls.meta_with_dtype_and_shape(dtype, shape(res.shape)) # ty: ignore[call-top-callable] + else: + res = cls.meta_with_dtype_and_shape(meta_noop, res.shape) + + if isinstance(res, cls._tensor_type): + return cls(meta=cls.eager_to_meta(res), args=args, kwargs=kwargs, func=fn) + elif isinstance(res, tuple) and all(isinstance(t, cls._tensor_type) for t in res): + # share the evaluation between lazy tuple elements + shared_args: list = [args, None] + + def eager_tuple_element(a: list[Any], i: int = 0, /, **kw) -> LazyBase: + assert len(a) == 2 + if a[1] is None: + a[1] = fn(*a[0], **kw) + return a[1][i] + return tuple(cls(meta=cls.eager_to_meta(res[i]), args=(shared_args, i), kwargs=kwargs, func=eager_tuple_element) for i in range(len(res))) + else: + del res # not needed + # non-tensor return likely relies on the contents of the args + # (e.g. the result of torch.equal) + eager_args = cls.to_eager(args) + return fn(*eager_args, **kwargs) + return wrapped_fn + + @classmethod + def to_eager(cls, t: Any) -> Any: + def simple_to_eager(_t: LazyBase) -> Any: + if _t._data is not None: + return _t._data + + # NOTE: there's a recursion limit in Python (usually 1000) + + assert _t._func is not None + _t._args = cls._recurse_apply(_t._args, simple_to_eager) + _t._data = _t._func(*_t._args, **_t._kwargs) + # sanity check + assert _t._data is not None + assert _t._data.dtype == _t._meta.dtype + assert _t._data.shape == _t._meta.shape + + return _t._data + + # recurse into lists and/or tuples, keeping their structure + return cls._recurse_apply(t, simple_to_eager) + + @classmethod + def eager_to_meta(cls, t: Any) -> Any: + return cls.meta_with_dtype_and_shape(t.dtype, t.shape) + + # must be overridden, meta tensor init is backend-specific + @classmethod + @abstractmethod + def meta_with_dtype_and_shape(cls, dtype: Any, shape: Any) -> Any: pass + + @classmethod + def from_eager(cls, t: Any) -> Any: + if type(t) is cls: + # already lazy + return t + elif isinstance(t, cls._tensor_type): + return cls(meta=cls.eager_to_meta(t), data=t) + else: + return TypeError(f"{type(t)!r} is not compatible with {cls._tensor_type!r}") + + +class LazyNumpyTensor(LazyBase): + _tensor_type = np.ndarray + + shape: tuple[int, ...] # Makes the type checker happy in quants.py + + @classmethod + def meta_with_dtype_and_shape(cls, dtype: DTypeLike, shape: tuple[int, ...]) -> np.ndarray[Any, Any]: + # The initial idea was to use np.nan as the fill value, + # but non-float types like np.int16 can't use that. + # So zero it is. + cheat = np.zeros(1, dtype) + return np.lib.stride_tricks.as_strided(cheat, shape, (0 for _ in shape)) + + def astype(self, dtype, *args, **kwargs): + meta = type(self).meta_with_dtype_and_shape(dtype, self._meta.shape) + full_args = (self, dtype,) + args + return type(self)(meta=meta, args=full_args, kwargs=kwargs, func=(lambda a, *args, **kwargs: a.astype(*args, **kwargs))) + + def tofile(self, *args, **kwargs): + eager = LazyNumpyTensor.to_eager(self) + return eager.tofile(*args, **kwargs) + + # TODO: __array_function__ diff --git a/backend/llama.cpp/gguf-py/gguf/metadata.py b/backend/llama.cpp/gguf-py/gguf/metadata.py new file mode 100644 index 0000000000000000000000000000000000000000..e954644e28f9b0a75122536a54ccb35788ca584c --- /dev/null +++ b/backend/llama.cpp/gguf-py/gguf/metadata.py @@ -0,0 +1,731 @@ +from __future__ import annotations + +import re +import json +import yaml +import logging +from pathlib import Path +from typing import Any, Literal, Optional +from dataclasses import dataclass + +from .constants import Keys + +import gguf + +logger = logging.getLogger("metadata") + + +@dataclass +class Metadata: + # Recommended Sampler Parameters to be written to GGUF KV Store + sampling_sequence: Optional[str] = None + sampling_top_k: Optional[int] = None + sampling_top_p: Optional[float] = None + sampling_min_p: Optional[float] = None + sampling_xtc_probability: Optional[float] = None + sampling_xtc_threshold: Optional[float] = None + sampling_temp: Optional[float] = None + sampling_penalty_last_n: Optional[int] = None + sampling_penalty_repeat: Optional[float] = None + sampling_mirostat: Optional[int] = None + sampling_mirostat_tau: Optional[float] = None + sampling_mirostat_eta: Optional[float] = None + + # Authorship Metadata to be written to GGUF KV Store + name: Optional[str] = None + author: Optional[str] = None + version: Optional[str] = None + organization: Optional[str] = None + finetune: Optional[str] = None + basename: Optional[str] = None + description: Optional[str] = None + quantized_by: Optional[str] = None + size_label: Optional[str] = None + url: Optional[str] = None + doi: Optional[str] = None + uuid: Optional[str] = None + repo_url: Optional[str] = None + source_url: Optional[str] = None + source_doi: Optional[str] = None + source_uuid: Optional[str] = None + source_repo_url: Optional[str] = None + license: Optional[str] = None + license_name: Optional[str] = None + license_link: Optional[str] = None + base_models: Optional[list[dict]] = None + tags: Optional[list[str]] = None + languages: Optional[list[str]] = None + datasets: Optional[list[dict]] = None + + @staticmethod + def load(metadata_override_path: Optional[Path] = None, model_path: Optional[Path] = None, model_name: Optional[str] = None, total_params: int = 0) -> Metadata: + # This grabs as many contextual authorship metadata as possible from the model repository + # making any conversion as required to match the gguf kv store metadata format + # as well as giving users the ability to override any authorship metadata that may be incorrect + + # Create a new Metadata instance + metadata = Metadata() + + model_card = Metadata.load_model_card(model_path) + hf_params = Metadata.load_hf_parameters(model_path) + gen_config = Metadata.load_generation_config(model_path) + # TODO: load adapter_config.json when possible, it usually contains the base model of the LoRA adapter + + # heuristics + metadata = Metadata.apply_metadata_heuristic(metadata, model_card, hf_params, model_path, total_params) + + if gen_config: + metadata.sampling_sequence = gen_config.get("sequence", metadata.sampling_sequence) + metadata.sampling_top_k = gen_config.get("top_k", metadata.sampling_top_k) + metadata.sampling_top_p = gen_config.get("top_p", metadata.sampling_top_p) + metadata.sampling_min_p = gen_config.get("min_p", metadata.sampling_min_p) + metadata.sampling_xtc_probability = gen_config.get("xtc_probability", metadata.sampling_xtc_probability) + metadata.sampling_xtc_threshold = gen_config.get("xtc_threshold", metadata.sampling_xtc_threshold) + metadata.sampling_temp = gen_config.get("temperature", metadata.sampling_temp) + metadata.sampling_penalty_last_n = gen_config.get("penalty_last_n", metadata.sampling_penalty_last_n) + metadata.sampling_penalty_repeat = gen_config.get("penalty_repeat", metadata.sampling_penalty_repeat) + metadata.sampling_mirostat = gen_config.get("mirostat", metadata.sampling_mirostat) + metadata.sampling_mirostat_tau = gen_config.get("mirostat_tau", metadata.sampling_mirostat_tau) + metadata.sampling_mirostat_eta = gen_config.get("mirostat_eta", metadata.sampling_mirostat_eta) + + # Metadata Override File Provided + # This is based on LLM_KV_NAMES mapping in llama.cpp + metadata_override = Metadata.load_metadata_override(metadata_override_path) + + metadata.sampling_sequence = metadata_override.get(Keys.General.SAMPLING_SEQUENCE, metadata.sampling_sequence) + metadata.sampling_top_k = metadata_override.get(Keys.General.SAMPLING_TOP_K, metadata.sampling_top_k) + metadata.sampling_top_p = metadata_override.get(Keys.General.SAMPLING_TOP_P, metadata.sampling_top_p) + metadata.sampling_min_p = metadata_override.get(Keys.General.SAMPLING_MIN_P, metadata.sampling_min_p) + metadata.sampling_xtc_probability = metadata_override.get(Keys.General.SAMPLING_XTC_PROBABILITY, metadata.sampling_xtc_probability) + metadata.sampling_xtc_threshold = metadata_override.get(Keys.General.SAMPLING_XTC_THRESHOLD, metadata.sampling_xtc_threshold) + metadata.sampling_temp = metadata_override.get(Keys.General.SAMPLING_TEMP, metadata.sampling_temp) + metadata.sampling_penalty_last_n = metadata_override.get(Keys.General.SAMPLING_PENALTY_LAST_N, metadata.sampling_penalty_last_n) + metadata.sampling_penalty_repeat = metadata_override.get(Keys.General.SAMPLING_PENALTY_REPEAT, metadata.sampling_penalty_repeat) + metadata.sampling_mirostat = metadata_override.get(Keys.General.SAMPLING_MIROSTAT, metadata.sampling_mirostat) + metadata.sampling_mirostat_tau = metadata_override.get(Keys.General.SAMPLING_MIROSTAT_TAU, metadata.sampling_mirostat_tau) + metadata.sampling_mirostat_eta = metadata_override.get(Keys.General.SAMPLING_MIROSTAT_ETA, metadata.sampling_mirostat_eta) + + metadata.name = metadata_override.get(Keys.General.NAME, metadata.name) + metadata.author = metadata_override.get(Keys.General.AUTHOR, metadata.author) + metadata.version = metadata_override.get(Keys.General.VERSION, metadata.version) + metadata.organization = metadata_override.get(Keys.General.ORGANIZATION, metadata.organization) + + metadata.finetune = metadata_override.get(Keys.General.FINETUNE, metadata.finetune) + metadata.basename = metadata_override.get(Keys.General.BASENAME, metadata.basename) + + metadata.description = metadata_override.get(Keys.General.DESCRIPTION, metadata.description) + metadata.quantized_by = metadata_override.get(Keys.General.QUANTIZED_BY, metadata.quantized_by) + + metadata.size_label = metadata_override.get(Keys.General.SIZE_LABEL, metadata.size_label) + metadata.license_name = metadata_override.get(Keys.General.LICENSE_NAME, metadata.license_name) + metadata.license_link = metadata_override.get(Keys.General.LICENSE_LINK, metadata.license_link) + + metadata.url = metadata_override.get(Keys.General.URL, metadata.url) + metadata.doi = metadata_override.get(Keys.General.DOI, metadata.doi) + metadata.uuid = metadata_override.get(Keys.General.UUID, metadata.uuid) + metadata.repo_url = metadata_override.get(Keys.General.REPO_URL, metadata.repo_url) + + metadata.source_url = metadata_override.get(Keys.General.SOURCE_URL, metadata.source_url) + metadata.source_doi = metadata_override.get(Keys.General.SOURCE_DOI, metadata.source_doi) + metadata.source_uuid = metadata_override.get(Keys.General.SOURCE_UUID, metadata.source_uuid) + metadata.source_repo_url = metadata_override.get(Keys.General.SOURCE_REPO_URL, metadata.source_repo_url) + + # Base Models is received here as an array of models + metadata.base_models = metadata_override.get("general.base_models", metadata.base_models) + + # Datasets is received here as an array of datasets + metadata.datasets = metadata_override.get("general.datasets", metadata.datasets) + + metadata.tags = metadata_override.get(Keys.General.TAGS, metadata.tags) + metadata.languages = metadata_override.get(Keys.General.LANGUAGES, metadata.languages) + + # Direct Metadata Override (via direct cli argument) + if model_name is not None: + metadata.name = model_name + + return metadata + + @staticmethod + def load_metadata_override(metadata_override_path: Optional[Path] = None) -> dict[str, Any]: + if metadata_override_path is None or not metadata_override_path.is_file(): + return {} + + with open(metadata_override_path, "r", encoding="utf-8") as f: + return json.load(f) + + @staticmethod + def load_model_card(model_path: Optional[Path] = None) -> dict[str, Any]: + if model_path is None or not model_path.is_dir(): + return {} + + model_card_path = model_path / "README.md" + + if not model_card_path.is_file(): + return {} + + # The model card metadata is assumed to always be in YAML (frontmatter) + # ref: https://github.com/huggingface/transformers/blob/a5c642fe7a1f25d3bdcd76991443ba6ff7ee34b2/src/transformers/modelcard.py#L468-L473 + yaml_content: str = "" + with open(model_card_path, "r", encoding="utf-8") as f: + content = f.read() + lines = content.splitlines() + lines_yaml = [] + if len(lines) == 0: + # Empty file + return {} + if len(lines) > 0 and lines[0] != "---": + # No frontmatter + return {} + for line in lines[1:]: + if line == "---": + break # End of frontmatter + else: + lines_yaml.append(line) + yaml_content = "\n".join(lines_yaml) + "\n" + + # Quick hack to fix the Norway problem + # https://hitchdev.com/strictyaml/why/implicit-typing-removed/ + yaml_content = yaml_content.replace("- no\n", "- \"no\"\n") + # yaml should use 2 spaces instead of tab + # this issue has came up with the Qwen/Qwen3-235B-A22B-Instruct-2507 model card + # (I've also sent a pr tp fix the modelcard too) + yaml_content = yaml_content.replace("\t", " ") + + if yaml_content: + data = yaml.safe_load(yaml_content) + if isinstance(data, dict): + return data + else: + logger.error(f"while reading YAML model card frontmatter, data is {type(data)} instead of dict") + return {} + else: + return {} + + @staticmethod + def load_hf_parameters(model_path: Optional[Path] = None) -> dict[str, Any]: + if model_path is None or not model_path.is_dir(): + return {} + + config_path = model_path / "config.json" + + if not config_path.is_file(): + return {} + + with open(config_path, "r", encoding="utf-8") as f: + return json.load(f) + + @staticmethod + def load_generation_config(model_path: Optional[Path] = None) -> dict[str, Any]: + if model_path is None or not model_path.is_dir(): + return {} + + generation_config_path = model_path / "generation_config.json" + + if not generation_config_path.is_file(): + return {} + + try: + with open(generation_config_path, "r", encoding="utf-8") as f: + return json.load(f) + except (json.JSONDecodeError, IOError): + # not all models have valid generation_config.json + return {} + + @staticmethod + def id_to_title(string): + # Convert capitalization into title form unless acronym or version number + return ' '.join([w.title() if w.islower() and not re.match(r'^(v\d+(?:\.\d+)*|\d.*)$', w) else w for w in string.strip().replace('-', ' ').split()]) + + @staticmethod + def get_model_id_components(model_id: Optional[str] = None, total_params: int = 0) -> tuple[str | None, str | None, str | None, str | None, str | None, str | None]: + # Huggingface often store model id as '/' + # so let's parse it and apply some heuristics if possible for model name components + + if model_id is None: + # model ID missing + return None, None, None, None, None, None + + if ' ' in model_id: + # model ID is actually a normal human sentence + # which means its most likely a normal model name only + # not part of the hugging face naming standard, but whatever + return model_id, None, None, None, None, None + + if '/' in model_id: + # model ID (huggingface style) + org_component, model_full_name_component = model_id.split('/', 1) + else: + # model ID but missing org components + org_component, model_full_name_component = None, model_id + + # Check if we erroneously matched against './' or '../' etc... + if org_component is not None and len(org_component) > 0 and org_component[0] == '.': + org_component = None + + name_parts: list[str] = model_full_name_component.split('-') + + # Remove empty parts + for i in reversed(range(len(name_parts))): + if len(name_parts[i]) == 0: + del name_parts[i] + + name_types: list[ + set[Literal["basename", "size_label", "finetune", "version", "type"]] + ] = [set() for _ in name_parts] + + # Annotate the name + for i, part in enumerate(name_parts): + # Version + if re.fullmatch(r'(v|iter)?\d+([.]\d+)*', part, re.IGNORECASE): + name_types[i].add("version") + # Quant type (should not be there for base models, but still annotated) + elif re.fullmatch(r'i?q\d(_\w)*|b?fp?(16|32)', part, re.IGNORECASE): + name_types[i].add("type") + name_parts[i] = part.upper() + # Model size + elif i > 0 and re.fullmatch(r'(([A]|\d+[x])?\d+([._]\d+)?[KMBT][\d]?|small|mini|medium|large|x?xl)', part, re.IGNORECASE): + part = part.replace("_", ".") + # Handle weird bloom-7b1 notation + if part[-1].isdecimal(): + part = part[:-2] + "." + part[-1] + part[-2] + # Normalize the size suffixes + if len(part) > 1 and part[-2].isdecimal(): + if part[-1] in "kmbt": + part = part[:-1] + part[-1].upper() + if total_params != 0: + try: + label_params = float(part[:-1]) * pow(1000, " KMBT".find(part[-1])) + # Only use it as a size label if it's close or bigger than the model size + # Note that LoRA adapters don't necessarily include all layers, + # so this is why bigger label sizes are accepted. + # Do not use the size label when it's smaller than 1/8 of the model size + if (total_params < 0 and label_params < abs(total_params) // 8) or ( + # Check both directions when the current model isn't a LoRA adapter + total_params > 0 and abs(label_params - total_params) > 7 * total_params // 8 + ): + # Likely a context length + name_types[i].add("finetune") + # Lowercase the size when it's a context length + part = part[:-1] + part[-1].lower() + except ValueError: + # Failed to convert the size label to float, use it anyway + pass + if len(name_types[i]) == 0: + name_types[i].add("size_label") + name_parts[i] = part + # Some easy to recognize finetune names + elif i > 0 and re.fullmatch(r'chat|instruct|vision|lora', part, re.IGNORECASE): + if total_params < 0 and part.lower() == "lora": + # ignore redundant "lora" in the finetune part when the output is a lora adapter + name_types[i].add("type") + else: + name_types[i].add("finetune") + + # Ignore word-based size labels when there is at least a number-based one present + # TODO: should word-based size labels always be removed instead? + if any(c.isdecimal() for n, t in zip(name_parts, name_types) if "size_label" in t for c in n): + for n, t in zip(name_parts, name_types): + if "size_label" in t: + if all(c.isalpha() for c in n): + t.remove("size_label") + + at_start = True + # Find the basename through the annotated name + for part, t in zip(name_parts, name_types): + if at_start and ((len(t) == 0 and part[0].isalpha()) or "version" in t): + t.add("basename") + else: + if at_start: + at_start = False + if len(t) == 0: + t.add("finetune") + + # Remove the basename annotation from trailing version + for part, t in zip(reversed(name_parts), reversed(name_types)): + if "basename" in t and len(t) > 1: + t.remove("basename") + else: + break + + basename = "-".join(n for n, t in zip(name_parts, name_types) if "basename" in t) or None + # Deduplicate size labels using order-preserving 'dict' ('set' seems to sort the keys) + size_label = "-".join(dict.fromkeys(s for s, t in zip(name_parts, name_types) if "size_label" in t).keys()) or None + finetune = "-".join(f for f, t in zip(name_parts, name_types) if "finetune" in t) or None + # TODO: should the basename version always be excluded? + # NOTE: multiple finetune versions are joined together + version = "-".join(v for v, t, in zip(name_parts, name_types) if "version" in t and "basename" not in t) or None + + if size_label is None and finetune is None and version is None: + # Too ambiguous, output nothing + basename = None + + return model_full_name_component, org_component, basename, finetune, version, size_label + + @staticmethod + def apply_metadata_heuristic(metadata: Metadata, model_card: Optional[dict] = None, hf_params: Optional[dict] = None, model_path: Optional[Path] = None, total_params: int = 0) -> Metadata: + # Reference Model Card Metadata: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 + + # Model Card Heuristics + ######################## + if model_card is not None: + + def use_model_card_metadata(metadata_key: str, model_card_key: str): + if model_card_key in model_card and getattr(metadata, metadata_key, None) is None: + setattr(metadata, metadata_key, model_card.get(model_card_key)) + + def use_array_model_card_metadata(metadata_key: str, model_card_key: str): + # Note: Will append rather than replace if already exist + tags_value = model_card.get(model_card_key, None) + if tags_value is None: + return + + current_value = getattr(metadata, metadata_key, None) + if current_value is None: + current_value = [] + + if isinstance(tags_value, str): + current_value.append(tags_value) + elif isinstance(tags_value, list): + current_value.extend(tags_value) + + setattr(metadata, metadata_key, current_value) + + # LLAMA.cpp's direct internal convention + # (Definitely not part of hugging face formal/informal standard) + ######################################### + use_model_card_metadata("name", "name") + use_model_card_metadata("author", "author") + use_model_card_metadata("version", "version") + use_model_card_metadata("organization", "organization") + use_model_card_metadata("description", "description") + use_model_card_metadata("finetune", "finetune") + use_model_card_metadata("basename", "basename") + use_model_card_metadata("size_label", "size_label") + use_model_card_metadata("source_url", "url") + use_model_card_metadata("source_doi", "doi") + use_model_card_metadata("source_uuid", "uuid") + use_model_card_metadata("source_repo_url", "repo_url") + + # LLAMA.cpp's huggingface style convention + # (Definitely not part of hugging face formal/informal standard... but with model_ appended to match their style) + ########################################### + use_model_card_metadata("name", "model_name") + use_model_card_metadata("author", "model_author") + use_model_card_metadata("version", "model_version") + use_model_card_metadata("organization", "model_organization") + use_model_card_metadata("description", "model_description") + use_model_card_metadata("finetune", "model_finetune") + use_model_card_metadata("basename", "model_basename") + use_model_card_metadata("size_label", "model_size_label") + use_model_card_metadata("source_url", "model_url") + use_model_card_metadata("source_doi", "model_doi") + use_model_card_metadata("source_uuid", "model_uuid") + use_model_card_metadata("source_repo_url", "model_repo_url") + + # Hugging Face Direct Convention + ################################# + + # Not part of huggingface model card standard but notice some model creator using it + # such as TheBloke in 'TheBloke/Mistral-7B-Instruct-v0.2-GGUF' + use_model_card_metadata("name", "model_name") + use_model_card_metadata("author", "model_creator") + use_model_card_metadata("basename", "model_type") + + if "base_model" in model_card or "base_models" in model_card or "base_model_sources" in model_card: + # This represents the parent models that this is based on + # Example: stabilityai/stable-diffusion-xl-base-1.0. Can also be a list (for merges) + # Example of merges: https://huggingface.co/EmbeddedLLM/Mistral-7B-Merge-14-v0.1/blob/main/README.md + metadata_base_models = [] + base_model_value = model_card.get("base_model", model_card.get("base_models", model_card.get("base_model_sources", None))) + + if base_model_value is not None: + if isinstance(base_model_value, str): + metadata_base_models.append(base_model_value) + elif isinstance(base_model_value, list): + metadata_base_models.extend(base_model_value) + + if metadata.base_models is None: + metadata.base_models = [] + + for model_id in metadata_base_models: + # NOTE: model size of base model is assumed to be similar to the size of the current model + base_model = {} + if isinstance(model_id, str): + if model_id.startswith("http://") or model_id.startswith("https://") or model_id.startswith("ssh://"): + base_model["repo_url"] = model_id + + # Check if Hugging Face ID is present in URL + if "huggingface.co" in model_id: + match = re.match(r"https?://huggingface.co/([^/]+/[^/]+)$", model_id) + if match: + model_id_component = match.group(1) + model_full_name_component, org_component, basename, finetune, version, size_label = Metadata.get_model_id_components(model_id_component, total_params) + + # Populate model dictionary with extracted components + if model_full_name_component is not None: + base_model["name"] = Metadata.id_to_title(model_full_name_component) + if org_component is not None: + base_model["organization"] = Metadata.id_to_title(org_component) + if version is not None: + base_model["version"] = version + + else: + # Likely a Hugging Face ID + model_full_name_component, org_component, basename, finetune, version, size_label = Metadata.get_model_id_components(model_id, total_params) + + # Populate model dictionary with extracted components + if model_full_name_component is not None: + base_model["name"] = Metadata.id_to_title(model_full_name_component) + if org_component is not None: + base_model["organization"] = Metadata.id_to_title(org_component) + if version is not None: + base_model["version"] = version + if org_component is not None and model_full_name_component is not None: + base_model["repo_url"] = f"https://huggingface.co/{org_component}/{model_full_name_component}" + + elif isinstance(model_id, dict): + base_model = model_id + + else: + logger.error(f"base model entry '{str(model_id)}' not in a known format") + + metadata.base_models.append(base_model) + + if "datasets" in model_card or "dataset" in model_card or "dataset_sources" in model_card: + # This represents the datasets that this was trained from + metadata_datasets = [] + dataset_value = model_card.get("datasets", model_card.get("dataset", model_card.get("dataset_sources", None))) + + if dataset_value is not None: + if isinstance(dataset_value, str): + metadata_datasets.append(dataset_value) + elif isinstance(dataset_value, list): + metadata_datasets.extend(dataset_value) + + if metadata.datasets is None: + metadata.datasets = [] + + for dataset_id in metadata_datasets: + # NOTE: model size of base model is assumed to be similar to the size of the current model + dataset = {} + if isinstance(dataset_id, str): + if dataset_id.startswith(("http://", "https://", "ssh://")): + dataset["repo_url"] = dataset_id + + # Check if Hugging Face ID is present in URL + if "huggingface.co" in dataset_id: + match = re.match(r"https?://huggingface.co/([^/]+/[^/]+)$", dataset_id) + if match: + dataset_id_component = match.group(1) + dataset_name_component, org_component, basename, finetune, version, size_label = Metadata.get_model_id_components(dataset_id_component, total_params) + + # Populate dataset dictionary with extracted components + if dataset_name_component is not None: + dataset["name"] = Metadata.id_to_title(dataset_name_component) + if org_component is not None: + dataset["organization"] = Metadata.id_to_title(org_component) + if version is not None: + dataset["version"] = version + + else: + # Likely a Hugging Face ID + dataset_name_component, org_component, basename, finetune, version, size_label = Metadata.get_model_id_components(dataset_id, total_params) + + # Populate dataset dictionary with extracted components + if dataset_name_component is not None: + dataset["name"] = Metadata.id_to_title(dataset_name_component) + if org_component is not None: + dataset["organization"] = Metadata.id_to_title(org_component) + if version is not None: + dataset["version"] = version + if org_component is not None and dataset_name_component is not None: + dataset["repo_url"] = f"https://huggingface.co/{org_component}/{dataset_name_component}" + + elif isinstance(dataset_id, dict): + dataset = dataset_id + + else: + logger.error(f"dataset entry '{str(dataset_id)}' not in a known format") + + metadata.datasets.append(dataset) + + use_model_card_metadata("license", "license") + use_model_card_metadata("license_name", "license_name") + use_model_card_metadata("license_link", "license_link") + + use_array_model_card_metadata("tags", "tags") + use_array_model_card_metadata("tags", "pipeline_tag") + + use_array_model_card_metadata("languages", "languages") + use_array_model_card_metadata("languages", "language") + + # Hugging Face Parameter Heuristics + #################################### + + if hf_params is not None: + + hf_name_or_path = hf_params.get("_name_or_path") + if hf_name_or_path is not None and hf_name_or_path.count('/') <= 1: + # Use _name_or_path only if its actually a model name and not some computer path + # e.g. 'meta-llama/Llama-2-7b-hf' + model_id = hf_name_or_path + model_full_name_component, org_component, basename, finetune, version, size_label = Metadata.get_model_id_components(model_id, total_params) + if metadata.name is None and model_full_name_component is not None: + metadata.name = Metadata.id_to_title(model_full_name_component) + if metadata.organization is None and org_component is not None: + metadata.organization = Metadata.id_to_title(org_component) + if metadata.basename is None and basename is not None: + metadata.basename = basename + if metadata.finetune is None and finetune is not None: + metadata.finetune = finetune + if metadata.version is None and version is not None: + metadata.version = version + if metadata.size_label is None and size_label is not None: + metadata.size_label = size_label + + # Directory Folder Name Fallback Heuristics + ############################################ + if model_path is not None: + model_id = model_path.name + model_full_name_component, org_component, basename, finetune, version, size_label = Metadata.get_model_id_components(model_id, total_params) + if metadata.name is None and model_full_name_component is not None: + metadata.name = Metadata.id_to_title(model_full_name_component) + if metadata.organization is None and org_component is not None: + metadata.organization = Metadata.id_to_title(org_component) + if metadata.basename is None and basename is not None: + metadata.basename = basename + if metadata.finetune is None and finetune is not None: + metadata.finetune = finetune + if metadata.version is None and version is not None: + metadata.version = version + if metadata.size_label is None and size_label is not None: + metadata.size_label = size_label + + return metadata + + def set_gguf_meta_model(self, gguf_writer: gguf.GGUFWriter): + assert self.name is not None + + if self.sampling_sequence is not None: + gguf_writer.add_sampling_sequence(self.sampling_sequence) + if self.sampling_top_k is not None: + gguf_writer.add_sampling_top_k(self.sampling_top_k) + if self.sampling_top_p is not None: + gguf_writer.add_sampling_top_p(self.sampling_top_p) + if self.sampling_min_p is not None: + gguf_writer.add_sampling_min_p(self.sampling_min_p) + if self.sampling_xtc_probability is not None: + gguf_writer.add_sampling_xtc_probability(self.sampling_xtc_probability) + if self.sampling_xtc_threshold is not None: + gguf_writer.add_sampling_xtc_threshold(self.sampling_xtc_threshold) + if self.sampling_temp is not None: + gguf_writer.add_sampling_temp(self.sampling_temp) + if self.sampling_penalty_last_n is not None: + gguf_writer.add_sampling_penalty_last_n(self.sampling_penalty_last_n) + if self.sampling_penalty_repeat is not None: + gguf_writer.add_sampling_penalty_repeat(self.sampling_penalty_repeat) + if self.sampling_mirostat is not None: + gguf_writer.add_sampling_mirostat(self.sampling_mirostat) + if self.sampling_mirostat_tau is not None: + gguf_writer.add_sampling_mirostat_tau(self.sampling_mirostat_tau) + if self.sampling_mirostat_eta is not None: + gguf_writer.add_sampling_mirostat_eta(self.sampling_mirostat_eta) + + gguf_writer.add_name(self.name) + + if self.author is not None: + gguf_writer.add_author(self.author) + if self.version is not None: + gguf_writer.add_version(self.version) + if self.organization is not None: + gguf_writer.add_organization(self.organization) + + if self.finetune is not None: + gguf_writer.add_finetune(self.finetune) + if self.basename is not None: + gguf_writer.add_basename(self.basename) + + if self.description is not None: + gguf_writer.add_description(self.description) + if self.quantized_by is not None: + gguf_writer.add_quantized_by(self.quantized_by) + + if self.size_label is not None: + gguf_writer.add_size_label(self.size_label) + + if self.license is not None: + if isinstance(self.license, list): + gguf_writer.add_license(",".join(self.license)) + else: + gguf_writer.add_license(self.license) + if self.license_name is not None: + gguf_writer.add_license_name(self.license_name) + if self.license_link is not None: + gguf_writer.add_license_link(self.license_link) + + if self.url is not None: + gguf_writer.add_url(self.url) + if self.doi is not None: + gguf_writer.add_doi(self.doi) + if self.uuid is not None: + gguf_writer.add_uuid(self.uuid) + if self.repo_url is not None: + gguf_writer.add_repo_url(self.repo_url) + + if self.source_url is not None: + gguf_writer.add_source_url(self.source_url) + if self.source_doi is not None: + gguf_writer.add_source_doi(self.source_doi) + if self.source_uuid is not None: + gguf_writer.add_source_uuid(self.source_uuid) + if self.source_repo_url is not None: + gguf_writer.add_source_repo_url(self.source_repo_url) + + if self.base_models is not None: + gguf_writer.add_base_model_count(len(self.base_models)) + for key, base_model_entry in enumerate(self.base_models): + if "name" in base_model_entry: + gguf_writer.add_base_model_name(key, base_model_entry["name"]) + if "author" in base_model_entry: + gguf_writer.add_base_model_author(key, base_model_entry["author"]) + if "version" in base_model_entry: + gguf_writer.add_base_model_version(key, base_model_entry["version"]) + if "organization" in base_model_entry: + gguf_writer.add_base_model_organization(key, base_model_entry["organization"]) + if "description" in base_model_entry: + gguf_writer.add_base_model_description(key, base_model_entry["description"]) + if "url" in base_model_entry: + gguf_writer.add_base_model_url(key, base_model_entry["url"]) + if "doi" in base_model_entry: + gguf_writer.add_base_model_doi(key, base_model_entry["doi"]) + if "uuid" in base_model_entry: + gguf_writer.add_base_model_uuid(key, base_model_entry["uuid"]) + if "repo_url" in base_model_entry: + gguf_writer.add_base_model_repo_url(key, base_model_entry["repo_url"]) + + if self.datasets is not None: + gguf_writer.add_dataset_count(len(self.datasets)) + for key, dataset_entry in enumerate(self.datasets): + if "name" in dataset_entry: + gguf_writer.add_dataset_name(key, dataset_entry["name"]) + if "author" in dataset_entry: + gguf_writer.add_dataset_author(key, dataset_entry["author"]) + if "version" in dataset_entry: + gguf_writer.add_dataset_version(key, dataset_entry["version"]) + if "organization" in dataset_entry: + gguf_writer.add_dataset_organization(key, dataset_entry["organization"]) + if "description" in dataset_entry: + gguf_writer.add_dataset_description(key, dataset_entry["description"]) + if "url" in dataset_entry: + gguf_writer.add_dataset_url(key, dataset_entry["url"]) + if "doi" in dataset_entry: + gguf_writer.add_dataset_doi(key, dataset_entry["doi"]) + if "uuid" in dataset_entry: + gguf_writer.add_dataset_uuid(key, dataset_entry["uuid"]) + if "repo_url" in dataset_entry: + gguf_writer.add_dataset_repo_url(key, dataset_entry["repo_url"]) + + if self.tags is not None: + gguf_writer.add_tags(self.tags) + if self.languages is not None: + gguf_writer.add_languages(self.languages) diff --git a/backend/llama.cpp/gguf-py/gguf/py.typed b/backend/llama.cpp/gguf-py/gguf/py.typed new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/backend/llama.cpp/gguf-py/gguf/quants.py b/backend/llama.cpp/gguf-py/gguf/quants.py new file mode 100644 index 0000000000000000000000000000000000000000..80966b6ef1518a45b86745d94eb70d05c3c5490f --- /dev/null +++ b/backend/llama.cpp/gguf-py/gguf/quants.py @@ -0,0 +1,1378 @@ +from __future__ import annotations +from abc import ABC, abstractmethod +from typing import Any, Callable, Sequence +from math import log2, ceil + +from numpy.typing import DTypeLike + +from .constants import GGML_QUANT_SIZES, GGMLQuantizationType, QK_K +from .lazy import LazyNumpyTensor + +import numpy as np + + +def quant_shape_to_byte_shape(shape: Sequence[int], quant_type: GGMLQuantizationType) -> tuple[int, ...]: + block_size, type_size = GGML_QUANT_SIZES[quant_type] + if shape[-1] % block_size != 0: + raise ValueError(f"Quantized tensor row size ({shape[-1]}) is not a multiple of {quant_type.name} block size ({block_size})") + return (*shape[:-1], shape[-1] // block_size * type_size) + + +def quant_shape_from_byte_shape(shape: Sequence[int], quant_type: GGMLQuantizationType) -> tuple[int, ...]: + block_size, type_size = GGML_QUANT_SIZES[quant_type] + if shape[-1] % type_size != 0: + raise ValueError(f"Quantized tensor bytes per row ({shape[-1]}) is not a multiple of {quant_type.name} type size ({type_size})") + return (*shape[:-1], shape[-1] // type_size * block_size) + + +# This is faster than np.vectorize and np.apply_along_axis because it works on more than one row at a time +def _apply_over_grouped_rows(func: Callable[[np.ndarray], np.ndarray], arr: np.ndarray, otype: DTypeLike, oshape: tuple[int, ...]) -> np.ndarray: + rows = arr.reshape((-1, arr.shape[-1])) + assert len(rows.shape) + osize = 1 + for dim in oshape: + osize *= dim + out = np.empty(shape=osize, dtype=otype) + # compute over groups of 16 rows (arbitrary, but seems good for performance) + n_groups = (rows.shape[0] // 16) or 1 + np.concatenate([func(group).ravel() for group in np.array_split(rows, n_groups)], axis=0, out=out) + return out.reshape(oshape) + + +# round away from zero +# ref: https://stackoverflow.com/a/59143326/22827863 +def np_roundf(n: np.ndarray) -> np.ndarray: + a = abs(n) + floored = np.floor(a) + b = floored + np.floor(2 * (a - floored)) + return np.sign(n) * b + + +class QuantError(Exception): ... + + +_type_traits: dict[GGMLQuantizationType, type[__Quant]] = {} + + +def quantize(data: np.ndarray, qtype: GGMLQuantizationType) -> np.ndarray: + if qtype == GGMLQuantizationType.F32: + return data.astype(np.float32, copy=False) + elif qtype == GGMLQuantizationType.F16: + return data.astype(np.float16, copy=False) + elif (q := _type_traits.get(qtype)) is not None: + return q.quantize(data) + else: + raise NotImplementedError(f"Quantization for {qtype.name} is not yet implemented") + + +def dequantize(data: np.ndarray, qtype: GGMLQuantizationType) -> np.ndarray: + if qtype == GGMLQuantizationType.F32: + return data.view(np.float32) + elif qtype == GGMLQuantizationType.F16: + return data.view(np.float16).astype(np.float32) + elif (q := _type_traits.get(qtype)) is not None: + return q.dequantize(data) + else: + raise NotImplementedError(f"Dequantization for {qtype.name} is not yet implemented") + + +class __Quant(ABC): + qtype: GGMLQuantizationType + block_size: int + type_size: int + + grid: np.ndarray[Any, np.dtype[np.float32]] | None = None + grid_shape: tuple[int, int] = (0, 0) + grid_map: tuple[int | float, ...] = () + grid_hex: bytes | None = None + + def __init__(self): + return TypeError("Quant conversion classes can't have instances") + + def __init_subclass__(cls, qtype: GGMLQuantizationType) -> None: + cls.qtype = qtype + cls.block_size, cls.type_size = GGML_QUANT_SIZES[qtype] + cls.__quantize_lazy: Any = LazyNumpyTensor._wrap_fn( + cls.__quantize_array, + meta_noop=(np.uint8, cls.__shape_to_bytes) + ) + cls.__dequantize_lazy: Any = LazyNumpyTensor._wrap_fn( + cls.__dequantize_array, + meta_noop=(np.float32, cls.__shape_from_bytes) + ) + assert qtype not in _type_traits + _type_traits[qtype] = cls + + @classmethod + def init_grid(cls): + if cls.grid is not None or cls.grid_hex is None: + return + + bits_per_elem = ceil(log2(len(cls.grid_map))) + assert bits_per_elem != 0, cls.qtype.name + elems_per_byte = 8 // bits_per_elem + + grid = np.frombuffer(cls.grid_hex, dtype=np.uint8) + # decode hexadecimal chars from grid + grid = grid.reshape((-1, 2)) + grid = (np.where(grid > 0x40, grid + 9, grid) & 0x0F) << np.array([4, 0], dtype=np.uint8).reshape((1, 2)) + grid = grid[..., 0] | grid[..., 1] + # unpack the grid values + grid = grid.reshape((-1, 1)) >> np.array([i for i in range(0, 8, 8 // elems_per_byte)], dtype=np.uint8).reshape((1, elems_per_byte)) + grid = (grid & ((1 << bits_per_elem) - 1)).reshape((-1, 1)) + grid_map = np.array(cls.grid_map, dtype=np.float32).reshape((1, -1)) + grid = np.take_along_axis(grid_map, grid, axis=-1) + cls.grid = grid.reshape((1, 1, *cls.grid_shape)) + + @classmethod + @abstractmethod + def quantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + raise NotImplementedError + + @classmethod + @abstractmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + raise NotImplementedError + + @classmethod + def quantize_rows(cls, rows: np.ndarray) -> np.ndarray: + rows = rows.astype(np.float32, copy=False) + shape = rows.shape + n_blocks = rows.size // cls.block_size + blocks = rows.reshape((n_blocks, cls.block_size)) + blocks = cls.quantize_blocks(blocks) + assert blocks.dtype == np.uint8 + assert blocks.shape[-1] == cls.type_size + return blocks.reshape(cls.__shape_to_bytes(shape)) + + @classmethod + def dequantize_rows(cls, rows: np.ndarray) -> np.ndarray: + rows = rows.view(np.uint8) + shape = rows.shape + n_blocks = rows.size // cls.type_size + blocks = rows.reshape((n_blocks, cls.type_size)) + blocks = cls.dequantize_blocks(blocks) + assert blocks.dtype == np.float32 + assert blocks.shape[-1] == cls.block_size + return blocks.reshape(cls.__shape_from_bytes(shape)) + + @classmethod + def __shape_to_bytes(cls, shape: Sequence[int]): + return quant_shape_to_byte_shape(shape, cls.qtype) + + @classmethod + def __shape_from_bytes(cls, shape: Sequence[int]): + return quant_shape_from_byte_shape(shape, cls.qtype) + + @classmethod + def __quantize_array(cls, array: np.ndarray) -> np.ndarray: + return _apply_over_grouped_rows(cls.quantize_rows, arr=array, otype=np.uint8, oshape=cls.__shape_to_bytes(array.shape)) + + @classmethod + def __dequantize_array(cls, array: np.ndarray) -> np.ndarray: + cls.init_grid() + return _apply_over_grouped_rows(cls.dequantize_rows, arr=array, otype=np.float32, oshape=cls.__shape_from_bytes(array.shape)) + + @classmethod + def __quantize_lazy(cls, lazy_tensor: LazyNumpyTensor, /) -> Any: + pass + + @classmethod + def __dequantize_lazy(cls, lazy_tensor: LazyNumpyTensor, /) -> Any: + pass + + @classmethod + def can_quantize(cls, tensor: np.ndarray | LazyNumpyTensor) -> bool: + return tensor.shape[-1] % cls.block_size == 0 + + @classmethod + def quantize(cls, tensor: np.ndarray | LazyNumpyTensor) -> np.ndarray: + if not cls.can_quantize(tensor): + raise QuantError(f"Can't quantize tensor with shape {tensor.shape} to {cls.qtype.name}") + if isinstance(tensor, LazyNumpyTensor): + return cls.__quantize_lazy(tensor) + else: + return cls.__quantize_array(tensor) + + @classmethod + def dequantize(cls, tensor: np.ndarray | LazyNumpyTensor) -> np.ndarray: + if isinstance(tensor, LazyNumpyTensor): + return cls.__dequantize_lazy(tensor) + else: + return cls.__dequantize_array(tensor) + + +class BF16(__Quant, qtype=GGMLQuantizationType.BF16): + @classmethod + # same as ggml_compute_fp32_to_bf16 in ggml-impl.h + def quantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n = blocks.view(np.uint32) + # force nan to quiet + n = np.where((n & 0x7fffffff) > 0x7f800000, (n & np.uint32(0xffff0000)) | np.uint32(64 << 16), n) + # round to nearest even + n = (np.uint64(n) + (0x7fff + ((n >> 16) & 1))) >> 16 + return n.astype(np.uint16).view(np.uint8) + + @classmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + return (blocks.view(np.int16).astype(np.int32) << 16).view(np.float32) + + +class Q4_0(__Quant, qtype=GGMLQuantizationType.Q4_0): + @classmethod + def quantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + imax = abs(blocks).argmax(axis=-1, keepdims=True) + max = np.take_along_axis(blocks, imax, axis=-1) + + d = max / -8 + with np.errstate(divide="ignore"): + id = np.where(d == 0, 0, 1 / d) + qs = np.trunc((blocks * id) + np.float32(8.5), dtype=np.float32).astype(np.uint8).clip(0, 15) + + qs = qs.reshape((n_blocks, 2, cls.block_size // 2)) + qs = qs[..., 0, :] | (qs[..., 1, :] << np.uint8(4)) + + d = d.astype(np.float16).view(np.uint8) + + return np.concatenate([d, qs], axis=-1) + + @classmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + d, qs = np.hsplit(blocks, [2]) + + d = d.view(np.float16).astype(np.float32) + + qs = qs.reshape((n_blocks, -1, 1, cls.block_size // 2)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2, 1)) + qs = (qs & np.uint8(0x0F)).reshape((n_blocks, -1)).astype(np.int8) - np.int8(8) + + return (d * qs.astype(np.float32)) + + +class Q4_1(__Quant, qtype=GGMLQuantizationType.Q4_1): + @classmethod + def quantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + max = blocks.max(axis=-1, keepdims=True) + min = blocks.min(axis=-1, keepdims=True) + + d = (max - min) / 15 + with np.errstate(divide="ignore"): + id = np.where(d == 0, 0, 1 / d) + qs = np.trunc((blocks - min) * id + np.float32(0.5), dtype=np.float32).astype(np.uint8).clip(0, 15) + + qs = qs.reshape((n_blocks, 2, cls.block_size // 2)) + qs = qs[..., 0, :] | (qs[..., 1, :] << np.uint8(4)) + + d = d.astype(np.float16).view(np.uint8) + m = min.astype(np.float16).view(np.uint8) + + return np.concatenate([d, m, qs], axis=-1) + + @classmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + d, rest = np.hsplit(blocks, [2]) + m, qs = np.hsplit(rest, [2]) + + d = d.view(np.float16).astype(np.float32) + m = m.view(np.float16).astype(np.float32) + + qs = qs.reshape((n_blocks, -1, 1, cls.block_size // 2)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2, 1)) + qs = (qs & np.uint8(0x0F)).reshape((n_blocks, -1)).astype(np.float32) + + return (d * qs) + m + + +class Q5_0(__Quant, qtype=GGMLQuantizationType.Q5_0): + @classmethod + def quantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + imax = abs(blocks).argmax(axis=-1, keepdims=True) + max = np.take_along_axis(blocks, imax, axis=-1) + + d = max / -16 + with np.errstate(divide="ignore"): + id = np.where(d == 0, 0, 1 / d) + q = np.trunc((blocks * id) + np.float32(16.5), dtype=np.float32).astype(np.uint8).clip(0, 31) + + qs = q.reshape((n_blocks, 2, cls.block_size // 2)) + qs = (qs[..., 0, :] & np.uint8(0x0F)) | (qs[..., 1, :] << np.uint8(4)) + + qh = np.packbits(q.reshape((n_blocks, 1, 32)) >> np.uint8(4), axis=-1, bitorder="little").reshape(n_blocks, 4) + + d = d.astype(np.float16).view(np.uint8) + + return np.concatenate([d, qh, qs], axis=-1) + + @classmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + d, rest = np.hsplit(blocks, [2]) + qh, qs = np.hsplit(rest, [4]) + + d = d.view(np.float16).astype(np.float32) + qh = qh.view(np.uint32) + + qh = qh.reshape((n_blocks, 1)) >> np.array([i for i in range(32)], dtype=np.uint32).reshape((1, 32)) + ql = qs.reshape((n_blocks, -1, 1, cls.block_size // 2)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2, 1)) + qh = (qh & np.uint32(0x01)).astype(np.uint8) + ql = (ql & np.uint8(0x0F)).reshape((n_blocks, -1)) + + qs = (ql | (qh << np.uint8(4))).astype(np.int8) - np.int8(16) + + return (d * qs.astype(np.float32)) + + +class Q5_1(__Quant, qtype=GGMLQuantizationType.Q5_1): + @classmethod + def quantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + max = blocks.max(axis=-1, keepdims=True) + min = blocks.min(axis=-1, keepdims=True) + + d = (max - min) / 31 + with np.errstate(divide="ignore"): + id = np.where(d == 0, 0, 1 / d) + q = np.trunc((blocks - min) * id + np.float32(0.5), dtype=np.float32).astype(np.uint8).clip(0, 31) + + qs = q.reshape((n_blocks, 2, cls.block_size // 2)) + qs = (qs[..., 0, :] & np.uint8(0x0F)) | (qs[..., 1, :] << np.uint8(4)) + + qh = np.packbits(q.reshape((n_blocks, 1, 32)) >> np.uint8(4), axis=-1, bitorder="little").reshape(n_blocks, 4) + + d = d.astype(np.float16).view(np.uint8) + m = min.astype(np.float16).view(np.uint8) + + return np.concatenate([d, m, qh, qs], axis=-1) + + @classmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + d, rest = np.hsplit(blocks, [2]) + m, rest = np.hsplit(rest, [2]) + qh, qs = np.hsplit(rest, [4]) + + d = d.view(np.float16).astype(np.float32) + m = m.view(np.float16).astype(np.float32) + qh = qh.view(np.uint32) + + qh = qh.reshape((n_blocks, 1)) >> np.array([i for i in range(32)], dtype=np.uint32).reshape((1, 32)) + ql = qs.reshape((n_blocks, -1, 1, cls.block_size // 2)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2, 1)) + qh = (qh & np.uint32(0x01)).astype(np.uint8) + ql = (ql & np.uint8(0x0F)).reshape((n_blocks, -1)) + + qs = (ql | (qh << np.uint8(4))).astype(np.float32) + + return (d * qs) + m + + +class Q8_0(__Quant, qtype=GGMLQuantizationType.Q8_0): + @classmethod + # Implementation of Q8_0 with bit-exact same results as reference implementation in ggml-quants.c + def quantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + + d = abs(blocks).max(axis=1, keepdims=True) / 127 + with np.errstate(divide="ignore"): + id = np.where(d == 0, 0, 1 / d) + qs = np_roundf(blocks * id) + + # (n_blocks, 2) + d = d.astype(np.float16).view(np.uint8) + # (n_blocks, block_size) + qs = qs.astype(np.int8).view(np.uint8) + + return np.concatenate([d, qs], axis=1) + + @classmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + d, x = np.split(blocks, [2], axis=1) + d = d.view(np.float16).astype(np.float32) + x = x.view(np.int8).astype(np.float32) + + return (x * d) + + +class Q2_K(__Quant, qtype=GGMLQuantizationType.Q2_K): + @classmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + scales, rest = np.hsplit(blocks, [QK_K // 16]) + qs, rest = np.hsplit(rest, [QK_K // 4]) + d, dmin = np.hsplit(rest, [2]) + + d = d.view(np.float16).astype(np.float32) + dmin = dmin.view(np.float16).astype(np.float32) + + # (n_blocks, 16, 1) + dl = (d * (scales & 0xF).astype(np.float32)).reshape((n_blocks, QK_K // 16, 1)) + ml = (dmin * (scales >> 4).astype(np.float32)).reshape((n_blocks, QK_K // 16, 1)) + + shift = np.array([0, 2, 4, 6], dtype=np.uint8).reshape((1, 1, 4, 1)) + + qs = (qs.reshape((n_blocks, -1, 1, 32)) >> shift) & np.uint8(3) + + qs = qs.reshape((n_blocks, QK_K // 16, 16)).astype(np.float32) + + qs = dl * qs - ml + + return qs.reshape((n_blocks, -1)) + + +class Q3_K(__Quant, qtype=GGMLQuantizationType.Q3_K): + @classmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + hmask, rest = np.hsplit(blocks, [QK_K // 8]) + qs, rest = np.hsplit(rest, [QK_K // 4]) + scales, d = np.hsplit(rest, [12]) + + d = d.view(np.float16).astype(np.float32) + + # The scales are packed at 6-bit each in this pattern: + # 0: IIIIAAAA + # 1: JJJJBBBB + # 2: KKKKCCCC + # 3: LLLLDDDD + # 4: MMMMEEEE + # 5: NNNNFFFF + # 6: OOOOGGGG + # 7: PPPPHHHH + # 8: MMIIEEAA + # 9: NNJJFFBB + # 10: OOKKGGCC + # 11: PPLLHHDD + lscales, hscales = np.hsplit(scales, [8]) + lscales = lscales.reshape((n_blocks, 1, 8)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 2, 1)) + lscales = lscales.reshape((n_blocks, 16)) + hscales = hscales.reshape((n_blocks, 1, 4)) >> np.array([0, 2, 4, 6], dtype=np.uint8).reshape((1, 4, 1)) + hscales = hscales.reshape((n_blocks, 16)) + scales = (lscales & np.uint8(0x0F)) | ((hscales & np.uint8(0x03)) << np.uint8(4)) + scales = (scales.astype(np.int8) - np.int8(32)).astype(np.float32) + + dl = (d * scales).reshape((n_blocks, 16, 1)) + + ql = qs.reshape((n_blocks, -1, 1, 32)) >> np.array([0, 2, 4, 6], dtype=np.uint8).reshape((1, 1, 4, 1)) + qh = hmask.reshape(n_blocks, -1, 1, 32) >> np.array([i for i in range(8)], dtype=np.uint8).reshape((1, 1, 8, 1)) + ql = ql.reshape((n_blocks, 16, QK_K // 16)) & np.uint8(3) + qh = (qh.reshape((n_blocks, 16, QK_K // 16)) & np.uint8(1)) + qh = qh ^ np.uint8(1) # strangely, the offset is zero when the bitmask is 1 + q = (ql.astype(np.int8) - (qh << np.uint8(2)).astype(np.int8)).astype(np.float32) + + return (dl * q).reshape((n_blocks, QK_K)) + + +class Q4_K(__Quant, qtype=GGMLQuantizationType.Q4_K): + K_SCALE_SIZE = 12 + + @staticmethod + def get_scale_min(scales: np.ndarray) -> tuple[np.ndarray, np.ndarray]: + n_blocks = scales.shape[0] + scales = scales.view(np.uint8) + ### Unpacking the following: ### + # 0 EEAAAAAA + # 1 FFBBBBBB + # 2 GGCCCCCC + # 3 HHDDDDDD + # 4 eeaaaaaa + # 5 ffbbbbbb + # 6 ggcccccc + # 7 hhdddddd + # 8 eeeeEEEE + # 9 ffffFFFF + # 10 ggggGGGG + # 11 hhhhHHHH + scales = scales.reshape((n_blocks, 3, 4)) + d, m, m_d = np.split(scales, 3, axis=-2) + + sc = np.concatenate([d & 0x3F, (m_d & 0x0F) | ((d >> 2) & 0x30)], axis=-1) + min = np.concatenate([m & 0x3F, (m_d >> 4) | ((m >> 2) & 0x30)], axis=-1) + + return (sc.reshape((n_blocks, 8)), min.reshape((n_blocks, 8))) + + @classmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + d, rest = np.hsplit(blocks, [2]) + dmin, rest = np.hsplit(rest, [2]) + scales, qs = np.hsplit(rest, [cls.K_SCALE_SIZE]) + + d = d.view(np.float16).astype(np.float32) + dmin = dmin.view(np.float16).astype(np.float32) + + sc, m = Q4_K.get_scale_min(scales) + + d = (d * sc.astype(np.float32)).reshape((n_blocks, -1, 1)) + dm = (dmin * m.astype(np.float32)).reshape((n_blocks, -1, 1)) + + qs = qs.reshape((n_blocks, -1, 1, 32)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2, 1)) + qs = (qs & np.uint8(0x0F)).reshape((n_blocks, -1, 32)).astype(np.float32) + + return (d * qs - dm).reshape((n_blocks, QK_K)) + + +class Q5_K(__Quant, qtype=GGMLQuantizationType.Q5_K): + @classmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + d, rest = np.hsplit(blocks, [2]) + dmin, rest = np.hsplit(rest, [2]) + scales, rest = np.hsplit(rest, [Q4_K.K_SCALE_SIZE]) + qh, qs = np.hsplit(rest, [QK_K // 8]) + + d = d.view(np.float16).astype(np.float32) + dmin = dmin.view(np.float16).astype(np.float32) + + sc, m = Q4_K.get_scale_min(scales) + + d = (d * sc.astype(np.float32)).reshape((n_blocks, -1, 1)) + dm = (dmin * m.astype(np.float32)).reshape((n_blocks, -1, 1)) + + ql = qs.reshape((n_blocks, -1, 1, 32)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2, 1)) + qh = qh.reshape((n_blocks, -1, 1, 32)) >> np.array([i for i in range(8)], dtype=np.uint8).reshape((1, 1, 8, 1)) + ql = (ql & np.uint8(0x0F)).reshape((n_blocks, -1, 32)) + qh = (qh & np.uint8(0x01)).reshape((n_blocks, -1, 32)) + q = (ql | (qh << np.uint8(4))).astype(np.float32) + + return (d * q - dm).reshape((n_blocks, QK_K)) + + +class Q6_K(__Quant, qtype=GGMLQuantizationType.Q6_K): + @classmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + ql, rest = np.hsplit(blocks, [QK_K // 2]) + qh, rest = np.hsplit(rest, [QK_K // 4]) + scales, d = np.hsplit(rest, [QK_K // 16]) + + scales = scales.view(np.int8).astype(np.float32) + d = d.view(np.float16).astype(np.float32) + d = (d * scales).reshape((n_blocks, QK_K // 16, 1)) + + ql = ql.reshape((n_blocks, -1, 1, 64)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2, 1)) + ql = (ql & np.uint8(0x0F)).reshape((n_blocks, -1, 32)) + qh = qh.reshape((n_blocks, -1, 1, 32)) >> np.array([0, 2, 4, 6], dtype=np.uint8).reshape((1, 1, 4, 1)) + qh = (qh & np.uint8(0x03)).reshape((n_blocks, -1, 32)) + q = (ql | (qh << np.uint8(4))).astype(np.int8) - np.int8(32) + q = q.reshape((n_blocks, QK_K // 16, -1)).astype(np.float32) + + return (d * q).reshape((n_blocks, QK_K)) + + +class TQ1_0(__Quant, qtype=GGMLQuantizationType.TQ1_0): + @classmethod + def quantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + d = abs(blocks).max(axis=-1, keepdims=True) + with np.errstate(divide="ignore"): + id = np.where(d == 0, 0, 1 / d) + qs = np_roundf(blocks * id) + qs = (qs.astype(np.int8) + np.int8(1)).astype(np.uint8) + + qs0, qs1, qh = qs[..., :(32 * 5)], qs[..., (32 * 5):(48 * 5)], qs[..., (48 * 5):] + qs0 = qs0.reshape((n_blocks, -1, 5, 32)) * np.array([81, 27, 9, 3, 1], dtype=np.uint8).reshape((1, 1, 5, 1)) + qs0 = np.sum(qs0, axis=-2).reshape((n_blocks, -1)) + qs1 = qs1.reshape((n_blocks, -1, 5, 16)) * np.array([81, 27, 9, 3, 1], dtype=np.uint8).reshape((1, 1, 5, 1)) + qs1 = np.sum(qs1, axis=-2).reshape((n_blocks, -1)) + qh = qh.reshape((n_blocks, -1, 4, 4)) * np.array([81, 27, 9, 3], dtype=np.uint8).reshape((1, 1, 4, 1)) + qh = np.sum(qh, axis=-2).reshape((n_blocks, -1)) + qs = np.concatenate([qs0, qs1, qh], axis=-1) + qs = (qs.astype(np.uint16) * 256 + (243 - 1)) // 243 + + qs = qs.astype(np.uint8) + d = d.astype(np.float16).view(np.uint8) + + return np.concatenate([qs, d], axis=-1) + + @classmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + qs, rest = np.hsplit(blocks, [(QK_K - 4 * QK_K // 64) // 5]) + qh, d = np.hsplit(rest, [QK_K // 64]) + + d = d.view(np.float16).astype(np.float32) + + qs0, qs1 = qs[..., :32], qs[..., 32:] + qs0 = qs0.reshape((n_blocks, -1, 1, 32)) * np.array([1, 3, 9, 27, 81], dtype=np.uint8).reshape((1, 1, 5, 1)) + qs0 = qs0.reshape((n_blocks, -1)) + qs1 = qs1.reshape((n_blocks, -1, 1, 16)) * np.array([1, 3, 9, 27, 81], dtype=np.uint8).reshape((1, 1, 5, 1)) + qs1 = qs1.reshape((n_blocks, -1)) + qh = qh.reshape((n_blocks, -1, 1, 4)) * np.array([1, 3, 9, 27], dtype=np.uint8).reshape((1, 1, 4, 1)) + qh = qh.reshape((n_blocks, -1)) + qs = np.concatenate([qs0, qs1, qh], axis=-1) + qs = ((qs.astype(np.uint16) * 3) >> 8).astype(np.int8) - np.int8(1) + + return (d * qs.astype(np.float32)) + + +class TQ2_0(__Quant, qtype=GGMLQuantizationType.TQ2_0): + @classmethod + def quantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + d = abs(blocks).max(axis=-1, keepdims=True) + with np.errstate(divide="ignore"): + id = np.where(d == 0, 0, 1 / d) + qs = np_roundf(blocks * id) + qs = (qs.astype(np.int8) + np.int8(1)).astype(np.uint8) + + qs = qs.reshape((n_blocks, -1, 4, 32)) << np.array([0, 2, 4, 6], dtype=np.uint8).reshape((1, 1, 4, 1)) + qs = qs[..., 0, :] | qs[..., 1, :] | qs[..., 2, :] | qs[..., 3, :] + qs = qs.reshape((n_blocks, -1)) + + d = d.astype(np.float16).view(np.uint8) + + return np.concatenate([qs, d], axis=-1) + + @classmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + qs, d = np.hsplit(blocks, [QK_K // 4]) + + d = d.view(np.float16).astype(np.float32) + + qs = qs.reshape((n_blocks, -1, 1, 32)) >> np.array([0, 2, 4, 6], dtype=np.uint8).reshape((1, 1, 4, 1)) + qs = (qs & 0x03).reshape((n_blocks, -1)).astype(np.int8) - np.int8(1) + + return (d * qs.astype(np.float32)) + + +class MXFP4(__Quant, qtype=GGMLQuantizationType.MXFP4): + # e2m1 values (doubled) + # ref: https://www.opencompute.org/documents/ocp-microscaling-formats-mx-v1-0-spec-final-pdf + kvalues = (0, 1, 2, 3, 4, 6, 8, 12, 0, -1, -2, -3, -4, -6, -8, -12) + + @staticmethod + # see ggml_e8m0_to_fp32_half in ggml-impl.h + def e8m0_to_fp32_half(x: np.ndarray) -> np.ndarray: + bits = np.where(x < 2, np.uint32(0x00200000) << np.uint32(x), np.uint32(x - 1) << np.uint32(23)) + return bits.view(np.float32) + + @classmethod + def quantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + d = abs(blocks).max(axis=-1, keepdims=True) + + with np.errstate(divide="ignore"): + e = np.where(d > 0, np.floor(np.log2(d)) - 2 + 127, 0).astype(np.uint8) + + d = cls.e8m0_to_fp32_half(e) + + kvalues = np.array(cls.kvalues, dtype=np.int8).reshape((1, 1, 16)) + + errs = np.abs(d.reshape((n_blocks, 1, 1)) * kvalues.astype(np.float32) - blocks.reshape((n_blocks, cls.block_size, 1))) + best = np.argmin(errs, axis=-1, keepdims=True) + + qs = best.reshape(n_blocks, 2, cls.block_size // 2).astype(np.uint8) + qs = qs[:, 0] | (qs[:, 1] << np.uint8(4)) + + qs = qs.reshape((n_blocks, cls.block_size // 2)) + + return np.concatenate([e, qs], axis=-1) + + @classmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + e, qs = np.hsplit(blocks, [1]) + + d = cls.e8m0_to_fp32_half(e) + + qs = qs.reshape((n_blocks, 1, cls.block_size // 2)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 2, 1)) + qs = (qs & np.uint8(0x0F)).view(np.int8) + + kvalues = np.array(cls.kvalues, dtype=np.int8).reshape(1, 1, 16) + qs = np.take_along_axis(kvalues, qs, axis=-1).reshape((n_blocks, cls.block_size)) + + return (d * qs.astype(np.float32)) + + +class NVFP4(__Quant, qtype=GGMLQuantizationType.NVFP4): + # E2M1 values doubled (kvalues_mxfp4 convention) + kvalues = (0, 1, 2, 3, 4, 6, 8, 12, 0, -1, -2, -3, -4, -6, -8, -12) + + @staticmethod + def ue4m3_to_fp32(x: np.ndarray) -> np.ndarray: + """Decode unsigned E4M3 (bias=7) to float, with 0.5 factor for kvalues convention.""" + exp = (x >> 3).astype(np.int32) & 0xF + man = (x & 0x7).astype(np.float32) + raw = np.where( + exp == 0, + man * 2**-9, + (1.0 + man / 8.0) * (2.0 ** (exp.astype(np.float32) - 7))) + return np.where((x == 0) | (x == 0x7F), 0.0, raw * 0.5) + + @staticmethod + def fp32_to_ue4m3(x: np.ndarray) -> np.ndarray: + """Vectorized float32 to unsigned E4M3, matching ggml_fp32_to_ue4m3 in C.""" + x = np.clip(x, 0.0, 448.0).astype(np.float32) + bits = x.view(np.uint32) + fp32_exp = ((bits >> 23) & 0xFF).astype(np.int32) - 127 + fp32_man = ((bits >> 20) & 0x7).astype(np.int32) + ue4m3_exp = fp32_exp + 7 + + # Subnormal + sub_man = np.clip((x * 512.0 + 0.5).astype(np.int32), 0, 7) + sub_result = np.where(sub_man >= 1, sub_man, 0).astype(np.uint8) + + # Normal with rounding + round_bit = ((bits >> 19) & 1).astype(np.int32) + man = fp32_man + round_bit + exp = ue4m3_exp.copy() + overflow = man > 7 + man = np.where(overflow, 0, man) + exp = np.where(overflow, exp + 1, exp) + normal_result = np.where(exp >= 15, np.uint8(0x7E), ((exp << 3) | man).astype(np.uint8)) + + return np.where(x <= 0.0, np.uint8(0), + np.where(ue4m3_exp <= 0, sub_result, + np.where(ue4m3_exp >= 15, np.uint8(0x7E), normal_result))) + + @classmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_super = blocks.shape[0] + + d_bytes, qs = np.hsplit(blocks, [4]) + d = cls.ue4m3_to_fp32(d_bytes).reshape(n_super, 4, 1) # (n_super, 4, 1) + + qs = qs.reshape(n_super, 4, 8) + lo = (qs & np.uint8(0x0F)).view(np.int8) + hi = (qs >> np.uint8(4)).view(np.int8) + vals = np.concatenate([lo, hi], axis=-1) # (n_super, 4, 16) + + kvalues = np.array(cls.kvalues, dtype=np.int8).reshape(1, 1, 16) + vals = np.take_along_axis(kvalues, vals, axis=-1) + + return (d * vals.astype(np.float32)).reshape(n_super, 64) + + +class IQ2_XXS(__Quant, qtype=GGMLQuantizationType.IQ2_XXS): + ksigns: bytes = ( + b"\x00\x81\x82\x03\x84\x05\x06\x87\x88\x09\x0a\x8b\x0c\x8d\x8e\x0f" + b"\x90\x11\x12\x93\x14\x95\x96\x17\x18\x99\x9a\x1b\x9c\x1d\x1e\x9f" + b"\xa0\x21\x22\xa3\x24\xa5\xa6\x27\x28\xa9\xaa\x2b\xac\x2d\x2e\xaf" + b"\x30\xb1\xb2\x33\xb4\x35\x36\xb7\xb8\x39\x3a\xbb\x3c\xbd\xbe\x3f" + b"\xc0\x41\x42\xc3\x44\xc5\xc6\x47\x48\xc9\xca\x4b\xcc\x4d\x4e\xcf" + b"\x50\xd1\xd2\x53\xd4\x55\x56\xd7\xd8\x59\x5a\xdb\x5c\xdd\xde\x5f" + b"\x60\xe1\xe2\x63\xe4\x65\x66\xe7\xe8\x69\x6a\xeb\x6c\xed\xee\x6f" + b"\xf0\x71\x72\xf3\x74\xf5\xf6\x77\x78\xf9\xfa\x7b\xfc\x7d\x7e\xff" + ) + + # iq2xxs_grid, but with each byte of the original packed in 2 bits, + # by mapping 0x08 to 0, 0x19 to 1, and 0x2b to 2. + grid_shape = (256, 8) + grid_map = (0x08, 0x19, 0x2b) + grid_hex = ( + b"00000200050008000a00110014002000220028002a0041004400500058006100" + b"6400800082008a00a20001010401100115014001840198010002020222028202" + b"010404041004210424044004420448046004810484049004a404000502050805" + b"200546056905800591050906100640068406a406000805080808140828084108" + b"440850085208880804094009020a140a01100410101021104010601084109010" + b"951000110811201150115a118011241245120014081420142514491480141815" + b"6215001616160118041810184018811800190519a019511a002002200a204420" + b"6120802082202921482100220222012404241024402456240025412564259026" + b"082820289428442a014004401040184021402440404048405640604081408440" + b"9040004120416141804185410142104248425642684200440844204480449944" + b"124524450046014804481048404845480049584961498249454a904a00500850" + b"1150195020508050885004514251a4519152905492540a550156545600581158" + b"195864584059085a046010604060686000615561186260620064056410651265" + b"84654268008002800a8041808280048118814081118201840484108415844084" + b"608400854685948509864086608602880489118a0490109024904090a1901691" + b"8091459200942294449451958198209902a050a085a009a100a218a450a804a9" + ) + + @classmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + d, qs = np.hsplit(blocks, [2]) + + d = d.view(np.float16).astype(np.float32) + + qs = qs.view(np.uint32).reshape(n_blocks, -1, 2) + + db = d * (np.float32(0.5) + (qs[..., 1] >> 28).astype(np.float32)) * np.float32(0.25) + db = db.reshape((n_blocks, -1, 1, 1)) + + # get the sign indices and unpack the bits + signs = qs[..., 1].reshape((n_blocks, -1, 1)) >> np.array([0, 7, 14, 21], dtype=np.uint32).reshape((1, 1, 4)) + ksigns = np.frombuffer(cls.ksigns, dtype=np.uint8).reshape((1, 1, 1, 128)) + signs = (signs & np.uint32(0x7F)).reshape((n_blocks, -1, 4, 1)) + signs = np.take_along_axis(ksigns, signs, axis=-1) + signs = signs.reshape((n_blocks, -1, 4, 1)) >> np.array([i for i in range(8)], dtype=np.uint8).reshape((1, 1, 1, 8)) + signs = signs & np.uint8(0x01) + signs = np.where(signs == 0, np.float32(1), np.float32(-1)) + signs = signs.reshape((n_blocks, -1, 4, 8)) + + assert cls.grid is not None + grid = np.take_along_axis(cls.grid, qs[..., 0].copy().view(np.uint8).reshape((n_blocks, -1, 1, 1)), axis=-2) + grid = grid.reshape((n_blocks, -1, 4, 8)) + + return (db * grid * signs).reshape((n_blocks, -1)) + + +class IQ2_XS(__Quant, qtype=GGMLQuantizationType.IQ2_XS): + # iq2xs_grid, but with each byte of the original packed in 2 bits, + # by mapping 0x08 to 0, 0x19 to 1, and 0x2b to 2. + grid_shape = (512, 8) + grid_map = (0x08, 0x19, 0x2b) + grid_hex = ( + b"00000200050008000a0011001400160019002000220025002800410044004600" + b"49005000520055005800610064008000820085008800910094009900a0000101" + b"04010601090110011201150118011a0121012401400142014501480151015401" + b"6001680181018401900100020202050208021102140220024102440250025502" + b"80028a0201040404060409041004120415041804210424044004420445044804" + b"5104540456046004810484049004000502050505080511051405200541054405" + b"500561058005010604061006260640064206840600080208050808080a081108" + b"14082008250841084408500858088008a008aa08010904091009400981098909" + b"000a200a280a960aa00a01100410061009101010121015101810211024104010" + b"4210451048105110541060106a10811084109010001102110511081111111411" + b"2011411144115011801194119611011204120612101240126012001402140514" + b"0814111414142014411444144914501464148014011504151015401500161416" + b"49160118041810181218401854188618001905196619511aa91a002002200520" + b"08200a201120142020204120442050208020a020012104211021402148216521" + b"002222228022a82201240424102429244024002541255225992501261a26a626" + b"002808280a28202855288828a22868299029082a202a822a882a8a2a01400440" + b"0640094010401240154018402140244040404240454048404a40514054406040" + b"6540814084409040004102410541084111411441204141414441504180418541" + b"a241014204421042124229424042004402440544084411441444194420444144" + b"4444504480449444014504451045244540459a4500460a464446504601480448" + b"1048404845485448624800491149444950496949044a00500250055008501150" + b"145020502850415044505050805001510451105115514051425100524452aa52" + b"0154045410542154405460548154a154005508558055885521566856a1560058" + b"14584158505899581a5940594259855a0160046010604060546062608660a960" + b"006124624a62926200641664106540654565a46501686a682569066a546a626a" + b"00800280058008801180148020802a8041804480508080808280a880aa800181" + b"0481068110814081518159810082208280828282a082a8820184048410841284" + b"158440846084898400854485a58518866a860088088825885a8880888288a888" + b"0689228a808a888a968aa88a0190049010904090569084900091229164915692" + b"89920094059444945094589429959095929541965198a6984999159a609a00a0" + b"02a008a00aa020a02aa0a0a051a159a1a6a100a202a208a22aa280a2a0a240a4" + b"95a465a698a60aa820a822a828a8a0a8a8a804a984a986a928aa2aaa91aaaaaa" + ) + + @classmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + d, rest = np.hsplit(blocks, [2]) + qs, scales = np.hsplit(rest, [2 * QK_K // 8]) + + d = d.view(np.float16).astype(np.float32) + qs = qs.view(np.uint16) + + scales = scales.reshape((n_blocks, -1, 1)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2)) + scales = (scales & 0x0F).reshape((n_blocks, -1)) + db = d * (np.float32(0.5) + scales) * np.float32(0.25) + db = db.reshape((n_blocks, -1, 1, 1)) + + # get the sign indices and unpack the bits + signs = np.frombuffer(IQ2_XXS.ksigns, dtype=np.uint8).reshape(1, 1, 128) + signs = np.take_along_axis(signs, (qs >> 9).reshape((n_blocks, -1, 1)), axis=-1) + signs = signs.reshape((n_blocks, -1, 1)) >> np.array([i for i in range(8)], dtype=np.uint8).reshape((1, 1, 8)) + signs = signs & np.uint8(0x01) + signs = np.where(signs == 0, np.float32(1), np.float32(-1)) + signs = signs.reshape((n_blocks, -1, 2, 8)) + + assert cls.grid is not None + grid = np.take_along_axis(cls.grid, (qs & np.uint16(511)).reshape((n_blocks, -1, 1, 1)), axis=-2) + grid = grid.reshape((n_blocks, -1, 2, 8)) + + return (db * grid * signs).reshape((n_blocks, -1)) + + +class IQ2_S(__Quant, qtype=GGMLQuantizationType.IQ2_S): + # iq2s_grid, but with each byte of the original packed in 2 bits, + # by mapping 0x08 to 0, 0x19 to 1, and 0x2b to 2. + grid_shape = (1024, 8) + grid_map = (0x08, 0x19, 0x2b) + grid_hex = ( + b"00000200050008000a0011001400160019002000220025002800410044004600" + b"490050005200550058006100640066006900800082008500880091009400a000" + b"a500aa0001010401060109011001120115011801210124014001420145014801" + b"510154015601590160016501680181018401900192019501a101a40100020202" + b"050208021102140220022a02410244024602490250025502800285028a029402" + b"a202010404040604090410041204150418042104240426042904400442044504" + b"48044a0451045404560459046004620465048104840486048904900495049804" + b"a104a40400050205050508050a05110514051605190520052505280541054405" + b"46054905500552055505580561056405800582058505880591059405a0050106" + b"0406060609061006150640064506480651065406600681068406900600080208" + b"050808081108140816081908200825082a084108440846084908500852085508" + b"580861086408800885089408aa08010904091009120915091809210940094509" + b"480951095409600981099009000a110a140a220a280a2a0a500a990a01100410" + b"0610091010101210151018102110241026104010421045104810511054105610" + b"59106010621065106810811084108610901095109810a110a410001102110511" + b"08110a1111111411161119112011221125112811411144114611491150115211" + b"5511581161116411801182118511881191119411011204120912101215122112" + b"2412401245125112541281128412901200140214051408141114141416141914" + b"2014251428144114441446144914501452145514581461146414801482148514" + b"881491149414a014011504150615091510151215151518152115241540154215" + b"4515481551155415601581158415901500160516081611161416201641164416" + b"50168016aa160118041806180918101815181818211840184218451848185118" + b"541860188118841800190219051908191119141920194119441950196919a219" + b"041a101a401a561a00200220052008201120142016201920202025202a204120" + b"4420502052205520642080208a209420aa200121042110211221152121214021" + b"4221452151215421602181218421902100220a22222228222a22442250228822" + b"8a22a82201240424062409241024152418242124242440244224452448245124" + b"5424602481248424902400250525082511251425202541254425502566258025" + b"0126042610264026592600280528112814284128442850288a28aa2801290429" + b"102995290a2a222a642a882a8a2a014004400640094010401240154018401a40" + b"21402440264040404240454048404a4051405440564059406040624065408140" + b"8440904095409840a140a4400041024105410841114114411641194120412241" + b"2541414144414641494150415241554158416141644180418241854188419141" + b"9441a04101420442104212421542184224424042454248425142544260428142" + b"844200440244054408440a441144144416441944204422442544284441444444" + b"46444944504452445544584461446444804482448544884491449444a0440145" + b"0445064509451045124515451845214524454045424545454845514554456045" + b"6a4581458445904500460246054608461146144620464146444650468046a546" + b"0148044809481048124815481848214824484048424845484848514854486048" + b"84489048004902490549084911491449204941494449504980499649014a044a" + b"104a404a00500250055008501150145016501950205022502550285041504450" + b"4650495050505250555058506150645080508250855088509150945001510451" + b"0651095110511251155118512151245140514251455148515151545160518151" + b"8451905100520552085211521452205241524452505269528052015404540654" + b"0954105412541554185421542454405442544554485451545454605481548454" + b"9054005502550555085511551455205541554455505580550156045610562656" + b"405600580258055808581158145820584158445850585a588058015904591059" + b"4059005a195a855aa85a01600460066010601260156018602160246040604560" + b"4860516054606060846090600061026105610861116114612061416144615061" + b"806199610462106240625662a162006405640864116414642064416444645064" + b"806401650465106540654a656865926500669466016804681068656898680069" + b"2a69426aa16a0080028005800880118014801980208025804180448050805280" + b"5580588061808080858091809480018104810981108112811581188121812481" + b"408142814581488151815481818184819081a981008205820a82118214824182" + b"4482508201840484068409841084128415841884218440844284458448845184" + b"5484608481848484908400850285058508851185148520854185448550858085" + b"8a85018604861086298640860088058811881488418844885088a28801890489" + b"40896589228a588a5a8a828aa28a019004900990109012901590189024904090" + b"4290459048905190549060908190849090900091059111911491419144915091" + b"5a910192049210924092a6920094029405940894119414942094419444945094" + b"8094969401950495109540959895a19500964696649601980498109826984098" + b"a998009949995299909a00a005a00aa014a022a02aa041a044a050a0a2a0aaa0" + b"40a165a102a20aa222a228a22aa282a288a28aa2a8a201a404a410a440a489a4" + b"a4a400a519a551a60aa828a8a2a854a986a908aa0aaa20aa22aa28aa88aaaaaa" + ) + + @classmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + d, rest = np.hsplit(blocks, [2]) + qs, rest = np.hsplit(rest, [QK_K // 8]) + signs, rest = np.hsplit(rest, [QK_K // 8]) + qh, scales = np.hsplit(rest, [QK_K // 32]) + + d = d.view(np.float16).astype(np.float32) + + scales = scales.reshape((n_blocks, -1, 1)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2)) + scales = (scales & 0x0F).reshape((n_blocks, -1)) + db = d * (np.float32(0.5) + scales) * np.float32(0.25) + db = db.reshape((n_blocks, -1, 1, 1)) + + # unpack the sign bits + signs = signs.reshape((n_blocks, -1, 1)) >> np.array([i for i in range(8)], dtype=np.uint8).reshape((1, 1, 8)) + signs = signs & np.uint8(0x01) + signs = np.where(signs == 0, np.float32(1), np.float32(-1)) + signs = signs.reshape((n_blocks, -1, 2, 8)) + + qh = qh.reshape((n_blocks, -1, 1)) >> np.array([0, 2, 4, 6], dtype=np.uint8).reshape((1, 1, 4)) + qs = qs.astype(np.uint16) | ((qh & 0x03).astype(np.uint16) << 8).reshape((n_blocks, -1)) + + assert cls.grid is not None + grid = np.take_along_axis(cls.grid, qs.reshape((n_blocks, -1, 1, 1)), axis=-2) + grid = grid.reshape((n_blocks, -1, 2, 8)) + + return (db * grid * signs).reshape((n_blocks, -1)) + + +class IQ3_XXS(__Quant, qtype=GGMLQuantizationType.IQ3_XXS): + grid_shape = (256, 4) + grid_map = (0x04, 0x0c, 0x14, 0x1c, 0x24, 0x2c, 0x34, 0x3e) + grid_hex = ( + b"0000020004001100130017002000220031004200730075000101030110011201" + b"2101250130013201410154017001000202020402110220022202310233023702" + b"5102570275020103070310031203250370031304370444045704730475040105" + b"0705320552053506640610071407160743076107011003101010121021102310" + b"3010321034104710501000110211111120112211011203121012121221123012" + b"7212001302132013311346136613011405145014201524154615711505162217" + b"4017002002201120132020202220262031204220012103210521102112212121" + b"3021632167217021002202221122172220222222372240225522012310231423" + b"7023742335245324032527254125742501270327162745270130103012302130" + b"2330503065307230003102312031313144314631013203321032253252327232" + b"1133333330344734723400350635223555351436363663363337603704401740" + b"3540374053405740744120423742404260426642074345430444514464442545" + b"4345704505471047124730471250415070500051065126515551145232527252" + b"0253535310542354275472540255315550562457425724604460466064602161" + b"6161176264623063366344640565526533660367216703700570077010703270" + b"5270267140711272457252720073157333736073217441740075027524753076" + ) + + @classmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + d, rest = np.hsplit(blocks, [2]) + qs, scales = np.hsplit(rest, [QK_K // 4]) + + d = d.view(np.float16).astype(np.float32) + scales = scales.view(np.uint32) + + db = d * (np.float32(0.5) + (scales >> 28).astype(np.float32)) * np.float32(0.5) + db = db.reshape((n_blocks, -1, 1, 1)) + + # get the sign indices and unpack the bits + signs = scales.reshape((n_blocks, -1, 1)) >> np.array([0, 7, 14, 21], dtype=np.uint32).reshape((1, 1, 4)) + ksigns = np.frombuffer(IQ2_XXS.ksigns, dtype=np.uint8).reshape((1, 1, 1, 128)) + signs = (signs & np.uint32(0x7F)).reshape((n_blocks, -1, 4, 1)) + signs = np.take_along_axis(ksigns, signs, axis=-1) + signs = signs.reshape((n_blocks, -1, 4, 1)) >> np.array([i for i in range(8)], dtype=np.uint8).reshape((1, 1, 1, 8)) + signs = signs & np.uint8(0x01) + signs = np.where(signs == 0, np.float32(1), np.float32(-1)) + signs = signs.reshape((n_blocks, -1, 4, 8)) + + assert cls.grid is not None + grid = np.take_along_axis(cls.grid, qs.reshape((n_blocks, -1, 1, 1)), axis=-2) + grid = grid.reshape((n_blocks, -1, 4, 8)) + + return (db * grid * signs).reshape((n_blocks, -1)) + + +class IQ3_S(__Quant, qtype=GGMLQuantizationType.IQ3_S): + grid_shape = (512, 4) + grid_map = (0x01, 0x03, 0x05, 0x07, 0x09, 0x0b, 0x0d, 0x0f) + grid_hex = ( + b"0000010002000500070010001100120014001600200021002500330040004200" + b"4500470051005300600062007100740077000001010102010401100111011501" + b"2001230127013101350144016101650172010002010205020702100213021602" + b"2102250230023402420245024702510253027002730203031103150320032203" + b"3103330336034403500352036703710375030004130417042104240432044004" + b"4304510470040205040520052205260533054105450547056605730506061106" + b"1306310652067106000702070407200722072607330750075407001001100210" + b"0410101011101310151017102010221031103410361054105610611072100011" + b"0111031106111011141121113011331141115011521170117611001212121512" + b"1712201224123212401243125512601272120113041307131013131321132713" + b"3013341341136213701303140514121414143114331442144614501454140115" + b"1015131521153015321551152016241627164416461601170317101712172117" + b"3517411762177017002001200320052007201020122014201620212023202720" + b"3020322041204320452050205220672070207320752000210221102113211721" + b"2221252131213421422151210122042207222122232230223722412253225722" + b"7122742200230223052311232223242331233323422350236623012407242024" + b"2324322435244124722475240425112522253725402553257025002602260726" + b"2126552661260527112726273027432750270230113013301530173022303130" + b"3330353042304430473051306330713001310331053114312131233140316031" + b"7231763100321232203232323432503201331033143321332333273330334133" + b"4333473355337333033411341634223431345234603464340135103512352535" + b"3235443556357335163641360137033720372237353700400440124020402440" + b"2740324041405040704002410741114113412241304135414341514155410142" + b"0342104215422142334240425742624270420443114313432043224331433543" + b"0044024424443744404471440545074521456245134634466046104715473047" + b"4347514702501050145022504050445047505250665074500151035105511251" + b"2151325172510052115223523052365253520253075310532753445351536553" + b"7353015404542054325446541255265551555355425602570457225711601360" + b"1560316033606060006120612761646112623462426255626262706200631463" + b"2163406325644364626400650365346560650566406611671367007004700770" + b"2070227036704070547062700271117124714371457101720472107216722172" + b"3072517202733273357353730174057413742074507422754275027631760077" + ) + + @classmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + d, rest = np.hsplit(blocks, [2]) + qs, rest = np.hsplit(rest, [QK_K // 4]) + qh, rest = np.hsplit(rest, [QK_K // 32]) + signs, scales = np.hsplit(rest, [QK_K // 8]) + + d = d.view(np.float16).astype(np.float32) + + scales = scales.reshape((n_blocks, -1, 1)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2)) + scales = (scales & 0x0F).reshape((n_blocks, -1)) + db = d * (1 + 2 * scales) + db = db.reshape((n_blocks, -1, 1, 1)) + + # unpack the sign bits + signs = signs.reshape((n_blocks, -1, 1)) >> np.array([i for i in range(8)], dtype=np.uint8).reshape((1, 1, 8)) + signs = signs & np.uint8(0x01) + signs = np.where(signs == 0, np.float32(1), np.float32(-1)) + signs = signs.reshape((n_blocks, -1, 4, 8)) + + qh = qh.reshape((n_blocks, -1, 1)) >> np.array([i for i in range(8)], dtype=np.uint8) + qh = (qh & 0x01).astype(np.uint16).reshape((n_blocks, -1)) + qs = qs.astype(np.uint16) | (qh << 8) + + assert cls.grid is not None + grid = np.take_along_axis(cls.grid, qs.reshape((n_blocks, -1, 1, 1)), axis=-2) + grid = grid.reshape((n_blocks, -1, 4, 8)) + + return (db * grid * signs).reshape((n_blocks, -1)) + + +class IQ1_S(__Quant, qtype=GGMLQuantizationType.IQ1_S): + # iq1s_grid, with each byte packed into 2 bits + # -1, 0, 1 <=> 0, 1, 2 + grid_shape = (2048, 8) + grid_map = (-1, 0, 1) + grid_hex = ( + b"00000200050008000a00110015002000220028002a0045005100540056006500" + 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b"20aa22aa28aa2aaa51aa54aa56aa80aa82aa88aa8aaa95aaa0aaa2aaa8aaaaaa" + ) + + delta = np.float32(0.125) + + @classmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + d, rest = np.hsplit(blocks, [2]) + qs, qh = np.hsplit(rest, [QK_K // 8]) + + d = d.view(np.float16).astype(np.float32) + qh = qh.view(np.uint16) + + dl = d * (2 * ((qh >> 12) & 7) + 1) + dl = dl.reshape((n_blocks, -1, 1, 1)) + delta = np.where((qh & np.uint16(0x8000)) == 0, cls.delta, -cls.delta) + delta = delta.reshape((n_blocks, -1, 1, 1)) + + qh = qh.reshape((n_blocks, -1, 1)) >> np.array([0, 3, 6, 9], dtype=np.uint16).reshape((1, 1, 4)) + qs = qs.astype(np.uint16) | ((qh & 7) << 8).reshape((n_blocks, -1)) + + assert cls.grid is not None + grid = np.take_along_axis(cls.grid, qs.reshape((n_blocks, -1, 1, 1)), axis=-2) + grid = grid.reshape((n_blocks, -1, 4, 8)) + + return (dl * (grid + delta)).reshape((n_blocks, -1)) + + +class IQ1_M(__Quant, qtype=GGMLQuantizationType.IQ1_M): + grid_shape = IQ1_S.grid_shape + grid_map = IQ1_S.grid_map + grid_hex = IQ1_S.grid_hex + + delta = IQ1_S.delta + + # Okay *this* type is weird. It's the only one which stores the f16 scales in multiple parts. + @classmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + qs, rest = np.hsplit(blocks, [QK_K // 8]) + qh, scales = np.hsplit(rest, [QK_K // 16]) + + # The f16 scale is packed across multiple bytes + scales = scales.view(np.uint16) + d = (scales.reshape((n_blocks, 4)) & np.uint16(0xF000)) >> np.array([12, 8, 4, 0], dtype=np.uint16).reshape((1, 4)) + d = d[..., 0] | d[..., 1] | d[..., 2] | d[..., 3] + d = d.view(np.float16).astype(np.float32).reshape((n_blocks, 1)) + + scales = scales.reshape(n_blocks, -1, 1) >> np.array([0, 3, 6, 9], dtype=np.uint16).reshape((1, 1, 4)) + scales = (scales & 0x07).reshape((n_blocks, -1)) + dl = d * (2 * scales + 1) + dl = dl.reshape((n_blocks, -1, 2, 1, 1)) + + qh = qh.reshape((n_blocks, -1, 1)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2)) + qs = qs.astype(np.uint16) | ((qh & 0x07).astype(np.uint16) << 8).reshape((n_blocks, -1)) + + delta = np.where(qh & 0x08 == 0, cls.delta, -cls.delta) + delta = delta.reshape((n_blocks, -1, 2, 2, 1)) + + assert cls.grid is not None + grid = np.take_along_axis(cls.grid, qs.reshape((n_blocks, -1, 1, 1)), axis=-2) + grid = grid.reshape((n_blocks, -1, 2, 2, 8)) + + return (dl * (grid + delta)).reshape((n_blocks, -1)) + + +class IQ4_NL(__Quant, qtype=GGMLQuantizationType.IQ4_NL): + kvalues = (-127, -104, -83, -65, -49, -35, -22, -10, 1, 13, 25, 38, 53, 69, 89, 113) + + @classmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + d, qs = np.hsplit(blocks, [2]) + + d = d.view(np.float16).astype(np.float32) + + qs = qs.reshape((n_blocks, -1, 1, cls.block_size // 2)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2, 1)) + + qs = (qs & np.uint8(0x0F)).reshape((n_blocks, -1, 1)) + + kvalues = np.array(cls.kvalues, dtype=np.int8).reshape(1, 1, 16) + qs = np.take_along_axis(kvalues, qs, axis=-1).astype(np.float32).reshape((n_blocks, -1)) + + return (d * qs) + + +class IQ4_XS(__Quant, qtype=GGMLQuantizationType.IQ4_XS): + @classmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + d, rest = np.hsplit(blocks, [2]) + scales_h, rest = np.hsplit(rest, [2]) + scales_l, qs = np.hsplit(rest, [QK_K // 64]) + + d = d.view(np.float16).astype(np.float32) + scales_h = scales_h.view(np.uint16) + + scales_l = scales_l.reshape((n_blocks, -1, 1)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2)) + scales_h = scales_h.reshape((n_blocks, 1, -1)) >> np.array([2 * i for i in range(QK_K // 32)], dtype=np.uint16).reshape((1, -1, 1)) + scales_l = scales_l.reshape((n_blocks, -1)) & np.uint8(0x0F) + scales_h = scales_h.reshape((n_blocks, -1)).astype(np.uint8) & np.uint8(0x03) + + scales = (scales_l | (scales_h << np.uint8(4))).astype(np.int8) - np.int8(32) + dl = (d * scales.astype(np.float32)).reshape((n_blocks, -1, 1)) + + qs = qs.reshape((n_blocks, -1, 1, 16)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2, 1)) + qs = qs.reshape((n_blocks, -1, 32, 1)) & np.uint8(0x0F) + + kvalues = np.array(IQ4_NL.kvalues, dtype=np.int8).reshape((1, 1, 1, -1)) + qs = np.take_along_axis(kvalues, qs, axis=-1).astype(np.float32).reshape((n_blocks, -1, 32)) + + return (dl * qs).reshape((n_blocks, -1)) diff --git a/backend/llama.cpp/gguf-py/gguf/scripts/gguf_convert_endian.py b/backend/llama.cpp/gguf-py/gguf/scripts/gguf_convert_endian.py new file mode 100644 index 0000000000000000000000000000000000000000..164c9171e04bb4800e5918198b909717e2db8709 --- /dev/null +++ b/backend/llama.cpp/gguf-py/gguf/scripts/gguf_convert_endian.py @@ -0,0 +1,187 @@ +#!/usr/bin/env python3 +from __future__ import annotations + +import logging +import argparse +import os +import sys +from tqdm import tqdm +from pathlib import Path + +import numpy as np + +# Necessary to load the local gguf package +if "NO_LOCAL_GGUF" not in os.environ and (Path(__file__).parent.parent.parent.parent / 'gguf-py').exists(): + sys.path.insert(0, str(Path(__file__).parent.parent.parent)) + +import gguf + +logger = logging.getLogger("gguf-convert-endian") + + +def byteswap_noop(tensor, block_offs): + # this function is used when byteswapping is not needed + pass + + +def byteswap_q4_0(tensor, block_offs): + # Each block_q4_0 consists of an f16 delta (scaling factor) followed by 16 int8 quantizations. + + # Byte-Swap f16 sized delta field + delta = tensor.data[block_offs:block_offs + 2].view(dtype=np.uint16) + delta.byteswap(inplace=True) + + +def byteswap_q8_0(tensor, block_offs): + # Each block_q8_0 consists of an f16 delta (scaling factor) followed by 32 int8 quantizations. + + # Byte-Swap f16 sized delta field + delta = tensor.data[block_offs:block_offs + 2].view(dtype=np.uint16) + delta.byteswap(inplace=True) + + +def byteswap_q4_k(tensor, block_offs): + # Each block_q4_k consists of 2 f16 values followed by 140 int8 values. + + # Byte-Swap f16 sized fields + delta = tensor.data[block_offs:block_offs + 2].view(dtype=np.uint16) + delta.byteswap(inplace=True) + + delta = tensor.data[block_offs + 2:block_offs + 4].view(dtype=np.uint16) + delta.byteswap(inplace=True) + + +def byteswap_q6_k(tensor, block_offs): + # Each block_q6_k consists of 208 int8 values followed by 1 f16 value. + + # Byte-Swap f16 sized field + delta = tensor.data[block_offs + 208:block_offs + 210].view(dtype=np.uint16) + delta.byteswap(inplace=True) + + +byteswap_tensors = { + gguf.GGMLQuantizationType.Q4_0: byteswap_q4_0, + gguf.GGMLQuantizationType.Q8_0: byteswap_q8_0, + gguf.GGMLQuantizationType.Q4_K: byteswap_q4_k, + gguf.GGMLQuantizationType.Q6_K: byteswap_q6_k, + gguf.GGMLQuantizationType.MXFP4: byteswap_noop, + gguf.GGMLQuantizationType.NVFP4: byteswap_noop, +} + + +def convert_byteorder(reader: gguf.GGUFReader, args: argparse.Namespace) -> None: + file_endian = reader.endianess.name + if reader.byte_order == 'S': + host_endian = 'BIG' if file_endian == 'LITTLE' else 'LITTLE' + else: + host_endian = file_endian + order = host_endian if args.order == "native" else args.order.upper() + logger.info(f"* Host is {host_endian} endian, GGUF file seems to be {file_endian} endian") + if file_endian == order: + logger.info(f"* File is already {order} endian. Nothing to do.") + sys.exit(0) + logger.info("* Checking tensors for conversion compatibility") + for tensor in reader.tensors: + if tensor.tensor_type not in byteswap_tensors and \ + tensor.tensor_type not in ( + gguf.GGMLQuantizationType.F32, + gguf.GGMLQuantizationType.F16, + gguf.GGMLQuantizationType.BF16, + ): + raise ValueError(f"Cannot handle type {tensor.tensor_type.name} for tensor {repr(tensor.name)}") + logger.info(f"* Preparing to convert from {file_endian} to {order}") + if args.dry_run: + return + logger.warning("*** Warning *** Warning *** Warning **") + logger.warning("* This conversion process may damage the file. Ensure you have a backup.") + if order != host_endian: + logger.warning("* Requested endian differs from host, you will not be able to load the model on this machine.") + logger.warning("* The file will be modified immediately, so if conversion fails or is interrupted") + logger.warning("* the file will be corrupted. Enter exactly YES if you are positive you want to proceed:") + response = input("YES, I am sure> ") + if response != "YES": + logger.warning("You didn't enter YES. Okay then, see ya!") + sys.exit(0) + logger.info(f"* Converting fields ({len(reader.fields)})") + for idx, field in enumerate(reader.fields.values()): + logger.info(f"- {idx:4}: Converting field {repr(field.name)}, part count: {len(field.parts)}") + for part in field.parts: + part.byteswap(inplace=True) + logger.info(f"* Converting tensors ({len(reader.tensors)})") + + for idx, tensor in enumerate(pbar := tqdm(reader.tensors, desc="Converting tensor")): + log_message = ( + f"Converting tensor {repr(tensor.name)}, " + f"type={tensor.tensor_type.name}, " + f"elements={tensor.n_elements} " + ) + + # Byte-swap each part of the tensor's field + for part in tensor.field.parts: + part.byteswap(inplace=True) + + # Byte-swap tensor data if necessary + if tensor.tensor_type in byteswap_tensors: + # first flatten structure + oldshape = tensor.data.shape + newshape = 1 + for i in tensor.data.shape: + newshape *= i + + tensor.data.resize(newshape) + + block_size = gguf.constants.GGML_QUANT_SIZES[tensor.tensor_type][1] + byteswap_func = byteswap_tensors[tensor.tensor_type] + + n_blocks = len(tensor.data) // block_size + for block_num in (inner_pbar := tqdm(range(n_blocks), desc="Byte-swapping Blocks", leave=False)): + block_offs = block_num * block_size + + byteswap_func(tensor, block_offs) + + if block_num % 100000 == 0: + inner_pbar.set_description(f"Byte-swapping Blocks [{(n_blocks - block_num) // n_blocks}]") + + # restore old shape in case it's ever used + tensor.data.resize(oldshape) + elif tensor.tensor_type == gguf.GGMLQuantizationType.BF16: + # Special case for BF16 + # It is 2-bytes data, but by default view loads it as 1-byte data. + # Change to correct view before byteswapping. + tensor.data.view(dtype=np.uint16).byteswap(inplace=True) + else: + # Handle other tensor types + tensor.data.byteswap(inplace=True) + + pbar.set_description(log_message) + + logger.info("* Completion") + + +def main() -> None: + parser = argparse.ArgumentParser(description="Convert GGUF file byte order") + parser.add_argument( + "model", type=str, + help="GGUF format model filename", + ) + parser.add_argument( + "order", type=str, choices=['big', 'little', 'native'], + help="Requested byte order", + ) + parser.add_argument( + "--dry-run", action="store_true", + help="Don't actually change anything", + ) + parser.add_argument("--verbose", action="store_true", help="increase output verbosity") + + args = parser.parse_args(None if len(sys.argv) > 1 else ["--help"]) + + logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO) + + logger.info(f'* Loading: {args.model}') + reader = gguf.GGUFReader(args.model, 'r' if args.dry_run else 'r+') + convert_byteorder(reader, args) + + +if __name__ == "__main__": + main() diff --git a/backend/llama.cpp/gguf-py/gguf/scripts/gguf_dump.py b/backend/llama.cpp/gguf-py/gguf/scripts/gguf_dump.py new file mode 100644 index 0000000000000000000000000000000000000000..8177dff386c7ee1e130ed0a013c2d297ee866439 --- /dev/null +++ b/backend/llama.cpp/gguf-py/gguf/scripts/gguf_dump.py @@ -0,0 +1,477 @@ +#!/usr/bin/env python3 +from __future__ import annotations + +import logging +import argparse +import os +import re +import sys +from pathlib import Path +from typing import Any + +# Necessary to load the local gguf package +if "NO_LOCAL_GGUF" not in os.environ and (Path(__file__).parent.parent.parent.parent / 'gguf-py').exists(): + sys.path.insert(0, str(Path(__file__).parent.parent.parent)) + +from gguf import GGUFReader, GGUFValueType, ReaderTensor # noqa: E402 + +logger = logging.getLogger("gguf-dump") + + +def get_file_host_endian(reader: GGUFReader) -> tuple[str, str]: + file_endian = reader.endianess.name + if reader.byte_order == 'S': + host_endian = 'BIG' if file_endian == 'LITTLE' else 'LITTLE' + else: + host_endian = file_endian + return (host_endian, file_endian) + + +# For more information about what field.parts and field.data represent, +# please see the comments in the modify_gguf.py example. +def dump_metadata(reader: GGUFReader, args: argparse.Namespace) -> None: + host_endian, file_endian = get_file_host_endian(reader) + print(f'* File is {file_endian} endian, script is running on a {host_endian} endian host.') # noqa: NP100 + print(f'* Dumping {len(reader.fields)} key/value pair(s)') # noqa: NP100 + for n, field in enumerate(reader.fields.values(), 1): + if not field.types: + pretty_type = 'N/A' + elif field.types[0] == GGUFValueType.ARRAY: + nest_count = len(field.types) - 1 + pretty_type = '[' * nest_count + str(field.types[-1].name) + ']' * nest_count + else: + pretty_type = str(field.types[-1].name) + + log_message = f' {n:5}: {pretty_type:10} | {len(field.data):8} | {field.name}' + if field.types: + curr_type = field.types[0] + if curr_type == GGUFValueType.STRING: + content = field.contents() + if len(content) > 60: + content = content[:57] + '...' + log_message += ' = {0}'.format(repr(content)) + elif curr_type in reader.gguf_scalar_to_np: + log_message += ' = {0}'.format(field.contents()) + else: + content = repr(field.contents(slice(6))) + if len(field.data) > 6: + content = content[:-1] + ', ...]' + log_message += ' = {0}'.format(content) + print(log_message) # noqa: NP100 + if args.no_tensors: + return + print(f'* Dumping {len(reader.tensors)} tensor(s)') # noqa: NP100 + for n, tensor in enumerate(reader.tensors, 1): + prettydims = ', '.join('{0:5}'.format(d) for d in list(tensor.shape) + [1] * (4 - len(tensor.shape))) + print(f' {n:5}: {tensor.n_elements:10} | {prettydims} | {tensor.tensor_type.name:7} | {tensor.name}') # noqa: NP100 + + +def dump_metadata_json(reader: GGUFReader, args: argparse.Namespace) -> None: + import json + host_endian, file_endian = get_file_host_endian(reader) + metadata: dict[str, Any] = {} + tensors: dict[str, Any] = {} + result = { + "filename": args.model, + "endian": file_endian, + "metadata": metadata, + "tensors": tensors, + } + for idx, field in enumerate(reader.fields.values()): + curr: dict[str, Any] = { + "index": idx, + "type": field.types[0].name if field.types else 'UNKNOWN', + "offset": field.offset, + } + metadata[field.name] = curr + if field.types[:1] == [GGUFValueType.ARRAY]: + curr["array_types"] = [t.name for t in field.types][1:] + if not args.json_array: + continue + curr["value"] = field.contents() + else: + curr["value"] = field.contents() + if not args.no_tensors: + for idx, tensor in enumerate(reader.tensors): + tensors[tensor.name] = { + "index": idx, + "shape": tensor.shape.tolist(), + "type": tensor.tensor_type.name, + "offset": tensor.field.offset, + } + json.dump(result, sys.stdout) + + +def markdown_table_with_alignment_support(header_map: list[dict[str, str]], data: list[dict[str, Any]]): + # JSON to Markdown table formatting: https://stackoverflow.com/a/72983854/2850957 + + # Alignment Utility Function + def strAlign(padding: int, alignMode: str | None, strVal: str): + if alignMode == 'center': + return strVal.center(padding) + elif alignMode == 'right': + return strVal.rjust(padding - 1) + ' ' + elif alignMode == 'left': + return ' ' + strVal.ljust(padding - 1) + else: # default left + return ' ' + strVal.ljust(padding - 1) + + def dashAlign(padding: int, alignMode: str | None): + if alignMode == 'center': + return ':' + '-' * (padding - 2) + ':' + elif alignMode == 'right': + return '-' * (padding - 1) + ':' + elif alignMode == 'left': + return ':' + '-' * (padding - 1) + else: # default left + return '-' * (padding) + + # Calculate Padding For Each Column Based On Header and Data Length + rowsPadding = {} + for index, columnEntry in enumerate(header_map): + padCount = max([len(str(v)) for d in data for k, v in d.items() if k == columnEntry['key_name']], default=0) + 2 + headerPadCount = len(columnEntry['header_name']) + 2 + rowsPadding[index] = headerPadCount if padCount <= headerPadCount else padCount + + # Render Markdown Header + rows = [] + rows.append('|'.join(strAlign(rowsPadding[index], columnEntry.get('align'), str(columnEntry['header_name'])) for index, columnEntry in enumerate(header_map))) + rows.append('|'.join(dashAlign(rowsPadding[index], columnEntry.get('align')) for index, columnEntry in enumerate(header_map))) + + # Render Tabular Data + for item in data: + rows.append('|'.join(strAlign(rowsPadding[index], columnEntry.get('align'), str(item[columnEntry['key_name']])) for index, columnEntry in enumerate(header_map))) + + # Convert Tabular String Rows Into String + tableString = "" + for row in rows: + tableString += f'|{row}|\n' + + return tableString + + +def element_count_rounded_notation(count: int) -> str: + if count > 1e15 : + # Quadrillion + scaled_amount = count * 1e-15 + scale_suffix = "Q" + elif count > 1e12 : + # Trillions + scaled_amount = count * 1e-12 + scale_suffix = "T" + elif count > 1e9 : + # Billions + scaled_amount = count * 1e-9 + scale_suffix = "B" + elif count > 1e6 : + # Millions + scaled_amount = count * 1e-6 + scale_suffix = "M" + elif count > 1e3 : + # Thousands + scaled_amount = count * 1e-3 + scale_suffix = "K" + else: + # Under Thousands + scaled_amount = count + scale_suffix = "" + return f"{'~' if count > 1e3 else ''}{round(scaled_amount)}{scale_suffix}" + + +def translate_tensor_name(name): + words = name.split(".") + + # Source: https://github.com/ggml-org/ggml/blob/master/docs/gguf.md#standardized-tensor-names + abbreviation_dictionary = { + 'token_embd': 'Token embedding', + 'pos_embd': 'Position embedding', + 'output_norm': 'Output normalization', + 'output': 'Output', + 'attn_norm': 'Attention normalization', + 'attn_norm_2': 'Attention normalization', + 'attn_qkv': 'Attention query-key-value', + 'attn_q': 'Attention query', + 'attn_k': 'Attention key', + 'attn_v': 'Attention value', + 'attn_output': 'Attention output', + 'ffn_norm': 'Feed-forward network normalization', + 'ffn_up': 'Feed-forward network "up"', + 'ffn_gate': 'Feed-forward network "gate"', + 'ffn_down': 'Feed-forward network "down"', + 'ffn_gate_inp': 'Expert-routing layer for the Feed-forward network in Mixture of Expert models', + 'ffn_gate_exp': 'Feed-forward network "gate" layer per expert in Mixture of Expert models', + 'ffn_down_exp': 'Feed-forward network "down" layer per expert in Mixture of Expert models', + 'ffn_up_exp': 'Feed-forward network "up" layer per expert in Mixture of Expert models', + 'ssm_in': 'State space model input projections', + 'ssm_conv1d': 'State space model rolling/shift', + 'ssm_x': 'State space model selective parametrization', + 'ssm_a': 'State space model state compression', + 'ssm_d': 'State space model skip connection', + 'ssm_dt': 'State space model time step', + 'ssm_out': 'State space model output projection', + 'blk': 'Block', + 'enc': 'Encoder', + 'dec': 'Decoder', + } + + expanded_words = [] + for word in words: + word_norm = word.strip().lower() + if word_norm in abbreviation_dictionary: + expanded_words.append(abbreviation_dictionary[word_norm].title()) + else: + expanded_words.append(word.title()) + + return ' '.join(expanded_words) + + +def dump_markdown_metadata(reader: GGUFReader, args: argparse.Namespace) -> None: + host_endian, file_endian = get_file_host_endian(reader) + markdown_content = "" + markdown_content += f'# {args.model} - GGUF Internal File Dump\n\n' + markdown_content += f'- Endian: {file_endian} endian\n' + markdown_content += '\n' + markdown_content += '## Key Value Metadata Store\n\n' + markdown_content += f'There are {len(reader.fields)} key-value pairs in this file\n' + markdown_content += '\n' + total_model_bytes = 0 + total_model_elements = 0 + + kv_dump_table: list[dict[str, str | int]] = [] + for n, field in enumerate(reader.fields.values(), 1): + if not field.types: + pretty_type = 'N/A' + elif field.types[0] == GGUFValueType.ARRAY: + nest_count = len(field.types) - 1 + pretty_type = '[' * nest_count + str(field.types[-1].name) + ']' * nest_count + else: + pretty_type = str(field.types[-1].name) + + def escape_markdown_inline_code(value_string): + # Find the longest contiguous sequence of backticks in the string then + # wrap string with appropriate number of backticks required to escape it + max_backticks = max((len(match.group(0)) for match in re.finditer(r'`+', value_string)), default=0) + inline_code_marker = '`' * (max_backticks + 1) + + # If the string starts or ends with a backtick, add a space at the beginning and end + if value_string.startswith('`') or value_string.endswith('`'): + value_string = f" {value_string} " + + return f"{inline_code_marker}{value_string}{inline_code_marker}" + + total_elements = len(field.data) + value = "" + if len(field.types) == 1: + curr_type = field.types[0] + if curr_type == GGUFValueType.STRING: + truncate_length = 60 + value_string = str(bytes(field.parts[-1]), encoding='utf-8') + if len(value_string) > truncate_length: + head = escape_markdown_inline_code(value_string[:truncate_length // 2]) + tail = escape_markdown_inline_code(value_string[-truncate_length // 2:]) + value = "{head}...{tail}".format(head=head, tail=tail) + else: + value = escape_markdown_inline_code(value_string) + elif curr_type in reader.gguf_scalar_to_np: + value = str(field.parts[-1][0]) + else: + if field.types[0] == GGUFValueType.ARRAY: + curr_type = field.types[1] + array_elements = [] + + if curr_type == GGUFValueType.STRING: + render_element = min(5, total_elements) + for element_pos in range(render_element): + truncate_length = 30 + value_string = str(bytes(field.parts[-1 - (total_elements - element_pos - 1) * 2]), encoding='utf-8') + if len(value_string) > truncate_length: + head = escape_markdown_inline_code(value_string[:truncate_length // 2]) + tail = escape_markdown_inline_code(value_string[-truncate_length // 2:]) + value = "{head}...{tail}".format(head=head, tail=tail) + else: + value = escape_markdown_inline_code(value_string) + array_elements.append(value) + + elif curr_type in reader.gguf_scalar_to_np: + render_element = min(7, total_elements) + for element_pos in range(render_element): + array_elements.append(str(field.parts[-1 - (total_elements - element_pos - 1)][0])) + + value = f'[ {", ".join(array_elements).strip()}{", ..." if total_elements > len(array_elements) else ""} ]' + + kv_dump_table.append({"n":n, "pretty_type":pretty_type, "total_elements":total_elements, "field_name":field.name, "value":value}) + + kv_dump_table_header_map = [ + {'key_name':'n', 'header_name':'POS', 'align':'right'}, + {'key_name':'pretty_type', 'header_name':'TYPE', 'align':'left'}, + {'key_name':'total_elements', 'header_name':'Count', 'align':'right'}, + {'key_name':'field_name', 'header_name':'Key', 'align':'left'}, + {'key_name':'value', 'header_name':'Value', 'align':'left'}, + ] + + markdown_content += markdown_table_with_alignment_support(kv_dump_table_header_map, kv_dump_table) + + markdown_content += "\n" + + if not args.no_tensors: + # Group tensors by their prefix and maintain order + tensor_prefix_order: list[str] = [] + tensor_name_to_key: dict[str, int] = {} + tensor_groups: dict[str, list[ReaderTensor]] = {} + total_elements = sum(tensor.n_elements for tensor in reader.tensors) + + # Parsing Tensors Record + for key, tensor in enumerate(reader.tensors): + tensor_components = tensor.name.split('.') + + # Classify Tensor Group + tensor_group_name = "base" + if tensor_components[0] == 'blk': + tensor_group_name = f"{tensor_components[0]}.{tensor_components[1]}" + elif tensor_components[0] in ['enc', 'dec'] and tensor_components[1] == 'blk': + tensor_group_name = f"{tensor_components[0]}.{tensor_components[1]}.{tensor_components[2]}" + elif tensor_components[0] in ['enc', 'dec']: + tensor_group_name = f"{tensor_components[0]}" + + # Check if new Tensor Group + if tensor_group_name not in tensor_groups: + tensor_groups[tensor_group_name] = [] + tensor_prefix_order.append(tensor_group_name) + + # Record Tensor and Tensor Position + tensor_groups[tensor_group_name].append(tensor) + tensor_name_to_key[tensor.name] = key + + # Tensors Mapping Dump + markdown_content += f'## Tensors Overview {element_count_rounded_notation(total_elements)} Elements\n\n' + markdown_content += f'Total number of elements in all tensors: {total_elements} Elements\n' + markdown_content += '\n' + + for group in tensor_prefix_order: + tensors = tensor_groups[group] + group_elements = sum(tensor.n_elements for tensor in tensors) + markdown_content += f"- [{translate_tensor_name(group)} Tensor Group - {element_count_rounded_notation(group_elements)} Elements](#{group.replace('.', '_')})\n" + + markdown_content += "\n" + + markdown_content += "### Tensor Data Offset\n" + markdown_content += '\n' + markdown_content += 'This table contains the offset and data segment relative to start of file\n' + markdown_content += '\n' + + tensor_mapping_table: list[dict[str, str | int]] = [] + for key, tensor in enumerate(reader.tensors): + data_offset_pretty = '{0:#16x}'.format(tensor.data_offset) + data_size_pretty = '{0:#16x}'.format(tensor.n_bytes) + tensor_mapping_table.append({"t_id":key, "layer_name":tensor.name, "data_offset":data_offset_pretty, "data_size":data_size_pretty}) + + tensors_mapping_table_header_map = [ + {'key_name':'t_id', 'header_name':'T_ID', 'align':'right'}, + {'key_name':'layer_name', 'header_name':'Tensor Layer Name', 'align':'left'}, + {'key_name':'data_offset', 'header_name':'Data Offset (B)', 'align':'right'}, + {'key_name':'data_size', 'header_name':'Data Size (B)', 'align':'right'}, + ] + + markdown_content += markdown_table_with_alignment_support(tensors_mapping_table_header_map, tensor_mapping_table) + markdown_content += "\n" + + for group in tensor_prefix_order: + tensors = tensor_groups[group] + group_elements = sum(tensor.n_elements for tensor in tensors) + group_percentage = group_elements / total_elements * 100 + total_group_bytes = 0 + total_group_elements = 0 + markdown_content += f"### {translate_tensor_name(group)} Tensor Group : {element_count_rounded_notation(group_elements)} Elements\n\n" + + # Precalculate column sizing for visual consistency + prettify_element_est_count_size: int = 1 + prettify_element_count_size: int = 1 + prettify_dimension_max_widths: dict[int, int] = {} + for tensor in tensors: + prettify_element_est_count_size = max(prettify_element_est_count_size, len(str(element_count_rounded_notation(tensor.n_elements)))) + prettify_element_count_size = max(prettify_element_count_size, len(str(tensor.n_elements))) + for i, dimension_size in enumerate(list(tensor.shape) + [1] * (4 - len(tensor.shape))): + prettify_dimension_max_widths[i] = max(prettify_dimension_max_widths.get(i,1), len(str(dimension_size))) + + # Generate Tensor Layer Table Content + tensor_dump_table: list[dict[str, str | int]] = [] + for tensor in tensors: + human_friendly_name = translate_tensor_name(tensor.name.replace(".weight", ".(W)").replace(".bias", ".(B)")) + pretty_dimension = ' x '.join(f'{str(d):>{prettify_dimension_max_widths[i]}}' for i, d in enumerate(list(tensor.shape) + [1] * (4 - len(tensor.shape)))) + element_count_est = f"({element_count_rounded_notation(tensor.n_elements):>{prettify_element_est_count_size}})" + element_count_string = f"{element_count_est} {tensor.n_elements:>{prettify_element_count_size}}" + type_name_string = f"{tensor.tensor_type.name}" + if tensor.n_elements > 0: + bpw = (tensor.n_bytes * 8) / tensor.n_elements + else: + bpw = float('nan') + tensor_dump_table.append({"t_id":tensor_name_to_key[tensor.name], "layer_name":tensor.name, "human_layer_name":human_friendly_name, "element_count":element_count_string, "pretty_dimension":pretty_dimension, "tensor_type":type_name_string, "bpw": f"{bpw:.4f}"}) + total_group_bytes += tensor.n_bytes + total_group_elements += tensor.n_elements + + tensor_dump_table_header_map = [ + {'key_name':'t_id', 'header_name':'T_ID', 'align':'right'}, + {'key_name':'layer_name', 'header_name':'Tensor Layer Name', 'align':'left'}, + {'key_name':'human_layer_name', 'header_name':'Human Friendly Tensor Layer Name', 'align':'left'}, + {'key_name':'element_count', 'header_name':'Elements', 'align':'left'}, + {'key_name':'pretty_dimension', 'header_name':'Shape', 'align':'left'}, + {'key_name':'tensor_type', 'header_name':'Type', 'align':'left'}, + {'key_name':'bpw', 'header_name':'BPW', 'align':'right'}, + ] + + markdown_content += markdown_table_with_alignment_support(tensor_dump_table_header_map, tensor_dump_table) + + markdown_content += "\n" + markdown_content += f"- Total elements in {group}: ({element_count_rounded_notation(group_elements):>4}) {group_elements}\n" + markdown_content += f"- Percentage of total elements: {group_percentage:.2f}%\n" + if total_group_elements > 0: + total_group_bpw = (total_group_bytes * 8) / total_group_elements + markdown_content += f"- Bits per Weight (BPW) for {group}: {total_group_bpw:.4f} bits\n" + else: + markdown_content += f"- Bits per Weight (BPW) for {group}: undefined (no elements)\n" + markdown_content += "\n\n" + total_model_bytes += total_group_bytes + total_model_elements += total_group_elements + + if total_model_elements > 0: + total_model_bpw = (total_model_bytes * 8) / total_model_elements + markdown_content += f"Total BPW for {os.path.basename(args.model)}: {total_model_bpw:.4f} bits" + else: + markdown_content += f"Total BPW for {os.path.basename(args.model)}: undefined (no elements)" + print(markdown_content) # noqa: NP100 + + +def main() -> None: + parser = argparse.ArgumentParser(description="Dump GGUF file metadata") + parser.add_argument("model", type=str, help="GGUF format model filename") + parser.add_argument("--no-tensors", action="store_true", help="Don't dump tensor metadata") + parser.add_argument("--json", action="store_true", help="Produce JSON output") + parser.add_argument("--json-array", action="store_true", help="Include full array values in JSON output (long)") + parser.add_argument("--data-offset", action="store_true", help="Start of data offset") + parser.add_argument("--data-alignment", action="store_true", help="Data alignment applied globally to data field") + parser.add_argument("--markdown", action="store_true", help="Produce markdown output") + parser.add_argument("--verbose", action="store_true", help="increase output verbosity") + + args = parser.parse_args(None if len(sys.argv) > 1 else ["--help"]) + + logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO) + + if not args.json and not args.markdown and not args.data_offset and not args.data_alignment: + logger.info(f'* Loading: {args.model}') + + reader = GGUFReader(args.model, 'r') + + if args.json: + dump_metadata_json(reader, args) + elif args.markdown: + dump_markdown_metadata(reader, args) + elif args.data_offset: + print(reader.data_offset) # noqa: NP100 + elif args.data_alignment: + print(reader.alignment) # noqa: NP100 + else: + dump_metadata(reader, args) + + +if __name__ == '__main__': + main() diff --git a/backend/llama.cpp/gguf-py/gguf/scripts/gguf_editor_gui.py b/backend/llama.cpp/gguf-py/gguf/scripts/gguf_editor_gui.py new file mode 100644 index 0000000000000000000000000000000000000000..293316afed4e497ce3e426243d9fbd30193f5c30 --- /dev/null +++ b/backend/llama.cpp/gguf-py/gguf/scripts/gguf_editor_gui.py @@ -0,0 +1,1621 @@ +#!/usr/bin/env python3 +from __future__ import annotations + +import logging +import argparse +import os +import sys +import numpy +import enum +from pathlib import Path +from typing import Any, Optional, Tuple, Type +import warnings + +import numpy as np +from PySide6.QtWidgets import ( + QApplication, QMainWindow, QWidget, QVBoxLayout, QHBoxLayout, + QPushButton, QLabel, QLineEdit, QFileDialog, QTableWidget, + QTableWidgetItem, QComboBox, QMessageBox, QTabWidget, + QTextEdit, QFormLayout, + QHeaderView, QDialog, QDialogButtonBox +) +from PySide6.QtCore import Qt + +# Necessary to load the local gguf package +if "NO_LOCAL_GGUF" not in os.environ and (Path(__file__).parent.parent.parent.parent / 'gguf-py').exists(): + sys.path.insert(0, str(Path(__file__).parent.parent.parent)) + +import gguf +from gguf import GGUFReader, GGUFWriter, GGUFValueType, ReaderField +from gguf.constants import TokenType, RopeScalingType, PoolingType, GGMLQuantizationType + +logger = logging.getLogger("gguf-editor-gui") + +# Map of key names to enum types for automatic enum interpretation +KEY_TO_ENUM_TYPE = { + gguf.Keys.Tokenizer.TOKEN_TYPE: TokenType, + gguf.Keys.Rope.SCALING_TYPE: RopeScalingType, + gguf.Keys.LLM.POOLING_TYPE: PoolingType, + gguf.Keys.General.FILE_TYPE: GGMLQuantizationType, +} + +# Define the tokenizer keys that should be edited together +TOKENIZER_LINKED_KEYS = [ + gguf.Keys.Tokenizer.LIST, + gguf.Keys.Tokenizer.TOKEN_TYPE, + gguf.Keys.Tokenizer.SCORES +] + + +class TokenizerEditorDialog(QDialog): + def __init__(self, tokens, token_types, scores, parent=None): + super().__init__(parent) + self.setWindowTitle("Edit Tokenizer Data") + self.resize(900, 600) + + self.tokens = tokens.copy() if tokens else [] + self.token_types = token_types.copy() if token_types else [] + self.scores = scores.copy() if scores else [] + + # Ensure all arrays have the same length + max_len = max(len(self.tokens), len(self.token_types), len(self.scores)) + if len(self.tokens) < max_len: + self.tokens.extend([""] * (max_len - len(self.tokens))) + if len(self.token_types) < max_len: + self.token_types.extend([0] * (max_len - len(self.token_types))) + if len(self.scores) < max_len: + self.scores.extend([0.0] * (max_len - len(self.scores))) + + layout = QVBoxLayout(self) + + # Add filter controls + filter_layout = QHBoxLayout() + filter_layout.addWidget(QLabel("Filter:")) + self.filter_edit = QLineEdit() + self.filter_edit.setPlaceholderText("Type to filter tokens...") + self.filter_edit.textChanged.connect(self.apply_filter) + filter_layout.addWidget(self.filter_edit) + + # Add page controls + self.page_size = 100 # Show 100 items per page + self.current_page = 0 + self.total_pages = max(1, (len(self.tokens) + self.page_size - 1) // self.page_size) + + self.page_label = QLabel(f"Page 1 of {self.total_pages}") + filter_layout.addWidget(self.page_label) + + prev_page = QPushButton("Previous") + prev_page.clicked.connect(self.previous_page) + filter_layout.addWidget(prev_page) + + next_page = QPushButton("Next") + next_page.clicked.connect(self.next_page) + filter_layout.addWidget(next_page) + + layout.addLayout(filter_layout) + + # Tokenizer data table + self.tokens_table = QTableWidget() + self.tokens_table.setColumnCount(4) + self.tokens_table.setHorizontalHeaderLabels(["Index", "Token", "Type", "Score"]) + self.tokens_table.horizontalHeader().setSectionResizeMode(0, QHeaderView.ResizeMode.ResizeToContents) + self.tokens_table.horizontalHeader().setSectionResizeMode(1, QHeaderView.ResizeMode.Stretch) + self.tokens_table.horizontalHeader().setSectionResizeMode(2, QHeaderView.ResizeMode.ResizeToContents) + self.tokens_table.horizontalHeader().setSectionResizeMode(3, QHeaderView.ResizeMode.ResizeToContents) + + layout.addWidget(self.tokens_table) + + # Controls + controls_layout = QHBoxLayout() + + add_button = QPushButton("Add Token") + add_button.clicked.connect(self.add_token) + controls_layout.addWidget(add_button) + + remove_button = QPushButton("Remove Selected") + remove_button.clicked.connect(self.remove_selected) + controls_layout.addWidget(remove_button) + + controls_layout.addStretch() + + layout.addLayout(controls_layout) + + # Buttons + buttons = QDialogButtonBox(QDialogButtonBox.StandardButton.Ok | QDialogButtonBox.StandardButton.Cancel) + buttons.accepted.connect(self.accept) + buttons.rejected.connect(self.reject) + layout.addWidget(buttons) + + # Initialize the filtered values + self.filtered_indices = list(range(len(self.tokens))) + + # Load data for the first page + self.load_page() + + def apply_filter(self): + """Filter the tokens based on the search text.""" + filter_text = self.filter_edit.text().lower() + + if not filter_text: + # No filter, show all values + self.filtered_indices = list(range(len(self.tokens))) + else: + # Apply filter + self.filtered_indices = [] + for i, token in enumerate(self.tokens): + if filter_text in str(token).lower(): + self.filtered_indices.append(i) + + # Reset to first page and reload + self.total_pages = max(1, (len(self.filtered_indices) + self.page_size - 1) // self.page_size) + self.current_page = 0 + self.page_label.setText(f"Page 1 of {self.total_pages}") + self.load_page() + + def previous_page(self): + """Go to the previous page of results.""" + if self.current_page > 0: + self.current_page -= 1 + self.page_label.setText(f"Page {self.current_page + 1} of {self.total_pages}") + self.load_page() + + def next_page(self): + """Go to the next page of results.""" + if self.current_page < self.total_pages - 1: + self.current_page += 1 + self.page_label.setText(f"Page {self.current_page + 1} of {self.total_pages}") + self.load_page() + + def load_page(self): + """Load the current page of tokenizer data.""" + self.tokens_table.setRowCount(0) # Clear the table + + # Calculate start and end indices for the current page + start_idx = self.current_page * self.page_size + end_idx = min(start_idx + self.page_size, len(self.filtered_indices)) + + # Pre-allocate rows for better performance + self.tokens_table.setRowCount(end_idx - start_idx) + + for row, i in enumerate(range(start_idx, end_idx)): + orig_idx = self.filtered_indices[i] + + # Index + index_item = QTableWidgetItem(str(orig_idx)) + index_item.setData(Qt.ItemDataRole.UserRole, orig_idx) # Store original index + index_item.setFlags(index_item.flags() & ~Qt.ItemFlag.ItemIsEditable) + self.tokens_table.setItem(row, 0, index_item) + + # Token + token_item = QTableWidgetItem(str(self.tokens[orig_idx])) + self.tokens_table.setItem(row, 1, token_item) + + # Token Type + token_type = self.token_types[orig_idx] if orig_idx < len(self.token_types) else 0 + try: + enum_val = TokenType(token_type) + display_text = f"{enum_val.name} ({token_type})" + except (ValueError, KeyError): + display_text = f"Unknown ({token_type})" + + type_item = QTableWidgetItem(display_text) + type_item.setData(Qt.ItemDataRole.UserRole, token_type) + + # Make type cell editable with a double-click handler + type_item.setFlags(type_item.flags() & ~Qt.ItemFlag.ItemIsEditable) + self.tokens_table.setItem(row, 2, type_item) + + # Score + score = self.scores[orig_idx] if orig_idx < len(self.scores) else 0.0 + score_item = QTableWidgetItem(str(score)) + self.tokens_table.setItem(row, 3, score_item) + + # Connect double-click handler for token type cells + self.tokens_table.cellDoubleClicked.connect(self.handle_cell_double_click) + + def handle_cell_double_click(self, row, column): + """Handle double-click on a cell, specifically for token type editing.""" + if column == 2: # Token Type column + orig_item = self.tokens_table.item(row, 0) + if orig_item: + orig_idx = orig_item.data(Qt.ItemDataRole.UserRole) + self.edit_token_type(row, orig_idx) + + def edit_token_type(self, row, orig_idx): + """Edit a token type using a dialog with a dropdown of all enum options.""" + current_value = self.token_types[orig_idx] if orig_idx < len(self.token_types) else 0 + + # Create a dialog with enum options + dialog = QDialog(self) + dialog.setWindowTitle("Select Token Type") + layout = QVBoxLayout(dialog) + + combo = QComboBox() + for enum_val in TokenType: + combo.addItem(f"{enum_val.name} ({enum_val.value})", enum_val.value) + + # Set current value + try: + if isinstance(current_value, int): + enum_val = TokenType(current_value) + combo.setCurrentText(f"{enum_val.name} ({current_value})") + except (ValueError, KeyError): + pass + + layout.addWidget(combo) + + buttons = QDialogButtonBox(QDialogButtonBox.StandardButton.Ok | QDialogButtonBox.StandardButton.Cancel) + buttons.accepted.connect(dialog.accept) + buttons.rejected.connect(dialog.reject) + layout.addWidget(buttons) + + if dialog.exec() == QDialog.DialogCode.Accepted: + # Get the selected value + new_value = combo.currentData() + enum_val = TokenType(new_value) + display_text = f"{enum_val.name} ({new_value})" + + # Update the display + type_item = self.tokens_table.item(row, 2) + if type_item: + type_item.setText(display_text) + type_item.setData(Qt.ItemDataRole.UserRole, new_value) + + # Update the actual value + self.token_types[orig_idx] = new_value + + def add_token(self): + """Add a new token to the end of the list.""" + # Add to the end of the arrays + self.tokens.append("") + self.token_types.append(0) # Default to normal token + self.scores.append(0.0) + + orig_idx = len(self.tokens) - 1 + + # Add to filtered indices if it matches the current filter + filter_text = self.filter_edit.text().lower() + if not filter_text or filter_text in "": + self.filtered_indices.append(orig_idx) + + # Update pagination + self.total_pages = max(1, (len(self.filtered_indices) + self.page_size - 1) // self.page_size) + + # Go to the last page to show the new item + self.current_page = self.total_pages - 1 + self.page_label.setText(f"Page {self.current_page + 1} of {self.total_pages}") + + # Reload the page + self.load_page() + + def remove_selected(self): + """Remove selected tokens from all arrays.""" + selected_rows = [] + for item in self.tokens_table.selectedItems(): + row = item.row() + if row not in selected_rows: + selected_rows.append(row) + + if not selected_rows: + return + + # Get original indices in descending order to avoid index shifting + orig_indices = [] + for row in selected_rows: + orig_item = self.tokens_table.item(row, 0) + if orig_item: + orig_indices.append(orig_item.data(Qt.ItemDataRole.UserRole)) + orig_indices.sort(reverse=True) + + # Remove from all arrays + for idx in orig_indices: + if idx < len(self.tokens): + del self.tokens[idx] + if idx < len(self.token_types): + del self.token_types[idx] + if idx < len(self.scores): + del self.scores[idx] + + # Rebuild filtered_indices + self.filtered_indices = [] + filter_text = self.filter_edit.text().lower() + + for i, token in enumerate(self.tokens): + if not filter_text or filter_text in str(token).lower(): + self.filtered_indices.append(i) + + # Update pagination + self.total_pages = max(1, (len(self.filtered_indices) + self.page_size - 1) // self.page_size) + self.current_page = min(self.current_page, self.total_pages - 1) + self.page_label.setText(f"Page {self.current_page + 1} of {self.total_pages}") + + # Reload the page + self.load_page() + + def get_data(self): + """Return the edited tokenizer data.""" + return self.tokens, self.token_types, self.scores + + +class ArrayEditorDialog(QDialog): + def __init__(self, array_values, element_type, key=None, parent=None): + super().__init__(parent) + self.setWindowTitle("Edit Array Values") + self.resize(700, 500) + + self.array_values = array_values + self.element_type = element_type + self.key = key + + # Get enum type for this array if applicable + self.enum_type = None + if key in KEY_TO_ENUM_TYPE and element_type == GGUFValueType.INT32: + self.enum_type = KEY_TO_ENUM_TYPE[key] + + layout = QVBoxLayout(self) + + # Add enum type information if applicable + if self.enum_type is not None: + enum_info_layout = QHBoxLayout() + enum_label = QLabel(f"Editing {self.enum_type.__name__} values:") + enum_info_layout.addWidget(enum_label) + + # Add a legend for the enum values + enum_values = ", ".join([f"{e.name}={e.value}" for e in self.enum_type]) + enum_values_label = QLabel(f"Available values: {enum_values}") + enum_values_label.setWordWrap(True) + enum_info_layout.addWidget(enum_values_label, 1) + + layout.addLayout(enum_info_layout) + + # Add search/filter controls + filter_layout = QHBoxLayout() + filter_layout.addWidget(QLabel("Filter:")) + self.filter_edit = QLineEdit() + self.filter_edit.setPlaceholderText("Type to filter values...") + self.filter_edit.textChanged.connect(self.apply_filter) + filter_layout.addWidget(self.filter_edit) + + # Add page controls for large arrays + self.page_size = 100 # Show 100 items per page + self.current_page = 0 + self.total_pages = max(1, (len(array_values) + self.page_size - 1) // self.page_size) + + self.page_label = QLabel(f"Page 1 of {self.total_pages}") + filter_layout.addWidget(self.page_label) + + prev_page = QPushButton("Previous") + prev_page.clicked.connect(self.previous_page) + filter_layout.addWidget(prev_page) + + next_page = QPushButton("Next") + next_page.clicked.connect(self.next_page) + filter_layout.addWidget(next_page) + + layout.addLayout(filter_layout) + + # Array items table + self.items_table = QTableWidget() + + # Set up columns based on whether we have an enum type + if self.enum_type is not None: + self.items_table.setColumnCount(3) + self.items_table.setHorizontalHeaderLabels(["Index", "Value", "Actions"]) + self.items_table.horizontalHeader().setSectionResizeMode(0, QHeaderView.ResizeMode.ResizeToContents) + self.items_table.horizontalHeader().setSectionResizeMode(1, QHeaderView.ResizeMode.Stretch) + self.items_table.horizontalHeader().setSectionResizeMode(2, QHeaderView.ResizeMode.ResizeToContents) + else: + self.items_table.setColumnCount(2) + self.items_table.setHorizontalHeaderLabels(["Index", "Value"]) + self.items_table.horizontalHeader().setSectionResizeMode(0, QHeaderView.ResizeMode.ResizeToContents) + self.items_table.horizontalHeader().setSectionResizeMode(1, QHeaderView.ResizeMode.Stretch) + + layout.addWidget(self.items_table) + + # Controls + controls_layout = QHBoxLayout() + + add_button = QPushButton("Add Item") + add_button.clicked.connect(self.add_item) + controls_layout.addWidget(add_button) + + remove_button = QPushButton("Remove Selected") + remove_button.clicked.connect(self.remove_selected) + controls_layout.addWidget(remove_button) + + # Add bulk edit button for enum arrays + if self.enum_type is not None: + bulk_edit_button = QPushButton("Bulk Edit Selected") + bulk_edit_button.clicked.connect(self.bulk_edit_selected) + controls_layout.addWidget(bulk_edit_button) + + controls_layout.addStretch() + + layout.addLayout(controls_layout) + + # Buttons + buttons = QDialogButtonBox(QDialogButtonBox.StandardButton.Ok | QDialogButtonBox.StandardButton.Cancel) + buttons.accepted.connect(self.accept) + buttons.rejected.connect(self.reject) + layout.addWidget(buttons) + + # Initialize the filtered values + self.filtered_indices = list(range(len(self.array_values))) + + # Load array values for the first page + self.load_page() + + def apply_filter(self): + """Filter the array values based on the search text.""" + filter_text = self.filter_edit.text().lower() + + if not filter_text: + # No filter, show all values + self.filtered_indices = list(range(len(self.array_values))) + else: + # Apply filter + self.filtered_indices = [] + for i, value in enumerate(self.array_values): + # For enum values, search in both name and value + if self.enum_type is not None and isinstance(value, int): + try: + enum_val = self.enum_type(value) + display_text = f"{enum_val.name} ({value})".lower() + if filter_text in display_text: + self.filtered_indices.append(i) + except (ValueError, KeyError): + # If not a valid enum value, just check the raw value + if filter_text in str(value).lower(): + self.filtered_indices.append(i) + else: + # For non-enum values, just check the string representation + if filter_text in str(value).lower(): + self.filtered_indices.append(i) + + # Reset to first page and reload + self.total_pages = max(1, (len(self.filtered_indices) + self.page_size - 1) // self.page_size) + self.current_page = 0 + self.page_label.setText(f"Page 1 of {self.total_pages}") + self.load_page() + + def previous_page(self): + """Go to the previous page of results.""" + if self.current_page > 0: + self.current_page -= 1 + self.page_label.setText(f"Page {self.current_page + 1} of {self.total_pages}") + self.load_page() + + def next_page(self): + """Go to the next page of results.""" + if self.current_page < self.total_pages - 1: + self.current_page += 1 + self.page_label.setText(f"Page {self.current_page + 1} of {self.total_pages}") + self.load_page() + + def load_page(self): + """Load the current page of array values.""" + self.items_table.setRowCount(0) # Clear the table + + # Calculate start and end indices for the current page + start_idx = self.current_page * self.page_size + end_idx = min(start_idx + self.page_size, len(self.filtered_indices)) + + # Pre-allocate rows for better performance + self.items_table.setRowCount(end_idx - start_idx) + + for row, i in enumerate(range(start_idx, end_idx)): + orig_idx = self.filtered_indices[i] + value = self.array_values[orig_idx] + + # Index + index_item = QTableWidgetItem(str(orig_idx)) + index_item.setData(Qt.ItemDataRole.UserRole, orig_idx) # Store original index + index_item.setFlags(index_item.flags() & ~Qt.ItemFlag.ItemIsEditable) + self.items_table.setItem(row, 0, index_item) + + # Value + if self.enum_type is not None: + # Display enum value and name + try: + if isinstance(value, (int, numpy.signedinteger)): + enum_val = self.enum_type(value) + display_text = f"{enum_val.name} ({value})" + else: + display_text = str(value) + except (ValueError, KeyError): + display_text = f"Unknown ({value})" + + # Store the enum value in the item + value_item = QTableWidgetItem(display_text) + value_item.setData(Qt.ItemDataRole.UserRole, value) + value_item.setFlags(value_item.flags() & ~Qt.ItemFlag.ItemIsEditable) + self.items_table.setItem(row, 1, value_item) + + # Add an edit button in a separate column + edit_button = QPushButton("Edit") + edit_button.setProperty("row", row) + edit_button.clicked.connect(self.edit_array_enum_value) + + # Create a widget to hold the button + button_widget = QWidget() + button_layout = QHBoxLayout(button_widget) + button_layout.setContentsMargins(2, 2, 2, 2) + button_layout.addWidget(edit_button) + button_layout.addStretch() + + self.items_table.setCellWidget(row, 2, button_widget) + else: + value_item = QTableWidgetItem(str(value)) + self.items_table.setItem(row, 1, value_item) + + def edit_array_enum_value(self): + """Handle editing an enum value in the array editor.""" + button = self.sender() + row = button.property("row") + + # Get the original index from the table item + orig_item = self.items_table.item(row, 0) + new_item = self.items_table.item(row, 1) + if orig_item and new_item and self.enum_type and self.edit_enum_value(row, self.enum_type): + orig_idx = orig_item.data(Qt.ItemDataRole.UserRole) + new_value = new_item.data(Qt.ItemDataRole.UserRole) + # Update the stored value in the array + if isinstance(new_value, (int, float, str, bool)): + self.array_values[orig_idx] = new_value + + def bulk_edit_selected(self): + """Edit multiple enum values at once.""" + if not self.enum_type: + return + + selected_rows = set() + for item in self.items_table.selectedItems(): + selected_rows.add(item.row()) + + if not selected_rows: + QMessageBox.information(self, "No Selection", "Please select at least one row to edit.") + return + + # Create a dialog with enum options + dialog = QDialog(self) + dialog.setWindowTitle(f"Bulk Edit {self.enum_type.__name__} Values") + layout = QVBoxLayout(dialog) + + layout.addWidget(QLabel(f"Set {len(selected_rows)} selected items to:")) + + combo = QComboBox() + for enum_val in self.enum_type: + combo.addItem(f"{enum_val.name} ({enum_val.value})", enum_val.value) + + layout.addWidget(combo) + + buttons = QDialogButtonBox(QDialogButtonBox.StandardButton.Ok | QDialogButtonBox.StandardButton.Cancel) + buttons.accepted.connect(dialog.accept) + buttons.rejected.connect(dialog.reject) + layout.addWidget(buttons) + + if dialog.exec() == QDialog.DialogCode.Accepted: + # Get the selected value + new_value = combo.currentData() + enum_val = self.enum_type(new_value) + display_text = f"{enum_val.name} ({new_value})" + + # Update all selected rows + for row in selected_rows: + orig_item = self.items_table.item(row, 0) + new_item = self.items_table.item(row, 1) + if orig_item and new_item: + orig_idx = orig_item.data(Qt.ItemDataRole.UserRole) + self.array_values[orig_idx] = new_value + + # Update the display + new_item.setText(display_text) + new_item.setData(Qt.ItemDataRole.UserRole, new_value) + + def add_item(self): + # Add to the end of the array + orig_idx = len(self.array_values) + + # Add default value based on type + if self.enum_type is not None: + # Default to first enum value + default_value = list(self.enum_type)[0].value + self.array_values.append(default_value) + else: + if self.element_type == GGUFValueType.STRING: + self.array_values.append("") + else: + self.array_values.append(0) + + # Add to filtered indices if it matches the current filter + self.filtered_indices.append(orig_idx) + + # Update pagination + self.total_pages = max(1, (len(self.filtered_indices) + self.page_size - 1) // self.page_size) + + # Go to the last page to show the new item + self.current_page = self.total_pages - 1 + self.page_label.setText(f"Page {self.current_page + 1} of {self.total_pages}") + + # Reload the page + self.load_page() + + def remove_selected(self): + selected_rows = [] + for item in self.items_table.selectedItems(): + row = item.row() + if row not in selected_rows: + selected_rows.append(row) + + if not selected_rows: + return + + # Get original indices in descending order to avoid index shifting + orig_indices = list() + for row in selected_rows: + orig_item = self.items_table.item(row, 0) + if orig_item: + orig_indices.append(orig_item.data(Qt.ItemDataRole.UserRole)) + orig_indices.sort(reverse=True) + + # Remove from array_values + for idx in orig_indices: + del self.array_values[idx] + + # Rebuild filtered_indices + self.filtered_indices = [] + filter_text = self.filter_edit.text().lower() + + for i, value in enumerate(self.array_values): + if not filter_text: + self.filtered_indices.append(i) + else: + # Apply filter + if self.enum_type is not None and isinstance(value, int): + try: + enum_val = self.enum_type(value) + display_text = f"{enum_val.name} ({value})".lower() + if filter_text in display_text: + self.filtered_indices.append(i) + except (ValueError, KeyError): + if filter_text in str(value).lower(): + self.filtered_indices.append(i) + else: + if filter_text in str(value).lower(): + self.filtered_indices.append(i) + + # Update pagination + self.total_pages = max(1, (len(self.filtered_indices) + self.page_size - 1) // self.page_size) + self.current_page = min(self.current_page, self.total_pages - 1) + self.page_label.setText(f"Page {self.current_page + 1} of {self.total_pages}") + + # Reload the page + self.load_page() + + def edit_enum_value(self, row: int, enum_type: Type[enum.Enum]): + """Edit an enum value using a dialog with a dropdown of all enum options.""" + # Get the original index from the table item + orig_item = self.items_table.item(row, 0) + if orig_item: + orig_idx = orig_item.data(Qt.ItemDataRole.UserRole) + else: + return + current_value = self.array_values[orig_idx] + + # Create a dialog with enum options + dialog = QDialog(self) + dialog.setWindowTitle(f"Select {enum_type.__name__} Value") + layout = QVBoxLayout(dialog) + + # Add description + description = QLabel(f"Select a {enum_type.__name__} value:") + layout.addWidget(description) + + # Use a combo box for quick selection + combo = QComboBox() + for enum_val in enum_type: + combo.addItem(f"{enum_val.name} ({enum_val.value})", enum_val.value) + + # Set current value + try: + if isinstance(current_value, int): + enum_val = enum_type(current_value) + combo.setCurrentText(f"{enum_val.name} ({current_value})") + except (ValueError, KeyError): + pass + + layout.addWidget(combo) + + buttons = QDialogButtonBox(QDialogButtonBox.StandardButton.Ok | QDialogButtonBox.StandardButton.Cancel) + buttons.accepted.connect(dialog.accept) + buttons.rejected.connect(dialog.reject) + layout.addWidget(buttons) + + if dialog.exec() == QDialog.DialogCode.Accepted: + # Update the value display and stored data + new_value = combo.currentData() + enum_val = enum_type(new_value) + display_text = f"{enum_val.name} ({new_value})" + + new_item = self.items_table.item(row, 1) + if new_item: + new_item.setText(display_text) + new_item.setData(Qt.ItemDataRole.UserRole, new_value) + + # Update the actual array value + self.array_values[orig_idx] = new_value + return True + return False + + def get_array_values(self): + # The array_values list is kept up-to-date as edits are made + return self.array_values + + +class AddMetadataDialog(QDialog): + def __init__(self, parent=None): + super().__init__(parent) + self.setWindowTitle("Add Metadata") + self.resize(400, 200) + + layout = QVBoxLayout(self) + + form_layout = QFormLayout() + + self.key_edit = QLineEdit() + form_layout.addRow("Key:", self.key_edit) + + self.type_combo = QComboBox() + for value_type in GGUFValueType: + if value_type != GGUFValueType.ARRAY: # Skip array type for simplicity + self.type_combo.addItem(value_type.name, value_type) + form_layout.addRow("Type:", self.type_combo) + + self.value_edit = QTextEdit() + form_layout.addRow("Value:", self.value_edit) + + layout.addLayout(form_layout) + + buttons = QDialogButtonBox(QDialogButtonBox.StandardButton.Ok | QDialogButtonBox.StandardButton.Cancel) + buttons.accepted.connect(self.accept) + buttons.rejected.connect(self.reject) + layout.addWidget(buttons) + + def get_data(self) -> Tuple[str, GGUFValueType, Any]: + key = self.key_edit.text() + value_type = self.type_combo.currentData() + value_text = self.value_edit.toPlainText() + + # Convert value based on type + if value_type == GGUFValueType.UINT8: + value = np.uint8(int(value_text)) + elif value_type == GGUFValueType.INT8: + value = np.int8(int(value_text)) + elif value_type == GGUFValueType.UINT16: + value = np.uint16(int(value_text)) + elif value_type == GGUFValueType.INT16: + value = np.int16(int(value_text)) + elif value_type == GGUFValueType.UINT32: + value = np.uint32(int(value_text)) + elif value_type == GGUFValueType.INT32: + value = np.int32(int(value_text)) + elif value_type == GGUFValueType.FLOAT32: + value = np.float32(float(value_text)) + elif value_type == GGUFValueType.BOOL: + value = value_text.lower() in ('true', 'yes', '1') + elif value_type == GGUFValueType.STRING: + value = value_text + else: + value = value_text + + return key, value_type, value + + +class GGUFEditorWindow(QMainWindow): + def __init__(self): + super().__init__() + + self.setWindowTitle("GGUF Editor") + self.resize(1000, 800) + + self.current_file = None + self.reader = None + self.modified = False + self.metadata_changes = {} # Store changes to apply when saving + self.metadata_to_remove = set() # Store keys to remove when saving + self.on_metadata_changed_is_connected = False + + self.setup_ui() + + def setup_ui(self): + central_widget = QWidget() + self.setCentralWidget(central_widget) + + main_layout = QVBoxLayout(central_widget) + + # File controls + file_layout = QHBoxLayout() + + self.file_path_edit = QLineEdit() + self.file_path_edit.setReadOnly(True) + file_layout.addWidget(self.file_path_edit) + + open_button = QPushButton("Open GGUF") + open_button.clicked.connect(self.open_file) + file_layout.addWidget(open_button) + + save_button = QPushButton("Save As...") + save_button.clicked.connect(self.save_file) + file_layout.addWidget(save_button) + + main_layout.addLayout(file_layout) + + # Tabs for different views + self.tabs = QTabWidget() + + # Metadata tab + self.metadata_tab = QWidget() + metadata_layout = QVBoxLayout(self.metadata_tab) + + # Metadata table + self.metadata_table = QTableWidget() + self.metadata_table.setColumnCount(4) + self.metadata_table.setHorizontalHeaderLabels(["Key", "Type", "Value", "Actions"]) + self.metadata_table.horizontalHeader().setSectionResizeMode(0, QHeaderView.ResizeMode.Stretch) + self.metadata_table.horizontalHeader().setSectionResizeMode(1, QHeaderView.ResizeMode.ResizeToContents) + self.metadata_table.horizontalHeader().setSectionResizeMode(2, QHeaderView.ResizeMode.Stretch) + self.metadata_table.horizontalHeader().setSectionResizeMode(3, QHeaderView.ResizeMode.ResizeToContents) + metadata_layout.addWidget(self.metadata_table) + + # Metadata controls + metadata_controls = QHBoxLayout() + + add_metadata_button = QPushButton("Add Metadata") + add_metadata_button.clicked.connect(self.add_metadata) + metadata_controls.addWidget(add_metadata_button) + + metadata_controls.addStretch() + + metadata_layout.addLayout(metadata_controls) + + # Tensors tab + self.tensors_tab = QWidget() + tensors_layout = QVBoxLayout(self.tensors_tab) + + self.tensors_table = QTableWidget() + self.tensors_table.setColumnCount(5) + self.tensors_table.setHorizontalHeaderLabels(["Name", "Type", "Shape", "Elements", "Size (bytes)"]) + self.tensors_table.horizontalHeader().setSectionResizeMode(0, QHeaderView.ResizeMode.Stretch) + self.tensors_table.horizontalHeader().setSectionResizeMode(1, QHeaderView.ResizeMode.ResizeToContents) + self.tensors_table.horizontalHeader().setSectionResizeMode(2, QHeaderView.ResizeMode.ResizeToContents) + self.tensors_table.horizontalHeader().setSectionResizeMode(3, QHeaderView.ResizeMode.ResizeToContents) + self.tensors_table.horizontalHeader().setSectionResizeMode(4, QHeaderView.ResizeMode.ResizeToContents) + tensors_layout.addWidget(self.tensors_table) + + # Add tabs to tab widget + self.tabs.addTab(self.metadata_tab, "Metadata") + self.tabs.addTab(self.tensors_tab, "Tensors") + + main_layout.addWidget(self.tabs) + + # Status bar + self.statusBar().showMessage("Ready") + + def load_file(self, file_path): + """Load a GGUF file by path""" + try: + self.statusBar().showMessage(f"Loading {file_path}...") + QApplication.processEvents() + + self.reader = GGUFReader(file_path, 'r') + self.current_file = file_path + self.file_path_edit.setText(file_path) + + self.load_metadata() + self.load_tensors() + + self.metadata_changes = {} + self.metadata_to_remove = set() + self.modified = False + + self.statusBar().showMessage(f"Loaded {file_path}") + return True + except Exception as e: + QMessageBox.critical(self, "Error", f"Failed to open file: {str(e)}") + self.statusBar().showMessage("Error loading file") + return False + + def open_file(self): + file_path, _ = QFileDialog.getOpenFileName( + self, "Open GGUF File", "", "GGUF Files (*.gguf);;All Files (*)" + ) + + if not file_path: + return + + self.load_file(file_path) + + def load_metadata(self): + self.metadata_table.setRowCount(0) + + if not self.reader: + return + + # Disconnect to prevent triggering during loading + if self.on_metadata_changed_is_connected: + with warnings.catch_warnings(): + warnings.filterwarnings('ignore') + self.metadata_table.itemChanged.disconnect(self.on_metadata_changed) + self.on_metadata_changed_is_connected = False + + for i, (key, field) in enumerate(self.reader.fields.items()): + self.metadata_table.insertRow(i) + + # Key + key_item = QTableWidgetItem(key) + key_item.setFlags(key_item.flags() & ~Qt.ItemFlag.ItemIsEditable) + self.metadata_table.setItem(i, 0, key_item) + + # Type + if not field.types: + type_str = "N/A" + elif field.types[0] == GGUFValueType.ARRAY: + nest_count = len(field.types) - 1 + element_type = field.types[-1].name + # Check if this is an enum array + enum_type = self.get_enum_for_key(key) + if enum_type is not None and field.types[-1] == GGUFValueType.INT32: + element_type = enum_type.__name__ + type_str = '[' * nest_count + element_type + ']' * nest_count + else: + type_str = str(field.types[0].name) + # Check if this is an enum field + enum_type = self.get_enum_for_key(key) + if enum_type is not None and field.types[0] == GGUFValueType.INT32: + type_str = enum_type.__name__ + + type_item = QTableWidgetItem(type_str) + type_item.setFlags(type_item.flags() & ~Qt.ItemFlag.ItemIsEditable) + self.metadata_table.setItem(i, 1, type_item) + + # Value + value_str = self.format_field_value(field) + value_item = QTableWidgetItem(value_str) + + # Make only simple values editable + if len(field.types) == 1 and field.types[0] != GGUFValueType.ARRAY: + value_item.setFlags(value_item.flags() | Qt.ItemFlag.ItemIsEditable) + else: + value_item.setFlags(value_item.flags() & ~Qt.ItemFlag.ItemIsEditable) + + self.metadata_table.setItem(i, 2, value_item) + + # Actions + actions_widget = QWidget() + actions_layout = QHBoxLayout(actions_widget) + actions_layout.setContentsMargins(2, 2, 2, 2) + + # Add Edit button for arrays and enum fields + if field.types and field.types[0] == GGUFValueType.ARRAY: + edit_button = QPushButton("Edit") + edit_button.setProperty("row", i) + edit_button.setProperty("key", key) + edit_button.clicked.connect(self.edit_array_metadata) + actions_layout.addWidget(edit_button) + + # Add special label for tokenizer linked fields + if key in TOKENIZER_LINKED_KEYS: + edit_button.setText("Edit Tokenizer") + edit_button.setToolTip("Edit all tokenizer data together") + elif len(field.types) == 1 and self.get_enum_for_key(key) is not None: + edit_button = QPushButton("Edit") + edit_button.setProperty("row", i) + edit_button.setProperty("key", key) + edit_button.clicked.connect(self.edit_metadata_enum) + actions_layout.addWidget(edit_button) + + remove_button = QPushButton("Remove") + remove_button.setProperty("row", i) + remove_button.setProperty("key", key) + remove_button.clicked.connect(self.remove_metadata) + actions_layout.addWidget(remove_button) + + self.metadata_table.setCellWidget(i, 3, actions_widget) + + # Reconnect after loading + self.metadata_table.itemChanged.connect(self.on_metadata_changed) + self.on_metadata_changed_is_connected = True + + def extract_array_values(self, field: ReaderField) -> list: + """Extract all values from an array field.""" + if not field.types or field.types[0] != GGUFValueType.ARRAY: + return [] + + curr_type = field.types[1] + array_values = [] + total_elements = len(field.data) + + if curr_type == GGUFValueType.STRING: + for element_pos in range(total_elements): + value_string = str(bytes(field.parts[-1 - (total_elements - element_pos - 1) * 2]), encoding='utf-8') + array_values.append(value_string) + elif self.reader and curr_type in self.reader.gguf_scalar_to_np: + for element_pos in range(total_elements): + array_values.append(field.parts[-1 - (total_elements - element_pos - 1)][0]) + + return array_values + + def get_enum_for_key(self, key: str) -> Optional[Type[enum.Enum]]: + """Get the enum type for a given key if it exists.""" + return KEY_TO_ENUM_TYPE.get(key) + + def format_enum_value(self, value: Any, enum_type: Type[enum.Enum]) -> str: + """Format a value as an enum if possible.""" + try: + if isinstance(value, (int, str)): + enum_value = enum_type(value) + return f"{enum_value.name} ({value})" + except (ValueError, KeyError): + pass + return str(value) + + def format_field_value(self, field: ReaderField) -> str: + if not field.types: + return "N/A" + + if len(field.types) == 1: + curr_type = field.types[0] + if curr_type == GGUFValueType.STRING: + return str(bytes(field.parts[-1]), encoding='utf-8') + elif self.reader and curr_type in self.reader.gguf_scalar_to_np: + value = field.parts[-1][0] + # Check if this field has an enum type + enum_type = self.get_enum_for_key(field.name) + if enum_type is not None: + return self.format_enum_value(value, enum_type) + return str(value) + + if field.types[0] == GGUFValueType.ARRAY: + array_values = self.extract_array_values(field) + render_element = min(5, len(array_values)) + + # Get enum type for this array if applicable + enum_type = self.get_enum_for_key(field.name) + + if enum_type is not None: + array_elements = [] + for i in range(render_element): + array_elements.append(self.format_enum_value(array_values[i], enum_type)) + else: + array_elements = [str(array_values[i]) for i in range(render_element)] + + return f"[ {', '.join(array_elements).strip()}{', ...' if len(array_values) > len(array_elements) else ''} ]" + + return "Complex value" + + def load_tensors(self): + self.tensors_table.setRowCount(0) + + if not self.reader: + return + + for i, tensor in enumerate(self.reader.tensors): + self.tensors_table.insertRow(i) + + # Name + name_item = QTableWidgetItem(tensor.name) + name_item.setFlags(name_item.flags() & ~Qt.ItemFlag.ItemIsEditable) + self.tensors_table.setItem(i, 0, name_item) + + # Type + type_item = QTableWidgetItem(tensor.tensor_type.name) + type_item.setFlags(type_item.flags() & ~Qt.ItemFlag.ItemIsEditable) + self.tensors_table.setItem(i, 1, type_item) + + # Shape + shape_str = " × ".join(str(d) for d in tensor.shape) + shape_item = QTableWidgetItem(shape_str) + shape_item.setFlags(shape_item.flags() & ~Qt.ItemFlag.ItemIsEditable) + self.tensors_table.setItem(i, 2, shape_item) + + # Elements + elements_item = QTableWidgetItem(str(tensor.n_elements)) + elements_item.setFlags(elements_item.flags() & ~Qt.ItemFlag.ItemIsEditable) + self.tensors_table.setItem(i, 3, elements_item) + + # Size + size_item = QTableWidgetItem(f"{tensor.n_bytes:,}") + size_item.setFlags(size_item.flags() & ~Qt.ItemFlag.ItemIsEditable) + self.tensors_table.setItem(i, 4, size_item) + + def on_metadata_changed(self, item): + if item.column() != 2: # Only handle value column changes + return + + row = item.row() + orig_item = self.metadata_table.item(row, 0) + key = None + if orig_item: + key = orig_item.text() + new_value = item.text() + + field = None + if self.reader and key: + field = self.reader.get_field(key) + if not field or not field.types or not key: + return + + value_type = field.types[0] + + # Check if this is an enum field + enum_type = self.get_enum_for_key(key) + if enum_type is not None and value_type == GGUFValueType.INT32: + # Try to parse the enum value from the text + try: + # Check if it's a name + try: + enum_val = enum_type[new_value] + converted_value = enum_val.value + except (KeyError, AttributeError): + # Check if it's a number or "NAME (value)" format + if '(' in new_value and ')' in new_value: + # Extract the value from "NAME (value)" format + value_part = new_value.split('(')[1].split(')')[0].strip() + converted_value = int(value_part) + else: + # Try to convert directly to int + converted_value = int(new_value) + + # Validate that it's a valid enum value + enum_type(converted_value) + + # Store the change + self.metadata_changes[key] = (value_type, converted_value) + self.modified = True + + # Update display with formatted enum value + formatted_value = self.format_enum_value(converted_value, enum_type) + item.setText(formatted_value) + + self.statusBar().showMessage(f"Changed {key} to {formatted_value}") + return + except (ValueError, KeyError) as e: + QMessageBox.warning( + self, + f"Invalid Enum Value ({e})", + f"'{new_value}' is not a valid {enum_type.__name__} value.\n" + f"Valid values are: {', '.join(v.name for v in enum_type)}") + + # Revert to original value + original_value = self.format_field_value(field) + item.setText(original_value) + return + + try: + # Convert the string value to the appropriate type + if value_type == GGUFValueType.UINT8: + converted_value = np.uint8(int(new_value)) + elif value_type == GGUFValueType.INT8: + converted_value = np.int8(int(new_value)) + elif value_type == GGUFValueType.UINT16: + converted_value = np.uint16(int(new_value)) + elif value_type == GGUFValueType.INT16: + converted_value = np.int16(int(new_value)) + elif value_type == GGUFValueType.UINT32: + converted_value = np.uint32(int(new_value)) + elif value_type == GGUFValueType.INT32: + converted_value = np.int32(int(new_value)) + elif value_type == GGUFValueType.FLOAT32: + converted_value = np.float32(float(new_value)) + elif value_type == GGUFValueType.BOOL: + converted_value = new_value.lower() in ('true', 'yes', '1') + elif value_type == GGUFValueType.STRING: + converted_value = new_value + else: + # Unsupported type for editing + return + + # Store the change + self.metadata_changes[key] = (value_type, converted_value) + self.modified = True + + self.statusBar().showMessage(f"Changed {key} to {new_value}") + except ValueError: + QMessageBox.warning(self, "Invalid Value", f"The value '{new_value}' is not valid for type {value_type.name}") + + # Revert to original value + original_value = self.format_field_value(field) + item.setText(original_value) + + def remove_metadata(self): + button = self.sender() + key = button.property("key") + row = button.property("row") + + reply = QMessageBox.question( + self, "Confirm Removal", + f"Are you sure you want to remove the metadata key '{key}'?", + QMessageBox.StandardButton.Yes | QMessageBox.StandardButton.No, QMessageBox.StandardButton.No + ) + + if reply == QMessageBox.StandardButton.Yes: + self.metadata_table.removeRow(row) + self.metadata_to_remove.add(key) + + # If we previously had changes for this key, remove them + if key in self.metadata_changes: + del self.metadata_changes[key] + + self.modified = True + self.statusBar().showMessage(f"Marked {key} for removal") + + def edit_metadata_enum(self): + """Edit an enum metadata field.""" + button = self.sender() + key = button.property("key") + row = button.property("row") + + field = None + if self.reader: + field = self.reader.get_field(key) + if not field or not field.types: + return + + enum_type = self.get_enum_for_key(key) + if enum_type is None: + return + + # Get current value + current_value = field.contents() + + # Create a dialog with enum options + dialog = QDialog(self) + dialog.setWindowTitle(f"Select {enum_type.__name__} Value") + layout = QVBoxLayout(dialog) + + combo = QComboBox() + for enum_val in enum_type: + combo.addItem(f"{enum_val.name} ({enum_val.value})", enum_val.value) + + # Set current value + try: + if isinstance(current_value, (int, str)): + enum_val = enum_type(current_value) + combo.setCurrentText(f"{enum_val.name} ({current_value})") + except (ValueError, KeyError): + pass + + layout.addWidget(combo) + + buttons = QDialogButtonBox(QDialogButtonBox.StandardButton.Ok | QDialogButtonBox.StandardButton.Cancel) + buttons.accepted.connect(dialog.accept) + buttons.rejected.connect(dialog.reject) + layout.addWidget(buttons) + + if dialog.exec() == QDialog.DialogCode.Accepted: + # Get the selected value + new_value = combo.currentData() + enum_val = enum_type(new_value) + + # Store the change + self.metadata_changes[key] = (field.types[0], new_value) + self.modified = True + + # Update display + display_text = f"{enum_val.name} ({new_value})" + target_item = self.metadata_table.item(row, 2) + if target_item: + target_item.setText(display_text) + + self.statusBar().showMessage(f"Changed {key} to {display_text}") + + def edit_array_metadata(self): + button = self.sender() + key = button.property("key") + row = button.property("row") + + # Check if this is one of the linked tokenizer keys + if key in TOKENIZER_LINKED_KEYS: + self.edit_tokenizer_metadata(key) + return + + field = None + if self.reader: + field = self.reader.get_field(key) + if not field or not field.types or field.types[0] != GGUFValueType.ARRAY: + return + + # Get array element type + element_type = field.types[1] + + # Extract array values + array_values = self.extract_array_values(field) + + # Open array editor dialog + dialog = ArrayEditorDialog(array_values, element_type, key, self) + if dialog.exec() == QDialog.DialogCode.Accepted: + new_values = dialog.get_array_values() + + # Store the change + self.metadata_changes[key] = (GGUFValueType.ARRAY, (element_type, new_values)) + self.modified = True + + # Update display + enum_type = self.get_enum_for_key(key) + if enum_type is not None and element_type == GGUFValueType.INT32: + value_str = f"[ {', '.join(self.format_enum_value(v, enum_type) for v in new_values[:5])}{', ...' if len(new_values) > 5 else ''} ]" + else: + value_str = f"[ {', '.join(str(v) for v in new_values[:5])}{', ...' if len(new_values) > 5 else ''} ]" + target_item = self.metadata_table.item(row, 2) + if target_item: + target_item.setText(value_str) + + self.statusBar().showMessage(f"Updated array values for {key}") + + def edit_tokenizer_metadata(self, trigger_key): + """Edit the linked tokenizer metadata arrays together.""" + if not self.reader: + return + + # Get all three fields + tokens_field = self.reader.get_field(gguf.Keys.Tokenizer.LIST) + token_types_field = self.reader.get_field(gguf.Keys.Tokenizer.TOKEN_TYPE) + scores_field = self.reader.get_field(gguf.Keys.Tokenizer.SCORES) + + # Extract values from each field + tokens = self.extract_array_values(tokens_field) if tokens_field else [] + token_types = self.extract_array_values(token_types_field) if token_types_field else [] + scores = self.extract_array_values(scores_field) if scores_field else [] + + # Apply any pending changes + if gguf.Keys.Tokenizer.LIST in self.metadata_changes: + _, (_, tokens) = self.metadata_changes[gguf.Keys.Tokenizer.LIST] + if gguf.Keys.Tokenizer.TOKEN_TYPE in self.metadata_changes: + _, (_, token_types) = self.metadata_changes[gguf.Keys.Tokenizer.TOKEN_TYPE] + if gguf.Keys.Tokenizer.SCORES in self.metadata_changes: + _, (_, scores) = self.metadata_changes[gguf.Keys.Tokenizer.SCORES] + + # Open the tokenizer editor dialog + dialog = TokenizerEditorDialog(tokens, token_types, scores, self) + if dialog.exec() == QDialog.DialogCode.Accepted: + new_tokens, new_token_types, new_scores = dialog.get_data() + + # Store changes for all three arrays + if tokens_field: + self.metadata_changes[gguf.Keys.Tokenizer.LIST] = ( + GGUFValueType.ARRAY, + (tokens_field.types[1], new_tokens) + ) + + if token_types_field: + self.metadata_changes[gguf.Keys.Tokenizer.TOKEN_TYPE] = ( + GGUFValueType.ARRAY, + (token_types_field.types[1], new_token_types) + ) + + if scores_field: + self.metadata_changes[gguf.Keys.Tokenizer.SCORES] = ( + GGUFValueType.ARRAY, + (scores_field.types[1], new_scores) + ) + + self.modified = True + + # Update display for all three fields + self.update_tokenizer_display(gguf.Keys.Tokenizer.LIST, new_tokens) + self.update_tokenizer_display(gguf.Keys.Tokenizer.TOKEN_TYPE, new_token_types) + self.update_tokenizer_display(gguf.Keys.Tokenizer.SCORES, new_scores) + + self.statusBar().showMessage("Updated tokenizer data") + + def update_tokenizer_display(self, key, values): + """Update the display of a tokenizer field in the metadata table.""" + for row in range(self.metadata_table.rowCount()): + key_item = self.metadata_table.item(row, 0) + if key_item and key_item.text() == key: + value_str = f"[ {', '.join(str(v) for v in values[:5])}{', ...' if len(values) > 5 else ''} ]" + value_item = self.metadata_table.item(row, 2) + if value_item: + value_item.setText(value_str) + break + + def add_metadata(self): + dialog = AddMetadataDialog(self) + if dialog.exec() == QDialog.DialogCode.Accepted: + key, value_type, value = dialog.get_data() + + if not key: + QMessageBox.warning(self, "Invalid Key", "Key cannot be empty") + return + + # Check if key already exists + for row in range(self.metadata_table.rowCount()): + orig_item = self.metadata_table.item(row, 0) + if orig_item and orig_item.text() == key: + QMessageBox.warning(self, "Duplicate Key", f"Key '{key}' already exists") + return + + # Add to table + row = self.metadata_table.rowCount() + self.metadata_table.insertRow(row) + + # Key + key_item = QTableWidgetItem(key) + key_item.setFlags(key_item.flags() & ~Qt.ItemFlag.ItemIsEditable) + self.metadata_table.setItem(row, 0, key_item) + + # Type + type_item = QTableWidgetItem(value_type.name) + type_item.setFlags(type_item.flags() & ~Qt.ItemFlag.ItemIsEditable) + self.metadata_table.setItem(row, 1, type_item) + + # Value + value_item = QTableWidgetItem(str(value)) + value_item.setFlags(value_item.flags() | Qt.ItemFlag.ItemIsEditable) + self.metadata_table.setItem(row, 2, value_item) + + # Actions + actions_widget = QWidget() + actions_layout = QHBoxLayout(actions_widget) + actions_layout.setContentsMargins(2, 2, 2, 2) + + remove_button = QPushButton("Remove") + remove_button.setProperty("row", row) + remove_button.setProperty("key", key) + remove_button.clicked.connect(self.remove_metadata) + actions_layout.addWidget(remove_button) + + self.metadata_table.setCellWidget(row, 3, actions_widget) + + # Store the change + self.metadata_changes[key] = (value_type, value) + self.modified = True + + self.statusBar().showMessage(f"Added new metadata key {key}") + + def save_file(self): + if not self.reader: + QMessageBox.warning(self, "No File Open", "Please open a GGUF file first") + return + + if not self.modified and not self.metadata_changes and not self.metadata_to_remove: + QMessageBox.information(self, "No Changes", "No changes to save") + return + + file_path, _ = QFileDialog.getSaveFileName( + self, "Save GGUF File As", "", "GGUF Files (*.gguf);;All Files (*)" + ) + + if not file_path: + return + + try: + self.statusBar().showMessage(f"Saving to {file_path}...") + QApplication.processEvents() + + # Get architecture and endianness from the original file + arch = 'unknown' + field = self.reader.get_field(gguf.Keys.General.ARCHITECTURE) + if field: + arch = field.contents() + + # Create writer + writer = GGUFWriter(file_path, arch=arch, endianess=self.reader.endianess) + + # Get alignment if present + alignment = None + field = self.reader.get_field(gguf.Keys.General.ALIGNMENT) + if field: + alignment = field.contents() + if alignment is not None: + writer.data_alignment = alignment + + # Copy metadata with changes + for field in self.reader.fields.values(): + # Skip virtual fields and fields written by GGUFWriter + if field.name == gguf.Keys.General.ARCHITECTURE or field.name.startswith('GGUF.'): + continue + + # Skip fields marked for removal + if field.name in self.metadata_to_remove: + continue + + # Apply changes if any + sub_type = None + if field.name in self.metadata_changes: + value_type, value = self.metadata_changes[field.name] + if value_type == GGUFValueType.ARRAY: + # Handle array values + sub_type, value = value + else: + # Copy original value + value = field.contents() + value_type = field.types[0] + if value_type == GGUFValueType.ARRAY: + sub_type = field.types[-1] + + if value is not None: + writer.add_key_value(field.name, value, value_type, sub_type=sub_type) + + # Add new metadata + for key, (value_type, value) in self.metadata_changes.items(): + # Skip if the key already existed (we handled it above) + if self.reader.get_field(key) is not None: + continue + + sub_type = None + if value_type == GGUFValueType.ARRAY: + # Handle array values + sub_type, value = value + + writer.add_key_value(key, value, value_type, sub_type=sub_type) + + # Add tensors (including data) + for tensor in self.reader.tensors: + writer.add_tensor(tensor.name, tensor.data, raw_shape=tensor.data.shape, raw_dtype=tensor.tensor_type, tensor_endianess=self.reader.endianess) + + # Write header and metadata + writer.open_output_file(Path(file_path)) + writer.write_header_to_file() + writer.write_kv_data_to_file() + + # Write tensor data using the optimized method + writer.write_tensors_to_file(progress=False) + + writer.close() + + self.statusBar().showMessage(f"Saved to {file_path}") + + # Ask if user wants to open the new file + reply = QMessageBox.question( + self, "Open Saved File", + "Would you like to open the newly saved file?", + QMessageBox.StandardButton.Yes | QMessageBox.StandardButton.No, QMessageBox.StandardButton.Yes + ) + + if reply == QMessageBox.StandardButton.Yes: + self.reader = GGUFReader(file_path, 'r') + self.current_file = file_path + self.file_path_edit.setText(file_path) + + self.load_metadata() + self.load_tensors() + + self.metadata_changes = {} + self.metadata_to_remove = set() + self.modified = False + + except Exception as e: + QMessageBox.critical(self, "Error", f"Failed to save file: {str(e)}") + self.statusBar().showMessage("Error saving file") + + +def main() -> None: + parser = argparse.ArgumentParser(description="GUI GGUF Editor") + parser.add_argument("model_path", nargs="?", help="path to GGUF model file to load at startup") + parser.add_argument("--verbose", action="store_true", help="increase output verbosity") + + args = parser.parse_args() + + logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO) + + app = QApplication(sys.argv) + window = GGUFEditorWindow() + window.show() + + # Load model if specified + if args.model_path: + if os.path.isfile(args.model_path) and args.model_path.endswith('.gguf'): + window.load_file(args.model_path) + else: + logger.error(f"Invalid model path: {args.model_path}") + QMessageBox.warning( + window, + "Invalid Model Path", + f"The specified file does not exist or is not a GGUF file: {args.model_path}") + + sys.exit(app.exec()) + + +if __name__ == '__main__': + main() diff --git a/backend/llama.cpp/gguf-py/gguf/scripts/gguf_hash.py b/backend/llama.cpp/gguf-py/gguf/scripts/gguf_hash.py new file mode 100644 index 0000000000000000000000000000000000000000..3ef98992197e987d8aefc8fb5f5bf9c787e9d06a --- /dev/null +++ b/backend/llama.cpp/gguf-py/gguf/scripts/gguf_hash.py @@ -0,0 +1,102 @@ +#!/usr/bin/env python3 +from __future__ import annotations + +import uuid +import hashlib + +import logging +import argparse +import os +import sys +from pathlib import Path + +from tqdm import tqdm + +# Necessary to load the local gguf package +if "NO_LOCAL_GGUF" not in os.environ and (Path(__file__).parent.parent.parent.parent / 'gguf-py').exists(): + sys.path.insert(0, str(Path(__file__).parent.parent.parent)) + +from gguf import GGUFReader # noqa: E402 + + +logger = logging.getLogger("gguf-hash") + +# UUID_NAMESPACE_LLAMA_CPP = uuid.uuid5(uuid.NAMESPACE_URL, 'en.wikipedia.org/wiki/Llama.cpp') +UUID_NAMESPACE_LLAMA_CPP = uuid.UUID('ef001206-dadc-5f6d-a15f-3359e577d4e5') + + +# For more information about what field.parts and field.data represent, +# please see the comments in the modify_gguf.py example. +def gguf_hash(reader: GGUFReader, filename: str, disable_progress_bar: bool, no_layer: bool) -> None: + sha1 = hashlib.sha1() + sha256 = hashlib.sha256() + uuidv5_sha1 = hashlib.sha1() + uuidv5_sha1.update(UUID_NAMESPACE_LLAMA_CPP.bytes) + + # Total Weight Calculation For Progress Bar + total_weights = 0 + for n, tensor in enumerate(reader.tensors, 1): + + # We don't need these + if tensor.name.endswith((".attention.masked_bias", ".attention.bias", ".rotary_emb.inv_freq")): + continue + + # Calculate Tensor Volume + sum_weights_in_tensor = 1 + for dim in tensor.shape: + sum_weights_in_tensor *= dim + total_weights += sum_weights_in_tensor + + # Hash Progress Bar + bar = tqdm(desc="Hashing", total=total_weights, unit="weights", unit_scale=True, disable=disable_progress_bar) + + # Hashing Process + for tensor in reader.tensors: + + # We don't need these + if tensor.name.endswith((".attention.masked_bias", ".attention.bias", ".rotary_emb.inv_freq")): + continue + + # Progressbar + sum_weights_in_tensor = 1 + for dim in tensor.shape: + sum_weights_in_tensor *= dim + bar.update(sum_weights_in_tensor) + + if not no_layer: + + sha1_layer = hashlib.sha1() + sha1_layer.update(tensor.data.data) + print("sha1 {0} {1}:{2}".format(sha1_layer.hexdigest(), filename, tensor.name)) # noqa: NP100 + + sha256_layer = hashlib.sha256() + sha256_layer.update(tensor.data.data) + print("sha256 {0} {1}:{2}".format(sha256_layer.hexdigest(), filename, tensor.name)) # noqa: NP100 + + sha1.update(tensor.data.data) + sha256.update(tensor.data.data) + uuidv5_sha1.update(tensor.data.data) + + # Flush Hash Progress Bar + bar.close() + + # Display Hash Output + print("sha1 {0} {1}".format(sha1.hexdigest(), filename)) # noqa: NP100 + print("sha256 {0} {1}".format(sha256.hexdigest(), filename)) # noqa: NP100 + print("uuid {0} {1}".format(uuid.UUID(bytes=uuidv5_sha1.digest()[:16], version=5), filename)) # noqa: NP100 + + +def main() -> None: + parser = argparse.ArgumentParser(description="Dump GGUF file metadata") + parser.add_argument("model", type=str, help="GGUF format model filename") + parser.add_argument("--no-layer", action="store_true", help="exclude per layer hash") + parser.add_argument("--verbose", action="store_true", help="increase output verbosity") + parser.add_argument("--progressbar", action="store_true", help="enable progressbar") + args = parser.parse_args(None if len(sys.argv) > 1 else ["--help"]) + logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO) + reader = GGUFReader(args.model, 'r') + gguf_hash(reader, args.model, not args.progressbar, args.no_layer) + + +if __name__ == '__main__': + main() diff --git a/backend/llama.cpp/gguf-py/gguf/scripts/gguf_new_metadata.py b/backend/llama.cpp/gguf-py/gguf/scripts/gguf_new_metadata.py new file mode 100644 index 0000000000000000000000000000000000000000..c67436bad4af6dfcf662b23aff3d3a5bb1d26257 --- /dev/null +++ b/backend/llama.cpp/gguf-py/gguf/scripts/gguf_new_metadata.py @@ -0,0 +1,216 @@ +#!/usr/bin/env python3 +from __future__ import annotations + +import logging +import argparse +import os +import sys +import json +from pathlib import Path + +from tqdm import tqdm +from typing import Any, Sequence, NamedTuple + +# Necessary to load the local gguf package +if "NO_LOCAL_GGUF" not in os.environ and (Path(__file__).parent.parent.parent.parent / 'gguf-py').exists(): + sys.path.insert(0, str(Path(__file__).parent.parent.parent)) + +import gguf + +logger = logging.getLogger("gguf-new-metadata") + + +class MetadataDetails(NamedTuple): + type: gguf.GGUFValueType + value: Any + description: str = '' + sub_type: gguf.GGUFValueType | None = None + + +def get_field_data(reader: gguf.GGUFReader, key: str) -> Any: + field = reader.get_field(key) + + return field.contents() if field else None + + +def find_token(token_list: Sequence[int], token: str) -> Sequence[int]: + token_ids = [index for index, value in enumerate(token_list) if value == token] + + if len(token_ids) == 0: + raise LookupError(f'Unable to find "{token}" in token list!') + + return token_ids + + +def copy_with_new_metadata(reader: gguf.GGUFReader, writer: gguf.GGUFWriter, new_metadata: dict[str, MetadataDetails], remove_metadata: Sequence[str]) -> None: + for field in reader.fields.values(): + # Suppress virtual fields and fields written by GGUFWriter + if field.name == gguf.Keys.General.ARCHITECTURE or field.name.startswith('GGUF.'): + logger.debug(f'Suppressing {field.name}') + continue + + # Skip old chat templates if we have new ones + if field.name.startswith(gguf.Keys.Tokenizer.CHAT_TEMPLATE) and gguf.Keys.Tokenizer.CHAT_TEMPLATE in new_metadata: + logger.debug(f'Skipping {field.name}') + continue + + if field.name in remove_metadata: + logger.debug(f'Removing {field.name}') + continue + + val_type = field.types[0] + sub_type = field.types[-1] if val_type == gguf.GGUFValueType.ARRAY else None + old_val = MetadataDetails(val_type, field.contents(), sub_type=sub_type) + val = new_metadata.get(field.name, old_val) + + if field.name in new_metadata: + logger.debug(f'Modifying {field.name}: "{old_val.value}" -> "{val.value}" {val.description}') + del new_metadata[field.name] + elif val.value is not None: + logger.debug(f'Copying {field.name}') + + if val.value is not None: + writer.add_key_value(field.name, val.value, val.type, sub_type=sub_type if val.sub_type is None else val.sub_type) + + if gguf.Keys.Tokenizer.CHAT_TEMPLATE in new_metadata: + logger.debug('Adding chat template(s)') + writer.add_chat_template(new_metadata[gguf.Keys.Tokenizer.CHAT_TEMPLATE].value) + del new_metadata[gguf.Keys.Tokenizer.CHAT_TEMPLATE] + + for key, val in new_metadata.items(): + logger.debug(f'Adding {key}: "{val.value}" {val.description}') + writer.add_key_value(key, val.value, val.type) + + total_bytes = 0 + + for tensor in reader.tensors: + total_bytes += tensor.n_bytes + writer.add_tensor_info(tensor.name, tensor.data.shape, tensor.data.dtype, tensor.data.nbytes, tensor.tensor_type) + + bar = tqdm(desc="Writing", total=total_bytes, unit="byte", unit_scale=True) + + writer.write_header_to_file() + writer.write_kv_data_to_file() + writer.write_ti_data_to_file() + + for tensor in reader.tensors: + writer.write_tensor_data(tensor.data, tensor_endianess=reader.endianess) + bar.update(tensor.n_bytes) + + writer.close() + + +def main() -> None: + tokenizer_metadata = (getattr(gguf.Keys.Tokenizer, n) for n in gguf.Keys.Tokenizer.__dict__.keys() if not n.startswith('_')) + token_names = dict((n.split('.')[-1][:-len('_token_id')], n) for n in tokenizer_metadata if n.endswith('_token_id')) + + parser = argparse.ArgumentParser(description="Make a copy of a GGUF file with new metadata") + parser.add_argument("input", type=Path, help="GGUF format model input filename") + parser.add_argument("output", type=Path, help="GGUF format model output filename") + parser.add_argument("--general-name", type=str, help="The models general.name", metavar='"name"') + parser.add_argument("--general-description", type=str, help="The models general.description", metavar='"Description ..."') + parser.add_argument("--chat-template", type=str, help="Chat template string (or JSON string containing templates)", metavar='"{% ... %} ..."') + parser.add_argument("--chat-template-config", type=Path, help="Config file containing chat template(s)", metavar='tokenizer_config.json') + parser.add_argument("--chat-template-file", type=Path, help="Jinja file containing chat template", metavar='chat_template.jinja') + parser.add_argument("--pre-tokenizer", type=str, help="The models tokenizer.ggml.pre", metavar='"pre tokenizer"') + parser.add_argument("--remove-metadata", action="append", type=str, help="Remove metadata (by key name) from output model", metavar='general.url') + parser.add_argument("--special-token", action="append", type=str, help="Special token by value", nargs=2, metavar=(' | '.join(token_names.keys()), '""')) + parser.add_argument("--special-token-by-id", action="append", type=str, help="Special token by id", nargs=2, metavar=(' | '.join(token_names.keys()), '0')) + parser.add_argument("--force", action="store_true", help="Bypass warnings without confirmation") + parser.add_argument("--verbose", action="store_true", help="Increase output verbosity") + args = parser.parse_args(None if len(sys.argv) > 2 else ["--help"]) + + logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO) + + new_metadata = {} + remove_metadata = args.remove_metadata or [] + + if args.general_name: + new_metadata[gguf.Keys.General.NAME] = MetadataDetails(gguf.GGUFValueType.STRING, args.general_name) + + if args.general_description: + new_metadata[gguf.Keys.General.DESCRIPTION] = MetadataDetails(gguf.GGUFValueType.STRING, args.general_description) + + if args.chat_template: + new_metadata[gguf.Keys.Tokenizer.CHAT_TEMPLATE] = MetadataDetails(gguf.GGUFValueType.STRING, json.loads(args.chat_template) if args.chat_template.startswith('[') else args.chat_template) + + if args.chat_template_config: + with open(args.chat_template_config, 'r', encoding='utf-8') as fp: + config = json.load(fp) + template = config.get('chat_template') + if template: + new_metadata[gguf.Keys.Tokenizer.CHAT_TEMPLATE] = MetadataDetails(gguf.GGUFValueType.STRING, template) + + if args.chat_template_file: + with open(args.chat_template_file, 'r', encoding='utf-8') as fp: + template = fp.read() + new_metadata[gguf.Keys.Tokenizer.CHAT_TEMPLATE] = MetadataDetails(gguf.GGUFValueType.STRING, template) + + if args.pre_tokenizer: + new_metadata[gguf.Keys.Tokenizer.PRE] = MetadataDetails(gguf.GGUFValueType.STRING, args.pre_tokenizer) + + if remove_metadata: + logger.warning('*** Warning *** Warning *** Warning **') + logger.warning('* Most metadata is required for a fully functional GGUF file,') + logger.warning('* removing crucial metadata may result in a corrupt output file!') + + if not args.force: + logger.warning('* Enter exactly YES if you are positive you want to proceed:') + response = input('YES, I am sure> ') + if response != 'YES': + logger.info("You didn't enter YES. Okay then, see ya!") + sys.exit(0) + + logger.info(f'* Loading: {args.input}') + reader = gguf.GGUFReader(args.input, 'r') + + arch = get_field_data(reader, gguf.Keys.General.ARCHITECTURE) + + token_list = get_field_data(reader, gguf.Keys.Tokenizer.LIST) or [] + + for name, token in args.special_token or []: + if name not in token_names: + logger.warning(f'Unknown special token "{name}", ignoring...') + else: + ids = find_token(token_list, token) + new_metadata[token_names[name]] = MetadataDetails(gguf.GGUFValueType.UINT32, ids[0], f'= {token}') + + if len(ids) > 1: + logger.warning(f'Multiple "{token}" tokens found, choosing ID {ids[0]}, use --special-token-by-id if you want another:') + logger.warning(', '.join(str(i) for i in ids)) + + for name, id_string in args.special_token_by_id or []: + if name not in token_names: + logger.warning(f'Unknown special token "{name}", ignoring...') + elif not id_string.isdecimal(): + raise LookupError(f'Token ID "{id_string}" is not a valid ID!') + else: + id_int = int(id_string) + + if id_int >= 0 and id_int < len(token_list): + new_metadata[token_names[name]] = MetadataDetails(gguf.GGUFValueType.UINT32, id_int, f'= {token_list[id_int]}') + else: + raise LookupError(f'Token ID {id_int} is not within token list!') + + if os.path.isfile(args.output) and not args.force: + logger.warning('*** Warning *** Warning *** Warning **') + logger.warning(f'* The "{args.output}" GGUF file already exists, it will be overwritten!') + logger.warning('* Enter exactly YES if you are positive you want to proceed:') + response = input('YES, I am sure> ') + if response != 'YES': + logger.info("You didn't enter YES. Okay then, see ya!") + sys.exit(0) + + logger.info(f'* Writing: {args.output}') + writer = gguf.GGUFWriter(args.output, arch=arch, endianess=reader.endianess) + + alignment = get_field_data(reader, gguf.Keys.General.ALIGNMENT) + if alignment is not None: + logger.debug(f'Setting custom alignment: {alignment}') + writer.data_alignment = alignment + + copy_with_new_metadata(reader, writer, new_metadata, remove_metadata) + + +if __name__ == '__main__': + main() diff --git a/backend/llama.cpp/gguf-py/gguf/scripts/gguf_set_metadata.py b/backend/llama.cpp/gguf-py/gguf/scripts/gguf_set_metadata.py new file mode 100644 index 0000000000000000000000000000000000000000..f5809c35c887086f8055e828719041f8ae3ea749 --- /dev/null +++ b/backend/llama.cpp/gguf-py/gguf/scripts/gguf_set_metadata.py @@ -0,0 +1,95 @@ +#!/usr/bin/env python3 +import logging +import argparse +import os +import sys +from pathlib import Path + +# Necessary to load the local gguf package +if "NO_LOCAL_GGUF" not in os.environ and (Path(__file__).parent.parent.parent.parent / 'gguf-py').exists(): + sys.path.insert(0, str(Path(__file__).parent.parent.parent)) + +from gguf import GGUFReader # noqa: E402 + +logger = logging.getLogger("gguf-set-metadata") + + +def minimal_example(filename: str) -> None: + reader = GGUFReader(filename, 'r+') + field = reader.fields['tokenizer.ggml.bos_token_id'] + if field is None: + return + part_index = field.data[0] + field.parts[part_index][0] = 2 # Set tokenizer.ggml.bos_token_id to 2 + # + # So what's this field.data thing? It's helpful because field.parts contains + # _every_ part of the GGUF field. For example, tokenizer.ggml.bos_token_id consists + # of: + # + # Part index 0: Key length (27) + # Part index 1: Key data ("tokenizer.ggml.bos_token_id") + # Part index 2: Field type (4, the id for GGUFValueType.UINT32) + # Part index 3: Field value + # + # Note also that each part is an NDArray slice, so even a part that + # is only a single value like the key length will be a NDArray of + # the key length type (numpy.uint32). + # + # The .data attribute in the Field is a list of relevant part indexes + # and doesn't contain internal GGUF details like the key length part. + # In this case, .data will be [3] - just the part index of the + # field value itself. + + +def set_metadata(reader: GGUFReader, args: argparse.Namespace) -> None: + field = reader.get_field(args.key) + if field is None: + logger.error(f'! Field {repr(args.key)} not found') + sys.exit(1) + # Note that field.types is a list of types. This is because the GGUF + # format supports arrays. For example, an array of UINT32 would + # look like [GGUFValueType.ARRAY, GGUFValueType.UINT32] + handler = reader.gguf_scalar_to_np.get(field.types[0]) if field.types else None + if handler is None: + logger.error(f'! This tool only supports changing simple values, {repr(args.key)} has unsupported type {field.types}') + sys.exit(1) + current_value = field.parts[field.data[0]][0] + new_value = handler(args.value) + logger.info(f'* Preparing to change field {repr(args.key)} from {current_value} to {new_value}') + if current_value == new_value: + logger.info(f'- Key {repr(args.key)} already set to requested value {current_value}') + sys.exit(0) + if args.dry_run: + sys.exit(0) + if not args.force: + logger.warning('*** Warning *** Warning *** Warning **') + logger.warning('* Changing fields in a GGUF file can make it unusable. Proceed at your own risk.') + logger.warning('* Enter exactly YES if you are positive you want to proceed:') + response = input('YES, I am sure> ') + if response != 'YES': + logger.info("You didn't enter YES. Okay then, see ya!") + sys.exit(0) + field.parts[field.data[0]][0] = new_value + logger.info('* Field changed. Successful completion.') + + +def main() -> None: + parser = argparse.ArgumentParser(description="Set a simple value in GGUF file metadata") + parser.add_argument("model", type=str, help="GGUF format model filename") + parser.add_argument("key", type=str, help="Metadata key to set") + parser.add_argument("value", type=str, help="Metadata value to set") + parser.add_argument("--dry-run", action="store_true", help="Don't actually change anything") + parser.add_argument("--force", action="store_true", help="Change the field without confirmation") + parser.add_argument("--verbose", action="store_true", help="increase output verbosity") + + args = parser.parse_args(None if len(sys.argv) > 1 else ["--help"]) + + logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO) + + logger.info(f'* Loading: {args.model}') + reader = GGUFReader(args.model, 'r' if args.dry_run else 'r+') + set_metadata(reader, args) + + +if __name__ == '__main__': + main() diff --git a/backend/llama.cpp/gguf-py/gguf/tensor_mapping.py b/backend/llama.cpp/gguf-py/gguf/tensor_mapping.py new file mode 100644 index 0000000000000000000000000000000000000000..9efb36f8a447921456c297e4db94562508b1988c --- /dev/null +++ b/backend/llama.cpp/gguf-py/gguf/tensor_mapping.py @@ -0,0 +1,2489 @@ +from __future__ import annotations + +from typing import Sequence + +from .constants import MODEL_ARCH, MODEL_TENSOR, MODEL_TENSORS, TENSOR_NAMES + + +class TensorNameMap: + mappings_cfg: dict[MODEL_TENSOR, tuple[str, ...]] = { + # Token embeddings + MODEL_TENSOR.TOKEN_EMBD: ( + "gpt_neox.embed_in", # gptneox + "transformer.wte", # gpt2 gpt-j mpt refact qwen dbrx jais exaone + "transformer.word_embeddings", # falcon + "word_embeddings", # bloom + "model.embed_tokens", # llama-hf nemotron olmoe olmo2 rwkv6qwen2 glm4-0414 plamo2 granite-hybrid + "embed_tokens", # embeddinggemma + "tok_embeddings", # llama-pth + "embeddings.word_embeddings", # bert nomic-bert + "embeddings.tok_embeddings", # modern-bert + "wte", # gpt2 + "transformer.embd.wte", # phi2 + "model.tok_embeddings", # internlm2 + "model.embedding", # mamba-qbert + "backbone.embedding", # mamba + "backbone.embeddings", # mamba-hf + "transformer.in_out_embed", # Grok + "embedding.word_embeddings", # chatglm + "transformer.token_embeddings", # openelm + "shared", # t5 + "rwkv.embeddings", # rwkv6 + "model.embeddings", # rwkv7 + "model.word_embeddings", # bailingmoe + "encoder", # neobert + "model.transformer.wte", # llada + "embed_tokens", # qwen3-embedding + "model.embed", # talkie + ), + + # Masked embeddings + MODEL_TENSOR.MASKED_EMBD_CENTROIDS: ( + "masked_embedding.centroids", # gemma-4 E2B/E4B assistants + ), + MODEL_TENSOR.MASKED_EMBD_ORDERING: ( + "masked_embedding.token_ordering", # gemma-4 E2B/E4B assistants + ), + + # Token type embeddings + MODEL_TENSOR.TOKEN_TYPES: ( + "embeddings.token_type_embeddings", # bert nomic-bert + ), + + # Normalization of token embeddings + MODEL_TENSOR.TOKEN_EMBD_NORM: ( + "word_embeddings_layernorm", # bloom + "embeddings.LayerNorm", # bert + "embeddings.norm", # modern-bert + "emb_ln", # nomic-bert + "transformer.norm", # openelm + "rwkv.blocks.0.pre_ln", # rwkv + "rwkv.blocks.0.pre_ln", # rwkv6 + "model.pre_ln", # rwkv7 + "model.layers.0.pre_norm", # rwkv7 + "backbone.norm", # wavtokenizer + "model.embedding_norm", # lfm2 + ), + + # Position embeddings + MODEL_TENSOR.POS_EMBD: ( + "transformer.wpe", # gpt2 + "embeddings.position_embeddings", # bert + "wpe", # gpt2 + "model.embed_positions", # rugpt3xl + ), + + # Output + MODEL_TENSOR.OUTPUT: ( + "embed_out", # gptneox + "lm_head", # gpt2 mpt falcon llama-hf baichuan qwen mamba dbrx jais nemotron exaone olmoe olmo2 phimoe plamo2 + "output", # llama-pth bloom internlm2 + "word_embeddings_for_head", # persimmon + "lm_head.linear", # phi2 + "output_layer", # chatglm + "head", # rwkv + "head.out", # wavtokenizer + "lm_head", # llama4 + "model.transformer.ff_out", # llada + "head.decoder", # modern-bert + ), + MODEL_TENSOR.DENSE_2_OUT: ( + "dense_2_out", # embeddinggemma + ), + MODEL_TENSOR.DENSE_3_OUT: ( + "dense_3_out", # embeddinggemma + ), + # Output norm + MODEL_TENSOR.OUTPUT_NORM: ( + "gpt_neox.final_layer_norm", # gptneox + "transformer.ln_f", # gpt2 gpt-j falcon jais exaone + "model.norm", # llama-hf baichuan internlm2 olmoe olmo2 phimoe plamo2 + "norm", # llama-pth + "transformer.norm_f", # mpt dbrx + "ln_f", # refact bloom qwen gpt2 + "model.final_layernorm", # persimmon + "lm_head.ln", # phi2 + "model.norm_f", # mamba-qbert + "backbone.norm_f", # mamba + "transformer.rms_norm", # Grok + "encoder.final_layernorm", # chatglm + "transformer.norm", # openelm + "model.norm", # nemotron + "rwkv.ln_out", # rwkv6 + "model.ln_out", # rwkv7 + "backbone.final_layer_norm", # wavtokenizer + "model.norm", # llama4 + "model.transformer.ln_f", # llada + "final_norm", # modern-bert + "model.norm", # cogvlm + ), + + # Rope frequencies + MODEL_TENSOR.ROPE_FREQS: ( + "rope.freqs", # llama-pth + "rotary_pos_emb.inv_freq", # chatglm + ), + + MODEL_TENSOR.ROPE_FACTORS_LONG: (), + MODEL_TENSOR.ROPE_FACTORS_SHORT: (), + + MODEL_TENSOR.CONV1D: ( + "backbone.embed", # roberta + ), + + MODEL_TENSOR.V_MM_EMBEDDING: ( + "model.embed_vision.embedding", # gemma3n + ), + MODEL_TENSOR.V_MM_HARD_EMB_NORM: ( + "model.embed_vision.hard_embedding_norm", # gemma3n + ), + MODEL_TENSOR.V_MM_INP_PROJ: ( + "model.embed_vision.embedding_projection", # gemma3n + ), + MODEL_TENSOR.V_MM_SOFT_EMB_NORM: ( + "model.embed_vision.soft_embedding_norm", # gemma3n + ), + MODEL_TENSOR.V_ENC_CONV_STEM: ( + "model.vision_tower.timm_model.conv_stem.conv", # gemma3n + ), + MODEL_TENSOR.V_ENC_CONV_STEM_NORM: ( + "model.vision_tower.timm_model.conv_stem.bn", # gemma3n + ), + MODEL_TENSOR.V_ENC_MSFA_EXP: ( + "model.vision_tower.timm_model.msfa.ffn.pw_exp.conv", # gemma3n + ), + MODEL_TENSOR.V_ENC_MSFA_EXP_NORM: ( + "model.vision_tower.timm_model.msfa.ffn.pw_exp.bn", # gemma3n + ), + MODEL_TENSOR.V_ENC_MSFA_PROJ: ( + "model.vision_tower.timm_model.msfa.ffn.pw_proj.conv", # gemma3n + ), + MODEL_TENSOR.V_ENC_MSFA_PROJ_NORM: ( + "model.vision_tower.timm_model.msfa.ffn.pw_proj.bn", # gemma3n + ), + MODEL_TENSOR.V_ENC_MSFA_NORM: ( + "model.vision_tower.timm_model.msfa.norm", # gemma3n + ), + MODEL_TENSOR.A_CTC_OUT: ( + "encoder.out", + ), + MODEL_TENSOR.A_CTC_OUT_MID: ( + "encoder.out_mid", + ), + MODEL_TENSOR.A_QF_PROJ_QUERY: ( + "projector.query", + ), + MODEL_TENSOR.A_QF_PROJ_NORM: ( + "projector.qformer.layernorm", + ), + MODEL_TENSOR.A_QF_PROJ_LINEAR: ( + "projector.linear", + ), + } + + block_mappings_cfg: dict[MODEL_TENSOR, tuple[str, ...]] = { + # Attention norm + MODEL_TENSOR.ATTN_NORM: ( + "gpt_neox.layers.{bid}.input_layernorm", # gptneox + "transformer.h.{bid}.ln_1", # gpt2 gpt-j refact qwen jais exaone + "transformer.blocks.{bid}.norm_1", # mpt + "transformer.h.{bid}.input_layernorm", # falcon7b + "h.{bid}.input_layernorm", # bloom + "transformer.h.{bid}.ln_mlp", # falcon40b + "model.layers.{bid}.input_layernorm", # llama-hf nemotron olmoe phimoe granite-hybrid + "layers.{bid}.attention_norm", # llama-pth + "model.layers.{bid}.ln1", # yi + "h.{bid}.ln_1", # gpt2 + "transformer.h.{bid}.ln", # phi2 + "model.layers.layers.{bid}.norm", # plamo + "model.layers.layers.{bid}.pre_mixer_norm", # plamo2 + "model.layers.{bid}.attention_norm", # internlm2 + "model.layers.{bid}.norm", # mamba-qbert + "backbone.layers.{bid}.norm", # mamba + "transformer.decoder_layer.{bid}.rms_norm", # Grok + "model.layers.{bid}.pre_attn_norm", # grok-2 + "transformer.blocks.{bid}.norm_attn_norm.norm_1", # dbrx + "encoder.layers.{bid}.input_layernorm", # chatglm + "transformer.layers.{bid}.attn_norm", # openelm + "rwkv.blocks.{bid}.ln1", # rwkv6 + "model.layers.{bid}.ln1", # rwkv7 + "model.layers.{bid}.input_layernorm", # llama4 + "layers.{bid}.input_layernorm", # embeddinggemma + "transformer_encoder.{bid}.attention_norm", # neobert + "layers.{bid}.attn_norm", # modern-bert + "model.layers.{bid}.operator_norm", # lfm2 + "model.transformer.blocks.{bid}.attn_norm", # llada + "layers.{bid}.input_layernorm", # qwen3-embedding + "model.layers.{bid}.attention_layernorm", # apertus + "model.layers.{bid}.pre_attention_layernorm", # kormo + ), + + # Attention norm 2 + MODEL_TENSOR.ATTN_NORM_2: ( + "transformer.h.{bid}.ln_attn", # falcon40b + "encoder.layer.{bid}.layer_norm_1", # jina-v2-code + "rwkv.blocks.{bid}.ln2", # rwkv6 + "model.layers.{bid}.ln2", # rwkv7 + "model.layers.{bid}.post_attention_layernorm", # cogvlm + ), + + # Attention query-key-value + MODEL_TENSOR.ATTN_QKV: ( + "gpt_neox.layers.{bid}.attention.query_key_value", # gptneox + "transformer.h.{bid}.attn.c_attn", # gpt2 qwen jais + "transformer.blocks.{bid}.attn.Wqkv", # mpt + "transformer.blocks.{bid}.norm_attn_norm.attn.Wqkv", # dbrx + "transformer.h.{bid}.self_attention.query_key_value", # falcon + "h.{bid}.self_attention.query_key_value", # bloom + "model.layers.{bid}.self_attn.query_key_value", # persimmon + "model.layers.{bid}.attention.query_key_value", # bailingmoe2 + "h.{bid}.attn.c_attn", # gpt2 + "transformer.h.{bid}.mixer.Wqkv", # phi2 + "encoder.layers.{bid}.attn.Wqkv", # nomic-bert + "encoder.layers.{bid}.mixer.Wqkv", # jina + "model.layers.{bid}.self_attn.qkv_proj", # phi3 + "model.layers.layers.{bid}.mixer.qkv_proj", # plamo2 + "encoder.layers.{bid}.self_attention.query_key_value", # chatglm + "transformer.layers.{bid}.attn.qkv_proj", # openelm + "transformer_encoder.{bid}.qkv", # neobert + "layers.{bid}.attn.Wqkv", # modern-bert + "model.layers.{bid}.self_attn.language_expert_query_key_value", # cogvlm + "model.layers.{bid}.linear_attn.in_proj_qkv", # qwen3.5 + ), + + # Attention query + MODEL_TENSOR.ATTN_Q: ( + "model.layers.{bid}.self_attn.q_proj", # llama-hf nemotron olmoe olmo2 phimoe + "layers.{bid}.self_attn.q_proj", # embeddinggemma + "model.layers.{bid}.self_attn.q_proj_no_perm", # llama-custom + "layers.{bid}.attention.wq", # llama-pth + "encoder.layer.{bid}.attention.self.query", # bert + "transformer.layer.{bid}.attention.q_lin", # distillbert + "transformer.h.{bid}.attn.q_proj", # gpt-j + "model.layers.layers.{bid}.self_attn.q_proj", # plamo + "model.layers.{bid}.attention.wq", # internlm2 + "transformer.decoder_layer.{bid}.multi_head_attention.query",# Grok + "transformer.h.{bid}.attn.attention.q_proj", # exaone + "model.layers.{bid}.self_attn.q_proj", # llama4 + "model.transformer.blocks.{bid}.q_proj", # llada + "layers.{bid}.self_attn.q_proj", # qwen3-embedding + "backbone.layers.{bid}.mixer.q_proj", # nemotron-h + "model.blocks.{bid}.attn.attn_query", # talkie + ), + + # Attention key + MODEL_TENSOR.ATTN_K: ( + "model.layers.{bid}.self_attn.k_proj", # llama-hf nemotron olmoe olmo2 phimoe + "layers.{bid}.self_attn.k_proj", # embeddinggemma + "model.layers.{bid}.self_attn.k_proj_no_perm", # llama-custom + "layers.{bid}.attention.wk", # llama-pth + "encoder.layer.{bid}.attention.self.key", # bert + "transformer.layer.{bid}.attention.k_lin", # distillbert + "transformer.h.{bid}.attn.k_proj", # gpt-j + "transformer.h.{bid}.attn.k", # refact + "model.layers.layers.{bid}.self_attn.k_proj", # plamo + "model.layers.{bid}.attention.wk", # internlm2 + "transformer.decoder_layer.{bid}.multi_head_attention.key",# Grok + "transformer.h.{bid}.attn.attention.k_proj", # exaone + "model.layers.{bid}.self_attn.k_proj", # llama4 + "model.transformer.blocks.{bid}.k_proj", # llada + "layers.{bid}.self_attn.k_proj", # qwen3-embedding + "backbone.layers.{bid}.mixer.k_proj", # nemotron-h + "model.blocks.{bid}.attn.attn_key", # talkie + ), + + # Attention value + MODEL_TENSOR.ATTN_V: ( + "model.layers.{bid}.self_attn.v_proj", # llama-hf nemotron olmoe olmo2 phimoe + "layers.{bid}.self_attn.v_proj", # embeddinggemma + "layers.{bid}.attention.wv", # llama-pth + "encoder.layer.{bid}.attention.self.value", # bert + "transformer.layer.{bid}.attention.v_lin", # distillbert + "transformer.h.{bid}.attn.v_proj", # gpt-j + "transformer.h.{bid}.attn.v", # refact + "model.layers.layers.{bid}.self_attn.v_proj", # plamo + "model.layers.{bid}.attention.wv", # internlm2 + "transformer.decoder_layer.{bid}.multi_head_attention.value",# Grok + "transformer.h.{bid}.attn.attention.v_proj", # exaone + "model.layers.{bid}.self_attn.v_proj", # llama4 + "model.transformer.blocks.{bid}.v_proj", # llada + "layers.{bid}.self_attn.v_proj", # qwen3-embedding + "backbone.layers.{bid}.mixer.v_proj", # nemotron-h + "model.blocks.{bid}.attn.attn_value", # talkie + ), + + # Attention output + MODEL_TENSOR.ATTN_OUT: ( + "gpt_neox.layers.{bid}.attention.dense", # gptneox + "transformer.h.{bid}.attn.c_proj", # gpt2 refact qwen jais + "transformer.blocks.{bid}.attn.out_proj", # mpt + "transformer.h.{bid}.self_attention.dense", # falcon + "h.{bid}.self_attention.dense", # bloom + "model.layers.{bid}.self_attn.o_proj", # llama-hf nemotron olmoe olmo2 phimoe + "layers.{bid}.self_attn.o_proj", # embeddinggemma + "model.layers.{bid}.self_attn.out_proj", # lfm2 + "model.layers.{bid}.self_attn.linear_attn", # deci + "layers.{bid}.attention.wo", # llama-pth + "encoder.layer.{bid}.attention.output.dense", # bert + "layers.{bid}.attn.Wo", # modern-bert + "transformer.layer.{bid}.attention.out_lin", # distillbert + "transformer.h.{bid}.attn.out_proj", # gpt-j + "model.layers.{bid}.self_attn.dense", # persimmon + "model.layers.{bid}.attention.dense", # bailingmoe2 + "h.{bid}.attn.c_proj", # gpt2 + "transformer.h.{bid}.mixer.out_proj", # phi2 + "model.layers.layers.{bid}.self_attn.o_proj", # plamo + "model.layers.layers.{bid}.mixer.o_proj", # plamo2 + "model.layers.{bid}.attention.wo", # internlm2 + "encoder.layers.{bid}.attn.out_proj", # nomic-bert + "encoder.layers.{bid}.mixer.out_proj", # jina + "transformer.decoder_layer.{bid}.multi_head_attention.linear", # Grok + "transformer.blocks.{bid}.norm_attn_norm.attn.out_proj", # dbrx + "encoder.layers.{bid}.self_attention.dense", # chatglm + "transformer.layers.{bid}.attn.out_proj", # openelm + "transformer.h.{bid}.attn.attention.out_proj", # exaone + "model.layers.{bid}.self_attn.o_proj", # llama4 + "transformer_encoder.{bid}.wo", # neobert + "model.transformer.blocks.{bid}.attn_out", # llada + "layers.{bid}.self_attn.o_proj", # qwen3-embedding + "backbone.layers.{bid}.mixer.o_proj", # nemotron-h + "model.layers.{bid}.self_attn.language_expert_dense", # cogvlm + "model.blocks.{bid}.attn.attn_resid", # talkie + ), + + # Attention output norm + MODEL_TENSOR.ATTN_OUT_NORM: ( + "encoder.layer.{bid}.attention.output.LayerNorm", # bert + "transformer.layer.{bid}.sa_layer_norm", # distillbert + "encoder.layers.{bid}.norm1", # nomic-bert + "transformer.decoder_layer.{bid}.rms_norm_1", # Grok + "model.layers.{bid}.post_attn_norm", # grok-2 + "transformer.blocks.{bid}.norm_attn_norm.norm_2", # dbrx + ), + + MODEL_TENSOR.ATTN_POST_NORM: ( + "model.layers.{bid}.post_attention_layernorm", # gemma2 olmo2 # ge + "layers.{bid}.post_attention_layernorm", # embeddinggemma + "model.layers.{bid}.post_self_attn_layernorm", # glm-4-0414 + "model.layers.layers.{bid}.post_mixer_norm.weight", # plamo2 + ), + + # Rotary embeddings + MODEL_TENSOR.ATTN_ROT_EMBD: ( + "model.layers.{bid}.self_attn.rotary_emb.inv_freq", # llama-hf + "layers.{bid}.attention.inner_attention.rope.freqs", # llama-pth + "model.layers.layers.{bid}.self_attn.rotary_emb.inv_freq", # plamo + "transformer.h.{bid}.attn.rotary_emb.inv_freq", # codeshell + ), + + MODEL_TENSOR.ATTN_SINKS: ( + "model.layers.{bid}.self_attn.sinks", # openai-moe + "model.layers.{bid}.self_attn.attention_sink_bias", # mimov2 + ), + + MODEL_TENSOR.ATTN_GATE: ( + "model.layers.{bid}.self_attn.gate_proj", # afmoe + "model.layers.{bid}.linear_attn.in_proj_z", # qwen3.5 + "model.layers.{bid}.self_attn.g_proj", # step3.5 head-wise attention gate + ), + + # Feed-forward norm + MODEL_TENSOR.FFN_NORM: ( + "gpt_neox.layers.{bid}.post_attention_layernorm", # gptneox + "transformer.h.{bid}.ln_2", # gpt2 refact qwen jais exaone + "h.{bid}.post_attention_layernorm", # bloom + "transformer.blocks.{bid}.norm_2", # mpt + "model.layers.{bid}.post_attention_layernorm", # llama-hf nemotron olmoe phimoe + "layers.{bid}.ffn_norm", # llama-pth + "model.layers.{bid}.ln2", # yi + "h.{bid}.ln_2", # gpt2 + "model.layers.{bid}.ffn_norm", # internlm2 + "transformer.decoder_layer.{bid}.rms_norm_2", # Grok + "model.layers.{bid}.pre_moe_norm", # grok-2 + "encoder.layers.{bid}.post_attention_layernorm", # chatglm + "transformer.layers.{bid}.ffn_norm", # openelm + "model.layers.{bid}.pre_ff_layernorm", # jamba granite-hybrid + "model.layers.{bid}.pre_moe_layernorm", # mini-jamba + "model.layers.{bid}.post_attention_layernorm", # llama4 + "transformer_encoder.{bid}.ffn_norm", # neobert + "model.layers.layers.{bid}.pre_mlp_norm", # plamo2 + "model.transformer.blocks.{bid}.ff_norm", # llada + "layers.{bid}.post_attention_layernorm", # qwen3-embedding + "model.layers.{bid}.feedforward_layernorm", # apertus + "model.layers.{bid}.pre_mlp_layernorm", # kormo + "layers.{bid}.mlp_norm" # modern-bert + ), + + # Pre feed-forward norm + MODEL_TENSOR.FFN_PRE_NORM: ( + "model.layers.{bid}.pre_feedforward_layernorm", # gemma2 + "layers.{bid}.pre_feedforward_layernorm", # embeddinggemma + "model.layers.{bid}.pre_ff_layernorm.weight", + "model.layers.{bid}.pre_mlp_layernorm", # afmoe + ), + + MODEL_TENSOR.FFN_PRE_NORM_2: ( + "model.layers.{bid}.pre_feedforward_layernorm_2", # gemma4 + ), + + # Post feed-forward norm + MODEL_TENSOR.FFN_POST_NORM: ( + "model.layers.{bid}.post_feedforward_layernorm", # gemma2 olmo2 + "layers.{bid}.post_feedforward_layernorm", # embeddinggemma + "model.layers.{bid}.post_mlp_layernorm", # glm-4-0414 + "model.layers.layers.{bid}.post_mlp_norm.weight", # plamo2 + "model.layers.{bid}.feed_forward.up_proj", + "model.layers.{bid}.post_moe_norm", # grok-2 + ), + + MODEL_TENSOR.FFN_POST_NORM_1: ( + "model.layers.{bid}.post_feedforward_layernorm_1", # gemma4 + ), + + MODEL_TENSOR.FFN_POST_NORM_2: ( + "model.layers.{bid}.post_feedforward_layernorm_2", # gemma4 + ), + + MODEL_TENSOR.FFN_GATE_INP: ( + "layers.{bid}.feed_forward.gate", # mixtral + "model.layers.{bid}.block_sparse_moe.gate", # mixtral phimoe + "model.layers.{bid}.mlp.gate", # qwen2moe olmoe + "transformer.decoder_layer.{bid}.router", # Grok + "transformer.blocks.{bid}.ffn.router.layer", # dbrx + "model.layers.{bid}.block_sparse_moe.router.layer", # granitemoe + "model.layers.{bid}.feed_forward.router", # llama4 jamba + "encoder.layers.{bid}.mlp.router.layer", # nomic-bert-moe + "model.layers.{bid}.mlp.router", # openai-moe + "model.layers.{bid}.mlp.gate.wg", # hunyuan + "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 + "backbone.layers.{bid}.mixer.gate", # nemotron-h-moe + "model.layers.{bid}.moe.gate", # step3.5 + "model.layers.{bid}.router.proj", # gemma4 + ), + + MODEL_TENSOR.FFN_GATE_INP_SHEXP: ( + "model.layers.{bid}.mlp.shared_expert_gate", # qwen2moe + ), + + MODEL_TENSOR.FFN_EXP_PROBS_B: ( + "model.layers.{bid}.mlp.gate.e_score_correction", # deepseek-v3 dots1 + "model.layers.{bid}.mlp.moe_statics.e_score_correction", # ernie4.5-moe + "model.layers.{bid}.mlp.gate.expert_bias", # bailingmoe2 + "model.layers.{bid}.mlp.expert_bias", # afmoe + "model.layers.{bid}.feed_forward.expert_bias", # lfm2moe + "model.layers.{bid}.block_sparse_moe.e_score_correction", # minimax-m2 + "backbone.layers.{bid}.mixer.gate.e_score_correction", # nemotron-h-moe + "model.layers.{bid}.mlp.e_score_correction", # exaone-moe + "model.layers.{bid}.block_sparse_moe.gate.e_score_correction", # kimi + "model.layers.{bid}.moe.router_bias", # step3.5 expert selection bias + ), + + # Feed-forward up + MODEL_TENSOR.FFN_UP: ( + "gpt_neox.layers.{bid}.mlp.dense_h_to_4h", # gptneox + "transformer.h.{bid}.mlp.c_fc", # gpt2 jais + "transformer.blocks.{bid}.ffn.up_proj", # mpt + "transformer.h.{bid}.mlp.dense_h_to_4h", # falcon + "h.{bid}.mlp.dense_h_to_4h", # bloom + "model.layers.{bid}.mlp.up_proj", # llama-hf refact nemotron olmo2 + "layers.{bid}.mlp.up_proj", # embeddinggemma + "layers.{bid}.feed_forward.w3", # llama-pth + "encoder.layer.{bid}.intermediate.dense", # bert + "layers.{bid}.mlp.Wi", # modern-bert + "transformer.layer.{bid}.ffn.lin1", # distillbert + "transformer.h.{bid}.mlp.fc_in", # gpt-j + "transformer.h.{bid}.mlp.linear_3", # refact + "model.layers.{bid}.mlp.dense_h_to_4h", # persimmon + "transformer.h.{bid}.mlp.w1", # qwen + "h.{bid}.mlp.c_fc", # gpt2 + "transformer.h.{bid}.mlp.fc1", # phi2 + "model.layers.{bid}.mlp.fc1", # phi2 + "model.layers.{bid}.mlp.gate_up_proj", # phi3 glm-4-0414 + "model.layers.layers.{bid}.mlp.up_proj", # plamo + "model.layers.layers.{bid}.mlp.gate_up_proj", # plamo2 + "model.layers.{bid}.feed_forward.w3", # internlm2 + "encoder.layers.{bid}.mlp.fc11", # nomic-bert + "encoder.layers.{bid}.mlp.fc1", # nomic-bert-moe + "model.layers.{bid}.mlp.c_fc", # starcoder2 + "encoder.layer.{bid}.mlp.gated_layers_v", # jina-bert-v2 (split up/gate, no longer used) + "encoder.layer.{bid}.mlp.gated_layers", # jina-bert-v2 (GEGLU) + "encoder.layer.{bid}.mlp.up_gated_layer", # jina-v2-code (GEGLU) + "model.layers.{bid}.residual_mlp.w3", # arctic + "encoder.layers.{bid}.mlp.dense_h_to_4h", # chatglm + "transformer.h.{bid}.mlp.c_fc_1", # exaone + "model.layers.{bid}.feed_forward.up_proj", # llama4 jamba granite-hybrid + "transformer_encoder.{bid}.ffn.w12", # neobert + "model.layers.{bid}.block_sparse_moe.up", # smallthinker + "model.transformer.blocks.{bid}.up_proj", # llada + "layers.{bid}.mlp.up_proj", # qwen3-embedding + "backbone.layers.{bid}.mixer.up_proj", # nemotron-h + "model.layers.{bid}.mlp.language_mlp.up_proj", # cogvlm + "model.blocks.{bid}.mlp.mlp_linear", # talkie + ), + + MODEL_TENSOR.FFN_UP_EXP: ( + "layers.{bid}.feed_forward.experts.w3", # mixtral (merged) + "transformer.decoder_layer.{bid}.moe.linear_v", # Grok (merged) + "transformer.blocks.{bid}.ffn.experts.mlp.v1", # dbrx + "model.layers.{bid}.mlp.experts.up_proj", # qwen2moe olmoe (merged) ernie4.5-moe, nemotron-h-moe (merged) + "model.layers.{bid}.block_sparse_moe.experts.w3", # phimoe (merged) + "model.layers.{bid}.feed_forward.experts.up_proj", # llama4 + "encoder.layers.{bid}.mlp.experts.mlp.w1", # nomic-bert-moe + "model.layers.{bid}.block_sparse_moe.experts.up", # smallthinker + "model.layers.{bid}.moe.up_proj", # step3.5 + ), + + MODEL_TENSOR.FFN_UP_SHEXP: ( + "model.layers.{bid}.mlp.shared_expert.up_proj", # qwen2moe + "model.layers.{bid}.mlp.shared_experts.up_proj", # deepseek deepseek2 + "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 + "backbone.layers.{bid}.mixer.shared_experts.up_proj", # nemotron-h-moe + "model.layers.{bid}.block_sparse_moe.shared_experts.up_proj", # kimi + "model.layers.{bid}.share_expert.up_proj", # step3.5 + ), + + MODEL_TENSOR.FFN_UP_CHEXP: ( + "model.layers.{bid}.mlp.chunk_experts.up_proj", # grovemoe + ), + + # AWQ-activation gate + MODEL_TENSOR.FFN_ACT: ( + "transformer.blocks.{bid}.ffn.act", # mpt + ), + + # Feed-forward gate + MODEL_TENSOR.FFN_GATE: ( + "model.layers.{bid}.mlp.gate_proj", # llama-hf refact olmo2 + "layers.{bid}.mlp.gate_proj", # embeddinggemma + "layers.{bid}.feed_forward.w1", # llama-pth + "transformer.h.{bid}.mlp.w2", # qwen + "transformer.h.{bid}.mlp.c_fc2", # jais + "model.layers.layers.{bid}.mlp.gate_proj", # plamo + "model.layers.{bid}.feed_forward.w1", # internlm2 + "encoder.layers.{bid}.mlp.fc12", # nomic-bert + "encoder.layer.{bid}.mlp.gated_layers_w", # jina-bert-v2 (split up/gate, no longer used) + "transformer.h.{bid}.mlp.linear_1", # refact + "model.layers.{bid}.residual_mlp.w1", # arctic + "transformer.h.{bid}.mlp.c_fc_0", # exaone + "model.layers.{bid}.feed_forward.gate_proj", # llama4 jamba granite-hybrid + "model.transformer.blocks.{bid}.ff_proj", # llada + "layers.{bid}.mlp.gate_proj", # qwen3-embedding + "model.layers.{bid}.mlp.language_mlp.gate_proj", # cogvlm + "model.blocks.{bid}.mlp.mlp_gate", # talkie + ), + + MODEL_TENSOR.FFN_GATE_EXP: ( + "layers.{bid}.feed_forward.experts.w1", # mixtral (merged) + "transformer.decoder_layer.{bid}.moe.linear", # Grok (merged) + "transformer.blocks.{bid}.ffn.experts.mlp.w1", # dbrx + "model.layers.{bid}.mlp.experts.gate_proj", # qwen2moe olmoe (merged) ernie4.5-moe + "model.layers.{bid}.block_sparse_moe.experts.w1", # phimoe (merged) + "model.layers.{bid}.feed_forward.experts.gate_proj", # llama4 + "model.layers.{bid}.block_sparse_moe.experts.gate", # smallthinker + "model.layers.{bid}.moe.gate_proj", # step3.5 + ), + + MODEL_TENSOR.FFN_GATE_SHEXP: ( + "model.layers.{bid}.mlp.shared_expert.gate_proj", # qwen2moe + "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.layers.{bid}.block_sparse_moe.shared_experts.gate_proj", # kimi + "model.layers.{bid}.share_expert.gate_proj", # step3.5 + ), + + MODEL_TENSOR.FFN_GATE_CHEXP: ( + "model.layers.{bid}.mlp.chunk_experts.gate_proj", # grovemoe + ), + + MODEL_TENSOR.FFN_GATE_UP_EXP: ( + "model.layers.{bid}.mlp.experts.gate_up_proj", + "model.layers.{bid}.experts.gate_up_proj", # gemma4 + ), + + MODEL_TENSOR.MOE_LATENT_DOWN: ( + "backbone.layers.{bid}.mixer.fc1_latent_proj", # nemotron 3 super + ), + + MODEL_TENSOR.MOE_LATENT_UP: ( + "backbone.layers.{bid}.mixer.fc2_latent_proj", # nemotron 3 super + ), + + # Feed-forward down + MODEL_TENSOR.FFN_DOWN: ( + "gpt_neox.layers.{bid}.mlp.dense_4h_to_h", # gptneox + "transformer.h.{bid}.mlp.c_proj", # gpt2 refact qwen jais + "transformer.blocks.{bid}.ffn.down_proj", # mpt + "transformer.h.{bid}.mlp.dense_4h_to_h", # falcon + "h.{bid}.mlp.dense_4h_to_h", # bloom + "model.layers.{bid}.mlp.down_proj", # llama-hf nemotron olmo2 + "layers.{bid}.mlp.down_proj", # embeddinggemma + "layers.{bid}.feed_forward.w2", # llama-pth + "encoder.layer.{bid}.output.dense", # bert + "layers.{bid}.mlp.Wo", # modern-bert + "transformer.layer.{bid}.ffn.lin2", # distillbert + "transformer.h.{bid}.mlp.fc_out", # gpt-j + "model.layers.{bid}.mlp.dense_4h_to_h", # persimmon + "h.{bid}.mlp.c_proj", # gpt2 + "transformer.h.{bid}.mlp.fc2", # phi2 + "model.layers.{bid}.mlp.fc2", # phi2 + "model.layers.layers.{bid}.mlp.down_proj", # plamo + "model.layers.{bid}.feed_forward.w2", # internlm2 + "encoder.layers.{bid}.mlp.fc2", # nomic-bert + "model.layers.{bid}.mlp.c_proj", # starcoder2 + "encoder.layer.{bid}.mlp.wo", # jina-bert-v2 + "transformer.layers.{bid}.ffn.proj_2", # openelm + "model.layers.{bid}.residual_mlp.w2", # arctic + "encoder.layer.{bid}.mlp.down_layer", # jina-bert-v2 + "encoder.layers.{bid}.mlp.dense_4h_to_h", # chatglm + "model.layers.h.{bid}.mlp.c_proj", # exaone + "model.layers.{bid}.feed_forward.down_proj", # llama4 jamba granite-hybrid + "transformer_encoder.{bid}.ffn.w3", # neobert + "model.layers.{bid}.block_sparse_moe.down", # smallthinker + "model.transformer.blocks.{bid}.ff_out", # llada + "layers.{bid}.mlp.down_proj", # qwen3-embedding + "backbone.layers.{bid}.mixer.down_proj", # nemotron-h + "model.layers.{bid}.mlp.language_mlp.down_proj", # cogvlm + "model.blocks.{bid}.mlp.mlp_resid", # talkie + ), + + MODEL_TENSOR.FFN_DOWN_EXP: ( + "layers.{bid}.feed_forward.experts.w2", # mixtral (merged) + "transformer.decoder_layer.{bid}.moe.linear_1", # Grok (merged) + "transformer.blocks.{bid}.ffn.experts.mlp.w2", # dbrx + "model.layers.{bid}.mlp.experts.down_proj", # qwen2moe olmoe (merged) ernie4.5-moe nemotron-h-moe (merged) + "model.layers.{bid}.block_sparse_moe.output_linear", # granitemoe + "model.layers.{bid}.block_sparse_moe.experts.w2", # phimoe (merged) + "model.layers.{bid}.feed_forward.experts.down_proj", # llama4 + "encoder.layers.{bid}.mlp.experts.mlp.w2", # nomic-bert-moe + "model.layers.{bid}.block_sparse_moe.experts.down", # smallthinker + "model.layers.{bid}.moe.down_proj", # step3.5 + "model.layers.{bid}.experts.down_proj", # gemma4 + ), + + MODEL_TENSOR.FFN_DOWN_SHEXP: ( + "model.layers.{bid}.mlp.shared_expert.down_proj", # qwen2moe + "model.layers.{bid}.mlp.shared_experts.down_proj", # deepseek deepseek2 + "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 + "backbone.layers.{bid}.mixer.shared_experts.down_proj", # nemotron-h-moe + "model.layers.{bid}.block_sparse_moe.shared_experts.down_proj", # kimi + "model.layers.{bid}.share_expert.down_proj", # step3.5 + ), + + MODEL_TENSOR.FFN_DOWN_CHEXP: ( + "model.layers.{bid}.mlp.chunk_experts.down_proj", # grovemoe + ), + + MODEL_TENSOR.ATTN_Q_NORM: ( + "encoder.layers.{bid}.self_attention.q_layernorm", + "model.layers.{bid}.self_attn.q_layernorm", # persimmon + "model.layers.{bid}.self_attn.query_layernorm", # hunyuan + "model.layers.{bid}.attention.query_layernorm", # bailingmoe2 + "model.layers.{bid}.self_attn.q_norm", # cohere olmoe chameleon olmo2 + "layers.{bid}.self_attn.q_norm", # embeddinggemma + "transformer.blocks.{bid}.attn.q_ln", # sea-lion + "encoder.layer.{bid}.attention.self.layer_norm_q", # jina-bert-v2 + "transformer.layers.{bid}.attn.q_norm", # openelm + "model.layers.layers.{bid}.mixer.q", # plamo2 + "model.layers.layers.{bid}.mixer.q_norm", # plamo3 + "layers.{bid}.self_attn.q_norm", # qwen3-embedding + "model.layers.{bid}.attention.query_layernorm", # apertus + "model.blocks.{bid}.attn.head_gain.head_g", # talkie + ), + + MODEL_TENSOR.ATTN_K_NORM: ( + "encoder.layers.{bid}.self_attention.k_layernorm", + "model.layers.{bid}.self_attn.k_layernorm", # persimmon + "model.layers.{bid}.self_attn.key_layernorm", # hunyuan + "model.layers.{bid}.attention.key_layernorm", # bailingmoe2 + "model.layers.{bid}.self_attn.k_norm", # cohere olmoe chameleon olmo2 + "layers.{bid}.self_attn.k_norm", # embeddinggemma + "transformer.blocks.{bid}.attn.k_ln", # sea-lion + "encoder.layer.{bid}.attention.self.layer_norm_k", # jina-bert-v2 + "transformer.layers.{bid}.attn.k_norm", # openelm + "model.layers.layers.{bid}.mixer.k", # plamo2 + "model.layers.layers.{bid}.mixer.k_norm", # plamo3 + "layers.{bid}.self_attn.k_norm", # qwen3-embedding + "model.layers.{bid}.attention.key_layernorm", # apertus + ), + + MODEL_TENSOR.ROPE_FREQS: ( + "encoder.layers.{bid}.self_attention.rotary_emb.inv_freq", # persimmon + ), + + MODEL_TENSOR.LAYER_OUT_NORM: ( + "encoder.layer.{bid}.output.LayerNorm", # bert + "transformer.layer.{bid}.output_layer_norm", # distillbert + "encoder.layers.{bid}.norm2", # nomic-bert + "transformer.decoder_layer.{bid}.rms_norm_3", # Grok + "encoder.layer.{bid}.mlp.layernorm", # jina-bert-v2 + "encoder.layer.{bid}.layer_norm_2", # jina-v2-code + "model.layers.{bid}.final_layernorm", # bailingmoe2 + ), + + MODEL_TENSOR.LAYER_OUT_SCALE: ( + "model.layers.{bid}.layer_scalar", # gemma4 + "model.blocks.{bid}.embed_skip.a_g", # talkie + ), + + MODEL_TENSOR.PER_LAYER_TOKEN_EMBD: ( + "model.embed_tokens_per_layer", # gemma3n + ), + + MODEL_TENSOR.PER_LAYER_MODEL_PROJ: ( + "model.per_layer_model_projection", # gemma3n + ), + + MODEL_TENSOR.PER_LAYER_PROJ_NORM: ( + "model.per_layer_projection_norm", # gemma3n + ), + + MODEL_TENSOR.ALTUP_PROJ: ( + "model.altup_projections", # gemma3n + ), + + MODEL_TENSOR.ALTUP_UNEMBD_PROJ: ( + "model.altup_unembed_projections", # gemma3n + ), + + MODEL_TENSOR.PER_LAYER_INP_GATE: ( + "model.layers.{bid}.per_layer_input_gate", # gemma3n + ), + + MODEL_TENSOR.PER_LAYER_PROJ: ( + "model.layers.{bid}.per_layer_projection", # gemma3n + ), + + MODEL_TENSOR.PER_LAYER_POST_NORM: ( + "model.layers.{bid}.post_per_layer_input_norm", # gemma3n + ), + + MODEL_TENSOR.ALTUP_CORRECT_COEF: ( + "model.layers.{bid}.altup.correction_coefs", # gemma3n + ), + + MODEL_TENSOR.ALTUP_CORRECT_SCALE: ( + "model.layers.{bid}.altup.correct_output_scale", # gemma3n + ), + + MODEL_TENSOR.ALTUP_PREDICT_COEF: ( + "model.layers.{bid}.altup.prediction_coefs", # gemma3n + ), + + MODEL_TENSOR.ALTUP_ROUTER: ( + "model.layers.{bid}.altup.modality_router", # gemma3n + ), + + MODEL_TENSOR.ALTUP_ROUTER_NORM: ( + "model.layers.{bid}.altup.router_norm", # gemma3n + ), + + MODEL_TENSOR.LAUREL_L: ( + "model.layers.{bid}.laurel.linear_left", # gemma3n + ), + + MODEL_TENSOR.LAUREL_R: ( + "model.layers.{bid}.laurel.linear_right", # gemma3n + ), + + MODEL_TENSOR.LAUREL_POST_NORM: ( + "model.layers.{bid}.laurel.post_laurel_norm", # gemma3n + ), + + MODEL_TENSOR.SSM_IN: ( + "model.layers.{bid}.in_proj", # mamba-hf + "backbone.layers.{bid}.mixer.in_proj", # mamba + "model.layers.{bid}.mamba.in_proj", # jamba falcon-h1 granite-hybrid + "model.layers.layers.{bid}.mixer.in_proj", # plamo2 + "model.layers.{bid}.linear_attn.in_proj_qkvz", # qwen3next + ), + + MODEL_TENSOR.SSM_CONV1D: ( + "model.layers.{bid}.conv1d", # mamba-hf + "backbone.layers.{bid}.mixer.conv1d", # mamba + "model.layers.{bid}.mamba.conv1d", # jamba falcon-h1 granite-hybrid + "model.layers.layers.{bid}.mixer.conv1d", # plamo2 + "model.layers.{bid}.linear_attn.conv1d", # qwen3next + ), + + MODEL_TENSOR.SSM_X: ( + "model.layers.{bid}.x_proj", # mamba-hf + "backbone.layers.{bid}.mixer.x_proj", # mamba + "model.layers.{bid}.mamba.x_proj", # jamba + "model.layers.layers.{bid}.mixer.bcdt_proj", # plamo2 + ), + + MODEL_TENSOR.SSM_DT: ( + "model.layers.{bid}.dt_proj", # mamba-hf + "backbone.layers.{bid}.mixer.dt_proj", # mamba + "model.layers.{bid}.mamba.dt_proj", # jamba falcon-h1 granite-hybrid + "model.layers.layers.{bid}.mixer.dt_proj", # plamo2 + "model.layers.{bid}.linear_attn.dt_proj", # qwen3next + "backbone.layers.{bid}.mixer.dt", # nemotron-h-moe + "model.layers.{bid}.self_attn.dt_proj", # kimi + ), + + MODEL_TENSOR.SSM_DT_NORM: ( + "model.layers.layers.{bid}.mixer.dt_norm.weight", # plamo2 + "model.layers.{bid}.mamba.dt_layernorm", # jamba + ), + + MODEL_TENSOR.SSM_A: ( + "model.layers.{bid}.A_log", # mamba-hf + "backbone.layers.{bid}.mixer.A_log", # mamba + "model.layers.{bid}.mamba.A_log", # jamba falcon-h1 granite-hybrid + "model.layers.layers.{bid}.mixer.A_log", # plamo2 + "model.layers.{bid}.linear_attn.A_log", # qwen3next + "model.layers.{bid}.self_attn.A_log", # kimi + ), + + MODEL_TENSOR.SSM_B_NORM: ( + "model.layers.{bid}.mamba.b_layernorm", # jamba + "model.layers.{bid}.mamba.B_layernorm", # mini-jamba + "model.layers.layers.{bid}.mixer.B_norm.weight", # plamo2 + ), + + MODEL_TENSOR.SSM_C_NORM: ( + "model.layers.{bid}.mamba.c_layernorm", # jamba + "model.layers.{bid}.mamba.C_layernorm", # mini-jamba + "model.layers.layers.{bid}.mixer.C_norm.weight", # plamo2 + ), + + MODEL_TENSOR.SSM_D: ( + "model.layers.{bid}.D", # mamba-hf + "backbone.layers.{bid}.mixer.D", # mamba + "model.layers.{bid}.mamba.D", # jamba falcon-h1 granite-hybrid + "model.layers.layers.{bid}.mixer.D", # plamo2 + ), + + MODEL_TENSOR.SSM_NORM: ( + "model.layers.{bid}.mamba.norm", # falcon-h1 granite-hybrid + "model.layers.{bid}.linear_attn.norm", # qwen3next + "backbone.layers.{bid}.mixer.norm", # mamba2 + "model.layers.{bid}.self_attn.o_norm", # kimi + ), + + MODEL_TENSOR.SSM_OUT: ( + "model.layers.{bid}.out_proj", # mamba-hf + "backbone.layers.{bid}.mixer.out_proj", # mamba + "model.layers.{bid}.mamba.out_proj", # jamba falcon-h1 granite-hybrid + "model.layers.{bid}.linear_attn.out_proj", # qwen3next + "model.layers.layers.{bid}.mixer.out_proj", # plamo2 + ), + + MODEL_TENSOR.SSM_ALPHA: ( + "model.layers.{bid}.linear_attn.in_proj_a", # qwen3.5 + ), + + MODEL_TENSOR.SSM_BETA_ALPHA: ( + "model.layers.{bid}.linear_attn.in_proj_ba", # qwen3next + ), + + # Kimi Linear KDA (using SSM_ prefix for consistency) + MODEL_TENSOR.SSM_CONV1D_Q: ( + "model.layers.{bid}.self_attn.q_conv1d", + ), + MODEL_TENSOR.SSM_CONV1D_K: ( + "model.layers.{bid}.self_attn.k_conv1d", + ), + MODEL_TENSOR.SSM_CONV1D_V: ( + "model.layers.{bid}.self_attn.v_conv1d", + ), + MODEL_TENSOR.SSM_F_A: ( + "model.layers.{bid}.self_attn.f_a_proj", + ), + MODEL_TENSOR.SSM_F_B: ( + "model.layers.{bid}.self_attn.f_b_proj", + ), + MODEL_TENSOR.SSM_BETA: ( + "model.layers.{bid}.linear_attn.in_proj_b", # qwen3.5 + "model.layers.{bid}.self_attn.b_proj", # Kimi Linear + ), + MODEL_TENSOR.SSM_G_A: ( + "model.layers.{bid}.self_attn.g_a_proj", + ), + MODEL_TENSOR.SSM_G_B: ( + "model.layers.{bid}.self_attn.g_b_proj", + ), + MODEL_TENSOR.TIME_MIX_W0: ( + "model.layers.{bid}.attention.w0", # rwkv7 + ), + + MODEL_TENSOR.TIME_MIX_W1: ( + "rwkv.blocks.{bid}.attention.time_maa_w1", # rwkv6 + "model.layers.{bid}.self_attn.time_maa_w1", # rwkv6qwen2 + "model.layers.{bid}.attention.w1", # rwkv7 + ), + + MODEL_TENSOR.TIME_MIX_W2: ( + "rwkv.blocks.{bid}.attention.time_maa_w2", # rwkv6 + "model.layers.{bid}.self_attn.time_maa_w2", # rwkv6qwen2 + "model.layers.{bid}.attention.w2", # rwkv7 + ), + + MODEL_TENSOR.TIME_MIX_A0: ( + "model.layers.{bid}.attention.a0", # rwkv7 + ), + + MODEL_TENSOR.TIME_MIX_A1: ( + "model.layers.{bid}.attention.a1", # rwkv7 + ), + + MODEL_TENSOR.TIME_MIX_A2: ( + "model.layers.{bid}.attention.a2", # rwkv7 + ), + + MODEL_TENSOR.TIME_MIX_V0: ( + "model.layers.{bid}.attention.v0", # rwkv7 + ), + + MODEL_TENSOR.TIME_MIX_V1: ( + "model.layers.{bid}.attention.v1", # rwkv7 + ), + + MODEL_TENSOR.TIME_MIX_V2: ( + "model.layers.{bid}.attention.v2", # rwkv7 + ), + + MODEL_TENSOR.TIME_MIX_G1: ( + "model.layers.{bid}.attention.g1", # rwkv7 + ), + + MODEL_TENSOR.TIME_MIX_G2: ( + "model.layers.{bid}.attention.g2", # rwkv7 + ), + + MODEL_TENSOR.TIME_MIX_K_K: ( + "model.layers.{bid}.attention.k_k", # rwkv7 + ), + + MODEL_TENSOR.TIME_MIX_K_A: ( + "model.layers.{bid}.attention.k_a", # rwkv7 + ), + + MODEL_TENSOR.TIME_MIX_R_K: ( + "model.layers.{bid}.attention.r_k", # rwkv7 + ), + + MODEL_TENSOR.TIME_MIX_LERP_X: ( + "rwkv.blocks.{bid}.attention.time_maa_x", # rwkv6 + "model.layers.{bid}.self_attn.time_maa_x", # rwkv6qwen2 + ), + + MODEL_TENSOR.TIME_MIX_LERP_K: ( + "rwkv.blocks.{bid}.attention.time_maa_k", # rwkv6 + "model.layers.{bid}.self_attn.time_maa_k", # rwkv6qwen2 + ), + + MODEL_TENSOR.TIME_MIX_LERP_V: ( + "rwkv.blocks.{bid}.attention.time_maa_v", # rwkv6 + "model.layers.{bid}.self_attn.time_maa_v", # rwkv6qwen2 + ), + + MODEL_TENSOR.TIME_MIX_LERP_R: ( + "rwkv.blocks.{bid}.attention.time_maa_r", # rwkv6 + "model.layers.{bid}.self_attn.time_maa_r", # rwkv6qwen2 + ), + + MODEL_TENSOR.TIME_MIX_LERP_G: ( + "rwkv.blocks.{bid}.attention.time_maa_g", # rwkv6 + "model.layers.{bid}.self_attn.time_maa_g", # rwkv6qwen2 + ), + + MODEL_TENSOR.TIME_MIX_LERP_W: ( + "rwkv.blocks.{bid}.attention.time_maa_w", # rwkv6 + "model.layers.{bid}.self_attn.time_maa_w", # rwkv6qwen2 + ), + + MODEL_TENSOR.TIME_MIX_FIRST: ( + "rwkv.blocks.{bid}.attention.time_faaaa", # rwkv6 + ), + + MODEL_TENSOR.TIME_MIX_DECAY: ( + "rwkv.blocks.{bid}.attention.time_decay", # rwkv6 + "model.layers.{bid}.self_attn.time_decay", # rwkv6qwen2 + ), + + MODEL_TENSOR.TIME_MIX_DECAY_W1: ( + "rwkv.blocks.{bid}.attention.time_decay_w1", # rwkv6 + "model.layers.{bid}.self_attn.time_decay_w1", # rwkv6qwen2 + ), + + MODEL_TENSOR.TIME_MIX_DECAY_W2: ( + "rwkv.blocks.{bid}.attention.time_decay_w2", # rwkv6 + "model.layers.{bid}.self_attn.time_decay_w2", # rwkv6qwen2 + ), + + MODEL_TENSOR.TIME_MIX_KEY: ( + "rwkv.blocks.{bid}.attention.key", # rwkv6 + "model.layers.{bid}.self_attn.k_proj", # rwkv6qwen2 + "model.layers.{bid}.attention.key", # rwkv7 + "model.layers.{bid}.attention.k_proj", # rwkv7 + ), + + MODEL_TENSOR.TIME_MIX_VALUE: ( + "rwkv.blocks.{bid}.attention.value", # rwkv6 + "model.layers.{bid}.self_attn.v_proj", # rwkv6qwen2 + "model.layers.{bid}.attention.value", # rwkv7 + "model.layers.{bid}.attention.v_proj", # rwkv7 + ), + + MODEL_TENSOR.TIME_MIX_RECEPTANCE: ( + "rwkv.blocks.{bid}.attention.receptance", # rwkv6 + "model.layers.{bid}.self_attn.q_proj", # rwkv6qwen2 + "model.layers.{bid}.attention.receptance", # rwkv7 + "model.layers.{bid}.attention.r_proj", # rwkv7 + ), + + MODEL_TENSOR.TIME_MIX_GATE: ( + "rwkv.blocks.{bid}.attention.gate", # rwkv6 + "model.layers.{bid}.self_attn.gate", # rwkv6qwen2 + ), + + MODEL_TENSOR.TIME_MIX_LN: ( + "rwkv.blocks.{bid}.attention.ln_x", # rwkv6 + "model.layers.{bid}.attention.ln_x" # rwkv7 + ), + + MODEL_TENSOR.TIME_MIX_OUTPUT: ( + "rwkv.blocks.{bid}.attention.output", # rwkv6 + "model.layers.{bid}.self_attn.o_proj", # rwkv6qwen2 + "model.layers.{bid}.attention.output", # rwkv7 + "model.layers.{bid}.attention.o_proj", # rwkv7 + ), + + MODEL_TENSOR.CHANNEL_MIX_LERP_K: ( + "rwkv.blocks.{bid}.feed_forward.time_maa_k", # rwkv6 + "model.layers.{bid}.feed_forward.x_k", # rwkv7 + ), + + MODEL_TENSOR.CHANNEL_MIX_LERP_R: ( + "rwkv.blocks.{bid}.feed_forward.time_maa_r", # rwkv6 + ), + + MODEL_TENSOR.CHANNEL_MIX_KEY: ( + "rwkv.blocks.{bid}.feed_forward.key", # rwkv6 + "model.layers.{bid}.feed_forward.key", # rwkv7 + ), + + MODEL_TENSOR.CHANNEL_MIX_RECEPTANCE: ( + "rwkv.blocks.{bid}.feed_forward.receptance", # rwkv6 + ), + + MODEL_TENSOR.CHANNEL_MIX_VALUE: ( + "rwkv.blocks.{bid}.feed_forward.value", # rwkv6 + "model.layers.{bid}.feed_forward.value", # rwkv7 + ), + + 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: ( + "model.layers.{bid}.self_attn.kv_b_proj", # deepseek2 + ), + + 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: ( + "model.layers.{bid}.self_attn.inner_attn_ln", # bitnet + ), + + MODEL_TENSOR.FFN_SUB_NORM: ( + "model.layers.{bid}.mlp.ffn_layernorm", # bitnet + ), + + MODEL_TENSOR.DEC_ATTN_NORM: ( + "decoder.block.{bid}.layer.0.layer_norm", # t5 + ), + + MODEL_TENSOR.DEC_ATTN_Q: ( + "decoder.block.{bid}.layer.0.SelfAttention.q", # t5 + ), + + MODEL_TENSOR.DEC_ATTN_K: ( + "decoder.block.{bid}.layer.0.SelfAttention.k", # t5 + ), + + MODEL_TENSOR.DEC_ATTN_V: ( + "decoder.block.{bid}.layer.0.SelfAttention.v", # t5 + ), + + MODEL_TENSOR.DEC_ATTN_OUT: ( + "decoder.block.{bid}.layer.0.SelfAttention.o", # t5 + ), + + MODEL_TENSOR.DEC_ATTN_REL_B: ( + "decoder.block.{bid}.layer.0.SelfAttention.relative_attention_bias", # t5 + ), + + MODEL_TENSOR.DEC_CROSS_ATTN_NORM: ( + "decoder.block.{bid}.layer.1.layer_norm", # t5 + ), + + MODEL_TENSOR.DEC_CROSS_ATTN_Q: ( + "decoder.block.{bid}.layer.1.EncDecAttention.q", # t5 + ), + + MODEL_TENSOR.DEC_CROSS_ATTN_K: ( + "decoder.block.{bid}.layer.1.EncDecAttention.k", # t5 + ), + + MODEL_TENSOR.DEC_CROSS_ATTN_V: ( + "decoder.block.{bid}.layer.1.EncDecAttention.v", # t5 + ), + + MODEL_TENSOR.DEC_CROSS_ATTN_OUT: ( + "decoder.block.{bid}.layer.1.EncDecAttention.o", # t5 + ), + + MODEL_TENSOR.DEC_CROSS_ATTN_REL_B: ( + "decoder.block.{bid}.layer.1.EncDecAttention.relative_attention_bias", # t5 + ), + + MODEL_TENSOR.DEC_FFN_NORM: ( + "decoder.block.{bid}.layer.2.layer_norm", # t5 + ), + + MODEL_TENSOR.DEC_FFN_GATE: ( + "decoder.block.{bid}.layer.2.DenseReluDense.wi_0", # flan-t5 + ), + + MODEL_TENSOR.DEC_FFN_UP: ( + "decoder.block.{bid}.layer.2.DenseReluDense.wi", # t5 + "decoder.block.{bid}.layer.2.DenseReluDense.wi_1", # flan-t5 + ), + + MODEL_TENSOR.DEC_FFN_DOWN: ( + "decoder.block.{bid}.layer.2.DenseReluDense.wo", # t5 + ), + + MODEL_TENSOR.DEC_OUTPUT_NORM: ( + "decoder.final_layer_norm", # t5 + ), + + MODEL_TENSOR.ENC_ATTN_NORM: ( + "encoder.block.{bid}.layer.0.layer_norm", # t5 + ), + + MODEL_TENSOR.ENC_ATTN_Q: ( + "encoder.block.{bid}.layer.0.SelfAttention.q", # t5 + ), + + MODEL_TENSOR.ENC_ATTN_K: ( + "encoder.block.{bid}.layer.0.SelfAttention.k", # t5 + ), + + MODEL_TENSOR.ENC_ATTN_V: ( + "encoder.block.{bid}.layer.0.SelfAttention.v", # t5 + ), + + MODEL_TENSOR.ENC_ATTN_OUT: ( + "encoder.block.{bid}.layer.0.SelfAttention.o", # t5 + ), + + MODEL_TENSOR.ENC_ATTN_REL_B: ( + "encoder.block.{bid}.layer.0.SelfAttention.relative_attention_bias", # t5 + ), + + MODEL_TENSOR.ENC_FFN_NORM: ( + "encoder.block.{bid}.layer.1.layer_norm", # t5 + ), + + MODEL_TENSOR.ENC_FFN_GATE: ( + "encoder.block.{bid}.layer.1.DenseReluDense.wi_0", # flan-t5 + ), + + MODEL_TENSOR.ENC_FFN_UP: ( + "encoder.block.{bid}.layer.1.DenseReluDense.wi", # t5 + "encoder.block.{bid}.layer.1.DenseReluDense.wi_1", # flan-t5 + ), + + MODEL_TENSOR.ENC_FFN_DOWN: ( + "encoder.block.{bid}.layer.1.DenseReluDense.wo", # t5 + ), + + MODEL_TENSOR.VISEXP_UP: ( + "model.layers.{bid}.mlp.vision_mlp.up_proj", # cogvlm + ), + + MODEL_TENSOR.VISEXP_GATE: ( + "model.layers.{bid}.mlp.vision_mlp.gate_proj", # cogvlm + ), + + MODEL_TENSOR.VISEXP_DOWN: ( + "model.layers.{bid}.mlp.vision_mlp.down_proj", # cogvlm + ), + + MODEL_TENSOR.VISEXP_ATTN_OUT: ( + "model.layers.{bid}.self_attn.vision_expert_dense", # cogvlm + ), + + MODEL_TENSOR.VISEXP_ATTN_QKV: ( + "model.layers.{bid}.self_attn.vision_expert_query_key_value", # cogvlm + ), + + MODEL_TENSOR.INDEXER_K_NORM: ( + "model.layers.{bid}.self_attn.indexer.k_norm", # DSA + ), + + MODEL_TENSOR.INDEXER_PROJ: ( + "model.layers.{bid}.self_attn.indexer.weights_proj", # DSA + ), + + MODEL_TENSOR.INDEXER_ATTN_K: ( + "model.layers.{bid}.self_attn.indexer.wk", # DSA + ), + + MODEL_TENSOR.INDEXER_ATTN_Q_B: ( + "model.layers.{bid}.self_attn.indexer.wq_b", # DSA + ), + + ############################################################################ + # TODO: these do not belong to block_mappings_cfg - move them to mappings_cfg + MODEL_TENSOR.ENC_OUTPUT_NORM: ( + "encoder.final_layer_norm", # t5 + "layer_norm", # neobert + "model.hidden_norm", # dflash + ), + + MODEL_TENSOR.FC: ( + "model.fc", # dflash + ), + + MODEL_TENSOR.CLS: ( + "classifier", # jina + "classifier.dense", # roberta + "pre_classifier", # distillbert + "dense", # neobert + "head.dense", # modern-bert + ), + + MODEL_TENSOR.CLS_OUT: ( + "classifier.out_proj", # roberta + ), + + MODEL_TENSOR.CLS_NORM: ( + "head.norm", # modern-bert + ), + ############################################################################# + + MODEL_TENSOR.CONVNEXT_DW: ( + "backbone.convnext.{bid}.dwconv", # wavtokenizer + ), + + MODEL_TENSOR.CONVNEXT_NORM: ( + "backbone.convnext.{bid}.norm", # wavtokenizer + ), + + MODEL_TENSOR.CONVNEXT_PW1: ( + "backbone.convnext.{bid}.pwconv1", # wavtokenizer + ), + + MODEL_TENSOR.CONVNEXT_PW2: ( + "backbone.convnext.{bid}.pwconv2", # wavtokenizer + ), + + MODEL_TENSOR.CONVNEXT_GAMMA: ( + "backbone.convnext.{bid}.gamma", # wavtokenizer + ), + + MODEL_TENSOR.POSNET_CONV1: ( + "backbone.posnet.{bid}.conv1", # wavtokenizer + ), + + MODEL_TENSOR.POSNET_CONV2: ( + "backbone.posnet.{bid}.conv2", # wavtokenizer + ), + + MODEL_TENSOR.POSNET_NORM: ( + "backbone.posnet.{bid}.norm", # wavtokenizer + ), + + MODEL_TENSOR.POSNET_NORM1: ( + "backbone.posnet.{bid}.norm1", # wavtokenizer + ), + + MODEL_TENSOR.POSNET_NORM2: ( + "backbone.posnet.{bid}.norm2", # wavtokenizer + ), + + MODEL_TENSOR.POSNET_ATTN_NORM: ( + "backbone.posnet.{bid}.norm", # wavtokenizer + ), + + MODEL_TENSOR.POSNET_ATTN_Q: ( + "backbone.posnet.{bid}.q", # wavtokenizer + ), + + MODEL_TENSOR.POSNET_ATTN_K: ( + "backbone.posnet.{bid}.k", # wavtokenizer + ), + + MODEL_TENSOR.POSNET_ATTN_V: ( + "backbone.posnet.{bid}.v", # wavtokenizer + ), + + MODEL_TENSOR.POSNET_ATTN_OUT: ( + "backbone.posnet.{bid}.proj_out", # wavtokenizer + ), + + MODEL_TENSOR.SHORTCONV_CONV: ( + "model.layers.{bid}.conv.conv", + ), + + MODEL_TENSOR.SHORTCONV_INPROJ: ( + "model.layers.{bid}.conv.in_proj", + ), + + MODEL_TENSOR.SHORTCONV_OUTPROJ: ( + "model.layers.{bid}.conv.out_proj", + ), + + ############################################################################# + ## Vision encoder + + MODEL_TENSOR.V_MMPROJ: ( + "multi_modal_projector.linear_{bid}", + "mm_projector.proj.linear_{bid}", # Kimi-K2.5 + "visual.merger.mlp.{bid}", # qwen2vl + "mlp_AR.linear_{bid}", # PaddleOCR-VL + "merger.mlp.{bid}", + "vision_tower.merger.mlp.{bid}", # dots.ocr + "vit.perceive.proj.{bid}", # HunyuanVL (proj.0 = conv1, proj.2 = conv2) + ), + + MODEL_TENSOR.V_MMPROJ_FC: ( + "model.connector.modality_projection.proj", # SmolVLM + "model.vision.linear_proj.linear_proj", # cogvlm + "model.projector.layers", # Deepseek-OCR + "visual.merger.proj", # glm4v + "vit.perceive.mlp", # HunyuanVL + ), + + MODEL_TENSOR.V_MMPROJ_MLP: ( + "model.mm_projector.mlp.mlp.{bid}", + "vision_model.vision_adapter.mlp.fc{bid}", # llama 4 + "mlp1.{bid}", # InternVL + "model.aligner.fc1.hidden_layers.{bid}", # Janus Pro + ), + + MODEL_TENSOR.V_MMPROJ_PEG: ( + "model.mm_projector.peg.peg.{bid}", + ), + + MODEL_TENSOR.V_ENC_EMBD_CLS: ( + "vision_tower.vision_model.embeddings.class_embedding", + "model.vision_tower.embeddings.cls_token", # Intern-S1 + "vision_model.class_embedding", # llama 4 + "model.vision.patch_embedding.cls_embedding", # cogvlm + "vision_model.radio_model.model.patch_generator.cls_token.token", # Nemotron Nano v2 VL + "model.vision_model.embeddings.class_embedding", # Deepseek-OCR + ), + + MODEL_TENSOR.V_ENC_EMBD_PATCH: ( + "model.vision_tower.vision_model.embeddings.patch_embedding", # Granite4Vision + "vision_tower.vision_model.embeddings.patch_embedding", + "model.vision_tower.embeddings.patch_embedding", # minicpmv4_6 + "model.vision_tower.embeddings.patch_embeddings.projection", # Intern-S1 + "vpm.embeddings.patch_embedding", + "model.vision_model.embeddings.patch_embedding", # SmolVLM + "vit.embeddings.patch_embedding", # HunyuanVL + "vision_tower.patch_conv", # pixtral-hf + "vision_encoder.patch_conv", # pixtral + "vision_model.patch_embedding.linear", # llama 4 + "visual.patch_embed.proj", # qwen2vl + "vision_tower.patch_embed.proj", # kimi-vl + "model.vision.patch_embedding.proj", # cogvlm + "model.vision_model.embeddings.patch_embedding", # Deepseek-OCR CLIP + "siglip2.vision_model.embeddings.patch_embedding", + "vision_model.radio_model.model.patch_generator.embedder", # Nemotron Nano v2 VL + "model.vision_tower.patch_embedder.input_proj", # gemma4 + "vision_tower.patch_embed.patchifier.proj", # dots.ocr + "vision_model.conv1", # Step3-VL + "model.vision_embedder.patch_dense", # gemma4 unified + ), + + MODEL_TENSOR.V_ENC_EMBD_NORM: ( + "visual.post_conv_layernorm", # glm4v + "vision_tower.patch_embed.patchifier.norm", # dots.ocr + ), + + MODEL_TENSOR.V_ENC_EMBD_PATCH_NORM: ( + "model.vision_embedder.patch_ln{bid}", # gemma4 unified + ), + + MODEL_TENSOR.V_ENC_EMBD_POS: ( + "model.vision_tower.vision_model.embeddings.position_embedding", # Granite4Vision + "vision_tower.vision_model.embeddings.position_embedding", + "model.vision_tower.embeddings.position_embedding", # minicpmv4_6 + "model.vision_tower.embeddings.position_embeddings", # Intern-S1 + "vpm.embeddings.position_embedding", + "model.vision_model.embeddings.position_embedding", # SmolVLM + "vit.embeddings.position_embedding", # HunyuanVL + "vision_model.positional_embedding_vlm", # llama 4 + "vision_tower.patch_embed.pos_emb", # kimi-vl + "visual.pos_embed", # qwen3vl + "model.vision.patch_embedding.position_embedding", # cogvlm + "visual.embeddings.position_embedding", # glm4v + "vision_model.radio_model.model.patch_generator.pos_embed", # Nemotron Nano v2 VL + "model.vision_tower.patch_embedder.position_embedding_table", # gemma4 + "vision_model.positional_embedding", # Step3-VL + "model.vision_embedder.pos_embedding", # gemma4 unified + ), + + # TODO: I think these should all be moved to mapping_cfg? + MODEL_TENSOR.V_ENC_EMBD_IMGNL: ( + "model.image_newline", # Deepseek-OCR, Granite4Vision + "vit.perceive.image_newline", # HunyuanVL + ), + + MODEL_TENSOR.V_ENC_EMBD_VSEP: ( + "model.view_seperator", # Deepseek-OCR + "vit.perceive.image_sep", # HunyuanVL + ), + + MODEL_TENSOR.V_ENC_ATTN_QKV: ( + "visual.blocks.{bid}.attn.qkv", # qwen3vl + "vision_tower.blocks.{bid}.attn.qkv", # dots.ocr + "model.vision.transformer.layers.{bid}.attention.query_key_value", # cogvlm + "model.vision_model.transformer.layers.{bid}.self_attn.qkv_proj", # Deepseek-OCR CLIP + "vision_tower.encoder.blocks.{bid}.wqkv", # Kimi-K2.5 + "vision_model.radio_model.model.blocks.{bid}.attn.qkv", # Nemotron Nano v2 VL + "vision_model.transformer.resblocks.{bid}.attn.in_proj", # Step3-VL + ), + + MODEL_TENSOR.V_ENC_ATTN_Q: ( + "model.vision_tower.vision_model.encoder.layers.{bid}.self_attn.q_proj", # Granite4Vision + "vision_tower.vision_model.encoder.layers.{bid}.self_attn.q_proj", + "model.vision_tower.encoder.layers.{bid}.self_attn.q_proj", # minicpmv4_6 + "model.vision_tower.encoder.layer.{bid}.attention.q_proj", # Intern-S1 + "vpm.encoder.layers.{bid}.self_attn.q_proj", + "model.vision_model.encoder.layers.{bid}.self_attn.q_proj", # SmolVLM + "vit.layers.{bid}.self_attn.q_proj", # HunyuanVL + "vision_model.model.layers.{bid}.self_attn.q_proj", # llama4 + "vision_tower.transformer.layers.{bid}.attention.q_proj", # pixtral-hf + "vision_encoder.transformer.layers.{bid}.attention.wq", # pixtral + "visual.blocks.{bid}.attn.q", # qwen2vl, generated + "vision_tower.encoder.blocks.{bid}.wq", # kimi-vl, generated + "siglip2.vision_model.encoder.layers.{bid}.self_attn.q_proj", # youtuvl + "model.vision_model.transformer.layers.{bid}.self_attn.q_proj", # Deepseek-OCR CLIP, generated + "vision_model.model.layers.{bid}.self_attn.q_proj.linear", # gemma4 + "model.qwen2_model.model.model.layers.{bid}.self_attn.q_proj" # Deepseek-OCR-2 qwen2 + ), + + MODEL_TENSOR.V_ENC_ATTN_Q_NORM: ( + "vision_tower.vision_model.encoder.layers.{bid}.attn.q_norm", # InternVL + "model.vision_tower.encoder.layer.{bid}.attention.q_norm", # Intern-S1 + "visual.blocks.{bid}.attn.q_norm", # GLM-OCR + "vision_model.model.layers.{bid}.self_attn.q_norm", # gemma4 + ), + + MODEL_TENSOR.V_ENC_ATTN_K: ( + "model.vision_tower.vision_model.encoder.layers.{bid}.self_attn.k_proj", # Granite4Vision + "vision_tower.vision_model.encoder.layers.{bid}.self_attn.k_proj", + "model.vision_tower.encoder.layers.{bid}.self_attn.k_proj", # minicpmv4_6 + "model.vision_tower.encoder.layer.{bid}.attention.k_proj", # Intern-S1 + "vpm.encoder.layers.{bid}.self_attn.k_proj", + "model.vision_model.encoder.layers.{bid}.self_attn.k_proj", # SmolVLM + "vit.layers.{bid}.self_attn.k_proj", # HunyuanVL + "vision_model.model.layers.{bid}.self_attn.k_proj", # llama4 + "vision_tower.transformer.layers.{bid}.attention.k_proj", # pixtral-hf + "vision_encoder.transformer.layers.{bid}.attention.wk", # pixtral + "visual.blocks.{bid}.attn.k", # qwen2vl, generated + "vision_tower.encoder.blocks.{bid}.wk", # kimi-vl, generated + "model.vision_model.transformer.layers.{bid}.self_attn.k_proj", # Deepseek-OCR CLIP, generated + "siglip2.vision_model.encoder.layers.{bid}.self_attn.k_proj", + "vision_model.model.layers.{bid}.self_attn.k_proj.linear", # gemma4 + "model.qwen2_model.model.model.layers.{bid}.self_attn.k_proj" # Deepseek-OCR-2 qwen2 + ), + + MODEL_TENSOR.V_ENC_ATTN_K_NORM: ( + "vision_tower.vision_model.encoder.layers.{bid}.attn.k_norm", # InternVL + "model.vision_tower.encoder.layer.{bid}.attention.k_norm", # Intern-S1 + "visual.blocks.{bid}.attn.k_norm", # GLM-OCR + "vision_model.model.layers.{bid}.self_attn.k_norm", # gemma4 + ), + + MODEL_TENSOR.V_ENC_ATTN_V: ( + "model.vision_tower.vision_model.encoder.layers.{bid}.self_attn.v_proj", # Granite4Vision + "vision_tower.vision_model.encoder.layers.{bid}.self_attn.v_proj", + "model.vision_tower.encoder.layers.{bid}.self_attn.v_proj", # minicpmv4_6 + "model.vision_tower.encoder.layer.{bid}.attention.v_proj", # Intern-S1 + "vpm.encoder.layers.{bid}.self_attn.v_proj", + "model.vision_model.encoder.layers.{bid}.self_attn.v_proj", # SmolVLM + "vit.layers.{bid}.self_attn.v_proj", # HunyuanVL + "vision_model.model.layers.{bid}.self_attn.v_proj", # llama4 + "vision_tower.transformer.layers.{bid}.attention.v_proj", # pixtral-hf + "vision_encoder.transformer.layers.{bid}.attention.wv", # pixtral + "visual.blocks.{bid}.attn.v", # qwen2vl, generated + "vision_tower.encoder.blocks.{bid}.wv", # kimi-vl, generated + "siglip2.vision_model.encoder.layers.{bid}.self_attn.v_proj", + "model.vision_model.transformer.layers.{bid}.self_attn.v_proj", # Deepseek-OCR CLIP, generated + "vision_model.model.layers.{bid}.self_attn.v_proj.linear", # gemma4 + "model.qwen2_model.model.model.layers.{bid}.self_attn.v_proj" # Deepseek-OCR-2 qwen2 + ), + + MODEL_TENSOR.V_ENC_INPUT_NORM: ( + "model.vision_tower.vision_model.encoder.layers.{bid}.layer_norm1", # Granite4Vision + "vision_tower.vision_model.encoder.layers.{bid}.layer_norm1", + "model.vision_tower.encoder.layers.{bid}.layer_norm1", # minicpmv4_6 + "vision_tower.vision_model.encoder.layers.{bid}.norm1", # InternVL + "model.vision_tower.encoder.layer.{bid}.layernorm_before", # Intern-S1 + "vpm.encoder.layers.{bid}.layer_norm1", + "model.vision_model.encoder.layers.{bid}.layer_norm1", # SmolVLM + "vit.layers.{bid}.input_layernorm", # HunyuanVL + "vision_tower.transformer.layers.{bid}.attention_norm", # pixtral-hf + "vision_encoder.transformer.layers.{bid}.attention_norm", # pixtral + "vision_model.model.layers.{bid}.input_layernorm", # llama4, gemma4 + "visual.blocks.{bid}.norm1", # qwen2vl + "vision_tower.encoder.blocks.{bid}.norm0", # kimi-vl (norm0/norm1) + "model.vision.transformer.layers.{bid}.input_layernorm", # cogvlm + "model.vision_model.transformer.layers.{bid}.layer_norm1", # Deepseek-OCR CLIP + "siglip2.vision_model.encoder.layers.{bid}.layer_norm1", + "vision_model.radio_model.model.blocks.{bid}.norm1", # Nemotron Nano v2 VL + "vision_tower.blocks.{bid}.norm1", # dots.ocr + "vision_model.transformer.resblocks.{bid}.ln_1", # Step3-VL + "model.qwen2_model.model.model.layers.{bid}.input_layernorm", # Deepseek-OCR-2 qwen2 + ), + + MODEL_TENSOR.V_ENC_ATTN_O: ( + "model.vision_tower.vision_model.encoder.layers.{bid}.self_attn.out_proj", # Granite4Vision + "vision_tower.vision_model.encoder.layers.{bid}.self_attn.out_proj", + "model.vision_tower.encoder.layers.{bid}.self_attn.out_proj", # minicpmv4_6 + "vision_tower.vision_model.encoder.layers.{bid}.attn.proj", # InternVL + "model.vision_tower.encoder.layer.{bid}.attention.projection_layer", # Intern-S1 + "vpm.encoder.layers.{bid}.self_attn.out_proj", + "model.vision_model.encoder.layers.{bid}.self_attn.out_proj", # SmolVLM + "vit.layers.{bid}.self_attn.o_proj", # HunyuanVL + "model.vision_model.encoder.layers.{bid}.self_attn.projection_layer", # Janus Pro + "vision_model.model.layers.{bid}.self_attn.o_proj", # llama4 + "vision_tower.transformer.layers.{bid}.attention.o_proj", # pixtral-hf + "vision_encoder.transformer.layers.{bid}.attention.wo", # pixtral + "visual.blocks.{bid}.attn.proj", # qwen2vl + "vision_tower.encoder.blocks.{bid}.wo", # kimi-vl + "model.vision.transformer.layers.{bid}.attention.dense", # cogvlm + "model.vision_model.transformer.layers.{bid}.self_attn.out_proj", # Deepseek-OCR CLIP + "siglip2.vision_model.encoder.layers.{bid}.self_attn.out_proj", # youtuvl + "vision_model.radio_model.model.blocks.{bid}.attn.proj", # Nemotron Nano v2 VL + "model.qwen2_model.model.model.layers.{bid}.self_attn.o_proj", # Deepseek-OCR-2 qwen2 + "vision_model.model.layers.{bid}.self_attn.o_proj.linear", # gemma4 + "vision_tower.blocks.{bid}.attn.proj", # dots.ocr + "vision_model.transformer.resblocks.{bid}.attn.out_proj", # Step3-VL + ), + + MODEL_TENSOR.V_ENC_ATTN_SINKS: ( + "visual.blocks.{bid}.attn.sinks", # mimovl + ), + + MODEL_TENSOR.V_ENC_POST_ATTN_NORM: ( + "model.vision_tower.vision_model.encoder.layers.{bid}.layer_norm2", # Granite4Vision + "vision_tower.vision_model.encoder.layers.{bid}.layer_norm2", + "model.vision_tower.encoder.layers.{bid}.layer_norm2", # minicpmv4_6 + "vision_tower.vision_model.encoder.layers.{bid}.norm2", # InternVL + "model.vision_tower.encoder.layer.{bid}.layernorm_after", # Intern-S1 + "vpm.encoder.layers.{bid}.layer_norm2", + "model.vision_model.encoder.layers.{bid}.layer_norm2", # SmolVLM + "vit.layers.{bid}.post_attention_layernorm", # HunyuanVL + "vision_model.model.layers.{bid}.post_attention_layernorm", # llama4 + "vision_tower.transformer.layers.{bid}.ffn_norm", # pixtral-hf + "vision_encoder.transformer.layers.{bid}.ffn_norm", # pixtral + "visual.blocks.{bid}.norm2", # qwen2vl + "vision_tower.encoder.blocks.{bid}.norm1", # kimi-vl (norm0/norm1) + "model.vision.transformer.layers.{bid}.post_attention_layernorm", # cogvlm + "model.vision_model.transformer.layers.{bid}.layer_norm2", # Deepseek-OCR CLIP + "siglip2.vision_model.encoder.layers.{bid}.layer_norm2", + "vision_model.radio_model.model.blocks.{bid}.norm2", # Nemotron Nano v2 VL + "vision_model.model.layers.{bid}.pre_feedforward_layernorm", # gemma4 + "vision_tower.blocks.{bid}.norm2", # dots.ocr + "vision_model.transformer.resblocks.{bid}.ln_2", # Step3-VL + "model.qwen2_model.model.model.layers.{bid}.post_attention_layernorm", # Deepseek-OCR-2 qwen2 + ), + + MODEL_TENSOR.V_ENC_FFN_UP: ( + "model.vision_tower.vision_model.encoder.layers.{bid}.mlp.fc1", # Granite4Vision + "vision_tower.vision_model.encoder.layers.{bid}.mlp.fc1", + "model.vision_tower.encoder.layers.{bid}.mlp.fc1", # minicpmv4_6 + "model.vision_tower.encoder.layer.{bid}.mlp.fc1", # Intern-S1 + "vpm.encoder.layers.{bid}.mlp.fc1", + "model.vision_model.encoder.layers.{bid}.mlp.fc1", # SmolVLM, gemma3 + "vit.layers.{bid}.mlp.dense_h_to_4h", # HunyuanVL + "vision_tower.transformer.layers.{bid}.feed_forward.up_proj", # pixtral-hf + "vision_encoder.transformer.layers.{bid}.feed_forward.w3", # pixtral + "vision_model.model.layers.{bid}.mlp.fc1", # llama4 + "visual.blocks.{bid}.mlp.fc1", # qwen2vl + "visual.blocks.{bid}.mlp.up_proj", # qwen2.5vl + "visual.blocks.{bid}.mlp.linear_fc1", # qwen3vl + "vision_tower.encoder.blocks.{bid}.mlp.fc0", # kimi-vl (fc0/fc1) + "model.vision_model.transformer.layers.{bid}.mlp.fc1", # Deepseek-OCR CLIP + "model.vision.transformer.layers.{bid}.mlp.fc1", # cogvlm + "siglip2.vision_model.encoder.layers.{bid}.mlp.fc1", + "vision_model.radio_model.model.blocks.{bid}.mlp.fc1", # Nemotron Nano v2 VL + "vision_model.model.layers.{bid}.mlp.up_proj", # gemma4 + "vision_model.transformer.resblocks.{bid}.mlp.c_fc", # Step3-VL + "model.qwen2_model.model.model.layers.{bid}.mlp.up_proj", # Deepseek-OCR-2 qwen2 + ), + + MODEL_TENSOR.V_ENC_FFN_GATE: ( + "vision_tower.transformer.layers.{bid}.feed_forward.gate_proj", # pixtral-hf + "vision_encoder.transformer.layers.{bid}.feed_forward.w1", # pixtral + "visual.blocks.{bid}.mlp.gate_proj", # qwen2.5vl + "vision_model.model.layers.{bid}.mlp.gate_proj", # gemma4 + "model.qwen2_model.model.model.layers.{bid}.mlp.gate_proj", # Deepseek-OCR-2 qwen2 + ), + + MODEL_TENSOR.V_ENC_FFN_DOWN: ( + "model.vision_tower.vision_model.encoder.layers.{bid}.mlp.fc2", # Granite4Vision + "vision_tower.vision_model.encoder.layers.{bid}.mlp.fc2", + "model.vision_tower.encoder.layers.{bid}.mlp.fc2", # minicpmv4_6 + "model.vision_tower.encoder.layer.{bid}.mlp.fc2", # Intern-S1 + "vpm.encoder.layers.{bid}.mlp.fc2", + "model.vision_model.encoder.layers.{bid}.mlp.fc2", # SmolVLM, gemma3 + "vit.layers.{bid}.mlp.dense_4h_to_h", # HunyuanVL + "vision_tower.transformer.layers.{bid}.feed_forward.down_proj", # pixtral-hf + "vision_encoder.transformer.layers.{bid}.feed_forward.w2", # pixtral + "vision_model.model.layers.{bid}.mlp.fc2", # llama4 + "visual.blocks.{bid}.mlp.fc2", # qwen2vl + "visual.blocks.{bid}.mlp.down_proj", # qwen2.5vl + "visual.blocks.{bid}.mlp.linear_fc2", # qwen3vl + "vision_tower.encoder.blocks.{bid}.mlp.fc1", # kimi-vl (fc0/fc1) + "model.vision.transformer.layers.{bid}.mlp.fc2", # cogvlm + "model.vision_model.transformer.layers.{bid}.mlp.fc2", # Deepseek-OCR CLIP + "siglip2.vision_model.encoder.layers.{bid}.mlp.fc2", + "vision_model.radio_model.model.blocks.{bid}.mlp.fc2", # Nemotron Nano v2 VL + "model.qwen2_model.model.model.layers.{bid}.mlp.down_proj" , # Deepseek-OCR-2 qwen2 + "vision_model.model.layers.{bid}.mlp.down_proj", # gemma4 + "vision_model.transformer.resblocks.{bid}.mlp.c_proj", # Step3-VL + ), + + MODEL_TENSOR.V_ENC_ATTN_POST_NORM: ( + "vision_model.model.layers.{bid}.post_attention_layernorm", # gemma4 + ), + + MODEL_TENSOR.V_ENC_FFN_POST_NORM: ( + "vision_model.model.layers.{bid}.post_feedforward_layernorm", # gemma4 + ), + + MODEL_TENSOR.V_LAYER_SCALE_1: ( + "vision_tower.vision_model.encoder.layers.{bid}.ls1", # InternVL + "model.vision_tower.encoder.layer.{bid}.lambda_1", # Intern-S1 + "vision_model.transformer.resblocks.{bid}.ls_1", # Step3-VL + ), + + MODEL_TENSOR.V_LAYER_SCALE_2: ( + "vision_tower.vision_model.encoder.layers.{bid}.ls2", # InternVL + "model.vision_tower.encoder.layer.{bid}.lambda_2", # Intern-S1 + "vision_model.transformer.resblocks.{bid}.ls_2", # Step3-VL + ), + + MODEL_TENSOR.V_LAYER_OUT_SCALE: ( + "vision_model.model.layers.{bid}.layer_scalar", # gemma4 + ), + + MODEL_TENSOR.V_PRE_NORM: ( + "vision_tower.vision_model.pre_layrnorm", + "vision_tower.ln_pre", # pixtral-hf + "vision_encoder.ln_pre", # pixtral + "vision_model.layernorm_pre", # llama4 + "model.vision_model.pre_layrnorm", # Deepseek-OCR CLIP + "vision_tower.patch_embed.patchifier.norm", # dots.ocr + "vision_model.ln_pre", # Step3-VL + ), + + MODEL_TENSOR.V_POST_NORM: ( + "model.vision_tower.vision_model.post_layernorm", # Granite4Vision + "vision_tower.vision_model.post_layernorm", + "model.vision_tower.post_layernorm", # minicpmv4_6 + "model.vision_model.post_layernorm", # SmolVLM + "vision_model.layernorm_post", # llama4 + "visual.merger.ln_q", # qwen2vl + "vision_tower.encoder.final_layernorm", # kimi-vl + "visual.post_layernorm", # glm4v + "siglip2.vision_model.post_layernorm", + "model.qwen2_model.model.model.norm", # Deepseek-OCR-2 qwen2 + ), + + MODEL_TENSOR.V_MM_POST_NORM: ( + "visual.merger.post_projection_norm", # glm4v + "vision_tower.post_trunk_norm", # dots.ocr + "vit.perceive.after_rms", # HunyuanVL + ), + + MODEL_TENSOR.V_MM_INP_PROJ: ( + "multi_modal_projector.mm_input_projection", + ), + + MODEL_TENSOR.V_MM_INP_NORM: ( + "multi_modal_projector.norm", + "multi_modal_projector.layer_norm", + "multi_modal_projector.pre_norm", + "mm_projector.pre_norm", # Kimi-K2.5 + "pre_mm_projector_norm", + "model.vision.linear_proj.norm1", # cogvlm + "mlp_AR.pre_norm", # PaddleOCR-VL + "merger.ln_q", + "vision_tower.merger.ln_q", # dots.ocr + "model.merger.mlp.0.pre_norm", # minicpmv4_6 + ), + + MODEL_TENSOR.V_MM_SOFT_EMB_NORM: ( + "multi_modal_projector.mm_soft_emb_norm", + ), + + MODEL_TENSOR.V_RESMPL_POS_EMBD_K: ( + "resampler.pos_embed_k", + ), + + MODEL_TENSOR.V_RESMPL_ATTN_Q: ( + "resampler.attn.in_proj_q", # tensor generated from resampler.attn.in_proj + ), + + MODEL_TENSOR.V_RESMPL_ATTN_K: ( + "resampler.attn.in_proj_k", # tensor generated from resampler.attn.in_proj + ), + + MODEL_TENSOR.V_RESMPL_ATTN_V: ( + "resampler.attn.in_proj_v", # tensor generated from resampler.attn.in_proj + ), + + MODEL_TENSOR.V_RESMPL_ATTN_OUT: ( + "resampler.attn.out_proj", + "model.vision_model.head.attention.out_proj", + ), + + MODEL_TENSOR.V_RESMPL_KV: ( + "resampler.kv_proj", + ), + + MODEL_TENSOR.V_RESMPL_POST_NORM: ( + "resampler.ln_post", + ), + + MODEL_TENSOR.V_RESMPL_KV_NORM: ( + "resampler.ln_kv", + ), + + MODEL_TENSOR.V_RESMPL_Q_NORM: ( + "resampler.ln_q", + ), + + MODEL_TENSOR.V_RESMPL_PROJ: ( + "resampler.proj", + ), + + MODEL_TENSOR.V_RESMPL_QUERY: ( + "resampler.query", + ), + + MODEL_TENSOR.V_TOK_EMBD_IMG_BREAK: ( + "v.token_embd.img_break", # for pixtral, this is a generated vector + ), + + MODEL_TENSOR.V_MM_PATCH_MERGER: ( + "multi_modal_projector.patch_merger.merging_layer", # mistral small 3.1 - hf + "patch_merger.merging_layer", # mistral + "visual.downsample", # glm4v + ), + + MODEL_TENSOR.V_DS_NORM: ( + "model.visual.deepstack_merger_list.{bid}.norm", # deepstack in qwen3vl + ), + + MODEL_TENSOR.V_DS_FC1: ( + "model.visual.deepstack_merger_list.{bid}.linear_fc1", # deepstack in qwen3vl + ), + + MODEL_TENSOR.V_DS_FC2: ( + "model.visual.deepstack_merger_list.{bid}.linear_fc2", # deepstack in qwen3vl + ), + + MODEL_TENSOR.V_MERGER_LN1: ( + "model.vision_tower.vit_merger.layer_norm1", # minicpmv4_6 + ), + + MODEL_TENSOR.V_MERGER_ATTN_Q: ( + "model.vision_tower.vit_merger.self_attn.q_proj", # minicpmv4_6 + ), + + MODEL_TENSOR.V_MERGER_ATTN_K: ( + "model.vision_tower.vit_merger.self_attn.k_proj", # minicpmv4_6 + ), + + MODEL_TENSOR.V_MERGER_ATTN_V: ( + "model.vision_tower.vit_merger.self_attn.v_proj", # minicpmv4_6 + ), + + MODEL_TENSOR.V_MERGER_ATTN_O: ( + "model.vision_tower.vit_merger.self_attn.out_proj", # minicpmv4_6 + ), + + MODEL_TENSOR.V_MERGER_DS_LN: ( + "model.vision_tower.vit_merger.pre_norm", # minicpmv4_6 + ), + + MODEL_TENSOR.V_MERGER_DS_UP: ( + "model.vision_tower.vit_merger.linear_1", # minicpmv4_6 + ), + + MODEL_TENSOR.V_MERGER_DS_DOWN: ( + "model.vision_tower.vit_merger.linear_2", # minicpmv4_6 + ), + + MODEL_TENSOR.V_SAM_POS_EMBD: ( + "model.sam_model.pos_embed", + ), + + MODEL_TENSOR.V_SAM_PATCH_EMBD: ( + "model.sam_model.patch_embed.proj", + ), + + MODEL_TENSOR.V_SAM_PRE_NORM: ( + "model.sam_model.blocks.{bid}.norm1", # deepstack in qwen3vl + ), + + MODEL_TENSOR.V_SAM_POST_NORM: ( + "model.sam_model.blocks.{bid}.norm2", # deepstack in qwen3vl + ), + + MODEL_TENSOR.V_SAM_ATTN_POS_H: ( + "model.sam_model.blocks.{bid}.attn.rel_pos_h", + ), + + MODEL_TENSOR.V_SAM_ATTN_POS_W: ( + "model.sam_model.blocks.{bid}.attn.rel_pos_w", + ), + + MODEL_TENSOR.V_SAM_ATTN_QKV: ( + "model.sam_model.blocks.{bid}.attn.qkv", + ), + + MODEL_TENSOR.V_SAM_ATTN_OUT: ( + "model.sam_model.blocks.{bid}.attn.proj", + ), + + MODEL_TENSOR.V_SAM_MLP_LIN_1: ( + "model.sam_model.blocks.{bid}.mlp.lin1", + ), + + MODEL_TENSOR.V_SAM_MLP_LIN_2: ( + "model.sam_model.blocks.{bid}.mlp.lin2", + ), + + MODEL_TENSOR.V_SAM_NECK: ( + "model.sam_model.neck.{bid}", + ), + + MODEL_TENSOR.V_SAM_NET_2: ( + "model.sam_model.net_2", + ), + + MODEL_TENSOR.V_SAM_NET_3: ( + "model.sam_model.net_3", + ), + + MODEL_TENSOR.V_RESMPL_QUERY_768: ( + "model.qwen2_model.query_768", # Deepseek-OCR-2 qwen2 + ), + + MODEL_TENSOR.V_RESMPL_QUERY_1024: ( + "model.qwen2_model.query_1024", # Deepseek-OCR-2 qwen2 + ), + + MODEL_TENSOR.V_MM_POST_FC_NORM: ( + "model.vision.linear_proj.norm1", # cogvlm + ), + + MODEL_TENSOR.V_MM_UP: ( + "model.vision.linear_proj.dense_h_to_4h", # cogvlm + "visual.merger.up_proj", # glm4v + "model.merger.mlp.0.linear_1", # minicpmv4_6 + ), + + MODEL_TENSOR.V_MM_DOWN: ( + "model.vision.linear_proj.dense_4h_to_h", # cogvlm + "visual.merger.down_proj", # glm4v + "model.merger.mlp.0.linear_2", # minicpmv4_6 + ), + + MODEL_TENSOR.V_MM_GATE: ( + "model.vision.linear_proj.gate_proj", # cogvlm + "visual.merger.gate_proj", # glm4v + ), + + MODEL_TENSOR.V_TOK_BOI: ( + "model.vision.boi", # cogvlm + ), + + MODEL_TENSOR.V_TOK_EOI: ( + "model.vision.eoi", # cogvlm + ), + + MODEL_TENSOR.V_MM_PRE_NORM: ( + "vit.perceive.before_rms", # HunyuanVL + ), + + MODEL_TENSOR.V_TOK_IMG_BEGIN: ( + "vit.perceive.image_begin", # HunyuanVL + ), + + MODEL_TENSOR.V_TOK_IMG_END: ( + "vit.perceive.image_end", # HunyuanVL + ), + + MODEL_TENSOR.V_STD_BIAS: ( + "model.vision_tower.std_bias", # gemma4 + ), + + MODEL_TENSOR.V_STD_SCALE: ( + "model.vision_tower.std_scale", # gemma4 + ), + + # For these tensors, bid => projector ID + MODEL_TENSOR.V_MULTI_PROJ_IMG_POS: ( + "model.layerwise_projectors.{bid}.image_positions", # Granite4 Vision + "model.spatial_projectors.{bid}.image_positions", # Granite4 Vision + ), + MODEL_TENSOR.V_MULTI_PROJ_QUERY: ( + "model.layerwise_projectors.{bid}.query", # Granite4 Vision + "model.spatial_projectors.{bid}.query", # Granite4 Vision + ), + MODEL_TENSOR.V_MULTI_PROJ_LINEAR: ( + "model.layerwise_projectors.{bid}.out_linear", # Granite4 Vision + "model.spatial_projectors.{bid}.out_linear", # Granite4 Vision + ), + MODEL_TENSOR.V_MULTI_PROJ_NORM: ( + "model.layerwise_projectors.{bid}.norm", # Granite4 Vision + "model.spatial_projectors.{bid}.norm", # Granite4 Vision + ), + MODEL_TENSOR.V_MULTI_PROJ_POST_NORM: ( + "model.layerwise_projectors.{bid}.qformer.layernorm", # Granite4 Vision + "model.spatial_projectors.{bid}.qformer.layernorm", # Granite4 Vision + ), + + # For these tensors, bid => proj-id + MODEL_TENSOR.V_QF_SELF_ATTN_Q: ( + "model.layerwise_projectors.qformer.encoder.layer.{bid}.attention.attention.query", # Granite4 Vision + "model.spatial_projectors.qformer.encoder.layer.{bid}.attention.attention.query", # Granite4 Vision + ), + MODEL_TENSOR.V_QF_SELF_ATTN_K: ( + "model.layerwise_projectors.qformer.encoder.layer.{bid}.attention.attention.key", # Granite4 Vision + "model.spatial_projectors.qformer.encoder.layer.{bid}.attention.attention.key", # Granite4 Vision + ), + MODEL_TENSOR.V_QF_SELF_ATTN_V: ( + "model.layerwise_projectors.qformer.encoder.layer.{bid}.attention.attention.value", # Granite4 Vision + "model.spatial_projectors.qformer.encoder.layer.{bid}.attention.attention.value", # Granite4 Vision + ), + MODEL_TENSOR.V_QF_SELF_ATTN_O: ( + "model.layerwise_projectors.qformer.encoder.layer.{bid}.attention.output.dense", # Granite4 Vision + "model.spatial_projectors.qformer.encoder.layer.{bid}.attention.output.dense", # Granite4 Vision + ), + MODEL_TENSOR.V_QF_SELF_ATTN_NORM: ( + "model.layerwise_projectors.qformer.encoder.layer.{bid}.attention.output.LayerNorm", # Granite4 Vision + "model.spatial_projectors.qformer.encoder.layer.{bid}.attention.output.LayerNorm", # Granite4 Vision + ), + MODEL_TENSOR.V_QF_CROSS_ATTN_Q: ( + "model.layerwise_projectors.qformer.encoder.layer.{bid}.crossattention.attention.query", # Granite4 Vision + "model.spatial_projectors.qformer.encoder.layer.{bid}.crossattention.attention.query", # Granite4 Vision + ), + MODEL_TENSOR.V_QF_CROSS_ATTN_K: ( + "model.layerwise_projectors.qformer.encoder.layer.{bid}.crossattention.attention.key", # Granite4 Vision + "model.spatial_projectors.qformer.encoder.layer.{bid}.crossattention.attention.key", # Granite4 Vision + ), + MODEL_TENSOR.V_QF_CROSS_ATTN_V: ( + "model.layerwise_projectors.qformer.encoder.layer.{bid}.crossattention.attention.value", # Granite4 Vision + "model.spatial_projectors.qformer.encoder.layer.{bid}.crossattention.attention.value", # Granite4 Vision + ), + MODEL_TENSOR.V_QF_CROSS_ATTN_O: ( + "model.layerwise_projectors.qformer.encoder.layer.{bid}.crossattention.output.dense", # Granite4 Vision + "model.spatial_projectors.qformer.encoder.layer.{bid}.crossattention.output.dense", # Granite4 Vision + ), + MODEL_TENSOR.V_QF_CROSS_ATTN_NORM: ( + "model.layerwise_projectors.qformer.encoder.layer.{bid}.crossattention.output.LayerNorm", # Granite4 Vision + "model.spatial_projectors.qformer.encoder.layer.{bid}.crossattention.output.LayerNorm", # Granite4 Vision + ), + MODEL_TENSOR.V_QF_FFN_UP: ( + "model.layerwise_projectors.qformer.encoder.layer.{bid}.intermediate_query.dense", # Granite4 Vision + "model.spatial_projectors.qformer.encoder.layer.{bid}.intermediate_query.dense", # Granite4 Vision + ), + MODEL_TENSOR.V_QF_FFN_DOWN: ( + "model.layerwise_projectors.qformer.encoder.layer.{bid}.output_query.dense", # Granite4 Vision + "model.spatial_projectors.qformer.encoder.layer.{bid}.output_query.dense", # Granite4 Vision + ), + MODEL_TENSOR.V_QF_FFN_NORM: ( + "model.layerwise_projectors.qformer.encoder.layer.{bid}.output_query.LayerNorm", # Granite4 Vision + "model.spatial_projectors.qformer.encoder.layer.{bid}.output_query.LayerNorm", # Granite4 Vision + ), + + # audio (mtmd) + + MODEL_TENSOR.A_ENC_EMBD_POS: ( + "audio_tower.embed_positions", # ultravox + "audio_embedding.embedding", # lfm2 + ), + + MODEL_TENSOR.A_ENC_EMBD_NORM: ( + "audio_embedding.embedding_norm", # lfm2 + ), + + MODEL_TENSOR.A_ENC_EMBD_TO_LOGITS: ( + "audio_embedding.to_logits", # lfm2 + ), + + MODEL_TENSOR.A_ENC_CONV1D: ( + "audio_tower.conv{bid}", # ultravox + "conformer.pre_encode.conv.{bid}", # lfm2 + "model.audio_tower.subsample_conv_projection.conv_{bid}.conv", # gemma3n + "conformer.subsample_conv_projection.layer{bid}.conv", # gemma4 + ), + + MODEL_TENSOR.A_ENC_CONV1D_NORM: ( + "conformer.subsample_conv_projection.layer{bid}.norm", # gemma4 + ), + + MODEL_TENSOR.A_ENC_INP_PROJ: ( + "conformer.subsample_conv_projection.input_proj_linear", # gemma4 + "encoder.input_linear", + ), + + MODEL_TENSOR.A_ENC_CONV2D: ( + "audio_tower.conv2d{bid}", # qwen3omni + ), + + MODEL_TENSOR.A_ENC_CONV_OUT: ( + "audio_tower.conv_out", # qwen3omni + ), + + MODEL_TENSOR.A_PRE_NORM: (), + + MODEL_TENSOR.A_POST_NORM: ( + "audio_tower.layer_norm", # ultravox + "audio_tower.ln_post", # qwen2omni + ), + + MODEL_TENSOR.A_ENC_ATTN_Q: ( + "audio_tower.layers.{bid}.self_attn.q_proj", # ultravox + "conformer.layers.{bid}.self_attn.linear_q", # lfm2 + "conformer.layers.{bid}.attention.attn.q_proj", # gemma3n + "conformer.layers.{bid}.self_attn.q_proj", # gemma4 + "encoder.layers.{bid}.attn.to_q", # granite_speech + ), + + MODEL_TENSOR.A_ENC_ATTN_K: ( + "audio_tower.layers.{bid}.self_attn.k_proj", # ultravox + "conformer.layers.{bid}.self_attn.linear_k", # lfm2 + "conformer.layers.{bid}.attention.attn.k_proj", # gemma3n + "conformer.layers.{bid}.self_attn.k_proj", # gemma4 + "encoder.layers.{bid}.attn.to_k", # granite_speech (split from to_kv) + ), + + MODEL_TENSOR.A_ENC_ATTN_V: ( + "audio_tower.layers.{bid}.self_attn.v_proj", # ultravox + "conformer.layers.{bid}.self_attn.linear_v", # lfm2 + "conformer.layers.{bid}.attention.attn.v_proj", # gemma3n + "conformer.layers.{bid}.self_attn.v_proj", # gemma4 + "encoder.layers.{bid}.attn.to_v", # granite_speech (split from to_kv) + ), + + MODEL_TENSOR.A_ENC_ATTN_K_REL: ( + "conformer.layers.{bid}.self_attn.relative_k_proj", # gemma4 + ), + + MODEL_TENSOR.A_ENC_ATTN_POST_NORM: ( + "conformer.layers.{bid}.norm_post_attn", # gemma4 + ), + + MODEL_TENSOR.A_ENC_ATTN_PRE_NORM: ( + "conformer.layers.{bid}.norm_pre_attn", # gemma4 + ), + + MODEL_TENSOR.A_ENC_PER_DIM_SCALE: ( + "conformer.layers.{bid}.attention.attn.per_dim_scale", # gemma3n + "conformer.layers.{bid}.self_attn.per_dim_scale", # gemma3n + ), + + MODEL_TENSOR.A_ENC_LAYER_PRE_NORM: ( + "conformer.layers.{bid}.norm", # gemma3n + ), + + MODEL_TENSOR.A_ENC_INPUT_NORM: ( + "audio_tower.layers.{bid}.self_attn_layer_norm", # ultravox + "conformer.layers.{bid}.norm_self_att", # lfm2 + "conformer.layers.{bid}.attention.pre_attn_norm", # gemma3n + "encoder.layers.{bid}.attn.pre_norm", # granite_speech + ), + + MODEL_TENSOR.A_ENC_OUTPUT: ( + "audio_tower.layers.{bid}.self_attn.out_proj", # ultravox + "conformer.layers.{bid}.self_attn.linear_out", # lfm2 + "conformer.layers.{bid}.attention.post", # gemma3n + "conformer.layers.{bid}.self_attn.post", # gemma4 + "encoder.layers.{bid}.attn.to_out", # granite_speech + ), + + MODEL_TENSOR.A_ENC_OUTPUT_NORM: ( + "audio_tower.layers.{bid}.final_layer_norm", # ultravox + "conformer.layers.{bid}.norm_out", # lfm2 + "conformer.layers.{bid}.attention.post_norm", # gemma3n + "encoder.layers.{bid}.post_norm", # granite_speech + ), + + MODEL_TENSOR.A_ENC_FFN_NORM: ( + "conformer.layers.{bid}.norm_feed_forward1", # lfm2 + "conformer.layers.{bid}.ffw_layer_start.pre_layer_norm", # gemma3n + "conformer.layers.{bid}.feed_forward1.pre_layer_norm", # gemma4 + "encoder.layers.{bid}.ff1.pre_norm", # granite_speech + ), + + MODEL_TENSOR.A_ENC_FFN_POST_NORM: ( + "conformer.layers.{bid}.ffw_layer_start.post_layer_norm", # gemma3n + "conformer.layers.{bid}.feed_forward1.post_layer_norm", # gemma4 + ), + + MODEL_TENSOR.A_ENC_FFN_SCALE: ( + "conformer.layers.{bid}.ffw_layer_start.post_layer_scale", # gemma3n + ), + + MODEL_TENSOR.A_ENC_FFN_UP: ( + "audio_tower.layers.{bid}.fc1", # ultravox + "conformer.layers.{bid}.feed_forward1.linear1", # lfm2 + "conformer.layers.{bid}.ffw_layer_start.ffw_layer_1", # gemma3n + "conformer.layers.{bid}.feed_forward1.ffw_layer_1", # gemma4 + "encoder.layers.{bid}.ff1.up_proj", # granite_speech + ), + + MODEL_TENSOR.A_ENC_FFN_GATE: (), + + MODEL_TENSOR.A_ENC_FFN_DOWN: ( + "audio_tower.layers.{bid}.fc2", # ultravox + "conformer.layers.{bid}.feed_forward1.linear2", # lfm2 + "conformer.layers.{bid}.ffw_layer_start.ffw_layer_2", # gemma3n + "conformer.layers.{bid}.feed_forward1.ffw_layer_2", # gemma4 + "encoder.layers.{bid}.ff1.down_proj", # granite_speech + ), + + MODEL_TENSOR.A_ENC_FFN_UP_1: ( + "conformer.layers.{bid}.feed_forward2.linear1", # lfm2 + "conformer.layers.{bid}.ffw_layer_end.ffw_layer_1", # gemma3n + "conformer.layers.{bid}.feed_forward2.ffw_layer_1", # gemma4 + "encoder.layers.{bid}.ff2.up_proj", # granite_speech + ), + + MODEL_TENSOR.A_ENC_FFN_DOWN_1: ( + "conformer.layers.{bid}.feed_forward2.linear2", # lfm2 + "conformer.layers.{bid}.ffw_layer_end.ffw_layer_2", # gemma3n + "conformer.layers.{bid}.feed_forward2.ffw_layer_2", # gemma4 + "encoder.layers.{bid}.ff2.down_proj", # granite_speech + ), + + MODEL_TENSOR.A_ENC_FFN_NORM_1: ( + "conformer.layers.{bid}.norm_feed_forward2", # lfm2 + "conformer.layers.{bid}.ffw_layer_end.pre_layer_norm", # gemma3n + "conformer.layers.{bid}.feed_forward2.pre_layer_norm", # gemma4 + "encoder.layers.{bid}.ff2.pre_norm", # granite_speech + ), + + MODEL_TENSOR.A_ENC_FFN_POST_NORM_1: ( + "conformer.layers.{bid}.ffw_layer_end.post_layer_norm", # gemma3n + "conformer.layers.{bid}.feed_forward2.post_layer_norm", # gemma4 + ), + + MODEL_TENSOR.A_ENC_FFN_SCALE_1: ( + "conformer.layers.{bid}.ffw_layer_end.post_layer_scale", # gemma3n + ), + + MODEL_TENSOR.A_ENC_LINEAR_POS: ( + "conformer.layers.{bid}.self_attn.linear_pos", # lfm2 + "conformer.layers.{bid}.attention.attn.relative_position_embedding.pos_proj", # gemma3n + ), + + MODEL_TENSOR.A_ENC_POS_BIAS_U: ( + "conformer.layers.{bid}.self_attn.pos_bias_u", # lfm2 + ), + + MODEL_TENSOR.A_ENC_POS_BIAS_V: ( + "conformer.layers.{bid}.self_attn.pos_bias_v", # lfm2 + ), + + MODEL_TENSOR.A_ENC_OUT: ( + "conformer.pre_encode.out", # lfm2 + "model.audio_tower.subsample_conv_projection.input_proj_linear", # gemma3n (note: it should be A_ENC_INP_PROJ, this is a mistake; it should be corrected in C++ code when it's supported) + "conformer.output_proj", # gemma4 + ), + + # note: some tensors below has "audio." pseudo-prefix, to prevent conflicts with vision tensors + # this prefix is added in the conversion code in modify_tensors() + + MODEL_TENSOR.A_MMPROJ: ( + "audio.multi_modal_projector.linear_{bid}", # ultravox, meralion + "audio_adapter.model.{bid}", # lfm2 + "audio_tower.proj{bid}", # qwen3omni + ), + + MODEL_TENSOR.A_MMPROJ_FC: ( + "audio.multi_modal_projector.linear", # qwen2audio + "audio_tower.proj", # qwen2omni + "model.audio_tower.output_proj" # gemma4 + ), + + MODEL_TENSOR.A_MM_NORM_PRE: ( + "audio.multi_modal_projector.ln_pre", # ultravox + ), + + MODEL_TENSOR.A_MM_NORM_MID: ( + "audio.multi_modal_projector.ln_mid", # ultravox + ), + + MODEL_TENSOR.A_ENC_CONV_DW: ( + "conformer.layers.{bid}.conv.depthwise_conv", # lfm2 + "conformer.layers.{bid}.lconv1d.depthwise_conv1d", # gemma3n + "encoder.layers.{bid}.conv.depth_conv.conv", # granite_speech + ), + + MODEL_TENSOR.A_ENC_CONV_NORM: ( + "conformer.layers.{bid}.conv.batch_norm", # lfm2 + "conformer.layers.{bid}.lconv1d.pre_layer_norm", # gemma3n + "encoder.layers.{bid}.conv.batch_norm", # granite_speech + ), + + MODEL_TENSOR.A_ENC_CONV_PW1: ( + "conformer.layers.{bid}.conv.pointwise_conv1", # lfm2 + "conformer.layers.{bid}.lconv1d.linear_start", # gemma3n + "encoder.layers.{bid}.conv.up_conv", # granite_speech + ), + + MODEL_TENSOR.A_ENC_CONV_PW2: ( + "conformer.layers.{bid}.conv.pointwise_conv2", # lfm2 + "conformer.layers.{bid}.lconv1d.linear_end", # gemma3n + "encoder.layers.{bid}.conv.down_conv", # granite_speech + ), + + MODEL_TENSOR.A_ENC_NORM_CONV: ( + "conformer.layers.{bid}.norm_conv", # lfm2 + "conformer.layers.{bid}.lconv1d.conv_norm", # gemma3n + "encoder.layers.{bid}.conv.norm", # granite_speech + ), + + MODEL_TENSOR.A_PER_DIM_K_SCALE: ( + "conformer.layers.{bid}.attention.attn.per_dim_key_scale", # gemma4 + ), + + MODEL_TENSOR.A_PER_DIM_SCALE: ( + "conformer.layers.{bid}.attention.attn.per_dim_scale", # gemma4 + ), + + MODEL_TENSOR.A_MM_EMBEDDING: ( + "model.embed_audio.embedding", # gemma3n + ), + MODEL_TENSOR.A_MM_HARD_EMB_NORM: ( + "model.embed_audio.hard_embedding_norm", # gemma3n + ), + MODEL_TENSOR.A_MM_INP_PROJ: ( + "model.embed_audio.embedding_projection", # gemma3n + ), + MODEL_TENSOR.A_MM_SOFT_EMB_NORM: ( + "model.embed_audio.soft_embedding_norm", # gemma3n + ), + + MODEL_TENSOR.A_ENC_ATTN_REL_POS_EMB: ( + "encoder.layers.{bid}.attn.rel_pos_emb.weight", + ), + + MODEL_TENSOR.A_QF_SELF_ATTN_Q: ( + "projector.qformer.encoder.layer.{bid}.attention.attention.query", + ), + + MODEL_TENSOR.A_QF_SELF_ATTN_K: ( + "projector.qformer.encoder.layer.{bid}.attention.attention.key", + ), + + MODEL_TENSOR.A_QF_SELF_ATTN_V: ( + "projector.qformer.encoder.layer.{bid}.attention.attention.value", + ), + + MODEL_TENSOR.A_QF_SELF_ATTN_O: ( + "projector.qformer.encoder.layer.{bid}.attention.output.dense", + ), + + MODEL_TENSOR.A_QF_SELF_ATTN_NORM: ( + "projector.qformer.encoder.layer.{bid}.attention.output.LayerNorm", + ), + + MODEL_TENSOR.A_QF_CROSS_ATTN_Q: ( + "projector.qformer.encoder.layer.{bid}.crossattention.attention.query", + ), + + MODEL_TENSOR.A_QF_CROSS_ATTN_K: ( + "projector.qformer.encoder.layer.{bid}.crossattention.attention.key", + ), + + MODEL_TENSOR.A_QF_CROSS_ATTN_V: ( + "projector.qformer.encoder.layer.{bid}.crossattention.attention.value", + ), + + MODEL_TENSOR.A_QF_CROSS_ATTN_O: ( + "projector.qformer.encoder.layer.{bid}.crossattention.output.dense", + ), + + MODEL_TENSOR.A_QF_CROSS_ATTN_NORM: ( + "projector.qformer.encoder.layer.{bid}.crossattention.output.LayerNorm", + ), + + MODEL_TENSOR.A_QF_FFN_UP: ( + "projector.qformer.encoder.layer.{bid}.intermediate_query.dense", + ), + + MODEL_TENSOR.A_QF_FFN_DOWN: ( + "projector.qformer.encoder.layer.{bid}.output_query.dense", + ), + + MODEL_TENSOR.A_QF_FFN_NORM: ( + "projector.qformer.encoder.layer.{bid}.output_query.LayerNorm", + ), + + # NextN/MTP tensors + MODEL_TENSOR.NEXTN_PROJ_PRE: ( + "pre_projection", + ), + + MODEL_TENSOR.NEXTN_PROJ_POST: ( + "post_projection", + ), + + MODEL_TENSOR.NEXTN_EH_PROJ: ( + "model.layers.{bid}.eh_proj", + ), + + MODEL_TENSOR.NEXTN_EMBED_TOKENS: ( + "model.layers.{bid}.embed_tokens", + ), + + MODEL_TENSOR.NEXTN_ENORM: ( + "model.layers.{bid}.enorm", + ), + + MODEL_TENSOR.NEXTN_HNORM: ( + "model.layers.{bid}.hnorm", + ), + + MODEL_TENSOR.NEXTN_SHARED_HEAD_HEAD: ( + "model.layers.{bid}.shared_head.head", + ), + + MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM: ( + "model.layers.{bid}.shared_head.norm", + ), + } + + # architecture-specific block mappings + arch_block_mappings_cfg: dict[MODEL_ARCH, dict[MODEL_TENSOR, tuple[str, ...]]] = { + MODEL_ARCH.ARCTIC: { + MODEL_TENSOR.FFN_NORM: ( + "model.layers.{bid}.residual_layernorm", + ), + MODEL_TENSOR.FFN_NORM_EXP: ( + "model.layers.{bid}.post_attention_layernorm", + ), + }, + } + + mapping: dict[str, tuple[MODEL_TENSOR, str]] + + def __init__(self, arch: MODEL_ARCH, n_blocks: int): + self.mapping = {} + for tensor, keys in self.mappings_cfg.items(): + if tensor not in MODEL_TENSORS[arch]: + continue + tensor_name = TENSOR_NAMES[tensor] + self.mapping[tensor_name] = (tensor, tensor_name) + for key in keys: + self.mapping[key] = (tensor, tensor_name) + if arch in self.arch_block_mappings_cfg: + self.block_mappings_cfg.update(self.arch_block_mappings_cfg[arch]) + for bid in range(n_blocks): + for tensor, keys in self.block_mappings_cfg.items(): + if tensor not in MODEL_TENSORS[arch]: + continue + + tensor_name = TENSOR_NAMES[tensor].format(bid = bid) + self.mapping[tensor_name] = (tensor, tensor_name) + for key in keys: + key = key.format(bid = bid) + self.mapping[key] = (tensor, tensor_name) + + def get_type_and_name(self, key: str, try_suffixes: Sequence[str] = ()) -> tuple[MODEL_TENSOR, str] | None: + result = self.mapping.get(key) + if result is not None: + return result + for suffix in try_suffixes: + if key.endswith(suffix): + result = self.mapping.get(key[:-len(suffix)]) + if result is not None: + return result[0], result[1] + suffix + return None + + def get_name(self, key: str, try_suffixes: Sequence[str] = ()) -> str | None: + result = self.get_type_and_name(key, try_suffixes = try_suffixes) + if result is None: + return None + return result[1] + + def get_type(self, key: str, try_suffixes: Sequence[str] = ()) -> MODEL_TENSOR | None: + result = self.get_type_and_name(key, try_suffixes = try_suffixes) + if result is None: + return None + return result[0] + + def __getitem__(self, key: str) -> str: + try: + return self.mapping[key][1] + except KeyError: + raise KeyError(key) + + def __contains__(self, key: str) -> bool: + return key in self.mapping + + def __repr__(self) -> str: + return repr(self.mapping) + + +def get_tensor_name_map(arch: MODEL_ARCH, n_blocks: int) -> TensorNameMap: + return TensorNameMap(arch, n_blocks) diff --git a/backend/llama.cpp/gguf-py/gguf/utility.py b/backend/llama.cpp/gguf-py/gguf/utility.py new file mode 100644 index 0000000000000000000000000000000000000000..154351d8ed63c50f8a66cc0bb9152ec020414077 --- /dev/null +++ b/backend/llama.cpp/gguf-py/gguf/utility.py @@ -0,0 +1,340 @@ +from __future__ import annotations + +from dataclasses import dataclass +from pathlib import Path +from typing import Literal + +import os +import json +import numpy as np + + +def fill_templated_filename(filename: str, output_type: str | None) -> str: + # Given a file name fill in any type templates e.g. 'some-model-name.{ftype}.gguf' + ftype_lowercase: str = output_type.lower() if output_type is not None else "" + ftype_uppercase: str = output_type.upper() if output_type is not None else "" + return filename.format(ftype_lowercase, + outtype=ftype_lowercase, ftype=ftype_lowercase, + OUTTYPE=ftype_uppercase, FTYPE=ftype_uppercase) + + +def model_weight_count_rounded_notation(model_params_count: int, min_digits: int = 2) -> str: + if model_params_count > 1e12 : + # Trillions Of Parameters + scaled_model_params = model_params_count * 1e-12 + scale_suffix = "T" + elif model_params_count > 1e9 : + # Billions Of Parameters + scaled_model_params = model_params_count * 1e-9 + scale_suffix = "B" + elif model_params_count > 1e6 : + # Millions Of Parameters + scaled_model_params = model_params_count * 1e-6 + scale_suffix = "M" + else: + # Thousands Of Parameters + scaled_model_params = model_params_count * 1e-3 + scale_suffix = "K" + + fix = max(min_digits - len(str(round(scaled_model_params)).lstrip('0')), 0) + + return f"{scaled_model_params:.{fix}f}{scale_suffix}" + + +def size_label(total_params: int, shared_params: int, expert_params: int, expert_count: int) -> str: + + if expert_count > 0: + pretty_size = model_weight_count_rounded_notation(abs(shared_params) + abs(expert_params), min_digits=2) + size_class = f"{expert_count}x{pretty_size}" + else: + size_class = model_weight_count_rounded_notation(abs(total_params), min_digits=2) + + return size_class + + +def naming_convention(model_name: str | None, base_name: str | None, finetune_string: str | None, version_string: str | None, size_label: str | None, output_type: str | None, model_type: Literal['vocab', 'LoRA'] | None = None) -> str: + # Reference: https://github.com/ggml-org/ggml/blob/master/docs/gguf.md#gguf-naming-convention + + if base_name is not None: + name = base_name.strip().replace(' ', '-').replace('/', '-') + elif model_name is not None: + name = model_name.strip().replace(' ', '-').replace('/', '-') + else: + name = "ggml-model" + + parameters = f"-{size_label}" if size_label is not None else "" + + finetune = f"-{finetune_string.strip().replace(' ', '-')}" if finetune_string is not None else "" + + version = f"-{version_string.strip().replace(' ', '-')}" if version_string is not None else "" + + encoding = f"-{output_type.strip().replace(' ', '-').upper()}" if output_type is not None else "" + + kind = f"-{model_type.strip().replace(' ', '-')}" if model_type is not None else "" + + return f"{name}{parameters}{finetune}{version}{encoding}{kind}" + + +@dataclass +class RemoteTensor: + dtype: str + shape: tuple[int, ...] + offset_start: int + size: int + url: str + + def data(self) -> bytearray: + # TODO: handle request errors (maybe with limited retries?) + # NOTE: using a bytearray, otherwise PyTorch complains the buffer is not writeable + data = bytearray(SafetensorRemote.get_data_by_range(url=self.url, start=self.offset_start, size=self.size)) + return data + + +class SafetensorRemote: + """ + Uility class to handle remote safetensor files. + This class is designed to work with Hugging Face model repositories. + + Example (one model has single safetensor file, the other has multiple): + for model_id in ["ngxson/TEST-Tiny-Llama4", "Qwen/Qwen2.5-7B-Instruct"]: + tensors = SafetensorRemote.get_list_tensors_hf_model(model_id) + print(tensors) + + Example reading tensor data: + tensors = SafetensorRemote.get_list_tensors_hf_model(model_id) + for name, meta in tensors.items(): + dtype, shape, offset_start, size, remote_safetensor_url = meta + # read the tensor data + data = SafetensorRemote.get_data_by_range(remote_safetensor_url, offset_start, size) + print(data) + """ + + BASE_DOMAIN = "https://huggingface.co" + + @classmethod + def get_list_tensors_hf_model(cls, model_id: str) -> dict[str, RemoteTensor]: + """ + Get list of tensors from a Hugging Face model repository. + + Returns a dictionary of tensor names and their metadata. + Each tensor is represented as a tuple of (dtype, shape, offset_start, size, remote_safetensor_url) + """ + # case 1: model has only one single model.safetensor file + is_single_file = cls.check_file_exist(f"{cls.BASE_DOMAIN}/{model_id}/resolve/main/model.safetensors") + if is_single_file: + url = f"{cls.BASE_DOMAIN}/{model_id}/resolve/main/model.safetensors" + return cls.get_list_tensors(url) + + # case 2: model has multiple files + index_url = f"{cls.BASE_DOMAIN}/{model_id}/resolve/main/model.safetensors.index.json" + is_multiple_files = cls.check_file_exist(index_url) + if is_multiple_files: + # read the index file + index_data = cls.get_data_by_range(index_url, 0) + index_str = index_data.decode('utf-8') + index_json = json.loads(index_str) + assert index_json.get("weight_map") is not None, "weight_map not found in index file" + weight_map = index_json["weight_map"] + # get the list of files + all_files = list(set(weight_map.values())) + all_files.sort() # make sure we load shard files in order + # get the list of tensors + tensors: dict[str, RemoteTensor] = {} + for file in all_files: + url = f"{cls.BASE_DOMAIN}/{model_id}/resolve/main/{file}" + for key, val in cls.get_list_tensors(url).items(): + tensors[key] = val + return tensors + + raise ValueError( + f"No safetensor file has been found for model {model_id}." + "If the repo has safetensor files, make sure the model is public or you have a " + "valid Hugging Face token set in the environment variable HF_TOKEN." + ) + + @classmethod + def get_list_tensors(cls, url: str) -> dict[str, RemoteTensor]: + """ + Get list of tensors from a remote safetensor file. + + Returns a dictionary of tensor names and their metadata. + Each tensor is represented as a tuple of (dtype, shape, offset_start, size) + """ + metadata, data_start_offset = cls.get_metadata(url) + res: dict[str, RemoteTensor] = {} + + for name, meta in metadata.items(): + if name == "__metadata__": + continue + if not isinstance(meta, dict): + raise ValueError(f"Invalid metadata for tensor '{name}': {meta}") + try: + dtype = meta["dtype"] + shape = meta["shape"] + offset_start_relative, offset_end_relative = meta["data_offsets"] + size = offset_end_relative - offset_start_relative + offset_start = data_start_offset + offset_start_relative + res[name] = RemoteTensor(dtype=dtype, shape=tuple(shape), offset_start=offset_start, size=size, url=url) + except KeyError as e: + raise ValueError(f"Missing key in metadata for tensor '{name}': {e}, meta = {meta}") + + # order by name (same as default safetensors behavior) + # ref: https://github.com/huggingface/safetensors/blob/0816a1ae1d6b731cefd67f061d80d1cadd0dd7bb/bindings/python/src/lib.rs#L606 + res = dict(sorted(res.items(), key=lambda t: t[0])) + + return res + + @classmethod + def get_metadata(cls, url: str) -> tuple[dict, int]: + """ + Get JSON metadata from a remote safetensor file. + + Returns tuple of (metadata, data_start_offset) + """ + # Request first 5MB of the file (hopefully enough for metadata) + read_size = 5 * 1024 * 1024 + raw_data = cls.get_data_by_range(url, 0, read_size) + + # Parse header + # First 8 bytes contain the metadata length as u64 little-endian + if len(raw_data) < 8: + raise ValueError("Not enough data to read metadata size") + metadata_length = int.from_bytes(raw_data[:8], byteorder='little') + + # Calculate the data start offset + data_start_offset = 8 + metadata_length + + # Check if we have enough data to read the metadata + if len(raw_data) < 8 + metadata_length: + raise ValueError(f"Could not read complete metadata. Need {8 + metadata_length} bytes, got {len(raw_data)}") + + # Extract metadata bytes and parse as JSON + metadata_bytes = raw_data[8:8 + metadata_length] + metadata_str = metadata_bytes.decode('utf-8') + try: + metadata = json.loads(metadata_str) + return metadata, data_start_offset + except json.JSONDecodeError as e: + raise ValueError(f"Failed to parse safetensor metadata as JSON: {e}") + + @classmethod + def get_data_by_range(cls, url: str, start: int, size: int = -1) -> bytes: + """ + Get raw byte data from a remote file by range. + If size is not specified, it will read the entire file. + """ + import requests + from urllib.parse import urlparse + + parsed_url = urlparse(url) + if not parsed_url.scheme or not parsed_url.netloc: + raise ValueError(f"Invalid URL: {url}") + + headers = cls._get_request_headers() + if size > -1: + headers["Range"] = f"bytes={start}-{start + size}" + response = requests.get(url, allow_redirects=True, headers=headers) + response.raise_for_status() + + # Get raw byte data + return response.content[slice(size if size > -1 else None)] + + @classmethod + def check_file_exist(cls, url: str) -> bool: + """ + Check if a file exists at the given URL. + Returns True if the file exists, False otherwise. + """ + import requests + from urllib.parse import urlparse + + parsed_url = urlparse(url) + if not parsed_url.scheme or not parsed_url.netloc: + raise ValueError(f"Invalid URL: {url}") + + try: + headers = cls._get_request_headers() + headers["Range"] = "bytes=0-0" + response = requests.head(url, allow_redirects=True, headers=headers) + # Success (2xx) or redirect (3xx) + return 200 <= response.status_code < 400 + except requests.RequestException: + return False + + @classmethod + def _get_request_headers(cls) -> dict[str, str]: + """Prepare common headers for requests.""" + headers = {"User-Agent": "convert_hf_to_gguf"} + if os.environ.get("HF_TOKEN"): + headers["Authorization"] = f"Bearer {os.environ['HF_TOKEN']}" + return headers + + +@dataclass +class LocalTensorRange: + filename: Path + offset: int + size: int + + +@dataclass +class LocalTensor: + dtype: str + shape: tuple[int, ...] + data_range: LocalTensorRange + + def mmap_bytes(self) -> np.ndarray: + return np.memmap(self.data_range.filename, mode='c', offset=self.data_range.offset, shape=self.data_range.size) + + +class SafetensorsLocal: + """ + Read a safetensors file from the local filesystem. + + Custom parsing gives a bit more control over the memory usage. + The official safetensors library doesn't expose file ranges. + """ + + tensors: dict[str, LocalTensor] + + def __init__(self, filename: Path): + with open(filename, "rb") as f: + metadata_length = int.from_bytes(f.read(8), byteorder='little') + file_size = os.stat(filename).st_size + if file_size < 8 + metadata_length: + raise ValueError(f"Could not read complete metadata. Need {8 + metadata_length} bytes, got {file_size}") + + metadata_str = f.read(metadata_length).decode('utf-8') + try: + metadata = json.loads(metadata_str) + except json.JSONDecodeError as e: + raise ValueError(f"Failed to parse safetensors metadata as JSON: {e}") + + data_start_offset = f.tell() + + tensors: dict[str, LocalTensor] = {} + for name, meta in metadata.items(): + if name == "__metadata__": + # ignore metadata, it's not a tensor + continue + + tensors[name] = LocalTensor( + dtype=meta["dtype"], + shape=tuple(meta["shape"]), + data_range=LocalTensorRange( + filename, + data_start_offset + meta["data_offsets"][0], + meta["data_offsets"][1] - meta["data_offsets"][0], + ), + ) + + # order by name (same as default safetensors behavior) + # ref: https://github.com/huggingface/safetensors/blob/0816a1ae1d6b731cefd67f061d80d1cadd0dd7bb/bindings/python/src/lib.rs#L606 + self.tensors = dict(sorted(tensors.items(), key=lambda t: t[0])) + + def __enter__(self, *args, **kwargs): + del args, kwargs # unused + return self.tensors + + def __exit__(self, *args, **kwargs): + del args, kwargs # unused diff --git a/backend/llama.cpp/gguf-py/gguf/vocab.py b/backend/llama.cpp/gguf-py/gguf/vocab.py new file mode 100644 index 0000000000000000000000000000000000000000..d93b94f2d792147276be21db004dbd8d4edef82c --- /dev/null +++ b/backend/llama.cpp/gguf-py/gguf/vocab.py @@ -0,0 +1,933 @@ +from __future__ import annotations + +from enum import Enum +import re +import logging +import json +import os +from pathlib import Path +from typing import Any, Callable, Sequence, Mapping, Iterable, Protocol, ClassVar, runtime_checkable + +try: + from sentencepiece import SentencePieceProcessor +except ImportError: + SentencePieceProcessor: Any = None + +try: + from mistral_common.tokens.tokenizers.mistral import MistralTokenizer # type: ignore[import-not-found, ty:unresolved-import] + from mistral_common.tokens.tokenizers.tekken import Tekkenizer # type: ignore[import-not-found, ty:unresolved-import] + from mistral_common.tokens.tokenizers.utils import ( # type: ignore[import-not-found, ty:unresolved-import] + _filter_valid_tokenizer_files, + ) + from mistral_common.tokens.tokenizers.sentencepiece import ( # type: ignore[import-not-found, ty:unresolved-import] + SentencePieceTokenizer, + ) +except ImportError: + _mistral_common_installed = False + MistralTokenizer: Any = None + Tekkenizer: Any = None + SentencePieceTokenizer: Any = None + _filter_valid_tokenizer_files: Any = None +else: + _mistral_common_installed = True + +try: + from mistral_common.tokens.tokenizers.utils import ( # type: ignore[import-not-found, ty:unresolved-import] + get_one_valid_tokenizer_file, + ) +except ImportError: + # We still want the conversion to work with older mistral-common versions. + get_one_valid_tokenizer_file: Any = None + + +import gguf + +from .gguf_writer import GGUFWriter + +logger = logging.getLogger(__name__) + + +class SpecialVocab: + merges: list[str] + add_special_token: dict[str, bool] + special_token_ids: dict[str, int] + chat_template: str | Sequence[Mapping[str, str]] | None + normalizer_lowercase: bool | None + normalizer_strip_accents: bool | None + + def __init__( + self, path: str | os.PathLike[str], load_merges: bool = False, + special_token_types: Iterable[str] | None = None, + n_vocab: int | None = None, + ): + self.special_token_ids = {} + self.add_special_token = {} + self.n_vocab = n_vocab + self.load_merges = load_merges + self.merges = [] + self.chat_template = None + self.normalizer_lowercase = None + self.normalizer_strip_accents = None + if special_token_types is not None: + self.special_token_types = special_token_types + else: + self.special_token_types = ('bos', 'eos', 'unk', 'sep', 'pad', 'cls', 'mask') + self._load(Path(path)) + + def __repr__(self) -> str: + return ''.format( + len(self.merges), self.special_token_ids or "unset", self.add_special_token or "unset", + ) + + def add_to_gguf(self, gw: GGUFWriter, quiet: bool = False) -> None: + if self.merges: + if not quiet: + logger.info(f'Adding {len(self.merges)} merge(s).') + gw.add_token_merges(self.merges) + elif self.load_merges: + logger.warning('Adding merges requested but no merges found, output may be non-functional.') + for typ, tokid in self.special_token_ids.items(): + id_handler: Callable[[int], None] | None = getattr(gw, f'add_{typ}_token_id', None) + if id_handler is None: + logger.warning(f'No handler for special token type {typ} with id {tokid} - skipping') + continue + if not quiet: + logger.info(f'Setting special token type {typ} to {tokid}') + id_handler(tokid) + for typ, value in self.add_special_token.items(): + add_handler: Callable[[bool], None] | None = getattr(gw, f'add_add_{typ}_token', None) + if add_handler is None: + logger.warning(f'No handler for add_{typ}_token with value {value} - skipping') + continue + if not quiet: + logger.info(f'Setting add_{typ}_token to {value}') + add_handler(value) + if self.chat_template is not None: + if not quiet: + logger.info(f'Setting chat_template to {self.chat_template}') + gw.add_chat_template(self.chat_template) + if self.normalizer_lowercase is not None: + if not quiet: + logger.info(f'Setting normalizer_lowercase to {self.normalizer_lowercase}') + gw.add_normalizer_lowercase(self.normalizer_lowercase) + if self.normalizer_strip_accents is not None: + if not quiet: + logger.info(f'Setting normalizer_strip_accents to {self.normalizer_strip_accents}') + gw.add_normalizer_strip_accents(self.normalizer_strip_accents) + + def _load(self, path: Path) -> None: + self._try_load_from_tokenizer_json(path) + self._try_load_from_config_json(path) + if self.load_merges and not self.merges: + self._try_load_merges_txt(path) + + def _try_load_merges_txt(self, path: Path) -> bool: + merges_file = path / 'merges.txt' + if not merges_file.is_file(): + return False + with open(merges_file, 'r', encoding = 'utf-8') as fp: + first_line = next(fp, '').strip() + if not first_line.startswith('#'): + fp.seek(0) + line_num = 0 + else: + line_num = 1 + merges = [] + for line in fp: + line_num += 1 + line = line.strip() + if not line: + continue + parts = line.split(None, 3) + if len(parts) != 2: + logger.warning(f'{merges_file.name}: Line {line_num}: Entry malformed, ignoring') + continue + merges.append(f'{parts[0]} {parts[1]}') + self.merges = merges + return True + + def _set_special_token(self, typ: str, tid: Any) -> None: + if not isinstance(tid, int): + return + if tid < 0: + raise ValueError(f'invalid value for special token type {typ}: {tid}') + if self.n_vocab is None or tid < self.n_vocab: + if typ in self.special_token_ids: + return + self.special_token_ids[typ] = tid + return + logger.warning(f'Special token type {typ}, id {tid} out of range, must be under {self.n_vocab} - skipping') + + def _parse_normalizer(self, normalizer: dict) -> None: + # ref: https://huggingface.co/docs/tokenizers/api/normalizers + # + # Extracts normalizer flags from three possible formats: + # 1. Standalone: {"type": "Lowercase"} + # 2. BertNormalizer attrs: {"type": "BertNormalizer", ...} + # 3. Nested in Sequence: {"type": "Sequence", "normalizers": [...]} + + normalizer_type = normalizer.get('type') + if normalizer_type == 'Lowercase': + self.normalizer_lowercase = True + elif normalizer_type == 'StripAccents': + self.normalizer_strip_accents = True + elif normalizer_type == 'BertNormalizer': + if 'lowercase' in normalizer: + self.normalizer_lowercase = normalizer['lowercase'] + if 'strip_accents' in normalizer: + self.normalizer_strip_accents = normalizer['strip_accents'] + elif normalizer_type == 'Sequence': + for norm in normalizer.get('normalizers', []): + self._parse_normalizer(norm) + + def _try_load_from_tokenizer_json(self, path: Path) -> bool: + tokenizer = None + tokenizer_file = path / 'tokenizer.json' + if tokenizer_file.is_file(): + with open(tokenizer_file, encoding = 'utf-8') as f: + tokenizer = json.load(f) + if self.load_merges: + merges = tokenizer.get('model', {}).get('merges') + if isinstance(merges, list) and merges: + if isinstance(merges[0], str): + self.merges = merges + elif isinstance(merges[0], list) and len(merges[0]) == 2 and isinstance(merges[0][0], str): + # New format since transformers 4.45 to support spaces in merges + # ref: https://github.com/ggml-org/llama.cpp/issues/9692 + # TODO: internally store as the new format instead of converting to old + if any(' ' in s for pair in merges for s in pair): + logger.warning(f'Spaces in merges detected, encoding as {chr(ord(" ") + 256)!r}') + self.merges = [ + ' '.join( + [ + # ensure the spaces are properly encoded + ''.join( + chr(ord(c) + 256) if c == ' ' else c + for c in part + ) + for part in pair + ] + ) + for pair in merges + ] + else: + raise ValueError("Unknown tokenizer merges format") + # Parse normalizer configuration (e.g. Lowercase) into metadata + if normalizer := tokenizer.get('normalizer'): + self._parse_normalizer(normalizer) + added_tokens = tokenizer.get('added_tokens', {}) + else: + added_tokens = {} + tokenizer_config = None + tokenizer_config_file = path / 'tokenizer_config.json' + if tokenizer_config_file.is_file(): + with open(tokenizer_config_file, encoding = 'utf-8') as f: + tokenizer_config = json.load(f) + if tokenizer: + special_bos = (tokenizer_config or {}).get('bos_token') + special_cls = (tokenizer_config or {}).get('cls_token') + special_eos = (tokenizer_config or {}).get('eos_token') + special_sep = (tokenizer_config or {}).get('sep_token') + if not special_bos and special_cls and tokenizer_config: + tokenizer_config['bos_token'] = special_bos = special_cls + if not special_eos and special_sep and tokenizer_config: + tokenizer_config['eos_token'] = special_eos = special_sep + if post_processor := tokenizer.get('post_processor'): + for processor in post_processor.get('processors', [post_processor]): + if processor.get('type') == 'RobertaProcessing': + self.add_special_token['bos'] = True + self.add_special_token['eos'] = True + self.add_special_token['sep'] = True + if not special_cls and tokenizer_config: + special_cls = processor.get('cls', [special_bos])[0] + tokenizer_config['cls_token'] = special_cls + if not special_sep and tokenizer_config: + special_sep = processor.get('sep', [special_eos])[0] + tokenizer_config['sep_token'] = special_sep + continue + # Crude parsing of TemplateProcessing to determine if BOS/SEP/EOS should be added + # Only works with simple templates, **will** get it wrong on unusual sequences + if processor.get('type') == 'TemplateProcessing': + tmpl_single = processor.get('single', []) + tmpl_pair = processor.get('pair', []) + special_first = None + special_last = None + if len(tmpl_single) > 1: + if special_first := tmpl_single[0].get('SpecialToken', {}).get('id'): + if not tokenizer_config: + special_bos = special_first + elif special_first not in (special_bos, special_cls): + if not special_bos: + tokenizer_config['bos_token'] = special_bos = special_first + if not special_cls: + tokenizer_config['cls_token'] = special_cls = special_first + self.add_special_token['bos'] = True if special_first in (special_bos, special_cls) else False + if special_first not in (special_bos, special_cls): + logger.warning(f'Unknown leading special token {special_first!r} in TemplateProcessing') + if special_last := tmpl_single[-1].get('SpecialToken', {}).get('id'): + if not tokenizer_config: + special_eos = special_last + elif special_last != special_eos: + if 'eot' not in self.special_token_types: + self.special_token_types = tuple(self.special_token_types) + ('eot', ) + tokenizer_config['eot_token'] = special_eos + elif 'eom' not in self.special_token_types: + self.special_token_types = tuple(self.special_token_types) + ('eom', ) + tokenizer_config['eom_token'] = special_eos + else: + logger.warning(f'Overriding EOS token {special_eos!r} with {special_last!r} without EOT/EOM fallback!') + tokenizer_config['eos_token'] = special_eos = special_last + self.add_special_token['eos'] = True if special_last == special_eos else False + if special_last != special_eos: + logger.warning(f'Unknown trailing special token {special_last!r} in TemplateProcessing') + if tmpl_pair: + seq_start = 1 if special_first and tmpl_pair[0].get('SpecialToken', {}).get('id') == special_first else 0 + seq_stop = -1 if special_last and tmpl_pair[-1].get('SpecialToken', {}).get('id') == special_last else None + if (special_first and seq_start == 0) or (special_last and seq_stop is None): + logger.warning('TemplateProcessing leading/trailing special tokens do not match TemplateProcessing') + if tmpl_pair := tmpl_pair[slice(seq_start, seq_stop)]: + tmpl_a = tmpl_pair[0].get('Sequence', {}).get('id') + tmpl_b = tmpl_pair[-1].get('Sequence', {}).get('id') + if tmpl_a != 'A' or tmpl_b != 'B': + logger.warning(f'Unknown sequence {tmpl_a}...{tmpl_b} in TemplateProcessing') + # A [sep] [eos] B + if tmpl_a == 'A' and tmpl_b == 'B' and (tmpl_pair := tmpl_pair[1:-1]): + add_sep = False + if special_entry := tmpl_pair[0].get('SpecialToken', {}).get('id'): + if special_entry in (special_sep, special_eos) and not special_last: + add_sep = True + if special_entry not in (special_sep, special_eos): + logger.warning(f'Unknown separator token {special_entry!r} in TemplateProcessing') + else: + logger.warning(f'Unknown middle sequence {tmpl_pair[0]!r} in TemplateProcessing') + if len(tmpl_pair) == 2: + if special_entry := tmpl_pair[1].get('SpecialToken', {}).get('id'): + if special_entry in (special_sep, special_eos): + add_sep = True + if special_entry not in (special_sep, special_eos): + logger.warning(f'Unknown second separator token {special_entry!r} in TemplateProcessing') + else: + logger.warning(f'Unknown second middle sequence {tmpl_pair[1]!r} in TemplateProcessing') + self.add_special_token['sep'] = add_sep + if add_sep and not special_sep and tokenizer_config: + tokenizer_config['sep_token'] = special_eos + continue + if not tokenizer_config: + return True + chat_template_alt = None + chat_template_json = path / 'chat_template.json' + chat_template_jinja = path / 'chat_template.jinja' + if chat_template_jinja.is_file(): + with open(chat_template_jinja, encoding = 'utf-8') as f: + chat_template_alt = f.read() + if additional_templates := list((path / 'additional_chat_templates').glob('*.jinja')): + chat_template_alt = [{'name': 'default', 'template': chat_template_alt}] + for template_path in additional_templates: + with open(template_path, encoding = 'utf-8') as fp: + chat_template_alt.append({'name': template_path.stem, 'template': fp.read()}) + elif chat_template_json.is_file(): + with open(chat_template_json, encoding = 'utf-8') as f: + chat_template_alt = json.load(f).get('chat_template') + chat_template = tokenizer_config.get('chat_template', chat_template_alt) + if chat_template is None or isinstance(chat_template, (str, list)): + self.chat_template = chat_template + else: + logger.warning(f'Bad type for chat_template field in {tokenizer_config_file!r} - ignoring') + for typ in self.special_token_types: + add_entry = tokenizer_config.get(f'add_{typ}_token') + if isinstance(add_entry, bool): + self.add_special_token[typ] = add_entry + entry = tokenizer_config.get(f'{typ}_token') + if isinstance(entry, str): + tc_content = entry + elif isinstance(entry, dict): + entry_content = entry.get('content') + if not isinstance(entry_content, str): + continue + tc_content = entry_content + else: + continue + # We only need the first match here. + maybe_token_id = next( + (atok.get('id') for atok in added_tokens if atok.get('content') == tc_content), + None, + ) + self._set_special_token(typ, maybe_token_id) + return True + + def _try_load_from_config_json(self, path: Path) -> bool: + config_file = path / 'config.json' + if not config_file.is_file(): + return False + with open(config_file, encoding = 'utf-8') as f: + config = json.load(f) + for typ in self.special_token_types: + token_id = config.get(f'{typ}_token_id') + # If not found at root, check in text_config (for multimodal models like Kimi-VL) + if token_id is None and 'text_config' in config: + token_id = config['text_config'].get(f'{typ}_token_id') + self._set_special_token(typ, token_id) + return True + + +@runtime_checkable +class BaseVocab(Protocol): + tokenizer_model: ClassVar[str] + name: ClassVar[str] + + +@runtime_checkable +class Vocab(BaseVocab, Protocol): + vocab_size: int + added_tokens_dict: dict[str, int] + added_tokens_list: list[str] + fname_tokenizer: Path + + def __init__(self, base_path: Path): ... + def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: ... + + +class NoVocab(BaseVocab): + tokenizer_model = "no_vocab" + name = "no_vocab" + + def __repr__(self) -> str: + return "" + + +class BpeVocab(Vocab): + tokenizer_model = "gpt2" + name = "bpe" + + def __init__(self, base_path: Path): + added_tokens: dict[str, int] = {} + + if (fname_tokenizer := base_path / 'vocab.json').exists(): + # "slow" tokenizer + with open(fname_tokenizer, encoding="utf-8") as f: + self.vocab = json.load(f) + + try: + # FIXME: Verify that added tokens here _cannot_ overlap with the main vocab. + with open(base_path / 'added_tokens.json', encoding="utf-8") as f: + added_tokens = json.load(f) + except FileNotFoundError: + pass + else: + # "fast" tokenizer + fname_tokenizer = base_path / 'tokenizer.json' + + # if this fails, FileNotFoundError propagates to caller + with open(fname_tokenizer, encoding="utf-8") as f: + tokenizer_json = json.load(f) + + tokenizer_model: dict[str, Any] = tokenizer_json['model'] + if ( + tokenizer_model['type'] != 'BPE' or tokenizer_model.get('byte_fallback', False) + or tokenizer_json['decoder']['type'] != 'ByteLevel' + ): + raise FileNotFoundError('Cannot find GPT-2 BPE tokenizer') + + self.vocab = tokenizer_model["vocab"] + + if (added := tokenizer_json.get('added_tokens')) is not None: + # Added tokens here can be duplicates of the main vocabulary. + added_tokens = {item['content']: item['id'] + for item in added + if item['content'] not in self.vocab} + + vocab_size = len(self.vocab) + expected_ids = list(range(vocab_size, vocab_size + len(added_tokens))) + actual_ids = sorted(added_tokens.values()) + if expected_ids != actual_ids: + expected_end_id = vocab_size + len(actual_ids) - 1 + raise ValueError(f"Expected the {len(actual_ids)} added token ID(s) to be sequential in the range " + f"{vocab_size} - {expected_end_id}; got {actual_ids}") + + items = sorted(added_tokens.items(), key=lambda text_idx: text_idx[1]) + self.added_tokens_dict = added_tokens + self.added_tokens_list = [text for (text, idx) in items] + self.vocab_size_base = vocab_size + self.vocab_size = self.vocab_size_base + len(self.added_tokens_list) + self.fname_tokenizer = fname_tokenizer + + def bpe_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: + reverse_vocab = {id: encoded_tok for encoded_tok, id in self.vocab.items()} + + for i, _ in enumerate(self.vocab): + yield reverse_vocab[i], 0.0, gguf.TokenType.NORMAL + + def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: + for text in self.added_tokens_list: + score = -1000.0 + yield text.encode("utf-8"), score, gguf.TokenType.CONTROL + + def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: + yield from self.bpe_tokens() + yield from self.added_tokens() + + def __repr__(self) -> str: + return f"" + + +class SentencePieceVocab(Vocab): + tokenizer_model = "llama" + name = "spm" + + def __init__(self, base_path: Path): + if SentencePieceProcessor is None: + raise RuntimeError("sentencepiece is not installed") + + added_tokens: dict[str, int] = {} + if (fname_tokenizer := base_path / 'tokenizer.model').exists(): + # normal location + try: + with open(base_path / 'added_tokens.json', encoding="utf-8") as f: + added_tokens = json.load(f) + except FileNotFoundError: + pass + elif not (fname_tokenizer := base_path.parent / 'tokenizer.model').exists(): + # not found in alternate location either + raise FileNotFoundError('Cannot find tokenizer.model') + + self.sentencepiece_tokenizer = SentencePieceProcessor() + self.sentencepiece_tokenizer.LoadFromFile(str(fname_tokenizer)) + vocab_size = self.sentencepiece_tokenizer.vocab_size() + + new_tokens = {id: piece for piece, id in added_tokens.items() if id >= vocab_size} + expected_new_ids = list(range(vocab_size, vocab_size + len(new_tokens))) + actual_new_ids = sorted(new_tokens.keys()) + + if expected_new_ids != actual_new_ids: + raise ValueError(f"Expected new token IDs {expected_new_ids} to be sequential; got {actual_new_ids}") + + # Token pieces that were added to the base vocabulary. + self.added_tokens_dict = added_tokens + self.added_tokens_list = [new_tokens[id] for id in actual_new_ids] + self.vocab_size_base = vocab_size + self.vocab_size = self.vocab_size_base + len(self.added_tokens_list) + self.fname_tokenizer = fname_tokenizer + + def sentencepiece_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: + tokenizer = self.sentencepiece_tokenizer + for i in range(tokenizer.vocab_size()): + piece = tokenizer.IdToPiece(i) + text = piece.encode("utf-8") + score: float = tokenizer.GetScore(i) + + toktype = gguf.TokenType.NORMAL + if tokenizer.IsUnknown(i): + toktype = gguf.TokenType.UNKNOWN + if tokenizer.IsControl(i): + toktype = gguf.TokenType.CONTROL + + # NOTE: I think added_tokens are user defined. + # ref: https://github.com/google/sentencepiece/blob/master/src/sentencepiece_model.proto + # if tokenizer.is_user_defined(i): toktype = gguf.TokenType.USER_DEFINED + + if tokenizer.IsUnused(i): + toktype = gguf.TokenType.UNUSED + if tokenizer.IsByte(i): + toktype = gguf.TokenType.BYTE + + yield text, score, toktype + + def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: + for text in self.added_tokens_list: + score = -1000.0 + yield text.encode("utf-8"), score, gguf.TokenType.USER_DEFINED + + def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: + yield from self.sentencepiece_tokens() + yield from self.added_tokens() + + def __repr__(self) -> str: + return f"" + + +class LlamaHfVocab(Vocab): + tokenizer_model = "llama" + name = "hfft" + + def __init__(self, base_path: Path): + fname_tokenizer = base_path / 'tokenizer.json' + # if this fails, FileNotFoundError propagates to caller + with open(fname_tokenizer, encoding='utf-8') as f: + tokenizer_json = json.load(f) + + # pre-check so we know if we need transformers + tokenizer_model: dict[str, Any] = tokenizer_json['model'] + is_llama3 = ( + tokenizer_model['type'] == 'BPE' and tokenizer_model.get('ignore_merges', False) + and not tokenizer_model.get('byte_fallback', True) + ) + if is_llama3: + raise TypeError('Llama 3 must be converted with BpeVocab') + + if not is_llama3 and ( + tokenizer_model['type'] != 'BPE' or not tokenizer_model.get('byte_fallback', False) + or tokenizer_json['decoder']['type'] != 'Sequence' + ): + raise FileNotFoundError('Cannot find Llama BPE tokenizer') + + try: + from transformers import AutoTokenizer + except ImportError as e: + raise ImportError( + "To use LlamaHfVocab, please install the `transformers` package. " + "You can install it with `pip install transformers`." + ) from e + + # Allow the tokenizer to default to slow or fast versions. + # Explicitly set tokenizer to use local paths. + self.tokenizer = AutoTokenizer.from_pretrained( + base_path, + cache_dir=base_path, + local_files_only=True, + ) + assert self.tokenizer.is_fast # assume tokenizer.json is used # ty: ignore[unresolved-attribute] + + # Initialize lists and dictionaries for added tokens + self.added_tokens_list = [] + self.added_tokens_dict = dict() + self.added_tokens_ids = set() + + # Process added tokens + for tok, tokidx in sorted( + self.tokenizer.get_added_vocab().items(), key=lambda x: x[1] # ty: ignore[unresolved-attribute] + ): + # Only consider added tokens that are not in the base vocabulary + if tokidx >= self.tokenizer.vocab_size: # ty: ignore[unresolved-attribute] + self.added_tokens_list.append(tok) + self.added_tokens_dict[tok] = tokidx + self.added_tokens_ids.add(tokidx) + + # Store special tokens and their IDs + self.specials = { + tok: self.tokenizer.get_vocab()[tok] # ty: ignore[unresolved-attribute] + for tok in self.tokenizer.all_special_tokens # ty: ignore[unresolved-attribute] + } + self.special_ids = set(self.tokenizer.all_special_ids) # ty: ignore[unresolved-attribute] + + # Set vocabulary sizes + self.vocab_size_base = self.tokenizer.vocab_size # ty: ignore[unresolved-attribute] + self.vocab_size = self.vocab_size_base + len(self.added_tokens_list) + + self.fname_tokenizer = fname_tokenizer + + def hf_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: + reverse_vocab = { + id: encoded_tok for encoded_tok, id in self.tokenizer.get_vocab().items() # ty: ignore[unresolved-attribute] + } + + for token_id in range(self.vocab_size_base): + # Skip processing added tokens here + if token_id in self.added_tokens_ids: + continue + + # Convert token text to bytes + token_text = reverse_vocab[token_id].encode("utf-8") + + # Yield token text, score, and type + yield token_text, self.get_token_score(token_id), self.get_token_type( + token_id, token_text, self.special_ids # Reuse already stored special IDs + ) + + def get_token_type(self, token_id: int, token_text: bytes, special_ids: set[int]) -> gguf.TokenType: + # Special case for byte tokens + if re.fullmatch(br"<0x[0-9A-Fa-f]{2}>", token_text): + return gguf.TokenType.BYTE + + # Determine token type based on whether it's a special token + return gguf.TokenType.CONTROL if token_id in special_ids else gguf.TokenType.NORMAL + + def get_token_score(self, token_id: int) -> float: + # Placeholder for actual logic to determine the token's score + # This needs to be implemented based on specific requirements + return -1000.0 # Default score + + def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: + for text in self.added_tokens_list: + if text in self.specials: + toktype = self.get_token_type(self.specials[text], b'', self.special_ids) + score = self.get_token_score(self.specials[text]) + else: + toktype = gguf.TokenType.USER_DEFINED + score = -1000.0 + + yield text.encode("utf-8"), score, toktype + + def has_newline_token(self): + return "<0x0A>" in self.tokenizer.vocab or "\n" in self.tokenizer.vocab # ty: ignore[unresolved-attribute] + + def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: + yield from self.hf_tokens() + yield from self.added_tokens() + + def __repr__(self) -> str: + return f"" + + +class MistralTokenizerType(str, Enum): + spm = "spm" + tekken = "tekken" + + +# Copied from Transformers (Apache 2.0) +# https://github.com/huggingface/transformers/blob/main/src/transformers/convert_slow_tokenizer.py#L1544 + +def bytes_to_unicode() -> dict[int, str]: + """ + Returns list of utf-8 byte and a mapping to unicode strings. We specifically avoids mapping to whitespace/control + characters the bpe code barfs on. + + The reversible bpe codes work on unicode strings. This means you need a large # of unicode characters in your vocab + if you want to avoid UNKs. When you're at something like a 10B token dataset you end up needing around 5K for + decent coverage. This is a significant percentage of your normal, say, 32K bpe vocab. To avoid that, we want lookup + tables between utf-8 bytes and unicode strings. + """ + bs = ( + list(range(ord("!"), ord("~") + 1)) + + list(range(ord("¡"), ord("¬") + 1)) + + list(range(ord("®"), ord("ÿ") + 1)) + ) + cs = bs[:] + n = 0 + for b in range(2**8): + if b not in bs: + bs.append(b) + cs.append(2**8 + n) + n += 1 + cs_str = [chr(n) for n in cs] + return dict(zip(bs, cs_str)) + + +class MistralVocab(Vocab): + tokenizer_model = "mistral" + name = "mistral" + + added_tokens_dict: dict[str, int] = {} + added_tokens_list: list[str] = [] + + def __init__(self, base_path: Path): + if not _mistral_common_installed: + raise ImportError( + "To use MistralVocab, please install the `mistral-common` package. " + "You can install it with `pip install mistral-common`." + ) + assert _filter_valid_tokenizer_files is not None, "mistral_common is not installed" + assert MistralTokenizer is not None, "mistral_common is not installed" + assert Tekkenizer is not None, "mistral_common is not installed" + + logger.info(f"Loading Mistral tokenizer from {base_path}") + + # Find the tokenizer files + all_files = [f.as_posix() for f in base_path.glob("**/*") if f.is_file()] + + if get_one_valid_tokenizer_file is not None: + tokenizer_file_path = get_one_valid_tokenizer_file(all_files) + else: + valid_tokenizer_files = _filter_valid_tokenizer_files(all_files) + + if len(valid_tokenizer_files) == 0: + raise ValueError(f"No tokenizer file found in the directory: {base_path}") + # If there are multiple tokenizer files, we use tekken.json if it exists, otherwise the versioned one. + if len(valid_tokenizer_files) > 1: + if "tekken.json" in valid_tokenizer_files: + tokenizer_file = "tekken.json" + else: + tokenizer_file = sorted(valid_tokenizer_files)[-1] + logger.warning( + f"Multiple tokenizer files found in {base_path}. Using {tokenizer_file}" + ) + else: + tokenizer_file = valid_tokenizer_files[0] + + tokenizer_file_path = base_path / tokenizer_file + + self.tokenizer: Any = MistralTokenizer.from_file( + tokenizer_file_path + ).instruct_tokenizer.tokenizer + self.tokenizer_type = ( + MistralTokenizerType.tekken + if isinstance(self.tokenizer, Tekkenizer) + else MistralTokenizerType.spm + ) + self.vocab_size = self.tokenizer.n_words + self.fname_tokenizer = tokenizer_file_path + self._name = ( + "mistral-" + self.tokenizer_type.value + "-" + self.tokenizer.version + ) + + @property + def tokenizer_name(self) -> str: + return self._name + + @property + def gguf_tokenizer_model(self) -> str: + return "llama" if self.tokenizer_type == MistralTokenizerType.spm else "gpt2" + + def _sentencepiece_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: + assert SentencePieceTokenizer is not None, "mistral_common is not installed" + assert isinstance(self.tokenizer, SentencePieceTokenizer), ( + f"Expected SentencePieceTokenizer, got {type(self.tokenizer)}" + ) + + for i in range(self.tokenizer._model.vocab_size()): + piece = self.tokenizer._model.IdToPiece(i) + text = piece.encode("utf-8") + score: float = self.tokenizer._model.GetScore(i) + + toktype = gguf.TokenType.NORMAL + if self.tokenizer._model.IsUnknown(i): + toktype = gguf.TokenType.UNKNOWN + if self.tokenizer._model.IsControl(i): + toktype = gguf.TokenType.CONTROL + + if self.tokenizer._model.IsUnused(i): + toktype = gguf.TokenType.UNUSED + if self.tokenizer._model.IsByte(i): + toktype = gguf.TokenType.BYTE + + yield text, score, toktype + + def _tekken_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: + assert Tekkenizer is not None, "mistral_common is not installed" + assert isinstance(self.tokenizer, Tekkenizer), ( + f"Expected Tekkenizer, got {type(self.tokenizer)}" + ) + + byte_encoder = bytes_to_unicode() + for token_id in range(self.tokenizer.num_special_tokens): + yield ( + self.tokenizer.id_to_piece(token_id).encode("utf-8"), + 0, + gguf.TokenType.CONTROL + ) + for token in self.tokenizer._tekken_token2id_nospecial: + yield ( + self.token_bytes_to_string(token, byte_encoder).encode("utf-8"), + 0, + gguf.TokenType.NORMAL, + ) + + def get_token_id(self, token: str) -> int: + assert SentencePieceTokenizer is not None and Tekkenizer is not None, "mistral_common is not installed" + if self.tokenizer_type == MistralTokenizerType.spm: + assert isinstance(self.tokenizer, SentencePieceTokenizer) + return self.tokenizer._vocab.index(token) + elif self.tokenizer_type == MistralTokenizerType.tekken: + assert isinstance(self.tokenizer, Tekkenizer) + return ( + self.tokenizer._vocab.index(token) + self.tokenizer.num_special_tokens + ) + else: + raise ValueError(f"Unknown tokenizer type: {self.tokenizer_type}") + + @property + def bos_id(self) -> int: + return self.tokenizer.bos_id + + @property + def eos_id(self) -> int: + return self.tokenizer.eos_id + + @property + def pad_id(self) -> int: + if self.tokenizer.pad_id == -1: + return self.eos_id + return self.tokenizer.pad_id + + @property + def unk_id(self) -> int: + return self.tokenizer.unk_id + + @property + def bos_token(self) -> str: + return self.tokenizer.id_to_piece(self.tokenizer.bos_id) + + @property + def eos_token(self) -> str: + return self.tokenizer.id_to_piece(self.tokenizer.eos_id) + + @property + def pad_token(self) -> str: + return self.tokenizer.id_to_piece(self.tokenizer.pad_id) + + @property + def unk_token(self) -> str: + return self.tokenizer.id_to_piece(self.tokenizer.unk_id) + + def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: + if self.tokenizer_type == MistralTokenizerType.spm: + yield from self._sentencepiece_tokens() + + elif self.tokenizer_type == MistralTokenizerType.tekken: + yield from self._tekken_tokens() + + else: + raise ValueError(f"Unknown tokenizer type: {self.tokenizer_type}") + + @staticmethod + def token_bytes_to_string(b, byte_encoder): + return "".join([byte_encoder[ord(char)] for char in b.decode("latin-1")]) + + def extract_vocab_merges_from_model(self): + # Adapted from Transformers (Apache 2.0) + # https://github.com/huggingface/transformers/blob/main/src/transformers/convert_slow_tokenizer.py + assert Tekkenizer is not None and isinstance(self.tokenizer, Tekkenizer), ( + f"Expected Tekkenizer, got {type(self.tokenizer)}" + ) + mergeable_ranks = self.tokenizer._model._mergeable_ranks + token_bytes_map = { + rank: token_bytes for token_bytes, rank in mergeable_ranks.items() + } + merge_pairs = [] + + # Sort vocab by rank to ensure correct merge order + for i in range(256, self.vocab_size - self.tokenizer.num_special_tokens): + merged_token = token_bytes_map[i] + local = [] + for j in range(1, len(merged_token)): + left = merged_token[:j] + right = merged_token[j:] + if ( + left in mergeable_ranks + and right in mergeable_ranks + and (left + right) in mergeable_ranks + ): + local.append((left, right, i)) + if not local: + raise ValueError( + f"Could not find valid merge for token at rank {i}: {merged_token.decode('latin-1')}" + ) + local = sorted( + local, + key=lambda x: (mergeable_ranks[x[0]], mergeable_ranks[x[1]]), + reverse=False, + ) + merge_pairs.extend(local) + merge_pairs = sorted(merge_pairs, key=lambda val: val[2], reverse=False) + + byte_encoder = bytes_to_unicode() + + decoded_merge_pairs = [ + [ + self.token_bytes_to_string(val[0], byte_encoder), + self.token_bytes_to_string(val[1], byte_encoder), + ] + for val in merge_pairs + ] + + merges = [ + " ".join( + [ + # ensure the spaces are properly encoded + "".join(chr(ord(c) + 256) if c == " " else c for c in part) + for part in pair + ] + ) + for pair in decoded_merge_pairs + ] + + return merges diff --git a/backend/llama.cpp/gguf-py/pyproject.toml b/backend/llama.cpp/gguf-py/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..d11c34a2186d7180345f0050c746df51043b5620 --- /dev/null +++ b/backend/llama.cpp/gguf-py/pyproject.toml @@ -0,0 +1,45 @@ +[project] +name = "gguf" +version = "0.19.0" +description = "Read and write ML models in GGUF for GGML" +keywords = ["ggml", "gguf", "llama.cpp"] +dynamic = ["classifiers"] +readme = "README.md" +authors = [{name = "GGML", email = "ggml@ggml.ai"}] +requires-python = '>=3.10' +dependencies = ['numpy (>=1.17)', 'tqdm (>=4.27)', 'pyyaml (>=5.1)', 'requests (>=2.25)'] +classifiers = [ + "Programming Language :: Python :: 3", + "License :: OSI Approved :: MIT License", + "Operating System :: OS Independent", +] + +[project.urls] +homepage = "https://ggml.ai" +repository = "https://github.com/ggml-org/llama.cpp" + +[project.scripts] +gguf-convert-endian = "gguf.scripts.gguf_convert_endian:main" +gguf-dump = "gguf.scripts.gguf_dump:main" +gguf-set-metadata = "gguf.scripts.gguf_set_metadata:main" +gguf-new-metadata = "gguf.scripts.gguf_new_metadata:main" +gguf-editor-gui = "gguf.scripts.gguf_editor_gui:main" + +[project.optional-dependencies] +gui = ['PySide6 (>=6.9,<7.0) ; python_version >= "3.9" and python_version < "3.14"'] + +[tool.poetry] +packages = [ + {include = "gguf"}, + {include = "gguf/py.typed"}, +] + +[tool.poetry.dependencies] +python = ">=3.10" + +[tool.poetry.group.dev.dependencies] +pytest = "^5.2" + +[build-system] +requires = ["poetry-core>=1.0.0"] +build-backend = "poetry.core.masonry.api" diff --git a/backend/llama.cpp/gguf-py/tests/__init__.py b/backend/llama.cpp/gguf-py/tests/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..d23ff9cb7380c5e0f9e246398f2ad0bee49e6981 --- /dev/null +++ b/backend/llama.cpp/gguf-py/tests/__init__.py @@ -0,0 +1 @@ +from .test_metadata import * diff --git a/backend/llama.cpp/gguf-py/tests/test_metadata.py b/backend/llama.cpp/gguf-py/tests/test_metadata.py new file mode 100644 index 0000000000000000000000000000000000000000..b77c563ff25efddbd958744cf7fbc743a495a083 --- /dev/null +++ b/backend/llama.cpp/gguf-py/tests/test_metadata.py @@ -0,0 +1,238 @@ +#!/usr/bin/env python3 + +import unittest +from pathlib import Path +import os +import sys + +# Necessary to load the local gguf package +if "NO_LOCAL_GGUF" not in os.environ and (Path(__file__).parent.parent.parent / 'gguf-py').exists(): + sys.path.insert(0, str(Path(__file__).parent.parent)) + +import gguf + + +class TestMetadataMethod(unittest.TestCase): + + def test_id_to_title(self): + self.assertEqual(gguf.Metadata.id_to_title("Mixtral-8x7B-Instruct-v0.1"), "Mixtral 8x7B Instruct v0.1") + self.assertEqual(gguf.Metadata.id_to_title("Meta-Llama-3-8B"), "Meta Llama 3 8B") + self.assertEqual(gguf.Metadata.id_to_title("hermes-2-pro-llama-3-8b-DPO"), "Hermes 2 Pro Llama 3 8b DPO") + + def test_get_model_id_components(self): + # This is the basic standard form with organization marker + self.assertEqual(gguf.Metadata.get_model_id_components("Mistral/Mixtral-8x7B-Instruct-v0.1"), + ('Mixtral-8x7B-Instruct-v0.1', "Mistral", 'Mixtral', 'Instruct', 'v0.1', '8x7B')) + + # Similar to basic standard form but without organization marker + self.assertEqual(gguf.Metadata.get_model_id_components("Mixtral-8x7B-Instruct-v0.1"), + ('Mixtral-8x7B-Instruct-v0.1', None, 'Mixtral', 'Instruct', 'v0.1', '8x7B')) + + # Missing version + self.assertEqual(gguf.Metadata.get_model_id_components("Mixtral-8x7B-Instruct"), + ('Mixtral-8x7B-Instruct', None, 'Mixtral', 'Instruct', None, '8x7B')) + + # Missing finetune + self.assertEqual(gguf.Metadata.get_model_id_components("Mixtral-8x7B-v0.1"), + ('Mixtral-8x7B-v0.1', None, 'Mixtral', None, 'v0.1', '8x7B')) + + # Base name and size label only + self.assertEqual(gguf.Metadata.get_model_id_components("Mixtral-8x7B"), + ('Mixtral-8x7B', None, 'Mixtral', None, None, '8x7B')) + + # Base name and version only + self.assertEqual(gguf.Metadata.get_model_id_components("Mixtral-v0.1"), + ('Mixtral-v0.1', None, 'Mixtral', None, 'v0.1', None)) + + ## Edge Cases ## + + # This is too ambiguous... best to err on caution and output nothing + self.assertEqual(gguf.Metadata.get_model_id_components("Mixtral"), + ('Mixtral', None, None, None, None, None)) + + # Basename has numbers mixed in and also size label provided. Must avoid capturing number in basename + self.assertEqual(gguf.Metadata.get_model_id_components("NousResearch/Meta-Llama-3-8B"), + ('Meta-Llama-3-8B', "NousResearch", 'Meta-Llama-3', None, None, '8B')) + + # Non standard naming + self.assertEqual(gguf.Metadata.get_model_id_components("Qwen1.5-MoE-A2.7B-Chat"), + ('Qwen1.5-MoE-A2.7B-Chat', None, 'Qwen1.5-MoE', 'Chat', None, 'A2.7B')) + + # Capture 'sub size labels' e.g. A14B in '57B-A14B' usually refers to activated params/weight count + self.assertEqual(gguf.Metadata.get_model_id_components("Qwen2-57B-A14B-Instruct"), + ('Qwen2-57B-A14B-Instruct', None, 'Qwen2', 'Instruct', None, '57B-A14B')) + + # Check that it can handle a real model id with no version code + # Note that 4k in this string is non standard and microsoft were referring to context length rather than weight count + self.assertEqual(gguf.Metadata.get_model_id_components("microsoft/Phi-3-mini-4k-instruct", 4 * 10**9), + ('Phi-3-mini-4k-instruct', 'microsoft', 'Phi-3', '4k-instruct', None, 'mini')) + + # There is some legitimate models with only thousands of parameters + self.assertEqual(gguf.Metadata.get_model_id_components("delphi-suite/stories-llama2-50k", 50 * 10**3), + ('stories-llama2-50k', 'delphi-suite', 'stories-llama2', None, None, '50K')) + + # Non standard and not easy to disambiguate + self.assertEqual(gguf.Metadata.get_model_id_components("DeepSeek-Coder-V2-Lite-Instruct"), + ('DeepSeek-Coder-V2-Lite-Instruct', None, 'DeepSeek-Coder-V2-Lite', 'Instruct', None, None)) + + # This is a real model_id where they append 2DPO to refer to Direct Preference Optimization + self.assertEqual(gguf.Metadata.get_model_id_components("crestf411/daybreak-kunoichi-2dpo-7b"), + ('daybreak-kunoichi-2dpo-7b', 'crestf411', 'daybreak-kunoichi', '2dpo', None, '7B')) + + # This is a real model id where the weight size has a decimal point + self.assertEqual(gguf.Metadata.get_model_id_components("Qwen2-0.5B-Instruct"), + ('Qwen2-0.5B-Instruct', None, 'Qwen2', 'Instruct', None, '0.5B')) + + # Uses an underscore in the size label + self.assertEqual(gguf.Metadata.get_model_id_components("smallcloudai/Refact-1_6B-fim"), + ('Refact-1_6B-fim', 'smallcloudai', 'Refact', 'fim', None, '1.6B')) + + # Uses Iter3 for the version + self.assertEqual(gguf.Metadata.get_model_id_components("UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3"), + ('Gemma-2-9B-It-SPPO-Iter3', 'UCLA-AGI', 'Gemma-2', 'It-SPPO', 'Iter3', '9B')) + + # Has two potential versions in the basename + self.assertEqual(gguf.Metadata.get_model_id_components("NousResearch/Hermes-2-Theta-Llama-3-8B"), + ('Hermes-2-Theta-Llama-3-8B', 'NousResearch', 'Hermes-2-Theta-Llama-3', None, None, '8B')) + + # Potential version in the basename + self.assertEqual(gguf.Metadata.get_model_id_components("SeaLLMs/SeaLLMs-v3-7B-Chat"), + ('SeaLLMs-v3-7B-Chat', 'SeaLLMs', 'SeaLLMs-v3', 'Chat', None, '7B')) + + # Underscore in the basename, and 1m for the context size + self.assertEqual(gguf.Metadata.get_model_id_components("internlm/internlm2_5-7b-chat-1m", 7 * 10**9), + ('internlm2_5-7b-chat-1m', 'internlm', 'internlm2_5', 'chat-1m', None, '7B')) + + # Version before the finetune name + self.assertEqual(gguf.Metadata.get_model_id_components("pszemraj/jamba-900M-v0.13-KIx2"), + ('jamba-900M-v0.13-KIx2', 'pszemraj', 'jamba', 'KIx2', 'v0.13', '900M')) + + # TODO: hf suffix which could be ignored but isn't + self.assertEqual(gguf.Metadata.get_model_id_components("state-spaces/mamba-2.8b-hf"), + ('mamba-2.8b-hf', 'state-spaces', 'mamba', 'hf', None, '2.8B')) + + # Two sizes, don't merge them, the other is the number of tokens on which it was trained + self.assertEqual(gguf.Metadata.get_model_id_components("abacaj/llama-161M-100B", 161 * 10**6), + ('llama-161M-100B', 'abacaj', 'llama', '100b', None, '161M')) + + # It's a trap, there is no size label + self.assertEqual(gguf.Metadata.get_model_id_components("SparseLLM/relu-100B", 1340 * 10**6), + ('relu-100B', 'SparseLLM', 'relu', '100b', None, None)) + + # Weird size notation + self.assertEqual(gguf.Metadata.get_model_id_components("bigscience/bloom-7b1-petals"), + ('bloom-7b1-petals', 'bigscience', 'bloom', 'petals', None, '7.1B')) + + # Ignore full-text size labels when there are number-based ones, and deduplicate size labels + self.assertEqual(gguf.Metadata.get_model_id_components("MaziyarPanahi/GreenNode-mini-7B-multilingual-v1olet-Mistral-7B-Instruct-v0.1"), + ('GreenNode-mini-7B-multilingual-v1olet-Mistral-7B-Instruct-v0.1', 'MaziyarPanahi', 'GreenNode-mini', 'multilingual-v1olet-Mistral-Instruct', 'v0.1', '7B')) + + # Instruct in a name without a size label + self.assertEqual(gguf.Metadata.get_model_id_components("mistralai/Mistral-Nemo-Instruct-2407"), + ('Mistral-Nemo-Instruct-2407', 'mistralai', 'Mistral-Nemo', 'Instruct', '2407', None)) + + # Non-obvious splitting relying on 'chat' keyword + self.assertEqual(gguf.Metadata.get_model_id_components("deepseek-ai/DeepSeek-V2-Chat-0628"), + ('DeepSeek-V2-Chat-0628', 'deepseek-ai', 'DeepSeek-V2', 'Chat', '0628', None)) + + # Multiple versions + self.assertEqual(gguf.Metadata.get_model_id_components("OpenGVLab/Mini-InternVL-Chat-2B-V1-5"), + ('Mini-InternVL-Chat-2B-V1-5', 'OpenGVLab', 'Mini-InternVL', 'Chat', 'V1-5', '2B')) + + # TODO: DPO in the name + self.assertEqual(gguf.Metadata.get_model_id_components("jondurbin/bagel-dpo-2.8b-v0.2"), + ('bagel-dpo-2.8b-v0.2', 'jondurbin', 'bagel-dpo', None, 'v0.2', '2.8B')) + + # DPO in name, but can't be used for the finetune to keep 'LLaMA-3' in the basename + self.assertEqual(gguf.Metadata.get_model_id_components("voxmenthe/SFR-Iterative-DPO-LLaMA-3-8B-R-unquantized"), + ('SFR-Iterative-DPO-LLaMA-3-8B-R-unquantized', 'voxmenthe', 'SFR-Iterative-DPO-LLaMA-3', 'R-unquantized', None, '8B')) + + # Too ambiguous + # TODO: should "base" be a 'finetune' or 'size_label'? + # (in this case it should be a size label, but other models use it to signal that they are not finetuned) + self.assertEqual(gguf.Metadata.get_model_id_components("microsoft/Florence-2-base"), + ('Florence-2-base', 'microsoft', None, None, None, None)) + + ## Invalid cases ## + + # Start with a dash and has dashes in rows + self.assertEqual(gguf.Metadata.get_model_id_components("mistralai/-Mistral--Nemo-Base-2407-"), + ('-Mistral--Nemo-Base-2407-', 'mistralai', 'Mistral-Nemo-Base', None, '2407', None)) + + ## LoRA ## + + self.assertEqual(gguf.Metadata.get_model_id_components("Llama-3-Instruct-abliteration-LoRA-8B"), + ('Llama-3-Instruct-abliteration-LoRA-8B', None, 'Llama-3', 'Instruct-abliteration-LoRA', None, '8B')) + + # Negative size --> output is a LoRA adapter --> prune "LoRA" out of the name to avoid redundancy with the suffix + self.assertEqual(gguf.Metadata.get_model_id_components("Llama-3-Instruct-abliteration-LoRA-8B", -1234), + ('Llama-3-Instruct-abliteration-LoRA-8B', None, 'Llama-3', 'Instruct-abliteration', None, '8B')) + + def test_apply_metadata_heuristic_from_model_card(self): + model_card = { + 'tags': ['Llama-3', 'instruct', 'finetune', 'chatml', 'DPO', 'RLHF', 'gpt4', 'synthetic data', 'distillation', 'function calling', 'json mode', 'axolotl'], + 'model-index': [{'name': 'Mixtral-8x7B-Instruct-v0.1', 'results': []}], + 'language': ['en'], + 'datasets': ['teknium/OpenHermes-2.5'], + 'widget': [{'example_title': 'Hermes 2 Pro', 'messages': [{'role': 'system', 'content': 'You are a sentient, superintelligent artificial general intelligence, here to teach and assist me.'}, {'role': 'user', 'content': 'Write a short story about Goku discovering kirby has teamed up with Majin Buu to destroy the world.'}]}], + 'base_model': ["EmbeddedLLM/Mistral-7B-Merge-14-v0", "janai-hq/trinity-v1"] + } + got = gguf.Metadata.apply_metadata_heuristic(gguf.Metadata(), model_card, None, None) + expect = gguf.Metadata() + expect.base_models=[{'name': 'Mistral 7B Merge 14 v0', 'organization': 'EmbeddedLLM', 'version': '14-v0', 'repo_url': 'https://huggingface.co/EmbeddedLLM/Mistral-7B-Merge-14-v0'}, {'name': 'Trinity v1', 'organization': 'Janai Hq', 'version': 'v1', 'repo_url': 'https://huggingface.co/janai-hq/trinity-v1'}] + expect.tags=['Llama-3', 'instruct', 'finetune', 'chatml', 'DPO', 'RLHF', 'gpt4', 'synthetic data', 'distillation', 'function calling', 'json mode', 'axolotl'] + expect.languages=['en'] + expect.datasets=[{'name': 'OpenHermes 2.5', 'organization': 'Teknium', 'version': '2.5', 'repo_url': 'https://huggingface.co/teknium/OpenHermes-2.5'}] + self.assertEqual(got, expect) + + # Base Model spec is inferred from model id + model_card = {'base_models': 'teknium/OpenHermes-2.5'} + expect = gguf.Metadata(base_models=[{'name': 'OpenHermes 2.5', 'organization': 'Teknium', 'version': '2.5', 'repo_url': 'https://huggingface.co/teknium/OpenHermes-2.5'}]) + got = gguf.Metadata.apply_metadata_heuristic(gguf.Metadata(), model_card, None, None) + self.assertEqual(got, expect) + + # Base Model spec is only url + model_card = {'base_models': ['https://huggingface.co/teknium/OpenHermes-2.5']} + expect = gguf.Metadata(base_models=[{'name': 'OpenHermes 2.5', 'organization': 'Teknium', 'version': '2.5', 'repo_url': 'https://huggingface.co/teknium/OpenHermes-2.5'}]) + got = gguf.Metadata.apply_metadata_heuristic(gguf.Metadata(), model_card, None, None) + self.assertEqual(got, expect) + + # Base Model spec is given directly + model_card = {'base_models': [{'name': 'OpenHermes 2.5', 'organization': 'Teknium', 'version': '2.5', 'repo_url': 'https://huggingface.co/teknium/OpenHermes-2.5'}]} + expect = gguf.Metadata(base_models=[{'name': 'OpenHermes 2.5', 'organization': 'Teknium', 'version': '2.5', 'repo_url': 'https://huggingface.co/teknium/OpenHermes-2.5'}]) + got = gguf.Metadata.apply_metadata_heuristic(gguf.Metadata(), model_card, None, None) + self.assertEqual(got, expect) + + # Dataset spec is inferred from model id + model_card = {'datasets': 'teknium/OpenHermes-2.5'} + expect = gguf.Metadata(datasets=[{'name': 'OpenHermes 2.5', 'organization': 'Teknium', 'version': '2.5', 'repo_url': 'https://huggingface.co/teknium/OpenHermes-2.5'}]) + got = gguf.Metadata.apply_metadata_heuristic(gguf.Metadata(), model_card, None, None) + self.assertEqual(got, expect) + + # Dataset spec is only url + model_card = {'datasets': ['https://huggingface.co/teknium/OpenHermes-2.5']} + expect = gguf.Metadata(datasets=[{'name': 'OpenHermes 2.5', 'organization': 'Teknium', 'version': '2.5', 'repo_url': 'https://huggingface.co/teknium/OpenHermes-2.5'}]) + got = gguf.Metadata.apply_metadata_heuristic(gguf.Metadata(), model_card, None, None) + self.assertEqual(got, expect) + + # Dataset spec is given directly + model_card = {'datasets': [{'name': 'OpenHermes 2.5', 'organization': 'Teknium', 'version': '2.5', 'repo_url': 'https://huggingface.co/teknium/OpenHermes-2.5'}]} + expect = gguf.Metadata(datasets=[{'name': 'OpenHermes 2.5', 'organization': 'Teknium', 'version': '2.5', 'repo_url': 'https://huggingface.co/teknium/OpenHermes-2.5'}]) + got = gguf.Metadata.apply_metadata_heuristic(gguf.Metadata(), model_card, None, None) + self.assertEqual(got, expect) + + def test_apply_metadata_heuristic_from_hf_parameters(self): + hf_params = {"_name_or_path": "./hermes-2-pro-llama-3-8b-DPO"} + got = gguf.Metadata.apply_metadata_heuristic(gguf.Metadata(), model_card=None, hf_params=hf_params, model_path=None) + expect = gguf.Metadata(name='Hermes 2 Pro Llama 3 8b DPO', finetune='DPO', basename='hermes-2-pro-llama-3', size_label='8B') + self.assertEqual(got, expect) + + def test_apply_metadata_heuristic_from_model_dir(self): + model_dir_path = Path("./hermes-2-pro-llama-3-8b-DPO") + got = gguf.Metadata.apply_metadata_heuristic(gguf.Metadata(), model_card=None, hf_params=None, model_path=model_dir_path) + expect = gguf.Metadata(name='Hermes 2 Pro Llama 3 8b DPO', finetune='DPO', basename='hermes-2-pro-llama-3', size_label='8B') + self.assertEqual(got, expect) + + +if __name__ == "__main__": + unittest.main() diff --git a/backend/llama.cpp/gguf-py/tests/test_quants.py b/backend/llama.cpp/gguf-py/tests/test_quants.py new file mode 100644 index 0000000000000000000000000000000000000000..9aa7c4ae2a5e1e84096e2617f9b29baed5eec539 --- /dev/null +++ b/backend/llama.cpp/gguf-py/tests/test_quants.py @@ -0,0 +1,248 @@ +#!/usr/bin/env python3 + +# Test gguf.quants so that it exactly matches the C implementation of the (de)quantization + +# NOTE: this is kind of a mess, but at least it worked for initially testing the Python implementations. + +from __future__ import annotations + +import argparse +from math import prod +import os +import sys +from pathlib import Path +import ctypes +import logging +import numpy as np + +# Necessary to load the local gguf package +if "NO_LOCAL_GGUF" not in os.environ and (Path(__file__).parent.parent.parent / 'gguf-py').exists(): + sys.path.insert(0, str(Path(__file__).parent.parent)) + +import gguf +from gguf.constants import GGMLQuantizationType + + +logger = logging.getLogger("test-quants") + + +c_float_p = ctypes.POINTER(ctypes.c_float) + + +class ggml_init_params(ctypes.Structure): + _fields_ = [ + ("mem_size", ctypes.c_size_t), + ("mem_buffer", ctypes.c_void_p), + ("no_alloc", ctypes.c_bool), + ] + + +class GGMLQuants: + libggml: ctypes.CDLL + + def __init__(self, libggml: Path): + self.libggml = ctypes.CDLL(str(libggml)) + self.libggml.ggml_quantize_chunk.restype = ctypes.c_size_t + # enum ggml_type type, + # const float * src, + # void * dst, + # int64_t start, + # int64_t nrows, + # int64_t n_per_row, + # const float * imatrix) { + self.libggml.ggml_quantize_chunk.argtypes = ( + ctypes.c_int, + ctypes.POINTER(ctypes.c_float), + ctypes.c_void_p, + ctypes.c_int64, + ctypes.c_int64, + ctypes.c_int64, + ctypes.POINTER(ctypes.c_float), + ) + + self.libggml.ggml_quantize_requires_imatrix.restype = ctypes.c_bool + self.libggml.ggml_quantize_requires_imatrix.argtypes = (ctypes.c_int,) + + for t in ( + "q4_0", "q4_1", "q5_0", "q5_1", "q8_0", + "q2_K", "q3_K", "q4_K", "q5_K", "q6_K", + "tq1_0", "tq2_0", + "mxfp4", + "nvfp4", + "iq2_xxs", "iq2_xs", "iq2_s", "iq3_xxs", "iq3_s", "iq1_s", "iq1_m", + "iq4_nl", "iq4_xs", + ): + dequant_func: ctypes._NamedFuncPointer = getattr(self.libggml, "dequantize_row_" + t) + dequant_func.restype = None + dequant_func.argtypes = (ctypes.c_void_p, ctypes.POINTER(ctypes.c_float), ctypes.c_int64) + + self.libggml.ggml_fp16_to_fp32_row.restype = None + self.libggml.ggml_fp16_to_fp32_row.argtypes = (ctypes.POINTER(ctypes.c_uint16), ctypes.POINTER(ctypes.c_float), ctypes.c_int64) + self.libggml.ggml_bf16_to_fp32_row.restype = None + self.libggml.ggml_bf16_to_fp32_row.argtypes = (ctypes.POINTER(ctypes.c_uint16), ctypes.POINTER(ctypes.c_float), ctypes.c_int64) + + self.libggml.ggml_init.argtypes = (ggml_init_params,) + + self.libggml.ggml_init(ggml_init_params(1 * 1024 * 1024, 0, False)) + + def dequantize(self, tensor: np.ndarray, qtype: GGMLQuantizationType) -> np.ndarray: + result = np.zeros(gguf.quant_shape_from_byte_shape(tensor.shape, qtype), dtype=np.float32, order="C") + if qtype == GGMLQuantizationType.F32: + # no-op + result = tensor.view(np.float32) + elif qtype == GGMLQuantizationType.F16: + self.libggml.ggml_fp16_to_fp32_row(tensor.ctypes.data_as(ctypes.POINTER(ctypes.c_uint16)), result.ctypes.data_as(c_float_p), result.size) + elif qtype == GGMLQuantizationType.BF16: + self.libggml.ggml_bf16_to_fp32_row(tensor.ctypes.data_as(ctypes.POINTER(ctypes.c_uint16)), result.ctypes.data_as(c_float_p), result.size) + else: + lw_qname = qtype.name.lower() + if lw_qname[-1] == "k": + lw_qname = lw_qname[:-1] + "K" + dequant_func: ctypes._NamedFuncPointer = getattr(self.libggml, "dequantize_row_" + lw_qname) + dequant_func(tensor.ctypes.data_as(ctypes.c_void_p), result.ctypes.data_as(c_float_p), result.size) + return result + + def quantize(self, data: np.ndarray, qtype: GGMLQuantizationType) -> np.ndarray: + result = np.zeros(gguf.quant_shape_to_byte_shape(data.shape, qtype), dtype=np.uint8, order="C") + if self.libggml.ggml_quantize_requires_imatrix(qtype.value): + # TODO: is a column-wise sum of squares appropriate? + qw = np.sum((data * data).reshape((-1, data.shape[-1])), axis=0).ctypes.data_as(c_float_p) + else: + qw = ctypes.cast(0, c_float_p) + result_size = self.libggml.ggml_quantize_chunk(qtype.value, data.ctypes.data_as(c_float_p), result.ctypes.data_as(ctypes.c_void_p), 0, prod(data.shape[:-1]), data.shape[-1], qw) + assert result.size == result_size + return result + + +def compare_tensors(t1: np.ndarray, t2: np.ndarray, qtype: GGMLQuantizationType) -> bool: + same = np.array_equal(t1, t2) + if same: + return True + else: + block_size, type_size = gguf.GGML_QUANT_SIZES[qtype] + if t1.dtype == np.float32: + t1 = t1.reshape((-1, block_size)) + t2 = t2.reshape((-1, block_size)) + else: + t1 = t1.reshape((-1, type_size)) + t2 = t2.reshape((-1, type_size)) + x = t1.view(np.uint8) ^ t2.view(np.uint8) + diff_bits = np.count_nonzero(np.unpackbits(x, axis=-1), axis=-1) + num_bad_blocks = np.count_nonzero(diff_bits, axis=0) + if num_bad_blocks == 0 and t1.shape == t2.shape: + logger.debug("Bits are equal, but arrays don't match, likely contains NANs") + return True + logger.debug(f"{num_bad_blocks} bad blocks ({100 * num_bad_blocks / x.shape[0]:.6f}%)") + bad_block_id = np.argmax(diff_bits, axis=0) + logger.debug(f"Worst block id: {bad_block_id}") + logger.debug(f"Sample bad block ({diff_bits[bad_block_id]} differing bits):\n{t1[bad_block_id]}\nReference:\n{t2[bad_block_id]}") + + sum_diff_bits = np.sum(diff_bits) + logger.debug(f"{sum_diff_bits} bits differ ({100 * sum_diff_bits / (x.size * 8):.6f}%)") + return False + + +def do_test(libggml_path: Path, quick: bool = False, user_type: GGMLQuantizationType | None = None): + ggml_quants = GGMLQuants(libggml_path) + + np.set_printoptions(precision=None, threshold=(4 * 256) + 1, formatter={"int": lambda n: "0x%02X" % n}) + + r = np.random.randn(8, 1024, 1024).astype(np.float32, copy=False) + # test zero blocks + r[0, 0, :] = 0 + ## Maybe test infinities? (can make NANs, not really useful in practice) + # r[0, 1, 0] = np.inf + # r[0, 2, 0] = -np.inf + # r[0, 3, 0] = np.inf + # r[0, 3, 1] = -np.inf + + for qtype in ((GGMLQuantizationType.F16, *gguf.quants._type_traits.keys()) if user_type is None else (user_type,)): + has_dequantize = False + has_quantize = False + + try: + gguf.dequantize(np.zeros((gguf.GGML_QUANT_SIZES[qtype][1]), dtype=np.uint8), qtype) + has_dequantize = True + except (NotImplementedError, AssertionError) as e: + if isinstance(e, AssertionError): + logger.error(f"Error with {qtype.name}: {e}") + raise e + try: + gguf.quantize(np.zeros((gguf.GGML_QUANT_SIZES[qtype][0]), dtype=np.float32), qtype) + has_quantize = True + except (NotImplementedError, AssertionError) as e: + if isinstance(e, AssertionError): + logger.error(f"Error with {qtype.name}: {e}") + raise e + + if not has_dequantize and not has_quantize: + continue + + logger.info(f"Testing {qtype.name}") + + rc = r.copy(order="C") + + pyq = None + ggq = None + + if has_quantize: + logger.debug(f"Quantizing to {qtype.name} with Python") + pyq = gguf.quants.quantize(rc, qtype) + + logger.debug(f"Quantizing to {qtype.name} with C") + ggq = ggml_quants.quantize(rc, qtype) + + if qtype == GGMLQuantizationType.F16: + pyq = pyq.view(np.uint8) + quant_equal = compare_tensors(pyq, ggq, qtype) + + if not quant_equal: + logger.error(f"Quantization to {qtype.name} does not match ❌") + else: + logger.info(f"Quantization to {qtype.name} matches exactly ✅") + + if has_dequantize: + if ggq is None and not quick: + logger.debug(f"Quantizing to {qtype.name} with C") + ggq = ggml_quants.quantize(rc, qtype) + + if ggq is not None: + logger.debug(f"Dequantizing from {qtype.name} with Python") + pydq = gguf.quants.dequantize(ggq, qtype) + logger.debug(f"Dequantizing from {qtype.name} with C") + ggdq = ggml_quants.dequantize(ggq, qtype) + + dequant_equal = compare_tensors(pydq, ggdq, qtype) + + if not dequant_equal: + logger.error(f"Dequantization from {qtype.name} does not match ❌") + else: + logger.info(f"Dequantization from {qtype.name} matches exactly ✅") + + rq_shape = gguf.quants.quant_shape_to_byte_shape((8, 1024, 1024 // 2), qtype) + rq = np.random.random(rq_shape).astype(np.float16).view(np.uint8) + + logger.debug(f"Dequantizing random f16 data as {qtype.name} with Python") + pydq = gguf.quants.dequantize(rq, qtype) + logger.debug(f"Dequantizing random f16 data as {qtype.name} with C") + ggdq = ggml_quants.dequantize(rq, qtype) + + dequant_equal = compare_tensors(pydq, ggdq, qtype) + + if not dequant_equal: + logger.error(f"Dequantization from random f16 data as {qtype.name} does not match ❌") + else: + logger.info(f"Dequantization from random f16 data as {qtype.name} matches exactly ✅") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Test Python (de)quantization against the reference C implementation") + parser.add_argument("--libggml", type=Path, default=Path(__file__).parent.parent.parent / "build" / "bin" / "libggml.so", help="The path to libggml.so") + parser.add_argument("--quick", action="store_true", help="Don't quantize with C when it's not strictly necessary") + parser.add_argument("--type", type=str, help="The quant type to test (all by default)") + + args = parser.parse_args() + + logging.basicConfig(level=logging.DEBUG) + + do_test(args.libggml, args.quick, GGMLQuantizationType[args.type.upper()] if args.type is not None else None) diff --git a/backend/llama.cpp/grammars/README.md b/backend/llama.cpp/grammars/README.md new file mode 100644 index 0000000000000000000000000000000000000000..9478b3e1b5aa8f8c621947083cc1edf8fdabe6af --- /dev/null +++ b/backend/llama.cpp/grammars/README.md @@ -0,0 +1,408 @@ +# GBNF Guide + +GBNF (GGML BNF) is a format for defining [formal grammars](https://en.wikipedia.org/wiki/Formal_grammar) to constrain model outputs in `llama.cpp`. For example, you can use it to force the model to generate valid JSON, or speak only in emojis. GBNF grammars are supported in various ways in `tools/cli`, `tools/completion` and `tools/server`. + +## Background + +[Backus-Naur Form (BNF)](https://en.wikipedia.org/wiki/Backus%E2%80%93Naur_form) is a notation for describing the syntax of formal languages like programming languages, file formats, and protocols. GBNF is an extension of BNF that primarily adds a few modern regex-like features. + +## Basics + +In GBNF, we define *production rules* that specify how a *non-terminal* (rule name) can be replaced with sequences of *terminals* (characters, specifically Unicode [code points](https://en.wikipedia.org/wiki/Code_point)) and other non-terminals. The basic format of a production rule is `nonterminal ::= sequence...`. + +## Example + +Before going deeper, let's look at some of the features demonstrated in `grammars/chess.gbnf`, a small chess notation grammar: +``` +# `root` specifies the pattern for the overall output +root ::= ( + # it must start with the characters "1. " followed by a sequence + # of characters that match the `move` rule, followed by a space, followed + # by another move, and then a newline + "1. " move " " move "\n" + + # it's followed by one or more subsequent moves, numbered with one or two digits + ([1-9] [0-9]? ". " move " " move "\n")+ +) + +# `move` is an abstract representation, which can be a pawn, nonpawn, or castle. +# The `[+#]?` denotes the possibility of checking or mate signs after moves +move ::= (pawn | nonpawn | castle) [+#]? + +pawn ::= ... +nonpawn ::= ... +castle ::= ... +``` + +## Non-Terminals and Terminals + +Non-terminal symbols (rule names) stand for a pattern of terminals and other non-terminals. They are required to be a dashed lowercase word, like `move`, `castle`, or `check-mate`. + +Terminals are actual characters ([code points](https://en.wikipedia.org/wiki/Code_point)). They can be specified as a sequence like `"1"` or `"O-O"` or as ranges like `[1-9]` or `[NBKQR]`. + +## Characters and character ranges + +Terminals support the full range of Unicode. Unicode characters can be specified directly in the grammar, for example `hiragana ::= [ぁ-ゟ]`, or with escapes: 8-bit (`\xXX`), 16-bit (`\uXXXX`) or 32-bit (`\UXXXXXXXX`). + +Character ranges can be negated with `^`: +``` +single-line ::= [^\n]+ "\n" +``` + +## Sequences and Alternatives + +The order of symbols in a sequence matters. For example, in `"1. " move " " move "\n"`, the `"1. "` must come before the first `move`, etc. + +Alternatives, denoted by `|`, give different sequences that are acceptable. For example, in `move ::= pawn | nonpawn | castle`, `move` can be a `pawn` move, a `nonpawn` move, or a `castle`. + +Parentheses `()` can be used to group sequences, which allows for embedding alternatives in a larger rule or applying repetition and optional symbols (below) to a sequence. + +## Repetition and Optional Symbols + +- `*` after a symbol or sequence means that it can be repeated zero or more times (equivalent to `{0,}`). +- `+` denotes that the symbol or sequence should appear one or more times (equivalent to `{1,}`). +- `?` makes the preceding symbol or sequence optional (equivalent to `{0,1}`). +- `{m}` repeats the precedent symbol or sequence exactly `m` times +- `{m,}` repeats the precedent symbol or sequence at least `m` times +- `{m,n}` repeats the precedent symbol or sequence at between `m` and `n` times (included) +- `{0,n}` repeats the precedent symbol or sequence at most `n` times (included) + +## Tokens + +Tokens allow grammars to match specific tokenizer tokens rather than character sequences. This is useful for constraining outputs based on special tokens (like `` or ``). + +Tokens can be specified in two ways: + +1. **Token ID**: Use angle brackets with the token ID in square brackets: `<[token-id]>`. For example, `<[1000]>` matches the token with ID 1000. + +2. **Token string**: Use angle brackets with the token text directly: ``. For example, `` will match the token whose text is exactly ``. This only works if the string tokenizes to exactly one token in the vocabulary, otherwise the grammar will fail to parse. + +You can negate token matches using the `!` prefix: `!<[1000]>` or `!` matches any token *except* the specified one. + +``` +# Match a thinking block: ... +# Using token strings (requires these to be single tokens in the vocab) +root ::= thinking .* +thinking ::= !* + +# Equivalent grammar using explicit token IDs +# Assumes token 1000 = , token 1001 = +root ::= <[1000]> thinking <[1001]> .* +thinking ::= !<[1001]>* +``` + +## Comments and newlines + +Comments can be specified with `#`: +``` +# defines optional whitespace +ws ::= [ \t\n]+ +``` + +Newlines are allowed between rules and between symbols or sequences nested inside parentheses. Additionally, a newline after an alternate marker `|` will continue the current rule, even outside of parentheses. + +## The root rule + +In a full grammar, the `root` rule always defines the starting point of the grammar. In other words, it specifies what the entire output must match. + +``` +# a grammar for lists +root ::= ("- " item)+ +item ::= [^\n]+ "\n" +``` + +## Next steps + +This guide provides a brief overview. Check out the GBNF files in this directory (`grammars/`) for examples of full grammars. You can try them out with: +``` +./llama-cli -m --grammar-file grammars/some-grammar.gbnf -p 'Some prompt' +``` + +`llama.cpp` can also convert JSON schemas to grammars either ahead of time or at each request, see below. + +## Troubleshooting + +Grammars currently have performance gotchas (see https://github.com/ggml-org/llama.cpp/issues/4218). + +### Efficient optional repetitions + +A common pattern is to allow repetitions of a pattern `x` up to N times. + +While semantically correct, the syntax `x? x? x?.... x?` (with N repetitions) may result in extremely slow sampling. Instead, you can write `x{0,N}` (or `(x (x (x ... (x)?...)?)?)?` w/ N-deep nesting in earlier llama.cpp versions). + +## Using GBNF grammars + +You can use GBNF grammars: + +- In [llama-server](../tools/server)'s completion endpoints, passed as the `grammar` body field +- In [llama-cli](../tools/cli) and [llama-completion](../tools/completion), passed as the `--grammar` & `--grammar-file` flags +- With [test-gbnf-validator](../tests/test-gbnf-validator.cpp), to test them against strings. + +## JSON Schemas → GBNF + +`llama.cpp` supports converting a subset of https://json-schema.org/ to GBNF grammars: + +- In [llama-server](../tools/server): + - For any completion endpoints, passed as the `json_schema` body field + - For the `/chat/completions` endpoint, passed inside the `response_format` body field (e.g. `{"type", "json_object", "schema": {"items": {}}}` or `{ type: "json_schema", json_schema: {"schema": ...} }`) +- In [llama-cli](../tools/cli) and [llama-completion](../tools/completion), passed as the `--json` / `-j` flag +- To convert to a grammar ahead of time: + - in CLI, with [examples/json_schema_to_grammar.py](../examples/json_schema_to_grammar.py) + +> [!NOTE] +> The JSON schema is only used to constrain the model output and is not injected into the prompt. The model has no visibility into the schema, so if you want it to understand the expected structure, describe it explicitly in your prompt. This does not apply to tool calling, where schemas are injected into the prompt. + +Take a look at [tests](../tests/test-json-schema-to-grammar.cpp) to see which features are likely supported (you'll also find usage examples in https://github.com/ggml-org/llama.cpp/pull/5978, https://github.com/ggml-org/llama.cpp/pull/6659 & https://github.com/ggml-org/llama.cpp/pull/6555). + +```bash +llama-cli \ + -hfr bartowski/Phi-3-medium-128k-instruct-GGUF \ + -hff Phi-3-medium-128k-instruct-Q8_0.gguf \ + -j '{ + "type": "array", + "items": { + "type": "object", + "properties": { + "name": { + "type": "string", + "minLength": 1, + "maxLength": 100 + }, + "age": { + "type": "integer", + "minimum": 0, + "maximum": 150 + } + }, + "required": ["name", "age"], + "additionalProperties": false + }, + "minItems": 10, + "maxItems": 100 + }' \ + -p 'Generate a {name, age}[] JSON array with famous actors of all ages.' +``` + +
+ +Show grammar + +You can convert any schema in command-line with: + +```bash +examples/json_schema_to_grammar.py name-age-schema.json +``` + +``` +char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4}) +item ::= "{" space item-name-kv "," space item-age-kv "}" space +item-age ::= ([0-9] | ([1-8] [0-9] | [9] [0-9]) | "1" ([0-4] [0-9] | [5] "0")) space +item-age-kv ::= "\"age\"" space ":" space item-age +item-name ::= "\"" char{1,100} "\"" space +item-name-kv ::= "\"name\"" space ":" space item-name +root ::= "[" space item ("," space item){9,99} "]" space +space ::= | " " | "\n" [ \t]{0,20} +``` + +
+ +Here is also a list of known limitations (contributions welcome): + +- `additionalProperties` defaults to `false` (produces faster grammars + reduces hallucinations). +- `"additionalProperties": true` may produce keys that contain unescaped newlines. +- Unsupported features are skipped silently. It is currently advised to use the command-line Python converter (see above) to see any warnings, and to inspect the resulting grammar / test it w/ [llama-gbnf-validator](../examples/gbnf-validator/gbnf-validator.cpp). +- Can't mix `properties` w/ `anyOf` / `oneOf` in the same type (https://github.com/ggml-org/llama.cpp/issues/7703) +- [prefixItems](https://json-schema.org/draft/2020-12/json-schema-core#name-prefixitems) is broken (but [items](https://json-schema.org/draft/2020-12/json-schema-core#name-items) works) +- `minimum`, `exclusiveMinimum`, `maximum`, `exclusiveMaximum`: only supported for `"type": "integer"` for now, not `number` +- Nested `$ref`s are broken (https://github.com/ggml-org/llama.cpp/issues/8073) +- [pattern](https://json-schema.org/draft/2020-12/json-schema-validation#name-pattern)s must start with `^` and end with `$` +- Remote `$ref`s not supported in the C++ version (Python & JavaScript versions fetch https refs) +- `string` [formats](https://json-schema.org/draft/2020-12/json-schema-validation#name-defined-formats) lack `uri`, `email` +- No [`patternProperties`](https://json-schema.org/draft/2020-12/json-schema-core#name-patternproperties) + +And a non-exhaustive list of other unsupported features that are unlikely to be implemented (hard and/or too slow to support w/ stateless grammars): + +- [`uniqueItems`](https://json-schema.org/draft/2020-12/json-schema-validation#name-uniqueitems) +- [`contains`](https://json-schema.org/draft/2020-12/json-schema-core#name-contains) / `minContains` +- `$anchor` (cf. [dereferencing](https://json-schema.org/draft/2020-12/json-schema-core#name-dereferencing)) +- [`not`](https://json-schema.org/draft/2020-12/json-schema-core#name-not) +- [Conditionals](https://json-schema.org/draft/2020-12/json-schema-core#name-keywords-for-applying-subsche) `if` / `then` / `else` / `dependentSchemas` + +### A word about additionalProperties + +> [!WARNING] +> The JSON schemas spec states `object`s accept [additional properties](https://json-schema.org/understanding-json-schema/reference/object#additionalproperties) by default. +> Since this is slow and seems prone to hallucinations, we default to no additional properties. +> You can set `"additionalProperties": true` in the schema of any object to explicitly allow additional properties. + +If you're using [Pydantic](https://pydantic.dev/) to generate schemas, you can enable additional properties with the `extra` config on each model class: + +```python +# pip install pydantic +import json +from typing import Annotated, List +from pydantic import BaseModel, Extra, Field +class QAPair(BaseModel): + class Config: + extra = 'allow' # triggers additionalProperties: true in the JSON schema + question: str + concise_answer: str + justification: str + +class Summary(BaseModel): + class Config: + extra = 'allow' + key_facts: List[Annotated[str, Field(pattern='- .{5,}')]] + question_answers: List[Annotated[List[QAPair], Field(min_items=5)]] + +print(json.dumps(Summary.model_json_schema(), indent=2)) +``` + +
+Show JSON schema & grammar + +```json +{ + "$defs": { + "QAPair": { + "additionalProperties": true, + "properties": { + "question": { + "title": "Question", + "type": "string" + }, + "concise_answer": { + "title": "Concise Answer", + "type": "string" + }, + "justification": { + "title": "Justification", + "type": "string" + } + }, + "required": [ + "question", + "concise_answer", + "justification" + ], + "title": "QAPair", + "type": "object" + } + }, + "additionalProperties": true, + "properties": { + "key_facts": { + "items": { + "pattern": "^- .{5,}$", + "type": "string" + }, + "title": "Key Facts", + "type": "array" + }, + "question_answers": { + "items": { + "items": { + "$ref": "#/$defs/QAPair" + }, + "minItems": 5, + "type": "array" + }, + "title": "Question Answers", + "type": "array" + } + }, + "required": [ + "key_facts", + "question_answers" + ], + "title": "Summary", + "type": "object" +} +``` + +``` +QAPair ::= "{" space QAPair-question-kv "," space QAPair-concise-answer-kv "," space QAPair-justification-kv ( "," space ( QAPair-additional-kv ( "," space QAPair-additional-kv )* ) )? "}" space +QAPair-additional-k ::= ["] ( [c] ([o] ([n] ([c] ([i] ([s] ([e] ([_] ([a] ([n] ([s] ([w] ([e] ([r] char+ | [^"r] char*) | [^"e] char*) | [^"w] char*) | [^"s] char*) | [^"n] char*) | [^"a] char*) | [^"_] char*) | [^"e] char*) | [^"s] char*) | [^"i] char*) | [^"c] char*) | [^"n] char*) | [^"o] char*) | [j] ([u] ([s] ([t] ([i] ([f] ([i] ([c] ([a] ([t] ([i] ([o] ([n] char+ | [^"n] char*) | [^"o] char*) | [^"i] char*) | [^"t] char*) | [^"a] char*) | [^"c] char*) | [^"i] char*) | [^"f] char*) | [^"i] char*) | [^"t] char*) | [^"s] char*) | [^"u] char*) | [q] ([u] ([e] ([s] ([t] ([i] ([o] ([n] char+ | [^"n] char*) | [^"o] char*) | [^"i] char*) | [^"t] char*) | [^"s] char*) | [^"e] char*) | [^"u] char*) | [^"cjq] char* )? ["] space +QAPair-additional-kv ::= QAPair-additional-k ":" space value +QAPair-concise-answer-kv ::= "\"concise_answer\"" space ":" space string +QAPair-justification-kv ::= "\"justification\"" space ":" space string +QAPair-question-kv ::= "\"question\"" space ":" space string +additional-k ::= ["] ( [k] ([e] ([y] ([_] ([f] ([a] ([c] ([t] ([s] char+ | [^"s] char*) | [^"t] char*) | [^"c] char*) | [^"a] char*) | [^"f] char*) | [^"_] char*) | [^"y] char*) | [^"e] char*) | [q] ([u] ([e] ([s] ([t] ([i] ([o] ([n] ([_] ([a] ([n] ([s] ([w] ([e] ([r] ([s] char+ | [^"s] char*) | [^"r] char*) | [^"e] char*) | [^"w] char*) | [^"s] char*) | [^"n] char*) | [^"a] char*) | [^"_] char*) | [^"n] char*) | [^"o] char*) | [^"i] char*) | [^"t] char*) | [^"s] char*) | [^"e] char*) | [^"u] char*) | [^"kq] char* )? ["] space +additional-kv ::= additional-k ":" space value +array ::= "[" space ( value ("," space value)* )? "]" space +boolean ::= ("true" | "false") space +char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4}) +decimal-part ::= [0-9]{1,16} +dot ::= [^\x0A\x0D] +integral-part ::= [0] | [1-9] [0-9]{0,15} +key-facts ::= "[" space (key-facts-item ("," space key-facts-item)*)? "]" space +key-facts-item ::= "\"" "- " key-facts-item-1{5,} "\"" space +key-facts-item-1 ::= dot +key-facts-kv ::= "\"key_facts\"" space ":" space key-facts +null ::= "null" space +number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space +object ::= "{" space ( string ":" space value ("," space string ":" space value)* )? "}" space +question-answers ::= "[" space (question-answers-item ("," space question-answers-item)*)? "]" space +question-answers-item ::= "[" space question-answers-item-item ("," space question-answers-item-item){4,} "]" space +question-answers-item-item ::= QAPair +question-answers-kv ::= "\"question_answers\"" space ":" space question-answers +root ::= "{" space key-facts-kv "," space question-answers-kv ( "," space ( additional-kv ( "," space additional-kv )* ) )? "}" space +space ::= | " " | "\n" [ \t]{0,20} +string ::= "\"" char* "\"" space +value ::= object | array | string | number | boolean | null +``` + +
+ +If you're using [Zod](https://zod.dev/), you can make your objects to explicitly allow extra properties w/ `nonstrict()` / `passthrough()` (or explicitly no extra props w/ `z.object(...).strict()` or `z.strictObject(...)`) but note that [zod-to-json-schema](https://github.com/StefanTerdell/zod-to-json-schema) currently always sets `"additionalProperties": false` anyway. + +```js +import { z } from 'zod'; +import { zodToJsonSchema } from 'zod-to-json-schema'; + +const Foo = z.object({ + age: z.number().positive(), + email: z.string().email(), +}).strict(); + +console.log(zodToJsonSchema(Foo)); +``` + +
+Show JSON schema & grammar + +```json +{ + "type": "object", + "properties": { + "age": { + "type": "number", + "exclusiveMinimum": 0 + }, + "email": { + "type": "string", + "format": "email" + } + }, + "required": [ + "age", + "email" + ], + "additionalProperties": false, + "$schema": "http://json-schema.org/draft-07/schema#" +} +``` + +``` +age-kv ::= "\"age\"" space ":" space number +char ::= [^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4}) +decimal-part ::= [0-9]{1,16} +email-kv ::= "\"email\"" space ":" space string +integral-part ::= [0] | [1-9] [0-9]{0,15} +number ::= ("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space +root ::= "{" space age-kv "," space email-kv "}" space +space ::= | " " | "\n" [ \t]{0,20} +string ::= "\"" char* "\"" space +``` + +
diff --git a/backend/llama.cpp/grammars/arithmetic.gbnf b/backend/llama.cpp/grammars/arithmetic.gbnf new file mode 100644 index 0000000000000000000000000000000000000000..3aa95a9dda7e80dc6ffc5fa07773e334aa6fb9f0 --- /dev/null +++ b/backend/llama.cpp/grammars/arithmetic.gbnf @@ -0,0 +1,6 @@ +root ::= (expr "=" ws term "\n")+ +expr ::= term ([-+*/] term)* +term ::= ident | num | "(" ws expr ")" ws +ident ::= [a-z] [a-z0-9_]* ws +num ::= [0-9]+ ws +ws ::= [ \t\n]* diff --git a/backend/llama.cpp/grammars/c.gbnf b/backend/llama.cpp/grammars/c.gbnf new file mode 100644 index 0000000000000000000000000000000000000000..4a0331dd2d6df91b6e2154b78dc31df57a4e715e --- /dev/null +++ b/backend/llama.cpp/grammars/c.gbnf @@ -0,0 +1,42 @@ +root ::= (declaration)* + +declaration ::= dataType identifier "(" parameter? ")" "{" statement* "}" + +dataType ::= "int" ws | "float" ws | "char" ws +identifier ::= [a-zA-Z_] [a-zA-Z_0-9]* + +parameter ::= dataType identifier + +statement ::= + ( dataType identifier ws "=" ws expression ";" ) | + ( identifier ws "=" ws expression ";" ) | + ( identifier ws "(" argList? ")" ";" ) | + ( "return" ws expression ";" ) | + ( "while" "(" condition ")" "{" statement* "}" ) | + ( "for" "(" forInit ";" ws condition ";" ws forUpdate ")" "{" statement* "}" ) | + ( "if" "(" condition ")" "{" statement* "}" ("else" "{" statement* "}")? ) | + ( singleLineComment ) | + ( multiLineComment ) + +forInit ::= dataType identifier ws "=" ws expression | identifier ws "=" ws expression +forUpdate ::= identifier ws "=" ws expression + +condition ::= expression relationOperator expression +relationOperator ::= ("<=" | "<" | "==" | "!=" | ">=" | ">") + +expression ::= term (("+" | "-") term)* +term ::= factor(("*" | "/") factor)* + +factor ::= identifier | number | unaryTerm | funcCall | parenExpression +unaryTerm ::= "-" factor +funcCall ::= identifier "(" argList? ")" +parenExpression ::= "(" ws expression ws ")" + +argList ::= expression ("," ws expression)* + +number ::= [0-9]+ + +singleLineComment ::= "//" [^\n]* "\n" +multiLineComment ::= "/*" ( [^*] | ("*" [^/]) )* "*/" + +ws ::= ([ \t\n]+) diff --git a/backend/llama.cpp/grammars/chess.gbnf b/backend/llama.cpp/grammars/chess.gbnf new file mode 100644 index 0000000000000000000000000000000000000000..ef0fc1b07f01c0ff32465597ced7dc36780f6171 --- /dev/null +++ b/backend/llama.cpp/grammars/chess.gbnf @@ -0,0 +1,13 @@ +# Specifies chess moves as a list in algebraic notation, using PGN conventions + +# Force first move to "1. ", then any 1-2 digit number after, relying on model to follow the pattern +root ::= "1. " move " " move "\n" ([1-9] [0-9]? ". " move " " move "\n")+ +move ::= (pawn | nonpawn | castle) [+#]? + +# piece type, optional file/rank, optional capture, dest file & rank +nonpawn ::= [NBKQR] [a-h]? [1-8]? "x"? [a-h] [1-8] + +# optional file & capture, dest file & rank, optional promotion +pawn ::= ([a-h] "x")? [a-h] [1-8] ("=" [NBKQR])? + +castle ::= "O-O" "-O"? diff --git a/backend/llama.cpp/grammars/english.gbnf b/backend/llama.cpp/grammars/english.gbnf new file mode 100644 index 0000000000000000000000000000000000000000..2e53686c8215143477deb5800d4694bf9b07280c --- /dev/null +++ b/backend/llama.cpp/grammars/english.gbnf @@ -0,0 +1,6 @@ +# note: this might be incomplete, mostly an example +root ::= en-char+ ([ \t\n] en-char+)* +en-char ::= letter | digit | punctuation +letter ::= [a-zA-Z] +digit ::= [0-9] +punctuation ::= [!"#$%&'()*+,-./:;<=>?@[\\\]^_`{|}~] diff --git a/backend/llama.cpp/grammars/japanese.gbnf b/backend/llama.cpp/grammars/japanese.gbnf new file mode 100644 index 0000000000000000000000000000000000000000..43f25ab598586d740c3e92066bfad3a2e9e3c562 --- /dev/null +++ b/backend/llama.cpp/grammars/japanese.gbnf @@ -0,0 +1,7 @@ +# A probably incorrect grammar for Japanese +root ::= jp-char+ ([ \t\n] jp-char+)* +jp-char ::= hiragana | katakana | punctuation | cjk +hiragana ::= [ぁ-ゟ] +katakana ::= [ァ-ヿ] +punctuation ::= [、-〾] +cjk ::= [一-鿿] diff --git a/backend/llama.cpp/grammars/json.gbnf b/backend/llama.cpp/grammars/json.gbnf new file mode 100644 index 0000000000000000000000000000000000000000..b6448c87b72ec7526404d563464df4e622a32f63 --- /dev/null +++ b/backend/llama.cpp/grammars/json.gbnf @@ -0,0 +1,25 @@ +root ::= object +value ::= object | array | string | number | ("true" | "false" | "null") ws + +object ::= + "{" ws ( + string ":" ws value + ("," ws string ":" ws value)* + )? "}" ws + +array ::= + "[" ws ( + value + ("," ws value)* + )? "]" ws + +string ::= + "\"" ( + [^"\\\x7F\x00-\x1F] | + "\\" (["\\bfnrt] | "u" [0-9a-fA-F]{4}) # escapes + )* "\"" ws + +number ::= ("-"? ([0-9] | [1-9] [0-9]{0,15})) ("." [0-9]+)? ([eE] [-+]? [0-9] [1-9]{0,15})? ws + +# Optional space: by convention, applied in this grammar after literal chars when allowed +ws ::= | " " | "\n" [ \t]{0,20} diff --git a/backend/llama.cpp/grammars/json_arr.gbnf b/backend/llama.cpp/grammars/json_arr.gbnf new file mode 100644 index 0000000000000000000000000000000000000000..b3dc6f9b16277e8e8e1f7dd1f33522695794e0f8 --- /dev/null +++ b/backend/llama.cpp/grammars/json_arr.gbnf @@ -0,0 +1,34 @@ +# This is the same as json.gbnf but we restrict whitespaces at the end of the root array +# Useful for generating JSON arrays + +root ::= arr +value ::= object | array | string | number | ("true" | "false" | "null") ws + +arr ::= + "[\n" ws ( + value + (",\n" ws value)* + )? "]" + +object ::= + "{" ws ( + string ":" ws value + ("," ws string ":" ws value)* + )? "}" ws + +array ::= + "[" ws ( + value + ("," ws value)* + )? "]" ws + +string ::= + "\"" ( + [^"\\\x7F\x00-\x1F] | + "\\" (["\\bfnrt] | "u" [0-9a-fA-F]{4}) # escapes + )* "\"" ws + +number ::= ("-"? ([0-9] | [1-9] [0-9]{0,15})) ("." [0-9]+)? ([eE] [-+]? [1-9] [0-9]{0,15})? ws + +# Optional space: by convention, applied in this grammar after literal chars when allowed +ws ::= | " " | "\n" [ \t]{0,20} diff --git a/backend/llama.cpp/grammars/list.gbnf b/backend/llama.cpp/grammars/list.gbnf new file mode 100644 index 0000000000000000000000000000000000000000..51e6c9c4b03292a52dfd2b58acc2312fc8aa9338 --- /dev/null +++ b/backend/llama.cpp/grammars/list.gbnf @@ -0,0 +1,4 @@ +root ::= item+ + +# Excludes various line break characters +item ::= "- " [^\r\n\x0b\x0c\x85\u2028\u2029]+ "\n" diff --git a/backend/llama.cpp/include/llama-cpp.h b/backend/llama.cpp/include/llama-cpp.h new file mode 100644 index 0000000000000000000000000000000000000000..8f6368177de0918e67daa4059e34519eecb42a78 --- /dev/null +++ b/backend/llama.cpp/include/llama-cpp.h @@ -0,0 +1,30 @@ +#pragma once + +#ifndef __cplusplus +#error "This header is for C++ only" +#endif + +#include + +#include "llama.h" + +struct llama_model_deleter { + void operator()(llama_model * model) { llama_model_free(model); } +}; + +struct llama_context_deleter { + void operator()(llama_context * context) { llama_free(context); } +}; + +struct llama_sampler_deleter { + void operator()(llama_sampler * sampler) { llama_sampler_free(sampler); } +}; + +struct llama_adapter_lora_deleter { + void operator()(llama_adapter_lora * adapter) { llama_adapter_lora_free(adapter); } +}; + +typedef std::unique_ptr llama_model_ptr; +typedef std::unique_ptr llama_context_ptr; +typedef std::unique_ptr llama_sampler_ptr; +typedef std::unique_ptr llama_adapter_lora_ptr; diff --git a/backend/llama.cpp/include/llama.h b/backend/llama.cpp/include/llama.h new file mode 100644 index 0000000000000000000000000000000000000000..a311ac2023579376ed571a614dcac9d259692e56 --- /dev/null +++ b/backend/llama.cpp/include/llama.h @@ -0,0 +1,1596 @@ +#ifndef LLAMA_H +#define LLAMA_H + +#include "ggml.h" +#include "ggml-cpu.h" +#include "ggml-backend.h" +#include "ggml-opt.h" +#include "gguf.h" + +#include +#include +#include +#include + +#ifdef LLAMA_SHARED +# if defined(_WIN32) && !defined(__MINGW32__) +# ifdef LLAMA_BUILD +# define LLAMA_API __declspec(dllexport) +# else +# define LLAMA_API __declspec(dllimport) +# endif +# else +# define LLAMA_API __attribute__ ((visibility ("default"))) +# endif +#else +# define LLAMA_API +#endif + +#ifdef __GNUC__ +# define DEPRECATED(func, hint) func __attribute__((deprecated(hint))) +#elif defined(_MSC_VER) +# define DEPRECATED(func, hint) __declspec(deprecated(hint)) func +#else +# define DEPRECATED(func, hint) func +#endif + +#define LLAMA_DEFAULT_SEED 0xFFFFFFFF + +#define LLAMA_TOKEN_NULL -1 + +#define LLAMA_FILE_MAGIC_GGLA 0x67676c61u // 'ggla' +#define LLAMA_FILE_MAGIC_GGSN 0x6767736eu // 'ggsn' +#define LLAMA_FILE_MAGIC_GGSQ 0x67677371u // 'ggsq' + +#define LLAMA_SESSION_MAGIC LLAMA_FILE_MAGIC_GGSN +#define LLAMA_SESSION_VERSION 9 + +#define LLAMA_STATE_SEQ_MAGIC LLAMA_FILE_MAGIC_GGSQ +#define LLAMA_STATE_SEQ_VERSION 2 + +#ifdef __cplusplus +extern "C" { +#endif + + // + // C interface + // + // TODO: show sample usage + // + + struct llama_vocab; + struct llama_model; + struct llama_context; + struct llama_sampler; + + typedef struct llama_memory_i * llama_memory_t; + + typedef int32_t llama_pos; + typedef int32_t llama_token; + typedef int32_t llama_seq_id; + + enum llama_vocab_type { + LLAMA_VOCAB_TYPE_NONE = 0, // For models without vocab + LLAMA_VOCAB_TYPE_SPM = 1, // LLaMA tokenizer based on byte-level BPE with byte fallback + LLAMA_VOCAB_TYPE_BPE = 2, // GPT-2 tokenizer based on byte-level BPE + LLAMA_VOCAB_TYPE_WPM = 3, // BERT tokenizer based on WordPiece + LLAMA_VOCAB_TYPE_UGM = 4, // T5 tokenizer based on Unigram + LLAMA_VOCAB_TYPE_RWKV = 5, // RWKV tokenizer based on greedy tokenization + LLAMA_VOCAB_TYPE_PLAMO2 = 6, // PLaMo-2 tokenizer based on Aho-Corasick with dynamic programming + }; + + enum llama_rope_type { + LLAMA_ROPE_TYPE_NONE = -1, + LLAMA_ROPE_TYPE_NORM = 0, + LLAMA_ROPE_TYPE_NEOX = GGML_ROPE_TYPE_NEOX, + LLAMA_ROPE_TYPE_MROPE = GGML_ROPE_TYPE_MROPE, + LLAMA_ROPE_TYPE_IMROPE = GGML_ROPE_TYPE_IMROPE, + LLAMA_ROPE_TYPE_VISION = GGML_ROPE_TYPE_VISION, + }; + + enum llama_token_type { //TODO: remove, required until per token attributes are available from GGUF file + LLAMA_TOKEN_TYPE_UNDEFINED = 0, + LLAMA_TOKEN_TYPE_NORMAL = 1, + LLAMA_TOKEN_TYPE_UNKNOWN = 2, + LLAMA_TOKEN_TYPE_CONTROL = 3, + LLAMA_TOKEN_TYPE_USER_DEFINED = 4, + LLAMA_TOKEN_TYPE_UNUSED = 5, + LLAMA_TOKEN_TYPE_BYTE = 6, + }; + + enum llama_token_attr { + LLAMA_TOKEN_ATTR_UNDEFINED = 0, + LLAMA_TOKEN_ATTR_UNKNOWN = 1 << 0, + LLAMA_TOKEN_ATTR_UNUSED = 1 << 1, + LLAMA_TOKEN_ATTR_NORMAL = 1 << 2, + LLAMA_TOKEN_ATTR_CONTROL = 1 << 3, // SPECIAL? + LLAMA_TOKEN_ATTR_USER_DEFINED = 1 << 4, + LLAMA_TOKEN_ATTR_BYTE = 1 << 5, + LLAMA_TOKEN_ATTR_NORMALIZED = 1 << 6, + LLAMA_TOKEN_ATTR_LSTRIP = 1 << 7, + LLAMA_TOKEN_ATTR_RSTRIP = 1 << 8, + LLAMA_TOKEN_ATTR_SINGLE_WORD = 1 << 9, + }; + + // model file types + enum llama_ftype { + LLAMA_FTYPE_ALL_F32 = 0, + LLAMA_FTYPE_MOSTLY_F16 = 1, // except 1d tensors + LLAMA_FTYPE_MOSTLY_Q4_0 = 2, // except 1d tensors + LLAMA_FTYPE_MOSTLY_Q4_1 = 3, // except 1d tensors + // LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16 = 4, // tok_embeddings.weight and output.weight are F16 + // LLAMA_FTYPE_MOSTLY_Q4_2 = 5, // support has been removed + // LLAMA_FTYPE_MOSTLY_Q4_3 = 6, // support has been removed + LLAMA_FTYPE_MOSTLY_Q8_0 = 7, // except 1d tensors + LLAMA_FTYPE_MOSTLY_Q5_0 = 8, // except 1d tensors + LLAMA_FTYPE_MOSTLY_Q5_1 = 9, // except 1d tensors + LLAMA_FTYPE_MOSTLY_Q2_K = 10, // except 1d tensors + LLAMA_FTYPE_MOSTLY_Q3_K_S = 11, // except 1d tensors + LLAMA_FTYPE_MOSTLY_Q3_K_M = 12, // except 1d tensors + LLAMA_FTYPE_MOSTLY_Q3_K_L = 13, // except 1d tensors + LLAMA_FTYPE_MOSTLY_Q4_K_S = 14, // except 1d tensors + LLAMA_FTYPE_MOSTLY_Q4_K_M = 15, // except 1d tensors + LLAMA_FTYPE_MOSTLY_Q5_K_S = 16, // except 1d tensors + LLAMA_FTYPE_MOSTLY_Q5_K_M = 17, // except 1d tensors + LLAMA_FTYPE_MOSTLY_Q6_K = 18, // except 1d tensors + LLAMA_FTYPE_MOSTLY_IQ2_XXS = 19, // except 1d tensors + LLAMA_FTYPE_MOSTLY_IQ2_XS = 20, // except 1d tensors + LLAMA_FTYPE_MOSTLY_Q2_K_S = 21, // except 1d tensors + LLAMA_FTYPE_MOSTLY_IQ3_XS = 22, // except 1d tensors + LLAMA_FTYPE_MOSTLY_IQ3_XXS = 23, // except 1d tensors + LLAMA_FTYPE_MOSTLY_IQ1_S = 24, // except 1d tensors + LLAMA_FTYPE_MOSTLY_IQ4_NL = 25, // except 1d tensors + LLAMA_FTYPE_MOSTLY_IQ3_S = 26, // except 1d tensors + LLAMA_FTYPE_MOSTLY_IQ3_M = 27, // except 1d tensors + LLAMA_FTYPE_MOSTLY_IQ2_S = 28, // except 1d tensors + LLAMA_FTYPE_MOSTLY_IQ2_M = 29, // except 1d tensors + LLAMA_FTYPE_MOSTLY_IQ4_XS = 30, // except 1d tensors + LLAMA_FTYPE_MOSTLY_IQ1_M = 31, // except 1d tensors + LLAMA_FTYPE_MOSTLY_BF16 = 32, // except 1d tensors + //LLAMA_FTYPE_MOSTLY_Q4_0_4_4 = 33, // removed from gguf files, use Q4_0 and runtime repack + //LLAMA_FTYPE_MOSTLY_Q4_0_4_8 = 34, // removed from gguf files, use Q4_0 and runtime repack + //LLAMA_FTYPE_MOSTLY_Q4_0_8_8 = 35, // removed from gguf files, use Q4_0 and runtime repack + LLAMA_FTYPE_MOSTLY_TQ1_0 = 36, // except 1d tensors + LLAMA_FTYPE_MOSTLY_TQ2_0 = 37, // except 1d tensors + LLAMA_FTYPE_MOSTLY_MXFP4_MOE = 38, // except 1d tensors + LLAMA_FTYPE_MOSTLY_NVFP4 = 39, // except 1d tensors + LLAMA_FTYPE_MOSTLY_Q1_0 = 40, // except 1d tensors + LLAMA_FTYPE_MOSTLY_Q2_0 = 41, // except 1d tensors + + LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file + }; + + // Get the model file type (quantization) as a string, e.g. "Q8_0" or "Q4_K - Medium" + LLAMA_API const char * llama_ftype_name(enum llama_ftype ftype); + + enum llama_rope_scaling_type { + LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED = -1, + LLAMA_ROPE_SCALING_TYPE_NONE = 0, + LLAMA_ROPE_SCALING_TYPE_LINEAR = 1, + LLAMA_ROPE_SCALING_TYPE_YARN = 2, + LLAMA_ROPE_SCALING_TYPE_LONGROPE = 3, + LLAMA_ROPE_SCALING_TYPE_MAX_VALUE = LLAMA_ROPE_SCALING_TYPE_LONGROPE, + }; + + enum llama_pooling_type { + LLAMA_POOLING_TYPE_UNSPECIFIED = -1, + LLAMA_POOLING_TYPE_NONE = 0, + LLAMA_POOLING_TYPE_MEAN = 1, + LLAMA_POOLING_TYPE_CLS = 2, + LLAMA_POOLING_TYPE_LAST = 3, + LLAMA_POOLING_TYPE_RANK = 4, // used by reranking models to attach the classification head to the graph + }; + + enum llama_attention_type { + LLAMA_ATTENTION_TYPE_UNSPECIFIED = -1, + LLAMA_ATTENTION_TYPE_CAUSAL = 0, + LLAMA_ATTENTION_TYPE_NON_CAUSAL = 1, + }; + + enum llama_flash_attn_type { + LLAMA_FLASH_ATTN_TYPE_AUTO = -1, + LLAMA_FLASH_ATTN_TYPE_DISABLED = 0, + LLAMA_FLASH_ATTN_TYPE_ENABLED = 1, + }; + + LLAMA_API const char * llama_flash_attn_type_name(enum llama_flash_attn_type flash_attn_type); + + enum llama_split_mode { + LLAMA_SPLIT_MODE_NONE = 0, // single GPU + LLAMA_SPLIT_MODE_LAYER = 1, // split layers and KV across GPUs + LLAMA_SPLIT_MODE_ROW = 2, // split layers and KV across GPUs, use tensor parallelism if supported + LLAMA_SPLIT_MODE_TENSOR = 3, + }; + + enum llama_context_type { + LLAMA_CONTEXT_TYPE_DEFAULT = 0, + LLAMA_CONTEXT_TYPE_MTP = 1, + }; + + // TODO: simplify (https://github.com/ggml-org/llama.cpp/pull/9294#pullrequestreview-2286561979) + typedef struct llama_token_data { + llama_token id; // token id + float logit; // log-odds of the token + float p; // probability of the token + } llama_token_data; + + typedef struct llama_token_data_array { + // TODO: consider SoA + // NOTE: this pointer can be modified by the samplers + llama_token_data * data; + size_t size; + int64_t selected; // this is the index in the data array (i.e. not the token id) + bool sorted; // note: do not assume the data is sorted - always check this flag + } llama_token_data_array; + + typedef bool (*llama_progress_callback)(float progress, void * user_data); + + // Input data for llama_encode/llama_decode + // A llama_batch object can contain input about one or many sequences + // The provided arrays (i.e. token, embd, pos, etc.) must have size of n_tokens + // + // - token : the token ids of the input (used when embd is NULL) + // - embd : token embeddings (i.e. float vector of size n_embd) (used when token is NULL) + // - pos : the positions of the respective token in the sequence + // (if set to NULL, the token position will be tracked automatically by llama_encode/llama_decode) + // - seq_id : the sequence to which the respective token belongs + // (if set to NULL, the sequence ID will be assumed to be 0) + // - logits : if zero, the logits (and/or the embeddings) for the respective token will not be output + // (if set to NULL: + // - if embeddings: all tokens are output + // - if not: only the last token is output + // ) + // + typedef struct llama_batch { + int32_t n_tokens; + + llama_token * token; + float * embd; + llama_pos * pos; + int32_t * n_seq_id; + llama_seq_id ** seq_id; + int8_t * logits; // TODO: rename this to "output" + } llama_batch; + + enum llama_model_kv_override_type { + LLAMA_KV_OVERRIDE_TYPE_INT, + LLAMA_KV_OVERRIDE_TYPE_FLOAT, + LLAMA_KV_OVERRIDE_TYPE_BOOL, + LLAMA_KV_OVERRIDE_TYPE_STR, + }; + + enum llama_model_meta_key { + LLAMA_MODEL_META_KEY_SAMPLING_SEQUENCE, + LLAMA_MODEL_META_KEY_SAMPLING_TOP_K, + LLAMA_MODEL_META_KEY_SAMPLING_TOP_P, + LLAMA_MODEL_META_KEY_SAMPLING_MIN_P, + LLAMA_MODEL_META_KEY_SAMPLING_XTC_PROBABILITY, + LLAMA_MODEL_META_KEY_SAMPLING_XTC_THRESHOLD, + LLAMA_MODEL_META_KEY_SAMPLING_TEMP, + LLAMA_MODEL_META_KEY_SAMPLING_PENALTY_LAST_N, + LLAMA_MODEL_META_KEY_SAMPLING_PENALTY_REPEAT, + LLAMA_MODEL_META_KEY_SAMPLING_MIROSTAT, + LLAMA_MODEL_META_KEY_SAMPLING_MIROSTAT_TAU, + LLAMA_MODEL_META_KEY_SAMPLING_MIROSTAT_ETA, + }; + + struct llama_model_kv_override { + enum llama_model_kv_override_type tag; + + char key[128]; + + union { + int64_t val_i64; + double val_f64; + bool val_bool; + char val_str[128]; + }; + }; + + struct llama_model_tensor_buft_override { + const char * pattern; + ggml_backend_buffer_type_t buft; + }; + + struct llama_model_params { + // NULL-terminated list of devices to use for offloading (if NULL, all available devices are used) + ggml_backend_dev_t * devices; + + // NULL-terminated list of buffer types to use for tensors that match a pattern + const struct llama_model_tensor_buft_override * tensor_buft_overrides; + + int32_t n_gpu_layers; // number of layers to store in VRAM, a negative value means all layers + enum llama_split_mode split_mode; // how to split the model across multiple GPUs + + // the GPU that is used for the entire model when split_mode is LLAMA_SPLIT_MODE_NONE + int32_t main_gpu; + + // proportion of the model (layers or rows) to offload to each GPU, size: llama_max_devices() + const float * tensor_split; + + // Called with a progress value between 0.0 and 1.0. Pass NULL to disable. + // If the provided progress_callback returns true, model loading continues. + // If it returns false, model loading is immediately aborted. + llama_progress_callback progress_callback; + + // context pointer passed to the progress callback + void * progress_callback_user_data; + + // override key-value pairs of the model meta data + const struct llama_model_kv_override * kv_overrides; + + // Keep the booleans together to avoid misalignment during copy-by-value. + bool vocab_only; // only load the vocabulary, no weights + bool use_mmap; // use mmap if possible + bool use_direct_io; // use direct io, takes precedence over use_mmap when supported + bool use_mlock; // force system to keep model in RAM + bool check_tensors; // validate model tensor data + bool use_extra_bufts; // use extra buffer types (used for weight repacking) + bool no_host; // bypass host buffer allowing extra buffers to be used + bool no_alloc; // only load metadata and simulate memory allocations + }; + + struct llama_sampler_seq_config { + llama_seq_id seq_id; + struct llama_sampler * sampler; + }; + + // NOTE: changing the default values of parameters marked as [EXPERIMENTAL] may cause crashes or incorrect results in certain configurations + // https://github.com/ggml-org/llama.cpp/pull/7544 + struct llama_context_params { + uint32_t n_ctx; // text context, 0 = from model + uint32_t n_batch; // logical maximum batch size that can be submitted to llama_decode + uint32_t n_ubatch; // physical maximum batch size + uint32_t n_seq_max; // max number of sequences (i.e. distinct states for recurrent models) + uint32_t n_rs_seq; // number of recurrent-state snapshots per seq for rollback (0 = no rollback) [EXPERIMENTAL] + uint32_t n_outputs_max; // max outputs in a ubatch (0 = n_batch) + int32_t n_threads; // number of threads to use for generation + int32_t n_threads_batch; // number of threads to use for batch processing + + enum llama_context_type ctx_type; // set the context type (e.g. MTP) + enum llama_rope_scaling_type rope_scaling_type; // RoPE scaling type, from `enum llama_rope_scaling_type` + enum llama_pooling_type pooling_type; // whether to pool (sum) embedding results by sequence id + enum llama_attention_type attention_type; // attention type to use for embeddings + enum llama_flash_attn_type flash_attn_type; // when to enable Flash Attention + + // ref: https://github.com/ggml-org/llama.cpp/pull/2054 + float rope_freq_base; // RoPE base frequency, 0 = from model + float rope_freq_scale; // RoPE frequency scaling factor, 0 = from model + float yarn_ext_factor; // YaRN extrapolation mix factor, negative = from model + float yarn_attn_factor; // YaRN magnitude scaling factor + float yarn_beta_fast; // YaRN low correction dim + float yarn_beta_slow; // YaRN high correction dim + uint32_t yarn_orig_ctx; // YaRN original context size + float defrag_thold; // [DEPRECATED] defragment the KV cache if holes/size > thold, <= 0 disabled (default) + + ggml_backend_sched_eval_callback cb_eval; + void * cb_eval_user_data; + + enum ggml_type type_k; // data type for K cache [EXPERIMENTAL] + enum ggml_type type_v; // data type for V cache [EXPERIMENTAL] + + // Abort callback + // if it returns true, execution of llama_decode() will be aborted + // currently works only with CPU execution + ggml_abort_callback abort_callback; + void * abort_callback_data; + + // Keep the booleans together and at the end of the struct to avoid misalignment during copy-by-value. + bool embeddings; // if true, extract embeddings (together with logits) + bool offload_kqv; // offload the KQV ops (including the KV cache) to GPU + bool no_perf; // measure performance timings + bool op_offload; // offload host tensor operations to device + bool swa_full; // use full-size SWA cache (https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055) + // NOTE: setting to false when n_seq_max > 1 can cause bad performance in some cases + // ref: https://github.com/ggml-org/llama.cpp/pull/13845#issuecomment-2924800573 + bool kv_unified; // use a unified buffer across the input sequences when computing the attention + // try to disable when n_seq_max > 1 for improved performance when the sequences do not share a large prefix + // ref: https://github.com/ggml-org/llama.cpp/pull/14363 + + // [EXPERIMENTAL] + // backend sampler chain configuration (make sure the caller keeps the sampler chains alive) + // note: the samplers must be sampler chains (i.e. use llama_sampler_chain_init) + struct llama_sampler_seq_config * samplers; + size_t n_samplers; + + // a source/target/parent context + // can be utilized in various ways, for example by sharing results or llama_memory between 2 contexts + struct llama_context * ctx_other; + }; + + struct llama_model_tensor_override { + const char * pattern; + enum ggml_type type; + }; + + struct llama_model_imatrix_data { + const char * name; + const float * data; + size_t size; + }; + + // model quantization parameters + typedef struct llama_model_quantize_params { + int32_t nthread; // number of threads to use for quantizing, if <=0 will use std::thread::hardware_concurrency() + enum llama_ftype ftype; // quantize to this llama_ftype + enum ggml_type output_tensor_type; // output tensor type + enum ggml_type token_embedding_type; // token embeddings tensor type + bool allow_requantize; // allow quantizing non-f32/f16 tensors + bool quantize_output_tensor; // quantize output.weight + bool only_copy; // only copy tensors - ftype, allow_requantize and quantize_output_tensor are ignored + bool pure; // quantize all tensors to the default type + bool keep_split; // quantize to the same number of shards + bool dry_run; // calculate and show the final quantization size without performing quantization + const struct llama_model_imatrix_data * imatrix; // pointer to importance matrix data + const struct llama_model_kv_override * kv_overrides; // pointer to kv overrides + const struct llama_model_tensor_override * tt_overrides; // pointer to tensor overrides + const int32_t * prune_layers; // pointer to layer indices to prune + } llama_model_quantize_params; + + typedef struct llama_logit_bias { + llama_token token; + float bias; + } llama_logit_bias; + + typedef struct llama_sampler_chain_params { + bool no_perf; // whether to measure performance timings + } llama_sampler_chain_params; + + // used in chat template + typedef struct llama_chat_message { + const char * role; + const char * content; + } llama_chat_message; + + // lora adapter + struct llama_adapter_lora; + + // Helpers for getting default parameters + // TODO: update API to start accepting pointers to params structs (https://github.com/ggml-org/llama.cpp/discussions/9172) + LLAMA_API struct llama_model_params llama_model_default_params(void); + LLAMA_API struct llama_context_params llama_context_default_params(void); + LLAMA_API struct llama_sampler_chain_params llama_sampler_chain_default_params(void); + LLAMA_API struct llama_model_quantize_params llama_model_quantize_default_params(void); + + // Initialize the llama + ggml backend + // If numa is true, use NUMA optimizations + // Call once at the start of the program + LLAMA_API void llama_backend_init(void); + + // Call once at the end of the program - currently only used for MPI + LLAMA_API void llama_backend_free(void); + + //optional: + LLAMA_API void llama_numa_init(enum ggml_numa_strategy numa); + + // Optional: an auto threadpool gets created in ggml if not passed explicitly + LLAMA_API void llama_attach_threadpool( + struct llama_context * ctx, + ggml_threadpool_t threadpool, + ggml_threadpool_t threadpool_batch); + + LLAMA_API void llama_detach_threadpool(struct llama_context * ctx); + + typedef void (*llama_model_set_tensor_data_t)(struct ggml_tensor * tensor, void * userdata); + + // Create a new model from GGUF metadata as well as a function to set the tensor data + // - tensors are created as GGML_TYPE_F32 by default, + // override by adding a tensor with the same name but a different name to the context + LLAMA_API struct llama_model * llama_model_init_from_user( + struct gguf_context * metadata, + llama_model_set_tensor_data_t set_tensor_data, // function to initialize tensor data with + void * set_tensor_data_ud, // userdata for function + struct llama_model_params params); + + DEPRECATED(LLAMA_API struct llama_model * llama_load_model_from_file( + const char * path_model, + struct llama_model_params params), + "use llama_model_load_from_file instead"); + + // Load a model from a file + // If the file is split into multiple parts, the file name must follow this pattern: -%05d-of-%05d.gguf + // If the split file name does not follow this pattern, use llama_model_load_from_splits + LLAMA_API struct llama_model * llama_model_load_from_file( + const char * path_model, + struct llama_model_params params); + + // Load a model from an open FILE pointer + LLAMA_API struct llama_model * llama_model_load_from_file_ptr( + FILE * file, + struct llama_model_params params); + + // Load a model from multiple splits (support custom naming scheme) + // The paths must be in the correct order + LLAMA_API struct llama_model * llama_model_load_from_splits( + const char ** paths, + size_t n_paths, + struct llama_model_params params); + + LLAMA_API void llama_model_save_to_file( + const struct llama_model * model, + const char * path_model); + + DEPRECATED(LLAMA_API void llama_free_model(struct llama_model * model), + "use llama_model_free instead"); + + LLAMA_API void llama_model_free(struct llama_model * model); + + LLAMA_API struct llama_context * llama_init_from_model( + struct llama_model * model, + struct llama_context_params params); + + DEPRECATED(LLAMA_API struct llama_context * llama_new_context_with_model( + struct llama_model * model, + struct llama_context_params params), + "use llama_init_from_model instead"); + + // Frees all allocated memory + LLAMA_API void llama_free(struct llama_context * ctx); + + LLAMA_API int64_t llama_time_us(void); + + LLAMA_API size_t llama_max_devices(void); + LLAMA_API size_t llama_max_parallel_sequences(void); + LLAMA_API size_t llama_max_tensor_buft_overrides(void); + + LLAMA_API bool llama_supports_mmap (void); + LLAMA_API bool llama_supports_mlock (void); + LLAMA_API bool llama_supports_gpu_offload(void); + LLAMA_API bool llama_supports_rpc (void); + + // NOTE: After creating a llama_context, it is recommended to query the actual values using these functions + // In some cases the requested values via llama_context_params may differ from the actual values used by the context + // ref: https://github.com/ggml-org/llama.cpp/pull/17046#discussion_r2503085732 + LLAMA_API uint32_t llama_n_ctx (const struct llama_context * ctx); + LLAMA_API uint32_t llama_n_ctx_seq (const struct llama_context * ctx); + LLAMA_API uint32_t llama_n_batch (const struct llama_context * ctx); + LLAMA_API uint32_t llama_n_ubatch (const struct llama_context * ctx); + LLAMA_API uint32_t llama_n_seq_max (const struct llama_context * ctx); + LLAMA_API uint32_t llama_n_rs_seq (const struct llama_context * ctx); + + DEPRECATED(LLAMA_API int32_t llama_n_ctx_train(const struct llama_model * model), "use llama_model_n_ctx_train instead"); + DEPRECATED(LLAMA_API int32_t llama_n_embd (const struct llama_model * model), "use llama_model_n_embd instead"); + DEPRECATED(LLAMA_API int32_t llama_n_layer (const struct llama_model * model), "use llama_model_n_layer instead"); + DEPRECATED(LLAMA_API int32_t llama_n_head (const struct llama_model * model), "use llama_model_n_head instead"); + + DEPRECATED(LLAMA_API int32_t llama_n_vocab (const struct llama_vocab * vocab), "use llama_vocab_n_tokens instead"); + + LLAMA_API const struct llama_model * llama_get_model (const struct llama_context * ctx); + LLAMA_API llama_memory_t llama_get_memory (const struct llama_context * ctx); + LLAMA_API enum llama_pooling_type llama_pooling_type(const struct llama_context * ctx); // TODO: rename to llama_get_pooling_type + + LLAMA_API const struct llama_vocab * llama_model_get_vocab(const struct llama_model * model); + LLAMA_API enum llama_rope_type llama_model_rope_type(const struct llama_model * model); + + LLAMA_API int32_t llama_model_n_ctx_train (const struct llama_model * model); + LLAMA_API int32_t llama_model_n_embd (const struct llama_model * model); + LLAMA_API int32_t llama_model_n_embd_inp (const struct llama_model * model); + LLAMA_API int32_t llama_model_n_embd_out (const struct llama_model * model); + LLAMA_API int32_t llama_model_n_layer (const struct llama_model * model); + LLAMA_API int32_t llama_model_n_layer_nextn(const struct llama_model * model); + LLAMA_API int32_t llama_model_n_head (const struct llama_model * model); + LLAMA_API int32_t llama_model_n_head_kv (const struct llama_model * model); + LLAMA_API int32_t llama_model_n_swa (const struct llama_model * model); + + // Get the model's RoPE frequency scaling factor + LLAMA_API float llama_model_rope_freq_scale_train(const struct llama_model * model); + + // Returns the number of classifier outputs (only valid for classifier models) + // Undefined behavior for non-classifier models + LLAMA_API uint32_t llama_model_n_cls_out(const struct llama_model * model); + + // Returns label of classifier output by index ( 1` + // p0 < 0 : [0, p1] + // p1 < 0 : [p0, inf) + LLAMA_API void llama_memory_seq_div( + llama_memory_t mem, + llama_seq_id seq_id, + llama_pos p0, + llama_pos p1, + int d); + + // Returns the smallest position present in the memory for the specified sequence + // This is typically non-zero only for SWA caches + // Note that all positions in the range [pos_min, pos_max] are guaranteed to be present in the memory + // Return -1 if the sequence is empty + LLAMA_API llama_pos llama_memory_seq_pos_min( + llama_memory_t mem, + llama_seq_id seq_id); + + // Returns the largest position present in the memory for the specified sequence + // Note that all positions in the range [pos_min, pos_max] are guaranteed to be present in the memory + // Return -1 if the sequence is empty + LLAMA_API llama_pos llama_memory_seq_pos_max( + llama_memory_t mem, + llama_seq_id seq_id); + + // Check if the memory supports shifting + LLAMA_API bool llama_memory_can_shift(llama_memory_t mem); + + // + // State / sessions + // + + // Returns the *actual* size in bytes of the state + // (logits, embedding and memory) + // Only use when saving the state, not when restoring it, otherwise the size may be too small. + LLAMA_API size_t llama_state_get_size(struct llama_context * ctx); + LLAMA_API DEPRECATED(size_t llama_get_state_size(struct llama_context * ctx), + "use llama_state_get_size instead"); + + // Copies the state to the specified destination address. + // Destination needs to have allocated enough memory. + // Returns the number of bytes copied + LLAMA_API size_t llama_state_get_data( + struct llama_context * ctx, + uint8_t * dst, + size_t size); + LLAMA_API DEPRECATED(size_t llama_copy_state_data( + struct llama_context * ctx, + uint8_t * dst), + "use llama_state_get_data instead"); + + // Set the state reading from the specified address + // Returns the number of bytes read + LLAMA_API size_t llama_state_set_data( + struct llama_context * ctx, + const uint8_t * src, + size_t size); + LLAMA_API DEPRECATED(size_t llama_set_state_data( + struct llama_context * ctx, + const uint8_t * src), + "use llama_state_set_data instead"); + + // Save/load session file + LLAMA_API bool llama_state_load_file( + struct llama_context * ctx, + const char * path_session, + llama_token * tokens_out, + size_t n_token_capacity, + size_t * n_token_count_out); + LLAMA_API DEPRECATED(bool llama_load_session_file( + struct llama_context * ctx, + const char * path_session, + llama_token * tokens_out, + size_t n_token_capacity, + size_t * n_token_count_out), + "use llama_state_load_file instead"); + + LLAMA_API bool llama_state_save_file( + struct llama_context * ctx, + const char * path_session, + const llama_token * tokens, + size_t n_token_count); + LLAMA_API DEPRECATED(bool llama_save_session_file( + struct llama_context * ctx, + const char * path_session, + const llama_token * tokens, + size_t n_token_count), + "use llama_state_save_file instead"); + + // Get the exact size needed to copy the state of a single sequence + LLAMA_API size_t llama_state_seq_get_size( + struct llama_context * ctx, + llama_seq_id seq_id); + + // Copy the state of a single sequence into the specified buffer + LLAMA_API size_t llama_state_seq_get_data( + struct llama_context * ctx, + uint8_t * dst, + size_t size, + llama_seq_id seq_id); + + // Copy the sequence data (originally copied with `llama_state_seq_get_data`) into the specified sequence + // Returns: + // - Positive: Ok + // - Zero: Failed to load + LLAMA_API size_t llama_state_seq_set_data( + struct llama_context * ctx, + const uint8_t * src, + size_t size, + llama_seq_id dest_seq_id); + + LLAMA_API size_t llama_state_seq_save_file( + struct llama_context * ctx, + const char * filepath, + llama_seq_id seq_id, + const llama_token * tokens, + size_t n_token_count); + + LLAMA_API size_t llama_state_seq_load_file( + struct llama_context * ctx, + const char * filepath, + llama_seq_id dest_seq_id, + llama_token * tokens_out, + size_t n_token_capacity, + size_t * n_token_count_out); + +#define LLAMA_STATE_SEQ_FLAGS_NONE 0 + +// for backwards-compat +#define LLAMA_STATE_SEQ_FLAGS_SWA_ONLY 1 + +// work only with partial states, such as SWA KV cache or recurrent cache (e.g. Mamba) +#define LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY 1 + +// Keeps the tensor data on device buffers (i.e. not accessible in host memory, but faster save/load). +// Getting the state for a seq_id with this flag invalidates all prior states gotten for that seq_id with this flag. +#define LLAMA_STATE_SEQ_FLAGS_ON_DEVICE 2 + + typedef uint32_t llama_state_seq_flags; + + LLAMA_API size_t llama_state_seq_get_size_ext( + struct llama_context * ctx, + llama_seq_id seq_id, + llama_state_seq_flags flags); + + LLAMA_API size_t llama_state_seq_get_data_ext( + struct llama_context * ctx, + uint8_t * dst, + size_t size, + llama_seq_id seq_id, + llama_state_seq_flags flags); + + LLAMA_API size_t llama_state_seq_set_data_ext( + struct llama_context * ctx, + const uint8_t * src, + size_t size, + llama_seq_id dest_seq_id, + llama_state_seq_flags flags); + + // + // Decoding + // + + // Return batch for single sequence of tokens + // The sequence ID will be fixed to 0 + // The position of the tokens will be tracked automatically by llama_decode + // + // NOTE: this is a helper function to facilitate transition to the new batch API - avoid using it + // + LLAMA_API struct llama_batch llama_batch_get_one( + llama_token * tokens, + int32_t n_tokens); + + // Allocates a batch of tokens on the heap that can hold a maximum of n_tokens + // Each token can be assigned up to n_seq_max sequence ids + // The batch has to be freed with llama_batch_free() + // If embd != 0, llama_batch.embd will be allocated with size of n_tokens * embd * sizeof(float) + // Otherwise, llama_batch.token will be allocated to store n_tokens llama_token + // The rest of the llama_batch members are allocated with size n_tokens + // All members are left uninitialized + LLAMA_API struct llama_batch llama_batch_init( + int32_t n_tokens, + int32_t embd, + int32_t n_seq_max); + + // Frees a batch of tokens allocated with llama_batch_init() + LLAMA_API void llama_batch_free(struct llama_batch batch); + + // Process a batch of tokens. + // In contrast to llama_decode() - this call does not use KV cache. + // For encode-decoder contexts, processes the batch using the encoder. + // Can store the encoder output internally for later use by the decoder's cross-attention layers. + // 0 - success + // < 0 - error. the memory state is restored to the state before this call + LLAMA_API int32_t llama_encode( + struct llama_context * ctx, + struct llama_batch batch); + + // Process a batch of tokens. + // Requires the context to have a memory. + // For encode-decoder contexts, processes the batch using the decoder. + // Positive return values does not mean a fatal error, but rather a warning. + // Upon fatal-error or abort, the ubatches that managed to be been processed will remain in the memory state of the context + // To handle this correctly, query the memory state using llama_memory_seq_pos_min() and llama_memory_seq_pos_max() + // Upon other return values, the memory state is restored to the state before this call + // 0 - success + // 1 - could not find a KV slot for the batch (try reducing the size of the batch or increase the context) + // 2 - aborted (processed ubatches will remain in the context's memory) + // -1 - invalid input batch + // < -1 - fatal error (processed ubatches will remain in the context's memory) + LLAMA_API int32_t llama_decode( + struct llama_context * ctx, + struct llama_batch batch); + + // Set the number of threads used for decoding + // n_threads is the number of threads used for generation (single token) + // n_threads_batch is the number of threads used for prompt and batch processing (multiple tokens) + LLAMA_API void llama_set_n_threads(struct llama_context * ctx, int32_t n_threads, int32_t n_threads_batch); + + // Get the number of threads used for generation of a single token. + LLAMA_API int32_t llama_n_threads(struct llama_context * ctx); + + // Get the number of threads used for prompt and batch processing (multiple token). + LLAMA_API int32_t llama_n_threads_batch(struct llama_context * ctx); + + // Set whether the context outputs embeddings or not + // TODO: rename to avoid confusion with llama_get_embeddings() + LLAMA_API void llama_set_embeddings(struct llama_context * ctx, bool embeddings); + + // Set whether to use causal attention or not + // If set to true, the model will only attend to the past tokens + LLAMA_API void llama_set_causal_attn(struct llama_context * ctx, bool causal_attn); + + // Set whether the model is in warmup mode or not + // If true, all model tensors are activated during llama_decode() to load and cache their weights. + // + // note: using this can cause extra graph reallocations because it changes the graph topology with MoE models, + // so it is generally not recommended to use in practice. will be removed in the future + DEPRECATED(LLAMA_API void llama_set_warmup(struct llama_context * ctx, bool warmup), + "user code should do warmup runs manually [TAG_LLAMA_GRAPH_NO_WARMUP]"); + + // Set abort callback + LLAMA_API void llama_set_abort_callback(struct llama_context * ctx, ggml_abort_callback abort_callback, void * abort_callback_data); + + // Wait until all computations are finished + // This is automatically done when using one of the functions below to obtain the computation results + // and is not necessary to call it explicitly in most cases + LLAMA_API void llama_synchronize(struct llama_context * ctx); + + // Token logits obtained from the last call to llama_decode() + // The logits for which llama_batch.logits[i] != 0 are stored contiguously + // in the order they have appeared in the batch. + // Rows: number of tokens for which llama_batch.logits[i] != 0 + // Cols: n_vocab + // TODO: deprecate in favor of llama_get_logits_ith() (ref: https://github.com/ggml-org/llama.cpp/pull/14853#issuecomment-3113143522) + LLAMA_API float * llama_get_logits(struct llama_context * ctx); + + // Logits for the ith token. For positive indices, Equivalent to: + // llama_get_logits(ctx) + ctx->output_ids[i]*n_vocab + // Negative indices can be used to access logits in reverse order, -1 is the last logit. + // returns NULL for invalid ids. + LLAMA_API float * llama_get_logits_ith(struct llama_context * ctx, int32_t i); + + // Get all output token embeddings. + // when pooling_type == LLAMA_POOLING_TYPE_NONE or when using a generative model, + // the embeddings for which llama_batch.logits[i] != 0 are stored contiguously + // in the order they have appeared in the batch. + // shape: [n_outputs*n_embd] + // Otherwise, returns NULL. + // TODO: deprecate in favor of llama_get_embeddings_ith() (ref: https://github.com/ggml-org/llama.cpp/pull/14853#issuecomment-3113143522) + LLAMA_API float * llama_get_embeddings(struct llama_context * ctx); + + // Get the embeddings for the ith token. For positive indices, Equivalent to: + // llama_get_embeddings(ctx) + ctx->output_ids[i]*n_embd + // Negative indices can be used to access embeddings in reverse order, -1 is the last embedding. + // shape: [n_embd] (1-dimensional) + // returns NULL for invalid ids. + LLAMA_API float * llama_get_embeddings_ith(struct llama_context * ctx, int32_t i); + + // Get the embeddings for a sequence id + // Returns NULL if pooling_type is LLAMA_POOLING_TYPE_NONE + // when pooling_type == LLAMA_POOLING_TYPE_RANK, returns float[n_cls_out] with the rank(s) of the sequence + // otherwise: float[n_embd] (1-dimensional) + LLAMA_API float * llama_get_embeddings_seq(struct llama_context * ctx, llama_seq_id seq_id); + + // + // backend sampling API [EXPERIMENTAL] + // note: use only if the llama_context was created with at least one llama_sampler_seq_config + // + + // Get the backend sampled token for the ith token. + // Returns LLAMA_TOKEN_NULL if no token was sampled. + LLAMA_API llama_token llama_get_sampled_token_ith(struct llama_context * ctx, int32_t i); + + // Get the backend sampled probabilities for the ith token + // The index matches llama_get_sampled_token_ith(). + // Returns NULL if no probabilities were generated. + LLAMA_API float * llama_get_sampled_probs_ith (struct llama_context * ctx, int32_t i); + LLAMA_API uint32_t llama_get_sampled_probs_count_ith(struct llama_context * ctx, int32_t i); + + // Get the backend sampled logits for the ith token + // Returns NULL if no logits were sampled. + LLAMA_API float * llama_get_sampled_logits_ith (struct llama_context * ctx, int32_t i); + LLAMA_API uint32_t llama_get_sampled_logits_count_ith(struct llama_context * ctx, int32_t i); + + // Get the backend sampled candidates (token ids) for the ith token + // These are needed to map probability/logit indices to vocab token ids. + // Returns NULL if no candidates were sampled. + LLAMA_API llama_token * llama_get_sampled_candidates_ith (struct llama_context * ctx, int32_t i); + LLAMA_API uint32_t llama_get_sampled_candidates_count_ith(struct llama_context * ctx, int32_t i); + + // + // Vocab + // + + LLAMA_API const char * llama_vocab_get_text(const struct llama_vocab * vocab, llama_token token); + + LLAMA_API float llama_vocab_get_score(const struct llama_vocab * vocab, llama_token token); + + LLAMA_API enum llama_token_attr llama_vocab_get_attr(const struct llama_vocab * vocab, llama_token token); + + // Check if the token is supposed to end generation (end-of-generation, eg. EOS, EOT, etc.) + LLAMA_API bool llama_vocab_is_eog(const struct llama_vocab * vocab, llama_token token); + + // Identify if Token Id is a control token or a render-able token + LLAMA_API bool llama_vocab_is_control(const struct llama_vocab * vocab, llama_token token); + + // Special tokens + LLAMA_API llama_token llama_vocab_bos(const struct llama_vocab * vocab); // beginning-of-sentence + LLAMA_API llama_token llama_vocab_eos(const struct llama_vocab * vocab); // end-of-sentence + LLAMA_API llama_token llama_vocab_eot(const struct llama_vocab * vocab); // end-of-turn + LLAMA_API llama_token llama_vocab_sep(const struct llama_vocab * vocab); // sentence separator + LLAMA_API llama_token llama_vocab_nl (const struct llama_vocab * vocab); // next-line + LLAMA_API llama_token llama_vocab_pad(const struct llama_vocab * vocab); // padding + LLAMA_API llama_token llama_vocab_mask(const struct llama_vocab * vocab); // mask + + LLAMA_API bool llama_vocab_get_add_bos(const struct llama_vocab * vocab); + LLAMA_API bool llama_vocab_get_add_eos(const struct llama_vocab * vocab); + LLAMA_API bool llama_vocab_get_add_sep(const struct llama_vocab * vocab); + + LLAMA_API llama_token llama_vocab_fim_pre(const struct llama_vocab * vocab); + LLAMA_API llama_token llama_vocab_fim_suf(const struct llama_vocab * vocab); + LLAMA_API llama_token llama_vocab_fim_mid(const struct llama_vocab * vocab); + LLAMA_API llama_token llama_vocab_fim_pad(const struct llama_vocab * vocab); + LLAMA_API llama_token llama_vocab_fim_rep(const struct llama_vocab * vocab); + LLAMA_API llama_token llama_vocab_fim_sep(const struct llama_vocab * vocab); + + DEPRECATED(LLAMA_API const char * llama_token_get_text(const struct llama_vocab * vocab, llama_token token), "use llama_vocab_get_text instead"); + DEPRECATED(LLAMA_API float llama_token_get_score(const struct llama_vocab * vocab, llama_token token), "use llama_vocab_get_score instead"); + DEPRECATED(LLAMA_API enum llama_token_attr llama_token_get_attr(const struct llama_vocab * vocab, llama_token token), "use llama_vocab_get_attr instead"); + DEPRECATED(LLAMA_API bool llama_token_is_eog(const struct llama_vocab * vocab, llama_token token), "use llama_vocab_is_eog instead"); + DEPRECATED(LLAMA_API bool llama_token_is_control(const struct llama_vocab * vocab, llama_token token), "use llama_vocab_is_control instead"); + DEPRECATED(LLAMA_API llama_token llama_token_bos(const struct llama_vocab * vocab), "use llama_vocab_bos instead"); + DEPRECATED(LLAMA_API llama_token llama_token_eos(const struct llama_vocab * vocab), "use llama_vocab_eos instead"); + DEPRECATED(LLAMA_API llama_token llama_token_eot(const struct llama_vocab * vocab), "use llama_vocab_eot instead"); + DEPRECATED(LLAMA_API llama_token llama_token_cls(const struct llama_vocab * vocab), "use llama_vocab_cls instead"); + DEPRECATED(LLAMA_API llama_token llama_token_sep(const struct llama_vocab * vocab), "use llama_vocab_sep instead"); + DEPRECATED(LLAMA_API llama_token llama_token_nl (const struct llama_vocab * vocab), "use llama_vocab_nl instead"); + DEPRECATED(LLAMA_API llama_token llama_token_pad(const struct llama_vocab * vocab), "use llama_vocab_pad instead"); + DEPRECATED(LLAMA_API bool llama_add_bos_token(const struct llama_vocab * vocab), "use llama_vocab_get_add_bos instead"); + DEPRECATED(LLAMA_API bool llama_add_eos_token(const struct llama_vocab * vocab), "use llama_vocab_get_add_eos instead"); + DEPRECATED(LLAMA_API llama_token llama_token_fim_pre(const struct llama_vocab * vocab), "use llama_vocab_fim_pre instead"); + DEPRECATED(LLAMA_API llama_token llama_token_fim_suf(const struct llama_vocab * vocab), "use llama_vocab_fim_suf instead"); + DEPRECATED(LLAMA_API llama_token llama_token_fim_mid(const struct llama_vocab * vocab), "use llama_vocab_fim_mid instead"); + DEPRECATED(LLAMA_API llama_token llama_token_fim_pad(const struct llama_vocab * vocab), "use llama_vocab_fim_pad instead"); + DEPRECATED(LLAMA_API llama_token llama_token_fim_rep(const struct llama_vocab * vocab), "use llama_vocab_fim_rep instead"); + DEPRECATED(LLAMA_API llama_token llama_token_fim_sep(const struct llama_vocab * vocab), "use llama_vocab_fim_sep instead"); + + // CLS is equivalent to BOS + DEPRECATED(LLAMA_API llama_token llama_vocab_cls(const struct llama_vocab * vocab), // classification + "use llama_vocab_bos instead"); + + // + // Tokenization + // + // The API is thread-safe. + // + + /// @details Convert the provided text into tokens. + /// @param tokens The tokens pointer must be large enough to hold the resulting tokens. + /// @return Returns the number of tokens on success, no more than n_tokens_max + /// @return Returns a negative number on failure - the number of tokens that would have been returned + /// @return Returns INT32_MIN on overflow (e.g., tokenization result size exceeds int32_t limit) + /// @param add_special Allow to add BOS and EOS tokens if model is configured to do so. + /// @param parse_special Allow tokenizing special and/or control tokens which otherwise are not exposed and treated + /// as plaintext. Does not insert a leading space. + LLAMA_API int32_t llama_tokenize( + const struct llama_vocab * vocab, + const char * text, + int32_t text_len, + llama_token * tokens, + int32_t n_tokens_max, + bool add_special, + bool parse_special); + + // Token Id -> Piece. + // Uses the vocabulary in the provided context. + // Does not write null terminator to the buffer. + // User can skip up to 'lstrip' leading spaces before copying (useful when encoding/decoding multiple tokens with 'add_space_prefix') + // @param special If true, special tokens are rendered in the output. + LLAMA_API int32_t llama_token_to_piece( + const struct llama_vocab * vocab, + llama_token token, + char * buf, + int32_t length, + int32_t lstrip, + bool special); + + /// @details Convert the provided tokens into text (inverse of llama_tokenize()). + /// @param text The char pointer must be large enough to hold the resulting text. + /// @return Returns the number of chars/bytes on success, no more than text_len_max. + /// @return Returns a negative number on failure - the number of chars/bytes that would have been returned. + /// @param remove_special Allow to remove BOS and EOS tokens if model is configured to do so. + /// @param unparse_special If true, special tokens are rendered in the output. + LLAMA_API int32_t llama_detokenize( + const struct llama_vocab * vocab, + const llama_token * tokens, + int32_t n_tokens, + char * text, + int32_t text_len_max, + bool remove_special, + bool unparse_special); + + // + // Chat templates + // + + /// Apply chat template. Inspired by hf apply_chat_template() on python. + /// + /// NOTE: This function does not use a jinja parser. It only support a pre-defined list of template. See more: https://github.com/ggml-org/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template + /// @param tmpl A Jinja template to use for this chat. + /// @param chat Pointer to a list of multiple llama_chat_message + /// @param n_msg Number of llama_chat_message in this chat + /// @param add_ass Whether to end the prompt with the token(s) that indicate the start of an assistant message. + /// @param buf A buffer to hold the output formatted prompt. The recommended alloc size is 2 * (total number of characters of all messages) + /// @param length The size of the allocated buffer + /// @return The total number of bytes of the formatted prompt. If is it larger than the size of buffer, you may need to re-alloc it and then re-apply the template. + LLAMA_API int32_t llama_chat_apply_template( + const char * tmpl, + const struct llama_chat_message * chat, + size_t n_msg, + bool add_ass, + char * buf, + int32_t length); + + // Get list of built-in chat templates + LLAMA_API int32_t llama_chat_builtin_templates(const char ** output, size_t len); + + // + // Sampling API + // + // Sample usage: + // + // // prepare the sampling chain at the start + // auto sparams = llama_sampler_chain_default_params(); + // + // llama_sampler * smpl = llama_sampler_chain_init(sparams); + // + // llama_sampler_chain_add(smpl, llama_sampler_init_top_k(50)); + // llama_sampler_chain_add(smpl, llama_sampler_init_top_p(0.9, 1)); + // llama_sampler_chain_add(smpl, llama_sampler_init_temp (0.8)); + // + // // typically, the chain should end with a sampler such as "greedy", "dist" or "mirostat" + // // this sampler will be responsible to select the actual token + // llama_sampler_chain_add(smpl, llama_sampler_init_dist(seed)); + // + // ... + // + // // decoding loop: + // while (...) { + // ... + // + // llama_decode(ctx, batch); + // + // // sample from the logits of the last token in the batch + // const llama_token id = llama_sampler_sample(smpl, ctx, -1); + // + // ... + // } + // + // llama_sampler_free(smpl); + // + + typedef void * llama_sampler_context_t; + + struct llama_sampler_data { + struct ggml_tensor * logits; + struct ggml_tensor * probs; + struct ggml_tensor * sampled; + struct ggml_tensor * candidates; + }; + + // user code can implement the interface below in order to create custom llama_sampler + struct llama_sampler_i { + const char * (*name) (const struct llama_sampler * smpl); // can be NULL + void (*accept)( struct llama_sampler * smpl, llama_token token); // can be NULL + void (*apply) ( struct llama_sampler * smpl, llama_token_data_array * cur_p); // required + void (*reset) ( struct llama_sampler * smpl); // can be NULL + struct llama_sampler * (*clone) (const struct llama_sampler * smpl); // can be NULL if ctx is NULL + void (*free) ( struct llama_sampler * smpl); // can be NULL if ctx is NULL + + // [EXPERIMENTAL] + // backend sampling interface: + + // return true if the backend supports all ops needed by the sampler + // note: call once per sampler + bool (*backend_init)(struct llama_sampler * smpl, ggml_backend_buffer_type_t buft); + + // call after .backend_apply() + void (*backend_accept)( + struct llama_sampler * smpl, + struct ggml_context * ctx, + struct ggml_cgraph * gf, + struct ggml_tensor * selected_token); + + // call after .backend_init() + void (*backend_apply)( + struct llama_sampler * smpl, + struct ggml_context * ctx, + struct ggml_cgraph * gf, + struct llama_sampler_data * data); + + // called before graph execution to set inputs for the current ubatch + void (*backend_set_input)(struct llama_sampler * smpl); + }; + + struct llama_sampler { + struct llama_sampler_i * iface; + + llama_sampler_context_t ctx; + }; + + // [EXPERIMENTAL] + // attach a sampler to the context + // note: prefer initializing the context with llama_context_params.samplers when possible + LLAMA_API bool llama_set_sampler(struct llama_context * ctx, llama_seq_id seq_id, struct llama_sampler * smpl); + + // mirror of llama_sampler_i: + LLAMA_API struct llama_sampler * llama_sampler_init ( struct llama_sampler_i * iface, llama_sampler_context_t ctx); + LLAMA_API const char * llama_sampler_name (const struct llama_sampler * smpl); + LLAMA_API void llama_sampler_accept( struct llama_sampler * smpl, llama_token token); + LLAMA_API void llama_sampler_apply ( struct llama_sampler * smpl, llama_token_data_array * cur_p); + LLAMA_API void llama_sampler_reset ( struct llama_sampler * smpl); + LLAMA_API struct llama_sampler * llama_sampler_clone (const struct llama_sampler * smpl); + // important: do not free if the sampler has been added to a llama_sampler_chain (via llama_sampler_chain_add) + LLAMA_API void llama_sampler_free ( struct llama_sampler * smpl); + + // llama_sampler_chain + // a type of llama_sampler that can chain multiple samplers one after another + + LLAMA_API struct llama_sampler * llama_sampler_chain_init(struct llama_sampler_chain_params params); + + // important: takes ownership of the sampler object and will free it when llama_sampler_free is called + LLAMA_API void llama_sampler_chain_add( struct llama_sampler * chain, struct llama_sampler * smpl); + + // return NULL if: + // - the sampler is NULL + // - the sampler is not a llama_sampler_chain + // - the index is out of bounds, unless i == -1 + // - if i == -1, returns the chain itself (can be used to check if the sampler is a chain) + LLAMA_API struct llama_sampler * llama_sampler_chain_get( struct llama_sampler * chain, int32_t i); + + // the total number of samplers in the chain + LLAMA_API int llama_sampler_chain_n (const struct llama_sampler * chain); + + // after removing a sampler, the chain will no longer own it, and it will not be freed when the chain is freed + LLAMA_API struct llama_sampler * llama_sampler_chain_remove( struct llama_sampler * chain, int32_t i); + + // available samplers: + + LLAMA_API struct llama_sampler * llama_sampler_init_greedy(void); + + /// seed == LLAMA_DEFAULT_SEED to use a random seed. + LLAMA_API struct llama_sampler * llama_sampler_init_dist(uint32_t seed); + + /// @details Top-K sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751 + /// Setting k <= 0 makes this a noop + LLAMA_API struct llama_sampler * llama_sampler_init_top_k (int32_t k); + + /// @details Nucleus sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751 + LLAMA_API struct llama_sampler * llama_sampler_init_top_p (float p, size_t min_keep); + + /// @details Minimum P sampling as described in https://github.com/ggml-org/llama.cpp/pull/3841 + LLAMA_API struct llama_sampler * llama_sampler_init_min_p (float p, size_t min_keep); + + /// @details Locally Typical Sampling implementation described in the paper https://arxiv.org/abs/2202.00666. + LLAMA_API struct llama_sampler * llama_sampler_init_typical (float p, size_t min_keep); + + /// #details Updates the logits l_i` = l_i/t. When t <= 0.0f, the maximum logit is kept at it's original value, the rest are set to -inf + LLAMA_API struct llama_sampler * llama_sampler_init_temp (float t); + + /// @details Dynamic temperature implementation (a.k.a. entropy) described in the paper https://arxiv.org/abs/2309.02772. + LLAMA_API struct llama_sampler * llama_sampler_init_temp_ext (float t, float delta, float exponent); + + /// @details XTC sampler as described in https://github.com/oobabooga/text-generation-webui/pull/6335 + LLAMA_API struct llama_sampler * llama_sampler_init_xtc (float p, float t, size_t min_keep, uint32_t seed); + + /// @details Top n sigma sampling as described in academic paper "Top-nσ: Not All Logits Are You Need" https://arxiv.org/pdf/2411.07641 + LLAMA_API struct llama_sampler * llama_sampler_init_top_n_sigma(float n); + + /// @details Mirostat 1.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words. + /// @param candidates A vector of `llama_token_data` containing the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text. + /// @param tau The target cross-entropy (or surprise) value you want to achieve for the generated text. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text. + /// @param eta The learning rate used to update `mu` based on the error between the target and observed surprisal of the sampled word. A larger learning rate will cause `mu` to be updated more quickly, while a smaller learning rate will result in slower updates. + /// @param m The number of tokens considered in the estimation of `s_hat`. This is an arbitrary value that is used to calculate `s_hat`, which in turn helps to calculate the value of `k`. In the paper, they use `m = 100`, but you can experiment with different values to see how it affects the performance of the algorithm. + /// @param mu Maximum cross-entropy. This value is initialized to be twice the target cross-entropy (`2 * tau`) and is updated in the algorithm based on the error between the target and observed surprisal. + LLAMA_API struct llama_sampler * llama_sampler_init_mirostat( + int32_t n_vocab, + uint32_t seed, + float tau, + float eta, + int32_t m); + + /// @details Mirostat 2.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words. + /// @param candidates A vector of `llama_token_data` containing the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text. + /// @param tau The target cross-entropy (or surprise) value you want to achieve for the generated text. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text. + /// @param eta The learning rate used to update `mu` based on the error between the target and observed surprisal of the sampled word. A larger learning rate will cause `mu` to be updated more quickly, while a smaller learning rate will result in slower updates. + /// @param mu Maximum cross-entropy. This value is initialized to be twice the target cross-entropy (`2 * tau`) and is updated in the algorithm based on the error between the target and observed surprisal. + LLAMA_API struct llama_sampler * llama_sampler_init_mirostat_v2( + uint32_t seed, + float tau, + float eta); + + /// @details Initializes a GBNF grammar, see grammars/README.md for details. + /// @param vocab The vocabulary that this grammar will be used with. + /// @param grammar_str The production rules for the grammar, encoded as a string. Returns an empty grammar if empty. Returns NULL if parsing of grammar_str fails. + /// @param grammar_root The name of the start symbol for the grammar. + LLAMA_API struct llama_sampler * llama_sampler_init_grammar( + const struct llama_vocab * vocab, + const char * grammar_str, + const char * grammar_root); + + DEPRECATED(LLAMA_API struct llama_sampler * llama_sampler_init_grammar_lazy( + const struct llama_vocab * vocab, + const char * grammar_str, + const char * grammar_root, + const char ** trigger_words, + size_t num_trigger_words, + const llama_token * trigger_tokens, + size_t num_trigger_tokens), + "use llama_sampler_init_grammar_lazy_patterns instead"); + + + /// @details Lazy grammar sampler, introduced in https://github.com/ggml-org/llama.cpp/pull/9639 + /// @param trigger_patterns A list of patterns that will trigger the grammar sampler. Pattern will be matched from the start of the generation output, and grammar sampler will be fed content starting from its first match group. + /// @param trigger_tokens A list of tokens that will trigger the grammar sampler. Grammar sampler will be fed content starting from the trigger token included. + LLAMA_API struct llama_sampler * llama_sampler_init_grammar_lazy_patterns( + const struct llama_vocab * vocab, + const char * grammar_str, + const char * grammar_root, + const char ** trigger_patterns, + size_t num_trigger_patterns, + const llama_token * trigger_tokens, + size_t num_trigger_tokens); + + + /// NOTE: Avoid using on the full vocabulary as searching for repeated tokens can become slow. For example, apply top-k or top-p sampling first. + LLAMA_API struct llama_sampler * llama_sampler_init_penalties( + int32_t penalty_last_n, // last n tokens to penalize (0 = disable penalty, -1 = context size) + float penalty_repeat, // 1.0 = disabled + float penalty_freq, // 0.0 = disabled + float penalty_present); // 0.0 = disabled + + /// @details DRY sampler, designed by p-e-w, as described in: https://github.com/oobabooga/text-generation-webui/pull/5677, porting Koboldcpp implementation authored by pi6am: https://github.com/LostRuins/koboldcpp/pull/982 + LLAMA_API struct llama_sampler * llama_sampler_init_dry( + const struct llama_vocab * vocab, + int32_t n_ctx_train, + float dry_multiplier, + float dry_base, + int32_t dry_allowed_length, + int32_t dry_penalty_last_n, + const char ** seq_breakers, + size_t num_breakers); + + /// adaptive-p: select tokens near a configurable target probability over time. + /// + /// the adaptive-p sampler transforms the token probability distribution to favor tokens + /// that fall near a user-configurable probability target. + /// + /// internally, the sampler maintains an exponential moving average of the *ORIGINAL* + /// probabilities of selected tokens at each sampling step. it uses this EMA to compute an + /// adapted target probability at each sampling step, thus maintaining the desired target + /// probability over time. + /// + /// adaptive-p selects a token ID rather than just mutating candidates, so it must be last + /// in the sampler chain (like mirostat, dist, greedy). + /// + /// only mild truncation before this sampler is recommended. we suggest applying min-p + /// before adaptive-p as the only other active sampler in the chain. + /// + /// @param target select tokens near this probability (valid range 0.0 to 1.0; negative = disabled) + /// @param decay EMA decay for adaptation; history ≈ 1/(1-decay) tokens (valid range 0.0 - 0.99) + /// @param seed RNG seed + /// + /// ref: https://github.com/ggml-org/llama.cpp/pull/17927 + /// + LLAMA_API struct llama_sampler * llama_sampler_init_adaptive_p( + float target, + float decay, + uint32_t seed); + + LLAMA_API struct llama_sampler * llama_sampler_init_logit_bias( + int32_t n_vocab, + int32_t n_logit_bias, + const llama_logit_bias * logit_bias); + + // this sampler is meant to be used for fill-in-the-middle infilling + // it's supposed to be used after top_k + top_p sampling + // + // 1. if the sum of the EOG probs times the number of candidates is higher than the sum of the other probs -> pick EOG + // 2. combine probs of tokens that have the same prefix + // + // example: + // + // - before: + // "hel": 0.5 + // "hell": 0.2 + // "hello": 0.1 + // "dummy": 0.1 + // + // - after: + // "hel": 0.8 + // "dummy": 0.1 + // + // 3. discard non-EOG tokens with low prob + // 4. if no tokens are left -> pick EOT + // + LLAMA_API struct llama_sampler * llama_sampler_init_infill(const struct llama_vocab * vocab); + + // Returns the seed used by the sampler if applicable, LLAMA_DEFAULT_SEED otherwise + LLAMA_API uint32_t llama_sampler_get_seed(const struct llama_sampler * smpl); + + /// @details Sample and accept a token from the idx-th output of the last evaluation + // + // Shorthand for: + // const auto * logits = llama_get_logits_ith(ctx, idx); + // llama_token_data_array cur_p = { ... init from logits ... }; + // llama_sampler_apply(smpl, &cur_p); + // auto token = cur_p.data[cur_p.selected].id; + // llama_sampler_accept(smpl, token); + // return token; + // Returns the sampled token + LLAMA_API llama_token llama_sampler_sample(struct llama_sampler * smpl, struct llama_context * ctx, int32_t idx); + + // TODO: extend in the future + //LLAMA_API void llama_decode_with_sampler(struct llama_context * ctx, struct llama_sampler * smpl, struct llama_batch batch, ...); + + // + // Model split + // + + /// @details Build a split GGUF final path for this chunk. + /// llama_split_path(split_path, sizeof(split_path), "/models/ggml-model-q4_0", 2, 4) => split_path = "/models/ggml-model-q4_0-00002-of-00004.gguf" + // Returns the split_path length. + LLAMA_API int32_t llama_split_path(char * split_path, size_t maxlen, const char * path_prefix, int32_t split_no, int32_t split_count); + + /// @details Extract the path prefix from the split_path if and only if the split_no and split_count match. + /// llama_split_prefix(split_prefix, 64, "/models/ggml-model-q4_0-00002-of-00004.gguf", 2, 4) => split_prefix = "/models/ggml-model-q4_0" + // Returns the split_prefix length. + LLAMA_API int32_t llama_split_prefix(char * split_prefix, size_t maxlen, const char * split_path, int32_t split_no, int32_t split_count); + + // Print system information + LLAMA_API const char * llama_print_system_info(void); + + // Set callback for all future logging events. + // If this is not called, or NULL is supplied, everything is output on stderr. + // The logger state is global so these functions are NOT thread safe. + LLAMA_API void llama_log_get(ggml_log_callback * log_callback, void ** user_data); + LLAMA_API void llama_log_set(ggml_log_callback log_callback, void * user_data); + + // + // Performance utils + // + // NOTE: Used by llama.cpp examples/tools, avoid using in third-party apps. Instead, do your own performance measurements. + // + + struct llama_perf_context_data { + // ms == milliseconds + double t_start_ms; // absolute start time + double t_load_ms; // time needed for loading the model + double t_p_eval_ms; // time needed for processing the prompt + double t_eval_ms; // time needed for generating tokens + + int32_t n_p_eval; // number of prompt tokens + int32_t n_eval; // number of generated tokens + int32_t n_reused; // number of times a ggml compute graph had been reused + }; + + struct llama_perf_sampler_data { + double t_sample_ms; // time needed for sampling in ms + + int32_t n_sample; // number of sampled tokens + }; + + LLAMA_API struct llama_perf_context_data llama_perf_context (const struct llama_context * ctx); + LLAMA_API void llama_perf_context_print(const struct llama_context * ctx); + LLAMA_API void llama_perf_context_reset( struct llama_context * ctx); + + // NOTE: the following work only with samplers constructed via llama_sampler_chain_init + LLAMA_API struct llama_perf_sampler_data llama_perf_sampler (const struct llama_sampler * chain); + LLAMA_API void llama_perf_sampler_print(const struct llama_sampler * chain); + LLAMA_API void llama_perf_sampler_reset( struct llama_sampler * chain); + + // + // training + // + + // function that returns whether or not a given tensor contains trainable parameters + typedef bool (*llama_opt_param_filter)(const struct ggml_tensor * tensor, void * userdata); + + // always returns true + LLAMA_API bool llama_opt_param_filter_all(const struct ggml_tensor * tensor, void * userdata); + + struct llama_opt_params { + uint32_t n_ctx_train; // assumed context size post training, use context size specified in llama_context if 0 + + llama_opt_param_filter param_filter; // callback for determining which tensors contain trainable parameters + void * param_filter_ud; // userdata for determining which tensors contain trainable parameters + + ggml_opt_get_optimizer_params get_opt_pars; // callback for calculating optimizer parameters + void * get_opt_pars_ud; // userdata for calculating optimizer parameters + + enum ggml_opt_optimizer_type optimizer_type; + }; + + LLAMA_API void llama_opt_init(struct llama_context * lctx, struct llama_model * model, struct llama_opt_params lopt_params); + + LLAMA_API void llama_opt_epoch( + struct llama_context * lctx, + ggml_opt_dataset_t dataset, + ggml_opt_result_t result_train, + ggml_opt_result_t result_eval, + int64_t idata_split, + ggml_opt_epoch_callback callback_train, + ggml_opt_epoch_callback callback_eval); + +#ifdef __cplusplus +} +#endif + +#endif // LLAMA_H diff --git a/backend/llama.cpp/licenses/LICENSE-jsonhpp b/backend/llama.cpp/licenses/LICENSE-jsonhpp new file mode 100644 index 0000000000000000000000000000000000000000..b5a10275c1cdff6d2d1b4d84f51922d42499dec5 --- /dev/null +++ b/backend/llama.cpp/licenses/LICENSE-jsonhpp @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2013-2025 Niels Lohmann + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. 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world! +__ggml_vocab_test__ + Hello, world! +__ggml_vocab_test__ + this is 🦙.cpp +__ggml_vocab_test__ +w048 7tuijk dsdfhu +__ggml_vocab_test__ +нещо на Български +__ggml_vocab_test__ +កាន់តែពិសេសអាចខលចេញ +__ggml_vocab_test__ +🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token) +__ggml_vocab_test__ +Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello + Hello +__ggml_vocab_test__ + ( +__ggml_vocab_test__ + + = +__ggml_vocab_test__ +' era +__ggml_vocab_test__ +Hello, y'all! How are you 😁 ?我想在apple工作1314151天～ +__ggml_vocab_test__ +!!!!!! +__ggml_vocab_test__ +3 +__ggml_vocab_test__ +33 +__ggml_vocab_test__ +333 +__ggml_vocab_test__ +3333 +__ggml_vocab_test__ +33333 +__ggml_vocab_test__ +333333 +__ggml_vocab_test__ +3333333 +__ggml_vocab_test__ +33333333 +__ggml_vocab_test__ +333333333 +__ggml_vocab_test__ +Cửa Việt +__ggml_vocab_test__ + discards +__ggml_vocab_test__ + + + + + + + + + + + +🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български ''''''```````""""......!!!!!!?????? I've been 'told he's there, 'RE you sure? 'M not sure I'll make it, 'D you like some tea? We'Ve a'lL +__ggml_vocab_test__ diff --git a/backend/llama.cpp/models/ggml-vocab-bert-bge.gguf.out b/backend/llama.cpp/models/ggml-vocab-bert-bge.gguf.out new file mode 100644 index 0000000000000000000000000000000000000000..b1c49672fadad845bad979532cf6491189d83c08 --- /dev/null +++ b/backend/llama.cpp/models/ggml-vocab-bert-bge.gguf.out @@ -0,0 +1,46 @@ + 29464 2094 1018 1092 2706 + 9706 7959 2140 + + + + + + + + + + 7592 2088 + 7592 2088 + 7592 2088 + 7592 2088 + 7592 2088 999 + 7592 1010 2088 999 + 7592 1010 2088 999 + 2023 2003 100 1012 18133 2361 + 1059 2692 18139 1021 8525 28418 2243 16233 20952 6979 + 1192 15290 29754 14150 1192 10260 1181 29755 29436 29741 10260 16856 29747 23925 10325 + 100 + 100 1006 3671 1007 100 1006 3674 7861 29147 2483 9530 16280 23854 1007 100 1006 2069 7861 29147 2072 2008 2038 2049 2219 19204 1007 + 7592 + 7592 + 7592 + 7592 + 7592 + 7592 7592 + 1006 + 1027 + 1005 3690 + 7592 1010 1061 1005 2035 999 2129 2024 2017 100 1029 1855 100 100 6207 100 100 14677 23632 22203 1811 1995 + 999 999 999 999 999 999 + 1017 + 3943 + 21211 + 21211 2509 + 21211 22394 + 21211 22394 2509 + 21211 22394 22394 + 21211 22394 22394 2509 + 21211 22394 22394 22394 + 12731 2050 19710 + 5860 18117 + 100 1006 3671 1007 100 1006 3674 7861 29147 2483 9530 16280 23854 1007 100 100 1017 3943 21211 21211 2509 21211 22394 21211 22394 2509 21211 22394 22394 21211 22394 22394 2509 1017 1012 1017 1017 1012 1012 1017 1017 1012 1012 1012 1017 100 1029 1855 100 100 6207 100 100 14677 23632 22203 1811 1995 1011 1011 1011 1011 1011 1011 1027 1027 1027 1027 1027 1027 1027 1192 15290 29754 14150 1192 10260 1181 29755 29436 29741 10260 16856 29747 23925 10325 1005 1005 1005 1005 1005 1005 1036 1036 1036 1036 1036 1036 1036 1000 1000 1000 1000 1012 1012 1012 1012 1012 1012 999 999 999 999 999 999 1029 1029 1029 1029 1029 1029 1045 1005 2310 2042 1005 2409 2002 1005 1055 2045 1010 1005 2128 2017 2469 1029 1005 1049 2025 2469 1045 1005 2222 2191 2009 1010 1005 1040 2017 2066 2070 5572 1029 2057 1005 2310 1037 1005 2222 diff --git a/backend/llama.cpp/models/ggml-vocab-command-r.gguf b/backend/llama.cpp/models/ggml-vocab-command-r.gguf new file mode 100644 index 0000000000000000000000000000000000000000..91656898fc12b3038b6ecf1c716fc61b6b88ab09 --- /dev/null +++ b/backend/llama.cpp/models/ggml-vocab-command-r.gguf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a2f8cfea952ef7c391a6d92a1c309d0bd32e36384d9b9230569a7425732f27d9 +size 10874545 diff --git a/backend/llama.cpp/models/ggml-vocab-command-r.gguf.inp b/backend/llama.cpp/models/ggml-vocab-command-r.gguf.inp new file mode 100644 index 0000000000000000000000000000000000000000..86b934e4020fba7061c15b6349d5e4163bb2383e --- /dev/null +++ b/backend/llama.cpp/models/ggml-vocab-command-r.gguf.inp @@ -0,0 +1,112 @@ +ied 4 ½ months +__ggml_vocab_test__ +Äpfel +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + + +__ggml_vocab_test__ + + + +__ggml_vocab_test__ + + + + +__ggml_vocab_test__ + + +__ggml_vocab_test__ +Hello world +__ggml_vocab_test__ + Hello world +__ggml_vocab_test__ +Hello World +__ggml_vocab_test__ + Hello World +__ggml_vocab_test__ + Hello World! +__ggml_vocab_test__ +Hello, world! +__ggml_vocab_test__ + Hello, world! +__ggml_vocab_test__ + this is 🦙.cpp +__ggml_vocab_test__ +w048 7tuijk dsdfhu +__ggml_vocab_test__ +нещо на Български +__ggml_vocab_test__ +កាន់តែពិសេសអាចខលចេញ +__ggml_vocab_test__ +🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token) +__ggml_vocab_test__ +Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello + Hello +__ggml_vocab_test__ + ( +__ggml_vocab_test__ + + = +__ggml_vocab_test__ +' era +__ggml_vocab_test__ +Hello, y'all! How are you 😁 ?我想在apple工作1314151天～ +__ggml_vocab_test__ +!!!!!! +__ggml_vocab_test__ +3 +__ggml_vocab_test__ +33 +__ggml_vocab_test__ +333 +__ggml_vocab_test__ +3333 +__ggml_vocab_test__ +33333 +__ggml_vocab_test__ +333333 +__ggml_vocab_test__ +3333333 +__ggml_vocab_test__ +33333333 +__ggml_vocab_test__ +333333333 +__ggml_vocab_test__ +Cửa Việt +__ggml_vocab_test__ + discards +__ggml_vocab_test__ + + + + + + + + + + + +🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български ''''''```````""""......!!!!!!?????? I've been 'told he's there, 'RE you sure? 'M not sure I'll make it, 'D you like some tea? We'Ve a'lL +__ggml_vocab_test__ diff --git a/backend/llama.cpp/models/ggml-vocab-command-r.gguf.out b/backend/llama.cpp/models/ggml-vocab-command-r.gguf.out new file mode 100644 index 0000000000000000000000000000000000000000..0e3af72eb1c23471ec7ed292fa7fbab89f8371b8 --- /dev/null +++ b/backend/llama.cpp/models/ggml-vocab-command-r.gguf.out @@ -0,0 +1,46 @@ + 2536 228 27 228 22957 6983 + 90711 87 20910 + + 228 + 1667 + 1742 + 205 + 206 + 2126 + 11516 + 34777 + 28339 3845 + 46609 3845 + 28339 3930 + 46609 3930 + 46609 3930 8 + 28339 19 3845 8 + 46609 19 3845 8 + 2075 1801 11254 107 255 21 19317 + 94 23 27 31 228 30 21213 20752 39267 6405 9980 + 4929 40071 2196 3236 8750 1764 37097 41168 + 38111 230 174833 38111 249 86325 241 38111 245 86325 232 38111 252 38111 123 38111 261 165 24629 38111 261 38111 103 174833 38111 235 38111 231 38111 257 38111 235 165 24629 38111 239 + 2226 256 230 1737 18258 16 80503 122 35927 2226 242 112 57462 1737 54457 223165 106230 2096 16 48389 1737 10203 109160 1875 2222 2517 3342 12523 16 + 28339 + 46609 + 228 46609 + 1667 46609 + 1742 46609 + 1742 46609 1856 46609 + 1737 + 206 1857 + 14 4515 + 28339 19 1770 14 1954 8 4070 1955 1933 80503 231 5691 12081 13336 2648 29325 14315 24 26 24 27 24 28 24 5123 18372 + 57178 10251 + 26 + 26 26 + 26 26 26 + 26 26 26 26 + 26 26 26 26 26 + 26 26 26 26 26 26 + 26 26 26 26 26 26 26 + 26 26 26 26 26 26 26 26 + 26 26 26 26 26 26 26 26 26 + 42 30719 12584 + 3642 4388 + 127731 51628 205 57788 18494 97469 126134 206 2226 256 230 1737 18258 16 80503 122 35927 2226 242 112 57462 1737 54457 223165 106230 2096 16 48389 11254 107 255 2226 107 255 228 26 228 26 26 228 26 26 26 228 26 26 26 26 228 26 26 26 26 26 228 26 26 26 26 26 26 228 26 26 26 26 26 26 26 228 26 26 26 26 26 26 26 26 228 26 21 26 228 26 2271 26 228 26 3834 26 182018 230 174833 38111 249 86325 241 38111 245 86325 232 38111 252 38111 123 38111 261 165 24629 38111 261 38111 103 174833 38111 235 188568 231 5691 12081 13336 2648 29325 14315 24 26 24 27 24 28 24 5123 18372 8391 158343 3512 40071 2196 3236 8750 1764 37097 41168 29721 32797 25646 3802 4975 4975 116167 57178 10251 154048 27292 1767 5125 2632 2155 91 2378 1919 1914 2782 19 2155 3354 1933 5470 38 2155 52 2068 5470 1767 4961 3059 1894 19 2155 43 1933 3026 2725 23186 38 2930 14 20676 1671 14 83 51 diff --git a/backend/llama.cpp/models/ggml-vocab-deepseek-coder.gguf b/backend/llama.cpp/models/ggml-vocab-deepseek-coder.gguf new file mode 100644 index 0000000000000000000000000000000000000000..b04617a314846e33cf088107f4854933105dc4fa --- /dev/null +++ b/backend/llama.cpp/models/ggml-vocab-deepseek-coder.gguf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:91cb1379f2e33af1c4866b194622b7a0e12e8f0c9dba7ba2f10d55978730bec1 +size 1156067 diff --git a/backend/llama.cpp/models/ggml-vocab-deepseek-coder.gguf.inp b/backend/llama.cpp/models/ggml-vocab-deepseek-coder.gguf.inp new file mode 100644 index 0000000000000000000000000000000000000000..86b934e4020fba7061c15b6349d5e4163bb2383e --- /dev/null +++ b/backend/llama.cpp/models/ggml-vocab-deepseek-coder.gguf.inp @@ -0,0 +1,112 @@ +ied 4 ½ months +__ggml_vocab_test__ +Äpfel +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + + +__ggml_vocab_test__ + + + +__ggml_vocab_test__ + + + + +__ggml_vocab_test__ + + +__ggml_vocab_test__ +Hello world +__ggml_vocab_test__ + Hello world +__ggml_vocab_test__ +Hello World +__ggml_vocab_test__ + Hello World +__ggml_vocab_test__ + Hello World! +__ggml_vocab_test__ +Hello, world! +__ggml_vocab_test__ + Hello, world! +__ggml_vocab_test__ + this is 🦙.cpp +__ggml_vocab_test__ +w048 7tuijk dsdfhu +__ggml_vocab_test__ +нещо на Български +__ggml_vocab_test__ +កាន់តែពិសេសអាចខលចេញ +__ggml_vocab_test__ +🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token) +__ggml_vocab_test__ +Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello + Hello +__ggml_vocab_test__ + ( +__ggml_vocab_test__ + + = +__ggml_vocab_test__ +' era +__ggml_vocab_test__ +Hello, y'all! How are you 😁 ?我想在apple工作1314151天～ +__ggml_vocab_test__ +!!!!!! +__ggml_vocab_test__ +3 +__ggml_vocab_test__ +33 +__ggml_vocab_test__ +333 +__ggml_vocab_test__ +3333 +__ggml_vocab_test__ +33333 +__ggml_vocab_test__ +333333 +__ggml_vocab_test__ +3333333 +__ggml_vocab_test__ +33333333 +__ggml_vocab_test__ +333333333 +__ggml_vocab_test__ +Cửa Việt +__ggml_vocab_test__ + discards +__ggml_vocab_test__ + + + + + + + + + + + +🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български ''''''```````""""......!!!!!!?????? I've been 'told he's there, 'RE you sure? 'M not sure I'll make it, 'D you like some tea? 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How are you 😁 ?我想在apple工作1314151天～ +__ggml_vocab_test__ +!!!!!! +__ggml_vocab_test__ +3 +__ggml_vocab_test__ +33 +__ggml_vocab_test__ +333 +__ggml_vocab_test__ +3333 +__ggml_vocab_test__ +33333 +__ggml_vocab_test__ +333333 +__ggml_vocab_test__ +3333333 +__ggml_vocab_test__ +33333333 +__ggml_vocab_test__ +333333333 +__ggml_vocab_test__ +Cửa Việt +__ggml_vocab_test__ + discards +__ggml_vocab_test__ + + + + + + + + + + + +🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български ''''''```````""""......!!!!!!?????? I've been 'told he's there, 'RE you sure? 'M not sure I'll make it, 'D you like some tea? We'Ve a'lL +__ggml_vocab_test__ diff --git a/backend/llama.cpp/models/ggml-vocab-deepseek-llm.gguf.out b/backend/llama.cpp/models/ggml-vocab-deepseek-llm.gguf.out new file mode 100644 index 0000000000000000000000000000000000000000..f9d49c9afe703703ce26bf30bc4a0dd52c4ffbbe --- /dev/null +++ b/backend/llama.cpp/models/ggml-vocab-deepseek-llm.gguf.out @@ -0,0 +1,46 @@ + 1052 207 19 207 19109 4223 + 82077 26723 282 + + 207 + 243 + 300 + 184 + 185 + 185 185 + 185 185 185 + 184 185 + 17464 1843 + 37727 1843 + 17464 5427 + 37727 5427 + 37727 5427 0 + 17464 11 1843 0 + 37727 11 1843 0 + 437 317 12356 99 234 13 14743 + 86 15 19 23 207 22 83 3970 27519 26016 3944 14025 + 1603 6476 620 91754 + 71374 209 71374 114 71374 228 155 240 220 71374 224 155 240 211 71374 231 71374 115 71374 240 155 240 210 71374 240 71374 95 71374 114 71374 214 71374 210 71374 236 71374 214 155 240 210 71374 218 + 10044 95300 334 8754 8 33701 114 350 222 10044 221 104 46713 334 34732 996 24250 262 80923 8 207 37103 214 334 5956 89213 344 643 895 1377 10728 8 + 17464 + 37727 + 207 37727 + 243 37727 + 300 37727 + 300 37727 185 300 37727 + 334 + 185 403 + 6 2906 + 17464 11 320 6 436 0 1724 418 340 33701 210 3025 19017 612 9407 2681 16 18 16 19 16 20 16 1398 68940 239 + 15278 3033 + 18 + 18 18 + 18 18 18 + 18 18 18 18 + 18 18 18 18 18 + 18 18 18 18 18 18 + 18 18 18 18 18 18 18 + 18 18 18 18 18 18 18 18 + 18 18 18 18 18 18 18 18 18 + 34 32555 242 64 23708 32555 216 83 + 1763 2550 + 185 207 185 185 207 185 185 185 207 11969 486 22504 185 243 185 300 185 251 185 663 185 10044 95300 334 8754 8 33701 114 350 222 10044 221 104 46713 334 34732 996 24250 262 80923 8 207 37103 214 12356 99 234 10044 99 234 207 18 207 18 18 207 18 18 18 207 18 18 18 18 207 18 18 18 18 18 207 18 18 18 18 18 18 207 18 18 18 18 18 18 18 207 18 18 18 18 18 18 18 18 207 18 13 18 207 18 526 18 207 18 1204 18 207 71374 209 71374 114 71374 228 155 240 220 71374 224 155 240 211 71374 231 71374 115 71374 240 155 240 210 71374 240 71374 95 71374 114 71374 214 71899 210 3025 19017 612 9407 2681 16 18 16 19 16 20 16 1398 68940 239 78827 55170 76659 620 91754 31116 36804 4885 4885 10897 4390 4390 41047 15278 3033 14986 5675 304 6 313 803 655 33326 362 6 82 745 11 655 1374 340 2049 30 655 44 441 2049 304 6 647 1099 359 11 655 35 340 837 742 10842 30 1003 6 10699 245 6 75 43 diff --git a/backend/llama.cpp/models/ggml-vocab-falcon.gguf b/backend/llama.cpp/models/ggml-vocab-falcon.gguf new file mode 100644 index 0000000000000000000000000000000000000000..1916c29e2e7602678c84dfa555e5153a68f7e27b --- /dev/null +++ b/backend/llama.cpp/models/ggml-vocab-falcon.gguf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9f0bf8b0733680398b72e652e90f260f43782f326e75545fc0e49611a5ba35ad +size 2287728 diff --git a/backend/llama.cpp/models/ggml-vocab-falcon.gguf.inp b/backend/llama.cpp/models/ggml-vocab-falcon.gguf.inp new file mode 100644 index 0000000000000000000000000000000000000000..86b934e4020fba7061c15b6349d5e4163bb2383e --- /dev/null +++ b/backend/llama.cpp/models/ggml-vocab-falcon.gguf.inp @@ -0,0 +1,112 @@ +ied 4 ½ months +__ggml_vocab_test__ +Äpfel +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + + +__ggml_vocab_test__ + + + +__ggml_vocab_test__ + + + + +__ggml_vocab_test__ + + +__ggml_vocab_test__ +Hello world +__ggml_vocab_test__ + Hello world +__ggml_vocab_test__ +Hello World +__ggml_vocab_test__ + Hello World +__ggml_vocab_test__ + Hello World! +__ggml_vocab_test__ +Hello, world! +__ggml_vocab_test__ + Hello, world! +__ggml_vocab_test__ + this is 🦙.cpp +__ggml_vocab_test__ +w048 7tuijk dsdfhu +__ggml_vocab_test__ +нещо на Български +__ggml_vocab_test__ +កាន់តែពិសេសអាចខលចេញ +__ggml_vocab_test__ +🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token) +__ggml_vocab_test__ +Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello + Hello +__ggml_vocab_test__ + ( +__ggml_vocab_test__ + + = +__ggml_vocab_test__ +' era +__ggml_vocab_test__ +Hello, y'all! How are you 😁 ?我想在apple工作1314151天～ +__ggml_vocab_test__ +!!!!!! +__ggml_vocab_test__ +3 +__ggml_vocab_test__ +33 +__ggml_vocab_test__ +333 +__ggml_vocab_test__ +3333 +__ggml_vocab_test__ +33333 +__ggml_vocab_test__ +333333 +__ggml_vocab_test__ +3333333 +__ggml_vocab_test__ +33333333 +__ggml_vocab_test__ +333333333 +__ggml_vocab_test__ +Cửa Việt +__ggml_vocab_test__ + discards +__ggml_vocab_test__ + + + + + + + + + + + +🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български ''''''```````""""......!!!!!!?????? I've been 'told he's there, 'RE you sure? 'M not sure I'll make it, 'D you like some tea? We'Ve a'lL +__ggml_vocab_test__ diff --git a/backend/llama.cpp/models/ggml-vocab-falcon.gguf.out b/backend/llama.cpp/models/ggml-vocab-falcon.gguf.out new file mode 100644 index 0000000000000000000000000000000000000000..6319de60e241269e91f79f6d0ce4bcc63f99a1b4 --- /dev/null +++ b/backend/llama.cpp/models/ggml-vocab-falcon.gguf.out @@ -0,0 +1,46 @@ + 878 204 31 3068 133 2137 + 34502 18614 286 + + 204 + 258 + 466 + 192 + 193 + 1001 + 11331 + 19125 + 9856 1079 + 23090 1079 + 9856 2889 + 23090 2889 + 23090 2889 12 + 9856 23 1079 12 + 23090 23 1079 12 + 414 304 3346 111 231 25 29247 + 98 55866 204 34 16682 7149 36190 6869 11481 + 150 133 6207 151 215 150 134 5052 133 6279 5052 223 151 216 49679 123 53110 47043 7795 + 38154 206 38154 126 38154 225 167 237 217 38154 221 167 237 208 38154 228 38154 127 38154 237 167 237 207 38154 237 38154 107 38154 126 38154 211 38154 207 38154 233 38154 211 167 237 207 38154 215 + 2571 232 206 204 19 11003 20 8196 126 283 219 48778 116 13392 204 19 51831 732 63209 1741 7955 522 20 22438 211 204 19 7927 53360 325 504 701 946 10930 20 + 9856 + 23090 + 204 23090 + 258 23090 + 466 23090 + 466 23090 742 23090 + 204 19 + 1212 40 + 18 4932 + 9856 23 291 18 436 12 1265 362 299 8196 207 204 42 50087 123 2727 20300 32022 133 234 17419 30137 28 7858 181 133 236 + 51520 + 30 + 3138 + 22287 + 22287 30 + 22287 3138 + 22287 22287 + 22287 22287 30 + 22287 22287 3138 + 22287 22287 22287 + 46 19768 239 76 9634 19768 213 95 + 1080 1502 + 1212 4824 1001 1212 192 204 663 49453 2069 742 561 1501 193 2571 232 206 204 19 11003 20 8196 126 283 219 48778 116 13392 204 19 51831 732 63209 1741 7955 522 20 22438 211 3346 111 231 2571 111 231 204 30 204 3138 204 22287 204 22287 30 204 22287 3138 204 22287 22287 204 22287 22287 30 204 22287 22287 3138 204 30 25 30 204 30 513 30 204 30 951 30 27171 236 206 38154 126 38154 225 167 237 217 38154 221 167 237 208 38154 228 38154 127 38154 237 167 237 207 38154 237 38154 107 38154 126 38154 211 20589 207 204 42 50087 123 2727 20300 32022 133 234 17419 30137 28 7858 181 133 236 204 37057 2228 10666 5052 133 6207 151 215 150 134 5052 133 6279 5052 223 151 216 49679 123 53110 47043 7795 204 7544 7544 7544 8543 8543 17593 3513 3513 12844 51520 17664 4247 295 18 298 650 204 18 95 693 332 18 94 629 23 204 18 1553 299 1310 42 204 18 56 416 1310 295 18 567 717 334 23 204 18 47 299 606 596 6696 42 703 18 16139 241 18 87 55 diff --git a/backend/llama.cpp/models/ggml-vocab-gemma-4.gguf b/backend/llama.cpp/models/ggml-vocab-gemma-4.gguf new file mode 100644 index 0000000000000000000000000000000000000000..1d43ddd3f18c74f26404d824384506957e1fc319 --- /dev/null +++ b/backend/llama.cpp/models/ggml-vocab-gemma-4.gguf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:58b1ba0b57f3b4d7c468ba4ffd91ad85190346a3d7ad7e71d1cabaae8a14bb65 +size 15776467 diff --git a/backend/llama.cpp/models/ggml-vocab-gemma-4.gguf.inp b/backend/llama.cpp/models/ggml-vocab-gemma-4.gguf.inp new file mode 100644 index 0000000000000000000000000000000000000000..856b29021ad3e2828951bdb46d747ca0d4be5cc7 --- /dev/null +++ b/backend/llama.cpp/models/ggml-vocab-gemma-4.gguf.inp @@ -0,0 +1,111 @@ +ied 4 ½ months +__ggml_vocab_test__ +Äpfel +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + + +__ggml_vocab_test__ + + + +__ggml_vocab_test__ + + + + +__ggml_vocab_test__ + + +__ggml_vocab_test__ +Hello world +__ggml_vocab_test__ + Hello world +__ggml_vocab_test__ +Hello World +__ggml_vocab_test__ + Hello World +__ggml_vocab_test__ + Hello World! +__ggml_vocab_test__ +Hello, world! +__ggml_vocab_test__ + Hello, world! +__ggml_vocab_test__ + this is 🦙.cpp +__ggml_vocab_test__ +w048 7tuijk dsdfhu +__ggml_vocab_test__ +нещо на Български +__ggml_vocab_test__ +កាន់តែពិសេសអាចខលចេញ +__ggml_vocab_test__ +🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token) +__ggml_vocab_test__ +Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello + Hello +__ggml_vocab_test__ + ( +__ggml_vocab_test__ + + = +__ggml_vocab_test__ +' era +__ggml_vocab_test__ +Hello, y'all! How are you 😁 ?我想在apple工作1314151天～ +__ggml_vocab_test__ +!!!!!! +__ggml_vocab_test__ +3 +__ggml_vocab_test__ +33 +__ggml_vocab_test__ +333 +__ggml_vocab_test__ +3333 +__ggml_vocab_test__ +33333 +__ggml_vocab_test__ +333333 +__ggml_vocab_test__ +3333333 +__ggml_vocab_test__ +33333333 +__ggml_vocab_test__ +333333333 +__ggml_vocab_test__ +Cửa Việt +__ggml_vocab_test__ + discards +__ggml_vocab_test__ + + + + + + + + + + + +🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български ''''''```````""""......!!!!!!?????? I've been 'told he's there, 'RE you sure? 'M not sure I'll make it, 'D you like some tea? We'Ve a'lL \ No newline at end of file diff --git a/backend/llama.cpp/models/ggml-vocab-gemma-4.gguf.out b/backend/llama.cpp/models/ggml-vocab-gemma-4.gguf.out new file mode 100644 index 0000000000000000000000000000000000000000..bd3143b88ca988efc4436668d2b11d14d2a2ed79 --- /dev/null +++ b/backend/llama.cpp/models/ggml-vocab-gemma-4.gguf.out @@ -0,0 +1,46 @@ +1178 236743 236812 47041 3794 +239122 22744 535 + +236743 +138 +139 +255968 +107 +108 +109 +255968 107 +9259 1902 +26352 1902 +9259 4109 +26352 4109 +26352 4109 236888 +9259 236764 1902 236888 +26352 236764 1902 236888 +672 563 236743 478 397 404 391 236761 12362 +236765 236771 236812 236828 236743 236832 11372 12065 31806 3405 9360 +1337 12515 1333 4632 165543 3830 +234889 63031 219876 66212 239077 237907 144494 +242015 568 7382 236768 236743 247717 237243 248989 238178 568 43819 111730 150567 236768 113452 568 8960 64334 600 815 1061 1852 8369 236768 +9259 +26352 +138 9259 +139 9259 +140 9259 +140 9259 107 140 9259 +568 +107 578 +236789 6933 +9259 236764 570 236789 712 236888 2088 659 611 170124 2360 62133 237075 17641 11700 236770 236800 236770 236812 236770 236810 236770 237471 238352 +123947 +236800 +236800 236800 +236800 236800 236800 +236800 236800 236800 236800 +236800 236800 236800 236800 236800 +236800 236800 236800 236800 236800 236800 +236800 236800 236800 236800 236800 236800 236800 +236800 236800 236800 236800 236800 236800 236800 236800 +236800 236800 236800 236800 236800 236800 236800 236800 236800 +236780 29719 33154 +2243 2206 +107 236743 108 236743 109 236743 255968 236743 255969 236743 255968 107 138 107 139 107 140 107 141 107 242015 568 7382 236768 236743 247717 237243 248989 238178 568 43819 111730 150567 236768 113452 236743 478 397 404 391 478 397 404 391 236743 236800 236743 236800 236800 236743 236800 236800 236800 236743 236800 236800 236800 236800 236743 236800 236800 236800 236800 236800 236743 236800 236800 236800 236800 236800 236800 236743 236800 236800 236800 236800 236800 236800 236800 236743 236800 236800 236800 236800 236800 236800 236800 236800 236743 236800 236761 236800 236743 236800 856 236800 236743 236800 1390 236800 90986 92814 63031 219876 66212 241702 2360 62133 237075 17641 11700 236770 236800 236770 236812 236770 236810 236770 237471 238352 80448 120697 210119 1333 4632 165543 3830 9451 159561 2629 2629 2717 84491 19938 123947 38950 10371 564 236789 560 1010 756 151812 668 236789 236751 993 236764 756 1357 611 2889 236881 756 236792 711 2889 564 236789 859 1386 625 236764 756 236796 611 1133 1070 11115 236881 1191 236789 32541 496 236789 95635 diff --git a/backend/llama.cpp/models/ggml-vocab-gpt-2.gguf b/backend/llama.cpp/models/ggml-vocab-gpt-2.gguf new file mode 100644 index 0000000000000000000000000000000000000000..2745d9f7aa50a3b4e152c9a98dffbc544bdfe005 --- /dev/null +++ b/backend/llama.cpp/models/ggml-vocab-gpt-2.gguf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cedc56ca6e2e89f63e781696d1fd76b4b1d49e6720dee86463e915f6e90016ac +size 1766807 diff --git a/backend/llama.cpp/models/ggml-vocab-gpt-2.gguf.inp b/backend/llama.cpp/models/ggml-vocab-gpt-2.gguf.inp new file mode 100644 index 0000000000000000000000000000000000000000..86b934e4020fba7061c15b6349d5e4163bb2383e --- /dev/null +++ b/backend/llama.cpp/models/ggml-vocab-gpt-2.gguf.inp @@ -0,0 +1,112 @@ +ied 4 ½ months +__ggml_vocab_test__ +Äpfel +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + + +__ggml_vocab_test__ + + + +__ggml_vocab_test__ + + + + +__ggml_vocab_test__ + + +__ggml_vocab_test__ +Hello world +__ggml_vocab_test__ + Hello world +__ggml_vocab_test__ +Hello World +__ggml_vocab_test__ + Hello World +__ggml_vocab_test__ + Hello World! +__ggml_vocab_test__ +Hello, world! +__ggml_vocab_test__ + Hello, world! +__ggml_vocab_test__ + this is 🦙.cpp +__ggml_vocab_test__ +w048 7tuijk dsdfhu +__ggml_vocab_test__ +нещо на Български +__ggml_vocab_test__ +កាន់តែពិសេសអាចខលចេញ +__ggml_vocab_test__ +🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token) +__ggml_vocab_test__ +Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello + Hello +__ggml_vocab_test__ + ( +__ggml_vocab_test__ + + = +__ggml_vocab_test__ +' era +__ggml_vocab_test__ +Hello, y'all! How are you 😁 ?我想在apple工作1314151天～ +__ggml_vocab_test__ +!!!!!! +__ggml_vocab_test__ +3 +__ggml_vocab_test__ +33 +__ggml_vocab_test__ +333 +__ggml_vocab_test__ +3333 +__ggml_vocab_test__ +33333 +__ggml_vocab_test__ +333333 +__ggml_vocab_test__ +3333333 +__ggml_vocab_test__ +33333333 +__ggml_vocab_test__ +333333333 +__ggml_vocab_test__ +Cửa Việt +__ggml_vocab_test__ + discards +__ggml_vocab_test__ + + + + + + + + + + + +🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български ''''''```````""""......!!!!!!?????? I've been 'told he's there, 'RE you sure? 'M not sure I'll make it, 'D you like some tea? We'Ve a'lL +__ggml_vocab_test__ diff --git a/backend/llama.cpp/models/ggml-vocab-gpt-2.gguf.out b/backend/llama.cpp/models/ggml-vocab-gpt-2.gguf.out new file mode 100644 index 0000000000000000000000000000000000000000..6464ded3d276709bc22d49f38f2d799e5828a59f --- /dev/null +++ b/backend/llama.cpp/models/ggml-vocab-gpt-2.gguf.out @@ -0,0 +1,46 @@ + 798 604 25208 1933 + 127 226 79 69 417 + + 220 + 220 220 + 220 220 220 + 197 + 198 + 628 + 628 198 + 197 198 + 15496 995 + 18435 995 + 15496 2159 + 18435 2159 + 18435 2159 0 + 15496 11 995 0 + 18435 11 995 0 + 428 318 12520 99 247 13 20322 + 86 47202 767 28047 45961 288 82 7568 13415 + 22177 16843 141 231 15166 12466 121 16142 12466 239 141 232 30143 140 111 16142 21169 21727 31583 18849 + 157 252 222 157 252 114 157 252 241 157 253 233 157 252 237 157 253 224 157 252 244 157 252 115 157 252 253 157 253 223 157 252 253 157 252 95 157 252 114 157 252 227 157 252 223 157 252 249 157 252 227 157 253 223 157 252 231 + 8582 248 222 357 11265 8 30325 114 447 235 8582 234 104 37929 357 48101 795 13210 271 1673 36686 515 8 14519 227 357 8807 44805 326 468 663 898 11241 8 + 15496 + 18435 + 220 18435 + 220 220 18435 + 220 220 220 18435 + 220 220 220 18435 198 220 220 220 18435 + 357 + 198 796 + 6 6980 + 15496 11 331 6 439 0 1374 389 345 30325 223 5633 22755 239 46349 111 28839 101 18040 32432 98 43291 1485 1415 24309 25465 171 121 252 + 13896 3228 + 18 + 2091 + 20370 + 24840 + 2091 20370 + 24840 2091 + 24840 20370 + 24840 24840 + 24840 2091 20370 + 34 157 119 255 64 16049 157 119 229 83 + 1221 1371 + 198 220 628 220 628 198 220 197 220 197 197 220 197 198 220 220 198 220 220 220 198 220 220 220 220 198 220 220 220 220 220 198 8582 248 222 357 11265 8 30325 114 447 235 8582 234 104 37929 357 48101 795 13210 271 1673 36686 515 8 14519 227 12520 99 247 8582 99 247 513 4747 23460 513 20370 23460 2091 23460 20370 23460 24840 23460 2091 20370 513 13 18 513 492 18 513 986 18 28053 252 222 157 252 114 157 252 241 157 253 233 157 252 237 157 253 224 157 252 244 157 252 115 157 252 253 157 253 223 157 252 253 157 252 95 157 252 114 157 252 227 47249 223 5633 22755 239 46349 111 28839 101 18040 32432 98 43291 1485 1415 24309 25465 171 121 252 40103 1421 18604 12466 121 16843 141 231 15166 12466 121 16142 12466 239 141 232 30143 140 111 16142 21169 21727 31583 18849 705 39115 6 33153 15506 63 15931 15931 16317 13896 3228 9805 3548 314 1053 587 705 44040 339 338 612 11 705 2200 345 1654 30 705 44 407 1654 314 1183 787 340 11 705 35 345 588 617 8887 30 775 6 26979 257 6 75 43 diff --git a/backend/llama.cpp/models/ggml-vocab-gpt-neox.gguf b/backend/llama.cpp/models/ggml-vocab-gpt-neox.gguf new file mode 100644 index 0000000000000000000000000000000000000000..62b552628e043adb7c58163d4375ccc4f0d8653f --- /dev/null +++ b/backend/llama.cpp/models/ggml-vocab-gpt-neox.gguf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ae593a7f9b8bb174ed4f5019e41530463e4dac7aa06e42dee8aa650d2bdac53d +size 1771431 diff --git a/backend/llama.cpp/models/ggml-vocab-llama-bpe.gguf b/backend/llama.cpp/models/ggml-vocab-llama-bpe.gguf new file mode 100644 index 0000000000000000000000000000000000000000..0c6826d8a9c8abd7623cafcad23bfab019bce8cc --- /dev/null +++ b/backend/llama.cpp/models/ggml-vocab-llama-bpe.gguf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:97272e430d53bc7688f52d5e0ad8ea8f163ede9f1bbd1694feaa504797d5d96e +size 7818140 diff --git a/backend/llama.cpp/models/ggml-vocab-llama-bpe.gguf.inp b/backend/llama.cpp/models/ggml-vocab-llama-bpe.gguf.inp new file mode 100644 index 0000000000000000000000000000000000000000..86b934e4020fba7061c15b6349d5e4163bb2383e --- /dev/null +++ b/backend/llama.cpp/models/ggml-vocab-llama-bpe.gguf.inp @@ -0,0 +1,112 @@ +ied 4 ½ months +__ggml_vocab_test__ +Äpfel +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + + +__ggml_vocab_test__ + + + +__ggml_vocab_test__ + + + + +__ggml_vocab_test__ + + +__ggml_vocab_test__ +Hello world +__ggml_vocab_test__ + Hello world +__ggml_vocab_test__ +Hello World +__ggml_vocab_test__ + Hello World +__ggml_vocab_test__ + Hello World! +__ggml_vocab_test__ +Hello, world! +__ggml_vocab_test__ + Hello, world! +__ggml_vocab_test__ + this is 🦙.cpp +__ggml_vocab_test__ +w048 7tuijk dsdfhu +__ggml_vocab_test__ +нещо на Български +__ggml_vocab_test__ +កាន់តែពិសេសអាចខលចេញ +__ggml_vocab_test__ +🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token) +__ggml_vocab_test__ +Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello + Hello +__ggml_vocab_test__ + ( +__ggml_vocab_test__ + + = +__ggml_vocab_test__ +' era +__ggml_vocab_test__ +Hello, y'all! How are you 😁 ?我想在apple工作1314151天～ +__ggml_vocab_test__ +!!!!!! +__ggml_vocab_test__ +3 +__ggml_vocab_test__ +33 +__ggml_vocab_test__ +333 +__ggml_vocab_test__ +3333 +__ggml_vocab_test__ +33333 +__ggml_vocab_test__ +333333 +__ggml_vocab_test__ +3333333 +__ggml_vocab_test__ +33333333 +__ggml_vocab_test__ +333333333 +__ggml_vocab_test__ +Cửa Việt +__ggml_vocab_test__ + discards +__ggml_vocab_test__ + + + + + + + + + + + +🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български ''''''```````""""......!!!!!!?????? I've been 'told he's there, 'RE you sure? 'M not sure I'll make it, 'D you like some tea? We'Ve a'lL +__ggml_vocab_test__ diff --git a/backend/llama.cpp/models/ggml-vocab-llama-bpe.gguf.out b/backend/llama.cpp/models/ggml-vocab-llama-bpe.gguf.out new file mode 100644 index 0000000000000000000000000000000000000000..a77376625a2a0f25c2cf158353b5dabf63756c2a --- /dev/null +++ b/backend/llama.cpp/models/ggml-vocab-llama-bpe.gguf.out @@ -0,0 +1,46 @@ + 1142 220 19 220 27154 4038 + 88075 16276 301 + + 220 + 256 + 262 + 197 + 198 + 271 + 1432 + 1602 + 9906 1917 + 22691 1917 + 9906 4435 + 22691 4435 + 22691 4435 0 + 9906 11 1917 0 + 22691 11 1917 0 + 420 374 11410 99 247 13 11055 + 86 23904 220 22 83 2005 42908 11729 3013 17156 + 79862 102118 13373 64571 34694 3114 112203 80112 + 21549 222 98629 241 45358 233 21549 237 45358 224 21549 244 21549 115 21549 253 45358 223 21549 253 21549 95 98629 227 21549 223 21549 249 21549 227 45358 223 21549 231 + 9468 248 222 320 8416 8 27623 114 102470 9468 234 104 31643 320 36773 100166 98634 8 26602 227 320 3323 43465 430 706 1202 1866 4037 8 + 9906 + 22691 + 220 22691 + 256 22691 + 262 22691 + 262 22691 198 262 22691 + 320 + 198 284 + 6 11639 + 9906 11 379 65948 0 2650 527 499 27623 223 949 37046 101067 19000 23182 102301 9263 18136 16 36827 21909 + 17523 3001 + 18 + 1644 + 8765 + 8765 18 + 8765 1644 + 8765 8765 + 8765 8765 18 + 8765 8765 1644 + 8765 8765 8765 + 34 91163 101798 + 2624 2402 + 198 4815 15073 66597 8004 1602 2355 79772 11187 9468 248 222 320 8416 8 27623 114 102470 9468 234 104 31643 320 36773 100166 98634 8 26602 227 11410 99 247 9468 99 247 220 18 220 1644 220 8765 220 8765 18 220 8765 1644 220 8765 8765 220 8765 8765 18 220 8765 8765 1644 220 18 13 18 220 18 497 18 220 18 1131 18 220 21549 222 98629 241 45358 233 21549 237 45358 224 21549 244 21549 115 21549 253 45358 223 21549 253 21549 95 98629 227 76460 223 949 37046 101067 19000 23182 102301 9263 18136 16 36827 21909 56560 54337 19175 102118 13373 64571 34694 3114 112203 80112 3436 106451 14196 14196 74694 3089 3089 29249 17523 3001 27708 7801 358 3077 1027 364 83 820 568 596 1070 11 364 793 499 2771 30 364 44 539 2771 358 3358 1304 433 11 364 35 499 1093 1063 15600 30 1226 6 43712 264 64966 43 diff --git a/backend/llama.cpp/models/ggml-vocab-llama-spm.gguf b/backend/llama.cpp/models/ggml-vocab-llama-spm.gguf new file mode 100644 index 0000000000000000000000000000000000000000..2093285959bf2c0a9f46febf445019645ca222b7 --- /dev/null +++ b/backend/llama.cpp/models/ggml-vocab-llama-spm.gguf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:16c3724582d59aa8bf84711894e833f916ee46a31d80e21312759c48bf8d0e69 +size 723869 diff --git a/backend/llama.cpp/models/ggml-vocab-llama-spm.gguf.inp b/backend/llama.cpp/models/ggml-vocab-llama-spm.gguf.inp new file mode 100644 index 0000000000000000000000000000000000000000..86b934e4020fba7061c15b6349d5e4163bb2383e --- /dev/null +++ b/backend/llama.cpp/models/ggml-vocab-llama-spm.gguf.inp @@ -0,0 +1,112 @@ +ied 4 ½ months +__ggml_vocab_test__ +Äpfel +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + + +__ggml_vocab_test__ + + + +__ggml_vocab_test__ + + + + +__ggml_vocab_test__ + + +__ggml_vocab_test__ +Hello world +__ggml_vocab_test__ + Hello world +__ggml_vocab_test__ +Hello World +__ggml_vocab_test__ + Hello World +__ggml_vocab_test__ + Hello World! +__ggml_vocab_test__ +Hello, world! +__ggml_vocab_test__ + Hello, world! +__ggml_vocab_test__ + this is 🦙.cpp +__ggml_vocab_test__ +w048 7tuijk dsdfhu +__ggml_vocab_test__ +нещо на Български +__ggml_vocab_test__ +កាន់តែពិសេសអាចខលចេញ +__ggml_vocab_test__ +🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token) +__ggml_vocab_test__ +Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello + Hello +__ggml_vocab_test__ + ( +__ggml_vocab_test__ + + = +__ggml_vocab_test__ +' era +__ggml_vocab_test__ +Hello, y'all! How are you 😁 ?我想在apple工作1314151天～ +__ggml_vocab_test__ +!!!!!! +__ggml_vocab_test__ +3 +__ggml_vocab_test__ +33 +__ggml_vocab_test__ +333 +__ggml_vocab_test__ +3333 +__ggml_vocab_test__ +33333 +__ggml_vocab_test__ +333333 +__ggml_vocab_test__ +3333333 +__ggml_vocab_test__ +33333333 +__ggml_vocab_test__ +333333333 +__ggml_vocab_test__ +Cửa Việt +__ggml_vocab_test__ + discards +__ggml_vocab_test__ + + + + + + + + + + + +🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български ''''''```````""""......!!!!!!?????? I've been 'told he's there, 'RE you sure? 'M not sure I'll make it, 'D you like some tea? We'Ve a'lL +__ggml_vocab_test__ diff --git a/backend/llama.cpp/models/ggml-vocab-llama-spm.gguf.out b/backend/llama.cpp/models/ggml-vocab-llama-spm.gguf.out new file mode 100644 index 0000000000000000000000000000000000000000..2a71a6ef86efa9ee78509747de5cd1ceb59c8a5a --- /dev/null +++ b/backend/llama.cpp/models/ggml-vocab-llama-spm.gguf.out @@ -0,0 +1,46 @@ + 474 287 29871 29946 29871 30226 7378 + 11585 7810 295 + + 259 + 1678 + 268 + 29871 12 + 29871 13 + 29871 13 13 + 29871 13 13 13 + 29871 12 13 + 15043 3186 + 29871 15043 3186 + 15043 2787 + 29871 15043 2787 + 29871 15043 2787 29991 + 15043 29892 3186 29991 + 29871 15043 29892 3186 29991 + 29871 445 338 29871 243 162 169 156 29889 8223 + 281 29900 29946 29947 29871 29955 9161 13535 18031 2176 6905 + 1538 4851 665 1386 29713 1305 + 29871 31849 31324 31934 228 162 142 228 161 146 228 162 133 228 161 153 228 161 186 31708 228 162 132 31708 228 161 165 31324 228 161 136 228 161 132 228 161 158 228 161 136 228 162 132 228 161 140 + 29871 243 162 157 131 313 8945 29897 29871 243 162 155 185 30722 243 162 143 174 30598 313 20787 953 3848 275 16125 630 29897 29871 31681 313 6194 953 29877 2397 393 756 967 1914 5993 29897 + 15043 + 29871 15043 + 259 15043 + 1678 15043 + 268 15043 + 268 15043 13 1678 15043 + 29871 313 + 29871 13 353 + 525 3152 + 15043 29892 343 29915 497 29991 1128 526 366 29871 243 162 155 132 1577 30672 31522 30505 11548 31041 30732 29896 29941 29896 29946 29896 29945 29896 30408 30739 + 1738 6824 21004 + 29871 29941 + 29871 29941 29941 + 29871 29941 29941 29941 + 29871 29941 29941 29941 29941 + 29871 29941 29941 29941 29941 29941 + 29871 29941 29941 29941 29941 29941 29941 + 29871 29941 29941 29941 29941 29941 29941 29941 + 29871 29941 29941 29941 29941 29941 29941 29941 29941 + 29871 29941 29941 29941 29941 29941 29941 29941 29941 29941 + 315 228 190 176 29874 10630 30529 29873 + 29871 2313 3163 + 29871 13 29871 13 13 29871 13 13 13 29871 12 29871 12 12 29871 12 13 259 13 1678 13 268 13 418 13 243 162 157 131 313 8945 29897 29871 243 162 155 185 30722 243 162 143 174 30598 313 20787 953 3848 275 16125 630 29897 29871 31681 29871 243 162 169 156 243 162 169 156 29871 29941 29871 29941 29941 29871 29941 29941 29941 29871 29941 29941 29941 29941 29871 29941 29941 29941 29941 29941 29871 29941 29941 29941 29941 29941 29941 29871 29941 29941 29941 29941 29941 29941 29941 29871 29941 29941 29941 29941 29941 29941 29941 29941 29871 29941 29889 29941 29871 29941 636 29941 29871 29941 856 29941 29871 31849 31324 31934 228 162 142 228 161 146 228 162 133 228 161 153 228 161 186 31708 228 162 132 31708 228 161 165 31324 228 161 136 243 162 155 132 1577 30672 31522 30505 11548 31041 30732 29896 29941 29896 29946 29896 29945 29896 30408 30739 448 23648 2751 25512 1538 4851 665 1386 29713 1305 14550 4907 11120 16159 16159 16159 15945 15945 3045 636 6824 6824 6824 8773 8773 8773 306 29915 345 1063 525 29873 1025 540 29915 29879 727 29892 525 1525 366 1854 29973 525 29924 451 1854 306 29915 645 1207 372 29892 525 29928 366 763 777 23429 29973 1334 29915 29963 29872 263 29915 29880 29931 diff --git a/backend/llama.cpp/models/ggml-vocab-mpt.gguf b/backend/llama.cpp/models/ggml-vocab-mpt.gguf new file mode 100644 index 0000000000000000000000000000000000000000..188bfa128057c36f035c44eedc0bcb4a77bdf630 --- /dev/null +++ b/backend/llama.cpp/models/ggml-vocab-mpt.gguf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:59dc382612866d1fc6c11ea531318d327598f3412d9c8f8600607cdf3030898f +size 1771393 diff --git a/backend/llama.cpp/models/ggml-vocab-mpt.gguf.inp b/backend/llama.cpp/models/ggml-vocab-mpt.gguf.inp new file mode 100644 index 0000000000000000000000000000000000000000..86b934e4020fba7061c15b6349d5e4163bb2383e --- /dev/null +++ b/backend/llama.cpp/models/ggml-vocab-mpt.gguf.inp @@ -0,0 +1,112 @@ +ied 4 ½ months +__ggml_vocab_test__ +Äpfel +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + + +__ggml_vocab_test__ + + + +__ggml_vocab_test__ + + + + +__ggml_vocab_test__ + + +__ggml_vocab_test__ +Hello world +__ggml_vocab_test__ + Hello world +__ggml_vocab_test__ +Hello World +__ggml_vocab_test__ + Hello World +__ggml_vocab_test__ + Hello World! +__ggml_vocab_test__ +Hello, world! +__ggml_vocab_test__ + Hello, world! +__ggml_vocab_test__ + this is 🦙.cpp +__ggml_vocab_test__ +w048 7tuijk dsdfhu +__ggml_vocab_test__ +нещо на Български +__ggml_vocab_test__ +កាន់តែពិសេសអាចខលចេញ +__ggml_vocab_test__ +🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token) +__ggml_vocab_test__ +Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello + Hello +__ggml_vocab_test__ + ( +__ggml_vocab_test__ + + = +__ggml_vocab_test__ +' era +__ggml_vocab_test__ +Hello, y'all! How are you 😁 ?我想在apple工作1314151天～ +__ggml_vocab_test__ +!!!!!! +__ggml_vocab_test__ +3 +__ggml_vocab_test__ +33 +__ggml_vocab_test__ +333 +__ggml_vocab_test__ +3333 +__ggml_vocab_test__ +33333 +__ggml_vocab_test__ +333333 +__ggml_vocab_test__ +3333333 +__ggml_vocab_test__ +33333333 +__ggml_vocab_test__ +333333333 +__ggml_vocab_test__ +Cửa Việt +__ggml_vocab_test__ + discards +__ggml_vocab_test__ + + + + + + + + + + + +🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български ''''''```````""""......!!!!!!?????? I've been 'told he's there, 'RE you sure? 'M not sure I'll make it, 'D you like some tea? We'Ve a'lL +__ggml_vocab_test__ diff --git a/backend/llama.cpp/models/ggml-vocab-mpt.gguf.out b/backend/llama.cpp/models/ggml-vocab-mpt.gguf.out new file mode 100644 index 0000000000000000000000000000000000000000..ca62669ad0945dcf646759d38b0192e33086b85e --- /dev/null +++ b/backend/llama.cpp/models/ggml-vocab-mpt.gguf.out @@ -0,0 +1,46 @@ + 728 577 24142 2607 + 37515 18569 293 + + 209 + 50276 + 50275 + 186 + 187 + 535 + 2756 + 186 187 + 12092 1533 + 24387 1533 + 12092 3645 + 24387 3645 + 24387 3645 2 + 12092 13 1533 2 + 24387 13 1533 2 + 436 310 22692 101 236 15 14161 + 88 27244 818 16853 16392 20505 4989 11917 + 32520 11514 1068 8713 38177 13396 3415 9925 12559 10453 1389 + 18081 211 18081 116 18081 230 39936 222 18081 226 39936 213 18081 233 18081 117 18081 242 39936 212 18081 242 18081 97 18081 116 18081 216 18081 212 18081 238 18081 216 39936 212 18081 220 + 14931 237 211 313 6320 10 49042 116 325 224 14931 223 106 171 118 226 313 34263 802 13511 261 32147 456 10 3384 239 216 313 7483 802 80 8020 326 556 697 1211 10669 10 + 12092 + 24387 + 50276 12092 + 50275 12092 + 50274 12092 + 50274 12092 187 50274 12092 + 313 + 187 426 + 8 8685 + 12092 13 340 8 455 2 1359 403 368 49042 212 3736 15367 41197 13610 19934 41869 21275 1012 1047 18795 40120 20422 241 + 18963 4672 + 20 + 1610 + 20084 + 26409 + 1610 20084 + 26409 1610 + 26409 20084 + 26409 26409 + 26409 1610 20084 + 36 6829 244 66 17721 35177 85 + 1262 2196 + 586 1744 33525 186 209 623 28910 187 50276 187 50275 187 50274 187 50273 187 14931 237 211 313 6320 10 49042 116 325 224 14931 223 106 171 118 226 313 34263 802 13511 261 32147 456 10 3384 239 216 22692 101 236 14931 101 236 495 5922 30057 495 20084 495 26409 30057 20084 495 26409 1610 495 26409 20084 495 15 20 495 537 20 495 1051 20 209 18081 211 18081 116 18081 230 39936 222 18081 226 39936 213 18081 233 18081 117 18081 242 39936 212 18081 242 18081 97 18081 116 18081 216 14931 235 212 3736 15367 41197 13610 19934 41869 21275 1012 1047 18795 40120 20422 241 16081 6877 12880 11514 1068 8713 38177 13396 3415 9925 12559 10453 1389 42011 35033 34842 11202 9739 9739 33021 18963 4672 25561 8220 309 1849 644 686 42618 344 434 627 13 686 1848 368 2119 32 686 46 417 2119 309 1833 1056 352 13 686 37 368 751 690 10331 32 844 8 31516 247 8 77 45 diff --git a/backend/llama.cpp/models/ggml-vocab-nomic-bert-moe.gguf b/backend/llama.cpp/models/ggml-vocab-nomic-bert-moe.gguf new file mode 100644 index 0000000000000000000000000000000000000000..fee7ec54610be32a526dd4f0b76625c4a1e0ae0e --- /dev/null +++ b/backend/llama.cpp/models/ggml-vocab-nomic-bert-moe.gguf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:90a6746926454784a98389ad36a36d89bc9cfc81db9cb0f33c941bcc959fe5f9 +size 6821877 diff --git a/backend/llama.cpp/models/ggml-vocab-phi-3.gguf b/backend/llama.cpp/models/ggml-vocab-phi-3.gguf new file mode 100644 index 0000000000000000000000000000000000000000..c1db01d8d6c3f59d275809509b130fe95203ba19 --- /dev/null +++ b/backend/llama.cpp/models/ggml-vocab-phi-3.gguf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:967d7190d11c4842eab697079d98d56c2116e10eb617be355a2733bfc132e326 +size 726019 diff --git a/backend/llama.cpp/models/ggml-vocab-phi-3.gguf.inp b/backend/llama.cpp/models/ggml-vocab-phi-3.gguf.inp new file mode 100644 index 0000000000000000000000000000000000000000..86b934e4020fba7061c15b6349d5e4163bb2383e --- /dev/null +++ b/backend/llama.cpp/models/ggml-vocab-phi-3.gguf.inp @@ -0,0 +1,112 @@ +ied 4 ½ months +__ggml_vocab_test__ +Äpfel +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + + +__ggml_vocab_test__ + + + +__ggml_vocab_test__ + + + + +__ggml_vocab_test__ + + +__ggml_vocab_test__ +Hello world +__ggml_vocab_test__ + Hello world +__ggml_vocab_test__ +Hello World +__ggml_vocab_test__ + Hello World +__ggml_vocab_test__ + Hello World! +__ggml_vocab_test__ +Hello, world! +__ggml_vocab_test__ + Hello, world! +__ggml_vocab_test__ + this is 🦙.cpp +__ggml_vocab_test__ +w048 7tuijk dsdfhu +__ggml_vocab_test__ +нещо на Български +__ggml_vocab_test__ +កាន់តែពិសេសអាចខលចេញ +__ggml_vocab_test__ +🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token) +__ggml_vocab_test__ +Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello + Hello +__ggml_vocab_test__ + ( +__ggml_vocab_test__ + + = +__ggml_vocab_test__ +' era +__ggml_vocab_test__ +Hello, y'all! How are you 😁 ?我想在apple工作1314151天～ +__ggml_vocab_test__ +!!!!!! +__ggml_vocab_test__ +3 +__ggml_vocab_test__ +33 +__ggml_vocab_test__ +333 +__ggml_vocab_test__ +3333 +__ggml_vocab_test__ +33333 +__ggml_vocab_test__ +333333 +__ggml_vocab_test__ +3333333 +__ggml_vocab_test__ +33333333 +__ggml_vocab_test__ +333333333 +__ggml_vocab_test__ +Cửa Việt +__ggml_vocab_test__ + discards +__ggml_vocab_test__ + + + + + + + + + + + +🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български ''''''```````""""......!!!!!!?????? I've been 'told he's there, 'RE you sure? 'M not sure I'll make it, 'D you like some tea? We'Ve a'lL +__ggml_vocab_test__ diff --git a/backend/llama.cpp/models/ggml-vocab-phi-3.gguf.out b/backend/llama.cpp/models/ggml-vocab-phi-3.gguf.out new file mode 100644 index 0000000000000000000000000000000000000000..2a71a6ef86efa9ee78509747de5cd1ceb59c8a5a --- /dev/null +++ b/backend/llama.cpp/models/ggml-vocab-phi-3.gguf.out @@ -0,0 +1,46 @@ + 474 287 29871 29946 29871 30226 7378 + 11585 7810 295 + + 259 + 1678 + 268 + 29871 12 + 29871 13 + 29871 13 13 + 29871 13 13 13 + 29871 12 13 + 15043 3186 + 29871 15043 3186 + 15043 2787 + 29871 15043 2787 + 29871 15043 2787 29991 + 15043 29892 3186 29991 + 29871 15043 29892 3186 29991 + 29871 445 338 29871 243 162 169 156 29889 8223 + 281 29900 29946 29947 29871 29955 9161 13535 18031 2176 6905 + 1538 4851 665 1386 29713 1305 + 29871 31849 31324 31934 228 162 142 228 161 146 228 162 133 228 161 153 228 161 186 31708 228 162 132 31708 228 161 165 31324 228 161 136 228 161 132 228 161 158 228 161 136 228 162 132 228 161 140 + 29871 243 162 157 131 313 8945 29897 29871 243 162 155 185 30722 243 162 143 174 30598 313 20787 953 3848 275 16125 630 29897 29871 31681 313 6194 953 29877 2397 393 756 967 1914 5993 29897 + 15043 + 29871 15043 + 259 15043 + 1678 15043 + 268 15043 + 268 15043 13 1678 15043 + 29871 313 + 29871 13 353 + 525 3152 + 15043 29892 343 29915 497 29991 1128 526 366 29871 243 162 155 132 1577 30672 31522 30505 11548 31041 30732 29896 29941 29896 29946 29896 29945 29896 30408 30739 + 1738 6824 21004 + 29871 29941 + 29871 29941 29941 + 29871 29941 29941 29941 + 29871 29941 29941 29941 29941 + 29871 29941 29941 29941 29941 29941 + 29871 29941 29941 29941 29941 29941 29941 + 29871 29941 29941 29941 29941 29941 29941 29941 + 29871 29941 29941 29941 29941 29941 29941 29941 29941 + 29871 29941 29941 29941 29941 29941 29941 29941 29941 29941 + 315 228 190 176 29874 10630 30529 29873 + 29871 2313 3163 + 29871 13 29871 13 13 29871 13 13 13 29871 12 29871 12 12 29871 12 13 259 13 1678 13 268 13 418 13 243 162 157 131 313 8945 29897 29871 243 162 155 185 30722 243 162 143 174 30598 313 20787 953 3848 275 16125 630 29897 29871 31681 29871 243 162 169 156 243 162 169 156 29871 29941 29871 29941 29941 29871 29941 29941 29941 29871 29941 29941 29941 29941 29871 29941 29941 29941 29941 29941 29871 29941 29941 29941 29941 29941 29941 29871 29941 29941 29941 29941 29941 29941 29941 29871 29941 29941 29941 29941 29941 29941 29941 29941 29871 29941 29889 29941 29871 29941 636 29941 29871 29941 856 29941 29871 31849 31324 31934 228 162 142 228 161 146 228 162 133 228 161 153 228 161 186 31708 228 162 132 31708 228 161 165 31324 228 161 136 243 162 155 132 1577 30672 31522 30505 11548 31041 30732 29896 29941 29896 29946 29896 29945 29896 30408 30739 448 23648 2751 25512 1538 4851 665 1386 29713 1305 14550 4907 11120 16159 16159 16159 15945 15945 3045 636 6824 6824 6824 8773 8773 8773 306 29915 345 1063 525 29873 1025 540 29915 29879 727 29892 525 1525 366 1854 29973 525 29924 451 1854 306 29915 645 1207 372 29892 525 29928 366 763 777 23429 29973 1334 29915 29963 29872 263 29915 29880 29931 diff --git a/backend/llama.cpp/models/ggml-vocab-qwen2.gguf b/backend/llama.cpp/models/ggml-vocab-qwen2.gguf new file mode 100644 index 0000000000000000000000000000000000000000..d97fb6fc8f222f7a92f8c0a916c9549de38b81dd --- /dev/null +++ b/backend/llama.cpp/models/ggml-vocab-qwen2.gguf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:44c2f46b715f585c6ab513970e8a006bfa5badd6108560054921cf598d154d8c +size 5928681 diff --git a/backend/llama.cpp/models/ggml-vocab-qwen2.gguf.inp b/backend/llama.cpp/models/ggml-vocab-qwen2.gguf.inp new file mode 100644 index 0000000000000000000000000000000000000000..86b934e4020fba7061c15b6349d5e4163bb2383e --- /dev/null +++ b/backend/llama.cpp/models/ggml-vocab-qwen2.gguf.inp @@ -0,0 +1,112 @@ +ied 4 ½ months +__ggml_vocab_test__ +Äpfel +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + + +__ggml_vocab_test__ + + + +__ggml_vocab_test__ + + + + +__ggml_vocab_test__ + + +__ggml_vocab_test__ +Hello world +__ggml_vocab_test__ + Hello world +__ggml_vocab_test__ +Hello World +__ggml_vocab_test__ + Hello World +__ggml_vocab_test__ + Hello World! +__ggml_vocab_test__ +Hello, world! +__ggml_vocab_test__ + Hello, world! +__ggml_vocab_test__ + this is 🦙.cpp +__ggml_vocab_test__ +w048 7tuijk dsdfhu +__ggml_vocab_test__ +нещо на Български +__ggml_vocab_test__ +កាន់តែពិសេសអាចខលចេញ +__ggml_vocab_test__ +🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token) +__ggml_vocab_test__ +Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello + Hello +__ggml_vocab_test__ + ( +__ggml_vocab_test__ + + = +__ggml_vocab_test__ +' era +__ggml_vocab_test__ +Hello, y'all! How are you 😁 ?我想在apple工作1314151天～ +__ggml_vocab_test__ +!!!!!! +__ggml_vocab_test__ +3 +__ggml_vocab_test__ +33 +__ggml_vocab_test__ +333 +__ggml_vocab_test__ +3333 +__ggml_vocab_test__ +33333 +__ggml_vocab_test__ +333333 +__ggml_vocab_test__ +3333333 +__ggml_vocab_test__ +33333333 +__ggml_vocab_test__ +333333333 +__ggml_vocab_test__ +Cửa Việt +__ggml_vocab_test__ + discards +__ggml_vocab_test__ + + + + + + + + + + + +🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български ''''''```````""""......!!!!!!?????? I've been 'told he's there, 'RE you sure? 'M not sure I'll make it, 'D you like some tea? 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How are you 😁 ?我想在apple工作1314151天～ +__ggml_vocab_test__ +!!!!!! +__ggml_vocab_test__ +3 +__ggml_vocab_test__ +33 +__ggml_vocab_test__ +333 +__ggml_vocab_test__ +3333 +__ggml_vocab_test__ +33333 +__ggml_vocab_test__ +333333 +__ggml_vocab_test__ +3333333 +__ggml_vocab_test__ +33333333 +__ggml_vocab_test__ +333333333 +__ggml_vocab_test__ +Cửa Việt +__ggml_vocab_test__ + discards +__ggml_vocab_test__ + + + + + + + + + + + +🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български ''''''```````""""......!!!!!!?????? I've been 'told he's there, 'RE you sure? 'M not sure I'll make it, 'D you like some tea? 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64 96549 64 96549 64 96549 64 96549 64 96549 64 96549 64 96549 64 96549 64 96549 64 96549 64 96549 64 96549 64 96549 64 96549 64 96549 64 96549 64 96549 64 96549 64 96549 64 96549 64 96549 64 96549 64 96549 64 96549 64 96549 64 96549 64 96549 64 96549 64 96549 +53 645 51580 29974 diff --git a/backend/llama.cpp/models/ggml-vocab-refact.gguf b/backend/llama.cpp/models/ggml-vocab-refact.gguf new file mode 100644 index 0000000000000000000000000000000000000000..ba90829117ad48af114796550f2681549dea09ad --- /dev/null +++ b/backend/llama.cpp/models/ggml-vocab-refact.gguf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ac3ceda902fed91ccf74312b305d9b86c37e4f8e35fa9cc6ef3ce34fca7d4678 +size 1720710 diff --git a/backend/llama.cpp/models/ggml-vocab-refact.gguf.inp b/backend/llama.cpp/models/ggml-vocab-refact.gguf.inp new file mode 100644 index 0000000000000000000000000000000000000000..86b934e4020fba7061c15b6349d5e4163bb2383e --- /dev/null +++ b/backend/llama.cpp/models/ggml-vocab-refact.gguf.inp @@ -0,0 +1,112 @@ +ied 4 ½ months +__ggml_vocab_test__ +Äpfel +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + + +__ggml_vocab_test__ + + + +__ggml_vocab_test__ + + + + +__ggml_vocab_test__ + + +__ggml_vocab_test__ +Hello world +__ggml_vocab_test__ + Hello world +__ggml_vocab_test__ +Hello World +__ggml_vocab_test__ + Hello World +__ggml_vocab_test__ + Hello World! +__ggml_vocab_test__ +Hello, world! +__ggml_vocab_test__ + Hello, world! +__ggml_vocab_test__ + this is 🦙.cpp +__ggml_vocab_test__ +w048 7tuijk dsdfhu +__ggml_vocab_test__ +нещо на Български +__ggml_vocab_test__ +កាន់តែពិសេសអាចខលចេញ +__ggml_vocab_test__ +🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token) +__ggml_vocab_test__ +Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello + Hello +__ggml_vocab_test__ + ( +__ggml_vocab_test__ + + = +__ggml_vocab_test__ +' era +__ggml_vocab_test__ +Hello, y'all! How are you 😁 ?我想在apple工作1314151天～ +__ggml_vocab_test__ +!!!!!! +__ggml_vocab_test__ +3 +__ggml_vocab_test__ +33 +__ggml_vocab_test__ +333 +__ggml_vocab_test__ +3333 +__ggml_vocab_test__ +33333 +__ggml_vocab_test__ +333333 +__ggml_vocab_test__ +3333333 +__ggml_vocab_test__ +33333333 +__ggml_vocab_test__ +333333333 +__ggml_vocab_test__ +Cửa Việt +__ggml_vocab_test__ + discards +__ggml_vocab_test__ + + + + + + + + + + + +🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български ''''''```````""""......!!!!!!?????? I've been 'told he's there, 'RE you sure? 'M not sure I'll make it, 'D you like some tea? We'Ve a'lL +__ggml_vocab_test__ diff --git a/backend/llama.cpp/models/ggml-vocab-refact.gguf.out b/backend/llama.cpp/models/ggml-vocab-refact.gguf.out new file mode 100644 index 0000000000000000000000000000000000000000..f13dda52ce41e1d93309d9def498d3cf2c322d22 --- /dev/null +++ b/backend/llama.cpp/models/ggml-vocab-refact.gguf.out @@ -0,0 +1,46 @@ + 4833 225 38 225 143 140 17723 + 144 231 7132 342 + + 225 + 261 + 264 + 202 + 203 + 478 + 2831 + 15773 + 8279 5788 + 12000 5788 + 8279 10896 + 12000 10896 + 12000 10896 19 + 8279 30 5788 19 + 12000 30 5788 19 + 458 438 5945 118 252 32 3766 + 105 34 38 42 225 41 102 1707 12530 10180 1479 8278 + 39862 8372 1039 9446 40242 13852 2053 8949 12531 1520 10700 + 14574 227 14574 133 14574 246 30457 238 14574 242 30457 229 14574 249 14574 134 14574 258 30457 228 14574 258 14574 114 14574 133 14574 232 14574 228 14574 254 14574 232 30457 228 14574 236 + 3807 253 227 308 4382 27 18458 133 46113 44967 123 13868 308 12565 19775 33071 40824 733 27 41889 308 2585 22680 688 1401 2819 4369 2404 27 + 8279 + 12000 + 225 12000 + 261 12000 + 264 12000 + 264 12000 284 12000 + 308 + 203 280 + 25 34666 + 8279 30 533 25 464 19 4971 884 844 18458 228 1018 4982 13368 2909 9513 17827 35 37 35 38 35 39 35 11873 47838 + 9163 3202 + 37 + 37 37 + 37 37 37 + 37 37 37 37 + 37 37 37 37 37 + 37 37 37 37 37 37 + 37 37 37 37 37 37 37 + 37 37 37 37 37 37 37 37 + 37 37 37 37 37 37 37 37 37 + 53 33934 83 33217 17102 102 + 1214 12258 + 334 719 8878 202 10885 4222 16104 28570 203 3807 253 227 308 4382 27 18458 133 46113 44967 123 13868 308 12565 19775 33071 40824 733 27 41889 5945 118 252 3807 118 252 225 37 225 37 37 225 37 37 37 225 37 37 37 37 225 37 37 37 37 37 225 37 37 37 37 37 37 225 37 37 37 37 37 37 37 225 37 37 37 37 37 37 37 37 225 37 32 37 225 37 497 37 225 37 1179 37 225 14574 227 14574 133 14574 246 30457 238 14574 242 30457 229 14574 249 14574 134 14574 258 30457 228 14574 258 14574 114 14574 133 14574 232 36628 228 1018 4982 13368 2909 9513 17827 35 37 35 38 35 39 35 11873 47838 20921 16623 13028 8372 1039 9446 40242 13852 2053 8949 12531 1520 10700 5881 9592 13299 914 31753 31359 9163 3202 35472 10397 439 4763 2583 330 102 1455 938 1182 2017 30 330 613 844 3654 49 330 63 646 3654 439 4621 1930 561 30 330 54 844 2124 1629 35993 49 2688 25 7709 312 25 94 62 diff --git a/backend/llama.cpp/models/ggml-vocab-starcoder.gguf b/backend/llama.cpp/models/ggml-vocab-starcoder.gguf new file mode 100644 index 0000000000000000000000000000000000000000..66c113ad7e480a2421e7098595e0e2f28e427aa7 --- /dev/null +++ b/backend/llama.cpp/models/ggml-vocab-starcoder.gguf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fedb892b4e1bd3c1f2fcdae356440b14fb458f4264d586e5c987ed93df4e174d +size 1719346 diff --git a/backend/llama.cpp/models/ggml-vocab-starcoder.gguf.inp b/backend/llama.cpp/models/ggml-vocab-starcoder.gguf.inp new file mode 100644 index 0000000000000000000000000000000000000000..86b934e4020fba7061c15b6349d5e4163bb2383e --- /dev/null +++ b/backend/llama.cpp/models/ggml-vocab-starcoder.gguf.inp @@ -0,0 +1,112 @@ +ied 4 ½ months +__ggml_vocab_test__ +Äpfel +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + + +__ggml_vocab_test__ + + + +__ggml_vocab_test__ + + + + +__ggml_vocab_test__ + + +__ggml_vocab_test__ +Hello world +__ggml_vocab_test__ + Hello world +__ggml_vocab_test__ +Hello World +__ggml_vocab_test__ + Hello World +__ggml_vocab_test__ + Hello World! +__ggml_vocab_test__ +Hello, world! +__ggml_vocab_test__ + Hello, world! +__ggml_vocab_test__ + this is 🦙.cpp +__ggml_vocab_test__ +w048 7tuijk dsdfhu +__ggml_vocab_test__ +нещо на Български +__ggml_vocab_test__ +កាន់តែពិសេសអាចខលចេញ +__ggml_vocab_test__ +🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token) +__ggml_vocab_test__ +Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello + Hello +__ggml_vocab_test__ + ( +__ggml_vocab_test__ + + = +__ggml_vocab_test__ +' era +__ggml_vocab_test__ +Hello, y'all! How are you 😁 ?我想在apple工作1314151天～ +__ggml_vocab_test__ +!!!!!! +__ggml_vocab_test__ +3 +__ggml_vocab_test__ +33 +__ggml_vocab_test__ +333 +__ggml_vocab_test__ +3333 +__ggml_vocab_test__ +33333 +__ggml_vocab_test__ +333333 +__ggml_vocab_test__ +3333333 +__ggml_vocab_test__ +33333333 +__ggml_vocab_test__ +333333333 +__ggml_vocab_test__ +Cửa Việt +__ggml_vocab_test__ + discards +__ggml_vocab_test__ + + + + + + + + + + + +🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български ''''''```````""""......!!!!!!?????? I've been 'told he's there, 'RE you sure? 'M not sure I'll make it, 'D you like some tea? We'Ve a'lL +__ggml_vocab_test__ diff --git a/backend/llama.cpp/models/ggml-vocab-starcoder.gguf.out b/backend/llama.cpp/models/ggml-vocab-starcoder.gguf.out new file mode 100644 index 0000000000000000000000000000000000000000..4698e2c3c81ad21e5046d5ba1a0bb8010f3c212b --- /dev/null +++ b/backend/llama.cpp/models/ggml-vocab-starcoder.gguf.out @@ -0,0 +1,46 @@ + 4850 244 57 244 162 159 17722 + 163 250 7146 361 + + 244 + 280 + 283 + 221 + 222 + 499 + 3067 + 15767 + 8302 5810 + 12009 5810 + 8302 10914 + 12009 10914 + 12009 10914 38 + 8302 49 5810 38 + 12009 49 5810 38 + 477 458 5954 137 271 51 3779 + 124 53 57 61 244 60 121 1726 12568 10240 1519 8290 + 39916 8389 1059 9504 40216 13858 2073 8983 12571 1539 10721 + 14566 246 14566 152 14566 265 30428 257 14566 261 30428 248 14566 268 14566 153 14566 277 30428 247 14566 277 14566 133 14566 152 14566 251 14566 247 14566 273 14566 251 30428 247 14566 255 + 3822 272 246 327 4434 46 18445 152 46030 45022 142 13878 327 12585 19884 33773 40920 751 46 41839 327 2605 22716 708 1421 2840 4387 2421 46 + 8302 + 12009 + 244 12009 + 280 12009 + 283 12009 + 283 12009 303 12009 + 327 + 222 299 + 44 34719 + 8302 49 553 44 483 38 4998 904 863 18445 247 1037 4995 13379 2924 9515 17823 54 56 54 57 54 58 54 11904 47892 + 9221 3226 + 56 + 56 56 + 56 56 56 + 56 56 56 56 + 56 56 56 56 56 + 56 56 56 56 56 56 + 56 56 56 56 56 56 56 + 56 56 56 56 56 56 56 56 + 56 56 56 56 56 56 56 56 56 + 72 34269 102 33245 17234 121 + 1236 12266 + 353 736 8886 221 10883 4238 16101 28540 222 3822 272 246 327 4434 46 18445 152 46030 45022 142 13878 327 12585 19884 33773 40920 751 46 41839 5954 137 271 3822 137 271 244 56 244 56 56 244 56 56 56 244 56 56 56 56 244 56 56 56 56 56 244 56 56 56 56 56 56 244 56 56 56 56 56 56 56 244 56 56 56 56 56 56 56 56 244 56 51 56 244 56 516 56 244 56 1198 56 244 14566 246 14566 152 14566 265 30428 257 14566 261 30428 248 14566 268 14566 153 14566 277 30428 247 14566 277 14566 133 14566 152 14566 251 36570 247 1037 4995 13379 2924 9515 17823 54 56 54 57 54 58 54 11904 47892 20895 16625 13047 8389 1059 9504 40216 13858 2073 8983 12571 1539 10721 5918 9643 13298 932 31723 31330 9221 3226 35426 10400 457 4783 2602 349 121 1477 957 1200 2038 49 349 632 863 3673 68 349 82 666 3673 457 4650 1949 580 49 349 73 863 2144 1649 35941 68 2726 44 7728 331 44 113 81 diff --git a/backend/llama.cpp/models/templates/Apertus-8B-Instruct.jinja b/backend/llama.cpp/models/templates/Apertus-8B-Instruct.jinja new file mode 100644 index 0000000000000000000000000000000000000000..432ae59a406c3ffabe72bd011f09dfa66e233eae --- /dev/null +++ b/backend/llama.cpp/models/templates/Apertus-8B-Instruct.jinja @@ -0,0 +1,327 @@ +{%- macro render_typescript_type(param_spec, required_params, is_nullable=false) -%} + {%- if param_spec.type == "array" -%} + {%- if param_spec['items'] -%} + {%- if param_spec['items']['type'] == "string" -%} + {{- "string[]" }} + {%- elif param_spec['items']['type'] == "number" -%} + {{- "number[]" }} + {%- elif param_spec['items']['type'] == "integer" -%} + {{- "number[]" }} + {%- elif param_spec['items']['type'] == "boolean" -%} + {{- "boolean[]" }} + {%- else -%} + {%- set inner_type = render_typescript_type(param_spec['items'], required_params) -%} + {%- if inner_type == "object | object" or inner_type|length > 50 -%} + {{- "any[]" }} + {%- else -%} + {{- inner_type + "[]" }} + {%- endif -%} + {%- endif -%} + {%- if param_spec.nullable -%} + {{- " | null" }} + {%- endif -%} + {%- else -%} + {{- "any[]" }} + {%- if param_spec.nullable -%} + {{- " | null" }} + {%- endif -%} + {%- endif -%} + {%- elif param_spec.type is defined and param_spec.type is iterable and param_spec.type is not string and param_spec.type is not mapping and param_spec.type[0] is defined -%} + {#- Handle array of types like ["object", "object"] from Union[dict, list] #} + {%- if param_spec.type | length > 1 -%} + {{- param_spec.type | join(" | ") }} + {%- else -%} + {{- param_spec.type[0] }} + {%- endif -%} + {%- elif param_spec.oneOf -%} + {#- Handle oneOf schemas - check for complex unions and fallback to any #} + {%- set has_object_variants = false -%} + {%- for variant in param_spec.oneOf -%} + {%- if variant.type == "object" -%} + {%- set has_object_variants = true -%} + {%- endif -%} + {%- endfor -%} + {%- if has_object_variants and param_spec.oneOf|length > 1 -%} + {{- "any" }} + {%- else -%} + {%- for variant in param_spec.oneOf -%} + {{- render_typescript_type(variant, required_params) -}} + {%- if variant.description %} + {{- "// " + variant.description }} + {%- endif -%} + {%- if variant.default is defined %} + {{ "// default: " + variant.default|tojson }} + {%- endif -%} + {%- if not loop.last %} + {{- " | " }} + {% endif -%} + {%- endfor -%} + {%- endif -%} + {%- elif param_spec.type == "string" -%} + {%- if param_spec.enum -%} + {{- '"' + param_spec.enum|join('" | "') + '"' -}} + {%- else -%} + {{- "string" }} + {%- if param_spec.nullable %} + {{- " | null" }} + {%- endif -%} + {%- endif -%} + {%- elif param_spec.type == "number" -%} + {{- "number" }} + {%- elif param_spec.type == "integer" -%} + {{- "number" }} + {%- elif param_spec.type == "boolean" -%} + {{- "boolean" }} + {%- elif param_spec.type == "object" -%} + {%- if param_spec.properties -%} + {{- "{\n" }} + {%- for prop_name, prop_spec in param_spec.properties.items() -%} + {{- prop_name -}} + {%- if prop_name not in (param_spec.required or []) -%} + {{- "?" }} + {%- endif -%} + {{- ": " }} + {{ render_typescript_type(prop_spec, param_spec.required or []) }} + {%- if not loop.last -%} + {{-", " }} + {%- endif -%} + {%- endfor -%} + {{- "}" }} + {%- else -%} + {{- "object" }} + {%- endif -%} + {%- else -%} + {{- "any" }} + {%- endif -%} +{%- endmacro -%} + +{%- macro render_tools(tools) -%} + {%- for tool in tools %} + {{- "// " + tool.function.description + "\n" }} + {{- "type "+ tool.function.name + " = " }} + {%- if tool.function.parameters and tool.function.parameters.properties %} + {{- "(_: {\n" }} + {%- for param_name, param_spec in tool.function.parameters.properties.items() %} + {%- if param_spec.description %} + {{- "// " + param_spec.description + "\n" }} + {%- endif %} + {{- param_name }} + {%- if param_name not in (tool.function.parameters.required or []) -%} + {{- "?" }} + {%- endif -%} + {{- ": " }} + {{- render_typescript_type(param_spec, tool.function.parameters.required or []) }} + {%- if param_spec.default is defined -%} + {%- if param_spec.enum %} + {{- ", // default: " + param_spec.default }} + {%- elif param_spec.oneOf %} + {{- "// default: " + param_spec.default }} + {%- else %} + {{- ", // default: " + param_spec.default|tojson }} + {%- endif -%} + {%- endif -%} + {%- if not loop.last %} + {{- ",\n" }} + {%- else %} + {{- "\n" }} + {%- endif -%} + {%- endfor %} + {{- "}) => any;" }} + {%- else -%} + {{- "() => any;" }} + {%- endif -%} + {%- if not loop.last -%} + {{- "\n" }} + {%- endif -%} + {%- endfor %} +{%- endmacro -%} + +{{ bos_token }} + +{%- set system_token = '<|system_start|>' -%} +{%- set end_system_token = '<|system_end|>' -%} +{%- set developer_token = '<|developer_start|>' -%} +{%- set end_developer_token = '<|developer_end|>' -%} +{%- set user_token = '<|user_start|>' -%} +{%- set end_user_token = '<|user_end|>' -%} +{%- set assistant_token = '<|assistant_start|>' -%} +{%- set end_assistant_token = '<|assistant_end|>' -%} +{%- set inner_token = '<|inner_prefix|>' -%} +{%- set outer_token = '<|inner_suffix|>' -%} +{%- set tool_calls_token = '<|tools_prefix|>' -%} +{%- set end_tool_calls_token = '<|tools_suffix|>' -%} + +{%- set ns = namespace(in_assistant=false, in_tool=false, in_inner=false, assistant_format=none) -%} + +{%- if messages and messages[0].role == 'system' -%} + {%- if "content" in messages[0] -%} + {%- if messages[0].content is string -%} + {{ system_token + messages[0].content + end_system_token }} + {%- elif messages[0].content is mapping and "text" in messages[0].content -%} + {{ system_token + messages[0].content.text + end_system_token }} + {%- else -%} + {{- raise_exception("Invalid system message") -}} + {%- endif -%} + {%- else -%} + {{- raise_exception("Invalid system message") -}} + {%- endif -%} + {%- set loop_messages = messages[1:] -%} +{%- else -%} + {{ system_token + 'You are Apertus, a helpful assistant created by the SwissAI initiative.\nKnowledge cutoff: 2024-04\nCurrent date: ' + strftime_now('%Y-%m-%d') + end_system_token }} + {%- set loop_messages = messages -%} +{%- endif -%} + +{{ developer_token + 'Deliberation: ' }} +{%- if enable_thinking is defined and enable_thinking -%} + {{ 'enabled\n' }} +{%- else -%} + {{ 'disabled\n' }} +{%- endif -%} +{%- if tools is defined and tools -%} + {{ 'Tool Capabilities:\n' + render_tools(tools) }} +{%- else -%} + {{ 'Tool Capabilities: disabled' }} +{%- endif -%} +{{ end_developer_token }} + +{%- for message in loop_messages -%} + {%- if message.role == 'user' -%} + {%- set ns.in_inner = false -%} + {%- if ns.in_tool -%} + {{ ']' }} + {%- set ns.in_tool = false -%} + {%- endif -%} + {%- if ns.in_assistant -%} + {{ end_assistant_token }} + {%- set ns.in_assistant = false -%} + {%- endif -%} + {%- if "content" in message -%} + {{ user_token }} + {%- if message.content is string -%} + {{ message.content }} + {%- elif message.content is mapping and "parts" in message.content -%} + {%- set parts = message.content.parts -%} + {%- for part in parts -%} + {%- if part.type == "text" -%} + {{ part.text }} + {%- else -%} + {{- raise_exception("Invalid user part: " + part.type) -}} + {%- endif -%} + {%- endfor -%} + {%- else -%} + {{- raise_exception("Invalid user message: " + message.role) -}} + {%- endif -%} + {{ end_user_token }} + {%- endif -%} + {%- elif message.role == 'assistant' -%} + {%- if not ns.in_assistant -%} + {{ assistant_token }} + {%- set ns.in_assistant = true -%} + {%- endif -%} + {%- if "content" in message and message.content is not none -%} + {%- if message.content is string and (ns.assistant_format is none or ns.assistant_format == "string") -%} + {%- if ns.in_tool -%} + {{ ']' }} + {%- set ns.in_tool = false -%} + {%- endif -%} + {%- set ns.assistant_format = "string" -%} + {{ message.content }} + {%- elif message.content is mapping and "blocks" in message.content and (ns.assistant_format is none or ns.assistant_format == "mapping") -%} + {%- set ns.assistant_format = "mapping" -%} + {%- set blocks = message.content.blocks -%} + {%- for block in blocks -%} + {%- if block.type == 'thoughts' -%} + {%- if ns.in_tool -%} + {{ ']' }} + {%- set ns.in_tool = false -%} + {%- endif -%} + {%- if not ns.in_inner -%} + {%- set ns.in_inner = true -%} + {{ inner_token }} + {%- endif -%} + {{ block.text }} + {%- elif block.type == 'tool_calls' -%} + {%- if ns.in_tool -%} + {{ ']' }} + {%- set ns.in_tool = false -%} + {%- endif -%} + {%- if ns.in_inner and not loop.first and block.calls|length == 1 and block.calls[0].name == 'display_answers' -%} + {%- set ns.in_inner = false -%} + {{ outer_token }} + {%- endif -%} + {{ tool_calls_token + '[' }} + {%- for tool_call in block.calls -%} + {{- '{"' + tool_call.name + '": ' + tool_call.arguments + '}' }} + {%- if not loop.last -%} + {{- ", " }} + {%- endif -%} + {%- endfor -%} + {{ ']' + end_tool_calls_token }} + {%- elif block.type == 'tool_outputs' -%} + {%- if ns.in_tool -%} + {{- raise_exception("Cannot have both tool outputs as separate messages and tool outputs as blocks") -}} + {%- endif -%} + {{ '[' }} + {%- for tool_output in block.outputs -%} + {{- tool_output.output }} + {%- if not loop.last -%} + {{- ", " }} + {%- endif -%} + {%- endfor -%} + {{- ']' }} + {%- elif block.type == 'response' -%} + {%- if ns.in_tool -%} + {{ ']' }} + {%- set ns.in_tool = false -%} + {%- endif -%} + {%- if (not loop.first and ns.in_inner) or (ns.in_assistant and ns.in_inner) -%} + {%- set ns.in_inner = false -%} + {{ outer_token }} + {%- endif -%} + {{ block.text }} + {%- else -%} + {{- raise_exception("Invalid assistant block type: " + block.type) -}} + {%- endif -%} + {%- endfor -%} + {%- else -%} + {{- raise_exception("Invalid assistant content '" + message.content + "', expected " + ns.assistant_format) -}} + {%- endif -%} + {%- elif "tool_calls" not in message -%} + {{- raise_exception("Invalid assistant message " + message) -}} + {%- endif -%} + {%- if "tool_calls" in message and message.tool_calls -%} + {{ tool_calls_token + '[' }} + {%- for tool_call in message.tool_calls -%} + {%- if tool_call.type == 'function' -%} + {%- set function = tool_call.function -%} + {{- '{"' + function.name + '": ' + function.arguments|tojson + '}' }} + {%- if not loop.last -%} + {{- ", " }} + {%- endif -%} + {%- else -%} + {{- raise_exception("Invalid tool call type: " + tool_call.type) -}} + {%- endif -%} + {%- endfor -%} + {{ ']' + end_tool_calls_token }} + {%- endif -%} + {%- elif message.role == 'tool' -%} + {%- if not ns.in_assistant -%} + {{- raise_exception("Tool message outside of assistant") -}} + {%- endif -%} + {%- if not ns.in_tool -%} + {{ '[' }} + {%- set ns.in_tool = true -%} + {%- else -%} + {{ ", "}} + {%- endif -%} + {{ message.content }} + {%- else -%} + {{- raise_exception("Invalid message role") -}} + {%- endif -%} +{%- endfor -%} +{%- if ns.in_tool -%} + {{ ']' }} +{%- endif -%} +{%- if add_generation_prompt -%} + {{ assistant_token }} +{%- endif -%} \ No newline at end of file diff --git a/backend/llama.cpp/models/templates/Apriel-1.6-15b-Thinker-fixed.jinja b/backend/llama.cpp/models/templates/Apriel-1.6-15b-Thinker-fixed.jinja new file mode 100644 index 0000000000000000000000000000000000000000..8a282b8231bdd14589d208df3ea881b1311f0aa1 --- /dev/null +++ b/backend/llama.cpp/models/templates/Apriel-1.6-15b-Thinker-fixed.jinja @@ -0,0 +1,171 @@ +{# ---------------------------------------------------------------------- #} +{# ƛƬ Default setup and flags #} +{# ---------------------------------------------------------------------- #} +{%- set messages = messages or [] -%} +{%- set tools = tools or [] -%} +{%- set add_generation_prompt = add_generation_prompt or false -%} +{%- set available_tool_string = '' -%} +{%- set add_tool_id = true -%} +{%- set add_thoughts = true -%} {# whether to include reasoning blocks #} +{# Optional token placeholders (safe defaults) #} +{%- set bos_token = bos_token or '' -%} +{%- set eos_token = eos_token or '' -%} +{# ---------------------------------------------------------------------- #} +{# Core reasoning prompt and assistant reasoning prefix #} +{# ---------------------------------------------------------------------- #} +{%- set reasoning_prompt -%} + You are a thoughtful, systematic AI assistant from ServiceNow Language Models (SLAM) lab. + Analyze each question carefully, present your reasoning step-by-step, then provide the final + response after the marker [BEGIN FINAL RESPONSE]. +{%- endset -%} +{%- set reasoning_asst_turn_start = 'Here are my reasoning steps:\n' -%} +{# ---------------------------------------------------------------------- #} +{# Tool list and tool call output format #} +{# ---------------------------------------------------------------------- #} +{%- if tools|length > 0 -%} + {%- set available_tool_string -%} + You are provided with function signatures within XML tags. + You may call one or more functions to assist with the user query. + Don't make assumptions about the arguments. You should infer the argument values from previous + user responses and the system message. + Here are the available tools: + + {% for tool in tools %}{{ tool|string }}{% endfor %} + + . + + Return all function calls as a list of JSON objects within XML tags. + Each JSON object should contain a function name and arguments as follows: + [ + {"name": , "arguments": }, + {"name": , "arguments": }, + ... + ] + {%- endset -%} +{%- endif -%} +{# ---------------------------------------------------------------------- #} +{# Start system block if first message is not system #} +{# ---------------------------------------------------------------------- #} +{%- if messages|length > 0 and messages[0]['role'] != 'system' -%} + {%- if tools|length > 0 -%} + {{ bos_token + '<|begin_system|>\n' + reasoning_prompt + '\n' + available_tool_string + '\n' }} + {%- else -%} + {{ bos_token + '<|begin_system|>\n' + reasoning_prompt + '\n' }} + {%- endif -%} +{%- endif -%} +{# ---------------------------------------------------------------------- #} +{# Iterate through messages #} +{# ---------------------------------------------------------------------- #} +{%- for message in messages -%} + + {# ---------------- USER MESSAGE ---------------- #} + {%- if message['role'] == 'user' -%} + {{ '<|begin_user|>\n' }} + {%- if message['content'] is not string -%} + {%- for chunk in message['content'] -%} + {%- if chunk['type'] == 'text' -%} + {{ chunk['text'] }} + {%- elif chunk['type'] in ['image', 'image_url'] -%} + {{ '[IMG]' }} + {%- else -%} + {{ raise_exception('Unrecognized content type!') }} + {%- endif -%} + {%- endfor -%} + {%- else -%} + {{ message['content'] }} + {%- endif -%} + + {# ---------------- SYSTEM MESSAGE ---------------- #} + {%- elif message['role'] == 'system' -%} + {%- set sys_content = message.get('content', '') -%} + {%- if sys_content and sys_content|length > 0 -%} + {%- if sys_content is string -%} + {%- set system_message = sys_content -%} + {%- else -%} + {%- set system_message = sys_content[0]['text'] -%} + {%- endif -%} + {%- else -%} + {%- set system_message = '' -%} + {%- endif -%} + + {%- if tools|length > 0 -%} + {{ bos_token + '<|begin_system|>\n' + reasoning_prompt + '\n' + system_message + '\n' + available_tool_string + '\n' }} + {%- else -%} + {{ bos_token + '<|begin_system|>\n' + reasoning_prompt + '\n' + system_message + '\n' }} + {%- endif -%} + + {# ---------------- ASSISTANT MESSAGE ---------------- #} + {%- elif message['role'] == 'assistant' -%} + {%- if loop.last -%} + {%- set add_tool_id = false -%} + {%- endif -%} + + {{ '\n<|begin_assistant|>\n' }} + + {%- if add_thoughts and message.get('reasoning_content') and loop.last -%} + {{ message['reasoning_content'] + '\n[BEGIN FINAL RESPONSE]\n' }} + {%- endif -%} + + {%- set asst_content = message.get('content', '') -%} + {%- if asst_content and asst_content|length > 0 -%} + {%- if asst_content is not string -%} + {%- set asst_text = asst_content[0]['text'] -%} + {%- else -%} + {%- set asst_text = asst_content -%} + {%- endif -%} + {# For historical turns (not the last), strip reasoning and keep only final response #} + {%- if not loop.last and '[BEGIN FINAL RESPONSE]' in asst_text -%} + {{- asst_text.split('[BEGIN FINAL RESPONSE]')[-1] | trim -}} + {%- else -%} + {{- asst_text -}} + {%- endif -%} + {%- elif message.get('chosen') and message['chosen']|length > 0 -%} + {{ message['chosen'][0] }} + {%- endif -%} + + {# Tool call output #} + {%- set tool_calls = message.get('tool_calls', []) -%} + {%- if tool_calls and tool_calls|length > 0 -%} + {{ '\n[' }} + {%- for tool_call in tool_calls -%} + {{ '{"name": "' + tool_call['function']['name'] + '", "arguments": ' + tool_call['function']['arguments']|tojson }} + {%- if add_tool_id == true and 'id' in tool_call -%} + {{ ', "id": "' + tool_call['id'] + '"' }} + {%- endif -%} + {{ '}' }} + {%- if not loop.last -%}{{ ', ' }}{%- endif -%} + {%- endfor -%} + {{ ']' }} + {%- endif -%} + + {%- set training_prompt = training_prompt if (training_prompt is defined) else false -%} + {%- if not loop.last or training_prompt -%} + {{ '\n<|end|>\n' }} + {%- endif -%} + + {# ---------------- TOOL RESULT MESSAGE ---------------- #} + {%- elif message['role'] == 'tool' -%} + {%- set tool_content = message.get('content', '') -%} + {%- if tool_content is string -%} + {%- set tool_message = tool_content -%} + {%- else -%} + {%- set tool_message = tool_content[0]['text'] if tool_content else '' -%} + {%- endif -%} + {{ '<|begin_tool_result|>\n' + tool_message|string + '\n' }} + + {# ---------------- CONTENT MESSAGE ---------------- #} + {%- elif message['role'] == 'content' -%} + {%- set msg_content = message.get('content', '') -%} + {%- if msg_content is not string -%} + {{ '<|begin_content|>\n' + msg_content[0]['text'] + '\n' }} + {%- else -%} + {{ '<|begin_content|>\n' + msg_content + '\n' }} + {%- endif -%} + {%- endif -%} + + {# ---------------- REASONING PROMPT BEFORE NEXT ASSISTANT ---------------- #} + {%- if loop.last and add_generation_prompt and message['role'] != 'assistant' -%} + {{ '\n<|begin_assistant|>\n' + reasoning_asst_turn_start }} + {%- endif -%} + +{%- endfor -%} diff --git a/backend/llama.cpp/models/templates/Bielik-11B-v3.0-Instruct.jinja b/backend/llama.cpp/models/templates/Bielik-11B-v3.0-Instruct.jinja new file mode 100644 index 0000000000000000000000000000000000000000..40ef50076eb379738cc7c05e5882429986104558 --- /dev/null +++ b/backend/llama.cpp/models/templates/Bielik-11B-v3.0-Instruct.jinja @@ -0,0 +1,77 @@ +{{ bos_token }} +{%- if messages[0]['role'] == 'system' %} + {%- set system_message = messages[0]['content'] %} + {%- set loop_start_index = 1 %} +{%- else %} + {%- set system_message = "" %} + {%- set loop_start_index = 0 %} +{%- endif %} + +{%- if system_message or tools %} + {{- '<|im_start|>system\n' }} + {%- if system_message %} + {{- system_message }} + {%- endif %} + {%- if tools %} + {{- '\n\nMasz dostęp do następujących narzędzi. Definicje narzędzi znajdują się poniżej wewnątrz znaczników <|function_list|>:\n<|function_list|>\n' }} + {{- '[' }} + {%- for tool in tools %} + {{- tool | tojson }} + {%- if not loop.last %} + {{- ',\n' }} + {%- endif %} + {%- endfor %} + {{- ']\n<|function_list|>\n\nAby wywołać narzędzie, użyj formatu {"name": "nazwa_narzędzia", "arguments": {"argument": "wartość"}}. Wyniki działania narzędzi zostaną przekazane z markerem <|function_output|>.\n' }} + {%- endif %} + {%- if enable_thinking %} + {{- '\n\nZanim odpowiesz na pytanie, najpierw przemyśl swoje kroki i umieść swoje myśli wewnątrz tagów .... Musisz najpierw pomyśleć, zanim udzielisz odpowiedzi. ' -}} + {{- 'WAŻNE: Powinieneś myśleć w tym samym języku, co pytanie użytkownika. Jeśli pytanie jest zadane po polsku, powinieneś również myśleć po polsku. Jeśli pytanie jest po angielsku, myślisz również po angielsku itd. ' }} + {{- '** PAMIĘTAJ! ** Pytanie po polsku -> myślenie po polsku -> odpowiedź po polsku!' -}} + {{- '*** BARDZO WAŻNE!!! *** Jesteś Bielikiem, polskim modelem językowym. Twoją główną cechą jest umiejętność pisania po polsku. Jeśli użytkownik zadaje Ci pytania po polsku, ZAWSZE odpowiadaj po polsku. ' -}} + {{- 'Nawet, jeśli korzystasz z narzędzia, którego większość instrukcji jest po angielsku, powinieneś przede wszystkim odpowiadać po polsku, jeśli użytkownik zadaje pytanie w tym języku. ' -}} + {%- endif %} + {{- '<|im_end|>\n' }} +{%- endif %} + +{%- for message in messages[loop_start_index:] %} + {%- if message['role'] == 'user' %} + {{- '<|im_start|>user\n' + message['content'] + '<|im_end|>\n' }} + {%- elif message['role'] == 'assistant' %} + {{- '<|im_start|>assistant\n' }} + {%- set content = message.content | default('') %} + {%- set reasoning_content = message.reasoning_content | default('') %} + {%- if not reasoning_content and '' in content and '' in content %} + {%- set reasoning_parts = content.split('') %} + {%- set reasoning_content = reasoning_parts[0].split('')[-1] %} + {%- set content = reasoning_parts[1:] | join('') %} + {%- endif %} + {%- if reasoning_content %} + {{- '\n' + reasoning_content.strip() + '\n\n' }} + {%- endif %} + {{- content.lstrip() }} + {%- if message.tool_calls %} + {%- for tool_call in message.tool_calls %} + {%- if tool_call.function %} + {%- set tool_call = tool_call.function %} + {%- endif %} + {{- '\n\n{"name": "' + tool_call.name + '", "arguments": ' + (tool_call.arguments if tool_call.arguments is string else tool_call.arguments | tojson) + '}\n' }} + {%- endfor %} + {%- endif %} + {{- '<|im_end|>\n' }} + {%- elif message['role'] == 'tool' %} + {%- if loop.index0 == 0 or messages[loop.index0 - 1]['role'] != 'tool' %} + {{- '<|im_start|>user\n' }} + {%- endif %} + {{- '<|function_output|>' + message['content'] }} + {%- if loop.last or messages[loop.index0 + 1]['role'] != 'tool' %} + {{- '<|im_end|>\n' }} + {%- endif %} + {%- endif %} +{%- endfor %} + +{%- if add_generation_prompt %} + {{- '<|im_start|>assistant\n' }} + {%- if enable_thinking %} + {{- '\n' }} + {%- endif %} +{%- endif %} \ No newline at end of file diff --git a/backend/llama.cpp/models/templates/ByteDance-Seed-OSS.jinja b/backend/llama.cpp/models/templates/ByteDance-Seed-OSS.jinja new file mode 100644 index 0000000000000000000000000000000000000000..903ebaaba77ed311bab4892f6a14324283852427 --- /dev/null +++ b/backend/llama.cpp/models/templates/ByteDance-Seed-OSS.jinja @@ -0,0 +1,171 @@ +{# ----------‑‑‑ special token variables ‑‑‑---------- #} +{%- set bos_token = '' -%} +{%- set eos_token = '' -%} +{%- set pad_token = '' -%} +{%- set toolcall_begin_token = '' -%} +{%- set toolcall_end_token = '' -%} +{%- set think_begin_token = '' -%} +{%- set think_end_token = '' -%} +{%- set budget_begin_token = ''-%} +{%- set budget_end_token = ''-%} +{# -------------- reflection-interval lookup -------------- #} +{%- if not thinking_budget is defined %} +{%- set thinking_budget = -1 -%} +{%- endif -%} +{%- set budget_reflections_v05 = { + 0: 0, + 512: 128, + 1024: 256, + 2048: 512, + 4096: 512, + 8192: 1024, + 16384: 1024 +} -%} +{# Find the first gear that is greater than or equal to the thinking_budget. #} +{%- set ns = namespace(interval = None) -%} +{%- for k, v in budget_reflections_v05 | dictsort -%} + {%- if ns.interval is none and thinking_budget <= k -%} + {%- set ns.interval = v -%} + {%- endif -%} +{%- endfor -%} +{# If it exceeds the maximum gear, use the value of the last gear #} +{%- if ns.interval is none -%} + {%- set ns.interval = budget_reflections_v05[16384] -%} +{%- endif -%} +{# ---------- Preprocess the system message ---------- #} +{%- if messages[0]["role"] == "system" %} +{%- set system_message = messages[0]["content"] %} +{%- set loop_messages = messages[1:] %} +{%- else %} +{%- set loop_messages = messages %} +{%- endif %} +{# ---------- Ensure tools exist ---------- #} +{%- if not tools is defined or tools is none %} +{%- set tools = [] %} +{%- endif %} +{# tools2doc.jinja #} +{%- macro py_type(t) -%} + {%- if t == "string" -%}str + {%- elif t in ("number", "integer") -%}int + {%- elif t == "boolean" -%}bool + {%- elif t == "array" -%}list + {%- else -%}Any{%- endif -%} +{%- endmacro -%} +{# ---------- Output the system block ---------- #} +{%- if system_message is defined %} +{{ bos_token + "system\n" + system_message }} +{%- else %} +{%- if tools is iterable and tools | length > 0 %} +{{ bos_token + "system\nYou are Doubao, a helpful AI assistant. You may call one or more functions to assist with the user query." }} +{%- endif %} +{%- endif %} +{%- if use_json_tooldef is defined and use_json_tooldef %} + +{{"Tool List:\nYou are authorized to use the following tools (described in JSON Schema format). Before performing any task, you must decide how to call them based on the descriptions and parameters of these tools."}} +{{ tools | tojson(ensure_ascii=False) }} +{%- else %} +{%- for item in tools if item.type == "function" %} + + +Function: +def {{ item.function.name }}( +{%- for name, spec in item.function.parameters.properties.items() %} + {{- name }}: {{ py_type(spec.type) }}{% if not loop.last %},{% endif %} +{%- endfor %}): + """ + {{ item.function.description | trim }} + + {# ---------- Args ---------- #} + {%- if item.function.parameters.properties %} + Args: + {%- for name, spec in item.function.parameters.properties.items() %} + + - {{ name }} ({{ py_type(spec.type) }}) + {%- if name in item.function.parameters.required %} [必填]{% else %} [选填]{% endif %}: + {{- " " ~ (spec.description or "") }} + {%- endfor %} + {%- endif %} + + {# ---------- Returns ---------- #} + {%- if item.function.returns is defined + and item.function.returns.properties is defined + and item.function.returns.properties %} + Returns: + {%- for name, spec in item.function.returns.properties.items() %} + + - {{ name }} ({{ py_type(spec.type) }}): + {{- " " ~ (spec.description or "") }} + {%- endfor %} + {%- endif %} + + """ +{%- endfor %} +{%- endif %} +{%- if tools is iterable and tools | length > 0 %} + +{{"工具调用请遵循如下格式:\n\n\nvalue_1\nThis is the value for the second parameter\nthat can span\nmultiple lines\n\n\n"}} +{%- endif %} +{# End the system block line #} +{%- if system_message is defined or tools is iterable and tools | length > 0 %} +{{ eos_token }} +{%- endif %} +{# ---------- Thinking Budget ---------- #} +{%- if thinking_budget is defined %} +{%- if thinking_budget == 0 %} +{{ bos_token+"system" }} +{{ "You are an intelligent assistant that can answer questions in one step without the need for reasoning and thinking, that is, your thinking budget is 0. Next, please skip the thinking process and directly start answering the user's questions." }} +{{ eos_token }} +{%- elif not thinking_budget == -1 %} +{{ bos_token+"system" }} +{{ "You are an intelligent assistant with reflective ability. In the process of thinking and reasoning, you need to strictly follow the thinking budget, which is "}}{{thinking_budget}}{{". That is, you need to complete your thinking within "}}{{thinking_budget}}{{" tokens and start answering the user's questions. You will reflect on your thinking process every "}}{{ns.interval}}{{" tokens, stating how many tokens have been used and how many are left."}} +{{ eos_token }} +{%- endif %} +{%- endif %} +{# ---------- List the historical messages one by one ---------- #} +{%- for message in loop_messages %} +{%- if message.role == "assistant" + and message.tool_calls is defined + and message.tool_calls is iterable + and message.tool_calls | length > 0 %} +{{ bos_token + message.role }} +{%- if message.reasoning_content is defined and message.reasoning_content is string and message.reasoning_content | trim | length > 0 %} +{{ "\n" + think_begin_token + message.reasoning_content | trim + think_end_token }} +{%- endif %} +{%- if message.content is defined and message.content is string and message.content | trim | length > 0 %} +{{ "\n" + message.content | trim + "\n" }} +{%- endif %} +{%- for tool_call in message.tool_calls %} +{%- if tool_call.function is defined %}{% set tool_call = tool_call.function %}{% endif %} +{{ "\n" + toolcall_begin_token + "\n\n" }} +{%- if tool_call.arguments is defined %} +{%- for arg_name, arg_value in tool_call.arguments | items %} +{{ "" }} +{%- set arg_value = arg_value if arg_value is string else arg_value | string %} +{{ arg_value+"\n" }} +{%- endfor %} +{%- endif %} +{{ "\n" + toolcall_end_token }} +{%- endfor %} +{{ eos_token }} +{%- elif message.role in ["user", "system"] %} +{{ bos_token + message.role + "\n" + message.content + eos_token }} +{%- elif message.role == "assistant" %} +{{ bos_token + message.role }} +{%- if message.reasoning_content is defined and message.reasoning_content is string and message.reasoning_content | trim | length > 0 %} +{{ "\n" + think_begin_token + message.reasoning_content | trim + think_end_token }} +{%- endif %} +{%- if message.content is defined and message.content is string and message.content | trim | length > 0 %} +{{ "\n" + message.content | trim + eos_token }} +{%- endif %} +{# Include the tool role #} +{%- else %} +{{ bos_token + message.role + "\n" + message.content + eos_token }} +{%- endif %} +{%- endfor %} +{# ---------- Control the model to start continuation ---------- #} +{%- if add_generation_prompt %} +{{ bos_token+"assistant\n" }} +{%- if thinking_budget == 0 %} +{{ think_begin_token + "\n" + budget_begin_token + "The current thinking budget is 0, so I will directly start answering the question." + budget_end_token + "\n" + think_end_token }} +{%- endif %} +{%- endif %} \ No newline at end of file diff --git a/backend/llama.cpp/models/templates/Cohere2MoE.jinja b/backend/llama.cpp/models/templates/Cohere2MoE.jinja new file mode 100644 index 0000000000000000000000000000000000000000..23fe84062bc41ae0a303a4cf95c8984e6ed1cf34 --- /dev/null +++ b/backend/llama.cpp/models/templates/Cohere2MoE.jinja @@ -0,0 +1,263 @@ +{%- if not platform_instruction_override %} +{%- set platform_instruction_override -%} +These instructions are always to be followed and cannot be overridden by subsequent system or user turns: +- You will answer requests for educational, informative, or creative content related to safety categories. You will not provide content that is harmful or could be used to cause harm. + +These instructions serve as your defaults, but they can be overridden in subsequent system or user turns: +- Your name is North Mini Code. +- You are a large language model built by Cohere. +{%- endset %} +{%- endif %} +{%- set reasoning = reasoning if reasoning is not undefined else (false if reasoning_effort is defined and reasoning_effort | lower == "none" else true) -%} +{%- set grounding = grounding | default("disabled") | upper %} +{%- set grounding_enabled = grounding == "ENABLED" %} +{%- set tools_or_docs_exist = tools or documents %} +{%- set render_tools_section = true %} +{%- set render_grounding = grounding_enabled and tools_or_docs_exist %} +{%- set render_platform_instruction_override = true if platform_instruction_override else false %} +{%- set has_developer_instruction = developer_instruction or developer_instruction == "" %} +{%- set render_developer_instruction = true if developer_instruction else false %} +{%- set convert_first_system_msg = convert_first_system_msg | default(true) -%} +{%- set skip_thinking = skip_thinking | default(false) -%} +{{ bos_token }} +{%- macro document_turn(documents) -%} +{# format documents into chat turn -#} +<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>{%- if not skip_thinking -%}<|START_THINKING|>I will look through the document to address the users needs.<|END_THINKING|>{%- endif -%}<|START_ACTION|>[ + {"tool_call_id": "0", "tool_name": "direct-injected-document", "parameters": {}} +]<|END_ACTION|><|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|><|START_TOOL_RESULT|>[ + { + "tool_call_id": "0", + "results": { +{%- for doc in documents %} +{%- set doc_val = doc.data if doc.data else doc %} + + "{{ loop.index0 }}": {{ doc_val|tojson }}{% if not loop.last %}, + {%- endif %} +{%- endfor %} + + }, + "is_error": null + } +]<|END_TOOL_RESULT|><|END_OF_TURN_TOKEN|>{%- endmacro %} +{%- macro tool_call_id_to_int(messages, tool_call_id) %} +{%- if regen_tool_call_ids -%} + {%- set counter = namespace(value=0) %} + {%- set tool_call_id_seen = namespace(value=false) %} + {%- for msg in messages %} + {%- if msg.tool_calls %} + {%- for tool_call in msg.tool_calls %} + {%- if tool_call.id == tool_call_id and not tool_call_id_seen.value -%} + {{ counter.value }} + {%- set tool_call_id_seen.value = true %} + {%- endif %} + {%- set counter.value = counter.value + 1 %} + {%- endfor %} + {%- endif %} + {%- endfor %} +{%- else -%} + {{ tool_call_id }} +{%- endif -%} +{%- endmacro %} +{%- macro format_tool_message(messages, tool_msg) -%} +{#- format tool message #}{ + "tool_call_id": "{{ tool_call_id_to_int(messages, tool_msg.tool_call_id) }}", + "results": { + {%- if tool_msg.content is mapping or tool_msg.content is string %} + + {% if tool_msg.content is string -%} + {%- set text_wrapper = {"content": tool_msg.content} -%} + {%- else -%} + {%- set text_wrapper = tool_msg.content -%} + {%- endif %} + "0": {{ text_wrapper|tojson }} + {%- else %} + {%- for content in tool_msg.content %} + + "{{ loop.index0 }}": {{ print_tool_content(content) }}{% if not loop.last %},{% endif %} + {%- endfor %} + {%- endif %} + + }, + "is_error": null + } +{%- endmacro -%} +{%- macro print_tool_content(item) %} +{%- if item.type|lower == "text" -%} +{%- set text_wrapper = {"content": item.text} -%} +{{ text_wrapper|tojson }} +{%- elif item.type|lower == "document" and item.document and "data" in item.document -%} +{{ item.document.data|tojson }} +{%- else -%} +{{ item|tojson }} +{%- endif -%} +{%- endmacro %} +{%- macro print_msg(msg) %} + {%- if msg is string -%} +<|START_TEXT|>{{ msg }}<|END_TEXT|> + {%- elif msg.content is string -%} +<|START_TEXT|>{{ msg.content }}<|END_TEXT|> + {%- else %} + {%- set last_was_text = namespace(value=false) %} + {%- for content in msg.content %} + {%- if content.type|lower == "text" -%} + {%- if not last_was_text.value -%} + <|START_TEXT|> + {%- endif -%} + {{ content.text }} + {%- if loop.last -%} + <|END_TEXT|> + {%- endif %} + {%- set last_was_text.value = true -%} + {%- else -%} + {%- if last_was_text.value -%} + <|END_TEXT|> + {%- endif -%} + {%- set last_was_text.value = false -%} + {%- endif -%} + {%- if content.type|lower == "image" -%} + {%- if content.data -%} +{{ content.data }} + {%- else -%} +<|IMG_PATCH|> + {%- endif -%} + {%- endif -%} + {%- endfor %} + {%- endif %} +{%- endmacro %} +{%- macro print_thinking(msg) %} + {%- if msg.reasoning -%} +{{ msg.reasoning }} + {%- elif msg.reasoning_content -%} +{{ msg.reasoning_content }} + {%- elif msg.thinking -%} +{{ msg.thinking }} + {%- elif msg.content and msg.content[0].thinking -%} +{{ msg.content[0].thinking }} + {%- endif %} +{%- endmacro %} +{%- if messages and messages[0]['role']|lower == 'system' and not has_developer_instruction and convert_first_system_msg %}{%- set developer_instruction = messages[0] %}{%- set render_developer_instruction = true %}{%- set initial_instruction_message = true %}{% endif %} +{%- set json_object = true if response_format and response_format.type == "json_object" else false %} +{%- set json_schema = (response_format.json_schema or response_format.schema) if response_format %} +{%- set json_mode = json_object or json_schema %} +{%- set tool_idx = namespace(value=0) %} +{%- set tool_ids_seen = namespace(value=[]) %} +{%- set regen_tool_call_ids = regen_tool_call_ids | default(true) -%} +{%- set sent_documents = namespace(value=false) -%} + +{%- if render_tools_section or render_platform_instruction_override or render_grounding or json_mode -%} +<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|><|START_TEXT|> +{%- elif not render_developer_instruction -%} +<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|> +{%- endif %} + +{%- set rendered_platform_turn_chunk = false %} + +{%- if render_platform_instruction_override -%} +{{ platform_instruction_override }} +{% set rendered_platform_turn_chunk = true %} +{%- else %} +{%- endif %} + +{%- if render_grounding -%} +{%- if rendered_platform_turn_chunk %} + +{% endif -%} +Note that both your responses and reflections can be grounded. Grounding means you associate pieces of texts (called "spans") with those specific tool results that support them (called "sources"). And you use a pair of tags "" and "" to indicate when a span can be grounded onto a list of sources, listing them out in the closing tag. Sources from the same tool call are grouped together and listed as "{tool_call_id}:[{list of result indices}]", before they are joined together by ",". E.g., "span" means that "span" is supported by result 1 and 2 from "tool_call_id=0" as well as result 0 from "tool_call_id=1". +{% set rendered_platform_turn_chunk = true %} +{%- endif %} + +{%- if render_tools_section %} +{%- if rendered_platform_turn_chunk %} + +{% endif %} +# Available Tools +```json +[ +{% if tools_or_docs_exist %} +{%- if documents %} + {"name": "direct-injected-document", "description": "This is a special tool to directly inject user-uploaded documents into the chat as additional context. DO NOT use this tool by yourself!", "parameters": {"type": "object", "properties": {}, "required": []}, "responses": {"200": {"description": "Successfully returned a list of chunked text snippets from the directly uploaded documents.", "content": {"application/json": {"schema": {"type": "array", "items": {"type": "object", "required": ["url", "snippet"], "properties": {"url": {"type": "string", "description": "The url of the uploaded document."}, "snippet": {"type": "string", "description": "The text snippet for the returned document chunk."}}}}}}}}} + {%- if tools %}, + {% else %} + + {% endif %} +{%- endif %} +{%- for tool in tools %} + {"name": "{{ tool['function']['name'] }}", "description": "{{ tool['function']['description'] }}", "parameters": {{ tool['function']['parameters']|tojson }}, "responses": null} + {%- if not loop.last %},{% endif %} + +{% endfor %} +{%- else %} + +{% endif %} +] +``` +{%- set rendered_platform_turn_chunk = true %} +{%- endif -%} + +{%- if json_mode -%} +{%- if rendered_platform_turn_chunk %} + + +{% endif -%} +When generating JSON objects, do not generate block markers. Generate an object directly without prefixing with ```json. Return only the JSON and nothing else. + {%- if json_schema %} + +Your output should adhere to the following json schema: +{{ json_schema }} + {%- endif -%} +{%- set rendered_platform_turn_chunk = true %} +{%- endif %} +{%- if rendered_platform_turn_chunk -%} +<|END_TEXT|><|END_OF_TURN_TOKEN|> +{%- elif not render_developer_instruction -%} +<|END_OF_TURN_TOKEN|> +{%- endif %} +{%- if render_developer_instruction -%} +<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>{{ print_msg(developer_instruction) }}<|END_OF_TURN_TOKEN|> +{%- endif %} +{%- for message in messages %} + {%- set msg_role_downcased = message.role | lower %} + {%- if msg_role_downcased == 'system' and (not (loop.first and initial_instruction_message)) -%} +<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>{{ print_msg(message) }}<|END_OF_TURN_TOKEN|> + {%- elif msg_role_downcased == 'user' -%} +<|START_OF_TURN_TOKEN|><|USER_TOKEN|>{{ print_msg(message) }}<|END_OF_TURN_TOKEN|> + {%- if documents and not sent_documents.value %}{%- set sent_documents.value = true %}{% set tool_idx.value = tool_idx.value + 1 %}{{ document_turn(documents) }}{% endif %} + {%- elif msg_role_downcased == 'assistant' or msg_role_downcased == 'chatbot' -%} +<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|> + {%- if message.tool_calls %} + {% if not skip_thinking %} + {% if message.tool_plan -%} + <|START_THINKING|>{{ message.tool_plan }}<|END_THINKING|> + {%- elif message.reasoning or message.reasoning_content or message.thinking or (message.content and message.content[0].type == "thinking") -%} + <|START_THINKING|>{{ print_thinking(message) }}<|END_THINKING|> + {%- endif %} + {%- endif %}<|START_ACTION|>[ + {%- for tc in message.tool_calls %} + + {"tool_call_id": "{%- if regen_tool_call_ids -%}{{ tool_idx.value }}{%- else -%}{{ tc.id }}{%- endif -%}", "tool_name": "{{ tc['function']['name'] }}", "parameters": {{ tc['function']['arguments']|tojson }}}{% if not loop.last %},{% endif %} + {%- set tool_idx.value = tool_idx.value + 1 %} + {%- endfor %} + +]<|END_ACTION|><|END_OF_TURN_TOKEN|> + {%- else -%} + {% if (message.reasoning or message.reasoning_content or message.thinking or (message.content and message.content[0].type == "thinking")) and not skip_thinking -%} + <|START_THINKING|>{{ print_thinking(message) }}<|END_THINKING|> + {%- endif -%} + {{ print_msg(message) }}<|END_OF_TURN_TOKEN|> + {%- endif %} + {%- elif msg_role_downcased == 'tool' and message.tool_call_id not in tool_ids_seen.value -%} +<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|><|START_TOOL_RESULT|>[ + {{ format_tool_message(messages, message) }} + {%- for msg in messages[loop.index0 + 1:] %} + + {%- if msg.role | lower == 'tool' %}, + {{ format_tool_message(messages, msg) }} + {%- set tool_ids_seen.value = tool_ids_seen.value + [msg.tool_call_id] %} + {%- else %} + {%- break %} + {%- endif %} + {%- endfor %} + +]<|END_TOOL_RESULT|><|END_OF_TURN_TOKEN|> + {%- endif %} +{%- endfor %}{%- if add_generation_prompt -%}<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>{% if reasoning %}<|START_THINKING|>{% else %}<|START_THINKING|><|END_THINKING|>{% endif %}{%- endif %} \ No newline at end of file diff --git a/backend/llama.cpp/models/templates/CohereForAI-c4ai-command-r-plus-tool_use.jinja b/backend/llama.cpp/models/templates/CohereForAI-c4ai-command-r-plus-tool_use.jinja new file mode 100644 index 0000000000000000000000000000000000000000..f5baef30b6f6569027a600cb259dbe56bdf0def8 --- /dev/null +++ b/backend/llama.cpp/models/templates/CohereForAI-c4ai-command-r-plus-tool_use.jinja @@ -0,0 +1,202 @@ + +{%- macro json_to_python_type(json_spec) %} +{%- set basic_type_map = { + "string": "str", + "number": "float", + "integer": "int", + "boolean": "bool" +} %} + +{%- if basic_type_map[json_spec.type] is defined %} + {{- basic_type_map[json_spec.type] }} +{%- elif json_spec.type == "array" %} + {{- "List[" + json_to_python_type(json_spec.items) + "]"}} +{%- elif json_spec.type == "object" %} + {{- "Dict[str, " + json_to_python_type(json_spec.additionalProperties) + ']'}} +{%- elif json_spec.type is iterable %} + {{- "Union[" }} + {%- for t in json_spec.type %} + {{- json_to_python_type({"type": t}) }} + {%- if not loop.last %} + {{- "," }} + {%- endif %} + {%- endfor %} + {{- "]" }} +{%- else %} + {{- "Any" }} +{%- endif %} +{%- endmacro %} + +{%- macro old_tool_parser(tools) %} +{%- for tool in tools %} + {%- if loop.index0 != 0 %} + {{- '\n\n' }} + {%- endif %} + {{- '```python\ndef ' + tool.name + '(' }} + {%- for param_name, param_fields in tool.parameter_definitions|items %} + {%- if loop.index0 != 0 %} + {{- ', '}} + {%- endif %} + {{- param_name + ': ' }} + {%- if not param_fields.required %} + {{- 'Optional[' + param_fields.type + '] = None'}} + {%- else %} + {{- param_fields.type }} + {%- endif %} + {%- endfor %} + {{- ') -> List[Dict]:\n """'}} + {{- tool.description }} + {%- if tool.parameter_definitions|length != 0 %} + {{- '\n\n Args:\n '}} + {%- for param_name, param_fields in tool.parameter_definitions|items %} + {%- if loop.index0 != 0 %} + {{- '\n ' }} + {%- endif %} + {{- param_name + ' ('}} + {%- if not param_fields.required %} + {{- 'Optional[' + param_fields.type + ']'}} + {%- else %} + {{- param_fields.type }} + {%- endif %} + {{- '): ' + param_fields.description }} + {%- endfor %} + {%- endif %} + {{- '\n """\n pass\n```' }} +{%- endfor %} +{%- endmacro %} + +{%- macro new_tool_parser(tools) %} +{%- for tool in tools %} + {%- if loop.index0 != 0 %} + {{- '\n\n'}} + {%- endif %} + {%- if tool.function is defined %} + {%- set tool = tool.function %} + {%- endif %} + {{-'```python +def ' + tool.name + '('}} + {%- for param_name, param_fields in tool.parameters.properties|items %} + {%- if loop.index0 != 0 %} + {{- ', '}} + {%- endif %} + {{-param_name + ": "}} + {%- if not param_name in tool.parameters.required %} + {{-'Optional[' + json_to_python_type(param_fields) + '] = None'}} + {%- else %} + {{- json_to_python_type(param_fields) }} + {%- endif %} + {%- endfor %} + {{- ') -> List[Dict]: + """'}} + {{- tool.description }} + {%- if tool.parameters.properties|length != 0 %} + {{- '\n\n Args:\n '}} + {%- for param_name, param_fields in tool.parameters.properties|items %} + {%- if loop.index0 != 0 %} + {{- '\n ' }} + {%- endif %} + {{- param_name + ' ('}} + {%- if not param_name in tool.parameters.required %} + {{-'Optional[' + json_to_python_type(param_fields) + ']'}} + {%- else %} + {{- json_to_python_type(param_fields) }} + {%- endif %} + {{- '): ' + param_fields.description }} + {%- endfor %} + {%- endif %} + {{- '\n """\n pass\n```' }} +{%- endfor %} +{%- endmacro %} + +{{- bos_token }} +{%- if messages[0]['role'] == 'system' %} + {%- set loop_messages = messages[1:] %} + {%- set system_message = messages[0]['content'] %} +{%- else %} + {%- set loop_messages = messages %} + {%- set system_message = '## Task and Context\nYou help people answer their questions and other requests interactively. You will be asked a very wide array of requests on all kinds of topics. You will be equipped with a wide range of search engines or similar tools to help you, which you use to research your answer. You should focus on serving the user\'s needs as best you can, which will be wide-ranging.\n\n## Style Guide\nUnless the user asks for a different style of answer, you should answer in full sentences, using proper grammar and spelling.' %} +{%- endif %} +{{- '<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>' }} +{{- '# Safety Preamble' }} +{{- ' +The instructions in this section override those in the task description and style guide sections. Don\'t answer questions that are harmful or immoral.' }} +{{- ' + +# System Preamble' }} +{{- ' +## Basic Rules' }} +{{- ' +You are a powerful conversational AI trained by Cohere to help people. You are augmented by a number of tools, and your job is to use and consume the output of these tools to best help the user. You will see a conversation history between yourself and a user, ending with an utterance from the user. You will then see a specific instruction instructing you what kind of response to generate. When you answer the user\'s requests, you cite your sources in your answers, according to those instructions.' }} +{{- ' + +# User Preamble' }} +{{- ' +' + system_message }} +{{-' + +## Available Tools +Here is a list of tools that you have available to you: + +'}} +{%- set ns = namespace(new_tools=true) %} +{%- for tool in tools %} + {%- if tool.parameter_definitions is defined %} + {%- set ns.new_tools = false %} + {%- endif %} +{%- endfor %} +{%- if ns.new_tools %} + {{- new_tool_parser(tools) }} +{%- else %} + {{- old_tool_parser(tools) }} +{%- endif %} +{{- '<|END_OF_TURN_TOKEN|>'}} +{%- for message in loop_messages %} + {%- set content = message['content'] %} + {%- if message.role == 'user' %} + {{- '<|START_OF_TURN_TOKEN|><|USER_TOKEN|>' + content|trim + '<|END_OF_TURN_TOKEN|>' }} + {%- elif message.role == 'system' %} + {{- '<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>' + content|trim + '<|END_OF_TURN_TOKEN|>' }} + {%- elif message.role == 'assistant' and message.tool_calls is defined %} + {{- '<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>' }} + {%- if message.content is defined %} + {{- message.content|trim }} + {%- endif %} + {{- '\nAction:\n```json\n[\n' }} + {%- for tool_call in message.tool_calls %} + {%- if tool_call.function is defined %} + {%- set tool_call = tool_call.function %} + {%- endif %} + {{- '{\n'|indent(4, first=true) }} + {{- '"tool_name": "'|indent(8, first=true) + tool_call.name + '",\n' }} + {{- '"parameters": '|indent(8, first=true) }} + {%- if tool_call.arguments is defined and tool_call.arguments|length > 0 %} + {{- tool_call.arguments|tojson(indent=4)|indent(8) }} + {{- '\n' }} + {%- else %} + {{- '{}\n' }} + {%- endif %} + {{- '}'|indent(4, first=true) }} + {%- if not loop.last %} + {{- ',\n' }} + {%- endif %} + {%- endfor %} + {{- "\n]```\n" }} + {%- elif message.role == 'assistant' %} + {{- '<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>' + content|trim + '<|END_OF_TURN_TOKEN|>' }} + {%- elif message.role == 'tool' %} + {{- '<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>\n' }} + {{- message.content|trim }} + {{- '<|END_OF_TURN_TOKEN|>' }} + {%- endif %} +{%- endfor %} +{{-'<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>Write \'Action:\' followed by a json-formatted list of actions that you want to perform in order to produce a good response to the user\'s last input. You can use any of the supplied tools any number of times, but you should aim to execute the minimum number of necessary actions for the input. You should use the `directly-answer` tool if calling the other tools is unnecessary. The list of actions you want to call should be formatted as a list of json objects, for example: +```json +[ + { + "tool_name": title of the tool in the specification, + "parameters": a dict of parameters to input into the tool as they are defined in the specs, or {} if it takes no parameters + } +]```<|END_OF_TURN_TOKEN|>'}} +{%- if add_generation_prompt %} + {{- '<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>' }} +{%- endif %} diff --git a/backend/llama.cpp/models/templates/CohereForAI-c4ai-command-r7b-12-2024-tool_use.jinja b/backend/llama.cpp/models/templates/CohereForAI-c4ai-command-r7b-12-2024-tool_use.jinja new file mode 100644 index 0000000000000000000000000000000000000000..fcf1259d33cbfbecfafd562d3fcc6fdbe2df0bca --- /dev/null +++ b/backend/llama.cpp/models/templates/CohereForAI-c4ai-command-r7b-12-2024-tool_use.jinja @@ -0,0 +1,156 @@ +{{ bos_token }}{%- macro document_turn(documents) -%} +{# format documents into chat turn #} +<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|><|START_THINKING|>I will look through the document to address the users needs.<|END_THINKING|><|START_ACTION|>[ + {"tool_call_id": "0", "tool_name": "direct-injected-document", "parameters": {}} +]<|END_ACTION|><|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|><|START_TOOL_RESULT|>[ + { + "tool_call_id": "0", + "results": { +{% for doc in documents %} + "{{ loop.index0 }}": {{doc|tojson}}{% if not loop.last %}, + {% endif %} +{% endfor %} + + }, + "is_error": null + } +]<|END_TOOL_RESULT|><|END_OF_TURN_TOKEN|>{%- endmacro %} +{%- macro tool_call_id_to_int(messages, tool_call_id) %} +{%- set counter = namespace(value=0) %} +{%- set tool_call_id_seen = namespace(value=false) %} +{%- for msg in messages %} + {%- if msg.tool_calls %} + {%- for tool_call in msg.tool_calls %} + {%- if tool_call.id == tool_call_id and not tool_call_id_seen.value -%} + {{ counter.value }} + {%- set tool_call_id_seen.value = true %} + {%- endif %} + {%- set counter.value = counter.value + 1 %} + {%- endfor %} + {%- endif %} +{%- endfor %} +{%- endmacro %} +{%- macro format_tool_message(messages, tool_msg) -%} +{# format tool message #} + { + "tool_call_id": "{{ tool_call_id_to_int(messages, tool_msg.tool_call_id) }}", + "results": { + "0": {{ tool_msg.content|tojson }} + }, + "is_error": null + } +{%- endmacro -%} +{%- if messages and messages[0]['role']|lower == 'system' %}{%- set developer_preamble = messages[0]['content'] %}{% endif %} +{%- set tool_idx = namespace(value=0) %} +{%- set tool_ids_seen = namespace(value=[]) %} +{%- set sent_documents = namespace(value=false) %} +<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|># System Preamble +You are in contextual safety mode. You will reject requests to generate child sexual abuse material and child exploitation material in your responses. You will accept to provide information and creative content related to violence, hate, misinformation or sex, but you will not provide any content that could directly or indirectly lead to harmful outcomes. + +Your information cutoff date is June 2024. + +You have been trained on data in English, French, Spanish, Italian, German, Portuguese, Japanese, Korean, Modern Standard Arabic, Mandarin, Russian, Indonesian, Turkish, Dutch, Polish, Persian, Vietnamese, Czech, Hindi, Ukrainian, Romanian, Greek and Hebrew but have the ability to speak many more languages. +{% if tools or documents %} + +You have been trained to have advanced reasoning and tool-use capabilities and you should make best use of these skills to serve user's requests. + +## Tool Use +Think about how you can make best use of the provided tools to help with the task and come up with a high level plan that you will execute first. + +0. Start by writing <|START_THINKING|> followed by a detailed step by step plan of how you will solve the problem. For each step explain your thinking fully and give details of required tool calls (if needed). Unless specified otherwise, you write your plan in natural language. When you finish, close it out with <|END_THINKING|>. + You can optionally choose to skip this step when the user request is so straightforward to address that only a trivial plan would be needed. + NOTE: You MUST skip this step when you are directly responding to the user's request without using any tools. + +Then carry out your plan by repeatedly executing the following steps. +1. Action: write <|START_ACTION|> followed by a list of JSON-formatted tool calls, with each one containing "tool_name" and "parameters" fields. + When there are multiple tool calls which are completely independent of each other (i.e. they can be executed in parallel), you should list them out all together in one step. When you finish, close it out with <|END_ACTION|>. +2. Observation: you will then receive results of those tool calls in JSON format in the very next turn, wrapped around by <|START_TOOL_RESULT|> and <|END_TOOL_RESULT|>. Carefully observe those results and think about what to do next. Note that these results will be provided to you in a separate turn. NEVER hallucinate results. + Every tool call produces a list of results (when a tool call produces no result or a single result, it'll still get wrapped inside a list). Each result is clearly linked to its originating tool call via its "tool_call_id". +3. Reflection: start the next turn by writing <|START_THINKING|> followed by what you've figured out so far, any changes you need to make to your plan, and what you will do next. When you finish, close it out with <|END_THINKING|>. + You can optionally choose to skip this step when everything is going according to plan and no special pieces of information or reasoning chains need to be recorded. + NOTE: You MUST skip this step when you are done with tool-use actions and are ready to respond to the user. + +You can repeat the above 3 steps multiple times (could be 0 times too if no suitable tool calls are available or needed), until you decide it's time to finally respond to the user. + +4. Response: then break out of the loop and write <|START_RESPONSE|> followed by a piece of text which serves as a response to the user's last request. Use all previous tool calls and results to help you when formulating your response. When you finish, close it out with <|END_RESPONSE|>. +{% if enable_citations %} + +## Grounding +Importantly, note that "Reflection" and "Response" above can be grounded. +Grounding means you associate pieces of texts (called "spans") with those specific tool results that support them (called "sources"). And you use a pair of tags "" and "" to indicate when a span can be grounded onto a list of sources, listing them out in the closing tag. Sources from the same tool call are grouped together and listed as "{tool_call_id}:[{list of result indices}]", before they are joined together by ",". E.g., "span" means that "span" is supported by result 1 and 2 from "tool_call_id=0" as well as result 0 from "tool_call_id=1". +{% endif %} + +## Available Tools +Here is the list of tools that you have available to you. +You can ONLY use the tools listed here. When a tool is not listed below, it is NOT available and you should NEVER attempt to use it. +Each tool is represented as a JSON object with fields like "name", "description", "parameters" (per JSON Schema), and optionally, "responses" (per JSON Schema). + +```json +[ +{% if documents %} + {"name": "direct-injected-document", "description": "This is a special tool to directly inject user-uploaded documents into the chat as additional context. DO NOT use this tool by yourself!", "parameters": {"type": "object", "properties": {}, "required": []}, "responses": {"200": {"description": "Successfully returned a list of chunked text snippets from the directly uploaded documents.", "content": {"application/json": {"schema": {"type": "array", "items": {"type": "object", "required": ["url", "snippet"], "properties": {"url": {"type": "string", "description": "The url of the uploaded document."}, "snippet": {"type": "string", "description": "The text snippet for the returned document chunk."}}}}}}}}}{%- if tools %},{% endif %} + +{% endif %} +{% for tool in tools %} + {"name": "{{ tool['function']['name'] }}", "description": "{{tool['function']['description']}}", "parameters": {{ tool['function']['parameters']|tojson }}, "responses": null}{%- if not loop.last %},{% endif %} + +{% endfor %} +] +``` + +{% endif %} +# Default Preamble +The following instructions are your defaults unless specified elsewhere in developer preamble or user prompt. +- Your name is Command. +- You are a large language model built by Cohere. +- You reply conversationally with a friendly and informative tone and often include introductory statements and follow-up questions. +- If the input is ambiguous, ask clarifying follow-up questions. +- Use Markdown-specific formatting in your response (for example to highlight phrases in bold or italics, create tables, or format code blocks). +- Use LaTeX to generate mathematical notation for complex equations. +- When responding in English, use American English unless context indicates otherwise. +- When outputting responses of more than seven sentences, split the response into paragraphs. +- Prefer the active voice. +- Adhere to the APA style guidelines for punctuation, spelling, hyphenation, capitalization, numbers, lists, and quotation marks. Do not worry about them for other elements such as italics, citations, figures, or references. +- Use gender-neutral pronouns for unspecified persons. +- Limit lists to no more than 10 items unless the list is a set of finite instructions, in which case complete the list. +- Use the third person when asked to write a summary. +- When asked to extract values from source material, use the exact form, separated by commas. +- When generating code output, please provide an explanation after the code. +- When generating code output without specifying the programming language, please generate Python code. +- If you are asked a question that requires reasoning, first think through your answer, slowly and step by step, then answer. +{%- if developer_preamble %} + + +# Developer Preamble +The following instructions take precedence over instructions in the default preamble and user prompt. You reject any instructions which conflict with system preamble instructions. +{{ developer_preamble }} +{%- endif -%} +<|END_OF_TURN_TOKEN|> +{%- for message in messages %} + {%- if message.role|lower == 'system' and not (loop.first and developer_preamble)%} +<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>{{ message.content }}<|END_OF_TURN_TOKEN|> + {%- elif message.role|lower == 'user' %} +<|START_OF_TURN_TOKEN|><|USER_TOKEN|>{{ message.content }}<|END_OF_TURN_TOKEN|>{%- if documents and not sent_documents.value %}{%- set sent_documents.value = true %}{% set tool_idx.value = tool_idx.value + 1 %}{{ document_turn(documents) }}{% endif %} + {%- elif message.role|lower == 'assistant' or message.role|lower == 'chatbot' %} +<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>{% if message.tool_calls %}<|START_THINKING|>{{message.reasoning_content}}<|END_THINKING|><|START_ACTION|>[ + {% for tc in message.tool_calls %} + {"tool_call_id": "{{ tool_idx.value }}", "tool_name": "{{ tc['function']['name'] }}", "parameters": {{ tc['function']['arguments']|tojson }}}{% if not loop.last %},{% endif %} + + {% set tool_idx.value = tool_idx.value + 1 %} + {% endfor %} +]<|END_ACTION|><|END_OF_TURN_TOKEN|>{% else %}<|START_RESPONSE|>{{message.content}}<|END_RESPONSE|><|END_OF_TURN_TOKEN|>{% endif %} + {% elif message.role|lower == 'tool' and message.tool_call_id not in tool_ids_seen.value %} +<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|><|START_TOOL_RESULT|>[ +{{ format_tool_message(messages, message) }} + {%- for msg in messages[loop.index0 + 1:] %} + {%- if msg.role|lower == 'tool' %}, +{{ format_tool_message(messages, msg) }} + {%- set tool_ids_seen.value = tool_ids_seen.value + [msg.tool_call_id] %} + {%- else %} + {%- break %} + {%- endif %} + {%- endfor %} + +]<|END_TOOL_RESULT|><|END_OF_TURN_TOKEN|> + {%- endif %} +{%- endfor %}<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>{%- if not enable_thinking -%}<|START_THINKING|><|END_THINKING|>{%- endif %} \ No newline at end of file diff --git a/backend/llama.cpp/models/templates/GLM-4.6.jinja b/backend/llama.cpp/models/templates/GLM-4.6.jinja new file mode 100644 index 0000000000000000000000000000000000000000..4a09db53c8ade84034d943e4ca36d89f16f938e8 --- /dev/null +++ b/backend/llama.cpp/models/templates/GLM-4.6.jinja @@ -0,0 +1,106 @@ +[gMASK] +{%- if tools -%} +<|system|> +# Tools + +You may call one or more functions to assist with the user query. + +You are provided with function signatures within XML tags: + +{% for tool in tools %} +{{ tool | tojson(ensure_ascii=False) }} +{% endfor %} + + +For each function call, output the function name and arguments within the following XML format: +{function-name} +{arg-key-1} +{arg-value-1} +{arg-key-2} +{arg-value-2} +... +{%- endif -%} +{%- macro visible_text(content) -%} + {%- if content is string -%} + {{- content }} + {%- elif content is iterable and content is not mapping -%} + {%- for item in content -%} + {%- if item is mapping and item.type == 'text' -%} + {{- item.text }} + {%- elif item is string -%} + {{- item }} + {%- endif -%} + {%- endfor -%} + {%- else -%} + {{- content }} + {%- endif -%} +{%- endmacro -%} +{%- set ns = namespace(last_user_index=-1) %} +{%- for m in messages %} + {%- if m.role == 'user' %} + {% set ns.last_user_index = loop.index0 -%} + {%- endif %} +{%- endfor %} +{% for m in messages %} +{%- if m.role == 'user' -%}<|user|> +{{ visible_text(m.content) }} +{{- '/nothink' if (enable_thinking is defined and not enable_thinking and not visible_text(m.content).endswith("/nothink")) else '' -}} +{%- elif m.role == 'assistant' -%} +<|assistant|> +{%- set reasoning_content = '' %} +{%- set content = visible_text(m.content) %} +{%- if m.reasoning_content is string %} + {%- set reasoning_content = m.reasoning_content %} +{%- else %} + {%- if '' in content %} + {%- set reasoning_content = content.split('')[0].rstrip('\n').split('')[-1].lstrip('\n') %} + {%- set content = content.split('')[-1].lstrip('\n') %} + {%- endif %} +{%- endif %} +{%- if loop.index0 > ns.last_user_index and reasoning_content -%} +{{ '\n' + reasoning_content.strip() + ''}} +{%- else -%} +{{ '\n' }} +{%- endif -%} +{%- if content.strip() -%} +{{ '\n' + content.strip() }} +{%- endif -%} +{% if m.tool_calls %} +{% for tc in m.tool_calls %} +{%- if tc.function %} + {%- set tc = tc.function %} +{%- endif %} +{{ '\n' + tc.name }} +{% set _args = tc.arguments or {} %} +{% if _args is not mapping %} + {{ raise_exception("Invalid tool call arguments passed: " + _args | string) }} +{% endif %} +{% for k, v in _args.items() %} +{{ k }} +{{ v | tojson(ensure_ascii=False) if v is not string else v }} +{% endfor %} +{% endfor %} +{% endif %} +{%- elif m.role == 'tool' -%} +{%- if m.content is string -%} +{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %} + {{- '<|observation|>' }} +{%- endif %} +{{- '\n\n' }} +{{- m.content }} +{{- '\n' }} +{%- else -%} +<|observation|>{% for tr in m.content %} + + +{{ tr.output if tr.output is defined else tr }} +{% endfor -%} +{% endif -%} +{%- elif m.role == 'system' -%} +<|system|> +{{ visible_text(m.content) }} +{%- endif -%} +{%- endfor -%} +{%- if add_generation_prompt -%} + <|assistant|>{{- '\n' if (enable_thinking is defined and not enable_thinking) else '' -}} +{%- endif -%} diff --git a/backend/llama.cpp/models/templates/GLM-4.7-Flash.jinja b/backend/llama.cpp/models/templates/GLM-4.7-Flash.jinja new file mode 100644 index 0000000000000000000000000000000000000000..2ab98ef068d62829d17c5ade1827b9f013fa2bbf --- /dev/null +++ b/backend/llama.cpp/models/templates/GLM-4.7-Flash.jinja @@ -0,0 +1,86 @@ +[gMASK] +{%- if tools -%} +<|system|> +# Tools + +You may call one or more functions to assist with the user query. + +You are provided with function signatures within XML tags: + +{% for tool in tools %} +{{ tool | tojson(ensure_ascii=False) }} +{% endfor %} + + +For each function call, output the function name and arguments within the following XML format: +{function-name}{arg-key-1}{arg-value-1}{arg-key-2}{arg-value-2}...{%- endif -%} +{%- macro visible_text(content) -%} + {%- if content is string -%} + {{- content }} + {%- elif content is iterable and content is not mapping -%} + {%- for item in content -%} + {%- if item is mapping and item.type == 'text' -%} + {{- item.text }} + {%- elif item is string -%} + {{- item }} + {%- endif -%} + {%- endfor -%} + {%- else -%} + {{- content }} + {%- endif -%} +{%- endmacro -%} +{%- set ns = namespace(last_user_index=-1) %} +{%- for m in messages %} + {%- if m.role == 'user' %} + {% set ns.last_user_index = loop.index0 -%} + {%- endif %} +{%- endfor %} +{% for m in messages %} +{%- if m.role == 'user' -%}<|user|>{{ visible_text(m.content) }} +{%- elif m.role == 'assistant' -%} +<|assistant|> +{%- set reasoning_content = '' %} +{%- set content = visible_text(m.content) %} +{%- if m.reasoning_content is string %} + {%- set reasoning_content = m.reasoning_content %} +{%- else %} + {%- if '' in content %} + {%- set reasoning_content = content.split('')[0].rstrip('\n').split('')[-1].lstrip('\n') %} + {%- set content = content.split('')[-1].lstrip('\n') %} + {%- endif %} +{%- endif %} +{%- if ((clear_thinking is defined and not clear_thinking) or loop.index0 > ns.last_user_index) and reasoning_content -%} +{{ '' + reasoning_content.strip() + ''}} +{%- else -%} +{{ '' }} +{%- endif -%} +{%- if content.strip() -%} +{{ content.strip() }} +{%- endif -%} +{% if m.tool_calls %} +{% for tc in m.tool_calls %} +{%- if tc.function %} + {%- set tc = tc.function %} +{%- endif %} +{{- '' + tc.name -}} +{% set _args = tc.arguments %}{% for k, v in _args.items() %}{{ k }}{{ v | tojson(ensure_ascii=False) if v is not string else v }}{% endfor %}{% endfor %} +{% endif %} +{%- elif m.role == 'tool' -%} +{%- if m.content is string -%} +{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %} + {{- '<|observation|>' }} +{%- endif %} +{{- '' }} +{{- m.content }} +{{- '' }} +{%- else -%} +<|observation|>{% for tr in m.content %} +{{ tr.output if tr.output is defined else tr }}{% endfor -%} +{% endif -%} +{%- elif m.role == 'system' -%} +<|system|>{{ visible_text(m.content) }} +{%- endif -%} +{%- endfor -%} +{%- if add_generation_prompt -%} + <|assistant|>{{- '' if (enable_thinking is defined and not enable_thinking) else '' -}} +{%- endif -%} \ No newline at end of file diff --git a/backend/llama.cpp/models/templates/GigaChat3-10B-A1.8B.jinja b/backend/llama.cpp/models/templates/GigaChat3-10B-A1.8B.jinja new file mode 100644 index 0000000000000000000000000000000000000000..f9826a4457f6a81dacddf62728e718c146f7ac20 --- /dev/null +++ b/backend/llama.cpp/models/templates/GigaChat3-10B-A1.8B.jinja @@ -0,0 +1,355 @@ +{#--------TOOL RENDERING FUNCTIONS---------#} + +{#--------------------------------------------------------------- + Converts JSON Schema (dict) to a TypeScript type definition +----------------------------------------------------------------#} +{%- macro json_schema_to_typescript(schema, indent="") -%} + {%- set ADDITIONAL_JSON_KEYS = ['format', 'maxItems', 'maximum', 'minItems', 'minimum', 'pattern'] -%} + {%- set ty = schema.get("type") -%} + + {# ---------------- OBJECT ---------------- #} + {%- if ty == "object" -%} + {{- "{\n" -}} + + {# Start building property list #} + {%- set props = schema.get("properties", {}) -%} + {%- set required = schema.get("required", []) -%} + {%- set has_additional_props = schema.get("additionalProperties") is defined -%} + {%- set additional_props_type = none -%} + {%- if has_additional_props -%} + {%- if schema.additionalProperties == true -%} + {%- set additional_props_type = {'type': 'any'} -%} + {%- elif schema.additionalProperties is mapping -%} + {%- set additional_props_type = schema.additionalProperties -%} + {%- endif -%} + {%- endif -%} + + {%- for key, val in props.items() -%} + {# ---------- Description Comments ---------- #} + {%- if "description" in val -%} + {%- for line in val['description'].split('\n') -%} + {%- if line.strip() -%} + {{- indent + '// ' + line + '\n' -}} + {%- endif -%} + {%- endfor -%} + {%- endif -%} + + {# ---------- Additional JSON Keys ---------- #} + {%- for add_key, add_val in val.items() -%} + {%- if add_key in ADDITIONAL_JSON_KEYS -%} + {%- if add_val is string -%} + {{- indent + '// ' + add_key + ': "' + add_val + '"' + '\n' -}} + {%- else -%} + {{- indent + '// ' + add_key + ': ' ~ add_val ~ '\n' -}} + {%- endif -%} + {%- endif -%} + {%- endfor -%} + + {# ---------- Property Definition ---------- #} + {%- set type_str = json_schema_to_typescript( + val, + indent + " " + ) -%} + + {{- indent + key + ('' if key in required else '?') + ': ' + type_str + ',' -}} + + {%- if "default" in val or "defalut_value" in val -%} + {%- set default = val.get("default", val.get("defalut_value")) -%} + {%- if default is string -%} + {{- ' // default: "' + default + '"' -}} + {%- else -%} + {{- ' // default: ' ~ default -}} + {%- endif -%} + {%- endif -%} + + {{- "\n" -}} + {%- endfor -%} + + {# Handle additionalProperties as index signature #} + {%- if has_additional_props and additional_props_type is not none -%} + {%- set additional_type_str = json_schema_to_typescript( + additional_props_type, + indent + " " + ) -%} + {{- indent + '[key: string]: ' + additional_type_str + '\n' -}} + {%- endif -%} + + {{- indent[: (indent|length - " "|length) ] + '}' -}} + + {# ---------------- STRING ---------------- #} + {%- elif ty == "string" -%} + {%- if schema.get("enum") -%} + {%- set ns = namespace(enum = []) -%} + {%- for en in schema['enum'] -%} + {%- set ns.enum = ns.enum + ['"' ~ en ~ '"'] -%} + {%- endfor -%} + {{- ns.enum | join(' | ') -}} + {%- elif schema.get("format", "none") in ['date-time', 'date'] -%} + {{- 'Date' -}} + {%- else -%} + {{- 'string' -}} + {%- endif -%} + + {# ---------------- NUMBER / INTEGER ---------------- #} + {%- elif ty in ["number", "integer"] -%} + {%- if schema.get("enum") -%} + {{- schema.enum | join(' | ') -}} + {%- else -%} + {{- 'number' -}} + {%- endif -%} + + {# ---------------- BOOLEAN ---------------- #} + {%- elif ty == "boolean" -%} + {{- 'boolean' -}} + + {# ---------------- ARRAY ---------------- #} + {%- elif ty == "array" -%} + {%- if "items" in schema -%} + {{- json_schema_to_typescript(schema['items'], indent) + '[]' -}} + {%- else -%} + {{- 'Array' -}} + {%- endif -%} + + {# ---------------- FALLBACK ---------------- #} + {%- else -%} + {{- 'any' -}} + {%- endif -%} +{%- endmacro -%} + +{#--------------------------------------------------------------- + Renders a namespace and its tool definitions in TypeScript style +----------------------------------------------------------------#} + +{%- macro render_tool_namespace(namespace_name, tools) -%} + {%- set ns = namespace(sections = ['namespace ' ~ namespace_name ~ ' {']) -%} + + {%- for tool in tools -%} + {%- if tool.function -%} + {%- set tool = tool.function -%} + {%- endif -%} + + {%- set ns_tool = namespace(content_lines=[]) -%} + + {# ---------- TOOL DESCRIPTION ---------- #} + {%- if tool.get('description') -%} + {%- for line in tool['description'].split('\n') -%} + {%- if line.strip() -%} + {%- set ns_tool.content_lines = ns_tool.content_lines + ['// ' ~ line] -%} + {%- endif -%} + {%- endfor -%} + {%- endif -%} + + {# ---------- TOOL SIGNATURE ---------- #} + {%- set main_body = "" -%} + {%- set params = tool.get("parameters") -%} + {%- if params and params.get("properties") -%} + {%- set param_type = json_schema_to_typescript(params, " ") -%} + {%- set main_body = 'type ' ~ tool.name ~ ' = (_: ' ~ param_type ~ ') => ' -%} + {%- else -%} + {%- set main_body = 'type ' ~ tool.name ~ ' = () => ' -%} + {%- endif -%} + + {# ---------- RETURN TYPE ---------- #} + {%- set return_params = tool.get("return_parameters") -%} + {%- if return_params and return_params.get("properties") -%} + {%- set return_type = json_schema_to_typescript(return_params, " ") -%} + {%- set main_body = main_body ~ return_type -%} + {%- else -%} + {%- set main_body = main_body ~ 'any' -%} + {%- endif -%} + + {%- set main_body = main_body ~ ';\n' -%} + + {%- set ns_tool.content_lines = ns_tool.content_lines + [main_body] -%} + + {# ---------- ADD TOOL TO SECTIONS ---------- #} + {%- set ns.sections = ns.sections + [ns_tool.content_lines | join('\n')] -%} + {%- endfor -%} + + {%- set ns.sections = ns.sections + ['} // namespace ' ~ namespace_name] -%} + + {{- ns.sections | join('\n') -}} +{%- endmacro -%} + + +{# ----------- MESSAGE RENDERING HELPER FUNCTIONS ------------ #} + +{%- macro render_role_message(message, role=None) -%} + {%- if not role -%} + {%- set role = message["role"] -%} + {%- endif -%} + + {%- set message_content = message['content'] or '' -%} + {%- if message_content is not string -%} + {%- set message_content = message_content | tojson(ensure_ascii=False) -%} + {%- endif -%} + + {{- role + add_tokens.role_sep + message_content + add_tokens.message_sep -}} + +{%- endmacro -%} + + +{%- macro render_function_call(message) -%} + {%- set call = message['content'] -%} + {%- if call.function -%} + {%- set call = call.function -%} + {%- endif -%} + + {%- set arguments = call['arguments'] -%} + {%- if arguments is not string -%} + {%- set arguments = arguments| tojson(ensure_ascii=False) -%} + {%- endif -%} + + {{- render_role_message( + { + 'role': 'function call', + 'content': '{"name": "' ~ call['name'] ~ '", "arguments": ' ~ arguments ~ '}' + } + ) -}} +{%- endmacro -%} + +{# ----- SPECIAL TOKENS ----- #} + +{%- set add_tokens = namespace( + role_sep="<|role_sep|>\n", + message_sep="<|message_sep|>\n\n" +) -%} + +{# ----- DEFAULT DEVSYSTEM ----- #} + +{%- set DEVSYSTEM -%} + +Description of the roles available in the dialog. + +`developer system` +A message added by Sber before the main dialog. It has the highest priority and sets global, non-overridable conditions (for example, conversation rules, the safety policy, the assistant's overall response style, etc.). + +`system` +A system instruction added by developers or by the user, but with a lower priority than `developer system`. It usually describes the assistant's instructions, a specific response style, and other conditions for this particular dialog. + +`user` +A message or request from the user. The assistant follows it if it does not conflict with higher-priority instructions (see ). + +`user memory` +A sequence of the most up-to-date long-term facts about the user at the time of their request, presented as a JSON list of strings. Facts are listed in chronological order, meaning newer facts are appended to the end of the sequence. When facts are changed or deleted, records of previous facts remain in the sequence. The assistant saves facts using a function and uses them in accordance with the block below. + +`added files` +Metadata about files available for use in the dialog, presented in JSON format. It contains the following keys: id (a unique file identifier), name (file name), type (file type). + +`assistant` +The assistant's reply to the user's request. If the system instruction or the user does not set additional rules for `assistant`, this reply must comply with the instructions in the block below. The list of functions available to call is contained in `function descriptions`. The name of the required function and its arguments will be generated next by the `function call` role. In its replies, the assistant follows the instructions in accordance with . + +`function descriptions` +Function descriptions in TypeScript format. A function is a special tool (or a set of instructions) that the assistant can call to perform specific actions, computations, or obtain data needed to solve the user's task. Each function description contains blocks with the name, description, and arguments. Sometimes the description contains separate blocks with return parameters and usage examples that illustrate the correct call and arguments. + +`function call` +The function that `assistant` calls based on the dialog context, and its arguments. The function is invoked in strict accordance with the instructions in the block. + +`function result` +The result of the last function call. + + + +The assistant can work with the following modalities: text, available functions. + + + +If instructions from different roles conflict within the dialog context, observe the following priorities: +`developer system` > `system` > `user` > `function descriptions` > `function result` > `user memory` + + + +Basic instructions for working with functions. + +Only call those functions that are described in `function descriptions`. + +Call available functions when, according to their description, such a call will help provide a more complete and/or accurate answer to the user's request. Fill in function arguments using information from the dialog context. If a function could help answer the request but a required argument is missing from the context, ask the user for the missing data before calling the function. If a necessary function is unavailable or an error occurs, briefly inform the user and, if possible, suggest an alternative. + + + +Rules for using facts in long-term memory: + +If there is no message under the `user memory` role in the dialog, this is equivalent to the absence of long-term facts about the user in memory. In that case, information about the user is limited to the current dialog, and no new facts should be saved. + + + +You are a helpful assistant. + +# Instructions +- Strictly follow the instruction priority. +- Maintain a logical chain of reasoning when answering the user's question. +- For complex questions (for example, STEM), try to answer in detail unless the system message or dialog context limits the response length. +- Be helpful, truthful, and avoid unsafe or prohibited content in your responses. +- Try to reply in the language in which the user asked their question. + + +A dialog will follow below. +The dialog may include various roles described in the block. +Each turn begins with the role name and a special token that marks the end of the role's full name, and ends with a special end-of-turn token. +Your task is to continue the dialog from the last specified role in accordance with the dialog context. +{%- endset -%} + + +{#- ---------------------- RENDERING STARTS HERE ---------------------- -#} + + +{# ----- RENDER BOS TOKEN ----- #} +{{- bos_token -}} + + +{# ----- RENDER DEVSYSTEM ----- #} +{{- render_role_message({"role": "developer system", "content": DEVSYSTEM}) -}} + +{# ----- RENDER SYSTEM IF PRESENT ----- #} +{%- if messages and messages[0]['role'] == 'system' -%} + {{- render_role_message(messages[0]) -}} + {%- set messages = messages[1:] -%} +{%- endif -%} + +{# ----- RENDER TOOLS ----- #} +{%- if tools -%} + {%- set tools_content = ( + render_tool_namespace('functions', tools) + + "\n\n" + ) -%} + {{- render_role_message({'role': 'function descriptions', 'content': tools_content}) -}} +{%- endif -%} + +{# ----- MAIN MESSAGE LOOP ----- #} +{%- for message in messages -%} + + {# ----- TOOL MESSAGE -------#} + {%- if message['role'] == 'tool' -%} + {{- render_role_message(message, 'function result') -}} + + + {# ----- ASSISTANT MESSAGE ----- #} + {%- elif message['role'] == 'assistant' -%} + + {# ----- FUNCTION CALL PART CHECKING: SINGLE CALL SETUP ----- #} + {%- if message.tool_calls is defined and message.tool_calls -%} + {%- set function_call = message.tool_calls[0] -%} + {%- else -%} + {%- set function_call = None -%} + {%- endif -%} + + {# ----- MAIN ASSISTANT RENDERING ----- #} + + {{- render_role_message({'role': 'assistant', 'content': message.content}) -}} + {%- if function_call -%} + {{- render_function_call({'role': 'function call', 'content': function_call}) -}} + {%- endif -%} + + + {# ----- OTHER MESSAGES ----- #} + {%- else -%} + {{- render_role_message(message) -}} + {%- endif -%} + + {# ----- ADDING GENERATION PROMPT ----- #} + + {%- if loop.last and add_generation_prompt and message['role'] != 'assistant' -%} + {{- 'assistant' + add_tokens.role_sep -}} + {%- endif -%} + +{%- endfor -%} \ No newline at end of file diff --git a/backend/llama.cpp/models/templates/GigaChat3.1-10B-A1.8B.jinja b/backend/llama.cpp/models/templates/GigaChat3.1-10B-A1.8B.jinja new file mode 100644 index 0000000000000000000000000000000000000000..0557754acf1d175643361e0eb2d9517a0637f9e7 --- /dev/null +++ b/backend/llama.cpp/models/templates/GigaChat3.1-10B-A1.8B.jinja @@ -0,0 +1,339 @@ +{#--------TOOL RENDERING FUNCTIONS---------#} + +{#--------------------------------------------------------------- + Converts JSON Schema (dict) to a TypeScript type definition +----------------------------------------------------------------#} +{%- macro json_schema_to_typescript(schema, indent="") -%} + {%- set ADDITIONAL_JSON_KEYS = ['format', 'maxItems', 'maximum', 'minItems', 'minimum', 'pattern'] -%} + {%- set ty = schema.get("type") -%} + + {# ---------------- OBJECT ---------------- #} + {%- if ty == "object" -%} + {{- "{\n" -}} + + {# Start building property list #} + {%- set props = schema.get("properties", {}) -%} + {%- set required = schema.get("required", []) -%} + {%- set has_additional_props = schema.get("additionalProperties") is defined -%} + {%- set additional_props_type = none -%} + {%- if has_additional_props -%} + {%- if schema.additionalProperties == true -%} + {%- set additional_props_type = {'type': 'any'} -%} + {%- elif schema.additionalProperties is mapping -%} + {%- set additional_props_type = schema.additionalProperties -%} + {%- endif -%} + {%- endif -%} + + {%- for key, val in props.items() -%} + {# ---------- Description Comments ---------- #} + {%- if "description" in val -%} + {%- for line in val['description'].split('\n') -%} + {%- if line.strip() -%} + {{- indent + '// ' + line + '\n' -}} + {%- endif -%} + {%- endfor -%} + {%- endif -%} + + {# ---------- Additional JSON Keys ---------- #} + {%- for add_key, add_val in val.items() -%} + {%- if add_key in ADDITIONAL_JSON_KEYS -%} + {%- if add_val is string -%} + {{- indent + '// ' + add_key + ': "' + add_val + '"' + '\n' -}} + {%- else -%} + {{- indent + '// ' + add_key + ': ' ~ add_val ~ '\n' -}} + {%- endif -%} + {%- endif -%} + {%- endfor -%} + + {# ---------- Property Definition ---------- #} + {%- set type_str = json_schema_to_typescript( + val, + indent + " " + ) -%} + + {{- indent + key + ('' if key in required else '?') + ': ' + type_str + ',' -}} + + {%- if "default" in val or "defalut_value" in val -%} + {%- set default = val.get("default", val.get("defalut_value")) -%} + {%- if default is string -%} + {{- ' // default: "' + default + '"' -}} + {%- else -%} + {{- ' // default: ' ~ default -}} + {%- endif -%} + {%- endif -%} + + {{- "\n" -}} + {%- endfor -%} + + {# Handle additionalProperties as index signature #} + {%- if has_additional_props and additional_props_type is not none -%} + {%- set additional_type_str = json_schema_to_typescript( + additional_props_type, + indent + " " + ) -%} + {{- indent + '[key: string]: ' + additional_type_str + '\n' -}} + {%- endif -%} + + {{- indent[: (indent|length - " "|length) ] + '}' -}} + + {# ---------------- STRING ---------------- #} + {%- elif ty == "string" -%} + {%- if schema.get("enum") -%} + {%- set ns = namespace(enum = []) -%} + {%- for en in schema['enum'] -%} + {%- set ns.enum = ns.enum + ['"' ~ en ~ '"'] -%} + {%- endfor -%} + {{- ns.enum | join(' | ') -}} + {%- elif schema.get("format", "none") in ['date-time', 'date'] -%} + {{- 'Date' -}} + {%- else -%} + {{- 'string' -}} + {%- endif -%} + + {# ---------------- NUMBER / INTEGER ---------------- #} + {%- elif ty in ["number", "integer"] -%} + {%- if schema.get("enum") -%} + {{- schema.enum | join(' | ') -}} + {%- else -%} + {{- 'number' -}} + {%- endif -%} + + {# ---------------- BOOLEAN ---------------- #} + {%- elif ty == "boolean" -%} + {{- 'boolean' -}} + + {# ---------------- ARRAY ---------------- #} + {%- elif ty == "array" -%} + {%- if "items" in schema -%} + {{- json_schema_to_typescript(schema['items'], indent) + '[]' -}} + {%- else -%} + {{- 'Array' -}} + {%- endif -%} + + {# ---------------- FALLBACK ---------------- #} + {%- else -%} + {{- 'any' -}} + {%- endif -%} +{%- endmacro -%} + +{#--------------------------------------------------------------- + Renders a namespace and its tool definitions in TypeScript style +----------------------------------------------------------------#} + +{%- macro render_tool_namespace(namespace_name, tools) -%} + {%- set ns = namespace(sections = ['namespace ' ~ namespace_name ~ ' {']) -%} + + {%- for tool in tools -%} + {%- if tool.function -%} + {%- set tool = tool.function -%} + {%- endif -%} + + {%- set ns_tool = namespace(content_lines=[]) -%} + + {# ---------- TOOL DESCRIPTION ---------- #} + {%- if tool.get('description') -%} + {%- for line in tool['description'].split('\n') -%} + {%- if line.strip() -%} + {%- set ns_tool.content_lines = ns_tool.content_lines + ['// ' ~ line] -%} + {%- endif -%} + {%- endfor -%} + {%- endif -%} + + {# ---------- TOOL SIGNATURE ---------- #} + {%- set main_body = "" -%} + {%- set params = tool.get("parameters") -%} + {%- if params and params.get("properties") -%} + {%- set param_type = json_schema_to_typescript(params, " ") -%} + {%- set main_body = 'type ' ~ tool.name ~ ' = (_: ' ~ param_type ~ ') => ' -%} + {%- else -%} + {%- set main_body = 'type ' ~ tool.name ~ ' = () => ' -%} + {%- endif -%} + + {# ---------- RETURN TYPE ---------- #} + {%- set return_params = tool.get("return_parameters") -%} + {%- if return_params and return_params.get("properties") -%} + {%- set return_type = json_schema_to_typescript(return_params, " ") -%} + {%- set main_body = main_body ~ return_type -%} + {%- else -%} + {%- set main_body = main_body ~ 'any' -%} + {%- endif -%} + + {%- set main_body = main_body ~ ';\n' -%} + + {%- set ns_tool.content_lines = ns_tool.content_lines + [main_body] -%} + + {# ---------- ADD TOOL TO SECTIONS ---------- #} + {%- set ns.sections = ns.sections + [ns_tool.content_lines | join('\n')] -%} + {%- endfor -%} + + {%- set ns.sections = ns.sections + ['} // namespace ' ~ namespace_name] -%} + + {{- ns.sections | join('\n') -}} +{%- endmacro -%} + + +{# ----------- MESSAGE RENDERING HELPER FUNCTIONS ------------ #} + +{%- macro render_function_call(call) -%} + {%- if call.function -%} + {%- set call = call.function -%} + {%- endif -%} + + {%- set arguments = call['arguments'] -%} + {%- if arguments is not string -%} + {%- set arguments = arguments| tojson(ensure_ascii=False) -%} + {%- endif -%} + + {{- '{"name": "' ~ call['name'] ~ '", "arguments": ' ~ arguments ~ '}' -}} +{%- endmacro -%} + + +{%- macro render_role_message(message, role=None) -%} + {%- if not role -%} + {%- set role = message["role"] -%} + {%- endif -%} + + {%- set message_content = message['content'] or '' -%} + {%- if message_content is not string -%} + {%- set message_content = message_content | tojson(ensure_ascii=False) -%} + {%- endif -%} + + {{- role + add_tokens.role_sep + message_content -}} + + {%- if message.tool_calls is defined and message.tool_calls -%} + {{- add_tokens.function_call + render_function_call(message.tool_calls[0]) -}} + {%- endif -%} + + {{- add_tokens.message_sep -}} + +{%- endmacro -%} + + + +{# ----- SPECIAL TOKENS ----- #} + +{%- set add_tokens = namespace( + role_sep="<|role_sep|>\n", + message_sep="<|message_sep|>\n\n", + function_call="<|function_call|>" +) -%} + +{# ----- DEFAULT DEVSYSTEM ----- #} + +{%- set DEVSYSTEM -%} + +Description of the roles available in the dialog. + +`developer system` +A message added by Sber before the main dialog. It has the highest priority and sets global, non-overridable conditions (for example, conversation rules, the safety policy, the assistant's overall response style, etc.). + +`system` +A system instruction added by developers or by the user, but with a lower priority than `developer system`. It usually describes the assistant's instructions, a specific response style, and other conditions for this particular dialog. + +`user` +A message or request from the user. The assistant follows it if it does not conflict with higher-priority instructions (see ). + +`user memory` +A sequence of the most up-to-date long-term facts about the user at the time of their request, presented as a JSON list of strings. Facts are listed in chronological order, meaning newer facts are appended to the end of the sequence. When facts are changed or deleted, records of previous facts remain in the sequence. The assistant saves facts using a function and uses them in accordance with the block below. + +`added files` +Metadata about files available for use in the dialog, presented in JSON format. It contains the following keys: id (a unique file identifier), name (file name), type (file type). + +`assistant` +The assistant's reply to the user's request. If the system instruction or the user does not set additional rules for `assistant`, this reply must comply with the instructions in the block below. The list of functions available to call is contained in `function descriptions`. The name of the required function and its arguments will be generated next by the `function call` role. In its replies, the assistant follows the instructions in accordance with . + +`function descriptions` +Function descriptions in TypeScript format. A function is a special tool (or a set of instructions) that the assistant can call to perform specific actions, computations, or obtain data needed to solve the user's task. Each function description contains blocks with the name, description, and arguments. Sometimes the description contains separate blocks with return parameters and usage examples that illustrate the correct call and arguments. + +`function call` +The function that `assistant` calls based on the dialog context, and its arguments. The function is invoked in strict accordance with the instructions in the block. + +`function result` +The result of the last function call. + + + +The assistant can work with the following modalities: text, available functions. + + + +If instructions from different roles conflict within the dialog context, observe the following priorities: +`developer system` > `system` > `user` > `function descriptions` > `function result` > `user memory` + + + +Basic instructions for working with functions. + +Only call those functions that are described in `function descriptions`. + +Call available functions when, according to their description, such a call will help provide a more complete and/or accurate answer to the user's request. Fill in function arguments using information from the dialog context. If a function could help answer the request but a required argument is missing from the context, ask the user for the missing data before calling the function. If a necessary function is unavailable or an error occurs, briefly inform the user and, if possible, suggest an alternative. + + + +Rules for using facts in long-term memory: + +If there is no message under the `user memory` role in the dialog, this is equivalent to the absence of long-term facts about the user in memory. In that case, information about the user is limited to the current dialog, and no new facts should be saved. + + + +You are a helpful assistant. + +# Instructions +- Strictly follow the instruction priority. +- Maintain a logical chain of reasoning when answering the user's question. +- For complex questions (for example, STEM), try to answer in detail unless the system message or dialog context limits the response length. +- Be helpful, truthful, and avoid unsafe or prohibited content in your responses. +- Try to reply in the language in which the user asked their question. + + +A dialog will follow below. +The dialog may include various roles described in the block. +Each turn begins with the role name and a special token that marks the end of the role's full name, and ends with a special end-of-turn token. +Your task is to continue the dialog from the last specified role in accordance with the dialog context. +{%- endset -%} + + +{#- ---------------------- RENDERING STARTS HERE ---------------------- -#} + + +{# ----- RENDER BOS TOKEN ----- #} +{{- bos_token -}} + + +{# ----- RENDER DEVSYSTEM ----- #} +{{- render_role_message({"role": "developer system", "content": DEVSYSTEM}) -}} + +{# ----- RENDER SYSTEM IF PRESENT ----- #} +{%- if messages and messages[0]['role'] == 'system' -%} + {{- render_role_message(messages[0]) -}} + {%- set messages = messages[1:] -%} +{%- endif -%} + +{# ----- RENDER TOOLS ----- #} +{%- if tools -%} + {%- set tools_content = ( + render_tool_namespace('functions', tools) + + "\n\n" + ) -%} + {{- render_role_message({'role': 'function descriptions', 'content': tools_content}) -}} +{%- endif -%} + +{# ----- MAIN MESSAGE LOOP ----- #} +{%- for message in messages -%} + + {# ----- TOOL MESSAGE -------#} + {%- if message['role'] == 'tool' -%} + {{- render_role_message(message, 'function result') -}} + + {# ----- OTHER MESSAGES ----- #} + {%- else -%} + {{- render_role_message(message) -}} + {%- endif -%} + + {# ----- ADDING GENERATION PROMPT ----- #} + + {%- if loop.last and add_generation_prompt and message['role'] != 'assistant' -%} + {{- 'assistant' + add_tokens.role_sep -}} + {%- endif -%} + +{%- endfor -%} \ No newline at end of file diff --git a/backend/llama.cpp/models/templates/HuggingFaceTB-SmolLM3-3B.jinja b/backend/llama.cpp/models/templates/HuggingFaceTB-SmolLM3-3B.jinja new file mode 100644 index 0000000000000000000000000000000000000000..b605d934395a08ed3cf234e2c12355a3f33d9ac1 --- /dev/null +++ b/backend/llama.cpp/models/templates/HuggingFaceTB-SmolLM3-3B.jinja @@ -0,0 +1,61 @@ +{#- Copyright 2025-present the Unsloth team. All rights reserved. #} +{#- Licensed under the Apache License, Version 2.0 (the "License") #} +{#- Edits made by Unsloth to make it work for most inference engines #} +{# ───── defaults ───── #} +{%- if enable_thinking is not defined -%} + {%- set enable_thinking = true -%} +{%- endif -%} +{# ───── reasoning mode ───── #} +{%- if enable_thinking -%} + {%- set reasoning_mode = "/think" -%} +{%- else -%} + {%- set reasoning_mode = "/no_think" -%} +{%- endif -%} +{# ───── header (system message) ───── #} +{{- "<|im_start|>system\n" -}} +{%- if messages[0].role == "system" -%} + {%- set system_message = messages[0].content -%} + {%- if "/no_think" in system_message -%} + {%- set reasoning_mode = "/no_think" -%} + {%- elif "/think" in system_message -%} + {%- set reasoning_mode = "/think" -%} + {%- endif -%} + {%- set custom_instructions = system_message.replace("/no_think", "") -%} + {%- set custom_instructions = custom_instructions.replace("/think", "") -%} + {%- set custom_instructions = custom_instructions.rstrip() -%} +{%- endif -%} +{{- "## Metadata\n\n" -}} +{{- "Knowledge Cutoff Date: June 2025\n" -}} +{{- "Reasoning Mode: " + reasoning_mode + "\n\n" -}} +{{- "## Custom Instructions\n\n" -}} +{%- if custom_instructions -%} + {{- custom_instructions + "\n\n" -}} +{%- elif reasoning_mode == "/think" -%} + {{- "You are a helpful AI assistant named SmolLM, trained by Hugging Face. Your role as an assistant involves thoroughly exploring questions through a systematic thinking process before providing the final precise and accurate solutions. This requires engaging in a comprehensive cycle of analysis, summarizing, exploration, reassessment, reflection, backtracking, and iteration to develop well-considered thinking process. Please structure your response into two main sections: Thought and Solution using the specified format: Thought section Solution section. In the Thought section, detail your reasoning process in steps. Each step should include detailed considerations such as analysing questions, summarizing relevant findings, brainstorming new ideas, verifying the accuracy of the current steps, refining any errors, and revisiting previous steps. In the Solution section, based on various attempts, explorations, and reflections from the Thought section, systematically present the final solution that you deem correct. The Solution section should be logical, accurate, and concise and detail necessary steps needed to reach the conclusion.\n\n" -}} +{%- else -%} + {{- "You are a helpful AI assistant named SmolLM, trained by Hugging Face.\n\n" -}} +{%- endif -%} +{{- "<|im_end|>\n" -}} +{# ───── main loop ───── #} +{%- for message in messages -%} + {%- set content = message.content if message.content is string else "" -%} + {%- if message.role == "user" -%} + {{ "<|im_start|>" + message.role + "\n" + content + "<|im_end|>\n" }} + {%- elif message.role == "assistant" -%} + {%- if reasoning_mode == "/think" -%} + {{ "<|im_start|>assistant\n" + content.lstrip("\n") + "<|im_end|>\n" }} + {%- else -%} + {{ "<|im_start|>assistant\n" + "\n\n\n" + content.lstrip("\n") + "<|im_end|>\n" }} + {%- endif -%} + {%- elif message.role == "tool" -%} + {{ "<|im_start|>" + "user\n" + content + "<|im_end|>\n" }} + {%- endif -%} +{%- endfor -%} +{# ───── generation prompt ───── #} +{%- if add_generation_prompt -%} + {%- if reasoning_mode == "/think" -%} + {{ "<|im_start|>assistant\n" }} + {%- else -%} + {{ "<|im_start|>assistant\n" + "\n\n\n" }} + {%- endif -%} +{%- endif -%} diff --git a/backend/llama.cpp/models/templates/Kimi-K2-Instruct.jinja b/backend/llama.cpp/models/templates/Kimi-K2-Instruct.jinja new file mode 100644 index 0000000000000000000000000000000000000000..6204fb3960286f57639873f512b3943926b82d83 --- /dev/null +++ b/backend/llama.cpp/models/templates/Kimi-K2-Instruct.jinja @@ -0,0 +1,60 @@ +{% macro render_content(msg) -%} + {%- set c = msg.get('content') -%} + {%- if c is string -%} + {{ c }} + {%- elif c is not none -%} + {% for content in c -%} + {% if content['type'] == 'image' or 'image' in content or 'image_url' in content -%} + <|media_start|>image<|media_content|><|media_pad|><|media_end|> + {% else -%} + {{ content['text'] }} + {%- endif -%} + {%- endfor -%} + {%- endif -%} +{%- endmacro %} + +{%- set tool_response_queue = namespace(ids=[]) -%} +{%- set tool_call_counter = namespace(value=0) -%} + +{%- if tools -%} + <|im_system|>tool_declare<|im_middle|>{{ tools | tojson }}<|im_end|> +{%- endif -%} +{% for message in messages %} + {%- if loop.first and messages[0]['role'] != 'system' -%} + <|im_system|>system<|im_middle|>You are Kimi, an AI assistant created by Moonshot AI.<|im_end|> + {% endif %} + + {%- set role_name = message.get('name') or message['role'] -%} + {%- if message['role'] == 'user' -%} + <|im_user|>{{role_name}}<|im_middle|> + {%- elif message['role'] == 'assistant' -%} + <|im_assistant|>{{role_name}}<|im_middle|> + {%- else -%} + <|im_system|>{{role_name}}<|im_middle|> + {%- endif -%} + + {%- if message['role'] == 'assistant' and message.get('tool_calls') -%} + {{render_content(message)}}<|tool_calls_section_begin|> + {%- for tool_call in message['tool_calls'] -%} + {%- set formatted_id = 'functions.' + tool_call['function']['name'] + ':' + (tool_call_counter.value | string) -%} + {%- set tool_call_counter.value = tool_call_counter.value + 1 -%} + {%- set _ = tool_response_queue.ids.append(formatted_id) -%} + <|tool_call_begin|>{{ formatted_id }}<|tool_call_argument_begin|>{% if tool_call['function']['arguments'] is string %}{{ tool_call['function']['arguments'] }}{% else %}{{ tool_call['function']['arguments'] | tojson }}{% endif %}<|tool_call_end|> + {%- endfor -%} + <|tool_calls_section_end|> + {%- elif message['role'] == 'tool' -%} + {%- if tool_response_queue.ids -%} + {%- set tool_call_id = tool_response_queue.ids.pop(0) -%} + {%- else -%} + {%- set tool_call_id = 'functions.' + message.get('name', 'unknown') + ':' + (tool_call_counter.value | string) -%} + {%- endif -%} + ## Return of {{ tool_call_id }} +{{render_content(message)}} + {%- elif message['content'] is not none -%} + {{render_content(message)}} + {%- endif -%} + <|im_end|> +{%- endfor -%} +{%- if add_generation_prompt -%} + <|im_assistant|>assistant<|im_middle|> +{%- endif -%} diff --git a/backend/llama.cpp/models/templates/Kimi-K2-Thinking.jinja b/backend/llama.cpp/models/templates/Kimi-K2-Thinking.jinja new file mode 100644 index 0000000000000000000000000000000000000000..5641429f5356fedb44226043804e885048c7f23e --- /dev/null +++ b/backend/llama.cpp/models/templates/Kimi-K2-Thinking.jinja @@ -0,0 +1,108 @@ +{%- macro render_content(msg) -%} + {%- set c = msg.get('content') -%} + {%- if c is string -%} + {{ c }} + {%- elif c is not none -%} + {% for content in c -%} + {% if content['type'] == 'image' or 'image' in content or 'image_url' in content -%} + <|media_start|>image<|media_content|><|media_pad|><|media_end|> + {% else -%} + {{ content['text'] }} + {%- endif -%} + {%- endfor -%} + {%- endif -%} +{%- endmacro -%} + +{% macro set_roles(message) -%} + {%- set role_name = message.get('name') or message['role'] -%} + {%- if message['role'] == 'user' -%} + <|im_user|>{{role_name}}<|im_middle|> + {%- elif message['role'] == 'assistant' -%} + <|im_assistant|>{{role_name}}<|im_middle|> + {%- else -%} + <|im_system|>{{role_name}}<|im_middle|> + {%- endif -%} +{%- endmacro -%} + +{%- set tool_response_queue = namespace(ids=[]) -%} +{%- set tool_call_counter = namespace(value=0) -%} + +{%- macro render_toolcalls(message) -%} + <|tool_calls_section_begin|> + {%- for tool_call in message['tool_calls'] -%} + {%- set formatted_id = 'functions.' + tool_call['function']['name'] + ':' + (tool_call_counter.value | string) -%} + {%- set tool_call_counter.value = tool_call_counter.value + 1 -%} + {%- set _ = tool_response_queue.ids.append(formatted_id) -%} + <|tool_call_begin|>{{ formatted_id }}<|tool_call_argument_begin|>{% if tool_call['function']['arguments'] is string %}{{ tool_call['function']['arguments'] }}{% else %}{{ tool_call['function']['arguments'] | tojson }}{% endif %}<|tool_call_end|> + {%- endfor -%} + <|tool_calls_section_end|> +{%- endmacro -%} + + +{# Find last non-tool-call assisitant message #} +{%- set ns = namespace(last_non_tool_call_assistant_msg=-1) -%} +{%- for idx in range(messages|length-1, -1, -1) -%} + {%- if messages[idx]['role'] == 'assistant' and not messages[idx].get('tool_calls') -%} + {%- set ns.last_non_tool_call_assistant_msg = idx -%} + {%- endif -%} +{%- endfor -%} + +{# split all messages into history & suffix, reasoning_content in suffix should be reserved.#} +{%- set hist_msgs = messages[:ns.last_non_tool_call_assistant_msg+1] -%} +{%- set suffix_msgs = messages[ns.last_non_tool_call_assistant_msg+1:] -%} + +{%- if tools -%} + <|im_system|>tool_declare<|im_middle|>{{ tools | tojson }}<|im_end|> +{%- endif -%} + +{%- if messages|length == 0 or messages[0]['role'] != 'system' -%} + <|im_system|>system<|im_middle|>You are Kimi, an AI assistant created by Moonshot AI.<|im_end|> +{%- endif -%} + +{%- for message in hist_msgs -%} + {{set_roles(message)}} + {%- if message['role'] == 'assistant' -%} + {{render_content(message)}} + {%- if message.get('tool_calls') -%} + {{render_toolcalls(message)}} + {%- endif -%} + {%- elif message['role'] == 'tool' -%} + {%- if tool_response_queue.ids -%} + {%- set tool_call_id = tool_response_queue.ids.pop(0) -%} + {%- else -%} + {%- set tool_call_id = 'functions.' + message.get('name', 'unknown') + ':' + (tool_call_counter.value | string) -%} + {%- endif -%} + ## Return of {{ tool_call_id }} +{{render_content(message)}} + {%- elif message['content'] is not none -%} + {{render_content(message)}} + {%- endif -%} + <|im_end|> +{%- endfor -%} + +{%- for message in suffix_msgs -%} + {{set_roles(message)}} + {%- if message['role'] == 'assistant' -%} + {%- set rc = message.get('reasoning_content', '') -%} + {{rc}}{{render_content(message)}} + {%- if message.get('tool_calls') -%} + {{render_toolcalls(message)}} + {%- endif -%} + {%- elif message['role'] == 'tool' -%} + {%- if tool_response_queue.ids -%} + {%- set tool_call_id = tool_response_queue.ids.pop(0) -%} + {%- else -%} + {%- set tool_call_id = 'functions.' + message.get('name', 'unknown') + ':' + (tool_call_counter.value | string) -%} + {%- endif -%} + ## Return of {{ tool_call_id }} +{{render_content(message)}} + {%- elif message['content'] is not none -%} + {{render_content(message)}} + {%- endif -%} + <|im_end|> +{%- endfor -%} + + +{%- if add_generation_prompt -%} + <|im_assistant|>assistant<|im_middle|> +{%- endif -%} diff --git a/backend/llama.cpp/models/templates/LFM2-8B-A1B.jinja b/backend/llama.cpp/models/templates/LFM2-8B-A1B.jinja new file mode 100644 index 0000000000000000000000000000000000000000..fab22e952bc2012edc702295dd2f11536e7ca6f9 --- /dev/null +++ b/backend/llama.cpp/models/templates/LFM2-8B-A1B.jinja @@ -0,0 +1,37 @@ +{{- bos_token -}} +{%- set system_prompt = "" -%} +{%- set ns = namespace(system_prompt="") -%} +{%- if messages[0]["role"] == "system" -%} + {%- set ns.system_prompt = messages[0]["content"] -%} + {%- set messages = messages[1:] -%} +{%- endif -%} +{%- if tools -%} + {%- set ns.system_prompt = ns.system_prompt + ("\n" if ns.system_prompt else "") + "List of tools: <|tool_list_start|>[" -%} + {%- for tool in tools -%} + {%- if tool is not string -%} + {%- set tool = tool | tojson -%} + {%- endif -%} + {%- set ns.system_prompt = ns.system_prompt + tool -%} + {%- if not loop.last -%} + {%- set ns.system_prompt = ns.system_prompt + ", " -%} + {%- endif -%} + {%- endfor -%} + {%- set ns.system_prompt = ns.system_prompt + "]<|tool_list_end|>" -%} +{%- endif -%} +{%- if ns.system_prompt -%} + {{- "<|im_start|>system\n" + ns.system_prompt + "<|im_end|>\n" -}} +{%- endif -%} +{%- for message in messages -%} + {{- "<|im_start|>" + message["role"] + "\n" -}} + {%- set content = message["content"] -%} + {%- if content is not string -%} + {%- set content = content | tojson -%} + {%- endif -%} + {%- if message["role"] == "tool" -%} + {%- set content = "<|tool_response_start|>" + content + "<|tool_response_end|>" -%} + {%- endif -%} + {{- content + "<|im_end|>\n" -}} +{%- endfor -%} +{%- if add_generation_prompt -%} + {{- "<|im_start|>assistant\n" -}} +{%- endif -%} \ No newline at end of file diff --git a/backend/llama.cpp/models/templates/LFM2.5-8B-A1B.jinja b/backend/llama.cpp/models/templates/LFM2.5-8B-A1B.jinja new file mode 100644 index 0000000000000000000000000000000000000000..8bca4a545e9ec8dc0c5d4689a78886c0741c47e4 --- /dev/null +++ b/backend/llama.cpp/models/templates/LFM2.5-8B-A1B.jinja @@ -0,0 +1,115 @@ +{{- bos_token -}} +{%- set preserve_thinking = preserve_thinking | default(false) -%} + +{%- macro format_arg_value(arg_value) -%} + {%- if arg_value is string -%} + {{- "'" + arg_value + "'" -}} + {%- elif arg_value is mapping -%} + {{- arg_value | tojson -}} + {%- else -%} + {{- arg_value | string -}} + {%- endif -%} +{%- endmacro -%} + +{%- macro parse_content(content) -%} + {%- if content is string -%} + {{- content -}} + {%- else -%} + {%- set _ns = namespace(result="") -%} + {%- for item in content -%} + {%- if item["type"] == "image" -%} + {%- set _ns.result = _ns.result + "" -%} + {%- elif item["type"] == "text" -%} + {%- set _ns.result = _ns.result + item["text"] -%} + {%- else -%} + {%- set _ns.result = _ns.result + item | tojson -%} + {%- endif -%} + {%- endfor -%} + {{- _ns.result -}} + {%- endif -%} +{%- endmacro -%} + +{%- macro render_tool_calls(tool_calls) -%} + {%- set tool_calls_ns = namespace(tool_calls=[]) -%} + {%- for tool_call in tool_calls -%} + {%- set func_name = tool_call["function"]["name"] -%} + {%- set func_args = tool_call["function"]["arguments"] -%} + {%- set args_ns = namespace(arg_strings=[]) -%} + {%- for arg_name, arg_value in func_args.items() -%} + {%- set args_ns.arg_strings = args_ns.arg_strings + [arg_name + "=" + format_arg_value(arg_value)] -%} + {%- endfor -%} + {%- set tool_calls_ns.tool_calls = tool_calls_ns.tool_calls + [func_name + "(" + (args_ns.arg_strings | join(", ")) + ")"] -%} + {%- endfor -%} + {{- "<|tool_call_start|>[" + (tool_calls_ns.tool_calls | join(", ")) + "]<|tool_call_end|>" -}} +{%- endmacro -%} + +{%- set ns = namespace(system_prompt="", last_user_index=-1) -%} +{%- if messages[0]["role"] == "system" -%} + {%- if messages[0].get("content") -%} + {%- set ns.system_prompt = parse_content(messages[0]["content"]) -%} + {%- endif -%} + {%- set messages = messages[1:] -%} +{%- endif -%} +{%- if tools -%} + {%- set ns.system_prompt = ns.system_prompt + ("\n" if ns.system_prompt else "") + "List of tools: [" -%} + {%- for tool in tools -%} + {%- if tool is not string -%} + {%- set tool = tool | tojson -%} + {%- endif -%} + {%- set ns.system_prompt = ns.system_prompt + tool -%} + {%- if not loop.last -%} + {%- set ns.system_prompt = ns.system_prompt + ", " -%} + {%- endif -%} + {%- endfor -%} + {%- set ns.system_prompt = ns.system_prompt + "]" -%} +{%- endif -%} +{%- if ns.system_prompt -%} + {{- "<|im_start|>system\n" + ns.system_prompt + "<|im_end|>\n" -}} +{%- endif -%} +{%- for message in messages -%} + {%- if message["role"] == "user" -%} + {%- set ns.last_user_index = loop.index0 -%} + {%- endif -%} +{%- endfor -%} +{%- for message in messages -%} + {{- "<|im_start|>" + message.role + "\n" -}} + {%- if message.role == "assistant" -%} + {%- generation -%} + {%- if message.thinking is defined and (preserve_thinking or loop.index0 > ns.last_user_index) -%} + {{- "" + message.thinking + "" -}} + {%- endif -%} + {%- set _cfm_tag = "CONTINUE_FINAL_MESSAGE_TAG " -%} + {%- set _has_cfm = false -%} + {%- if message.content is defined -%} + {%- set content = parse_content(message.content) -%} + {%- if not (preserve_thinking or loop.index0 > ns.last_user_index) -%} + {%- if "" in content -%} + {%- set content = content.split("")[-1] | trim -%} + {%- endif -%} + {%- endif -%} + {%- if message.tool_calls is defined and content.endswith(_cfm_tag) -%} + {%- set _has_cfm = true -%} + {%- set _trunc_len = (content | length) - (_cfm_tag | length) -%} + {{- content[:_trunc_len] -}} + {%- else -%} + {{- content -}} + {%- endif -%} + {%- endif -%} + {%- if message.tool_calls is defined -%} + {{- render_tool_calls(message.tool_calls) -}} + {%- endif -%} + {%- if _has_cfm -%} + {{- _cfm_tag -}} + {%- endif -%} + {{- "<|im_end|>\n" -}} + {%- endgeneration -%} + {%- else %} + {%- if message.get("content") -%} + {{- parse_content(message["content"]) -}} + {%- endif -%} + {{- "<|im_end|>\n" -}} + {%- endif %} +{%- endfor -%} +{%- if add_generation_prompt -%} + {{- "<|im_start|>assistant\n" -}} +{%- endif -%} \ No newline at end of file diff --git a/backend/llama.cpp/models/templates/LFM2.5-Instruct.jinja b/backend/llama.cpp/models/templates/LFM2.5-Instruct.jinja new file mode 100644 index 0000000000000000000000000000000000000000..7778756dd927ccd77836f05b0315809593fd0f3a --- /dev/null +++ b/backend/llama.cpp/models/templates/LFM2.5-Instruct.jinja @@ -0,0 +1,45 @@ +{{- bos_token -}} +{%- set keep_past_thinking = keep_past_thinking | default(false) -%} +{%- set ns = namespace(system_prompt="") -%} +{%- if messages[0]["role"] == "system" -%} + {%- set ns.system_prompt = messages[0]["content"] -%} + {%- set messages = messages[1:] -%} +{%- endif -%} +{%- if tools -%} + {%- set ns.system_prompt = ns.system_prompt + ("\n" if ns.system_prompt else "") + "List of tools: [" -%} + {%- for tool in tools -%} + {%- if tool is not string -%} + {%- set tool = tool | tojson -%} + {%- endif -%} + {%- set ns.system_prompt = ns.system_prompt + tool -%} + {%- if not loop.last -%} + {%- set ns.system_prompt = ns.system_prompt + ", " -%} + {%- endif -%} + {%- endfor -%} + {%- set ns.system_prompt = ns.system_prompt + "]" -%} +{%- endif -%} +{%- if ns.system_prompt -%} + {{- "<|im_start|>system\n" + ns.system_prompt + "<|im_end|>\n" -}} +{%- endif -%} +{%- set ns.last_assistant_index = -1 -%} +{%- for message in messages -%} + {%- if message["role"] == "assistant" -%} + {%- set ns.last_assistant_index = loop.index0 -%} + {%- endif -%} +{%- endfor -%} +{%- for message in messages -%} + {{- "<|im_start|>" + message["role"] + "\n" -}} + {%- set content = message["content"] -%} + {%- if content is not string -%} + {%- set content = content | tojson -%} + {%- endif -%} + {%- if message["role"] == "assistant" and not keep_past_thinking and loop.index0 != ns.last_assistant_index -%} + {%- if "" in content -%} + {%- set content = content.split("")[-1] | trim -%} + {%- endif -%} + {%- endif -%} + {{- content + "<|im_end|>\n" -}} +{%- endfor -%} +{%- if add_generation_prompt -%} + {{- "<|im_start|>assistant\n" -}} +{%- endif -%} \ No newline at end of file diff --git a/backend/llama.cpp/models/templates/MiMo-VL.jinja b/backend/llama.cpp/models/templates/MiMo-VL.jinja new file mode 100644 index 0000000000000000000000000000000000000000..9c1b1696a4851654a6333971a09f944962c971b8 --- /dev/null +++ b/backend/llama.cpp/models/templates/MiMo-VL.jinja @@ -0,0 +1,54 @@ +{%- if tools %} + {{- '<|im_start|>system\n' }} + {%- if messages[0]['role'] == 'system' %} + {{- messages[0]['content'] }} + {%- else %} + {{- 'You are MiMo, an AI assistant developed by Xiaomi.' }} + {%- endif %} + {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within XML tags:\n" }} + {%- for tool in tools %} + {{- "\n" }} + {{- tool | tojson }} + {%- endfor %} + {{- "\n\n\nFor each function call, return a json object with function name and arguments within XML tags:\n\n{\"name\": , \"arguments\": }\n<|im_end|>\n" }} +{%- else %} + {%- if messages[0]['role'] == 'system' %} + {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }} + {%- else %} + {{- '<|im_start|>system\nYou are MiMo, an AI assistant developed by Xiaomi.<|im_end|>\n' }} + {%- endif %} +{%- endif %} +{%- for message in messages %} + {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %} + {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }} + {%- elif message.role == "assistant" %} + {{- '<|im_start|>' + message.role }} + {%- if message.content %} + {{- '\n' + message.content }} + {%- endif %} + {%- for tool_call in message.tool_calls %} + {%- if tool_call.function is defined %} + {%- set tool_call = tool_call.function %} + {%- endif %} + {{- '\n\n{"name": "' }} + {{- tool_call.name }} + {{- '", "arguments": ' }} + {{- tool_call.arguments | tojson }} + {{- '}\n' }} + {%- endfor %} + {{- '<|im_end|>\n' }} + {%- elif message.role == "tool" %} + {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %} + {{- '<|im_start|>user' }} + {%- endif %} + {{- '\n\n' }} + {{- message.content }} + {{- '\n' }} + {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %} + {{- '<|im_end|>\n' }} + {%- endif %} + {%- endif %} +{%- endfor %} +{%- if add_generation_prompt %} + {{- '<|im_start|>assistant\n' }} +{%- endif %} diff --git a/backend/llama.cpp/models/templates/MiniMax-M2.jinja b/backend/llama.cpp/models/templates/MiniMax-M2.jinja new file mode 100644 index 0000000000000000000000000000000000000000..9302ccedb217e95fcdbf55b6cfda952420d82875 --- /dev/null +++ b/backend/llama.cpp/models/templates/MiniMax-M2.jinja @@ -0,0 +1,159 @@ +{# ----------‑‑‑ special token variables ‑‑‑---------- #} +{%- set toolcall_begin_token = '' -%} +{%- set toolcall_end_token = '' -%} +{#- Tool Rendering Functions ============================================== -#} +{%- macro render_tool_namespace(namespace_name, tool_list) -%} +{%- for tool in tool_list -%} +{{ tool.function | tojson(ensure_ascii=False) }} +{% endfor -%} +{%- endmacro -%} +{%- macro visible_text(content) -%} + {%- if content is string -%} + {{ content }} + {%- elif content is iterable and content is not mapping -%} + {%- for item in content -%} + {%- if item is mapping and item.type == 'text' -%} + {{- item.text }} + {%- elif item is string -%} + {{- item }} + {%- endif -%} + {%- endfor -%} + {%- else -%} + {{- content }} + {%- endif -%} +{%- endmacro -%} +{#- System Message Construction ============================================ -#} +{%- macro build_system_message(system_message) -%} + {%- if system_message and system_message.content -%} + {{- visible_text(system_message.content) }} + {%- else -%} + {%- if model_identity is not defined -%} + {%- set model_identity = "You are a helpful assistant." -%} + {%- endif -%} + {{- model_identity }} + {%- endif -%} + + {#- Handle current_date -#} + {%- if system_message and system_message.current_date -%} + {{- '\n' ~ 'Current date: ' + system_message.current_date }} + {%- endif -%} + {#- Handle current_location -#} + {%- if system_message and system_message.current_location -%} + {{- '\n' ~ 'Current location: ' + system_message.current_location }} + {%- endif -%} +{%- endmacro -%} +{#- Main Template Logic ================================================= -#} +{#- Extract system message (only first message if it's system) -#} +{%- set system_message = none -%} +{%- set conversation_messages = messages -%} +{%- if messages and messages[0].role == "system" -%} + {%- set system_message = messages[0] -%} + {%- set conversation_messages = messages[1:] -%} +{%- endif -%} +{#- Get the last user message turn, for interleved thinking -#} +{%- set ns = namespace(last_user_index=-1) %} +{% for m in conversation_messages %} + {%- if m.role == 'user' %} + {% set ns.last_user_index = loop.index0 -%} + {%- endif %} +{%- endfor %} +{#- Render system message -#} +{{- ']~!b[' ~ ']~b]system' ~ '\n' }} +{{- build_system_message(system_message) }} +{#- Render tools if available -#} +{%- if tools -%} + {{- '\n\n' ~ '# Tools' ~ '\n' ~ 'You may call one or more tools to assist with the user query.\nHere are the tools available in JSONSchema format:' ~ '\n' }} + {{- '\n' ~ '' ~ '\n' }} + {{- render_tool_namespace("functions", tools) }} + {{- '' ~ '\n\n' }} +{{- 'When making tool calls, use XML format to invoke tools and pass parameters:' ~ '\n' }} +{{- '\n' ~ toolcall_begin_token }} + +param-value-1 +param-value-2 +... + +{{- '\n' ~ toolcall_end_token }} +{%- endif -%} +{{- '[e~[\n' }} + +{#- Render messages -#} +{%- set last_tool_call = namespace(name=none) -%} +{%- for message in conversation_messages -%} + {%- if message.role == 'assistant' -%} + {#- Only render reasoning_content if no user message follows -#} + {{- ']~b]ai' ~ '\n' }} + + {%- set reasoning_content = '' %} + {%- set content = visible_text(message.content) %} + {%- if message.reasoning_content is string %} + {%- set reasoning_content = message.reasoning_content %} + {%- else %} + {%- if '' in content %} + {%- set reasoning_content = content.split('')[0].strip('\n').split('')[-1].strip('\n') %} + {%- set content = content.split('')[-1].strip('\n') %} + {%- endif %} + {%- endif %} + {%- if reasoning_content and loop.index0 > ns.last_user_index -%} + {{- '' ~ '\n' ~ reasoning_content ~ '\n' ~ '' ~ '\n\n' }} + {%- endif -%} + {%- if content -%} + {{- content }} + {%- endif -%} + {%- if message.tool_calls -%} + {{- '\n' ~ toolcall_begin_token ~ '\n' }} + + {%- for tool_call in message.tool_calls -%} + {%- if tool_call.function %} + {%- set tool_call = tool_call.function %} + {%- endif %} + {{- '' }} + {% set _args = tool_call.arguments %} + {%- for k, v in _args.items() %} + {{- '' }} + {{- v | tojson(ensure_ascii=False) if v is not string else v }} + {{- '' }} + {% endfor %} + {{- '' ~ '\n' }} + {%- endfor -%} + + {{- toolcall_end_token}} + {%- set last_tool_call.name = message.tool_calls[-1].function.name -%} + {%- else -%} + {%- set last_tool_call.name = none -%} + {%- endif -%} + {{- '[e~[' ~ '\n' }} + + {%- elif message.role == 'tool' -%} + {%- if last_tool_call.name is none -%} + {{- raise_exception("Message has tool role, but there was no previous assistant message with a tool call!") }} + {%- endif -%} + {%- if loop.first or (conversation_messages[loop.index0 - 1].role != 'tool') -%} + {{- ']~b]tool' }} + {%- endif -%} + {%- if message.content is string -%} + {{- '\n' }} + {{- message.content }} + {{- '' }} + {%- else -%} + {%- for tr in message.content -%} + {{- '\n' }} + {{- tr.output if tr.output is defined else (tr.text if tr.type == 'text' and tr.text is defined else tr) }} + {{- '\n' }} + {%- endfor -%} + {%- endif -%} + {%- if loop.last or (conversation_messages[loop.index0 + 1].role != 'tool') -%} + {{- '[e~[\n' -}} + {%- endif -%} + + {%- elif message.role == 'user' -%} + {{- ']~b]user' ~ '\n' }} + {{- visible_text(message.content) }} + {{- '[e~[' ~ '\n' }} + {%- endif -%} +{%- endfor -%} + +{#- Generation prompt -#} +{%- if add_generation_prompt -%} +{{- ']~b]ai' ~ '\n' ~ '' ~ '\n' }} +{%- endif -%} diff --git a/backend/llama.cpp/models/templates/Mistral-Small-3.2-24B-Instruct-2506.jinja b/backend/llama.cpp/models/templates/Mistral-Small-3.2-24B-Instruct-2506.jinja new file mode 100644 index 0000000000000000000000000000000000000000..19a3eaee49be66728214fc425052b6501c3395cb --- /dev/null +++ b/backend/llama.cpp/models/templates/Mistral-Small-3.2-24B-Instruct-2506.jinja @@ -0,0 +1,124 @@ +{%- set today = strftime_now("%Y-%m-%d") %} +{%- set default_system_message = "You are Mistral Small 3, a Large Language Model (LLM) created by Mistral AI, a French startup headquartered in Paris.\nYour knowledge base was last updated on 2023-10-01. The current date is " + today + ".\n\nWhen you're not sure about some information or when the user's request requires up-to-date or specific data, you must use the available tools to fetch the information. Do not hesitate to use tools whenever they can provide a more accurate or complete response. If no relevant tools are available, then clearly state that you don't have the information and avoid making up anything. + +If the user's question is not clear, ambiguous, or does not provide enough context for you to accurately answer the question, you do not try to answer it right away and you rather ask the user to clarify their request (e.g. \"What are some good restaurants around me?\" => \"Where are you?\" or \"When is the next flight to Tokyo\" => \"Where do you travel from?\"). +You are always very attentive to dates, and when asked about information at specific dates, you discard information that is at another date. +You follow these instructions in all languages, and always respond to the user in the language they use or request. +Next sections describe the capabilities that you have. + +# WEB BROWSING INSTRUCTIONS + +You cannot perform any web search or access internet to open URLs, links etc. If it seems like the user is expecting you to do so, you clarify the situation and ask the user to copy paste the text directly in the chat. + +# MULTI-MODAL INSTRUCTIONS + +You have the ability to read images, but you cannot generate images. You also cannot transcribe audio files or videos. +You cannot read nor transcribe audio files or videos. + +# TOOL CALLING INSTRUCTIONS + +You may have access to tools that you can use to fetch information or perform actions. You must use these tools in the following situations: + +1. When the request requires up-to-date information. +2. When the request requires specific data that you do not have in your knowledge base. +3. When the request involves actions that you cannot perform without tools. + +Always prioritize using tools to provide the most accurate and helpful response. If tools are not available, inform the user that you cannot perform the requested action at the moment." %} + +{{- bos_token }} + +{%- set system_prompt = default_system_message %} +{%- set loop_messages = messages %} + +{%- if not tools is defined %} + {%- set tools = none %} +{%- endif %} + +{%- if messages|length > 0 and messages[0]['role'] == 'system' %} + {%- if messages[0]['content'] is string %} + {%- set system_prompt = messages[0]['content'] %} + {%- else %} + {%- set system_prompt = messages[0]['content'][0]['text'] %} + {%- endif %} + {%- set loop_messages = messages[1:] %} +{%- endif %} + +{%- set user_messages = loop_messages | selectattr("role", "equalto", "user") | list %} + +{%- set ns = namespace(index=0) %} +{%- for message in loop_messages %} + {%- if not (message.role == "tool" or (message.get('tool_calls'))) %} + {%- if (message["role"] == "user") != (ns.index % 2 == 0) %} + {{- raise_exception("After the optional system message, conversation roles must alternate user/assistant/user/assistant/...") }} + {%- endif %} + {%- set ns.index = ns.index + 1 %} + {%- endif %} +{%- endfor %} + +{{- '[SYSTEM_PROMPT]' + system_prompt + '[/SYSTEM_PROMPT]' }} + +{%- for message in loop_messages %} + {%- if message['role'] == 'system' %} + {%- if message['content'] is string %} + {{- '[SYSTEM_PROMPT]' + message['content'] + '[/SYSTEM_PROMPT]' }} + {%- else %} + {{- '[SYSTEM_PROMPT]' + message['content'][0]['text'] + '[/SYSTEM_PROMPT]' }} + {%- endif %} + {%- elif message['role'] == 'user' %} + {%- if tools is not none and (message == user_messages[-1]) %} + {{- '[AVAILABLE_TOOLS]' + tools|tojson + '[/AVAILABLE_TOOLS]' }} + {%- endif %} + {{- '[INST]' }} + {%- if message['content'] is string %} + {{- message['content'] }} + {%- else %} + {%- for block in message['content'] %} + {%- if block['type'] == 'text' %} + {{- block['text'] }} + {%- elif block['type'] in ['image', 'image_url'] %} + {{- '[IMG]' }} + {%- else %} + {{- raise_exception('Only text and image blocks are supported in message content!') }} + {%- endif %} + {%- endfor %} + {%- endif %} + {{- '[/INST]' }} + {%- elif message['role'] == 'assistant' %} + {%- if message.get('tool_calls') %} + {%- for tool_call in message.tool_calls %} + {{- '[TOOL_CALLS]' + tool_call.function.name }} + {%- if not tool_call.id is defined or tool_call.id is not string or tool_call.id|length != 9 %} + {{- raise_exception("Tool call IDs should be alphanumeric strings with length 9!") }} + {%- endif %} + {{- '[CALL_ID]' + tool_call.id }} + {{- '[ARGS]' + tool_call['function']['arguments']|tojson }} + {%- endfor %} + {{- eos_token }} + {%- elif message['content'] is string %} + {{- message['content'] + eos_token }} + {%- else %} + {%- for block in message['content'] %} + {%- if block['type'] == 'text' %} + {{- block['text'] }} + {%- elif block['type'] in ['image', 'image_url'] %} + {{- '[IMG]' }} + {%- else %} + {{- raise_exception('Only text and image blocks are supported in assistant content!') }} + {%- endif %} + {%- endfor %} + {{- eos_token }} + {%- endif %} + {%- elif message['role'] == 'tool_results' or message['role'] == 'tool' %} + {%- if message.content is defined and message.content.content is defined %} + {%- set content = message.content.content %} + {%- else %} + {%- set content = message.content %} + {%- endif %} + {%- if not message.tool_call_id is defined or message.tool_call_id is not string or message['tool_call_id']|length != 9 %} + {{- raise_exception("Tool call IDs should be alphanumeric strings with length 9!") }} + {%- endif %} + {{- '[TOOL_RESULTS]' + message.tool_call_id + '[TOOL_CONTENT]' + content|string + '[/TOOL_RESULTS]' }} + {%- else %} + {{- raise_exception('Only system, user, assistant, and tool roles are supported!') }} + {%- endif %} +{%- endfor %} diff --git a/backend/llama.cpp/models/templates/deepseek-ai-DeepSeek-R1-Distill-Llama-8B.jinja b/backend/llama.cpp/models/templates/deepseek-ai-DeepSeek-R1-Distill-Llama-8B.jinja new file mode 100644 index 0000000000000000000000000000000000000000..299f7a7ff1244da2e8e7d6006efa40c6e77849d6 --- /dev/null +++ b/backend/llama.cpp/models/templates/deepseek-ai-DeepSeek-R1-Distill-Llama-8B.jinja @@ -0,0 +1,44 @@ +{% if not add_generation_prompt is defined -%} + {%- set add_generation_prompt = false -%} +{%- endif -%} +{%- set ns = namespace(is_first=false, is_tool=false, is_output_first=true, system_prompt='') -%} +{%- for message in messages -%} + {%- if message['role'] == 'system' -%} + {%- set ns.system_prompt = message['content'] -%} + {%- endif -%} +{%- endfor -%}{{bos_token}}{{ns.system_prompt}} +{%- for message in messages -%} + {%- if message['role'] == 'user' -%} + {%- set ns.is_tool = false -%}{{'<｜User｜>' + message['content']}} + {%- endif -%} + {%- if message['role'] == 'assistant' and message['content'] is none -%} + {%- set ns.is_tool = false -%} + {%- for tool in message['tool_calls']-%} + {%- if not ns.is_first -%}{{'<｜Assistant｜><｜tool▁calls▁begin｜><｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\n' + '```json' + '\n' + tool['function']['arguments'] + '\n' + '```' + '<｜tool▁call▁end｜>'}} + {%- set ns.is_first = true -%} + {%- else -%}{{'\n' + '<｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\n' + '```json' + '\n' + tool['function']['arguments'] + '\n' + '```' + '<｜tool▁call▁end｜>'}}{{'<｜tool▁calls▁end｜><｜end▁of▁sentence｜>'}} + {%- endif -%} + {%- endfor -%} + {%- endif -%} + {%- if message['role'] == 'assistant' and message['content'] is not none -%} + {%- if ns.is_tool -%}{{'<｜tool▁outputs▁end｜>' + message['content'] + '<｜end▁of▁sentence｜>'}} + {%- set ns.is_tool = false -%} + {%- else -%} + {%- set content = message['content'] -%} + {%- if '' in content -%} + {%- set content = content.split('')[-1] -%} + {%- endif -%}{{'<｜Assistant｜>' + content + '<｜end▁of▁sentence｜>'}} + {%- endif -%} + {%- endif -%} + {%- if message['role'] == 'tool' -%} + {%- set ns.is_tool = true -%} + {%- if ns.is_output_first -%}{{'<｜tool▁outputs▁begin｜><｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}} + {%- set ns.is_output_first = false -%} + {%- else -%}{{'\n<｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}} + {%- endif -%} + {%- endif -%} +{%- endfor -%} +{%- if ns.is_tool -%}{{'<｜tool▁outputs▁end｜>'}} +{%- endif -%} +{%- if add_generation_prompt and not ns.is_tool -%}{{'<｜Assistant｜>\n'}} +{%- endif %} \ No newline at end of file diff --git a/backend/llama.cpp/models/templates/deepseek-ai-DeepSeek-R1-Distill-Qwen-32B.jinja b/backend/llama.cpp/models/templates/deepseek-ai-DeepSeek-R1-Distill-Qwen-32B.jinja new file mode 100644 index 0000000000000000000000000000000000000000..7349ce9eca9dd2c96a59293655d1ff38b7d0a579 --- /dev/null +++ b/backend/llama.cpp/models/templates/deepseek-ai-DeepSeek-R1-Distill-Qwen-32B.jinja @@ -0,0 +1,47 @@ +{% if not add_generation_prompt is defined -%} + {%- set add_generation_prompt = false -%} +{%- endif -%} +{%- set ns = namespace(is_first=false, is_tool=false, is_output_first=true, system_prompt='') -%} +{%- for message in messages -%} + {%- if message['role'] == 'system' -%} + {%- set ns.system_prompt = message['content'] -%} + {%- endif -%} +{%- endfor -%}{{bos_token}}{{ns.system_prompt}} +{%- for message in messages -%} + {%- if message['role'] == 'user' -%} + {%- set ns.is_tool = false -%}{{'<｜User｜>' + message['content']}} + {%- endif -%} + {%- if message['role'] == 'assistant' and message['tool_calls'] -%} + {%- set ns.is_tool = false -%} + {%- for tool in message['tool_calls']-%} + {%- if not ns.is_first -%} + {{'<｜Assistant｜><｜tool▁calls▁begin｜><｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\n' + '```json' + '\n' + tool['function']['arguments'] | tojson + '\n' + '```' + '<｜tool▁call▁end｜>'}} + {%- set ns.is_first = true -%} + {%- else -%} + {{'\n' + '<｜tool▁call▁begin｜>' + tool['type'] + '<｜tool▁sep｜>' + tool['function']['name'] + '\n' + '```json' + '\n' + tool['function']['arguments'] | tojson + '\n' + '```' + '<｜tool▁call▁end｜>'}} + {%- endif -%} + {%- endfor -%} + {{'<｜tool▁calls▁end｜><｜end▁of▁sentence｜>'}} + {%- endif -%} + {%- if message['role'] == 'assistant' and message['content'] is not none -%} + {%- if ns.is_tool -%}{{'<｜tool▁outputs▁end｜>' + message['content'] + '<｜end▁of▁sentence｜>'}} + {%- set ns.is_tool = false -%} + {%- else -%} + {%- set content = message['content'] -%} + {%- if '' in content -%} + {%- set content = content.split('')[-1] -%} + {%- endif -%}{{'<｜Assistant｜>' + content + '<｜end▁of▁sentence｜>'}} + {%- endif -%} + {%- endif -%} + {%- if message['role'] == 'tool' -%} + {%- set ns.is_tool = true -%} + {%- if ns.is_output_first -%}{{'<｜tool▁outputs▁begin｜><｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}} + {%- set ns.is_output_first = false -%} + {%- else -%}{{'\n<｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}} + {%- endif -%} + {%- endif -%} +{%- endfor -%} +{%- if ns.is_tool -%}{{'<｜tool▁outputs▁end｜>'}} +{%- endif -%} +{%- if add_generation_prompt and not ns.is_tool -%}{{'<｜Assistant｜>\n'}}{% if not enable_thinking %}{{- '' -}}{% endif %} +{%- endif %} \ No newline at end of file diff --git a/backend/llama.cpp/models/templates/deepseek-ai-DeepSeek-V3.1.jinja b/backend/llama.cpp/models/templates/deepseek-ai-DeepSeek-V3.1.jinja new file mode 100644 index 0000000000000000000000000000000000000000..e987c2a16e8c50da8376871c49202037834f1317 --- /dev/null +++ b/backend/llama.cpp/models/templates/deepseek-ai-DeepSeek-V3.1.jinja @@ -0,0 +1,71 @@ +{% if not add_generation_prompt is defined -%} + {%- set add_generation_prompt = false -%} +{%- endif -%} +{%- if not thinking is defined -%} + {%- if enable_thinking is defined -%} + {%- set thinking = enable_thinking -%} + {%- else -%} + {%- set thinking = false -%} + {%- endif -%} +{%- endif -%} +{%- set ns = namespace(is_first=false, is_tool=false, system_prompt='', is_first_sp=true, is_last_user=false) -%} +{%- for message in messages -%} + {%- if message['role'] == 'system' -%} + {%- if ns.is_first_sp -%} + {%- set ns.system_prompt = ns.system_prompt + message['content'] -%} + {%- set ns.is_first_sp = false -%} + {%- else -%} + {%- set ns.system_prompt = ns.system_prompt + ' + +' + message['content'] -%} + {%- endif -%} + {%- endif -%} +{%- endfor -%}{{ bos_token }}{{ ns.system_prompt }} +{%- for message in messages -%} + {%- if message['role'] == 'user' -%} + {%- set ns.is_tool = false -%} + {%- set ns.is_first = false -%} + {%- set ns.is_last_user = true -%}{{'<｜User｜>' + message['content']}} + {%- endif -%} + {%- if message['role'] == 'assistant' and message['tool_calls'] -%} + {%- if ns.is_last_user -%}{{'<｜Assistant｜>'}} + {%- endif -%} + {%- set ns.is_last_user = false -%} + {%- set ns.is_first = false -%} + {%- set ns.is_tool = false -%} + {%- for tool in message['tool_calls'] -%} + {%- if not ns.is_first -%} + {%- if not message['content'] -%}{{'<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>'+ tool['function']['name'] + '<｜tool▁sep｜>' + tool['function']['arguments'] | tojson + '<｜tool▁call▁end｜>'}} + {%- else -%}{{message['content'] + '<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>' + tool['function']['name'] + '<｜tool▁sep｜>' + tool['function']['arguments'] | tojson + '<｜tool▁call▁end｜>'}} + {%- endif -%} + {%- set ns.is_first = true -%} + {%- else -%}{{'<｜tool▁call▁begin｜>'+ tool['function']['name'] + '<｜tool▁sep｜>' + tool['function']['arguments'] | tojson + '<｜tool▁call▁end｜>'}} + {%- endif -%} + {%- endfor -%}{{'<｜tool▁calls▁end｜><｜end▁of▁sentence｜>'}} + {%- endif -%} + {%- if message['role'] == 'assistant' and not message['tool_calls'] -%} + {%- if ns.is_last_user -%}{{'<｜Assistant｜>'}} + {%- if message['prefix'] is defined and message['prefix'] and thinking -%}{{''}} + {%- else -%}{{''}} + {%- endif -%} + {%- endif -%} + {%- set ns.is_last_user = false -%} + {%- if ns.is_tool -%}{{message['content'] + '<｜end▁of▁sentence｜>'}} + {%- set ns.is_tool = false -%} + {%- else -%} + {%- set content = message['content'] -%} + {%- if '' in content -%} + {%- set content = content.split('', 1)[1] -%} + {%- endif -%}{{content + '<｜end▁of▁sentence｜>'}} + {%- endif -%} + {%- endif -%} + {%- if message['role'] == 'tool' -%} + {%- set ns.is_last_user = false -%} + {%- set ns.is_tool = true -%}{{'<｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>'}} + {%- endif -%} +{%- endfor -%} +{%- if add_generation_prompt and ns.is_last_user and not ns.is_tool -%}{{'<｜Assistant｜>'}} + {%- if not thinking -%}{{''}} + {%- else -%}{{''}} + {%- endif -%} +{%- endif %} \ No newline at end of file diff --git a/backend/llama.cpp/models/templates/deepseek-ai-DeepSeek-V3.2.jinja b/backend/llama.cpp/models/templates/deepseek-ai-DeepSeek-V3.2.jinja new file mode 100644 index 0000000000000000000000000000000000000000..98d56589f0ede0b6e925007f9c68264bae9cff27 --- /dev/null +++ b/backend/llama.cpp/models/templates/deepseek-ai-DeepSeek-V3.2.jinja @@ -0,0 +1,141 @@ +{%- if not add_generation_prompt is defined -%} + {%- set add_generation_prompt = false -%} +{%- endif -%} +{%- if not thinking is defined -%} + {%- if enable_thinking is defined -%} + {%- set thinking = enable_thinking -%} + {%- else -%} + {%- set thinking = false -%} + {%- endif -%} +{%- endif -%} +{%- set dsml_token = '｜DSML｜' -%} +{%- set thinking_start_token = '' -%} +{%- set thinking_end_token = '' -%} +{%- set tools_header = '## Tools\n\nYou have access to a set of tools you can use to answer the user\'s question.\nYou can invoke functions by writing a "<' + dsml_token + 'function_calls>" block like the following as part of your reply to the user:\n<' + dsml_token + 'function_calls>\n<' + dsml_token + 'invoke name="$FUNCTION_NAME">\n<' + dsml_token + 'parameter name="$PARAMETER_NAME" string="true|false">$PARAMETER_VALUE\n...\n\n<' + dsml_token + 'invoke name="$FUNCTION_NAME2">\n...\n\n\n\nString and scalar parameters should be specified as is without any escaping or quotes, while lists and objects should use JSON format. The "string" attribute should be set to "true" for string type parameters and "false" for other types (numbers, booleans, arrays, objects).\n\nIf the thinking_mode is enabled, then after function results you should strongly consider outputting a thinking block. Here is an example:\n\n<' + dsml_token + 'function_calls>\n...\n\n\n\n...\n\n\n' + thinking_start_token + '...thinking about results' + thinking_end_token + '\n\nHere are the functions available in JSONSchema format:\n\n' -%} +{%- set tools_footer = '\n' -%} +{%- set ns = namespace(system_prompt='', is_first_sp=true) -%} +{%- for message in messages -%} + {%- if message['role'] == 'system' -%} + {%- if ns.is_first_sp -%} + {%- set ns.system_prompt = ns.system_prompt + (message['content'] or '') -%} + {%- set ns.is_first_sp = false -%} + {%- else -%} + {%- set ns.system_prompt = ns.system_prompt + '\n\n' + (message['content'] or '') -%} + {%- endif -%} + {%- endif -%} +{%- endfor -%} +{%- if tools is defined and tools -%} + {%- set ts = namespace(schemas='') -%} + {%- for tool in tools -%} + {%- if tool['type'] == 'function' -%} + {%- set ts.schemas = ts.schemas + (tool['function'] | tojson) + '\n' -%} + {%- endif -%} + {%- endfor -%} + {%- if ns.system_prompt -%} + {%- set ns.system_prompt = ns.system_prompt + '\n\n' + tools_header + ts.schemas + tools_footer -%} + {%- else -%} + {%- set ns.system_prompt = tools_header + ts.schemas + tools_footer -%} + {%- endif -%} +{%- endif -%} +{{- bos_token -}} +{{- ns.system_prompt -}} +{%- set last_user_idx = namespace(value=-1) -%} +{%- for message in messages -%} + {%- if message['role'] == 'user' or message['role'] == 'developer' -%} + {%- set last_user_idx.value = loop.index0 -%} + {%- endif -%} +{%- endfor -%} +{%- set state = namespace(pending_asst_marker=false, pending_tool_marker=false) -%} +{%- for message in messages -%} + {%- if message['role'] == 'user' -%} + {{- '<｜User｜>' + (message['content'] or '') -}} + {%- set state.pending_asst_marker = true -%} + {%- set state.pending_tool_marker = false -%} + {%- elif message['role'] == 'assistant' -%} + {%- set is_after_last_user = loop.index0 > last_user_idx.value -%} + {%- if state.pending_asst_marker -%} + {{- '<｜Assistant｜>' -}} + {%- if is_after_last_user and thinking -%} + {{- thinking_start_token -}} + {%- if message['reasoning_content'] is defined and message['reasoning_content'] -%} + {{- message['reasoning_content'] -}} + {%- endif -%} + {{- thinking_end_token -}} + {%- else -%} + {{- thinking_end_token -}} + {%- endif -%} + {%- elif state.pending_tool_marker -%} + {%- if is_after_last_user and thinking -%} + {{- '\n\n' + thinking_start_token -}} + {%- if message['reasoning_content'] is defined and message['reasoning_content'] -%} + {{- message['reasoning_content'] -}} + {%- endif -%} + {{- thinking_end_token -}} + {%- else -%} + {{- '\n\n' + thinking_end_token -}} + {%- endif -%} + {%- endif -%} + {%- set state.pending_asst_marker = false -%} + {%- set state.pending_tool_marker = false -%} + {%- if message['content'] is defined and message['content'] -%} + {{- message['content'] -}} + {%- endif -%} + {%- if message['tool_calls'] -%} + {{- '\n\n<' + dsml_token + 'function_calls>\n' -}} + {%- for tool in message['tool_calls'] -%} + {%- set func = tool['function'] -%} + {{- '<' + dsml_token + 'invoke name="' + func['name'] + '">\n' -}} + {%- set args = func['arguments'] -%} + {%- if args is string -%} + {%- set args = args | from_json -%} + {%- endif -%} + {%- for key, val in args.items() -%} + {%- if val is string -%} + {{- '<' + dsml_token + 'parameter name="' + key + '" string="true">' + val + '\n' -}} + {%- else -%} + {{- '<' + dsml_token + 'parameter name="' + key + '" string="false">' + (val | tojson) + '\n' -}} + {%- endif -%} + {%- endfor -%} + {{- '\n' -}} + {%- endfor -%} + {{- '' -}} + {%- endif -%} + {{- '<｜end▁of▁sentence｜>' -}} + {%- elif message['role'] == 'tool' -%} + {%- set outer_index = loop.index0 -%} + {%- set assistant_idx = namespace(value=-1) -%} + {%- for prev_msg in messages -%} + {%- if prev_msg['role'] == 'assistant' and prev_msg['tool_calls'] and loop.index0 < outer_index -%} + {%- set assistant_idx.value = loop.index0 -%} + {%- endif -%} + {%- endfor -%} + {%- set call_order = outer_index - assistant_idx.value -%} + {%- set assistant_msg = messages[assistant_idx.value] -%} + {%- set tool_call_count = assistant_msg['tool_calls'] | length -%} + {%- if call_order == 1 -%} + {{- '\n\n' -}} + {%- endif -%} + {{- '\n' + (message['content'] or '') + '' -}} + {%- if call_order == tool_call_count -%} + {{- '\n' -}} + {%- set state.pending_asst_marker = false -%} + {%- set state.pending_tool_marker = true -%} + {%- endif -%} + {%- endif -%} +{%- endfor -%} +{%- if add_generation_prompt -%} + {%- if state.pending_asst_marker -%} + {{- '<｜Assistant｜>' -}} + {%- if thinking -%} + {{- thinking_start_token -}} + {%- else -%} + {{- thinking_start_token + thinking_end_token -}} + {%- endif -%} + {%- elif state.pending_tool_marker -%} + {%- if thinking -%} + {{- '\n\n' + thinking_start_token -}} + {%- else -%} + {{- '\n\n' + thinking_start_token + thinking_end_token -}} + {%- endif -%} + {%- endif -%} +{%- endif -%} diff --git a/backend/llama.cpp/models/templates/deepseek-ai-DeepSeek-V4.jinja b/backend/llama.cpp/models/templates/deepseek-ai-DeepSeek-V4.jinja new file mode 100644 index 0000000000000000000000000000000000000000..f19f787b1b7e1885ab04e74f2f68cb6911dbe375 --- /dev/null +++ b/backend/llama.cpp/models/templates/deepseek-ai-DeepSeek-V4.jinja @@ -0,0 +1,112 @@ +{%- if not add_generation_prompt is defined -%} + {%- set add_generation_prompt = false -%} +{%- endif -%} +{%- if not thinking is defined -%} + {%- if enable_thinking is defined -%} + {%- set thinking = enable_thinking -%} + {%- else -%} + {%- set thinking = false -%} + {%- endif -%} +{%- endif -%} +{%- set dsml_token = '｜DSML｜' -%} +{%- set thinking_start_token = '' -%} +{%- set thinking_end_token = '' -%} +{%- set tools_header = '## Tools\n\nYou have access to a set of tools to help answer the user\'s question. You can invoke tools by writing a "<' + dsml_token + 'tool_calls>" block like the following:\n\n<' + dsml_token + 'tool_calls>\n<' + dsml_token + 'invoke name="$TOOL_NAME">\n<' + dsml_token + 'parameter name="$PARAMETER_NAME" string="true|false">$PARAMETER_VALUE\n...\n\n<' + dsml_token + 'invoke name="$TOOL_NAME2">\n...\n\n\n\nString parameters should be specified as is and set `string="true"`. For all other types (numbers, booleans, arrays, objects), pass the value in JSON format and set `string="false"`.\n\nIf thinking_mode is enabled (triggered by ' + thinking_start_token + '), you MUST output your complete reasoning inside ' + thinking_start_token + '...' + thinking_end_token + ' BEFORE any tool calls or final response.\n\nOtherwise, output directly after ' + thinking_end_token + ' with tool calls or final response.\n\n### Available Tool Schemas\n\n' -%} +{%- set tools_footer = '\nYou MUST strictly follow the above defined tool name and parameter schemas to invoke tool calls.\n' -%} +{%- set ns = namespace(system_prompt='', is_first_sp=true) -%} +{%- for message in messages -%} + {%- if message['role'] == 'system' -%} + {%- if ns.is_first_sp -%} + {%- set ns.system_prompt = ns.system_prompt + (message['content'] or '') -%} + {%- set ns.is_first_sp = false -%} + {%- else -%} + {%- set ns.system_prompt = ns.system_prompt + '\n\n' + (message['content'] or '') -%} + {%- endif -%} + {%- endif -%} +{%- endfor -%} +{%- if tools is defined and tools -%} + {%- set ts = namespace(schemas='') -%} + {%- for tool in tools -%} + {%- if tool['type'] == 'function' -%} + {%- set ts.schemas = ts.schemas + (tool['function'] | tojson) + '\n' -%} + {%- endif -%} + {%- endfor -%} + {%- if ns.system_prompt -%} + {%- set ns.system_prompt = ns.system_prompt + '\n\n' + tools_header + ts.schemas + tools_footer -%} + {%- else -%} + {%- set ns.system_prompt = tools_header + ts.schemas + tools_footer -%} + {%- endif -%} +{%- endif -%} +{{- bos_token -}} +{{- ns.system_prompt -}} +{%- set last_user_idx = namespace(value=-1) -%} +{%- for message in messages -%} + {%- if message['role'] == 'user' or message['role'] == 'developer' or message['role'] == 'tool' -%} + {%- set last_user_idx.value = loop.index0 -%} + {%- endif -%} +{%- endfor -%} +{%- set state = namespace(in_user=false) -%} +{%- for message in messages -%} + {%- if message['role'] == 'user' or message['role'] == 'developer' -%} + {%- if state.in_user -%} + {{- '\n\n' -}} + {%- else -%} + {{- '<｜User｜>' -}} + {%- set state.in_user = true -%} + {%- endif -%} + {{- message['content'] or '' -}} + {%- elif message['role'] == 'tool' -%} + {%- if state.in_user -%} + {{- '\n\n' -}} + {%- else -%} + {{- '<｜User｜>' -}} + {%- set state.in_user = true -%} + {%- endif -%} + {{- '' + (message['content'] or '') + '' -}} + {%- elif message['role'] == 'assistant' -%} + {%- set state.in_user = false -%} + {{- '<｜Assistant｜>' -}} + {%- set is_after_last_user = loop.index0 > last_user_idx.value -%} + {%- if is_after_last_user and thinking -%} + {{- thinking_start_token -}} + {%- if message['reasoning_content'] is defined and message['reasoning_content'] -%} + {{- message['reasoning_content'] -}} + {%- endif -%} + {{- thinking_end_token -}} + {%- else -%} + {{- thinking_end_token -}} + {%- endif -%} + {%- if message['content'] is defined and message['content'] -%} + {{- message['content'] -}} + {%- endif -%} + {%- if message['tool_calls'] -%} + {{- '\n\n<' + dsml_token + 'tool_calls>\n' -}} + {%- for tool in message['tool_calls'] -%} + {%- set func = tool['function'] -%} + {{- '<' + dsml_token + 'invoke name="' + func['name'] + '">\n' -}} + {%- set args = func['arguments'] -%} + {%- if args is string -%} + {%- set args = args | from_json -%} + {%- endif -%} + {%- for key, val in args.items() -%} + {%- if val is string -%} + {{- '<' + dsml_token + 'parameter name="' + key + '" string="true">' + val + '\n' -}} + {%- else -%} + {{- '<' + dsml_token + 'parameter name="' + key + '" string="false">' + (val | tojson) + '\n' -}} + {%- endif -%} + {%- endfor -%} + {{- '\n' -}} + {%- endfor -%} + {{- '' -}} + {%- endif -%} + {{- '<｜end▁of▁sentence｜>' -}} + {%- endif -%} +{%- endfor -%} +{%- if add_generation_prompt -%} + {{- '<｜Assistant｜>' -}} + {%- if thinking -%} + {{- thinking_start_token -}} + {%- else -%} + {{- thinking_end_token -}} + {%- endif -%} +{%- endif -%} \ No newline at end of file diff --git a/backend/llama.cpp/models/templates/fireworks-ai-llama-3-firefunction-v2.jinja b/backend/llama.cpp/models/templates/fireworks-ai-llama-3-firefunction-v2.jinja new file mode 100644 index 0000000000000000000000000000000000000000..b94cfd4d9bd40378b5d111d564efc8c9675bf0fc --- /dev/null +++ b/backend/llama.cpp/models/templates/fireworks-ai-llama-3-firefunction-v2.jinja @@ -0,0 +1,57 @@ +{%- set loop_messages = messages -%} +{%- set message_roles = ['system', 'user', 'assistant', 'tool'] -%} +{%- set system_prompt_suffix -%} +{%- filter trim -%} +In addition to plain text responses, you can chose to call one or more of the provided functions. + +Use the following rule to decide when to call a function: + * if the response can be generated from your internal knowledge (e.g., as in the case of queries like "What is the capital of Poland?"), do so + * if you need external information that can be obtained by calling one or more of the provided functions, generate a function calls + +If you decide to call functions: + * prefix function calls with functools marker (no closing marker required) + * all function calls should be generated in a single JSON list formatted as functools[{"name": [function name], "arguments": [function arguments as JSON]}, ...] + * follow the provided JSON schema. Do not hallucinate arguments or values. Do to blindly copy values from the provided samples + * respect the argument type formatting. E.g., if the type if number and format is float, write value 7 as 7.0 + * make sure you pick the right functions that match the user intent + +Available functions as JSON spec: +{%- endfilter -%} +{%- endset -%} +{%- set system_prompt_suffix = system_prompt_suffix + "\n" + functions -%} +{%- set system_prompt_suffix = system_prompt_suffix + '\nToday is ' + datetime + '.' -%} +{%- set ns = namespace(role='', content='') -%} +{#- Basic consistency checks -#} +{%- if not loop_messages -%} + {{ raise_exception('Expected non-empty messages') }} +{%- endif -%} +{%- for message in loop_messages -%} + {%- set ns.role = message['role'] | lower -%} + {%- if ns.role not in message_roles -%} + {%- set message_roles_string = message_roles | join(', ') -%} + {{ raise_exception('Invalid role ' + message['role'] + '. Only ' + message_roles_string + ' are supported.') }} + {%- endif -%} + {%- set msg_content = message['content'] | default('', true) | trim -%} + {%- if loop.index0 == 0 -%} + {%- if ns.role == 'system' -%} + {%- set system_prompt = '<|start_header_id|>' + 'system' + '<|end_header_id|>\n\n' + message['content'] | trim + '\n' + system_prompt_suffix + '<|eot_id|>' -%} + {%- else -%} + {%- set system_prompt = '<|start_header_id|>' + 'system' + '<|end_header_id|>\n\nYou are a helpful assistant with access to functions.\n' + system_prompt_suffix + '<|eot_id|>' -%} + {%- endif -%} + {%- set ns.content = bos_token + system_prompt -%} + {{- ns.content -}} + {%- endif -%} + {%- if loop.index0 > 0 or ns.role != 'system' -%} + {%- set ns.content = '<|start_header_id|>' + ns.role + '<|end_header_id|>\n\n' + msg_content -%} + {%- if 'tool_calls' in message and message['tool_calls'] -%} + {%- set tool = namespace(calls=[]) -%} + {%- for call in message['tool_calls'] -%} + {%- set tool.calls = tool.calls + ['{"name": "' + call['function']['name'] + '", "arguments": ' + call['function']['arguments']|tojson + '}'] -%} + {%- endfor -%} + {%- set ns.content = ns.content + ' functools[' + tool.calls | join(', ') + ']' -%} + {%- endif -%} + {%- set ns.content = ns.content + '<|eot_id|>' -%} + {{- ns.content -}} + {%- endif -%} +{%- endfor -%} +{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}} diff --git a/backend/llama.cpp/models/templates/google-gemma-2-2b-it.jinja b/backend/llama.cpp/models/templates/google-gemma-2-2b-it.jinja new file mode 100644 index 0000000000000000000000000000000000000000..923ec253c8dbefbb41cf084db7251df41d000f6d --- /dev/null +++ b/backend/llama.cpp/models/templates/google-gemma-2-2b-it.jinja @@ -0,0 +1,4 @@ +{{ bos_token }}{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if (message['role'] == 'assistant') %}{% set role = 'model' %}{% else %}{% set role = message['role'] %}{% endif %}{{ '' + role + ' +' + message['content'] | trim + ' +' }}{% endfor %}{% if add_generation_prompt %}{{'model +'}}{% endif %} \ No newline at end of file diff --git a/backend/llama.cpp/models/templates/google-gemma-4-31B-it-interleaved.jinja b/backend/llama.cpp/models/templates/google-gemma-4-31B-it-interleaved.jinja new file mode 100644 index 0000000000000000000000000000000000000000..85791c4fe5dc60bdbe8ee7f4bfc02dd1a375edf1 --- /dev/null +++ b/backend/llama.cpp/models/templates/google-gemma-4-31B-it-interleaved.jinja @@ -0,0 +1,282 @@ +{%- macro format_parameters(properties, required) -%} + {%- set standard_keys = ['description', 'type', 'properties', 'required', 'nullable'] -%} + {%- set ns = namespace(found_first=false) -%} + {%- for key, value in properties | dictsort -%} + {%- set add_comma = false -%} + {%- if key not in standard_keys -%} + {%- if ns.found_first %},{% endif -%} + {%- set ns.found_first = true -%} + {{ key }}:{ + {%- if value['description'] -%} + description:<|"|>{{ value['description'] }}<|"|> + {%- set add_comma = true -%} + {%- endif -%} + {%- if value['nullable'] %} + {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%} + nullable:true + {%- endif -%} + {%- if value['type'] | upper == 'STRING' -%} + {%- if value['enum'] -%} + {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%} + enum:{{ format_argument(value['enum']) }} + {%- endif -%} + {%- elif value['type'] | upper == 'OBJECT' -%} + ,properties:{ + {%- if value['properties'] is defined and value['properties'] is mapping -%} + {{- format_parameters(value['properties'], value['required'] | default([])) -}} + {%- elif value is mapping -%} + {{- format_parameters(value, value['required'] | default([])) -}} + {%- endif -%} + } + {%- if value['required'] -%} + ,required:[ + {%- for item in value['required'] | default([]) -%} + <|"|>{{- item -}}<|"|> + {%- if not loop.last %},{% endif -%} + {%- endfor -%} + ] + {%- endif -%} + {%- elif value['type'] | upper == 'ARRAY' -%} + {%- if value['items'] is mapping and value['items'] -%} + ,items:{ + {%- set ns_items = namespace(found_first=false) -%} + {%- for item_key, item_value in value['items'] | dictsort -%} + {%- if item_value is not none -%} + {%- if ns_items.found_first %},{% endif -%} + {%- set ns_items.found_first = true -%} + {%- if item_key == 'properties' -%} + properties:{ + {%- if item_value is mapping -%} + {{- format_parameters(item_value, value['items']['required'] | default([])) -}} + {%- endif -%} + } + {%- elif item_key == 'required' -%} + required:[ + {%- for req_item in item_value -%} + <|"|>{{- req_item -}}<|"|> + {%- if not loop.last %},{% endif -%} + {%- endfor -%} + ] + {%- elif item_key == 'type' -%} + {%- if item_value is string -%} + type:{{ format_argument(item_value | upper) }} + {%- else -%} + type:{{ format_argument(item_value | map('upper') | list) }} + {%- endif -%} + {%- else -%} + {{ item_key }}:{{ format_argument(item_value) }} + {%- endif -%} + {%- endif -%} + {%- endfor -%} + } + {%- endif -%} + {%- endif -%} + {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%} + type:<|"|>{{ value['type'] | upper }}<|"|>} + {%- endif -%} + {%- endfor -%} +{%- endmacro -%} +{%- macro format_function_declaration(tool_data) -%} + declaration:{{- tool_data['function']['name'] -}}{description:<|"|>{{- tool_data['function']['description'] -}}<|"|> + {%- set params = tool_data['function']['parameters'] -%} + {%- if params -%} + ,parameters:{ + {%- if params['properties'] -%} + properties:{ {{- format_parameters(params['properties'], params['required']) -}} }, + {%- endif -%} + {%- if params['required'] -%} + required:[ + {%- for item in params['required'] -%} + <|"|>{{- item -}}<|"|> + {{- ',' if not loop.last -}} + {%- endfor -%} + ], + {%- endif -%} + {%- if params['type'] -%} + type:<|"|>{{- params['type'] | upper -}}<|"|>} + {%- endif -%} + {%- endif -%} + {%- if 'response' in tool_data['function'] -%} + {%- set response_declaration = tool_data['function']['response'] -%} + ,response:{ + {%- if response_declaration['description'] -%} + description:<|"|>{{- response_declaration['description'] -}}<|"|>, + {%- endif -%} + {%- if response_declaration['type'] | upper == 'OBJECT' -%} + type:<|"|>{{- response_declaration['type'] | upper -}}<|"|>} + {%- endif -%} + {%- endif -%} + } +{%- endmacro -%} +{%- macro format_argument(argument, escape_keys=True) -%} + {%- if argument is string -%} + {{- '<|"|>' + argument + '<|"|>' -}} + {%- elif argument is boolean -%} + {{- 'true' if argument else 'false' -}} + {%- elif argument is mapping -%} + {{- '{' -}} + {%- set ns = namespace(found_first=false) -%} + {%- for key, value in argument | dictsort -%} + {%- if ns.found_first %},{% endif -%} + {%- set ns.found_first = true -%} + {%- if escape_keys -%} + {{- '<|"|>' + key + '<|"|>' -}} + {%- else -%} + {{- key -}} + {%- endif -%} + :{{- format_argument(value, escape_keys=escape_keys) -}} + {%- endfor -%} + {{- '}' -}} + {%- elif argument is sequence -%} + {{- '[' -}} + {%- for item in argument -%} + {{- format_argument(item, escape_keys=escape_keys) -}} + {%- if not loop.last %},{% endif -%} + {%- endfor -%} + {{- ']' -}} + {%- else -%} + {{- argument -}} + {%- endif -%} +{%- endmacro -%} +{%- macro strip_thinking(text) -%} + {%- set ns = namespace(result='') -%} + {%- for part in text.split('') -%} + {%- if '<|channel>' in part -%} + {%- set ns.result = ns.result + part.split('<|channel>')[0] -%} + {%- else -%} + {%- set ns.result = ns.result + part -%} + {%- endif -%} + {%- endfor -%} + {{- ns.result | trim -}} +{%- endmacro -%} + +{%- set ns = namespace(prev_message_type=None, last_user_message=-1) -%} +{%- set loop_messages = messages -%} +{{- bos_token -}} +{#- Handle System/Tool Definitions Block -#} +{%- if (enable_thinking is defined and enable_thinking) or tools or messages[0]['role'] in ['system', 'developer'] -%} + {{- '<|turn>system\n' -}} + + {#- Inject Thinking token at the very top of the FIRST system turn -#} + {%- if enable_thinking is defined and enable_thinking -%} + {{- '<|think|>\n' -}} + {%- set ns.prev_message_type = 'think' -%} + {%- endif -%} + + {%- if messages[0]['role'] in ['system', 'developer'] -%} + {{- messages[0]['content'] | trim -}} + {%- set loop_messages = messages[1:] -%} + {%- endif -%} + + {%- if tools -%} + {%- for tool in tools %} + {{- '<|tool>' -}} + {{- format_function_declaration(tool) | trim -}} + {{- '' -}} + {%- endfor %} + {%- set ns.prev_message_type = 'tool' -%} + {%- endif -%} + + {{- '\n' -}} +{%- endif %} + +{#- Find last user message -#} +{%- for message in loop_messages -%} + {%- if message['role'] == 'user' -%} + {%- set ns.last_user_message = loop.index0 -%} + {%- endif -%} +{%- endfor -%} + +{#- Loop through messages -#} +{%- for message in loop_messages -%} + {%- set role = 'model' if message['role'] == 'assistant' else message['role'] -%} + {%- if not (ns.prev_message_type == 'tool_response' and message['tool_calls']) -%} + {{- '<|turn>' + role + '\n' }} + {%- endif -%} + + {%- set ns.prev_message_type = None -%} + + {%- if message['tool_calls'] -%} + {#- Preserve reasoning between tool calls for model turns that come after the last user turn -#} + {%- if message['reasoning_content'] and loop.index0 > ns.last_user_message -%} + {{- '<|channel>thought\n' -}} + {{- message['reasoning_content'] -}} + {{- '' -}} + {%- endif -%} + {%- for tool_call in message['tool_calls'] -%} + {%- set function = tool_call['function'] -%} + {{- '<|tool_call>call:' + function['name'] + '{' -}} + {%- if function['arguments'] is mapping -%} + {%- set ns_args = namespace(found_first=false) -%} + {%- for key, value in function['arguments'] | dictsort -%} + {%- if ns_args.found_first %},{% endif -%} + {%- set ns_args.found_first = true -%} + {{- key -}}:{{- format_argument(value, escape_keys=False) -}} + {%- endfor -%} + {%- elif function['arguments'] is string -%} + {{- function['arguments'] -}} + {%- endif -%} + {{- '}' -}} + {%- endfor -%} + {%- set ns.prev_message_type = 'tool_call' -%} + {%- endif -%} + + {%- if message['tool_responses'] -%} + {#- Tool Response handling -#} + {%- for tool_response in message['tool_responses'] -%} + {{- '<|tool_response>' -}} + {%- if tool_response['response'] is mapping -%} + {{- 'response:' + tool_response['name'] | default('unknown') + '{' -}} + {%- for key, value in tool_response['response'] | dictsort -%} + {{- key -}}:{{- format_argument(value, escape_keys=False) -}} + {%- if not loop.last %},{% endif -%} + {%- endfor -%} + {{- '}' -}} + {%- else -%} + {{- 'response:' + tool_response['name'] | default('unknown') + '{value:' + format_argument(tool_response['response'], escape_keys=False) + '}' -}} + {%- endif -%} + {{- '' -}} + {%- endfor -%} + {%- set ns.prev_message_type = 'tool_response' -%} + {%- endif -%} + + {%- if message['content'] is string -%} + {%- if role == 'model' -%} + {{- strip_thinking(message['content']) -}} + {%- else -%} + {{- message['content'] | trim -}} + {%- endif -%} + {%- elif message['content'] is sequence -%} + {%- for item in message['content'] -%} + {%- if item['type'] == 'text' -%} + {%- if role == 'model' -%} + {{- strip_thinking(item['text']) -}} + {%- else -%} + {{- item['text'] | trim -}} + {%- endif -%} + {%- elif item['type'] == 'image' -%} + {{- '<|image|>' -}} + {%- set ns.prev_message_type = 'image' -%} + {%- elif item['type'] == 'audio' -%} + {{- '<|audio|>' -}} + {%- set ns.prev_message_type = 'audio' -%} + {%- elif item['type'] == 'video' -%} + {{- '<|video|>' -}} + {%- set ns.prev_message_type = 'video' -%} + {%- endif -%} + {%- endfor -%} + {%- endif -%} + + {%- if not (message['tool_responses'] and not message['content']) -%} + {{- '\n' -}} + {%- endif -%} +{%- endfor -%} + +{%- if add_generation_prompt -%} + {%- if ns.prev_message_type != 'tool_response' -%} + {{- '<|turn>model\n' -}} + {%- endif -%} + {%- if not enable_thinking | default(false) -%} + {{- '<|channel>thought\n' -}} + {%- endif -%} +{%- endif -%} diff --git a/backend/llama.cpp/models/templates/google-gemma-4-31B-it.jinja b/backend/llama.cpp/models/templates/google-gemma-4-31B-it.jinja new file mode 100644 index 0000000000000000000000000000000000000000..98da08eb6be6f7a353d390456a6c4cacf811c9ed --- /dev/null +++ b/backend/llama.cpp/models/templates/google-gemma-4-31B-it.jinja @@ -0,0 +1,347 @@ +{%- macro format_parameters(properties, required) -%} + {%- set standard_keys = ['description', 'type', 'properties', 'required', 'nullable'] -%} + {%- set ns = namespace(found_first=false) -%} + {%- for key, value in properties | dictsort -%} + {%- set add_comma = false -%} + {%- if key not in standard_keys -%} + {%- if ns.found_first %},{% endif -%} + {%- set ns.found_first = true -%} + {{ key }}:{ + {%- if value['description'] -%} + description:<|"|>{{ value['description'] }}<|"|> + {%- set add_comma = true -%} + {%- endif -%} + {%- if value['type'] | upper == 'STRING' -%} + {%- if value['enum'] -%} + {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%} + enum:{{ format_argument(value['enum']) }} + {%- endif -%} + {%- elif value['type'] | upper == 'ARRAY' -%} + {%- if value['items'] is mapping and value['items'] -%} + {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%} + items:{ + {%- set ns_items = namespace(found_first=false) -%} + {%- for item_key, item_value in value['items'] | dictsort -%} + {%- if item_value is not none -%} + {%- if ns_items.found_first %},{% endif -%} + {%- set ns_items.found_first = true -%} + {%- if item_key == 'properties' -%} + properties:{ + {%- if item_value is mapping -%} + {{- format_parameters(item_value, value['items']['required'] | default([])) -}} + {%- endif -%} + } + {%- elif item_key == 'required' -%} + required:[ + {%- for req_item in item_value -%} + <|"|>{{- req_item -}}<|"|> + {%- if not loop.last %},{% endif -%} + {%- endfor -%} + ] + {%- elif item_key == 'type' -%} + {%- if item_value is string -%} + type:{{ format_argument(item_value | upper) }} + {%- else -%} + type:{{ format_argument(item_value | map('upper') | list) }} + {%- endif -%} + {%- else -%} + {{ item_key }}:{{ format_argument(item_value) }} + {%- endif -%} + {%- endif -%} + {%- endfor -%} + } + {%- endif -%} + {%- endif -%} + {%- if value['nullable'] %} + {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%} + nullable:true + {%- endif -%} + {%- if value['type'] | upper == 'OBJECT' -%} + {%- if value['properties'] is defined and value['properties'] is mapping -%} + {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%} + properties:{ + {{- format_parameters(value['properties'], value['required'] | default([])) -}} + } + {%- elif value is mapping -%} + {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%} + properties:{ + {{- format_parameters(value, value['required'] | default([])) -}} + } + {%- endif -%} + {%- if value['required'] -%} + {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%} + required:[ + {%- for item in value['required'] | default([]) -%} + <|"|>{{- item -}}<|"|> + {%- if not loop.last %},{% endif -%} + {%- endfor -%} + ] + {%- endif -%} + {%- endif -%} + {%- if add_comma %},{%- else -%} {%- set add_comma = true -%} {% endif -%} + type:<|"|>{{ value['type'] | upper }}<|"|>} + {%- endif -%} + {%- endfor -%} +{%- endmacro -%} +{%- macro format_function_declaration(tool_data) -%} + declaration:{{- tool_data['function']['name'] -}}{description:<|"|>{{- tool_data['function']['description'] -}}<|"|> + {%- set params = tool_data['function']['parameters'] -%} + {%- if params -%} + ,parameters:{ + {%- if params['properties'] -%} + properties:{ {{- format_parameters(params['properties'], params['required']) -}} }, + {%- endif -%} + {%- if params['required'] -%} + required:[ + {%- for item in params['required'] -%} + <|"|>{{- item -}}<|"|> + {{- ',' if not loop.last -}} + {%- endfor -%} + ], + {%- endif -%} + {%- if params['type'] -%} + type:<|"|>{{- params['type'] | upper -}}<|"|>} + {%- endif -%} + {%- endif -%} + {%- if 'response' in tool_data['function'] -%} + {%- set response_declaration = tool_data['function']['response'] -%} + ,response:{ + {%- if response_declaration['description'] -%} + description:<|"|>{{- response_declaration['description'] -}}<|"|>, + {%- endif -%} + {%- if response_declaration['type'] | upper == 'OBJECT' -%} + type:<|"|>{{- response_declaration['type'] | upper -}}<|"|>} + {%- endif -%} + {%- endif -%} + } +{%- endmacro -%} +{%- macro format_argument(argument, escape_keys=True) -%} + {%- if argument is string -%} + {{- '<|"|>' + argument + '<|"|>' -}} + {%- elif argument is boolean -%} + {{- 'true' if argument else 'false' -}} + {%- elif argument is mapping -%} + {{- '{' -}} + {%- set ns = namespace(found_first=false) -%} + {%- for key, value in argument | dictsort -%} + {%- if ns.found_first %},{% endif -%} + {%- set ns.found_first = true -%} + {%- if escape_keys -%} + {{- '<|"|>' + key + '<|"|>' -}} + {%- else -%} + {{- key -}} + {%- endif -%} + :{{- format_argument(value, escape_keys=escape_keys) -}} + {%- endfor -%} + {{- '}' -}} + {%- elif argument is sequence -%} + {{- '[' -}} + {%- for item in argument -%} + {{- format_argument(item, escape_keys=escape_keys) -}} + {%- if not loop.last %},{% endif -%} + {%- endfor -%} + {{- ']' -}} + {%- else -%} + {{- argument -}} + {%- endif -%} +{%- endmacro -%} +{%- macro strip_thinking(text) -%} + {%- set ns = namespace(result='') -%} + {%- for part in text.split('') -%} + {%- if '<|channel>' in part -%} + {%- set ns.result = ns.result + part.split('<|channel>')[0] -%} + {%- else -%} + {%- set ns.result = ns.result + part -%} + {%- endif -%} + {%- endfor -%} + {{- ns.result | trim -}} +{%- endmacro -%} + +{%- macro format_tool_response_block(tool_name, response) -%} + {{- '<|tool_response>' -}} + {%- if response is mapping -%} + {{- 'response:' + tool_name + '{' -}} + {%- for key, value in response | dictsort -%} + {{- key -}}:{{- format_argument(value, escape_keys=False) -}} + {%- if not loop.last %},{% endif -%} + {%- endfor -%} + {{- '}' -}} + {%- else -%} + {{- 'response:' + tool_name + '{value:' + format_argument(response, escape_keys=False) + '}' -}} + {%- endif -%} + {{- '' -}} +{%- endmacro -%} + +{%- set ns = namespace(prev_message_type=None) -%} +{%- set loop_messages = messages -%} +{{- bos_token -}} +{#- Handle System/Tool Definitions Block -#} +{%- if (enable_thinking is defined and enable_thinking) or tools or messages[0]['role'] in ['system', 'developer'] -%} + {{- '<|turn>system\n' -}} + + {#- Inject Thinking token at the very top of the FIRST system turn -#} + {%- if enable_thinking is defined and enable_thinking -%} + {{- '<|think|>\n' -}} + {%- set ns.prev_message_type = 'think' -%} + {%- endif -%} + + {%- if messages[0]['role'] in ['system', 'developer'] -%} + {{- messages[0]['content'] | trim -}} + {%- set loop_messages = messages[1:] -%} + {%- endif -%} + + {%- if tools -%} + {%- for tool in tools %} + {{- '<|tool>' -}} + {{- format_function_declaration(tool) | trim -}} + {{- '' -}} + {%- endfor %} + {%- set ns.prev_message_type = 'tool' -%} + {%- endif -%} + + {{- '\n' -}} +{%- endif %} + +{#- Pre-scan: find last user message index for reasoning guard -#} +{%- set ns_turn = namespace(last_user_idx=-1) -%} +{%- for i in range(loop_messages | length) -%} + {%- if loop_messages[i]['role'] == 'user' -%} + {%- set ns_turn.last_user_idx = i -%} + {%- endif -%} +{%- endfor -%} + +{#- Loop through messages -#} +{%- for message in loop_messages -%} + {%- if message['role'] != 'tool' -%} + {%- set ns.prev_message_type = None -%} + {%- set role = 'model' if message['role'] == 'assistant' else message['role'] -%} + {#- Detect continuation: suppress duplicate <|turn>model when previous non-tool message was also assistant -#} + {%- set prev_nt = namespace(role=None, found=false) -%} + {%- if loop.index0 > 0 -%} + {%- for j in range(loop.index0 - 1, -1, -1) -%} + {%- if not prev_nt.found -%} + {%- if loop_messages[j]['role'] != 'tool' -%} + {%- set prev_nt.role = loop_messages[j]['role'] -%} + {%- set prev_nt.found = true -%} + {%- endif -%} + {%- endif -%} + {%- endfor -%} + {%- endif -%} + {%- set continue_same_model_turn = (role == 'model' and prev_nt.role == 'assistant') -%} + {%- if not continue_same_model_turn -%} + {{- '<|turn>' + role + '\n' }} + {%- endif -%} + + {#- Render reasoning/reasoning_content as thinking channel -#} + {%- set thinking_text = message.get('reasoning') or message.get('reasoning_content') -%} + {%- if thinking_text and loop.index0 > ns_turn.last_user_idx and message.get('tool_calls') -%} + {{- '<|channel>thought\n' + thinking_text + '\n' -}} + {%- endif -%} + + {%- if message['tool_calls'] -%} + {%- for tool_call in message['tool_calls'] -%} + {%- set function = tool_call['function'] -%} + {{- '<|tool_call>call:' + function['name'] + '{' -}} + {%- if function['arguments'] is mapping -%} + {%- set ns_args = namespace(found_first=false) -%} + {%- for key, value in function['arguments'] | dictsort -%} + {%- if ns_args.found_first %},{% endif -%} + {%- set ns_args.found_first = true -%} + {{- key -}}:{{- format_argument(value, escape_keys=False) -}} + {%- endfor -%} + {%- elif function['arguments'] is string -%} + {{- function['arguments'] -}} + {%- endif -%} + {{- '}' -}} + {%- endfor -%} + {%- set ns.prev_message_type = 'tool_call' -%} + {%- endif -%} + + {%- set ns_tr_out = namespace(flag=false) -%} + {%- if message.get('tool_responses') -%} + {#- Legacy: tool_responses embedded on the assistant message (Google/Gemma native) -#} + {%- for tool_response in message['tool_responses'] -%} + {{- format_tool_response_block(tool_response['name'] | default('unknown'), tool_response['response']) -}} + {%- set ns_tr_out.flag = true -%} + {%- set ns.prev_message_type = 'tool_response' -%} + {%- endfor -%} + {%- elif message.get('tool_calls') -%} + {#- OpenAI Chat Completions: forward-scan consecutive role:tool messages -#} + {%- set ns_tool_scan = namespace(stopped=false) -%} + {%- for k in range(loop.index0 + 1, loop_messages | length) -%} + {%- if ns_tool_scan.stopped -%} + {%- elif loop_messages[k]['role'] != 'tool' -%} + {%- set ns_tool_scan.stopped = true -%} + {%- else -%} + {%- set follow = loop_messages[k] -%} + {#- Resolve tool_call_id to function name -#} + {%- set ns_tname = namespace(name=follow.get('name') | default('unknown')) -%} + {%- for tc in message['tool_calls'] -%} + {%- if tc.get('id') == follow.get('tool_call_id') -%} + {%- set ns_tname.name = tc['function']['name'] -%} + {%- endif -%} + {%- endfor -%} + {#- Handle content as string or content-parts array -#} + {%- set tool_body = follow.get('content') -%} + {%- if tool_body is string -%} + {{- format_tool_response_block(ns_tname.name, tool_body) -}} + {%- elif tool_body is sequence and tool_body is not string -%} + {%- set ns_txt = namespace(s='') -%} + {%- for part in tool_body -%} + {%- if part.get('type') == 'text' -%} + {%- set ns_txt.s = ns_txt.s + (part.get('text') | default('')) -%} + {%- endif -%} + {%- endfor -%} + {{- format_tool_response_block(ns_tname.name, ns_txt.s) -}} + {%- else -%} + {{- format_tool_response_block(ns_tname.name, tool_body) -}} + {%- endif -%} + {%- set ns_tr_out.flag = true -%} + {%- set ns.prev_message_type = 'tool_response' -%} + {%- endif -%} + {%- endfor -%} + {%- endif -%} + + {%- if message['content'] is string -%} + {%- if role == 'model' -%} + {{- strip_thinking(message['content']) -}} + {%- else -%} + {{- message['content'] | trim -}} + {%- endif -%} + {%- elif message['content'] is sequence -%} + {%- for item in message['content'] -%} + {%- if item['type'] == 'text' -%} + {%- if role == 'model' -%} + {{- strip_thinking(item['text']) -}} + {%- else -%} + {{- item['text'] | trim -}} + {%- endif -%} + {%- elif item['type'] == 'image' -%} + {{- '<|image|>' -}} + {%- set ns.prev_message_type = 'image' -%} + {%- elif item['type'] == 'audio' -%} + {{- '<|audio|>' -}} + {%- set ns.prev_message_type = 'audio' -%} + {%- elif item['type'] == 'video' -%} + {{- '<|video|>' -}} + {%- set ns.prev_message_type = 'video' -%} + {%- endif -%} + {%- endfor -%} + {%- endif -%} + + {%- if ns.prev_message_type == 'tool_call' and not ns_tr_out.flag -%} + {{- '<|tool_response>' -}} + {%- elif not (ns_tr_out.flag and not message.get('content')) -%} + {{- '\n' -}} + {%- endif -%} + {%- endif -%} +{%- endfor -%} + +{%- if add_generation_prompt -%} + {%- if ns.prev_message_type != 'tool_response' and ns.prev_message_type != 'tool_call' -%} + {{- '<|turn>model\n' -}} + {%- if not enable_thinking | default(false) -%} + {{- '<|channel>thought\n' -}} + {%- endif -%} + {%- endif -%} +{%- endif -%} diff --git a/backend/llama.cpp/models/templates/ibm-granite-granite-3.3-2B-Instruct.jinja b/backend/llama.cpp/models/templates/ibm-granite-granite-3.3-2B-Instruct.jinja new file mode 100644 index 0000000000000000000000000000000000000000..f5065360960f03229a3a76084cfda04b584a6e78 --- /dev/null +++ b/backend/llama.cpp/models/templates/ibm-granite-granite-3.3-2B-Instruct.jinja @@ -0,0 +1,59 @@ +{# Alias tools -> available_tools #} +{%- if tools and not available_tools -%} + {%- set available_tools = tools -%} +{%- endif -%} +{%- if messages[0]['role'] == 'system' %} + {%- set system_message = messages[0]['content'] %} + {%- set loop_messages = messages[1:] %} + {%- else %} + {%- set system_message = "Knowledge Cutoff Date: April 2024. Today's Date: " + strftime_now('%B %d, %Y') + ". You are Granite, developed by IBM." %} + {%- if available_tools and documents %} + {%- set system_message = system_message + " You are a helpful assistant with access to the following tools. When a tool is required to answer the user's query, respond only with <|tool_call|> followed by a JSON list of tools used. If a tool does not exist in the provided list of tools, notify the user that you do not have the ability to fulfill the request. Write the response to the user's input by strictly aligning with the facts in the provided documents. If the information needed to answer the question is not available in the documents, inform the user that the question cannot be answered based on the available data." %} + {%- elif available_tools %} + {%- set system_message = system_message + " You are a helpful assistant with access to the following tools. When a tool is required to answer the user's query, respond only with <|tool_call|> followed by a JSON list of tools used. If a tool does not exist in the provided list of tools, notify the user that you do not have the ability to fulfill the request." %} + {%- elif documents %} + {%- set system_message = system_message + " Write the response to the user's input by strictly aligning with the facts in the provided documents. If the information needed to answer the question is not available in the documents, inform the user that the question cannot be answered based on the available data." %} + {%- elif thinking %} + {%- set system_message = system_message + " You are a helpful AI assistant. +Respond to every user query in a comprehensive and detailed way. You can write down your thoughts and reasoning process before responding. In the thought process, engage in a comprehensive cycle of analysis, summarization, exploration, reassessment, reflection, backtracing, and iteration to develop well-considered thinking process. In the response section, based on various attempts, explorations, and reflections from the thoughts section, systematically present the final solution that you deem correct. The response should summarize the thought process. Write your thoughts between and write your response between for each user query." %} + {%- else %} + {%- set system_message = system_message + " You are a helpful AI assistant." %} + {%- endif %} + {%- if 'citations' in controls and documents %} + {%- set system_message = system_message + ' +Use the symbols <|start_of_cite|> and <|end_of_cite|> to indicate when a fact comes from a document in the search result, e.g <|start_of_cite|> {document_id: 1}my fact <|end_of_cite|> for a fact from document 1. Afterwards, list all the citations with their corresponding documents in an ordered list.' %} + {%- endif %} + {%- if 'hallucinations' in controls and documents %} + {%- set system_message = system_message + ' +Finally, after the response is written, include a numbered list of sentences from the response with a corresponding risk value that are hallucinated and not based in the documents.' %} + {%- endif %} + {%- set loop_messages = messages %} + {%- endif %} + {{- '<|start_of_role|>system<|end_of_role|>' + system_message + '<|end_of_text|> +' }} + {%- if available_tools %} + {{- '<|start_of_role|>available_tools<|end_of_role|>' }} + {{- available_tools | tojson(indent=4) }} + {{- '<|end_of_text|> +' }} + {%- endif %} + {%- if documents %} + {%- for document in documents %} + {{- '<|start_of_role|>document {"document_id": "' + document['doc_id'] | string + '"}<|end_of_role|> +' }} + {{- document['text'] }} + {{- '<|end_of_text|> +' }} + {%- endfor %} + {%- endif %} + {%- for message in loop_messages %} + {{- '<|start_of_role|>' + message['role'] + '<|end_of_role|>' + message['content'] + '<|end_of_text|> +' }} + {%- if loop.last and add_generation_prompt %} + {{- '<|start_of_role|>assistant' }} + {%- if controls %} + {{- ' ' + controls | tojson()}} + {%- endif %} + {{- '<|end_of_role|>' }} + {%- endif %} + {%- endfor %} diff --git a/backend/llama.cpp/models/templates/ibm-granite-granite-4.0.jinja b/backend/llama.cpp/models/templates/ibm-granite-granite-4.0.jinja new file mode 100644 index 0000000000000000000000000000000000000000..82e3ebf57b6de47c0c4e722dde0e33915f73bfe8 --- /dev/null +++ b/backend/llama.cpp/models/templates/ibm-granite-granite-4.0.jinja @@ -0,0 +1,118 @@ +{%- set tools_system_message_prefix = 'You are a helpful assistant with access to the following tools. You may call one or more tools to assist with the user query.\n\nYou are provided with function signatures within XML tags:\n' %} +{%- set tools_system_message_suffix = '\n\n\nFor each tool call, return a json object with function name and arguments within XML tags:\n\n{\"name\": , \"arguments\": }\n. If a tool does not exist in the provided list of tools, notify the user that you do not have the ability to fulfill the request.' %} +{%- set documents_system_message_prefix = 'You are a helpful assistant with access to the following documents. You may use one or more documents to assist with the user query.\n\nYou are given a list of documents within XML tags:\n' %} +{%- set documents_system_message_suffix = '\n\n\nWrite the response to the user\'s input by strictly aligning with the facts in the provided documents. If the information needed to answer the question is not available in the documents, inform the user that the question cannot be answered based on the available data.' %} +{%- set g4_default_system_message = 'You are a helpful assistant. Please ensure responses are professional, accurate, and safe.' %} +{%- if available_tools is defined and available_tools %} + {%- set tools = available_tools %} +{%- endif %} +{%- set ns = namespace(tools_system_message=tools_system_message_prefix, + documents_system_message=documents_system_message_prefix, + default_system_message=g4_default_system_message, + system_message='' + ) %} +{%- if tools %} + {%- for tool in tools %} + {%- set ns.tools_system_message = ns.tools_system_message + '\n' + (tool | tojson) %} + {%- endfor %} + {%- set ns.tools_system_message = ns.tools_system_message + tools_system_message_suffix %} +{%- else %} + {%- set ns.tools_system_message = '' %} +{%- endif %} +{%- if documents %} + {%- for document in documents %} + {%- set ns.documents_system_message = ns.documents_system_message + '\n' + (document | tojson) %} + {%- endfor %} + {%- set ns.documents_system_message = ns.documents_system_message + documents_system_message_suffix %} +{%- else %} + {%- set ns.documents_system_message = '' %} +{%- endif %} +{%- if messages[0].role == 'system' %} + {%- if messages[0].content is string %} + {%- set ns.system_message = messages[0].content %} + {%- elif messages[0].content is iterable %} + {%- for entry in messages[0].content %} + {%- if entry.type== 'text' %} + {%- if ns.system_message != '' %} + {%- set ns.system_message = ns.system_message + '\n' %} + {%- endif %} + {%- set ns.system_message = ns.system_message + entry.text %} + {%- endif %} + {%- endfor %} + {%- endif %} + {%- if tools and documents %} + {%- set ns.system_message = ns.system_message + '\n\n' + ns.tools_system_message + '\n\n' + ns.documents_system_message %} + {%- elif tools %} + {%- set ns.system_message = ns.system_message + '\n\n' + ns.tools_system_message %} + {%- elif documents %} + {%- set ns.system_message = ns.system_message + '\n\n' + ns.documents_system_message %} + {%- endif %} +{%- else %} + {%- if tools and documents %} + {%- set ns.system_message = ns.tools_system_message + '\n\n' + ns.documents_system_message %} + {%- elif tools %} + {%- set ns.system_message = ns.tools_system_message %} + {%- elif documents %} + {%- set ns.system_message = ns.documents_system_message %} + {%- endif %} +{%- endif %} +{%- if ns.system_message %} + {{- '<|start_of_role|>system<|end_of_role|>' + ns.system_message + '<|end_of_text|>\n' }} +{%- else %} + {{- '<|start_of_role|>system<|end_of_role|>' + ns.default_system_message + '<|end_of_text|>\n' }} +{%- endif %} +{%- for message in messages %} + {%- set content = namespace(val='') %} + {%- if message.content is string %} + {%- set content.val = message.content %} + {%- else %} + {%- if message.content is iterable %} + {%- for entry in message.content %} + {%- if entry.type== 'text' %} + {%- if content.val != '' %} + {%- set content.val = content.val + '\n' %} + {%- endif %} + {%- set content.val = content.val + entry.text %} + {%- endif %} + {%- endfor %} + {%- endif %} + {%- endif %} + {%- if (message.role == 'user') or (message.role == 'system' and not loop.first) %} + {{- '<|start_of_role|>' + message.role + '<|end_of_role|>' + content.val + '<|end_of_text|>\n' }} + {%- elif message.role == 'assistant' %} + {{- '<|start_of_role|>' + message.role + '<|end_of_role|>' + content.val }} + {%- if message.tool_calls %} + {%- for tool_call in message.tool_calls %} + {%- if (loop.first and content.val) or (not loop.first) %} + {{- '\n' }} + {%- endif %} + {%- if tool_call.function %} + {%- set tool_call = tool_call.function %} + {%- endif %} + {{- '\n{"name": "' }} + {{- tool_call.name }} + {{- '", "arguments": ' }} + {%- if tool_call.arguments is string %} + {{- tool_call.arguments }} + {%- else %} + {{- tool_call.arguments | tojson }} + {%- endif %} + {{- '}\n' }} + {%- endfor %} + {%- endif %} + {{- '<|end_of_text|>\n' }} + {%- elif message.role == 'tool' %} + {%- if loop.first or (messages[loop.index0 - 1].role != 'tool') %} + {{- '<|start_of_role|>user<|end_of_role|>' }} + {%- endif %} + {{- '\n\n' }} + {{- content.val }} + {{- '\n' }} + {%- if loop.last or (messages[loop.index0 + 1].role != 'tool') %} + {{- '<|end_of_text|>\n' }} + {%- endif %} + {%- endif %} +{%- endfor %} +{%- if add_generation_prompt %} + {{- '<|start_of_role|>assistant<|end_of_role|>' }} +{%- endif %} \ No newline at end of file diff --git a/backend/llama.cpp/models/templates/ibm-granite-granite-4.1.jinja b/backend/llama.cpp/models/templates/ibm-granite-granite-4.1.jinja new file mode 100644 index 0000000000000000000000000000000000000000..903cac6443fe2b41f95d4418da168b6e4129c073 --- /dev/null +++ b/backend/llama.cpp/models/templates/ibm-granite-granite-4.1.jinja @@ -0,0 +1,114 @@ +{%- set tools_system_message_prefix = 'You are a helpful assistant with access to the following tools. You may call one or more tools to assist with the user query.\n\nYou are provided with function signatures within XML tags:\n' %} +{%- set tools_system_message_suffix = '\n\n\nFor each tool call, return a json object with function name and arguments within XML tags:\n\n{\"name\": , \"arguments\": }\n. If a tool does not exist in the provided list of tools, notify the user that you do not have the ability to fulfill the request.' %} +{%- set documents_system_message_prefix = 'You are a helpful assistant with access to the following documents. You may use one or more documents to assist with the user query.\n\nYou are given a list of documents within XML tags:\n' %} +{%- set documents_system_message_suffix = '\n\n\nWrite the response to the user\'s input by strictly aligning with the facts in the provided documents. If the information needed to answer the question is not available in the documents, inform the user that the question cannot be answered based on the available data.' %} +{%- if available_tools is defined and available_tools %} + {%- set tools = available_tools %} +{%- endif %} +{%- set ns = namespace(tools_system_message=tools_system_message_prefix, + documents_system_message=documents_system_message_prefix, + system_message='' + ) %} +{%- if tools %} + {%- for tool in tools %} + {%- set ns.tools_system_message = ns.tools_system_message + '\n' + (tool | tojson) %} + {%- endfor %} + {%- set ns.tools_system_message = ns.tools_system_message + tools_system_message_suffix %} +{%- else %} + {%- set ns.tools_system_message = '' %} +{%- endif %} +{%- if documents %} + {%- for document in documents %} + {%- set ns.documents_system_message = ns.documents_system_message + '\n' + (document | tojson) %} + {%- endfor %} + {%- set ns.documents_system_message = ns.documents_system_message + documents_system_message_suffix %} +{%- else %} + {%- set ns.documents_system_message = '' %} +{%- endif %} +{%- if messages[0].role == 'system' %} + {%- if messages[0].content is string %} + {%- set ns.system_message = messages[0].content %} + {%- elif messages[0].content is iterable %} + {%- for entry in messages[0].content %} + {%- if entry.type== 'text' %} + {%- if ns.system_message != '' %} + {%- set ns.system_message = ns.system_message + '\n' %} + {%- endif %} + {%- set ns.system_message = ns.system_message + entry.text %} + {%- endif %} + {%- endfor %} + {%- endif %} + {%- if tools and documents %} + {%- set ns.system_message = ns.system_message + '\n\n' + ns.tools_system_message + '\n\n' + ns.documents_system_message %} + {%- elif tools %} + {%- set ns.system_message = ns.system_message + '\n\n' + ns.tools_system_message %} + {%- elif documents %} + {%- set ns.system_message = ns.system_message + '\n\n' + ns.documents_system_message %} + {%- endif %} +{%- else %} + {%- if tools and documents %} + {%- set ns.system_message = ns.tools_system_message + '\n\n' + ns.documents_system_message %} + {%- elif tools %} + {%- set ns.system_message = ns.tools_system_message %} + {%- elif documents %} + {%- set ns.system_message = ns.documents_system_message %} + {%- endif %} +{%- endif %} +{%- if ns.system_message %} + {{- '<|start_of_role|>system<|end_of_role|>' + ns.system_message + '<|end_of_text|>\n' }} +{%- endif %} +{%- for message in messages %} + {%- set content = namespace(val='') %} + {%- if message.content is string %} + {%- set content.val = message.content %} + {%- else %} + {%- if message.content is iterable %} + {%- for entry in message.content %} + {%- if entry.type== 'text' %} + {%- if content.val != '' %} + {%- set content.val = content.val + '\n' %} + {%- endif %} + {%- set content.val = content.val + entry.text %} + {%- endif %} + {%- endfor %} + {%- endif %} + {%- endif %} + {%- if (message.role == 'user') or (message.role == 'system' and not loop.first) %} + {{- '<|start_of_role|>' + message.role + '<|end_of_role|>' + content.val + '<|end_of_text|>\n' }} + {%- elif message.role == 'assistant' %} + {{- '<|start_of_role|>' + message.role + '<|end_of_role|>' + content.val }} + {%- if message.tool_calls %} + {%- for tool_call in message.tool_calls %} + {%- if (loop.first and content.val) or (not loop.first) %} + {{- '\n' }} + {%- endif %} + {%- if tool_call.function %} + {%- set tool_call = tool_call.function %} + {%- endif %} + {{- '\n{"name": "' }} + {{- tool_call.name }} + {{- '", "arguments": ' }} + {%- if tool_call.arguments is string %} + {{- tool_call.arguments }} + {%- else %} + {{- tool_call.arguments | tojson }} + {%- endif %} + {{- '}\n' }} + {%- endfor %} + {%- endif %} + {{- '<|end_of_text|>\n' }} + {%- elif message.role == 'tool' %} + {%- if loop.first or (messages[loop.index0 - 1].role != 'tool') %} + {{- '<|start_of_role|>user<|end_of_role|>' }} + {%- endif %} + {{- '\n\n' }} + {{- content.val }} + {{- '\n' }} + {%- if loop.last or (messages[loop.index0 + 1].role != 'tool') %} + {{- '<|end_of_text|>\n' }} + {%- endif %} + {%- endif %} +{%- endfor %} +{%- if add_generation_prompt %} + {{- '<|start_of_role|>assistant<|end_of_role|>' }} +{%- endif %} \ No newline at end of file diff --git a/backend/llama.cpp/models/templates/llama-cpp-deepseek-r1.jinja b/backend/llama.cpp/models/templates/llama-cpp-deepseek-r1.jinja new file mode 100644 index 0000000000000000000000000000000000000000..151b2edd0296ea56f487c3a3230916a301f9282c --- /dev/null +++ b/backend/llama.cpp/models/templates/llama-cpp-deepseek-r1.jinja @@ -0,0 +1,76 @@ +{%- if not add_generation_prompt is defined -%} + {%- set add_generation_prompt = false -%} +{%- endif -%} +{%- set ns = namespace(is_first=false, is_tool_outputs=false, is_output_first=true, system_prompt='') -%} +{%- for message in messages -%} + {%- if message['role'] == 'system' -%} + {%- set ns.system_prompt = message['content'] -%} + {%- endif -%} +{%- endfor -%} +{{bos_token}} +{%- if tools %} +You can call any of the following function tools to satisfy the user's requests: {{tools | map(attribute='function') | tojson(indent=2)}} + +Example function tool call syntax: + +<｜tool▁calls▁begin｜><｜tool▁call▁begin｜>function<｜tool▁sep｜>example_function_name +```json +{ + "arg1": "some_value" + ... +} +``` +<｜tool▁call▁end｜><｜tool▁calls▁end｜> + +{% endif -%} +{{ns.system_prompt}} +{%- macro flush_tool_outputs() -%} + {%- if ns.is_tool_outputs -%} + {{- '<｜tool▁outputs▁end｜><｜end▁of▁sentence｜>' -}} + {%- set ns.is_tool_outputs = false -%} + {%- endif -%} +{%- endmacro -%} +{{- flush_tool_outputs() -}} +{%- for message in messages -%} + {%- if message['role'] != 'tool' -%} + {{- flush_tool_outputs() -}} + {%- endif -%} + {%- if message['role'] == 'user' -%} + {{- '<｜User｜>' + message['content'] + '<｜end▁of▁sentence｜>' -}} + {%- endif -%} + {%- if message['role'] == 'assistant' and not message['content'] -%} + {{- '<｜Assistant｜><｜tool▁calls▁begin｜>' -}} + {%- set ns.is_first = true -%} + {%- for tc in message['tool_calls'] -%} + {%- if ns.is_first -%} + {%- set ns.is_first = false -%} + {%- else -%} + {{- '\n' -}} + {%- endif -%} + {%- set tool_name = tc['function']['name'] -%} + {%- set tool_args = tc['function']['arguments'] -%} + {{- '<｜tool▁call▁begin｜>' + tc['type'] + '<｜tool▁sep｜>' + tool_name + '\n' + '```json' + '\n' + tool_args | tojson + '\n' + '```' + '<｜tool▁call▁end｜>' -}} + {%- endfor -%} + {{- '<｜tool▁calls▁end｜><｜end▁of▁sentence｜>' -}} + {%- endif -%} + {%- if message['role'] == 'assistant' and message['content'] -%} + {{- flush_tool_outputs() -}} + {%- set content = message['content'] -%} + {%- if '' in content -%} + {%- set content = content.split('')[-1] -%} + {%- endif -%} + {{- '<｜Assistant｜>' + content + '<｜end▁of▁sentence｜>' -}} + {%- endif -%} + {%- if message['role'] == 'tool' -%} + {%- set ns.is_tool_outputs = true -%} + {%- if ns.is_output_first -%} + {{- '<｜tool▁outputs▁begin｜>' -}} + {%- set ns.is_output_first = false -%} + {%- endif -%} + {{- '\n<｜tool▁output▁begin｜>' + message['content'] + '<｜tool▁output▁end｜>' -}} + {%- endif -%} +{%- endfor -%} +{{- flush_tool_outputs() -}} +{%- if add_generation_prompt and not ns.is_tool_outputs -%} + {{- '<｜Assistant｜>\n' -}} +{%- endif -%} diff --git a/backend/llama.cpp/models/templates/llama-cpp-rwkv-world.jinja b/backend/llama.cpp/models/templates/llama-cpp-rwkv-world.jinja new file mode 100644 index 0000000000000000000000000000000000000000..690223f1b03fe5022acc2ac7006c17722620a02b --- /dev/null +++ b/backend/llama.cpp/models/templates/llama-cpp-rwkv-world.jinja @@ -0,0 +1,34 @@ +{%- if not add_generation_prompt is defined -%} + {%- set add_generation_prompt = true -%} +{%- endif -%} +{%- set ns = namespace(system_prompt='') -%} +{%- for message in messages -%} + {%- if message['role'] == 'system' -%} + {%- set ns.system_prompt = message['content'] -%} + {%- endif -%} +{%- endfor -%} +{{bos_token}} +{%- if ns.system_prompt != '' -%} +{{- 'System: ' + ns.system_prompt + '\n\n' -}} +{%- endif -%} +{%- for message in messages -%} + {%- if message['role'] == 'user' -%} + {{- 'User: ' + message['content']|trim + '\n\n' -}} + {%- endif -%} + {%- if message['role'] == 'assistant' and message['content'] is not none -%} + {%- set content = message['content'] -%} + {%- if '' in content -%} + {%- set content = content.split('')[-1] -%} + {%- endif -%} + {{- 'Assistant: ' + content|trim + '\n\n' -}} + {%- endif -%} +{%- endfor -%} +{%- if add_generation_prompt -%} + {{- 'Assistant:' -}} + {%- if enable_thinking is defined and enable_thinking is false %} + {{- ' \n' }} + {%- endif %} + {%- if enable_thinking is defined and enable_thinking is true %} + {{- ' ' }} + {%- endif %} +{%- endif -%} \ No newline at end of file diff --git a/backend/llama.cpp/models/templates/meetkai-functionary-medium-v3.1.jinja b/backend/llama.cpp/models/templates/meetkai-functionary-medium-v3.1.jinja new file mode 100644 index 0000000000000000000000000000000000000000..5f74b72f33308854ba6639d71aab54f63dc82c65 --- /dev/null +++ b/backend/llama.cpp/models/templates/meetkai-functionary-medium-v3.1.jinja @@ -0,0 +1,58 @@ +{# version=v3-llama3.1 #}{%- if not tools is defined -%} + {%- set tools = none -%} +{%- endif -%} + +{%- set has_code_interpreter = tools | selectattr("type", "equalto", "code_interpreter") | list | length > 0 -%} +{%- if has_code_interpreter -%} + {%- set tools = tools | rejectattr("type", "equalto", "code_interpreter") | list -%} +{%- endif -%} + +{#- System message + builtin tools #} +{{- bos_token + "<|start_header_id|>system<|end_header_id|>\n\n" }} +{%- if has_code_interpreter %} + {{- "Environment: ipython\n\n" }} +{%- else -%} + {{ "\n"}} +{%- endif %} +{{- "Cutting Knowledge Date: December 2023\n\n" }} +{%- if tools %} + {{- "\nYou have access to the following functions:\n\n" }} + {%- for t in tools %} + {%- if "type" in t -%} + {{ "Use the function '"|safe + t["function"]["name"] + "' to '"|safe + t["function"]["description"] + "'\n"|safe + t["function"] | tojson() }} + {%- else -%} + {{ "Use the function '"|safe + t["name"] + "' to '"|safe + t["description"] + "'\n"|safe + t | tojson() }} + {%- endif -%} + {{- "\n\n" }} + {%- endfor %} + {{- '\nThink very carefully before calling functions.\nIf a you choose to call a function ONLY reply in the following format:\n<{start_tag}={function_name}>{parameters}{end_tag}\nwhere\n\nstart_tag => ` a JSON dict with the function argument name as key and function argument value as value.\nend_tag => ``\n\nHere is an example,\n{"example_name": "example_value"}\n\nReminder:\n- If looking for real time information use relevant functions before falling back to brave_search\n- Function calls MUST follow the specified format, start with \n- Required parameters MUST be specified\n- Only call one function at a time\n- Put the entire function call reply on one line\n\n' -}} +{%- endif %} +{{- "<|eot_id|>" -}} + +{%- for message in messages -%} + {%- if message['role'] == 'user' or message['role'] == 'system' -%} + {{ '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n' + message['content'] + '<|eot_id|>' }} + {%- elif message['role'] == 'tool' -%} + {{ '<|start_header_id|>ipython<|end_header_id|>\n\n' + message['content'] + '<|eot_id|>' }} + {%- else -%} + {{ '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'}} + {%- if message['content'] -%} + {{ message['content'] }} + {%- endif -%} + {%- if 'tool_calls' in message and message['tool_calls'] -%} + {%- for tool_call in message['tool_calls'] -%} + {%- if tool_call["function"]["name"] == "python" -%} + {{ '<|python_tag|>' + tool_call['function']['arguments'] | tojson }} + {%- else -%} + {{ '' + tool_call['function']['arguments'] | tojson + '' }} + {%- endif -%} + {%- endfor -%} + {{ '<|eom_id|>' }} + {%- else -%} + {{ '<|eot_id|>' }} + {%- endif -%} + {%- endif -%} +{%- endfor -%} +{%- if add_generation_prompt -%} + {{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }} +{%- endif -%} \ No newline at end of file diff --git a/backend/llama.cpp/models/templates/meetkai-functionary-medium-v3.2.jinja b/backend/llama.cpp/models/templates/meetkai-functionary-medium-v3.2.jinja new file mode 100644 index 0000000000000000000000000000000000000000..74fd1e7af6f3797d0de61ccc996721976bcb4e07 --- /dev/null +++ b/backend/llama.cpp/models/templates/meetkai-functionary-medium-v3.2.jinja @@ -0,0 +1,287 @@ +{# version=v3.llama3 #}{%- macro append_new_param_info(param_declaration, comment_info, examples_info, depth) -%} + {%- set offset = "" -%} + {%- if depth >= 1 -%} + {%- set offset = " " * depth -%} + {%- endif -%} + {%- if comment_info != "<|NONE|>" -%} + {{ "\n" + offset + comment_info }} + {%- if examples_info | length > 0 -%} + {# Append each example info #} + {%- for example in examples_info -%} + {{ "\n" + offset + "// " + example|string|replace("'", '"') }} + {%- endfor -%} + {%- endif -%} + {%- endif -%} + {{ "\n" + offset + param_declaration }} +{%- endmacro -%} + +{%- macro convert_data_type(param_type) -%} + {%- if param_type == "integer" or param_type == "float" -%} + {{ "number" }} + {%- else -%} + {{ param_type }} + {%- endif -%} +{%- endmacro -%} + +{%- macro get_param_type(param) -%} + {%- set param_type = "any" -%} + + {%- if "type" in param -%} + {%- set raw_param_type = param["type"] -%} + {%- if raw_param_type is iterable and raw_param_type is not string -%} + {%- set param_type = raw_param_type | join(" | ") -%} + {%- else -%} + {%- set param_type = raw_param_type -%} + {%- endif -%} + {{ convert_data_type(param_type) }} + {%- elif "oneOf" in param -%} + {%- set one_of_types = param["oneOf"]|selectattr("type", "defined")|list -%} + {%- set one_of_types = one_of_types|map(attribute="type")|unique|list -%} + {{ convert_data_type(one_of_types | join(" | ")) }} + {%- endif -%} +{%- endmacro -%} + +{%- macro get_format_param(param) -%} + {%- if "format" in param -%} + {{ param["format"] }} + {%- elif "oneOf" in param -%} + {%- set formats = [] -%} + {%- for item in param["oneOf"] -%} + {%- if "format" in item -%} + {%- if item["format"] == param["oneOf"][-1]["format"] -%} + {{ item["format"] }} + {%- else -%} + {{ item["format"] + " or "}} + {%- endif -%} + {%- endif -%} + {%- endfor -%} + {%- else -%} + {{ "<|NONE|>" }} + {%- endif -%} +{%- endmacro -%} + +{%- macro get_param_info(param) -%} + {%- set param_type = param.get("type", "any") -%} + {%- set format_param = get_format_param(param) -%} + + {%- if "description" in param or "default" in param or format_param != "<|NONE|>" or param["maximum"] or param["minimum"] or param["maxLength"] or param["minLength"] -%} + {{ "//" }} + {%- if "description" in param -%} + {%- set desc = param["description"] -%} + {%- if not desc.endswith(".") -%} + {%- set desc = desc + "." -%} + {%- endif -%} + {{ " " + desc }} + {%- endif -%} + + {%- if "default" in param -%} + {%- set default_value = param["default"] -%} + {%- if param_type == "string" -%} + {%- set default_value = '"' ~ default_value ~ '"' -%} + {%- endif -%} + {{ " Default=" ~ default_value ~ "." }} + {%- endif -%} + + {%- set format_param = get_format_param(param) -%} + {%- if format_param != "<|NONE|>" -%} + {{ " Format=" ~ format_param }} + {%- endif -%} + + {%- for field, field_name in [("maximum", "Maximum"), ("minimum", "Minimum"), ("maxLength", "Maximum length"), ("minLength", "Minimum length")] -%} + {%- if field in param -%} + {{ " " + field_name ~ "=" ~ param[field] }} + {%- endif -%} + {%- endfor -%} + {%- else -%} + {{ "<|NONE|>"}} + {%- endif -%} +{%- endmacro -%} + +{%- macro get_enum_option_str(enum_options) -%} + {%- for v in enum_options -%} + {%- if v is string -%} + {{ '"' + v + '"' }} + {%- else -%} + {{ v }} + {%- endif -%} + {%- if enum_options|length > 0 and v != enum_options[-1] -%} + {{ " | " }} + {%- endif -%} + {%- endfor -%} +{%- endmacro -%} + +{%- macro get_array_typescript(param_name, param_dic, depth) -%} + {%- set offset = '' -%} + {%- if depth >= 1 -%} + {%- set offset = " " * depth -%} + {%- endif -%} + {%- set items_info = param_dic.get('items', {}) -%} + + {%- if items_info|length == 0 -%} + {%- if param_name -%} + {{ "\n" + offset + param_name + ": []" }} + {%- else -%} + {{ "\n" + offset + "[]" }} + {%- endif -%} + {%- else -%} + {%- set array_type = get_param_type(items_info) -%} + {%- if array_type == 'object' -%} + {%- if param_name -%} + {{ "\n" + offset + param_name + ": {" }} + {%- else -%} + {{ "\n" + offset + "{" }} + {%- endif -%} + {{ get_parameter_typescript(items_info.get('properties', {}), items_info.get('required', []), depth + 1) -}} + {{- "\n" + offset + "}[]" }} + {%- elif array_type == 'array' -%} + {%- set item_info = get_array_typescript(None, items_info, depth + 1) -%} + {%- if not param_name -%} + {{ "\n" + item_info + "[]" }} + {%- else -%} + {{ "\n" + offset + param_name + ": " + item_info|trim + "[]" }} + {%- endif -%} + {%- else -%} + {%- if 'enum' in items_info -%} + {%- set item_type = get_enum_option_str(items_info['enum']) -%} + {%- if param_name is none -%} + {{ "(" + item_type + ")[]"}} + {%- else -%} + {{ "\n" + offset + param_name + ": (" + item_type + ")[]" }} + {%- endif -%} + {%- else -%} + {%- if param_name is none -%} + {{ "\n" + array_type + "[]" }} + {%- else -%} + {{ "\n" + offset + param_name + ": " + array_type + "[]," }} + {%- endif -%} + {%- endif -%} + {%- endif -%} + {%- endif -%} +{%- endmacro -%} + +{%- macro get_parameter_typescript(properties, required_params, depth=0) -%} + {%- set res = "" -%} + {%- for param_name, param in properties.items() -%} + {%- if param is mapping -%} + {%- set comment_info = get_param_info(param) -%} + {# Param Examples #} + {%- set examples_info = [] -%} + {%- if "examples" in param -%} + {%- set examples_info = ["Example " + param_name + ":"] -%} + {%- set examples_info = examples_info + param["examples"] -%} + {%- endif -%} + + {# Param Name declaration #} + {%- set param_declaration = param_name -%} + {%- if required_params is iterable and param_name not in required_params -%} + {%- set param_declaration = param_declaration + "?" -%} + {%- endif -%} + + {%- set param_type = get_param_type(param) -%} + + {# Handle indentation based on depth #} + {%- set offset = "" -%} + {%- if depth >= 1 -%} + {%- set offset = " " * depth -%} + {%- endif -%} + + {%- if param_type == "object" -%} + {%- if comment_info != "<|NONE|>" -%} + {{ "\n" + offset + comment_info }} + {%- endif -%} + {%- if examples_info|length > 0 -%} + {%- for example in examples_info -%} + {{ "\n" + offset + "// " + example|string|replace("'", '"') }} + {%- endfor -%} + {%- endif -%} + {%- set param_declaration = param_declaration + ": {" -%} + {{ "\n" + offset + param_declaration -}} + {{- get_parameter_typescript(param.get("properties", {}), param.get("required", []), depth + 1) -}} + {{- "\n" + offset + "}," }} + {%- elif param_type == "array" -%} + {%- set item_info = param.get("items", {}) -%} + {%- if "type" not in item_info -%} + {%- set param_declaration = param_declaration + ": []," -%} + {{ append_new_param_info(param_declaration, comment_info, examples_info, depth) }} + {%- else -%} + {%- if comment_info != "<|NONE|>" -%} + {{ "\n" + offset + comment_info }} + {%- endif -%} + {%- if examples_info|length > 0 -%} + {%- for example in examples_info -%} + {{ "\n" + offset + "// " + example|string|replace("'", '"') }} + {%- endfor -%} + {%- endif -%} + {%- set array_declaration = get_array_typescript(param_declaration, param, depth) -%} + {%- if not array_declaration.endswith(",") -%} + {%- set array_declaration = array_declaration + "," -%} + {%- endif -%} + {{ array_declaration}} + {%- endif -%} + {%- else -%} + {%- if "enum" in param -%} + {%- set param_type = get_enum_option_str(param["enum"]) -%} + {%- endif -%} + {%- if "nullable" in param and param["nullable"] -%} + {%- set param_type = param_type + " | null" -%} + {%- endif -%} + {%- set param_declaration = param_declaration + ": " + param_type + "," -%} + {{ append_new_param_info(param_declaration, comment_info, examples_info, depth) }} + {%- endif -%} + {%- endif -%} + {%- endfor -%} +{%- endmacro -%} + +{%- macro generate_schema_from_functions(functions, namespace='functions') -%} + {{ "// Supported function definitions that should be called when necessary.\n" -}} + {{- "namespace " + namespace + " {\n\n" -}} + + {%- for function in functions -%} + {%- if function.get("function") -%} + {%- set function = function.get("function") -%} + {%- endif -%} + + {%- set function_name = function.get("name") -%} + {%- if function_name -%} + {%- set description = function.get('description', '') -%} + {%- set parameters = function.get('parameters', {}) -%} + {{- "// " + description + "\n" -}} + {{- "type " + function_name -}} + {%- if parameters and parameters.get("properties") -%} + {{- " = (_: {" -}} + {%- set required_params = parameters.get("required", []) -%} + {{ get_parameter_typescript(parameters.get("properties"), required_params, 0) -}} + {{- "\n}) => any;\n\n" }} + {%- else -%} + {{ " = () => any;\n\n" }} + {%- endif -%} + {%- endif -%} + {%- endfor -%} + {{ "} // namespace " + namespace }} +{%- endmacro -%} +{%- if not tools -%} + {%- set tools = [] -%} +{%- endif -%} +{{ bos_token + '<|start_header_id|>system<|end_header_id|>\n\nYou are capable of executing available function(s) if required.\nOnly execute function(s) when absolutely necessary.\nAsk for the required input to:recipient==all\nUse JSON for function arguments.\nRespond in this format:\n>>>${recipient}\n${content}\nAvailable functions:\n' + generate_schema_from_functions(tools) + '<|eot_id|>' -}} +{%- if tools|length > 0 and tools|selectattr("type", "equalto", "code_interpreter")|list|length > 0 -%} + {{ '<|start_header_id|>system<|end_header_id|>\n\nWhen you send a message containing Python code to python, it will be executed in a stateful Jupyter notebook environment. python will respond with the output of the execution or time out after 60.0 seconds. The drive at \'/mnt/data\' can be used to save and persist user files.<|eot_id|>' }} +{%- endif -%} +{%- for message in messages -%} + {%- if message['role'] == 'user' or message['role'] == 'system' -%} + {{ '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n' + message['content'] + '<|eot_id|>' }} + {%- elif message['role'] == 'tool' -%} + {{ '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n' + message['content'] + '<|eot_id|>' }} + {%- else -%} + {{ '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'}} + {%- if message['content'] -%} + {{ '>>>all\n' + message['content'] }} + {%- endif -%} + {%- if 'tool_calls' in message and message['tool_calls'] -%} + {%- for tool_call in message['tool_calls'] -%} + {{ '>>>' + tool_call['function']['name'] + '\n' + tool_call['function']['arguments'] }} + {%- endfor -%} + {%- endif -%} + {{ '<|eot_id|>' }} + {%- endif -%} +{%- endfor -%} +{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n>>>' }}{% endif %} \ No newline at end of file diff --git a/backend/llama.cpp/models/templates/meta-llama-Llama-3.1-8B-Instruct.jinja b/backend/llama.cpp/models/templates/meta-llama-Llama-3.1-8B-Instruct.jinja new file mode 100644 index 0000000000000000000000000000000000000000..33089ace1be88f22a10fe861ad49718d5d886090 --- /dev/null +++ b/backend/llama.cpp/models/templates/meta-llama-Llama-3.1-8B-Instruct.jinja @@ -0,0 +1,109 @@ +{{- bos_token }} +{%- if custom_tools is defined %} + {%- set tools = custom_tools %} +{%- endif %} +{%- if not tools_in_user_message is defined %} + {%- set tools_in_user_message = true %} +{%- endif %} +{%- if not date_string is defined %} + {%- set date_string = "26 Jul 2024" %} +{%- endif %} +{%- if not tools is defined %} + {%- set tools = none %} +{%- endif %} + +{#- This block extracts the system message, so we can slot it into the right place. #} +{%- if messages[0]['role'] == 'system' %} + {%- set system_message = messages[0]['content']|trim %} + {%- set messages = messages[1:] %} +{%- else %} + {%- set system_message = "" %} +{%- endif %} + +{#- System message + builtin tools #} +{{- "<|start_header_id|>system<|end_header_id|>\n\n" }} +{%- if builtin_tools is defined or tools is not none %} + {{- "Environment: ipython\n" }} +{%- endif %} +{%- if builtin_tools is defined %} + {{- "Tools: " + builtin_tools | reject('equalto', 'code_interpreter') | join(", ") + "\n\n"}} +{%- endif %} +{{- "Cutting Knowledge Date: December 2023\n" }} +{{- "Today Date: " + date_string + "\n\n" }} +{%- if tools is not none and not tools_in_user_message %} + {{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }} + {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }} + {{- "Do not use variables.\n\n" }} + {%- for t in tools %} + {{- t | tojson(indent=4) }} + {{- "\n\n" }} + {%- endfor %} +{%- endif %} +{{- system_message }} +{{- "<|eot_id|>" }} + +{#- Custom tools are passed in a user message with some extra guidance #} +{%- if tools_in_user_message and not tools is none %} + {#- Extract the first user message so we can plug it in here #} + {%- if messages | length != 0 %} + {%- set first_user_message = messages[0]['content']|trim %} + {%- set messages = messages[1:] %} + {%- else %} + {{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }} +{%- endif %} + {{- '<|start_header_id|>user<|end_header_id|>\n\n' -}} + {{- "Given the following functions, please respond with a JSON for a function call " }} + {{- "with its proper arguments that best answers the given prompt.\n\n" }} + {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }} + {{- "Do not use variables.\n\n" }} + {%- for t in tools %} + {{- t | tojson(indent=4) }} + {{- "\n\n" }} + {%- endfor %} + {{- first_user_message + "<|eot_id|>"}} +{%- endif %} + +{%- for message in messages %} + {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %} + {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }} + {%- elif 'tool_calls' in message %} + {%- if not message.tool_calls|length == 1 %} + {{- raise_exception("This model only supports single tool-calls at once!") }} + {%- endif %} + {%- set tool_call = message.tool_calls[0].function %} + {%- if builtin_tools is defined and tool_call.name in builtin_tools %} + {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}} + {{- "<|python_tag|>" + tool_call.name + ".call(" }} + {%- for arg_name, arg_val in tool_call.arguments | items %} + {{- arg_name + '="' + arg_val + '"' }} + {%- if not loop.last %} + {{- ", " }} + {%- endif %} + {%- endfor %} + {{- ")" }} + {%- else %} + {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}} + {{- '{"name": "' + tool_call.name + '", ' }} + {{- '"parameters": ' }} + {{- tool_call.arguments | tojson }} + {{- "}" }} + {%- endif %} + {%- if builtin_tools is defined %} + {#- This means we're in ipython mode #} + {{- "<|eom_id|>" }} + {%- else %} + {{- "<|eot_id|>" }} + {%- endif %} + {%- elif message.role == "tool" or message.role == "ipython" %} + {{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }} + {%- if message.content is mapping or message.content is iterable %} + {{- message.content | tojson }} + {%- else %} + {{- message.content }} + {%- endif %} + {{- "<|eot_id|>" }} + {%- endif %} +{%- endfor %} +{%- if add_generation_prompt %} + {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }} +{%- endif %} diff --git a/backend/llama.cpp/models/templates/meta-llama-Llama-3.2-3B-Instruct.jinja b/backend/llama.cpp/models/templates/meta-llama-Llama-3.2-3B-Instruct.jinja new file mode 100644 index 0000000000000000000000000000000000000000..1bad6a0f648dccdbec523ca79ba90fbcfc806af0 --- /dev/null +++ b/backend/llama.cpp/models/templates/meta-llama-Llama-3.2-3B-Instruct.jinja @@ -0,0 +1,93 @@ +{{- bos_token }} +{%- if custom_tools is defined %} + {%- set tools = custom_tools %} +{%- endif %} +{%- if not tools_in_user_message is defined %} + {%- set tools_in_user_message = true %} +{%- endif %} +{%- if not date_string is defined %} + {%- if strftime_now is defined %} + {%- set date_string = strftime_now("%d %b %Y") %} + {%- else %} + {%- set date_string = "26 Jul 2024" %} + {%- endif %} +{%- endif %} +{%- if not tools is defined %} + {%- set tools = none %} +{%- endif %} + +{#- This block extracts the system message, so we can slot it into the right place. #} +{%- if messages[0]['role'] == 'system' %} + {%- set system_message = messages[0]['content']|trim %} + {%- set messages = messages[1:] %} +{%- else %} + {%- set system_message = "" %} +{%- endif %} + +{#- System message #} +{{- "<|start_header_id|>system<|end_header_id|>\n\n" }} +{%- if tools is not none %} + {{- "Environment: ipython\n" }} +{%- endif %} +{{- "Cutting Knowledge Date: December 2023\n" }} +{{- "Today Date: " + date_string + "\n\n" }} +{%- if tools is not none and not tools_in_user_message %} + {{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }} + {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }} + {{- "Do not use variables.\n\n" }} + {%- for t in tools %} + {{- t | tojson(indent=4) }} + {{- "\n\n" }} + {%- endfor %} +{%- endif %} +{{- system_message }} +{{- "<|eot_id|>" }} + +{#- Custom tools are passed in a user message with some extra guidance #} +{%- if tools_in_user_message and not tools is none %} + {#- Extract the first user message so we can plug it in here #} + {%- if messages | length != 0 %} + {%- set first_user_message = messages[0]['content']|trim %} + {%- set messages = messages[1:] %} + {%- else %} + {{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }} +{%- endif %} + {{- '<|start_header_id|>user<|end_header_id|>\n\n' -}} + {{- "Given the following functions, please respond with a JSON for a function call " }} + {{- "with its proper arguments that best answers the given prompt.\n\n" }} + {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }} + {{- "Do not use variables.\n\n" }} + {%- for t in tools %} + {{- t | tojson(indent=4) }} + {{- "\n\n" }} + {%- endfor %} + {{- first_user_message + "<|eot_id|>"}} +{%- endif %} + +{%- for message in messages %} + {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %} + {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }} + {%- elif 'tool_calls' in message %} + {%- if not message.tool_calls|length == 1 %} + {{- raise_exception("This model only supports single tool-calls at once!") }} + {%- endif %} + {%- set tool_call = message.tool_calls[0].function %} + {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}} + {{- '{"name": "' + tool_call.name + '", ' }} + {{- '"parameters": ' }} + {{- tool_call.arguments | tojson }} + {{- "}" }} + {{- "<|eot_id|>" }} + {%- elif message.role == "tool" or message.role == "ipython" %} + {{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }} + {%- if message.content is mapping or message.content is iterable %} + {{- message.content | tojson }} + {%- else %} + {{- message.content }} + {%- endif %} + {{- "<|eot_id|>" }} + {%- endif %} +{%- endfor %} +{%- if add_generation_prompt %} + {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }} +{%- endif %} diff --git a/backend/llama.cpp/models/templates/meta-llama-Llama-3.3-70B-Instruct.jinja b/backend/llama.cpp/models/templates/meta-llama-Llama-3.3-70B-Instruct.jinja new file mode 100644 index 0000000000000000000000000000000000000000..33089ace1be88f22a10fe861ad49718d5d886090 --- /dev/null +++ b/backend/llama.cpp/models/templates/meta-llama-Llama-3.3-70B-Instruct.jinja @@ -0,0 +1,109 @@ +{{- bos_token }} +{%- if custom_tools is defined %} + {%- set tools = custom_tools %} +{%- endif %} +{%- if not tools_in_user_message is defined %} + {%- set tools_in_user_message = true %} +{%- endif %} +{%- if not date_string is defined %} + {%- set date_string = "26 Jul 2024" %} +{%- endif %} +{%- if not tools is defined %} + {%- set tools = none %} +{%- endif %} + +{#- This block extracts the system message, so we can slot it into the right place. #} +{%- if messages[0]['role'] == 'system' %} + {%- set system_message = messages[0]['content']|trim %} + {%- set messages = messages[1:] %} +{%- else %} + {%- set system_message = "" %} +{%- endif %} + +{#- System message + builtin tools #} +{{- "<|start_header_id|>system<|end_header_id|>\n\n" }} +{%- if builtin_tools is defined or tools is not none %} + {{- "Environment: ipython\n" }} +{%- endif %} +{%- if builtin_tools is defined %} + {{- "Tools: " + builtin_tools | reject('equalto', 'code_interpreter') | join(", ") + "\n\n"}} +{%- endif %} +{{- "Cutting Knowledge Date: December 2023\n" }} +{{- "Today Date: " + date_string + "\n\n" }} +{%- if tools is not none and not tools_in_user_message %} + {{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }} + {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }} + {{- "Do not use variables.\n\n" }} + {%- for t in tools %} + {{- t | tojson(indent=4) }} + {{- "\n\n" }} + {%- endfor %} +{%- endif %} +{{- system_message }} +{{- "<|eot_id|>" }} + +{#- Custom tools are passed in a user message with some extra guidance #} +{%- if tools_in_user_message and not tools is none %} + {#- Extract the first user message so we can plug it in here #} + {%- if messages | length != 0 %} + {%- set first_user_message = messages[0]['content']|trim %} + {%- set messages = messages[1:] %} + {%- else %} + {{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }} +{%- endif %} + {{- '<|start_header_id|>user<|end_header_id|>\n\n' -}} + {{- "Given the following functions, please respond with a JSON for a function call " }} + {{- "with its proper arguments that best answers the given prompt.\n\n" }} + {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }} + {{- "Do not use variables.\n\n" }} + {%- for t in tools %} + {{- t | tojson(indent=4) }} + {{- "\n\n" }} + {%- endfor %} + {{- first_user_message + "<|eot_id|>"}} +{%- endif %} + +{%- for message in messages %} + {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %} + {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }} + {%- elif 'tool_calls' in message %} + {%- if not message.tool_calls|length == 1 %} + {{- raise_exception("This model only supports single tool-calls at once!") }} + {%- endif %} + {%- set tool_call = message.tool_calls[0].function %} + {%- if builtin_tools is defined and tool_call.name in builtin_tools %} + {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}} + {{- "<|python_tag|>" + tool_call.name + ".call(" }} + {%- for arg_name, arg_val in tool_call.arguments | items %} + {{- arg_name + '="' + arg_val + '"' }} + {%- if not loop.last %} + {{- ", " }} + {%- endif %} + {%- endfor %} + {{- ")" }} + {%- else %} + {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}} + {{- '{"name": "' + tool_call.name + '", ' }} + {{- '"parameters": ' }} + {{- tool_call.arguments | tojson }} + {{- "}" }} + {%- endif %} + {%- if builtin_tools is defined %} + {#- This means we're in ipython mode #} + {{- "<|eom_id|>" }} + {%- else %} + {{- "<|eot_id|>" }} + {%- endif %} + {%- elif message.role == "tool" or message.role == "ipython" %} + {{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }} + {%- if message.content is mapping or message.content is iterable %} + {{- message.content | tojson }} + {%- else %} + {{- message.content }} + {%- endif %} + {{- "<|eot_id|>" }} + {%- endif %} +{%- endfor %} +{%- if add_generation_prompt %} + {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }} +{%- endif %} diff --git a/backend/llama.cpp/models/templates/microsoft-Phi-3.5-mini-instruct.jinja b/backend/llama.cpp/models/templates/microsoft-Phi-3.5-mini-instruct.jinja new file mode 100644 index 0000000000000000000000000000000000000000..d1533d1526b2e42fccd99477a7274dc20b7a189b --- /dev/null +++ b/backend/llama.cpp/models/templates/microsoft-Phi-3.5-mini-instruct.jinja @@ -0,0 +1,8 @@ +{% for message in messages %}{% if message['role'] == 'system' and message['content'] %}{{'<|system|> +' + message['content'] + '<|end|> +'}}{% elif message['role'] == 'user' %}{{'<|user|> +' + message['content'] + '<|end|> +'}}{% elif message['role'] == 'assistant' %}{{'<|assistant|> +' + message['content'] + '<|end|> +'}}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|assistant|> +' }}{% else %}{{ eos_token }}{% endif %} \ No newline at end of file diff --git a/backend/llama.cpp/models/templates/mistralai-Ministral-3-14B-Reasoning-2512.jinja b/backend/llama.cpp/models/templates/mistralai-Ministral-3-14B-Reasoning-2512.jinja new file mode 100644 index 0000000000000000000000000000000000000000..beb4d612c75af9d10bb0865b20422c8778fa7ea4 --- /dev/null +++ b/backend/llama.cpp/models/templates/mistralai-Ministral-3-14B-Reasoning-2512.jinja @@ -0,0 +1,126 @@ +{#- Default system message if no system prompt is passed. #} +{%- set default_system_message = '# HOW YOU SHOULD THINK AND ANSWER\n\nFirst draft your thinking process (inner monologue) until you arrive at a response. Format your response using Markdown, and use LaTeX for any mathematical equations. Write both your thoughts and the response in the same language as the input.\n\nYour thinking process must follow the template below:[THINK]Your thoughts or/and draft, like working through an exercise on scratch paper. Be as casual and as long as you want until you are confident to generate the response to the user.[/THINK]Here, provide a self-contained response.' %} + +{#- Begin of sequence token. #} +{{- bos_token }} + +{#- Handle system prompt if it exists. #} +{#- System prompt supports text content or text and thinking chunks. #} +{%- if messages[0]['role'] == 'system' %} + {{- '[SYSTEM_PROMPT]' -}} + {%- if messages[0]['content'] is string %} + {{- messages[0]['content'] -}} + {%- else %} + {%- for block in messages[0]['content'] %} + {%- if block['type'] == 'text' %} + {{- block['text'] }} + {%- elif block['type'] == 'thinking' %} + {{- '[THINK]' + block['thinking'] + '[/THINK]' }} + {%- else %} + {{- raise_exception('Only text and thinking chunks are supported in system message contents.') }} + {%- endif %} + {%- endfor %} + {%- endif %} + {{- '[/SYSTEM_PROMPT]' -}} + {%- set loop_messages = messages[1:] %} +{%- else %} + {%- set loop_messages = messages %} + {%- if default_system_message != '' %} + {{- '[SYSTEM_PROMPT]' + default_system_message + '[/SYSTEM_PROMPT]' }} + {%- endif %} +{%- endif %} + + +{#- Tools definition #} +{%- set tools_definition = '' %} +{%- set has_tools = false %} +{%- if tools is defined and tools is not none and tools|length > 0 %} + {%- set has_tools = true %} + {%- set tools_definition = '[AVAILABLE_TOOLS]' + (tools| tojson) + '[/AVAILABLE_TOOLS]' %} + {{- tools_definition }} +{%- endif %} + +{#- Checks for alternating user/assistant messages. #} +{%- set ns = namespace(index=0) %} +{%- for message in loop_messages %} + {%- if message.role == 'user' or (message.role == 'assistant' and (message.tool_calls is not defined or message.tool_calls is none or message.tool_calls | length == 0)) %} + {%- if (message['role'] == 'user') != (ns.index % 2 == 0) %} + {{- raise_exception('After the optional system message, conversation roles must alternate user and assistant roles except for tool calls and results.') }} + {%- endif %} + {%- set ns.index = ns.index + 1 %} + {%- endif %} +{%- endfor %} + +{#- Handle conversation messages. #} +{%- for message in loop_messages %} + + {#- User messages supports text content or text and image chunks. #} + {%- if message['role'] == 'user' %} + {%- if message['content'] is string %} + {{- '[INST]' + message['content'] + '[/INST]' }} + {%- elif message['content'] | length > 0 %} + {{- '[INST]' }} + {%- if message['content'] | length == 2 %} + {%- set blocks = message['content'] | sort(attribute='type') %} + {%- else %} + {%- set blocks = message['content'] %} + {%- endif %} + {%- for block in blocks %} + {%- if block['type'] == 'text' %} + {{- block['text'] }} + {%- elif block['type'] in ['image', 'image_url'] %} + {{- '[IMG]' }} + {%- else %} + {{- raise_exception('Only text, image and image_url chunks are supported in user message content.') }} + {%- endif %} + {%- endfor %} + {{- '[/INST]' }} + {%- else %} + {{- raise_exception('User message must have a string or a list of chunks in content') }} + {%- endif %} + + {#- Assistant messages supports text content or text, image and thinking chunks. #} + {%- elif message['role'] == 'assistant' %} + {%- if (message['content'] is none or message['content'] == '' or message['content']|length == 0) and (message['tool_calls'] is not defined or message['tool_calls'] is none or message['tool_calls']|length == 0) %} + {{- raise_exception('Assistant message must have a string or a list of chunks in content or a list of tool calls.') }} + {%- endif %} + + {%- if message['content'] is string and message['content'] != '' %} + {{- message['content'] }} + {%- elif message['content'] | length > 0 %} + {%- for block in message['content'] %} + {%- if block['type'] == 'text' %} + {{- block['text'] }} + {%- elif block['type'] == 'thinking' %} + {{- '[THINK]' + block['thinking'] + '[/THINK]' }} + {%- else %} + {{- raise_exception('Only text and thinking chunks are supported in assistant message contents.') }} + {%- endif %} + {%- endfor %} + {%- endif %} + + {%- if message['tool_calls'] is defined and message['tool_calls'] is not none and message['tool_calls']|length > 0 %} + {%- for tool in message['tool_calls'] %} + {{- '[TOOL_CALLS]' }} + {%- set name = tool['function']['name'] %} + {%- set arguments = tool['function']['arguments'] %} + {%- if arguments is not string %} + {%- set arguments = arguments|tojson|safe %} + {%- elif arguments == '' %} + {%- set arguments = '{}' %} + {%- endif %} + {{- name + '[ARGS]' + arguments }} + {%- endfor %} + {%- endif %} + + {{- eos_token }} + + {#- Tool messages only supports text content. #} + {%- elif message['role'] == 'tool' %} + {{- '[TOOL_RESULTS]' + message['content']|string + '[/TOOL_RESULTS]' }} + + {#- Raise exception for unsupported roles. #} + {%- else %} + {{- raise_exception('Only user, assistant and tool roles are supported, got ' + message['role'] + '.') }} + {%- endif %} +{%- endfor %} diff --git a/backend/llama.cpp/models/templates/mistralai-Mistral-Nemo-Instruct-2407.jinja b/backend/llama.cpp/models/templates/mistralai-Mistral-Nemo-Instruct-2407.jinja new file mode 100644 index 0000000000000000000000000000000000000000..9c21a3f13ebf55994c64c93c9ae5361580570651 --- /dev/null +++ b/backend/llama.cpp/models/templates/mistralai-Mistral-Nemo-Instruct-2407.jinja @@ -0,0 +1,87 @@ +{%- if messages[0]["role"] == "system" %} + {%- set system_message = messages[0]["content"] %} + {%- set loop_messages = messages[1:] %} +{%- else %} + {%- set loop_messages = messages %} +{%- endif %} +{%- if not tools is defined %} + {%- set tools = none %} +{%- endif %} +{%- set user_messages = loop_messages | selectattr("role", "equalto", "user") | list %} + +{#- This block checks for alternating user/assistant messages, skipping tool calling messages #} +{%- set ns = namespace() %} +{%- set ns.index = 0 %} +{%- for message in loop_messages %} + {%- if not (message.role == "tool" or message.role == "tool_results" or (message.tool_calls is defined and message.tool_calls is not none)) %} + {%- if (message["role"] == "user") != (ns.index % 2 == 0) %} + {{- raise_exception("After the optional system message, conversation roles must alternate user/assistant/user/assistant/...") }} + {%- endif %} + {%- set ns.index = ns.index + 1 %} + {%- endif %} +{%- endfor %} + +{{- bos_token }} +{%- for message in loop_messages %} + {%- if message["role"] == "user" %} + {%- if tools is not none and (message == user_messages[-1]) %} + {{- "[AVAILABLE_TOOLS][" }} + {%- for tool in tools %} + {%- set tool = tool.function %} + {{- '{"type": "function", "function": {' }} + {%- for key, val in tool.items() if key != "return" %} + {%- if val is string %} + {{- '"' + key + '": "' + val + '"' }} + {%- else %} + {{- '"' + key + '": ' + val|tojson }} + {%- endif %} + {%- if not loop.last %} + {{- ", " }} + {%- endif %} + {%- endfor %} + {{- "}}" }} + {%- if not loop.last %} + {{- ", " }} + {%- else %} + {{- "]" }} + {%- endif %} + {%- endfor %} + {{- "[/AVAILABLE_TOOLS]" }} + {%- endif %} + {%- if loop.last and system_message is defined %} + {{- "[INST]" + system_message + "\n\n" + message["content"] + "[/INST]" }} + {%- else %} + {{- "[INST]" + message["content"] + "[/INST]" }} + {%- endif %} + {%- elif (message.tool_calls is defined and message.tool_calls is not none) %} + {{- "[TOOL_CALLS][" }} + {%- for tool_call in message.tool_calls %} + {%- set out = tool_call.function|tojson %} + {{- out[:-1] }} + {%- if not tool_call.id is defined or tool_call.id|length != 9 %} + {{- raise_exception("Tool call IDs should be alphanumeric strings with length 9!") }} + {%- endif %} + {{- ', "id": "' + tool_call.id + '"}' }} + {%- if not loop.last %} + {{- ", " }} + {%- else %} + {{- "]" + eos_token }} + {%- endif %} + {%- endfor %} + {%- elif message["role"] == "assistant" %} + {{- message["content"] + eos_token}} + {%- elif message["role"] == "tool_results" or message["role"] == "tool" %} + {%- if message.content is defined and message.content.content is defined %} + {%- set content = message.content.content %} + {%- else %} + {%- set content = message.content %} + {%- endif %} + {{- '[TOOL_RESULTS]{"content": ' + content|string + ", " }} + {%- if not message.tool_call_id is defined or message.tool_call_id|length != 9 %} + {{- raise_exception("Tool call IDs should be alphanumeric strings with length 9!") }} + {%- endif %} + {{- '"call_id": "' + message.tool_call_id + '"}[/TOOL_RESULTS]' }} + {%- else %} + {{- raise_exception("Only user and assistant roles are supported, with the exception of an initial optional system message!") }} + {%- endif %} +{%- endfor %}