--- /dev/null
+#include "ggml-webgpu.h"
+
+#include <webgpu/webgpu_cpp.h>
+
+#include "ggml-impl.h"
+#include "ggml-backend-impl.h"
+
+#include "ggml-wgsl-shaders.hpp"
+
+#include <cstring>
+#include <iostream>
+#include <mutex>
+#include <vector>
+
+#ifdef GGML_WEBGPU_DEBUG
+#define WEBGPU_LOG_DEBUG(msg) std::cout << msg << std::endl
+#else
+#define WEBGPU_LOG_DEBUG(msg) ((void) 0)
+#endif // GGML_WEBGPU_DEBUG
+
+/* Constants */
+
+#define WEBGPU_MUL_MAT_WG_SIZE 64
+#define WEBGPU_MUL_MAT_PARAMS_SIZE (13 * sizeof(uint32_t)) // M, N, K, batch sizes, broadcasts
+#define WEBGPU_CPY_PARAMS_SIZE (15 * sizeof(uint32_t)) // strides and offsets
+#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
+
+// Always returns the base offset of a tensor, regardless of views.
+static uint64_t webgpu_tensor_offset(const ggml_tensor * tensor) {
+ if (tensor->view_src) {
+ return (uint8_t *) tensor->view_src->data - (uint8_t *) webgpu_ptr_base;
+ }
+ return (uint8_t *) tensor->data - (uint8_t *) webgpu_ptr_base;
+}
+
+/* Struct definitions */
+
+// All the base objects needed to run operations on a WebGPU device
+struct webgpu_context_struct {
+ wgpu::Instance instance;
+ wgpu::Adapter adapter;
+ wgpu::Device device;
+ wgpu::Queue queue;
+ wgpu::Limits limits;
+ wgpu::SupportedFeatures features;
+
+ std::mutex mutex;
+ bool device_initialized = false;
+
+ // pipelines and parameter buffers
+ // TODO: reuse params buffers for different pipelines when possible
+ wgpu::ComputePipeline memset_pipeline;
+ wgpu::Buffer memset_params_dev_buf;
+ wgpu::Buffer memset_params_host_buf;
+ wgpu::ComputePipeline mul_mat_pipeline;
+ wgpu::Buffer mul_mat_params_dev_buf;
+ wgpu::Buffer mul_mat_params_host_buf;
+ wgpu::ComputePipeline cpy_pipeline;
+ wgpu::Buffer cpy_params_dev_buf;
+ wgpu::Buffer cpy_params_host_buf;
+
+ size_t memset_bytes_per_thread;
+
+ // Staging buffer for reading data from the GPU
+ wgpu::Buffer get_tensor_staging_buf;
+};
+
+typedef std::shared_ptr<webgpu_context_struct> webgpu_context;
+
+struct ggml_backend_webgpu_reg_context {
+ webgpu_context webgpu_ctx;
+
+ size_t device_count;
+ const char * name;
+};
+
+struct ggml_backend_webgpu_device_context {
+ webgpu_context webgpu_ctx;
+
+ std::string device_name;
+ std::string device_desc;
+};
+
+struct ggml_backend_webgpu_context {
+ webgpu_context webgpu_ctx;
+
+ std::string name;
+};
+
+struct ggml_backend_webgpu_buffer_context {
+ webgpu_context webgpu_ctx;
+
+ wgpu::Buffer buffer;
+
+ ggml_backend_webgpu_buffer_context(webgpu_context ctx, wgpu::Buffer buf) :
+ webgpu_ctx(ctx), buffer(buf) {
+ }
+};
+
+/* End struct definitions */
+
+/* WebGPU object initializations */
+
+static void ggml_webgpu_create_pipeline(wgpu::Device &device, wgpu::ComputePipeline &pipeline, const char * shader_code, const char * label, const std::vector<wgpu::ConstantEntry> &constants = {}) {
+ WEBGPU_LOG_DEBUG("ggml_webgpu_create_pipeline()");
+ wgpu::ShaderSourceWGSL shader_source;
+ shader_source.code = shader_code;
+ wgpu::ShaderModuleDescriptor shader_desc;
+ shader_desc.nextInChain = &shader_source;
+ wgpu::ShaderModule shader_module = device.CreateShaderModule(&shader_desc);
+
+ wgpu::ComputePipelineDescriptor pipeline_desc;
+ pipeline_desc.label = label;
+ pipeline_desc.compute.module = shader_module;
+ pipeline_desc.compute.entryPoint = "main"; // Entry point in the WGSL code
+ pipeline_desc.layout = nullptr; // nullptr means auto layout
+ if (constants.size() > 0) {
+ pipeline_desc.compute.constants = constants.data();
+ pipeline_desc.compute.constantCount = constants.size();
+ }
+ pipeline = device.CreateComputePipeline(&pipeline_desc);
+}
+
+static void ggml_webgpu_create_buffer(wgpu::Device &device, wgpu::Buffer &buffer, size_t size, wgpu::BufferUsage usage, const char* label) {
+ WEBGPU_LOG_DEBUG("ggml_webgpu_create_buffer()");
+
+ 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);
+}
+
+/** End WebGPU object initializations */
+
+/** WebGPU Actions */
+
+static void ggml_backend_webgpu_map_buffer(webgpu_context ctx, wgpu::Buffer buffer, wgpu::MapMode mode, size_t offset, size_t size) {
+ ctx->instance.WaitAny(buffer.MapAsync(
+ mode, offset, size, wgpu::CallbackMode::WaitAnyOnly,
+ [](wgpu::MapAsyncStatus status, wgpu::StringView message) {
+ if (status != wgpu::MapAsyncStatus::Success) {
+ GGML_LOG_ERROR("ggml_webgpu: Failed to map buffer: %s\n", message.data);
+ }
+ }),
+ UINT64_MAX
+ );
+}
+
+static void ggml_backend_webgpu_buffer_memset(webgpu_context ctx, wgpu::Buffer buf, uint32_t value, size_t offset, size_t size) {
+ std::lock_guard<std::mutex> lock(ctx->mutex);
+ wgpu::Device device = ctx->device;
+
+ // map the host parameters buffer
+ ggml_backend_webgpu_map_buffer(ctx, ctx->memset_params_host_buf, wgpu::MapMode::Write, 0, ctx->memset_params_host_buf.GetSize());
+ uint32_t * params = (uint32_t *) ctx->memset_params_host_buf.GetMappedRange();
+
+ params[0] = (uint32_t)offset;
+ params[1] = (uint32_t)size;
+ params[2] = value;
+ ctx->memset_params_host_buf.Unmap();
+
+ wgpu::BindGroupEntry entries[2];
+ entries[0].binding = 0; // binding for the buffer to memset
+ entries[0].buffer = buf;
+ entries[0].offset = 0;
+ entries[0].size = buf.GetSize();
+ entries[1].binding = 1; // binding for the parameters
+ entries[1].buffer = ctx->memset_params_dev_buf;
+ entries[1].offset = 0;
+ entries[1].size = ctx->memset_params_dev_buf.GetSize();
+
+ wgpu::BindGroupDescriptor bind_group_desc;
+ bind_group_desc.layout = ctx->memset_pipeline.GetBindGroupLayout(0);
+ bind_group_desc.entryCount = 2;
+ bind_group_desc.label = "ggml_memset";
+ bind_group_desc.entries = entries;
+ wgpu::BindGroup bind_group = device.CreateBindGroup(&bind_group_desc);
+
+ wgpu::CommandEncoder encoder = device.CreateCommandEncoder();
+ encoder.CopyBufferToBuffer(
+ ctx->memset_params_host_buf, 0,
+ ctx->memset_params_dev_buf, 0,
+ ctx->memset_params_dev_buf.GetSize()
+ );
+ wgpu::ComputePassEncoder pass = encoder.BeginComputePass();
+ pass.SetPipeline(ctx->memset_pipeline);
+ pass.SetBindGroup(0, bind_group);
+ size_t bytes_per_wg = ctx->limits.maxComputeWorkgroupSizeX * ctx->memset_bytes_per_thread;
+ pass.DispatchWorkgroups(((size + 3) + bytes_per_wg - 1) / bytes_per_wg, 1, 1);
+ pass.End();
+ wgpu::CommandBuffer commands = encoder.Finish();
+
+ ctx->queue.Submit(1, &commands);
+}
+
+static void ggml_backend_webgpu_wait_on_submission(webgpu_context ctx) {
+ // Wait for the queue to finish processing all commands
+ ctx->instance.WaitAny(ctx->queue.OnSubmittedWorkDone(wgpu::CallbackMode::WaitAnyOnly,
+ [](wgpu::QueueWorkDoneStatus status, wgpu::StringView message) {
+ if (status != wgpu::QueueWorkDoneStatus::Success) {
+ GGML_LOG_ERROR("ggml_webgpu: Failed to wait on queue: %s\n", message.data);
+ }
+ }),
+ UINT64_MAX
+ );
+}
+
+/** 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 << ")");
+
+ // TODO: cleanup
+ GGML_UNUSED(ctx);
+}
+
+// Returns true if node has enqueued work into the queue, false otherwise
+static bool ggml_webgpu_encode_node(webgpu_context ctx, ggml_tensor * node){
+ if (ggml_is_empty(node)) {
+ return false;
+ }
+
+ WEBGPU_LOG_DEBUG("ggml_webgpu_encode_node(" << node << ", " << ggml_op_name(node->op) << ")");
+
+
+ switch (node->op) {
+ // no-ops
+ case GGML_OP_NONE:
+ case GGML_OP_VIEW:
+ case GGML_OP_PERMUTE:
+ return false;
+
+ case GGML_OP_CPY: {
+ std::lock_guard<std::mutex> lock(ctx->mutex);
+ const ggml_tensor * src = node->src[0];
+ ggml_backend_webgpu_buffer_context * src_ctx = (ggml_backend_webgpu_buffer_context *) src->buffer->context;
+ size_t src_offset = webgpu_tensor_offset(src) + src->view_offs;
+ // assumes power of 2 offset alignment
+ size_t src_misalignment = src_offset & (ctx->limits.minStorageBufferOffsetAlignment - 1);
+ // align to minimum offset alignment
+ src_offset &= ~(ctx->limits.minStorageBufferOffsetAlignment - 1);
+ ggml_backend_webgpu_buffer_context * dst_ctx = (ggml_backend_webgpu_buffer_context *) node->buffer->context;
+ size_t dst_offset = webgpu_tensor_offset(node) + node->view_offs;
+ size_t dst_misalignment = dst_offset & (ctx->limits.minStorageBufferOffsetAlignment - 1);
+ dst_offset &= ~(ctx->limits.minStorageBufferOffsetAlignment - 1);
+
+ wgpu::Device device = ctx->device;
+ ggml_backend_webgpu_map_buffer(ctx, ctx->cpy_params_host_buf,
+ wgpu::MapMode::Write, 0, ctx->cpy_params_host_buf.GetSize());
+ uint32_t * params = (uint32_t *) ctx->cpy_params_host_buf.GetMappedRange();
+ uint32_t ne = (uint32_t)ggml_nelements(node);
+ params[0] = ne;
+ params[1] = src_misalignment/ggml_type_size(src->type);
+ params[2] = dst_misalignment/ggml_type_size(node->type);
+
+ // Convert byte-strides to element-strides
+ params[3] = (uint32_t)src->nb[0]/ggml_type_size(src->type);
+ params[4] = (uint32_t)src->nb[1]/ggml_type_size(src->type);
+ params[5] = (uint32_t)src->nb[2]/ggml_type_size(src->type);
+ params[6] = (uint32_t)src->nb[3]/ggml_type_size(src->type);
+ params[7] = (uint32_t)node->nb[0]/ggml_type_size(node->type);
+ params[8] = (uint32_t)node->nb[1]/ggml_type_size(node->type);
+ params[9] = (uint32_t)node->nb[2]/ggml_type_size(node->type);
+ params[10] = (uint32_t)node->nb[3]/ggml_type_size(node->type);
+ // Logical shape — same for both tensors even if permuted
+ params[11] = (uint32_t)(src->ne[0]);
+ params[12] = (uint32_t)(src->ne[1]);
+ params[13] = (uint32_t)(src->ne[2]);
+ params[14] = (uint32_t)(src->ne[3]);
+
+ ctx->cpy_params_host_buf.Unmap();
+
+ wgpu::BindGroupEntry entries[3];
+ entries[0].binding = 0;
+ entries[0].buffer = src_ctx->buffer;
+ entries[0].offset = src_offset;
+ entries[0].size = (ggml_nbytes(src) + src_misalignment + WEBGPU_STORAGE_BUF_BINDING_MULT - 1) & ~(WEBGPU_STORAGE_BUF_BINDING_MULT - 1);
+
+ entries[1].binding = 1;
+ entries[1].buffer = dst_ctx->buffer;
+ entries[1].offset = dst_offset;
+ entries[1].size = (ggml_nbytes(node) + dst_misalignment + WEBGPU_STORAGE_BUF_BINDING_MULT - 1) & ~(WEBGPU_STORAGE_BUF_BINDING_MULT - 1);
+
+ entries[2].binding = 2;
+ entries[2].buffer = ctx->cpy_params_dev_buf;
+ entries[2].offset = 0;
+ entries[2].size = ctx->cpy_params_dev_buf.GetSize();
+
+ wgpu::BindGroupDescriptor bind_group_desc;
+ bind_group_desc.layout = ctx->cpy_pipeline.GetBindGroupLayout(0);
+ bind_group_desc.label = "ggml_op_cpy";
+ bind_group_desc.entryCount = 3;
+ bind_group_desc.entries = entries;
+ wgpu::BindGroup bind_group = device.CreateBindGroup(&bind_group_desc);
+
+ wgpu::CommandEncoder encoder = device.CreateCommandEncoder();
+ encoder.CopyBufferToBuffer(
+ ctx->cpy_params_host_buf, 0,
+ ctx->cpy_params_dev_buf, 0,
+ ctx->cpy_params_dev_buf.GetSize()
+ );
+ wgpu::ComputePassEncoder pass = encoder.BeginComputePass();
+ pass.SetPipeline(ctx->cpy_pipeline);
+ pass.SetBindGroup(0, bind_group);
+ size_t max_wg_size = ctx->limits.maxComputeWorkgroupSizeX;
+ pass.DispatchWorkgroups((ne + max_wg_size - 1) / max_wg_size);
+ pass.End();
+ wgpu::CommandBuffer commands = encoder.Finish();
+
+ // TODO, don't submit here, batch submissions
+ ctx->queue.Submit(1, &commands);
+ // TODO, don't wait on submission here
+ ggml_backend_webgpu_wait_on_submission(ctx);
+ return true;
+ }
+
+ case GGML_OP_MUL_MAT:
+ {
+ const ggml_tensor * src0 = node->src[0];
+ ggml_backend_webgpu_buffer_context * src0_ctx = (ggml_backend_webgpu_buffer_context *) src0->buffer->context;
+ size_t src0_offset = webgpu_tensor_offset(src0) + src0->view_offs;
+ const ggml_tensor * src1 = node->src[1];
+ ggml_backend_webgpu_buffer_context * src1_ctx = (ggml_backend_webgpu_buffer_context *) src1->buffer->context;
+ size_t src1_offset = webgpu_tensor_offset(src1) + src1->view_offs;
+ ggml_backend_webgpu_buffer_context * dst_ctx = (ggml_backend_webgpu_buffer_context *) node->buffer->context;
+
+ size_t dst_offset = webgpu_tensor_offset(node) + node->view_offs;
+
+ wgpu::Device device = ctx->device;
+
+ // map the host parameters buffer
+ ggml_backend_webgpu_map_buffer(ctx, ctx->mul_mat_params_host_buf,
+ wgpu::MapMode::Write, 0, ctx->mul_mat_params_host_buf.GetSize());
+ uint32_t * params = (uint32_t *) ctx->mul_mat_params_host_buf.GetMappedRange();
+
+ params[0] = (uint32_t)node->ne[1]; // number of rows in result (M)
+ params[1] = (uint32_t)node->ne[0]; // number of columns in result (N)
+ params[2] = (uint32_t)src0->ne[0]; // number of columns in src0/src1 (K)
+
+ params[3] = (uint32_t)src0->nb[1]/ggml_type_size(src0->type); // stride (elements) of src0 in dimension 1
+ params[4] = (uint32_t)src1->nb[1]/ggml_type_size(src1->type); // stride (elements) of src1 in dimension 1
+ params[5] = (uint32_t)src0->nb[2]/ggml_type_size(src0->type); // stride (elements) of src0 in dimension 2
+ params[6] = (uint32_t)src1->nb[2]/ggml_type_size(src1->type); // stride (elements) of src1 in dimension 2
+ params[7] = (uint32_t)src0->nb[3]/ggml_type_size(src0->type); // stride (elements) of src0 in dimension 3
+ params[8] = (uint32_t)src1->nb[3]/ggml_type_size(src1->type); // stride (elements) of src1 in dimension 3
+
+ params[9] = (uint32_t)src0->ne[2]; // batch size in dimension 2
+ params[10] = (uint32_t)src0->ne[3]; // batch size in dimension 3
+ params[11] = (uint32_t)(src1->ne[2]/src0->ne[2]); // broadcast in dimension 2
+ params[12] = (uint32_t)(src1->ne[3]/src0->ne[3]); // broadcast in dimension 3
+
+ ctx->mul_mat_params_host_buf.Unmap();
+
+ wgpu::BindGroupEntry entries[4];
+ entries[0].binding = 0;
+ entries[0].buffer = src0_ctx->buffer;
+ entries[0].offset = src0_offset;
+ entries[0].size = ggml_nbytes(src0);
+
+ entries[1].binding = 1;
+ entries[1].buffer = src1_ctx->buffer;
+ entries[1].offset = src1_offset;
+ entries[1].size = ggml_nbytes(src1);
+
+ entries[2].binding = 2;
+ entries[2].buffer = dst_ctx->buffer;
+ entries[2].offset = dst_offset;
+ entries[2].size = ggml_nbytes(node);
+
+ entries[3].binding = 3;
+ entries[3].buffer = ctx->mul_mat_params_dev_buf;
+ entries[3].offset = 0;
+ entries[3].size = ctx->mul_mat_params_dev_buf.GetSize();
+
+ wgpu::BindGroupDescriptor bind_group_desc;
+ bind_group_desc.layout = ctx->mul_mat_pipeline.GetBindGroupLayout(0);
+ bind_group_desc.entryCount = 4;
+ bind_group_desc.label = "ggml_op_mul_mat";
+ bind_group_desc.entries = entries;
+ wgpu::BindGroup bind_group = device.CreateBindGroup(&bind_group_desc);
+
+ wgpu::CommandEncoder encoder = device.CreateCommandEncoder();
+ encoder.CopyBufferToBuffer(
+ ctx->mul_mat_params_host_buf, 0,
+ ctx->mul_mat_params_dev_buf, 0,
+ ctx->mul_mat_params_dev_buf.GetSize()
+ );
+ wgpu::ComputePassEncoder pass = encoder.BeginComputePass();
+ pass.SetPipeline(ctx->mul_mat_pipeline);
+ pass.SetBindGroup(0, bind_group);
+ pass.DispatchWorkgroups((node->ne[0] * node->ne[1] * node->ne[2] * node->ne[3] + WEBGPU_MUL_MAT_WG_SIZE - 1) / WEBGPU_MUL_MAT_WG_SIZE);
+ pass.End();
+ wgpu::CommandBuffer commands = encoder.Finish();
+
+ // TODO, don't submit here, batch submissions
+ ctx->queue.Submit(1, &commands);
+ // TODO, don't wait on submission here
+ ggml_backend_webgpu_wait_on_submission(ctx);
+ return true;
+ }
+
+ default:
+ return false;
+ }
+}
+
+static ggml_status ggml_backend_webgpu_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
+ WEBGPU_LOG_DEBUG("ggml_backend_webgpu_graph_compute(" << cgraph->n_nodes << " nodes)");
+
+ ggml_backend_webgpu_context * backend_ctx = static_cast<ggml_backend_webgpu_context *>(backend->context);
+ webgpu_context ctx = backend_ctx->webgpu_ctx;
+
+ for (int i = 0; i < cgraph->n_nodes; i++) {
+ ggml_webgpu_encode_node(ctx, cgraph->nodes[i]);
+ }
+
+ return GGML_STATUS_SUCCESS;
+}
+
+static ggml_backend_i ggml_backend_webgpu_i = {
+ /* .get_name = */ ggml_backend_webgpu_name,
+ /* .free = */ ggml_backend_webgpu_free,
+ /* .set_tensor_async = */ NULL,
+ /* .get_tensor_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_webgpu_graph_compute,
+ /* .event_record = */ NULL,
+ /* .event_wait = */ NULL,
+};
+
+/* End GGML Backend Interface */
+
+/* GGML Backend Buffer Interface */
+
+static void ggml_backend_webgpu_buffer_free_buffer(ggml_backend_buffer_t buffer) {
+ WEBGPU_LOG_DEBUG("ggml_backend_webgpu_buffer_free_buffer()");
+ ggml_backend_webgpu_buffer_context * ctx = static_cast<ggml_backend_webgpu_buffer_context *>(buffer->context);
+ ctx->buffer.Destroy();
+}
+
+// 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_LOG_DEBUG("ggml_backend_webgpu_buffer_memset_tensor(" << buffer << ", " << tensor << ", " << value << ", " << offset << ", " << size << ")");
+
+ ggml_backend_webgpu_buffer_context * buf_ctx = (ggml_backend_webgpu_buffer_context *) buffer->context;
+ size_t total_offset = webgpu_tensor_offset(tensor) + tensor->view_offs + 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->webgpu_ctx, buf_ctx->buffer, val32, total_offset, size);
+}
+
+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_LOG_DEBUG("ggml_backend_webgpu_buffer_set_tensor(" << buffer << ", " << tensor << ", " << data << ", " << offset << ", " << size << ")");
+ ggml_backend_webgpu_buffer_context * buf_ctx = (ggml_backend_webgpu_buffer_context *) buffer->context;
+ webgpu_context webgpu_ctx = buf_ctx->webgpu_ctx;
+
+ size_t total_offset = webgpu_tensor_offset(tensor) + tensor->view_offs + offset;
+
+ webgpu_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(webgpu_ctx, buf_ctx->buffer, val32, total_offset + (size - remaining_size), remaining_size);
+ }
+}
+
+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_LOG_DEBUG("ggml_backend_webgpu_buffer_get_tensor(" << buffer << ", " << tensor << ", " << data << ", " << offset << ", " << size << ")");
+
+ ggml_backend_webgpu_buffer_context * buf_ctx = (ggml_backend_webgpu_buffer_context *) buffer->context;
+ webgpu_context webgpu_ctx = buf_ctx->webgpu_ctx;
+ wgpu::Device device = webgpu_ctx->device;
+
+ size_t total_offset = webgpu_tensor_offset(tensor) + tensor->view_offs + 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<std::mutex> lock(webgpu_ctx->mutex);
+
+ if (webgpu_ctx->get_tensor_staging_buf == nullptr ||
+ webgpu_ctx->get_tensor_staging_buf.GetSize() < final_size) {
+ // Create a new staging buffer if it doesn't exist or is too small
+ if (webgpu_ctx->get_tensor_staging_buf) {
+ webgpu_ctx->get_tensor_staging_buf.Destroy();
+ }
+ ggml_webgpu_create_buffer(device, webgpu_ctx->get_tensor_staging_buf, final_size,
+ wgpu::BufferUsage::CopyDst | wgpu::BufferUsage::MapRead, "get_tensor_staging_buf");
+ }
+
+ // Copy the data from the buffer to the staging buffer
+ wgpu::CommandEncoder encoder = device.CreateCommandEncoder();
+ encoder.CopyBufferToBuffer(buf_ctx->buffer, total_offset, webgpu_ctx->get_tensor_staging_buf, 0, final_size);
+ wgpu::CommandBuffer commands = encoder.Finish();
+ // Submit the command buffer to the queue
+ webgpu_ctx->queue.Submit(1, &commands);
+
+ // Map the staging buffer to read the data
+ ggml_backend_webgpu_map_buffer(webgpu_ctx, webgpu_ctx->get_tensor_staging_buf, wgpu::MapMode::Read, 0, final_size);
+ // Must specify size here since the staging buffer might be larger than the tensor size
+ const void * mapped_range = webgpu_ctx->get_tensor_staging_buf.GetConstMappedRange(0, final_size);
+
+ // Copy the data from the mapped range to the output buffer
+ std::memcpy(data, mapped_range, size);
+ webgpu_ctx->get_tensor_staging_buf.Unmap();
+}
+
+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 << ")");
+
+ ggml_backend_webgpu_buffer_context * buf_ctx = (ggml_backend_webgpu_buffer_context *) buffer->context;
+ ggml_backend_webgpu_buffer_memset(buf_ctx->webgpu_ctx, buf_ctx->buffer, value, 0, buffer->size);
+}
+
+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,
+ /* .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<ggml_backend_webgpu_device_context *>(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) {
+ WEBGPU_LOG_DEBUG("ggml_backend_webgpu_buffer_type_alloc_buffer(" << size << ")");
+ ggml_backend_webgpu_device_context * ctx = static_cast<ggml_backend_webgpu_device_context *>(buft->device->context);
+
+ wgpu::Buffer buf;
+ ggml_webgpu_create_buffer(ctx->webgpu_ctx->device, buf, size,
+ wgpu::BufferUsage::Storage | wgpu::BufferUsage::CopySrc | wgpu::BufferUsage::CopyDst, "allocated_buffer");
+
+ ggml_backend_webgpu_buffer_context * buf_ctx = new ggml_backend_webgpu_buffer_context(ctx->webgpu_ctx, buf);
+
+ return ggml_backend_buffer_init(buft, ggml_backend_webgpu_buffer_interface, buf_ctx, size);
+}
+
+static size_t ggml_backend_webgpu_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) {
+ ggml_backend_webgpu_device_context * ctx = static_cast<ggml_backend_webgpu_device_context *>(buft->device->context);
+ return ctx->webgpu_ctx->limits.minStorageBufferOffsetAlignment;
+}
+
+// maxBufferSize might be larger, but you can't bind more than maxStorageBufferBindingSize to a single binding.
+static size_t ggml_backend_webgpu_buffer_type_get_max_size(ggml_backend_buffer_type_t buft) {
+ ggml_backend_webgpu_device_context * ctx = static_cast<ggml_backend_webgpu_device_context *>(buft->device->context);
+ return ctx->webgpu_ctx->limits.maxStorageBufferBindingSize;
+}
+
+/* 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<ggml_backend_webgpu_device_context *>(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<ggml_backend_webgpu_device_context *>(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<ggml_backend_webgpu_device_context *>(dev->context);
+ // TODO: what do we actually want to return here? maxBufferSize might not be the full available memory.
+ *free = ctx->webgpu_ctx->limits.maxBufferSize;
+ *total = ctx->webgpu_ctx->limits.maxBufferSize;
+}
+
+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 const char * guid_str = "__ggml_webgpu :)";
+ return reinterpret_cast<ggml_guid_t>((void *)guid_str);
+}
+
+static void ggml_webgpu_init_memset_pipeline(webgpu_context webgpu_ctx) {
+ // we use the maximum workgroup size for the memset pipeline
+ size_t max_wg_size = webgpu_ctx->limits.maxComputeWorkgroupSizeX;
+ size_t max_threads = max_wg_size * webgpu_ctx->limits.maxComputeWorkgroupsPerDimension;
+ // Size the bytes_per_thread so that the largest buffer size can be handled
+ webgpu_ctx->memset_bytes_per_thread = (webgpu_ctx->limits.maxStorageBufferBindingSize + max_threads - 1) / max_threads;
+ std::vector<wgpu::ConstantEntry> constants(2);
+ constants[0].key = "wg_size";
+ constants[0].value = max_wg_size;
+ constants[1].key = "bytes_per_thread";
+ constants[1].value = webgpu_ctx->memset_bytes_per_thread;
+ ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->memset_pipeline, wgsl_memset, "memset", constants);
+ ggml_webgpu_create_buffer(webgpu_ctx->device, webgpu_ctx->memset_params_dev_buf,
+ 3 * sizeof(uint32_t), // 3 parameters: buffer size, offset, value
+ wgpu::BufferUsage::Uniform | wgpu::BufferUsage::CopyDst, "memset_params_dev_buf");
+ ggml_webgpu_create_buffer(webgpu_ctx->device, webgpu_ctx->memset_params_host_buf,
+ 3 * sizeof(uint32_t), wgpu::BufferUsage::MapWrite | wgpu::BufferUsage::CopySrc, "memset_params_host_buf");
+}
+
+static void ggml_webgpu_init_mul_mat_pipeline(webgpu_context webgpu_ctx) {
+ ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->mul_mat_pipeline, wgsl_mul_mat, "mul_mat");
+ ggml_webgpu_create_buffer(webgpu_ctx->device, webgpu_ctx->mul_mat_params_dev_buf, WEBGPU_MUL_MAT_PARAMS_SIZE,
+ wgpu::BufferUsage::Uniform | wgpu::BufferUsage::CopyDst, "mul_mat_params_dev_buf");
+ ggml_webgpu_create_buffer(webgpu_ctx->device, webgpu_ctx->mul_mat_params_host_buf, WEBGPU_MUL_MAT_PARAMS_SIZE,
+ wgpu::BufferUsage::MapWrite | wgpu::BufferUsage::CopySrc, "mul_mat_params_host_buf");
+}
+
+static void ggml_webgpu_init_cpy_pipeline(webgpu_context webgpu_ctx) {
+ std::vector<wgpu::ConstantEntry> constants(1);
+ constants[0].key = "wg_size";
+ constants[0].value = webgpu_ctx->limits.maxComputeWorkgroupSizeX;
+
+ ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->cpy_pipeline, wgsl_cpy, "cpy", constants);
+ ggml_webgpu_create_buffer(webgpu_ctx->device, webgpu_ctx->cpy_params_dev_buf, WEBGPU_CPY_PARAMS_SIZE,
+ wgpu::BufferUsage::Uniform | wgpu::BufferUsage::CopyDst, "cpy_params_dev_buf");
+ ggml_webgpu_create_buffer(webgpu_ctx->device, webgpu_ctx->cpy_params_host_buf, WEBGPU_CPY_PARAMS_SIZE,
+ wgpu::BufferUsage::MapWrite | wgpu::BufferUsage::CopySrc, "cpy_params_host_buf");
+}
+
+// TODO: Make thread safe if multiple devices are used
+static ggml_backend_t ggml_backend_webgpu_device_init(ggml_backend_dev_t dev, const char * params) {
+ GGML_UNUSED(params);
+
+ WEBGPU_LOG_DEBUG("ggml_backend_webgpu_device_init()");
+
+ ggml_backend_webgpu_device_context * dev_ctx = static_cast<ggml_backend_webgpu_device_context *>(dev->context);
+ webgpu_context webgpu_ctx = dev_ctx->webgpu_ctx;
+
+ std::lock_guard<std::mutex> lock(webgpu_ctx->mutex);
+
+ if (!webgpu_ctx->device_initialized) {
+ // Initialize device
+ wgpu::DeviceDescriptor dev_desc;
+ dev_desc.requiredLimits = &webgpu_ctx->limits;
+ dev_desc.requiredFeatures = webgpu_ctx->features.features;
+ dev_desc.requiredFeatureCount = webgpu_ctx->features.featureCount;
+ dev_desc.SetDeviceLostCallback(wgpu::CallbackMode::AllowSpontaneous,
+ [](const wgpu::Device& device, wgpu::DeviceLostReason reason, wgpu::StringView message) {
+ GGML_UNUSED(device);
+ GGML_LOG_ERROR("ggml_webgpu: Device lost! Reason: %d, Message: %s\n", static_cast<int>(reason), message.data);
+ });
+ dev_desc.SetUncapturedErrorCallback(
+ [](const wgpu::Device& device, wgpu::ErrorType reason, wgpu::StringView message) {
+ GGML_UNUSED(device);
+ GGML_LOG_ERROR("ggml_webgpu: Device error! Reason: %d, Message: %s\n", static_cast<int>(reason), message.data);
+ });
+ webgpu_ctx->instance.WaitAny(webgpu_ctx->adapter.RequestDevice(&dev_desc, wgpu::CallbackMode::WaitAnyOnly,
+ [webgpu_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", message.data);
+ return;
+ }
+ webgpu_ctx->device = device;
+ }),
+ UINT64_MAX
+ );
+ GGML_ASSERT(webgpu_ctx->device != nullptr);
+
+ // Initialize (compute) queue
+ webgpu_ctx->queue = webgpu_ctx->device.GetQueue();
+
+ ggml_webgpu_init_memset_pipeline(webgpu_ctx);
+ ggml_webgpu_init_mul_mat_pipeline(webgpu_ctx);
+ ggml_webgpu_init_cpy_pipeline(webgpu_ctx);
+ webgpu_ctx->device_initialized = true;
+ }
+
+ static ggml_backend_webgpu_context backend_ctx;
+ backend_ctx.name = GGML_WEBGPU_NAME + std::string(": ") + dev_ctx->device_name;
+ backend_ctx.webgpu_ctx = webgpu_ctx;
+
+ // See GGML Backend Interface section
+ static 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 = */ NULL, // defaults to ggml_nbytes
+ /* .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_backend_webgpu_device_supports_op(ggml_backend_dev_t dev, const ggml_tensor * op) {
+ GGML_UNUSED(dev);
+
+ switch (op->op) {
+ case GGML_OP_NONE:
+ case GGML_OP_VIEW:
+ case GGML_OP_PERMUTE:
+ return true;
+ case GGML_OP_CPY:
+ return op->type == GGML_TYPE_F16 && op->src[0]->type == GGML_TYPE_F32;
+ case GGML_OP_MUL_MAT:
+ return op->src[0]->type == GGML_TYPE_F32 && op->src[1]->type == GGML_TYPE_F32;
+ default:
+ return false;
+ }
+}
+
+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_device_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 = */ NULL,
+ /* .event_free = */ NULL,
+ /* .event_synchronize = */ NULL,
+};
+
+/* 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<ggml_backend_webgpu_reg_context *>(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<ggml_backend_webgpu_reg_context *>(reg->context);
+ return ctx->device_count;
+}
+
+// TODO: Does this need to be thread safe? Is it only called once?
+// 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()");
+
+ ggml_backend_webgpu_reg_context * reg_ctx = static_cast<ggml_backend_webgpu_reg_context *>(reg->context);
+
+ webgpu_context ctx = reg_ctx->webgpu_ctx;
+
+ wgpu::RequestAdapterOptions options = {};
+ auto callback = [](wgpu::RequestAdapterStatus status, wgpu::Adapter adapter, const char *message, void *userdata) {
+ if (status != wgpu::RequestAdapterStatus::Success) {
+ GGML_LOG_ERROR("ggml_webgpu: Failed to get an adapter: %s\n", message);
+ return;
+ }
+ *static_cast<wgpu::Adapter *>(userdata) = adapter;
+ };
+ void *userdata = &ctx->adapter;
+ ctx->instance.WaitAny(ctx->instance.RequestAdapter(&options, wgpu::CallbackMode::WaitAnyOnly, callback, userdata), UINT64_MAX);
+ GGML_ASSERT(ctx->adapter != nullptr);
+
+ ctx->adapter.GetLimits(&ctx->limits);
+ ctx->adapter.GetFeatures(&ctx->features);
+
+ wgpu::AdapterInfo info{};
+ ctx->adapter.GetInfo(&info);
+
+ static ggml_backend_webgpu_device_context device_ctx;
+ device_ctx.webgpu_ctx = ctx;
+ device_ctx.device_name = GGML_WEBGPU_NAME;
+ device_ctx.device_desc = std::string(info.description.data);
+
+ 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, info.vendor.data, info.architecture.data, info.deviceID, info.device.data, info.description.data);
+
+ // See GGML Backend Device Interface section
+ static ggml_backend_device device = {
+ /* .iface = */ ggml_backend_webgpu_device_i,
+ /* .reg = */ reg,
+ /* .context = */ &device_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 */
+
+// TODO: Does this need to be thread safe? Is it only called once?
+ggml_backend_reg_t ggml_backend_webgpu_reg() {
+ WEBGPU_LOG_DEBUG("ggml_backend_webgpu_reg()");
+
+ webgpu_context webgpu_ctx = std::make_shared<webgpu_context_struct>();
+ webgpu_ctx->device_initialized = false;
+
+ static ggml_backend_webgpu_reg_context ctx;
+ ctx.webgpu_ctx = webgpu_ctx;
+ ctx.name = GGML_WEBGPU_NAME;
+ ctx.device_count = 1;
+
+ wgpu::InstanceDescriptor instance_descriptor{};
+ std::vector<wgpu::InstanceFeatureName> instance_features = {wgpu::InstanceFeatureName::TimedWaitAny};
+ instance_descriptor.requiredFeatures = instance_features.data();
+ instance_descriptor.requiredFeatureCount = instance_features.size();
+ webgpu_ctx->instance = wgpu::CreateInstance(&instance_descriptor);
+ GGML_ASSERT(webgpu_ctx->instance != nullptr);
+
+ static ggml_backend_reg reg = {
+ /* .api_version = */ GGML_BACKEND_API_VERSION,
+ /* .iface = */ ggml_backend_webgpu_reg_i,
+ /* .context = */ &ctx,
+ };
+ return ®
+}
+
+ggml_backend_t ggml_backend_webgpu_init(void) {
+ ggml_backend_dev_t dev = ggml_backend_reg_dev_get(ggml_backend_webgpu_reg(), 0);
+
+ return ggml_backend_webgpu_device_init(dev, nullptr);
+}
+
+GGML_BACKEND_DL_IMPL(ggml_backend_webgpu_reg)
overview / pkg / ggml / sources / llama.cpp / commitdiff