diff --git "a/bindings/ruby/ext/ggml-backend.c" "b/bindings/ruby/ext/ggml-backend.c" --- "a/bindings/ruby/ext/ggml-backend.c" +++ "b/bindings/ruby/ext/ggml-backend.c" @@ -9,31 +9,76 @@ #include #include -#define UNUSED GGML_UNUSED #define MAX(a, b) ((a) > (b) ? (a) : (b)) +// backend buffer type + +const char * ggml_backend_buft_name(ggml_backend_buffer_type_t buft) { + return buft->iface.get_name(buft); +} + +GGML_CALL ggml_backend_buffer_t ggml_backend_buft_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { + return buft->iface.alloc_buffer(buft, size); +} + +size_t ggml_backend_buft_get_alignment(ggml_backend_buffer_type_t buft) { + return buft->iface.get_alignment(buft); +} + +size_t ggml_backend_buft_get_max_size(ggml_backend_buffer_type_t buft) { + // get_max_size is optional, defaults to SIZE_MAX + if (buft->iface.get_max_size) { + return buft->iface.get_max_size(buft); + } + return SIZE_MAX; +} + +GGML_CALL size_t ggml_backend_buft_get_alloc_size(ggml_backend_buffer_type_t buft, struct ggml_tensor * tensor) { + // get_alloc_size is optional, defaults to ggml_nbytes + if (buft->iface.get_alloc_size) { + size_t size = buft->iface.get_alloc_size(buft, tensor); + assert(size >= ggml_nbytes(tensor)); + return size; + } + return ggml_nbytes(tensor); +} + +bool ggml_backend_buft_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) { + return buft->iface.supports_backend(buft, backend); +} + +bool ggml_backend_buft_is_host(ggml_backend_buffer_type_t buft) { + if (buft->iface.is_host) { + return buft->iface.is_host(buft); + } + return false; +} + // backend buffer -ggml_backend_buffer_t ggml_backend_buffer_init( - struct ggml_backend * backend, +GGML_CALL ggml_backend_buffer_t ggml_backend_buffer_init( + ggml_backend_buffer_type_t buft, struct ggml_backend_buffer_i iface, ggml_backend_buffer_context_t context, size_t size) { ggml_backend_buffer_t buffer = malloc(sizeof(struct ggml_backend_buffer)); - GGML_ASSERT(iface.get_base != NULL); - (*buffer) = (struct ggml_backend_buffer) { /* .interface = */ iface, - /* .backend = */ backend, + /* .buft = */ buft, /* .context = */ context, /* .size = */ size, + /* .usage = */ GGML_BACKEND_BUFFER_USAGE_ANY }; return buffer; } +const char * ggml_backend_buffer_name(ggml_backend_buffer_t buffer) { + return buffer->iface.get_name(buffer); +} + void ggml_backend_buffer_free(ggml_backend_buffer_t buffer) { if (buffer == NULL) { return; @@ -45,10 +90,6 @@ void ggml_backend_buffer_free(ggml_backend_buffer_t buffer) { free(buffer); } -size_t ggml_backend_buffer_get_alignment(ggml_backend_buffer_t buffer) { - return ggml_backend_get_alignment(buffer->backend); -} - size_t ggml_backend_buffer_get_size(ggml_backend_buffer_t buffer) { return buffer->size; } @@ -61,32 +102,67 @@ void * ggml_backend_buffer_get_base(ggml_backend_buffer_t buffer) { return base; } +GGML_CALL void ggml_backend_buffer_init_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) { + // init_tensor is optional + if (buffer->iface.init_tensor) { + buffer->iface.init_tensor(buffer, tensor); + } +} + +size_t ggml_backend_buffer_get_alignment (ggml_backend_buffer_t buffer) { + return ggml_backend_buft_get_alignment(ggml_backend_buffer_get_type(buffer)); +} + +size_t ggml_backend_buffer_get_max_size(ggml_backend_buffer_t buffer) { + return ggml_backend_buft_get_max_size(ggml_backend_buffer_get_type(buffer)); +} + size_t ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) { - // get_alloc_size is optional, defaults to ggml_nbytes - if (buffer->iface.get_alloc_size) { - return buffer->iface.get_alloc_size(buffer, tensor); + return ggml_backend_buft_get_alloc_size(ggml_backend_buffer_get_type(buffer), tensor); +} + +void ggml_backend_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { + buffer->iface.clear(buffer, value); +} + +bool ggml_backend_buffer_is_host(ggml_backend_buffer_t buffer) { + return ggml_backend_buft_is_host(ggml_backend_buffer_get_type(buffer)); +} + +void ggml_backend_buffer_set_usage(ggml_backend_buffer_t buffer, enum ggml_backend_buffer_usage usage) { + buffer->usage = usage; + + // FIXME: add a generic callback to the buffer interface + if (ggml_backend_buffer_is_multi_buffer(buffer)) { + ggml_backend_multi_buffer_set_usage(buffer, usage); } - return ggml_nbytes(tensor); } -void ggml_backend_buffer_init_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) { - // init_tensor is optional - if (buffer->iface.init_tensor) { - buffer->iface.init_tensor(buffer, tensor); +ggml_backend_buffer_type_t ggml_backend_buffer_get_type(ggml_backend_buffer_t buffer) { + return buffer->buft; +} + +void ggml_backend_buffer_reset(ggml_backend_buffer_t buffer) { + if (buffer->iface.reset) { + buffer->iface.reset(buffer); } } -void ggml_backend_buffer_free_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) { - // free_tensor is optional - if (buffer->iface.free_tensor) { - buffer->iface.free_tensor(buffer, tensor); +bool ggml_backend_buffer_copy_tensor(const struct ggml_tensor * src, struct ggml_tensor * dst) { + ggml_backend_buffer_t dst_buf = dst->view_src ? dst->view_src->buffer : dst->buffer; + if (dst_buf->iface.cpy_tensor) { + return src->buffer->iface.cpy_tensor(dst_buf, src, dst); } + return false; } // backend -ggml_backend_t ggml_get_backend(const struct ggml_tensor * tensor) { - return tensor->buffer ? tensor->buffer->backend : NULL; +ggml_guid_t ggml_backend_guid(ggml_backend_t backend) { + if (backend == NULL) { + return NULL; + } + return backend->guid; } const char * ggml_backend_name(ggml_backend_t backend) { @@ -104,59 +180,105 @@ void ggml_backend_free(ggml_backend_t backend) { backend->iface.free(backend); } +ggml_backend_buffer_type_t ggml_backend_get_default_buffer_type(ggml_backend_t backend) { + return backend->iface.get_default_buffer_type(backend); +} + ggml_backend_buffer_t ggml_backend_alloc_buffer(ggml_backend_t backend, size_t size) { - return backend->iface.alloc_buffer(backend, size); + return ggml_backend_buft_alloc_buffer(ggml_backend_get_default_buffer_type(backend), size); } size_t ggml_backend_get_alignment(ggml_backend_t backend) { - return backend->iface.get_alignment(backend); + return ggml_backend_buft_get_alignment(ggml_backend_get_default_buffer_type(backend)); +} + +size_t ggml_backend_get_max_size(ggml_backend_t backend) { + return ggml_backend_buft_get_max_size(ggml_backend_get_default_buffer_type(backend)); } -void ggml_backend_tensor_set_async(struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) { - ggml_get_backend(tensor)->iface.set_tensor_async(ggml_get_backend(tensor), tensor, data, offset, size); +void ggml_backend_tensor_set_async(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) { + GGML_ASSERT(tensor->data != NULL && "tensor not allocated"); + GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor write out of bounds"); + + if (backend->iface.set_tensor_async == NULL) { + ggml_backend_tensor_set(tensor, data, offset, size); + } else { + backend->iface.set_tensor_async(backend, tensor, data, offset, size); + } } -void ggml_backend_tensor_get_async(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) { - ggml_get_backend(tensor)->iface.get_tensor_async(ggml_get_backend(tensor), tensor, data, offset, size); +void ggml_backend_tensor_get_async(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) { + GGML_ASSERT(tensor->data != NULL && "tensor not allocated"); + GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor read out of bounds"); + + if (backend->iface.get_tensor_async == NULL) { + ggml_backend_tensor_get(tensor, data, offset, size); + } else { + backend->iface.get_tensor_async(backend, tensor, data, offset, size); + } } -void ggml_backend_tensor_set(struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) { - ggml_backend_t backend = ggml_get_backend(tensor); +GGML_CALL void ggml_backend_tensor_set(struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) { + ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer; + GGML_ASSERT(buf != NULL && "tensor buffer not set"); GGML_ASSERT(tensor->data != NULL && "tensor not allocated"); - GGML_ASSERT(backend != NULL && "tensor backend not set"); + GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor write out of bounds"); - backend->iface.set_tensor_async(backend, tensor, data, offset, size); - backend->iface.synchronize(backend); + if (!size) { + return; + } + + buf->iface.set_tensor(buf, tensor, data, offset, size); } -void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) { - ggml_backend_t backend = ggml_get_backend(tensor); +GGML_CALL void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) { + ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer; + GGML_ASSERT(buf != NULL && "tensor buffer not set"); GGML_ASSERT(tensor->data != NULL && "tensor not allocated"); - GGML_ASSERT(backend != NULL && "tensor backend not set"); + GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor read out of bounds"); - backend->iface.get_tensor_async(backend, tensor, data, offset, size); - backend->iface.synchronize(backend); + if (!size) { + return; + } + + buf->iface.get_tensor(buf, tensor, data, offset, size); } void ggml_backend_synchronize(ggml_backend_t backend) { + if (backend->iface.synchronize == NULL) { + return; + } + backend->iface.synchronize(backend); } ggml_backend_graph_plan_t ggml_backend_graph_plan_create(ggml_backend_t backend, struct ggml_cgraph * cgraph) { + GGML_ASSERT(backend->iface.graph_plan_create != NULL); + return backend->iface.graph_plan_create(backend, cgraph); } void ggml_backend_graph_plan_free(ggml_backend_t backend, ggml_backend_graph_plan_t plan) { + GGML_ASSERT(backend->iface.graph_plan_free != NULL); + backend->iface.graph_plan_free(backend, plan); } -void ggml_backend_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) { - backend->iface.graph_plan_compute(backend, plan); +enum ggml_status ggml_backend_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) { + GGML_ASSERT(backend->iface.graph_plan_compute != NULL); + + return backend->iface.graph_plan_compute(backend, plan); +} + +enum ggml_status ggml_backend_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { + enum ggml_status err = ggml_backend_graph_compute_async(backend, cgraph); + ggml_backend_synchronize(backend); + return err; } -bool ggml_backend_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { +enum ggml_status ggml_backend_graph_compute_async(ggml_backend_t backend, struct ggml_cgraph * cgraph) { return backend->iface.graph_compute(backend, cgraph); } @@ -164,6 +286,13 @@ bool ggml_backend_supports_op(ggml_backend_t backend, const struct ggml_tensor * return backend->iface.supports_op(backend, op); } +bool ggml_backend_offload_op(ggml_backend_t backend, const struct ggml_tensor * op) { + if (backend->iface.offload_op != NULL) { + return backend->iface.offload_op(backend, op); + } + return false; +} + // backend copy static bool ggml_are_same_layout(const struct ggml_tensor * a, const struct ggml_tensor * b) { @@ -182,27 +311,20 @@ static bool ggml_are_same_layout(const struct ggml_tensor * a, const struct ggml } void ggml_backend_tensor_copy(struct ggml_tensor * src, struct ggml_tensor * dst) { - //printf("src: %s ne: [%d %d %d %d] nb: [%d %d %d %d]\n", src->name, (int)src->ne[0], (int)src->ne[1], (int)src->ne[2], (int)src->ne[3], (int)src->nb[0], (int)src->nb[1], (int)src->nb[2], (int)src->nb[3]); - //printf("dst: %s ne: [%d %d %d %d] nb: [%d %d %d %d]\n", dst->name, (int)dst->ne[0], (int)dst->ne[1], (int)dst->ne[2], (int)dst->ne[3], (int)dst->nb[0], (int)dst->nb[1], (int)dst->nb[2], (int)dst->nb[3]); GGML_ASSERT(ggml_are_same_layout(src, dst) && "cannot copy tensors with different layouts"); - // fprintf(stderr, "cpy tensor %s from %s to %s (%lu bytes)\n", src->name, ggml_backend_name(src->backend), ggml_backend_name(dst->backend), ggml_nbytes(src)); - if (src == dst) { return; } - // TODO: allow backends to support copy to/from same backend - - if (ggml_get_backend(dst)->iface.cpy_tensor_from != NULL) { - ggml_get_backend(dst)->iface.cpy_tensor_from(ggml_get_backend(dst)->context, src, dst); - } else if (ggml_get_backend(src)->iface.cpy_tensor_to != NULL) { - ggml_get_backend(src)->iface.cpy_tensor_to(ggml_get_backend(src)->context, src, dst); - } else { - // shouldn't be hit when copying from/to CPU - #ifndef NDEBUG - fprintf(stderr, "ggml_backend_tensor_copy: neither cpy_tensor_from nor cpy_tensor_to are implemented for backends %s and %s, falling back to get/set\n", ggml_backend_name(src->buffer->backend), ggml_backend_name(dst->buffer->backend)); - #endif + if (ggml_backend_buffer_is_host(src->buffer)) { + ggml_backend_tensor_set(dst, src->data, 0, ggml_nbytes(src)); + } else if (ggml_backend_buffer_is_host(dst->buffer)) { + ggml_backend_tensor_get(src, dst->data, 0, ggml_nbytes(src)); + } else if (!ggml_backend_buffer_copy_tensor(src, dst)) { +#ifndef NDEBUG + fprintf(stderr, "%s: warning: slow copy from %s to %s\n", __func__, ggml_backend_buffer_name(src->buffer), ggml_backend_buffer_name(dst->buffer)); +#endif size_t nbytes = ggml_nbytes(src); void * data = malloc(nbytes); ggml_backend_tensor_get(src, data, 0, nbytes); @@ -211,318 +333,846 @@ void ggml_backend_tensor_copy(struct ggml_tensor * src, struct ggml_tensor * dst } } -// backend CPU +void ggml_backend_tensor_copy_async(ggml_backend_t backend_src, ggml_backend_t backend_dst, struct ggml_tensor * src, struct ggml_tensor * dst) { + GGML_ASSERT(ggml_are_same_layout(src, dst) && "cannot copy tensors with different layouts"); -struct ggml_backend_cpu_context { - int n_threads; - void * work_data; - size_t work_size; -}; + if (src == dst) { + return; + } -static const char * ggml_backend_cpu_name(ggml_backend_t backend) { - return "CPU"; + if (backend_dst->iface.cpy_tensor_async != NULL) { + if (backend_dst->iface.cpy_tensor_async(backend_src, backend_dst, src, dst)) { + return; + } + } - UNUSED(backend); + // an async copy would normally happen after all the queued operations on both backends are completed + // sync src, set_async dst + if (ggml_backend_buffer_is_host(src->buffer)) { + ggml_backend_synchronize(backend_src); + ggml_backend_tensor_set_async(backend_dst, dst, src->data, 0, ggml_nbytes(src)); + } else { + ggml_backend_synchronize(backend_src); + ggml_backend_tensor_copy(src, dst); + ggml_backend_synchronize(backend_dst); + } } -static void ggml_backend_cpu_free(ggml_backend_t backend) { - struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context; - free(cpu_ctx->work_data); - free(cpu_ctx); - free(backend); +// events + +ggml_backend_event_t ggml_backend_event_new(ggml_backend_t backend) { + if (backend->iface.event_new == NULL) { + return NULL; + } + return backend->iface.event_new(backend); +} + +void ggml_backend_event_free(ggml_backend_event_t event) { + if (event == NULL) { + return; + } + event->backend->iface.event_free(event); } -static void * ggml_backend_cpu_buffer_get_base(ggml_backend_buffer_t buffer) { - return (void *)buffer->context; +void ggml_backend_event_record(ggml_backend_event_t event) { + GGML_ASSERT(event->backend->iface.event_record != NULL); + + event->backend->iface.event_record(event); } -static void ggml_backend_cpu_buffer_free_buffer(ggml_backend_buffer_t buffer) { - free(buffer->context); - UNUSED(buffer); +void ggml_backend_event_synchronize(ggml_backend_event_t event) { + GGML_ASSERT(event->backend->iface.event_synchronize != NULL); + + event->backend->iface.event_synchronize(event); } -static struct ggml_backend_buffer_i cpu_backend_buffer_i = { - /* .free_buffer = */ ggml_backend_cpu_buffer_free_buffer, - /* .get_base = */ ggml_backend_cpu_buffer_get_base, - /* .get_alloc_size = */ NULL, // defaults to ggml_nbytes - /* .init_tensor = */ NULL, // no initialization required - /* .free_tensor = */ NULL, // no cleanup required -}; +void ggml_backend_event_wait(ggml_backend_t backend, ggml_backend_event_t event) { + GGML_ASSERT(backend->iface.event_wait != NULL); -// for buffers from ptr, free is not called -static struct ggml_backend_buffer_i cpu_backend_buffer_i_from_ptr = { - /* .free_buffer = */ NULL, // ptr is not owned by the buffer, so it does not need to be freed - /* .get_base = */ ggml_backend_cpu_buffer_get_base, - /* .get_alloc_size = */ NULL, // defaults to ggml_nbytes - /* .init_tensor = */ NULL, - /* .free_tensor = */ NULL, + backend->iface.event_wait(backend, event); +} + +// backend registry + +#define GGML_REG_MAX_BACKENDS 16 + +struct ggml_backend_reg { + char name[128]; + ggml_backend_init_fn init_fn; + ggml_backend_buffer_type_t default_buffer_type; + void * user_data; }; -static const size_t TENSOR_ALIGNMENT = 64; // should be enough for AVX 512 +static struct ggml_backend_reg ggml_backend_registry[GGML_REG_MAX_BACKENDS]; +static size_t ggml_backend_registry_count = 0; -static ggml_backend_buffer_t ggml_backend_cpu_alloc_buffer(ggml_backend_t backend, size_t size) { - size += TENSOR_ALIGNMENT; // malloc may return an address that is not aligned - void * data = malloc(size); // TODO: maybe use GGML_ALIGNED_MALLOC? +GGML_CALL static ggml_backend_t ggml_backend_reg_cpu_init(const char * params, void * user_data); + +GGML_CALL static void ggml_backend_registry_init(void) { + static bool initialized = false; - GGML_ASSERT(data != NULL && "failed to allocate buffer"); + if (initialized) { + return; + } + + initialized = true; + + ggml_backend_register("CPU", ggml_backend_reg_cpu_init, ggml_backend_cpu_buffer_type(), NULL); + + // add forward decls here to avoid including the backend headers +#ifdef GGML_USE_CUDA + extern GGML_CALL void ggml_backend_cuda_reg_devices(void); + ggml_backend_cuda_reg_devices(); +#endif + +#ifdef GGML_USE_SYCL + extern void ggml_backend_sycl_reg_devices(void); + ggml_backend_sycl_reg_devices(); +#endif - return ggml_backend_buffer_init(backend, cpu_backend_buffer_i, data, size); +#ifdef GGML_USE_METAL + extern GGML_CALL ggml_backend_t ggml_backend_reg_metal_init(const char * params, void * user_data); + extern GGML_CALL ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void); + ggml_backend_register("Metal", ggml_backend_reg_metal_init, ggml_backend_metal_buffer_type(), NULL); +#endif + +#ifdef GGML_USE_VULKAN + extern GGML_CALL int ggml_backend_vk_reg_devices(void); + ggml_backend_vk_reg_devices(); +#endif + +#ifdef GGML_USE_KOMPUTE + extern GGML_CALL void ggml_backend_kompute_reg_devices(void); + ggml_backend_kompute_reg_devices(); +#endif } -static size_t ggml_backend_cpu_get_alignment(ggml_backend_t backend) { - return TENSOR_ALIGNMENT; - UNUSED(backend); +GGML_CALL void ggml_backend_register(const char * name, ggml_backend_init_fn init_fn, ggml_backend_buffer_type_t default_buffer_type, void * user_data) { + GGML_ASSERT(ggml_backend_registry_count < GGML_REG_MAX_BACKENDS); + + size_t id = ggml_backend_registry_count; + + ggml_backend_registry[id] = (struct ggml_backend_reg) { + /* .name = */ {0}, + /* .fn = */ init_fn, + /* .default_buffer_type = */ default_buffer_type, + /* .user_data = */ user_data, + }; + + snprintf(ggml_backend_registry[id].name, sizeof(ggml_backend_registry[id].name), "%s", name); + +#ifndef NDEBUG + fprintf(stderr, "%s: registered backend %s\n", __func__, name); +#endif + + ggml_backend_registry_count++; } -static void ggml_backend_cpu_set_tensor_async(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) { - GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor write out of bounds"); - GGML_ASSERT(tensor->data != NULL && "tensor not allocated"); +size_t ggml_backend_reg_get_count(void) { + ggml_backend_registry_init(); - memcpy((char *)tensor->data + offset, data, size); + return ggml_backend_registry_count; +} + +size_t ggml_backend_reg_find_by_name(const char * name) { + ggml_backend_registry_init(); + + for (size_t i = 0; i < ggml_backend_registry_count; i++) { + // TODO: case insensitive in a portable way + if (strcmp(ggml_backend_registry[i].name, name) == 0) { + return i; + } + } - UNUSED(backend); + // not found + return SIZE_MAX; } -static void ggml_backend_cpu_get_tensor_async(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) { - GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor read out of bounds"); - GGML_ASSERT(tensor->data != NULL && "tensor not allocated"); +// init from backend:params string +ggml_backend_t ggml_backend_reg_init_backend_from_str(const char * backend_str) { + ggml_backend_registry_init(); - memcpy(data, (const char *)tensor->data + offset, size); + const char * params = strchr(backend_str, ':'); + char backend_name[128]; + if (params == NULL) { + snprintf(backend_name, sizeof(backend_name), "%s", backend_str); + params = ""; + } else { + snprintf(backend_name, sizeof(backend_name), "%.*s", (int)(params - backend_str), backend_str); + params++; + } + + size_t backend_i = ggml_backend_reg_find_by_name(backend_name); - UNUSED(backend); + if (backend_i == SIZE_MAX) { + fprintf(stderr, "%s: backend %s not found\n", __func__, backend_name); + return NULL; + } + + return ggml_backend_reg_init_backend(backend_i, params); } -static void ggml_backend_cpu_synchronize(ggml_backend_t backend) { - UNUSED(backend); +const char * ggml_backend_reg_get_name(size_t i) { + ggml_backend_registry_init(); + + GGML_ASSERT(i < ggml_backend_registry_count); + return ggml_backend_registry[i].name; } -static void ggml_backend_cpu_cpy_tensor_from(ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst) { - ggml_backend_tensor_get(src, dst->data, 0, ggml_nbytes(src)); +ggml_backend_t ggml_backend_reg_init_backend(size_t i, const char * params) { + ggml_backend_registry_init(); - UNUSED(backend); + GGML_ASSERT(i < ggml_backend_registry_count); + return ggml_backend_registry[i].init_fn(params, ggml_backend_registry[i].user_data); } -static void ggml_backend_cpu_cpy_tensor_to(ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst) { - ggml_backend_tensor_set(dst, src->data, 0, ggml_nbytes(src)); +ggml_backend_buffer_type_t ggml_backend_reg_get_default_buffer_type(size_t i) { + ggml_backend_registry_init(); - UNUSED(backend); + GGML_ASSERT(i < ggml_backend_registry_count); + return ggml_backend_registry[i].default_buffer_type; } -struct ggml_backend_plan_cpu { - struct ggml_cplan cplan; - struct ggml_cgraph cgraph; -}; +ggml_backend_buffer_t ggml_backend_reg_alloc_buffer(size_t i, size_t size) { + ggml_backend_registry_init(); -static ggml_backend_graph_plan_t ggml_backend_cpu_graph_plan_create(ggml_backend_t backend, struct ggml_cgraph * cgraph) { - struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context; + GGML_ASSERT(i < ggml_backend_registry_count); + return ggml_backend_buft_alloc_buffer(ggml_backend_registry[i].default_buffer_type, size); +} - struct ggml_backend_plan_cpu * cpu_plan = malloc(sizeof(struct ggml_backend_plan_cpu)); +// backend CPU - cpu_plan->cplan = ggml_graph_plan(cgraph, cpu_ctx->n_threads); - cpu_plan->cgraph = *cgraph; +static const size_t TENSOR_ALIGNMENT = 32; // required for mmap as gguf only guarantees 32-byte alignment - if (cpu_plan->cplan.work_size > 0) { - cpu_plan->cplan.work_data = malloc(cpu_plan->cplan.work_size); - } +GGML_CALL static const char * ggml_backend_cpu_buffer_name(ggml_backend_buffer_t buffer) { + return "CPU"; - return cpu_plan; + GGML_UNUSED(buffer); } -static void ggml_backend_cpu_graph_plan_free(ggml_backend_t backend, ggml_backend_graph_plan_t plan) { - struct ggml_backend_plan_cpu * cpu_plan = (struct ggml_backend_plan_cpu *)plan; +GGML_CALL static void * ggml_backend_cpu_buffer_get_base(ggml_backend_buffer_t buffer) { + uintptr_t data = (uintptr_t)buffer->context; - free(cpu_plan->cplan.work_data); - free(cpu_plan); + // align the buffer + if (data % TENSOR_ALIGNMENT != 0) { + data = GGML_PAD(data, TENSOR_ALIGNMENT); + } - UNUSED(backend); + return (void *)data; } -static void ggml_backend_cpu_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) { - struct ggml_backend_plan_cpu * cpu_plan = (struct ggml_backend_plan_cpu *)plan; +GGML_CALL static void ggml_backend_cpu_buffer_free_buffer(ggml_backend_buffer_t buffer) { + free(buffer->context); +} - ggml_graph_compute(&cpu_plan->cgraph, &cpu_plan->cplan); +GGML_CALL static void ggml_backend_cpu_buffer_set_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) { + memcpy((char *)tensor->data + offset, data, size); - UNUSED(backend); + GGML_UNUSED(buffer); } -static void ggml_backend_cpu_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { - struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context; +GGML_CALL static void ggml_backend_cpu_buffer_get_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) { + memcpy(data, (const char *)tensor->data + offset, size); - struct ggml_cplan cplan = ggml_graph_plan(cgraph, cpu_ctx->n_threads); + GGML_UNUSED(buffer); +} - if (cpu_ctx->work_size < cplan.work_size) { - // TODO: may be faster to free and use malloc to avoid the copy - cpu_ctx->work_data = realloc(cpu_ctx->work_data, cplan.work_size); - cpu_ctx->work_size = cplan.work_size; +GGML_CALL static bool ggml_backend_cpu_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const struct ggml_tensor * src, struct ggml_tensor * dst) { + if (ggml_backend_buffer_is_host(src->buffer)) { + memcpy(dst->data, src->data, ggml_nbytes(src)); + return true; } + return false; - cplan.work_data = cpu_ctx->work_data; - - ggml_graph_compute(cgraph, &cplan); + GGML_UNUSED(buffer); } -static bool ggml_backend_cpu_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) { - return true; - UNUSED(backend); - UNUSED(op); +GGML_CALL static void ggml_backend_cpu_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { + memset(buffer->context, value, buffer->size); } -static struct ggml_backend_i cpu_backend_i = { - /* .get_name = */ ggml_backend_cpu_name, - /* .free = */ ggml_backend_cpu_free, - /* .alloc_buffer = */ ggml_backend_cpu_alloc_buffer, - /* .get_alignment = */ ggml_backend_cpu_get_alignment, - /* .set_tensor_async = */ ggml_backend_cpu_set_tensor_async, - /* .get_tensor_async = */ ggml_backend_cpu_get_tensor_async, - /* .synchronize = */ ggml_backend_cpu_synchronize, - /* .cpy_tensor_from = */ ggml_backend_cpu_cpy_tensor_from, - /* .cpy_tensor_to = */ ggml_backend_cpu_cpy_tensor_to, - /* .graph_plan_create = */ ggml_backend_cpu_graph_plan_create, - /* .graph_plan_free = */ ggml_backend_cpu_graph_plan_free, - /* .graph_plan_compute = */ ggml_backend_cpu_graph_plan_compute, - /* .graph_compute = */ ggml_backend_cpu_graph_compute, - /* .supports_op = */ ggml_backend_cpu_supports_op, +static struct ggml_backend_buffer_i cpu_backend_buffer_i = { + /* .get_name = */ ggml_backend_cpu_buffer_name, + /* .free_buffer = */ ggml_backend_cpu_buffer_free_buffer, + /* .get_base = */ ggml_backend_cpu_buffer_get_base, + /* .init_tensor = */ NULL, // no initialization required + /* .set_tensor = */ ggml_backend_cpu_buffer_set_tensor, + /* .get_tensor = */ ggml_backend_cpu_buffer_get_tensor, + /* .cpy_tensor = */ ggml_backend_cpu_buffer_cpy_tensor, + /* .clear = */ ggml_backend_cpu_buffer_clear, + /* .reset = */ NULL, }; -ggml_backend_t ggml_backend_cpu_init(void) { - struct ggml_backend_cpu_context * ctx = malloc(sizeof(struct ggml_backend_cpu_context)); - - ctx->n_threads = GGML_DEFAULT_N_THREADS; - ctx->work_data = NULL; - ctx->work_size = 0; +// for buffers from ptr, free is not called +static struct ggml_backend_buffer_i cpu_backend_buffer_i_from_ptr = { + /* .get_name = */ ggml_backend_cpu_buffer_name, + /* .free_buffer = */ NULL, // ptr is not owned by the buffer, so it does not need to be freed + /* .get_base = */ ggml_backend_cpu_buffer_get_base, + /* .init_tensor = */ NULL, // no initialization required + /* .set_tensor = */ ggml_backend_cpu_buffer_set_tensor, + /* .get_tensor = */ ggml_backend_cpu_buffer_get_tensor, + /* .cpy_tensor = */ ggml_backend_cpu_buffer_cpy_tensor, + /* .clear = */ ggml_backend_cpu_buffer_clear, + /* .reset = */ NULL, +}; - ggml_backend_t cpu_backend = malloc(sizeof(struct ggml_backend)); +GGML_CALL static const char * ggml_backend_cpu_buffer_type_get_name(ggml_backend_buffer_type_t buft) { + return "CPU"; - *cpu_backend = (struct ggml_backend) { - /* .interface = */ cpu_backend_i, - /* .context = */ ctx - }; - return cpu_backend; + GGML_UNUSED(buft); } -bool ggml_backend_is_cpu(ggml_backend_t backend) { - return backend->iface.get_name == ggml_backend_cpu_name; +GGML_CALL static ggml_backend_buffer_t ggml_backend_cpu_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { + size += TENSOR_ALIGNMENT; // malloc may return an address that is not aligned + void * data = malloc(size); // TODO: use GGML_ALIGNED_MALLOC (move to ggml-impl.h) + if (data == NULL) { + fprintf(stderr, "%s: failed to allocate buffer of size %zu\n", __func__, size); + return NULL; + } + + return ggml_backend_buffer_init(buft, cpu_backend_buffer_i, data, size); } -void ggml_backend_cpu_set_n_threads(ggml_backend_t backend_cpu, int n_threads) { - GGML_ASSERT(ggml_backend_is_cpu(backend_cpu)); +GGML_CALL static size_t ggml_backend_cpu_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { + return TENSOR_ALIGNMENT; - struct ggml_backend_cpu_context * ctx = (struct ggml_backend_cpu_context *)backend_cpu->context; - ctx->n_threads = n_threads; + GGML_UNUSED(buft); } -ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(ggml_backend_t backend_cpu, void * ptr, size_t size) { - return ggml_backend_buffer_init(backend_cpu, cpu_backend_buffer_i_from_ptr, ptr, size); +GGML_CALL static bool ggml_backend_cpu_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) { + return ggml_backend_is_cpu(backend); + + GGML_UNUSED(buft); } -// scheduler +GGML_CALL static bool ggml_backend_cpu_buffer_type_is_host(ggml_backend_buffer_type_t buft) { + return true; -#define GGML_MAX_BACKENDS 4 -#define GGML_MAX_SPLITS 256 -#define GGML_MAX_SPLIT_INPUTS 16 + GGML_UNUSED(buft); +} -struct ggml_backend_sched_split { - ggml_tallocr_t tallocr; - int i_start; - int i_end; - struct ggml_tensor * inputs[GGML_MAX_SPLIT_INPUTS]; - int n_inputs; - struct ggml_cgraph * graph; -}; +GGML_CALL ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void) { + static struct ggml_backend_buffer_type ggml_backend_cpu_buffer_type = { + /* .iface = */ { + /* .get_name = */ ggml_backend_cpu_buffer_type_get_name, + /* .alloc_buffer = */ ggml_backend_cpu_buffer_type_alloc_buffer, + /* .get_alignment = */ ggml_backend_cpu_buffer_type_get_alignment, + /* .get_max_size = */ NULL, // defaults to SIZE_MAX + /* .get_alloc_size = */ NULL, // defaults to ggml_nbytes + /* .supports_backend = */ ggml_backend_cpu_buffer_type_supports_backend, + /* .is_host = */ ggml_backend_cpu_buffer_type_is_host, + }, + /* .context = */ NULL, + }; -struct ggml_backend_sched { - int n_backends; - ggml_backend_t backends[GGML_MAX_BACKENDS]; - ggml_tallocr_t tallocs[GGML_MAX_BACKENDS]; + return &ggml_backend_cpu_buffer_type; +} - ggml_gallocr_t galloc; +#ifdef GGML_USE_CPU_HBM - struct ggml_hash_set hash_set; - ggml_tallocr_t * node_talloc; // [hash_set.size] - struct ggml_tensor * (* node_copies)[GGML_MAX_BACKENDS]; // [hash_set.size][GGML_MAX_BACKENDS] +// buffer type HBM - struct ggml_cgraph * graph; - struct ggml_backend_sched_split splits[GGML_MAX_SPLITS]; - int n_splits; +#include - struct ggml_context * ctx; +GGML_CALL static const char * ggml_backend_cpu_hbm_buffer_type_get_name(ggml_backend_buffer_type_t buft) { + return "CPU_HBM"; - // align context_buffer to GGML_MEM_ALIGN - #ifdef _MSC_VER - __declspec(align(GGML_MEM_ALIGN)) - #else - __attribute__((aligned(GGML_MEM_ALIGN))) - #endif - char context_buffer[GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS*sizeof(struct ggml_tensor) + GGML_MAX_SPLITS*sizeof(struct ggml_cgraph)]; -}; + GGML_UNUSED(buft); +} -#define hash_id(node) ggml_hash_find_or_insert(sched->hash_set, node) -#define node_allocr(node) sched->node_talloc[hash_id(node)] +GGML_CALL static const char * ggml_backend_cpu_hbm_buffer_get_name(ggml_backend_buffer_t buf) { + return "CPU_HBM"; -static bool ggml_is_view_op(enum ggml_op op) { - return op == GGML_OP_VIEW || op == GGML_OP_RESHAPE || op == GGML_OP_PERMUTE || op == GGML_OP_TRANSPOSE; + GGML_UNUSED(buf); } -// returns the priority of the backend, lower is better -static int sched_backend_prio(ggml_backend_sched_t sched, ggml_backend_t backend) { - for (int i = 0; i < sched->n_backends; i++) { - if (sched->backends[i] == backend) { - return i; - } - } - return INT_MAX; +GGML_CALL static void ggml_backend_cpu_hbm_buffer_free_buffer(ggml_backend_buffer_t buffer) { + hbw_free(buffer->context); } -static int sched_allocr_prio(ggml_backend_sched_t sched, ggml_tallocr_t allocr) { - for (int i = 0; i < sched->n_backends; i++) { - if (sched->tallocs[i] == allocr) { - return i; - } +GGML_CALL static ggml_backend_buffer_t ggml_backend_cpu_hbm_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { + //void * ptr = hbw_malloc(size); + void * ptr; + int result = hbw_posix_memalign(&ptr, ggml_backend_cpu_buffer_type_get_alignment(buft), size); + if (result != 0) { + fprintf(stderr, "failed to allocate HBM buffer of size %zu\n", size); + return NULL; } - return INT_MAX; -} + + ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size); + buffer->buft = buft; + buffer->iface.get_name = ggml_backend_cpu_hbm_buffer_get_name; + buffer->iface.free_buffer = ggml_backend_cpu_hbm_buffer_free_buffer; + + return buffer; +} + +ggml_backend_buffer_type_t ggml_backend_cpu_hbm_buffer_type(void) { + static struct ggml_backend_buffer_type ggml_backend_cpu_buffer_type_hbm = { + /* .iface = */ { + /* .get_name = */ ggml_backend_cpu_hbm_buffer_type_get_name, + /* .alloc_buffer = */ ggml_backend_cpu_hbm_buffer_type_alloc_buffer, + /* .get_alignment = */ ggml_backend_cpu_buffer_type_get_alignment, + /* .get_max_size = */ NULL, // defaults to SIZE_MAX + /* .get_alloc_size = */ NULL, // defaults to ggml_nbytes + /* .supports_backend = */ ggml_backend_cpu_buffer_type_supports_backend, + /* .is_host = */ ggml_backend_cpu_buffer_type_is_host, + }, + /* .context = */ NULL, + }; + + return &ggml_backend_cpu_buffer_type_hbm; +} +#endif + +struct ggml_backend_cpu_context { + int n_threads; + void * work_data; + size_t work_size; + + ggml_abort_callback abort_callback; + void * abort_callback_data; +}; + +GGML_CALL static const char * ggml_backend_cpu_name(ggml_backend_t backend) { + return "CPU"; + + GGML_UNUSED(backend); +} + +GGML_CALL static void ggml_backend_cpu_free(ggml_backend_t backend) { + struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context; + free(cpu_ctx->work_data); + free(cpu_ctx); + free(backend); +} + +GGML_CALL static ggml_backend_buffer_type_t ggml_backend_cpu_get_default_buffer_type(ggml_backend_t backend) { + return ggml_backend_cpu_buffer_type(); + + GGML_UNUSED(backend); +} + +struct ggml_backend_plan_cpu { + struct ggml_cplan cplan; + struct ggml_cgraph cgraph; +}; + +GGML_CALL static ggml_backend_graph_plan_t ggml_backend_cpu_graph_plan_create(ggml_backend_t backend, const struct ggml_cgraph * cgraph) { + struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context; + + struct ggml_backend_plan_cpu * cpu_plan = malloc(sizeof(struct ggml_backend_plan_cpu)); + + cpu_plan->cplan = ggml_graph_plan(cgraph, cpu_ctx->n_threads); + cpu_plan->cgraph = *cgraph; // FIXME: deep copy + + if (cpu_plan->cplan.work_size > 0) { + cpu_plan->cplan.work_data = malloc(cpu_plan->cplan.work_size); + if (cpu_plan->cplan.work_data == NULL) { + free(cpu_plan); + return NULL; + } + } + + cpu_plan->cplan.abort_callback = cpu_ctx->abort_callback; + cpu_plan->cplan.abort_callback_data = cpu_ctx->abort_callback_data; + + return cpu_plan; +} + +GGML_CALL static void ggml_backend_cpu_graph_plan_free(ggml_backend_t backend, ggml_backend_graph_plan_t plan) { + struct ggml_backend_plan_cpu * cpu_plan = (struct ggml_backend_plan_cpu *)plan; + + free(cpu_plan->cplan.work_data); + free(cpu_plan); + + GGML_UNUSED(backend); +} + +GGML_CALL static enum ggml_status ggml_backend_cpu_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) { + struct ggml_backend_plan_cpu * cpu_plan = (struct ggml_backend_plan_cpu *)plan; + + return ggml_graph_compute(&cpu_plan->cgraph, &cpu_plan->cplan); + + GGML_UNUSED(backend); +} + +GGML_CALL static enum ggml_status ggml_backend_cpu_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { + struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context; + + struct ggml_cplan cplan = ggml_graph_plan(cgraph, cpu_ctx->n_threads); + + if (cpu_ctx->work_size < cplan.work_size) { + free(cpu_ctx->work_data); + cpu_ctx->work_data = malloc(cplan.work_size); + if (cpu_ctx->work_data == NULL) { + cpu_ctx->work_size = 0; + return GGML_STATUS_ALLOC_FAILED; + } + cpu_ctx->work_size = cplan.work_size; + } + cplan.work_data = cpu_ctx->work_data; + + cplan.abort_callback = cpu_ctx->abort_callback; + cplan.abort_callback_data = cpu_ctx->abort_callback_data; + + return ggml_graph_compute(cgraph, &cplan); +} + +GGML_CALL static bool ggml_backend_cpu_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) { + switch (op->op) { + case GGML_OP_CPY: + return op->type != GGML_TYPE_IQ2_XXS && op->type != GGML_TYPE_IQ2_XS && op->type != GGML_TYPE_IQ1_S; // missing type_traits.from_float + case GGML_OP_MUL_MAT: + return op->src[1]->type == GGML_TYPE_F32 || op->src[1]->type == ggml_internal_get_type_traits(op->src[0]->type).vec_dot_type; + default: + return true; + } + + GGML_UNUSED(backend); +} + +static struct ggml_backend_i cpu_backend_i = { + /* .get_name = */ ggml_backend_cpu_name, + /* .free = */ ggml_backend_cpu_free, + /* .get_default_buffer_type = */ ggml_backend_cpu_get_default_buffer_type, + /* .set_tensor_async = */ NULL, + /* .get_tensor_async = */ NULL, + /* .cpy_tensor_async = */ NULL, + /* .synchronize = */ NULL, + /* .graph_plan_create = */ ggml_backend_cpu_graph_plan_create, + /* .graph_plan_free = */ ggml_backend_cpu_graph_plan_free, + /* .graph_plan_compute = */ ggml_backend_cpu_graph_plan_compute, + /* .graph_compute = */ ggml_backend_cpu_graph_compute, + /* .supports_op = */ ggml_backend_cpu_supports_op, + /* .offload_op = */ NULL, + /* .event_new = */ NULL, + /* .event_free = */ NULL, + /* .event_record = */ NULL, + /* .event_wait = */ NULL, + /* .event_synchronize = */ NULL, +}; + +static ggml_guid_t ggml_backend_cpu_guid(void) { + static ggml_guid guid = { 0xaa, 0x67, 0xc7, 0x43, 0x96, 0xe6, 0xa3, 0x8a, 0xe3, 0xaf, 0xea, 0x92, 0x36, 0xbc, 0xfc, 0x89 }; + return &guid; +} + +ggml_backend_t ggml_backend_cpu_init(void) { + struct ggml_backend_cpu_context * ctx = malloc(sizeof(struct ggml_backend_cpu_context)); + if (ctx == NULL) { + return NULL; + } + + ctx->n_threads = GGML_DEFAULT_N_THREADS; + ctx->work_data = NULL; + ctx->work_size = 0; + ctx->abort_callback = NULL; + ctx->abort_callback_data = NULL; + + ggml_backend_t cpu_backend = malloc(sizeof(struct ggml_backend)); + if (cpu_backend == NULL) { + free(ctx); + return NULL; + } + + *cpu_backend = (struct ggml_backend) { + /* .guid = */ ggml_backend_cpu_guid(), + /* .interface = */ cpu_backend_i, + /* .context = */ ctx + }; + return cpu_backend; +} + +GGML_CALL bool ggml_backend_is_cpu(ggml_backend_t backend) { + return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_cpu_guid()); +} + +void ggml_backend_cpu_set_n_threads(ggml_backend_t backend_cpu, int n_threads) { + GGML_ASSERT(ggml_backend_is_cpu(backend_cpu)); + + struct ggml_backend_cpu_context * ctx = (struct ggml_backend_cpu_context *)backend_cpu->context; + ctx->n_threads = n_threads; +} + +void ggml_backend_cpu_set_abort_callback(ggml_backend_t backend_cpu, ggml_abort_callback abort_callback, void * abort_callback_data) { + GGML_ASSERT(ggml_backend_is_cpu(backend_cpu)); + + struct ggml_backend_cpu_context * ctx = (struct ggml_backend_cpu_context *)backend_cpu->context; + ctx->abort_callback = abort_callback; + ctx->abort_callback_data = abort_callback_data; +} + +GGML_CALL ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(void * ptr, size_t size) { + GGML_ASSERT((uintptr_t)ptr % TENSOR_ALIGNMENT == 0 && "buffer pointer must be aligned"); + return ggml_backend_buffer_init(ggml_backend_cpu_buffer_type(), cpu_backend_buffer_i_from_ptr, ptr, size); +} + +GGML_CALL static ggml_backend_t ggml_backend_reg_cpu_init(const char * params, void * user_data) { + return ggml_backend_cpu_init(); + + GGML_UNUSED(params); + GGML_UNUSED(user_data); +} + +// multi-buffer buffer + +struct ggml_backend_multi_buffer_context { + ggml_backend_buffer_t * buffers; + size_t n_buffers; +}; + +typedef struct ggml_backend_multi_buffer_context * ggml_backend_multi_buffer_context_t; + +GGML_CALL static const char * ggml_backend_multi_buffer_get_name(ggml_backend_buffer_t buffer) { + ggml_backend_multi_buffer_context_t ctx = (ggml_backend_multi_buffer_context_t) buffer->context; + + return ctx->buffers[0]->iface.get_name(ctx->buffers[0]); +} + +GGML_CALL static void ggml_backend_multi_buffer_free_buffer(ggml_backend_buffer_t buffer) { + ggml_backend_multi_buffer_context_t ctx = (ggml_backend_multi_buffer_context_t) buffer->context; + for (size_t i = 0; i < ctx->n_buffers; i++) { + ggml_backend_buffer_free(ctx->buffers[i]); + } + + free(ctx->buffers); + free(ctx); +} + +GGML_CALL static void ggml_backend_multi_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { + ggml_backend_multi_buffer_context_t ctx = (ggml_backend_multi_buffer_context_t) buffer->context; + for (size_t i = 0; i < ctx->n_buffers; i++) { + ggml_backend_buffer_clear(ctx->buffers[i], value); + } +} + +static struct ggml_backend_buffer_i ggml_backend_multi_buffer_context_interface(void) { + static struct ggml_backend_buffer_i multi_backend_buffer_i = { + /* .get_name = */ ggml_backend_multi_buffer_get_name, + /* .free_buffer = */ ggml_backend_multi_buffer_free_buffer, + /* .get_base = */ NULL, + /* .init_tensor = */ NULL, + /* .set_tensor = */ NULL, + /* .get_tensor = */ NULL, + /* .cpy_tensor = */ NULL, + /* .clear = */ ggml_backend_multi_buffer_clear, + /* .reset = */ NULL, + }; + + return multi_backend_buffer_i; +} + +GGML_CALL ggml_backend_buffer_t ggml_backend_multi_buffer_alloc_buffer(ggml_backend_buffer_t * buffers, size_t n_buffers) { + ggml_backend_multi_buffer_context_t ctx = (ggml_backend_multi_buffer_context_t) malloc(sizeof(struct ggml_backend_multi_buffer_context)); + ctx->n_buffers = n_buffers; + ctx->buffers = (ggml_backend_buffer_t *) malloc(n_buffers * sizeof(ggml_backend_buffer_t)); + + GGML_ASSERT(ctx->buffers != NULL); + + size_t total_size = 0; + for (size_t i = 0; i < n_buffers; i++) { + ctx->buffers[i] = buffers[i]; + total_size += ggml_backend_buffer_get_size(buffers[i]); + } + + return ggml_backend_buffer_init(buffers[0]->buft, ggml_backend_multi_buffer_context_interface(), ctx, total_size); +} + +GGML_CALL bool ggml_backend_buffer_is_multi_buffer(ggml_backend_buffer_t buffer) { + return buffer->iface.get_name == ggml_backend_multi_buffer_get_name; +} + +GGML_CALL void ggml_backend_multi_buffer_set_usage(ggml_backend_buffer_t buffer, enum ggml_backend_buffer_usage usage) { + GGML_ASSERT(ggml_backend_buffer_is_multi_buffer(buffer)); + ggml_backend_multi_buffer_context_t ctx = (ggml_backend_multi_buffer_context_t) buffer->context; + for (size_t i = 0; i < ctx->n_buffers; i++) { + ggml_backend_buffer_set_usage(ctx->buffers[i], usage); + } +} + +// creates a copy of the tensor with the same memory layout +static struct ggml_tensor * ggml_dup_tensor_layout(struct ggml_context * ctx, const struct ggml_tensor * tensor) { + struct ggml_tensor * dup = ggml_dup_tensor(ctx, tensor); + for (int i = 0; i < GGML_MAX_DIMS; i++) { + dup->nb[i] = tensor->nb[i]; + } + return dup; +} + +static bool ggml_is_view_op(enum ggml_op op) { + return op == GGML_OP_VIEW || op == GGML_OP_RESHAPE || op == GGML_OP_PERMUTE || op == GGML_OP_TRANSPOSE; +} + +// scheduler + +#ifndef GGML_SCHED_MAX_BACKENDS +#define GGML_SCHED_MAX_BACKENDS 16 +#endif + +#ifndef GGML_SCHED_MAX_SPLITS +#define GGML_SCHED_MAX_SPLITS 2048 +#endif + +#ifndef GGML_SCHED_MAX_SPLIT_INPUTS +#define GGML_SCHED_MAX_SPLIT_INPUTS GGML_MAX_SRC +#endif + +#ifndef GGML_SCHED_MAX_COPIES +#define GGML_SCHED_MAX_COPIES 4 +#endif + +struct ggml_backend_sched_split { + int backend_id; + int i_start; + int i_end; + struct ggml_tensor * inputs[GGML_SCHED_MAX_SPLIT_INPUTS]; + int n_inputs; + // graph view of this split + struct ggml_cgraph graph; +}; + +struct ggml_backend_sched { + bool is_reset; // true if the scheduler has been reset since the last graph split + bool is_alloc; + + int n_backends; + + ggml_backend_t backends[GGML_SCHED_MAX_BACKENDS]; + ggml_backend_buffer_type_t bufts[GGML_SCHED_MAX_BACKENDS]; + ggml_gallocr_t galloc; + + // hash keys of the nodes in the graph + struct ggml_hash_set hash_set; + // hash values + int * tensor_backend_id; + struct ggml_tensor * (* tensor_copies)[GGML_SCHED_MAX_BACKENDS][GGML_SCHED_MAX_COPIES]; + + int * node_backend_ids; // [graph_size] + int * leaf_backend_ids; // [graph_size] + + // copy of the graph with modified inputs + struct ggml_cgraph * graph; + + // graph splits + struct ggml_backend_sched_split * splits; + int n_splits; + int splits_capacity; + + // pipeline parallelism support + int n_copies; + int cur_copy; + ggml_backend_event_t events[GGML_SCHED_MAX_BACKENDS][GGML_SCHED_MAX_COPIES]; + struct ggml_tensor * graph_inputs[GGML_SCHED_MAX_SPLIT_INPUTS]; + int n_graph_inputs; + + struct ggml_context * ctx; + + ggml_backend_sched_eval_callback callback_eval; + void * callback_eval_user_data; + + // align context_buffer to GGML_MEM_ALIGN +#ifdef _MSC_VER + __declspec(align(GGML_MEM_ALIGN)) +#else + __attribute__((aligned(GGML_MEM_ALIGN))) +#endif + char context_buffer[GGML_SCHED_MAX_SPLITS*GGML_SCHED_MAX_SPLIT_INPUTS*2*sizeof(struct ggml_tensor) + sizeof(struct ggml_cgraph)]; +}; + +#define hash_id(tensor) ggml_hash_find_or_insert(sched->hash_set, tensor) +#define tensor_backend_id(tensor) sched->tensor_backend_id[hash_id(tensor)] + +// returns the priority of the backend, lower id is higher priority +static int ggml_backend_sched_backend_id(ggml_backend_sched_t sched, ggml_backend_t backend) { + for (int i = 0; i < sched->n_backends; i++) { + if (sched->backends[i] == backend) { + return i; + } + } + return -1; +} + +static int ggml_backend_sched_backend_from_buffer(ggml_backend_sched_t sched, const struct ggml_tensor * tensor) { + ggml_backend_buffer_t buffer = tensor->buffer; + if (buffer == NULL) { + return -1; + } + + // find highest prio backend that supports the buffer type + for (int i = 0; i < sched->n_backends; i++) { + if (ggml_backend_buft_supports_backend(buffer->buft, sched->backends[i])) { + return i; + } + } + + fprintf(stderr, "%s: error: no backend supports buffer type %s used in tensor %s\n", + __func__, ggml_backend_buffer_name(buffer), tensor->name); + GGML_ASSERT(false); + + return -1; +} + +#if 0 +static char causes[GGML_DEFAULT_GRAPH_SIZE*16 + GGML_SCHED_MAX_SPLITS*GGML_SCHED_MAX_SPLIT_INPUTS][128]; // debug only +#define SET_CAUSE(node, ...) sprintf(causes[hash_id(node)], __VA_ARGS__) +#define GET_CAUSE(node) causes[hash_id(node)] +#else +#define SET_CAUSE(node, ...) +#define GET_CAUSE(node) "" +#endif // returns the backend that should be used for the node based on the current locations -char causes[GGML_DEFAULT_GRAPH_SIZE*4 + GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS][128]; // debug, remove -static ggml_backend_t sched_backend_from_cur(ggml_backend_sched_t sched, struct ggml_tensor * node) { - // if the dst tensor is already allocated in a buffer, we must assume that it is critical to keep it there - // ie. kv cache updates - // note that this doesn't allow fallback to CPU. need to add output tensors to the splits to copy the data back to the original backend. - // dst - ggml_backend_t cur_backend = ggml_get_backend(node); - if (cur_backend != NULL) { - sprintf(causes[hash_id(node)], "1.dst"); - return cur_backend; +static int ggml_backend_sched_backend_id_from_cur(ggml_backend_sched_t sched, struct ggml_tensor * tensor) { + // TODO: use supports_op to check if the backend supports the op + + // assign pre-allocated nodes to their backend + int cur_backend_id = ggml_backend_sched_backend_from_buffer(sched, tensor); + if (cur_backend_id != -1) { + SET_CAUSE(tensor, "1.dst"); + return cur_backend_id; } // view_src - if (node->view_src != NULL && ggml_get_backend(node->view_src) != NULL) { - sprintf(causes[hash_id(node)], "1.vsrc"); - return ggml_get_backend(node->view_src); + if (tensor->view_src != NULL) { + cur_backend_id = ggml_backend_sched_backend_from_buffer(sched, tensor->view_src); + if (cur_backend_id != -1) { + SET_CAUSE(tensor, "1.vsrc"); + return cur_backend_id; + } } - // src - int cur_prio = INT_MAX; - size_t cur_size = 0; + // graph input + if (tensor->flags & GGML_TENSOR_FLAG_INPUT) { + cur_backend_id = sched->n_backends - 1; // last backend (assumed CPU) + SET_CAUSE(tensor, "1.inp"); + return cur_backend_id; + } + // assign nodes that use weights to the backend of the weights + // operations with weights are preferably run on the same backend as the weights for (int i = 0; i < GGML_MAX_SRC; i++) { - const struct ggml_tensor * src = node->src[i]; + const struct ggml_tensor * src = tensor->src[i]; if (src == NULL) { - break; + continue; } - ggml_backend_t src_backend = ggml_get_backend(src); - if (src_backend != NULL) { - int src_prio = sched_backend_prio(sched, src_backend); - size_t src_size = ggml_nbytes(src); - if (src_prio < cur_prio && src_size >= cur_size) { - cur_prio = src_prio; - cur_size = src_size; - cur_backend = src_backend; - sprintf(causes[hash_id(node)], "1.src%d", i); + if (src->buffer != NULL && src->buffer->usage == GGML_BACKEND_BUFFER_USAGE_WEIGHTS) { + int src_backend_id = ggml_backend_sched_backend_from_buffer(sched, src); + // check if a backend with higher prio wants to offload the op + if (src_backend_id == sched->n_backends - 1) { + for (int b = 0; b < src_backend_id; b++) { + if (ggml_backend_offload_op(sched->backends[b], tensor)) { + SET_CAUSE(tensor, "1.off"); + return b; + } + } } + SET_CAUSE(tensor, "1.wgt%d", i); + return src_backend_id; } } - return cur_backend; + + return -1; } static char * fmt_size(size_t size) { @@ -535,14 +1185,16 @@ static char * fmt_size(size_t size) { return buffer; } -static void sched_print_assignments(ggml_backend_sched_t sched, struct ggml_cgraph * graph) { +static void ggml_backend_sched_print_assignments(ggml_backend_sched_t sched, struct ggml_cgraph * graph) { int cur_split = 0; for (int i = 0; i < graph->n_nodes; i++) { if (cur_split < sched->n_splits && i == sched->splits[cur_split].i_start) { - ggml_backend_t split_backend = ggml_tallocr_get_buffer(sched->splits[cur_split].tallocr)->backend; - fprintf(stderr, "\n## SPLIT #%d: %s # %d inputs: ", cur_split, ggml_backend_name(split_backend), sched->splits[cur_split].n_inputs); + ggml_backend_t split_backend = sched->backends[sched->splits[cur_split].backend_id]; + fprintf(stderr, "\n## SPLIT #%d: %s # %d inputs: ", cur_split, ggml_backend_name(split_backend), + sched->splits[cur_split].n_inputs); for (int j = 0; j < sched->splits[cur_split].n_inputs; j++) { - fprintf(stderr, "[%s (%5.5s)] ", sched->splits[cur_split].inputs[j]->name, fmt_size(ggml_nbytes(sched->splits[cur_split].inputs[j]))); + fprintf(stderr, "[%s (%5.5s)] ", sched->splits[cur_split].inputs[j]->name, + fmt_size(ggml_nbytes(sched->splits[cur_split].inputs[j]))); } fprintf(stderr, "\n"); cur_split++; @@ -551,341 +1203,558 @@ static void sched_print_assignments(ggml_backend_sched_t sched, struct ggml_cgra if (ggml_is_view_op(node->op)) { continue; } - ggml_tallocr_t node_allocr = node_allocr(node); - ggml_backend_t node_backend = node_allocr ? ggml_tallocr_get_buffer(node_allocr)->backend : NULL; - fprintf(stderr, "node #%3d (%10.10s): %20.20s (%4.4s) [%4.4s %8.8s]:", i, ggml_op_name(node->op), node->name, fmt_size(ggml_nbytes(node)), node_allocr ? ggml_backend_name(node_backend) : "NULL", causes[hash_id(node)]); + ggml_backend_t tensor_backend = ggml_backend_sched_get_tensor_backend(sched, node); + fprintf(stderr, "node #%3d (%10.10s): %20.20s (%5.5s) [%5.5s %8.8s]:", i, ggml_op_name(node->op), node->name, + fmt_size(ggml_nbytes(node)), tensor_backend ? ggml_backend_name(tensor_backend) : "NULL", GET_CAUSE(node)); for (int j = 0; j < GGML_MAX_SRC; j++) { struct ggml_tensor * src = node->src[j]; if (src == NULL) { - break; + continue; } - ggml_tallocr_t src_allocr = node_allocr(src); - ggml_backend_t src_backend = src_allocr ? ggml_tallocr_get_buffer(src_allocr)->backend : NULL; - fprintf(stderr, " %20.20s (%4.4s) [%4.4s %8.8s]", src->name, fmt_size(ggml_nbytes(src)), src_backend ? ggml_backend_name(src_backend) : "NULL", causes[hash_id(src)]); + ggml_backend_t src_backend = ggml_backend_sched_get_tensor_backend(sched, src); + fprintf(stderr, " %20.20s (%5.5s) [%5.5s %8.8s]", src->name, + fmt_size(ggml_nbytes(src)), src_backend ? ggml_backend_name(src_backend) : "NULL", GET_CAUSE(src)); } fprintf(stderr, "\n"); } } -// creates a copy of the tensor with the same memory layout -static struct ggml_tensor * ggml_dup_tensor_layout(struct ggml_context * ctx, const struct ggml_tensor * tensor) { - struct ggml_tensor * dup = ggml_dup_tensor(ctx, tensor); - for (int i = 0; i < GGML_MAX_DIMS; i++) { - dup->nb[i] = tensor->nb[i]; - } - return dup; -} +//#define DEBUG_PASS1 +//#define DEBUG_PASS2 +//#define DEBUG_PASS3 +//#define DEBUG_PASS4 // assigns backends to ops and splits the graph into subgraphs that can be computed on the same backend -// TODO: merge passes -static void sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph) { - // reset state - size_t hash_size = sched->hash_set.size; - memset(sched->hash_set.keys, 0, sizeof(sched->hash_set.keys[0]) * hash_size); - memset(sched->node_talloc, 0, sizeof(sched->node_talloc[0]) * hash_size); - memset(sched->node_copies, 0, sizeof(sched->node_copies[0]) * hash_size); +static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph) { + // reset splits sched->n_splits = 0; + sched->n_graph_inputs = 0; + sched->is_reset = false; struct ggml_init_params params = { - /*.mem_size = */ sizeof(sched->context_buffer), - /*.mem_buffer = */ sched->context_buffer, - /*.no_alloc = */ true + /* .mem_size = */ sizeof(sched->context_buffer), + /* .mem_buffer = */ sched->context_buffer, + /* .no_alloc = */ true }; - if (sched->ctx != NULL) { - ggml_free(sched->ctx); - } + ggml_free(sched->ctx); sched->ctx = ggml_init(params); + if (sched->ctx == NULL) { + fprintf(stderr, "%s: failed to initialize context\n", __func__); + GGML_ASSERT(false); + } - // pass 1: assign backends to ops with allocated inputs + // pass 1: assign backends to ops with pre-allocated inputs for (int i = 0; i < graph->n_leafs; i++) { struct ggml_tensor * leaf = graph->leafs[i]; - if (node_allocr(leaf) != NULL) { + int * leaf_backend_id = &tensor_backend_id(leaf); + if (*leaf_backend_id != -1) { // do not overwrite user assignments continue; } - ggml_backend_t leaf_backend = ggml_get_backend(leaf); - if (leaf_backend == NULL && leaf->view_src != NULL) { - leaf_backend = ggml_get_backend(leaf->view_src); - } - if (leaf_backend != NULL) { - node_allocr(leaf) = ggml_backend_sched_get_tallocr(sched, leaf_backend); - } + *leaf_backend_id = ggml_backend_sched_backend_id_from_cur(sched, leaf); } for (int i = 0; i < graph->n_nodes; i++) { struct ggml_tensor * node = graph->nodes[i]; - if (node_allocr(node) != NULL) { + int * node_backend_id = &tensor_backend_id(node); + if (*node_backend_id != -1) { // do not overwrite user assignments continue; } - ggml_backend_t node_backend = sched_backend_from_cur(sched, node); - if (node_backend != NULL) { - node_allocr(node) = ggml_backend_sched_get_tallocr(sched, node_backend); + *node_backend_id = ggml_backend_sched_backend_id_from_cur(sched, node); + // src + for (int j = 0; j < GGML_MAX_SRC; j++) { + struct ggml_tensor * src = node->src[j]; + if (src == NULL) { + continue; + } + int * src_backend_id = &tensor_backend_id(src); + if (*src_backend_id == -1) { + *src_backend_id = ggml_backend_sched_backend_id_from_cur(sched, src); + } } } - //printf("PASS 1 ASSIGNMENTS\n"); sched_print_assignments(sched, graph); +#ifdef DEBUG_PASS1 + fprintf(stderr, "PASS 1 ASSIGNMENTS\n"); ggml_backend_sched_print_assignments(sched, graph); +#endif - // pass 2: assign backends to ops from current assignments - // TODO: - // - reuse sched_backend_from_cur - for (int i = 0; i < graph->n_nodes; i++) { - struct ggml_tensor * node = graph->nodes[i]; - ggml_tallocr_t node_allocr = node_allocr(node); - if (node_allocr == NULL) { - int cur_prio = INT_MAX; - size_t cur_size = 0; - for (int j = 0; j < GGML_MAX_SRC; j++) { - struct ggml_tensor * src = node->src[j]; - if (src == NULL) { - break; + // pass 2: expand current backend assignments + // assign the same backend to adjacent nodes + // expand gpu backends (i.e. non last prio) up and down, ignoring cpu (the lowest priority backend) + // thus, cpu will never be used unless weights are on cpu, or there are no gpu ops between cpu ops + + + // pass 2.2 expand gpu down + { + int cur_backend_id = -1; + for (int i = 0; i < graph->n_nodes; i++) { + struct ggml_tensor * node = graph->nodes[i]; + if (ggml_is_view_op(node->op)) { + continue; + } + int * node_backend_id = &tensor_backend_id(node); + if (*node_backend_id != -1) { + if (*node_backend_id == sched->n_backends - 1) { + // skip cpu (lowest prio backend) + cur_backend_id = -1; + } else { + cur_backend_id = *node_backend_id; } - ggml_tallocr_t src_allocr = node_allocr(src); - if (src_allocr != NULL) { - int src_prio = sched_allocr_prio(sched, src_allocr); - size_t src_size = ggml_nbytes(src); - if (src_prio < cur_prio && src_size >= cur_size) { - cur_prio = src_prio; - cur_size = src_size; - node_allocr = src_allocr; - sprintf(causes[hash_id(node)], "2.src%d", j); - } + } else { + *node_backend_id = cur_backend_id; + SET_CAUSE(node, "2.2"); + } + } + } + // pass 2.1 expand gpu up + { + int cur_backend_id = -1; + for (int i = graph->n_nodes - 1; i >= 0; i--) { + struct ggml_tensor * node = graph->nodes[i]; + if (ggml_is_view_op(node->op)) { + continue; + } + int * node_backend_id = &tensor_backend_id(node); + if (*node_backend_id != -1) { + if (*node_backend_id == sched->n_backends - 1) { + // skip cpu (lowest prio backend) + cur_backend_id = -1; + } else { + cur_backend_id = *node_backend_id; } + } else { + *node_backend_id = cur_backend_id; + SET_CAUSE(node, "2.1"); } - if (node_allocr != NULL) { - node_allocr(node) = node_allocr; + } + } + // pass 2.4 expand rest down + { + int cur_backend_id = -1; + for (int i = 0; i < graph->n_nodes; i++) { + struct ggml_tensor * node = graph->nodes[i]; + if (ggml_is_view_op(node->op)) { + continue; + } + int * node_backend_id = &tensor_backend_id(node); + if (*node_backend_id != -1) { + cur_backend_id = *node_backend_id; + } else { + *node_backend_id = cur_backend_id; + SET_CAUSE(node, "2.4"); + } + } + } + // pass 2.3 expand rest up + { + int cur_backend_id = -1; + for (int i = graph->n_nodes - 1; i >= 0; i--) { + struct ggml_tensor * node = graph->nodes[i]; + if (ggml_is_view_op(node->op)) { + continue; + } + int * node_backend_id = &tensor_backend_id(node); + if (*node_backend_id != -1) { + cur_backend_id = *node_backend_id; + } else { + *node_backend_id = cur_backend_id; + SET_CAUSE(node, "2.3"); } } } - //printf("PASS 2 ASSIGNMENTS\n"); sched_print_assignments(sched, graph); - // pass 3: assign backends to remaining src from dst (should only be leafs) +#ifdef DEBUG_PASS2 + fprintf(stderr, "PASS 2 ASSIGNMENTS\n"); ggml_backend_sched_print_assignments(sched, graph); +#endif + + // pass 3: assign backends to remaining src from dst and view_src for (int i = 0; i < graph->n_nodes; i++) { struct ggml_tensor * node = graph->nodes[i]; - ggml_tallocr_t node_allocr = node_allocr(node); + int * cur_backend_id = &tensor_backend_id(node); + if (node->view_src != NULL && *cur_backend_id == -1) { + *cur_backend_id = tensor_backend_id(node->view_src); + SET_CAUSE(node, "3.vsrc"); + } for (int j = 0; j < GGML_MAX_SRC; j++) { struct ggml_tensor * src = node->src[j]; if (src == NULL) { - break; + continue; } - ggml_tallocr_t src_allocr = node_allocr(src); - if (src_allocr == NULL) { - node_allocr(src) = node_allocr; + int * src_backend_id = &tensor_backend_id(src); + if (*src_backend_id == -1) { + if (src->view_src != NULL) { + // views are always on the same backend as the source + *src_backend_id = tensor_backend_id(src->view_src); + SET_CAUSE(src, "3.vsrc"); + } else { + *src_backend_id = *cur_backend_id; + SET_CAUSE(src, "3.cur"); + } } } } - //printf("PASS 3 ASSIGNMENTS\n"); sched_print_assignments(sched, graph); +#ifdef DEBUG_PASS3 + fprintf(stderr, "PASS 3 ASSIGNMENTS\n"); ggml_backend_sched_print_assignments(sched, graph); +#endif // pass 4: split graph, find tensors that need to be copied - // TODO: - // - when switching from a less preferred backend to a more preferred backend, check if it is possible to move the switch to an earlier point for the same cost - // find first backend - int cur_split = 0; - for (int i = 0; i < graph->n_nodes; i++) { - struct ggml_tensor * node = graph->nodes[i]; - if (node->view_src == NULL) { - sched->splits[0].tallocr = node_allocr(node); - break; - } - } - sched->splits[0].i_start = 0; - sched->splits[0].n_inputs = 0; - memset(sched->splits[0].inputs, 0, sizeof(sched->splits[0].inputs)); //HACK - ggml_tallocr_t cur_allocr = sched->splits[0].tallocr; - size_t cur_backend_id = sched_allocr_prio(sched, cur_allocr); - for (int i = 0; i < graph->n_nodes; i++) { - struct ggml_tensor * node = graph->nodes[i]; - - if (ggml_is_view_op(node->op)) { - continue; + { + int i_split = 0; + struct ggml_backend_sched_split * split = &sched->splits[0]; + // find the backend of the first split, skipping view ops + for (int i = 0; i < graph->n_nodes; i++) { + struct ggml_tensor * node = graph->nodes[i]; + if (!ggml_is_view_op(node->op)) { + split->backend_id = tensor_backend_id(node); + break; + } } + split->i_start = 0; + split->n_inputs = 0; + memset(split->inputs, 0, sizeof(split->inputs)); //HACK + int cur_backend_id = split->backend_id; + for (int i = 0; i < graph->n_nodes; i++) { + struct ggml_tensor * node = graph->nodes[i]; + + if (ggml_is_view_op(node->op)) { + continue; + } - ggml_tallocr_t node_allocr = node_allocr(node); + const int node_backend_id = tensor_backend_id(node); - if (node_allocr != cur_allocr) { - sched->splits[cur_split].i_end = i; - cur_split++; - GGML_ASSERT(cur_split < GGML_MAX_SPLITS); - sched->splits[cur_split].tallocr = node_allocr; - sched->splits[cur_split].i_start = i; - sched->splits[cur_split].n_inputs = 0; - memset(sched->splits[cur_split].inputs, 0, sizeof(sched->splits[cur_split].inputs)); //HACK - cur_allocr = node_allocr; - cur_backend_id = sched_allocr_prio(sched, cur_allocr); - } + GGML_ASSERT(node_backend_id != -1); // all nodes should be assigned by now - // find inputs that are not on the same backend - for (int j = 0; j < GGML_MAX_SRC; j++) { - struct ggml_tensor * src = node->src[j]; - if (src == NULL) { - break; + // check if we should start a new split based on the sources of the current node + bool need_new_split = false; + if (node_backend_id == cur_backend_id && split->n_inputs > 0) { + for (int j = 0; j < GGML_MAX_SRC; j++) { + struct ggml_tensor * src = node->src[j]; + if (src == NULL) { + continue; + } + // check if a weight is on a different backend + // by starting a new split, the memory of the previously offloaded weights can be reused + if (src->buffer != NULL && src->buffer->usage == GGML_BACKEND_BUFFER_USAGE_WEIGHTS) { + int src_backend_id = tensor_backend_id(src); + if (src_backend_id != -1 && src_backend_id != cur_backend_id) { + need_new_split = true; + break; + } + } + // check if the split has too many inputs + if (split->n_inputs == GGML_SCHED_MAX_SPLIT_INPUTS) { + const size_t id = hash_id(src); + int src_backend_id = sched->tensor_backend_id[id]; + if (src_backend_id != cur_backend_id && sched->tensor_copies[hash_id(src)][cur_backend_id][0] == NULL) { + //printf("starting new split because of too many inputs: node %s, input %s\n", node->name, src->name); + need_new_split = true; + break; + } + } + } } - ggml_tallocr_t src_allocr = node_allocr(src); - if (src_allocr != node_allocr) { - int n_inputs = sched->splits[cur_split].n_inputs++; - GGML_ASSERT(n_inputs < GGML_MAX_SPLIT_INPUTS); - sched->splits[cur_split].inputs[n_inputs] = (struct ggml_tensor *)src; - - // create copies - size_t id = hash_id(src); - if (sched->node_copies[id][cur_backend_id] == NULL) { - struct ggml_tensor * tensor_copy = ggml_dup_tensor_layout(sched->ctx, src); - sched->node_copies[id][cur_backend_id] = tensor_copy; - node_allocr(tensor_copy) = cur_allocr; - ggml_backend_t backend = ggml_tallocr_get_buffer(cur_allocr)->backend; - ggml_format_name(tensor_copy, "%s#%s", ggml_backend_name(backend), src->name); + + if (node_backend_id != cur_backend_id || need_new_split) { + split->i_end = i; + i_split++; + if (i_split >= sched->splits_capacity) { + sched->splits_capacity *= 2; + sched->splits = realloc(sched->splits, sched->splits_capacity * sizeof(struct ggml_backend_sched_split)); + GGML_ASSERT(sched->splits != NULL); } - node->src[j] = sched->node_copies[id][cur_backend_id]; + GGML_ASSERT(i_split < GGML_SCHED_MAX_SPLITS); + split = &sched->splits[i_split]; + split->backend_id = node_backend_id; + split->i_start = i; + split->n_inputs = 0; + cur_backend_id = node_backend_id; } - } - } - sched->splits[cur_split].i_end = graph->n_nodes; - sched->n_splits = cur_split + 1; - //fprintf(stderr, "PASS 4 ASSIGNMENTS\n"); sched_print_assignments(sched, graph); fflush(stdout); + // find inputs that are not on the same backend + for (int j = 0; j < GGML_MAX_SRC; j++) { + struct ggml_tensor * src = node->src[j]; + if (src == NULL) { + continue; + } -#if 1 - // sanity check: all sources should have the same backend as the node - for (int i = 0; i < graph->n_nodes; i++) { - struct ggml_tensor * node = graph->nodes[i]; - ggml_tallocr_t node_allocr = node_allocr(node); - if (node_allocr == NULL) { - fprintf(stderr, "!!!!!!! %s has no backend\n", node->name); - } - for (int j = 0; j < GGML_MAX_SRC; j++) { - struct ggml_tensor * src = node->src[j]; - if (src == NULL) { - break; - } - ggml_tallocr_t src_allocr = node_allocr(src); - if (src_allocr != node_allocr /* && src_backend != NULL */) { // ignore nulls for now - fprintf(stderr, "!!!! %s has backend %s, src %d (%s) has backend %s\n", - node->name, node_allocr ? ggml_backend_name(ggml_tallocr_get_buffer(node_allocr)->backend) : "NULL", - j, src->name, src_allocr ? ggml_backend_name(ggml_tallocr_get_buffer(src_allocr)->backend) : "NULL"); + const int src_backend_id = tensor_backend_id(src); + assert(src_backend_id != -1); // all inputs should be assigned by now + + if (src->flags & GGML_TENSOR_FLAG_INPUT && sched->n_copies > 1) { + size_t id = hash_id(src); + if (sched->tensor_copies[id][src_backend_id][0] == NULL) { + ggml_backend_t backend = sched->backends[src_backend_id]; + for (int c = 0; c < sched->n_copies; c++) { + struct ggml_tensor * tensor_copy; + if (c == sched->cur_copy) { + tensor_copy = src; // use the original tensor as the current copy + } else { + tensor_copy = ggml_dup_tensor_layout(sched->ctx, src); + ggml_format_name(tensor_copy, "%s#%s#%d", ggml_backend_name(backend), src->name, c); + } + if (sched->n_copies > 1) { + ggml_set_input(tensor_copy); + ggml_set_output(tensor_copy); // prevent ggml-alloc from overwriting the tensor + } + sched->tensor_copies[id][src_backend_id][c] = tensor_copy; + SET_CAUSE(tensor_copy, "4.cpy"); + } + int n_graph_inputs = sched->n_graph_inputs++; + GGML_ASSERT(n_graph_inputs < GGML_SCHED_MAX_SPLIT_INPUTS); + sched->graph_inputs[n_graph_inputs] = src; + } + } + + if (src_backend_id != node_backend_id) { + // create a copy of the input in the split's backend + const size_t id = hash_id(src); + if (sched->tensor_copies[id][cur_backend_id][0] == NULL) { + ggml_backend_t backend = sched->backends[cur_backend_id]; + for (int c = 0; c < sched->n_copies; c++) { + struct ggml_tensor * tensor_copy = ggml_dup_tensor_layout(sched->ctx, src); + ggml_format_name(tensor_copy, "%s#%s#%d", ggml_backend_name(backend), src->name, c); + if (sched->n_copies > 1) { + ggml_set_input(tensor_copy); + ggml_set_output(tensor_copy); // prevent ggml-alloc from overwriting the tensor + } + sched->tensor_copies[id][cur_backend_id][c] = tensor_copy; + SET_CAUSE(tensor_copy, "4.cpy"); + } + int n_inputs = split->n_inputs++; + GGML_ASSERT(n_inputs < GGML_SCHED_MAX_SPLIT_INPUTS); + split->inputs[n_inputs] = src; + } + node->src[j] = sched->tensor_copies[id][cur_backend_id][sched->cur_copy]; + } } } + split->i_end = graph->n_nodes; + sched->n_splits = i_split + 1; } +#ifdef DEBUG_PASS4 + fprintf(stderr, "PASS 4 ASSIGNMENTS\n"); ggml_backend_sched_print_assignments(sched, graph); #endif // create copies of the graph for each split - // FIXME: avoid this copy, pass split inputs to ggml_gallocr_alloc_graph_n in some other way - struct ggml_cgraph * graph_copy = ggml_new_graph_custom(sched->ctx, graph->n_nodes + sched->n_splits*GGML_MAX_SPLIT_INPUTS, false); + // TODO: avoid this copy + struct ggml_cgraph * graph_copy = ggml_new_graph_custom(sched->ctx, graph->n_nodes + sched->n_splits*GGML_SCHED_MAX_SPLIT_INPUTS*2, false); for (int i = 0; i < sched->n_splits; i++) { struct ggml_backend_sched_split * split = &sched->splits[i]; - split->graph = ggml_graph_view(sched->ctx, graph, split->i_start, split->i_end); + split->graph = ggml_graph_view(graph, split->i_start, split->i_end); // add inputs to the graph copy so that they are allocated by ggml-alloc at the start of the split for (int j = 0; j < split->n_inputs; j++) { + assert(graph_copy->size > (graph_copy->n_nodes + 1)); + struct ggml_tensor * input = split->inputs[j]; - struct ggml_tensor * input_cpy = sched->node_copies[hash_id(input)][sched_allocr_prio(sched, split->tallocr)]; - input_cpy->src[0] = input; + const size_t input_id = hash_id(input); + struct ggml_tensor * input_cpy = sched->tensor_copies[input_id][split->backend_id][sched->cur_copy]; + + // add a dependency to the input source so that it is not freed before the copy is done + struct ggml_tensor * input_dep = ggml_view_tensor(sched->ctx, input); + input_dep->src[0] = input; + sched->node_backend_ids[graph_copy->n_nodes] = sched->tensor_backend_id[input_id]; + graph_copy->nodes[graph_copy->n_nodes++] = input_dep; + + // add a dependency to the input copy so that it is allocated at the start of the split + sched->node_backend_ids[graph_copy->n_nodes] = split->backend_id; graph_copy->nodes[graph_copy->n_nodes++] = input_cpy; } for (int j = split->i_start; j < split->i_end; j++) { + assert(graph_copy->size > graph_copy->n_nodes); + sched->node_backend_ids[graph_copy->n_nodes] = tensor_backend_id(graph->nodes[j]); graph_copy->nodes[graph_copy->n_nodes++] = graph->nodes[j]; } } + + if (sched->n_copies > 1) { + // add input copies as leafs so that they are allocated first + for (int i = 0; i < sched->n_graph_inputs; i++) { + struct ggml_tensor * input = sched->graph_inputs[i]; + size_t id = hash_id(input); + int backend_id = tensor_backend_id(input); + for (int c = 0; c < sched->n_copies; c++) { + struct ggml_tensor * input_cpy = sched->tensor_copies[id][backend_id][c]; + sched->leaf_backend_ids[graph_copy->n_leafs] = backend_id; + graph_copy->leafs[graph_copy->n_leafs++] = input_cpy; + } + } + + for (int i = 0; i < sched->n_splits; i++) { + struct ggml_backend_sched_split * split = &sched->splits[i]; + int backend_id = split->backend_id; + for (int j = 0; j < split->n_inputs; j++) { + struct ggml_tensor * input = split->inputs[j]; + size_t id = hash_id(input); + for (int c = 0; c < sched->n_copies; c++) { + struct ggml_tensor * input_cpy = sched->tensor_copies[id][backend_id][c]; + sched->leaf_backend_ids[graph_copy->n_leafs] = backend_id; + graph_copy->leafs[graph_copy->n_leafs++] = input_cpy; + } + } + } + } + + // add leafs from the original graph + for (int i = 0; i < graph->n_leafs; i++) { + struct ggml_tensor * leaf = graph->leafs[i]; + sched->leaf_backend_ids[graph_copy->n_leafs] = tensor_backend_id(leaf); + graph_copy->leafs[graph_copy->n_leafs++] = leaf; + } + sched->graph = graph_copy; } -static void sched_alloc_splits(ggml_backend_sched_t sched) { - ggml_gallocr_alloc_graph_n( - sched->galloc, - sched->graph, - sched->hash_set, - sched->node_talloc); -} +static bool ggml_backend_sched_alloc_splits(ggml_backend_sched_t sched) { + // allocate graph + if (!ggml_gallocr_alloc_graph(sched->galloc, sched->graph)) { + // the re-allocation may cause the split inputs to be moved to a different address + ggml_backend_sched_synchronize(sched); +#ifndef NDEBUG + fprintf(stderr, "%s: failed to allocate graph, reserving\n", __func__); +#endif + ggml_gallocr_reserve_n(sched->galloc, sched->graph, sched->node_backend_ids, sched->leaf_backend_ids); + if (!ggml_gallocr_alloc_graph(sched->galloc, sched->graph)) { + fprintf(stderr, "%s: failed to allocate graph\n", __func__); + return false; + } + } -static void sched_compute_splits(ggml_backend_sched_t sched) { - uint64_t copy_us[GGML_MAX_BACKENDS] = {0}; - uint64_t compute_us[GGML_MAX_BACKENDS] = {0}; + return true; +} +static enum ggml_status ggml_backend_sched_compute_splits(ggml_backend_sched_t sched) { struct ggml_backend_sched_split * splits = sched->splits; for (int i = 0; i < sched->n_splits; i++) { struct ggml_backend_sched_split * split = &splits[i]; - ggml_backend_t split_backend = ggml_tallocr_get_buffer(split->tallocr)->backend; - int split_backend_id = sched_backend_prio(sched, split_backend); + int split_backend_id = split->backend_id; + ggml_backend_t split_backend = sched->backends[split_backend_id]; // copy the input tensors to the split backend - uint64_t copy_start_us = ggml_time_us(); for (int j = 0; j < split->n_inputs; j++) { - struct ggml_tensor * input_cpy = sched->node_copies[hash_id(split->inputs[j])][sched_backend_prio(sched, split_backend)]; - if (split->inputs[j]->buffer == NULL) { - if (split->inputs[j]->view_src == NULL) { - fprintf(stderr, "input %s has no buffer and no view_src\n", split->inputs[j]->name); - exit(1); + ggml_backend_t input_backend = ggml_backend_sched_get_tensor_backend(sched, split->inputs[j]); + struct ggml_tensor * input = split->inputs[j]; + struct ggml_tensor * input_cpy = sched->tensor_copies[hash_id(input)][split_backend_id][sched->cur_copy]; + + if (input->flags & GGML_TENSOR_FLAG_INPUT) { + // inputs from the user must be copied immediately to prevent the user overwriting the data before the copy is done + if (sched->events[split_backend_id][sched->cur_copy] != NULL) { + ggml_backend_event_synchronize(sched->events[split_backend_id][sched->cur_copy]); + } else { + ggml_backend_synchronize(split_backend); } - struct ggml_tensor * view = split->inputs[j]; - view->backend = view->view_src->backend; - view->buffer = view->view_src->buffer; - view->data = (char *)view->view_src->data + view->view_offs; - ggml_backend_buffer_init_tensor(ggml_backend_sched_get_buffer(sched, view->buffer->backend), view); - } - if (input_cpy->buffer == NULL) { - fprintf(stderr, "input_cpy %s has no buffer\n", input_cpy->name); - exit(1); + ggml_backend_tensor_copy(input, input_cpy); + } else { + // wait for the split backend to finish using the input before overwriting it + if (sched->events[split_backend_id][sched->cur_copy] != NULL) { + ggml_backend_event_wait(split_backend, sched->events[split_backend_id][sched->cur_copy]); + } else { + ggml_backend_synchronize(split_backend); + } + ggml_backend_tensor_copy_async(input_backend, split_backend, input, input_cpy); } - GGML_ASSERT(split->inputs[j]->buffer->backend != input_cpy->buffer->backend); - GGML_ASSERT(input_cpy->buffer->backend == split_backend); - ggml_backend_tensor_copy(split->inputs[j], input_cpy); } - // ggml_backend_synchronize(split_backend); - int64_t copy_end_us = ggml_time_us(); - copy_us[split_backend_id] += copy_end_us - copy_start_us; -#if 0 - char split_filename[GGML_MAX_NAME]; - snprintf(split_filename, GGML_MAX_NAME, "split_%i_%s.dot", i, ggml_backend_name(split_backend)); - ggml_graph_dump_dot(split->graph, NULL, split_filename); -#endif + if (!sched->callback_eval) { + enum ggml_status ec = ggml_backend_graph_compute_async(split_backend, &split->graph); + if (ec != GGML_STATUS_SUCCESS) { + return ec; + } + } else { + // similar to ggml_backend_compare_graph_backend + for (int j0 = 0; j0 < split->graph.n_nodes; j0++) { + struct ggml_tensor * t = split->graph.nodes[j0]; - uint64_t compute_start_us = ggml_time_us(); - ggml_backend_graph_compute(split_backend, split->graph); - // ggml_backend_synchronize(split_backend); - uint64_t compute_end_us = ggml_time_us(); - compute_us[split_backend_id] += compute_end_us - compute_start_us; - } + // check if the user needs data from this node + bool need = sched->callback_eval(t, true, sched->callback_eval_user_data); -#if 0 - // per-backend timings - fprintf(stderr, "sched_compute_splits times (%d splits):\n", sched->n_splits); - for (int i = 0; i < sched->n_backends; i++) { - if (copy_us[i] > 0 || compute_us[i] > 0) { - fprintf(stderr, "\t%5.5s: %lu us copy, %lu us compute\n", ggml_backend_name(sched->backends[i]), copy_us[i], compute_us[i]); + int j1 = j0; + + // determine the range [j0, j1] of nodes that can be computed together + while (!need && j1 < split->graph.n_nodes - 1) { + t = split->graph.nodes[++j1]; + need = sched->callback_eval(t, true, sched->callback_eval_user_data); + } + + struct ggml_cgraph gv = ggml_graph_view(&split->graph, j0, j1 + 1); + + enum ggml_status ec = ggml_backend_graph_compute_async(split_backend, &gv); + if (ec != GGML_STATUS_SUCCESS) { + return ec; + } + + // TODO: pass backend to the callback, then the user can decide if they want to synchronize + ggml_backend_synchronize(split_backend); + + if (need && !sched->callback_eval(t, false, sched->callback_eval_user_data)) { + break; + } + + j0 = j1; + } } - } -#endif -} -static void sched_reset(ggml_backend_sched_t sched) { - for (int i = 0; i < sched->n_backends; i++) { - ggml_tallocr_reset(sched->tallocs[i]); + // record the event of this copy + if (split->n_inputs > 0) { + if (sched->events[split_backend_id][sched->cur_copy] != NULL) { + ggml_backend_event_record(sched->events[split_backend_id][sched->cur_copy]); + } + } } + + sched->cur_copy = (sched->cur_copy + 1) % sched->n_copies; + + return GGML_STATUS_SUCCESS; } -ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, int n_backends) { - GGML_ASSERT(n_backends <= GGML_MAX_BACKENDS); +ggml_backend_sched_t ggml_backend_sched_new( + ggml_backend_t * backends, + ggml_backend_buffer_type_t * bufts, + int n_backends, + size_t graph_size, + bool parallel) { + GGML_ASSERT(n_backends > 0); + GGML_ASSERT(n_backends <= GGML_SCHED_MAX_BACKENDS); + GGML_ASSERT(ggml_backend_is_cpu(backends[n_backends - 1])); // last backend must be CPU + + struct ggml_backend_sched * sched = calloc(sizeof(struct ggml_backend_sched), 1); - struct ggml_backend_sched * sched = malloc(sizeof(struct ggml_backend_sched)); - memset(sched, 0, sizeof(struct ggml_backend_sched)); + // initialize hash table + sched->hash_set = ggml_hash_set_new(graph_size); + sched->tensor_backend_id = calloc(sizeof(sched->tensor_backend_id[0]), sched->hash_set.size); + sched->tensor_copies = calloc(sizeof(sched->tensor_copies[0]), sched->hash_set.size); - fprintf(stderr, "ggml_backend_sched size: %lu KB\n", sizeof(struct ggml_backend_sched)/1024); + const size_t nodes_size = graph_size + GGML_SCHED_MAX_SPLITS*GGML_SCHED_MAX_SPLIT_INPUTS*2; + sched->node_backend_ids = calloc(sizeof(sched->node_backend_ids[0]), nodes_size); + sched->leaf_backend_ids = calloc(sizeof(sched->leaf_backend_ids[0]), nodes_size); sched->n_backends = n_backends; - for (int i = 0; i < n_backends; i++) { - sched->backends[i] = backends[i]; - } - sched->galloc = ggml_gallocr_new(); + sched->n_copies = parallel ? GGML_SCHED_MAX_COPIES : 1; - // init measure allocs for each backend - for (int i = 0; i < n_backends; i++) { - sched->tallocs[i] = ggml_tallocr_new_measure_from_backend(backends[i]); + const int initial_splits_capacity = 16; + sched->splits = calloc(sizeof(sched->splits[0]), initial_splits_capacity); + sched->splits_capacity = initial_splits_capacity; + + for (int b = 0; b < n_backends; b++) { + sched->backends[b] = backends[b]; + sched->bufts[b] = bufts ? bufts[b] : ggml_backend_get_default_buffer_type(backends[b]); + GGML_ASSERT(ggml_backend_buft_supports_backend(sched->bufts[b], backends[b])); + if (sched->n_copies > 1) { + for (int c = 0; c < sched->n_copies; c++) { + sched->events[b][c] = ggml_backend_event_new(backends[b]); + } + } } + sched->galloc = ggml_gallocr_new_n(sched->bufts, n_backends); + + ggml_backend_sched_reset(sched); + return sched; } @@ -893,58 +1762,334 @@ void ggml_backend_sched_free(ggml_backend_sched_t sched) { if (sched == NULL) { return; } - for (int i = 0; i < sched->n_backends; i++) { - ggml_tallocr_free(sched->tallocs[i]); + for (int b = 0; b < sched->n_backends; b++) { + for (int c = 0; c < sched->n_copies; c++) { + ggml_backend_event_free(sched->events[b][c]); + } } ggml_gallocr_free(sched->galloc); + ggml_free(sched->ctx); + free(sched->splits); free(sched->hash_set.keys); - free(sched->node_talloc); - free(sched->node_copies); + free(sched->tensor_backend_id); + free(sched->tensor_copies); + free(sched->node_backend_ids); + free(sched->leaf_backend_ids); free(sched); } -void ggml_backend_sched_init_measure(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph) { - // initialize hash tables - size_t hash_size = measure_graph->visited_hash_table.size + GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS; - sched->hash_set.size = hash_size; - sched->hash_set.keys = malloc(sizeof(sched->hash_set.keys[0]) * hash_size); - sched->node_talloc = malloc(sizeof(sched->node_talloc[0]) * hash_size); - sched->node_copies = malloc(sizeof(sched->node_copies[0]) * hash_size); +void ggml_backend_sched_reset(ggml_backend_sched_t sched) { + // reset state for the next run + size_t hash_size = sched->hash_set.size; + memset(sched->hash_set.keys, 0, sizeof(sched->hash_set.keys[0]) * hash_size); // NOLINT + memset(sched->tensor_backend_id, -1, sizeof(sched->tensor_backend_id[0]) * hash_size); + memset(sched->tensor_copies, 0, sizeof(sched->tensor_copies[0]) * hash_size); + + sched->is_reset = true; + sched->is_alloc = false; +} + +bool ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph) { + GGML_ASSERT((int)sched->hash_set.size >= measure_graph->n_nodes); - sched_split_graph(sched, measure_graph); - sched_alloc_splits(sched); + ggml_backend_sched_split_graph(sched, measure_graph); - // allocate buffers and reset allocators - for (int i = 0; i < sched->n_backends; i++) { - size_t size = ggml_tallocr_max_size(sched->tallocs[i]); - ggml_tallocr_free(sched->tallocs[i]); - sched->tallocs[i] = ggml_tallocr_new_from_backend(sched->backends[i], size); + // TODO: extract this to a separate function + if (!ggml_gallocr_reserve_n(sched->galloc, sched->graph, sched->node_backend_ids, sched->leaf_backend_ids)) { + return false; + } + + ggml_backend_sched_reset(sched); + ggml_backend_sched_synchronize(sched); + + return true; +} + +bool ggml_backend_sched_alloc_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph) { + GGML_ASSERT((int)sched->hash_set.size >= graph->n_nodes); + + ggml_backend_sched_split_graph(sched, graph); + + if (!ggml_backend_sched_alloc_splits(sched)) { + return false; + } + + sched->is_alloc = true; + + return true; +} + +enum ggml_status ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, struct ggml_cgraph * graph) { + enum ggml_status err = ggml_backend_sched_graph_compute_async(sched, graph); + ggml_backend_sched_synchronize(sched); + return err; +} + +enum ggml_status ggml_backend_sched_graph_compute_async(ggml_backend_sched_t sched, struct ggml_cgraph * graph) { + if (!sched->is_reset && !sched->is_alloc) { + ggml_backend_sched_reset(sched); + } + + if (!sched->is_alloc) { + if (!ggml_backend_sched_alloc_graph(sched, graph)) { + return GGML_STATUS_ALLOC_FAILED; + } } - sched_reset(sched); + return ggml_backend_sched_compute_splits(sched); +} + +void ggml_backend_sched_synchronize(ggml_backend_sched_t sched) { + for (int i = 0; i < sched->n_backends; i++) { + ggml_backend_synchronize(sched->backends[i]); + } } -void ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, struct ggml_cgraph * graph) { - GGML_ASSERT(sched->hash_set.size >= graph->visited_hash_table.size + GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS); +void ggml_backend_sched_set_eval_callback(ggml_backend_sched_t sched, ggml_backend_sched_eval_callback callback, void * user_data) { + sched->callback_eval = callback; + sched->callback_eval_user_data = user_data; +} - sched_split_graph(sched, graph); - sched_alloc_splits(sched); - sched_compute_splits(sched); - sched_reset(sched); +int ggml_backend_sched_get_n_splits(ggml_backend_sched_t sched) { + return sched->n_splits; } -ggml_tallocr_t ggml_backend_sched_get_tallocr(ggml_backend_sched_t sched, ggml_backend_t backend) { - int backend_index = sched_backend_prio(sched, backend); - return sched->tallocs[backend_index]; +int ggml_backend_sched_get_n_copies(ggml_backend_sched_t sched) { + return sched->n_copies; } -ggml_backend_buffer_t ggml_backend_sched_get_buffer(ggml_backend_sched_t sched, ggml_backend_t backend) { - int backend_index = sched_backend_prio(sched, backend); - return ggml_tallocr_get_buffer(sched->tallocs[backend_index]); +size_t ggml_backend_sched_get_buffer_size(ggml_backend_sched_t sched, ggml_backend_t backend) { + int backend_index = ggml_backend_sched_backend_id(sched, backend); + GGML_ASSERT(backend_index >= 0 && backend_index < sched->n_backends); + + return ggml_gallocr_get_buffer_size(sched->galloc, backend_index); } -void ggml_backend_sched_set_node_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend) { - int backend_index = sched_backend_prio(sched, backend); +void ggml_backend_sched_set_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend) { + int backend_index = ggml_backend_sched_backend_id(sched, backend); GGML_ASSERT(backend_index >= 0 && backend_index < sched->n_backends); - node_allocr(node) = sched->tallocs[backend_index]; + tensor_backend_id(node) = backend_index; +} + +ggml_backend_t ggml_backend_sched_get_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node) { + int backend_index = tensor_backend_id(node); + if (backend_index == -1) { + return NULL; + } + return sched->backends[backend_index]; +} + +// utils + +void ggml_backend_view_init(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) { + GGML_ASSERT(tensor->buffer == NULL); + GGML_ASSERT(tensor->view_src != NULL); + GGML_ASSERT(tensor->view_src->buffer != NULL); + GGML_ASSERT(tensor->view_src->data != NULL); + + tensor->buffer = buffer; + tensor->data = (char *)tensor->view_src->data + tensor->view_offs; + tensor->backend = tensor->view_src->backend; + ggml_backend_buffer_init_tensor(buffer, tensor); +} + +void ggml_backend_tensor_alloc(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, void * addr) { + GGML_ASSERT(tensor->buffer == NULL); + GGML_ASSERT(tensor->data == NULL); + GGML_ASSERT(tensor->view_src == NULL); + GGML_ASSERT(addr >= ggml_backend_buffer_get_base(buffer)); + GGML_ASSERT((char *)addr + ggml_backend_buffer_get_alloc_size(buffer, tensor) <= + (char *)ggml_backend_buffer_get_base(buffer) + ggml_backend_buffer_get_size(buffer)); + + tensor->buffer = buffer; + tensor->data = addr; + ggml_backend_buffer_init_tensor(buffer, tensor); +} + +static struct ggml_tensor * graph_copy_dup_tensor(struct ggml_hash_set hash_set, struct ggml_tensor ** node_copies, + struct ggml_context * ctx_allocated, struct ggml_context * ctx_unallocated, struct ggml_tensor * src) { + + GGML_ASSERT(src != NULL); + GGML_ASSERT(src->data && "graph must be allocated"); + + size_t id = ggml_hash_insert(hash_set, src); + if (id == GGML_HASHTABLE_ALREADY_EXISTS) { + return node_copies[ggml_hash_find(hash_set, src)]; + } + + struct ggml_tensor * dst = ggml_dup_tensor_layout(src->data && !src->view_src ? ctx_allocated : ctx_unallocated, src); + if (src->view_src != NULL) { + dst->view_src = graph_copy_dup_tensor(hash_set, node_copies, ctx_allocated, ctx_unallocated, src->view_src); + dst->view_offs = src->view_offs; + } + dst->op = src->op; + memcpy(dst->op_params, src->op_params, sizeof(dst->op_params)); + ggml_set_name(dst, src->name); + + // copy src + for (int i = 0; i < GGML_MAX_SRC; i++) { + struct ggml_tensor * s = src->src[i]; + if (s == NULL) { + continue; + } + dst->src[i] = graph_copy_dup_tensor(hash_set, node_copies, ctx_allocated, ctx_unallocated, s); + } + + node_copies[id] = dst; + return dst; +} + +static void graph_copy_init_tensor(struct ggml_hash_set hash_set, struct ggml_tensor ** node_copies, bool * node_init, struct ggml_tensor * src) { + size_t id = ggml_hash_find(hash_set, src); + if (node_init[id]) { + return; + } + node_init[id] = true; + + struct ggml_tensor * dst = node_copies[id]; + if (dst->view_src != NULL) { + graph_copy_init_tensor(hash_set, node_copies, node_init, src->view_src); + ggml_backend_view_init(dst->view_src->buffer, dst); + } + else { + ggml_backend_tensor_copy(src, dst); + } + + // init src + for (int i = 0; i < GGML_MAX_SRC; i++) { + struct ggml_tensor * s = src->src[i]; + if (s == NULL) { + continue; + } + graph_copy_init_tensor(hash_set, node_copies, node_init, s); + } +} + +struct ggml_backend_graph_copy ggml_backend_graph_copy(ggml_backend_t backend, struct ggml_cgraph * graph) { + struct ggml_hash_set hash_set = { + /* .size = */ graph->visited_hash_table.size, + /* .keys = */ calloc(sizeof(hash_set.keys[0]), graph->visited_hash_table.size) // NOLINT + }; + struct ggml_tensor ** node_copies = calloc(sizeof(node_copies[0]), hash_set.size); // NOLINT + bool * node_init = calloc(sizeof(node_init[0]), hash_set.size); + + struct ggml_init_params params = { + /* .mem_size = */ ggml_tensor_overhead()*hash_set.size + ggml_graph_overhead_custom(graph->size, false), + /* .mem_buffer = */ NULL, + /* .no_alloc = */ true + }; + + struct ggml_context * ctx_allocated = ggml_init(params); + struct ggml_context * ctx_unallocated = ggml_init(params); + + if (ctx_allocated == NULL || ctx_unallocated == NULL) { + fprintf(stderr, "failed to allocate context for graph copy\n"); + free(hash_set.keys); + free(node_copies); + free(node_init); + ggml_free(ctx_allocated); + ggml_free(ctx_unallocated); + return (struct ggml_backend_graph_copy) { + /* .buffer = */ NULL, + /* .ctx_allocated = */ NULL, + /* .ctx_unallocated = */ NULL, + /* .graph = */ NULL, + }; + } + + // dup nodes + for (int i = 0; i < graph->n_nodes; i++) { + struct ggml_tensor * node = graph->nodes[i]; + graph_copy_dup_tensor(hash_set, node_copies, ctx_allocated, ctx_unallocated, node); + } + + // allocate nodes + ggml_backend_buffer_t buffer = ggml_backend_alloc_ctx_tensors(ctx_allocated, backend); + if (buffer == NULL) { + fprintf(stderr, "failed to allocate buffer for graph copy\n"); + free(hash_set.keys); + free(node_copies); + free(node_init); + ggml_free(ctx_allocated); + ggml_free(ctx_unallocated); + return (struct ggml_backend_graph_copy) { + /* .buffer = */ NULL, + /* .ctx_allocated = */ NULL, + /* .ctx_unallocated = */ NULL, + /* .graph = */ NULL, + }; + } + + //printf("copy buffer size: %zu MB\n", ggml_backend_buffer_get_size(buffer) / 1024 / 1024); + + // copy data and init views + for (int i = 0; i < graph->n_nodes; i++) { + struct ggml_tensor * node = graph->nodes[i]; + graph_copy_init_tensor(hash_set, node_copies, node_init, node); + } + + // build graph copy + struct ggml_cgraph * graph_copy = ggml_new_graph_custom(ctx_allocated, graph->size, false); + for (int i = 0; i < graph->n_nodes; i++) { + struct ggml_tensor * node = graph->nodes[i]; + struct ggml_tensor * node_copy = node_copies[ggml_hash_find(hash_set, node)]; + graph_copy->nodes[i] = node_copy; + } + graph_copy->n_nodes = graph->n_nodes; + + free(hash_set.keys); + free(node_copies); + free(node_init); + + return (struct ggml_backend_graph_copy) { + /* .buffer = */ buffer, + /* .ctx_allocated = */ ctx_allocated, + /* .ctx_unallocated = */ ctx_unallocated, + /* .graph = */ graph_copy, + }; +} + +void ggml_backend_graph_copy_free(struct ggml_backend_graph_copy copy) { + ggml_backend_buffer_free(copy.buffer); + ggml_free(copy.ctx_allocated); + ggml_free(copy.ctx_unallocated); +} + +bool ggml_backend_compare_graph_backend(ggml_backend_t backend1, ggml_backend_t backend2, struct ggml_cgraph * graph, ggml_backend_eval_callback callback, void * user_data) { + struct ggml_backend_graph_copy copy = ggml_backend_graph_copy(backend2, graph); + if (copy.buffer == NULL) { + return false; + } + + struct ggml_cgraph * g1 = graph; + struct ggml_cgraph * g2 = copy.graph; + + assert(g1->n_nodes == g2->n_nodes); + + for (int i = 0; i < g1->n_nodes; i++) { + //printf("eval %d/%d\n", i, g1->n_nodes); + struct ggml_tensor * t1 = g1->nodes[i]; + struct ggml_tensor * t2 = g2->nodes[i]; + + assert(t1->op == t2->op && ggml_are_same_layout(t1, t2)); + + struct ggml_cgraph g1v = ggml_graph_view(g1, i, i + 1); + struct ggml_cgraph g2v = ggml_graph_view(g2, i, i + 1); + + ggml_backend_graph_compute(backend1, &g1v); + ggml_backend_graph_compute(backend2, &g2v); + + if (ggml_is_view_op(t1->op)) { + continue; + } + + // compare results, calculate rms etc + if (!callback(i, t1, t2, user_data)) { + break; + } + } + + ggml_backend_graph_copy_free(copy); + + return true; }