mirror of
https://github.com/ggerganov/whisper.cpp.git
synced 2024-12-27 09:08:55 +01:00
sync : ggml (new ops, new backend, etc) (#1602)
* sync : ggml (new ops, new backend, etc) * whisper : remove obsolete broadcasting code * ggml : remove backend self-registers + fix ggml_concat + n_task logic * metal : fix assert * metal : print resource path * whisper : fix bug if metal init fails
This commit is contained in:
Georgi Gerganov
GitHub
parent
3163090d89
commit
afce6fa113
51
ggml-alloc.c
51
ggml-alloc.c
@ -137,7 +137,7 @@ void ggml_tallocr_alloc(ggml_tallocr_t alloc, struct ggml_tensor * tensor) {
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#ifdef GGML_ALLOCATOR_DEBUG
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add_allocated_tensor(alloc, tensor);
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size_t cur_max = (char*)addr - (char*)alloc->data + size;
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size_t cur_max = (char*)addr - (char*)alloc->base + size;
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if (cur_max > alloc->max_size) {
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printf("max_size = %.2f MB: tensors: ", cur_max / 1024.0 / 1024.0);
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for (int i = 0; i < 1024; i++) {
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@ -168,10 +168,6 @@ static void ggml_tallocr_free_tensor(ggml_tallocr_t alloc, struct ggml_tensor *
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size = aligned_offset(NULL, size, alloc->alignment);
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AT_PRINTF("%s: freeing %s at %p (%zu bytes) - n_free_blocks = %d\n", __func__, tensor->name, ptr, size, alloc->n_free_blocks);
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if (!alloc->measure) {
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ggml_backend_buffer_free_tensor(alloc->buffer, tensor);
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}
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#ifdef GGML_ALLOCATOR_DEBUG
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remove_allocated_tensor(alloc, tensor);
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#endif
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@ -237,7 +233,7 @@ void ggml_tallocr_reset(ggml_tallocr_t alloc) {
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}
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ggml_tallocr_t ggml_tallocr_new(void * data, size_t size, size_t alignment) {
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struct ggml_backend_buffer * buffer = ggml_backend_cpu_buffer_from_ptr(NULL, data, size);
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struct ggml_backend_buffer * buffer = ggml_backend_cpu_buffer_from_ptr(data, size);
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ggml_tallocr_t alloc = (ggml_tallocr_t)malloc(sizeof(struct ggml_tallocr));
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@ -449,7 +445,6 @@ static ggml_tallocr_t node_tallocr(ggml_gallocr_t galloc, struct ggml_tensor * n
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static void init_view(ggml_gallocr_t galloc, struct ggml_tensor * view, bool update_backend) {
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ggml_tallocr_t alloc = node_tallocr(galloc, view);
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//printf("init_view: %s from src %s\n", view->name, view->view_src->name);
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GGML_ASSERT(view->view_src != NULL && view->view_src->data != NULL);
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if (update_backend) {
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view->backend = view->view_src->backend;
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@ -459,7 +454,7 @@ static void init_view(ggml_gallocr_t galloc, struct ggml_tensor * view, bool upd
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// FIXME: the view should be initialized by the owning buffer, but currently this breaks the CUDA backend
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// due to the ggml_tensor_extra_gpu ring buffer overwriting the KV cache extras
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assert(ggml_tallocr_is_measure(alloc) || !view->buffer || view->buffer->backend == alloc->buffer->backend);
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assert(ggml_tallocr_is_measure(alloc) || !view->buffer || view->buffer->buft == alloc->buffer->buft);
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if (!alloc->measure) {
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ggml_backend_buffer_init_tensor(alloc->buffer, view);
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@ -765,3 +760,43 @@ size_t ggml_allocr_max_size(ggml_allocr_t alloc) {
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size_t ggml_allocr_alloc_graph(ggml_allocr_t alloc, struct ggml_cgraph * graph) {
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return ggml_gallocr_alloc_graph(alloc->galloc, alloc->talloc, graph);
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}
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// utils
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ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_context * ctx, ggml_backend_buffer_type_t buft) {
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GGML_ASSERT(ggml_get_no_alloc(ctx) == true);
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size_t alignment = ggml_backend_buft_get_alignment(buft);
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size_t nbytes = 0;
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for (struct ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
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if (t->data == NULL && t->view_src == NULL) {
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nbytes += GGML_PAD(ggml_backend_buft_get_alloc_size(buft, t), alignment);
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}
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}
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if (nbytes == 0) {
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fprintf(stderr, "%s: no tensors to allocate\n", __func__);
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return NULL;
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}
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ggml_backend_buffer_t buffer = ggml_backend_buft_alloc_buffer(buft, nbytes);
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ggml_tallocr_t tallocr = ggml_tallocr_new_from_buffer(buffer);
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for (struct ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
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if (t->data == NULL) {
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if (t->view_src == NULL) {
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ggml_tallocr_alloc(tallocr, t);
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} else {
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ggml_backend_view_init(buffer, t);
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}
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}
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}
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ggml_tallocr_free(tallocr);
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return buffer;
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}
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ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors(struct ggml_context * ctx, ggml_backend_t backend) {
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return ggml_backend_alloc_ctx_tensors_from_buft(ctx, ggml_backend_get_default_buffer_type(backend));
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}
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@ -8,6 +8,7 @@ extern "C" {
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struct ggml_backend;
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struct ggml_backend_buffer;
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struct ggml_backend_buffer_type;
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//
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// Legacy API
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@ -80,6 +81,12 @@ GGML_API void ggml_gallocr_alloc_graph_n(
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struct ggml_hash_set hash_set,
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ggml_tallocr_t * hash_node_talloc);
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// Utils
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// Create a buffer and allocate all the tensors in a ggml_context
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GGML_API struct ggml_backend_buffer * ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_context * ctx, struct ggml_backend_buffer_type * buft);
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GGML_API struct ggml_backend_buffer * ggml_backend_alloc_ctx_tensors(struct ggml_context * ctx, struct ggml_backend * backend);
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#ifdef __cplusplus
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}
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#endif
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@ -12,31 +12,50 @@ extern "C" {
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// Backend buffer
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//
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// buffer type
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typedef void * ggml_backend_buffer_type_context_t;
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struct ggml_backend_buffer_type_i {
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ggml_backend_buffer_t (*alloc_buffer) (ggml_backend_buffer_type_t buft, size_t size);
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size_t (*get_alignment) (ggml_backend_buffer_type_t buft); // tensor alignment
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size_t (*get_alloc_size) (ggml_backend_buffer_type_t buft, struct ggml_tensor * tensor); // data size needed to allocate the tensor, including padding
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bool (*supports_backend)(ggml_backend_buffer_type_t buft, ggml_backend_t backend); // check if the buffer type is usable by the backend
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};
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struct ggml_backend_buffer_type {
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struct ggml_backend_buffer_type_i iface;
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ggml_backend_buffer_type_context_t context;
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};
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// buffer
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typedef void * ggml_backend_buffer_context_t;
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struct ggml_backend_buffer_i {
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void (*free_buffer)(ggml_backend_buffer_t buffer);
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void * (*get_base) (ggml_backend_buffer_t buffer); // get base pointer
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size_t (*get_alloc_size)(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); // pre-allocation callback
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void (*init_tensor) (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); // post-allocation callback
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void (*free_tensor) (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); // pre-free callback
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//void (*reset) (ggml_backend_buffer_t buffer); // reset any internal state due to tensor initialization, such as tensor extras
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void * (*get_base) (ggml_backend_buffer_t buffer);
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void (*init_tensor)(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
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void (*set_tensor) (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
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void (*get_tensor) (ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);
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// (optional) copy tensor between different buffer-type, allow for single-copy tranfers
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void (*cpy_tensor_from)(ggml_backend_buffer_t buffer, struct ggml_tensor * src, struct ggml_tensor * dst);
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void (*cpy_tensor_to) (ggml_backend_buffer_t buffer, struct ggml_tensor * src, struct ggml_tensor * dst);
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};
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struct ggml_backend_buffer {
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struct ggml_backend_buffer_i iface;
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ggml_backend_t backend;
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ggml_backend_buffer_type_t buft;
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ggml_backend_buffer_context_t context;
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size_t size;
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};
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GGML_API ggml_backend_buffer_t ggml_backend_buffer_init(
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struct ggml_backend * backend,
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ggml_backend_buffer_t ggml_backend_buffer_init(
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ggml_backend_buffer_type_t buft,
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struct ggml_backend_buffer_i iface,
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ggml_backend_buffer_context_t context,
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size_t size);
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//
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// Backend
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//
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@ -49,20 +68,17 @@ extern "C" {
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void (*free)(ggml_backend_t backend);
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// buffer allocation
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ggml_backend_buffer_t (*alloc_buffer)(ggml_backend_t backend, size_t size);
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ggml_backend_buffer_type_t (*get_default_buffer_type)(ggml_backend_t backend);
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// get buffer alignment
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size_t (*get_alignment)(ggml_backend_t backend);
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// tensor data access
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// these functions can be asynchronous, helper functions are provided for synchronous access that automatically call synchronize
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// (optional) asynchroneous tensor data access
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void (*set_tensor_async)(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
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void (*get_tensor_async)(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);
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void (*synchronize) (ggml_backend_t backend);
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// (optional) copy tensor between different backends, allow for single-copy tranfers
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void (*cpy_tensor_from)(ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst);
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void (*cpy_tensor_to) (ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst);
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// (optional) asynchroneous tensor copy
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void (*cpy_tensor_from_async)(ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst);
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void (*cpy_tensor_to_async) (ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst);
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void (*synchronize) (ggml_backend_t backend);
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// compute graph with a plan
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ggml_backend_graph_plan_t (*graph_plan_create) (ggml_backend_t backend, struct ggml_cgraph * cgraph);
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@ -82,6 +98,15 @@ extern "C" {
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ggml_backend_context_t context;
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};
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//
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// Backend registry
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//
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typedef ggml_backend_t (*ggml_backend_init_fn)(const char * params, void * user_data);
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void ggml_backend_register(const char * name, ggml_backend_init_fn init_fn, ggml_backend_buffer_type_t default_buffer_type, void * user_data);
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#ifdef __cplusplus
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}
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#endif
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749
ggml-backend.c
749
ggml-backend.c
File diff suppressed because it is too large
Load Diff
@ -7,41 +7,44 @@
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extern "C" {
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#endif
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typedef struct ggml_backend_buffer_type * ggml_backend_buffer_type_t;
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typedef struct ggml_backend_buffer * ggml_backend_buffer_t;
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typedef struct ggml_backend * ggml_backend_t;
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typedef void * ggml_backend_graph_plan_t;
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//
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// Backend buffer
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//
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struct ggml_backend_buffer;
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typedef struct ggml_backend_buffer * ggml_backend_buffer_t;
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// buffer type
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GGML_API ggml_backend_buffer_t ggml_backend_buft_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size);
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GGML_API size_t ggml_backend_buft_get_alignment (ggml_backend_buffer_type_t buft);
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GGML_API size_t ggml_backend_buft_get_alloc_size(ggml_backend_buffer_type_t buft, struct ggml_tensor * tensor);
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GGML_API bool ggml_backend_buft_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend);
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// backend buffer functions
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// buffer
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GGML_API void ggml_backend_buffer_free (ggml_backend_buffer_t buffer);
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GGML_API size_t ggml_backend_buffer_get_alignment (ggml_backend_buffer_t buffer);
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GGML_API void * ggml_backend_buffer_get_base (ggml_backend_buffer_t buffer);
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GGML_API size_t ggml_backend_buffer_get_size (ggml_backend_buffer_t buffer);
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GGML_API size_t ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
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GGML_API void ggml_backend_buffer_init_tensor (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
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GGML_API void ggml_backend_buffer_free_tensor (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
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GGML_API size_t ggml_backend_buffer_get_alignment (ggml_backend_buffer_t buffer);
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GGML_API size_t ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
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GGML_API ggml_backend_buffer_type_t ggml_backend_buffer_type(ggml_backend_buffer_t buffer);
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//
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// Backend
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//
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struct ggml_backend;
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typedef struct ggml_backend * ggml_backend_t;
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typedef void * ggml_backend_graph_plan_t;
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GGML_API ggml_backend_t ggml_get_backend(const struct ggml_tensor * tensor);
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GGML_API const char * ggml_backend_name(ggml_backend_t backend);
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GGML_API void ggml_backend_free(ggml_backend_t backend);
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GGML_API ggml_backend_buffer_type_t ggml_backend_get_default_buffer_type(ggml_backend_t backend);
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GGML_API ggml_backend_buffer_t ggml_backend_alloc_buffer(ggml_backend_t backend, size_t size);
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GGML_API size_t ggml_backend_get_alignment(ggml_backend_t backend);
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GGML_API void ggml_backend_tensor_set_async( struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
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GGML_API void ggml_backend_tensor_get_async(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);
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GGML_API void ggml_backend_tensor_set_async(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
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GGML_API void ggml_backend_tensor_get_async(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);
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GGML_API void ggml_backend_tensor_set( struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
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GGML_API void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);
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@ -57,6 +60,7 @@ extern "C" {
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// tensor copy between different backends
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GGML_API void ggml_backend_tensor_copy(struct ggml_tensor * src, struct ggml_tensor * dst);
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GGML_API void ggml_backend_tensor_copy_async(ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst); // automatic fallback to sync copy
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//
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// CPU backend
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@ -68,8 +72,23 @@ extern "C" {
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GGML_API void ggml_backend_cpu_set_n_threads(ggml_backend_t backend_cpu, int n_threads);
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// Create a backend buffer from an existing pointer
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GGML_API ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(ggml_backend_t backend_cpu, void * ptr, size_t size);
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GGML_API ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(void * ptr, size_t size);
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GGML_API ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void);
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//
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// Backend registry
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//
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// The backend registry is a registry of all the available backends, and allows initializing backends in a generic way
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GGML_API size_t ggml_backend_reg_get_count(void);
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GGML_API size_t ggml_backend_reg_find_by_name(const char * name);
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GGML_API ggml_backend_t ggml_backend_reg_init_backend_from_str(const char * backend_str); // str is name[:params]
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GGML_API const char * ggml_backend_reg_get_name(size_t i);
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GGML_API ggml_backend_t ggml_backend_reg_init_backend(size_t i, const char * params); // params is backend-specific
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GGML_API ggml_backend_buffer_type_t ggml_backend_reg_get_default_buffer_type(size_t i);
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GGML_API ggml_backend_buffer_t ggml_backend_reg_alloc_buffer(size_t i, size_t size);
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//
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// Backend scheduler
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@ -131,6 +150,32 @@ extern "C" {
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ggml_backend_sched_t sched,
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struct ggml_cgraph * graph);
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//
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// Utils
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//
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struct ggml_backend_graph_copy {
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ggml_backend_buffer_t buffer;
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struct ggml_context * ctx_allocated;
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struct ggml_context * ctx_unallocated;
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struct ggml_cgraph * graph;
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};
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// Copy a graph to a different backend
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GGML_API struct ggml_backend_graph_copy ggml_backend_graph_copy(ggml_backend_t backend, struct ggml_cgraph * graph);
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GGML_API void ggml_backend_graph_copy_free(struct ggml_backend_graph_copy copy);
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typedef bool (*ggml_backend_eval_callback)(int node_index, struct ggml_tensor * t1, struct ggml_tensor * t2, void * user_data);
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// Compare the output of two backends
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GGML_API void 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);
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// Tensor initialization
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GGML_API void ggml_backend_tensor_alloc(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, void * addr);
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GGML_API void ggml_backend_view_init(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
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#ifdef __cplusplus
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}
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#endif
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1659
ggml-cuda.cu
1659
ggml-cuda.cu
File diff suppressed because it is too large
Load Diff
10
ggml-cuda.h
10
ggml-cuda.h
@ -49,7 +49,15 @@ GGML_API int ggml_cuda_get_device_count(void);
|
||||
GGML_API void ggml_cuda_get_device_description(int device, char * description, size_t description_size);
|
||||
|
||||
// backend API
|
||||
GGML_API ggml_backend_t ggml_backend_cuda_init(void); // TODO: take a list of devices to use
|
||||
GGML_API ggml_backend_t ggml_backend_cuda_init(int device);
|
||||
|
||||
GGML_API bool ggml_backend_is_cuda(ggml_backend_t backend);
|
||||
GGML_API int ggml_backend_cuda_get_device(ggml_backend_t backend);
|
||||
|
||||
GGML_API ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device);
|
||||
|
||||
// pinned host buffer for use with CPU backend for faster copies between CPU and GPU
|
||||
GGML_API ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type(void);
|
||||
|
||||
#ifdef __cplusplus
|
||||
}
|
||||
|
||||
@ -232,7 +232,7 @@ bool ggml_hash_contains (const struct ggml_hash_set hash_set, struct ggml
|
||||
// returns GGML_HASHTABLE_FULL if table is full, otherwise the current index of the key or where it should be inserted
|
||||
size_t ggml_hash_find (const struct ggml_hash_set hash_set, struct ggml_tensor * key);
|
||||
|
||||
// returns GGML_HAHSHTABLE_ALREADY_EXISTS if key already exists, index otherwise, asserts if table is full
|
||||
// returns GGML_HASHTABLE_ALREADY_EXISTS if key already exists, index otherwise, asserts if table is full
|
||||
size_t ggml_hash_insert ( struct ggml_hash_set hash_set, struct ggml_tensor * key);
|
||||
|
||||
// return index, asserts if table is full
|
||||
|
||||
@ -52,11 +52,6 @@ void ggml_metal_free(struct ggml_metal_context * ctx);
|
||||
void * ggml_metal_host_malloc(size_t n);
|
||||
void ggml_metal_host_free (void * data);
|
||||
|
||||
// helper to check if the device supports a specific family
|
||||
// ideally, the user code should be doing these checks
|
||||
// ref: https://developer.apple.com/metal/Metal-Feature-Set-Tables.pdf
|
||||
bool ggml_metal_supports_family(struct ggml_metal_context * ctx, int family);
|
||||
|
||||
// set the number of command buffers to use
|
||||
void ggml_metal_set_n_cb(struct ggml_metal_context * ctx, int n_cb);
|
||||
|
||||
@ -104,7 +99,11 @@ GGML_API ggml_backend_t ggml_backend_metal_init(void);
|
||||
GGML_API bool ggml_backend_is_metal(ggml_backend_t backend);
|
||||
|
||||
GGML_API void ggml_backend_metal_set_n_cb(ggml_backend_t backend, int n_cb);
|
||||
GGML_API ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void);
|
||||
|
||||
// helper to check if the device supports a specific family
|
||||
// ideally, the user code should be doing these checks
|
||||
// ref: https://developer.apple.com/metal/Metal-Feature-Set-Tables.pdf
|
||||
GGML_API bool ggml_backend_metal_supports_family(ggml_backend_t backend, int family);
|
||||
|
||||
#ifdef __cplusplus
|
||||
|
||||
740
ggml-metal.m
740
ggml-metal.m
File diff suppressed because it is too large
Load Diff
1005
ggml-metal.metal
1005
ggml-metal.metal
File diff suppressed because it is too large
Load Diff
@ -1,20 +1,18 @@
|
||||
#include "ggml.h"
|
||||
#include "ggml-opencl.h"
|
||||
|
||||
#include <array>
|
||||
#include <atomic>
|
||||
#include <cstdio>
|
||||
#include <cstdlib>
|
||||
#include <cstring>
|
||||
#include <limits>
|
||||
#include <sstream>
|
||||
#include <vector>
|
||||
#include <limits>
|
||||
|
||||
#define CL_TARGET_OPENCL_VERSION 110
|
||||
#include <clblast.h>
|
||||
|
||||
#include <stdlib.h>
|
||||
#include <stdio.h>
|
||||
#include <string.h>
|
||||
|
||||
#include "ggml.h"
|
||||
|
||||
#if defined(_MSC_VER)
|
||||
#pragma warning(disable: 4244 4267) // possible loss of data
|
||||
#endif
|
||||
|
||||
@ -19,7 +19,7 @@
|
||||
#ifdef __wasm_simd128__
|
||||
#include <wasm_simd128.h>
|
||||
#else
|
||||
#ifdef __POWER9_VECTOR__
|
||||
#if defined(__POWER9_VECTOR__) || defined(__powerpc64__)
|
||||
#include <altivec.h>
|
||||
#undef bool
|
||||
#define bool _Bool
|
||||
|
||||
67
ggml.h
67
ggml.h
@ -244,11 +244,10 @@
|
||||
#define GGML_ASSERT(x) \
|
||||
do { \
|
||||
if (!(x)) { \
|
||||
fprintf(stderr, "GGML_ASSERT: %s:%d: %s\n", __FILE__, __LINE__, #x); \
|
||||
fflush(stderr); \
|
||||
fflush(stdout); \
|
||||
fprintf(stderr, "GGML_ASSERT: %s:%d: %s\n", __FILE__, __LINE__, #x); \
|
||||
ggml_print_backtrace(); \
|
||||
exit(1); \
|
||||
abort(); \
|
||||
} \
|
||||
} while (0)
|
||||
|
||||
@ -284,6 +283,20 @@
|
||||
const type prefix##3 = (pointer)->array[3]; \
|
||||
GGML_UNUSED(prefix##3);
|
||||
|
||||
#define GGML_TENSOR_UNARY_OP_LOCALS \
|
||||
GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) \
|
||||
GGML_TENSOR_LOCALS(size_t, nb0, src0, nb) \
|
||||
GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) \
|
||||
GGML_TENSOR_LOCALS(size_t, nb, dst, nb)
|
||||
|
||||
#define GGML_TENSOR_BINARY_OP_LOCALS \
|
||||
GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) \
|
||||
GGML_TENSOR_LOCALS(size_t, nb0, src0, nb) \
|
||||
GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne) \
|
||||
GGML_TENSOR_LOCALS(size_t, nb1, src1, nb) \
|
||||
GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) \
|
||||
GGML_TENSOR_LOCALS(size_t, nb, dst, nb)
|
||||
|
||||
#ifdef __cplusplus
|
||||
extern "C" {
|
||||
#endif
|
||||
@ -382,6 +395,7 @@ extern "C" {
|
||||
GGML_OP_GROUP_NORM,
|
||||
|
||||
GGML_OP_MUL_MAT,
|
||||
GGML_OP_MUL_MAT_ID,
|
||||
GGML_OP_OUT_PROD,
|
||||
|
||||
GGML_OP_SCALE,
|
||||
@ -408,8 +422,8 @@ extern "C" {
|
||||
GGML_OP_CONV_TRANSPOSE_2D,
|
||||
GGML_OP_POOL_1D,
|
||||
GGML_OP_POOL_2D,
|
||||
|
||||
GGML_OP_UPSCALE, // nearest interpolate
|
||||
GGML_OP_ARGSORT,
|
||||
|
||||
GGML_OP_FLASH_ATTN,
|
||||
GGML_OP_FLASH_FF,
|
||||
@ -449,7 +463,9 @@ extern "C" {
|
||||
GGML_UNARY_OP_GELU,
|
||||
GGML_UNARY_OP_GELU_QUICK,
|
||||
GGML_UNARY_OP_SILU,
|
||||
GGML_UNARY_OP_LEAKY
|
||||
GGML_UNARY_OP_LEAKY,
|
||||
|
||||
GGML_UNARY_OP_COUNT,
|
||||
};
|
||||
|
||||
enum ggml_object_type {
|
||||
@ -632,6 +648,9 @@ extern "C" {
|
||||
GGML_API const char * ggml_op_name (enum ggml_op op);
|
||||
GGML_API const char * ggml_op_symbol(enum ggml_op op);
|
||||
|
||||
GGML_API const char * ggml_unary_op_name(enum ggml_unary_op op);
|
||||
GGML_API const char * ggml_op_desc(const struct ggml_tensor * t); // unary or op name
|
||||
|
||||
GGML_API size_t ggml_element_size(const struct ggml_tensor * tensor);
|
||||
|
||||
GGML_API bool ggml_is_quantized(enum ggml_type type);
|
||||
@ -1028,6 +1047,15 @@ extern "C" {
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b);
|
||||
|
||||
// indirect matrix multiplication
|
||||
// ggml_mul_mat_id(ctx, as, ids, id, b) ~= ggml_mul_mat(as[ids[id]], b)
|
||||
GGML_API struct ggml_tensor * ggml_mul_mat_id(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * as[],
|
||||
struct ggml_tensor * ids,
|
||||
int id,
|
||||
struct ggml_tensor * b);
|
||||
|
||||
// A: m columns, n rows,
|
||||
// B: p columns, n rows,
|
||||
// result is m columns, p rows
|
||||
@ -1283,6 +1311,14 @@ extern "C" {
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a);
|
||||
|
||||
// fused soft_max(a*scale + mask)
|
||||
// mask is optional
|
||||
GGML_API struct ggml_tensor * ggml_soft_max_ext(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * mask,
|
||||
float scale);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_soft_max_back(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
@ -1513,6 +1549,23 @@ extern "C" {
|
||||
struct ggml_tensor * a,
|
||||
int scale_factor);
|
||||
|
||||
// sort rows
|
||||
enum ggml_sort_order {
|
||||
GGML_SORT_ASC,
|
||||
GGML_SORT_DESC,
|
||||
};
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_argsort(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
enum ggml_sort_order order);
|
||||
|
||||
// top k elements per row
|
||||
GGML_API struct ggml_tensor * ggml_top_k(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
int k);
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_flash_attn(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * q,
|
||||
@ -1574,7 +1627,6 @@ extern "C" {
|
||||
int kh);
|
||||
|
||||
// used in sam
|
||||
|
||||
GGML_API struct ggml_tensor * ggml_add_rel_pos(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
@ -1749,7 +1801,7 @@ extern "C" {
|
||||
GGML_API struct ggml_cgraph * ggml_new_graph (struct ggml_context * ctx); // size = GGML_DEFAULT_GRAPH_SIZE, grads = false
|
||||
GGML_API struct ggml_cgraph * ggml_new_graph_custom (struct ggml_context * ctx, size_t size, bool grads);
|
||||
GGML_API struct ggml_cgraph * ggml_graph_dup (struct ggml_context * ctx, struct ggml_cgraph * cgraph);
|
||||
GGML_API struct ggml_cgraph * ggml_graph_view (struct ggml_context * ctx, struct ggml_cgraph * cgraph, int i0, int i1);
|
||||
GGML_API struct ggml_cgraph ggml_graph_view (struct ggml_cgraph * cgraph, int i0, int i1);
|
||||
GGML_API void ggml_graph_cpy (struct ggml_cgraph * src, struct ggml_cgraph * dst);
|
||||
GGML_API void ggml_graph_reset (struct ggml_cgraph * cgraph); // zero grads
|
||||
GGML_API void ggml_graph_clear (struct ggml_cgraph * cgraph);
|
||||
@ -2045,6 +2097,7 @@ extern "C" {
|
||||
GGML_API double gguf_get_val_f64 (const struct gguf_context * ctx, int key_id);
|
||||
GGML_API bool gguf_get_val_bool(const struct gguf_context * ctx, int key_id);
|
||||
GGML_API const char * gguf_get_val_str (const struct gguf_context * ctx, int key_id);
|
||||
GGML_API const void * gguf_get_val_data(const struct gguf_context * ctx, int key_id);
|
||||
GGML_API int gguf_get_arr_n (const struct gguf_context * ctx, int key_id);
|
||||
GGML_API const void * gguf_get_arr_data(const struct gguf_context * ctx, int key_id);
|
||||
GGML_API const char * gguf_get_arr_str (const struct gguf_context * ctx, int key_id, int i);
|
||||
|
||||
40
whisper.cpp
40
whisper.cpp
@ -1063,7 +1063,7 @@ static ggml_backend_t whisper_backend_init(const whisper_context_params & params
|
||||
#ifdef GGML_USE_CUBLAS
|
||||
if (params.use_gpu && ggml_cublas_loaded()) {
|
||||
WHISPER_LOG_INFO("%s: using CUDA backend\n", __func__);
|
||||
backend_gpu = ggml_backend_cuda_init();
|
||||
backend_gpu = ggml_backend_cuda_init(0);
|
||||
if (!backend_gpu) {
|
||||
WHISPER_LOG_ERROR("%s: ggml_backend_cuda_init() failed\n", __func__);
|
||||
}
|
||||
@ -1077,8 +1077,7 @@ static ggml_backend_t whisper_backend_init(const whisper_context_params & params
|
||||
backend_gpu = ggml_backend_metal_init();
|
||||
if (!backend_gpu) {
|
||||
WHISPER_LOG_ERROR("%s: ggml_backend_metal_init() failed\n", __func__);
|
||||
}
|
||||
if (!ggml_backend_metal_supports_family(backend_gpu, 7)) {
|
||||
} else if (!ggml_backend_metal_supports_family(backend_gpu, 7)) {
|
||||
WHISPER_LOG_ERROR("%s: Metal GPU does not support family 7 - falling back to CPU\n", __func__);
|
||||
ggml_backend_free(backend_gpu);
|
||||
backend_gpu = NULL;
|
||||
@ -1346,10 +1345,10 @@ static bool whisper_model_load(struct whisper_model_loader * loader, whisper_con
|
||||
model.e_pe = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_audio_state, n_audio_ctx);
|
||||
|
||||
model.e_conv_1_w = ggml_new_tensor_3d(ctx, vtype, 3, n_mels, n_audio_state);
|
||||
model.e_conv_1_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, 2*n_audio_ctx, n_audio_state);
|
||||
model.e_conv_1_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, 1, n_audio_state);
|
||||
|
||||
model.e_conv_2_w = ggml_new_tensor_3d(ctx, vtype, 3, n_audio_state, n_audio_state);
|
||||
model.e_conv_2_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_audio_ctx, n_audio_state);
|
||||
model.e_conv_2_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, 1, n_audio_state);
|
||||
|
||||
model.e_ln_w = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_audio_state);
|
||||
model.e_ln_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_audio_state);
|
||||
@ -1579,9 +1578,6 @@ static bool whisper_model_load(struct whisper_model_loader * loader, whisper_con
|
||||
|
||||
auto tensor = model.tensors[name.data()];
|
||||
|
||||
const bool is_conv_bias = (name == "encoder.conv1.bias" || name == "encoder.conv2.bias");
|
||||
|
||||
if (!is_conv_bias) {
|
||||
if (ggml_nelements(tensor) != nelements) {
|
||||
WHISPER_LOG_ERROR("%s: tensor '%s' has wrong size in model file\n", __func__, name.data());
|
||||
WHISPER_LOG_ERROR("%s: shape: [%d, %d, %d], expected: [%d, %d, %d]\n",
|
||||
@ -1602,7 +1598,6 @@ static bool whisper_model_load(struct whisper_model_loader * loader, whisper_con
|
||||
__func__, name.data(), ggml_nbytes(tensor), nelements*bpe);
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
ggml_backend_t backend = wctx.backend;
|
||||
|
||||
@ -1612,7 +1607,7 @@ static bool whisper_model_load(struct whisper_model_loader * loader, whisper_con
|
||||
#ifdef GGML_USE_METAL
|
||||
|| ggml_backend_is_metal(backend)
|
||||
#endif
|
||||
) && !is_conv_bias) {
|
||||
)) {
|
||||
// for the CPU and Metal backend, we can read directly into the tensor
|
||||
loader->read(loader->context, tensor->data, ggml_nbytes(tensor));
|
||||
BYTESWAP_TENSOR(tensor);
|
||||
@ -1620,24 +1615,7 @@ static bool whisper_model_load(struct whisper_model_loader * loader, whisper_con
|
||||
// read into a temporary buffer first, then copy to device memory
|
||||
read_buf.resize(ggml_nbytes(tensor));
|
||||
|
||||
// we repeat the 2 bias tensors along dim 0:
|
||||
// [1, 512] -> [3000, 512] (conv1.bias)
|
||||
// [1, 512] -> [1500, 512] (conv2.bias)
|
||||
if (is_conv_bias) {
|
||||
loader->read(loader->context, read_buf.data(), read_buf.size() / tensor->ne[0]);
|
||||
|
||||
float * data_f32 = (float *) read_buf.data();
|
||||
for (int64_t y = 0; y < tensor->ne[1]; ++y) {
|
||||
const int64_t yy = tensor->ne[1] - y - 1;
|
||||
const float val = data_f32[yy];
|
||||
|
||||
for (int64_t x = 0; x < tensor->ne[0]; ++x) {
|
||||
data_f32[yy*tensor->ne[0] + x] = val;
|
||||
}
|
||||
}
|
||||
} else {
|
||||
loader->read(loader->context, read_buf.data(), read_buf.size());
|
||||
}
|
||||
|
||||
ggml_backend_tensor_set(tensor, read_buf.data(), 0, ggml_nbytes(tensor));
|
||||
}
|
||||
@ -1737,20 +1715,12 @@ static struct ggml_cgraph * whisper_build_graph_conv(
|
||||
// convolution + gelu
|
||||
{
|
||||
cur = ggml_conv_1d_ph(ctx0, model.e_conv_1_w, mel, 1, 1);
|
||||
if (n_ctx == hparams.n_audio_ctx) {
|
||||
cur = ggml_add(ctx0, cur, model.e_conv_1_b);
|
||||
} else {
|
||||
cur = ggml_add(ctx0, cur, ggml_cont(ctx0, ggml_view_2d(ctx0, model.e_conv_1_b, cur->ne[0], cur->ne[1], model.e_conv_1_b->nb[1], 0)));
|
||||
}
|
||||
|
||||
cur = ggml_gelu(ctx0, cur);
|
||||
|
||||
cur = ggml_conv_1d_ph(ctx0, model.e_conv_2_w, cur, 2, 1);
|
||||
if (n_ctx == hparams.n_audio_ctx) {
|
||||
cur = ggml_add(ctx0, cur, model.e_conv_2_b);
|
||||
} else {
|
||||
cur = ggml_add(ctx0, cur, ggml_cont(ctx0, ggml_view_2d(ctx0, model.e_conv_2_b, cur->ne[0], cur->ne[1], model.e_conv_2_b->nb[1], 0)));
|
||||
}
|
||||
|
||||
cur = ggml_gelu(ctx0, cur);
|
||||
}
|
||||
|
||||
Loading…
Reference in New Issue
Block a user