Merge 3100a05ba1 into 902368a06b
This commit is contained in:
Herman Semenoff
GitHub
commit
c5c9676583
7 changed files with 80 additions and 79 deletions
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@ -46,17 +46,17 @@ GGML_API void ggml_tallocr_alloc(struct ggml_tallocr * talloc, st
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typedef struct ggml_gallocr * ggml_gallocr_t;
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GGML_API ggml_gallocr_t ggml_gallocr_new(ggml_backend_buffer_type_t buft);
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GGML_API ggml_gallocr_t ggml_gallocr_new_n(ggml_backend_buffer_type_t * bufts, int n_bufs);
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GGML_API ggml_gallocr_t ggml_gallocr_new_n(const ggml_backend_buffer_type_t * bufts, int n_bufs);
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GGML_API void ggml_gallocr_free(ggml_gallocr_t galloc);
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// pre-allocate buffers from a measure graph - does not allocate or modify the graph
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// call with a worst-case graph to avoid buffer reallocations
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// not strictly required for single buffer usage: ggml_gallocr_alloc_graph will reallocate the buffers automatically if needed
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// returns false if the buffer allocation failed
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GGML_API bool ggml_gallocr_reserve(ggml_gallocr_t galloc, struct ggml_cgraph * graph);
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GGML_API bool ggml_gallocr_reserve(ggml_gallocr_t galloc, const struct ggml_cgraph * graph);
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GGML_API bool ggml_gallocr_reserve_n(
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ggml_gallocr_t galloc,
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struct ggml_cgraph * graph,
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const struct ggml_cgraph * graph,
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const int * node_buffer_ids,
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const int * leaf_buffer_ids);
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@ -698,7 +698,7 @@ extern "C" {
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GGML_API size_t ggml_used_mem(const struct ggml_context * ctx);
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GGML_API bool ggml_get_no_alloc(struct ggml_context * ctx);
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GGML_API bool ggml_get_no_alloc(const struct ggml_context * ctx);
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GGML_API void ggml_set_no_alloc(struct ggml_context * ctx, bool no_alloc);
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GGML_API void * ggml_get_mem_buffer (const struct ggml_context * ctx);
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@ -745,7 +745,7 @@ extern "C" {
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// Context tensor enumeration and lookup
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GGML_API struct ggml_tensor * ggml_get_first_tensor(const struct ggml_context * ctx);
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GGML_API struct ggml_tensor * ggml_get_next_tensor (const struct ggml_context * ctx, struct ggml_tensor * tensor);
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GGML_API struct ggml_tensor * ggml_get_tensor(struct ggml_context * ctx, const char * name);
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GGML_API struct ggml_tensor * ggml_get_tensor(const struct ggml_context * ctx, const char * name);
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// Converts a flat index into coordinates
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GGML_API void ggml_unravel_index(const struct ggml_tensor * tensor, int64_t i, int64_t * i0, int64_t * i1, int64_t * i2, int64_t * i3);
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@ -763,7 +763,7 @@ extern "C" {
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// Tensor flags
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GGML_API void ggml_set_input(struct ggml_tensor * tensor);
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GGML_API void ggml_set_output(struct ggml_tensor * tensor);
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GGML_API void ggml_set_param(struct ggml_context * ctx, struct ggml_tensor * tensor);
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GGML_API void ggml_set_param(const struct ggml_context * ctx, struct ggml_tensor * tensor);
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GGML_API void ggml_set_loss(struct ggml_tensor * tensor);
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//
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@ -927,13 +927,13 @@ extern "C" {
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GGML_API struct ggml_tensor * ggml_repeat(
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struct ggml_context * ctx,
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struct ggml_tensor * a,
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struct ggml_tensor * b);
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const struct ggml_tensor * b);
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// sums repetitions in a into shape of b
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GGML_API struct ggml_tensor * ggml_repeat_back(
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struct ggml_context * ctx,
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struct ggml_tensor * a,
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struct ggml_tensor * b);
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const struct ggml_tensor * b);
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// concat a and b along dim
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// used in stable-diffusion
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@ -1243,7 +1243,7 @@ extern "C" {
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GGML_API struct ggml_tensor * ggml_reshape(
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struct ggml_context * ctx,
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struct ggml_tensor * a,
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struct ggml_tensor * b);
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const struct ggml_tensor * b);
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// return view(a)
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// TODO: when we start computing gradient, make a copy instead of view
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@ -1335,7 +1335,7 @@ extern "C" {
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struct ggml_context * ctx,
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struct ggml_tensor * a, // gradients of ggml_get_rows result
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struct ggml_tensor * b, // row indices
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struct ggml_tensor * c); // data for ggml_get_rows, only used for its shape
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const struct ggml_tensor * c); // data for ggml_get_rows, only used for its shape
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GGML_API struct ggml_tensor * ggml_diag(
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struct ggml_context * ctx,
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@ -1563,7 +1563,7 @@ extern "C" {
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struct ggml_context * ctx,
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struct ggml_tensor * a, // convolution kernel
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struct ggml_tensor * b, // gradient of im2col output
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int64_t * ne, // shape of im2col input
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const int64_t * ne, // shape of im2col input
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int s0, // stride dimension 0
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int s1, // stride dimension 1
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int p0, // padding dimension 0
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@ -2062,15 +2062,16 @@ extern "C" {
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// graph allocation in a context
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GGML_API struct ggml_cgraph * ggml_new_graph (struct ggml_context * ctx); // size = GGML_DEFAULT_GRAPH_SIZE, grads = false
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GGML_API struct ggml_cgraph * ggml_new_graph_custom(struct ggml_context * ctx, size_t size, bool grads);
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GGML_API struct ggml_cgraph * ggml_graph_dup (struct ggml_context * ctx, struct ggml_cgraph * cgraph);
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GGML_API void ggml_graph_cpy (struct ggml_cgraph * src, struct ggml_cgraph * dst);
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GGML_API void ggml_graph_reset (struct ggml_cgraph * cgraph); // set regular grads + optimizer momenta to 0, set loss grad to 1
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GGML_API struct ggml_cgraph * ggml_graph_dup (struct ggml_context * ctx, const struct ggml_cgraph * cgraph);
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GGML_API void ggml_graph_cpy (const struct ggml_cgraph * src, struct ggml_cgraph * dst);
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GGML_API void ggml_graph_reset (
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const struct ggml_cgraph * cgraph); // set regular grads + optimizer momenta to 0, set loss grad to 1
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GGML_API void ggml_graph_clear (struct ggml_cgraph * cgraph);
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GGML_API int ggml_graph_size (struct ggml_cgraph * cgraph);
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GGML_API struct ggml_tensor * ggml_graph_node (struct ggml_cgraph * cgraph, int i); // if i < 0, returns nodes[n_nodes + i]
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GGML_API struct ggml_tensor ** ggml_graph_nodes (struct ggml_cgraph * cgraph);
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GGML_API int ggml_graph_n_nodes(struct ggml_cgraph * cgraph);
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GGML_API int ggml_graph_size (const struct ggml_cgraph * cgraph);
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GGML_API struct ggml_tensor * ggml_graph_node (const struct ggml_cgraph * cgraph, int i); // if i < 0, returns nodes[n_nodes + i]
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GGML_API struct ggml_tensor ** ggml_graph_nodes (const struct ggml_cgraph * cgraph);
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GGML_API int ggml_graph_n_nodes(const struct ggml_cgraph * cgraph);
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GGML_API void ggml_graph_add_node(struct ggml_cgraph * cgraph, struct ggml_tensor * tensor);
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@ -377,7 +377,7 @@ struct ggml_gallocr {
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int n_leafs;
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};
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ggml_gallocr_t ggml_gallocr_new_n(ggml_backend_buffer_type_t * bufts, int n_bufs) {
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ggml_gallocr_t ggml_gallocr_new_n(const ggml_backend_buffer_type_t * bufts, int n_bufs) {
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ggml_gallocr_t galloc = (ggml_gallocr_t)calloc(1, sizeof(struct ggml_gallocr));
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GGML_ASSERT(galloc != NULL);
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@ -563,7 +563,7 @@ static int get_node_buffer_id(const int * node_buffer_ids, int i) {
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return node_buffer_ids ? node_buffer_ids[i] : 0;
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}
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static void ggml_gallocr_alloc_graph_impl(ggml_gallocr_t galloc, struct ggml_cgraph * graph, const int * node_buffer_ids, const int * leaf_buffer_ids) {
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static void ggml_gallocr_alloc_graph_impl(ggml_gallocr_t galloc, const struct ggml_cgraph * graph, const int * node_buffer_ids, const int * leaf_buffer_ids) {
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// clear hash tables
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ggml_hash_set_reset(&galloc->hash_set);
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memset(galloc->hash_values, 0, sizeof(struct hash_node) * galloc->hash_set.size);
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@ -670,7 +670,7 @@ static void ggml_gallocr_alloc_graph_impl(ggml_gallocr_t galloc, struct ggml_cgr
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}
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}
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bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, const int * node_buffer_ids, const int * leaf_buffer_ids) {
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bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, const struct ggml_cgraph * graph, const int * node_buffer_ids, const int * leaf_buffer_ids) {
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size_t min_hash_size = graph->n_nodes + graph->n_leafs;
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// add 25% margin to avoid hash collisions
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min_hash_size += min_hash_size / 4;
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@ -780,11 +780,11 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c
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return true;
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}
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bool ggml_gallocr_reserve(ggml_gallocr_t galloc, struct ggml_cgraph *graph) {
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bool ggml_gallocr_reserve(ggml_gallocr_t galloc, const struct ggml_cgraph *graph) {
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return ggml_gallocr_reserve_n(galloc, graph, NULL, NULL);
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}
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static void ggml_gallocr_init_tensor(ggml_gallocr_t galloc, struct ggml_tensor * tensor, struct tensor_alloc * tensor_alloc) {
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static void ggml_gallocr_init_tensor(ggml_gallocr_t galloc, struct ggml_tensor * tensor, const struct tensor_alloc * tensor_alloc) {
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int buffer_id = tensor_alloc->buffer_id;
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assert(tensor->data || tensor->view_src || ggml_backend_buffer_get_alloc_size(galloc->buffers[buffer_id], tensor) <= tensor_alloc->size_max);
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@ -813,7 +813,7 @@ static void ggml_gallocr_init_tensor(ggml_gallocr_t galloc, struct ggml_tensor *
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}
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}
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static bool ggml_gallocr_node_needs_realloc(ggml_gallocr_t galloc, struct ggml_tensor * node, struct tensor_alloc * talloc) {
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static bool ggml_gallocr_node_needs_realloc(ggml_gallocr_t galloc, struct ggml_tensor * node, const struct tensor_alloc * talloc) {
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size_t node_size = 0;
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if (!node->data && !node->view_src) {
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GGML_ASSERT(talloc->buffer_id >= 0); // prevent segfault when misusing the API
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@ -822,7 +822,7 @@ static bool ggml_gallocr_node_needs_realloc(ggml_gallocr_t galloc, struct ggml_t
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return talloc->size_max >= node_size;
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}
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static bool ggml_gallocr_needs_realloc(ggml_gallocr_t galloc, struct ggml_cgraph * graph) {
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static bool ggml_gallocr_needs_realloc(ggml_gallocr_t galloc, const struct ggml_cgraph * graph) {
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if (galloc->n_nodes != graph->n_nodes) {
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#ifndef NDEBUG
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GGML_LOG_DEBUG("%s: graph has different number of nodes\n", __func__);
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@ -933,8 +933,8 @@ size_t ggml_gallocr_get_buffer_size(ggml_gallocr_t galloc, int buffer_id) {
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// utils
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static bool alloc_tensor_range(struct ggml_context * ctx,
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struct ggml_tensor * first, struct ggml_tensor * last,
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static bool alloc_tensor_range(const struct ggml_context * ctx,
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struct ggml_tensor * first, const struct ggml_tensor * last,
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ggml_backend_buffer_type_t buft, size_t size,
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ggml_backend_buffer_t ** buffers, size_t * n_buffers) {
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ggml_backend_buffer_t buffer = ggml_backend_buft_alloc_buffer(buft, size);
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@ -124,7 +124,7 @@ static void * dl_get_sym(dl_handle * handle, const char * name) {
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using dl_handle = void;
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struct dl_handle_deleter {
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void operator()(void * handle) {
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void operator()(void * handle) const {
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dlclose(handle);
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}
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};
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@ -188,13 +188,13 @@ struct ggml_hash_set {
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};
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struct ggml_hash_set ggml_hash_set_new(size_t size);
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void ggml_hash_set_free(struct ggml_hash_set * hash_set);
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void ggml_hash_set_free(const struct ggml_hash_set * hash_set);
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// returns the minimum size for a hash set that can hold min_sz elements
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size_t ggml_hash_size(size_t min_sz);
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// remove all elements from the hash set
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void ggml_hash_set_reset(struct ggml_hash_set * hash_set);
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void ggml_hash_set_reset(const struct ggml_hash_set * hash_set);
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// returns true if key is in the hash set
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static bool ggml_hash_contains(const struct ggml_hash_set * hash_set, struct ggml_tensor * key);
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@ -302,7 +302,7 @@ struct ggml_cgraph {
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// returns a slice of cgraph with nodes [i0, i1)
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// the slice does not have leafs or gradients
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// if you need the gradients, get them from the original graph
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struct ggml_cgraph ggml_graph_view(struct ggml_cgraph * cgraph, int i0, int i1);
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struct ggml_cgraph ggml_graph_view(const struct ggml_cgraph * cgraph, int i0, int i1);
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// Memory allocation
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@ -1478,7 +1478,7 @@ size_t ggml_used_mem(const struct ggml_context * ctx) {
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return ctx->objects_end == NULL ? 0 : ctx->objects_end->offs + ctx->objects_end->size;
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}
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bool ggml_get_no_alloc(struct ggml_context * ctx) {
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bool ggml_get_no_alloc(const struct ggml_context * ctx) {
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return ctx->no_alloc;
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}
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@ -1789,7 +1789,7 @@ struct ggml_tensor * ggml_get_next_tensor(const struct ggml_context * ctx, struc
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return NULL;
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}
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struct ggml_tensor * ggml_get_tensor(struct ggml_context * ctx, const char * name) {
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struct ggml_tensor * ggml_get_tensor(const struct ggml_context * ctx, const char * name) {
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struct ggml_object * obj = ctx->objects_begin;
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char * const mem_buffer = ctx->mem_buffer;
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@ -1952,7 +1952,7 @@ static struct ggml_tensor * ggml_acc_impl(
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struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
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int32_t params[] = { nb1, nb2, nb3, offset, inplace ? 1 : 0 };
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const int32_t params[] = { nb1, nb2, nb3, offset, inplace ? 1 : 0 };
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ggml_set_op_params(result, params, sizeof(params));
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result->op = GGML_OP_ACC;
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@ -2292,8 +2292,8 @@ struct ggml_tensor * ggml_count_equal(
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struct ggml_tensor * ggml_repeat(
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struct ggml_context * ctx,
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struct ggml_tensor * a,
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struct ggml_tensor * b) {
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struct ggml_tensor * a,
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const struct ggml_tensor * b) {
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GGML_ASSERT(ggml_can_repeat(a, b));
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struct ggml_tensor * result = ggml_new_tensor(ctx, a->type, GGML_MAX_DIMS, b->ne);
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@ -2308,8 +2308,8 @@ struct ggml_tensor * ggml_repeat(
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struct ggml_tensor * ggml_repeat_back(
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struct ggml_context * ctx,
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struct ggml_tensor * a,
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struct ggml_tensor * b) {
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struct ggml_tensor * a,
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const struct ggml_tensor * b) {
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GGML_ASSERT(ggml_can_repeat(b, a));
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struct ggml_tensor * result = ggml_new_tensor(ctx, a->type, GGML_MAX_DIMS, b->ne);
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@ -2836,7 +2836,7 @@ static struct ggml_tensor * ggml_set_impl(
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struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
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GGML_ASSERT(offset < (size_t)(1 << 30));
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int32_t params[] = { nb1, nb2, nb3, offset, inplace ? 1 : 0 };
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const int32_t params[] = { nb1, nb2, nb3, offset, inplace ? 1 : 0 };
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ggml_set_op_params(result, params, sizeof(params));
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result->op = GGML_OP_SET;
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@ -3014,7 +3014,7 @@ struct ggml_tensor * ggml_cont_4d(
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struct ggml_tensor * ggml_reshape(
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struct ggml_context * ctx,
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struct ggml_tensor * a,
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struct ggml_tensor * b) {
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const struct ggml_tensor * b) {
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GGML_ASSERT(ggml_is_contiguous(a));
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// as only the shape of b is relevant, and not its memory layout, b is allowed to be non contiguous.
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GGML_ASSERT(ggml_nelements(a) == ggml_nelements(b));
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@ -3247,7 +3247,7 @@ struct ggml_tensor * ggml_permute(
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result->op = GGML_OP_PERMUTE;
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result->src[0] = a;
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int32_t params[] = { axis0, axis1, axis2, axis3 };
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const int32_t params[] = { axis0, axis1, axis2, axis3 };
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ggml_set_op_params(result, params, sizeof(params));
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return result;
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@ -3302,8 +3302,8 @@ struct ggml_tensor * ggml_get_rows(
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struct ggml_tensor * ggml_get_rows_back(
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struct ggml_context * ctx,
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struct ggml_tensor * a,
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struct ggml_tensor * b,
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struct ggml_tensor * c) {
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struct ggml_tensor * b,
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const struct ggml_tensor * c) {
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GGML_ASSERT(ggml_is_matrix(a) && ggml_is_vector(b) && b->type == GGML_TYPE_I32);
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GGML_ASSERT(ggml_is_matrix(c) && (a->ne[0] == c->ne[0]));
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@ -3343,7 +3343,7 @@ static struct ggml_tensor * ggml_diag_mask_inf_impl(
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bool inplace) {
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struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
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||||
|
||||
int32_t params[] = { n_past };
|
||||
const int32_t params[] = { n_past };
|
||||
ggml_set_op_params(result, params, sizeof(params));
|
||||
|
||||
result->op = GGML_OP_DIAG_MASK_INF;
|
||||
|
|
@ -3375,7 +3375,7 @@ static struct ggml_tensor * ggml_diag_mask_zero_impl(
|
|||
bool inplace) {
|
||||
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
|
||||
|
||||
int32_t params[] = { n_past };
|
||||
const int32_t params[] = { n_past };
|
||||
ggml_set_op_params(result, params, sizeof(params));
|
||||
|
||||
result->op = GGML_OP_DIAG_MASK_ZERO;
|
||||
|
|
@ -3423,7 +3423,7 @@ static struct ggml_tensor * ggml_soft_max_impl(
|
|||
|
||||
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
|
||||
|
||||
float params[] = { scale, max_bias };
|
||||
const float params[] = { scale, max_bias };
|
||||
ggml_set_op_params(result, params, sizeof(params));
|
||||
|
||||
result->op = GGML_OP_SOFT_MAX;
|
||||
|
|
@ -3758,7 +3758,7 @@ struct ggml_tensor * ggml_clamp(
|
|||
// TODO: when implement backward, fix this:
|
||||
struct ggml_tensor * result = ggml_view_tensor(ctx, a);
|
||||
|
||||
float params[] = { min, max };
|
||||
const float params[] = { min, max };
|
||||
ggml_set_op_params(result, params, sizeof(params));
|
||||
|
||||
result->op = GGML_OP_CLAMP;
|
||||
|
|
@ -3809,7 +3809,7 @@ struct ggml_tensor * ggml_im2col(
|
|||
};
|
||||
|
||||
struct ggml_tensor * result = ggml_new_tensor(ctx, dst_type, 4, ne);
|
||||
int32_t params[] = { s0, s1, p0, p1, d0, d1, (is_2D ? 1 : 0) };
|
||||
const int32_t params[] = { s0, s1, p0, p1, d0, d1, (is_2D ? 1 : 0) };
|
||||
ggml_set_op_params(result, params, sizeof(params));
|
||||
|
||||
result->op = GGML_OP_IM2COL;
|
||||
|
|
@ -3822,8 +3822,8 @@ struct ggml_tensor * ggml_im2col(
|
|||
struct ggml_tensor * ggml_im2col_back(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b,
|
||||
int64_t * ne,
|
||||
struct ggml_tensor * b,
|
||||
const int64_t * ne,
|
||||
int s0,
|
||||
int s1,
|
||||
int p0,
|
||||
|
|
@ -3832,7 +3832,7 @@ struct ggml_tensor * ggml_im2col_back(
|
|||
int d1,
|
||||
bool is_2D) {
|
||||
struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);
|
||||
int32_t params[] = { s0, s1, p0, p1, d0, d1, (is_2D ? 1 : 0) };
|
||||
const int32_t params[] = { s0, s1, p0, p1, d0, d1, (is_2D ? 1 : 0) };
|
||||
ggml_set_op_params(result, params, sizeof(params));
|
||||
|
||||
result->op = GGML_OP_IM2COL_BACK;
|
||||
|
|
@ -3932,7 +3932,7 @@ GGML_API struct ggml_tensor * ggml_conv_transpose_1d(
|
|||
};
|
||||
struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);
|
||||
|
||||
int32_t params[] = { s0, p0, d0 };
|
||||
const int32_t params[] = { s0, p0, d0 };
|
||||
ggml_set_op_params(result, params, sizeof(params));
|
||||
|
||||
result->op = GGML_OP_CONV_TRANSPOSE_1D;
|
||||
|
|
@ -4067,7 +4067,7 @@ struct ggml_tensor * ggml_pool_1d(
|
|||
};
|
||||
struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);
|
||||
|
||||
int32_t params[] = { op, k0, s0, p0 };
|
||||
const int32_t params[] = { op, k0, s0, p0 };
|
||||
ggml_set_op_params(result, params, sizeof(params));
|
||||
|
||||
result->op = GGML_OP_POOL_1D;
|
||||
|
|
@ -4097,7 +4097,7 @@ struct ggml_tensor * ggml_pool_2d(
|
|||
};
|
||||
result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);
|
||||
|
||||
int32_t params[] = { op, k0, k1, s0, s1, p0, p1 };
|
||||
const int32_t params[] = { op, k0, k1, s0, s1, p0, p1 };
|
||||
ggml_set_op_params(result, params, sizeof(params));
|
||||
|
||||
result->op = GGML_OP_POOL_2D;
|
||||
|
|
@ -4116,11 +4116,10 @@ struct ggml_tensor * ggml_pool_2d_back(
|
|||
int s0,
|
||||
int s1,
|
||||
float p0,
|
||||
float p1) {
|
||||
struct ggml_tensor * result;
|
||||
result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, af->ne);
|
||||
float p1) {
|
||||
struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, af->ne);
|
||||
|
||||
int32_t params[] = { op, k0, k1, s0, s1, p0, p1 };
|
||||
const int32_t params[] = { op, k0, k1, s0, s1, p0, p1 };
|
||||
ggml_set_op_params(result, params, sizeof(params));
|
||||
|
||||
result->op = GGML_OP_POOL_2D_BACK;
|
||||
|
|
@ -4206,13 +4205,9 @@ struct ggml_tensor * ggml_pad_reflect_1d(
|
|||
GGML_ASSERT(ggml_is_contiguous(a));
|
||||
GGML_ASSERT(a->type == GGML_TYPE_F32);
|
||||
|
||||
struct ggml_tensor * result = ggml_new_tensor_4d(ctx, a->type,
|
||||
a->ne[0] + p0 + p1,
|
||||
a->ne[1],
|
||||
a->ne[2],
|
||||
a->ne[3]);
|
||||
struct ggml_tensor * result = ggml_new_tensor_4d(ctx, a->type, a->ne[0] + p0 + p1, a->ne[1], a->ne[2], a->ne[3]);
|
||||
|
||||
int32_t params[] = { p0, p1 };
|
||||
const int32_t params[] = { p0, p1 };
|
||||
ggml_set_op_params(result, params, sizeof(params));
|
||||
|
||||
result->op = GGML_OP_PAD_REFLECT_1D;
|
||||
|
|
@ -4332,7 +4327,7 @@ struct ggml_tensor * ggml_flash_attn_ext(
|
|||
int64_t ne[4] = { q->ne[0], q->ne[2], q->ne[1], q->ne[3] };
|
||||
struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);
|
||||
|
||||
float params[] = { scale, max_bias, logit_softcap };
|
||||
const float params[] = { scale, max_bias, logit_softcap };
|
||||
ggml_set_op_params(result, params, sizeof(params));
|
||||
|
||||
result->op = GGML_OP_FLASH_ATTN_EXT;
|
||||
|
|
@ -4531,10 +4526,13 @@ struct ggml_tensor * ggml_win_part(
|
|||
const int npy = (py + a->ne[2])/w;
|
||||
const int np = npx*npy;
|
||||
|
||||
const int64_t ne[4] = { a->ne[0], w, w, np, };
|
||||
const int64_t ne[4] = { a->ne[0], w,
|
||||
w,
|
||||
np,
|
||||
};
|
||||
struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);
|
||||
|
||||
int32_t params[] = { npx, npy, w };
|
||||
const int32_t params[] = { npx, npy, w };
|
||||
ggml_set_op_params(result, params, sizeof(params));
|
||||
|
||||
result->op = GGML_OP_WIN_PART;
|
||||
|
|
@ -4556,7 +4554,7 @@ struct ggml_tensor * ggml_win_unpart(
|
|||
const int64_t ne[4] = { a->ne[0], w0, h0, 1, };
|
||||
struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 3, ne);
|
||||
|
||||
int32_t params[] = { w };
|
||||
const int32_t params[] = { w };
|
||||
ggml_set_op_params(result, params, sizeof(params));
|
||||
|
||||
result->op = GGML_OP_WIN_UNPART;
|
||||
|
|
@ -5141,11 +5139,11 @@ struct ggml_hash_set ggml_hash_set_new(size_t size) {
|
|||
return result;
|
||||
}
|
||||
|
||||
void ggml_hash_set_reset(struct ggml_hash_set * hash_set) {
|
||||
void ggml_hash_set_reset(const struct ggml_hash_set * hash_set) {
|
||||
memset(hash_set->used, 0, sizeof(ggml_bitset_t) * ggml_bitset_size(hash_set->size));
|
||||
}
|
||||
|
||||
void ggml_hash_set_free(struct ggml_hash_set * hash_set) {
|
||||
void ggml_hash_set_free(const struct ggml_hash_set * hash_set) {
|
||||
GGML_FREE(hash_set->used);
|
||||
GGML_FREE(hash_set->keys);
|
||||
}
|
||||
|
|
@ -5966,7 +5964,7 @@ struct ggml_cgraph * ggml_new_graph(struct ggml_context * ctx) {
|
|||
return ggml_new_graph_custom(ctx, GGML_DEFAULT_GRAPH_SIZE, false);
|
||||
}
|
||||
|
||||
struct ggml_cgraph ggml_graph_view(struct ggml_cgraph * cgraph0, int i0, int i1) {
|
||||
struct ggml_cgraph ggml_graph_view(const struct ggml_cgraph * cgraph0, int i0, int i1) {
|
||||
struct ggml_cgraph cgraph = {
|
||||
/*.size =*/ 0,
|
||||
/*.n_nodes =*/ i1 - i0,
|
||||
|
|
@ -5982,7 +5980,7 @@ struct ggml_cgraph ggml_graph_view(struct ggml_cgraph * cgraph0, int i0, int i1)
|
|||
return cgraph;
|
||||
}
|
||||
|
||||
void ggml_graph_cpy(struct ggml_cgraph * src, struct ggml_cgraph * dst) {
|
||||
void ggml_graph_cpy(const struct ggml_cgraph * src, struct ggml_cgraph * dst) {
|
||||
GGML_ASSERT(dst->size >= src->n_leafs);
|
||||
GGML_ASSERT(dst->size >= src->n_nodes);
|
||||
GGML_ASSERT(dst->visited_hash_set.size >= src->visited_hash_set.size);
|
||||
|
|
@ -6028,7 +6026,7 @@ void ggml_graph_cpy(struct ggml_cgraph * src, struct ggml_cgraph * dst) {
|
|||
}
|
||||
}
|
||||
|
||||
struct ggml_cgraph * ggml_graph_dup(struct ggml_context * ctx, struct ggml_cgraph * cgraph) {
|
||||
struct ggml_cgraph * ggml_graph_dup(struct ggml_context * ctx, const struct ggml_cgraph * cgraph) {
|
||||
struct ggml_cgraph * result = ggml_new_graph_custom(ctx, cgraph->size, cgraph->grads != NULL);
|
||||
ggml_graph_cpy(cgraph, result);
|
||||
return result;
|
||||
|
|
@ -6047,7 +6045,7 @@ struct ggml_tensor * ggml_set_zero(struct ggml_tensor * tensor) {
|
|||
return tensor;
|
||||
}
|
||||
|
||||
void ggml_graph_reset(struct ggml_cgraph * cgraph) {
|
||||
void ggml_graph_reset(const struct ggml_cgraph * cgraph) {
|
||||
GGML_ASSERT(cgraph->grads != NULL);
|
||||
|
||||
for (int i = 0; i < cgraph->n_nodes; i++) {
|
||||
|
|
@ -6086,11 +6084,11 @@ void ggml_graph_clear(struct ggml_cgraph * cgraph) {
|
|||
ggml_hash_set_reset(&cgraph->visited_hash_set);
|
||||
}
|
||||
|
||||
int ggml_graph_size(struct ggml_cgraph * cgraph) {
|
||||
int ggml_graph_size(const struct ggml_cgraph * cgraph) {
|
||||
return cgraph->size;
|
||||
}
|
||||
|
||||
struct ggml_tensor * ggml_graph_node(struct ggml_cgraph * cgraph, int i) {
|
||||
struct ggml_tensor * ggml_graph_node(const struct ggml_cgraph * cgraph, int i) {
|
||||
if (i < 0) {
|
||||
GGML_ASSERT(cgraph->n_nodes + i >= 0);
|
||||
return cgraph->nodes[cgraph->n_nodes + i];
|
||||
|
|
@ -6100,11 +6098,11 @@ struct ggml_tensor * ggml_graph_node(struct ggml_cgraph * cgraph, int i) {
|
|||
return cgraph->nodes[i];
|
||||
}
|
||||
|
||||
struct ggml_tensor ** ggml_graph_nodes(struct ggml_cgraph * cgraph) {
|
||||
struct ggml_tensor ** ggml_graph_nodes(const struct ggml_cgraph * cgraph) {
|
||||
return cgraph->nodes;
|
||||
}
|
||||
|
||||
int ggml_graph_n_nodes(struct ggml_cgraph * cgraph) {
|
||||
int ggml_graph_n_nodes(const struct ggml_cgraph * cgraph) {
|
||||
return cgraph->n_nodes;
|
||||
}
|
||||
|
||||
|
|
@ -6357,7 +6355,7 @@ void ggml_set_output(struct ggml_tensor * tensor) {
|
|||
tensor->flags |= GGML_TENSOR_FLAG_OUTPUT;
|
||||
}
|
||||
|
||||
void ggml_set_param(struct ggml_context * ctx, struct ggml_tensor * tensor) {
|
||||
void ggml_set_param(const struct ggml_context * ctx, struct ggml_tensor * tensor) {
|
||||
GGML_UNUSED(ctx); // TODO: remove this parameter
|
||||
tensor->flags |= GGML_TENSOR_FLAG_PARAM;
|
||||
}
|
||||
|
|
|
|||
|
|
@ -1144,6 +1144,7 @@ struct gguf_writer {
|
|||
|
||||
template <typename T>
|
||||
void write(const T & val) const {
|
||||
buf.reserve(sizeof(val));
|
||||
for (size_t i = 0; i < sizeof(val); ++i) {
|
||||
buf.push_back(reinterpret_cast<const int8_t *>(&val)[i]);
|
||||
}
|
||||
|
|
@ -1163,6 +1164,7 @@ struct gguf_writer {
|
|||
const uint64_t n = val.length();
|
||||
write(n);
|
||||
}
|
||||
buf.reserve(val.length());
|
||||
for (size_t i = 0; i < val.length(); ++i) {
|
||||
buf.push_back(reinterpret_cast<const int8_t *>(val.data())[i]);
|
||||
}
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue