};
static_assert(GGML_TYPE_COUNT == 13, "GGML_IS_QUANTIZED is outdated");
-static const char * GGML_OP_LABEL[GGML_OP_COUNT] = {
+static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
"NONE",
"DUP",
return GGML_TYPE_NAME[type];
}
+const char * ggml_op_name(enum ggml_op op) {
+ return GGML_OP_NAME[op];
+}
size_t ggml_element_size(const struct ggml_tensor * tensor) {
return GGML_TYPE_SIZE[tensor->type];
return result;
}
+void ggml_set_no_alloc(struct ggml_context * ctx, bool no_alloc) {
+ ctx->no_alloc = no_alloc;
+}
+
// IMPORTANT:
// when creating "opt" tensors, always save and load the scratch buffer
// this is an error prone process, but it is necessary to support inplace
struct ggml_object * const obj_new = (struct ggml_object *)(mem_buffer + cur_end);
if (ctx->scratch.data == NULL || data != NULL) {
- size_needed += sizeof(struct ggml_tensor);
+ size_needed += GGML_TENSOR_SIZE;
if (cur_end + size_needed + GGML_OBJECT_SIZE > ctx->mem_size) {
GGML_PRINT("%s: not enough space in the context's memory pool (needed %zu, available %zu)\n",
};
} else {
if (ctx->scratch.offs + size_needed > ctx->scratch.size) {
- GGML_PRINT("%s: not enough space in the scratch memory\n", __func__);
+ GGML_PRINT("%s: not enough space in the scratch memory pool (needed %zu, available %zu)\n",
+ __func__, ctx->scratch.offs + size_needed, ctx->scratch.size);
assert(false);
return NULL;
}
- if (cur_end + sizeof(struct ggml_tensor) + GGML_OBJECT_SIZE > ctx->mem_size) {
+ if (cur_end + GGML_TENSOR_SIZE + GGML_OBJECT_SIZE > ctx->mem_size) {
GGML_PRINT("%s: not enough space in the context's memory pool (needed %zu, available %zu)\n",
- __func__, cur_end + sizeof(struct ggml_tensor) + GGML_OBJECT_SIZE, ctx->mem_size);
+ __func__, cur_end + GGML_TENSOR_SIZE + GGML_OBJECT_SIZE, ctx->mem_size);
assert(false);
return NULL;
}
*obj_new = (struct ggml_object) {
.offs = cur_end + GGML_OBJECT_SIZE,
- .size = sizeof(struct ggml_tensor),
+ .size = GGML_TENSOR_SIZE,
.next = NULL,
};
// reached a leaf node, not part of the gradient graph (e.g. a constant)
GGML_ASSERT(cgraph->n_leafs < GGML_MAX_NODES);
+ if (strlen(node->name) == 0) {
+ snprintf(node->name, sizeof(node->name), "leaf_%d", cgraph->n_leafs);
+ }
+
cgraph->leafs[cgraph->n_leafs] = node;
cgraph->n_leafs++;
} else {
GGML_ASSERT(cgraph->n_nodes < GGML_MAX_NODES);
+ if (strlen(node->name) == 0) {
+ snprintf(node->name, sizeof(node->name), "node_%d", cgraph->n_nodes);
+ }
+
cgraph->nodes[cgraph->n_nodes] = node;
cgraph->grads[cgraph->n_nodes] = node->grad;
cgraph->n_nodes++;
}
}
+struct ggml_tensor * ggml_get_tensor_by_name(struct ggml_cgraph * cgraph, const char * name) {
+ for (int i = 0; i < cgraph->n_nodes; i++) {
+ struct ggml_tensor * node = cgraph->nodes[i];
+
+ if (strcmp(node->name, name) == 0) {
+ return node;
+ }
+ }
+
+ return NULL;
+}
+
void ggml_graph_print(const struct ggml_cgraph * cgraph) {
int64_t perf_total_per_op_us[GGML_OP_COUNT] = {0};
GGML_PRINT(" - %3d: [ %5" PRId64 ", %5" PRId64 ", %5" PRId64 "] %16s %s (%3d) cpu = %7.3f / %7.3f ms, wall = %7.3f / %7.3f ms\n",
i,
node->ne[0], node->ne[1], node->ne[2],
- GGML_OP_LABEL[node->op], node->is_param ? "x" : node->grad ? "g" : " ", node->perf_runs,
+ GGML_OP_NAME[node->op], node->is_param ? "x" : node->grad ? "g" : " ", node->perf_runs,
(double) node->perf_cycles / (double) ggml_cycles_per_ms(),
(double) node->perf_cycles / (double) ggml_cycles_per_ms() / (double) node->perf_runs,
(double) node->perf_time_us / 1000.0,
GGML_PRINT(" - %3d: [ %5" PRId64 ", %5" PRId64 "] %8s\n",
i,
node->ne[0], node->ne[1],
- GGML_OP_LABEL[node->op]);
+ GGML_OP_NAME[node->op]);
}
for (int i = 0; i < GGML_OP_COUNT; i++) {
continue;
}
- GGML_PRINT("perf_total_per_op_us[%16s] = %7.3f ms\n", GGML_OP_LABEL[i], (double) perf_total_per_op_us[i] / 1000.0);
+ GGML_PRINT("perf_total_per_op_us[%16s] = %7.3f ms\n", GGML_OP_NAME[i], (double) perf_total_per_op_us[i] / 1000.0);
}
GGML_PRINT("========================================\n");
#define GGML_MAX_PARAMS 256
#define GGML_MAX_CONTEXTS 64
#define GGML_MAX_OPT 4
+#define GGML_MAX_NAME 32
#define GGML_DEFAULT_N_THREADS 4
#define GGML_ASSERT(x) \
void * data;
- char name[32];
+ char name[GGML_MAX_NAME];
char padding[16];
};
+ static const size_t GGML_TENSOR_SIZE = sizeof(struct ggml_tensor);
+
+ // use this to compute the memory overhead of a tensor
+ static const size_t GGML_TENSOR_OVERHEAD = (GGML_OBJECT_SIZE + GGML_TENSOR_SIZE + 16);
+
// computation graph
struct ggml_cgraph {
int n_nodes;
GGML_API float ggml_type_sizef(enum ggml_type type); // ggml_type_size()/ggml_blck_size() as float
GGML_API const char * ggml_type_name(enum ggml_type type);
+ GGML_API const char * ggml_op_name (enum ggml_op op);
GGML_API size_t ggml_element_size(const struct ggml_tensor * tensor);
GGML_API size_t ggml_used_mem(const struct ggml_context * ctx);
GGML_API size_t ggml_set_scratch(struct ggml_context * ctx, struct ggml_scratch scratch);
+ GGML_API void ggml_set_no_alloc(struct ggml_context * ctx, bool no_alloc);
GGML_API struct ggml_tensor * ggml_new_tensor(
struct ggml_context * ctx,
GGML_API void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph);
GGML_API void ggml_graph_reset (struct ggml_cgraph * cgraph);
+ GGML_API struct ggml_tensor * ggml_get_tensor_by_name(struct ggml_cgraph * cgraph, const char * name);
+
// print info and performance information for the graph
GGML_API void ggml_graph_print(const struct ggml_cgraph * cgraph);