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ggml : sync ggml core (minor additions, e.g. ggml_get_tensor_by_name())

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Georgi Gerganov 2023-05-27 12:22:05 +03:00
parent 66874d4fbc
commit bdbda1b17a
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GPG Key ID: 449E073F9DC10735
2 changed files with 48 additions and 10 deletions
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46
ggml.c
View File

@ -3494,7 +3494,7 @@ static bool GGML_IS_QUANTIZED[GGML_TYPE_COUNT] = {
};
static_assert(GGML_TYPE_COUNT == 13, "GGML_IS_QUANTIZED is outdated");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",
@ -3749,6 +3749,9 @@ const char * ggml_type_name(enum ggml_type type) {
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];
@ -4017,6 +4020,10 @@ size_t ggml_set_scratch(struct ggml_context * ctx, struct ggml_scratch scratch)
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
@ -4061,7 +4068,7 @@ struct ggml_tensor * ggml_new_tensor_impl(
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",
@ -4077,14 +4084,15 @@ struct ggml_tensor * ggml_new_tensor_impl(
};
} 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;
}
@ -4093,7 +4101,7 @@ struct ggml_tensor * ggml_new_tensor_impl(
*obj_new = (struct ggml_object) {
.offs = cur_end + GGML_OBJECT_SIZE,
.size = sizeof(struct ggml_tensor),
.size = GGML_TENSOR_SIZE,
.next = NULL,
};
@ -13792,11 +13800,19 @@ static void ggml_visit_parents(struct ggml_cgraph * cgraph, struct ggml_tensor *
// 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++;
@ -14510,6 +14526,18 @@ void ggml_graph_reset(struct ggml_cgraph * cgraph) {
}
}
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};
@ -14527,7 +14555,7 @@ void ggml_graph_print(const struct ggml_cgraph * cgraph) {
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,
@ -14541,7 +14569,7 @@ void ggml_graph_print(const struct ggml_cgraph * cgraph) {
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++) {
@ -14549,7 +14577,7 @@ void ggml_graph_print(const struct ggml_cgraph * cgraph) {
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");

12
ggml.h
View File

@ -198,6 +198,7 @@
#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) \
@ -372,11 +373,16 @@ extern "C" {
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;
@ -429,6 +435,7 @@ extern "C" {
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);
@ -445,6 +452,7 @@ extern "C" {
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,
@ -970,6 +978,8 @@ extern "C" {
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);