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ggml : sync ggml core (minor additions, e.g. ggml_get_tensor_by_name())
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@ggerganov
ggerganov committed May 27, 2023
1 parent 66874d4 commit bdbda1b
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Showing 2 changed files with 48 additions and 10 deletions.
46 changes: 37 additions & 9 deletions ggml.c
View file
Original file line number Diff line number Diff line change
Expand Up @@ -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static_assert(GGML_TYPE_COUNT == 13, "GGML_IS_QUANTIZED is outdated");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",
Expand Down Expand Up @@ -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];
Expand Down Expand Up @@ -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
Expand Down Expand Up @@ -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",
Expand All @@ -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;
}
Expand All @@ -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,
};

Expand Down Expand Up @@ -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++;
Expand Down Expand Up @@ -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};

Expand All @@ -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,
Expand All @@ -14541,15 +14569,15 @@ 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++) {
if (perf_total_per_op_us[i] == 0) {
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");
Expand Down
12 changes: 11 additions & 1 deletion ggml.h
View file
Original file line number Diff line number Diff line change
Expand Up @@ -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) \
Expand Down Expand Up @@ -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);

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@Green-Sky

Green-Sky May 27, 2023

Collaborator

cuda for some reason does not like this.

D:\a\llama.cpp\llama.cpp\ggml.h(384,25): error C2099: initializer is not a constant

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@howard0su

howard0su May 27, 2023

Collaborator

@ggerganov this line cannot compile under Windows + VS2022:
C:\GPT\llama.cpp\ggml.h(384,25): error C2099: initializer is not a constant [C:\GPT\llama.cpp\build\ggml.vcxproj]

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@ggerganov

ggerganov May 27, 2023

Author Owner

Thanks for notifying - will fix this now

// computation graph
struct ggml_cgraph {
int n_nodes;
Expand Down Expand Up @@ -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);

Expand All @@ -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,
Expand Down Expand Up @@ -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);

Expand Down

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