ikawrakow/ik_llama.cpp
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Faster tensor name formatting

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We gain ~1% for Ling-mini-2.0 when running on CUDA.
This commit is contained in:
Iwan Kawrakow
2025-10-23 18:37:27 +03:00
parent 159051183b
commit 1f96fc97c6
2 changed files with 59 additions and 15 deletions
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55
ggml/src/ggml.c
View File

@@ -5941,11 +5941,24 @@ struct ggml_tensor * ggml_format_name(struct ggml_tensor * tensor, const char *
return tensor;
}
static inline void ggml_format_name_fast(const char * name, const char * suffix, int suffix_len, char * new_name) {
int j = 0;
for (; j < GGML_MAX_NAME-1; ++j) {
new_name[j] = name[j];
if (!name[j]) break;
}
for (int k = 0; k < suffix_len && j < GGML_MAX_NAME-1; ++k) {
new_name[j++] = suffix[k];
}
new_name[j] = 0;
}
struct ggml_tensor * ggml_view_tensor(
struct ggml_context * ctx,
struct ggml_tensor * src) {
struct ggml_tensor * result = ggml_new_tensor_impl(ctx, src->type, GGML_MAX_DIMS, src->ne, src, 0);
ggml_format_name(result, "%s (view)", src->name);
//ggml_format_name(result, "%s (view)", src->name);
ggml_format_name_fast(src->name, " (view)", 7, result->name);
for (int i = 0; i < GGML_MAX_DIMS; i++) {
result->nb[i] = src->nb[i];
@@ -7894,7 +7907,8 @@ static struct ggml_tensor * ggml_cpy_impl(
if (strlen(b->name) > 0) {
ggml_format_name(result, "%s (copy of %s)", b->name, a->name);
} else {
ggml_format_name(result, "%s (copy)", a->name);
//ggml_format_name(result, "%s (copy)", a->name);
ggml_format_name_fast(a->name, " (copy)", 7, result->name);
}
result->op = GGML_OP_CPY;
@@ -7919,7 +7933,8 @@ struct ggml_tensor * ggml_cast(
bool is_node = false;
struct ggml_tensor * result = ggml_new_tensor(ctx, type, GGML_MAX_DIMS, a->ne);
ggml_format_name(result, "%s (copy)", a->name);
//ggml_format_name(result, "%s (copy)", a->name);
ggml_format_name_fast(a->name, " (copy)", 7, result->name);
result->op = GGML_OP_CPY;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
@@ -7941,7 +7956,8 @@ static struct ggml_tensor * ggml_cont_impl(
}
struct ggml_tensor * result = ggml_dup_tensor(ctx, a);
ggml_format_name(result, "%s (cont)", a->name);
//ggml_format_name(result, "%s (cont)", a->name);
ggml_format_name_fast(a->name, " (cont)", 7, result->name);
result->op = GGML_OP_CONT;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
@@ -7993,7 +8009,8 @@ struct ggml_tensor * ggml_cont_4d(
bool is_node = false;
struct ggml_tensor * result = ggml_new_tensor_4d(ctx, a->type, ne0, ne1, ne2, ne3);
ggml_format_name(result, "%s (cont)", a->name);
//ggml_format_name(result, "%s (cont)", a->name);
ggml_format_name_fast(a->name, " (cont)", 7, result->name);
result->op = GGML_OP_CONT;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
@@ -8024,7 +8041,8 @@ struct ggml_tensor * ggml_reshape(
}
struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, GGML_MAX_DIMS, b->ne, a, 0);
ggml_format_name(result, "%s (reshaped)", a->name);
//ggml_format_name(result, "%s (reshaped)", a->name);
ggml_format_name_fast(a->name, " (reshaped)", 11, result->name);
result->op = GGML_OP_RESHAPE;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
@@ -8048,7 +8066,8 @@ struct ggml_tensor * ggml_reshape_1d(
const int64_t ne[1] = { ne0 };
struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 1, ne, a, 0);
ggml_format_name(result, "%s (reshaped)", a->name);
//ggml_format_name(result, "%s (reshaped)", a->name);
ggml_format_name_fast(a->name, " (reshaped)", 11, result->name);
result->op = GGML_OP_RESHAPE;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
@@ -8073,7 +8092,8 @@ struct ggml_tensor * ggml_reshape_2d(
const int64_t ne[2] = { ne0, ne1 };
struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 2, ne, a, 0);
ggml_format_name(result, "%s (reshaped)", a->name);
//ggml_format_name(result, "%s (reshaped)", a->name);
ggml_format_name_fast(a->name, " (reshaped)", 11, result->name);
result->op = GGML_OP_RESHAPE;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
@@ -8099,7 +8119,8 @@ struct ggml_tensor * ggml_reshape_3d(
const int64_t ne[3] = { ne0, ne1, ne2 };
struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 3, ne, a, 0);
ggml_format_name(result, "%s (reshaped)", a->name);
//ggml_format_name(result, "%s (reshaped)", a->name);
ggml_format_name_fast(a->name, " (reshaped)", 11, result->name);
result->op = GGML_OP_RESHAPE;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
@@ -8126,7 +8147,8 @@ struct ggml_tensor * ggml_reshape_4d(
const int64_t ne[4] = { ne0, ne1, ne2, ne3 };
struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 4, ne, a, 0);
ggml_format_name(result, "%s (reshaped)", a->name);
//ggml_format_name(result, "%s (reshaped)", a->name);
ggml_format_name_fast(a->name, " (reshaped)", 11, result->name);
result->op = GGML_OP_RESHAPE;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
@@ -8149,7 +8171,8 @@ static struct ggml_tensor * ggml_view_impl(
}
struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, n_dims, ne, a, offset);
ggml_format_name(result, "%s (view)", a->name);
//ggml_format_name(result, "%s (view)", a->name);
ggml_format_name_fast(a->name, " (view)", 7, result->name);
ggml_set_op_params(result, &offset, sizeof(offset));
@@ -8270,7 +8293,8 @@ struct ggml_tensor * ggml_permute(
}
struct ggml_tensor * result = ggml_view_tensor(ctx, a);
ggml_format_name(result, "%s (permuted)", a->name);
//ggml_format_name(result, "%s (permuted)", a->name);
ggml_format_name_fast(a->name, " (permuted)", 11, result->name);
int ne[GGML_MAX_DIMS];
int nb[GGML_MAX_DIMS];
@@ -8317,7 +8341,8 @@ struct ggml_tensor * ggml_transpose(
}
struct ggml_tensor * result = ggml_view_tensor(ctx, a);
ggml_format_name(result, "%s (transposed)", a->name);
//ggml_format_name(result, "%s (transposed)", a->name);
ggml_format_name_fast(a->name, " (transposed)", 13, result->name);
result->ne[0] = a->ne[1];
result->ne[1] = a->ne[0];
@@ -9510,6 +9535,7 @@ struct ggml_tensor * ggml_top_k(
GGML_ASSERT(a->ne[0] >= k);
struct ggml_tensor * result = ggml_argsort(ctx, a, GGML_SORT_ORDER_DESC);
ggml_format_name_fast(a->name, " (sort)", 7, result->name);
ggml_set_op_params_i32(result, 1, k);
result = ggml_view_4d(ctx, result,
@@ -10439,7 +10465,8 @@ void ggml_set_param(
GGML_ASSERT(tensor->grad == NULL);
tensor->grad = ggml_dup_tensor(ctx, tensor);
ggml_format_name(tensor->grad, "%s (grad)", tensor->name);
//ggml_format_name(tensor->grad, "%s (grad)", tensor->name);
ggml_format_name_fast(tensor->name, " (grad)", 7, tensor->grad->name);
}
// ggml_compute_forward_dup

19
src/llama-build-context.cpp
View File

@@ -1342,6 +1342,8 @@ ggml_cgraph * llm_build_context::build_llama() {
n_tokens = n_outputs;
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
cb(cur, "last_attn", il);
cb(inpSA, "last_ffn_inp", il);
}
// For Granite architecture
@@ -5942,6 +5944,8 @@ ggml_cgraph * llm_build_context::build_deepseek2() {
n_tokens = n_outputs;
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
cb(cur, "last_attn", il);
cb(inpSA, "last_ffn_inp", il);
}
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
@@ -8040,7 +8044,20 @@ ggml_cgraph * llm_build_context::llama_build_graph(
// this callback allows us to apply custom logic to each tensor (e.g. ggml-alloc, offloading, etc.)
llm_build_cb cb = [&](struct ggml_tensor * cur, const char * name, int il) {
if (il >= 0) {
ggml_format_name(cur, "%s-%d", name, il);
int j = 0;
for (; j < GGML_MAX_NAME - 1; ++j) {
cur->name[j] = name[j];
if (!name[j]) break;
}
if (j < GGML_MAX_NAME - 3) {
cur->name[j++] = '-';
auto sil = std::to_string(il);
for (int k = 0; k < (int)sil.size() && j < GGML_MAX_NAME - 1; ++k) {
cur->name[j++] = sil[k];
}
}
cur->name[j] = 0;
//ggml_format_name(cur, "%s-%d", name, il);
} else {
ggml_set_name(cur, name);
}