haraldwolff/llama.cpp
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add google magika inference example (ggml/748)

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* add magika inference example

* ggml : fix unaligned accesses in custom ops

* ggml : fix FP32 GELU for values that exceed the FP16 range

* use ggml_pool_1d

* add README

* Update README.md

* pad inputs if the files are too small

* cleanup

ggml-ci
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slaren 2024-02-25 20:41:35 +01:00 committed by Georgi Gerganov
parent 5f70671856
commit 2774b0c974
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GPG key ID: BF970631944C16B7
1 changed files with 34 additions and 20 deletions
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ggml.c
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@ -1608,9 +1608,15 @@ inline static void ggml_vec_gelu_f16(const int n, ggml_fp16_t * y, const ggml_fp
inline static void ggml_vec_gelu_f32(const int n, float * y, const float * x) {
uint16_t t;
for (int i = 0; i < n; ++i) {
ggml_fp16_t fp16 = GGML_FP32_TO_FP16(x[i]);
memcpy(&t, &fp16, sizeof(uint16_t));
y[i] = GGML_FP16_TO_FP32(ggml_table_gelu_f16[t]);
if (x[i] <= -10.0f) {
y[i] = 0.0f;
} else if (x[i] >= 10.0f) {
y[i] = x[i];
} else {
ggml_fp16_t fp16 = GGML_FP32_TO_FP16(x[i]);
memcpy(&t, &fp16, sizeof(uint16_t));
y[i] = GGML_FP16_TO_FP32(ggml_table_gelu_f16[t]);
}
}
}
#else
@ -5780,11 +5786,13 @@ struct ggml_tensor * ggml_pool_1d(
is_node = true;
}
const int64_t ne[2] = {
const int64_t ne[4] = {
ggml_calc_pool_output_size(a->ne[0], k0, s0, p0),
a->ne[1],
a->ne[2],
a->ne[3],
};
struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 2, ne);
struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);
int32_t params[] = { op, k0, s0, p0 };
ggml_set_op_params(result, params, sizeof(params));
@ -15081,9 +15089,10 @@ static void ggml_compute_forward_map_custom1(
return;
}
struct ggml_map_custom1_op_params * p = (struct ggml_map_custom1_op_params *) dst->op_params;
struct ggml_map_custom1_op_params p;
memcpy(&p, dst->op_params, sizeof(p));
p->fun(dst, a, params->ith, params->nth, p->userdata);
p.fun(dst, a, params->ith, params->nth, p.userdata);
}
// ggml_compute_forward_map_custom2
@ -15099,9 +15108,10 @@ static void ggml_compute_forward_map_custom2(
return;
}
struct ggml_map_custom2_op_params * p = (struct ggml_map_custom2_op_params *) dst->op_params;
struct ggml_map_custom2_op_params p;
memcpy(&p, dst->op_params, sizeof(p));
p->fun(dst, a, b, params->ith, params->nth, p->userdata);
p.fun(dst, a, b, params->ith, params->nth, p.userdata);
}
// ggml_compute_forward_map_custom3
@ -15118,9 +15128,10 @@ static void ggml_compute_forward_map_custom3(
return;
}
struct ggml_map_custom3_op_params * p = (struct ggml_map_custom3_op_params *) dst->op_params;
struct ggml_map_custom3_op_params p;
memcpy(&p, dst->op_params, sizeof(p));
p->fun(dst, a, b, c, params->ith, params->nth, p->userdata);
p.fun(dst, a, b, c, params->ith, params->nth, p.userdata);
}
// ggml_compute_forward_cross_entropy_loss
@ -17386,29 +17397,32 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
} break;
case GGML_OP_MAP_CUSTOM1:
{
struct ggml_map_custom1_op_params * p = (struct ggml_map_custom1_op_params *) node->op_params;
if (p->n_tasks == GGML_N_TASKS_MAX) {
struct ggml_map_custom1_op_params p;
memcpy(&p, node->op_params, sizeof(p));
if (p.n_tasks == GGML_N_TASKS_MAX) {
n_tasks = n_threads;
} else {
n_tasks = MIN(p->n_tasks, n_threads);
n_tasks = MIN(p.n_tasks, n_threads);
}
} break;
case GGML_OP_MAP_CUSTOM2:
{
struct ggml_map_custom2_op_params * p = (struct ggml_map_custom2_op_params *) node->op_params;
if (p->n_tasks == GGML_N_TASKS_MAX) {
struct ggml_map_custom2_op_params p;
memcpy(&p, node->op_params, sizeof(p));
if (p.n_tasks == GGML_N_TASKS_MAX) {
n_tasks = n_threads;
} else {
n_tasks = MIN(p->n_tasks, n_threads);
n_tasks = MIN(p.n_tasks, n_threads);
}
} break;
case GGML_OP_MAP_CUSTOM3:
{
struct ggml_map_custom3_op_params * p = (struct ggml_map_custom3_op_params *) node->op_params;
if (p->n_tasks == GGML_N_TASKS_MAX) {
struct ggml_map_custom3_op_params p;
memcpy(&p, node->op_params, sizeof(p));
if (p.n_tasks == GGML_N_TASKS_MAX) {
n_tasks = n_threads;
} else {
n_tasks = MIN(p->n_tasks, n_threads);
n_tasks = MIN(p.n_tasks, n_threads);
}
} break;
case GGML_OP_CROSS_ENTROPY_LOSS: