Mirrors/llama.cpp
1
0
Fork 0
mirror of https://github.com/ggerganov/llama.cpp.git synced 2024-12-27 06:39:25 +01:00
Code Issues Packages Projects Releases Wiki Activity

ggml: refactor cross entropy loss CPU impl. (ggml/976)

Browse Source
This commit is contained in:
Johannes Gäßler 2024-10-02 15:32:39 +02:00 committed by Georgi Gerganov
parent 5d5ab1e5cc
commit eee39bdc96
No known key found for this signature in database
GPG Key ID: 449E073F9DC10735
2 changed files with 36 additions and 32 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
ggml/include/ggml-backend.h
View File

@ -247,7 +247,7 @@ extern "C" {
GGML_API void ggml_backend_sched_free(ggml_backend_sched_t sched);
// Initialize backend buffers from a measure graph
GGML_API bool ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph);
GGML_API bool ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph); // returns success
GGML_API int ggml_backend_sched_get_n_backends(ggml_backend_sched_t sched);
GGML_API ggml_backend_t ggml_backend_sched_get_backend(ggml_backend_sched_t sched, int i);
@ -262,7 +262,7 @@ extern "C" {
GGML_API ggml_backend_t ggml_backend_sched_get_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node);
// Allocate and compute graph on the backend scheduler
GGML_API bool ggml_backend_sched_alloc_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph);
GGML_API bool ggml_backend_sched_alloc_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph); // returns success
GGML_API enum ggml_status ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, struct ggml_cgraph * graph);
GGML_API enum ggml_status ggml_backend_sched_graph_compute_async(ggml_backend_sched_t sched, struct ggml_cgraph * graph);
GGML_API void ggml_backend_sched_synchronize(ggml_backend_sched_t sched);

64
ggml/src/ggml.c
View File

@ -4232,9 +4232,13 @@ static void ggml_set_op_params_f32(struct ggml_tensor * tensor, uint32_t i, floa
}
struct ggml_tensor * ggml_set_zero(struct ggml_tensor * tensor) {
if (ggml_is_empty(tensor)) {
return tensor;
}
if (tensor->buffer) {
ggml_backend_tensor_memset(tensor, 0, 0, ggml_nbytes(tensor));
} else {
GGML_ASSERT(tensor->data);
memset(tensor->data, 0, ggml_nbytes(tensor));
}
return tensor;
@ -16851,41 +16855,40 @@ static void ggml_compute_forward_cross_entropy_loss_f32(
const struct ggml_tensor * src0 = dst->src[0];
const struct ggml_tensor * src1 = dst->src[1];
GGML_ASSERT(ggml_is_contiguous(src0));
GGML_ASSERT(ggml_is_contiguous(src1));
GGML_ASSERT(ggml_is_scalar(dst));
GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT(src1->type == GGML_TYPE_F32);
GGML_ASSERT(src0->nb[0] == ggml_type_size(src0->type));
GGML_ASSERT(src1->nb[0] == ggml_type_size(src1->type));
GGML_ASSERT(ggml_are_same_shape(src0, src1));
GGML_ASSERT(ggml_is_scalar(dst));
GGML_ASSERT(dst->type == GGML_TYPE_F32);
// TODO: handle transposed/permuted matrices
const int64_t nc = src0->ne[0];
const int64_t nr = ggml_nrows(src0);
const int ith = params->ith;
const int nth = params->nth;
float * sums = (float *) params->wdata;
// TODO: handle transposed/permuted matrices
const int nc = src0->ne[0];
const int nr = ggml_nrows(src0);
float * sums = (float *) params->wdata;
float * st = ((float *) params->wdata) + nth + ith*nc;
float sum_thread = 0.0f;
GGML_ASSERT(params->wsize >= sizeof(float) * (nth + nth * nc));
if (ith == 0) {
memset(sums, 0, sizeof(float) * (nth + nth * nc));
}
ggml_barrier(params->threadpool);
// rows per thread
const int dr = (nr + nth - 1)/nth;
const int64_t dr = (nr + nth - 1)/nth;
// row range for this thread
const int ir0 = dr*ith;
const int ir1 = MIN(ir0 + dr, nr);
const int64_t ir0 = dr*ith;
const int64_t ir1 = MIN(ir0 + dr, nr);
for (int i1 = ir0; i1 < ir1; i1++) {
float * s0 = (float *)((char *) src0->data + i1*src0->nb[1]);
float * s1 = (float *)((char *) src1->data + i1*src1->nb[1]);
float * st = ((float *) params->wdata) + nth + ith*nc;
for (int64_t i1 = ir0; i1 < ir1; ++i1) {
const float * s0 = (const float *)((const char *) src0->data + i1*src0->nb[1]);
const float * s1 = (const float *)((const char *) src1->data + i1*src1->nb[1]);
#ifndef NDEBUG
for (int i = 0; i < nc; ++i) {
for (int64_t i = 0; i < nc; ++i) {
//printf("p[%d] = %f\n", i, p[i]);
assert(!isnan(s0[i]));
assert(!isnan(s1[i]));
@ -16894,23 +16897,24 @@ static void ggml_compute_forward_cross_entropy_loss_f32(
float max = -INFINITY;
ggml_vec_max_f32(nc, &max, s0);
ggml_float sum = ggml_vec_log_soft_max_f32(nc, st, s0, max);
assert(sum >= 0.0);
const ggml_float sum_softmax = ggml_vec_log_soft_max_f32(nc, st, s0, max);
assert(sum_softmax >= 0.0);
ggml_vec_add1_f32(nc, st, st, -sum);
ggml_vec_add1_f32(nc, st, st, -sum_softmax);
ggml_vec_mul_f32(nc, st, st, s1);
float st_sum = 0.0f;
ggml_vec_sum_f32(nc, &st_sum, st);
sums[ith] += st_sum;
float sum_st = 0.0f;
ggml_vec_sum_f32(nc, &sum_st, st);
sum_thread += sum_st;
#ifndef NDEBUG
for (int i = 0; i < nc; ++i) {
for (int64_t i = 0; i < nc; ++i) {
assert(!isnan(st[i]));
assert(!isinf(st[i]));
}
#endif
}
sums[ith] = sum_thread;
ggml_barrier(params->threadpool);
if (ith == 0) {
@ -16976,7 +16980,7 @@ static void ggml_compute_forward_cross_entropy_loss_back_f32(
float * s1 = (float *)((char *) src1->data + i1*src1->nb[1]);
#ifndef NDEBUG
for (int i = 0; i < nc; ++i) {
for (int64_t i = 0; i < nc; ++i) {
//printf("p[%d] = %f\n", i, p[i]);
assert(!isnan(s0[i]));
assert(!isnan(s1[i]));
@ -16995,7 +16999,7 @@ static void ggml_compute_forward_cross_entropy_loss_back_f32(
ggml_vec_scale_f32(nc, ds0, d_by_nr);
#ifndef NDEBUG
for (int i = 0; i < nc; ++i) {
for (int64_t i = 0; i < nc; ++i) {
assert(!isnan(ds0[i]));
assert(!isinf(ds0[i]));
}