ver4a/llama.cpp
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ggml : change ggml_graph_compute() API to not require context (#1999)

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* ggml_graph_compute: deprecate using ggml_context, try resolve issue #287

* rewrite: no longer consider backward compitability; plan and make_plan

* minor: rename ctx as plan; const

* remove ggml_graph_compute from tests/test-grad0.c, but current change breaks backward

* add static ggml_graph_compute_sugar()

* minor: update comments

* reusable buffers

* ggml : more consistent naming + metal fixes

* ggml : fix docs

* tests : disable grad / opt + minor naming changes

* ggml : add ggml_graph_compute_with_ctx()

- backwards compatible API
- deduplicates a lot of copy-paste

* ci : enable test-grad0

* examples : factor out plan allocation into a helper function

* llama : factor out plan stuff into a helper function

* ci : fix env

* llama : fix duplicate symbols + refactor example benchmark

* ggml : remove obsolete assert + refactor n_tasks section

* ggml : fix indentation in switch

* llama : avoid unnecessary bool

* ggml : remove comments from source file and match order in header

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
This commit is contained in:
Qingyou Meng 2023-07-08 00:24:01 +08:00 • committed by GitHub
parent 7242140283
commit 1d656d6360
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GPG key ID: 4AEE18F83AFDEB23
13 changed files with 571 additions and 449 deletions
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37
tests/test-grad0.c
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@ -10,6 +10,8 @@
#pragma warning(disable: 4244 4267) // possible loss of data
#endif
#pragma GCC diagnostic ignored "-Wdouble-promotion"
#define MAX_NARGS 3
#undef MIN
@ -49,7 +51,7 @@ float frand(void) {
int irand(int n) {
if (n == 0) return 0;
else return rand()%n;
return rand()%n;
}
void get_random_dims(int64_t * dims, int ndims) {
@ -159,12 +161,14 @@ struct ggml_tensor * get_random_tensor_int(
float get_element(const struct ggml_tensor * t, int idx) {
if (t->type == GGML_TYPE_F32) {
return ((float *)t->data)[idx];
} else if (t->type == GGML_TYPE_I32) {
return ((int32_t *)t->data)[idx];
} else {
assert(false);
return INFINITY;
}
if (t->type == GGML_TYPE_I32) {
return ((int32_t *)t->data)[idx];
}
assert(false);
return INFINITY;
}
void set_element(struct ggml_tensor * t, int idx, float value) {
@ -215,15 +219,14 @@ bool check_gradient(
}
struct ggml_cgraph gf = ggml_build_forward (f);
gf.n_threads = n_threads;
struct ggml_cgraph gb = ggml_build_backward(ctx0, &gf, false);
gb.n_threads = n_threads;
ggml_graph_compute(ctx0, &gf);
ggml_graph_compute_with_ctx(ctx0, &gf, n_threads);
ggml_graph_reset (&gf);
ggml_set_f32 (f->grad, 1.0f);
ggml_graph_compute(ctx0, &gb);
ggml_graph_compute_with_ctx(ctx0, &gb, n_threads);
// ggml_graph_dump_dot(&gf, NULL, "test-grad0-forward.dot");
// ggml_graph_dump_dot(&gb, &gf, "test-grad0-backward.dot");
@ -236,15 +239,16 @@ bool check_gradient(
const float xm = x0 - eps;
const float xp = x0 + eps;
set_element(x[i], k, xp);
ggml_graph_compute(ctx0, &gf);
ggml_graph_compute_with_ctx(ctx0, &gf, n_threads);
const float f0 = ggml_get_f32_1d(f, 0);
set_element(x[i], k, xm);
ggml_graph_compute(ctx0, &gf);
ggml_graph_compute_with_ctx(ctx0, &gf, n_threads);
const float f1 = ggml_get_f32_1d(f, 0);
const float g0 = (f0 - f1)/(2.0f*eps);
set_element(x[i], k, x0);
@ -252,12 +256,13 @@ bool check_gradient(
// compute gradient using backward graph
ggml_graph_reset (&gf);
ggml_set_f32 (f->grad, 1.0f);
ggml_graph_compute(ctx0, &gb);
ggml_graph_compute_with_ctx(ctx0, &gb, n_threads);
const float g1 = get_element(x[i]->grad, k);
const float error_abs = fabsf(g0 - g1);
const float error_rel = g0 != 0 ? fabsf(g0 - g1)/fabs(g0) : 0;
const float error_rel = g0 != 0 ? fabsf(g0 - g1)/fabsf(g0) : 0;
if (error_abs > max_error_abs || error_rel > max_error_rel) {
printf("%s: ndims=%d, i=%d, k=%d, x0=%f, xm=%f, xp=%f, f0=%f, f1=%f, g0=%f, g1=%f, eps=%f, error_abs=%f, error_rel=%f\n",