mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-26 14:20:31 +01:00
1526 lines
50 KiB
C
1526 lines
50 KiB
C
#define _CRT_SECURE_NO_DEPRECATE // Disables ridiculous "unsafe" warnigns on Windows
|
|
#include "ggml.h"
|
|
|
|
#include <math.h>
|
|
#include <stdio.h>
|
|
#include <stdlib.h>
|
|
#include <assert.h>
|
|
|
|
#if defined(_MSC_VER)
|
|
#pragma warning(disable: 4244 4267) // possible loss of data
|
|
#endif
|
|
|
|
#if defined(__GNUC__)
|
|
#pragma GCC diagnostic ignored "-Wdouble-promotion"
|
|
#endif
|
|
|
|
#define MAX_NARGS 3
|
|
|
|
#undef MIN
|
|
#undef MAX
|
|
#define MIN(a, b) ((a) < (b) ? (a) : (b))
|
|
#define MAX(a, b) ((a) > (b) ? (a) : (b))
|
|
|
|
#define GGML_SILU_FP16
|
|
|
|
//
|
|
// logging
|
|
//
|
|
|
|
#if (GGML_DEBUG >= 1)
|
|
#define GGML_PRINT_DEBUG(...) printf(__VA_ARGS__)
|
|
#else
|
|
#define GGML_PRINT_DEBUG(...)
|
|
#endif
|
|
|
|
#if (GGML_DEBUG >= 5)
|
|
#define GGML_PRINT_DEBUG_5(...) printf(__VA_ARGS__)
|
|
#else
|
|
#define GGML_PRINT_DEBUG_5(...)
|
|
#endif
|
|
|
|
#if (GGML_DEBUG >= 10)
|
|
#define GGML_PRINT_DEBUG_10(...) printf(__VA_ARGS__)
|
|
#else
|
|
#define GGML_PRINT_DEBUG_10(...)
|
|
#endif
|
|
|
|
#define GGML_PRINT(...) printf(__VA_ARGS__)
|
|
|
|
float frand(void) {
|
|
return (float)rand()/(float)RAND_MAX;
|
|
}
|
|
|
|
int irand(int n) {
|
|
if (n == 0) return 0;
|
|
return rand()%n;
|
|
}
|
|
|
|
void get_random_dims(int64_t * dims, int ndims) {
|
|
dims[0] = dims[1] = dims[2] = dims[3] = 1;
|
|
|
|
for (int i = 0; i < ndims; i++) {
|
|
dims[i] = 1 + irand(4);
|
|
}
|
|
}
|
|
|
|
struct ggml_tensor * get_random_tensor_f32(
|
|
struct ggml_context * ctx0,
|
|
int ndims,
|
|
int64_t ne[],
|
|
float fmin,
|
|
float fmax) {
|
|
struct ggml_tensor * result = ggml_new_tensor(ctx0, GGML_TYPE_F32, ndims, ne);
|
|
|
|
switch (ndims) {
|
|
case 1:
|
|
for (int i0 = 0; i0 < ne[0]; i0++) {
|
|
((float *)result->data)[i0] = frand()*(fmax - fmin) + fmin;
|
|
}
|
|
break;
|
|
case 2:
|
|
for (int i1 = 0; i1 < ne[1]; i1++) {
|
|
for (int i0 = 0; i0 < ne[0]; i0++) {
|
|
((float *)result->data)[i1*ne[0] + i0] = frand()*(fmax - fmin) + fmin;
|
|
}
|
|
}
|
|
break;
|
|
case 3:
|
|
for (int i2 = 0; i2 < ne[2]; i2++) {
|
|
for (int i1 = 0; i1 < ne[1]; i1++) {
|
|
for (int i0 = 0; i0 < ne[0]; i0++) {
|
|
((float *)result->data)[i2*ne[1]*ne[0] + i1*ne[0] + i0] = frand()*(fmax - fmin) + fmin;
|
|
}
|
|
}
|
|
}
|
|
break;
|
|
case 4:
|
|
for (int i3 = 0; i3 < ne[3]; i3++) {
|
|
for (int i2 = 0; i2 < ne[2]; i2++) {
|
|
for (int i1 = 0; i1 < ne[1]; i1++) {
|
|
for (int i0 = 0; i0 < ne[0]; i0++) {
|
|
((float *)result->data)[i3*ne[2]*ne[1]*ne[0] + i2*ne[1]*ne[0] + i1*ne[0] + i0] = frand()*(fmax - fmin) + fmin;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
break;
|
|
default:
|
|
assert(false);
|
|
};
|
|
|
|
return result;
|
|
}
|
|
|
|
struct ggml_tensor * get_random_tensor_f16(
|
|
struct ggml_context * ctx0,
|
|
int ndims,
|
|
int64_t ne[],
|
|
float fmin,
|
|
float fmax) {
|
|
struct ggml_tensor * result = ggml_new_tensor(ctx0, GGML_TYPE_F16, ndims, ne);
|
|
|
|
switch (ndims) {
|
|
case 1:
|
|
for (int i0 = 0; i0 < ne[0]; i0++) {
|
|
((ggml_fp16_t *)result->data)[i0] = ggml_fp32_to_fp16(frand()*(fmax - fmin) + fmin);
|
|
}
|
|
break;
|
|
case 2:
|
|
for (int i1 = 0; i1 < ne[1]; i1++) {
|
|
for (int i0 = 0; i0 < ne[0]; i0++) {
|
|
((ggml_fp16_t *)result->data)[i1*ne[0] + i0] = ggml_fp32_to_fp16(frand()*(fmax - fmin) + fmin);
|
|
}
|
|
}
|
|
break;
|
|
case 3:
|
|
for (int i2 = 0; i2 < ne[2]; i2++) {
|
|
for (int i1 = 0; i1 < ne[1]; i1++) {
|
|
for (int i0 = 0; i0 < ne[0]; i0++) {
|
|
((ggml_fp16_t *)result->data)[i2*ne[1]*ne[0] + i1*ne[0] + i0] = ggml_fp32_to_fp16(frand()*(fmax - fmin) + fmin);
|
|
}
|
|
}
|
|
}
|
|
break;
|
|
case 4:
|
|
for (int i3 = 0; i3 < ne[3]; i3++) {
|
|
for (int i2 = 0; i2 < ne[2]; i2++) {
|
|
for (int i1 = 0; i1 < ne[1]; i1++) {
|
|
for (int i0 = 0; i0 < ne[0]; i0++) {
|
|
((ggml_fp16_t *)result->data)[i3*ne[2]*ne[1]*ne[0] + i2*ne[1]*ne[0] + i1*ne[0] + i0] = ggml_fp32_to_fp16(frand()*(fmax - fmin) + fmin);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
break;
|
|
default:
|
|
assert(false);
|
|
};
|
|
|
|
return result;
|
|
}
|
|
|
|
struct ggml_tensor * get_random_tensor_i32(
|
|
struct ggml_context * ctx0,
|
|
int ndims,
|
|
int64_t ne[],
|
|
int32_t imin,
|
|
int32_t imax) {
|
|
struct ggml_tensor * result = ggml_new_tensor(ctx0, GGML_TYPE_I32, ndims, ne);
|
|
|
|
switch (ndims) {
|
|
case 1:
|
|
for (int i0 = 0; i0 < ne[0]; i0++) {
|
|
((int32_t *)result->data)[i0] = irand(imax - imin) + imin;
|
|
}
|
|
break;
|
|
case 2:
|
|
for (int i1 = 0; i1 < ne[1]; i1++) {
|
|
for (int i0 = 0; i0 < ne[0]; i0++) {
|
|
((int32_t *)result->data)[i1*ne[0] + i0] = irand(imax - imin) + imin;
|
|
}
|
|
}
|
|
break;
|
|
case 3:
|
|
for (int i2 = 0; i2 < ne[2]; i2++) {
|
|
for (int i1 = 0; i1 < ne[1]; i1++) {
|
|
for (int i0 = 0; i0 < ne[0]; i0++) {
|
|
((int32_t *)result->data)[i2*ne[1]*ne[0] + i1*ne[0] + i0] = irand(imax - imin) + imin;
|
|
}
|
|
}
|
|
}
|
|
break;
|
|
case 4:
|
|
for (int i3 = 0; i3 < ne[3]; i3++) {
|
|
for (int i2 = 0; i2 < ne[2]; i2++) {
|
|
for (int i1 = 0; i1 < ne[1]; i1++) {
|
|
for (int i0 = 0; i0 < ne[0]; i0++) {
|
|
((int32_t *)result->data)[i3*ne[2]*ne[1]*ne[0] + i2*ne[1]*ne[0] + i1*ne[0] + i0] = irand(imax - imin) + imin;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
break;
|
|
default:
|
|
assert(false);
|
|
};
|
|
|
|
return result;
|
|
}
|
|
|
|
void print_elements(const char* label, const struct ggml_tensor * t) {
|
|
if (!t) {
|
|
printf("%s: %s = null\n", __func__, label);
|
|
return;
|
|
}
|
|
const int nelements = ggml_nelements(t);
|
|
printf("%s: %s = [", __func__, label);
|
|
for (int k = 0; k < nelements; ++k) {
|
|
if (k > 0) { printf(", "); }
|
|
printf("%.5f", ggml_get_f32_1d(t, k));
|
|
}
|
|
printf("] shape: [");
|
|
for (int k = 0; k < t->n_dims; ++k) {
|
|
if (k > 0) { printf(", "); }
|
|
printf("%d", (int)t->ne[k]);
|
|
}
|
|
printf("]\n");
|
|
|
|
}
|
|
|
|
bool check_gradient(
|
|
const char * op_name,
|
|
struct ggml_context * ctx0,
|
|
struct ggml_tensor * x[],
|
|
struct ggml_tensor * f,
|
|
int ndims,
|
|
int nargs,
|
|
float eps,
|
|
float max_error_abs,
|
|
float max_error_rel) {
|
|
|
|
static int n_threads = -1;
|
|
if (n_threads < 0) {
|
|
n_threads = GGML_DEFAULT_N_THREADS;
|
|
|
|
const char *env = getenv("GGML_N_THREADS");
|
|
if (env) {
|
|
n_threads = atoi(env);
|
|
}
|
|
|
|
printf("GGML_N_THREADS = %d\n", n_threads);
|
|
}
|
|
|
|
struct ggml_cgraph gf = ggml_build_forward (f);
|
|
struct ggml_cgraph gb = ggml_build_backward(ctx0, &gf, false);
|
|
|
|
ggml_graph_compute_with_ctx(ctx0, &gf, n_threads);
|
|
|
|
ggml_graph_reset (&gf);
|
|
ggml_set_f32 (f->grad, 1.0f);
|
|
|
|
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");
|
|
|
|
for (int i = 0; i < nargs; ++i) {
|
|
const int nelements = ggml_nelements(x[i]);
|
|
for (int k = 0; k < nelements; ++k) {
|
|
// compute gradient using finite differences
|
|
const float x0 = ggml_get_f32_1d(x[i], k);
|
|
const float xm = x0 - eps;
|
|
const float xp = x0 + eps;
|
|
ggml_set_f32_1d(x[i], k, xp);
|
|
|
|
ggml_graph_compute_with_ctx(ctx0, &gf, n_threads);
|
|
|
|
const float f0 = ggml_get_f32_1d(f, 0);
|
|
|
|
ggml_set_f32_1d(x[i], k, xm);
|
|
|
|
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);
|
|
|
|
ggml_set_f32_1d(x[i], k, x0);
|
|
|
|
// compute gradient using backward graph
|
|
ggml_graph_reset (&gf);
|
|
ggml_set_f32 (f->grad, 1.0f);
|
|
|
|
ggml_graph_compute_with_ctx(ctx0, &gb, n_threads);
|
|
|
|
const float g1 = ggml_get_f32_1d(x[i]->grad, k);
|
|
|
|
const float error_abs = fabsf(g0 - g1);
|
|
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",
|
|
op_name, ndims, i, k, x0, xm, xp, f0, f1, g0, g1, eps, error_abs, error_rel);
|
|
//assert(false);
|
|
return false;
|
|
}
|
|
}
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
// TODO: clean-up this ..
|
|
bool check_mat_mul(
|
|
const struct ggml_tensor * y,
|
|
const struct ggml_tensor * x0,
|
|
const struct ggml_tensor * x1) {
|
|
float * dst = (float *) y->data;
|
|
float * src0 = (float *) x0->data;
|
|
float * src1 = (float *) x1->data;
|
|
|
|
const int nc = x0->ne[1];
|
|
const int nr = x1->ne[1];
|
|
const int nk = x0->ne[0];
|
|
|
|
GGML_PRINT_DEBUG("check_mat_mul: nc=%d, nr=%d, nk=%d\n", nc, nr, nk);
|
|
|
|
GGML_PRINT_DEBUG("x0:\n");
|
|
for (int j = 0; j < x0->ne[1]; ++j) {
|
|
for (int i = 0; i < x0->ne[0]; ++i) {
|
|
GGML_PRINT_DEBUG("%6.3f ", src0[j*nk + i]);
|
|
}
|
|
GGML_PRINT_DEBUG("\n");
|
|
}
|
|
GGML_PRINT_DEBUG("\n");
|
|
|
|
GGML_PRINT_DEBUG("x1:\n");
|
|
for (int j = 0; j < x1->ne[1]; ++j) {
|
|
for (int i = 0; i < x1->ne[0]; ++i) {
|
|
GGML_PRINT_DEBUG("%6.3f ", src1[j*nk + i]);
|
|
}
|
|
GGML_PRINT_DEBUG("\n");
|
|
}
|
|
GGML_PRINT_DEBUG("\n");
|
|
|
|
GGML_PRINT_DEBUG("y: n_dims = %d, (%lld, %lld)\n", y->n_dims, y->ne[0], y->ne[1]);
|
|
for (int j = 0; j < y->ne[1]; ++j) {
|
|
for (int i = 0; i < y->ne[0]; ++i) {
|
|
GGML_PRINT_DEBUG("%6.3f ", dst[j*nr + i]);
|
|
}
|
|
GGML_PRINT_DEBUG("\n");
|
|
}
|
|
|
|
for (int i = 0; i < nr; ++i) {
|
|
for (int j = 0; j < nc; ++j) {
|
|
float sum = 0.0f;
|
|
|
|
for (int k = 0; k < nk; ++k) {
|
|
sum += src0[j*nk + k]*src1[i*nk + k];
|
|
}
|
|
|
|
if (fabsf(dst[i*nc + j] - sum) > 1e-5f) {
|
|
fprintf(stderr, "check_mat_mul: dst[%d] = %f, sum = %f\n", i*nc + j, dst[i*nc + j], sum);
|
|
assert(false);
|
|
return false;
|
|
}
|
|
}
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
#define NUM_PERMUTATIONS (4*3*2*1)
|
|
|
|
int main(int argc, const char ** argv) {
|
|
struct ggml_init_params params = {
|
|
.mem_size = 128*1024*1024,
|
|
.mem_buffer = NULL,
|
|
.no_alloc = false,
|
|
};
|
|
|
|
int64_t ne[4];
|
|
|
|
int all_permutations[4 * NUM_PERMUTATIONS];
|
|
{
|
|
int count = 0;
|
|
for (int ax0=0; ax0<4; ++ax0) {
|
|
for (int ax1=0; ax1<4; ++ax1) {
|
|
if (ax1 == ax0) continue;
|
|
for (int ax2=0; ax2<4; ++ax2) {
|
|
if (ax2 == ax0) continue;
|
|
if (ax2 == ax1) continue;
|
|
for (int ax3=0; ax3<4; ++ax3) {
|
|
if (ax3 == ax0) continue;
|
|
if (ax3 == ax1) continue;
|
|
if (ax3 == ax2) continue;
|
|
assert(count < NUM_PERMUTATIONS);
|
|
all_permutations[count*4+0] = ax0;
|
|
all_permutations[count*4+1] = ax1;
|
|
all_permutations[count*4+2] = ax2;
|
|
all_permutations[count*4+3] = ax3;
|
|
++count;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
|
|
// original loop: 1000
|
|
int niter = 4;
|
|
const char *env = getenv("GGML_NLOOP");
|
|
if (env != NULL) {
|
|
niter = atoi(env);
|
|
}
|
|
if (argc > 1) {
|
|
niter = atoi(argv[1]);
|
|
}
|
|
for (int iter = 0; iter < niter; ++iter) {
|
|
printf("test-grad0: iter:%d/%d\n", iter, niter);
|
|
struct ggml_context * ctx0 = ggml_init(params);
|
|
|
|
get_random_dims(ne, 4);
|
|
|
|
struct ggml_tensor * x[MAX_NARGS];
|
|
|
|
// add f32
|
|
{
|
|
const int nargs = 2;
|
|
|
|
for (int ndims = 1; ndims <= 4; ++ndims) {
|
|
for (int i = 0; i < nargs; ++i) {
|
|
x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[i]);
|
|
}
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_add(ctx0, x[0], x[1]));
|
|
|
|
check_gradient("add f32", ctx0, x, f, ndims, nargs, 1e-3f, 2e-3f, 2e-3f);
|
|
}
|
|
}
|
|
|
|
// add f16
|
|
{
|
|
const int nargs = 2;
|
|
|
|
for (int ndims = 1; ndims <= 4; ++ndims) {
|
|
for (int i = 0; i < nargs; ++i) {
|
|
x[i] = get_random_tensor_f16(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[i]);
|
|
}
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_add(ctx0, x[0], x[1]));
|
|
|
|
check_gradient("add f16", ctx0, x, f, ndims, nargs, 1e-1f, 2e-1f, 2e-1f);
|
|
}
|
|
}
|
|
|
|
// sub
|
|
{
|
|
const int nargs = 2;
|
|
|
|
for (int ndims = 1; ndims <= 4; ++ndims) {
|
|
for (int i = 0; i < nargs; ++i) {
|
|
x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[i]);
|
|
}
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_sub(ctx0, x[0], x[1]));
|
|
|
|
check_gradient("sub", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, 1e-3f);
|
|
}
|
|
}
|
|
|
|
// mul
|
|
{
|
|
const int nargs = 2;
|
|
|
|
for (int ndims = 1; ndims <= 4; ++ndims) {
|
|
for (int i = 0; i < nargs; ++i) {
|
|
x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[i]);
|
|
}
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_mul(ctx0, x[0], x[1]));
|
|
|
|
check_gradient("mul", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
|
|
}
|
|
}
|
|
|
|
// div
|
|
{
|
|
const int nargs = 2;
|
|
|
|
for (int ndims = 1; ndims <= 4; ++ndims) {
|
|
for (int i = 0; i < nargs; ++i) {
|
|
x[i] = get_random_tensor_f32(ctx0, ndims, ne, 0.5f, 1.0f);
|
|
ggml_set_param(ctx0, x[i]);
|
|
}
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_div(ctx0, x[0], x[1]));
|
|
|
|
check_gradient("div", ctx0, x, f, ndims, nargs, 1e-3f, 1e-1f, 1e-1f);
|
|
}
|
|
}
|
|
|
|
// sqr
|
|
{
|
|
const int nargs = 1;
|
|
|
|
for (int ndims = 1; ndims <= 2; ++ndims) {
|
|
for (int i = 0; i < nargs; ++i) {
|
|
x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[i]);
|
|
}
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_sqr(ctx0, x[0]));
|
|
|
|
check_gradient("sqr", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
|
|
}
|
|
}
|
|
|
|
// sqrt
|
|
{
|
|
const int nargs = 1;
|
|
|
|
for (int ndims = 1; ndims <= 2; ++ndims) {
|
|
for (int i = 0; i < nargs; ++i) {
|
|
x[i] = get_random_tensor_f32(ctx0, ndims, ne, 2.0f*1e-3f, 1.0f);
|
|
ggml_set_param(ctx0, x[i]);
|
|
}
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_sqrt(ctx0, x[0]));
|
|
|
|
check_gradient("sqrt", ctx0, x, f, ndims, nargs, 1e-3f, INFINITY, 1e-1f);
|
|
}
|
|
}
|
|
|
|
// log
|
|
{
|
|
const int nargs = 1;
|
|
|
|
for (int ndims = 1; ndims <= 2; ++ndims) {
|
|
for (int i = 0; i < nargs; ++i) {
|
|
x[i] = get_random_tensor_f32(ctx0, ndims, ne, 2.0f*1e-3f, 1.0f);
|
|
ggml_set_param(ctx0, x[i]);
|
|
}
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_log(ctx0, x[0]));
|
|
|
|
check_gradient("log", ctx0, x, f, ndims, nargs, 1e-3f, INFINITY, 1e-1f);
|
|
}
|
|
}
|
|
|
|
// sum
|
|
{
|
|
const int nargs = 1;
|
|
|
|
for (int ndims = 1; ndims <= 2; ++ndims) {
|
|
for (int i = 0; i < nargs; ++i) {
|
|
x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[i]);
|
|
}
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, x[0]);
|
|
|
|
check_gradient("sum", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, 1e-3f);
|
|
}
|
|
}
|
|
|
|
|
|
// sum_rows
|
|
{
|
|
const int nargs = 1;
|
|
|
|
for (int ndims = 1; ndims <= 4; ++ndims) {
|
|
for (int i = 0; i < nargs; ++i) {
|
|
x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[i]);
|
|
}
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_sqr(ctx0, ggml_sum_rows(ctx0, x[0])));
|
|
|
|
check_gradient("sum_rows", ctx0, x, f, ndims, nargs, 1e-3f, 1e-2f, INFINITY);
|
|
}
|
|
}
|
|
|
|
// mean, not yet fully implemented
|
|
if(0)
|
|
{
|
|
const int nargs = 1;
|
|
|
|
for (int ndims = 1; ndims <= 4; ++ndims) {
|
|
for (int i = 0; i < nargs; ++i) {
|
|
x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[i]);
|
|
}
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_mean(ctx0, x[0]));
|
|
|
|
check_gradient("mean", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, 1e-3f);
|
|
}
|
|
}
|
|
|
|
// argmax
|
|
if (0)
|
|
{
|
|
const int nargs = 1;
|
|
|
|
for (int ndims = 1; ndims <= 4; ++ndims) {
|
|
for (int i = 0; i < nargs; ++i) {
|
|
x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[i]);
|
|
}
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_argmax(ctx0, x[0]));
|
|
|
|
check_gradient("argmax", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, 1e-3f);
|
|
}
|
|
}
|
|
|
|
// repeat
|
|
{
|
|
int64_t ne2[4];
|
|
get_random_dims(ne2, 4);
|
|
|
|
ne2[0] = ne[0] * ne2[0];
|
|
ne2[1] = ne[1] * ne2[1];
|
|
ne2[2] = 1;
|
|
ne2[3] = 1;
|
|
|
|
const int nargs = 1;
|
|
for (int ndims = 1; ndims <= 2; ++ndims) {
|
|
x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
x[1] = get_random_tensor_f32(ctx0, ndims, ne2, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[0]);
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_sqr(ctx0, ggml_sub(ctx0, x[1], ggml_repeat(ctx0, x[0], x[1]))));
|
|
|
|
check_gradient("repeat", ctx0, x, f, ndims, nargs, 1e-3f, 1e-2f, INFINITY);
|
|
}
|
|
}
|
|
|
|
// repeat back
|
|
{
|
|
int64_t ne2[4];
|
|
get_random_dims(ne2, 4);
|
|
|
|
ne2[0] = ne[0] * ne2[0];
|
|
ne2[1] = ne[1] * ne2[1];
|
|
ne2[2] = 1;
|
|
ne2[3] = 1;
|
|
|
|
const int nargs = 1;
|
|
for (int ndims = 1; ndims <= 2; ++ndims) {
|
|
x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
x[1] = get_random_tensor_f32(ctx0, ndims, ne2, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[0]);
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_sqr(ctx0, ggml_sub(ctx0, x[0], ggml_repeat_back(ctx0, x[1], x[0]))));
|
|
|
|
check_gradient("repeat back", ctx0, x, f, ndims, nargs, 1e-3f, 1e-2f, INFINITY);
|
|
}
|
|
}
|
|
|
|
// abs (finite differences do not work)
|
|
//{
|
|
// const int nargs = 1;
|
|
|
|
// for (int ndims = 1; ndims <= 2; ++ndims) {
|
|
// for (int i = 0; i < nargs; ++i) {
|
|
// x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
// ggml_set_param(ctx0, x[i]);
|
|
// }
|
|
|
|
// struct ggml_tensor * f = ggml_sum(ctx0, ggml_abs(ctx0, x[0]));
|
|
|
|
// check_gradient("abs", ctx0, x, f, ndims, nargs, 1e-3f, INFINITY, 1e-3f);
|
|
// }
|
|
//}
|
|
|
|
// sgn
|
|
{
|
|
const int nargs = 1;
|
|
|
|
for (int ndims = 1; ndims <= 4; ++ndims) {
|
|
for (int i = 0; i < nargs; ++i) {
|
|
x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[i]);
|
|
}
|
|
|
|
struct ggml_tensor* f = ggml_sum(ctx0, ggml_sgn(ctx0, x[0]));
|
|
|
|
check_gradient("sgn", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, 1e-3f);
|
|
}
|
|
}
|
|
|
|
// neg
|
|
{
|
|
const int nargs = 1;
|
|
|
|
for (int ndims = 1; ndims <= 4; ++ndims) {
|
|
for (int i = 0; i < nargs; ++i) {
|
|
x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[i]);
|
|
}
|
|
|
|
struct ggml_tensor* f = ggml_sum(ctx0, ggml_neg(ctx0, x[0]));
|
|
|
|
check_gradient("neg", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, 1e-3f);
|
|
}
|
|
}
|
|
|
|
// step
|
|
{
|
|
const int nargs = 1;
|
|
|
|
for (int ndims = 1; ndims <= 4; ++ndims) {
|
|
for (int i = 0; i < nargs; ++i) {
|
|
x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[i]);
|
|
}
|
|
|
|
struct ggml_tensor* f = ggml_sum(ctx0, ggml_step(ctx0, x[0]));
|
|
|
|
check_gradient("step", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, 1e-3f);
|
|
}
|
|
}
|
|
|
|
// tanh, not yet fully implemented
|
|
if(0)
|
|
{
|
|
const int nargs = 1;
|
|
|
|
for (int ndims = 1; ndims <= 4; ++ndims) {
|
|
for (int i = 0; i < nargs; ++i) {
|
|
x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[i]);
|
|
}
|
|
|
|
struct ggml_tensor* f = ggml_sum(ctx0, ggml_tanh(ctx0, x[0]));
|
|
|
|
check_gradient("tanh", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, 1e-3f);
|
|
}
|
|
}
|
|
|
|
// mul_mat
|
|
{
|
|
const int nargs = 2;
|
|
|
|
for (int ndims = 2; ndims <= 2; ++ndims) {
|
|
x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
{
|
|
int64_t ne2[4];
|
|
get_random_dims(ne2, 4);
|
|
ne2[0] = ne[0];
|
|
x[1] = get_random_tensor_f32(ctx0, ndims, ne2, -1.0f, 1.0f);
|
|
}
|
|
|
|
ggml_set_param(ctx0, x[0]);
|
|
ggml_set_param(ctx0, x[1]);
|
|
|
|
struct ggml_tensor * m = ggml_mul_mat(ctx0, x[1], x[0]);
|
|
struct ggml_tensor * f = ggml_sum(ctx0, m);
|
|
|
|
GGML_PRINT_DEBUG("testing: mul_mat, [%lld, %lld] (%d) * [%lld, %lld] (%d)\n", x[1]->ne[0], x[1]->ne[1], x[1]->n_dims, x[0]->ne[0], x[0]->ne[1], x[0]->n_dims);
|
|
|
|
check_gradient("mul_mat", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
|
|
check_mat_mul(m, x[1], x[0]);
|
|
}
|
|
}
|
|
|
|
// elu, not yet fully implemented
|
|
if(0)
|
|
{
|
|
const int nargs = 1;
|
|
|
|
for (int ndims = 1; ndims <= 4; ++ndims) {
|
|
for (int i = 0; i < nargs; ++i) {
|
|
x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[i]);
|
|
}
|
|
|
|
struct ggml_tensor* f = ggml_sum(ctx0, ggml_elu(ctx0, x[0]));
|
|
|
|
check_gradient("elu", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, 1e-3f);
|
|
}
|
|
}
|
|
|
|
// relu
|
|
{
|
|
const int nargs = 1;
|
|
|
|
for (int ndims = 1; ndims <= 4; ++ndims) {
|
|
for (int i = 0; i < nargs; ++i) {
|
|
x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[i]);
|
|
}
|
|
|
|
struct ggml_tensor* f = ggml_sum(ctx0, ggml_relu(ctx0, x[0]));
|
|
|
|
check_gradient("relu", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
|
|
}
|
|
}
|
|
|
|
// gelu, not yet fully implemented
|
|
if(0)
|
|
{
|
|
const int nargs = 1;
|
|
|
|
for (int ndims = 1; ndims <= 4; ++ndims) {
|
|
for (int i = 0; i < nargs; ++i) {
|
|
x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[i]);
|
|
}
|
|
|
|
struct ggml_tensor* f = ggml_sum(ctx0, ggml_gelu(ctx0, x[0]));
|
|
|
|
check_gradient("gelu", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, 1e-3f);
|
|
}
|
|
}
|
|
|
|
// silu
|
|
{
|
|
const int nargs = 1;
|
|
|
|
for (int ndims = 1; ndims <= 2; ++ndims) {
|
|
for (int i = 0; i < nargs; ++i) {
|
|
x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[i]);
|
|
}
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_silu(ctx0, x[0]));
|
|
|
|
#ifdef GGML_SILU_FP16
|
|
// due to GGML_SILU_FP16 the finite difference method will be slightly wrong -> increase error bounds.
|
|
check_gradient("silu", ctx0, x, f, ndims, nargs, 1e-3f, 0.5, INFINITY);
|
|
#else
|
|
check_gradient("silu", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
|
|
#endif
|
|
}
|
|
}
|
|
|
|
// rms_norm
|
|
{
|
|
const int nargs = 1;
|
|
|
|
for (int ndims = 1; ndims <= 2; ++ndims) {
|
|
for (int i = 0; i < nargs; ++i) {
|
|
x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[i]);
|
|
}
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_rms_norm(ctx0, x[0]));
|
|
|
|
check_gradient("rms_norm", ctx0, x, f, ndims, nargs, 1e-4f, 1.0f, INFINITY);
|
|
}
|
|
}
|
|
|
|
// scale
|
|
{
|
|
const int nargs = 2;
|
|
|
|
int64_t ne2[4];
|
|
ne2[0] = 1;
|
|
|
|
for (int ndims = 1; ndims <= 2; ++ndims) {
|
|
x[1] = get_random_tensor_f32(ctx0, 1, ne2, -1.0f, 1.0f);
|
|
x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
|
|
ggml_set_param(ctx0, x[0]);
|
|
ggml_set_param(ctx0, x[1]);
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_scale(ctx0, x[0], x[1]));
|
|
|
|
check_gradient("scale", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
|
|
}
|
|
}
|
|
|
|
// cpy f32
|
|
{
|
|
const int nargs = 2;
|
|
|
|
for (int ndims = 1; ndims <= 2; ++ndims) {
|
|
for (int i = 0; i < nargs; ++i) {
|
|
x[i] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[i]);
|
|
}
|
|
// x[1] is overwritten by x[0], so the gradients don't propagate to x[1]
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_cpy(ctx0, x[0], x[1]));
|
|
|
|
check_gradient("cpy f32", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
|
|
}
|
|
}
|
|
|
|
// cpy f16
|
|
{
|
|
const int nargs = 2;
|
|
|
|
for (int ndims = 1; ndims <= 2; ++ndims) {
|
|
for (int i = 0; i < nargs; ++i) {
|
|
x[i] = get_random_tensor_f16(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[i]);
|
|
}
|
|
// x[1] is overwritten by x[0], so the gradients don't propagate to x[1]
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_cpy(ctx0, x[0], x[1]));
|
|
|
|
check_gradient("cpy f16", ctx0, x, f, ndims, nargs, 1e-1f, 1e-1f, INFINITY);
|
|
}
|
|
}
|
|
|
|
// reshape (1d->nd)
|
|
{
|
|
const int nargs = 1;
|
|
|
|
for (int ndims = 1; ndims <= 2; ++ndims) {
|
|
int64_t ne2[4];
|
|
ne2[0] = 1;
|
|
ne2[1] = 1;
|
|
ne2[2] = 1;
|
|
ne2[3] = 1;
|
|
for (int i = 0; i < ndims; ++i) {
|
|
ne2[0] *= ne[i];
|
|
}
|
|
x[0] = get_random_tensor_f32(ctx0, 1, ne2, -1.0f, 1.0f);
|
|
x[1] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[0]);
|
|
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_reshape(ctx0, x[0], x[1]));
|
|
check_gradient("reshape", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
|
|
}
|
|
}
|
|
|
|
// reshape (nd->1d)
|
|
{
|
|
const int nargs = 1;
|
|
|
|
for (int ndims = 1; ndims <= 2; ++ndims) {
|
|
int64_t ne2[4];
|
|
ne2[0] = 1;
|
|
ne2[1] = 1;
|
|
ne2[2] = 1;
|
|
ne2[3] = 1;
|
|
for (int i = 0; i < ndims; ++i) {
|
|
ne2[0] *= ne[i];
|
|
}
|
|
x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
x[1] = get_random_tensor_f32(ctx0, 1, ne2, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[0]);
|
|
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_reshape(ctx0, x[0], x[1]));
|
|
check_gradient("reshape", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
|
|
}
|
|
}
|
|
|
|
// acc 1d
|
|
{
|
|
int64_t ne2[4] = { 1, 1, 1, 1 };
|
|
|
|
const int nargs = 2;
|
|
for (int ndims = 1; ndims <= 4; ++ndims) {
|
|
|
|
x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[0]);
|
|
|
|
get_random_dims(ne2, 1);
|
|
while ((ne2[0] > ne[0]) || (ne2[0] > ggml_nelements(x[0]))) {
|
|
get_random_dims(ne2, 1);
|
|
}
|
|
|
|
x[1] = get_random_tensor_f32(ctx0, 1, ne2, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[1]);
|
|
|
|
const int max_offset = MAX(0, ggml_nelements(x[0]) - ggml_nelements(x[1]));
|
|
const int offset = irand(max_offset) * ggml_element_size(x[0]);
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_acc(ctx0, x[0], x[1], x[0]->nb[1], x[0]->nb[2], x[0]->nb[3], offset));
|
|
|
|
check_gradient("acc 1d", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
|
|
}
|
|
}
|
|
|
|
// acc 2d
|
|
{
|
|
int64_t ne2[4] = { 1, 1, 1, 1 };
|
|
int64_t max_offsets[4] = { 0, 0, 0, 0 };
|
|
int64_t offsets[4] = { 0, 0, 0, 0 };
|
|
|
|
const int nargs = 2;
|
|
for (int ndims = 2; ndims <= 4; ++ndims) {
|
|
|
|
x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[0]);
|
|
|
|
get_random_dims(ne2, 2);
|
|
while ((ne2[0] > ne[0]) || (ne2[1] > ne[1]) || (ne2[0]*ne2[1] > ggml_nelements(x[0]))) {
|
|
get_random_dims(ne2, 2);
|
|
}
|
|
|
|
x[1] = get_random_tensor_f32(ctx0, 2, ne2, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[1]);
|
|
|
|
max_offsets[0] = MAX(0, x[0]->ne[0] - x[1]->ne[0]);
|
|
max_offsets[1] = MAX(0, x[0]->ne[1] - x[1]->ne[1]);
|
|
offsets[0] = irand(max_offsets[0]) * x[0]->nb[0];
|
|
offsets[1] = irand(max_offsets[1]) * x[0]->nb[1];
|
|
const int offset = offsets[0] + offsets[1];
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_acc(ctx0, x[0], x[1], x[0]->nb[1], x[0]->nb[2], x[0]->nb[3], offset));
|
|
|
|
check_gradient("acc 2d", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
|
|
}
|
|
}
|
|
|
|
// acc 3d
|
|
{
|
|
int64_t ne2[4] = { 1, 1, 1, 1 };
|
|
int64_t max_offsets[4] = { 0, 0, 0, 0 };
|
|
int64_t offsets[4] = { 0, 0, 0, 0 };
|
|
|
|
const int nargs = 2;
|
|
for (int ndims = 3; ndims <= 4; ++ndims) {
|
|
|
|
x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[0]);
|
|
|
|
get_random_dims(ne2, 3);
|
|
while ((ne2[0] > ne[0]) || (ne2[1] > ne[1]) || (ne2[2] > ne[2]) || (ne2[0]*ne2[1]*ne2[2] > ggml_nelements(x[0]))) {
|
|
get_random_dims(ne2, 3);
|
|
}
|
|
|
|
x[1] = get_random_tensor_f32(ctx0, 3, ne2, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[1]);
|
|
|
|
max_offsets[0] = MAX(0, x[0]->ne[0] - x[1]->ne[0]);
|
|
max_offsets[1] = MAX(0, x[0]->ne[1] - x[1]->ne[1]);
|
|
max_offsets[2] = MAX(0, x[0]->ne[2] - x[1]->ne[2]);
|
|
offsets[0] = irand(max_offsets[0]) * x[0]->nb[0];
|
|
offsets[1] = irand(max_offsets[1]) * x[0]->nb[1];
|
|
offsets[2] = irand(max_offsets[2]) * x[0]->nb[2];
|
|
const int offset = offsets[0] + offsets[1] + offsets[2];
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_acc(ctx0, x[0], x[1], x[0]->nb[1], x[0]->nb[2], x[0]->nb[3], offset));
|
|
|
|
check_gradient("acc 3d", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
|
|
}
|
|
}
|
|
|
|
// acc 4d
|
|
{
|
|
int64_t ne2[4] = { 1, 1, 1, 1 };
|
|
int64_t max_offsets[4] = { 0, 0, 0, 0 };
|
|
int64_t offsets[4] = { 0, 0, 0, 0 };
|
|
|
|
const int nargs = 2;
|
|
for (int ndims = 4; ndims <= 4; ++ndims) {
|
|
|
|
x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[0]);
|
|
|
|
get_random_dims(ne2, 4);
|
|
while ((ne2[0] > ne[0]) || (ne2[1] > ne[1]) || (ne2[2] > ne[2]) || (ne2[3] > ne[3]) || (ne2[0]*ne2[1]*ne2[2]*ne2[3] > ggml_nelements(x[0]))) {
|
|
get_random_dims(ne2, 4);
|
|
}
|
|
|
|
x[1] = get_random_tensor_f32(ctx0, 4, ne2, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[1]);
|
|
|
|
max_offsets[0] = MAX(0, x[0]->ne[0] - x[1]->ne[0]);
|
|
max_offsets[1] = MAX(0, x[0]->ne[1] - x[1]->ne[1]);
|
|
max_offsets[2] = MAX(0, x[0]->ne[2] - x[1]->ne[2]);
|
|
max_offsets[3] = MAX(0, x[0]->ne[3] - x[1]->ne[3]);
|
|
offsets[0] = irand(max_offsets[0]) * x[0]->nb[0];
|
|
offsets[1] = irand(max_offsets[1]) * x[0]->nb[1];
|
|
offsets[2] = irand(max_offsets[2]) * x[0]->nb[2];
|
|
offsets[3] = irand(max_offsets[3]) * x[0]->nb[3];
|
|
const int offset = offsets[0] + offsets[1] + offsets[2] + offsets[3];
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_acc(ctx0, x[0], x[1], x[0]->nb[1], x[0]->nb[2], x[0]->nb[3], offset));
|
|
|
|
check_gradient("acc 4d", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
|
|
}
|
|
}
|
|
|
|
// set_1d
|
|
{
|
|
int64_t ne2[4];
|
|
|
|
const int nargs = 2;
|
|
for (int ndims = 1; ndims <= 4; ++ndims) {
|
|
|
|
x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[0]);
|
|
|
|
get_random_dims(ne2, 1);
|
|
while ((ne2[0] > ne[0]) || (ne2[0] > ggml_nelements(x[0]))) {
|
|
get_random_dims(ne2, 1);
|
|
}
|
|
|
|
x[1] = get_random_tensor_f32(ctx0, 1, ne2, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[1]);
|
|
|
|
const int max_offset = MAX(0, ggml_nelements(x[0]) - ggml_nelements(x[1]));
|
|
const int offset = irand(max_offset) * ggml_element_size(x[0]);
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_set_1d(ctx0, x[0], x[1], offset));
|
|
|
|
check_gradient("set_1d", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
|
|
}
|
|
}
|
|
|
|
// set_2d
|
|
{
|
|
int64_t ne2[4];
|
|
int64_t max_offsets[4] = { 0, 0, 0, 0 };
|
|
int64_t offsets[4] = { 0, 0, 0, 0 };
|
|
|
|
const int nargs = 1;
|
|
for (int ndims = 2; ndims <= 4; ++ndims) {
|
|
|
|
x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[0]);
|
|
|
|
get_random_dims(ne2, 2);
|
|
while ((ne2[0] > ne[0]) || (ne2[1] > ne[1]) || (ne2[0]*ne2[1] > ggml_nelements(x[0]))) {
|
|
get_random_dims(ne2, 2);
|
|
}
|
|
|
|
x[1] = get_random_tensor_f32(ctx0, 2, ne2, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[1]);
|
|
|
|
max_offsets[0] = MAX(0, x[0]->ne[0] - x[1]->ne[0]);
|
|
max_offsets[1] = MAX(0, x[0]->ne[1] - x[1]->ne[1]);
|
|
offsets[0] = irand(max_offsets[0]) * x[0]->nb[0];
|
|
offsets[1] = irand(max_offsets[1]) * x[0]->nb[1];
|
|
const int offset = offsets[0] + offsets[1];
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_set_2d(ctx0, x[0], x[1], x[1]->nb[1], offset));
|
|
|
|
check_gradient("set_2d", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
|
|
}
|
|
}
|
|
|
|
// view_1d
|
|
{
|
|
const int nargs = 1;
|
|
for (int ndims = 1; ndims <= 4; ++ndims) {
|
|
|
|
x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
|
|
ggml_set_param(ctx0, x[0]);
|
|
|
|
const int k0 = irand(ggml_nelements(x[0]));
|
|
const int k1 = irand(ggml_nelements(x[0]));
|
|
const int i0 = MIN(k0, k1);
|
|
const int i1 = MAX(k0, k1);
|
|
|
|
const int offset = i0 * sizeof(float);
|
|
const int nelem = i1 - i0;
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_view_1d(ctx0, x[0], nelem, offset));
|
|
|
|
check_gradient("view_1d", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
|
|
}
|
|
}
|
|
|
|
// view_2d
|
|
{
|
|
int64_t ne2[4];
|
|
int64_t nb2[4];
|
|
|
|
const int nargs = 1;
|
|
for (int ndims = 1; ndims <= 4; ++ndims) {
|
|
|
|
x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
|
|
get_random_dims(ne2, 2);
|
|
while (ne2[0]*ne2[1] > ggml_nelements(x[0])) {
|
|
get_random_dims(ne2, 2);
|
|
}
|
|
const int count = ne2[0]*ne2[1];
|
|
|
|
nb2[0] = sizeof(float);
|
|
nb2[1] = nb2[0]*ne2[0];
|
|
|
|
ggml_set_param(ctx0, x[0]);
|
|
|
|
const int max_offset = ggml_nelements(x[0]) - count;
|
|
const int offset = irand(max_offset+1) * sizeof(float);
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_view_2d(ctx0, x[0], ne2[0], ne2[1], nb2[1], offset));
|
|
|
|
check_gradient("view_2d", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
|
|
}
|
|
}
|
|
|
|
// view_3d
|
|
{
|
|
int64_t ne2[4] = {1,1,1,1};
|
|
int64_t nb2[4] = {0,0,0,0};
|
|
|
|
const int nargs = 1;
|
|
for (int ndims = 1; ndims <= 4; ++ndims) {
|
|
|
|
x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
|
|
get_random_dims(ne2, 3);
|
|
while (ne2[0]*ne2[1]*ne2[2] > ggml_nelements(x[0])) {
|
|
get_random_dims(ne2, 3);
|
|
}
|
|
const int count = ne2[0]*ne2[1]*ne2[2];
|
|
|
|
nb2[0] = sizeof(float);
|
|
nb2[1] = nb2[0]*ne2[0];
|
|
nb2[2] = nb2[1]*ne2[1];
|
|
|
|
ggml_set_param(ctx0, x[0]);
|
|
|
|
const int max_offset = ggml_nelements(x[0]) - count;
|
|
const int offset = irand(max_offset+1) * sizeof(float);
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_view_3d(ctx0, x[0], ne2[0], ne2[1], ne2[2], nb2[1], nb2[2], offset));
|
|
|
|
check_gradient("view_3d", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
|
|
}
|
|
}
|
|
|
|
// permute
|
|
{
|
|
int64_t ne2[4];
|
|
|
|
const int nargs = 1;
|
|
for (int ndims = 1; ndims <= 4; ++ndims)
|
|
{
|
|
// ggml_permute will set axes of dimensions below n_dims to 1.
|
|
// to make ggml_permute work correctly on all axes,
|
|
// the input tensor needs maximal n_dim of 4.
|
|
for (int i=0; i<ndims; ++i) {
|
|
ne2[i] = ne[i];
|
|
}
|
|
for (int i=ndims; i<4; ++i) {
|
|
ne2[i] = 1;
|
|
}
|
|
x[0] = get_random_tensor_f32(ctx0, 4, ne2, -1.0f, 1.0f);
|
|
|
|
ggml_set_param(ctx0, x[0]);
|
|
|
|
const int p = irand(NUM_PERMUTATIONS);
|
|
const int ax0 = all_permutations[p*4+0];
|
|
const int ax1 = all_permutations[p*4+1];
|
|
const int ax2 = all_permutations[p*4+2];
|
|
const int ax3 = all_permutations[p*4+3];
|
|
|
|
// sum requires contiguous tensor rows
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_cont(ctx0, ggml_permute(ctx0, x[0], ax0, ax1, ax2, ax3)));
|
|
|
|
check_gradient("permute", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
|
|
}
|
|
}
|
|
|
|
// transpose
|
|
{
|
|
int64_t ne2[4];
|
|
|
|
const int nargs = 1;
|
|
for (int ndims = 1; ndims <= 4; ++ndims)
|
|
{
|
|
// ggml_transpose will set axes of dimensions below n_dims to 1.
|
|
// to make ggml_transpose work correctly on all axes,
|
|
// the input tensor needs maximal n_dim of 4.
|
|
for (int i=0; i<ndims; ++i) {
|
|
ne2[i] = ne[i];
|
|
}
|
|
for (int i=ndims; i<4; ++i) {
|
|
ne2[i] = 1;
|
|
}
|
|
x[0] = get_random_tensor_f32(ctx0, 4, ne2, -1.0f, 1.0f);
|
|
|
|
ggml_set_param(ctx0, x[0]);
|
|
|
|
// sum requires contiguous tensor rows
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, x[0])));
|
|
|
|
check_gradient("transpose", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
|
|
}
|
|
}
|
|
|
|
// get_rows
|
|
{
|
|
int64_t ne2[4] = {ne[0], ne[1], 1, 1};
|
|
int64_t ne3[4] = {1+irand(ne[1]), 1, 1, 1};
|
|
const int nargs = 1;
|
|
const int ndims = 2;
|
|
x[0] = get_random_tensor_f32(ctx0, ndims, ne2, -1.0f, 1.0f);
|
|
x[1] = get_random_tensor_i32(ctx0, 1, ne3, 0, ne2[1]);
|
|
|
|
ggml_set_param(ctx0, x[0]);
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_get_rows(ctx0, x[0], x[1]));
|
|
|
|
check_gradient("get_rows", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
|
|
}
|
|
|
|
// diag_mask_inf
|
|
{
|
|
const int nargs = 1;
|
|
const int ndims = 2;
|
|
|
|
x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[0]);
|
|
|
|
int n_past = irand(ne[0]);
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_diag_mask_inf(ctx0, x[0], n_past));
|
|
|
|
check_gradient("diag_mask_inf", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
|
|
}
|
|
|
|
// diag_mask_zero
|
|
{
|
|
const int nargs = 1;
|
|
const int ndims = 2;
|
|
|
|
x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[0]);
|
|
|
|
int n_past = irand(ne[0]);
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_diag_mask_zero(ctx0, x[0], n_past));
|
|
|
|
check_gradient("diag_mask_zero", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
|
|
}
|
|
|
|
// softmax
|
|
{
|
|
const int nargs = 1;
|
|
|
|
int64_t ne2[4];
|
|
get_random_dims(ne2, 4);
|
|
|
|
for (int ndims = 1; ndims <= 3; ++ndims) {
|
|
x[0] = get_random_tensor_f32(ctx0, ndims, ne2, -1.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[0]);
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_soft_max(ctx0, x[0]));
|
|
|
|
check_gradient("softmax", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
|
|
}
|
|
}
|
|
|
|
// cross_entropy_loss
|
|
{
|
|
const int nargs = 1;
|
|
|
|
int64_t ne2[4];
|
|
get_random_dims(ne2, 4);
|
|
|
|
for (int ndims = 1; ndims <= 3; ++ndims) {
|
|
x[0] = get_random_tensor_f32(ctx0, ndims, ne2, -1.0f, 1.0f);
|
|
x[1] = get_random_tensor_f32(ctx0, ndims, ne2, 0.0f, 1.0f);
|
|
ggml_set_param(ctx0, x[0]);
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_cross_entropy_loss(ctx0, x[0], x[1]));
|
|
|
|
check_gradient("cross_entropy_loss", ctx0, x, f, ndims, nargs, 1e-1f, 1e-2f, INFINITY);
|
|
// finite differences regularly fails!
|
|
}
|
|
}
|
|
|
|
// rope f32
|
|
{
|
|
const int nargs = 1;
|
|
|
|
int64_t ne2[4];
|
|
get_random_dims(ne2, 4);
|
|
ne2[0] += ne2[0] % 2;
|
|
int n_rot = ne2[0];
|
|
|
|
for (int ndims = 3; ndims <= 4; ++ndims) {
|
|
for (int mode = 0; mode < 4; ++mode) {
|
|
for (int n_past = 1; n_past < ne2[2]; ++n_past) {
|
|
x[0] = get_random_tensor_f32(ctx0, ndims, ne2, -1.0f, 1.0f);
|
|
|
|
ggml_set_param(ctx0, x[0]);
|
|
|
|
const bool skip_past = (mode & 1);
|
|
if (skip_past) {
|
|
// we have no past, so this would have to work on uninitialized memory.
|
|
// we only test the gradients here;
|
|
// skip_past should have no influence on gradient computation.
|
|
// so when other modes work, we assume that this does as well.
|
|
continue;
|
|
}
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_rope(ctx0, x[0], n_past, n_rot, mode, 0));
|
|
|
|
GGML_PRINT_DEBUG("rope f32: n_past: %d n_rot: %d mode: %d\n", n_past, n_rot, mode);
|
|
check_gradient("rope f32", ctx0, x, f, ndims, nargs, 1e-2f, 1e-3f, INFINITY);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// rope f16
|
|
{
|
|
const int nargs = 1;
|
|
|
|
int64_t ne2[4];
|
|
get_random_dims(ne2, 4);
|
|
ne2[0] += ne2[0] % 2;
|
|
int n_rot = ne2[0];
|
|
|
|
for (int ndims = 3; ndims <= 4; ++ndims) {
|
|
for (int mode = 0; mode < 4; ++mode) {
|
|
for (int n_past = 1; n_past < ne2[2]; ++n_past) {
|
|
x[0] = get_random_tensor_f16(ctx0, ndims, ne2, -1.0f, 1.0f);
|
|
|
|
ggml_set_param(ctx0, x[0]);
|
|
|
|
const bool skip_past = (mode & 1);
|
|
if (skip_past) {
|
|
// we have no past, so this would have to work on uninitialized memory.
|
|
// we only test the gradients here;
|
|
// skip_past should have no influence on gradient computation.
|
|
// so when other modes work, we assume that this does as well.
|
|
continue;
|
|
}
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_rope(ctx0, x[0], n_past, n_rot, mode, 0));
|
|
|
|
GGML_PRINT_DEBUG("rope f16: n_past: %d n_rot: %d mode: %d\n", n_past, n_rot, mode);
|
|
check_gradient("rope f16", ctx0, x, f, ndims, nargs, 1e-1f, 1e-1f, INFINITY);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// flash_attn f32
|
|
{
|
|
const int nargs = 3;
|
|
|
|
int64_t ne2[4];
|
|
|
|
get_random_dims(ne2, 4);
|
|
int64_t D = ne2[0];
|
|
int64_t N = ne2[1];
|
|
int64_t M = ne2[2] + N;
|
|
int64_t B = ne2[3];
|
|
|
|
for (int masked = 0; masked <= 1; ++masked) {
|
|
for (int ndims = 2; ndims <= 4; ++ndims) {
|
|
int64_t neq[4] = { D, N, B, ne[3] };
|
|
int64_t nek[4] = { D, M, B, ne[3] };
|
|
int64_t nev[4] = { M, D, B, ne[3] };
|
|
if (ndims == 2) {
|
|
neq[2] = 1; neq[3] = 1;
|
|
nek[2] = 1; nek[3] = 1;
|
|
nev[2] = 1; nev[3] = 1;
|
|
} else if (ndims == 3) {
|
|
neq[3] = 1;
|
|
nek[3] = 1;
|
|
nev[3] = 1;
|
|
}
|
|
x[0] = get_random_tensor_f32(ctx0, ndims, neq, -0.1250f, 0.1250f);
|
|
x[1] = get_random_tensor_f32(ctx0, ndims, nek, -0.1250f, 0.1250f);
|
|
x[2] = get_random_tensor_f32(ctx0, ndims, nev, -0.1250f, 0.1250f);
|
|
ggml_set_param(ctx0, x[0]);
|
|
ggml_set_param(ctx0, x[1]);
|
|
ggml_set_param(ctx0, x[2]);
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_flash_attn(ctx0, x[0], x[1], x[2], (masked == 0)));
|
|
|
|
check_gradient("flash_attn f32", ctx0, x, f, ndims, nargs, 1.5e-4f, INFINITY, 3.5f);
|
|
}
|
|
}
|
|
}
|
|
|
|
// flash_attn f16, not yet fully implemented
|
|
if(0)
|
|
{
|
|
const int nargs = 3;
|
|
|
|
int64_t ne2[4];
|
|
|
|
get_random_dims(ne2, 4);
|
|
int64_t D = ne2[0];
|
|
int64_t N = ne2[1];
|
|
int64_t M = ne2[2] + N;
|
|
int64_t B = ne2[3];
|
|
|
|
for (int masked = 0; masked <= 1; ++masked) {
|
|
for (int ndims = 2; ndims <= 4; ++ndims) {
|
|
int64_t neq[4] = { D, N, B, ne[3] };
|
|
int64_t nek[4] = { D, M, B, ne[3] };
|
|
int64_t nev[4] = { M, D, B, ne[3] };
|
|
if (ndims == 2) {
|
|
neq[2] = 1; neq[3] = 1;
|
|
nek[2] = 1; nek[3] = 1;
|
|
nev[2] = 1; nev[3] = 1;
|
|
} else if (ndims == 3) {
|
|
neq[3] = 1;
|
|
nek[3] = 1;
|
|
nev[3] = 1;
|
|
}
|
|
x[0] = get_random_tensor_f16(ctx0, ndims, neq, -0.1250f, 0.1250f);
|
|
x[1] = get_random_tensor_f16(ctx0, ndims, nek, -0.1250f, 0.1250f);
|
|
x[2] = get_random_tensor_f16(ctx0, ndims, nev, -0.1250f, 0.1250f);
|
|
ggml_set_param(ctx0, x[0]);
|
|
ggml_set_param(ctx0, x[1]);
|
|
ggml_set_param(ctx0, x[2]);
|
|
|
|
struct ggml_tensor * f = ggml_sum(ctx0, ggml_flash_attn(ctx0, x[0], x[1], x[2], (masked == 0)));
|
|
|
|
check_gradient("flash_attn f16", ctx0, x, f, ndims, nargs, 1.5e-4f, INFINITY, 3.5f);
|
|
}
|
|
}
|
|
}
|
|
ggml_free(ctx0);
|
|
}
|
|
|
|
return 0;
|
|
}
|