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379 lines
11 KiB
379 lines
11 KiB
#include "ggml/ggml.h"
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#include <math.h>
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#include <stdio.h>
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#include <stdlib.h>
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#include <assert.h>
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#define MAX_NARGS 2
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float frand() {
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return (float)rand()/(float)RAND_MAX;
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}
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int irand(int n) {
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return rand()%n;
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}
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void get_random_dims(int * dims, int ndims) {
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dims[0] = dims[1] = dims[2] = dims[3] = 1;
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for (int i = 0; i < ndims; i++) {
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dims[i] = 1 + irand(4);
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}
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}
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struct ggml_tensor * get_random_tensor(
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struct ggml_context * ctx0,
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int ndims,
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int ne[],
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float fmin,
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float fmax) {
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struct ggml_tensor * result = ggml_new_tensor(ctx0, GGML_TYPE_F32, ndims, ne);
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switch (ndims) {
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case 1:
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for (int i0 = 0; i0 < ne[0]; i0++) {
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((float *)result->data)[i0] = frand()*(fmax - fmin) + fmin;
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}
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break;
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case 2:
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for (int i1 = 0; i1 < ne[1]; i1++) {
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for (int i0 = 0; i0 < ne[0]; i0++) {
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((float *)result->data)[i1*ne[0] + i0] = frand()*(fmax - fmin) + fmin;
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}
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}
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break;
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case 3:
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for (int i2 = 0; i2 < ne[2]; i2++) {
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for (int i1 = 0; i1 < ne[1]; i1++) {
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for (int i0 = 0; i0 < ne[0]; i0++) {
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((float *)result->data)[i2*ne[1]*ne[0] + i1*ne[0] + i0] = frand()*(fmax - fmin) + fmin;
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}
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}
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}
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break;
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case 4:
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for (int i3 = 0; i3 < ne[3]; i3++) {
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for (int i2 = 0; i2 < ne[2]; i2++) {
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for (int i1 = 0; i1 < ne[1]; i1++) {
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for (int i0 = 0; i0 < ne[0]; i0++) {
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((float *)result->data)[i3*ne[2]*ne[1]*ne[0] + i2*ne[1]*ne[0] + i1*ne[0] + i0] = frand()*(fmax - fmin) + fmin;
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}
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}
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}
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}
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break;
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default:
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assert(false);
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};
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return result;
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}
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float get_element(const struct ggml_tensor * t, int idx) {
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return ((float *)t->data)[idx];
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}
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void set_element(struct ggml_tensor * t, int idx, float value) {
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((float *)t->data)[idx] = value;
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}
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bool check_gradient(
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const char * op_name,
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struct ggml_context * ctx0,
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struct ggml_tensor * x[],
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struct ggml_tensor * f,
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int ndims,
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int nargs,
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float eps,
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float max_error_abs,
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float max_error_rel) {
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struct ggml_cgraph gf = ggml_build_forward (f);
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struct ggml_cgraph gb = ggml_build_backward(ctx0, &gf, false);
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ggml_graph_compute(ctx0, &gf);
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ggml_graph_reset (&gf);
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ggml_set_f32 (f->grad, 1.0f);
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ggml_graph_compute(ctx0, &gb);
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ggml_graph_dump_dot(&gf, NULL, "test-grad0-forward.dot");
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ggml_graph_dump_dot(&gb, &gf, "test-grad0-backward.dot");
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for (int i = 0; i < nargs; ++i) {
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const int nelements = ggml_nelements(x[i]);
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for (int k = 0; k < nelements; ++k) {
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// compute gradient using finite differences
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const float x0 = get_element(x[i], k);
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set_element(x[i], k, x0 + eps);
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ggml_graph_compute(ctx0, &gf);
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const float f0 = ggml_get_f32_1d(f, 0);
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set_element(x[i], k, x0 - eps);
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ggml_graph_compute(ctx0, &gf);
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const float f1 = ggml_get_f32_1d(f, 0);
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const float g0 = (f0 - f1)/(2.0f*eps);
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set_element(x[i], k, x0);
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// compute gradient using backward graph
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ggml_graph_reset (&gf);
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ggml_set_f32 (f->grad, 1.0f);
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ggml_graph_compute(ctx0, &gb);
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const float g1 = get_element(x[i]->grad, k);
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const float error_abs = fabsf(g0 - g1);
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const float error_rel = g0 != 0 ? fabsf(g0 - g1)/fabs(g0) : 0;
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if (error_abs > max_error_abs || error_rel > max_error_rel) {
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printf("%s: ndims=%d, i=%d, k=%d, g0=%f, g1=%f, error_abs=%f, error_rel=%f\n",
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op_name, ndims, i, k, g0, g1, error_abs, error_rel);
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assert(false);
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}
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}
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}
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return true;
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}
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// TODO: clean-up this ..
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bool check_mat_mul(
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const struct ggml_tensor * y,
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const struct ggml_tensor * x0,
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const struct ggml_tensor * x1) {
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float * dst = (float *) y->data;
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float * src0 = (float *) x0->data;
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float * src1 = (float *) x1->data;
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const int nc = x0->ne[1];
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const int nr = x1->ne[1];
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const int nk = x0->ne[0];
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printf("check_mat_mul: nc=%d, nr=%d, nk=%d\n", nc, nr, nk);
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printf("x0:\n");
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for (int j = 0; j < x0->ne[1]; ++j) {
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for (int i = 0; i < x0->ne[0]; ++i) {
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printf("%6.3f ", src0[j*nk + i]);
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}
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printf("\n");
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}
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printf("\n");
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printf("x1:\n");
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for (int j = 0; j < x1->ne[1]; ++j) {
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for (int i = 0; i < x1->ne[0]; ++i) {
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printf("%6.3f ", src1[j*nk + i]);
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}
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printf("\n");
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}
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printf("\n");
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printf("y: n_dims = %d, (%d, %d)\n", y->n_dims, y->ne[0], y->ne[1]);
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for (int j = 0; j < y->ne[1]; ++j) {
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for (int i = 0; i < y->ne[0]; ++i) {
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printf("%6.3f ", dst[j*nr + i]);
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}
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printf("\n");
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}
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for (int i = 0; i < nr; ++i) {
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for (int j = 0; j < nc; ++j) {
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float sum = 0.0f;
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for (int k = 0; k < nk; ++k) {
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sum += src0[j*nk + k]*src1[i*nk + k];
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}
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if (fabsf(dst[i*nc + j] - sum) > 1e-5f) {
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printf("check_mat_mul: dst[%d] = %f, sum = %f\n", i*nc + j, dst[i*nc + j], sum);
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assert(false);
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return false;
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}
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}
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}
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return true;
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}
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int main(int argc, const char ** argv) {
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struct ggml_init_params params = {
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.mem_size = 128*1024*1024,
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.mem_buffer = NULL,
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};
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int ne[4];
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for (int iter = 0; iter < 1000; ++iter) {
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struct ggml_context * ctx0 = ggml_init(params);
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get_random_dims(ne, 4);
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struct ggml_tensor * x[MAX_NARGS];
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// add
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{
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const int nargs = 2;
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for (int ndims = 1; ndims <= 2; ++ndims) {
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for (int i = 0; i < nargs; ++i) {
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x[i] = get_random_tensor(ctx0, ndims, ne, -1.0f, 1.0f);
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ggml_set_param(ctx0, x[i]);
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}
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struct ggml_tensor * f = ggml_sum(ctx0, ggml_add(ctx0, x[0], x[1]));
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check_gradient("add", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, 1e-3f);
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}
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}
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// sub
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{
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const int nargs = 2;
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for (int ndims = 1; ndims <= 2; ++ndims) {
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for (int i = 0; i < nargs; ++i) {
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x[i] = get_random_tensor(ctx0, ndims, ne, -1.0f, 1.0f);
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ggml_set_param(ctx0, x[i]);
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}
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struct ggml_tensor * f = ggml_sum(ctx0, ggml_sub(ctx0, x[0], x[1]));
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check_gradient("sub", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, 1e-3f);
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}
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}
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// mul
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{
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const int nargs = 2;
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for (int ndims = 1; ndims <= 2; ++ndims) {
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for (int i = 0; i < nargs; ++i) {
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x[i] = get_random_tensor(ctx0, ndims, ne, -1.0f, 1.0f);
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ggml_set_param(ctx0, x[i]);
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}
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struct ggml_tensor * f = ggml_sum(ctx0, ggml_mul(ctx0, x[0], x[1]));
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check_gradient("mul", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
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}
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}
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// div
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{
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const int nargs = 2;
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for (int ndims = 1; ndims <= 2; ++ndims) {
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for (int i = 0; i < nargs; ++i) {
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x[i] = get_random_tensor(ctx0, ndims, ne, 0.5f, 1.0f);
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ggml_set_param(ctx0, x[i]);
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}
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struct ggml_tensor * f = ggml_sum(ctx0, ggml_div(ctx0, x[0], x[1]));
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check_gradient("div", ctx0, x, f, ndims, nargs, 1e-3f, INFINITY, 1e-2f);
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}
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}
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// sqr
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{
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const int nargs = 1;
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for (int ndims = 1; ndims <= 2; ++ndims) {
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for (int i = 0; i < nargs; ++i) {
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x[i] = get_random_tensor(ctx0, ndims, ne, -1.0f, 1.0f);
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ggml_set_param(ctx0, x[i]);
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}
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struct ggml_tensor * f = ggml_sum(ctx0, ggml_sqr(ctx0, x[0]));
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check_gradient("sqr", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
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}
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}
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// sqrt
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{
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const int nargs = 1;
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for (int ndims = 1; ndims <= 2; ++ndims) {
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for (int i = 0; i < nargs; ++i) {
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x[i] = get_random_tensor(ctx0, ndims, ne, 2.0f*1e-3f, 1.0f);
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ggml_set_param(ctx0, x[i]);
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}
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struct ggml_tensor * f = ggml_sum(ctx0, ggml_sqrt(ctx0, x[0]));
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check_gradient("sqrt", ctx0, x, f, ndims, nargs, 1e-3f, INFINITY, 1e-1f);
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}
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}
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// sum
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{
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const int nargs = 1;
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for (int ndims = 1; ndims <= 2; ++ndims) {
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for (int i = 0; i < nargs; ++i) {
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x[i] = get_random_tensor(ctx0, ndims, ne, -1.0f, 1.0f);
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ggml_set_param(ctx0, x[i]);
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}
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struct ggml_tensor * f = ggml_sum(ctx0, x[0]);
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check_gradient("sum", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, 1e-3f);
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}
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}
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// abs (finite differences do not work)
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//{
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// const int nargs = 1;
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// for (int ndims = 1; ndims <= 2; ++ndims) {
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// for (int i = 0; i < nargs; ++i) {
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// x[i] = get_random_tensor(ctx0, ndims, ne, -1.0f, 1.0f);
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// ggml_set_param(ctx0, x[i]);
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// }
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// struct ggml_tensor * f = ggml_sum(ctx0, ggml_abs(ctx0, x[0]));
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// check_gradient("abs", ctx0, x, f, ndims, nargs, 1e-3f, INFINITY, 1e-3f);
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// }
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//}
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// mul_mat
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{
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const int nargs = 1;
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for (int ndims = 1; ndims <= 2; ++ndims) {
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x[0] = get_random_tensor(ctx0, ndims, ne, -1.0f, 1.0f);
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{
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int ne2[4];
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get_random_dims(ne2, 4);
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ne2[0] = ne[0];
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x[1] = get_random_tensor(ctx0, ndims, ne2, -1.0f, 1.0f);
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}
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ggml_set_param(ctx0, x[0]);
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struct ggml_tensor * m = ggml_mul_mat(ctx0, x[1], x[0]);
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struct ggml_tensor * f = ggml_sum(ctx0, m);
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printf("testing: mul_mat, [%d, %d] * [%d, %d]\n",
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x[1]->ne[0], x[1]->ne[1], x[0]->ne[0], x[0]->ne[1]);
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check_gradient("mul_mat", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
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check_mat_mul(m, x[1], x[0]);
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}
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}
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ggml_free(ctx0);
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}
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return 0;
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}
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