+++ /dev/null
-#define _CRT_SECURE_NO_DEPRECATE // Disables ridiculous "unsafe" warnings on Windows
-#include "ggml.h"
-#include "ggml-cpu.h"
-
-#include <cfloat>
-#include <cmath>
-#include <cstdint>
-#include <cstdio>
-#include <cstdlib>
-#include <cassert>
-#include <initializer_list>
-#include <vector>
-
-#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__)
-
-static float frand(void) {
- return (float)rand()/(float)RAND_MAX;
-}
-
-static int irand(int n) {
- if (n == 0) return 0;
- return rand()%n;
-}
-
-static 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);
- }
-}
-
-static 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;
-}
-
-static 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;
-}
-
-static 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;
-}
-
-static 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,
- std::vector<double> expected_vals) {
-
- 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_new_graph_custom(ctx0, GGML_DEFAULT_GRAPH_SIZE, true);
- struct ggml_cgraph * gb = ggml_new_graph_custom(ctx0, GGML_DEFAULT_GRAPH_SIZE, true);
- ggml_build_forward_expand(gf, f);
- ggml_graph_cpy(gf, gb);
- ggml_build_backward_expand(ctx0, gf, gb, false);
-
- ggml_graph_compute_with_ctx(ctx0, gf, n_threads);
-
- ggml_graph_reset(gb);
- if (f->grad) {
- 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) {
- bool all_g0_bad = true;
- const int nelements = ggml_nelements(x[i]);
- for (int k = 0; k < nelements; ++k) {
- // Calculate gradient numerically:
- 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 double 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 double f1 = ggml_get_f32_1d(f, 0);
- const double g0 = (f0 - f1)/(2.0*(double) eps);
-
- // The numerical calculation of the gradient fails around noncontinuities (e.g. 0 for ReLU).
- // In such cases, provide a vector of expected values and skip the comparison for failed calculations.
- if (!expected_vals.empty()) {
- bool matches_any = false;
- for (const double & ev : expected_vals) {
- const double error_abs = std::fabs(g0 - ev);
- if (error_abs > max_error_abs) {
- continue;
- }
- const double error_rel = g0 != 0.0 ? fabs(g0 - ev)/fabs(g0) : 0.0;
- if (error_rel > max_error_rel) {
- continue;
- }
- matches_any = true;
- break;
- }
- if (!matches_any) {
- continue;
- }
- }
- all_g0_bad = false;
-
- ggml_set_f32_1d(x[i], k, x0);
-
- // compute gradient using backward graph
- ggml_graph_reset(gb);
- if (f->grad) {
- ggml_set_f32(f->grad, 1.0f);
- }
-
- ggml_graph_compute_with_ctx(ctx0, gb, n_threads);
-
- const double g1 = ggml_get_f32_1d(x[i]->grad, k);
-
- const double error_abs = fabs(g0 - g1);
- const double error_rel = g0 != 0.0 ? fabs(g0 - g1)/fabs(g0) : 0.0;
-
- 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;
- }
- }
- if (all_g0_bad) {
- printf("%s: numerical calculation of the gradient failed for all values\n", op_name);
- return false;
- }
- }
-
- return true;
-}
-
-// TODO: clean-up this ..
-static 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 = */ 256*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;
- }
- }
- }
- }
- }
-
- unsigned seed_iter = 1;
-
- // 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) {
- srand(seed_iter);
- seed_iter = rand();
- unsigned seed = rand();
-
- printf("test-grad0: iter:%d/%d\n", (iter+1), niter);
- struct ggml_context * ctx0 = ggml_init(params);
-
- get_random_dims(ne, 4);
-
- struct ggml_tensor * x[MAX_NARGS];
-
- // add f32
- {
- srand(seed);
- 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
- {
- srand(seed);
- 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
- {
- srand(seed);
- 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
- {
- srand(seed);
- 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
- {
- srand(seed);
- 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
- {
- srand(seed);
- 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
- {
- srand(seed);
- 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, 2e-2f, 1e-1f, {});
- }
- }
-
- // log
- {
- srand(seed);
- 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
- {
- srand(seed);
- 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
- {
- srand(seed);
- 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)
- {
- srand(seed);
- 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)
- {
- srand(seed);
- 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
- {
- srand(seed);
- 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
- {
- srand(seed);
- 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
- {
- 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_abs(ctx0, x[0]));
-
- check_gradient("abs", ctx0, x, f, ndims, nargs, 1e-3f, INFINITY, 1e-3f, {-1.0, 1.0});
- }
- }
-
- // sgn
- {
- srand(seed);
- 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, {0.0});
- }
- }
-
- // neg
- {
- srand(seed);
- 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
- {
- srand(seed);
- 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, {0.0});
- }
- }
-
- // tanh, not yet fully implemented
- if(0)
- {
- srand(seed);
- 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
- {
- srand(seed);
- const int nargs = 2;
-
- for (int ndims = 2; ndims <= 4; ++ndims) {
- int max_nrep = (ndims >= 3) ? 2 : 1;
- x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
- for (int nrep2 = 1; nrep2 < max_nrep; ++nrep2) {
- for (int nrep3 = 1; nrep3 < max_nrep; ++nrep3) {
- {
- int64_t ne2[4];
- get_random_dims(ne2, 4);
- ne2[0] = ne[0];
- ne2[2] = nrep2 * ne[2];
- ne2[3] = nrep3 * ne[3];
- 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, {});
- if (ndims == 2) {
- // check_mat_mul does not support ndims > 2
- check_mat_mul(m, x[1], x[0]);
- }
- }
- }
- }
- }
-
- // elu, not yet fully implemented
- if(0)
- {
- srand(seed);
- 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
- {
- srand(seed);
- 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, {0.0, 1.0});
- }
- }
-
- // gelu, not yet fully implemented
- if(0)
- {
- srand(seed);
- 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
- {
- srand(seed);
- 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
- {
- srand(seed);
- 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], 1e-6f));
-
- check_gradient("rms_norm", ctx0, x, f, ndims, nargs, 1e-4f, 1.0f, INFINITY, {});
- }
- }
-
- // scale
- {
- srand(seed);
- const int nargs = 1;
-
- for (int ndims = 1; ndims <= 2; ++ndims) {
- x[0] = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
-
- const float s = -1.0f + 2.0f*frand();
-
- ggml_set_param(ctx0, x[0]);
-
- struct ggml_tensor * f = ggml_sum(ctx0, ggml_scale(ctx0, x[0], s));
-
- check_gradient("scale", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {});
- }
- }
-
- // cpy f32
- {
- srand(seed);
- 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
- {
- srand(seed);
- 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)
- {
- srand(seed);
- 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)
- {
- srand(seed);
- 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
- {
- srand(seed);
- 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
- {
- srand(seed);
- 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
- {
- srand(seed);
- 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
- {
- srand(seed);
- 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
- {
- srand(seed);
- 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
- {
- srand(seed);
- 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
- {
- srand(seed);
- 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
- {
- srand(seed);
- 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
- {
- srand(seed);
- 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
- {
- srand(seed);
- 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
- {
- srand(seed);
- 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
- {
- srand(seed);
- 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
- {
- srand(seed);
- 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
- {
- srand(seed);
- 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
- {
- srand(seed);
- 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]);
-
- float eps = 1e-6f;
- // dont use only sum as aggregation, because sum of softmax is always 1 -> finite differences should not work
- // instead use sum(log(soft_max()*(1-eps)+eps)); use eps to avoid log(0)
- struct ggml_tensor * f = ggml_sum(ctx0,
- ggml_log(ctx0,
- ggml_add1(ctx0,
- ggml_scale(ctx0,
- ggml_soft_max(ctx0, x[0]),
- 1.0f - eps),
- ggml_new_f32(ctx0, eps))));
-
- check_gradient("softmax", ctx0, x, f, ndims, nargs, 1e-3f, 2e-1f, INFINITY, {});
- // NOTE: softmax forward is computed using f16 table lookup instead of using actual expf, but backward assumes actual expf.
- // this may result in different gradients too finite differences.
- // when this test reports errors, first try to replace the table lookup with actual expf and test again to see if just that was the cause.
- // if only the table lookup causes gradients to differ this is acceptable.
- }
- }
-
- // cross_entropy_loss
- {
- srand(seed);
- const int nargs = 1;
-
- int64_t ne2[4];
- get_random_dims(ne2, 4);
-
- for (int ndims = 1; ndims <= 4; ++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);
- // the second argument to cross_entropy_loss must sum up to 1 for each row
- int nr = ggml_nrows(x[1]);
- int nc = ggml_nelements(x[1]) / nr;
- for (int ir = 0; ir < nr; ++ir) {
- float sum = 0;
- for (int ic = 0; ic < nc; ++ic) {
- sum += ((float *) x[1]->data)[ic + ir*nc];
- }
- for (int ic = 0; ic < nc; ++ic) {
- ((float *) x[1]->data)[ic + ir*nc] /= sum;
- }
- }
- ggml_set_param(ctx0, x[0]);
-
- struct ggml_tensor * f = ggml_cross_entropy_loss(ctx0, x[0], x[1]);
-
- check_gradient("cross_entropy_loss", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, {});
- }
- }
-
- // rope f32
- {
- srand(seed);
- 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);
-
- struct ggml_tensor * p = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, ne2[2]);
- for (int i = 0; i < ne2[2]; ++i) {
- ((int32_t *) p->data)[i] = n_past + i;
- }
-
- 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], p, n_rot, mode));
-
- 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
- {
- srand(seed);
- 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);
-
- struct ggml_tensor * p = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, ne2[2]);
- for (int i = 0; i < ne2[2]; ++i) {
- ((int32_t *) p->data)[i] = n_past + i;
- }
-
- 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], p, n_rot, mode));
-
- 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, {});
- }
- }
- }
- }
-
- // im2col f32
- {
- srand(seed);
- const int nargs = 1;
- const int ndims = 4;
-
- for (const bool is_2D : {false, true}) {
- int64_t ne0[ndims];
- int64_t ne1[ndims];
- get_random_dims(ne0, ndims);
- get_random_dims(ne1, ndims);
-
- // // Ensure that the output is not zero-sized:
- ne1[0] += 8;
- ne1[1] += 8;
-
- if (is_2D) {
- ne1[2] = ne0[2];
- } else {
- ne1[1] = ne0[1];
- ne0[3] = 1;
- ne1[3] = 1;
- }
-
- // The order of arguments is swapped because the first tensor is only used for its shape.
- x[1] = get_random_tensor_f16(ctx0, ndims, ne0, -1.0f, 1.0f);
- x[0] = get_random_tensor_f32(ctx0, ndims, ne1, -1.0f, 1.0f);
-
- ggml_set_param(ctx0, x[0]);
-
- const int s0 = 1 + irand(2);
- const int s1 = is_2D ? 1 + irand(2) : 0;
- const int p0 = 0 + irand(2);
- const int p1 = is_2D ? 0 + irand(2) : 0;
- const int d0 = 1 + irand(2);
- const int d1 = is_2D ? 1 + irand(2) : 0;
-
- struct ggml_tensor * f = ggml_sum(ctx0, ggml_im2col(ctx0, x[1], x[0], s0, s1, p0, p1, d0, d1, is_2D, GGML_TYPE_F32));
-
- GGML_PRINT_DEBUG("im2col f32: is_2D=%s, s0=%d, s1=%d, p0=%d, p1=%d, d0=%d, d1=%d\n", is_2D ? "yes" : "no", s0, s1, p0, p1, d0, d1);
- check_gradient("im2col f32", ctx0, x, f, ndims, nargs, 1e-2f, 1e-3f, INFINITY, {});
- }
- }
-
- // pool_2d f32
- {
- srand(seed);
- const int nargs = 1;
- const int ndims = 4;
-
- for (const enum ggml_op_pool op : {GGML_OP_POOL_AVG, GGML_OP_POOL_MAX}) {
- int64_t ne0[ndims];
- get_random_dims(ne0, ndims);
-
- ne0[0] += 8;
- ne0[1] += 8;
-
- x[0] = get_random_tensor_f32(ctx0, ndims, ne0, -1.0f, 1.0f);
-
- ggml_set_param(ctx0, x[0]);
-
- const int k0 = 2 + irand(2);
- const int k1 = 2 + irand(2);
- const int s0 = 2 + irand(2);
- const int s1 = 2 + irand(2);
- const int p0 = 0 + irand(2);
- const int p1 = 0 + irand(2);
-
- struct ggml_tensor * f = ggml_sum(ctx0, ggml_pool_2d(ctx0, x[0], op, k0, k1, s0, s1, p0, p1));
-
- GGML_PRINT_DEBUG("ggml_pool_2d f32: op=%s k0=%d, k1=%d, s0=%d, s1=%d, p0=%d, p1=%d\n",
- op == GGML_OP_POOL_MAX ? "max" : "avg", k0, k1, s0, s1, p0, p1);
- std::vector<double> expected_vals;
- if (op == GGML_OP_POOL_MAX) {
- expected_vals.push_back(0.0);
- expected_vals.push_back(1.0);
- }
- check_gradient("ggml_pool_2d f32", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY, expected_vals);
- }
- }
-
- // flash_attn f32
- // TODO: adapt to ggml_flash_attn_ext() changes
- //{
- // srand(seed);
- // 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) {
- // int max_nrep = (ndims >= 3) ? 2 : 1;
- // for (int nrep = 1; nrep < max_nrep; ++nrep) {
- // int64_t neq[4] = { D, N, B*nrep, 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, 1e-3f, INFINITY, {});
- // }
- // }
- // }
- //}
-
- ggml_free(ctx0);
- }
-
- return 0;
-}
overview / pkg / ggml / sources / llama.cpp / commitdiff