resolve merge conflict

1 files changed, 1525 insertions, 0 deletions

diff --git a/tests/test-grad0.cpp b/tests/test-grad0.cpp
new file mode 100644
index 0000000..75a698d
--- /dev/null
+++ b/tests/test-grad0.cpp
@@ -0,0 +1,1525 @@
+#define _CRT_SECURE_NO_DEPRECATE // Disables ridiculous "unsafe" warnigns on Windows
+#include "ggml.h"
+
+#include <cmath>
+#include <cstdio>
+#include <cstdlib>
+#include <cassert>
+
+#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 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");
+
+}
+
+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) {
+
+    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 ..
+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   = */ 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], 1e-6f));
+
+                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;
+}