successfully test get_rows backward

tests/test-grad0.c

@@ -104,8 +104,63 @@ struct ggml_tensor * get_random_tensor(
     return result;
 }
 
+struct ggml_tensor * get_random_tensor_int(
+        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;
+}
+
 float get_element(const struct ggml_tensor * t, int idx) {
+    if (t->type == GGML_TYPE_F32) {
         return ((float *)t->data)[idx];
+    } else if (t->type == GGML_TYPE_I32) { 
+        return ((int32_t *)t->data)[idx];
+    } else {
+        assert(false);
+        return INFINITY;
+    }
 }
 
 void set_element(struct ggml_tensor * t, int idx, float value) {
@@ -371,7 +426,7 @@ int main(int argc, const char ** argv) {
 
                 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-2f, 1e-1f);
+                check_gradient("div", ctx0, x, f, ndims, nargs, 1e-3f, 1e-1f, 1e-1f);
             }
         }
 
@@ -654,6 +709,22 @@ int main(int argc, const char ** argv) {
             }
         }
 
+        // 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(ctx0, ndims, ne2, -1.0f, 1.0f);
+            x[1] = get_random_tensor_int(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;