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-rw-r--r--ggml-cuda.cu2
-rw-r--r--ggml-cuda/unary.cu20
-rw-r--r--ggml-kompute.cpp2
-rw-r--r--ggml-metal.m2
-rw-r--r--ggml-sycl.cpp2
-rw-r--r--ggml-vulkan.cpp2
-rw-r--r--ggml.c97
-rw-r--r--tests/test-backend-ops.cpp29
8 files changed, 90 insertions, 66 deletions
diff --git a/ggml-cuda.cu b/ggml-cuda.cu
index af10f21a..c6bc3f64 100644
--- a/ggml-cuda.cu
+++ b/ggml-cuda.cu
@@ -2740,7 +2740,7 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons
case GGML_UNARY_OP_HARDSWISH:
case GGML_UNARY_OP_GELU_QUICK:
case GGML_UNARY_OP_TANH:
- return true;
+ return ggml_is_contiguous(op->src[0]);
default:
return false;
}
diff --git a/ggml-cuda/unary.cu b/ggml-cuda/unary.cu
index ac03d5c6..a5ff9632 100644
--- a/ggml-cuda/unary.cu
+++ b/ggml-cuda/unary.cu
@@ -148,6 +148,8 @@ void ggml_cuda_op_gelu(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
float * dst_d = (float *)dst->data;
cudaStream_t stream = ctx.stream();
+ GGML_ASSERT(ggml_is_contiguous(src0));
+
GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT( dst->type == GGML_TYPE_F32);
@@ -160,6 +162,8 @@ void ggml_cuda_op_silu(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
float * dst_d = (float *)dst->data;
cudaStream_t stream = ctx.stream();
+ GGML_ASSERT(ggml_is_contiguous(src0));
+
GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT( dst->type == GGML_TYPE_F32);
@@ -172,6 +176,8 @@ void ggml_cuda_op_gelu_quick(ggml_backend_cuda_context & ctx, ggml_tensor * dst)
float * dst_d = (float *)dst->data;
cudaStream_t stream = ctx.stream();
+ GGML_ASSERT(ggml_is_contiguous(src0));
+
GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT( dst->type == GGML_TYPE_F32);
@@ -184,6 +190,8 @@ void ggml_cuda_op_tanh(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
float * dst_d = (float *)dst->data;
cudaStream_t stream = ctx.stream();
+ GGML_ASSERT(ggml_is_contiguous(src0));
+
GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT( dst->type == GGML_TYPE_F32);
@@ -196,6 +204,8 @@ void ggml_cuda_op_relu(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
float * dst_d = (float *)dst->data;
cudaStream_t stream = ctx.stream();
+ GGML_ASSERT(ggml_is_contiguous(src0));
+
GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT( dst->type == GGML_TYPE_F32);
@@ -208,6 +218,8 @@ void ggml_cuda_op_sigmoid(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
float * dst_d = (float *)dst->data;
cudaStream_t stream = ctx.stream();
+ GGML_ASSERT(ggml_is_contiguous(src0));
+
GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT( dst->type == GGML_TYPE_F32);
@@ -220,6 +232,8 @@ void ggml_cuda_op_hardsigmoid(ggml_backend_cuda_context & ctx, ggml_tensor * dst
float * dst_d = (float *)dst->data;
cudaStream_t stream = ctx.stream();
+ GGML_ASSERT(ggml_is_contiguous(src0));
+
GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT( dst->type == GGML_TYPE_F32);
@@ -232,6 +246,8 @@ void ggml_cuda_op_hardswish(ggml_backend_cuda_context & ctx, ggml_tensor * dst)
float * dst_d = (float *)dst->data;
cudaStream_t stream = ctx.stream();
+ GGML_ASSERT(ggml_is_contiguous(src0));
+
GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT( dst->type == GGML_TYPE_F32);
@@ -244,6 +260,8 @@ void ggml_cuda_op_leaky_relu(ggml_backend_cuda_context & ctx, ggml_tensor * dst)
float * dst_d = (float *)dst->data;
cudaStream_t stream = ctx.stream();
+ GGML_ASSERT(ggml_is_contiguous(src0));
+
GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT( dst->type == GGML_TYPE_F32);
@@ -259,6 +277,8 @@ void ggml_cuda_op_sqr(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
float * dst_d = (float *)dst->data;
cudaStream_t stream = ctx.stream();
+ GGML_ASSERT(ggml_is_contiguous(src0));
+
GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT( dst->type == GGML_TYPE_F32);
diff --git a/ggml-kompute.cpp b/ggml-kompute.cpp
index 5592741b..18c6f4a1 100644
--- a/ggml-kompute.cpp
+++ b/ggml-kompute.cpp
@@ -1340,7 +1340,7 @@ static bool ggml_vk_supports_op(const struct ggml_tensor * op) {
case GGML_UNARY_OP_RELU:
case GGML_UNARY_OP_GELU:
case GGML_UNARY_OP_SILU:
- return true;
+ return ggml_is_contiguous(op->src[0]);
default:
;
}
diff --git a/ggml-metal.m b/ggml-metal.m
index 946f1181..b5c28734 100644
--- a/ggml-metal.m
+++ b/ggml-metal.m
@@ -744,7 +744,7 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const
case GGML_UNARY_OP_GELU:
case GGML_UNARY_OP_GELU_QUICK:
case GGML_UNARY_OP_SILU:
- return true;
+ return ggml_is_contiguous(op->src[0]);
default:
return false;
}
diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp
index 42fc0df2..e7d260bd 100644
--- a/ggml-sycl.cpp
+++ b/ggml-sycl.cpp
@@ -17190,7 +17190,7 @@ GGML_CALL static bool ggml_backend_sycl_supports_op(ggml_backend_t backend, cons
case GGML_UNARY_OP_HARDSWISH:
case GGML_UNARY_OP_GELU_QUICK:
case GGML_UNARY_OP_TANH:
- return true;
+ return ggml_is_contiguous(op->src[0]);
default:
return false;
}
diff --git a/ggml-vulkan.cpp b/ggml-vulkan.cpp
index 06ba2331..5b928049 100644
--- a/ggml-vulkan.cpp
+++ b/ggml-vulkan.cpp
@@ -6439,7 +6439,7 @@ GGML_CALL static bool ggml_backend_vk_supports_op(ggml_backend_t backend, const
case GGML_UNARY_OP_GELU:
case GGML_UNARY_OP_SILU:
case GGML_UNARY_OP_RELU:
- return true;
+ return ggml_is_contiguous(op->src[0]);
default:
return false;
}
diff --git a/ggml.c b/ggml.c
index 5fb9e9a3..2ea1d767 100644
--- a/ggml.c
+++ b/ggml.c
@@ -7345,6 +7345,8 @@ static struct ggml_tensor * ggml_unary_impl(
struct ggml_tensor * a,
enum ggml_unary_op op,
bool inplace) {
+ GGML_ASSERT(ggml_is_contiguous_1(a));
+
bool is_node = false;
if (!inplace && (a->grad)) {
@@ -11009,6 +11011,8 @@ static void ggml_compute_forward_abs_f32(
const struct ggml_tensor * src0 = dst->src[0];
assert(params->ith == 0);
+ assert(ggml_is_contiguous_1(src0));
+ assert(ggml_is_contiguous_1(dst));
assert(ggml_are_same_shape(src0, dst));
if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
@@ -11018,9 +11022,6 @@ static void ggml_compute_forward_abs_f32(
const int n = ggml_nrows(src0);
const int nc = src0->ne[0];
- assert(dst->nb[0] == sizeof(float));
- assert(src0->nb[0] == sizeof(float));
-
for (int i = 0; i < n; i++) {
ggml_vec_abs_f32(nc,
(float *) ((char *) dst->data + i*( dst->nb[1])),
@@ -11055,6 +11056,8 @@ static void ggml_compute_forward_sgn_f32(
const struct ggml_tensor * src0 = dst->src[0];
assert(params->ith == 0);
+ assert(ggml_is_contiguous_1(src0));
+ assert(ggml_is_contiguous_1(dst));
assert(ggml_are_same_shape(src0, dst));
if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
@@ -11064,9 +11067,6 @@ static void ggml_compute_forward_sgn_f32(
const int n = ggml_nrows(src0);
const int nc = src0->ne[0];
- assert(dst->nb[0] == sizeof(float));
- assert(src0->nb[0] == sizeof(float));
-
for (int i = 0; i < n; i++) {
ggml_vec_sgn_f32(nc,
(float *) ((char *) dst->data + i*( dst->nb[1])),
@@ -11101,6 +11101,8 @@ static void ggml_compute_forward_neg_f32(
const struct ggml_tensor * src0 = dst->src[0];
assert(params->ith == 0);
+ assert(ggml_is_contiguous_1(src0));
+ assert(ggml_is_contiguous_1(dst));
assert(ggml_are_same_shape(src0, dst));
if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
@@ -11110,9 +11112,6 @@ static void ggml_compute_forward_neg_f32(
const int n = ggml_nrows(src0);
const int nc = src0->ne[0];
- assert(dst->nb[0] == sizeof(float));
- assert(src0->nb[0] == sizeof(float));
-
for (int i = 0; i < n; i++) {
ggml_vec_neg_f32(nc,
(float *) ((char *) dst->data + i*( dst->nb[1])),
@@ -11147,6 +11146,8 @@ static void ggml_compute_forward_step_f32(
const struct ggml_tensor * src0 = dst->src[0];
assert(params->ith == 0);
+ assert(ggml_is_contiguous_1(src0));
+ assert(ggml_is_contiguous_1(dst));
assert(ggml_are_same_shape(src0, dst));
if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
@@ -11156,9 +11157,6 @@ static void ggml_compute_forward_step_f32(
const int n = ggml_nrows(src0);
const int nc = src0->ne[0];
- assert(dst->nb[0] == sizeof(float));
- assert(src0->nb[0] == sizeof(float));
-
for (int i = 0; i < n; i++) {
ggml_vec_step_f32(nc,
(float *) ((char *) dst->data + i*( dst->nb[1])),
@@ -11193,6 +11191,8 @@ static void ggml_compute_forward_tanh_f32(
const struct ggml_tensor * src0 = dst->src[0];
assert(params->ith == 0);
+ assert(ggml_is_contiguous_1(src0));
+ assert(ggml_is_contiguous_1(dst));
assert(ggml_are_same_shape(src0, dst));
if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
@@ -11202,9 +11202,6 @@ static void ggml_compute_forward_tanh_f32(
const int n = ggml_nrows(src0);
const int nc = src0->ne[0];
- assert(dst->nb[0] == sizeof(float));
- assert(src0->nb[0] == sizeof(float));
-
for (int i = 0; i < n; i++) {
ggml_vec_tanh_f32(nc,
(float *) ((char *) dst->data + i*( dst->nb[1])),
@@ -11239,6 +11236,8 @@ static void ggml_compute_forward_elu_f32(
const struct ggml_tensor * src0 = dst->src[0];
assert(params->ith == 0);
+ assert(ggml_is_contiguous_1(src0));
+ assert(ggml_is_contiguous_1(dst));
assert(ggml_are_same_shape(src0, dst));
if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
@@ -11248,9 +11247,6 @@ static void ggml_compute_forward_elu_f32(
const int n = ggml_nrows(src0);
const int nc = src0->ne[0];
- assert(dst->nb[0] == sizeof(float));
- assert(src0->nb[0] == sizeof(float));
-
for (int i = 0; i < n; i++) {
ggml_vec_elu_f32(nc,
(float *) ((char *) dst->data + i*( dst->nb[1])),
@@ -11285,6 +11281,8 @@ static void ggml_compute_forward_relu_f32(
const struct ggml_tensor * src0 = dst->src[0];
assert(params->ith == 0);
+ assert(ggml_is_contiguous_1(src0));
+ assert(ggml_is_contiguous_1(dst));
assert(ggml_are_same_shape(src0, dst));
if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
@@ -11294,9 +11292,6 @@ static void ggml_compute_forward_relu_f32(
const int n = ggml_nrows(src0);
const int nc = src0->ne[0];
- assert(dst->nb[0] == sizeof(float));
- assert(src0->nb[0] == sizeof(float));
-
for (int i = 0; i < n; i++) {
ggml_vec_relu_f32(nc,
(float *) ((char *) dst->data + i*( dst->nb[1])),
@@ -11331,6 +11326,8 @@ static void ggml_compute_forward_sigmoid_f32(
const struct ggml_tensor * src0 = dst->src[0];
assert(params->ith == 0);
+ assert(ggml_is_contiguous_1(src0));
+ assert(ggml_is_contiguous_1(dst));
assert(ggml_are_same_shape(src0, dst));
if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
@@ -11340,9 +11337,6 @@ static void ggml_compute_forward_sigmoid_f32(
const int n = ggml_nrows(src0);
const int nc = src0->ne[0];
- assert(dst->nb[0] == sizeof(float));
- assert(src0->nb[0] == sizeof(float));
-
for (int i = 0; i < n; i++) {
ggml_vec_sigmoid_f32(nc,
(float *) ((char *) dst->data + i*( dst->nb[1])),
@@ -11376,9 +11370,9 @@ static void ggml_compute_forward_gelu_f32(
const struct ggml_tensor * src0 = dst->src[0];
- GGML_ASSERT(ggml_is_contiguous_1(src0));
- GGML_ASSERT(ggml_is_contiguous_1(dst));
- GGML_ASSERT(ggml_are_same_shape(src0, dst));
+ assert(ggml_is_contiguous_1(src0));
+ assert(ggml_is_contiguous_1(dst));
+ assert(ggml_are_same_shape(src0, dst));
if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
return;
@@ -11439,9 +11433,9 @@ static void ggml_compute_forward_gelu_quick_f32(
const struct ggml_tensor * src0 = dst->src[0];
- GGML_ASSERT(ggml_is_contiguous_1(src0));
- GGML_ASSERT(ggml_is_contiguous_1(dst));
- GGML_ASSERT(ggml_are_same_shape(src0, dst));
+ assert(ggml_is_contiguous_1(src0));
+ assert(ggml_is_contiguous_1(dst));
+ assert(ggml_are_same_shape(src0, dst));
if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
return;
@@ -11502,9 +11496,9 @@ static void ggml_compute_forward_silu_f32(
const struct ggml_tensor * src0 = dst->src[0];
- GGML_ASSERT(ggml_is_contiguous_1(src0));
- GGML_ASSERT(ggml_is_contiguous_1(dst));
- GGML_ASSERT(ggml_are_same_shape(src0, dst));
+ assert(ggml_is_contiguous_1(src0));
+ assert(ggml_is_contiguous_1(dst));
+ assert(ggml_are_same_shape(src0, dst));
if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
return;
@@ -11565,6 +11559,8 @@ static void ggml_compute_forward_leaky_relu_f32(
const struct ggml_tensor * src0 = dst->src[0];
assert(params->ith == 0);
+ assert(ggml_is_contiguous_1(src0));
+ assert(ggml_is_contiguous_1(dst));
assert(ggml_are_same_shape(src0, dst));
if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
@@ -11614,11 +11610,11 @@ static void ggml_compute_forward_silu_back_f32(
const struct ggml_tensor * src0 = dst->src[0];
const struct ggml_tensor * grad = dst->src[1];
- GGML_ASSERT(ggml_is_contiguous_1(grad));
- GGML_ASSERT(ggml_is_contiguous_1(src0));
- GGML_ASSERT(ggml_is_contiguous_1(dst));
- GGML_ASSERT(ggml_are_same_shape(src0, dst));
- GGML_ASSERT(ggml_are_same_shape(src0, grad));
+ assert(ggml_is_contiguous_1(grad));
+ assert(ggml_is_contiguous_1(src0));
+ assert(ggml_is_contiguous_1(dst));
+ assert(ggml_are_same_shape(src0, dst));
+ assert(ggml_are_same_shape(src0, grad));
if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
return;
@@ -11680,6 +11676,8 @@ static void ggml_compute_forward_hardswish_f32(
const struct ggml_tensor * src0 = dst->src[0];
assert(params->ith == 0);
+ assert(ggml_is_contiguous_1(src0));
+ assert(ggml_is_contiguous_1(dst));
assert(ggml_are_same_shape(src0, dst));
if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
@@ -11689,9 +11687,6 @@ static void ggml_compute_forward_hardswish_f32(
const int n = ggml_nrows(src0);
const int nc = src0->ne[0];
- assert(dst->nb[0] == sizeof(float));
- assert(src0->nb[0] == sizeof(float));
-
for (int i = 0; i < n; i++) {
ggml_vec_hardswish_f32(nc,
(float *) ((char *) dst->data + i*( dst->nb[1])),
@@ -11723,6 +11718,8 @@ static void ggml_compute_forward_hardsigmoid_f32(
const struct ggml_tensor * src0 = dst->src[0];
assert(params->ith == 0);
+ assert(ggml_is_contiguous_1(src0));
+ assert(ggml_is_contiguous_1(dst));
assert(ggml_are_same_shape(src0, dst));
if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
@@ -11732,9 +11729,6 @@ static void ggml_compute_forward_hardsigmoid_f32(
const int n = ggml_nrows(src0);
const int nc = src0->ne[0];
- assert(dst->nb[0] == sizeof(float));
- assert(src0->nb[0] == sizeof(float));
-
for (int i = 0; i < n; i++) {
ggml_vec_hardsigmoid_f32(nc,
(float *) ((char *) dst->data + i*( dst->nb[1])),
@@ -16681,7 +16675,10 @@ static void ggml_compute_forward_map_unary_f32(
const struct ggml_tensor * src0 = dst->src[0];
- GGML_ASSERT(ggml_are_same_shape(src0, dst));
+ assert(params->ith == 0);
+ assert(ggml_is_contiguous_1(src0));
+ assert(ggml_is_contiguous_1(dst));
+ assert(ggml_are_same_shape(src0, dst));
if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
return;
@@ -16690,9 +16687,6 @@ static void ggml_compute_forward_map_unary_f32(
const int n = ggml_nrows(src0);
const int nc = src0->ne[0];
- assert( dst->nb[0] == sizeof(float));
- assert(src0->nb[0] == sizeof(float));
-
for (int i = 0; i < n; i++) {
fun(nc,
(float *) ((char *) dst->data + i*( dst->nb[1])),
@@ -16730,6 +16724,9 @@ static void ggml_compute_forward_map_binary_f32(
const struct ggml_tensor * src1 = dst->src[1];
assert(params->ith == 0);
+ assert(ggml_is_contiguous_1(src0));
+ assert(ggml_is_contiguous_1(src1));
+ assert(ggml_is_contiguous_1(dst));
assert(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst));
if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) {
@@ -16739,10 +16736,6 @@ static void ggml_compute_forward_map_binary_f32(
const int n = ggml_nrows(src0);
const int nc = src0->ne[0];
- assert( dst->nb[0] == sizeof(float));
- assert(src0->nb[0] == sizeof(float));
- assert(src1->nb[0] == sizeof(float));
-
for (int i = 0; i < n; i++) {
fun(nc,
(float *) ((char *) dst->data + i*( dst->nb[1])),
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index ce406a8a..2b48e623 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -642,20 +642,29 @@ struct test_case {
struct test_unary : public test_case {
const ggml_unary_op op;
const ggml_type type;
- const std::array<int64_t, 4> ne;
+ const std::array<int64_t, 4> ne_a;
+ int v; // view (1 : non-contiguous a)
std::string vars() override {
- return VARS_TO_STR2(type, ne);
+ return VARS_TO_STR3(type, ne_a, v);
}
test_unary(ggml_unary_op op,
ggml_type type = GGML_TYPE_F32,
- std::array<int64_t, 4> ne = {128, 10, 10, 10})
- : op(op), type(type), ne(ne) {}
+ std::array<int64_t, 4> ne_a = {128, 10, 10, 10},
+ int v = 0)
+ : op(op), type(type), ne_a(ne_a), v(v) {}
ggml_tensor * build_graph(ggml_context * ctx) override {
- ggml_tensor * in = ggml_new_tensor(ctx, type, 4, ne.data());
- ggml_tensor * out = ggml_unary(ctx, in, op);
+ ggml_tensor * a;
+ if (v & 1) {
+ auto ne = ne_a; ne[0] *= 3;
+ a = ggml_new_tensor(ctx, type, 4, ne.data());
+ a = ggml_view_4d(ctx, a, ne_a[0], ne_a[1], ne_a[2], ne_a[3], a->nb[1], a->nb[2], a->nb[3], 0);
+ } else {
+ a = ggml_new_tensor(ctx, type, 4, ne_a.data());
+ }
+ ggml_tensor * out = ggml_unary(ctx, a, op);
return out;
}
@@ -2016,9 +2025,11 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
};
// unary ops
- for (int op = 0; op < GGML_UNARY_OP_COUNT; op++) {
- test_cases.emplace_back(new test_unary((ggml_unary_op) op));
- test_cases.emplace_back(new test_unary((ggml_unary_op) op, GGML_TYPE_F32, { 7, 13, 19, 23 }));
+ for (int v : {0, 1}) {
+ for (int op = 0; op < GGML_UNARY_OP_COUNT; op++) {
+ test_cases.emplace_back(new test_unary((ggml_unary_op) op, GGML_TYPE_F32, { 128, 10, 10, 10 }, v));
+ test_cases.emplace_back(new test_unary((ggml_unary_op) op, GGML_TYPE_F32, { 7, 13, 19, 23 }, v));
+ }
}
test_cases.emplace_back(new test_get_rows(GGML_TYPE_F32, 1, 8, 2, 1, false));
generated by cgit v1.2.3 (git 2.25.1) at 2025-08-16 23:10:04 +0000