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cpu : add FLOOR, CEIL, ROUND and TRUNC unary operators (ggml-org#16083)
* CPU: Add support for FLOOR,CEIL,ROUND and TRUNC unary operators - Added the operators to unary op enum - Implemented API functions - Implemented forward and unary-op logic in CPU backend - Updated ggml_get_n_tasks - Updated operators names array and static_assert - Updated docs and enabled automatic tests * docs: add documentation for ggml_trunc and ggml_trunc_inplace in ggml.h * chore: remove trailing whitespace from ggml.h * Remove unresolved merge markers * Apply review suggestions: cleanup formatting, enum order and leftover artifacts * Regenerate ops.md using create_ops_docs.py
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‎docs/ops.md‎

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@@ -22,6 +22,7 @@ Legend:
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| ARANGE | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ |
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| ARGMAX | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ |
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| ARGSORT | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ |
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| CEIL | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
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| CLAMP | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ❌ |
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| CONCAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | 🟡 | ✅ | ❌ |
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| CONT | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ❌ |
@@ -41,6 +42,7 @@ Legend:
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| ELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ | ❌ |
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| EXP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | ❌ | ❌ |
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| FLASH_ATTN_EXT | ❌ | 🟡 | ✅ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ |
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| FLOOR | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
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| GATED_LINEAR_ATTN | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ |
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| GEGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ |
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| GEGLU_ERF | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ |
@@ -82,6 +84,7 @@ Legend:
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| ROLL | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ |
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| ROPE | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ |
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| ROPE_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ |
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| ROUND | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
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| RWKV_WKV6 | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ |
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| RWKV_WKV7 | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ |
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| SCALE | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ |
@@ -108,5 +111,6 @@ Legend:
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| TANH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | 🟡 | ❌ |
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| TIMESTEP_EMBEDDING | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ |
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| TOPK_MOE | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ |
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| TRUNC | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
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| UPSCALE | ❌ | 🟡 | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ❌ |
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| XIELU | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |

‎docs/ops/CPU.csv‎

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@@ -59,6 +59,14 @@
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"CPU","EXP","type=f16,ne_a=[5,7,11,13],v=1","support","1","yes","CPU"
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"CPU","GELU_ERF","type=f16,ne_a=[128,2,2,2],v=1","support","1","yes","CPU"
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"CPU","GELU_ERF","type=f16,ne_a=[5,7,11,13],v=1","support","1","yes","CPU"
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"CPU","FLOOR","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","CPU"
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"CPU","FLOOR","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","CPU"
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"CPU","CEIL","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","CPU"
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"CPU","CEIL","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","CPU"
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"CPU","ROUND","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","CPU"
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"CPU","ROUND","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","CPU"
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"CPU","TRUNC","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","CPU"
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"CPU","TRUNC","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","CPU"
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"CPU","ABS","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","CPU"
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"CPU","ABS","type=f32,ne_a=[5,7,11,13],v=0","support","1","yes","CPU"
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"CPU","SGN","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","CPU"
@@ -119,6 +127,14 @@
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"CPU","EXP","type=f32,ne_a=[5,7,11,13],v=1","support","1","yes","CPU"
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"CPU","GELU_ERF","type=f32,ne_a=[128,2,2,2],v=1","support","1","yes","CPU"
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"CPU","GELU_ERF","type=f32,ne_a=[5,7,11,13],v=1","support","1","yes","CPU"
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"CPU","FLOOR","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","CPU"
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"CPU","FLOOR","type=f32,ne_a=[5,7,11,13],v=0","support","1","yes","CPU"
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"CPU","CEIL","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","CPU"
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"CPU","CEIL","type=f32,ne_a=[5,7,11,13],v=0","support","1","yes","CPU"
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"CPU","ROUND","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","CPU"
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"CPU","ROUND","type=f32,ne_a=[5,7,11,13],v=0","support","1","yes","CPU"
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"CPU","TRUNC","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","CPU"
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"CPU","TRUNC","type=f32,ne_a=[5,7,11,13],v=0","support","1","yes","CPU"
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"CPU","REGLU","type=f16,ne_a=[128,2,2,2],v=0,swapped=0","support","1","yes","CPU"
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"CPU","REGLU","type=f16,ne_a=[5,7,11,13],v=0,swapped=0","support","1","yes","CPU"
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"CPU","REGLU","type=f16,ne_a=[128,2,2,2],v=0,swapped=1","support","1","yes","CPU"

‎ggml/include/ggml.h‎

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@@ -577,6 +577,10 @@ extern "C" {
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GGML_UNARY_OP_EXP,
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GGML_UNARY_OP_GELU_ERF,
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GGML_UNARY_OP_XIELU,
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GGML_UNARY_OP_FLOOR,
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GGML_UNARY_OP_CEIL,
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GGML_UNARY_OP_ROUND,
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GGML_UNARY_OP_TRUNC,
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GGML_UNARY_OP_COUNT,
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};
@@ -1151,6 +1155,46 @@ extern "C" {
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struct ggml_context * ctx,
11521156
struct ggml_tensor * a);
11531157

1158+
GGML_API struct ggml_tensor * ggml_floor(
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struct ggml_context * ctx,
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struct ggml_tensor * a);
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GGML_API struct ggml_tensor * ggml_floor_inplace(
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struct ggml_context * ctx,
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struct ggml_tensor * a);
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GGML_API struct ggml_tensor * ggml_ceil(
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struct ggml_context * ctx,
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struct ggml_tensor * a);
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GGML_API struct ggml_tensor * ggml_ceil_inplace(
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struct ggml_context * ctx,
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struct ggml_tensor * a);
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GGML_API struct ggml_tensor * ggml_round(
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struct ggml_context * ctx,
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struct ggml_tensor * a);
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GGML_API struct ggml_tensor * ggml_round_inplace(
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struct ggml_context * ctx,
1180+
struct ggml_tensor * a);
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1182+
/**
1183+
* Truncates the fractional part of each element in the tensor (towards zero).
1184+
* For example: trunc(3.7) = 3.0, trunc(-2.9) = -2.0
1185+
* Similar to std::trunc in C/C++.
1186+
*/
1187+
1188+
GGML_API struct ggml_tensor * ggml_trunc(
1189+
struct ggml_context * ctx,
1190+
struct ggml_tensor * a);
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1192+
GGML_API struct ggml_tensor * ggml_trunc_inplace(
1193+
struct ggml_context * ctx,
1194+
struct ggml_tensor * a);
1195+
1196+
1197+
11541198
// xIELU activation function
11551199
// x = x * (c_a(alpha_n) + c_b(alpha_p, beta) * sigmoid(beta * x)) + eps * (x > 0)
11561200
// where c_a = softplus and c_b(a, b) = softplus(a) + b are constraining functions

‎ggml/src/ggml-cpu/ggml-cpu.c‎

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@@ -2184,6 +2184,10 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
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case GGML_UNARY_OP_HARDSWISH:
21852185
case GGML_UNARY_OP_HARDSIGMOID:
21862186
case GGML_UNARY_OP_EXP:
2187+
case GGML_UNARY_OP_FLOOR:
2188+
case GGML_UNARY_OP_CEIL:
2189+
case GGML_UNARY_OP_ROUND:
2190+
case GGML_UNARY_OP_TRUNC:
21872191
{
21882192
n_tasks = 1;
21892193
} break;

‎ggml/src/ggml-cpu/ops.cpp‎

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@@ -8993,6 +8993,22 @@ void ggml_compute_forward_unary(
89938993
{
89948994
ggml_compute_forward_exp(params, dst);
89958995
} break;
8996+
case GGML_UNARY_OP_FLOOR:
8997+
{
8998+
ggml_compute_forward_floor(params, dst);
8999+
} break;
9000+
case GGML_UNARY_OP_CEIL:
9001+
{
9002+
ggml_compute_forward_ceil(params, dst);
9003+
} break;
9004+
case GGML_UNARY_OP_ROUND:
9005+
{
9006+
ggml_compute_forward_round(params, dst);
9007+
} break;
9008+
case GGML_UNARY_OP_TRUNC:
9009+
{
9010+
ggml_compute_forward_trunc(params, dst);
9011+
} break;
89969012
case GGML_UNARY_OP_XIELU:
89979013
{
89989014
ggml_compute_forward_xielu(params, dst);

‎ggml/src/ggml-cpu/unary-ops.cpp‎

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@@ -73,6 +73,22 @@ static inline float op_log(float x) {
7373
return logf(x);
7474
}
7575

76+
static inline float op_floor(float x) {
77+
return floorf(x);
78+
}
79+
80+
static inline float op_ceil(float x) {
81+
return ceilf(x);
82+
}
83+
84+
static inline float op_round(float x) {
85+
return roundf(x);
86+
}
87+
88+
static inline float op_trunc(float x) {
89+
return truncf(x);
90+
}
91+
7692
template <float (*op)(float), typename src0_t, typename dst_t>
7793
static inline void vec_unary_op(int64_t n, dst_t * y, const src0_t * x) {
7894
constexpr auto src0_to_f32 = type_conversion_table<src0_t>::to_f32;
@@ -274,6 +290,22 @@ void ggml_compute_forward_log(const ggml_compute_params * params, ggml_tensor *
274290
unary_op<op_log>(params, dst);
275291
}
276292

293+
void ggml_compute_forward_floor(const ggml_compute_params * params, ggml_tensor * dst) {
294+
unary_op<op_floor>(params, dst);
295+
}
296+
297+
void ggml_compute_forward_ceil(const ggml_compute_params * params, ggml_tensor * dst) {
298+
unary_op<op_ceil>(params, dst);
299+
}
300+
301+
void ggml_compute_forward_round(const ggml_compute_params * params, ggml_tensor * dst) {
302+
unary_op<op_round>(params, dst);
303+
}
304+
305+
void ggml_compute_forward_trunc(const ggml_compute_params * params, ggml_tensor * dst) {
306+
unary_op<op_trunc>(params, dst);
307+
}
308+
277309
void ggml_compute_forward_xielu(const ggml_compute_params * params, ggml_tensor * dst) {
278310
const float alpha_n = ggml_get_op_params_f32(dst, 1);
279311
const float alpha_p = ggml_get_op_params_f32(dst, 2);

‎ggml/src/ggml-cpu/unary-ops.h‎

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@@ -22,6 +22,10 @@ void ggml_compute_forward_sqrt(const struct ggml_compute_params * params, struct
2222
void ggml_compute_forward_sin(const struct ggml_compute_params * params, struct ggml_tensor * dst);
2323
void ggml_compute_forward_cos(const struct ggml_compute_params * params, struct ggml_tensor * dst);
2424
void ggml_compute_forward_log(const struct ggml_compute_params * params, struct ggml_tensor * dst);
25+
void ggml_compute_forward_floor(const struct ggml_compute_params * params, struct ggml_tensor * dst);
26+
void ggml_compute_forward_ceil(const struct ggml_compute_params * params, struct ggml_tensor * dst);
27+
void ggml_compute_forward_round(const struct ggml_compute_params * params, struct ggml_tensor * dst);
28+
void ggml_compute_forward_trunc(const struct ggml_compute_params * params, struct ggml_tensor * dst);
2529
void ggml_compute_forward_xielu(const struct ggml_compute_params * params, struct ggml_tensor * dst);
2630

2731
#ifdef __cplusplus

‎ggml/src/ggml.c‎

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@@ -1144,9 +1144,13 @@ static const char * GGML_UNARY_OP_NAME[GGML_UNARY_OP_COUNT] = {
11441144
"EXP",
11451145
"GELU_ERF",
11461146
"XIELU",
1147+
"FLOOR",
1148+
"CEIL",
1149+
"ROUND",
1150+
"TRUNC",
11471151
};
11481152

1149-
static_assert(GGML_UNARY_OP_COUNT == 16, "GGML_UNARY_OP_COUNT != 16");
1153+
static_assert(GGML_UNARY_OP_COUNT == 20, "GGML_UNARY_OP_COUNT != 20");
11501154

11511155
static const char * GGML_GLU_OP_NAME[GGML_GLU_OP_COUNT] = {
11521156
"REGLU",
@@ -2749,6 +2753,62 @@ static struct ggml_tensor * ggml_glu_impl(
27492753
return result;
27502754
}
27512755

2756+
// ggml_floor
2757+
2758+
struct ggml_tensor * ggml_floor(
2759+
struct ggml_context * ctx,
2760+
struct ggml_tensor * a) {
2761+
return ggml_unary(ctx, a, GGML_UNARY_OP_FLOOR);
2762+
}
2763+
2764+
struct ggml_tensor * ggml_floor_inplace(
2765+
struct ggml_context * ctx,
2766+
struct ggml_tensor * a) {
2767+
return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_FLOOR);
2768+
}
2769+
2770+
// ggml_ceil
2771+
2772+
struct ggml_tensor * ggml_ceil(
2773+
struct ggml_context * ctx,
2774+
struct ggml_tensor * a) {
2775+
return ggml_unary(ctx, a, GGML_UNARY_OP_CEIL);
2776+
}
2777+
2778+
struct ggml_tensor * ggml_ceil_inplace(
2779+
struct ggml_context * ctx,
2780+
struct ggml_tensor * a) {
2781+
return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_CEIL);
2782+
}
2783+
2784+
//ggml_round
2785+
2786+
struct ggml_tensor * ggml_round(
2787+
struct ggml_context * ctx,
2788+
struct ggml_tensor * a) {
2789+
return ggml_unary(ctx, a, GGML_UNARY_OP_ROUND);
2790+
}
2791+
2792+
struct ggml_tensor * ggml_round_inplace(
2793+
struct ggml_context * ctx,
2794+
struct ggml_tensor * a) {
2795+
return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_ROUND);
2796+
}
2797+
2798+
//ggml_trunc
2799+
2800+
struct ggml_tensor * ggml_trunc(
2801+
struct ggml_context * ctx,
2802+
struct ggml_tensor * a) {
2803+
return ggml_unary(ctx, a, GGML_UNARY_OP_TRUNC);
2804+
}
2805+
2806+
struct ggml_tensor * ggml_trunc_inplace(
2807+
struct ggml_context * ctx,
2808+
struct ggml_tensor * a) {
2809+
return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_TRUNC);
2810+
}
2811+
27522812
struct ggml_tensor * ggml_glu(
27532813
struct ggml_context * ctx,
27542814
struct ggml_tensor * a,

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