1#include "binary-ops.h"
2
3#if defined(GGML_USE_ACCELERATE)
4#include <Accelerate/Accelerate.h>
5
6using vDSP_fn_t = void (*)(const float *, vDSP_Stride, const float *, vDSP_Stride, float *, vDSP_Stride, vDSP_Length);
7#endif
8
9static inline float op_add(float a, float b) {
10 return a + b;
11}
12
13static inline float op_sub(float a, float b) {
14 return a - b;
15}
16
17static inline float op_mul(float a, float b) {
18 return a * b;
19}
20
21static inline float op_div(float a, float b) {
22 return a / b;
23}
24
25template <float (*op)(float, float), typename src0_t, typename src1_t, typename dst_t>
26static inline void vec_binary_op_contiguous(const int64_t n, dst_t * z, const src0_t * x, const src1_t * y) {
27 constexpr auto src0_to_f32 = type_conversion_table<src0_t>::to_f32;
28 constexpr auto src1_to_f32 = type_conversion_table<src1_t>::to_f32;
29 constexpr auto f32_to_dst = type_conversion_table<dst_t >::from_f32;
30
31 for (int i = 0; i < n; i++) {
32 z[i] = f32_to_dst(op(src0_to_f32(x[i]), src1_to_f32(y[i])));
33 }
34}
35
36template <float (*op)(float, float), typename src0_t, typename src1_t, typename dst_t>
37static inline void vec_binary_op_non_contiguous(const int64_t n, const int64_t ne10, const int64_t nb10, dst_t * z, const src0_t * x, const src1_t * y) {
38 constexpr auto src0_to_f32 = type_conversion_table<src0_t>::to_f32;
39 constexpr auto src1_to_f32 = type_conversion_table<src1_t>::to_f32;
40 constexpr auto f32_to_dst = type_conversion_table<dst_t >::from_f32;
41
42 for (int i = 0; i < n; i++) {
43 int i10 = i % ne10;
44 const src1_t * y_ptr = (const src1_t *)((const char *)y + i10*nb10);
45 z[i] = f32_to_dst(op(src0_to_f32(x[i]), src1_to_f32(*y_ptr)));
46 }
47}
48
49template <float (*op)(float, float), typename src0_t, typename src1_t, typename dst_t>
50static void apply_binary_op(const ggml_compute_params * params, ggml_tensor * dst) {
51 const ggml_tensor * src0 = dst->src[0];
52 const ggml_tensor * src1 = dst->src[1];
53
54 GGML_ASSERT(ggml_can_repeat(src1, src0) && ggml_are_same_shape(src0, dst));
55
56 GGML_TENSOR_BINARY_OP_LOCALS
57
58 GGML_ASSERT( nb0 == sizeof(dst_t));
59 GGML_ASSERT(nb00 == sizeof(src0_t));
60
61 const auto [ir0, ir1] = get_thread_range(params, src0);
62 const bool is_src1_contiguous = (nb10 == sizeof(src1_t));
63
64 if (!is_src1_contiguous) { // broadcast not implemented yet for non-contiguous
65 GGML_ASSERT(ggml_are_same_shape(src0, src1));
66 }
67
68#ifdef GGML_USE_ACCELERATE
69 vDSP_fn_t vDSP_op = nullptr;
70 // TODO - avoid the f32-only check using type 'trait' lookup tables and row-based src-to-float conversion functions
71 if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
72 if (op == op_add) {
73 vDSP_op = vDSP_vadd;
74 } else if (op == op_sub) {
75 vDSP_op = vDSP_vsub;
76 } else if (op == op_mul) {
77 vDSP_op = vDSP_vmul;
78 } else if (op == op_div) {
79 vDSP_op = vDSP_vdiv;
80 }
81 }
82#endif
83
84 for (int64_t ir = ir0; ir < ir1; ++ir) {
85 const int64_t i03 = ir/(ne02*ne01);
86 const int64_t i02 = (ir - i03*ne02*ne01)/ne01;
87 const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01);
88
89 const int64_t i13 = i03 % ne13;
90 const int64_t i12 = i02 % ne12;
91 const int64_t i11 = i01 % ne11;
92
93 dst_t * dst_ptr = (dst_t *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 );
94 const src0_t * src0_ptr = (const src0_t *) ((const char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01);
95 const src1_t * src1_ptr = (const src1_t *) ((const char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11);
96
97 if (is_src1_contiguous) {
98 // src1 is broadcastable across src0 and dst in i1, i2, i3
99 const int64_t nr0 = ne00 / ne10;
100
101 for (int64_t r = 0; r < nr0; ++r) {
102#ifdef GGML_USE_ACCELERATE
103 if constexpr (std::is_same_v<src0_t, float> && std::is_same_v<src1_t, float> && std::is_same_v<dst_t, float>) {
104 if (vDSP_op != nullptr) {
105 vDSP_op(src1_ptr, 1, src0_ptr + r*ne10, 1, dst_ptr + r*ne10, 1, ne10);
106 continue;
107 }
108 }
109#endif
110 vec_binary_op_contiguous<op>(ne10, dst_ptr + r*ne10, src0_ptr + r*ne10, src1_ptr);
111 }
112 } else {
113 vec_binary_op_non_contiguous<op>(ne0, ne10, nb10, dst_ptr, src0_ptr, src1_ptr);
114 }
115 }
116}
117
118// TODO: Use the 'traits' lookup table (for type conversion fns), instead of a mass of 'if' conditions with long templates
119template <float (*op)(float, float)>
120static void binary_op(const ggml_compute_params * params, ggml_tensor * dst) {
121 const ggml_tensor * src0 = dst->src[0];
122 const ggml_tensor * src1 = dst->src[1];
123
124 /* */ if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { // all f32
125 apply_binary_op<op, float, float, float>(params, dst);
126 } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) { // all f16
127 apply_binary_op<op, ggml_fp16_t, ggml_fp16_t, ggml_fp16_t>(params, dst);
128 } else if (src0->type == GGML_TYPE_BF16 && src1->type == GGML_TYPE_BF16 && dst->type == GGML_TYPE_BF16) { // all bf16
129 apply_binary_op<op, ggml_bf16_t, ggml_bf16_t, ggml_bf16_t>(params, dst);
130 } else if (src0->type == GGML_TYPE_BF16 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_BF16) {
131 apply_binary_op<op, ggml_bf16_t, float, ggml_bf16_t>(params, dst);
132 } else if (src0->type == GGML_TYPE_BF16 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
133 apply_binary_op<op, ggml_bf16_t, float, float>(params, dst);
134 } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F16) {
135 apply_binary_op<op, ggml_fp16_t, float, ggml_fp16_t>(params, dst);
136 } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
137 apply_binary_op<op, ggml_fp16_t, float, float>(params, dst);
138 } else {
139 GGML_ABORT("%s: unsupported types: dst: %s, src0: %s, src1: %s\n", __func__,
140 ggml_type_name(dst->type), ggml_type_name(src0->type), ggml_type_name(src1->type));
141 }
142}
143
144void ggml_compute_forward_add_non_quantized(const ggml_compute_params * params, ggml_tensor * dst) {
145 binary_op<op_add>(params, dst);
146}
147
148void ggml_compute_forward_sub(const ggml_compute_params * params, ggml_tensor * dst) {
149 binary_op<op_sub>(params, dst);
150}
151
152void ggml_compute_forward_mul(const ggml_compute_params * params, ggml_tensor * dst) {
153 binary_op<op_mul>(params, dst);
154}
155
156void ggml_compute_forward_div(const ggml_compute_params * params, ggml_tensor * dst) {
157 binary_op<op_div>(params, dst);
158}
159