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16 | |
17 | /// @example sum.cpp |
18 | /// > Annotated version: @ref sum_example_cpp |
19 | /// |
20 | /// @page sum_example_cpp_short |
21 | /// |
22 | /// This C++ API example demonstrates how to create and execute a |
23 | /// [Sum](@ref dev_guide_sum) primitive. |
24 | /// |
25 | /// Key optimizations included in this example: |
26 | /// - Identical memory formats for source (src) and destination (dst) tensors. |
27 | /// |
28 | /// @page sum_example_cpp Sum Primitive Example |
29 | /// @copydetails sum_example_cpp_short |
30 | /// |
31 | /// @include sum.cpp |
32 | |
33 | #include <algorithm> |
34 | #include <cmath> |
35 | #include <iostream> |
36 | #include <string> |
37 | #include <vector> |
38 | |
39 | #include "example_utils.hpp" |
40 | #include "oneapi/dnnl/dnnl.hpp" |
41 | |
42 | using namespace dnnl; |
43 | |
44 | using tag = memory::format_tag; |
45 | using dt = memory::data_type; |
46 | |
47 | void sum_example(dnnl::engine::kind engine_kind) { |
48 | |
49 | // Create execution dnnl::engine. |
50 | dnnl::engine engine(engine_kind, 0); |
51 | |
52 | // Create dnnl::stream. |
53 | dnnl::stream engine_stream(engine); |
54 | |
55 | // Tensor dimensions. |
56 | const memory::dim N = 3, // batch size |
57 | IC = 3, // channels |
58 | IH = 227, // tensor height |
59 | IW = 227; // tensor width |
60 | |
61 | // Source (src) and destination (dst) tensors dimensions. |
62 | memory::dims src_dims = {N, IC, IH, IW}; |
63 | |
64 | // Allocate buffers. |
65 | std::vector<float> src_data(product(src_dims)); |
66 | std::vector<float> dst_data(product(src_dims)); |
67 | |
68 | // Initialize src. |
69 | std::generate(src_data.begin(), src_data.end(), []() { |
70 | static int i = 0; |
71 | return std::cos(i++ / 10.f); |
72 | }); |
73 | |
74 | // Number of src tensors. |
75 | const int num_src = 10; |
76 | |
77 | // Scaling factors. |
78 | std::vector<float> scales(num_src); |
79 | std::generate(scales.begin(), scales.end(), |
80 | [](int n = 0) { return sin(float(n)); }); |
81 | |
82 | // Create an array of memory descriptors and memory objects for src tensors. |
83 | std::vector<memory::desc> src_md; |
84 | std::vector<memory> src_mem; |
85 | |
86 | for (int n = 0; n < num_src; ++n) { |
87 | auto md = memory::desc(src_dims, dt::f32, tag::nchw); |
88 | auto mem = memory(md, engine); |
89 | |
90 | // Write data to memory object's handle. |
91 | write_to_dnnl_memory(src_data.data(), mem); |
92 | |
93 | src_md.push_back(md); |
94 | src_mem.push_back(mem); |
95 | } |
96 | |
97 | // Create primitive descriptor. |
98 | auto sum_pd = sum::primitive_desc(engine, scales, src_md); |
99 | |
100 | // Create the primitive. |
101 | auto sum_prim = sum(sum_pd); |
102 | |
103 | // Create memory object for dst. |
104 | auto dst_mem = memory(sum_pd.dst_desc(), engine); |
105 | |
106 | // Primitive arguments. |
107 | std::unordered_map<int, memory> sum_args; |
108 | sum_args.insert({DNNL_ARG_DST, dst_mem}); |
109 | for (int n = 0; n < num_src; ++n) { |
110 | sum_args.insert({DNNL_ARG_MULTIPLE_SRC + n, src_mem[n]}); |
111 | } |
112 | |
113 | // Primitive execution: sum. |
114 | sum_prim.execute(engine_stream, sum_args); |
115 | |
116 | // Wait for the computation to finalize. |
117 | engine_stream.wait(); |
118 | |
119 | // Read data from memory object's handle. |
120 | read_from_dnnl_memory(dst_data.data(), dst_mem); |
121 | } |
122 | |
123 | int main(int argc, char **argv) { |
124 | return handle_example_errors(sum_example, parse_engine_kind(argc, argv)); |
125 | } |
126 | |