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16 | |
17 | /// @example reorder.cpp |
18 | /// > Annotated version: @ref reorder_example_cpp |
19 | /// |
20 | /// @page reorder_example_cpp_short |
21 | /// |
22 | /// This C++ API demonstrates how to create and execute a |
23 | /// [Reorder](@ref dev_guide_reorder) primitive. |
24 | /// |
25 | /// Key optimizations included in this example: |
26 | /// - Primitive attributes for output scaling. |
27 | /// |
28 | /// @page reorder_example_cpp Reorder Primitive Example |
29 | /// @copydetails reorder_example_cpp_short |
30 | /// |
31 | /// @include reorder.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 reorder_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<int8_t> dst_data(product(src_dims)); |
67 | |
68 | // Initialize src tensor. |
69 | std::generate(src_data.begin(), src_data.end(), []() { |
70 | static int i = 0; |
71 | return std::cos(i++ / 10.f); |
72 | }); |
73 | |
74 | // Create memory descriptors and memory objects for src and dst. |
75 | auto src_md = memory::desc(src_dims, dt::f32, tag::nchw); |
76 | auto dst_md = memory::desc(src_dims, dt::s8, tag::nhwc); |
77 | |
78 | auto src_mem = memory(src_md, engine); |
79 | auto dst_mem = memory(dst_md, engine); |
80 | |
81 | // Write data to memory object's handle. |
82 | write_to_dnnl_memory(src_data.data(), src_mem); |
83 | |
84 | // Per-channel scales. |
85 | std::vector<float> scales(IC); |
86 | std::generate(scales.begin(), scales.end(), []() { |
87 | static int i = 0; |
88 | return 64.f + 5.f * i++; |
89 | }); |
90 | |
91 | // Dimension of the dst tensor where the output scales will be applied |
92 | const int ic_dim = 1; |
93 | |
94 | // Create primitive post-ops (per-channel output scales) |
95 | primitive_attr reorder_attr; |
96 | reorder_attr.set_scales_mask(DNNL_ARG_DST, 1 << ic_dim); |
97 | auto dst_scales_mem = memory({{IC}, dt::f32, tag::x}, engine); |
98 | write_to_dnnl_memory(scales.data(), dst_scales_mem); |
99 | |
100 | // Create primitive descriptor. |
101 | auto reorder_pd = reorder::primitive_desc( |
102 | engine, src_md, engine, dst_md, reorder_attr); |
103 | |
104 | // Create the primitive. |
105 | auto reorder_prim = reorder(reorder_pd); |
106 | |
107 | // Primitive arguments. |
108 | std::unordered_map<int, memory> reorder_args; |
109 | reorder_args.insert({DNNL_ARG_SRC, src_mem}); |
110 | reorder_args.insert({DNNL_ARG_DST, dst_mem}); |
111 | reorder_args.insert({DNNL_ARG_ATTR_SCALES | DNNL_ARG_DST, dst_scales_mem}); |
112 | |
113 | // Primitive execution: reorder with scaled sum. |
114 | reorder_prim.execute(engine_stream, reorder_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( |
125 | reorder_example, parse_engine_kind(argc, argv)); |
126 | } |
127 | |