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16
17/// @example inner_product.cpp
18/// > Annotated version: @ref inner_product_example_cpp
19///
20/// @page inner_product_example_cpp_short
21///
22/// This C++ API example demonstrates how to create and execute an
23/// [Inner Product](@ref dev_guide_inner_product) primitive.
24///
25/// Key optimizations included in this example:
26/// - Primitive attributes with fused post-ops;
27/// - Creation of optimized memory format from the primitive descriptor.
28///
29/// @page inner_product_example_cpp Inner Product Primitive Example
30/// @copydetails inner_product_example_cpp_short
31///
32/// @include inner_product.cpp
33
34#include <algorithm>
35#include <cmath>
36#include <iostream>
37#include <string>
38#include <vector>
39
40#include "example_utils.hpp"
41#include "oneapi/dnnl/dnnl.hpp"
42
43using namespace dnnl;
44
45using tag = memory::format_tag;
46using dt = memory::data_type;
47
48void inner_product_example(dnnl::engine::kind engine_kind) {
49
50 // Create execution dnnl::engine.
51 dnnl::engine engine(engine_kind, 0);
52
53 // Create dnnl::stream.
54 dnnl::stream engine_stream(engine);
55
56 // Tensor dimensions.
57 const memory::dim N = 3, // batch size
58 IC = 3, // input channels
59 IH = 227, // tensor height
60 IW = 227, // tensor width
61 OC = 96; // output channels
62
63 // Source (src), weights, bias, and destination (dst) tensors
64 // dimensions.
65 memory::dims src_dims = {N, IC, IH, IW};
66 memory::dims weights_dims = {OC, IC, IH, IW};
67 memory::dims bias_dims = {OC};
68 memory::dims dst_dims = {N, OC};
69
70 // Allocate buffers.
71 std::vector<float> src_data(product(src_dims));
72 std::vector<float> weights_data(product(weights_dims));
73 std::vector<float> bias_data(OC);
74 std::vector<float> dst_data(product(dst_dims));
75
76 // Initialize src, weights, and bias tensors.
77 std::generate(src_data.begin(), src_data.end(), []() {
78 static int i = 0;
79 return std::cos(i++ / 10.f);
80 });
81 std::generate(weights_data.begin(), weights_data.end(), []() {
82 static int i = 0;
83 return std::sin(i++ * 2.f);
84 });
85 std::generate(bias_data.begin(), bias_data.end(), []() {
86 static int i = 0;
87 return std::tanh(float(i++));
88 });
89
90 // Create memory descriptors and memory objects for src and dst. In this
91 // example, NCHW layout is assumed.
92 auto src_md = memory::desc(src_dims, dt::f32, tag::nchw);
93 auto bias_md = memory::desc(bias_dims, dt::f32, tag::a);
94 auto dst_md = memory::desc(dst_dims, dt::f32, tag::nc);
95
96 auto src_mem = memory(src_md, engine);
97 auto bias_mem = memory(bias_md, engine);
98 auto dst_mem = memory(dst_md, engine);
99
100 // Create memory object for user's layout for weights. In this example, OIHW
101 // is assumed.
102 auto user_weights_mem = memory({weights_dims, dt::f32, tag::oihw}, engine);
103
104 // Write data to memory object's handles.
105 write_to_dnnl_memory(src_data.data(), src_mem);
106 write_to_dnnl_memory(bias_data.data(), bias_mem);
107 write_to_dnnl_memory(weights_data.data(), user_weights_mem);
108
109 // Create memory descriptor for weights with format_tag::any. This enables
110 // the inner product primitive to choose the memory layout for an optimized
111 // primitive implementation, and this format may differ from the one
112 // provided by the user.
113 auto inner_product_weights_md
114 = memory::desc(weights_dims, dt::f32, tag::any);
115
116 // Create primitive post-ops (ReLU).
117 const float alpha = 0.f;
118 const float beta = 0.f;
119 post_ops inner_product_ops;
120 inner_product_ops.append_eltwise(algorithm::eltwise_relu, alpha, beta);
121 primitive_attr inner_product_attr;
122 inner_product_attr.set_post_ops(inner_product_ops);
123
124 // Create inner product primitive descriptor.
125 auto inner_product_pd = inner_product_forward::primitive_desc(engine,
126 prop_kind::forward_training, src_md, inner_product_weights_md,
127 bias_md, dst_md, inner_product_attr);
128
129 // For now, assume that the weights memory layout generated by the primitive
130 // and the one provided by the user are identical.
131 auto inner_product_weights_mem = user_weights_mem;
132
133 // Reorder the data in case the weights memory layout generated by the
134 // primitive and the one provided by the user are different. In this case,
135 // we create additional memory objects with internal buffers that will
136 // contain the reordered data.
137 if (inner_product_pd.weights_desc() != user_weights_mem.get_desc()) {
138 inner_product_weights_mem
139 = memory(inner_product_pd.weights_desc(), engine);
140 reorder(user_weights_mem, inner_product_weights_mem)
141 .execute(engine_stream, user_weights_mem,
142 inner_product_weights_mem);
143 }
144
145 // Create the primitive.
146 auto inner_product_prim = inner_product_forward(inner_product_pd);
147
148 // Primitive arguments.
149 std::unordered_map<int, memory> inner_product_args;
150 inner_product_args.insert({DNNL_ARG_SRC, src_mem});
151 inner_product_args.insert({DNNL_ARG_WEIGHTS, inner_product_weights_mem});
152 inner_product_args.insert({DNNL_ARG_BIAS, bias_mem});
153 inner_product_args.insert({DNNL_ARG_DST, dst_mem});
154
155 // Primitive execution: inner-product with ReLU.
156 inner_product_prim.execute(engine_stream, inner_product_args);
157
158 // Wait for the computation to finalize.
159 engine_stream.wait();
160
161 // Read data from memory object's handle.
162 read_from_dnnl_memory(dst_data.data(), dst_mem);
163}
164
165int main(int argc, char **argv) {
166 return handle_example_errors(
167 inner_product_example, parse_engine_kind(argc, argv));
168}
169