oneAPI Deep Neural Network Library (oneDNN)  1.6.0
Performance library for Deep Learning
Concat Primitive Example

This C++ API example demonstrates how to create and execute a Concat primitive.

Key optimizations included in this example:

  • Identical source (src) memory formats.
  • Creation of optimized memory format for destination (dst) from the primitive descriptor
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#include <algorithm>
#include <cmath>
#include <iostream>
#include <string>
#include <vector>
#include "dnnl.hpp"
#include "example_utils.hpp"
using namespace dnnl;
void concat_example(dnnl::engine::kind engine_kind) {
// Create execution dnnl::engine.
dnnl::engine engine(engine_kind, 0);
// Create dnnl::stream.
dnnl::stream engine_stream(engine);
// Tensor dimensions.
const memory::dim N = 3, // batch size
IC = 3, // channels
IH = 120, // tensor height
IW = 120; // tensor width
// Number of source (src) tensors.
const int num_src = 10;
// Concatenation axis.
const int axis = 1;
// src tensors dimensions
memory::dims src_dims = {N, IC, IH, IW};
// Allocate buffers.
std::vector<float> src_data(product(src_dims));
// Initialize src.
// NOTE: In this example, the same src memory buffer is used to demonstrate
// concatenation for simplicity
std::generate(src_data.begin(), src_data.end(), []() {
static int i = 0;
return std::cos(i++ / 10.f);
});
// Create a memory descriptor and memory object for each src tensor.
std::vector<memory::desc> src_mds;
std::vector<memory> src_mems;
for (int n = 0; n < num_src; ++n) {
auto md = memory::desc(src_dims, dt::f32, tag::nchw);
auto mem = memory(md, engine);
// Write data to memory object's handle.
write_to_dnnl_memory(src_data.data(), mem);
src_mds.push_back(md);
src_mems.push_back(mem);
}
// Create primitive descriptor.
auto concat_pd = concat::primitive_desc(axis, src_mds, engine);
// Create destination (dst) memory object using the memory descriptor
// created by the primitive.
auto dst_mem = memory(concat_pd.dst_desc(), engine);
// Create the primitive.
auto concat_prim = concat(concat_pd);
// Primitive arguments.
std::unordered_map<int, memory> concat_args;
for (int n = 0; n < num_src; ++n)
concat_args.insert({DNNL_ARG_MULTIPLE_SRC + n, src_mems[n]});
concat_args.insert({DNNL_ARG_DST, dst_mem});
// Primitive execution: concatenation.
concat_prim.execute(engine_stream, concat_args);
// Wait for the computation to finalize.
engine_stream.wait();
}
int main(int argc, char **argv) {
return handle_example_errors(concat_example, parse_engine_kind(argc, argv));
}
dnnl::stream
An execution stream.
Definition: dnnl.hpp:1043
dnnl::engine
An execution engine.
Definition: dnnl.hpp:840
dnnl::engine::kind
kind
Kinds of engines.
Definition: dnnl.hpp:845
DNNL_ARG_DST
#define DNNL_ARG_DST
A special mnemonic for destination argument for primitives that have a single destination.
Definition: dnnl_types.h:1897
dnnl::concat
Tensor concatenation (concat) primitive.
Definition: dnnl.hpp:3332
dnnl.hpp
dnnl::memory::data_type
data_type
Data type specification.
Definition: dnnl.hpp:1204
dnnl::memory::format_tag
format_tag
Memory format tag specification.
Definition: dnnl.hpp:1278
dnnl::concat::primitive_desc
Primitive descriptor for a concat primitive.
Definition: dnnl.hpp:3334
dnnl::memory::dim
dnnl_dim_t dim
Integer type for representing dimension sizes and indices.
Definition: dnnl.hpp:1186
dnnl::memory
Memory object.
Definition: dnnl.hpp:1184
dnnl::memory::dims
std::vector< dim > dims
Vector of dimensions.
Definition: dnnl.hpp:1189
dnnl::memory::desc
A memory descriptor.
Definition: dnnl.hpp:1766
dnnl
oneDNN namespace
Definition: dnnl.hpp:81