#include <algorithm>
#include <cmath>
#include <iostream>
#include <string>
#include <vector>
#include "example_utils.hpp"
using tag = memory::format_tag;
using dt = memory::data_type;
const memory::dim T = 12,
N = 3,
C = 227;
const memory::dims src_dims = {T, N, C};
memory::dims scale_shift_dims = {2, C};
std::vector<float> src_data(product(src_dims));
std::vector<float> scale_shift_data(product(scale_shift_dims));
std::generate(src_data.begin(), src_data.end(), []() {
static int i = 0;
return std::cos(i++ / 10.f);
});
auto mid = scale_shift_data.begin() + C;
std::generate(scale_shift_data.begin(), mid, []() {
static int i = 0;
return std::sin(i++ * 2.f);
});
std::generate(mid, scale_shift_data.end(), []() {
static int i = 0;
return std::tanh(i++);
});
auto src_md = memory::desc(src_dims, dt::f32, tag::tnc);
auto src_mem = memory(src_md, engine);
auto scale_shift_mem = memory({scale_shift_dims, dt::f32, tag::nc},
engine);
write_to_dnnl_memory(src_data.data(), src_mem);
write_to_dnnl_memory(scale_shift_data.data(), scale_shift_mem);
const float epsilon = 1.e-10f;
auto lnorm_desc
= layer_normalization_forward::desc(prop_kind::forward_training,
src_md, epsilon, normalization_flags::use_scale_shift);
auto lnorm_pd
= layer_normalization_forward::primitive_desc(lnorm_desc, engine);
auto mean_mem = memory(lnorm_pd.mean_desc(), engine);
auto variance_mem = memory(lnorm_pd.variance_desc(), engine);
auto lnorm_prim = layer_normalization_forward(lnorm_pd);
std::unordered_map<int, memory> lnorm_args;
lnorm_prim.execute(engine_stream, lnorm_args);
engine_stream.wait();
read_from_dnnl_memory(src_data.data(), src_mem);
}
int main(int argc, char **argv) {
return handle_example_errors(
layer_normalization_example, parse_engine_kind(argc, argv));
}
#define DNNL_ARG_DST
A special mnemonic for destination argument for primitives that have a single destination.
Definition: dnnl_types.h:2307
#define DNNL_ARG_SCALE_SHIFT
A special mnemonic for scale and shift argument of normalization primitives.
Definition: dnnl_types.h:2333
#define DNNL_ARG_MEAN
Mean values tensor argument.
Definition: dnnl_types.h:2360
#define DNNL_ARG_VARIANCE
Variance values tensor argument.
Definition: dnnl_types.h:2362
#define DNNL_ARG_SRC
A special mnemonic for source argument for primitives that have a single source.
Definition: dnnl_types.h:2283
@ src_md
source memory desc
oneDNN namespace
Definition: dnnl.hpp:74
An execution engine.
Definition: dnnl.hpp:869
kind
Kinds of engines.
Definition: dnnl.hpp:874
An execution stream.
Definition: dnnl.hpp:985