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/*******************************************************************************
* Copyright 2020-2025 Intel Corporation
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*******************************************************************************/
/// @example inner_product.cpp
/// > Annotated version: @ref inner_product_example_cpp
///
/// @page inner_product_example_cpp_short
///
/// This C++ API example demonstrates how to create and execute an
/// [Inner Product](@ref dev_guide_inner_product) primitive.
///
/// Key optimizations included in this example:
/// - Primitive attributes with fused post-ops;
/// - Creation of optimized memory format from the primitive descriptor.
///
/// @page inner_product_example_cpp Inner Product Primitive Example
/// @copydetails inner_product_example_cpp_short
///
/// @include inner_product.cpp
#include <algorithm>
#include <cmath>
#include <iostream>
#include <string>
#include <vector>
#include "example_utils.hpp"
#include "oneapi/dnnl/dnnl.hpp"
using namespace dnnl;
void inner_product_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, // input channels
IH = 227, // tensor height
IW = 227, // tensor width
OC = 96; // output channels
// Source (src), weights, bias, and destination (dst) tensors
// dimensions.
memory::dims src_dims = {N, IC, IH, IW};
memory::dims weights_dims = {OC, IC, IH, IW};
memory::dims bias_dims = {OC};
memory::dims dst_dims = {N, OC};
// Allocate buffers.
std::vector<float> src_data(product(src_dims));
std::vector<float> weights_data(product(weights_dims));
std::vector<float> bias_data(OC);
std::vector<float> dst_data(product(dst_dims));
// Initialize src, weights, and bias tensors.
std::generate(src_data.begin(), src_data.end(), []() {
static int i = 0;
return std::cos(i++ / 10.f);
});
std::generate(weights_data.begin(), weights_data.end(), []() {
static int i = 0;
return std::sin(i++ * 2.f);
});
std::generate(bias_data.begin(), bias_data.end(), []() {
static int i = 0;
return std::tanh(float(i++));
});
// Create memory descriptors and memory objects for src and dst. In this
// example, NCHW layout is assumed.
auto src_md = memory::desc(
src_dims, memory::data_type::f32, memory::format_tag::nchw);
auto bias_md = memory::desc(
bias_dims, memory::data_type::f32, memory::format_tag::a);
auto dst_md = memory::desc(
dst_dims, memory::data_type::f32, memory::format_tag::nc);
auto src_mem = memory(src_md, engine);
auto bias_mem = memory(bias_md, engine);
auto dst_mem = memory(dst_md, engine);
// Create memory object for user's layout for weights. In this example, OIHW
// is assumed.
auto user_weights_mem = memory(
{weights_dims, memory::data_type::f32, memory::format_tag::oihw},
engine);
// Write data to memory object's handles.
write_to_dnnl_memory(src_data.data(), src_mem);
write_to_dnnl_memory(bias_data.data(), bias_mem);
write_to_dnnl_memory(weights_data.data(), user_weights_mem);
// Create memory descriptor for weights with format_tag::any. This enables
// the inner product primitive to choose the memory layout for an optimized
// primitive implementation, and this format may differ from the one
// provided by the user.
auto inner_product_weights_md = memory::desc(
weights_dims, memory::data_type::f32, memory::format_tag::any);
// Create primitive post-ops (ReLU).
const float alpha = 0.f;
const float beta = 0.f;
post_ops inner_product_ops;
inner_product_ops.append_eltwise(algorithm::eltwise_relu, alpha, beta);
primitive_attr inner_product_attr;
inner_product_attr.set_post_ops(inner_product_ops);
// Create inner product primitive descriptor.
auto inner_product_pd = inner_product_forward::primitive_desc(engine,
prop_kind::forward_training, src_md, inner_product_weights_md,
bias_md, dst_md, inner_product_attr);
// For now, assume that the weights memory layout generated by the primitive
// and the one provided by the user are identical.
auto inner_product_weights_mem = user_weights_mem;
// Reorder the data in case the weights memory layout generated by the
// primitive and the one provided by the user are different. In this case,
// we create additional memory objects with internal buffers that will
// contain the reordered data.
if (inner_product_pd.weights_desc() != user_weights_mem.get_desc()) {
inner_product_weights_mem
= memory(inner_product_pd.weights_desc(), engine);
reorder(user_weights_mem, inner_product_weights_mem)
.execute(engine_stream, user_weights_mem,
inner_product_weights_mem);
}
// Create the primitive.
auto inner_product_prim = inner_product_forward(inner_product_pd);
// Primitive arguments.
std::unordered_map<int, memory> inner_product_args;
inner_product_args.insert({DNNL_ARG_SRC, src_mem});
inner_product_args.insert({DNNL_ARG_WEIGHTS, inner_product_weights_mem});
inner_product_args.insert({DNNL_ARG_BIAS, bias_mem});
inner_product_args.insert({DNNL_ARG_DST, dst_mem});
// Primitive execution: inner-product with ReLU.
inner_product_prim.execute(engine_stream, inner_product_args);
// Wait for the computation to finalize.
engine_stream.wait();
// Read data from memory object's handle.
read_from_dnnl_memory(dst_data.data(), dst_mem);
}
int main(int argc, char **argv) {
return handle_example_errors(
inner_product_example, parse_engine_kind(argc, argv));
}
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