oneAPI Deep Neural Network Library (oneDNN)
Performance library for Deep Learning
1.96.0
convolution.cpp

Annotated version: Convolution Primitive Example

/*******************************************************************************
* Copyright 2020 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.
*******************************************************************************/
#include <algorithm>
#include <cmath>
#include <iostream>
#include <string>
#include <vector>
#include "example_utils.hpp"
using namespace dnnl;
void convolution_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 = 32, // input channels
IH = 13, // input height
IW = 13, // input width
OC = 64, // output channels
KH = 3, // weights height
KW = 3, // weights width
PH_L = 1, // height padding: left
PH_R = 1, // height padding: right
PW_L = 1, // width padding: left
PW_R = 1, // width padding: right
SH = 4, // height-wise stride
SW = 4, // width-wise stride
OH = (IH - KH + PH_L + PH_R) / SH + 1, // output height
OW = (IW - KW + PW_L + PW_R) / SW + 1; // output width
// Source (src), weights, bias, and destination (dst) tensors
// dimensions.
memory::dims src_dims = {N, IC, IH, IW};
memory::dims weights_dims = {OC, IC, KH, KW};
memory::dims bias_dims = {OC};
memory::dims dst_dims = {N, OC, OH, OW};
// Strides, padding dimensions.
memory::dims strides_dims = {SH, SW};
memory::dims padding_dims_l = {PH_L, PW_L};
memory::dims padding_dims_r = {PH_R, PW_R};
// 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 dst 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(i++);
});
// Create memory objects for tensor data (src, weights, dst). In this
// example, NCHW layout is assumed for src and dst, and OIHW for weights.
auto user_src_mem = memory({src_dims, dt::f32, tag::nchw}, engine);
auto user_weights_mem = memory({weights_dims, dt::f32, tag::oihw}, engine);
auto user_dst_mem = memory({dst_dims, dt::f32, tag::nchw}, engine);
// Create memory descriptors with format_tag::any for the primitive. This
// enables the convolution primitive to choose memory layouts for an
// optimized primitive implementation, and these layouts may differ from the
// ones provided by the user.
auto conv_src_md = memory::desc(src_dims, dt::f32, tag::any);
auto conv_weights_md = memory::desc(weights_dims, dt::f32, tag::any);
auto conv_dst_md = memory::desc(dst_dims, dt::f32, tag::any);
// Create memory descriptor and memory object for input bias.
auto user_bias_md = memory::desc(bias_dims, dt::f32, tag::a);
auto user_bias_mem = memory(user_bias_md, engine);
// Write data to memory object's handle.
write_to_dnnl_memory(src_data.data(), user_src_mem);
write_to_dnnl_memory(weights_data.data(), user_weights_mem);
write_to_dnnl_memory(bias_data.data(), user_bias_mem);
// Create operation descriptor.
algorithm::convolution_direct, conv_src_md, conv_weights_md,
user_bias_md, conv_dst_md, strides_dims, padding_dims_l,
padding_dims_r);
// Create primitive post-ops (ReLU).
const float scale = 1.f;
const float alpha = 0.f;
const float beta = 0.f;
post_ops conv_ops;
conv_ops.append_eltwise(scale, algorithm::eltwise_relu, alpha, beta);
primitive_attr conv_attr;
conv_attr.set_post_ops(conv_ops);
// Create primitive descriptor.
auto conv_pd
= convolution_forward::primitive_desc(conv_desc, conv_attr, engine);
// For now, assume that the src, weights, and dst memory layouts generated
// by the primitive and the ones provided by the user are identical.
auto conv_src_mem = user_src_mem;
auto conv_weights_mem = user_weights_mem;
auto conv_dst_mem = user_dst_mem;
// Reorder the data in case the src and weights memory layouts generated by
// the primitive and the ones provided by the user are different. In this
// case, we create additional memory objects with internal buffers that will
// contain the reordered data. The data in dst will be reordered after the
// convolution computation has finalized.
if (conv_pd.src_desc() != user_src_mem.get_desc()) {
conv_src_mem = memory(conv_pd.src_desc(), engine);
reorder(user_src_mem, conv_src_mem)
.execute(engine_stream, user_src_mem, conv_src_mem);
}
if (conv_pd.weights_desc() != user_weights_mem.get_desc()) {
conv_weights_mem = memory(conv_pd.weights_desc(), engine);
reorder(user_weights_mem, conv_weights_mem)
.execute(engine_stream, user_weights_mem, conv_weights_mem);
}
if (conv_pd.dst_desc() != user_dst_mem.get_desc()) {
conv_dst_mem = memory(conv_pd.dst_desc(), engine);
}
// Create the primitive.
auto conv_prim = convolution_forward(conv_pd);
// Primitive arguments.
std::unordered_map<int, memory> conv_args;
conv_args.insert({DNNL_ARG_SRC, conv_src_mem});
conv_args.insert({DNNL_ARG_WEIGHTS, conv_weights_mem});
conv_args.insert({DNNL_ARG_BIAS, user_bias_mem});
conv_args.insert({DNNL_ARG_DST, conv_dst_mem});
// Primitive execution: convolution with ReLU.
conv_prim.execute(engine_stream, conv_args);
// Reorder the data in case the dst memory descriptor generated by the
// primitive and the one provided by the user are different.
if (conv_pd.dst_desc() != user_dst_mem.get_desc()) {
reorder(conv_dst_mem, user_dst_mem)
.execute(engine_stream, conv_dst_mem, user_dst_mem);
} else
user_dst_mem = conv_dst_mem;
// Wait for the computation to finalize.
engine_stream.wait();
// Read data from memory object's handle.
read_from_dnnl_memory(dst_data.data(), user_dst_mem);
}
int main(int argc, char **argv) {
return handle_example_errors(
convolution_example, parse_engine_kind(argc, argv));
}