Visible to Intel only — GUID: GUID-3F89B7F6-4386-4C9B-8CD2-3E78B70D3AD7
Abs
AbsBackward
Add
AvgPool
AvgPoolBackward
BatchNormForwardTraining
BatchNormInference
BatchNormTrainingBackward
BiasAdd
BiasAddBackward
Clamp
ClampBackward
Concat
Convolution
ConvolutionBackwardData
ConvolutionBackwardWeights
ConvTranspose
ConvTransposeBackwardData
ConvTransposeBackwardWeights
Dequantize
Divide
DynamicDequantize
DynamicQuantize
Elu
EluBackward
End
Exp
GELU
GELUBackward
HardSigmoid
HardSigmoidBackward
HardSwish
HardSwishBackward
Interpolate
InterpolateBackward
LayerNorm
LayerNormBackward
LeakyReLU
Log
LogSoftmax
LogSoftmaxBackward
MatMul
Maximum
MaxPool
MaxPoolBackward
Minimum
Mish
MishBackward
Multiply
Pow
PReLU
PReLUBackward
Quantize
Reciprocal
ReduceL1
ReduceL2
ReduceMax
ReduceMean
ReduceMin
ReduceProd
ReduceSum
ReLU
ReLUBackward
Reorder
Round
Select
Sigmoid
SigmoidBackward
SoftMax
SoftMaxBackward
SoftPlus
SoftPlusBackward
Sqrt
SqrtBackward
Square
SquaredDifference
StaticReshape
StaticTranspose
Subtract
Tanh
TanhBackward
TypeCast
Wildcard
enum dnnl_alg_kind_t
enum dnnl_normalization_flags_t
enum dnnl_primitive_kind_t
enum dnnl_prop_kind_t
enum dnnl_query_t
enum dnnl::normalization_flags
enum dnnl::query
struct dnnl_exec_arg_t
struct dnnl_primitive
struct dnnl_primitive_desc
struct dnnl::primitive
struct dnnl::primitive_desc
struct dnnl::primitive_desc_base
enum dnnl_rnn_direction_t
enum dnnl_rnn_flags_t
enum dnnl::rnn_direction
enum dnnl::rnn_flags
struct dnnl::augru_backward
struct dnnl::augru_forward
struct dnnl::gru_backward
struct dnnl::gru_forward
struct dnnl::lbr_augru_backward
struct dnnl::lbr_augru_forward
struct dnnl::lbr_gru_backward
struct dnnl::lbr_gru_forward
struct dnnl::lstm_backward
struct dnnl::lstm_forward
struct dnnl::rnn_primitive_desc_base
struct dnnl::vanilla_rnn_backward
struct dnnl::vanilla_rnn_forward
Visible to Intel only — GUID: GUID-3F89B7F6-4386-4C9B-8CD2-3E78B70D3AD7
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
/*******************************************************************************
* Copyright 2020-2022 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"
#include "oneapi/dnnl/dnnl.hpp"
using namespace dnnl;
using tag = memory::format_tag;
using dt = memory::data_type;
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(engine, axis, src_mds);
// 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));
}