oneAPI Deep Neural Network Library (oneDNN)  1.95.0
Performance library for Deep Learning
matmul.cpp

Annotated version: Matmul Primitive Example

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* Copyright 2020 Intel Corporation
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* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
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* http://www.apache.org/licenses/LICENSE-2.0
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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 matmul_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 MB = 3, // batch size
M = 128, K = 256, N = 512;
// Source (src), weights, bias, and destination (dst) tensors dimensions.
memory::dims src_dims = {MB, M, K};
memory::dims weights_dims = {MB, K, N};
memory::dims bias_dims = {1, 1, N};
memory::dims dst_dims = {MB, M, N};
// Allocate buffers.
std::vector<float> src_data(product(src_dims));
std::vector<float> weights_data(product(weights_dims));
std::vector<float> bias_data(product(bias_dims));
std::vector<float> dst_data(product(dst_dims));
// Initialize src, weights, bias.
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 descriptors and memory objects for src, weights, bias, and
// dst.
auto src_md = memory::desc(src_dims, dt::f32, tag::abc);
auto weights_md = memory::desc(weights_dims, dt::f32, tag::abc);
auto bias_md = memory::desc(bias_dims, dt::f32, tag::abc);
auto dst_md = memory::desc(dst_dims, dt::f32, tag::abc);
auto src_mem = memory(src_md, engine);
auto weights_mem = memory(weights_md, engine);
auto bias_mem = memory(bias_md, engine);
auto dst_mem = memory(dst_md, engine);
// Write data to memory object's handles.
write_to_dnnl_memory(src_data.data(), src_mem);
write_to_dnnl_memory(weights_data.data(), weights_mem);
write_to_dnnl_memory(bias_data.data(), bias_mem);
// Create operation descriptor
// Create primitive post-ops (ReLU).
const float scale = 1.0f;
const float alpha = 0.f;
const float beta = 0.f;
post_ops matmul_ops;
matmul_ops.append_eltwise(scale, algorithm::eltwise_relu, alpha, beta);
primitive_attr matmul_attr;
matmul_attr.set_post_ops(matmul_ops);
// Create primitive descriptor.
auto matmul_pd = matmul::primitive_desc(matmul_d, matmul_attr, engine);
// Create the primitive.
auto matmul_prim = matmul(matmul_pd);
// Primitive arguments.
std::unordered_map<int, memory> matmul_args;
matmul_args.insert({DNNL_ARG_SRC, src_mem});
matmul_args.insert({DNNL_ARG_WEIGHTS, weights_mem});
matmul_args.insert({DNNL_ARG_BIAS, bias_mem});
matmul_args.insert({DNNL_ARG_DST, dst_mem});
// Primitive execution: matrix multiplication with ReLU.
matmul_prim.execute(engine_stream, matmul_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(matmul_example, parse_engine_kind(argc, argv));
}
dnnl::stream
An execution stream.
Definition: dnnl.hpp:1086
dnnl::engine
An execution engine.
Definition: dnnl.hpp:865
dnnl::stream::wait
stream & wait()
Waits for all primitives executing in the stream to finish.
Definition: dnnl.hpp:1158
dnnl::matmul
Matrix multiplication (matmul) primitive.
Definition: dnnl.hpp:9655
dnnl::matmul::desc
Descriptor for a matmul primitive.
Definition: dnnl.hpp:9657
dnnl::engine::kind
kind
Kinds of engines.
Definition: dnnl.hpp:870
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::primitive_attr::set_post_ops
void set_post_ops(const post_ops ops)
Sets post-ops.
Definition: dnnl.hpp:2918
dnnl::post_ops
Post-ops.
Definition: dnnl.hpp:2386
dnnl.hpp
dnnl::memory::data_type
data_type
Data type specification.
Definition: dnnl.hpp:1261
dnnl::query::weights_md
@ weights_md
weights memory descriptor desc
dnnl::memory::format_tag
format_tag
Memory format tag specification.
Definition: dnnl.hpp:1335
dnnl::query::dst_md
@ dst_md
destination memory desc
DNNL_ARG_BIAS
#define DNNL_ARG_BIAS
Bias tensor argument.
Definition: dnnl_types.h:1947
dnnl::query::src_md
@ src_md
source memory desc
DNNL_ARG_SRC
#define DNNL_ARG_SRC
A special mnemonic for source argument for primitives that have a single source.
Definition: dnnl_types.h:1873
dnnl::matmul::primitive_desc
Primitive descriptor for a matmul primitive.
Definition: dnnl.hpp:9689
dnnl::memory::dim
dnnl_dim_t dim
Integer type for representing dimension sizes and indices.
Definition: dnnl.hpp:1243
dnnl::algorithm::eltwise_relu
@ eltwise_relu
Elementwise: rectified linear unit (ReLU)
dnnl::primitive_attr
Primitive attributes.
Definition: dnnl.hpp:2688
dnnl::memory
Memory object.
Definition: dnnl.hpp:1241
dnnl::memory::dims
std::vector< dim > dims
Vector of dimensions.
Definition: dnnl.hpp:1246
dnnl::memory::desc
A memory descriptor.
Definition: dnnl.hpp:1823
dnnl
oneDNN namespace
Definition: dnnl.hpp:86
DNNL_ARG_WEIGHTS
#define DNNL_ARG_WEIGHTS
A special mnemonic for primitives that have a single weights argument.
Definition: dnnl_types.h:1920
dnnl::post_ops::append_eltwise
void append_eltwise(float scale, algorithm aalgorithm, float alpha, float beta)
Appends an elementwise post-op.
Definition: dnnl.hpp:2487
dnnl::query::matmul_d
@ matmul_d
matmul descriptor