Getting started with SYCL extensions API and Graph API

Intel® oneAPI Deep Neural Network Developer Guide and Reference

Download PDF

ID 768875

Date 6/20/2024

Version

Public

A newer version of this document is available. Customers should click here to go to the newest version.

Visible to Intel only — GUID: GUID-B80EAE6B-620B-4D11-B6E0-CD2F1E2449D3

View Details

Getting started with SYCL extensions API and Graph API

This is an example to demonstrate how to build a simple graph and run on SYCL device.

Example code: sycl_getting_started.cpp

Some key take-aways included in this example:

how to build a graph and get several partitions
how to create engine, allocator and stream
how to compile a partition
how to execute a compiled partition

Some assumptions in this example:

Only workflow is demonstrated without checking correctness
Unsupported partitions should be handled by users themselves

Public headers

To start using oneDNN graph, we must include the dnnl_graph.hpp header file into the application. If you also want to run with SYCL device, you need include dnnl_graph_sycl.hpp header as well. All the C++ APIs reside in namespace dnnl::graph.

#include "oneapi/dnnl/dnnl_graph.hpp"
#include "oneapi/dnnl/dnnl_graph_sycl.hpp"
#include "oneapi/dnnl/dnnl_sycl.hpp"
using namespace dnnl::graph;
using namespace sycl;

#include <assert.h>
#include <iostream>
#include <memory>
#include <vector>
#include <unordered_map>
#include <unordered_set>

#include "example_utils.hpp"
#include "graph_example_utils.hpp"

using data_type = logical_tensor::data_type;
using layout_type = logical_tensor::layout_type;
using dim = logical_tensor::dim;
using dims = logical_tensor::dims;

sycl_getting_started_tutorial() function

Build Graph and Get Partitions.

In this section, we are trying to build a graph containing the pattern like conv0->relu0->conv1->relu1. After that, we can get all of partitions which are determined by backend.

To build a graph, the connection relationship of different ops must be known.In oneDNN graph, dnnl::graph::logical_tensor is used to express such relationship.So, next step is to create logical tensors for these ops including inputs and outputs.

NOTE:

It’s not necessary to provide concrete shape/layout information at graph partitioning stage. Users can provide these information till compilation stage.

Create input/output dnnl::graph::logical_tensor for the first Convolution op.

logical_tensor conv0_src_desc {0, data_type::f32};
logical_tensor conv0_weight_desc {1, data_type::f32};
logical_tensor conv0_dst_desc {2, data_type::f32};

Create first Convolution op (dnnl::graph::op) and attaches attributes to it, such as strides, pads_begin, pads_end, data_format, etc.

op conv0(0, op::kind::Convolution, {conv0_src_desc, conv0_weight_desc},
        {conv0_dst_desc}, "conv0");
conv0.set_attr<dims>(op::attr::strides, {4, 4});
conv0.set_attr<dims>(op::attr::pads_begin, {0, 0});
conv0.set_attr<dims>(op::attr::pads_end, {0, 0});
conv0.set_attr<dims>(op::attr::dilations, {1, 1});
conv0.set_attr<int64_t>(op::attr::groups, 1);
conv0.set_attr<std::string>(op::attr::data_format, "NCX");
conv0.set_attr<std::string>(op::attr::weights_format, "OIX");

Create input/output logical tensors for first BiasAdd op and create the first BiasAdd op

logical_tensor conv0_bias_desc {3, data_type::f32};
logical_tensor conv0_bias_add_dst_desc {
        4, data_type::f32, layout_type::undef};
op conv0_bias_add(1, op::kind::BiasAdd, {conv0_dst_desc, conv0_bias_desc},
        {conv0_bias_add_dst_desc}, "conv0_bias_add");
conv0_bias_add.set_attr<std::string>(op::attr::data_format, "NCX");

Create output logical tensors for first Relu op and create the op.

logical_tensor relu0_dst_desc {5, data_type::f32};
op relu0(2, op::kind::ReLU, {conv0_bias_add_dst_desc}, {relu0_dst_desc},
        "relu0");

Create input/output logical tensors for second Convolution op and create the second Convolution op.

logical_tensor conv1_weight_desc {6, data_type::f32};
logical_tensor conv1_dst_desc {7, data_type::f32};
op conv1(3, op::kind::Convolution, {relu0_dst_desc, conv1_weight_desc},
        {conv1_dst_desc}, "conv1");
conv1.set_attr<dims>(op::attr::strides, {1, 1});
conv1.set_attr<dims>(op::attr::pads_begin, {0, 0});
conv1.set_attr<dims>(op::attr::pads_end, {0, 0});
conv1.set_attr<dims>(op::attr::dilations, {1, 1});
conv1.set_attr<int64_t>(op::attr::groups, 1);
conv1.set_attr<std::string>(op::attr::data_format, "NCX");
conv1.set_attr<std::string>(op::attr::weights_format, "OIX");

Create input/output logical tensors for second BiasAdd op and create the op.

logical_tensor conv1_bias_desc {8, data_type::f32};
logical_tensor conv1_bias_add_dst_desc {9, data_type::f32};
op conv1_bias_add(4, op::kind::BiasAdd, {conv1_dst_desc, conv1_bias_desc},
        {conv1_bias_add_dst_desc}, "conv1_bias_add");
conv1_bias_add.set_attr<std::string>(op::attr::data_format, "NCX");

Create output logical tensors for second Relu op and create the op

logical_tensor relu1_dst_desc {10, data_type::f32};
op relu1(5, op::kind::ReLU, {conv1_bias_add_dst_desc}, {relu1_dst_desc},
        "relu1");

Finally, those created ops will be added into the graph. The graph internally will maintain a list to store all of these ops. To create a graph, dnnl::engine::kind is needed because the returned partitions maybe vary on different devices.

NOTE:

The order of adding op doesn’t matter. The connection will be obtained through logical tensors.

graph g(ekind);

g.add_op(conv0);
g.add_op(conv0_bias_add);
g.add_op(relu0);
g.add_op(conv1);
g.add_op(conv1_bias_add);
g.add_op(relu1);

After adding all ops into the graph, call dnnl::graph::graph::get_partitions() to indicate that the graph building is over and is ready for partitioning. Adding new ops into a finalized graph or partitioning a unfinalized graph will both lead to a failure.

g.finalize();

After finished above operations, we can get partitions by calling dnnl::graph::graph::get_partitions(). Here we can also specify the dnnl::graph::partition::policy to get different partitions.

In this example, the graph will be partitioned into two partitions:

conv0 + conv0_bias_add + relu0
conv1 + conv1_bias_add + relu1

auto partitions = g.get_partitions();

Below codes are to create runtime objects like allocator, engine and stream. Unlike CPU example, users need to provide sycl device, sycl context, and sycl queue. oneDNN Graph provides different interoperability APIs which are defined at dnnl_graph_sycl.hpp.

Compile and Execute Partition

In the real case, users like framework should provide device information at this stage. But in this example, we just use a self-defined device to simulate the real behavior.

Create a dnnl::graph::allocator with two user-defined dnnl_graph_sycl_allocate_f and dnnl_graph_sycl_deallocate_f call-back functions.

allocator alloc = sycl_interop::make_allocator(
        sycl_malloc_wrapper, sycl_free_wrapper);

Define SYCL queue (code outside of oneDNN graph)

sycl::queue q = (ekind == engine::kind::gpu)
        ? sycl::queue(
                sycl::gpu_selector_v, sycl::property::queue::in_order {})
        : sycl::queue(
                sycl::cpu_selector_v, sycl::property::queue::in_order {});

Create a dnnl::engine based on SYCL device and context. Also, set a user-defined dnnl::graph::allocator to this engine.

dnnl::engine eng = sycl_interop::make_engine_with_allocator(
        q.get_device(), q.get_context(), alloc);

Create a dnnl::stream on the given engine

dnnl::stream strm = dnnl::sycl_interop::make_stream(eng, q);

Compile the partition to generate compiled partition with the input and output logical tensors.

compiled_partition cp = partition.compile(inputs, outputs, eng);

Execute the compiled partition on the specified stream.

sycl_interop::execute(cp, strm, inputs_ts, outputs_ts);

Select Your Language

Using Intel.com Search

Quick Links

Recent Searches

Advanced Search

Only search in

Intel® oneAPI Deep Neural Network Developer Guide and Reference

Getting started with SYCL extensions API and Graph API

Public headers

sycl_getting_started_tutorial() function