Convolution int8 inference example with Graph API

Intel® oneAPI Deep Neural Network Developer Guide and Reference

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ID 768875

Date 6/20/2024

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Convolution int8 inference example with Graph API

This is an example to demonstrate how to build an int8 graph with Graph API and run it on CPU.

Example code: cpu_inference_int8.cpp

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 in the application. All the C++ APIs reside in namespace dnnl::graph.

#include <iostream>
#include <memory>
#include <vector>
#include <unordered_map>
#include <unordered_set>

#include <assert.h>

#include "oneapi/dnnl/dnnl_graph.hpp"

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

using namespace dnnl::graph;
using data_type = logical_tensor::data_type;
using layout_type = logical_tensor::layout_type;
using property_type = logical_tensor::property_type;
using dim = logical_tensor::dim;
using dims = logical_tensor::dims;

simple_pattern_int8() function

Build Graph and Get Partitions

In this section, we are trying to build a graph indicating an int8 convolution with relu post-op. After that, we can get all of partitions which are determined by backend.

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

logical_tensor dequant0_src_desc {0, data_type::u8};
logical_tensor conv_src_desc {1, data_type::f32};
op dequant0(2, op::kind::Dequantize, {dequant0_src_desc}, {conv_src_desc},
        "dequant0");
dequant0.set_attr<std::string>(op::attr::qtype, "per_tensor");
dequant0.set_attr<std::vector<float>>(op::attr::scales, {0.1f});
dequant0.set_attr<std::vector<int64_t>>(op::attr::zps, {10});

Create input/output dnnl::graph::logical_tensor and op for the second Dequantize.

NOTE:

It’s necessary to provide scale and weight information on the Dequantize on weight.

NOTE:

Users can set weight property type to constant to enable dnnl weight cache for better performance

logical_tensor dequant1_src_desc {3, data_type::s8};
logical_tensor conv_weight_desc {
        4, data_type::f32, 4, layout_type::undef, property_type::constant};
op dequant1(5, op::kind::Dequantize, {dequant1_src_desc},
        {conv_weight_desc}, "dequant1");
dequant1.set_attr<std::string>(op::attr::qtype, "per_channel");
// the memory format of weight is XIO, which indicates channel equals
// to 64 for the convolution.
std::vector<float> wei_scales(64, 0.1f);
dims wei_zps(64, 0);
dequant1.set_attr<std::vector<float>>(op::attr::scales, wei_scales);
dequant1.set_attr<std::vector<int64_t>>(op::attr::zps, wei_zps);
dequant1.set_attr<int64_t>(op::attr::axis, 1);

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

logical_tensor conv_bias_desc {
        6, data_type::f32, 1, layout_type::undef, property_type::constant};
logical_tensor conv_dst_desc {7, data_type::f32, layout_type::undef};

// create the convolution op
op conv(8, op::kind::Convolution,
        {conv_src_desc, conv_weight_desc, conv_bias_desc}, {conv_dst_desc},
        "conv");
conv.set_attr<dims>(op::attr::strides, {1, 1});
conv.set_attr<dims>(op::attr::pads_begin, {0, 0});
conv.set_attr<dims>(op::attr::pads_end, {0, 0});
conv.set_attr<dims>(op::attr::dilations, {1, 1});
conv.set_attr<std::string>(op::attr::data_format, "NXC");
conv.set_attr<std::string>(op::attr::weights_format, "XIO");
conv.set_attr<int64_t>(op::attr::groups, 1);

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

logical_tensor relu_dst_desc {9, data_type::f32, layout_type::undef};
op relu(10, op::kind::ReLU, {conv_dst_desc}, {relu_dst_desc}, "relu");

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

logical_tensor quant_dst_desc {11, data_type::u8, layout_type::undef};
op quant(
        12, op::kind::Quantize, {relu_dst_desc}, {quant_dst_desc}, "quant");
quant.set_attr<std::string>(op::attr::qtype, "per_tensor");
quant.set_attr<std::vector<float>>(op::attr::scales, {0.1f});
quant.set_attr<std::vector<int64_t>>(op::attr::zps, {10});

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

NOTE:

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

Create graph and add ops to the graph

graph g(dnnl::engine::kind::cpu);

g.add_op(dequant0);
g.add_op(dequant1);
g.add_op(conv);
g.add_op(relu);
g.add_op(quant);

After finished above operations, we can get partitions by calling dnnl::graph::graph::get_partitions().

In this example, the graph will be partitioned into one partition.

auto partitions = g.get_partitions();

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::engine. Also, set a user-defined dnnl::graph::allocator to this engine.

allocator alloc {};
dnnl::engine eng
        = make_engine_with_allocator(dnnl::engine::kind::cpu, 0, alloc);
dnnl::stream strm {eng};

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.

cp.execute(strm, inputs_ts, outputs_ts);

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Intel® oneAPI Deep Neural Network Developer Guide and Reference

Convolution int8 inference example with Graph API

Public headers

simple_pattern_int8() function