OpenVINO optimizations for Knowledge graphs

This Jupyter notebook can be launched on-line, opening an interactive environment in a browser window. You can also make a local installation. Choose one of the following options:

The goal of this notebook is to showcase performance optimizations for the ConvE knowledge graph embeddings model using the Intel® Distribution of OpenVINO™ Toolkit. The optimizations process contains the following steps:

1. Export the trained model to a format suitable for OpenVINO optimizations and inference

2. Report the inference performance speedup obtained with the optimized OpenVINO model

The ConvE model is an implementation of the paper - “Convolutional 2D Knowledge Graph Embeddings” (https://arxiv.org/abs/1707.01476). The sample dataset can be downloaded from: TimDettmers/ConvE

Table of contents:

• Windows specific settings

• Import the packages needed for successful execution

  • Settings: Including path to the serialized model files and input data files

  • Download Model Checkpoint

  • Defining the ConvE model class

  • Defining the dataloader

  • Evaluate the trained ConvE model

  • Prediction on the Knowledge graph.

  • Convert the trained PyTorch model to IR format for OpenVINO inference

  • Evaluate the model performance with OpenVINO

• Select inference device

  • Determine the platform specific speedup obtained through OpenVINO graph optimizations

  • Benchmark the converted OpenVINO model using benchmark app

  • Conclusions

  • References

Installation Instructions

This is a self-contained example that relies solely on its own code.

We recommend running the notebook in a virtual environment. You only need a Jupyter server to start. For details, please refer to Installation Guide.

%pip install -q "openvino>=2023.1.0" torch scikit-learn tqdm --extra-index-url https://download.pytorch.org/whl/cpu

Note: you may need to restart the kernel to use updated packages.

Windows specific settings

# On Windows, add the directory that contains cl.exe to the PATH
# to enable PyTorch to find the required C++ tools.
# This code assumes that Visual Studio 2019 is installed in the default directory.
# If you have a different C++ compiler, please add the correct path
# to os.environ["PATH"] directly.
# Note that the C++ Redistributable is not enough to run this notebook.

# Adding the path to os.environ["LIB"] is not always required
# - it depends on the system's configuration

import sys

if sys.platform == "win32":
    import distutils.command.build_ext
    import os
    from pathlib import Path

    VS_INSTALL_DIR = r"C:/Program Files (x86)/Microsoft Visual Studio"
    cl_paths = sorted(list(Path(VS_INSTALL_DIR).glob("**/Hostx86/x64/cl.exe")))
    if len(cl_paths) == 0:
        raise ValueError(
            "Cannot find Visual Studio. This notebook requires a C++ compiler. If you installed "
            "a C++ compiler, please add the directory that contains"
            "cl.exe to `os.environ['PATH']`."
        )
    else:
        # If multiple versions of MSVC are installed, get the most recent version
        cl_path = cl_paths[-1]
        vs_dir = str(cl_path.parent)
        os.environ["PATH"] += f"{os.pathsep}{vs_dir}"
        # Code for finding the library dirs from
        # https://stackoverflow.com/questions/47423246/get-pythons-lib-path
        d = distutils.core.Distribution()
        b = distutils.command.build_ext.build_ext(d)
        b.finalize_options()
        os.environ["LIB"] = os.pathsep.join(b.library_dirs)
        print(f"Added {vs_dir} to PATH")

Import the packages needed for successful execution

import json
from pathlib import Path
import sys
import time

import numpy as np
import torch
from sklearn.metrics import accuracy_score
from torch.nn import functional as F, Parameter
from torch.nn.init import xavier_normal_

import openvino as ov

# Fetch `notebook_utils` module
import requests

r = requests.get(
    url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
)

open("notebook_utils.py", "w").write(r.text)
from notebook_utils import download_file, device_widget

Settings: Including path to the serialized model files and input data files

# Path to the pretrained model checkpoint
modelpath = Path("models/conve.pt")

# Entity and relation embedding dimensions
EMB_DIM = 300

# Top K vals to consider from the predictions
TOP_K = 2

# Required for OpenVINO conversion
output_dir = Path("models")
base_model_name = "conve"

output_dir.mkdir(exist_ok=True)

# Paths where PyTorch and OpenVINO IR models will be stored
ir_path = Path(output_dir / base_model_name).with_suffix(".xml")

data_folder = "data"

# Download the file containing the entities and entity IDs
entdatapath = download_file(
    "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/text/countries_S1/kg_training_entids.txt",
    directory=data_folder,
)

# Download the file containing the relations and relation IDs
reldatapath = download_file(
    "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/text/countries_S1/kg_training_relids.txt",
    directory=data_folder,
)

# Download the test data file
testdatapath = download_file(
    "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/json/countries_S1/e1rel_to_e2_ranking_test.json",
    directory=data_folder,
)

data/kg_training_entids.txt:   0%|          | 0.00/3.79k [00:00<?, ?B/s]

data/kg_training_relids.txt:   0%|          | 0.00/62.0 [00:00<?, ?B/s]

data/e1rel_to_e2_ranking_test.json:   0%|          | 0.00/19.1k [00:00<?, ?B/s]

Download Model Checkpoint

model_url = "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/models/knowledge-graph-embeddings/conve.pt"

download_file(model_url, filename=modelpath.name, directory=modelpath.parent)

models/conve.pt:   0%|          | 0.00/18.8M [00:00<?, ?B/s]

PosixPath('/opt/home/k8sworker/ci-ai/cibuilds/jobs/ov-notebook/jobs/OVNotebookOps/builds/810/archive/.workspace/scm/ov-notebook/notebooks/knowledge-graphs-conve/models/conve.pt')

Defining the ConvE model class

# Model implementation reference: https://github.com/TimDettmers/ConvE
class ConvE(torch.nn.Module):
    def __init__(self, num_entities, num_relations, emb_dim):
        super(ConvE, self).__init__()
        # Embedding tables for entity and relations with num_uniq_ent in y-dim, emb_dim in x-dim
        self.emb_e = torch.nn.Embedding(num_entities, emb_dim, padding_idx=0)
        self.ent_weights_matrix = torch.ones([num_entities, emb_dim], dtype=torch.float64)
        self.emb_rel = torch.nn.Embedding(num_relations, emb_dim, padding_idx=0)
        self.ne = num_entities
        self.nr = num_relations
        self.inp_drop = torch.nn.Dropout(0.2)
        self.hidden_drop = torch.nn.Dropout(0.3)
        self.feature_map_drop = torch.nn.Dropout2d(0.2)
        self.loss = torch.nn.BCELoss()
        self.conv1 = torch.nn.Conv2d(1, 32, (3, 3), 1, 0, bias=True)
        self.bn0 = torch.nn.BatchNorm2d(1)
        self.bn1 = torch.nn.BatchNorm2d(32)
        self.ln0 = torch.nn.LayerNorm(emb_dim)
        self.register_parameter("b", Parameter(torch.zeros(num_entities)))
        self.fc = torch.nn.Linear(16128, emb_dim)

    def init(self):
        """Initializes the model"""
        # Xavier initialization
        xavier_normal_(self.emb_e.weight.data)
        xavier_normal_(self.emb_rel.weight.data)

    def forward(self, e1, rel):
        """Forward pass on the model.
        :param e1: source entity
        :param rel: relation between the source and target entities
        Returns the model predictions for the target entities
        """
        e1_embedded = self.emb_e(e1).view(-1, 1, 10, 30)
        rel_embedded = self.emb_rel(rel).view(-1, 1, 10, 30)
        stacked_inputs = torch.cat([e1_embedded, rel_embedded], 2)
        stacked_inputs = self.bn0(stacked_inputs)
        x = self.inp_drop(stacked_inputs)
        x = self.conv1(x)
        x = self.bn1(x)
        x = F.relu(x)
        x = self.feature_map_drop(x)
        x = x.view(1, -1)
        x = self.fc(x)
        x = self.hidden_drop(x)
        x = self.ln0(x)
        x = F.relu(x)
        x = torch.mm(x, self.emb_e.weight.transpose(1, 0))
        x = self.hidden_drop(x)
        x += self.b.expand_as(x)
        pred = torch.nn.functional.softmax(x, dim=1)
        return pred

Defining the dataloader

class DataLoader:
    def __init__(self):
        super(DataLoader, self).__init__()

        self.ent_path = entdatapath
        self.rel_path = reldatapath
        self.test_file = testdatapath
        self.entity_ids, self.ids2entities = self.load_data(data_path=self.ent_path)
        self.rel_ids, self.ids2rel = self.load_data(data_path=self.rel_path)
        self.test_triples_list = self.convert_triples(data_path=self.test_file)

    def load_data(self, data_path):
        """Creates a dictionary of data items with corresponding ids"""
        item_dict, ids_dict = {}, {}
        fp = open(data_path, "r")
        lines = fp.readlines()
        for line in lines:
            name, id = line.strip().split("\t")
            item_dict[name] = int(id)
            ids_dict[int(id)] = name
        fp.close()
        return item_dict, ids_dict

    def convert_triples(self, data_path):
        """Creates a triple of source entity, relation and target entities"""
        triples_list = []
        dp = open(data_path, "r")
        lines = dp.readlines()
        for line in lines:
            item_dict = json.loads(line.strip())
            h = item_dict["e1"]
            r = item_dict["rel"]
            t = item_dict["e2_multi1"].split("\t")
            hrt_list = []
            hrt_list.append(self.entity_ids[h])
            hrt_list.append(self.rel_ids[r])
            t_ents = []
            for t_idx in t:
                t_ents.append(self.entity_ids[t_idx])
            hrt_list.append(t_ents)
            triples_list.append(hrt_list)
        dp.close()
        return triples_list

Evaluate the trained ConvE model

First, we will evaluate the model performance using PyTorch. The goal is to make sure there are no accuracy differences between the original model inference and the model converted to OpenVINO intermediate representation inference results. Here, we use a simple accuracy metric to evaluate the model performance on a test dataset. However, it is typical to use metrics such as Mean Reciprocal Rank, Hits@10 etc.

data = DataLoader()
num_entities = len(data.entity_ids)
num_relations = len(data.rel_ids)

model = ConvE(num_entities=num_entities, num_relations=num_relations, emb_dim=EMB_DIM)
model.load_state_dict(torch.load(modelpath))
model.eval()

pt_inf_times = []

triples_list = data.test_triples_list
num_test_samples = len(triples_list)
pt_acc = 0.0
for i in range(num_test_samples):
    test_sample = triples_list[i]
    h, r, t = test_sample
    start_time = time.time()
    logits = model.forward(e1=torch.tensor(h), rel=torch.tensor(r))
    end_time = time.time()
    pt_inf_times.append(end_time - start_time)
    score, pred = torch.topk(logits, TOP_K, 1)

    gt = np.array(sorted(t))
    pred = np.array(sorted(pred[0].cpu().detach()))
    pt_acc += accuracy_score(gt, pred)

avg_pt_time = np.mean(pt_inf_times) * 1000
print(f"Average time taken for inference: {avg_pt_time} ms")
print(f"Mean accuracy of the model on the test dataset: {pt_acc/num_test_samples}")

Average time taken for inference: 0.7430613040924072 ms
Mean accuracy of the model on the test dataset: 0.875

Prediction on the Knowledge graph.

Here, we perform the entity prediction on the knowledge graph, as a sample evaluation task. We pass the source entity san_marino and relation locatedIn to the knowledge graph and obtain the target entity predictions. Expected predictions are target entities that form a factual triple with the entity and relation passed as inputs to the knowledge graph.

entitynames_dict = data.ids2entities

ent = "san_marino"
rel = "locatedin"

h_idx = data.entity_ids[ent]
r_idx = data.rel_ids[rel]

logits = model.forward(torch.tensor(h_idx), torch.tensor(r_idx))
score, pred = torch.topk(logits, TOP_K, 1)

for j, id in enumerate(pred[0].cpu().detach().numpy()):
    pred_entity = entitynames_dict[id]
    print(f"Source Entity: {ent}, Relation: {rel}, Target entity prediction: {pred_entity}")

Source Entity: san_marino, Relation: locatedin, Target entity prediction: southern_europe
Source Entity: san_marino, Relation: locatedin, Target entity prediction: europe

Convert the trained PyTorch model to IR format for OpenVINO inference

To evaluate performance with OpenVINO, we can either convert the trained PyTorch model to an intermediate representation (IR) format. ov.convert_model function can be used for conversion PyTorch models to OpenVINO Model class instance, that is ready to load on device or can be saved on disk in OpenVINO Intermediate Representation (IR) format using ov.save_model.

print("Converting the trained conve model to IR format")

ov_model = ov.convert_model(model, example_input=(torch.tensor(1), torch.tensor(1)))
ov.save_model(ov_model, ir_path)

Converting the trained conve model to IR format

Evaluate the model performance with OpenVINO

Now, we evaluate the model performance with the OpenVINO framework. In order to do so, make three main API calls:

1. Initialize the Inference engine with Core()

2. Load the model with read_model()

3. Compile the model with compile_model()

Then, the model can be inferred on by using the create_infer_request() API call.

core = ov.Core()
ov_model = core.read_model(model=ir_path)

Select inference device

select device from dropdown list for running inference using OpenVINO

device = device_widget()
device

Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO')

compiled_model = core.compile_model(model=ov_model, device_name=device.value)
input_layer_source = compiled_model.inputs[0]
input_layer_relation = compiled_model.inputs[1]
output_layer = compiled_model.output(0)

ov_acc = 0.0
ov_inf_times = []
for i in range(num_test_samples):
    test_sample = triples_list[i]
    source, relation, target = test_sample
    model_inputs = {
        input_layer_source: np.int64(source),
        input_layer_relation: np.int64(relation),
    }
    start_time = time.time()
    result = compiled_model(model_inputs)[output_layer]
    end_time = time.time()
    ov_inf_times.append(end_time - start_time)
    top_k_idxs = list(np.argpartition(result[0], -TOP_K)[-TOP_K:])

    gt = np.array(sorted(t))
    pred = np.array(sorted(top_k_idxs))
    ov_acc += accuracy_score(gt, pred)

avg_ov_time = np.mean(ov_inf_times) * 1000
print(f"Average time taken for inference: {avg_ov_time} ms")
print(f"Mean accuracy of the model on the test dataset: {ov_acc/num_test_samples}")

Average time taken for inference: 1.0752081871032715 ms
Mean accuracy of the model on the test dataset: 0.10416666666666667

Determine the platform specific speedup obtained through OpenVINO graph optimizations

# prevent division by zero
delimiter = max(avg_ov_time, np.finfo(float).eps)

print(f"Speedup with OpenVINO optimizations: {round(float(avg_pt_time)/float(delimiter),2)} X")

Speedup with OpenVINO optimizations: 0.69 X

Benchmark the converted OpenVINO model using benchmark app

The OpenVINO toolkit provides a benchmarking application to gauge the platform specific runtime performance that can be obtained under optimal configuration parameters for a given model. For more details refer to: https://docs.openvino.ai/2024/learn-openvino/openvino-samples/benchmark-tool.html

Here, we use the benchmark application to obtain performance estimates under optimal configuration for the knowledge graph model inference. We obtain the average (AVG), minimum (MIN) as well as maximum (MAX) latency as well as the throughput performance (in samples/s) observed while running the benchmark application. The platform specific optimal configuration parameters determined by the benchmarking app for OpenVINO inference can also be obtained by looking at the benchmark app results.

print("Benchmark OpenVINO model using the benchmark app")
! benchmark_app -m $ir_path -d $device.value -api async -t 10 -shape "input.1[1],input.2[1]"

Benchmark OpenVINO model using the benchmark app
[Step 1/11] Parsing and validating input arguments
[ INFO ] Parsing input parameters
[Step 2/11] Loading OpenVINO Runtime
[ INFO ] OpenVINO:
[ INFO ] Build ................................. 2024.4.0-16579-c3152d32c9c-releases/2024/4
[ INFO ]
[ INFO ] Device info:
[ INFO ] AUTO
[ INFO ] Build ................................. 2024.4.0-16579-c3152d32c9c-releases/2024/4
[ INFO ]
[ INFO ]
[Step 3/11] Setting device configuration
[ WARNING ] Performance hint was not explicitly specified in command line. Device(AUTO) performance hint will be set to PerformanceMode.THROUGHPUT.
[Step 4/11] Reading model files
[ INFO ] Loading model files
[ INFO ] Read model took 4.36 ms
[ INFO ] Original model I/O parameters:
[ INFO ] Model inputs:
[ INFO ]     e1 (node: e1) : i64 / [...] / []
[ INFO ]     rel (node: rel) : i64 / [...] / []
[ INFO ] Model outputs:
[ INFO ]     *NO_NAME* (node: aten::softmax/Softmax) : f32 / [...] / [1,271]
[Step 5/11] Resizing model to match image sizes and given batch
[ INFO ] Model batch size: 1
[Step 6/11] Configuring input of the model
[ INFO ] Model inputs:
[ INFO ]     e1 (node: e1) : i64 / [...] / []
[ INFO ]     rel (node: rel) : i64 / [...] / []
[ INFO ] Model outputs:
[ INFO ]     *NO_NAME* (node: aten::softmax/Softmax) : f32 / [...] / [1,271]
[Step 7/11] Loading the model to the device
[ INFO ] Compile model took 54.92 ms
[Step 8/11] Querying optimal runtime parameters
[ INFO ] Model:
[ INFO ]   NETWORK_NAME: Model0
[ INFO ]   EXECUTION_DEVICES: ['CPU']
[ INFO ]   PERFORMANCE_HINT: PerformanceMode.THROUGHPUT
[ INFO ]   OPTIMAL_NUMBER_OF_INFER_REQUESTS: 12
[ INFO ]   MULTI_DEVICE_PRIORITIES: CPU
[ INFO ]   CPU:
[ INFO ]     AFFINITY: Affinity.CORE
[ INFO ]     CPU_DENORMALS_OPTIMIZATION: False
[ INFO ]     CPU_SPARSE_WEIGHTS_DECOMPRESSION_RATE: 1.0
[ INFO ]     DYNAMIC_QUANTIZATION_GROUP_SIZE: 32
[ INFO ]     ENABLE_CPU_PINNING: True
[ INFO ]     ENABLE_HYPER_THREADING: True
[ INFO ]     EXECUTION_DEVICES: ['CPU']
[ INFO ]     EXECUTION_MODE_HINT: ExecutionMode.PERFORMANCE
[ INFO ]     INFERENCE_NUM_THREADS: 24
[ INFO ]     INFERENCE_PRECISION_HINT: <Type: 'float32'>
[ INFO ]     KV_CACHE_PRECISION: <Type: 'float16'>
[ INFO ]     LOG_LEVEL: Level.NO
[ INFO ]     MODEL_DISTRIBUTION_POLICY: set()
[ INFO ]     NETWORK_NAME: Model0
[ INFO ]     NUM_STREAMS: 12
[ INFO ]     OPTIMAL_NUMBER_OF_INFER_REQUESTS: 12
[ INFO ]     PERFORMANCE_HINT: THROUGHPUT
[ INFO ]     PERFORMANCE_HINT_NUM_REQUESTS: 0
[ INFO ]     PERF_COUNT: NO
[ INFO ]     SCHEDULING_CORE_TYPE: SchedulingCoreType.ANY_CORE
[ INFO ]   MODEL_PRIORITY: Priority.MEDIUM
[ INFO ]   LOADED_FROM_CACHE: False
[ INFO ]   PERF_COUNT: False
[Step 9/11] Creating infer requests and preparing input tensors
[ WARNING ] No input files were given for input 'e1'!. This input will be filled with random values!
[ WARNING ] No input files were given for input 'rel'!. This input will be filled with random values!
[ INFO ] Fill input 'e1' with random values
[ INFO ] Fill input 'rel' with random values
[Step 10/11] Measuring performance (Start inference asynchronously, 12 inference requests, limits: 10000 ms duration)
[ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
[ INFO ] First inference took 1.66 ms
[Step 11/11] Dumping statistics report
[ INFO ] Execution Devices:['CPU']
[ INFO ] Count:            94596 iterations
[ INFO ] Duration:         10001.69 ms
[ INFO ] Latency:
[ INFO ]    Median:        1.07 ms
[ INFO ]    Average:       1.09 ms
[ INFO ]    Min:           0.79 ms
[ INFO ]    Max:           8.57 ms
[ INFO ] Throughput:   9458.00 FPS

Conclusions

In this notebook, we convert the trained PyTorch knowledge graph embeddings model to the OpenVINO format. We confirm that there are no accuracy differences post conversion. We also perform a sample evaluation on the knowledge graph. Then, we determine the platform specific speedup in runtime performance that can be obtained through OpenVINO graph optimizations. To learn more about the OpenVINO performance optimizations, refer to: https://docs.openvino.ai/2024/openvino-workflow/running-inference/optimize-inference.html

References

1. Convolutional 2D Knowledge Graph Embeddings, Tim Dettmers et al. (https://arxiv.org/abs/1707.01476)

2. Model implementation: TimDettmers/ConvE

The ConvE model implementation used in this notebook is licensed under the MIT License. The license is displayed below: MIT License

Copyright (c) 2017 Tim Dettmers

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.