OpenVINO optimizations for Knowledge graphs

This tutorial is also available as a Jupyter notebook that can be cloned directly from GitHub. See the installation guide for instructions to run this tutorial locally on Windows, Linux or macOS. To run without installing anything, click the launch binder button.

Binder Github

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: https://github.com/TimDettmers/ConvE/tree/master/countries/countries_S1

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 numpy as np
import time
import json

import torch
from torch.nn import functional as F, Parameter
from torch.nn.init import xavier_normal_

from pathlib import Path

from sklearn.metrics import accuracy_score

from openvino.runtime import Core

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

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Using {device} device")

# Path to the trained model
modelpath = Path('models/conve.pt')

# Path to the file containing the entities and entity IDs
entdatapath = Path('../data/text/countries_S1/kg_training_entids.txt')

# Path to the file containing the relations and relation IDs
reldatapath = Path('../data/text/countries_S1/kg_training_relids.txt')

# Path to the test data file
testdatapath = Path('../data/json/countries_S1/e1rel_to_e2_ranking_test.json')

# 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, ONNX and OpenVINO IR models will be stored
fp32_onnx_path = Path(output_dir / (base_model_name + "_fp32")).with_suffix(".onnx")

Using cpu device

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: 0.5890130996704102 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 ONNX format for OpenVINO inference

To evaluate performance with OpenVINO, we can either convert the trained PyTorch model to an intermediate representation (IR) format or to an ONNX representation. This notebook uses the ONNX format. For more details on model optimization, refer to: https://docs.openvino.ai/latest/openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html

print('Converting the trained conve model to ONNX format')
torch.onnx.export(model, (torch.tensor(1), torch.tensor(1)),
                  fp32_onnx_path, input_names=['input.1', 'input.2'], verbose=False, opset_version=11)

Converting the trained conve model to ONNX 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.

ie = Core()
ir_net = ie.read_model(model=fp32_onnx_path)
compiled_model = ie.compile_model(model=ir_net)
input_layer_source = compiled_model.input('input.1')
input_layer_relation = compiled_model.input('input.2')
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: 1.2496610482533772 ms
Mean accuracy of the model on the test dataset: 0.10416666666666667

Determine the platform specific speedup obtained through OpenVINO graph optimizations

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

Speedup with OpenVINO optimizations: 0.47 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/latest/openvino_inference_engine_tools_benchmark_tool_README.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 "$fp32_onnx_path" -d CPU -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 ................................. 2022.3.0-9052-9752fafe8eb-releases/2022/3
[ INFO ]
[ INFO ] Device info:
[ INFO ] CPU
[ INFO ] Build ................................. 2022.3.0-9052-9752fafe8eb-releases/2022/3
[ INFO ]
[ INFO ]
[Step 3/11] Setting device configuration
[ WARNING ] Performance hint was not explicitly specified in command line. Device(CPU) performance hint will be set to THROUGHPUT.
[Step 4/11] Reading model files
[ INFO ] Loading model files
[ INFO ] Read model took 34.13 ms
[ INFO ] Original model I/O parameters:
[ INFO ] Model inputs:
[ INFO ]     input.1 (node: input.1) : i64 / [...] / []
[ INFO ]     input.2 (node: input.2) : i64 / [...] / []
[ INFO ] Model outputs:
[ INFO ]     51 (node: 51) : f32 / [...] / [1,271]
[Step 5/11] Resizing model to match image sizes and given batch
[ INFO ] Model batch size: 1
[ INFO ] Reshaping model: 'input.1': [1], 'input.2': [1]
[ INFO ] Reshape model took 1.61 ms
[Step 6/11] Configuring input of the model
[ INFO ] Model inputs:
[ INFO ]     input.1 (node: input.1) : i64 / [...] / [1]
[ INFO ]     input.2 (node: input.2) : i64 / [...] / [1]
[ INFO ] Model outputs:
[ INFO ]     51 (node: 51) : f32 / [...] / [1,271]
[Step 7/11] Loading the model to the device
[ INFO ] Compile model took 80.57 ms
[Step 8/11] Querying optimal runtime parameters
[ INFO ] Model:
[ INFO ]   NETWORK_NAME: torch_jit
[ INFO ]   OPTIMAL_NUMBER_OF_INFER_REQUESTS: 12
[ INFO ]   NUM_STREAMS: 12
[ INFO ]   AFFINITY: Affinity.CORE
[ INFO ]   INFERENCE_NUM_THREADS: 24
[ INFO ]   PERF_COUNT: False
[ INFO ]   INFERENCE_PRECISION_HINT: <Type: 'float32'>
[ INFO ]   PERFORMANCE_HINT: PerformanceMode.THROUGHPUT
[ INFO ]   PERFORMANCE_HINT_NUM_REQUESTS: 0
[Step 9/11] Creating infer requests and preparing input tensors
[ WARNING ] No input files were given for input 'input.1'!. This input will be filled with random values!
[ WARNING ] No input files were given for input 'input.2'!. This input will be filled with random values!
[ INFO ] Fill input 'input.1' with random values
[ INFO ] Fill input 'input.2' 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 4.55 ms
[Step 11/11] Dumping statistics report
[ INFO ] Count:            98424 iterations
[ INFO ] Duration:         10001.17 ms
[ INFO ] Latency:
[ INFO ]    Median:        1.07 ms
[ INFO ]    Average:       1.09 ms
[ INFO ]    Min:           0.62 ms
[ INFO ]    Max:           2.73 ms
[ INFO ] Throughput:   9841.25 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/latest/openvino_docs_optimization_guide_dldt_optimization_guide.html

References

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

  2. Model implementation: https://github.com/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

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