qprof 1.2.0

From Gitlab

git clone https://gitlab.com/qcomputing/qprof/qprof.git
pip install qprof/

From PyPi

qprof is now available on PyPi! To download and install the last version, just type

pip install qprof

Plugins for library support are not installed by default in order to avoid pulling silently huge dependencies like qiskit in your project. In order to install the plugins you can either do it afterwards with pip:

pip install qprof_qiskit  # replace "qiskit" by your library

or use the appropriate target when installing qprof:

pip install qprof[qiskit]
pip install qprof[myqlm]
pip install qprof[all_plugin]

For developers

Developers might want to install qprof as an editable project. To do so you need to clone the git repository and install the library in editable mode with the -e option:

git clone https://gitlab.com/qcomputing/qprof/qprof.git
pip install -e qprof/

Note that the dependencies will not be installed in editable mode. If you want all the qprof stack in editable mode, you need to clone and install everything “by hand”:

git clone https://gitlab.com/qcomputing/qprof/qprof.git
git clone https://gitlab.com/qcomputing/qprof/qprof_interfaces.git
# Plugins?
# git clone https://gitlab.com/qcomputing/qprof/qprof_qiskit.git
pip install -e qprof_interfaces/
pip install -e qprof/
# Plugins?
# pip install -e qprof_qiskit/

Plugin organisation

The qprof library is organised as follow:

1. A main qprof library containing all the code related to computing routine execution time, call graph, exporting, …

2. A qprof_interfaces plugin providing interfaces for the data structures used by qprof to communicate with the plugins.

3. Several qprof_XXX libraries that are used to adapt a library XXX to qprof by implementing the interfaces of qprof_interfaces.

Plugins are automatically discovered the first time qprof.frameworks is imported and are arranged in a dictionary-like data-structure with the following structure:

frameworks = {
    "interfaces": <module 'qprof_interfaces' from '[path]'>, # always present
    "plugin1": <module 'qprof_plugin1' from '[path]'>,
    # ...
    "pluginN": <module 'qprof_pluginN' from '[path]'>,
}

Plugins are lazy-imported, meaning that the plugin module is imported at the first access to the dictionary key.

Profiling

The profiling is performed with the qprof.profile function.

The qprof.profile function needs a quantum routine implemented with one of the supported frameworks along with the “base” gate times provided as a dictionary.

Example of profiling:

# Import the qprof tools
from qprof import profile

# Import the framework tools to generate a quantum routine
from qiskit.aqua.algorithms import Grover
from qiskit.aqua.components.oracles import LogicalExpressionOracle


# Generate the routine to benchmark.
input_3sat = """
c example DIMACS-CNF 3-SAT
p cnf 3 5
-1 -2 -3 0
1 -2 3 0
1 2 -3 0
1 -2 -3 0
-1 2 3 0
"""

oracle = LogicalExpressionOracle(input_3sat)
grover = Grover(oracle)

# Hard-coded gate times retrieved by hand
gate_times = {"U1": 0, "U2": 89, "U3": 178, "CX": 930, "BARRIER": 0}

# Profile the resulting quantum routine and use the "gprof" exporter
qprof_out = profile(rout, gate_times, "gprof")

# Print to stdout the analysis report
print(qprof_out)

Requirements for the example

You should have the dot tool installed on your machine, along with the gprof2dot tool that can be installed with pip install gprof2dot.

Profile the code

Let save the code of the previous section in a file profile.py.

You can generate the following graph with the command

python3 profile.py | gprof2dot | dot -Tpng -o profiling_result.png