MacOS arm64 wheels installed via pip result in ImportError

[JoelHBierman]

Issue description

Running the following code:

from numpy import gradient
import pennylane as qml
from pennylane import qchem
from pennylane import numpy as np
from functools import partial
from time import perf_counter

# Build the electronic Hamiltonian
symbols, coordinates = (['H', 'H'], np.array([0., 0., -0.66140414, 0., 0., 0.66140414]))
h, qubits = qml.qchem.molecular_hamiltonian(symbols, coordinates)

# Number of electrons
electrons = 2

# Define the HF state
ref_state = qchem.hf_state(electrons, qubits)

# Generate single and double excitations
singles, doubles = qchem.excitations(electrons, qubits)

# Map excitations to the wires the UCCSD circuit will act on
s_wires, d_wires = qchem.excitations_to_wires(singles, doubles)

# Define the device
dev = qml.device('lightning.qubit', wires=qubits)

# Define the UCCSD ansatz
ansatz = partial(qml.UCCSD, init_state=ref_state, s_wires=s_wires, d_wires=d_wires)

# Define the cost function
cost_fn = qml.ExpvalCost(ansatz, h, dev)

# Compute the expectation value of 'h' for given set of parameters 'params'
params = np.array(np.zeros(len(singles) + len(doubles)), requires_grad=True)
print(cost_fn(params))

opt = qml.GradientDescentOptimizer(stepsize=0.4)
max_iterations = 100
conv_tol = 1e-06


energy = [cost_fn(params)]


for n in range(max_iterations):
    start_time = perf_counter()
    theta, prev_energy = opt.step_and_cost(cost_fn, params)

    energy.append(cost_fn(theta))

    conv = np.abs(energy[-1] - prev_energy)

    #if n % 2 == 0:
    print(f"Step = {n},  Energy = {energy[-1]:.8f} Ha")

    if conv <= conv_tol:
        break

print("\n" f"Final value of the ground-state energy = {energy[-1]:.8f} Ha")

Results in the following error traceback:

Traceback (most recent call last):
  File "/Users/joelbierman/Desktop/pennylane/tutorial_vqe.py", line 26, in <module>
    dev = qml.device('lightning.qubit', wires=qubits)
  File "/Users/joelbierman/miniforge3/envs/pennylane-test/lib/python3.9/site-packages/pennylane/__init__.py", line 297, in device
    plugin_device_class = plugin_devices[name].load()
  File "/Users/joelbierman/miniforge3/envs/pennylane-test/lib/python3.9/site-packages/pkg_resources/__init__.py", line 2472, in load
    return self.resolve()
  File "/Users/joelbierman/miniforge3/envs/pennylane-test/lib/python3.9/site-packages/pkg_resources/__init__.py", line 2478, in resolve
    module = __import__(self.module_name, fromlist=['__name__'], level=0)
  File "/Users/joelbierman/miniforge3/envs/pennylane-test/lib/python3.9/site-packages/pennylane_lightning/__init__.py", line 17, in <module>
    from .lightning_qubit import LightningQubit
  File "/Users/joelbierman/miniforge3/envs/pennylane-test/lib/python3.9/site-packages/pennylane_lightning/lightning_qubit.py", line 48, in <module>
    from .lightning_qubit_ops import (
ImportError: dlopen(/Users/joelbierman/miniforge3/envs/pennylane-test/lib/python3.9/site-packages/pennylane_lightning/lightning_qubit_ops.cpython-39-darwin.so, 2): no suitable image found.  Did find:
        /Users/joelbierman/miniforge3/envs/pennylane-test/lib/python3.9/site-packages/pennylane_lightning/lightning_qubit_ops.cpython-39-darwin.so: mach-o, but wrong architecture
        /Users/joelbierman/miniforge3/envs/pennylane-test/lib/python3.9/site-packages/pennylane_lightning/lightning_qubit_ops.cpython-39-darwin.so: mach-o, but wrong architecture*

Setting "default.qubit" instead of "lightning.qubit" for the device works fine. There seems to be an issue with the lightning builds.

Expected behavior:

No ImportError

• Actual behavior:

ImportError.

• Reproduces how often:

100%

• System information:

Name: PennyLane
Version: 0.23.1
Summary: PennyLane is a Python quantum machine learning library by Xanadu Inc.
Home-page: https://github.com/XanaduAI/pennylane
Author: None
Author-email: None
License: Apache License 2.0
Location: /Users/joelbierman/miniforge3/envs/pennylane-test/lib/python3.9/site-packages
Requires: autoray, numpy, scipy, semantic-version, pennylane-lightning, networkx, retworkx, appdirs, cachetools, toml, autograd
Required-by: PennyLane-Qchem, PennyLane-Lightning

Platform info:           macOS-11.6.2-arm64-arm-64bit
Python version:          3.9.6
Numpy version:           1.21.0
Scipy version:           1.7.0
Installed devices:
- default.gaussian (PennyLane-0.23.1)
- default.mixed (PennyLane-0.23.1)
- default.qubit (PennyLane-0.23.1)
- default.qubit.autograd (PennyLane-0.23.1)
- default.qubit.jax (PennyLane-0.23.1)
- default.qubit.tf (PennyLane-0.23.1)
- default.qubit.torch (PennyLane-0.23.1)
- lightning.qubit (PennyLane-Lightning-0.23.0)

Source code and tracebacks

See above.

Additional information

In the past when I have encountered this sort of error for other packages, it was because while the wheel names indicated that they were built for arm64, the actual wheel contents contained code compiled to x86. My guess is that there's something buggy with the build process for these published wheels.

[mlxd]

HI @JoelHBierman thanks for reporting this. We will check this out and report back shortly

[mlxd]

Hi again @JoelHBierman
We can reproduce the error locally. The issue seems to arise from the builder we are using for the wheels being unable to correctly output ARM64 targeted binaries, as we build using GH Actions. We are looking into a strategy to fix this, but as an interim we can suggest the following:

• Install the XCode command line tools and C++ compiler
• Install Lightning locally with:

git clone [email protected]:PennyLaneAI/pennylane-lightning.git
cd pennylane-lightning && git checkout v0.23.0
python -m pip install cmake ninja
python -m pip install -e .
cd ..

We can confirm when built natively, this will work, though we aim to have a fix for the built wheels shortly.

[mlxd]

As an update, we have a fix for this coming with PR #306

[mlxd]

The next release of PennyLane (v0.24) will arrive on Tuesday 21st June. The above mentioned fix will be available as of that release. I will close this issue now, but feel free to reopen if there are any updates you would like to share.