Hi, I'm trying to follow the instruction on how to compile tensornet_example.cu . https://github.com/NVIDIA/cuQuantum/tree/main/samples/cutensornet I think it is inconsistent because it says: export CUTENSORNET_ROOT=<path_to_custatevec_root>

but currently, custatevec is not part of CUTENSORNET anymore. It is a part of CUQUANTUM. I'm referring to this pair:

https://developer.download.nvidia.com/compute/cuquantum/redist/cuquantum/linux-x86_64/cuquantum-linux-x86_64-0.1.0.30-archive.tar.xz

https://developer.download.nvidia.com/compute/cutensor/redist/libcutensor/linux-x86_64/libcutensor-linux-x86_64-1.4.0.6-archive.tar.xz Can you please clarify how to configure the Makefile to work with these 2 libs and compile tensornet_example.cu ? Thanks Jan Balewski, NERSC

I'm unsure whether this is an issue on my side, or just a missing compatibility. **Problem: ** When installing custatevec on Ubuntu 20.04 (on WSL2), it fails on the last of the following commands:

$ wget https://developer.download.nvidia.com/compute/cuquantum/22.07.1/local_installers/cuquantum-local-repo-ubuntu2004-22.07.1_1.0-1_amd64.deb
$ sudo dpkg -i cuquantum-local-repo-ubuntu2004-22.07.1_1.0-1_amd64.deb
$ sudo cp /var/cuquantum-local-repo-ubuntu2004-22.07.1/cuquantum-*-keyring.gpg /usr/share/keyrings/
$ sudo apt-get update
$ sudo apt-get -y install cuquantum cuquantum-dev cuquantum-doc

with the following cuBLAS error:

$ sudo apt-get -y install cuquantum cuquantum-dev cuquantum-doc
Reading package lists... Done
Building dependency tree
Reading state information... Done
Some packages could not be installed. This may mean that you have
requested an impossible situation or if you are using the unstable
distribution that some required packages have not yet been created
or been moved out of Incoming.
The following information may help to resolve the situation:

The following packages have unmet dependencies:
 cuquantum : Depends: libcublaslt.so.11 but it is not installable or
                      libcublas-11-0 but it is not installable or
                      libcublas-11-1 but it is not installable or
                      libcublas-11-2 but it is not installable or
                      libcublas-11-3 but it is not installable or
                      libcublas-11-4 but it is not installable or
                      libcublas-11-5 but it is not installable or
                      libcublas-11-6 but it is not installable
             Depends: libcutensor1 but it is not installable
E: Unable to correct problems, you have held broken packages.

Any idea what may be causing this? I looked into cublas directly but I thought it may be too invasive for such a simple issue. I already have custatevec working on an ubuntu GCP instance so I'm wondering if this is due to an incompatibility with Window's Subsystem for Linux 2.

I am trying to compile the examples in the /samples/cutensornet directory and the compilation is successful, however it fails at runtime with the error in the subject.

According to https://github.com/NVIDIA/cuQuantum/blob/main/samples/cutensornet/README.md and https://docs.nvidia.com/cuda/cuquantum/cutensornet/index.html as of today, I must have

• cuTensorNet v1.1.0

• CUDA Toolkit 11.x and compatible driver r450+ (see CUDA Driver Release Notes).

• cuTENSOR v1.5.0 (or above)

I actually have

• cuTensorNet v1.1.0
• CUDA Version: 11.7 with Driver Version: 515.48.07
• cuTENSOR v1.6.0.3 (from libcutensor-linux-x86_64-1.6.0.3-archive)

So I meet all the requirements but still get that error. The only thing I can think is that libcutensor libraries are split in 3 directories for 10.2, 11, and 11.0 so perhaps CUDA v11.7 is too new? Does anybody has any insight?

Environment:

• Ubuntu 20.04 LTS
• Python 3.8

Symptom:

>>> import cuquantum
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/local/lib/python3.8/dist-packages/cuquantum/__init__.py", line 1, in <module>
    from cuquantum import custatevec
  File "/usr/local/lib/python3.8/dist-packages/cuquantum/custatevec/__init__.py", line 1, in <module>
    from cuquantum.custatevec.custatevec import *
ImportError: libcublas.so.11: cannot open shared object file: No such file or directory

I guess that pyculib should be converted to pip, too.

Hi,

I am trying to build an image with cuquantum and the code samples installed. Here is what I have so far, compiled from the README here and in the documentation :

FROM nvcr.io/nvidia/pytorch:22.01-py3

# Get cuquantum
ENV CUQUANTUM_ROOT=/opt/cuquantum0.1.0.30
ARG TARFILE=cuquantum-linux-x86_64-0.1.0.30-archive.tar.xz
RUN wget -O /tmp/${TARFILE} \
    https://developer.download.nvidia.com/compute/cuquantum/redist/linux-x86_64/${TARFILE} && \
    mkdir -p ${CUQUANTUM_ROOT} && \
    tar xvf /tmp/${TARFILE} -C ${CUQUANTUM_ROOT} --strip-components=1 && \
    #lib64/ is missing, symlink it to lib/
    ln -s ${CUQUANTUM_ROOT}/lib ${CUQUANTUM_ROOT}/lib64 && \
    rm /tmp/${TARFILE}
ENV LD_LIBRARY_PATH=${CUQUANTUM_ROOT}/lib:${LD_LIBRARY_PATH}

# Install cuquantum python bindings, remove previous cupy version
# TODO verify
RUN pip uninstall -y cupy-cuda115 && \
    conda install -c conda-forge cuquantum-python


ENV CUSTATEVEC_ROOT=${CUQUANTUM_ROOT}
ENV CUTENSORNET_ROOT=${CUQUANTUM_ROOT}
ENV PATH=/usr/local/cuda/bin/:${PATH}

# Get samples repo
ARG TARFILE=v0.1.0.0.tar.gz
RUN wget -O /tmp/${TARFILE} https://github.com/NVIDIA/cuQuantum/archive/refs/tags/${TARFILE} && \
    mkdir -p ${CUSTATEVEC_ROOT}/code_samples && \
    tar xvf /tmp/${TARFILE} -C ${CUSTATEVEC_ROOT}/code_samples --strip-components=1 && \
    rm /tmp/${TARFILE}

The image has cupy-cuda115, the conda install of cuquantum-python installs another version of cupy as a dependency so I uninstall the old one (it will complain during import if both are available). make all builds successfully (though the lib64->lib symlink is needed for it to work), but I am unable to run the python samples without hitting import errors.

I am running on an intel-chip mac, just trying to clear up the import errors before we run this on a cloud instance with an nvidia GPU mounted in.

Before posting any stacktraces, am I on the right track here? Maybe I should use a different base image that has an equivalent version of cupy. I'm also not sure if the cuda version is incompatible.

I am happy to submit a PR with the working Dockerfile once we figure this all out :)

Under the following setup.

Hardware: INSPUR NF5488M5 (V100 version) environments: Ubuntu 22.04.1 LTS Python 3.9.15 Nvidia driver: 525.60.13 cuda_12.0.r12.0 mpich-4.0.3 mpi4py 3.1.4 cuquantum 22.11.0

When I run /cuQuantum/python/samples/cutensornet/tensornet_example_mpi.py , I got. It works .

*** Printing is done only from the root process to prevent jumbled messages ***
The number of processes is 1
cuTensorNet-vers: 20000
===== root process device info ======
GPU-name: Tesla V100-SXM3-32GB
GPU-clock: 1597000
GPU-memoryClock: 958000
GPU-nSM: 80
GPU-major: 7
GPU-minor: 0
========================
Include headers and define data types.
Define network, modes, and extents.
Initialize the cuTensorNet library and create a network descriptor.
Process 0 has the path with the lowest FLOP count 4299161600.0.
Find an optimized contraction path with cuTensorNet optimizer.
Allocate workspace.
Create a contraction plan for cuTENSOR and optionally auto-tune it.
Contract the network, each slice uses the same contraction plan.
Check cuTensorNet result against that of cupy.einsum().
num_slices: 1
0.8309440016746521 ms / slice
5173.82831013358 GFLOPS/s
Free resource and exit.

But when I run /cuQuantum/python/samples/cutensornet/tensornet_example_mpi_auto.py I got the following error.

*** Printing is done only from the root process to prevent jumbled messages ***
The number of processes is 1
cuTensorNet-vers: 20000
===== root process device info ======
GPU-name: Tesla V100-SXM3-32GB
GPU-clock: 1597000
GPU-memoryClock: 958000
GPU-nSM: 80
GPU-major: 7
GPU-minor: 0
========================
Include headers and define data types.
Define network, modes, and extents.
Initialize the cuTensorNet library and create a network descriptor.
python: distributed_interfaces/cutensornet_distributed_interface_mpi.c:44: unpackMpiCommunicator: Assertion `sizeof(MPI_Comm) == comm->commSize' failed.
[suneo:06467] *** Process received signal ***
[suneo:06467] Signal: Aborted (6)
[suneo:06467] Signal code:  (-6)
[suneo:06467] [ 0] /lib/x86_64-linux-gnu/libc.so.6(+0x42520)[0x7f55bbd22520]
[suneo:06467] [ 1] /lib/x86_64-linux-gnu/libc.so.6(pthread_kill+0x12c)[0x7f55bbd76a7c]
[suneo:06467] [ 2] /lib/x86_64-linux-gnu/libc.so.6(raise+0x16)[0x7f55bbd22476]
[suneo:06467] [ 3] /lib/x86_64-linux-gnu/libc.so.6(abort+0xd3)[0x7f55bbd087f3]
[suneo:06467] [ 4] /lib/x86_64-linux-gnu/libc.so.6(+0x2871b)[0x7f55bbd0871b]
[suneo:06467] [ 5] /lib/x86_64-linux-gnu/libc.so.6(+0x39e96)[0x7f55bbd19e96]
[suneo:06467] [ 6] /home/tsujino/anaconda3/envs/cu/lib/libcutensornet_distributed_interface_mpi.so(+0x123c)[0x7f553de1223c]
[suneo:06467] [ 7] /home/tsujino/anaconda3/envs/cu/lib/libcutensornet_distributed_interface_mpi.so(cutensornetMpiCommRank+0x23)[0x7f553de122ae]
[suneo:06467] [ 8] /home/tsujino/anaconda3/envs/cu/lib/python3.9/site-packages/cuquantum/cutensornet/../../../../libcutensornet.so.2(+0x105462)[0x7f554c705462]
[suneo:06467] [ 9] /home/tsujino/anaconda3/envs/cu/lib/python3.9/site-packages/cuquantum/cutensornet/../../../../libcutensornet.so.2(+0x1056bd)[0x7f554c7056bd]
[suneo:06467] [10] /home/tsujino/anaconda3/envs/cu/lib/python3.9/site-packages/cuquantum/cutensornet/../../../../libcutensornet.so.2(+0x1058ed)[0x7f554c7058ed]
[suneo:06467] [11] /home/tsujino/anaconda3/envs/cu/lib/python3.9/site-packages/cuquantum/cutensornet/../../../../libcutensornet.so.2(cutensornetDistributedResetConfiguration+0xd3)[0x7f554c703633]
[suneo:06467] [12] /home/tsujino/anaconda3/envs/cu/lib/python3.9/site-packages/cuquantum/cutensornet/cutensornet.cpython-39-x86_64-linux-gnu.so(+0x26063)[0x7f554e65c063]
[suneo:06467] [13] python[0x507457]
[suneo:06467] [14] python(_PyObject_MakeTpCall+0x2ec)[0x4f068c]
[suneo:06467] [15] python(_PyEval_EvalFrameDefault+0x525b)[0x4ec9fb]
[suneo:06467] [16] python[0x4e689a]
[suneo:06467] [17] python(_PyEval_EvalCodeWithName+0x47)[0x4e6527]
[suneo:06467] [18] python(PyEval_EvalCodeEx+0x39)[0x4e64d9]
[suneo:06467] [19] python(PyEval_EvalCode+0x1b)[0x59329b]
[suneo:06467] [20] python[0x5c0ad7]
[suneo:06467] [21] python[0x5bcb00]
[suneo:06467] [22] python[0x4566f4]
[suneo:06467] [23] python(PyRun_SimpleFileExFlags+0x1a2)[0x5b67e2]
[suneo:06467] [24] python(Py_RunMain+0x37e)[0x5b3d5e]
[suneo:06467] [25] python(Py_BytesMain+0x39)[0x587349]
[suneo:06467] [26] /lib/x86_64-linux-gnu/libc.so.6(+0x29d90)[0x7f55bbd09d90]
[suneo:06467] [27] /lib/x86_64-linux-gnu/libc.so.6(__libc_start_main+0x80)[0x7f55bbd09e40]
[suneo:06467] [28] python[0x5871fe]
[suneo:06467] *** End of error message ***
Aborted (core dumped)

I have tried other smaples and those works.

Following the well designed documentation I was able to successfully get this to run. I am using terminal to write my Quantum experiments.I want to know how to run a jupyter notebook inside docker container. After installing jupyter in docker and running it gives a link that doesn't open in a browser of my linux system.I then installed firefox inside cuQuantum docker but it doesn't launch the browser. Gives a display not found error.How can I run my experiments in jupyter ?

Workaround: please install typing_extensions via pip or conda:

pip install typing_extensions

or

conda install -c conda-forge typing_extensions

Symptom:

leof:~$ python
Python 3.9.10 | packaged by conda-forge | (main, Feb  1 2022, 21:24:37) 
[GCC 9.4.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import cuquantum
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/leof/miniforge3/envs/ppqwqq/lib/python3.9/site-packages/cuquantum/__init__.py", line 2, in <module>
    from cuquantum import cutensornet
  File "/home/leof/miniforge3/envs/ppqwqq/lib/python3.9/site-packages/cuquantum/cutensornet/__init__.py", line 2, in <module>
    from cuquantum.cutensornet.configuration import *
  File "/home/leof/miniforge3/envs/ppqwqq/lib/python3.9/site-packages/cuquantum/cutensornet/configuration.py", line 19, in <module>
    from .memory import BaseCUDAMemoryManager
  File "/home/leof/miniforge3/envs/ppqwqq/lib/python3.9/site-packages/cuquantum/cutensornet/memory.py", line 7, in <module>
    from typing_extensions import Protocol, runtime_checkable
ModuleNotFoundError: No module named 'typing_extensions'

I was trying to reproduce the statement in https://developer.nvidia.com/blog/nvidia-announces-cuquantum-beta-availability-record-quantum-benchmark-and-quantum-container/, in particular

Quantum Fourier Transform – accelerated from 29 mins down to 19 secs

I'm running the following minimal reproducer on Python 3.8, cuQuantum 22.11, NVIDIA A100 40 GB (on a GCP instance)

import time

import cirq
import qsimcirq
import cupy
from cuquantum import contract
from cuquantum import CircuitToEinsum

simulator = qsimcirq.QSimSimulator()

# See https://quantumai.google/cirq/experiments/textbook_algorithms
def make_qft(qubits):
    """Generator for the QFT on a list of qubits."""
    qreg = list(qubits)
    while len(qreg) > 0:
        q_head = qreg.pop(0)
        yield cirq.H(q_head)
        for i, qubit in enumerate(qreg):
            yield (cirq.CZ ** (1 / 2 ** (i + 1)))(qubit, q_head)


def simulate_and_measure(nqubits):
    qubits = cirq.LineQubit.range(nqubits)
    qft = cirq.Circuit(make_qft(qubits))

    myconverter = CircuitToEinsum(qft, backend=cupy)

    tic = time.time()
    simulator.simulate(qft)
    elapsed_qsim = time.time() - tic
    out = {"qsim": elapsed_qsim}

    # CUDA expectation
    pauli_string = {qubits[0]: 'Z'}
    expression, operands = myconverter.expectation(pauli_string, lightcone=True)
    tic = time.time()
    contract(expression, *operands)
    elapsed = time.time() - tic
    out["cu_expectation"] = elapsed

    # CUDA Batched amplitudes
    # Fix everything but last qubit
    fixed_states = "0" * (nqubits - 1)
    fixed_index = tuple(map(int, fixed_states))
    num_fixed = len(fixed_states)
    fixed = dict(zip(myconverter.qubits[:num_fixed], fixed_states))
    expression, operands = myconverter.batched_amplitudes(fixed)
    tic = time.time()
    contract(expression, *operands)
    elapsed = time.time() - tic
    out["cu_batched"] = elapsed

    return out

for i in [10, 15, 20, 25, 30]:
    print(i, simulate_and_measure(i))

Output (the numbers are elapsed in seconds; 10, 15, ... are number of qubits for QFT):

10 {'qsim': 0.9677999019622803, 'cu_expectation': 0.29337143898010254, 'cu_batched': 0.07590365409851074}
15 {'qsim': 0.023270368576049805, 'cu_expectation': 0.019628524780273438, 'cu_batched': 0.3687710762023926}
20 {'qsim': 0.03504538536071777, 'cu_expectation': 0.023822784423828125, 'cu_batched': 0.9347813129425049}
25 {'qsim': 0.14235782623291016, 'cu_expectation': 0.02486586570739746, 'cu_batched': 2.39030122756958}
30 {'qsim': 3.4044816493988037, 'cu_expectation': 0.028923749923706055, 'cu_batched': 4.6819908618927}
35 {'cu_expectation': 1.0615959167480469, 'cu_batched': 10.964831829071045}
40 {'cu_expectation': 0.03381609916687012, 'cu_batched': 82.43729209899902}

I wasn't able to go to 35 qubits for qsim, because I got CUDA OOM for qsim. The much reduced memory usage alone is sufficient to prefer cuQuantum for this use case.

But, I was hoping that batched_amplitudes is going to be faster than a full statevector simulation, because some qubits are fixed. But it doesn't seem to be the case. I have also tried reduced_density_matrix (not shown, so that the code snippet is short). The only one that is consistently fast is expectation. I wonder if I did it wrongly?

`TypeError Traceback (most recent call last) Cell In [9], line 2 1 ngpus = 1 ----> 2 qsim_options = qsimcirq.QSimOptions( 3 max_fused_gate_size = 2 4 , cpu_threads = 1 5 , gpu_mode = ngpus 6 , use_sampler = True 7 , disable_gpu = False 8 ) 9 qsim_simulator = qsimcirq.QSimSimulator(qsim_options)

TypeError: init() got an unexpected keyword argument 'use_sampler'`

It's working fine when these 2 options are removed. I am using windows wsl to run it locally using conda installation. The documentation clearly has these options. Link to QSimOptions

Hi,

I am using cuQuantum Appliance 22.07-Cirq to experiment with cirq + custatevec simulator. I am able to run up to 32 qubit simulations with single NVidia A100 40GB gpu as expected.

However I am having trouble getting it to run on multiple GPUs. I am using QSimOptions.gpu_mode = 2 to achieve this as explained in cuquantum docs but I only see one gpu being used thourgh nvidia-smi command, and I run out of memory for 33 qubits.

here is a minimal reproducer:

import cirq
import qsimcirq

def load_test(num_gpus = 1, depth = 4, num_qubits = 30):
	circuit = cirq.testing.random_circuit(
		qubits = num_qubits,
		n_moments = depth,
		op_density = 1.0,
		random_state = 1)
	num_gates = len(list(circuit.all_operations()))
	options = {"gpu_mode": num_gpus, "n_subsvs": num_gpus}
	qsim_simulator = qsimcirq.QSimSimulator(options)
	result = qsim_simulator.simulate(circuit)
	print (f"DONE with qubits: {num_qubits} \t gates: {num_gates} \t depth: {depth} \t")

load_test(num_gpus = 2, depth = 2, num_qubits = 30) # uses  ~8GB memory on 1 gpu
load_test(num_gpus = 2, depth = 2, num_qubits = 32) # uses ~32GB memory on 1 gpu
load_test(num_gpus = 2, depth = 2, num_qubits = 33) # CUDA error: out of memory vector_mgpu.h 116

I have 2 NVidia A100 gpu's on my machine and here is the output of nvidia-smi for 32 qubit case:

+-----------------------------------------------------------------------------+
| NVIDIA-SMI 510.47.03    Driver Version: 510.47.03    CUDA Version: 11.6     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  NVIDIA A100-SXM...  Off  | 00000000:00:04.0 Off |                    0 |
| N/A   35C    P0    82W / 400W |  33561MiB / 40960MiB |     23%      Default |
|                               |                      |             Disabled |
+-------------------------------+----------------------+----------------------+
|   1  NVIDIA A100-SXM...  Off  | 00000000:00:05.0 Off |                    0 |
| N/A   43C    P0    75W / 400W |      2MiB / 40960MiB |      0%      Default |
|                               |                      |             Disabled |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|    0   N/A  N/A     26614      C   python                          33559MiB |
+-----------------------------------------------------------------------------+

Am I missing something?