diff --git a/.github/workflows/nightly_ci.yaml b/.github/workflows/nightly_ci.yaml index b976b0b1..93c90665 100644 --- a/.github/workflows/nightly_ci.yaml +++ b/.github/workflows/nightly_ci.yaml @@ -17,8 +17,10 @@ jobs: include: - qutip-version: '4' qutip-branch: 'qutip-4.7.X' + qip-branch: 'qutip-qip-0.4.X' - qutip-version: '5' qutip-branch: 'master' + qip-branch: 'master' - name: Setup Conda uses: conda-incubator/setup-miniconda@v3 @@ -54,7 +56,7 @@ jobs: pip install -r requirements.txt pip install . cd .. - python -m pip install git+https://github.com/qutip/qutip-qip + python -m pip install git+https://github.com/qutip/qutip-qip@${{ matrix.qip-branch }} python -m pip install --no-deps git+https://github.com/qutip/qutip-jax python -m pip install --no-deps git+https://github.com/qutip/qutip-qoc @@ -81,6 +83,11 @@ jobs: find . -name '*.md' -exec jupytext --to notebook {} + find . -name '*.md' -delete + - name: Remove cuQuantum notebook from tests + # Without GPU, this notebook can't be ran. + if: ${{ matrix.qutip-version == '5' }} + run: rm notebooks/miscellaneous/cuQuantum_backend.ipynb + - name: Check PEP8 formatting run: | pip install nbqa flake8 @@ -93,6 +100,12 @@ jobs: find . -name '*.ipynb' -exec pytest --nbmake --overwrite --nbmake-timeout=900 {} + rm template.ipynb + - name: Copy cuQuantum notebook + # Without GPU, this notebook can't be ran. + # Copy the already executed notebook after the tests. + if: ${{ matrix.qutip-version == '5' }} + run: cp tutorials-v5/miscellaneous/cuQuantum_backend.ipynb notebooks/miscellaneous/ + - name: Create Notebook Artifact uses: actions/upload-artifact@v4 with: diff --git a/.github/workflows/notebook_ci.yaml b/.github/workflows/notebook_ci.yaml index 1981a437..2e2f735c 100644 --- a/.github/workflows/notebook_ci.yaml +++ b/.github/workflows/notebook_ci.yaml @@ -86,6 +86,11 @@ jobs: find . -name '*.md' -exec jupytext --to notebook {} + find . -name '*.md' -delete + - name: Remove cuQuantum notebook from tests + # Without GPU, this notebook can't be ran. + if: ${{ matrix.qutip-version == '5' }} + run: rm notebooks/miscellaneous/cuQuantum_backend.ipynb + - name: Check PEP8 formatting run: | pip install nbqa flake8 @@ -98,6 +103,12 @@ jobs: find . -name '*.ipynb' -exec pytest --nbmake --overwrite --nbmake-timeout=900 {} + rm template.ipynb + - name: Copy cuQuantum notebook + # Without GPU, this notebook can't be ran. + # Copy the already executed notebook after the tests. + if: ${{ matrix.qutip-version == '5' }} + run: cp tutorials-v5/miscellaneous/cuQuantum_backend.ipynb notebooks/miscellaneous/ + - name: Create Notebook Artifact uses: actions/upload-artifact@v4 with: diff --git a/tutorials-v5/miscellaneous/cuQuantum_backend.ipynb b/tutorials-v5/miscellaneous/cuQuantum_backend.ipynb new file mode 100644 index 00000000..1687d346 --- /dev/null +++ b/tutorials-v5/miscellaneous/cuQuantum_backend.ipynb @@ -0,0 +1,531 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "9fa249ef-1691-4158-8000-1599940adea5", + "metadata": {}, + "source": [ + "# Dynamics of a Spin Chain using cuQuantum backend" + ] + }, + { + "cell_type": "markdown", + "id": "377dc9e7-c780-40e3-85a5-fa17980bf85c", + "metadata": {}, + "source": [ + "### Introduction\n", + "\n", + "In this tutorial, we will simulate a spin chain (also called the Heisenberg model), which consists of $N$ $\\frac{1}{2}$-spins/qubits in a magnetic field. Each spin can interact with its direct neighbors. This model is often used for the study of magnetic systems.\n", + "\n", + "This roughly follows the tutorial **Master Equation Solver: Dynamics of a Spin Chain**, but focuses on different ways to perform the computation on a GPU. For further details on the physics of the simulated problem, please refer to the former notebook.\n", + "\n", + "### Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "1a2f034a-6263-4996-9138-006ec97719f9", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import qutip_cuquantum\n", + "from cuquantum.densitymat import WorkStream\n", + "from qutip import (about, basis, expand_operator, mcsolve, mesolve, qeye, # noqa F401\n", + " sesolve, sigmax, sigmay, sigmaz, tensor)\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "01f80754-956d-4bbd-a56c-a99f0a0682b8", + "metadata": {}, + "source": [ + "We first prepare a GPU context to run the simulation on. If only one GPU is available, the default workstream can be used. But for multi-GPU or MPI processes, more options are available. See NVIDIA's documentation for more details." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "7d09cbd3-4e57-4e1d-a639-90ed05e0b688", + "metadata": {}, + "outputs": [], + "source": [ + "ctx = WorkStream()" + ] + }, + { + "cell_type": "markdown", + "id": "2b959475-9f94-45c7-9d56-5e0f9ea5ca9e", + "metadata": {}, + "source": [ + "### Creating the system:" + ] + }, + { + "cell_type": "markdown", + "id": "4ca61b83-a174-47a4-ae04-eb2b189a1e7b", + "metadata": {}, + "source": [ + "**cuQuantum** does not use a sparse matrix representation, as QuTiP does. Instead, it keeps track of each elementary matrix constructing the operators and the mode of the Hilbert space they act on.\n", + "\n", + "With the QuTiP interface, this means we must choose to use the backend before compounding the modes into a larger operator (before any calls to `tensor` or the creation of superoperators). This can be done manually by using identity matrices in the cuQuantum format for tensor operations:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "60a88ffb-9c1c-457c-a23b-aad31bc59096", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "qutip_cuquantum.operator.CuOperator" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "N = 8\n", + "\n", + "eye = qeye(2, dtype=\"CuOperator\")\n", + "sz_list = []\n", + "for i in range(N):\n", + " op_list = [eye] * N\n", + " op_list[i] = sigmaz(dtype=\"Dia\")\n", + " sz_list.append(tensor(op_list))\n", + "\n", + "sz_list[0].dtype" + ] + }, + { + "cell_type": "markdown", + "id": "a62c10fb-b5c2-4290-b0fb-2c8e14cb4ffe", + "metadata": {}, + "source": [ + "While it is not needed to use the ``cuquantum.densitymat`` format directly to use it in QuTiP, we can take a quick look at it to see that our operator contains a single 2x2 matrix acting on a single mode:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "76e80c60-0c70-4aa2-9935-39338920a5ce", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "[[(0,)]]\n", + "[[]]\n", + "(2, 2)\n" + ] + } + ], + "source": [ + "oper = sz_list[0].data_as()\n", + "print(type(oper))\n", + "print(oper.modes)\n", + "print(oper.terms)\n", + "print(oper.terms[0][0].shape)" + ] + }, + { + "cell_type": "markdown", + "id": "bb804bc4-0a77-40c6-97df-d14fe50eb804", + "metadata": {}, + "source": [ + "There is also a context that, when activated, automatically creates operators in the cuQuantum format:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "ab108927-dfc9-43bc-be22-cd19a92159cf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "qutip_cuquantum.operator.CuOperator" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def create_spin_chain_Hamiltonian(N: int, h: float, J: float):\n", + " sx_list = [expand_operator(sigmax(), [2] * N, i) for i in range(N)]\n", + " sy_list = [expand_operator(sigmay(), [2] * N, i) for i in range(N)]\n", + " sz_list = [expand_operator(sigmaz(), [2] * N, i) for i in range(N)]\n", + "\n", + " H = -0.5 * h * sum(sz_list)\n", + " for n in range(N - 1):\n", + " H += -0.5 * J * sx_list[n] * sx_list[n + 1]\n", + " H += -0.5 * J * sy_list[n] * sy_list[n + 1]\n", + " H += -0.5 * J * sz_list[n] * sz_list[n + 1]\n", + "\n", + " return H\n", + "\n", + "\n", + "h = 0.2 * np.pi\n", + "J = 0.2 * np.pi\n", + "\n", + "with qutip_cuquantum.CuQuantumBackend(ctx):\n", + " H = create_spin_chain_Hamiltonian(N, h, J)\n", + "\n", + "H.dtype" + ] + }, + { + "cell_type": "markdown", + "id": "c9475ab1-2d1b-48aa-8c01-98619bbf2a57", + "metadata": {}, + "source": [ + "This context changes how QuTiP creates `Qobj` from functions (`qeye`, `sigmax`, `destroy`, `basis`, etc.) and alters solver options. Inside this context, manually setting the `dtype` as ``CuOperator`` is not needed, but using other formats can still be useful. For example, CSR matrices are converted to dense arrays (``cuquantum.densitymat`` supports diagonal and dense elementary operators), so manually choosing diagonal matrix it still usefull." + ] + }, + { + "cell_type": "markdown", + "id": "281d0206-f0b3-4de2-9ee5-75b2ff451aff", + "metadata": {}, + "source": [ + "These operators can be used normally for most QuTiP operations, but this often means that they are converted to a dense array and then used as normal operators. They can be used in QuTiP's main solvers to accelerate simulation by leveraging the GPU. This is done by calling the solver and using the result inside the context window:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "2786496c-97a4-40a8-bb52-2a61fd279888", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Dynamics of spin chain')" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "with qutip_cuquantum.CuQuantumBackend(ctx):\n", + " psi0 = basis([2] * N, [1] + [0] * (N - 1))\n", + " times = np.linspace(0, 100, 200)\n", + " result = sesolve(H, psi0, times, e_ops=sz_list)\n", + "\n", + " exp_sz = np.array([exp.get() for exp in result.expect])[..., 0]\n", + "\n", + "plt.plot(times, exp_sz[0, :], label=r\"$\\langle \\sigma_z^{0} \\rangle$\")\n", + "plt.plot(times, exp_sz[-1, :], label=r\"$\\langle \\sigma_z^{-1} \\rangle$\")\n", + "plt.legend(loc=\"lower right\")\n", + "plt.xlabel(\"Time\"), plt.ylabel(r\"$\\langle \\sigma_z \\rangle$\")\n", + "plt.title(\"Dynamics of spin chain\")" + ] + }, + { + "cell_type": "markdown", + "id": "64dab6ab-3870-4491-8a9e-dc3858f459b9", + "metadata": {}, + "source": [ + "As shown, the resulting expectation values are not numpy arrays, but CuPy arrays. The method ``get`` is required to transfer them from the GPU to the CPU. \n", + "\n", + "Outside the context, the expectation values can be obtained from the result with `e_data` as a list of single GPU values:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "79bb6a9d-9183-4d9b-807a-2e0f75f0565e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "print(type(result.e_data[0][0]))\n", + "exp_sz = np.array([[value.get() for value in result.e_data[i]] for i in result.e_data])[\n", + " ..., 0\n", + "] # remove the extra dimension from each value being a 1 element 1D array." + ] + }, + { + "cell_type": "markdown", + "id": "51458f06-01e0-44dc-bb80-c12268706381", + "metadata": {}, + "source": [ + "### Dephasing and trajectory solver\n", + "\n", + "Let's add the dissipation process and use the Monte-Carlo solver to simulate it." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "b33d479d-2724-4a6c-bfa2-0dc312e9630e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10.0%. Run time: 0.00s. Est. time left: 00:00:00:00\n", + "20.0%. Run time: 19.91s. Est. time left: 00:00:01:19\n", + "30.0%. Run time: 39.19s. Est. time left: 00:00:01:31\n", + "40.0%. Run time: 59.57s. Est. time left: 00:00:01:29\n", + "50.0%. Run time: 79.33s. Est. time left: 00:00:01:19\n", + "60.0%. Run time: 98.91s. Est. time left: 00:00:01:05\n", + "70.0%. Run time: 119.47s. Est. time left: 00:00:00:51\n", + "80.0%. Run time: 139.08s. Est. time left: 00:00:00:34\n", + "90.0%. Run time: 158.88s. Est. time left: 00:00:00:17\n", + "100.0%. Run time: 180.28s. Est. time left: 00:00:00:00\n", + "Total run time: 202.45s\n" + ] + } + ], + "source": [ + "gamma = 0.02\n", + "\n", + "# sz_list operators are already in the cuQuantum format,\n", + "# so further modification can be done outside the context\n", + "c_ops = [np.sqrt(gamma) * sz_list[i] for i in range(N)]\n", + "\n", + "# However the solver must be called inside the context\n", + "with qutip_cuquantum.CuQuantumBackend(ctx):\n", + " psi0 = basis([2] * N, [1] + [0] * (N - 1))\n", + " times = np.linspace(0, 100, 200)\n", + " result = mcsolve(\n", + " H, psi0, times, c_ops, e_ops=sz_list, options={\"map\": \"serial\"}, ntraj=10\n", + " )\n", + "\n", + " exp_sz_mc = np.array([exp.get() for exp in result.average_expect])" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "85c7c7dc-ec7a-4734-92d2-2f3dc3fe27d8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Dynamics of spin chain')" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(times, exp_sz[0, :], label=r\"$\\langle \\sigma_z^{0} \\rangle$\")\n", + "plt.plot(times, exp_sz[-1, :], label=r\"$\\langle \\sigma_z^{-1} \\rangle$\")\n", + "plt.legend(loc=\"lower right\")\n", + "plt.xlabel(\"Time\"), plt.ylabel(r\"$\\langle \\sigma_z \\rangle$\")\n", + "plt.title(\"Dynamics of spin chain\")" + ] + }, + { + "cell_type": "markdown", + "id": "6c0d6c5d-2afc-47b7-ae07-36ada0e4482a", + "metadata": {}, + "source": [ + "## Tricks and best practice\n", + "\n", + "QuTiP and ``cuQuantum.densitymat`` have quite different approaches and do not always play well together. This results in multiple limitations and requires care to be taken.\n", + "\n", + "``cuQuantum.densitymat`` is there to optimize the ``Operator @ State`` operation for large compound systems. QuTiP uses this operation to optimize solvers, but little effort has been made to support other features with this backend. So using it blindly will often not give any benefits.\n", + "\n", + "It is best to create the elementary operators first in pure QuTiP mode if any of the following apply:\n", + "* Rectangular operators are used for intermediate states. For example, creating an operator using `ket @ bra`. They are not supported in cuQuantum operators.\n", + "* **Dense** transformations (`expm`, `sqrtm`, `sinm`, etc.) are used. When applied to a cuQuantum operator, the internal structure will be lost." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "da2eee91-ca36-4fe5-810a-c21ca0fe47ed", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "converting to Dense\n", + "operator shape pre computed (2, 2)\n", + "operator shape post computed (8, 8)\n" + ] + } + ], + "source": [ + "# Do\n", + "H1 = (0.2 * sigmax() + 0.5 * sigmay()).expm() & qeye([2, 2], dtype=\"CuOperator\")\n", + "\n", + "# Don't\n", + "with qutip_cuquantum.CuQuantumBackend(ctx):\n", + " sx = sigmax() & qeye(2) & qeye(2)\n", + " sy = sigmay() & qeye(2) & qeye(2)\n", + " H2 = (0.2 * sx + 0.5 * sy).expm(dtype=\"CuOperator\")\n", + "\n", + "print(\"operator shape pre computed\", H1.data_as().terms[0][0].shape)\n", + "print(\"operator shape post computed\", H2.data_as().terms[0][0].shape)" + ] + }, + { + "cell_type": "markdown", + "id": "a7bac92d-bb27-4e49-8b9b-332ee6b2ef95", + "metadata": {}, + "source": [ + "## Solver Methods and Optimization\n", + "\n", + "Specialized integrator methods are needed for the GPU backend: ``CuTsit5``, ``CuVern7`` (new default set in the context), and ``CuVern9``. In most cases, the default is good enough, but ``CuTsit5`` uses fewer derivatives, and therefore less GPU memory. Using it can be needed for larger simulations. It is also often the fastest with normal tolerance. ``CuVern9`` can be good with very small error tolerance, but uses a lot more memory. ``CuVern7`` is the in-between: never the best, never the worst.\n", + "\n", + "Setting ``options={\"interpolate\": False}`` can save close to 40% memory for the Verner methods and is recommended for large simulations." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "3d3b469d-1dd8-47ea-84af-f9926f6a8861", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 15 s, sys: 47.9 s, total: 1min 2s\n", + "Wall time: 1min 2s\n", + "CPU times: user 25.2 s, sys: 1min 25s, total: 1min 50s\n", + "Wall time: 1min 49s\n", + "CPU times: user 33.6 s, sys: 1min 52s, total: 2min 25s\n", + "Wall time: 2min 24s\n" + ] + } + ], + "source": [ + "with qutip_cuquantum.CuQuantumBackend(ctx):\n", + " psi0 = basis([2] * N, [1] + [0] * (N - 1))\n", + " times = np.linspace(0, 100, 200)\n", + " %time result = mesolve(H, psi0, times, c_ops, e_ops=sz_list, options={\"method\": \"CuTsit5\"})\n", + " %time result = mesolve(H, psi0, times, c_ops, e_ops=sz_list, options={\"method\": \"CuVern7\"})\n", + " %time result = mesolve(H, psi0, times, c_ops, e_ops=sz_list, options={\"method\": \"CuVern9\"})" + ] + }, + { + "cell_type": "markdown", + "id": "1175b281-3420-443f-9d78-193359d3af5d", + "metadata": {}, + "source": [ + "#### About performance\n", + "This backend can allow to use powerful hardware and to scale simulation to multiple GPU. It is however not great with small simulations. It has a large overhead and will only perform well for large simulations that use a significant fraction of the GPU memory. Also, a CUDA-aware MPI is needed to be efficient on multiple nodes." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "d21b6057-6054-488f-b792-3efcc8c9cb3d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "QuTiP: Quantum Toolbox in Python\n", + "================================\n", + "Copyright (c) QuTiP team 2011 and later.\n", + "Current admin team: Alexander Pitchford, Nathan Shammah, Shahnawaz Ahmed, Neill Lambert, Eric Giguère, Boxi Li, Simon Cross, Asier Galicia, Paul Menczel, and Patrick Hopf.\n", + "Board members: Daniel Burgarth, Robert Johansson, Anton F. Kockum, Franco Nori and Will Zeng.\n", + "Original developers: R. J. Johansson & P. D. Nation.\n", + "Previous lead developers: Chris Granade & A. Grimsmo.\n", + "Currently developed through wide collaboration. See https://github.com/qutip for details.\n", + "\n", + "QuTiP Version: 5.3.0.dev0+f15c8f2\n", + "Numpy Version: 2.3.4\n", + "Scipy Version: 1.16.2\n", + "Cython Version: 3.1.6\n", + "Matplotlib Version: 3.10.7\n", + "Python Version: 3.13.9\n", + "Number of CPUs: 12\n", + "BLAS Info: scipy-openblas\n", + "INTEL MKL Ext: None\n", + "Platform Info: Linux (x86_64)\n", + "Installation path: \n", + "\n", + "Installed QuTiP family packages\n", + "-------------------------------\n", + "\n", + "qutip-cuquantum: 0.2.0.dev5+dd5394a\n", + "\n", + "================================================================================\n", + "Please cite QuTiP in your publication.\n", + "================================================================================\n", + "For your convenience a bibtex reference can be easily generated using `qutip.cite()`\n" + ] + } + ], + "source": [ + "about()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}