{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Gradient-based optimisation using _Cheetah_ and _PyTorch_\n", "\n", "_Cheetah_ is a differentiable beam dynamics simulation engine, making it ideally suited to gradient-based optimisation, for example for optimisation magnet settings, lattice geometries or even for system identification. _Cheetah_'s tight integration with _PyTorch_ makes this particularly easy as it opens up the use of _PyTorch_'s automatic differentiation capabilities and toolchain.\n", "\n", "In this example, we demonstrate how to use _Cheetah_ for **magnet setting optimisation** and how to **add custom normalisation** to that same task.\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "\n", "import cheetah" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Simple example (without normalisation)\n", "\n", "We start by creating the lattice section and incoming beam.\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "ares_ea = cheetah.Segment.from_lattice_json(\"ARESlatticeStage3v1_9.json\").subcell(\n", " \"AREASOLA1\", \"AREABSCR1\"\n", ")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "ParticleBeam(particles=tensor([[ 5.0896e-08, 5.9917e-09, -5.4731e-07, ..., -0.0000e+00,\n", " 2.2205e-16, 1.0000e+00],\n", " [ 6.1503e-05, 6.2810e-07, -5.5022e-07, ..., 2.1870e-06,\n", " -1.0257e-03, 1.0000e+00],\n", " [ 2.3025e-08, 6.3879e-09, 4.4044e-05, ..., 6.8509e-07,\n", " 1.1856e-03, 1.0000e+00],\n", " ...,\n", " [ 2.0862e-04, 5.5063e-06, 2.0189e-04, ..., 8.3149e-07,\n", " -5.4731e-04, 1.0000e+00],\n", " [ 5.6475e-05, 1.2176e-06, 2.7788e-04, ..., 1.1890e-06,\n", " 1.4368e-03, 1.0000e+00],\n", " [-6.2661e-05, -2.3784e-06, 2.1643e-04, ..., 6.5793e-06,\n", " -1.8158e-03, 1.0000e+00]]), energy=107315904.0, particle_charges=tensor([5.0000e-18, 5.0000e-18, 5.0000e-18, ..., 5.0000e-18, 5.0000e-18,\n", " 5.0000e-18]), survival_probabilities=tensor([1., 1., 1., ..., 1., 1., 1.]), s=0.0, species=Species(name='electron', num_elementary_charges=tensor(-1.), mass_eV=tensor(510998.9375)))" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "incoming_beam = cheetah.ParticleBeam.from_astra(\n", " \"../../tests/resources/ACHIP_EA1_2021.1351.001\"\n", ")\n", "incoming_beam" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By default, _Cheetah_ assumes that no part of its simulation requires differentiation and therefore does not track gradients, all parameters are of type `torch.Tensor`. To enable gradient tracking for parameters you would like to optimise over, you need to wrap them in `torch.nn.Parameter`, either when creating your elements and beams, or later on.\n", "\n", "In this example, we would like to optimise over the settings of three quadrupoles and two steerers in the experimental area at the _ARES_ accelerator facility at DESY. In this case, we will need to redefine the `k1` and `angle` parameters of the magnets as `torch.nn.Parameter`.\n", "\n", "**Note:** You could simply wrap the value of the parameters as the value it already has, e.g.\n", "\n", "```python\n", "ares_ea.AREAMQZM1.k1 = nn.Parameter(ares_ea.AREAMQZM1.k1)\n", "```\n", "\n", "However, in this specific case, the `k1` of the quadrupoles is set to `0.0` in the original lattice file. This will cause their gradients to be undefined. It is therefore necessary to set them to a non-zero value for gradient-based optimisation. Here we choose random values in reasonable ranges for the quadrupole strengths and steerer angles.\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "ares_ea.AREAMQZM1.k1 = nn.Parameter(torch.rand(1).squeeze() * 60.0 - 30.0)\n", "ares_ea.AREAMQZM2.k1 = nn.Parameter(torch.rand(1).squeeze() * 60.0 - 30.0)\n", "ares_ea.AREAMCVM1.angle = nn.Parameter(torch.rand(1).squeeze() * 6e-6 - 3e-6)\n", "ares_ea.AREAMQZM3.k1 = nn.Parameter(torch.rand(1).squeeze() * 60.0 - 30.0)\n", "ares_ea.AREAMCHM1.angle = nn.Parameter(torch.rand(1).squeeze() * 6e-6 - 3e-6)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we define the function that will do the actual optimisation. The goal of our optimisation is to tune the transverse beam parameters `[mu_x, sigma_x, mu_y, sigma_y]` towards some target beam parameters on a diagnostic screen at the end of the considered lattice segment. Hence, we pass the target beam parameters to the `train` function and make use of _PyTorch_'s `torch.nn.function.mse_loss` function. Note that we can easily make use of _PyTorch_'s `Adam` optimiser implementation. As a result the following code looks very similar to a standard _PyTorch_ optimisation loop for the training of neural networks.\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "def train(num_steps: int, target_beam_parameters: torch.Tensor, lr=0.1) -> dict:\n", " beam_parameter_history = []\n", " magnet_setting_history = []\n", " loss_history = []\n", "\n", " optimizer = torch.optim.Adam(ares_ea.parameters(), lr=lr)\n", "\n", " for _ in range(num_steps):\n", " optimizer.zero_grad()\n", "\n", " outgoing_beam = ares_ea.track(incoming_beam)\n", "\n", " observed_beam_parameters = torch.stack(\n", " [\n", " outgoing_beam.mu_x,\n", " outgoing_beam.sigma_x,\n", " outgoing_beam.mu_y,\n", " outgoing_beam.sigma_y,\n", " ]\n", " )\n", " loss = F.mse_loss(observed_beam_parameters, target_beam_parameters)\n", "\n", " loss.backward()\n", "\n", " # Log magnet settings and beam parameters\n", " loss_history.append(loss.item())\n", " beam_parameter_history.append(observed_beam_parameters.detach().numpy())\n", " magnet_setting_history.append(\n", " torch.stack(\n", " [\n", " ares_ea.AREAMQZM1.k1,\n", " ares_ea.AREAMQZM2.k1,\n", " ares_ea.AREAMCVM1.angle,\n", " ares_ea.AREAMQZM3.k1,\n", " ares_ea.AREAMCHM1.angle,\n", " ]\n", " )\n", " .detach()\n", " .numpy()\n", " )\n", " optimizer.step()\n", "\n", " history = {\n", " \"loss\": loss_history,\n", " \"beam_parameters\": beam_parameter_history,\n", " \"magnet_settings\": magnet_setting_history,\n", " }\n", " return history" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now simply run the optimisation function with a target beam that is centred on the origin and focused to be as small as possible.\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "history = train(num_steps=100, target_beam_parameters=torch.zeros(4))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The returned `history` dictionary allows us to plot the evolution of the optimisation process.\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(16, 3))\n", "\n", "plt.subplot(1, 4, 1)\n", "plt.plot(history[\"loss\"])\n", "plt.xlabel(\"Iteration\")\n", "plt.ylabel(\"Loss\")\n", "plt.title(\"Loss\")\n", "\n", "plt.subplot(1, 4, 2)\n", "plt.plot([record[0] for record in history[\"beam_parameters\"]], label=\"mu_x\")\n", "plt.plot([record[1] for record in history[\"beam_parameters\"]], label=\"sigma_x\")\n", "plt.plot([record[2] for record in history[\"beam_parameters\"]], label=\"mu_y\")\n", "plt.plot([record[3] for record in history[\"beam_parameters\"]], label=\"sigma_y\")\n", "plt.xlabel(\"Iteration\")\n", "plt.ylabel(\"Beam parameter (m)\")\n", "plt.title(\"Beam parameters\")\n", "plt.legend()\n", "\n", "plt.subplot(1, 4, 3)\n", "plt.plot([record[0] for record in history[\"magnet_settings\"]], label=\"AREAMQZM1\")\n", "plt.plot([record[1] for record in history[\"magnet_settings\"]], label=\"AREAMQZM2\")\n", "plt.plot([record[3] for record in history[\"magnet_settings\"]], label=\"AREAMQZM3\")\n", "plt.xlabel(\"Iteration\")\n", "plt.ylabel(\"Quadrupole strength (1/m^2)\")\n", "plt.title(\"Quadrupole settings\")\n", "plt.legend()\n", "\n", "plt.subplot(1, 4, 4)\n", "plt.plot([record[2] for record in history[\"magnet_settings\"]], label=\"AREAMCVM1\")\n", "plt.plot([record[4] for record in history[\"magnet_settings\"]], label=\"AREAMCHM1\")\n", "plt.xlabel(\"Iteration\")\n", "plt.ylabel(\"Steering angle (rad)\")\n", "plt.title(\"Steerer settings\")\n", "plt.legend()\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Success! 🎉 We can observe that the optimisation converges to a solution that is close to the target beam parameters.\n", "\n", "**However**, we can also observe that the quadrupole converges very slowly, indicating that the learning rate is too small, while the steerers keep overshooting the target, indicating that the learning rate is too large. This is a common problem in gradient-based optimisation caused by the very different scales of `k1` and `angle`, and can be solved by **normalising** the parameters under optimisation.\n", "\n", "## Normalising parameters in gradient-based optimisation\n", "\n", "In the following example we demonstrate how to **normalise** the parameters under optimisation with _Cheetah_. The same principle can also be applied to other custom mechanisms one might like to build around the lattice optimisation process, e.g. to add custom constraints, coupled parameters, etc.\n", "\n", "To achieve this, we wrap the lattice section in a `torch.nn.Module` and define a `forward` function that applies the normalisation to the parameters before passing them to the lattice section.\n", "\n", "**Note** that this time, simply for the fun of it, we also start with randomly initialised magnet settings.\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "class NormalizedARESExperimentalArea(nn.Module):\n", " \"\"\"\n", " Wrapper around the AREA experimental area that holds normalised versions of the\n", " magnet settings as its trainable parameters.\n", " \"\"\"\n", "\n", " QUADRUPOLE_LIMIT = 5.0\n", " STEERER_LIMIT = 6.1782e-3\n", "\n", " def __init__(self) -> None:\n", " super().__init__()\n", " self.ares_ea = cheetah.Segment.from_lattice_json(\n", " \"ARESlatticeStage3v1_9.json\"\n", " ).subcell(\"AREASOLA1\", \"AREABSCR1\")\n", "\n", " # self.normalized_quadrupole_strengths = nn.Parameter(\n", " # torch.tensor([10.0, -10.0, 10.0]) / self.QUADRUPOLE_LIMIT\n", " # )\n", " # self.normalized_steering_angles = nn.Parameter(\n", " # torch.tensor([1e-3, -1e-3]) / self.STEERER_LIMIT\n", " # )\n", "\n", " self.normalized_quadrupole_strengths = nn.Parameter(torch.randn(3) * 2 - 1)\n", " self.normalized_steering_angles = nn.Parameter(torch.randn(2) * 2 - 1)\n", "\n", " def forward(self, incoming_beam: cheetah.Beam):\n", " self.ares_ea.AREAMQZM1.k1 = (\n", " self.normalized_quadrupole_strengths[0] * self.QUADRUPOLE_LIMIT\n", " )\n", " self.ares_ea.AREAMQZM2.k1 = (\n", " self.normalized_quadrupole_strengths[1] * self.QUADRUPOLE_LIMIT\n", " )\n", " self.ares_ea.AREAMCVM1.angle = (\n", " self.normalized_steering_angles[0] * self.STEERER_LIMIT\n", " )\n", " self.ares_ea.AREAMQZM3.k1 = (\n", " self.normalized_quadrupole_strengths[2] * self.QUADRUPOLE_LIMIT\n", " )\n", " self.ares_ea.AREAMCHM1.angle = (\n", " self.normalized_steering_angles[1] * self.STEERER_LIMIT\n", " )\n", "\n", " return self.ares_ea.track(incoming_beam)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "normalized_ares_ea = NormalizedARESExperimentalArea()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We then redefine the `train` function to use the `torch.nn.Module` instead of the lattice section directly.\n", "\n", "**Note** that we also chose to apply normalisation to the beam parameters. This is not strictly necessary, but can help to improve the stability of the optimisation process.\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "def train_normalized(num_steps: int, target_beam_parameters: torch.Tensor):\n", " beam_parameter_history = []\n", " magnet_setting_history = []\n", " loss_history = []\n", "\n", " optimizer = torch.optim.Adam(normalized_ares_ea.parameters(), lr=1e-1)\n", "\n", " for _ in range(num_steps):\n", " optimizer.zero_grad()\n", "\n", " outgoing_beam = normalized_ares_ea(incoming_beam)\n", " observed_beam_parameters = torch.stack(\n", " [\n", " outgoing_beam.mu_x,\n", " outgoing_beam.sigma_x,\n", " outgoing_beam.mu_y,\n", " outgoing_beam.sigma_y,\n", " ]\n", " )\n", " loss = F.mse_loss(\n", " observed_beam_parameters / 2e-3, target_beam_parameters / 2e-3\n", " )\n", "\n", " loss.backward()\n", "\n", " # Log magnet settings and beam parameters\n", " loss_history.append(loss.item())\n", " beam_parameter_history.append(observed_beam_parameters.detach().numpy())\n", " magnet_setting_history.append(\n", " torch.stack(\n", " [\n", " normalized_ares_ea.ares_ea.AREAMQZM1.k1,\n", " normalized_ares_ea.ares_ea.AREAMQZM2.k1,\n", " normalized_ares_ea.ares_ea.AREAMCVM1.angle,\n", " normalized_ares_ea.ares_ea.AREAMQZM3.k1,\n", " normalized_ares_ea.ares_ea.AREAMCHM1.angle,\n", " ]\n", " )\n", " .detach()\n", " .numpy()\n", " )\n", "\n", " optimizer.step()\n", "\n", " history = {\n", " \"loss\": loss_history,\n", " \"beam_parameters\": beam_parameter_history,\n", " \"magnet_settings\": magnet_setting_history,\n", " }\n", " return history" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we run or new `train_normalized` function with the same target beam as before.\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "history = train_normalized(num_steps=200, target_beam_parameters=torch.zeros(4))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we plot the evolution of the optimisation process again.\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(16, 3))\n", "\n", "plt.subplot(1, 4, 1)\n", "plt.plot(history[\"loss\"])\n", "# plt.yscale(\"log\")\n", "plt.xlabel(\"Iteration\")\n", "plt.ylabel(\"Loss\")\n", "plt.title(\"Loss\")\n", "\n", "plt.subplot(1, 4, 2)\n", "plt.plot([record[0] for record in history[\"beam_parameters\"]], label=\"mu_x\")\n", "plt.plot([record[1] for record in history[\"beam_parameters\"]], label=\"sigma_x\")\n", "plt.plot([record[2] for record in history[\"beam_parameters\"]], label=\"mu_y\")\n", "plt.plot([record[3] for record in history[\"beam_parameters\"]], label=\"sigma_y\")\n", "plt.xlabel(\"Iteration\")\n", "plt.ylabel(\"Beam parameter (m)\")\n", "plt.title(\"Beam parameters\")\n", "plt.legend()\n", "\n", "plt.subplot(1, 4, 3)\n", "plt.plot([record[0] for record in history[\"magnet_settings\"]], label=\"AREAMQZM1\")\n", "plt.plot([record[1] for record in history[\"magnet_settings\"]], label=\"AREAMQZM2\")\n", "plt.plot([record[3] for record in history[\"magnet_settings\"]], label=\"AREAMQZM3\")\n", "plt.xlabel(\"Iteration\")\n", "plt.ylabel(\"Quadrupole strength (1/m^2)\")\n", "plt.title(\"Quadrupole settings\")\n", "plt.legend()\n", "\n", "plt.subplot(1, 4, 4)\n", "plt.plot([record[2] for record in history[\"magnet_settings\"]], label=\"AREAMCVM1\")\n", "plt.plot([record[4] for record in history[\"magnet_settings\"]], label=\"AREAMCHM1\")\n", "plt.xlabel(\"Iteration\")\n", "plt.ylabel(\"Steering angle (rad)\")\n", "plt.title(\"Steerer settings\")\n", "plt.legend()\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see this already looks much better than it did without normalisation.\n" ] } ], "metadata": { "kernelspec": { "display_name": "cheetah-dev", "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": "3.14.0" }, "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 }