From 8ec2799515563fb450b94fc8d8f75fa7b7c8e8bd Mon Sep 17 00:00:00 2001 From: Martin Schubert Date: Thu, 26 Oct 2023 12:37:51 -0700 Subject: [PATCH 1/5] Import challenges with invrs_gym --- src/invrs_gym/__init__.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/src/invrs_gym/__init__.py b/src/invrs_gym/__init__.py index a45400d..3a315e4 100644 --- a/src/invrs_gym/__init__.py +++ b/src/invrs_gym/__init__.py @@ -5,3 +5,5 @@ __version__ = "v0.0.0" __author__ = "Martin F. Schubert " + +from invrs_gym import challenges as challenges From 2a7aff0ab9a4165492f42f29dfa0c7b428985801 Mon Sep 17 00:00:00 2001 From: Martin Schubert Date: Thu, 26 Oct 2023 12:38:22 -0700 Subject: [PATCH 2/5] Update readme --- README.md | 37 ++++++++++++++++++++++++++++++++++--- 1 file changed, 34 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index aa55b08..fb30fe7 100644 --- a/README.md +++ b/README.md @@ -6,16 +6,47 @@ The `invrs_gym` package is an open-source gym containing a diverse set of photon Each of the challenges consists of a high-dimensional problem in which a physical structure (the photonic device) is optimized. The structure includes typically >10,000 degrees of freedom (DoF), generally including one or more arrays representing the structure or patterning of a layer, and may also include scalar variables representing e.g. layer thickness. In general, the DoF must satisfy certain constraints to be physical: thicknesses must be positive, and layer patterns must be _manufacturable_---they must not include features that are too small, or too closely spaced. -In general, we seek optimization techniques that _reliably_ produce manufacturable, high-quality solutions and require reasonable compute resources. +In general, we seek optimization techniques that _reliably_ produce manufacturable, high-quality solutions and require reasonable compute resources. Among the techniques that could be applied are topology optimization, inverse design, and AI-guided design. +`invrs_gym` is intended to facilitate research on such methods within the jax ecosystem. It includes several challenges that have been used in previous works, so that researchers may directly compare their results to those of the literature. While some challenges are test problems (e.g. where the structure is two-dimensional, which is unphysical but allows fast simulation), others are actual problems that are relevant e.g. for quantum computing or 3D sensing. +## Key concepts +The key types of the challenge are the `Challenge` and `Component` objects. -## Example +The `Component` represents the physical structure to be optimized, and has some intended excitation or operating condition (e.g. illumination with a particular wavelength from a particular direction). The `Component` includes methods to obtain initial parameters, and to compute the _response_ of a component to the excitation. +Each `Challenge` has a `Component` as an attribute, and also has a target that can be used to determine whether particular parameters "solve" the challenge. The `Challenge` also provides functions to compute a scalar loss for use with gradient-based optimization, and additional metrics. +## Example +```python +challenge = invrs_gym.challenges.ceviche_lightweight_waveguide_bend() + +def loss_fn(params): + response, aux = challenge.component.response(params) + loss = challenge.loss(response) + distance = challenge.distance_to_target(response) + metrics = challenge.metrics(response, params, aux) + return loss, (response, distance, aux) + +value_and_grad_fn = jax.value_and_grad(loss_fn, hax_aux=True) + +opt = invrs_opt.density_lbfgsb(beta=4) +params = challenge.component.init(jax.random.PRNGKey(0)) +state = opt.init(params) + +for _ in range(steps): + params = opt.params(state) + (value, (response, distance, aux)), grad = value_and_grad_fn(params) + state = opt.update(grad=grad, value=value, params=params, state=state) +``` +## Challenges +The current list of challenges is below. Check out the notebooks for ready-to-go examples of each. -Traditionally, a designer faced with such challenges would use their knowledge to define a low-dimensional solution space, and use gradient-free methods such as particle swarms to find a local optimum. +- The **ceviche** challenges are jax-wrapped versions of the [Ceviche Challenges](https://github.com/google/ceviche-challenges) open-sourced by Google, with defaults matching [Inverse Design of Photonic Devices with Strict Foundry Fabrication Constraints](https://pubs.acs.org/doi/10.1021/acsphotonics.2c00313) by Schubert et al. +- The **metagrating** challenge is a re-implementation of the [Metagrating3D](https://github.com/NanoComp/photonics-opt-testbed/tree/main/Metagrating3D) problem using the [fmmax](https://github.com/facebookresearch/fmmax) simulator. +- The **diffractive splitter** challenge involves designing a non-paraxial diffractive beamsplitter useful for 3D sensing, as discussed in [LightTrans documentation](https://www.lighttrans.com/use-cases/application/design-and-rigorous-analysis-of-non-paraxial-diffractive-beam-splitter.html). +- The **photon extractor** challenge is based on [Inverse-designed photon extractors for optically addressable defect qubits](https://opg.optica.org/optica/fulltext.cfm?uri=optica-7-12-1805) by Chakravarthi et al., and aims to create structures that increase photon extraction efficiency for quantum applications. ## Install From 51bc2cf62d7f209b0a8c2c6b083b2b8e8af1bf57 Mon Sep 17 00:00:00 2001 From: Martin Schubert Date: Thu, 26 Oct 2023 12:57:53 -0700 Subject: [PATCH 3/5] Add animation to readme --- README.md | 6 +- docs/img/waveguide_bend.gif | Bin 0 -> 464758 bytes notebooks/readme_example.ipynb | 122 +++++++++++++++++++++++++++++++++ 3 files changed, 127 insertions(+), 1 deletion(-) create mode 100644 docs/img/waveguide_bend.gif create mode 100644 notebooks/readme_example.ipynb diff --git a/README.md b/README.md index fb30fe7..b9695a5 100644 --- a/README.md +++ b/README.md @@ -28,9 +28,10 @@ def loss_fn(params): metrics = challenge.metrics(response, params, aux) return loss, (response, distance, aux) -value_and_grad_fn = jax.value_and_grad(loss_fn, hax_aux=True) +value_and_grad_fn = jax.value_and_grad(loss_fn, has_aux=True) opt = invrs_opt.density_lbfgsb(beta=4) + params = challenge.component.init(jax.random.PRNGKey(0)) state = opt.init(params) @@ -39,6 +40,9 @@ for _ in range(steps): (value, (response, distance, aux)), grad = value_and_grad_fn(params) state = opt.update(grad=grad, value=value, params=params, state=state) ``` +With some plotting (see the [example notebook](notebooks/readme_example.ipynb)), this code will produce the following waveguide bend: + +![Animated evolution of waveguide bend design](docs/img/waveguide_bend.gif) ## Challenges The current list of challenges is below. 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zdsY16sWpF84eI7Isi9{&lfP*HBiQiNq+8p8jgwc?U{*Z)^&)76>&~^b@>BR?D&%n{ z=SfHmYH)%QmOR8QDj2Vrw1rlZ{h>e_t*?r|nV8+VSpL1tQ4SZ9Oy}5Ed_9=PDq3LU z?&74R8ttUDwaQAC?8hO}wGUPFTU$uzOk-#Bmef3-CCRTEx(KOOuvs&rZmb#<;6o@` z$u82~6@PsawHwc2PR0@+If@i@_BA&qr2RygD`n!Oo12Sf)+im`^R4Z!-;r0J4Y7+6US@5cXKA1^3?dBBd+nq_CtmdFff$axe PY&6ku;>REmaQFWJjEpX@ literal 0 HcmV?d00001 diff --git a/notebooks/readme_example.ipynb b/notebooks/readme_example.ipynb new file mode 100644 index 0000000..6a0e65c --- /dev/null +++ b/notebooks/readme_example.ipynb @@ -0,0 +1,122 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "e4f6b9c2-e829-46b1-bfd1-de01c690ef27", + "metadata": {}, + "outputs": [], + "source": [ + "import jax\n", + "import jax.numpy as jnp\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as onp\n", + "from skimage import measure\n", + "import gifcm\n", + "\n", + "import invrs_gym\n", + "import invrs_opt" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "340fab20-b508-4d47-8c7d-ae884282dce8", + "metadata": {}, + "outputs": [], + "source": [ + "challenge = invrs_gym.challenges.ceviche_lightweight_waveguide_bend()\n", + "\n", + "def loss_fn(params):\n", + " response, aux = challenge.component.response(params)\n", + " loss = challenge.loss(response)\n", + " distance = challenge.distance_to_target(response)\n", + " metrics = challenge.metrics(response, params, aux)\n", + " return loss, (response, distance, aux)\n", + "\n", + "value_and_grad_fn = jax.value_and_grad(loss_fn, has_aux=True)\n", + "\n", + "opt = invrs_opt.density_lbfgsb(beta=4)\n", + "\n", + "params = challenge.component.init(jax.random.PRNGKey(0))\n", + "state = opt.init(params)\n", + "\n", + "data = []\n", + "for i in range(36):\n", + " params = opt.params(state)\n", + " (value, (response, distance, aux)), grad = value_and_grad_fn(params)\n", + " state = opt.update(grad=grad, value=value, params=params, state=state)\n", + " data.append((i, value, params, aux))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6f6c6fc3-11b5-4f4e-91ab-191c123f5d06", + "metadata": {}, + "outputs": [], + "source": [ + "anim = gifcm.AnimatedFigure(figure=plt.figure(figsize=(8, 4)))\n", + "\n", + "for (i, _, params, aux) in data:\n", + " with anim.frame():\n", + " # Plot fields, using some of the methods specific to the underlying ceviche model.\n", + " density = challenge.component.ceviche_model.density(params.array)\n", + " \n", + " ax = plt.subplot(121)\n", + " ax.imshow(density, cmap=\"gray\")\n", + " plt.text(100, 90, f\"step {i:02}\", color=\"w\", fontsize=20)\n", + " ax.axis(False)\n", + " ax.set_xlim(ax.get_xlim()[::-1])\n", + " ax.set_ylim(ax.get_ylim()[::-1])\n", + " \n", + " # Plot the field, which is a part of the `aux` returned with the challenge response.\n", + " # The field will be overlaid with contours of the binarized design.\n", + " field = onp.real(aux[\"fields\"])\n", + " field = field[0, 0, :, :] # First wavelength, first excitation port.\n", + " contours = measure.find_contours(density)\n", + " \n", + " ax = plt.subplot(122)\n", + " im = ax.imshow(field, cmap=\"bwr\")\n", + " im.set_clim([-onp.amax(field), onp.amax(field)])\n", + " for c in contours:\n", + " plt.plot(c[:, 1], c[:, 0], \"k\", lw=1)\n", + " ax.axis(False)\n", + " ax.set_xlim(ax.get_xlim()[::-1])\n", + " ax.set_ylim(ax.get_ylim()[::-1])\n", + "\n", + "anim.save_gif(\"waveguide_bend.gif\", duration=200)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f75622f1-b207-4ba1-aeec-2c989985b1eb", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 4b1796331fe6081ab1859dabdb10b5c12147393f Mon Sep 17 00:00:00 2001 From: Martin Schubert Date: Thu, 26 Oct 2023 12:58:44 -0700 Subject: [PATCH 4/5] Notebook formatting --- notebooks/readme_example.ipynb | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/notebooks/readme_example.ipynb b/notebooks/readme_example.ipynb index 6a0e65c..7a5de06 100644 --- a/notebooks/readme_example.ipynb +++ b/notebooks/readme_example.ipynb @@ -28,6 +28,7 @@ "source": [ "challenge = invrs_gym.challenges.ceviche_lightweight_waveguide_bend()\n", "\n", + "\n", "def loss_fn(params):\n", " response, aux = challenge.component.response(params)\n", " loss = challenge.loss(response)\n", @@ -35,6 +36,7 @@ " metrics = challenge.metrics(response, params, aux)\n", " return loss, (response, distance, aux)\n", "\n", + "\n", "value_and_grad_fn = jax.value_and_grad(loss_fn, has_aux=True)\n", "\n", "opt = invrs_opt.density_lbfgsb(beta=4)\n", @@ -59,24 +61,24 @@ "source": [ "anim = gifcm.AnimatedFigure(figure=plt.figure(figsize=(8, 4)))\n", "\n", - "for (i, _, params, aux) in data:\n", + "for i, _, params, aux in data:\n", " with anim.frame():\n", " # Plot fields, using some of the methods specific to the underlying ceviche model.\n", " density = challenge.component.ceviche_model.density(params.array)\n", - " \n", + "\n", " ax = plt.subplot(121)\n", " ax.imshow(density, cmap=\"gray\")\n", " plt.text(100, 90, f\"step {i:02}\", color=\"w\", fontsize=20)\n", " ax.axis(False)\n", " ax.set_xlim(ax.get_xlim()[::-1])\n", " ax.set_ylim(ax.get_ylim()[::-1])\n", - " \n", + "\n", " # Plot the field, which is a part of the `aux` returned with the challenge response.\n", " # The field will be overlaid with contours of the binarized design.\n", " field = onp.real(aux[\"fields\"])\n", " field = field[0, 0, :, :] # First wavelength, first excitation port.\n", " contours = measure.find_contours(density)\n", - " \n", + "\n", " ax = plt.subplot(122)\n", " im = ax.imshow(field, cmap=\"bwr\")\n", " im.set_clim([-onp.amax(field), onp.amax(field)])\n", From 204a67031d0a093046a123ca46a8c845bfb6f46d Mon Sep 17 00:00:00 2001 From: Martin Schubert Date: Thu, 26 Oct 2023 13:02:46 -0700 Subject: [PATCH 5/5] Tweak readme and notebook --- README.md | 7 ++++++- notebooks/readme_example.ipynb | 7 +++++-- 2 files changed, 11 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index b9695a5..b63039a 100644 --- a/README.md +++ b/README.md @@ -19,8 +19,10 @@ Each `Challenge` has a `Component` as an attribute, and also has a target that c ## Example ```python +# Select the challenge. challenge = invrs_gym.challenges.ceviche_lightweight_waveguide_bend() +# Define loss function, which also returns auxilliary quantities. def loss_fn(params): response, aux = challenge.component.response(params) loss = challenge.loss(response) @@ -30,12 +32,15 @@ def loss_fn(params): value_and_grad_fn = jax.value_and_grad(loss_fn, has_aux=True) +# Select an optimizer. opt = invrs_opt.density_lbfgsb(beta=4) +# Generate initial parameters, and use these to initialize the optimizer state. params = challenge.component.init(jax.random.PRNGKey(0)) state = opt.init(params) -for _ in range(steps): +# Carry out the optimization. +for i in range(steps): params = opt.params(state) (value, (response, distance, aux)), grad = value_and_grad_fn(params) state = opt.update(grad=grad, value=value, params=params, state=state) diff --git a/notebooks/readme_example.ipynb b/notebooks/readme_example.ipynb index 7a5de06..8977693 100644 --- a/notebooks/readme_example.ipynb +++ b/notebooks/readme_example.ipynb @@ -28,7 +28,7 @@ "source": [ "challenge = invrs_gym.challenges.ceviche_lightweight_waveguide_bend()\n", "\n", - "\n", + "# Define loss function, which also returns auxilliary quantities.\n", "def loss_fn(params):\n", " response, aux = challenge.component.response(params)\n", " loss = challenge.loss(response)\n", @@ -36,14 +36,16 @@ " metrics = challenge.metrics(response, params, aux)\n", " return loss, (response, distance, aux)\n", "\n", - "\n", "value_and_grad_fn = jax.value_and_grad(loss_fn, has_aux=True)\n", "\n", + "# Select an optimizer.\n", "opt = invrs_opt.density_lbfgsb(beta=4)\n", "\n", + "# Generate initial parameters, and use these to initialize the optimizer state.\n", "params = challenge.component.init(jax.random.PRNGKey(0))\n", "state = opt.init(params)\n", "\n", + "# Carry out the optimization.\n", "data = []\n", "for i in range(36):\n", " params = opt.params(state)\n", @@ -59,6 +61,7 @@ "metadata": {}, "outputs": [], "source": [ + "# Create an animated gif showing the evolution of the waveguide bend design.\n", "anim = gifcm.AnimatedFigure(figure=plt.figure(figsize=(8, 4)))\n", "\n", "for i, _, params, aux in data:\n",