Implement improved demo build pipeline

.gitignore

@@ -11,6 +11,7 @@ backreferences
 .DS_Store
 demos_community.rst
 .venv/
+.venv-build/
 **/*.egg-info/
 
 # Generated after installation

.vscode/settings.json

@@ -0,0 +1,7 @@
+{
+    "python.analysis.ignore": [
+        ".venv/**",
+        ".venv-build/**",
+        "_build/**"
+    ]
+}

Makefile

@@ -81,3 +81,7 @@ environment:
 			$$PYTHON_VENV_PATH/bin/python -m pip install --upgrade git+https://github.com/PennyLaneAI/pennylane.git#egg=pennylane;\
 		fi;\
 	fi
+
+fmt:
+	poetry run ruff check --fix lib/
+	poetry run ruff format lib/

conf.py

@@ -19,6 +19,7 @@
 from jinja2 import FileSystemLoader, Environment
 import yaml
 from pennylane import PennyLaneDeprecationWarning
+from pathlib import Path
 
 sys.path.insert(0, os.path.abspath("."))
 sys.path.insert(0, os.path.join(os.path.dirname(__file__)))
@@ -56,12 +57,12 @@
     "extension",
 ]
 
-
 html_baseurl = "https://pennylane.ai/qml/"
+demo_staging_dir = os.getenv("DEMO_STAGING_DIR", "demonstrations")
 
 sphinx_gallery_conf = {
     # path to your example scripts
-    "examples_dirs": ["demonstrations"],
+    "examples_dirs": [demo_staging_dir],
     # path where to save gallery generated examples
     "gallery_dirs": ["demos"],
     # execute files that match the following filename pattern,
@@ -85,6 +86,8 @@
     "doc_module"          : ("pennylane"),
     "junit": "../test-results/sphinx-gallery/junit.xml",
     "reset_modules": ("module_resets.reset_jax", "matplotlib", "seaborn"),
+    "show_signature": False,
+    'download_all_examples': False,
 }
 
 
@@ -136,7 +139,7 @@
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.
 # This pattern also affects html_static_path and html_extra_path.
-exclude_patterns = ["_build", "Thumbs.db", ".DS_Store", "*venv", "sphinxext"]
+exclude_patterns = ["_build", "Thumbs.db", ".DS_Store", "*venv", "*venv-build", "sphinxext"]
 
 # The name of the Pygments (syntax highlighting) style to use.
 pygments_style = "sphinx"

demonstrations_v2/adjoint_diff_benchmarking/demo.py

@@ -0,0 +1,144 @@
+r"""
+
+.. _adjoint_differentiation_benchmarking:
+
+Adjoint Differentiation
+=======================
+
+.. meta::
+    :property="og:description": Benchmarking file for adjoint diff demonstration.
+    :property="og:image": https://pennylane.ai/qml/_static/demo_thumbnails/opengraph_demo_thumbnails/code.png
+
+
+*Author: Christina Lee — Posted: 23 November 2021. Last updated: 04 July 2024.*
+
+"""
+
+##############################################################################
+# This page is supplementary material to the
+# `Adjoint Differentiation <https://pennylane.ai/qml/demos/tutorial_adjoint_diff.html>`__
+# demonstration.  The below script produces the benchmarking images used.
+
+import timeit
+import matplotlib.pyplot as plt
+import pennylane as qml
+import jax
+
+jax.config.update("jax_platform_name", "cpu")
+jax.config.update('jax_enable_x64', True)
+
+plt.style.use("bmh")
+
+n_samples = 5
+
+
+def get_time(qnode, params):
+    globals_dict = {'grad': jax.grad, 'circuit': qnode, 'params': params}
+    return timeit.timeit("grad(circuit)(params)", globals=globals_dict, number=n_samples)
+
+
+def wires_scaling(n_wires, n_layers):
+    key = jax.random.PRNGKey(42)
+
+    t_adjoint = []
+    t_ps = []
+    t_backprop = []
+
+    def circuit(params, wires):
+        qml.StronglyEntanglingLayers(params, wires=range(wires))
+        return qml.expval(qml.PauliZ(0) @ qml.PauliZ(1) @ qml.PauliZ(2))
+
+    for i_wires in n_wires:
+        dev = qml.device("lightning.qubit", wires=i_wires)
+        dev_python = qml.device("default.qubit", wires=i_wires)
+
+        circuit_adjoint = qml.QNode(lambda x: circuit(x, wires=i_wires), dev, diff_method="adjoint")
+        circuit_ps = qml.QNode(lambda x: circuit(x, wires=i_wires), dev, diff_method="parameter-shift")
+        circuit_backprop = qml.QNode(lambda x: circuit(x, wires=i_wires), dev_python, diff_method="backprop")
+
+        # set up the parameters
+        param_shape = qml.StronglyEntanglingLayers.shape(n_wires=i_wires, n_layers=n_layers)
+        params = jax.random.normal(key, param_shape)
+
+        t_adjoint.append(get_time(circuit_adjoint, params))
+        t_backprop.append(get_time(circuit_backprop, params))
+        t_ps.append(get_time(circuit_ps, params))
+
+    return t_adjoint, t_backprop, t_ps
+
+
+def layers_scaling(n_wires, n_layers):
+    key = jax.random.PRNGKey(42)
+
+    dev = qml.device("lightning.qubit", wires=n_wires)
+    dev_python = qml.device('default.qubit', wires=n_wires)
+
+    t_adjoint = []
+    t_ps = []
+    t_backprop = []
+
+    def circuit(params):
+        qml.StronglyEntanglingLayers(params, wires=range(n_wires))
+        return qml.expval(qml.PauliZ(0) @ qml.PauliZ(1) @ qml.PauliZ(2))
+
+    circuit_adjoint = qml.QNode(circuit, dev, diff_method="adjoint")
+    circuit_ps = qml.QNode(circuit, dev, diff_method="parameter-shift")
+    circuit_backprop = qml.QNode(circuit, dev_python, diff_method="backprop")
+
+    for i_layers in n_layers:
+        # set up the parameters
+        param_shape = qml.StronglyEntanglingLayers.shape(n_wires=n_wires, n_layers=i_layers)
+        params = jax.random.normal(key, param_shape)
+
+        t_adjoint.append(get_time(circuit_adjoint, params))
+        t_backprop.append(get_time(circuit_backprop, params))
+        t_ps.append(get_time(circuit_ps, params))
+
+    return t_adjoint, t_backprop, t_ps
+
+
+if __name__ == "__main__":
+
+    wires_list = [3, 6, 9, 12, 15]
+    n_layers = 6
+    adjoint_wires, backprop_wires, ps_wires = wires_scaling(wires_list, n_layers)
+
+    layers_list = [3, 9, 15, 21, 27]
+    n_wires = 12
+    adjoint_layers, backprop_layers, ps_layers = layers_scaling(n_wires, layers_list)
+
+    # Generating the graphic
+    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4))
+
+    ax1.plot(wires_list, adjoint_wires, '.-', label="adjoint")
+    ax1.plot(wires_list, ps_wires, '.-', label="parameter-shift")
+    ax1.plot(wires_list, backprop_wires, '.-', label="backprop")
+
+    ax1.legend()
+
+    ax1.set_xlabel("Number of wires")
+    ax1.set_xticks(wires_list)
+    ax1.set_ylabel("Log Time")
+    ax1.set_yscale("log")
+    ax1.set_title("Scaling with wires")
+
+    ax2.plot(layers_list, adjoint_layers, '.-', label="adjoint")
+    ax2.plot(layers_list, ps_layers, '.-', label="parameter-shift")
+    ax2.plot(layers_list, backprop_layers, '.-', label="backprop")
+
+    ax2.legend()
+
+    ax2.set_xlabel("Number of layers")
+    ax2.set_xticks(layers_list)
+    ax2.set_ylabel("Time")
+    ax2.set_title("Scaling with Layers")
+
+    plt.savefig("scaling.png")
+
+##############################################################################
+#
+# .. figure:: ../_static/demonstration_assets/adjoint_diff/scaling.png
+#     :width: 80%
+#     :align: center
+#
+#

demonstrations_v2/adjoint_diff_benchmarking/metadata.json

@@ -0,0 +1,24 @@
+{
+  "title": "Adjoint Differentiation \u2013 Supplementary Material",
+  "authors": [
+    {
+      "username": "christina"
+    }
+  ],
+  "dateOfPublication": "2021-11-23T00:00:00+00:00",
+  "dateOfLastModification": "2024-10-07T00:00:00+00:00",
+  "categories": [],
+  "tags": [],
+  "previewImages": [
+    {
+      "type": "thumbnail",
+      "uri": "/_static/demo_thumbnails/regular_demo_thumbnails/thumbnail_adjoint_differentiation.png"
+    }
+  ],
+  "seoDescription": "Benchmarking file for adjoint diff demonstration.",
+  "doi": "",
+  "references": [],
+  "basedOnPapers": [],
+  "referencedByPapers": [],
+  "relatedContent": []
+}

demonstrations_v2/adjoint_diff_benchmarking/requirements.in

@@ -0,0 +1,4 @@
+jax
+jaxlib
+matplotlib
+pennylane