diff --git a/.github/workflows/build_dist.yml b/.github/workflows/build_dist.yml index 54dda61..0e52588 100644 --- a/.github/workflows/build_dist.yml +++ b/.github/workflows/build_dist.yml @@ -26,13 +26,16 @@ jobs: with: python-version: "3.10" + - name: Install uv + uses: astral-sh/setup-uv@v6 + with: + version: "0.9.2" + python-version: "3.10" + enable-cache: true - name: Install dependencies run: | - python -m venv ./venv - . ./venv/bin/activate - python -m pip install --upgrade pip - pip install poetry - poetry install --with=dev --no-interaction --no-ansi + uv venv .venv + uv sync --locked --dev --extra cpu - name: Gather new package version id: version @@ -45,14 +48,12 @@ jobs: - name: Bump package version in pyproject.toml run: | - . ./venv/bin/activate - poetry version ${{steps.version.outputs.new_tag}} + uv version ${{steps.version.outputs.new_tag}} - name: Build dist run: | - . ./venv/bin/activate - python -m build --sdist --wheel --no-isolation --outdir dist/ . - twine check dist/* + uv run python -m build --sdist --wheel --no-isolation --outdir dist/ . + uv run twine check dist/* - name: Commit updated pyproject.toml run: | diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml index 3f4b4d5..94ac0a2 100644 --- a/.github/workflows/docs.yml +++ b/.github/workflows/docs.yml @@ -17,21 +17,23 @@ jobs: with: python-version: "3.10" + - name: Install uv + uses: astral-sh/setup-uv@v6 + with: + version: "0.9.2" + python-version: "3.10" + enable-cache: true - name: Install dependencies run: | - python -m venv ./venv - . ./venv/bin/activate - python -m pip install --upgrade pip - pip install poetry - poetry install --with=docs --no-interaction --no-ansi + uv venv .venv + uv sync --locked --dev --group docs --extra cpu - name: Build the sphinx docs run: | - . ./venv/bin/activate - sphinx-apidoc -f -o ./sphinx/source ./src/bolightningpipeline - make -C sphinx clean - python sphinx/clean_html_files.py - make -C sphinx html + uv run sphinx-apidoc -f -o ./sphinx/source ./src/bolightningpipeline + uv run make -C sphinx clean + uv run python sphinx/clean_html_files.py + uv run make -C sphinx html touch sphinx/build/html/.nojekyll - name: Checkout gh-pages branch @@ -41,9 +43,8 @@ jobs: - name: Copy build files to docs folder run: | - . ./venv/bin/activate cp -a sphinx/build/html/. docs/ - python sphinx/make_html_files_list.py + uv run python sphinx/make_html_files_list.py rm -rf sphinx/build - name: Commit to gh-pages branch diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 8145198..bd73724 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -27,31 +27,30 @@ jobs: uses: actions/setup-python@v3 with: python-version: ${{ matrix.python-version }} + - name: Install uv + uses: astral-sh/setup-uv@v6 + with: + version: "0.9.2" + python-version: ${{ matrix.python-version }} + enable-cache: true - name: Install dependencies run: | - python -m venv ./venv - . ./venv/bin/activate - python -m pip install --upgrade pip - pip install poetry - poetry install --with=dev --no-interaction --no-ansi + uv venv .venv + uv sync --locked --dev --extra cpu - name: Test Linting run: | - . ./venv/bin/activate - black src tests --check --diff - isort src tests --check-only --diff + uv run black src tests --check --diff + uv run isort src tests --check-only --diff - name: Test Typing run: | - . ./venv/bin/activate - mypy src tests + uv run mypy src tests - name: Test Notebooks run: | - . ./venv/bin/activate - pytest --nbmake notebooks -n=auto --nbmake-kernel=python3 --nbmake-timeout=600 # 10 minutes timeout + uv run pytest --nbmake notebooks -n=auto --nbmake-kernel=python3 --nbmake-timeout=600 # 10 minutes timeout - name: Test Unittests with pytest run: | - . ./venv/bin/activate - pytest tests -n=auto --cov=src --cov-report="xml:tests/.tmp/coverage.xml" --cov-report=term-missing --durations=10 + uv run pytest tests -n=auto --cov=src --cov-report="xml:tests/.tmp/coverage.xml" --cov-report=term-missing --durations=10 - name: Code Coverage uses: orgoro/coverage@v3.2 @@ -65,9 +64,8 @@ jobs: - name: Test Build if: ${{ matrix.python-version == '3.10' }} run: | - . ./venv/bin/activate - python -m build --sdist --wheel --no-isolation --outdir dist/ . - twine check dist/* + uv run python -m build --sdist --wheel --no-isolation --outdir dist/ . + uv run twine check dist/* Run-tests-on-Windows: name: Run tests on Windows-latest @@ -82,14 +80,16 @@ jobs: uses: actions/setup-python@v3 with: python-version: ${{ matrix.python-version }} + - name: Install uv + uses: astral-sh/setup-uv@v6 + with: + version: "0.9.2" + python-version: ${{ matrix.python-version }} + enable-cache: true - name: Install dependencies run: | - python -m venv ./venv - . ./venv/Scripts/activate - python -m pip install --upgrade pip - pip install poetry - poetry install --with=dev --no-interaction --no-ansi + uv venv .venv + uv sync --locked --dev --extra cpu - name: Test Unittests with pytest run: | - . ./venv/Scripts/activate - pytest tests -n=auto + uv run pytest tests -n=auto diff --git a/.gitignore b/.gitignore index 2b5a0ff..a136d41 100644 --- a/.gitignore +++ b/.gitignore @@ -303,3 +303,5 @@ tmp_report_dir .tmp_report.json coverage.json /notebooks/Digits2D +/notebooks/Cifar10 +/notebooks/data diff --git a/README.md b/README.md index 7a1a580..843a9f5 100644 --- a/README.md +++ b/README.md @@ -30,11 +30,13 @@ With `python` and `pip` installed, pip install git+https://github.com/MatchCake/MatchCake-Opt ``` -With `poetry` installed, +With `uv` installed, ```bash -poetry add "git+https://github.com/MatchCake/MatchCake-Opt" +uv add "git+https://github.com/MatchCake/MatchCake-Opt" ``` +To install the package with cu128 (CUDA), add `--extra cu128` to the installation commands above. + ### For developers @@ -42,17 +44,18 @@ With `python` and `pip` installed, run the following commands to install the dep ```bash python -m venv .venv source .venv/bin/activate -pip install poetry -python -m poetry install +pip install uv +uv sync --dev ``` -With `poetry` installed, run the following commands to install the dependencies: +With `uv` installed, run the following commands to install the dependencies: ```bash -python -m venv .venv -source .venv/bin/activate -poetry install +uv venv .venv +uv sync --dev ``` +If you'd like to contribute to this repository, please do so by submitting pull requests to the `dev` branch. Thank you! + ## Quick Usage Exemple ```python @@ -78,7 +81,7 @@ class LinearNN(ClassificationModel): RangeParameterConfig( name="n_neurons", parameter_type="int", - bounds=(4, 16), + bounds=(4, 2048), ), ] @@ -92,7 +95,7 @@ class LinearNN(ClassificationModel): ): super().__init__(input_shape=input_shape, output_shape=output_shape, learning_rate=learning_rate, **kwargs) self.save_hyperparameters("learning_rate", "n_neurons") - self.nn = torch.Sequential( + self.nn = torch.nn.Sequential( torch.nn.Flatten(), torch.nn.LazyLinear(n_neurons), torch.nn.ReLU(), @@ -106,7 +109,7 @@ class LinearNN(ClassificationModel): def output_size(self): return int(np.prod(self.output_shape)) -datamodule = DataModule.from_dataset_name("Digits2D") +datamodule = DataModule.from_dataset_name("Digits2D", fold_id=0) automl_pipeline = AutoMLPipeline(model_cls=LinearNN, datamodule=datamodule) automl_pipeline.run() lt_pipeline, metrics = automl_pipeline.run_best_pipeline() diff --git a/notebooks/automl_pipeline_tutorial.ipynb b/notebooks/automl_pipeline_tutorial.ipynb index 05323d0..d023d8e 100644 --- a/notebooks/automl_pipeline_tutorial.ipynb +++ b/notebooks/automl_pipeline_tutorial.ipynb @@ -24,6 +24,8 @@ "from pathlib import Path\n", "from typing import Optional\n", "import json\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")\n", "\n", "import numpy as np\n", "import pennylane as qml\n", @@ -53,6 +55,18 @@ "cell_type": "code", "source": [ "class NIFCNN(ClassificationModel):\n", + " \"\"\"\n", + " Non-Interacting fermions classifier through CNN hamiltonian encoding. The flow of information\n", + " in the model goes as follows.\n", + "\n", + " 1. CNN(X_{bchw}) -> X_{be}: Build the embeddings with the CNN encoder\n", + " 2. Linear(X_{be}) -> W_{bn}: Build the local fields weights from the embeddings\n", + " 3. Linear(X_{be}) -> W_{bt}: Build the ZZ couplings weights from the embeddings\n", + " - We now have the hamiltonian: H(X_{be}) = W_{bn} Z_{n} + W_{bt} ZZ_{t}\n", + " 4. P_{knu}, P_{ktv}: We compute the probabilities to get the eigenstates of Z and ZZ\n", + " 5. E_{k} = W_{bn} P_{knu} Lambda_{u} + W_{bt} P_{ktv} Lambda_{v}: We now compute the expected values with Lambda_{u} and Lambda_{v} the eigenvalues of Z and ZZ respectively.\n", + " 6. Softmax(E_{k}) -> Y_{k}: Finally, we apply a softmax to get the probabilities of each class.\n", + " \"\"\"\n", " MODEL_NAME = \"NIFCNN\"\n", " DEFAULT_N_QUBITS = 16\n", " DEFAULT_LEARNING_RATE = 2e-4\n", @@ -68,7 +82,7 @@ " RangeParameterConfig(\n", " name=\"n_qubits\",\n", " parameter_type=\"int\",\n", - " bounds=(4, 16),\n", + " bounds=(4, 92),\n", " ),\n", " ChoiceParameterConfig(\n", " name=\"encoder_output_activation\",\n", @@ -118,21 +132,13 @@ " torch.nn.LazyLinear(self._hamiltonian_n_params),\n", " getattr(torch.nn, encoder_output_activation)()\n", " )\n", - " self.xx_body_couplings_head = torch.nn.Sequential(\n", - " torch.nn.Flatten(),\n", - " torch.nn.LazyLinear(self._hamiltonian_n_params),\n", - " getattr(torch.nn, encoder_output_activation)()\n", - " )\n", "\n", " self.zz_body_coupling_weights = torch.nn.Parameter(torch.randn(self._hamiltonian_n_params), requires_grad=True)\n", - " self.xx_body_coupling_weights = torch.nn.Parameter(torch.randn(self._hamiltonian_n_params), requires_grad=True)\n", "\n", " self.local_fields_op_eigvals = torch.nn.Parameter(torch.from_numpy(np.array([1.0, -1.0])).float(),\n", " requires_grad=False) # eigvals(Z)\n", " self.zz_eigvals = torch.nn.Parameter(torch.from_numpy(np.array([1.0, -1.0, -1.0, 1.0])).float(),\n", " requires_grad=False) # eigvals(ZZ)\n", - " self.xx_eigvals = torch.nn.Parameter(torch.from_numpy(np.array([1.0, -1.0, 1.0, -1.0])).float(),\n", - " requires_grad=False) # eigvals(XX)\n", "\n", " self.local_fields_wires = [[i] for i in range(self.n_qubits)]\n", " self.couplings_wires = [[i, j] for i, j in np.vstack(np.triu_indices(self.n_qubits, k=1)).T]\n", @@ -153,7 +159,6 @@ "\n", " local_fields = self.local_fields_head(embeddings)\n", " zz_couplings = self.zz_body_couplings_head(embeddings)\n", - " xx_couplings = self.xx_body_couplings_head(embeddings)\n", " self.q_device.execute_generator(self.circuit_gen(), reset=True)\n", "\n", " local_fields_probs = (\n", @@ -170,11 +175,8 @@ " weighted_zz_eigvals = torch.einsum(\n", " \"bi,kij,j->bk\", zz_couplings, couplings_probs, self.zz_eigvals\n", " )\n", - " weighted_xx_eigvals = torch.einsum(\n", - " \"bi,kij,j->bk\", xx_couplings, couplings_probs, self.xx_eigvals\n", - " )\n", "\n", - " expval = weighted_local_eigvals + weighted_zz_eigvals + weighted_xx_eigvals\n", + " expval = weighted_local_eigvals + weighted_zz_eigvals\n", " return expval\n", "\n", " def circuit_gen(self):\n", @@ -253,12 +255,11 @@ "model_cls = NIFCNN\n", "\n", "# Pipeline\n", - "job_output_folder_root = Path(os.getcwd()) / \"data\" / \"automl\"\n", - "job_output_folder = Path(dataset_name) / model_cls.MODEL_NAME\n", + "job_output_folder = Path(os.getcwd()) / \"data\" / \"automl\" / dataset_name / model_cls.MODEL_NAME\n", "checkpoint_folder = Path(job_output_folder) / \"checkpoints\"\n", "pipeline_args = dict(\n", - " max_epochs=100,\n", - " max_time=\"00:00:03:00\", # DD:HH:MM:SS\n", + " max_epochs=100, # increase at least to 256\n", + " max_time=\"00:00:02:00\", # DD:HH:MM:SS, increase at least to \"00:01:00:00\"\n", ")" ], "id": "d8db16a0825411", @@ -292,9 +293,9 @@ " model_cls=model_cls,\n", " datamodule=datamodule,\n", " checkpoint_folder=checkpoint_folder,\n", - " automl_iterations=5,\n", - " inner_max_epochs=10,\n", - " inner_max_time=\"00:00:01:00\",\n", + " automl_iterations=5, # increase at least to 32\n", + " inner_max_epochs=10, # increase at least to 128\n", + " inner_max_time=\"00:00:01:00\", # increase at least to \"00:00:10:00\"\n", " automl_overwrite_fit=True,\n", " **pipeline_args\n", ")" @@ -310,19 +311,36 @@ "id": "af11b75e95e08c0c" }, { - "metadata": {}, + "metadata": { + "jupyter": { + "is_executing": true + } + }, "cell_type": "code", "source": [ "start_time = time.perf_counter()\n", "automl_pipeline.run()\n", "end_time = time.perf_counter()\n", - "print(f\"Time taken: {end_time - start_time:.4f} seconds\")\n", - "print(f\"Best Hyperparameters:\\n{json.dumps(automl_pipeline.get_best_params(), indent=2, default=str)}\")" + "print(f\"Time taken: {end_time - start_time:.4f} seconds\")" ], "id": "cf7202cce5b169d9", "outputs": [], "execution_count": null }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "We can then check the best hyperparameters found.", + "id": "cb990e91a45ed0b7" + }, + { + "metadata": {}, + "cell_type": "code", + "source": "print(f\"Best Hyperparameters:\\n{json.dumps(automl_pipeline.get_best_params(), indent=2, default=str)}\")", + "id": "dd640fc80cf084de", + "outputs": [], + "execution_count": null + }, { "metadata": {}, "cell_type": "markdown", diff --git a/notebooks/ligthning_pipeline_tutorial.ipynb b/notebooks/ligthning_pipeline_tutorial.ipynb index a278e2c..a00bda0 100644 --- a/notebooks/ligthning_pipeline_tutorial.ipynb +++ b/notebooks/ligthning_pipeline_tutorial.ipynb @@ -60,6 +60,18 @@ "cell_type": "code", "source": [ "class NIFCNN(ClassificationModel):\n", + " \"\"\"\n", + " Non-Interacting fermions classifier through CNN hamiltonian encoding. The flow of information\n", + " in the model goes as follows.\n", + "\n", + " 1. CNN(X_{bchw}) -> X_{be}: Build the embeddings with the CNN encoder\n", + " 2. Linear(X_{be}) -> W_{bn}: Build the local fields weights from the embeddings\n", + " 3. Linear(X_{be}) -> W_{bt}: Build the ZZ couplings weights from the embeddings\n", + " - We now have the hamiltonian: H(X_{be}) = W_{bn} Z_{n} + W_{bt} ZZ_{t}\n", + " 4. P_{knu}, P_{ktv}: We compute the probabilities to get the eigenstates of Z and ZZ\n", + " 5. E_{k} = W_{bn} P_{knu} Lambda_{u} + W_{bt} P_{ktv} Lambda_{v}: We now compute the expected values with Lambda_{u} and Lambda_{v} the eigenvalues of Z and ZZ respectively.\n", + " 6. Softmax(E_{k}) -> Y_{k}: Finally, we apply a softmax to get the probabilities of each class.\n", + " \"\"\"\n", " MODEL_NAME = \"NIFCNN\"\n", " DEFAULT_N_QUBITS = 16\n", " DEFAULT_LEARNING_RATE = 2e-4\n", @@ -106,21 +118,13 @@ " torch.nn.LazyLinear(self._hamiltonian_n_params),\n", " getattr(torch.nn, encoder_output_activation)()\n", " )\n", - " self.xx_body_couplings_head = torch.nn.Sequential(\n", - " torch.nn.Flatten(),\n", - " torch.nn.LazyLinear(self._hamiltonian_n_params),\n", - " getattr(torch.nn, encoder_output_activation)()\n", - " )\n", "\n", " self.zz_body_coupling_weights = torch.nn.Parameter(torch.randn(self._hamiltonian_n_params), requires_grad=True)\n", - " self.xx_body_coupling_weights = torch.nn.Parameter(torch.randn(self._hamiltonian_n_params), requires_grad=True)\n", "\n", " self.local_fields_op_eigvals = torch.nn.Parameter(torch.from_numpy(np.array([1.0, -1.0])).float(),\n", " requires_grad=False) # eigvals(Z)\n", " self.zz_eigvals = torch.nn.Parameter(torch.from_numpy(np.array([1.0, -1.0, -1.0, 1.0])).float(),\n", " requires_grad=False) # eigvals(ZZ)\n", - " self.xx_eigvals = torch.nn.Parameter(torch.from_numpy(np.array([1.0, -1.0, 1.0, -1.0])).float(),\n", - " requires_grad=False) # eigvals(XX)\n", "\n", " self.local_fields_wires = [[i] for i in range(self.n_qubits)]\n", " self.couplings_wires = [[i, j] for i, j in np.vstack(np.triu_indices(self.n_qubits, k=1)).T]\n", @@ -141,7 +145,6 @@ "\n", " local_fields = self.local_fields_head(embeddings)\n", " zz_couplings = self.zz_body_couplings_head(embeddings)\n", - " xx_couplings = self.xx_body_couplings_head(embeddings)\n", " self.q_device.execute_generator(self.circuit_gen(), reset=True)\n", "\n", " local_fields_probs = (\n", @@ -158,11 +161,8 @@ " weighted_zz_eigvals = torch.einsum(\n", " \"bi,kij,j->bk\", zz_couplings, couplings_probs, self.zz_eigvals\n", " )\n", - " weighted_xx_eigvals = torch.einsum(\n", - " \"bi,kij,j->bk\", xx_couplings, couplings_probs, self.xx_eigvals\n", - " )\n", "\n", - " expval = weighted_local_eigvals + weighted_zz_eigvals + weighted_xx_eigvals\n", + " expval = weighted_local_eigvals + weighted_zz_eigvals\n", " return expval\n", "\n", " def circuit_gen(self):\n", @@ -246,8 +246,7 @@ ")\n", "\n", "# Pipeline\n", - "job_output_folder_root = Path(os.getcwd()) / \"data\" / \"lightning\"\n", - "job_output_folder = Path(dataset_name) / model_cls.MODEL_NAME\n", + "job_output_folder = Path(os.getcwd()) / \"data\" / \"lightning\" / dataset_name / model_cls.MODEL_NAME\n", "checkpoint_folder = Path(job_output_folder) / \"checkpoints\"" ], "id": "7650c0c97d2d27a8", diff --git a/notebooks/nif_deep_learning.ipynb b/notebooks/nif_deep_learning.ipynb new file mode 100644 index 0000000..12890b5 --- /dev/null +++ b/notebooks/nif_deep_learning.ipynb @@ -0,0 +1,335 @@ +{ + "cells": [ + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "# NIF Deep Learning (NIFDL) for Classification Tasks\n", + "This notebook demonstrates the implementation of a quantum-classical hybrid model called NIF Deep Learning (NIFDL) using the Matchcake library. The model is designed for classification tasks and leverages the Non-Interacting Fermionic Device (NIFD) for quantum computations. The notebook also showcases the use of PyTorch Lightning for training and evaluation, as well as the Ax library for hyperparameter optimization." + ], + "id": "c11b257a04e4cb48" + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "import os\n", + "import datetime\n", + "import time\n", + "from pathlib import Path\n", + "from typing import Optional, Any\n", + "import json\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "import numpy as np\n", + "import pennylane as qml\n", + "import torch\n", + "from ax import RangeParameterConfig\n", + "from matchcake import NonInteractingFermionicDevice\n", + "from matchcake.operations import SptmAngleEmbedding, SptmfRxRx, SptmFHH\n", + "\n", + "from matchcake_opt.datasets import *\n", + "from matchcake_opt.modules.classification_model import ClassificationModel\n", + "from matchcake_opt.tr_pipeline.automl_pipeline import AutoMLPipeline\n", + "from matchcake_opt.tr_pipeline.lightning_pipeline import LightningPipeline" + ], + "id": "3b95af772cefb5b3", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "In the following cell we define the NIFDL model class, which inherits from `ClassificationModel`. The model consists of a quantum circuit implemented using PennyLane, and it includes methods for building the model, defining the quantum circuit, and performing forward passes.", + "id": "e1f9ef65d39494b7" + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "class NIFDL(ClassificationModel):\n", + " MODEL_NAME = \"NIFDL\"\n", + " DEFAULT_N_QUBITS = 16\n", + " DEFAULT_LEARNING_RATE = 2e-4\n", + " DEFAULT_N_LAYERS = 6\n", + "\n", + " HP_CONFIGS = [\n", + " RangeParameterConfig(\n", + " name=\"learning_rate\",\n", + " parameter_type=\"float\",\n", + " bounds=(1e-5, 0.1),\n", + " ),\n", + " RangeParameterConfig(\n", + " name=\"n_qubits\",\n", + " parameter_type=\"int\",\n", + " bounds=(4, 32),\n", + " step_size=2,\n", + " ),\n", + " RangeParameterConfig(\n", + " name=\"n_layers\",\n", + " parameter_type=\"int\",\n", + " bounds=(1, 10),\n", + " ),\n", + " ]\n", + "\n", + " def __init__(\n", + " self,\n", + " input_shape: Optional[tuple[int, ...]],\n", + " output_shape: Optional[tuple[int, ...]],\n", + " learning_rate: float = DEFAULT_LEARNING_RATE,\n", + " n_qubits: int = DEFAULT_N_QUBITS,\n", + " n_layers: int = DEFAULT_N_LAYERS,\n", + " **kwargs,\n", + " ):\n", + " super().__init__(input_shape=input_shape, output_shape=output_shape, learning_rate=learning_rate, **kwargs)\n", + " self.save_hyperparameters(\"learning_rate\", \"n_qubits\", \"n_layers\")\n", + " self.n_qubits = n_qubits\n", + " self.n_layers = n_layers\n", + " self.n_encoders = 8\n", + " self.R_DTYPE = torch.float32\n", + " self.C_DTYPE = torch.cfloat\n", + " self.q_device = NonInteractingFermionicDevice(\n", + " wires=self.n_qubits, r_dtype=self.R_DTYPE, c_dtype=self.C_DTYPE, show_progress=False\n", + " )\n", + " self.q_node = qml.QNode(self.circuit, self.q_device, interface=\"torch\", diff_method=\"backprop\")\n", + " self._weight_shapes = {\"weights\": (self.n_layers, (self.n_qubits - 1) * 2)}\n", + " self.flatten = torch.nn.Flatten()\n", + " self.encoders = torch.nn.ModuleList(\n", + " [\n", + " qml.qnn.TorchLayer(self.q_node, self._weight_shapes)\n", + " for _ in range(self.n_encoders)\n", + " ]\n", + " )\n", + " self.readout = torch.nn.LazyLinear(self.output_size)\n", + " self._build()\n", + "\n", + " def _build(self):\n", + " dummy_input = torch.randn((3, *self.input_shape)).to(device=self.device)\n", + " with torch.no_grad():\n", + " self(dummy_input)\n", + " return self\n", + "\n", + " def circuit(self, inputs, weights):\n", + " SptmAngleEmbedding(inputs, wires=range(self.n_qubits))\n", + " for i in range(self.n_layers):\n", + " for j in range(self.n_qubits - 1):\n", + " SptmfRxRx(weights[i, j*2 : j*2+2], wires=[j, j+1])\n", + " SptmFHH(wires=[j, j+1])\n", + " return [qml.expval(qml.PauliZ(wires=i)) for i in range(self.n_qubits)]\n", + "\n", + " def forward(self, x) -> Any:\n", + " x = self.flatten(x).to(self.device)\n", + " x_split = torch.split(x, self.n_qubits, dim=1)\n", + " x_out = [layer(x_chunk) for layer, x_chunk in zip(self.encoders, x_split)]\n", + " x = torch.cat(x_out, dim=1).to(self.device)\n", + " x = self.readout(x)\n", + " return x\n", + "\n", + " @property\n", + " def input_size(self):\n", + " return int(np.prod(self.input_shape))\n", + "\n", + " @property\n", + " def output_size(self):\n", + " return int(np.prod(self.output_shape))" + ], + "id": "586f30eed53a5ae", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "# Dataset\n", + "dataset_name = \"Digits2D\"\n", + "fold_id = 0\n", + "batch_size = 32\n", + "random_state = 0\n", + "num_workers = 0\n", + "\n", + "# Model\n", + "model_cls = NIFDL\n", + "\n", + "# Pipeline\n", + "job_output_folder = Path(os.getcwd()) / \"data\" / \"automl\" / dataset_name / model_cls.MODEL_NAME\n", + "checkpoint_folder = Path(job_output_folder) / \"checkpoints\"\n", + "pipeline_args = dict(\n", + " max_epochs=128, # increase at least to 256\n", + " max_time=\"00:00:02:00\", # DD:HH:MM:SS, increase at least to \"00:01:00:00\"\n", + ")" + ], + "id": "412328c44c55e453", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "Then, we set up the data module using the specified dataset and parameters.", + "id": "3847ed18ac6e57fd" + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "datamodule = DataModule.from_dataset_name(\n", + " dataset_name,\n", + " fold_id=fold_id,\n", + " batch_size=batch_size,\n", + " random_state=random_state,\n", + " num_workers=num_workers,\n", + ")" + ], + "id": "42cb94221bd39753", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "## Lightning Pipeline - Model Training and Evaluation\n", + "In this section, we create a Lightning Pipeline for training and evaluating the NIFDL model." + ], + "id": "d13cf82e118d84eb" + }, + { + "metadata": {}, + "cell_type": "code", + "outputs": [], + "execution_count": null, + "source": [ + "model_args = dict(\n", + " n_qubits=16,\n", + " learning_rate=2e-4,\n", + " n_layers=6,\n", + ")\n", + "lightning_pipeline = LightningPipeline(\n", + " model_cls=model_cls,\n", + " datamodule=datamodule,\n", + " checkpoint_folder=checkpoint_folder,\n", + " max_epochs=10,\n", + " max_time=\"00:00:03:00\", # DD:HH:MM:SS\n", + " overwrite_fit=True,\n", + " verbose=True,\n", + " **model_args,\n", + ")" + ], + "id": "8832b1d06113ac48" + }, + { + "metadata": {}, + "cell_type": "code", + "outputs": [], + "execution_count": null, + "source": [ + "start_time = time.perf_counter()\n", + "metrics = lightning_pipeline.run()\n", + "end_time = time.perf_counter()\n", + "elapsed_time = datetime.timedelta(seconds=end_time - start_time)\n", + "print(f\"Time taken: {elapsed_time}\")\n", + "print(\"⚡\" * 20, \"\\nValidation Metrics:\\n\", metrics, \"\\n\", \"⚡\" * 20)\n", + "test_metrics = lightning_pipeline.run_test()\n", + "print(\"⚡\" * 20, \"\\nTest Metrics:\\n\", test_metrics, \"\\n\", \"⚡\" * 20)" + ], + "id": "7d8a499f05426a0d" + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "# AutoML Pipeline - Hyperparameter Optimization", + "id": "688b66fca9889c36" + }, + { + "metadata": {}, + "cell_type": "code", + "outputs": [], + "execution_count": null, + "source": [ + "automl_pipeline = AutoMLPipeline(\n", + " model_cls=model_cls,\n", + " datamodule=datamodule,\n", + " checkpoint_folder=checkpoint_folder,\n", + " automl_iterations=5, # increase at least to 32\n", + " inner_max_epochs=10, # increase at least to 128\n", + " inner_max_time=\"00:00:01:00\", # increase at least to \"00:00:10:00\"\n", + " automl_overwrite_fit=True,\n", + " **pipeline_args\n", + ")" + ], + "id": "16001dd8071bc8b0" + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "start_time = time.perf_counter()\n", + "automl_pipeline.run()\n", + "end_time = time.perf_counter()\n", + "print(f\"Time taken: {end_time - start_time:.4f} seconds\")" + ], + "id": "8c84ee5352220970", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "code", + "source": "print(f\"Best Hyperparameters:\\n{json.dumps(automl_pipeline.get_best_params(), indent=2, default=str)}\")", + "id": "9e966f925f2b5ad3", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "lt_pipeline, metrics = automl_pipeline.run_best_pipeline()\n", + "print(\"⚡\" * 20, \"\\nValidation Metrics:\\n\", metrics, \"\\n\", \"⚡\" * 20)" + ], + "id": "3e536e4ced33a442", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "code", + "source": [ + "test_metrics = lt_pipeline.run_test()\n", + "print(\"⚡\" * 20, \"\\nTest Metrics:\\n\", test_metrics, \"\\n\", \"⚡\" * 20)" + ], + "id": "4bdef94d9cfb1944", + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "-------------------------------------------------------", + "id": "c2551cd1690867ec" + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/poetry.lock b/poetry.lock deleted file mode 100644 index 5fe6ef8..0000000 --- a/poetry.lock +++ /dev/null @@ -1,7183 +0,0 @@ -# This file is automatically @generated by Poetry 2.0.1 and should not be changed by hand. - 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"0.0.1" -dynamic = ["readme"] description = "" -authors = [{name = "Jérémie Gince", email = "gincejeremie@gmail.com"},] +authors = [{ name = "Jérémie Gince", email = "gincejeremie@gmail.com" }] +requires-python = ">=3.10,<3.13" +license = { file = "LICENSE" } classifiers = [ "Development Status :: 3 - Alpha", "Intended Audience :: Science/Research", @@ -14,7 +15,6 @@ classifiers = [ "Topic :: Software Development :: Libraries :: Python Modules", "Operating System :: OS Independent", ] -requires-python = ">=3.10,<3.13" dependencies = [ "numpy (>=1.23,<3.0.0)", "setuptools (>=65.5.1,<=80.9.0)", @@ -33,8 +33,33 @@ dependencies = [ "autoray (<=0.7.2)", "medmnist (>=3.0.2,<4.0.0)", ] -license={file="LICENSE"} +dynamic = ["readme"] +[dependency-groups] +dev = [ + "pytest-cov>=6.1.1,<7", + "pytest-json-report>=1.5.0,<2", + "pytest>=8.3.5,<9", + "docutils>=0.18.1,<0.20", + "mypy>=1.15.0,<2", + "black>=25.1.0,<26", + "nbmake>=1.5.5,<2", + "wheel>=0.45.1,<0.46", + "build>=1.2.2.post1,<2", + 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= "pytorch-cpu" +url = "https://download.pytorch.org/whl/cpu" +explicit = true + +[[tool.uv.index]] +name = "pytorch-cu128" +url = "https://download.pytorch.org/whl/cu128" +explicit = true [tool.setuptools.dynamic] readme = {file = "README.md", content-type = "text/markdown"} +[tool.hatch.build.targets.sdist] +include = ["src/matchcake_opt"] + +[tool.hatch.build.targets.wheel] +include = ["src/matchcake_opt"] + +[tool.hatch.build.targets.wheel.sources] +"src/matchcake_opt" = "matchcake_opt" [tool.setuptools] package-dir = {"" = "src"} @@ -57,36 +132,6 @@ package-dir = {"" = "src"} [tool.setuptools.packages.find] where = ["src"] -[tool.poetry] -package-mode = true -packages = [{include = "matchcake_opt", from="src"}] - -[tool.poetry.group.dev.dependencies] -pytest-cov = "^6.1.1" -pytest-json-report = "^1.5.0" -pytest = "^8.3.5" -docutils = ">=0.18.1,<0.20" -mypy = "^1.15.0" -black = "^25.1.0" -nbmake = "^1.5.5" -wheel = "^0.45.1" -build = "^1.2.2.post1" -twine = "^6.1.0" -pytest-xdist = 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