From cfd1630fe6dd78d8a5c9629507b5f74400b17c3b Mon Sep 17 00:00:00 2001 From: flowerthrower Date: Thu, 27 Aug 2026 09:48:09 +0200 Subject: [PATCH 1/9] =?UTF-8?q?=E2=9C=A8=20Add=20GNN=20for=20RL=20circuit?= =?UTF-8?q?=20embedding?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Antonio Tudisco Signed-off-by: flowerthrower Assisted-by: GPT 5.6 via Codex --- pyproject.toml | 5 + src/mqt/predictor/rl/gnn.py | 617 +++++++++++++++++++ src/mqt/predictor/rl/predictor.py | 99 ++- uv.lock | 981 ++++++++++++++++++++++++++++-- 4 files changed, 1628 insertions(+), 74 deletions(-) create mode 100644 src/mqt/predictor/rl/gnn.py diff --git a/pyproject.toml b/pyproject.toml index 567e13b6c..6504ed3e2 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -83,6 +83,11 @@ Issues = "https://github.com/munich-quantum-toolkit/predictor/issues" Discussions = "https://github.com/munich-quantum-toolkit/predictor/discussions" Research = "https://www.cda.cit.tum.de/research/quantum/" +[project.optional-dependencies] +gnn = [ + "torch-geometric>=2.7.0; python_version < '3.14'", + "torch-geometric>=2.8.0; python_version >= '3.14'", +] [dependency-groups] dev = [ diff --git a/src/mqt/predictor/rl/gnn.py b/src/mqt/predictor/rl/gnn.py new file mode 100644 index 000000000..8a3444ce5 --- /dev/null +++ b/src/mqt/predictor/rl/gnn.py @@ -0,0 +1,617 @@ +# Copyright (c) 2023 - 2026 Chair for Design Automation, TUM +# Copyright (c) 2025 - 2026 Munich Quantum Software Company GmbH +# All rights reserved. +# +# SPDX-License-Identifier: MIT +# +# Licensed under the MIT License + +"""Graph neural network support for the reinforcement learning predictor.""" + +from __future__ import annotations + +from dataclasses import dataclass +from importlib import import_module +from math import cos, pi +from pathlib import Path +from typing import TYPE_CHECKING, Any, Self, cast + +import gymnasium as gym +import numpy as np +import torch +import torch.nn.functional as functional +from gymnasium import spaces +from qiskit import QuantumCircuit +from qiskit.converters import circuit_to_dag +from qiskit.dagcircuit import DAGOpNode +from qiskit.transpiler import PassManager +from qiskit.transpiler.passes import RemoveBarriers +from sb3_contrib import MaskablePPO +from sb3_contrib.common.maskable.policies import MaskableMultiInputActorCriticPolicy +from stable_baselines3.common.torch_layers import BaseFeaturesExtractor +from torch import nn + +try: + _torch_geometric_nn = import_module("torch_geometric.nn") +except ModuleNotFoundError as error: + msg = "GNN support requires the optional dependencies. Install MQT Predictor with the 'gnn' extra." + raise ImportError(msg) from error + +AttentionalAggregation = _torch_geometric_nn.AttentionalAggregation +GraphNorm = _torch_geometric_nn.GraphNorm +SAGEConv = _torch_geometric_nn.SAGEConv + +if TYPE_CHECKING: + from numpy.typing import NDArray + from stable_baselines3.common.type_aliases import Schedule + from torch.optim import Optimizer + + from mqt.predictor.rl.predictorenv import PredictorEnv + + +NODE_OPERATION_NAMES = ( + "id", + "x", + "y", + "z", + "h", + "s", + "sdg", + "t", + "tdg", + "sx", + "rx", + "ry", + "rz", + "u", + "u1", + "u2", + "u3", + "cx", + "cy", + "cz", + "ch", + "crx", + "cry", + "crz", + "cp", + "cu", + "cu1", + "cu2", + "cu3", + "swap", + "iswap", + "ecr", + "rzz", + "rxx", + "ryy", + "zz", + "ccx", + "reset", + "measure", + "other", +) +GLOBAL_FEATURE_NAMES = ( + "num_qubits", + "depth", + "program_communication", + "critical_depth", + "entanglement_ratio", + "parallelism", + "liveness", + "measure", + "cx", + "id", + "p", + "x", + "y", + "z", + "h", + "s", + "sdg", + "t", + "tdg", + "rx", + "ry", + "rz", + "sx", + "sxdg", + "cz", + "cy", + "swap", + "ch", + "crx", + "cry", + "crz", + "cp", + "cu3", + "csx", + "rxx", + "rzz", +) +NODE_FEATURE_DIM = 52 +GLOBAL_FEATURE_DIM = 36 +NODE_SCALAR_DIM = NODE_FEATURE_DIM - len(NODE_OPERATION_NAMES) + +_GATE_INDICES = "gate_indices" +_NODE_SCALARS = "node_scalars" +_EDGE_INDEX = "edge_index" +_NUM_NODES = "num_nodes" +_NUM_EDGES = "num_edges" +_GLOBAL_FEATURES = "global_features" + + +@dataclass(frozen=True, slots=True) +class GNNConfig: + """Configuration for the opt-in GNN policy.""" + + max_nodes: int = 512 + max_edges: int = 2048 + hidden_dim: int = 128 + num_conv_wo_resnet: int = 3 + num_resnet_layers: int = 5 + dropout_p: float = 0.2 + bidirectional: bool = True + learning_rate: float = 3e-4 + gnn_learning_rate: float = 1e-4 + minimum_learning_rate_factor: float = 0.1 + n_steps: int = 2048 + batch_size: int = 64 + n_epochs: int = 10 + gamma: float = 0.98 + gae_lambda: float = 0.95 + clip_range: float = 0.2 + clip_range_vf: float = 0.2 + ent_coef: float = 0.01 + vf_coef: float = 0.5 + max_grad_norm: float = 0.5 + target_kl: float = 0.01 + + def __post_init__(self) -> None: + """Validate the configuration.""" + if self.max_nodes < 1 or self.max_edges < 1: + msg = "max_nodes and max_edges must be positive" + raise ValueError(msg) + if self.hidden_dim < 1 or self.num_conv_wo_resnet < 1 or self.num_resnet_layers < 0: + msg = "hidden_dim and num_conv_wo_resnet must be positive, and num_resnet_layers must be non-negative" + raise ValueError(msg) + if not 0 <= self.dropout_p < 1: + msg = "dropout_p must be in the interval [0, 1)" + raise ValueError(msg) + if self.learning_rate <= 0 or self.gnn_learning_rate <= 0: + msg = "learning rates must be positive" + raise ValueError(msg) + if not 0 < self.minimum_learning_rate_factor <= 1: + msg = "minimum_learning_rate_factor must be in the interval (0, 1]" + raise ValueError(msg) + + @classmethod + def paper(cls, *, max_nodes: int = 512, max_edges: int = 2048) -> Self: + """Return the tuned configuration used for the paper model.""" + return cls( + max_nodes=max_nodes, + max_edges=max_edges, + hidden_dim=119, + num_conv_wo_resnet=1, + num_resnet_layers=5, + dropout_p=0.1, + bidirectional=True, + learning_rate=1e-3, + gnn_learning_rate=1e-3, + ) + + +def _safe_float(value: object) -> float: + try: + return float(cast("Any", value)) + except (TypeError, ValueError): + return 0.0 + + +def _create_sparse_dag( + qc: QuantumCircuit, +) -> tuple[NDArray[np.int32], NDArray[np.float32], NDArray[np.int32]]: + dag = circuit_to_dag(PassManager(RemoveBarriers()).run(qc)) + nodes = list(dag.op_nodes()) + node_indices = {node: index for index, node in enumerate(nodes)} + operation_indices = {name: index for index, name in enumerate(NODE_OPERATION_NAMES)} + + gate_indices = np.zeros(len(nodes), dtype=np.int32) + node_scalars = np.zeros((len(nodes), NODE_SCALAR_DIM), dtype=np.float32) + + for index, node in enumerate(nodes): + gate_indices[index] = operation_indices.get(node.op.name, operation_indices["other"]) + raw_parameters = list(getattr(node.op, "params", ())) + parameters = [_safe_float(value) for value in raw_parameters[:3]] + parameters.extend([0.0] * (3 - len(parameters))) + node_scalars[index, :6] = [value for parameter in parameters for value in (np.sin(parameter), np.cos(parameter))] + node_scalars[index, 6] = len(node.qargs) + node_scalars[index, 7] = getattr(node.op, "num_ctrl_qubits", 0) + node_scalars[index, 8] = len(raw_parameters) + node_scalars[index, 10] = sum(isinstance(predecessor, DAGOpNode) for predecessor in dag.predecessors(node)) + node_scalars[index, 11] = sum(isinstance(successor, DAGOpNode) for successor in dag.successors(node)) + + edges = [ + (node_indices[source], node_indices[target]) + for source, target, _ in dag.edges() + if source in node_indices and target in node_indices + ] + edge_index = np.asarray(edges, dtype=np.int32).T if edges else np.empty((2, 0), dtype=np.int32) + + topological_nodes = list(dag.topological_op_nodes()) + distance_from_start: dict[DAGOpNode, int] = dict.fromkeys(topological_nodes, 0) + for node in topological_nodes: + predecessors = [predecessor for predecessor in dag.predecessors(node) if isinstance(predecessor, DAGOpNode)] + if predecessors: + distance_from_start[node] = max(distance_from_start[predecessor] + 1 for predecessor in predecessors) + + distance_to_end: dict[DAGOpNode, int] = dict.fromkeys(topological_nodes, 0) + for node in reversed(topological_nodes): + successors = [successor for successor in dag.successors(node) if isinstance(successor, DAGOpNode)] + if successors: + distance_to_end[node] = max(distance_to_end[successor] + 1 for successor in successors) + + if topological_nodes: + longest_path = max(distance_from_start[node] + distance_to_end[node] for node in topological_nodes) + for index, node in enumerate(nodes): + node_scalars[index, 9] = distance_from_start[node] + distance_to_end[node] == longest_path + + return gate_indices, node_scalars, edge_index + + +def _observation_scalar(observation: dict[str, Any], name: str) -> float: + value = np.asarray(observation[name], dtype=np.float32).reshape(-1) + if value.size != 1: + msg = f"Expected one scalar for RL feature '{name}', got shape {value.shape}." + raise ValueError(msg) + return float(value[0]) + + +def create_graph_observation( + qc: QuantumCircuit, + flat_observation: dict[str, Any], + *, + max_nodes: int, + max_edges: int, +) -> dict[str, NDArray[np.generic]]: + """Pack a circuit graph into the fixed-shape observation consumed by Stable-Baselines3.""" + gate_indices, node_scalars, edge_index = _create_sparse_dag(qc) + num_nodes = gate_indices.shape[0] + num_edges = edge_index.shape[1] + if num_nodes > max_nodes or num_edges > max_edges: + msg = ( + f"Circuit graph has {num_nodes} nodes and {num_edges} edges, but the configured GNN capacity is " + f"{max_nodes} nodes and {max_edges} edges. Increase GNNConfig.max_nodes or GNNConfig.max_edges." + ) + raise ValueError(msg) + + padded_gate_indices = np.zeros(max_nodes, dtype=np.int32) + padded_gate_indices[:num_nodes] = gate_indices + padded_node_scalars = np.zeros((max_nodes, NODE_SCALAR_DIM), dtype=np.float32) + padded_node_scalars[:num_nodes] = node_scalars + padded_edge_index = np.zeros((2, max_edges), dtype=np.int32) + padded_edge_index[:, :num_edges] = edge_index + + global_features = np.asarray( + [_observation_scalar(flat_observation, name) for name in GLOBAL_FEATURE_NAMES], dtype=np.float32 + ) + global_features[0] = qc.num_qubits + global_features[1] = qc.depth() + + return { + _GATE_INDICES: padded_gate_indices, + _NODE_SCALARS: padded_node_scalars, + _EDGE_INDEX: padded_edge_index, + _NUM_NODES: np.asarray([num_nodes], dtype=np.int32), + _NUM_EDGES: np.asarray([num_edges], dtype=np.int32), + _GLOBAL_FEATURES: global_features, + } + + +def _graph_observation_space(max_nodes: int, max_edges: int) -> spaces.Dict: + return spaces.Dict( + { + _GATE_INDICES: spaces.Box(0, len(NODE_OPERATION_NAMES) - 1, (max_nodes,), dtype=np.int32), + _NODE_SCALARS: spaces.Box(-np.inf, np.inf, (max_nodes, NODE_SCALAR_DIM), dtype=np.float32), + _EDGE_INDEX: spaces.Box(0, max_nodes - 1, (2, max_edges), dtype=np.int32), + _NUM_NODES: spaces.Box(0, max_nodes, (1,), dtype=np.int32), + _NUM_EDGES: spaces.Box(0, max_edges, (1,), dtype=np.int32), + _GLOBAL_FEATURES: spaces.Box(-np.inf, np.inf, (GLOBAL_FEATURE_DIM,), dtype=np.float32), + } + ) + + +def _graph_capacities(observation_space: spaces.Space[Any]) -> tuple[int, int]: + if not isinstance(observation_space, spaces.Dict): + msg = "The GNN policy requires a dictionary observation space." + raise TypeError(msg) + try: + node_space = observation_space[_NODE_SCALARS] + edge_space = observation_space[_EDGE_INDEX] + global_space = observation_space[_GLOBAL_FEATURES] + except KeyError as error: + msg = "The observation space does not contain the required GNN graph fields." + raise ValueError(msg) from error + if node_space.shape is None or edge_space.shape is None or global_space.shape != (GLOBAL_FEATURE_DIM,): + msg = "The observation space contains incompatible GNN graph shapes." + raise ValueError(msg) + return node_space.shape[0], edge_space.shape[1] + + +class GNNObservationWrapper(gym.Wrapper): + """Adapt the flat predictor observation to a sparse, fixed-capacity graph observation.""" + + def __init__( + self, + env: PredictorEnv, + config: GNNConfig | None = None, + *, + observation_space: spaces.Space[Any] | None = None, + ) -> None: + """Initialize the graph observation wrapper.""" + super().__init__(env) + if observation_space is None: + if config is None: + msg = "A GNN configuration is required when no saved observation space is provided." + raise ValueError(msg) + observation_space = _graph_observation_space(config.max_nodes, config.max_edges) + self.max_nodes, self.max_edges = _graph_capacities(observation_space) + self.observation_space = cast("spaces.Dict", observation_space) + + def observation(self, observation: dict[str, Any]) -> dict[str, NDArray[np.generic]]: + """Convert a flat observation and its circuit state to the graph representation.""" + predictor_env = cast("PredictorEnv", self.unwrapped) + return create_graph_observation( + predictor_env.state, + observation, + max_nodes=self.max_nodes, + max_edges=self.max_edges, + ) + + def reset( + self, + qc: Path | str | QuantumCircuit | None = None, + seed: int | None = None, + options: dict[str, Any] | None = None, + ) -> tuple[dict[str, NDArray[np.generic]], dict[str, Any]]: + """Reset the underlying predictor and return a graph observation.""" + predictor_env = cast("PredictorEnv", self.unwrapped) + observation, info = predictor_env.reset(qc, seed=seed, options=options) + return self.observation(observation), info + + def step( + self, action: Any + ) -> tuple[dict[str, NDArray[np.generic]], float, bool, bool, dict[str, Any]]: + """Step the underlying predictor and return a graph observation.""" + observation, reward, terminated, truncated, info = self.env.step(action) + return self.observation(observation), float(reward), terminated, truncated, info + + def action_masks(self) -> list[bool]: + """Forward the action mask required by MaskablePPO.""" + predictor_env = cast("PredictorEnv", self.unwrapped) + return predictor_env.action_masks() + + +class GraphSAGEEncoder(nn.Module): + """Encode variable-size circuit graphs with the prototype GraphSAGE topology.""" + + def __init__( + self, + hidden_dim: int, + num_conv_wo_resnet: int, + num_resnet_layers: int, + dropout_p: float, + *, + bidirectional: bool, + ) -> None: + """Initialize the GraphSAGE encoder.""" + super().__init__() + layer_count = num_conv_wo_resnet + num_resnet_layers + self.convs = nn.ModuleList( + [SAGEConv(NODE_FEATURE_DIM if index == 0 else hidden_dim, hidden_dim) for index in range(layer_count)] + ) + self.norms = nn.ModuleList(GraphNorm(hidden_dim) for _ in range(layer_count)) + self.activation = nn.LeakyReLU() + self.dropout = nn.Dropout(dropout_p) + self.residual_start = num_conv_wo_resnet + self.bidirectional = bidirectional + self.hidden_dim = hidden_dim + self.pool = AttentionalAggregation( + gate_nn=nn.Sequential( + nn.Linear(hidden_dim, hidden_dim // 2), + nn.Tanh(), + nn.Linear(hidden_dim // 2, 1), + ) + ) + + def forward( + self, + node_features: torch.Tensor, + edge_index: torch.Tensor, + batch: torch.Tensor, + num_graphs: int, + ) -> torch.Tensor: + """Return one embedding per circuit graph.""" + if node_features.shape[0] == 0: + return node_features.new_zeros((num_graphs, self.hidden_dim)) + + features = node_features + for index, (conv, norm) in enumerate(zip(self.convs, self.norms, strict=True)): + updated = conv(features, edge_index) + if self.bidirectional: + updated = 0.5 * (updated + conv(features, edge_index.flip(0))) + updated = self.dropout(self.activation(norm(updated, batch=batch))) + features = updated if index < self.residual_start else features + updated + return self.pool(features, index=batch, dim_size=num_graphs) + + +class GNNFeaturesExtractor(BaseFeaturesExtractor): + """Stable-Baselines3 feature extractor implementing the prototype GNN and shared trunk.""" + + def __init__( + self, + observation_space: spaces.Dict, + hidden_dim: int = 128, + num_conv_wo_resnet: int = 3, + num_resnet_layers: int = 5, + dropout_p: float = 0.2, + *, + bidirectional: bool = True, + ) -> None: + """Initialize the GNN feature extractor.""" + _graph_capacities(observation_space) + super().__init__(observation_space, features_dim=hidden_dim) + self.encoder = GraphSAGEEncoder( + hidden_dim, + num_conv_wo_resnet, + num_resnet_layers, + dropout_p, + bidirectional=bidirectional, + ) + self.trunk = nn.Sequential( + nn.Linear(hidden_dim + GLOBAL_FEATURE_DIM, hidden_dim), + nn.LayerNorm(hidden_dim), + nn.GELU(), + nn.Dropout(dropout_p), + nn.Linear(hidden_dim, hidden_dim), + nn.LayerNorm(hidden_dim), + nn.GELU(), + nn.Dropout(dropout_p), + ) + + @staticmethod + def _batch_graphs(observations: dict[str, torch.Tensor]) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + batch_size = observations[_GLOBAL_FEATURES].shape[0] + node_features: list[torch.Tensor] = [] + edge_indices: list[torch.Tensor] = [] + batches: list[torch.Tensor] = [] + node_offset = 0 + for graph_index in range(batch_size): + num_nodes = int(observations[_NUM_NODES][graph_index].reshape(-1)[0].item()) + num_edges = int(observations[_NUM_EDGES][graph_index].reshape(-1)[0].item()) + gate_indices = observations[_GATE_INDICES][graph_index, :num_nodes].long() + scalars = observations[_NODE_SCALARS][graph_index, :num_nodes] + one_hot = functional.one_hot(gate_indices, num_classes=len(NODE_OPERATION_NAMES)).to(scalars.dtype) + node_features.append(torch.cat((one_hot, scalars), dim=1)) + edge_indices.append(observations[_EDGE_INDEX][graph_index, :, :num_edges].long() + node_offset) + batches.append(torch.full((num_nodes,), graph_index, dtype=torch.long, device=scalars.device)) + node_offset += num_nodes + + return torch.cat(node_features), torch.cat(edge_indices, dim=1), torch.cat(batches) + + def forward(self, observations: dict[str, torch.Tensor]) -> torch.Tensor: + """Extract a shared latent feature vector for the actor and critic.""" + node_features, edge_index, batch = self._batch_graphs(observations) + global_features = observations[_GLOBAL_FEATURES].reshape(-1, GLOBAL_FEATURE_DIM) + graph_embedding = self.encoder(node_features, edge_index, batch, global_features.shape[0]) + return self.trunk(torch.cat((graph_embedding, global_features), dim=1)) + + +class GNNMaskableMultiInputActorCriticPolicy(MaskableMultiInputActorCriticPolicy): + """Maskable multi-input policy with a separate learning rate for the GNN encoder.""" + + def __init__( + self, + observation_space: spaces.Dict, + action_space: spaces.Space[Any], + lr_schedule: Schedule, + *, + gnn_learning_rate: float, + **kwargs: Any, + ) -> None: + """Initialize the maskable GNN policy.""" + self.gnn_learning_rate = gnn_learning_rate + super().__init__(observation_space, action_space, lr_schedule, **kwargs) + + def _build(self, lr_schedule: Schedule) -> None: + super()._build(lr_schedule) + extractor = cast("GNNFeaturesExtractor", self.features_extractor) + encoder_parameters = list(extractor.encoder.parameters()) + encoder_parameter_ids = {id(parameter) for parameter in encoder_parameters} + policy_parameters = [parameter for parameter in self.parameters() if id(parameter) not in encoder_parameter_ids] + policy_learning_rate = float(lr_schedule(1.0)) + self.optimizer = self.optimizer_class( + [ + { + "params": encoder_parameters, + "lr": self.gnn_learning_rate, + "learning_rate_scale": self.gnn_learning_rate / policy_learning_rate, + }, + {"params": policy_parameters, "lr": policy_learning_rate, "learning_rate_scale": 1.0}, + ], + **self.optimizer_kwargs, + ) + + def _get_constructor_parameters(self) -> dict[str, Any]: + data = super()._get_constructor_parameters() + data["gnn_learning_rate"] = self.gnn_learning_rate + return data + + +class GNNMaskablePPO(MaskablePPO): + """MaskablePPO variant that retains the encoder-to-policy learning-rate ratio.""" + + def _update_learning_rate(self, optimizers: list[Optimizer] | Optimizer) -> None: + current_learning_rate = self.lr_schedule(self._current_progress_remaining) + self.logger.record("train/learning_rate", current_learning_rate) + optimizer_sequence = optimizers if isinstance(optimizers, list) else (optimizers,) + for optimizer in optimizer_sequence: + for parameter_group in optimizer.param_groups: + parameter_group["lr"] = current_learning_rate * parameter_group.get("learning_rate_scale", 1.0) + + +def _cosine_schedule(initial_value: float, minimum_factor: float) -> Schedule: + def schedule(progress_remaining: float) -> float: + completed_fraction = 1.0 - progress_remaining + factor = minimum_factor + (1.0 - minimum_factor) * 0.5 * (1.0 + cos(pi * completed_fraction)) + return initial_value * factor + + return schedule + + +def create_gnn_model( + env: GNNObservationWrapper, + config: GNNConfig, + *, + verbose: int, + tensorboard_log: str, + n_steps: int, + batch_size: int, + n_epochs: int, + seed: int | None, +) -> GNNMaskablePPO: + """Create the GNN policy on top of the existing MaskablePPO implementation.""" + return GNNMaskablePPO( + GNNMaskableMultiInputActorCriticPolicy, + env, + learning_rate=_cosine_schedule(config.learning_rate, config.minimum_learning_rate_factor), + n_steps=n_steps, + batch_size=batch_size, + n_epochs=n_epochs, + gamma=config.gamma, + gae_lambda=config.gae_lambda, + clip_range=config.clip_range, + clip_range_vf=config.clip_range_vf, + ent_coef=config.ent_coef, + vf_coef=config.vf_coef, + max_grad_norm=config.max_grad_norm, + target_kl=config.target_kl, + verbose=verbose, + tensorboard_log=tensorboard_log, + seed=seed, + policy_kwargs={ + "features_extractor_class": GNNFeaturesExtractor, + "features_extractor_kwargs": { + "hidden_dim": config.hidden_dim, + "num_conv_wo_resnet": config.num_conv_wo_resnet, + "num_resnet_layers": config.num_resnet_layers, + "dropout_p": config.dropout_p, + "bidirectional": config.bidirectional, + }, + "net_arch": {"pi": [config.hidden_dim], "vf": [config.hidden_dim]}, + "activation_fn": nn.GELU, + "optimizer_class": torch.optim.AdamW, + "optimizer_kwargs": {"eps": 1e-5}, + "gnn_learning_rate": config.gnn_learning_rate, + }, + ) diff --git a/src/mqt/predictor/rl/predictor.py b/src/mqt/predictor/rl/predictor.py index 10908291c..f9dc99897 100644 --- a/src/mqt/predictor/rl/predictor.py +++ b/src/mqt/predictor/rl/predictor.py @@ -27,6 +27,7 @@ from qiskit.transpiler import Target from mqt.predictor.reward import figure_of_merit + from mqt.predictor.rl.gnn import GNNConfig class Predictor: @@ -41,6 +42,8 @@ def __init__( max_steps: int | None = None, tracer_output_path: str | Path | None = None, mdp: MDPPolicy = "v3", + graph: bool = False, + gnn_config: GNNConfig | None = None, ) -> None: """Initializes the Predictor object. @@ -53,6 +56,8 @@ def __init__( tracer_output_path: Path to export the compilation trace JSON. Defaults to None. mdp: The MDP transition policy. ``v2`` is the original strategy and ``v3`` is the default. + graph: Whether to use the opt-in GNN policy. Defaults to False. + gnn_config: Configuration for the GNN policy. Defaults to the prototype training configuration. """ logger.setLevel(logger_level) @@ -66,7 +71,15 @@ def __init__( ) self.device_name = device.description self.figure_of_merit = figure_of_merit - self.model_name = f"model_{self.figure_of_merit}_{self.device_name}_{mdp}" + self.graph = graph + if graph: + from mqt.predictor.rl.gnn import GNNConfig # ruff: ignore[import-outside-top-level] + + self.gnn_config = gnn_config or GNNConfig() + else: + self.gnn_config = gnn_config + model_prefix = "gnn" if graph else "model" + self.model_name = f"{model_prefix}_{self.figure_of_merit}_{self.device_name}_{mdp}" def compile_as_predicted( self, @@ -101,15 +114,21 @@ def compile_as_predicted( self.env.tracer_output_path = tracer_output_path self.env.pass_timeout = pass_timeout - trained_rl_model = load_model(self.model_name) + trained_rl_model = load_model(self.model_name, graph=self.graph) + + policy_env = self.env + if self.graph: + from mqt.predictor.rl.gnn import GNNObservationWrapper # ruff: ignore[import-outside-top-level] - obs, _ = self.env.reset(qc, seed=seed) + policy_env = GNNObservationWrapper(self.env, observation_space=trained_rl_model.observation_space) + + obs, _ = policy_env.reset(qc, seed=seed) used_compilation_passes = [] terminated = False truncated = False while not (terminated or truncated): - action_masks = get_action_masks(self.env) + action_masks = get_action_masks(policy_env) action, _ = trained_rl_model.predict( obs, deterministic=seed is not None, @@ -118,7 +137,7 @@ def compile_as_predicted( action = int(action) action_item = self.env.action_set[action] used_compilation_passes.append(action_item.name) - obs, _reward_val, terminated, truncated, _info = self.env.step(action) + obs, _reward_val, terminated, truncated, _info = policy_env.step(action) if not self.env.error_occurred: return self.env.state, used_compilation_passes @@ -138,17 +157,20 @@ def train_model( test: bool = False, seed: int | None = None, pass_timeout: float | None = None, + iterations: int | None = None, ) -> None: """Trains all models for the given reward functions and device. Arguments: - timesteps: The number of timesteps to train the model. Defaults to 1000. + timesteps: The number of timesteps for flat-policy training. Ignored by the GNN policy. Defaults to 1000. verbose: The verbosity level. Defaults to 2. test: Whether to train the model for testing purposes. Defaults to False. seed: The random seed to use for reproducible training. Set to None to use true randomness. Defaults to None. pass_timeout: Maximum duration in seconds for one compilation pass. Defaults to None, which disables pass timeouts. + iterations: The number of GNN rollout iterations. Defaults to 1000, or 10 in test mode. + Ignored by the flat policy. Raises: ValueError: If ``pass_timeout`` is not positive. @@ -158,7 +180,7 @@ def train_model( set_random_seed(seed) if test: # minimum training overhead - n_steps = max(timesteps, 2) + n_steps = 20 if self.graph else max(timesteps, 2) n_epochs = 1 batch_size = n_steps progress_bar = False @@ -173,30 +195,59 @@ def train_model( self.env.pass_timeout = pass_timeout try: logger.debug("Start training for: " + self.figure_of_merit + " on " + self.device_name) - model = MaskablePPO( - MaskableMultiInputActorCriticPolicy, - self.env, - verbose=verbose, - tensorboard_log=f"./{self.model_name}", - gamma=0.98, - n_steps=n_steps, - batch_size=batch_size, - n_epochs=n_epochs, - seed=seed, - ) + if self.graph: + from mqt.predictor.rl.gnn import ( # ruff: ignore[import-outside-top-level] + GNNObservationWrapper, + create_gnn_model, + ) + + assert self.gnn_config is not None + if not test: + n_steps = self.gnn_config.n_steps + n_epochs = self.gnn_config.n_epochs + batch_size = self.gnn_config.batch_size + graph_env = GNNObservationWrapper(self.env, self.gnn_config) + model = create_gnn_model( + graph_env, + self.gnn_config, + verbose=verbose, + tensorboard_log=f"./{self.model_name}", + n_steps=n_steps, + batch_size=batch_size, + n_epochs=n_epochs, + seed=seed, + ) + else: + model = MaskablePPO( + MaskableMultiInputActorCriticPolicy, + self.env, + verbose=verbose, + tensorboard_log=f"./{self.model_name}", + gamma=0.98, + n_steps=n_steps, + batch_size=batch_size, + n_epochs=n_epochs, + seed=seed, + ) # Training Loop: In each iteration, the agent collects n_steps steps (rollout), # updates the policy for n_epochs, and then repeats the process until total_timesteps steps have been taken. - model.learn(total_timesteps=timesteps, progress_bar=progress_bar) + total_timesteps = ( + n_steps * (iterations if iterations is not None else (10 if test else 1000)) + if self.graph + else timesteps + ) + model.learn(total_timesteps=total_timesteps, progress_bar=progress_bar) model.save(get_path_trained_model() / self.model_name) finally: self.env.pass_timeout = original_pass_timeout -def load_model(model_name: str) -> MaskablePPO: +def load_model(model_name: str, *, graph: bool = False) -> MaskablePPO: """Loads a trained model from the trained model folder. Arguments: model_name: The name of the model to be loaded. + graph: Whether the model uses the GNN policy. Defaults to False. Returns: The loaded model. @@ -206,6 +257,10 @@ def load_model(model_name: str) -> MaskablePPO: """ path = get_path_trained_model() if Path(path / (model_name + ".zip")).is_file(): + if graph: + from mqt.predictor.rl.gnn import GNNMaskablePPO # ruff: ignore[import-outside-top-level] + + return GNNMaskablePPO.load(path / (model_name + ".zip")) return MaskablePPO.load(path / (model_name + ".zip")) error_msg = f"The RL model '{model_name}' is not trained yet. Please train the model before using it." @@ -222,6 +277,7 @@ def rl_compile( mdp: MDPPolicy = "v3", seed: int | None = None, pass_timeout: float | None = None, + graph: bool = False, ) -> tuple[QuantumCircuit, list[str]]: """Compiles a given quantum circuit to a device optimizing for the given figure of merit. @@ -237,6 +293,8 @@ def rl_compile( seed: The seed for reproducible deterministic inference and randomized Qiskit actions. Defaults to None. pass_timeout: Maximum duration in seconds for one compilation pass. Defaults to None, which disables pass timeouts. + graph: Whether to use the opt-in GNN policy. Ignored when ``predictor_singleton`` is provided. + Defaults to False. Returns: A tuple containing the compiled quantum circuit and the compilation information. 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b/src/mqt/predictor/rl/gnn.py @@ -13,7 +13,6 @@ from dataclasses import dataclass from importlib import import_module from math import cos, pi -from pathlib import Path from typing import TYPE_CHECKING, Any, Self, cast import gymnasium as gym @@ -21,7 +20,6 @@ import torch import torch.nn.functional as functional from gymnasium import spaces -from qiskit import QuantumCircuit from qiskit.converters import circuit_to_dag from qiskit.dagcircuit import DAGOpNode from qiskit.transpiler import PassManager @@ -42,7 +40,10 @@ SAGEConv = _torch_geometric_nn.SAGEConv if TYPE_CHECKING: + from pathlib import Path + from numpy.typing import NDArray + from qiskit import QuantumCircuit from stable_baselines3.common.type_aliases import Schedule from torch.optim import Optimizer @@ -224,7 +225,9 @@ def _create_sparse_dag( raw_parameters = list(getattr(node.op, "params", ())) parameters = [_safe_float(value) for value in raw_parameters[:3]] parameters.extend([0.0] * (3 - len(parameters))) - node_scalars[index, :6] = [value for parameter in parameters for value in (np.sin(parameter), np.cos(parameter))] + node_scalars[index, :6] = [ + value for parameter in parameters for value in (np.sin(parameter), np.cos(parameter)) + ] node_scalars[index, 6] = len(node.qargs) node_scalars[index, 7] = getattr(node.op, "num_ctrl_qubits", 0) node_scalars[index, 8] = len(raw_parameters) @@ -309,16 +312,14 @@ def create_graph_observation( def _graph_observation_space(max_nodes: int, max_edges: int) -> spaces.Dict: - return spaces.Dict( - { - _GATE_INDICES: spaces.Box(0, len(NODE_OPERATION_NAMES) - 1, (max_nodes,), dtype=np.int32), - _NODE_SCALARS: spaces.Box(-np.inf, np.inf, (max_nodes, NODE_SCALAR_DIM), dtype=np.float32), - _EDGE_INDEX: spaces.Box(0, max_nodes - 1, (2, max_edges), dtype=np.int32), - _NUM_NODES: spaces.Box(0, max_nodes, (1,), dtype=np.int32), - _NUM_EDGES: spaces.Box(0, max_edges, (1,), dtype=np.int32), - _GLOBAL_FEATURES: spaces.Box(-np.inf, np.inf, (GLOBAL_FEATURE_DIM,), dtype=np.float32), - } - ) + return spaces.Dict({ + _GATE_INDICES: spaces.Box(0, len(NODE_OPERATION_NAMES) - 1, (max_nodes,), dtype=np.int32), + _NODE_SCALARS: spaces.Box(-np.inf, np.inf, (max_nodes, NODE_SCALAR_DIM), dtype=np.float32), + _EDGE_INDEX: spaces.Box(0, max_nodes - 1, (2, max_edges), dtype=np.int32), + _NUM_NODES: spaces.Box(0, max_nodes, (1,), dtype=np.int32), + _NUM_EDGES: spaces.Box(0, max_edges, (1,), dtype=np.int32), + _GLOBAL_FEATURES: spaces.Box(-np.inf, np.inf, (GLOBAL_FEATURE_DIM,), dtype=np.float32), + }) def _graph_capacities(observation_space: spaces.Space[Any]) -> tuple[int, int]: @@ -379,9 +380,7 @@ def reset( observation, info = predictor_env.reset(qc, seed=seed, options=options) return self.observation(observation), info - def step( - self, action: Any - ) -> tuple[dict[str, NDArray[np.generic]], float, bool, bool, dict[str, Any]]: + def step(self, action: Any) -> tuple[dict[str, NDArray[np.generic]], float, bool, bool, dict[str, Any]]: """Step the underlying predictor and return a graph observation.""" observation, reward, terminated, truncated, info = self.env.step(action) return self.observation(observation), float(reward), terminated, truncated, info @@ -407,9 +406,9 @@ def __init__( """Initialize the GraphSAGE encoder.""" super().__init__() layer_count = num_conv_wo_resnet + num_resnet_layers - self.convs = nn.ModuleList( - [SAGEConv(NODE_FEATURE_DIM if index == 0 else hidden_dim, hidden_dim) for index in range(layer_count)] - ) + self.convs = nn.ModuleList([ + SAGEConv(NODE_FEATURE_DIM if index == 0 else hidden_dim, hidden_dim) for index in range(layer_count) + ]) self.norms = nn.ModuleList(GraphNorm(hidden_dim) for _ in range(layer_count)) self.activation = nn.LeakyReLU() self.dropout = nn.Dropout(dropout_p) From 137059eb806aff664d666d7b1d3166654b626c50 Mon Sep 17 00:00:00 2001 From: flowerthrower Date: Thu, 27 Aug 2026 12:41:27 +0200 Subject: [PATCH 3/9] =?UTF-8?q?=E2=99=BB=EF=B8=8F=20Preserve=20exact=20rag?= =?UTF-8?q?ged=20GNN=20observations?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: flowerthrower Assisted-by: GPT 5.6 via Codex --- pyproject.toml | 2 +- src/mqt/predictor/rl/gnn.py | 413 ++++++++++++++++++++---------- src/mqt/predictor/rl/predictor.py | 27 +- uv.lock | 2 +- 4 files changed, 300 insertions(+), 144 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 6504ed3e2..5d44301ef 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -57,7 +57,7 @@ dependencies = [ "pytket-qiskit>=0.78; python_version>='3.14'", "qiskit>=2.1.1", "rich>=12.6", - "sb3-contrib>=2", + "sb3-contrib>=2.3", "scikit-learn>=1.5.1", "scikit-learn>=1.7.2; python_version>='3.14'", "tensorboard>=2.17", diff --git a/src/mqt/predictor/rl/gnn.py b/src/mqt/predictor/rl/gnn.py index 7011161fc..82682a0e8 100644 --- a/src/mqt/predictor/rl/gnn.py +++ b/src/mqt/predictor/rl/gnn.py @@ -13,7 +13,7 @@ from dataclasses import dataclass from importlib import import_module from math import cos, pi -from typing import TYPE_CHECKING, Any, Self, cast +from typing import TYPE_CHECKING, Any, NamedTuple, Self, cast import gymnasium as gym import numpy as np @@ -25,30 +25,60 @@ from qiskit.transpiler import PassManager from qiskit.transpiler.passes import RemoveBarriers from sb3_contrib import MaskablePPO +from sb3_contrib.common.maskable.buffers import MaskableDictRolloutBuffer from sb3_contrib.common.maskable.policies import MaskableMultiInputActorCriticPolicy +from sb3_contrib.common.maskable.utils import get_action_masks, is_masking_supported from stable_baselines3.common.torch_layers import BaseFeaturesExtractor from torch import nn try: + _torch_geometric_data = import_module("torch_geometric.data") _torch_geometric_nn = import_module("torch_geometric.nn") except ModuleNotFoundError as error: msg = "GNN support requires the optional dependencies. Install MQT Predictor with the 'gnn' extra." raise ImportError(msg) from error AttentionalAggregation = _torch_geometric_nn.AttentionalAggregation +_PyGBatch: Any = _torch_geometric_data.Batch +_PyGData: Any = _torch_geometric_data.Data GraphNorm = _torch_geometric_nn.GraphNorm SAGEConv = _torch_geometric_nn.SAGEConv if TYPE_CHECKING: + from collections.abc import Generator, Sequence from pathlib import Path + from typing import Protocol from numpy.typing import NDArray from qiskit import QuantumCircuit + from sb3_contrib.common.maskable.distributions import MaskableDistribution + from stable_baselines3.common.buffers import RolloutBuffer + from stable_baselines3.common.callbacks import BaseCallback from stable_baselines3.common.type_aliases import Schedule + from stable_baselines3.common.vec_env import VecEnv, VecNormalize from torch.optim import Optimizer from mqt.predictor.rl.predictorenv import PredictorEnv + class GraphData(Protocol): + """Typed subset of the optional PyG Data API used here.""" + + num_nodes: int + + def __getitem__(self, key: str) -> torch.Tensor: + """Return a graph tensor by key.""" + ... + + def to(self, device: torch.device | str) -> Self: + """Move the graph tensors to a device.""" + ... + + class GraphBatch(GraphData, Protocol): + """Typed subset of the optional PyG Batch API used here.""" + + batch: torch.Tensor + num_graphs: int + NODE_OPERATION_NAMES = ( "id", @@ -137,17 +167,14 @@ _GATE_INDICES = "gate_indices" _NODE_SCALARS = "node_scalars" _EDGE_INDEX = "edge_index" -_NUM_NODES = "num_nodes" -_NUM_EDGES = "num_edges" _GLOBAL_FEATURES = "global_features" +_TERMINAL_GRAPH_OBSERVATION = "_mqt_gnn_terminal_observation" @dataclass(frozen=True, slots=True) class GNNConfig: """Configuration for the opt-in GNN policy.""" - max_nodes: int = 512 - max_edges: int = 2048 hidden_dim: int = 128 num_conv_wo_resnet: int = 3 num_resnet_layers: int = 5 @@ -170,9 +197,6 @@ class GNNConfig: def __post_init__(self) -> None: """Validate the configuration.""" - if self.max_nodes < 1 or self.max_edges < 1: - msg = "max_nodes and max_edges must be positive" - raise ValueError(msg) if self.hidden_dim < 1 or self.num_conv_wo_resnet < 1 or self.num_resnet_layers < 0: msg = "hidden_dim and num_conv_wo_resnet must be positive, and num_resnet_layers must be non-negative" raise ValueError(msg) @@ -187,11 +211,9 @@ def __post_init__(self) -> None: raise ValueError(msg) @classmethod - def paper(cls, *, max_nodes: int = 512, max_edges: int = 2048) -> Self: + def paper(cls) -> Self: """Return the tuned configuration used for the paper model.""" return cls( - max_nodes=max_nodes, - max_edges=max_edges, hidden_dim=119, num_conv_wo_resnet=1, num_resnet_layers=5, @@ -273,101 +295,33 @@ def _observation_scalar(observation: dict[str, Any], name: str) -> float: def create_graph_observation( qc: QuantumCircuit, flat_observation: dict[str, Any], - *, - max_nodes: int, - max_edges: int, -) -> dict[str, NDArray[np.generic]]: - """Pack a circuit graph into the fixed-shape observation consumed by Stable-Baselines3.""" +) -> GraphData: + """Create an exact, variable-size graph alongside the regular RL observation.""" gate_indices, node_scalars, edge_index = _create_sparse_dag(qc) - num_nodes = gate_indices.shape[0] - num_edges = edge_index.shape[1] - if num_nodes > max_nodes or num_edges > max_edges: - msg = ( - f"Circuit graph has {num_nodes} nodes and {num_edges} edges, but the configured GNN capacity is " - f"{max_nodes} nodes and {max_edges} edges. Increase GNNConfig.max_nodes or GNNConfig.max_edges." - ) - raise ValueError(msg) - - padded_gate_indices = np.zeros(max_nodes, dtype=np.int32) - padded_gate_indices[:num_nodes] = gate_indices - padded_node_scalars = np.zeros((max_nodes, NODE_SCALAR_DIM), dtype=np.float32) - padded_node_scalars[:num_nodes] = node_scalars - padded_edge_index = np.zeros((2, max_edges), dtype=np.int32) - padded_edge_index[:, :num_edges] = edge_index - global_features = np.asarray( [_observation_scalar(flat_observation, name) for name in GLOBAL_FEATURE_NAMES], dtype=np.float32 ) global_features[0] = qc.num_qubits global_features[1] = qc.depth() - return { - _GATE_INDICES: padded_gate_indices, - _NODE_SCALARS: padded_node_scalars, - _EDGE_INDEX: padded_edge_index, - _NUM_NODES: np.asarray([num_nodes], dtype=np.int32), - _NUM_EDGES: np.asarray([num_edges], dtype=np.int32), - _GLOBAL_FEATURES: global_features, - } - - -def _graph_observation_space(max_nodes: int, max_edges: int) -> spaces.Dict: - return spaces.Dict({ - _GATE_INDICES: spaces.Box(0, len(NODE_OPERATION_NAMES) - 1, (max_nodes,), dtype=np.int32), - _NODE_SCALARS: spaces.Box(-np.inf, np.inf, (max_nodes, NODE_SCALAR_DIM), dtype=np.float32), - _EDGE_INDEX: spaces.Box(0, max_nodes - 1, (2, max_edges), dtype=np.int32), - _NUM_NODES: spaces.Box(0, max_nodes, (1,), dtype=np.int32), - _NUM_EDGES: spaces.Box(0, max_edges, (1,), dtype=np.int32), - _GLOBAL_FEATURES: spaces.Box(-np.inf, np.inf, (GLOBAL_FEATURE_DIM,), dtype=np.float32), - }) - - -def _graph_capacities(observation_space: spaces.Space[Any]) -> tuple[int, int]: - if not isinstance(observation_space, spaces.Dict): - msg = "The GNN policy requires a dictionary observation space." - raise TypeError(msg) - try: - node_space = observation_space[_NODE_SCALARS] - edge_space = observation_space[_EDGE_INDEX] - global_space = observation_space[_GLOBAL_FEATURES] - except KeyError as error: - msg = "The observation space does not contain the required GNN graph fields." - raise ValueError(msg) from error - if node_space.shape is None or edge_space.shape is None or global_space.shape != (GLOBAL_FEATURE_DIM,): - msg = "The observation space contains incompatible GNN graph shapes." - raise ValueError(msg) - return node_space.shape[0], edge_space.shape[1] + return cast( + "GraphData", + _PyGData( + gate_indices=torch.as_tensor(gate_indices, dtype=torch.long), + node_scalars=torch.as_tensor(node_scalars, dtype=torch.float32), + edge_index=torch.as_tensor(edge_index, dtype=torch.long), + global_features=torch.as_tensor(global_features, dtype=torch.float32).reshape(1, -1), + num_nodes=gate_indices.shape[0], + ), + ) class GNNObservationWrapper(gym.Wrapper): - """Adapt the flat predictor observation to a sparse, fixed-capacity graph observation.""" + """Maintain a variable-size graph sidecar while preserving the regular RL interface.""" - def __init__( - self, - env: PredictorEnv, - config: GNNConfig | None = None, - *, - observation_space: spaces.Space[Any] | None = None, - ) -> None: - """Initialize the graph observation wrapper.""" - super().__init__(env) - if observation_space is None: - if config is None: - msg = "A GNN configuration is required when no saved observation space is provided." - raise ValueError(msg) - observation_space = _graph_observation_space(config.max_nodes, config.max_edges) - self.max_nodes, self.max_edges = _graph_capacities(observation_space) - self.observation_space = cast("spaces.Dict", observation_space) - - def observation(self, observation: dict[str, Any]) -> dict[str, NDArray[np.generic]]: - """Convert a flat observation and its circuit state to the graph representation.""" + def _update_graph_observation(self, observation: dict[str, Any]) -> None: predictor_env = cast("PredictorEnv", self.unwrapped) - return create_graph_observation( - predictor_env.state, - observation, - max_nodes=self.max_nodes, - max_edges=self.max_edges, - ) + self.graph_observation = create_graph_observation(predictor_env.state, observation) def reset( self, @@ -375,15 +329,19 @@ def reset( seed: int | None = None, options: dict[str, Any] | None = None, ) -> tuple[dict[str, NDArray[np.generic]], dict[str, Any]]: - """Reset the underlying predictor and return a graph observation.""" + """Reset the predictor and refresh the graph sidecar.""" predictor_env = cast("PredictorEnv", self.unwrapped) observation, info = predictor_env.reset(qc, seed=seed, options=options) - return self.observation(observation), info + self._update_graph_observation(observation) + return observation, info - def step(self, action: Any) -> tuple[dict[str, NDArray[np.generic]], float, bool, bool, dict[str, Any]]: - """Step the underlying predictor and return a graph observation.""" + def step(self, action: int) -> tuple[dict[str, NDArray[np.generic]], float, bool, bool, dict[str, Any]]: + """Step the predictor and refresh the graph sidecar.""" observation, reward, terminated, truncated, info = self.env.step(action) - return self.observation(observation), float(reward), terminated, truncated, info + self._update_graph_observation(observation) + if truncated and not terminated: + info[_TERMINAL_GRAPH_OBSERVATION] = self.graph_observation + return observation, float(reward), terminated, truncated, info def action_masks(self) -> list[bool]: """Forward the action mask required by MaskablePPO.""" @@ -458,7 +416,6 @@ def __init__( bidirectional: bool = True, ) -> None: """Initialize the GNN feature extractor.""" - _graph_capacities(observation_space) super().__init__(observation_space, features_dim=hidden_dim) self.encoder = GraphSAGEEncoder( hidden_dim, @@ -478,34 +435,101 @@ def __init__( nn.Dropout(dropout_p), ) - @staticmethod - def _batch_graphs(observations: dict[str, torch.Tensor]) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: - batch_size = observations[_GLOBAL_FEATURES].shape[0] - node_features: list[torch.Tensor] = [] - edge_indices: list[torch.Tensor] = [] - batches: list[torch.Tensor] = [] - node_offset = 0 - for graph_index in range(batch_size): - num_nodes = int(observations[_NUM_NODES][graph_index].reshape(-1)[0].item()) - num_edges = int(observations[_NUM_EDGES][graph_index].reshape(-1)[0].item()) - gate_indices = observations[_GATE_INDICES][graph_index, :num_nodes].long() - scalars = observations[_NODE_SCALARS][graph_index, :num_nodes] - one_hot = functional.one_hot(gate_indices, num_classes=len(NODE_OPERATION_NAMES)).to(scalars.dtype) - node_features.append(torch.cat((one_hot, scalars), dim=1)) - edge_indices.append(observations[_EDGE_INDEX][graph_index, :, :num_edges].long() + node_offset) - batches.append(torch.full((num_nodes,), graph_index, dtype=torch.long, device=scalars.device)) - node_offset += num_nodes - - return torch.cat(node_features), torch.cat(edge_indices, dim=1), torch.cat(batches) - - def forward(self, observations: dict[str, torch.Tensor]) -> torch.Tensor: + def forward(self, observations: GraphBatch) -> torch.Tensor: """Extract a shared latent feature vector for the actor and critic.""" - node_features, edge_index, batch = self._batch_graphs(observations) + gate_indices = observations[_GATE_INDICES] + node_scalars = observations[_NODE_SCALARS] + edge_index = observations[_EDGE_INDEX] + batch = observations.batch global_features = observations[_GLOBAL_FEATURES].reshape(-1, GLOBAL_FEATURE_DIM) - graph_embedding = self.encoder(node_features, edge_index, batch, global_features.shape[0]) + one_hot = functional.one_hot(gate_indices, num_classes=len(NODE_OPERATION_NAMES)).to(node_scalars.dtype) + node_features = torch.cat((one_hot, node_scalars), dim=1) + graph_embedding = self.encoder(node_features, edge_index, batch, observations.num_graphs) return self.trunk(torch.cat((graph_embedding, global_features), dim=1)) +class GNNMaskableDictRolloutBufferSamples(NamedTuple): + """A MaskablePPO minibatch with a variable-size graph batch.""" + + observations: GraphBatch + actions: torch.Tensor + old_values: torch.Tensor + old_log_prob: torch.Tensor + advantages: torch.Tensor + returns: torch.Tensor + action_masks: torch.Tensor + + +class GNNMaskableDictRolloutBuffer(MaskableDictRolloutBuffer): + """Store graph sidecars while retaining MaskablePPO's numeric rollout data and GAE.""" + + graph_observations: NDArray[np.object_] + + def reset(self) -> None: + """Reset both the standard rollout data and graph sidecars.""" + self.graph_observations = np.empty((self.buffer_size, self.n_envs), dtype=object) + super().reset() + + def add( # ty: ignore[invalid-method-override] + self, + obs: dict[str, NDArray[np.generic]], + action: NDArray[np.generic], + reward: NDArray[np.generic], + episode_start: NDArray[np.generic], + value: torch.Tensor, + log_prob: torch.Tensor, + *, + action_masks: NDArray[np.generic] | None = None, + graph_observations: Sequence[GraphData] | None = None, + ) -> None: + """Add one vectorized transition and its graph sidecars.""" + if graph_observations is None: + msg = "GNN rollouts require graph observations." + raise ValueError(msg) + if len(graph_observations) != self.n_envs: + msg = f"Expected {self.n_envs} graph observations, got {len(graph_observations)}." + raise ValueError(msg) + self.graph_observations[self.pos] = graph_observations + super().add(obs, action, reward, episode_start, value, log_prob, action_masks=action_masks) + + def get( # ty: ignore[invalid-method-override] + self, batch_size: int | None = None + ) -> Generator[GNNMaskableDictRolloutBufferSamples, None, None]: + """Yield shuffled minibatches with PyG's exact ragged batching.""" + assert self.full + indices = np.random.permutation(self.buffer_size * self.n_envs) # ruff: ignore[numpy-legacy-random] + if not self.generator_ready: + self.graph_observations = self.swap_and_flatten(self.graph_observations).reshape(-1) + for tensor_name in ("actions", "values", "log_probs", "advantages", "returns", "action_masks"): + self.__dict__[tensor_name] = self.swap_and_flatten(self.__dict__[tensor_name]) + self.generator_ready = True + + if batch_size is None: + batch_size = self.buffer_size * self.n_envs + + start_index = 0 + while start_index < self.buffer_size * self.n_envs: + yield self._get_samples(indices[start_index : start_index + batch_size]) + start_index += batch_size + + def _get_samples( # ty: ignore[invalid-method-override] + self, + batch_inds: NDArray[np.int64], + _env: VecNormalize | None = None, + ) -> GNNMaskableDictRolloutBufferSamples: + graphs = [cast("GraphData", self.graph_observations[index]) for index in batch_inds] + observations = cast("GraphBatch", _PyGBatch.from_data_list(graphs).to(self.device)) + return GNNMaskableDictRolloutBufferSamples( + observations=observations, + actions=self.to_torch(self.actions[batch_inds]), + old_values=self.to_torch(self.values[batch_inds].flatten()), + old_log_prob=self.to_torch(self.log_probs[batch_inds].flatten()), + advantages=self.to_torch(self.advantages[batch_inds].flatten()), + returns=self.to_torch(self.returns[batch_inds].flatten()), + action_masks=self.to_torch(self.action_masks[batch_inds].reshape(-1, self.mask_dims)), + ) + + class GNNMaskableMultiInputActorCriticPolicy(MaskableMultiInputActorCriticPolicy): """Maskable multi-input policy with a separate learning rate for the GNN encoder.""" @@ -516,12 +540,59 @@ def __init__( lr_schedule: Schedule, *, gnn_learning_rate: float, - **kwargs: Any, + **kwargs: Any, # ruff: ignore[any-type] ) -> None: """Initialize the maskable GNN policy.""" self.gnn_learning_rate = gnn_learning_rate super().__init__(observation_space, action_space, lr_schedule, **kwargs) + def obs_to_tensor( # ty: ignore[invalid-method-override] + self, + observation: GraphData | GraphBatch | Sequence[GraphData], + ) -> tuple[GraphBatch, bool]: + """Move one graph or an exact ragged graph batch to the policy device.""" + if isinstance(observation, _PyGBatch): + batch = observation + vectorized = True + elif isinstance(observation, _PyGData): + batch = _PyGBatch.from_data_list([observation]) + vectorized = False + else: + batch = _PyGBatch.from_data_list(list(observation)) + vectorized = True + return cast("GraphBatch", batch.to(self.device)), vectorized + + def extract_features( # ty: ignore[invalid-method-override] + self, + obs: GraphBatch, + features_extractor: BaseFeaturesExtractor | None = None, + ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]: + """Apply the GNN extractor directly, without SB3's fixed-shape preprocessing.""" + if features_extractor is not None: + return features_extractor(obs) + if self.share_features_extractor: + return self.features_extractor(obs) + return self.pi_features_extractor(obs), self.vf_features_extractor(obs) + + def get_distribution( # ty: ignore[invalid-method-override] + self, + obs: GraphBatch, + action_masks: NDArray[np.bool_] | None = None, + ) -> MaskableDistribution: + """Return the masked action distribution for a graph batch.""" + features = cast("torch.Tensor", self.extract_features(obs, self.pi_features_extractor)) + latent_pi = self.mlp_extractor.forward_actor(features) + distribution = self._get_action_dist_from_latent(latent_pi) + if action_masks is not None: + distribution.apply_masking(action_masks) + return distribution + + def predict_values(self, obs: GraphBatch) -> torch.Tensor: # ty: ignore[invalid-method-override] + """Estimate values for a graph batch.""" + features = cast("torch.Tensor", self.extract_features(obs, self.vf_features_extractor)) + latent_vf = self.mlp_extractor.forward_critic(features) + return self.value_net(latent_vf) + def _build(self, lr_schedule: Schedule) -> None: super()._build(lr_schedule) extractor = cast("GNNFeaturesExtractor", self.features_extractor) @@ -550,6 +621,90 @@ def _get_constructor_parameters(self) -> dict[str, Any]: class GNNMaskablePPO(MaskablePPO): """MaskablePPO variant that retains the encoder-to-policy learning-rate ratio.""" + def collect_rollouts( + self, + env: VecEnv, + callback: BaseCallback, + rollout_buffer: RolloutBuffer, + n_rollout_steps: int, + use_masking: bool = True, + ) -> bool: + """Collect rollouts with graph sidecars while retaining MaskablePPO's rollout logic.""" + assert isinstance(rollout_buffer, GNNMaskableDictRolloutBuffer) + assert self._last_obs is not None + policy = cast("GNNMaskableMultiInputActorCriticPolicy", self.policy) + policy.set_training_mode(False) + n_steps = 0 + action_masks = None + rollout_buffer.reset() + + if use_masking and not is_masking_supported(env): + msg = "Environment does not support action masking. Consider using ActionMasker wrapper." + raise ValueError(msg) + + graph_observations = cast("list[GraphData]", env.get_attr("graph_observation")) + callback.on_rollout_start() + + while n_steps < n_rollout_steps: + with torch.no_grad(): + obs_tensor, _ = policy.obs_to_tensor(graph_observations) + if use_masking: + action_masks = get_action_masks(env) + actions, values, log_probs = policy(obs_tensor, action_masks=action_masks) + + actions = actions.cpu().numpy() + new_obs, rewards, dones, infos = env.step(actions) + terminal_graph_observations = [info.pop(_TERMINAL_GRAPH_OBSERVATION, None) for info in infos] + new_graph_observations = cast("list[GraphData]", env.get_attr("graph_observation")) + + self.num_timesteps += env.num_envs + callback.update_locals(locals()) + if not callback.on_step(): + return False + + self._update_info_buffer(infos, dones) + n_steps += 1 + + if isinstance(self.action_space, spaces.Discrete): + actions = actions.reshape(-1, 1) + + for index, done in enumerate(dones): + if ( + done + and infos[index].get("terminal_observation") is not None + and infos[index].get("TimeLimit.truncated", False) + ): + terminal_graph_observation = terminal_graph_observations[index] + if terminal_graph_observation is None: + msg = "The GNN environment did not provide the graph for a truncated episode." + raise RuntimeError(msg) + terminal_obs, _ = policy.obs_to_tensor(cast("GraphData", terminal_graph_observation)) + with torch.no_grad(): + terminal_value = policy.predict_values(terminal_obs)[0] + rewards[index] += self.gamma * float(terminal_value.item()) + + rollout_buffer.add( + cast("dict[str, NDArray[np.generic]]", self._last_obs), + actions, + rewards, + cast("NDArray[np.generic]", self._last_episode_starts), + values, + log_probs, + action_masks=action_masks, + graph_observations=graph_observations, + ) + self._last_obs = new_obs + self._last_episode_starts = dones + graph_observations = new_graph_observations + + with torch.no_grad(): + final_obs, _ = policy.obs_to_tensor(graph_observations) + values = policy.predict_values(final_obs) + + rollout_buffer.compute_returns_and_advantage(last_values=values, dones=dones) + callback.on_rollout_end() + return True + def _update_learning_rate(self, optimizers: list[Optimizer] | Optimizer) -> None: current_learning_rate = self.lr_schedule(self._current_progress_remaining) self.logger.record("train/learning_rate", current_learning_rate) @@ -574,9 +729,6 @@ def create_gnn_model( *, verbose: int, tensorboard_log: str, - n_steps: int, - batch_size: int, - n_epochs: int, seed: int | None, ) -> GNNMaskablePPO: """Create the GNN policy on top of the existing MaskablePPO implementation.""" @@ -584,9 +736,9 @@ def create_gnn_model( GNNMaskableMultiInputActorCriticPolicy, env, learning_rate=_cosine_schedule(config.learning_rate, config.minimum_learning_rate_factor), - n_steps=n_steps, - batch_size=batch_size, - n_epochs=n_epochs, + n_steps=config.n_steps, + batch_size=config.batch_size, + n_epochs=config.n_epochs, gamma=config.gamma, gae_lambda=config.gae_lambda, clip_range=config.clip_range, @@ -598,6 +750,7 @@ def create_gnn_model( verbose=verbose, tensorboard_log=tensorboard_log, seed=seed, + rollout_buffer_class=GNNMaskableDictRolloutBuffer, policy_kwargs={ "features_extractor_class": GNNFeaturesExtractor, "features_extractor_kwargs": { diff --git a/src/mqt/predictor/rl/predictor.py b/src/mqt/predictor/rl/predictor.py index f9dc99897..b84891e18 100644 --- a/src/mqt/predictor/rl/predictor.py +++ b/src/mqt/predictor/rl/predictor.py @@ -11,6 +11,7 @@ from __future__ import annotations import logging +from dataclasses import replace from pathlib import Path from typing import TYPE_CHECKING @@ -27,7 +28,7 @@ from qiskit.transpiler import Target from mqt.predictor.reward import figure_of_merit - from mqt.predictor.rl.gnn import GNNConfig + from mqt.predictor.rl.gnn import GNNConfig, GNNObservationWrapper class Predictor: @@ -116,11 +117,13 @@ def compile_as_predicted( trained_rl_model = load_model(self.model_name, graph=self.graph) + graph_env: GNNObservationWrapper | None = None policy_env = self.env if self.graph: from mqt.predictor.rl.gnn import GNNObservationWrapper # ruff: ignore[import-outside-top-level] - policy_env = GNNObservationWrapper(self.env, observation_space=trained_rl_model.observation_space) + graph_env = GNNObservationWrapper(self.env) + policy_env = graph_env obs, _ = policy_env.reset(qc, seed=seed) @@ -129,8 +132,9 @@ def compile_as_predicted( truncated = False while not (terminated or truncated): action_masks = get_action_masks(policy_env) + policy_observation = graph_env.graph_observation if graph_env is not None else obs action, _ = trained_rl_model.predict( - obs, + policy_observation, # ty: ignore[invalid-argument-type] deterministic=seed is not None, action_masks=action_masks, ) @@ -202,19 +206,18 @@ def train_model( ) assert self.gnn_config is not None - if not test: - n_steps = self.gnn_config.n_steps - n_epochs = self.gnn_config.n_epochs - batch_size = self.gnn_config.batch_size - graph_env = GNNObservationWrapper(self.env, self.gnn_config) + effective_gnn_config = ( + replace(self.gnn_config, n_steps=n_steps, n_epochs=n_epochs, batch_size=batch_size) + if test + else self.gnn_config + ) + n_steps = effective_gnn_config.n_steps + graph_env = GNNObservationWrapper(self.env) model = create_gnn_model( graph_env, - self.gnn_config, + effective_gnn_config, verbose=verbose, tensorboard_log=f"./{self.model_name}", - n_steps=n_steps, - batch_size=batch_size, - n_epochs=n_epochs, seed=seed, ) else: diff --git a/uv.lock b/uv.lock index c7bc4499f..e734ec7fa 100644 --- a/uv.lock +++ b/uv.lock @@ -1726,7 +1726,7 @@ requires-dist = [ { name = "pytket-qiskit", marker = "python_full_version >= '3.14'", specifier = ">=0.78" }, { name = "qiskit", specifier = ">=2.1.1" }, { name = "rich", specifier = ">=12.6" }, - { name = "sb3-contrib", specifier = ">=2" }, + { name = "sb3-contrib", specifier = ">=2.3" }, { name = "scikit-learn", specifier = ">=1.5.1" }, { name = "scikit-learn", marker = "python_full_version >= '3.14'", specifier = ">=1.7.2" }, { name = "tensorboard", specifier = ">=2.17" }, From 3a147c14aed963f8143a0138cfcd08f32d3fb506 Mon Sep 17 00:00:00 2001 From: flowerthrower Date: Thu, 27 Aug 2026 12:47:40 +0200 Subject: [PATCH 4/9] =?UTF-8?q?=F0=9F=94=A7=20Recognize=20the=20GAE=20doma?= =?UTF-8?q?in=20term?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: flowerthrower Assisted-by: GPT 5.6 via Codex --- pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/pyproject.toml b/pyproject.toml index 5d44301ef..7d76365de 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -231,6 +231,7 @@ wille = "wille" anc = "anc" aer = "aer" fom = "fom" +gae = "gae" [tool.ty.src] From edbeeb47bd1d111be26a38a1f784d8e1d2c139e3 Mon Sep 17 00:00:00 2001 From: flowerthrower Date: Thu, 27 Aug 2026 12:49:43 +0200 Subject: [PATCH 5/9] =?UTF-8?q?=F0=9F=90=9B=20Preserve=20the=20flat=20mode?= =?UTF-8?q?l=20loader=20call?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: flowerthrower Assisted-by: GPT 5.6 via Codex --- src/mqt/predictor/rl/predictor.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/mqt/predictor/rl/predictor.py b/src/mqt/predictor/rl/predictor.py index b84891e18..09a16492e 100644 --- a/src/mqt/predictor/rl/predictor.py +++ b/src/mqt/predictor/rl/predictor.py @@ -115,7 +115,7 @@ def compile_as_predicted( self.env.tracer_output_path = tracer_output_path self.env.pass_timeout = pass_timeout - trained_rl_model = load_model(self.model_name, graph=self.graph) + trained_rl_model = load_model(self.model_name, graph=True) if self.graph else load_model(self.model_name) graph_env: GNNObservationWrapper | None = None policy_env = self.env From 5143ddd5b0271e2fc46355f528ea2134fa7f02f6 Mon Sep 17 00:00:00 2001 From: flowerthrower Date: Tue, 22 Sep 2026 16:09:21 +0200 Subject: [PATCH 6/9] =?UTF-8?q?=F0=9F=90=9B=20Honor=20GNN=20training=20tim?= =?UTF-8?q?estep=20budgets?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: flowerthrower Assisted-by: GPT 5.6 via Codex --- CHANGELOG.md | 3 +++ UPGRADING.md | 19 +++++++++++++ docs/setup.md | 37 ++++++++++++++++++++++++++ src/mqt/predictor/rl/predictor.py | 17 ++++-------- tests/compilation/test_predictor_rl.py | 21 +++++++++++++++ 5 files changed, 85 insertions(+), 12 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 4e556b1e3..d1bf28e6b 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -12,6 +12,8 @@ releases may include breaking changes. ### Added +- ✨ Add an opt-in GNN policy with a tuned paper preset and SB3 training using + the shared timestep budget ([#800]) ([**@flowerthrower**]) - ✨ Add opt-in per-pass timeouts for RL training and inference ([#798]) ([**@flowerthrower**]) - ✨ Add TKET's `KAKDecomposition`, `GraphPlacement`, and `NoiseAwarePlacement` @@ -119,6 +121,7 @@ for previous changelogs._ [#831]: https://github.com/munich-quantum-toolkit/predictor/pull/831 [#758]: https://github.com/munich-quantum-toolkit/predictor/pull/758 [#825]: https://github.com/munich-quantum-toolkit/predictor/pull/825 +[#800]: https://github.com/munich-quantum-toolkit/predictor/pull/800 [#798]: https://github.com/munich-quantum-toolkit/predictor/pull/798 [#773]: https://github.com/munich-quantum-toolkit/predictor/pull/771 [#769]: https://github.com/munich-quantum-toolkit/predictor/pull/769 diff --git a/UPGRADING.md b/UPGRADING.md index ea592cb62..fc693b33f 100644 --- a/UPGRADING.md +++ b/UPGRADING.md @@ -6,6 +6,25 @@ of changes including minor and patch releases, please refer to the ## [Unreleased] +### GNN RL models + +GNN support requires the optional `gnn` extra and `graph=True`. Select +`GNNConfig.paper()` explicitly to use the tuned network and PPO settings. +Training uses SB3's masked PPO and the same `timesteps` argument as the flat +policy; the draft-only `iterations` argument has been removed. To convert an +iteration budget, pass `timesteps=iterations * gnn_config.n_steps`. SB3 +completes whole rollouts, so the actual step count may exceed the requested +budget. + +Prototype `.pt` checkpoints cannot be loaded because the action-output schema +has changed. Retrain the GNN model to create an SB3 `.zip` checkpoint. The SB3 +trainer uses different advantage normalization, value clipping, KL stopping, and +rollout semantics; the new model is not an exact reproduction of the old +training implementation. See [GNN setup](docs/setup.md#gnn-policy). + +The minimum `sb3_contrib` version is now 2.3.0. The optional extra requires +Torch Geometric 2.7.0 or newer, or 2.8.0 or newer on Python 3.14. + ### Intermediate RL rewards `PredictorEnv` now enables intermediate rewards by default. For diff --git a/docs/setup.md b/docs/setup.md index b7bda1f18..1d327aeeb 100644 --- a/docs/setup.md +++ b/docs/setup.md @@ -89,6 +89,43 @@ register_action(action) For other compilation sources, a new `CompilationOrigin` must be defined and conversions to/from Qiskit's `QuantumCircuit` must be implemented. +### GNN Policy + +Install the optional graph dependencies with +`uv pip install 'mqt.predictor[gnn]'`. Select the tuned network and PPO settings +explicitly: + +```python +from mqt.predictor.rl.gnn import GNNConfig + +rl_pred = RL_Predictor( + device=device, + figure_of_merit="expected_fidelity", + graph=True, + gnn_config=GNNConfig.paper(), +) +rl_pred.train_model(timesteps=100000, seed=0) +``` + +`graph=True` alone uses `GNNConfig()`, which has different network dimensions +and learning rates. The paper preset configures the network and PPO; choose the +device calibration, training circuits, MDP, reward settings, and `max_steps` +separately for the experiment. + +Both policies use `timesteps` as the training budget. SB3 completes whole +rollouts: with the preset's 2,048 steps per rollout, a requested budget of +100,000 steps produces 100,352 steps. Record this effective budget with the +experiment settings. + +The GNN uses SB3's masked PPO instead of the prototype's custom trainer. It +retains the graph architecture and configured hyperparameters, while accepting +SB3's minibatch advantage normalization, clipped-prediction value loss, and +minibatch KL stopping. SB3 also continues unfinished episodes across rollouts +and bootstraps truncated episodes from their final value estimate; terminated +episodes are not bootstrapped. Rewards and termination flags come from +`PredictorEnv`. This preserves the overall method but can produce different +trained policies from the prototype. + ## Step 3: Generate Training Data and Train ML Model Once the RL models are trained, generate the training data and train the diff --git a/src/mqt/predictor/rl/predictor.py b/src/mqt/predictor/rl/predictor.py index 09a16492e..eb87846d5 100644 --- a/src/mqt/predictor/rl/predictor.py +++ b/src/mqt/predictor/rl/predictor.py @@ -58,7 +58,8 @@ def __init__( mdp: The MDP transition policy. ``v2`` is the original strategy and ``v3`` is the default. graph: Whether to use the opt-in GNN policy. Defaults to False. - gnn_config: Configuration for the GNN policy. Defaults to the prototype training configuration. + gnn_config: Configuration for the GNN policy. Defaults to ``GNNConfig()``. + Select ``GNNConfig.paper()`` explicitly for the tuned paper configuration. """ logger.setLevel(logger_level) @@ -161,20 +162,18 @@ def train_model( test: bool = False, seed: int | None = None, pass_timeout: float | None = None, - iterations: int | None = None, ) -> None: """Trains all models for the given reward functions and device. Arguments: - timesteps: The number of timesteps for flat-policy training. Ignored by the GNN policy. Defaults to 1000. + timesteps: The number of timesteps to train either policy. Defaults to 1000. + SB3 completes full rollouts, so the actual number can exceed this budget. verbose: The verbosity level. Defaults to 2. test: Whether to train the model for testing purposes. Defaults to False. seed: The random seed to use for reproducible training. Set to None to use true randomness. Defaults to None. pass_timeout: Maximum duration in seconds for one compilation pass. Defaults to None, which disables pass timeouts. - iterations: The number of GNN rollout iterations. Defaults to 1000, or 10 in test mode. - Ignored by the flat policy. Raises: ValueError: If ``pass_timeout`` is not positive. @@ -211,7 +210,6 @@ def train_model( if test else self.gnn_config ) - n_steps = effective_gnn_config.n_steps graph_env = GNNObservationWrapper(self.env) model = create_gnn_model( graph_env, @@ -234,12 +232,7 @@ def train_model( ) # Training Loop: In each iteration, the agent collects n_steps steps (rollout), # updates the policy for n_epochs, and then repeats the process until total_timesteps steps have been taken. - total_timesteps = ( - n_steps * (iterations if iterations is not None else (10 if test else 1000)) - if self.graph - else timesteps - ) - model.learn(total_timesteps=total_timesteps, progress_bar=progress_bar) + model.learn(total_timesteps=timesteps, progress_bar=progress_bar) model.save(get_path_trained_model() / self.model_name) finally: self.env.pass_timeout = original_pass_timeout diff --git a/tests/compilation/test_predictor_rl.py b/tests/compilation/test_predictor_rl.py index 78306b4b5..be9a66160 100644 --- a/tests/compilation/test_predictor_rl.py +++ b/tests/compilation/test_predictor_rl.py @@ -151,6 +151,27 @@ def save(self, _path: Path) -> None: assert predictor.env.pass_timeout is None +@pytest.mark.parametrize("test_mode", [False, True]) +def test_gnn_training_uses_requested_timesteps(monkeypatch: pytest.MonkeyPatch, test_mode: bool) -> None: + """Test that GNN training forwards the requested timestep budget in both modes.""" + pytest.importorskip("torch_geometric") + gnn_module = import_module("mqt.predictor.rl.gnn") + config = gnn_module.GNNConfig.paper() + model = Mock() + create_model = Mock(return_value=model) + monkeypatch.setattr(gnn_module, "create_gnn_model", create_model) + predictor = Predictor( + figure_of_merit="expected_fidelity", device=get_device("ibm_falcon_27"), graph=True, gnn_config=config + ) + + predictor.train_model(timesteps=100_000, test=test_mode) + + model.learn.assert_called_once_with(total_timesteps=100_000, progress_bar=not test_mode) + create_model.assert_called_once() + if not test_mode: + assert create_model.call_args.args[1] is config + + def test_inference_uses_temporary_pass_timeout(monkeypatch: pytest.MonkeyPatch) -> None: """Test that inference applies and restores its independent pass timeout.""" observed_timeouts: list[float | None] = [] From e6b0a8dd4861ec919960cb2a372dbd2dc04ac798 Mon Sep 17 00:00:00 2001 From: flowerthrower Date: Tue, 22 Sep 2026 16:50:28 +0200 Subject: [PATCH 7/9] =?UTF-8?q?=E2=9C=85=20Exercise=20masked=20GNN=20train?= =?UTF-8?q?ing=20and=20checkpoint=20inference?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: flowerthrower Assisted-by: GPT 5.6 via Codex --- noxfile.py | 2 +- tests/compilation/test_gnn.py | 198 ++++++++++++++++++++++++++++++++++ 2 files changed, 199 insertions(+), 1 deletion(-) create mode 100644 tests/compilation/test_gnn.py diff --git a/noxfile.py b/noxfile.py index 4543d6767..f6ce417aa 100755 --- a/noxfile.py +++ b/noxfile.py @@ -87,7 +87,7 @@ def _run_tests( @nox.session(python=PYTHON_ALL_VERSIONS, reuse_venv=True, default=True) def tests(session: nox.Session) -> None: """Run the test suite.""" - _run_tests(session, pytest_run_args=["-m", "not model_training"]) + _run_tests(session, install_args=["--extra", "gnn"], pytest_run_args=["-m", "not model_training"]) @nox.session(python=PYTHON_ALL_VERSIONS, reuse_venv=True, venv_backend="uv", default=True) diff --git a/tests/compilation/test_gnn.py b/tests/compilation/test_gnn.py new file mode 100644 index 000000000..e4f39660e --- /dev/null +++ b/tests/compilation/test_gnn.py @@ -0,0 +1,198 @@ +# Copyright (c) 2023 - 2026 Chair for Design Automation, TUM +# Copyright (c) 2025 - 2026 Munich Quantum Software Company GmbH +# All rights reserved. +# +# SPDX-License-Identifier: MIT +# +# Licensed under the MIT License + +"""Tests for variable-size circuit graphs and masked GNN training.""" + +from __future__ import annotations + +from importlib import import_module +from typing import TYPE_CHECKING, cast + +import numpy as np +import pytest +import torch +from mqt.bench.targets import get_device +from qiskit import QuantumCircuit +from qiskit.qasm2 import dump +from qiskit.quantum_info import Operator + +from mqt.predictor.rl import Predictor +from mqt.predictor.rl import predictor as predictor_module +from mqt.predictor.rl.helper import create_feature_dict + +if TYPE_CHECKING: + from pathlib import Path + + from mqt.predictor.rl.gnn import ( + GNNFeaturesExtractor, + GNNMaskableDictRolloutBuffer, + GNNMaskableMultiInputActorCriticPolicy, + GraphBatch, + ) + +pytest.importorskip("torch_geometric") +gnn = import_module("mqt.predictor.rl.gnn") + +# PyG 2.8.0.post1 uses the deprecated typing._eval_type signature on Python 3.14. +pytestmark = pytest.mark.filterwarnings( + "ignore:Failing to pass a value to the 'type_params' parameter:DeprecationWarning:torch_geometric.inspector" +) + + +@pytest.fixture +def gnn_predictor(tmp_path: Path) -> Predictor: + """Create a small predictor with a circuit that changes size when optimized.""" + circuit = QuantumCircuit(2) + circuit.h(0) + circuit.h(0) + circuit.cx(0, 1) + circuit.rz(0.5, 1) + dump(circuit, tmp_path / "circuit_2.qasm") + return Predictor( + figure_of_merit="critical_depth", + device=get_device("ibm_falcon_27"), + path_training_circuits=tmp_path, + max_steps=2, + graph=True, + gnn_config=gnn.GNNConfig( + hidden_dim=8, + num_conv_wo_resnet=1, + num_resnet_layers=1, + dropout_p=0, + learning_rate=1e-3, + gnn_learning_rate=2e-3, + n_steps=4, + batch_size=2, + n_epochs=1, + ), + ) + + +def test_graph_observation_preserves_dag_features() -> None: + """Keep gate parameters, qubit dependencies, and raw global sizes in the graph.""" + circuit = QuantumCircuit(2) + circuit.h(0) + circuit.barrier() + circuit.cx(0, 1) + circuit.rz(np.pi / 2, 1) + flat_observation = create_feature_dict(circuit, 27) + + graph = gnn.create_graph_observation(circuit, flat_observation) + + assert graph.num_nodes == 3 + assert graph["gate_indices"].tolist() == [gnn.NODE_OPERATION_NAMES.index(gate) for gate in ("h", "cx", "rz")] + assert graph["edge_index"].tolist() == [[0, 1], [1, 2]] + torch.testing.assert_close(graph["node_scalars"][2, :2], torch.tensor([1.0, 0.0]), atol=1e-7, rtol=0) + assert graph["node_scalars"][:, 6:9].tolist() == [[1, 0, 0], [2, 1, 0], [1, 0, 1]] + assert graph["node_scalars"][:, 9:].tolist() == [[1, 0, 1], [1, 1, 1], [1, 1, 0]] + assert graph["global_features"][0, :2].tolist() == [2, 3] + assert graph["global_features"][0, 2:].tolist() == pytest.approx([ + flat_observation[name][0] for name in gnn.GLOBAL_FEATURE_NAMES[2:] + ]) + + +def test_gnn_masked_training_and_saved_inference( + gnn_predictor: Predictor, tmp_path: Path, monkeypatch: pytest.MonkeyPatch +) -> None: + """Train real graph minibatches, preserve masks, and reload the resulting compiler.""" + predictor = gnn_predictor + env = predictor.env + action_mask = [ + action.name in {"CommutativeCancellation", "InverseCancellation"} for action in env.action_set.values() + ] + monkeypatch.setattr(env, "action_masks", lambda: action_mask) + graph_env = gnn.GNNObservationWrapper(env) + assert predictor.gnn_config is not None + model = gnn.create_gnn_model(graph_env, predictor.gnn_config, verbose=0, tensorboard_log=str(tmp_path), seed=7) + policy = cast("GNNMaskableMultiInputActorCriticPolicy", model.policy) + encoder = cast("GNNFeaturesExtractor", policy.features_extractor).encoder + buffer = cast("GNNMaskableDictRolloutBuffer", model.rollout_buffer) + before = [parameter.detach().clone() for parameter in encoder.parameters()] + + model.learn(total_timesteps=4) + + assert model.num_timesteps == 4 + assert not env.error_occurred + assert all(action_mask[int(action)] for action in buffer.actions.flatten()) + assert {graph.num_nodes for graph in buffer.graph_observations} == {2, 4} + assert any(not torch.equal(old, new) for old, new in zip(before, encoder.parameters(), strict=True)) + assert any(parameter.grad is not None and torch.count_nonzero(parameter.grad) for parameter in encoder.parameters()) + assert all(torch.isfinite(parameter).all() for parameter in model.policy.parameters()) + assert [group["lr"] for group in model.policy.optimizer.param_groups] == pytest.approx([2e-4, 1e-4]) + + graph_env.reset() + circuit = env.state.copy() + empty_circuit = QuantumCircuit(2) + graphs = [ + gnn.create_graph_observation(empty_circuit, create_feature_dict(empty_circuit, env.device.num_qubits)), + graph_env.graph_observation, + ] + batch, vectorized = policy.obs_to_tensor(graphs) + assert vectorized + policy.set_training_mode(False) + with torch.no_grad(): + features = policy.features_extractor(batch) + values = policy.predict_values(batch) + assert features.shape == (2, 8) + assert torch.isfinite(features).all() + masks = np.tile(action_mask, (2, 1)) + expected_actions, _ = model.predict(graphs, deterministic=True, action_masks=masks) # ty: ignore[invalid-argument-type] + assert all(action_mask[int(action)] for action in expected_actions) + empty_action, _ = model.predict(graphs[0], deterministic=True, action_masks=masks[0]) # ty: ignore[invalid-argument-type] + assert action_mask[int(empty_action)] + + model.save(tmp_path / predictor.model_name) + monkeypatch.setattr(predictor_module, "get_path_trained_model", lambda: tmp_path) + loaded = predictor_module.load_model(predictor.model_name, graph=True) + actual_actions, _ = loaded.predict(batch, deterministic=True, action_masks=masks) # ty: ignore[invalid-argument-type] + np.testing.assert_array_equal(actual_actions, expected_actions) + loaded_policy = cast("GNNMaskableMultiInputActorCriticPolicy", loaded.policy) + loaded_policy.set_training_mode(False) + with torch.no_grad(): + torch.testing.assert_close(loaded_policy.predict_values(batch), values) + compiled, passes = predictor.compile_as_predicted(circuit) + assert len(passes) == 2 + assert set(passes) <= {"CommutativeCancellation", "InverseCancellation"} + assert Operator(compiled).equiv(Operator(circuit)) + + +@pytest.mark.parametrize("truncated", [False, True]) +def test_gnn_bootstraps_only_the_truncated_terminal_graph( + gnn_predictor: Predictor, tmp_path: Path, monkeypatch: pytest.MonkeyPatch, truncated: bool +) -> None: + """Use the final graph on truncation, without bootstrapping a terminated episode.""" + env = gnn_predictor.env + env.intermediate_reward = False + action_mask = [index == env.action_terminate_index for index in env.action_set] + monkeypatch.setattr(env, "action_masks", lambda: action_mask) + + def apply_action(_action: int) -> QuantumCircuit: + env.state.x(0) + if truncated: + msg = "Compilation failed after changing the circuit." + raise RuntimeError(msg) + return env.state + + monkeypatch.setattr(env, "apply_action", apply_action) + assert gnn_predictor.gnn_config is not None + model = gnn.create_gnn_model( + gnn.GNNObservationWrapper(env), gnn_predictor.gnn_config, verbose=0, tensorboard_log=str(tmp_path), seed=7 + ) + + def graph_value(graphs: GraphBatch) -> torch.Tensor: + return torch.bincount(graphs.batch, minlength=graphs.num_graphs).float().reshape(-1, 1) + + monkeypatch.setattr(model.policy, "predict_values", graph_value) + + model.learn(total_timesteps=4) + + expected_reward = env.no_effect_penalty + model.gamma * 5 if truncated else 0 + np.testing.assert_allclose(model.rollout_buffer.rewards, expected_reward) + assert model.rollout_buffer.episode_starts.all() + buffer = cast("GNNMaskableDictRolloutBuffer", model.rollout_buffer) + assert {graph.num_nodes for graph in buffer.graph_observations} == {4} From dffba065fc50db622c1ab3bff2b044a809e0b4e9 Mon Sep 17 00:00:00 2001 From: flowerthrower Date: Tue, 22 Sep 2026 16:59:18 +0200 Subject: [PATCH 8/9] =?UTF-8?q?=F0=9F=8E=A8=20Match=20GNN=20metadata=20to?= =?UTF-8?q?=20base=20formatting?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Assisted-by: GPT 5.6 via Codex Signed-off-by: flowerthrower --- pyproject.toml | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 7d76365de..233981d8a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -77,17 +77,18 @@ email = "lukas.burgholzer@tum.de" name = "Patrick Hopf" email = "patrick.hopf@tum.de" +[project.optional-dependencies] +gnn = [ + "torch-geometric>=2.7; python_version<'3.14'", + "torch-geometric>=2.8; python_version>='3.14'", +] + [project.urls] Homepage = "https://github.com/munich-quantum-toolkit/predictor" Issues = "https://github.com/munich-quantum-toolkit/predictor/issues" Discussions = "https://github.com/munich-quantum-toolkit/predictor/discussions" Research = "https://www.cda.cit.tum.de/research/quantum/" -[project.optional-dependencies] -gnn = [ - "torch-geometric>=2.7.0; python_version < '3.14'", - "torch-geometric>=2.8.0; python_version >= '3.14'", -] [dependency-groups] dev = [ From 16fa358111583f7f32e388e752af1fc880bd2f5e Mon Sep 17 00:00:00 2001 From: flowerthrower Date: Tue, 22 Sep 2026 17:00:39 +0200 Subject: [PATCH 9/9] =?UTF-8?q?=F0=9F=94=92=20Preserve=20GNN=20dependency?= =?UTF-8?q?=20versions=20after=20rebase?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Assisted-by: GPT 5.6 via Codex Signed-off-by: flowerthrower --- uv.lock | 720 ++++++++++++++++++++++++-------------------------------- 1 file changed, 314 insertions(+), 406 deletions(-) diff --git a/uv.lock b/uv.lock index e734ec7fa..585f9c476 100644 --- a/uv.lock +++ b/uv.lock @@ -1731,8 +1731,8 @@ requires-dist = [ { name = "scikit-learn", marker = "python_full_version >= '3.14'", specifier = ">=1.7.2" }, { name = "tensorboard", specifier = ">=2.17" }, { name = "tensorboard", marker = "python_full_version >= '3.14'", specifier = ">=2.18" }, - { name = "torch-geometric", marker = "python_full_version >= '3.14' and extra == 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