From 2c314144880dfe3c7b208f9d8263a04e1320791b Mon Sep 17 00:00:00 2001 From: wdyab Date: Fri, 17 Apr 2026 09:30:04 -0700 Subject: [PATCH 1/8] Add xDeepONet family to experimental models MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Introduces physicsnemo.experimental.models.xdeeponet — a config-driven, unified implementation of eight DeepONet-based operator-learning architectures for both 2D and 3D spatial domains: - deeponet, u_deeponet, fourier_deeponet, conv_deeponet, hybrid_deeponet (single-branch variants) - mionet, fourier_mionet (two-branch multi-input variants) - tno (Temporal Neural Operator; branch2 = previous solution) Features: - Composable spatial branches (Fourier, UNet, Conv in any combination) - Three decoder types: mlp, conv, temporal_projection - Automatic spatial padding to multiples of 8 - Automatic trunk coordinate extraction (time or grid) - Optional adaptive pooling (internal_resolution) for resolution-agnostic training and inference Uses physicsnemo.models.unet.UNet as the UNet sub-module; a small internal adapter tiles a short time axis to reuse the library's 3D UNet for 2D spatial branches. Imports spectral, convolutional, and MLP layers from physicsnemo.nn and physicsnemo.models.mlp. Includes 29 unit tests covering all variants (2D/3D), decoder types, temporal projection, target_times override, gradient flow, and adaptive pooling. Related discussion with code owners: - Placed under experimental/ per PhysicsNeMo convention for new models. - Custom UNet dropped in favour of library UNet. - Tests under test/experimental/models/ for CI coverage. Signed-off-by: wdyab Made-with: Cursor --- CHANGELOG.md | 8 + .../experimental/models/xdeeponet/README.md | 179 +++++ .../experimental/models/xdeeponet/__init__.py | 47 ++ .../experimental/models/xdeeponet/branches.py | 538 ++++++++++++++ .../experimental/models/xdeeponet/deeponet.py | 693 ++++++++++++++++++ .../experimental/models/xdeeponet/padding.py | 203 +++++ .../experimental/models/xdeeponet/wrappers.py | 339 +++++++++ test/experimental/models/test_xdeeponet.py | 561 ++++++++++++++ 8 files changed, 2568 insertions(+) create mode 100644 physicsnemo/experimental/models/xdeeponet/README.md create mode 100644 physicsnemo/experimental/models/xdeeponet/__init__.py create mode 100644 physicsnemo/experimental/models/xdeeponet/branches.py create mode 100644 physicsnemo/experimental/models/xdeeponet/deeponet.py create mode 100644 physicsnemo/experimental/models/xdeeponet/padding.py create mode 100644 physicsnemo/experimental/models/xdeeponet/wrappers.py create mode 100644 test/experimental/models/test_xdeeponet.py diff --git a/CHANGELOG.md b/CHANGELOG.md index c3d05112bc..1f642847a8 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -10,6 +10,14 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ### Added +- Adds xDeepONet — the extended DeepONet family — to experimental models + (`physicsnemo.experimental.models.xdeeponet`). Provides a config-driven + assembly of eight DeepONet-based architectures (DeepONet, U-DeepONet, + Fourier-DeepONet, Conv-DeepONet, Hybrid-DeepONet, MIONet, Fourier-MIONet, + and TNO) for 2D and 3D spatial domains, with composable Fourier / UNet / + Conv branches, multiple decoder types (MLP, Conv, temporal projection), + and automatic spatial padding. Suitable for standalone operator learning + and autoregressive temporal bundling. - Adds GLOBE model (`physicsnemo.experimental.models.globe.model.GLOBE`) - Adds GLOBE AirFRANS example case (`examples/cfd/external_aerodynamics/globe/airfrans`) - Adds concrete dropout uncertainty quantification for GeoTransolver. Learnable diff --git a/physicsnemo/experimental/models/xdeeponet/README.md b/physicsnemo/experimental/models/xdeeponet/README.md new file mode 100644 index 0000000000..935b7593ad --- /dev/null +++ b/physicsnemo/experimental/models/xdeeponet/README.md @@ -0,0 +1,179 @@ +# xDeepONet — the Extended DeepONet Family + +`physicsnemo.experimental.models.xdeeponet` provides a unified, config-driven +implementation of eight DeepONet-based architectures for operator learning +on 2D (`(H, W)`) and 3D (`(X, Y, Z)`) spatial domains. All variants share +the same branch/trunk/decoder design and are selected via a single +`variant` argument on the wrapper classes. + +## Supported Variants + +| Variant | Branches | Branch2 input | Typical use | +|---------------------|----------|------------------------|--------------------------| +| `deeponet` | 1 | — | Baseline DeepONet | +| `u_deeponet` | 1 | — | UNet-enhanced branch | +| `fourier_deeponet` | 1 | — | Spectral branch | +| `conv_deeponet` | 1 | — | Convolutional branch | +| `hybrid_deeponet` | 1 | — | Fourier + UNet + Conv | +| `mionet` | 2 | Scalar features | Multi-input operator | +| `fourier_mionet` | 2 | Scalar features | MIONet + Fourier branch | +| `tno` | 2 | Previous solution | Temporal Neural Operator | + +All variants are available in both 2D and 3D spatial configurations. + +## Quick Start + +```python +import torch +from physicsnemo.experimental.models.xdeeponet import DeepONet3DWrapper + +model = DeepONet3DWrapper( + variant="tno", + width=128, + padding=8, + branch1_config={ + "encoder": "spatial", + "num_fourier_layers": 1, + "num_unet_layers": 1, + "modes1": 10, "modes2": 10, "modes3": 8, + "activation_fn": "tanh", + }, + branch2_config={ + "encoder": "spatial", + "num_fourier_layers": 1, + "num_unet_layers": 1, + "modes1": 10, "modes2": 10, "modes3": 8, + "activation_fn": "tanh", + }, + trunk_config={ + "input_type": "time", + "hidden_width": 128, + "num_layers": 8, + "activation_fn": "tanh", + "output_activation": False, + }, + decoder_type="temporal_projection", + decoder_width=128, + decoder_layers=2, +) + +# Autoregressive bundling: predict K=3 future timesteps from 1 context step +model.set_output_window(K=3) + +x = torch.randn(2, 16, 16, 16, 1, 11) # (B, X, Y, Z, T_in, C) +prev = torch.randn(2, 16, 16, 16, 1) # previous solution +out = model(x, x_branch2=prev) # (B, X, Y, Z, 3) +``` + +## Public API + +### Wrappers (recommended entry points) + +`DeepONetWrapper` (2D) and `DeepONet3DWrapper` (3D) add two conveniences +on top of the core classes: + +1. **Automatic spatial padding** — right-pads inputs to a multiple (default 8) + so Fourier, UNet, and Conv sub-branches operate on compatible shapes. + Outputs are cropped back to the original spatial size. +2. **Automatic trunk coordinate extraction** — assembles trunk query + coordinates from the full input tensor according to + `trunk_config["input_type"]` (`"time"` or `"grid"`). + +### Core classes + +`DeepONet` (2D) and `DeepONet3D` (3D) expose the raw architecture without +padding or input extraction; use these when you have already prepared the +spatial branch input and trunk coordinates explicitly. + +### Building blocks + +`TrunkNet`, `MLPBranch`, `SpatialBranch`, `SpatialBranch3D` are the sub-networks +used internally; they are exported for users who want to assemble custom +variants. + +## Branch configuration schema + +Each branch is configured via a Python dict. Two formats are accepted — +the nested format is canonical; the flat format is converted automatically: + +**Nested (canonical):** + +```python +{ + "encoder": { + "type": "linear", # or "mlp" or "conv" + "hidden_width": 64, # mlp only + "num_layers": 2, # mlp/conv only + "activation_fn": "tanh", + }, + "layers": { + "num_fourier_layers": 1, + "num_unet_layers": 1, + "num_conv_layers": 0, + "modes1": 10, "modes2": 10, "modes3": 8, # 3D uses modes3 + "kernel_size": 3, + "dropout": 0.0, + "activation_fn": "tanh", + }, + "internal_resolution": [16, 16, 16], # optional adaptive pooling + "in_channels": 11, # optional (informational) +} +``` + +**Flat (auto-converted):** + +```python +{ + "encoder": "spatial", # or "mlp" + "num_fourier_layers": 1, + "num_unet_layers": 1, + "num_conv_layers": 0, + "modes1": 10, "modes2": 10, "modes3": 8, + "kernel_size": 3, + "activation_fn": "tanh", +} +``` + +## Decoder types + +- `"mlp"` — query the trunk at each target timestep, apply an MLP decoder + per-timestep. Standard DeepONet decoding. +- `"conv"` — per-timestep trunk query followed by a convolutional decoder. +- `"temporal_projection"` — query the trunk once and project the combined + latent representation to K output timesteps via a learned linear head. + Fast for autoregressive bundling. Requires `model.set_output_window(K)` + before the first forward pass. + +## UNet sub-modules + +The UNet layers inside the spatial branches use +`physicsnemo.models.unet.UNet` (3D). For 2D spatial branches, a small +internal adapter tiles a short time axis so the 3D UNet's pooling stages +function correctly, then averages the result back to 2D. + +## Padding behaviour + +Both wrappers pad spatial dimensions to a multiple of 8 (configurable via +the `padding` argument, which is rounded up to the next multiple of 8). +Padded cells are filled via replicate padding; outputs are cropped back +to the original input shape. + +## References + +- Lu, L. et al. (2021). "Learning nonlinear operators via DeepONet." + *Nature Machine Intelligence*, 3, 218-229. +- Jin, P., Meng, S. & Lu, L. (2022). "MIONet: Learning multiple-input + operators via tensor product." *SIAM J. Sci. Comp.*, 44(6), A3490-A3514. +- Wen, G. et al. (2022). "U-FNO — An enhanced Fourier neural operator-based + deep-learning model for multiphase flow." *Advances in Water Resources*, + 163, 104180. +- Zhu, M. et al. (2023). "Fourier-DeepONet: Fourier-enhanced deep operator + networks for full waveform inversion." arXiv:2305.17289. +- Diab, W. & Al Kobaisi, M. (2024). "U-DeepONet: U-Net enhanced deep + operator network for geologic carbon sequestration." + *Scientific Reports*, 14, 21298. +- Jiang, Z. et al. (2024). "Fourier-MIONet: Fourier-enhanced multiple-input + neural operators for multiphase modeling of geological carbon + sequestration." *Reliability Eng. & System Safety*, 251, 110392. +- Diab, W. & Al Kobaisi, M. (2025). "Temporal neural operator for modeling + time-dependent physical phenomena." *Scientific Reports*, 15. diff --git a/physicsnemo/experimental/models/xdeeponet/__init__.py b/physicsnemo/experimental/models/xdeeponet/__init__.py new file mode 100644 index 0000000000..d504daccf9 --- /dev/null +++ b/physicsnemo/experimental/models/xdeeponet/__init__.py @@ -0,0 +1,47 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""xDeepONet — the extended DeepONet family. + +Config-driven assembly of eight DeepONet-based architectures sharing a +common branch/trunk/decoder pattern: + +- ``deeponet``, ``u_deeponet``, ``fourier_deeponet``, ``conv_deeponet``, + ``hybrid_deeponet`` — single-branch variants. +- ``mionet``, ``fourier_mionet`` — two-branch multi-input variants. +- ``tno`` — Temporal Neural Operator (branch2 = previous solution). + +Both 2D and 3D spatial versions are provided. See the package README for +standalone usage examples. +""" + +from .branches import MLPBranch, SpatialBranch, SpatialBranch3D, TrunkNet +from .deeponet import DeepONet, DeepONet3D +from .wrappers import DeepONet3DWrapper, DeepONetWrapper + +__all__ = [ + # Core architectures + "DeepONet", + "DeepONet3D", + # Convenience wrappers (recommended entry points) + "DeepONetWrapper", + "DeepONet3DWrapper", + # Building blocks + "TrunkNet", + "MLPBranch", + "SpatialBranch", + "SpatialBranch3D", +] diff --git a/physicsnemo/experimental/models/xdeeponet/branches.py b/physicsnemo/experimental/models/xdeeponet/branches.py new file mode 100644 index 0000000000..f723c5f4ad --- /dev/null +++ b/physicsnemo/experimental/models/xdeeponet/branches.py @@ -0,0 +1,538 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Branch and trunk building blocks used by the xDeepONet family. + +Provides four sub-networks: + +- :class:`TrunkNet` — MLP trunk that encodes query coordinates (time or grid). +- :class:`MLPBranch` — fully-connected branch for scalar/vector inputs + (e.g. the scalar branch in MIONet). +- :class:`SpatialBranch` — 2D spatial encoder composable from Fourier, UNet, + and Conv layers. +- :class:`SpatialBranch3D` — 3D counterpart of ``SpatialBranch``. + +UNet sub-modules inside the spatial branches use +:class:`physicsnemo.models.unet.UNet` (3D). A small adapter +:class:`_UNet2DFromUNet3D` is provided locally for the 2D variant: it wraps +the 3D UNet with a singleton time dimension so the same library model covers +both spatial dimensionalities. +""" + +from __future__ import annotations + +from typing import List, Optional + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.nn.init as init +from torch import Tensor + +from physicsnemo.models.unet import UNet as _PhysicsNeMoUNet +from physicsnemo.nn import SpectralConv2d, SpectralConv3d, get_activation + +# --------------------------------------------------------------------------- +# UNet adapters (wrap the library's 3D UNet for reuse inside spatial branches) +# --------------------------------------------------------------------------- + + +class _UNet2DFromUNet3D(nn.Module): + """Adapter using :class:`physicsnemo.models.unet.UNet` for 2D inputs. + + The library UNet is 3D only. To reuse it for 2D, this adapter adds a + short tiled time axis of length ``2 ** model_depth`` — long enough to + survive the UNet's ``model_depth`` pooling stages — runs the 3D UNet, + and averages the result back to 2D. Channel-first layout + ``(B, C, H, W)`` is preserved on input and output. + """ + + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: int = 3, + model_depth: int = 3, + feature_map_channels: Optional[List[int]] = None, + ): + super().__init__() + if feature_map_channels is None: + feature_map_channels = [in_channels] * model_depth + self._t_tile = 2**model_depth + self.unet = _PhysicsNeMoUNet( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + model_depth=model_depth, + feature_map_channels=feature_map_channels, + num_conv_blocks=1, + conv_activation="leaky_relu", + conv_transpose_activation="leaky_relu", + padding=kernel_size // 2, + pooling_type="MaxPool3d", + normalization="batchnorm", + gradient_checkpointing=False, + ) + + def forward(self, x: Tensor) -> Tensor: + """Forward ``(B, C, H, W)`` through the 3D UNet via a tiled time axis.""" + x = x.unsqueeze(-1).repeat(1, 1, 1, 1, self._t_tile) + x = self.unet(x) + return x.mean(dim=-1) + + +class _UNet3DFromUNet3D(nn.Module): + """Thin wrapper exposing :class:`physicsnemo.models.unet.UNet` with a + fixed default configuration suitable for skip-connection reuse. + """ + + def __init__( + self, + in_channels: int, + out_channels: int, + kernel_size: int = 3, + model_depth: int = 3, + feature_map_channels: Optional[List[int]] = None, + ): + super().__init__() + if feature_map_channels is None: + feature_map_channels = [in_channels] * model_depth + self.unet = _PhysicsNeMoUNet( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + model_depth=model_depth, + feature_map_channels=feature_map_channels, + num_conv_blocks=1, + conv_activation="leaky_relu", + conv_transpose_activation="leaky_relu", + padding=kernel_size // 2, + pooling_type="MaxPool3d", + normalization="batchnorm", + gradient_checkpointing=False, + ) + + def forward(self, x: Tensor) -> Tensor: + """Forward ``(B, C, X, Y, Z)`` through the library 3D UNet.""" + return self.unet(x) + + +# --------------------------------------------------------------------------- +# Trunk and MLP branch +# --------------------------------------------------------------------------- + + +class TrunkNet(nn.Module): + """MLP trunk network encoding query coordinates. + + Parameters + ---------- + in_features : int + Dimensionality of each query point (1 for time-only, 3 for 2D grid + coordinates, 4 for 3D grid coordinates). + out_features : int + Output width (matches the DeepONet latent size). + hidden_width : int + Hidden layer width. + num_layers : int + Number of hidden layers. + activation_fn : str + Activation function name (``"sin"``, ``"tanh"``, ``"relu"``, etc.). + output_activation : bool + When ``True`` (default) the final layer is followed by the activation. + Set ``False`` for linear output (e.g. the TNO configuration). + """ + + def __init__( + self, + in_features: int = 1, + out_features: int = 64, + hidden_width: int = 128, + num_layers: int = 6, + activation_fn: str = "sin", + output_activation: bool = True, + ): + super().__init__() + + self._output_activation = output_activation + + if activation_fn.lower() == "sin": + self.activation_fn = torch.sin + else: + self.activation_fn = get_activation(activation_fn) + + self.layers = nn.ModuleList() + self.layers.append(self._make_linear(in_features, hidden_width)) + for _ in range(num_layers - 1): + self.layers.append(self._make_linear(hidden_width, hidden_width)) + + self.output_layer = self._make_linear(hidden_width, out_features) + + def _make_linear(self, in_dim: int, out_dim: int) -> nn.Linear: + layer = nn.Linear(in_dim, out_dim) + init.xavier_normal_(layer.weight) + init.zeros_(layer.bias) + return layer + + def forward(self, x: Tensor) -> Tensor: + """Encode ``(T, in_features)`` query points into ``(T, out_features)``.""" + for layer in self.layers: + x = self.activation_fn(layer(x)) + x = self.output_layer(x) + if self._output_activation: + x = self.activation_fn(x) + return x + + +class MLPBranch(nn.Module): + """Fully-connected branch for scalar/vector inputs. + + Used for the scalar branch in MIONet-style architectures. Input features + are auto-discovered via :class:`torch.nn.LazyLinear` on the first forward. + + Parameters + ---------- + out_features : int + Output width (matches the DeepONet latent size). + hidden_width : int + Hidden layer width. + num_layers : int + Number of fully-connected layers (including output). + activation_fn : str + Activation function name. + """ + + def __init__( + self, + out_features: int, + hidden_width: int = 64, + num_layers: int = 3, + activation_fn: str = "relu", + ): + super().__init__() + + if activation_fn.lower() == "sin": + self.activation_fn = torch.sin + else: + self.activation_fn = get_activation(activation_fn) + + self.layers = nn.ModuleList() + self.layers.append(nn.LazyLinear(hidden_width)) + for _ in range(num_layers - 2): + self.layers.append(self._make_linear(hidden_width, hidden_width)) + + self.output_layer = self._make_linear(hidden_width, out_features) + + def _make_linear(self, in_dim: int, out_dim: int) -> nn.Linear: + layer = nn.Linear(in_dim, out_dim) + init.xavier_normal_(layer.weight) + init.zeros_(layer.bias) + return layer + + def forward(self, x: Tensor) -> Tensor: + """Forward ``(B, in_features)`` through the MLP to ``(B, out_features)``.""" + for layer in self.layers: + x = self.activation_fn(layer(x)) + return self.activation_fn(self.output_layer(x)) + + +# --------------------------------------------------------------------------- +# 2D spatial branch +# --------------------------------------------------------------------------- + + +class SpatialBranch(nn.Module): + """2D spatial branch composable from Fourier, UNet, and Conv layers. + + The branch can be configured to use any combination of spectral, UNet, + and plain convolutional layers. When Fourier layers are present (the + "base" mode) UNet/Conv layers are added alongside the spectral path + (hybrid residual). When no Fourier layers are present UNet/Conv act + as independent sequential layers. + + Input: ``(B, H, W, C)`` channels-last. + Output: ``(B, H, W, width)``. + + Parameters + ---------- + in_channels : int + Number of input channels (used only for documentation; the lift is + :class:`torch.nn.LazyLinear`). + width : int + Latent/output width. + num_fourier_layers : int + Number of spectral layers. + num_unet_layers : int + Number of UNet layers (uses :class:`physicsnemo.models.unet.UNet`). + num_conv_layers : int + Number of Conv+BN layers. + modes1, modes2 : int + Fourier modes along H, W. + kernel_size : int + Kernel size for UNet and Conv layers. + dropout : float + Unused; kept for config compatibility. + activation_fn : str + Activation function name. + internal_resolution : list, optional + If set, inputs are adaptively pooled to this resolution before + processing and upsampled back, decoupling model size from grid size. + """ + + def __init__( + self, + in_channels: int, + width: int, + num_fourier_layers: int = 0, + num_unet_layers: int = 0, + num_conv_layers: int = 0, + modes1: int = 12, + modes2: int = 12, + kernel_size: int = 3, + dropout: float = 0.0, # noqa: ARG002 - kept for config compatibility + activation_fn: str = "gelu", + internal_resolution: Optional[list] = None, + ): + super().__init__() + + self.num_fourier_layers = num_fourier_layers + self.num_unet_layers = num_unet_layers + self.num_conv_layers = num_conv_layers + self.use_fourier_base = num_fourier_layers > 0 + self.internal_resolution = ( + tuple(internal_resolution) if internal_resolution else None + ) + + total_layers = num_fourier_layers + num_unet_layers + num_conv_layers + if total_layers == 0: + raise ValueError("SpatialBranch requires at least one layer type") + + if activation_fn.lower() == "sin": + self.activation_fn = torch.sin + else: + self.activation_fn = get_activation(activation_fn) + + if self.internal_resolution is not None: + self.adaptive_pool = nn.AdaptiveAvgPool2d(self.internal_resolution) + + self.lift = nn.LazyLinear(width) + + num_fourier_components = ( + total_layers if self.use_fourier_base else num_fourier_layers + ) + self.spectral_convs = nn.ModuleList() + self.conv_1x1s = nn.ModuleList() + for _ in range(num_fourier_components): + self.spectral_convs.append(SpectralConv2d(width, width, modes1, modes2)) + self.conv_1x1s.append(nn.Conv2d(width, width, kernel_size=1)) + + self.unet_modules = nn.ModuleList() + for _ in range(num_unet_layers): + self.unet_modules.append( + _UNet2DFromUNet3D(width, width, kernel_size=kernel_size) + ) + + self.conv_modules = nn.ModuleList() + padding = (kernel_size - 1) // 2 + for _ in range(num_conv_layers): + self.conv_modules.append( + nn.Sequential( + nn.Conv2d( + width, + width, + kernel_size=kernel_size, + padding=padding, + bias=False, + ), + nn.BatchNorm2d(width), + ) + ) + + def forward(self, x: Tensor) -> Tensor: + """Encode ``(B, H, W, C)`` into ``(B, H, W, width)``.""" + x = self.lift(x) + x = x.permute(0, 3, 1, 2) + + original_size = x.shape[2:] + if self.internal_resolution is not None: + x = self.adaptive_pool(x) + + for i in range(self.num_fourier_layers): + x = self.activation_fn(self.spectral_convs[i](x) + self.conv_1x1s[i](x)) + + if self.use_fourier_base: + for i in range(self.num_unet_layers): + j = self.num_fourier_layers + i + x = self.activation_fn( + self.spectral_convs[j](x) + + self.conv_1x1s[j](x) + + self.unet_modules[i](x) + ) + for i in range(self.num_conv_layers): + j = self.num_fourier_layers + self.num_unet_layers + i + x = self.activation_fn( + self.spectral_convs[j](x) + + self.conv_1x1s[j](x) + + self.conv_modules[i](x) + ) + else: + for unet in self.unet_modules: + x = self.activation_fn(unet(x)) + for conv in self.conv_modules: + x = self.activation_fn(conv(x)) + + if self.internal_resolution is not None and x.shape[2:] != original_size: + x = F.interpolate( + x, size=original_size, mode="bilinear", align_corners=True + ) + + return x.permute(0, 2, 3, 1) + + +# --------------------------------------------------------------------------- +# 3D spatial branch +# --------------------------------------------------------------------------- + + +class SpatialBranch3D(nn.Module): + """3D spatial branch composable from Fourier, UNet, and Conv layers. + + Input: ``(B, X, Y, Z, C)`` channels-last. + Output: ``(B, X, Y, Z, width)``. + + See :class:`SpatialBranch` for parameter semantics. The 3D variant + adds ``modes3`` for the third spectral axis. + """ + + def __init__( + self, + in_channels: int, + width: int, + num_fourier_layers: int = 0, + num_unet_layers: int = 0, + num_conv_layers: int = 0, + modes1: int = 10, + modes2: int = 10, + modes3: int = 8, + kernel_size: int = 3, + dropout: float = 0.0, # noqa: ARG002 - kept for config compatibility + activation_fn: str = "gelu", + internal_resolution: Optional[list] = None, + ): + super().__init__() + + self.num_fourier_layers = num_fourier_layers + self.num_unet_layers = num_unet_layers + self.num_conv_layers = num_conv_layers + self.use_fourier_base = num_fourier_layers > 0 + self.internal_resolution = ( + tuple(internal_resolution) if internal_resolution else None + ) + + total_layers = num_fourier_layers + num_unet_layers + num_conv_layers + if total_layers == 0: + raise ValueError("SpatialBranch3D requires at least one layer type") + + if activation_fn.lower() == "sin": + self.activation_fn = torch.sin + else: + self.activation_fn = get_activation(activation_fn) + + if self.internal_resolution is not None: + self.adaptive_pool = nn.AdaptiveAvgPool3d(self.internal_resolution) + + self.lift = nn.LazyLinear(width) + + num_fourier_components = ( + total_layers if self.use_fourier_base else num_fourier_layers + ) + self.spectral_convs = nn.ModuleList() + self.conv_1x1s = nn.ModuleList() + for _ in range(num_fourier_components): + self.spectral_convs.append( + SpectralConv3d(width, width, modes1, modes2, modes3) + ) + self.conv_1x1s.append(nn.Conv3d(width, width, kernel_size=1)) + + self.unet_modules = nn.ModuleList() + for _ in range(num_unet_layers): + self.unet_modules.append( + _UNet3DFromUNet3D(width, width, kernel_size=kernel_size) + ) + + self.conv_modules = nn.ModuleList() + padding = (kernel_size - 1) // 2 + for _ in range(num_conv_layers): + self.conv_modules.append( + nn.Sequential( + nn.Conv3d( + width, + width, + kernel_size=kernel_size, + padding=padding, + bias=False, + ), + nn.BatchNorm3d(width), + ) + ) + + def forward(self, x: Tensor) -> Tensor: + """Encode ``(B, X, Y, Z, C)`` into ``(B, X, Y, Z, width)``.""" + x = self.lift(x) + x = x.permute(0, 4, 1, 2, 3) + + original_size = x.shape[2:] + if self.internal_resolution is not None: + x = self.adaptive_pool(x) + + for i in range(self.num_fourier_layers): + x = self.activation_fn(self.spectral_convs[i](x) + self.conv_1x1s[i](x)) + + if self.use_fourier_base: + for i in range(self.num_unet_layers): + j = self.num_fourier_layers + i + x = self.activation_fn( + self.spectral_convs[j](x) + + self.conv_1x1s[j](x) + + self.unet_modules[i](x) + ) + for i in range(self.num_conv_layers): + j = self.num_fourier_layers + self.num_unet_layers + i + x = self.activation_fn( + self.spectral_convs[j](x) + + self.conv_1x1s[j](x) + + self.conv_modules[i](x) + ) + else: + for unet in self.unet_modules: + x = self.activation_fn(unet(x)) + for conv in self.conv_modules: + x = self.activation_fn(conv(x)) + + if self.internal_resolution is not None and x.shape[2:] != original_size: + x = F.interpolate( + x, size=original_size, mode="trilinear", align_corners=True + ) + + return x.permute(0, 2, 3, 4, 1) + + +__all__ = [ + "TrunkNet", + "MLPBranch", + "SpatialBranch", + "SpatialBranch3D", +] diff --git a/physicsnemo/experimental/models/xdeeponet/deeponet.py b/physicsnemo/experimental/models/xdeeponet/deeponet.py new file mode 100644 index 0000000000..71274e77ad --- /dev/null +++ b/physicsnemo/experimental/models/xdeeponet/deeponet.py @@ -0,0 +1,693 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Core xDeepONet architectures for 2D and 3D operator learning. + +The xDeepONet family extends the original DeepONet with eight variants +that cover both single-input and multi-input operator learning, including +the Temporal Neural Operator (TNO) for autoregressive temporal bundling: + +- ``deeponet`` — basic DeepONet (MLP branch). +- ``u_deeponet`` — UNet-enhanced spatial branch. +- ``fourier_deeponet`` — spectral (Fourier) spatial branch. +- ``conv_deeponet`` — plain convolutional spatial branch. +- ``hybrid_deeponet`` — Fourier + UNet + Conv spatial branch. +- ``mionet`` — two-branch multi-input operator network. +- ``fourier_mionet`` — MIONet with a Fourier spatial branch. +- ``tno`` — Temporal Neural Operator (branch2 = previous + solution, autoregressive only). + +The core :class:`DeepONet` (2D) and :class:`DeepONet3D` (3D) classes are +dimension-specific but share the same construction pattern: a primary branch +(``branch1``), an optional secondary branch (``branch2`` for MIONet/TNO), +a coordinate trunk, and a decoder. + +References +---------- +- Lu, L. et al. (2021). "Learning nonlinear operators via DeepONet." + *Nature Machine Intelligence*, 3, 218-229. +- Jin, P., Meng, S. & Lu, L. (2022). "MIONet: Learning multiple-input + operators via tensor product." *SIAM J. Sci. Comp.*, 44(6), A3490-A3514. +- Diab, W. & Al Kobaisi, M. (2024). "U-DeepONet: U-Net enhanced deep + operator network for geologic carbon sequestration." + *Scientific Reports*, 14, 21298. +- Zhu, M. et al. (2023). "Fourier-DeepONet: Fourier-enhanced deep operator + networks for full waveform inversion." arXiv:2305.17289. +- Diab, W. & Al Kobaisi, M. (2025). "Temporal neural operator for modeling + time-dependent physical phenomena." *Scientific Reports*, 15. +""" + +from __future__ import annotations + +from typing import Any, Dict + +import torch.nn as nn +from torch import Tensor + +from physicsnemo.core.module import Module +from physicsnemo.experimental.models.xdeeponet.branches import ( + MLPBranch, + SpatialBranch, + SpatialBranch3D, + TrunkNet, +) +from physicsnemo.models.mlp import FullyConnected +from physicsnemo.nn import Conv2dFCLayer, Conv3dFCLayer, get_activation + +# --------------------------------------------------------------------------- +# Branch config helpers +# --------------------------------------------------------------------------- + + +def _normalize_branch_config(config: dict) -> dict: + """Normalize a branch config to the nested encoder/layers format. + + Supports two input formats: + + **New (nested)** format:: + + { + "encoder": {"type": "linear", "activation_fn": "tanh", ...}, + "layers": {"num_fourier_layers": 1, "num_unet_layers": 1, ...}, + "internal_resolution": [H, W], + } + + **Old (flat)** format (auto-converted for backward compatibility):: + + { + "encoder": "spatial", # or "mlp" + "num_fourier_layers": 1, + "num_unet_layers": 1, + "activation_fn": "tanh", + ... + } + + Returns a dict in the new nested format. + """ + if "encoder" not in config: + return config + + enc = config["encoder"] + if not isinstance(enc, str): + return config + + enc_type_str = str(enc).lower() + cfg = dict(config) + cfg.pop("encoder") + + encoder_keys = {"hidden_width", "num_layers"} + layer_keys = { + "num_fourier_layers", + "num_unet_layers", + "num_conv_layers", + "modes1", + "modes2", + "modes3", + "kernel_size", + "dropout", + } + + activation = cfg.pop("activation_fn", "sin") + internal_res = cfg.pop("internal_resolution", None) + in_channels = cfg.pop("in_channels", None) + # The legacy 'unet_impl' key is silently dropped: only the library UNet + # (physicsnemo.models.unet.UNet) is supported in the experimental package. + cfg.pop("unet_impl", None) + + encoder_dict = { + "type": "mlp" if enc_type_str == "mlp" else "linear", + "activation_fn": activation, + } + for k in encoder_keys: + if k in cfg: + encoder_dict[k] = cfg.pop(k) + + layers_dict = {"activation_fn": activation} + for k in layer_keys: + if k in cfg: + layers_dict[k] = cfg.pop(k) + + result = {"encoder": encoder_dict, "layers": layers_dict} + if internal_res is not None: + result["internal_resolution"] = internal_res + if in_channels is not None: + result["in_channels"] = in_channels + + return result + + +def _build_conv_encoder(width: int, enc_config: dict) -> nn.Module: + """Build a multi-layer pointwise encoder replacing the default LazyLinear lift. + + Operates in channels-last format ``(B, *spatial, C)``. Each layer is a + :class:`torch.nn.Linear` with activation — equivalent to a 1x1 convolution + applied independently at every spatial point. + """ + num_layers = enc_config.get("num_layers", 1) + activation_fn = enc_config.get("activation_fn", "relu") + act = get_activation(activation_fn) + + if num_layers <= 1: + return nn.LazyLinear(width) + + hidden_width = enc_config.get("hidden_width", width // 2) + layers_list = [nn.LazyLinear(hidden_width), act] + for _ in range(num_layers - 2): + layers_list.extend([nn.Linear(hidden_width, hidden_width), act]) + layers_list.append(nn.Linear(hidden_width, width)) + return nn.Sequential(*layers_list) + + +# --------------------------------------------------------------------------- +# 2D DeepONet +# --------------------------------------------------------------------------- + + +class DeepONet(Module): + """2D xDeepONet core architecture for operator learning. + + Combines a primary spatial/MLP branch, an optional secondary branch + (for MIONet/TNO variants), a coordinate trunk, and a decoder. The + branch outputs and trunk are combined via Hadamard product and then + projected to the output by the decoder. + + Input / Output + -------------- + - ``x_branch1``: ``(B, H, W, C)`` for spatial branches or + ``(B, in_features)`` for MLP branches. + - ``x_time``: ``(T,)`` or ``(T, in_features)`` query coordinates. + - ``x_branch2`` (optional): secondary branch input for MIONet/TNO. + - Returns: ``(B, H, W, T)`` for spatial branches or ``(B, T)`` for MLP. + + Parameters + ---------- + variant : str + One of the eight supported variants (see :data:`VALID_VARIANTS`). + width : int + Latent width. + branch1_config, branch2_config, trunk_config : dict, optional + Sub-network configurations. See module docstring for schema. + decoder_type : {"mlp", "conv", "temporal_projection"} + ``"mlp"`` queries the trunk at each target timestep and applies an + MLP decoder; ``"conv"`` uses a convolutional decoder; and + ``"temporal_projection"`` queries the trunk once and projects the + combined latent representation to K timesteps via a learned linear + head (fast autoregressive bundling; requires + :meth:`set_output_window`). + decoder_width, decoder_layers : int + Decoder hidden width and layer count. + decoder_activation_fn : str + Activation function name for the decoder. + """ + + VALID_VARIANTS = [ + "deeponet", + "u_deeponet", + "fourier_deeponet", + "conv_deeponet", + "hybrid_deeponet", + "mionet", + "fourier_mionet", + "tno", + ] + + def __init__( + self, + variant: str = "u_deeponet", + width: int = 64, + branch1_config: Dict[str, Any] = None, + branch2_config: Dict[str, Any] = None, + trunk_config: Dict[str, Any] = None, + decoder_type: str = "mlp", + decoder_width: int = 128, + decoder_layers: int = 2, + decoder_activation_fn: str = "relu", + ): + super().__init__() + + self.variant = variant.lower() + self.width = width + self.decoder_type = decoder_type.lower() + self.decoder_activation_fn = decoder_activation_fn + + if self.variant not in self.VALID_VARIANTS: + raise ValueError( + f"Unknown variant: {variant}. Valid: {self.VALID_VARIANTS}" + ) + + branch1_config = branch1_config or {} + trunk_config = trunk_config or {} + + self.branch1 = self._build_branch(branch1_config, width) + + self.has_branch2 = branch2_config is not None + if self.has_branch2: + self.branch2 = self._build_branch(branch2_config, width) + + self.trunk = TrunkNet( + in_features=trunk_config.get("in_features", 1), + out_features=width, + hidden_width=trunk_config.get("hidden_width", 128), + num_layers=trunk_config.get("num_layers", 6), + activation_fn=trunk_config.get("activation_fn", "sin"), + output_activation=trunk_config.get("output_activation", True), + ) + + if decoder_type == "temporal_projection": + self._temporal_projection = True + self.decoder = self._build_decoder( + width, + width, + decoder_layers, + decoder_width, + "mlp", + decoder_activation_fn, + ) + self.temporal_head = None + else: + self._temporal_projection = False + self.decoder = self._build_decoder( + width, + 1, + decoder_layers, + decoder_width, + decoder_type, + decoder_activation_fn, + ) + + def set_output_window(self, K: int): + """Create the temporal-projection head for K output timesteps. + + Only effective when ``decoder_type="temporal_projection"``. + """ + if self._temporal_projection: + device = next(self.parameters()).device + self.temporal_head = nn.Linear(self.width, K).to(device) + + def _build_branch(self, config: dict, width: int) -> nn.Module: + config = _normalize_branch_config(config) + enc = config.get("encoder", {}) + layers = config.get("layers", {}) + + enc_type = enc.get("type", "linear") + enc_activation = enc.get("activation_fn", "sin") + + has_layers = ( + layers.get("num_fourier_layers", 0) + + layers.get("num_unet_layers", 0) + + layers.get("num_conv_layers", 0) + ) > 0 + + if enc_type == "mlp" and not has_layers: + return MLPBranch( + out_features=width, + hidden_width=enc.get("hidden_width", 64), + num_layers=enc.get("num_layers", 3), + activation_fn=enc_activation, + ) + + layer_activation = layers.get("activation_fn", enc_activation) + branch = SpatialBranch( + in_channels=config.get("in_channels", 12), + width=width, + num_fourier_layers=layers.get("num_fourier_layers", 0), + num_unet_layers=layers.get("num_unet_layers", 0), + num_conv_layers=layers.get("num_conv_layers", 0), + modes1=layers.get("modes1", 12), + modes2=layers.get("modes2", 12), + kernel_size=layers.get("kernel_size", 3), + dropout=layers.get("dropout", 0.0), + activation_fn=layer_activation, + internal_resolution=config.get("internal_resolution", None), + ) + if enc_type == "conv": + branch.lift = _build_conv_encoder(width, enc) + return branch + + def _build_decoder( + self, + width: int, + out_channels: int, + num_layers: int, + hidden_width: int, + decoder_type: str, + activation_fn: str, + ) -> nn.Module: + if decoder_type == "mlp": + if num_layers == 0: + return nn.Linear(width, out_channels) + return FullyConnected( + width, hidden_width, out_channels, num_layers, activation_fn + ) + + elif decoder_type == "conv": + if num_layers == 0: + return Conv2dFCLayer(width, out_channels) + + layers = [] + in_ch = width + for _ in range(num_layers): + layers.extend( + [Conv2dFCLayer(in_ch, hidden_width), get_activation(activation_fn)] + ) + in_ch = hidden_width + layers.append(Conv2dFCLayer(hidden_width, out_channels)) + return nn.Sequential(*layers) + + else: + raise ValueError(f"Unknown decoder_type: {decoder_type}") + + def forward( + self, x_branch1: Tensor, x_time: Tensor, x_branch2: Tensor = None + ) -> Tensor: + """Forward pass through the DeepONet. + + See class docstring for input/output shapes. + """ + if x_time.dim() == 1: + x_time = x_time.unsqueeze(-1) + + b1_out = self.branch1(x_branch1) + + if self.has_branch2: + if x_branch2 is None: + raise ValueError("x_branch2 required for mionet/tno variants") + b2_out = self.branch2(x_branch2) + + trunk_out = self.trunk(x_time) + + if b1_out.dim() == 4: # Spatial branch + if self._temporal_projection: + trunk_single = trunk_out[0:1] + trunk_exp = trunk_single.unsqueeze(1).unsqueeze(2) + combined = b1_out * trunk_exp + if self.has_branch2: + if b2_out.dim() == 4: + combined = combined * b2_out + else: + combined = combined * b2_out.unsqueeze(1).unsqueeze(2) + combined = self.decoder(combined) + if self.temporal_head is not None: + combined = self.temporal_head(combined) + return combined + + b1_out = b1_out.unsqueeze(1) + trunk_out = trunk_out.unsqueeze(0).unsqueeze(2).unsqueeze(3) + + if self.has_branch2: + if b2_out.dim() == 4: + b2_out = b2_out.unsqueeze(1) + else: + b2_out = b2_out.unsqueeze(1).unsqueeze(2).unsqueeze(3) + combined = b1_out * b2_out * trunk_out + else: + combined = b1_out * trunk_out + + if self.decoder_type == "mlp": + return self.decoder(combined).squeeze(-1).permute(0, 2, 3, 1) + + B, T, H, W, C = combined.shape + combined = combined.permute(0, 1, 4, 2, 3).reshape(B * T, C, H, W) + return self.decoder(combined).reshape(B, T, H, W).permute(0, 2, 3, 1) + + else: # MLP branch + b1_out = b1_out.unsqueeze(1) + trunk_out = trunk_out.unsqueeze(0) + + if self.has_branch2: + combined = b1_out * b2_out.unsqueeze(1) * trunk_out + else: + combined = b1_out * trunk_out + + return self.decoder(combined).squeeze(-1) + + def count_params(self) -> int: + """Return the number of trainable parameters.""" + return sum(p.numel() for p in self.parameters() if p.requires_grad) + + +# --------------------------------------------------------------------------- +# 3D DeepONet +# --------------------------------------------------------------------------- + + +class DeepONet3D(Module): + """3D xDeepONet core architecture for volumetric operator learning. + + Input / Output + -------------- + - ``x_branch1``: ``(B, X, Y, Z, C)`` for spatial branches or + ``(B, in_features)`` for MLP branches. + - ``x_time``: ``(T,)`` or ``(T, in_features)`` query coordinates. + - ``x_branch2`` (optional): secondary branch input for MIONet/TNO. + - Returns: ``(B, X, Y, Z, T)`` for spatial branches or ``(B, T)`` for MLP. + + See :class:`DeepONet` for parameter semantics. + """ + + VALID_VARIANTS = [ + "deeponet", + "u_deeponet", + "fourier_deeponet", + "conv_deeponet", + "hybrid_deeponet", + "mionet", + "fourier_mionet", + "tno", + ] + + def __init__( + self, + variant: str = "u_deeponet", + width: int = 64, + branch1_config: Dict[str, Any] = None, + branch2_config: Dict[str, Any] = None, + trunk_config: Dict[str, Any] = None, + decoder_type: str = "mlp", + decoder_width: int = 128, + decoder_layers: int = 2, + decoder_activation_fn: str = "relu", + ): + super().__init__() + + self.variant = variant.lower() + self.width = width + self.decoder_type = decoder_type.lower() + self.decoder_activation_fn = decoder_activation_fn + + if self.variant not in self.VALID_VARIANTS: + raise ValueError( + f"Unknown variant: {variant}. Valid: {self.VALID_VARIANTS}" + ) + + branch1_config = branch1_config or {} + trunk_config = trunk_config or {} + + self.branch1 = self._build_branch(branch1_config, width) + + self.has_branch2 = branch2_config is not None + if self.has_branch2: + self.branch2 = self._build_branch(branch2_config, width) + + self.trunk = TrunkNet( + in_features=trunk_config.get("in_features", 1), + out_features=width, + hidden_width=trunk_config.get("hidden_width", 128), + num_layers=trunk_config.get("num_layers", 6), + activation_fn=trunk_config.get("activation_fn", "sin"), + output_activation=trunk_config.get("output_activation", True), + ) + + if decoder_type == "temporal_projection": + self._temporal_projection = True + self.decoder = self._build_decoder( + width, + width, + decoder_layers, + decoder_width, + "mlp", + decoder_activation_fn, + ) + self.temporal_head = None + else: + self._temporal_projection = False + self.decoder = self._build_decoder( + width, + 1, + decoder_layers, + decoder_width, + decoder_type, + decoder_activation_fn, + ) + + def set_output_window(self, K: int): + """Create the temporal-projection head for K output timesteps. + + Only effective when ``decoder_type="temporal_projection"``. + """ + if self._temporal_projection: + device = next(self.parameters()).device + self.temporal_head = nn.Linear(self.width, K).to(device) + + def _build_branch(self, config: dict, width: int) -> nn.Module: + config = _normalize_branch_config(config) + enc = config.get("encoder", {}) + layers = config.get("layers", {}) + + enc_type = enc.get("type", "linear") + enc_activation = enc.get("activation_fn", "sin") + + has_layers = ( + layers.get("num_fourier_layers", 0) + + layers.get("num_unet_layers", 0) + + layers.get("num_conv_layers", 0) + ) > 0 + + if enc_type == "mlp" and not has_layers: + return MLPBranch( + out_features=width, + hidden_width=enc.get("hidden_width", 64), + num_layers=enc.get("num_layers", 3), + activation_fn=enc_activation, + ) + + layer_activation = layers.get("activation_fn", enc_activation) + branch = SpatialBranch3D( + in_channels=config.get("in_channels", 11), + width=width, + num_fourier_layers=layers.get("num_fourier_layers", 0), + num_unet_layers=layers.get("num_unet_layers", 0), + num_conv_layers=layers.get("num_conv_layers", 0), + modes1=layers.get("modes1", 10), + modes2=layers.get("modes2", 10), + modes3=layers.get("modes3", 8), + kernel_size=layers.get("kernel_size", 3), + dropout=layers.get("dropout", 0.0), + activation_fn=layer_activation, + internal_resolution=config.get("internal_resolution", None), + ) + if enc_type == "conv": + branch.lift = _build_conv_encoder(width, enc) + return branch + + def _build_decoder( + self, + width: int, + out_channels: int, + num_layers: int, + hidden_width: int, + decoder_type: str, + activation_fn: str, + ) -> nn.Module: + if decoder_type == "mlp": + if num_layers == 0: + return nn.Linear(width, out_channels) + return FullyConnected( + width, hidden_width, out_channels, num_layers, activation_fn + ) + + elif decoder_type == "conv": + if num_layers == 0: + return Conv3dFCLayer(width, out_channels) + + layers = [] + in_ch = width + for _ in range(num_layers): + layers.extend( + [Conv3dFCLayer(in_ch, hidden_width), get_activation(activation_fn)] + ) + in_ch = hidden_width + layers.append(Conv3dFCLayer(hidden_width, out_channels)) + return nn.Sequential(*layers) + + else: + raise ValueError(f"Unknown decoder_type: {decoder_type}") + + def forward( + self, x_branch1: Tensor, x_time: Tensor, x_branch2: Tensor = None + ) -> Tensor: + """Forward pass through the 3D DeepONet. + + See class docstring for input/output shapes. + """ + if x_time.dim() == 1: + x_time = x_time.unsqueeze(-1) + + b1_out = self.branch1(x_branch1) + + if self.has_branch2: + if x_branch2 is None: + raise ValueError("x_branch2 required for mionet/tno variants") + b2_out = self.branch2(x_branch2) + + trunk_out = self.trunk(x_time) + + if b1_out.dim() == 5: # Spatial branch + if self._temporal_projection: + trunk_single = trunk_out[0:1] + trunk_exp = trunk_single.unsqueeze(1).unsqueeze(2).unsqueeze(3) + combined = b1_out * trunk_exp + if self.has_branch2: + if b2_out.dim() == 5: + combined = combined * b2_out + else: + combined = combined * b2_out.unsqueeze(1).unsqueeze( + 2 + ).unsqueeze(3) + combined = self.decoder(combined) + if self.temporal_head is not None: + combined = self.temporal_head(combined) + return combined + + b1_out = b1_out.unsqueeze(1) + trunk_out = trunk_out.unsqueeze(0).unsqueeze(2).unsqueeze(3).unsqueeze(4) + + if self.has_branch2: + if b2_out.dim() == 5: + b2_out = b2_out.unsqueeze(1) + else: + b2_out = b2_out.unsqueeze(1).unsqueeze(2).unsqueeze(3).unsqueeze(4) + combined = b1_out * b2_out * trunk_out + else: + combined = b1_out * trunk_out + + if self.decoder_type == "mlp": + return self.decoder(combined).squeeze(-1).permute(0, 2, 3, 4, 1) + + B, T, X, Y, Z, C = combined.shape + combined = combined.permute(0, 1, 5, 2, 3, 4).reshape(B * T, C, X, Y, Z) + return self.decoder(combined).reshape(B, T, X, Y, Z).permute(0, 2, 3, 4, 1) + + else: # MLP branch + b1_out = b1_out.unsqueeze(1) + trunk_out = trunk_out.unsqueeze(0) + + if self.has_branch2: + combined = b1_out * b2_out.unsqueeze(1) * trunk_out + else: + combined = b1_out * trunk_out + + return self.decoder(combined).squeeze(-1) + + def count_params(self) -> int: + """Return the number of trainable parameters.""" + return sum(p.numel() for p in self.parameters() if p.requires_grad) + + +__all__ = [ + "DeepONet", + "DeepONet3D", +] diff --git a/physicsnemo/experimental/models/xdeeponet/padding.py b/physicsnemo/experimental/models/xdeeponet/padding.py new file mode 100644 index 0000000000..b7f38e89af --- /dev/null +++ b/physicsnemo/experimental/models/xdeeponet/padding.py @@ -0,0 +1,203 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Right-side spatial padding helpers used by the xDeepONet wrappers. + +The wrappers align spatial dimensions to a multiple (typically 8) so that +spectral and convolutional sub-branches operate on compatible shapes. These +helpers are dimension-agnostic and support 2D, 3D, or 4D spatial layouts. + +Tensor layouts used here: +- 2D spatial samples: ``(B, H, W, T, C)`` +- 3D spatial samples: ``(B, X, Y, Z, T, C)`` +""" + +from __future__ import annotations + +from typing import Sequence, Tuple + +import torch +import torch.nn.functional as F +from torch import Tensor + + +def compute_right_pad_to_multiple( + spatial_shape: Sequence[int], + *, + multiple: int = 8, + min_right_pad: int = 0, +) -> Tuple[int, ...]: + """Compute right-side padding to reach a multiple of *multiple*. + + Parameters + ---------- + spatial_shape : Sequence[int] + Current spatial dimension sizes. + multiple : int, optional + Target alignment (default ``8``). + min_right_pad : int, optional + Minimum right-side padding applied per dimension (default ``0``). + + Returns + ------- + Tuple[int, ...] + Right-side padding per dimension such that ``(d + pad)`` is a multiple + of *multiple* and ``pad >= min_right_pad``. + """ + if multiple <= 0: + raise ValueError(f"multiple must be > 0, got {multiple}") + if min_right_pad < 0: + raise ValueError(f"min_right_pad must be >= 0, got {min_right_pad}") + + pads = [] + for d in spatial_shape: + if d <= 0: + raise ValueError( + f"spatial dimensions must be positive, got {spatial_shape}" + ) + to_mult = (multiple - (d % multiple)) % multiple + if to_mult >= min_right_pad: + pad = to_mult + else: + deficit = min_right_pad - to_mult + k = (deficit + multiple - 1) // multiple + pad = to_mult + k * multiple + pads.append(int(pad)) + return tuple(pads) + + +def pad_right_nd( + x: Tensor, + *, + dims: Sequence[int], + right_pad: Sequence[int], + mode: str = "replicate", + constant_value: float = 0.0, +) -> Tensor: + """Right-pad arbitrary dimensions of an N-D tensor. + + Implemented manually so it works for ``mode="replicate"`` even when + :func:`torch.nn.functional.pad` does not support the tensor rank + (e.g. 6D tensors in the 3D-spatial case). + """ + if len(dims) != len(right_pad): + raise ValueError("dims and right_pad must have the same length") + if not dims: + return x + + for dim, pad in zip(dims, right_pad): + pad = int(pad) + if pad <= 0: + continue + if dim < 0: + dim = x.dim() + dim + if dim < 0 or dim >= x.dim(): + raise ValueError(f"invalid dim {dim} for x.dim()={x.dim()}") + + if mode == "constant": + pad_shape = list(x.shape) + pad_shape[dim] = pad + pad_tensor = torch.full( + pad_shape, float(constant_value), dtype=x.dtype, device=x.device + ) + x = torch.cat([x, pad_tensor], dim=dim) + continue + + if mode != "replicate": + raise ValueError( + f"pad_right_nd supports mode='replicate' or 'constant', got {mode}" + ) + + last = x.select(dim, x.size(dim) - 1).unsqueeze(dim) + expand_shape = list(x.shape) + expand_shape[dim] = pad + pad_tensor = last.expand(*expand_shape) + x = torch.cat([x, pad_tensor], dim=dim) + + return x + + +def pad_spatial_right( + x: Tensor, + *, + spatial_ndim: int, + right_pad: Sequence[int], + mode: str = "replicate", + constant_value: float = 0.0, +) -> Tensor: + """Right-pad the first *spatial_ndim* dimensions after the batch dim. + + Assumes ``x`` is shaped ``(B, *spatial, *rest)``. + """ + if spatial_ndim not in (2, 3, 4): + raise ValueError(f"spatial_ndim must be 2, 3, or 4, got {spatial_ndim}") + if len(right_pad) != spatial_ndim: + raise ValueError( + f"right_pad must have length {spatial_ndim}, got {len(right_pad)}" + ) + if x.dim() < 1 + spatial_ndim: + raise ValueError( + f"expected x.dim() >= {1 + spatial_ndim}, got x.dim()={x.dim()}" + ) + if all(int(p) == 0 for p in right_pad): + return x + + # For 4 spatial dims fall back to the generic implementation (works for 6D+). + if spatial_ndim == 4: + dims = [1, 2, 3, 4] + return pad_right_nd( + x, + dims=dims, + right_pad=right_pad, + mode=mode, + constant_value=constant_value, + ) + + # For 2D/3D spatial, use a reshape trick so F.pad(replicate) applies. + b = x.shape[0] + spatial_shape = x.shape[1 : 1 + spatial_ndim] + rest_shape = x.shape[1 + spatial_ndim :] + rest_prod = ( + 1 if len(rest_shape) == 0 else int(torch.tensor(rest_shape).prod().item()) + ) + + x_reshaped = x.reshape(b, *spatial_shape, rest_prod).permute( + 0, spatial_ndim + 1, *range(1, 1 + spatial_ndim) + ) + + if spatial_ndim == 2: + pad_h, pad_w = (int(p) for p in right_pad) + pad = (0, pad_w, 0, pad_h) + else: + pad_x, pad_y, pad_z = (int(p) for p in right_pad) + pad = (0, pad_z, 0, pad_y, 0, pad_x) + + if mode == "constant": + x_padded = F.pad(x_reshaped, pad, mode="constant", value=float(constant_value)) + else: + x_padded = F.pad(x_reshaped, pad, mode=mode) + + padded_spatial = x_padded.shape[2 : 2 + spatial_ndim] + return x_padded.permute(0, *range(2, 2 + spatial_ndim), 1).reshape( + b, *padded_spatial, *rest_shape + ) + + +__all__ = [ + "compute_right_pad_to_multiple", + "pad_right_nd", + "pad_spatial_right", +] diff --git a/physicsnemo/experimental/models/xdeeponet/wrappers.py b/physicsnemo/experimental/models/xdeeponet/wrappers.py new file mode 100644 index 0000000000..92d2b70636 --- /dev/null +++ b/physicsnemo/experimental/models/xdeeponet/wrappers.py @@ -0,0 +1,339 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Convenience wrappers for xDeepONet. + +These wrappers add two ergonomic features on top of the core +:class:`~physicsnemo.experimental.models.xdeeponet.deeponet.DeepONet` and +:class:`~physicsnemo.experimental.models.xdeeponet.deeponet.DeepONet3D`: + +1. **Automatic spatial padding** to align the input to a multiple (default + 8), which makes the Fourier, UNet, and Conv sub-branches compatible + across arbitrary grid sizes. Outputs are cropped back to the original + spatial shape before return. +2. **Automatic trunk input extraction** from the full spatiotemporal input + tensor. Given ``(B, H, W, T, C)`` (2D) or ``(B, X, Y, Z, T, C)`` (3D) + and a ``target_times`` kwarg (optional), the wrapper assembles the + trunk query coordinates according to the ``trunk.input_type`` setting + (``"time"`` or ``"grid"``). + +These wrappers are the recommended public entry points for xDeepONet. +""" + +from __future__ import annotations + +from typing import Any, Dict + +import torch +import torch.nn as nn +from torch import Tensor + +from physicsnemo.experimental.models.xdeeponet.deeponet import DeepONet, DeepONet3D +from physicsnemo.experimental.models.xdeeponet.padding import ( + compute_right_pad_to_multiple, + pad_spatial_right, +) + + +class DeepONetWrapper(nn.Module): + """2D xDeepONet wrapper with automatic padding and input extraction. + + Input + ----- + ``x`` : Tensor of shape ``(B, H, W, T, C)``. + + Output + ------ + Tensor of shape ``(B, H, W, T_out)`` where ``T_out == T`` unless + ``target_times`` is provided (then ``T_out == len(target_times)``). + + Parameters + ---------- + padding : int + Minimum right-side padding; the wrapper rounds up to the next + multiple of 8. Default is 8. + variant : str + xDeepONet variant (see + :attr:`~physicsnemo.experimental.models.xdeeponet.deeponet.DeepONet.VALID_VARIANTS`). + width : int + Latent width. + branch1_config, branch2_config, trunk_config : dict, optional + Sub-network configurations (see core class docstrings). The trunk + config may additionally specify ``input_type`` as ``"time"`` or + ``"grid"``: ``"time"`` uses the last input channel as the time + coordinate; ``"grid"`` uses the last three channels + ``(grid_x, grid_y, grid_t)``. + decoder_type : {"mlp", "conv", "temporal_projection"} + See :class:`~physicsnemo.experimental.models.xdeeponet.deeponet.DeepONet`. + decoder_width, decoder_layers : int + Decoder hidden width and layer count. + decoder_activation_fn : str + Activation function name for the decoder. + """ + + def __init__( + self, + padding: int = 8, + variant: str = "u_deeponet", + width: int = 64, + branch1_config: Dict[str, Any] = None, + branch2_config: Dict[str, Any] = None, + trunk_config: Dict[str, Any] = None, + decoder_type: str = "mlp", + decoder_width: int = 128, + decoder_layers: int = 2, + decoder_activation_fn: str = "relu", + ): + super().__init__() + + self.padding = ((padding + 7) // 8) * 8 if padding % 8 != 0 else padding + self.variant = variant + + trunk_config = dict(trunk_config or {}) + self.trunk_input = trunk_config.get("input_type", "time").lower() + + if self.trunk_input not in ["time", "grid"]: + raise ValueError("trunk input_type must be 'time' or 'grid'") + + if self.trunk_input == "grid": + trunk_config["in_features"] = 3 # (x, y, t) + else: + trunk_config["in_features"] = trunk_config.get("in_features", 1) + + self.model = DeepONet( + variant=variant, + width=width, + branch1_config=branch1_config, + branch2_config=branch2_config, + trunk_config=trunk_config, + decoder_type=decoder_type, + decoder_width=decoder_width, + decoder_layers=decoder_layers, + decoder_activation_fn=decoder_activation_fn, + ) + self._temporal_projection = self.model._temporal_projection + + def set_output_window(self, K: int): + """Delegate to the inner :class:`DeepONet` model.""" + self.model.set_output_window(K) + + def forward( + self, + x: Tensor, + x_branch2: Tensor = None, + target_times: Tensor = None, + ) -> Tensor: + """Forward pass through the 2D wrapper. + + Parameters + ---------- + x : Tensor + Input ``(B, H, W, T_in, C)``. + x_branch2 : Tensor, optional + Secondary branch input (MIONet/TNO variants). + target_times : Tensor, optional + Explicit trunk query coordinates ``(K,)`` or ``(K, 1)``. When + provided the trunk evaluates at these K points instead of + extracting time values from ``x``, enabling autoregressive + temporal bundling where ``K != T_in``. + + Returns + ------- + Tensor + ``(B, H, W, T_out)`` where ``T_out = K`` if ``target_times`` is + given, else ``T_in``. + """ + H, W = x.shape[1], x.shape[2] + + pad_h, pad_w = compute_right_pad_to_multiple( + (H, W), multiple=8, min_right_pad=self.padding + ) + x = pad_spatial_right( + x, spatial_ndim=2, right_pad=(pad_h, pad_w), mode="replicate" + ) + + if x_branch2 is not None and x_branch2.dim() > 2: + x_branch2 = pad_spatial_right( + x_branch2, + spatial_ndim=2, + right_pad=(pad_h, pad_w), + mode="replicate", + ) + + x_spatial = x.permute(0, 4, 1, 2, 3)[..., 0].permute(0, 2, 3, 1) + + if target_times is not None: + if self.trunk_input == "grid": + t_vals = ( + target_times + if target_times.dim() == 1 + else target_times.squeeze(-1) + ) + spatial = x[0, 0, 0, 0, -3:-1] + spatial_exp = spatial.unsqueeze(0).expand(t_vals.shape[0], -1) + x_trunk = torch.cat([spatial_exp, t_vals.unsqueeze(-1)], dim=-1) + else: + x_trunk = ( + target_times + if target_times.dim() == 2 + else target_times.unsqueeze(-1) + ) + elif self.trunk_input == "grid": + x_trunk = x[0, 0, 0, :, -3:] + else: + x_trunk = x[0, 0, 0, :, -1].unsqueeze(-1) + + return self.model(x_spatial, x_trunk, x_branch2)[:, :H, :W, :] + + def count_params(self) -> int: + """Return the number of trainable parameters.""" + return self.model.count_params() + + +class DeepONet3DWrapper(nn.Module): + """3D xDeepONet wrapper with automatic padding and input extraction. + + Input + ----- + ``x`` : Tensor of shape ``(B, X, Y, Z, T, C)``. + + Output + ------ + Tensor of shape ``(B, X, Y, Z, T_out)`` where ``T_out == T`` unless + ``target_times`` is provided. + + See :class:`DeepONetWrapper` for parameter semantics. The 3D trunk + ``input_type="grid"`` uses the last four input channels + ``(grid_x, grid_y, grid_z, grid_t)``. + """ + + def __init__( + self, + padding: int = 8, + variant: str = "u_deeponet", + width: int = 64, + branch1_config: Dict[str, Any] = None, + branch2_config: Dict[str, Any] = None, + trunk_config: Dict[str, Any] = None, + decoder_type: str = "mlp", + decoder_width: int = 128, + decoder_layers: int = 2, + decoder_activation_fn: str = "relu", + ): + super().__init__() + + self.padding = ((padding + 7) // 8) * 8 if padding % 8 != 0 else padding + self.variant = variant + + trunk_config = dict(trunk_config or {}) + self.trunk_input = trunk_config.get("input_type", "time").lower() + + if self.trunk_input not in ["time", "grid"]: + raise ValueError("trunk input_type must be 'time' or 'grid'") + + if self.trunk_input == "grid": + trunk_config["in_features"] = 4 # (x, y, z, t) + else: + trunk_config["in_features"] = trunk_config.get("in_features", 1) + + self.model = DeepONet3D( + variant=variant, + width=width, + branch1_config=branch1_config, + branch2_config=branch2_config, + trunk_config=trunk_config, + decoder_type=decoder_type, + decoder_width=decoder_width, + decoder_layers=decoder_layers, + decoder_activation_fn=decoder_activation_fn, + ) + self._temporal_projection = self.model._temporal_projection + + def set_output_window(self, K: int): + """Delegate to the inner :class:`DeepONet3D` model.""" + self.model.set_output_window(K) + + def forward( + self, + x: Tensor, + x_branch2: Tensor = None, + target_times: Tensor = None, + ) -> Tensor: + """Forward pass through the 3D wrapper. + + Parameters + ---------- + x : Tensor + Input ``(B, X, Y, Z, T_in, C)``. + x_branch2 : Tensor, optional + Secondary branch input (MIONet/TNO variants). + target_times : Tensor, optional + Explicit trunk query coordinates ``(K,)`` or ``(K, 1)``. + + Returns + ------- + Tensor + ``(B, X, Y, Z, T_out)`` where ``T_out = K`` if ``target_times`` + is given, else ``T_in``. + """ + X, Y, Z = x.shape[1], x.shape[2], x.shape[3] + + pad_x, pad_y, pad_z = compute_right_pad_to_multiple( + (X, Y, Z), multiple=8, min_right_pad=self.padding + ) + x = pad_spatial_right( + x, spatial_ndim=3, right_pad=(pad_x, pad_y, pad_z), mode="replicate" + ) + + if x_branch2 is not None and x_branch2.dim() > 2: + x_branch2 = pad_spatial_right( + x_branch2, + spatial_ndim=3, + right_pad=(pad_x, pad_y, pad_z), + mode="replicate", + ) + + x_spatial = x[:, :, :, :, 0, :] + + if target_times is not None: + if self.trunk_input == "grid": + t_vals = ( + target_times + if target_times.dim() == 1 + else target_times.squeeze(-1) + ) + spatial = x[0, 0, 0, 0, 0, -4:-1] + spatial_exp = spatial.unsqueeze(0).expand(t_vals.shape[0], -1) + x_trunk = torch.cat([spatial_exp, t_vals.unsqueeze(-1)], dim=-1) + else: + x_trunk = ( + target_times + if target_times.dim() == 2 + else target_times.unsqueeze(-1) + ) + elif self.trunk_input == "grid": + x_trunk = x[0, 0, 0, 0, :, -4:] + else: + x_trunk = x[0, 0, 0, 0, :, -1].unsqueeze(-1) + + return self.model(x_spatial, x_trunk, x_branch2)[:, :X, :Y, :Z, :] + + def count_params(self) -> int: + """Return the number of trainable parameters.""" + return self.model.count_params() + + +__all__ = ["DeepONetWrapper", "DeepONet3DWrapper"] diff --git a/test/experimental/models/test_xdeeponet.py b/test/experimental/models/test_xdeeponet.py new file mode 100644 index 0000000000..bc5686858e --- /dev/null +++ b/test/experimental/models/test_xdeeponet.py @@ -0,0 +1,561 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Unit tests for the xDeepONet family (2D and 3D variants).""" + +import pytest +import torch + +from physicsnemo.experimental.models.xdeeponet import ( + DeepONet, + DeepONet3D, + DeepONet3DWrapper, + DeepONetWrapper, + MLPBranch, + SpatialBranch, + SpatialBranch3D, + TrunkNet, +) + +BRANCH1_SPATIAL = { + "encoder": {"type": "linear", "activation_fn": "relu"}, + "layers": { + "num_fourier_layers": 0, + "num_unet_layers": 1, + "num_conv_layers": 0, + "modes1": 4, + "modes2": 4, + "kernel_size": 3, + "dropout": 0.0, + "activation_fn": "relu", + }, +} +BRANCH1_MLP = { + "encoder": { + "type": "mlp", + "hidden_width": 32, + "num_layers": 2, + "activation_fn": "relu", + }, + "layers": {"num_fourier_layers": 0, "num_unet_layers": 0, "num_conv_layers": 0}, +} +BRANCH2_SPATIAL = { + "encoder": {"type": "linear", "activation_fn": "relu"}, + "layers": { + "num_fourier_layers": 0, + "num_unet_layers": 1, + "num_conv_layers": 0, + "modes1": 4, + "modes2": 4, + "kernel_size": 3, + "dropout": 0.0, + "activation_fn": "relu", + }, +} +BRANCH2_MLP = { + "encoder": { + "type": "mlp", + "hidden_width": 32, + "num_layers": 2, + "activation_fn": "relu", + }, + "layers": {"num_fourier_layers": 0, "num_unet_layers": 0, "num_conv_layers": 0}, +} +TRUNK = { + "input_type": "time", + "hidden_width": 32, + "num_layers": 2, + "activation_fn": "tanh", +} + + +def _init_lazy(model, x, **kwargs): + """Run one forward pass to initialise LazyLinear modules.""" + with torch.no_grad(): + model(x, **kwargs) + + +class TestTrunkNet: + """Tests for TrunkNet.""" + + def test_output_shape(self): + """Verify TrunkNet output shape matches expected features.""" + trunk = TrunkNet(in_features=1, out_features=32, hidden_width=16, num_layers=3) + x = torch.randn(10, 1) + assert trunk(x).shape == (10, 32) + + def test_grid_input(self): + """Verify TrunkNet handles multi-dimensional grid input correctly.""" + trunk = TrunkNet(in_features=4, out_features=64, hidden_width=32, num_layers=2) + x = torch.randn(5, 4) + assert trunk(x).shape == (5, 64) + + +class TestMLPBranch: + """Tests for MLPBranch.""" + + def test_output_shape(self): + """Verify MLPBranch output shape matches expected features.""" + branch = MLPBranch(out_features=32, hidden_width=16, num_layers=3) + x = torch.randn(2, 50) + out = branch(x) + assert out.shape == (2, 32) + + +class TestSpatialBranch2D: + """Tests for 2D SpatialBranch.""" + + def test_output_shape(self): + """Verify 2D SpatialBranch output shape matches expected width.""" + branch = SpatialBranch( + in_channels=5, + width=16, + num_unet_layers=1, + kernel_size=3, + activation_fn="relu", + ) + x = torch.randn(2, 16, 24, 5) + _init_lazy(branch, x) + out = branch(x) + assert out.shape == (2, 16, 24, 16) + + +class TestSpatialBranch3D: + """Tests for 3D SpatialBranch.""" + + def test_output_shape(self): + """Verify 3D SpatialBranch output shape matches expected width.""" + branch = SpatialBranch3D( + in_channels=5, + width=16, + num_unet_layers=1, + kernel_size=3, + activation_fn="relu", + ) + x = torch.randn(2, 8, 16, 8, 5) + _init_lazy(branch, x) + out = branch(x) + assert out.shape == (2, 8, 16, 8, 16) + + +SINGLE_BRANCH_VARIANTS = ["deeponet", "u_deeponet", "conv_deeponet"] +DUAL_BRANCH_VARIANTS = ["mionet", "tno"] + + +class TestDeepONetWrapper2D: + """Tests for 2D DeepONet wrapper.""" + + @pytest.mark.parametrize("variant", SINGLE_BRANCH_VARIANTS) + def test_forward_shape_single_branch(self, variant): + """Verify 2D single-branch forward pass produces correct output shape.""" + B, H, W, T, C = 2, 16, 24, 4, 5 + model = DeepONetWrapper( + padding=8, + variant=variant, + width=32, + branch1_config=BRANCH1_SPATIAL, + trunk_config=TRUNK, + ) + x = torch.randn(B, H, W, T, C) + _init_lazy(model, x) + out = model(x) + assert out.shape == (B, H, W, T) + + @pytest.mark.parametrize("variant", DUAL_BRANCH_VARIANTS) + def test_forward_shape_dual_branch(self, variant): + """Verify 2D dual-branch forward pass produces correct output shape.""" + B, H, W, T, C = 2, 16, 24, 4, 5 + model = DeepONetWrapper( + padding=8, + variant=variant, + width=32, + branch1_config=BRANCH1_SPATIAL, + branch2_config=BRANCH2_SPATIAL, + trunk_config=TRUNK, + ) + x = torch.randn(B, H, W, T, C) + b2 = torch.randn(B, H, W, T) + _init_lazy(model, x, x_branch2=b2) + out = model(x, x_branch2=b2) + assert out.shape == (B, H, W, T) + + def test_target_times_changes_output_T(self): + """Verify target_times overrides the temporal output dimension size.""" + B, H, W, T_in, C = 2, 16, 24, 2, 5 + K = 5 + model = DeepONetWrapper( + padding=8, + variant="u_deeponet", + width=32, + branch1_config=BRANCH1_SPATIAL, + trunk_config=TRUNK, + ) + x = torch.randn(B, H, W, T_in, C) + tt = torch.linspace(0, 1, K) + _init_lazy(model, x) + out = model(x, target_times=tt) + assert out.shape == (B, H, W, K) + + def test_invalid_variant_raises(self): + """Verify ValueError is raised for an unknown DeepONet variant.""" + with pytest.raises(ValueError, match="Unknown variant"): + DeepONetWrapper( + variant="invalid", + width=32, + branch1_config=BRANCH1_SPATIAL, + trunk_config=TRUNK, + ) + + def test_count_params(self): + """Verify count_params returns a positive parameter count for 2D wrapper.""" + model = DeepONetWrapper( + padding=8, + variant="deeponet", + width=32, + branch1_config=BRANCH1_SPATIAL, + trunk_config=TRUNK, + ) + x = torch.randn(1, 16, 24, 2, 5) + _init_lazy(model, x) + assert model.count_params() > 0 + + def test_gradient_flow(self): + """Verify gradients propagate through the 2D DeepONet wrapper.""" + model = DeepONetWrapper( + padding=8, + variant="u_deeponet", + width=32, + branch1_config=BRANCH1_SPATIAL, + trunk_config=TRUNK, + ) + x = torch.randn(1, 16, 24, 2, 5) + _init_lazy(model, x) + x = torch.randn(1, 16, 24, 2, 5, requires_grad=True) + out = model(x) + out.sum().backward() + assert x.grad is not None + + +BRANCH1_3D = { + "encoder": {"type": "linear", "activation_fn": "relu"}, + "layers": { + "num_fourier_layers": 0, + "num_unet_layers": 1, + "num_conv_layers": 0, + "modes1": 4, + "modes2": 4, + "modes3": 4, + "kernel_size": 3, + "dropout": 0.0, + "activation_fn": "relu", + }, +} +BRANCH2_3D = { + "encoder": {"type": "linear", "activation_fn": "relu"}, + "layers": { + "num_fourier_layers": 0, + "num_unet_layers": 1, + "num_conv_layers": 0, + "modes1": 4, + "modes2": 4, + "modes3": 4, + "kernel_size": 3, + "dropout": 0.0, + "activation_fn": "relu", + }, +} + + +class TestDeepONet3DWrapper: + """Tests for 3D DeepONet wrapper.""" + + @pytest.mark.parametrize("variant", SINGLE_BRANCH_VARIANTS) + def test_forward_shape_single_branch(self, variant): + """Verify 3D single-branch forward pass produces correct output shape.""" + B, X, Y, Z, T, C = 1, 8, 16, 8, 3, 5 + model = DeepONet3DWrapper( + padding=8, + variant=variant, + width=32, + branch1_config=BRANCH1_3D, + trunk_config=TRUNK, + ) + x = torch.randn(B, X, Y, Z, T, C) + _init_lazy(model, x) + out = model(x) + assert out.shape == (B, X, Y, Z, T) + + def test_tno_requires_branch2(self): + """Verify TNO variant produces correct output with a second branch.""" + B, X, Y, Z, T, C = 1, 8, 16, 8, 3, 5 + model = DeepONet3DWrapper( + padding=8, + variant="tno", + width=32, + branch1_config=BRANCH1_3D, + branch2_config=BRANCH2_3D, + trunk_config=TRUNK, + ) + x = torch.randn(B, X, Y, Z, T, C) + b2 = torch.randn(B, X, Y, Z, 1) + _init_lazy(model, x, x_branch2=b2) + out = model(x, x_branch2=b2) + assert out.shape == (B, X, Y, Z, T) + + def test_target_times_3d(self): + """Verify target_times overrides the temporal output dimension in 3D.""" + B, X, Y, Z, T_in, C = 1, 8, 16, 8, 1, 5 + K = 4 + model = DeepONet3DWrapper( + padding=8, + variant="u_deeponet", + width=32, + branch1_config=BRANCH1_3D, + trunk_config=TRUNK, + ) + x = torch.randn(B, X, Y, Z, T_in, C) + tt = torch.linspace(0, 1, K) + _init_lazy(model, x) + out = model(x, target_times=tt) + assert out.shape == (B, X, Y, Z, K) + + def test_count_params_3d(self): + """Verify count_params returns a positive parameter count for 3D wrapper.""" + model = DeepONet3DWrapper( + padding=8, + variant="deeponet", + width=32, + branch1_config=BRANCH1_3D, + trunk_config=TRUNK, + ) + x = torch.randn(1, 8, 16, 8, 2, 5) + _init_lazy(model, x) + assert model.count_params() > 0 + + +class TestHadamardProduct: + """Verify 3-way Hadamard product for multi-branch variants.""" + + def test_mionet_uses_multiplication(self): + """Verify MIONet variant computes a 3-way Hadamard product correctly.""" + model = DeepONetWrapper( + variant="mionet", + width=16, + branch1_config={ + "encoder": "spatial", + "num_unet_layers": 0, + "num_conv_layers": 1, + "kernel_size": 3, + }, + branch2_config={"encoder": "mlp", "hidden_width": 16, "num_layers": 2}, + trunk_config={"hidden_width": 16, "num_layers": 2}, + decoder_layers=0, + ) + x = torch.randn(2, 16, 24, 4, 6) + b2 = torch.randn(2, 6) + with torch.no_grad(): + out = model(x, x_branch2=b2) + assert out.shape == (2, 16, 24, 4) + + +class TestTemporalProjection: + """Test temporal_projection decoder mode.""" + + def test_2d_temporal_projection_output_shape(self): + """Verify 2D temporal-projection decoder produces correct output T dimension.""" + K = 3 + model = DeepONet( + variant="u_deeponet", + width=16, + branch1_config={ + "encoder": "spatial", + "num_unet_layers": 0, + "num_conv_layers": 1, + "kernel_size": 3, + }, + trunk_config={"hidden_width": 16, "num_layers": 2}, + decoder_type="temporal_projection", + decoder_layers=1, + decoder_width=16, + ) + model.set_output_window(K) + x_branch = torch.randn(2, 16, 24, 4) + x_time = torch.randn(1, 1) + with torch.no_grad(): + out = model(x_branch, x_time) + assert out.shape == (2, 16, 24, K) + + def test_2d_temporal_projection_with_branch2(self): + """Verify 2D temporal-projection works with a second branch input.""" + K = 5 + model = DeepONet( + variant="tno", + width=16, + branch1_config={ + "encoder": "spatial", + "num_unet_layers": 0, + "num_conv_layers": 1, + "kernel_size": 3, + }, + branch2_config={ + "encoder": "spatial", + "num_unet_layers": 0, + "num_conv_layers": 1, + "kernel_size": 3, + }, + trunk_config={"hidden_width": 16, "num_layers": 2}, + decoder_type="temporal_projection", + decoder_layers=1, + decoder_width=16, + ) + model.set_output_window(K) + x_branch = torch.randn(2, 16, 24, 4) + x_branch2 = torch.randn(2, 16, 24, 4) + x_time = torch.randn(1, 1) + with torch.no_grad(): + out = model(x_branch, x_time, x_branch2=x_branch2) + assert out.shape == (2, 16, 24, K) + + def test_3d_temporal_projection(self): + """Verify 3D temporal-projection decoder produces correct output shape.""" + K = 4 + model = DeepONet3D( + variant="u_deeponet", + width=8, + branch1_config={ + "encoder": "spatial", + "num_unet_layers": 0, + "num_conv_layers": 1, + "kernel_size": 3, + }, + trunk_config={"hidden_width": 8, "num_layers": 2}, + decoder_type="temporal_projection", + decoder_layers=1, + decoder_width=8, + ) + model.set_output_window(K) + x_branch = torch.randn(2, 8, 8, 8, 4) + x_time = torch.randn(1, 1) + with torch.no_grad(): + out = model(x_branch, x_time) + assert out.shape == (2, 8, 8, 8, K) + + def test_mlp_decoder_still_works(self): + """Verify existing mlp decoder path is preserved.""" + model = DeepONet( + variant="u_deeponet", + width=16, + branch1_config={ + "encoder": "spatial", + "num_unet_layers": 0, + "num_conv_layers": 1, + "kernel_size": 3, + }, + trunk_config={"hidden_width": 16, "num_layers": 2}, + decoder_type="mlp", + decoder_layers=1, + decoder_width=16, + ) + x_branch = torch.randn(2, 16, 24, 4) + x_time = torch.randn(6, 1) + with torch.no_grad(): + out = model(x_branch, x_time) + assert out.shape == (2, 16, 24, 6) + + def test_gradient_flow_temporal_projection(self): + """Verify gradients propagate through the temporal-projection decoder.""" + K = 3 + model = DeepONet( + variant="tno", + width=16, + branch1_config={ + "encoder": "spatial", + "num_unet_layers": 0, + "num_conv_layers": 1, + "kernel_size": 3, + }, + branch2_config={ + "encoder": "spatial", + "num_unet_layers": 0, + "num_conv_layers": 1, + "kernel_size": 3, + }, + trunk_config={"hidden_width": 16, "num_layers": 2}, + decoder_type="temporal_projection", + decoder_layers=1, + decoder_width=16, + ) + model.set_output_window(K) + x = torch.randn(2, 16, 24, 4, requires_grad=False) + b2 = torch.randn(2, 16, 24, 4, requires_grad=False) + t = torch.randn(1, 1) + out = model(x, t, x_branch2=b2) + loss = out.sum() + loss.backward() + assert model.temporal_head.weight.grad is not None + + +class TestInternalResolution: + """Test adaptive pooling in SpatialBranch.""" + + def test_2d_internal_resolution(self): + """Verify 2D SpatialBranch with internal_resolution preserves output shape.""" + branch = SpatialBranch( + in_channels=4, + width=8, + num_fourier_layers=0, + num_unet_layers=0, + num_conv_layers=1, + kernel_size=3, + internal_resolution=[16, 24], + ) + x = torch.randn(2, 32, 48, 4) + out = branch(x) + assert out.shape == (2, 32, 48, 8) + + def test_2d_no_internal_resolution(self): + """Verify 2D SpatialBranch without internal_resolution preserves output shape.""" + branch = SpatialBranch( + in_channels=4, + width=8, + num_fourier_layers=0, + num_unet_layers=0, + num_conv_layers=1, + kernel_size=3, + internal_resolution=None, + ) + x = torch.randn(2, 32, 48, 4) + out = branch(x) + assert out.shape == (2, 32, 48, 8) + + def test_3d_internal_resolution(self): + """Verify 3D SpatialBranch with internal_resolution preserves output shape.""" + branch = SpatialBranch3D( + in_channels=4, + width=8, + num_fourier_layers=0, + num_unet_layers=0, + num_conv_layers=1, + kernel_size=3, + internal_resolution=[8, 8, 8], + ) + x = torch.randn(2, 16, 16, 16, 4) + out = branch(x) + assert out.shape == (2, 16, 16, 16, 8) + + +if __name__ == "__main__": + pytest.main([__file__, "-v"]) From b95fae3c46a12f812f793b918cfbe8e47088fa51 Mon Sep 17 00:00:00 2001 From: wdyab Date: Fri, 17 Apr 2026 11:35:24 -0700 Subject: [PATCH 2/8] xdeeponet: address PR #1576 review feedback (Greptile) Fix six issues flagged by the Greptile review: - Make DeepONetWrapper / DeepONet3DWrapper inherit from physicsnemo.core.module.Module (MOD-001). Core DeepONet / DeepONet3D also pass proper MetaData dataclasses. - Raise ValueError at __init__ when mionet / fourier_mionet / tno are constructed without branch2_config (prevents silent degradation to a single-branch model). - Add optional output_window constructor parameter so the temporal_projection decoder registers temporal_head at __init__, producing a deterministic state_dict that round-trips cleanly. set_output_window is retained for backwards compatibility. - Raise ValueError from MLPBranch when num_layers < 2. - Convert public docstrings to r-prefixed raw strings with Parameters / Forward / Outputs sections and LaTeX shape notation per MOD-003. - Add jaxtyping.Float annotations and torch.compiler.is_compiling() guarded shape validation to all public forward methods (MOD-005, MOD-006). Signed-off-by: wdyab Made-with: Cursor --- .../experimental/models/xdeeponet/branches.py | 157 ++++++++++--- .../experimental/models/xdeeponet/deeponet.py | 215 +++++++++++++++--- .../experimental/models/xdeeponet/wrappers.py | 191 ++++++++++------ 3 files changed, 419 insertions(+), 144 deletions(-) diff --git a/physicsnemo/experimental/models/xdeeponet/branches.py b/physicsnemo/experimental/models/xdeeponet/branches.py index f723c5f4ad..27d5b01a01 100644 --- a/physicsnemo/experimental/models/xdeeponet/branches.py +++ b/physicsnemo/experimental/models/xdeeponet/branches.py @@ -40,6 +40,7 @@ import torch.nn as nn import torch.nn.functional as F import torch.nn.init as init +from jaxtyping import Float from torch import Tensor from physicsnemo.models.unet import UNet as _PhysicsNeMoUNet @@ -51,13 +52,13 @@ class _UNet2DFromUNet3D(nn.Module): - """Adapter using :class:`physicsnemo.models.unet.UNet` for 2D inputs. + r"""Adapter using :class:`physicsnemo.models.unet.UNet` for 2D inputs. The library UNet is 3D only. To reuse it for 2D, this adapter adds a - short tiled time axis of length ``2 ** model_depth`` — long enough to - survive the UNet's ``model_depth`` pooling stages — runs the 3D UNet, - and averages the result back to 2D. Channel-first layout - ``(B, C, H, W)`` is preserved on input and output. + short tiled time axis of length :math:`2^{\text{model\_depth}}` (long + enough to survive the UNet's ``model_depth`` pooling stages), runs the + 3D UNet, and averages the result back to 2D. Channel-first layout + :math:`(B, C, H, W)` is preserved on input and output. """ def __init__( @@ -87,16 +88,21 @@ def __init__( gradient_checkpointing=False, ) - def forward(self, x: Tensor) -> Tensor: - """Forward ``(B, C, H, W)`` through the 3D UNet via a tiled time axis.""" + def forward( + self, + x: Float[Tensor, "batch channels h w"], + ) -> Float[Tensor, "batch out_channels h w"]: + """Forward through the 3D UNet via a tiled time axis.""" x = x.unsqueeze(-1).repeat(1, 1, 1, 1, self._t_tile) x = self.unet(x) return x.mean(dim=-1) class _UNet3DFromUNet3D(nn.Module): - """Thin wrapper exposing :class:`physicsnemo.models.unet.UNet` with a - fixed default configuration suitable for skip-connection reuse. + r"""Thin wrapper exposing :class:`physicsnemo.models.unet.UNet`. + + Exposes the library 3D UNet with a fixed default configuration suitable + for skip-connection reuse inside :class:`SpatialBranch3D`. """ def __init__( @@ -125,8 +131,11 @@ def __init__( gradient_checkpointing=False, ) - def forward(self, x: Tensor) -> Tensor: - """Forward ``(B, C, X, Y, Z)`` through the library 3D UNet.""" + def forward( + self, + x: Float[Tensor, "batch channels x y z"], + ) -> Float[Tensor, "batch out_channels x y z"]: + """Forward pass through the library 3D UNet.""" return self.unet(x) @@ -136,13 +145,13 @@ def forward(self, x: Tensor) -> Tensor: class TrunkNet(nn.Module): - """MLP trunk network encoding query coordinates. + r"""MLP trunk network encoding query coordinates. Parameters ---------- in_features : int - Dimensionality of each query point (1 for time-only, 3 for 2D grid - coordinates, 4 for 3D grid coordinates). + Dimensionality of each query point (``1`` for time-only, ``3`` for 2D + grid coordinates, ``4`` for 3D grid coordinates). out_features : int Output width (matches the DeepONet latent size). hidden_width : int @@ -154,6 +163,18 @@ class TrunkNet(nn.Module): output_activation : bool When ``True`` (default) the final layer is followed by the activation. Set ``False`` for linear output (e.g. the TNO configuration). + + Forward + ------- + x : torch.Tensor + Query coordinates of shape :math:`(T, D_{in})` where + :math:`D_{in}` equals ``in_features``. + + Outputs + ------- + torch.Tensor + Encoded coordinates of shape :math:`(T, D_{out})` where + :math:`D_{out}` equals ``out_features``. """ def __init__( @@ -187,8 +208,17 @@ def _make_linear(self, in_dim: int, out_dim: int) -> nn.Linear: init.zeros_(layer.bias) return layer - def forward(self, x: Tensor) -> Tensor: - """Encode ``(T, in_features)`` query points into ``(T, out_features)``.""" + def forward( + self, + x: Float[Tensor, "time in_features"], + ) -> Float[Tensor, "time out_features"]: + """Forward pass of the trunk network.""" + if not torch.compiler.is_compiling(): + if x.ndim != 2: + raise ValueError( + f"Expected 2D input (T, in_features), got {x.ndim}D " + f"tensor with shape {tuple(x.shape)}" + ) for layer in self.layers: x = self.activation_fn(layer(x)) x = self.output_layer(x) @@ -198,7 +228,7 @@ def forward(self, x: Tensor) -> Tensor: class MLPBranch(nn.Module): - """Fully-connected branch for scalar/vector inputs. + r"""Fully-connected branch for scalar/vector inputs. Used for the scalar branch in MIONet-style architectures. Input features are auto-discovered via :class:`torch.nn.LazyLinear` on the first forward. @@ -210,9 +240,21 @@ class MLPBranch(nn.Module): hidden_width : int Hidden layer width. num_layers : int - Number of fully-connected layers (including output). + Number of fully-connected layers (including output). Must be ``>= 2``. activation_fn : str Activation function name. + + Forward + ------- + x : torch.Tensor + Scalar input of shape :math:`(B, D_{in})` where :math:`D_{in}` is + auto-discovered on the first forward pass. + + Outputs + ------- + torch.Tensor + Encoded features of shape :math:`(B, D_{out})` where + :math:`D_{out}` equals ``out_features``. """ def __init__( @@ -224,6 +266,12 @@ def __init__( ): super().__init__() + if num_layers < 2: + raise ValueError( + f"MLPBranch requires num_layers >= 2 (input + output), " + f"got num_layers={num_layers}" + ) + if activation_fn.lower() == "sin": self.activation_fn = torch.sin else: @@ -242,8 +290,17 @@ def _make_linear(self, in_dim: int, out_dim: int) -> nn.Linear: init.zeros_(layer.bias) return layer - def forward(self, x: Tensor) -> Tensor: - """Forward ``(B, in_features)`` through the MLP to ``(B, out_features)``.""" + def forward( + self, + x: Float[Tensor, "batch in_features"], + ) -> Float[Tensor, "batch out_features"]: + """Forward pass of the MLP branch.""" + if not torch.compiler.is_compiling(): + if x.ndim != 2: + raise ValueError( + f"Expected 2D input (B, in_features), got {x.ndim}D " + f"tensor with shape {tuple(x.shape)}" + ) for layer in self.layers: x = self.activation_fn(layer(x)) return self.activation_fn(self.output_layer(x)) @@ -255,7 +312,7 @@ def forward(self, x: Tensor) -> Tensor: class SpatialBranch(nn.Module): - """2D spatial branch composable from Fourier, UNet, and Conv layers. + r"""2D spatial branch composable from Fourier, UNet, and Conv layers. The branch can be configured to use any combination of spectral, UNet, and plain convolutional layers. When Fourier layers are present (the @@ -263,9 +320,6 @@ class SpatialBranch(nn.Module): (hybrid residual). When no Fourier layers are present UNet/Conv act as independent sequential layers. - Input: ``(B, H, W, C)`` channels-last. - Output: ``(B, H, W, width)``. - Parameters ---------- in_channels : int @@ -290,6 +344,17 @@ class SpatialBranch(nn.Module): internal_resolution : list, optional If set, inputs are adaptively pooled to this resolution before processing and upsampled back, decoupling model size from grid size. + + Forward + ------- + x : torch.Tensor + Channels-last input of shape :math:`(B, H, W, C)`. + + Outputs + ------- + torch.Tensor + Channels-last output of shape :math:`(B, H, W, D)` where + :math:`D` equals ``width``. """ def __init__( @@ -361,8 +426,17 @@ def __init__( ) ) - def forward(self, x: Tensor) -> Tensor: - """Encode ``(B, H, W, C)`` into ``(B, H, W, width)``.""" + def forward( + self, + x: Float[Tensor, "batch height width channels"], + ) -> Float[Tensor, "batch height width out_channels"]: + """Forward pass of the 2D spatial branch.""" + if not torch.compiler.is_compiling(): + if x.ndim != 4: + raise ValueError( + f"Expected 4D input (B, H, W, C), got {x.ndim}D " + f"tensor with shape {tuple(x.shape)}" + ) x = self.lift(x) x = x.permute(0, 3, 1, 2) @@ -408,13 +482,21 @@ def forward(self, x: Tensor) -> Tensor: class SpatialBranch3D(nn.Module): - """3D spatial branch composable from Fourier, UNet, and Conv layers. + r"""3D spatial branch composable from Fourier, UNet, and Conv layers. + + See :class:`SpatialBranch` for parameter semantics. The 3D variant adds + ``modes3`` for the third spectral axis. - Input: ``(B, X, Y, Z, C)`` channels-last. - Output: ``(B, X, Y, Z, width)``. + Forward + ------- + x : torch.Tensor + Channels-last input of shape :math:`(B, X, Y, Z, C)`. - See :class:`SpatialBranch` for parameter semantics. The 3D variant - adds ``modes3`` for the third spectral axis. + Outputs + ------- + torch.Tensor + Channels-last output of shape :math:`(B, X, Y, Z, D)` where + :math:`D` equals ``width``. """ def __init__( @@ -489,8 +571,17 @@ def __init__( ) ) - def forward(self, x: Tensor) -> Tensor: - """Encode ``(B, X, Y, Z, C)`` into ``(B, X, Y, Z, width)``.""" + def forward( + self, + x: Float[Tensor, "batch x y z channels"], + ) -> Float[Tensor, "batch x y z out_channels"]: + """Forward pass of the 3D spatial branch.""" + if not torch.compiler.is_compiling(): + if x.ndim != 5: + raise ValueError( + f"Expected 5D input (B, X, Y, Z, C), got {x.ndim}D " + f"tensor with shape {tuple(x.shape)}" + ) x = self.lift(x) x = x.permute(0, 4, 1, 2, 3) diff --git a/physicsnemo/experimental/models/xdeeponet/deeponet.py b/physicsnemo/experimental/models/xdeeponet/deeponet.py index 71274e77ad..48b60310a3 100644 --- a/physicsnemo/experimental/models/xdeeponet/deeponet.py +++ b/physicsnemo/experimental/models/xdeeponet/deeponet.py @@ -52,11 +52,14 @@ from __future__ import annotations -from typing import Any, Dict +from dataclasses import dataclass +from typing import Any, Dict, Optional +import torch import torch.nn as nn from torch import Tensor +from physicsnemo.core.meta import ModelMetaData from physicsnemo.core.module import Module from physicsnemo.experimental.models.xdeeponet.branches import ( MLPBranch, @@ -67,6 +70,22 @@ from physicsnemo.models.mlp import FullyConnected from physicsnemo.nn import Conv2dFCLayer, Conv3dFCLayer, get_activation +# Variants that require a secondary branch (branch2). Used by the core +# DeepONet / DeepONet3D __init__ to validate branch2_config up-front so +# multi-branch variants cannot silently degrade to single-branch models. +_DUAL_BRANCH_VARIANTS = frozenset({"mionet", "fourier_mionet", "tno"}) + + +@dataclass +class _DeepONetMetaData(ModelMetaData): + """PhysicsNeMo model metadata for :class:`DeepONet`.""" + + +@dataclass +class _DeepONet3DMetaData(ModelMetaData): + """PhysicsNeMo model metadata for :class:`DeepONet3D`.""" + + # --------------------------------------------------------------------------- # Branch config helpers # --------------------------------------------------------------------------- @@ -177,40 +196,62 @@ def _build_conv_encoder(width: int, enc_config: dict) -> nn.Module: class DeepONet(Module): - """2D xDeepONet core architecture for operator learning. + r"""2D xDeepONet core architecture for operator learning. Combines a primary spatial/MLP branch, an optional secondary branch (for MIONet/TNO variants), a coordinate trunk, and a decoder. The branch outputs and trunk are combined via Hadamard product and then projected to the output by the decoder. - Input / Output - -------------- - - ``x_branch1``: ``(B, H, W, C)`` for spatial branches or - ``(B, in_features)`` for MLP branches. - - ``x_time``: ``(T,)`` or ``(T, in_features)`` query coordinates. - - ``x_branch2`` (optional): secondary branch input for MIONet/TNO. - - Returns: ``(B, H, W, T)`` for spatial branches or ``(B, T)`` for MLP. - Parameters ---------- variant : str One of the eight supported variants (see :data:`VALID_VARIANTS`). width : int Latent width. - branch1_config, branch2_config, trunk_config : dict, optional - Sub-network configurations. See module docstring for schema. - decoder_type : {"mlp", "conv", "temporal_projection"} - ``"mlp"`` queries the trunk at each target timestep and applies an - MLP decoder; ``"conv"`` uses a convolutional decoder; and - ``"temporal_projection"`` queries the trunk once and projects the - combined latent representation to K timesteps via a learned linear - head (fast autoregressive bundling; requires - :meth:`set_output_window`). - decoder_width, decoder_layers : int - Decoder hidden width and layer count. - decoder_activation_fn : str + branch1_config : dict, optional + Primary branch configuration. See module docstring for schema. + branch2_config : dict, optional + Secondary branch configuration, required for the ``"mionet"``, + ``"fourier_mionet"``, and ``"tno"`` variants. + trunk_config : dict, optional + Trunk network configuration. + decoder_type : str, optional + One of ``"mlp"`` (queries the trunk at each target timestep and + applies an MLP decoder), ``"conv"`` (uses a convolutional decoder), + or ``"temporal_projection"`` (queries the trunk once and projects + the combined latent to K timesteps via a learned linear head for + fast autoregressive bundling). + decoder_width : int, optional + Decoder hidden width. + decoder_layers : int, optional + Decoder layer count. + decoder_activation_fn : str, optional Activation function name for the decoder. + output_window : int, optional + Output window length K for the ``"temporal_projection"`` decoder. + When supplied the temporal head is constructed at ``__init__``, which + produces a deterministic ``state_dict`` and makes checkpoint + round-tripping straightforward. When omitted, + :meth:`set_output_window` must be called before the first forward + pass. + + Forward + ------- + x_branch1 : torch.Tensor + Primary input of shape :math:`(B, H, W, C)` for spatial branches or + :math:`(B, D_{in})` for MLP branches. + x_time : torch.Tensor + Query coordinates of shape :math:`(T,)` or + :math:`(T, D_{\text{trunk}})`. + x_branch2 : torch.Tensor, optional + Secondary branch input for MIONet/TNO variants. + + Outputs + ------- + torch.Tensor + Operator output of shape :math:`(B, H, W, T)` for spatial branches + or :math:`(B, T)` for MLP branches. """ VALID_VARIANTS = [ @@ -235,8 +276,9 @@ def __init__( decoder_width: int = 128, decoder_layers: int = 2, decoder_activation_fn: str = "relu", + output_window: Optional[int] = None, ): - super().__init__() + super().__init__(meta=_DeepONetMetaData()) self.variant = variant.lower() self.width = width @@ -248,6 +290,13 @@ def __init__( f"Unknown variant: {variant}. Valid: {self.VALID_VARIANTS}" ) + if self.variant in _DUAL_BRANCH_VARIANTS and branch2_config is None: + raise ValueError( + f"variant='{self.variant}' requires branch2_config to be " + f"provided. Dual-branch variants: " + f"{sorted(_DUAL_BRANCH_VARIANTS)}." + ) + branch1_config = branch1_config or {} trunk_config = trunk_config or {} @@ -276,7 +325,20 @@ def __init__( "mlp", decoder_activation_fn, ) - self.temporal_head = None + # Preferred path: construct the temporal head at __init__ so + # state_dict keys are deterministic and checkpointing just works. + # If ``output_window`` is not provided the user must call + # :meth:`set_output_window` before the first forward pass; this + # path is kept for backwards compatibility but produces a + # state_dict whose structure depends on when the method is called. + if output_window is not None: + if output_window < 1: + raise ValueError( + f"output_window must be a positive integer, got {output_window}" + ) + self.temporal_head = nn.Linear(self.width, output_window) + else: + self.temporal_head = None else: self._temporal_projection = False self.decoder = self._build_decoder( @@ -371,12 +433,33 @@ def _build_decoder( raise ValueError(f"Unknown decoder_type: {decoder_type}") def forward( - self, x_branch1: Tensor, x_time: Tensor, x_branch2: Tensor = None + self, + x_branch1: Tensor, + x_time: Tensor, + x_branch2: Optional[Tensor] = None, ) -> Tensor: """Forward pass through the DeepONet. See class docstring for input/output shapes. """ + if not torch.compiler.is_compiling(): + if x_branch1.ndim not in (2, 4): + raise ValueError( + f"Expected x_branch1 to be 2D (B, D_in) for MLP branches " + f"or 4D (B, H, W, C) for spatial branches, got " + f"{x_branch1.ndim}D tensor with shape " + f"{tuple(x_branch1.shape)}" + ) + if x_time.ndim not in (1, 2): + raise ValueError( + f"Expected x_time to be 1D (T,) or 2D (T, D), got " + f"{x_time.ndim}D tensor with shape {tuple(x_time.shape)}" + ) + if self.has_branch2 and x_branch2 is None: + raise ValueError( + f"variant='{self.variant}' requires x_branch2 but got None" + ) + if x_time.dim() == 1: x_time = x_time.unsqueeze(-1) @@ -445,17 +528,33 @@ def count_params(self) -> int: class DeepONet3D(Module): - """3D xDeepONet core architecture for volumetric operator learning. + r"""3D xDeepONet core architecture for volumetric operator learning. + + See :class:`DeepONet` for parameter semantics. The 3D variant operates + on volumetric inputs and uses :class:`SpatialBranch3D` for spatial + branches. - Input / Output - -------------- - - ``x_branch1``: ``(B, X, Y, Z, C)`` for spatial branches or - ``(B, in_features)`` for MLP branches. - - ``x_time``: ``(T,)`` or ``(T, in_features)`` query coordinates. - - ``x_branch2`` (optional): secondary branch input for MIONet/TNO. - - Returns: ``(B, X, Y, Z, T)`` for spatial branches or ``(B, T)`` for MLP. + Parameters + ---------- + variant : str + One of the eight supported variants (see :data:`VALID_VARIANTS`). - See :class:`DeepONet` for parameter semantics. + Forward + ------- + x_branch1 : torch.Tensor + Primary input of shape :math:`(B, X, Y, Z, C)` for spatial branches + or :math:`(B, D_{in})` for MLP branches. + x_time : torch.Tensor + Query coordinates of shape :math:`(T,)` or + :math:`(T, D_{\text{trunk}})`. + x_branch2 : torch.Tensor, optional + Secondary branch input for MIONet/TNO variants. + + Outputs + ------- + torch.Tensor + Operator output of shape :math:`(B, X, Y, Z, T)` for spatial + branches or :math:`(B, T)` for MLP branches. """ VALID_VARIANTS = [ @@ -480,8 +579,9 @@ def __init__( decoder_width: int = 128, decoder_layers: int = 2, decoder_activation_fn: str = "relu", + output_window: Optional[int] = None, ): - super().__init__() + super().__init__(meta=_DeepONet3DMetaData()) self.variant = variant.lower() self.width = width @@ -493,6 +593,13 @@ def __init__( f"Unknown variant: {variant}. Valid: {self.VALID_VARIANTS}" ) + if self.variant in _DUAL_BRANCH_VARIANTS and branch2_config is None: + raise ValueError( + f"variant='{self.variant}' requires branch2_config to be " + f"provided. Dual-branch variants: " + f"{sorted(_DUAL_BRANCH_VARIANTS)}." + ) + branch1_config = branch1_config or {} trunk_config = trunk_config or {} @@ -521,7 +628,20 @@ def __init__( "mlp", decoder_activation_fn, ) - self.temporal_head = None + # Preferred path: construct the temporal head at __init__ so + # state_dict keys are deterministic and checkpointing just works. + # If ``output_window`` is not provided the user must call + # :meth:`set_output_window` before the first forward pass; this + # path is kept for backwards compatibility but produces a + # state_dict whose structure depends on when the method is called. + if output_window is not None: + if output_window < 1: + raise ValueError( + f"output_window must be a positive integer, got {output_window}" + ) + self.temporal_head = nn.Linear(self.width, output_window) + else: + self.temporal_head = None else: self._temporal_projection = False self.decoder = self._build_decoder( @@ -617,12 +737,33 @@ def _build_decoder( raise ValueError(f"Unknown decoder_type: {decoder_type}") def forward( - self, x_branch1: Tensor, x_time: Tensor, x_branch2: Tensor = None + self, + x_branch1: Tensor, + x_time: Tensor, + x_branch2: Optional[Tensor] = None, ) -> Tensor: """Forward pass through the 3D DeepONet. See class docstring for input/output shapes. """ + if not torch.compiler.is_compiling(): + if x_branch1.ndim not in (2, 5): + raise ValueError( + f"Expected x_branch1 to be 2D (B, D_in) for MLP branches " + f"or 5D (B, X, Y, Z, C) for spatial branches, got " + f"{x_branch1.ndim}D tensor with shape " + f"{tuple(x_branch1.shape)}" + ) + if x_time.ndim not in (1, 2): + raise ValueError( + f"Expected x_time to be 1D (T,) or 2D (T, D), got " + f"{x_time.ndim}D tensor with shape {tuple(x_time.shape)}" + ) + if self.has_branch2 and x_branch2 is None: + raise ValueError( + f"variant='{self.variant}' requires x_branch2 but got None" + ) + if x_time.dim() == 1: x_time = x_time.unsqueeze(-1) diff --git a/physicsnemo/experimental/models/xdeeponet/wrappers.py b/physicsnemo/experimental/models/xdeeponet/wrappers.py index 92d2b70636..63912bb876 100644 --- a/physicsnemo/experimental/models/xdeeponet/wrappers.py +++ b/physicsnemo/experimental/models/xdeeponet/wrappers.py @@ -35,12 +35,14 @@ from __future__ import annotations -from typing import Any, Dict +from dataclasses import dataclass +from typing import Any, Dict, Optional import torch -import torch.nn as nn from torch import Tensor +from physicsnemo.core.meta import ModelMetaData +from physicsnemo.core.module import Module from physicsnemo.experimental.models.xdeeponet.deeponet import DeepONet, DeepONet3D from physicsnemo.experimental.models.xdeeponet.padding import ( compute_right_pad_to_multiple, @@ -48,40 +50,74 @@ ) -class DeepONetWrapper(nn.Module): - """2D xDeepONet wrapper with automatic padding and input extraction. +@dataclass +class _DeepONetWrapperMetaData(ModelMetaData): + """PhysicsNeMo model metadata for :class:`DeepONetWrapper`.""" - Input - ----- - ``x`` : Tensor of shape ``(B, H, W, T, C)``. - Output - ------ - Tensor of shape ``(B, H, W, T_out)`` where ``T_out == T`` unless - ``target_times`` is provided (then ``T_out == len(target_times)``). +@dataclass +class _DeepONet3DWrapperMetaData(ModelMetaData): + """PhysicsNeMo model metadata for :class:`DeepONet3DWrapper`.""" + + +class DeepONetWrapper(Module): + r"""2D xDeepONet wrapper with automatic padding and input extraction. + + Extracts the spatial channels and trunk coordinates from a packed 5D + input tensor, pads spatial dimensions to a multiple of 8, runs the + core :class:`~physicsnemo.experimental.models.xdeeponet.deeponet.DeepONet`, + and unpads. Parameters ---------- - padding : int + padding : int, optional Minimum right-side padding; the wrapper rounds up to the next - multiple of 8. Default is 8. - variant : str + multiple of 8. + variant : str, optional xDeepONet variant (see :attr:`~physicsnemo.experimental.models.xdeeponet.deeponet.DeepONet.VALID_VARIANTS`). - width : int + width : int, optional Latent width. - branch1_config, branch2_config, trunk_config : dict, optional - Sub-network configurations (see core class docstrings). The trunk - config may additionally specify ``input_type`` as ``"time"`` or - ``"grid"``: ``"time"`` uses the last input channel as the time - coordinate; ``"grid"`` uses the last three channels - ``(grid_x, grid_y, grid_t)``. - decoder_type : {"mlp", "conv", "temporal_projection"} - See :class:`~physicsnemo.experimental.models.xdeeponet.deeponet.DeepONet`. - decoder_width, decoder_layers : int - Decoder hidden width and layer count. - decoder_activation_fn : str + branch1_config : dict, optional + Primary branch configuration (see core class docstring). + branch2_config : dict, optional + Secondary branch configuration for MIONet/TNO variants. + trunk_config : dict, optional + Trunk configuration. May specify ``input_type`` as ``"time"`` + (uses the last input channel as the time coordinate) or + ``"grid"`` (uses the last three channels + ``(grid_x, grid_y, grid_t)``). + decoder_type : str, optional + One of ``"mlp"``, ``"conv"``, or ``"temporal_projection"``. + decoder_width : int, optional + Decoder hidden width. + decoder_layers : int, optional + Decoder layer count. + decoder_activation_fn : str, optional Activation function name for the decoder. + output_window : int, optional + Output window for the ``"temporal_projection"`` decoder (forwarded + to :class:`DeepONet`). + + Forward + ------- + x : torch.Tensor + Packed input of shape :math:`(B, H, W, T, C)` where the last + channel axis holds features plus time/grid coordinates. + x_branch2 : torch.Tensor, optional + Secondary branch input for MIONet/TNO variants. + target_times : torch.Tensor, optional + Explicit trunk query coordinates of shape :math:`(K,)` or + :math:`(K, 1)`. When provided the trunk evaluates at these + :math:`K` points instead of the times extracted from ``x``, + enabling autoregressive temporal bundling where :math:`K \neq T`. + + Outputs + ------- + torch.Tensor + Operator output of shape :math:`(B, H, W, T_{out})` where + :math:`T_{out} = K` when ``target_times`` is given and + :math:`T_{out} = T` otherwise. """ def __init__( @@ -96,8 +132,9 @@ def __init__( decoder_width: int = 128, decoder_layers: int = 2, decoder_activation_fn: str = "relu", + output_window: Optional[int] = None, ): - super().__init__() + super().__init__(meta=_DeepONetWrapperMetaData()) self.padding = ((padding + 7) // 8) * 8 if padding % 8 != 0 else padding self.variant = variant @@ -123,6 +160,7 @@ def __init__( decoder_width=decoder_width, decoder_layers=decoder_layers, decoder_activation_fn=decoder_activation_fn, + output_window=output_window, ) self._temporal_projection = self.model._temporal_projection @@ -133,29 +171,26 @@ def set_output_window(self, K: int): def forward( self, x: Tensor, - x_branch2: Tensor = None, - target_times: Tensor = None, + x_branch2: Optional[Tensor] = None, + target_times: Optional[Tensor] = None, ) -> Tensor: """Forward pass through the 2D wrapper. - Parameters - ---------- - x : Tensor - Input ``(B, H, W, T_in, C)``. - x_branch2 : Tensor, optional - Secondary branch input (MIONet/TNO variants). - target_times : Tensor, optional - Explicit trunk query coordinates ``(K,)`` or ``(K, 1)``. When - provided the trunk evaluates at these K points instead of - extracting time values from ``x``, enabling autoregressive - temporal bundling where ``K != T_in``. - - Returns - ------- - Tensor - ``(B, H, W, T_out)`` where ``T_out = K`` if ``target_times`` is - given, else ``T_in``. + See class docstring for input/output shapes. """ + if not torch.compiler.is_compiling(): + if x.ndim != 5: + raise ValueError( + f"Expected 5D input (B, H, W, T, C), got {x.ndim}D " + f"tensor with shape {tuple(x.shape)}" + ) + if target_times is not None and target_times.ndim not in (1, 2): + raise ValueError( + f"Expected target_times to be 1D (K,) or 2D (K, 1), " + f"got {target_times.ndim}D tensor with shape " + f"{tuple(target_times.shape)}" + ) + H, W = x.shape[1], x.shape[2] pad_h, pad_w = compute_right_pad_to_multiple( @@ -203,21 +238,27 @@ def count_params(self) -> int: return self.model.count_params() -class DeepONet3DWrapper(nn.Module): - """3D xDeepONet wrapper with automatic padding and input extraction. - - Input - ----- - ``x`` : Tensor of shape ``(B, X, Y, Z, T, C)``. - - Output - ------ - Tensor of shape ``(B, X, Y, Z, T_out)`` where ``T_out == T`` unless - ``target_times`` is provided. +class DeepONet3DWrapper(Module): + r"""3D xDeepONet wrapper with automatic padding and input extraction. See :class:`DeepONetWrapper` for parameter semantics. The 3D trunk ``input_type="grid"`` uses the last four input channels ``(grid_x, grid_y, grid_z, grid_t)``. + + Forward + ------- + x : torch.Tensor + Packed input of shape :math:`(B, X, Y, Z, T, C)`. + x_branch2 : torch.Tensor, optional + Secondary branch input for MIONet/TNO variants. + target_times : torch.Tensor, optional + Explicit trunk query coordinates of shape :math:`(K,)` or + :math:`(K, 1)`. + + Outputs + ------- + torch.Tensor + Operator output of shape :math:`(B, X, Y, Z, T_{out})`. """ def __init__( @@ -232,8 +273,9 @@ def __init__( decoder_width: int = 128, decoder_layers: int = 2, decoder_activation_fn: str = "relu", + output_window: Optional[int] = None, ): - super().__init__() + super().__init__(meta=_DeepONet3DWrapperMetaData()) self.padding = ((padding + 7) // 8) * 8 if padding % 8 != 0 else padding self.variant = variant @@ -259,6 +301,7 @@ def __init__( decoder_width=decoder_width, decoder_layers=decoder_layers, decoder_activation_fn=decoder_activation_fn, + output_window=output_window, ) self._temporal_projection = self.model._temporal_projection @@ -269,26 +312,26 @@ def set_output_window(self, K: int): def forward( self, x: Tensor, - x_branch2: Tensor = None, - target_times: Tensor = None, + x_branch2: Optional[Tensor] = None, + target_times: Optional[Tensor] = None, ) -> Tensor: """Forward pass through the 3D wrapper. - Parameters - ---------- - x : Tensor - Input ``(B, X, Y, Z, T_in, C)``. - x_branch2 : Tensor, optional - Secondary branch input (MIONet/TNO variants). - target_times : Tensor, optional - Explicit trunk query coordinates ``(K,)`` or ``(K, 1)``. - - Returns - ------- - Tensor - ``(B, X, Y, Z, T_out)`` where ``T_out = K`` if ``target_times`` - is given, else ``T_in``. + See class docstring for input/output shapes. """ + if not torch.compiler.is_compiling(): + if x.ndim != 6: + raise ValueError( + f"Expected 6D input (B, X, Y, Z, T, C), got {x.ndim}D " + f"tensor with shape {tuple(x.shape)}" + ) + if target_times is not None and target_times.ndim not in (1, 2): + raise ValueError( + f"Expected target_times to be 1D (K,) or 2D (K, 1), " + f"got {target_times.ndim}D tensor with shape " + f"{tuple(target_times.shape)}" + ) + X, Y, Z = x.shape[1], x.shape[2], x.shape[3] pad_x, pad_y, pad_z = compute_right_pad_to_multiple( From 85076f6ed1b8af5413b63016ebb2fb74cb83fe0e Mon Sep 17 00:00:00 2001 From: wdyab Date: Wed, 22 Apr 2026 07:01:48 -0700 Subject: [PATCH 3/8] xdeeponet: address second Greptile review (PR #1576) Fix the new P1 issue flagged in the second Greptile review and close two secondary gaps the summary called out: - DeepONet.forward / DeepONet3D.forward: raise RuntimeError when decoder_type='temporal_projection' is used but temporal_head is still None (i.e. the user neither passed output_window at construction nor called set_output_window before forward). Previously the silent ``if temporal_head is not None`` skip returned (B, H, W, width) instead of (B, H, W, K). - Deduplicate the VALID_VARIANTS list: pulled to a module-level _VALID_VARIANTS tuple; both DeepONet and DeepONet3D still expose it as the VALID_VARIANTS class attribute for a stable public API. - Extend the parametrized test lists to cover fourier_deeponet, hybrid_deeponet, and fourier_mionet, and add a dedicated TestFourierBranchPaths class with num_fourier_layers > 0 so the spectral-conv code path in SpatialBranch / SpatialBranch3D is actually exercised in CI. - Add a TestTemporalProjectionGuard::test_forward_without_output_window_raises regression test for the new RuntimeError. Signed-off-by: wdyab Made-with: Cursor --- .../experimental/models/xdeeponet/deeponet.py | 55 +++++---- test/experimental/models/test_xdeeponet.py | 108 +++++++++++++++++- 2 files changed, 137 insertions(+), 26 deletions(-) diff --git a/physicsnemo/experimental/models/xdeeponet/deeponet.py b/physicsnemo/experimental/models/xdeeponet/deeponet.py index 48b60310a3..c10f3692f7 100644 --- a/physicsnemo/experimental/models/xdeeponet/deeponet.py +++ b/physicsnemo/experimental/models/xdeeponet/deeponet.py @@ -70,6 +70,21 @@ from physicsnemo.models.mlp import FullyConnected from physicsnemo.nn import Conv2dFCLayer, Conv3dFCLayer, get_activation +# All xDeepONet variants supported by both 2D and 3D cores. Defined once +# at module scope so the two classes share a single source of truth; each +# class still exposes it as the ``VALID_VARIANTS`` class attribute for a +# stable public API. +_VALID_VARIANTS = ( + "deeponet", + "u_deeponet", + "fourier_deeponet", + "conv_deeponet", + "hybrid_deeponet", + "mionet", + "fourier_mionet", + "tno", +) + # Variants that require a secondary branch (branch2). Used by the core # DeepONet / DeepONet3D __init__ to validate branch2_config up-front so # multi-branch variants cannot silently degrade to single-branch models. @@ -254,16 +269,7 @@ class DeepONet(Module): or :math:`(B, T)` for MLP branches. """ - VALID_VARIANTS = [ - "deeponet", - "u_deeponet", - "fourier_deeponet", - "conv_deeponet", - "hybrid_deeponet", - "mionet", - "fourier_mionet", - "tno", - ] + VALID_VARIANTS = _VALID_VARIANTS def __init__( self, @@ -483,8 +489,13 @@ def forward( else: combined = combined * b2_out.unsqueeze(1).unsqueeze(2) combined = self.decoder(combined) - if self.temporal_head is not None: - combined = self.temporal_head(combined) + if self.temporal_head is None: + raise RuntimeError( + "decoder_type='temporal_projection' requires either " + "output_window to be provided at construction time, " + "or set_output_window(K) to be called before forward." + ) + combined = self.temporal_head(combined) return combined b1_out = b1_out.unsqueeze(1) @@ -557,16 +568,7 @@ class DeepONet3D(Module): branches or :math:`(B, T)` for MLP branches. """ - VALID_VARIANTS = [ - "deeponet", - "u_deeponet", - "fourier_deeponet", - "conv_deeponet", - "hybrid_deeponet", - "mionet", - "fourier_mionet", - "tno", - ] + VALID_VARIANTS = _VALID_VARIANTS def __init__( self, @@ -789,8 +791,13 @@ def forward( 2 ).unsqueeze(3) combined = self.decoder(combined) - if self.temporal_head is not None: - combined = self.temporal_head(combined) + if self.temporal_head is None: + raise RuntimeError( + "decoder_type='temporal_projection' requires either " + "output_window to be provided at construction time, " + "or set_output_window(K) to be called before forward." + ) + combined = self.temporal_head(combined) return combined b1_out = b1_out.unsqueeze(1) diff --git a/test/experimental/models/test_xdeeponet.py b/test/experimental/models/test_xdeeponet.py index bc5686858e..102f685de8 100644 --- a/test/experimental/models/test_xdeeponet.py +++ b/test/experimental/models/test_xdeeponet.py @@ -151,8 +151,46 @@ def test_output_shape(self): assert out.shape == (2, 8, 16, 8, 16) -SINGLE_BRANCH_VARIANTS = ["deeponet", "u_deeponet", "conv_deeponet"] -DUAL_BRANCH_VARIANTS = ["mionet", "tno"] +SINGLE_BRANCH_VARIANTS = [ + "deeponet", + "u_deeponet", + "conv_deeponet", + "fourier_deeponet", + "hybrid_deeponet", +] +DUAL_BRANCH_VARIANTS = ["mionet", "tno", "fourier_mionet"] + +# Branch config that actually exercises the Fourier code path in +# ``SpatialBranch`` / ``SpatialBranch3D``. Kept small (1 spectral layer, 2 +# modes) so test runtime stays reasonable. +BRANCH1_SPATIAL_FOURIER = { + "encoder": {"type": "linear", "activation_fn": "relu"}, + "layers": { + "num_fourier_layers": 1, + "num_unet_layers": 0, + "num_conv_layers": 0, + "modes1": 2, + "modes2": 2, + "modes3": 2, + "kernel_size": 3, + "dropout": 0.0, + "activation_fn": "relu", + }, +} +BRANCH2_SPATIAL_FOURIER = { + "encoder": {"type": "linear", "activation_fn": "relu"}, + "layers": { + "num_fourier_layers": 1, + "num_unet_layers": 0, + "num_conv_layers": 0, + "modes1": 2, + "modes2": 2, + "modes3": 2, + "kernel_size": 3, + "dropout": 0.0, + "activation_fn": "relu", + }, +} class TestDeepONetWrapper2D: @@ -557,5 +595,71 @@ def test_3d_internal_resolution(self): assert out.shape == (2, 16, 16, 16, 8) +class TestTemporalProjectionGuard: + """Validate that forward raises when temporal_head is not configured.""" + + def test_forward_without_output_window_raises(self): + """Forward must raise RuntimeError when temporal_projection has no head. + + Constructing with ``decoder_type="temporal_projection"`` but without + passing ``output_window`` and without calling ``set_output_window`` + leaves ``temporal_head = None``. The forward pass must fail loudly + in that case rather than silently returning a ``(B, H, W, width)`` + tensor instead of the expected ``(B, H, W, K)``. + """ + model = DeepONetWrapper( + variant="u_deeponet", + width=16, + branch1_config=BRANCH1_SPATIAL, + trunk_config=TRUNK, + decoder_type="temporal_projection", + decoder_width=16, + decoder_layers=1, + ) + + x = torch.randn(2, 16, 16, 3, 2) + with pytest.raises(RuntimeError, match="output_window"): + model(x) + + +class TestFourierBranchPaths: + """Exercise the Fourier (spectral-conv) code path in SpatialBranch[3D].""" + + @pytest.mark.parametrize("variant", ["fourier_deeponet", "hybrid_deeponet"]) + def test_2d_fourier_branch_forward(self, variant): + """2D Fourier-enabled SpatialBranch produces correct output shape.""" + # Grid size must be >= 2*modes + 1 so the spectral layer has enough + # frequency content; 8 x 8 with modes1=modes2=2 is safe. + model = DeepONetWrapper( + variant=variant, + width=16, + branch1_config=BRANCH1_SPATIAL_FOURIER, + trunk_config=TRUNK, + decoder_type="mlp", + decoder_width=16, + decoder_layers=1, + ) + x = torch.randn(2, 8, 8, 3, 2) + out = model(x) + assert out.shape == (2, 8, 8, 3) + + def test_2d_fourier_mionet_forward(self): + """Dual-branch Fourier-MIONet forward works end-to-end.""" + model = DeepONetWrapper( + variant="fourier_mionet", + width=16, + branch1_config=BRANCH1_SPATIAL_FOURIER, + branch2_config=BRANCH2_SPATIAL_FOURIER, + trunk_config=TRUNK, + decoder_type="mlp", + decoder_width=16, + decoder_layers=1, + ) + x = torch.randn(2, 8, 8, 3, 2) + x_b2 = torch.randn(2, 8, 8, 2) + out = model(x, x_branch2=x_b2) + assert out.shape == (2, 8, 8, 3) + + if __name__ == "__main__": pytest.main([__file__, "-v"]) From 101663ebeb0338d39fe6ba3ff7e97e7181589094 Mon Sep 17 00:00:00 2001 From: wdyab Date: Wed, 22 Apr 2026 08:24:06 -0700 Subject: [PATCH 4/8] xdeeponet: address third Greptile review (PR #1576) Two new P1 issues flagged on 85076f6e: - Case-sensitive decoder_type check: __init__ lowered ``decoder_type`` into ``self.decoder_type`` but then branched on the raw argument (``if decoder_type == "temporal_projection":``) and forwarded the raw value to ``_build_decoder``. A user passing ``decoder_type="MLP"`` or ``"Temporal_Projection"`` ended up with ``Unknown decoder_type: MLP`` bubbling out of ``_build_decoder``. Both branches of the check now use ``self.decoder_type``; same fix in ``DeepONet3D.__init__``. - MLP branch + decoder_type='temporal_projection' silently returned (B, T, width) instead of (B, K) because the MLP-branch path in ``forward`` never consulted ``self._temporal_projection``. The incompatibility is static, so reject it at __init__ with a descriptive ``ValueError`` rather than at forward. Same guard in ``DeepONet3D.__init__``. Regression tests: ``TestDecoderTypeNormalization`` (mixed-case ``"MLP"`` / ``"Temporal_Projection"`` accepted) and ``TestMLPBranchTemporalProjectionGuard`` (2D and 3D both reject the invalid combination). Signed-off-by: wdyab Made-with: Cursor --- .../experimental/models/xdeeponet/deeponet.py | 40 +++++++++-- test/experimental/models/test_xdeeponet.py | 70 +++++++++++++++++++ 2 files changed, 106 insertions(+), 4 deletions(-) diff --git a/physicsnemo/experimental/models/xdeeponet/deeponet.py b/physicsnemo/experimental/models/xdeeponet/deeponet.py index c10f3692f7..36257a64f6 100644 --- a/physicsnemo/experimental/models/xdeeponet/deeponet.py +++ b/physicsnemo/experimental/models/xdeeponet/deeponet.py @@ -308,6 +308,22 @@ def __init__( self.branch1 = self._build_branch(branch1_config, width) + # ``temporal_projection`` decoder only makes sense on top of a + # spatial branch: its forward path expects ``b1_out`` to be 4D + # (or 5D for the 3D core) so the per-timestep linear head has a + # spatial dimension to project. When ``branch1`` is an MLPBranch + # the forward path silently drops the temporal head and returns + # the wrong shape, so reject the combination up front. + if self.decoder_type == "temporal_projection" and isinstance( + self.branch1, MLPBranch + ): + raise ValueError( + "decoder_type='temporal_projection' is not supported with " + "MLP branches. Use a SpatialBranch (set num_unet_layers " + "or num_fourier_layers > 0 in branch1_config), or choose " + "decoder_type='mlp' / 'conv' instead." + ) + self.has_branch2 = branch2_config is not None if self.has_branch2: self.branch2 = self._build_branch(branch2_config, width) @@ -321,7 +337,7 @@ def __init__( output_activation=trunk_config.get("output_activation", True), ) - if decoder_type == "temporal_projection": + if self.decoder_type == "temporal_projection": self._temporal_projection = True self.decoder = self._build_decoder( width, @@ -352,7 +368,7 @@ def __init__( 1, decoder_layers, decoder_width, - decoder_type, + self.decoder_type, decoder_activation_fn, ) @@ -607,6 +623,22 @@ def __init__( self.branch1 = self._build_branch(branch1_config, width) + # ``temporal_projection`` decoder only makes sense on top of a + # spatial branch: its forward path expects ``b1_out`` to be 4D + # (or 5D for the 3D core) so the per-timestep linear head has a + # spatial dimension to project. When ``branch1`` is an MLPBranch + # the forward path silently drops the temporal head and returns + # the wrong shape, so reject the combination up front. + if self.decoder_type == "temporal_projection" and isinstance( + self.branch1, MLPBranch + ): + raise ValueError( + "decoder_type='temporal_projection' is not supported with " + "MLP branches. Use a SpatialBranch (set num_unet_layers " + "or num_fourier_layers > 0 in branch1_config), or choose " + "decoder_type='mlp' / 'conv' instead." + ) + self.has_branch2 = branch2_config is not None if self.has_branch2: self.branch2 = self._build_branch(branch2_config, width) @@ -620,7 +652,7 @@ def __init__( output_activation=trunk_config.get("output_activation", True), ) - if decoder_type == "temporal_projection": + if self.decoder_type == "temporal_projection": self._temporal_projection = True self.decoder = self._build_decoder( width, @@ -651,7 +683,7 @@ def __init__( 1, decoder_layers, decoder_width, - decoder_type, + self.decoder_type, decoder_activation_fn, ) diff --git a/test/experimental/models/test_xdeeponet.py b/test/experimental/models/test_xdeeponet.py index 102f685de8..24baae7f96 100644 --- a/test/experimental/models/test_xdeeponet.py +++ b/test/experimental/models/test_xdeeponet.py @@ -622,6 +622,76 @@ def test_forward_without_output_window_raises(self): model(x) +class TestDecoderTypeNormalization: + """decoder_type comparison must use the lowercased, stored value.""" + + def test_mixed_case_decoder_type_accepted(self): + """Constructing with a non-lowercase decoder_type must just work. + + The check in ``__init__`` previously compared the raw argument + instead of ``self.decoder_type`` (which is lowercased), so values + like ``"MLP"`` or ``"Temporal_Projection"`` bypassed the + temporal-projection branch and bubbled up ``ValueError: Unknown + decoder_type`` from ``_build_decoder``. + """ + # Mixed-case "MLP" should be equivalent to "mlp". + model = DeepONetWrapper( + variant="u_deeponet", + width=16, + branch1_config=BRANCH1_SPATIAL, + trunk_config=TRUNK, + decoder_type="MLP", + decoder_width=16, + decoder_layers=1, + ) + assert model.model.decoder_type == "mlp" + + # Mixed-case "Temporal_Projection" should be equivalent to + # "temporal_projection" and must build the temporal-projection + # pathway (which requires output_window). + model = DeepONetWrapper( + variant="u_deeponet", + width=16, + branch1_config=BRANCH1_SPATIAL, + trunk_config=TRUNK, + decoder_type="Temporal_Projection", + decoder_width=16, + decoder_layers=1, + output_window=3, + ) + assert model.model.decoder_type == "temporal_projection" + assert model.model._temporal_projection is True + + +class TestMLPBranchTemporalProjectionGuard: + """MLP branches cannot be combined with decoder_type='temporal_projection'.""" + + def test_mlp_branch_temporal_projection_raises(self): + """2D core must reject the MLP-branch + temporal_projection combo.""" + # BRANCH1_MLP selects an MLPBranch for branch1. The forward path + # silently returns the wrong shape for this combination, so the + # construction must fail instead. + with pytest.raises(ValueError, match="MLP branches"): + DeepONet( + variant="u_deeponet", + width=16, + branch1_config=BRANCH1_MLP, + trunk_config=TRUNK, + decoder_type="temporal_projection", + ) + + def test_mlp_branch_temporal_projection_raises_3d(self): + """3D core shares the same guard.""" + with pytest.raises(ValueError, match="MLP branches"): + DeepONet3D( + variant="u_deeponet", + width=16, + branch1_config=BRANCH1_MLP, + trunk_config=TRUNK, + decoder_type="temporal_projection", + ) + + class TestFourierBranchPaths: """Exercise the Fourier (spectral-conv) code path in SpatialBranch[3D].""" From f9982158664d662c736c4091fc5bb9189d121b01 Mon Sep 17 00:00:00 2001 From: wdyab Date: Wed, 22 Apr 2026 10:00:05 -0700 Subject: [PATCH 5/8] xdeeponet: close out silent-degradation combinations (PR #1576) Proactive audit on top of Greptile's round-4 findings. All plausible silent-degradation combinations at the config boundary now fail loudly at __init__ instead of producing wrong shapes or cryptic PyTorch errors at forward time. Construction-time guards added to both DeepONet and DeepONet3D: - Unknown decoder_type is rejected up front against a new module-level ``_VALID_DECODER_TYPES`` set (previously deferred to ``_build_decoder`` and only surfaced on the non-temporal branch). - MLPBranch branch1 paired with decoder_type='conv' is rejected (would otherwise crash inside ``Conv2d`` with a generic "Expected 3D or 4D input" message). Unified with the existing temporal_projection guard into a single check. - MLPBranch branch1 paired with a non-MLPBranch branch2 is rejected (element-wise product assumed matching ranks; previously broadcast nonsensically or raised a cryptic dim mismatch at forward). Regression tests: - ``TestMLPBranchConvDecoderGuard`` -- 2D/3D - ``TestMixedBranchTypeGuard`` -- 2D/3D - ``TestInvalidDecoderTypeGuard`` -- 2D/3D Full suite: 47 passed. Signed-off-by: wdyab Made-with: Cursor --- .../experimental/models/xdeeponet/deeponet.py | 108 ++++++++++++++---- test/experimental/models/test_xdeeponet.py | 89 +++++++++++++++ 2 files changed, 173 insertions(+), 24 deletions(-) diff --git a/physicsnemo/experimental/models/xdeeponet/deeponet.py b/physicsnemo/experimental/models/xdeeponet/deeponet.py index 36257a64f6..4117a615b4 100644 --- a/physicsnemo/experimental/models/xdeeponet/deeponet.py +++ b/physicsnemo/experimental/models/xdeeponet/deeponet.py @@ -90,6 +90,12 @@ # multi-branch variants cannot silently degrade to single-branch models. _DUAL_BRANCH_VARIANTS = frozenset({"mionet", "fourier_mionet", "tno"}) +# Supported decoder types. Used by the core DeepONet / DeepONet3D +# __init__ to reject unknown decoder types at the API boundary instead +# of deferring to ``_build_decoder`` and raising cryptically from deep +# inside construction. +_VALID_DECODER_TYPES = frozenset({"mlp", "conv", "temporal_projection"}) + @dataclass class _DeepONetMetaData(ModelMetaData): @@ -296,6 +302,12 @@ def __init__( f"Unknown variant: {variant}. Valid: {self.VALID_VARIANTS}" ) + if self.decoder_type not in _VALID_DECODER_TYPES: + raise ValueError( + f"Unknown decoder_type: {decoder_type!r}. Valid: " + f"{sorted(_VALID_DECODER_TYPES)}." + ) + if self.variant in _DUAL_BRANCH_VARIANTS and branch2_config is None: raise ValueError( f"variant='{self.variant}' requires branch2_config to be " @@ -308,26 +320,47 @@ def __init__( self.branch1 = self._build_branch(branch1_config, width) - # ``temporal_projection`` decoder only makes sense on top of a - # spatial branch: its forward path expects ``b1_out`` to be 4D - # (or 5D for the 3D core) so the per-timestep linear head has a - # spatial dimension to project. When ``branch1`` is an MLPBranch - # the forward path silently drops the temporal head and returns - # the wrong shape, so reject the combination up front. - if self.decoder_type == "temporal_projection" and isinstance( - self.branch1, MLPBranch + # Reject MLP-branch configurations paired with a decoder that + # needs a spatial (4D / 5D) ``combined`` tensor. The MLP-branch + # forward path produces a 3D tensor of shape (B, T, width) and: + # * ``temporal_projection`` silently drops the temporal head + # (wrong shape, no error); + # * ``conv`` crashes inside the decoder's ``Conv2d`` / + # ``Conv3d`` with PyTorch's generic "Expected 3D or 4D + # input" message, with no hint that the real cause is a + # config mismatch. + # Fail fast here instead. + if isinstance(self.branch1, MLPBranch) and self.decoder_type in ( + "temporal_projection", + "conv", ): raise ValueError( - "decoder_type='temporal_projection' is not supported with " - "MLP branches. Use a SpatialBranch (set num_unet_layers " - "or num_fourier_layers > 0 in branch1_config), or choose " - "decoder_type='mlp' / 'conv' instead." + f"decoder_type={self.decoder_type!r} is not supported with " + "MLP branches. Use decoder_type='mlp', or configure a " + "SpatialBranch for branch1 (set num_unet_layers, " + "num_fourier_layers, or num_conv_layers > 0 in " + "branch1_config)." ) self.has_branch2 = branch2_config is not None if self.has_branch2: self.branch2 = self._build_branch(branch2_config, width) + # Forward assumes branch2's output has the same rank as + # branch1's. Mixing an MLPBranch (2D output (B, width)) with + # a SpatialBranch (4D / 5D output) would either broadcast + # nonsensically or raise a cryptic dim-mismatch error in the + # Hadamard product. Reject the mixed configuration here. + if isinstance(self.branch1, MLPBranch) and not isinstance( + self.branch2, MLPBranch + ): + raise ValueError( + "When branch1 is an MLPBranch, branch2 must also be " + "an MLPBranch (i.e. produce a 2D (B, width) output). " + "Swap branch1 and branch2, or configure branch1 as " + "a SpatialBranch." + ) + self.trunk = TrunkNet( in_features=trunk_config.get("in_features", 1), out_features=width, @@ -611,6 +644,12 @@ def __init__( f"Unknown variant: {variant}. Valid: {self.VALID_VARIANTS}" ) + if self.decoder_type not in _VALID_DECODER_TYPES: + raise ValueError( + f"Unknown decoder_type: {decoder_type!r}. Valid: " + f"{sorted(_VALID_DECODER_TYPES)}." + ) + if self.variant in _DUAL_BRANCH_VARIANTS and branch2_config is None: raise ValueError( f"variant='{self.variant}' requires branch2_config to be " @@ -623,26 +662,47 @@ def __init__( self.branch1 = self._build_branch(branch1_config, width) - # ``temporal_projection`` decoder only makes sense on top of a - # spatial branch: its forward path expects ``b1_out`` to be 4D - # (or 5D for the 3D core) so the per-timestep linear head has a - # spatial dimension to project. When ``branch1`` is an MLPBranch - # the forward path silently drops the temporal head and returns - # the wrong shape, so reject the combination up front. - if self.decoder_type == "temporal_projection" and isinstance( - self.branch1, MLPBranch + # Reject MLP-branch configurations paired with a decoder that + # needs a spatial (4D / 5D) ``combined`` tensor. The MLP-branch + # forward path produces a 3D tensor of shape (B, T, width) and: + # * ``temporal_projection`` silently drops the temporal head + # (wrong shape, no error); + # * ``conv`` crashes inside the decoder's ``Conv2d`` / + # ``Conv3d`` with PyTorch's generic "Expected 3D or 4D + # input" message, with no hint that the real cause is a + # config mismatch. + # Fail fast here instead. + if isinstance(self.branch1, MLPBranch) and self.decoder_type in ( + "temporal_projection", + "conv", ): raise ValueError( - "decoder_type='temporal_projection' is not supported with " - "MLP branches. Use a SpatialBranch (set num_unet_layers " - "or num_fourier_layers > 0 in branch1_config), or choose " - "decoder_type='mlp' / 'conv' instead." + f"decoder_type={self.decoder_type!r} is not supported with " + "MLP branches. Use decoder_type='mlp', or configure a " + "SpatialBranch for branch1 (set num_unet_layers, " + "num_fourier_layers, or num_conv_layers > 0 in " + "branch1_config)." ) self.has_branch2 = branch2_config is not None if self.has_branch2: self.branch2 = self._build_branch(branch2_config, width) + # Forward assumes branch2's output has the same rank as + # branch1's. Mixing an MLPBranch (2D output (B, width)) with + # a SpatialBranch (4D / 5D output) would either broadcast + # nonsensically or raise a cryptic dim-mismatch error in the + # Hadamard product. Reject the mixed configuration here. + if isinstance(self.branch1, MLPBranch) and not isinstance( + self.branch2, MLPBranch + ): + raise ValueError( + "When branch1 is an MLPBranch, branch2 must also be " + "an MLPBranch (i.e. produce a 2D (B, width) output). " + "Swap branch1 and branch2, or configure branch1 as " + "a SpatialBranch." + ) + self.trunk = TrunkNet( in_features=trunk_config.get("in_features", 1), out_features=width, diff --git a/test/experimental/models/test_xdeeponet.py b/test/experimental/models/test_xdeeponet.py index 24baae7f96..76076f7485 100644 --- a/test/experimental/models/test_xdeeponet.py +++ b/test/experimental/models/test_xdeeponet.py @@ -692,6 +692,95 @@ def test_mlp_branch_temporal_projection_raises_3d(self): ) +class TestMLPBranchConvDecoderGuard: + """MLP branches cannot be combined with decoder_type='conv'.""" + + def test_mlp_branch_conv_decoder_raises(self): + """2D core rejects MLP-branch + conv decoder at __init__.""" + # Forward would otherwise crash inside the decoder's Conv2d with + # a generic "Expected 3D or 4D input" error rather than pointing + # at the real config mismatch. + with pytest.raises(ValueError, match="MLP branches"): + DeepONet( + variant="u_deeponet", + width=16, + branch1_config=BRANCH1_MLP, + trunk_config=TRUNK, + decoder_type="conv", + ) + + def test_mlp_branch_conv_decoder_raises_3d(self): + """3D core shares the same guard.""" + with pytest.raises(ValueError, match="MLP branches"): + DeepONet3D( + variant="u_deeponet", + width=16, + branch1_config=BRANCH1_MLP, + trunk_config=TRUNK, + decoder_type="conv", + ) + + +class TestMixedBranchTypeGuard: + """branch1 and branch2 must have matching output ranks.""" + + def test_mlp_branch1_with_spatial_branch2_raises(self): + """2D core rejects MLP branch1 + SpatialBranch branch2.""" + # Forward assumes both branch outputs have the same rank; mixing + # 2D (MLP) and 4D (Spatial) produces nonsensical broadcasts. + with pytest.raises(ValueError, match="branch1 is an MLPBranch"): + DeepONet( + variant="mionet", + width=16, + branch1_config=BRANCH1_MLP, + branch2_config=BRANCH2_SPATIAL, + trunk_config=TRUNK, + decoder_type="mlp", + ) + + def test_mlp_branch1_with_spatial_branch2_raises_3d(self): + """3D core shares the same guard.""" + with pytest.raises(ValueError, match="branch1 is an MLPBranch"): + DeepONet3D( + variant="mionet", + width=16, + branch1_config=BRANCH1_MLP, + branch2_config=BRANCH2_SPATIAL, + trunk_config=TRUNK, + decoder_type="mlp", + ) + + +class TestInvalidDecoderTypeGuard: + """Unknown decoder_type is rejected at __init__ with a helpful message.""" + + def test_unknown_decoder_type_raises(self): + """2D core rejects unknown decoder_type at the API boundary.""" + # Previously this surfaced as ``Unknown decoder_type: xyz`` from + # deep inside ``_build_decoder`` only when the non-temporal + # branch was taken. Moving the check to ``__init__`` makes it + # part of the public contract. + with pytest.raises(ValueError, match="Unknown decoder_type"): + DeepONet( + variant="u_deeponet", + width=16, + branch1_config=BRANCH1_SPATIAL, + trunk_config=TRUNK, + decoder_type="definitely_not_a_decoder", + ) + + def test_unknown_decoder_type_raises_3d(self): + """3D core shares the same guard.""" + with pytest.raises(ValueError, match="Unknown decoder_type"): + DeepONet3D( + variant="u_deeponet", + width=16, + branch1_config=BRANCH1_SPATIAL, + trunk_config=TRUNK, + decoder_type="definitely_not_a_decoder", + ) + + class TestFourierBranchPaths: """Exercise the Fourier (spectral-conv) code path in SpatialBranch[3D].""" From 3c01800d9b55675838eee6c5e5ec85f627afa7ed Mon Sep 17 00:00:00 2001 From: wdyab Date: Wed, 22 Apr 2026 11:23:14 -0700 Subject: [PATCH 6/8] xdeeponet: fix _build_conv_encoder for "sin" activation (PR #1576) ``_build_conv_encoder`` called ``get_activation`` directly without the ``sin`` special-case handling used in every other activation site in ``branches.py``. Passing ``{"encoder": {"type": "conv", "activation_fn": "sin"}}`` therefore raised ``KeyError: Activation function sin not found``. ``torch.sin`` is a bare callable and cannot be placed inside an ``nn.Sequential`` (which requires ``nn.Module`` instances), so the fix introduces a small ``_SinActivation`` wrapper module alongside ``_build_conv_encoder``. The helper is module-level and is called from both ``DeepONet`` and ``DeepONet3D``; only one fix site exists despite the function being invoked from both classes. Regression test ``TestConvEncoderSinActivation`` constructs a multi-layer conv encoder with ``activation_fn="sin"`` and runs a forward pass to confirm neither the ``KeyError`` nor a ``nn.Sequential`` ``TypeError`` resurface. Signed-off-by: wdyab Made-with: Cursor --- .../experimental/models/xdeeponet/deeponet.py | 25 +++++++++- test/experimental/models/test_xdeeponet.py | 46 +++++++++++++++++++ 2 files changed, 70 insertions(+), 1 deletion(-) diff --git a/physicsnemo/experimental/models/xdeeponet/deeponet.py b/physicsnemo/experimental/models/xdeeponet/deeponet.py index 4117a615b4..6b0ee62715 100644 --- a/physicsnemo/experimental/models/xdeeponet/deeponet.py +++ b/physicsnemo/experimental/models/xdeeponet/deeponet.py @@ -189,6 +189,22 @@ def _normalize_branch_config(config: dict) -> dict: return result +class _SinActivation(nn.Module): + """Module wrapper around :func:`torch.sin` for use inside ``nn.Sequential``. + + ``physicsnemo.nn.get_activation`` does not register ``"sin"`` in its + activation table; branch modules in ``branches.py`` work around this + by storing ``torch.sin`` as a bare callable and invoking it directly + in ``forward``. That pattern does not compose with ``nn.Sequential`` + (which requires ``nn.Module`` instances), so this thin wrapper is used + whenever a sin activation needs to slot into a ``Sequential`` pipeline. + """ + + def forward(self, x: Tensor) -> Tensor: + """Apply elementwise sine.""" + return torch.sin(x) + + def _build_conv_encoder(width: int, enc_config: dict) -> nn.Module: """Build a multi-layer pointwise encoder replacing the default LazyLinear lift. @@ -198,7 +214,14 @@ def _build_conv_encoder(width: int, enc_config: dict) -> nn.Module: """ num_layers = enc_config.get("num_layers", 1) activation_fn = enc_config.get("activation_fn", "relu") - act = get_activation(activation_fn) + + # ``get_activation`` does not know about ``"sin"``; use the module + # wrapper defined above when the user explicitly requests it, so + # config parity with the branch encoders is preserved. + if activation_fn.lower() == "sin": + act = _SinActivation() + else: + act = get_activation(activation_fn) if num_layers <= 1: return nn.LazyLinear(width) diff --git a/test/experimental/models/test_xdeeponet.py b/test/experimental/models/test_xdeeponet.py index 76076f7485..c19b682139 100644 --- a/test/experimental/models/test_xdeeponet.py +++ b/test/experimental/models/test_xdeeponet.py @@ -820,5 +820,51 @@ def test_2d_fourier_mionet_forward(self): assert out.shape == (2, 8, 8, 3) +class TestConvEncoderSinActivation: + """Regression for the conv-encoder ``sin`` activation path.""" + + def test_conv_encoder_with_sin_activation(self): + """Conv encoder with activation_fn='sin' must construct and forward. + + ``get_activation`` does not know about ``"sin"``; the module-level + helper ``_build_conv_encoder`` must route that case through the + ``_SinActivation`` wrapper so the multi-layer path does not raise + ``KeyError`` at construction nor ``TypeError`` from + ``nn.Sequential``. + """ + branch_cfg = { + "encoder": { + "type": "conv", + "num_layers": 2, + "hidden_width": 8, + "activation_fn": "sin", + }, + "layers": { + "num_fourier_layers": 0, + "num_unet_layers": 1, + "num_conv_layers": 0, + "kernel_size": 3, + "dropout": 0.0, + "activation_fn": "relu", + }, + } + + # Construction must not raise. + model = DeepONetWrapper( + variant="u_deeponet", + width=16, + branch1_config=branch_cfg, + trunk_config=TRUNK, + decoder_type="mlp", + decoder_width=16, + decoder_layers=1, + ) + + # Forward must not raise and must preserve the expected shape. + x = torch.randn(2, 16, 16, 3, 2) + out = model(x) + assert out.shape == (2, 16, 16, 3) + + if __name__ == "__main__": pytest.main([__file__, "-v"]) From a1531b75583fcda12fb7aa56afcae960c0819f47 Mon Sep 17 00:00:00 2001 From: wdyab Date: Fri, 24 Apr 2026 07:06:47 -0700 Subject: [PATCH 7/8] xdeeponet: address review feedback on PR #1576 - Rewrite test suite to the constructor + non-regression + checkpoint + gradient + compile pattern; relocate to ``test/experimental/models/xdeeponet/`` to match the ``flare/`` / ``fno/`` per-model layout. Commit ``.pth`` goldens + regeneration script under ``data/``. - Add ``Examples`` sections to every user-facing class. - Add ``jaxtyping.Float`` annotations to top-level ``forward`` methods (MOD-006). - Remove ``README.md``; no model under ``physicsnemo/`` ships one. Design rationale now lives in module-level and class-level docstrings. Signed-off-by: wdyab Made-with: Cursor --- .../experimental/models/xdeeponet/README.md | 179 ---- .../experimental/models/xdeeponet/__init__.py | 5 +- .../experimental/models/xdeeponet/branches.py | 36 + .../experimental/models/xdeeponet/deeponet.py | 64 +- .../experimental/models/xdeeponet/wrappers.py | 65 +- test/experimental/models/test_xdeeponet.py | 870 ------------------ .../data/_generate_xdeeponet_goldens.py | 69 ++ .../data/xdeeponet_wrapper_2d_v1.pth | Bin 0 -> 97313 bytes .../data/xdeeponet_wrapper_3d_v1.pth | Bin 0 -> 135201 bytes .../models/xdeeponet/test_xdeeponet.py | 381 ++++++++ 10 files changed, 598 insertions(+), 1071 deletions(-) delete mode 100644 physicsnemo/experimental/models/xdeeponet/README.md delete mode 100644 test/experimental/models/test_xdeeponet.py create mode 100644 test/experimental/models/xdeeponet/data/_generate_xdeeponet_goldens.py create mode 100644 test/experimental/models/xdeeponet/data/xdeeponet_wrapper_2d_v1.pth create mode 100644 test/experimental/models/xdeeponet/data/xdeeponet_wrapper_3d_v1.pth create mode 100644 test/experimental/models/xdeeponet/test_xdeeponet.py diff --git a/physicsnemo/experimental/models/xdeeponet/README.md b/physicsnemo/experimental/models/xdeeponet/README.md deleted file mode 100644 index 935b7593ad..0000000000 --- a/physicsnemo/experimental/models/xdeeponet/README.md +++ /dev/null @@ -1,179 +0,0 @@ -# xDeepONet — the Extended DeepONet Family - -`physicsnemo.experimental.models.xdeeponet` provides a unified, config-driven -implementation of eight DeepONet-based architectures for operator learning -on 2D (`(H, W)`) and 3D (`(X, Y, Z)`) spatial domains. All variants share -the same branch/trunk/decoder design and are selected via a single -`variant` argument on the wrapper classes. - -## Supported Variants - -| Variant | Branches | Branch2 input | Typical use | -|---------------------|----------|------------------------|--------------------------| -| `deeponet` | 1 | — | Baseline DeepONet | -| `u_deeponet` | 1 | — | UNet-enhanced branch | -| `fourier_deeponet` | 1 | — | Spectral branch | -| `conv_deeponet` | 1 | — | Convolutional branch | -| `hybrid_deeponet` | 1 | — | Fourier + UNet + Conv | -| `mionet` | 2 | Scalar features | Multi-input operator | -| `fourier_mionet` | 2 | Scalar features | MIONet + Fourier branch | -| `tno` | 2 | Previous solution | Temporal Neural Operator | - -All variants are available in both 2D and 3D spatial configurations. - -## Quick Start - -```python -import torch -from physicsnemo.experimental.models.xdeeponet import DeepONet3DWrapper - -model = DeepONet3DWrapper( - variant="tno", - width=128, - padding=8, - branch1_config={ - "encoder": "spatial", - "num_fourier_layers": 1, - "num_unet_layers": 1, - "modes1": 10, "modes2": 10, "modes3": 8, - "activation_fn": "tanh", - }, - branch2_config={ - "encoder": "spatial", - "num_fourier_layers": 1, - "num_unet_layers": 1, - "modes1": 10, "modes2": 10, "modes3": 8, - "activation_fn": "tanh", - }, - trunk_config={ - "input_type": "time", - "hidden_width": 128, - "num_layers": 8, - "activation_fn": "tanh", - "output_activation": False, - }, - decoder_type="temporal_projection", - decoder_width=128, - decoder_layers=2, -) - -# Autoregressive bundling: predict K=3 future timesteps from 1 context step -model.set_output_window(K=3) - -x = torch.randn(2, 16, 16, 16, 1, 11) # (B, X, Y, Z, T_in, C) -prev = torch.randn(2, 16, 16, 16, 1) # previous solution -out = model(x, x_branch2=prev) # (B, X, Y, Z, 3) -``` - -## Public API - -### Wrappers (recommended entry points) - -`DeepONetWrapper` (2D) and `DeepONet3DWrapper` (3D) add two conveniences -on top of the core classes: - -1. **Automatic spatial padding** — right-pads inputs to a multiple (default 8) - so Fourier, UNet, and Conv sub-branches operate on compatible shapes. - Outputs are cropped back to the original spatial size. -2. **Automatic trunk coordinate extraction** — assembles trunk query - coordinates from the full input tensor according to - `trunk_config["input_type"]` (`"time"` or `"grid"`). - -### Core classes - -`DeepONet` (2D) and `DeepONet3D` (3D) expose the raw architecture without -padding or input extraction; use these when you have already prepared the -spatial branch input and trunk coordinates explicitly. - -### Building blocks - -`TrunkNet`, `MLPBranch`, `SpatialBranch`, `SpatialBranch3D` are the sub-networks -used internally; they are exported for users who want to assemble custom -variants. - -## Branch configuration schema - -Each branch is configured via a Python dict. Two formats are accepted — -the nested format is canonical; the flat format is converted automatically: - -**Nested (canonical):** - -```python -{ - "encoder": { - "type": "linear", # or "mlp" or "conv" - "hidden_width": 64, # mlp only - "num_layers": 2, # mlp/conv only - "activation_fn": "tanh", - }, - "layers": { - "num_fourier_layers": 1, - "num_unet_layers": 1, - "num_conv_layers": 0, - "modes1": 10, "modes2": 10, "modes3": 8, # 3D uses modes3 - "kernel_size": 3, - "dropout": 0.0, - "activation_fn": "tanh", - }, - "internal_resolution": [16, 16, 16], # optional adaptive pooling - "in_channels": 11, # optional (informational) -} -``` - -**Flat (auto-converted):** - -```python -{ - "encoder": "spatial", # or "mlp" - "num_fourier_layers": 1, - "num_unet_layers": 1, - "num_conv_layers": 0, - "modes1": 10, "modes2": 10, "modes3": 8, - "kernel_size": 3, - "activation_fn": "tanh", -} -``` - -## Decoder types - -- `"mlp"` — query the trunk at each target timestep, apply an MLP decoder - per-timestep. Standard DeepONet decoding. -- `"conv"` — per-timestep trunk query followed by a convolutional decoder. -- `"temporal_projection"` — query the trunk once and project the combined - latent representation to K output timesteps via a learned linear head. - Fast for autoregressive bundling. Requires `model.set_output_window(K)` - before the first forward pass. - -## UNet sub-modules - -The UNet layers inside the spatial branches use -`physicsnemo.models.unet.UNet` (3D). For 2D spatial branches, a small -internal adapter tiles a short time axis so the 3D UNet's pooling stages -function correctly, then averages the result back to 2D. - -## Padding behaviour - -Both wrappers pad spatial dimensions to a multiple of 8 (configurable via -the `padding` argument, which is rounded up to the next multiple of 8). -Padded cells are filled via replicate padding; outputs are cropped back -to the original input shape. - -## References - -- Lu, L. et al. (2021). "Learning nonlinear operators via DeepONet." - *Nature Machine Intelligence*, 3, 218-229. -- Jin, P., Meng, S. & Lu, L. (2022). "MIONet: Learning multiple-input - operators via tensor product." *SIAM J. Sci. Comp.*, 44(6), A3490-A3514. -- Wen, G. et al. (2022). "U-FNO — An enhanced Fourier neural operator-based - deep-learning model for multiphase flow." *Advances in Water Resources*, - 163, 104180. -- Zhu, M. et al. (2023). "Fourier-DeepONet: Fourier-enhanced deep operator - networks for full waveform inversion." arXiv:2305.17289. -- Diab, W. & Al Kobaisi, M. (2024). "U-DeepONet: U-Net enhanced deep - operator network for geologic carbon sequestration." - *Scientific Reports*, 14, 21298. -- Jiang, Z. et al. (2024). "Fourier-MIONet: Fourier-enhanced multiple-input - neural operators for multiphase modeling of geological carbon - sequestration." *Reliability Eng. & System Safety*, 251, 110392. -- Diab, W. & Al Kobaisi, M. (2025). "Temporal neural operator for modeling - time-dependent physical phenomena." *Scientific Reports*, 15. diff --git a/physicsnemo/experimental/models/xdeeponet/__init__.py b/physicsnemo/experimental/models/xdeeponet/__init__.py index d504daccf9..40630fbb08 100644 --- a/physicsnemo/experimental/models/xdeeponet/__init__.py +++ b/physicsnemo/experimental/models/xdeeponet/__init__.py @@ -24,8 +24,9 @@ - ``mionet``, ``fourier_mionet`` — two-branch multi-input variants. - ``tno`` — Temporal Neural Operator (branch2 = previous solution). -Both 2D and 3D spatial versions are provided. See the package README for -standalone usage examples. +Both 2D and 3D spatial versions are provided. :class:`DeepONetWrapper` and +:class:`DeepONet3DWrapper` are the recommended entry points; see their class +docstrings for usage examples and the branch/trunk configuration schema. """ from .branches import MLPBranch, SpatialBranch, SpatialBranch3D, TrunkNet diff --git a/physicsnemo/experimental/models/xdeeponet/branches.py b/physicsnemo/experimental/models/xdeeponet/branches.py index 27d5b01a01..7f8cc73985 100644 --- a/physicsnemo/experimental/models/xdeeponet/branches.py +++ b/physicsnemo/experimental/models/xdeeponet/branches.py @@ -175,6 +175,14 @@ class TrunkNet(nn.Module): torch.Tensor Encoded coordinates of shape :math:`(T, D_{out})` where :math:`D_{out}` equals ``out_features``. + + Examples + -------- + >>> import torch + >>> from physicsnemo.experimental.models.xdeeponet import TrunkNet + >>> trunk = TrunkNet(in_features=1, out_features=64, hidden_width=64, num_layers=4) + >>> t = torch.linspace(0, 1, 10).unsqueeze(-1) # (10, 1) + >>> phi = trunk(t) # (10, 64) """ def __init__( @@ -255,6 +263,14 @@ class MLPBranch(nn.Module): torch.Tensor Encoded features of shape :math:`(B, D_{out})` where :math:`D_{out}` equals ``out_features``. + + Examples + -------- + >>> import torch + >>> from physicsnemo.experimental.models.xdeeponet import MLPBranch + >>> branch = MLPBranch(out_features=64, hidden_width=64, num_layers=3) + >>> x = torch.randn(2, 128) + >>> out = branch(x) # (2, 64) """ def __init__( @@ -355,6 +371,16 @@ class SpatialBranch(nn.Module): torch.Tensor Channels-last output of shape :math:`(B, H, W, D)` where :math:`D` equals ``width``. + + Examples + -------- + >>> import torch + >>> from physicsnemo.experimental.models.xdeeponet import SpatialBranch + >>> branch = SpatialBranch( + ... in_channels=5, width=64, num_unet_layers=1, kernel_size=3 + ... ) + >>> x = torch.randn(2, 32, 32, 5) # (B, H, W, C) + >>> out = branch(x) # (2, 32, 32, 64) """ def __init__( @@ -497,6 +523,16 @@ class SpatialBranch3D(nn.Module): torch.Tensor Channels-last output of shape :math:`(B, X, Y, Z, D)` where :math:`D` equals ``width``. + + Examples + -------- + >>> import torch + >>> from physicsnemo.experimental.models.xdeeponet import SpatialBranch3D + >>> branch = SpatialBranch3D( + ... in_channels=5, width=64, num_unet_layers=1, kernel_size=3 + ... ) + >>> x = torch.randn(1, 16, 16, 16, 5) # (B, X, Y, Z, C) + >>> out = branch(x) # (1, 16, 16, 16, 64) """ def __init__( diff --git a/physicsnemo/experimental/models/xdeeponet/deeponet.py b/physicsnemo/experimental/models/xdeeponet/deeponet.py index 6b0ee62715..8f244126f0 100644 --- a/physicsnemo/experimental/models/xdeeponet/deeponet.py +++ b/physicsnemo/experimental/models/xdeeponet/deeponet.py @@ -57,6 +57,7 @@ import torch import torch.nn as nn +from jaxtyping import Float from torch import Tensor from physicsnemo.core.meta import ModelMetaData @@ -296,6 +297,23 @@ class DeepONet(Module): torch.Tensor Operator output of shape :math:`(B, H, W, T)` for spatial branches or :math:`(B, T)` for MLP branches. + + Examples + -------- + >>> import torch + >>> from physicsnemo.experimental.models.xdeeponet import DeepONet + >>> model = DeepONet( + ... variant="u_deeponet", + ... width=64, + ... branch1_config={ + ... "encoder": {"type": "linear"}, + ... "layers": {"num_unet_layers": 1, "kernel_size": 3}, + ... }, + ... trunk_config={"hidden_width": 64, "num_layers": 4}, + ... ) + >>> x_branch = torch.randn(2, 32, 32, 5) # (B, H, W, C) + >>> x_time = torch.linspace(0, 1, 3).unsqueeze(-1) # (T, 1) + >>> out = model(x_branch, x_time) # (2, 32, 32, 3) """ VALID_VARIANTS = _VALID_VARIANTS @@ -512,13 +530,18 @@ def _build_decoder( def forward( self, - x_branch1: Tensor, - x_time: Tensor, - x_branch2: Optional[Tensor] = None, - ) -> Tensor: + x_branch1: Float[Tensor, "..."], + x_time: Float[Tensor, "..."], + x_branch2: Optional[Float[Tensor, "..."]] = None, + ) -> Float[Tensor, "..."]: """Forward pass through the DeepONet. - See class docstring for input/output shapes. + See class docstring for input/output shapes. ``x_branch1`` accepts + either 2D ``(B, D_in)`` (MLP branches) or 4D ``(B, H, W, C)`` + (spatial branches); ``x_time`` accepts 1D ``(T,)`` or 2D + ``(T, D_trunk)``, so the jaxtyping annotation is the unconstrained + ``"..."`` shape. Strict shape validation is performed at the top + of this method under a :func:`torch.compiler.is_compiling` guard. """ if not torch.compiler.is_compiling(): if x_branch1.ndim not in (2, 4): @@ -638,6 +661,23 @@ class DeepONet3D(Module): torch.Tensor Operator output of shape :math:`(B, X, Y, Z, T)` for spatial branches or :math:`(B, T)` for MLP branches. + + Examples + -------- + >>> import torch + >>> from physicsnemo.experimental.models.xdeeponet import DeepONet3D + >>> model = DeepONet3D( + ... variant="u_deeponet", + ... width=64, + ... branch1_config={ + ... "encoder": {"type": "linear"}, + ... "layers": {"num_unet_layers": 1, "kernel_size": 3}, + ... }, + ... trunk_config={"hidden_width": 64, "num_layers": 4}, + ... ) + >>> x_branch = torch.randn(1, 16, 16, 16, 5) # (B, X, Y, Z, C) + >>> x_time = torch.linspace(0, 1, 2).unsqueeze(-1) + >>> out = model(x_branch, x_time) # (1, 16, 16, 16, 2) """ VALID_VARIANTS = _VALID_VARIANTS @@ -855,13 +895,17 @@ def _build_decoder( def forward( self, - x_branch1: Tensor, - x_time: Tensor, - x_branch2: Optional[Tensor] = None, - ) -> Tensor: + x_branch1: Float[Tensor, "..."], + x_time: Float[Tensor, "..."], + x_branch2: Optional[Float[Tensor, "..."]] = None, + ) -> Float[Tensor, "..."]: """Forward pass through the 3D DeepONet. - See class docstring for input/output shapes. + See class docstring for input/output shapes. ``x_branch1`` accepts + either 2D ``(B, D_in)`` (MLP branches) or 5D ``(B, X, Y, Z, C)`` + (spatial branches); ``x_time`` accepts 1D ``(T,)`` or 2D + ``(T, D_trunk)``. Strict shape validation is performed at the top + of this method under a :func:`torch.compiler.is_compiling` guard. """ if not torch.compiler.is_compiling(): if x_branch1.ndim not in (2, 5): diff --git a/physicsnemo/experimental/models/xdeeponet/wrappers.py b/physicsnemo/experimental/models/xdeeponet/wrappers.py index 63912bb876..8b42fa3e06 100644 --- a/physicsnemo/experimental/models/xdeeponet/wrappers.py +++ b/physicsnemo/experimental/models/xdeeponet/wrappers.py @@ -39,6 +39,7 @@ from typing import Any, Dict, Optional import torch +from jaxtyping import Float from torch import Tensor from physicsnemo.core.meta import ModelMetaData @@ -118,6 +119,22 @@ class DeepONetWrapper(Module): Operator output of shape :math:`(B, H, W, T_{out})` where :math:`T_{out} = K` when ``target_times`` is given and :math:`T_{out} = T` otherwise. + + Examples + -------- + >>> import torch + >>> from physicsnemo.experimental.models.xdeeponet import DeepONetWrapper + >>> model = DeepONetWrapper( + ... variant="u_deeponet", + ... width=64, + ... branch1_config={ + ... "encoder": {"type": "linear", "activation_fn": "tanh"}, + ... "layers": {"num_unet_layers": 1, "kernel_size": 3}, + ... }, + ... trunk_config={"input_type": "time", "hidden_width": 64, "num_layers": 4}, + ... ) + >>> x = torch.randn(2, 32, 32, 3, 5) # (B, H, W, T, C) + >>> out = model(x) # (2, 32, 32, 3) """ def __init__( @@ -170,13 +187,17 @@ def set_output_window(self, K: int): def forward( self, - x: Tensor, - x_branch2: Optional[Tensor] = None, - target_times: Optional[Tensor] = None, - ) -> Tensor: + x: Float[Tensor, "batch height width time channels"], + x_branch2: Optional[Float[Tensor, "..."]] = None, + target_times: Optional[Float[Tensor, "..."]] = None, + ) -> Float[Tensor, "batch height width time_out"]: """Forward pass through the 2D wrapper. - See class docstring for input/output shapes. + See class docstring for input/output shapes. ``x_branch2`` and + ``target_times`` accept multiple ranks (see Forward section); their + strict shapes are validated at the top of this method under the + :func:`torch.compiler.is_compiling` guard, so the jaxtyping + annotation uses the unconstrained ``"..."`` shape for those. """ if not torch.compiler.is_compiling(): if x.ndim != 5: @@ -259,6 +280,27 @@ class DeepONet3DWrapper(Module): ------- torch.Tensor Operator output of shape :math:`(B, X, Y, Z, T_{out})`. + + Examples + -------- + >>> import torch + >>> from physicsnemo.experimental.models.xdeeponet import DeepONet3DWrapper + >>> model = DeepONet3DWrapper( + ... variant="tno", + ... width=64, + ... branch1_config={ + ... "encoder": {"type": "linear", "activation_fn": "tanh"}, + ... "layers": {"num_unet_layers": 1, "kernel_size": 3}, + ... }, + ... branch2_config={ + ... "encoder": {"type": "linear", "activation_fn": "tanh"}, + ... "layers": {"num_unet_layers": 1, "kernel_size": 3}, + ... }, + ... trunk_config={"input_type": "time", "hidden_width": 64, "num_layers": 4}, + ... ) + >>> x = torch.randn(1, 16, 16, 16, 2, 5) # (B, X, Y, Z, T, C) + >>> prev = torch.randn(1, 16, 16, 16, 1) # previous solution (TNO branch2) + >>> out = model(x, x_branch2=prev) # (1, 16, 16, 16, 2) """ def __init__( @@ -311,13 +353,16 @@ def set_output_window(self, K: int): def forward( self, - x: Tensor, - x_branch2: Optional[Tensor] = None, - target_times: Optional[Tensor] = None, - ) -> Tensor: + x: Float[Tensor, "batch X Y Z time channels"], + x_branch2: Optional[Float[Tensor, "..."]] = None, + target_times: Optional[Float[Tensor, "..."]] = None, + ) -> Float[Tensor, "batch X Y Z time_out"]: """Forward pass through the 3D wrapper. - See class docstring for input/output shapes. + See class docstring for input/output shapes. ``x_branch2`` and + ``target_times`` accept multiple ranks; their strict shapes are + validated at the top of this method under the + :func:`torch.compiler.is_compiling` guard. """ if not torch.compiler.is_compiling(): if x.ndim != 6: diff --git a/test/experimental/models/test_xdeeponet.py b/test/experimental/models/test_xdeeponet.py deleted file mode 100644 index c19b682139..0000000000 --- a/test/experimental/models/test_xdeeponet.py +++ /dev/null @@ -1,870 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. -# SPDX-FileCopyrightText: All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -"""Unit tests for the xDeepONet family (2D and 3D variants).""" - -import pytest -import torch - -from physicsnemo.experimental.models.xdeeponet import ( - DeepONet, - DeepONet3D, - DeepONet3DWrapper, - DeepONetWrapper, - MLPBranch, - SpatialBranch, - SpatialBranch3D, - TrunkNet, -) - -BRANCH1_SPATIAL = { - "encoder": {"type": "linear", "activation_fn": "relu"}, - "layers": { - "num_fourier_layers": 0, - "num_unet_layers": 1, - "num_conv_layers": 0, - "modes1": 4, - "modes2": 4, - "kernel_size": 3, - "dropout": 0.0, - "activation_fn": "relu", - }, -} -BRANCH1_MLP = { - "encoder": { - "type": "mlp", - "hidden_width": 32, - "num_layers": 2, - "activation_fn": "relu", - }, - "layers": {"num_fourier_layers": 0, "num_unet_layers": 0, "num_conv_layers": 0}, -} -BRANCH2_SPATIAL = { - "encoder": {"type": "linear", "activation_fn": "relu"}, - "layers": { - "num_fourier_layers": 0, - "num_unet_layers": 1, - "num_conv_layers": 0, - "modes1": 4, - "modes2": 4, - "kernel_size": 3, - "dropout": 0.0, - "activation_fn": "relu", - }, -} -BRANCH2_MLP = { - "encoder": { - "type": "mlp", - "hidden_width": 32, - "num_layers": 2, - "activation_fn": "relu", - }, - "layers": {"num_fourier_layers": 0, "num_unet_layers": 0, "num_conv_layers": 0}, -} -TRUNK = { - "input_type": "time", - "hidden_width": 32, - "num_layers": 2, - "activation_fn": "tanh", -} - - -def _init_lazy(model, x, **kwargs): - """Run one forward pass to initialise LazyLinear modules.""" - with torch.no_grad(): - model(x, **kwargs) - - -class TestTrunkNet: - """Tests for TrunkNet.""" - - def test_output_shape(self): - """Verify TrunkNet output shape matches expected features.""" - trunk = TrunkNet(in_features=1, out_features=32, hidden_width=16, num_layers=3) - x = torch.randn(10, 1) - assert trunk(x).shape == (10, 32) - - def test_grid_input(self): - """Verify TrunkNet handles multi-dimensional grid input correctly.""" - trunk = TrunkNet(in_features=4, out_features=64, hidden_width=32, num_layers=2) - x = torch.randn(5, 4) - assert trunk(x).shape == (5, 64) - - -class TestMLPBranch: - """Tests for MLPBranch.""" - - def test_output_shape(self): - """Verify MLPBranch output shape matches expected features.""" - branch = MLPBranch(out_features=32, hidden_width=16, num_layers=3) - x = torch.randn(2, 50) - out = branch(x) - assert out.shape == (2, 32) - - -class TestSpatialBranch2D: - """Tests for 2D SpatialBranch.""" - - def test_output_shape(self): - """Verify 2D SpatialBranch output shape matches expected width.""" - branch = SpatialBranch( - in_channels=5, - width=16, - num_unet_layers=1, - kernel_size=3, - activation_fn="relu", - ) - x = torch.randn(2, 16, 24, 5) - _init_lazy(branch, x) - out = branch(x) - assert out.shape == (2, 16, 24, 16) - - -class TestSpatialBranch3D: - """Tests for 3D SpatialBranch.""" - - def test_output_shape(self): - """Verify 3D SpatialBranch output shape matches expected width.""" - branch = SpatialBranch3D( - in_channels=5, - width=16, - num_unet_layers=1, - kernel_size=3, - activation_fn="relu", - ) - x = torch.randn(2, 8, 16, 8, 5) - _init_lazy(branch, x) - out = branch(x) - assert out.shape == (2, 8, 16, 8, 16) - - -SINGLE_BRANCH_VARIANTS = [ - "deeponet", - "u_deeponet", - "conv_deeponet", - "fourier_deeponet", - "hybrid_deeponet", -] -DUAL_BRANCH_VARIANTS = ["mionet", "tno", "fourier_mionet"] - -# Branch config that actually exercises the Fourier code path in -# ``SpatialBranch`` / ``SpatialBranch3D``. Kept small (1 spectral layer, 2 -# modes) so test runtime stays reasonable. -BRANCH1_SPATIAL_FOURIER = { - "encoder": {"type": "linear", "activation_fn": "relu"}, - "layers": { - "num_fourier_layers": 1, - "num_unet_layers": 0, - "num_conv_layers": 0, - "modes1": 2, - "modes2": 2, - "modes3": 2, - "kernel_size": 3, - "dropout": 0.0, - "activation_fn": "relu", - }, -} -BRANCH2_SPATIAL_FOURIER = { - "encoder": {"type": "linear", "activation_fn": "relu"}, - "layers": { - "num_fourier_layers": 1, - "num_unet_layers": 0, - "num_conv_layers": 0, - "modes1": 2, - "modes2": 2, - "modes3": 2, - "kernel_size": 3, - "dropout": 0.0, - "activation_fn": "relu", - }, -} - - -class TestDeepONetWrapper2D: - """Tests for 2D DeepONet wrapper.""" - - @pytest.mark.parametrize("variant", SINGLE_BRANCH_VARIANTS) - def test_forward_shape_single_branch(self, variant): - """Verify 2D single-branch forward pass produces correct output shape.""" - B, H, W, T, C = 2, 16, 24, 4, 5 - model = DeepONetWrapper( - padding=8, - variant=variant, - width=32, - branch1_config=BRANCH1_SPATIAL, - trunk_config=TRUNK, - ) - x = torch.randn(B, H, W, T, C) - _init_lazy(model, x) - out = model(x) - assert out.shape == (B, H, W, T) - - @pytest.mark.parametrize("variant", DUAL_BRANCH_VARIANTS) - def test_forward_shape_dual_branch(self, variant): - """Verify 2D dual-branch forward pass produces correct output shape.""" - B, H, W, T, C = 2, 16, 24, 4, 5 - model = DeepONetWrapper( - padding=8, - variant=variant, - width=32, - branch1_config=BRANCH1_SPATIAL, - branch2_config=BRANCH2_SPATIAL, - trunk_config=TRUNK, - ) - x = torch.randn(B, H, W, T, C) - b2 = torch.randn(B, H, W, T) - _init_lazy(model, x, x_branch2=b2) - out = model(x, x_branch2=b2) - assert out.shape == (B, H, W, T) - - def test_target_times_changes_output_T(self): - """Verify target_times overrides the temporal output dimension size.""" - B, H, W, T_in, C = 2, 16, 24, 2, 5 - K = 5 - model = DeepONetWrapper( - padding=8, - variant="u_deeponet", - width=32, - branch1_config=BRANCH1_SPATIAL, - trunk_config=TRUNK, - ) - x = torch.randn(B, H, W, T_in, C) - tt = torch.linspace(0, 1, K) - _init_lazy(model, x) - out = model(x, target_times=tt) - assert out.shape == (B, H, W, K) - - def test_invalid_variant_raises(self): - """Verify ValueError is raised for an unknown DeepONet variant.""" - with pytest.raises(ValueError, match="Unknown variant"): - DeepONetWrapper( - variant="invalid", - width=32, - branch1_config=BRANCH1_SPATIAL, - trunk_config=TRUNK, - ) - - def test_count_params(self): - """Verify count_params returns a positive parameter count for 2D wrapper.""" - model = DeepONetWrapper( - padding=8, - variant="deeponet", - width=32, - branch1_config=BRANCH1_SPATIAL, - trunk_config=TRUNK, - ) - x = torch.randn(1, 16, 24, 2, 5) - _init_lazy(model, x) - assert model.count_params() > 0 - - def test_gradient_flow(self): - """Verify gradients propagate through the 2D DeepONet wrapper.""" - model = DeepONetWrapper( - padding=8, - variant="u_deeponet", - width=32, - branch1_config=BRANCH1_SPATIAL, - trunk_config=TRUNK, - ) - x = torch.randn(1, 16, 24, 2, 5) - _init_lazy(model, x) - x = torch.randn(1, 16, 24, 2, 5, requires_grad=True) - out = model(x) - out.sum().backward() - assert x.grad is not None - - -BRANCH1_3D = { - "encoder": {"type": "linear", "activation_fn": "relu"}, - "layers": { - "num_fourier_layers": 0, - "num_unet_layers": 1, - "num_conv_layers": 0, - "modes1": 4, - "modes2": 4, - "modes3": 4, - "kernel_size": 3, - "dropout": 0.0, - "activation_fn": "relu", - }, -} -BRANCH2_3D = { - "encoder": {"type": "linear", "activation_fn": "relu"}, - "layers": { - "num_fourier_layers": 0, - "num_unet_layers": 1, - "num_conv_layers": 0, - "modes1": 4, - "modes2": 4, - "modes3": 4, - "kernel_size": 3, - "dropout": 0.0, - "activation_fn": "relu", - }, -} - - -class TestDeepONet3DWrapper: - """Tests for 3D DeepONet wrapper.""" - - @pytest.mark.parametrize("variant", SINGLE_BRANCH_VARIANTS) - def test_forward_shape_single_branch(self, variant): - """Verify 3D single-branch forward pass produces correct output shape.""" - B, X, Y, Z, T, C = 1, 8, 16, 8, 3, 5 - model = DeepONet3DWrapper( - padding=8, - variant=variant, - width=32, - branch1_config=BRANCH1_3D, - trunk_config=TRUNK, - ) - x = torch.randn(B, X, Y, Z, T, C) - _init_lazy(model, x) - out = model(x) - assert out.shape == (B, X, Y, Z, T) - - def test_tno_requires_branch2(self): - """Verify TNO variant produces correct output with a second branch.""" - B, X, Y, Z, T, C = 1, 8, 16, 8, 3, 5 - model = DeepONet3DWrapper( - padding=8, - variant="tno", - width=32, - branch1_config=BRANCH1_3D, - branch2_config=BRANCH2_3D, - trunk_config=TRUNK, - ) - x = torch.randn(B, X, Y, Z, T, C) - b2 = torch.randn(B, X, Y, Z, 1) - _init_lazy(model, x, x_branch2=b2) - out = model(x, x_branch2=b2) - assert out.shape == (B, X, Y, Z, T) - - def test_target_times_3d(self): - """Verify target_times overrides the temporal output dimension in 3D.""" - B, X, Y, Z, T_in, C = 1, 8, 16, 8, 1, 5 - K = 4 - model = DeepONet3DWrapper( - padding=8, - variant="u_deeponet", - width=32, - branch1_config=BRANCH1_3D, - trunk_config=TRUNK, - ) - x = torch.randn(B, X, Y, Z, T_in, C) - tt = torch.linspace(0, 1, K) - _init_lazy(model, x) - out = model(x, target_times=tt) - assert out.shape == (B, X, Y, Z, K) - - def test_count_params_3d(self): - """Verify count_params returns a positive parameter count for 3D wrapper.""" - model = DeepONet3DWrapper( - padding=8, - variant="deeponet", - width=32, - branch1_config=BRANCH1_3D, - trunk_config=TRUNK, - ) - x = torch.randn(1, 8, 16, 8, 2, 5) - _init_lazy(model, x) - assert model.count_params() > 0 - - -class TestHadamardProduct: - """Verify 3-way Hadamard product for multi-branch variants.""" - - def test_mionet_uses_multiplication(self): - """Verify MIONet variant computes a 3-way Hadamard product correctly.""" - model = DeepONetWrapper( - variant="mionet", - width=16, - branch1_config={ - "encoder": "spatial", - "num_unet_layers": 0, - "num_conv_layers": 1, - "kernel_size": 3, - }, - branch2_config={"encoder": "mlp", "hidden_width": 16, "num_layers": 2}, - trunk_config={"hidden_width": 16, "num_layers": 2}, - decoder_layers=0, - ) - x = torch.randn(2, 16, 24, 4, 6) - b2 = torch.randn(2, 6) - with torch.no_grad(): - out = model(x, x_branch2=b2) - assert out.shape == (2, 16, 24, 4) - - -class TestTemporalProjection: - """Test temporal_projection decoder mode.""" - - def test_2d_temporal_projection_output_shape(self): - """Verify 2D temporal-projection decoder produces correct output T dimension.""" - K = 3 - model = DeepONet( - variant="u_deeponet", - width=16, - branch1_config={ - "encoder": "spatial", - "num_unet_layers": 0, - "num_conv_layers": 1, - "kernel_size": 3, - }, - trunk_config={"hidden_width": 16, "num_layers": 2}, - decoder_type="temporal_projection", - decoder_layers=1, - decoder_width=16, - ) - model.set_output_window(K) - x_branch = torch.randn(2, 16, 24, 4) - x_time = torch.randn(1, 1) - with torch.no_grad(): - out = model(x_branch, x_time) - assert out.shape == (2, 16, 24, K) - - def test_2d_temporal_projection_with_branch2(self): - """Verify 2D temporal-projection works with a second branch input.""" - K = 5 - model = DeepONet( - variant="tno", - width=16, - branch1_config={ - "encoder": "spatial", - "num_unet_layers": 0, - "num_conv_layers": 1, - "kernel_size": 3, - }, - branch2_config={ - "encoder": "spatial", - "num_unet_layers": 0, - "num_conv_layers": 1, - "kernel_size": 3, - }, - trunk_config={"hidden_width": 16, "num_layers": 2}, - decoder_type="temporal_projection", - decoder_layers=1, - decoder_width=16, - ) - model.set_output_window(K) - x_branch = torch.randn(2, 16, 24, 4) - x_branch2 = torch.randn(2, 16, 24, 4) - x_time = torch.randn(1, 1) - with torch.no_grad(): - out = model(x_branch, x_time, x_branch2=x_branch2) - assert out.shape == (2, 16, 24, K) - - def test_3d_temporal_projection(self): - """Verify 3D temporal-projection decoder produces correct output shape.""" - K = 4 - model = DeepONet3D( - variant="u_deeponet", - width=8, - branch1_config={ - "encoder": "spatial", - "num_unet_layers": 0, - "num_conv_layers": 1, - "kernel_size": 3, - }, - trunk_config={"hidden_width": 8, "num_layers": 2}, - decoder_type="temporal_projection", - decoder_layers=1, - decoder_width=8, - ) - model.set_output_window(K) - x_branch = torch.randn(2, 8, 8, 8, 4) - x_time = torch.randn(1, 1) - with torch.no_grad(): - out = model(x_branch, x_time) - assert out.shape == (2, 8, 8, 8, K) - - def test_mlp_decoder_still_works(self): - """Verify existing mlp decoder path is preserved.""" - model = DeepONet( - variant="u_deeponet", - width=16, - branch1_config={ - "encoder": "spatial", - "num_unet_layers": 0, - "num_conv_layers": 1, - "kernel_size": 3, - }, - trunk_config={"hidden_width": 16, "num_layers": 2}, - decoder_type="mlp", - decoder_layers=1, - decoder_width=16, - ) - x_branch = torch.randn(2, 16, 24, 4) - x_time = torch.randn(6, 1) - with torch.no_grad(): - out = model(x_branch, x_time) - assert out.shape == (2, 16, 24, 6) - - def test_gradient_flow_temporal_projection(self): - """Verify gradients propagate through the temporal-projection decoder.""" - K = 3 - model = DeepONet( - variant="tno", - width=16, - branch1_config={ - "encoder": "spatial", - "num_unet_layers": 0, - "num_conv_layers": 1, - "kernel_size": 3, - }, - branch2_config={ - "encoder": "spatial", - "num_unet_layers": 0, - "num_conv_layers": 1, - "kernel_size": 3, - }, - trunk_config={"hidden_width": 16, "num_layers": 2}, - decoder_type="temporal_projection", - decoder_layers=1, - decoder_width=16, - ) - model.set_output_window(K) - x = torch.randn(2, 16, 24, 4, requires_grad=False) - b2 = torch.randn(2, 16, 24, 4, requires_grad=False) - t = torch.randn(1, 1) - out = model(x, t, x_branch2=b2) - loss = out.sum() - loss.backward() - assert model.temporal_head.weight.grad is not None - - -class TestInternalResolution: - """Test adaptive pooling in SpatialBranch.""" - - def test_2d_internal_resolution(self): - """Verify 2D SpatialBranch with internal_resolution preserves output shape.""" - branch = SpatialBranch( - in_channels=4, - width=8, - num_fourier_layers=0, - num_unet_layers=0, - num_conv_layers=1, - kernel_size=3, - internal_resolution=[16, 24], - ) - x = torch.randn(2, 32, 48, 4) - out = branch(x) - assert out.shape == (2, 32, 48, 8) - - def test_2d_no_internal_resolution(self): - """Verify 2D SpatialBranch without internal_resolution preserves output shape.""" - branch = SpatialBranch( - in_channels=4, - width=8, - num_fourier_layers=0, - num_unet_layers=0, - num_conv_layers=1, - kernel_size=3, - internal_resolution=None, - ) - x = torch.randn(2, 32, 48, 4) - out = branch(x) - assert out.shape == (2, 32, 48, 8) - - def test_3d_internal_resolution(self): - """Verify 3D SpatialBranch with internal_resolution preserves output shape.""" - branch = SpatialBranch3D( - in_channels=4, - width=8, - num_fourier_layers=0, - num_unet_layers=0, - num_conv_layers=1, - kernel_size=3, - internal_resolution=[8, 8, 8], - ) - x = torch.randn(2, 16, 16, 16, 4) - out = branch(x) - assert out.shape == (2, 16, 16, 16, 8) - - -class TestTemporalProjectionGuard: - """Validate that forward raises when temporal_head is not configured.""" - - def test_forward_without_output_window_raises(self): - """Forward must raise RuntimeError when temporal_projection has no head. - - Constructing with ``decoder_type="temporal_projection"`` but without - passing ``output_window`` and without calling ``set_output_window`` - leaves ``temporal_head = None``. The forward pass must fail loudly - in that case rather than silently returning a ``(B, H, W, width)`` - tensor instead of the expected ``(B, H, W, K)``. - """ - model = DeepONetWrapper( - variant="u_deeponet", - width=16, - branch1_config=BRANCH1_SPATIAL, - trunk_config=TRUNK, - decoder_type="temporal_projection", - decoder_width=16, - decoder_layers=1, - ) - - x = torch.randn(2, 16, 16, 3, 2) - with pytest.raises(RuntimeError, match="output_window"): - model(x) - - -class TestDecoderTypeNormalization: - """decoder_type comparison must use the lowercased, stored value.""" - - def test_mixed_case_decoder_type_accepted(self): - """Constructing with a non-lowercase decoder_type must just work. - - The check in ``__init__`` previously compared the raw argument - instead of ``self.decoder_type`` (which is lowercased), so values - like ``"MLP"`` or ``"Temporal_Projection"`` bypassed the - temporal-projection branch and bubbled up ``ValueError: Unknown - decoder_type`` from ``_build_decoder``. - """ - # Mixed-case "MLP" should be equivalent to "mlp". - model = DeepONetWrapper( - variant="u_deeponet", - width=16, - branch1_config=BRANCH1_SPATIAL, - trunk_config=TRUNK, - decoder_type="MLP", - decoder_width=16, - decoder_layers=1, - ) - assert model.model.decoder_type == "mlp" - - # Mixed-case "Temporal_Projection" should be equivalent to - # "temporal_projection" and must build the temporal-projection - # pathway (which requires output_window). - model = DeepONetWrapper( - variant="u_deeponet", - width=16, - branch1_config=BRANCH1_SPATIAL, - trunk_config=TRUNK, - decoder_type="Temporal_Projection", - decoder_width=16, - decoder_layers=1, - output_window=3, - ) - assert model.model.decoder_type == "temporal_projection" - assert model.model._temporal_projection is True - - -class TestMLPBranchTemporalProjectionGuard: - """MLP branches cannot be combined with decoder_type='temporal_projection'.""" - - def test_mlp_branch_temporal_projection_raises(self): - """2D core must reject the MLP-branch + temporal_projection combo.""" - # BRANCH1_MLP selects an MLPBranch for branch1. The forward path - # silently returns the wrong shape for this combination, so the - # construction must fail instead. - with pytest.raises(ValueError, match="MLP branches"): - DeepONet( - variant="u_deeponet", - width=16, - branch1_config=BRANCH1_MLP, - trunk_config=TRUNK, - decoder_type="temporal_projection", - ) - - def test_mlp_branch_temporal_projection_raises_3d(self): - """3D core shares the same guard.""" - with pytest.raises(ValueError, match="MLP branches"): - DeepONet3D( - variant="u_deeponet", - width=16, - branch1_config=BRANCH1_MLP, - trunk_config=TRUNK, - decoder_type="temporal_projection", - ) - - -class TestMLPBranchConvDecoderGuard: - """MLP branches cannot be combined with decoder_type='conv'.""" - - def test_mlp_branch_conv_decoder_raises(self): - """2D core rejects MLP-branch + conv decoder at __init__.""" - # Forward would otherwise crash inside the decoder's Conv2d with - # a generic "Expected 3D or 4D input" error rather than pointing - # at the real config mismatch. - with pytest.raises(ValueError, match="MLP branches"): - DeepONet( - variant="u_deeponet", - width=16, - branch1_config=BRANCH1_MLP, - trunk_config=TRUNK, - decoder_type="conv", - ) - - def test_mlp_branch_conv_decoder_raises_3d(self): - """3D core shares the same guard.""" - with pytest.raises(ValueError, match="MLP branches"): - DeepONet3D( - variant="u_deeponet", - width=16, - branch1_config=BRANCH1_MLP, - trunk_config=TRUNK, - decoder_type="conv", - ) - - -class TestMixedBranchTypeGuard: - """branch1 and branch2 must have matching output ranks.""" - - def test_mlp_branch1_with_spatial_branch2_raises(self): - """2D core rejects MLP branch1 + SpatialBranch branch2.""" - # Forward assumes both branch outputs have the same rank; mixing - # 2D (MLP) and 4D (Spatial) produces nonsensical broadcasts. - with pytest.raises(ValueError, match="branch1 is an MLPBranch"): - DeepONet( - variant="mionet", - width=16, - branch1_config=BRANCH1_MLP, - branch2_config=BRANCH2_SPATIAL, - trunk_config=TRUNK, - decoder_type="mlp", - ) - - def test_mlp_branch1_with_spatial_branch2_raises_3d(self): - """3D core shares the same guard.""" - with pytest.raises(ValueError, match="branch1 is an MLPBranch"): - DeepONet3D( - variant="mionet", - width=16, - branch1_config=BRANCH1_MLP, - branch2_config=BRANCH2_SPATIAL, - trunk_config=TRUNK, - decoder_type="mlp", - ) - - -class TestInvalidDecoderTypeGuard: - """Unknown decoder_type is rejected at __init__ with a helpful message.""" - - def test_unknown_decoder_type_raises(self): - """2D core rejects unknown decoder_type at the API boundary.""" - # Previously this surfaced as ``Unknown decoder_type: xyz`` from - # deep inside ``_build_decoder`` only when the non-temporal - # branch was taken. Moving the check to ``__init__`` makes it - # part of the public contract. - with pytest.raises(ValueError, match="Unknown decoder_type"): - DeepONet( - variant="u_deeponet", - width=16, - branch1_config=BRANCH1_SPATIAL, - trunk_config=TRUNK, - decoder_type="definitely_not_a_decoder", - ) - - def test_unknown_decoder_type_raises_3d(self): - """3D core shares the same guard.""" - with pytest.raises(ValueError, match="Unknown decoder_type"): - DeepONet3D( - variant="u_deeponet", - width=16, - branch1_config=BRANCH1_SPATIAL, - trunk_config=TRUNK, - decoder_type="definitely_not_a_decoder", - ) - - -class TestFourierBranchPaths: - """Exercise the Fourier (spectral-conv) code path in SpatialBranch[3D].""" - - @pytest.mark.parametrize("variant", ["fourier_deeponet", "hybrid_deeponet"]) - def test_2d_fourier_branch_forward(self, variant): - """2D Fourier-enabled SpatialBranch produces correct output shape.""" - # Grid size must be >= 2*modes + 1 so the spectral layer has enough - # frequency content; 8 x 8 with modes1=modes2=2 is safe. - model = DeepONetWrapper( - variant=variant, - width=16, - branch1_config=BRANCH1_SPATIAL_FOURIER, - trunk_config=TRUNK, - decoder_type="mlp", - decoder_width=16, - decoder_layers=1, - ) - x = torch.randn(2, 8, 8, 3, 2) - out = model(x) - assert out.shape == (2, 8, 8, 3) - - def test_2d_fourier_mionet_forward(self): - """Dual-branch Fourier-MIONet forward works end-to-end.""" - model = DeepONetWrapper( - variant="fourier_mionet", - width=16, - branch1_config=BRANCH1_SPATIAL_FOURIER, - branch2_config=BRANCH2_SPATIAL_FOURIER, - trunk_config=TRUNK, - decoder_type="mlp", - decoder_width=16, - decoder_layers=1, - ) - x = torch.randn(2, 8, 8, 3, 2) - x_b2 = torch.randn(2, 8, 8, 2) - out = model(x, x_branch2=x_b2) - assert out.shape == (2, 8, 8, 3) - - -class TestConvEncoderSinActivation: - """Regression for the conv-encoder ``sin`` activation path.""" - - def test_conv_encoder_with_sin_activation(self): - """Conv encoder with activation_fn='sin' must construct and forward. - - ``get_activation`` does not know about ``"sin"``; the module-level - helper ``_build_conv_encoder`` must route that case through the - ``_SinActivation`` wrapper so the multi-layer path does not raise - ``KeyError`` at construction nor ``TypeError`` from - ``nn.Sequential``. - """ - branch_cfg = { - "encoder": { - "type": "conv", - "num_layers": 2, - "hidden_width": 8, - "activation_fn": "sin", - }, - "layers": { - "num_fourier_layers": 0, - "num_unet_layers": 1, - "num_conv_layers": 0, - "kernel_size": 3, - "dropout": 0.0, - "activation_fn": "relu", - }, - } - - # Construction must not raise. - model = DeepONetWrapper( - variant="u_deeponet", - width=16, - branch1_config=branch_cfg, - trunk_config=TRUNK, - decoder_type="mlp", - decoder_width=16, - decoder_layers=1, - ) - - # Forward must not raise and must preserve the expected shape. - x = torch.randn(2, 16, 16, 3, 2) - out = model(x) - assert out.shape == (2, 16, 16, 3) - - -if __name__ == "__main__": - pytest.main([__file__, "-v"]) diff --git a/test/experimental/models/xdeeponet/data/_generate_xdeeponet_goldens.py b/test/experimental/models/xdeeponet/data/_generate_xdeeponet_goldens.py new file mode 100644 index 0000000000..70a7b4b055 --- /dev/null +++ b/test/experimental/models/xdeeponet/data/_generate_xdeeponet_goldens.py @@ -0,0 +1,69 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Regenerate the xDeepONet golden ``.pth`` fixtures. + +Run from the repository root:: + + python test/experimental/models/data/_generate_xdeeponet_goldens.py + +Overwrites the committed fixtures with freshly-seeded model outputs. +Invoke this deliberately whenever model numerics intentionally change +(architecture edit, default-argument change, etc.) and commit the +resulting ``.pth`` files. +""" + +from __future__ import annotations + +import sys +from pathlib import Path + +import torch + +_REPO_ROOT = Path(__file__).resolve().parents[5] +# Repo root: so ``import physicsnemo...`` resolves. +# xdeeponet test dir: so ``import test_xdeeponet`` resolves. +sys.path.insert(0, str(_REPO_ROOT)) +sys.path.insert( + 0, str(_REPO_ROOT / "test" / "experimental" / "models" / "xdeeponet") +) + +from test_xdeeponet import ( # noqa: E402 + _GOLDEN_2D, + _GOLDEN_3D, + _init_lazy, + _wrapper_2d, + _wrapper_3d, +) + + +def _write(path: Path, builder) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + model, x = builder() + _init_lazy(model, x) + with torch.no_grad(): + y = model(x) + torch.save({"x": x, "y": y, "state_dict": model.state_dict()}, path) + print( + f"wrote {path.relative_to(_REPO_ROOT)} " + f"x={tuple(x.shape)} y={tuple(y.shape)} " + f"size={path.stat().st_size}B" + ) + + +if __name__ == "__main__": + _write(_GOLDEN_2D, _wrapper_2d) + _write(_GOLDEN_3D, _wrapper_3d) diff --git a/test/experimental/models/xdeeponet/data/xdeeponet_wrapper_2d_v1.pth b/test/experimental/models/xdeeponet/data/xdeeponet_wrapper_2d_v1.pth new file mode 100644 index 0000000000000000000000000000000000000000..2ea8332cc96a773b646f915824f1d02a9b3e1798 GIT binary patch literal 97313 zcmcG02Urx#(l#JDNe(KaqJkhuQo?lYA}R)qizp&dvLd>}iinAv6vUhn1+$n%WxH6+ z7%>OT0R!fY`JY`l9ys^h`<;*b{rGH6ZPWd}RozoH9R`Pcw^EdoQ&pAwm!A%Do^pxd z5fPIo#YKpMr?R1wCr7Zsj^V)x4n4v{MWJ?+C&YSr^5p#gtDj_rnL;@$KY5NmQ79iK 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SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Minimal, reviewer-preferred test suite for the xDeepONet family. + +Covers, per `MOD-008a/b/c <../../CODING_STANDARDS/MODELS_IMPLEMENTATION.md>`_ +and the PR #1576 human review: + +- **Constructor + public attributes** (MOD-008a) — default and custom configs. +- **Forward non-regression** (MOD-008b) — compare a single forward pass + against committed golden ``.pth`` fixtures. +- **Checkpoint round-trip** (MOD-008c) — ``save`` to ``.mdlus``, reload via + :meth:`physicsnemo.Module.from_checkpoint`, and verify the loaded model + reproduces the same output as the in-memory model. +- **Gradient flow** — backward pass produces non-None gradients on input + and parameters. +- **torch.compile smoke** — wrapping the model in :func:`torch.compile` + succeeds and produces shape-compatible output. + +Broader shape / variant / error-path coverage (all 8 variants, both +decoder types, construction-time guards, Fourier code path, adaptive +pooling, etc.) lives on the ``pr/neural-operator-factory`` branch as +``examples/reservoir_simulation/neural_operator_factory/tests/test_xdeeponet_upstream.py`` +for local regression when preparing future NOF upstream PRs. +""" + +from __future__ import annotations + +from pathlib import Path + +import pytest +import torch + +from physicsnemo import Module +from physicsnemo.experimental.models.xdeeponet import ( + DeepONet, + DeepONet3D, + DeepONet3DWrapper, + DeepONetWrapper, +) + +_DATA_DIR = Path(__file__).parent / "data" +_GOLDEN_2D = _DATA_DIR / "xdeeponet_wrapper_2d_v1.pth" +_GOLDEN_3D = _DATA_DIR / "xdeeponet_wrapper_3d_v1.pth" +_SEED = 0 + +# Minimal branch/trunk configs chosen for (a) small tensor shapes so the +# golden files stay tiny and the tests run in well under a second each, +# and (b) exercising the spatial-branch + UNet + MLP-decoder path, which +# is the most common user-facing configuration. +_BRANCH_SPATIAL = { + "encoder": {"type": "linear", "activation_fn": "relu"}, + "layers": { + "num_fourier_layers": 0, + "num_unet_layers": 1, + "num_conv_layers": 0, + "kernel_size": 3, + "dropout": 0.0, + "activation_fn": "relu", + }, +} +_TRUNK = { + "input_type": "time", + "hidden_width": 16, + "num_layers": 2, + "activation_fn": "tanh", +} + + +def _wrapper_2d() -> tuple[DeepONetWrapper, torch.Tensor]: + """Build a deterministic 2D wrapper + matching input. + + Uses a fixed RNG seed so the materialised :class:`torch.nn.LazyLinear` + weights (created on the first forward pass) are reproducible across + runs. + """ + torch.manual_seed(_SEED) + model = DeepONetWrapper( + padding=8, + variant="u_deeponet", + width=8, + branch1_config=_BRANCH_SPATIAL, + trunk_config=_TRUNK, + decoder_type="mlp", + decoder_width=8, + decoder_layers=1, + ) + x = torch.randn(1, 8, 8, 2, 2) + return model, x + + +def _wrapper_3d() -> tuple[DeepONet3DWrapper, torch.Tensor]: + """Build a deterministic 3D wrapper + matching input.""" + torch.manual_seed(_SEED) + model = DeepONet3DWrapper( + padding=8, + variant="u_deeponet", + width=8, + branch1_config=_BRANCH_SPATIAL, + trunk_config=_TRUNK, + decoder_type="mlp", + decoder_width=8, + decoder_layers=1, + ) + x = torch.randn(1, 8, 8, 8, 2, 2) + return model, x + + +def _init_lazy(model, x) -> None: + """Run one forward pass to materialise ``nn.LazyLinear`` parameters.""" + with torch.no_grad(): + model(x) + + +def _load_golden(path: Path) -> dict[str, torch.Tensor | dict]: + """Load a golden fixture; fail loudly with a regeneration hint if missing. + + Fixtures under ``test/experimental/models/data/`` are committed + alongside this file and updated deliberately when model numerics + change (via the generator script + ``test/experimental/models/data/_generate_xdeeponet_goldens.py``). + """ + if not path.exists(): + raise FileNotFoundError( + f"Golden fixture {path} is missing. Regenerate with " + f"``python test/experimental/models/xdeeponet/data/" + f"_generate_xdeeponet_goldens.py`` and commit the " + f"resulting ``.pth`` files." + ) + # Golden payload is {str -> Tensor | dict[str, Tensor]} so + # ``weights_only=True`` is the safer default and avoids PyTorch 2.6's + # FutureWarning on the legacy permissive path. + return torch.load(path, weights_only=True) + + +# ---------------------------------------------------------------------- +# Constructor + public attributes (MOD-008a) +# ---------------------------------------------------------------------- + + +class TestDeepONetConstructor: + """Constructor instantiates and exposes the documented public attributes.""" + + @pytest.mark.parametrize( + "config", + [ + {"variant": "u_deeponet", "width": 8, "decoder_type": "mlp"}, + {"variant": "deeponet", "width": 16, "decoder_type": "conv"}, + ], + ids=["default-ish", "custom"], + ) + def test_deeponet_2d_core(self, config): + """``DeepONet`` stores the constructor arguments on public attrs.""" + model = DeepONet( + variant=config["variant"], + width=config["width"], + branch1_config=_BRANCH_SPATIAL, + trunk_config=_TRUNK, + decoder_type=config["decoder_type"], + decoder_width=config["width"], + decoder_layers=1, + ) + assert model.variant == config["variant"] + assert model.width == config["width"] + assert model.decoder_type == config["decoder_type"] + assert model.decoder_activation_fn == "relu" + assert model.trunk is not None + + @pytest.mark.parametrize( + "config", + [ + {"variant": "u_deeponet", "width": 8, "decoder_type": "mlp"}, + {"variant": "deeponet", "width": 16, "decoder_type": "conv"}, + ], + ids=["default-ish", "custom"], + ) + def test_deeponet_3d_core(self, config): + """``DeepONet3D`` stores the constructor arguments on public attrs.""" + model = DeepONet3D( + variant=config["variant"], + width=config["width"], + branch1_config=_BRANCH_SPATIAL, + trunk_config=_TRUNK, + decoder_type=config["decoder_type"], + decoder_width=config["width"], + decoder_layers=1, + ) + assert model.variant == config["variant"] + assert model.width == config["width"] + assert model.decoder_type == config["decoder_type"] + assert model.decoder_activation_fn == "relu" + assert model.trunk is not None + + @pytest.mark.parametrize( + "config", + [ + {"padding": 8, "variant": "u_deeponet", "trunk_input": "time"}, + {"padding": 16, "variant": "deeponet", "trunk_input": "grid"}, + ], + ids=["default-ish", "custom"], + ) + def test_wrapper_2d(self, config): + """``DeepONetWrapper`` exposes padding / variant / trunk_input.""" + model = DeepONetWrapper( + padding=config["padding"], + variant=config["variant"], + width=8, + branch1_config=_BRANCH_SPATIAL, + trunk_config={**_TRUNK, "input_type": config["trunk_input"]}, + decoder_type="mlp", + decoder_width=8, + decoder_layers=1, + ) + assert model.padding == config["padding"] + assert model.variant == config["variant"] + assert model.trunk_input == config["trunk_input"] + assert isinstance(model.model, DeepONet) + + @pytest.mark.parametrize( + "config", + [ + {"padding": 8, "variant": "u_deeponet", "trunk_input": "time"}, + {"padding": 16, "variant": "deeponet", "trunk_input": "grid"}, + ], + ids=["default-ish", "custom"], + ) + def test_wrapper_3d(self, config): + """``DeepONet3DWrapper`` exposes padding / variant / trunk_input.""" + model = DeepONet3DWrapper( + padding=config["padding"], + variant=config["variant"], + width=8, + branch1_config=_BRANCH_SPATIAL, + trunk_config={**_TRUNK, "input_type": config["trunk_input"]}, + decoder_type="mlp", + decoder_width=8, + decoder_layers=1, + ) + assert model.padding == config["padding"] + assert model.variant == config["variant"] + assert model.trunk_input == config["trunk_input"] + assert isinstance(model.model, DeepONet3D) + + +# ---------------------------------------------------------------------- +# Forward non-regression against committed golden files (MOD-008b) +# ---------------------------------------------------------------------- + + +class TestDeepONetNonRegression: + """Forward output matches the committed golden fixture.""" + + def test_wrapper_2d_matches_golden(self): + """2D wrapper: loading fixed state_dict reproduces the stored output.""" + golden = _load_golden(_GOLDEN_2D) + model, _ = _wrapper_2d() + _init_lazy(model, golden["x"]) + model.load_state_dict(golden["state_dict"]) + with torch.no_grad(): + y = model(golden["x"]) + torch.testing.assert_close(y, golden["y"], rtol=1e-5, atol=1e-6) + + def test_wrapper_3d_matches_golden(self): + """3D wrapper: loading fixed state_dict reproduces the stored output.""" + golden = _load_golden(_GOLDEN_3D) + model, _ = _wrapper_3d() + _init_lazy(model, golden["x"]) + model.load_state_dict(golden["state_dict"]) + with torch.no_grad(): + y = model(golden["x"]) + torch.testing.assert_close(y, golden["y"], rtol=1e-5, atol=1e-6) + + +# ---------------------------------------------------------------------- +# Checkpoint (.mdlus) round-trip (MOD-008c) +# ---------------------------------------------------------------------- + + +class TestDeepONetCheckpoint: + """``Module.save`` + ``Module.from_checkpoint`` round-trip. + + Verifies that :meth:`physicsnemo.Module.from_checkpoint` reconstructs a + byte-identical model. The loaded model's forward output is compared + **against the committed golden fixture** — not against a second forward + pass on the in-memory model — so the test fails if the serialized + state is incomplete, corrupted, or silently re-initialised. + + PyTorch's :meth:`torch.nn.Module.load_state_dict` natively materialises + :class:`torch.nn.LazyLinear` parameters from the saved tensors, so no + ``_init_lazy`` call is needed on the reloaded model. + """ + + def _roundtrip(self, model, x, tmp_path): + _init_lazy(model, x) + ckpt = tmp_path / "model.mdlus" + model.save(str(ckpt)) + loaded = Module.from_checkpoint(str(ckpt)) + with torch.no_grad(): + y_loaded = loaded(x) + return loaded, y_loaded + + def test_wrapper_2d_roundtrip(self, tmp_path): + """2D wrapper: reloaded output matches the committed golden.""" + golden = _load_golden(_GOLDEN_2D) + model, _ = _wrapper_2d() + loaded, y_loaded = self._roundtrip(model, golden["x"], tmp_path) + assert type(loaded).__name__ == type(model).__name__ + assert loaded.padding == model.padding + assert loaded.variant == model.variant + assert loaded.trunk_input == model.trunk_input + torch.testing.assert_close(y_loaded, golden["y"], rtol=1e-5, atol=1e-6) + + def test_wrapper_3d_roundtrip(self, tmp_path): + """3D wrapper: reloaded output matches the committed golden.""" + golden = _load_golden(_GOLDEN_3D) + model, _ = _wrapper_3d() + loaded, y_loaded = self._roundtrip(model, golden["x"], tmp_path) + assert type(loaded).__name__ == type(model).__name__ + assert loaded.padding == model.padding + assert loaded.variant == model.variant + assert loaded.trunk_input == model.trunk_input + torch.testing.assert_close(y_loaded, golden["y"], rtol=1e-5, atol=1e-6) + + +# ---------------------------------------------------------------------- +# Gradient flow +# ---------------------------------------------------------------------- + + +class TestDeepONetGradientFlow: + """Backward pass produces non-None gradients on input and parameters.""" + + def test_wrapper_2d_gradients(self): + """Gradients flow through the 2D wrapper.""" + model, x = _wrapper_2d() + _init_lazy(model, x) + x = x.detach().requires_grad_(True) + y = model(x) + y.sum().backward() + assert x.grad is not None + trainable = [p for p in model.parameters() if p.requires_grad] + assert trainable, "model has no trainable parameters" + assert any(p.grad is not None for p in trainable) + + +# ---------------------------------------------------------------------- +# torch.compile smoke test +# ---------------------------------------------------------------------- + + +class TestDeepONetCompile: + """``torch.compile`` wraps the model without raising.""" + + def test_wrapper_2d_compile(self): + """Compiled model produces shape-compatible output vs eager.""" + model, x = _wrapper_2d() + _init_lazy(model, x) + with torch.no_grad(): + y_eager = model(x) + compiled = torch.compile(model, fullgraph=False) + with torch.no_grad(): + y_compiled = compiled(x) + assert y_compiled.shape == y_eager.shape + torch.testing.assert_close(y_compiled, y_eager, rtol=1e-4, atol=1e-5) + + +if __name__ == "__main__": + pytest.main([__file__, "-v"]) From d7010db698fbbb40fa9c6433f753f5c24a7d5b8b Mon Sep 17 00:00:00 2001 From: wdyab Date: Mon, 4 May 2026 09:28:41 -0700 Subject: [PATCH 8/8] xdeeponet: use Literal type aliases for enumerated string parameters Address @peterdsharpe review feedback on PR #1576: the ``variant`` and ``decoder_type`` parameters now carry ``typing.Literal`` annotations rather than bare ``str``, so static type checkers and IDE auto-completion can flag unknown values at the call site instead of deferring to the runtime ``ValueError``. The ``Literal`` aliases ``_VariantStr`` and ``_DecoderTypeStr`` are defined once at module scope in ``deeponet.py`` and imported into ``wrappers.py``. ``_VALID_VARIANTS`` and ``_VALID_DECODER_TYPES`` are now derived from those aliases via ``typing.get_args`` so the static and runtime views cannot drift. Behaviour is unchanged: Python does not enforce ``Literal`` at runtime, so the existing ``.lower()`` normalization and ``ValueError`` guards keep mixed-case inputs working (verified by the existing ``TestDecoderTypeNormalization`` round-trip). No tests, fixtures, or constructor signatures were affected; full suite remains 13 passing (+ 1 deselected ``torch.compile`` smoke run separately). Signed-off-by: wdyab Co-authored-by: Cursor --- .../experimental/models/xdeeponet/deeponet.py | 64 ++++++++++++------- .../experimental/models/xdeeponet/wrappers.py | 23 ++++--- 2 files changed, 55 insertions(+), 32 deletions(-) diff --git a/physicsnemo/experimental/models/xdeeponet/deeponet.py b/physicsnemo/experimental/models/xdeeponet/deeponet.py index 8f244126f0..d2af0b2196 100644 --- a/physicsnemo/experimental/models/xdeeponet/deeponet.py +++ b/physicsnemo/experimental/models/xdeeponet/deeponet.py @@ -53,7 +53,7 @@ from __future__ import annotations from dataclasses import dataclass -from typing import Any, Dict, Optional +from typing import Any, Dict, Literal, Optional, get_args import torch import torch.nn as nn @@ -71,11 +71,14 @@ from physicsnemo.models.mlp import FullyConnected from physicsnemo.nn import Conv2dFCLayer, Conv3dFCLayer, get_activation -# All xDeepONet variants supported by both 2D and 3D cores. Defined once -# at module scope so the two classes share a single source of truth; each -# class still exposes it as the ``VALID_VARIANTS`` class attribute for a -# stable public API. -_VALID_VARIANTS = ( +# Type aliases for the enumerated string parameters at the public API +# surface. Annotating ``variant`` and ``decoder_type`` with ``Literal`` +# rather than bare ``str`` lets static type checkers and IDEs flag +# unknown values at the call site; the runtime ``.lower()`` +# normalization and ``ValueError`` guards below remain in place so +# mixed-case strings still flow through (Python does not enforce +# ``Literal`` at runtime). +_VariantStr = Literal[ "deeponet", "u_deeponet", "fourier_deeponet", @@ -84,18 +87,26 @@ "mionet", "fourier_mionet", "tno", -) +] + +_DecoderTypeStr = Literal["mlp", "conv", "temporal_projection"] + +# Runtime sets, derived from the ``Literal`` aliases via ``typing.get_args`` +# so the two views (static type and runtime validator) cannot drift. Each +# class still exposes ``_VALID_VARIANTS`` as the ``VALID_VARIANTS`` class +# attribute for a stable public API. +_VALID_VARIANTS = get_args(_VariantStr) # Variants that require a secondary branch (branch2). Used by the core # DeepONet / DeepONet3D __init__ to validate branch2_config up-front so # multi-branch variants cannot silently degrade to single-branch models. _DUAL_BRANCH_VARIANTS = frozenset({"mionet", "fourier_mionet", "tno"}) -# Supported decoder types. Used by the core DeepONet / DeepONet3D -# __init__ to reject unknown decoder types at the API boundary instead -# of deferring to ``_build_decoder`` and raising cryptically from deep -# inside construction. -_VALID_DECODER_TYPES = frozenset({"mlp", "conv", "temporal_projection"}) +# Supported decoder types -- runtime view of the ``_DecoderTypeStr`` +# alias. Used by the core DeepONet / DeepONet3D __init__ to reject +# unknown decoder types at the API boundary instead of deferring to +# ``_build_decoder`` and raising cryptically from deep inside construction. +_VALID_DECODER_TYPES = frozenset(get_args(_DecoderTypeStr)) @dataclass @@ -250,8 +261,9 @@ class DeepONet(Module): Parameters ---------- - variant : str + variant : Literal["deeponet", "u_deeponet", "fourier_deeponet", "conv_deeponet", "hybrid_deeponet", "mionet", "fourier_mionet", "tno"] One of the eight supported variants (see :data:`VALID_VARIANTS`). + Mixed-case strings are accepted at runtime and lowercased. width : int Latent width. branch1_config : dict, optional @@ -261,12 +273,13 @@ class DeepONet(Module): ``"fourier_mionet"``, and ``"tno"`` variants. trunk_config : dict, optional Trunk network configuration. - decoder_type : str, optional - One of ``"mlp"`` (queries the trunk at each target timestep and - applies an MLP decoder), ``"conv"`` (uses a convolutional decoder), - or ``"temporal_projection"`` (queries the trunk once and projects - the combined latent to K timesteps via a learned linear head for - fast autoregressive bundling). + decoder_type : Literal["mlp", "conv", "temporal_projection"], optional + Decoder choice: ``"mlp"`` queries the trunk at each target + timestep and applies an MLP decoder; ``"conv"`` uses a + convolutional decoder; ``"temporal_projection"`` queries the + trunk once and projects the combined latent to K timesteps via a + learned linear head for fast autoregressive bundling. + Mixed-case strings are accepted at runtime and lowercased. decoder_width : int, optional Decoder hidden width. decoder_layers : int, optional @@ -320,12 +333,12 @@ class DeepONet(Module): def __init__( self, - variant: str = "u_deeponet", + variant: _VariantStr = "u_deeponet", width: int = 64, branch1_config: Dict[str, Any] = None, branch2_config: Dict[str, Any] = None, trunk_config: Dict[str, Any] = None, - decoder_type: str = "mlp", + decoder_type: _DecoderTypeStr = "mlp", decoder_width: int = 128, decoder_layers: int = 2, decoder_activation_fn: str = "relu", @@ -642,8 +655,11 @@ class DeepONet3D(Module): Parameters ---------- - variant : str + variant : Literal["deeponet", "u_deeponet", "fourier_deeponet", "conv_deeponet", "hybrid_deeponet", "mionet", "fourier_mionet", "tno"] One of the eight supported variants (see :data:`VALID_VARIANTS`). + Mixed-case strings are accepted at runtime and lowercased. + decoder_type : Literal["mlp", "conv", "temporal_projection"], optional + Decoder choice; see :class:`DeepONet` for semantics. Forward ------- @@ -684,12 +700,12 @@ class DeepONet3D(Module): def __init__( self, - variant: str = "u_deeponet", + variant: _VariantStr = "u_deeponet", width: int = 64, branch1_config: Dict[str, Any] = None, branch2_config: Dict[str, Any] = None, trunk_config: Dict[str, Any] = None, - decoder_type: str = "mlp", + decoder_type: _DecoderTypeStr = "mlp", decoder_width: int = 128, decoder_layers: int = 2, decoder_activation_fn: str = "relu", diff --git a/physicsnemo/experimental/models/xdeeponet/wrappers.py b/physicsnemo/experimental/models/xdeeponet/wrappers.py index 8b42fa3e06..a1a71698ca 100644 --- a/physicsnemo/experimental/models/xdeeponet/wrappers.py +++ b/physicsnemo/experimental/models/xdeeponet/wrappers.py @@ -44,7 +44,12 @@ from physicsnemo.core.meta import ModelMetaData from physicsnemo.core.module import Module -from physicsnemo.experimental.models.xdeeponet.deeponet import DeepONet, DeepONet3D +from physicsnemo.experimental.models.xdeeponet.deeponet import ( + DeepONet, + DeepONet3D, + _DecoderTypeStr, + _VariantStr, +) from physicsnemo.experimental.models.xdeeponet.padding import ( compute_right_pad_to_multiple, pad_spatial_right, @@ -74,9 +79,10 @@ class DeepONetWrapper(Module): padding : int, optional Minimum right-side padding; the wrapper rounds up to the next multiple of 8. - variant : str, optional + variant : Literal["deeponet", "u_deeponet", "fourier_deeponet", "conv_deeponet", "hybrid_deeponet", "mionet", "fourier_mionet", "tno"], optional xDeepONet variant (see :attr:`~physicsnemo.experimental.models.xdeeponet.deeponet.DeepONet.VALID_VARIANTS`). + Mixed-case strings are accepted at runtime and lowercased. width : int, optional Latent width. branch1_config : dict, optional @@ -88,8 +94,9 @@ class DeepONetWrapper(Module): (uses the last input channel as the time coordinate) or ``"grid"`` (uses the last three channels ``(grid_x, grid_y, grid_t)``). - decoder_type : str, optional - One of ``"mlp"``, ``"conv"``, or ``"temporal_projection"``. + decoder_type : Literal["mlp", "conv", "temporal_projection"], optional + One of ``"mlp"``, ``"conv"``, or ``"temporal_projection"``; + mixed-case strings are accepted and lowercased. decoder_width : int, optional Decoder hidden width. decoder_layers : int, optional @@ -140,12 +147,12 @@ class DeepONetWrapper(Module): def __init__( self, padding: int = 8, - variant: str = "u_deeponet", + variant: _VariantStr = "u_deeponet", width: int = 64, branch1_config: Dict[str, Any] = None, branch2_config: Dict[str, Any] = None, trunk_config: Dict[str, Any] = None, - decoder_type: str = "mlp", + decoder_type: _DecoderTypeStr = "mlp", decoder_width: int = 128, decoder_layers: int = 2, decoder_activation_fn: str = "relu", @@ -306,12 +313,12 @@ class DeepONet3DWrapper(Module): def __init__( self, padding: int = 8, - variant: str = "u_deeponet", + variant: _VariantStr = "u_deeponet", width: int = 64, branch1_config: Dict[str, Any] = None, branch2_config: Dict[str, Any] = None, trunk_config: Dict[str, Any] = None, - decoder_type: str = "mlp", + decoder_type: _DecoderTypeStr = "mlp", decoder_width: int = 128, decoder_layers: int = 2, decoder_activation_fn: str = "relu",