Add xDeepONet family to experimental models

CHANGELOG.md

@@ -10,6 +10,17 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Added
 
+- Adds xDeepONet to experimental models
+  (`physicsnemo.experimental.models.xdeeponet.DeepONet`).  A single
+  dimension-generic (2D/3D) DeepONet that accepts a spatial or MLP branch,
+  an optional trunk, and an optional second branch as `nn.Module` inputs
+  (dependency injection).  Six forward-call conventions cover trunked,
+  trunkless, packed/auto-padded, and xFNO-style time-axis-extend modes.
+  Supports multi-channel output, multiple decoder types (MLP, Conv,
+  temporal projection), composable Fourier / UNet / Conv spatial branches
+  (`SpatialBranch`), and coordinate features.
+- Adds `Sin` elementwise sine activation to `physicsnemo.nn`, registered
+  in `ACT2FN` so it can be looked up by name (`get_activation("sin")`).
 - Adds GLOBE model (`physicsnemo.experimental.models.globe.model.GLOBE`),
   including new variant that uses a dual tree traversal algorithm to reduce the
   complexity of the kernel evaluations from O(N^2) to O(N).

physicsnemo/experimental/models/xdeeponet/__init__.py

@@ -0,0 +1,49 @@
+# 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.
+
+A single :class:`DeepONet` class assembles operator-learning
+architectures spanning the DeepONet and FNO families:
+
+- ``deeponet``, ``u_deeponet``, ``fourier_deeponet``, ``conv_deeponet``,
+  ``hybrid_deeponet`` — single-branch + trunk variants.
+- ``mionet``, ``fourier_mionet`` — two-branch multi-input + trunk variants.
+- ``tno`` — Temporal Neural Operator (branch2 = previous solution) + trunk.
+- ``ufno`` / xFNO-style trunkless operators — trunkless spatial branch
+  with composable Fourier / UNet / Conv layers; the last spatial axis
+  can be interpreted as time for autoregressive bundling via the
+  :attr:`DeepONet.time_modes` parameter.
+
+The :class:`DeepONet` class is dimension-generic (``dimension=2|3``
+constructor argument; per-dimension primitives are dispatched
+internally) and dispatches forward by two flags
+(:attr:`auto_pad`, :attr:`trunk`-is-None) over six valid call
+conventions: packed-input vs core-input × trunked vs trunkless,
+plus the ``temporal_projection`` decoder variant.  See the
+:class:`DeepONet` class docstring for the full matrix and worked
+examples; see :class:`SpatialBranch` for the spatial-encoder
+composition options (Fourier / UNet / Conv layers, multi-layer
+pointwise lift, optional coordinate-feature channels).
+"""
+
+from .branches import SpatialBranch
+from .deeponet import DeepONet
+
+__all__ = [
+    "DeepONet",
+    "SpatialBranch",
+]

physicsnemo/experimental/models/xdeeponet/_padding.py

@@ -0,0 +1,254 @@
+# 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 packed-input
+forward path.
+
+When :class:`~physicsnemo.experimental.models.xdeeponet.DeepONet` is
+constructed with ``auto_pad=True`` it aligns 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)``
+
+This module is private (leading underscore): the helpers are part of the
+xdeeponet package's internal API surface only and may be renamed or
+restructured without notice.
+"""
+
+from __future__ import annotations
+
+from typing import Sequence
+
+import torch
+import torch.nn.functional as F
+from jaxtyping import Shaped
+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: Shaped[Tensor, "..."],
+    *,
+    dims: Sequence[int],
+    right_pad: Sequence[int],
+    mode: str = "replicate",
+    constant_value: float = 0.0,
+) -> Shaped[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).
+
+    Parameters
+    ----------
+    x : torch.Tensor
+        Input tensor of any rank and dtype.
+    dims : Sequence[int]
+        Dimensions to right-pad.  Negative indices are supported.
+    right_pad : Sequence[int]
+        Right-side padding amounts per ``dims`` entry.  Non-positive
+        entries are no-ops.
+    mode : str, optional
+        ``"replicate"`` (default) repeats the last slice along each
+        padded dim; ``"constant"`` uses ``constant_value``.
+    constant_value : float, optional
+        Fill value when ``mode="constant"`` (default ``0.0``).
+
+    Returns
+    -------
+    torch.Tensor
+        Tensor of the same rank and dtype as ``x`` with the specified
+        dimensions right-padded.
+    """
+    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: Shaped[Tensor, "..."],
+    *,
+    spatial_ndim: int,
+    right_pad: Sequence[int],
+    mode: str = "replicate",
+    constant_value: float = 0.0,
+) -> Shaped[Tensor, "..."]:
+    """Right-pad the first *spatial_ndim* dimensions after the batch dim.
+
+    Assumes ``x`` is shaped ``(B, *spatial, *rest)``.
+
+    Parameters
+    ----------
+    x : torch.Tensor
+        Input tensor shaped ``(B, *spatial, *rest)``; any dtype is
+        accepted.  Must satisfy ``x.dim() >= 1 + spatial_ndim``.
+    spatial_ndim : int
+        Number of spatial dimensions immediately following the batch
+        dim.  Must be ``2``, ``3``, or ``4``.
+    right_pad : Sequence[int]
+        Right-side padding amounts per spatial dimension; must have
+        length ``spatial_ndim``.  Non-positive entries are no-ops.
+    mode : str, optional
+        ``"replicate"`` (default) or ``"constant"``.
+    constant_value : float, optional
+        Fill value when ``mode="constant"`` (default ``0.0``).
+
+    Returns
+    -------
+    torch.Tensor
+        Tensor of the same rank and dtype as ``x`` with the spatial
+        dimensions right-padded.
+    """
+    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",
+]