diff --git a/CHANGELOG.md b/CHANGELOG.md index 054a36fd..c01c247c 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -56,6 +56,20 @@ ### Models +- **PET (metatrain) wrapper** — new `PETWrapper` exposes PET + (Point-Edge Transformer) foundation models through the `BaseModelMixin` + interface, ready for any dynamics engine or standalone inference. + PETWrapper supports all the PET-MAD models, as well as other PET-based + models from the `https://github.com/lab-cosmo/upet` repository. + The interface is fully torch-native and torch-compileable with + `fullgraph=True` for both forward energies and autograd forces. + PETWrapper can be installed with `pip install 'nvalchemi-toolkit[pet]'`, + that will pull an extra `metatrain` dependency. See more details on + how to run simulations with PET in the `examples/advanced/10_pet_nvt.py`. + `PETWrapper.from_checkpoint` can also fetch named checkpoints (e.g. + `pet-mad-xs-v1.6.0`) directly from the `lab-cosmo/upet` HuggingFace + repository via the new `upet` dependency (also pulled by the `pet` extra), + in addition to loading local checkpoint files. - **UMA (fairchem-core) wrapper** — new `UMAWrapper` exposes UMA (Universal Models for Atoms) foundation models (`uma-s-1p1`, `uma-s-1p2`, `uma-m-1p1`) through the `BaseModelMixin` interface, diff --git a/Makefile b/Makefile index b2ed25eb..8334a642 100644 --- a/Makefile +++ b/Makefile @@ -21,9 +21,16 @@ # Keep `uv run` aligned with the selected CUDA stack. Bare `uv run` performs a # sync without extras, which can replace a CUDA 12 environment with the default. +# The cuXX extras only ship CUDA wheels for Linux, so only default to cu13 there; +# other platforms (e.g. macOS) fall back to the native (non-CUDA) torch build. +UNAME_S := $(shell uname -s) +ifeq ($(UNAME_S),Linux) CUDA_EXTRA ?= cu13 +else +CUDA_EXTRA ?= +endif OPTIONAL_EXTRAS ?= -UV_EXTRA_FLAGS = --extra $(CUDA_EXTRA) $(foreach extra,$(OPTIONAL_EXTRAS),--extra $(extra)) +UV_EXTRA_FLAGS = $(if $(CUDA_EXTRA),--extra $(CUDA_EXTRA)) $(foreach extra,$(OPTIONAL_EXTRAS),--extra $(extra)) UV_SYNC ?= uv sync $(UV_EXTRA_FLAGS) UV_RUN ?= uv run $(UV_EXTRA_FLAGS) diff --git a/docs/models/index.md b/docs/models/index.md index bc56c9d0..01fdb1b7 100644 --- a/docs/models/index.md +++ b/docs/models/index.md @@ -57,6 +57,15 @@ mechanical reference data. - ✓ - charge, spin - COO +* - {py:class}`~nvalchemi.models.pet.PETWrapper` + - ✓ + - ✓ + - ✓ + - ✓ + - ✗ + - ✓ + - — + - COO * - {py:class}`~nvalchemi.models.demo.DemoModelWrapper` - ✓ - ✓ @@ -175,6 +184,15 @@ the original publications for the underlying methods. - Batatia, I. *et al.* "A foundation model for atomistic materials chemistry." *arXiv:2401.00096*, 2023. [doi:10.48550/arXiv.2401.00096](https://doi.org/10.48550/arXiv.2401.00096) +* - **PET** (Point-Edge Transformer) + - Pozdnyakov, S. N. & Ceriotti, M. "Smooth, exact rotational symmetrization + for deep learning on point clouds." *Advances in Neural Information + Processing Systems (NeurIPS)*, 2023. + [openreview.net/forum?id=WYlNCXFbis](https://openreview.net/forum?id=WYlNCXFbis) +* - **PET-MAD** (foundation) + - Mazitov, A. *et al.* "PET-MAD, a universal interatomic potential for + advanced materials modeling." *arXiv:2503.14118*, 2025. + [doi:10.48550/arXiv.2503.14118](https://doi.org/10.48550/arXiv.2503.14118) * - **AIMNet2** - Anstine, D. M., Zubatyuk, R. & Isayev, O. "AIMNet2: a neural network potential to meet your neutral, charged, organic, and elemental-organic needs." diff --git a/docs/modules/models.rst b/docs/modules/models.rst index 255cb4ae..4fc3f68d 100644 --- a/docs/modules/models.rst +++ b/docs/modules/models.rst @@ -60,6 +60,15 @@ Machine-learned potentials UMAWrapper +.. currentmodule:: nvalchemi.models.pet + +.. autosummary:: + :toctree: generated + :template: class.rst + :nosignatures: + + PETWrapper + Physical / classical models --------------------------- diff --git a/docs/userguide/models.md b/docs/userguide/models.md index df8e9658..78a5d420 100644 --- a/docs/userguide/models.md +++ b/docs/userguide/models.md @@ -37,9 +37,10 @@ potentials: | {py:class}`~nvalchemi.models.aimnet2.AIMNet2Wrapper` | {py:class}`~aimnet.calculators.AIMNet2Calculator` | Requires the `aimnet2` optional dependency | | {py:class}`~nvalchemi.models.mace.MACEWrapper` | Any MACE variant | Requires the `mace` optional dependency with a CUDA extra, such as `cu13` or `cu12` | | {py:class}`~nvalchemi.models.uma.UMAWrapper` | fairchem-core UMA (`MLIPPredictUnit`) | Requires the `uma` optional dependency; conflicts with `mace` (incompatible `e3nn` pins) | +| {py:class}`~nvalchemi.models.pet.PETWrapper` | metatrain's pure-torch `PETBackend` (PET-MAD foundation models) | Requires the `pet` optional dependency (`metatrain` + `upet`) | {py:class}`~nvalchemi.models.aimnet2.AIMNet2Wrapper`, {py:class}`~nvalchemi.models.mace.MACEWrapper`, -and {py:class}`~nvalchemi.models.uma.UMAWrapper` +{py:class}`~nvalchemi.models.uma.UMAWrapper`, and {py:class}`~nvalchemi.models.pet.PETWrapper` are lazily imported --- they only load when accessed, so missing dependencies will not break other imports. @@ -188,6 +189,71 @@ fast = UMAWrapper.from_checkpoint( See {doc}`the UMA NVE/NVT example ` for a runnable end-to-end molecular-dynamics walkthrough. +### Using PET (metatrain) + +PET (Point-Edge Transformer) is a graph-transformer foundation model from the +[metatrain](https://github.com/metatensor/metatrain) project. +{py:class}`~nvalchemi.models.pet.PETWrapper` wraps metatrain's pure-torch +`PETBackend` --- structure preprocessing, featurization, and prediction, with +no `metatomic.torch.System` / `metatensor.torch.TensorMap` at call time --- so +the forward pass is `torch.compile`-friendly. It supports all PET-MAD models +as well as other PET-based checkpoints from the +[`lab-cosmo/upet`](https://github.com/lab-cosmo/upet) repository. + +**1. Install the optional dependency:** + +```bash +uv sync --extra pet +# or, with pip: pip install 'nvalchemi-toolkit[pet]' +``` + +The `pet` extra pulls in `metatrain` (currently sourced from its GitHub `main` +branch until the pure-torch `PETBackend` lands in a tagged PyPI release --- see +`[tool.uv.sources].metatrain` in `pyproject.toml`) and `upet`, the package used +to resolve and fetch named checkpoints from HuggingFace. + +**2. Load a checkpoint.** Fetch a named model directly from the +`lab-cosmo/upet` HuggingFace repository (list available names via +`upet.list_upet()`): + +```python +from nvalchemi.models.pet import PETWrapper +import torch + +model = PETWrapper.from_checkpoint(device=torch.device("cuda"), dtype=torch.float32) +# or, pick a specific model/version: +model = PETWrapper.from_checkpoint(model="pet-mad-xs", version="1.6.0") +``` + +Or load a local checkpoint file directly (older layouts are auto-upgraded to +the current metatrain checkpoint format): + +```python +model = PETWrapper.from_checkpoint( + checkpoint_path="pet-mad-xs-v1.6.0.ckpt", + device=torch.device("cuda"), + dtype=torch.float32, +) +``` + +Only the `energy` output is registered on the backend --- forces and stress +are always derived via autograd (`autograd_outputs = {"forces", "stress"}`); +the non-conservative PET heads and the long-range module are skipped. + +**`torch.compile`.** `PETWrapper.from_checkpoint(..., compile_model=True, +**compile_kwargs)` compiles the three backend building blocks (`preprocess`, +`calculate_features`, `predict`), forwarding `compile_kwargs` (e.g. +`fullgraph=True`) to each `torch.compile` call. This requires a checkpoint +trained with the `'solver'` adaptive-cutoff method (`pet-mad` >= v1.6.0) --- +`'grid'`-method checkpoints (`pet-mad` <= v1.5.0) raise `ValueError` because +autograd backward through the compiled grid cutoff aborts; run those eagerly +instead. `torch.compile` has a fixed per-call overhead and warmup, so it only +pays off for larger systems, on GPU, and over long trajectories. + +See {doc}`the PET NVT example ` for a +runnable end-to-end molecular-dynamics walkthrough (falls back to a +Lennard-Jones potential in CI when no checkpoint / `metatrain` is available). + ## Architecture overview A wrapped model uses **multiple inheritance**: your existing {py:class}`~torch.nn.Module` diff --git a/examples/advanced/10_pet_nvt.py b/examples/advanced/10_pet_nvt.py new file mode 100644 index 00000000..dfc3d3ed --- /dev/null +++ b/examples/advanced/10_pet_nvt.py @@ -0,0 +1,267 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. 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. +""" +PET (Point Edge Transformer): NVT Molecular Dynamics +==================================================== + +PET is a graph transformer interatomic potential from the +`metatrain `_ project. +:class:`~nvalchemi.models.pet.PETWrapper` wraps metatrain's pure-torch +``PETBackend`` (structure preprocessing, featurization, prediction) as a +:class:`~nvalchemi.models.base.BaseModelMixin` model and computes conservative +forces and stress from the energy via autograd. It loads a ``pet-mad``-style +checkpoint (e.g. ``pet-mad-xs-v1.5.0.ckpt``) through +:meth:`~nvalchemi.models.pet.PETWrapper.from_checkpoint`. + +This example drives PET through nvalchemi's Langevin (NVT) integrator on a +small water cluster. The simulation code is model-agnostic: the neighbor list +is wired automatically from +:attr:`~nvalchemi.models.base.ModelConfig.neighbor_config`, so the same loop +runs whether PET or the Lennard-Jones fallback supplies the forces. + +Set ``PET_MODEL_PATH=/path/to/checkpoint.ckpt`` to run with PET; otherwise the +example falls back to a Lennard-Jones argon cluster so it works in CI without a +checkpoint or the optional ``metatrain`` dependency. + +Key concepts demonstrated +------------------------- +* Loading a PET checkpoint with + ``PETWrapper.from_checkpoint(checkpoint_path=..., device=...)`` (old + checkpoints are auto-upgraded to the current metatrain layout). +* Automatic neighbor-list wiring via ``ModelConfig.neighbor_config``. +* A model-agnostic NVT loop with an LJ fallback for CI. + +.. note:: + + ``PETWrapper.from_checkpoint(..., compile_model=True, **compile_kwargs)`` + ``torch.compile``\\ s the three backend blocks (pass e.g. + ``fullgraph=True``; requires a ``solver``-method checkpoint such as + pet-mad >= v1.6.0). ``torch.compile`` has a fixed per-call overhead and a + one-time warmup (seconds), so it is **slower than eager for small systems on + CPU** — like the few-atom clusters here — and only pays off for larger cells, + on GPU, and over long trajectories. Benchmark on your target system before + enabling it for production MD. +""" + +from __future__ import annotations + +import sys +import types + +if "hostlist" not in sys.modules: + try: + import hostlist # noqa: F401 + except ImportError: + sys.modules["hostlist"] = types.ModuleType("hostlist") + +import logging +import math +import os + +import torch + +from nvalchemi.data import AtomicData, Batch +from nvalchemi.dynamics import NVTLangevin +from nvalchemi.dynamics.base import DynamicsStage + +# KB_EV and kinetic_energy_per_graph are internal helpers used by the built-in +# integrators. A stable public re-export may be added in a future release. +from nvalchemi.dynamics.hooks._utils import KB_EV, kinetic_energy_per_graph +from nvalchemi.hooks import NeighborListHook + +logging.basicConfig(level=logging.INFO) + +# %% +# Model selection: PET or LJ fallback +# -------------------------------------- +# The ``PET_MODEL_PATH`` environment variable selects the model. If it is +# unset (or metatrain is not installed / the checkpoint fails to load), we fall +# back to the LJ potential so the example works in CI without any PET +# dependency. +# +# Both code paths produce a ``model`` object that satisfies the +# :class:`~nvalchemi.models.base.BaseModelMixin` interface, so all simulation +# code below is model-agnostic. + +PET_MODEL_PATH = os.environ.get("PET_MODEL_PATH", "") +USE_PET = False + +if PET_MODEL_PATH: + try: + from nvalchemi.models.pet import PETWrapper + + model = PETWrapper.from_checkpoint( + checkpoint_path=PET_MODEL_PATH, + device=torch.device("cuda" if torch.cuda.is_available() else "cpu"), + ) + print(f"Using PET model from {PET_MODEL_PATH}") + USE_PET = True + except Exception as exc: + print(f"Failed to load PET ({exc}). Falling back to LJ.") + +if not USE_PET: + from nvalchemi.models.lj import LennardJonesModelWrapper + + model = LennardJonesModelWrapper( # type: ignore[assignment] + epsilon=0.0104, # eV — argon ε + sigma=3.40, # Å — argon σ + cutoff=8.5, # Å + ) + print("Using LJ model (set PET_MODEL_PATH=/path/to/model.ckpt to use PET)") + +# %% +# Neighbor list: automatic wiring via ModelConfig +# ------------------------------------------------ +# :attr:`~nvalchemi.models.base.ModelConfig.neighbor_config` encodes the cutoff, +# list format (COO or MATRIX), and whether to use a half-list. +# :class:`~nvalchemi.hooks.NeighborListHook` reads this automatically. +# +# If ``neighbor_config`` is ``None`` (e.g. a demo model that does its own +# neighbour search), no hook is needed. + +neighbor_hook = NeighborListHook( + model.model_config.neighbor_config, + stage=DynamicsStage.BEFORE_COMPUTE, +) + +# %% +# Building the system +# -------------------- +# When PET is active we use a small water cluster (3 molecules, 9 atoms). +# With LJ we use an 8-atom argon cluster. Both result in the same simulation +# code below. + +_R_MIN_AR = 2 ** (1 / 6) * 3.40 # ≈ 3.82 Å + + +def _make_argon_cluster(n_per_side: int = 2, seed: int = 0) -> AtomicData: + """8-atom argon cluster on a cubic lattice.""" + n = n_per_side**3 + spacing = _R_MIN_AR * 1.05 + coords = torch.arange(n_per_side, dtype=torch.float32) * spacing + gx, gy, gz = torch.meshgrid(coords, coords, coords, indexing="ij") + positions = torch.stack([gx.flatten(), gy.flatten(), gz.flatten()], dim=-1) + torch.manual_seed(seed) + positions = positions + 0.05 * torch.randn_like(positions) + # Maxwell-Boltzmann at 300 K: v_std = sqrt(kB * T / m), m_Ar = 39.948 amu + _v_std = math.sqrt(KB_EV * 300.0 / 39.948) + return AtomicData( + positions=positions, + atomic_numbers=torch.full((n,), 18, dtype=torch.long), # Ar Z=18 + forces=torch.zeros(n, 3), + energy=torch.zeros(1, 1), + velocities=_v_std * torch.randn(n, 3), + ) + + +def _make_water_cluster(n_molecules: int = 3, seed: int = 0) -> AtomicData: + """Small water cluster (H₂O): n_molecules × {O, H, H} atoms. + + Geometry: O at the origin of each molecule; H atoms at ±104.5° / 0.96 Å. + Molecules are spaced 3.5 Å apart along the x-axis. + """ + torch.manual_seed(seed) + positions_list = [] + atomic_numbers_list = [] + + o_h_bond = 0.96 # Å + half_angle = math.radians(104.5 / 2) + + for i in range(n_molecules): + ox = float(i) * 3.5 + # Oxygen + o_pos = torch.tensor([ox, 0.0, 0.0]) + # Two hydrogens in the xz-plane + h1_pos = o_pos + o_h_bond * torch.tensor( + [math.sin(half_angle), 0.0, math.cos(half_angle)] + ) + h2_pos = o_pos + o_h_bond * torch.tensor( + [-math.sin(half_angle), 0.0, math.cos(half_angle)] + ) + positions_list.extend([o_pos, h1_pos, h2_pos]) + atomic_numbers_list.extend([8, 1, 1]) # O=8, H=1 + + positions = torch.stack(positions_list) + 0.02 * torch.randn(len(positions_list), 3) + n_atoms = len(atomic_numbers_list) + # Maxwell-Boltzmann at 300 K per species: O m=15.999 amu, H m=1.008 amu + _masses = torch.tensor([15.999 if z == 8 else 1.008 for z in atomic_numbers_list]) + _v_stds = (KB_EV * 300.0 / _masses).sqrt().unsqueeze(-1) # (n_atoms, 1) + return AtomicData( + positions=positions.float(), + atomic_numbers=torch.tensor(atomic_numbers_list, dtype=torch.long), + forces=torch.zeros(n_atoms, 3), + energy=torch.zeros(1, 1), + velocities=(_v_stds * torch.randn(n_atoms, 3)).float(), + ) + + +if USE_PET: + data = _make_water_cluster(n_molecules=3) + system_label = "water cluster (9 atoms, 3 H₂O)" +else: + data = _make_argon_cluster(n_per_side=2, seed=0) + system_label = "argon cluster (8 atoms)" + +sim_device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +batch = Batch.from_data_list([data], device=sim_device) +print(f"\nSystem: {system_label} → {batch.num_nodes} atoms on {sim_device}") + +# %% +# NVT MD simulation +# ------------------ +# 100 steps of NVTLangevin at 300 K. The neighbor hook (if any) fires at +# BEFORE_COMPUTE to rebuild the neighbor list before each forward pass. + +nvt = NVTLangevin( + model=model, + dt=0.1, # fs + temperature=300.0, + friction=0.5, + n_steps=100, + random_seed=99, +) + +if neighbor_hook is not None: + nvt.register_hook(neighbor_hook) + +print("\nRunning 100 NVT steps …") +batch = nvt.run(batch) +print(f"Run complete: {nvt.step_count} steps") + +# %% +# Model-agnostic observation +# --------------------------- +# Temperature and mean energy are computed the same way regardless of whether +# PET or LJ provided the forces. + +# Kinetic energy per graph (eV) → temperature. +# 2 KE = 3 N k_B T → T = 2 KE / (3 N k_B) +ke_per_graph = kinetic_energy_per_graph( + velocities=batch.velocities, + masses=batch.atomic_masses, + batch_idx=batch.batch_idx, + num_graphs=batch.num_graphs, +) # [B, 1] + +n_atoms_per_graph = batch.num_nodes_per_graph.float() # [B] +T_inst = (2.0 * ke_per_graph.squeeze(-1)) / (3.0 * n_atoms_per_graph * KB_EV) + +mean_E = batch.energy.squeeze(-1).mean().item() +mean_T = T_inst.mean().item() + +print("\nFinal observables (model-agnostic):") +print(f" Mean energy : {mean_E:+.4f} eV") +print(f" Inst. temp. : {mean_T:.1f} K (target = 300 K)") +print(f" Model used : {'PET' if USE_PET else 'Lennard-Jones (fallback)'}") diff --git a/examples/advanced/README.rst b/examples/advanced/README.rst index c72f90c7..8c968b20 100644 --- a/examples/advanced/README.rst +++ b/examples/advanced/README.rst @@ -29,3 +29,8 @@ electrostatics and DFTD3 dispersion in a multi-group pipeline. **09 — UMA NVE/NVT**: Driving the fairchem UMA foundation model through NVE / NVT dynamics with energy-drift tracking; OMat crystals and OMol molecules via task selection on ``UMAWrapper.from_checkpoint``. + +**10 — PET NVT**: Driving the metatrain PET graph-transformer potential +through NVT dynamics; loading a ``pet-mad`` checkpoint via +``PETWrapper.from_checkpoint`` with automatic neighbor-list wiring; LJ +fallback for CI. diff --git a/nvalchemi/_optional.py b/nvalchemi/_optional.py index 583ea92c..62ab592f 100644 --- a/nvalchemi/_optional.py +++ b/nvalchemi/_optional.py @@ -96,6 +96,7 @@ def needs_pymatgen(): TENSORBOARD = ("tensorboard", "nvalchemi-toolkit[tensorboard]") UMA = ("fairchem.core", "nvalchemi-toolkit[uma]") + PET = ("metatrain", "nvalchemi-toolkit[pet]") def __init__(self, import_name: str, install_target: str) -> None: self.import_name = import_name diff --git a/nvalchemi/models/__init__.py b/nvalchemi/models/__init__.py index a6b3220e..caae8dc5 100644 --- a/nvalchemi/models/__init__.py +++ b/nvalchemi/models/__init__.py @@ -24,6 +24,7 @@ from nvalchemi.models.ewald import EwaldModelWrapper from nvalchemi.models.lj import LennardJonesModelWrapper from nvalchemi.models.mace import MACEWrapper + from nvalchemi.models.pet import PETWrapper from nvalchemi.models.pipeline import ( PipelineGroup, PipelineModelWrapper, @@ -41,6 +42,7 @@ "AIMNet2Wrapper", "MACEWrapper", "UMAWrapper", + "PETWrapper", # Pipeline composition "PipelineModelWrapper", "PipelineStep", @@ -74,6 +76,10 @@ def __getattr__(name: str): from nvalchemi.models.mace import MACEWrapper return MACEWrapper + elif name == "PETWrapper": + from nvalchemi.models.pet import PETWrapper + + return PETWrapper elif name == "PMEModelWrapper": from nvalchemi.models.pme import PMEModelWrapper diff --git a/nvalchemi/models/pet.py b/nvalchemi/models/pet.py new file mode 100644 index 00000000..1f21b971 --- /dev/null +++ b/nvalchemi/models/pet.py @@ -0,0 +1,1129 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. 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. +"""PET (Point Edge Transformer) model wrapper. + +Wraps the pure-torch :class:`metatrain.pet.modules.backend.PETBackend` as a +:class:`~nvalchemi.models.base.BaseModelMixin`-compatible model. ``PETBackend`` +is the structure-preprocessing / featurization / prediction core of the PET +architecture, operating purely on :class:`torch.Tensor` objects (no +``metatomic.torch.System`` / ``metatensor.torch.TensorMap`` at call time), so it +is ``torch.compile``-friendly. + +:class:`PETWrapper` owns a ``PETBackend`` (built from hypers + atomic types) and +adds only the nvalchemi-specific glue: + +* translating a :class:`~nvalchemi.data.Batch` into the concatenated plain + tensors the backend expects (:meth:`PETWrapper.adapt_input`, mirroring + :func:`metatrain.pet.modules.structures.concatenate_structures`); +* driving the three backend building blocks + (:meth:`~metatrain.pet.modules.backend.PETBackend.preprocess`, + :meth:`~metatrain.pet.modules.backend.PETBackend.calculate_features`, + :meth:`~metatrain.pet.modules.backend.PETBackend.predict`); +* gradient / affine-strain wiring for conservative forces and stress; +* the flat composition / scaler buffers decoded from the checkpoint. + +Usage +----- +Fetch a named model directly from the ``lab-cosmo/upet`` HuggingFace +repository (requires the optional ``upet`` dependency; see +https://github.com/lab-cosmo/upet for available names, or list them +programmatically via ``upet.list_upet()``):: + + from nvalchemi.models.pet import PETWrapper + import torch + + model = PETWrapper.from_checkpoint( + device=torch.device("cuda"), + dtype=torch.float32, + ) + # or, pick a specific model/version: + model = PETWrapper.from_checkpoint(model="pet-mad-xs", version="1.5.0") + +Or load a local checkpoint file directly (e.g. ``pet-mad-xs-v1.5.0.ckpt``):: + + model = PETWrapper.from_checkpoint( + checkpoint_path="pet-mad-xs-v1.5.0.ckpt", + device=torch.device("cuda"), + dtype=torch.float32, + ) + +Notes +----- +* Forces and stress are derived from the energy via autograd + (``autograd_outputs = {"forces", "stress"}``). The non-conservative PET + heads are intentionally skipped. +* The upstream composition model and scaler — originally wrapped as + ``metatomic.torch.AtomisticModel`` with serialized ``TensorMap`` buffers + — are decoded once at :meth:`PETWrapper.from_checkpoint` time into two + flat torch buffers (``composition_energy``, ``scale_energy``) so the + forward path has no metatensor dependency. +* Only the ``energy`` output is registered on the backend; the long-range + module is skipped entirely. +""" + +from __future__ import annotations + +import contextlib +import sys +import types +import warnings + +# metatrain.pet.__init__ imports metatrain.pet.trainer, which imports +# metatrain.utils.distributed.slurm, which pulls in `hostlist`. That package +# is a SLURM-only helper that the nvalchemi dev environment explicitly +# disables via the `override-dependencies` block in `pyproject.toml`. When +# it isn't present, stub a minimal module so the import chain resolves and +# the tests can still exercise PET's pure-torch code paths. +if "hostlist" not in sys.modules: + try: + import hostlist # noqa: F401 + except ImportError: + sys.modules["hostlist"] = types.ModuleType("hostlist") + +from pathlib import Path +from typing import TYPE_CHECKING, Any + +import torch +from torch import nn + +from nvalchemi._optional import OptionalDependency +from nvalchemi._typing import ModelOutputs +from nvalchemi.data import AtomicData, Batch +from nvalchemi.models._utils import ( + autograd_forces_and_stresses, + autograd_stresses, + prepare_strain, +) +from nvalchemi.models.base import ( + BaseModelMixin, + ModelConfig, + NeighborConfig, + NeighborListFormat, +) + +if TYPE_CHECKING: + from collections.abc import Sequence + +__all__ = ["PETWrapper"] + + +# --------------------------------------------------------------------------- +# Hyper-parameter normalisation +# --------------------------------------------------------------------------- + + +_REQUIRED_HYPERS: tuple[str, ...] = ( + "cutoff", + "cutoff_function", + "cutoff_width", + "d_pet", + "d_node", + "d_head", + "d_feedforward", + "num_heads", + "num_gnn_layers", + "num_attention_layers", + "normalization", + "activation", + "attention_temperature", + "transformer_type", + "featurizer_type", +) + +# The single-block, scalar ``energy`` output shape passed to +# ``PETBackend.add_output``. The block key ``energy___0`` and shape ``[1]`` are +# what ``metatrain.pet.model.PET._add_output`` derives for a standard scalar +# energy target (a single TensorMap block with key name ``"_"`` / value ``0`` +# and one property). +_ENERGY_OUTPUT_SHAPES: dict[str, list[int]] = {"energy___0": [1]} + + +def _normalize_hypers(hypers: dict[str, Any]) -> dict[str, Any]: + """Validate *hypers* and fill in the keys ``PETBackend`` reads directly. + + Returns a copy with ``num_neighbors_adaptive`` / ``adaptive_cutoff_method`` / + ``cutoff_width_adaptive`` / ``system_conditioning`` defaulted when absent + (checkpoints already carry these after + :meth:`metatrain.pet.PET.upgrade_checkpoint`; hand-built hypers may not). + + Parameters + ---------- + hypers : dict[str, Any] + Hyper-parameter dict pulled from the checkpoint's ``model_hypers`` (or + constructed by hand for tests). + + Returns + ------- + dict[str, Any] + Normalised copy. + + Raises + ------ + ValueError + When a required key is missing. + """ + missing = [key for key in _REQUIRED_HYPERS if key not in hypers] + if missing: + raise ValueError(f"PET hypers are missing required keys: {missing}") + normalized = dict(hypers) + normalized.setdefault("num_neighbors_adaptive", None) + normalized.setdefault("adaptive_cutoff_method", "grid") + normalized.setdefault("cutoff_width_adaptive", 1.0) + normalized.setdefault("system_conditioning", False) + return normalized + + +# --------------------------------------------------------------------------- +# State-dict filtering helpers +# --------------------------------------------------------------------------- + + +_HEAD_PREFIXES: tuple[str, ...] = ( + "node_heads.", + "edge_heads.", + "node_last_layers.", + "edge_last_layers.", +) + + +def _filter_state_dict(raw_sd: dict[str, torch.Tensor]) -> dict[str, torch.Tensor]: + """Filter a (upgraded) PET checkpoint state dict down to the backend. + + The current metatrain checkpoint layout (>= v14) nests the pure-torch core + under a ``backend.`` prefix and keeps the additive composition model, + scaler, long-range featurizer and ``finetune_config`` outside it. This keeps + only the ``backend.*`` keys (with the prefix stripped) and, among those, + only the ``energy`` readout heads/last-layers — dropping any other output + (e.g. ``non_conservative_forces`` / ``non_conservative_stress``) so the + result loads into an energy-only :class:`PETBackend` with ``strict=True``. + + Parameters + ---------- + raw_sd : dict[str, torch.Tensor] + State dict from the upgraded ``wrapped_model_checkpoint``'s + ``model_state_dict``. + + Returns + ------- + dict[str, torch.Tensor] + Filtered state dict, keyed for ``PETBackend.load_state_dict``. + """ + filtered: dict[str, torch.Tensor] = {} + for key, value in raw_sd.items(): + if not key.startswith("backend."): + # Drops additive_models.*, scaler.*, long_range_featurizer.*, + # finetune_config, etc. + continue + name = key[len("backend.") :] + if name.startswith(_HEAD_PREFIXES): + # name == "_heads..<...>"; keep only energy. + output_name = name.split(".")[1] + if output_name != "energy": + continue + filtered[name] = value + return filtered + + +def _decode_tensor_map_values(buffer: torch.Tensor) -> torch.Tensor: + """Decode a serialized ``TensorMap`` buffer to a flat tensor. + + The metatrain composition / scaler modules store their weights as + ``TensorMap`` byte buffers (uint8 tensors). Decoding with + ``metatensor.torch.load_buffer`` returns a :class:`TensorMap`; the + single block's ``values`` tensor carries the actual numeric weights. + + Parameters + ---------- + buffer : torch.Tensor + ``uint8`` tensor holding the serialized TensorMap. + + Returns + ------- + torch.Tensor + The decoded ``values`` tensor from the single block of the + TensorMap. + """ + import metatensor.torch as mts + + tensor_map = mts.load_buffer(buffer) + return tensor_map.block(0).values + + +@contextlib.contextmanager +def _ignore_nonleaf_grad_warning(): + """Silence the benign non-leaf ``.grad`` warning emitted under compile. + + When Dynamo's builder wraps a grad-tracking (non-leaf, ``requires_grad``) + tensor as a graph input, it reads its ``.grad`` and PyTorch emits a harmless + ``UserWarning``. The autograd graph is left intact, so forces via + ``autograd.grad`` still work. Mirrors the helper in + ``metatrain/pet/tests/test_backend.py``. + """ + with warnings.catch_warnings(): + warnings.filterwarnings( + "ignore", + message=".*grad attribute of a Tensor that is not a leaf Tensor.*", + category=UserWarning, + ) + yield + + +# --------------------------------------------------------------------------- +# HuggingFace checkpoint resolution +# --------------------------------------------------------------------------- + +_UPET_HF_REPO_ID = "lab-cosmo/upet" +_UPET_HF_SUBFOLDER = "models" +_UPET_INSTALL_HINT = ( + "Fetching a named PET checkpoint from HuggingFace requires the 'upet' package. " + "Install it with: pip install 'nvalchemi-toolkit[pet]'" +) + + +def _fetch_pet_checkpoint( + model: str | None, + version: str | None, +) -> Path: + """Fetch a PET checkpoint from HuggingFace, and caching it locally + for subsequent runs. + + Parameters + ---------- + model : str | None + Model name (e.g. ``"pet-mad-s"`` or bare ``"pet-mad"``), used when + *checkpoint_path* isn't a local file or doesn't parse as a magic + string. + version : str | None + Model version, or ``"latest"`` / ``None`` for the newest available. + + Returns + ------- + Path + Local path to the checkpoint file. + + Raises + ------ + ImportError + When ``upet`` is not installed. + FileNotFoundError + When *checkpoint_path* is neither an existing local file nor a + parseable ``--v.ckpt`` name. + ValueError + When neither *checkpoint_path* nor *model* is given, or when the + requested model/size/version isn't available on HuggingFace. + """ + try: + from huggingface_hub import hf_hub_download + from upet._models import upet_resolve_model + from upet._version import DEPRECATED_MODELS as _UPET_DEPRECATED_MODELS + except ImportError as e: + raise ImportError(_UPET_INSTALL_HINT) from e + + model_name, size = model.rsplit("-", 1) + + requested_version = ( + None if (version is None or version == "latest") else str(version) + ) + size, version = upet_resolve_model( + model=model_name, + requested_size=size, + requested_version=requested_version, + ) + + upet_model_name = f"{model_name}-{size}-v{version}" + + if upet_model_name in _UPET_DEPRECATED_MODELS: + warn_msg = ( + f"Model {upet_model_name} is deprecated and may not be supported in " + "future versions. Please switch to a newer model for better " + "performance and support." + ) + warnings.warn(warn_msg, category=DeprecationWarning, stacklevel=2) + + model_string = f"{upet_model_name}.ckpt" + + path = hf_hub_download( + repo_id=_UPET_HF_REPO_ID, + filename=model_string, + subfolder=_UPET_HF_SUBFOLDER, + ) + return Path(path) + + +# --------------------------------------------------------------------------- +# Wrapper +# --------------------------------------------------------------------------- + + +@OptionalDependency.PET.require +class PETWrapper(nn.Module, BaseModelMixin): + """:class:`~nvalchemi.models.base.BaseModelMixin` wrapper around PET. + + Builds and owns a :class:`metatrain.pet.modules.backend.PETBackend` (from + *hypers* and *atomic_types*) and drives its three building blocks. Handles: + + * translating a :class:`~nvalchemi.data.Batch` into the concatenated plain + tensors consumed by :meth:`PETBackend.preprocess` + (:meth:`adapt_input`); + * enabling gradients on ``positions`` when autograd outputs are active, and + wiring the affine strain trick + (:func:`~nvalchemi.models._utils.prepare_strain`) for stress; + * applying the flat composition / scaler buffers decoded from the + checkpoint at load time; + * producing :class:`~nvalchemi._typing.ModelOutputs` with ``energy``, + ``forces``, and ``stress``. + + Parameters + ---------- + atomic_types : Sequence[int] + Atomic numbers in species-index order. + hypers : dict[str, Any] + PET hyper-parameters. Must include the keys listed in + :data:`_REQUIRED_HYPERS`. + composition_energy : torch.Tensor + Per-species reference energy, shape ``[num_species]``. Indexed by the + species index (not by atomic number). + scale_energy : torch.Tensor + Scalar (0-dim) tensor used as the global energy scale. + + Attributes + ---------- + backend : PETBackend + Underlying pure-torch PET core. + atomic_types : list[int] + Copy of the atomic-number list used to build the backend. + hypers : dict[str, Any] + Normalised copy of the hyper-parameters. + model_config : ModelConfig + Capability declaration with ``active_outputs`` defaulting to + ``{"energy", "forces", "stress"}``. + """ + + def __init__( + self, + atomic_types: Sequence[int], + hypers: dict[str, Any], + composition_energy: torch.Tensor, + scale_energy: torch.Tensor, + ) -> None: + from metatrain.pet.modules.backend import PETBackend + + super().__init__() + + self.atomic_types = list(atomic_types) + self.hypers = _normalize_hypers(hypers) + # Width of the adaptive-cutoff smooth taper; not cached on ``PETBackend`` + # itself (unlike the other hypers), so the wrapper keeps its own copy + # to pass into ``preprocess`` at call time. + self._cutoff_width_adaptive = float(self.hypers["cutoff_width_adaptive"]) + + # Build the core from hypers + atomic species and register the single + # scalar energy output (heads + last layers). + self.backend = PETBackend(self.hypers, self.atomic_types) + self.backend.add_output("energy", _ENERGY_OUTPUT_SHAPES) + + # Set to True by `from_checkpoint(compile_model=True)`, which compiles + # the three backend methods; controls the Dynamo config patching applied + # around the backend calls in `forward` / `compute_embeddings`. + self._compiled = False + + # Per-species reference energy (shape [num_species]) indexed by species + # index (backend.species_to_species_index lookup), not atomic number. + # Non-persistent: decoded at `from_checkpoint` time from the raw + # metatensor buffer; saved back via `export_model` using the same route. + self.register_buffer( + "composition_energy", composition_energy.clone(), persistent=False + ) + # Scalar global scaler. Kept as a 0-dim tensor for broadcast. + self.register_buffer( + "scale_energy", scale_energy.clone().reshape(()), persistent=False + ) + + self.model_config = ModelConfig( + outputs=frozenset({"energy", "forces", "stress"}), + autograd_outputs=frozenset({"forces", "stress"}), + autograd_inputs=frozenset({"positions"}), + required_inputs=frozenset(), + optional_inputs=frozenset({"cell", "neighbor_list_shifts"}), + supports_pbc=True, + needs_pbc=False, + neighbor_config=NeighborConfig( + cutoff=float(self.hypers["cutoff"]), + format=NeighborListFormat.COO, + half_list=False, + ), + ) + + # ------------------------------------------------------------------ + # BaseModelMixin required properties + # ------------------------------------------------------------------ + + @property + def embedding_shapes(self) -> dict[str, tuple[int, ...]]: + """Return node/graph embedding shapes. + + Embeddings concatenate the per-layer node features with the + cutoff-weighted, neighbor-summed per-layer edge features (see + :meth:`compute_embeddings`), so the dimension is + ``num_readout_layers * (d_node + d_pet)``. + """ + dim = self.backend.num_readout_layers * ( + self.backend.d_node + self.backend.d_pet + ) + return {"node_embeddings": (dim,), "graph_embeddings": (dim,)} + + # ------------------------------------------------------------------ + # Convenience properties + # ------------------------------------------------------------------ + + @property + def cutoff(self) -> float: + """Interaction cutoff in Angstroms.""" + return float(self.backend.cutoff) + + @property + def _model_dtype(self) -> torch.dtype: + """Return the current dtype of the backend's parameters. + + Read live from ``parameters()`` so it stays correct after + ``.to(dtype=...)`` calls. + """ + try: + return next(self.backend.parameters()).dtype + except StopIteration: + return torch.float32 + + # ------------------------------------------------------------------ + # Backend invocation (compile-aware) + # ------------------------------------------------------------------ + + @contextlib.contextmanager + def _backend_ctx(self): + """Context for calling the backend building blocks. + + When the backend methods have been ``torch.compile``-d + (``self._compiled``), the Dynamo flags required to capture the + data-dependent ``max_edges_per_node`` size must be active while the + compiled functions trace (lazily, on first call) — so the calls + themselves run inside the ``config.patch`` context, matching + ``metatrain/pet/tests/test_backend.py``. The benign non-leaf ``.grad`` + warning Dynamo emits while building grad-tracking graph inputs (when + autograd forces / stress are active) is silenced. In eager mode this is + a no-op. + """ + if self._compiled: + with ( + torch._dynamo.config.patch( + capture_scalar_outputs=True, + capture_dynamic_output_shape_ops=True, + specialize_int=True, + ), + _ignore_nonleaf_grad_warning(), + ): + yield + else: + yield + + # ------------------------------------------------------------------ + # Input preparation + # ------------------------------------------------------------------ + + def adapt_input( + self, data: AtomicData | Batch, **_kwargs: Any + ) -> dict[str, torch.Tensor]: + """Translate a :class:`~nvalchemi.data.Batch` into backend input tensors. + + Produces the concatenated, plain-tensor structure representation that + :meth:`PETBackend.preprocess` consumes — the nvalchemi analogue of + :func:`metatrain.pet.modules.structures.concatenate_structures`. All the + edge manipulation (NEF reshaping, adaptive cutoffs, reversed-neighbor + indexing) then happens inside the backend. + + Handles ``AtomicData -> Batch`` promotion and gradient enabling on + ``positions`` when an autograd output is active. Strain handling (for + stress) is done by :meth:`forward` **before** calling this method, so + that the scaled positions/cell flow through the full featurisation. + + Parameters + ---------- + data : AtomicData | Batch + Input data. ``AtomicData`` inputs are promoted to a single-graph + ``Batch``. + **_kwargs : Any + Ignored — kept for interface compatibility. + + Returns + ------- + dict[str, torch.Tensor] + Keyword arguments for :meth:`PETBackend.preprocess`: + ``positions``, ``centers``, ``neighbors``, ``species``, ``cells``, + ``cell_shifts``, ``system_indices``. + """ + if isinstance(data, AtomicData): + data = Batch.from_data_list([data]) + + dtype = self._model_dtype + + # Cast positions to model dtype and enable gradients when needed. + # Clone so the original batch tensor is never mutated in-place. + positions = data.positions.to(dtype=dtype) + if self.model_config.autograd_outputs & self.model_config.active_outputs: + positions = positions.clone() + positions.requires_grad_(True) + # Store the prepared positions back on the batch so that downstream + # autograd calls in `forward` can reach them via `data.positions`. + data["positions"] = positions + + return self._collect_backend_inputs(data, dtype) + + def _collect_backend_inputs( + self, data: Batch, dtype: torch.dtype + ) -> dict[str, torch.Tensor]: + """Gather the :meth:`PETBackend.preprocess` kwargs from a prepared batch. + + Reads ``data.positions`` as-is (the caller is responsible for any dtype + cast / gradient setup) and assembles the remaining structure tensors, + mirroring :func:`metatrain.pet.modules.structures.concatenate_structures`. + + Parameters + ---------- + data : Batch + Batch whose ``positions`` are already prepared. + dtype : torch.dtype + Model dtype, used to cast ``cells``. + + Returns + ------- + dict[str, torch.Tensor] + Keyword arguments for :meth:`PETBackend.preprocess`. + """ + positions = data.positions + device = positions.device + num_graphs = int(data.num_graphs) + + centers = data.neighbor_list[:, 0].long() + neighbors = data.neighbor_list[:, 1].long() + species = data.atomic_numbers.long() + system_indices = data.batch_idx.long() + + # Integer PBC shifts [E, 3] — zero for non-PBC systems. + raw_shifts = getattr(data, "neighbor_list_shifts", None) + if raw_shifts is None: + cell_shifts = torch.zeros( + centers.shape[0], 3, dtype=torch.long, device=device + ) + else: + cell_shifts = raw_shifts.to(dtype=torch.long, device=device) + + # Cell [B, 3, 3] — identity for non-PBC systems. + raw_cell = getattr(data, "cell", None) + if raw_cell is None: + cells = ( + torch.eye(3, dtype=dtype, device=device) + .unsqueeze(0) + .expand(num_graphs, -1, -1) + .contiguous() + ) + else: + cells = raw_cell.to(dtype=dtype, device=device) + + return { + "positions": positions, + "centers": centers, + "neighbors": neighbors, + "species": species, + "cells": cells, + "cell_shifts": cell_shifts, + "system_indices": system_indices, + } + + def adapt_output( + self, + raw_output: dict[str, torch.Tensor | None], + data: AtomicData | Batch, + ) -> ModelOutputs: + """Map raw PET outputs to the standard :class:`ModelOutputs` layout. + + Parameters + ---------- + raw_output : dict[str, torch.Tensor | None] + Dict with optional ``energy``, ``forces``, ``stress`` tensors. + data : AtomicData | Batch + Original input batch (unused here but forwarded to + :meth:`BaseModelMixin.adapt_output`). + + Returns + ------- + ModelOutputs + Ordered dict keyed by the wrapper's active outputs. + """ + mapped: dict[str, torch.Tensor] = {} + energy = raw_output.get("energy") + if energy is not None: + mapped["energy"] = energy.unsqueeze(-1) if energy.ndim == 1 else energy + if raw_output.get("forces") is not None: + mapped["forces"] = raw_output["forces"] + if raw_output.get("stress") is not None: + mapped["stress"] = raw_output["stress"] + return super().adapt_output(mapped, data) + + # ------------------------------------------------------------------ + # Forward pass + # ------------------------------------------------------------------ + + def forward(self, data: AtomicData | Batch, **kwargs: Any) -> ModelOutputs: + """Run the PET backend and return energy / forces / stress. + + The energy comes from + :meth:`PETBackend.preprocess` -> + :meth:`PETBackend.calculate_features` -> + :meth:`PETBackend.predict` (the latter already sums the node and + cutoff-weighted edge contributions over all readout layers). The flat + scaler / composition buffers are then applied. + + Conservative forces are derived via :func:`torch.autograd.grad` of the + total energy with respect to positions. Stresses use the affine-strain + trick from :func:`~nvalchemi.models._utils.prepare_strain` / + :func:`~nvalchemi.models._utils.autograd_stresses`. + + Parameters + ---------- + data : AtomicData | Batch + Input batch. + **kwargs : Any + Forwarded to :meth:`adapt_input`. + + Returns + ------- + ModelOutputs + Dict with the active output keys populated. + """ + if isinstance(data, AtomicData): + data = Batch.from_data_list([data]) + + active = self.model_config.active_outputs & self.model_config.outputs + compute_forces = "forces" in active + compute_stresses = "stress" in active + + # Set up the affine strain BEFORE adapt_input so the scaled positions + # and cell flow through the full featurisation. + displacement: torch.Tensor | None = None + orig_positions: torch.Tensor | None = None + orig_cell: torch.Tensor | None = None + if compute_stresses and getattr(data, "cell", None) is not None: + scaled_pos, scaled_cell, displacement = prepare_strain( + data.positions.to(self._model_dtype), + data.cell.to(self._model_dtype), + data.batch_idx, + ) + orig_positions = data.positions + orig_cell = data.cell + data["positions"] = scaled_pos + data["cell"] = scaled_cell + + inputs = self.adapt_input(data, **kwargs) + positions = data.positions # updated in-place by adapt_input + + with self._backend_ctx(): + batch_data = self.backend.preprocess( + inputs["positions"], + inputs["centers"], + inputs["neighbors"], + inputs["species"], + inputs["cells"], + inputs["cell_shifts"], + inputs["system_indices"], + self._cutoff_width_adaptive, + ) + node_features_list, edge_features_list = self.backend.calculate_features( + batch_data + ) + atomic_predictions, _, _ = self.backend.predict( + node_features_list, + edge_features_list, + batch_data, + inputs["cells"], + inputs["system_indices"], + ["energy"], + ) + + per_atom = atomic_predictions["energy"][0] # [N, 1] + species_idx = self.backend.species_to_species_index[inputs["species"]] + # Scaler first, then composition (matches upstream PET ordering). + per_atom = self.scale_energy * per_atom + per_atom = per_atom + self.composition_energy[species_idx].unsqueeze(-1) + + num_graphs = int(data.num_graphs) + energy = torch.zeros( + num_graphs, 1, dtype=per_atom.dtype, device=per_atom.device + ) + energy.scatter_add_(0, inputs["system_indices"].unsqueeze(-1), per_atom) + + result: dict[str, torch.Tensor] = {"energy": energy} + + need_stress = ( + compute_stresses and displacement is not None and orig_cell is not None + ) + if compute_forces and need_stress: + # A single backward for both forces and stress. Two separate + # ``autograd.grad`` calls would run backward twice over the same + # graph, which clashes with ``torch.compile``'s donated-buffer + # optimization (it requires create_graph=retain_graph=False). + forces, stress = autograd_forces_and_stresses( + energy, + positions, + displacement, + orig_cell, + num_graphs, + ) + result["forces"] = forces + result["stress"] = stress + elif compute_forces: + (grad,) = torch.autograd.grad(energy.sum(), positions) + result["forces"] = -grad + elif need_stress: + result["stress"] = autograd_stresses( + energy, + displacement, + orig_cell, + num_graphs, + ) + + # Restore the batch's original positions/cell if strain was applied, + # so the caller sees no mutation from the stress trick. + if orig_positions is not None and orig_cell is not None: + data["positions"] = orig_positions + data["cell"] = orig_cell + + return self.adapt_output(result, data) + + # ------------------------------------------------------------------ + # Embeddings + # ------------------------------------------------------------------ + + def compute_embeddings( + self, data: AtomicData | Batch, **kwargs: Any + ) -> AtomicData | Batch: + """Compute node and graph embeddings without autograd. + + The node embedding is the concatenation of the per-layer node features + with the cutoff-weighted, neighbor-summed per-layer edge features — + matching ``metatrain.pet.model.PET._get_output_features``:: + + node = cat(node_features_list, dim=1) + edge = (cat(edge_features_list, dim=2) * cutoff_factors).sum(neighbors) + feats = cat([node, edge], dim=1) + + Writes ``node_embeddings`` + (``[N, num_readout_layers*(d_node+d_pet)]``) and ``graph_embeddings`` + (``[B, ...]``, sum-pooled over atoms) into *data* and returns it. Does + **not** mutate ``model_config``. + + Parameters + ---------- + data : AtomicData | Batch + Input data. + **kwargs : Any + Forwarded to :meth:`adapt_input`. + + Returns + ------- + AtomicData | Batch + The same batch with ``node_embeddings`` and ``graph_embeddings`` + attached. + """ + if isinstance(data, AtomicData): + data = Batch.from_data_list([data]) + + with torch.no_grad(): + # Build inputs without enabling gradients on positions (embeddings + # are autograd-free), so adapt_input's grad toggle is bypassed. + data["positions"] = data.positions.to(dtype=self._model_dtype) + inputs = self._collect_backend_inputs(data, self._model_dtype) + with self._backend_ctx(): + batch_data = self.backend.preprocess( + inputs["positions"], + inputs["centers"], + inputs["neighbors"], + inputs["species"], + inputs["cells"], + inputs["cell_shifts"], + inputs["system_indices"], + self._cutoff_width_adaptive, + ) + node_features_list, edge_features_list = ( + self.backend.calculate_features(batch_data) + ) + + node_features = torch.cat(node_features_list, dim=1) + edge_features = torch.cat(edge_features_list, dim=2) + edge_features = ( + edge_features * batch_data["cutoff_factors"][:, :, None] + ).sum(dim=1) + node_feats = torch.cat([node_features, edge_features], dim=1) + + # Write node embeddings directly to the atoms group to avoid the + # default "system" routing used by `setattr` on unknown keys. + atoms_group = data._atoms_group + if atoms_group is not None: + atoms_group["node_embeddings"] = node_feats + else: + data.node_embeddings = node_feats + + hidden_dim = node_feats.shape[-1] + graph_embeddings = torch.zeros( + data.num_graphs, + hidden_dim, + device=node_feats.device, + dtype=node_feats.dtype, + ) + graph_embeddings.scatter_add_( + 0, + data.batch_idx.long().unsqueeze(-1).expand(-1, hidden_dim), + node_feats, + ) + data.graph_embeddings = graph_embeddings + return data + + # ------------------------------------------------------------------ + # Checkpoint loading + # ------------------------------------------------------------------ + + @classmethod + def from_checkpoint( + cls, + model: str | None = None, + version: str | None = "latest", + dtype: torch.dtype | None = None, + checkpoint_path: Path | str | None = None, + device: torch.device = torch.device("cpu"), + compile_model: bool = False, + **compile_kwargs: Any, + ) -> "PETWrapper": + """Load a PET checkpoint from disk or HuggingFace and return a wrapped instance. + + Expects a model and, optionally, a version to fetch the model checkpoint and + expose it as a :class:`PETWrapper` instance. The list of available models and + their versions can be found in the `upet repository `_. + The full list of models and versions can be listed programmatically via ``upet.list_upet()``. + + Alternatively, a local checkpoint file can be provided via the *checkpoint_path* argument. + In this case the *model* and *version* arguments are ignored. + + :class:`PETWrapper` supports the compilation of the PET backend building blocks + (``preprocess`` / ``calculate_features`` / ``predict``) via ``torch.compile``, which is + controlled by the *compile_model* argument. The ``fullgraph`` compilation mode can be + enabled by passing ``fullgraph=True`` as an argument. Note that models using the + ``'grid'`` adaptive-cutoff method (for example, ``pet-mad`` v1.5.0) cannot be compiled + because of a break in the autograd backward. + + + + Parameters + ---------- + model : str + Model name to fetch from HuggingFace, either a combined + ``-`` name (e.g. ``"pet-mad-s"``, ``"pet-mad-xs"``) + or a bare base name (e.g. ``"pet-mad"``, resolved to ``upet``'s + own default size preference). Used when *checkpoint_path* is + ``None``; ignored otherwise. + version : str | None, optional + Model version to fetch, or ``"latest"`` / ``None`` for the newest + available. Ignored when *checkpoint_path* is given. + Defaults to ``"latest"``. + dtype : torch.dtype | None, optional + If set, cast the backend and composition/scaler buffers to this + dtype before returning. + checkpoint_path : Path | str | None + Path to a local PET checkpoint file (``.ckpt`` / ``.pt``), or a + named model such as ``"pet-mad-xs-v1.6.0"`` to fetch from + HuggingFace. If ``None``, *model* must be given instead. + device : torch.device, optional + Target device. Defaults to CPU. + compile_model : bool, optional + ``torch.compile`` the three backend building blocks + (``preprocess`` / ``calculate_features`` / ``predict``). Sets eval + mode and freezes parameters; the model is **inference-only** after + this step. Conservative forces and stress via autograd still work + through the compiled graph (the combined single-backward in + :meth:`forward` keeps it compatible with ``torch.compile``'s + donated-buffer optimization). Note that the backend's + data-dependent ``max_edges_per_node`` size requires + ``capture_scalar_outputs`` / ``capture_dynamic_output_shape_ops`` to + be set, which :meth:`forward` applies via ``torch._dynamo.config`` + patching at call time (see :meth:`_backend_ctx`). + + Models using the ``'grid'`` adaptive-cutoff method (for example, + ``pet-mad v1.5.0``) cannot be compiled at all — autograd backward + through the compiled grid cutoff aborts — so ``compile_model=True`` + for such a model raises ``ValueError``. Use a ``'solver'``-method + checkpoint (pet-mad >= v1.6.0) to compile, or run the grid model eagerly. + **compile_kwargs + Forwarded verbatim to each ``torch.compile`` call, so the caller + chooses the compilation options (e.g. ``fullgraph=True``, + ``mode=...``, ``dynamic=...``). + + Returns + ------- + PETWrapper + + Raises + ------ + OptionalDependencyError + When :mod:`metatrain` is not installed. + ImportError + When fetching a named model and ``upet`` is not installed. + ValueError + When ``compile_model`` is requested for a ``'grid'`` adaptive-cutoff + model (see ``compile_model`` above), or when neither + *checkpoint_path* nor *model* is given. + FileNotFoundError + When *checkpoint_path* is neither an existing local file nor a + parseable named model. + """ + if not OptionalDependency.PET.is_available(): + OptionalDependency.PET._raise_error(f"{cls.__qualname__}.from_checkpoint") + # Make sure metatomic's custom torch ops are registered before + # torch.load, otherwise the ScriptObject metadata unpickling fails. + import metatomic.torch # noqa: F401 + from metatrain.pet import PET + + if checkpoint_path is not None and Path(checkpoint_path).is_file(): + checkpoint_path = str(checkpoint_path) + elif model is not None and version is not None: + checkpoint_path = _fetch_pet_checkpoint(model, version) + else: + raise ValueError( + "PETWrapper.from_checkpoint requires either `checkpoint_path` (a " + "local file path, or `model` (e.g. 'pet-mad-s', optionally with `version`). " + ) + + raw = torch.load(str(checkpoint_path), weights_only=False, map_location=device) + + if isinstance(raw, dict) and "wrapped_model_checkpoint" in raw: + raw = raw["wrapped_model_checkpoint"] + + # Bring an old checkpoint up to the current model version (adds the + # `backend.` prefix and any missing hypers). Mutates `raw` in place. + raw = PET.upgrade_checkpoint(raw) + + model_data = raw["model_data"] + hypers = dict(model_data["model_hypers"]) + atomic_types = list(model_data["dataset_info"].atomic_types) + # Prefer the best weights (``best_model_state_dict``); fall back to the last + # epoch (``model_state_dict``). Exported / best-only checkpoints + # (e.g. pet-mad-xs-v1.6.0) carry only the former. + raw_sd = raw.get("best_model_state_dict") or raw.get("model_state_dict") + if raw_sd is None: + raise KeyError( + "Checkpoint has neither 'best_model_state_dict' nor 'model_state_dict' " + "keys. Checkpoint may be corrupted or not a PET checkpoint." + ) + + composition_values = _decode_tensor_map_values( + raw_sd["additive_models.0.energy_composition_buffer"] + ) # [num_species, 1] + composition_energy = composition_values.squeeze(-1).clone() + scale_values = _decode_tensor_map_values( + raw_sd["scaler.energy_scaler_buffer"] + ) # [1, 1] + scale_energy = scale_values.reshape(()).clone() + + backend_sd = _filter_state_dict(raw_sd) + wrapper = cls( + atomic_types=atomic_types, + hypers=hypers, + composition_energy=composition_energy, + scale_energy=scale_energy, + ) + wrapper.backend.load_state_dict(backend_sd, strict=True) + + if dtype is not None: + wrapper.backend = wrapper.backend.to(dtype=dtype) + wrapper.composition_energy = wrapper.composition_energy.to(dtype=dtype) + wrapper.scale_energy = wrapper.scale_energy.to(dtype=dtype) + + wrapper = wrapper.to(device) + + if compile_model: + wrapper.eval() + for param in wrapper.parameters(): + param.requires_grad = False + # The 'grid' adaptive-cutoff method (what pet-mad <= v1.5.0 was + # trained with) cannot be safely compiled: autograd backward through + # the compiled grid cutoff aborts at the C++ level (with + # ``fullgraph=True`` always, and intermittently even without it). + # The 'solver' method (pet-mad >= v1.6.0) is fully compatible, + # including ``fullgraph=True``. Refuse to compile a grid model so the + # user gets a clear error instead of a hard crash at the first + # backward. + uses_grid_adaptive = ( + wrapper.backend.num_neighbors_adaptive is not None + and str(wrapper.backend.adaptive_cutoff_method).lower() == "grid" + ) + if uses_grid_adaptive: + raise ValueError( + "compile_model=True is not supported for PET models using " + "the 'grid' adaptive-cutoff method (e.g. pet-mad-xs " + "<= v1.5.0): autograd backward through the compiled grid " + "cutoff aborts. Load a checkpoint trained with the 'solver' " + "method (e.g. pet-mad-xs >= v1.6.0) to use torch.compile, or " + "run the grid model in eager mode (compile_model=False)." + ) + wrapper.backend.preprocess = torch.compile( + wrapper.backend.preprocess, **compile_kwargs + ) + wrapper.backend.calculate_features = torch.compile( + wrapper.backend.calculate_features, **compile_kwargs + ) + wrapper.backend.predict = torch.compile( + wrapper.backend.predict, **compile_kwargs + ) + wrapper._compiled = True + return wrapper + + # ------------------------------------------------------------------ + # Export + # ------------------------------------------------------------------ + + def export_model(self, path: Path, as_state_dict: bool = False) -> None: + """Serialize the wrapper to disk in a pure-torch layout. + + Writes a plain dict containing the backend ``state_dict``, the + hyper-parameters, the atomic-type list, and the composition/scaler + buffers. The output is **not** a metatrain / metatomic checkpoint — it + is a self-contained snapshot that can be reloaded by constructing + ``PETWrapper(atomic_types, hypers, ...)`` and calling + ``load_state_dict`` on its backend. + + Parameters + ---------- + path : Path + Output path. + as_state_dict : bool, optional + If ``True``, save only the backend's ``state_dict``. Defaults to + ``False`` (saves the full snapshot). + """ + if as_state_dict: + torch.save(self.backend.state_dict(), path) + else: + snapshot = { + "backend_state_dict": self.backend.state_dict(), + "hypers": self.hypers, + "atomic_types": self.atomic_types, + "composition_energy": self.composition_energy.detach().cpu(), + "scale_energy": self.scale_energy.detach().cpu(), + } + torch.save(snapshot, path) diff --git a/pyproject.toml b/pyproject.toml index e4a4e90f..3d1303fa 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -95,6 +95,12 @@ tensorboard = [ uma = [ "fairchem-core>=2.0.0", ] +pet = [ + # Sourced from upstream main until released on PyPI — see + # `[tool.uv.sources].metatrain`. + "metatrain", + "upet", +] [tool.hatch.build.targets.wheel] packages = ["nvalchemi"] @@ -142,6 +148,11 @@ dependency-metadata = [ [tool.uv.sources] nvalchemi-toolkit-ops = { git = "https://github.com/NVIDIA/nvalchemi-toolkit-ops.git", rev = "0.4.0-rc" } +# Temporary: PETWrapper needs the pure-torch `PETBackend`, which was merged +# into upstream metatrain's `main` in metatensor/metatrain#1180. Drop this +# source pin (keep the plain `metatrain>=...` requirement in the `pet` +# extra) once those changes land in a tagged metatrain release on PyPI. +metatrain = { git = "https://github.com/metatensor/metatrain.git", rev = "main" } torch = [ { index = "pytorch-cu126", extra = "cu12", marker = "sys_platform != 'darwin'" }, { index = "pytorch-cu130", extra = "cu13", marker = "sys_platform != 'darwin'" }, @@ -245,6 +256,13 @@ markers = [ "slow: marks tests as slow (deselect with: -m 'not slow')", "cli: marks tests which run CLIs" ] +# metatensor.torch decorates dozens of ops with @torch.jit.script at import +# time, producing hundreds of duplicate DeprecationWarnings. Suppress them +# here so test output stays readable; the issue is upstream. +filterwarnings = [ + "ignore:`torch.jit.script` is deprecated.*:DeprecationWarning", + "ignore:`torch.jit.script_method` is deprecated.*:DeprecationWarning", +] asyncio_mode = "auto" # ==== Coverage.py configuration (ran using pytest-cov) ==== diff --git a/test/models/test_pet.py b/test/models/test_pet.py new file mode 100644 index 00000000..79230267 --- /dev/null +++ b/test/models/test_pet.py @@ -0,0 +1,1012 @@ +# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. 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. +"""Tests for :class:`~nvalchemi.models.pet.PETWrapper`. + +All tests in this module require :mod:`metatrain` (and the ``metatomic`` / +``metatensor`` packages that ride along with it) and are automatically +skipped when they are not installed. Install with:: + + pip install 'nvalchemi-toolkit[pet]' +""" + +from __future__ import annotations + +import sys +import types + +import pytest +import torch + +# metatrain.pet.__init__ imports metatrain.pet.trainer, which imports +# metatrain.utils.distributed.slurm, which pulls in `hostlist`. That package +# is a SLURM-only helper that the nvalchemi dev environment explicitly +# disables via the `override-dependencies` block in `pyproject.toml`. When +# it isn't present, stub a minimal module so the import chain resolves and +# the tests can still exercise PET's pure-torch code paths. +if "hostlist" not in sys.modules: + try: + import hostlist # noqa: F401 + except ImportError: + sys.modules["hostlist"] = types.ModuleType("hostlist") + +# Skip the entire module when metatrain is not installed. +pytest.importorskip("metatrain", reason="metatrain not installed; skipping PET tests") + +from metatrain.pet.modules.backend import PETBackend # noqa: E402 + +from nvalchemi.data import AtomicData, Batch # noqa: E402 +from nvalchemi.models.base import NeighborListFormat # noqa: E402 +from nvalchemi.models.pet import PETWrapper # noqa: E402 +from nvalchemi.neighbors import compute_neighbors + +# --------------------------------------------------------------------------- +# Shared constants +# --------------------------------------------------------------------------- + +_ATOMIC_NUMBERS = [1, 6, 8] # H, C, O — keeps species_to_species_index small +_CUTOFF = 5.0 +_D_NODE = 16 +_D_PET = 8 + + +def _tiny_hypers() -> dict: + """Return a minimal PET hyper-parameter dict for fast unit tests.""" + return { + "cutoff": _CUTOFF, + "cutoff_width": 0.5, + "cutoff_function": "Bump", + "d_pet": _D_PET, + "d_node": _D_NODE, + "d_head": 8, + "d_feedforward": 8, + "num_heads": 2, + "num_gnn_layers": 1, + "num_attention_layers": 1, + "normalization": "RMSNorm", + "activation": "SwiGLU", + "attention_temperature": 1.0, + "transformer_type": "PreLN", + "featurizer_type": "feedforward", + "num_neighbors_adaptive": None, + "adaptive_cutoff_method": "grid", + "system_conditioning": False, + } + + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- + + +def _make_water(device: str = "cpu") -> AtomicData: + """Single H2O molecule with a pre-computed full edge list (no PBC).""" + positions = torch.tensor( + [[0.0, 0.0, 0.0], [0.96, 0.0, 0.0], [0.0, 0.96, 0.0]], + dtype=torch.float32, + device=device, + ) + numbers = torch.tensor([8, 1, 1], dtype=torch.long, device=device) + neighbor_list = torch.tensor( + [[0, 1], [1, 0], [0, 2], [2, 0], [1, 2], [2, 1]], + dtype=torch.long, + device=device, + ) + neighbor_list_shifts = torch.zeros( + neighbor_list.shape[0], 3, dtype=torch.long, device=device + ) + return AtomicData( + positions=positions, + atomic_numbers=numbers, + neighbor_list=neighbor_list, + neighbor_list_shifts=neighbor_list_shifts, + ) + + +def _make_two_atoms(device: str = "cpu") -> AtomicData: + """Two H atoms at (0, 0, 0) and (1.1, 0, 0) with a symmetric edge pair.""" + positions = torch.tensor( + [[0.0, 0.0, 0.0], [1.1, 0.0, 0.0]], dtype=torch.float32, device=device + ) + numbers = torch.tensor([1, 1], dtype=torch.long, device=device) + neighbor_list = torch.tensor([[0, 1], [1, 0]], dtype=torch.long, device=device) + neighbor_list_shifts = torch.zeros(2, 3, dtype=torch.long, device=device) + return AtomicData( + positions=positions, + atomic_numbers=numbers, + neighbor_list=neighbor_list, + neighbor_list_shifts=neighbor_list_shifts, + ) + + +def _make_pbc_water(device: str = "cpu") -> AtomicData: + """H2O in a 10 Å cubic periodic box.""" + positions = torch.tensor( + [[0.0, 0.0, 0.0], [0.96, 0.0, 0.0], [0.0, 0.96, 0.0]], + dtype=torch.float32, + device=device, + ) + numbers = torch.tensor([8, 1, 1], dtype=torch.long, device=device) + neighbor_list = torch.tensor( + [[0, 1], [1, 0], [0, 2], [2, 0], [1, 2], [2, 1]], + dtype=torch.long, + device=device, + ) + cell = (torch.eye(3, dtype=torch.float32, device=device) * 10.0).unsqueeze(0) + neighbor_list_shifts = torch.zeros(6, 3, dtype=torch.long, device=device) + pbc = torch.tensor([[True, True, True]], device=device) + return AtomicData( + positions=positions, + atomic_numbers=numbers, + neighbor_list=neighbor_list, + cell=cell, + neighbor_list_shifts=neighbor_list_shifts, + pbc=pbc, + ) + + +# --------------------------------------------------------------------------- +# Fixtures +# --------------------------------------------------------------------------- + + +@pytest.fixture +def wrapper() -> PETWrapper: + """PETWrapper with zero composition and unit scaler.""" + torch.manual_seed(0) + composition = torch.zeros(len(_ATOMIC_NUMBERS), dtype=torch.float32) + scale = torch.tensor(1.0, dtype=torch.float32) + return PETWrapper( + atomic_types=_ATOMIC_NUMBERS, + hypers=_tiny_hypers(), + composition_energy=composition, + scale_energy=scale, + ) + + +@pytest.fixture +def single_batch() -> Batch: + return Batch.from_data_list([_make_water()]) + + +@pytest.fixture +def multi_batch() -> Batch: + """Two H2O molecules as a batched system (B=2, N=6).""" + return Batch.from_data_list([_make_water(), _make_water()]) + + +@pytest.fixture +def pbc_batch() -> Batch: + return Batch.from_data_list([_make_pbc_water()]) + + +# --------------------------------------------------------------------------- +# Instantiation +# --------------------------------------------------------------------------- + + +class TestInstantiation: + def test_wrapper_builds_backend(self, wrapper): + # The wrapper builds and owns a metatrain PETBackend from hypers. + assert isinstance(wrapper.backend, PETBackend) + assert wrapper.backend.d_node == _D_NODE + assert wrapper.backend.d_pet == _D_PET + # The single scalar energy output is registered. + assert "energy" in wrapper.backend.node_last_layers + + def test_default_model_config(self, wrapper): + assert "forces" in wrapper.model_config.active_outputs + assert "stress" in wrapper.model_config.active_outputs + + def test_composition_buffer_shape(self, wrapper): + assert wrapper.composition_energy.shape == (len(_ATOMIC_NUMBERS),) + + def test_scale_buffer_scalar(self, wrapper): + assert wrapper.scale_energy.shape == () + + def test_buffers_not_in_state_dict(self, wrapper): + # composition_energy / scale_energy are non-persistent. + sd_keys = wrapper.state_dict().keys() + assert not any(k.endswith("composition_energy") for k in sd_keys) + assert not any(k.endswith("scale_energy") for k in sd_keys) + + def test_validate_hypers_rejects_missing(self): + bad = _tiny_hypers() + del bad["cutoff"] + with pytest.raises(ValueError, match="missing required keys"): + PETWrapper( + atomic_types=_ATOMIC_NUMBERS, + hypers=bad, + composition_energy=torch.zeros(len(_ATOMIC_NUMBERS)), + scale_energy=torch.tensor(1.0), + ) + + def test_residual_featurizer_builds(self): + # Both 'feedforward' and 'residual' featurizers are now supported. + torch.manual_seed(0) + hypers = _tiny_hypers() + hypers["featurizer_type"] = "residual" + w = PETWrapper( + atomic_types=_ATOMIC_NUMBERS, + hypers=hypers, + composition_energy=torch.zeros(len(_ATOMIC_NUMBERS)), + scale_energy=torch.tensor(1.0), + ) + # Residual featurization keeps one readout layer per GNN layer. + assert w.backend.num_readout_layers == hypers["num_gnn_layers"] + + def test_import_error_without_metatrain(self, monkeypatch): + from nvalchemi._optional import OptionalDependency + + monkeypatch.setattr(OptionalDependency.PET, "_available", False) + with pytest.raises(ImportError): + PETWrapper( + atomic_types=_ATOMIC_NUMBERS, + hypers=_tiny_hypers(), + composition_energy=torch.zeros(len(_ATOMIC_NUMBERS)), + scale_energy=torch.tensor(1.0), + ) + + +# --------------------------------------------------------------------------- +# ModelConfig capability checks +# --------------------------------------------------------------------------- + + +class TestModelConfigCapabilities: + def test_forces_via_autograd(self, wrapper): + assert "forces" in wrapper.model_config.autograd_outputs + + def test_stress_via_autograd(self, wrapper): + assert "stress" in wrapper.model_config.autograd_outputs + + def test_outputs_include_energies_forces_stresses(self, wrapper): + cfg = wrapper.model_config + assert "energy" in cfg.outputs + assert "forces" in cfg.outputs + assert "stress" in cfg.outputs + + def test_autograd_inputs(self, wrapper): + assert "positions" in wrapper.model_config.autograd_inputs + + def test_supports_pbc(self, wrapper): + assert wrapper.model_config.supports_pbc is True + + def test_embedding_shapes_available(self, wrapper): + shapes = wrapper.embedding_shapes + assert "node_embeddings" in shapes + assert "graph_embeddings" in shapes + + def test_neighbor_config_coo(self, wrapper): + nc = wrapper.model_config.neighbor_config + assert nc is not None + assert nc.format == NeighborListFormat.COO + assert nc.cutoff == pytest.approx(_CUTOFF) + assert nc.half_list is False + + def test_needs_pbc_false(self, wrapper): + assert wrapper.model_config.needs_pbc is False + + +# --------------------------------------------------------------------------- +# Properties +# --------------------------------------------------------------------------- + + +class TestProperties: + def test_cutoff(self, wrapper): + assert wrapper.cutoff == pytest.approx(_CUTOFF) + assert isinstance(wrapper.cutoff, float) + + def test_embedding_shapes(self, wrapper): + # Embeddings concat node + cutoff-weighted edge features (one readout + # layer for the feedforward featurizer): d_node + d_pet. + shapes = wrapper.embedding_shapes + assert shapes["node_embeddings"] == (_D_NODE + _D_PET,) + assert shapes["graph_embeddings"] == (_D_NODE + _D_PET,) + + def test_model_dtype(self, wrapper): + assert wrapper._model_dtype == torch.float32 + + +# --------------------------------------------------------------------------- +# adapt_input +# --------------------------------------------------------------------------- + + +class TestAdaptInput: + def test_required_keys_present(self, wrapper, single_batch): + # adapt_input now returns the concatenated, plain-tensor structure + # representation consumed by PETBackend.preprocess. + inp = wrapper.adapt_input(single_batch) + for key in ( + "positions", + "centers", + "neighbors", + "species", + "cells", + "cell_shifts", + "system_indices", + ): + assert key in inp, f"Missing key: {key}" + + def test_species_are_raw_atomic_numbers(self, wrapper, single_batch): + # H2O = [8, 1, 1]; the species->index map is applied inside the backend, + # so adapt_input passes raw atomic numbers through. + inp = wrapper.adapt_input(single_batch) + assert inp["species"].tolist() == [8, 1, 1] + + def test_centers_neighbors_from_neighbor_list(self, wrapper, single_batch): + inp = wrapper.adapt_input(single_batch) + assert inp["centers"].tolist() == [0, 1, 0, 2, 1, 2] + assert inp["neighbors"].tolist() == [1, 0, 2, 0, 2, 1] + + def test_cells_shape(self, wrapper, single_batch): + # Non-PBC batch → identity cell [B, 3, 3]. + inp = wrapper.adapt_input(single_batch) + assert inp["cells"].shape == (1, 3, 3) + + def test_positions_requires_grad_when_forces_active(self, wrapper, single_batch): + wrapper.model_config.active_outputs = {"energy", "forces"} + wrapper.adapt_input(single_batch) + assert single_batch.positions.requires_grad + + def test_positions_no_grad_energy_only(self, wrapper, single_batch): + wrapper.model_config.active_outputs = {"energy"} + wrapper.adapt_input(single_batch) + assert not single_batch.positions.requires_grad + + def test_atomic_data_promoted_to_batch(self, wrapper): + data = _make_water() + inp = wrapper.adapt_input(data) + assert inp["species"].shape == (3,) + + def test_multi_batch_shapes(self, wrapper, multi_batch): + inp = wrapper.adapt_input(multi_batch) + # 6 atoms total across 2 water molecules; 2 cells. + assert inp["species"].shape == (6,) + assert inp["cells"].shape == (2, 3, 3) + assert inp["system_indices"].tolist() == [0, 0, 0, 1, 1, 1] + + def test_pbc_runs(self, wrapper, pbc_batch): + inp = wrapper.adapt_input(pbc_batch) + assert inp["positions"].shape == (3, 3) + assert inp["cells"].shape == (1, 3, 3) + + +# --------------------------------------------------------------------------- +# adapt_output +# --------------------------------------------------------------------------- + + +class TestAdaptOutput: + def test_energy_key_in_output(self, wrapper, single_batch): + raw = {"energy": torch.randn(1, 1)} + out = wrapper.adapt_output(raw, single_batch) + assert "energy" in out + + def test_energies_shape(self, wrapper, single_batch): + raw = {"energy": torch.randn(1, 1)} + out = wrapper.adapt_output(raw, single_batch) + assert out["energy"].shape == (1, 1) + + def test_1d_energy_unsqueezed(self, wrapper, single_batch): + raw = {"energy": torch.randn(1)} + out = wrapper.adapt_output(raw, single_batch) + assert out["energy"].shape == (1, 1) + + def test_forces_passed_through(self, wrapper, single_batch): + wrapper.model_config.active_outputs = {"energy", "forces"} + raw = {"energy": torch.randn(1, 1), "forces": torch.randn(3, 3)} + out = wrapper.adapt_output(raw, single_batch) + assert out["forces"].shape == (3, 3) + + def test_stress_passed_through(self, wrapper, single_batch): + wrapper.model_config.active_outputs = {"energy", "forces", "stress"} + raw = { + "energy": torch.randn(1, 1), + "forces": torch.randn(3, 3), + "stress": torch.randn(1, 3, 3), + } + out = wrapper.adapt_output(raw, single_batch) + assert out["stress"].shape == (1, 3, 3) + + +# --------------------------------------------------------------------------- +# forward +# --------------------------------------------------------------------------- + + +class TestForward: + def test_energies_shape_single(self, wrapper, single_batch): + out = wrapper.forward(single_batch) + assert out["energy"].shape == (1, 1) + + def test_energies_shape_multi(self, wrapper, multi_batch): + out = wrapper.forward(multi_batch) + assert out["energy"].shape == (2, 1) + + def test_energies_dtype(self, wrapper, single_batch): + out = wrapper.forward(single_batch) + assert out["energy"].dtype == wrapper._model_dtype + + def test_forces_shape(self, wrapper, single_batch): + out = wrapper.forward(single_batch) + assert out["forces"].shape == (3, 3) + + def test_forces_shape_multi(self, wrapper, multi_batch): + out = wrapper.forward(multi_batch) + assert out["forces"].shape == (6, 3) + + def test_no_forces_when_disabled(self, wrapper, single_batch): + wrapper.model_config.active_outputs = {"energy"} + out = wrapper.forward(single_batch) + assert out.get("forces") is None + + def test_atomic_data_input(self, wrapper): + data = _make_water() + out = wrapper.forward(data) + assert out["energy"].shape == (1, 1) + + def test_pbc_stress_shape(self, wrapper, pbc_batch): + out = wrapper.forward(pbc_batch) + assert out["stress"].shape == (1, 3, 3) + + def test_forces_match_finite_difference(self, wrapper): + """Conservative forces agree with a numerical gradient. + + Uses a small two-atom system so the finite-difference evaluation is + cheap, and a ``float64`` copy of the wrapper so the FD comparison + isn't dominated by float32 rounding. + """ + torch.manual_seed(0) + w = PETWrapper( + atomic_types=_ATOMIC_NUMBERS, + hypers=_tiny_hypers(), + composition_energy=torch.zeros(len(_ATOMIC_NUMBERS), dtype=torch.float64), + scale_energy=torch.tensor(1.0, dtype=torch.float64), + ) + w.backend = w.backend.to(torch.float64) + data = _make_two_atoms() + data["positions"] = data.positions.to(torch.float64) + batch = Batch.from_data_list([data]) + out = w.forward(batch) + analytic_force = out["forces"][0, 0].item() + + eps = 1e-4 + base_pos = torch.tensor([[0.0, 0.0, 0.0], [1.1, 0.0, 0.0]], dtype=torch.float64) + + def energy_at(pos): + d = AtomicData( + positions=pos, + atomic_numbers=torch.tensor([1, 1], dtype=torch.long), + neighbor_list=torch.tensor([[0, 1], [1, 0]], dtype=torch.long), + neighbor_list_shifts=torch.zeros(2, 3, dtype=torch.long), + ) + b = Batch.from_data_list([d]) + w.model_config.active_outputs = {"energy"} + return w.forward(b)["energy"].item() + + # Restore autograd-output expectation after the energy-only sanity calls. + w.model_config.active_outputs = {"energy", "forces", "stress"} + + pos_p = base_pos.clone() + pos_p[0, 0] += eps + pos_m = base_pos.clone() + pos_m[0, 0] -= eps + fd = -(energy_at(pos_p) - energy_at(pos_m)) / (2 * eps) + + assert analytic_force == pytest.approx(fd, abs=1e-3) + + +# --------------------------------------------------------------------------- +# compute_embeddings +# --------------------------------------------------------------------------- + + +class TestComputeEmbeddings: + # Embeddings concat node + cutoff-weighted edge features: d_node + d_pet. + _EMB_DIM = _D_NODE + _D_PET + + def test_node_embeddings_shape(self, wrapper, single_batch): + result = wrapper.compute_embeddings(single_batch) + assert result.node_embeddings.shape == (3, self._EMB_DIM) + + def test_graph_embeddings_shape(self, wrapper, single_batch): + result = wrapper.compute_embeddings(single_batch) + assert result.graph_embeddings.shape == (1, self._EMB_DIM) + + def test_graph_embeddings_shape_multi(self, wrapper, multi_batch): + result = wrapper.compute_embeddings(multi_batch) + assert result.graph_embeddings.shape == (2, self._EMB_DIM) + + def test_graph_embeddings_is_sum_of_node_embeddings(self, wrapper, single_batch): + result = wrapper.compute_embeddings(single_batch) + expected_graph = result.node_embeddings.sum(dim=0) + assert torch.allclose(result.graph_embeddings[0], expected_graph) + + def test_does_not_mutate_model_config(self, wrapper, single_batch): + wrapper.model_config.active_outputs = {"energy", "forces", "stress"} + wrapper.compute_embeddings(single_batch) + assert "forces" in wrapper.model_config.active_outputs + assert "stress" in wrapper.model_config.active_outputs + + def test_atomic_data_input(self, wrapper): + data = _make_water() + result = wrapper.compute_embeddings(data) + assert result.node_embeddings.shape == (3, self._EMB_DIM) + + def test_no_grad_on_positions_after_embeddings(self, wrapper, single_batch): + wrapper.compute_embeddings(single_batch) + assert not single_batch.positions.requires_grad + + +# --------------------------------------------------------------------------- +# torch.compile of the backend building blocks +# --------------------------------------------------------------------------- + + +@pytest.mark.slow +class TestCompiledBackend: + """The compiled backend (preprocess/calculate_features/predict) matches eager.""" + + @staticmethod + def _compile_backend(w: PETWrapper) -> None: + """Compile the three backend methods in place, as `from_checkpoint` does.""" + w.eval() + for p in w.parameters(): + p.requires_grad = False + w.backend.preprocess = torch.compile(w.backend.preprocess, fullgraph=True) + w.backend.calculate_features = torch.compile( + w.backend.calculate_features, fullgraph=True + ) + w.backend.predict = torch.compile(w.backend.predict, fullgraph=True) + w._compiled = True + + @staticmethod + def _make_pair() -> tuple[PETWrapper, PETWrapper]: + """Build identical eager + compiled float64 wrappers.""" + torch.manual_seed(0) + kwargs = dict( + atomic_types=_ATOMIC_NUMBERS, + hypers=_tiny_hypers(), + composition_energy=torch.zeros(len(_ATOMIC_NUMBERS), dtype=torch.float64), + scale_energy=torch.tensor(1.0, dtype=torch.float64), + ) + eager = PETWrapper(**kwargs) + eager.backend = eager.backend.to(torch.float64) + + torch.manual_seed(0) + compiled = PETWrapper(**kwargs) + compiled.backend = compiled.backend.to(torch.float64) + compiled.load_state_dict(eager.state_dict()) + TestCompiledBackend._compile_backend(compiled) + return eager, compiled + + @staticmethod + def _water64() -> Batch: + d = _make_water() + d["positions"] = d.positions.to(torch.float64) + return Batch.from_data_list([d]) + + @staticmethod + def _pbc_water64() -> Batch: + d = _make_pbc_water() + d["positions"] = d.positions.to(torch.float64) + d["cell"] = d.cell.to(torch.float64) + return Batch.from_data_list([d]) + + def test_compiled_energy_matches_eager(self): + eager, compiled = self._make_pair() + eager.model_config.active_outputs = {"energy"} + compiled.model_config.active_outputs = {"energy"} + e_eager = eager.forward(self._water64())["energy"] + e_compiled = compiled.forward(self._water64())["energy"] + torch.testing.assert_close(e_compiled.detach(), e_eager.detach()) + + def test_compiled_forces_match_eager(self): + # Forces via autograd backprop through the compiled backend and match + # eager (no PBC → no stress / strain path). + eager, compiled = self._make_pair() + eager.model_config.active_outputs = {"energy", "forces"} + compiled.model_config.active_outputs = {"energy", "forces"} + f_eager = eager.forward(self._water64())["forces"] + f_compiled = compiled.forward(self._water64())["forces"] + torch.testing.assert_close(f_compiled.detach(), f_eager.detach()) + + def test_compiled_forces_and_stress_match_eager(self): + # Forces + stress share a single backward (autograd_forces_and_stresses), + # which is what keeps the compiled graph compatible with torch.compile's + # donated-buffer optimization. + eager, compiled = self._make_pair() + eager.model_config.active_outputs = {"energy", "forces", "stress"} + compiled.model_config.active_outputs = {"energy", "forces", "stress"} + out_eager = eager.forward(self._pbc_water64()) + out_compiled = compiled.forward(self._pbc_water64()) + torch.testing.assert_close( + out_compiled["forces"].detach(), out_eager["forces"].detach() + ) + torch.testing.assert_close( + out_compiled["stress"].detach(), out_eager["stress"].detach() + ) + + +# --------------------------------------------------------------------------- +# export_model +# --------------------------------------------------------------------------- + + +class TestExportModel: + def test_export_snapshot(self, wrapper, tmp_path): + path = tmp_path / "pet.pt" + wrapper.export_model(path) + assert path.exists() + loaded = torch.load(path, weights_only=False) + assert isinstance(loaded, dict) + assert "backend_state_dict" in loaded + assert "hypers" in loaded + assert "atomic_types" in loaded + assert "composition_energy" in loaded + assert "scale_energy" in loaded + + def test_export_state_dict(self, wrapper, tmp_path): + path = tmp_path / "pet_sd.pt" + wrapper.export_model(path, as_state_dict=True) + assert path.exists() + sd = torch.load(path, weights_only=True) + assert isinstance(sd, dict) + assert any("gnn_layers" in k for k in sd.keys()) + + def test_reload_snapshot_into_new_backend(self, wrapper, tmp_path): + path = tmp_path / "pet.pt" + wrapper.export_model(path) + snapshot = torch.load(path, weights_only=False) + + new_backend = PETBackend(snapshot["hypers"], snapshot["atomic_types"]) + new_backend.add_output("energy", {"energy___0": [1]}) + new_backend.load_state_dict(snapshot["backend_state_dict"], strict=True) + # Check every parameter matches. + for key in wrapper.backend.state_dict(): + assert torch.allclose( + new_backend.state_dict()[key], wrapper.backend.state_dict()[key] + ) + + +# --------------------------------------------------------------------------- +# from_checkpoint error path +# --------------------------------------------------------------------------- + + +class TestFromCheckpointErrors: + def test_raises_import_error_when_metatrain_unavailable(self, monkeypatch): + from nvalchemi._optional import OptionalDependency + + monkeypatch.setattr(OptionalDependency.PET, "_available", False) + with pytest.raises(ImportError): + PETWrapper.from_checkpoint(checkpoint_path="nonexistent.ckpt") + + +# --------------------------------------------------------------------------- +# Integration tests — real PET checkpoint (marked slow) +# --------------------------------------------------------------------------- + + +# Fetched from the `lab-cosmo/upet` HuggingFace repo (see PETWrapper.from_checkpoint). +_CHECKPOINT_MODEL = "pet-mad-xs" +_CHECKPOINT_VERSION = "1.5.0" # 'grid' adaptive cutoff +# Not yet published on HuggingFace; must be provided locally to run this test. +_SOLVER_CHECKPOINT_PATH = "pet-mad-xs-v1.6.0.ckpt" # 'solver' adaptive cutoff +_CUTOFF = 7.5 + + +def _crystal(device: str = "cpu") -> AtomicData: + return AtomicData( + positions=torch.tensor( + [[0.0, 0.0, 0.0], [0.5, 0.5, 0.5]], dtype=torch.float32 + ).to(device=device), + atomic_numbers=torch.tensor([6, 6], dtype=torch.long).to(device=device), + cell=torch.eye(3, dtype=torch.float32).reshape(1, 3, 3).to(device=device), + pbc=torch.tensor([True] * 3, dtype=torch.bool).reshape(1, 3).to(device=device), + ) + + +def _batch(dtype: torch.dtype = torch.float32) -> Batch: + data = _crystal() + batch = Batch.from_data_list([data]) + compute_neighbors(batch, cutoff=_CUTOFF, format=NeighborListFormat.COO) + return batch + + +@pytest.fixture(scope="session") +def real_wrapper_cpu(): + """Fetch the pet-mad-xs checkpoint from HuggingFace once per session.""" + try: + return PETWrapper.from_checkpoint( + model=_CHECKPOINT_MODEL, + version=_CHECKPOINT_VERSION, + device=torch.device("cpu"), + dtype=torch.float32, + ) + except Exception as e: + pytest.skip(f"Could not fetch/load PET checkpoint: {e}") + + +@pytest.mark.slow +class TestRealCheckpoint: + """Integration tests against the pet-mad-xs-v1.5.0 checkpoint.""" + + def test_is_pet_wrapper(self, real_wrapper_cpu): + assert isinstance(real_wrapper_cpu, PETWrapper) + + def test_model_config_matches_wrapper(self, real_wrapper_cpu): + cfg = real_wrapper_cpu.model_config + assert "forces" in cfg.autograd_outputs + assert "stress" in cfg.autograd_outputs + assert "energy" in cfg.outputs + assert cfg.neighbor_config is not None + assert cfg.neighbor_config.format == NeighborListFormat.COO + + def test_cutoff_positive(self, real_wrapper_cpu): + assert real_wrapper_cpu.cutoff > 0.0 + + def test_inference_energy_finite(self, real_wrapper_cpu): + batch = _batch() + out = real_wrapper_cpu.forward(batch) + e = out["energy"] + assert e.shape == (1, 1) + assert torch.isfinite(e).all() + + def test_inference_forces_shape(self, real_wrapper_cpu): + batch = _batch() + out = real_wrapper_cpu.forward(batch) + assert out["forces"].shape == (2, 3) + assert torch.isfinite(out["forces"]).all() + + def test_inference_stress_shape(self, real_wrapper_cpu): + """Non-PBC system produces a stress tensor of zeros with shape [B, 3, 3].""" + batch = _batch() + out = real_wrapper_cpu.forward(batch) + assert out["stress"].shape == (1, 3, 3) + assert torch.isfinite(out["stress"]).all() + + def test_batch_determinism(self, real_wrapper_cpu): + """Same input → same output across two consecutive calls.""" + b1 = _batch() + b2 = _batch() + e1 = real_wrapper_cpu.forward(b1)["energy"] + e2 = real_wrapper_cpu.forward(b2)["energy"] + assert torch.allclose(e1, e2, atol=1e-6) + + def test_batched_matches_single(self, real_wrapper_cpu): + """Two-water batch energies equal the single-water energy.""" + single = real_wrapper_cpu.forward(_batch()) + multi_batch = Batch.from_data_list([_crystal(), _crystal()]) + compute_neighbors(multi_batch, cutoff=_CUTOFF, format=NeighborListFormat.COO) + multi = real_wrapper_cpu.forward(multi_batch) + assert torch.allclose( + multi["energy"][0], single["energy"][0], atol=1e-4, rtol=1e-4 + ) + assert torch.allclose( + multi["energy"][1], single["energy"][0], atol=1e-4, rtol=1e-4 + ) + + def test_compute_embeddings_run(self, real_wrapper_cpu): + batch = _batch() + result = real_wrapper_cpu.compute_embeddings(batch) + assert result.node_embeddings.shape[0] == 2 + assert result.graph_embeddings.shape == (1, result.node_embeddings.shape[1]) + + def test_embeddings_match_metatrain_features(self): + """compute_embeddings reproduces metatrain PET's per-atom ``feature`` output. + + The metatrain ``feature`` output is built by + ``metatrain.pet.model.PET._get_output_features`` as the concatenation of + the per-layer node features with the cutoff-weighted, neighbor-summed + per-layer edge features — exactly what :meth:`PETWrapper.compute_embeddings` + computes. This verifies they agree value-for-value (in float64). + """ + ase = pytest.importorskip("ase") + from metatomic.torch import ModelOutput, systems_to_torch + from metatrain.pet import PET + from metatrain.utils.neighbor_lists import get_system_with_neighbor_lists + + from nvalchemi.models.pet import _fetch_pet_checkpoint + + try: + checkpoint_path = _fetch_pet_checkpoint( + model=_CHECKPOINT_MODEL, version=_CHECKPOINT_VERSION + ) + except Exception as e: + pytest.skip(f"Could not fetch PET checkpoint: {e}") + + # metatrain PET (float64) with its native "feature" output. + raw = torch.load(checkpoint_path, weights_only=False, map_location="cpu") + wrapped = raw.get("wrapped_model_checkpoint", raw) + PET.upgrade_checkpoint(wrapped) + pet = PET.load_checkpoint(wrapped, context="export").to(torch.float64).eval() + + data = _crystal() + atoms = ase.Atoms( + positions=data.positions.numpy(), + numbers=data.atomic_numbers.numpy(), + cell=data.cell.squeeze().numpy(), + pbc=[True, True, True], + ) + system = systems_to_torch(atoms, dtype=torch.float64) + system = get_system_with_neighbor_lists(system, pet.requested_neighbor_lists()) + mt_feature = ( + pet([system], {"feature": ModelOutput(sample_kind="atom")})["feature"] + .block() + .values.detach() + ) + + # nvalchemi embeddings (float64) on the same structure. + wrapper = PETWrapper.from_checkpoint( + checkpoint_path=checkpoint_path, dtype=torch.float64 + ) + batch = Batch.from_data_list([_crystal()]) + batch["positions"] = batch.positions.to(torch.float64) + batch["cell"] = batch.cell.to(torch.float64) + compute_neighbors(batch, cutoff=_CUTOFF, format=NeighborListFormat.COO) + nv_embeddings = wrapper.compute_embeddings(batch).node_embeddings.detach() + + assert nv_embeddings.shape == mt_feature.shape + torch.testing.assert_close(nv_embeddings, mt_feature, atol=1e-8, rtol=1e-8) + + def test_export_and_reload(self, real_wrapper_cpu, tmp_path): + path = tmp_path / "pet_snapshot.pt" + real_wrapper_cpu.export_model(path) + snapshot = torch.load(path, weights_only=False) + new_backend = PETBackend(snapshot["hypers"], snapshot["atomic_types"]) + new_backend.add_output("energy", {"energy___0": [1]}) + new_backend.load_state_dict(snapshot["backend_state_dict"], strict=True) + + def test_grid_compile_raises(self): + """Compiling a 'grid' adaptive-cutoff model is rejected up front. + + Autograd backward through the compiled grid cutoff aborts at the C++ + level (always with ``fullgraph=True``, intermittently without), so + ``from_checkpoint`` raises a clear error rather than letting it crash at + the first backward. Both the plain and ``fullgraph=True`` requests raise. + """ + from nvalchemi.models.pet import _fetch_pet_checkpoint + + try: + checkpoint_path = _fetch_pet_checkpoint( + model=_CHECKPOINT_MODEL, version=_CHECKPOINT_VERSION + ) + except Exception as e: + pytest.skip(f"Could not fetch PET checkpoint: {e}") + + for extra in ({}, {"fullgraph": True}): + with pytest.raises(ValueError, match="grid"): + PETWrapper.from_checkpoint( + checkpoint_path=checkpoint_path, + device=torch.device("cpu"), + dtype=torch.float32, + compile_model=True, + **extra, + ) + + def test_solver_compile_fullgraph_forces_stress(self): + """A 'solver' model (pet-mad >= v1.6.0) compiles with ``fullgraph=True``. + + Verifies the ``compile_kwargs`` forwarding and that autograd forces + + stress through the fullgraph-compiled solver backbone match eager. + """ + import os + import warnings + + if not os.path.exists(_SOLVER_CHECKPOINT_PATH): + pytest.skip(f"Checkpoint {_SOLVER_CHECKPOINT_PATH} not found.") + + eager = PETWrapper.from_checkpoint( + checkpoint_path=_SOLVER_CHECKPOINT_PATH, + device=torch.device("cpu"), + dtype=torch.float64, + ) + assert eager.backend.adaptive_cutoff_method.lower() == "solver" + out_eager = eager.forward(_batch(torch.float64)) + + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + compiled = PETWrapper.from_checkpoint( + checkpoint_path=_SOLVER_CHECKPOINT_PATH, + device=torch.device("cpu"), + dtype=torch.float64, + compile_model=True, + fullgraph=True, + ) + assert compiled._compiled is True + out_compiled = compiled.forward(_batch(torch.float64)) + + torch.testing.assert_close( + out_compiled["forces"].detach(), out_eager["forces"].detach() + ) + torch.testing.assert_close( + out_compiled["stress"].detach(), out_eager["stress"].detach() + ) + + def test_metatrain_model_compatibility(self, real_wrapper_cpu): + """Predictions from the PETWrapper match those from the original metatrain model.""" + ase = pytest.importorskip("ase") + from metatomic.torch import NeighborListOptions, systems_to_torch + from metatrain.utils.data.target_info import get_energy_target_info + from metatrain.utils.evaluate_model import evaluate_model + from metatrain.utils.io import load_model + from metatrain.utils.neighbor_lists import get_system_with_neighbor_lists + + from nvalchemi.models.pet import _fetch_pet_checkpoint + + try: + checkpoint_path = _fetch_pet_checkpoint( + model=_CHECKPOINT_MODEL, version=_CHECKPOINT_VERSION + ) + except Exception as e: + pytest.skip(f"Could not fetch PET checkpoint: {e}") + + mt_model = load_model(checkpoint_path) + data = _crystal() + atoms = ase.Atoms( + positions=data.positions.cpu().numpy(), + numbers=data.atomic_numbers.cpu().numpy(), + cell=data.cell.cpu().squeeze().numpy() if data.cell is not None else None, + pbc=data.pbc.cpu().squeeze().numpy() if data.pbc is not None else None, + ) + system = systems_to_torch(atoms) + neighbor_options = NeighborListOptions( + cutoff=_CUTOFF, full_list=True, strict=True + ) + system = get_system_with_neighbor_lists(system, [neighbor_options]) + targets = { + "energy": get_energy_target_info( + "energy", + {"unit": "eV"}, + add_position_gradients=True, + add_strain_gradients=True, + ) + } + mt_output = evaluate_model( + mt_model, [system], targets=targets, is_training=False + ) + + mt_energy = mt_output["energy"].block().values.detach().squeeze() + mt_forces = -mt_output["energy"].block().gradient("positions").values.squeeze() + mt_stress = ( + -mt_output["energy"].block().gradient("strain").values.squeeze() + / atoms.get_volume() + ) + + nv_output = real_wrapper_cpu.forward(_batch()) + nv_energy = nv_output["energy"].detach().squeeze().item() + nv_forces = nv_output["forces"].detach() + nv_stress = nv_output["stress"].detach().squeeze() + + assert abs(nv_energy - mt_energy) < 1e-4, ( + f"Energy mismatch: nvalchemi={nv_energy}, metatrain={mt_energy}" + ) + assert torch.allclose(nv_forces, mt_forces, atol=1e-4, rtol=1e-4), ( + f"Force mismatch: max |dF|={float((nv_forces - mt_forces).abs().max())}" + ) + + # nvalchemi's shared `autograd_stresses` returns *tensile-positive* + # Cauchy stress (+1/V * dE/dstrain), the opposite sign to metatrain's + # strain gradient convention (mt_stress = -1/V * dE/dstrain), so + # nv_stress == -mt_stress. The looser tolerance absorbs float32 noise in + # the near-zero off-diagonal terms (metatrain returns the raw, + # unsymmetrized strain gradient while nvalchemi applies a symmetric + # strain); the dominant diagonal stress matches to < 1e-2 / ~324. + assert torch.allclose(nv_stress, -mt_stress, atol=2e-2, rtol=1e-4), ( + f"Stress mismatch: max |dS|={float((nv_stress + mt_stress).abs().max())}" + ) diff --git a/uv.lock b/uv.lock index b3af3f8c..2771382f 100644 --- a/uv.lock +++ b/uv.lock @@ -2,168 +2,43 @@ version = 1 revision = 3 requires-python = ">=3.11, <3.14" resolution-markers = [ - "python_full_version >= '3.13' and platform_machine != 's390x' and sys_platform == 'win32' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-mace' and extra != 'extra-17-nvalchemi-toolkit-uma'", - "python_full_version >= '3.13' and platform_machine != 's390x' and sys_platform == 'win32' and extra != 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"python_full_version == '3.12.*' and platform_machine == 's390x' and sys_platform == 'win32' and extra == 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-mace' and extra != 'extra-17-nvalchemi-toolkit-uma'", - "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform == 'emscripten' and extra == 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-mace' and extra != 'extra-17-nvalchemi-toolkit-uma'", - "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform == 'emscripten' and extra == 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-mace' and extra != 'extra-17-nvalchemi-toolkit-uma'", - "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform == 'emscripten' and extra == 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-mace' and extra != 'extra-17-nvalchemi-toolkit-uma'", - "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform == 'emscripten' and extra == 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-mace' and extra != 'extra-17-nvalchemi-toolkit-uma'", - "python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'emscripten' and extra == 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-mace' and extra != 'extra-17-nvalchemi-toolkit-uma'", - "python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'emscripten' and extra == 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-mace' and extra != 'extra-17-nvalchemi-toolkit-uma'", - "python_full_version == '3.12.*' and platform_machine == 's390x' and sys_platform == 'emscripten' and extra == 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-mace' and extra != 'extra-17-nvalchemi-toolkit-uma'", - "python_full_version == '3.12.*' and platform_machine == 's390x' and sys_platform == 'emscripten' and extra == 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-mace' and extra != 'extra-17-nvalchemi-toolkit-uma'", - "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform != 'emscripten' and sys_platform != 'win32' and extra == 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-mace' and extra != 'extra-17-nvalchemi-toolkit-uma'", - "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform != 'emscripten' and sys_platform != 'win32' and extra == 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-mace' and extra != 'extra-17-nvalchemi-toolkit-uma'", - "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32' and extra == 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-mace' and extra != 'extra-17-nvalchemi-toolkit-uma'", - "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32' and extra == 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-mace' and extra 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!= 'extra-17-nvalchemi-toolkit-uma'", - "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'emscripten' and extra == 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-mace' and extra != 'extra-17-nvalchemi-toolkit-uma'", - "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'emscripten' and extra == 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-mace' and extra != 'extra-17-nvalchemi-toolkit-uma'", - "python_full_version < '3.12' and platform_machine == 's390x' and sys_platform == 'emscripten' and extra == 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-mace' and extra != 'extra-17-nvalchemi-toolkit-uma'", - "python_full_version < '3.12' and platform_machine == 's390x' and sys_platform == 'emscripten' and extra == 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-mace' and extra != 'extra-17-nvalchemi-toolkit-uma'", - "python_full_version < '3.12' and platform_machine == 'aarch64' and sys_platform != 'emscripten' and sys_platform != 'win32' and extra == 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-mace' and extra != 'extra-17-nvalchemi-toolkit-uma'", - "python_full_version < '3.12' and platform_machine == 'aarch64' and sys_platform != 'emscripten' and sys_platform != 'win32' and extra == 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-mace' and extra != 'extra-17-nvalchemi-toolkit-uma'", - "python_full_version < '3.12' and platform_machine == 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32' and extra == 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-mace' and extra != 'extra-17-nvalchemi-toolkit-uma'", - "python_full_version < '3.12' and platform_machine == 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32' and extra == 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-mace' and extra != 'extra-17-nvalchemi-toolkit-uma'", - "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32' and extra == 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-mace' and extra != 'extra-17-nvalchemi-toolkit-uma'", - "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32' and extra == 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-mace' and extra != 'extra-17-nvalchemi-toolkit-uma'", - "python_full_version < '3.12' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32' and extra == 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-mace' and extra != 'extra-17-nvalchemi-toolkit-uma'", - "python_full_version < '3.12' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32' and extra == 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-mace' and extra != 'extra-17-nvalchemi-toolkit-uma'", - "python_full_version >= '3.13' and platform_machine != 's390x' and sys_platform == 'win32' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma'", - "python_full_version >= '3.13' and platform_machine != 's390x' and sys_platform == 'emscripten' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma'", - "python_full_version >= '3.13' and platform_machine != 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma'", - "python_full_version >= '3.13' and platform_machine == 's390x' and sys_platform == 'win32' and extra != 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and extra == 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-uma'", + "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform == 'win32' and extra == 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-uma'", + "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform != 'win32' and extra == 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-uma'", + "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform == 'win32' and extra == 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-uma'", + "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform != 'win32' and extra == 'extra-17-nvalchemi-toolkit-cu12' 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'emscripten'", - "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform == 'emscripten'", - "python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'emscripten'", - "python_full_version == '3.12.*' and platform_machine == 's390x' and sys_platform == 'emscripten'", - "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version == '3.12.*' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform != 'win32'", + "python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'win32'", + "python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'win32'", "python_full_version < '3.12' and platform_machine == 'aarch64' and sys_platform == 'win32'", + "python_full_version < '3.12' and platform_machine == 'aarch64' and sys_platform != 'win32'", "python_full_version < '3.12' and platform_machine == 'x86_64' and sys_platform == 'win32'", - "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'win32'", - "python_full_version < '3.12' and platform_machine == 's390x' and sys_platform == 'win32'", - "python_full_version < '3.12' and platform_machine == 'aarch64' and sys_platform == 'emscripten'", - "python_full_version < '3.12' and platform_machine == 'x86_64' and sys_platform == 'emscripten'", - "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'emscripten'", - "python_full_version < '3.12' and platform_machine == 's390x' and sys_platform == 'emscripten'", - "python_full_version < '3.12' and platform_machine == 'aarch64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version < '3.12' and platform_machine == 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version < '3.12' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version < '3.12' and platform_machine == 'x86_64' and sys_platform != 'win32'", + "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'win32'", + "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'win32'", ] wheels = [ { url = "https://pypi.nvidia.com/cuda-toolkit/cuda_toolkit-12.6.3-py2.py3-none-any.whl", hash = "sha256:79d8605baeb6c2f695761e0efb54bc62dbc3c9e32eb0742df7669c07befaa8f7" }, @@ -1258,24 +1061,16 @@ name = "cuda-toolkit" version = "13.0.2" source = { registry = "https://pypi.nvidia.com/" } resolution-markers = [ - "python_full_version >= '3.13' and platform_machine != 's390x' and sys_platform == 'win32'", - "python_full_version >= '3.13' and platform_machine == 's390x' and sys_platform == 'win32'", - "python_full_version >= '3.13' and platform_machine != 's390x' and sys_platform == 'emscripten'", - "python_full_version >= '3.13' and platform_machine == 's390x' and sys_platform == 'emscripten'", - "python_full_version >= '3.13' and platform_machine != 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version >= '3.13' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version == '3.12.*' and platform_machine != 's390x' and sys_platform == 'win32'", - "python_full_version == '3.12.*' and platform_machine == 's390x' and sys_platform == 'win32'", - "python_full_version == '3.12.*' and platform_machine != 's390x' and sys_platform == 'emscripten'", - "python_full_version == '3.12.*' and platform_machine == 's390x' and sys_platform == 'emscripten'", - "python_full_version == '3.12.*' and platform_machine != 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version == '3.12.*' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version < '3.12' and platform_machine != 's390x' and sys_platform == 'win32'", - "python_full_version < '3.12' and platform_machine == 's390x' and sys_platform == 'win32'", - "python_full_version < '3.12' and platform_machine != 's390x' and sys_platform == 'emscripten'", - "python_full_version < '3.12' and platform_machine == 's390x' and sys_platform == 'emscripten'", - "python_full_version < '3.12' and platform_machine != 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version < '3.12' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version >= '3.13' and sys_platform == 'win32' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-uma'", + "python_full_version >= '3.13' and sys_platform == 'emscripten' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-uma'", + "python_full_version >= '3.13' and sys_platform != 'emscripten' and sys_platform != 'win32' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-uma'", + "python_full_version == '3.12.*' and sys_platform == 'win32' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-uma'", + "python_full_version == '3.12.*' and sys_platform != 'win32' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-uma'", + "python_full_version < '3.12' and sys_platform == 'win32' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-uma'", + "python_full_version < '3.12' and sys_platform != 'win32' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-uma'", + "python_full_version >= '3.13' and sys_platform != 'win32' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-uma'", + "python_full_version == '3.12.*' and sys_platform != 'win32' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-uma'", + "python_full_version < '3.12' and sys_platform != 'win32' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-uma'", ] wheels = [ { url = "https://pypi.nvidia.com/cuda-toolkit/cuda_toolkit-13.0.2-py2.py3-none-any.whl", hash = "sha256:b198824cf2f54003f50d64ada3a0f184b42ca0846c1c94192fa269ecd97a66eb" }, @@ -1331,17 +1126,17 @@ version = "26.2.1" source = { registry = "https://pypi.nvidia.com/" } dependencies = [ { name = "cachetools" }, - { name = "cuda-python", version = "12.9.6", source = { registry = "https://pypi.org/simple" } }, + { name = "cuda-python", version = "12.9.7", source = { registry = "https://pypi.org/simple" } }, { name = "cuda-toolkit", version = "12.6.3", source = { registry = "https://pypi.nvidia.com/" }, extra = ["nvcc", "nvrtc"], marker = "extra == 'extra-17-nvalchemi-toolkit-cu12' or (extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma')" }, { name = "cupy-cuda12x" }, { name = "fsspec" }, { name = "libcudf-cu12" }, { name = "numba", version = "0.61.2", source = { registry = "https://pypi.org/simple" } }, { name = "numba-cuda", version = "0.22.2", source = { registry = "https://pypi.org/simple" }, extra = ["cu12"], marker = "extra == 'extra-17-nvalchemi-toolkit-cu12' or (extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma')" }, - { name = "numpy" }, + { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" } }, { name = "nvtx" }, - { name = "packaging" }, - { name = "pandas", version = "2.3.3", source = { registry = "https://pypi.org/simple" } }, + { name = "packaging", version = "26.0", source = { registry = "https://pypi.org/simple" } }, + { name = "pandas" }, { name = "pyarrow", marker = "platform_machine == 'aarch64' or platform_machine == 'x86_64'" }, { name = "pylibcudf-cu12" }, { name = "rich" }, @@ -1359,7 +1154,7 @@ wheels = [ [[package]] name = "cudf-cu13" -version = "26.4.0" +version = "26.6.0" source = { registry = "https://pypi.nvidia.com/" } dependencies = [ { name = "cachetools" }, @@ -1370,18 +1165,18 @@ dependencies = [ { name = "libcudf-cu13" }, { name = "numba", version = "0.64.0", source = { registry = "https://pypi.org/simple" } }, { name = "numba-cuda", version = "0.28.2", source = { registry = "https://pypi.org/simple" }, extra = ["cu13"], marker = "extra == 'extra-17-nvalchemi-toolkit-cu13' or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma')" }, - { name = "numpy" }, + { name = "numpy", version = "2.3.5", source = { registry = "https://pypi.org/simple" } }, { name = "nvtx" }, - { name = "packaging" }, - { name = "pandas", version = "2.3.3", source = { registry = "https://pypi.org/simple" } }, + { name = "packaging", version = "26.2", source = { registry = "https://pypi.org/simple" } }, + { name = "pandas" }, { name = "pyarrow" }, { name = "pylibcudf-cu13" }, { name = "rich" }, { name = "rmm-cu13" }, ] wheels = [ - { url = "https://pypi.nvidia.com/cudf-cu13/cudf_cu13-26.4.0-cp311-abi3-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:7c090c9809ea5b5c5620797f6273c42fb746e150f3b5ccfa6bbf53b11d8889db" }, - { url = "https://pypi.nvidia.com/cudf-cu13/cudf_cu13-26.4.0-cp311-abi3-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a6960b78c5748dfc426ea690d3dbd2eb3544c10280125d541ceb0e2cf2583562" }, + { url = "https://pypi.nvidia.com/cudf-cu13/cudf_cu13-26.6.0-cp311-abi3-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:e11edd3e11d1748461ee5894b0d84aa7cce267315240044a24d50e546c195727" }, + { url = "https://pypi.nvidia.com/cudf-cu13/cudf_cu13-26.6.0-cp311-abi3-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:9c2f093fb2a563e7fe7b8f8c99b7130ed572f469acbfc35a793da3aa6fb064c7" }, ] [[package]] @@ -1390,9 +1185,11 @@ version = "0.10.0" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "networkx" }, - { name = "numpy" }, + { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-17-nvalchemi-toolkit-cu12' or (extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 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'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma')" }, { name = "sympy" }, ] sdist = { url = "https://files.pythonhosted.org/packages/9e/df/eaad918ad320805c08ec29ca5f3df8f89b1d0b6c828668d0c10e94ae6b6e/cuequivariance-0.10.0.tar.gz", hash = "sha256:8be785f84a0c25b6b3e927e551078be261f0900c583a7c8c95744bcd5fa1967d", size = 214826, upload-time = "2026-04-22T01:20:40.317Z" } @@ -1436,7 +1233,8 @@ version = "0.10.0" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "cuequivariance-ops-cu12" }, - { name = "scipy" }, + { name = "scipy", version = "1.17.1", source = { registry = "https://pypi.org/simple" }, marker = "(python_full_version < '3.12' and extra == 'extra-17-nvalchemi-toolkit-cu12') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (extra == 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!= 'win32' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma'", ] dependencies = [ { name = "llvmlite", version = "0.46.0", source = { registry = "https://pypi.org/simple" } }, - { name = "numpy" }, + { name = "numpy", version = "2.3.5", source = { registry = "https://pypi.org/simple" } }, ] sdist = { url = "https://files.pythonhosted.org/packages/23/c9/a0fb41787d01d621046138da30f6c2100d80857bf34b3390dd68040f27a3/numba-0.64.0.tar.gz", hash = "sha256:95e7300af648baa3308127b1955b52ce6d11889d16e8cfe637b4f85d2fca52b1", size = 2765679, upload-time = "2026-02-18T18:41:20.974Z" } wheels = [ @@ -3660,47 +3669,29 @@ version = "0.22.2" source = { registry = "https://pypi.org/simple" } resolution-markers = [ "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform == 'win32'", + "python_full_version >= '3.13' and platform_machine == 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"python_full_version < '3.12' and platform_machine == 'x86_64' and sys_platform == 'win32'", - "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'win32'", - "python_full_version < '3.12' and platform_machine == 's390x' and sys_platform == 'win32'", - "python_full_version < '3.12' and platform_machine == 'aarch64' and sys_platform == 'emscripten'", - "python_full_version < '3.12' and platform_machine == 'x86_64' and sys_platform == 'emscripten'", - "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'emscripten'", - "python_full_version < '3.12' and platform_machine == 's390x' and sys_platform == 'emscripten'", - "python_full_version < '3.12' and platform_machine == 'aarch64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version < '3.12' and platform_machine == 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"cuda-core" }, { name = "numba", version = "0.61.2", source = { registry = "https://pypi.org/simple" } }, - { name = "packaging" }, + { name = "packaging", version = "26.0", source = { registry = "https://pypi.org/simple" } }, ] sdist = { url = "https://files.pythonhosted.org/packages/aa/cd/9017506815047ee30ad404e3c469788676a6abeaaff8014d07a0180cdfbc/numba_cuda-0.22.2.tar.gz", hash = "sha256:e8c19bc1174dfc3596259381fa708f1c3397a618bdbbaa5d068bcc56af8fd921", size = 1340447, upload-time = "2025-12-19T01:08:57.73Z" } wheels = [ @@ -3717,9 +3708,9 @@ wheels = [ [package.optional-dependencies] cu12 = [ - { name = "cuda-bindings", version = "12.9.6", source = { registry = "https://pypi.org/simple" } }, + { name = "cuda-bindings", version = "12.9.7", source = { registry = "https://pypi.org/simple" } }, { name = "cuda-core" }, - { name = "cuda-python", version = "12.9.6", source = { registry = "https://pypi.org/simple" } }, + { name = "cuda-python", version = "12.9.7", source = { registry = 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'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma')" }, + { name = "zarr", version = "3.1.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12' or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma')" }, + { name = "zarr", version = "3.2.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12' or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma')" }, ] [package.optional-dependencies] @@ -3889,21 +3972,25 @@ cu12 = [ { name = "cuml-cu12", marker = "sys_platform != 'darwin'" }, { name = "nvalchemi-toolkit-ops", extra = ["torch-cu12"], marker = "(sys_platform != 'darwin' and extra == 'extra-17-nvalchemi-toolkit-cu12') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma')" }, { name = "nvidia-physicsnemo", version = "2.1.1", source = { registry = "https://pypi.nvidia.com/" }, extra = ["cu12"], marker = "(sys_platform != 'darwin' and extra == 'extra-17-nvalchemi-toolkit-cu12') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma')" }, - { name = "torch", version = "2.12.0+cu126", source = { registry = "https://download.pytorch.org/whl/cu126" }, marker = "sys_platform != 'darwin'" }, - { name = "torchvision", version = "0.27.0+cu126", source = { registry = "https://download.pytorch.org/whl/cu126" }, marker = "sys_platform != 'darwin'" }, + { name = "torch", version = "2.12.1+cu126", source = { registry = "https://download.pytorch.org/whl/cu126" }, marker = "sys_platform != 'darwin'" }, + { name = "torchvision", version = "0.27.1+cu126", source = { registry = "https://download.pytorch.org/whl/cu126" }, marker = "sys_platform != 'darwin'" }, ] cu13 = [ { name = "cuequivariance-ops-torch-cu13", marker = "sys_platform != 'darwin'" }, { name = "cuml-cu13", marker = "sys_platform != 'darwin'" }, { name = "nvalchemi-toolkit-ops", extra = ["torch-cu13"], marker = "(sys_platform != 'darwin' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma')" }, { name = "nvidia-physicsnemo", version = "2.1.1", source = { registry = "https://pypi.nvidia.com/" }, extra = ["cu13"], marker = "(sys_platform != 'darwin' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma')" }, - { name = "torch", version = "2.12.0+cu130", source = { registry = "https://download.pytorch.org/whl/cu130" }, marker = "sys_platform != 'darwin'" }, - { name = "torchvision", version = "0.27.0+cu130", source = { registry = "https://download.pytorch.org/whl/cu130" }, marker = "sys_platform != 'darwin'" }, + { name = "torch", version = "2.12.1+cu130", source = { registry = "https://download.pytorch.org/whl/cu130" }, marker = "sys_platform != 'darwin'" }, + { name = "torchvision", version = "0.27.1+cu130", source = { registry = "https://download.pytorch.org/whl/cu130" }, marker = "sys_platform != 'darwin'" }, ] mace = [ { name = "cuequivariance-torch" }, { name = "mace-torch" }, ] +pet = [ + { name = "metatrain" }, + { name = "upet" }, +] pymatgen = [ { name = "pymatgen" }, ] @@ -3951,7 +4038,7 @@ docs = [ { name = "sphinx", version = "9.0.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12' or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma')" }, { name = "sphinx", version = "9.1.0", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12' or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma')" }, { name = "sphinx-autodoc-typehints", version = "3.6.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.12' or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma')" }, - { name = "sphinx-autodoc-typehints", version = "3.9.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12' or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma')" }, + { name = "sphinx-autodoc-typehints", version = "3.12.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.12' or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma')" }, { name = "sphinx-design" }, { name = "sphinx-favicon" }, { name = "sphinx-gallery" }, @@ -3973,6 +4060,7 @@ requires-dist = [ { name = "jaxtyping", specifier = ">=0.3.2" }, { name = "loguru" }, { name = "mace-torch", marker = "extra == 'mace'", specifier = "==0.3.15" }, + { name = "metatrain", marker = "extra == 'pet'", git = "https://github.com/metatensor/metatrain.git?rev=main" }, { name = "numpy", specifier = "<2.4" }, { name = "nvalchemi-toolkit-ops", git = "https://github.com/NVIDIA/nvalchemi-toolkit-ops.git?rev=0.4.0-rc" }, { name = "nvalchemi-toolkit-ops", extras = ["torch-cu12"], marker = "sys_platform != 'darwin' and extra == 'cu12'", git = "https://github.com/NVIDIA/nvalchemi-toolkit-ops.git?rev=0.4.0-rc" }, @@ -3993,9 +4081,10 @@ requires-dist = [ { name = "torch", marker = "sys_platform != 'darwin' and extra == 'cu13'", index = "https://download.pytorch.org/whl/cu130", conflict = { package = "nvalchemi-toolkit", extra = "cu13" } }, { name = "torchvision", marker = "sys_platform != 'darwin' and extra == 'cu12'", index = "https://download.pytorch.org/whl/cu126", conflict = { package = "nvalchemi-toolkit", extra = "cu12" } }, { name = "torchvision", marker = "sys_platform != 'darwin' and extra == 'cu13'", index = "https://download.pytorch.org/whl/cu130", conflict = { package = "nvalchemi-toolkit", extra = "cu13" } }, + { name = "upet", marker = "extra == 'pet'" }, { name = "zarr", specifier = ">=3" }, ] -provides-extras = ["aimnet", "ase", "cu12", "cu13", "mace", "pymatgen", "tensorboard", "uma"] +provides-extras = ["aimnet", "ase", "cu12", "cu13", "mace", "pet", "pymatgen", "tensorboard", "uma"] [package.metadata.requires-dev] build = [ @@ -4042,18 +4131,37 @@ docs = [ [[package]] name = "nvalchemi-toolkit-ops" version = "0.4.0" -source = { git = "https://github.com/NVIDIA/nvalchemi-toolkit-ops.git?rev=0.4.0-rc#ef898c6e3372032842788f6672f84bef33f311ac" } +source = { git = "https://github.com/NVIDIA/nvalchemi-toolkit-ops.git?rev=0.4.0-rc#90af513a66537cbab8d836ea0c064d137a7b4a05" } dependencies = [ - { name = "numpy" }, + { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-17-nvalchemi-toolkit-cu12' or (extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma')" }, + { name = "numpy", version = "2.3.5", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-17-nvalchemi-toolkit-cu13' or extra == 'extra-17-nvalchemi-toolkit-uma' or extra != 'extra-17-nvalchemi-toolkit-cu12'" }, { name = "warp-lang" }, ] [package.optional-dependencies] torch-cu12 = [ - { name = "torch", version = "2.12.0+cu126", source = { registry = "https://download.pytorch.org/whl/cu126" } }, + { name = "torch", version = "2.12.1+cu126", source = { registry = "https://download.pytorch.org/whl/cu126" } }, ] torch-cu13 = [ - { name = "torch", version = "2.12.0+cu130", source = { registry = "https://download.pytorch.org/whl/cu130" } }, + { name = "torch", version = "2.12.1+cu130", source = { registry = "https://download.pytorch.org/whl/cu130" } }, +] + +[[package]] +name = "nvforest-cu13" +version = "26.6.0" +source = { registry = "https://pypi.nvidia.com/" } +dependencies = [ + { name = "cuda-python", version = "13.2.0", source = { registry = "https://pypi.org/simple" } }, + { name = "cupy-cuda13x" }, + { name = "libnvforest-cu13" }, + { name = "numpy", version = "2.3.5", source = { registry = "https://pypi.org/simple" } }, + { name = "pylibraft-cu13" }, + { name = "scikit-learn" }, + { name = "treelite" }, +] +wheels = [ + { url = "https://pypi.nvidia.com/nvforest-cu13/nvforest_cu13-26.6.0-cp311-abi3-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:482a73d63b7fa9c1862988926ce4bac4fa40637087587831bb5d4e2cf43ae322" }, + { url = "https://pypi.nvidia.com/nvforest-cu13/nvforest_cu13-26.6.0-cp311-abi3-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:34b754e6332b0725f16e7f6d1c309ecacec4f8da74c39198cf148db5ab2b3ee0" }, ] [[package]] @@ -4072,41 +4180,23 @@ version = "12.6.4.1" source = { registry = "https://pypi.nvidia.com/" } resolution-markers = [ "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform == 'win32'", + "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform != 'win32'", "python_full_version >= '3.13' and platform_machine == 'x86_64' and sys_platform == 'win32'", - "python_full_version >= '3.13' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'win32'", - "python_full_version >= '3.13' and platform_machine == 's390x' and sys_platform == 'win32'", - "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform == 'emscripten'", - "python_full_version >= '3.13' and platform_machine == 'x86_64' and sys_platform == 'emscripten'", - "python_full_version >= '3.13' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'emscripten'", - "python_full_version >= '3.13' and platform_machine == 's390x' and sys_platform == 'emscripten'", - "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version >= '3.13' and platform_machine == 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version >= '3.13' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version >= '3.13' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version >= '3.13' and platform_machine == 'x86_64' and sys_platform != 'win32'", + "python_full_version >= '3.13' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'win32'", + "python_full_version >= '3.13' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'win32'", "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform == 'win32'", + "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform != 'win32'", "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform == 'win32'", - "python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'win32'", - "python_full_version == '3.12.*' and platform_machine == 's390x' and sys_platform == 'win32'", - "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform == 'emscripten'", - "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform == 'emscripten'", - "python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'emscripten'", - "python_full_version == '3.12.*' and platform_machine == 's390x' and sys_platform == 'emscripten'", - "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version == '3.12.*' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform != 'win32'", + "python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'win32'", + "python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'win32'", "python_full_version < '3.12' and platform_machine == 'aarch64' and sys_platform == 'win32'", + "python_full_version < '3.12' and platform_machine == 'aarch64' and sys_platform != 'win32'", "python_full_version < '3.12' and platform_machine == 'x86_64' and sys_platform == 'win32'", - "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'win32'", - "python_full_version < '3.12' and platform_machine == 's390x' and sys_platform == 'win32'", - "python_full_version < '3.12' and platform_machine == 'aarch64' and sys_platform == 'emscripten'", - "python_full_version < '3.12' and platform_machine == 'x86_64' and sys_platform == 'emscripten'", - "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'emscripten'", - "python_full_version < '3.12' and platform_machine == 's390x' and sys_platform == 'emscripten'", - "python_full_version < '3.12' and platform_machine == 'aarch64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version < '3.12' and platform_machine == 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version < '3.12' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version < '3.12' and platform_machine == 'x86_64' and sys_platform != 'win32'", + "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'win32'", + "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'win32'", ] wheels = [ { url = "https://pypi.nvidia.com/nvidia-cublas-cu12/nvidia_cublas_cu12-12.6.4.1-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:08ed2686e9875d01b58e3cb379c6896df8e76c75e0d4a7f7dace3d7b6d9ef8eb" }, @@ -4119,9 +4209,9 @@ name = "nvidia-cublas-cu12" version = "12.8.4.1" source = { registry = "https://pypi.nvidia.com/" } resolution-markers = [ - "python_full_version >= '3.13' and platform_machine != 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version == '3.12.*' and platform_machine != 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version < '3.12' and platform_machine != 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version >= '3.13' and sys_platform != 'win32'", + "python_full_version == '3.12.*' and sys_platform != 'win32'", + "python_full_version < '3.12' and sys_platform != 'win32'", ] wheels = [ { url = "https://pypi.nvidia.com/nvidia-cublas-cu12/nvidia_cublas_cu12-12.8.4.1-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:b86f6dd8935884615a0683b663891d43781b819ac4f2ba2b0c9604676af346d0" }, @@ -4151,12 +4241,12 @@ wheels = [ [[package]] name = "nvidia-cuda-crt" -version = "13.2.78" +version = "13.3.73" source = { registry = "https://pypi.nvidia.com/" } wheels = [ - { url = "https://pypi.nvidia.com/nvidia-cuda-crt/nvidia_cuda_crt-13.2.78-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:3f5f1d7bf8a89e98f19f45a2f18bb5df99a806433bfb6f0bc487d9e8f4b3677b" }, - { url = "https://pypi.nvidia.com/nvidia-cuda-crt/nvidia_cuda_crt-13.2.78-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:2c8615ee30ed466cb6298ecb8ffe9e6ea8b252ca833206152d155750bf831608" }, - { url = "https://pypi.nvidia.com/nvidia-cuda-crt/nvidia_cuda_crt-13.2.78-py3-none-win_amd64.whl", hash = "sha256:6a5ac267680aecbec0405884c81b12db48f9baadfa67a62f230ab2f47f6ccaf1" }, + { url = "https://pypi.nvidia.com/nvidia-cuda-crt/nvidia_cuda_crt-13.3.73-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:60aacc0b5e1e8b40c62abe4d1ab16440add91b99bd2f17f62dd091586b73d166" }, + { url = "https://pypi.nvidia.com/nvidia-cuda-crt/nvidia_cuda_crt-13.3.73-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:df14a17ae1c5c3171265411212246654d780f89344ea85344466c6b955247543" }, + { url = "https://pypi.nvidia.com/nvidia-cuda-crt/nvidia_cuda_crt-13.3.73-py3-none-win_amd64.whl", hash = "sha256:af04e75148db1f0eea30958f33a9ec5a5a2dc2afa99ca4323f9a93b840602ca5" }, ] [[package]] @@ -4174,18 +4264,15 @@ name = "nvidia-cuda-cupti-cu12" version = "12.6.80" source = { registry = "https://pypi.nvidia.com/" } resolution-markers = [ - "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version >= '3.13' and platform_machine == 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version >= '3.13' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version >= '3.13' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version == '3.12.*' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version < '3.12' and platform_machine == 'aarch64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version < '3.12' and platform_machine == 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version < '3.12' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform != 'win32'", + "python_full_version >= '3.13' and platform_machine == 'x86_64' and sys_platform != 'win32'", + "python_full_version >= '3.13' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'win32'", + "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform != 'win32'", + "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform != 'win32'", + "python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'win32'", + "python_full_version < '3.12' and platform_machine == 'aarch64' and sys_platform != 'win32'", + "python_full_version < '3.12' and platform_machine == 'x86_64' and sys_platform != 'win32'", + "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'win32'", ] wheels = [ { url = "https://pypi.nvidia.com/nvidia-cuda-cupti-cu12/nvidia_cuda_cupti_cu12-12.6.80-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:166ee35a3ff1587f2490364f90eeeb8da06cd867bd5b701bf7f9a02b78bc63fc" }, @@ -4200,9 +4287,9 @@ name = "nvidia-cuda-cupti-cu12" version = "12.8.90" source = { registry = "https://pypi.nvidia.com/" } resolution-markers = [ - "python_full_version >= '3.13' and platform_machine != 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version == '3.12.*' and platform_machine != 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version < '3.12' and platform_machine != 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version >= '3.13' and sys_platform != 'win32'", + "python_full_version == '3.12.*' and sys_platform != 'win32'", + "python_full_version < '3.12' and sys_platform != 'win32'", ] wheels = [ { url = "https://pypi.nvidia.com/nvidia-cuda-cupti-cu12/nvidia_cuda_cupti_cu12-12.8.90-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:4412396548808ddfed3f17a467b104ba7751e6b58678a4b840675c56d21cf7ed" }, @@ -4215,9 +4302,9 @@ name = "nvidia-cuda-nvcc" version = "13.0.88" source = { registry = "https://pypi.nvidia.com/" } dependencies = [ - { name = "nvidia-cuda-crt", marker = "sys_platform != 'emscripten'" }, - { name = "nvidia-cuda-runtime", marker = "sys_platform != 'emscripten'" }, - { name = "nvidia-nvvm", marker = "sys_platform != 'emscripten'" }, + { name = "nvidia-cuda-crt", marker = "python_full_version < '3.13' or sys_platform != 'emscripten'" }, + { name = "nvidia-cuda-runtime", marker = "python_full_version < '3.13' or sys_platform != 'emscripten'" }, + { name = "nvidia-nvvm", marker = "python_full_version < '3.13' or sys_platform != 'emscripten'" }, ] wheels = [ { url = "https://pypi.nvidia.com/nvidia-cuda-nvcc/nvidia_cuda_nvcc-13.0.88-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:c7ff28f86a24effdc6c034fa15230c549a273e4771b10a7fec14996f8cf3307f" }, @@ -4251,41 +4338,23 @@ version = "12.6.85" source = { registry = "https://pypi.nvidia.com/" } resolution-markers = [ "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform == 'win32'", + "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform != 'win32'", "python_full_version >= '3.13' and platform_machine == 'x86_64' and sys_platform == 'win32'", - "python_full_version >= '3.13' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'win32'", - "python_full_version >= '3.13' and platform_machine == 's390x' and sys_platform == 'win32'", - "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform == 'emscripten'", - "python_full_version >= '3.13' and platform_machine == 'x86_64' and sys_platform == 'emscripten'", - "python_full_version >= '3.13' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'emscripten'", - "python_full_version >= '3.13' and platform_machine == 's390x' and sys_platform == 'emscripten'", - "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version >= '3.13' and platform_machine == 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version >= '3.13' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version >= '3.13' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version >= '3.13' and platform_machine == 'x86_64' and sys_platform != 'win32'", + "python_full_version >= '3.13' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'win32'", + "python_full_version >= '3.13' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'win32'", "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform == 'win32'", + "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform != 'win32'", "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform == 'win32'", - "python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'win32'", - "python_full_version == '3.12.*' and platform_machine == 's390x' and sys_platform == 'win32'", - "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform == 'emscripten'", - "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform == 'emscripten'", - "python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'emscripten'", - "python_full_version == '3.12.*' and platform_machine == 's390x' and sys_platform == 'emscripten'", - "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version == '3.12.*' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform != 'win32'", + "python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'win32'", + "python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'win32'", "python_full_version < '3.12' and platform_machine == 'aarch64' and sys_platform == 'win32'", + "python_full_version < '3.12' and platform_machine == 'aarch64' and sys_platform != 'win32'", "python_full_version < '3.12' and platform_machine == 'x86_64' and sys_platform == 'win32'", - "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'win32'", - "python_full_version < '3.12' and platform_machine == 's390x' and sys_platform == 'win32'", - "python_full_version < '3.12' and platform_machine == 'aarch64' and sys_platform == 'emscripten'", - "python_full_version < '3.12' and platform_machine == 'x86_64' and sys_platform == 'emscripten'", - "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'emscripten'", - "python_full_version < '3.12' and platform_machine == 's390x' and sys_platform == 'emscripten'", - "python_full_version < '3.12' and platform_machine == 'aarch64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version < '3.12' and platform_machine == 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version < '3.12' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version < '3.12' and platform_machine == 'x86_64' and sys_platform != 'win32'", + "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'win32'", + "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'win32'", ] wheels = [ { url = "https://pypi.nvidia.com/nvidia-cuda-nvrtc-cu12/nvidia_cuda_nvrtc_cu12-12.6.85-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl", hash = "sha256:800927308ccc5dd6246d3f61f7fcef2ed7ec4e59e199090d360d3293f78bd5a2" }, @@ -4298,9 +4367,9 @@ name = "nvidia-cuda-nvrtc-cu12" version = "12.8.93" source = { registry = "https://pypi.nvidia.com/" } resolution-markers = [ - "python_full_version >= '3.13' and platform_machine != 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version == '3.12.*' and platform_machine != 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version < '3.12' and platform_machine != 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version >= '3.13' and sys_platform != 'win32'", + "python_full_version == '3.12.*' and sys_platform != 'win32'", + "python_full_version < '3.12' and sys_platform != 'win32'", ] wheels = [ { url = "https://pypi.nvidia.com/nvidia-cuda-nvrtc-cu12/nvidia_cuda_nvrtc_cu12-12.8.93-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl", hash = "sha256:a7756528852ef889772a84c6cd89d41dfa74667e24cca16bb31f8f061e3e9994" }, @@ -4324,41 +4393,23 @@ version = "12.6.77" source = { registry = "https://pypi.nvidia.com/" } resolution-markers = [ "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform == 'win32'", + "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform != 'win32'", "python_full_version >= '3.13' and platform_machine == 'x86_64' and sys_platform == 'win32'", - "python_full_version >= '3.13' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'win32'", - "python_full_version >= '3.13' and platform_machine == 's390x' and sys_platform == 'win32'", - "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform == 'emscripten'", - "python_full_version >= '3.13' and platform_machine == 'x86_64' and sys_platform == 'emscripten'", - "python_full_version >= '3.13' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'emscripten'", - "python_full_version >= '3.13' and platform_machine == 's390x' and sys_platform == 'emscripten'", - "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version >= '3.13' and platform_machine == 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version >= '3.13' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version >= '3.13' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version >= '3.13' and platform_machine == 'x86_64' and sys_platform != 'win32'", + "python_full_version >= '3.13' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'win32'", + "python_full_version >= '3.13' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'win32'", "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform == 'win32'", + "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform != 'win32'", "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform == 'win32'", - "python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'win32'", - "python_full_version == '3.12.*' and platform_machine == 's390x' and sys_platform == 'win32'", - "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform == 'emscripten'", - "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform == 'emscripten'", - "python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'emscripten'", - "python_full_version == '3.12.*' and platform_machine == 's390x' and sys_platform == 'emscripten'", - "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version == '3.12.*' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform != 'win32'", + "python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'win32'", + "python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'win32'", "python_full_version < '3.12' and platform_machine == 'aarch64' and sys_platform == 'win32'", + "python_full_version < '3.12' and platform_machine == 'aarch64' and sys_platform != 'win32'", "python_full_version < '3.12' and platform_machine == 'x86_64' and sys_platform == 'win32'", - "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'win32'", - "python_full_version < '3.12' and platform_machine == 's390x' and sys_platform == 'win32'", - "python_full_version < '3.12' and platform_machine == 'aarch64' and sys_platform == 'emscripten'", - "python_full_version < '3.12' and platform_machine == 'x86_64' and sys_platform == 'emscripten'", - "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'emscripten'", - "python_full_version < '3.12' and platform_machine == 's390x' and sys_platform == 'emscripten'", - "python_full_version < '3.12' and platform_machine == 'aarch64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version < '3.12' and platform_machine == 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version < '3.12' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version < '3.12' and platform_machine == 'x86_64' and sys_platform != 'win32'", + "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'win32'", + "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'win32'", ] wheels = [ { url = "https://pypi.nvidia.com/nvidia-cuda-runtime-cu12/nvidia_cuda_runtime_cu12-12.6.77-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:6116fad3e049e04791c0256a9778c16237837c08b27ed8c8401e2e45de8d60cd" }, @@ -4373,9 +4424,9 @@ name = "nvidia-cuda-runtime-cu12" version = "12.8.90" source = { registry = "https://pypi.nvidia.com/" } resolution-markers = [ - "python_full_version >= '3.13' and platform_machine != 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version == '3.12.*' and platform_machine != 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version < '3.12' and platform_machine != 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version >= '3.13' and sys_platform != 'win32'", + "python_full_version == '3.12.*' and sys_platform != 'win32'", + "python_full_version < '3.12' and sys_platform != 'win32'", ] wheels = [ { url = "https://pypi.nvidia.com/nvidia-cuda-runtime-cu12/nvidia_cuda_runtime_cu12-12.8.90-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:52bf7bbee900262ffefe5e9d5a2a69a30d97e2bc5bb6cc866688caa976966e3d" }, @@ -4388,8 +4439,8 @@ name = "nvidia-cudnn-cu12" version = "9.10.2.21" source = { registry = "https://pypi.nvidia.com/" } dependencies = [ - { name = "nvidia-cublas-cu12", version = "12.6.4.1", source = { registry = "https://pypi.nvidia.com/" }, marker = "(sys_platform != 'emscripten' and sys_platform != 'win32' and extra == 'extra-17-nvalchemi-toolkit-cu12') or (sys_platform == 'emscripten' and extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (sys_platform == 'emscripten' and extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-uma') or (sys_platform == 'win32' and extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (sys_platform == 'win32' and extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra != 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra != 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma')" }, - { name = "nvidia-cublas-cu12", version = "12.8.4.1", source = { registry = "https://pypi.nvidia.com/" }, marker = "(platform_machine != 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32' and extra == 'extra-17-nvalchemi-toolkit-uma') or (platform_machine == 's390x' and extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (platform_machine == 's390x' and extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma') or (sys_platform == 'emscripten' and extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (sys_platform == 'emscripten' and extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma') or (sys_platform == 'win32' and extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (sys_platform == 'win32' and extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-uma')" }, + { name = "nvidia-cublas-cu12", version = "12.6.4.1", source = { registry = "https://pypi.nvidia.com/" }, marker = "(sys_platform != 'win32' and extra == 'extra-17-nvalchemi-toolkit-cu12') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma')" }, + { name = "nvidia-cublas-cu12", version = "12.8.4.1", source = { registry = "https://pypi.nvidia.com/" }, marker = "(sys_platform != 'win32' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma')" }, ] wheels = [ { url = "https://pypi.nvidia.com/nvidia-cudnn-cu12/nvidia_cudnn_cu12-9.10.2.21-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:c9132cc3f8958447b4910a1720036d9eff5928cc3179b0a51fb6d167c6cc87d8" }, @@ -4402,7 +4453,7 @@ name = "nvidia-cudnn-cu13" version = "9.20.0.48" source = { registry = "https://pypi.nvidia.com/" } dependencies = [ - { name = "nvidia-cublas", marker = "(sys_platform != 'emscripten' and sys_platform != 'win32' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (sys_platform != 'emscripten' and sys_platform != 'win32' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-mace') or (sys_platform != 'emscripten' and sys_platform != 'win32' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-uma') or (sys_platform == 'emscripten' and extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (sys_platform == 'emscripten' and extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma') or (sys_platform == 'win32' and extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (sys_platform == 'win32' and extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-uma')" }, + { name = "nvidia-cublas", marker = "(python_full_version < '3.13' and sys_platform == 'emscripten' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (sys_platform != 'emscripten' and sys_platform != 'win32' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (sys_platform != 'emscripten' and sys_platform != 'win32' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-mace') or (sys_platform != 'emscripten' and sys_platform != 'win32' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-uma') or (sys_platform == 'emscripten' and extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (sys_platform == 'win32' and extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (sys_platform == 'win32' and extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma') or (sys_platform == 'emscripten' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-mace') or (sys_platform == 'emscripten' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-uma')" }, ] wheels = [ { url = "https://pypi.nvidia.com/nvidia-cudnn-cu13/nvidia_cudnn_cu13-9.20.0.48-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:e31454ae00094b0c55319d9d15b6fa2fc50a9e1c0f5c8c80fb75258234e731e1" }, @@ -4415,7 +4466,7 @@ name = "nvidia-cufft" version = "12.0.0.61" source = { registry = "https://pypi.nvidia.com/" } dependencies = [ - { name = "nvidia-nvjitlink", marker = "(sys_platform != 'emscripten' and sys_platform != 'win32' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-mace') or (sys_platform != 'emscripten' and sys_platform != 'win32' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-uma') or (sys_platform != 'emscripten' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (sys_platform == 'win32' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (sys_platform == 'emscripten' and extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma') or (sys_platform == 'win32' and extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-uma')" }, + { name = "nvidia-nvjitlink", marker = "(python_full_version < '3.13' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (sys_platform != 'emscripten' and sys_platform != 'win32' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-mace') or (sys_platform != 'emscripten' and sys_platform != 'win32' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-uma') or (sys_platform != 'emscripten' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (sys_platform == 'win32' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (sys_platform == 'win32' and extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma') or (sys_platform == 'emscripten' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-mace') or (sys_platform == 'emscripten' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-uma')" }, ] wheels = [ { url = "https://pypi.nvidia.com/nvidia-cufft/nvidia_cufft-12.0.0.61-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:2708c852ef8cd89d1d2068bdbece0aa188813a0c934db3779b9b1faa8442e5f5" }, @@ -4429,32 +4480,26 @@ version = "11.3.0.4" source = { registry = "https://pypi.nvidia.com/" } resolution-markers = [ "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform == 'win32'", + "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform != 'win32'", "python_full_version >= '3.13' and platform_machine == 'x86_64' and sys_platform == 'win32'", - "python_full_version >= '3.13' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'win32'", - "python_full_version >= '3.13' and platform_machine == 's390x' and sys_platform == 'win32'", - "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version >= '3.13' and platform_machine == 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version >= '3.13' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version >= '3.13' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version >= '3.13' and platform_machine == 'x86_64' and sys_platform != 'win32'", + "python_full_version >= '3.13' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'win32'", + "python_full_version >= '3.13' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'win32'", "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform == 'win32'", + "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform != 'win32'", "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform == 'win32'", - "python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'win32'", - "python_full_version == '3.12.*' and platform_machine == 's390x' and sys_platform == 'win32'", - "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version == '3.12.*' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform != 'win32'", + "python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'win32'", + "python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'win32'", "python_full_version < '3.12' and platform_machine == 'aarch64' and sys_platform == 'win32'", + "python_full_version < '3.12' and platform_machine == 'aarch64' and sys_platform != 'win32'", "python_full_version < '3.12' and platform_machine == 'x86_64' and sys_platform == 'win32'", - "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'win32'", - "python_full_version < '3.12' and platform_machine == 's390x' and sys_platform == 'win32'", - "python_full_version < '3.12' and platform_machine == 'aarch64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version < '3.12' and platform_machine == 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version < '3.12' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version < '3.12' and platform_machine == 'x86_64' and sys_platform != 'win32'", + "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'win32'", + "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'win32'", ] dependencies = [ - { name = "nvidia-nvjitlink-cu12", version = "12.6.85", source = { registry = "https://pypi.nvidia.com/" }, marker = "(sys_platform != 'emscripten' and extra == 'extra-17-nvalchemi-toolkit-cu12') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma')" }, + { name = "nvidia-nvjitlink-cu12", version = "12.6.85", source = { registry = "https://pypi.nvidia.com/" }, marker = "extra == 'extra-17-nvalchemi-toolkit-cu12' or (extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma')" }, ] wheels = [ { url = "https://pypi.nvidia.com/nvidia-cufft-cu12/nvidia_cufft_cu12-11.3.0.4-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:d16079550df460376455cba121db6564089176d9bac9e4f360493ca4741b22a6" }, @@ -4469,12 +4514,12 @@ name = "nvidia-cufft-cu12" version = "11.3.3.83" source = { registry = "https://pypi.nvidia.com/" } resolution-markers = [ - "python_full_version >= '3.13' and platform_machine != 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version == '3.12.*' and platform_machine != 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version < '3.12' and platform_machine != 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version >= '3.13' and sys_platform != 'win32'", + "python_full_version == '3.12.*' and sys_platform != 'win32'", + "python_full_version < '3.12' and sys_platform != 'win32'", ] dependencies = [ - { name = "nvidia-nvjitlink-cu12", version = "12.8.93", source = { registry = "https://pypi.nvidia.com/" }, marker = "(platform_machine != 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32' and extra == 'extra-17-nvalchemi-toolkit-uma') or (platform_machine == 's390x' and extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (platform_machine == 's390x' and extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma') or (sys_platform == 'emscripten' and extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (sys_platform == 'emscripten' and extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma') or (sys_platform == 'win32' and extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (sys_platform == 'win32' and extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-uma')" }, + { name = "nvidia-nvjitlink-cu12", version = "12.8.93", source = { registry = "https://pypi.nvidia.com/" }, marker = "(sys_platform != 'win32' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma')" }, ] wheels = [ { url = "https://pypi.nvidia.com/nvidia-cufft-cu12/nvidia_cufft_cu12-11.3.3.83-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:848ef7224d6305cdb2a4df928759dca7b1201874787083b6e7550dd6765ce69a" }, @@ -4496,18 +4541,15 @@ name = "nvidia-cufile-cu12" version = "1.11.1.6" source = { registry = "https://pypi.nvidia.com/" } resolution-markers = [ - "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version >= '3.13' and platform_machine == 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version >= '3.13' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version >= '3.13' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version == '3.12.*' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version < '3.12' and platform_machine == 'aarch64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version < '3.12' and platform_machine == 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version < '3.12' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform != 'win32'", + "python_full_version >= '3.13' and platform_machine == 'x86_64' and sys_platform != 'win32'", + "python_full_version >= '3.13' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'win32'", + "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform != 'win32'", + "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform != 'win32'", + "python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'win32'", + "python_full_version < '3.12' and platform_machine == 'aarch64' and sys_platform != 'win32'", + "python_full_version < '3.12' and platform_machine == 'x86_64' and sys_platform != 'win32'", + "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'win32'", ] wheels = [ { url = "https://pypi.nvidia.com/nvidia-cufile-cu12/nvidia_cufile_cu12-1.11.1.6-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:cc23469d1c7e52ce6c1d55253273d32c565dd22068647f3aa59b3c6b005bf159" }, @@ -4519,9 +4561,9 @@ name = "nvidia-cufile-cu12" version = "1.13.1.3" source = { registry = "https://pypi.nvidia.com/" } resolution-markers = [ - "python_full_version >= '3.13' and platform_machine != 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version == '3.12.*' and platform_machine != 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version < '3.12' and platform_machine != 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version >= '3.13' and sys_platform != 'win32'", + "python_full_version == '3.12.*' and sys_platform != 'win32'", + "python_full_version < '3.12' and sys_platform != 'win32'", ] wheels = [ { url = "https://pypi.nvidia.com/nvidia-cufile-cu12/nvidia_cufile_cu12-1.13.1.3-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:1d069003be650e131b21c932ec3d8969c1715379251f8d23a1860554b1cb24fc" }, @@ -4544,29 +4586,23 @@ version = "10.3.7.77" source = { registry = "https://pypi.nvidia.com/" } resolution-markers = [ "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform == 'win32'", + "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform != 'win32'", "python_full_version >= '3.13' and platform_machine == 'x86_64' and sys_platform == 'win32'", - "python_full_version >= '3.13' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'win32'", - "python_full_version >= '3.13' and platform_machine == 's390x' and sys_platform == 'win32'", - "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version >= '3.13' and platform_machine == 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version >= '3.13' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version >= '3.13' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version >= '3.13' and platform_machine == 'x86_64' and sys_platform != 'win32'", + "python_full_version >= '3.13' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'win32'", + "python_full_version >= '3.13' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'win32'", "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform == 'win32'", + "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform != 'win32'", "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform == 'win32'", - "python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'win32'", - "python_full_version == '3.12.*' and platform_machine == 's390x' and sys_platform == 'win32'", - "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version == '3.12.*' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform != 'win32'", + "python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'win32'", + "python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'win32'", "python_full_version < '3.12' and platform_machine == 'aarch64' and sys_platform == 'win32'", + "python_full_version < '3.12' and platform_machine == 'aarch64' and sys_platform != 'win32'", "python_full_version < '3.12' and platform_machine == 'x86_64' and sys_platform == 'win32'", - "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'win32'", - "python_full_version < '3.12' and platform_machine == 's390x' and sys_platform == 'win32'", - "python_full_version < '3.12' and platform_machine == 'aarch64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version < '3.12' and platform_machine == 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version < '3.12' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version < '3.12' and platform_machine == 'x86_64' and sys_platform != 'win32'", + "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'win32'", + "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'win32'", ] wheels = [ { url = "https://pypi.nvidia.com/nvidia-curand-cu12/nvidia_curand_cu12-10.3.7.77-py3-none-manylinux2014_aarch64.whl", hash = "sha256:6e82df077060ea28e37f48a3ec442a8f47690c7499bff392a5938614b56c98d8" }, @@ -4581,9 +4617,9 @@ name = "nvidia-curand-cu12" version = "10.3.9.90" source = { registry = "https://pypi.nvidia.com/" } resolution-markers = [ - "python_full_version >= '3.13' and platform_machine != 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version == '3.12.*' and platform_machine != 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version < '3.12' and platform_machine != 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version >= '3.13' and sys_platform != 'win32'", + "python_full_version == '3.12.*' and sys_platform != 'win32'", + "python_full_version < '3.12' and sys_platform != 'win32'", ] wheels = [ { url = "https://pypi.nvidia.com/nvidia-curand-cu12/nvidia_curand_cu12-10.3.9.90-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:dfab99248034673b779bc6decafdc3404a8a6f502462201f2f31f11354204acd" }, @@ -4596,9 +4632,9 @@ name = "nvidia-cusolver" version = "12.0.4.66" source = { registry = "https://pypi.nvidia.com/" } dependencies = [ - { name = "nvidia-cublas", marker = "(sys_platform != 'emscripten' and sys_platform != 'win32' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-mace') or (sys_platform != 'emscripten' and sys_platform != 'win32' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-uma') or (sys_platform != 'emscripten' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (sys_platform == 'win32' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (sys_platform == 'emscripten' and extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma') or (sys_platform == 'win32' and extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-uma')" }, - { name = "nvidia-cusparse", marker = "(sys_platform != 'emscripten' and sys_platform != 'win32' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-mace') or (sys_platform != 'emscripten' and sys_platform != 'win32' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-uma') or (sys_platform != 'emscripten' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (sys_platform == 'win32' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (sys_platform == 'emscripten' and extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma') or (sys_platform == 'win32' and extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-uma')" }, - { name = "nvidia-nvjitlink", marker = "(sys_platform != 'emscripten' and sys_platform != 'win32' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-mace') or (sys_platform != 'emscripten' and sys_platform != 'win32' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-uma') or (sys_platform != 'emscripten' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (sys_platform == 'win32' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (sys_platform == 'emscripten' and extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma') or (sys_platform == 'win32' and extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-uma')" }, + { name = "nvidia-cublas", marker = "(python_full_version < '3.13' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (sys_platform != 'emscripten' and sys_platform != 'win32' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-mace') or (sys_platform != 'emscripten' and sys_platform != 'win32' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-uma') or (sys_platform != 'emscripten' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (sys_platform == 'win32' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (sys_platform == 'win32' and extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma') or (sys_platform == 'emscripten' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-mace') or (sys_platform == 'emscripten' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-uma')" }, + { name = "nvidia-cusparse", marker = "(python_full_version < '3.13' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (sys_platform != 'emscripten' and sys_platform != 'win32' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-mace') or (sys_platform != 'emscripten' and sys_platform != 'win32' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-uma') or (sys_platform != 'emscripten' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (sys_platform == 'win32' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (sys_platform == 'win32' and extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma') or (sys_platform == 'emscripten' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-mace') or (sys_platform == 'emscripten' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-uma')" }, + { name = "nvidia-nvjitlink", marker = "(python_full_version < '3.13' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (sys_platform != 'emscripten' and sys_platform != 'win32' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-mace') or (sys_platform != 'emscripten' and sys_platform != 'win32' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-uma') or (sys_platform != 'emscripten' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (sys_platform == 'win32' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (sys_platform == 'win32' and extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma') or (sys_platform == 'emscripten' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-mace') or (sys_platform == 'emscripten' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-uma')" }, ] wheels = [ { url = "https://pypi.nvidia.com/nvidia-cusolver/nvidia_cusolver-12.0.4.66-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:02c2457eaa9e39de20f880f4bd8820e6a1cfb9f9a34f820eb12a155aa5bc92d2" }, @@ -4612,34 +4648,28 @@ version = "11.7.1.2" source = { registry = "https://pypi.nvidia.com/" } resolution-markers = [ "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform == 'win32'", + "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform != 'win32'", "python_full_version >= '3.13' and platform_machine == 'x86_64' and sys_platform == 'win32'", - "python_full_version >= '3.13' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'win32'", - "python_full_version >= '3.13' and platform_machine == 's390x' and sys_platform == 'win32'", - "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version >= '3.13' and platform_machine == 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version >= '3.13' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version >= '3.13' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version >= '3.13' and platform_machine == 'x86_64' and sys_platform != 'win32'", + "python_full_version >= '3.13' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'win32'", + "python_full_version >= '3.13' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'win32'", "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform == 'win32'", + "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform != 'win32'", "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform == 'win32'", - "python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'win32'", - "python_full_version == '3.12.*' and platform_machine == 's390x' and sys_platform == 'win32'", - "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version == '3.12.*' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform != 'win32'", + "python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'win32'", + "python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'win32'", "python_full_version < '3.12' and platform_machine == 'aarch64' and sys_platform == 'win32'", + "python_full_version < '3.12' and platform_machine == 'aarch64' and sys_platform != 'win32'", "python_full_version < '3.12' and platform_machine == 'x86_64' and sys_platform == 'win32'", - "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'win32'", - "python_full_version < '3.12' and platform_machine == 's390x' and sys_platform == 'win32'", - "python_full_version < '3.12' and platform_machine == 'aarch64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version < '3.12' and platform_machine == 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version < '3.12' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version < '3.12' and platform_machine == 'x86_64' and sys_platform != 'win32'", + "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'win32'", + "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'win32'", ] dependencies = [ - { name = "nvidia-cublas-cu12", version = "12.6.4.1", source = { registry = "https://pypi.nvidia.com/" }, marker = "(sys_platform != 'emscripten' and extra == 'extra-17-nvalchemi-toolkit-cu12') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma')" }, - { name = "nvidia-cusparse-cu12", version = "12.5.4.2", source = { registry = "https://pypi.nvidia.com/" }, marker = "(sys_platform != 'emscripten' and extra == 'extra-17-nvalchemi-toolkit-cu12') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma')" }, - { name = "nvidia-nvjitlink-cu12", version = "12.6.85", source = { registry = "https://pypi.nvidia.com/" }, marker = "(sys_platform != 'emscripten' and extra == 'extra-17-nvalchemi-toolkit-cu12') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma')" }, + { name = "nvidia-cublas-cu12", version = "12.6.4.1", source = { registry = "https://pypi.nvidia.com/" }, marker = "extra == 'extra-17-nvalchemi-toolkit-cu12' or (extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma')" }, + { name = "nvidia-cusparse-cu12", version = "12.5.4.2", source = { registry = "https://pypi.nvidia.com/" }, marker = "extra == 'extra-17-nvalchemi-toolkit-cu12' or (extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma')" }, + { name = "nvidia-nvjitlink-cu12", version = "12.6.85", source = { registry = "https://pypi.nvidia.com/" }, marker = "extra == 'extra-17-nvalchemi-toolkit-cu12' or (extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma')" }, ] wheels = [ { url = "https://pypi.nvidia.com/nvidia-cusolver-cu12/nvidia_cusolver_cu12-11.7.1.2-py3-none-manylinux2014_aarch64.whl", hash = "sha256:0ce237ef60acde1efc457335a2ddadfd7610b892d94efee7b776c64bb1cac9e0" }, @@ -4654,14 +4684,14 @@ name = "nvidia-cusolver-cu12" version = "11.7.3.90" source = { registry = "https://pypi.nvidia.com/" } resolution-markers = [ - "python_full_version >= '3.13' and platform_machine != 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version == '3.12.*' and platform_machine != 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version < '3.12' and platform_machine != 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version >= '3.13' and sys_platform != 'win32'", + "python_full_version == '3.12.*' and sys_platform != 'win32'", + "python_full_version < '3.12' and sys_platform != 'win32'", ] dependencies = [ - { name = "nvidia-cublas-cu12", version = "12.8.4.1", source = { registry = "https://pypi.nvidia.com/" }, marker = "(platform_machine != 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32' and extra == 'extra-17-nvalchemi-toolkit-uma') or (platform_machine == 's390x' and extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (platform_machine == 's390x' and extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma') or (sys_platform == 'emscripten' and extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (sys_platform == 'emscripten' and extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma') or (sys_platform == 'win32' and extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (sys_platform == 'win32' and extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-uma')" }, - { name = "nvidia-cusparse-cu12", version = "12.5.8.93", source = { registry = "https://pypi.nvidia.com/" }, marker = "(platform_machine != 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32' and extra == 'extra-17-nvalchemi-toolkit-uma') or (platform_machine == 's390x' and extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (platform_machine == 's390x' and extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma') or (sys_platform == 'emscripten' and extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (sys_platform == 'emscripten' and extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma') or (sys_platform == 'win32' and extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (sys_platform == 'win32' and extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-uma')" }, - { name = "nvidia-nvjitlink-cu12", version = "12.8.93", source = { registry = "https://pypi.nvidia.com/" }, marker = "(platform_machine != 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32' and extra == 'extra-17-nvalchemi-toolkit-uma') or (platform_machine == 's390x' and extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (platform_machine == 's390x' and extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma') or (sys_platform == 'emscripten' and extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (sys_platform == 'emscripten' and extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma') or (sys_platform == 'win32' and extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (sys_platform == 'win32' and extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-uma')" }, + { name = "nvidia-cublas-cu12", version = "12.8.4.1", source = { registry = "https://pypi.nvidia.com/" }, marker = "(sys_platform != 'win32' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma')" }, + { name = "nvidia-cusparse-cu12", version = "12.5.8.93", source = { registry = "https://pypi.nvidia.com/" }, marker = "(sys_platform != 'win32' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma')" }, + { name = "nvidia-nvjitlink-cu12", version = "12.8.93", source = { registry = "https://pypi.nvidia.com/" }, marker = "(sys_platform != 'win32' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma')" }, ] wheels = [ { url = "https://pypi.nvidia.com/nvidia-cusolver-cu12/nvidia_cusolver_cu12-11.7.3.90-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:db9ed69dbef9715071232caa9b69c52ac7de3a95773c2db65bdba85916e4e5c0" }, @@ -4674,7 +4704,7 @@ name = "nvidia-cusparse" version = "12.6.3.3" source = { registry = "https://pypi.nvidia.com/" } dependencies = [ - { name = "nvidia-nvjitlink", marker = "(sys_platform != 'emscripten' and sys_platform != 'win32' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-mace') or (sys_platform != 'emscripten' and sys_platform != 'win32' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-uma') or (sys_platform != 'emscripten' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (sys_platform == 'win32' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (sys_platform == 'emscripten' and extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma') or (sys_platform == 'win32' and extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-uma')" }, + { name = "nvidia-nvjitlink", marker = "(python_full_version < '3.13' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (sys_platform != 'emscripten' and sys_platform != 'win32' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-mace') or (sys_platform != 'emscripten' and sys_platform != 'win32' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-uma') or (sys_platform != 'emscripten' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (sys_platform == 'win32' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (sys_platform == 'win32' and extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma') or (sys_platform == 'emscripten' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-mace') or (sys_platform == 'emscripten' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-uma')" }, ] wheels = [ { url = "https://pypi.nvidia.com/nvidia-cusparse/nvidia_cusparse-12.6.3.3-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:80bcc4662f23f1054ee334a15c72b8940402975e0eab63178fc7e670aa59472c" }, @@ -4688,32 +4718,26 @@ version = "12.5.4.2" source = { registry = "https://pypi.nvidia.com/" } resolution-markers = [ "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform == 'win32'", + "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform != 'win32'", "python_full_version >= '3.13' and platform_machine == 'x86_64' and sys_platform == 'win32'", - "python_full_version >= '3.13' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'win32'", - "python_full_version >= '3.13' and platform_machine == 's390x' and sys_platform == 'win32'", - "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version >= '3.13' and platform_machine == 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version >= '3.13' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version >= '3.13' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version >= '3.13' and platform_machine == 'x86_64' and sys_platform != 'win32'", + "python_full_version >= '3.13' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'win32'", + "python_full_version >= '3.13' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'win32'", "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform == 'win32'", + "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform != 'win32'", "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform == 'win32'", - "python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'win32'", - "python_full_version == '3.12.*' and platform_machine == 's390x' and sys_platform == 'win32'", - "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version == '3.12.*' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform != 'win32'", + "python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'win32'", + "python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'win32'", "python_full_version < '3.12' and platform_machine == 'aarch64' and sys_platform == 'win32'", + "python_full_version < '3.12' and platform_machine == 'aarch64' and sys_platform != 'win32'", "python_full_version < '3.12' and platform_machine == 'x86_64' and sys_platform == 'win32'", - "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'win32'", - "python_full_version < '3.12' and platform_machine == 's390x' and sys_platform == 'win32'", - "python_full_version < '3.12' and platform_machine == 'aarch64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version < '3.12' and platform_machine == 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version < '3.12' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version < '3.12' and platform_machine == 'x86_64' and sys_platform != 'win32'", + "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'win32'", + "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'win32'", ] dependencies = [ - { name = "nvidia-nvjitlink-cu12", version = "12.6.85", source = { registry = "https://pypi.nvidia.com/" }, marker = "(sys_platform != 'emscripten' and extra == 'extra-17-nvalchemi-toolkit-cu12') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma')" }, + { name = "nvidia-nvjitlink-cu12", version = "12.6.85", source = { registry = "https://pypi.nvidia.com/" }, marker = "extra == 'extra-17-nvalchemi-toolkit-cu12' or (extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-mace' and 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hash = "sha256:d6d7537990ef2df14e50275b79e9a4d80a19b08cea705c7eb1e30b5c887924e9" }, + { url = "https://pypi.nvidia.com/nvidia-dali-cuda130/nvidia_dali_cuda130-2.2.0-py3-none-manylinux_2_28_aarch64.whl", hash = "sha256:115b2f19de9b01c49f66f020ee97fca209ea7c6636b402b874e5230c329ffdd4" }, + { url = "https://pypi.nvidia.com/nvidia-dali-cuda130/nvidia_dali_cuda130-2.2.0-py3-none-manylinux_2_28_x86_64.whl", hash = "sha256:a957b654bd851f36b65ab8c99705bba40cf699d10a11cccecdc995523f739524" }, ] [[package]] @@ -4815,21 +4840,21 @@ wheels = [ [[package]] name = "nvidia-libnvcomp-cu13" -version = "5.1.0.21" +version = "5.2.0.13" source = { registry = "https://pypi.nvidia.com/" } wheels = [ - { url = "https://pypi.nvidia.com/nvidia-libnvcomp-cu13/nvidia_libnvcomp_cu13-5.1.0.21-py3-none-manylinux_2_28_aarch64.whl", hash = "sha256:131c0d375dd739ce0bdcec484f9c6322600c5cf28bd0e3bc520d6d88c1f71cd4" }, - { url = 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'3.13' and sys_platform != 'win32'", + "python_full_version == '3.12.*' and sys_platform != 'win32'", + "python_full_version < '3.12' and sys_platform != 'win32'", ] wheels = [ { url = "https://pypi.nvidia.com/nvidia-nccl-cu12/nvidia_nccl_cu12-2.27.3-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:9ddf1a245abc36c550870f26d537a9b6087fb2e2e3d6e0ef03374c6fd19d984f" }, @@ -4852,41 +4877,23 @@ version = "2.29.3" source = { registry = "https://pypi.nvidia.com/" } resolution-markers = [ "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform == 'win32'", + "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform != 'win32'", "python_full_version >= '3.13' and platform_machine == 'x86_64' and sys_platform == 'win32'", - "python_full_version >= '3.13' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'win32'", - "python_full_version >= '3.13' and 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== '3.12.*' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform != 'win32'", + "python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'win32'", + "python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'win32'", "python_full_version < '3.12' and platform_machine == 'aarch64' and sys_platform == 'win32'", + "python_full_version < '3.12' and platform_machine == 'aarch64' and sys_platform != 'win32'", "python_full_version < '3.12' and platform_machine == 'x86_64' and sys_platform == 'win32'", - "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'win32'", - "python_full_version < '3.12' and platform_machine == 's390x' and sys_platform == 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sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version < '3.12' and platform_machine == 'x86_64' and sys_platform != 'win32'", + "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'win32'", + "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'win32'", ] wheels = [ { url = "https://pypi.nvidia.com/nvidia-nccl-cu12/nvidia_nccl_cu12-2.29.3-py3-none-manylinux_2_18_aarch64.whl", hash = "sha256:6351b79dc7d2cc3d654ea1523616b9eeded71fe9c8da66b71eef9a5d1b2adad4" }, @@ -4922,12 +4929,12 @@ all = [ [[package]] name = "nvidia-nvimgcodec-cu13" -version = "0.7.0.11" +version = "0.8.0.22" source = { registry = "https://pypi.nvidia.com/" } wheels = [ - { url = "https://pypi.nvidia.com/nvidia-nvimgcodec-cu13/nvidia_nvimgcodec_cu13-0.7.0.11-py3-none-manylinux_2_28_aarch64.whl", hash = "sha256:65ee61c93aaed80e21dc5db428bc7641fca6dcc319c166835a961359f7703736" }, - { url = "https://pypi.nvidia.com/nvidia-nvimgcodec-cu13/nvidia_nvimgcodec_cu13-0.7.0.11-py3-none-manylinux_2_28_x86_64.whl", hash = "sha256:6075220b7ece40b5d975969f423e4ff9bc6d02bae4ac64ff8c8bf67d1234b12e" }, - { url = "https://pypi.nvidia.com/nvidia-nvimgcodec-cu13/nvidia_nvimgcodec_cu13-0.7.0.11-py3-none-win_amd64.whl", hash = "sha256:39ef829c17e7bba7eb26c067604b0af77445ed611b5443b51579a913bbfa776f" }, + { url = "https://pypi.nvidia.com/nvidia-nvimgcodec-cu13/nvidia_nvimgcodec_cu13-0.8.0.22-py3-none-manylinux_2_28_aarch64.whl", hash = "sha256:3b525a78bdfb4d9baa0cb9798883f36efca524807b37453c43a95f2d9e3476bb" }, + { url = "https://pypi.nvidia.com/nvidia-nvimgcodec-cu13/nvidia_nvimgcodec_cu13-0.8.0.22-py3-none-manylinux_2_28_x86_64.whl", hash = "sha256:0efee8404d36a857e9d713635f8076b4e50c5b5446c526e569dd6ba5e6455b4a" }, + { url = "https://pypi.nvidia.com/nvidia-nvimgcodec-cu13/nvidia_nvimgcodec_cu13-0.8.0.22-py3-none-win_amd64.whl", hash = "sha256:3895ad7f73a496356b6e9028354359d10a7e815b8edc67346f8fd99cc21198b8" }, ] [package.optional-dependencies] @@ -4954,41 +4961,23 @@ version = "12.6.85" source = { registry = "https://pypi.nvidia.com/" } resolution-markers = [ "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform == 'win32'", + "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform != 'win32'", "python_full_version >= '3.13' and platform_machine == 'x86_64' and sys_platform == 'win32'", - "python_full_version >= '3.13' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'win32'", - "python_full_version >= '3.13' and platform_machine == 's390x' and sys_platform == 'win32'", - "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform == 'emscripten'", - "python_full_version >= '3.13' and platform_machine == 'x86_64' and sys_platform == 'emscripten'", - "python_full_version >= '3.13' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'emscripten'", - "python_full_version >= '3.13' and platform_machine == 's390x' and sys_platform == 'emscripten'", - "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version >= '3.13' and platform_machine == 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version >= '3.13' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version >= '3.13' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version >= '3.13' and platform_machine == 'x86_64' and sys_platform != 'win32'", + "python_full_version >= '3.13' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'win32'", + "python_full_version >= '3.13' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'win32'", "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform == 'win32'", + "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform != 'win32'", "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform == 'win32'", - "python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'win32'", - "python_full_version == '3.12.*' and platform_machine == 's390x' and sys_platform == 'win32'", - "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform == 'emscripten'", - "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform == 'emscripten'", - "python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'emscripten'", - "python_full_version == '3.12.*' and platform_machine == 's390x' and sys_platform == 'emscripten'", - "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version == '3.12.*' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform != 'win32'", + "python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'win32'", + "python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'win32'", "python_full_version < '3.12' and platform_machine == 'aarch64' and sys_platform == 'win32'", + "python_full_version < '3.12' and platform_machine == 'aarch64' and sys_platform != 'win32'", "python_full_version < '3.12' and platform_machine == 'x86_64' and sys_platform == 'win32'", - "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'win32'", - "python_full_version < '3.12' and platform_machine == 's390x' and sys_platform == 'win32'", - "python_full_version < '3.12' and platform_machine == 'aarch64' and sys_platform == 'emscripten'", - "python_full_version < '3.12' and platform_machine == 'x86_64' and sys_platform == 'emscripten'", - "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform == 'emscripten'", - "python_full_version < '3.12' and platform_machine == 's390x' and sys_platform == 'emscripten'", - "python_full_version < '3.12' and platform_machine == 'aarch64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version < '3.12' and platform_machine == 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version < '3.12' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version < '3.12' and platform_machine == 'x86_64' and sys_platform != 'win32'", + "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'win32'", + "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'win32'", ] wheels = [ { url = "https://pypi.nvidia.com/nvidia-nvjitlink-cu12/nvidia_nvjitlink_cu12-12.6.85-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl", hash = "sha256:eedc36df9e88b682efe4309aa16b5b4e78c2407eac59e8c10a6a47535164369a" }, @@ -5001,9 +4990,9 @@ name = "nvidia-nvjitlink-cu12" version = "12.8.93" source = { registry = "https://pypi.nvidia.com/" } resolution-markers = [ - "python_full_version >= '3.13' and platform_machine != 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version == '3.12.*' and platform_machine != 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version < '3.12' and platform_machine != 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version >= '3.13' and sys_platform != 'win32'", + "python_full_version == '3.12.*' and sys_platform != 'win32'", + "python_full_version < '3.12' and sys_platform != 'win32'", ] wheels = [ { url = "https://pypi.nvidia.com/nvidia-nvjitlink-cu12/nvidia_nvjitlink_cu12-12.8.93-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl", hash = "sha256:81ff63371a7ebd6e6451970684f916be2eab07321b73c9d244dc2b4da7f73b88" }, @@ -5013,12 +5002,12 @@ wheels = [ [[package]] name = "nvidia-nvjpeg" -version = "13.1.0.48" +version = "13.2.1.68" source = { registry = "https://pypi.nvidia.com/" } wheels = [ - { url = "https://pypi.nvidia.com/nvidia-nvjpeg/nvidia_nvjpeg-13.1.0.48-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:b4d4b7da5f6d2baa9ad42d6b4b3e55e3a203422e2f7bfa3fc21ba510afe01838" }, - { url = "https://pypi.nvidia.com/nvidia-nvjpeg/nvidia_nvjpeg-13.1.0.48-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:605ad70cd040104d58535a7b42d1f6423845c43548b7ac841ec0bdbb8ac282b8" }, - { url = "https://pypi.nvidia.com/nvidia-nvjpeg/nvidia_nvjpeg-13.1.0.48-py3-none-win_amd64.whl", hash = "sha256:ad4693d42102b75c0dfe0d5a6f2d4fd30ce68602e8ef8fc0d4cd6dc36a191853" }, + { url = "https://pypi.nvidia.com/nvidia-nvjpeg/nvidia_nvjpeg-13.2.1.68-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:e3f0d88b7e8b952919a954d67e5c7935ffcd27ddcaec76da8619b463e241a9d6" }, + { url = "https://pypi.nvidia.com/nvidia-nvjpeg/nvidia_nvjpeg-13.2.1.68-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:2c292f2219909bfcc56f2184dd134b7c7dd65bafa7247f3287e26ef42dbd1453" }, + { url = "https://pypi.nvidia.com/nvidia-nvjpeg/nvidia_nvjpeg-13.2.1.68-py3-none-win_amd64.whl", hash = "sha256:22396d67132217c47b33341df103202693fc1095fa44c8432e6adc3c7dd963b7" }, ] [[package]] @@ -5104,18 +5093,15 @@ name = "nvidia-nvtx-cu12" version = "12.6.77" source = { registry = "https://pypi.nvidia.com/" } resolution-markers = [ - "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version >= '3.13' and platform_machine == 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version >= '3.13' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version >= '3.13' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version == '3.12.*' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version < '3.12' and platform_machine == 'aarch64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version < '3.12' and platform_machine == 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 's390x' and platform_machine != 'x86_64' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version < '3.12' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform != 'win32'", + "python_full_version >= '3.13' and platform_machine == 'x86_64' and sys_platform != 'win32'", + "python_full_version >= '3.13' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'win32'", + "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform != 'win32'", + "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform != 'win32'", + "python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'win32'", + "python_full_version < '3.12' and platform_machine == 'aarch64' and sys_platform != 'win32'", + "python_full_version < '3.12' and platform_machine == 'x86_64' and sys_platform != 'win32'", + "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'win32'", ] wheels = [ { url = "https://pypi.nvidia.com/nvidia-nvtx-cu12/nvidia_nvtx_cu12-12.6.77-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:f44f8d86bb7d5629988d61c8d3ae61dddb2015dee142740536bc7481b022fe4b" }, @@ -5130,9 +5116,9 @@ name = "nvidia-nvtx-cu12" version = "12.8.90" source = { registry = "https://pypi.nvidia.com/" } resolution-markers = [ - "python_full_version >= '3.13' and platform_machine != 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version == '3.12.*' and platform_machine != 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version < '3.12' and platform_machine != 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version >= '3.13' and sys_platform != 'win32'", + "python_full_version == '3.12.*' and sys_platform != 'win32'", + "python_full_version < '3.12' and sys_platform != 'win32'", ] wheels = [ { url = "https://pypi.nvidia.com/nvidia-nvtx-cu12/nvidia_nvtx_cu12-12.8.90-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:d7ad891da111ebafbf7e015d34879f7112832fc239ff0d7d776b6cb685274615" }, @@ -5155,24 +5141,12 @@ name = "nvidia-physicsnemo" version = "2.0.0" source = { registry = "https://pypi.nvidia.com/" } resolution-markers = [ - "python_full_version >= '3.13' and platform_machine != 's390x' and sys_platform == 'win32'", - "python_full_version >= '3.13' and platform_machine != 's390x' and sys_platform == 'emscripten'", - "python_full_version >= '3.13' and platform_machine != 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version >= '3.13' and platform_machine == 's390x' and sys_platform == 'win32'", - "python_full_version >= '3.13' and platform_machine == 's390x' and sys_platform == 'emscripten'", - "python_full_version >= '3.13' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version == '3.12.*' and platform_machine != 's390x' and sys_platform == 'win32'", - "python_full_version == '3.12.*' and platform_machine != 's390x' and sys_platform == 'emscripten'", - "python_full_version == '3.12.*' and platform_machine != 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version == '3.12.*' and platform_machine == 's390x' and sys_platform == 'win32'", - "python_full_version == '3.12.*' and platform_machine == 's390x' and sys_platform == 'emscripten'", - "python_full_version == '3.12.*' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version < '3.12' and platform_machine != 's390x' and sys_platform == 'win32'", - "python_full_version < '3.12' and platform_machine != 's390x' and sys_platform == 'emscripten'", - "python_full_version < '3.12' and platform_machine != 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version < '3.12' and platform_machine == 's390x' and sys_platform == 'win32'", - "python_full_version < '3.12' and platform_machine == 's390x' and sys_platform == 'emscripten'", - "python_full_version < '3.12' and platform_machine == 's390x' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version >= '3.13' and sys_platform == 'win32'", + "python_full_version >= '3.13' and sys_platform != 'win32'", + "python_full_version == '3.12.*' and sys_platform == 'win32'", + "python_full_version == '3.12.*' and sys_platform != 'win32'", + "python_full_version < '3.12' and sys_platform == 'win32'", + "python_full_version < '3.12' and sys_platform != 'win32'", ] dependencies = [ { name = "cftime", marker = "(extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (extra != 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra != 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-uma')" }, @@ -5182,12 +5156,12 @@ dependencies = [ { name = "hydra-core", marker = "(extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (extra != 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra != 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-uma')" }, { name = "importlib-metadata", marker = "(extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (extra != 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra != 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-uma')" }, { name = "jaxtyping", marker = "(extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (extra != 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra != 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-uma')" }, - { name = "numpy", marker = "(extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 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and sys_platform == 'win32' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-mace' and extra != 'extra-17-nvalchemi-toolkit-uma'", - "python_full_version == '3.12.*' and platform_machine != 's390x' and sys_platform == 'emscripten' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-mace' and extra != 'extra-17-nvalchemi-toolkit-uma'", - "python_full_version == '3.12.*' and platform_machine != 's390x' and sys_platform == 'emscripten' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-mace' and extra != 'extra-17-nvalchemi-toolkit-uma'", - "python_full_version == '3.12.*' and platform_machine == 's390x' and sys_platform == 'emscripten' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra 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platform_machine != 's390x' and sys_platform == 'win32' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-mace' and extra != 'extra-17-nvalchemi-toolkit-uma'", - "python_full_version < '3.12' and platform_machine == 's390x' and sys_platform == 'win32' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-mace' and extra != 'extra-17-nvalchemi-toolkit-uma'", - "python_full_version < '3.12' and platform_machine == 's390x' and sys_platform == 'win32' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-mace' and extra != 'extra-17-nvalchemi-toolkit-uma'", - "python_full_version < '3.12' and platform_machine != 's390x' and sys_platform == 'emscripten' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' 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'emscripten' and sys_platform != 'win32' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-mace' and extra != 'extra-17-nvalchemi-toolkit-uma'", + "python_full_version >= '3.13' and sys_platform == 'win32' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-uma'", + "python_full_version >= '3.13' and sys_platform == 'emscripten' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-uma'", + "python_full_version >= '3.13' and sys_platform != 'emscripten' and sys_platform != 'win32' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-uma'", + "python_full_version == '3.12.*' and sys_platform == 'win32' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-uma'", + "python_full_version == '3.12.*' and sys_platform != 'win32' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-uma'", + "python_full_version < '3.12' and sys_platform == 'win32' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-uma'", + "python_full_version < '3.12' and sys_platform != 'win32' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-uma'", + "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform == 'win32' and extra == 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-uma'", + "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform != 'win32' and extra == 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-uma'", + "python_full_version >= '3.13' and platform_machine == 'x86_64' and sys_platform == 'win32' and extra == 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-uma'", + "python_full_version >= '3.13' and platform_machine == 'x86_64' and sys_platform != 'win32' and extra == 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-uma'", + "python_full_version >= '3.13' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'win32' and extra == 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-uma'", + "python_full_version >= '3.13' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'win32' and extra == 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-uma'", + "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform == 'win32' and extra == 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-uma'", + "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform != 'win32' and extra == 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-uma'", + "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform == 'win32' and extra == 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-uma'", + "python_full_version == '3.12.*' and platform_machine == 'x86_64' and sys_platform != 'win32' and extra == 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-uma'", + "python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'win32' and extra == 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-uma'", + "python_full_version == '3.12.*' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'win32' and extra == 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-uma'", + "python_full_version < '3.12' and platform_machine == 'aarch64' and sys_platform == 'win32' and extra == 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-uma'", + "python_full_version < '3.12' and platform_machine == 'aarch64' and sys_platform != 'win32' and extra == 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-uma'", + "python_full_version < '3.12' and platform_machine == 'x86_64' and sys_platform == 'win32' and extra == 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-uma'", + "python_full_version < '3.12' and platform_machine == 'x86_64' and sys_platform != 'win32' and extra == 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-uma'", + "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'win32' and extra == 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-uma'", + "python_full_version < '3.12' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform != 'win32' and extra == 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-uma'", + "python_full_version >= '3.13' and sys_platform == 'win32' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-uma'", + "python_full_version >= '3.13' and sys_platform != 'win32' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-uma'", + "python_full_version == '3.12.*' and sys_platform == 'win32' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-uma'", + "python_full_version == '3.12.*' and sys_platform != 'win32' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-uma'", + "python_full_version < '3.12' and sys_platform == 'win32' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-uma'", + "python_full_version < '3.12' and sys_platform != 'win32' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-uma'", ] dependencies = [ { name = "cftime", marker = "extra == 'extra-17-nvalchemi-toolkit-cu12' or extra == 'extra-17-nvalchemi-toolkit-cu13' or extra == 'extra-17-nvalchemi-toolkit-mace' or extra != 'extra-17-nvalchemi-toolkit-uma'" }, @@ -5361,24 +5222,25 @@ dependencies = [ { name = "hydra-core", marker = "extra == 'extra-17-nvalchemi-toolkit-cu12' or extra == 'extra-17-nvalchemi-toolkit-cu13' or extra == 'extra-17-nvalchemi-toolkit-mace' or extra != 'extra-17-nvalchemi-toolkit-uma'" }, { name = "importlib-metadata", marker = "extra == 'extra-17-nvalchemi-toolkit-cu12' or extra == 'extra-17-nvalchemi-toolkit-cu13' or extra == 'extra-17-nvalchemi-toolkit-mace' or extra != 'extra-17-nvalchemi-toolkit-uma'" }, { name = "jaxtyping", marker = "extra == 'extra-17-nvalchemi-toolkit-cu12' or extra == 'extra-17-nvalchemi-toolkit-cu13' or extra == 'extra-17-nvalchemi-toolkit-mace' or extra != 'extra-17-nvalchemi-toolkit-uma'" }, - { name = "numpy", marker = "extra == 'extra-17-nvalchemi-toolkit-cu12' or extra == 'extra-17-nvalchemi-toolkit-cu13' or extra == 'extra-17-nvalchemi-toolkit-mace' or extra != 'extra-17-nvalchemi-toolkit-uma'" }, + { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "extra == 'extra-17-nvalchemi-toolkit-cu12' or (extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma')" }, + { name = "numpy", version = "2.3.5", source = { registry = "https://pypi.org/simple" }, marker = "extra 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dependencies = [ { name = "alabaster", marker = "python_full_version >= '3.12' or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma')" }, @@ -7667,7 +7347,8 @@ dependencies = [ { name = "docutils", marker = "python_full_version >= '3.12' or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-cu13') or (extra == 'extra-17-nvalchemi-toolkit-cu12' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-uma') or (extra == 'extra-17-nvalchemi-toolkit-mace' and extra == 'extra-17-nvalchemi-toolkit-uma')" }, { name = "imagesize", marker = "python_full_version >= 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and platform_machine == 's390x' and sys_platform == 'win32' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-mace' and extra != 'extra-17-nvalchemi-toolkit-uma'", - "python_full_version == '3.12.*' and platform_machine == 's390x' and sys_platform == 'win32' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra != 'extra-17-nvalchemi-toolkit-mace' and extra != 'extra-17-nvalchemi-toolkit-uma'", - "python_full_version == '3.12.*' and platform_machine != 's390x' and sys_platform == 'emscripten' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra != 'extra-17-nvalchemi-toolkit-cu13' and extra == 'extra-17-nvalchemi-toolkit-mace' and extra != 'extra-17-nvalchemi-toolkit-uma'", - "python_full_version == '3.12.*' and platform_machine != 's390x' and sys_platform == 'emscripten' and extra != 'extra-17-nvalchemi-toolkit-cu12' and extra != 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