[weather] Add Fgn model

CHANGELOG.md

@@ -149,6 +149,15 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Added support for Batched radius search, which enables Domino
   and GeoTransolver with local features and batch size > 1.
 - Added the underfill recipe.
+- Adds Functional Generative Networks (FGN) weather training example
+  (`examples/weather/fgn`). Implements the latent-conditioned U-Net
+  stochastic generator from
+  `arXiv:2506.10772 <https://arxiv.org/abs/2506.10772>`_ (WeatherNext 2)
+  as a PhysicsNeMo ``Module``, trained with fair-CRPS loss on ERA5 via the
+  earth2studio ARCO data source. Supports autoregressive rollout training
+  with per-channel normalization, FSDP + ShardTensor domain parallelism,
+  deep-ensemble inference (paper §2.2.1), and validation diagnostics
+  (CRPS, RMSE, spread-skill, rank histograms, power spectra).
 
 ### Changed
 

examples/weather/fgn/.gitignore

@@ -0,0 +1,12 @@
+*.mlus
+*.png
+*.pt
+*.tfevents*
+*wandb/
+rundir/
+logs/
+*.npz
+FGN.md
+# SLURM launcher scripts are cluster-specific; keep locally, don't track.
+scripts/train_fgn.sh
+scripts/eval_fgn.sh

examples/weather/fgn/README.md

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+<!-- markdownlint-disable -->
+# Functional Generative Networks for Weather Forecasting
+
+A PhysicsNeMo implementation of Functional Generative Networks (FGN) for
+probabilistic global weather forecasting, following the approach of:
+
+> Alet et al., "Skillful joint probabilistic weather forecasting from marginals"
+> ([arXiv:2506.10772](https://arxiv.org/abs/2506.10772))
+
+FGN is the architecture behind the production
+[WeatherNext 2](https://developers.google.com/weathernext/guides/models) model,
+which delivers 64-member ensemble forecasts (4 independently trained seeds ×
+16 trajectories each) at 0.25° global resolution. The full variable schema is
+described in the
+[WeatherNext 2 model specs](https://developers.google.com/weathernext/guides/model-specs-vmg).
+
+FGN generates ensemble weather forecasts by perturbing a deterministic backbone
+with a low-dimensional latent noise vector `z ~ N(0, I_32)` injected through
+conditional layer normalization (CLN) at every layer, producing globally coherent
+ensemble spread from a marginal (fair-CRPS) training loss. Multiple independently
+trained model seeds form a deep ensemble (J=4 seeds, 16 trajectories each = 64
+members in production) capturing both aleatoric and epistemic uncertainty.
+
+## Problem Overview
+
+FGN autoregressively predicts the next 6-hour atmospheric state from the two
+previous states (`X_{t-2}`, `X_{t-1}`), sampled from ERA5 (pre-training) and
+HRES-fc0 (fine-tuning) at 0.25° global resolution. Each forward pass is
+non-diffusive: one pass per forecast step, with a fresh `z` drawn per step per
+ensemble member. AR fine-tuning with rollouts up to 8 steps (Table A.2) improves
+temporal coherence without requiring a diffusion sampler.
+
+This example implements:
+
+- Latent-conditioned `FGNUNet` backbone (`utils/nn.py`) with AdaGN modulation
+- ARCO-backed real dataset using `earth2studio.data.ARCO` (`datasets/arco.py`)
+- Fair-CRPS training loss with paper-faithful per-variable and area weights (`utils/loss.py`)
+- Autoregressive rollout training with BPTT (`utils/trainer.py`)
+- Multi-stage AR schedule runner (Table A.2: `8k·1AR → 4k·2AR → 1k·{3..8}AR`)
+- Validation metrics and plots: CRPS, RMSE, spread-skill, rank histograms,
+  power spectra (`utils/metrics.py`)
+- FSDP + ShardTensor distributed training via `ParallelHelper` (`utils/parallel.py`)
+- Deep ensemble inference across multiple independently trained checkpoints
+- Per-channel normalization stats with Welford online estimation
+
+## Dataset
+
+Training data is fetched live from the [ARCO ERA5](https://cloud.google.com/storage/docs/public-datasets/era5)
+dataset via `earth2studio.data.ARCO`. No local download is required for training.
+
+The dataset covers the full 83-channel Table A.1 schema: 78 atmospheric channels
+(6 variables × 13 pressure levels: 50–1000 hPa) plus 5 input/predicted surface
+channels (`t2m`, `u10m`, `v10m`, `msl`, `sst`) and `tp06` (6-h accumulated
+precipitation, predicted-only). Static inputs (surface geopotential, land-sea mask)
+and clock features (local time, year progress sin/cos) are added automatically.
+
+All variables use compact Earth2Studio / PhysicsNeMo names: `u10m`, `v10m`, `t2m`,
+`msl`, `sst`, `tp06`, `z{level}`, `q{level}`, `t{level}`, `u{level}`, `v{level}`,
+`w{level}`.
+
+> **Note:** The production WeatherNext 2 output also includes `u100m` / `v100m`
+> (100 m wind components). ERA5 via ARCO does not provide 100 m winds, so they
+> are omitted from this ERA5-based training example.
+
+### Normalization Stats
+
+Pre-compute per-channel mean and standard deviation before training:
+
+```bash
+python scripts/compute_arco_stats.py \
+    --start 2020-01-01 --end 2023-12-31 \
+    --output rundir/fgn_2024_val/stats_2024.npz
+```
+
+Pass the resulting `.npz` file to the trainer via `dataset.stats_path`.
+
+## Getting Started
+
+### Requirements
+
+```bash
+pip install -r requirements.txt
+```
+
+PyTorch 2.10 or higher is required for domain parallelism.
+
+### Smoke Test
+
+Run the self-contained synthetic test suite (no GPU, no network access):
+
+```bash
+pytest test_training.py
+```
+
+Multi-GPU tests require `torchrun`:
+
+```bash
+torchrun --standalone --nproc_per_node=2 --no-python pytest test_training.py
+```
+
+## Configuration
+
+Training is configured with [Hydra](https://hydra.cc) and validated with Pydantic
+(`utils/config.py`). Configs live under `config/`:
+
+- `config/fgn.yaml` — base defaults (model, training, dataset structure)
+- `config/fgn_arco.yaml` — ARCO real-data training config (inherits from `fgn.yaml`)
+- `config/test_fgn.yaml` — fast synthetic smoke-test config
+
+Key config knobs:
+
+| Setting | Description |
+|---|---|
+| `model.hidden_channels` | U-Net channel width (64 for quick runs, 256+ for full scale) |
+| `model.latent_dim` | Latent noise dimension (32, per paper) |
+| `training.batch_size` | Global batch size; local per-GPU = `batch_size / data_parallel_size` |
+| `training.ar_steps` | AR rollout length for loss (1 = single-step pre-training) |
+| `training.loss.num_samples` | Ensemble members per training example (N=2 per paper) |
+| `training.domain_parallel_size` | GPUs per sample for domain parallelism (1 = pure DDP) |
+| `dataset.stats_path` | Path to `.npz` normalization stats |
+
+Training outputs (checkpoints, logs, plots) are saved to:
+
+```
+rundir/{training.experiment_name}/{training.run_id}/
+```
+
+## Training
+
+### Single GPU
+
+```bash
+python train.py --config-name fgn_arco \
+    dataset.stats_path=rundir/fgn_2024_val/stats_2024.npz \
+    training.experiment_name=fgn_run \
+    training.batch_size=1
+```
+
+### Multi-GPU (torchrun)
+
+```bash
+torchrun --standalone --nnodes=1 --nproc_per_node=2 \
+    train.py --config-name fgn_arco \
+    dataset.stats_path=rundir/fgn_2024_val/stats_2024.npz \
+    training.experiment_name=fgn_run \
+    training.batch_size=2
+```
+
+With 2 GPUs and `domain_parallel_size=1` (DDP), `batch_size` is the global batch
+size — each GPU processes `batch_size / 2` samples.
+
+### SLURM
+
+```bash
+sbatch scripts/train_fgn.sh
+```
+
+Override defaults via environment variables:
+
+```bash
+sbatch --export=ALL,EXP_NAME=fgn_2024,RUN_ID=1,STEPS=10000 scripts/train_fgn.sh
+```
+
+See `scripts/train_fgn.sh` for all overridable variables (`EXP_NAME`, `RUN_ID`,
+`STEPS`, `CFG`, `STATS_PATH`, `NGPU`).
+
+### Resuming
+
+Set `training.resume_checkpoint=latest` (default) to automatically resume from
+the most recent checkpoint in the run directory.
+
+### Domain Parallelism
+
+For models too large to fit one sample on a single GPU, enable domain parallelism:
+
+```bash
+torchrun --standalone --nproc_per_node=4 \
+    train.py --config-name fgn_arco \
+    training.domain_parallel_size=2 \
+    training.batch_size=2
+```
+
+With `domain_parallel_size=2` and 4 GPUs: 2 domain-parallel pairs, each handling
+1 sample (`batch_size / data_parallel_size = 2 / 2 = 1`).
+
+### AR Fine-Tuning Schedule (Table A.2)
+
+The trainer implements the paper's multi-stage AR schedule automatically when
+`training.ar_steps` increases across runs. Start with single-step pre-training,
+then resume with progressively longer rollouts:
+
+| Stage | `ar_steps` | Steps | Notes |
+|---|---|---|---|
+| 1 | 1 | 8000 | Single-step pre-train |
+| 2 | 2 | 4000 | Resume from stage 1 |
+| 3–8 | 3–8 | 1000 each | Resume from previous |
+
+## Inference
+
+Run stochastic ensemble inference from a trained checkpoint:
+
+```bash
+python inference.py --config-name inference_fgn \
+    inference.checkpoint=rundir/fgn_run/0/checkpoints/FGNUNet.mdlus
+```
+
+For deep ensemble inference across multiple independently trained seeds:
+
+```bash
+python inference.py --config-name inference_fgn \
+    "inference.checkpoints=[seed0/FGNUNet.mdlus, seed1/FGNUNet.mdlus, seed2/FGNUNet.mdlus, seed3/FGNUNet.mdlus]"
+```
+
+Trajectories are distributed across checkpoints following paper §2.2.1.
+
+### Bad-Seed Detection
+
+Before including a checkpoint in a deep ensemble, check its spectral properties:
+
+```bash
+python scripts/check_spectra.py \
+    --checkpoint rundir/fgn_run/0/checkpoints/FGNUNet.mdlus \
+    --stats rundir/fgn_2024_val/stats_2024.npz
+```
+
+## Adding Custom Datasets
+
+Implement the `FGNDataset` interface from `datasets/dataset.py`:
+
+```python
+class MyDataset(FGNDataset):
+    def state_channels(self) -> list[str]: ...
+    def background_channels(self) -> list[str]: ...
+    def image_shape(self) -> tuple[int, int]: ...
+    def __len__(self) -> int: ...
+    def __getitem__(self, idx): ...
+    # Optional:
+    def get_invariants(self) -> np.ndarray | None: ...
+    def output_only_channels(self) -> list[int]: ...
+```
+
+`__getitem__` should return a dict with keys `history` (shape `(T, C, H, W)`),
+`target` (shape `(K, C, H, W)`), and optionally `background`. Register your
+dataset by placing it in `datasets/` — it is discovered automatically via
+`pkgutil.iter_modules` at import time.
+
+## Memory Management
+
+At 0.25° (721×1440), each training sample is large. Recommended settings for an
+80 GB H100:
+
+- `training.batch_size=2` (1 per GPU) with 2 GPUs, `domain_parallel_size=1`
+- bf16 AMP is enabled automatically (`torch.autocast(bfloat16)`)
+- `PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True` (set in `train_fgn.sh`)
+
+For larger models (hidden_channels ≥ 256), use `domain_parallel_size=2` with
+4+ GPUs, or enable gradient checkpointing via `model.checkpoint_level`.
+
+## References
+
+- [Skillful joint probabilistic weather forecasting from marginals](https://arxiv.org/abs/2506.10772)
+- [WeatherNext 2 model overview](https://developers.google.com/weathernext/guides/models)
+- [WeatherNext 2 variable schema](https://developers.google.com/weathernext/guides/model-specs-vmg)
+- [Generative Ensemble Downscaling with Diffusion Models (CorrDiff)](https://arxiv.org/abs/2308.14453)
+- [Kilometer-Scale Convection Allowing Model Emulation (StormCast)](https://arxiv.org/abs/2408.10958)
+- [GraphCast: Learning skillful medium-range global weather forecasting](https://arxiv.org/abs/2212.12794)

examples/weather/fgn/config/eval_fgn.yaml

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+# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES.
+# SPDX-FileCopyrightText: All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+# Standalone evaluation config for FGN (§4 of arXiv:2506.10772).
+#
+# Usage:
+#   python eval.py --config-name eval_fgn \
+#     dataset.stats_path=rundir/fgn_2024_val/stats_2024.npz \
+#     eval.checkpoint=rundir/fgn_2024_long/0/checkpoints/FGNUNet.0.5000.mdlus
+#
+# To evaluate a deep ensemble pass a list:
+#   eval.checkpoints=[seed0/FGNUNet.mdlus,seed1/FGNUNet.mdlus]
+
+defaults:
+  - fgn         # model + dataset schema defaults
+  - _self_
+
+dataset:
+  name: arco.ArcoFGNDataset
+  state_variables: null
+  invariant_variables:
+    - z
+    - lsm
+  step_hours: 6
+  history_frames: 2
+  # future_frames is overridden by eval.future_steps at runtime
+  future_frames: ${eval.future_steps}
+  val_start: "2024-10-01"
+  val_end:   "2025-01-01"
+  # train_{start,end} unused in eval but required by the dataset schema
+  train_start: "2024-01-01"
+  train_end:   "2024-10-01"
+  spatial_stride: 1
+  static_date: "2016-01-01"
+  arco_cache: true
+  stats_path: ???
+  tp_accumulation_hours: null
+
+model:
+  latent_dim: 16
+  hidden_channels: 64
+
+# training section is required by TrainMainConfig but unused during eval;
+# keep it minimal so Pydantic passes validation.
+training:
+  experiment_name: fgn_eval
+  run_id: "0"
+  batch_size: 1
+  total_train_steps: 1
+  ar_steps: ${eval.future_steps}
+
+eval:
+  # Path to a single .mdlus checkpoint, or "latest" to auto-detect.
+  checkpoint: "latest"
+  # For deep-ensemble eval, list multiple checkpoints; overrides checkpoint.
+  checkpoints: null
+  # Number of AR steps forward (each step = step_hours hours).
+  # 20 steps = 5 days at 6h.  Max ~40 steps (10 days) fits in memory.
+  future_steps: 20
+  # Ensemble members per checkpoint.  Paper uses 56 total (14×4 seeds).
+  ensemble_size: 8
+  # Batch size for the eval DataLoader.  Keep at 1 for full-resolution.
+  batch_size: 1
+  # Number of DataLoader workers.
+  num_workers: 0
+  # Output directory for plots + eval_metrics.npz.
+  outdir: "rundir/fgn_2024_long/0/eval"
+  # Pooled-CRPS cell sizes (number of 0.25° grid cells per side).
+  # [4,8,16,32] ≈ [120, 240, 480, 960] km.
+  pool_sizes: [4, 8, 16, 32]

examples/weather/fgn/config/fgn.yaml

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+# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES.
+# SPDX-FileCopyrightText: All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+# Base FGN config — required by train.py's @hydra.main(config_name="fgn").
+# Provides model defaults and a skeleton dataset section; override dataset
+# and training fields on the command line or via a derived config such as
+# fgn_arco.yaml.
+#
+# Minimal usage (all dataset fields required as overrides):
+#   python train.py \
+#     dataset.name=arco.ArcoFGNDataset \
+#     dataset.stats_path=/path/to/stats.npz \
+#     [dataset.train_start=... dataset.train_end=... ...]
+
+defaults:
+  - training: default
+  - _self_
+
+dataset:
+  name: ???   # required — e.g. arco.ArcoFGNDataset
+
+model:
+  model_name: fgn
+  history_frames: 2
+  latent_dim: 16
+  hidden_channels: 32
+  background_channels: auto
+  invariant_channels: auto
+  group_norm_groups: 8