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FluxNet-ShallowWater-LAP

+ +# Model Introduction + +FluxNet-ShallowWater-LAP reproduces the two-dimensional shallow-water FluxNet-LAP pushforward experiment in the [FluxNet paper](https://arxiv.org/pdf/2602.01941). It forecasts water depth and two momentum fields on a 64x64 grid. + +# Model Description + +The model takes `(h, mx, my)` and predicts the next state. The configuration uses 64 channels, six residual blocks, 5x5 kernels, a 3x3 transport neighborhood and five-step pushforward training. + +## Use Cases + +| Use case | Description | +| --- | --- | +| Shallow-water forecasting | Forecast depth and momentum on a regular grid | +| Simulation acceleration | Compare surrogate rollouts against numerical reference trajectories | + +# Usage + +## 1. OneCode + +[Open OneCode](https://web-2069360198568017922-iaaj.ksai.scnet.cn:58043/home) + +## 2. Manual Setup + +**Hardware requirements** + +- Training and inference require a Slurm-allocated HIP/DCU compute node; there is no silent CPU fallback. +- The formal run used one DCU, eight CPU cores and 30 GB of host memory. GPU execution was not validated. + +### Download the Model + +```bash +hf download OneScience-Group/FluxNet-ShallowWater-LAP --local-dir ./FluxNet-ShallowWater-LAP +cd FluxNet-ShallowWater-LAP +``` + +### Install Dependencies + +**DCU environment** + +```bash +conda create -n onescience311 python=3.11 -y +conda activate onescience311 +pip install onescience[cfd-dcu] -i http://mirrors.onescience.ai:3141/pypi/simple/ --trusted-host mirrors.onescience.ai +pip install numpy h5py PyYAML matplotlib +``` + +**GPU environment** + +```bash +conda create -n onescience311 python=3.11 -y libstdcxx-ng=12 libgcc-ng=12 gcc_linux-64=12 gxx_linux-64=12 +conda activate onescience311 +pip install onescience[cfd-gpu] -i http://mirrors.onescience.ai:3141/pypi/simple/ --trusted-host mirrors.onescience.ai +pip install numpy h5py PyYAML matplotlib +``` + +The GPU commands describe dependency installation only; the provided scripts were validated on Slurm HIP/DCU. + +### Training Data + +The [OneScience/FluxNet-ShallowWater dataset on ModelScope](https://www.modelscope.cn/datasets/OneScience/FluxNet-ShallowWater) contains 50 training, 20 validation, 50 short-test and 50 long-test trajectories produced with the paper's official numerical generator. Each HDF5 trajectory contains `h`, `mx`, `my` and `t`. The dataset is not mirrored on Hugging Face. Download it into the model directory: + +```bash +modelscope download --dataset OneScience/FluxNet-ShallowWater --local_dir ./data +``` + +Run commands from the package root. `data.root` in `config/config.yaml` defaults to `./data`. + +### Training + +On a Slurm-allocated DCU node: + +```bash +python scripts/train.py +``` + +The full run trains for 300 epochs and writes `weight/best_model.pth`. Back up the included `best_model.pth` and `last_model.pth` before retraining if they must be preserved. + +### Trained Weights + +The included checkpoint is the best validation-MSE model at epoch 283 of a completed 300-epoch run. `weight/best_model.pth` is 15636928 bytes; SHA256: `47fb0a029b0c5baa10b2452c70b12f54c958b548cb9aff300b85183d55f1ae70`. + +### Inference + +```bash +python scripts/inference.py +``` + +### Evaluation and Visualization + +```bash +python scripts/result.py +``` + +Outputs are written to `results/`. The long-horizon (4.8 time units) MAE for `(h, mx, my)` is `(1.4063e-3, 1.8558e-3, 1.7632e-3)`; the paper's Table 3 two-times extrapolation means are `(1.75e-3, 2.38e-3, 2.30e-3)`. Our result is one seed, not a reproduction of the paper's multi-seed mean and standard deviation. + +# OneScience Repositories + +| Platform | Main repository | Skills repository | +| --- | --- | --- | +| Gitee | https://gitee.com/onescience-ai/onescience | https://gitee.com/onescience-ai/oneskills | +| GitHub | https://github.com/onescience-ai/OneScience | https://github.com/onescience-ai/oneskills | + +# Citation and License + +- Paper: [FluxNet](https://arxiv.org/pdf/2602.01941). +- [Original public repository](https://github.com/Lan-zs/FluxNet); [public dataset and its provenance](https://www.modelscope.cn/datasets/OneScience/FluxNet-ShallowWater). This model package is an independent reproduction. diff --git a/config.json b/config.json new file mode 100644 index 0000000000000000000000000000000000000000..7b1ca2ae2baac2801c486a24e602ec4ea275e944 --- /dev/null +++ b/config.json @@ -0,0 +1,11 @@ +{ + "model_type": "fluxnet_shallow_water_lap", + "architectures": ["FluxNetShallowWater"], + "task": "two-dimensional shallow-water rollout", + "framework": "pytorch", + "model_file": "model/FluxNetShallowWater.py", + "checkpoint": "weight/best_model.pth", + "config_file": "config/config.yaml", + "dataset_url": "https://www.modelscope.cn/datasets/OneScience/FluxNet-ShallowWater", + "paper_url": "https://arxiv.org/pdf/2602.01941" +} diff --git a/config/config.yaml b/config/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8ea4e4c85d674187f9246c115178ca5230ca5111 --- /dev/null +++ b/config/config.yaml @@ -0,0 +1,44 @@ +experiment: FluxNet-ShallowWater-LAP-pushforward +paper: https://arxiv.org/pdf/2602.01941v2 +seed: 42 +data: + root: ./data + fields: [h, mx, my] + shape: [64, 64] + dt: 0.04 + expected_counts: {train: 50, val: 20, test: 50, test_long: 50} + expected_frames: {train: 61, val: 61, test: 61, test_long: 121} + normalization: none + reference_negative_tolerance: 1.0e-9 +model: + base_channels: 64 + num_blocks: 6 + kernel_size: 5 + neighborhood_size: 3 + epsilon: 1.0e-6 +training: + epochs: 300 + pushforward_steps: 5 + batch_size: 16 + num_workers: 4 + optimizer: AdamW + learning_rate: 1.0e-3 + weight_decay: 1.0e-2 + scheduler: ReduceLROnPlateau + scheduler_factor: 0.5 + scheduler_patience: 15 + loss: mse_step1_plus_mse_step5 + early_stopping: false + gradient_clip: null + precision: float32 + print_every_batches: 10 + checkpoint_every_epochs: 10 +evaluation: + batch_size: 10 + long_test_every_epochs: 50 + splits: [test, test_long] + example_cases: [A1, A2, B1, B2] +paths: + best_checkpoint: weight/best_model.pth + last_checkpoint: weight/last_model.pth + results: results diff --git a/model/FluxNetShallowWater.py b/model/FluxNetShallowWater.py new file mode 100644 index 0000000000000000000000000000000000000000..55e256e6ce62d4f40163d6fff97d944003035686 --- /dev/null +++ b/model/FluxNetShallowWater.py @@ -0,0 +1,84 @@ +"""Independent implementation of FluxNet-LAP (arXiv:2602.01941v2). + +The transport equations and epsilon follow Sections 3.2, 3.3 and 3.5; +the backbone dimensions follow Appendix C.2. See reproduction_spec.md +for the explicitly identified gaps filled by read-only official references. +""" +import torch +from torch import nn +from torch.nn import functional as F + + +class FeatureBlock(nn.Module): + """Two periodic convolutions, with a learned concatenation skip fusion.""" + + def __init__(self, width, kernel_size): + super().__init__() + self.layers = nn.Sequential( + nn.Conv2d(width, width, kernel_size, padding=kernel_size // 2, + padding_mode="circular"), + nn.BatchNorm2d(width), nn.GELU(), + nn.Conv2d(width, width, kernel_size, padding=kernel_size // 2, + padding_mode="circular"), + nn.BatchNorm2d(width), nn.GELU(), + ) + self.fuse = nn.Conv2d(2 * width, width, 1) + + def forward(self, features): + return self.fuse(torch.cat((self.layers(features), features), dim=1)) + + +class FluxNetShallowWater(nn.Module): + """Map physical states [B,3,H,W] ordered h,mx,my to the next state. + + This operator redistributes cumulative amounts, with no dt multiplier, + clipping, mass projection, or numerical-solver substeps. + """ + + def __init__(self, base_channels=64, num_blocks=6, kernel_size=5, + neighborhood_size=3, epsilon=1e-6): + super().__init__() + if kernel_size % 2 != 1 or neighborhood_size % 2 != 1: + raise ValueError("Convolution and transport sizes must be odd") + if neighborhood_size < 3 or epsilon <= 0: + raise ValueError("Invalid transport size or epsilon") + self.epsilon = float(epsilon) + radius = neighborhood_size // 2 + self.offsets = tuple((dy, dx) for dy in range(-radius, radius + 1) + for dx in range(-radius, radius + 1) + if (dy, dx) != (0, 0)) + self.encoder = nn.Sequential( + nn.Conv2d(3, base_channels, kernel_size, padding=kernel_size // 2, + padding_mode="circular"), + nn.BatchNorm2d(base_channels), nn.GELU(), + *(FeatureBlock(base_channels, kernel_size) for _ in range(num_blocks)), + ) + directions = len(self.offsets) + self.depth_head = nn.Conv2d(base_channels, directions + 1, 1) + self.pressure_x = nn.Conv2d(base_channels, directions, 1) + self.pressure_y = nn.Conv2d(base_channels, directions, 1) + + def transport(self, state): + features = self.encoder(state) + h = state[:, :1] + raw = self.depth_head(features) + outgoing_depth = h * raw[:, :1].sigmoid() + depth_transfers = outgoing_depth * raw[:, 1:].softmax(dim=1) + velocity = state[:, 1:] / (h + self.epsilon) + gate = h.square() + momentum_x = depth_transfers * velocity[:, :1] + gate * F.softplus(self.pressure_x(features)) + momentum_y = depth_transfers * velocity[:, 1:] + gate * F.softplus(self.pressure_y(features)) + transfers = torch.stack((depth_transfers, momentum_x, momentum_y), dim=1) + return outgoing_depth, transfers + + def forward(self, state): + outgoing_depth, transfers = self.transport(state) + # For h, use the analytically summed alpha*h to retain the lower bound + # in floating point. Both forms are algebraically the same (Prop. 2). + outgoing = torch.cat((outgoing_depth, transfers[:, 1:].sum(dim=2)), dim=1) + updated = state - outgoing + for index, offset in enumerate(self.offsets): + # Offset denotes receiver minus sender; rolling moves sender's + # outgoing field to its receiver on the periodic torus. + updated = updated + torch.roll(transfers[:, :, index], offset, dims=(-2, -1)) + return updated diff --git a/model/__init__.py b/model/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..f5818d09ba40838271db92e86d180ff185482e6a --- /dev/null +++ b/model/__init__.py @@ -0,0 +1,3 @@ +from .FluxNetShallowWater import FluxNetShallowWater + +__all__ = ["FluxNetShallowWater"] diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..016521a719cdef75981dccab0c7716a7944ff1be --- /dev/null +++ b/requirements.txt @@ -0,0 +1,5 @@ +torch +numpy +h5py +PyYAML +matplotlib diff --git 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a/results/report.md b/results/report.md new file mode 100644 index 0000000000000000000000000000000000000000..13f2561ad41dfed8ae5d4e85aedee0908924b36a --- /dev/null +++ b/results/report.md @@ -0,0 +1,37 @@ +# FluxNet shallow-water LAP pushforward reproduction + +Source: https://arxiv.org/pdf/2602.01941v2 (Sections 3.2–3.7, Appendix C.2). + +One independent training run (seed 42), 300 complete epochs. This is not a five-seed mean/std replication. +64 channels, 6 residual blocks, kernel 5, transport stencil 3×3, detached pushforward length 5. +AdamW lr=1e-3, weight_decay=1e-2; ReduceLROnPlateau factor=0.5, patience=15; float32 states. + +Selected checkpoint epoch: 283; validation MSE: 1.11785405e-06. +Checkpoint SHA256: `47fb0a029b0c5baa10b2452c70b12f54c958b548cb9aff300b85183d55f1ae70`. + +| Split | Horizon | MAE h | MAE mx | MAE my | Negative h (%) | +| --- | ---: | ---: | ---: | ---: | ---: | +| test | 2.4 | 1.30214413e-03 | 1.79798426e-03 | 1.72321738e-03 | 0.00000000e+00 | +| test_long | 4.8 | 1.40631581e-03 | 1.85576825e-03 | 1.76324718e-03 | 0.00000000e+00 | + +MAE averages all forecast steps, grid cells, and 50 trajectories, excluding the initial frame. +Final-time and T=2/T=2.4/T=4.8 prefix metrics are separately stored in metrics.json. +The paper labels Table 3 as T=2 while Appendix C.1 specifies 2.4/4.8; both time conventions are retained. + +Conservation is computed using float64 reductions of float32 predictions relative to the initial state. +Relative drift divides by |initial signed total|+1e-8; zero-total momentum makes this statistic ill-conditioned. +Absolute sum drift and initial-L1-scaled drift are therefore also reported. No postprocessing correction is used. + +test maximum absolute sum drift [h,mx,my]: [2.2027743398211896e-05, 3.510061651468277e-05, 3.0668867111671716e-05] +test maximum relative drift [h,mx,my]: [2.168669351450529e-08, 3510.061651468277, 2132.3561668395996] +test minimum depth: 0.00000000e+00 + +test_long maximum absolute sum drift [h,mx,my]: [2.8165319235995412e-05, 4.894798621535301e-05, 3.981318513979204e-05] +test_long maximum relative drift [h,mx,my]: [2.574266788559366e-08, 4894.798621535301, 2548.028714954853] +test_long minimum depth: 0.00000000e+00 + +All arrays, checkpoints, training history, and saved-case MAE recomputations were checked. +The original numerical-generator datasets were read directly; no data generation or official repository code was downloaded. + +Reference data contains small negative roundoff depths (about 1e-11); these are retained unchanged. +Data validation tolerates reference values down to -1e-9; model violation metrics use the strict condition h<0, 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1.1429121415216872e-06, + "train_step1_mse": 1.3349117338634642e-07, + "train_step5_mse": 1.0094209681353407e-06, + "val_mse": 3.335701327945756e-06, + "val_step1_mse": 2.986339289076889e-07, + "val_step5_mse": 3.0370673990380666e-06, + "val_mae_h": 0.00026025866888536526, + "val_mae_mx": 0.0002710300020574084, + "val_mae_my": 0.0002517975028997163, + "val_negative_h_ratio": 0.00099945068359375, + "lr": 3.90625e-06, + "seconds": 24.114831797021907 + }, + { + "epoch": 293, + "train_loss": 1.0512262058983067e-06, + "train_step1_mse": 1.2755675099031447e-07, + "train_step5_mse": 9.236694549079922e-07, + "val_mse": 1.3399930645193868e-06, + "val_step1_mse": 1.8959825570007948e-07, + "val_step5_mse": 1.1503948088193073e-06, + "val_mae_h": 0.0001677188824553838, + "val_mae_mx": 0.00019225249229027434, + "val_mae_my": 0.00018135096419995405, + "val_negative_h_ratio": 0.0010014125279017858, + "lr": 3.90625e-06, + "seconds": 24.068644613958895 + }, + { + "epoch": 294, + "train_loss": 1.0631339244631429e-06, + "train_step1_mse": 1.2834884927315215e-07, + "train_step5_mse": 9.347850751899907e-07, + "val_mse": 3.164506812111441e-06, + "val_step1_mse": 2.932287349895853e-07, + "val_step5_mse": 2.871278077121855e-06, + "val_mae_h": 0.0002563841739744004, + "val_mae_mx": 0.0002686411106049711, + "val_mae_my": 0.00025164467049986275, + "val_negative_h_ratio": 0.0010009765625, + "lr": 3.90625e-06, + "seconds": 24.102019790036138 + }, + { + "epoch": 295, + "train_loss": 1.1175310697605516e-06, + "train_step1_mse": 1.3368258519465468e-07, + "train_step5_mse": 9.838484845658967e-07, + "val_mse": 1.7140620711916655e-06, + "val_step1_mse": 2.1625377624184657e-07, + "val_step5_mse": 1.497808294949819e-06, + "val_mae_h": 0.00018371256087957528, + "val_mae_mx": 0.00021299599667996312, + "val_mae_my": 0.00019467857171012726, + "val_negative_h_ratio": 0.001002284458705357, + "lr": 3.90625e-06, + "seconds": 24.07523840898648 + }, + { + "epoch": 296, + "train_loss": 1.0779038715164852e-06, + "train_step1_mse": 1.3091028579635478e-07, + "train_step5_mse": 9.469935857201303e-07, + "val_mse": 1.4053953515541816e-06, + "val_step1_mse": 1.944586381906785e-07, + "val_step5_mse": 1.210936713363503e-06, + "val_mae_h": 0.00017187260515393602, + "val_mae_mx": 0.00019498100735323943, + "val_mae_my": 0.00018305499739105095, + "val_negative_h_ratio": 0.0010046822684151786, + "lr": 3.90625e-06, + "seconds": 24.0590361939976 + }, + { + "epoch": 297, + "train_loss": 1.0540519434698581e-06, + "train_step1_mse": 1.2886219004875395e-07, + "train_step5_mse": 9.251897534211042e-07, + "val_mse": 1.4557078707347438e-06, + "val_step1_mse": 1.9852101357512058e-07, + "val_step5_mse": 1.257186857159623e-06, + "val_mae_h": 0.00017688093727698769, + "val_mae_mx": 0.00020262925228143588, + "val_mae_my": 0.00019247185676843107, + "val_negative_h_ratio": 0.0010033743722098214, + "lr": 3.90625e-06, + "seconds": 24.079705893003847 + }, + { + "epoch": 298, + "train_loss": 1.101530653033121e-06, + "train_step1_mse": 1.3146949894706137e-07, + "train_step5_mse": 9.700611540860595e-07, + "val_mse": 1.286140490167626e-06, + "val_step1_mse": 1.9586338678967691e-07, + "val_step5_mse": 1.090277103377949e-06, + "val_mae_h": 0.0001628726230415524, + "val_mae_mx": 0.000191488903320784, + "val_mae_my": 0.0001830022568388811, + "val_negative_h_ratio": 0.0010064261300223214, + "lr": 3.90625e-06, + "seconds": 24.092913430999033 + }, + { + "epoch": 299, + "train_loss": 1.107275991643064e-06, + "train_step1_mse": 1.3099912469069101e-07, + "train_step5_mse": 9.762768669523732e-07, + "val_mse": 1.3516160574070087e-06, + "val_step1_mse": 2.0785354703222762e-07, + "val_step5_mse": 1.143762510374781e-06, + "val_mae_h": 0.00017387585729131683, + "val_mae_mx": 0.00019829974406663828, + "val_mae_my": 0.0001874889419011067, + "val_negative_h_ratio": 0.0010110037667410714, + "lr": 3.90625e-06, + "seconds": 24.091813115985133 + }, + { + "epoch": 300, + "train_loss": 1.0880015733606894e-06, + "train_step1_mse": 1.2892730171846063e-07, + "train_step5_mse": 9.590742716422289e-07, + "val_mse": 1.1431634973723978e-06, + "val_step1_mse": 1.8239083979518212e-07, + "val_step5_mse": 9.607726575772157e-07, + "val_mae_h": 0.0001583335669408068, + "val_mae_mx": 0.00018144697773240205, + "val_mae_my": 0.0001757563024094196, + "val_negative_h_ratio": 0.0010079520089285715, + "lr": 1.953125e-06, + "seconds": 24.062847763998434 + } +] diff --git a/results/training_status.json b/results/training_status.json new file mode 100644 index 0000000000000000000000000000000000000000..33ac42bdae0f670f2411820bc1ad78e1c5eed794 --- /dev/null +++ b/results/training_status.json @@ -0,0 +1,27 @@ +{ + "state": "completed", + "completed_epochs": 300, + "best_epoch": 283, + "best_val_loss": 1.117854045685592e-06, + "elapsed_seconds": 7284.017323547974, + "provenance": { + "hostname": "a01r3n06", + "slurm_job_id": "941230", + "torch": "2.4.1", + "hip": "6.1.25065", + "python": "3.10.16 (main, Dec 11 2024, 16:24:50) [GCC 11.2.0]", + "device": "BW", + "device_count": 1, + "source_sha256": { + "model/FluxNetShallowWater.py": "1cda2ca318e512fcc1bfe3679cc97437251cb279ed8402f2eaf0596550181dc2", + "model/__init__.py": "5549ab1fe3ffaf66757b8288968a9a672ac349a5766a226f2c32193a35920591", + "scripts/common.py": "75c681036e8d423633118289f6b5dfbd4edfe73f23e938f31f0dd3347c9df443", + "scripts/inference.py": "2ba1e3d18267e02d7b670324610d17419179edffca72661d1180f5e689e60d66", + "scripts/preflight.py": "0101b19e9520e938bb25b38f734cbfe9b13d3f8d38ecbe09fcf4d8a0a5a5f05d", + "scripts/result.py": "ed44119146213d3a3054694ce996f47971363690e385b8396b092c97bb9ef2b5", + "scripts/train.py": "c8ed7e90f42e79f36729683d0128f574dba7c22dc72e52679eb0428ff0bbf405" + } + }, + "formal_training": true, + "stopped_early": false +} diff --git a/results/verification.json b/results/verification.json new file mode 100644 index 0000000000000000000000000000000000000000..a7ecbc24e2f4d6135eb734a3aed62583899a971e --- /dev/null +++ b/results/verification.json @@ -0,0 +1,54 @@ +{ + "completed_epochs": 300, + "best_checkpoint_hash_matches": true, + "selected_epoch_is_val_minimum": true, + "checkpoint_sha256": "47fb0a029b0c5baa10b2452c70b12f54c958b548cb9aff300b85183d55f1ae70", + "splits": { + "test": { + "all_trajectories": true, + "all_steps": true, + "finite": true, + "metrics_recomputed": true, + "example_fields_recomputed": true, + "zero_negative_depth": true + }, + "test_long": { + "all_trajectories": true, + "all_steps": true, + "finite": true, + "metrics_recomputed": true, + "example_fields_recomputed": true, + "zero_negative_depth": true + } + }, + "figures": [ + "training_curve.png", + "test_rollout_metrics.png", + "test_A1_h.png", + "test_A1_mx.png", + "test_A1_my.png", + "test_A2_h.png", + "test_A2_mx.png", + "test_A2_my.png", + "test_B1_h.png", + "test_B1_mx.png", + "test_B1_my.png", + "test_B2_h.png", + "test_B2_mx.png", + "test_B2_my.png", + "test_long_rollout_metrics.png", + "test_long_A1_h.png", + "test_long_A1_mx.png", + "test_long_A1_my.png", + "test_long_A2_h.png", + "test_long_A2_mx.png", + "test_long_A2_my.png", + "test_long_B1_h.png", + "test_long_B1_mx.png", + "test_long_B1_my.png", + "test_long_B2_h.png", + "test_long_B2_mx.png", + "test_long_B2_my.png" + ], + "report": "report.md" +} diff --git a/scripts/common.py b/scripts/common.py new file mode 100644 index 0000000000000000000000000000000000000000..102861f142c6f3a1ff2e3920d8e91da5af00ff45 --- /dev/null +++ b/scripts/common.py @@ -0,0 +1,271 @@ +"""Data, provenance, and metrics shared by the independent reproduction.""" +import argparse +import hashlib +import json +import os +import platform +import random +import sys +from pathlib import Path + +import h5py +import numpy as np +import torch +import yaml +from torch.utils.data import Dataset + +ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(ROOT)) +from model import FluxNetShallowWater + +FIELDS = ("h", "mx", "my") + + +def load_config(path=None): + with open(path or ROOT / "config/config.yaml", encoding="utf-8") as stream: + cfg = yaml.safe_load(stream) + for name, expected in {"epochs": 300, "pushforward_steps": 5}.items(): + if cfg["training"][name] != expected: + raise ValueError(f"Formal experiment requires {name}={expected}") + if cfg["training"]["early_stopping"] or cfg["data"]["normalization"] != "none": + raise ValueError("Formal run requires 300 full epochs and physical states") + return cfg + + +def checkpoint_config_matches(saved, current): + # The published dataset can be relocated; all experiment settings must still match. + saved = {**saved, "data": {**saved["data"], "root": None}} + current = {**current, "data": {**current["data"], "root": None}} + return saved == current + + +def setup(seed): + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + torch.set_num_threads(min(8, int(os.environ.get("SLURM_CPUS_PER_TASK", 8)))) + torch.backends.cudnn.benchmark = True + torch.backends.cuda.matmul.allow_tf32 = False + torch.backends.cudnn.allow_tf32 = False + + +def device(): + if not os.environ.get("SLURM_JOB_ID"): + raise RuntimeError("Run training/inference inside a SCNet DCU SLURM allocation") + if not torch.cuda.is_available() or not torch.version.hip: + raise RuntimeError("A HIP/DCU PyTorch device is required; CPU fallback is disabled") + return torch.device("cuda") + + +def output_dir(cfg): + path = ROOT / cfg["paths"]["results"] + path.mkdir(parents=True, exist_ok=True) + return path + + +def save_json(path, obj): + path = Path(path) + path.parent.mkdir(parents=True, exist_ok=True) + temporary = path.with_suffix(path.suffix + ".tmp") + temporary.write_text(json.dumps(obj, indent=2, ensure_ascii=False, allow_nan=False) + "\n") + temporary.replace(path) + + +def sha256(path): + digest = hashlib.sha256() + with open(path, "rb") as stream: + for chunk in iter(lambda: stream.read(1024 * 1024), b""): + digest.update(chunk) + return digest.hexdigest() + + +def provenance(): + return {"hostname": platform.node(), "slurm_job_id": os.environ.get("SLURM_JOB_ID"), + "torch": torch.__version__, "hip": torch.version.hip, + "python": sys.version, "device": torch.cuda.get_device_name(), + "device_count": torch.cuda.device_count(), + "source_sha256": {str(p.relative_to(ROOT)): sha256(p) + for p in sorted(ROOT.rglob("*.py")) if "__pycache__" not in str(p)}} + + +class Trajectories: + def __init__(self, cfg, split, validate=True): + self.split = split + self.files = sorted((Path(cfg["data"]["root"]) / split).glob("*.h5")) + if len(self.files) != cfg["data"]["expected_counts"][split]: + raise ValueError(f"Unexpected {split} trajectory count: {len(self.files)}") + self.values = [] + self.metadata = [] + expected = (cfg["data"]["expected_frames"][split], *cfg["data"]["shape"]) + for path in self.files: + with h5py.File(path, "r") as source: + fields = [] + for field in FIELDS: + array = source[field][...] + if array.dtype != np.float32 or array.shape != expected: + raise ValueError(f"{path}:{field}: {array.dtype} {array.shape}") + if validate and not np.isfinite(array).all(): + raise ValueError(f"Nonfinite data: {path}:{field}") + fields.append(array) + state = np.stack(fields, axis=1) + if validate and state[:, 0].min() < -cfg["data"]["reference_negative_tolerance"]: + raise ValueError(f"Substantive negative reference depth: {path}") + if validate and state[0, 0].min() < 0: + raise ValueError(f"Negative initial reference depth: {path}") + times = source["t"][...].astype(np.float64) + if not np.allclose(np.diff(times), cfg["data"]["dt"], atol=2e-6): + raise ValueError(f"Unexpected snapshot interval: {path}") + self.times = times + self.metadata.append({"file": str(path), "bytes": path.stat().st_size, + "frames": len(times), "t_start": float(times[0]), + "t_end": float(times[-1]), "min_h": float(state[:, 0].min()), + "reference_negative_h_count": int((state[:, 0] < 0).sum()), + "min": state.min(axis=(0, 2, 3)).tolist(), + "max": state.max(axis=(0, 2, 3)).tolist()}) + self.values.append(torch.from_numpy(state)) + self.values = torch.stack(self.values) + reference_negatives = sum(m["reference_negative_h_count"] for m in self.metadata) + print(f"DATA {split}: {tuple(self.values.shape)}; dt={cfg['data']['dt']} " + f"t_end={self.times[-1]:.2f}; normalization=none; " + f"raw_reference_negative_cells={reference_negatives} (roundoff retained, no clipping)", flush=True) + + +class Windows(Dataset): + def __init__(self, trajectories, steps): + self.trajectories = trajectories + self.steps = steps + self.starts = trajectories.values.shape[1] - steps + + def __len__(self): + return self.trajectories.values.shape[0] * self.starts + + def __getitem__(self, index): + trajectory, start = divmod(index, self.starts) + return self.trajectories.values[trajectory, start:start + self.steps + 1] + + +def build_model(cfg, target_device): + return FluxNetShallowWater(**cfg["model"]).to(target_device) + + +@torch.no_grad() +def validate(model, loader, target_device): + model.eval() + square = torch.zeros(3, device=target_device, dtype=torch.float64) + absolute = torch.zeros_like(square) + count = 0 + negative = 0 + terminal_square = torch.zeros_like(square) + for window in loader: + window = window.to(target_device, non_blocking=True) + prediction = model(window[:, 0]) + error = prediction.double() - window[:, 1].double() + square += error.square().sum(dim=(0, 2, 3)) + absolute += error.abs().sum(dim=(0, 2, 3)) + current = prediction + for _ in range(1, window.shape[1] - 1): + current = model(current) + terminal_error = current.double() - window[:, -1].double() + terminal_square += terminal_error.square().sum(dim=(0, 2, 3)) + count += prediction.shape[0] * prediction.shape[2] * prediction.shape[3] + negative += int((prediction[:, 0] < 0).sum()) + return {"mse": float(((square + terminal_square) / count).mean()), + "step1_mse": float((square / count).mean()), + "step5_mse": float((terminal_square / count).mean()), + "mae": (absolute / count).tolist(), "negative_h_ratio": negative / count} + + +@torch.no_grad() +def rollout_metrics(model, trajectories, target_device, batch_size=10, + save_path=None, example_cases=("A1", "A2", "B1", "B2")): + model.eval() + values = trajectories.values + n, frames, channels, height, width = values.shape + steps = frames - 1 + mae = np.empty((n, steps, channels), dtype=np.float64) + cons_relative = np.empty_like(mae) + cons_abs = np.empty_like(mae) + cons_scaled = np.empty_like(mae) + negatives = np.empty((n, steps), dtype=np.float64) + minima = np.empty((n, steps), dtype=np.float64) + examples = {} + example_indices = {} + for case in example_cases: + matches = [i for i, p in enumerate(trajectories.files) if p.name.startswith("Case" + case + "_")] + if matches: + example_indices[matches[0]] = case + for begin in range(0, n, batch_size): + end = min(begin + batch_size, n) + current = values[begin:end, 0].to(target_device) + initial_sum = current.double().sum(dim=(-2, -1)) + initial_abs_sum = current.double().abs().sum(dim=(-2, -1)) + traces = [] + keep = any(begin <= idx < end for idx in example_indices) + if keep: + traces.append(current.cpu().numpy()) + for step in range(steps): + current = model(current) + if not torch.isfinite(current).all(): + raise FloatingPointError(f"Nonfinite {trajectories.split} rollout at step {step + 1}") + target = values[begin:end, step + 1].to(target_device) + mae[begin:end, step] = (current.double() - target.double()).abs().mean((-2, -1)).cpu().numpy() + drift = (current.double().sum((-2, -1)) - initial_sum).abs() + cons_abs[begin:end, step] = drift.cpu().numpy() + # Paper's signed-total denominator is ill-conditioned for zero-net + # momentum; preserve it and also report absolute and L1-scaled drift. + cons_relative[begin:end, step] = (drift / (initial_sum.abs() + 1e-8)).cpu().numpy() + cons_scaled[begin:end, step] = (drift / (initial_abs_sum + 1e-8)).cpu().numpy() + negatives[begin:end, step] = (current[:, 0] < 0).double().mean((-2, -1)).cpu().numpy() + minima[begin:end, step] = current[:, 0].amin((-2, -1)).cpu().numpy() + if keep: + traces.append(current.cpu().numpy()) + if keep: + traces = np.stack(traces, axis=1) + for idx, case in example_indices.items(): + if begin <= idx < end: + examples[case + "_prediction"] = traces[idx - begin] + examples[case + "_target"] = values[idx].numpy() + examples[case + "_file"] = np.array(trajectories.files[idx].name) + print(f"ROLLOUT {trajectories.split} {end}/{n}: " + f"MAE={mae[:end].mean((0, 1)).tolist()} " + f"cons_abs_max={cons_abs[:end].max((0, 1)).tolist()} " + f"negative_h_ratio={negatives[:end].mean():.6e}", flush=True) + summary = {"split": trajectories.split, "trajectories": n, "steps": steps, + "t_end": float(trajectories.times[-1]), "fields": list(FIELDS), + "mae": mae.mean((0, 1)).tolist(), + "mae_std_across_trajectories": mae.mean(1).std(0).tolist(), + "mae_final": mae[:, -1].mean(0).tolist(), + "conservation_relative_mean": cons_relative.mean((0, 1)).tolist(), + "conservation_relative_max": cons_relative.max((0, 1)).tolist(), + "conservation_absolute_sum_mean": cons_abs.mean((0, 1)).tolist(), + "conservation_absolute_sum_max": cons_abs.max((0, 1)).tolist(), + "conservation_initial_l1_scaled_max": cons_scaled.max((0, 1)).tolist(), + "negative_h_ratio": float(negatives.mean()), + "negative_h_percent": float(100 * negatives.mean()), + "min_h": float(minima.min()), + "reduction": "all trajectories, forecast steps 1..T, all grid cells; initial frame excluded", + "accumulation_dtype": "float64 (model states float32)", + "zero_momentum_note": "Relative drift uses |sum(m0)|+1e-8; inspect absolute drift and initial-L1-scaled drift for zero-total cases."} + for horizon in (2.0, 2.4, 4.8): + count = int(np.searchsorted(trajectories.times[1:], horizon + 1e-6, side="right")) + if count and horizon <= trajectories.times[-1] + 1e-6: + summary[f"mae_through_t{horizon:.1f}"] = mae[:, :count].mean((0, 1)).tolist() + if save_path: + save_path = Path(save_path) + np.savez_compressed(save_path.with_suffix(".npz"), time=trajectories.times[1:], + mae=mae, conservation_relative=cons_relative, + conservation_absolute_sum=cons_abs, + conservation_l1_scaled=cons_scaled, + negative_h_ratio=negatives, min_h=minima, + files=np.array([p.name for p in trajectories.files])) + np.savez_compressed(save_path.parent / (save_path.stem + "_examples.npz"), + time=trajectories.times, **examples) + save_json(save_path.with_suffix(".json"), summary) + return summary + + +def parser(description): + arg = argparse.ArgumentParser(description=description) + arg.add_argument("--config", type=Path, default=ROOT / "config/config.yaml") + return arg diff --git a/scripts/inference.py b/scripts/inference.py new file mode 100644 index 0000000000000000000000000000000000000000..77bf51538ce0237c3bf7710c0eccd8f17cce9e3d --- /dev/null +++ b/scripts/inference.py @@ -0,0 +1,51 @@ +"""Reload the selected checkpoint and evaluate every short/long test trajectory.""" +import json +from pathlib import Path + +import torch + +from common import (ROOT, Trajectories, build_model, checkpoint_config_matches, device, load_config, output_dir, + parser, provenance, rollout_metrics, save_json, setup, sha256) + + +def main(): + arg = parser(__doc__) + arg.add_argument("--checkpoint", type=Path) + args = arg.parse_args() + cfg = load_config(args.config) + setup(cfg["seed"]) + target_device = device() + out = output_dir(cfg) + checkpoint_path = args.checkpoint or ROOT / cfg["paths"]["best_checkpoint"] + checkpoint = torch.load(checkpoint_path, map_location="cpu", weights_only=False) + status = json.loads((out / "training_status.json").read_text()) + if status["state"] != "completed" or status["completed_epochs"] != 300: + raise RuntimeError("Final inference requires evidence of completed 300-epoch training") + if not checkpoint_config_matches(checkpoint["config"], cfg) or checkpoint["epoch"] != status["best_epoch"]: + raise ValueError("Checkpoint/config/best-epoch provenance mismatch") + model = build_model(cfg, target_device) + model.load_state_dict(checkpoint["model_state_dict"], strict=True) + if not all(torch.isfinite(p).all() for p in model.parameters()): + raise ValueError("Nonfinite checkpoint parameters") + information = {"checkpoint": str(checkpoint_path), "sha256": sha256(checkpoint_path), + "selected_epoch": checkpoint["epoch"], "completed_epochs": 300, + "best_val_mse": checkpoint["best_val_loss"], + "parameter_count": sum(p.numel() for p in model.parameters()), + "provenance": provenance()} + print("CHECKPOINT_VERIFIED " + json.dumps(information), flush=True) + metrics, manifests = {}, {} + for split in cfg["evaluation"]["splits"]: + data = Trajectories(cfg, split) + manifests[split] = data.metadata + metrics[split] = rollout_metrics(model, data, target_device, + batch_size=cfg["evaluation"]["batch_size"], + save_path=out / split, + example_cases=cfg["evaluation"]["example_cases"]) + print("FINAL_METRICS " + json.dumps(metrics[split]), flush=True) + save_json(out / "inference_manifest.json", {**information, "data": manifests}) + save_json(out / "metrics.json", metrics) + print("INFERENCE_COMPLETED all=100 trajectories long_steps=120", flush=True) + + +if __name__ == "__main__": + main() diff --git a/scripts/preflight.py b/scripts/preflight.py new file mode 100644 index 0000000000000000000000000000000000000000..0fae9620aa89fd0865bf37e29bc546b504b43d73 --- /dev/null +++ b/scripts/preflight.py @@ -0,0 +1,80 @@ +"""OneScience installer preflight and independent structural checks; no formal weights.""" +import ast +import importlib +import json +import os +import shutil +import time + +import torch +from torch.utils.data import DataLoader + +from common import (ROOT, Trajectories, Windows, build_model, device, load_config, + output_dir, parser, provenance, save_json, setup, validate) +from train import train_batch + + +def main(): + args = parser(__doc__).parse_args() + cfg = load_config(args.config) + setup(cfg["seed"]) + target_device = device() + for module in ("onescience", "torch", "numpy", "h5py", "yaml", "matplotlib"): + importlib.import_module(module) + print("DEPENDENCY_OK " + module, flush=True) + for path in ROOT.rglob("*.py"): + ast.parse(path.read_text()) + for command in ("sbatch", "squeue", "sacct"): + assert shutil.which(command), command + datasets = {split: Trajectories(cfg, split) for split in cfg["data"]["expected_counts"]} + out = output_dir(cfg) + save_json(out / "dataset_validation.json", {s: d.metadata for s, d in datasets.items()}) + model = build_model(cfg, target_device) + sample = datasets["train"].values[0, :6].unsqueeze(0).to(target_device) + model.eval() + with torch.no_grad(): + initial = sample[:, 0] + predicted = model(initial) + assert predicted.shape == initial.shape + assert torch.isfinite(predicted).all() + assert predicted[:, 0].min() >= 0 + drift = (predicted.double().sum((-2, -1)) - initial.double().sum((-2, -1))).abs() + scale = initial.double().abs().sum((-2, -1)).clamp_min(1.0) + assert (drift / scale).max() < 2e-6 + # A uniform state remains uniform for all learned transport plans. + uniform = torch.ones_like(initial) + assert torch.allclose(model(uniform), uniform, atol=2e-6) + # Every channel conserves its sum for arbitrary signed momentum. + random_state = torch.rand_like(initial) + random_state[:, 1:] -= 0.5 + result = model(random_state) + random_drift = (result.double().sum((-2, -1)) - random_state.double().sum((-2, -1))).abs() + assert (random_drift / random_state.double().abs().sum((-2, -1)).clamp_min(1)).max() < 2e-6 + print("STRUCTURE_OK forward, positivity, uniform state, per-channel conservation", flush=True) + model.train() + optimizer = torch.optim.AdamW(model.parameters(), lr=1e-3, weight_decay=1e-2) + first, terminal = train_batch(model, sample, optimizer, 5) + assert any(p.grad is not None for p in model.parameters()) + validation = validate(model, DataLoader(Windows(datasets["val"], 5), batch_size=2), target_device) + assert validation["mse"] >= 0 + # Independent stop-gradient check on an explicit two-state feedback graph. + x = sample[:, 0].detach().requires_grad_(True) + intermediate = model(x) + final = model(intermediate.detach()) + assert torch.autograd.grad(final.sum(), x, allow_unused=True)[0] is None + props = torch.cuda.get_device_properties(target_device) + result = {"status": "passed", "preflight_passed": True, "execution_readiness": "ready", + "evidence": {"preflight": {"status": "passed"}}, "installer_reason": "preflight_validation", + "execution_channel": "local_slurm", "provenance": provenance(), + "gpu_memory_gb": props.total_memory / 2**30, + "model_parameters": sum(p.numel() for p in model.parameters()), + "structural_checks": ["shape", "finite", "positivity", "uniform_state", "conservation", "detached_feedback"], + "real_data_train_batch_loss": {"step1": first, "step5": terminal}, + "real_data_validation": validation, + "formal_training_completed": False, "formal_weights_written": False} + save_json(out / "preflight_validation.json", result) + print("PREFLIGHT_PASSED " + json.dumps(result), flush=True) + + +if __name__ == "__main__": + main() diff --git a/scripts/result.py b/scripts/result.py new file mode 100644 index 0000000000000000000000000000000000000000..f7e77e4b1e661bb7d3e458835930d59ba12c4398 --- /dev/null +++ b/scripts/result.py @@ -0,0 +1,141 @@ +"""Create standalone scientific figures and audit actual inference artifacts.""" +import json + +import matplotlib +matplotlib.use("Agg") +import matplotlib.pyplot as plt +import numpy as np + +from common import FIELDS, ROOT, load_config, output_dir, parser, save_json, sha256 + + +def main(): + args = parser(__doc__).parse_args() + cfg = load_config(args.config) + out = output_dir(cfg) + history = json.loads((out / "training_history.json").read_text()) + status = json.loads((out / "training_status.json").read_text()) + manifest = json.loads((out / "inference_manifest.json").read_text()) + metrics = json.loads((out / "metrics.json").read_text()) + assert status["completed_epochs"] == 300 and status["state"] == "completed" + assert [r["epoch"] for r in history] == list(range(1, 301)) + assert all(np.isfinite(r["train_loss"]) and np.isfinite(r["val_mse"]) for r in history) + selected = min(history, key=lambda r: r["val_mse"]) + assert selected["epoch"] == manifest["selected_epoch"] == status["best_epoch"] + assert sha256(ROOT / cfg["paths"]["best_checkpoint"]) == manifest["sha256"] + figures = [] + plt.rcParams.update({"font.size": 10, "figure.dpi": 140, "savefig.dpi": 180}) + fig, ax = plt.subplots(figsize=(7, 4)) + for key, label in [("train_step1_mse", "Train step 1"), + ("train_step5_mse", "Train step 5"), ("val_mse", "Validation step 1 + 5")]: + ax.semilogy([r["epoch"] for r in history], [r[key] for r in history], label=label) + ax.axvline(status["best_epoch"], color="0.6", lw=1, ls=":", label="Selected checkpoint") + ax.set(xlabel="Epoch", ylabel="MSE in physical units", title="FluxNet-LAP: 300-epoch pushforward training") + ax.legend() + fig.tight_layout() + fig.savefig(out / "training_curve.png") + plt.close(fig) + figures.append("training_curve.png") + checks = {} + for split in ("test", "test_long"): + data = np.load(out / f"{split}.npz") + expected_steps = cfg["data"]["expected_frames"][split] - 1 + assert data["mae"].shape == (50, expected_steps, 3) + assert np.isfinite(data["mae"]).all() + assert np.allclose(data["mae"].mean((0, 1)), metrics[split]["mae"], atol=1e-12) + assert np.allclose(data["mae"][:, -1].mean(0), metrics[split]["mae_final"], atol=1e-12) + assert float(data["negative_h_ratio"].mean()) == metrics[split]["negative_h_ratio"] + assert np.allclose(data["conservation_absolute_sum"].max((0, 1)), + metrics[split]["conservation_absolute_sum_max"]) + fig, axes = plt.subplots(1, 3, figsize=(13, 3.6)) + for c, field in enumerate(FIELDS): + axes[0].plot(data["time"], data["mae"].mean(0)[:, c], label=field) + axes[1].plot(data["time"], data["conservation_absolute_sum"].max(0)[:, c], label=field) + axes[0].set(xlabel="Time", ylabel="MAE", title="Autoregressive error") + axes[1].set(xlabel="Time", ylabel="max |sum(u)-sum(u0)|", title="Conservation drift (float64 reduction)") + axes[2].plot(data["time"], 100 * data["negative_h_ratio"].mean(0)) + axes[2].set(xlabel="Time", ylabel="Negative depth (%)", title="Depth bound violations") + for ax in axes[:2]: + ax.legend() + fig.tight_layout() + name = f"{split}_rollout_metrics.png" + fig.savefig(out / name) + figures.append(name) + plt.close(fig) + examples = np.load(out / f"{split}_examples.npz") + for case in cfg["evaluation"]["example_cases"]: + prediction = examples[case + "_prediction"] + target = examples[case + "_target"] + assert prediction.shape == target.shape == (expected_steps + 1, 3, 64, 64) + assert np.array_equal(prediction[0], target[0]) + # Independent recomputation from saved physical fields. + errors = np.abs(prediction[1:].astype(np.float64) - target[1:]).mean((0, 2, 3)) + file = str(examples[case + "_file"]) + index = list(data["files"]).index(file) + assert np.allclose(errors, data["mae"][index].mean(0), atol=1e-12) + frames = np.round(np.linspace(0, expected_steps, 6)).astype(int) + for c, field in enumerate(FIELDS): + fig, axes = plt.subplots(3, 6, figsize=(14, 7.3), layout="constrained") + minimum = min(target[frames, c].min(), prediction[frames, c].min()) + maximum = max(target[frames, c].max(), prediction[frames, c].max()) + error_max = np.abs(prediction[frames, c] - target[frames, c]).max() + for j, frame in enumerate(frames): + for r, values in enumerate((target, prediction)): + image = axes[r, j].imshow(values[frame, c], origin="lower", extent=(0, 10, 0, 10), + cmap="viridis", vmin=minimum, vmax=maximum) + err_image = axes[2, j].imshow(np.abs(prediction[frame, c] - target[frame, c]), + origin="lower", extent=(0, 10, 0, 10), cmap="magma", + vmin=0, vmax=max(float(error_max), 1e-12)) + axes[0, j].set_title(f"t={examples['time'][frame]:.2f}") + for r in range(3): + axes[r, j].set_xticks([]) + axes[r, j].set_yticks([]) + for r, label in enumerate(("Reference", "FluxNet-LAP", "Absolute error")): + axes[r, 0].set_ylabel(label) + fig.colorbar(image, ax=axes[:2, :], shrink=0.75) + fig.colorbar(err_image, ax=axes[2, :], shrink=0.75) + fig.suptitle(f"{split} Case {case}: {field}") + name = f"{split}_{case}_{field}.png" + fig.savefig(out / name) + figures.append(name) + plt.close(fig) + checks[split] = {"all_trajectories": True, "all_steps": True, "finite": True, + "metrics_recomputed": True, "example_fields_recomputed": True, + "zero_negative_depth": metrics[split]["negative_h_ratio"] == 0} + report = ["# FluxNet shallow-water LAP pushforward reproduction", "", + "Source: https://arxiv.org/pdf/2602.01941v2 (Sections 3.2–3.7, Appendix C.2).", "", + "One independent training run (seed 42), 300 complete epochs. This is not a five-seed mean/std replication.", + "64 channels, 6 residual blocks, kernel 5, transport stencil 3×3, detached pushforward length 5.", + "AdamW lr=1e-3, weight_decay=1e-2; ReduceLROnPlateau factor=0.5, patience=15; float32 states.", "", + f"Selected checkpoint epoch: {manifest['selected_epoch']}; validation MSE: {manifest['best_val_mse']:.8e}.", + f"Checkpoint SHA256: `{manifest['sha256']}`.", "", + "| Split | Horizon | MAE h | MAE mx | MAE my | Negative h (%) |", + "| --- | ---: | ---: | ---: | ---: | ---: |"] + for split, values in metrics.items(): + report.append(f"| {split} | {values['t_end']:.1f} | " + " | ".join(f"{v:.8e}" for v in values["mae"]) + + f" | {values['negative_h_percent']:.8e} |") + report += ["", "MAE averages all forecast steps, grid cells, and 50 trajectories, excluding the initial frame.", + "Final-time and T=2/T=2.4/T=4.8 prefix metrics are separately stored in metrics.json.", + "The paper labels Table 3 as T=2 while Appendix C.1 specifies 2.4/4.8; both time conventions are retained.", + "", "Conservation is computed using float64 reductions of float32 predictions relative to the initial state.", + "Relative drift divides by |initial signed total|+1e-8; zero-total momentum makes this statistic ill-conditioned.", + "Absolute sum drift and initial-L1-scaled drift are therefore also reported. No postprocessing correction is used.", ""] + for split, values in metrics.items(): + report += [f"{split} maximum absolute sum drift [h,mx,my]: {values['conservation_absolute_sum_max']}", + f"{split} maximum relative drift [h,mx,my]: {values['conservation_relative_max']}", + f"{split} minimum depth: {values['min_h']:.8e}", ""] + report += ["All arrays, checkpoints, training history, and saved-case MAE recomputations were checked.", + "The original numerical-generator datasets were read directly; no data generation or official repository code was downloaded."] + report += ["", "Reference data contains small negative roundoff depths (about 1e-11); these are retained unchanged.", + "Data validation tolerates reference values down to -1e-9; model violation metrics use the strict condition h<0, with no tolerance or clipping."] + (out / "report.md").write_text("\n".join(report) + "\n") + save_json(out / "verification.json", {"completed_epochs": 300, + "best_checkpoint_hash_matches": True, "selected_epoch_is_val_minimum": True, + "checkpoint_sha256": manifest["sha256"], "splits": checks, + "figures": figures, "report": "report.md"}) + print("RESULT_VERIFIED " + json.dumps({"completed_epochs": 300, "checks": checks, + "figures": len(figures), "results": str(out)}), flush=True) + + +if __name__ == "__main__": + main() diff --git a/scripts/train.py b/scripts/train.py new file mode 100644 index 0000000000000000000000000000000000000000..6b0bacf6db36c0e8c72676934bc1068227d53779 --- /dev/null +++ b/scripts/train.py @@ -0,0 +1,162 @@ +"""Formal 300-epoch FluxNet-LAP pushforward training on SCNet DCU.""" +import csv +import json +import random +import time +from pathlib import Path + +import numpy as np +import torch +from torch.nn import functional as F +from torch.utils.data import DataLoader + +from common import (ROOT, Trajectories, Windows, build_model, checkpoint_config_matches, device, load_config, + output_dir, parser, provenance, rollout_metrics, save_json, + setup, validate) + + +def train_batch(model, window, optimizer, steps): + """Eq. (6): first-step MSE + terminal MSE; every feedback is detached.""" + optimizer.zero_grad(set_to_none=True) + prediction = model(window[:, 0]) + first_loss = F.mse_loss(prediction, window[:, 1]) + current = prediction.detach() + # Free the first graph before the terminal graph to reduce memory without + # changing gradients or updating parameters between the two losses. + first_loss.backward() + with torch.no_grad(): + for _ in range(1, steps - 1): + current = model(current).detach() + prediction = model(current.detach()) + terminal_loss = F.mse_loss(prediction, window[:, steps]) + terminal_loss.backward() + if not torch.isfinite(first_loss + terminal_loss): + raise FloatingPointError("Nonfinite training loss") + if not torch.stack([torch.isfinite(parameter.grad).all() for parameter in model.parameters() + if parameter.grad is not None]).all(): + raise FloatingPointError("Nonfinite parameter gradient") + optimizer.step() + return float(first_loss.detach()), float(terminal_loss.detach()) + + +def atomic_checkpoint(path, payload): + path.parent.mkdir(parents=True, exist_ok=True) + temporary = path.with_suffix(".tmp") + torch.save(payload, temporary) + temporary.replace(path) + + +def main(): + args_parser = parser(__doc__) + args_parser.add_argument("--resume", type=Path) + args = args_parser.parse_args() + cfg = load_config(args.config) + setup(cfg["seed"]) + target_device = device() + out = output_dir(cfg) + settings = cfg["training"] + run_info = provenance() + print("FORMAL_TRAINING " + json.dumps({**run_info, "config": cfg}), flush=True) + train = Trajectories(cfg, "train") + val = Trajectories(cfg, "val") + save_json(out / "data_manifest.json", {"train": train.metadata, "val": val.metadata}) + generator = torch.Generator().manual_seed(cfg["seed"]) + train_loader = DataLoader(Windows(train, settings["pushforward_steps"]), + batch_size=settings["batch_size"], shuffle=True, + num_workers=settings["num_workers"], pin_memory=True, + persistent_workers=settings["num_workers"] > 0, + generator=generator, drop_last=False) + val_loader = DataLoader(Windows(val, settings["pushforward_steps"]), batch_size=cfg["evaluation"]["batch_size"], + shuffle=False, num_workers=0, pin_memory=True) + model = build_model(cfg, target_device) + optimizer = torch.optim.AdamW(model.parameters(), lr=settings["learning_rate"], + weight_decay=settings["weight_decay"]) + scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( + optimizer, factor=settings["scheduler_factor"], patience=settings["scheduler_patience"]) + epoch_start, best_loss, best_epoch, history = 1, float("inf"), 0, [] + if args.resume: + checkpoint = torch.load(args.resume, map_location="cpu", weights_only=False) + if not checkpoint_config_matches(checkpoint["config"], cfg): + raise ValueError("Resume config differs from checkpoint") + model.load_state_dict(checkpoint["model_state_dict"], strict=True) + optimizer.load_state_dict(checkpoint["optimizer_state_dict"]) + scheduler.load_state_dict(checkpoint["scheduler_state_dict"]) + epoch_start = checkpoint["epoch"] + 1 + best_loss, best_epoch = checkpoint["best_val_loss"], checkpoint["best_epoch"] + history = checkpoint["history"] + random.setstate(checkpoint["random_state"]) + np.random.set_state(checkpoint["numpy_state"]) + torch.set_rng_state(checkpoint["torch_state"]) + torch.cuda.set_rng_state_all(checkpoint["cuda_state"]) + generator.set_state(checkpoint["loader_state"]) + print(f"TRAIN samples={len(train_loader.dataset)} batches/epoch={len(train_loader)} " + f"parameters={sum(p.numel() for p in model.parameters())} " + f"epochs={settings['epochs']} start_epoch={epoch_start}", flush=True) + started = time.monotonic() + long_data = None + for epoch in range(epoch_start, settings["epochs"] + 1): + before = time.monotonic() + model.train() + losses = np.zeros(2) + samples = 0 + lr = optimizer.param_groups[0]["lr"] + for batch_index, window in enumerate(train_loader, 1): + window = window.to(target_device, non_blocking=True) + first, terminal = train_batch(model, window, optimizer, settings["pushforward_steps"]) + losses += np.array([first, terminal]) * len(window) + samples += len(window) + if batch_index % settings["print_every_batches"] == 0 or batch_index == len(train_loader): + print(f"epoch={epoch:03d}/300 batch={batch_index:03d}/{len(train_loader)} " + f"loss={(losses.sum()/samples):.8e} one_step={losses[0]/samples:.8e} " + f"pushforward5={losses[1]/samples:.8e} lr={lr:.3e}", flush=True) + validation = validate(model, val_loader, target_device) + scheduler.step(validation["mse"]) + if validation["mse"] < best_loss: + best_loss, best_epoch = validation["mse"], epoch + row = {"epoch": epoch, "train_loss": float(losses.sum()/samples), + "train_step1_mse": float(losses[0]/samples), + "train_step5_mse": float(losses[1]/samples), + "val_mse": validation["mse"], + "val_step1_mse": validation["step1_mse"], + "val_step5_mse": validation["step5_mse"], + **{f"val_mae_{name}": x for name, x in zip(("h", "mx", "my"), validation["mae"])}, + "val_negative_h_ratio": validation["negative_h_ratio"], "lr": lr, + "seconds": time.monotonic() - before} + history.append(row) + print("EPOCH " + json.dumps(row), flush=True) + save_json(out / "training_history.json", history) + with open(out / "training_history.csv", "w", newline="") as stream: + writer = csv.DictWriter(stream, fieldnames=row.keys()) + writer.writeheader() + writer.writerows(history) + payload = {"model_state_dict": model.state_dict(), "optimizer_state_dict": optimizer.state_dict(), + "scheduler_state_dict": scheduler.state_dict(), "epoch": epoch, + "best_val_loss": best_loss, "best_epoch": best_epoch, "config": cfg, + "history": history, "provenance": run_info, + "random_state": random.getstate(), "numpy_state": np.random.get_state(), + "torch_state": torch.get_rng_state(), "cuda_state": torch.cuda.get_rng_state_all(), + "loader_state": generator.get_state()} + if best_epoch == epoch: + atomic_checkpoint(ROOT / cfg["paths"]["best_checkpoint"], payload) + print(f"BEST checkpoint epoch={best_epoch} val_mse={best_loss:.8e}", flush=True) + if epoch % settings["checkpoint_every_epochs"] == 0 or epoch == settings["epochs"]: + atomic_checkpoint(ROOT / cfg["paths"]["last_checkpoint"], payload) + save_json(out / "training_status.json", {"state": "running", "completed_epochs": epoch, + "best_epoch": best_epoch, "best_val_loss": best_loss, + "elapsed_seconds": time.monotonic() - started, "provenance": run_info}) + if epoch % cfg["evaluation"]["long_test_every_epochs"] == 0: + if long_data is None: + long_data = Trajectories(cfg, "test_long") + diagnostic = rollout_metrics(model, long_data, target_device, cfg["evaluation"]["batch_size"]) + save_json(out / f"diagnostic_long_epoch{epoch:03d}.json", diagnostic) + print("LONG_TEST_DIAGNOSTIC " + json.dumps({"epoch": epoch, **diagnostic}), flush=True) + assert len(history) == settings["epochs"] and history[-1]["epoch"] == 300 + save_json(out / "training_status.json", {"state": "completed", "completed_epochs": 300, + "best_epoch": best_epoch, "best_val_loss": best_loss, + "elapsed_seconds": time.monotonic() - started, "provenance": run_info, + "formal_training": True, "stopped_early": False}) + print(f"TRAINING_COMPLETED epochs=300 best_epoch={best_epoch} best_val_mse={best_loss:.8e}", flush=True) + + +if __name__ == "__main__": + main() diff --git a/weight/best_model.pth b/weight/best_model.pth new file mode 100644 index 0000000000000000000000000000000000000000..c32ded02e10abd9a41ca36998e86158075d1201c --- /dev/null +++ b/weight/best_model.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:47fb0a029b0c5baa10b2452c70b12f54c958b548cb9aff300b85183d55f1ae70 +size 15636928 diff --git a/weight/last_model.pth b/weight/last_model.pth new file mode 100644 index 0000000000000000000000000000000000000000..04933ff5406bd0b5e742a863eb6d1e4655b795f6 --- /dev/null +++ b/weight/last_model.pth @@ -0,0 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