diff --git a/examples/weather/diffusion_sda/data.py b/examples/weather/diffusion_sda/data.py new file mode 100644 index 0000000000..7401a76c1b --- /dev/null +++ b/examples/weather/diffusion_sda/data.py @@ -0,0 +1,221 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import csv +import datetime +import os + +import numpy as np +import torch +import zarr +from torch.utils.data import Dataset + +from physicsnemo.utils.zenith_angle import cos_zenith_angle + + +class HRRRSurfaceDataset(Dataset): + """HRRR Surface dataset on S3 + + Parameters + ---------- + zarr_url : str + URL to Zarr group (e.g., s3://bucket/path) + storage_options : dict, optional + Backend/storage kwargs passed to Zarr opener (e.g., endpoint_url) + time_indices : np.array + Index array of times to use as part of dataset + stats_csv : str, optional + Stats CSV location, by default "stats/stats.csv" + """ + + VARIABLES = [ + "u10m", + "v10m", + "u80m", + "v80m", + "t2m", + "d2m", + "q2m", + "sp", + "fg10m", + "tcc", + "sde", + "snowc", + "refc", + "rsds", + "tp", + "aerot", + ] + LOG_VARIABLES = ("tp", "aerot") # Make sure is consistent with stats CSV + EPSILON = 1e-8 + + def __init__( + self, + zarr_url: str, + time_indices: np.array, + stats_csv: str = "stats/stats.csv", + storage_options: dict | None = None, + ): + self.zarr_url = zarr_url + self.storage_options = storage_options or {} + self.idx = np.asarray(time_indices, dtype=int).ravel() + + # Verify bounds against available time coordinate in zarr + _root = zarr.open_group( + store=self.zarr_url, mode="r", storage_options=self.storage_options + ) + n_time = _root["time"].size + out_of_bounds_mask = (self.idx < 0) | (self.idx >= n_time) + if np.any(out_of_bounds_mask): + invalid_values = np.unique(self.idx[out_of_bounds_mask]) + raise IndexError( + f"time_indices contain out-of-bounds values for zarr_root['time'] " + f"(n_time={n_time}): {invalid_values}" + ) + + # Load normalization stats and log-scaling flags from summary_stats.csv + stats_csv = os.path.join(os.path.dirname(__file__), stats_csv) + means = [] + stds = [] + stats_map = {} + with open(stats_csv, "r", newline="") as f: + reader = csv.DictReader(f) + for row in reader: + var = row.get("variable") + mu = float(row.get("mean", "nan")) + variance = float(row.get("variance", "nan")) + stats_map[var] = (mu, variance) + + # Order based on VARIABLES; convert variance -> std + for var in self.VARIABLES: + mu, variance = stats_map[var] + means.append(mu) + stds.append(np.sqrt(variance)) + + # Instance-level overrides for normalization and log variables + self.target_means = ( + torch.tensor(means, dtype=torch.float32).unsqueeze(-1).unsqueeze(-1) + ) + self.target_stds = ( + torch.tensor(stds, dtype=torch.float32).unsqueeze(-1).unsqueeze(-1) + ) + # Save zarr coords to memory for use + self.grid_lat = _root["lat"][:] + self.grid_lon = _root["lon"][:] + self.time_array = _root["time"][:] + + # Cache of dataset indices known to contain NaN samples + self._nan_indices: set = set() + + def __len__(self): + return self.idx.shape[0] + + def _get_root(self): + if not hasattr(self, "_root_cache"): + self._root_cache = zarr.open_group( + store=self.zarr_url, mode="r", storage_options=self.storage_options + ) + return self._root_cache + + def _get_array(self, idx): + root = self._get_root() + time_idx = self.idx[idx] + data_arrays = np.empty( + (len(self.VARIABLES), self.grid_lat.shape[0], self.grid_lat.shape[1]) + ) + for i, var in enumerate(self.VARIABLES): + arr = root[var][time_idx, :, :] + if var in self.LOG_VARIABLES: + data_arrays[i] = np.log(np.clip(arr, a_min=0, a_max=1e8) + self.EPSILON) + else: + data_arrays[i] = arr + return data_arrays + + MAX_SKIP_RETRIES = 10 + + def __getitem__(self, idx): + for attempt in range(self.MAX_SKIP_RETRIES): + if attempt == 0 and idx not in self._nan_indices: + current_idx = idx + else: + current_idx = np.random.randint(0, len(self)) + while current_idx in self._nan_indices: + current_idx = np.random.randint(0, len(self)) + target, condition_spatial, condition_time = self._load_sample(current_idx) + if not torch.isnan(target).any(): + return target, condition_spatial, condition_time + self._nan_indices.add(current_idx) + raise RuntimeError( + f"Could not find a valid sample after {self.MAX_SKIP_RETRIES} attempts " + f"starting from idx={idx}" + ) + + def _load_sample(self, idx): + time_idx = self.idx[idx] + time_stamp = self.time_array[time_idx] + data_arrays = self._get_array(idx) + + target = torch.Tensor(data_arrays) + target = (target - self.target_means) / self.target_stds + # Conditional encoding + data_arrays = np.empty( + (3, self.grid_lat.shape[0], self.grid_lat.shape[1]), dtype=np.float32 + ) + ts = (time_stamp - np.datetime64("1970-01-01T00:00:00Z")) / np.timedelta64( + 1, "s" + ) + data_arrays[0] = cos_zenith_angle( + datetime.datetime.utcfromtimestamp(ts), self.grid_lat, self.grid_lon + ) + data_arrays[1] = self.grid_lat / 90.0 + data_arrays[2] = self.grid_lon / 360.0 + condition_time = np.array( + [ + ( + time_stamp.astype("datetime64[D]") + - time_stamp.astype("datetime64[Y]") + + 1 + ).astype(int) + ], + dtype=np.int32, + ) + + condition_spatial = torch.Tensor(data_arrays) + return target, condition_spatial, condition_time + + +if __name__ == "__main__": + root = zarr.open_group( + store="s3://hrrr-surface-sda/zarr-v2", + mode="r", + storage_options={"endpoint_url": "https://pdx.s8k.io"}, + ) + time = root["time"][:] + sidx = np.where(time == np.datetime64("2023-01-01T00:00:00"))[0][0] + eidx = np.where(time == np.datetime64("2023-02-01T00:00:00"))[0][0] + + time_idx = np.arange(sidx, eidx) + + dataset = HRRRSurfaceDataset( + "s3://hrrr-surface-sda/zarr-v2", + time_idx, + storage_options={"endpoint_url": "https://pdx.s8k.io"}, + ) + target, cond_spatial, cond_time = dataset[30] + + print(target.shape) + print(cond_spatial.shape) + print(cond_time) diff --git a/examples/weather/diffusion_sda/etl.py b/examples/weather/diffusion_sda/etl.py new file mode 100644 index 0000000000..fed58a2282 --- /dev/null +++ b/examples/weather/diffusion_sda/etl.py @@ -0,0 +1,281 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import asyncio +import csv +import os +import shutil +import sys +from datetime import timedelta + +import numpy as np +import zarr +from earth2studio.data import HRRR, HRRR_FX +from loguru import logger + +ENDPOINT_URL = "https://pdx.s8k.io" + + +def _append_missing_dates_to_csv(datetimes: np.ndarray, csv_path: str) -> None: + """Append an array of numpy datetime64 values to a CSV file. + + Creates the file with a header if it does not exist. + """ + file_exists = os.path.exists(csv_path) + # Ensure 1-D array iteration + flat_times = np.asarray(datetimes).ravel() + with open(csv_path, "a", newline="") as f: + writer = csv.writer(f) + if not file_exists: + writer.writerow(["datetime"]) + for t in flat_times: + # Convert numpy datetime64 to ISO string (second precision) + writer.writerow([np.datetime_as_string(t, unit="s")]) + + +def init_zarr_store(zarr_root: str, time: np.ndarray, variable: str): + """Initizes the HRRR Zarr store for training, will overwrite the coordinates every + run but will not overwrite variable arrays if they exist. Each variable will be in + a seperate array and chunked along the time dimension + + + Parameters + ---------- + zarr_path : str + Location of zarr store + time : np.ndarray + Time array + variable : str + Variables to create arrays for + """ + zarr_root = zarr.open_group( + zarr_root, mode="a", storage_options={"endpoint_url": ENDPOINT_URL} + ) + lat, lon = HRRR.grid() + + # Write the coordinates + zarr_root.create_array( + "time", + shape=time.shape, + chunks=time.shape, + dtype=time.dtype, + dimension_names=["time"], + overwrite=True, + ) + zarr_root["time"][:] = time + zarr_root.create_array( + "hrrr_y", + shape=(lat.shape[0],), + dtype=lat.dtype, + dimension_names=["hrrr_y"], + overwrite=True, + ) + zarr_root["hrrr_y"][:] = np.arange(lat.shape[0]) + zarr_root.create_array( + "hrrr_x", + shape=(lat.shape[1],), + dtype=lat.dtype, + dimension_names=["hrrr_x"], + overwrite=True, + ) + zarr_root["hrrr_x"][:] = np.arange(lat.shape[1]) + + zarr_root.create_array( + "lat", + shape=lat.shape, + dtype=lat.dtype, + dimension_names=["hrrr_y", "hrrr_x"], + overwrite=True, + ) + zarr_root["lat"][:] = lat + zarr_root.create_array( + "lon", + shape=lon.shape, + dtype=lon.dtype, + dimension_names=["hrrr_y", "hrrr_x"], + overwrite=True, + ) + zarr_root["lon"][:] = lon + + # Set up chunking of variable arrays + shape = (time.size, lat.shape[0], lat.shape[1]) + chunks = (128, lat.shape[0], lat.shape[1]) # adjust as needed + + # Create 3D variable arrays (time, hrrr_y, hrrr_x) + for v in variable: + try: + zarr_root.create_array( + v, + shape=shape, + chunks=chunks, + dtype="float32", + fill_value=np.nan, + dimension_names=["time", "hrrr_y", "hrrr_x"], + ) + except zarr.errors.ContainsArrayError: + logger.debug(f"Array {v} alread exists!") + pass + + +async def pull_hrrr_data( + zarr_root: str, time: np.ndarray, variable: str, batch_size: int = 1 +): + root = await zarr.api.asynchronous.open_group( + zarr_root, mode="a", storage_options={"endpoint_url": ENDPOINT_URL} + ) + zarr_time_coord = await (await root.get("time")).getitem(slice(None)) + ds = HRRR(verbose=False, cache=False) + # Batch over times present + for sidx in range(0, len(time), batch_size): + eidx = min([time.shape[0] - 1, sidx + batch_size]) + logger.warning(f"Fetching times between {time[sidx]} to {time[eidx]}") + batch_t = time[sidx : eidx + 1] + + retries = 4 + for attempt in range(retries + 1): + try: + da = await ds.fetch(batch_t, variable) + shutil.rmtree(ds.cache) + except Exception: + logger.error("Failed to download data, re-attempting...") + if attempt < retries: + await asyncio.sleep(4 * attempt + 1) + else: + raise + + time_indices = np.where(np.isin(zarr_time_coord, da["time"].values))[0] + + # Save to zarr store using async open_array and concurrent writes + logger.warning("Uploading data (async)") + store = root.store + + async def _write_one(i: int, j: int, t: int, v: str): + retries = 4 + for attempt in range(retries + 1): + try: + arr = await root.get(v) + await arr.setitem((t, slice(None), slice(None)), da.values[i, j]) + except Exception as e: + logger.error(f"Write failed {e}, re-attempting...") + if attempt < retries: + await asyncio.sleep(4) + else: + raise + + jobs = [] + for i, t in enumerate(time_indices): + for j, v in enumerate(variable): + jobs.append(_write_one(i, j, t, v)) + + await asyncio.gather(*jobs) + + +async def pull_hrrr_fx_data( + zarr_root: str, time: np.ndarray, variable: str, batch_size: int = 1 +): + root = await zarr.api.asynchronous.open_group( + zarr_root, mode="a", storage_options={"endpoint_url": ENDPOINT_URL} + ) + zarr_time_coord = await (await root.get("time")).getitem(slice(None)) + ds = HRRR_FX(verbose=False, cache=False) + # Batch over times present + for sidx in range(0, len(time), batch_size): + eidx = min([time.shape[0] - 1, sidx + batch_size]) + logger.warning(f"Fetching times between {time[sidx]} to {time[eidx]}") + batch_t = time[sidx : eidx + 1] + + try: + da = (await ds.fetch(batch_t, timedelta(hours=1), variable)).isel( + lead_time=0 + ) + except FileNotFoundError as exc: + logger.warning( + f"File not found for times {batch_t[0]} to {batch_t[-1]} - recording and continuing. Details: {exc}" + ) + _append_missing_dates_to_csv(batch_t, "missing_dates_hrrr.csv") + continue + time_indices = np.where(np.isin(zarr_time_coord, da["time"].values))[0] + + # Save to zarr store using async open_array and concurrent writes + logger.warning("Uploading data (async)") + store = root.store + + async def _write_one(i: int, j: int, t: int, v: str): + retries = 4 + for attempt in range(retries + 1): + try: + arr = await root.get(v) + await arr.setitem((t, slice(None), slice(None)), da.values[i, j]) + return + except Exception: + logger.error("Write failed, re-attempting...") + if attempt < retries: + await asyncio.sleep(4) + else: + raise + + jobs = [] + for i, t in enumerate(time_indices): + for j, v in enumerate(variable): + jobs.append(_write_one(i, j, t, v)) + + await asyncio.gather(*jobs) + + +async def main(): + store = "s3://hrrr-surface-sda/zarr-v2" + logger.remove() + logger.add(sys.stderr, level="INFO") + logger.add("output.log", level="INFO") + hrrr_variables = [ + "u10m", + "v10m", + "u80m", + "v80m", + "t2m", + "d2m", + "q2m", + "sp", + "fg10m", + "tcc", + "sde", + "snowc", + "refc", + "rsds", + ] + hrrr_fx_variables = ["tp", "aerot"] + variable = hrrr_variables + hrrr_fx_variables + time = np.arange( + "2017-01-01T00:00:00", "2027-01-01T00:00:00", dtype="datetime64[h]" + ) + + init_zarr_store(store, time, hrrr_variables) + + # Set start time + start_date = np.datetime64("2021-01-01T00:00:00") + end_date = np.datetime64("2021-06-01T00:00:00") + sidx = np.where(time == start_date)[0][0] + eidx = np.where(time == end_date)[0][0] + step = 24 * 5 # 5-day batches (in hours) + for bstart in range(sidx, eidx + 1, step): + bend = min(eidx, bstart + step - 1) + batch_time = time[bstart : bend + 1] + (await pull_hrrr_data(store, batch_time, hrrr_variables, batch_size=12),) + await pull_hrrr_fx_data(store, batch_time, hrrr_fx_variables, batch_size=12) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/examples/weather/diffusion_sda/nn.py b/examples/weather/diffusion_sda/nn.py new file mode 100644 index 0000000000..ed7dd6c062 --- /dev/null +++ b/examples/weather/diffusion_sda/nn.py @@ -0,0 +1,135 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import Any, List + +import torch +from jaxtyping import Float +from tensordict import TensorDict + +from physicsnemo.core import Module +from physicsnemo.models.diffusion_unets import SongUNet +from physicsnemo.nn import PositionalEmbedding + + +class HRRRUnconditionalUNet(Module): + r"""Backbone wrapping SongUNet with temporal embedding for the HRRR + surface diffusion model. + + This wrapper sits between the preconditioner and the raw SongUNet backbone. + It consumes a TensorDict condition produced by MultiDiffusionModel2D and: + + 1. Embeds the scalar temporal conditioning via a learnable + :class:`~physicsnemo.nn.PositionalEmbedding`. + 2. Concatenates spatial conditioning and positional embeddings (from the + multi-diffusion wrapper) directly to the input ``x`` along the channel + dimension. + 3. Calls :class:`~physicsnemo.models.diffusion_unets.SongUNet` with the + concatenated input and the temporal embedding as ``class_labels``. + + Parameters + ---------- + img_resolution : list[int] + Spatial resolution :math:`[H, W]` of the input patches. + img_channels : int + Number of image channels (used as ``out_channels`` for the UNet). + num_condition_channels : int + Number of spatial conditioning channels concatenated to ``x``. + num_grid_channels : int + Number of positional-embedding channels concatenated to ``x``. + time_embed_channels : int + Dimensionality of the temporal embedding vector (passed as + ``label_dim`` to the internal + :class:`~physicsnemo.models.diffusion_unets.SongUNet`). + model_channels : int, optional + Base channel count for the UNet, by default 128. + channel_mult : list[int], optional + Per-level channel multipliers, by default ``[1, 2, 2, 2, 2]``. + use_apex_gn : bool, optional + Whether to use Apex fused group-norm kernels, by default ``False``. + amp_mode : bool, optional + Whether mixed-precision (AMP) training is enabled, by default ``False``. + Propagated to the internal + :class:`~physicsnemo.models.diffusion_unets.SongUNet`. + + Forward + ------- + x : torch.Tensor + Noisy latent state of shape :math:`(B, C, H, W)`. + t : torch.Tensor + Diffusion time tensor of shape :math:`(B,)`. + condition : TensorDict or None + TensorDict with keys ``"cond_concat"`` (spatial conditioning, + shape :math:`(B, C_{cond}, H, W)`) and ``"cond_time"`` (scalar + temporal conditioning, shape :math:`(B, 1)`). Optionally contains + ``"positional_embedding"`` (shape :math:`(B, C_{PE}, H, W)`) + injected by :class:`~physicsnemo.diffusion.multi_diffusion.MultiDiffusionModel2D`. + + Outputs + ------- + torch.Tensor + Model output of shape :math:`(B, C_{out}, H, W)`. + """ + + def __init__( + self, + img_resolution: List[int], + img_channels: int, + num_condition_channels: int, + num_grid_channels: int, + time_embed_channels: int, + model_channels: int = 128, + channel_mult: List[int] = [1, 2, 2, 2, 2], + use_apex_gn: bool = False, + amp_mode: bool = False, + ) -> None: + super().__init__() + self.unet = SongUNet( + img_resolution=list(img_resolution), + in_channels=img_channels + num_condition_channels + num_grid_channels, + out_channels=img_channels, + label_dim=time_embed_channels, + model_channels=model_channels, + channel_mult=channel_mult, + attn_resolutions=[img_resolution[0] >> len(channel_mult)], + use_apex_gn=use_apex_gn, + amp_mode=amp_mode, + ) + self.time_embedding = PositionalEmbedding( + num_channels=time_embed_channels, + max_positions=365, + endpoint=True, + learnable=True, + ) + + def forward( + self, + x: Float[torch.Tensor, "B C H W"], + t: Float[torch.Tensor, " B"], + condition: TensorDict, + ) -> Float[torch.Tensor, "B C H W"]: + cond_time = condition["cond_time"] + cond_concat = condition["cond_concat"] + pos_embd = condition["positional_embedding"] + + ct_embed = self.time_embedding(cond_time.squeeze(-1)) + x_and_cond = torch.cat([x, cond_concat, pos_embd], dim=1) + + return self.unet( + x_and_cond, + noise_labels=t, + class_labels=ct_embed, + ) diff --git a/examples/weather/diffusion_sda/plot_hrrr.py b/examples/weather/diffusion_sda/plot_hrrr.py new file mode 100644 index 0000000000..b2b6275804 --- /dev/null +++ b/examples/weather/diffusion_sda/plot_hrrr.py @@ -0,0 +1,112 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import math +from typing import List, Sequence + +import matplotlib.pyplot as plt +import numpy as np +import zarr +from matplotlib.colors import LogNorm + + +def detect_variable_keys(root: zarr.group) -> List[str]: + """Return variable array names to plot. + + A variable is defined as a 3D array with shape (time, hrrr_y, hrrr_x). + Coordinate and non-3D arrays are skipped. + """ + skip_names = {"time", "lat", "lon", "hrrr_y", "hrrr_x"} + variable_keys: List[str] = [] + for key, arr in root.arrays(): + if key in skip_names: + continue + try: + shape = arr.shape + except Exception: + continue + if len(shape) == 3: + variable_keys.append(key) + # Keep a deterministic order + variable_keys.sort() + return variable_keys + + +def make_subplot_grid(n: int) -> (int, int): + """Compute a pleasant rows/cols grid for n subplots.""" + if n <= 0: + return 0, 0 + cols = min(4, max(1, int(math.ceil(math.sqrt(n))))) + rows = int(math.ceil(n / cols)) + return rows, cols + + +def plot_time_slice(root: zarr.group, idx: int, variables: Sequence[str]) -> None: + """Plot each variable slice at time index idx in subplots.""" + rows, cols = make_subplot_grid(len(variables)) + if rows == 0: + raise ValueError("No variables to plot.") + + fig, axes = plt.subplots( + rows, cols, figsize=(4.5 * cols, 3.8 * rows), squeeze=False + ) + axes_flat = axes.ravel() + + for ax in axes_flat[len(variables) :]: + ax.axis("off") + + for ax, var in zip(axes_flat, variables): + data = root[var][idx, :, :] + if var in {"aerot", "sde", "tp"}: + positive = data[data > 0] + if positive.size == 0: + im = ax.imshow(data, origin="lower", cmap="viridis") + else: + vmin = float(np.nanmin(positive)) + vmax = float(np.nanmax(data)) + vmax = vmin * 10 if not np.isfinite(vmax) or vmax <= vmin else vmax + im = ax.imshow( + data, + origin="lower", + cmap="viridis", + norm=LogNorm(vmin=max(vmin, 1e-6), vmax=vmax), + ) + else: + im = ax.imshow(data, origin="lower", cmap="viridis") + ax.set_title(var) + ax.set_xlabel("hrrr_x") + ax.set_ylabel("hrrr_y") + plt.colorbar(im, ax=ax, fraction=0.046, pad=0.04) + + plt.tight_layout() + plt.savefig(f"zarr_plot_{idx:05d}.jpg") + + +def main() -> None: + root = zarr.open_group( + store="s3://hrrr-surface-sda/zarr-v1", + mode="r", + storage_options={"endpoint_url": "https://pdx.s8k.io"}, + ) + time = root["time"][:] + idx = np.where(time == np.datetime64("2023-03-30T00:00:00"))[0][0] + variables = detect_variable_keys(root) + + plot_time_slice(root, idx, variables) + + +if __name__ == "__main__": + main() diff --git a/examples/weather/diffusion_sda/pyproject.toml b/examples/weather/diffusion_sda/pyproject.toml new file mode 100644 index 0000000000..33d26faeec --- /dev/null +++ b/examples/weather/diffusion_sda/pyproject.toml @@ -0,0 +1,18 @@ +[project] +name = "diffusion-sda" +version = "0.1.0" +description = "Add your description here" +readme = "README.md" +requires-python = ">=3.12, <3.14" +dependencies = [ + "earth2studio[data]", + "matplotlib>=3.10.8", + "nvidia-physicsnemo", + "zarr>=3.1.5", +] + +[tool.uv.sources] +nvidia-physicsnemo = { path = "../../../" } + +[tool.setuptools] +package-dir = {"" = "."} diff --git a/examples/weather/diffusion_sda/stats/aggregate_stats.py b/examples/weather/diffusion_sda/stats/aggregate_stats.py new file mode 100644 index 0000000000..33b88f073c --- /dev/null +++ b/examples/weather/diffusion_sda/stats/aggregate_stats.py @@ -0,0 +1,94 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import csv +from collections import defaultdict +from typing import Dict, List, Tuple + +import numpy as np +import zarr + + +def read_daily_stats(csv_path: str) -> Dict[str, List[Tuple[float, float]]]: + grouped: Dict[str, List[Tuple[float, float]]] = defaultdict(list) + with open(csv_path, "r", newline="") as f: + reader = csv.DictReader(f) + for row in reader: + var = row["variable"] + x_val = float(row["x"]) + x2_val = float(row["x2"]) + grouped[var].append((x_val, x2_val)) + return grouped + + +def pooled_mean_variance( + entries: List[Tuple[float, float]], n_per_entry: int +) -> Tuple[float, float, int]: + """Compute pooled mean/variance across entries with equal sample size per entry. + + - entries: list of (sum_x_i, sum_x2_i) where each statistic was computed over n_per_entry samples + - returns: (mean, variance, total_samples) + """ + if not entries: + return float("nan"), float("nan"), 0 + + x = np.array([m for m, _ in entries], dtype=np.float64) + x2 = np.array([s for _, s in entries], dtype=np.float64) + n = int(n_per_entry) + k = int(len(entries)) + total_n = n * k + + mu = np.sum(x) / total_n + variance = np.sum(x2) / total_n - mu**2 + return mu, variance, total_n + + +def main() -> None: + root = zarr.open_group( + store="s3://hrrr-surface-sda/zarr-v1", + mode="r", + storage_options={ + "endpoint_url": "https://pdx.s8k.io", + "profile": "physicsnemo" + }, + ) + n_grid = root["lat"].shape[0] * root["lat"].shape[1] + grouped = read_daily_stats("daily_random_moments.csv") + + out_path = "stats.csv" + with open(out_path, "w", newline="") as f: + writer = csv.writer(f) + writer.writerow( + ["variable", "log_scale", "n_samples", "mean", "variance", "eps"] + ) + for var, stats in sorted(grouped.items()): + mu, var_val, total_n = pooled_mean_variance(stats, n_grid) + writer.writerow( + [ + var, + (var in ["tp", "aerot"]), + f"{total_n:.16g}", + f"{mu:.16g}", + f"{var_val:.16g}", + 1e-8, + ] + ) + + print(f"Wrote {out_path}") + + +if __name__ == "__main__": + main() diff --git a/examples/weather/diffusion_sda/stats/collect_stat_moments.py b/examples/weather/diffusion_sda/stats/collect_stat_moments.py new file mode 100644 index 0000000000..38cf0e96bd --- /dev/null +++ b/examples/weather/diffusion_sda/stats/collect_stat_moments.py @@ -0,0 +1,116 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import csv +import os +from typing import List, Tuple + +import numpy as np +import zarr + + +def pick_random_index_for_day(time: np.ndarray, day: np.datetime64) -> int: + """Pick a random index from the time array that belongs to the given day.""" + day_mask = time.astype("datetime64[D]") == day + indices = np.flatnonzero(day_mask) + if indices.size == 0: + raise ValueError("No timestamps available for day {}".format(day)) + choice = int(np.random.choice(indices)) + return choice + + +def compute_stats_for_index( + root: zarr.group, variables: List[str], idx: int +) -> List[Tuple[str, float, float]]: + """Compute spatial sum and sum-of-squares for each variable at time index idx.""" + results: List[Tuple[str, float, float]] = [] + print(f"Fetching times for index {idx}") + for var in variables: + if var == "tp" or var == "aerot": + data = np.log(np.clip(root[var][idx, :, :], a_min=0, a_max=1e8) + 1e-8) + else: + data = root[var][idx, :, :] + x_val = float(np.nansum(data)) + x2_val = float(np.nansum(data**2)) + results.append((var, x_val, x2_val)) + return results + + +def main() -> None: + root = zarr.open_group( + store="s3://hrrr-surface-sda/zarr-v1", + mode="r", + storage_options={ + "endpoint_url": "https://pdx.s8k.io", + "profile": "physicsnemo" + }, + ) + variables = [ + "u10m", + "v10m", + "u80m", + "v80m", + "t2m", + "d2m", + "q2m", + "sp", + "fg10m", + "tcc", + "sde", + "snowc", + "refc", + "rsds", + "tp", + "aerot", + ] + time = root["time"][:] + + time_2023 = time[ + (time >= np.datetime64("2023-01-01T00:00:00")) + & (time < np.datetime64("2024-01-01T00:00:00")) + ] + days_2023 = np.unique(time_2023.astype("datetime64[D]")) + + output_path = "daily_random_moments.csv" + write_header = not os.path.exists(output_path) + with open(output_path, "a", newline="") as f: + writer = csv.writer(f) + if write_header: + writer.writerow(["date", "timestamp", "variable", "x", "x2"]) + + for day in days_2023: + try: + idx = pick_random_index_for_day(time, day) + except ValueError: + continue + ts = time[idx] + stats = compute_stats_for_index(root, variables, idx) + with open(output_path, "a", newline="") as f: + writer = csv.writer(f) + for var, sum_x, sum_x2 in stats: + writer.writerow( + [ + str(day), + np.datetime_as_string(ts, unit="s"), + var, + f"{sum_x:.16g}", + f"{sum_x2:.16g}", + ] + ) + + +if __name__ == "__main__": + main() diff --git a/examples/weather/diffusion_sda/stats/stats.csv b/examples/weather/diffusion_sda/stats/stats.csv new file mode 100644 index 0000000000..6a71742d1e --- /dev/null +++ b/examples/weather/diffusion_sda/stats/stats.csv @@ -0,0 +1,17 @@ +variable,log_scale,n_samples,mean,variance,eps +aerot,True,535344621,-8.690861943861766,28.25828962076244,1e-08 +d2m,False,535344621,280.9546111793285,136.4877790103637,1e-08 +fg10m,False,535344621,6.735405900342464,16.32258730394337,1e-08 +q2m,False,535344621,0.008538280337315419,2.849202323332603e-05,1e-08 +refc,False,535344621,-7.700364276192102,58.72793806412655,1e-08 +rsds,False,535344621,218.7404028892579,92585.29485092769,1e-08 +sde,False,535344621,0.03776848884231479,0.0392588369099757,1e-08 +snowc,False,535344621,11.67816570066929,1028.27526395731,1e-08 +sp,False,535344621,96229.42131419305,51015544.99170685,1e-08 +t2m,False,535344621,287.6137238110028,140.5676143535238,1e-08 +tcc,False,535344621,0.4201268632434807,0.1871557763786935,1e-08 +tp,True,535344621,-17.43471427538636,7.825264106976022,1e-08 +u10m,False,535344621,0.4374201557031359,12.20200191833407,1e-08 +u80m,False,535344621,0.7271753224553098,20.84472656710777,1e-08 +v10m,False,535344621,-0.2648801015983047,17.35289476156986,1e-08 +v80m,False,535344621,-0.1101839541846023,28.99252924896015,1e-08 diff --git a/examples/weather/diffusion_sda/train.py b/examples/weather/diffusion_sda/train.py new file mode 100644 index 0000000000..b6a5773721 --- /dev/null +++ b/examples/weather/diffusion_sda/train.py @@ -0,0 +1,410 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2026 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse +import importlib +import logging +import random +import time + +import numpy as np +import torch +import zarr +from data import HRRRSurfaceDataset +from nn import HRRRUnconditionalUNet +from tensordict import TensorDict +from torch.nn.parallel import DistributedDataParallel +from torch.optim.lr_scheduler import CosineAnnealingLR +from torch.utils.data import DataLoader + +from physicsnemo.diffusion.multi_diffusion import ( + MultiDiffusionModel2D, + MultiDiffusionMSEDSMLoss, +) +from physicsnemo.diffusion.noise_schedulers import EDMNoiseScheduler +from physicsnemo.diffusion.preconditioners import EDMPreconditioner +from physicsnemo.diffusion.utils.utils import InfiniteSampler +from physicsnemo.distributed import DistributedManager +from physicsnemo.distributed.utils import reduce_loss +from physicsnemo.utils import load_checkpoint, save_checkpoint +from physicsnemo.utils.logging import PythonLogger, RankZeroLoggingWrapper + +# Compilation settings +torch._dynamo.reset() +torch._dynamo.config.cache_size_limit = 264 +torch._dynamo.config.verbose = True +torch._dynamo.config.suppress_errors = False +torch._logging.set_logs(recompiles=True, graph_breaks=True) + + +def parse_args(): + """Parse command-line arguments with sensible defaults.""" + p = argparse.ArgumentParser(description="HRRR surface diffusion SDA training") + p.add_argument("--load-checkpoint", action="store_true", default=False) + p.add_argument("--checkpoint-dir", type=str, default="./checkpoints") + p.add_argument("--batch-size-per-gpu", type=int, default=2) + p.add_argument("--P-mean", type=float, default=-0.8) + p.add_argument("--P-std", type=float, default=1.6) + return p.parse_args() + + +def main(): + args = parse_args() + + # Configuration + img_resolution = [1059, 1799] + img_channels = 16 + num_condition_channels = 3 + batch_size_per_gpu = args.batch_size_per_gpu + num_patches_per_sample = 4 + patch_shape = (448, 448) + load_checkpoint_from_file = args.load_checkpoint + checkpoint_dir = args.checkpoint_dir + max_training_samples = 10000000 + checkpoint_frequency = 100000 + validation_frequency = 1000 + num_validation_samples = 100 + logging_frequency = 1000 + use_apex = True + + # Set seeds for reproducibility + seed = 1234 + random.seed(seed) + torch.manual_seed(seed) + + # Initialize distributed environment + DistributedManager.initialize() + dist = DistributedManager() + + # Setup logging + logger = PythonLogger("main") + logger.logger.setLevel(logging.INFO) + rank_zero_logger = RankZeroLoggingWrapper(logger, dist) + + # Setup model + num_grid_channels, time_embed_channels = 20, 8 + backbone = HRRRUnconditionalUNet( + img_resolution=list(patch_shape), + img_channels=img_channels, + num_condition_channels=num_condition_channels, + num_grid_channels=num_grid_channels, + time_embed_channels=time_embed_channels, + use_apex_gn=use_apex, + amp_mode=True, + ) + preconditioner = EDMPreconditioner(backbone, sigma_data=1.0) + + md_model = MultiDiffusionModel2D( + model=preconditioner, + global_spatial_shape=(img_resolution[0], img_resolution[1]), + positional_embedding="learnable", + channels_positional_embedding=num_grid_channels, + condition_patch={"cond_concat": True}, + ) + md_model.set_random_patching( + patch_shape=patch_shape, patch_num=num_patches_per_sample + ) + + model = md_model.to(dist.device).to(memory_format=torch.channels_last) + rank_zero_logger.info(f"Training model with {model.num_parameters()} parameters.") + + if load_checkpoint_from_file: + load_checkpoint(checkpoint_dir, models=model) + + # Compile inner model before DDP (recommended by PyTorch docs) + model = torch.compile(model) + + # Setup DDP for multi-GPU training + if dist.world_size > 1: + model = DistributedDataParallel( + model, + device_ids=[dist.local_rank], + broadcast_buffers=True, + output_device=dist.device, + find_unused_parameters=True, + bucket_cap_mb=35, + gradient_as_bucket_view=True, + static_graph=True, + ) + + # Determine resume point for the data sampler + current_samples_trained = 0 + if load_checkpoint_from_file: + metadata = {} + load_checkpoint(checkpoint_dir, metadata_dict=metadata) + current_samples_trained = metadata.get("current_samples_trained", 0) + rank_zero_logger.info( + f"Resuming from samples trained: {current_samples_trained}" + ) + sampler_start_idx = current_samples_trained // num_patches_per_sample + + # Create data loaders + # Needs zarr 3.0 + root = zarr.open_group( + store="s3://hrrr-surface-sda/zarr-v2", + mode="r", + storage_options={ + "endpoint_url": "https://pdx.s8k.io", + "profile": "physicsnemo", + }, + ) + time_coord = root["time"][:] + sidx = np.where(time_coord == np.datetime64("2021-01-01T00:00:00"))[0][0] + eidx = np.where(time_coord == np.datetime64("2024-12-31T23:00:00"))[0][0] + time_idx = np.arange(sidx, eidx) + dataset = HRRRSurfaceDataset( + "s3://hrrr-surface-sda/zarr-v2", + time_idx, + storage_options={ + "endpoint_url": "https://pdx.s8k.io", + "profile": "physicsnemo", + }, + ) + train_iter = iter( + DataLoader( + dataset, + batch_size=batch_size_per_gpu, + sampler=InfiniteSampler( + dataset=dataset, + shuffle=True, + rank=dist.rank, + num_replicas=dist.world_size, + start_idx=sampler_start_idx, + ), + num_workers=8, + pin_memory=True, + drop_last=True, + timeout=0, + prefetch_factor=4, + persistent_workers=False, + ) + ) + num_training_samples = len(dataset) + + sidx = np.where(time_coord == np.datetime64("2025-01-01T00:00:00"))[0][0] + eidx = np.where(time_coord == np.datetime64("2025-12-31T00:00:00"))[0][0] + time_idx = np.arange(sidx, eidx, 25) + val_dataset = HRRRSurfaceDataset( + "s3://hrrr-surface-sda/zarr-v2", + time_idx, + storage_options={ + "endpoint_url": "https://pdx.s8k.io", + "profile": "physicsnemo", + }, + ) + val_iter = iter( + DataLoader( + val_dataset, + batch_size=batch_size_per_gpu, + sampler=InfiniteSampler( + dataset=val_dataset, + shuffle=True, + rank=dist.rank, + num_replicas=dist.world_size, + ), + num_workers=8, + pin_memory=True, + drop_last=True, + timeout=0, + prefetch_factor=4, + persistent_workers=False, + ) + ) + + # Create loss function with multi-diffusion support + noise_scheduler = EDMNoiseScheduler(P_mean=args.P_mean, P_std=args.P_std, sigma_data=1.0) + loss_fn = MultiDiffusionMSEDSMLoss( + model=model, + noise_scheduler=noise_scheduler, + ) + + # Initialize optimizer + if use_apex: + FusedAdam = getattr(importlib.import_module("apex.optimizers"), "FusedAdam") + optimizer = FusedAdam( + model.parameters(), + lr=5e-4, + weight_decay=0.0, + ) + else: + optimizer = torch.optim.AdamW( + model.parameters(), + lr=5e-4, + weight_decay=0.0, + ) + + # Initialize learning rate scheduler + scheduler = CosineAnnealingLR( + optimizer, + T_max=max_training_samples // num_training_samples, + eta_min=5e-6, + ) + + # Load optimizer/scheduler state from checkpoint + if dist.world_size > 1: + torch.distributed.barrier() + if load_checkpoint_from_file: + load_checkpoint( + checkpoint_dir, + optimizer=optimizer, + scheduler=scheduler, + device=dist.device, + ) + + # Training loop (batch-based with InfiniteSampler) + rank_zero_logger.info("Training started...") + + # Running average for loss + loss_running_mean = 0.0 + n_loss_running_mean = 1 + + total_batch_size = batch_size_per_gpu * dist.world_size * num_patches_per_sample + rank_zero_logger.info(f"Total batch size: {total_batch_size}") + + # Counters for periodic tasks + samples_since_scheduler_update = 0 + samples_since_logging = 0 + samples_since_validation = 0 + samples_since_checkpoint = 0 + + tick_start_time = time.time() + while current_samples_trained < max_training_samples: + + model.train() + + # Get next batch from infinite sampler + x, cond_spatial, cond_time = next(train_iter) + x = x.to(dist.device, non_blocking=True).to(memory_format=torch.channels_last) + cond_spatial = cond_spatial.to(dist.device, non_blocking=True).to( + memory_format=torch.channels_last + ) + cond_time = cond_time.to(dist.device, non_blocking=True).float() + batch_size = x.shape[0] + + condition = TensorDict( + {"cond_concat": cond_spatial, "cond_time": cond_time}, + batch_size=[batch_size], + ) + + # Forward pass + optimizer.zero_grad(**({} if use_apex else {"set_to_none": True})) + with torch.autocast("cuda", dtype=torch.bfloat16): + loss = loss_fn(x, condition=condition) + + # Backward pass and optimize + loss.backward() + optimizer.step() + + mean_loss = reduce_loss(loss.item() * batch_size, dst_rank=0) + + # Update running mean of loss (only meaningful on rank 0) + if dist.rank == 0: + loss_running_mean += ( + mean_loss / total_batch_size - loss_running_mean + ) / n_loss_running_mean + n_loss_running_mean += 1 + current_samples_trained += total_batch_size + + # Update scheduler periodically + samples_since_scheduler_update += total_batch_size + if samples_since_scheduler_update >= num_training_samples: + scheduler.step() + samples_since_scheduler_update = 0 + + # Periodic logging + samples_since_logging += total_batch_size + if samples_since_logging >= logging_frequency: + elapsed = time.time() - tick_start_time + steps = samples_since_logging / total_batch_size + rank_zero_logger.info( + f"Samples trained: {current_samples_trained}, " + f"loss: {loss_running_mean:.3e}, " + f"learning rate: {optimizer.param_groups[0]['lr']:.2e}, " + f"throughput: {samples_since_logging / elapsed / 1000:.4f} ksamples/s, " + f"time/step: {elapsed / steps:.3f}s" + ) + # Reset running mean after logging + loss_running_mean = 0.0 + n_loss_running_mean = 1 + tick_start_time = time.time() + samples_since_logging = 0 + + # Validation step + samples_since_validation += total_batch_size + if samples_since_validation >= validation_frequency: + model.eval() + val_loss_running_mean = 0.0 + n_val_loss_running_mean = 1 + current_validation_samples = 0 + with torch.no_grad(): + while current_validation_samples < num_validation_samples: + x, cs, ct = next(val_iter) + x = x.to(dist.device, non_blocking=True).to( + memory_format=torch.channels_last + ) + cs = cs.to(dist.device, non_blocking=True).to( + memory_format=torch.channels_last + ) + ct = ct.to(dist.device, non_blocking=True).float() + batch_size = x.shape[0] + + val_condition = TensorDict( + {"cond_concat": cs, "cond_time": ct}, + batch_size=[batch_size], + ) + with torch.autocast("cuda", dtype=torch.bfloat16): + val_loss = loss_fn(x, condition=val_condition) + mean_val_loss = reduce_loss( + val_loss.item() * batch_size, dst_rank=0 + ) + if dist.rank == 0: + val_loss_running_mean += ( + mean_val_loss / total_batch_size - val_loss_running_mean + ) / n_val_loss_running_mean + n_val_loss_running_mean += 1 + current_validation_samples += total_batch_size + rank_zero_logger.info( + f"Samples trained: {current_samples_trained}, " + f"val_loss: {val_loss_running_mean:.3e}, " + ) + samples_since_validation = 0 + + # Periodic checkpoint + samples_since_checkpoint += total_batch_size + if samples_since_checkpoint >= checkpoint_frequency: + if dist.world_size > 1: + torch.distributed.barrier() + if dist.rank == 0: + save_checkpoint( + checkpoint_dir, + models=model, + optimizer=optimizer, + scheduler=scheduler, + metadata={ + "current_samples_trained": current_samples_trained, + }, + ) + rank_zero_logger.info( + f"Saved checkpoint at samples trained: {current_samples_trained}" + ) + samples_since_checkpoint = 0 + + # Cleanup + rank_zero_logger.info("Training completed!") + + +if __name__ == "__main__": + main() diff --git a/examples/weather/diffusion_sda/uv.lock b/examples/weather/diffusion_sda/uv.lock new file mode 100644 index 0000000000..678bf078f4 --- /dev/null +++ b/examples/weather/diffusion_sda/uv.lock @@ -0,0 +1,3530 @@ +version = 1 +revision = 3 +requires-python = ">=3.12, <3.14" +resolution-markers = [ + "python_full_version >= '3.13'", + "python_full_version < '3.13'", +] + +[[package]] +name = "affine" +version = "2.4.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/69/98/d2f0bb06385069e799fc7d2870d9e078cfa0fa396dc8a2b81227d0da08b9/affine-2.4.0.tar.gz", hash = "sha256:a24d818d6a836c131976d22f8c27b8d3ca32d0af64c1d8d29deb7bafa4da1eea", size = 17132, upload-time = "2023-01-19T23:44:30.696Z" } +wheels = [ + { url = 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m.module + else: + raise TypeError( + f"Could not unwrap a MultiDiffusionModel2D from " + f"{type(model).__name__}. Found leaf type " + f"{type(m).__name__}." + ) + return m + + class _CompiledPatchX: """Cached ``torch.compile``-d wrapper around :meth:`~MultiDiffusionModel2D.patch_x`. @@ -213,8 +236,9 @@ def __init__( reduction: Literal["none", "mean", "sum"] = "mean", ) -> None: self.model = model + self._md_model = _unwrap_multi_diffusion(model) self.noise_scheduler = noise_scheduler - self._compiled_patch_x = _CompiledPatchX(model) + self._compiled_patch_x = _CompiledPatchX(self._md_model) if prediction_type == "x0": self._to_x0 = lambda prediction, x_t, t: prediction @@ -269,7 +293,7 @@ def __call__( :math:`(P \times B, C, H_p, W_p)`. Otherwise a scalar tensor. """ if reset_patch_indices: - self.model.reset_patch_indices() + self._md_model.reset_patch_indices() # Patch x0 and sample per-patch noise x0_patched = self._compiled_patch_x(x0) # (P*B, C, Hp, Wp) @@ -436,8 +460,9 @@ def __init__( reduction: Literal["none", "mean", "sum"] = "mean", ) -> None: self.model = model + self._md_model = _unwrap_multi_diffusion(model) self.noise_scheduler = noise_scheduler - self._compiled_patch_x = _CompiledPatchX(model) + self._compiled_patch_x = _CompiledPatchX(self._md_model) if prediction_type == "x0": self._to_x0 = lambda prediction, x_t, t: prediction @@ -502,7 +527,7 @@ def __call__( ) if reset_patch_indices: - self.model.reset_patch_indices() + self._md_model.reset_patch_indices() # Patch x0 and weight, then sample per-patch noise x0_patched = self._compiled_patch_x(x0) # (P*B, C, Hp, Wp) diff --git a/physicsnemo/diffusion/multi_diffusion/patching.py b/physicsnemo/diffusion/multi_diffusion/patching.py index c1b3805990..102893cd11 100644 --- a/physicsnemo/diffusion/multi_diffusion/patching.py +++ b/physicsnemo/diffusion/multi_diffusion/patching.py @@ -17,7 +17,6 @@ """Utilities for multi-diffusion (patching and fusion).""" import math -import random import warnings from abc import ABC, abstractmethod from typing import Optional, Tuple, Union @@ -150,13 +149,20 @@ def global_index( def _compute_global_index(self) -> Int[Tensor, "P 2 Hp Wp"]: r"""Compute the global-index tensor from current patch positions. + The ``global_index`` tensor is created and computed on the same device + as ``patch_indices`` (if the buffer exists) to avoid cross-device + indexing errors. + Returns ------- Tensor Integer tensor of shape :math:`(P, 2, H_p, W_p)`. """ - Ny = torch.arange(self.img_shape[0]).int() - Nx = torch.arange(self.img_shape[1]).int() + device = None + if hasattr(self, "patch_indices") and isinstance(self.patch_indices, Tensor): + device = self.patch_indices.device + Ny = torch.arange(self.img_shape[0], device=device).int() + Nx = torch.arange(self.img_shape[1], device=device).int() grid = torch.stack(torch.meshgrid(Ny, Nx, indexing="ij"), dim=0).unsqueeze( 0 ) # (1, 2, H, W) @@ -237,10 +243,20 @@ def set_patch_num(self, value: int) -> None: self._patch_num = value self.reset_patch_indices() - def reset_patch_indices(self) -> None: + def reset_patch_indices( + self, + *, + generator: torch.Generator | None = None, + ) -> None: r"""Re-draw random upper-left corner positions for all patches. - Also refreshes the cached ``_global_index`` buffer. + The cached ``_global_index`` buffer is invalidated and will be + lazily recomputed on the next call to :meth:`global_index`. + + Parameters + ---------- + generator : torch.Generator, optional + Pseudo-random number generator for reproducible sampling. """ has_buffer = hasattr(self, "patch_indices") and isinstance( self.patch_indices, Tensor @@ -249,36 +265,65 @@ def reset_patch_indices(self) -> None: max_y = self.img_shape[0] - self.patch_shape[0] max_x = self.img_shape[1] - self.patch_shape[1] - # TODO: use torch.randint instead of random.randint to create - # patch indices directly on right device. Note: this will break - # non-regression tests because torch.randint and random.randint do not - # use the same random number generation process. But for an object taht - # is deliberately designed to be random, breaking these non-regression - # tests might not be a problem. - new_indices = torch.tensor( - [ - (random.randint(0, max_y), random.randint(0, max_x)) - for _ in range(self.patch_num) - ], + + py = torch.randint( + 0, + max_y + 1, + (self.patch_num,), dtype=torch.long, device=device, + generator=generator, ) + px = torch.randint( + 0, + max_x + 1, + (self.patch_num,), + dtype=torch.long, + device=device, + generator=generator, + ) + new_indices = torch.stack([py, px], dim=1) if has_buffer and new_indices.shape == self.patch_indices.shape: self.patch_indices.copy_(new_indices) else: self.register_buffer("patch_indices", new_indices, persistent=False) - # Refresh cached global index - new_global_index = self._compute_global_index() - if ( - hasattr(self, "_global_index") - and isinstance(self._global_index, Tensor) - and new_global_index.shape == self._global_index.shape - ): - self._global_index.copy_(new_global_index) - else: - self.register_buffer("_global_index", new_global_index, persistent=False) + self._global_index_needs_update = True + + def global_index( + self, batch_size: int = 1, device: Union[torch.device, str] = "cpu" + ) -> Int[Tensor, "P 2 Hp Wp"]: + r"""Return global :math:`(y, x)` grid coordinates for each patch. + Recomputes lazily if patch positions have changed since the last call. + + Parameters + ---------- + batch_size : int, default=1 + Kept for backward compatibility. Ignored. + device : Union[torch.device, str], default="cpu" + Kept for backward compatibility. The buffer follows the module + device (use ``.to(device)`` to move the module). + + Returns + ------- + Tensor + Integer tensor of shape :math:`(P, 2, H_p, W_p)`. + """ + if self._global_index_needs_update: + new_global_index = self._compute_global_index() + if ( + hasattr(self, "_global_index") + and isinstance(self._global_index, Tensor) + and new_global_index.shape == self._global_index.shape + ): + self._global_index.copy_(new_global_index) + else: + self.register_buffer( + "_global_index", new_global_index, persistent=False + ) + self._global_index_needs_update = False + return self._global_index.clone() def forward( self, @@ -286,23 +331,28 @@ def forward( additional_input: Optional[Float[Tensor, "B C_add H_add W_add"]] = None, ) -> Float[Tensor, "P_times_B C_out Hp Wp"]: r"""Extract random patches from the input tensor.""" - B = input.shape[0] + B, C, H, W = input.shape Hp, Wp = self.patch_shape P = self.patch_num + K = Hp * Wp - # Unfold creates a view of all stride-1 patches (no copy) - patches = input.unfold(2, Hp, 1).unfold(3, Wp, 1) - # patches: (B, C, H-Hp+1, W-Wp+1, Hp, Wp) - - # Gather the P patches at stored random positions py = self.patch_indices[:, 0] # (P,) px = self.patch_indices[:, 1] # (P,) - gathered = patches[:, :, py, px] # (B, C, P, Hp, Wp) - # Reorder to patch-major layout - out = gathered.permute(2, 0, 1, 3, 4).reshape( - P * B, -1, Hp, Wp - ) # (P*B, C, Hp, Wp) + dy = torch.arange(Hp, device=input.device) + dx = torch.arange(Wp, device=input.device) + base = (py * W + px).reshape(P, 1, 1) # (P, 1, 1) + rel = (dy[:, None] * W + dx[None, :]).reshape(1, 1, K) # (1, 1, K) + idx = (base + rel).expand(P, B, K) # (P, B, Hp*Wp) + + x_flat = input.reshape(B, C, H * W) # (B, C, HW) + gathered = torch.gather( + x_flat.unsqueeze(0).expand(P, B, C, H * W), + dim=3, + index=idx.unsqueeze(2).expand(P, B, C, K), + ) # (P, B, C, Hp*Wp) + + out = gathered.reshape(P * B, C, Hp, Wp) if input.is_contiguous(memory_format=torch.channels_last): out = out.to(memory_format=torch.channels_last) diff --git a/physicsnemo/diffusion/utils/model_wrappers.py b/physicsnemo/diffusion/utils/model_wrappers.py index 401a3700f6..2101b09167 100644 --- a/physicsnemo/diffusion/utils/model_wrappers.py +++ b/physicsnemo/diffusion/utils/model_wrappers.py @@ -13,6 +13,7 @@ # 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. + import warnings from typing import Any