diff --git a/apax/config/train_config.py b/apax/config/train_config.py index df6e9f6e..d7836c38 100644 --- a/apax/config/train_config.py +++ b/apax/config/train_config.py @@ -76,14 +76,16 @@ class PBPDatset(DatasetConfig, extra="forbid"): ---------- num_workers : int | Number of batches to be processed in parallel. - reset_every : int - | Number of epochs before reinitializing the ProcessPoolExcecutor. - | Avoids memory leaks. + atom_padding : int + | Next nearest integer to which to pad per-atom arrays (positions, forces, ...). + nl_padding: int + | Next nearest integer to which to pad neighborlists. """ processing: Literal["pbp"] = "pbp" num_workers: PositiveInt = 10 - reset_every: PositiveInt = 10 + atom_padding: PositiveInt = 10 + nl_padding: PositiveInt = 2000 class DataConfig(BaseModel, extra="forbid"): diff --git a/apax/data/input_pipeline.py b/apax/data/input_pipeline.py index 3994d883..f4b8a4e1 100644 --- a/apax/data/input_pipeline.py +++ b/apax/data/input_pipeline.py @@ -1,10 +1,13 @@ import logging import multiprocessing +import time import uuid from collections import deque -from concurrent.futures import ProcessPoolExecutor +from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor, as_completed from pathlib import Path +from queue import Queue from random import shuffle +from threading import Event from typing import Dict, Iterator, Optional import jax @@ -358,87 +361,101 @@ def next_power_of_two(x): return 1 << (int(x) - 1).bit_length() +def round_up_to_multiple(value, multiple): + """ + Rounds up the given integer `value` to the next multiple of `multiple`. + + Parameters: + - value (int): The integer to round up. + - multiple (int): The multiple to round up to. + + Returns: + - int: The rounded-up value. + """ + return int(np.ceil(value / multiple) * multiple) + + class BatchProcessor: - def __init__(self, cutoff, forces=True, stress=False) -> None: + def __init__( + self, cutoff, atom_padding: int, nl_padding: int, forces=True, stress=False + ) -> None: self.cutoff = cutoff + self.atom_padding = atom_padding + self.nl_padding = nl_padding + self.forces = forces self.stress = stress def __call__(self, samples: list[dict]): + n_samples = len(samples) + max_atoms = np.max([inp[0]["n_atoms"] for inp in samples]) + max_atoms = round_up_to_multiple(max_atoms, self.atom_padding) + inputs = { - "numbers": [], - "n_atoms": [], - "positions": [], - "box": [], - "idx": [], - "offsets": [], + "numbers": np.zeros((n_samples, max_atoms), dtype=np.int16), + "n_atoms": np.zeros(n_samples, dtype=np.int16), + "positions": np.zeros((n_samples, max_atoms, 3), dtype=np.float64), + "box": np.zeros((n_samples, 3, 3), dtype=np.float32), } labels = { - "energy": [], + "energy": np.zeros(n_samples, dtype=np.float64), } if self.forces: - labels["forces"] = [] + labels["forces"] = np.zeros((n_samples, max_atoms, 3), dtype=np.float64) if self.stress: - labels["stress"] = [] + labels["stress"] = np.zeros((n_samples, 3, 3), dtype=np.float64) - for sample in samples: - inp, lab = sample + idxs = [] + offsets = [] + for i, (inp, lab) in enumerate(samples): + inputs["numbers"][i, : inp["n_atoms"]] = inp["numbers"] + inputs["n_atoms"][i] = inp["n_atoms"] + inputs["positions"][i, : inp["n_atoms"]] = inp["positions"] + inputs["box"][i] = inp["box"] - inputs["numbers"].append(inp["numbers"]) - inputs["n_atoms"].append(inp["n_atoms"]) - inputs["positions"].append(inp["positions"]) - inputs["box"].append(inp["box"]) - idx, offsets = compute_nl(inp["positions"], inp["box"], self.cutoff) - inputs["idx"].append(idx) - inputs["offsets"].append(offsets) + idx, offset = compute_nl(inp["positions"], inp["box"], self.cutoff) + idxs.append(idx) + offsets.append(offset) - labels["energy"].append(lab["energy"]) + labels["energy"][i] = lab["energy"] if self.forces: - labels["forces"].append(lab["forces"]) + labels["forces"][i, : inp["n_atoms"]] = lab["forces"] if self.stress: - labels["stress"].append(lab["stress"]) - - max_atoms = np.max(inputs["n_atoms"]) - max_nbrs = np.max([idx.shape[1] for idx in inputs["idx"]]) + labels["stress"][i] = lab["stress"] - max_atoms = next_power_of_two(max_atoms) - max_nbrs = next_power_of_two(max_nbrs) + max_nbrs = np.max([idx.shape[1] for idx in idxs]) + max_nbrs = round_up_to_multiple(max_nbrs, self.nl_padding) - for i in range(len(inputs["n_atoms"])): - inputs["idx"][i], inputs["offsets"][i] = pad_nl( - inputs["idx"][i], inputs["offsets"][i], max_nbrs - ) - - zeros_to_add = max_atoms - inputs["numbers"][i].shape[0] - inputs["positions"][i] = np.pad( - inputs["positions"][i], ((0, zeros_to_add), (0, 0)), "constant" - ) - inputs["numbers"][i] = np.pad( - inputs["numbers"][i], (0, zeros_to_add), "constant" - ).astype(np.int16) + inputs["idx"] = np.zeros((n_samples, 2, max_nbrs), dtype=np.int16) + inputs["offsets"] = np.zeros((n_samples, max_nbrs, 3), dtype=np.float64) - if "forces" in labels: - labels["forces"][i] = np.pad( - labels["forces"][i], ((0, zeros_to_add), (0, 0)), "constant" - ) + for i, (idx, offset) in enumerate(zip(idxs, offsets)): + inputs["idx"][i, :, : idx.shape[1]] = idx + inputs["offsets"][i, : offset.shape[0], :] = offset - inputs = {k: np.array(v) for k, v in inputs.items()} - labels = {k: np.array(v) for k, v in labels.items()} return inputs, labels class PerBatchPaddedDataset(InMemoryDataset): - """Dataset which pads everything (atoms, neighbors) - to the next larges power of two. - This limits the compute wasted due to padding at the (negligible) - cost of some recompilations. - The NL is computed on-the-fly in parallel for `num_workers` of batches. + """Dataset with padding that leverages multiprocessing and optimized buffering. + + Per-atom and per-neighbor arrays are padded to the next multiple of a user specified integer. + This limits the compute wasted due to padding at the (negligible) cost of some recompilations. + Since the padding occurs on a per-batch basis, it is the most performant option for datasets with significantly differently sized systems (e.g. MaterialsProject, SPICE). + + Further, the neighborlist is computed on-the-fly in parallel on a side thread. Does not use tf.data. - Most performant option for datasets with significantly differently sized systems - (e.g. MaterialsProject, SPICE). + Attributes + ---------- + num_workers : int + Number of processes to use for preprocessing batches. + atom_padding : int + Pad extensive arrays (positions, etc.) to next multiple of this integer. + nl_padding : int + Pad neighborlist arrays to next multiple of this integer. """ def __init__( @@ -449,18 +466,13 @@ def __init__( n_epochs, n_jit_steps=1, num_workers: Optional[int] = None, - reset_every: int = 10, + atom_padding: int = 10, + nl_padding: int = 2000, pos_unit: str = "Ang", energy_unit: str = "eV", pre_shuffle=False, ) -> None: self.cutoff = cutoff - - if n_jit_steps > 1: - raise NotImplementedError( - "PerBatchPaddedDataset is not yet compatible with multi step jit" - ) - self.n_jit_steps = n_jit_steps self.n_epochs = n_epochs self.n_data = len(atoms_list) @@ -470,13 +482,12 @@ def __init__( if num_workers: self.num_workers = num_workers else: - self.num_workers = multiprocessing.cpu_count() - self.buffer_size = num_workers * 2 - self.batch_size = bs + self.num_workers = multiprocessing.cpu_count() - 1 self.sample_atoms = atoms_list[0] - self.inputs = atoms_to_inputs(atoms_list, pos_unit) + # Transform atoms into inputs and labels + self.inputs = atoms_to_inputs(atoms_list, pos_unit) self.labels = atoms_to_labels(atoms_list, pos_unit, energy_unit) label_keys = self.labels.keys() @@ -488,61 +499,85 @@ def __init__( forces = "forces" in label_keys stress = "stress" in label_keys - self.prepare_batch = BatchProcessor(cutoff, forces, stress) + self.prepare_batch = BatchProcessor( + cutoff, atom_padding, nl_padding, forces, stress + ) self.count = 0 - self.reset_every = reset_every + self.max_count = self.n_epochs * self.steps_per_epoch() - self.buffer = deque() + + self.buffer_size = min(600, self.steps_per_epoch()) + self.buffer = Queue(maxsize=self.buffer_size) self.process_pool = ProcessPoolExecutor(self.num_workers) + self.thread_pool = ThreadPoolExecutor(1) # Single thread for buffering batches + self.epoch_finished = False + self.enqueue_future = None + self.needs_data = Event() + + def enqueue_batches(self): + """Function to enqueue batches on a side thread.""" + while self.count < self.steps_per_epoch() * self.n_epochs: + self.needs_data.wait() + if self.epoch_finished: + break + num_batches = min( + self.buffer_size - self.buffer.qsize(), + self.steps_per_epoch() - self.count, + ) + if num_batches > 0: + self.enqueue(num_batches) + self.needs_data.clear() # Reset event def enqueue(self, num_batches): start = self.count * self.batch_size + # Split data into chunks and submit tasks to the process pool dataset_chunks = [ self.data[start + self.batch_size * i : start + self.batch_size * (i + 1)] - for i in range(0, num_batches) + for i in range(num_batches) + ] + + # Using submit and as_completed for faster batch retrieval + futures = [ + self.process_pool.submit(self.prepare_batch, chunk) + for chunk in dataset_chunks ] - for batch in self.process_pool.map(self.prepare_batch, dataset_chunks): - self.buffer.append(batch) + for future in as_completed(futures): + batch = future.result() + self.buffer.put(batch) self.count += num_batches def __iter__(self): for n in range(self.n_epochs): self.count = 0 - self.buffer = deque() - - # reinitialize PPE from time to time to avoid memory leak - if n % self.reset_every == 0: - self.process_pool = ProcessPoolExecutor(self.num_workers) + self.buffer.queue.clear() # Reset buffer + self.epoch_finished = False if self.should_shuffle: shuffle(self.data) - self.enqueue(min(self.buffer_size, self.n_data // self.batch_size)) + # Start pre-filling the buffer + self.enqueue_future = self.thread_pool.submit(self.enqueue_batches) for i in range(self.steps_per_epoch()): - batch = self.buffer.popleft() - yield batch - - current_buffer_len = len(self.buffer) - space = self.buffer_size - current_buffer_len + if self.buffer.qsize() < (self.buffer_size * 0.75): + self.needs_data.set() # Trigger buffer refill + while self.buffer.empty(): + time.sleep(0.001) + yield self.buffer.get() - if space >= self.num_workers: - more_data = min(space, self.steps_per_epoch() - self.count) - more_data = max(more_data, 0) - if more_data > 0: - self.enqueue(more_data) + self.epoch_finished = True + self.needs_data.set() + self.enqueue_future.result() def shuffle_and_batch(self, sharding): self.should_shuffle = True - ds = prefetch_to_single_device( iter(self), 2, sharding, n_step_jit=self.n_jit_steps > 1 ) - return ds def batch(self, sharding) -> Iterator[jax.Array]: @@ -555,6 +590,15 @@ def batch(self, sharding) -> Iterator[jax.Array]: def make_signature(self) -> None: pass + def cleanup(self): + self.epoch_finished = True + self.needs_data.set() + self.enqueue_future.result() + self.needs_data.clear() + self.thread_pool.shutdown(wait=True, cancel_futures=True) + self.process_pool.shutdown(wait=True, cancel_futures=True) + self.buffer.queue.clear() + dataset_dict = { "cached": CachedInMemoryDataset, diff --git a/apax/data/preprocessing.py b/apax/data/preprocessing.py index 13b7302b..3cc0c668 100644 --- a/apax/data/preprocessing.py +++ b/apax/data/preprocessing.py @@ -5,7 +5,7 @@ import jax import jax.numpy as jnp import numpy as np -from matscipy.neighbours import neighbour_list +from vesin import NeighborList log = logging.getLogger(__name__) @@ -32,14 +32,10 @@ def compute_nl(positions, box, r_max): """ if np.all(box < 1e-6): - box, box_origin = get_shrink_wrapped_cell(positions) - idxs_i, idxs_j = neighbour_list( - "ij", - positions=positions, - cutoff=r_max, - cell=box, - cell_origin=box_origin, - pbc=[False, False, False], + box, _ = get_shrink_wrapped_cell(positions) + calculator = NeighborList(cutoff=r_max, full_list=True) + idxs_i, idxs_j = calculator.compute( + points=positions, box=box, periodic=False, quantities="ij" ) neighbor_idxs = np.array([idxs_i, idxs_j], dtype=np.int16) @@ -49,10 +45,11 @@ def compute_nl(positions, box, r_max): else: positions = positions @ box - idxs_i, idxs_j, offsets = neighbour_list( - "ijS", positions=positions, cutoff=r_max, cell=box, pbc=[True, True, True] + calculator = NeighborList(cutoff=r_max, full_list=True) + idxs_i, idxs_j, offsets = calculator.compute( + points=positions, box=box, periodic=True, quantities="ijS" ) - neighbor_idxs = np.array([idxs_i, idxs_j], dtype=np.int16) + neighbor_idxs = np.array([idxs_i, idxs_j], dtype=np.int32) offsets = np.matmul(offsets, box) return neighbor_idxs, offsets diff --git a/apax/md/ase_calc.py b/apax/md/ase_calc.py index 6a178c7d..5f62609a 100644 --- a/apax/md/ase_calc.py +++ b/apax/md/ase_calc.py @@ -9,8 +9,8 @@ from ase.calculators.calculator import Calculator, all_changes from ase.calculators.singlepoint import SinglePointCalculator from flax.core.frozen_dict import freeze, unfreeze -from matscipy.neighbours import neighbour_list from tqdm import trange +from vesin import NeighborList from apax.data.input_pipeline import ( CachedInMemoryDataset, @@ -149,7 +149,7 @@ def __init__( Function transformations applied on top of the EnergyDerivativeModel. Transfomrations are implemented under `apax.md.transformations`. padding_factor: - Multiple of the fallback Matscipy NL's amount of neighbors. + Multiple of the fallback vesin's amount of neighbors. This NL will be padded to `len(neighbors) * padding_factor` on NL initialization. """ @@ -209,15 +209,25 @@ def initialize(self, atoms): else: self.neighbors = self.neighbor_fn.allocate(positions) else: - idxs_i = neighbour_list("i", atoms, self.r_max) + calculator = NeighborList(cutoff=self.r_max, full_list=True) + idxs_i, _, _ = calculator.compute( + points=atoms.positions, + box=atoms.cell.array, + periodic=np.any(atoms.pbc), + quantities="ijS", + ) self.padded_length = int(len(idxs_i) * self.padding_factor) def set_neighbours_and_offsets(self, atoms, box): - idxs_i, idxs_j, offsets = neighbour_list("ijS", atoms, self.r_max) - + calculator = NeighborList(cutoff=self.r_max, full_list=True) + idxs_i, idxs_j, offsets = calculator.compute( + points=atoms.positions, + box=atoms.cell.array, + periodic=np.any(atoms.pbc), + quantities="ijS", + ) if len(idxs_i) > self.padded_length: print("neighbor list overflowed, extending.") - self.padded_length = int(len(idxs_i) * self.padding_factor) self.initialize(atoms) zeros_to_add = self.padded_length - len(idxs_i) diff --git 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description = "Backport of pathlib-compatible object wrapper for zip files" optional = false -python-versions = ">=3.8" +python-versions = ">=3.9" files = [ - {file = "zipp-3.20.2-py3-none-any.whl", hash = "sha256:a817ac80d6cf4b23bf7f2828b7cabf326f15a001bea8b1f9b49631780ba28350"}, - {file = "zipp-3.20.2.tar.gz", hash = "sha256:bc9eb26f4506fda01b81bcde0ca78103b6e62f991b381fec825435c836edbc29"}, + {file = "zipp-3.21.0-py3-none-any.whl", hash = "sha256:ac1bbe05fd2991f160ebce24ffbac5f6d11d83dc90891255885223d42b3cd931"}, + {file = "zipp-3.21.0.tar.gz", hash = "sha256:2c9958f6430a2040341a52eb608ed6dd93ef4392e02ffe219417c1b28b5dd1f4"}, ] [package.extras] @@ -7474,4 +7290,4 @@ zntrack = ["zntrack"] [metadata] lock-version = "2.0" python-versions = ">=3.10,<3.12" -content-hash = "a5d79feec4c98a470c9f4797cf704118a68b73b534953e294cfc365da5beda96" +content-hash = "3f6a847095567e2225b3f21641105362a43e63794ecc18fa3aa39982938dcadb" diff --git a/pyproject.toml b/pyproject.toml index 838d4193..4164b2ea 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