diff --git a/moll/core/distance.py b/moll/core/distance.py index cb931e4..19d0b0d 100644 --- a/moll/core/distance.py +++ b/moll/core/distance.py @@ -5,7 +5,6 @@ import jax import jax.numpy as jnp from jax import lax -from loguru import logger from moll.core.utils import fill_diagonal @@ -30,15 +29,12 @@ def tanimoto(a: jnp.ndarray, b: jnp.ndarray) -> float: bitwise_and = jnp.bitwise_and(a, b).sum().astype(float) # Check for the case where both vectors are all zeros and return 0.0 in that case - dist = jax.lax.cond( + return jax.lax.cond( bitwise_or == 0.0, - lambda _: 0.0, - lambda _: 1 - (bitwise_and / bitwise_or), - None, + lambda: 0.0, + lambda: 1 - (bitwise_and / bitwise_or), ) - return dist - @jax.jit def euclidean(p1, p2): @@ -48,25 +44,7 @@ def euclidean(p1, p2): return jnp.linalg.norm(p1 - p2) -@jax.jit -def is_in_bag(x: jnp.ndarray, X: jnp.ndarray) -> jnp.ndarray: - """ - Checks if a point is already in the bag. - """ - return (X == x).all(axis=1).any() - - -@partial(jax.jit, static_argnames="dist_fn") -def one_to_many_dists(x: jnp.ndarray, X: jnp.ndarray, dist_fn: Callable) -> jnp.ndarray: - """ - Computes the distance between one point and many points. - """ - dists_from_x_to_X = jax.vmap(dist_fn, in_axes=(None, 0))(x, X) - return dists_from_x_to_X - - -@partial(jax.jit, static_argnames="dist_fn") -def pairwise_distances(X, dist_fn): +def _pairwise_distances(X, dist_fn: Callable): """Compute pairwise distances between points in X using a custom distance function.""" def x_to_X_dists(x): @@ -77,43 +55,35 @@ def x_to_X_dists(x): return dists -def submatrix(X: jnp.ndarray, remove_row: int, remove_col: int) -> jnp.ndarray: +def _matrix_cross_sum(X: jnp.ndarray, i: int, j: int, row_only=False): """ - Returns a submatrix of `X` removing the specified row and column. + Computes the sum of the elements in the row `i` and the column `j` of the matrix `X`. """ - return jnp.delete( - jnp.delete(X, remove_row, axis=0, assume_unique_indices=True), - remove_col, - axis=1, - assume_unique_indices=True, + + return lax.cond( + row_only, + lambda X: X[i, :].sum(), + lambda X: X[i, :].sum() + X[:, j].sum() - X[i, j], + X, ) -def matrix_cross_sum(X: jnp.ndarray, i: int, j: int, row_only=False): - """ - Computes the sum of the elements in the row `i` and the column `j` of the matrix `X`. - """ - X_row_i = X[i, :] - if row_only: - return X_row_i.sum() - X_col_j = X[:, j] - X_element_ij = X[i, j] - return X_row_i.sum() + X_col_j.sum() - X_element_ij +matrix_cross_sum = jax.jit(_matrix_cross_sum, static_argnames=["row_only"]) -def needless_point_idx( +def _needless_point_idx( X: jnp.ndarray, dist_fn: Callable, potential_fn: Callable ) -> int: """ Find a point in `X` removing which would decrease the total potential the most. """ - dists = pairwise_distances(X, dist_fn) + dists = _pairwise_distances(X, dist_fn) potentials = jax.vmap(potential_fn)(dists) potentials = fill_diagonal(potentials, 0) # replace diagonal elements with 0 total_potentials_without_each_point = jax.vmap( - lambda i: matrix_cross_sum(potentials, i, i, row_only=True) + lambda i: _matrix_cross_sum(potentials, i, i, row_only=True) )(jnp.arange(X.shape[0])) # Compute the decrease in the total potential @@ -125,64 +95,19 @@ def needless_point_idx( return idx -def _min_dist(x, X, dist_fn, n_valid, threshold=0.0): - # # Initialize the distances with the first distance calculation - dists = jnp.full((X.shape[0],), jnp.inf) - - # TODO: test both implementations: GPU, CPU, datasets - - def early_stop(dists): - # Loop condition function - def cond_fun(args): - i, _dists, min_dist = args - return (min_dist > threshold) & (i < n_valid) - - # Loop body function - def body_fun(args): - i, dists, min_dist = args - disti = dist_fn(x, X[i]) - dists = dists.at[i].set(disti) - min_dist = lax.min(min_dist, disti) - return i + 1, dists, min_dist - - # Run the while loop - _, dists, min_dist = lax.while_loop(cond_fun, body_fun, (0, dists, jnp.inf)) - - return dists, min_dist - - def no_early_stop(dists): - def body_fun(i, args): - min_dist, dists = args - disti = dist_fn(x, X[i]) - min_dist = lax.min(min_dist, disti) - dists = dists.at[i].set(disti) - return min_dist, dists - - min_dist, dists = lax.fori_loop(0, n_valid, body_fun, (jnp.inf, dists)) - return dists, min_dist +needless_point_idx = jax.jit( + _needless_point_idx, static_argnames=["dist_fn", "potential_fn"] +) - dists, min_dist = lax.cond( - threshold > 0.0, - lambda: early_stop(dists), - lambda: no_early_stop(dists), - ) - is_above_threshold = min_dist > threshold - return is_above_threshold, dists, min_dist +def dists(x, X, dist_fn, n_valid, threshold=0.0): + ds = jax.vmap(dist_fn, in_axes=(None, 0))(x, X) + mask = jnp.arange(X.shape[0]) < n_valid + ds = jnp.where(mask, ds, jnp.inf) + return ds.min() > threshold, ds, ds.min() -@partial( - jax.jit, - static_argnames=[ - "dist_fn", - "k_neighbors", - "threshold", - "power", - "approx_min", - "n_valid_points", - ], -) -def add_point_to_bag( +def _add_point_to_bag( x: jnp.ndarray, X: jnp.ndarray, dist_fn: Callable, @@ -197,77 +122,71 @@ def add_point_to_bag( of the replaced point (or -1 if no point was replaced). """ - assert k_neighbors > 0 - - is_full = X.shape[0] == n_valid_points - def below_threshold(X): return X, False, -1 - def above_threshold(X): - def above_and_not_full(X): - changed_item_idx = n_valid_points - X = X.at[changed_item_idx].set(x) - return X, True, changed_item_idx - - def above_and_full(X): - # Find closest points in `X` to `x` - # TODO: test approx_min_k vs argpartition - if approx_min: - k_closest_points_dists, k_closest_points_indices = lax.approx_min_k( - dists_from_x_to_X, k=k_neighbors - ) - else: - k_closest_points_dists, k_closest_points_indices = lax.top_k( - -dists_from_x_to_X, k_neighbors - ) - k_closest_points_dists = -k_closest_points_dists - - # Define a neighborhood of `x` - N = jnp.concatenate((jnp.array([x]), X[k_closest_points_indices])) - - # Find a point in `N` removing which would decrease the total potential the most - needless_point_local_idx = needless_point_idx( - N, dist_fn, lambda d: jnp.power(d, -power) - ) - - # If the needless point is not `x`, replace it with `x` - is_accepted = needless_point_local_idx > 0 - changed_item_idx = k_closest_points_indices[needless_point_local_idx - 1] - - X, changed_item_idx = lax.cond( - is_accepted, - lambda X, idx: (X.at[changed_item_idx].set(x), idx), - lambda X, idx: (X, -1), - X, - changed_item_idx, - ) - - return X, is_accepted, changed_item_idx - - result = lax.cond( - is_full, - lambda: above_and_full(X), - lambda: above_and_not_full(X), + def above_and_not_full(X): + changed_item_idx = n_valid_points + X = X.at[changed_item_idx].set(x) + return X, True, changed_item_idx + + def above_and_full(X): + # Find closest points in `X` to `x` + # TODO: test approx_min_k vs argpartition + + k_closest_points_dists, k_closest_points_indices = lax.cond( + approx_min, + lambda ds: lax.approx_min_k(ds, k=k_neighbors), + lambda ds: lax.top_k(-ds, k=k_neighbors), + dists_from_x_to_X, + ) + + k_closest_points_dists = lax.abs(k_closest_points_dists) # for top_k + + # Define a neighborhood of `x` + N = jnp.concatenate((jnp.array([x]), X[k_closest_points_indices])) + + # Find a point in `N` removing which would decrease the total potential the most + needless_point_local_idx = _needless_point_idx( + N, dist_fn, lambda d: d**-power + ) + + # If the needless point is not `x`, replace it with `x` + is_accepted = needless_point_local_idx > 0 + changed_item_idx = k_closest_points_indices[needless_point_local_idx - 1] + + X, changed_item_idx = lax.cond( + is_accepted, + lambda X, idx: (X.at[changed_item_idx].set(x), idx), + lambda X, _: (X, -1), + X, + changed_item_idx, ) - return result + return X, is_accepted, changed_item_idx + + is_full = X.shape[0] == n_valid_points - is_above_threshold, dists_from_x_to_X, min_dist = _min_dist( + is_above_threshold, dists_from_x_to_X, min_dist = dists( x, X, dist_fn, n_valid_points, threshold=threshold ) - X, is_accepted, updated_idx = lax.cond( - is_above_threshold, - above_threshold, - below_threshold, - X, - ) - return X, is_accepted, updated_idx + branches = [below_threshold, above_and_not_full, above_and_full] + branch_idx = 0 + is_above_threshold + (is_full & is_above_threshold) + return lax.switch(branch_idx, branches, X) -@jax.jit -def finalize_updates(changes: jnp.ndarray) -> jnp.ndarray: + +add_point_to_bag = jax.jit( + _add_point_to_bag, + static_argnames=[ + "dist_fn", + "k_neighbors", + ], +) + + +def _finalize_updates(changes: jnp.ndarray) -> jnp.ndarray: """ Given an array where each element represents whether a change occurred or not, -1 means no change, and a positive integer represents ID of changed @@ -285,26 +204,15 @@ def finalize_updates(changes: jnp.ndarray) -> jnp.ndarray: changes_reversed, return_index=True, size=changes.shape[0] ) - def keep_only_unique_change(i, changes_reversed): - return lax.cond( - jnp.isin(i, unique_idxs), - lambda _: changes_reversed, - lambda _: changes_reversed.at[i].set(-1), - changes_reversed, - ) + idxs = jnp.arange(changes.shape[0]) + mask = jnp.isin(idxs, unique_idxs)[::-1] + return jnp.where(mask, changes, -1) - changes_reversed = lax.fori_loop( - 0, changes_reversed.shape[0], keep_only_unique_change, changes_reversed - ) - return changes_reversed[::-1] +finalize_updates = jax.jit(_finalize_updates) -@partial( - jax.jit, - static_argnames=["dist_fn", "k_neighbors", "power", "threshold", "n_valid_points"], -) -def add_points_to_bag( +def _add_points_to_bag( *, X: jnp.ndarray, xs: jnp.ndarray, @@ -314,25 +222,15 @@ def add_points_to_bag( power: float, n_valid_points: int, ) -> tuple[jnp.ndarray, jnp.ndarray, jnp.ndarray]: - assert X.shape[0] >= n_valid_points assert xs.shape[0] > 0 - assert k_neighbors > 0 - assert power > 0 - assert threshold >= 0 - assert n_valid_points >= 0 - - if X.dtype != xs.dtype: - logger.warning( - f"X and xs have different dtypes: {X.dtype} and {xs.dtype}, casting to {X.dtype}" - ) - xs = xs.astype(X.dtype) + assert X.dtype == xs.dtype # Initialize array to store the information about the changes changed_item_idxs = -jnp.ones(xs.shape[0], dtype=int) # -1 means not changed def body_fun(i, args): X, changed_items_idxs, n_valid_points = args - X_updated, is_accepted, changed_item_idx = add_point_to_bag( + X_updated, is_accepted, changed_item_idx = _add_point_to_bag( xs[i], X, dist_fn, @@ -359,7 +257,14 @@ def body_fun(i, args): ) # Some points might have been accepted and then replaced by another point - changed_item_idxs = finalize_updates(changed_item_idxs) + changed_item_idxs = _finalize_updates(changed_item_idxs) acceptance_mask = changed_item_idxs >= 0 return changed_item_idxs, X_new, acceptance_mask + + +add_points_to_bag = jax.jit( + _add_points_to_bag, + static_argnames=["dist_fn", "k_neighbors"], + donate_argnames=["X"], +) diff --git a/moll/core/diversity.py b/moll/core/diversity.py index 089e4f1..0795e43 100644 --- a/moll/core/diversity.py +++ b/moll/core/diversity.py @@ -1,6 +1,7 @@ from typing import Callable import jax.numpy as jnp +from loguru import logger from moll.core.distance import add_points_to_bag @@ -19,6 +20,7 @@ def __init__( k_neighbors: int = 5, p: float = 1.0, threshold: float = 0.0, + dtype: jnp.dtype = None, ): self.capacity: int = capacity self.dist_fn = dist_fn @@ -27,49 +29,64 @@ def __init__( self.threshold = threshold self._data: jnp.ndarray | None = None + self.dtype: jnp.dtype | None = dtype self._labels: list = [None] * capacity self.n_seen: int = 0 self.n_accepted: int = 0 self.n_valid_points: int = 0 - def append(self, point, label=None) -> bool: + def _init(self, point: jnp.ndarray, label=None) -> int: """ - Add a point to the picker. + Initialize the picker with the first point. """ - points = jnp.array([point]) - labels = [label] if label else None - n_accepted = self.extend(points, labels) - is_accepted = n_accepted > 0 - return is_accepted + + dim = point.shape[0] + self.dtype = point.dtype + self._data = jnp.zeros((self.capacity, dim), dtype=self.dtype) + self._data = self._data.at[0].set(point) + self._labels[0] = label + + self.n_valid_points += 1 + self.n_accepted += 1 + self.n_seen += 1 def extend(self, points: jnp.ndarray, labels=None) -> int: """ Add a batch of points to the picker. """ + batch_size = len(points) + n_accepted = 0 + + # Check labels + if not labels: labels = range(self.n_seen, self.n_seen + len(points)) + else: + assert len(labels) == batch_size - batch_size = len(points) - n_accepted = 0 + # Check dtype - if self.is_empty(): - # compute dim and init data container, add first point - dim = points.shape[1] - dtype = points.dtype - self._data = jnp.zeros((self.capacity, dim), dtype=dtype) - self._data = self._data.at[0].set(points[0]) - self._labels[0] = labels[0] - - self.n_valid_points += 1 - n_accepted += 1 + if points.dtype is jnp.float64: + points = points.astype(self.dtype) + logger.warning( + "Downcasting to float32. If float64 is needed, set dtype=jnp.float64" + " and configure JAX to support it." + ) + # Init if empty + + if was_empty := self.is_empty(): + self._init(points[0], labels[0]) points = points[1:] labels = labels[1:] + n_accepted += 1 + + # Process remaining points if points.shape[0] > 0: - update_idxs, data_updated, _acceptance_mask = add_points_to_bag( + update_idxs, data_updated, acceptance_mask = add_points_to_bag( X=self._data, xs=points, dist_fn=self.dist_fn, @@ -87,7 +104,6 @@ def extend(self, points: jnp.ndarray, labels=None) -> int: ) n_appended = (update_idxs > last_valid_idx).sum().item() self.n_valid_points += n_appended - n_accepted += n_appended + n_updated # Update labels @@ -96,11 +112,44 @@ def extend(self, points: jnp.ndarray, labels=None) -> int: self._labels[updated_idx] = label n_accepted = min(n_accepted, self.capacity) - self.n_accepted += n_accepted - self.n_seen += batch_size + self.n_accepted += n_accepted - was_empty + self.n_seen += batch_size - was_empty return n_accepted + def append(self, point, label=None) -> bool: + """ + Add a point to the picker. + """ + points = jnp.array([point]) + labels = [label] if label else None + n_accepted = self.extend(points, labels) + is_accepted = n_accepted > 0 + return is_accepted + + def fast_init(self, points: jnp.ndarray, labels=None) -> int: + """ + Initialize the picker with a set of points. + """ + assert self.is_empty() + + batch_size = points.shape[0] + assert batch_size <= self.capacity + + if labels: + assert len(labels) == batch_size + else: + labels = range(len(points)) + + self._init(points[0], labels[0]) + + self._data = self._data.at[1:batch_size].set(points[1:]) + self._labels[1:batch_size] = labels[1:] + + self.n_accepted += batch_size - 1 + self.n_seen += batch_size - 1 + self.n_valid_points += batch_size - 1 + @property def n_rejected(self) -> int: """ diff --git a/poetry.lock b/poetry.lock index b9ae7ca..15b68d1 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,114 +1,16 @@ -# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. 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["Vsevolod Shegolev "] readme = "README.md" +# Main dependencies + [tool.poetry.dependencies] -python = ">=3.9,<3.13" -seaborn = "^0.12.2" +python = ">=3.9,<3.12" tqdm = "^4.66.1" -papermill = "^2.5.0" loguru = "^0.7.2" +# Dev dependencies + [tool.poetry.group.dev.dependencies] +seaborn = "^0.12.2" black = "^23.3.0" pylint = "^2.17.4" pytest = "^7.3.2" -jax = "0.4.20" -jaxlib = {version = "0.4.20", source = "jax_cpu"} rdkit = "^2023.3.1" ipykernel = "^6.25.2" ipython = "^8.15.0" @@ -28,6 +29,19 @@ ipywidgets = "^8.1.1" scikit-learn = "^1.3.2" jupyter = "^1.0.0" pytest-cov = "^4.1.0" +jax = "0.4.20" +# TODO: add option to install jaxlib for different hardware and platforms +jaxlib = [ + { version = "0.4.20", source = "jax_cpu", markers = "sys_platform == 'darwin'" }, + { version = "0.4.20+cuda12.cudnn89", source = "jax_cuda", markers = "sys_platform == 'linux'" }, +] + + +# Sources + + +[tool.poetry.group.profiler.dependencies] +tensorflow = "^2.15.0" [[tool.poetry.source]] name = "jax_cpu" @@ -45,15 +59,15 @@ priority = "primary" name = "PyPI" priority = "primary" +# Tools + [build-system] requires = ["poetry-core"] build-backend = "poetry.core.masonry.api" [tool.pytest.ini_options] -addopts="-n auto" +addopts = "-n auto" [tool.pylint.messages_control] max-line-length = 120 -disable = [ - "missing-docstring", -] \ No newline at end of file +disable = ["missing-docstring"] diff --git a/tests/core/test_distance.py b/tests/core/test_distance.py index 667e529..e70d39c 100644 --- a/tests/core/test_distance.py +++ b/tests/core/test_distance.py @@ -2,8 +2,8 @@ import pytest from moll.core.distance import ( + _add_points_to_bag, add_point_to_bag, - add_points_to_bag, euclidean, finalize_updates, matrix_cross_sum, @@ -165,7 +165,7 @@ def test_add_points_to_bag(X, xs, acc_mask): xs = jnp.array(xs) acc_mask = jnp.array(acc_mask) - _updated_idxs, X_updated, acceptance_mask = add_points_to_bag( + _updated_idxs, X_updated, acceptance_mask = _add_points_to_bag( X=X, xs=xs, dist_fn=euclidean, diff --git a/tests/core/test_diversity.py b/tests/core/test_diversity.py index 9cd682f..4e0ea78 100644 --- a/tests/core/test_diversity.py +++ b/tests/core/test_diversity.py @@ -201,7 +201,7 @@ def test_extend_same_points(picker_euclidean: OnlineDiversityPicker): @pytest.fixture -def circles(factor=0.1, random_state=42, n_samples=10): +def circles(factor=0.1, random_state=42, n_samples=20): """ Generate 2 circles: a small one and a large one. Points in the small circle are not favorable because of repulsion. @@ -249,7 +249,7 @@ def test_auto_labels_extend(picker_euclidean: OnlineDiversityPicker, circles): assert picker_euclidean.labels labels_generated = set(picker_euclidean.labels) - assert large_circle_idxs == labels_generated + assert large_circle_idxs.issuperset(labels_generated) assert small_circle_idxs & labels_generated == set() @@ -270,3 +270,25 @@ def test_tanimoto_picker(picker_tanimoto: OnlineDiversityPicker): assert picker_tanimoto.n_seen == len(points) assert picker_tanimoto.n_accepted == 5 + + +@pytest.mark.parametrize( + "init_batch_size", + [1, 2, 3, 4, 5], +) +def test_fast_init(picker_euclidean: OnlineDiversityPicker, circles, init_batch_size): + points, labels = circles + + batch_init = points[:init_batch_size] + batch = points[init_batch_size:] + labels_init = labels[:init_batch_size] + labels = labels[init_batch_size:] + + picker_euclidean.fast_init(batch_init, labels_init) + picker_euclidean.extend(batch, labels) + + assert picker_euclidean.labels + counts = Counter(circle for circle, idx in picker_euclidean.labels) + + assert counts["large"] >= 4 + assert counts["small"] <= 1