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z5b9a{>}5$b@vNGf+GJ0rIy}c*fzgXiOFJ-=fFQe_xb?wDnO0`HU3ei%Lv0sQb65Dd zg~OJj3!%p5L|$$oPX61;x(kBb1KI^A8hE&c|HDdF#q(J&Pe`7ZbmMk_ Nfv)MrM;GjW{Wm9QFYN#T literal 0 HcmV?d00001 diff --git a/examples/rosenbrock.py b/examples/rosenbrock.py index 5e9f92b..9e70d13 100644 --- a/examples/rosenbrock.py +++ b/examples/rosenbrock.py @@ -103,10 +103,11 @@ def execute_experiments(optimizers, objective, func, plot_func, initial_state): all_steps.append(steps) all_labels.append(name) - plot_func(all_steps, name="all", labels=all_labels) + # plot_func(all_steps, name="all", labels=all_labels) if __name__ == "__main__": + mx.random.seed(42) optimizers = [ # default (Adam, 0, 0.2, {}), @@ -120,6 +121,7 @@ def execute_experiments(optimizers, objective, func, plot_func, initial_state): (optim.Lamb, 0, 0.25, {}), (optim.Muon, 0, 0.2, {"alternate_optimizer": AdamW(learning_rate=0.0842)}), # fixed lr (optim.Shampoo, 0, 2, {}), + (optim.Kron, 0, 0.5, {}), ] execute_experiments( optimizers, objective_rosenbrock, rosenbrock, plot_rosenbrock, ROSENBROCK_INITIAL diff --git a/mlx_optimizers/__init__.py b/mlx_optimizers/__init__.py index e6b72d2..788583a 100644 --- a/mlx_optimizers/__init__.py +++ b/mlx_optimizers/__init__.py @@ -4,6 +4,7 @@ from .adopt import ADOPT from .diffgrad import DiffGrad +from .kron import Kron from .lamb import Lamb from .madgrad import MADGRAD from .muon import Muon @@ -13,6 +14,7 @@ __all__ = [ "ADOPT", "DiffGrad", + "Kron", "Lamb", "MADGRAD", "Muon", diff --git a/mlx_optimizers/kron.py b/mlx_optimizers/kron.py new file mode 100644 index 0000000..63618ee --- /dev/null +++ b/mlx_optimizers/kron.py @@ -0,0 +1,347 @@ +# original: https://github.com/lixilinx/psgd_torch +# kron torch: https://github.com/evanatyourservice/kron_torch +# heavyball: https://github.com/ClashLuke/HeavyBall + +import random +import string +from functools import partial +from typing import Callable, Optional, Union + +import mlx.core as mx +import numpy as np +from mlx.optimizers import Optimizer +from tqdm import tqdm + + +def flat_exponential_schedule( + max_prob: float = 1.0, + min_prob: float = 0.03, + decay: float = 0.001, + flat_start: float = 250, +) -> Callable: + """Anneal preconditioner update probability during beginning of training. + + PSGD benefits from more preconditioner updates at the beginning of training, + but once the preconditioner is learned the update probability can drop low. + + This schedule is an exponential anneal with a flat start. Default settings keep + update probability at 1.0 for 200 steps then exponentially anneal down to + `min_prob` by 4000 steps. Default settings work very well for most models and + training regimes. + """ + + def schedule(step): + """Exponential anneal with flat start.""" + step = mx.array(step, dtype=mx.float32) + prob = max_prob * mx.exp(-decay * (step - flat_start)) + prob = mx.clip(prob, min_prob, max_prob) + return prob + + return schedule + + +class Kron(Optimizer): + """Implements PSGD Kron from https://github.com/lixilinx/psgd_torch. + + Kron is a preconditioned SGD optimizer that uses a Kronecker-factored + approximation of the Fisher information matrix to precondition the gradient. + """ + + rng = random.Random(5318008) + + def __init__( + self, + learning_rate: Union[float, Callable[[mx.array], mx.array]], + b1: float = 0.9, + weight_decay: float = 0.0, + precond_update_prob: Optional[Union[float, Callable[[mx.array], mx.array]]] = None, + max_size_triangular: int = 8192, + min_ndim_triangular: int = 2, + memory_save_mode: Optional[str] = None, + momentum_into_precond_update: bool = True, + ): + super().__init__() + + self._maybe_schedule("learning_rate", learning_rate) + self.b1 = b1 + self.weight_decay = weight_decay + self.precond_update_prob = precond_update_prob + + self.max_size_triangular = max_size_triangular + self.min_ndim_triangular = min_ndim_triangular + self.memory_save_mode = memory_save_mode + self.momentum_into_precond_update = momentum_into_precond_update + self.precond_learning_rate = 0.1 # hardcode + self.precond_init_scale = 1.0 # hardcode + + self._tiny = 1e-8 + self.trust_region = lambda x: 0.1 * mx.sign(x) * mx.log(mx.abs(x) + 1) + 0.9 * mx.tanh(x) + + self._maybe_schedule("do_update_balance", self._do_update_balance) + + def _do_update_balance(self, step): + precond_update_prob = ( + flat_exponential_schedule() + if self.precond_update_prob is None + else self.precond_update_prob + ) + if isinstance(precond_update_prob, Callable): + precond_update_prob = precond_update_prob(step) + do_update = self.rng.random() < precond_update_prob + balance = self.rng.random() < 0.01 and do_update + return mx.array((do_update, balance)) + + def init_single(self, parameter: mx.array, state: dict): + """Initialize optimizer state""" + state["momentum_buffer"] = mx.zeros_like(parameter) + state["Q"], state["exprs"] = init_Q_exprs( + parameter, + self.precond_init_scale, + self.max_size_triangular, + self.min_ndim_triangular, + self.memory_save_mode, + ) + + def apply_single(self, gradient: mx.array, parameter: mx.array, state: dict): + """Performs a single optimization step, updating :math:`m` and :math:`v`""" + lr = self.learning_rate.astype(gradient.dtype) + b1 = self.b1 + + state["momentum_buffer"] = b1 * state["momentum_buffer"] + (1 - b1) * gradient + debiased_momentum = state["momentum_buffer"] / (1 - b1 ** self.state["step"]) + do_update, balance = self.state["do_update_balance"] + + if gradient.ndim > 1: + for i, (balanced, original) in enumerate(zip(_balance_Q(state["Q"]), state["Q"])): + state["Q"][i] = mx.where(balance, balanced, original) + + for i, (balanced, original) in enumerate( + zip( + _update_precond( + state["Q"], + state["exprs"], + mx.random.normal(debiased_momentum.shape, dtype=debiased_momentum.dtype), + debiased_momentum if self.momentum_into_precond_update else gradient, + self.precond_learning_rate, + self._tiny, + ), + state["Q"], + ) + ): + state["Q"][i] = mx.where(do_update, balanced, original) + + pre_grad = _precond_grad(state["Q"], state["exprs"], debiased_momentum) + pre_grad = mx.clip(self.trust_region(pre_grad / 1.5) * 1.5, -2, 2) + + if self.weight_decay != 0 and parameter.ndim > 1: + pre_grad = pre_grad + self.weight_decay * parameter + + return parameter - lr * pre_grad + + +def init_Q_exprs(t, scale, max_size, min_ndim_triangular, memory_save_mode): + """For a scalar or tensor t, we initialize its preconditioner Q and + reusable einsum expressions for updating Q and preconditioning gradient. + """ + letters = string.ascii_lowercase + string.ascii_uppercase + + dtype = t.dtype + shape = t.shape + if len(shape) == 0: # scalar + Q = [scale * mx.ones_like(t)] + exprA = ",->" + exprGs = [",->"] + exprP = ",,->" + else: # tensor + if len(shape) > 13: + raise ValueError(f"Got tensor with dim {len(t.shape)}; Einstein runs out of letters!") + + scale = scale ** (1 / len(shape)) + + if memory_save_mode is None: + dim_diag = [False for _ in shape] + elif memory_save_mode == "one_diag": + rev_sorted_dims = mx.argsort(shape)[::-1] + dim_diag = [False for _ in shape] + dim_diag[int(rev_sorted_dims[0])] = True + elif memory_save_mode == "all_diag": + dim_diag = [True for _ in shape] + else: + raise ValueError( + f"Invalid memory_save_mode: {memory_save_mode}, must be one of " + "[None, 'one_diag', 'all_diag']" + ) + + Q = [] + piece1A, piece2A, piece3A = ([], "", "") + exprGs = [] + piece1P, piece2P, piece3P, piece4P = ([], [], "", "") + for i, (size, dim_d) in enumerate(zip(shape, dim_diag)): + if size == 1 or size > max_size or len(shape) < min_ndim_triangular or dim_d: + # use diagonal matrix as preconditioner for this dim + Q.append(scale * mx.ones(size, dtype=dtype)) + + piece1A.append(letters[i]) + piece2A = piece2A + letters[i] + piece3A = piece3A + letters[i] + + piece1 = "".join( + [(letters[i + 13] if j == i else letters[j]) for j in range(len(shape))] + ) + subscripts = piece1 + "," + piece1 + "->" + letters[i + 13] + exprGs.append(subscripts) + + piece1P.append(letters[i + 13]) + piece2P.append(letters[i + 13]) + piece3P = piece3P + letters[i + 13] + piece4P = piece4P + letters[i + 13] + else: + # use triangular matrix as preconditioner for this dim + Q.append(scale * mx.eye(size, dtype=dtype)) + + piece1A.append(letters[i] + letters[i + 13]) + piece2A = piece2A + letters[i + 13] + piece3A = piece3A + letters[i] + + piece1 = "".join( + [(letters[i + 13] if j == i else letters[j]) for j in range(len(shape))] + ) + piece2 = "".join( + [(letters[i + 26] if j == i else letters[j]) for j in range(len(shape))] + ) + subscripts = piece1 + "," + piece2 + "->" + letters[i + 13] + letters[i + 26] + exprGs.append(subscripts) + + a, b, c = (letters[i], letters[i + 13], letters[i + 26]) + piece1P.append(a + b) + piece2P.append(a + c) + piece3P = piece3P + c + piece4P = piece4P + b + + exprA = ",".join(piece1A) + "," + piece2A + "->" + piece3A + exprP = ",".join(piece1P) + "," + ",".join(piece2P) + "," + piece3P + "->" + piece4P + + exprGs = tuple(exprGs) + return [Q, (exprA, exprGs, exprP)] + + +def _balance_Q(Q): + norms = mx.stack([mx.linalg.norm(q, float("inf")) for q in Q]) + geometric_mean = norms.prod() ** (1 / len(Q)) + return [q * (geometric_mean / norms[i]) for i, q in enumerate(Q)] + + +def _solve_triangular(a, b, *, upper, left=True): + """ + Simplified triangular solve implementation. + Args: + a: A batch of triangular matrices with shape (..., m, m). + b: A batch of matrices with shape (..., m, n) if left is True, + or shape (..., n, m) otherwise. + upper: If True, uses the upper triangular part of `a`. Otherwise, uses the lower part. + left: If True, solves A * X = B. If False, solves X * A = B. + Returns: + The solution matrix with the same shape as `b`. + """ + a = np.asarray(a) + b = np.asarray(b) + + if a.shape[-1] != a.shape[-2]: + raise ValueError("Matrix `a` must be square in its last two dimensions.") + if left and a.shape[-1] != b.shape[-2]: + raise ValueError("Shapes of `a` and `b` are incompatible for left-side solve.") + if not left and a.shape[-1] != b.shape[-1]: + raise ValueError("Shapes of `a` and `b` are incompatible for right-side solve.") + + a = np.triu(a) if upper else np.tril(a) + if left: + solution = np.linalg.solve(a, b) # Solve A * X = B + else: + solution = np.linalg.solve(a.swapaxes(-1, -2), b.swapaxes(-1, -2)).swapaxes( + -1, -2 + ) # Solve X * A = B + + return mx.array(solution) + + +@mx.compile +def _calc_A_and_conjB(exprA, G, Q, V): + A = mx.einsum(exprA, *Q, G) + order = G.ndim + p = list(range(order)) + conjB = mx.transpose(mx.conj(V), p[1:] + p[:1]) + for i, q in enumerate(Q): + conjB = ( + conjB / q + if q.ndim < 2 + # else _solve_triangular(q, conjB[None, :], upper=True, left=False)[0] + else conjB @ mx.linalg.inv(q, stream=mx.cpu) # type: ignore + ) + j = order - 1 + if i < j: + # Swap i-th and j-th dimensions of conjB + conjB = mx.transpose(conjB, p[:i] + [p[j]] + p[i + 1 : j] + [p[i]] + p[j + 1 :]) + return A, conjB + + +def _q_terms(exprGs, A, conjB): + terms = [] + for exprG in exprGs: + term1 = mx.einsum(exprG, A, mx.conj(A)) + term2 = mx.einsum(exprG, mx.conj(conjB), conjB) + terms.append((term1, term2)) + return terms + + +# @mx.compile +def _lb(A, max_abs): + H = lambda a: mx.conj(mx.transpose(a)) + imax = lambda a: (mx.max(a), mx.argmax(a)) + + A = A / max_abs + aa = mx.real(A * mx.conj(A)) + vcol, i = imax(mx.sum(aa, axis=0)) + vrow, j = imax(mx.sum(aa, axis=1)) + + return mx.where( + vcol > vrow, + max_abs * mx.linalg.norm(((x := mx.conj(A[:, i]) @ A) / mx.linalg.norm(x)) @ H(A)), + max_abs * mx.linalg.norm(H(x := A @ mx.conj(A[j])) @ (x / mx.linalg.norm(x))), + ) + + +def _norm_lower_bound(A): + """Cheap lower bound for the spectral norm of A.""" + max_abs = mx.linalg.norm(A, float("inf")) + return mx.where(max_abs > 0, _lb(A, max_abs), max_abs) + + +def _update_precond(Q, exprs, V, G, step, tiny): + """Update Kronecker product preconditioner Q with pair (V, G).""" + exprA, exprGs, _ = exprs + + A, conjB = _calc_A_and_conjB(exprA, G, Q, V) + terms = _q_terms(exprGs, A, conjB) + + updated_Q = [] + for q, (term1, term2) in zip(Q, terms): + tmp = term1 - term2 + tmp *= step + if q.ndim < 2: + tmp *= q + tmp /= mx.linalg.norm(term1 + term2, float("inf")) + tiny + q_updated = q - tmp + else: + tmp = mx.triu(tmp, k=0) + tmp /= _norm_lower_bound(term1 + term2) + tiny + q_updated = q - (tmp @ q) + + updated_Q.append(q_updated) + + return updated_Q + + +@mx.compile +def _precond_grad(Q, exprs, G): + """Precondition gradient G with preconditioner Q.""" + return mx.einsum(exprs[-1], *[mx.conj(q) for q in Q], *Q, G) diff --git a/tests/__init__.py b/tests/__init__.py index e69de29..55e5f84 100644 --- a/tests/__init__.py +++ b/tests/__init__.py @@ -0,0 +1 @@ +from .common import * diff --git a/tests/common.py b/tests/common.py new file mode 100644 index 0000000..e103171 --- /dev/null +++ b/tests/common.py @@ -0,0 +1,22 @@ +import mlx.nn as nn + + +class MLP(nn.Module): + def __init__(self, n_inputs: int = 2, n_hiddens_list: list = [10, 10], n_outputs: int = 2): + super().__init__() + assert len(n_hiddens_list) > 0 and all(n > 0 for n in n_hiddens_list) + activation = nn.ReLU + self.layers = [ + layer + for ni, no in zip([n_inputs] + n_hiddens_list, n_hiddens_list) + for layer in (nn.Linear(ni, no), activation()) + ] + [nn.Linear(n_hiddens_list[-1], n_outputs)] + + def __call__(self, x): + for layer in self.layers: + x = layer(x) + return x + + +def ids(v): + return f"{v[0].__name__, } {v[1:]}" diff --git a/tests/test_basic.py b/tests/test_basic.py index 72055dc..96dbaf1 100644 --- a/tests/test_basic.py +++ b/tests/test_basic.py @@ -3,6 +3,7 @@ from mlx.optimizers import AdamW import mlx_optimizers as optim +from tests import ids def rosenbrock(xy): @@ -24,28 +25,25 @@ def beale(xy): cases = [ - (rosenbrock, (1.5, 1.5), (1, 1)), - (quadratic, (1.5, 1.5), (0, 0)), - (beale, (1.5, 1.5), (3, 0.5)), + (rosenbrock, (-1.5, 2), (1, 1)), + (quadratic, (5.0, -3), (0, 0)), + (beale, (1.0, 0), (3, 0.5)), ] -def ids(v): - return f"{v[0].__name__, } {v[1:]}" - - optimizers = [ - (optim.QHAdam, {"learning_rate": 0.25}, 300), - (optim.DiffGrad, {"learning_rate": 0.3}, 300), - (optim.MADGRAD, {"learning_rate": 0.03}, 300), - (optim.ADOPT, {"learning_rate": 0.8}, 1200), + (optim.QHAdam, {"learning_rate": 0.25}, 150), + (optim.DiffGrad, {"learning_rate": 0.3}, 150), + (optim.MADGRAD, {"learning_rate": 0.03}, 150), + (optim.ADOPT, {"learning_rate": 0.17}, 150), ( optim.Muon, # using alternate for ndim < 2 { - "alternate_optimizer": AdamW(learning_rate=0.01, betas=[0.9, 0.95]), + "alternate_optimizer": AdamW(learning_rate=0.15, betas=[0.9, 0.99]), }, - 800, + 500, ), + (optim.Kron, {"learning_rate": 0.015, "precond_update_prob": 0.75}, 700), # TODO: Lamb & Shampoo tests ] @@ -56,6 +54,7 @@ def test_benchmark_function(case, optimizer_config): func, initial_state, min_loc = case optimizer_class, config, iterations = optimizer_config optimizer = optimizer_class(**config) + mx.random.seed(42) x = mx.array(initial_state) @@ -65,7 +64,8 @@ def test_benchmark_function(case, optimizer_config): min_loc = mx.array(min_loc) error = mx.sqrt(mx.sum((x - min_loc) ** 2)).item() - assert mx.allclose(x, min_loc, atol=0.001), f"x=({x[0]:.4f},{x[1]:.4f}), {error=:.4f}" + assert mx.allclose(x, min_loc, atol=0.01), f"x=({x[0]:.4f},{x[1]:.4f}), {error=:.4f}" name = optimizer.__class__.__name__ assert name in optimizer.__repr__() + assert name in optimizer.__repr__() diff --git a/tests/test_functional.py b/tests/test_functional.py new file mode 100644 index 0000000..d8c9958 --- /dev/null +++ b/tests/test_functional.py @@ -0,0 +1,9 @@ +import mlx.core as mx +from mlx.utils import tree_flatten, tree_unflatten + +import mlx_optimizers as optim +from tests import MLP + + +def test_init_from_state(): + pass diff --git a/tests/test_neuralnet.py b/tests/test_neuralnet.py index 587ea1f..de33cb2 100644 --- a/tests/test_neuralnet.py +++ b/tests/test_neuralnet.py @@ -6,6 +6,7 @@ from mlx.optimizers import AdamW import mlx_optimizers as optim +from tests import MLP, ids def generate_moons(n_samples: int = 100, noise: float = 0.2): @@ -37,27 +38,6 @@ def generate_moons(n_samples: int = 100, noise: float = 0.2): return X, T -class MLP(nn.Module): - def __init__(self, n_inputs: int = 2, n_hiddens_list: list = [10, 10], n_outputs: int = 2): - super().__init__() - assert len(n_hiddens_list) > 0 and all(n > 0 for n in n_hiddens_list) - activation = nn.ReLU - self.layers = [ - layer - for ni, no in zip([n_inputs] + n_hiddens_list, n_hiddens_list) - for layer in (nn.Linear(ni, no), activation()) - ] + [nn.Linear(n_hiddens_list[-1], n_outputs)] - - def __call__(self, x): - for layer in self.layers: - x = layer(x) - return x - - -def ids(v): - return f"{v[0].__name__, } {v[1:]}" - - optimizers = [ (optim.QHAdam, {"learning_rate": 0.01}, 50), (optim.DiffGrad, {"learning_rate": 0.01}, 100), @@ -70,6 +50,7 @@ def ids(v): (optim.ADOPT, {"learning_rate": 0.01}, 50), (optim.Lamb, {"learning_rate": 0.01}, 50), (optim.Shampoo, {"learning_rate": 0.03}, 50), + (optim.Kron, {"learning_rate": 0.03}, 50), ] @@ -105,3 +86,4 @@ def step(X, T): acc = mx.sum(mx.argmax(model(X), axis=1) == T) / T.shape[0] # type: ignore assert losses[0] > 2 * losses[-1], f"loss={losses[-1]:.5f}, {acc=:.3f}" + assert losses[0] > 2 * losses[-1], f"loss={losses[-1]:.5f}, {acc=:.3f}" From 542bd741adc2eff3f8943428bf306fa7586f509f Mon Sep 17 00:00:00 2001 From: Jason Stock Date: Sun, 17 Nov 2024 20:17:57 -0700 Subject: [PATCH 2/4] optim: kron psgd cleanup --- mlx_optimizers/kron.py | 34 ---------------------------------- 1 file changed, 34 deletions(-) diff --git a/mlx_optimizers/kron.py b/mlx_optimizers/kron.py index 63618ee..04e0bc2 100644 --- a/mlx_optimizers/kron.py +++ b/mlx_optimizers/kron.py @@ -231,39 +231,6 @@ def _balance_Q(Q): return [q * (geometric_mean / norms[i]) for i, q in enumerate(Q)] -def _solve_triangular(a, b, *, upper, left=True): - """ - Simplified triangular solve implementation. - Args: - a: A batch of triangular matrices with shape (..., m, m). - b: A batch of matrices with shape (..., m, n) if left is True, - or shape (..., n, m) otherwise. - upper: If True, uses the upper triangular part of `a`. Otherwise, uses the lower part. - left: If True, solves A * X = B. If False, solves X * A = B. - Returns: - The solution matrix with the same shape as `b`. - """ - a = np.asarray(a) - b = np.asarray(b) - - if a.shape[-1] != a.shape[-2]: - raise ValueError("Matrix `a` must be square in its last two dimensions.") - if left and a.shape[-1] != b.shape[-2]: - raise ValueError("Shapes of `a` and `b` are incompatible for left-side solve.") - if not left and a.shape[-1] != b.shape[-1]: - raise ValueError("Shapes of `a` and `b` are incompatible for right-side solve.") - - a = np.triu(a) if upper else np.tril(a) - if left: - solution = np.linalg.solve(a, b) # Solve A * X = B - else: - solution = np.linalg.solve(a.swapaxes(-1, -2), b.swapaxes(-1, -2)).swapaxes( - -1, -2 - ) # Solve X * A = B - - return mx.array(solution) - - @mx.compile def _calc_A_and_conjB(exprA, G, Q, V): A = mx.einsum(exprA, *Q, G) @@ -293,7 +260,6 @@ def _q_terms(exprGs, A, conjB): return terms -# @mx.compile def _lb(A, max_abs): H = lambda a: mx.conj(mx.transpose(a)) imax = lambda a: (mx.max(a), mx.argmax(a)) From 1f4b7d59438edfb16f13cc3d6ca5c7490ce2a79e Mon Sep 17 00:00:00 2001 From: Jason Stock Date: Mon, 18 Nov 2024 08:16:27 -0700 Subject: [PATCH 3/4] tests: added functional test --- tests/test_functional.py | 97 +++++++++++++++++++++++++++++++++++++++- 1 file changed, 95 insertions(+), 2 deletions(-) diff --git a/tests/test_functional.py b/tests/test_functional.py index d8c9958..f10ba83 100644 --- a/tests/test_functional.py +++ b/tests/test_functional.py @@ -1,9 +1,102 @@ +from functools import partial + import mlx.core as mx +import mlx.nn as nn +import pytest from mlx.utils import tree_flatten, tree_unflatten import mlx_optimizers as optim from tests import MLP +optimizers = [ + optim.QHAdam, + optim.DiffGrad, + optim.MADGRAD, + optim.ADOPT, + optim.Muon, + optim.Lamb, + optim.Kron, + # optim.Shampoo, +] + + +@pytest.mark.parametrize("optclass", optimizers) +def test_init_from_state(optclass): + model = MLP() + optimizer = optclass(learning_rate=3e-4) + optimizer.init(model.trainable_parameters()) + + # Flatten the state for serialization + state = tree_flatten(optimizer.state) + + # Make a new optimizer and load the state + optimizer = optclass(learning_rate=3e-4) + optimizer.state = tree_unflatten(state) + + # This should work without any errors + grads = model.trainable_parameters() + optimizer.update(model, grads) + + +@pytest.mark.parametrize("optclass", optimizers) +def test_compiled_optimizer(optclass): + model = nn.Linear(10, 10) + x = mx.random.uniform(shape=(2, 10)) + + mx.random.seed(42) + optimizer = optclass(learning_rate=1e-2) + + orig_params = model.parameters() + + def loss(model, x): # type: ignore + return model(x).sum() + + # Uncompiled version + def step(x): # type: ignore + _, grad = nn.value_and_grad(model, loss)(model, x) + optimizer.update(model, grad) + + step(x) + print(optimizer.state) + uncompiled_params = model.parameters() + + # Pure version + def loss(params, x): # type: ignore + model.update(params) + return model(x).sum() + + model.update(orig_params) + mx.random.seed(42) + optimizer = optclass(learning_rate=1e-2) + + @mx.compile + def step(params, opt_state, x): # type: ignore + grad = mx.grad(loss)(params, x) + optimizer.state = opt_state + params = optimizer.apply_gradients(grad, params) + return params, optimizer.state + + optimizer.init(model.parameters()) + pure_params, _ = step(model.parameters(), optimizer.state, x) + assert mx.allclose(pure_params["weight"], uncompiled_params["weight"]) # type: ignore + assert mx.allclose(pure_params["bias"], uncompiled_params["bias"]) # type: ignore + + # Impure version + def loss(model, x): + return model(x).sum() + + model.update(orig_params) + mx.random.seed(42) + optimizer = optclass(learning_rate=1e-2) + state = [model.state, optimizer.state] + + @partial(mx.compile, inputs=state, outputs=state) + def step(x): + _, grad = nn.value_and_grad(model, loss)(model, x) + optimizer.update(model, grad) -def test_init_from_state(): - pass + step(x) + print(optimizer.state) + impure_params = model.parameters() + assert mx.allclose(impure_params["weight"], uncompiled_params["weight"]) # type: ignore + assert mx.allclose(impure_params["bias"], uncompiled_params["bias"]) # type: ignore From 3d2c0b6fa93c79615a441580cf0fce84741cc9de Mon Sep 17 00:00:00 2001 From: Jason Stock Date: Mon, 18 Nov 2024 08:46:27 -0700 Subject: [PATCH 4/4] optim: kron psgd added with tests --- CHANGELOG.md | 4 +++ mlx_optimizers/kron.py | 62 ++++++++++++++++++++++++++-------------- tests/__init__.py | 1 - tests/test_basic.py | 10 ++++--- tests/test_functional.py | 28 +++++++++--------- tests/test_neuralnet.py | 4 ++- 6 files changed, 69 insertions(+), 40 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index e786f8a..57f7965 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,10 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## Unreleased +### Added +- optim: Kron PSGD +- tests: functional compilation (uncompiled, pure, and impure) and init from state + ## [v0.2.0](https://github.com/stockeh/mlx-optimizers/releases/tag/v0.2.0) - 2024-11-11 ### Added diff --git a/mlx_optimizers/kron.py b/mlx_optimizers/kron.py index 04e0bc2..c97cd63 100644 --- a/mlx_optimizers/kron.py +++ b/mlx_optimizers/kron.py @@ -1,16 +1,9 @@ -# original: https://github.com/lixilinx/psgd_torch -# kron torch: https://github.com/evanatyourservice/kron_torch -# heavyball: https://github.com/ClashLuke/HeavyBall - import random import string -from functools import partial from typing import Callable, Optional, Union import mlx.core as mx -import numpy as np from mlx.optimizers import Optimizer -from tqdm import tqdm def flat_exponential_schedule( @@ -41,10 +34,34 @@ def schedule(step): class Kron(Optimizer): - """Implements PSGD Kron from https://github.com/lixilinx/psgd_torch. - - Kron is a preconditioned SGD optimizer that uses a Kronecker-factored - approximation of the Fisher information matrix to precondition the gradient. + """Kronecker-Factored Preconditioned Stochastic Gradient Descent [1]. + + PSGD is a second-order optimizer that uses Hessian- or whitening-based :math:`(gg^T)` + preconditioners and Lie groups to improve convergence. Kron uses Kronecker-factored + preconditioners for tensors of any number dimension. + + [1] Xi-Lin Li, 2015. Preconditioned Stochastic Gradient Descent. + https://arxiv.org/abs/1512.04202 + https://github.com/lixilinx/psgd_torch + + Args: + learning_rate (float or callable): the learning rate. + b1 (float, optional): coefficient used for computing running averages of the + gradient. Default: ``0.9`` + weight_decay (float, optional): weight decay factor. Default: ``0.0`` + precond_update_prob (float or callable, optional): probability of updating the + preconditioner. Default: ``None`` (flat exponential schedule) + max_size_triangular (int, optional): maximum size for dim's preconditioner to be + triangular. Default: ``8192`` + min_ndim_triangular (int, optional): minimum number of dimensions a layer needs + to have triangular preconditioners. Default: ``2`` + memory_save_mode (str, optional): (None, 'one_diag', or 'all_diag'). None: set all + preconditioners to be triangular, 'one_diag': sets the largest or last dim to + be diagonal per layer, and 'all_diag': sets all preconditioners to be diagonal. + Default: ``None`` + momentum_into_precond_update (bool, optional): whether to use momentum in + preconditioner update. Default: ``True`` + .. """ rng = random.Random(5318008) @@ -85,9 +102,9 @@ def _do_update_balance(self, step): if self.precond_update_prob is None else self.precond_update_prob ) - if isinstance(precond_update_prob, Callable): - precond_update_prob = precond_update_prob(step) - do_update = self.rng.random() < precond_update_prob + if callable(precond_update_prob): + precond_update_prob = precond_update_prob(step) # type: ignore + do_update = self.rng.random() < precond_update_prob # type: ignore balance = self.rng.random() < 0.01 and do_update return mx.array((do_update, balance)) @@ -221,7 +238,6 @@ def init_Q_exprs(t, scale, max_size, min_ndim_triangular, memory_save_mode): exprA = ",".join(piece1A) + "," + piece2A + "->" + piece3A exprP = ",".join(piece1P) + "," + ",".join(piece2P) + "," + piece3P + "->" + piece4P - exprGs = tuple(exprGs) return [Q, (exprA, exprGs, exprP)] @@ -241,13 +257,12 @@ def _calc_A_and_conjB(exprA, G, Q, V): conjB = ( conjB / q if q.ndim < 2 - # else _solve_triangular(q, conjB[None, :], upper=True, left=False)[0] + # TODO: better to use `solve_triangular(q, conjB, upper=True, left=False)` else conjB @ mx.linalg.inv(q, stream=mx.cpu) # type: ignore ) j = order - 1 if i < j: - # Swap i-th and j-th dimensions of conjB - conjB = mx.transpose(conjB, p[:i] + [p[j]] + p[i + 1 : j] + [p[i]] + p[j + 1 :]) + conjB = mx.swapaxes(conjB, i, j) return A, conjB @@ -260,10 +275,15 @@ def _q_terms(exprGs, A, conjB): return terms -def _lb(A, max_abs): - H = lambda a: mx.conj(mx.transpose(a)) - imax = lambda a: (mx.max(a), mx.argmax(a)) +def H(a): + return mx.conj(mx.transpose(a)) + +def imax(a): + return mx.max(a), mx.argmax(a) + + +def _lb(A, max_abs): A = A / max_abs aa = mx.real(A * mx.conj(A)) vcol, i = imax(mx.sum(aa, axis=0)) diff --git a/tests/__init__.py b/tests/__init__.py index 55e5f84..e69de29 100644 --- a/tests/__init__.py +++ b/tests/__init__.py @@ -1 +0,0 @@ -from .common import * diff --git a/tests/test_basic.py b/tests/test_basic.py index 96dbaf1..1ef58cb 100644 --- a/tests/test_basic.py +++ b/tests/test_basic.py @@ -3,7 +3,8 @@ from mlx.optimizers import AdamW import mlx_optimizers as optim -from tests import ids + +from .common import ids def rosenbrock(xy): @@ -39,11 +40,11 @@ def beale(xy): ( optim.Muon, # using alternate for ndim < 2 { - "alternate_optimizer": AdamW(learning_rate=0.15, betas=[0.9, 0.99]), + "alternate_optimizer": AdamW(learning_rate=0.12, betas=[0.9, 0.99]), }, - 500, + 700, ), - (optim.Kron, {"learning_rate": 0.015, "precond_update_prob": 0.75}, 700), + (optim.Kron, {"learning_rate": 0.015, "precond_update_prob": 0.75}, 800), # TODO: Lamb & Shampoo tests ] @@ -69,3 +70,4 @@ def test_benchmark_function(case, optimizer_config): name = optimizer.__class__.__name__ assert name in optimizer.__repr__() assert name in optimizer.__repr__() + assert name in optimizer.__repr__() diff --git a/tests/test_functional.py b/tests/test_functional.py index f10ba83..3767520 100644 --- a/tests/test_functional.py +++ b/tests/test_functional.py @@ -6,7 +6,8 @@ from mlx.utils import tree_flatten, tree_unflatten import mlx_optimizers as optim -from tests import MLP + +from .common import MLP optimizers = [ optim.QHAdam, @@ -48,20 +49,20 @@ def test_compiled_optimizer(optclass): orig_params = model.parameters() - def loss(model, x): # type: ignore + def uncompiled_loss(model, x): return model(x).sum() # Uncompiled version - def step(x): # type: ignore - _, grad = nn.value_and_grad(model, loss)(model, x) + def uncompiled_step(x): + _, grad = nn.value_and_grad(model, uncompiled_loss)(model, x) optimizer.update(model, grad) - step(x) + uncompiled_step(x) print(optimizer.state) uncompiled_params = model.parameters() # Pure version - def loss(params, x): # type: ignore + def pure_loss(params, x): model.update(params) return model(x).sum() @@ -70,19 +71,19 @@ def loss(params, x): # type: ignore optimizer = optclass(learning_rate=1e-2) @mx.compile - def step(params, opt_state, x): # type: ignore - grad = mx.grad(loss)(params, x) + def pure_step(params, opt_state, x): # type: ignore + grad = mx.grad(pure_loss)(params, x) optimizer.state = opt_state params = optimizer.apply_gradients(grad, params) return params, optimizer.state optimizer.init(model.parameters()) - pure_params, _ = step(model.parameters(), optimizer.state, x) + pure_params, _ = pure_step(model.parameters(), optimizer.state, x) assert mx.allclose(pure_params["weight"], uncompiled_params["weight"]) # type: ignore assert mx.allclose(pure_params["bias"], uncompiled_params["bias"]) # type: ignore # Impure version - def loss(model, x): + def impure_loss(model, x): return model(x).sum() model.update(orig_params) @@ -91,12 +92,13 @@ def loss(model, x): state = [model.state, optimizer.state] @partial(mx.compile, inputs=state, outputs=state) - def step(x): - _, grad = nn.value_and_grad(model, loss)(model, x) + def impure_step(x): + _, grad = nn.value_and_grad(model, impure_loss)(model, x) optimizer.update(model, grad) - step(x) + impure_step(x) print(optimizer.state) impure_params = model.parameters() assert mx.allclose(impure_params["weight"], uncompiled_params["weight"]) # type: ignore assert mx.allclose(impure_params["bias"], uncompiled_params["bias"]) # type: ignore + assert mx.allclose(impure_params["bias"], uncompiled_params["bias"]) # type: ignore diff --git a/tests/test_neuralnet.py b/tests/test_neuralnet.py index de33cb2..190c03b 100644 --- a/tests/test_neuralnet.py +++ b/tests/test_neuralnet.py @@ -6,7 +6,8 @@ from mlx.optimizers import AdamW import mlx_optimizers as optim -from tests import MLP, ids + +from .common import MLP, ids def generate_moons(n_samples: int = 100, noise: float = 0.2): @@ -87,3 +88,4 @@ def step(X, T): acc = mx.sum(mx.argmax(model(X), axis=1) == T) / T.shape[0] # type: ignore assert losses[0] > 2 * losses[-1], f"loss={losses[-1]:.5f}, {acc=:.3f}" assert losses[0] > 2 * losses[-1], f"loss={losses[-1]:.5f}, {acc=:.3f}" + assert losses[0] > 2 * losses[-1], f"loss={losses[-1]:.5f}, {acc=:.3f}"